diff --git a/.gitignore b/.gitignore index 1c243f1b..fff7e20c 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ orbitize/example_data/rebound*.csv orbitize/example_data/*test.hdf5 .vscode/launch.json .vscode/settings.json +*.DS_Store # images & storage files generated by tutorials *.hdf5 diff --git a/docs/tutorials/dynesty_tutorial.ipynb b/docs/tutorials/dynesty_tutorial.ipynb new file mode 100644 index 00000000..db3406cf --- /dev/null +++ b/docs/tutorials/dynesty_tutorial.ipynb @@ -0,0 +1,329 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Nested Sampler Introduction\n", + "\n", + "by Thea McKenna & Sarah Blunt, with advice & wisdom from Josh Speagle (2024) \n", + "\n", + "Here, we will explain how to sample an orbit posterior using nested sampling from the `dynesty` package. An advantage of nested sampling is that it computes the evidence and posterior at the same time. To compute the evidence, Dynesty first uses live points to make iso-likelihood contours with probability greater than that of the previous contour. It then takes the integral of these contour shells and calculates prior “volumes” of these shells. As each contour is made, the live point with the least probability is removed (it “dies”), and it is replaced with a new live point sampled from the prior, which must have a higher probability. `dynesty` then uses these “dead” points to approximate the evidence integral by weighting each point and summing them. To estimate the posterior, dynesty then uses the calculated “dead” point weighting and evidence to get its importance weight, or the probability of the parameter set. For more info on how nested sampling works in `dynesty`, go to their [website](https://dynesty.readthedocs.io/en/stable/index.html).\n", + "\n", + "In this tutorial, we will demonstrate how to generate a synthetic data set with a user-controlled orbit fraction (fraction of orbit covered by the synthetic data points) to feed into the nested sampler. We will also show plots using the sampler results from `dynesty` and save these results.\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate Synthetic Data\n", + "We generate synthetic data using the `generate_synthetic_data` function in `orbitize.system` to feed into the nested sampler. This function sets ground truth values for all of the parameters, the desired fractional orbit coverage, the number of observations, and the desired observational uncertainty. The observations are uniformly spaced synthetic data points covering the years 2001-2003 such that the orbital period and semi-major axis are determined by selecting the orbital fraction. A three year period is used for all orbits, so that for smaller orbit fraction, the orbit has a larger semi-major axis. The function then adds Gaussian noise to the observations to make them more realistic. This approach simulates similar observing strategies for orbits of varying lengths." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " epoch object quant1 ... quant_type instrument\n", + "------------------ ------ ------------------- ... ---------- ----------\n", + " 51550.0 1 26.80749409680015 ... radec defrd\n", + " 51587.93103448276 1 29.108164753506628 ... radec defrd\n", + "51625.862068965514 1 48.910894541835965 ... radec defrd\n", + "51663.793103448275 1 48.89113297812603 ... radec defrd\n", + "51701.724137931036 1 54.902728166363005 ... radec defrd\n", + " 51739.65517241379 1 -3.2294339867550867 ... radec defrd\n", + " 51777.58620689655 1 6.75909265680874 ... radec defrd\n", + " 51815.51724137931 1 -47.876270799215646 ... radec defrd\n", + " 51853.44827586207 1 -63.97156459001942 ... radec defrd\n", + "51891.379310344826 1 -93.38916349992081 ... radec defrd\n", + " ... ... ... ... ... ...\n", + " 52270.68965517241 1 -168.59492862364965 ... radec defrd\n", + "52308.620689655174 1 -180.67267309707097 ... radec defrd\n", + " 52346.55172413793 1 -151.2813788006864 ... radec defrd\n", + " 52384.48275862069 1 -144.5979028523999 ... radec defrd\n", + " 52422.41379310345 1 -137.29145594487034 ... radec defrd\n", + " 52460.34482758621 1 -119.81483177573747 ... radec defrd\n", + "52498.275862068964 1 -119.95641701153806 ... radec defrd\n", + "52536.206896551725 1 -96.42312286797939 ... radec defrd\n", + "52574.137931034486 1 -84.42086242715266 ... radec defrd\n", + " 52612.06896551724 1 -55.48017221398401 ... radec defrd\n", + " 52650.0 1 -23.3209833700658 ... radec defrd\n", + "Length = 30 rows\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import orbitize\n", + "from orbitize import read_input, system, priors, sampler\n", + "import matplotlib.pyplot as plt\n", + "from orbitize.system import generate_synthetic_data\n", + "import numpy as np\n", + "\n", + "np.random.seed(1)\n", + "\n", + "# generate data\n", + "plx = 60.0 # [mas]\n", + "mtot = 1.2 # [M_sol]\n", + "orbit_frac = 95.0\n", + "ecc = 0.5\n", + "inc = np.pi / 4\n", + "aop = np.pi / 4\n", + "pan = np.pi / 4\n", + "tau = 0.8\n", + "unc = 10 # [mas]\n", + "\n", + "data_table, sma = generate_synthetic_data(\n", + " orbit_frac,\n", + " mtot,\n", + " plx,\n", + " num_obs=30,\n", + " inc=inc,\n", + " tau=tau,\n", + " unc=unc,\n", + " lan=pan,\n", + " argp=aop,\n", + " ecc=ecc,\n", + ")\n", + "\n", + "# initialize orbitize.system.System object\n", + "num_secondary_bodies = 1\n", + "sys = system.System(\n", + " num_secondary_bodies, data_table, mtot, plx, restrict_angle_ranges=True\n", + ")\n", + "print(data_table)\n", + "lab = sys.param_idx\n", + "\n", + "# plot the generated data\n", + "plt.figure()\n", + "plt.errorbar(\n", + " data_table[\"quant1\"],\n", + " data_table[\"quant2\"],\n", + " data_table[\"quant1_err\"],\n", + " data_table[\"quant2_err\"],\n", + " ls=\"\",\n", + " color=\"rebeccapurple\",\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Running the Dynesty Sampler\n", + "`dynesty's` nested sampler has multiple prior bound shape options, as well as a static and dynamic nested sampler, more details of which can be found [here](https://dynesty.readthedocs.io/). These can also be configured in `run_sampler` keywords. Here we stick with the default options of a static sampler and 'multi' prior bound shape." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "iter: 18941 | batch: 6 | bound: 8 | nc: 1 | ncall: 78925 | eff(%): 23.720 | loglstar: -223.183 < -217.852 < -219.047 | logz: -234.384 +/- 0.111 | stop: 0.892 " + ] + } + ], + "source": [ + "# fix the values of semimajor axis and argument of periastron to speed up convergence for ease of tutorial use\n", + "sys.sys_priors[lab[\"sma1\"]] = sma\n", + "sys.sys_priors[lab[\"aop1\"]] = aop\n", + "\n", + "threads = 8 # number of parallel threads to use\n", + "nested_sampler = sampler.NestedSampler(sys)\n", + "samples, num_iter = nested_sampler.run_sampler(num_threads=threads, static=False)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plotting\n", + "Full plotting capabilities of orbitize (including advanced corner and orbit plots) can be found in the [orbitize plotting tutorial](https://orbitize.readthedocs.io/en/latest/tutorials/Plotting_tutorial.html).\n", + "\n", + "Here, we will plot the posterior samples from dynesty against the ground truth variables used to generate the synthetic data set." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAC6UAAAHvCAYAAAAWvpggAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAADCUklEQVR4nOzdeVhUdf//8dcACoosbqgo7qa5m1uu4ZJKaZlL6W25lpVLKpVJ9+3WRraolYYt5lL5NS01MzOXREM0F8SyzMxQcMEdUFQkOL8//DExAQrDwDDD83Fd57qcs837HIbX53Phm4PJMAxDAAAAAAAAAAAAAAAAAAAAAABYwcXeBQAAAAAAAAAAAAAAAAAAAAAAHBdN6QAAAAAAAAAAAAAAAAAAAAAAq9GUDgAAAAAAAAAAAAAAAAAAAACwGk3pAAAAAAAAAAAAAAAAAAAAAACr0ZQOAAAAAAAAAAAAAAAAAAAAALAaTekAAAAAAAAAAAAAAAAAAAAAAKvRlA4AAAAAAAAAAAAAAAAAAAAAsBpN6QAAAAAAAAAAAAAAAAAAAAAAq9GUDgAAAAAAAAAAAAAAAAAAAACwGk3pAAAAAAAAsMqmTZs0YsQI3XHHHfL29pa7u7uqVKmie++9V3PmzNG5c+fsXaJDOXbsmEwmk2rWrGnvUoqEyMhI9ejRQ+XKlZOLi4tMJpMWL1582+MCAwNlMpkslpIlS6pKlSrq06ePvvnmm4IvvogJDw+XyWRSYGCgvUuxEBUVpZEjR6pu3boqVaqUSpcurRo1aqhDhw567rnntGnTJnuXWCBmzJghk8mkGTNm2LuUIs0WmRgeHq4nnnhCDRs2VNmyZVWiRAmVL19ebdq00bhx47R582YZhmG7oh1QRk7mRs2aNbPk6+0We45pts6+oUOHymQyadCgQbnaf86cOTKZTGrYsKHV75kxpoWHh1t9DgAAAAAAAKCwuNm7AAAAAAAAADiW8+fPa/Dgwdq8ebOkm01qXbp0kaenp+Lj4xUZGanNmzdr2rRp2rx5s9q2bWvniuFoTp06pfvvv1+JiYnq2LGjatasKRcXF9WtWzfX52jWrJmaN28uSbpy5Yr279+vdevWad26dXrmmWf0zjvvFFD12atZs6aOHz+umJgYfvFA0nvvvaeJEycqPT1dVatWVZcuXVS2bFmdO3dOUVFRioyMVHh4uO699157lwoHdP78eQ0ZMkQbN26UJFWtWlUdOnSQj4+PEhMTdfDgQc2fP1/z589XixYtFBUVZeeKHcOAAQN0/vx5i3VXrlzRV199JUnq37+/ypQpY7G9QoUKBVZPYefqqFGj9Omnn2rNmjW6dOmSypYte8v9Fy1aZD4OAAAAAAAAKA5oSgcAAAAAAECuZTQJHz58WA0aNNCHH36oTp06WeyTkpKiJUuWaPr06Tp9+rSdKnU8VatW1aFDh1SiRAl7l2J3GzduVEJCgv7zn//o888/t+ocffv2tXgSdXp6uv73v/8pNDRU7777rh588EF17drVRhUXbW3atNGhQ4dUunRpe5ciSfr555/NDelz5szR+PHj5erqat6enp6uiIgIRURE2LFKOKqEhASLcer9999Xly5dsux38OBBzZkzR8uXL7dDlY7prbfeyrLu2LFj5qb0t956y6l/6aZz586qW7eu/vzzT33++ecaN25cjvvu2bNHv/zyi0qUKKHHHnusEKsEAAAAAAAA7MfF3gUAAAAAAADAcYwfP16HDx9WzZo1tWPHjiwN6ZLk7u6u0aNHKzo6WnfeeacdqnRMJUqUUIMGDVSnTh17l2J3sbGxkqR69erZ7JwuLi56+eWXVbt2bUnSihUrbHbuoq506dJq0KCBqlevbu9SJEkrV65Uenq62rVrp4kTJ1o0pEs3v1adO3fWiy++aKcK4cgyxqnatWsrMjIy24Z0SWrcuLEWLlyorVu3FnKFcFQmk0kjR46U9M9T0HOSsb13797y8/Mr8NoAAAAAAACAooCmdAAAAAAAAOTKX3/9pWXLlkmSZs+erXLlyt1y/0qVKql+/fpZ1i9fvlzdunVTuXLl5O7urho1amjkyJH6448/sj1PzZo1ZTKZdOzYMX333XcKDAyUj4+PypYtq969e+uXX34x77ts2TK1a9dOXl5e8vX1Vb9+/XT06NEs5wwPD5fJZFJgYKCuXr2qF198UXXr1pWHh4f8/f01atQonTx5Mtt6Nm/erPHjx6t58+aqUKGC3N3dVa1aNT3yyCPas2dPtsfMmDFDJpNJM2bMUGxsrEaNGqWAgACVKFFCw4cPl3TzabMmkynbp8weOXJEI0eOVK1ateTu7q4yZcqoRo0auv/++3NsjPv+++/NzXAlS5aUv7+/HnnkEe3duzfb/QMDA2UymRQeHq7o6Gj169fPfH0NGzbU22+/LcMwsj32dnL7NV+8eLFMJpOmT58uSZo5c6ZMJlOO9yWvXF1d1bx5c0k373dmu3fv1sMPPyx/f3+VLFlSfn5+6tOnjzZt2pTtuVJSUvTmm2+qZcuW8vLyUsmSJVW5cmW1bt1akydP1sWLFy2u6fjx45KkWrVqma8p435ndurUKQUHB+vOO+9U6dKl5eXlpdatW2vevHn6+++/s9QxfPhwmUwmLV68WAcPHtQjjzyiKlWqyNXV1fyk+Myf9+z8/vvvGjFihGrUqCF3d3eVK1dO3bp1y7FxPzef51s5c+aMJFnVqLl7925NnjxZbdq0UeXKlVWyZElVqlRJffr00ebNm7M9JuNrMHz4cCUmJio4OFg1a9aUh4eH6tWrp1mzZik9PV2SdPLkST355JMKCAiQu7u76tevr/feey/b82b+ntm2bZt69OihcuXKqXTp0mrTpo0+/fTTPF+fJP3xxx968sknVadOHXl4eMjHx0edO3fWZ599lu3+iYmJ+t///qcmTZrI09NT7u7u8vf3V4cOHTRt2jSlpqbm+r3ze3+Tk5MVEhKiunXryt3dXZUrV9awYcNyzFNJWrdune655x55eXnJx8dHnTp10tdff53rmjM7evSoeZyaM2eOypYte9tj2rRpk2Vd5q/tjz/+qD59+qhixYpycXHR4sWLzfudOHFC48ePV7169cxfqw4dOuiDDz5QWlpalvNm/t7JTk7fq5nXp6amatasWWrUqJFKlSql8uXLq1+/fjp06FCO17hz504FBQXJ19dXZcqUUatWrfTJJ5/c9t7kV+bPxsWLFzVx4kTVqVNH7u7u5mu8XT5JMuflv8+b21yVZNV9y87w4cPl6uqqqKgo/fzzz9nuc/36df3f//2fJGnUqFGSpMuXL+ujjz5Sv379VK9ePXl6esrT01NNmjTRf//7XyUkJOSpjsyf0ezc7rO2b98+DRkyRNWrVzfnfs+ePbV+/fps9z99+rQmTJigO+64Qx4eHipdurQCAgLUrVu3bJ+gDwAAAAAAgOLJzd4FAAAAAAAAwDGsW7dOaWlp8vX11QMPPJDn4w3D0PDhw7V06VK5ubmpc+fO8vPzU1RUlBYtWqQvvvhCX331lXr16pXt8R988IFmzZql9u3bq1evXoqOjta3336rHTt2aO/evfrggw80Z84cde7cWb169dJPP/2k1atX66efftLBgwezbU68ceOGunXrpp9//lmBgYG66667FBERoU8++UTr16/X9u3bszyt+6mnnlJcXJwaNWqkDh06yM3NTb///rtWrFihVatWafny5erfv3+213DkyBG1aNFCJUuWVIcOHWQYhipUqHDL+3bw4EF16NBBSUlJql+/vnr37i1XV1edOHFC27dv18mTJzVixAiLY6ZOnapXXnlFJpNJ7du3V/Xq1XXo0CGtWLFCX331lT788EPz017/7fvvv9fs2bNVp04d3XvvvTp9+rQiIiL03HPPKS4uTnPnzr1lvZnl9Wtet25dDRs2TNHR0Tpw4ICaNWtmbiK/3X3KraSkJEk3n+if4aOPPtJTTz2l9PR0tWjRQoGBgTp+/LjWrVundevWacaMGeZGeUlKT0/X/fffry1btsjb21udOnWSr6+vzp07pyNHjujNN9/Uf/7zH5UrV858TV9++aWSk5PVv39/lSlTxnyuypUrm/+9fft29e3bV5cuXVLNmjV17733KiUlRbt379b48eP1zTffaN26dSpRokSW64qMjNRTTz2lKlWqqHPnzrp27Zq8vLxuez++/fZbDRgwQNevX1f9+vXVr18/nT17Vtu2bdMPP/yg77//XgsXLsz2WGs+z5LMT2zfsmWLDh48qMaNG9/2mAwvvviitm7dqkaNGqlly5by9PTU0aNHzV+ruXPnasKECdkem5CQoHbt2unChQvq1KmTLl++rB9//FFTpkzRiRMnNHHiRHXs2FElSpRQ+/btde7cOW3fvl3PPPOMrl69qhdeeCHb865evVrz5s1TgwYN1LNnT506dUoREREaOnSooqOj9fbbb+f6+lauXKmhQ4fq+vXratCgge677z4lJibqp59+0mOPPaYffvjBopn46tWr6tixow4ePKiKFSuqW7du8vT0VHx8vH7//XdFRkYqODhYvr6+BX5/ExMT1b59e8XGxqpTp05q3Lixdu7cqaVLl2rbtm06cOCAfHx8LI6ZM2eOgoODJd1sDq9Tp46OHDmivn37mtfnxbp165Senm7+paX8WrlypRYsWKAGDRqoe/fuunjxojk79uzZo169eunixYuqXr26+vbtq8TERIWHhysyMlKrV6/W2rVrVbJkyXzXkSE1NVX33XefIiMj1blzZ915553avXu3Vq9era1bt2r//v1ZfoFn5cqVGjx4sNLS0tS4cWM1adJEcXFxevzxx/Xrr7/arLZbOX/+vFq1aqWEhAR16tRJLVu2zNd9yUuuStbdt5xUqVJF9913n7755hstXLhQ77zzTpZ9Vq1apYSEBPn7+5vHtwMHDmj06NGqWLGi6tevr5YtW+rSpUvat2+fXnvtNa1YsUK7du1S+fLlrb4vufXOO+8oODhY6enpat68udq2bav4+HiFh4dr48aNmjlzpqZNm2bePz4+Xq1atdKpU6dUvXp19erVSx4eHjp16pSio6O1b98+PffccwVeNwAAAAAAAByAAQAAAAAAAOTCY489ZkgyunbtatXxYWFhhiSjQoUKxv79+83r09PTjenTpxuSDF9fX+Ps2bMWx9WoUcOQZLi7uxubN282r//777+NgQMHGpKMxo0bG+XLlzeio6PN25OTk4327dsbkoxXXnnF4pxbt241JBmSjLp16xrHjx83b7t27ZrRv39/Q5Jx9913Z7mO1atXGxcvXsx2vZubm1G+fHnj6tWrFtsyrk+S8eijjxrXr1/PcnxMTIwhyahRo4bF+hEjRmR7DYZhGFevXjW2bdtmse67774zJBkeHh7Gxo0bLbZ9/PHHhiSjRIkSxsGDBy223XPPPeYaFyxYYLFty5YthslkMlxdXY24uLgsdeTE2q95xrbp06fn+r3+fR3ZHXvq1CnDy8vLkGRMmzbNMAzD+Pnnnw03NzfDZDIZS5cutdh//fr1RsmSJQ1JFvdy27ZthiSjRYsWRlJSUpb32bNnj3H+/HmLdRmf45iYmGzrPn36tFG+fHnDZDIZ77//vpGWlmbedv78eaNr166GJGPmzJkWxw0bNsz8dZsyZYrFcRkyPu/33HOPxfr4+HjDx8fH/PlKT0+3uIayZcsakowPP/zQ4rjcfJ5vJTY21vx1cHNzM+677z5j1qxZxqZNm4yEhIRbHrt+/Xrj1KlTWdZHRkYa3t7eRokSJYwTJ05YbFu0aJG53j59+hjJycnmbfv27TPc3NwMFxcXo2HDhsZTTz1lpKammrevWbPGkGR4e3tbHGcYlt8zr732msW28PBwo1SpUoYkY8OGDRbbcvp8//zzz4a7u7vh4eFhfPXVVxbbjh07ZjRp0sSQZCxZssS8fsmSJYYkIygoyLhx44bFMWlpaUZ4eLiRkpKS5X7lJL/3t2fPnkZiYqJ528WLF43mzZtne48OHDhguLq6Gi4uLsbKlSsttn322WeGyWTKNhNvJWOc6tatW66PyU7mr+38+fOzbL9+/br5e/qpp56yuPdHjx41atasaUgyXnzxRYvjbpdtOX2vZh6zWrRoYZw+fdq87dq1a0bPnj0NScbo0aMtjjt9+rT5e2327NkW2zZv3mx4eHiYz2utjLEru3zL/Nno1q2bxWfjdtecWU413i5Xrb1vt5ORC+XLl8/2+6t79+5Zvv5xcXHG5s2bs2R0cnKyMXToUEOSMWbMmCznyvgsbt26NVfrM+T0WduwYYNhMpmMChUqZJk//Pzzz0a1atUMSUZ4eLh5/cyZM833KfM4YRiGcePGDYu5GQAAAAAAAIo3Fyv62AEAAAAAAFAMnTt3TpLk5+dn1fFvvfWWJGnatGnmp19Lkslk0vTp09W0aVMlJCToo48+yvb4Z555Rt26dTO/dnV1VUhIiKSbTxN/6aWX1KxZM/P20qVL69lnn5V084nMt6or48nNkuTh4aH3339fpUuX1q5duxQZGWmxf9++fbN96nrfvn01cOBAXbhwQVu3bs32vcqVK6d58+ZZPKX7ds6cOSNJuu+++7JsK1WqlDp37pzleiRpzJgxuvfeey22jRo1Sr1791Zqamq2T3eVpH79+unJJ5+0WNe1a1f17NlTaWlpOV5bdvL7NbeV5ORkbd++Xb1799bly5fl6empxx9/XNLNJ8b+/fffeuihh/TYY49ZHBcUFKTRo0dLkt58803z+oyvSadOnbJ9GnmrVq3y/LTbuXPn6sKFCxo7dqyefvppubj886Pb8uXLa+nSpSpRooTmzZsnwzCyHH/HHXfolVdesTjudj766CMlJiaqZcuW+u9//yuTyWRxDf/9738lWV57ZtZ8niUpICBAGzduVIMGDfT3339r/fr1euGFF3TvvfeqXLly6tChg7744otsjw0KClKVKlWyrG/Xrp3Gjh2r1NRUff3119keW6ZMGX388ccqXbq0ed1dd92l++67T+np6bpy5YrmzJkjN7d//sDogw8+qCZNmigpKUl79+7N9rwtWrQwZ1GGe+65R2PGjJGkXD8p/dVXX1VKSopeeeUV9evXz2JbjRo1zE+sf/fdd83rMz6L9957b5Yn6Lu4uOiee+7J0xOp83N/PT09tWjRInl7e5vXlS1bVlOmTJEkbd682WL/9957T2lpaRo4cKAGDBhgsW3IkCFW/UWO8+fPS5IqVqyY7fYDBw5o+PDhWZaIiIhs9+/atav565jZypUrdfz4cfn7+2vu3LkW97527drm7Hvvvfd0/fr1PF9HTkwmkxYtWmTxJHAPDw/NnDlTUtZ7vHDhQl2+fFl33323Jk2aZLGtW7duWbK+oJQoUUIffvihxWejMOX1vt3O/fffr8qVK+vChQtau3atxbbY2Fj98MMPkmTxV0yqVaumbt26Zcno0qVLKywsTG5ublq5cmWe6rDG9OnTZRiGFixYkGX+0KRJE82ePVvSzc9uhoyc6dWrl8U4Id382maemwEAAAAAAKB4oykdAAAAAAAABe7EiRM6evSoJGnYsGFZtptMJnPzVk5Nz9k1ZderVy9X20+dOpXtOX19fbNtfPTz81OvXr0kSeHh4Vm2nzp1Sh999JGeffZZPf744+bGxl9//VWSdPjw4Wzfr3v37vLx8cl2W07atGkjSXr66af1/fff37LB8e+//9aOHTskScOHD892n1GjRknK+T736dMn2/V33nmnJOnkyZO5qtsWX/P8mDlzpkwmk0wmk8qUKaN77rlHUVFR8vPz09dff62AgABJ/3x9b3e/fvzxR6WlpUm62cjs6uqqTz75RPPnz9fp06fzXe+3334rSXrkkUey3V61alXVq1dP586d05EjR7Js79u3r1xdXfP0nhnXnt3XR/rn2o8cOZLt95A1n+cMd999t3799Vf98MMPmjx5srp06SIfHx+lp6crMjJSgwYNyvFrcuHCBS1dulSTJ0/WE088Yf7+27Ztm6Scv/9atmyZ7S/VZOREly5d5OHhkeP2nHJk6NCh2a7PuK8RERHmz05O0tPT9d1330nK+TPQqlUrlSlTRvv37zfnQOvWrSVJb7zxhpYuXaqLFy/e8n1yw9r726pVq2wb2nPKjozP36OPPprt+XL6XOZHXFyclixZkmX5888/s93/383yGTJqHzRoULa/lNGvXz+VLVtWly9f1r59+2xWf/Xq1S1++SrD7e7xkCFDsj1fQdzj7LRo0UK1a9culPfKTl7v2+24ubmZ790nn3xisW3RokVKT0/XPffco7p162Y5NjIyUrNmzdLYsWM1YsQIDR8+XGPGjFHJkiV17tw5Xbp0KU+15MX58+e1e/dulSpVKsexPjAw0Fxnhox5yJQpU7Rq1SpduXKlwGoEAAAAAACAY3O7/S4AAAAAAADAP0+ePXv2bJ6PzWj4Kl++fI5PSq1Tp47Fvv+W+WnmGcqUKXPL7RlPsc6pkbtmzZpZnvqZoVatWpJuNldnNnPmTL366qtKTU3N9jhJSkpKyvH98ur5559XRESENm/erF69eqlEiRJq1qyZOnfurEGDBpmbUqWbzaQZ15pR/79Zc58lmb9uuX3qry2+5vnRrFkz89PZS5QooXLlyqlly5bq06ePSpUqlaXO292v69ev68KFC/Lz81OdOnU0Z84cPf/88xo3bpzGjRunGjVqqF27durdu7cGDhyYp6dTS9Jff/0l6ebT12/n3LlzuuOOOyzWWfPZut21+/r6qly5crp48aJOnDghf3//fL9nZi4uLurSpYu6dOkiSUpLS9POnTv10ksvadOmTVqyZInuv/9+DRw40HzMRx99pEmTJik5OTnH8+b0/ZfTZzsjR3Lafrscyen+Zay/du2a+bOTkwsXLpjrzviFiVu5cOGCqlatqsDAQL3wwgt68803NWzYMJlMJtWrV08dOnTQgw8+qD59+uT56fm2vr85ZUdGtt7u/uVFhQoVJP3zlz3+rXfv3hZ/aaB79+63/EsaOX3Gb/e9YzKZVKtWLV26dMmm+Xa7e5ySkmKxviDusTXymxX5ldf7lhsjR47UrFmztHHjRp08eVJVq1aVYRhavHixpH9+qSfD2bNn1b9//xyfyp8hKSkp27/GYgsxMTEyDEPXrl277V+4yPw99Nhjj2nTpk36/PPP1b9/f7m6uqphw4bq2LGjBgwYoK5duxZIvQAAAAAAAHA8NKUDAAAAAAAgV1q2bKlPP/1UUVFRSktLy/NTmfPrdo2VeWm8zIvMDYyrVq3SjBkzVKZMGc2bN09du3aVv7+/SpUqJZPJpBdffFGhoaEWx2SWuRk6t0qXLq1NmzZpz5492rBhgyIjIxUZGam9e/dq9uzZGjNmjObPn2/19f1bQd3Hwta3b1/NmDGjwM4/fvx4Pfzww1q7dq0iIiIUERGh5cuXa/ny5Zo+fbp+/PHHbJ8anZP09HRJN5/M7Onpect9y5cvn2WdNZ+t/LL1e7q6uqpjx4767rvv1KZNG0VFRWnNmjXmpvR9+/bpySeflKurq2bNmqU+ffqoevXqKl26tEwmkz788EM9+eSTOX7/2StDJOVYU4aMr7+Uu6dXZ24off311/XUU0/pm2++UUREhHbs2KFFixZp0aJFat26tbZu3Xrbz5RU8Pe3MNx1113mcSo9PT3fNRX291Xmz0F2isI9tkZ+7uPt7kluFMR9u+OOO9SpUyf9+OOPWrp0qUJCQrR161YdO3ZMPj4+WZ6y//jjjysiIkLt2rXTzJkz1axZM5UtW1YlSpSQJPn7++v06dO3zYrcyu6+ZawrU6aM+vfvn+tzubi46LPPPtOLL76ob7/9Vjt27NCOHTsUFhamsLAw9enTR6tXry70eSEAAAAAAACKHprSAQAAAAAAkCu9e/dWcHCwEhIStHbtWj300EO5PrZq1aqS/nkacHZPzs54UnTGvoXh2LFjt91WrVo187oVK1ZIkl599VWNHj06yzFHjhyxaX2ZtW7d2vxU9L///ltr1qzR0KFD9f7772vAgAHq0qWLypcvL3d3d6WkpOivv/5S06ZNs5ynsO5zUf2a/1vVqlV19OhR/fXXX2rcuHGW7Rk1enh4qFy5chbbKlWqpCeeeEJPPPGEJOn333/XyJEjtXPnTk2ZMkVLlizJdR0BAQE6cuSIXnjhBbVq1SofV5R7VatW1e+//26+xn9LTEzUxYsXzfsWFldXV3Xt2lVRUVE6f/68ef3KlStlGIbGjx+vyZMnZzmuIL//biUmJibb9RkZ4uHhke0vEmRWoUIFlSpVSteuXdNbb71lfuJ3btWsWVPjx4/X+PHjJUl79uzRo48+qj179uiNN97QzJkzb3uOwr6/Gd97x44dU6NGjbJsv1U+56R379569tlndenSJa1fv169e/e2QaVZZXw/5PS9I/3zucj8vZPxFxQuX76c7THHjx+3VYnm9/79999zvJfW3GNbK+x7YkujRo3Sjz/+qEWLFikkJESffPKJJGnQoEEWjfjJyclav369XFxctH79evn6+lqcJzk5WfHx8Xl6b2vuW8ZfYTCZTPrkk0/y3KzfsGFDNWzYUM8//7wMw9APP/yg//znP/rmm2+0dOlSjRgxIk/nAwAAAAAAgPNxzMdqAAAAAAAAoNDVqVNHgwcPliQ9++yz5mbVnJw9e1aHDx+WdLOxu06dOpKkxYsXZ9nXMAzz+i5dutiu6NtISEjQN998k2X9uXPntGHDBklSYGCgeX3GNdeoUSPLMWfPntWmTZsKptB/cXNz04ABA9SzZ09JUnR0tHl9x44dJWV/nyWZm+YK+j4X1a/5v2V8fW93vzp16iQ3t1s/46NBgwZ64YUXJP3zNcmQ0UD4999/Z3tsUFCQpH9+8aEwZFx7Ts3zGdder149mzal5+ZJwLGxsZIsfynkVt9/169f11dffWWjCvPms88+y3b90qVLJUkdO3a87WfH1dVV9957ryTbfAZat26tMWPGSMr6WcxJYd/fe+65R5L0+eefZ7s94/7lRd26dfXII49IkoKDg5WYmGh9gbeQ8b3zxRdf6Pr161m2r169WpcuXZKXl5datmxpXp/xfXTo0KFsz/vtt9/atM6CuMe2lrnB/8aNG1m23+qe3C5XC9rAgQPl7e2tI0eOaN26dVq1apWkm83qmSUmJiotLU3e3t5ZGtKlmxmS1yek3+qzdPXqVW3dujXLen9/fzVt2lSXL182z3GsZTKZ1K1bN/3nP/+RlPucAQAAAAAAgHOjKR0AAAAAAAC59t5776lu3bqKiYlRx44dFRERkWWfGzdu6JNPPlGLFi0smqWee+45SdLLL7+sAwcOmNcbhqFXXnlF0dHR8vX1NT91urA8++yzOnHihPl1SkqKxo4dq+TkZLVp00YdOnQwb7vzzjslSR9++KFF81xiYqKGDRtWIA2Q77//vrm5P7P4+Hjt3btXkmUT6bPPPitJCgsL05YtWyyOWbx4sdauXasSJUpowoQJNq/134rq1zyzCRMmyM3NTWvWrMnSXLxx40Z98MEHkv65Fkn64YcftH79eqWmplrsbxiG1q1bJylrY29Gc/Wvv/6abR3PP/+8fH19NXv2bL399tvZNmfGxMTk2ABtjSeeeELe3t6KiorSa6+9ZtEUuX//fr3yyivm2mzpv//9r8aPH6+ff/45y7a///5bH3zwgb788ktJN584nCHj+2/JkiUWTwe+fv26xowZk+MTywvavn379MYbb1isi4iI0Pz58yVJkyZNytV5pk+frpIlS+r555/XkiVLlJ6enmWfgwcPmhtfpZvNz9u3b8+yb2pqqrnpNLsm8+wU9v0dP368XF1dtWLFCq1evdpi2/Lly7VmzRqrzjt//nzVrVtXR44cUfv27bVt27Zs9zt27JhF9ufFwIEDVb16dZ06dUrBwcEWTdExMTHmHB4/frw8PDzM27p27SoXFxd9//33FnUZhqF3333X5o3/o0aNUpkyZbRz5069++67FtvCw8O1YMECm76fNWrUqKF69eopISFBs2bNstgWHh6uadOm5Xjs7XK1oJUuXdr8y3ojR47UtWvX1KRJE/NfNclQqVIllS1bVgkJCfr0008ttu3atUshISF5fu/u3btLuvl5P3nypHl9cnKyRo8erbi4uGyPy8j1ESNGZPtLeYZh6KefftLGjRvN65YuXap9+/Zl2ffy5csKDw+XlPucAQAAAAAAgHO79eNZAAAAAAAAgEzKli2rHTt26JFHHlF4eLg6deqkWrVqqWnTpipdurTOnDmj3bt368qVK/L29pa/v7/52CeffFKRkZH69NNP1apVK91zzz3y8/NTVFSUDh8+rFKlSmnZsmWqWLFioV1Pu3btlJ6ervr166tr164qXbq0IiIidOrUKfn5+WV5iuzEiRO1dOlSrV+/XrVr19bdd9+t1NRUbdu2TaVLl9bIkSPNT5e2lQ8//FBjx45VrVq11LhxY3l7e+vcuXP68ccfde3aNXXt2lUPPPCAef+goCD973//0yuvvKJ7771XHTp0UPXq1fX7778rKipKrq6uWrBggRo1amTTOrNTFL/m/9akSRPNnz9fTz/9tB577DHNmTNHDRo00PHjxxUZGSnDMDRjxgz16NHDfMzPP/+sSZMmydvbW3fddZf8/f117do1RUVF6fjx4/Lx8dFLL71k8T79+/fX1q1b9eijj6pHjx4qW7aspJsN3/Xr11e1atX09ddfq3///nruuef0xhtvqHHjxqpSpYoSExN16NAhHT16VG3bttWjjz5qk2uvVKmSPv/8cw0cOFD//e9/9emnn6pFixY6e/astm3bpr///lsjRoyw+S8NXL16VfPmzdO8efNUtWpVNWvWTL6+vrpw4YIOHDig+Ph4SVJISIj56eHSzSbKd955R/v371etWrXUqVMnubq6mr8XJkyYoHfeecemtebGM888o5CQEC1dulRNmzbVqVOn9OOPPyo9PV0TJkzQfffdl6vz3HXXXfrss880fPhwDR8+XP/73//UsGFDVaxYURcvXtQvv/yiEydO6JFHHlG/fv0kSdu2bdM777yjChUqqEWLFvLz89Ply5e1a9cunT17VlWrVtXkyZNz9f6FfX+bN2+u0NBQTZ48Wf369VPbtm1Vp04dHTlyRHv27NGkSZM0Z86cPJ83Y5z6z3/+oy1btigwMFDVqlVT8+bN5evrq2vXrunIkSP65ZdfZBiGmjRpolatWuXpPdzd3fXll1+qV69eCgsL0/r163X33Xfr8uXL+uGHH3T9+nX17NlT06dPtzguICBA48eP1zvvvKNu3bqpU6dOKleunA4cOKDY2FhNmTJFr7/+ep6vOSf+/v766KOP9Oijj2rChAn6+OOP1bhxY508eVI//vijJk6caNU9trXXX39dAwYM0LRp07Rq1SrVq1dPf/31l6KiojR16tQseZrhdrlaGEaNGqUPPvhA586dM7/+N1dXV02bNk2TJk3S0KFDNX/+fNWuXVuxsbGKjIzUo48+qu3bt+v48eO5ft+HH35Yc+fO1d69e9WoUSN17NhR6enp2rt3r0qWLJnjfKRPnz5655139Oyzz+qBBx5Q3bp1Vb9+ffn4+OjcuXM6cOCAzp49qxdeeME87q1atUrDhg2Tv7+/mjdvrrJly+rSpUvasWOHEhMT1bhxY7v+chkAAAAAAACKDp6UDgAAAAAAgDzx8/PT1q1b9d1332no0KFydXXVli1b9OWXX+q3335Tu3btNHfuXMXExKhNmzbm40wmk5YuXaply5apY8eO2rdvn7788ktdvXpVw4cP1/79+xUUFFSo11KyZElt2bJFY8eO1a+//qo1a9YoLS1Nw4cP1969e7M0tdWqVUv79+/XkCFD5OrqqnXr1unAgQMaPHiw9u/fr4CAAJvX+Oqrr+rpp5+Wr6+vdu3apZUrV+q3335T27ZttWTJEm3YsEFubpbPnnj55Zf13XffKSgoSIcOHdKKFSt06tQpDRw4UJGRkRo5cqTN68xOUfyaZ2f06NGKjIzUgAEDdOrUKa1YsUK///677rvvPm3cuDFLY2mfPn00Y8YMtW7dWn/99ZdWrVql8PBw+fj4aMqUKTp48KCaN29ucczTTz+t0NBQ1ahRQ+vXr9fChQu1cOFCnT592rxP586d9euvv2rq1KmqVq2a9uzZo5UrVyo6OlqVKlXS9OnT9dFHH9n02nv37q2oqCgNGzZMV65c0Zdffql9+/apU6dOWr58uc1/yUKSpk6dquXLl+uJJ55QpUqVtH//fq1YsUIRERHy9fXVyJEjFRkZqddee83iOF9fX+3du1djxoyRr6+vvvvuO+3cuVM9evRQVFRUlnteWB566CFt2rRJlStX1vr167V7927dddddWrx4sebOnZuncw0cOFC//vqrJk2aJF9fX+3YsUNfffWVfvvtN9WtW1evv/66Xn31VfP+w4cP15QpU9SgQQP99ttvWrlypXbu3KmAgAC99tprOnDggPlp0rdjj/v7/PPP6+uvv1bHjh118OBB819y+PLLL/XMM89YfV4/Pz9t3rxZmzdv1siRI+Xp6ant27dr+fLl+uGHH+Tm5qbRo0dr06ZNio6OVuPGjfP8Hq1bt1Z0dLTGjh0rV1dXrV69Wj/++KNatGihsLAwrVu3TiVLlsxy3Jw5c/T222/rjjvuUGRkpMLDw9WwYUPt2rVLPXv2tPqaczJo0CCFh4erZ8+eOn78uL7++mtdvnxZCxYs0OzZs23+ftbo16+f1q1bpw4dOuiPP/7Q+vXrVaJECS1fvlwzZ87M8bjc5GpBa926tZo0aSLp5pwip18amjhxotasWaP27dvr8OHD+uabb5SSkqL58+dryZIleX7fEiVKaNOmTRo3bpy8vLy0ceNG/fzzz3rooYcUFRV1y/nIM888o/3792v06NEymUzasmWL1qxZo6NHj6pFixZ69913Lb7/nn32WU2cOFHVqlVTVFSUVq5cqaioKDVs2FDvvfeedu3aJS8vrzxfAwAAAAAAAJyPycj8N1kBAAAAAACAYiA8PFxdunTRPffco/DwcHuXA8ABBQYGatu2bdq6dasCAwPtXQ4AAAAAAAAAAHbFk9IBAAAAAAAAAAAAAAAAAAAAAFajKR0AAAAAAAAAAAAAAAAAAAAAYDWa0gEAAAAAAAAAAAAAAAAAAAAAVjMZhmHYuwgAAAAAAAAAAAAAAAAAAAAAgGPiSekAAAAAAAAAAAAAAAAAAAAAAKvRlA4AAAAAAAAAAAAAAAAAAAAAsBpN6QAAAAAAAAAAAAAAAAAAAAAAq9GUDgAAAAAAAAAAAAAAAAAAAACwGk3pAAAAAAAAAAAAAAAAAAAAAACr0ZQOAAAAAAAAAAAAAAAAAAAAALAaTekAAAAAAAAAAAAAAAAAAAAAAKvRlA4AAAAAAAAAAAAAAAAAAAAAsBpN6QAAAAAAAAAAAAAAAAAAAAAAq9GUDgAAAAAAAAAAAAAAAAAAAACwGk3pAAAAAAAAAAAAAAAAAAAAAACr0ZQOAAAAAAAAAAAAAAAAAAAAALAaTekAAAAAAAAAAAAAAAAAAAAAAKvRlA4AAAAAAAAAAAAAAAAAAAAAsBpN6QAAAAAAAAAAAAAAAAAAAAAAq9GUDgAAAAAAAAAAAAAAAAAAAACwGk3pAAAAAAAAAAAAAAAAAAAAAACr0ZQOAAAAAAAAAAAAAAAAAAAAALCam70LyI/09HSdOnVKXl5eMplM9i4HQDFgGIYuX74sf39/ubjwez0FiYwHUNjI+MJDxgMobGR84SDfAdgDGV84yHgAhY18LzxkPIDCRsYXDvIdgD2Q8YWDjAdQ2PKS7w7dlH7q1CkFBATYuwwAxVBcXJyqVatm7zKcGhkPwF7I+IJHxgOwFzK+YJHvAOyJjC9YZDwAeyHfCx4ZD8BeyPiCRb4DsCcyvmCR8QDsJTf57tBN6V5eXpJuXqi3t7edq4HDSE6W/P1v/vvUKcnT0771wKEkJSUpICDAnD8oOGR8HpBrgE2Q8YWHjIdVGO+QD2R84SDfgULG2CiJjC8sZDysQk4hH8j3wkPGW4F8A/KFjC8c5DusxjiHfCDjCwcZD6uQ78iHvOS7QzelZ/z5CW9vbwIWuefq+s+/vb0JWFiFP39T8Mj4PCDXAJsi4wseGQ+rMN7BBsj4gkW+A4WMsdECGV+wyHhYhZyCDZDvBY+MtwL5BtgEGV+wyHdYjXEONkDGFywyHlYh32EDucl3l0KoAwAAAAAAAAAAAAAAAAAAAADgpGhKBwAAAAAAAAAAAAAAAAAAAABYjaZ0AAAAAAAAAAAAAAAAAAAAAIDVaEoHAAAAAAAAAAAAAAAAAAAAAFiNpnQAAAAAAAAAAAAAAAAAAAAAgNVoSgcAAAAAAAAAAAAAAAAAAAAAWI2mdAAAAAAAAAAAAAAAAAAAAACA1WhKBwAAAAAAAAAAAAAAAAAAAABYjaZ0AAAAAAAAAAAAAAAAAAAAAIDVaEoHAAAAAAAAAAAAAAAAAAAAAFiNpnQAAAAAAAAAAAAAAAAAAAAAgNVoSgcAAACcUGhoqFq3bi0vLy/5+fmpb9++Onz4sMU+169f19ixY1W+fHmVKVNG/fv315kzZ255XsMwNG3aNFWpUkWlSpVS9+7ddeTIkYK8FAAAAKDYYB4PAAAAOBbm8ADgvMh4AMg7mtIBAAAAJ7Rt2zaNHTtWu3bt0qZNm5SamqoePXooOTnZvM+kSZP0zTffaOXKldq2bZtOnTqlfv363fK8b7zxht59910tWLBAP/30kzw9PdWzZ09dv369oC8JAAAAcHrM4wEAAADHwhweAJwXGQ8AeWcyDMOwdxHWSkpKko+PjxITE+Xt7W3vcuAokpOlMmVu/vvKFcnT0771wKGQO4WHe50H5BpgE86eO+fOnZOfn5+2bdumzp07KzExURUrVtSyZcs0YMAASdLvv/+uO++8Uzt37tTdd9+d5RyGYcjf31/PPvusnnvuOUlSYmKiKlWqpMWLF2vQoEG5qsXZ7zUKCOMd8oHcKRzcZ6CQMTZKcv7sKSrzeGe/zygg5BTygdwpPNxrK5BvQL44e+4wh4fDY5xDPjh79pDxcGjkO/IhL7nDk9IBAACAYiAxMVGSVK5cOUnSvn37lJqaqu7du5v3adCggapXr66dO3dme46YmBjFx8dbHOPj46O2bdvmeAwAAAAA6zGPBwAAABwLc3gAcF5kPADcnpu9CwBs4WTCNV1KvpGrfU1Xk9WogOsBAFvJTb5lzrVfTyXKt6KLqvqWKvjiADiM9PR0TZw4UR06dFDjxo0lSfHx8SpZsqR8fX0t9q1UqZLi4+OzPU/G+kqVKuX6GElKSUlRSkqK+XVSUpI1lwGY/XoqUUbpv3O9f1nPkoyNAACHwM+4kJk95/HM4WFrzOEBOCvyDUBmzOHhbBjngH+Q8XAm5DsKEk3pcHgnE66p+9vbdC01LVf7l7pxXYf+/79PJVyTP3+KAkARldt8y5xrA8J2Sp6e2vzsPUwIAZiNHTtWBw8eVEREhF3ePzQ0VDNnzrTLe8M5DQjbqWslPXK9f6kSroyNAIAij59x4d/sOY9nDg9bYw4PwFmRbwAyYw4PZ8M4B/yDjIczId9RkGhKh8O7lHxD11LTNPeR5qrrV+a2+5uuJktz/v+xV2/Iv4DrAwBr5TbfMufaGwOaavzaP3Qp+QaTQQCSpHHjxmndunXavn27qlWrZl5fuXJl3bhxQwkJCRa/vX/mzBlVrlw523NlrD9z5oyqVKlicUzz5s1zrCEkJETBwcHm10lJSQoICLDyioCb412tGn652vfPs1c08YtoxkYAQJHHz7iQmb3n8czhYWvM4QE4K/INQAbm8HBGjHPATWQ8nA35joJEUzqcRl2/Mmpc1ef2OybzsQfgWG6bb5lyrXZFnowH4CbDMDR+/HitXr1a4eHhqlWrlsX2li1bqkSJEtqyZYv69+8vSTp8+LBiY2PVrl27bM9Zq1YtVa5cWVu2bDH/UCQpKUk//fSTnn766RxrcXd3l7u7u20uDNDN8a5Rbub+AAA4IH7GVbwVlXk8c3jYGnN4AM6KfAPAHB7OjHEOxR0ZD2dFvqMgudjzzdPS0jR16lTVqlVLpUqVUp06dfTyyy/LMAx7lgUAAAA4vLFjx+qzzz7TsmXL5OXlpfj4eMXHx+vatWuSJB8fH40aNUrBwcHaunWr9u3bpxEjRqhdu3a6++67zedp0KCBVq9eLUkymUyaOHGiXnnlFa1du1a//PKLhg4dKn9/f/Xt29celwkAAAA4FebxAAAAgGNhDg8AzouMB4C8s+vjdGbNmqWwsDAtWbJEjRo10t69ezVixAj5+PjomWeesWdpAAAAgEMLCwuTJAUGBlqsX7RokYYPHy5JmjNnjlxcXNS/f3+lpKSoZ8+eev/99y32P3z4sBITE82vJ0+erOTkZI0ePVoJCQnq2LGjNmzYIA8PjwK9HgAAAKA4YB4PAAAAOBbm8ADgvMh4AMg7uzalR0ZG6sEHH9T9998vSapZs6b+7//+T7t377ZnWQAAAIDDy81fH/Lw8ND8+fM1f/78XJ/HZDLppZde0ksvvZTvGgEAAABYYh4PAAAAOBbm8ADgvMh4AMg7F3u+efv27bVlyxb98ccfkqQDBw4oIiJCQUFB2e6fkpKipKQkiwUAAAAAAAAAAAAAAAAAAAAAYD92fVL6lClTlJSUpAYNGsjV1VVpaWl69dVXNWTIkGz3Dw0N1cyZMwu5SgAAAAAAAAAAAAAAAAAAAABATuz6pPQVK1bo888/17JlyxQVFaUlS5borbfe0pIlS7LdPyQkRImJieYlLi6ukCsGAAAAAAAAAAAAAAAAAAAAAGRm1yelP//885oyZYoGDRokSWrSpImOHz+u0NBQDRs2LMv+7u7ucnd3L+wyAQAAAAAAAAAAAAAAAAAAAAA5sOuT0q9evSoXF8sSXF1dlZ6ebqeKAAAAAAAAAAAAAAAAAAAAAAB5Ydcnpffp00evvvqqqlevrkaNGmn//v2aPXu2Ro4cac+yAAAAAAAAAAAAAAAAAAAAAAC5ZNem9Pfee09Tp07VmDFjdPbsWfn7++vJJ5/UtGnT7FkWAAAAAAAAAAAAAAAAAAAAACCX7NqU7uXlpblz52ru3Ln2LAMAAAAAAAAAAAAAAAAAAAAAYCUXexcAAAAAAAAAAAAAAAAAAAAAAHBcNKUDAAAAAAAAAAAAAAAAAAAAAKxGUzoAAAAAAAAAAAAAAAAAAAAAwGo0pQMAAAAAAAAAAAAAAAAAAAAArEZTOgAAAAAAAAAAAAAAAAAAAADAam72LgCwp7/OJcsonZjr/ct6llRV31IFWBEAAAAAAAAAAAAAAAAAAADgWGhKR7E2+cufda2kR673L1XCVZufvYfGdCCPXn/9dYWEhGjChAmaO3euvcsBAAAAAAAAAAAAAAAAAAA2RFM6irUvn24no7Rnrvb98+wVTfwiWpeSb9CUDuTBnj179MEHH6hp06b2LgUAAAAAAAAAAAAAAAAAABQAmtJRrDXy95E8c9eUDiDvrly5oiFDhuijjz7SK6+8Yu9yAAAAAAAAAAAAAAAAAABAAXCxdwEAAOc1duxY3X///erevbu9SwEAAAAAAAAAAAAAAAAAAAWEJ6UDAArE8uXLFRUVpT179uRq/5SUFKWkpJhfJyUlFVRpAAAAAAAAAAAAAAAAAADAhnhSOgDA5uLi4jRhwgR9/vnn8vDwyNUxoaGh8vHxMS8BAQEFXCUAAAAAAAAAAAAAAAAAALAFmtIBADa3b98+nT17VnfddZfc3Nzk5uambdu26d1335Wbm5vS0tKyHBMSEqLExETzEhcXZ4fKAQAAAOe1fft29enTR/7+/jKZTFqzZo15W2pqql544QU1adJEnp6e8vf319ChQ3Xq1Cn7FQwAAAAAAAAAAADAYdCUDgCwuW7duumXX35RdHS0eWnVqpWGDBmi6Ohoubq6ZjnG3d1d3t7eFgsAAAAA20lOTlazZs00f/78LNuuXr2qqKgoTZ06VVFRUVq1apUOHz6sBx54wA6VAgAAAAAAAAAAAHA0bvYuAADgfLy8vNS4cWOLdZ6enipfvnyW9QAAAAAKR1BQkIKCgrLd5uPjo02bNlmsmzdvntq0aaPY2FhVr169MEoEAAAAAAAAAAAA4KB4UjoAAAAAAACySExMlMlkkq+vr71LAQAAAAAAAAAAAFDE8aR0AEChCA8Pt3cJAAAAAHLp+vXreuGFFzR48GB5e3tnu09KSopSUlLMr5OSkgqrPAAAAAAAAAAAAABFDE9KBwAAAAAAgFlqaqoefvhhGYahsLCwHPcLDQ2Vj4+PeQkICCjEKgEAAAAAAAAAAAAUJTSlAwAAAAAAQNI/DenHjx/Xpk2bcnxKuiSFhIQoMTHRvMTFxRVipQAAAIBzCQ0NVevWreXl5SU/Pz/17dtXhw8fvuUxixcvlslkslg8PDwKqWIAAAAAAABLbvYuAAAAAAAAAPaX0ZB+5MgRbd26VeXLl7/l/u7u7nJ3dy+k6gAAAADntm3bNo0dO1atW7fW33//rRdffFE9evTQb7/9Jk9PzxyP8/b2tmheN5lMhVEuAAAAAABAFjSlAwAAAAAAFANXrlzRn3/+aX4dExOj6OholStXTlWqVNGAAQMUFRWldevWKS0tTfHx8ZKkcuXKqWTJkvYqGwAAACgWNmzYYPF68eLF8vPz0759+9S5c+ccjzOZTKpcuXJBl+f0TiZc06XkG7na13Q1WY0KuB4AAAAAABwRTekAAAAAAADFwN69e9WlSxfz6+DgYEnSsGHDNGPGDK1du1aS1Lx5c4vjtm7dqsDAwMIqEwAAAICkxMRESTd/SfRWrly5oho1aig9PV133XWXXnvtNTVqRMt0XpxMuKbub2/TtdS0XO1f6sZ1Hfr//y5bml/gBQAAAAAgA03pAAAAAAAAxUBgYKAMw8hx+622AQAAACg86enpmjhxojp06KDGjRvnuF/9+vX1ySefqGnTpkpMTNRbb72l9u3b69dff1W1atWyPSYlJUUpKSnm10lJSTav39FcSr6ha6lpmvtIc9X1K3Pb/U1Xk6U5N//t71uqgKsDAAAAAMBx0JQOAAAAAAAAAAAAAEXE2LFjdfDgQUVERNxyv3bt2qldu3bm1+3bt9edd96pDz74QC+//HK2x4SGhmrmzJk2rddZ1PUro8ZVfW6/YzL/xQ4AAAAAQHZc7F0AAAAAAAAAAAAAAEAaN26c1q1bp61bt+b4tPOclChRQi1atNCff/6Z4z4hISFKTEw0L3FxcfktGQAAAAAAQBJPSgcAAAAAAAAAAAAAuzIMQ+PHj9fq1asVHh6uWrVq5fkcaWlp+uWXX3TffffluI+7u7vc3d3zUyoAAAAAAEC2aEoHAAAAAAAAAAAAADsaO3asli1bpq+//lpeXl6Kj4+XJPn4+KhUqVKSpKFDh6pq1aoKDQ2VJL300ku6++67VbduXSUkJOjNN9/U8ePH9fjjj9vtOgAAAAAAQPFFUzoAAAAAAAAAAAAA2FFYWJgkKTAw0GL9okWLNHz4cElSbGysXFxczNsuXbqkJ554QvHx8SpbtqxatmypyMhINWzYsLDKBgAAAAAAMHO5/S4AAAAAHNH27dvVp08f+fv7y2Qyac2aNRbbTSZTtsubb76Z4zlnzJiRZf8GDRoU8JUAAAAAxQNzeAAovgzDyHbJaEiXpPDwcC1evNj8es6cOTp+/LhSUlIUHx+vb7/9Vi1atCj84gGgmGMeDwDOiXwHgLyjKR0AAABwUsnJyWrWrJnmz5+f7fbTp09bLJ988olMJpP69+9/y/M2atTI4riIiIiCKB8AAAAodpjDAwAAAI6HeTwAOCfyHQDyzs3eBQAAAAAoGEFBQQoKCspxe+XKlS1ef/311+rSpYtq1659y/O6ubllORYAAABA/jGHBwAAABwP83gAcE7kOwDkHU9KBwAAAKAzZ87o22+/1ahRo26775EjR+Tv76/atWtryJAhio2NveX+KSkpSkpKslgAAAAA5A9zeAAAAMDxFNQ8njk8ANgXP6cBgJtoSgcAAACgJUuWyMvLS/369bvlfm3bttXixYu1YcMGhYWFKSYmRp06ddLly5dzPCY0NFQ+Pj7mJSAgwNblAwAAAMUOc3gAAADA8RTUPJ45PADYFz+nAYCbaEoHAAAAoE8++URDhgyRh4fHLfcLCgrSwIED1bRpU/Xs2VPr169XQkKCVqxYkeMxISEhSkxMNC9xcXG2Lh8AAAAodpjDAwAAAI6noObxzOEBwL74OQ0A3ORmzzevWbOmjh8/nmX9mDFjNH/+fDtUBAAAABQ/P/74ow4fPqwvvvgiz8f6+vrqjjvu0J9//pnjPu7u7nJ3d89PiQAAAAAyYQ4PAAAAOJ6CnMczhwcA++HnNADwD7s+KX3Pnj06ffq0edm0aZMkaeDAgfYsCwAAAChWFi5cqJYtW6pZs2Z5PvbKlSs6evSoqlSpUgCVAQAAAMgOc3gAAADA8TCPBwDnRL4DwD/s2pResWJFVa5c2bysW7dOderU0T333GPPsgAAAACncOXKFUVHRys6OlqSFBMTo+joaMXGxpr3SUpK0sqVK/X4449ne45u3bpp3rx55tfPPfectm3bpmPHjikyMlIPPfSQXF1dNXjw4AK9FgAAAKA4YA4PAAAAOB7m8QDgnMh3AMg7N3sXkOHGjRv67LPPFBwcLJPJZO9yAAAAAIe3d+9edenSxfw6ODhYkjRs2DAtXrxYkrR8+XIZhpHjDzqOHj2q8+fPm1+fOHFCgwcP1oULF1SxYkV17NhRu3btUsWKFQvuQgAAAIBigjk8AAAA4HiYxwOAcyLfASDvikxT+po1a5SQkKDhw4fnuE9KSopSUlLMr5OSkgqhMgAAAMAxBQYGyjCMW+4zevRojR49Osftx44ds3i9fPlyW5QGAAAAIBvM4QEAAADHwzweAJwT+Q4Aeedi7wIyLFy4UEFBQfL3989xn9DQUPn4+JiXgICAQqwQAAAAAAAAAAAAAAAAAAAAAPBvRaIp/fjx49q8ebMef/zxW+4XEhKixMRE8xIXF1dIFQIAAAAAAAAAAAAAAAAAAAAAsuNm7wIkadGiRfLz89P9999/y/3c3d3l7u5eSFUBAAAAAAAAAAAAAAAAAAAAAG7H7k9KT09P16JFizRs2DC5uRWJHnkAAAAAAAAAAAAAAAAAAAAAQC7ZvSl98+bNio2N1ciRI+1dCgAAAAAAAAAAAAAAAAAAAAAgj+z+aPIePXrIMAx7lwEAAAAAAAAAAAAAAAAAAAAAsILdn5QOAAAAAAAAAAAAAAAAAAAAAHBcNKUDAAAAAAAAAAAAAAAAAAAAAKxGUzoAAAAAAAAAAAAAAAAAAAAAwGo0pQMAAAAAAAAAAAAAAAAAAAAArEZTOgAAAAAAAAAAAAAAAAAAAADAajSlAwAAAAAAAAAAAAAAAAAAAACsRlM6AAAAAAAAAAAAAAAAAAAAAMBqNKUDAAAAAAAAAAAAAAAAAAAAAKxGUzoAAAAAAAAAAAAAAAAAAAAAwGo0pQMAAAAAAAAAAAAAAAAAAAAArEZTOgAAAAAAAAAAAAAAAAAAAADAajSlAwAAAAAAAAAAAAAAAAAAAACsRlM6AAAAAAAAAAAAAAAAAAAAAMBqNKUDAAAAAAAAAAAAAAAAAAAAAKxGUzoAAAAAAAAAAAAAAAAAAAAAwGo0pQMAAAAAAAAAAAAAAAAAAAAArEZTOgAAAAAAAAAAAAAAAAAAAADAajSlAwAAAAAAAAAAAAAAAAAAAACsRlM6AAAAAAAAAAAAAAAAAAAAAMBqNKUDAAAAAAAAAAAAAAAAAAAAAKxGUzoAAAAAAAAAAAAAAAAAAAAAwGo0pQMAAAAAAAAAAAAAAAAAAAAArEZTOgAAAAAAAAAAAAAAAAAAAADAajSlAwAAAAAAAAAAAAAAAAAAAACsRlM6AAAAAABAMbB9+3b16dNH/v7+MplMWrNmjcV2wzA0bdo0ValSRaVKlVL37t115MgR+xQLAAAAAAAAAAAAwKHQlA4AAAAAAFAMJCcnq1mzZpo/f36229944w29++67WrBggX766Sd5enqqZ8+eun79eiFXCgAAAAAAAAAAAMDRuNm7AAAAAAAAABS8oKAgBQUFZbvNMAzNnTtX//vf//Tggw9KkpYuXapKlSppzZo1GjRoUGGWCgAAAAAAAAAAAMDB8KR0AAAAAACAYi4mJkbx8fHq3r27eZ2Pj4/atm2rnTt32rEyAAAAAAAAAAAAAI6AJ6UDAAAAAAAUc/Hx8ZKkSpUqWayvVKmSedu/paSkKCUlxfw6KSmp4AoEAAAAAAAAAAAAUKTxpHQAAADASW3fvl19+vSRv7+/TCaT1qxZY7F9+PDhMplMFkuvXr1ue9758+erZs2a8vDwUNu2bbV79+4CugIAQFEWGhoqHx8f8xIQEGDvkgDA4TGHBwAAABwP83gAcE7kOwDkHU3pAAAAgJNKTk5Ws2bNNH/+/Bz36dWrl06fPm1e/u///u+W5/ziiy8UHBys6dOnKyoqSs2aNVPPnj119uxZW5cPAChElStXliSdOXPGYv2ZM2fM2/4tJCREiYmJ5iUuLq7A6wQAZ8ccHgAAAHA8zOMBwDmR7wCQd272LgAAAABAwQgKClJQUNAt93F3d8+x2TA7s2fP1hNPPKERI0ZIkhYsWKBvv/1Wn3zyiaZMmZKvegEA9lOrVi1VrlxZW7ZsUfPmzSVJSUlJ+umnn/T0009ne4y7u7vc3d0LsUoAcH7M4QEAAADHwzweAJwT+Q4AeceT0gEAAIBiLDw8XH5+fqpfv76efvppXbhwIcd9b9y4oX379ql79+7mdS4uLurevbt27txZGOUCAPLhypUrio6OVnR0tCQpJiZG0dHRio2Nlclk0sSJE/XKK69o7dq1+uWXXzR06FD5+/urb9++dq0bAGCJOTwAAADgeJjHA4BzIt8BwBJPSgcAAACKqV69eqlfv36qVauWjh49qhdffFFBQUHauXOnXF1ds+x//vx5paWlqVKlShbrK1WqpN9//z3H90lJSVFKSor5dVJSku0uAgCQa3v37lWXLl3Mr4ODgyVJw4YN0+LFizV58mQlJydr9OjRSkhIUMeOHbVhwwZ5eHjYq2QAwL8whwcAAAAcT2HM45nDA0Dh4+c0AJAVTekAAABAMTVo0CDzv5s0aaKmTZuqTp06Cg8PV7du3Wz2PqGhoZo5c6bNzgcAsE5gYKAMw8hxu8lk0ksvvaSXXnqpEKsCAOQFc3gAAADA8RTGPJ45PAAUPn5OAwBZudi7gJMnT+rRRx9V+fLlVapUKTVp0kR79+61d1kAAABAsVO7dm1VqFBBf/75Z7bbK1SoIFdXV505c8Zi/ZkzZ1S5cuUczxsSEqLExETzEhcXZ9O6AQAAgOKKOTwAAADgeApiHs8cHgDsj5/TAICdm9IvXbqkDh06qESJEvruu+/022+/6e2331bZsmXtWRYAAABQLJ04cUIXLlxQlSpVst1esmRJtWzZUlu2bDGvS09P15YtW9SuXbscz+vu7i5vb2+LBQAAAED+MYcHAAAAHE9BzOOZwwOA/fFzGgCQ3Oz55rNmzVJAQIAWLVpkXlerVi07VgQAAAA4jytXrlj8Jn5MTIyio6NVrlw5lStXTjNnzlT//v1VuXJlHT16VJMnT1bdunXVs2dP8zHdunXTQw89pHHjxkmSgoODNWzYMLVq1Upt2rTR3LlzlZycrBEjRhT69QEAAADOhjk8AAAA4HiYxwOAcyLfASDv7NqUvnbtWvXs2VMDBw7Utm3bVLVqVY0ZM0ZPPPGEPcsCAAAAnMLevXvVpUsX8+vg4GBJ0rBhwxQWFqaff/5ZS5YsUUJCgvz9/dWjRw+9/PLLcnd3Nx9z9OhRnT9/3vz6kUce0blz5zRt2jTFx8erefPm2rBhgypVqlR4FwYAAAA4KebwAAAAgONhHg8Azol8B4C8s2tT+l9//aWwsDAFBwfrxRdf1J49e/TMM8+oZMmSGjZsWJb9U1JSlJKSYn6dlJRUmOUCAAAADiUwMFCGYeS4/fvvv7/tOY4dO5Zl3bhx48y/zQ8AAADAdpjDAwAAAI6HeTwAOCfyHQDyzsWeb56enq677rpLr732mlq0aKHRo0friSee0IIFC7LdPzQ0VD4+PuYlICCgkCsGAAAAAAAAAAAAANsKDQ1V69at5eXlJT8/P/Xt21eHDx++7XErV65UgwYN5OHhoSZNmmj9+vWFUC0AAAAAAEBWdm1Kr1Kliho2bGix7s4771RsbGy2+4eEhCgxMdG8xMXFFUaZAAAAAAAAAAAAAFBgtm3bprFjx2rXrl3atGmTUlNT1aNHDyUnJ+d4TGRkpAYPHqxRo0Zp//796tu3r/r27auDBw8WYuUAAAAAAAA3udnzzTt06JDlN/z/+OMP1ahRI9v93d3d5e7uXhilAQAAAAAAAAAAAECh2LBhg8XrxYsXy8/PT/v27VPnzp2zPeadd95Rr1699Pzzz0uSXn75ZW3atEnz5s3L8S9TAwAAAAAAFBS7Pil90qRJ2rVrl1577TX9+eefWrZsmT788EONHTvWnmUBAAAAAAAAAAAAgN0kJiZKksqVK5fjPjt37lT37t0t1vXs2VM7d+4s0NoAAAAAAACyY9cnpbdu3VqrV69WSEiIXnrpJdWqVUtz587VkCFD7FkWAAAAAAAAAAAAANhFenq6Jk6cqA4dOqhx48Y57hcfH69KlSpZrKtUqZLi4+NzPCYlJUUpKSnm10lJSfkvGAAAAAAAQHZuSpek3r17q3fv3vYuAwAAAAAAAAAAAADsbuzYsTp48KAiIiJsfu7Q0FDNnDnT5ucFAAAAAABwsXcBAAAAAAAAAAAAAABp3LhxWrdunbZu3apq1ardct/KlSvrzJkzFuvOnDmjypUr53hMSEiIEhMTzUtcXJxN6gYAAAAAAKApHQAAAAAAAAAAAADsyDAMjRs3TqtXr9YPP/ygWrVq3faYdu3aacuWLRbrNm3apHbt2uV4jLu7u7y9vS0WAAAAAAAAW6ApHQBgc2FhYWratKn5B9rt2rXTd999Z++yAAAAAAAAAAAoksaOHavPPvtMy5Ytk5eXl+Lj4xUfH69r166Z9xk6dKhCQkLMrydMmKANGzbo7bff1u+//64ZM2Zo7969GjdunD0uAQAAAAAAFHM0pQMAbK5atWp6/fXXtW/fPu3du1ddu3bVgw8+qF9//dXepQEAAAAAAAAAUOSEhYUpMTFRgYGBqlKlinn54osvzPvExsbq9OnT5tft27fXsmXL9OGHH6pZs2b68ssvtWbNGjVu3NgelwAAAAAAAIo5N3sXAABwPn369LF4/eqrryosLEy7du1So0aN7FQVAAAAAAAAAABFk2EYt90nPDw8y7qBAwdq4MCBBVARAAAAAABA3tCUDgAoUGlpaVq5cqWSk5PVrl07e5cDAAAAAAAAAAAAAAAAAABsjKZ0AECB+OWXX9SuXTtdv35dZcqU0erVq9WwYcMc909JSVFKSor5dVJSUmGUCQAAAAAAAAAAAAAAAAAA8snF3gUAAJxT/fr1FR0drZ9++klPP/20hg0bpt9++y3H/UNDQ+Xj42NeAgICCrFaAAAAAAAAAAAAAAAAAABgLZrSAQAFomTJkqpbt65atmyp0NBQNWvWTO+8806O+4eEhCgxMdG8xMXFFWK1AAAAAAAAAAAAAAAAAADAWm72LgAAUDykp6crJSUlx+3u7u5yd3cvxIoAAAAAAAAAAAAAAAAAAIAt0JQOALC5kJAQBQUFqXr16rp8+bKWLVum8PBwff/99/YuDQAAAAAAAAAAAAAAAAAA2BhN6QAAmzt79qyGDh2q06dPy8fHR02bNtX333+ve++9196lAQAAAAAAAAAAAAAAAAAAG6MpHQBgcwsXLrR3CQAAAAAAAAAAAAAAAAAAoJC42LsAAAAAAAAAAAAAAAAAAAAAAIDjoikdAAAAAAAAAAAAAAAAAAAAAGA1mtIBAAAAAAAAAAAAAAAAAAAAAFajKR0AAAAAAAAAAAAAAAAAAAAAYDWa0gEAAAAAAAAAAAAAAAAAAAAAVqMpHQAAAAAAAAAAAAAAAAAAAABgNZrSAQAAAAAAAAAAAAAAAAAAAABWoykdAAAAAAAAAAAAAAAAAAAAAGA1mtIBAAAAAAAAAAAAAAAAAAAAAFajKR0AAAAAAAAAAAAAAAAAAAAAYDWa0gEAAAAAAAAAAAAAAAAAAAAAVqMpHQAAAAAAAAAAAAAAAAAAAABgNZrSAQAAAAAAAAAAAAAAAAAAAABWoykdAAAAAAAAAAAAAAAAAAAAAGA1mtIBAAAAAAAAAAAAAAAAAAAAAFajKR0AAAAAAAAAAAAAAAAAAAAAYDWa0gEAAAAAAAAAAAAAAAAAAAAAVqMpHQAAAHBS27dvV58+feTv7y+TyaQ1a9aYt6WmpuqFF15QkyZN5OnpKX9/fw0dOlSnTp265TlnzJghk8lksTRo0KCArwQAAAAoHpjDAwAAAI6HeTwAOCfyHQDyjqZ0AAAAwEklJyerWbNmmj9/fpZtV69eVVRUlKZOnaqoqCitWrVKhw8f1gMPPHDb8zZq1EinT582LxEREQVRPgAAAFDsMIcHAAAAHA/zeABwTuQ7AOSdm70LAAAAAFAwgoKCFBQUlO02Hx8fbdq0yWLdvHnz1KZNG8XGxqp69eo5ntfNzU2VK1e2aa0AAAAAmMMDAAAAjoh5PAA4J/IdAPKOJ6UDAAAAkCQlJibKZDLJ19f3lvsdOXJE/v7+ql27toYMGaLY2NjCKRAAAACABebwAAAAgONhHg8Azol8BwCelA4AAABA0vXr1/XCCy9o8ODB8vb2znG/tm3bavHixapfv75Onz6tmTNnqlOnTjp48KC8vLyyPSYlJUUpKSnm10lJSTavHwAAAChumMMDAAAAjqeg5vHM4QHAvvg5DQDcZNcnpc+YMUMmk8liadCggT1LAgAAAIqd1NRUPfzwwzIMQ2FhYbfcNygoSAMHDlTTpk3Vs2dPrV+/XgkJCVqxYkWOx4SGhsrHx8e8BAQE2PoSAAA2kJaWpqlTp6pWrVoqVaqU6tSpo5dfflmGYdi7NADAvzCHBwAAABxPQc7jmcMDgP3wcxoA+Iddm9IlqVGjRjp9+rR5iYiIsHdJAAAAQLGR8UOS48ePa9OmTbf8zf3s+Pr66o477tCff/6Z4z4hISFKTEw0L3FxcfktGwBQAGbNmqWwsDDNmzdPhw4d0qxZs/TGG2/ovffes3dpAIBMmMMDAAAAjqeg5/HM4QHAPvg5DQBYsqopPTk52WYFuLm5qXLlyualQoUKNjs3ACDvbJnxAADrFFYWZ/yQ5MiRI9q8ebPKly+f53NcuXJFR48eVZUqVXLcx93dXd7e3hYLACD3CmtciIyM1IMPPqj7779fNWvW1IABA9SjRw/t3r27UN4fABwZc3gAKN74uToAOCZnmsczhweAfzhTvktkPADHYlVTeqVKlTRy5EibPNX8yJEj8vf3V+3atTVkyBDFxsbmuG9KSoqSkpIsFgCAbdky4wEA1rFVFl+5ckXR0dGKjo6WJMXExCg6OlqxsbFKTU3VgAEDtHfvXn3++edKS0tTfHy84uPjdePGDfM5unXrpnnz5plfP/fcc9q2bZuOHTumyMhIPfTQQ3J1ddXgwYPzVSsAIGeFNUdv3769tmzZoj/++EOSdODAAUVERCgoKKhA3xcAnAFzeAAo3vi5OgA4JubxAOCcyHcAsB+rmtI/++wzXbx4UV27dtUdd9yh119/XadOncrzedq2bavFixdrw4YNCgsLU0xMjDp16qTLly9nu39oaKh8fHzMS0BAgDXlAwBuwVYZDwCwnq2yeO/evWrRooVatGghSQoODlaLFi00bdo0nTx5UmvXrtWJEyfUvHlzValSxbxERkaaz3H06FGdP3/e/PrEiRMaPHiw6tevr4cffljly5fXrl27VLFixfxfOAAgW4U1R58yZYoGDRqkBg0aqESJEmrRooUmTpyoIUOGZLs/Dw8AgH8whweA4o2fqwOAY2IeDwDOiXwHAPtxs+agvn37qm/fvjp37pw+/fRTLV68WFOnTlXPnj01cuRIPfDAA3Jzu/2pMz9pq2nTpmrbtq1q1KihFStWaNSoUVn2DwkJUXBwsPl1UlISjekAYGO2yngAgPVslcWBgYEyDCPH7bfaluHYsWMWr5cvX37bYwAAtlVYc/QVK1bo888/17Jly9SoUSNFR0dr4sSJ8vf317Bhw7LsHxoaqpkzZ+b7fQHAGTCHB4DijZ+rA4BjYh4PAM6JfAcA+7HqSekZKlasqODgYP3888+aPXu2Nm/erAEDBsjf31/Tpk3T1atX83Q+X19f3XHHHfrzzz+z3e7u7i5vb2+LBQBQMGyd8QCAvCOLAQCZFfS48Pzzz5uflt6kSRM99thjmjRpkkJDQ7PdPyQkRImJieYlLi4uX+8PAM6AOTwAFG+MAwDgmMhvAHBO5DsAFL58/Ur+mTNntGTJEi1evFjHjx/XgAEDNGrUKJ04cUKzZs3Srl27tHHjxlyf78qVKzp69Kgee+yx/JQFALABW2c8ACDvyGIAQGYFPS5cvXpVLi6Wzy9wdXVVenp6tvu7u7vL3d3d6vcDAGfEHB4AijfGAQBwTOQ3ADgn8h0ACp9VTemrVq3SokWL9P3336thw4YaM2aMHn30Ufn6+pr3ad++ve68885bnue5555Tnz59VKNGDZ06dUrTp0+Xq6urBg8ebE1ZAAAbsFXGAwCsRxYDADIrrHGhT58+evXVV1W9enU1atRI+/fv1+zZszVy5Mh8XgEAOD/m8ABQvDEOAIBjIr8BwDmR7wBgP1Y1pY8YMUKDBg3Sjh071Lp162z38ff313//+99bnufEiRMaPHiwLly4oIoVK6pjx47atWuXKlasaE1ZAAAbsFXGAwCsRxYDADIrrHHhvffe09SpUzVmzBidPXtW/v7+evLJJzVt2rR8nRcAigPm8ABQvDEOAIBjIr8BwDmR7wBgP1Y1pZ8+fVqlS5e+5T6lSpXS9OnTb7nP8uXLrXl7AEABslXGAwCsRxYDADIrrHHBy8tLc+fO1dy5c/N1HgAojpjDA0DxxjgAAI6J/AYA50S+A4D9uFhzkJeXl86ePZtl/YULF+Tq6prvogAA9kPGA4D9kcUAgMwYFwCg6COrAaB4YxwAAMdEfgOAcyLfAcB+rGpKNwwj2/UpKSkqWbJkvgoCANgXGQ8A9kcWAwAyY1wAgKKPrAaA4o1xAAAcE/kNAM6JfAcA+3HLy87vvvuuJMlkMunjjz9WmTJlzNvS0tK0fft2NWjQwLYVAgAKBRkPAPZHFgMAMmNcAICij6wGgOKNcQAAHBP5DQDOiXwHAPvLU1P6nDlzJN38baIFCxZY/DmLkiVLqmbNmlqwYIFtKwQAFAoyHgDsjywGAGTGuAAARR9ZDQDFG+MAADgm8hsAnBP5DgD2l6em9JiYGElSly5dtGrVKpUtW7ZAigIAFD4yHgDsjywGAGTGuAAARR9ZDQDFG+MAADgm8hsAnBP5DgD2l6em9Axbt261dR0AgCKCjAcA+yOLAQCZMS4AQNFHVgNA8cY4AACOifwGAOdEvgOA/eS6KT04OFgvv/yyPD09FRwcfMt9Z8+ene/CAACFh4wHAPsjiwEAmTEuAEDRR1YDQPHGOAAAjon8BgDnRL4DQNGQ66b0/fv3KzU11fzvnJhMpvxXBQAoVGQ8ANgfWQwAyIxxAQCKPrIaAIo3xgEAcEzkNwA4J/IdAIqGXDelZ/6zFvyJCwBwLmQ8ANgfWQwAyIxxAQCKPrIaAIo3xgEAcEzkNwA4J/IdAIoGF3sXAAAAAAAAAAAAAAAAAAAAAABwXLl+Unq/fv1yfdJVq1ZZVQwAwD7IeACwP7IYAJAZ4wIAFH1kNQAUb4wDAOCYyG8AcE7kOwAUDbluSvfx8SnIOgAAdkTGA4D9kcUAgMwYFwCg6COrAaB4YxwAAMdEfgOAcyLfAaBoyHVT+qJFiwqyDgCAHZHxAGB/ZDEAIDPGBQAo+shqACjeGAcAwDGR3wDgnMh3ACgaXOxdAAAAAAAAAAAAAAAAAAAAAADAceX6Sel33XWXtmzZorJly6pFixYymUw57hsVFWWT4gAAhYOMBwD7I4sBAJkxLgBA0UdWA0DxxjgAAI6J/AYA50S+A0DRkOum9AcffFDu7u6SpL59+xZUPQAAOyDjAcD+yGIAQGaMCwBQ9JHVAFC8FcQ4sH37dr355pvat2+fTp8+rdWrV9/y3OHh4erSpUuW9adPn1blypVtUhMAOBvm8QDgnMh3ACgact2UPn369Gz/DQBwfGQ8ANgfWQwAyIxxAQCKPrIaAIq3ghgHkpOT1axZM40cOVL9+vXL9XGHDx+Wt7e3+bWfn59N6gEAZ8Q8HgCcE/kOAEVDrpvSs7N3714dOnRIktSwYUO1bNnSJkUBAOyPjAcA+yOLAQCZMS4AQNFHVgNA8ZbfcSAoKEhBQUF5fl8/Pz/5+vrm+TgAwE3M4wHAOZHvAFD4rGpKP3HihAYPHqwdO3aYf8CRkJCg9u3ba/ny5apWrZotawQAFCIyHgDsjywGAGTGuAAARR9ZDQDFm73HgebNmyslJUWNGzfWjBkz1KFDhwJ9PwBwFvbObwBAwSDfAcB+XKw56PHHH1dqaqoOHTqkixcv6uLFizp06JDS09P1+OOP27pGAEAhIuMBwP7IYgBAZowLAFD0kdUAULzZaxyoUqWKFixYoK+++kpfffWVAgICFBgYqKioqByPSUlJUVJSksUCAMUV83gAcE7kOwDYj1VPSt+2bZsiIyNVv35987r69evrvffeU6dOnWxWHACg8JHxAGB/ZDEAIDPGBQAo+shqACje7DUO1K9f3+I927dvr6NHj2rOnDn69NNPsz0mNDRUM2fOLLCaAMCRMI8HAOdEvgOA/Vj1pPSAgAClpqZmWZ+WliZ/f/98FwUAsB8yHgDsjywGAGTGuAAARR9ZDQDFW1EaB9q0aaM///wzx+0hISFKTEw0L3FxcYVYHQAULUUpvwEAtkO+A4D9WNWU/uabb2r8+PHau3eved3evXs1YcIEvfXWWzYrDgBQ+Mh4ALA/shgAkBnjAgAUfWQ1ABRvRWkciI6OVpUqVXLc7u7uLm9vb4sFAIqropTfAADbId8BwH7ccrtj2bJlZTKZzK+Tk5PVtm1bubndPMXff/8tNzc3jRw5Un379rV5oQCAgkPGA4D9kcUAgMwYFwCg6COrAaB4K4hx4MqVKxZPOY+JiVF0dLTKlSun6tWrKyQkRCdPntTSpUslSXPnzlWtWrXUqFEjXb9+XR9//LF++OEHbdy40XYXCgBOhnk8ADgn8h0AioZcN6XPnTu3AMsAANgTGQ8A9kcWAwAyY1wAgKKPrAaA4q0gxoG9e/eqS5cu5tfBwcGSpGHDhmnx4sU6ffq0YmNjzdtv3LihZ599VidPnlTp0qXVtGlTbd682eIcAABLzOMBwDmR7wBQNOS6KX3YsGEFWQcAwI7IeACwP7IYAJAZ4wIAFH1kNQAUbwUxDgQGBsowjBy3L1682OL15MmTNXnyZJvXAQDOjHk8ADgn8h0AioZcN6Xn5Pr167px44bFOm9v7/yeFgBQBJDxAGB/ZDEAIDPGBQAo+shqACjeGAcAwDGR3wDgnMh3AChcLtYclJycrHHjxsnPz0+enp4qW7asxQIAcFxkPADYH1kMAMiMcQEAij6yGgCKN8YBAHBM5DcAOCfyHQDsx6qm9MmTJ+uHH35QWFiY3N3d9fHHH2vmzJny9/fX0qVLbV0jAKAQkfEAYH9kMQAgM8YFACj6yGoAKN4YBwDAMZHfAOCcyHcAsB83aw765ptvtHTpUgUGBmrEiBHq1KmT6tatqxo1aujzzz/XkCFDbF0nAKCQkPEAYH9kMQAgM8YFACj6yGoAKN4YBwDAMZHfAOCcyHcAsB+rnpR+8eJF1a5dW5Lk7e2tixcvSpI6duyo7du32646AEChI+MBwP7IYgBAZowLAFD0kdUAULwxDgCAYyK/AcA5ke8AYD9WNaXXrl1bMTExkqQGDRpoxYoVkm7+lpGvr6/NigMAFD4yHgDsz1ZZvH37dvXp00f+/v4ymUxas2aNxXbDMDRt2jRVqVJFpUqVUvfu3XXkyJHbnnf+/PmqWbOmPDw81LZtW+3evTvXNQEA8o45OgAUfczhAaB4Y84OAI6JeTwAOCfyHQDsx6qm9BEjRujAgQOSpClTpmj+/Pny8PDQpEmT9Pzzz9u0QABA4SLjAcD+bJXFycnJatasmebPn5/t9jfeeEPvvvuuFixYoJ9++kmenp7q2bOnrl+/nuM5v/jiCwUHB2v69OmKiopSs2bN1LNnT509ezZvFwkAyDXm6ABQ9DGHB4DijTk7ADgm5vEA4JzIdwCwHzdrDpo0aZL53927d9ehQ4cUFRWlunXrqmnTpjYrDgBQ+Mh4ALA/W2VxUFCQgoKCst1mGIbmzp2r//3vf3rwwQclSUuXLlWlSpW0Zs0aDRo0KNvjZs+erSeeeEIjRoyQJC1YsEDffvutPvnkE02ZMiXXtQEAco85OgAUfczhAaB4Y84OAI6JeTwAOCfyHQDsx6qm9H+rWbOmatasma9zvP766woJCdGECRM0d+5cW5QFALABW2Q8ACB/CiKLY2JiFB8fr+7du5vX+fj4qG3bttq5c2e2Pyi5ceOG9u3bp5CQEPM6FxcXde/eXTt37szxvVJSUpSSkmJ+nZSUZKOrAIDiiTk6ABR9zOEBoHhjzg4AjsmR5/HM4QEgZ46c7xIZD8CxuFh74JYtW9S7d2/VqVNHderUUe/evbV582arzrVnzx598MEHPCkAAIoIW2Y8AMA6BZ3F8fHxkqRKlSpZrK9UqZJ527+dP39eaWlpeTpGkkJDQ+Xj42NeAgIC8lk9ABQ/zNEBoOhjDg8AxRtzdgBwTM4yj2cODwCWnCXfJTIegGOxqin9/fffV69eveTl5aUJEyZowoQJ8vb21n333af58+fn6VxXrlzRkCFD9NFHH6ls2bLWlAMAsCFbZjwAwDrOlsUhISFKTEw0L3FxcfYuCQAcirONCwDgjJwtq5nDA0DeONs4AADFhTPlN3N4APiHM+W7RMYDcCxu1hz02muvac6cORo3bpx53TPPPKMOHTrotdde09ixY3N9rrFjx+r+++9X9+7d9corr1hTDgDAhmyZ8QAA6xRGFleuXFmSdObMGVWpUsW8/syZM2revHm2x1SoUEGurq46c+aMxfozZ86Yz5cdd3d3ubu757tmACiumKMDQNHHHB4Aijfm7ADgmJxpHs8cHgD+4Uz5LpHxAByLVU9KT0hIUK9evbKs79GjhxITE3N9nuXLlysqKkqhoaG52j8lJUVJSUkWCwDAtmyV8QAA6xVGFteqVUuVK1fWli1bzOuSkpL0008/qV27dtkeU7JkSbVs2dLimPT0dG3ZsiXHYwAA+cccHQCKPubwAFC8MWcHAMfEPB4AnBP5DgD2Y1VT+gMPPKDVq1dnWf/111+rd+/euTpHXFycJkyYoM8//1weHh65OiY0NFQ+Pj7mJSAgIE91AwBuzxYZDwDIH1tl8ZUrVxQdHa3o6GhJUkxMjKKjoxUbGyuTyaSJEyfqlVde0dq1a/XLL79o6NCh8vf3V9++fc3n6Natm+bNm2d+HRwcrI8++khLlizRoUOH9PTTTys5OVkjRoyw+noBALfGHB0Aij7m8ABQvDFnBwDHxDweAJwT+Q4A9uOW2x3fffdd878bNmyoV199VeHh4ebf0tm1a5d27NihZ599Nlfn27dvn86ePau77rrLvC4tLU3bt2/XvHnzlJKSIldXV4tjQkJCFBwcbH6dlJREYzoA2ICtMx4AkHcFkcV79+5Vly5dzK8z5tLDhg3T4sWLNXnyZCUnJ2v06NFKSEhQx44dtWHDBotfGj169KjOnz9vfv3II4/o3LlzmjZtmuLj49W8eXNt2LBBlSpVsvraAQBZMUcHgKKPOTwAFG/M2QHAMTGPBwDnRL4DQNFgMgzDyM2OtWrVyt0JTSb99ddft93v8uXLOn78uMW6ESNGqEGDBnrhhRfUuHHj254jKSlJPj4+SkxMlLe3d67qg/M5eDJRvd+L0LrxHdW4qs/tD0hOlsqUufnvK1ckT8+CeR84JWfNHVtnvC04673Oi1znTqZc+/WPk7p/4X6yCrCCvXOnKGZxQbH3vYaD+td416ief64OYx4PyTFzxxHHBUe8z0BRkp+fceVlbHQ29sweR8xqa5HxsApzeOSDI+SOs4wDjnCvCxr/1wgULnvnjrPk9+3Y+z7DgTGPRz7wc5rCQcbDKuQ78iEvuZPrJ6XHxMTku7DMvLy8sjSee3p6qnz58rlqSAcA2I6tMx4AkHdkMQAgM8YFACj6yGoAKN4YBwDAMZHfAOCcyHcAKBpc8nsCwzCUy4etAwAcjLUZHxoaqtatW8vLy0t+fn7q27evDh8+XAAVAoDzY74NAMiMcQEAij6yGgCKN8YBAHBM5DcAOCfyHQAKl9VN6UuXLlWTJk1UqlQplSpVSk2bNtWnn36ar2LCw8M1d+7cfJ0DAJB/+c34bdu2aezYsdq1a5c2bdqk1NRU9ejRQ8nJyQVYNQA4l4KYbwMAHBfjAgAUfWQ1ABRvjAMA4JjIbwBwTuQ7ANiHmzUHzZ49W1OnTtW4cePUoUMHSVJERISeeuopnT9/XpMmTbJpkQCAwmOLjN+wYYPF68WLF8vPz0/79u1T586dC6RuAHAmzLcBAJkxLgBA0UdWA0DxxjgAAI6J/AYA50S+A4D9WNWU/t577yksLExDhw41r3vggQfUqFEjzZgxg+AGAAdWEBmfmJgoSSpXrpzN6gQAZ8Z8GwCQWWGOCydPntQLL7yg7777TlevXlXdunW1aNEitWrVymbvAQDOiDk8ABRvjAMA4JjIbwBwTuQ7ANiPVU3pp0+fVvv27bOsb9++vU6fPp3vogAA9mPrjE9PT9fEiRPVoUMHNW7cOMf9UlJSlJKSYn6dlJSU5/cCAGfBfBsAkFlhjQuXLl1Shw4d1KVLF3333XeqWLGijhw5orJly9rsPQDAWTGHB4DijXEAABwT+Q0Azol8BwD7cbHmoLp162rFihVZ1n/xxReqV69evosCANiPrTN+7NixOnjwoJYvX37L/UJDQ+Xj42NeAgIC8vxeAOAsmG8DADIrrHFh1qxZCggI0KJFi9SmTRvVqlVLPXr0UJ06dWz2HgDgrJjDA0DxxjgAAI6J/AYA50S+A4D9WPWk9JkzZ+qRRx7R9u3b1aFDB0nSjh07tGXLlmwDHQDgOGyZ8ePGjdO6deu0fft2VatW7Zb7hoSEKDg42Pw6KSmJxnQAxRbzbQBAZoU1Lqxdu1Y9e/bUwIEDtW3bNlWtWlVjxozRE088YbP3AABnxRweAIo3xgEAcEzkNwA4J/IdAOzHqiel9+/fX7t371aFChW0Zs0arVmzRhUqVNDu3bv10EMP2bpGAEAhskXGG4ahcePGafXq1frhhx9Uq1at2x7j7u4ub29viwUAiivm2wCAzAprXPjrr78UFhamevXq6fvvv9fTTz+tZ555RkuWLMl2/5SUFCUlJVksAFBcMYcHgOKNcQAAHBP5DQDOiXwHAPvJ85PSU1NT9eSTT2rq1Kn67LPPCqImAICd2Crjx44dq2XLlunrr7+Wl5eX4uPjJUk+Pj4qVaqUrcoFAKfEfBsAkFlhjgvp6elq1aqVXnvtNUlSixYtdPDgQS1YsEDDhg3Lsn9oaKhmzpxZoDUBgCNgDg8AxRvjAAA4JvIbAJwT+Q4A9pXnJ6WXKFFCX331VUHUAgCwM1tlfFhYmBITExUYGKgqVaqYly+++MIGVQKAc2O+DQDIrDDHhSpVqqhhw4YW6+68807FxsZmu39ISIgSExPNS1xcXGGUCQBFDnN4ACjeGAcAwDGR3wDgnMh3ALCvPDelS1Lfvn21Zs0aG5cCACgKbJHxhmFkuwwfPtwmNQKAs2O+DQDIrLDGhQ4dOujw4cMW6/744w/VqFEj2/3d3d3l7e1tsQBAccUcHgCKN8YBAHBM5DcAOCfyHQDsx82ag+rVq6eXXnpJO3bsUMuWLeXp6Wmx/ZlnnrFJcQCAwkfGA4D9kcUAgMwKa1yYNGmS2rdvr9dee00PP/ywdu/erQ8//FAffvihTc4PAM6MOTwAFG+MAwDgmMhvAHBO5DsA2I9VTekLFy6Ur6+v9u3bp3379llsM5lMBDcAODAyHgDsjywGAGRWWONC69attXr1aoWEhOill15SrVq1NHfuXA0ZMsQm5wcAZ8YcHgCKN8YBAHBM5DcAOCfyHQDsx6qm9JiYGPO/DcOQdDOwAQCOj4wHAPsjiwEAmRXmuNC7d2/17t27QM4NAM6MOTwAFG+MAwDgmMhvAHBO5DsA2I+LtQcuXLhQjRs3loeHhzw8PNS4cWN9/PHHtqwNAGAnZDwA2B9ZDADIjHEBAIo+shoAijfGAQBwTOQ3ADgn8h0A7MOqJ6VPmzZNs2fP1vjx49WuXTtJ0s6dOzVp0iTFxsbqpZdesmmRAIDCQ8YDgP2RxQCAzBgXAKDoI6sBoHhjHAAAx0R+A4BzIt8BwH6sakoPCwvTRx99pMGDB5vXPfDAA2ratKnGjx9PcAOAAyPjAcD+yGIAQGaMCwBQ9JHVAFC8MQ4AgGMivwHAOZHvAGA/LtYclJqaqlatWmVZ37JlS/3999/5LgoAYD9kPADYH1kMAMiMcQEAij6yGgCKN8YBAHBM5DcAOCfyHQDsx6qm9Mcee0xhYWFZ1n/44YcaMmRIvosCANgPGQ8A9kcWAwAyY1wAgKKPrAaA4o1xAAAcE/kNAM6JfAcA+3Gz9sCFCxdq48aNuvvuuyVJP/30k2JjYzV06FAFBweb95s9e3b+qwQAFCoyHgDsjywGAGTGuAAARR9ZDQDFG+MAADgm8hsAnBP5DgD28f/au/PwqOqrceAnLAmorMomoqAg7oCoiGhBRalSKu3vtdZawf1VwVekWsGNWltDq9at1rWKbVVc6lbXKhZ5VVRAYsGFCqKgJeDGkqgBk/v7oy/RyJYMycxk8vk8zzwPM7l35pxJODn3Oyf3pjSUPnfu3Nh7770jImLBggUREbHNNtvENttsE3Pnzq3cLi8vrxZCBCCd1HiAzFOLAfgmvxcAsp9aDdCw+T0AUD+p3wC5SX0HyJyUhtL/8Y9/1HYcAGQJNR4g89RiAL7J7wWA7KdWAzRsfg8A1E/qN0BuUt8BMqdRpgMAAAAAAAAAAAAAAKD+MpQOAAAAAAAAAAAAAEDKDKUDAAAAAAAAAAAAAJAyQ+kAAAAAAAAAAAAAAKTMUDoAAAAAAAAAAAAAACkzlA4AAAAAAAAAAAAAQMoMpQMAAAAAAAAAAAAAkDJD6QAAAAAAAAAAAAAApMxQOgAAAAAAAAAAAAAAKTOUDgAAAAAAAAAAAABAygylAwAAAAAAAAAAAACQMkPpAAAAAAAAAAAAAACkzFA6AAA0UF27do28vLx1bqNGjVrv9pMmTVpn22bNmqU5agAAaNj08QAAUL/o4QFylxoPUFWTTAcAAABkxowZM6K8vLzy/ty5c+Owww6Lo48+eoP7tGzZMubNm1d5Py8vr05jBAAAqtLHAwBA/aKHB8hdajxAVYbSAQCggWrXrl2V+xMnToyddtopBg4cuMF98vLyomPHjnUdGgAAsAH6eAAAqF/08AC5S40HqKpRJl/8xhtvjL322itatmwZLVu2jP79+8eTTz6ZyZAAAKBBWr16dfzlL3+Jk046aaN/jV9SUhI77LBDdOnSJY466qh444030hglAADwTfp4AACoX/TwALlLjQfI8FD6dtttFxMnToxZs2bFzJkz45BDDlFoAQAgAx5++OFYvnx5nHDCCRvcpmfPnnH77bfHI488En/5y1+ioqIiDjjggPjggw82+txlZWWxcuXKKjcAAGDz1VUfr4cHAIC6oYcHyF1qPECGh9KHDRsWRx55ZPTo0SN23nnn+PWvfx1bbbVVvPzyy5kMCwAAGpw//vGPccQRR8S22267wW369+8fI0aMiN69e8fAgQPjwQcfjHbt2sXNN9+80ecuLCyMVq1aVd66dOlS2+EDAECDVFd9vB4eAADqhh4eIHep8QAZHkr/pvLy8pg8eXKUlpZG//79Mx0OAAA0GO+//348++yzccopp9Rov6ZNm0afPn1i/vz5G91u/PjxsWLFisrb4sWLNydcAAAg6raP18MDZMa0adNi2LBhse2220ZeXl48/PDDm9xn6tSpsffee0dBQUF07949Jk2aVOdxApAaPTxA7lLjAf4j40Ppc+bMia222ioKCgri9NNPj4ceeih222239W7rUhQAAFD77rjjjmjfvn0MHTq0RvuVl5fHnDlzolOnThvdrqCgIFq2bFnlBgAAbJ667OP18ACZUVpaGr169YobbrihWtsvXLgwhg4dGgcffHAUFRXFmDFj4pRTTomnn366jiMFIBV6eIDcpcYD/EeTTAfQs2fPKCoqihUrVsQDDzwQI0eOjOeff369g+mFhYVx6aWXZiBKAADITRUVFXHHHXfEyJEjo0mTqocHI0aMiM6dO0dhYWFERPzyl7+M/fffP7p37x7Lly+PK664It5///0a/8U/AACwefTxALnpiCOOiCOOOKLa2990003RrVu3uOqqqyIiYtddd40XXnghrr766hgyZEhdhQlACvTwALlLjQf4WsaH0vPz86N79+4REdG3b9+YMWNGXHvttXHzzTevs+348eNj7NixlfdXrlwZXbp0SVusAACQa5599tlYtGhRnHTSSet8bdGiRdGo0dcXV/rss8/i1FNPjeLi4mjTpk307ds3XnrppQ1e6QgAAKgb+ngAIiKmT58egwcPrvLYkCFDYsyYMRvcp6ysLMrKyirvuzI1QHro4QFylxoP8LWMD6V/W0VFRZWFkG8qKCiIgoKCNEcEAAC56/DDD48kSdb7talTp1a5f/XVV8fVV1+dhqgAAICN0ccDEBFRXFwcHTp0qPJYhw4dYuXKlfHFF19E8+bN19nHlakBMkMPD5C71HiArzXa9CZ1Z/z48TFt2rR47733Ys6cOTF+/PiYOnVqHHfccZkMCwAAAAAAACDnjB8/PlasWFF5W7x4caZDAgAAAHJERs+UvmzZshgxYkQsWbIkWrVqFXvttVc8/fTTcdhhh2UyLAAAAAAAAICs1rFjx1i6dGmVx5YuXRotW7Zc71nSI1yZGgAAAKg7GR1K/+Mf/5jJlwcAAAAAAACol/r37x9PPPFElceeeeaZ6N+/f4YiAgAAABqyRpkOAAAAAAAAAKChKykpiaKioigqKoqIiIULF0ZRUVEsWrQoIiLGjx8fI0aMqNz+9NNPj3fffTd+/vOfx9tvvx1/+MMf4r777otzzjknE+EDAAAADZyhdAAAAAAAAIAMmzlzZvTp0yf69OkTERFjx46NPn36xCWXXBIREUuWLKkcUI+I6NatWzz++OPxzDPPRK9eveKqq66K2267LYYMGZKR+AEAAICGrUmmAwAAAAAAAABo6AYNGhRJkmzw65MmTVrvPrNnz67DqAAAAACqx5nSAQAAAAAAAAAAAABImTOlAwAAAADUY+9+VBrJFiuqvX2bLfOjc+vmdRgRAAAAAADQ0BhKBwAAAACox37+wD/ji/xm1d6+edPG8ezPBhpMBwAAAAAAao2hdAAAAACAeuyBM/pHssWW1dp2/rKSGHNvUXxWutpQOgAAAAAAUGsMpQMAAAAA1GO7b9sqYsvqDaUDAAAAAADUhUaZDgAAAAAAAAAAAAAAgPrLUDoAAAAAAAAAAAAAACkzlA4AAAAAAAAAAAAAQMoMpQMAAAAAAAAAAAAAkDJD6QAAAACsY+LEiZGXlxdjxozJdCgAAAAAAABAljOUDgAAAEAVM2bMiJtvvjn22muvTIcCAAAAAAAA1AOG0gEAAACoVFJSEscdd1zceuut0aZNm0yHAwAAAAAAANQDhtIBAAAAqDRq1KgYOnRoDB48ONOhAAAAAAAAAPVEk0wHAAAAAEB2mDx5crz22msxY8aMTW5bVlYWZWVllfdXrlxZl6EBAAAAAAAAWcyZ0gEAAACIxYsXx9lnnx133XVXNGvWbJPbFxYWRqtWrSpvXbp0SUOUAAAAAAAAQDYylA4AAABAzJo1K5YtWxZ77713NGnSJJo0aRLPP/98XHfdddGkSZMoLy+vsv348eNjxYoVlbfFixdnKHIAAAAAAAAg05pkOgAAAAAAMu/QQw+NOXPmVHnsxBNPjF122SXOP//8aNy4cZWvFRQUREFBQTpDBAAAAAAAALKUoXQAAAAAokWLFrHHHntUeWzLLbeMrbfeep3HAQAAAAAAAL6pUaYDAAAAAAAAAAAAAACg/nKmdAAAAADWa+rUqZkOAQAAAAAAAKgHnCkdAAAAAAAAAAAAAICUGUoHAAAAAAAAAAAAACBlhtIBAAAAAAAAAAAAAEiZoXQAAAAAAAAAAAAAAFJmKB0AAAAAAAAAAAAAgJQZSgcAAAAAAAAAAAAAIGWG0gEAAAAAAAAAAAAASJmhdAAAAAAAAAAAAAAAUmYoHQAAAAAAAAAAAACAlBlKBwAAAAAAAAAAAAAgZYbSAQCggfrFL34ReXl5VW677LLLRve5//77Y5dddolmzZrFnnvuGU888USaogUAAPTwAABQ/+jjAXKXGg9QlaF0AABowHbfffdYsmRJ5e2FF17Y4LYvvfRSHHvssXHyySfH7NmzY/jw4TF8+PCYO3duGiMGAICGTQ8PAAD1jz4eIHep8QBfy+hQemFhYey7777RokWLaN++fQwfPjzmzZuXyZAAAKBBadKkSXTs2LHyts0222xw22uvvTa++93vxnnnnRe77rprXHbZZbH33nvH73//+zRGDAAADZseHgAA6h99PEDuUuMBvpbRofTnn38+Ro0aFS+//HI888wzsWbNmjj88MOjtLQ0k2EBAECD8c4778S2224bO+64Yxx33HGxaNGiDW47ffr0GDx4cJXHhgwZEtOnT9/oa5SVlcXKlSur3AAAgNTo4QEAoP6p6z5eDw+QOWo8wNcyOpT+1FNPxQknnBC777579OrVKyZNmhSLFi2KWbNmZTIsAABoEPr16xeTJk2Kp556Km688cZYuHBhHHTQQbFq1ar1bl9cXBwdOnSo8liHDh2iuLh4o69TWFgYrVq1qrx16dKl1nIAAICGRA8PAAD1Tzr6eD08QGao8QBVZXQo/dtWrFgRERFt27Zd79f91Q8AANSeI444Io4++ujYa6+9YsiQIfHEE0/E8uXL47777qvV1xk/fnysWLGi8rZ48eJafX4AAGgo9PAAAFD/pKOP18MDZIYaD1BV1gylV1RUxJgxY2LAgAGxxx57rHcbf/UDUH9MmzYthg0bFttuu23k5eXFww8/nOmQANiE1q1bx8477xzz589f79c7duwYS5curfLY0qVLo2PHjht93oKCgmjZsmWVGwAAsPn08AAAUP/URR+vhwfIDmo80NBlzVD6qFGjYu7cuTF58uQNbuOvfgDqj9LS0ujVq1fccMMNmQ4FgGoqKSmJBQsWRKdOndb79f79+8eUKVOqPPbMM89E//790xEeAADwLXp4AACof/TxALlLjQcauiaZDiAiYvTo0fHYY4/FtGnTYrvtttvgdgUFBVFQUJDGyABI1RFHHBFHHHFEpsMAYCPOPffcGDZsWOywww7x73//OyZMmBCNGzeOY489NiIiRowYEZ07d47CwsKIiDj77LNj4MCBcdVVV8XQoUNj8uTJMXPmzLjlllsymQYAADQYengAAKh/9PEAuUuNB6gqo0PpSZLEWWedFQ899FBMnTo1unXrlslwAACgQfnggw/i2GOPjU8++STatWsXBx54YLz88svRrl27iIhYtGhRNGr09cWVDjjggLj77rvjoosuigsuuCB69OgRDz/8cOyxxx6ZSgEAABoUPTwAANQ/+niA3KXGA1SV0aH0UaNGxd133x2PPPJItGjRIoqLiyMiolWrVtG8efNMhgZAmpWVlUVZWVnl/ZUrV2YwGoCGYfLkyRv9+tSpU9d57Oijj46jjz66jiICAAA2Rg8PAAD1jz4eIHep8QBVZXQo/cYbb4yIiEGDBlV5/I477ogTTjgh/QEBkDGFhYVx6aWXZjqMnDF/WUmNtm+zZX50bu0PwgAAAAAAAAAAAKi5jA6lJ0mSyZcHIIuMHz8+xo4dW3l/5cqV0aVLlwxGVD+12SI/mjdtHGPuLarRfs2bNo5nfzbQYDoAAAAAAAAAAAA1ltGhdABYq6CgIAoKCjIdRr23bevm8ezPBsZnpaurvc/8ZSUx5t6i+Kx0taF0AAAAAAAAAAAAasxQOgB1oqSkJObPn195f+HChVFUVBRt27aN7bffPoOR5b7OrZsbLgcAAAAAAAAAACBtDKUDUCdmzpwZBx98cOX9sWPHRkTEyJEjY9KkSRmKCgAAAAAAAAAAAKhthtIBqBODBg2KJEkyHQYAAAAAAAAAAABQxxplOgAAAAAAAAAAAAAAAOovQ+kAAAAAAAAAAAAAAKTMUDoAAAAAAAAAAAAAACkzlA4AAAAAAAAAAAAAQMoMpQMAAAAAAAAAAAAAkDJD6QAAAAAAAAAAAAAApMxQOgAAAAAAAAAAAAAAKTOUDgAAAAAAAAAAAABAygylAwAAAAAAAAAAAACQMkPpAAAAAAAAAFnghhtuiK5du0azZs2iX79+8eqrr25w20mTJkVeXl6VW7NmzdIYLQAAAMDXDKUDAAAAAAAAZNi9994bY8eOjQkTJsRrr70WvXr1iiFDhsSyZcs2uE/Lli1jyZIllbf3338/jREDAAAAfM1QOgAAAAAAAECG/e53v4tTTz01TjzxxNhtt93ipptuii222CJuv/32De6Tl5cXHTt2rLx16NAhjREDAAAAfM1QOgAAAAAAAEAGrV69OmbNmhWDBw+ufKxRo0YxePDgmD59+gb3KykpiR122CG6dOkSRx11VLzxxhsbfZ2ysrJYuXJllRsAAABAbTCUDgAAAAAAAJBBH3/8cZSXl69zpvMOHTpEcXHxevfp2bNn3H777fHII4/EX/7yl6ioqIgDDjggPvjggw2+TmFhYbRq1ary1qVLl1rNAwAAAGi4DKUDAAAAAAAA1DP9+/ePESNGRO/evWPgwIHx4IMPRrt27eLmm2/e4D7jx4+PFStWVN4WL16cxogBAACAXNYk0wEAAAAAAAAANGTbbLNNNG7cOJYuXVrl8aVLl0bHjh2r9RxNmzaNPn36xPz58ze4TUFBQRQUFGxWrAAAAADr40zpAAAAAAAAABmUn58fffv2jSlTplQ+VlFREVOmTIn+/ftX6znKy8tjzpw50alTp7oKEwAAAGCDnCkdAAAAAAAAIMPGjh0bI0eOjH322Sf222+/uOaaa6K0tDROPPHEiIgYMWJEdO7cOQoLCyMi4pe//GXsv//+0b1791i+fHlcccUV8f7778cpp5ySyTQAAACABspQOgAAAAAAAECGHXPMMfHRRx/FJZdcEsXFxdG7d+946qmnokOHDhERsWjRomjU6OsLYX/22Wdx6qmnRnFxcbRp0yb69u0bL730Uuy2226ZSgEAAABowAylAwAAABCFhYXx4IMPxttvvx3NmzePAw44IH7zm99Ez549Mx0aAAA0GKNHj47Ro0ev92tTp06tcv/qq6+Oq6++Og1RAQAAAGxao01vAgAAAECue/7552PUqFHx8ssvxzPPPBNr1qyJww8/PEpLSzMdGgAAAAAAAJDlnCkdAAAAgHjqqaeq3J80aVK0b98+Zs2aFd/5zncyFBUAAAAAAABQHzhTOgAAAADrWLFiRUREtG3bNsORAAAAAAAAANnOmdIBAAAAqKKioiLGjBkTAwYMiD322GO925SVlUVZWVnl/ZUrV6YrPAAAAAAAACDLOFM6AAAAAFWMGjUq5s6dG5MnT97gNoWFhdGqVavKW5cuXdIYIQAAAAAAAJBNDKUDAAAAUGn06NHx2GOPxT/+8Y/YbrvtNrjd+PHjY8WKFZW3xYsXpzFKAAAAAAAAIJs0yXQAAAAAAGRekiRx1llnxUMPPRRTp06Nbt26bXT7goKCKCgoSFN0AAAAAAAAQDYzlA4AAABAjBo1Ku6+++545JFHokWLFlFcXBwREa1atYrmzZtnODoAAAAAAAAgmzXKdAAAAAAAZN6NN94YK1asiEGDBkWnTp0qb/fee2+mQwMAAAAAAACynDOlAwAAABBJkmQ6BAAAAAAAAKCecqZ0AABooAoLC2PfffeNFi1aRPv27WP48OExb968je4zadKkyMvLq3Jr1qxZmiIGAICGTQ8PAAD1jz4eIHep8QBVZXQofdq0aTFs2LDYdtttIy8vLx5++OFMhgMAAA3K888/H6NGjYqXX345nnnmmVizZk0cfvjhUVpautH9WrZsGUuWLKm8vf/++2mKGAAAGjY9PAAA1D/6eIDcpcYDVNUkky9eWloavXr1ipNOOil++MMfZjIUAABocJ566qkq9ydNmhTt27ePWbNmxXe+850N7peXlxcdO3as6/AAAIBv0cMDAED9o48HyF1qPEBVGR1KP+KII+KII47IZAgAAMD/WbFiRUREtG3bdqPblZSUxA477BAVFRWx9957x+WXXx677777BrcvKyuLsrKyyvsrV66snYABAKCB08MDAED9Uxd9vB4eIDuo8UBD1yjTAdREWVlZrFy5ssoNAADYfBUVFTFmzJgYMGBA7LHHHhvcrmfPnnH77bfHI488En/5y1+ioqIiDjjggPjggw82uE9hYWG0atWq8talS5e6SAEAABoUPTwAANQ/ddXH6+EBMk+NB6hnQ+kKLAAA1I1Ro0bF3LlzY/LkyRvdrn///jFixIjo3bt3DBw4MB588MFo165d3HzzzRvcZ/z48bFixYrK2+LFi2s7fAAAaHD08AAAUP/UVR+vhwfIPDUeIKJJpgOoifHjx8fYsWMr769cudJgOgAAbKbRo0fHY489FtOmTYvtttuuRvs2bdo0+vTpE/Pnz9/gNgUFBVFQULC5YQIAAP9HDw8AAPVPXfbxeniAzFLjAf6jXp0pvaCgIFq2bFnlBgAApCZJkhg9enQ89NBD8dxzz0W3bt1q/Bzl5eUxZ86c6NSpUx1ECAAAfJMeHgAA6h99PEDuUuMBqqpXZ0oHAABqz6hRo+Luu++ORx55JFq0aBHFxcUREdGqVato3rx5RESMGDEiOnfuHIWFhRER8ctf/jL233//6N69eyxfvjyuuOKKeP/99+OUU07JWB4AANBQ6OEBAKD+0ccD5C41HqCqjA6ll5SUVLnsxMKFC6OoqCjatm0b22+/fQYjAwCA3HfjjTdGRMSgQYOqPH7HHXfECSecEBERixYtikaNvr7A0meffRannnpqFBcXR5s2baJv377x0ksvxW677ZausAEAoMHSwwMAQP2jjwfIXWo8QFUZHUqfOXNmHHzwwZX3x44dGxERI0eOjEmTJmUoKgAAaBiSJNnkNlOnTq1y/+qrr46rr766jiICAAA2Rg8PAAD1jz4eIHep8QBVZXQofdCgQdUqzAAAAAAAAAAAAAAAZKdGm94EAAAAAAAAAAAAAADWz1A6AAAAAAAAAAAAAAApM5QOAAAAAAAAAAAAAEDKDKUDAAAAAAAAAAAAAJAyQ+kAAAAAAAAAAAAAAKTMUDoAAAAAAAAAAAAAAClrkukA4Ns+XP5FfFa6utrbz19WUofRAAAAAAAAAAAAAAAbYyidrPLh8i9i8FXPxxdrymu0X/OmjaPNlvl1FBUAAAAAAADA5qvpCbfabJkfnVs3r6NoAAAAoPYYSierfFa6Or5YUx7XHNM7urffqtr7WYwBAAAAAAAAslWbLfOjedPGMebeohrt17xp43j2ZwN9FgoAAEDWM5ROVurefqvYo3OrTIcBAAAAAAAAsNk6t24ez/5sYHxWurra+8xfVhJj7i2Kz0pXG0oHAAAg6xlKhxpyST0AAAAAAACgpjq3bu5zQwAAAHKWoXSoJpfUAwAAAAAAAAAAAIB1GUqHanJJPQAAAAAAAAAAAABYl6F0qAGX1AMAAAAAAAAAAACAqhplOgAAAAAAAAAAAAAAAOovZ0oHAAAAAAAAICd8uPyL+Kx0dbW3n7+spA6jAQAAgIbDUDoAAAAAAAAA9d6Hy7+IwVc9H1+sKa/Rfs2bNo42W+bXUVQAAADQMBhKBwAAAAAAAKDe+6x0dXyxpjyuOaZ3dG+/VbX3a7NlfnRu3bwOIwMAAIDcZygdANIk2y8ZWtPXs0gPAAAAAEA26t5+q9ijc6tMhwEAAAANiqF0AEiDbL5kaJst86N508Yx5t6iGu3XvGnjePZnAw2mAwAAAAAAAAAANHCG0qlT2X5WYIB0yeZLhnZu3Tye/dnAGtfrMfcWxWelqw2lAwAAAAAAAAAANHCG0qkz2XxW4HSr6bB9OoZQgczI1kuGdm7dXN0BAAAAAAAAAAAgJYbSqTPZfFbgdGmzZX40b9o4xtxbVKP9mjdtHM/+bGDOvA8AAAAAZBcnUQAAqD/0bgAAANQHhtKpc9l6VuB06Ny6eTz7s4HxWenqau8zf1lJjLm3KD4rXW2xCAAAAIBa5SQKAAD1h94NAACA+sRQOtSxzq2bW/ABAAAAICs4iQIAQP2hdwMAAKA+MZROtX24/IsaL3gAkNtcMhQAAGD9snktzUkUAADqD70bAAAA9YWhdKrlw+VfxOCrno8v1pTXaL/mTRtHmy3z6yiq3GbQE8hmLhkKAACwYdbSAADINJ81AgAAkG6G0qmWz0pXxxdryuOaY3pH9/ZbVXs/ixc1Z9ATqA9cMhQAAGDDrKUBANSObL76TLbyWSMAAACZYiidGunefqvYo3OrTIeR0wx6AvVFqpcMdXYWAACgobCWBgCQOlefSY3PGgEAAMgUQ+kNlLMKZLdUBz2B9FFHa87ZWQAAAAAAqC5Xn0mdzxoBAADIBEPpDZCzCuSudAy9WsgDdTRVm3N2lhkLP43PfOgAAACQUa58BQBkgqvPpI9+DwAAgM1hKD0HpHK2XmcVyC2pnn04Fc2bNo6bju8bW9dgsNbPDrnG2VlSV9Ozszi7OgAAQOY5NgMAaoMrkGavzen3fG4IAADAWobSs0xNF2M+KV0dp/95Vkpn6923W1sH/DkilbMPp2Ltz9vI21+t0X4+gCRXOTtL3XN2dQAAIBs09AGqzTk2+6x0tWMtAMAVSLNcKv3e5nxuaJAdAAAgNzXIofSafoiULpszYH7nSfs5cG/ganr24VSlazg0XfxfgOyXzrOrWwgHAAC+zQDVf6S69lTTAX3HWQCQm1yBNPul0u8ZZAcAAOCbsmIo/YYbbogrrrgiiouLo1evXnH99dfHfvvtVyevleqHSOliwJxsl67h0HRxFve6lc76vjka+hnvco0zukDN1bRe33///XHxxRfHe++9Fz169Ijf/OY3ceSRR6YxYgDqUrr7+FROHqCnIp0MUKVmc/5g2FrNpunhAXJXfanxqa6ruwJpbsm1QfZUNPS+n5qpLzUegJpR3wG+lvGh9HvvvTfGjh0bN910U/Tr1y+uueaaGDJkSMybNy/at29f66+X6odI6eKglVyTynBouriMdN1Kd31PlTPe5aZcWwjXH1CXalqvX3rppTj22GOjsLAwvve978Xdd98dw4cPj9deey322GOPDGQAQG1Kdx+/Of244QLSdTVEA1SpSWVNyFpN9ejhAXJXfanx1tXZHNm8fp+KdB2fpsIxbXapLzUegJpR3wGqykuSJMlkAP369Yt99903fv/730dEREVFRXTp0iXOOuusGDdu3Eb3XblyZbRq1SpWrFgRLVu2rNbrzf1wRXzv+hfisbMO9CFSQ1VaGrHV//1BQklJxJZbZjYeMiaVepBK3WmoNqe+R6T+XqdydpYx9xbV7zPeqWsZU9Oft7UL4YauslNDrfE1rdfHHHNMlJaWxmOPPVb52P777x+9e/eOm266qVqv2VDfazbTN37fvfGvD2P3HttWazfHgESoOzWRqXWamvTjqfZUqcjm4YJ0SaVHTMeweDp/DiKy9OzdOXgsaK2mevTw1Bt6eDZDQ6079aXGp9LHR+TQ+mMO9mG5KBePS2oqWz9bUOPTU+Mb6vtMLdDHsxkaYu2pLz08qO9sjprUnYyeKX316tUxa9asGD9+fOVjjRo1isGDB8f06dMzGBkAmyNT9X1zzs6yb7e2ubEYTlo5o0v65MwHVlkmlXo9ffr0GDt2bJXHhgwZEg8//HBdhgpAGmRynaamZ6BOxxW50tmHZbOa9ojp/qOBO0/aLy39q36UbKGHB8hd9bHGu5IM2SyV9ftUZOsVo9P92ULW/RFvlqmPNR6ATVPfAdaV0aH0jz/+OMrLy6NDhw5VHu/QoUO8/fbb62xfVlYWZWVllfdXrFgREf+Zwq+uklUro6Ls8yhZtTJWrsxLMXLqtdLSr/+9cmVEeXb+5Tp1L5V6sLbeZPgiE1mvpvU9onZq/OLiFVFasiom/nDP2LFd9c9M0nqL/GjRaE2sXLmm2vtkFXWtXmnRKKJFi+r3INu3KIiHTu0Tyz+v20XtTz9fE2Mmz47jb5xap6+TqmZNG8U1P+4TbbdoWu192m1VEO1aNqv29g2xxqdSr4uLi9e7fXFx8QZfpzZqfETERyu/jI9Kyja9ITkp74vPY9f/+3dJyapq//ys7fn++e6SKFlVs585spcaXzfq0zpNTXuqVKSrD8tmqfaIzZo2ij/UsHdLRest8mPb1gV1+hpfy8Jjxhw8FrRWs2l6eOoTPTzfpIfftPpU4xv856052IeRunQcn6YiXce0735UGuMenBOLiz+OFo2qf7WjCDU+onZrvB6e2qKP55v08RtXn3r4CDW+oVPf+aa6rO8ZHUqvqcLCwrj00kvXebxLly41fq7+19RCQNR/21bvMhTktlTqwapVq6JVK2f/qE21WeOPu6YWAqqv1DVy2NAr0vM6anztq80aDxERsfcuNd6lQfcHVFLja5d1GjYmXb0b/yfHjgWt1WSeHp5ap4cnRep77dPH17Ic68MgVXr4zNPDUyf08aRIja9dajy1Tn0nRdWp7xkdSt9mm22icePGsXTp0iqPL126NDp27LjO9uPHj69y+YqKior49NNPY+utt468vOqfOadLly6xePHiaNmy5eYlkAXkk93kk91SySdJkli1alVsa5Fxo2pa3yNSr/G59nOZad7P2uX9rH11+Z42xBqfSr3u2LFjjbaPqJ0+PiL3/k/JJ/vlWk4NOZ+GWONTYZ1m88knu+VaPhG5l5O1mk2rbz18Jvh/kd3kk92yKZ+GVt8j6leNz6aflfrOe1l7vJe1p67fSzX+a7VZ4+tbD59r/2flk93kkz4NrcbXpx4+XbL55zMV8slu8kmfmtT3jA6l5+fnR9++fWPKlCkxfPjwiPhP0ZwyZUqMHj16ne0LCgqioKDqJXlbt26d0mu3bNky675xm0M+2U0+2a2m+fhrzk2raX2P2Pwan2s/l5nm/axd3s/aV1fvaUOr8anU6/79+8eUKVNizJgxlY8988wz0b9//w2+Tm328RG5939KPtkv13JqqPk0tBqfCus0tUc+2S3X8onIvZys1WxYfe3hM6Gh/7/IdvLJbtmST0Oq7xH1s8Zny89KLvBe1h7vZe2py/dSja/9Gl9fe/hc+z8rn+wmn/RoSDW+Pvbw6ZKtP5+pkk92k096VLe+Z3QoPSJi7NixMXLkyNhnn31iv/32i2uuuSZKS0vjxBNPzHRoAGwG9R2gfthUvR4xYkR07tw5CgsLIyLi7LPPjoEDB8ZVV10VQ4cOjcmTJ8fMmTPjlltuyWQaANQSfTxA9tPDA+QuNR4gd6nxALlJfQeoKuND6cccc0x89NFHcckll0RxcXH07t07nnrqqejQoUOmQwNgM6jvAPXDpur1okWLolGjRpXbH3DAAXH33XfHRRddFBdccEH06NEjHn744dhjjz0ylQIAtUgfD5D99PAAuUuNB8hdajxAblLfAarK+FB6RMTo0aM3eMmK2lZQUBATJkxY55IW9ZV8spt8sluu5ZON0lHffR9rl/ezdnk/a5/3tG5srF5PnTp1nceOPvroOProo+s4qnXl2vdfPtkv13KSD9VlnSZ18sluuZZPRO7llGv51KX60sNnQq79HMknu8mHulAfaryfldrjvaw93sva472sO/WhxqdLrv2cySe7yYe6pr5/Ldd+PuWT3eSTnfKSJEkyHQQAAAAAAAAAAAAAAPVTo01vAgAAAAAAAAAAAAAA62coHQAAAAAAAAAAAACAlBlKBwAAAAAAAAAAAAAgZTk5lH7DDTdE165do1mzZtGvX7949dVXN7r9/fffH7vssks0a9Ys9txzz3jiiSfSFGn11CSfW2+9NQ466KBo06ZNtGnTJgYPHrzJ/NOtpt+ftSZPnhx5eXkxfPjwug2whmqaz/Lly2PUqFHRqVOnKCgoiJ133jmrfuZqms8111wTPXv2jObNm0eXLl3inHPOiS+//DJN0W7ctGnTYtiwYbHttttGXl5ePPzww5vcZ+rUqbH33ntHQUFBdO/ePSZNmlTncZK6VOsJ60rl/wsbVlhYGPvuu2+0aNEi2rdvH8OHD4958+ZlOqx668Ybb4y99torWrZsGS1btoz+/fvHk08+memwqAOp1KK77rorevXqFVtssUV06tQpTjrppPjkk0/qPthNSLUOZPOxSSo5ZfPxyebW6mw7Pkk1n2w9Pkk1n2w+PmnIrNNkZx1cyzpNdtbBtazTWKdh/dQutStdcq121TSfBx98MA477LBo165d5ZrM008/nZ5gyVrW5WuHNfnaYS2+9liHp7bpCfWE6bI5v1NffPHFaNKkSfTu3bvO4qupVPIpKyuLCy+8MHbYYYcoKCiIrl27xu233173wZLzfvGLX0ReXl6V2y677FL59QULFsQPfvCDyvrwox/9KJYuXbrJ5/3www/jpz/9aWy99dbRvHnz2HPPPWPmzJl1mUpE1E0+5eXlcfHFF0e3bt2iefPmsdNOO8Vll10WSZLUdToRsen3MkmSuOSSS6JTp07RvHnzGDx4cLzzzjubfN5MHffVRT6ZPGaoq+/PWhMnToy8vLwYM2ZMHUSfupwbSr/33ntj7NixMWHChHjttdeiV69eMWTIkFi2bNl6t3/ppZfi2GOPjZNPPjlmz54dw4cPj+HDh8fcuXPTHPn61TSfqVOnxrHHHhv/+Mc/Yvr06dGlS5c4/PDD48MPP0xz5OtX03zWeu+99+Lcc8+Ngw46KE2RVk9N81m9enUcdthh8d5778UDDzwQ8+bNi1tvvTU6d+6c5sjXr6b53H333TFu3LiYMGFCvPXWW/HHP/4x7r333rjgggvSHPn6lZaWRq9eveKGG26o1vYLFy6MoUOHxsEHHxxFRUUxZsyYOOWUU7LqgIqvpVpPWL+a/n9h455//vkYNWpUvPzyy/HMM8/EmjVr4vDDD4/S0tJMh1YvbbfddjFx4sSYNWtWzJw5Mw455JA46qij4o033sh0aNSymtaiF198MUaMGBEnn3xyvPHGG3H//ffHq6++GqeeemodR7ppqdSBbD82SSWnbD4+2ZxanY3HJ6nkk83HJ6nkk+3HJw2VdZrsrYMR1mmyuQ5GWKexTsOGqF1qVzrlWu2qaT7Tpk2Lww47LJ544omYNWtWHHzwwTFs2LCYPXt2HUdKtrIuX3usydcOa/G1xzo8tUlPqCdMp1R/py5fvjxGjBgRhx56aB1FlppU8vnRj34UU6ZMiT/+8Y8xb968uOeee6Jnz551GCUNye677x5LliypvL3wwgsR8Z+f1cMPPzzy8vLiueeeixdffDFWr14dw4YNi4qKig0+32effRYDBgyIpk2bxpNPPhlvvvlmXHXVVdGmTZt6mc9vfvObuPHGG+P3v/99vPXWW/Gb3/wmfvvb38b1119f57lU57387W9/G9ddd13cdNNN8corr8SWW24ZQ4YM2egffmXquK+u8snUMUNd5bPWjBkz4uabb4699tqrLtNITZJj9ttvv2TUqFGV98vLy5Ntt902KSwsXO/2P/rRj5KhQ4dWeaxfv37Jf//3f9dpnNVV03y+7auvvkpatGiR3HnnnXUVYo2kks9XX32VHHDAAcltt92WjBw5MjnqqKPSEGn11DSfG2+8Mdlxxx2T1atXpyvEGqlpPqNGjUoOOeSQKo+NHTs2GTBgQJ3GmYqISB566KGNbvPzn/882X333as8dswxxyRDhgypw8hI1ebWRzasOv9fqJlly5YlEZE8//zzmQ4lZ7Rp0ya57bbbMh0Gdag6teiKK65IdtxxxyqPXXfddUnnzp3rMLLUVKcOZPuxybelUtuy7fjkm6qbTzYfn3xTdfLJ9uOTb6pOPvXp+KQhsU5TVbbVQes02V0HrdNYp2H91C61K1NyrXaluga42267JZdeemntB0S9YF2+bliTrz3W4muXdXhSpSfUE2ZKTfI55phjkosuuiiZMGFC0qtXrzqNK1XVyefJJ59MWrVqlXzyySfpCYoGZWP/P55++umkUaNGyYoVKyofW758eZKXl5c888wzG3zO888/PznwwANrO9RqqYt8hg4dmpx00klVHvvhD3+YHHfccbUS88Zs6r2sqKhIOnbsmFxxxRWVjy1fvjwpKChI7rnnng3ul6njvrrK59vSdcxQl/msWrUq6dGjR/LMM88kAwcOTM4+++zaCrtW5NSZ0levXh2zZs2KwYMHVz7WqFGjGDx4cEyfPn29+0yfPr3K9hERQ4YM2eD26ZRKPt/2+eefx5o1a6Jt27Z1FWa1pZrPL3/5y2jfvn2cfPLJ6Qiz2lLJ59FHH43+/fvHqFGjokOHDrHHHnvE5ZdfHuXl5ekKe4NSyeeAAw6IWbNmVV6i4913340nnngijjzyyLTEXNuyuR5QVW3UR0inFStWRERkxe/j+q68vDwmT54cpaWl0b9//0yHQ4b1798/Fi9eHE888UQkSRJLly6NBx54ICt7kerUgfrWi6RS27Lp+OTbqptPth6ffFt18snm45Nvq04+uXZ8kgus06wrm+qgdZrsroPWabK7HpA5apfale1yvXZVVFTEqlWrsqKXIf2sy1MfWIuvHdbh2Rx6Qj1hfXDHHXfEu+++GxMmTMh0KJvt0UcfjX322Sd++9vfRufOnWPnnXeOc889N7744otMh0aOeOedd2LbbbeNHXfcMY477rhYtGhRRESUlZVFXl5eFBQUVG7brFmzaNSoUeXZx9dn7c/s0UcfHe3bt48+ffrErbfeWud5rFXb+RxwwAExZcqU+Ne//hUREa+//nq88MILccQRR9RtIrHp93LhwoVRXFxc5XdSq1atol+/fhv8nZTJ4766yGd90nXMUJf5jBo1KoYOHbpOv5EtmmQ6gNr08ccfR3l5eXTo0KHK4x06dIi33357vfsUFxevd/vi4uI6i7O6Usnn284///zYdttts+IHMJV8XnjhhfjjH/8YRUVFaYiwZlLJ5913343nnnsujjvuuHjiiSdi/vz5ceaZZ8aaNWsy3uymks9PfvKT+Pjjj+PAAw+MJEniq6++itNPPz1rLqNVUxuqBytXrowvvvgimjdvnqHI+LbaqI+QLhUVFTFmzJgYMGBA7LHHHpkOp96aM2dO9O/fP7788svYaqut4qGHHorddtst02GRYQMGDIi77rorjjnmmPjyyy/jq6++imHDhmXdZY+rWwey+djk21Ktbdl0fPJN1c0nm49Pvqm6+WTz8ck3VTefXDs+yQXWadaVTXXQOk1210HrNNZpWD+1S+3Kdrleu6688sooKSmJH/3oR5kOhQywLk+2sxa/+azDUxv0hHrCbPfOO+/EuHHj4n//93+jSZP6Pzb37rvvxgsvvBDNmjWLhx56KD7++OM488wz45NPPok77rgj0+FRz/Xr1y8mTZoUPXv2jCVLlsSll14aBx10UMydOzf233//2HLLLeP888+Pyy+/PJIkiXHjxkV5eXksWbJkg8/57rvvxo033hhjx46NCy64IGbMmBH/8z//E/n5+TFy5Mh6l8+4ceNi5cqVscsuu0Tjxo2jvLw8fv3rX8dxxx1Xp7lEbPq9XPu5Sk0+c8nkcV9d5PNt6TxmqKt8Jk+eHK+99lrMmDGjTuPfHDl1pnSqmjhxYkyePDkeeuihaNasWabDqbFVq1bF8ccfH7feemtss802mQ6nVlRUVET79u3jlltuib59+8YxxxwTF154Ydx0002ZDi0lU6dOjcsvvzz+8Ic/xGuvvRYPPvhgPP7443HZZZdlOjSArDFq1KiYO3duTJ48OdOh1Gs9e/aMoqKieOWVV+KMM86IkSNHxptvvpnpsMiwN998M84+++y45JJLYtasWfHUU0/Fe++9F6effnqmQ6siF+tAKjll8/FJdfKpT8cn1f3+1Jfjk+rm4/iEbJfNdbA66lMdrK76UgerSx2Edald2U/tqj/uvvvuuPTSS+O+++6L9u3bZzocgHXk4hpculmHJ1P0hPVHfe8Jy8vL4yc/+UlceumlsfPOO2c6nFpRUVEReXl5cdddd8V+++0XRx55ZPzud7+LO++809nS2WxHHHFEHH300bHXXnvFkCFD4oknnojly5fHfffdF+3atYv7778//va3v8VWW20VrVq1iuXLl8fee+8djRpteCS1oqIi9t5777j88sujT58+cdppp8Wpp56alnWNusjnvvvui7vuuivuvvvueO211+LOO++MK6+8Mu688846zyeT72VdSEc+6TxmqIt8Fi9eHGeffXbcddddWf05U/3/k69v2GabbaJx48axdOnSKo8vXbo0OnbsuN59OnbsWKPt0ymVfNa68sorY+LEifHss8/GXnvtVZdhVltN81mwYEG89957MWzYsMrHKioqIiKiSZMmMW/evNhpp53qNuiNSOX706lTp2jatGk0bty48rFdd901iouLY/Xq1ZGfn1+nMW9MKvlcfPHFcfzxx8cpp5wSERF77rlnlJaWxmmnnRYXXnjhRn8pZ6MN1YOWLVvW+79WzjWbUx8hnUaPHh2PPfZYTJs2LbbbbrtMh1Ov5efnR/fu3SMiom/fvjFjxoy49tpr4+abb85wZGRSYWFhDBgwIM4777yIiNhrr71iyy23jIMOOih+9atfRadOnTIcYc3qQDYfm3xTKrUtG49P1qpuPtl+fLJWTb4/2Xx8slZN8sm145NcYJ3ma9lYB63TZHcdtE5jnYb1U7vUrmyXq7Vr8uTJccopp8T999+fFVd8ITOsy5PNrMXXDuvw1AY9oZ4wm61atSpmzpwZs2fPjtGjR0fEf44RkySJJk2axN///vc45JBDMhxlzXTq1Ck6d+4crVq1qnxs1113jSRJ4oMPPogePXpkMDpyTevWrWPnnXeO+fPnR0TE4YcfHgsWLIiPP/44mjRpEq1bt46OHTvGjjvuuMHn6NSp0zpXYtl1113jr3/9a53Gvj61kc95550X48aNix//+McR8Z/fYe+//34UFhbW+ZnfN/Verv29u3Tp0iqfmy9dujR69+693ufM5HFfXeTzTek+ZqiLfGbNmhXLli2Lvffeu/Kx8vLymDZtWvz+97+PsrKyKmuGmVK/OrdNyM/Pj759+8aUKVMqH6uoqIgpU6ZE//7917tP//79q2wfEfHMM89scPt0SiWfiIjf/va3cdlll8VTTz0V++yzTzpCrZaa5rPLLrvEnDlzoqioqPL2/e9/Pw4++OAoKiqKLl26pDP8daTy/RkwYEDMnz+/8oOPiIh//etf0alTp4wPfKSSz+eff77OAeDawpYkSd0FW0eyuR5QVar1EdIlSZIYPXp0PPTQQ/Hcc89Ft27dMh1SzqmoqIiysrJMh0GGZXMvkkodyPZeJNXalq3HJzXNJ9uPT1L5/mTz8Ukq+WRzTWiorNP8R7bWQes02V0HrdNkdz0gc9QutSvb5WLtuueee+LEE0+Me+65J4YOHZrpcMgg6/JkI2vxdcs6PKnQE+oJs1nLli3XOUY8/fTTK68U0a9fv0yHWGMDBgyIf//731FSUlL52L/+9a9o1KiRP9Si1pWUlMSCBQvWOTHYNttsE61bt47nnnsuli1bFt///vc3+BwDBgyIefPmVXnsX//6V+ywww51EvPG1EY+G/od9s11m7qyqfeyW7du0bFjxyq/k1auXBmvvPLKBn8nZfK4ry7yicjcMUNd5HPooYeu83tsn332ieOOOy6KioqyYiA9IiKSHDN58uSkoKAgmTRpUvLmm28mp512WtK6deukuLg4SZIkOf7445Nx48ZVbv/iiy8mTZo0Sa688srkrbfeSiZMmJA0bdo0mTNnTqZSqKKm+UycODHJz89PHnjggWTJkiWVt1WrVmUqhSpqms+3jRw5MjnqqKPSFO2m1TSfRYsWJS1atEhGjx6dzJs3L3nssceS9u3bJ7/61a8ylUIVNc1nwoQJSYsWLZJ77rkneffdd5O///3vyU477ZT86Ec/ylQKVaxatSqZPXt2Mnv27CQikt/97nfJ7Nmzk/fffz9JkiQZN25ccvzxx1du/+677yZbbLFFct555yVvvfVWcsMNNySNGzdOnnrqqUylwEZs6ueVmtnU/xdq5owzzkhatWqVTJ06tcrv488//zzTodVL48aNS55//vlk4cKFyT//+c9k3LhxSV5eXvL3v/8906FRy2r6u/uOO+5ImjRpkvzhD39IFixYkLzwwgvJPvvsk+y3336ZSqFSdepAfTs2SSWnbD4+SSWfb8um45NU8snm45NU8sn245OGyjpN9tbBJLFOk811MEms01inYUPULrUrnXKtdtU0n7vuuitp0qRJcsMNN1TpZZYvX56pFMgw6/K1x5p87bAWX3usw1Ob9IR6wnSqaT7fNmHChKRXr15pinbTaprPqlWrku222y75r//6r+SNN95Inn/++aRHjx7JKaeckqkUyCE/+9nPkqlTpyYLFy5MXnzxxWTw4MHJNttskyxbtixJkiS5/fbbk+nTpyfz589P/vznPydt27ZNxo4dW+U5DjnkkOT666+vvP/qq68mTZo0SX79618n77zzTnLXXXclW2yxRfKXv/ylXuYzcuTIpHPnzsljjz2WLFy4MHnwwQeTbbbZJvn5z39e5/lU572cOHFi0rp16+SRRx5J/vnPfyZHHXVU0q1bt+SLL77YYE6ZOu6rq3wydcxQV/l828CBA5Ozzz67LlOpsZwbSk+SJLn++uuT7bffPsnPz0/222+/5OWXX6782sCBA5ORI0dW2f6+++5Ldt555yQ/Pz/Zfffdk8cffzzNEW9cTfLZYYcdkohY5zZhwoT0B74BNf3+fFO2fWCQJDXP56WXXkr69euXFBQUJDvuuGPy61//Ovnqq6/SHPWG1SSfNWvWJL/4xS+SnXbaKWnWrFnSpUuX5Mwzz0w+++yz9Ae+Hv/4xz/W+/9hbQ4jR45MBg4cuM4+vXv3TvLz85Mdd9wxueOOO9IeN9W3sZ9XamZT/1+omfW9lxGhpqTopJNOSnbYYYckPz8/adeuXXLooYdaCM9Rqfzuvu6665Lddtstad68edKpU6fkuOOOSz744IP0B/8t1akD9e3YJJWcsvn4JNXv0Tdl0/FJqvlk6/FJKvlk+/FJQ2adJjvr4FrWabKzDq5lncY6Deundqld6ZJrtaum+QwcONCaIeuwLl87rMnXDmvxtcc6PLVNT6gnTJdUvj/flG1D6ank89ZbbyWDBw9Omjdvnmy33XbJ2LFj/YEWteKYY45JOnXqlOTn5yedO3dOjjnmmGT+/PmVXz///POTDh06JE2bNk169OiRXHXVVUlFRUWV59hhhx3WWQ//29/+luyxxx5JQUFBsssuuyS33HJLOtKpk3xWrlyZnH322cn222+fNGvWLNlxxx2TCy+8MCkrK0tLTpt6LysqKpKLL7446dChQ1JQUJAceuihybx586pss77vUaaO++oin0weM9TV9+ebsnEoPS9J6uG1bgAAAAAAAAAAAAAAyAqNMh0AAAAAAAAAAAAAAAD1l6F0AAAAAAAAAAAAAABSZigdAAAAAAAAAAAAAICUGUoHAAAAAAAAAAAAACBlhtIBAAAAAAAAAAAAAEiZoXQAAAAAAAAAAAAAAFJmKB0AAAAAAAAAAAAAgJQZSgcAAAAAAAAAAAAAIGWG0gGArDZo0KAYM2ZMpsMAAAAAAAAAAIB6qWvXrnHNNddkOgxynKF02IhbbrklBg0aFC1btoy8vLxYvnx5pkMCaHAefPDBuOyyy2rluZYsWRI/+clPYuedd45GjRoZdgfIYWo+QGbU1R+VPvjgg3H44YfH1ltvHXl5eVFUVFTrrwFAZk2bNi2GDRsW2267beTl5cXDDz+c6ZAAqAXWaADqh6lTp6Y0F5PqfgBAbjKUDhvx+eefx3e/+9244IILMh0KQIPVtm3baNGiRa08V1lZWbRr1y4uuuii6NWrV608JwDZSc0HyC2lpaVx4IEHxm9+85tMhwJAHSktLY1evXrFDTfckOlQAKhF1mgAAAAaDkPp1HsVFRVRWFgY3bp1i+bNm0evXr3igQceqPz6G2+8Ed/73veiZcuW0aJFizjooINiwYIFlV+//fbbY/fdd4+CgoLo1KlTjB49uvJrY8aMiXHjxsX++++f1pwA+No3z7TYtWvXuPzyy+Okk06KFi1axPbbbx+33HJLle0/+OCDOPbYY6Nt27ax5ZZbxj777BOvvPJK5f7XXnttjBgxIlq1apXuVAAatAceeCD23HPPaN68eWy99dYxePDgKC0tjRNOOCGGDx8el19+eXTo0CFat24dv/zlL+Orr76K8847L9q2bRvbbbdd3HHHHVWe7/zzz4+dd945tthii9hxxx3j4osvjjVr1lR+Xc0HSL8TTjghnn/++bj22msjLy8v8vLyYsGCBXHyySdXrtv07Nkzrr322ir7re/s6sOHD48TTjih8v7xxx8fl1xySQwePDgNmQDwbU899VQceOCB0bp169h6663je9/7XpV19jlz5sQhhxxS2e+fdtppUVJSUvn1tX3/pZdeGu3atYuWLVvG6aefHqtXr67c5ogjjohf/epX8YMf/CCtuQE0ZIMGDYrRo0fH6NGjo1WrVrHNNtvExRdfHEmSRETEn//859hnn32iRYsW0bFjx/jJT34Sy5Ytq9x/7dlxp0yZEvvss09sscUWccABB8S8efMqt7FGA5AZgwYNirPOOivGjBkTbdq0iQ4dOsStt94apaWlceKJJ0aLFi2ie/fu8eSTT8Z7770XBx98cEREtGnTJvLy8irXZcrKyuJ//ud/on379tGsWbM48MADY8aMGRERG90PgPTbVH//TVOnTo38/Pz43//938rHfvvb30b79u1j6dKl6QybHGMonXqvsLAw/vSnP8VNN90Ub7zxRpxzzjnx05/+NJ5//vn48MMP4zvf+U4UFBTEc889F7NmzYqTTjopvvrqq4iIuPHGG2PUqFFx2mmnxZw5c+LRRx+N7t27ZzgjADbmqquuin322Sdmz54dZ555ZpxxxhmVC9wlJSUxcODA+PDDD+PRRx+N119/PX7+859HRUVFhqMGaNiWLFkSxx57bJx00knx1ltvxdSpU+OHP/xh5QLIc889F//+979j2rRp8bvf/S4mTJgQ3/ve96JNmzbxyiuvxOmnnx7//d//HR988EHlc7Zo0SImTZoUb775Zlx77bVx6623xtVXX52pFAGIiGuvvTb69+8fp556aixZsiSWLFkS2223XWy33XZx//33x5tvvhmXXHJJXHDBBXHfffdlOlwAaqC0tDTGjh0bM2fOjClTpkSjRo3iBz/4QVRUVERpaWkMGTIk2rRpEzNmzIj7778/nn322SongImImDJlSuXxwD333BMPPvhgXHrppRnKCIC17rzzzmjSpEm8+uqrce2118bvfve7uO222yIiYs2aNXHZZZfF66+/Hg8//HC899576x02vPDCC+Oqq66KmTNnRpMmTeKkk05KcxYArM+dd94Z22yzTbz66qtx1llnxRlnnBFHH310HHDAAfHaa6/F4YcfHscff3y0a9cu/vrXv0ZExLx582LJkiWVJxX4+c9/Hn/961/jzjvvjNdeey26d+8eQ4YMiU8//TS6dOmywf0AyIyN9ffftPZkMccff3ysWLEiZs+eHRdffHHcdttt0aFDhwxETq7IS9b3ZxBQT5SVlUXbtm3j2Wefjf79+1c+fsopp8Tnn38eXbt2jcmTJ8e8efOiadOm6+zfuXPnOPHEE+NXv/rVRl9n6tSpcfDBB8dnn30WrVu3ru00ANiIQYMGRe/eveOaa66Jrl27xkEHHRR//vOfIyIiSZLo2LFjXHrppXH66afHLbfcEueee26899570bZt22o/LwB167XXXou+ffvGe++9FzvssEOVr51wwgkxderUePfdd6NRo//83fQuu+wS7du3j2nTpkVERHl5ebRq1Spuu+22+PGPf7ze17jyyitj8uTJMXPmzHW+puYDpE91au7o0aOjuLi48kp369tn+PDh0bp165g0aVKVfd97773o1q1bzJ49O3r37l37CQBQLR9//HG0a9cu5syZE9OnT4/zzz8/Fi9eHFtuuWVERDzxxBMxbNiw+Pe//x0dOnSIE044If72t7/F4sWLY4sttoiIiJtuuinOO++8WLFiReWxwFp5eXnx0EMPxfDhw9OdGkCDMmjQoFi2bFm88cYbkZeXFxER48aNi0cffTTefPPNdbafOXNm7LvvvrFq1arYaqutKj9DffbZZ+PQQw+NiP/8Dhg6dGh88cUX0axZs3VezxoNQHoMGjQoysvLK8+Au3ad/Yc//GH86U9/ioiI4uLi6NSpU0yfPj2+/PLLdeZiSktLo02bNjFp0qT4yU9+EhH/+YOlrl27xpgxY+K8884zTwOQRTbV36+t32uvXLp69ero169f7LzzzjF37twYMGBA3HLLLRnMgFzgTOnUa/Pnz4/PP/88DjvssNhqq60qb3/6059iwYIFUVRUFAcddNB6B9KXLVsW//73vysXSACoH/baa6/Kf+fl5UXHjh0rLxdaVFQUffr02eRAOgDp1atXrzj00ENjzz33jKOPPjpuvfXW+Oyzzyq/vvvuu1cZQunQoUPsueeelfcbN24cW2+9dZXLQ997770xYMCA6NixY2y11VZx0UUXxaJFi9KTEAA1csMNN0Tfvn2jXbt2sdVWW8Utt9yiZgPUM++8804ce+yxseOOO0bLli2ja9euERGxaNGieOutt6JXr16VA+kREQMGDIiKiorKq9tF/Oe4YO1AekRE//79o6SkJBYvXpy2PABY1/777185sBLxn/r8zjvvRHl5ecyaNSuGDRsW22+/fbRo0SIGDhwYEbFOP//NdftOnTpFRFRZxwEgM75Zn9eus39z7X3tmXA3VLMXLFgQa9asiQEDBlQ+1rRp09hvv/3irbfeqqOoAdgcG+vvvy0/Pz/uuuuu+Otf/xpffvmlq1JTKwylU6+VlJRERMTjjz8eRUVFlbc333wzHnjggWjevPkG993Y1wDIXt/+Q6O8vLyoqKiICLUdIFs1btw4nnnmmXjyySdjt912i+uvvz569uwZCxcujIj11/aN1fvp06fHcccdF0ceeWQ89thjMXv27Ljwwgtj9erV6UkIgGqbPHlynHvuuXHyySfH3//+9ygqKooTTzyxSs1u1KhRfPtijmvWrEl3qABsxLBhw+LTTz+NW2+9NV555ZV45ZVXIiL04AA57Msvv4whQ4ZEy5Yt46677ooZM2bEQw89FBHr1v9vruOsHYBZu44DQOZsau1dzQbgpZdeioiITz/9ND799NMMR0MuMJROvbbbbrtFQUFBLFq0KLp3717l1qVLl9hrr73if//3f9f7QWaLFi2ia9euMWXKlAxEDkBd2GuvvaKoqEijDJCF8vLyYsCAAXHppZfG7NmzIz8/v/KDzJp66aWXYocddogLL7ww9tlnn+jRo0e8//77tRwxAKnIz8+vcsaVF198MQ444IA488wzo0+fPtG9e/dYsGBBlX3atWsXS5YsqbxfXl4ec+fOTVvMAGzcJ598EvPmzYuLLrooDj300Nh1112rXPlo1113jddffz1KS0srH3vxxRejUaNG0bNnz8rHXn/99fjiiy8q77/88sux1VZbRZcuXdKTCADrtfYPjdZ6+eWXo0ePHvH222/HJ598EhMnToyDDjoodtllF2c/B8hh+fn5ERFV1nV22mmnyM/PjxdffLHysTVr1sSMGTNit9122+B+AGTOhvr7xo0br7PtggUL4pxzzolbb701+vXrFyNHjvSHSmw2Q+nUay1atIhzzz03zjnnnLjzzjtjwYIF8dprr8X1118fd955Z4wePTpWrlwZP/7xj2PmzJnxzjvvxJ///OfKS4b+4he/iKuuuiquu+66eOeddyr3Xau4uDiKiopi/vz5ERExZ84cw44AWezYY4+Njh07xvDhw+PFF1+Md999N/7617/G9OnTK7dZe1WNkpKS+OijjyqvsAFA3XnllVfi8ssvj5kzZ8aiRYviwQcfjI8++ih23XXXlJ6vR48esWjRopg8eXIsWLAgrrvuuvUOuKv5AOnXtWvXeOWVV+K9996Ljz/+OHr06BEzZ86Mp59+Ov71r3/FxRdfHDNmzKiyzyGHHBKPP/54PP744/H222/HGWecEcuXL6+yzaefflqljs+bNy+KioqiuLg4XakBNFht2rSJrbfeOm655ZaYP39+PPfcczF27NjKrx933HHRrFmzGDlyZMydOzf+8Y9/xFlnnRXHH398dOjQoXK71atXx8knnxxvvvlmPPHEEzFhwoQYPXp0NGr0n4+qSkpKKnv4iIiFCxdGUVFRLFq0KK35AjQ0ixYtirFjx8a8efPinnvuieuvvz7OPvvs2H777SM/Pz+uv/76ePfdd+PRRx+Nyy67LKXXsEYDkP122GGHyMvLi8ceeyw++uijKCkpiS233DLOOOOMOO+88+Kpp56KN998M0499dT4/PPP4+STT97gfgBkzob6+28rLy+Pn/70pzFkyJA48cQT44477oh//vOfcdVVV2UganKJoXTqvcsuuywuvvjiKCwsjF133TW++93vxuOPPx7dunWLrbfeOp577rkoKSmJgQMHRt++fePWW2+tvBzRyJEj45prrok//OEPsfvuu8f3vve9eOeddyqf+6abboo+ffrEqaeeGhER3/nOd6JPnz7x6KOPZiRXADYuPz8//v73v0f79u3jyCOPjD333DMmTpxY5S8++/TpE3369IlZs2bF3XffHX369Ikjjzwyg1ED5L6WLVvGtGnT4sgjj4ydd945LrroorjqqqviiCOOSOn5vv/978c555wTo0ePjt69e8dLL70UF1988TrbqfkA6XfuuedG48aNY7fddot27drFkCFD4oc//GEcc8wx0a9fv/jkk0/izDPPrLLPSSedFCNHjowRI0bEwIEDY8cdd4yDDz64yjaPPvpo9OnTJ4YOHRoRET/+8Y+jT58+cdNNN6UtN4CGqlGjRjF58uSYNWtW7LHHHnHOOefEFVdcUfn1LbbYIp5++un49NNPY999943/+q//ikMPPTR+//vfV3meQw89NHr06BHf+c534phjjonvf//78Ytf/KLy6zNnzqzs4SMixo4dG3369IlLLrkkLXkCNFQjRoyIL774Ivbbb78YNWpUnH322XHaaadFu3btYtKkSXH//ffHbrvtFhMnTowrr7wypdewRgOQ/Tp37hyXXnppjBs3Ljp06BCjR4+OiIiJEyfG//t//y+OP/742HvvvWP+/Pnx9NNPR5s2bTa6HwCZsaH+/tt+/etfx/vvvx8333xzRER06tQpbrnllrjooovi9ddfT3fY5JC8JEmSTAcBAAAAAABAbjrhhBNi+fLl8fDDD2c6FAC+YdCgQdG7d++45pprMh0KAACwmfT3ZANnSgcAAAAAAAAAAAAAIGWG0gEAAAAAAAAAAAAASFlekiRJpoMAAAAAAAAAAAAAAKB+cqZ0AAAAAAAAAAAAAABSZigdAAAAAAAAAAAAAICUGUoHAAAAAAAAAAAAACBlhtIBAAAAAAAAAAAAAEiZoXQAAAAAAAAAAAAAAFJmKB0AAAAAAAAAAAAAgJQZSgcAAAAAAAAAAAAAIGWG0gEAAAAAAAAAAAAASJmhdAAAAAAAAAAAAAAAUvb/Ab88PRmMFnd0AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot posterior samples of each parameter with the groound truth value for comparison\n", + "fig, ax = plt.subplots(1, 8, figsize=(30, 5))\n", + "labels = [\"ecc1\", \"inc1\", \"sma1\", \"tau1\", \"aop1\", \"pan1\", \"mtot\", \"plx\"]\n", + "truth = [ecc, inc, sma, tau, aop, pan, mtot, plx]\n", + "for i in range(0, 8):\n", + " ax[i].hist(\n", + " nested_sampler.results.post[:, lab[labels[i]]],\n", + " bins=20,\n", + " density=True,\n", + " histtype=\"step\",\n", + " )\n", + " ax[i].set_ylabel(\"probability\")\n", + " ax[i].set_xlabel(labels[i])\n", + " ax[i].axvline(truth[i], color=\"red\")\n", + "fig.suptitle(\"Comparison of Posterior Samples and Ground Truth Values\", fontsize=16)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize the run using `dynesty` plotting tools (see [this page](https://dynesty.readthedocs.io/en/stable/dynamic.html)) for info about interpreting these plots." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/sblunt/miniconda3/envs/python3.12/lib/python3.12/site-packages/dynesty/plotting.py:706: UserWarning: Attempting to set identical low and high ylims makes transformation singular; automatically expanding.\n", + " ax.set_ylim([min(x), max(x)])\n", + "/home/sblunt/miniconda3/envs/python3.12/lib/python3.12/site-packages/dynesty/plotting.py:749: UserWarning: Attempting to set identical low and high xlims makes transformation singular; automatically expanding.\n", + " ax.set_xlim(span[i])\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from dynesty import plotting as dyplot\n", + "\n", + "fig, axes = dyplot.runplot(nested_sampler.dynesty_sampler.results)\n", + "fig, axes = dyplot.traceplot(nested_sampler.dynesty_sampler.results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tips for Faster Convergence\n", + "\n", + "Here are some tips that (in our experience) will help your sampler converge more quickly:\n", + "\n", + "1. Set the `orbitize.system.System.restrict_angle_ranges` keyword. This sets the prior on $\\Omega$ (position angle of nodes) to be 0-$\\pi$, rather than the usual 0-$2\\pi$ (unless you have data in addition to your relative astrometry, like RV measurements, that will uniquely identify one of the two degenerate $\\Omega$ peaks.)\n", + "\n", + "2. We recommend restricting your prior on semimajor axis as much as possible. A good rule of thumb is as follows:\n", + "\n", + "- step 1: fit a circular orbit (fix e=0, or chi-by-eye) to your data, and note the semimajor axis\n", + "- step 2: set your semimajor axis prior limits between 0 and 5 x your circular orbit solution\n", + "- step 3: once your fit is finished, check if your semimajor axis posterior doesn't drop off sharply to zero at the upper bound. If it does, repeat the fit with a wider prior.\n", + "\n", + "**It's important to note that this method isn't reliable for computing the Bayesian evidence, just for computing the posterior!**\n", + "\n", + "3. Parallelization (which you can use by passing `num_threads`>1 into `nested_sampler.run_sampler()`) helps, so use it if you can!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Saving Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# save results in .hdf5 format\n", + "filename = \"myposterior.hdf5\"\n", + "nested_sampler.results.save_results(filename)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + }, + "kernelspec": { + "display_name": "Python 3.9.6 64-bit", + "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": "3.12.2" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/orbitize/basis.py b/orbitize/basis.py index fd9f097e..672a0228 100644 --- a/orbitize/basis.py +++ b/orbitize/basis.py @@ -1408,14 +1408,14 @@ def standard_to_xyz( self, epoch, elems, tau_ref_epoch=58849, tolerance=1e-9, max_iter=100 ): """ - Converts array of orbital elements from the regular base of Keplerian orbits to positions and velocities in xyz - Uses code from orbitize.kepler + Converts array of orbital elements from the regular base of Keplerian orbits + to positions and velocities in xyz. Uses code from orbitize.kepler Args: epoch (float): Date in MJD of observation to calculate time of periastron passage (tau). elems (np.array of floats): Orbital elements (sma, ecc, inc, aop, pan, tau, plx, mtot). If more than 1 set of parameters is passed, the first dimension must be - the number of orbital parameter sets, and the second the orbital elements. + the number of the orbital elements, and the second the number of orbital parameter sets. Return: np.array: Orbital elements in xyz (x-coordinate [au], y-coordinate [au], z-coordinate [au], diff --git a/orbitize/priors.py b/orbitize/priors.py index 7f83cff4..60a7fdab 100644 --- a/orbitize/priors.py +++ b/orbitize/priors.py @@ -4,6 +4,8 @@ from orbitize import basis from orbitize.kepler import _calc_ecc_anom +import scipy.special +import scipy.stats """ This module defines priors with methods to draw samples and compute log(probability) @@ -61,6 +63,13 @@ def increment_param_num(self): self.param_num = self.param_num % (self.total_params + 1) self.param_num = self.param_num % self.total_params + def transform_samples(self): + raise NotImplementedError( + """The transform_samples() method is not implemented for this Prior + class yet. We're working on it! + """ + ) + def draw_samples(self, num_samples): """ Draw positive samples from the ND interpolator. @@ -160,6 +169,13 @@ def increment_param_num(self): self.param_num = self.param_num % (self.total_params + 1) self.param_num = self.param_num % self.total_params + def transform_samples(self): + raise NotImplementedError( + """The transform_samples() method is not implemented for this Prior + class yet. We're working on it! + """ + ) + def draw_samples(self, num_samples): """ Draw positive samples from the KDE. @@ -244,7 +260,8 @@ class GaussianPrior(Prior): mu (float): mean of the distribution sigma (float): standard deviation of the distribution no_negatives (bool): if True, only positive values will be drawn from - this prior, and the probability of negative values will be 0 (default:True). + this prior, and the probability of negative values will be 0 + (default:True). (written) Sarah Blunt, 2018 """ @@ -257,6 +274,30 @@ def __init__(self, mu, sigma, no_negatives=True): def __repr__(self): return "Gaussian" + def transform_samples(self, u): + """ + Transform uniform 1D samples, u, to samples drawn + from a Gaussian distribution. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a Gaussian + distribution. + """ + # a is the # of standard deviations at which 0 occurs + a = -self.mu / self.sigma + + if self.no_negatives: + samples = scipy.stats.truncnorm.isf( + u, a, np.inf, loc=self.mu, scale=self.sigma + ) + else: + z = scipy.special.ndtri(u) + samples = z * self.sigma + self.mu + return samples + def draw_samples(self, num_samples): """ Draw positive samples from a Gaussian distribution. @@ -269,20 +310,8 @@ def draw_samples(self, num_samples): numpy array of float: samples drawn from the appropriate Gaussian distribution. Array has length `num_samples`. """ - - samples = np.random.normal(loc=self.mu, scale=self.sigma, size=num_samples) - bad = np.inf - - if self.no_negatives: - - while bad != 0: - - bad_samples = np.where(samples < 0)[0] - bad = len(bad_samples) - - samples[bad_samples] = np.random.normal( - loc=self.mu, scale=self.sigma, size=bad - ) + samples = np.random.uniform(0, 1, num_samples) + samples = self.transform_samples(samples) return samples @@ -304,7 +333,6 @@ def compute_lnprob(self, element_array): lnprob = -0.5 * ((element_array - self.mu) / self.sigma) ** 2 if self.no_negatives: - bad_samples = np.where(element_array < 0)[0] lnprob[bad_samples] = -np.inf @@ -336,6 +364,25 @@ def __init__(self, minval, maxval): def __repr__(self): return "Log Uniform" + def transform_samples(self, u): + """ + Transform uniform 1D samples, u, to samples drawn + from a Log Uniform distribution. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a Log Uniform + distribution. + """ + samples = (self.logmax - self.logmin) * u + self.logmin + + # generate samples following a log uniform distribution + samples = np.exp(samples) + + return samples + def draw_samples(self, num_samples): """ Draw samples from this 1/x distribution. @@ -347,10 +394,10 @@ def draw_samples(self, num_samples): np.array: samples ranging from [``minval``, ``maxval``) as floats. """ # sample from a uniform distribution in log space - samples = np.random.uniform(self.logmin, self.logmax, num_samples) + samples = np.random.uniform(0, 1, num_samples) # convert from log space to linear space - samples = np.exp(samples) + samples = self.transform_samples(samples) return samples @@ -397,6 +444,23 @@ def __init__(self, minval, maxval): def __repr__(self): return "Uniform" + def transform_samples(self, u): + """ + Transform uniform 1D samples, u, to samples drawn + from a uniform distribution. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a uniform + distribution. + """ + # generate samples following a uniform distribution + samples = (self.maxval - self.minval) * u + self.minval + + return samples + def draw_samples(self, num_samples): """ Draw samples from this uniform distribution. @@ -408,7 +472,8 @@ def draw_samples(self, num_samples): np.array: samples ranging from [0, pi) as floats. """ # sample from a uniform distribution in log space - samples = np.random.uniform(self.minval, self.maxval, num_samples) + samples = np.random.uniform(0, 1, num_samples) + samples = self.transform_samples(samples) return samples @@ -449,6 +514,23 @@ def __init__(self): def __repr__(self): return "Sine" + def transform_samples(self, u): + """ + Transform uniform 1D samples, u, to samples drawn + from a Sine distribution. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a Sine + distribution. + """ + # generate samples following a sin distribution + samples = np.arccos(1 - 2 * u) + + return samples + def draw_samples(self, num_samples): """ Draw samples from a Sine distribution. @@ -461,9 +543,9 @@ def draw_samples(self, num_samples): """ # draw uniform from -1 to 1 - samples = np.random.uniform(-1, 1, num_samples) + samples = np.random.uniform(0, 1, num_samples) - samples = np.arccos(samples) % np.pi + samples = self.transform_samples(samples) return samples @@ -515,6 +597,27 @@ def __init__(self, m, b): def __repr__(self): return "Linear" + def transform_samples(self, u): + """ + Transform uniform 1D samples, u, to samples drawn + from a Linear distribution. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a Linear + distribution. + """ + norm = -0.5 * self.b**2 / self.m + + # generate samples following a linear distribution + linear_samples = -np.sqrt(2.0 * norm * u / self.m + (self.b / self.m) ** 2) - ( + self.b / self.m + ) + + return linear_samples + def draw_samples(self, num_samples): """ Draw samples from a descending linear distribution. @@ -525,20 +628,16 @@ def draw_samples(self, num_samples): Returns: np.array: samples ranging from [0, -b/m) as floats. """ - norm = -0.5 * self.b**2 / self.m # draw uniform from 0 to 1 samples = np.random.uniform(0, 1, num_samples) # generate samples following a linear distribution - linear_samples = -np.sqrt( - 2.0 * norm * samples / self.m + (self.b / self.m) ** 2 - ) - (self.b / self.m) + linear_samples = self.transform_samples(samples) return linear_samples def compute_lnprob(self, element_array): - x_intercept = -self.b / self.m normalizer = -0.5 * self.b**2 / self.m @@ -730,22 +829,31 @@ def all_lnpriors(params, priors): for param, prior in zip(params, priors): param = np.array([param]) - logp += prior.compute_lnprob(param) # retrun a float + logp += prior.compute_lnprob(param) # return a float return logp if __name__ == "__main__": + # myPrior = LinearPrior(-1.0, 1.0) + # mySamples = myPrior.draw_samples(1000) + # print(mySamples) + # myProbs = myPrior.compute_lnprob(mySamples) + # print(myProbs) + + # myPrior = GaussianPrior(1.3, 0.2) + # mySamples = myPrior.draw_samples(1) + # print(mySamples) + + # myProbs = myPrior.compute_lnprob(mySamples) + # print(myProbs) - myPrior = LinearPrior(-1.0, 1.0) - mySamples = myPrior.draw_samples(1000) - print(mySamples) - myProbs = myPrior.compute_lnprob(mySamples) - print(myProbs) + myPrior = GaussianPrior(-10, 0.5, no_negatives=True) + u = np.random.uniform(0, 1, int(1e4)) + samps = myPrior.transform_samples(u) + print(samps.min(), samps.max()) - myPrior = GaussianPrior(1.3, 0.2) - mySamples = myPrior.draw_samples(1) - print(mySamples) + import matplotlib.pyplot as plt - myProbs = myPrior.compute_lnprob(mySamples) - print(myProbs) + plt.hist(samps, bins=50) + plt.show() diff --git a/orbitize/sampler.py b/orbitize/sampler.py index 764abe50..5ee50f21 100644 --- a/orbitize/sampler.py +++ b/orbitize/sampler.py @@ -5,9 +5,12 @@ import time from astropy.time import Time +import dynesty + import emcee import ptemcee import multiprocessing as mp + from multiprocessing import Pool import orbitize.lnlike @@ -142,7 +145,6 @@ def _logl(self, params): params, self.system.param_idx, ) - return lnlikes_sum @@ -512,9 +514,9 @@ def _sampler_process( else: n_accepted = len(accepted_orbits) maxindex2save = np.min([n_accepted, total_orbits - n_orbits_saved]) - output_orbits[ - n_orbits_saved : n_orbits_saved + n_accepted - ] = accepted_orbits[0:maxindex2save] + output_orbits[n_orbits_saved : n_orbits_saved + n_accepted] = ( + accepted_orbits[0:maxindex2save] + ) output_lnlikes[n_orbits_saved : n_orbits_saved + n_accepted] = lnlikes[ 0:maxindex2save ] @@ -644,12 +646,12 @@ def run_sampler( n_accepted = len(accepted_orbits) maxindex2save = np.min([n_accepted, total_orbits - n_orbits_saved]) - output_orbits[ - n_orbits_saved : n_orbits_saved + n_accepted - ] = accepted_orbits[0:maxindex2save] - output_lnlikes[ - n_orbits_saved : n_orbits_saved + n_accepted - ] = lnlikes[0:maxindex2save] + output_orbits[n_orbits_saved : n_orbits_saved + n_accepted] = ( + accepted_orbits[0:maxindex2save] + ) + output_lnlikes[n_orbits_saved : n_orbits_saved + n_accepted] = ( + lnlikes[0:maxindex2save] + ) n_orbits_saved += maxindex2save # print progress statement @@ -657,7 +659,6 @@ def run_sampler( str(n_orbits_saved) + "/" + str(total_orbits) + " orbits found", end="\r", ) - self.results.add_samples(np.array(output_orbits), output_lnlikes) return output_orbits @@ -1309,3 +1310,140 @@ def check_prior_support(self): # otherwise exit the function and continue. return + + +class NestedSampler(Sampler): + """ + Implements nested sampling using Dynesty package. + + Thea McKenna, Sarah Blunt, & Lea Hirsch 2024 + """ + + def __init__(self, system): + super(NestedSampler, self).__init__(system) + + # create an empty results object + self.results = orbitize.results.Results( + self.system, + sampler_name=self.__class__.__name__, + post=None, + lnlike=None, + version_number=orbitize.__version__, + ) + self.start = time.time() + self.dynesty_sampler = None + + def ptform(self, u): + """ + Prior transform function. + + Args: + u (array of floats): list of samples with values 0 < u < 1. + + Returns: + numpy array of floats: 1D u samples transformed to a chosen Prior + Class distribution. + """ + utform = np.zeros(len(u)) + for i in range(len(u)): + try: + utform[i] = self.system.sys_priors[i].transform_samples(u[i]) + except AttributeError: # prior is a fixed number + utform[i] = self.system.sys_priors[i] + return utform + + def run_sampler( + self, + static=False, + bound="multi", + pfrac=1.0, + num_threads=1, + start_method="fork", + run_nested_kwargs={}, + ): + """Runs the nested sampler from the Dynesty package. + + Args: + static (bool): True if using static nested sampling, + False if using dynamic. + bound (str): Method used to approximately bound the prior + using the current set of live points. Conditions the + sampling methods used to propose new live points. See + https://dynesty.readthedocs.io/en/latest/quickstart.html#bounding-options + for complete list of options. + pfrac (float): posterior weight, between 0 and 1. Can only be + altered for the Dynamic nested sampler, otherwise this + keyword is unused. + num_threads (int): number of threads to use for parallelization + (default=1) + start_method (str): multiprocessing start method. Default "fork," which + won't work on all OS. Change to "spawn" if you get an error, + and make sure you run your orbitize! script inside an + if __name__=='__main__' condition to protect entry points. + run_nested_kwargs (dict): dictionary of keywords to be passed into dynesty.Sampler.run_nested() + + Returns: + tuple: + + numpy.array of float: posterior samples + + int: number of iterations it took to converge + """ + + mp.set_start_method(start_method, force=True) + if static and pfrac != 1.0: + raise ValueError( + """The static nested sampler does not take alternate values for pfrac.""" + ) + + if num_threads > 1: + with dynesty.pool.Pool(num_threads, self._logl, self.ptform) as pool: + if static: + self.dynesty_sampler = dynesty.NestedSampler( + pool.loglike, + pool.prior_transform, + len(self.system.sys_priors), + pool=pool, + bound=bound, + bootstrap=False, + ) + self.dynesty_sampler.run_nested(**run_nested_kwargs) + else: + self.dynesty_sampler = dynesty.DynamicNestedSampler( + pool.loglike, + pool.prior_transform, + len(self.system.sys_priors), + pool=pool, + bound=bound, + bootstrap=False, + ) + self.dynesty_sampler.run_nested( + wt_kwargs={"pfrac": pfrac}, **run_nested_kwargs + ) + else: + if static: + self.dynesty_sampler = dynesty.NestedSampler( + self._logl, + self.ptform, + len(self.system.sys_priors), + bound=bound, + ) + self.dynesty_sampler.run_nested(**run_nested_kwargs) + else: + self.dynesty_sampler = dynesty.DynamicNestedSampler( + self._logl, + self.ptform, + len(self.system.sys_priors), + bound=bound, + ) + self.dynesty_sampler.run_nested( + wt_kwargs={"pfrac": pfrac}, **run_nested_kwargs + ) + + self.results.add_samples( + self.dynesty_sampler.results["samples"], + self.dynesty_sampler.results["logl"], + ) + num_iter = self.dynesty_sampler.results["niter"] + + return self.dynesty_sampler.results["samples"], num_iter diff --git a/orbitize/system.py b/orbitize/system.py index 4f1dddd9..97c77276 100644 --- a/orbitize/system.py +++ b/orbitize/system.py @@ -1,6 +1,8 @@ import numpy as np from orbitize import nbody, kepler, basis from astropy import table +from orbitize.read_input import read_file + class System(object): """ @@ -11,11 +13,13 @@ class System(object): Args: num_secondary_bodies (int): number of secondary bodies in the system. Should be at least 1. - data_table (astropy.table.Table): output from + data_table (astropy.table.Table): output from ``orbitize.read_input.read_file()`` stellar_or_system_mass (float): mass of the primary star (if fitting for - dynamical masses of both components) or total system mass (if - fitting using relative astrometry only) [M_sol] + dynamical masses of both components, for example when you have both + astrometry and RVs) or total system mass (if fitting for total system + mass only, as in the case of a vanilla 2-body fit using relative + astrometry only ) [M_sol] plx (float): mean parallax of the system, in mas mass_err (float, optional): uncertainty on ``stellar_or_system_mass``, in M_sol plx_err (float, optional): uncertainty on ``plx``, in mas @@ -25,35 +29,45 @@ class System(object): tau_ref_epoch (float, optional): reference epoch for defining tau (MJD). Default is 58849 (Jan 1, 2020). fit_secondary_mass (bool, optional): if True, include the dynamical - mass of the orbiting body as a fitted parameter. If this is set to - False, ``stellar_or_system_mass`` is taken to be the total mass of the system. + mass of the orbiting body as a fitted parameter. If this is set to + False, ``stellar_or_system_mass`` is taken to be the total mass of the system. (default: False) - hipparcos_IAD (orbitize.hipparcos.HipparcosLogProb): an object + hipparcos_IAD (orbitize.hipparcos.HipparcosLogProb): an object containing information & precomputed values relevant to Hipparcos IAD fitting. See hipparcos.py for more details. - gaia (orbitize.gaia.GaiaLogProb): an object + gaia (orbitize.gaia.GaiaLogProb): an object containing information & precomputed values relevant to Gaia astrometrry fitting. See gaia.py for more details. - fitting_basis (str): the name of the class corresponding to the fitting + fitting_basis (str): the name of the class corresponding to the fitting basis to be used. See basis.py for a list of implemented fitting bases. use_rebound (bool): if True, use an n-body backend solver instead of a Keplerian solver. Priors are initialized as a list of orbitize.priors.Prior objects and stored - in the variable ``System.sys_priors``. You should initialize this class, - then overwrite priors you wish to customize. You can use the - ``System.param_idx`` attribute to figure out which indices correspond to - which fitting parameters. See the "changing priors" tutorial for more detail. + in the variable ``System.sys_priors``. You should initialize this class, + then overwrite priors you wish to customize. You can use the + ``System.param_idx`` attribute to figure out which indices correspond to + which fitting parameters. See the "changing priors" tutorial for more detail. Written: Sarah Blunt, Henry Ngo, Jason Wang, 2018 """ - def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, - plx, mass_err=0, plx_err=0, restrict_angle_ranges=False, - tau_ref_epoch=58849, fit_secondary_mass=False, - hipparcos_IAD=None, gaia=None, fitting_basis='Standard', use_rebound=False, - ): - + def __init__( + self, + num_secondary_bodies, + data_table, + stellar_or_system_mass, + plx, + mass_err=0, + plx_err=0, + restrict_angle_ranges=False, + tau_ref_epoch=58849, + fit_secondary_mass=False, + hipparcos_IAD=None, + gaia=None, + fitting_basis="Standard", + use_rebound=False, + ): self.num_secondary_bodies = num_secondary_bodies self.data_table = data_table self.stellar_or_system_mass = stellar_or_system_mass @@ -84,40 +98,40 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, # List of index arrays corresponding to each rv for each body self.rv = [] - self.fit_astrometry=True - radec_indices = np.where(self.data_table['quant_type'] == 'radec') - seppa_indices = np.where(self.data_table['quant_type'] == 'seppa') + self.fit_astrometry = True + radec_indices = np.where(self.data_table["quant_type"] == "radec") + seppa_indices = np.where(self.data_table["quant_type"] == "seppa") - if len(radec_indices[0])==0 and len(seppa_indices[0])==0: - self.fit_astrometry=False - rv_indices = np.where(self.data_table['quant_type'] == 'rv') + if len(radec_indices[0]) == 0 and len(seppa_indices[0]) == 0: + self.fit_astrometry = False + rv_indices = np.where(self.data_table["quant_type"] == "rv") # defining all indices to loop through the unique rv instruments to get different offsets and jitters - instrument_list = np.unique(self.data_table['instrument']) + instrument_list = np.unique(self.data_table["instrument"]) inst_indices_all = [] for inst in instrument_list: - inst_indices = np.where(self.data_table['instrument'] == inst) + inst_indices = np.where(self.data_table["instrument"] == inst) inst_indices_all.append(inst_indices) # defining indices for unique instruments in the data table - self.rv_instruments = np.unique(self.data_table['instrument'][rv_indices]) + self.rv_instruments = np.unique(self.data_table["instrument"][rv_indices]) self.rv_inst_indices = [] for inst in self.rv_instruments: - inst_indices = np.where(self.data_table['instrument'] == inst) + inst_indices = np.where(self.data_table["instrument"] == inst) self.rv_inst_indices.append(inst_indices) # astrometry instruments same for radec and seppa: self.astr_instruments = np.unique( - self.data_table['instrument'][np.where(self.data_table['quant_type'] != 'rv')]) + self.data_table["instrument"][ + np.where(self.data_table["quant_type"] != "rv") + ] + ) # save indicies for all of the ra/dec, sep/pa measurements for convenience self.all_radec = radec_indices self.all_seppa = seppa_indices - for body_num in np.arange(self.num_secondary_bodies+1): - - self.body_indices.append( - np.where(self.data_table['object'] == body_num) - ) + for body_num in np.arange(self.num_secondary_bodies + 1): + self.body_indices.append(np.where(self.data_table["object"] == body_num)) self.radec.append( np.intersect1d(self.body_indices[body_num], radec_indices) @@ -125,15 +139,12 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, self.seppa.append( np.intersect1d(self.body_indices[body_num], seppa_indices) ) - self.rv.append( - np.intersect1d(self.body_indices[body_num], rv_indices) - ) + self.rv.append(np.intersect1d(self.body_indices[body_num], rv_indices)) # we should track the influence of the planet(s) on each other/the star if: - # we are not fitting massless planets and + # we are not fitting massless planets and # we have more than 1 companion OR we have stellar astrometry - self.track_planet_perturbs = ( - self.fit_secondary_mass and + self.track_planet_perturbs = self.fit_secondary_mass and ( ( ((len(self.radec[0]) + len(self.seppa[0]) > 0) or (self.num_secondary_bodies > 1) or @@ -149,7 +160,7 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, if self.restrict_angle_ranges: angle_upperlim = np.pi else: - angle_upperlim = 2.*np.pi + angle_upperlim = 2.0 * np.pi # Check for rv data contains_rv = False @@ -161,30 +172,38 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, basis_obj = getattr(basis, self.fitting_basis) # Obtain extra necessary data to assign priors for XYZ - if self.fitting_basis == 'XYZ': + if self.fitting_basis == "XYZ": # Get epochs with least uncertainty, as is done in sampler.py convert_warning_print = False for body_num in np.arange(self.num_secondary_bodies) + 1: if len(self.radec[body_num]) > 0: - # only print the warning once. + # only print the warning once. if not convert_warning_print: - print('Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table.') + print( + "Converting ra/dec data points in data_table to sep/pa. Original data are stored in input_table." + ) convert_warning_print = True self.convert_data_table_radec2seppa(body_num=body_num) - sep_err = self.data_table[np.where( - self.data_table['quant_type'] == 'seppa')]['quant1_err'].copy() - meas_object = self.data_table[np.where( - self.data_table['quant_type'] == 'seppa')]['object'].copy() + sep_err = self.data_table[ + np.where(self.data_table["quant_type"] == "seppa") + ]["quant1_err"].copy() + meas_object = self.data_table[ + np.where(self.data_table["quant_type"] == "seppa") + ]["object"].copy() - astr_inds = np.where(self.input_table['object'] > 0)[0] + astr_inds = np.where(self.input_table["object"] > 0)[0] astr_data = self.input_table[astr_inds] - epochs = astr_data['epoch'] + epochs = astr_data["epoch"] self.best_epochs = [] self.best_epoch_idx = [] - min_sep_indices = np.argsort(sep_err) # indices of sep err sorted from smallest to higheset - min_sep_indices_body = meas_object[min_sep_indices] # the corresponding body_num that these sorted measurements correspond to + min_sep_indices = np.argsort( + sep_err + ) # indices of sep err sorted from smallest to higheset + min_sep_indices_body = meas_object[ + min_sep_indices + ] # the corresponding body_num that these sorted measurements correspond to for i in range(self.num_secondary_bodies): body_num = i + 1 this_object_meas = np.where(min_sep_indices_body == body_num)[0] @@ -193,64 +212,76 @@ def __init__(self, num_secondary_bodies, data_table, stellar_or_system_mass, self.best_epochs.append(None) continue # get the smallest measurement belonging to this body - this_best_epoch_idx = min_sep_indices[this_object_meas][0] # already sorted by argsort + this_best_epoch_idx = min_sep_indices[this_object_meas][ + 0 + ] # already sorted by argsort self.best_epoch_idx.append(this_best_epoch_idx) this_best_epoch = epochs[this_best_epoch_idx] self.best_epochs.append(this_best_epoch) - self.extra_basis_kwargs = {'data_table':astr_data, 'best_epoch_idx':self.best_epoch_idx, 'epochs':epochs} + self.extra_basis_kwargs = { + "data_table": astr_data, + "best_epoch_idx": self.best_epoch_idx, + "epochs": epochs, + } self.basis = basis_obj( - self.stellar_or_system_mass, self.mass_err, self.plx, self.plx_err, self.num_secondary_bodies, - self.fit_secondary_mass, angle_upperlim=angle_upperlim, - hipparcos_IAD=self.hipparcos_IAD, rv=contains_rv, - rv_instruments=self.rv_instruments, **self.extra_basis_kwargs + self.stellar_or_system_mass, + self.mass_err, + self.plx, + self.plx_err, + self.num_secondary_bodies, + self.fit_secondary_mass, + angle_upperlim=angle_upperlim, + hipparcos_IAD=self.hipparcos_IAD, + rv=contains_rv, + rv_instruments=self.rv_instruments, + **self.extra_basis_kwargs ) self.basis.verify_params() self.sys_priors, self.labels = self.basis.construct_priors() self.secondary_mass_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('m') and - not i.endswith('0') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("m") and not i.endswith("0")) ] - + self.sma_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('sma') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("sma")) ] self.ecc_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('ecc') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("ecc")) ] self.inc_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('inc') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("inc")) ] self.aop_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('aop') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("aop")) ] self.pan_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('pan') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("pan")) ] self.tau_indx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('tau') - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("tau")) ] self.mpl_idx = [ - self.basis.standard_basis_idx[i] for i in self.basis.standard_basis_idx.keys() if ( - i.startswith('m') and i[1:] not in ['tot', '0'] - ) + self.basis.standard_basis_idx[i] + for i in self.basis.standard_basis_idx.keys() + if (i.startswith("m") and i[1:] not in ["tot", "0"]) ] self.param_idx = self.basis.param_idx @@ -261,29 +292,27 @@ def save(self, hf): Args: hf (h5py._hl.files.File): a currently open hdf5 file in which - to save the object. + to save the object. """ - hf.attrs['num_secondary_bodies'] = self.num_secondary_bodies + hf.attrs["num_secondary_bodies"] = self.num_secondary_bodies - hf.create_dataset('data', data=self.input_table) + hf.create_dataset("data", data=self.input_table) - hf.attrs['restrict_angle_ranges'] = self.restrict_angle_ranges - hf.attrs['tau_ref_epoch'] = self.tau_ref_epoch - hf.attrs['stellar_or_system_mass'] = self.stellar_or_system_mass - hf.attrs['plx'] = self.plx - hf.attrs['mass_err'] = self.mass_err - hf.attrs['plx_err'] = self.plx_err - hf.attrs['fit_secondary_mass'] = self.fit_secondary_mass + hf.attrs["restrict_angle_ranges"] = self.restrict_angle_ranges + hf.attrs["tau_ref_epoch"] = self.tau_ref_epoch + hf.attrs["stellar_or_system_mass"] = self.stellar_or_system_mass + hf.attrs["plx"] = self.plx + hf.attrs["mass_err"] = self.mass_err + hf.attrs["plx_err"] = self.plx_err + hf.attrs["fit_secondary_mass"] = self.fit_secondary_mass if self.gaia is not None: self.gaia._save(hf) elif self.hipparcos_IAD is not None: self.hipparcos_IAD._save(hf) - hf.attrs['fitting_basis'] = self.fitting_basis - hf.attrs['use_rebound'] = self.use_rebound - - + hf.attrs["fitting_basis"] = self.fitting_basis + hf.attrs["use_rebound"] = self.use_rebound def compute_all_orbits(self, params_arr, epochs=None, comp_rebound=False): """ @@ -298,10 +327,10 @@ def compute_all_orbits(self, params_arr, epochs=None, comp_rebound=False): documented in ``System()`` above. If M=1, this can be a 1d array. epochs (np.array of float): epochs (in mjd) at which to compute orbit predictions. - comp_rebound (bool, optional): A secondary optional input for + comp_rebound (bool, optional): A secondary optional input for use of N-body solver Rebound; by default, this will be set - to false and a Kepler solver will be used instead. - + to false and a Kepler solver will be used instead. + Returns: tuple: @@ -310,14 +339,14 @@ def compute_all_orbits(self, params_arr, epochs=None, comp_rebound=False): decoff (np.array of float): N_epochs x N_bodies x N_orbits array of Dec offsets from barycenter at each epoch. - + vz (np.array of float): N_epochs x N_bodies x N_orbits array of radial velocities at each epoch. """ if epochs is None: - epochs = self.data_table['epoch'] + epochs = self.data_table["epoch"] n_epochs = len(epochs) @@ -326,10 +355,12 @@ def compute_all_orbits(self, params_arr, epochs=None, comp_rebound=False): else: n_orbits = params_arr.shape[1] - ra_kepler = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) # N_epochs x N_bodies x N_orbits + ra_kepler = np.zeros( + (n_epochs, self.num_secondary_bodies + 1, n_orbits) + ) # N_epochs x N_bodies x N_orbits dec_kepler = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) - ra_perturb = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) + ra_perturb = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) dec_perturb = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) vz = np.zeros((n_epochs, self.num_secondary_bodies + 1, n_orbits)) @@ -340,155 +371,199 @@ def compute_all_orbits(self, params_arr, epochs=None, comp_rebound=False): mtots = np.zeros((self.num_secondary_bodies + 1, n_orbits)) if comp_rebound or self.use_rebound: - sma = params_arr[self.sma_indx] ecc = params_arr[self.ecc_indx] inc = params_arr[self.inc_indx] argp = params_arr[self.aop_indx] lan = params_arr[self.pan_indx] tau = params_arr[self.tau_indx] - plx = params_arr[self.basis.standard_basis_idx['plx']] + plx = params_arr[self.basis.standard_basis_idx["plx"]] if self.fit_secondary_mass: m_pl = params_arr[self.mpl_idx] - m0 = params_arr[self.basis.param_idx['m0']] + m0 = params_arr[self.basis.param_idx["m0"]] mtot = m0 + sum(m_pl) else: m_pl = np.zeros(self.num_secondary_bodies) # if not fitting for secondary mass, then total mass must be stellar mass - mtot = params_arr[self.basis.param_idx['mtot']] - - raoff, deoff, vz = nbody.calc_orbit(epochs, sma, ecc, inc, argp, lan, tau, plx, mtot, tau_ref_epoch=self.tau_ref_epoch, m_pl=m_pl, output_star=True) + mtot = params_arr[self.basis.param_idx["mtot"]] + + raoff, deoff, vz = nbody.calc_orbit( + epochs, + sma, + ecc, + inc, + argp, + lan, + tau, + plx, + mtot, + tau_ref_epoch=self.tau_ref_epoch, + m_pl=m_pl, + output_star=True, + ) else: - for body_num in np.arange(self.num_secondary_bodies)+1: - - sma = params_arr[self.basis.standard_basis_idx['sma{}'.format(body_num)]] - ecc = params_arr[self.basis.standard_basis_idx['ecc{}'.format(body_num)]] - inc = params_arr[self.basis.standard_basis_idx['inc{}'.format(body_num)]] - argp = params_arr[self.basis.standard_basis_idx['aop{}'.format(body_num)]] - lan = params_arr[self.basis.standard_basis_idx['pan{}'.format(body_num)]] - tau = params_arr[self.basis.standard_basis_idx['tau{}'.format(body_num)]] - plx = params_arr[self.basis.standard_basis_idx['plx']] - - if self.fit_secondary_mass: - # mass of secondary bodies are in order from -1-num_bodies until -2 in order. - mass = params_arr[self.basis.standard_basis_idx['m{}'.format(body_num)]] - m0 = params_arr[self.basis.standard_basis_idx['m0']] - - # For what mtot to use to calculate central potential, we should use the mass enclosed in a sphere with r <= distance of planet. - # We need to select all planets with sma < this planet. - all_smas = params_arr[self.sma_indx] - within_orbit = np.where(all_smas <= sma) - outside_orbit = np.where(all_smas > sma) - all_pl_masses = params_arr[self.secondary_mass_indx] - inside_masses = all_pl_masses[within_orbit] - mtot = np.sum(inside_masses) + m0 + for body_num in np.arange(self.num_secondary_bodies) + 1: + sma = params_arr[ + self.basis.standard_basis_idx["sma{}".format(body_num)] + ] + ecc = params_arr[ + self.basis.standard_basis_idx["ecc{}".format(body_num)] + ] + inc = params_arr[ + self.basis.standard_basis_idx["inc{}".format(body_num)] + ] + argp = params_arr[ + self.basis.standard_basis_idx["aop{}".format(body_num)] + ] + lan = params_arr[ + self.basis.standard_basis_idx["pan{}".format(body_num)] + ] + tau = params_arr[ + self.basis.standard_basis_idx["tau{}".format(body_num)] + ] + plx = params_arr[self.basis.standard_basis_idx["plx"]] + + if self.fit_secondary_mass: + # mass of secondary bodies are in order from -1-num_bodies until -2 in order. + mass = params_arr[ + self.basis.standard_basis_idx["m{}".format(body_num)] + ] + m0 = params_arr[self.basis.standard_basis_idx["m0"]] + + # For what mtot to use to calculate central potential, we should use the mass enclosed in a sphere with r <= distance of planet. + # We need to select all planets with sma < this planet. + all_smas = params_arr[self.sma_indx] + within_orbit = np.where(all_smas <= sma) + outside_orbit = np.where(all_smas > sma) + all_pl_masses = params_arr[self.secondary_mass_indx] + inside_masses = all_pl_masses[within_orbit] + mtot = np.sum(inside_masses) + m0 + + else: + m_pl = np.zeros(self.num_secondary_bodies) + # if not fitting for secondary mass, then total mass must be stellar mass + mass = None + m0 = None + mtot = params_arr[self.basis.standard_basis_idx["mtot"]] - else: - m_pl = np.zeros(self.num_secondary_bodies) - # if not fitting for secondary mass, then total mass must be stellar mass - mass = None - m0 = None - mtot = params_arr[self.basis.standard_basis_idx['mtot']] - - if self.track_planet_perturbs: - masses[body_num] = mass - mtots[body_num] = mtot - - # solve Kepler's equation - raoff, decoff, vz_i = kepler.calc_orbit( - epochs, sma, ecc, inc, argp, lan, tau, plx, mtot, - mass_for_Kamp=m0, tau_ref_epoch=self.tau_ref_epoch - ) - - # raoff, decoff, vz are scalers if the length of epochs is 1 - if len(epochs) == 1: - raoff = np.array([raoff]) - decoff = np.array([decoff]) - vz_i = np.array([vz_i]) - - # add Keplerian ra/deoff for this body to storage arrays - ra_kepler[:, body_num, :] = np.reshape(raoff, (n_epochs, n_orbits)) - dec_kepler[:, body_num, :] = np.reshape(decoff, (n_epochs, n_orbits)) - vz[:, body_num, :] = np.reshape(vz_i, (n_epochs, n_orbits)) - - # vz_i is the ith companion radial velocity - if self.fit_secondary_mass: - vz0 = np.reshape(vz_i * -(mass / m0), (n_epochs, n_orbits)) # calculating stellar velocity due to ith companion - vz[:, 0, :] += vz0 # adding stellar velocity and gamma - - # if we are fitting for the mass of the planets, then they will perturb the star - # add the perturbation on the star due to this planet on the relative astrometry of the planet that was measured - # We are superimposing the Keplerian orbits, so we can add it linearly, scaled by the mass. - # Because we are in Jacobi coordinates, for companions, we only should model the effect of planets interior to it. - # (Jacobi coordinates mean that separation for a given companion is measured relative to the barycenter of all interior companions) if self.track_planet_perturbs: - for body_num in np.arange(self.num_secondary_bodies + 1): + masses[body_num] = mass + mtots[body_num] = mtot + + # solve Kepler's equation + raoff, decoff, vz_i = kepler.calc_orbit( + epochs, + sma, + ecc, + inc, + argp, + lan, + tau, + plx, + mtot, + mass_for_Kamp=m0, + tau_ref_epoch=self.tau_ref_epoch, + ) - if body_num > 0: - # for companions, only perturb companion orbits at larger SMAs than this one. - sma = params_arr[self.basis.standard_basis_idx['sma{}'.format(body_num)]] - all_smas = params_arr[self.sma_indx] - outside_orbit = np.where(all_smas > sma)[0] - which_perturb_bodies = outside_orbit + 1 + # raoff, decoff, vz are scalers if the length of epochs is 1 + if len(epochs) == 1: + raoff = np.array([raoff]) + decoff = np.array([decoff]) + vz_i = np.array([vz_i]) + + # add Keplerian ra/deoff for this body to storage arrays + ra_kepler[:, body_num, :] = np.reshape(raoff, (n_epochs, n_orbits)) + dec_kepler[:, body_num, :] = np.reshape(decoff, (n_epochs, n_orbits)) + vz[:, body_num, :] = np.reshape(vz_i, (n_epochs, n_orbits)) + + # vz_i is the ith companion radial velocity + if self.fit_secondary_mass: + vz0 = np.reshape( + vz_i * -(mass / m0), (n_epochs, n_orbits) + ) # calculating stellar velocity due to ith companion + vz[:, 0, :] += vz0 # adding stellar velocity and gamma + + # if we are fitting for the mass of the planets, then they will perturb the star + # add the perturbation on the star due to this planet on the relative astrometry of the planet that was measured + # We are superimposing the Keplerian orbits, so we can add it linearly, scaled by the mass. + # Because we are in Jacobi coordinates, for companions, we only should model the effect of planets interior to it. + # (Jacobi coordinates mean that separation for a given companion is measured relative to the barycenter of all interior companions) + if self.track_planet_perturbs: + for body_num in np.arange(self.num_secondary_bodies + 1): + if body_num > 0: + # for companions, only perturb companion orbits at larger SMAs than this one. + sma = params_arr[ + self.basis.standard_basis_idx["sma{}".format(body_num)] + ] + all_smas = params_arr[self.sma_indx] + outside_orbit = np.where(all_smas > sma)[0] + which_perturb_bodies = outside_orbit + 1 + + # the planet will also perturb the star + which_perturb_bodies = np.append([0], which_perturb_bodies) - # the planet will also perturb the star - which_perturb_bodies = np.append([0], which_perturb_bodies) + else: + # for the star, what we are measuring is its position relative to the system barycenter + # so we want to account for all of the bodies. + which_perturb_bodies = np.arange(self.num_secondary_bodies + 1) + + for other_body_num in which_perturb_bodies: + # skip itself since the the 2-body problem is measuring the planet-star separation already + if (body_num == other_body_num) | (body_num == 0): + continue + + ## NOTE: we are only handling astrometry right now (TODO: integrate RV into this) + # this computes the perturbation on the other body due to the current body + + # star is perturbed in opposite direction + if other_body_num == 0: + ra_perturb[:, other_body_num, :] -= ( + masses[body_num] / mtots[body_num] + ) * ra_kepler[:, body_num, :] + dec_perturb[:, other_body_num, :] -= ( + masses[body_num] / mtots[body_num] + ) * dec_kepler[:, body_num, :] else: - # for the star, what we are measuring is its position relative to the system barycenter - # so we want to account for all of the bodies. - which_perturb_bodies = np.arange(self.num_secondary_bodies+1) - - for other_body_num in which_perturb_bodies: - # skip itself since the the 2-body problem is measuring the planet-star separation already - if (body_num == other_body_num) | (body_num == 0): - continue - - ## NOTE: we are only handling astrometry right now (TODO: integrate RV into this) - # this computes the perturbation on the other body due to the current body - - # star is perturbed in opposite direction - if other_body_num == 0: - ra_perturb[:, other_body_num, :] -= (masses[body_num]/mtots[body_num]) * ra_kepler[:, body_num, :] - dec_perturb[:, other_body_num, :] -= (masses[body_num]/mtots[body_num]) * dec_kepler[:, body_num, :] - - else: - ra_perturb[:, other_body_num, :] += (masses[body_num]/mtots[body_num]) * ra_kepler[:, body_num, :] - dec_perturb[:, other_body_num, :] += (masses[body_num]/mtots[body_num]) * dec_kepler[:, body_num, :] - - raoff = ra_kepler + ra_perturb - deoff = dec_kepler + dec_perturb - - if self.fitting_basis == 'XYZ': + ra_perturb[:, other_body_num, :] += ( + masses[body_num] / mtots[body_num] + ) * ra_kepler[:, body_num, :] + dec_perturb[:, other_body_num, :] += ( + masses[body_num] / mtots[body_num] + ) * dec_kepler[:, body_num, :] + + raoff = ra_kepler + ra_perturb + deoff = dec_kepler + dec_perturb + + if self.fitting_basis == "XYZ": # Find and filter out unbound orbits - bad_orbits = np.where(np.logical_or(ecc >= 1., ecc < 0.))[0] - if (bad_orbits.size != 0): - raoff[:,:, bad_orbits] = np.inf - deoff[:,:, bad_orbits] = np.inf - vz[:,:, bad_orbits] = np.inf + bad_orbits = np.where(np.logical_or(ecc >= 1.0, ecc < 0.0))[0] + if bad_orbits.size != 0: + raoff[:, :, bad_orbits] = np.inf + deoff[:, :, bad_orbits] = np.inf + vz[:, :, bad_orbits] = np.inf + return raoff, deoff, vz + else: return raoff, deoff, vz - else: - return raoff, deoff, vz else: return raoff, deoff, vz - def compute_model(self, params_arr, use_rebound=False): """ - Compute model predictions for an array of fitting parameters. + Compute model predictions for an array of fitting parameters. Calls the above compute_all_orbits() function, adds jitter/gamma to RV measurements, and propagates these predictions to a model array that - can be subtracted from a data array to compute chi2. - + can be subtracted from a data array to compute chi2. + Args: params_arr (np.array of float): RxM array of fitting parameters, where R is the number of parameters being fit, and M is the number of orbits we need model predictions for. Must be in the same order documented in ``System()`` above. If M=1, this can be a 1d array. - use_rebound (bool, optional): A secondary optional input for + use_rebound (bool, optional): A secondary optional input for use of N-body solver Rebound; by default, this will be set to false and a Kepler solver will be used instead. @@ -501,10 +576,12 @@ def compute_model(self, params_arr, use_rebound=False): """ to_convert = np.copy(params_arr) - standard_params_arr = self.basis.to_standard_basis(to_convert) + standard_params_arr = self.basis.to_standard_basis(to_convert) if use_rebound: - raoff, decoff, vz = self.compute_all_orbits(standard_params_arr, comp_rebound=True) + raoff, decoff, vz = self.compute_all_orbits( + standard_params_arr, comp_rebound=True + ) else: raoff, decoff, vz = self.compute_all_orbits(standard_params_arr) @@ -518,29 +595,33 @@ def compute_model(self, params_arr, use_rebound=False): jitter = np.zeros((n_epochs, 2, n_orbits)) gamma = np.zeros((n_epochs, 2, n_orbits)) - if len(self.rv[0]) > 0 and self.fit_secondary_mass: - + if len(self.rv[0]) > 0 and self.fit_secondary_mass: # looping through instruments to get the gammas & jitters for rv_idx in range(len(self.rv_instruments)): - - jitter[self.rv_inst_indices[rv_idx], 0] = standard_params_arr[ # [km/s] - self.basis.standard_basis_idx['sigma_{}'.format(self.rv_instruments[rv_idx])] + jitter[self.rv_inst_indices[rv_idx], 0] = standard_params_arr[ # [km/s] + self.basis.standard_basis_idx[ + "sigma_{}".format(self.rv_instruments[rv_idx]) + ] ] jitter[self.rv_inst_indices[rv_idx], 1] = np.nan - gamma[self.rv_inst_indices[rv_idx], 0] = standard_params_arr[ - self.basis.standard_basis_idx['gamma_{}'.format(self.rv_instruments[rv_idx])] - ] + self.basis.standard_basis_idx[ + "gamma_{}".format(self.rv_instruments[rv_idx]) + ] + ] gamma[self.rv_inst_indices[rv_idx], 1] = np.nan for body_num in np.arange(self.num_secondary_bodies + 1): - # for the model points that correspond to this planet's orbit, add the model prediction # RA/Dec - if len(self.radec[body_num]) > 0: # (prevent empty array dimension errors) - model[self.radec[body_num], 0] = raoff[self.radec[body_num], body_num, :] # N_epochs x N_bodies x N_orbits - model[self.radec[body_num], 1] = decoff[self.radec[body_num], body_num, :] + if len(self.radec[body_num]) > 0: # (prevent empty array dimension errors) + model[self.radec[body_num], 0] = raoff[ + self.radec[body_num], body_num, : + ] # N_epochs x N_bodies x N_orbits + model[self.radec[body_num], 1] = decoff[ + self.radec[body_num], body_num, : + ] # Sep/PA if len(self.seppa[body_num]) > 0: @@ -572,34 +653,39 @@ def convert_data_table_radec2seppa(self, body_num=1): Args: body_num (int): which object to convert (1 = first planet) """ - for i in self.radec[body_num]: # Loop through rows where input provided in radec + for i in self.radec[ + body_num + ]: # Loop through rows where input provided in radec # Get ra/dec values - ra = self.data_table['quant1'][i] - ra_err = self.data_table['quant1_err'][i] - dec = self.data_table['quant2'][i] - dec_err = self.data_table['quant2_err'][i] - radec_corr = self.data_table['quant12_corr'][i] + ra = self.data_table["quant1"][i] + ra_err = self.data_table["quant1_err"][i] + dec = self.data_table["quant2"][i] + dec_err = self.data_table["quant2_err"][i] + radec_corr = self.data_table["quant12_corr"][i] # Convert to sep/PA sep, pa = radec2seppa(ra, dec) - if np.isnan(radec_corr): + if np.isnan(radec_corr): # E-Z - sep_err = 0.5*(ra_err+dec_err) - pa_err = np.degrees(sep_err/sep) + sep_err = 0.5 * (ra_err + dec_err) + pa_err = np.degrees(sep_err / sep) seppa_corr = np.nan else: - sep_err, pa_err, seppa_corr = transform_errors(ra, dec, ra_err, dec_err, radec_corr, radec2seppa) + sep_err, pa_err, seppa_corr = transform_errors( + ra, dec, ra_err, dec_err, radec_corr, radec2seppa + ) # Update data_table - self.data_table['quant1'][i] = sep - self.data_table['quant1_err'][i] = sep_err - self.data_table['quant2'][i] = pa - self.data_table['quant2_err'][i] = pa_err - self.data_table['quant12_corr'][i] = seppa_corr - self.data_table['quant_type'][i] = 'seppa' + self.data_table["quant1"][i] = sep + self.data_table["quant1_err"][i] = sep_err + self.data_table["quant2"][i] = pa + self.data_table["quant2_err"][i] = pa_err + self.data_table["quant12_corr"][i] = seppa_corr + self.data_table["quant_type"][i] = "seppa" # Update self.radec and self.seppa arrays self.radec[body_num] = np.delete( - self.radec[body_num], np.where(self.radec[body_num] == i)[0]) + self.radec[body_num], np.where(self.radec[body_num] == i)[0] + ) self.seppa[body_num] = np.append(self.seppa[body_num], i) @@ -623,13 +709,14 @@ def radec2seppa(ra, dec, mod180=False): """ sep = np.sqrt((ra**2) + (dec**2)) - pa = np.degrees(np.arctan2(ra, dec)) % 360. + pa = np.degrees(np.arctan2(ra, dec)) % 360.0 if mod180: pa[pa < 180] += 360 return sep, pa + def seppa2radec(sep, pa): """ Convenience function to convert sep/pa to ra/dec @@ -649,39 +736,104 @@ def seppa2radec(sep, pa): def transform_errors(x1, x2, x1_err, x2_err, x12_corr, transform_func, nsamps=100000): """ - Transform errors and covariances from one basis to another using a Monte Carlo - apporach - - Args: - x1 (float): planet location in first coordinate (e.g., RA, sep) before - transformation - x2 (float): planet location in the second coordinate (e.g., Dec, PA) - before transformation) - x1_err (float): error in x1 - x2_err (float): error in x2 - x12_corr (float): correlation between x1 and x2 - transform_func (function): function that transforms between (x1, x2) - and (x1p, x2p) (the transformed coordinates). The function signature - should look like: `x1p, x2p = transform_func(x1, x2)` - nsamps (int): number of samples to draw more the Monte Carlo approach. - More is slower but more accurate. - Returns: - tuple (x1p_err, x2p_err, x12p_corr): the errors and correlations for - x1p,x2p (the transformed coordinates) + Transform errors and covariances from one basis to another using a Monte Carlo + apporach + + Args: + x1 (float): planet location in first coordinate (e.g., RA, sep) before + transformation + x2 (float): planet location in the second coordinate (e.g., Dec, PA) + before transformation) + x1_err (float): error in x1 + x2_err (float): error in x2 + x12_corr (float): correlation between x1 and x2 + transform_func (function): function that transforms between (x1, x2) + and (x1p, x2p) (the transformed coordinates). The function signature + should look like: `x1p, x2p = transform_func(x1, x2)` + nsamps (int): number of samples to draw more the Monte Carlo approach. + More is slower but more accurate. + Returns: + tuple (x1p_err, x2p_err, x12p_corr): the errors and correlations for + x1p,x2p (the transformed coordinates) """ if np.isnan(x12_corr): - x12_corr = 0. + x12_corr = 0.0 # construct covariance matrix from the terms provided - cov = np.array([[x1_err**2, x1_err*x2_err*x12_corr], [x1_err*x2_err*x12_corr, x2_err**2]]) + cov = np.array( + [ + [x1_err**2, x1_err * x2_err * x12_corr], + [x1_err * x2_err * x12_corr, x2_err**2], + ] + ) samps = np.random.multivariate_normal([x1, x2], cov, size=nsamps) - x1p, x2p = transform_func(samps[:,0], samps[:, 1]) + x1p, x2p = transform_func(samps[:, 0], samps[:, 1]) x1p_err = np.std(x1p) x2p_err = np.std(x2p) - x12_corr = np.corrcoef([x1p, x2p])[0,1] + x12_corr = np.corrcoef([x1p, x2p])[0, 1] return x1p_err, x2p_err, x12_corr + + +def generate_synthetic_data( + orbit_frac, + mtot, + plx, + ecc=0.5, + inc=np.pi / 4, + argp=np.pi / 4, + lan=np.pi / 4, + tau=0.8, + num_obs=4, + unc=2, +): + """Generate an orbitize-table of synethic data + + Args: + orbit_frac (float): percentage of orbit covered by synthetic data + mtot (float): total mass of the system [M_sol] + plx (float): parallax of system [mas] + num_obs (int): number of observations to generate + unc (float): uncertainty on all simulated RA & Dec measurements [mas] + + Returns: + 2-tuple: + - `astropy.table.Table`: data table of generated synthetic data + - float: the semimajor axis of the generated data + """ + + # calculate RA/Dec at three observation epochs + # `num_obs` epochs between ~2000 and ~2003 [MJD] + observation_epochs = np.linspace(51550.0, 52650.0, num_obs) + num_obs = len(observation_epochs) + + # calculate the orbital fraction + orbit_coverage = (max(observation_epochs) - min(observation_epochs)) / 365.25 + period = 100 * orbit_coverage / orbit_frac + sma = (period**2 * mtot) ** (1 / 3) + + # calculate RA/Dec at three observation epochs + # `num_obs` epochs between ~2000 and ~2003 [MJD] + ra, dec, _ = kepler.calc_orbit( + observation_epochs, sma, ecc, inc, argp, lan, tau, plx, mtot + ) + + # add Gaussian noise to simulate measurement + ra += np.random.normal(scale=unc, size=num_obs) + dec += np.random.normal(scale=unc, size=num_obs) + + # define observational uncertainties + ra_err = dec_err = np.ones(num_obs) * unc + + data_table = table.Table( + [observation_epochs, [1] * num_obs, ra, ra_err, dec, dec_err], + names=("epoch", "object", "raoff", "raoff_err", "decoff", "decoff_err"), + ) + # read into orbitize format + data_table = read_file(data_table) + + return data_table, sma diff --git a/requirements.txt b/requirements.txt index e3f39298..ddc47100 100644 --- a/requirements.txt +++ b/requirements.txt @@ -15,3 +15,4 @@ astroquery sphinx docutils<0.17 rebound +dynesty diff --git a/tests/end-to-end-tests/betaPic_hipIAD.py b/tests/end-to-end-tests/betaPic_hipIAD.py index 3e9d7a9d..38b1c771 100644 --- a/tests/end-to-end-tests/betaPic_hipIAD.py +++ b/tests/end-to-end-tests/betaPic_hipIAD.py @@ -24,12 +24,12 @@ Begin keywords << """ -fit_IAD = True +fit_IAD = True if fit_IAD: - savedir = '/data/user/{}/betaPic/hipIAD'.format(os.getlogin()) + savedir = "/data/user/{}/betaPic/hipIAD".format(os.getlogin()) else: - savedir = '/data/user/{}/betaPic/noIAD'.format(os.getlogin()) + savedir = "/data/user/{}/betaPic/noIAD".format(os.getlogin()) """ >> End keywords """ @@ -37,50 +37,54 @@ if not os.path.exists(savedir): os.mkdir(savedir) -input_file = os.path.join(orbitize.DATADIR, 'betaPic.csv') +input_file = os.path.join(orbitize.DATADIR, "betaPic.csv") plx = 51.5 num_secondary_bodies = 1 data_table = read_input.read_file(input_file) if fit_IAD: - hipparcos_number='027321' + hipparcos_number = "027321" gaia_edr3_number = 4792774797545800832 gaia_dr2_number = 4792774797545105664 - fit_secondary_mass=True - hipparcos_filename=os.path.join(orbitize.DATADIR, 'HIP027321.d') + fit_secondary_mass = True + hipparcos_filename = os.path.join(orbitize.DATADIR, "HIP027321.d") betaPic_Hip = HipparcosLogProb( hipparcos_filename, hipparcos_number, num_secondary_bodies ) - betaPic_gaia = GaiaLogProb( - gaia_dr2_number, betaPic_Hip, dr='dr2' - ) + betaPic_gaia = GaiaLogProb(gaia_dr2_number, betaPic_Hip, dr="dr2") # betaPic_gaia = GaiaLogProb( # gaia_edr3_number, betaPic_Hip, dr='edr3' # ) else: - fit_secondary_mass=False + fit_secondary_mass = False betaPic_Hip = None betaPic_gaia = None betaPic_system = system.System( - num_secondary_bodies, data_table, 1.75, plx, hipparcos_IAD=betaPic_Hip, - gaia=betaPic_gaia, fit_secondary_mass=fit_secondary_mass, mass_err=0.01, - plx_err=0.01 + num_secondary_bodies, + data_table, + 1.75, + plx, + hipparcos_IAD=betaPic_Hip, + gaia=betaPic_gaia, + fit_secondary_mass=fit_secondary_mass, + mass_err=0.01, + plx_err=0.01, ) m0_or_mtot_prior = priors.UniformPrior(1.5, 2.0) # set uniform parallax prior -plx_index = betaPic_system.param_idx['plx'] +plx_index = betaPic_system.param_idx["plx"] betaPic_system.sys_priors[plx_index] = priors.UniformPrior(plx - 1.0, plx + 1.0) # set prior on Omega, since we know that know direction of orbital motion from RV -pan_index = betaPic_system.param_idx['pan1'] +pan_index = betaPic_system.param_idx["pan1"] betaPic_system.sys_priors[pan_index] = priors.UniformPrior(0, np.pi) # set uniform prior on m1 as Nielsen+ 2020 do -m1_index = betaPic_system.param_idx['m1'] +m1_index = betaPic_system.param_idx["m1"] betaPic_system.sys_priors[m1_index] = priors.UniformPrior(0, 0.1) if fit_IAD: @@ -88,7 +92,7 @@ assert betaPic_system.track_planet_perturbs # set uniform m0 prior - m0_index = betaPic_system.param_idx['m0'] + m0_index = betaPic_system.param_idx["m0"] betaPic_system.sys_priors[m0_index] = m0_or_mtot_prior else: @@ -96,32 +100,32 @@ assert not betaPic_system.track_planet_perturbs # set uniform mtot prior - mtot_index = betaPic_system.param_idx['mtot'] + mtot_index = betaPic_system.param_idx["mtot"] betaPic_system.sys_priors[mtot_index] = m0_or_mtot_prior # run MCMC num_threads = 100 num_temps = 20 num_walkers = 1000 -num_steps = 1000000 # n_walkers x n_steps_per_walker +num_steps = 1000000 # n_walkers x n_steps_per_walker burn_steps = 1000 thin = 100 betaPic_sampler = sampler.MCMC( - betaPic_system, num_threads=num_threads, num_temps=num_temps, - num_walkers=num_walkers + betaPic_system, + num_threads=num_threads, + num_temps=num_temps, + num_walkers=num_walkers, ) betaPic_sampler.run_sampler(num_steps, burn_steps=burn_steps, thin=thin) # save chains -betaPic_sampler.results.save_results( - '{}/betaPic_IAD{}.hdf5'.format(savedir, fit_IAD) -) +betaPic_sampler.results.save_results("{}/betaPic_IAD{}.hdf5".format(savedir, fit_IAD)) # make corner plot fig = betaPic_sampler.results.plot_corner() -plt.savefig('{}/corner_IAD{}.png'.format(savedir, fit_IAD), dpi=250) +plt.savefig("{}/corner_IAD{}.png".format(savedir, fit_IAD), dpi=250) # make orbit plot fig = betaPic_sampler.results.plot_orbits() -plt.savefig('{}/orbit_IAD{}.png'.format(savedir, fit_IAD), dpi=250) \ No newline at end of file +plt.savefig("{}/orbit_IAD{}.png".format(savedir, fit_IAD), dpi=250) diff --git a/tests/end-to-end-tests/hr8799e_only.py b/tests/end-to-end-tests/hr8799e_only.py index c3e31390..497302fc 100644 --- a/tests/end-to-end-tests/hr8799e_only.py +++ b/tests/end-to-end-tests/hr8799e_only.py @@ -18,20 +18,20 @@ # System parameters -datafile='hr8799e_1epochgravity.csv' -num_secondary_bodies=1 -system_mass=1.52 # Msol -plx=24.2175 #mas -mass_err=0.15 # Msol -plx_err=0.0881 #mas +datafile = "hr8799e_1epochgravity.csv" +num_secondary_bodies = 1 +system_mass = 1.52 # Msol +plx = 24.2175 # mas +mass_err = 0.15 # Msol +plx_err = 0.0881 # mas # Sampler parameters -likelihood_func_name='chi2_lnlike' -n_temps=20 -n_walkers=1000 -n_threads=mp.cpu_count() -total_orbits=10000000 # n_walkers x num_steps_per_walker -burn_steps=50000 +likelihood_func_name = "chi2_lnlike" +n_temps = 20 +n_walkers = 1000 +n_threads = mp.cpu_count() +total_orbits = 10000000 # n_walkers x num_steps_per_walker +burn_steps = 50000 tau_ref_epoch = 50000 @@ -41,8 +41,13 @@ # Initialize System object which stores data & sets priors my_system = system.System( - num_secondary_bodies, data_table, system_mass, - plx, mass_err=mass_err, plx_err=plx_err, tau_ref_epoch=tau_ref_epoch + num_secondary_bodies, + data_table, + system_mass, + plx, + mass_err=mass_err, + plx_err=plx_err, + tau_ref_epoch=tau_ref_epoch, ) my_sampler = sampler.MCMC(my_system, n_temps, n_walkers, n_threads) @@ -53,22 +58,26 @@ my_sampler.results.save_results("hr8799e_gravity_chains.hdf5") -#import orbitize.results as results -#my_sampler.results = results.Results() -#my_sampler.results.load_results("hr8799e_gravity_chains.hdf5") +# import orbitize.results as results +# my_sampler.results = results.Results() +# my_sampler.results.load_results("hr8799e_gravity_chains.hdf5") # make corner plot fig = my_sampler.results.plot_corner() -plt.savefig('corner_hr8799e_gravity.png', dpi=250) +plt.savefig("corner_hr8799e_gravity.png", dpi=250) # print SMA, ecc, inc labels = ["sma", "ecc", "inc"] paper_vals = ["16.4 (+2.1/-1.1)", "0.15 +/- 0.08", "25 +/- 8"] for i in range(len(labels)): - med_val = np.median(my_sampler.results.post[:,i]) - ci_vals = np.percentile(my_sampler.results.post[:,i], [84, 16]) - med_val - if labels[i] == 'inc': + med_val = np.median(my_sampler.results.post[:, i]) + ci_vals = np.percentile(my_sampler.results.post[:, i], [84, 16]) - med_val + if labels[i] == "inc": med_val = np.degrees(med_val) ci_vals = np.degrees(ci_vals) print("{0}: paper value is {1}".format(labels[i], paper_vals[i])) - print("{3}: this fit obtained {0:.2f} (+{1:.2f}/-{2:.2f})".format(med_val, ci_vals[0], ci_vals[1], labels[i])) + print( + "{3}: this fit obtained {0:.2f} (+{1:.2f}/-{2:.2f})".format( + med_val, ci_vals[0], ci_vals[1], labels[i] + ) + ) diff --git a/tests/end-to-end-tests/nested_test.py b/tests/end-to-end-tests/nested_test.py new file mode 100644 index 00000000..6ea521f9 --- /dev/null +++ b/tests/end-to-end-tests/nested_test.py @@ -0,0 +1,72 @@ +import orbitize +from orbitize import read_input, system, sampler, priors +import matplotlib.pyplot as plt +from dynesty import plotting as dyplot +import time + + +savedir = "." + +""" +Runs the GJ504 fit (from the quickstart tutorial) using dynesty as a backend + +Written: Thea McKenna, 2023 +""" + + +def dynesty_e2e_test(): + + data_table = read_input.read_file("{}/GJ504.csv".format(orbitize.DATADIR)) + + # number of secondary bodies in system + num_planets = 1 + + # total mass & error [msol] + total_mass = 1.22 + mass_err = 0 # 0.08 + + # parallax & error[mas] + plx = 56.95 + plx_err = 0 # 0.26 + + sys = system.System( + num_planets, + data_table, + total_mass, + plx, + mass_err=mass_err, + plx_err=plx_err, + restrict_angle_ranges=True, + ) + # alias for convenience + lab = sys.param_idx + + # set prior on semimajor axis + sys.sys_priors[lab["sma1"]] = priors.LogUniformPrior(10, 300) + + nested_sampler = sampler.NestedSampler(sys) + + start = time.time() + + samples, num_iter = nested_sampler.run_sampler(num_threads=50) + nested_sampler.results.save_results("{}/nested_sampler_test.hdf5".format(savedir)) + print("iteration number is: " + str(num_iter)) + + print("iteration time: {:.f} mins".format((time.time() - start) / 60.0)) + + fig, ax = plt.subplots(2, 1) + accepted_eccentricities = nested_sampler.results.post[:, lab["ecc1"]] + accepted_inclinations = nested_sampler.results.post[:, lab["inc1"]] + ax[0].hist(accepted_eccentricities, bins=50) + ax[1].hist(accepted_inclinations, bins=50) + ax[0].set_xlabel("ecc") + ax[1].set_xlabel("inc") + plt.tight_layout() + plt.savefig("{}/nested_sampler_test.png".format(savedir)) + + fig, axes = dyplot.traceplot(nested_sampler.dynesty_sampler.results) + plt.savefig("{}/nested_sampler_traceplot.png".format(savedir)) + + +if __name__ == "__main__": + dynesty_e2e_test() diff --git a/tests/end-to-end-tests/rv_end_to_end.py b/tests/end-to-end-tests/rv_end_to_end.py index 7dd53424..f1870f97 100644 --- a/tests/end-to-end-tests/rv_end_to_end.py +++ b/tests/end-to-end-tests/rv_end_to_end.py @@ -1,4 +1,4 @@ -from orbitize import read_input, system, priors, sampler,results,kepler +from orbitize import read_input, system, priors, sampler, results, kepler import numpy as np @@ -15,194 +15,226 @@ Written: Vighnesh Nagpal, 2021 """ -def plot_rv(epochs,rvs): - plt.plot(epochs,rvs) - plt.savefig('rv_trend') + +def plot_rv(epochs, rvs): + plt.plot(epochs, rvs) + plt.savefig("rv_trend") plt.close() -def plot_astro(ras,decs): - plt.plot(ras,decs) + +def plot_astro(ras, decs): + plt.plot(ras, decs) plt.axis("equal") - plt.savefig('orbit_trend') + plt.savefig("orbit_trend") plt.close() + def gen_data(): - ''' + """ Simulates radial velocity and astrometric data for a test system. - Returns: - (rvs,rv_epochs): Tuple of generated radial velocity measurements (rvs) and their corresponding + Returns: + (rvs,rv_epochs): Tuple of generated radial velocity measurements (rvs) and their corresponding measurement epochs (rv_epochs) - (ras,decs,astro_epochs): Tuple containing simulated astrometric measurements (ras, decs) + (ras,decs,astro_epochs): Tuple containing simulated astrometric measurements (ras, decs) and the corresponding measurement epochs (astro_epochs) - ''' - #set parameters for the synthetic data - sma=1 - inc=np.pi/2 - ecc=0.2 - aop=np.pi/4 - pan=np.pi/4 - tau=0.4 - plx=50 - mass_for_kamp=0.1 - mtot=1.1 - #epochs and errors for rv - rv_epochs=np.linspace(51544,52426,200) - #epochs and errors for astrometry - astro_epochs=np.linspace(51500,52500,10) - astro_err=0 - #generate rv trend - rvset=kepler.calc_orbit(rv_epochs,sma,ecc,inc,aop,pan,tau,plx,mtot,mass_for_Kamp=mass_for_kamp) - rvs=rvset[2] - #generate predictions for astrometric epochs - astro_set=kepler.calc_orbit(astro_epochs,sma,ecc,inc,aop,pan,tau,plx,mtot,mass_for_Kamp=mass_for_kamp) - ras,decs=astro_set[0],astro_set[1] - #return model generations - return (rvs,rv_epochs),(ras,decs,astro_epochs) - - -def scat_model(rvs,calibration_terms): - ''' + """ + # set parameters for the synthetic data + sma = 1 + inc = np.pi / 2 + ecc = 0.2 + aop = np.pi / 4 + pan = np.pi / 4 + tau = 0.4 + plx = 50 + mass_for_kamp = 0.1 + mtot = 1.1 + # epochs and errors for rv + rv_epochs = np.linspace(51544, 52426, 200) + # epochs and errors for astrometry + astro_epochs = np.linspace(51500, 52500, 10) + astro_err = 0 + # generate rv trend + rvset = kepler.calc_orbit( + rv_epochs, sma, ecc, inc, aop, pan, tau, plx, mtot, mass_for_Kamp=mass_for_kamp + ) + rvs = rvset[2] + # generate predictions for astrometric epochs + astro_set = kepler.calc_orbit( + astro_epochs, + sma, + ecc, + inc, + aop, + pan, + tau, + plx, + mtot, + mass_for_Kamp=mass_for_kamp, + ) + ras, decs = astro_set[0], astro_set[1] + # return model generations + return (rvs, rv_epochs), (ras, decs, astro_epochs) + + +def scat_model(rvs, calibration_terms): + """ Function that adds scatter to RV data based on provided calibration terms (gamma, sigma) that are unique for each instrument in the dataset. - Args: + Args: rvs (array): Array of radial velocity measurements calibration_terms (tuple): Tuple of the form: (gamma_instrument1,jit_instrument1, - gamma_instrument2,jit_instrument2, + gamma_instrument2,jit_instrument2, rv_err) returns: - scat_rvs (array): Array of RV measurements with scatter added + scat_rvs (array): Array of RV measurements with scatter added errors (array): Array of measurement uncertainties the RV measurements - ''' - gam1,jit1,gam2,jit2,rv_err=calibration_terms - #create empty arrays to be filled with data from each inst +respective jit and sigmas - length=int(len(rvs)/2) - off_1=np.zeros(length) - off_2=np.zeros(length) - #create an array of normally sampled jitters for each instruments - - errors1=np.abs(rv_err*np.random.randn(length)) - errors2=np.abs(rv_err*np.random.randn(length)) - - - jscat1=np.random.randn(length)*np.sqrt(jit1**2+errors1**2) - jscat2=np.random.randn(length)*np.sqrt(jit2**2+errors2**2) - #fill off_1 and off_2 - off_1[:]=rvs[:length] - off_2[:]=rvs[length:] - #add scatters and gammas for first instrument - off_1+=gam1 - off_1+=jscat1 - #add scatters and gammas for second instrument - off_2+=gam2 - off_2+=jscat2 - #put em together - scat_rvs=np.concatenate([off_1,off_2]) - #put measurement uncertainties together - errors=np.concatenate([errors1,errors2]) - return scat_rvs,errors - - -def make_csv(fname,rv_epochs,model,astr_info,errors): - ''' + """ + gam1, jit1, gam2, jit2, rv_err = calibration_terms + # create empty arrays to be filled with data from each inst +respective jit and sigmas + length = int(len(rvs) / 2) + off_1 = np.zeros(length) + off_2 = np.zeros(length) + # create an array of normally sampled jitters for each instruments + + errors1 = np.abs(rv_err * np.random.randn(length)) + errors2 = np.abs(rv_err * np.random.randn(length)) + + jscat1 = np.random.randn(length) * np.sqrt(jit1**2 + errors1**2) + jscat2 = np.random.randn(length) * np.sqrt(jit2**2 + errors2**2) + # fill off_1 and off_2 + off_1[:] = rvs[:length] + off_2[:] = rvs[length:] + # add scatters and gammas for first instrument + off_1 += gam1 + off_1 += jscat1 + # add scatters and gammas for second instrument + off_2 += gam2 + off_2 += jscat2 + # put em together + scat_rvs = np.concatenate([off_1, off_2]) + # put measurement uncertainties together + errors = np.concatenate([errors1, errors2]) + return scat_rvs, errors + + +def make_csv(fname, rv_epochs, model, astr_info, errors): + """ Takes the data generated and saves it as an orbitize-compatible csv file. - ''' - #unpack astrometric info - ras,decs,astro_epochs=astr_info - #actually make csv - frame=[] - for i,val in enumerate(rv_epochs): - if i<100: - obs=[val,0,model[i],errors[i],None,None,None,None,'tel_1'] + """ + # unpack astrometric info + ras, decs, astro_epochs = astr_info + # actually make csv + frame = [] + for i, val in enumerate(rv_epochs): + if i < 100: + obs = [val, 0, model[i], errors[i], None, None, None, None, "tel_1"] else: - obs=[val,0,model[i],errors[i],None,None,None,None,'tel_2'] + obs = [val, 0, model[i], errors[i], None, None, None, None, "tel_2"] frame.append(obs) - for i,val in enumerate(astro_epochs): - obs=[val,1,None,None,ras[i],0,decs[i],0,'default'] + for i, val in enumerate(astro_epochs): + obs = [val, 1, None, None, ras[i], 0, decs[i], 0, "default"] frame.append(obs) - df=pd.DataFrame(frame, columns = ['epoch', 'object','rv','rv_err','raoff','raoff_err','decoff','decoff_err','instrument']) - df.set_index('epoch', inplace=True) + df = pd.DataFrame( + frame, + columns=[ + "epoch", + "object", + "rv", + "rv_err", + "raoff", + "raoff_err", + "decoff", + "decoff_err", + "instrument", + ], + ) + df.set_index("epoch", inplace=True) df.to_csv(fname) + def run_fit(fname): - ''' + """ Runs the orbit fit! Saves the resultant posterior, orbit plot and corner plot - args: - fname (str): Path to the data file. + args: + fname (str): Path to the data file. + + """ - ''' - - #parameters for the system - num_planets=1 + # parameters for the system + num_planets = 1 data_table = read_input.read_file(fname) m0 = 1.0 mass_err = 0.01 - plx=50 - plx_err=0.01 - #initialise a system object + plx = 50 + plx_err = 0.01 + # initialise a system object sys = system.System( - num_planets, data_table, m0, - plx, mass_err=mass_err, plx_err=plx_err,fit_secondary_mass=True + num_planets, + data_table, + m0, + plx, + mass_err=mass_err, + plx_err=plx_err, + fit_secondary_mass=True, ) - #MCMC parameters - n_temps=5 - n_walkers=1000 - n_threads=5 - total_orbits_MCMC=5000 # n_walkers x num_steps_per_walker - burn_steps=1 - thin=1 - #set up sampler object and run it - mcmc_sampler = sampler.MCMC(sys,n_temps,n_walkers,n_threads) - orbits = mcmc_sampler.run_sampler(total_orbits_MCMC, burn_steps=burn_steps, thin=thin) - myResults=mcmc_sampler.results + # MCMC parameters + n_temps = 5 + n_walkers = 1000 + n_threads = 5 + total_orbits_MCMC = 5000 # n_walkers x num_steps_per_walker + burn_steps = 1 + thin = 1 + # set up sampler object and run it + mcmc_sampler = sampler.MCMC(sys, n_temps, n_walkers, n_threads) + orbits = mcmc_sampler.run_sampler( + total_orbits_MCMC, burn_steps=burn_steps, thin=thin + ) + myResults = mcmc_sampler.results try: - save_path = '.' - filename = 'post.hdf5' - hdf5_filename=os.path.join(save_path,filename) + save_path = "." + filename = "post.hdf5" + hdf5_filename = os.path.join(save_path, filename) myResults.save_results(hdf5_filename) # saves results object as an hdf5 file except: print("Something went wrong while saving the results") - finally: - corner_figure=myResults.plot_corner() - corner_name='corner.png' + finally: + corner_figure = myResults.plot_corner() + corner_name = "corner.png" corner_figure.savefig(corner_name) - orbit_figure=myResults.plot_orbits(rv_time_series=True) - orbit_name='joint_orbit.png' - orbit_figure.savefig(orbit_name) + orbit_figure = myResults.plot_orbits(rv_time_series=True) + orbit_name = "joint_orbit.png" + orbit_figure.savefig(orbit_name) print("Done!") -if __name__=='__main__': - - rv_info,astr_info=gen_data() - rvs,rv_epochs=rv_info + +if __name__ == "__main__": + rv_info, astr_info = gen_data() + rvs, rv_epochs = rv_info # set gammas and jitters - calibration_terms=(0.7,0.009,-0.3,0.006,0.002) + calibration_terms = (0.7, 0.009, -0.3, 0.006, 0.002) - #add scatter to model - model,errors=scat_model(rvs,calibration_terms) - - #save this to a new file - fname='./simulated_data.csv' - make_csv(fname,rv_epochs,model,astr_info,errors) + # add scatter to model + model, errors = scat_model(rvs, calibration_terms) + + # save this to a new file + fname = "./simulated_data.csv" + make_csv(fname, rv_epochs, model, astr_info, errors) # run orbit fit run_fit(fname) # delete CSV os.remove("demofile.txt") - - - - \ No newline at end of file diff --git a/tests/test_nested_sampler.py b/tests/test_nested_sampler.py new file mode 100644 index 00000000..a6130c35 --- /dev/null +++ b/tests/test_nested_sampler.py @@ -0,0 +1,64 @@ +""" +Tests the NestedSampler class by fixing all parameters except for eccentricity. +""" + +from orbitize import system, sampler +import numpy as np +import pytest +from orbitize.system import generate_synthetic_data + + +def test_nested_sampler(): + # generate data + mtot = 1.2 # total system mass [M_sol] + plx = 60.0 # parallax [mas] + orbit_frac = 95 + data_table, sma = generate_synthetic_data( + orbit_frac, + mtot, + plx, + num_obs=30, + ) + + # assumed ecc value + ecc = 0.5 + + # initialize orbitize `System` object + sys = system.System(1, data_table, mtot, plx) + lab = sys.param_idx + + ecc = 0.5 # eccentricity + + # set all parameters except eccentricity to fixed values (same as used to generate data) + sys.sys_priors[lab["inc1"]] = np.pi / 4 + sys.sys_priors[lab["sma1"]] = sma + sys.sys_priors[lab["aop1"]] = np.pi / 4 + sys.sys_priors[lab["pan1"]] = np.pi / 4 + sys.sys_priors[lab["tau1"]] = 0.8 + sys.sys_priors[lab["plx"]] = plx + sys.sys_priors[lab["mtot"]] = mtot + + # run both static & dynamic nested samplers + mysampler = sampler.NestedSampler(sys) + _ = mysampler.run_sampler(bound="multi", pfrac=0.95, static=False, num_threads=8) + print("Finished first run!") + + dynamic_eccentricities = mysampler.results.post[:, lab["ecc1"]] + assert np.median(dynamic_eccentricities) == pytest.approx(ecc, abs=0.1) + + _ = mysampler.run_sampler(bound="multi", static=True, num_threads=8) + print("Finished second run!") + + static_eccentricities = mysampler.results.post[:, lab["ecc1"]] + assert np.median(static_eccentricities) == pytest.approx(ecc, abs=0.1) + + # check that the static sampler raises an error when user tries to set pfrac + # for static sampler + try: + mysampler.run_sampler(pfrac=0.1, static=True) + except ValueError: + pass + + +if __name__ == "__main__": + test_nested_sampler() diff --git a/tests/test_priors.py b/tests/test_priors.py index 2c81b98e..47aa15b4 100644 --- a/tests/test_priors.py +++ b/tests/test_priors.py @@ -121,6 +121,7 @@ def test_obsprior(): if __name__ == "__main__": - # test_compute_lnprob() - # test_draw_samples() test_obsprior() + test_compute_lnprob() + test_draw_samples() + print("All tests passed!") diff --git a/tests/test_secondary_rvs.py b/tests/test_secondary_rvs.py index 8b2ef9ab..f5064734 100644 --- a/tests/test_secondary_rvs.py +++ b/tests/test_secondary_rvs.py @@ -4,7 +4,7 @@ from pandas import DataFrame from orbitize.kepler import calc_orbit -from orbitize import read_input, system, sampler +from orbitize import read_input, system, sampler, DATADIR def test_secondary_rv_lnlike_calc(): @@ -64,6 +64,16 @@ def test_secondary_rv_lnlike_calc(): assert np.all(rv0 == -m1 / m0 * rv1) +def test_read_input(): + """ + Test that reading in a data file with only a companion RV and relative astrometry + works. Added in response to issue #351. + """ + + input_data = read_input.read_file('{}/HD4747.csv'.format(DATADIR)) + input_data['object'] = 1 # make sure all astrometry and RV is marked as of the secondary + mySystem = system.System(1, input_data, 1, 1, fit_secondary_mass=False) if __name__ == "__main__": - test_secondary_rv_lnlike_calc() + # test_secondary_rv_lnlike_calc() + test_read_input()