From f47fb8ebb467da3634c1c6380149de14f1e010a8 Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Sun, 13 Oct 2019 03:39:39 -0700 Subject: [PATCH 01/11] Made a draft for the periodograph code. --- .ipynb_checkpoints/Research-checkpoint.ipynb | 143 +++++++++++++++++++ Research.ipynb | 143 +++++++++++++++++++ 2 files changed, 286 insertions(+) create mode 100644 .ipynb_checkpoints/Research-checkpoint.ipynb create mode 100644 Research.ipynb diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb new file mode 100644 index 0000000..2f4ba9d --- /dev/null +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import lightkurve as lk" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#just setting up sample data\n", + "search_result = lk.search_targetpixelfile('Pi Mensae', mission='TESS', sector=1)\n", + "tpf = search_result.download(quality_bitmask='default')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def plotperiodograph(targetpixelfile):\n", + " #This is the mask from lightkurve for the aperture\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #This is a postage stamp x and y values for pixels that are part of the aperture\n", + " postagestamp = np.where(aperture == True)\n", + " \n", + " #Setting up the pixel subplots so They create a rectangle of pixels based off the min and max of the row and column values from the aperture mask.\n", + " #Could make ploting functions optional for the user and set this to default\n", + " fig,ax = plt.subplots(len(np.arange(postagestamp[0].min(),postagestamp[0].max()+1)),\n", + " len(np.arange(postagestamp[1].min(),postagestamp[1].max()+1)),\n", + " figsize=(20,20),sharex='col', sharey='row')\n", + "\n", + " #Just making the subplot spacings 0 pixel width and height separation\n", + " fig.subplots_adjust(wspace=0,hspace=0)\n", + "\n", + " #iterating through the columns of the postage stamp pixels\n", + " for i in np.arange(postagestamp[0].min(),postagestamp[0].max()+1):\n", + " \n", + " #iterating through the rows of the postage stamp pixels\n", + " for j in np.arange(postagestamp[1].min(),postagestamp[1].max()+1):\n", + " \n", + " #Creating a false mask to alter each iteration\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", + " mask[i][j] = True\n", + " \n", + " #extracting the light curve for the target pixel\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " \n", + " #Grabbing periodogram frequency and power data and storing as a tuple\n", + " periodogram = np.asarray([lightcurve.to_periodogram(oversample_factor=1).frequency,\n", + " lightcurve.to_periodogram(oversample_factor=1).power])\n", + " \n", + " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", + " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram[0],periodogram[1]);\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "data": { + "image/png": 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j4kiSVHHhEB5JkiRpRjJxJEmSJEmSpFwmjiRJkqaJAzIlSdJsY+JIkiRpGjgiU5IkzUYmjiRJkiRJkpTLxJEkaUZwCI8kSZI085g4kiRVnEN4VA2O9A2x/cVjlW6GJEnSKamrdAMkSZKqwf1Pd1a6CZIkSafMHkeSJEmSJEnKZeJIkiRJkiRJuUwcSZJmhOTs2JIkSdKMY+JIklRxEU6PLUmSJM1EJo4kSZIkSZKUa1KJo4i4OSL2R8SjJWWLI2JTRGzNvrdl5RERn4iIbRGxJSLeUHLMhqz+1ojYUP7bkSRJkiRJUrlMtsfRZ4CrTir7AHBvSmk9cG/2GOAdwPrsayPwSSgmmoCbgEuBS4CbRpNNkiRJkiRJmnkmlThKKX0L6Dqp+Brglmz7FuDakvJbU9H3gNaIWAlcCWxKKXWllA4Cm3hpMkqSJEmSJEkzxFTmOFqeUtoLkH0/IytfBewqqbc7KxurXJJU5fYc6mVnV0+lmyFJkiTpJHWn4Zx5S+OkccpfeoKIjRSHubFmzZrytUyaQdrb22lvbwegs7Ozwq2RTo/Jxvn3t5/cqVWaPXw+VzUwzlUNjHMp31R6HO3LhqCRfd+fle8GziqptxrYM075S6SU2lNKhZRSYdmyZVNoojRzbdy4kY6ODjo6OjDONVcZ56oGxrmqgXGuamCcS/mmkji6ExhdGW0D8OWS8ndlq6tdBhzOhrLdA1wREW3ZpNhXZGWSJEmSJEmagSY1VC0ibgMuB5ZGxG6Kq6N9DLg9It4D7ASuy6rfDVwNbAN6gHcDpJS6IuIjwINZvQ+nlBybIEmSJEmSNENNKnGUUrphjF1vy6mbgBvHOM/NwM2Tbp0kSZIkSZIqZipD1SRJkiRJkjSHmTiSJEmSJElSLhNHkiRJkiRJymXiSJIkaRoVp4OUJEmaHUwcSZIkTaP+oZFKN0E6bR7bc5gtuw9VuhmSpDIycSRJkiSpLD721Se56c7HKt0MSVIZmTiSJEmaRo5U01wWEYwY45I0p5g4kiRJmkYJ31Vr7qoJzI5K0hxj4kiSJElSWQTY40iS5hgTR5IkSdPIzhiay2oi7FUnSXOMiSNJkqRpNGLmSHNYRDDiwoGSNKeYOJIkSZpGB48NVroJ0mkTYXJUkuYaE0eSpBnjxaP9lW6CdNq9eMw419xVE5VugSSp3EwcSZJmjEM99sTQ3PfcgWOVboJ02gRhjyNJmmNMHEmSZgw/qVY1+MKDuyrdBOm0qalxAnhJmmtMHEmSZoyaMHOkue+Fw32VboJ02kTY40iS5hoTR5KkivuDq18NFCdVlea6HQd6Kt0E6bQJ7HGkue/zD+zkM9/ZXulmSNPGxJEkqeK2Z3O+fGvrixVuiSRpKmoiMG+kue7uR1/gSw/vqXQzpGlj4kiSVHHfe/YAAH/4z49WuCWSpKmIwKFqmvNqApJxripi4kiSVHE3/NiaSjdBmlaDwyOVboJ0WtREOFRNc15tBMMjBrqqh4kjSVLFLV/UdHz7+UO9FWyJdPq87ydecXx7/R98tYItkU6fY/1D7OxyHi/NbTU1gXkjVRMTR5Kkimuprz2+/aaPfYNdvumYkya7mtiznUcZmeQr8pQSN372B3x7a+cJ5Z1H+hkcHmFojJ49HTu62Pxc15jXGR5JZR+GcPl5y054bK8jzUVff3xfpZsgnXY1waT/T0lzQd10XzAirgL+GqgFPpVS+th0t0GaTT7zne30DY3wG289t9JNkU6b+roTP8d485998/j2b15+Llv3HeVfn9jHTf/xfJYvbKKwro0P3vEIf/jT57OgqY4l8xtPS7v2dfdRE8GyBVM7/55DvdTX1rzkPH2Dw3zt0Re49vWrco/bf6SPMxYUe2MdPDbA84d6uXDVIr76yF5evXIh65bO42j/ED/cdYgLzlxIa0sDB48NMJwSS+c38mznUQ71DtI3OMwv/v33Wbukhd0He7n/dy9ndVsLH/vqk/zaf1jHjhd7+IX27/JXv3ARj+/p5neuOI+Guhoeff4wX3roeX73yvP4m/ue4bKzF/PVR1+grja4/+lO/uFXf4y3/vl9ANTVBMsXNrF4XgOtLfV8e+uL/M7bX8kvXrqGB3d08alvb6fjuYO887Ur2fTYPhY01XHg2AC/9qazufk72/np167kFy9dw/vv2MKurh/1OvupVy/nX5/40RvRi85q5eFdhzhzUROvWrmQbzy5H4CvPLKXz/76pfzSp77/kp/jL166hs99f+eYv5/Xr2nloZ2HJvw9/vi5S/jcf75swnpjuXht2wmPS3sdPfw/3k5TfS3ferqTt5+/nChZYvD+pztZMq+BC1ctetnXVnU53DvIgsY6amomXqpyaHiE4ZRorKudsK6kotqacC4vVZWYzkm9IqIWeBp4O7AbeBC4IaX0+FjHFAqF1NHRkbtv460dHOkb4raNL/9FnDQTFAoFxorzd//DA3Qe7eeu//rmaW6VVF7jxfnBYwO8/iObTuv1l85vYFVrMz/cfZgNb1xLRPCZf98BwCuXz+c/vWE1f/n1p2msq+FI/9CUr/e2V53BN5/ab1f2OWbHx9457v7x4hxg3Qe+MqXrn7d8AVdesJznunpoaaijqb6GwtrFFNa1EQEHjw3S0lDL/U938suXrQXgge1dNNbV0NpSz/zGOtpaGqipCR7f000isXxhE0tLkq8pJZ7ad4TDPYO8fk0bg8MjHOodZMm8Brr7Blk6r1h3cGSE7t4hlswrnu/Fo/3s6+7jVSsW8tyBY9z4uYe4eG0rq1pbuK6wmkXN9ew/0s/Q8AjH+ofZsvsQbz1vWfG13AM7WdXazCuXL2D7i8foPNLP1a9ZyaGeAb7yyF5+/NylXHbOYo72D9HSUMf//sZWnnrhCId6BlnUUs+5y+axZvE8/vRrT7KgqY6+wWE+8I5Xs23/ERrranl8bzcPbO/iU+8q8PF/fZoVC5vYfuAY9TU1PLXvyAk/48a6GvqHRviz/+e1fOCftjCSoKGuhoGhH/UQq68NBodP/OM+uc543vvWc/i7+589oezitW38cNchhkqeNGprpn8eld+/+lVsfMv4H1aNF+ejMf74h6+kpWHaP6OWymYycT7R/wRppouIzSmlwoT1pjlx9EbgQymlK7PHHwRIKf3JWMeMlzh69R9+jd7BYe74zR+nriaO/3ONgCCya/6ofkqQSNTExJ++zBQpnXgPlT7P6LlGTfac411/dN/oefPqjf7ugiCNscjr6O/8JeVjnO/4dnbesdow0c9u9NOG0bgaSSeer7G+hlcuXzD2CRj/H9NP/K/72P7iMT74jldx8do26mqLPTOC4gvKlPJXLzm5zaNtmsw9naqXExNTPVfp76p0eyRN/Dd+qvd/coy+ZP84Cw+XxmVejI3GdmndsX5Po8eOd72x2n+qSp9H8/adrL62hvNWvPw4h6m/oZamQ6UTR9J0mEqcTzXGf+7i1Xxx8+7jj9+8findfUM8vucwIwkKa9t43VmtLGyq47YHdvG6sxbRXF/HHT/YzVteuYyfOG8ZB44OsLOrh5WLmmioq+F/f2Mb1128mi27D3P5ect4/lAvj+/t5rkDPaxd3MIbz13CqrZmbv3354iARc31nLdiAe98zUoa6mpICe557AXWLpnHo88f5vwzF7L5uYN848n9nLGgkb/4+dfxnW0H+Nv7n+G/v/PVPLC9i/uf7jz+/3dgeISFTXX0Dg5z7rL5PPnCERY0/iipdsbCRp7pPHbCz+GGS87iwR0HOWNBI//+zAH+x0+fz4fvepy2lnoO9gz+6Hexto2O5w4C8Ks/vu74ByITOX/lQgaGR6iNOCF52tpSz6GS85daOr+RF4/2T+r84/n0hsLx1xIf/coTL7n36fC3v3wxV124Ytw6pxrn5y6bx7ol8473CF6zuIXewWFSSiyZ18jA8DAP7jhIU10NrzurlYhgeGSE4ZHia6ujfUMsbC7GxWiv08GhEQ72DLJyUdPx17hjvYbNe91W+prxxPc/o2UnvkisiWAoez9d3BfUBPQPjTA4PMLCpvoTjh8cHqGuZvQ9UHbFVBzu3d03RE0Uk+oLmuqprw2O9Q/TXF9LIjGSitfoHRxmcDixsKmOmgiODQzRVF9L55F+AlgyvwEIuo4NcLh3kHOXzWMkJfoGRwigf3iExroamutr2Xu4j6P9Q6xqbaYh680+2v7DPYM01tfQXF/HwPAIA0MjzGuoZV72t3iwZ4DamqCpvpaUYGhkhKb6Wna8eIxVrc0MDI8wPJIYGk5Zm07UNzhC3+AwQyOJ2hpY2FRPz8AwR/qGWNnadPznlPf7KHVsYIjGutrstThs23+UdUvnsftgD2uXzCMleKbzKMsWNLKgsY6+wRH6h4bZ1dVDS2Mdh3oGuODMRaQEF64q9kYfz0xNHP0ccFVK6dezx78CXJpSet9Yx4yXOPLFl2aD1W3N/Nv7f3LcOqfzBZg0HaYa5wDt33qGP777yXI3TSqrqSaOuvsGee2Hvl7uZkllNZU4/8hdj/Ppf9t+OpolldWzf3z1uMM5fX2u2W7Fwibu/73Lxx2KPNnE0XT3H837y3xJ5ioiNgIbAdasGXuJ5of+8O3c/J3tXLy2jeGRxPDIj3oa5GVRI4p52JFUzL2O1+Fgov0T1RndN9nzlPaQKPZY+VGbU0rHv5ceQ8lxo/vGul7e8WP9MmKcOqPZ79Hy8cb2lh4fY6TGR+8tbzvvmsWfVd55xm7DWNcsPc3ofeRdv9TJ+xPF5TgBhrPf28n32lSfPwd9e3s77e3tAHR2dubWgWJX71/7zIO89ZVnsHZJC41Z9rzYU6WoJvJ7ppQazeqPxlvez3g8pfd8ctyN/jxTTv2xnByTeW1JY/ytnlyn9PdSE/GS3+fJ91F67ZPr5P0tjdYfryfT2D3lxjb6yVFNSU+kxIk/15N/RpHVP1Wn2sMq79c33r001+f/Q5psnANsfMu5x4dH9A8NA7DvcD/dfYOsXdJCTQRPvnCEw70DvGrFQlYsbOLZF4/SdWyQH+46xCvOmM/g8Ah7DvVyxsImFjXXs6q1mc6j/Rw42s9Zi1vY191H17FBntzbzX983ZkcONbPma3N7Orq5VDPAAua6ujuG+IVZ8xnybwGHt51iNaWBpbOb+DFowPs7+7jNasXcax/iLqaGhY01bH7YC+vXL6AZzqP0lBXw5N7uxkeSVx27hKeeuEIl569hJ1dPbQ01LKouZ7B7BOrrmPF+7g/m9Q5pURDbQ0XrlrED3cf4nWrW1nYVE/v4DAvHu1naCTROzDM6rZmhkcSew/3cbh3kNVtzaxY1ETvwDDHBoY41j/E689qY1vnURrramisq+Vo/yALmurZ1dXD8oVN9A8N89QLRzln2Tzqa4OaCJbMa+T5Q73sPdx7fF6n0aE3vYPDvHrlQp7c280rVyzg4LEBWpsbqKmBh3YeYnVbM0MjiYGhEXoGhrl4bRsDQyMc7Cl+Otg3OMy6JfPoHyrOpdLdO8hZi1vYc6iXmoCegWEWNtXT2lLPgWMDrFzUxP7uftYtnQcUV9rr7h2kZ2CI9csXHH/e3br/KMMjiVWtzbTNq2f7i8dYu7j4qVzPwDArFjXR2lJfXDY5Fdu3r7ufloZaEtA7MMyZrU0823mMJfMbWNBUHMp1qGeAoZHip6aHeweBRGtLA0vm5X9qdypxvrCpnh0feye7uoptPG/FAvYc6qWuNujuHaKxroZdXT38Uza3FMBDOw/SOzjMioXN7Ovu41tbO3nTuUvZd6SPhU31zGus5YHtB7Of/xBntjazeF4D//rEPt68fhmP7znM4nkN9AwM0zdY/HT4zNZmagJWLGriaP8Q+7r72N/dz6XnLGbHgR4aamt4pvMob1jTxtIFjTTW1tB5tJ+dB3pobqhlYVMdq9taaGqo5R++s51fvnQt3X2DXHr2Ev7pB7tZ3dZM/9AIi+c10NbSwNb9R1k8r56dXT28cLifBU11LGiq48kXjnCkb5DrLj6LBLQ01B6fC2zPoV6e3neErmMDrFsyj4GhEfYc7mN4ZITLzin+fa1fvoCBoRGe6zrGDT+2hod3HWLJ/AZam4vD6n64+xApcXyY3vzGOvYc6qUhG462clEztTXwmlWtvHCGWToDAAAgAElEQVS4j0O9A+zq6mX5wkaO9A1xzrJ57OrqpetYsZfFkvmNXHDmQp7Ye4T9R/q45OzFxeN6BhkYGuHsZfP4P9/Yxn96wypWtRb/Vl88OsDQyAj7uvvoGRhmX3cfV5y/guaGWn646xArW5tZ3dZMd+8gLQ11LGyuY9v+o6xdPI+dXT1s23+UFYsaGUmwdnELQyOJweERWlsaaKqv4VPf3s51F69mflMdD+44yGXnLOZI3xCtLfU8tPMQKxY1sbCpeM43r1/GA9u7GMnmNCrOEdfAK5cv4P6nOqmvq+GCMxdOKc7/8KfP57JzlvDtrZ3c+t3nxhxul9eTd1FzPf/hFUv5yiN7j5e99y3n8EJ3H1t2H2b7i8d44zlLuGhNK6vbmvnb+5/hlWcs4HDvIE/s7eb8Mxdy0VmtNDfUsePFY4ykRHN9Lfc93cl/ufxc/u83t/HGc5fyxN5uegeGOdgzwLnL5nP20nmct2IBdz+yl7aWBg71DtB1dIDf+qn1nNXWQkNdDd98aj/7uvtZtqCRnQd6aJtXzz8/tIezFrfwB1e/ms3PHeT2jl381tvWs63zKF/Zspd93X20ttTz4tEBLjl7MQ/vPMSaJS3U19awuq2Zh3cd4g1rWqmvreGuLcV7Hp3H7d1vWgcUFxX4+uP7+OOfvZD33/EIC5rqONI3xLyGWkYS/PJla/j7bxcTdRveuJYvbt5N/9AIC5vrqasJ9h/5UQ+hMxc1sWJREz/YeYjfvfI89hzqZWdXD4d7B9my+zBwYo+v0iGZr1m1iIvXtvGZf9/B/MY6jp40pHv0/yTAa1cv4tnOY6xY1MR5KxbQ2d3PAzu6jtf95xvfdPw11t9/+1m++8wBzlk2jwd3HKSloZZXnDGfLbsPs/6M+WzdfzQ3zs5eOo/tLxZ7KrW11LNmcQs/3H2Y16xaxCPPH6ahtoaFzfUMjYzQ1tJwvO6ov/mlN+S+NppsnO/42Dt5/xe38IWOXcfLLlm3mDNbm2hprOO85QtYt7T4P7bzSD/dfUOsXNjEjgPHSAnWLmkpGS0T9A8N88LhPs5a3HK8p0xQ7Cnzg50HuWTd4glHcLykLDtH6TGj2ye8Eyp58zeS0vH5m46/vk/QdWyAI32DrFjUTF1NUFMDA0MjdPcNsbCpjoa6mqw3VPF16vBI4oXDfRzsGaQm4Nxl8+kbKva+WTq/8fh7l4igu3eQo/3F/10jI4mGuhqGRoqvFRrraogIegeGGRga4bE9h3nrecuoramhJoo9nnoHiq8tegeGis+3w4n5TXWsW9LCkb4htr94jAvOXMjR/iGWLWgkCHoHh+nuHczmjiw+5+3v7mdgeITlC5sYHkk01dcwPAJPvdDN2UvnMzQywkhKHO4ZZG32+qTU0HDisT2HaW2uZ8Wipuw9CTy97wivWrGA2pwX8Hm/zwNHB2isLz4/k+Dxvd2sWzKP2hqO90R6YHsXr13denx6hZ5sNctVbc1879kurr3oTHYc6OGSdYvLNn/drB6qJs0VE31CLc0FxrmqgXGuamCcqxoY56oGk+1xlN8V4vR5EFgfEWdHRANwPXDnNLdBkiRJkiRJkzCtQ9VSSkMR8T7gHqAWuDml9Nh0tkGSJEmSJEmTM+1rZKaU7gbunu7rSpIkSZIk6dRM91A1SZIkSZIkzRImjiRJkiRJkpTLxJEkSZIkSZJyRUqp0m0Y19KlS9O6desq3QzptNqxYwfGueY641zVwDhXNTDOVQ2Mc1WDzZs3p5TShB2KJpwcOyLOAm4FVgAjQHtK6a8jYjHwBWAdsAP4+ZTSwYgI4K+Bq4Ee4FdTSj/IzrUB+O/Zqf8opXTLRNdft24dHR0dE1WTZrVCoWCca84zzlUNjHNVA+Nc1cA4VzWIiB9Mpt5khqoNAb+TUno1cBlwY0ScD3wAuDeltB64N3sM8A5gffa1Efhk1qDFwE3ApcAlwE0R0TbpO5IkSZIkSdK0mjBxlFLaO9pjKKV0BHgCWAVcA4z2GLoFuDbbvga4NRV9D2iNiJXAlcCmlFJXSukgsAm4qqx3I0mSJEmSpLI5pcmxI2Id8Hrg+8DylNJeKCaXgDOyaquAXSWH7c7KxiqXJEmSJEnSDDThHEejImI+cAfw2yml7uJURvlVc8rSOOV519pIcZgba9asmWwTpVmlvb2d9vZ2ADo7OyvcGun0MM5VDYxzVQPjXNXAOJfyTWpVtYioB+4C7kkp/WVW9hRweUppbzYU7b6U0nkR8XfZ9m2l9Ua/UkrvzcpPqDeWQqGQnJRMc52T76kaGOeqBsa5qoFxrmpgnKsaRMTmlFJhonoTDlXLVkn7NPDEaNIocyewIdveAHy5pPxdUXQZcDgbynYPcEVEtGWTYl+RlUmSJEmaA/78nif58L88XulmSJLKaDJD1d4E/ArwSEQ8nJX9PvAx4PaIeA+wE7gu23c3cDWwDegB3g2QUuqKiI8AD2b1PpxS6irLXUiSJEmquC27D3O0f6jSzZAkldGEiaOU0r+RPz8RwNty6ifgxjHOdTNw86k0UJIkSZIkSZVxSquqSZIkSZIkqXqYOJIkSZIkSVIuE0eSJEmSJEnKZeJIkiRJkiRJuUwcSZIkSSqblCrdAklSOZk4kiRJklQWEWMtxixJmq1MHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknKZOJIkSZJUNqnSDZAkldWEiaOIuDki9kfEoyVlX4iIh7OvHRHxcFa+LiJ6S/b9bckxF0fEIxGxLSI+Ea7VKUmSJM0pvsCXpLmnbhJ1PgP8H+DW0YKU0i+MbkfEXwCHS+o/k1K6KOc8nwQ2At8D7gauAr566k2WJEmSJEnSdJiwx1FK6VtAV96+rNfQzwO3jXeOiFgJLEwpfTellCgmoa499eZKkiRJkiRpukx1jqM3A/tSSltLys6OiIci4v6IeHNWtgrYXVJnd1YmSZIkSZKkGWoyQ9XGcwMn9jbaC6xJKR2IiIuBf46IC8gf7jzmvHkRsZHisDbWrFkzxSZKM1N7ezvt7e0AdHZ2Vrg10ulhnKsaGOeqBsa5qoFxLuWL4sixCSpFrAPuSildWFJWBzwPXJxS2j3GcfcB/y2r982U0quy8huAy1NK753o2oVCIXV0dEzYRmk2KxQKGOea64xzVQPjXNVgvDjfcPMDHOod5Ms3vmmaWyWVl8/nqgYRsTmlVJio3lSGqv0U8GRp0igilkVEbbZ9DrAeeDaltBc4EhGXZfMivQv48hSuLUmSJEmSpNNswsRRRNwGfBc4LyJ2R8R7sl3X89JJsd8CbImIHwJfBH4jpTQ6sfZvAp8CtgHP4IpqkiRJ0twziRENkqTZY8I5jlJKN4xR/qs5ZXcAd4xRvwO4MG+fJEmSpNkv8mY2lSTNalNdVU2SJEmSJElzlIkjSZIkSZIk5TJxJEmSJEmSpFwmjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSVDap0g2QJJWViSNJkiRJZRGVboAkqexMHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknJNmDiKiJsjYn9EPFpS9qGIeD4iHs6+ri7Z98GI2BYRT0XElSXlV2Vl2yLiA+W/FUmSJEmVllKlWyBJKqfJ9Dj6DHBVTvnHU0oXZV93A0TE+cD1wAXZMX8TEbURUQv8X+AdwPnADVldSZIkSXNERFS6CZKkMqubqEJK6VsRsW6S57sG+HxKqR/YHhHbgEuyfdtSSs8CRMTns7qPn3KLJUmSJEmSNC2mMsfR+yJiSzaUrS0rWwXsKqmzOysbq1ySJEmSJEkz1MtNHH0SOBe4CNgL/EVWntc3NY1TnisiNkZER0R0dHZ2vswmSjNbe3s7hUKBQqGAca65yjhXNTDOVQ2Mc1UD41zKF2kSs9dlQ9XuSildON6+iPggQErpT7J99wAfyqp+KKV0ZVZ+Qr3xFAqF1NHRMfGdSLNYoVDAONdcZ5yrGhjnqgbjxfmvfeZBOo/08y//9T9Mc6uk8vL5XNUgIjanlAoT1XtZPY4iYmXJw58FRldcuxO4PiIaI+JsYD3wAPAgsD4izo6IBooTaN/5cq4tSZIkSZKk6THh5NgRcRtwObA0InYDNwGXR8RFFIeb7QDeC5BSeiwibqc46fUQcGNKaTg7z/uAe4Ba4OaU0mNlvxtJkiRJkiSVzWRWVbshp/jT49T/KPDRnPK7gbtPqXWSJEmSZpU09lSmkqRZaCqrqkmSJEnScXkr4kiSZjcTR5IkSZIkScpl4kiSJEmSJEm5TBxJkiRJkiQpl4kjSZIkSZIk5TJxJEmSJEmSpFwmjiRJkiSVTUqVboEkqZxMHEmSJEkqi4hKt0CSVG4mjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSJElSLhNHkiRJkiRJyjVh4igibo6I/RHxaEnZn0fEkxGxJSK+FBGtWfm6iOiNiIezr78tOebiiHgkIrZFxCcinDpPkiRJkiRpJptMj6PPAFedVLYJuDCl9FrgaeCDJfueSSldlH39Rkn5J4GNwPrs6+RzSpIkSZrlUqp0CyRJ5TRh4iil9C2g66Syr6eUhrKH3wNWj3eOiFgJLEwpfTellIBbgWtfXpMlSZIkzUwOKpCkuaYccxz9GvDVksdnR8RDEXF/RLw5K1sF7C6pszsrkyRJkiRJ0gxVN5WDI+IPgCHgs1nRXmBNSulARFwM/HNEXED+Rw9jdmKNiI0Uh7WxZs2aqTRRmrHa29tpb28HoLOzs8KtkU4P41zVwDhXNTDOVQ2McylfpEkMQo6IdcBdKaULS8o2AL8BvC2l1DPGcfcB/w14HvhmSulVWfkNwOUppfdOdO1CoZA6OjombKM0mxUKBYxzzXXGuaqBca5qMF6c//otHew51Mvdv/Xm3P3SbOHzuapBRGxOKRUmqveyhqpFxFXA+4GfKU0aRcSyiKjNts+hOAn2symlvcCRiLgsW03tXcCXX861JUmSJEmSND0mHKoWEbcBlwNLI2I3cBPFVdQagU3FPBDfy1ZQewvw4YgYAoaB30gpjU6s/ZsUV2hrpjgnUum8SJIkSZIkSZphJkwcpZRuyCn+9Bh17wDuGGNfB3Bh3j5JkiRJkiTNPOVYVU2SJEmSgHFWwJEkzUomjiRJkiSVReStpSxJmtVMHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknKZOJIkSZJUNimlSjdBklRGJo4kSZIklUVUugGSpLIzcSRJkiRJkqRcJo4kSZIkSZKUy8SRJEmSJEmSck0qcRQRN0fE/oh4tKRscURsioit2fe2rDwi4hMRsS0itkTEG0qO2ZDV3xoRG8p/O5IkSZIkSSqXyfY4+gxw1UllHwDuTSmtB+7NHgO8A1iffW0EPgnFRBNwE3ApcAlw02iySZIkSZIkSTPPpBJHKaVvAV0nFV8D3JJt3wJcW1J+ayr6HtAaESuBK4FNKaWulNJBYBMvTUZJkiRJkiRphpjKHEfLU0p7AbLvZ2Tlq4BdJfV2Z2VjlUuSJEmaAyIq3QJJUrnVnYZz5v27SOOUv/QEERspDnNjzZo15WuZNIO0t7fT3t4OQGdnZ4VbI50exrmqgXGuamCcqxoY51K+SCk3d/PSihHrgLtSShdmj58CLk8p7c2Got2XUjovIv4u276ttN7oV0rpvVn5CfXGUigUUkdHx8u4NWn2KBQKGOea64xzVQPjXNVgvDh/7z928NyBHr7222+Z5lZJ5eXzuapBRGxOKRUmqjeVoWp3AqMro20AvlxS/q5sdbXLgMPZULZ7gCsioi2bFPuKrEySJEmSJEkz0KSGqkXEbRR7DC2NiN0UV0f7GHB7RLwH2Alcl1W/G7ga2Ab0AO8GSCl1RcRHgAezeh9OKZ084bYkSZIkSZJmiEkljlJKN4yx6205dRNw4xjnuRm4edKtkyRJkiRJUsVMZaiaJEmSJEmS5jATR5IkSZLKZpJr70iSZgkTR5IkSZLKIohKN0GSVGYmjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSJElSLhNHkiRJkiRJymXiSJIkSZIkSblMHEmSJEkqm0SqdBMkSWVk4kiSJElSWURUugWSpHIzcSRJkiRJkqRcJo4kSZIkSZKU62UnjiLivIh4uOSrOyJ+OyI+FBHPl5RfXXLMByNiW0Q8FRFXlucWJEmSJEmSdDrUvdwDU0pPARcBREQt8DzwJeDdwMdTSv+rtH5EnA9cD1wAnAn8a0S8MqU0/HLbIEmSJEmSpNOnXEPV3gY8k1J6bpw61wCfTyn1p5S2A9uAS8p0fUmSJEmSJJVZuRJH1wO3lTx+X0RsiYibI6ItK1sF7CqpszsrkyRJkjRHpFTpFkiSymnKiaOIaAB+Bvj/sqJPAudSHMa2F/iL0ao5h+f+W4mIjRHREREdnZ2dU22iNCO1t7dTKBQoFAoY55qrjHNVA+Nc1WCycR55r/ilWcLncylfpCl+JBAR1wA3ppSuyNm3DrgrpXRhRHwQIKX0J9m+e4APpZS+O975C4VC6ujomFIbpZmuUChgnGuuM85VDYxzVYPx4vy/fHYzW/cdZdP/+9ZpbpVUXj6fqxpExOaUUmGieuUYqnYDJcPUImJlyb6fBR7Ntu8Ero+Ixog4G1gPPFCG60uSJEmSJOk0eNmrqgFERAvwduC9JcV/FhEXURyGtmN0X0rpsYi4HXgcGKLYS8kV1SRJkiRJkmaoKSWOUko9wJKTyn5lnPofBT46lWtKkiRJkiRpepRrVTVJkiRJkiTNMSaOJEmSJEmSlMvEkSRJkqSymdqazZKkmcbEkSRJkqSyCKLSTZAklZmJI0mSJEmSJOUycSRJkiRJkqRcJo4kSZIkSZKUy8SRJEmSJEmScpk4kiRJkiRJUi4TR5IkSZLKJqVU6SZIksrIxJEkSZKk8ohKN0CSVG4mjiRJkiRJkpRryomjiNgREY9ExMMR0ZGVLY6ITRGxNfvelpVHRHwiIrZFxJaIeMNUry9JkiRJkqTTo1w9jn4ipXRRSqmQPf4AcG9KaT1wb/YY4B3A+uxrI/DJMl1fkiRJkiRJZXa6hqpdA9ySbd8CXFtSfmsq+h7QGhErT1MbJEmSJEmSNAXlSBwl4OsRsTkiNmZly1NKewGy72dk5auAXSXH7s7KJEmSJEmSNMPUleEcb0op7YmIM4BNEfHkOHXz1ll4yXqdWQJqI8CaNWvK0ERp5mlvb6e9vR2Azs7OCrdGOj2Mc1UD41zV4FTi/CUv7qVZwudzKV+kVL6n9oj4EHAU+M/A5SmlvdlQtPtSSudFxN9l27dl9Z8arTfWOQuFQuro6ChbG6WZqFAoYJxrrjPOVQ2Mc1WD8eL8fZ/7AY/v7eYbv3P59DZKKjOfz1UNImJzyVzVY5rSULWImBcRC0a3gSuAR4E7gQ1ZtQ3Al7PtO4F3ZaurXQYcHi9pJEmSJEmSpMqZ6lC15cCXImL0XJ9LKX0tIh4Ebo+I9wA7geuy+ncDVwPbgB7g3VO8viRJkiRJkk6T/5+9O4+T46rvvf/99T49+6aRNFpGsiTvYJvGGLDB2BjbmMSQFyQmBEgwUbgPBAjZTAKYPECA++SGGy5LmGBfTEIMhLA42ECMF2yDt5EX2bJla5dG6+z79Fbn+aNr2pLcksaaHvV09+f9eumlrqpTVadbR1WnfnWWOQWOnHPbJb28wPoBSZcXWO8kfXAu5wQAAAAAAMCpUYxZ1QAAAAAAAFCBCBwBAAAAAACgIAJHAAAAAIrCc06pjFfqbAAAimiug2MDAAAAgCTpjqcOlDoLAIAio8URAAAAAAAACiJwBAAAAAAAgIIIHAEAAAAAAKAgAkcAAAAAAAAoiMARAKDk/p/vbNB7bn6k1NkAAAAAcBRmVQMAlNzYdEZj05lSZwMAAADAUWhxBCxw9z3fp7s3Hyx1NoB5ZWZypc4EAAAAgBehxRGwwP3L/ds1Np3RZWd0lDorwLyxUmcAAAAAQEEn3eLIzJab2T1m9qyZbTKzj/jrP21me83sCf/Pmw/b5+NmttXMnjOzK4vxBYBKR0sMVA1HSQcAAAAWmrm0OMpI+nPn3GNmVi9pg5nd6W/7knPuHw5PbGZnSbpO0tmSlkr6pZmtc85l55AHoOIFTDxQo+KZiQApAAAAsACddIsj59x+59xj/ucxSc9K6jzOLtdK+q5zLumc2yFpq6QLT/b8QLUwSR5P1KhwxEcBAACAhakog2ObWZek8yU97K/6kJltNLObzazZX9cpac9hu/Xq+IEmAJrpqsYTNSqbGaMcAQAAAAvRnANHZlYn6T8lfdQ5Nyrp65JOk3SepP2S/tdM0gK7F3waNrP1ZtZjZj19fX1zzSKwIHV3dyuRSCiRSOh45ZyWGChnsy3nkgiQomy9lHIOlCvKOaoB5RwobE6BIzMLKxc0+o5z7oeS5Jw76JzLOuc8Sf+iF7qj9UpaftjuyyTtK3Rc51y3cy7hnEu0t7fPJYvAgrV+/Xr19PSop6dHxyvnZkbgCGVr1uVcBEhRvmZbzoFyRjlHNaCcA4XNZVY1k3STpGedc/942PolhyV7m6Sn/c+3SbrOzKJmtkrSWkmPnOz5gWphJnk8UaPC0VMNACpLOuuVOgsAgCKZy6xqr5X0bklPmdkT/rq/kfROMztPuW5oOyX9iSQ55zaZ2fclPaPcjGwfZEY14MR4nka1ID4KAJWDazoAVI6TDhw55x5Q4WfaO46zz+ckfe5kzwlUIzMqX6gGxghHqHifv+NZTaQy+uxbzy11VoB5R0tSAKgcc2lxBOAUCDCrGqpALkBKOUdle/bAmEam0qXOBgAAwEsy51nVAMyv3BhHpc4FML94MY1qYBJNSFE1KOoAUDkIHAELnMloiQEAFSBgov0oAAAoOwSOgIWOBw1UAcbyQjUwM2bJRNV4eMdAqbMAACgSAkfAAhcwIkeofCbG8kLlMxEgRfU4MDJd6iwAAIqkrANH92w+pF9sOlDqbADzyiTeUKPiMfsOqoGZEThC1fjhY3tLnQUAQJGU9axqN/96h8amM7ry7MWlzgowb2hwhGrBAzUqXW6yAwo6qsOO/olSZwGYN+PJjLKeU2NNuNRZAU6Jsg4chQKMFYDKR9cGVAMCpKgGNKxDNTkwSlc1VK4LPnOnUhlPO79wTamzApwSZd1VLRgwZbI8aqCyBYyxX1D5jEdqVIEAXdUAoCKkMl6pswCcUmUdOAowOwmqgUke9yZUAcf1HBWOrmqoNslMttRZAAAUQVkHjkJBU9ajAobK9sPH9mrv8FSpswHMq2TG0+BEqtTZAOYVXTJRbU7/xM9LnQUAQBGUdeAoYASOAKAS/PLZgxqaTJc6G8C8ys2qRr0F1WVb37gOjU7rtif3lTorAICTdMoHxzazqyT9k6SgpG86575wsscKBUxZKmAAUDG6brhdT974JjXWhOWck3NSIMD4R6gMTHaAarC8pUZ7Bl9oKX35//pV/vOZi+s1MpVW/3hKV57dIbMXru93PXtQrXVRnbe86ZTmF9VhaCKl6UxWSxprSp0VoCyd0sCRmQUlfVXSFZJ6JT1qZrc55545meM9tH2QGRtQNfrGkmqvj5Y6G8C8e/nf/fcRy99bf5HOWNygmkhQw5MptdVFNTqd1rP7x1QbDerspY0KmDSZyiqd9VQTCep7j+5RPBLS21+xTFJu/KSs5xQKzr6h7UzLkMMfbI4l6zllPE/RUPC4x5vNseZb1nNy7sjfYv/IlCaSWS1vqdGjO4a0rqNOixpi+e2zyXsq4ykUsBcF+rKeU8Bm9zvOuO/5Pr1qdUv+9/Q8p6xzCh/j328qlVVN5Ni//Wz0Dk0q6zmtbK2VJI1MptUYL+40yz/duL+oxwMWoj++ZLU+9ZNNBbdd8aX7XrSuPhrSWDKTX375ska988IVemLPsN7ysqX67O3P6MJVLfrdxHKd09mYTzedzmrf8JRWt9cdcbyjr1e9Q5OKhYPKek6L6qOaTnuKhQMyMyUzWU2lsmqKRyTlrlfBAi8r9gxOqqMhpkgooF0DE9pycFyXnt6uUDCgvcNT6h2clCRduKpFU+ms4pFQPh9j02nVRkLKeE4/3bhPF61u1ZZD43pm36gSXc06Z2mjptJZjUylVRsNqqkmooBJE6msvnL3Fo0nM7rirA6ZTP/9zAG961Ur9c7uh3TJujYtb47r2vM61RgP698e2qUPvP407eyf0L/cv10NNWG9fFmj9o9Ma0VLXJ/88dOaSGX1h6/pUsBMK1pq9On/ekb3/sWl+u6je1QXDcrM9N1Hd2tFS1znLW9S1pP++VfbJEmXrG3Tx65Yp7d97TdH/DaXn7FId20+pPpYSC21Ee0ayP0W//wHr9DK1riu/qf7j0h/8Zo2XXFWh268bZMioYBSGU+vWNmsC1Y06VWrWrX5wKjMTP9877YjysWMxMpm9ewayi+/7fxO/ejxvS9KdyK/vuEydTbNPYg0OJFSS21kzscBFjo7lU2mzezVkj7tnLvSX/64JDnnPn+sfRKJhOvp6Sm4reuG2/Of//X6C9XREFNNOKiM59RUE1bGc0pnPTX4b67DwYCSfsXWc0414WC+xZLJFCgw9oBzuZZN0gsDWnpOClhuVresX5k9PG3Gc/l9nL+fyXS8OrPlfg95/ht2zzmZ5brj2VHHcXL5N5Zmyh/bOeW3Hb5+Ju+el1s/k++ZFlsmy/8mGc/lj+t5UiCQG3skclRlfeZ8E8mMYuGgIqHcbxsNBZT1nFJZz589xikeCWl0Kq2aSFChgCmZ8XLfU1L8qH+Dmd84GDCNTWcUNFPa8xSPBGUyZTxPnpMiwYDMlD9nOuspFMitS2c9RUKBI97qHv7bOOUqFamMp3gklP9NMlmntOfJlPvukVBA4ZfJo7MAACAASURBVKAdUfnIeJ7S2dxv55w0kcrlUZLCoUDuN8zmjl0TDioYzP22sfDxH2gSiYRmU86PVhMOakljTAMTKV24qkXprKc17XV6YGu/2uuj6mqt1ZvO7tDAeErRUEAZzylgpubasDoaYoqGAtozOKXaaFCL6mPaNzKlRfVRJTOeDoxMa0ljTFkvVxb3DE2ps6lGqYynTftG1BSPqKMhpsUNMe0dnpJzTrFwUMmMp6znFI8EtWHXkBprwgoGTA01YS1rzu3fWhtRxnManEgpEgrky+TugUktboxpIpnRwbGknHNa1lyj0emMkumsFjXENJXKykyaSGYVjwTVWhfRcwfGtKN/Qq9Y2aznDoypsSasZc1xHRqb1tBESm31US1vjuu+5/u0tqNOE8msljTGNDKV1uBkSl+7Z5suXNWiUMB0wcpmRUMBjU6ltaI1roHxlH7+9AFdsrZNixpi6htLqrUuoid2DysUDKijIarJVFadTTX6zbZ+ndvZqCf2jOialy3WY7uGlfY8vXxZk36x6YDOWtKgb9y3XWcsrte5nY167Zo2Pbh9QI/sGNSZS+olSfWxsJ7eO6LXrW3XvpEppbOeOupjmkxnFQkG1D+elOectvVNaGQyreGplM5e2qilTTE5J7XWRhQMBBQwacfAhJY1x9Wzc1BdbbXacnBM0VBQnnM6MDKtsWRGZy9t0NBESk/2jqirNa6JVFZnLK7XRDKj09rrVBcLad/wlJrjEfWNJfXIjkGd09moB7cPSJJe2dWstR31Wn/JanW11c5LOQcWkhNNvTzbcn56R73ekVimvcNTWtVWq6WNNYpHglrZVqtIMKDxZEYdDVE5Jw1PpTU+nVFrXUTOSfFIUKmMd8T9KxQ0hYOB/L398AfgoJkCZkplczMteM4pFMjdO51ydZrAYfe6mf1SGS//OWCWv78eXZ1xytVjMocNHxAKmCaSuYGRo+Hcdd653IN5xL9vH36+oclUrj4RzN2/sy53r01lZ+7zLn+/DQTkf39T0K87zeRz5h4UDQUUClq+TpXxnNIZT7XRUP53mPndMlknzzmFAwFNpbOKhAKKhQMKmCmZ9pTMZBWLBOWclExnVRcLKZ3J7ZP2cnWkmUBsNus0kcrITGqORzSVyspzuXtvIGCKhl6oTxWqgh89i+rRaQrV2lMZT2m/3iXlAirhYEBN8bCmUrl/g3y9TE510ZAmU1kNTqQUjwQVDQU1kcwo43lKZjytaqtVPHL8d8vHK+c/erxXf/a9J4+7P7AQFOt6LkkrW+MKBUy/9fKlao5HdE5noxpiueeMvrGUggHT03tH9MYzOxSLBOR5uf/vwYAplfHUFI9oIplRTSSYv9ZlXe66FQxYPtDpXO56WxsNKeucxqYzmkpl1VIb0f6RKUVDAU0kszptUZ1M0sHRadXFQjo0msw/OzXWhOV5TtFQUGnPU1ttVM8dHNOi+qjqY7kg5/6RadVGgkplPR0cnVY8ElJTPKy+saS2HRpXR0NM05ms1rTXa2gypeGptKKhgKKhgKZSuWtmKpO7Z/SPJdU3nlQsFFRDTViTqYxaaiMyme569qBOX1yv+lhYz+wf0fLmuCKh3PXrV8/1qX88pda6iFpro9pyaEwTyYze8rKl2rB7SMuaavTQjkG11kZ04aoWPbF7OP+strylRv1jKa3tqNPeoSkFAqYJPxB573N9ynie/uCilXpiz7BWtMQ1lcrqlV0t+vdHdqu9PqpYOKix6bRMUnt9VL/YdDAf6JSkVW21qo0G1V4XVUdDTN99dE++LCxuiOnA6LTqoqF8UHF0Oq3ho4ZkiIYCeut5ndo3MqXR6Yye3DP8QvlqjWsyldWhsaQk6Y9e26VfPd+n7X0TaquLqq0uos0HxhQw6eyljWqKh3X/ln611UXVP57bp7OpRv/+x6/KvxA7FjPb4JxLHDeRTn3g6O2SrnLOvd9ffrekVznnPnSsfWYbOCp3AcsFpE7WTCAHC8+y5ho98NeXHTfN8W5M77/lUf3y2UPzkTWgqHZ8/s3HbVVC4AiVoJgPGsBCFAkG9Nxnrzrp6/mO/gm94R/unafcAcUzl+v59r5xXXZYN0xgodr6uauP2+J+toGjUz3GUaE70IvCHWa2XtJ6SVqxYsUxD7bj82/Wqo/fMasTR0IB/fElq/TVe7bpjMX1ao5H9OrTWnOtjFzuTczAREpL/Kb5AxMp7RueUtZzOqezMd/6R/Kb4/tvsQJmmvl3yHhO49MZTaSyqgkHFY8EFfCjyAHLvSE8ll0DkxqeSqupJqyVrfH8zXpoIqVkxlNzbUThQK4F0eE38oAffZ4JOpmk4cmUPCd1NEQ1Op1R/3hSq9tq9fCOQaUynnYOTOi1p7Vp1+Ck+seTOm95k1pqI4qFg/I8l49o7xmcUmdTTA01YcXCQQXMtGHXoJ7dP6ZEV7MWN8b0jV9t19tfsUwdDVEdGElqa9+4ntwzrGgooPNXNGl0KqNQ0LSxd0SLG2K66pzF2tE/oaznNJ3O6nXr2vX47iElulq0a2BC3+/pVTwS1FQ6e0Qg7IIVTXpsdy4Ke/bSBmU9p/7xpKbTuah5/3hS775opRprwvr1tn5dvKZNZpZveZUrVzMtu3Lr7tp8UKFAQPWxkM5c0qBQ0LT14Lh++PjefMuTi9e0HfHvNJXO6tn9o9rZP6GlTTX6zbYBtdZGNDCR0ukd9Vq3uF7/5Q/++Mm3nCXPc4pHC/+7d3d3q7u7W5LU19d3zLLxL+9J6F3ffFi/2TZwzDTlaFF9NB9FR/m7eE1bvrXj4WZbzrf//Zv1tz9+Sjv7J/Xg9gGFg6Z7/uJS3ftcn7b1jWtxQ0yf/9lm/c75nXrdunZ99HtP6JqXLdHFa9r08R8+JUm67IxFuuHqM/Spnzydbw33jfu26/Xr2vWr53PnjkeC+v0LV+ibD+zQxWv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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotperiodograph(tpf);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Generalized way to convert each pixel into a light curve.\n", + "2. apply tpf mask to that (bool in def : true,false) to get desired pixels to graph\n", + "3. Make a periodogram for each relevant pixel\n", + "4. Graph that nicely." + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Research.ipynb b/Research.ipynb new file mode 100644 index 0000000..2f4ba9d --- /dev/null +++ b/Research.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import lightkurve as lk" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#just setting up sample data\n", + "search_result = lk.search_targetpixelfile('Pi Mensae', mission='TESS', sector=1)\n", + "tpf = search_result.download(quality_bitmask='default')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "def plotperiodograph(targetpixelfile):\n", + " #This is the mask from lightkurve for the aperture\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #This is a postage stamp x and y values for pixels that are part of the aperture\n", + " postagestamp = np.where(aperture == True)\n", + " \n", + " #Setting up the pixel subplots so They create a rectangle of pixels based off the min and max of the row and column values from the aperture mask.\n", + " #Could make ploting functions optional for the user and set this to default\n", + " fig,ax = plt.subplots(len(np.arange(postagestamp[0].min(),postagestamp[0].max()+1)),\n", + " len(np.arange(postagestamp[1].min(),postagestamp[1].max()+1)),\n", + " figsize=(20,20),sharex='col', sharey='row')\n", + "\n", + " #Just making the subplot spacings 0 pixel width and height separation\n", + " fig.subplots_adjust(wspace=0,hspace=0)\n", + "\n", + " #iterating through the columns of the postage stamp pixels\n", + " for i in np.arange(postagestamp[0].min(),postagestamp[0].max()+1):\n", + " \n", + " #iterating through the rows of the postage stamp pixels\n", + " for j in np.arange(postagestamp[1].min(),postagestamp[1].max()+1):\n", + " \n", + " #Creating a false mask to alter each iteration\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", + " mask[i][j] = True\n", + " \n", + " #extracting the light curve for the target pixel\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " \n", + " #Grabbing periodogram frequency and power data and storing as a tuple\n", + " periodogram = np.asarray([lightcurve.to_periodogram(oversample_factor=1).frequency,\n", + " lightcurve.to_periodogram(oversample_factor=1).power])\n", + " \n", + " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", + " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram[0],periodogram[1]);\n", + "\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", + "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", + " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" + ] + }, + { + "data": { + "image/png": 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j4kiSVHHhEB5JkiRpRjJxJEmSJEmSpFwmjiRJkqaJAzIlSdJsY+JIkiRpGjgiU5IkzUYmjiRJkiRJkpTLxJEkaUZwCI8kSZI085g4kiRVnEN4VA2O9A2x/cVjlW6GJEnSKamrdAMkSZKqwf1Pd1a6CZIkSafMHkeSJEmSJEnKZeJIkiRJkiRJuUwcSZJmhOTs2JIkSdKMY+JIklRxEU6PLUmSJM1EJo4kSZIkSZKUa1KJo4i4OSL2R8SjJWWLI2JTRGzNvrdl5RERn4iIbRGxJSLeUHLMhqz+1ojYUP7bkSRJkiRJUrlMtsfRZ4CrTir7AHBvSmk9cG/2GOAdwPrsayPwSSgmmoCbgEuBS4CbRpNNkiRJkiRJmnkmlThKKX0L6Dqp+Brglmz7FuDakvJbU9H3gNaIWAlcCWxKKXWllA4Cm3hpMkqSJEmSJEkzxFTmOFqeUtoLkH0/IytfBewqqbc7KxurXJJU5fYc6mVnV0+lmyFJkiTpJHWn4Zx5S+OkccpfeoKIjRSHubFmzZrytUyaQdrb22lvbwegs7Ozwq2RTo/Jxvn3t5/cqVWaPXw+VzUwzlUNjHMp31R6HO3LhqCRfd+fle8GziqptxrYM075S6SU2lNKhZRSYdmyZVNoojRzbdy4kY6ODjo6OjDONVcZ56oGxrmqgXGuamCcS/mmkji6ExhdGW0D8OWS8ndlq6tdBhzOhrLdA1wREW3ZpNhXZGWSJEmSJEmagSY1VC0ibgMuB5ZGxG6Kq6N9DLg9It4D7ASuy6rfDVwNbAN6gHcDpJS6IuIjwINZvQ+nlBybIEmSJEmSNENNKnGUUrphjF1vy6mbgBvHOM/NwM2Tbp0kSZIkSZIqZipD1SRJkiRJkjSHmTiSJEmSJElSLhNHkiRJkiRJymXiSJIkaRoVp4OUJEmaHUwcSZIkTaP+oZFKN0E6bR7bc5gtuw9VuhmSpDIycSRJkiSpLD721Se56c7HKt0MSVIZmTiSJEmaRo5U01wWEYwY45I0p5g4kiRJmkYJ31Vr7qoJzI5K0hxj4kiSJElSWQTY40iS5hgTR5IkSdPIzhiay2oi7FUnSXOMiSNJkqRpNGLmSHNYRDDiwoGSNKeYOJIkSZpGB48NVroJ0mkTYXJUkuYaE0eSpBnjxaP9lW6CdNq9eMw419xVE5VugSSp3EwcSZJmjEM99sTQ3PfcgWOVboJ02gRhjyNJmmNMHEmSZgw/qVY1+MKDuyrdBOm0qalxAnhJmmtMHEmSZoyaMHOkue+Fw32VboJ02kTY40iS5hoTR5KkivuDq18NFCdVlea6HQd6Kt0E6bQJ7HGkue/zD+zkM9/ZXulmSNPGxJEkqeK2Z3O+fGvrixVuiSRpKmoiMG+kue7uR1/gSw/vqXQzpGlj4kiSVHHfe/YAAH/4z49WuCWSpKmIwKFqmvNqApJxripi4kiSVHE3/NiaSjdBmlaDwyOVboJ0WtREOFRNc15tBMMjBrqqh4kjSVLFLV/UdHz7+UO9FWyJdPq87ydecXx7/R98tYItkU6fY/1D7OxyHi/NbTU1gXkjVRMTR5Kkimuprz2+/aaPfYNdvumYkya7mtiznUcZmeQr8pQSN372B3x7a+cJ5Z1H+hkcHmFojJ49HTu62Pxc15jXGR5JZR+GcPl5y054bK8jzUVff3xfpZsgnXY1waT/T0lzQd10XzAirgL+GqgFPpVS+th0t0GaTT7zne30DY3wG289t9JNkU6b+roTP8d485998/j2b15+Llv3HeVfn9jHTf/xfJYvbKKwro0P3vEIf/jT57OgqY4l8xtPS7v2dfdRE8GyBVM7/55DvdTX1rzkPH2Dw3zt0Re49vWrco/bf6SPMxYUe2MdPDbA84d6uXDVIr76yF5evXIh65bO42j/ED/cdYgLzlxIa0sDB48NMJwSS+c38mznUQ71DtI3OMwv/v33Wbukhd0He7n/dy9ndVsLH/vqk/zaf1jHjhd7+IX27/JXv3ARj+/p5neuOI+Guhoeff4wX3roeX73yvP4m/ue4bKzF/PVR1+grja4/+lO/uFXf4y3/vl9ANTVBMsXNrF4XgOtLfV8e+uL/M7bX8kvXrqGB3d08alvb6fjuYO887Ur2fTYPhY01XHg2AC/9qazufk72/np167kFy9dw/vv2MKurh/1OvupVy/nX5/40RvRi85q5eFdhzhzUROvWrmQbzy5H4CvPLKXz/76pfzSp77/kp/jL166hs99f+eYv5/Xr2nloZ2HJvw9/vi5S/jcf75swnpjuXht2wmPS3sdPfw/3k5TfS3ferqTt5+/nChZYvD+pztZMq+BC1ctetnXVnU53DvIgsY6amomXqpyaHiE4ZRorKudsK6kotqacC4vVZWYzkm9IqIWeBp4O7AbeBC4IaX0+FjHFAqF1NHRkbtv460dHOkb4raNL/9FnDQTFAoFxorzd//DA3Qe7eeu//rmaW6VVF7jxfnBYwO8/iObTuv1l85vYFVrMz/cfZgNb1xLRPCZf98BwCuXz+c/vWE1f/n1p2msq+FI/9CUr/e2V53BN5/ab1f2OWbHx9457v7x4hxg3Qe+MqXrn7d8AVdesJznunpoaaijqb6GwtrFFNa1EQEHjw3S0lDL/U938suXrQXgge1dNNbV0NpSz/zGOtpaGqipCR7f000isXxhE0tLkq8pJZ7ad4TDPYO8fk0bg8MjHOodZMm8Brr7Blk6r1h3cGSE7t4hlswrnu/Fo/3s6+7jVSsW8tyBY9z4uYe4eG0rq1pbuK6wmkXN9ew/0s/Q8AjH+ofZsvsQbz1vWfG13AM7WdXazCuXL2D7i8foPNLP1a9ZyaGeAb7yyF5+/NylXHbOYo72D9HSUMf//sZWnnrhCId6BlnUUs+5y+axZvE8/vRrT7KgqY6+wWE+8I5Xs23/ERrranl8bzcPbO/iU+8q8PF/fZoVC5vYfuAY9TU1PLXvyAk/48a6GvqHRviz/+e1fOCftjCSoKGuhoGhH/UQq68NBodP/OM+uc543vvWc/i7+589oezitW38cNchhkqeNGprpn8eld+/+lVsfMv4H1aNF+ejMf74h6+kpWHaP6OWymYycT7R/wRppouIzSmlwoT1pjlx9EbgQymlK7PHHwRIKf3JWMeMlzh69R9+jd7BYe74zR+nriaO/3ONgCCya/6ofkqQSNTExJ++zBQpnXgPlT7P6LlGTfac411/dN/oefPqjf7ugiCNscjr6O/8JeVjnO/4dnbesdow0c9u9NOG0bgaSSeer7G+hlcuXzD2CRj/H9NP/K/72P7iMT74jldx8do26mqLPTOC4gvKlPJXLzm5zaNtmsw9naqXExNTPVfp76p0eyRN/Dd+qvd/coy+ZP84Cw+XxmVejI3GdmndsX5Po8eOd72x2n+qSp9H8/adrL62hvNWvPw4h6m/oZamQ6UTR9J0mEqcTzXGf+7i1Xxx8+7jj9+8findfUM8vucwIwkKa9t43VmtLGyq47YHdvG6sxbRXF/HHT/YzVteuYyfOG8ZB44OsLOrh5WLmmioq+F/f2Mb1128mi27D3P5ect4/lAvj+/t5rkDPaxd3MIbz13CqrZmbv3354iARc31nLdiAe98zUoa6mpICe557AXWLpnHo88f5vwzF7L5uYN848n9nLGgkb/4+dfxnW0H+Nv7n+G/v/PVPLC9i/uf7jz+/3dgeISFTXX0Dg5z7rL5PPnCERY0/iipdsbCRp7pPHbCz+GGS87iwR0HOWNBI//+zAH+x0+fz4fvepy2lnoO9gz+6Hexto2O5w4C8Ks/vu74ByITOX/lQgaGR6iNOCF52tpSz6GS85daOr+RF4/2T+r84/n0hsLx1xIf/coTL7n36fC3v3wxV124Ytw6pxrn5y6bx7ol8473CF6zuIXewWFSSiyZ18jA8DAP7jhIU10NrzurlYhgeGSE4ZHia6ujfUMsbC7GxWiv08GhEQ72DLJyUdPx17hjvYbNe91W+prxxPc/o2UnvkisiWAoez9d3BfUBPQPjTA4PMLCpvoTjh8cHqGuZvQ9UHbFVBzu3d03RE0Uk+oLmuqprw2O9Q/TXF9LIjGSitfoHRxmcDixsKmOmgiODQzRVF9L55F+AlgyvwEIuo4NcLh3kHOXzWMkJfoGRwigf3iExroamutr2Xu4j6P9Q6xqbaYh680+2v7DPYM01tfQXF/HwPAIA0MjzGuoZV72t3iwZ4DamqCpvpaUYGhkhKb6Wna8eIxVrc0MDI8wPJIYGk5Zm07UNzhC3+AwQyOJ2hpY2FRPz8AwR/qGWNnadPznlPf7KHVsYIjGutrstThs23+UdUvnsftgD2uXzCMleKbzKMsWNLKgsY6+wRH6h4bZ1dVDS2Mdh3oGuODMRaQEF64q9kYfz0xNHP0ccFVK6dezx78CXJpSet9Yx4yXOPLFl2aD1W3N/Nv7f3LcOqfzBZg0HaYa5wDt33qGP777yXI3TSqrqSaOuvsGee2Hvl7uZkllNZU4/8hdj/Ppf9t+OpolldWzf3z1uMM5fX2u2W7Fwibu/73Lxx2KPNnE0XT3H837y3xJ5ioiNgIbAdasGXuJ5of+8O3c/J3tXLy2jeGRxPDIj3oa5GVRI4p52JFUzL2O1+Fgov0T1RndN9nzlPaQKPZY+VGbU0rHv5ceQ8lxo/vGul7e8WP9MmKcOqPZ79Hy8cb2lh4fY6TGR+8tbzvvmsWfVd55xm7DWNcsPc3ofeRdv9TJ+xPF5TgBhrPf28n32lSfPwd9e3s77e3tAHR2dubWgWJX71/7zIO89ZVnsHZJC41Z9rzYU6WoJvJ7ppQazeqPxlvez3g8pfd8ctyN/jxTTv2xnByTeW1JY/ytnlyn9PdSE/GS3+fJ91F67ZPr5P0tjdYfryfT2D3lxjb6yVFNSU+kxIk/15N/RpHVP1Wn2sMq79c33r001+f/Q5psnANsfMu5x4dH9A8NA7DvcD/dfYOsXdJCTQRPvnCEw70DvGrFQlYsbOLZF4/SdWyQH+46xCvOmM/g8Ah7DvVyxsImFjXXs6q1mc6j/Rw42s9Zi1vY191H17FBntzbzX983ZkcONbPma3N7Orq5VDPAAua6ujuG+IVZ8xnybwGHt51iNaWBpbOb+DFowPs7+7jNasXcax/iLqaGhY01bH7YC+vXL6AZzqP0lBXw5N7uxkeSVx27hKeeuEIl569hJ1dPbQ01LKouZ7B7BOrrmPF+7g/m9Q5pURDbQ0XrlrED3cf4nWrW1nYVE/v4DAvHu1naCTROzDM6rZmhkcSew/3cbh3kNVtzaxY1ETvwDDHBoY41j/E689qY1vnURrramisq+Vo/yALmurZ1dXD8oVN9A8N89QLRzln2Tzqa4OaCJbMa+T5Q73sPdx7fF6n0aE3vYPDvHrlQp7c280rVyzg4LEBWpsbqKmBh3YeYnVbM0MjiYGhEXoGhrl4bRsDQyMc7Cl+Otg3OMy6JfPoHyrOpdLdO8hZi1vYc6iXmoCegWEWNtXT2lLPgWMDrFzUxP7uftYtnQcUV9rr7h2kZ2CI9csXHH/e3br/KMMjiVWtzbTNq2f7i8dYu7j4qVzPwDArFjXR2lJfXDY5Fdu3r7ufloZaEtA7MMyZrU0823mMJfMbWNBUHMp1qGeAoZHip6aHeweBRGtLA0vm5X9qdypxvrCpnh0feye7uoptPG/FAvYc6qWuNujuHaKxroZdXT38Uza3FMBDOw/SOzjMioXN7Ovu41tbO3nTuUvZd6SPhU31zGus5YHtB7Of/xBntjazeF4D//rEPt68fhmP7znM4nkN9AwM0zdY/HT4zNZmagJWLGriaP8Q+7r72N/dz6XnLGbHgR4aamt4pvMob1jTxtIFjTTW1tB5tJ+dB3pobqhlYVMdq9taaGqo5R++s51fvnQt3X2DXHr2Ev7pB7tZ3dZM/9AIi+c10NbSwNb9R1k8r56dXT28cLifBU11LGiq48kXjnCkb5DrLj6LBLQ01B6fC2zPoV6e3neErmMDrFsyj4GhEfYc7mN4ZITLzin+fa1fvoCBoRGe6zrGDT+2hod3HWLJ/AZam4vD6n64+xApcXyY3vzGOvYc6qUhG462clEztTXwmlWtvHCGWToDAAAgAElEQVS4j0O9A+zq6mX5wkaO9A1xzrJ57OrqpetYsZfFkvmNXHDmQp7Ye4T9R/q45OzFxeN6BhkYGuHsZfP4P9/Yxn96wypWtRb/Vl88OsDQyAj7uvvoGRhmX3cfV5y/guaGWn646xArW5tZ3dZMd+8gLQ11LGyuY9v+o6xdPI+dXT1s23+UFYsaGUmwdnELQyOJweERWlsaaKqv4VPf3s51F69mflMdD+44yGXnLOZI3xCtLfU8tPMQKxY1sbCpeM43r1/GA9u7GMnmNCrOEdfAK5cv4P6nOqmvq+GCMxdOKc7/8KfP57JzlvDtrZ3c+t3nxhxul9eTd1FzPf/hFUv5yiN7j5e99y3n8EJ3H1t2H2b7i8d44zlLuGhNK6vbmvnb+5/hlWcs4HDvIE/s7eb8Mxdy0VmtNDfUsePFY4ykRHN9Lfc93cl/ufxc/u83t/HGc5fyxN5uegeGOdgzwLnL5nP20nmct2IBdz+yl7aWBg71DtB1dIDf+qn1nNXWQkNdDd98aj/7uvtZtqCRnQd6aJtXzz8/tIezFrfwB1e/ms3PHeT2jl381tvWs63zKF/Zspd93X20ttTz4tEBLjl7MQ/vPMSaJS3U19awuq2Zh3cd4g1rWqmvreGuLcV7Hp3H7d1vWgcUFxX4+uP7+OOfvZD33/EIC5rqONI3xLyGWkYS/PJla/j7bxcTdRveuJYvbt5N/9AIC5vrqasJ9h/5UQ+hMxc1sWJREz/YeYjfvfI89hzqZWdXD4d7B9my+zBwYo+v0iGZr1m1iIvXtvGZf9/B/MY6jp40pHv0/yTAa1cv4tnOY6xY1MR5KxbQ2d3PAzu6jtf95xvfdPw11t9/+1m++8wBzlk2jwd3HKSloZZXnDGfLbsPs/6M+WzdfzQ3zs5eOo/tLxZ7KrW11LNmcQs/3H2Y16xaxCPPH6ahtoaFzfUMjYzQ1tJwvO6ov/mlN+S+NppsnO/42Dt5/xe38IWOXcfLLlm3mDNbm2hprOO85QtYt7T4P7bzSD/dfUOsXNjEjgPHSAnWLmkpGS0T9A8N88LhPs5a3HK8p0xQ7Cnzg50HuWTd4glHcLykLDtH6TGj2ye8Eyp58zeS0vH5m46/vk/QdWyAI32DrFjUTF1NUFMDA0MjdPcNsbCpjoa6mqw3VPF16vBI4oXDfRzsGaQm4Nxl8+kbKva+WTq/8fh7l4igu3eQo/3F/10jI4mGuhqGRoqvFRrraogIegeGGRga4bE9h3nrecuoramhJoo9nnoHiq8tegeGis+3w4n5TXWsW9LCkb4htr94jAvOXMjR/iGWLWgkCHoHh+nuHczmjiw+5+3v7mdgeITlC5sYHkk01dcwPAJPvdDN2UvnMzQywkhKHO4ZZG32+qTU0HDisT2HaW2uZ8Wipuw9CTy97wivWrGA2pwX8Hm/zwNHB2isLz4/k+Dxvd2sWzKP2hqO90R6YHsXr13denx6hZ5sNctVbc1879kurr3oTHYc6OGSdYvLNn/drB6qJs0VE31CLc0FxrmqgXGuamCcqxoY56oGk+1xlN8V4vR5EFgfEWdHRANwPXDnNLdBkiRJkiRJkzCtQ9VSSkMR8T7gHqAWuDml9Nh0tkGSJEmSJEmTM+1rZKaU7gbunu7rSpIkSZIk6dRM91A1SZIkSZIkzRImjiRJkiRJkpTLxJEkSZIkSZJyRUqp0m0Y19KlS9O6desq3QzptNqxYwfGueY641zVwDhXNTDOVQ2Mc1WDzZs3p5TShB2KJpwcOyLOAm4FVgAjQHtK6a8jYjHwBWAdsAP4+ZTSwYgI4K+Bq4Ee4FdTSj/IzrUB+O/Zqf8opXTLRNdft24dHR0dE1WTZrVCoWCca84zzlUNjHNVA+Nc1cA4VzWIiB9Mpt5khqoNAb+TUno1cBlwY0ScD3wAuDeltB64N3sM8A5gffa1Efhk1qDFwE3ApcAlwE0R0TbpO5IkSZIkSdK0mjBxlFLaO9pjKKV0BHgCWAVcA4z2GLoFuDbbvga4NRV9D2iNiJXAlcCmlFJXSukgsAm4qqx3I0mSJEmSpLI5pcmxI2Id8Hrg+8DylNJeKCaXgDOyaquAXSWH7c7KxiqXJEmSJEnSDDThHEejImI+cAfw2yml7uJURvlVc8rSOOV519pIcZgba9asmWwTpVmlvb2d9vZ2ADo7OyvcGun0MM5VDYxzVQPjXNXAOJfyTWpVtYioB+4C7kkp/WVW9hRweUppbzYU7b6U0nkR8XfZ9m2l9Ua/UkrvzcpPqDeWQqGQnJRMc52T76kaGOeqBsa5qoFxrmpgnKsaRMTmlFJhonoTDlXLVkn7NPDEaNIocyewIdveAHy5pPxdUXQZcDgbynYPcEVEtGWTYl+RlUmSJEmaA/78nif58L88XulmSJLKaDJD1d4E/ArwSEQ8nJX9PvAx4PaIeA+wE7gu23c3cDWwDegB3g2QUuqKiI8AD2b1PpxS6irLXUiSJEmquC27D3O0f6jSzZAkldGEiaOU0r+RPz8RwNty6ifgxjHOdTNw86k0UJIkSZIkSZVxSquqSZIkSZIkqXqYOJIkSZIkSVIuE0eSJEmSJEnKZeJIkiRJkiRJuUwcSZIkSSqblCrdAklSOZk4kiRJklQWEWMtxixJmq1MHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknKZOJIkSZJUNqnSDZAkldWEiaOIuDki9kfEoyVlX4iIh7OvHRHxcFa+LiJ6S/b9bckxF0fEIxGxLSI+Ea7VKUmSJM0pvsCXpLmnbhJ1PgP8H+DW0YKU0i+MbkfEXwCHS+o/k1K6KOc8nwQ2At8D7gauAr566k2WJEmSJEnSdJiwx1FK6VtAV96+rNfQzwO3jXeOiFgJLEwpfTellCgmoa499eZKkiRJkiRpukx1jqM3A/tSSltLys6OiIci4v6IeHNWtgrYXVJnd1YmSZIkSZKkGWoyQ9XGcwMn9jbaC6xJKR2IiIuBf46IC8gf7jzmvHkRsZHisDbWrFkzxSZKM1N7ezvt7e0AdHZ2Vrg10ulhnKsaGOeqBsa5qoFxLuWL4sixCSpFrAPuSildWFJWBzwPXJxS2j3GcfcB/y2r982U0quy8huAy1NK753o2oVCIXV0dEzYRmk2KxQKGOea64xzVQPjXNVgvDjfcPMDHOod5Ms3vmmaWyWVl8/nqgYRsTmlVJio3lSGqv0U8GRp0igilkVEbbZ9DrAeeDaltBc4EhGXZfMivQv48hSuLUmSJEmSpNNswsRRRNwGfBc4LyJ2R8R7sl3X89JJsd8CbImIHwJfBH4jpTQ6sfZvAp8CtgHP4IpqkiRJ0twziRENkqTZY8I5jlJKN4xR/qs5ZXcAd4xRvwO4MG+fJEmSpNkv8mY2lSTNalNdVU2SJEmSJElzlIkjSZIkSZIk5TJxJEmSJEmSpFwmjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSVDap0g2QJJWViSNJkiRJZRGVboAkqexMHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknJNmDiKiJsjYn9EPFpS9qGIeD4iHs6+ri7Z98GI2BYRT0XElSXlV2Vl2yLiA+W/FUmSJEmVllKlWyBJKqfJ9Dj6DHBVTvnHU0oXZV93A0TE+cD1wAXZMX8TEbURUQv8X+AdwPnADVldSZIkSXNERFS6CZKkMqubqEJK6VsRsW6S57sG+HxKqR/YHhHbgEuyfdtSSs8CRMTns7qPn3KLJUmSJEmSNC2mMsfR+yJiSzaUrS0rWwXsKqmzOysbq1ySJEmSJEkz1MtNHH0SOBe4CNgL/EVWntc3NY1TnisiNkZER0R0dHZ2vswmSjNbe3s7hUKBQqGAca65yjhXNTDOVQ2Mc1UD41zKF2kSs9dlQ9XuSildON6+iPggQErpT7J99wAfyqp+KKV0ZVZ+Qr3xFAqF1NHRMfGdSLNYoVDAONdcZ5yrGhjnqgbjxfmvfeZBOo/08y//9T9Mc6uk8vL5XNUgIjanlAoT1XtZPY4iYmXJw58FRldcuxO4PiIaI+JsYD3wAPAgsD4izo6IBooTaN/5cq4tSZIkSZKk6THh5NgRcRtwObA0InYDNwGXR8RFFIeb7QDeC5BSeiwibqc46fUQcGNKaTg7z/uAe4Ba4OaU0mNlvxtJkiRJkiSVzWRWVbshp/jT49T/KPDRnPK7gbtPqXWSJEmSZpU09lSmkqRZaCqrqkmSJEnScXkr4kiSZjcTR5IkSZIkScpl4kiSJEmSJEm5TBxJkiRJkiQpl4kjSZIkSZIk5TJxJEmSJEmSpFwmjiRJkiSVTUqVboEkqZxMHEmSJEkqi4hKt0CSVG4mjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSJElSLhNHkiRJkiRJyjVh4igibo6I/RHxaEnZn0fEkxGxJSK+FBGtWfm6iOiNiIezr78tOebiiHgkIrZFxCcinDpPkiRJkiRpJptMj6PPAFedVLYJuDCl9FrgaeCDJfueSSldlH39Rkn5J4GNwPrs6+RzSpIkSZrlUqp0CyRJ5TRh4iil9C2g66Syr6eUhrKH3wNWj3eOiFgJLEwpfTellIBbgWtfXpMlSZIkzUwOKpCkuaYccxz9GvDVksdnR8RDEXF/RLw5K1sF7C6pszsrkyRJkiRJ0gxVN5WDI+IPgCHgs1nRXmBNSulARFwM/HNEXED+Rw9jdmKNiI0Uh7WxZs2aqTRRmrHa29tpb28HoLOzs8KtkU4P41zVwDhXNTDOVQ2McylfpEkMQo6IdcBdKaULS8o2AL8BvC2l1DPGcfcB/w14HvhmSulVWfkNwOUppfdOdO1CoZA6OjombKM0mxUKBYxzzXXGuaqBca5qMF6c//otHew51Mvdv/Xm3P3SbOHzuapBRGxOKRUmqveyhqpFxFXA+4GfKU0aRcSyiKjNts+hOAn2symlvcCRiLgsW03tXcCXX861JUmSJEmSND0mHKoWEbcBlwNLI2I3cBPFVdQagU3FPBDfy1ZQewvw4YgYAoaB30gpjU6s/ZsUV2hrpjgnUum8SJIkSZIkSZphJkwcpZRuyCn+9Bh17wDuGGNfB3Bh3j5JkiRJkiTNPOVYVU2SJEmSgHFWwJEkzUomjiRJkiSVReStpSxJmtVMHEmSJEmSJCmXiSNJkiRJkiTlMnEkSZIkSZKkXCaOJEmSJEmSlMvEkSRJkiRJknKZOJIkSZJUNimlSjdBklRGJo4kSZIklUVUugGSpLIzcSRJkiRJkqRcJo4kSZIkSZKUy8SRJEmSJEmSck0qcRQRN0fE/oh4tKRscURsioit2fe2rDwi4hMRsS0itkTEG0qO2ZDV3xoRG8p/O5IkSZIkSSqXyfY4+gxw1UllHwDuTSmtB+7NHgO8A1iffW0EPgnFRBNwE3ApcAlw02iySZIkSZIkSTPPpBJHKaVvAV0nFV8D3JJt3wJcW1J+ayr6HtAaESuBK4FNKaWulNJBYBMvTUZJkiRJkiRphpjKHEfLU0p7AbLvZ2Tlq4BdJfV2Z2VjlUuSJEmaAyIq3QJJUrnVnYZz5v27SOOUv/QEERspDnNjzZo15WuZNIO0t7fT3t4OQGdnZ4VbI50exrmqgXGuamCcqxoY51K+SCk3d/PSihHrgLtSShdmj58CLk8p7c2Got2XUjovIv4u276ttN7oV0rpvVn5CfXGUigUUkdHx8u4NWn2KBQKGOea64xzVQPjXNVgvDh/7z928NyBHr7222+Z5lZJ5eXzuapBRGxOKRUmqjeVoWp3AqMro20AvlxS/q5sdbXLgMPZULZ7gCsioi2bFPuKrEySJEmSJEkz0KSGqkXEbRR7DC2NiN0UV0f7GHB7RLwH2Alcl1W/G7ga2Ab0AO8GSCl1RcRHgAezeh9OKZ084bYkSZIkSZJmiEkljlJKN4yx6205dRNw4xjnuRm4edKtkyRJkiRJUsVMZaiaJEmSJEmS5jATR5IkSZLKZpJr70iSZgkTR5IkSZLKIohKN0GSVGYmjiRJkiRJkpTLxJEkSZIkSZJymTiSJEmSJElSLhNHkiRJkiRJymXiSJIkSZIkSblMHEmSJEkqm0SqdBMkSWVk4kiSJElSWURUugWSpHIzcSRJkiRJkqRcJo4kSZIkSZKU62UnjiLivIh4uOSrOyJ+OyI+FBHPl5RfXXLMByNiW0Q8FRFXlucWJEmSJEmSdDrUvdwDU0pPARcBREQt8DzwJeDdwMdTSv+rtH5EnA9cD1wAnAn8a0S8MqU0/HLbIEmSJEmSpNOnXEPV3gY8k1J6bpw61wCfTyn1p5S2A9uAS8p0fUmSJEmSJJVZuRJH1wO3lTx+X0RsiYibI6ItK1sF7CqpszsrkyRJkjRHpFTpFkiSymnKiaOIaAB+Bvj/sqJPAudSHMa2F/iL0ao5h+f+W4mIjRHREREdnZ2dU22iNCO1t7dTKBQoFAoY55qrjHNVA+Nc1WCycR55r/ilWcLncylfpCl+JBAR1wA3ppSuyNm3DrgrpXRhRHwQIKX0J9m+e4APpZS+O975C4VC6ujomFIbpZmuUChgnGuuM85VDYxzVYPx4vy/fHYzW/cdZdP/+9ZpbpVUXj6fqxpExOaUUmGieuUYqnYDJcPUImJlyb6fBR7Ntu8Ero+Ixog4G1gPPFCG60uSJEmSJOk0eNmrqgFERAvwduC9JcV/FhEXURyGtmN0X0rpsYi4HXgcGKLYS8kV1SRJkiRJkmaoKSWOUko9wJKTyn5lnPofBT46lWtKkiRJkiRpepRrVTVJkiRJkiTNMSaOJEmSJEmSlMvEkSRJkqSymdqazZKkmcbEkSRJkqSyCKLSTZAklZmJI0mSJEmSJOUycSRJkiRJkqRcJo4kSZIkSZKUy8SRJEmSJEmScpk4kiRJkiRJUi4TR5IkSZLKJqVU6SZIksrIxJEkSZKk8ohKN0CSVG4mjiRJkiRJkpRryomjiNgREY9ExMMR0ZGVLY6ITRGxNfvelpVHRHwiIrZFxJaIeMNUry9JkiRJkqTTo1w9jn4ipXRRSqmQPf4AcG9KaT1wb/YY4B3A+uxrI/DJMl1fkiRJkiRJZXa6hqpdA9ySbd8CXFtSfmsq+h7QGhErT1MbJEmSJEmSNAXlSBwl4OsRsTkiNmZly1NKewGy72dk5auAXSXH7s7KJEmSJEmSNMPUleEcb0op7YmIM4BNEfHkOHXz1ll4yXqdWQJqI8CaNWvK0ERp5mlvb6e9vR2Azs7OCrdGOj2Mc1UD41zV4FTi/CUv7qVZwudzKV+kVL6n9oj4EHAU+M/A5SmlvdlQtPtSSudFxN9l27dl9Z8arTfWOQuFQuro6ChbG6WZqFAoYJxrrjPOVQ2Mc1WD8eL8fZ/7AY/v7eYbv3P59DZKKjOfz1UNImJzyVzVY5rSULWImBcRC0a3gSuAR4E7gQ1ZtQ3Al7PtO4F3ZaurXQYcHi9pJEmSJEmSpMqZ6lC15cCXImL0XJ9LKX0tIh4Ebo+I9wA7geuy+ncDVwPbgB7g3VO8viRJkiRJkk6T/5+9O4+T46rvvf/99T49+6aRNFpGsiTvYJvGGLDB2BjbmMSQFyQmBEgwUbgPBAjZTAKYPECA++SGGy5LmGBfTEIMhLA42ECMF2yDt5EX2bJla5dG6+z79Fbn+aNr2pLcksaaHvV09+f9eumlrqpTVadbR1WnfnWWOQWOnHPbJb28wPoBSZcXWO8kfXAu5wQAAAAAAMCpUYxZ1QAAAAAAAFCBCBwBAAAAAACgIAJHAAAAAIrCc06pjFfqbAAAimiug2MDAAAAgCTpjqcOlDoLAIAio8URAAAAAAAACiJwBAAAAAAAgIIIHAEAAAAAAKAgAkcAAAAAAAAoiMARAKDk/p/vbNB7bn6k1NkAAAAAcBRmVQMAlNzYdEZj05lSZwMAAADAUWhxBCxw9z3fp7s3Hyx1NoB5ZWZypc4EAAAAgBehxRGwwP3L/ds1Np3RZWd0lDorwLyxUmcAAAAAQEEn3eLIzJab2T1m9qyZbTKzj/jrP21me83sCf/Pmw/b5+NmttXMnjOzK4vxBYBKR0sMVA1HSQcAAAAWmrm0OMpI+nPn3GNmVi9pg5nd6W/7knPuHw5PbGZnSbpO0tmSlkr6pZmtc85l55AHoOIFTDxQo+KZiQApAAAAsACddIsj59x+59xj/ucxSc9K6jzOLtdK+q5zLumc2yFpq6QLT/b8QLUwSR5P1KhwxEcBAACAhakog2ObWZek8yU97K/6kJltNLObzazZX9cpac9hu/Xq+IEmAJrpqsYTNSqbGaMcAQAAAAvRnANHZlYn6T8lfdQ5Nyrp65JOk3SepP2S/tdM0gK7F3waNrP1ZtZjZj19fX1zzSKwIHV3dyuRSCiRSOh45ZyWGChnsy3nkgiQomy9lHIOlCvKOaoB5RwobE6BIzMLKxc0+o5z7oeS5Jw76JzLOuc8Sf+iF7qj9UpaftjuyyTtK3Rc51y3cy7hnEu0t7fPJYvAgrV+/Xr19PSop6dHxyvnZkbgCGVr1uVcBEhRvmZbzoFyRjlHNaCcA4XNZVY1k3STpGedc/942PolhyV7m6Sn/c+3SbrOzKJmtkrSWkmPnOz5gWphJnk8UaPC0VMNACpLOuuVOgsAgCKZy6xqr5X0bklPmdkT/rq/kfROMztPuW5oOyX9iSQ55zaZ2fclPaPcjGwfZEY14MR4nka1ID4KAJWDazoAVI6TDhw55x5Q4WfaO46zz+ckfe5kzwlUIzMqX6gGxghHqHifv+NZTaQy+uxbzy11VoB5R0tSAKgcc2lxBOAUCDCrGqpALkBKOUdle/bAmEam0qXOBgAAwEsy51nVAMyv3BhHpc4FML94MY1qYBJNSFE1KOoAUDkIHAELnMloiQEAFSBgov0oAAAoOwSOgIWOBw1UAcbyQjUwM2bJRNV4eMdAqbMAACgSAkfAAhcwIkeofCbG8kLlMxEgRfU4MDJd6iwAAIqkrANH92w+pF9sOlDqbADzyiTeUKPiMfsOqoGZEThC1fjhY3tLnQUAQJGU9axqN/96h8amM7ry7MWlzgowb2hwhGrBAzUqXW6yAwo6qsOO/olSZwGYN+PJjLKeU2NNuNRZAU6Jsg4chQKMFYDKR9cGVAMCpKgGNKxDNTkwSlc1VK4LPnOnUhlPO79wTamzApwSZd1VLRgwZbI8aqCyBYyxX1D5jEdqVIEAXdUAoCKkMl6pswCcUmUdOAowOwmqgUke9yZUAcf1HBWOrmqoNslMttRZAAAUQVkHjkJBU9ajAobK9sPH9mrv8FSpswHMq2TG0+BEqtTZAOYVXTJRbU7/xM9LnQUAQBGUdeAoYASOAKAS/PLZgxqaTJc6G8C8ys2qRr0F1WVb37gOjU7rtif3lTorAICTdMoHxzazqyT9k6SgpG86575wsscKBUxZKmAAUDG6brhdT974JjXWhOWck3NSIMD4R6gMTHaAarC8pUZ7Bl9oKX35//pV/vOZi+s1MpVW/3hKV57dIbMXru93PXtQrXVRnbe86ZTmF9VhaCKl6UxWSxprSp0VoCyd0sCRmQUlfVXSFZJ6JT1qZrc55545meM9tH2QGRtQNfrGkmqvj5Y6G8C8e/nf/fcRy99bf5HOWNygmkhQw5MptdVFNTqd1rP7x1QbDerspY0KmDSZyiqd9VQTCep7j+5RPBLS21+xTFJu/KSs5xQKzr6h7UzLkMMfbI4l6zllPE/RUPC4x5vNseZb1nNy7sjfYv/IlCaSWS1vqdGjO4a0rqNOixpi+e2zyXsq4ykUsBcF+rKeU8Bm9zvOuO/5Pr1qdUv+9/Q8p6xzCh/j328qlVVN5Ni//Wz0Dk0q6zmtbK2VJI1MptUYL+40yz/duL+oxwMWoj++ZLU+9ZNNBbdd8aX7XrSuPhrSWDKTX375ska988IVemLPsN7ysqX67O3P6MJVLfrdxHKd09mYTzedzmrf8JRWt9cdcbyjr1e9Q5OKhYPKek6L6qOaTnuKhQMyMyUzWU2lsmqKRyTlrlfBAi8r9gxOqqMhpkgooF0DE9pycFyXnt6uUDCgvcNT6h2clCRduKpFU+ms4pFQPh9j02nVRkLKeE4/3bhPF61u1ZZD43pm36gSXc06Z2mjptJZjUylVRsNqqkmooBJE6msvnL3Fo0nM7rirA6ZTP/9zAG961Ur9c7uh3TJujYtb47r2vM61RgP698e2qUPvP407eyf0L/cv10NNWG9fFmj9o9Ma0VLXJ/88dOaSGX1h6/pUsBMK1pq9On/ekb3/sWl+u6je1QXDcrM9N1Hd2tFS1znLW9S1pP++VfbJEmXrG3Tx65Yp7d97TdH/DaXn7FId20+pPpYSC21Ee0ayP0W//wHr9DK1riu/qf7j0h/8Zo2XXFWh268bZMioYBSGU+vWNmsC1Y06VWrWrX5wKjMTP9877YjysWMxMpm9ewayi+/7fxO/ejxvS9KdyK/vuEydTbNPYg0OJFSS21kzscBFjo7lU2mzezVkj7tnLvSX/64JDnnPn+sfRKJhOvp6Sm4reuG2/Of//X6C9XREFNNOKiM59RUE1bGc0pnPTX4b67DwYCSfsXWc0414WC+xZLJFCgw9oBzuZZN0gsDWnpOClhuVresX5k9PG3Gc/l9nL+fyXS8OrPlfg95/ht2zzmZ5brj2VHHcXL5N5Zmyh/bOeW3Hb5+Ju+el1s/k++ZFlsmy/8mGc/lj+t5UiCQG3skclRlfeZ8E8mMYuGgIqHcbxsNBZT1nFJZz589xikeCWl0Kq2aSFChgCmZ8XLfU1L8qH+Dmd84GDCNTWcUNFPa8xSPBGUyZTxPnpMiwYDMlD9nOuspFMitS2c9RUKBI97qHv7bOOUqFamMp3gklP9NMlmntOfJlPvukVBA4ZfJo7MAACAASURBVKAdUfnIeJ7S2dxv55w0kcrlUZLCoUDuN8zmjl0TDioYzP22sfDxH2gSiYRmU86PVhMOakljTAMTKV24qkXprKc17XV6YGu/2uuj6mqt1ZvO7tDAeErRUEAZzylgpubasDoaYoqGAtozOKXaaFCL6mPaNzKlRfVRJTOeDoxMa0ljTFkvVxb3DE2ps6lGqYynTftG1BSPqKMhpsUNMe0dnpJzTrFwUMmMp6znFI8EtWHXkBprwgoGTA01YS1rzu3fWhtRxnManEgpEgrky+TugUktboxpIpnRwbGknHNa1lyj0emMkumsFjXENJXKykyaSGYVjwTVWhfRcwfGtKN/Qq9Y2aznDoypsSasZc1xHRqb1tBESm31US1vjuu+5/u0tqNOE8msljTGNDKV1uBkSl+7Z5suXNWiUMB0wcpmRUMBjU6ltaI1roHxlH7+9AFdsrZNixpi6htLqrUuoid2DysUDKijIarJVFadTTX6zbZ+ndvZqCf2jOialy3WY7uGlfY8vXxZk36x6YDOWtKgb9y3XWcsrte5nY167Zo2Pbh9QI/sGNSZS+olSfWxsJ7eO6LXrW3XvpEppbOeOupjmkxnFQkG1D+elOectvVNaGQyreGplM5e2qilTTE5J7XWRhQMBBQwacfAhJY1x9Wzc1BdbbXacnBM0VBQnnM6MDKtsWRGZy9t0NBESk/2jqirNa6JVFZnLK7XRDKj09rrVBcLad/wlJrjEfWNJfXIjkGd09moB7cPSJJe2dWstR31Wn/JanW11c5LOQcWkhNNvTzbcn56R73ekVimvcNTWtVWq6WNNYpHglrZVqtIMKDxZEYdDVE5Jw1PpTU+nVFrXUTOSfFIUKmMd8T9KxQ0hYOB/L398AfgoJkCZkplczMteM4pFMjdO51ydZrAYfe6mf1SGS//OWCWv78eXZ1xytVjMocNHxAKmCaSuYGRo+Hcdd653IN5xL9vH36+oclUrj4RzN2/sy53r01lZ+7zLn+/DQTkf39T0K87zeRz5h4UDQUUClq+TpXxnNIZT7XRUP53mPndMlknzzmFAwFNpbOKhAKKhQMKmCmZ9pTMZBWLBOWclExnVRcLKZ3J7ZP2cnWkmUBsNus0kcrITGqORzSVyspzuXtvIGCKhl6oTxWqgh89i+rRaQrV2lMZT2m/3iXlAirhYEBN8bCmUrl/g3y9TE510ZAmU1kNTqQUjwQVDQU1kcwo43lKZjytaqtVPHL8d8vHK+c/erxXf/a9J4+7P7AQFOt6LkkrW+MKBUy/9fKlao5HdE5noxpiueeMvrGUggHT03tH9MYzOxSLBOR5uf/vwYAplfHUFI9oIplRTSSYv9ZlXe66FQxYPtDpXO56WxsNKeucxqYzmkpl1VIb0f6RKUVDAU0kszptUZ1M0sHRadXFQjo0msw/OzXWhOV5TtFQUGnPU1ttVM8dHNOi+qjqY7kg5/6RadVGgkplPR0cnVY8ElJTPKy+saS2HRpXR0NM05ms1rTXa2gypeGptKKhgKKhgKZSuWtmKpO7Z/SPJdU3nlQsFFRDTViTqYxaaiMyme569qBOX1yv+lhYz+wf0fLmuCKh3PXrV8/1qX88pda6iFpro9pyaEwTyYze8rKl2rB7SMuaavTQjkG11kZ04aoWPbF7OP+strylRv1jKa3tqNPeoSkFAqYJPxB573N9ynie/uCilXpiz7BWtMQ1lcrqlV0t+vdHdqu9PqpYOKix6bRMUnt9VL/YdDAf6JSkVW21qo0G1V4XVUdDTN99dE++LCxuiOnA6LTqoqF8UHF0Oq3ho4ZkiIYCeut5ndo3MqXR6Yye3DP8QvlqjWsyldWhsaQk6Y9e26VfPd+n7X0TaquLqq0uos0HxhQw6eyljWqKh3X/ln611UXVP57bp7OpRv/+x6/KvxA7FjPb4JxLHDeRTn3g6O2SrnLOvd9ffrekVznnPnSsfWYbOCp3AcsFpE7WTCAHC8+y5ho98NeXHTfN8W5M77/lUf3y2UPzkTWgqHZ8/s3HbVVC4AiVoJgPGsBCFAkG9Nxnrzrp6/mO/gm94R/unafcAcUzl+v59r5xXXZYN0xgodr6uauP2+J+toGjUz3GUaE70IvCHWa2XtJ6SVqxYsUxD7bj82/Wqo/fMasTR0IB/fElq/TVe7bpjMX1ao5H9OrTWnOtjFzuTczAREpL/Kb5AxMp7RueUtZzOqezMd/6R/Kb4/tvsQJmmvl3yHhO49MZTaSyqgkHFY8EFfCjyAHLvSE8ll0DkxqeSqupJqyVrfH8zXpoIqVkxlNzbUThQK4F0eE38oAffZ4JOpmk4cmUPCd1NEQ1Op1R/3hSq9tq9fCOQaUynnYOTOi1p7Vp1+Ck+seTOm95k1pqI4qFg/I8l49o7xmcUmdTTA01YcXCQQXMtGHXoJ7dP6ZEV7MWN8b0jV9t19tfsUwdDVEdGElqa9+4ntwzrGgooPNXNGl0KqNQ0LSxd0SLG2K66pzF2tE/oaznNJ3O6nXr2vX47iElulq0a2BC3+/pVTwS1FQ6e0Qg7IIVTXpsdy4Ke/bSBmU9p/7xpKbTuah5/3hS775opRprwvr1tn5dvKZNZpZveZUrVzMtu3Lr7tp8UKFAQPWxkM5c0qBQ0LT14Lh++PjefMuTi9e0HfHvNJXO6tn9o9rZP6GlTTX6zbYBtdZGNDCR0ukd9Vq3uF7/5Q/++Mm3nCXPc4pHC/+7d3d3q7u7W5LU19d3zLLxL+9J6F3ffFi/2TZwzDTlaFF9NB9FR/m7eE1bvrXj4WZbzrf//Zv1tz9+Sjv7J/Xg9gGFg6Z7/uJS3ftcn7b1jWtxQ0yf/9lm/c75nXrdunZ99HtP6JqXLdHFa9r08R8+JUm67IxFuuHqM/Spnzydbw33jfu26/Xr2vWr53PnjkeC+v0LV+ibD+zQxWv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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotperiodograph(tpf);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Generalized way to convert each pixel into a light curve.\n", + "2. apply tpf mask to that (bool in def : true,false) to get desired pixels to graph\n", + "3. Make a periodogram for each relevant pixel\n", + "4. Graph that nicely." + ] + } + ], + "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": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From cd1c450828ba2603bbc2b03c72568c5b30e7157c Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Tue, 15 Oct 2019 14:13:52 -0700 Subject: [PATCH 02/11] Call the periodograph function once and need to make class --- .ipynb_checkpoints/Research-checkpoint.ipynb | 84 ++++++++++++++------ Research.ipynb | 84 ++++++++++++++------ 2 files changed, 116 insertions(+), 52 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index 2f4ba9d..b89d907 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,8 @@ "\n", " #Just making the subplot spacings 0 pixel width and height separation\n", " fig.subplots_adjust(wspace=0,hspace=0)\n", - "\n", + " \n", + " \n", " #iterating through the columns of the postage stamp pixels\n", " for i in np.arange(postagestamp[0].min(),postagestamp[0].max()+1):\n", " \n", @@ -59,41 +60,27 @@ " mask[i][j] = True\n", " \n", " #extracting the light curve for the target pixel\n", + " \n", " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", - " \n", - " #Grabbing periodogram frequency and power data and storing as a tuple\n", - " periodogram = np.asarray([lightcurve.to_periodogram(oversample_factor=1).frequency,\n", - " lightcurve.to_periodogram(oversample_factor=1).power])\n", + " #lightcurve = lightcurve[lightcurve.quality==0]\n", + " periodogram = lightcurve.to_periodogram(oversample_factor=5)\n", + " \n", " \n", " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", - " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram[0],periodogram[1]);\n", - "\n", + " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram.frequency,periodogram.power);\n", + " #ax[i-postagestamp[0].min()][j-postagestamp[1].min()].set_yscale('log')\n", "\n", " " ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" - ] - }, { "data": { - "image/png": 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\n", 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tiKdpL3Vfd1I8zY3Vq1dPN0SpqmzcuJGNGzcC0N3dXeZopHPDPNd8YJ5rPjDPNR+Y51JpIcYzXwA5hJABvg08GGP846RtG3BjjPFQciraIzHGi0MIX0yWvzq538QtxviLSfur+k2lq6srbt68+fWOT6oKXV1dmOc6350uz9fe/R0Adn/utrkMSZp1vp5rPjDPNR+Y55oPQghPxRi7ztRvOldVC8A9wNaJolHifmDiymh3AN+a1P7h5Opq1wJ9yalsDwI3hxDaky/FvjlpkyRJkiRJUgWazqlqPwF8CHg+hPDDpO03gc8B3wghfATYC3wg2fYA8B5gBzAM/DxAjLE3hPB7wJNJv0/FGHtnZRSSJEmSJEmadWcsHMUYH6X09xMB3FSifwTumuJY9wL3ziRASZIkSZIklceMrqomSZIkSZKk+cPCkSRJkiRJkkqycCRJkiRJkqSSLBxJkiRJkiSpJAtHkiRJkiRJKsnCkSRJkiRJkkqycCRJkiRJkqSSLBxJkiRJkiSpJAtHkiRJkiRJKsnCkSRJkiRJkkqycCRJkiRJkqSSLBxJkiRJkiSpJAtHkqSKMTSWK3cIkiRJkiaxcCRJqhi5fCx3CJIkSZImsXAkSZIkSZKkkiwcSZIqRsQZR5IkSVIlsXAkSaoYBetGkiRJUkWxcCRJqhih3AFIkiRJehULR5KkilGITjmSJEmSKomFI0lSxXjg+UPlDkGSJEnSJBaOJEkV46GtR8sdgiRJkqRJLBxJkirGv77cXe4QJEmSJE1i4UiSJEmSJEklWTiSJEmSJElSSRaOJEmSJEmSVJKFI0lSRekfzZY7BEmSJEkJC0dShfvDf36JP3pwW7nDkObM1zbtLXcIkiRJkhIWjqQK94VHdvJn39tR7jCkc+pTt192cvmzD7xE37CzjiSpGv3O/Vv4n/c9W+4wJEmzaM4LRyGEW0MI20IIO0IId8/1/UvV5uZLO3nD0pZyhyGdU7detvRV62/61L+w9u7v8N4/e5TxXIGhsRyH+0Zfs9+xwTH6RiwySVKl2Ns7zNbD/eUOQ5I0i9JzeWchhBrg88C7gP3AkyGE+2OML76e431t017G8wU+fN3aWYxSqiyZmhTZfKHcYUjn1JLW+pLtz+3vY8Mn/umM+//uey9j857jvPdNyzk6MEpzXZqV7Y2MjOe5aEkzdekUA6M56mtTfHfrUX726tUMjGZ5ZFs3ly1vZVVHI4f7RhnJ5rlocTP5GBkez7OgIQNAjJHRbIGG2hpeOtzP4uY6ChGe2tPL8rYGVrQ10FSXJkZoqK0Bit/VVFtT/P/MWK7A5/5pKx+9/gJa6zP8j/ue5fr1i2itz9Bcn+baCxbyb9u7uWz5Al481M9j24/x32/ewMETI3zlB3tYvbCRN69qYzSbZ0/PMG9dv4hv/fAgWw72cdvly8nmC2TzBX7mqpU8d6CPZ/ed4CuP76F7YIyf+/G1jOXyfHXTvpOP18fefhEH+0Z4bMcxUiFwuH+UX37bhXzhkZ1csqyVo/2j9AyNT/l4X722g027e0tuW7uwkd09w2d8zs5WS32agdHcOb+fyd7xhiXc+3M/9rr3/8WvbOZI/xj/cNdPzGJUUmWprUkxnvN9i85vuXyBgdEc7U215Q5FmhNzWjgCrgZ2xBhfAQghfA24HXhdhaO7v/k8AKs6GlnQkKEmBNI1gULytyqE1+4T49TbXq+JY00cezaPW+qYk2Of7fucK1ON7XxUm06xofP1zxj6zvOHAFh793f4qStXsPVQP5evWMD1GxZTl07RUp+mPlPD0f5Rlrc1cHw4S00IvHS4n1whcsXKBeQLkcbaNPlCpCFTQ65Q4HDfKNlCZHVHIzUhECk+IYFigkUigfCa/B7P56mtqTnZp3tgjMUtdQCkSvxiTew3cXyAnqFxWuszZGrCq+5zYvf+0SzZfKSjsZbxfAGI1NbUvCqWnqExOpI/1uO5AoNjOTqaaqlJhR/dZ4Te4XHGsnmWLqgnXyhGMZ4rkKlJUZdOkS8UCwRHB0ZZ2d5AIUJDpobRbJ7xXLFQMDiWo6U+QyrA0f4x2hoz1KZTBAL9o1lihNaGNKPZAo1J0SBXiIxm8zRkahgez9NYW0NNKtA7NM5oEk82H8kXIkPjueLzko801KYYyxU42j9Gc12aVAoWN9effPxihFeODTKeK/CGpa0nf5cKMTIynufowBgXLWk++XyM5wvUhMDRgVFaGzLUpVP0Do0zlitwYnicdYuaqU2nqAmBY4Nj1GVSbNrVyxuXLyCbL9BSn2Esl2dhcx27jw2xqqNYjGmqK45nQqYmxSXLWqef2LPsk/dvAeAfnz04rf4fT/5+zLXJxZvvv9x92r5f37xv6o2TammP7eg5ufzp72x9Tde//vfdr2krdfrrFx7ZCcDWQ2eeKTBV0QiYk6IRMOdFI4CHXzp6Vvs/uOUIUHw9B/jMT72Rv3psNzuODvLRt67jcP8oj+44xvolzSxva+BbPzzI1es6aK3P0DM0xsh4niP9xd/lGOEDb1lJfaaGSOTCxc280j1EfSbFN585wK2XLeWJXb385BXLaGvMcKR/jJb64tu+48NZVrTV8+1nD/Ht5w7xnsuXsmZhE+sWNdE/muWFA330j+S4cEkTn//eTt7/lpVcuLiZmlQxb/tGstz+phWs6migNXn/dXx4nGf2neCL33+F//wT62ipT/P03uN0D4yxbEE9l69so38kS8/QOMsW1PPo9mM016XZtLuXX3rbhaxob2DLgT4e3HKYi5e28OZV7bxtw2IO9Y3wg5093PfUfgB++soV/Ov2Y/zWT15CQ6aGgydGaKxNc+DECKPZPO1NtfzZwztY2FzLe9+0nC88spOrVrfRWp/hwuS1sXtgjOvXL+LvnznAj1+4iJ3dg1y5uo3PP7yDNQubqMukeOtFi3jxUD8nhrPc9fYLeX5/H2sWNtEzNM59m/fxoevWsPPoEHt7h2moTbGwqY57Ht3FbZcv41D/KMcGxnjHG5awor2Bv3tqPy8dHuDdb1zKmoWNXL6yjVSAbL7AgROjpFOBnsExdnYPcd0FCxkaz/HykUHWLmzkcP8o6xY1sf3IICvbG+hoqmVBQ4a9vcPs7B4imy+wqr2RXKHAjRcv4TvPHaKtMcPSBfX8+t8+xy9cv45XuofY3TPE9esX85Y17ew7PkyhELloSQvHh8cZGM3yzacP8K5LO3l673E+cdulZ/V6/uKhfvb2DvOJf3iele2NPLP3OP/hTctZ0dbA46/08uZVbdRnUuzpGT7592osVyAVfvQ+IhVe+/4jEonx1dtONV/eW87U5PdgKjrb9y23f/4xthzs575fuo6GTM2U/Wbzc9vZfOY83eficjsfPtvOxNk+fzOpY6RTKS6epTNXQpzDZyeE8H7g1hjjR5P1DwHXxBg/NtU+XV1dcfPmzSW3TbzxkirZyvYGHv2Nd5y2T1dXF+a5qtnZ5jlAz+AYb/n0/5vt0KRZtftzt512u6/nOh+Y5zrf+f5c88F08jyE8FSMsetMx5rrGUel6mOvqVyFEO4E7gRYvXr1lAd7+rfexYfueYKPvf0i6muL/60vxJjMniieWjD5TkLx2EDxv/OT16dSmHSMiZ5x0vqpwZ/a59R+k7eHU36W6j/V/Zxqqgd2qtGdbttU/aYa90wnb51p38n3c6bn52zNReG0for/QmzcuJGNGzcC0N099cyDZ3/7Zt70qX85ud7WmGHtwibetmEx9ZkaVrY3UJ+pYfexIS5YXPxvaP9Ilge3HObSZa1ctaadPT3DvHFFK4UC9I1kyRciqVTgcN8Ily1fQK4QS+bpRP5Pfh6y+eJsnRgjIQR2HB1kdUcjtcnsnYlJKJP3mViaaBocy9FcV3z5+dF/En9k17FBWuszLGouzmQazuapS6eIEWpSgXyhwAsH+nnzqjYiMDiWpXtgjDULm065v8DweI69PcNcsqyVmlQgV4j0DI7R1licnRSAAydG6B/J0tFcS2dLPfkYOdI/ysh4nrbGDAOjOdYsbCIAu3uGWNxSR1NtmgiMZPPkCwVa6jKMZPOEUJymP5zMDOhsrSdXKM7MWtXRQP9Iju6BMS5d3kqMkYMnRli6oIHjw+PUZ1Jk85FsvsCu7iHaGjMUImzobCadShGBfCHy6I5uOprquGLFglflSv9olm2HB7hiZRuRSF26hnQqMJrN8/yBPq5YuYB0KkWuENndM8Tjr/Twzks6GRnPs3ZREy8d6qeztZ6//vfdvOvSTo4Pj9PZWk8IsGFJCwdOjNBcl6Z3aJzWhjTtyWMIZ5/nAAub69j+mXefzNFCjDyyrZtUKM7U2LSrl2UL6mmsS1OXLp4CtuPoIO2NmWS2wdSnRrTUpRkYK85Qqa1JJTPZir9PJ87ii7gbMjWMZPPT6ntxZwvbjgzM6PjNdWkGx3LUpYsz0Yr5/9rXrVUdDezrHQFgYVMtPUPjXLi4iUKEgdEsCxoyZJLT5k4MZxkez9FfYsbO5MdjYjbbBYub6GypZ9mCer79/CHSqcAblrawp2eYkWyepro0g6M5RrJ5rlrdxs7uIcZyeVa2N7K4uY5svsDmPcdPnlr2zks6yeYLHDwxQiFGmurS7D42RGdrPduPDnJxZwttjRm6B8Z45dgQyxYU73vtoiZ+sLOHbD5yw4ZFPPFKLxcvbSEVoHdonFwh0lqfIV+IpGvCydeP5w/00dFUy6ZdvazqaOCG9Yt58VA/V65q5yuP72ZDZwtjuQI/deUKHn7pKPt6h6lJBW67fBkLGjI8tvMYFy5uZsvBfj79vjeWfJ5m+np+zboOdnYP8YnbLmFf7zC7eoa4Zl0Hu3uG2bSrl6vXdVAoRF442Mc7L+nk2X0n6B4cI5ePLG9r4MndvbTUZ/iF69edfF3sbK3n6b3HKRQim3b3cu0FCzk2OMYN6xczni8wms0TQmBoLMe+3mGuXN3O0YFRth8ZpLU+zcG+UbrWtDOSzbP96CB7e4Z524bF/M0Te7hoSQuNtTVctryVAyeKpzj+zFtW8uZVbaRCIJePHOwb4fsvd/Pc/hPcuGEJna11/NVju1nWVk9jJs1lK1o5OjBGe2OGfAHGcnnq0jUcHxrn3ZcvpX80x+Ov9DCWzdM/muPizhbedWkn2w4PcGxwjB+80kNNKlCXTrHr2BCfed/lABwZGCUVirM5dx0b4sRwlsP9I1y0uJn2plr2Hx8hxuLvSG06xZYD/eQLkVsu6+Rg3yi1NSm2HRngx9a284f/vI2bL+tkLFtgw9IWXjjQx8h4no9ev44ndvVyzboOhsfzfO3Jffyna9cwOJrj2OAYz+0/wcKmOo4Pj5+cMbvr2BBXrW7nbRcv5t9e7ubpvSf45RsvpLmuhouWNBMjDI/nOdQ3wt7eYZYtaODBLYf52atX8/1t3azvbKa1IcPjO3u4YuUC/uXFI6xb1MTV6zoYGc+z7cgAR/pHOTGcpWtNO22NtVywuImvP7mPtsZarr2gg4dePMLqjkb6R7M8tqOHrjXt3PrGpWzec5zVHcXfz6MDY7x8ZIB/fPYgH7puDd9/uZv/dtP6s8rzJ37zJq757HdZu7CRGzYs5ss/2MPP/fharrtwIfdt3s/V69pZtqCBFw/1n8yhGCPZfKQ+kyIEKBSK7wsKMZ6chRST5cJp3ruFEGb8vnS+OMdvqavO2b5v+bdffzs3/e/v89mfvpzWZDbnqZ8zofRnxVIZPK0PyVMc60xP7emOMxeft04nxtfOh5vtaE732TNMen0ptc9MYpnJ4ziT4068Fr4eE2dCzIa5nnF0HfA7McZbkvWPA8QYf3+qS2UmHwAABgFJREFUfU4340g6X5xpJoZ0PjDPNR+Y55oPzHPNB+a55oPpzjia66uqPQmsDyGsCyHUAh8E7p/jGCRJkiRJkjQNc3qqWowxF0L4GPAgUAPcG2PcMpcxSJIkSZIkaXrm+juOiDE+ADww1/crSZIkSZKkmZnrU9UkSZIkSZJUJSwcSZIkSZIkqSQLR5IkSZIkSSopxBjLHcNphRC6gT2n6bIIODZH4cw2Yy+PSoz9KuDp02yvxJiny9jLoxJjN88rk7HPLvO8Mhn77DLPK5Oxzy7zvDIZ++xaE2NcfKZOFV84OpMQwuYYY1e543g9jL08qjH2aox5grGXRzXGXo0xTzD28qjG2Ksx5gnGXh7VGHs1xjzB2MujGmOvxpgnGHt5VHPsnqomSZIkSZKkkiwcSZIkSZIkqaTzoXC0sdwBnAVjL49qjL0aY55g7OVRjbFXY8wTjL08qjH2aox5grGXRzXGXo0xTzD28qjG2Ksx5gnGXh5VG3vVf8eRJEmSJEmSzo3zYcaRJEmSJEmSzoGqLhyFEG4NIWwLIewIIdxd7nhOFUK4N4RwNITwwqS2jhDCQyGE7cnP9qQ9hBD+NBnLcyGEq8oY96oQwvdCCFtDCFtCCL9SRbHXhxA2hRCeTWL/3aR9XQjhiST2r4cQapP2umR9R7J9bblin4p5fs7iNs8riHl+zuI2zyuIeX7O4jbPK4h5fs7iNs8riHl+zuI2zytVjLEqb0ANsBO4AKgFngUuLXdcp8R4A3AV8MKktj8E7k6W7wb+IFl+D/BPQACuBZ4oY9zLgKuS5RbgZeDSKok9AM3JcgZ4IonpG8AHk/a/AH45Wf4vwF8kyx8Evl7uvDllPOb5uYvbPK+Qm3lunk8Ru3k+9zGa53Mfu3k+9zGa53Mfu3k+9zGa53Mf+3mV568ZX7kDOIsn5jrgwUnrHwc+Xu64SsS59pRf2G3AsmR5GbAtWf4i8LOl+pX7BnwLeFe1xQ40Ak8D1wDHgPSpuQM8CFyXLKeTfqHcsU8ag3k+d2Mwz8s3BvN87sZgnpdvDOb53I3BPC/fGMzzuRuDeV6+MZjnczcG87xCbtV8qtoKYN+k9f1JW6XrjDEeAkh+LknaK3I8yZS5KylWTKsi9hBCTQjhh8BR4CGK/xE4EWPMlYjvZOzJ9j5g4dxGfFoV9djOQFXkygTzvOwq6rGdgarIlQnmedlV1GM7A1WRKxPM87KrqMd2BqoiVyaY52VXUY/tDFRFrkwwzytLNReOQom2OOdRzJ6KG08IoRn4O+BXY4z9p+taoq1ssccY8zHGNwMrgauBS0p1S35WVOwlVHp8M1Vx4zHPK0KlxzdTFTce87wiVHp8M1Vx4zHPK0KlxzdTFTce87wiVHp8M1Vx4zHPK081F472A6smra8EDpYplpk4EkJYBpD8PJq0V9R4QggZir+sfxNj/GbSXBWxT4gxngAeoXhuaVsIIZ1smhzfydiT7QuA3rmN9LQq8rGdhqrIFfO8YlTkYzsNVZEr5nnFqMjHdhqqIlfM84pRkY/tNFRFrpjnFaMiH9tpqIpcMc8rUzUXjp4E1iffUl5L8Qul7i9zTNNxP3BHsnwHxfM2J9o/nHwz/LVA38R0vLkWQgjAPcDWGOMfT9pUDbEvDiG0JcsNwDuBrcD3gPcn3U6NfWJM7wcejsmJphXCPD9HzHPzfBZUQ66Y55XDPD9HzHPzfBZUQ66Y55XDPD9HzPOKyvNXK/eXLJ3NjeK3qL9M8dzB/1XueErE91XgEJClWFH8CMXzFr8LbE9+diR9A/D5ZCzPA11ljPutFKfJPQf8MLm9p0pivwJ4Jon9BeC3k/YLgE3ADuA+oC5pr0/WdyTbLyh33pQYk3l+buI2zyvoZp6b5yViN8/nPj7zfO5jN8/nPj7zfO5jN8/nPj7zfO5jP+/yfPItJEFLkiRJkiRJr1LNp6pJkiRJkiTpHLJwJEmSJEmSpJIsHEmSJEmSJKkkC0eSJEmSJEkqycKRJEmSJEmSSrJwJEmSJEmSpJIsHEmSJEmSJKkkC0eSJEmSJEkq6f8Dmg7UgYMzaG4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -117,6 +104,51 @@ "3. Make a periodogram for each relevant pixel\n", "4. Graph that nicely." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Class for the periodograph data/array\n", + "Pass kwargs through" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Normalization\n", + "x-xmin / (xmax -xmin)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[0.007173574,~0.014347148,~0.021520722,~\\dots,~359.99147,~359.99864,~360.00581] \\; \\mathrm{\\frac{1}{d}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/Research.ipynb b/Research.ipynb index 2f4ba9d..b89d907 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -45,7 +45,8 @@ "\n", " #Just making the subplot spacings 0 pixel width and height separation\n", " fig.subplots_adjust(wspace=0,hspace=0)\n", - "\n", + " \n", + " \n", " #iterating through the columns of the postage stamp pixels\n", " for i in np.arange(postagestamp[0].min(),postagestamp[0].max()+1):\n", " \n", @@ -59,41 +60,27 @@ " mask[i][j] = True\n", " \n", " #extracting the light curve for the target pixel\n", + " \n", " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", - " \n", - " #Grabbing periodogram frequency and power data and storing as a tuple\n", - " periodogram = np.asarray([lightcurve.to_periodogram(oversample_factor=1).frequency,\n", - " lightcurve.to_periodogram(oversample_factor=1).power])\n", + " #lightcurve = lightcurve[lightcurve.quality==0]\n", + " periodogram = lightcurve.to_periodogram(oversample_factor=5)\n", + " \n", " \n", " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", - " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram[0],periodogram[1]);\n", - "\n", + " ax[i-postagestamp[0].min()][j-postagestamp[1].min()].plot(periodogram.frequency,periodogram.power);\n", + " #ax[i-postagestamp[0].min()][j-postagestamp[1].min()].set_yscale('log')\n", "\n", " " ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 27, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n", - "C:\\Users\\higgi\\Anaconda3\\lib\\site-packages\\astropy\\units\\quantity.py:463: RuntimeWarning: invalid value encountered in sqrt\n", - " result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -117,6 +104,51 @@ "3. Make a periodogram for each relevant pixel\n", "4. Graph that nicely." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Class for the periodograph data/array\n", + "Pass kwargs through" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Normalization\n", + "x-xmin / (xmax -xmin)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[0.007173574,~0.014347148,~0.021520722,~\\dots,~359.99147,~359.99864,~360.00581] \\; \\mathrm{\\frac{1}{d}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From ed45b2469ed701602d792b869056933372b24bd9 Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Sat, 9 Nov 2019 18:09:37 -0800 Subject: [PATCH 03/11] Need to add documentation and clean up the notebook a lot --- .ipynb_checkpoints/Research-checkpoint.ipynb | 2841 ++++++++++++++++- Research.ipynb | 2854 +++++++++++++++++- 2 files changed, 5663 insertions(+), 32 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index b89d907..e00a492 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -10,23 +10,55 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import scipy as sp\n", - "import lightkurve as lk" + "import lightkurve as lk\n", + "from scipy import stats\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "from astropy import units as u\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "import bokeh \n", + "from bokeh.io import show, output_notebook, push_notebook\n", + "from bokeh.plotting import figure, ColumnDataSource\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker\n", + "from bokeh.layouts import layout, Spacer\n", + "from bokeh.models.tools import HoverTool\n", + "from bokeh.models.widgets import Button, Div\n", + "from bokeh.models.formatters import PrintfTickFormatter" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "#just setting up sample data\n", + "search_result = lk.search_targetpixelfile('TIC425064757', mission='TESS', sector=1)\n", + "tpf = search_result.download(quality_bitmask='default')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ "#just setting up sample data\n", - "search_result = lk.search_targetpixelfile('Pi Mensae', mission='TESS', sector=1)\n", + "search_result = lk.search_targetpixelfile('TIC142875987', mission='TESS', sector=4)\n", "tpf = search_result.download(quality_bitmask='default')\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +86,7 @@ " for j in np.arange(postagestamp[1].min(),postagestamp[1].max()+1):\n", " \n", " #Creating a false mask to alter each iteration\n", - " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask = np.empty((len(aperture),len(aperture[0])), dtype=bool)\n", " \n", " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", " mask[i][j] = True\n", @@ -75,12 +107,29 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplotperiodograph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtpf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mplotperiodograph\u001b[1;34m(targetpixelfile)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;31m#extracting the light curve for the target pixel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mlightcurve\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtargetpixelfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lightcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;31m#lightcurve = lightcurve[lightcurve.quality==0]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mperiodogram\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlightcurve\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_periodogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moversample_factor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mto_lightcurve\u001b[1;34m(self, method, **kwargs)\u001b[0m\n\u001b[0;32m 369\u001b[0m \"\"\"\n\u001b[0;32m 370\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'aperture'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 371\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract_aperture_photometry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 372\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'prf'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprf_lightcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mextract_aperture_photometry\u001b[1;34m(self, aperture_mask)\u001b[0m\n\u001b[0;32m 1562\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maperture_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1563\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Warning: aperture mask contains zero pixels.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1564\u001b[1;33m \u001b[0mcentroid_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcentroid_row\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_centroids\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture_mask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1565\u001b[0m \u001b[1;31m# Ignore warnings related to zero or negative errors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1566\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mestimate_centroids\u001b[1;34m(self, aperture_mask)\u001b[0m\n\u001b[0;32m 493\u001b[0m \"\"\"\n\u001b[0;32m 494\u001b[0m \u001b[0maperture_mask\u001b[0m \u001b[1;33m=\u001b[0m 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164\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mflux\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mflux\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;34m\"\"\"Returns the flux for all good-quality cadences.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhdu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FLUX'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquality_mask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, { "data": { - "image/png": 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m7y2qBNa5lO+sVlVLKR0EHgeuBmZFRE321GJgV7a9A1gCkD0/E2jp257zGklSBfMDtSRJkjQ6DWZVtbqImJVtTwZ+AXgJ+D7w0azbKuCRbHtt9pjs+e+llFLWfnu26tp5wHLgqVIdiCRJkiRJkkqr5sxdWAQ8kK2AVgU8nFL6VkRsBh6KiM8BzwBfzvp/GfhaRDRRPNPodoCU0osR8TCwGegG7kop9ZT2cCRJkiRJklQqZwyOUkrPA5fntL9GzqpoKaUO4LZ+9vV54PNnP0xJkqSxz6m8JEnSWHNWcxxJkiTprYncdUIkSZJGN4MjSZIkSZIk5TI4kiRJkiRJUi6DI0mSJEmSJOUyOJIkSRohKTk9tiRJGlsMjiRJkkaCc2NLkqQxyOBIkiRJkiRJuQyOJEmSJEmSlMvgSJIkSZIkSbkMjiRJkkaIU2NLkqSxxuBIkiRpBDg3tiRJGosMjiRJkiRJkpTL4EiSJEmSJEm5zhgcRcSSiPh+RLwUES9GxKey9jkRsS4itmT3s7P2iIj7IqIpIp6PiCv67GtV1n9LRKwavsOSJEmSJEnSUA3mjKNu4LdTShcDVwN3RcQlwN3A+pTScmB99hjgZmB5dlsN3A/FoAm4B7gKuBK450TYJEmSVBGcHVuSJI0xZwyOUkq7U0pPZ9uHgZeAemAl8EDW7QHg1mx7JfDVVPQkMCsiFgE3AutSSi0ppVZgHXBTSY9GkiRplIpwemxJkjT2nNUcRxGxDLgc+AmwIKW0G4rhEjA/61YPbO/zsh1ZW3/tkiRJkiRJGoVqBtsxIqYBXwd+K6XUNsC3ZnlPpAHa895rNcXL3Fi6dOlghyiNKQ0NDTQ0NADQ3Nxc5tFIw+Ns6jx5CY/GKH+eqxJY56oE1rmUb1BnHEXEBIqh0V+llP4+a96bXYJGdr8va98BLOnz8sXArgHaT5NSakgpFVJKhbq6usEeizSmrF69msbGRhobG7HONV4Nvs69hEdjlz/PVQmsc1UC61zKN5hV1QL4MvBSSulP+jy1FjixMtoq4JE+7R/PVle7GjiUXcr2GHBDRMzOJsW+IWuTJEmqCJ5YJ0mSxprBXKp2DfCrwAsR8WzW9vvAvcDDEXEnsA24LXvuUeAWoAloB+4ASCm1RMRngQ1Zv8+klFpKchSSJEmjnOfVSZKkseiMwVFK6Uf0/7vO9Tn9E3BXP/taA6w5mwFKkiRJkiSpPM5qVTVJkiRJkiRVDoMjSZIkSZIk5TI4kiRJGiHFK/olSZLGDoMjSZKkERDOji1JksYggyNJkiRJkiTlMjiSJEmSJElSLoMjSZIkSZIk5TI4kiRJGiFOjS1JksYagyNJkqQR4NzYkiRpLDI4kiRJGgFHO3t440B7uYchSZJ0VgyOJEmSRsC6zXvLPQRJkqSzZnAkSZIkSZKkXAZHkiRJkiRJymVwJEmSJEmSpFyDCo4iYk1E7IuITX3a5kTEuojYkt3PztojIu6LiKaIeD4irujzmlVZ/y0Rsar0hyNJkiRJkqRSGewZR18Bbjql7W5gfUppObA+ewxwM7A8u60G7odi0ATcA1wFXAnccyJskiRJkiRJ0ugzqOAopfQDoOWU5pXAA9n2A8Ctfdq/moqeBGZFxCLgRmBdSqklpdQKrOP0MEqSVKFSuQcgSZIk6TQ1Q3jtgpTSboCU0u6ImJ+11wPb+/TbkbX11y5JqnD/9JLLlEuSJEmj0VCCo/5ETlsaoP30HUSspniZG0uXLi3dyKRRpKGhgYaGBgCam5vLPBppeFjnqgTWuSqBda5KYJ1L+Yayqtre7BI0svt9WfsOYEmffouBXQO0nyal1JBSKqSUCnV1dUMYojR6rV69msbGRhobG7HONV5Z56oE1rkqgXWuSmCdS/mGEhytBU6sjLYKeKRP+8ez1dWuBg5ll7Q9BtwQEbOzSbFvyNokSZIkSZI0Cg3qUrWIeBC4FpgXETsoro52L/BwRNwJbANuy7o/CtwCNAHtwB0AKaWWiPgssCHr95mU0qkTbkuSJEkao9Y+t4uOrh7+dWHJmTtLksaEQQVHKaWP9fPU9Tl9E3BXP/tZA6wZ9OgkSZIkjRm/+eAzAAZHkjSODMfk2JIkSZIq0LUr6mg52lnuYUiSSmgocxxJkiTpLB3u6Cr3EKRhE0DKXTdZkjRWGRxJkiSNoN7eco9AGj5VESRMjiRpPDE4kiRJGkF+qNZ4FmE4KknjjcGRJEnSCPIyHo1nEWE0KknjjMGRJEnSCPJDtcaz4hxHVrkkjScGR5IkSSOop9cP1Rq/IjyrTpLGG4MjSdKo0eZqU6oA//WRTeUegjRsnBxbksYfgyNJ0qixdf/Rcg9BGnb/uGlPuYcgDZsI8KQ6SRpfDI4kSaNGt582JGlMC8I5jjTuPbf9IE+93lLuYUgjxuBIkjRq/N3GHeUegiRpCCKcAF7j38ovPsG//tKPyz0MacQYHEmSRo1vPrOz3EOQJA1BRDg5tsa961bUcX7d1HIPQxoxBkeSpFGjvbOn3EOQJA1BVeClahr3qquqmFRTXe5hSCPG4EiSNKpsb2kv9xA0DvT0plH34fXypbNObi+7+9tlHIk0fHoTHDneXe5hSMOquqr4/4xUKUY8OIqImyLilYhoioi7h7o//8FqvNve0s7rrjSlCvKBP/4+19z7Pb6zaQ8btrbQ3tnNgSPHeXpbKykleiv0535XT++QPoxt2nmIlBIHjhw/Gah09/SSUjFgefSF3Rzv7v+Mry/986s8va0VgN7exNFsLJt3tfHdF/ew6+AxOrp6ONTexU/3HuaVPYfp7O49+fojx7v5/Lc3s3lXGw9v2E5vb2J7SzttHV28uOsQ+9o6AOjs7uVX/uwJXm0+QtO+w3R09fBE037WPreLhzdsZ8+hDr6zaTetRzvZc6iDo8e7WXb3t/nLJ984eZzfe3kvb/v9Rznv04/SuLU4eem+tg52tLbzqYee4Yvfb+JP1v2UT/zFU2zaWXzv/+0vN/Lynjb+pWk/z20/yD2PbOLRF3az7O5vs/KLT/A3G7bxoy373/KfP8D//SvvfNPjS/7rd3j0hd184xnn9tL48Q/P7WL/kc5yD0MaVjVVVfSMsi8npOFUM5JvFhHVwBeBXwR2ABsiYm1KafNb2d/dX3+ezu5e/uTfXFbKYUqjygf++PtMmlDFy5+9udxDkUbMzoPH+I9/uXHQ/T91/XJqa6qYMamGI8d7mDN1Ar/39Rf4tWvOY/3Le7njfct479vmMX1SDetf2sutl9dTXRX8w3O7CIJ3Lp7JkjlT2Lyrjdb2Ti5fOouV//MJdh/q4D3LZvNr15xHZ08vlyyaQcvRTr79wm5e2HmIIx3dbNl3BIC//vdX8ZlvbeZT1y/nu5v3ctd1b6Oto5uXdrfxf35jE9etqOPWy+v51EPPnjb+6qrgo1cs5rkdB3l9/1EuWjSD57YfPPn8whmT2JMFK2Pd//fD1wH43a8/P2C/6//7P5/Vfv/LNzfxX7656bT2j/6vgScvffyV5pPb/7hpz5uee+DHxTDque0HT/59bL33Q2c1rr4uXjTjTY/bO3v4T3/1NAD/+W+eO63/hy89h+svnn+yZv7xUx+guydx7rwp9PYmDhzt5FhnD++onwlwMlTt7k3sbetgyZwpQPELiHNmTaa6Kt7y2KF4+VHEwPs40edgeye7D3WwePZkJlRXMaG66k3vXwwsoSpra9p3hGVzp1BTXUVXTy+HjnUxb9pEjh7vprsnMXFCFZMmVNPZ3UttTRVHjnezaechJtZUsbftOB+8aD69KfG3G3fQdqyL975tLsvnT2NqbQ2Hj3fTfLiDqgjOmTWZV5uPMLW2hmNdPbQe7aSto4ufvN7ClNpqJk+o5pfedQ4v72njvefPY8u+wzz24h7eUT+Trp7EtpZ2Llo4nbrpE/nNB5/hw5eewwXzpzFz8gTu/+dXmVBdxfL503it+SjvPnc2m3YdYtHMSSycMZn1L+/lWGcP937knUyaUM2PXz3A5779EhNrqnjPsjn8zo0reHb7QV5rPsLbz5nJt17Yzf9xw4UcONLJxJoqPvm1jbx/+Tz+cdMePnLFYl7a3cbm3W2cN28qS+dM4f0XzOPrT+/g5T2HgeIcLN/P6vucmZPYdaiDmqrgE+9bRndvYt3mvcybVstzOw696e/wn3/nWs6dO/S5W3p605BrThqt9h85TlP2/79UCWIkT+OOiPcCf5BSujF7/GmAlNIf9feaQqGQGhsbc5/70H0/5MVdbXztzivp6OplztRaJlQHVRH0pkRVzi83/bVLw6W2pooLF0wfsE+hUKC/Or/sM9/lYHsXH3/vuVx30XymT6yhNxX3WxXZsrcMX11HcHKSy77bQ3HqPlLO+itBnHy/kfgn2/cY38prB3pd3p/Zqf3z/oxPPf6hjHG4DbXOvWxHY8WZgqOB6hysdY0NQ6nzodb4whmTqJ89mY1vtDKhOpg6sYaFMyadDMQA6mdNBopfMly+dBbPbCsGu5cvncUvv+sc1j63i2ezsPd//bsr+O2Hn2Pe9In8xgeXs+3AUe77XhMfuWIxN759AZ/79kssmTOZJ5oOnDaWJXMm09HVS/Ph49z8joX8y6sHOHSsa0jHN21i8Xv7U88g/fClxXH35/+57VJmTKphb1sH/9cjLw5pDIL//AsX8qlfWD5gn7Ot85mTJ3DoWBc/f2Ed166o49Ils/jxqwfYdqCdlZedw5//6HUuWTSDt82fSldP4lhnD/WzJjNtUg1PNO3nuovms6/tOIeOdXLJopl09fYWz85tbR8wzD1wpJPW9k4umD/t5OeBE5/xdx48xk9ea2HFwumcO3cK0ydNKAb32WuL3YqPjh7v4VhXD729iePdvbR1dHH5ktl09/bywy37Ob9uKq81H+UDy+cxobqKLfsOc+BIJ1eeN4ddBzt48rUDzJhUwwcurOOVPYc51tnDhQuns7P1GO2d3SyePYUdre1ccs4MJlQX54jq7OnleHcPVRFMqa0++fv/P720lyMd3XzwovlMnzQB+NnvxQePddLdm3jq9RaefqOVpXOm8I76mZxfN5UptTV09/Ry8FgXHV09zJlaSxAcPNZJ/azJ7DnUwf6jnRzp6Ob8uqnUz5rMtpZ25kytpaunl+0tx3jsxT1UVwVXnTeH5Qum0dHVS3tn8UvJlOBYVw8v7DzE9Ik1vL1+Jk9s2c/7LpjLxjdamTW5lvPmTeW1/UeYM3Uiz+84yDUXzGPetFq27D1CW0cX76ifyTef2clV581l2bziFzyvNR/lbzfuYPn8acyYPIEb376QDa+38D++t4UPLK/jinNn855ls+nuTTQfPs5Tr7fwrsUz2dF6jLfVTaU3QUdXDzMmTWDOtFqC4kIFE2uqTvvS6lQRsTGlVBiwEyMfHH0UuCml9O+zx78KXJVS+vX+XjNQcOQvXxoLFs+ezI9+74MD9vEDtca6odb5hq0t3HaGM0Ok0WCowdEjz+7MPetMGk3KGRxJI2XL529mQnX/M7dY5xrrlsyZzA9/d+DfzwcbHI3opWpA3vfkpyVXEbEaWA2wdOnSfnf2409/kE899Cy/9K5FtB7tYuncyUyfOIHe7DTpU0OxiCAonnWUN4joc/9WlGIfGn8m1+avuNDQ0EBDQwMAzc3NuX0Anvz09Vz9R+s5v24qv3jJAi5eOIMZk2s41tnLhOqgqyfRtO8IFy8a+GwP4LRLDE78G+lbu/Dm+s2Lls+2vk/9N5F3dtSpTb3pZ5c89KZ08vV993Wmf2sDHdfp73/KJRR9+vf9eXKive+4Tjzft9+pf9an/pmeeG3f/Z147zjl26LeVFylJq9Pv8fez8/B4TLUOn/Psjm8/NmbaO/s4a9/8gadPcW5jFraO3n6jVZWLJzOioXT+dvGHUybWMP1F8/n9f1H2fhGK8vmTuWp11vo7Oll+qQaDnd0UxUwsaaapXOmsL21fcDV2mqrq+jsKc7Fc+LfFBTDsEUzJ7G95Rjdvb1vmrNj8oRqenoTc6fVcu7cKew62MG2lnbmTZvI/iPHmVAdBHFyvxfMn8aeQx3MmjKBc+dOYVtLOxcvnMFFi2bwzLZWGre2cqyrOMYTl5RctHA6NdXBa81H6U2Jc+cAOAQ4AAAgAElEQVRMZf+R4yxfMI36WVP4h+d2MbGmioUzJ/H2c2bwxKsHqK2uYtrEGqZNqqG6KpgxaQIR0N7ZzfsvqOOxF/dw9Hg3bR1dpAQfefditre007i1lesums8z21q5eNEMvv3CbupnTaYqYOmcKTQ1H+HdS2fzo6YDvLN+Budk39D1psSBI50snj2Fto4uLlo4nfUv7WP5gmlce2EdP9iyn32HO1g4YxKFZXN4aXcbvQle3XeEK86dxV8+uY23nzOD6ZNqeP8F89h9qIOWo51cvGgGx7p6mDaxhm0H2rlo0fTifEad3dRNm8QLOw9SN30iE2uq+cnrLVy3oo7Fs6fw+v4j7D7UwQXzp7FgxiR6sm/m9h3u4IL50/nOpt3MmlLLkY5uli8ofjN7vLuXc2ZOoiclXms+ymVLZvHj1w5QP2sy586dwr+8eoCjx7upiuAPV759SHUOsPKyet5/wTya9h2hqydx4cJp/NPmfew5dIx/emkfuw4do7a6in2Hj7N8/jQ6unuYO3Uim3e1nawnKJ61MBonIO77b2ikTZ5QzbGuHqbUVp/1Co0fWD6PZ7cfZMnsYr1fvmQWr+0/SvPh46f1ra2pIqXEghmTANjReuzkc32Pv7oqTpuLc/70iezL9nnevKkc7+ph16HiZajTJ9Vw7twpbNrZxszJE1g0s3iWzaWLZ7K37Th72jqYWlvNlIk1J89AnVAd7D7UQd30iScvXzxn5iSmTqxhy74j1FQF3b2J+lmT2XnwGB+5YjFff3oHtTVVdHb3snDGJC5fOosNW4tzyB042slX7nhP7p/RYOv89T+6hV/+nz9i0862wfzRn+a8eVM5cOQ4bR0/q+8TPxfzvKN+xpve619dXs/fP7Pz5OM//si7+N2vP8/0iTX87k0r+OGW/Xx3814uWjid37x+Of/pr55m3rTafudlOvH/yok/w+Fy62Xn8M1n+z/jaM0nCsyfPomD7V38uy//ZFjGsHRO8f+nSnD/v70iNzQabJ3/9HM3864/fIyOrp/9XJ4ztZaWo51MqA5+6V3nsPKyc/jCup+y7/BxfufGFfzvDz/H+y+Yx7J5U6ifNYWf7j3ML1y8gIk1VTz+031cungWm3e3URXB1efPpTcldrQeo7c3sXj25H5/19p9qIPndxziuhV11GS/fwAQsHX/Uf7kuz/ljmuWMam2mgvnT6e6Ok48ffLzMcCLu9qYOrH4Hjtaj7Fl72E++u4lTKgOHm7czjvqZ/LoC7v5N+9ZytI5U/jhlmb2tnVw27uX0NR8hK/9+A2mTqzm07dczNpndzFpQhXdPcWzlza+0crPr6jjx68e4CNX1HPhgulURdDd28u+w8epra5i3rSJJIqXuX7jmR1Mqa3hiqWzWDRz8ps+jxw93s3B9k7+4l+28saBds6fN5Vb3rmIto4ufm55HbU1VbS2d7Lz4DHmTq2lvbOHn+49zPveNo/9R46zo/UYL+w8xIfeuYi502o52N7F5AnVtLZ38vdP72Tz7jamTazh/LqprLysnjlTJ/Dc9kOcO3cKHV29TJ1YzXdf3Muz2w9yzy9fwk9eb+H8uqk8/UYrddMnMWlCFU9vO8gvXDSfx3/azPveNpdzZk3miab9dPX0css7F3Hf+i3c8s5FvGfZHAAOHD3OZ/5hM929iavOm8vHrlzC//heE89uP0j9rMm8/4J5/OIlC+juTWzedYgnXj3AtRfWMaGmiu0t7dTPnszW/Uf5+QvnM2ViNaTiFR2lXPlvTF+qJo0XZ/qGWhoPrHNVAutclcA6VyWwzlUJBnvG0UivqrYBWB4R50VELXA7sHaExyBJkiRJkqRBGNFL1VJK3RHx68BjQDWwJqXk7G6SJEmSJEmj0EjPcURK6VHg0ZF+X0mSJEmSJJ2dkb5UTZIkSZIkSWOEwZEkSZIkSZJyGRxJkiRJkiQpV6SUyj2GAc2bNy8tW7as3MOQhtXWrVuxzjXeWeeqBNa5KoF1rkpgnasSbNy4MaWUznhC0YhPjn22li1bRmNjY7mHIQ2rQqFgnWvcs85VCaxzVQLrXJXAOlcliIinB9PPS9UkSZIkSZKUy+BIkiRJkiRJuQyOJEmSJEmSlMvgSJIkSZIkSbkMjiRJkiRJkpTL4EiSJElSSXxn026+8cyOcg9DklRCNeUegCRJkqTx4T/+ZXFl51+5fHGZRyJJKhXPOJIkSZIkSVIuzziSJEmSVBI/d2Edh451lXsYkqQS8owjSZIkSSUR5R6AJKnkDI4kSZIkSZKUy+BIkiRJUumkVO4RSJJKyOBIkiRJUkmE16pJ0rhjcCRJkiRJkqRcBkeSJEmSJEnKZXAkSZIkSZKkXAZHkiRJkiRJymVwJEmSJKlkXFNNksYXgyNJkiRJJeGiapI0/hgcSZIkSZIkKZfBkSRJkiRJknIZHEmSJEmSJCmXwZEkSZIkSZJyGRxJkiRJkiQpl8GRJEmSpJJJqdwjkCSV0hmDo4hYEhHfj4iXIuLFiPhU1j4nItZFxJbsfnbWHhFxX0Q0RcTzEXFFn32tyvpviYhVw3dYkiRJkkZaRJR7CJKkEhvMGUfdwG+nlC4GrgbuiohLgLuB9Sml5cD67DHAzcDy7LYauB+KQRNwD3AVcCVwz4mwSZIkSZIkSaPPGYOjlNLulNLT2fZh4CWgHlgJPJB1ewC4NdteCXw1FT0JzIqIRcCNwLqUUktKqRVYB9xU0qORJEmSJElSyZzVHEcRsQy4HPgJsCCltBuK4RIwP+tWD2zv87IdWVt/7ZIkSZIkSRqFagbbMSKmAV8Hfiul1DbA9ct5T6QB2vPeazXFy9xYunTpYIcojSkNDQ00NDQA0NzcXObRSMPDOlclsM5VCaxzVQLrXMoXaRDLHkTEBOBbwGMppT/J2l4Brk0p7c4uRXs8pbQiIr6UbT/Yt9+JW0rpk1n7m/r1p1AopMbGxrd6fNKYUCgUsM413lnnqgTWuSrBQHX+a1/ZQPPh4/zDb7x/hEcllZY/z1UJImJjSqlwpn6DWVUtgC8DL50IjTJrgRMro60CHunT/vFsdbWrgUPZpWyPATdExOxsUuwbsjZJkiRJ40TKv6hAkjRGDeZStWuAXwVeiIhns7bfB+4FHo6IO4FtwG3Zc48CtwBNQDtwB0BKqSUiPgtsyPp9JqXUUpKjkCRJklR2/U5mIUkas84YHKWUfkT//wdcn9M/AXf1s681wJqzGaAkSZIkSZLK46xWVZMkSZIkSVLlMDiSJEmSJElSLoMjSZIkSZIk5TI4kiRJkiRJUi6DI0mSJEklk1K5RyBJKiWDI0mSJEklEf2txSxJGrMMjiRJkiRJkpTL4EiSJEmSJEm5DI4kSZIkSZKUy+BIkiRJkiRJuQyOJEmSJEmSlMvgSJIkSVLJpFTuEUiSSsngSJIkSVKJRLkHIEkqMYMjSZIkSZIk5TI4kiRJkiRJUi6DI0mSJEmSJOUyOJIkSZIkSVIugyNJkiRJJeOiapI0vhgcSZIkSSqJcFE1SRp3DI4kSZIkSZKUy+BIkiRJkiRJuQyOJEmSJEmSlMvgSJIkSZIkSbkMjiRJkiRJkpTL4EiSJElSyaSUyj0ESVIJGRxJkiRJKoko9wAkSSVncCRJkiRJkqRcZwyOImJNROyLiE192v4gInZGxLPZ7ZY+z306Ipoi4pWIuLFP+01ZW1NE3F36Q5EkSZIkSVIpDeaMo68AN+W0fyGldFl2exQgIi4Bbgfenr3mzyKiOiKqgS8CNwOXAB/L+kqSJEmSJGmUqjlTh5TSDyJi2SD3txJ4KKV0HHg9IpqAK7PnmlJKrwFExENZ381nPWJJkiRJkiSNiKHMcfTrEfF8dinb7KytHtjep8+OrK2/dkmSJEmSJI1SbzU4uh94G3AZsBv471l73kIKaYD2XBGxOiIaI6Kxubn5LQ5RGt0aGhooFAoUCgWsc41X1rkqgXWuSmCdqxJY51K+SKnf/OZnnYqXqn0rpfSOgZ6LiE8DpJT+KHvuMeAPsq5/kFK6MWt/U7+BFAqF1NjYeOYjkcawQqGAda7xzjpXJbDOVQkGqvNPfq2RNw60853f+rkRHpVUWv48VyWIiI0ppcKZ+r2lM44iYlGfh78CnFhxbS1we0RMjIjzgOXAU8AGYHlEnBcRtRQn0F77Vt5bkiRJkiRJI+OMk2NHxIPAtcC8iNgB3ANcGxGXUbzcbCvwSYCU0osR8TDFSa+7gbtSSj3Zfn4deAyoBtaklF4s+dFIkiRJkiSpZAazqtrHcpq/PED/zwOfz2l/FHj0rEYnSZIkSZKkshnKqmqSJEmSJEkaxwyOJEmSJEmSlMvgSJIkSVLJDGLRZknSGGJwJEmSJKkkgij3ECRJJWZwJEmSJEmSpFwGR5IkSZIkScplcCRJkiRJkqRcBkeSJEmSJEnKZXAkSZIkSZKkXAZHkiRJkkomkco9BElSCRkcSZIkSSqJiHKPQJJUagZHkiRJkiRJymVwJEmSJEmSpFwGR5IkSZIkScplcCRJkiRJkqRcBkeSJEmSSia5qJokjSsGR5IkSZJKwlXVJGn8MTiSJEmSJElSLoMjSZIkSZIk5TI4kiRJkiRJUi6DI0mSJEmSJOUyOJIkSZIkSVIugyNJkiRJJZPKPQBJUkkZHEmSJEkqiSDKPQRJUokZHEmSJEmSJCmXwZEkSZIkSZJyDSo4iog1EbEvIjb1aZsTEesiYkt2Pztrj4i4LyKaIuL5iLiiz2tWZf23RMSq0h+OJEmSJEmSSmWwZxx9BbjplLa7gfUppeXA+uwxwM3A8uy2GrgfikETcA9wFXAlcM+JsEmSJEmSJEmjz6CCo5TSD4CWU5pXAg9k2w8At/Zp/2oqehKYFRGLgBuBdSmllpRSK7CO08MoSZIkSZIkjRJDmeNoQUppN0B2Pz9rrwe29+m3I2vrr12SJEnSOJFSKvcQJEklVDMM+8xbgzMN0H76DiJWU7zMjaVLl5ZuZNIo0tDQQENDAwDNzc1lHo00PKxzVQLrXJVg0HWe9xu/NEb481zKF4P9RiAilgHfSim9I3v8CnBtSml3dina4ymlFRHxpWz7wb79TtxSSp/M2t/Urz+FQiE1Nja+hUOTxo5CoYB1rvHOOlclsM5VCQaq87v++mle3t3G+t++dmQHJZWYP89VCSJiY0qpcKZ+Q7lUbS1wYmW0VcAjfdo/nq2udjVwKLuU7THghoiYnU2KfUPWJkmSJEmSpFFoUJeqRcSDFM8YmhcROyiujnYv8HBE3AlsA27Luj8K3AI0Ae3AHQAppZaI+CywIev3mZTSqRNuS5IkSZIkaZQYVHCUUvpYP09dn9M3AXf1s581wJpBj06SJEmSJEllM5RL1SRJkiRJkjSOGRxJkiRJKpnBLb0jSRorDI4kSZIklUSUewCSpJIzOJIkSZIkSVIugyNJkiRJkiTlMjiSJEmSJElSLoMjSZIkSZIk5TI4kiRJkiRJUi6DI0mSJEmlk8o9AElSKRkcSZIkSSqJiCj3ECRJJWZwJEmSJEmSpFwGR5IkSZIkScplcCRJkiRJkqRcBkeSJEmSJEnKZXAkSZIkqWRcVE2SxheDI0mSJEkl4ZpqkjT+GBxJkiRJkiQpl8GRJEmSJEmSchkcSZIkSZIkKZfBkSRJkiRJknIZHEmSJEmSJCmXwZEkSZKkkkkplXsIkqQSMjiSJEmSVBIR5R6BJKnUDI4kSZIkSZKUy+BIkiRJkiRJuQyOJEmSJEmSlGvIwVFEbI2IFyLi2YhozNrmRMS6iNiS3c/O2iMi7ouIpoh4PiKuGOr7S5IkSZIkaXiU6oyj61JKl6WUCtnju4H1KaXlwPrsMcDNwPLsthq4v0TvL0mSJEmSpBIbrkvVVgIPZNsPALf2af9qKnoSmBURi4ZpDJIkSZJGWCr3ACRJJVWK4CgB342IjRGxOmtbkFLaDZDdz8/a64HtfV67I2uTJEmSNMZFuQcgSSq5mhLs45qU0q6ImA+si4iXB+ib93/JaV9KZAHUaoClS5eWYIjS6NPQ0EBDQwMAzc3NZR6NNDysc1UC61yVwDpXJbDOpXyRUulOJo2IPwCOAP8BuDaltDu7FO3xlNKKiPhStv1g1v+VE/3622ehUEiNjY0lG6M0GhUKBaxzjXfWuSqBda5KMFCd/9ZDz/DM9oP88+9cN8KjkkrLn+eqBBGxsc9c1f0a0qVqETE1Iqaf2AZuADYBa4FVWbdVwCPZ9lrg49nqalcDhwYKjSRJkiRJklQ+Q71UbQHwjYg4sa+/Til9JyI2AA9HxJ3ANuC2rP+jwC1AE9AO3DHE95ckSZIkSdIwGVJwlFJ6Dbg0p/0AcH1OewLuGsp7SpIkSZIkaWSUYlU1SZIkSQKghFOoSpJGAYMjSZIkSSWRTWEhSRpHhjrHkSRJkiQB8I1ndpZ7CJKkEvOMI2mUe277QTZsbSn3MKRh9eRrB3j8lX3lHoYkSZKkU3jGkTTKrfziEwBsvfdDZR6JNHxub3gSsM4lSZKk0cbgSBrlfv7COva2dZR7GJIkSZKkCmRwJI1yEVBb41WlGt+uXVFH69HOcg9DkiRJ0in8NCqNcoHL2qoyWOaSJEnS6GNwJI1yEUHyI7XGOQNSSZIkaXQyOJJGOT9QqxJERLmHIEmSJCmHwZE0ykUYHKkyeGadJEmSNPoYHEmjXvhxWuOe5xtJkiRJo5PBkTTKFc84MjrS+GeZS5IkSaOPwZE0ynkmhiqBUxxJkiRJo5PBkTTKOceRKoV1LkmSJI0+BkfSKBeEkwarAnjKkSSNJ0ePd5d7CJKkEjE4kkY5zzhSpbDMNd7d9Kc/oPC5deUehjQiunv9qS5J40VNuQcgaWARfqDW+Ock8KoEL+85XO4hSJIknTXPOJJGuSD8QC1J48BFC6czb1ptuYchjQgXPZCk8WNMn3HU1dPL8e5epk0c04chDcwzjlQB/HyhSrBo5iRqa/zOTpXB77wkafwY07+9rFrzFJ9Y81S5hyENqwCTI417fjOtShAR9PppWhXiO5t2l3sIkqQSGdPBUW1NFZ09veUehjSsIsLcSBXBz9Ma76pc7EAV5Adb9pd7CNKwub3hx7zvj9aXexjSiBnTwVFNVdDd429gGt8CJw3W+BderKYKUDzjqNyjkEbGt5/3jCONX0/+/+zdeXxc9Znn++9TizZLlizLuxEy2EDYAxVIAiQkENaeJpnbmYbcScjWJn1hknT33GlI96tDJyHQcydNJ7czdNSJA7kToLNMggMkxJCFkIZgmdUY8I4tW9iyJcvapap67h91JGT7aLFUUpWqPu/XSy9V/c7vnPMr+XHVqef8lu1t2tvRl+tmADNmVieOInT5RhFgVTUUg+ZDPXp9HytOobBFWD0QAADMQrM6cRSNmFLcukOBy/Q4ynUrgOm1cc/hXDcBmHbc8AIAALMRiSMAAIAZEGGoGopMa2d/rpsAAMiC2Z844s4dCtxPX9irXW09uW4GAGCKzESPIxSVd9zxuCSps29Qz2w/mOPWANmztLos100AZtSMJ47M7Coze93MtprZrVM5Fj2OUEyYFwPF4P99YkuumwBMm4gZQ49RdBpufURn3f5LXd/4jLbu79TPXtyrg13H9kTqGUiqbzCVgxYCx4+JsVFsYjN5MjOLSvqmpA9Iapa03szWuvumyRyvpz+l5vbebDYRyFuDKVdJjJWnUNi+tm6zvrZus/722rfpK4+8Kkl68YtXqLo8LimTQDXL3DToHkgqHomovCR6xDHcXdtau3S4L6nz6ufN+GuYjfqTKUXMFI9G1NWfVGksong0d52Sdx7o1onzK2Q2c+957d0Disciqiydvkuj53a1c92Conb5Pz55TNnS6rIjvoR/8yPnKR41tXUP6OqzluiHTbtVVRbTn5x/gqKRt94TUmlXR++gaueUyN2VSruiERv+jIhYZiXD3oGUykuiwzfgegdTKo9Hj+v9Ze+hXi2sKlUsGlEylZYrs7qze6YnYc9ASsmUa255TAe6BrSgqnR433TaZZZp744D3Wqom6Pu/qR2HuxRfW2FaueUSJKSqbQiZopETH2DKZXGImpu79XmfZ2qr61QNGJySSfMq9CdP39V+zv79ZEL6lVdHtfJCyq1t6NXJ9XN0cHuAa15aodWLqzU2+vnqbWzX2l3ff8Pu7R1f5fu//SFenr7QZ21rFrdA0mtXFCpN9p6tK+jT90DKb28p0MLqkr1nlV16ugd1I1rntW7V9bpE+9u0LJ55Xr3Xb/SxSvrVBaPandbj/7iA6dox4Fu1VWWKh41ff+ZXUq761sfPV+S9Mf//HvtOZR533vvKQv0Z5ecpIFUSpv2Htb2A93a3dajj76rQXWVJSqLR/XKng65pO2t3TplUZWe2X5Q6zbtU31thS48qVY3XFCvnz6/R996crsk6a7/eJYan9yu7Qe6ZSaVx6PqGcgkIP/o7CV6OGR1v1ULK/XN//M8nbKoasIxMJq+wZTK4tHxKwKznM1kLwYze5ek2939yuD5bZLk7neOtk8ikfCmpqbQbQ23PiJJOq++Rp+9bJVq55SoNBZVRUnmJxaJKJlOa05pTGn34Q+bwZSrNBYZXvx56C8w9KfwEWtYDZVFI3ZE/YiZIqYj5ioY+lu6MhMaR0Z8IPlR20eey484xlvnzzx+69hD+1WUROWe6e4+1A4Lfmc+KG14W9pdac+s5BId8QE3mMp8iEka/qCVhu6GulLBB/DQ8UZO6Okj2jP8NxrxeixYBqyi9K030f5kWiXRiOLRIz+kh9pmZhpIpjMfwpKS6bQGkmmVxCLDf5/u/qRK41HFgrbGIqbBVKatQ2VDf5eSWEQDyfRwW80y/35D/zbpoKw8HtVgKq1kOnOMoeP0J9OKRU3RYD6KnoGkIkE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\n", 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" ] @@ -123,32 +172,2790 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 229, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " #periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " #periodogram = periodogram.flatten()\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " #def plotpixels(self):\n", + " \n", + " \n", + " \n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "tpfperiod=PixelMapPeriodogram(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 210, "metadata": {}, "outputs": [ { "data": { - "text/latex": [ - "$[0.007173574,~0.014347148,~0.021520722,~\\dots,~359.99147,~359.99864,~360.00581] \\; \\mathrm{\\frac{1}{d}}$" - ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], - "source": [] + "source": [ + "lightcurve = tpf.to_lightcurve(aperture_mask=mask)\n", + "masks = np.where(lightcurve.quality == 0)" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 58.9 ms\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%%time \n", + "import time\n", + "\n", + "plt.plot(tpfperiod.periodogram[0][0].frequency, tpfperiod.periodogram[0][0].power)\n", + "plt.xlim(1,350)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679)],\n", + " 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TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679)]], dtype=object)" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpfperiod.periodogram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interactive plot\n", + "1. Allow for user defined frequency range selection (Slider)\n", + "2. Integrate the power of that range over the frequency width\n", + "3. Plot a heat map\n", + "4. Create sample data and test." + ] + }, + { + "cell_type": "code", + "execution_count": 50, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "aperture = tpf.pipeline_mask\n", + "mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + "mask[0][0] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "lcc=tpf.to_lightcurve(aperture_mask=mask)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2018-07-25 19:07:39.356', '2018-07-25 19:09:39.354',\n", + " '2018-07-25 19:11:39.352', ..., '2018-08-22 16:11:00.470',\n", + " '2018-08-22 16:13:00.467', '2018-08-22 16:15:00.464'], dtype=' low)].sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " \n", + " def frequency_heat_plot(self,low=1,high=2):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(self.aperture)):\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = self.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " plt.imshow(heat_stamp)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'PixelMapPeriodogram' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtpfperiod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPixelMapPeriodogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtpf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'PixelMapPeriodogram' is not defined" + ] + } + ], + "source": [ + "tpfperiod = PixelMapPeriodogram(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'aperture' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtpfperiod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency_heat_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mfrequency_heat_plot\u001b[1;34m(self, low, high)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[0mheat_stamp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msums\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[0mheat_stamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheat_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheat_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'aperture' is not defined" + ] + } + ], + "source": [ + "tpfperiod.frequency_heat_plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_plot(tpfperiod,low=0,high=1):\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " plt.imshow(heat_stamp)\n", + " #interact(frequency_heat_plot,low = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1), high = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "frequency_heat_plot(tpfperiod,65,72)\n", + "#freq =np.asarray(tpfperiod.periodogram[0][0].frequency)\n", + "#interact(frequency_heat_plot(tpfperiod),low = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1), high = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1));\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. if tuple, integrate over each value in tuple\n", + "2. if list use 1 over length of period as the width and integrate frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " \n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " #def interact(self):\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "\n", + "\n", + "def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + "def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + "def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + "def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + "def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + "def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + " \n", + "\n", + " \n", + " def origin(tpf):\n", + " tpfperiod=PixelMapPeriodogram(tpf)\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " return heat_stamp,tpfperiod\n", + " \n", + " heat_stamp,tpfperiod = origin(tpf)\n", + " fig.image(image = [heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + " \n", + " \n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + " \n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + "def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + "def show_interact_widget(tpf, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = tpf._parse_aperture_mask(aperture_mask)\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram= periodogram.flatten()\n", + " pg = periodogram\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.campaign, tpf.module, tpf.output)\n", + " elif tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.quarter, tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " tpf.targetid, tpf.sector, tpf.camera, tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "lc =tpf.to_lightcurve()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "pg =lc.to_periodogram()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "8a075029a3084ce783a80f31df1752e3" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8890')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Flatten\n", + "2. Remove outliers\n", + "periodograms\n", + "1. Divide periodogram values by the median" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tpf.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", + "lcc = lcc[np.where(lcc.quality == 0)]\n", + "lcc = lcc.flatten(window_length= 3001)\n", + "pgg = lcc.to_periodogram()\n", + "#pgg = pgg.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact(notebook_url='localhost:8890')" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "\n", + "\n", + "def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + "def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + "def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + "def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + "def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + "def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + " \n", + "\n", + " \n", + " def origin(tpf):\n", + " tpfperiod=PixelMapPeriodogram(tpf)\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " return heat_stamp,tpfperiod\n", + " \n", + " heat_stamp,tpfperiod = origin(tpf)\n", + " fig.image(image = [heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + " \n", + " \n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + " \n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + "def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + "def show_interact_widget(tpf, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = tpf._parse_aperture_mask(aperture_mask)\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method='bls')\n", + " periodogram.power= periodogram.power / np.median(periodogram.power)\n", + " pg = periodogram\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(method='bls')\n", + " pg_new.power= pg_new.power / np.median(pg_new.power)\n", + " #pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(method='bls')\n", + " #pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.campaign, tpf.module, tpf.output)\n", + " elif tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.quarter, tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " tpf.targetid, tpf.sector, tpf.camera, tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "428f1aba080c4ca5879c910462df8508" + } + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:bokeh.server.protocol_handler:error handling message Message 'PULL-DOC-REQ' (revision 1) content: {}: ValueError('Out of range float values are not JSON compliant',)\n" + ] + } + ], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8890')" + ] } ], "metadata": { diff --git a/Research.ipynb b/Research.ipynb index b89d907..c89cb83 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -10,23 +10,55 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import scipy as sp\n", - "import lightkurve as lk" + "import lightkurve as lk\n", + "from scipy import stats\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "from astropy import units as u\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 157, + "metadata": {}, + "outputs": [], + "source": [ + "import bokeh \n", + "from bokeh.io import show, output_notebook, push_notebook\n", + "from bokeh.plotting import figure, ColumnDataSource\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker\n", + "from bokeh.layouts import layout, Spacer\n", + "from bokeh.models.tools import HoverTool\n", + "from bokeh.models.widgets import Button, Div\n", + "from bokeh.models.formatters import PrintfTickFormatter" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [], + "source": [ + "#just setting up sample data\n", + "search_result = lk.search_targetpixelfile('TIC425064757', mission='TESS', sector=1)\n", + "tpf = search_result.download(quality_bitmask='default')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ "#just setting up sample data\n", - "search_result = lk.search_targetpixelfile('Pi Mensae', mission='TESS', sector=1)\n", + "search_result = lk.search_targetpixelfile('TIC142875987', mission='TESS', sector=4)\n", "tpf = search_result.download(quality_bitmask='default')\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -54,7 +86,7 @@ " for j in np.arange(postagestamp[1].min(),postagestamp[1].max()+1):\n", " \n", " #Creating a false mask to alter each iteration\n", - " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask = np.empty((len(aperture),len(aperture[0])), dtype=bool)\n", " \n", " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", " mask[i][j] = True\n", @@ -75,12 +107,29 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplotperiodograph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtpf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mplotperiodograph\u001b[1;34m(targetpixelfile)\u001b[0m\n\u001b[0;32m 30\u001b[0m \u001b[1;31m#extracting the light curve for the target pixel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[0mlightcurve\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtargetpixelfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_lightcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture_mask\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;31m#lightcurve = lightcurve[lightcurve.quality==0]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[0mperiodogram\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlightcurve\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_periodogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moversample_factor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mto_lightcurve\u001b[1;34m(self, method, **kwargs)\u001b[0m\n\u001b[0;32m 369\u001b[0m \"\"\"\n\u001b[0;32m 370\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'aperture'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 371\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextract_aperture_photometry\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 372\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'prf'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 373\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprf_lightcurve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mextract_aperture_photometry\u001b[1;34m(self, aperture_mask)\u001b[0m\n\u001b[0;32m 1562\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0maperture_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1563\u001b[0m \u001b[0mlog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Warning: aperture mask contains zero pixels.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1564\u001b[1;33m \u001b[0mcentroid_col\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcentroid_row\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_centroids\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture_mask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1565\u001b[0m \u001b[1;31m# Ignore warnings related to zero or negative errors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1566\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcatch_warnings\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mestimate_centroids\u001b[1;34m(self, aperture_mask)\u001b[0m\n\u001b[0;32m 493\u001b[0m \"\"\"\n\u001b[0;32m 494\u001b[0m \u001b[0maperture_mask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parse_aperture_mask\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture_mask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 495\u001b[1;33m \u001b[0myy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindices\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 496\u001b[0m \u001b[0myy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrow\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0myy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 497\u001b[0m \u001b[0mxx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumn\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mxx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mshape\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 160\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 161\u001b[0m \u001b[1;34m\"\"\"Return the cube dimension shape.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 162\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflux\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 164\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\lightkurve\\targetpixelfile.py\u001b[0m in \u001b[0;36mflux\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mflux\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 186\u001b[0m \u001b[1;34m\"\"\"Returns the flux for all good-quality cadences.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhdu\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'FLUX'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquality_mask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 189\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + }, { "data": { - "image/png": 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\n", 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" ] @@ -123,32 +172,2807 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 260, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " #periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " #periodogram = periodogram.flatten()\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " #def plotpixels(self):\n", + " \n", + " \n", + " \n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [], + "source": [ + "tpfperiod=PixelMapPeriodogram(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 258, "metadata": {}, "outputs": [ { "data": { "text/latex": [ - "$[0.007173574,~0.014347148,~0.021520722,~\\dots,~359.99147,~359.99864,~360.00581] \\; \\mathrm{\\frac{1}{d}}$" + "$[-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty] \\; \\mathrm{}$" ], "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" } ], - "source": [] + "source": [ + "tpfperiod.periodogram[0][0].power[np.where(tpfperiod.periodogram[0][0].power<0)]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 58.9 ms\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%%time \n", + "import time\n", + "\n", + "plt.plot(tpfperiod.periodogram[0][0].frequency, tpfperiod.periodogram[0][0].power)\n", + "plt.xlim(1,350)\n", + "plt.yscale('log')" + ] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679)],\n", + " 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TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679),\n", + " LombScarglePeriodogram(ID: TIC 261136679)]], dtype=object)" + ] + }, + "execution_count": 261, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpfperiod.periodogram" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Interactive plot\n", + "1. Allow for user defined frequency range selection (Slider)\n", + "2. Integrate the power of that range over the frequency width\n", + "3. Plot a heat map\n", + "4. Create sample data and test." + ] + }, + { + "cell_type": "code", + "execution_count": 50, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "aperture = tpf.pipeline_mask\n", + "mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + "mask[0][0] = True" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "lcc=tpf.to_lightcurve(aperture_mask=mask)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['2018-07-25 19:07:39.356', '2018-07-25 19:09:39.354',\n", + " '2018-07-25 19:11:39.352', ..., '2018-08-22 16:11:00.470',\n", + " '2018-08-22 16:13:00.467', '2018-08-22 16:15:00.464'], dtype=' low)].sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " \n", + " def frequency_heat_plot(self,low=1,high=2):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(self.aperture)):\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = self.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " plt.imshow(heat_stamp)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'PixelMapPeriodogram' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtpfperiod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPixelMapPeriodogram\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtpf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mNameError\u001b[0m: name 'PixelMapPeriodogram' is not defined" + ] + } + ], + "source": [ + "tpfperiod = PixelMapPeriodogram(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'aperture' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtpfperiod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency_heat_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m\u001b[0m in \u001b[0;36mfrequency_heat_plot\u001b[1;34m(self, low, high)\u001b[0m\n\u001b[0;32m 61\u001b[0m \u001b[0mheat_stamp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0msums\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m \u001b[0mheat_stamp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheat_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maperture\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 64\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheat_stamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'aperture' is not defined" + ] + } + ], + "source": [ + "tpfperiod.frequency_heat_plot()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_plot(tpfperiod,low=0,high=1):\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " plt.imshow(heat_stamp)\n", + " #interact(frequency_heat_plot,low = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1), high = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1))\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "frequency_heat_plot(tpfperiod,65,72)\n", + "#freq =np.asarray(tpfperiod.periodogram[0][0].frequency)\n", + "#interact(frequency_heat_plot(tpfperiod),low = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1), high = widgets.IntSlider(min=0,max=freq.max(),step=2,value=1));\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. if tuple, integrate over each value in tuple\n", + "2. if list use 1 over length of period as the width and integrate frequencies" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " \n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " #def interact(self):\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "\n", + "\n", + "def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + "def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + "def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + "def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + "def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + "def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + " \n", + "\n", + " \n", + " def origin(tpf):\n", + " tpfperiod=PixelMapPeriodogram(tpf)\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " return heat_stamp,tpfperiod\n", + " \n", + " heat_stamp,tpfperiod = origin(tpf)\n", + " fig.image(image = [heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + " \n", + " \n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + " \n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + "def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + "def show_interact_widget(tpf, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = tpf._parse_aperture_mask(aperture_mask)\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram= periodogram.flatten()\n", + " pg = periodogram\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.campaign, tpf.module, tpf.output)\n", + " elif tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.quarter, tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " tpf.targetid, tpf.sector, tpf.camera, tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "lc =tpf.to_lightcurve()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "pg =lc.to_periodogram()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "8a075029a3084ce783a80f31df1752e3" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8890')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Flatten\n", + "2. Remove outliers\n", + "periodograms\n", + "1. Divide periodogram values by the median" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 224, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tpf.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", + "lcc = lcc[np.where(lcc.quality == 0)]\n", + "lcc = lcc.flatten(window_length= 3001)\n", + "pgg = lcc.to_periodogram()\n", + "#pgg = pgg.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact(notebook_url='localhost:8890')" + ] + }, + { + "cell_type": "code", + "execution_count": 227, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 261, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "\n", + "\n", + "def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + "def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + "def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + "def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + "def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + "def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + " \n", + "\n", + " \n", + " def origin(tpf):\n", + " tpfperiod=PixelMapPeriodogram(tpf)\n", + " heat_stamp = []\n", + " aperture = tpf.pipeline_mask\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))\n", + " return heat_stamp,tpfperiod\n", + " \n", + " heat_stamp,tpfperiod = origin(tpf)\n", + " fig.image(image = [heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + " \n", + " \n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + " \n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = tpfperiod.periodogram[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + "def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + "def show_interact_widget(tpf, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = tpf._parse_aperture_mask(aperture_mask)\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method='bls')\n", + " periodogram.power= periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " pg = periodogram\n", + " \n", + " \n", + "\n", + " \n", + "\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(method='bls')\n", + " pg_new.power= pg_new.power / np.median(pg_new.power)\n", + " pg_new.power[np.where(pg_new.power<0)] = 0\n", + " #pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(method='bls')\n", + " pg_new = pg_new.power / np.median(pg_new.power)\n", + " pg_new.power[np.where(pg_new.power<0)] = 0\n", + " #pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.campaign, tpf.module, tpf.output)\n", + " elif tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " tpf.targetid, tpf.quarter, tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " tpf.targetid, tpf.sector, tpf.camera, tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 262, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "d6a42895392444e9a5565a3d04db5b47" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8890')" + ] + }, + { + "cell_type": "code", + "execution_count": 264, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[3.9779006,~3.9741829,~3.9704653,~\\dots,~0.12266673,~0.11894905,~0.11523137] \\; \\mathrm{\\frac{1}{d}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 264, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpfperiod.periodogram[0][0].frequency" + ] } ], "metadata": { From 1e0a34bd9142b010fb7e8daeaeefe2352e8fbf27 Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Fri, 22 Nov 2019 16:26:26 -0800 Subject: [PATCH 04/11] Draft --- .ipynb_checkpoints/Research-checkpoint.ipynb | 2155 ++++++++++++++---- Research.ipynb | 2128 +++++++++++++---- 2 files changed, 3454 insertions(+), 829 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index e00a492..27248b9 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,14 +20,14 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import bokeh \n", "from bokeh.io import show, output_notebook, push_notebook\n", "from bokeh.plotting import figure, ColumnDataSource\n", - "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker, Range1d\n", "from bokeh.layouts import layout, Spacer\n", "from bokeh.models.tools import HoverTool\n", "from bokeh.models.widgets import Button, Div\n", @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -204,10 +204,62 @@ " lc = lc.remove_outliers()\n", " \n", " #Making a periodogram for the pixel\n", - " #periodogram = lc.to_periodogram(oversample_factor=5)\n", " periodogram = lc.to_periodogram(method = 'bls')\n", - " #periodogram = periodogram.flatten()\n", " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " #def plotpixels(self):\n", + " \n", + " \n", + " \n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = periodogram.flatten()\n", " #Extending the list of periodogram data for each pixel\n", " pg.extend([periodogram])\n", " \n", @@ -227,7 +279,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 240, "metadata": {}, "outputs": [], "source": [ @@ -236,33 +288,28 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 258, "metadata": {}, "outputs": [ { "data": { + "text/latex": [ + "$[-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty,~-\\infty] \\; \\mathrm{}$" + ], "text/plain": [ - "" + "" ] }, - "execution_count": 210, + "execution_count": 258, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "lightcurve = tpf.to_lightcurve(aperture_mask=mask)\n", - "masks = np.where(lightcurve.quality == 0)" + "tpfperiod.periodogram[0][0].power[np.where(tpfperiod.periodogram[0][0].power<0)]" ] }, { @@ -675,6 +722,13 @@ " plt.imshow(heat_stamp)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make it so the frequency_data in the heat stamp can take a tuple of (freq,range centered) and store that in a sep. array, make plot function capable of plotting combination of heat stamps." + ] + }, { "cell_type": "code", "execution_count": 122, @@ -837,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1144,7 +1198,7 @@ " # Configure the stretch slider and its callback function\n", " stretch_slider = RangeSlider(start=low,\n", " end=high,\n", - " step=1,\n", + " step=.1,\n", " title='Frequency Range',\n", " value=(low, high),\n", " orientation='horizontal',\n", @@ -1154,7 +1208,11 @@ " show_value=True,\n", " sizing_mode='fixed',\n", " name='frequencyrange')\n", + " \n", + " \n", "\n", + "\n", + " \n", " def stretch_change_callback(attr, old, new):\n", " \"\"\"TPF stretch slider callback.\"\"\"\n", "\n", @@ -1420,10 +1478,72 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 22, "metadata": { "scrolled": false }, + "outputs": [], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8889')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Flatten\n", + "2. Remove outliers\n", + "periodograms\n", + "1. Divide periodogram values by the median" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tpf.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", + "lcc = lcc[np.where(lcc.quality == 0)]\n", + "lcc = lcc.flatten(window_length= 3001)\n", + "pgg = lcc.to_periodogram()\n", + "#pgg = pgg.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, "outputs": [ { "data": { @@ -1701,394 +1821,45 @@ "application/vnd.bokehjs_exec.v0+json": "", "text/html": [ "\n", - "" + "" ] }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "server_id": "8a075029a3084ce783a80f31df1752e3" + "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" } }, "output_type": "display_data" } ], "source": [ - "show_interact_widget(tpf,notebook_url='localhost:8890')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Flatten\n", - "2. Remove outliers\n", - "periodograms\n", - "1. Divide periodogram values by the median" + "tpf.interact(notebook_url='localhost:8890')" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 227, "metadata": {}, "outputs": [ { "data": { + "text/latex": [ + "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" + ], "text/plain": [ - "" + "" ] }, - "execution_count": 224, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], - "source": [ - "tpf.plot()" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [], - "source": [ - "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", - "lcc = lcc[np.where(lcc.quality == 0)]\n", - "lcc = lcc.flatten(window_length= 3001)\n", - "pgg = lcc.to_periodogram()\n", - "#pgg = pgg.flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.bokehjs_exec.v0+json": "", - "text/html": [ - "\n", - "" - ] - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "tpf.interact(notebook_url='localhost:8890')" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 237, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2395,7 +2166,7 @@ " # Configure the stretch slider and its callback function\n", " stretch_slider = RangeSlider(start=low,\n", " end=high,\n", - " step=1,\n", + " step=.1,\n", " title='Frequency Range',\n", " value=(low, high),\n", " orientation='horizontal',\n", @@ -2481,6 +2252,7 @@ " #Making a periodogram for the pixel\n", " periodogram = lc.to_periodogram(method='bls')\n", " periodogram.power= periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", " pg = periodogram\n", " \n", " \n", @@ -2527,6 +2299,7 @@ " #lc_new = lc_new.remove_outliers()\n", " pg_new = lc_new.to_periodogram(method='bls')\n", " pg_new.power= pg_new.power / np.median(pg_new.power)\n", + " pg_new.power[np.where(pg_new.power<0)] = 0\n", " #pg_new = pg_new.flatten()\n", " pg_source.data['power'] = pg_new.power\n", " pg_source.data['frequency'] = pg_new.frequency\n", @@ -2575,6 +2348,8 @@ " lc_new = lc_new.flatten(window_length=3001)\n", " #lc_new = lc_new.remove_outliers()\n", " pg_new = lc_new.to_periodogram(method='bls')\n", + " pg_new = pg_new.power / np.median(pg_new.power)\n", + " pg_new.power[np.where(pg_new.power<0)] = 0\n", " #pg_new = pg_new.flatten()\n", " pg_new.to_fits(exported_filename, overwrite=True,\n", " power_column_name='SAP_POWER',\n", @@ -2654,7 +2429,7 @@ }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -2935,46 +2710,1592 @@ "application/vnd.bokehjs_exec.v0+json": "", "text/html": [ "\n", - "" + "" ] }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "server_id": "428f1aba080c4ca5879c910462df8508" + "server_id": "db00256820434182a0023df69d629e05" } }, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR:bokeh.server.protocol_handler:error handling message Message 'PULL-DOC-REQ' (revision 1) content: {}: ValueError('Out of range float values are not JSON compliant',)\n" - ] } ], "source": [ - "show_interact_widget(tpf,notebook_url='localhost:8890')" + "show_interact_widget(tpf,notebook_url='localhost:8889')" ] - } - ], - "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": "3.6.8" + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "937c1070a2c24eb7a178bf6d1d51f1c7" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact_sky(notebook_url='localhost:8889')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tpf.to_lightcurve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# $$\\text{Putting it all together}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "import bokeh \n", + "from bokeh.io import show, output_notebook, push_notebook\n", + "from bokeh.plotting import figure, ColumnDataSource\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker, Range1d\n", + "from bokeh.layouts import layout, Spacer\n", + "from bokeh.models.tools import HoverTool\n", + "from bokeh.models.widgets import Button, Div\n", + "from bokeh.models.formatters import PrintfTickFormatter\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import lightkurve as lk\n", + "from scipy import stats\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "from astropy import units as u\n", + "\n", + "class PixelMapPeriodogram:\n", + " \n", + " \n", + " def __init__(self , targetpixelfile, method = 'LombScargle'):\n", + " \n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " self.aperture = targetpixelfile.pipeline_mask\n", + " self.tpf = targetpixelfile\n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(self.aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " \n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " if method == 'bls':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " elif method == 'LombScargle':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = periodogram.flatten()\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(self.aperture),len(self.aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " def plot(self):\n", + " fig,ax = plt.subplots(len(self.aperture[0]),\n", + " len(self.aperture[1]),\n", + " figsize=(20,20),sharex='col', sharey='row')\n", + "\n", + " #Just making the subplot spacings 0 pixel width and height separation\n", + " fig.subplots_adjust(wspace=0,hspace=0)\n", + " \n", + " \n", + " #iterating through the columns of the postage stamp pixels\n", + " for i in np.arange(0,len(self.aperture[0])):\n", + " \n", + " #iterating through the rows of the postage stamp pixels\n", + " for j in np.arange(0,len(self.aperture[1])):\n", + " \n", + " #Creating a false mask to alter each iteration\n", + " mask = np.empty((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " \n", + " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", + " mask[i][j] = True\n", + " \n", + " \n", + " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", + " ax[i][j].plot(self.periodogram[i][j].frequency,self.periodogram[i][j].power);\n", + " \n", + " def frequency_heat(self,low=0,high=1):\n", + " heat_stamp = []\n", + " for i in np.arange(0,len(self.aperture)):\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = self.periodogram[i][j]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " return heat_stamp\n", + " \n", + "\n", + "\n", + "\n", + " def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + " def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + " def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + " def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + " def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + " def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " fig.image(image = [self.heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + "\n", + "\n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=.1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + "\n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + "\n", + " period = self.periodogram[mask]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + " def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + " def show_interact_widget(self, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = self.tpf._parse_aperture_mask(aperture_mask)\n", + "\n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = self.tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + "\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram= periodogram.flatten()\n", + " pg = periodogram\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " npix = self.tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(self.tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(self.tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(self.tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = self.tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(self.tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(self.tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = self.tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(self.tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(self.tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if self.tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " self.tpf.targetid, self.tpf.campaign, self.tpf.module, self.tpf.output)\n", + " elif self.tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " self.tpf.targetid, self.tpf.quarter, self.tpf.module, self.tpf.output)\n", + " elif self.tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " self.tpf.targetid, self.tpf.sector, self.tpf.camera, self.tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 41.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "pmp =PixelMapPeriodogram(tpf[:1000])" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "47604b9bb5d24c468ffdac3104025500" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pmp.show_interact_widget(notebook_url = 'localhost:8889')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[358.03475074, 344.82649896, 344.54874566, 343.10694532,\n", + " 349.38352515, 348.87361353, 338.78543162, 344.55139163,\n", + " 342.53857564, 344.83478669, 335.72208425],\n", + " [360.76869663, 339.73901922, 371.75593382, 345.41684843,\n", + " 340.5694529 , 341.14464127, 350.16815091, 357.74581839,\n", + " 329.98507957, 339.85915529, 331.29377578],\n", + " [354.19234993, 341.03492116, 345.92900364, 339.17920826,\n", + " 354.07019195, 352.79611308, 342.24785325, 344.34490824,\n", + " 338.55856351, 337.10514697, 339.61990441],\n", + " [357.68029527, 345.32846716, 361.58705072, 353.02927928,\n", + " 327.9441054 , 337.13401256, 351.33801749, 356.24186195,\n", + " 334.14923355, 339.63186027, 349.08003986],\n", + " [358.64688387, 352.75600675, 339.18444002, 357.58795294,\n", + " 338.1068074 , 349.29338038, 338.60343706, 355.33835133,\n", + " 338.67106656, 340.32286218, 343.32330107],\n", + " [357.89569132, 360.4712817 , 347.27640094, 336.95947578,\n", + " 326.01253605, 348.50589242, 352.1833025 , 341.67838555,\n", + " 352.55565047, 364.36797615, 337.26181194],\n", + " [346.13077295, 355.87454334, 352.95454401, 346.26177658,\n", + " 336.78026505, 349.25199256, 331.87383201, 338.54176561,\n", + " 330.27261896, 352.05592264, 355.37231689],\n", + " [350.21848769, 339.08214238, 342.54113619, 350.00701687,\n", + " 351.35737273, 351.97036311, 355.98721942, 364.14477166,\n", + " 344.98051897, 343.27363221, 332.5237457 ],\n", + " [350.05866705, 353.96293736, 345.71909403, 342.91752648,\n", + " 347.74180979, 344.91142493, 326.9141297 , 348.74924481,\n", + " 345.89550866, 334.2600599 , 351.96470468],\n", + " [351.41203253, 357.58912519, 348.5259286 , 355.08294449,\n", + " 339.51763286, 357.38289578, 345.65995855, 336.38609732,\n", + " 336.94250552, 341.39160943, 359.27492029],\n", + " [342.72308272, 342.62003368, 348.4240653 , 352.46502913,\n", + " 350.80846724, 336.26307759, 338.76509312, 351.49683223,\n", + " 346.48931161, 330.12868115, 339.24940034]])" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmp.frequency_heat(low=276.34,high=343.54)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "frequency=np.linspace(0,400,69999)\n", + "frequency.size\n", + "tpf.flux.shape\n", + "power = np.zeros((frequency.size,tpf.flux.shape[1],tpf.flux.shape[2]))" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(69999, 11, 11)" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power[:,0,0] = .to_periodogrmam(frequency=frequency).power\n" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pmp.periodogram[5][5].frequency,pmp.periodogram[5][5].power)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "t= tpf.time" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "lc = tpf.to_lightcurve()" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "lc_new = lc[np.where(lc.quality == 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200739,)" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t= lc.time\n", + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)\n", + "freq.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200739,)" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t=lc_new.time\n", + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)\n", + "freq.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 0, 0, 0])" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf.quality" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 11, 11)" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf[:1000].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18104, 11, 11)" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Frequency range input for to_periodogram" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, diff --git a/Research.ipynb b/Research.ipynb index c89cb83..27248b9 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -20,14 +20,14 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import bokeh \n", "from bokeh.io import show, output_notebook, push_notebook\n", "from bokeh.plotting import figure, ColumnDataSource\n", - "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker, Range1d\n", "from bokeh.layouts import layout, Spacer\n", "from bokeh.models.tools import HoverTool\n", "from bokeh.models.widgets import Button, Div\n", @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 144, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 260, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -204,9 +204,7 @@ " lc = lc.remove_outliers()\n", " \n", " #Making a periodogram for the pixel\n", - " #periodogram = lc.to_periodogram(oversample_factor=5)\n", " periodogram = lc.to_periodogram(method = 'bls')\n", - " #periodogram = periodogram.flatten()\n", " periodogram.power = periodogram.power / np.median(periodogram.power)\n", " periodogram.power[np.where(periodogram.power<0)] = 0\n", " #Extending the list of periodogram data for each pixel\n", @@ -226,6 +224,59 @@ "\n" ] }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "class PixelMapPeriodogram:\n", + " def __init__(self , targetpixelfile):\n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " \n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " \n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = periodogram.flatten()\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " #def plotpixels(self):\n", + " \n", + " \n", + " \n", + " \n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 240, @@ -671,6 +722,13 @@ " plt.imshow(heat_stamp)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Make it so the frequency_data in the heat stamp can take a tuple of (freq,range centered) and store that in a sep. array, make plot function capable of plotting combination of heat stamps." + ] + }, { "cell_type": "code", "execution_count": 122, @@ -833,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1140,7 +1198,7 @@ " # Configure the stretch slider and its callback function\n", " stretch_slider = RangeSlider(start=low,\n", " end=high,\n", - " step=1,\n", + " step=.1,\n", " title='Frequency Range',\n", " value=(low, high),\n", " orientation='horizontal',\n", @@ -1150,7 +1208,11 @@ " show_value=True,\n", " sizing_mode='fixed',\n", " name='frequencyrange')\n", + " \n", + " \n", "\n", + "\n", + " \n", " def stretch_change_callback(attr, old, new):\n", " \"\"\"TPF stretch slider callback.\"\"\"\n", "\n", @@ -1416,10 +1478,72 @@ }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 22, "metadata": { "scrolled": false }, + "outputs": [], + "source": [ + "show_interact_widget(tpf,notebook_url='localhost:8889')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Flatten\n", + "2. Remove outliers\n", + "periodograms\n", + "1. Divide periodogram values by the median" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "tpf.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": {}, + "outputs": [], + "source": [ + "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", + "lcc = lcc[np.where(lcc.quality == 0)]\n", + "lcc = lcc.flatten(window_length= 3001)\n", + "pgg = lcc.to_periodogram()\n", + "#pgg = pgg.flatten()" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, "outputs": [ { "data": { @@ -1697,394 +1821,45 @@ "application/vnd.bokehjs_exec.v0+json": "", "text/html": [ "\n", - "" + "" ] }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "server_id": "8a075029a3084ce783a80f31df1752e3" + "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" } }, "output_type": "display_data" } ], "source": [ - "show_interact_widget(tpf,notebook_url='localhost:8890')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "1. Flatten\n", - "2. Remove outliers\n", - "periodograms\n", - "1. Divide periodogram values by the median" + "tpf.interact(notebook_url='localhost:8890')" ] }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 227, "metadata": {}, "outputs": [ { "data": { + "text/latex": [ + "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" + ], "text/plain": [ - "" + "" ] }, - "execution_count": 224, + "execution_count": 227, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], - "source": [ - "tpf.plot()" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 222, - "metadata": {}, - "outputs": [], - "source": [ - "lcc = tpf.to_lightcurve(aperture_mask = tpf.pipeline_mask)\n", - "lcc = lcc[np.where(lcc.quality == 0)]\n", - "lcc = lcc.flatten(window_length= 3001)\n", - "pgg = lcc.to_periodogram()\n", - "#pgg = pgg.flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 221, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " var toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " var events = require('base/js/events');\n", - " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - "\n", - " \n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " var el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " }\n", - " finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((root.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.bokehjs_exec.v0+json": "", - "text/html": [ - "\n", - "" - ] - }, - "metadata": { - "application/vnd.bokehjs_exec.v0+json": { - "server_id": "b3423fd1f0814a5bb263e0c7aafa5484" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "tpf.interact(notebook_url='localhost:8890')" - ] - }, - { - "cell_type": "code", - "execution_count": 227, - "metadata": {}, - "outputs": [ - { - "data": { - "text/latex": [ - "$9.6636799 \\times 10^{-5} \\; \\mathrm{\\frac{e^{-}}{s}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 227, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 261, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -2391,7 +2166,7 @@ " # Configure the stretch slider and its callback function\n", " stretch_slider = RangeSlider(start=low,\n", " end=high,\n", - " step=1,\n", + " step=.1,\n", " title='Frequency Range',\n", " value=(low, high),\n", " orientation='horizontal',\n", @@ -2654,7 +2429,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 34, "metadata": { "scrolled": true }, @@ -2935,63 +2710,1592 @@ "application/vnd.bokehjs_exec.v0+json": "", "text/html": [ "\n", - "" + "" ] }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "server_id": "d6a42895392444e9a5565a3d04db5b47" + "server_id": "db00256820434182a0023df69d629e05" } }, "output_type": "display_data" } ], "source": [ - "show_interact_widget(tpf,notebook_url='localhost:8890')" + "show_interact_widget(tpf,notebook_url='localhost:8889')" ] }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { - "text/latex": [ - "$[3.9779006,~3.9741829,~3.9704653,~\\dots,~0.12266673,~0.11894905,~0.11523137] \\; \\mathrm{\\frac{1}{d}}$" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 264, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tpfperiod.periodogram[0][0].frequency" - ] - } - ], - "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": "3.6.8" + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; 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\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "937c1070a2c24eb7a178bf6d1d51f1c7" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact_sky(notebook_url='localhost:8889')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tpf.to_lightcurve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# $$\\text{Putting it all together}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [], + "source": [ + "from __future__ import division, print_function\n", + "import logging\n", + "import warnings\n", + "import numpy as np\n", + "from astropy.stats import sigma_clip\n", + "import os\n", + "from astropy.coordinates import SkyCoord, Angle\n", + "import astropy.units as u\n", + "import bokeh \n", + "from bokeh.io import show, output_notebook, push_notebook\n", + "from bokeh.plotting import figure, ColumnDataSource\n", + "from bokeh.models import LogColorMapper,LinearColorMapper, Selection, Slider, RangeSlider, Span, ColorBar, LogTicker, Range1d, Ticker, BasicTicker, Range1d\n", + "from bokeh.layouts import layout, Spacer\n", + "from bokeh.models.tools import HoverTool\n", + "from bokeh.models.widgets import Button, Div\n", + "from bokeh.models.formatters import PrintfTickFormatter\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import scipy as sp\n", + "import lightkurve as lk\n", + "from scipy import stats\n", + "from ipywidgets import interact, interactive, fixed, interact_manual\n", + "import ipywidgets as widgets\n", + "from astropy import units as u\n", + "\n", + "class PixelMapPeriodogram:\n", + " \n", + " \n", + " def __init__(self , targetpixelfile, method = 'LombScargle'):\n", + " \n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " self.aperture = targetpixelfile.pipeline_mask\n", + " self.tpf = targetpixelfile\n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(self.aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " \n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " if method == 'bls':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " elif method == 'LombScargle':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram = periodogram.flatten()\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(self.aperture),len(self.aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " self.periodogram = pg\n", + " \n", + " def plot(self):\n", + " fig,ax = plt.subplots(len(self.aperture[0]),\n", + " len(self.aperture[1]),\n", + " figsize=(20,20),sharex='col', sharey='row')\n", + "\n", + " #Just making the subplot spacings 0 pixel width and height separation\n", + " fig.subplots_adjust(wspace=0,hspace=0)\n", + " \n", + " \n", + " #iterating through the columns of the postage stamp pixels\n", + " for i in np.arange(0,len(self.aperture[0])):\n", + " \n", + " #iterating through the rows of the postage stamp pixels\n", + " for j in np.arange(0,len(self.aperture[1])):\n", + " \n", + " #Creating a false mask to alter each iteration\n", + " mask = np.empty((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " \n", + " #setting one pixel in the postage stamp to have a lightcurve extracted and plotted\n", + " mask[i][j] = True\n", + " \n", + " \n", + " #Plotting the target pixel periodograph -- This can also be set up to have looks based on user input if desired\n", + " ax[i][j].plot(self.periodogram[i][j].frequency,self.periodogram[i][j].power);\n", + " \n", + " def frequency_heat(self,low=0,high=1):\n", + " heat_stamp = []\n", + " for i in np.arange(0,len(self.aperture)):\n", + " for j in np.arange(0,len(self.aperture[0])):\n", + " mask = np.zeros((len(self.aperture),len(self.aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = self.periodogram[i][j]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(self.aperture),len(self.aperture[0])))\n", + " return heat_stamp\n", + " \n", + "\n", + "\n", + "\n", + " def prepare_periodogram_datasource(pg):\n", + " \"\"\"Prepare a bokeh ColumnDataSource object for tool tips.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve object\n", + " The light curve to be shown.\n", + " Returns\n", + " -------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " \"\"\"\n", + " # Convert time into human readable strings, breaks with NaN time\n", + " # See https://github.com/KeplerGO/lightkurve/issues/116\n", + "\n", + "\n", + "\n", + "\n", + " pg_source = ColumnDataSource(data=dict(power=pg.power,frequency=pg.frequency))\n", + " return pg_source\n", + "\n", + "\n", + " def prepare_tpf_datasource(tpf, aperture_mask):\n", + " \"\"\"Prepare a bokeh DataSource object for selection glyphs\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to be shown.\n", + " aperture_mask : boolean numpy array\n", + " The Aperture mask applied at the startup of interact\n", + " Returns\n", + " -------\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object to be shown.\n", + " \"\"\"\n", + " npix = tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(tpf.flux[0].shape)\n", + " xx = tpf.column + np.arange(tpf.shape[2])\n", + " yy = tpf.row + np.arange(tpf.shape[1])\n", + " xa, ya = np.meshgrid(xx, yy)\n", + " preselected = Selection()\n", + " preselected.indices = pixel_index_array[aperture_mask].reshape(-1).tolist()\n", + " tpf_source = ColumnDataSource(data=dict(xx=xa+0.5, yy=ya+0.5),\n", + " selected=preselected)\n", + " return tpf_source\n", + "\n", + "\n", + " def get_periodogram_y_limits(pg_source):\n", + " \"\"\"Compute sensible defaults for the Y axis limits of the lightcurve plot.\n", + " Parameters\n", + " ----------\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " The lightcurve being shown.\n", + " Returns\n", + " -------\n", + " ymin, ymax : float, float\n", + " Flux min and max limits.\n", + " \"\"\"\n", + " #ask about this sigma clip\n", + " power = pg_source.data['power']\n", + "\n", + " low = float(power.min())\n", + " high = float(power.max())\n", + " margin = 0.10 * (high - low)\n", + " return low, high\n", + "\n", + "\n", + " def make_periodogram_figure_elements(pg, pg_source,lc):\n", + " \"\"\"Make the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " lc : LightCurve\n", + " Lightcurve to be shown.\n", + " lc_source : bokeh.plotting.ColumnDataSource\n", + " Bokeh object that enables the visualization.\n", + " Returns\n", + " ----------\n", + " fig : `bokeh.plotting.figure` instance\n", + " step_renderer : GlyphRenderer\n", + " vertical_line : Span\n", + " \"\"\"\n", + " if lc.mission == 'K2':\n", + " title = \"Periodogram for {} (K2)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'Kepler':\n", + " title = \"Periodogram for {} (Kepler)\".format(\n", + " pg.label)\n", + " elif lc.mission == 'TESS':\n", + " title = \"Periodogram for {} (TESS)\".format(\n", + " pg.label)\n", + " else:\n", + " title = \"Periodogram for target {}\".format(pg.label)\n", + "\n", + " fig = figure(title=title, plot_height=340, plot_width=600,\n", + " tools=\"pan,wheel_zoom,box_zoom,tap,reset\",\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + " fig.title.offset = -10\n", + " fig.yaxis.axis_label = 'Power (unit)'\n", + " fig.xaxis.axis_label = 'Frequency (unit)'\n", + "\n", + "\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig.y_range = Range1d(start=ylims[0], end=ylims[1])\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " # Add step lines, circles, and hover-over tooltips\n", + " fig.step('frequency', 'power', line_width=1, color='gray',\n", + " source=pg_source, nonselection_line_color='gray',\n", + " nonselection_line_alpha=1.0)\n", + " circ = fig.circle('frequency', 'power', source=pg_source, fill_alpha=0.3, size=8,\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0,\n", + " nonselection_fill_color=\"grey\",\n", + " nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0,\n", + " fill_color=None, hover_fill_color=\"firebrick\",\n", + " hover_alpha=0.9, hover_line_color=\"white\")\n", + " tooltips = [(\"frequency\", \"@frequency\"),\n", + " (\"power\", \"@power\")]\n", + " fig.add_tools(HoverTool(tooltips=tooltips, renderers=[circ],\n", + " mode='mouse', point_policy=\"snap_to_data\"))\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " # Vertical line to indicate the frequency\n", + " #vertical_line = Span(location=pg.frequency[0], dimension='height',\n", + " #line_color='firebrick', line_width=4, line_alpha=0.5)\n", + " #fig.add_layout(vertical_line)\n", + "\n", + " return fig#, vertical_line\n", + "\n", + "\n", + " def add_gaia_figure_elements(tpf, fig, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],\n", + " radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 1) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = ColumnDataSource(data=dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+tpf.column,\n", + " y=coords[:, 1]+tpf.row,\n", + " size=sizes))\n", + "\n", + " r = fig.circle('x', 'y', source=source, fill_alpha=0.3, size='size',\n", + " line_color=None, selection_color=\"firebrick\",\n", + " nonselection_fill_alpha=0.0, nonselection_line_color=None,\n", + " nonselection_line_alpha=0.0, fill_color=\"firebrick\",\n", + " hover_fill_color=\"firebrick\", hover_alpha=0.9,\n", + " hover_line_color=\"white\")\n", + "\n", + " fig.add_tools(HoverTool(tooltips=[(\"Gaia source\", \"@source\"),\n", + " (\"G\", \"@Gmag\"),\n", + " (\"Parallax (mas)\", \"@plx (~@one_over_plx{0,0} pc)\"),\n", + " (\"RA\", \"@ra{0,0.00000000}\"),\n", + " (\"DEC\", \"@dec{0,0.00000000}\"),\n", + " (\"x\", \"@x\"),\n", + " (\"y\", \"@y\")],\n", + " renderers=[r],\n", + " mode='mouse',\n", + " point_policy=\"snap_to_data\"))\n", + " return fig, r\n", + "\n", + "\n", + " def make_tpf_figure_elements(tpf, tpf_source,pg, pedestal=None, fiducial_frame=None,\n", + " plot_width=370, plot_height=340):\n", + " \"\"\"Returns the lightcurve figure elements.\n", + " Parameters\n", + " ----------\n", + " tpf : TargetPixelFile\n", + " TPF to show.\n", + " tpf_source : bokeh.plotting.ColumnDataSource\n", + " TPF data source.\n", + " pedestal: float\n", + " A scalar value to be added to the TPF flux values, often to avoid\n", + " taking the log of a negative number in colorbars.\n", + " Defaults to `-min(tpf.flux) + 1`\n", + " fiducial_frame: int\n", + " The tpf slice to start with by default, it is assumed the WCS\n", + " is exact for this frame.\n", + " Returns\n", + " -------\n", + " fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider\n", + " \"\"\"\n", + "\n", + " low = float(np.min(pg.frequency*u.d))\n", + " high = float(np.max(pg.frequency*u.d))\n", + "\n", + " if tpf.mission in ['Kepler', 'K2']:\n", + " title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)\n", + " elif tpf.mission == 'TESS':\n", + " title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)\n", + " else:\n", + " title = \"Pixel data\"\n", + "\n", + " fig = figure(plot_width=plot_width, plot_height=plot_height,\n", + " x_range=(tpf.column, tpf.column+tpf.shape[2]),\n", + " y_range=(tpf.row, tpf.row+tpf.shape[1]),\n", + " title=title, tools='tap,box_select,wheel_zoom,reset',\n", + " toolbar_location=\"below\",\n", + " border_fill_color=\"whitesmoke\")\n", + "\n", + " fig.yaxis.axis_label = 'Pixel Row Number'\n", + " fig.xaxis.axis_label = 'Pixel Column Number'\n", + "\n", + " color_mapper = LinearColorMapper(palette=\"Viridis256\", low=low, high=high)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " fig.image(image = [self.heat_stamp], x=tpf.column, y=tpf.row,\n", + " dw=tpf.shape[2], dh=tpf.shape[1], dilate=True,\n", + " color_mapper=color_mapper, name=\"tpfimg\")\n", + "\n", + "\n", + " # The colorbar will update with the screen stretch slider\n", + " # The colorbar margin increases as the length of the tick labels grows.\n", + " # This colorbar share of the plot window grows, shrinking plot area.\n", + " # This effect is known, some workarounds might work to fix the plot area:\n", + " # https://github.com/bokeh/bokeh/issues/5186\n", + " color_bar = ColorBar(color_mapper=color_mapper,\n", + " ticker=BasicTicker(desired_num_ticks=8), #LogTicker\n", + " label_standoff=-10, border_line_color=None,\n", + " location=(0, 0), background_fill_color='whitesmoke',\n", + " major_label_text_align='left',\n", + " major_label_text_baseline='middle',\n", + " title='Power', margin=0)\n", + " fig.add_layout(color_bar, 'right')\n", + "\n", + " color_bar.formatter = PrintfTickFormatter(format=\"%14u\")\n", + "\n", + " if tpf_source is not None:\n", + " fig.rect('xx', 'yy', 1, 1, source=tpf_source, fill_color='gray',\n", + " fill_alpha=0.4, line_color='white')\n", + "\n", + " # Configure the stretch slider and its callback function\n", + " stretch_slider = RangeSlider(start=low,\n", + " end=high,\n", + " step=.1,\n", + " title='Frequency Range',\n", + " value=(low, high),\n", + " orientation='horizontal',\n", + " width=200,\n", + " height=10,\n", + " direction='ltr',\n", + " show_value=True,\n", + " sizing_mode='fixed',\n", + " name='frequencyrange')\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " def stretch_change_callback(attr, old, new):\n", + " \"\"\"TPF stretch slider callback.\"\"\"\n", + "\n", + "\n", + " aperture = tpf.pipeline_mask\n", + " heat_stamp=[]\n", + " for i in np.arange(0,len(aperture)):\n", + " for j in np.arange(0,len(aperture[0])):\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " mask[i][j] = True\n", + "\n", + " period = self.periodogram[mask]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < new[1]) & (freq > new[0]))]).sum()\n", + " heat_stamp.extend([sums])\n", + " fig.select('tpfimg')[0].data_source.data['image'] = [np.reshape(np.asarray(heat_stamp),(len(aperture),len(aperture[0])))]\n", + " fig.select('tpfimg')[0].glyph.color_mapper.high = max(heat_stamp)\n", + " fig.select('tpfimg')[0].glyph.color_mapper.low = min(heat_stamp)\n", + "\n", + " stretch_slider.on_change('value', stretch_change_callback)\n", + "\n", + " return fig, stretch_slider\n", + "\n", + "\n", + " def make_default_export_name(tpf, suffix='custom-pg'):\n", + " \"\"\"makes the default name to save a custom intetract mask\"\"\"\n", + " fn = tpf.hdu.filename()\n", + " if fn is None:\n", + " outname = \"{}_{}_{}.fits\".format(tpf.mission, tpf.targetid, suffix)\n", + " else:\n", + " base = os.path.basename(fn)\n", + " outname = base.rsplit('.fits')[0] + '-{}.fits'.format(suffix)\n", + " return outname\n", + "\n", + "\n", + " def show_interact_widget(self, notebook_url='localhost:8888',\n", + " aperture_mask='pipeline',\n", + " exported_filename=None):\n", + " \"\"\"Display an interactive Jupyter Notebook widget to inspect the pixel data.\n", + " The widget will show both the lightcurve and pixel data. The pixel data\n", + " supports pixel selection via Bokeh tap and box select tools in an\n", + " interactive javascript user interface.\n", + " Note: at this time, this feature only works inside an active Jupyter\n", + " Notebook, and tends to be too slow when more than ~30,000 cadences\n", + " are contained in the TPF (e.g. short cadence data).\n", + " Parameters\n", + " ----------\n", + " tpf : lightkurve.TargetPixelFile\n", + " Target Pixel File to interact with\n", + " notebook_url: str\n", + " Location of the Jupyter notebook page (default: \"localhost:8888\")\n", + " When showing Bokeh applications, the Bokeh server must be\n", + " explicitly configured to allow connections originating from\n", + " different URLs. This parameter defaults to the standard notebook\n", + " host and port. If you are running on a different location, you\n", + " will need to supply this value for the application to display\n", + " properly. If no protocol is supplied in the URL, e.g. if it is\n", + " of the form \"localhost:8888\", then \"http\" will be used.\n", + " max_cadences : int\n", + " Raise a RuntimeError if the number of cadences shown is larger than\n", + " this value. This limit helps keep browsers from becoming unresponsive.\n", + " \"\"\"\n", + "\n", + "\n", + " aperture_mask = self.tpf._parse_aperture_mask(aperture_mask)\n", + "\n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = self.tpf.to_lightcurve(aperture_mask=aperture_mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " #lc = lc.remove_outliers()\n", + "\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(oversample_factor=5)\n", + " periodogram= periodogram.flatten()\n", + " pg = periodogram\n", + "\n", + "\n", + "\n", + "\n", + "\n", + " npix = self.tpf.flux[0, :, :].size\n", + " pixel_index_array = np.arange(0, npix, 1).reshape(self.tpf.flux[0].shape)\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " pg_source = prepare_periodogram_datasource(pg)\n", + " tpf_source = prepare_tpf_datasource(self.tpf, aperture_mask)\n", + "\n", + " # Create the lightcurve figure and its vertical marker\n", + " fig_pg = make_periodogram_figure_elements(pg, pg_source,lc)\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(self.tpf, tpf_source,pg,\n", + " fiducial_frame=0)\n", + "\n", + " r_button = Button(label=\">\", button_type=\"default\", width=30)\n", + " l_button = Button(label=\"<\", button_type=\"default\", width=30)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " message_on_save = Div(text=' ',width=600, height=15)\n", + "\n", + "\n", + " # Callbacks\n", + " def update_upon_pixel_selection(attr, old, new):\n", + " \"\"\"Callback to take action when pixels are selected.\"\"\"\n", + " # Check if a selection was \"re-clicked\", then de-select\n", + " if ((sorted(old) == sorted(new)) & (new != [])):\n", + " # Trigger recursion\n", + " tpf_source.selected.indices = new[1:]\n", + "\n", + " if new != []:\n", + " selected_indices = np.array(new)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = self.tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_source.data['power'] = pg_new.power\n", + " pg_source.data['frequency'] = pg_new.frequency\n", + " ylims = get_periodogram_y_limits(pg_source)\n", + " fig_pg.y_range.start = ylims[0]\n", + " fig_pg.y_range.end = ylims[1]\n", + " else:\n", + " pg_source.data['power'] = pg.power * 0.0\n", + " fig_pg.y_range.start = -1\n", + " fig_pg.y_range.end = 1\n", + "\n", + " message_on_save.text = \" \"\n", + " export_button.button_type = \"success\"\n", + "\n", + " #def update_upon_cadence_change(attr, old, new):\n", + " # \"\"\"Callback to take action when cadence slider changes\"\"\"\n", + " # if new in tpf.cadenceno:\n", + " # frameno = tpf_index_lookup[new]\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[frameno, :, :] + pedestal]\n", + " # vertical_line.update(location=tpf.time[frameno])\n", + " #else:\n", + " # fig_tpf.select('tpfimg')[0].data_source.data['image'] = \\\n", + " # [tpf.flux[0, :, :] * np.NaN]\n", + " #lc_source.selected.indices = []\n", + "\n", + " def go_right_by_one():\n", + " \"\"\"Step forward in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value < np.max(self.tpf.cadenceno):\n", + " cadence_slider.value = existing_value + 1\n", + "\n", + " def go_left_by_one():\n", + " \"\"\"Step back in time by a single cadence\"\"\"\n", + " existing_value = cadence_slider.value\n", + " if existing_value > np.min(self.tpf.cadenceno):\n", + " cadence_slider.value = existing_value - 1\n", + "\n", + " def save_periodogram():\n", + " \"\"\"Save the lightcurve as a fits file with mask as HDU extension\"\"\"\n", + " if tpf_source.selected.indices != []:\n", + " selected_indices = np.array(tpf_source.selected.indices)\n", + " selected_mask = np.isin(pixel_index_array, selected_indices)\n", + " lc_new = self.tpf.to_lightcurve(aperture_mask=selected_mask)\n", + " lc_new = lc_new[np.where(lc_new.quality == 0)]\n", + " lc_new = lc_new.flatten(window_length=3001)\n", + " #lc_new = lc_new.remove_outliers()\n", + " pg_new = lc_new.to_periodogram(oversample_factor=5)\n", + " pg_new = pg_new.flatten()\n", + " pg_new.to_fits(exported_filename, overwrite=True,\n", + " power_column_name='SAP_POWER',\n", + " aperture_mask=selected_mask.astype(np.int),\n", + " SOURCE='lightkurve interact',\n", + " NOTE='custom mask',\n", + " MASKNPIX=np.nansum(selected_mask))\n", + " if message_on_save.text == \" \":\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " export_button.button_type = \"success\"\n", + " else:\n", + " text = 'Saved file {} '\n", + " message_on_save.text = text.format(exported_filename)\n", + " else:\n", + " text = 'No pixels selected, no mask saved'\n", + " export_button.button_type = \"warning\"\n", + " message_on_save.text = text\n", + "\n", + " #def jump_to_lightcurve_position(attr, old, new):\n", + " # if new != []:\n", + " # cadence_slider.value = lc.cadenceno[new[0]]\n", + "\n", + " # Map changes to callbacks\n", + " r_button.on_click(go_right_by_one)\n", + " l_button.on_click(go_left_by_one)\n", + " export_button = Button(label=\"Save Periodogram\",\n", + " button_type=\"success\", width=120)\n", + " tpf_source.selected.on_change('indices', update_upon_pixel_selection)\n", + " export_button.on_click(save_periodogram)\n", + " #cadence_slider.on_change('value', update_upon_cadence_change)\n", + "\n", + " # Layout all of the plots\n", + " sp1, sp2, sp3, sp4 = (Spacer(width=15), Spacer(width=30),\n", + " Spacer(width=80), Spacer(width=60))\n", + " widgets_and_figures = layout([fig_pg, fig_tpf],\n", + " [l_button, sp1, r_button, sp2, sp3, stretch_slider],\n", + " [export_button, sp4, message_on_save])\n", + " #removed cadence slider\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n", + "\n", + " def create_interact_ui(doc):\n", + " # The data source includes metadata for hover-over tooltips\n", + " tpf_source = None\n", + "\n", + " # Create the TPF figure and its stretch slider\n", + " fig_tpf, stretch_slider = make_tpf_figure_elements(self.tpf, tpf_source,pg,\n", + " fiducial_frame=fiducial_frame,\n", + " plot_width=640, plot_height=600)\n", + " fig_tpf, r = add_gaia_figure_elements(self.tpf, fig_tpf,\n", + " magnitude_limit=magnitude_limit)\n", + "\n", + " # Optionally override the default title\n", + " if self.tpf.mission == 'K2':\n", + " fig_tpf.title.text = \"Skyview for EPIC {}, K2 Campaign {}, CCD {}.{}\".format(\n", + " self.tpf.targetid, self.tpf.campaign, self.tpf.module, self.tpf.output)\n", + " elif self.tpf.mission == 'Kepler':\n", + " fig_tpf.title.text = \"Skyview for KIC {}, Kepler Quarter {}, CCD {}.{}\".format(\n", + " self.tpf.targetid, self.tpf.quarter, self.tpf.module, self.tpf.output)\n", + " elif self.tpf.mission == 'TESS':\n", + " fig_tpf.title.text = 'Skyview for TESS {} Sector {}, Camera {}.{}'.format(\n", + " self.tpf.targetid, self.tpf.sector, self.tpf.camera, self.tpf.ccd)\n", + "\n", + " # Layout all of the plots\n", + " widgets_and_figures = layout([fig_tpf, stretch_slider])\n", + " doc.add_root(widgets_and_figures)\n", + "\n", + " output_notebook(verbose=False, hide_banner=True)\n", + " return show(create_interact_ui, notebook_url=notebook_url)\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 41.7 s\n" + ] + } + ], + "source": [ + "%%time\n", + "pmp =PixelMapPeriodogram(tpf[:1000])" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "47604b9bb5d24c468ffdac3104025500" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "pmp.show_interact_widget(notebook_url = 'localhost:8889')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[358.03475074, 344.82649896, 344.54874566, 343.10694532,\n", + " 349.38352515, 348.87361353, 338.78543162, 344.55139163,\n", + " 342.53857564, 344.83478669, 335.72208425],\n", + " [360.76869663, 339.73901922, 371.75593382, 345.41684843,\n", + " 340.5694529 , 341.14464127, 350.16815091, 357.74581839,\n", + " 329.98507957, 339.85915529, 331.29377578],\n", + " [354.19234993, 341.03492116, 345.92900364, 339.17920826,\n", + " 354.07019195, 352.79611308, 342.24785325, 344.34490824,\n", + " 338.55856351, 337.10514697, 339.61990441],\n", + " [357.68029527, 345.32846716, 361.58705072, 353.02927928,\n", + " 327.9441054 , 337.13401256, 351.33801749, 356.24186195,\n", + " 334.14923355, 339.63186027, 349.08003986],\n", + " [358.64688387, 352.75600675, 339.18444002, 357.58795294,\n", + " 338.1068074 , 349.29338038, 338.60343706, 355.33835133,\n", + " 338.67106656, 340.32286218, 343.32330107],\n", + " [357.89569132, 360.4712817 , 347.27640094, 336.95947578,\n", + " 326.01253605, 348.50589242, 352.1833025 , 341.67838555,\n", + " 352.55565047, 364.36797615, 337.26181194],\n", + " [346.13077295, 355.87454334, 352.95454401, 346.26177658,\n", + " 336.78026505, 349.25199256, 331.87383201, 338.54176561,\n", + " 330.27261896, 352.05592264, 355.37231689],\n", + " [350.21848769, 339.08214238, 342.54113619, 350.00701687,\n", + " 351.35737273, 351.97036311, 355.98721942, 364.14477166,\n", + " 344.98051897, 343.27363221, 332.5237457 ],\n", + " [350.05866705, 353.96293736, 345.71909403, 342.91752648,\n", + " 347.74180979, 344.91142493, 326.9141297 , 348.74924481,\n", + " 345.89550866, 334.2600599 , 351.96470468],\n", + " [351.41203253, 357.58912519, 348.5259286 , 355.08294449,\n", + " 339.51763286, 357.38289578, 345.65995855, 336.38609732,\n", + " 336.94250552, 341.39160943, 359.27492029],\n", + " [342.72308272, 342.62003368, 348.4240653 , 352.46502913,\n", + " 350.80846724, 336.26307759, 338.76509312, 351.49683223,\n", + " 346.48931161, 330.12868115, 339.24940034]])" + ] + }, + "execution_count": 223, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmp.frequency_heat(low=276.34,high=343.54)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "frequency=np.linspace(0,400,69999)\n", + "frequency.size\n", + "tpf.flux.shape\n", + "power = np.zeros((frequency.size,tpf.flux.shape[1],tpf.flux.shape[2]))" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(69999, 11, 11)" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "power[:,0,0] = .to_periodogrmam(frequency=frequency).power\n" + ] + }, + { + "cell_type": "code", + "execution_count": 189, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 189, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pmp.periodogram[5][5].frequency,pmp.periodogram[5][5].power)" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "metadata": {}, + "outputs": [], + "source": [ + "t= tpf.time" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "metadata": {}, + "outputs": [], + "source": [ + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "metadata": {}, + "outputs": [], + "source": [ + "lc = tpf.to_lightcurve()" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": {}, + "outputs": [], + "source": [ + "lc_new = lc[np.where(lc.quality == 0)]" + ] + }, + { + "cell_type": "code", + "execution_count": 206, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200739,)" + ] + }, + "execution_count": 206, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t= lc.time\n", + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)\n", + "freq.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 207, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(200739,)" + ] + }, + "execution_count": 207, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "t=lc_new.time\n", + "df = 1./(t[-1]-t[0])/5.\n", + "nyq = 2/np.median(np.diff(t))\n", + "freq = np.arange(df,nyq,df)\n", + "freq.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 211, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 0, 0, ..., 0, 0, 0])" + ] + }, + "execution_count": 211, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf.quality" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1000, 11, 11)" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf[:1000].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(18104, 11, 11)" + ] + }, + "execution_count": 214, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tpf.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Frequency range input for to_periodogram" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" } }, "nbformat": 4, From 4906d00d9a58ddf55931742a5cd6f02009020dfa Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Thu, 30 Jan 2020 21:26:24 -0800 Subject: [PATCH 05/11] Made a centroid finder, and made code function with the simulated lightcurves. Will have questions about the best way to find peak frequencies. --- .ipynb_checkpoints/Research-checkpoint.ipynb | 862 ++++++++++++++++++- Research.ipynb | 862 ++++++++++++++++++- 2 files changed, 1720 insertions(+), 4 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index 27248b9..7502e0e 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -4289,6 +4289,852 @@ "source": [ "## Frequency range input for to_periodogram" ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.33727637e-78, -7.49777622e-78, 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = Simulate_Random_Image(separation=5)\n", + "plt.imshow(x[0][0],origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m time = np.arange(1000)*1./7.64.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "def Simulate_Random_Image(imageshape=(30,30),separation=None):\n", + " time = np.arange(1000)*1./7.64.\n", + " freq1 = np.random.uniform()*15 #per day\n", + " freq2 = np.random.uniform()*15 #per day\n", + " relamp = 1\n", + " signal1 = relamp * np.sin(time*freq1)\n", + " signal2 = relamp * np.sin(time*freq2)\n", + " \n", + "\n", + " #Images\n", + " if(separation == None):\n", + " star1pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star2pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star1flux = np.random.randint(300,2000)\n", + " star2flux = np.random.randint(300,2000)\n", + " seeingsigma = 1.\n", + " \n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + " backgroundnoise = 10.\n", + "\n", + " #add starlight\n", + "\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + " distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal1[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux * (1. + signal2[i])\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + "\n", + " stars = imagestack[:,:,:].T\n", + " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]\n", + " \n", + " \n", + " else:\n", + " star1pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star2pos = [np.random.randint(star1pos[0]-separation,star1pos[0]+separation,size=1)]\n", + " star2pos.extend(np.sqrt(separation**2 - (star2pos[0]-star1pos[0])**2)+star1pos[1])\n", + " star1flux = np.random.randint(300,2000)\n", + " star2flux = np.random.randint(300,2000)\n", + " seeingsigma = 1.\n", + "\n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " ycoord,xcoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + " backgroundnoise = 10.\n", + "\n", + " #add starlight\n", + "\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + " distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal1[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux * (1. + signal2[i])\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + " \n", + " stars = imagestack[:,:,:].T\n", + " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "time = np.arange(1000)*1./48.\n", + "freq = 10. #per day\n", + "relamp = 1\n", + "signal = relamp * np.sin(time*freq)\n", + "plt.plot(time,signal)\n", + "\n", + "#Images\n", + "imageshape = (30,30) #pixels\n", + "star1pos = [10,10]\n", + "star2pos = [20,20]\n", + "star1flux = 1000.\n", + "star2flux = 750.\n", + "seeingsigma = 1.\n", + "\n", + "imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + "xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + "backgroundnoise = 10.\n", + "\n", + "#add starlight\n", + "distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + "distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + "for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " #imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)" + ] + }, + { + "cell_type": "code", + "execution_count": 489, + "metadata": {}, + "outputs": [], + "source": [ + "test = Simulate_Random_Image()" + ] + }, + { + "cell_type": "code", + "execution_count": 565, + "metadata": {}, + "outputs": [], + "source": [ + "def Create_LightCurve(*simulatedimage):\n", + " lc_array = np.zeros(shape = imageshape,dtype=object)\n", + " for i in np.arange(0,imageshape[0]):\n", + " for j in np.arange(0,imageshape[1]):\n", + " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", + " return lc_array" + ] + }, + { + "cell_type": "code", + "execution_count": 567, + "metadata": {}, + "outputs": [], + "source": [ + "lc = Create_LightCurve(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 568, + "metadata": {}, + "outputs": [], + "source": [ + "def Create_Periodogram(lc):\n", + " pg = np.zeros(shape = (len(lc[0]),len(lc[1])),dtype=object)\n", + " for i in np.arange(0,len(lc[0])):\n", + " for j in np.arange(0,len(lc[1])):\n", + " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5)\n", + " return pg" + ] + }, + { + "cell_type": "code", + "execution_count": 569, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "pg = Create_Periodogram(lc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Flatten lightcurve and pick peaks to look at on the periodogram image eventually" + ] + }, + { + "cell_type": "code", + "execution_count": 497, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_plot(pg,low=1,high=2):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(pg)):\n", + " for j in np.arange(0,len(pg[0])):\n", + " mask = np.zeros((len(pg),len(pg[0])), dtype=bool)\n", + " mask[j][i] = True\n", + " \n", + " period = pg[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", + " return heat_stamp" + ] + }, + { + "cell_type": "code", + "execution_count": 570, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 570, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fhp = frequency_heat_plot(pg,9.4,9.5)\n", + "plt.imshow(fhp,origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 522, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 522, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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80.25 , 80.375,\n", + " 80.5 , 80.625, 80.75 , 80.875, 81. , 81.125, 81.25 ,\n", + " 81.375, 81.5 , 81.625, 81.75 , 81.875, 82. , 82.125,\n", + " 82.25 , 82.375, 82.5 , 82.625, 82.75 , 82.875, 83. ,\n", + " 83.125, 83.25 , 83.375, 83.5 , 83.625, 83.75 , 83.875,\n", + " 84. , 84.125, 84.25 , 84.375, 84.5 , 84.625, 84.75 ,\n", + " 84.875, 85. , 85.125, 85.25 , 85.375, 85.5 , 85.625,\n", + " 85.75 , 85.875, 86. , 86.125, 86.25 , 86.375, 86.5 ,\n", + " 86.625, 86.75 , 86.875, 87. , 87.125, 87.25 , 87.375,\n", + " 87.5 , 87.625, 87.75 , 87.875, 88. , 88.125, 88.25 ,\n", + " 88.375, 88.5 , 88.625, 88.75 , 88.875, 89. , 89.125,\n", + " 89.25 , 89.375, 89.5 , 89.625, 89.75 , 89.875, 90. ,\n", + " 90.125, 90.25 , 90.375, 90.5 , 90.625, 90.75 , 90.875,\n", + " 91. , 91.125, 91.25 , 91.375, 91.5 , 91.625, 91.75 ,\n", + " 91.875, 92. , 92.125, 92.25 , 92.375, 92.5 , 92.625,\n", + " 92.75 , 92.875, 93. , 93.125, 93.25 , 93.375, 93.5 ,\n", + " 93.625, 93.75 , 93.875, 94. , 94.125, 94.25 , 94.375,\n", + " 94.5 , 94.625, 94.75 , 94.875, 95. , 95.125, 95.25 ,\n", + " 95.375, 95.5 , 95.625, 95.75 , 95.875, 96. , 96.125,\n", + " 96.25 , 96.375, 96.5 , 96.625, 96.75 , 96.875, 97. ,\n", + " 97.125, 97.25 , 97.375, 97.5 , 97.625, 97.75 , 97.875,\n", + " 98. , 98.125, 98.25 , 98.375, 98.5 , 98.625, 98.75 ,\n", + " 98.875, 99. , 99.125, 99.25 , 99.375, 99.5 , 99.625,\n", + " 99.75 , 99.875, 100. , 100.125, 100.25 , 100.375, 100.5 ,\n", + " 100.625, 100.75 , 100.875, 101. , 101.125, 101.25 , 101.375,\n", + " 101.5 , 101.625, 101.75 , 101.875, 102. , 102.125, 102.25 ,\n", + " 102.375, 102.5 , 102.625, 102.75 , 102.875, 103. , 103.125,\n", + " 103.25 , 103.375, 103.5 , 103.625, 103.75 , 103.875, 104. ,\n", + " 104.125, 104.25 , 104.375, 104.5 , 104.625, 104.75 , 104.875,\n", + " 105. , 105.125, 105.25 , 105.375, 105.5 , 105.625, 105.75 ,\n", + " 105.875, 106. , 106.125, 106.25 , 106.375, 106.5 , 106.625,\n", + " 106.75 , 106.875, 107. , 107.125, 107.25 , 107.375, 107.5 ,\n", + " 107.625, 107.75 , 107.875, 108. , 108.125, 108.25 , 108.375,\n", + " 108.5 , 108.625, 108.75 , 108.875, 109. , 109.125, 109.25 ,\n", + " 109.375, 109.5 , 109.625, 109.75 , 109.875, 110. , 110.125,\n", + " 110.25 , 110.375, 110.5 , 110.625, 110.75 , 110.875, 111. ,\n", + " 111.125, 111.25 , 111.375, 111.5 , 111.625, 111.75 , 111.875,\n", + " 112. , 112.125, 112.25 , 112.375, 112.5 , 112.625, 112.75 ,\n", + " 112.875, 113. , 113.125, 113.25 , 113.375, 113.5 , 113.625,\n", + " 113.75 , 113.875, 114. , 114.125, 114.25 , 114.375, 114.5 ,\n", + " 114.625, 114.75 , 114.875, 115. , 115.125, 115.25 , 115.375,\n", + " 115.5 , 115.625, 115.75 , 115.875, 116. , 116.125, 116.25 ,\n", + " 116.375, 116.5 , 116.625, 116.75 , 116.875, 117. , 117.125,\n", + " 117.25 , 117.375, 117.5 , 117.625, 117.75 , 117.875, 118. ,\n", + " 118.125, 118.25 , 118.375, 118.5 , 118.625, 118.75 , 118.875,\n", + " 119. , 119.125, 119.25 , 119.375, 119.5 , 119.625, 119.75 ,\n", + " 119.875, 120. , 120.125, 120.25 , 120.375, 120.5 , 120.625,\n", + " 120.75 , 120.875, 121. , 121.125, 121.25 , 121.375, 121.5 ,\n", + " 121.625, 121.75 , 121.875, 122. , 122.125, 122.25 , 122.375,\n", + " 122.5 , 122.625, 122.75 , 122.875, 123. , 123.125, 123.25 ,\n", + " 123.375, 123.5 , 123.625, 123.75 , 123.875, 124. , 124.125,\n", + " 124.25 , 124.375, 124.5 , 124.625, 124.75 , 124.875]),\n", + " (30, 30),\n", + " None]" + ] + }, + "execution_count": 492, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test[1::]" + ] + }, + { + "cell_type": "code", + "execution_count": 520, + "metadata": {}, + "outputs": [], + "source": [ + "def Find_Centroid(fhp):\n", + " x=0\n", + " y=0\n", + " for i in np.arange(0,len(fhp)):\n", + " for j in np.arange(0,len(fhp[0])):\n", + " x += j*fhp[i][j]/fhp.sum()\n", + " y += i*fhp[i][j]/fhp.sum()\n", + " return x,y" + ] + }, + { + "cell_type": "code", + "execution_count": 573, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20.869451398841548, 4.875739425117415)" + ] + }, + "execution_count": 573, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Find_Centroid(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 559, + "metadata": {}, + "outputs": [], + "source": [ + "def Flat_lc(*simulatedimage):\n", + " flux = np.zeros(shape=len(time))\n", + " for i in np.arange(0,len(time)):\n", + " flux[i] = simulatedimage[0][0][i].sum()\n", + " lc = lk.LightCurve(time = time, flux = flux)\n", + " return lc" + ] + }, + { + "cell_type": "code", + "execution_count": 574, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 574, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Flat_lc(test).plot()" + ] } ], "metadata": { @@ -4296,6 +5142,18 @@ "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": "3.6.8" } }, "nbformat": 4, diff --git a/Research.ipynb b/Research.ipynb index 27248b9..7502e0e 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -4289,6 +4289,852 @@ "source": [ "## Frequency range input for to_periodogram" ] + }, + { + "cell_type": "code", + "execution_count": 370, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 3.33727637e-78, -7.49777622e-78, 7.27343176e-78, -1.18772445e-77,\n", + " 1.22191312e-77, -3.22881077e-78, 1.19567507e-77, 2.86242851e-78,\n", + " -7.28946219e-78, 1.10000959e-78, -6.90345376e-78, -5.74038184e-78,\n", + " -1.20598920e-77, -9.99570452e-78, 2.38523015e-77, 4.32740548e-78,\n", + " -1.16059417e-77, 1.51751722e-77, -6.54130761e-78, -5.74356852e-78,\n", + " -9.58933822e-78, 3.42987076e-78, -4.84649606e-78, 6.26582401e-79,\n", + " 1.62993593e-78, 1.94074176e-79, 1.38817050e-78, 1.25242723e-78,\n", + " 3.88035509e-78, -2.67229337e-78, -2.67496144e-78, 3.38285851e-79,\n", + " -6.13591972e-78, -2.22273009e-77, 6.64275473e-78, -6.45027587e-78,\n", + " -1.99514235e-77, -2.37371531e-77, -1.05948302e-77, -3.53096896e-77,\n", + " 7.30488984e-78, -4.09355165e-77, -4.23495569e-78, 1.66828727e-77,\n", + " -1.33700171e-77, 1.81459943e-78, -3.21450901e-77, -4.43795247e-78,\n", + 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3.50953867e-79, -1.12459906e-77, 2.84091827e-78, 2.70265926e-77,\n", + " 3.12199990e-78, -1.24148956e-77, 2.80513998e-78, 6.10486176e-78,\n", + " 1.08046152e-77, -7.87316755e-78, 1.20168602e-77, -3.97507778e-78,\n", + " 1.07165825e-77, 2.40950842e-77, 7.33318233e-78, -1.37304506e-77,\n", + " 6.44764045e-78, -8.65398003e-78, -4.86298801e-78, -1.18569030e-77,\n", + " -4.67222141e-78, 2.88173772e-78, 4.77192127e-78, -6.28646954e-79,\n", + " 5.96146808e-80, 2.21537837e-78, 1.91376250e-79, 3.02410150e-78,\n", + " -2.23979175e-78, -2.16519843e-78, -6.89994747e-78, 2.16662103e-78,\n", + " 6.13313329e-78, 1.84277899e-77, -1.06976223e-77, 3.75918854e-78,\n", + " 4.89053320e-78, 5.10031774e-78, 8.14555541e-79, 5.79107115e-78,\n", + " -1.16539693e-77, -8.18856658e-78, -3.66140274e-78, -1.36757207e-77,\n", + " 1.63706005e-77, -2.00725479e-77, 1.52934451e-78, -3.40003819e-78,\n", + " -9.54515559e-78, -7.15712172e-78, -1.39736243e-77, 4.54471044e-78,\n", + " 4.27761649e-78, -2.84667972e-78, 6.78182004e-78, 1.50261568e-78,\n", + " 1.54774697e-78, -2.97418529e-79, -1.27540188e-78, -2.21518009e-78,\n", + " 1.21387517e-78, -2.91994522e-78, 3.25576553e-79, -4.37514429e-78,\n", + " -3.21802487e-78, 5.45306800e-78, -1.09433949e-77, -7.94145815e-78,\n", + " -1.04830847e-77, -1.22641364e-77, 9.90138235e-78, 4.75523827e-78,\n", + " -5.60228289e-78, 3.23879490e-78, -4.05381685e-77, 1.08895379e-77,\n", + " 1.30181119e-77, -6.31058865e-78, -1.26879998e-77, 1.24782625e-77,\n", + " -6.83138982e-78, -4.20836614e-78, 2.53498253e-78, -4.87459336e-78,\n", + " -1.38772498e-77, 8.62053275e-78, 1.14673529e-77, 1.11184967e+01])" + ] + }, + "execution_count": 370, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = Simulate_Random_Image(separation=5)\n", + "plt.imshow(x[0][0],origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 488, + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (, line 2)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m time = np.arange(1000)*1./7.64.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "def Simulate_Random_Image(imageshape=(30,30),separation=None):\n", + " time = np.arange(1000)*1./7.64.\n", + " freq1 = np.random.uniform()*15 #per day\n", + " freq2 = np.random.uniform()*15 #per day\n", + " relamp = 1\n", + " signal1 = relamp * np.sin(time*freq1)\n", + " signal2 = relamp * np.sin(time*freq2)\n", + " \n", + "\n", + " #Images\n", + " if(separation == None):\n", + " star1pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star2pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star1flux = np.random.randint(300,2000)\n", + " star2flux = np.random.randint(300,2000)\n", + " seeingsigma = 1.\n", + " \n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + " backgroundnoise = 10.\n", + "\n", + " #add starlight\n", + "\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + " distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal1[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux * (1. + signal2[i])\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + "\n", + " stars = imagestack[:,:,:].T\n", + " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]\n", + " \n", + " \n", + " else:\n", + " star1pos = [np.random.uniform()*imageshape[0],np.random.uniform()*imageshape[1]]\n", + " star2pos = [np.random.randint(star1pos[0]-separation,star1pos[0]+separation,size=1)]\n", + " star2pos.extend(np.sqrt(separation**2 - (star2pos[0]-star1pos[0])**2)+star1pos[1])\n", + " star1flux = np.random.randint(300,2000)\n", + " star2flux = np.random.randint(300,2000)\n", + " seeingsigma = 1.\n", + "\n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " ycoord,xcoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + " backgroundnoise = 10.\n", + "\n", + " #add starlight\n", + "\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + " distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal1[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux * (1. + signal2[i])\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + " \n", + " stars = imagestack[:,:,:].T\n", + " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "time = np.arange(1000)*1./48.\n", + "freq = 10. #per day\n", + "relamp = 1\n", + "signal = relamp * np.sin(time*freq)\n", + "plt.plot(time,signal)\n", + "\n", + "#Images\n", + "imageshape = (30,30) #pixels\n", + "star1pos = [10,10]\n", + "star2pos = [20,20]\n", + "star1flux = 1000.\n", + "star2flux = 750.\n", + "seeingsigma = 1.\n", + "\n", + "imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + "xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + "\n", + "\n", + "\n", + "backgroundnoise = 10.\n", + "\n", + "#add starlight\n", + "distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", + "distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + "\n", + "for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", + "\n", + " #star 2\n", + " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux\n", + "\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + "\n", + " #background\n", + " #imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)" + ] + }, + { + "cell_type": "code", + "execution_count": 489, + "metadata": {}, + "outputs": [], + "source": [ + "test = Simulate_Random_Image()" + ] + }, + { + "cell_type": "code", + "execution_count": 565, + "metadata": {}, + "outputs": [], + "source": [ + "def Create_LightCurve(*simulatedimage):\n", + " lc_array = np.zeros(shape = imageshape,dtype=object)\n", + " for i in np.arange(0,imageshape[0]):\n", + " for j in np.arange(0,imageshape[1]):\n", + " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", + " return lc_array" + ] + }, + { + "cell_type": "code", + "execution_count": 567, + "metadata": {}, + "outputs": [], + "source": [ + "lc = Create_LightCurve(test)" + ] + }, + { + "cell_type": "code", + "execution_count": 568, + "metadata": {}, + "outputs": [], + "source": [ + "def Create_Periodogram(lc):\n", + " pg = np.zeros(shape = (len(lc[0]),len(lc[1])),dtype=object)\n", + " for i in np.arange(0,len(lc[0])):\n", + " for j in np.arange(0,len(lc[1])):\n", + " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5)\n", + " return pg" + ] + }, + { + "cell_type": "code", + "execution_count": 569, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "pg = Create_Periodogram(lc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Flatten lightcurve and pick peaks to look at on the periodogram image eventually" + ] + }, + { + "cell_type": "code", + "execution_count": 497, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_plot(pg,low=1,high=2):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(pg)):\n", + " for j in np.arange(0,len(pg[0])):\n", + " mask = np.zeros((len(pg),len(pg[0])), dtype=bool)\n", + " mask[j][i] = True\n", + " \n", + " period = pg[mask][0]\n", + " freq = np.asarray(period.frequency)\n", + " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", + " return heat_stamp" + ] + }, + { + "cell_type": "code", + "execution_count": 570, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 570, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fhp = frequency_heat_plot(pg,9.4,9.5)\n", + "plt.imshow(fhp,origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 522, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 522, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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80.25 , 80.375,\n", + " 80.5 , 80.625, 80.75 , 80.875, 81. , 81.125, 81.25 ,\n", + " 81.375, 81.5 , 81.625, 81.75 , 81.875, 82. , 82.125,\n", + " 82.25 , 82.375, 82.5 , 82.625, 82.75 , 82.875, 83. ,\n", + " 83.125, 83.25 , 83.375, 83.5 , 83.625, 83.75 , 83.875,\n", + " 84. , 84.125, 84.25 , 84.375, 84.5 , 84.625, 84.75 ,\n", + " 84.875, 85. , 85.125, 85.25 , 85.375, 85.5 , 85.625,\n", + " 85.75 , 85.875, 86. , 86.125, 86.25 , 86.375, 86.5 ,\n", + " 86.625, 86.75 , 86.875, 87. , 87.125, 87.25 , 87.375,\n", + " 87.5 , 87.625, 87.75 , 87.875, 88. , 88.125, 88.25 ,\n", + " 88.375, 88.5 , 88.625, 88.75 , 88.875, 89. , 89.125,\n", + " 89.25 , 89.375, 89.5 , 89.625, 89.75 , 89.875, 90. ,\n", + " 90.125, 90.25 , 90.375, 90.5 , 90.625, 90.75 , 90.875,\n", + " 91. , 91.125, 91.25 , 91.375, 91.5 , 91.625, 91.75 ,\n", + " 91.875, 92. , 92.125, 92.25 , 92.375, 92.5 , 92.625,\n", + " 92.75 , 92.875, 93. , 93.125, 93.25 , 93.375, 93.5 ,\n", + " 93.625, 93.75 , 93.875, 94. , 94.125, 94.25 , 94.375,\n", + " 94.5 , 94.625, 94.75 , 94.875, 95. , 95.125, 95.25 ,\n", + " 95.375, 95.5 , 95.625, 95.75 , 95.875, 96. , 96.125,\n", + " 96.25 , 96.375, 96.5 , 96.625, 96.75 , 96.875, 97. ,\n", + " 97.125, 97.25 , 97.375, 97.5 , 97.625, 97.75 , 97.875,\n", + " 98. , 98.125, 98.25 , 98.375, 98.5 , 98.625, 98.75 ,\n", + " 98.875, 99. , 99.125, 99.25 , 99.375, 99.5 , 99.625,\n", + " 99.75 , 99.875, 100. , 100.125, 100.25 , 100.375, 100.5 ,\n", + " 100.625, 100.75 , 100.875, 101. , 101.125, 101.25 , 101.375,\n", + " 101.5 , 101.625, 101.75 , 101.875, 102. , 102.125, 102.25 ,\n", + " 102.375, 102.5 , 102.625, 102.75 , 102.875, 103. , 103.125,\n", + " 103.25 , 103.375, 103.5 , 103.625, 103.75 , 103.875, 104. ,\n", + " 104.125, 104.25 , 104.375, 104.5 , 104.625, 104.75 , 104.875,\n", + " 105. , 105.125, 105.25 , 105.375, 105.5 , 105.625, 105.75 ,\n", + " 105.875, 106. , 106.125, 106.25 , 106.375, 106.5 , 106.625,\n", + " 106.75 , 106.875, 107. , 107.125, 107.25 , 107.375, 107.5 ,\n", + " 107.625, 107.75 , 107.875, 108. , 108.125, 108.25 , 108.375,\n", + " 108.5 , 108.625, 108.75 , 108.875, 109. , 109.125, 109.25 ,\n", + " 109.375, 109.5 , 109.625, 109.75 , 109.875, 110. , 110.125,\n", + " 110.25 , 110.375, 110.5 , 110.625, 110.75 , 110.875, 111. ,\n", + " 111.125, 111.25 , 111.375, 111.5 , 111.625, 111.75 , 111.875,\n", + " 112. , 112.125, 112.25 , 112.375, 112.5 , 112.625, 112.75 ,\n", + " 112.875, 113. , 113.125, 113.25 , 113.375, 113.5 , 113.625,\n", + " 113.75 , 113.875, 114. , 114.125, 114.25 , 114.375, 114.5 ,\n", + " 114.625, 114.75 , 114.875, 115. , 115.125, 115.25 , 115.375,\n", + " 115.5 , 115.625, 115.75 , 115.875, 116. , 116.125, 116.25 ,\n", + " 116.375, 116.5 , 116.625, 116.75 , 116.875, 117. , 117.125,\n", + " 117.25 , 117.375, 117.5 , 117.625, 117.75 , 117.875, 118. ,\n", + " 118.125, 118.25 , 118.375, 118.5 , 118.625, 118.75 , 118.875,\n", + " 119. , 119.125, 119.25 , 119.375, 119.5 , 119.625, 119.75 ,\n", + " 119.875, 120. , 120.125, 120.25 , 120.375, 120.5 , 120.625,\n", + " 120.75 , 120.875, 121. , 121.125, 121.25 , 121.375, 121.5 ,\n", + " 121.625, 121.75 , 121.875, 122. , 122.125, 122.25 , 122.375,\n", + " 122.5 , 122.625, 122.75 , 122.875, 123. , 123.125, 123.25 ,\n", + " 123.375, 123.5 , 123.625, 123.75 , 123.875, 124. , 124.125,\n", + " 124.25 , 124.375, 124.5 , 124.625, 124.75 , 124.875]),\n", + " (30, 30),\n", + " None]" + ] + }, + "execution_count": 492, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test[1::]" + ] + }, + { + "cell_type": "code", + "execution_count": 520, + "metadata": {}, + "outputs": [], + "source": [ + "def Find_Centroid(fhp):\n", + " x=0\n", + " y=0\n", + " for i in np.arange(0,len(fhp)):\n", + " for j in np.arange(0,len(fhp[0])):\n", + " x += j*fhp[i][j]/fhp.sum()\n", + " y += i*fhp[i][j]/fhp.sum()\n", + " return x,y" + ] + }, + { + "cell_type": "code", + "execution_count": 573, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20.869451398841548, 4.875739425117415)" + ] + }, + "execution_count": 573, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Find_Centroid(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 559, + "metadata": {}, + "outputs": [], + "source": [ + "def Flat_lc(*simulatedimage):\n", + " flux = np.zeros(shape=len(time))\n", + " for i in np.arange(0,len(time)):\n", + " flux[i] = simulatedimage[0][0][i].sum()\n", + " lc = lk.LightCurve(time = time, flux = flux)\n", + " return lc" + ] + }, + { + "cell_type": "code", + "execution_count": 574, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 574, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Flat_lc(test).plot()" + ] } ], "metadata": { @@ -4296,6 +5142,18 @@ "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": "3.6.8" } }, "nbformat": 4, From 2a6f1c316719d200ab1bf0812cbf909971105c9d Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Sat, 1 Feb 2020 21:14:54 -0800 Subject: [PATCH 06/11] Some code changes with simulations --- .ipynb_checkpoints/Research-checkpoint.ipynb | 262 +++++-------------- Research.ipynb | 262 +++++-------------- 2 files changed, 120 insertions(+), 404 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index 7502e0e..c1d28d6 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -4574,26 +4574,17 @@ }, { "cell_type": "code", - "execution_count": 488, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m time = np.arange(1000)*1./7.64.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "def Simulate_Random_Image(imageshape=(30,30),separation=None):\n", - " time = np.arange(1000)*1./7.64.\n", + " time = np.arange(1000)*1./48.\n", " freq1 = np.random.uniform()*15 #per day\n", " freq2 = np.random.uniform()*15 #per day\n", " relamp = 1\n", - " signal1 = relamp * np.sin(time*freq1)\n", - " signal2 = relamp * np.sin(time*freq2)\n", + " signal1 = relamp * np.sin(time*2*np.pi*freq1)\n", + " signal2 = relamp * np.sin(time*2*np.pi*freq2)\n", " \n", "\n", " #Images\n", @@ -4736,7 +4727,7 @@ }, { "cell_type": "code", - "execution_count": 489, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -4745,12 +4736,14 @@ }, { "cell_type": "code", - "execution_count": 565, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def Create_LightCurve(*simulatedimage):\n", - " lc_array = np.zeros(shape = imageshape,dtype=object)\n", + " imageshape = simulatedimage[0][8]\n", + " time = simulatedimage[0][7]\n", + " lc_array = np.zeros(imageshape,dtype=object)\n", " for i in np.arange(0,imageshape[0]):\n", " for j in np.arange(0,imageshape[1]):\n", " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", @@ -4759,7 +4752,7 @@ }, { "cell_type": "code", - "execution_count": 567, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -4768,7 +4761,7 @@ }, { "cell_type": "code", - "execution_count": 568, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -4776,13 +4769,13 @@ " pg = np.zeros(shape = (len(lc[0]),len(lc[1])),dtype=object)\n", " for i in np.arange(0,len(lc[0])):\n", " for j in np.arange(0,len(lc[1])):\n", - " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5)\n", + " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5,)\n", " return pg" ] }, { "cell_type": "code", - "execution_count": 569, + "execution_count": 28, "metadata": { "scrolled": false }, @@ -4800,7 +4793,7 @@ }, { "cell_type": "code", - "execution_count": 497, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -4813,9 +4806,10 @@ " mask[j][i] = True\n", " \n", " period = pg[mask][0]\n", + " normperiod = np.asarray(period.power)/np.median(np.asarray(period.power))\n", " freq = np.asarray(period.frequency)\n", - " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", - " heat_stamp.extend([sums])\n", + " sums = np.asarray(normperiod[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums-len(np.where((freq < high) & (freq > low))[0])])\n", " \n", " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", " return heat_stamp" @@ -4823,22 +4817,22 @@ }, { "cell_type": "code", - "execution_count": 570, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 570, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -4850,28 +4844,28 @@ } ], "source": [ - "fhp = frequency_heat_plot(pg,9.4,9.5)\n", - "plt.imshow(fhp,origin=0)" + "fhp = frequency_heat_plot(pg,2,2.5)\n", + "plt.imshow(fhp,origin=0)\n" ] }, { "cell_type": "code", - "execution_count": 522, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 522, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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80.25 , 80.375,\n", - " 80.5 , 80.625, 80.75 , 80.875, 81. , 81.125, 81.25 ,\n", - " 81.375, 81.5 , 81.625, 81.75 , 81.875, 82. , 82.125,\n", - " 82.25 , 82.375, 82.5 , 82.625, 82.75 , 82.875, 83. ,\n", - " 83.125, 83.25 , 83.375, 83.5 , 83.625, 83.75 , 83.875,\n", - " 84. , 84.125, 84.25 , 84.375, 84.5 , 84.625, 84.75 ,\n", - " 84.875, 85. , 85.125, 85.25 , 85.375, 85.5 , 85.625,\n", - " 85.75 , 85.875, 86. , 86.125, 86.25 , 86.375, 86.5 ,\n", - " 86.625, 86.75 , 86.875, 87. , 87.125, 87.25 , 87.375,\n", - " 87.5 , 87.625, 87.75 , 87.875, 88. , 88.125, 88.25 ,\n", - " 88.375, 88.5 , 88.625, 88.75 , 88.875, 89. , 89.125,\n", - " 89.25 , 89.375, 89.5 , 89.625, 89.75 , 89.875, 90. ,\n", - " 90.125, 90.25 , 90.375, 90.5 , 90.625, 90.75 , 90.875,\n", - " 91. , 91.125, 91.25 , 91.375, 91.5 , 91.625, 91.75 ,\n", - " 91.875, 92. , 92.125, 92.25 , 92.375, 92.5 , 92.625,\n", - " 92.75 , 92.875, 93. , 93.125, 93.25 , 93.375, 93.5 ,\n", - " 93.625, 93.75 , 93.875, 94. , 94.125, 94.25 , 94.375,\n", - " 94.5 , 94.625, 94.75 , 94.875, 95. , 95.125, 95.25 ,\n", - " 95.375, 95.5 , 95.625, 95.75 , 95.875, 96. , 96.125,\n", - " 96.25 , 96.375, 96.5 , 96.625, 96.75 , 96.875, 97. ,\n", - " 97.125, 97.25 , 97.375, 97.5 , 97.625, 97.75 , 97.875,\n", - " 98. , 98.125, 98.25 , 98.375, 98.5 , 98.625, 98.75 ,\n", - " 98.875, 99. , 99.125, 99.25 , 99.375, 99.5 , 99.625,\n", - " 99.75 , 99.875, 100. , 100.125, 100.25 , 100.375, 100.5 ,\n", - " 100.625, 100.75 , 100.875, 101. , 101.125, 101.25 , 101.375,\n", - " 101.5 , 101.625, 101.75 , 101.875, 102. , 102.125, 102.25 ,\n", - " 102.375, 102.5 , 102.625, 102.75 , 102.875, 103. , 103.125,\n", - " 103.25 , 103.375, 103.5 , 103.625, 103.75 , 103.875, 104. ,\n", - " 104.125, 104.25 , 104.375, 104.5 , 104.625, 104.75 , 104.875,\n", - " 105. , 105.125, 105.25 , 105.375, 105.5 , 105.625, 105.75 ,\n", - " 105.875, 106. , 106.125, 106.25 , 106.375, 106.5 , 106.625,\n", - " 106.75 , 106.875, 107. , 107.125, 107.25 , 107.375, 107.5 ,\n", - " 107.625, 107.75 , 107.875, 108. , 108.125, 108.25 , 108.375,\n", - " 108.5 , 108.625, 108.75 , 108.875, 109. , 109.125, 109.25 ,\n", - " 109.375, 109.5 , 109.625, 109.75 , 109.875, 110. , 110.125,\n", - " 110.25 , 110.375, 110.5 , 110.625, 110.75 , 110.875, 111. ,\n", - " 111.125, 111.25 , 111.375, 111.5 , 111.625, 111.75 , 111.875,\n", - " 112. , 112.125, 112.25 , 112.375, 112.5 , 112.625, 112.75 ,\n", - " 112.875, 113. , 113.125, 113.25 , 113.375, 113.5 , 113.625,\n", - " 113.75 , 113.875, 114. , 114.125, 114.25 , 114.375, 114.5 ,\n", - " 114.625, 114.75 , 114.875, 115. , 115.125, 115.25 , 115.375,\n", - " 115.5 , 115.625, 115.75 , 115.875, 116. , 116.125, 116.25 ,\n", - " 116.375, 116.5 , 116.625, 116.75 , 116.875, 117. , 117.125,\n", - " 117.25 , 117.375, 117.5 , 117.625, 117.75 , 117.875, 118. ,\n", - " 118.125, 118.25 , 118.375, 118.5 , 118.625, 118.75 , 118.875,\n", - " 119. , 119.125, 119.25 , 119.375, 119.5 , 119.625, 119.75 ,\n", - " 119.875, 120. , 120.125, 120.25 , 120.375, 120.5 , 120.625,\n", - " 120.75 , 120.875, 121. , 121.125, 121.25 , 121.375, 121.5 ,\n", - " 121.625, 121.75 , 121.875, 122. , 122.125, 122.25 , 122.375,\n", - " 122.5 , 122.625, 122.75 , 122.875, 123. , 123.125, 123.25 ,\n", - " 123.375, 123.5 , 123.625, 123.75 , 123.875, 124. , 124.125,\n", - " 124.25 , 124.375, 124.5 , 124.625, 124.75 , 124.875]),\n", - " (30, 30),\n", - " None]" + "[2.362452968394556,\n", + " 1.3332392386246288,\n", + " [10.773326006527324, 22.279953907529595],\n", + " [18.6411113522353, 15.410720759799736],\n", + " 1606,\n", + " 482]" ] }, - "execution_count": 492, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test[1::]" + "test[1:7:]" ] }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -5074,16 +4923,16 @@ }, { "cell_type": "code", - "execution_count": 573, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(20.869451398841548, 4.875739425117415)" + "(11.23758581054665, 21.718837078137543)" ] }, - "execution_count": 573, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -5094,36 +4943,38 @@ }, { "cell_type": "code", - "execution_count": 559, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "def Flat_lc(*simulatedimage):\n", - " flux = np.zeros(shape=len(time))\n", - " for i in np.arange(0,len(time)):\n", + " flux = np.zeros(shape=len(simulatedimage[0][7]))\n", + " for i in np.arange(0,len(simulatedimage[0][7])):\n", " flux[i] = simulatedimage[0][0][i].sum()\n", - " lc = lk.LightCurve(time = time, flux = flux)\n", + " lc = lk.LightCurve(time = simulatedimage[0][7], flux = flux)\n", " return lc" ] }, { "cell_type": "code", - "execution_count": 574, - "metadata": {}, + "execution_count": 64, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 574, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -5135,6 +4986,13 @@ "source": [ "Flat_lc(test).plot()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulate one star in different locations and brightnesses many times" + ] } ], "metadata": { diff --git a/Research.ipynb b/Research.ipynb index 7502e0e..c1d28d6 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -4574,26 +4574,17 @@ }, { "cell_type": "code", - "execution_count": 488, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "ename": "SyntaxError", - "evalue": "invalid syntax (, line 2)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m2\u001b[0m\n\u001b[1;33m time = np.arange(1000)*1./7.64.\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] - } - ], + "outputs": [], "source": [ "def Simulate_Random_Image(imageshape=(30,30),separation=None):\n", - " time = np.arange(1000)*1./7.64.\n", + " time = np.arange(1000)*1./48.\n", " freq1 = np.random.uniform()*15 #per day\n", " freq2 = np.random.uniform()*15 #per day\n", " relamp = 1\n", - " signal1 = relamp * np.sin(time*freq1)\n", - " signal2 = relamp * np.sin(time*freq2)\n", + " signal1 = relamp * np.sin(time*2*np.pi*freq1)\n", + " signal2 = relamp * np.sin(time*2*np.pi*freq2)\n", " \n", "\n", " #Images\n", @@ -4736,7 +4727,7 @@ }, { "cell_type": "code", - "execution_count": 489, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -4745,12 +4736,14 @@ }, { "cell_type": "code", - "execution_count": 565, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "def Create_LightCurve(*simulatedimage):\n", - " lc_array = np.zeros(shape = imageshape,dtype=object)\n", + " imageshape = simulatedimage[0][8]\n", + " time = simulatedimage[0][7]\n", + " lc_array = np.zeros(imageshape,dtype=object)\n", " for i in np.arange(0,imageshape[0]):\n", " for j in np.arange(0,imageshape[1]):\n", " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", @@ -4759,7 +4752,7 @@ }, { "cell_type": "code", - "execution_count": 567, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -4768,7 +4761,7 @@ }, { "cell_type": "code", - "execution_count": 568, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -4776,13 +4769,13 @@ " pg = np.zeros(shape = (len(lc[0]),len(lc[1])),dtype=object)\n", " for i in np.arange(0,len(lc[0])):\n", " for j in np.arange(0,len(lc[1])):\n", - " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5)\n", + " pg[i][j] = lc[i][j].to_periodogram(oversample_factor = 5,)\n", " return pg" ] }, { "cell_type": "code", - "execution_count": 569, + "execution_count": 28, "metadata": { "scrolled": false }, @@ -4800,7 +4793,7 @@ }, { "cell_type": "code", - "execution_count": 497, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -4813,9 +4806,10 @@ " mask[j][i] = True\n", " \n", " period = pg[mask][0]\n", + " normperiod = np.asarray(period.power)/np.median(np.asarray(period.power))\n", " freq = np.asarray(period.frequency)\n", - " sums = np.asarray(period.power[np.where((freq < high) & (freq > low))]).sum()\n", - " heat_stamp.extend([sums])\n", + " sums = np.asarray(normperiod[np.where((freq < high) & (freq > low))]).sum()\n", + " heat_stamp.extend([sums-len(np.where((freq < high) & (freq > low))[0])])\n", " \n", " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", " return heat_stamp" @@ -4823,22 +4817,22 @@ }, { "cell_type": "code", - "execution_count": 570, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 570, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -4850,28 +4844,28 @@ } ], "source": [ - "fhp = frequency_heat_plot(pg,9.4,9.5)\n", - "plt.imshow(fhp,origin=0)" + "fhp = frequency_heat_plot(pg,2,2.5)\n", + "plt.imshow(fhp,origin=0)\n" ] }, { "cell_type": "code", - "execution_count": 522, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 522, + "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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80.25 , 80.375,\n", - " 80.5 , 80.625, 80.75 , 80.875, 81. , 81.125, 81.25 ,\n", - " 81.375, 81.5 , 81.625, 81.75 , 81.875, 82. , 82.125,\n", - " 82.25 , 82.375, 82.5 , 82.625, 82.75 , 82.875, 83. ,\n", - " 83.125, 83.25 , 83.375, 83.5 , 83.625, 83.75 , 83.875,\n", - " 84. , 84.125, 84.25 , 84.375, 84.5 , 84.625, 84.75 ,\n", - " 84.875, 85. , 85.125, 85.25 , 85.375, 85.5 , 85.625,\n", - " 85.75 , 85.875, 86. , 86.125, 86.25 , 86.375, 86.5 ,\n", - " 86.625, 86.75 , 86.875, 87. , 87.125, 87.25 , 87.375,\n", - " 87.5 , 87.625, 87.75 , 87.875, 88. , 88.125, 88.25 ,\n", - " 88.375, 88.5 , 88.625, 88.75 , 88.875, 89. , 89.125,\n", - " 89.25 , 89.375, 89.5 , 89.625, 89.75 , 89.875, 90. ,\n", - " 90.125, 90.25 , 90.375, 90.5 , 90.625, 90.75 , 90.875,\n", - " 91. , 91.125, 91.25 , 91.375, 91.5 , 91.625, 91.75 ,\n", - " 91.875, 92. , 92.125, 92.25 , 92.375, 92.5 , 92.625,\n", - " 92.75 , 92.875, 93. , 93.125, 93.25 , 93.375, 93.5 ,\n", - " 93.625, 93.75 , 93.875, 94. , 94.125, 94.25 , 94.375,\n", - " 94.5 , 94.625, 94.75 , 94.875, 95. , 95.125, 95.25 ,\n", - " 95.375, 95.5 , 95.625, 95.75 , 95.875, 96. , 96.125,\n", - " 96.25 , 96.375, 96.5 , 96.625, 96.75 , 96.875, 97. ,\n", - " 97.125, 97.25 , 97.375, 97.5 , 97.625, 97.75 , 97.875,\n", - " 98. , 98.125, 98.25 , 98.375, 98.5 , 98.625, 98.75 ,\n", - " 98.875, 99. , 99.125, 99.25 , 99.375, 99.5 , 99.625,\n", - " 99.75 , 99.875, 100. , 100.125, 100.25 , 100.375, 100.5 ,\n", - " 100.625, 100.75 , 100.875, 101. , 101.125, 101.25 , 101.375,\n", - " 101.5 , 101.625, 101.75 , 101.875, 102. , 102.125, 102.25 ,\n", - " 102.375, 102.5 , 102.625, 102.75 , 102.875, 103. , 103.125,\n", - " 103.25 , 103.375, 103.5 , 103.625, 103.75 , 103.875, 104. ,\n", - " 104.125, 104.25 , 104.375, 104.5 , 104.625, 104.75 , 104.875,\n", - " 105. , 105.125, 105.25 , 105.375, 105.5 , 105.625, 105.75 ,\n", - " 105.875, 106. , 106.125, 106.25 , 106.375, 106.5 , 106.625,\n", - " 106.75 , 106.875, 107. , 107.125, 107.25 , 107.375, 107.5 ,\n", - " 107.625, 107.75 , 107.875, 108. , 108.125, 108.25 , 108.375,\n", - " 108.5 , 108.625, 108.75 , 108.875, 109. , 109.125, 109.25 ,\n", - " 109.375, 109.5 , 109.625, 109.75 , 109.875, 110. , 110.125,\n", - " 110.25 , 110.375, 110.5 , 110.625, 110.75 , 110.875, 111. ,\n", - " 111.125, 111.25 , 111.375, 111.5 , 111.625, 111.75 , 111.875,\n", - " 112. , 112.125, 112.25 , 112.375, 112.5 , 112.625, 112.75 ,\n", - " 112.875, 113. , 113.125, 113.25 , 113.375, 113.5 , 113.625,\n", - " 113.75 , 113.875, 114. , 114.125, 114.25 , 114.375, 114.5 ,\n", - " 114.625, 114.75 , 114.875, 115. , 115.125, 115.25 , 115.375,\n", - " 115.5 , 115.625, 115.75 , 115.875, 116. , 116.125, 116.25 ,\n", - " 116.375, 116.5 , 116.625, 116.75 , 116.875, 117. , 117.125,\n", - " 117.25 , 117.375, 117.5 , 117.625, 117.75 , 117.875, 118. ,\n", - " 118.125, 118.25 , 118.375, 118.5 , 118.625, 118.75 , 118.875,\n", - " 119. , 119.125, 119.25 , 119.375, 119.5 , 119.625, 119.75 ,\n", - " 119.875, 120. , 120.125, 120.25 , 120.375, 120.5 , 120.625,\n", - " 120.75 , 120.875, 121. , 121.125, 121.25 , 121.375, 121.5 ,\n", - " 121.625, 121.75 , 121.875, 122. , 122.125, 122.25 , 122.375,\n", - " 122.5 , 122.625, 122.75 , 122.875, 123. , 123.125, 123.25 ,\n", - " 123.375, 123.5 , 123.625, 123.75 , 123.875, 124. , 124.125,\n", - " 124.25 , 124.375, 124.5 , 124.625, 124.75 , 124.875]),\n", - " (30, 30),\n", - " None]" + "[2.362452968394556,\n", + " 1.3332392386246288,\n", + " [10.773326006527324, 22.279953907529595],\n", + " [18.6411113522353, 15.410720759799736],\n", + " 1606,\n", + " 482]" ] }, - "execution_count": 492, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test[1::]" + "test[1:7:]" ] }, { "cell_type": "code", - "execution_count": 520, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -5074,16 +4923,16 @@ }, { "cell_type": "code", - "execution_count": 573, + "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(20.869451398841548, 4.875739425117415)" + "(11.23758581054665, 21.718837078137543)" ] }, - "execution_count": 573, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -5094,36 +4943,38 @@ }, { "cell_type": "code", - "execution_count": 559, + "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "def Flat_lc(*simulatedimage):\n", - " flux = np.zeros(shape=len(time))\n", - " for i in np.arange(0,len(time)):\n", + " flux = np.zeros(shape=len(simulatedimage[0][7]))\n", + " for i in np.arange(0,len(simulatedimage[0][7])):\n", " flux[i] = simulatedimage[0][0][i].sum()\n", - " lc = lk.LightCurve(time = time, flux = flux)\n", + " lc = lk.LightCurve(time = simulatedimage[0][7], flux = flux)\n", " return lc" ] }, { "cell_type": "code", - "execution_count": 574, - "metadata": {}, + "execution_count": 64, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 574, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -5135,6 +4986,13 @@ "source": [ "Flat_lc(test).plot()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simulate one star in different locations and brightnesses many times" + ] } ], "metadata": { From b5e1b1e62257fb74d2149360a75ce1be2435b12a Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Fri, 7 Feb 2020 03:21:20 -0800 Subject: [PATCH 07/11] Testing new way to find centroids --- .ipynb_checkpoints/Research-checkpoint.ipynb | 747 ++++++++++--------- Research.ipynb | 747 ++++++++++--------- 2 files changed, 786 insertions(+), 708 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index c1d28d6..92d7332 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -4292,289 +4292,7 @@ }, { "cell_type": "code", - "execution_count": 370, - "metadata": {}, - "outputs": [ - { - "data": { - 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = Simulate_Random_Image(separation=5)\n", - "plt.imshow(x[0][0],origin=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -4662,97 +4380,100 @@ " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", " \n", " stars = imagestack[:,:,:].T\n", - " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]" + " diction = {\n", + " 'stars':stars,\n", + " 'Frequencystar1':freq1,\n", + " 'Frequencystar2':freq2,\n", + " 'star1position':star1pos,\n", + " 'star2position':star2pos,\n", + " 'star1flux':star1flux,\n", + " 'star2flux':star2flux,\n", + " 'time':time,\n", + " 'imageshape':imageshape,\n", + " 'separation':separation\n", + " }\n", + " return diction" ] }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "time = np.arange(1000)*1./48.\n", - "freq = 10. #per day\n", - "relamp = 1\n", - "signal = relamp * np.sin(time*freq)\n", - "plt.plot(time,signal)\n", + "def Simulate_Image(imageshape=(30,30),star1pos = [15,15],star1flux = 1000 , freq=10):\n", + "\n", + " time = np.arange(1000)*1./48.\n", + " relamp = 1\n", + " signal = relamp * np.sin(time*freq*2*np.pi)\n", "\n", - "#Images\n", - "imageshape = (30,30) #pixels\n", - "star1pos = [10,10]\n", - "star2pos = [20,20]\n", - "star1flux = 1000.\n", - "star2flux = 750.\n", - "seeingsigma = 1.\n", + " #Images\n", + " star1flux = 1000.\n", + " seeingsigma = 1.\n", "\n", - "imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", - "xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", "\n", "\n", "\n", - "backgroundnoise = 10.\n", + " backgroundnoise = 10.\n", "\n", - "#add starlight\n", - "distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", - "distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + " #add starlight\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", "\n", - "for i in range(len(time)):\n", - " #star 1\n", - " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", "\n", - " #star 2\n", - " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux\n", "\n", - " #add measurement noise\n", - " #should probably be Poisson\n", - " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", "\n", - " #background\n", - " #imagestack[:,:,i] += backgroundnoise\n", - " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)" + " #background\n", + " #imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + " stars = imagestack[:,:,:].T\n", + " diction = {\n", + " 'stars':stars,\n", + " 'frequency':freq,\n", + " 'starposition':star1pos,\n", + " 'starflux':star1flux,\n", + " 'time':time,\n", + " 'imageshape':imageshape\n", + " }\n", + " return diction" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ - "test = Simulate_Random_Image()" + "test = Simulate_Image(imageshape=(31,31),star1pos=[5,5])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def Create_LightCurve(*simulatedimage):\n", - " imageshape = simulatedimage[0][8]\n", - " time = simulatedimage[0][7]\n", + " imageshape = simulatedimage[0]['imageshape']\n", + " time = simulatedimage[0]['time']\n", " lc_array = np.zeros(imageshape,dtype=object)\n", + " fluxtype = simulatedimage[0]['stars']\n", " for i in np.arange(0,imageshape[0]):\n", " for j in np.arange(0,imageshape[1]):\n", - " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", + " lc_array[j][i] = lk.LightCurve(time = time, flux = fluxtype.T[i,j,:])\n", " return lc_array" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -4761,7 +4482,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -4775,13 +4496,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 141, "metadata": { "scrolled": false }, "outputs": [], "source": [ - "pg = Create_Periodogram(lc)" + "pg = Create_Periodogram(lc) #21 seconds" ] }, { @@ -4793,7 +4514,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -4817,22 +4538,22 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 53, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -4844,28 +4565,28 @@ } ], "source": [ - "fhp = frequency_heat_plot(pg,2,2.5)\n", + "fhp = frequency_heat_plot(pg,9,11)\n", "plt.imshow(fhp,origin=0)\n" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -4875,7 +4596,7 @@ } ], "source": [ - "pg[11][22].plot()" + "pg[15][15].plot()" ] }, { @@ -4907,7 +4628,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -4923,16 +4644,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(11.23758581054665, 21.718837078137543)" + "(14.913443604667416, 14.756152587364843)" ] }, - "execution_count": 54, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -4991,7 +4712,325 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Simulate one star in different locations and brightnesses many times" + "## Simulate one star in different locations and brightnesses many times" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "xposition = []\n", + "yposition = []\n", + "for i in np.arange(0,100):\n", + " image = Simulate_Image(imageshape=(11,11),star1pos=[5,5])\n", + " lc = Create_LightCurve(image)\n", + " pg = Create_Periodogram(lc)\n", + " fhp = frequency_heat_plot(pg,low=9.5,high=10.5)\n", + " loc = Find_Centroid(fhp)\n", + " xposition.extend([loc[0]])\n", + " yposition.extend([loc[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 1., 2., 3., 6., 0.],\n", + " [ 1., 7., 9., 4., 1.],\n", + " [ 6., 7., 15., 7., 4.],\n", + " [ 5., 8., 5., 1., 1.],\n", + " [ 1., 1., 4., 0., 1.]]),\n", + " array([4.9704024 , 4.9829112 , 4.99542 , 5.0079288 , 5.02043761,\n", + " 5.03294641]),\n", + " array([4.97419894, 4.98561154, 4.99702413, 5.00843672, 5.01984932,\n", + " 5.03126191]),\n", + " )" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(xposition,yposition,bins=5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.97730812, 5.02160851]), 4.999971650040226)" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#xhist = np.histogram(xposition,bins=100)\n", + "#yhist = np.histogram(yposition,bins=100)\n", + "\n", + "np.quantile(xposition,[.05,.95]),np.mean(xposition)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.97901203, 5.02261124]), 5.000342892287856)" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(yposition,[.05,.95]),np.mean(yposition)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "xpos = []\n", + "ypos = []\n", + "for i in np.arange(0,100):\n", + " image = Simulate_Image(imageshape=(11,11),star1pos=[1,1])\n", + " lc = Create_LightCurve(image)\n", + " pg = Create_Periodogram(lc)\n", + " fhp = frequency_heat_plot(pg,low=9.5,high=10.5)\n", + " loc = Find_Centroid(fhp)\n", + " xpos.extend([loc[0]])\n", + " ypos.extend([loc[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 2., 2., 1., 1., 0.],\n", + " [ 2., 7., 3., 2., 0.],\n", + " [ 1., 5., 14., 6., 1.],\n", + " [ 3., 3., 16., 7., 4.],\n", + " [ 0., 0., 4., 10., 6.]]),\n", + " array([1.3216241 , 1.34295334, 1.36428258, 1.38561181, 1.40694105,\n", + " 1.42827029]),\n", + " array([1.33093094, 1.35291088, 1.37489083, 1.39687078, 1.41885072,\n", + " 1.44083067]),\n", + " )" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(xpos,ypos,bins=5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1.34123792, 1.42416661]), 1.3850751759378905)" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(xpos,[.05,.95]),np.mean(xpos)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1.35189323, 1.42869432]), 1.3898605964480095)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(ypos,[.05,.95]),np.mean(ypos)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.asarray([xpos,ypos])" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import centroids as cent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## New fitting with photutils" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.00584334, 4.99294139])" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_2dg(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7.5785385618535, 7.44127184156069)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Find_Centroid(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7.57853856, 7.44127184])" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_com(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" -5.60228289e-78, 3.23879490e-78, -4.05381685e-77, 1.08895379e-77,\n", - " 1.30181119e-77, -6.31058865e-78, -1.26879998e-77, 1.24782625e-77,\n", - " -6.83138982e-78, -4.20836614e-78, 2.53498253e-78, -4.87459336e-78,\n", - " -1.38772498e-77, 8.62053275e-78, 1.14673529e-77, 1.11184967e+01])" - ] - }, - "execution_count": 370, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "x = Simulate_Random_Image(separation=5)\n", - "plt.imshow(x[0][0],origin=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -4662,97 +4380,100 @@ " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", " \n", " stars = imagestack[:,:,:].T\n", - " return [stars,freq1,freq2,star1pos,star2pos,star1flux,star2flux,time,imageshape,separation]" + " diction = {\n", + " 'stars':stars,\n", + " 'Frequencystar1':freq1,\n", + " 'Frequencystar2':freq2,\n", + " 'star1position':star1pos,\n", + " 'star2position':star2pos,\n", + " 'star1flux':star1flux,\n", + " 'star2flux':star2flux,\n", + " 'time':time,\n", + " 'imageshape':imageshape,\n", + " 'separation':separation\n", + " }\n", + " return diction" ] }, { "cell_type": "code", - "execution_count": 285, + "execution_count": 32, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "time = np.arange(1000)*1./48.\n", - "freq = 10. #per day\n", - "relamp = 1\n", - "signal = relamp * np.sin(time*freq)\n", - "plt.plot(time,signal)\n", + "def Simulate_Image(imageshape=(30,30),star1pos = [15,15],star1flux = 1000 , freq=10):\n", + "\n", + " time = np.arange(1000)*1./48.\n", + " relamp = 1\n", + " signal = relamp * np.sin(time*freq*2*np.pi)\n", "\n", - "#Images\n", - "imageshape = (30,30) #pixels\n", - "star1pos = [10,10]\n", - "star2pos = [20,20]\n", - "star1flux = 1000.\n", - "star2flux = 750.\n", - "seeingsigma = 1.\n", + " #Images\n", + " star1flux = 1000.\n", + " seeingsigma = 1.\n", "\n", - "imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", - "xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", + " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", + " xcoord,ycoord = np.meshgrid(np.arange(imageshape[1]),np.arange(imageshape[0])) #strange order\n", "\n", "\n", "\n", - "backgroundnoise = 10.\n", + " backgroundnoise = 10.\n", "\n", - "#add starlight\n", - "distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", - "distance2 = np.sqrt((xcoord-star2pos[0])**2 + (ycoord-star2pos[1])**2)\n", + " #add starlight\n", + " distance1 = np.sqrt((xcoord-star1pos[0])**2 + (ycoord-star1pos[1])**2)\n", "\n", - "for i in range(len(time)):\n", - " #star 1\n", - " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", + " for i in range(len(time)):\n", + " #star 1\n", + " imagestack[:,:,i] += stats.norm.pdf(distance1,scale=seeingsigma) * star1flux * (1. + signal[i])\n", "\n", - " #star 2\n", - " imagestack[:,:,i] += stats.norm.pdf(distance2,scale=seeingsigma) * star2flux\n", "\n", - " #add measurement noise\n", - " #should probably be Poisson\n", - " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", + " #add measurement noise\n", + " #should probably be Poisson\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(imagestack[:,:,i])\n", "\n", - " #background\n", - " #imagestack[:,:,i] += backgroundnoise\n", - " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)" + " #background\n", + " #imagestack[:,:,i] += backgroundnoise\n", + " imagestack[:,:,i] += stats.norm.rvs(size=imagestack[:,:,i].size).reshape(imageshape) * np.sqrt(backgroundnoise)\n", + " stars = imagestack[:,:,:].T\n", + " diction = {\n", + " 'stars':stars,\n", + " 'frequency':freq,\n", + " 'starposition':star1pos,\n", + " 'starflux':star1flux,\n", + " 'time':time,\n", + " 'imageshape':imageshape\n", + " }\n", + " return diction" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 139, "metadata": {}, "outputs": [], "source": [ - "test = Simulate_Random_Image()" + "test = Simulate_Image(imageshape=(31,31),star1pos=[5,5])" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "def Create_LightCurve(*simulatedimage):\n", - " imageshape = simulatedimage[0][8]\n", - " time = simulatedimage[0][7]\n", + " imageshape = simulatedimage[0]['imageshape']\n", + " time = simulatedimage[0]['time']\n", " lc_array = np.zeros(imageshape,dtype=object)\n", + " fluxtype = simulatedimage[0]['stars']\n", " for i in np.arange(0,imageshape[0]):\n", " for j in np.arange(0,imageshape[1]):\n", - " lc_array[j][i] = lk.LightCurve(time = time, flux = simulatedimage[0][0].T[i,j,:])\n", + " lc_array[j][i] = lk.LightCurve(time = time, flux = fluxtype.T[i,j,:])\n", " return lc_array" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -4761,7 +4482,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -4775,13 +4496,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 141, "metadata": { "scrolled": false }, "outputs": [], "source": [ - "pg = Create_Periodogram(lc)" + "pg = Create_Periodogram(lc) #21 seconds" ] }, { @@ -4793,7 +4514,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 142, "metadata": {}, "outputs": [], "source": [ @@ -4817,22 +4538,22 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 143, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 53, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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+ "image/png": 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\n", 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" ] @@ -4844,28 +4565,28 @@ } ], "source": [ - "fhp = frequency_heat_plot(pg,2,2.5)\n", + "fhp = frequency_heat_plot(pg,9,11)\n", "plt.imshow(fhp,origin=0)\n" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 49, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -4875,7 +4596,7 @@ } ], "source": [ - "pg[11][22].plot()" + "pg[15][15].plot()" ] }, { @@ -4907,7 +4628,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -4923,16 +4644,16 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(11.23758581054665, 21.718837078137543)" + "(14.913443604667416, 14.756152587364843)" ] }, - "execution_count": 54, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -4991,7 +4712,325 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Simulate one star in different locations and brightnesses many times" + "## Simulate one star in different locations and brightnesses many times" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": {}, + "outputs": [], + "source": [ + "xposition = []\n", + "yposition = []\n", + "for i in np.arange(0,100):\n", + " image = Simulate_Image(imageshape=(11,11),star1pos=[5,5])\n", + " lc = Create_LightCurve(image)\n", + " pg = Create_Periodogram(lc)\n", + " fhp = frequency_heat_plot(pg,low=9.5,high=10.5)\n", + " loc = Find_Centroid(fhp)\n", + " xposition.extend([loc[0]])\n", + " yposition.extend([loc[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 1., 2., 3., 6., 0.],\n", + " [ 1., 7., 9., 4., 1.],\n", + " [ 6., 7., 15., 7., 4.],\n", + " [ 5., 8., 5., 1., 1.],\n", + " [ 1., 1., 4., 0., 1.]]),\n", + " array([4.9704024 , 4.9829112 , 4.99542 , 5.0079288 , 5.02043761,\n", + " 5.03294641]),\n", + " array([4.97419894, 4.98561154, 4.99702413, 5.00843672, 5.01984932,\n", + " 5.03126191]),\n", + " )" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(xposition,yposition,bins=5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.97730812, 5.02160851]), 4.999971650040226)" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#xhist = np.histogram(xposition,bins=100)\n", + "#yhist = np.histogram(yposition,bins=100)\n", + "\n", + "np.quantile(xposition,[.05,.95]),np.mean(xposition)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.97901203, 5.02261124]), 5.000342892287856)" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(yposition,[.05,.95]),np.mean(yposition)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [], + "source": [ + "xpos = []\n", + "ypos = []\n", + "for i in np.arange(0,100):\n", + " image = Simulate_Image(imageshape=(11,11),star1pos=[1,1])\n", + " lc = Create_LightCurve(image)\n", + " pg = Create_Periodogram(lc)\n", + " fhp = frequency_heat_plot(pg,low=9.5,high=10.5)\n", + " loc = Find_Centroid(fhp)\n", + " xpos.extend([loc[0]])\n", + " ypos.extend([loc[1]])" + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[ 2., 2., 1., 1., 0.],\n", + " [ 2., 7., 3., 2., 0.],\n", + " [ 1., 5., 14., 6., 1.],\n", + " [ 3., 3., 16., 7., 4.],\n", + " [ 0., 0., 4., 10., 6.]]),\n", + " array([1.3216241 , 1.34295334, 1.36428258, 1.38561181, 1.40694105,\n", + " 1.42827029]),\n", + " array([1.33093094, 1.35291088, 1.37489083, 1.39687078, 1.41885072,\n", + " 1.44083067]),\n", + " )" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist2d(xpos,ypos,bins=5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1.34123792, 1.42416661]), 1.3850751759378905)" + ] + }, + "execution_count": 106, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(xpos,[.05,.95]),np.mean(xpos)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([1.35189323, 1.42869432]), 1.3898605964480095)" + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.quantile(ypos,[.05,.95]),np.mean(ypos)" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "data = np.asarray([xpos,ypos])" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [], + "source": [ + "from photutils import centroids as cent" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## New fitting with photutils" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([5.00584334, 4.99294139])" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_2dg(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(7.5785385618535, 7.44127184156069)" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Find_Centroid(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([7.57853856, 7.44127184])" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_com(fhp)" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 150, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(fhp,origin=0)" ] } ], From 649057088fbce06ebdacb417b74bc20c7ff12d2a Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Mon, 17 Feb 2020 02:39:36 -0800 Subject: [PATCH 08/11] Some analysis code that will be moved to a more tidy branch --- .ipynb_checkpoints/Research-checkpoint.ipynb | 1942 ++++++++++++++- Research.ipynb | 2245 +++++++++++++++++- 2 files changed, 4055 insertions(+), 132 deletions(-) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index 92d7332..caf5d54 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -4292,7 +4292,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -4397,7 +4397,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -4408,7 +4408,6 @@ " signal = relamp * np.sin(time*freq*2*np.pi)\n", "\n", " #Images\n", - " star1flux = 1000.\n", " seeingsigma = 1.\n", "\n", " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", @@ -4447,7 +4446,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -4456,7 +4455,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -4473,7 +4472,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -4482,7 +4481,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -4496,7 +4495,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 49, "metadata": { "scrolled": false }, @@ -4514,7 +4513,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -4538,22 +4537,22 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 143, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -4571,22 +4570,22 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -4596,7 +4595,7 @@ } ], "source": [ - "pg[15][15].plot()" + "pg[5][5].plot()" ] }, { @@ -4628,7 +4627,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -4735,43 +4734,24 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 24, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(array([[ 1., 2., 3., 6., 0.],\n", - " [ 1., 7., 9., 4., 1.],\n", - " [ 6., 7., 15., 7., 4.],\n", - " [ 5., 8., 5., 1., 1.],\n", - " [ 1., 1., 4., 0., 1.]]),\n", - " array([4.9704024 , 4.9829112 , 4.99542 , 5.0079288 , 5.02043761,\n", - " 5.03294641]),\n", - " array([4.97419894, 4.98561154, 4.99702413, 5.00843672, 5.01984932,\n", - " 5.03126191]),\n", - " )" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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M3Arc3mfu28DV3fE7gG90x28F/gs4pnvcB7x93L2MoK/LgbuAJcAJwBRw8rh7mVX3buC0Q8xfBnwPCHAB8N/d+KuBx7vn5d3x8nH3M0xP3dw7gX/s9x6P+zHEe/UG4Ozu+HXAXuCUcfczZE/LgGO74xO7j/O6YWpxp79A3Q73cuDrcyx5E3B3d/wD4IruuIDj6N5MYCnw2yNX6fwM0debgB9W1YGqegZ4CHjPkax1xK4ANlXP/cApSU4H3g3cVVVPVdXv6X1h+2vpa66eqKq7gT+NtbqF69tXVf2iqh4DqKppYB9w2F9WmhBz9fR8VT3XrTmWEVydMfQX7qvAp4AX55h/CPhgd/x+4KQkp1bVffTCcm/3uLOqHj3Sxc7Dgvrqxi9N8sokpwEXA6uOdLHzUMC27lvl9X3mzwCemPV6Tzc21/gkWGhPk27ovpKcR29j9csjVuX8LLinJKuS7Ojmv9h9QVswQ38BkrwX2FdV2w+x7JPARUkeAC4CfgMcSPJ64BxgJb039R1JLjzSNQ9imL6qahtwB/AT4Jv0LlsdOMIlz8e6qloDXApc3+fPvN/PVeoQ45NgoT1NuqH66r6b+QZwbVXNtXlZbAvuqaqeqKrVwOuBq5OsGKYQQ39h1gHvS7Ib+Ba94L5l9oKqmq6qD1TVW4BPd2P76e2O76+qp6vqaXrX8S5Y1OrnNkxfVNXnq+rcqnoXvb/Ejy1q9Ycwszuqqn3AZuC8g5bs4aXfmawEpg8xPnZD9DTRhukrycnAd4HPdJdJJsIo3qvuYzwCvG2YWgz9BaiqG6pqZVWdBXwE+H5VXTl7TZLTksz8+d4A3Nwd/5reTnlJkqX0dssTcXlnmL66O35O7Y5XA6uBbYtW/CEkOSHJSTPHwCXAwwct2wJc1d1FcQGwv6r2AncClyRZnmR5d+6di1h+X0P2NLGG6SvJMnqBuqmqvr2ohR/CkD2tTHJ8d+5yehuznw9Tz5JhTtZLJbkRmKqqLcDbgS8kKeBe4Ppu2W307nrZSe/bt61V9Z0xlDuwAftaCvwovbtP/whcWVWTcnlnBbC5q20JcGtVbZ25/a2qNtC7NHUZsAt4Fri2m3sqyeeAn3Yf68aqemqR6+9nwT0BJPkR8EbgxCR7gI9W1di/mDFcXx8CLgROTXJNN3ZNVT24eOX3NUxP5wBf6f69BfhyVe0cphh/I1eSGuLlHUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JD/h+3JkjeRahhXgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'xposition' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxposition\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myposition\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#plt.scatter(xposition,yposition)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'xposition' is not defined" + ] } ], "source": [ - "plt.hist2d(xposition,yposition,bins=5)\n" + "plt.hist2d(xposition,yposition,bins=5)\n", + "#plt.scatter(xposition,yposition)" ] }, { @@ -4927,7 +4907,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -4943,36 +4923,36 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5.00584334, 4.99294139])" + "array([4.99532993, 4.9851858 ])" ] }, - "execution_count": 154, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cent.centroid_2dg(fhp)" + "cent.centroid_1dg(fhp)" ] }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(7.5785385618535, 7.44127184156069)" + "(7.317515242428074, 7.245791867238815)" ] }, - "execution_count": 148, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4983,16 +4963,16 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([7.57853856, 7.44127184])" + "array([7.31751524, 7.24579187])" ] }, - "execution_count": 155, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -5003,22 +4983,24 @@ }, { "cell_type": "code", - "execution_count": 150, - "metadata": {}, + "execution_count": 22, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 150, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -5032,6 +5014,1834 @@ "source": [ "plt.imshow(fhp,origin=0)" ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "nostar = Simulate_Image(imageshape=(30,30),star1flux=0,star1pos=[15,15],freq=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(nostar['stars'][0],origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0797886885266915" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(nostar['stars'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "search_result = lk.search_targetpixelfile('TIC239678779', mission='TESS', sector=19)\n", + "tpf = search_result.download(quality_bitmask='default')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def make_pg(targetpixelfile, method = 'LombScargle'):\n", + " \n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " tpf = targetpixelfile\n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " if method == 'bls':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " elif method == 'LombScargle':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(maximum_frequency=1000,freq_unit= u.microHertz)\n", + " periodogram = periodogram.flatten()\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " return [pg,(tpf.astropy_time.max()-tpf.astropy_time.min()).value]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "pg_image = make_pg(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_test(pg,timeserieslength,frequency):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(pg)):\n", + " for j in np.arange(0,len(pg[0])):\n", + " mask = np.zeros((len(pg),len(pg[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = pg[mask][0]\n", + " normperiod = np.asarray(period.power)/np.median(np.asarray(period.power))\n", + " freq = np.asarray(period.frequency)\n", + " sums = 0 \n", + " background = 0\n", + " for k in np.arange(len(frequency)):\n", + " sums += np.asarray(normperiod[np.where((freq < frequency[k]+(1/timeserieslength) ) & (freq > frequency[k]-(1/timeserieslength)))]).sum()\n", + " background += len(np.where((freq < frequency[k]+(1/timeserieslength) ) & (freq > frequency[k]-(1/timeserieslength)))[0])\n", + " heat_stamp.extend([sums-background])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", + " return heat_stamp" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "heatmap = frequency_heat_test(pg_image[0],pg_image[1],[97.87963868,92.03828453])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "X = np.arange(11)\n", + "Y = np.arange(11)\n", + "X,Y = np.meshgrid(X,Y)\n", + "ax.plot_surface(X,Y,heatmap)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.15641899, 7.4578977 ])" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_1dg(heatmap)" + ] } ], "metadata": { diff --git a/Research.ipynb b/Research.ipynb index 92d7332..52ff965 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -4292,7 +4292,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -4397,7 +4397,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -4408,7 +4408,6 @@ " signal = relamp * np.sin(time*freq*2*np.pi)\n", "\n", " #Images\n", - " star1flux = 1000.\n", " seeingsigma = 1.\n", "\n", " imagestack = np.zeros((imageshape[0],imageshape[1],len(time)))\n", @@ -4447,7 +4446,7 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ @@ -4456,7 +4455,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -4473,7 +4472,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -4482,7 +4481,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -4496,7 +4495,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 49, "metadata": { "scrolled": false }, @@ -4514,7 +4513,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -4538,22 +4537,22 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 143, + "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -4571,22 +4570,22 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 37, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -4596,7 +4595,7 @@ } ], "source": [ - "pg[15][15].plot()" + "pg[5][5].plot()" ] }, { @@ -4628,7 +4627,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -4735,43 +4734,24 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 24, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "(array([[ 1., 2., 3., 6., 0.],\n", - " [ 1., 7., 9., 4., 1.],\n", - " [ 6., 7., 15., 7., 4.],\n", - " [ 5., 8., 5., 1., 1.],\n", - " [ 1., 1., 4., 0., 1.]]),\n", - " array([4.9704024 , 4.9829112 , 4.99542 , 5.0079288 , 5.02043761,\n", - " 5.03294641]),\n", - " array([4.97419894, 4.98561154, 4.99702413, 5.00843672, 5.01984932,\n", - " 5.03126191]),\n", - " )" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "ename": "NameError", + "evalue": "name 'xposition' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist2d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxposition\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0myposition\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;31m#plt.scatter(xposition,yposition)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'xposition' is not defined" + ] } ], "source": [ - "plt.hist2d(xposition,yposition,bins=5)\n" + "plt.hist2d(xposition,yposition,bins=5)\n", + "#plt.scatter(xposition,yposition)" ] }, { @@ -4927,7 +4907,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -4943,36 +4923,36 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([5.00584334, 4.99294139])" + "array([4.99532993, 4.9851858 ])" ] }, - "execution_count": 154, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "cent.centroid_2dg(fhp)" + "cent.centroid_1dg(fhp)" ] }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(7.5785385618535, 7.44127184156069)" + "(7.317515242428074, 7.245791867238815)" ] }, - "execution_count": 148, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -4983,16 +4963,16 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([7.57853856, 7.44127184])" + "array([7.31751524, 7.24579187])" ] }, - "execution_count": 155, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -5003,22 +4983,24 @@ }, { "cell_type": "code", - "execution_count": 150, - "metadata": {}, + "execution_count": 22, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 150, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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V571x+TvFbZeeu7e47bGf/oIYTXlxog2vvl7c1nv33fIhRVefis9Up6CawFJq6xSU3YmjX+NQR52jv48xN5faT1EdjZgSk3seKU80Ogp+5TerlIvfrFIufrNKufjNKuXiN6uUi9+sUkNFfST3AXgLwCKAhYiYyR5LRjpr1xS3vfPx8v9fKs771qbZ4rbfmTu+uO3ZUz5V3LZBTQoqOsLkZJNry8uay8kfs5FVW3yW7GyTHWpy0kwxSWc6lhNdjWvKz7VxdBHKCUrV47tQ7pIc1Chy/l+PiFdHcBwz65B/7Ter1LDFHwC+T/JpktuWuwLJbSRnSc7Ox3g/sWRmgxv21/6LI+IAyVMB7CT5bxHxxNIrRMR2ANsB4GNTG0f/+U8zSxnqlT8iDjRfDwN4CMCWUQzKzMYvXfwk15M88ej3AC4BsGdUAzOz8Rrm1/7TADzUdI+tBvC3EfEPco8oxzoy1jhypLjt+J+V30k8+pNyLLd2qhyxPHPwrOK29a+Ldy4isoOaqFFNmqk65ZibwFNNiKom2gTynXRy0tDVoutNxYBiv+x9VNGblF1TcYi1EYeVLv6IeBnAL49wLGbWIUd9ZpVy8ZtVysVvVikXv1mlXPxmlep4Ak+Uo42p8lB6b5cn4jz5yQPFbYxNxW1///yvFredsL8cL538T+X1+BbFOLOTbfYWRfSUjInkunlta/Ulo75snCkjQtXxJ9ask2PJRm8UcWWWigg5/Idl/cpvVikXv1mlXPxmlXLxm1XKxW9WKRe/WaW6j/pK8UVycsTFAz8rbtv46BvlbWJiTNWdt/g/bw40rg8R6/H15sszHHFaTCipiMhKRV2qGw4Aeu+J2ZhENKW6ASkmPpUTVarnjOoSVfFhNgYU29LrDapzhsI4e4NHsX7lN6uUi9+sUi5+s0q5+M0q5eI3q5SL36xS3UZ9ZDlKSU6AqCZcXJx/u7htat0YJnFMknFe8rzo+Kx8/6IlcZWTbWYfw2wHXrJTUHUDKjGfi+xaOyVLUvH34N1+fuU3q5SL36xSLn6zSrn4zSrl4jerlIvfrFJDRX0kLwVwK4BVAP46Im6SO0QUO6rkZIxzc+UxrBExmVrnrpfr0JKRlewWKx8yO2mk7HiDiOSGWTtO7au6BcUkpSp6lJKdoDGXWzdwLBN/qv0yEeFCB119JFcB+CsAvwHgfABXkTw/ezwz69Ywv/ZvAfBiRLwcEXMA7gVw+WiGZWbjNkzxnwHgP5f8vL+57H1IbiM5S3J2HmIiCDPr1DDFv9ybiw+9yY6I7RExExEz0xCz55hZp4Yp/v0Azlry85kAysvnmNmKMkzx/wjAJ0meQ3INgC8BeGQ0wzKzcaPqqGrdmbwMwLfQj/rujIg/bbn+KwD+o/nxZACvpm989FbSeDyW5Xksy1s6lp+PiFMG2Wmo4h8GydmImJnIjS9jJY3HY1mex7K87Fj8CT+zSrn4zSo1yeLfPsHbXs5KGo/HsjyPZXmpsUzsPb+ZTZZ/7Ter1ESKn+SlJH9C8kWS109iDEvGso/kcyR3kZzt+LbvJHmY5J4ll20kuZPkC83XDRMcy40k/6s5N7uaaLeLsZxF8nGSe0k+T/JrzeWdnxsxls7PDcl1JH9I8tlmLH/cXH4Oyaea8/Ld5nM37SKi03/ofybgJQCfALAGwLMAzu96HEvGsw/AyRO67U8DuBDAniWX/TmA65vvrwfwZxMcy40Afn8C52UTgAub708E8O/od452fm7EWDo/N+h/pP6E5vtpAE8BuAjAfQC+1Fz+bQC/O8jxJvHK727ARkQ8AeC1D1x8OYAdzfc7AFwxwbFMREQcjIhnmu/fArAX/aaxzs+NGEvnou/ofPTTzb8A8FkA9zeXD3xeJlH8A3UDdigAfJ/k0yS3TXAcR50WEQeB/hMPwKkTHs+1JHc3bws6eQuyFMmzAVyA/qvcRM/NB8YCTODckFxFcheAwwB2ov9b9BsRcXRGlIHraRLFP1A3YIcujogL0Z+U5KskPz3Bsaw0twE4F8BmAAcB3NzljZM8AcADAK6LiDe7vO0BxjKRcxMRixGxGf1Gui0AzlvuaoMcaxLFv6K6ASPiQPP1MICH0D+hk3SI5CYAaL4entRAIuJQ82TrAbgdHZ4bktPoF9vdEfFgc/FEzs1yY5nkuWlu/w0AP0D/Pf9JJI/OgzdwPU2i+FdMNyDJ9SRPPPo9gEsA7NF7jd0jALY2328F8PCkBnK00BpXoqNzw/6Ef3cA2BsRtyzZ1Pm5KY1lEueG5CkkT2q+Pw7A59H/G8TjAL7YXG3w89LlXyuX/NXyMvT/avoSgD+cxBiacXwC/bThWQDPdz0WAPeg/yvjPPq/EV0D4OcAPAbghebrxgmO5W8APAdgN/qFt6mjsfwa+r+67gawq/l32STOjRhL5+cGwC8B+HFzm3sA/NGS5/EPAbwI4O8ArB3keP6En1ml/Ak/s0q5+M0q5eI3q5SL36xSLn6zSrn4zSrl4jerlIvfrFL/CwLbGDjDb9v3AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -5032,6 +5014,2137 @@ "source": [ "plt.imshow(fhp,origin=0)" ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "nostar = Simulate_Image(imageshape=(30,30),star1flux=0,star1pos=[15,15],freq=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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nsRFCpI8jNf9dAKYDmAegFsCP2BPd/W53r3b36sxRBUfYnBDiWHNE5nf3Onfvd/cBAPcAOPPYdksIcbw5IvOb2cSD/vdyAG8em+4IIdJFKlHfUgALAJSZ2U4AtwJYYGbzMBgs1AC4LpXGPAvoKkueRZx6xdvBbTfc8xGq5Yzhccqmr/GY69rqZ4NtPta6gGqhmWD7Z3RSbcovwotm7jmLl4e+W8+1zD6e8ZxQxUt2AWDhhLeo9tDABL5hcV9wvyXTeQRWl8mPpWA332cfr5AFADRdwBcsRS2Pk7fO4ZtV/ToclW6+mi8GO+ujvBy4pp6XjGeODrfZOyr5QPhhXM6HNL+7X5Xk4XtTb0IIMRLRHX5CRIrML0SkyPxCRIrML0SkyPxCRMqQv/YfU3IH4NOT3+L7xi8DWQuAjGweZXXM5fHOtF/wGPDBunODbfaewqOsKU/w7Rp6+bS1e84KNon+XH6cnZP4dqVv8+12NI4OtjmpklcvZn+ex4SZr48P7jdvIh+/vNm8zd4WPlNz4bnh2LL/Jd6ngcA5lLOPx4A7FvHoFgAM/Djbb5/M25zK+1PyaLjKteG/JffRwIpw1ejB6MovRKTI/EJEiswvRKTI/EJEiswvRKTI/EJEiswvRKSkNeefXdiIF+YnLwhcUHJlcNv2FbyGtmgdX/l29+I2qpX8IbxKb18+H54dn+f3Fsz8Z77i8Og7dwXbXLX+RKpVrOD3LDR8gbfZ2xkuI/4/P/8i37YwUCq8rD243y05/DXLaeXXnZyz+eq1za2BpX8BeKC/fWN4Hl+4mY/RqLXhNqsu4atEt/fxnL+DS5j53zcH22z5/clJH7fO1K/nuvILESkyvxCRIvMLESkyvxCRIvMLESkyvxCRktaob0dvIb61e35Sra6hJLjtmZfzhTpXv1tFtcytfGbVzonhRR/HnltLtbLv8zLZrZ/jbRY28YVDAaBwK39JGufy7cqX8riz9orwTLDF7/Ey0PbJ/PqQ+QO+qCgAlC3h45DRz9tszgyUIGcGm0RWN39NK57l2+26hpftlv4xPGXwW2urqDbwxcAMx718Mc5Vb08PtplRnnz8BsKp7vv3kfpThRD/lZD5hYgUmV+ISJH5hYgUmV+ISJH5hYiUVBbqXALgYgD17j4n8dgYAA8BqMLgQp1XuDsvxUrQ0ZuDl2unJtXOO2lTcNvXGwNT1w7weOf0+Xy/NXfNDLbZ+sREqo39u51Uy3yel2udPDY8++zq8WOoZoGJWbuLeAaWm9cbbLNocSPV8n7AF+rccFJFcL+ZC3nlY956Hp+FjjO/IdgkOip4VV/DPJ6DTf9HHvVV3LUl2ObGH/FFZPdfw6tKO97gr3XPuPAiqJX/njwmbGzlx38oqVz57wOw8JDHvgNghbvPALAi8f9CiA8RQ5rf3Z8H0HzIw5cCuD/x9/0ALjvG/RJCHGeO9Dt/ubvXAkDi3/DqDUKIEcdx/8HPzBab2RozW9PXEl75RAiRPo7U/HVmNhEAEv/SX7Hc/W53r3b36qyS8HRIQoj0caTmXwZgUeLvRQB+d2y6I4RIF6lEfUsBLABQZmY7AdwK4DYAD5vZVwBsB/CFVBrr35+FtreTxxvXzlka3PZrD91AtSJeHIXXtyaf6BAARo0KxyLXXvck1X5z6wVU657Ps6rXn5gVbNPH8W0LtvP36qZT+XaVPw1XpT1878+oNn/OTVQr4oWWAIC2E3kE23Uq/wro9bxCceJLgRcbQMFFvNKwYR2fULTp73kc2rAkUE4JoKCH96n7pbFU65/DJ13N3sHHAABapiUf2/7V4UrVgxnS/O5+FZHOS7kVIcSIQ3f4CREpMr8QkSLzCxEpMr8QkSLzCxEpMr8QkZLW2XttAMhuT55Dfvmh64PbFpx7aG3Rf1L6fwuo1jw7l2tnhktdl/7gQqq1XclLNUf/exHVctoD9aoAugKT+3aewbPxJWfdR7WNfx4ohwbwiTt5lj//qnVU290ZnnH53Waecff28hLk7F38mrTt6vBMxDNzuqnWPIMvLNofKAvf+7HweXJJ9YtUe+6ms6lWc1JgZuRwRS9KLtmdfLunwn09GF35hYgUmV+ISJH5hYgUmV+ISJH5hYgUmV+ISDH31Gf7PFpyT6jwSX+fPNLzZh7JAUD+Hv4+tX8Cj88y9/MIx6fwkkoAqBzHJyTevZrHZwOBxSS/dvEfg23eu4lHQwW/L6ba6E08Btxydbg8FIFTIK8hEMnx5AwAMONz71Bt399NoVpdNe9v++xw1Ff0Vg7VcgIz21qgUnj/xa3BNkfl8j7tfbOMatMeC7xmXwq/Zp6V/Fj2/MNP0L1tZ0p1vbryCxEpMr8QkSLzCxEpMr8QkSLzCxEpMr8QkZLWqr6MDEfeqOSxSO/OcLTRPYbHNLmTOqh285ynqfavt18ebLPpUl4h1lvI+zPrzlqq/XPBp4Nt5jYGcsJALLvlr/h2Y58Lv8d3l/JkqD+QwJav4uMOAO91zqBa85d42VpWU+C13skX2wSAjEASuPcjfL+Z5Tz2Hf+rwmCbddV8fAdyA/Hi9/hMwyWP8igUAPbNGaLsLwV05RciUmR+ISJF5hciUmR+ISJF5hciUmR+ISLlqKI+M6sB0AagH0Cfu1eHnj/Qk4mu7cknt/QhFs0sn9lAtfpGXu32/T9eRrVp7/EoDwDereMRz/i1PB7bvDgwYeZA+Dj7CgJxVC/XsvN49NN6Qrhisj8vUBXZzY+z8Lbkk0geYN+9J1LNevh159RzNlPt9Rd4fAgALbP5OJS/wNucfN0uqq2vDrdZuiHQnxl8/Fp/NplqXeEmUTW9LunjzYHz4FCORc7/SXdvPAb7EUKkEX3sFyJSjtb8DmC5ma01s8XHokNCiPRwtB/7z3H33WY2HsDTZrbR3Z8/+AmJN4XFAJBZWnqUzQkhjhVHdeV3992Jf+sBPArgzCTPudvdq929OrOQr6wjhEgvR2x+Mysws6IDfwP4DIA3j1XHhBDHl6P52F8O4FEzO7CfX7n7U8ekV0KI484Rm9/dtwI49XC2yewGit9N/mGj/HPbgtt2/ZBn53mn8jLPrnKeYTecFi4jHvsKz9XbpvD8tvKMnVTLOn97sM2tv5pHtfpJvM3MrfwrVX5TeDLXvjyud1bxhR8HPPzBsauM7/ekn7ZQ7YwH+bmwcd/MYJt9o7nWcDrX/G4erOeNC49feyXXLHBfxxk3raXak88GOgugcXlF0sf7WsIlzwejqE+ISJH5hYgUmV+ISJH5hYgUmV+ISJH5hYiUtC7UOWVOsf/Nb5JX/f5y2wduDnwf9U28bLdsOY/smk7hxxeaWRUIz6Tbn8+3za/l0dBAuLoW7Sfx6Wcz9/JkNqeFv4/3jOZxJwD0F/JVKkfV8Oho/Lrwopnt3+ALXLa+NpZqhTv4Piuufi/Y5pblJ/D97gpEt1NTWtsyOYHTqPsEXjZe9Co/GXJbwufmqPrkpbuvrvwntO3TQp1CiAAyvxCRIvMLESkyvxCRIvMLESkyvxCRktaFOutaSnDn4xcn1TK7wtv6VB6Z5DXzqConEI/tn8or1gCgfxZfvHHigzxe3P7nfJ85peEDLX6Jzxj82Wv/RLXV1/MqsJobwrFR5pZRVCt9h49tzRXB3SLnVR7nTXyRzzLbVslfs40vTQu22VvB9+vZfL/9OXyM+orCUWlhDY+EC/6UQ7WBS/lCnV3P8bEDgJYTkx9L76upR5a68gsRKTK/EJEi8wsRKTK/EJEi8wsRKTK/EJGS1qjP+oGsjuRRxKg94Thq0kr+PrV3Bj+MsRt4VNW1O3z4+8fzirbaqzuolreBT6Y5YTqfuBIA8h9vo9rjA/Op1n0e32dfIx8DACjcx+Oh/K/zxTgvLOaLpwLA25UTqLatbDzVxq4J7HSIItSyNTx2ywgsdNp0IY910RwuxbRAEjhl0Raq7biPL2R60f/ksS4APLj6rKSPe3bqVbq68gsRKTK/EJEi8wsRKTK/EJEi8wsRKTK/EJEi8wsRKUc1e6+ZLQTwEwCZAH7m7reFnl9QVumzPvutpFrLRTw3B4Bpt/GsevOiIqqFck/PDeff+aN5+e3+Np795hXyWW0n/CycGe/8FL+34JLzV1Ht98s/RrXstnCZZ28RH6PCwPqp7VXB3SIncP9AZiBW7ykN7HNvuM3xazuptn1hPtVO/CmfMviJVY8H25y+4stUG/MfvPS78WP8/Ct9nd+vAAD5TclfszeW34n25h3Hd/ZeM8sE8C8ALgQwG8BVZjb7SPcnhEgvR/Ox/0wAW9x9q7v3APg1gEuPTbeEEMebozF/BYCDPyvtTDz2PsxssZmtMbM1fV3hj/ZCiPRxNOZP9r3iA19E3P1ud6929+qsPH7PuxAivRyN+XcCqDzo/ycD4FUgQogRxdGY/xUAM8xsmpnlALgSwLJj0y0hxPHmaKO+iwDcicGob4m7/8MQz28AcHB4VAag8Yg7cOxRf8KMtP4AI69Pw92fqe4+LpUnpnWV3g80brbG3ZMv2zsMqD9hRlp/gJHXp5HWnxC6w0+ISJH5hYiU4Tb/3cPc/qGoP2FGWn+AkdenkdYfyrB+5xdCDB/DfeUXQgwTw2J+M1toZpvMbIuZfWc4+nBIf2rM7A0ze83MQnPHHs8+LDGzejN786DHxpjZ02a2OfFvoN4tLf35rpntSozTa4moN139qTSzZ8xsg5m9ZWbfTDw+LGMU6M+wjdHhkvaP/YlqwHcAfBqDdwm+AuAqd387rR15f59qAFS7+7Dls2b2ZwDaATzg7nMSj90OoNndb0u8SZa6+7eHsT/fBdDu7j9MRx8O6c9EABPdfZ2ZFQFYC+AyANdiGMYo0J8rMExjdLgMx5Vf1YBJcPfnATQf8vClAO5P/H0/Bk+u4ezPsOHute6+LvF3G4ANGCwkG5YxCvTnQ8NwmD+lasA04wCWm9laM1s8zH05mHJ3rwUGTzYAfKWL9HGDma1PfC1I29eQgzGzKgCnAViFETBGh/QHGAFjlArDYf6UqgHTzDnu/lEMTkxyfeIjr/ggdwGYDmAegFoAP0p3B8ysEMBvAdzo7q3pbj+F/gz7GKXKcJh/xFUDuvvuxL/1AB7F4FeTkUBd4rvlge+Y9cPZGXevc/d+dx8AcA/SPE5mlo1Boz3o7o8kHh62MUrWn+Eeo8NhOMw/oqoBzawg8YMNzKwAwGcAvBneKm0sA7Ao8fciAL8bxr4cMNcBLkcax8nMDMC9ADa4+x0HScMyRqw/wzlGh8uw3ORzuNWAx7kvJ2Dwag8MLlz6q+Hoj5ktBbAAg1VhdQBuBfAYgIcBTAGwHcAX3D0tP8KR/izA4MdZB1AD4LoD37fT0J9PAPgTgDcAHFga8xYMfs9O+xgF+nMVhmmMDhfd4SdEpOgOPyEiReYXIlJkfiEiReYXIlJkfiEiReYXIlJkfiEiReYXIlL+Pz5ttiyTw4XEAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(nostar['stars'][0],origin=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "-0.0797886885266915" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.mean(nostar['stars'][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "search_result = lk.search_targetpixelfile('TIC239678779', mission='TESS', sector=19)\n", + "tpf = search_result.download(quality_bitmask='default')" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "def make_pg(targetpixelfile, method = 'LombScargle'):\n", + " \n", + " #Defining an aperture that will be used in plotting and making empty 2-d arrays of the correct size for masks\n", + " aperture = targetpixelfile.pipeline_mask\n", + " tpf = targetpixelfile\n", + " #Initiating a python list since this is computational slightly faster than a numpy array when initially storing periodogram\n", + " pg = []\n", + "\n", + " \n", + " #Iterating through columns of pixels\n", + " for i in np.arange(0,len(aperture)):\n", + " \n", + " #Iterating through rws of pixels\n", + " for j in np.arange(0,len(aperture[0])):\n", + " \n", + " \n", + " #Making an empty 2-d array\n", + " mask = np.zeros((len(aperture),len(aperture[0])), dtype=bool)\n", + " \n", + " #Iterating to isolate pixel by pixel to get light curves\n", + " mask[i][j] = True\n", + " \n", + " #Getting the light curve for a pixel and excluding any flagged data\n", + " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", + " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", + " lc = lc.flatten(window_length=3001)\n", + " lc = lc.remove_outliers()\n", + " if method == 'bls':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(method = 'bls')\n", + " periodogram.power = periodogram.power / np.median(periodogram.power)\n", + " periodogram.power[np.where(periodogram.power<0)] = 0\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " elif method == 'LombScargle':\n", + " #Making a periodogram for the pixel\n", + " periodogram = lc.to_periodogram(maximum_frequency=1000,freq_unit= u.microHertz)\n", + " periodogram = periodogram.flatten()\n", + " #Extending the list of periodogram data for each pixel\n", + " pg.extend([periodogram])\n", + " \n", + " #Taking the final list and turning it into a 2-d numpy array witht he same dimensions of the full postage stamp \n", + " pg = np.reshape(np.asarray(pg),(len(aperture),len(aperture[0])))\n", + " \n", + " #Defining self.periodogram as this 2-d array of periodogram data\n", + " return [pg,(tpf.astropy_time.max()-tpf.astropy_time.min()).value]" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "pg_image = make_pg(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "def frequency_heat_test(pg,timeserieslength,frequency):\n", + " heat_stamp = []\n", + " \n", + " for i in np.arange(0,len(pg)):\n", + " for j in np.arange(0,len(pg[0])):\n", + " mask = np.zeros((len(pg),len(pg[0])), dtype=bool)\n", + " mask[i][j] = True\n", + " \n", + " period = pg[mask][0]\n", + " normperiod = np.asarray(period.power)/np.median(np.asarray(period.power))\n", + " freq = np.asarray(period.frequency)\n", + " sums = 0 \n", + " background = 0\n", + " for k in np.arange(len(frequency)):\n", + " sums += np.asarray(normperiod[np.where((freq < frequency[k]+(1/timeserieslength) ) & (freq > frequency[k]-(1/timeserieslength)))]).sum()\n", + " background += len(np.where((freq < frequency[k]+(1/timeserieslength) ) & (freq > frequency[k]-(1/timeserieslength)))[0])\n", + " heat_stamp.extend([sums-background])\n", + " \n", + " heat_stamp = np.reshape(np.asarray(heat_stamp),(len(pg),len(pg[0])))\n", + " return heat_stamp" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "heatmap = frequency_heat_test(pg_image[0],pg_image[1],[97.87963868,92.03828453])" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support.' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
')\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " event.shiftKey = false;\n", + " // Send a \"J\" for go to next cell\n", + " event.which = 74;\n", + " event.keyCode = 74;\n", + " manager.command_mode();\n", + " manager.handle_keydown(event);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib notebook\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "X = np.arange(11)\n", + "Y = np.arange(11)\n", + "X,Y = np.meshgrid(X,Y)\n", + "ax.plot_surface(X,Y,heatmap)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([4.15641899, 7.4578977 ])" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cent.centroid_1dg(heatmap)" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "88293a5af6ae48039290dfcc9073cc9a" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact_sky()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 2dd07dcd8b76d150210c1d54644cd82bedfd6cee Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Fri, 6 Mar 2020 22:49:48 -0800 Subject: [PATCH 09/11] Slight changes to periodogram array code, and adding the photutils guassian centroid fit --- .ipynb_checkpoints/Research-checkpoint.ipynb | 303 +++++++++++++++++++ 1 file changed, 303 insertions(+) diff --git a/.ipynb_checkpoints/Research-checkpoint.ipynb b/.ipynb_checkpoints/Research-checkpoint.ipynb index caf5d54..52ff965 100644 --- a/.ipynb_checkpoints/Research-checkpoint.ipynb +++ b/.ipynb_checkpoints/Research-checkpoint.ipynb @@ -6842,6 +6842,309 @@ "source": [ "cent.centroid_1dg(heatmap)" ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + "\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + "\n", + " \n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " var NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.3.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.3.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.3.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.bokehjs_exec.v0+json": "", + "text/html": [ + "\n", + "" + ] + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "server_id": "88293a5af6ae48039290dfcc9073cc9a" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "tpf.interact_sky()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 375b7dc32dc1878fbdfb9769e6aa7d85ae0494b5 Mon Sep 17 00:00:00 2001 From: Higgins00 <43622895+Higgins00@users.noreply.github.com> Date: Mon, 13 Apr 2020 22:29:31 -0700 Subject: [PATCH 10/11] just have the 2d guassian fit left to do --- Research.ipynb | 1283 ++++++++++++------------------------------------ 1 file changed, 312 insertions(+), 971 deletions(-) diff --git a/Research.ipynb b/Research.ipynb index 52ff965..100c7f5 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -15,12 +15,12 @@ "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "from astropy import units as u\n", - "\n" + "from astropy.coordinates import SkyCoord, Angle\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -4907,7 +4907,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -5085,7 +5085,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -5095,7 +5095,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -5149,7 +5149,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -5158,7 +5158,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -5186,7 +5186,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 7, "metadata": { "scrolled": true }, @@ -5197,7 +5197,59 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 105, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n", + "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 105, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.imshow(heatmap,origin=0)\n", + "c = cent.centroid_2dg(heatmap)\n", + "plt.scatter(c[0],c[1],marker='X',s=100,c='r')\n", + "\n", + "plt.scatter(gaia_data['x'][np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))],\n", + " gaia_data['y'][np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))],\n", + " marker='o',\n", + " alpha=.9,\n", + " s=np.asarray(gaia_data['size'])[np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))]*10,\n", + " c='white')" + ] + }, + { + "cell_type": "code", + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -5983,7 +6035,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -5991,1001 +6043,186 @@ }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "%matplotlib notebook\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "\n", + "fig = plt.figure()\n", + "ax = fig.gca(projection = '3d')\n", + "\n", + "X = np.arange(11)\n", + "Y = np.arange(11)\n", + "X,Y = np.meshgrid(X,Y)\n", + "ax.plot_surface(X,Y,heatmap)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "" + "array([4.15641899, 7.4578977 ])" ] }, - "execution_count": 97, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "fig = plt.figure()\n", - "plt.imshow(heatmap,origin=0)\n", - "c = cent.centroid_1dg(heatmap)\n", - "plt.scatter(c[0],c[1],marker='X',s=100,c='r')" + "cent.centroid_1dg(heatmap)" ] }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", + " var force = true;\n", "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", + " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", "\n", - " this.ws = websocket;\n", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", " }\n", + " });\n", + " // Destroy server and session\n", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", " }\n", + " }\n", "\n", - " this.imageObj = new Image();\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", "\n", - " this.image_mode = 'full';\n", + " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", - " this.root = $('
');\n", - " this._root_extra_style(this.root)\n", - " this.root.attr('style', 'display: inline-block');\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", "\n", - " $(parent_element).append(this.root);\n", + " function register_renderer(events, OutputArea) {\n", "\n", - " this._init_header(this);\n", - " this._init_canvas(this);\n", - " this._init_toolbar(this);\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " var toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", "\n", - " var fig = this;\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", "\n", - " this.waiting = false;\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", - " this.ws.onopen = function () {\n", - " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", - " fig.send_message(\"send_image_mode\", {});\n", - " if (mpl.ratio != 1) {\n", - " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", - " }\n", - " fig.send_message(\"refresh\", {});\n", - " }\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", "\n", - " this.imageObj.onload = function() {\n", - " if (fig.image_mode == 'full') {\n", - " // Full images could contain transparency (where diff images\n", - " // almost always do), so we need to clear the canvas so that\n", - " // there is no ghosting.\n", - " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", - " }\n", - " fig.context.drawImage(fig.imageObj, 0, 0);\n", - " };\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " var events = require('base/js/events');\n", + " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", - " this.imageObj.onunload = function() {\n", - " fig.ws.close();\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", " }\n", - "\n", - " this.ws.onmessage = this._make_on_message_function(this);\n", - "\n", - " this.ondownload = ondownload;\n", - "}\n", - "\n", - "mpl.figure.prototype._init_header = function() {\n", - " var titlebar = $(\n", - " '
');\n", - " var titletext = $(\n", - " '
');\n", - " titlebar.append(titletext)\n", - " this.root.append(titlebar);\n", - " this.header = titletext[0];\n", - "}\n", - "\n", - "\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._init_canvas = function() {\n", - " var fig = this;\n", - "\n", - " var canvas_div = $('
');\n", - "\n", - " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", - "\n", - " function canvas_keyboard_event(event) {\n", - " return fig.key_event(event, event['data']);\n", - " }\n", - "\n", - " canvas_div.keydown('key_press', canvas_keyboard_event);\n", - " canvas_div.keyup('key_release', canvas_keyboard_event);\n", - " this.canvas_div = canvas_div\n", - " this._canvas_extra_style(canvas_div)\n", - " this.root.append(canvas_div);\n", - "\n", - " var canvas = $('');\n", - " canvas.addClass('mpl-canvas');\n", - " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", - "\n", - " this.canvas = canvas[0];\n", - " this.context = canvas[0].getContext(\"2d\");\n", - "\n", - " var backingStore = this.context.backingStorePixelRatio ||\n", - "\tthis.context.webkitBackingStorePixelRatio ||\n", - "\tthis.context.mozBackingStorePixelRatio ||\n", - "\tthis.context.msBackingStorePixelRatio ||\n", - "\tthis.context.oBackingStorePixelRatio ||\n", - "\tthis.context.backingStorePixelRatio || 1;\n", - "\n", - " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", - "\n", - " var rubberband = $('');\n", - " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", - "\n", - " var pass_mouse_events = true;\n", - "\n", - " canvas_div.resizable({\n", - " start: function(event, ui) {\n", - " pass_mouse_events = false;\n", - " },\n", - " resize: function(event, ui) {\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " stop: function(event, ui) {\n", - " pass_mouse_events = true;\n", - " fig.request_resize(ui.size.width, ui.size.height);\n", - " },\n", - " });\n", - "\n", - " function mouse_event_fn(event) {\n", - " if (pass_mouse_events)\n", - " return fig.mouse_event(event, event['data']);\n", - " }\n", - "\n", - " rubberband.mousedown('button_press', mouse_event_fn);\n", - " rubberband.mouseup('button_release', mouse_event_fn);\n", - " // Throttle sequential mouse events to 1 every 20ms.\n", - " rubberband.mousemove('motion_notify', mouse_event_fn);\n", - "\n", - " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", - " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", - "\n", - " canvas_div.on(\"wheel\", function (event) {\n", - " event = event.originalEvent;\n", - " event['data'] = 'scroll'\n", - " if (event.deltaY < 0) {\n", - " event.step = 1;\n", - " } else {\n", - " event.step = -1;\n", - " }\n", - " mouse_event_fn(event);\n", - " });\n", - "\n", - " canvas_div.append(canvas);\n", - " canvas_div.append(rubberband);\n", - "\n", - " this.rubberband = rubberband;\n", - " this.rubberband_canvas = rubberband[0];\n", - " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", - " this.rubberband_context.strokeStyle = \"#000000\";\n", - "\n", - " this._resize_canvas = function(width, height) {\n", - " // Keep the size of the canvas, canvas container, and rubber band\n", - " // canvas in synch.\n", - " canvas_div.css('width', width)\n", - " canvas_div.css('height', height)\n", - "\n", - " canvas.attr('width', width * mpl.ratio);\n", - " canvas.attr('height', height * mpl.ratio);\n", - " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", - "\n", - " rubberband.attr('width', width);\n", - " rubberband.attr('height', height);\n", - " }\n", - "\n", - " // Set the figure to an initial 600x600px, this will subsequently be updated\n", - " // upon first draw.\n", - " this._resize_canvas(600, 600);\n", - "\n", - " // Disable right mouse context menu.\n", - " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", - " return false;\n", - " });\n", - "\n", - " function set_focus () {\n", - " canvas.focus();\n", - " canvas_div.focus();\n", - " }\n", - "\n", - " window.setTimeout(set_focus, 100);\n", - "}\n", - "\n", - "mpl.figure.prototype._init_toolbar = function() {\n", - " var fig = this;\n", - "\n", - " var nav_element = $('
')\n", - " nav_element.attr('style', 'width: 100%');\n", - " this.root.append(nav_element);\n", - "\n", - " // Define a callback function for later on.\n", - " function toolbar_event(event) {\n", - " return fig.toolbar_button_onclick(event['data']);\n", - " }\n", - " function toolbar_mouse_event(event) {\n", - " return fig.toolbar_button_onmouseover(event['data']);\n", - " }\n", - "\n", - " for(var toolbar_ind in mpl.toolbar_items) {\n", - " var name = mpl.toolbar_items[toolbar_ind][0];\n", - " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", - " var image = mpl.toolbar_items[toolbar_ind][2];\n", - " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", - "\n", - " if (!name) {\n", - " // put a spacer in here.\n", - " continue;\n", - " }\n", - " var button = $('');\n", - " button.click(method_name, toolbar_event);\n", - " button.mouseover(tooltip, toolbar_mouse_event);\n", - " nav_element.append(button);\n", - " }\n", - "\n", - " // Add the status bar.\n", - " var status_bar = $('');\n", - " nav_element.append(status_bar);\n", - " this.message = status_bar[0];\n", - "\n", - " // Add the close button to the window.\n", - " var buttongrp = $('
');\n", - " var button = $('');\n", - " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", - " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", - " buttongrp.append(button);\n", - " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", - " titlebar.prepend(buttongrp);\n", - "}\n", - "\n", - "mpl.figure.prototype._root_extra_style = function(el){\n", - " var fig = this\n", - " el.on(\"remove\", function(){\n", - "\tfig.close_ws(fig, {});\n", - " });\n", - "}\n", - "\n", - "mpl.figure.prototype._canvas_extra_style = function(el){\n", - " // this is important to make the div 'focusable\n", - " el.attr('tabindex', 0)\n", - " // reach out to IPython and tell the keyboard manager to turn it's self\n", - " // off when our div gets focus\n", - "\n", - " // location in version 3\n", - " if (IPython.notebook.keyboard_manager) {\n", - " IPython.notebook.keyboard_manager.register_events(el);\n", - " }\n", - " else {\n", - " // location in version 2\n", - " IPython.keyboard_manager.register_events(el);\n", - " }\n", - "\n", - "}\n", - "\n", - "mpl.figure.prototype._key_event_extra = function(event, name) {\n", - " var manager = IPython.notebook.keyboard_manager;\n", - " if (!manager)\n", - " manager = IPython.keyboard_manager;\n", - "\n", - " // Check for shift+enter\n", - " if (event.shiftKey && event.which == 13) {\n", - " this.canvas_div.blur();\n", - " event.shiftKey = false;\n", - " // Send a \"J\" for go to next cell\n", - " event.which = 74;\n", - " event.keyCode = 74;\n", - " manager.command_mode();\n", - " manager.handle_keydown(event);\n", - " }\n", - "}\n", - "\n", - "mpl.figure.prototype.handle_save = function(fig, msg) {\n", - " fig.ondownload(fig, null);\n", - "}\n", - "\n", - "\n", - "mpl.find_output_cell = function(html_output) {\n", - " // Return the cell and output element which can be found *uniquely* in the notebook.\n", - " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", - " // IPython event is triggered only after the cells have been serialised, which for\n", - " // our purposes (turning an active figure into a static one), is too late.\n", - " var cells = IPython.notebook.get_cells();\n", - " var ncells = cells.length;\n", - " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", - " data = data.data;\n", - " }\n", - " if (data['text/html'] == html_output) {\n", - " return [cell, data, j];\n", - " }\n", - " }\n", - " }\n", - " }\n", - "}\n", - "\n", - "// Register the function which deals with the matplotlib target/channel.\n", - "// The kernel may be null if the page has been refreshed.\n", - "if (IPython.notebook.kernel != null) {\n", - " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", - "}\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib notebook\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "\n", - "fig = plt.figure()\n", - "ax = fig.gca(projection = '3d')\n", - "\n", - "X = np.arange(11)\n", - "Y = np.arange(11)\n", - "X,Y = np.meshgrid(X,Y)\n", - "ax.plot_surface(X,Y,heatmap)\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([4.15641899, 7.4578977 ])" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cent.centroid_1dg(heatmap)" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "\n", - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - " var JS_MIME_TYPE = 'application/javascript';\n", - " var HTML_MIME_TYPE = 'text/html';\n", - " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " var CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " var script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " var cell = handle.cell;\n", - "\n", - " var id = cell.output_area._bokeh_element_id;\n", - " var server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " var id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " var output_area = handle.output_area;\n", - " var output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " var bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " var script_attrs = bk_div.children[0].attributes;\n", - " for (var i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", + " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", @@ -7124,12 +6482,12 @@ "application/vnd.bokehjs_exec.v0+json": "", "text/html": [ "\n", - "" + "" ] }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { - "server_id": "88293a5af6ae48039290dfcc9073cc9a" + "server_id": "ff0c1f5481684a21b5a09695dbff471b" } }, "output_type": "display_data" @@ -7141,10 +6499,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "def add_gaia_figure_elements(tpf, magnitude_limit=18):\n", + " \"\"\"Make the Gaia Figure Elements\"\"\"\n", + " # Get the positions of the Gaia sources\n", + " c1 = SkyCoord(tpf.ra, tpf.dec, frame='icrs', unit='deg')\n", + " # Use pixel scale for query size\n", + " pix_scale = 4.0 # arcseconds / pixel for Kepler, default\n", + " if tpf.mission == 'TESS':\n", + " pix_scale = 21.0\n", + " # We are querying with a diameter as the radius, overfilling by 2x.\n", + " from astroquery.vizier import Vizier\n", + " Vizier.ROW_LIMIT = -1\n", + " result = Vizier.query_region(c1, catalog=[\"I/345/gaia2\"],radius=Angle(np.max(tpf.shape[1:]) * pix_scale, \"arcsec\"))\n", + " \n", + " no_targets_found_message = ValueError('Either no sources were found in the query region '\n", + " 'or Vizier is unavailable')\n", + " too_few_found_message = ValueError('No sources found brighter than {:0.1f}'.format(magnitude_limit))\n", + " if result is None:\n", + " raise no_targets_found_message\n", + " elif len(result) == 0:\n", + " raise too_few_found_message\n", + " result = result[\"I/345/gaia2\"].to_pandas()\n", + " result = result[result.Gmag < magnitude_limit]\n", + " if len(result) == 0:\n", + " raise no_targets_found_message\n", + " radecs = np.vstack([result['RA_ICRS'], result['DE_ICRS']]).T\n", + " coords = tpf.wcs.all_world2pix(radecs, 0) ## TODO, is origin supposed to be zero or one?\n", + " year = ((tpf.astropy_time[0].jd - 2457206.375) * u.day).to(u.year)\n", + " pmra = ((np.nan_to_num(np.asarray(result.pmRA)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " pmdec = ((np.nan_to_num(np.asarray(result.pmDE)) * u.milliarcsecond/u.year) * year).to(u.arcsec).value\n", + " result.RA_ICRS += pmra\n", + " result.DE_ICRS += pmdec\n", + "\n", + " # Gently size the points by their Gaia magnitude\n", + " sizes = 64.0 / 2**(result['Gmag']/5.0)\n", + " one_over_parallax = 1.0 / (result['Plx']/1000.)\n", + " source = dict(ra=result['RA_ICRS'],\n", + " dec=result['DE_ICRS'],\n", + " source=result['Source'].astype(str),\n", + " Gmag=result['Gmag'],\n", + " plx=result['Plx'],\n", + " one_over_plx=one_over_parallax,\n", + " x=coords[:, 0]+.5,\n", + " y=coords[:, 1]+.5,\n", + " size=sizes)\n", + "\n", + "\n", + " return source" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "metadata": {}, + "outputs": [], + "source": [ + "gaia_data = add_gaia_figure_elements(tpf)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 7.28808609, 6.48218412, 5.72025714, 5.34355549, 5.91525351,\n", + " 8.09269374, 8.26949941, 5.40659391, 7.79830186, 5.89405286,\n", + " 6.48398161, 5.5505087 , 5.9758337 , 6.85558605, 5.34674177,\n", + " 10.88251422, 15.31167279, 8.58025188, 9.59311931, 5.63829905,\n", + " 6.92896563, 6.42457046, 6.50685317, 10.32679022])" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.asarray(gaia_data['size'])[np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))]" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([65.01419239]), array([32.54329521]))" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "closest_star_mask = np.where(np.square(c[0]-gaia_data['x'])+np.square(c[1]-gaia_data['y'])==(np.square(c[0]-gaia_data['x'])+np.square(c[1]-gaia_data['y'])).min())\n", + "np.asarray(gaia_data['ra'])[closest_star_mask],np.asarray(gaia_data['dec'])[closest_star_mask]" + ] } ], "metadata": { diff --git a/Research.ipynb b/Research.ipynb index 100c7f5..ee7abe6 100644 --- a/Research.ipynb +++ b/Research.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 14, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -15,12 +15,13 @@ "from ipywidgets import interact, interactive, fixed, interact_manual\n", "import ipywidgets as widgets\n", "from astropy import units as u\n", - "from astropy.coordinates import SkyCoord, Angle\n" + "from astropy.coordinates import SkyCoord, Angle\n", + "import scipy.optimize as opt" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -4907,7 +4908,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -5085,7 +5086,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -5095,7 +5096,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -5124,7 +5125,7 @@ " #Getting the light curve for a pixel and excluding any flagged data\n", " lightcurve = targetpixelfile.to_lightcurve(aperture_mask=mask)\n", " lc = lightcurve[np.where(lightcurve.quality == 0)]\n", - " lc = lc.flatten(window_length=3001)\n", + " lc = lc.flatten(window_length=3001)#ask about this\n", " lc = lc.remove_outliers()\n", " if method == 'bls':\n", " #Making a periodogram for the pixel\n", @@ -5135,7 +5136,7 @@ " pg.extend([periodogram])\n", " elif method == 'LombScargle':\n", " #Making a periodogram for the pixel\n", - " periodogram = lc.to_periodogram(maximum_frequency=1000,freq_unit= u.microHertz)\n", + " periodogram = lc.to_periodogram(minimum_frequency = 1,maximum_frequency=1000,freq_unit= u.microHertz)\n", " periodogram = periodogram.flatten()\n", " #Extending the list of periodogram data for each pixel\n", " pg.extend([periodogram])\n", @@ -5149,8 +5150,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 27, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "pg_image = make_pg(tpf)" @@ -5158,7 +5161,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -5171,7 +5174,7 @@ " mask[i][j] = True\n", " \n", " period = pg[mask][0]\n", - " normperiod = np.asarray(period.power)/np.median(np.asarray(period.power))\n", + " normperiod = np.asarray(period.power)/np.nanmedian(np.asarray(period.power))\n", " freq = np.asarray(period.frequency)\n", " sums = 0 \n", " background = 0\n", @@ -5186,7 +5189,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "metadata": { "scrolled": true }, @@ -5197,32 +5200,145 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 168, + "metadata": {}, + "outputs": [], + "source": [ + "def tdg_fit(heatmap_data):\n", + " \n", + " def two_dGaussian( shape , amplitude, xo, yo, sigma_x, sigma_y, offset):\n", + " x,y = np.meshgrid(shape[0],shape[1])\n", + " xo = float(xo)\n", + " yo = float(yo) \n", + " a = 1/(2*sigma_x**2)\n", + " b = 1/(2*sigma_y**2)\n", + " g = offset + amplitude*np.exp( - (a*((x-xo)**2) + b*((y-yo)**2)))\n", + " return g.flatten()\n", + " \n", + " c = cent.centroid_2dg(heatmap_data)\n", + " x = np.arange(0,np.shape(heatmap_data)[0])\n", + " y = np.arange(0,np.shape(heatmap_data)[1])\n", + "\n", + " initial_guess = (heatmap_data.max(),c[0],c[1],1,1,np.nanmedian(heatmap_data))\n", + " popt, pcov = opt.curve_fit(two_dGaussian, (x,y),heatmap_data.flatten(), p0=initial_guess)\n", + " \n", + " return popt,pcov\n" + ] + }, + { + "cell_type": "code", + "execution_count": 169, "metadata": { "scrolled": true }, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]\n", - "WARNING:astropy:The fit may be unsuccessful; check fit_info['message'] for more information.\n" - ] - }, + "data": { + "text/plain": [ + "array([[1. , 1.00000001, 1.00000033, 1.00000403, 1.00001808,\n", + " 1.00002981, 1.00001808, 1.00000403, 1.00000033, 1.00000001,\n", + " 1. ],\n", + " [1.00000001, 1.0000009 , 1.00002981, 1.0003632 , 1.00162775,\n", + " 1.0026837 , 1.00162775, 1.0003632 , 1.00002981, 1.0000009 ,\n", + " 1.00000001],\n", + " [1.00000033, 1.00002981, 1.00098728, 1.01202751, 1.05390358,\n", + " 1.08887197, 1.05390358, 1.01202751, 1.00098728, 1.00002981,\n", + " 1.00000033],\n", + " [1.00000403, 1.0003632 , 1.01202751, 1.14652511, 1.65667999,\n", + " 2.08268227, 1.65667999, 1.14652511, 1.01202751, 1.0003632 ,\n", + " 1.00000403],\n", + " [1.00001808, 1.00162775, 1.05390358, 1.65667999, 3.94303553,\n", + " 5.85224528, 3.94303553, 1.65667999, 1.05390358, 1.00162775,\n", + " 1.00001808],\n", + " [1.00002981, 1.0026837 , 1.08887197, 2.08268227, 5.85224528,\n", + " 9. , 5.85224528, 2.08268227, 1.08887197, 1.0026837 ,\n", + " 1.00002981],\n", + " [1.00001808, 1.00162775, 1.05390358, 1.65667999, 3.94303553,\n", + " 5.85224528, 3.94303553, 1.65667999, 1.05390358, 1.00162775,\n", + " 1.00001808],\n", + " [1.00000403, 1.0003632 , 1.01202751, 1.14652511, 1.65667999,\n", + " 2.08268227, 1.65667999, 1.14652511, 1.01202751, 1.0003632 ,\n", + " 1.00000403],\n", + " [1.00000033, 1.00002981, 1.00098728, 1.01202751, 1.05390358,\n", + " 1.08887197, 1.05390358, 1.01202751, 1.00098728, 1.00002981,\n", + " 1.00000033],\n", + " [1.00000001, 1.0000009 , 1.00002981, 1.0003632 , 1.00162775,\n", + " 1.0026837 , 1.00162775, 1.0003632 , 1.00002981, 1.0000009 ,\n", + " 1.00000001],\n", + " [1. , 1.00000001, 1.00000033, 1.00000403, 1.00001808,\n", + " 1.00002981, 1.00001808, 1.00000403, 1.00000033, 1.00000001,\n", + " 1. ]])" + ] + }, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def two_dGaussian( shape , amplitude, xo, yo, sigma_x, sigma_y, offset):\n", + " x,y = np.meshgrid(shape[0],shape[1])\n", + " xo = float(xo)\n", + " yo = float(yo) \n", + " a = 1/(2*sigma_x**2) \n", + " b = 1/(2*sigma_y**2)\n", + " g = offset + amplitude*np.exp( - (a*((x-xo)**2) + b*((y-yo)**2)))\n", + " return g\n", + "x = np.arange(11)\n", + "two_dGaussian((x,x),8,5,5,1,1,1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [], + "source": [ + "test = tdg_fit(heatmap)" + ] + }, + { + "cell_type": "code", + "execution_count": 176, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "" + "array([0.59470728, 0.07730971, 0.07370782, 0.08235371, 0.0784179 ,\n", + " 0.1103204 ])" ] }, - "execution_count": 105, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" + } + ], + "source": [ + "np.sqrt(np.diagonal(test[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'cent' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m 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\n", + "image/png": 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" ] @@ -5237,8 +5353,13 @@ "fig = plt.figure()\n", "plt.imshow(heatmap,origin=0)\n", "c = cent.centroid_2dg(heatmap)\n", + "\n", "plt.scatter(c[0],c[1],marker='X',s=100,c='r')\n", "\n", + "plt.scatter(test[0][1],test[0][2],marker='X',s=100,c='black')\n", + "\n", + "plt.contour(x,x,two_dGaussian((x,x),*test[0]),8,alpha=1,cmap='Greys')\n", + "\n", "plt.scatter(gaia_data['x'][np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))],\n", " gaia_data['y'][np.where((gaia_data['x']>=0)&(gaia_data['x']<=len(heatmap[0])-1)&(gaia_data['y']<=len(heatmap[0])-1)&(gaia_data['y']>=0))],\n", " marker='o',\n",