{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spatially-Resolved Mass-Metallicity Relation\n", "\n", "We're going to construct the spatially-resolved mass-metallicity relation (MZR) for a MaNGA galaxy, where mass refers to stellar mass and metallicity refers to gas-phase oxygen abundance.\n", "\n", "### Roadmap\n", "1. Compute metallicity.\n", "2. Select spaxels that are\n", " 1. star-forming, \n", " 2. not flagged as \"bad data,\" and\n", " 3. above a signal-to-noise ratio threshold.\n", "3. Compute stellar mass surface density.\n", "4. Plot metallicity as a function of stellar mass surface density." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "from os.path import join\n", "path_notebooks = os.path.abspath('.')\n", "path_data = join(path_notebooks, 'data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Maps for Galaxy\n", "\n", "Import the Marvin `Maps` class from `marvin.tools.maps` and initialize a `Maps` object for the galaxy 8077-6104." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO: No release version set. Setting default to MPL-6\n" ] } ], "source": [ "from marvin.tools.maps import Maps\n", "\n", "# REMOVE FROM NOTEBOOK\n", "filename = '/Users/andrews/hacks/galaxies-mzr/data/manga-8077-6104-MAPS-SPX-GAU-MILESHC.fits.gz'\n", "maps = Maps(filename=filename)\n", "\n", "# maps = Maps('8077-6104')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Measure Metallicity\n", "\n", "\n", "### Pettini & Pagel (2004) N2 metallicity calibration\n", "\n", "We are going to use the N2 metallicity calibration (their Equation 1) from Pettini & Pagel (2004):\n", "\n", "12 + log(O/H) = 8.90 + 0.57 $\\times$ log( $\\frac{F([NII])}{F(H\\alpha)}$ ).\n", "\n", "One of the benefits of this calibration is that the required lines are very close in wavelength, so the reddening correction is negligible.\n", "\n", "Get [NII] 6585 and Halpha flux maps from the Marvin `Maps` object. Note: MaNGA (and Marvin) use the wavelengths of lines in vaccuum, whereas they are usually reported in air, hence the slight offsets." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "nii = maps.emline_gflux_nii_6585\n", "ha = maps.emline_gflux_ha_6564" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the necessary line ratio.\n", "\n", "Marvin can do map arithmetic, which propagates the inverse variances and masks, so you can just do `+`, `-`, `*`, `/`, and `**` operations as normal. (Note: taking the log of a Marvin `Map` will work for the values but the inverse variance propagation does not correctly propagate the inverse variance yet.)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "n2 = nii / ha\n", "logn2 = np.log10(n2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, calculate the metallicity." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "oh = 8.90 + 0.57 * logn2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select Spaxels\n", "### Using the BPT Diagram to select star-forming spaxels\n", "\n", "Metallicity indicators only work for star-forming spaxels, so we need a way to select only these spaxels.\n", "\n", "The classic diagnostic diagram for classify the emission from galaxies (or galactic sub-regions) as star-forming or non-star-forming (i.e., from active galactic nuclei (AGN) or evolved stars) was originally proposed in Baldwin, Phillips, & Terlevich (1981) and is known as the **BPT diagram**.\n", "\n", "The BPT diagram uses ratios of emission lines to separate thermal and non-thermal emission.\n", "\n", "The classic BPT diagram uses [OIII]5007 / Hbeta vs. [NII]6583 / Halpha, but there are several versions of the BPT diagram that use different lines ratios." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### BPT Diagrams with Marvin\n", "\n", "Let's use Marvin's `maps.get_bpt()` method to make BPT diagrams for this galaxy.\n", "\n", "**red line**: maximal starbust (Kewley et al 2001) -- everything to the right is non-star-forming. \n", "**dashed black line**: conservative star-forming cut (Kauffmann et al. 2003) -- everything to the left is star-forming.\n", "\n", "Line ratios that fall in between these two lines are designated \"Composite\" with contributions from both star-forming and non-star-forming emission.\n", "\n", "**blue line**: separates non-star-forming spaxels into Seyferts and LINERs.\n", "\n", "Seyferts are a type of AGNs.\n", "\n", "LINERs (Low Ionization Nuclear Emission Regions) are not always nuclear (LIER is a better acronym) and not always AGN (oftern hot evolved stars).\n", "\n", "Sometimes these diagnostic diagrams disagree with each other, hence the \"Ambiguous\" designation.\n", "\n", "Try using `maps.get_bpt?` to read the documentation on how to use this function." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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Zs2fn77+t+PnlQhAEPDw8iYgIR6VS8/HHZQAoWrQE//vfaSIiIggICASiMnoeOrSPrl17sXLlEnbu3EbFipXi9OvBgwfcuHGN48d/wmIxU6RIMfvvgY3E1/7Ro0eEhDxg/PioVxw3blyjVKmPozOQenhEZSCVy+UoFFER+76+GaNPSM2VKw8vXjz/19ZDChQohCDIKFCgIGp1zOAiUYwKynz9+hVZsmSLzqFSsGAhnjx58l7dKIcHDhzC99/P482bN9Envr6P1SrGMZbiCniKOwgqIeIep/HZsUczdrko2tqPtKBlj10ZkyerCAoy4OlpfeeQJ+do2d4+fuKb35Ku6Yw+21fX9rEjaX1I69gxOTNmKNm3T4uXV+ygZfvs2j+/xUeqX2AkNxUrVmbw4K9p2rQlXl5e3Lhxjd27d5ApU2YsFgtGo5F//nmKj0+GD2bfvHPnTwBu3rxBnjx5CQt7wx9/XKdy5WrcuHGVnDn9+Omnw7Rq1ZbcufPQpUsHmjX777Cwt9uJ/PxyUbJkKb78siE3blwjNPSlU+9BXBw9eohevfrSqFETAFavXo5cLk/wHrx+/ZoXL56TIYMvjx79Rdas2YCow9Hu3buLKIqEhDzAaIyKkhZFK5GREf9mJg3FxycDz58/Q6/Xo1AouH37Jl269EClUhMa+hKlUsmTJ1Fp3I8cOcCoURPQarUEB/fliy8qO/mOSLgaly7JuHhRzsKF+g9XlpBIJdy8KaNvXw0rV+rJl8/5B5jZQ7pfYHwo5uJ9fH196datF0OGfB39ATpz5jx+/fU8vXt3xWKx0LlzD5sCHG/dukn//r1QKJSMHTsJo9HA5Mnj2LBhLV5eXowdO4nbt28xatQwPD09+eSTsri7e0S3L1asBOPGjWTChKlMnjyWvXt3YbVaGTlynN334V1sjbl4l+PHf2T+/MXRv9ep8yWBge3o1i12crS3eHl5MWfODJ49e4q/f1s0Gg0Q9WSjcuVq9OrVhVy5ckenTG/atCVBQd3Jly8/OXL4IZfL6dq1J/369UQURerVa0CmTJlp1qwFI0cOwc8vFzlyRL3OypEjJ127BuDh4UGrVnE/wZBI20ycqCI42EgcSYMlJFIl//wTtWNk/HgDFSq43lk6KZ7JM6mk1kyeBw7sJSIiPN7H9Y7UsgVX0jKZTOzbt5tmzVpy795dZs2axoIFS5yi9Za0mMlTceE8HpPGIQsNBdGKNacfEWMnYSlazG4tITwMn7YtEd68JnLICIyNm8TQiq+9EPYG78D2vNmxL0XzYJw8KWfIEA2nTkWiVDpXy95+SZk8Ja3E1NXroVUrDypVMjFsmDHBuvbYTVOZPCUk3kepVHLv3l26d++I2Wzm66+HpLRLqQ+DAZ8O/rzZsgtz6TLI5TIUmzbg07YFoReuglz+YRvvoLh2Fdnzfwg9d9mudsLr1ygvXbSrjaMRRZg0Sc3w4cZYiwsJidSIKMLXX2vIlUtk6NDkPcDMHlL9AsMRu0iSutsiMVoNGjT+YF1HadlS19W0goOHJmu/wLlR1sm9i0Rh1CO8eYNcp0WUy5DJBMyt26L18UGOiPLoIdxmfgcmI6KbO7rxkzCXK49PhbLovpuBsXotANy/7oulWDE0Sxcj+/sJvjUrEXboGIqrV3AbNwq5TocaAd3Q4Zjq1Ue1YR2a9WtAG4n471Zu9Dp8a1Yi4ufTkIR7kNhdDnv3yjGboVkzq9O1EtOvhJB2kUhacZVNm6bk4UMZhw5ZEATH9kHaRfIOjthFYk9di8XCggVzuHfvLlqtlgoVKtKlSw+b2v/00xHWrVtF7979YyTIiqvu/v17aPzvY+jE+prUXSTpSQvS1i4Ss5cPkaMn4OXfDGuWbJgrfI6xUhX0TVsgD3mA28SxvN65H1mWLHD9Oj4tvyL03GV0gV1QrlmFrkoNhPAwVAf3ETp6PKZiJfH89hteHTuN8PoV7n178WbzTjRFC6G/F0KGL2vyumhxrKKI7OYfhF68iujljexhCBmrfs6rY6eRC8m/i8RshsmTNYwfbwBsby/tInGuVvx6ktaH6m7frmDTJgUHDmjRaJRotY7ug7SLJMU4e/Z/CIKM2bMXAjBkyEBu3bpJkSJFP9j23LkzDB06kqJFi3+w7tatm+JcYEhI2Iqud1/0AZ1Q/u8XVOfO4DZ/Nm7zZ6Pv3A3Zs6dkaPHVfzlgZDLk9++hb9Me9xnTEF68QL13F4Y6XyL6ZIhhV3nhPLJnz/Dp1A5BJqC2iiAIKG5cA8BSogSil3fydzgOtm5VkCmTSI0aFhz1rUxCIqU4f17GqFFqtm/XpYpEcel+geHdIWYmz7B1Ce8qyZw5C5cuXeDcuTOUKVOWyZOn8zYj5buZNFesWEK5cuWpWLEy27dv4c2b15w9+z/u3r3DrFnzWbr0ex48uI9SqeTbb0djsVj49ttvcHNz47PPKvD48V+sX7+Gtm0DnNl9iTSK4txZlL+eQ9d3AMa69bHUb0jEt6PxrfY5Qng4xirVCV+6KjqgS/b4EdbsOUAux9ikKZptm1Dv2ErE1DjOlbFYsRQuzOtDx6MD2mRP/8aaKTPq7VsQk5BF1pHo9VFZO3/4QecSWQ0lJJJCSIhAly5uLFigp1gxxzxhcDbSkt5OihQpSs+efdi6dSPNmjVg+vTJ7N69k9Kly7BgwRJq1qzDtm2bqVOnPseO/QhEpRdv06Y9FSpUZNiwkVy9egWNxo0FC5bQtWtPVq5cCoDJZOT775fTpUsP/Pxy0759x5TsqkQqRsycCffZ01GcPRNdJnv2FEGrxVCvAaqfjyH/8zYAqh8P41v9i6hPZMDQtSduyxaDVcRctlws26ZPP0N+7y7KM6cBkF/9nYyff4LsvQR0ACgUYLUkPcNbIli9WkmJElbKl08dk7GERHy8PcBs4EAjNWu63nbU+Ej1TzAcEuRJ7GSe8dW9e/dPihQpxowZ8zAYDEyfPpn582dRqFCRGJk0ixUrRkjIA548eYyXl1eM/BUhIQ84e/Z/3Lx5AwBvbx8AcuXKbVN/00OQZ3JrQdoK8qRwESLWbsRzyjhkT56ARoPo5Y129nz4+GMiZ8/Du2dnBFFEVCiIWL8ZuXfUNjSxdGnEDBkwdu4aI522wL99yJaViFXr8Rw3CtkIA2qLhYhFSxHy5UP2v1/+qweQMyfm0mXIWKkcEYd/ggwZk9QvW4MQIyMF5s9XsX27PsGU4I7QkoI8E1c3rQVeOkvLbIauXTVUqWKlR4+Yr/qckapfCvJ8B4cEecZjNy7OnTtDaGgoQUEDUKvV5MqVm6CgAfj6+sbIpCmK8MUXlVmwYA5fftkwhg0/v1zUrl2XwMBuPH78iMuXLwFEJ5RKiJQOhkyrWpC2gjwtFiuWL6pg2H0IeG9vu8WKpVFT9I2axioHkN1/gPDiBdpmraLLLBUrYz79a3RdyxdVMBw6FnPPv8WKrnU7jO06vOOTwOt9R2L7kIR+2VJ30SI1VataKFLEQkIpwR2hJQV5Jq5uWgq8dJaWKMLQoWpkMpHx4/XvjGV7tezxwXFBnun+FUnYui0x/n2Ili3b8ObNGzp3bkfv3l14+vRvmjRpxi+/nKBPn+7Mnz+b/PkLAFHZLH/77SIVK1aKYaNq1Rr8/fcT+vbtwZgxw/noowKxdDw9PVmxInHJpSQkEov71Il4N6hN5NiJ4O6e0u4kipcvBZYsUTJkiCGlXZGQSBJLlyo5d07O0qUGbEgO7XJImTztxJ6Mlw8fhrBx4zqGDh3hdK2kkt610mImz/SgFVfd0aOjjqyeMkWfYD1HaDmiX+k5k+f58xdZvHgBYWFvsFqtZMuWnaCgATG+dNmqFRkZwTffDCA8PJxu3XpS/d9cLvH17W37iIgIhg//hnnzfnCpTJ5Hj8oZNEjDgQNa8uWL+wAzZ/z9SZk8UwHnz59l0aJ5jB8/OaVdkZBINzx+LLB5s5LTp3Up7YrEBzAajQwd+jWzZi2M3uZ/9OhBvvmmP1u37kFuZ7bZP/+8TWjoSzZv3mVXu/DwMP7447pdbZzN9esyBgzQsGaNjty5XesAM3uQFhhOonz5z2Mk05KQkHA+M2ao6NTJSLZsYqz31RKuhV6vJyIiAp1OG11Wr14D3NzcsVqtnDlzmtWrl2M2m9Fo1PTp8zUlSpSiXbsWDBw4hIoVvwBg2rSJ5M9fgO3bN/P8+XMCA9uxePEKbt++xfffz0ev1yGXywkM7E6lSlU4cGAv+/btRq/X4+ERFXxvMBgIDGzHqlXrSekP82fPBAICNEyebKBcudS9AyrVLzBSa6pwScu1tCBt7SJJL1rv1r19W+DQISW//qp1upYj+5UQaXkXiYeHD336DCA4uD+ZMmWidOmP+fTTz6hduy7Pnz9jyZJFLFq0BF9fX+7cuUP//r3ZunU3zZu3Yt++3VSqVInISC2//HKCfv0GUqRIEWbOnMbatZsICwtjypTxzJmzkBw5chIW9oqAgHYULlwYmUzg/v177Nq1H3d3D/7++wnt27di7dpN8b5STa5dJDodBASoCQgw06KFNfp6UrXs8UHaRfIO8UVZGwwGlEpV1Ja9d+rGZ8OWsoR8sLW9pOVaWqIoYjIZASHN7SJJH1r/1Z08WUNQkBFPTytWa/LtWEn6boT4Seu7SPz929GoURN+++0SV65cYu3aVaxZs5JmzVrx8uVz+vbtFf2FQBAEQkJC+PLLRixfvoSXL0P56aejVKxYGXd3j2gNi8XK779f5sWLFwwZMijKA1nU58Dt27exWkUKFCgY3ebddim5i8RqhaAgDfnzi3z9teG9J3BJ1bKnD2ksVfiVK1eYMWMGa9eujVF+7NgxFi5ciEKhoEWLFvj7+8djISbe3pl48+YFFovZ4b4KgkByxcVKWsmjJZMp8PbOmGAdCdfm8mUZv/4qZ/58/YcrS7gEly//xoULF2nXriOVKlWhUqUq9O7djw4d/NFqI/n00/KMHz8lOujw2bOnZM6cBblcTo0atTl0aD9Hjhxi0KDYpy1bLFby5s3H0qWrgahgyIcPH5Mhgy9HjhzE3QV3SE2bpuLvv2Xs2qVPM5lnU3yBsXTpUvbs2YObm1uMcpPJxJQpU9i2bRtubm60bduWmjVrkjlz5g/adHf3xMvL22mRz46IEJa0XEtLInUzaZKa4GBjat1Zmy7x9fVl9erllChRmo8/LgPAy5cv0Ol0VKpUlU2b1hMS8oCPPvqIM2d+Ydy4UezceQA3NzeaN2/F8OHf4OXlTfHiJWPZLlGiFI8e/cXly5coU6Yst27dpHPnjqxbtzVWXblcjsViTbYvPXGxebOC7duVHDqkRaMR0kz8UIovMPLkycP8+fMZMiTmKvTu3bvkyZMHH5+oLJeffvopv/76K/Xr108JNyUkJFyUkydlPHwoo107U0q7ImEHefPmY8qUmSxZspB//vkHtVqFp6cnQ4aMoFChwgwZMoIxY4YDIjKZnGnTZkV/ES1UqDBeXt40bdoiTtu+vr5MmvQdCxfOxWg0Iggio0aNJ3v2HLHqZsqUmSJFitC+fUuWLFmJp2fyHtR39qyccePU7NypI3Pm1LtjJC5cIg/Go0ePGDRoEFu2/Jfo6sKFC6xbt445c+YAMHfuXHLmzEmrVq1itNXrTXF+G43r0Xh8j8vtqatUyjCZrB+sK2mlLi177MbX3sNDHavsLXGNU0f0L65yZ/TZVbVEEWrUUBIUZMHf35pg+6Rq2WPXHi17x40jNJ3RZ3vrJmUe+Ouvv+jRows7dsR8+p2a5hxBELhzR6ROHRXLlpmoWVN0mpY9dhMzv8VHij/BiA9PT08iIyOjf4+MjMTLK3aiD4vFmuzJYVzt8b6k5ZhXJElNRJPQH2Bc4zStJr9KTq0DBxTo9SINGujRahNu76qJtuwdN47QdJVEW4mZB5Yt+4E9e3YycOA3iKI8ho3UNOeEh8to0ULDN98Y+PxzU/T4TY3zW3y47AKjQIEChISE8Pr1a9zd3blw4QJdu3aNVU928gQKQYHVLxfWLFnBzuQsEhISqROLBaZMUTFunAkbjvFJlSgnT0SdIxfmTz/D8lEB0mxH7aBbt15069YrVcdSmUwQGKihVi0LgYFp99Weyy0w9u7di1arpXXr1gwBxmSPAAAgAElEQVQbNoyuXbsiiiItWrQgW7Zsseor1q7G88YfyJ88Rnj9CmvWbFhz5MTq54cle06sOf2w5syJJYcfQu5cWLJkI1UmdZeQkIjB1q0KfH1Fate2YE2dnzMfRMySFdWPR/D4bjJCeBjmTz7FVLYc1s/KYy1TFjFjppR2UcJORBGGDVPj7i4ydmzaPi/HJT5pc+XKFR1/0bhx4+jymjVrUrNmzQTbGpeu+O8RkdGI7OnfyJ48QfnsCTx6jOzxXyh/PYfs78fInzxBePkCa/YcWPLmw5InL9a8+RDz5cOUOy+WPPkQM2cmzewRknAZhJAHkDmHNLYchMEA06erWbQo7Wzpiwtz9x5o2wcCIDx7hvLSBZSXLqBZOA+Py79hzZEDU8XKmCpVxlK5KmTOkrIOS3yQ779XcvGinAMH9Gn+gbtLLDAchkqFNU9erHnyIsb3bkmnR/boL+QPQ5CHPEAe8gDF/r2oHzxA/vABgsGIJW9erPk+wlygIOZChbEULISlUGHEDL4p0y+JVI+mRjXUcjmmCp9jqlARU/mKUKoUaSliPDlZs0ZJsWJWKlSw4Kisg66OmC0bxvoNMdZvGDWXGU0orl9FefoX1Nu3oBw8EGvWrJi+qIKpchWMVasj+kr5XVyJgwcV/PCDioMHtXh5pZ3tqPHhErtIkoJWa0Cvj51QK66UrwmlgX1bLoS9QRYSguLBPYTbt5H/eRv5nT+R/3kb0U2DWKQopgIFsRYsjKVwEcwlSiHkzMH7Zm3R+lBdjUaJXm/6YF1JK+la9tiNr33GjB6xyt6ijdRjvPknirNnUJw7g/Ls/5A9fYrps/KYK1XBVL0mltIfg0yW5HvhjD67klZ4OJQv78a2bXpKlBCT/P9ma7/ssWuPVoLjxp75TbQiXL2K4vQplKdOoDz9C+biJTDXqYexVh0spUpHx3A4o8/21nXFecCZWpcvC7RsqWHTJj1ly1rT1PwWH6n+CYbDU+l6eEHxklhKlY5ZVxSRPXuK+8N7mK/dQH73T9RHDuPxxzUQRczFS0b9K1ESc4lSWIsVw6JQJsmvxKaclbQSo2WPXfuDy0QETHnyYcqTD/zbAqB4HYrszBmUp37Go1dXZC9fYKxSHUuNmuirVMeaO0+ifE759N3O1fr+exVVqlgoWtTy7zfApP6/2dov52glhF3zm1yGpUQpjCVKQY8g0OtRnjmN5viPePTojBAWhrFWHYyNvsJSo1as+SklUoW73jzgHK0nT+S0b6/mu+/0fPyxOYFxm3StlJjf4iPVLzCSDUHAmj0H1o/yoi9f6b9yUUTx4jnC1Ssorl1D9fNPuC+cizzkAU99MvDELxfZmzTDrXotLIWLQByHCUmkT8RMmaMfeUcCssePUJ78GfXJ4/hOHIvVNyPGLxtiqN8Q8bPyKe2uS/DypcDSpUoOHtR+uHJ6R6PBVKMW1tp1sIyfguzBfdRHDuI2fw6KPj0x1q6L4atmGKvXBI0mpb1Ns0RGQocOajp3NtG4seOPr3BlpAVGUhEExOzZMWfJiqlmnehiucnI3U0bubNhDXcmjqXyd1PIIQhYynyC6ZNPMZUrj+nzitI7UolorH65MLTtgLlDRywmM4rLl1AdOoDXoH7IXoViqNsAY/0GGKvWAJUqpd1NEebNU9GkiZn8+VP1m90UwZovP7oeQeh6BKF4/g+KvbtxW7IIr369MH7ZALFTJyhbQdoK60DeHmBWrJiV/v1j5xhJ60gjyVloNBQJ6ETDgz/x+Z9/cWXLTkIvXmW2SsXxs/+DRfPI+GkpfGtUwn34EFQH9iG8Ck1pryVcBZkMc9lyaIeP5tXJc4TtO4KlQEHc58wkU+nCeAYPQHnmNGl2f2YcPHoEmzYpGTQo/U3UjkbMnh19l+682bGP0NMXMJcoiXpwMBnLf4z7d5ORhTxIaRfTBBMnqnj9WmD2bGOa3u0UH1KQp511kxos9PDhA5YvX8qGDWv5qn4j5gQEovrfKeS/nEJ5/hyWfPkw1aiNqU5dFFWroH8vythV+5Xateyxm6ggzzjGaWL9kD36C9X2rai2bUb25g2G5q0wtG2PtUhRIO0GeQYHa/DxsTB6dOLGTpoP8kzifdeoFZh+vYh64zpU27dg/vgTDN17YalbD+t7u53S6jzgSK116xTMnavk8GEdmTOn7fktPlL9AiMy0pAq0tu+X67Varl7909KlizN4MEDqFatFg3rfonqymVUx46i+ukoipD7GKrWwFi7LsaadRCzZnX5fqVWLXvsxtc+S5bYqezfEtc4dUT/VLduoNyyGfXmDVgKFETfqQvyVi3RWgSb2qeWVOF37gg0buzBmTMRZMiQOLuumirc3nHjCM0P9lmvR717B27LFiMLD0PXuRv6th0QvX0cohVLz4n9SgmtI0cs9OihYc8eLQULiineL3vsJmZ+iw/pFUkK4e7uTqlSHwNQt259FiyYTbXaVdj/KhTtsJG8PnoC3cXLGGvXRfXjETJWKodPk/qoly1GePYshb2XcBUsxUsSOWocob/dQNetJ5oN63AvUhCPcaOQPforpd1zGFOnqunf3xJrcSHhJDQaDK3b8frIz0QuWoLi0gUyflYaj0njEF68SGnvXJo//xTo0UPDkiV6ChZM1d/fk4y0wEhhBEGgfv2GHDp0nAkTpqJUKrFarZw5cxoxW3YMbdoTvmw1L6/eRte7H4oLv5Kx8mf4NG2AZsVShNCXKd0FCVdAqcTYuClvtu1G9+NxMJvxrVkJrz49kN+4ntLeJYkrV2ScPy+nd+80npXIFREEzJ9VIHzxSl4dPYnw5jUZvyiL+8hhyJ7+ndLeuRyvXkGrVgpGjDBSubI0XqUFhosgCALVq9ekZs3aPHnymIED+9KkSSOuX78WVUGjwfhlAyJ/WBa12OjVF+X5M2QsXwaPbp1QHv8J0npaOAmbEAsWJHLCFEJ//R1zkaL4tG6Gp38zFOfPpbRriWLSJDUDBxpxd09pT9I31jx5ifhuNq9ORo0j32qf4zFhDMKb1ynsmWtgNELnzm40amSlffu0e4CZPaT6GIzUFuRpa12TycT69auYMmUSO3fuo3jxknHWFV6/Qr1jG6p1axBePMfQoSOGwK6IWbO5ZL9cVcseuykd5Pmh8lh9NhjQbNmIesY0LMWKoxs+OiprqDO0HNyvU6dkDByo5swZHV5eyRd4mS6DPO3ss/j4CW5TJ6I6fADdwG8wdO6OTKNO1fNAYrVEEfr3V/HqlcDmzVZMJtfqlz12pSDPd0itQZ62aj158gwfnwzs2LEVLy8v6tdvGG97+bWruK1ajnr3Doz1G2Lo3RdjsRIu2S9X07LHrisFedoVeKnVoVm7Evc5MzFXqEjEiDEIhQq5bJCn2WylQQN3unUz0qKFOVkDL9N9kKcd7eV/3MBjwmjkd++gnT4HQ9XqNvnlivNAYrXmzVOxe7eCPXu0ZMniev2yx64U5JmOyJDBF0EQyJYtOyNHDqN//yAiIsLjrGspWYqIGXMIPfcb5gIF8WrTAp8WjVGePpXMXku4JGo1+m69CD13GVPpj/FtUAu3CWOjUg26IIcOKdDpoFmz9JX9MLVhKVacsA3biJw4FY9B/fDq1SVdBaLv26dgxQol69bp8LD/S36axuYFxoULF+jXrx9ffPEF1apVo2bNmgQHB3Pp0iVn+ifxL5UrV+XYsdMIgsCYMSMSrCtmzIRuQDCvf7uOvlUbPAf1w6dZQ2mhIRGFhwe6AcG8+vkMskd/kbFSOdS7tkc953URLBaYPFnFiBEGpMSSqQNjnS9588t5rH65yVijIupN611qTDmDy5dlDB6sZs0aHTlypO2+JgabUoVPmDABT09P+vXrR8GCBZH9+xd/69Yt9uzZw549exg7dqwz/ZQAPD09mTt3ITqdnidPHnPw4H66dOkefwOlEkOb9hhatka9bTNeA/tizZWbiHGTMP+7RTY9YTabadGiEQULFmLmzPkxru3bt4s9e3YRGRmByWQiZ04/uncPonTp0gD07dsDgHnzfkAujxr/r1+/plGj2vzyy4Xk7YiDsGbPQeTi5QinT+M1dCDqHVuJmD4HcuZMadfYulVBhgwitWtLgcupCg8PIkeNQ9+sJd59eqA+fBDt7HmQIe0difD4sUCnThpmzjRQunTsVwoSNj7B6NOnDwMHDqRw4cLRiwuAIkWKMHjwYPr27es0ByVio1KpsFqtbNiwlqCg7mi1Hzj4SaHA0KY9of+7iLFpc3zatMBzYN909RgT4MSJ4xQoUIhbt/7gwYP70eWLFy9k//69TJw4lfXrt7Fly24CAjozdOjXPH1nK96NG9dYs2ZFSrjuVMyfV+TVkROYi5fAt0YlVFs2peg3T4MBpk5VMmJE+kyvnBawlCzFq8PHseTOg0/VilG73NIQERHQvr2a7t2NNGggvcKLjw8GeW7dupUff/yRevXq0bBhQ1auXInFYqF27doUKVIkufyMl7S6i8QWLa1Wy8CB/fjrr4fs338EQfhwOl+ZTEB89QrNrOmoN6xFN2gIhh690XhoXKZfztIKCupO7dr1uH//LiaTiWHDRhIa+pLmzRuzbdtusmbNGsPGwYP7KF68BHnz5icoqDvly1dg/fo1zJmzkBIlSvH69Svq16/FmTNRrwlddheJHe3lVy7j2bcn5kKF0c5ZEJ250Rla8ZUtWaLg2DEFmzbpP6hlj11pF0nCZUntc3zlql9O4N6jK/ou3dEPGszbd16uNJfao2WxQMeOarJkgdmzDbEWwa7YL3vsOnIXyQdfkSxfvpx58+axbt06Vq5cSYkSJfjkk0+YOHEizZo1o3nz5naLOhJRJM6IV+w65972uqIo2ljX+VpqtYaFC5cQEnIfi8XK8+fPyZo164e1PL2JGD0BXfuOeAYPQLVzG6ZFi7HkL+QS/XKG1v3797h+/SqTJn1H0aLFCArqRo8eQVy5coW8efPh65sJqzWmX3XrNoiOqBZFkVy58hAUNIAxY0awYsU6LJa3E86HH4/GPU6T3r+4ym2/v7HLLSVL8+boCdxGDMW7RmXClq3GXLqMU7TiKouIgNmzlWzdarBRyza79vqVnFoJkZzzW9L7HHe5sXI1DEd+xrtLAPLfLhK+YDGit49LzaX2aI0erSYiAlauNGCN47BB1+yXPXbtG6MJ8cFXJEqlksKFCzNixAgePXrE+PHjad26NcuXL2fDhg1JdsBqtTJ69Ghat25NQEAAISEhMa5PnDiR5s2bExAQQEBAAOHhce+gSK8IgkCBAgW5ceM6NWp8wcWLv9rc1lKgEG927EPfNgC3BvVwnzktzSbr2rVrGxUrVsLb24fixUuQI4cfu3fvQBTFGE9+tNpIAgPbERjYjtatm/L99zFjNb76qhmFCxdh5sxpyd2F5EOjIeK72UR+Owqf1s3QrF2VbNJLlqioXNlCyZLSO+20hDVHTl7vPog1ew4y1K+F7K+HKe1SolizRsnRowpWrNChUqW0N67PBxcYderUoXfv3pw8eZIxY8ag+veuKhQKXr16lWQHfvzxR4xGI5s3byY4OJipU6fGuH79+nWWLVvG2rVrWbt2LV5e9u/FTQ+UKFGS2bPnExDQml9/tSNjo0yGvmNndGfOofzfL/i0aIzsyWPnOZoC6HQ6Dh3az++/X6Fly8Y0a9aQly9fsGPHVooUKcrDhw948282Qnd3D1at2sCqVRuoW7c+kXFs4Rw2bBSXL1/iyJEDyd2VZMXQrCWv9x3BbeFcPEYPd/riMzQUlixRMmSIwak6EimESkXEd7PRd+pChkZ1kV25nNIe2cWJE3KmTVOxfr1WOhPHRj64wOjfvz/t27fnzJkzbNy4kerVq9OpUyfat2+Pj48Pd+/ejfMxka1cvHiRKlWqAFCmTBmuXbsWfc1qtRISEsLo0aNp06YN27ZtS7ROeiDq0LTFbNiw1u62Yk4/3mzZhal6TXzrVEN15KATPEwZjhw5iI9PBnbtOsi2bXvZuXM/W7bsRqfT8vvvl2nZsg2jRg2LEdD59OlTrl69glwuj2XP29ubUaPGs3jxwuTsRopgKVCI1wd/QnHtdzwD2iDEk4PFEcybp6ZxYzMffSRt90vL6HoEETFxKpqvGqE8cTyl3bGJW7cEevfWsGyZXhqfdmDTNtXKlStTuXJlIOqdz7179/jjjz/4448/mDRpEvfv3+f48cQNlIiICDw9PaN/l8vlmM1mFAoFWq2WDh060LlzZywWCx07dqRkyZIULVr0nfoy3N1jP6sSBIH341fjKrO3rlIZW8+VtBo1akijRg25e/cuWq2WUqVK2a7l5QbDh2OoUR2vTgGY79zGFPwN70YxpcZ7uGfPDgICOuHl5RZdN1u2TLRt255t2zaxbt1GDhzYz/jxo9BqtZjNJlQqNXXr1qN167ZoNCrkchlqtQJ3dxWCIFCpUkU6dOjI8uVL4xx/7xPXOHVE/+Iqt/X+2qzlnh3Tnn2ov+6Pr39T9Dt2Q8aMDtV6/Bg2bVJy7pwx+h7b0i+b+2CnX8mplRDJOb8ltc921W3tjzmvHz5t2mBYtgJL7TrO04qnb7a2f/EC2rdXMWmSmVq15IDcaVrO6pc9du0dowlhU6rwXbt2xXutadOmSXJgypQpfPzxxzRo0ACAqlWrcvLkSQAsFgs6nS56AfLdd99RuHDhGJppPVV4YrX27dvDyJFDOXDgR3Lm9LNbS/b3E3w6tcNcoCDhsxeARuMS/XKWlj12U22q8KRqyQQ0o4aj+vkYr7fuRsya1WFawcFqMmQQGTXKaFe/7OmDlCo84bKk9tneuu7uKow/n8InsC1hCxZjqlknxeeB98sMBmjZ0o2KFa0MH274YPukaDmzX/bYTfZU4Xfv3o3+N2/evOif7927Z7fg+5QtWzZ6QXH58mUKFy4cfe3Bgwe0bdsWi8WCyWTi0qVLlChRIj5TEu/QqNFXdOvWk3btWhEeHmZ3e2uOnITtOwwWMxlafiWdmCgBgkDk2IkYGn1Fhq/qOey47rt3BQ4cUNCvX+wPN4m0jbl8Bd6s3oh3354u97pEFGHgQA1Zs4qMGCGdjpoYbHpFEhwcHP3z5cuXY/yeVOrUqcPp06dp06YNoigyefJkVq5cSZ48eahVqxZNmjTB398fpVJJkyZNKFTo/a2UEvHRv/9A/vnnH/788zZly5az34CbG+E/rMBj5FB8mjXizeadkD2b4x2VSD0IAtrB3yKqVPi0aoLhyE/glrTA66lT1fTqZZIC59Ip5s8qELZ8Ld5dAwjfuhtLiVIfbpQMzJ6t4u5dGTt3apHJZGl1g51TsWmB8S7vJ3NKKjKZjPHjx8coK1CgQPTP3bp1o1u3bg7VTC8IgsD48ZOxWq2cP3+O8uUr2G9EJiNy0ne4fzeZDF/VI3zXAciW3fHOSqQqdAOCkYWFoWn2FbqtuxG9vBNl58oVGWfPypkzR//hyhJpFlPFSoRPm41X25a82nsYa958KerP7t0K1q1TcvCgFnf3FHUlVSMdI5QOCA0NpWvXAE6e/DlxBgQB7dAR6Nt2wLt5I4QXLxzqn0TqJHLkWKxlPsE7sD0YE/d6Y9IkJQMHGqVTKCUwNm6C7utgfNq1REjEa11HcfGijG+/jTrALFs2acdIUrApyHPQoEHRkaVnz56lYsWK0ddmzpzpVAc/RHpOFW6P1smTP9OrVzdOnTpDpkxZEq3lPnk8iqOHCd+1H9EnQ4J1U9M9tMduYlLpppZU4XZrKWUoWrXAmj072pnzQBBsbn/6tIx+/dScPRs7aVFKp++WUoU7Vys+PZlMQDNoALKnT4lYswFksmSdBx49klGvnppZs4zUq2dJsK40v30Ym16RtGnTJs6fXYH0nCrcHq1Klari79+WTZs20qtX7MPpbNXSfjsKTVgYHu38ebN1N6jVKdovx2nZY9f+VLqpJVW43VpqBWHfLyNDwzqoFi9C1723Te1FEcaP1/Dtt0bkcmsc77dTNn23lCrcuVrx68kInzCVDM0aop4xDW3wUKdqvVsWHg5t27rRp4+R2rVN741JaX5LDDYtMHbu3Em1atWoXLlyjJwVEqmL4cNHo1Qq0OsN0RlZ7UYQiJwwFe+uHfEa/DXhcxfFyJMhkf4QPb14s3YzGRrUxly0ONbqNT7Y5vBhOZGR0KKFJSUPbpVwRVQqwlasJUPd6pjKlsP6b44MZ2I2Q8+ebpQvb6FHD2nHiKOwKQZjyJAh6PV6xo0bR//+/Vm1alWsM0MkXB+5XE54eBiVK3/Gs2dPE29IJiNswWLk16/htnCe4xyUSLVY8+QlfOESvPr0QPjnWYJ1LRaYMkXNiBGGtwdrSkjEwJotO+Hzf8BrQBDCS+fHfI0Zo8ZkgqlTjdL3JQdi05+3r68vTZs2Zfr06cyaNYuiRYuyceNGevXq5Wz/JByMt7cPjRs3Zdy4UUkz5OFB2NpNuC1ZhPLnY45xTiJVY6pWA33b9nj27g4JHB+wfbsCT0+oU0fa9ycRP6aq1TE0a4nHwH448zHXihVKTpyQs2yZDqXSaTLpEpuCPO/fvx9n+bsnUebPn9+xntmIFORpv1ZYWDgVK37KkiUrqFixUpK0FKdO4NmzK+EnTmPJku2D7Z3ZL1cOgkqzQZ7va5nNeDdpgLHul+gHDIrVXq8XqVDBjUWLDFSsaHXZwEspyNO5WvHpxaprMOBTtzq6Hr0xtu/ocK0ff5TRt6+KAwf05MsnuvS87crzW3zYFIPh7+9PsWLF4s1ZfuvWLc6fP2+3uCOQgjzt13Jzc2fx4pX4+eV6J+gucVqWL6og79AJ9x5deb15J8Q4HCw13UN77EpBnvFqCTIifliGd60q6OvWx1K4SIz2q1bJKVTISvny5n+D6Fwz8FIK8nSuVvx679VVKIlYtBSv5o3Q16iDmC1b/HXt1Lp5U07v3ipWrtSTO7fF7vEozW8fxqYFRr169Zg4cWK810eOHOkQZySSjwoVPufRo784ffoUlSpVSZIt7TfDULVohNsPC9H16e8gDyVSK9bceYgcMgKvAUG83ncketEZERGVHXHjRl0KeyiRmrCUKIm+TQc8Rw8jfPFKh9h8/lygbVsN48cbqFBBelXnLGyKwUhocWHLdQnX5OnTv+nTpwc6XRInfLmcyPk/4D5/FvJ7dxzjnESqRt+pC6JGg9viRdFlixcrqVTJQqlSjvl2JJF+iPxmGMqLF1EeO5pkW3o9dOrkhr+/mZYtY79+knAcNj3BaN26dYwjXN/+LAgCmzZtcqqDEs6jXLnylC5dhpUrl/HNN0k7X8aaLz/aQUPw/Lovb3YdQNoekM6RyQifNR/f+jUxNG3OSzc/Fi9Wsn9/ZEp7JpEacXcn/LuZeH47mFenzhMrM5uNiCJ8/bWGXLmsDBtmSigWWcIB2BTk+fjx47gbCwI5c+Z0uFP2IAV5Jk3r5s0btGvnz++/X8dsTmLwmsmMV6N6GP3bYOjcLVXdQ3vsSkGetmu5TRqH7GEIg3OsJTxcYOZMY7x1E6NlTx9cVUsK8rS9vad/M0y1amPo2SdRWtOmKTl2TM6uXXo8PFynX87Wssdusgd5vk0VHhcp/QRDCvJMmlahQkU5ceIscrkcg+H98yTs1EIgfNosMrRqgq5xU8icORXdQ3vsSkGetmpF9BtExkrleGI5y9gfy6WawEspyNO5WvHrJdw+YsxEMjRviK5lG8iUyS6tLVtkbNqk4MABLSqViNXqOv1yvpY9dpM5yHPWrFkOEZNwTRQKBUFBPZk0aQbq6NTficNSoiSGhl/hPmMq+qkzHOShRKrFw4PIMRPYOL8/4dlPYfng81IJifixFC2GoX5j3GdNRz9pqs3tzp4VGDVKzfbtOrJmlQZhcmHTi3I/P794/0mkflQqFY8ePWL79i0OsRc5dASaHVuR3fzDIfYkUjeGpi1AIUO5d1dKuyKRBogcMhzNpnUIT23LRhwSItC+vZIFC/QUKyYFXSQndgV5vosU5Jm2GDgwmEGDvqZNm/bIkhigKWbOjLZ/MO6TxvFm1QYHeSiRahEEIoeNwmvUMPT1G7+XK0VCwj7EbNnQ+7fFbf5swsdPSbBuWBh06ODG4MFmataUtqMmN9IrEgkAqlevQa5cuXn06C/y5MmbZHu6wK64fz8fxZXfMH/8iQM8lEjNmGrUwpo5M+qtmzC0aZ/S7kikcnR9v8a36ucIfb5+L/nWf5jN0K2bG5UrW+jVS0SrTWYnJWzbRfKWx48f8+TJE3LkyEGuXLmc6ZfNSLtIXFdLs2wxip+OErFxm8v3yx670i6SxGkpz5zGvU9P3py/DAqFQ7Ts6YOrakm7SBLX3mPYN1hVKnTjJ8eqK4oweLCKv/4SWL/egKen6805aWl+iw+bnmBERkYSHBzM69ev8fPzIyQkhIwZMzJr1qwUP75d2kXiOC2z2UJAQGtmzJhL9uw5kqylDwjEZ95shPPnMH/6WYr1yxWirNPrLpIYVKyENXsOFLt3RsVlOETLnj64plZCSLtI4m+v6/c13lU/J3LQEEQv7xh1lyxRcvasjH37tAiCq845aWd+iw+bXrbPnDmTL7/8kk2bNjFz5ky2bdtGnTp1+O6775LsgNVqZfTo0bRu3ZqAgIBYx8Bv2bKF5s2b4+/vz/Hjx5OsJxE/giCQPXtO1q9f4xiDajXaPv1xXzDXMfYkUj3aPgNwWzjPqadjSqQPrH65MFaviWbD2hjlR4/KmT9fxbp1Ory8Usg5CcDGBcbNmzdp2rRpjLJWrVpx69atJDvw448/YjQa2bx5M8HBwUyd+t/Wo+fPn7N27Vo2bdrE8uXLmTVrFkbj+7kaJBxJx46BbNy4LtbBdolF36YDyjO/ILt/zyH2JFI3xrpfImgjUZ4+ldKuSKQBdD2CcFu6GKJOKuP6dYEBAzSsXKkjd25pEZvS2PSKRKGIu5rcAdHgFy9epEqVqMO2ypQpwz9ZYXsAACAASURBVLVr16Kv/f7773zyySeoVCpUKhV58uTh5s2blC5dOsm66ZkOITHPC1mXt2D0z6VKfUyZMmX5559/yJkzR9LFPD3RdwjEben3RE6ennR7Ek7j7biIeqQcc1w4DJkMXe9+uC2ci6lyVcfbl0gz2DIezeXKY82SGdWRQ/xVthHt22uYPNlAuXLSdlRXwKYFRoYMGbh69SqlSpWKLrt69So+Pj5JdiAiIiJGHIdcLsdsNqNQKIiIiMDrnWdcHh4eRERExGgvl8twd4+dl/7ds1MSKrO3rlIZWy+1af2mjxlO7e6uiqG1YcNGx/arbx/cKpRDHDMWfH1d8h7aYze+9gkR1zh1RP/iKk9sn+MaF07R6hiAatI4PJ49go8KJKlf8dV1xj10llZCJOf8ltQ+21v3Q2NHLpdxIfK/+b7To3tsLVw0Vl1Ln764LV9C5/n+BAZaaddOBsS064pzTlqa3+LDpgXG4MGDCQoKokKFCuTOnZtHjx5x5swZvv/++yQ74OnpSWTkfwcgWa3W6Ccm71+LjIyMseCAqAAorTb2axO5PHagSlxl9tZ1d1fF0ktNWgEP7/DCbOLt8BGA5n/cYEex4tFakZGRtGrVhL17DyGXKz5o84N+ZciCrEYtxDVr0HXv7ZL30B678bX38Ig/C2pc49QR/Yur3N4+F7n+GwAvLFG7FVQIiGCTjcTdXxlCy9awdBn6MeOT1K/46jrjHjpLy95x4whNZ/T5Q3Xff3K6o1hxmv9xI0bZxo8K0/bebQAu6SJ5Y7GQQS5HJPa9iNaq3QCfAcHUrH+HAQP80GrT3rzt6vNbfNgUg5E7d262bdvGZ599hslkonTp0mzZsoXcuXPbLfg+ZcuW5eTJkwBcvnyZwoULR18rXbo0Fy9exGAwEB4ezt27d2Ncl7CdDiF38Lt+icPhb3h3bSoS9Yf8Lh4eHsjlco4d+9Fh+voOgWjWrZaC+1IBRkRMiGS7fhG/65fwu34p1odDUtF36oJm4zqQYqrSHZd0kVzSRdLq9s0Yc88lXSTt7t+Ovv7GYsGMyAuLmRcWM0cj3uB3/RLFb16JOR41GsytWzI69wriOTJLIoWw6QnGqlWrCAwMpF69egleTwx16tTh9OnTtGnTBlEUmTx5MitXriRPnjzUqlWLgIAA2rVrhyiKDBw4MMlnZaRnzMT94f7CYib/bxd4bTZTwzNqu9ebqlXZs2cXdep86RBt0xeVEXQ6FJcuQJVKDrEp4TjePr14F5H/xszbD4K4voUmBkvBQpgLF0F1YC+6xs0SZUMi9dAh5E6MRYMCgZ/CXiOKMb/gXNJG1YGosff+l6H4CK5enXHBAwn0b4lFJndO/JCE3di0wFi9ejWhoaFxXhNFkf379yd6gSGTyRg/fnyMsgIFCkT/7O/vj7+/f6JsS/zH8YiwBP9An5ujkrW8/WO3lP8M0927jnNAJkPXoROadauxSguMFOXdyV4g6olFfLy98tJipt392w71Qx8QiNuaVdICIw3wdkyFWSyIgM+/GwDKunkgCP/NK28XDSbE6MH17uJWhRC9AIkLEyJvLBaOR4TR7v7t/x6IFinCG98MlPn1PBcrVHRSLyXsxaYFRv/+/ZN0XcIFef9Vxb/PFt9YLPjI5cizZWPp0lUOS7gCoG/dnoyVyqGbNRsEpcPsStjH8YgwTHEtKuIZE/DvqzRtJCJRHxqOwPBlQzyHDEL27CnWbNkdYlMi+ekQcofjEWExnjj8n737jo+qyv8//r5TElIICKFDIAkdpIkUpYgNdXVRV2k/wLbuylcULICIhV0RG2Db1S0qKojYcNfdddeCBSmCAoIiRZAmTUBaAkkmM/f3R0ggJJNkkjO5M8nr+Xj4MMy9c865M2fufObczzn3wImgIf9xSQVBg6XgoxGnBrvB9ikYVTuWqa6n9MUPL/+1zn///SIBxtKlnxfKK+jdu3/ZDw4VUqYAY9WqVRoxYkSx+Q/r1q3T66+/XmSdjMpiWXlJKadzuSydnmJS3GOh7mtZVpH6oqGu2m639hUzDH46n2wd8OfKK0sXzX5JF+/Zq/HjJ5k5rsaN5D+ruzwf/E/uXw0yclyl7VvW1zCUcoM9vyTF9VMTx1fc46Udc7BLZaU5HPCrlstd6Dp3hV7fmonKvexXqvHeu8q+5dZSyyiurmD7huM1DFddJanM81t5j3nV8cziA1ap0DhESYHF6YKVFyNLtdzuvMsoVl7d+Xae01tL//q8YnKyCx1H/o068/+fv83pc05VOr8FU6YA484779RTTz2l7777TqmpqUpOTtaRI0e0fv16nXnmmRo3bpyRxpQHS4WXra5SP9in/XpN8nhUo2EDvTZjuu64Y8Ipd9Ot2HEdv/I3in/rTfkvuaJczw9130hZSjeSlgovc3hh24VGMXx23i/U2SktT9mlYq9v1jVDFDd1io7dPLrUfVkq3GSd5o65uP6U/9ipIxLBgoZQ5MjWIb9fPtnal3vyB5NXluzkZP3Yuo3OWrJEw2JiC/Iwfvllv3y+k/frWLTo0xOjGE6fc6rO+S2YMq+DMWXKFGVkZGj16tU6ePCg6tatq8mTJys+Pt5IQxBe37ftrPprV5RpX1t5l0o2NmqkDH+urvzkA/3zAjPJnjmXXa6a998j68hh2UkVX0cFoQvll+TpDvv9GrFtk7Ekuty+/eXatUvuzT/In97KSJmITBUNL/JD3eICldwTuRmv9e+nnh/8V0vPG1CmMpcu/bzQv/v0KdvzUDYhjYMkJibq3HPP1eWXX67evXsTXESZixPL/oWeK1tHAgE1vPAiHd+101gb7Fq15e/bTzH//Y+xMhGauu4y/a4oxFLer8RaBlbvLcTjUfaVVyv23XfMlotK0y0uQd4gSZkm2QoepOTPeJpz7rm6cOlSHdu9syB4OOOMuoqJiZHX65XXS+5XZQopwOjbt686dOigPn36qGPHjurWrZsuvvhiLV68OFztg0Pctq24gF/1RoyQ98yORsvO/c01iv3XP4yWibIrT5JmfuZ//hRCk3J+9WsCzig2p3lLDUhMqpQgozR7zjhDX7dpo+7LvtTBgwe0dOnnOnjwgNPNqrZC+ilz9tlna8yYMUpLS9P27dv1pz/9SbfeeqvGjx+vc891ZuohSZ5lr8uypIFJtfXBkUMnF/gPIlfSYcvSOjsgzyOPKHfuO4qNjTVyXIGBlyru9tvkzs6SToyCOf0ahlJutCd5vp7WWo3WfF3i9NQC+X3Eylvd0zrtWEy8voFeveXe9ZO8u35SoFlKSMdVUrmmX8Nw1VWSaEjylPL6VP4iWftzy5tGXDH5df6rd2/1++or/dKihX7+eY8kqVGjxgoEbJ177nn617/e0fvvvytJ8vly1KDByXsuReJ520RdoZRb6Ume+fbs2aO0tDRJUkpKinbv3q3mzZsbuelZeZHkWba68ueph/obI8PrlWvPHr344l/0+9/fVqa6SmuXXauWfJ27yP3Zp8oZeGnIzw/PaxhKudGd5ClJSW53sYtrBXUi4TPJ7dbslJYnE/xMvL6WSzkXXSLPf/6l4wXJniR55rUj8pM8881OaamR2zfpk6NBpkFXkn/16qWJr7+uS269VbfuyQswDhzYr9q165xo88kbJXi9XvXq1a/guYFARd/3SP2OCKVcc0meIYUp9erV0/Tp07VgwQJNnz5dycnJWrx4Mde1okihIW7LKjRLIJhA9+5avfobo+3IuegSxXz4P6Nlouy6xSUouRy5GOGSfenlXCapImq53c71LcvSj02b6lBiomrv2OFMG1AgpADj8ccfV40aNfTqq68qPj5ejz76qOLj4zVz5sxwtQ8GdYtLKF+SXteu2rZtq9G25Ay8RDEf/Y97kzhkTvOWIediWDK3yNbpcvoPkGf1N7IOFr9iMKLD3NTW6haXELZ+Ulb/7tVLA5cvl8vlkstlZrgfoQspzPzkk0/0r3/9S+np6Zo/f76aNGmiQYMGlf5EOO7U+wAUEWwUI//Lv0sX2R3P1Ihtm/R6mpmbzfnTW8mOj5fn29XK7dTFSJkIzZzmLQtPXT69HxQT/K08nml0mmqB+Hj5evVWzMLPlD3oarNlo1Ll942yTosPh/d799bM55/Xjt9cKykvj6VHj76SpMsuu7rgMVOXAlC8kAKMl19+WfPnz1dCQoIyMjJ03XXXOR5gkORZtrrOik/Iu5FQwK+cso4a5H/hWJZWfb5ASa1ay9WyjbHjyj3/QsUu/Ex2126Ov4ahlBvtSZ6F9lUJ6xOcFnDYko74/caTPPMfyx1wgWIWfqbcq69xPPGSJM+K11Vi3wqjGFmK732OWt93v1q07yS7Tl3VqOFVVpav0H5On3Oq0vktmJACDMuylJCQN/SVmJgYEXc2JcmzbHXZJ+4tVMtVtiXD81nKu4eA/4dNyt6/X4FfX2PsuLL79Ffcyy/IP2ZcuY+rrHWZLTf6kzylondGLQuf7ELHYvL1ze43QLWee1b+XL/kcZPkqehK8hyxbZNWHc8sCCo8J25cVplBhiXpvMQkvdy8pTy9z1Huws+Vc8WVUXveNldXKOU6lOTZrFkzPfroo/r444/16KOPKiUlxUgjELnyF7CxOnaUb80ao2X7zjlXnq+WS9nZRstF2aw85cugrML5ZeFv1VoKBOTeHHrgg8gS9HJsJfL1PU8xX3zuaBuqu5ACjEceeUTNmjXT0qVL1axZM02dOjVc7YJhc5q31PdtO2t9h64hP9eW5O/YQbnffadAwNw1S7v2GfK3aiXvyq+NlYnw8soyn3+Rz7KUc9758n7+SXjKR6UKdsv1yjBi2ybl9Okn76KFjrUBZbxEMmPGjFNudiXFx8drz549euaZZ3TnnXeGrXHVwdfHMjTt5106kJs3mNjYG6MpDZqqbY04JX61VO1ia8h1ymvfNS5BMxs3r/R2euvUVc3Zc+RymU2M8vXpL+/Cz5Tdp6+xMqPZd999q7/97c86fPiQAoGA6tdvqFtvHatWrVqpT5/uSktLl8t1ciZQu3btNXHifUHLy78MUty6agdCWQfjBONLhZ/G13+AYt95U77fnX7zM5gSrI917NiuXH3sdPmjF06NX3yacUS13G75O5wp1y8H5Nq9S0pv4VBrqrcyBRj5i2vBrOxAQCO2b9LbqW3UMTZOkvTWoQMatv0Hfd3qTEnS/BZtVNdz8m0ykfmc7PaEtsjSCYFDB7V27Xdq27Z9heo/VU7f/kqY+bi4SCLl5ORo4sRxevrp59SyZRtJ0gcfvK+7775d8+f/W5L0zDN/Ve3atQueU97+8GnGkZC/AMI5TTVfTp/+Spxwp+T3SxGw9HRVU1If+89/8talqUgf6xafoBXHMh0PMiRJLpd85/aT94vPCTAcUqYA46qrrgp3O8otmmeR5NgBHfb7dUyBgjKH1ElWLY9HcuWdXN3uwvWZOK7ynLZ9snVo+XLNXrJMjz46vcx1lfYaBnr1lufb1XL5ciRvTJmeH41Z1mWZReLz5SgjI0NZWccL9r300l+pZs2ays9+CNYfhm/ZePIxt0tzUlpq+JaNWnU880QDCgcH5bk+7rWsItOUjb++DRvITq4nz4Z1UvvC98BhFknF6yypj9l2yX2stLpWHc+UrBMzjZR3Oa1My9EbZp34z+12KbdPX8UuXSz/DddH3MyOqnR+CyZylvIrp2ieRVLTcumBBk117eaNqufxqEd8ovok1NSVterIfeJzOWjz+kKXSN5Ja6M61unD1KEdV7k/8q1b682XXtLDJl/DuHj5U9NlffON/N26l+n50ZhlXZZZJAkJiRo9+jaNHTtGderUVadOndS1a3ddeOFAuU+sjHjrrb8rNHydc9ddciXV0soTgUS3uATpRF35M4ckybKlT44eqdCvylquss7sqNjrm9Ozl9xLFiu7TeGRMmaRVLzOkvqYx1N8H3v66edUq1btoGUWtFV5/Sw/MdwJXuXdM6drXELeTLXuPRX74t+UG5EzO6rO+S0YRwOMrKwsjR8/XgcOHFBCQoIee+wx1alTp9A+o0eP1sGDB+X1ehUbG6sXXnjBodaGx+jkBrouub4WHT2spZkZenb/Hj27f48+SGsnKTyXSKRyzlFv1UqHf/hBubm5BScjE3xn95Dn6+XKKRJgVD9Dh47QlVf+RitWfK3Vq1fqtdde0WuvvaKXXpotqejw9cjtm0pcDDV/1GLV8cwK3x9ifYeuRvNvgvH1Okexny2Qbrg57HVVR8H62Jw5r0sK7RJJ/j2ODvv9jt5/JF8tt1u2Ti725W/XXq5du6SDB6VYZ1cXrY7MjIOU0+uvv67WrVtr7ty5uvLKK/Xcc88V2Wfbtm16/fXXNXv27CoXXCw7lqE/7d+jmm63Lq5ZWw82bKovWnaQS5Y+zzjidPOKSkhQrYenFQylmuLr3kPe5V8aLTMarVnzjebOfVUJCQk699y++r//G6vZs9+Uy+XS8uXLgj4v/wR/wJ+rjzIOa8GRQ2q/frU+zTiilccz8+5wWY6cG6f4evSSd+kSlpEPg5L62LJlS8tczvAtGwsFF05PSQ3K41Fu125yf7Xc6ZZUS44GGCtWrFDfvnmzB/r166elSwt38P379+vIkSO65ZZbNGzYMH366adONDNskt0ePblvt77MPFrw2N5cn47ZfrWrERe2ervFJZR7Ctmx1q31yy8HjLbHd3ZPeZYvq/ZfKLVrn6FXXnlRq1evKnjswIH9On78uNLTg08NPTWh7vRXMD/wqOgrW5k3rwqkpkkBv1zbt1VandVFSX2sZctW5S43Ej65wc5ovu5ny/Vl2YMnmFNpZ4233npLr7zySqHH6tateyKBTUpISNDRo0cLbff5fLrxxhs1atQoHT58WMOGDVOnTp1Ut27dgn3cbpfi4wsnB0p5yS+n/9Iu7rFQ9/V6i9ZX3rrOjI/RvFZtNW3XT9qZk61Yy6Vabrf+lJquTrWSJElxcV7Fn3K3WhPHNb9de6Wu+lr7cn1FnlOamA8/1NPH39NTTz1TprrK9Bq2byMrN1cJv+yV3Syl1OeH4/0Kpdxgzy9Jcf309HLatm2lmTOf1l/+8mf9/PNexcTEKjExUfff/6DatMk7+cfFeQuVM79dB7VY+ZUO+3Pls0/mV1iW2VUUjwT8qlHDW+S4w/H6SlLgnHOV+M1Xym13Mqm0uLpCKTeUdlVmXSUxfX4rqY+1apUuqWgfK65cl9sl2Xn9IhIujViSkj1edU9MlGwVar+7Tx95/vSMsfN2afua/I4wVVco5Zbn/BZMpQUY1157ra699tpCj40ZM0aZmXnJaZmZmUpKSiq0PTk5WUOHDpXH41HdunXVrl07bdmypVCA4fcHdOxYTpH6irtuGOxaYij7xsfHFKmvInWd7YnTe6ltiux77FiOMs7urWPHcnTMlxP0+eU9rvJ0IEtSu/bt9d0rr1boNSjuNfR27yHf4qXK+XXDUp8fjvcrlHKDPT8hIfjS+cX10+LK6dChi5577oUij2dl+bRoUd6CZKeW43a7ZNt2kcGfQ7lmB63PS0hSVpavSLvC8fpKktXlLGnZch0bdPKcUVxdoZQbSrsqs65Q+01F6wzWx3y+QNA+dvq+Xx89mjdi5kBskZ8/Zkmqe2JkrVtcguY0b1nQ1lPbb3Xsorpff6VjR49Lp6zjEi3fESbqCqXc8pzfgnH0Ekm3bt30+ed5S7kuXLhQZ511VqHtS5Ys0dixYyXlBSA//PADa3IYUt71DBLTW2rduu+N52Hkdu4s75rVRsusLrrFJaiW2626bo+S3R5dkFTb6IJYXgfWo8jt0lXeb1aVviMc0S2+8hMmvbIKgorkE/+V5dbw9hl1ZDdsKPeG9ZXTUBRwdBbJsGHDNHHiRA0bNkxer1czZsyQJD3++OO65JJL1L9/fy1atEiDBw+Wy+XSnXfeWWSWCSpPXbdH73Q6WzNuvV0+n08xMUWHbsvLf2Znxb74N2PlVSenL90dHx+jFiu/MnY3y3Cv3lmc3E5d5Pn+O8nnk065RIjIUVkzR2JkKcnt1vdtOxe6QV/BGi9lEOjSVZ5vV8vfvkM4moggHA0w4uLi9MwzzxR5fMKECQV/T548uTKbVG3Mad5S9deuKPP+lqTv23aWJI0bd7f5EYxOXZTw7eq8MVer8n8xV0X5ibwVycW4OLFW+O49UpKkJPmbNJV7w3r5O55Z+fWjRCuPZVbKzBGvLA2omVRwKebUvhjKlP1A5y7yfLta2UOGh6OZCMLRSySIHqfOOnnssYf1l7/82Wj5duPGkm3LtWe30XKrq25xCRqQmKRabne5Zww5Hebldukm7zcrHW4FTjdi2ybtMzAzqTL5O3eRh0uwlS7qV/KM5qXCI6muksTI0u5O3Que37RpUy1fvqzcS5gHOy5/py6KWfutfE2bGjuuSFlKtyxLhZenzmDLXL+e1lrDt2yUJam22619Ia6DEXPil+Pc1JOzOMq6pLap4/J37SbvmlXyuW8IWlco5bJUeMmPlfWYQ7k0URH55x2Xy1IgUDScCeU1sLt0lfe7b+W2JLlcIT8/Us/bkXJ+CybqA4xoXio8kuoqia1TX2OXWrduq1deeem0cip+XL6OneRa/Y38Fw4spa3Rt5RuWZYKL1+dwZe5zl8q/LDfH1JbJem8xKRi2lzWJbXNHFdO565KnDe3YDtLhZuss/zHfKgc/SkUXlkFeT959Ro4D5xxhgJ16kibfpA/vVUJ+0bXeTtSzm/BRH2AgfA7Pcmvbdt2ato0Jcje5Zd7ZifFvvuO8XIR2tLwMbKcybs4TW6HM+XZuJ5EzwiRv3JnOBM78xM6w3HX3tyOneT5ds0pAQbCjRwMlOr0D3vNmkkF98YwKbddB7k3rDNebnWWP4U1lKmm+ZfDHBcfL3/jJnL/uNnpluCE8oyGldWpIxfhkNupszyrvwlb+SiKEQyUyBvk1+zjj0/TBRdcpLPOOttYXf60dLl37ZSysqQaNYyVW13lv28jtm3SquOZpeZhWJL2djirxH0qm79dB3m+/07+Nm2dbgoMsiR5LUtJrsKjFZaVd1koHCNoue07Ku7lqnU/q0gX9QEGSZ5m6iqOJen8mknFJnP+/PMefffdGvXo0dPccdWIlb95C8Vs2Sx/xzOrVBJUZSd5nlrX62mt1fb70heturhmLbndrgonQ5o8rkCHDvKu/165bhdJngbrLM8xW1bFZxbFnCihltstWZa6xcVrbmpeQvKJRsiSXWoCebDHSzoP2O3by71hfUHZVeG8HSnnt2CiPsAgydNMXcXZ3+ls+f2BYhP9UlNbatOmTYWSP00cl791W1nrvpe/XQcjxxUpSVBOJHkWfjDvhmUlLY40O6VlyEl14Uzy9PsDymnbQTXeeO1E0ipJnubqDP2YZ6e01Ihtm/RpxhFZknwn1lfxnrLeileWckrI0TgvMalgdCJ/mev8sqWTa1uUnkBe9uPKPzZf0xS5Dv6iwKFDsmsmhfT8SD1vR8r5LZioDzBQfqdelz/9S6e0a/Zpaen6JgxrFOS2aSv3Rpb0DSevit4IzYnlwMvC3769POu+d7oZOCE/OMgPBE5dWfPTjCOF9s1PLM5P3Dz1+Y5wuZTbsrXcG9Yrt3sP59pRjRBgVGOnJlRZlqWuNeLL/NxLLrlMl176K+Nt8rdpy0ySMOgWn1DoxlQrT6xlkP8lkL9Ka6TxN0+Va/8+WUePSPHJTjcHJajlduus+AStOJbXt/JzK15Pa23sF3FF+du0lYcAo9IQYFRjp36pBLt7ZDCWZem5557VzTffIq/BKYS5rdsqnhEM4+amFn+SD2W5ZUe43XmjWuvWSQ36Ot0anOb0EYlI70+5bdrJvZ6ZapUl6gMMkjydq2vWrL/rsst+pfT0lubqat1a7h3b5c71yeWtUWnHFUq50ZbkGe11Bdq2V8wP62UN6EeSp6E6w3HMoe7rxPnNbt9e3r8vDJrMHK3HZbJckjxPQZKnc3WlpaXrhx82qkWLNHN1uT3yN2kq/fijAu3aVZkkKMeTPKO4Ll96S7k2biTJ02id4Tjm0PZ14vxmt2qj+PXrSkhmjs7jMluuuVEoM2EKqqWUlBbavn278XL9qWlyb/nReLmITv70VnJv/sHpZqAKCDRLkevwobycHoRd1I9gwDm33TZONWrEGS83L8DYrPDe8QDRwt+yldybflBot2sDiuFyyd8i7weM3bWb062p8ggwUG516tTRzp07Vb9+faPlBlLT5N7EL1bk8bdIlXvnT3n3JKmgnO3Z2tzve7Xb2rXQ45mLj2r3pO1qubCDMhcf1dqrN6r5G62UeF5SwT6779kuT7JX9e5upJ8f36VfZu2Tt6G30I1eEs9PUoP7m2rPYzt14MWf87Yr71JHIMOvWr86Q/WnNJFlRea04OrAn5Yu94+blVuGAGP37l0aNWqIPvlkcaHHV678Wk899YReffUNrVz5tcaOHa2ZM5/V2Wf3Kthn5szHVKtWbd100+/1wgt/0TvvvKnk5JPnSsuSevTordGjb9OLL/5V7777lpKT68uypEDA1rFjmerX7zyNGXOHuYOvZAQYKLcNG9Zr0qS79eGHnxst15+appiPPjBaJqJYbKwCjRrL2rJFatqiUqq0vJZ23rZV6Z+1l6du8afJWoPOUKNHU4LOnMjfns9/KFebz/teCf1rKvH8WmFrO0rmT03LCzAMlunxeDR16hS98so8xccX/4Pr/PMv0p13Tiz49+n9Jn97/uNHjhzR9dcPU48evXXOOecabG3lifoAg1kkztXVokUL7dixPeSM7NLqstNbyr11S5XKsmYWScXqyrtMslHu5mnlLtflOtHW094Ll8s6sb9V0JdrpNdQjW4J2jVuq1Lnti441vz30XJZBcde/OtS9LXxHfArcDwgbx1vsees4jCLxHxdgZYt5V2yWL4yPD/YsuL5j7vdLrndLjVrlqL21UD7swAAIABJREFU7TvqkUf+oD/96bm8PmJZBX0ur+9YRfpdfpmuYvrT4cMHlZWVpVq1akX8+S2YqA8wmEXiXF116iTr2LFjOnz4iGrVSjJWl79JM7l27VQgO0d+V3F3V4y+LGtmkVSsrtz0lvJs3Cj/eRdXoNwTjxV5L1wKBGxJeceS97fUcGpTbb5wnX7+2x7Vvam+bNsueB/tgK1D//hFmV8eLXSJpMH9TZR4fi3Ztgq2B44H5D+YqxpnxqvJzBaK7RJf5v7DLBLzdeW2SFPs7FcUCJRef/7fp+9bcK7y5y1rbtu2xo69Wzfe+P/0+utz9etf/0a2bRc8z7ZtLVjwoVafcjdXy5JuueU29ezZW4HAye3Z2Vk6fPiwWrduo/HjJ6lt2/ZB2ho557dgIiLA+Oijj/S///1PM2bMKLLtzTff1Lx58+TxeDR69GgNGDDAgRaiOJZl6YEHHpJtm+mMBWJiFGjYWK4d2+Vvnmq2bEQlf3oredeurtQ6XQluNf1LqrZevVEJ59Qssr2sl0gCOQHtmbRDWeuOK+mCWiXcqQOVwZ+WLveWzcbLjYuL04MPPqxx40arY8eiK+OW9RJJIODXE088qi1bNqtXr+i8NJLP8WmqU6dO1YwZMxQIFP2A7tu3T7Nnz9a8efP04osvaubMmcrJKftqkwi/m276nRITi558K8qfmspUVRTwp7eU64fKT/yN65ygenc00k+3/KhAdvkCaVeMSw0faaZApl+7p/xkuIUIVaB+AykrW9bhQ8bLbtu2nW666WZNmTK53N9VXq9Xd945QceOZeq5554x3MLK5XiA0a1bN02ZMqXYbWvWrFHXrl0VExOjmjVrKiUlRevXs4x0JHnwwcl6+eUXjZfrb54qFwEGTvA3byFr21ZH6q57awN56nl1+O1fyl2GK8alRo8114FXftbxNccMtg4hsyz5U9Pk+jE855dRo65XnTp19eGH/y13GV6vV3fdNUnvvTdfGzZE73depV0ieeutt/TKK68UemzatGm67LLLtGzZsmKfk5GRoZo1T/46TkhIUEZGRqF93G6X4uNjijzXsizZtl3qY6Hu6/UWra8619WwYX3t3v2TatTwGq3LndZCnt07pQq+t2U9rlDKDfb8khTXT028b8U9Ho5jdryulqmy9u1TvNuWYmPLVa5lWVJcrgLHAlqXuqrQtqZTUuSyLMXHxyg31iOd+Lug+pdaa23P1fJ48o7X63Xr4D/36fhXmaemYCi2WaxavdlWHo9L8hR+beLPr6OjQ+pp7+QdavdxxzJNVa3M81tx72W46gpWX2XVZbVqpRo7tsrTq/BNz05/flycV8ePH9f55xe+VHHbbWNlnegjNWp45XKd7C9er0sPP/yIhgy5Rl6vW/HxMfJ43Prkk4/13XdrCpXTsGFDPfXUs/J63fJ48vbNb0Pv3j106aWX6emnn9DLL88uckzFHZeJ16s857dgLNtUSRWwbNkyzZs3T08++WShxxcsWKAvvviiYITj1ltv1S233KIzzzyzYJ/MzOxib9JV3HXRYNdKQ9m3uJuCVee63nprnhYs+FB///vLRuuKfedNxX74Xx3566xKOa5Qyg32/Hr1gl8qKq6fmnjfins8HMccCXXV7dFJh+bNlz+t9BtsheO4wlVXqP3GRJ3hOOZQ93Xy/Jbw0INSUpIyx95VpY7LRLnlOb8F4/glkpJ06tRJK1asUHZ2to4eParNmzerdevWTjcLp2jVqrWaNGlmvFx/k2Zy/cT1apxkN28h17ZtTjcDVYC/WYpcO+hL4RYRs0hON2vWLKWkpOiCCy7QyJEjNXz4cNm2rTvuuEOxpw2PwlldunRTly7ml9wNNGsm9w7z9zlB9Aq0aCH39m2q+HqeqO4CzZrJ/d9/O92MKi8iAoyePXuqZ8+eBf++4YYbCv4ePHiwBg8e7ESzUAa5ubn63e9u0MsvzylxP3/Ar7+teV7zf3hLAfmVnZuti1tcqok9JivWXTRoDDRoKOvAfiknR4opel0Y1Y/dIlVuhxI9UbX4m6bIFYYbNaKwiL5Egsjn8Xj0+eef6tChgyXuN2HhHfp673K98+v3tPS65frgms+06dAPuuPTMcEKVqBBQ7l27wpDqxGNAs1byLWdYW1UnL9pM7l27shbyQxhExEjGBXBUuHO19WoUSPt3btHZ5xRp9h9tx3eqnc2vqm1N21SUkySLMtSUo2amjngGS3f/aUyc49qwoI79e2+NbIsSxc2v1j39Z4iu1mKmrx/lm7pdrs+3PpfHc05qj+cO1XvbfqH1u7/Tg0TG2nu5W8pwZug+n+qpVu63KpFPy1Upi9T9/WeoitaDoqYpXRZKrzidSk1VZ7t28rdTyt6XOGqqyQsFR6mupJqyk5IlOfAftkNGlSd4zJQLkuFn4Klwp2vq2HDxtqzZ69atWpb7L6r9q5SmzptleBOLFg21+8PKLlGPV2WeoXGLPi9zqhRR58P+VI5gRyNfH+Inl3xlO5r0lTZ9mLVj6uvz4d8qWdWPqmxC8Zo6YgVqlejvi5++zz9Z9O/9JvWg+W3/aoVU1sfXbtQa/d/p0H/uFQ9GvZWSmzjiFhKl6XCK16XP6W5YrZvrcB7VNHjCk9dJWGp8PDVFWjaTNq2Vf7kemGvy/nXMJRyQz+/BcMlElTYvHnv6Lzzgi/h7rJcCpQwFPnJ9o/0206/l2VZinXH6roON2nB9o/yTgCSLk8bJElKrZWqdnXbq3FiY7ksl1JqNteh7JOXZm4683eSpA7JHdWubnst3bW4aGWIXvXry8rKkpVx1OmWoAoIpKSQSB5mBBiosHXrvtfy5V8G3d6t/ln64eAGZeQU/mLYnbFLw/99jfwBf6HHA3ZAPr9PgWZ5AUbMKUmgXpc3aD1u6+SAXMAOyG0Vd6M0RC3Lkr9xE7l2kZeDisubqrrD6WZUaQQYqLDly7/UG2/MDbq9UWJj/ab1YI399FYdzTkiSTqac0QTF96pM2rU0YCUC/Ximr/Jtm1l+7M1+/tZ6t9sgAKNGofUjjc3vC5JWrPvG206uFHnNI7uGwWhqECjJnLtZH0UVFygWYrcrIURVlGfg0GSp/N1NWnSRJ99tqDE508f8JSmL39Uv5p/kbxur7Jys3RZ2hW6p+dkZeQc1T0L71b/N3rJF/Dp/JQLdXePidL330sbJbc7r20ulyXLOlmuZRVu91d7l2nOupcVsAN64ZJXVTehbsQkQZHkaaYuu0kTefbuVuCUOknyDL3Oap/kKclOaS73Jx8XerwqHBdJngaR5Ol8XfXrN9CePbtL3NeSS+PPvlfjz763yPK2tWLO0N8Gziry/Nx6DeX/Uz0d+L868vsD+lXqIP0qdZACgby2vjgwb33+/Of98ZxHVDeubsHzT00oLctrUPbXiyRPp+rKbdRI1k8/nbaNJM/Q6yTJ09+osVy7dpWhL1W8Ludfw1DKJckTEaR167Z65JEnjJdr16uXd0tlH2s3Ik+gcVPWRoERgYaN5NpDXwonAgxUWGJioho0aGjsDnwF3G4F6tSVa9/Ppe564LaMQqMXqJoCjRvLtWun081AFWDXqyfryBEpO9vpplRZBBgwon//c3T06BHj5QYaNJRr7x7j5SI6+Rs1kZtZJDDB5VKgfgPOL2FEgAEj6tWrp31lGGkIVaBhQ7n2cAJAnkDjxnLtZgQDZgQaNpRr926nm1FlRX2SJ7NIIqOuevXq65dfDqh16zZG67IbNpRn3175yzFrIJS6QimXWSTO1eVKTpaVlSV31nEpISGkcplFUvJj1W0WictlyW7UWJ59e2Sf2FZVjquyz2/BRH2AwSySyKhr6NDhqlXrjDJlZIdUV/2G0u7TZ6hEX5Y1s0gM1RWw5W/UWNr5k/zprUIsl1kkJT1W3WaRSC75GzSUtXPnKduqyHExiwRVyXXX3Vho9MIUcjBwugCrecIQf6PGXIINIwIMGPHCC3/V88//yXi5gYaNCDBQSKBhI2aSwIhAw0ZMew4jAgwYYdu2tmzZbLzcQIMG/MJAIYH6DeTat8/pZqAK4AdMeEV9DgZJnpFRV/369bV48RdlWnY3pLoaNpR738+llhvpSVAkeRqsq2FDuffuKaiXJM/Q6yTJ88S+TZrIvWd3iX0pKo+rks9vwUR9gEGSZ2TU1bhxUyUkJJpP8qxdR9YvB+TP9eedbcN2XKGUS5Knk3XlJtdTzOpvSknMI8kz1H2rY5JnoH4DuXbvOuX8UjWOK1KSPCMiwPjoo4/0v//9TzNmzCiyberUqVq5cqUSTkxJe+6551SzZs3KbiJKcfbZPdStW3fzBdeoITsmVtbRI7KTapkvH1EnUK8+l0hghF0zKe+PzEwpMdHZxlRBjgcYU6dO1aJFi9SuXbtit69du1YvvPCC6tSpU8ktQyiysrL00ENT9Mc/TjNetl23rlz798lPgAHl52DsdboZqCICyfXk2vezAgQYxjme5NmtWzdNmTKl2G2BQEDbtm3TAw88oKFDh+rtt9+u3MahzLxer/7+9+eVm5trvOxAcj1Z+w8YLxfRKVC/gVw/E2DAjEC9eoyIhYllG79DVfHeeustvfLKK4UemzZtmjp16qRly5Zp3rx5evLJJwttz8jI0KuvvqobbrhBfr9fo0aN0rRp09S2bdvKaDIAACinSrtEcu211+raa68N6TlxcXEaNWqU4uLiJEm9evXS+vXrCTAAAIhwjl8iKcnWrVs1bNgw+f1++Xw+rVy5Uh06dHC6WQAAoBSOJ3kWZ9asWUpJSdEFF1ygQYMGafDgwfJ6vRo0aJBatWpVegEAAMBRlZaDYcrRo0c1fvx4ZWRkyOfz6Z577lHXrl0L7fPmm29q3rx58ng8Gj16tAYMGFChOitzGm1JdZk6rqysLI0fP14HDhxQQkKCHnvssSKzdEaPHq2DBw/K6/UqNjZWL7zwQkh1BAIBTZkyRRs2bFBMTIymTp2q5s2bGz+WstTl5FRn+g59JxRlOb+ZahPnUj4PodYV8ntkR5mnn37anjVrlm3btr1582b7yiuvLLT9559/ti+//HI7OzvbPnLkSMHf5fXQQw/ZAwcOtMeNG1fs9qFDh9oHDhwod/llrcvkcb300kv2M888Y9u2bf/73/+2H3rooSL7XHrppXYgEChX+bZt2x988IE9ceJE27Zte9WqVfYtt9xSsM30e1RSXbZt9j0KBX2nfKpz3ynt/GayTZxL+TyEUpdth/4eRXQORnGuv/56DR06VJLk9/sVGxtbaPuaNWvUtWtXxcTEqGbNmkpJSdH69evLXV9lTqMtqS6Tx7VixQr17dtXktSvXz8tXbq00Pb9+/fryJEjuuWWWzRs2DB9+umnFaqjS5cu+u6778JyLKXV5eRUZ/oOfSdUpZ3fTLaJcymfh1DqKs97FJE5GPlKmtq6b98+jR8/Xvfee2+h7RkZGYWGbBISEpSRkVHuui677DItW7as2OccO3ZMI0aMKDSNtmPHjqXOcilPXSaPq27dugVlJSQk6OjRo4W2+3w+3XjjjRo1apQOHz6sYcOGqVOnTqpbt26p9Z3a3sRTFq5xu93Kzc2Vx+Mp97GUp67yvkehoO+cRN8pu/Kc30z3G86lfB7KWld53qOIDjCCTW3dsGGD7rzzTk2YMEE9evQotC0xMVGZmZkF/87MzCzTdbzKnEZbnrpMHteYMWMKysrMzFRSUlKh7cnJyRo6dKg8Ho/q1q2rdu3aacuWLSF9KE5vbyAQkMfjqdCxlKeuypjqTN85ib5TduU5v5nuN5xL+TyUta7yvEdRd4lk06ZNGjt2rGbMmKH+/fsX2d6pUyetWLFC2dnZOnr0qDZv3qzWrVuHpS2VOY3W5HF169ZNn3/+uSRp4cKFOuusswptX7JkicaOHSspr8P+8MMPSktLC7mOhQsXSpK++eabQm01/R6VVFekTnWm75RcR3XtO6Wd30y2iXMpn4dQ6irPexTRIxjFmTFjhnJycvTwww9Lyou4nn/++UJTW0eOHKnhw4fLtm3dcccdRa4tVlRlTqMNx3ENGzZMEydO1LBhw+T1eguyrB9//HFdcskl6t+/vxYtWqTBgwfL5XLpzjvvDPleMBdddJEWL16soUOHyrZtTZs2LWzvUWl1RdJUZ/pO6apz3ynL+c1UmziX8nkIta5Q36Oom6YKAAAiX9RdIgEAAJGPAAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHEEGAAAwDgCjAg1f/58TZ8+PeTnHTx4UA888IDmz5+v8847T7NmzdKyZct01llnaffu3QX7TZ8+XfPnz9dPP/2kwYMHS1KhvydPnqzu3btr8+bNkqRffvlF559/vu64445C9eWXU5L9+/frj3/8Y8jHgsplss9J0t/+9jddf/31GjFihEaOHFlw46T8flZaf5s0aZKWLVtGn6uiTPe3HTt26LbbbtPIkSM1dOhQTZkypeC+HMH6Gv0svAgwqpinnnpKw4cPlyRdfvnluuGGGyRJMTExmjRpksq6rtrDDz+sdu3aFfx74cKFuvvuu0t93g8//KDrr79eAwcO1J///Gc99NBD2rVrlxISErR8+fJyHBEiXXF9btOmTfrkk080a9YszZkzR/fee2+Rm2mdqrj+ln9Xx5IU19/WrFmj5ORk+lwVVVx/y8rK0v/93//pt7/9rWbPnq158+apc+fOuuuuu4o8/9S+Rj8LLwKMCOfz+XT33Xdr6NChuvbaa/X+++8rKytLt99+u4YOHao77rhDffr0kZR3J7xvv/222JvP9OrVS7Vq1dJrr71WrnYsWrRIMTExJe6TnZ2tsWPHavLkyfrnP/+pt99+W3v37lWnTp10+eWX69VXXy1X3ahcJvpczZo1tWvXroI+0K5du5Buwb1o0aJST/wl9TdJ9LkoYaK/ffbZZzr77LPVuXPngseuuuoqHTx4UDt27AhaN/0svKLuXiTVzRtvvKE6depo+vTpysjI0NVXX60ff/xRTZs21TPPPKPNmzfr8ssvl5R3c5rU1NSgZU2ZMkXXXnttmSL2U/l8PmVlZalmzZr68ssvNXLkyIJtO3bs0O233y4p78Y+7dq1K1if3ufzFYygtGzZUitWrAipXjjDRJ9r0KCBnn/+ec2ZM0d//vOfVaNGDd1xxx0aOHBgqfWf2t8kBe1zJfU3iT4XLUz0tx07diglJaXI402bNtWuXbvUpEmTIttyc3PpZ2FGgBHhNm/erHPOOUdS3s2I0tPTtWzZMl133XWSpPT09IKb5xw8eFDJyclByzrjjDN07733auLEierWrVuZ2/D1118X3CWwV69eevLJJwu2nXoNdd26dWrfvr0kae/evYqPjy94ntvtlsfjUSAQkMvFwFkkM9Hntm3bpsTERD3yyCOSpG+//VY333yzevbsWWr9p/Y3KXifK6m/SfS5aGGivzVo0EBr1qwp8vi2bdvUuHHjYuuln4Ufr0aES09P19dffy0pb3hw48aN6tmzp1atWiVJ2r59uw4ePChJqlu3ro4cOVJieeeff75SU1P17rvvlrkNn332mc4777xS9/N6vdq7d68kaebMmfL5fAXbbNuWx+PhAxgFTPS5DRs26I9//KNycnIkSampqUpKSpLb7S61fhP9TaLPRQsT/e2CCy7QkiVLCgUZb731ls444ww1a9as2HqXLl1KPwszXpEIN3jwYB06dEjDhg3TqFGjNGbMGP32t7/Vzp079f/+3//Ts88+W3B73s6dO2vDhg2lljl58mTVqFGjzG3YunVriZde8l1xxRX6+uuvNXDgQLVt21ZdunQpuBX0hg0b1KVLlzLXCeeY6HMXX3yxunfvrmuuuUZDhw7VTTfdpAkTJhQMR5fERH+T6HPRwkR/S0hI0F/+8hc999xzBbkcq1ev1syZM4PWm5GRQT8LNxtRZ8WKFfYXX3xh27Ztb9myxb7gggsKtt1///322rVr7Xfeecd+4oknKlTPiBEj7E2bNlWojHyPPfaY/dVXXxkpC5WvMvqcyf5m2/S5aBbu/sa5rXIwghGFmjVrpr/+9a8aOnSo7r77bj3wwAMF28aOHau5c+dKkv79738XzBEP1eTJk7Vu3Toj7d23b58yMjLUvXt3I+Wh8oW7z5nsbxJ9LtqFs79xbqs8lm2XcWEEAACAMmIEAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwAACAcQQYAADAOAIMAABgHAEGAAAwjgADAAAYR4ABAACMI8AAAADGEWAAAADjCDAAAIBxBBgAAMA4AgwAAGAcAQYAADCOAAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHEEGAAAwDgCDAAAYBwBBgAAMI4AAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwAACAcQQYAADAOAIMAABgHAEGAAAwjgADAAAYR4ABAACMI8AAAADGEWAAAADjCDAAAIBxBBgAAMA4AgwAAGAcAQYAADCOAAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHEEGAAAwDgCDAAAYBwBBgAAMI4AAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwAACAcQQYAADAOAIMAABgHAEGAAAwjgADAAAYR4ABAACMI8AAAADGEWAAAADjCDAAAIBxBBgAAMA4AgwAAGAcAQYAADDO43QDUD5XXXWVEhMTJUlNmzbVkCFD9PDDD8vtdqtPnz4aM2aMwy0EAFRnBBhRKDs7W7Zta/bs2QWPDRo0SM8++6yaNWum3/3ud/r+++/Vvn17B1sJAKjOuEQShdavX6/jx4/rxhtv1KhRo/TVV18pJydHKSkpsixLffr00ZIlS5xuJgCgGmMEIwrVqFFDN910k6699lpt3bpVN998s5KSkgq2JyQkaMeOHQ62EABQ3RFgRKHU1FQ1b95clmUpNTVVNWvW1KFDhwq2Z2ZmFgo4AACobFwiiUJvv/22Hn30UUnS3r17dfz4ccXHx2v79u2ybVuLFi1S9+7dHW4lAKA6s2zbtp1uBEKTk5OjSZMmadeuXbIsS3fffbdcLpemTZsmv9+vPn366I477nC6mQCAaowAAwAAGMclEgAAYBwBBgAAMI4AAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwAACAcQQYAADAOAIMAABgHAEGAAAwjrupAlWEFeRx7gUAwAmMYAAAAOMIMAAAgHEEGAAAwDgCDAAAYBwBBgAAMI5ZJAAqLNgMlmBMzWwpqV5mzwDOYgQDAAAYR4ABAACMI8AAAADGEWAAAADjCDAAAIBxzCIBHBTq7ItIrSNUptpUGTNFoqmtQCRhBAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHHMIgEqQSTO5EDlKk8fCDbzJFhZzFRBJGEEAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwAACAccwiAVCtRNOMnmhqK3A6RjAAAIBxBBgAAMA4AgwAAGAcAQYAADCOAAMAABhHgAEAAIxjmipgENMKASAPIxgAAMA4AgwAAGAcAQYAADCOAAMAABhHgAEAAIxjFgmAiGEHeZzZOWUT7HUK9roC4cQIBgAAMI4AAwAAGEeAAQAAjCPAAAAAxhFgAAAA4wgwotSBAwfUv39/bd68Wdu2bdOwYcM0fPhwPfjggwoEAk43DygXK8h/oe5f0n8AKgcBRhTy+Xx64IEHVKNGDUnSI488onHjxmnu3LmybVsLFixwuIUAgOqOACMKPfbYYxo6dKjq168vSVq7dq169OghSerXr5+WLFniZPMAACDAiDbz589XnTp11Ldv34LHbNuWZeUN/iYkJOjo0aNONQ8AAEms5Bl13nnnHVmWpaVLl2rdunWaOHGifvnll4LtmZmZSkpKcrCFAAAQYESd1157reDvkSNHasqUKXriiSe0bNky9ezZUwsXLlSvXr0cbCEAAFwiqRImTpyoZ599VkOGDJHP59PAgQOdbhKACMKMGjjBsm2b++AAhnDSRjTh5I9wYgQDAAAYR4ABAACMI8AAAADGEWAAAADjmKYKlICkTVSGYMmW9D9EM0YwAACAcQQYAADAOAIMAABgHAEGAAAwjgADAAAYxywSQGTrIzIxuwTRjBEMAABgHAEGAAAwjgADAAAYR4ABAACMI8AAAADGEWAAAADjmKYKAA5j2imqIkYwAACAcQQYAADAOAIMAABgHAEGAAAwjgADAAAYxywSVCtk6wMVE+wzFOzGbKi+GMEAAADGEWAAAADjCDAAAIBxBBgAAMA4AgwAAGAcs0gQ8aryzI/yZN5X5dcDkY/ZIigrRjAAAIBxBBgAAMA4AgwAAGAcAQYAADCOAAMAABjHLBJEvGBZ61VhNkV5jiHULP7KeJ1MzSyoCu9pNDH5ejO7BKdjBAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHHMIkHEc3IWRCTOagjWpsrI4g93HZE4QwZl42S/RGRiBAMAABhHgAEAAIwjwAAAAMYRYAAAAOMIMAAAgHHMIgGqCFMzKpzM+mdWCFB1MIIBAACMI8AAAADGEWAAAADjCDAAAIBxBBgAAMA4ZpEAqtqzF5gVAidxj5LqiwAjyvj9ft13333asmWLLMvSH/7wB8XGxuqee+6RZVlq1aqVHnzwQblcDE4BAJxDgBFlPv30U0nSvHnztGzZMj355JOybVvjxo1Tz5499cADD2jBggW66KKLHG4pAKA6I8BwyNChQ4Nus21blmVp3rx5RbZdeOGFOu+88yRJu3btUlJSkpYsWaIePXpIkvr166fFixcTYAAAHEWA4RDbtjVz5syg2+66666gz/V4PJo4caI++ugjPfPMM1q8eLEsK+9KZ0JCgo4ePRqWNgMAUFaWbdvk2jhg+/btSklJKfTY7t271ahRo6DbT7dv3z4NHjxYGRkZ+uqrryRJH3/8sZYsWaIHHnggPA13AImCFUOSJyL1Lsc5AAATIUlEQVQRXzxVHyMYDskPHl544QUlJSXpyJEjmj9/vvr27atJkyYFDS7+8Y9/aO/evfr973+vuLg4WZaljh07atmyZerZs6cWLlyoXr16VeahAABQBCMYDhs8eLDmzJmj3/72t3r11Vc1atQovfrqq0H3P3bsmCZNmqT9+/crNzdXN998s9LT03X//ffL5/MpLS1NU6dOldvtrsSjcAa/jsvG5Ae8Kr/mwV6nqnzMTuKLp+pjBMNhLpdL+/fvV3JysiQpKyurxP3j4+P19NNPF3l8zpw5YWkfAADlwWIJDuvZs6dGjhypESNGaNq0aerfv7/TTQIAoMK4RBJBfD6fvF6v082IGgxdlw2XSMqGSySViy+eqo9LJA4ZMmRIwdTS0xW3/gUAANGEEQyH7Ny5M+i2Jk2aVGJLohe/LMuGEYyyYQSjcvHFU/URYDhs7969euKJJ/TLL7/okksuUZs2bdS5c2enmxUVOPEXVhkf5Kr8mhNgRAa+kKoOkjwddv/99+s3v/mNfD6funfvrocfftjpJgEAUGEEGA7LyspS7969ZVmW0tLSFBsb63STAACoMAIMh8XGxuqLL75QIBDQN998o5iYGKebBABAhZGD4bA9e/boscce08aNG5Wenq4JEyaoadOmTjcrKnBtvDByMCqGHIzIwBdS1UGA4bB169apXbt2Bf/+9NNPNWDAAAdbFD048RdGgFExBBiRgS+kqoNLJA6bPHmy3nrrLeXk5Oihhx4q8T4kKMwO8h9QHlaQ/wCUDwGGw+bOnasvvvhCAwYMUL169TRr1iynmwQAQIURYDjsvffe05YtW3Tdddfpv//9r1asWOF0kwAAqDCWCnfY4sWLNXfuXNWsWVOXXnqpxo8fz1LhAICoR5JnhNm1a5caN27sdDOiWnW9bk6SJ6oCvpCqDkYwHPb000/r9ddfl8/nU1ZWllq0aKH//Oc/TjcLAIAKIQfDYZ988okWLlyoK664Qu+//74aNGjgdJMQpZgFASCSEGA4rF69eoqJiVFmZqaaN28un8/ndJMAAKgwAgyHNWz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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "masks_bpt, __, __ = maps.get_bpt()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The BPT masks are dictionaries of dictionaries of a boolean (True/False) arrays. We are interested in the spaxels that are classified as star-forming in all three BPT diagrams are designated as ``True``, which is designated with the `global` key. Print this mask." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " ...,\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False],\n", " [False, False, False, ..., False, False, False]])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "masks_bpt['sf']['global']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Masks\n", "MaNGA (and SDSS generally) use bitmasks to communicate data quality.\n", "\n", "Marvin has built-in methods to convert from the bitmasks integer values to individual bits or labels and to create new masks by specifying a set of labels.\n", "\n", "Show the mask schema with `n2.pixmask.schema`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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bitlabeldescription
00NOCOVNo coverage in this spaxel
11LOWCOVLow coverage in this spaxel
22DEADFIBERMajor contributing fiber is dead
33FORESTARForeground star
44NOVALUESpaxel was not fit because it did not meet sel...
55UNRELIABLEValue is deemed unreliable; see TRM for defini...
66MATHERRORMathematical error in computing value
77FITFAILEDAttempted fit for property failed
88NEARBOUNDFitted value is too near an imposed boundary; ...
99NOCORRECTIONAppropriate correction not available
1010MULTICOMPMulti-component velocity features present
1130DONOTUSEDo not use this spaxel for science
\n", "
" ], "text/plain": [ " bit label description\n", "0 0 NOCOV No coverage in this spaxel\n", "1 1 LOWCOV Low coverage in this spaxel\n", "2 2 DEADFIBER Major contributing fiber is dead\n", "3 3 FORESTAR Foreground star\n", "4 4 NOVALUE Spaxel was not fit because it did not meet sel...\n", "5 5 UNRELIABLE Value is deemed unreliable; see TRM for defini...\n", "6 6 MATHERROR Mathematical error in computing value\n", "7 7 FITFAILED Attempted fit for property failed\n", "8 8 NEARBOUND Fitted value is too near an imposed boundary; ...\n", "9 9 NOCORRECTION Appropriate correction not available\n", "10 10 MULTICOMP Multi-component velocity features present\n", "11 30 DONOTUSE Do not use this spaxel for science" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n2.pixmask.schema" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select non-star-forming spaxels (from the BPT mask) and set their mask value to the DAP's DONOTUSE value with the `n2.pixmask.labels_to_value()` method. Note that we are selecting spaxels that we want from the BPT mask (i.e., `True` is a spaxel to keep), whereas we are using the pixmask to select spaxels that we want to exclude (i.e., `True` is a spaxel to ignore)." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "mask_non_sf = ~masks_bpt['sf']['global'] * n2.pixmask.labels_to_value('DONOTUSE')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select spaxels classified by the DAP as bad data according to the masks for spaxels with no IFU coverage, with unreliable measurements, or otherwise unfit for science. Use the `n2.pixmask.get_mask` method." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "mask_bad_data = n2.pixmask.get_mask(['NOCOV', 'UNRELIABLE', 'DONOTUSE'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select spaxels with signal-to-noise ratios (SNRs) > 3 on both [NII] 6585 and Halpha.\n", "\n", "`ha.ivar` = inverse variance = $\\frac{1}{\\sigma^2}$, where $\\sigma$ is the error." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "min_snr = 3.\n", "mask_nii_low_snr = (np.abs(nii.value * np.sqrt(nii.ivar)) < min_snr)\n", "mask_ha_low_snr = (np.abs(ha.value * np.sqrt(ha.ivar)) < min_snr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do a [bitwise (binary) OR](https://www.tutorialspoint.com/python/bitwise_operators_example.htm) to create a master mask of spaxels to ignore." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "mask = mask_non_sf | mask_bad_data | mask_nii_low_snr | mask_ha_low_snr" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the Metallicity Map\n", "\n", "Plot the map of metallicity using the `plot()` method from your Marvin `Map` metallicity object. Also, mask undesirable spaxels and label the colorbar.\n", "\n", "Note: solar metallicity is about 8.7." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = oh.plot(mask=mask, cblabel='12+log(O/H)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Stellar Mass Surface Density\n", "\n", "1. Read in spaxel stellar mass measurements from the Firefly spectral fitting catalog (Goddard et al. 2017).\n", " - The [Firefly stellar population fitting results file](https://data.sdss.org/sas/dr14/manga/spectro/firefly/v1_0_3/manga_firefly-v2_1_2-STELLARPOP.fits) is large (1.8 GB), so we have extracted only the measurements needed for our purposes [DOWNLOAD CSV FILE](https://sdss-marvin.readthedocs.io/en/latest/_downloads/dcf4a43fc5baeb08e3b14b9cad60a3bc/manga-8077-6104_mstar.csv).\n", " - [Summary of the Firefly Value Added Catalog](http://www.sdss.org/dr14/manga/manga-data/manga-firefly-value-added-catalog/)\n", " - [Datamodel of the Firefly Value Added Catalog](https://data.sdss.org/datamodel/files/MANGA_FIREFLY/FIREFLY_VER/manga_firefly-STELLARPOP.html)\n", "2. Convert spaxel angular size to a physical scale in pc.\n", "3. Divide stellar mass by area to get stellar surface mass density.\n", "\n", "### Read in stellar masses\n", "\n", "Use [pandas](http://pandas.pydata.org/pandas-docs/stable/) to read in the csv file with stellar masses." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "mstar = pd.read_csv(join(path_data, 'manga-{}_mstar.csv'.format(maps.plateifu)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot stellar mass map using `ax.imshow()`. MaNGA maps are oriented such that you want to specify `origin='lower'`. Also include a labelled colorbar." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "p = ax.imshow(mstar, origin='lower')\n", "ax.set_xlabel('spaxel')\n", "ax.set_ylabel('spaxel')\n", "cb = fig.colorbar(p)\n", "cb.set_label('log(Mstar) [M$_\\odot$]')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate physical size of a spaxel\n", "\n", "MaNGA's maps (and data cubes) have a spaxel size of 0.5 arcsec. Let's convert that into a physical scale for our galaxy." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "spaxel_size = 0.5 # [arcsec]\n", "\n", "# or programmatically:\n", "# spaxel_size = float(maps.getCube().header['CD2_2']) * 3600" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the redshift of the galaxy from the `maps.nsa` attribute." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "redshift = maps.nsa['z']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the **small angle approximation** to estimate the physical scale:\n", "\n", "$\\theta = \\mathrm{tan}^{-1}(\\frac{d}{D}) \\approx \\frac{206,265 \\, \\mathrm{arcsec}}{1 \\, \\mathrm{radian}} \\frac{d}{D}$,\n", "\n", "where \n", "$\\theta$ is the angular size of the object (in our case spaxel) in arcsec, \n", "$d$ is the diameter of the object (spaxel), and \n", "$D$ is the angular diameter distance.\n", "\n", "\n", "The distance (via the **Hubble Law** --- which is fairly accurate for low redshift objects) is\n", "\n", "$D \\approx \\frac{cz}{H_0}$,\n", "\n", "where \n", "$c$ is the speed of light in km/s, \n", "$z$ is the redshift, and \n", "$H_0$ is the Hubble constant in km/s/Mpc.\n", "\n", "Calculate $D$." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "c = 299792 # speed of light [km/s]\n", "H0 = 70 # [km s^-1 Mpc^-1]\n", "D = c * redshift / H0 # approx. distance to galaxy [Mpc]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rearrange the small angle formula to solve for the scale ($\\frac{d}{\\theta}$) in pc / arcsec." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "scale = 1 / 206265 * D * 1e6 # 1 radian = 206265 arcsec [pc / arcsec]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now convert the spaxel size from arcsec to parsecs and calculate the area of a spaxel." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "spaxel_area = (scale * spaxel_size)**2 # [pc^2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we simply divide the stellar mass by the area to get the stellar mass surface density $\\Sigma_\\star$ in units of $\\frac{M_\\odot}{pc^2}$." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "sigma_star = np.log10(10**mstar / spaxel_area) # [Msun / pc^2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot metallicity as a function of $\\Sigma_\\star$! Remember to apply the mask. Also set the axis range to be `[0, 4, 8, 8.8]`." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 4, 8.0, 8.8]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n", "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n", "ax.set_ylabel('12+log(O/H)')\n", "ax.axis([0, 4, 8.0, 8.8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### MaNGA Spatially-Resolved Mass-Metallicity Relation\n", "\n", "We have constructed the spatially-resolved MZR for one galaxy, but we are interested in understanding the evolution of galaxies in general, so we want to repeat this exercise for many galaxies. In [Barrera-Ballesteros et al. (2016)](https://arxiv.org/pdf/1609.01740.pdf), Jorge Barrera-Ballesteros (who gave a talk at Pitt in November 2017) did just this, and here is the analogous figure for 653 disk galaxies.\n", "\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best fit line from Barrera-Ballesteros et al. (2016) is given in the next cell. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# fitting formula\n", "aa = 8.55\n", "bb = 0.014\n", "cc = 3.14\n", "xx = np.linspace(1, 3, 1000)\n", "yy = aa + bb * (xx - cc) * np.exp(-(xx - cc))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Remake the spatially-resolved MZR plot for our galaxy showing the he best fit line from Barrera-Ballesteros et al. (2016)." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[0, 4, 8.0, 8.8]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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O5zV51TAtDdPSUBvH/UnB9qRi1NHsTCuOCkM3khwuGqyv6cealSwiiySX+ikAu9OKYarJIkVpDLX1RFIwKRo+OZjz57eP6CSa+5OC2nk+P16AlBSF4eow4VI3ZlobysYQKwHes5NXvLmSsTerGHUiauO5PkyQQrCfV9wdFwwSzUY3Jos1d8clQniujzIiJR67al/txkRKMqsMRWNBwLx2aCm4Py5JIsnxouHN1ZA9dX2UcVBZrPPcOV5QGkeqZaiYtq9PJtqrpjUEF5xX4Tt90QHhJ7HaifmLz8cMM0WqFEVj2T0q+PU3H4QPT9xUe3mNJ6Ru5pVlUjZ851LvBawaT4xRQWU8iRZ0IsW0aE5XqXt5Q2NDNs7urKLbiVBSUpqGvVmFRHBtFKOF5Ed3p7y5mtFYx+2jBft5jXeexjuO8oZpZWgaSzdRdGIZJvRljKF2Dmc9lfKMS8NvXBkxLWr++KN9bh0tQlGah2uDBIHBeZASDmYVpXFobYM8dWOReHqZZr823D6yvLPeYSOK2cthVlasdiISqaidp7KOX7k+5JPDBQfzivvTmn4cfPg2VnxyWHB1GHN9lBBLybQwT1y1X+4nzCvDpV5MrCR/sztjUjR8a61DpAT7ec39aUmsBX/nnXW6seagrrh1uKAXazqRorGeW4cLbq79fBZvXURaQ3CBeVWl+V8WEH6cYQKey1hlsWKYRRzNKypTMeynDLPooZ1HUVukgI/2co7mNZ1YsZJFTArDT+5NSeOQyoh/cM1nMZar3Zh/d+uIkEgZLlM7SyeK+OyoIIskeW24PylprGNeOYwQfHowRyuBkoJUaw5mDZ1UsrU/587RnKvDlB/fDZk0iVYcFzWNDRO3BDpe89lRQdFYjPM4H36BYaxBeIQQKCkRAv797TF5bTDWEukgP51Fmm6quNqLSWPFd6/2+WRvwaSqcM4xLS39zHK5G7E/b/hwL+xwZoXB4UkjSawVUgiuDTKKxgXxO+DmSkpRW8ZFifcNkRLcPiywxrPeT5Ai+Pqf5C6UQnD7qKBsLB/u5Ywyzby2rHQi3lzpcLkXjN7JPRIsd4DCh7sgljezTS5/abSG4ALzVUHb8ySkij6Y4HdnFf3EsjMtHzJM729PAcEo089srIo6pFhKEWMd9LoJwliK2p4anE8O5hzNKyZlQ6olSgruTgqkEMTdmLKx3D4M1Z5vr3Ue0tp5KmPgg5yJ9x6BYF4bdmYlqx3LKNWsdGOO5jXT0pDoMEkdzSuEAOMc89pR2yYEPY8aYgVVbfj0aEFlHcoL9uc1xjoiKVhUBi0VPvXhzfHgwTiLc3DsPTjP5YHk1sGCTiTpJpJ5A4vaM1y6dXbzgssk0EvY6MYIIfhwd8bnRwv6scILAQLGRXCn3TqcUzeOfqKIteCoCUVoK5nGesFf3T3m3fUu9ycVUnjGhVnGGizfWkmZVobDeUPjHD+4sQLApDSMi5r3t6enldUnz8eNUcqtwwXWWhId47xjZ1JxZZggTib8k1vgw707XDQUjSWJFG+vJbjWELw0WkNwgXmVVbz7ecVf3hnjvaeXaAYu4sO9nKuD5CHDlJcFCHG6M3gWYzWvDWkkWe8FaYpeL2HnMOdoUZ/uhLJIUjQhp14BnURTNo7VjkZLSWWD/3m8aPirpVumn+gvvP/ZnQwCytpyuGj48d0xK52IXhpRGcdR0XBtmKGFoHKenVmF845uImksxFJinKMfK2a142o/5WhecetgRtFAN4FYK5JI4ZwnVgKPxzqPcQ7pJR7PpGoQQKoVtXCUFUTLHYDUkmnRhFx+BArwDvqZRmooSkfVBAnrsplRGstGP+HTw5DJ5GVEJxJksWR/UjItghEdpTHOeaZ1gxSSO+OS/+dne/zGt1a5udpFK8FqN6ZoDA2Wa4OUubEIKehGGoOnNPZU7C4Y3M7pAuCHd465MshItOLOuKCXKK6NOhzkNTdWNFqF/gu9RHNz9YHbp5MoqkJw44zrsTKhZqHl5dAaggvGF3XoY0ZZdHr8ZVTxzmvDD+8co4Sgm0Q0LgQ+i8YyLRpGy/x0AOtBePfQ+U9rrB7NMKmMw3sorTvdCdXWk2lJV0saH9wq89qgBQxSx7isaYzDeEfZOJSETqS4OkxPtY/OutikgB/dm/DpUcFGN2JeG4z1RCoUh3mgrCwowXf7CZOioTKw0U24Msz42c6UhW0wXjBINZOqYXdWUTYQKWgceOOpmrASL6Uk1QrroDCeTuTBCsrGhYleOTId/OLOezxhATA2UDkX7kGs8R4QnkUJizqkujYWrFP8bDcn2p8hhGS9GzNvHJGQKBGMlhSCXqxZ7ydMS8OsNtSNoZGCJlIUtcUYS2Uk14YZxwvJvHZkWjD0MQIYrGiE98wbx51xyd6sYq2jH2rE4zxMy4ZRFlEtK4+vD1PmtcV6h7XQOMe31roP+f+vDjN2DvLTZ+e8YlItT6Y1BBeIR2MCxkWnbo5hqp/5C3JiVNSsxtbNU/vObx8t2JlURFoyiwwrnZhYC0xhyeuHV2lKAOLhnz2tsXo0wyTrCi4PUnZnJfFSfO3z4wVFY1nrJYyLhl6i2Z6WTCuDkh7vPJ8eL7jcTeglGiUEe3lFrB+4HnZnFW7ZDeyj/Tm3jxakWnA0D8VP96cVDh8yVpSk8rCehoyeRAuUcOQ1HBc1VeO4Osq4a8LEdTCrmFYGwYNsK+ccxiyNJA6ThPM9sKggizyNdaxkGiUVkQq7BO9diBe44Dipaof1sCgtQkE/1eSloTYgFSRKI4WgNA3zyrOaxWiliL1j3rigC6Q0cbb8nA4XpxIUwoOXApRgWjZUS5fMuxsSJSLujhfszQzvbvS5vpKipOBw3jDM4OZqhsSjpeTOccGNlYwsUvTiEL8ASCJF4xxKSr693iGNFLPK0InVaY3G6fMZ10gpaKynsV+vSLHl+Wg/6QvEozGBURbz9nrw8zZLzZZuHNwel/t86RflrFEZpZqDonoq3/m8Nny8NyfVQRLBOdiZVFwehElGirBtP1m59VINiId+9rTG6tEMk6STcHA85/ooY1Ka0MZRScrGsj0tOcxrIg2NhcYZ9mc1tbMowkr06iikWmohqIw//X1+en/K/qwCAT+9P+VoXuMFXB2k3FztYNyC24dz1roxK1mEMo5ESW4f5hRN2O1c6ik+O1ygBBSVpZ9GoWLWWIwJcYbjIihvRgKMBwNkEsrmgUfcEHYNw0TTWCiNwUdyqUEExodraAWlC1/QwkHkYVYYlpsVYilwYml0lSKSnm6qqS1kWjCINVJKRqnEekFVWyaTEiUElXFhZ1VZLnU9H+zNuNSN+dZ6D+vgaGFY7cQMsoj1fnAngcD4sHv5aH/OZ8cLEiXpxRqB591LfQZZxLxxjIuasjF8crAgVpJfvj6gl2iGWXT6/D36fFZFGFdbRPZqaJ1wF4iitl+ovhymEd1Yk2jJ1UF6KuT1aGP0R3naHrqPO6+fakZdjVlG67SE++OSLFL84MYKeun60Ury3tUB713tP/Szp/0yn2QnPThX8PZ6l5urHbanJeDpxIrS+NN4yfHCIqXg6iBDK8HOtCbWgkkZCqg+OsjpxGFiDVXDUz4/nrM9K/mLz465fThnNy/Zn1V8uJ9zZ1xQGse4NMxrQ2Ucg07E50cLxgvDQV6z0olIY8WisYzLUFG7OympXbhfWgVD4Ah/wk4gfLkaB2Y5odvl8cZCZR2TsqFsLHntSKSispZ5DaUFa4Ncr/XL63pY68ZoCV6Ez06KULW7n1cUtUMiuNKLsc5RGEeiBe+s99m83CeOFVkiMS647LRWJFpwNK8xjUMJye3DsFta62reu9Lnaj9BChh1QvEeTlA7zyf7c6aF4bPDBZOi5rPjgnFRI4Xgu5f7bE8rGut5Z63DSifih3cmfLCXI8XDu7TneT5bzofW9F4gnlTINa8NgzR9puyh5w00F3WQIb47LlntRuRV0NCpjePXboxORdIe5XmzmM7KVZzVe1/vxhS1oWpCgdFGP+FoXpHFKmQPCUGiFKsdzb1xkG7oZYq8svzp52P+01+4dJrHL6Xk1m7O/XlFWYfsHCVhUVt+1EzY6IX8+MaC9Z7744L7s5pOJCgaw0/uT2iMBylIlaCfBe2dXqKZVSZkCi1tsufhdnyOYBTcSW7q8vi0sEgZVu/Oekpn8SGBCEOY+NWZLEoloTCOUaYxXrBoLMJahA+7jxOX1FFRk8Waq4OU33hnjVs7M7JYkSnJShYRS4VWgkXVoAQsmuC+6cSaLJYsKsPRPOgPbfQSLvUTGuc5yCv25yW39heksWKYKvqZ5v605PowY1w0/PqbK8tnskOiQ13IneOC7qoijdRDRWitnPnFojUEF4gnFXJ1Y/1EnZYn8bzVwVmsMNZxYyXjYF6jpWRluSJ8nAE4L1a7MSbV1Nbz5mrGn31+zLiwGOeYlI6qscQaZrUligSldRwe1Fjv0FLy/vaUReOIlaCsHYdlKOyKlKD0PgR1PSwqQ51qsjjEYO4cL8hrS1FZDlw4HwGV8cxLg5AwKR11Y5h4S9F4lhmdT8QD9SPHDRB7OC4ckQxBVGvDLsKd+fNQfYOxobZg+XrhBZEI0hyRUiyMZZDGrGaa//A7l2iEYFI2dBPNr74x4ic7Uw7yINPhkKc7viySrPUiFrU9rRHYz2veenOEdZ6P9+YUxrIzKTmY13QbRSQShIRISb613mG9G+JPRb04neAP8uDai5QIaaFnFjCvu5z5RaM1BBeIJxVy7c6qJ35pnkYu2nv/pTr1Zzk5L9GSN4bpqTF62VWeJ+MQQiCFJ9OKogmFVFJ45o3l4/0CJSCNBJ8fByXQbiTJOjL4pyPFahZRW/egYEoGH7snxLhjLcKkax2RkmghWVQ1eVWDEHSihGllmC4MNZAKmPmKxkDT+ODD5/lqn9wyjmAcVLV/7DVOMu61CFlHxgTj34nBOI93kGWSbiQoaksndigp+HA3RyWS71zqoaUMAWkfjImW4lTqWiBY78b0kojGBDVRT+g6d3dcsD+taLznzz8/4u64AAd5FVxxG72YtU7YNZ5M4Gcn+NK4UCnsHEl0IuERFjA31zrP9Xy2nA8X3hC8bvK0j1P2vNznsTuFlU78VHLRs9KglXyqTIwXKTP9LMxrw/7ejM92pqdB8SxSDFLNvXHBcV4zzCLy0mKtZdiJEUIyKSqmVRBTE0JQGs9+XjNMNceLmqJxfHqYEyvBtHHUJkyusQp/J0rhnccpj1ICpUFJgbFhgiqVpbHmtC2n8SBcWK1D+Pt5657ORnhOXEhyeb2zCbkCyA0MYtBRcEMVzTKGAIja4qxnbdkZbS+vSaKCXx6tkkk4XNSMl43tG+sZZRHXV1LGi4b7k4qiLlk0juuDjG4qmRQ1q1mCcZatvRm3jxZMS0OkwCOY1Db0R+5opAq6RP0kBICL2vLxfk4/0YA/bUpzYyWkHNc2/Ga7syrUbSxqnAo73jZT6NVxoT/1VyWxcNH4sp3C08hFP2uv1RclM/20nNznbjd5SFju8iDo+P/W22v8+P4E7zzr/ZhICsqlqJsSQX8okqHmIZKO2nkQnqJxTIqS40WDQKKkpW4euF36Hc1KJ+TJz2tD4zxdp2lsSNv0HhamwZoH7hlDCOSeTP6eB+6cr8vJezx6rRMDUZgQU9DCn2Yl4aGswShHL4HxImgkaSXYnZWspop5ZUl0COxrLSlrC96zaBxZLJgsDHoBAk+nVBTGUwwcd6YwLg15Heo8kijEEEKhl2NaGm5uaL5/bcjeMkU30WEXsj2tOMobkkhyc7XDojb81d1jdmc13Ujx/etDrvQTBqkmiiSXlkkRLa+GC/3Jv0qJhS/jVXTVetzkfNYfe8I3MeB2cp8npSGJghJobRyz0nCpnzCrDIM0WrZGFJSNxWPoxiEgmSoZcvllEIwbJRpjHceLGggO/BA7EDjlaWxY2fcSRTdSTMqaorH0lvcwVoJUSRrrqEzI8nnSqt8RGnI/ryE4meRPjNPjjjvCF7UXw6LyNP6BAUo0LAzUFu6NS5SEVMP1UcJ6P+OD3ZxhppmUnrIJDWykFHx+XLDeTZhVgs4wpHbeGxfcnxR87+og3BMV+hHXjUFpSYZEaclAg3WSXqr5u99eZyVNodONAAAgAElEQVSL+HB/zs3V7DRIHCtBPw3pxrOy4cf3pgyzoGRqneen96ckb45Y7cTEkXrl3+nXnQv9yV/EzIIvFH1Zz63jl7NL2c8rfrYzZVIYhpmmm2giJZ4r4HaRXG4n9/lwYYhkCFZGKmTGWOe5fZQjEBzPGy73Y7wSNFLROBikEaojELMCgSCLQoVr0cAbo5hxEdxi3QRqI5HCEgkHErqRZqMfs9KJuH1csNFLmJYN692ESCnyuiEvLZX9cufPk5N4vxxBcO+cTOqPPtVi+eck7XReQ+MfHLNAaR4YqQZOs4h+uj0FGVb/H+7lrPdChXo30VSNYS93mGWdxjvrXQ7mNRu9mONCcpg3HC6CYmgcCfpZRF5bjA9SF5UPO6aVzll3jz8tAvx4P2e+lJZunKM2BW+tZqx1E24fLeguhQI/OchZfXOVREuOvmGLl583LrQhuIiZBY/uUtLoQf7zea5o9vOK/29rP0gGEKpBnQ9VnlcG6RdjB4fz00n+O2ckIeBiudzmteFgXvH5scUriXKh8Xuz9M38ze6Mylj6qeIwF3xykLOUC2KQ6tCw3XpGWShOq62jn0SsdSPeWulyMD9m0YQAaS9SdGJNXjZoKZBSUhnP9VF6qoZpCZPyqBvx8Z7DRGCdobQPVuyPmoXnjRGcZNWfyM89CckypXQZm1A8iE08znh4D8cLwyd7c670E5yDadGQ145UheMdLUgihcSzk5fkpUMJTy9RTBaGTiSprKUbRyzi0P9gthSwU1Ly5krGtVHGn356xHvXBssiwIaf3J9yb1IySBT9JCKNFLcO5hTGMKtseG5jTSeWjItgQivTZgu9ai50QdnlfkJlHJWxD2UWnAicvQjmteHW4Zz3t6fcOvzyIi14fNFXrGQQMztHfnjnOIi0aUUnVqRa0VjHZBkIPinmujJI2ZmWmGUvWWMdH+3OHvq9Lkoxz4lBGmURSgjSSPH5UdDEr4wlrw2704rVTsrVQUqsJf00ZrUTIaUg1YKbqx0GcZCpSLXiW+sd3rvW563VzmlgdK0TYb1nWpvTArA4kiRRkDU4ntfEQrA9LYmloGxClhCANQZjHwRzH0Uu/6gzxxVf/cVShApkLR9cIzpz/OR8JR4OTD9u53AW64PBEMtOaJWxCAGHi4b7x3Puj0s8sDFI0FJwuDDcOy4oqlAoF2IBgrVuxHgRgr9KilB30VgipbjUi7nUS1hUQSX2s2UvgVsHC3amJYNE4r1gZ1aRaoUHdqc1mZb0YsXOskq8l0gqYykb+0K/0y3PzoXeEZx3BsvzrIxf1S7l7nFo5hF07UM+fKole9OKv/+97unrbh3OvxBXedQH+7Jcbl/lfjprkBKtKBCs9xIWteXqesrubsW1Ycq8NmxPSgZZRBIJ7hyVKCkQQnL3eEEnVtxYyYikYK0Tc3Otw6KyTIqG9V4oiCotzKsG7z3OyyAd4SVZHFQ6PztahHTOWPHZcUFjHFJ5SrfMMiKsyh9duWsgSwQSwaJxSB8qfyv3IPj7KAJIlsZIeM+iDk5/KcP1GvfA3XRSf3DW2Ngzf5+9vl7+vHIhzbUyIRU0iyWLOtROaO8xzpMXlmllwIeCsjiSeATOe0ZZzNw4nPc0xpJFmkQqJIJ31rqsdGJmlUHFcKWfMqstH+xOuTpM+XxcUBtPJ5FczWJ284I3Rim3DoJmVCdWDDPNwbzmF64M0Ery7uU+pqhpeXVcaENw3jxPMPrRoq+yeTn5z7VzfLZfECtFEkkyrRkXFavdh90+j5vkH/XBvgxj9jRG9uxYQ1P0jBUtmNeW964OuD8pOZ7XdFN16i+fLAydRPGLl3v82WcT9mYV10YJax2NlKFZ/GoWU1Qll/sJkQpN4jta4GxQ41QiNCForGUlSfn4YMG4aujFEfvzmqo25Mt56dH0UM3DMQG5lHuwDjwOqRQKUI3FNPDoHuukQKyxkMYSjz2VpEh0CGxX7ovndOOgn+R82EmI5eDOuodOxqXg1EdUNhZrIdIK1Xi8h4N5zZV+Ah6+/8aIyaIJGVHOLRcZUDcGJEHwrxshhMRLOFrUHJcNWaRZ68Vszyq0CoquqZYM0hi73IkpCfcmhsu9hN+4uYLzoc/BMNP8wuUBf+edNQB6iWbcGoJXyoU2BOfty36elfGju5RuX5y7b31em5DTbhypVjjn2J9XSAmb3Ye31I+b5B/1wb6MVpRPY2S/yiAlWrAwlkVuOSpCcxgFdFOFkgqBZ62jybRCSkUWKTqR4mhRc30l5aOdnMo6VrohRfTzcUFeGLwIFblHi5q/bhx53SB9qISdLiwNDzJ1JF+UjIh4sBo3PqRsnqz+uwoSFQq49ppw5slqHsL8XBNcPgJPsYz+OggTtw3XX+Y7nbqDnPckCiobjI8jZBEVTTAMjXtgEDoahllEHCms8+zOazItUCLsWmIX1E8HWYS1IeaEDLGDatmp5zuX+tybFNyeL/jJ9pRRljBIJLPaUjuPSz0f7xoqB9+/NqAfCwrjsM6xMJakCZLeWSTpZ5pfvDJYZn0F0ULd9hq4UFxoQ3De6aPPuzJ+kj7OebE7q7g2zJZSwDWLJlTIXuolXBumD732cj/h/e0ZeVXinENKyZU1wVtnfLAvo2jsaYzsV+2uurEGF1aq3VixO63oxpLVLGY/LxFScK2XIZRgJYsZZjHjoqGTaJyDQSfiaiz5eN/xk0lF3TjeWu1wXDRszwqsD5N21TiKOvQAPruCf1K0qCFM7HqpMur9A6VQCIJyQjwo+JJAvPTbGAvaLQvaXJCXsG6pLSQF0njK8FIyHSb9ooHGhLTQ1U6EsUF/yfhQsKUV9HSobzAO0liRxRohBTuTEmQwX0opvKlRUnC8aHh3o8ustGRR6Pw2tZ6iMqx3JXuzip1pzTCNyYua7dmC+5PwnZFCEjeOhfekkWKYRoDHLsypHIoWkk6ieHuty9Gi5tPDOb1YMcii06b1LReHC20IztuX/bKatH9ditqy1gl59Bu9lMoEDRwlxRdcQ4EwO/llDv3jhHDOu2jsaYzsV+2urPe8sWwEH2SlBYd5ye60YphFXB+lTAtDVyvWejGSpYRyN2aUaQZp8EUvGkfRNPSS5Sp50ZBpzayoOZiXiKW+TvMMeaDJsjJZEzLHqC2RgrKxmKXi6EmGTxaFdFglBSUOK6ATKxCQSYkyYcdXG48l7AhSHXr/ZrGkNiF1s/HhOsMkpnKOvXGFXo6lm2pmhSWVHusF14YpFsGsrDFLMT0lCOJyShJpCSIYS2cdpQ3JGMMsQuD5/GhOYaExjoVx4ATzylLWjhsrHVY6MdOyZl5ZPt6fcXOty7uXusyrhqLxXBkm/OobQ3amJf00Ylo25JVh3gTxwrZ47GJxoe/GefuyX5WcwrOSxYqBiyhNzVonJlKht671fCHbYndWMcoiLvcf7BTiLP7S1o1fVUfwPDUHT2tkzxqkSkr+9MPd0zqJxnq890EDx1gaGzRrrgxSEi25NylQUpBFmqKxaCG4MkxDkdKy49iNUcakaHh/OzSSb6xnXjY47zHeI5xglAUBtrz58rKwk+Ivu/yPsWHCts5iXdgVJCJk7rB8nSas1Bvj0So0h49jySiLsT5kDRWNY15bEh1cS0UT1EqV9OS1RatgHGIdVEOLelkbEYMQEuegH8Uo0TBeNBhj+JudGQYQxmKFJ9OawbKjWNU41nsRZeMZJHBQOmIJbwwzjhY129MaJyQrGdyd1BjrEXi6sQChMN6xPQ0xGCUN4zLISITWogkrnYSbax1mlTnd0Z902auMZVaZlypg2PLVXKwZ7xFexor9ZcspPA+nDVz6MdOiYVIapIBfu7HyhQn5aYLFzxJ7ed44zbMa2f284k/uTJjmJZLQaGY/D4YviWRwYWhFP1FoKVjUQYnzrWFKL1OMS8NKP+VXrg+5fTQP+jdpxHo3JtWSLFIczCt2pyWVCbLOWSTpJQqtFJVpHjuuEyIg0kHnRxIKqzTLWIIItcFahMm/4UEqqCS8V2MgltBPNIUN/Q9i5ZmYYEgk0I1UEM6Lgzz0vAytN1e6IV1WS8HBrCJSniQK7p9F7dEaKhtkrJsGlIbaBP99syw4W+nAShrTOE+/H7HRibg7qegkEdcjzfa0YGdasjOtcISCscOFwztPN5IkUQjoJjrENxbGoiRcG2TUNri4dicl/Sxio5dwuZ9w+/Dno/L9deBCz4DflBX7eXP2c9BScn3lyavypwkWP0vs5evEaZ7FyP7V3TGHeUNZGbwLyqDToqYylt+8uYZzPhiAxqGWDWys82xPKtaWPRIGWXBB4GFrNz9VK+3FCuuDLz7WEiGC+FxtYb2XcbRoQtN4npyjH8ngxz9xBxkHaQSIIEmxWNYEIMCZYCDiZWGBlIJEhSY7SazQNtR3TAqD8KAjRSpCGmeqJAd56KbmgW4a+hsPsoiQYwS2AU8o8hIChBdMnAEfKu2GmcbaoKjqrcP4kMdvXM5KFnGzE2osVjvh3hjjmFdhx1VbR20tdhm4xgVFUqVcCHQ7x7SwIe1WK5QS3Bh08MC8cXz/eoeba50gGngBC0JbHs+Fn1G/CSv2l8HTfg6P20W5Rwp2niX28rJqDj45mOOFQMtQuWoJ4nGuNuzNK7anIUtqlEbcPSrItKCXRLy1mnFjpYP3ng/35wxTzccHc0bZg+rVH97PybTgzZUuu7OCWWXpJmCM596kYK0bs9GN2R3XFGfGdJIdFMuwGygN9CKQWpIqgXUCRNilpjq8VklJZYKLyfplUFkFKW3jYJDEdJOgx2M8ZFqipESpsNo/XhqlbqQoG8ui9qSxxy4D/4kMrSvtUoJaSSisJ4uDEJ0UUC4zkRQCrYJhUgKc88yqhg+2Z9xczbg87FDUhu1pjRaC48rSOItAkGpFqhy1C1Xs1juuDjLy2lA1HucFhbFcG2Z8e6PLmytZ6E639qCmJSQuTMnLEJhXAnqp5r2rgxf67LR8fdoZ9ueMx+2iHi3YeZaVWhYrJmXDrDTL9FVJP9X00+gLr/061MYTJxK9rKnVKKSH49KG7mOx4sd3J+waRycJiprjwvCDGyOA0x7HH+zMMM6FAC6w0onYzxU7s4rVTNGJIgQS5y2xFAgpWMkinPEIXTOSIRWzWrpUhgkopVjtRJSNo2ocOpJkWpIoSdEY5sKGinMZDFlBcA+d9BJY1I5EQuMto0wzK80yphEmxto48sqglCR2FovAeHhzJWV/XlIWjrIKKaCNDzsR74MRODE2sQIlJIV1OOtCJbQMvRYiDZ0sCMtVjQ3ifEnEejfizlHBuKhCDwJrmRZhR9VLo9DDuRNzf1pQ1o557cALLg/iUD+RRGSxJK/Ml7hsBQiBONnmPbY+u+VV0xqCn0Me3T08WrDzLLGXfqL58d0JvSTk6C8ay35enxYDvSiuj1LuTGs89sGY3NJ94UNM4Mog5bioSZRCSsnlbtANkiK0QFzraD47mjOrGqaFJdUq+M+N5WhRk6mUfiqZ14Z55djoxfSS0EYxjkITdesFynm6CXQiRXfpVlIypJq6RLA2iMkXDXt5jfNBbM3rIOnQNP6hit8T+QipJP0sCq0zRcjpH6YxB3mNwZJKRd0YrAMhQnB8L1/6/X0IOBfWEwsY9jSTIlgqrcI4hRAksaQyNVIJlA9xBrM0FHVt2LWOeulm2p5VXO4nLIylbjzjsiFeChgKEWQqCuPoGkc30qx3FJV1xEqy1ou5McoomlC5/KSm8yFxQT+0G62MbZVGLyDt3XgNeZbYy6wyvL3eZVYZysaSxZpL/fSFZ35890ofHRfcO8iZLrNNrgxSNi91URJmpaGfat5cyXDAG6OM/Ty4Uhzw9nrok/vHt464N6mIhKCsLXEsmBaGlTTCeEfVCFLt8V6xaAzvXe0zKw15HYrVokgSKxny9J1DSMF//ktX6SWhUnZ7UvHR4Zz7Rwu8h0GmqRqHUQ5lJVY6Oir0CDiVlhYQa0VHhVTQtV4c3C+RIo6CfLQVYVJPtKayjqZxNC7ITDtCkZgLC3KKZZonBIG+snFY70mUJIskxodJ3PiwU7AuKJdqGVxWZWM5mJW8vysZpgopQ61GpjW9VFM2llhLLvViahMytd7e6IZkgcYjZND/2ugmrHQiVrvxY5+di6ge3PJ4zsUQbG5uRsAfAjcJrtPf29ra+uDM8b8F/EvCPnEH+CdbW1vleYzlPDnpqrV3OH8uKedXKQX9tDGHILL38HY+VuKFi+zdXO0gtGagxKk/eVYZ3hh1QuqhEFjnKBsXeg8AQgjeWe8uf5+wKo4kFJVh6kJwc+hDL96317t89/KAj/ZnWO9Z7YTV/lo3YaWb8MnhnChWaBl89saDVpKNfsJvfmuNTw8X7OcVo47C7Hsq4xlmiizSYYIV0I08s9pirKeRQXdILFtjGm+pnOKdtQ6l8Syqhm6seWPUoajDBJ0LT2U9zjuqpXxEY0KgWimBVkGwOtUhMG1siCgrCXKpZb3WSZBSUBiP9Z6itpR1cPekscJ7jySI7RVFg/Kw2omJlhN01YSqXy1CEDmNFL9+Y8Qgi0JAOXJMy4ZJYdi83Ge9m9BPL5YuV8uzc16zzu8Aemtr67c2Nzf/HvD7wD8E2NzcFMC/Bv6zra2tjzc3N/8L4C1g65zGci6cpFWur3SfS/7iIklBfxU/ujflaFFRNY4kkqx2En7p2osN+HVjzS/d6JAKf2oY+4lmZ1pSmVBQ9ze7OXuziptrKUoI5o3lcF5RmtCPuJ9oZrWjE2usczgPWitibZiXhu9dG/C9awPGRc2//+yIWWmZVg1aSC71U6ZF2F14Z+mlCVp6fuFSn71ZRTdRKJFwuAiTqpehACyJwqp8XDY0ztNYS16FVTiAsEEJNHZBJHCjnyAQNDahm2i2pyXDLOLz49AOUosHstSnMtU+SEykWpFpyWonYlKFFOIsVnQILrJYa4apZlo0jFIFUuAzx14umC6Ca3CQaIQIghghG0iQRhKx7PA2r2ompSFSiqujjDdXMt67NiDRinuTgs8OCwZpxHcu9bgxyr5UDfibUrDZcn6G4ENAb25uSmDAw3It3wEOgX+6ubn5PeD/3tra+kYZAXiQVplGilqIZ0qrPHv+Reu+9ijHRc1f3x9TL/3BArgbFdxYSb/q1Geml+iHsk4gVODuzioaa9FKkCnJ9qSml1jeGIXUTylACUHRBMVRIWC1lzBINFLCrgjiaXnVIIH3t6dBBjlVIeiqlg3cI8XCOI7yGiVCt7OVTJ8aaqclv/rGkJ/t5/zg2pB70xqBx1nPShJxZ1LgzlQVwwNxOOMcw1SjpGQl0+zmFdZ6upEi14pUS4pQoAyEQrXGBM0hPLg6NJ43kWW9n3JtGPPmSsZGP+HDnZxIhWcoSxSxFkRSspNXdCPJpV5EJAXOQz/VCCDSglhJvn9tQN043t+d0hgYpnrZmCe0nPztdzfYmZZIAe+sdVnvJmxPS0ZZ/JV9sNv0728O53VHcoJb6ANgHfjdM8fWgd8C/ivgY+D/2tzc/Iutra0/OnsBIYKOz0VFzWpGaWjePRiEFY73nllpnmrcJ+eH1RnPfP4zjVXJ577mRwcFQiq6mUYsFSuM83x0UPC7Pzj/cY6A65dCX+L784arqz0SLait57PDOZf6CVpK3lzrsDerWOnGHM0bhJTMGof3oSn9KEn54CAnL0Ou/A++tcrR3JBXDaNexlDArKnoZYo4Dn3DpJBMak+UxQzj4Dv/fLxgvZeEAHCkOZjVlMZihF/KMzck1lLaB6t6SXDL9LMYK6DbTejbIAcdoxl2Qy2BPMo5yhtqY4PWUBSqjA3L+gYH8wZu7edsXu1TWJj//+3deXxcdb3/8dfsk8lka5K2aZtSuvAFStkKioCIbCqigFzxouj1qoB6vf5Ef16F64JcUa8bIteNTQHF7acgVwREUVlcUBAQSr+FLlBKl+zLJLOe8/vjTNI0tNnIZCY57+fjwYPknJkzn54k53POd/l88y5mcS196QINySh9AzmqoiF60zn2X1DDQKbAc50pMgWXYNCbWJcoNoEtbUzw5pcv4/6n26ipidHWl6U/45Xi8FZui7JqSQMtmTzbewYZyBRYVBNn7cpmkrGJXTqGfn6T/blXotkS51SUKhFcDNxtrb3EGNMK3GuMWVPsB+gAnrHWPgVgjLkLOArYIxG4LiUv5vZSFLI52gczNM9L0tvrjT4fqqo4kbiH3r/nxK+Jv38yXkphvI1tfcSBZGz3cNH+wSwb2/pmJM6hfpQntvfyXMcAVdEQDYkoDVURBgZybEvnWDIvQT6dY14kyMrGKh7qz5DN5im4Ltm8Q6bgsqwhTNBxaYiFaEsV2NExQMGFzoEM/YM58o5Dz2CWSChEIhymP58nEQqxpa2fp56LsGp+Da7rsq09RWMiwiObO0hlHAIBl0QkRFfWoTEepjOVJl/YvbBMJOS18QeKzTvd/Vma4hECjktrbZxtPYMsrK6iczBHZ+8gTsIllfH6ZmKRIAXXIZDfPVTUcaA367Juey8D6QKdfVFCoQDzq2NUhaC1NkY8EqQzFKAmGaUK2H9enPXb+2lLpRnMFAjisrQuxusPnE/M8eaZNMdDLEnWDJ/3TD5PNlcY/nk0R0NezzOQH8xOa9nomSjcOB1mS5zNzTXjv2iUUiWCLnY3B3Xizc0ZuuJtApLGmJXW2meAVwLXlyiOkhlq/0znvNXTJtv+OVvaTxORIOmsQ94tEMKb6JV3IREtfRnhoX4Ux4W23gyRcIAtHQO092e8tvFIkAEHDi8W3hssFnxrSHhF5zpTWXZmCtTGwiysi1NwAuQdh1Q6T08mT108RCgQpCedI5XOEwgEWFzntd0nMiHSGa8PYWv3IKvm15AtOMQjQZ7rSuPiEgi4DGS95Rf70jmv+SoQJBlzyBQoDgX1OotzDjiOgxOAvkyO7sE8oYBX3yceCdJSV8W2njSh/ow3yawmQC4P2/IDhCJeldOqUBC34NCX90YBDT1tdKZyHDQ/ydH7NbCte5BsAcz8JIctbxqeP3LSqn0PTFhcF2dL5wCBgLdCWc7x5gwsmzc3737lxUr113wlcKQx5n68O/1LgTONMRdaa7PAu4FbjDF/BbZaa+8oURwlM9T+GQ4FhpeJnExH7+73B6f0/pmyZnEdoWCAbN6rTpnNu4SCAdYsriv5Zw/1o/Rl8kQjQQZzLo2JaLHgm0tHKkdDtTe5yXVdXuhJEwkFeeWKecyviVEAamIhltTHiYZCVEW9eQV92Ty4LvkCDGTzuK43lLPgFpdlDHgdqA5etc/edH54mdR51VE6BrIE3AD9mQJ9mRz9mQID2QKpnEMsHCQcChEJBYfXEphXHaM+HqGvuKZygADL5yUIBLwa/h0D3hOJaaomEgp4M5jjXvNMJBgiXxw5FCDAQN5LAGFgR1+GrnQexy3wyNZuls1LcNqBCzjtwPmsbqndo/nGGyVWzeqWWpY37vl7tqwxQXMyRsHxzkfBgeakVzhO/KEkVx1rbT9w7hj77wVeVorPnknV0TCL6xPeY/MU319JHcN7c8x+8+hKZelK5Sg4XhJoqI5wzH7zSv7ZQ+PQM8XKouAtCxktRGiuidA5kGd54+5kmnccljcmiIaDLKipIldw6U3nyBZcIqEgdVVhtnd5s3pbamJkXQjmIBjyhn32pb01oIPBgNeuHwnRl86SyubJOS7Lm6rZ0jFANOQtGZopFEjGwkTDQfozObK5ArXJGE6mQDDgzSb2isOFSEZC1FdFmV8bZ1GdV1q7LhwkEQ3RXCwqWB2PcMiiOtLZAtm8QzgUoGsgy9NtA2RyEAsXhvsdAgGvA7omHiSTdekdzPPk9l5Wt9RO+maiOhpmdUtN2YYyS/npJz0HjZ6fcEDV3tYsmJjmZIzXHbyQdTt6h8tDH7ywdkbKCA+PQ4+EyLsuLbUxOlNZXLxaNyuaElRFQsMjjaqiIfqKpSagwLxElJ7BHLm8Q1U45A3jjIRoSETIO67X6RwOEw/j3Xk7eOscV0dIFRdtNwtqWN6YwHHc4c8gCFnHpTYeJhryYquKhLyS2OEQjdVRulM5AiGv8ushLXVe2WvHK0OdKzjkCg7zqiPe+gDhEEe1NgDez25LxwAb21P0pvMkYxGe7/LWAXbxFsMZWtqy4HiF6zJ5l9b6OP3p/JRHnc2GmxIpHf3k55i9zU94emcf8+OhKd/hNSdjvGpl8zRHOr6hfpSaWBhcrzR1znFIRMLk8g41sfCLluD0ynXH6E3nvIXSE1FWNnnllwdyBRbVx1mzuIaHnu1kU1vKW+DdDVMTDXNEax07+rNs780wLxHxJs7lCwzm3eHSEMsaE9TFogSLi//kiuWfF9Un6B5Ik4xHOH55A3ZnP92ZAiesmMfBC2sZzBW417YxmC94w1urIwQDQWrjkRct1rO6pXa4MNumjhQ7etM8ubOXTNahOurQM1Dwag4FvNFU0bB3Bz+QK0z7RD/xByWCOWZv8xOikVDFzU+YiJHj0Fvq4vztuS7qqyLUV4WJR0K80JNhYW0VmzpSw08/C2u98hfhoLcc4nHLG+nL5Pdo8hjIFnj8+R7yToBYKEiu4NKfzbN0XoKm6ij/2N7P4voq0lmH2njYa6qJhHimrZ9ljQmOW9nEU9t7aO/PkAgFqI5HiQagqTpMPOytjbByfpL5ySjLGquJhoIEA3D0fvV0DeSJhgMkomFq4xGCgRcvLjTSYLbAiqYEDi5dg3mvmSyYpieTpyYWZmFdjCV1XtmNjlSWg1XZU6Zgdl0ZZFyD2QLBAGztHiSTKxCLhFgWC8/aO8WRTRbNyRh9mfzwvysSDPDUzj6WNyaGn3529KZf1Ok+uhlrZ1+G1S112LYUBcchFg7gFAI8tauPFY0J9m+oojYepqHKW4Q+5xToSee8UtV9GVYvaeBU08yfNnUMV/+MhIMsqU/wutULhj9vqIluaDLV2qUNw7k3Wy0AACAASURBVJ8/0bb4qmiIZHHhg6pwkGgwQH80RDQcZHlTgmWN1eB65TgGRpUbF5koJYI5aFPHAMlYiKpIiJzj8PTOfuZXze4f9WC2QF08PLzkIcBznSlc15307OzBbIFgEF62tJ6/bOkm6AaojkG2EGBz5wBnHLyALV2D1EQDuK5X938gm6dlUS2DWa+D+LjljTQmoqzb2Ud/Ok8yFmL/Rq84XyLqNcPtq919Mk9mC2pi9Ke95idch7zj1ViqjobY2ZtlR1+W6nCIVQuqWVynWbsyNfqtmWuGC9QEdv8/EKiYMvBTLbS3t3UR2lNZmkbd7U+kumVVNER7W5b+TJ5F9TH6MjkcN8C8RJCqaIydvVkCLuzqz5CMRYhHgrQ2JAgFvaYk8J5U1i5t4MCFNcN9MkPzQaazZlR1NIyLSzIaJBb2Ov1DQdjc7iX7Q1pqGMi6bO4Y1IIvMmWlnxUkM8uF5Y0JQkEYyHnryq6aX727QH4ZDXVk5wsO1VFvucZN7SlS2fy47+tKZXlgY6c38cl1GSyui1BwXLZ2DfB0Wz9buwboSefGrW5ZEwvT1p+lJ5OnMRFhcW0VTYkoK5trqI1H6M3kaKmLk4yGyTsOiUiI5mRsrwXWdvZlcFyXXX0ZnmlPsav4/c6+zEs+X0MCgQDBgLeAzoJkjFSmON8hGKTgBohHgzRVR9nRO32fKf6iRDDHVEW9YYytDQlWNSeLd7LBiij9O5WLZirrjY/fsCtFNOzNAn6mPUUgEMDMT7KhLcVgrkBVOMhgrsCm9gFvlNEY+jJ5DltUSzwUpC9TIBIK0pSM8EJPhuZknJXNSZLxCE01MRLRIC5esba93eV3prLs7M1QcLwFYgoO7OzN0JmavhIMkVCA+kSESDhE1vHKS8+vjhAOQbbgEgxCa4PXSS4yFUoEc8yCGu/ONZP3Sl9k8gXSFdKJOJWL5pZOb0z9hp29bOsapGcgR94FXJdgAJqTUaqiYdJ5h6riKKPxLoiDWe98vHJlE/WJKKlcgd5BrzppJue9N5PzFn85fHE9K5qqXzQbd0gqmycQgGg4SCAQKP6fcZ9yJmNxXRWuC8loiJbaGLVRr9z2/Jo4S+ri1FdF2N6XHTcBiuyLfnPmmImsWVwuIy+aANFwgGyhMOZFc932Pp7tGCAUDhIJQsENsL07TTjA8HDP1hH1mVzXnVAfQbbgUBUJ0ZyMUh0Jkck75AsOL/SmqYlHqYl5He2bOgbGrLlTHQ2TyWXJFnbX6XFdprXTdlF9nHAowPPdA3QP5mmujZBxvGUjHdchnfXWUlZJCJkqJYI5aLw1i8tlKhfNbT2D3kLxsRBdqRzhkNdU8kJ3mmWNCWrjkT1eP5EVsIYmqrX1Z6mJhaiNRcgWHBqTEXb0ZugezFITq9rd4T5GR/u8am91r76MV7oiFgmxoDa6z1W7pmJootyhi+qIhoI8tbOP1vocHaksHakctYkwp+7XRN1LmEEu/qZEIDNmKhfNWDhIujg3oj4RoWcwR7bgUB0LcWRrw/AKZpOp4FodDbOwNs5j23rI5l1q4iFWNifpSGVZ1lDFzr4sA7kC8XDQKy8xRkf78GzmZHSPGKazKW70U14iGmJRXdUeQ2mHSpiLTIUSgcyYqVw0VzRXs3FXioIDrgN1VRHq4hFWzK+mORkbXsFsMitgpbJ5dvSmWVRXRTDgEiBIe3+WQMBbWWxpY2K4uSmTLwwPGd2bmVqFa+RT3tDoq8kmQJF9USKQGTOVi+YRi+vpTGXJ512CgQCO6xIOBzhicf3uY06ydMZQGY5FdXG2dg0SDbtEggEGc3nSeZf5NfFJrTEx0wXbtASkTDf95siMmuxFszkZ4+QD5k9r9dOh8taBQIDWhiraU1nS+TyhUIjj96ujL5Ov+AusqoXKdNJvklS86a5+OlzeOuyV4WitrxpuY29OxmakxLYfFRyXrsEcHf1Z2lIZ2vuzdA3m+Jfjl2sce5kpEYjvzJZlQmeTvOPS3p9hR2+G7X1pdvR6X7f1Z2hPZWlPZekayFEbD9NYHaU5GaWpOkpzMkawQsqf+JkSgfiO2tgnz3VdugdzPNc1yNbuQbZ2p9nRm2Z7b4YdvWnaU1nqqyIsrInTUhtjYW2clc3VHLt/A03JGE3VURoTkb2ObKpPROmexgl4Mnn6zRdfUhv73qVzBTZ3DvBs5yBbuwZ5tmuArd1ptnYNAtDaUEVrfZzW+irWttbTUhujpTbOgpoYEQ1fnbX0lyDiQ/mCw7Ndg2xsT7GxY4BN7Sk2tqfY1Z+ltb6KZfMSLG2Ic8yyBt5cX8V+DQnqqsIEAmrHmYuUCETmuHSuwIa2FOt39vHUzn7W7+xna/cgC2pirGiqZkVjgtccOJ8VTdW01sc1Mc2HlAhE5pC847JhVz9PbO9lY1eax7d2s7V7kP3nJThwQZI1LTW8+fBFLG9MEI+UvyKtVAYlApFZrD+T5/EXennshV4e39bDuh39LKyNsWZRLWv3n8eZB89nZVP1cKE/kb1RIhCZRfrSeR7e2s1fn+vmked72NYzyEELajhscS3nH9XKmkU1w4X46usTdHcPlDlimQ2UCEQqWCbv8PgLPTz0rHfx39wxwJpFNRy9tIH/PG0VZn5So3XkJVMiEKkw23vTPLCpkwc2dfDo872saEpw9NJ6/v2E/VnTUqtmHpl2SgQiZVZwXJ7Y3lu8+HfSnspy3P4NvGH1Qq54/UEktfKYlJh+w0TKIO+4/P35bn5j2/nd0+00Vkc5fvk8Pn7KSg5pqSWkugsyg5QIRGZIwXH5+/M9/GZDG797up0FNTFOPqCZG956OEtU50jKSIlApMQ27OrnjnU7ueupXcxPxjjFNHP9ebr4S+VQIhApgc6BLHc9tYs7ntxJTzrP61cv4Np/PpylDbr4S+VRIhCZJo7r8uctXfz8se08/Hw3J6xo5EMnLmdtaz1B1eiRCqZEIPISdQ/k+N8nd/Czx7aTjIX5p8Na+MzpRmWtZdYoyW+qMSYC3AgsAwrABdba9SP2Xwy8B2grbrrIWmtLEYtIqTy5o4+f/n0b923s5IQV8/js6w9k9cIaVeiUWadUtyynA2Fr7bHGmFOBK4BzRuxfC7zDWvtwiT5fpCQc1+Veu4tv/34j23vSnHvEIj504grqqyLlDk1kykqVCDYAYWNMEKgFcqP2rwUuMcYsBO6w1n5+9AECAa9WSqULhYIVH+dsiBEqO85M3uH2x17g+gc3E4+EePdxy3jt6oUVXd6hks/nSIqz/EqVCPrxmoXWA03AGaP2/wj4BtAL3GqMOcNa+8uRL3BdZkXBrNlQ2Gs2xAiVGWc6V+Dnj2/n+397nhVN1Xz4Vcs59dBF9PQMkupLlzu8MVXi+dwbxTm9mptrJv2eUt3OXAzcba09ADgMuNEYEwcwxgSAr1lr2621WeAO4IgSxSEyJelcgVsefp6zr/8rj27r5cqzD+Hqc9bwsv0a1Acgc06pngi62N0c1AlEgKFVMGqBJ4wxBwEp4CTghhLFITIpmbzDrY9v56a/bmX1whq+9qZDMPOT5Q5LpKRKlQiuBG4wxtwPRIFLgTONMUlr7TXGmEuB3wEZ4LfW2l+VKA6RCSk4Lnc9tYtvPbgFMz/JlWcdglmgBCD+UJJEYK3tB84dY//NwM2l+GyRyfrLli6uum8T8XCIK15/IIctrit3SCIzSjNexLeebuvn63/YzLaeQT7wyv159aomtf+LLykRiO/0pfN8549b+PX6Nt7ziqW86dAWwhU8DFSk1JQIxDcc1+VX63byP/dv4YQV8/jJvx6liWAiKBGIT2zY1c8Xf/sM2YLDV85azeqFkx9rLTJXKRHInJbJO1z/52e57fEdvPe4/ThzTYtW/xIZRYlA5qzHX+jlv+62LJuX4JZ3HElTMlbukEQqkhKBzDmDuQLfemALv7Zt/N9Xr+DkAzQaSGQsSgQypzy5vZdP3Wk5aEGSH71jLfUJdQaLjEeJQOaEguNy40Nb+dEj2/iPk1dyimkud0gis4YSgcx6L/Sk+fSd6wkHA9z89iNZUKO+AJHJUCKQWe0e28aXfvsMbz96CW87aonWBhaZgnETQbFs9OuBE4FGYBfwW+Aea61b0uhE9iFXcLjqD5t4YFMnXz/nEA5coHkBIlM15rx6Y8xJwG+AVwGPA7cADwOvAX5jjDml5BGKjLKjN82FP36MHb0Zbj7/SCUBkZdovCeCVcBp1trCqO0/McaEgAvxEoXIjPjTlk4uu9PytrVLePvRSzQsVGQajJkIrLXfGWNfAfjWtEcksheu640K+smjL/C5Mw5ibWt9uUMSmTPGTATGmM3A6H6AAOBaa5eXLCqRETJ5h8/+egPPdg7wvbcewXyNChKZVuM1DR1Y/H8AuBs4rbThiOypvT/DR29fR0ttnGvechjxSGj8N4nIpIzXNJQZ+toYUxj5vUiprd/Zx//9xTrOWrOQdx+zVP0BIiWieQRSkf64uZNP32n5+CkrOfkAzRIWKaXx+ggOGPFtwhizCq+ZCGvthlIGJv71yyd3cPV9m/nymQdr/WCRGTDeE8HIUUODwDXFr13gpJJEJL7lui7f/ctz3Pr4dr597mHs35god0givjBeIrgJ+KW1tm0mghH/Kjgul9/xFH/Z1MH15x1Os9YOEJkx4yWCbuBzxphm4G94SeHR0oclfpIvOFx2l6U7U+CatxxGMqauK5GZNN6ooVuBWwGMMUcDZxtjLge2WWvfNwPxyRyXKzhc+sunyOQdrnvHUaRTGpgmMtPGrDU0xBizH1AFfNda+0bg8pJGJb6QzhX46C/WAfDlM1drjoBImYw3aigJ/BCv6ugWYKUxpg04r/ShyVw2mCvw4duepDER4bLXGsKhCd2TiEgJjNcY+wXgp9bam4Y2GGPeA3wJuKiUgcnclc4V+D8/f4IldXH+87QDCAU1UUyknMa7DTtsZBIAsNZeBxxaupBkLsvkHT5y25MsqovzidcoCYhUgvESQW4f2/PTHYjMfbmCw8duX0d9VYRPnnaAVhMTqRDjJYJOY8xRIzcUv+8sXUgyF+WLo4MioQCfeZ3Rk4BIBRmvj+CjwC+MMb8HNgL7A6cAbyhxXDKHFByXT99pyRVcvvjGg9UxLFJhxvyLtNZuBl4G/AGIAg8BLy9uFxmX67p89XcbaU9l+e83Hkw0rCQgUmnGGz56lrX2NuBn+9h/dnHS2ejtEeBGYBlQAC6w1q7fy+uuATqttR+fQuwyC3zvoa38fVsP17zlMGJKAiIVabymoYQx5k7g13iL1+8E6oFj8Bawv2kf7zsdCFtrjzXGnApcAZwz8gXGmIuANXhPGzIH3f6PHdz2+HauO+9wlY0QqWDjNQ3dgncB7wfeBXwFb/5AF3C2tfbmfbx1AxA2xgSBWkaNPjLGHAu8nD2rm8occv/GDr754Ba+fs4aFZATqXDj3qZZaweAa4v/TVQ/XrPQeqAJOGNohzGmBfg0cDZw7r4OEAhAfX3llyEOhYIVH+dMx/jo1m4+e8/TXHP+kRy2ZOKLzM+GcwmKc7opzvKb0PO6MWYbMB9ow7uwp/Gaid5vrb1nL2+5GLjbWnuJMaYVuNcYs8ZamwbeXDzGr4CFeM1P66213xt5ANeF7u6BKf6zZk59faLi45zJGLf3pnnfLY/yiVNXsV8yOqnPnQ3nEhTndFOc06u5uWbS75lo7919wCHW2kXAQcBtwOuA/9rH67uAnuLXnUAECAFYa79urV1rrT0Rr4TFLaOTgMxOqWyeD9/6JOcftYRXrmgsdzgiMkETTQRLrLUWwFq7EVhqrX2Gfc8wvhI40hhzP3AvcClwpjHmwpcasFSmguPyiTvWc0hLDW9du7jc4YjIJEx0KMd2Y8wXgD8CxwI7iqOBsnt7sbW2nzHa/0e87nsT/HypcF+/bxPpXIGPnbySgEpHiMwqE30ieAfwAvBa4DngnXgdwipHLdz+jx08sKmTL7xBs4ZFZqOJPhHk8CaGgdfe71hr/1SakGQ2eXJHH1ffv5lr33IYdVWRcocjIlMw0du3a4DleBPLlgHXlSogmT26BrJ8/PZ1XHLqKpY1zs1hdSJ+MNEnglXW2hOKX99mjPljqQKS2SHvuFx6x3pec9B8TlrVVO5wROQlmOgTQdwYkwAwxlRRHAoq/vWtBzYTBN533LJyhyIiL9FEnwiuAh4zxjwBHAxcVrKIpOLdu6GNe2wbN73tSK0rIDIHTCgRWGt/UCw+txzYbK3tKG1YUqme7x7k8795hqvedAj1CXUOi8wF45Wh/iHg7mU71tq3liwqqUi5gsMn7ljPv768lYMXTn4au4hUpvGeCL49I1HIrPDNB7bQkIhw3pGaOSwyl4yZCKy1w2sFGGM+Yq39SulDkkr04OZOfr1+Fz94+1rNHBaZYyYzDfT1JYtCKlpbf4b/unsDl59+oPoFROagCSWCYl/BwcaYW4wxt5Q4JqkgjuvyqTst5xzawtrWia8tICKzx0SHj34HMGhFMd/50SPbyOYd3nXM0nKHIiIlMtHho783xvSM7DOQuW9TR4ob/vwc33vbEZovIDKHTaaP4KKSRSEVJ19wuOxOy/uPX8aS+qpyhyMiJTTePIJm4OPAIN5iM0PbP22t/UyJY5MyuuEvz1FfFeHsQ1vKHYqIlNh4TwQ3ARZvLYL7jDH7Fbe/qqRRSVk9uaOPnz22nU++5gANFRXxgfH6CGLW2msAjDGPAr8wxpwI6OowR6VzBS67cz0fefUKmpOxcocjIjNgvCeCsDFmDYC19o/A54HbgbpSByblcf2fn2N5YzWnHTi/3KGIyAwZLxF8ELjaGLMAwFr7Y7xFavYb810yK9ld/fziHzv46Mkryx2KiMyg8UpMPAqcOGrb9zWpbO7JOy6fvXsDHzhhf5qqo+UOR0Rm0Hijhn4H7Kuh+NjpD0fK5YcPP09NPMwbVi8odygiMsPG6yz+OHAtcDaQL304Ug5buwa58aGtfO9tR2iUkIgPjdc09BdjzM3AodbaW2coJplBruvyuXs28M6XL9XEMRGfGrfEhLX2SzMRiJTHnU/toj9T4J+1xoCIb02mxITMMX3pPFfft5mPn7KSsGoJifiWEoGPffvBLbxyxTxWt9SWOxQRKSMlAp9av7OP32xo4/3H71/uUESkzJQIfMhxXb7422d433HLqK/SimMifqdE4EP/+8QOAN64ZmGZIxGRSqBE4DO96RzffGALHzt5FUHNGRARlAh857o/PceJK5swC5LlDkVEKoQSgY9s6RzgV+t2ctFxqhkoIrtNdPH6STHGRIAbgWVAAbjAWrt+xP5z8MpXuMAPrLVXlSIO2dPX/7CJdxzdyryEisqJyG6leiI4HQhba48FLgeuGNphjAkBXwBOAV4BvN8Y01SiOKTooWe72NQxoBnEIvIipUoEG/AWtQkCtUBuaIe1tgAcZK3tARqBEJAtURwCFByXr/1hEx88YX+iYbUGisieStI0BPTjNQutB5qAM0butNbmjTFvAr4B3AGkRh8gEID6+kSJwps+oVCw4uP86SPPU18d5eyjl1Z0ddHZcC5BcU43xVl+Add1p/2gxpivAhlr7SXGmFbgXmCNtTY96nVB4HvA76y13x25z3Fct6Ojf9pjm2719Qm6uwfKHcY+5R2XN177EF8562AOWlBT7nDGVOnncojinF6Kc3o1N9c8DBw1mfeU6omgi93NQZ1ABK8JCGNMLfC/wGnW2owxJgU4JYrD94IB+J/zDmd5rRaiF5G9K1WD8ZXAkcaY+/GeBi4FzjTGXGit7QV+ANxnjHkAb+TQ90sUh+8FAwGOXNpQ7jBEpIKV5InAWtsPnDvG/muAa0rx2SIiMjkaQiIi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic+FSHNQYEwFuBJYBBeACa+36EfvPAz4E5IF/AO+31jqliEVERMZWqieC04GwtfZY4HLgiqEdxpgq4LPAq621xwF1wBklikNERMZRqkSwAQgbY4JALZAbsS8DHGutHSh+HwbSJYpDRETGUZKmIaAfr1loPdDEiDv+YhPQTgBjzL8DSeCe0QcIBKC+PlGi8KZPKBSs+DhnQ4ygOKeb4pxesyXOqShVIrgYuNtae4kxphW41xizxlqbBig+KXwROAA4x1rrjj6A60J398DozRWnvj5R8XHOhhhBcU43xTm9Zkuczc01k35PqRJBF7ubgzqBCBAasf87eE1EZ6mTWESkvEqVCK4EbjDG3A9EgUuBM40xSeBvwLuB+/GeFACustbeWqJYRERkDCVJBNbafuDcMV6i+QsiIhVCF2QREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERn1MiEBHxOSUCERGfUyIQEfE5JQIREZ9TIhAR8TklAhERnwuX4qDGmAhwI7AMKAAXWGvXj3pNArgHePfofSIiMnNK9URwOhC21h4LXA5cMXKnMeYo4D5gRYk+X0REJqhUiWADEDbGBIFaIDdqfww4G9CTgIhImZWkaQjox2sWWg80AWeM3GmtfRDAGLPPAwQCUF+fKFF40ycUClZ8nLMhRlCc001xTq/ZEudUlCoRXAzcba29xBjTCtxrjFljrU1P9ACuC93dAyUKb/rU1ycqPs7ZECMozummOKfXbImzublm0u8pVSLoYndzUCcQAUIl+iwREXkJStVHcCVwpDHmfuBe4FLgTGPMhSX6PBERmaKSPBFYa/uBcyfwuhNL8fkiIjJxmlAmIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj4XLsVBjTER4EZgGVAALrDWrh+x/w3Ap4A8cIO19tpSxCEiIuMr1RPB6UDYWnsscDlwxdCOYpK4EjgNeBVwoTFmQYniEBGRcZQqEWwAwsaYIFAL5EbsOwh4xlrbZa3NAg8AJ5QoDhERGUdJmoaAfrxmofVAE3DGiH21QM+I7/uAutEHCAYD7c3NNc+WKL5p1dxcU+4QxjUbYgTFOd0U5/SaJXHuN9k3lCoRXAzcba29xBjTCtxrjFljrU0DvcDIs1kDdO/lGM0lik1EREYoVSLoYndzUCcQAULF758CVhlj5uE9OZwAfLlEcYiIyDgCrutO+0GNMUngBqAFiAJXFXclrbXXjBg1FMQbNfSNaQ9CREQmpCSJYKKKncnfBA4DMsB7rLXPjNh/AXAR3jDTz1prf1mhcV4FHI/X3wFwprW250UHmiHGmJcD/22tPXHU9ooZtjtGjBcD7wHaipsustbaGQ5vaHTbDXh9XTG837/bR+yviHM5gTgr5XyGgGsBA7jAe621T4zYXynnc7w4K+J8johnPvAwcOpLGaJfqqahiToLiFtrX2GMOQb4CnAmgDFmIfBB4CggDjxgjLnHWpuppDiL1gKvsda2lyG2PRhj/gN4O5AatX1o2O7RxX0PGmNut9burJQYi9YC77DWPjyzUb3I+UCHtfbtxWbMR4HbobLO5VhxFlXK+XwDgLX2OGPMiXhDyof+1ivpfO4zzqJKOZ9D5+07wOBetk/qfJZ7ZvHxwF0A1to/4130h7wMeNBamyneXT8DHDrzIQJjxFl8WlgFXGOMedAY867yhDhsI/CmvWyvpGG7+4oRvD+0S4wxDxhjLpnBmEb7KfDJ4tcBvDurIZV0LseKEyrkfFprbwMuLH67H3sOEKmY8zlOnFAh57Poy8C3gRdGbZ/0+Sx3Ihg9lLRgjAnvY99eh5nOkLHirAauxrszey3wfmNMuRIW1tqfsee8jSEVcz7HiBHgR8B7gZOA440xZ+zjdSVlre231vYZY2qA/wd8YsTuSjqXY8UJFXI+Aay1eWPMjXh/Lz8YsatizieMGSdUyPk0xrwTaLPW3r2X3ZM+n+VOBKOHkgattfl97NvXMNOZMFacA8BV1toBa20fcC9eX0KlqaTzuVfGmADwNWtte/FO5g7giDLG0wr8DrjZWnvLiF0VdS73FWelnU8Aa+2/AAcA1xpjqoubK+p8wt7jrLDz+S7gVGPM74HDgZuKzekwhfNZ7j6CB/Ha5H5SbHv/x4h9DwFXGGPieJ1gBwFPvPgQM2KsOA8AfmyMOQIvsR6PV2ep0syGYbu1wBPGmIPw2jZPwusInXHFsie/Bj5grf3tqN0Vcy7HibOSzufbgSXW2s/j3Tw5xf+gss7nWHFWzPm01g439RSTwXuttTuKmyZ9PsudCG7Fy2p/xGvf/FdjzIfx2rduN8Z8Hbgf7wL7n8UJaZUY583An/GaO26y1j5ZpjhfxBjzVnYP2/0wcDe7h+1uK290nlExXop3d5sBfmut/VWZwroUaAA+aYwZaoO/FqiusHM5XpyVcj5/DnzXGHMf3ryiDwFnG2Mq7XdzvDgr5Xy+yEv5Wy/r8FERESm/cvcRiIhImSkRiIj4nBKBSe3OGgAAA1NJREFUiIjPKRGIiPicEoGIiM8pEYiI+JwSgYiIz5V7QpnIhBRrqxxorf34JN/XCHwO+CHeRKDzrLU/GrH/ceARa+079/H+OHC+tfa6CXzWAuCT1toP7CX2y4GvAY9MJY5Rx7sO+CfgmJGlh0WmSk8EMtd9Fhha+Gg98M9DO4wxa/CKBo5lIV79+XEVy/z2GWNetZfdt1hrv/oS4hj5Oe/BKzctMi30RCCzSrHW+neB5XjLn34Vr/7+TcAiYCtwgrV2kTGmFjjaWvu+Ym35x7xDmLpiafPz8apLLi0e+4DisfN4N0lvBf4TONgY8yngEKC++DnfsNZ+q3i3/67i6z8N3AJ8BvjDGP+MMeMQmWl6IpDZ5iK88rvHAqfg3fH/B7DZWnsccBmwoPjaY4DRq0f9DHhTsZLky4A/jth3Kl6xw1PwLup1eAuTrAN+CfzIWnsacBrw4RHv67LWHl8s+rYOr/DgeMaKA/AyhTHmm8aYrxhjFk3gmCJTokQgs81BwH0AxbLf64ATKV5Ii23mQ8sINgGjV2W6Ba9Z5gS8goYjXY9Xrvcu4APsucjLTuAsY8z38Wr+R0bsG0421toCkCsuWDSWseLAGNMEXIKX2K4GvmyMiY5zTJEpUSKQ2eYp4JUAxcVY1uB1vr6iuG0FXgIA2IXXlDPMWrsJrz3+g8D3Rx37TOB+a+3JeCt/fQyvBHEQ+AjwJ2vt+cV9gRHvGypTPFSzPm+tdRjDOHGAlyCuAs7Ge8L5GbB6rGOKTJUSgcw21wCNxpgHgN/jtcd/CVhWLB18GTBUrvzP7H2RoB8DrdbaDaO2/w243BhzL94qVFfjJZMocBzwb8aYP+CVJs4bY2J7OfYa4E8T/LfsKw6Ap4GTrLXfsdb+BTgWr/9DZNqpDLXMesaYY/HqsP/aGLMKuMtau6K479vAd6y1f5+hWL4I3G6tfWDEtncytaGv78PrtwgA3y8u8Tm07/d4i5Fo+Ki8ZBo1JHPBJuCHxphP47Xd/9uIfZ/C6/C9oNRBFJcKrB2ZBEZ4qzFm14ghpOOy1n4L+NZePuc6vOUJRaaFnghERHxOfQQiIj6nRCAi4nNKBCIiPqdEICLic0oEIiI+p0QgIuJzSgQiIj6nRCAi4nP/Hwi+bVNOaq/7AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "ax.scatter(sigma_star.values[mask == 0], oh.value[mask == 0], alpha=0.15)\n", "ax.plot(xx, yy)\n", "ax.set_xlabel('log(Mstar) [M$_\\odot$]')\n", "ax.set_ylabel('12+log(O/H)')\n", "ax.axis([0, 4, 8.0, 8.8])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The spaxels in our galaxy are typically above the best fit relation. Part of the offset may be due to systematic differences in the metallicity calibrator used, but the overal trend of flat metallicity as stellar mass surface densities decreases seems to be in tension with their best fit. It would be worth investigating this effect for more galaxies to understand if individual galaxies typically obey the best fit relation or whether they typically exhibit a flat trend in this space.\n", "\n", "\n", "Ultimately, Barrera-Ballesteros et al. (2016) concluded that the spatially-resolved MZR is a scaled version of the global MZR (e.g., see Tremonti et al. 2004)." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 4 }