{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What are Light Curve Files?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the previous tutorial we looked at Target Pixel Files (TPFs), which show how much flux is recorded by each pixel for every cadence (time stamp). Now we are going to look at a lightcurve file. There are two ways to access these. You can either generate them yourself from a TPF or you can download them from (https://archive.stsci.edu/kepler/data_search/search.php)[MAST]. The files on MAST have been built from TPFs by summing up the pixels in the aperture and correcting for spacecraft systematics and cosmic rays. Lightcurve files are a little easier to work with and are smaller in file size than the TPFs.\n", "\n", "(Some more details of how TPFs are turned into light curve files can be found in [Demystifying Kepler Data](https://arxiv.org/pdf/1207.3093.pdf).)\n", "\n", "Firstly, let's see how to build one from the target pixel file. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lightcurves from Target Pixel Files" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lightkurve import KeplerTargetPixelFile, KeplerLightCurve\n", "%matplotlib inline\n", "\n", "#First we open the TPF\n", "tpf = KeplerTargetPixelFile('data/kplr006922244-2010078095331_lpd-targ.fits.gz')\n", "\n", "#Then we convert the target pixel file into a light curve.\n", "lc = tpf.to_lightcurve()\n", "lc.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've build a new lightcurve object 'lc'. We can use the plot method to take a look at the light curve. Note in this case we've not passed an **aperture** to the `to_lightcurve` method. In this case the default is to use the *Kepler* pipeline aperture.\n", "\n", "By summing all the pixels in the aperture we have created a Simple Aperture Photometry (SAP) lightcurve." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can access the meta data of the light curve in a similar way to TPFs from the previous tutorial:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([352.37632485, 352.39675805, 352.43762445, ..., 442.16263546,\n", " 442.18306983, 442.2035041 ])" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.time" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.quarter" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Kepler'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lc.mission" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PDCSAP from lightcurves from MAST\n", "\n", "Lightcurves from MAST have some level of processing (more details [here](https://arxiv.org/pdf/1207.3093.pdf)) and allow you to access the **PDCSAP** flux. This is the Pre-search Data Conditioning SAP flux. Long term trends have been removed from this data such as Cotrending Basis Vectors (CBVs). PDCSAP flux is usually slightly cleaner data than the SAP flux and will have fewer long term trends.\n", "\n", "We can read in a lightcurve file using `KeplerLightCurveFile`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lightkurve import KeplerLightCurveFile\n", "lcf = KeplerLightCurveFile('data/kplr006922244-2010078095331_llc.fits')\n", "lcf.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `plot` method on a `KeplerLightCurveFile` object will plot up both the SAP and PDCSAP flux. We can see that the PDCSAP flux is flatter. To work more with this data we must choose which type of flux we want to work with. Let's choose PDCSAP flux:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pdcsap = lcf.PDCSAP_FLUX" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This has created a `KeplerLightCurve` object. The only flux it contains is the PDCSAP flux. This has methods to investigate the quality of the lightcurve. For example you can check the cdpp:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "407.9620564267519" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pdcsap.cdpp()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a planet in this light curve. We might want to fold the light curve on a particular period to check the transit shape of this object. We can do that with the `fold` method. This target is Kepler-8b, and the period for this planet (3.5225 days) has already been found. We can pass this and optionally a phase to `fold`, which will then fold the `time` attribute of the light curve. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEKCAYAAAA4t9PUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAAIABJREFUeJzt3X2YJGV57/Hvb2DRHQdQGeILYwSis2bRzWRcQeLlCbi+rDGBCIlIRMSonEwkMSeSk/VKoicQAkbUY8QzHoKo5EiMwZigQUEHOGviguBmeFGy42aPL6AJrsqaYZa4y9znj66aramt7q6Znuqe7v59rmuu6a7X+6mufu56qqqfUkRgZma2XAOdDsDMzLqbE4mZmbXEicTMzFriRGJmZi1xIjEzs5Y4kZiZWUucSMzMrCVOJGZm1hInEjMza8mhnQ6gHYaHh+PYY4/tdBhLsn//fg49tC8+ngUuc39wmbvHV77yld0RcXSz6bqvZMtw7LHHcuedd3Y6jCXZvXs3w8PDnQ6jrVzm/uAydw9J3ywznU9tmZlZS5xIzMysJU4kZmbWEicSMzNriROJmZm1xInEzMxa4kRiZmYtcSIxM7OWOJFYx0xMTDAxMdHpMMysRU4kZmbWEicSMzNriROJmZm1xInEzCrla2G9z4nEzMxa0hfdyHeT9Mjt4osv7nAk1ZucnOx0CNbj0u+T97VquUViViGf1lm+KredP5eV5URi1oU6XRF2ev1WrFOfixPJEjT6kFb6A9yyZUvPfFFd6SzPat9uMzMzS45vYmKCV73qVYvmW0o56027kttqNW/31Rqbr5H0uKWeI+6mc8r1Ym1HGbppOwEHVT5FcXdTmVZiv04TYTeUt6yZmZmOrNeJZJmq+tKly3vjG9+4osvtRqu9YiuqiFZbzBMTEwuVy+joaNvXnZqZmWHTpk1Lmnd6eprBwcHS8+RbSCv5Gay2z3W1cSJZIUs5EihzdJifdjXvwNkYy8S7EmVKK8jlVI7tbKWl26Qo4czMzBARrFu3rtSy8xVz1Ymh3imUyclJpqenmZ+fP2jaonLMzMwwNzfH4OAgc3NzheOL1pkmkWathmyyLIo/u1/mYyqavqqj+nZ/l9u5PieSJvIfRnrUU2Zny86bHo1NTU3VnS61nNMOy91pys6XrbjrVRbLOXebXf+WLVtYs2ZN09MPZbd/0ToaTdOofEux3FMmzU7VpctuNm+j6dP3+fLmWwCbNm1aqPxnZ2cZGBhgbGys8HNO5y0Ta5p8sstJ15OdJ5skl7s9s+vPxzI3N8f8/DzT09OcdNJJC8OHhoYOKls2EbbbUr/37W51ppxIWjA3N8emTZsKP7xGR8zZHTP/hUmXedxxxxUeSdWTfpEbye902RhX+gLeUlsMExMT7Nq1i3Xr1i0aPjc3t6jyyw4vWsZy4oTatp+dnWX79u1s2rSpMOE3kh4oZMtb7wi3WSLMz5ddZlrusbGxRZ9jmgSy2z1/1J19v337dqC236Sxp5X8xMTEwnT57Zz9PLLLnZ+fZ2BgYGGZ2dYKwMDAwKJlzc7OLkpc6X6fJq28/AFcuk2yFX1emizSpDg/P89JJ510ULLIz1OUMOfn5wvX0Yrs8tNtkZYrm9wBTj755IXx6WnCdNp6B1tLPTXYCieSJUh32KIdN7tjZ6cFFn1B0i9pfses92VIx6XrqFdB57+4qfzRfRpP9lRDfifOlqPMUWCjL3J2Zy46qsxXDPkvCdQqnfwXKVtxFcWTl1aYaYJo1ILKl6UoEaSV5fT09MJy8pVjdr65ublSp07S5Q4NDTVNwvmElN030woou18UfUbz8/PMzs4umiaNIbt95+fnF7Z7dvrJyUlOPvnkhWnSBFW0niJpTNkKL92uWdnx2XLml5tu//y4/HZIy5e2uBpJx6dJqdVWa7q9xsbGmk6bjXt2dnYhwczMzCxs64GBgYX9IDu+nZxIVkjR0WP65av3PjtvdmeenZ1l165diyqsdGdJvwTbt29fON2QVe80UBpXoy9Y/suZP3JLK8qio+9sOQYGBhaWkVZG6Ze/0WmQhx9+eGG9+YokrcCyFXW+EskmtGzyyp+DT99nP7Ps9k8rjHwZs4kgXXe+0kvXm/3c8sNSAwMDrF27dlH89cpU1HpNt2t2WzQ6ai5TaWaXlX9dNO/JJ59cN0mUkY07v95038l+9vmYijQrY/azq3fNJh2+0hVy9jOuty/nvydZ6X6Z31+zBypA4cFqlRQRbVlRJ23cuDHuvPPOJc9XdMSX7uDZirLoCKis7LJWYnyjeIaGhhrGWbSu8fHxhaPO/HqARZXlUrfB+Pj4QUm0jGyc6ZF7fjlFR5vZSj07fGhoqG4M2WsD2S9lo21YNL5o+MDAACeccAJr1qwpPJJPE3ka2/j4eN0j/jJxNNuXVqPsZ1Nv2y7HcpeV7g9LbZHs3r2bs846q+G+nm0Blk369ebPluv2229f0nKyJH0lIjY2Xfey19AnZmdnF/6yRzKp9GhpuTt3s/mWOr5RPMuJc/v27Zx00kmF68luj+UsezlJJF1fanZ2tnA5RUdjaZxFR2n1TpOlLY60fNly15u+aHyj+eolh7m5uUXlKptE0vWVGbba5Y+8V6oMy11W/nTmUjRrHWT3r+V+L1ZyGy2FT23ZgnbvgMv5sixlOfXKkx9e9lTISpufn+eee+6pbL3dmDh6WS9/HpW2SCRdLelBSffWGS9Jfy5pp6S7JY1nxr1O0teTv9dlhj9X0j3JPH8uSVXF36lfiZrZ6lGvtWoHVL2FPgJsbjD+5cAzk7/zgUkASU8E3gGcBJwIvEPSE5J5JoE3ZeZrtPyWtOtClZmtXkWndZdqy5YtKxXOqlRpIomIrcAPGkxyOnBN1NwGPF7SU4CXAZ+PiB9ExA+BzwObk3FHRMRtUbtL4Brgl6uKv5eboma2fMv9AW6v6nSb7Rjg25n39yfDGg2/v2C4mVnbrNT1vV7RsxfbJZ1P7XQZIyMj7N69u8MRmVkv2bFjR+l6Zf/+/RVHU1876r5OJ5IHgKdl3o8kwx4ATskNvzUZPlIw/UEi4krgSqj9jmR4eHilYjYz4+GHH6ZsvfKtb32r4miKDQwMlI6xpfVUvobGrgfOTe7eej6wJyK+C9wIvFTSE5KL7C8FbkzG/UjS85O7tc4F/r5j0ZuZlXD88cd3OoRKVdoikfRX1FoWw5Lup3Yn1hqAiPggcAPwC8BOYA54fTLuB5IuBu5IFnVRRKQX7X+T2t1ga4HPJn+VaOWXwEudtxt+ddwNMa6EtJz9Ul6r3q5duzqy3nbtv5Umkog4u8n4AN5cZ9zVwNUFw+8Enr0iATaxnG4KoHk3JXDwxbqi7jzK7ATZ/rBW4gJgo+5CGm2PevM1Wl4rsVQpW87lxrDSsRftD8vp5qPMfuUEurp0w+fR6VNbq1qj3lcb/Ugp7Ugx7dcpO+3o6ChTU1OLhqWvBwYGGBoaYmhoaFFnfum4onWmv3XJx1ovvvHx8brdaA8MDDA6Osrtt99eOE1R1+ppzKOjowfNk48h+358fJzx8fG60660dPs1W0++LEXlSqcrs7x03Y26Li+aPm9wcHDROsfHx0v1HpuPN9uLbrqvld02S415ufN14geArZZ/KbL7QrN9I/3M0vjSz6uRbFnaVSYnkibGx8e5/fbbF1V6Q0NDdfv5HxgYYHJykqmpqYVKaGxs7KAdIDssXVbaw+vc3Bx79+5dtPOMjY0t2qHSOLKVSTp8fHx8UXzZ9aY9ARdV8tl5RkdHF8qeVjjpOrM7ab4yyybDfAxjY2ML4/Od3qVly36p0u03NDS0kHiyZc8uO7veehVxNrFnl5VdTtHBQ1GSnpqaWlhmdtuk609jr7eN6n3B859D/iAkOy7bk2x++2TXkw5L9690e+bLlZYlv/5sBZZ9n66vaDs2OlhZaiVXtKyVqCwbzVdlYslu97GxsYMOVrL7UfapkmNjY0xNTbFt27aGCWVwcHDhu9au55E4kSxT/sMvs9MNDg4uVKCTk5OMjo4u7Byjo6MLzzkYHBxk7dq1C8kj//yD9Auf7pDp+DQ5ZWNKk0qjL3yakNJedNOu6NN1Tk1NLbRG0sqs0U6aliWVTXhpefMxTE1NccIJJzRdVlH8Y2Nji7bj4ODgQsVaVGmmn0N2u2R7dS3q2TVbcaay2yVNHulnml1XWo50GWm8RRXx4ODgQos1n0Sy+0w2xjQxp2VK40n3n3qfU9qjcRpP2lrOV3TpuOzysvtcOjxdb7byyx6E5JNqdp58xZidp+i7lsaRvi5Kos2O3tPKNh9P+rrRweJSW5l79+5dVK7smYH0wDM9YE33o+wBZr0eh9ODvfSAL91e2X2hHZxIliD90LIVeFphpZV7vdMNjSpEYFEFNjo6unCXRzaJpEcj2YdAZfsDS3fIdPq0wswfyaYVQFohZOcpI7u8dPn54UXlzlfSaRxQ67L/sssuq7uN0nm3bdu2qHVYNF12fdnl5eNL3zf6omanzVac9bZHvbIXlWVycpITTjjhoJjgQAWePzjI7yf5ijLbCs6WJ3ug0iim9HW9U6D5MqX7YBpLmgjTSj6dfnx8nG3bti2sIx2Xbvt0mdkDlOzrbFnT/baoRZU9KEjjSOWTTVFfeun8Raegs+tZaiWdthzLftfSz6lRvZE9ME2Xnd3GzeqcldTp35GsavlKr974iYmJul/O5T5LvdE8ZZ6Clo8n/75eeZp1+1D0PPGiJzYu5THB+fmyzz5pFOdSHuVbb11Q/IjetHVWL4aieBots972uOyyyxgeHq67nyx1v8mvp95+mY2xTPmy6n2u2YdvZZN5XvoMlnys2aSRfzhbGUWPFx4dHV14CFyRNOGm+1M+QWRbV61c8B4YqF0wzz54ruhJi2U/7zL7fStPcVwqJ5KKNfowW/mgO/VIzUaW86UoqmC3bdtWKqGVqXwbJYtmcbbyRW8Uy1LnayT/5MR6y1lOUq/3SNl8Syc/bCmKnuZZb5r8UyuL9p38Ey0nJycX5mv2nUnLm13m1NTUQU/ybNdRfiufXbs5kSxDvcplpT7wycnJ0t0alPmyV31k0uozrJdrORVz2c+olYTRLo1arGWnXU2atWKLklaZzzVb8ddbR36ZExMTBz22OruupSaUlb59d7V9nk4kK2i1fbjdrKpt2a7PqJsTUbNxrS47PZ23kjE1Ov22Gr6Xj3vc4xYuuC/Vaoi/GSeSLlbFDtbJCs0da1o7tXNf37t376JWSTckh6VwIjGzZevGCnElWotLPZ27du1aJHXl9irDicRa0qtfDDMrz4nEzHpOldd4fPB0MP8g0czMWuIWiZlZxY4//njWrFnT6TAq4xaJmZm1xInEzMxa4lNbZmbLsJR+9Hbt2kXt6eC9yYnEzGwZltLPna+RmJmZNeAWiZlZxXr91JZbJGZm1hInEjMza4lPbZmZLUP2SYzN9PrFdicSM7NlWOpz23uZT22ZmVlL3CIxM6tYo6dC9gK3SMzMKrZly5aGz5XvdpUmEkmbJe2QtFPSloLxT5c0JeluSbdKGsmMe6eke5O/szLDN0naLmla0j9KekaVZTAzKzI6Osro6GipaXft2rWkX8J3m8oSiaRDgA8ALwfWA2dLWp+b7HLgmojYAFwEXJrM+wpgHBgDTgIulHREMs8k8JqIGAOuBf6wqjKYmVlzVbZITgR2RsSuiPgx8HHg9Nw064Gbk9e3ZMavB7ZGxP6IeBi4G9icjAsgTSpHAt+pKH4zMyuhykRyDPDtzPv7k2FZdwFnJK9fCRwu6ahk+GZJg5KGgVOBpyXTvRG4QdL9wGuByyqK38ysrpmZmVKnqyYmJti7d28bIuqcpndtSfqJiHgwN2xdROxYgfVfCFwh6TxgK/AA8GhE3CTpecCXgO8B24BHk3n+G/ALEXG7pN8D3kMtueTjPh84H2BkZITdu3evQLjts2fPnk6H0HYuc3/olTJHBEDTumXfvn0L03dbPVRWmdt/vyjpjyLiEwCS3gq8gdrpp0Ye4EArAmAkGbYgIr5D0iKRNAScGREPJeMuAS5Jxl0LzEg6GviZiLg9WcRfA58rWnlEXAlcCbBx48boxlvvujHmVrnM/aEXyrxu3TqgeVnSX7RL6olyFylzausU4LWS/kbSVmCU2vWPZu4AninpOEmHAa8Grs9OIGlYUhrD24Crk+GHJKe4kLQB2ADcBPwQOFJSeqvES4D7SsRiZtYxa9euLX2HVzdq2iKJiO9K+hy1in4e2BIRsyXm2y/pAuBG4BDg6oj4qqSLgDsj4npqSepSSUHt1Nabk9nXUGsJAfwIOCci9gNIehPwSUnz1BLLry+lwGZmtrLKXCP5ArU7o55N7VTVhyRtjYgLm80bETcAN+SGvT3z+jrguoL5HqHOqbOI+BTwqWbrNjOrUplH7PaLMqe2roiIcyPioYi4B/g5oDeulpmZVWxycpLjjz++02FUqsyprb/Lvd8PXFxZRGZmXSDt8qRMy6TX+9oqc2rrP6j9CBDgMGrXL2Yj4sgqAzMzs+5QpkVyePpatavfpwPPrzIoMzPrHkv6ZXvU/B3wsoriMTOzLlPm1NYZmbcDwEbgkcoiMjPrAmV7852YmGDfvn1cddVVFUfUOWV+2f5Lmdf7gW9wcOeLZmbWp8pcI3l9OwIxM+smvfxL9aWqm0gkvZ8Dd2sdJCJ+u5KIzMy6QC8/qGqpGrVI7mxbFGZm1rUaJZKPpf1bmZnZYj61dUCjRPJlao+7RdL7I+K32hOSmdnqV/bU1uTkZM8+hyTV6Hckyrx+QdWBmJlZd2rUIql7od3MrN/51NYBjRLJsyTdTa1l8lPJa5L3EREbKo/OzMxWvUaJ5KfbFoWZmXWtuokkIr7ZzkDMzLqJf0dyQJkuUszMLMfXSA5YUu+/ZmZmeU4kZmbWkkZ9bd1D4762fNeWmZk1vEbyi8n/Nyf//zL5/5rqwjEzs27T9K4tSS+JiJ/NjNoiaTuwpergzMxWq8nJyU6HsGqUuUYiSS/IvPm5kvOZmfWsiYkJJiYmSk23ZUtvH3eXuf33DcDVko5M3j8E/Hp1IZmZ9Y6ZmRkiervHqTJPSPwK8DNpIomIPZVHZWa2yvkHiQc0PUUl6UmSPgR8PCL2SFov6Q1tiM3MrOvNzc2xd+/eTodRqTLXOj4C3Ag8NXk/A/xOmYVL2ixph6Sdkg46SSjp6ZKmJN0t6VZJI5lx75R0b/J3Vma4JF0iaUbSfZL8yF8zsw4qk0iGI+ITwDxA8tTER5vNJOkQ4APAy4H1wNmS1ucmuxy4JvlNykXApcm8r6D2UK0x4CTgQklHJPOcBzwNeFZE/DTw8RJlMDPriMHBQdauXdvpMCpVJpE8LOkokh8nSno+UOY6yYnAzojYFRE/plbhn56bZj1wc/L6lsz49cDWiNgfEQ8DdwObk3ETwEURkSa2B0vEYma2okZHR93fVqLMXVtvBa6n9kySfwKOBn61xHzHAN/OvL+fWusi6y7gDOB9wCuBw5OkdRfwDknvBgaBU4GvJfP8FHCWpFcC3wN+OyK+XiIeM7MVU/Z3JKOjo+zbt6/iaDqr1F1bkn4eWEftoVY7ImKltsqFwBWSzgO2Ag8Aj0bETZKeB3yJWrLYxoHTaY8BHomIjZLOAK4GXphfsKTzgfMBRkZGuu6ZyXv29N/NcS5zf+ilMqe/D7nsssvqTrNjxw4iouvqoKVomkgk/Svwroj4YGbYZyLiFxvMBrWk8LTM+5Fk2IKI+A61FgmShoAzI+KhZNwlwCXJuGupXeSHWsvmb5PXnwI+XLTyiLgSuBJg48aNMTw83CTc1acbY26Vy9wfeqXMa9asARqXZ926dezbt69nylykzDWSfcCpkj4s6bBk2DEl5rsDeKak45L5Xk3tFNkCScOS0hjeRq11gaRDklNcSNoAbABuSqb7O2qnugB+ngMJxsxs1ZmZmWHXrl2dDqNSZRLJXEScBdwHfFHST9KgV+BUcnfXBdRuHb4P+EREfFXSRZJOSyY7BdghaQZ4EkkLBFiTrOtr1FoV5yTLA7gMODPpnfhS4I0lymBmZhUpc7FdABHxZ0lnjTcBTyyz8Ii4AbghN+ztmdfXAdcVzPcItTu3ipb5EPCKMus3M6vS9PR0p0NYFcokkmzF/wVJLwNeV11IZmbdYXBwsNMhrAqNHmz1rIj4F+ABSeO50Z+pNiwzs97Q77f/vhV4E/DugnEBvKiSiMzMesjk5GRP3/oLjR9s9abk/6n1pjEzs8YmJibYt28fV111VadDqUyjU1tnNJoxIv620XgzM+sPjU5t/VKDccGBHwWamfWlqampToewKjQ6tfX6dgZiZmbdqcztv2m37icAj02HRcRFVQVlZtYN0me2l+3AsVeV6Wvrgxzogfcq4FeAL1ccl5lZT+iHu7bKdJHycxFxLvDDiPhj4GTAnfCbmRlQLpGkDxuek/RUap04PqW6kMzMrJuUuUbyGUmPB94FbKd2x1bv3hBtZraC+vp3JKmIuDh5+UlJnwEeGxG982QaM7MKzczMENG0w/SuVuZi+yHUets9Np1eEhHxnmpDMzNb3fr9bq1UmVNbnwYeAe4B5qsNx8zMuk2ZRDISERsqj8TMrAfNzc11OoTKlblr67OSXlp5JGZm1pXKtEhuAz6VPFt9H7UnJkZEHFFpZGZmPWBsbKyvn0eSeg+1HyHeE71+64GZmS1ZmVNb3wbudRIxM7MiZVoku4BbJX0W+M90oG//NTMzKJdI/l/yd1jyZ2ZmlOv9tx86bWyYSJIfIx4eERe2KR4zs64xMzPT6RBWhYbXSCLiUeAFbYrFzMy6UJlTW9OSrgf+Bng4HehntpuZGZRLJI8Fvg+8KDPMz2w3MzOgXO+/fna7mVmB0dHmz/jbtGkTEcHNN9/chog6o+nvSCSNSPqUpAeTv09KGimzcEmbJe2QtFPSloLxT5c0JeluSbdmlyvpnZLuTf7OKpj3zyXNlonDzMyqU+YHiR8Grgeemvx9OhnWUHLH1weAlwPrgbMlrc9NdjlwTdIp5EXApcm8rwDGgTHgJOBCSUdklr0ReEKJ2M3MOmpubo69e/c2n7CLlUkkR0fEhyNif/L3EeDoEvOdCOyMiF0R8WPg48DpuWnWA2l775bM+PXA1mR9DwN3A5thIUG9C/jvJWIwM6vM9PQ009PTnQ6j48okku9LOkfSIcnfOdQuvjdzDLXuVVL3J8Oy7gLOSF6/Ejhc0lHJ8M2SBiUNA6cCT0umuwC4PiK+WyIGMzOrWJm7tn4deD/wXmp3a30JWKkL8BcCV0g6D9gKPAA8GhE3SXpesq7vAduARyU9FfhV4JRmC5Z0PnA+wMjISNf9snTPnv57mrHL3B96scyN6pcTTjiB/fv3d10dtBRl7tr6JnDaMpb9AAdaEQAjybDssr9D0iKRNAScGREPJeMuAS5Jxl0LzAA/CzwD2CkJYFDSzoh4RkHcVwJXAmzcuDGGh4eXUYTO6saYW+Uy94deK3Oj8qxZs6bpNN2ubiKR9PYG80VEXNxk2XcAz5R0HLUE8mrg13LrGAZ+EBHzwNuAq5PhhwCPj4jvS9oAbABuioj9wJMz888WJREzM2ufRi2ShwuGPQ54A3AU0DCRRMR+SRcANwKHAFdHxFclXQTcGRHXUztFdamkoHZq683J7GuALyatjh8B5yRJxMysq/R1p40R8e70taTDgbdQuzbyceDd9ebLLeMG4IbcsLdnXl8HXFcw3yPU7txqtvyhMnGYmVVhbGys0yGsCg3v2pL0REl/Qu3220OB8Yj4/Yh4sC3RmZl1sYmJiYWu5ntZo2sk76J2IfxK4DkR4V+Rm5ktQb90M9/oGslbqT0R8Q+BP0iuVwCI2sX2I+rNaGbWDxo90KqfNLpGUubHimZmfavMExL7QZkfJJqZWYF+OXXVjBOJmVlFynQz3wt8+srMzFriFomZWUXSaye9/oNEt0jMzKwlbpGYmS3T1NRUw/HpXV0XX9ysa8Lu5kRiZlaRfnnolU9tmZlZS5xIzMysJT61ZWbWgka/bp+fn293OB3hRGJmVpGBgf446eNEYmZWkX55Xkl/pEszM6uME4mZ2TJNTEw07LhxZmamLzp29KktM7OKzM3NdTqEtnCLxMzMWuJEYma2TDMzM33T6mjEicTMrAXz8/N9cR2kEScSM7NlGh0dbfhbkcHBQQYHB9sYUWc4kZiZtWBwcLBvnoRYj+/aMjNrwdzcXN1TW/2SYNwiMTNbpsnJyb44ddWMWyRmZi2o1+qYmJhgenq6LxJNpS0SSZsl7ZC0U9KWgvFPlzQl6W5Jt0oayYx7p6R7k7+zMsM/lizzXklXS1pTZRnMzBrpl1+vN1JZIpF0CPAB4OXAeuBsSetzk10OXBMRG4CLgEuTeV8BjANjwEnAhZKOSOb5GPAs4DnAWuCNVZXBzKyZubm5vv8tSZUtkhOBnRGxKyJ+DHwcOD03zXrg5uT1LZnx64GtEbE/Ih4G7gY2A0TEDZEAvgyMYGbWIfPz833z3JF6qkwkxwDfzry/PxmWdRdwRvL6lcDhko5Khm+WNChpGDgVeFp2xuSU1muBz1UQu5lZS2ZmZvomwXT6YvuFwBWSzgO2Ag8Aj0bETZKeB3wJ+B6wDXg0N+//otZq+WLRgiWdD5wPMDIywu7du6spQUX27NnT6RDazmXuD71a5nwdUztpUvvfq2VOVZlIHmBxK2IkGbYgIr5D0iKRNAScGREPJeMuAS5Jxl0LLFzNkvQO4Gjgv9ZbeURcCVwJsHHjxhgeHm69RG3WjTG3ymXuD71Y5nyZ1q1bx/T0NJI48sgje7LMqSpPbd0BPFPScZIOA14NXJ+dQNKwpDSGtwFXJ8MPSU5xIWkDsAG4KXn/RuBlwNkR0R/tRjPrOv10aquyRBIR+4ELgBuB+4BPRMRXJV0k6bRkslOAHZJmgCeRtECANcAXJX2NWqvinGR5AB9Mpt0maVrS26sqg5nZcqV3cvXDr9srvUYSETcAN+SGvT3z+jrguoL5HqF251bRMjt9XcfMbEG9Thv7pTUCnb/YbmbW1fopYdTjvrbMzFbIxMQEExMTi4ZNTk52KJr2cYvEzGwFTEyOpOdZAAAK/klEQVRMLDwxMZ9Mep0TiZnZCti+fXvDh1z1sv4stZlZBfr1eokTiZnZCpqfn2f79u2dDqOtnEjMzFowNDS08NqntszMbMmmpqYWvc8mk35JLP1RSjOzDhgbG+t0CG3hu7bMzFaIL7abmVnLsqez+uHHiOBEYmbWsuwF92yrpF9+mOhEYmZmLXEiMTNrUdplfL9yIjEzs5Y4kZiZWUucSMzMWjQ4OFg43HdtmZmZleBEYmbWoqLnsvdL9yjgRGJmZi1yIjEzq0C96ya9yInEzGwF5E9lFZ3u6lVOJGZmKyDbAumn6yPgRGJm1rLJyUmmpqYYHx9nYGCAwcHBvrn1F5xIzMxWzOTkJGNjY311Wgv8PBIzsxXVTy2RlFskZmbWkkoTiaTNknZI2ilpS8H4p0uaknS3pFsljWTGvVPSvcnfWZnhx0m6PVnmX0s6rMoymJlZY5UlEkmHAB8AXg6sB86WtD432eXANRGxAbgIuDSZ9xXAODAGnARcKOmIZJ53Au+NiGcAPwTeUFUZzMysuSpbJCcCOyNiV0T8GPg4cHpumvXAzcnrWzLj1wNbI2J/RDwM3A1sliTgRcB1yXQfBX65wjKYmVkTVSaSY4BvZ97fnwzLugs4I3n9SuBwSUclwzdLGpQ0DJwKPA04CngoIvY3WKaZmbVRp+/auhC4QtJ5wFbgAeDRiLhJ0vOALwHfA7YBjy5lwZLOB84HGBkZYffu3SsZd+X27NnT6RDazmXuDy5z76kykTxArRWRGkmGLYiI75C0SCQNAWdGxEPJuEuAS5Jx1wIzwPeBx0s6NGmVHLTMzLKvBK4E2LhxYwwPD69cydqkG2NulcvcH1zm3lLlqa07gGcmd1kdBrwauD47gaRhSWkMbwOuToYfkpziQtIGYANwU0QEtWspv5LM8zrg7yssg5mZNVFZIklaDBcANwL3AZ+IiK9KukjSaclkpwA7JM0ATyJpgQBrgC9K+hq1VsU5mesivw/8rqSd1K6ZfKiqMpiZWXOqHeT3NknfA77Z6TiWaBjorgs7rXOZ+4PL3D2eHhFHN5uoLxJJN5J0Z0Rs7HQc7eQy9weXufe4ixQzM2uJE4mZmbXEiWT1urLTAXSAy9wfXOYe42skZmbWErdIzMysJU4kq4SkJ0r6vKSvJ/+f0GDaIyTdL+mKdsa40sqUWdKYpG2Svpo8buCsomWtdiUeqfCY5LEIO5PHJBzb/ihXVoky/66kryWf65Skp3cizpXUrMyZ6c6UFJJ64k4uJ5LVYwswFRHPBKaS9/VcTK1vsm5XpsxzwLkRcQKwGfifkh7fxhhbVvKRCm8Afpg8HuG91B6X0LVKlvmfgY3JYySuA/6svVGurJJlRtLhwFuA29sbYXWcSFaP06l1iw8NuseX9FxqvQDc1Ka4qtS0zBExExFfT15/B3gQaPoDqVWmzCMVstviOmBT8tiEbtW0zBFxS0TMJW9vo9Z3Xjcr8zlD7UDwncAj7QyuSk4kq8eTIuK7yet/o5YsFkn6JXs3tV6Te0HTMmdJOhE4DPjXqgNbYWUeqbAwTdId0B5qXQB1qzJlznoD8NlKI6pe0zJLGgeeFhH/0M7AqtbpbuT7iqQvAE8uGPUH2TcREZKKbqf7TeCGiLi/Ww5WV6DM6XKeAvwl8LqImF/ZKK2TJJ0DbAR+vtOxVCk5EHwPcF6HQ1lxTiRtFBEvrjdO0r9LekpEfDepNB8smOxk4IWSfhMYAg6TNBsRja6ndNQKlJnkMcv/APxBRNxWUahVavpIhcw090s6FDiS2mMTulWZMiPpxdQOKn4+Iv6zTbFVpVmZDweeDdyaHAg+Gbhe0mkRcWfboqyAT22tHtdT6xYf6nSPHxGviYifjIhjqZ3eumY1J5ESmpY5eQTBp6iV9br8+C7R9JEKLN4WvwLcHN39I68yj5H4WeB/A6dFROFBRJdpWOaI2BMRwxFxbPIdvo1a2bs6iYATyWpyGfASSV8HXpy8R9JGSVd1NLLqlCnzq4D/ApwnaTr5G+tMuMtT8pEKHwKOSh6P8Ls0vmtv1StZ5ndRa1n/TfK55pNrVylZ5p7kX7abmVlL3CIxM7OWOJGYmVlLnEjMzKwlTiRmZtYSJxIzM2uJE4mtapKOytz2+2+SHsi8/1KbY2nYW21Rr8ySbk16g01j/oncPIt6gZW0RtJHJd0j6T5Jb0uGP1bSlyXdlfSE/MeZZXwoGX63pOskDdWJ/5clvb3OuNnlb5nC5X1BDXqwtt7i23+ta0j6H8BsRFzeofWfCtweEXOSJoBTIuKszPj3UetQ8gcRcUEy7FbgwqIfnSW9wP4Dtf7DLoiIOyX9GrUfqb1a0iDwNeAU4JvA4yJiVtIa4B+Bt0TEbZKOiIgfJct8D/BgRFxWsL4vJcveXTBuNiIKE9BySHodMBIRl6zUMm31covEulZ6FC3pFEn/V9LfS9ol6TJJr0mO4O+R9FPJdEdL+qSkO5K/FyxlfY16q9XyemUu6gU2gMcl3aSsBX4M/Chq0lbDmuQvkrjSJKJknoOODiWNAv+ZJpHk19fbku3zJ5nphpLW1vZk3OnJ8Isk/U5mukskvUXSUyRtTVpb90p6YTLJ9cDZS9gW1sWcSKxX/AzwG8BPA68FRiPiROAq4LeSad4HvDcingecmYxbroXeatW8V+YPJxXtHyWVfaNeYK8DHga+C3wLuDwifpDMc4ikaWp9kn0+IhaeZyHpw9R6UH4W8P6CGF4AbM+8fx8wGRHPSdaVegR4ZUSMA6cC705ivho4N1PeVwP/B/g14MaIGKP2GUwDRMQPgcdI6uYejK0kJxLrFXdExHeTjv/+lQMtg3uAY5PXLwauSCrj64Ej6l1PaEQHeqt9VzJooVfmgslfk1TWL0z+XqsDvcC+tWD6E4FHgacCxwFvlXQ8QEQ8mlTYI8CJkp6dzhQRr0/muQ8oeorkU4DvZd6/APir5PVfZosH/Kmku4EvUOsG/UkR8Q3g+6r1j/VS4J8j4vvU+pd6fXLa8TkR8R+ZZT2YxGQ9zonEekW259j5zPt5DvRyPQA8PyLGkr9jMqeLgNqRfdJ6uKFoJTrQW+1pmd5qTwYukPQN4HLgXEmXAUTEA8n//wCupZYosr3AfgN4PrVeYDdSO8L/XETsSzoy/CdqSWtBRDwE3ELtiZHZ4Y9Se5jSmQWh7wUemxtWdIH0NdSu8zw3SVr/npnvKmpdoL+eWguFiNhKrS+0B4CPSDo3s6zHJuu1HudEYv3kJg6c5kIFnT9GxOuTJPML+XGq01ttvV6ZJR0qaTiZdw3wi8C9TXqB/RbwomSex1FLMv+SXN95fDJ8LfCSZLgkPSMZLuA04F8Kyn4f8IzM+3+idnoKaskjdSS1i/X7kpsLsnemfYpa8noetY4JUe3OtX+PiL+glmjGM7E8GfhGQSzWY/w8Eusnvw18IDltcyi1597/xhLmz/ZWC/CtiGjUq+tjgBuTJHIItVNFf9FkHR+gdk3lq9ROM304Iu6WtAH4qGrPBR+g1rPsZ5LTZB9V7ZktAu4CJgqWu5XkekfSPf1bgGsl/T6Lu+//GPBpSfcAd5JJShHxY0m3AA8lrR+o3VH2e5L2AbMk11GA5wK3JT3iWo/z7b9mfSK5PfnTEfGFZc4/QO2C/a9GxNdLrOv6iJhazrqsu/jUlln/+FNgcDkzSloP7ASmmiWRxL1OIv3DLRIzM2uJWyRmZtYSJxIzM2uJE4mZmbXEicTMzFriRGJmZi1xIjEzs5b8f62KZd6ohLvdAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pdcsap.fold(period=3.5225, phase=0).plot()" ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }