{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
      "/home/eolson/anaconda3/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as dt\n",
    "mpl.rc('xtick', labelsize=14) \n",
    "mpl.rc('ytick', labelsize=14) \n",
    "mpl.rc('legend', fontsize=16) \n",
    "mpl.rc('axes', titlesize=16) \n",
    "mpl.rc('figure', titlesize=16) \n",
    "mpl.rc('axes', labelsize=16) \n",
    "mpl.rc('font', size=16) \n",
    "mpl.rc('legend', numpoints= 1)\n",
    "mpl.rc('lines', markersize= 8)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temperature Response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Q10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.0137527074704766, 2.0137527074704766, 2.0137527074704766)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSSoI3ZcfAJhZX6CKtPq6+0bgRUqnvg8Aj7n7C+k7y6BuZwMzzWycma0ws7+a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43ad7abb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#EXP( 0.07 * (tsn(:,:,:,jp_tem)-20.0_wp) )\n",
    "T=np.arange(-5,100,.1)\n",
    "f=np.exp(0.07*(T-20))\n",
    "plt.plot(T,f)\n",
    "np.exp(0.07*(30-20))/np.exp(0.07*(20-20)),np.exp(0.07*(35-20))/np.exp(0.07*(25-20)),np.exp(0.07*(10-20))/np.exp(0.07*(0-20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### phyto growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "T=np.arange(-5,100,.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "R1=1.0*np.exp(0.07 * (T - 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f438d69e400>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lSSoI3ZcfAJhZX6CKtPq6+0bgRUqnvg8Aj7n7C+k7y6BuZwMzzWycma0ws7+a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f43ad7939b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T,R1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "R=[1.0*np.exp(0.07 * (TT - 20)) * min(max((35 - TT), 0.0),5) / (5 + 1e-10) for TT in T]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f438d81d780>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9gd9GxMAu1nmiZQ389Kfwf/8Hd98NgwY1O5rm+/e/YYcd4Nlnmx2JWX3UeqJl\n3iaynwFfkvRnYA9SM9koSeeRmq1yN55ExFzgIaD4gbrbk/pz8tqINDDA6uDXv07PdbnlFieXTr6D\nMatMrgQTERNIiWQDUpOUgFOBz5OqLFeSGMj2/bqkb0paW9LppH6ccwEknSxpQufGkvaVtHe27ZqS\nfkAacXZGhee1HM46C845B26/vY8MR87ZWT94cCqRs2BBZfuZ9VW5msje31gSsC4wFHgdeDwiFpTf\nq+SxDiD1oSwPPAEc1pmoJF0EjIqI1bL3+wKHAyuS+oKeAU6LiCtKHNtNZFW68MLUNHbnnbDyys2O\npkEqqCk2aBC8+GJKNq5FZu2mYU+0lPQssHtEPFqrkzWKE0x1zj8/VUe+4w5YY41mR9NAFSSKFVeE\nu+7Kkq8TjLWZRha7XBk/A6bP+PWvU7PYnXfCaqs1O5rea8gQ98OY5VXJMGVrQxFpQvqf/pQ+mQ8v\nV6zH3u+HMbPudZdgfP/fxubPh+99Lw1DvvPO9EhgK88Jxiy/7hLM8ZJey3GciIiv1SIga4yZM+HL\nX4a33059LkOGNDuiJqqgptiHEoxrkZmV1V2C2RCYneM4vtNpIVOmpNpi66+fZugvumizI2qyCoYb\nDx6cknKl+5n1Rd0lmC9ExAMNicQa4rHHYNddYf/94eijXf6lUm4iM8vPnfx9yGWXwWGHwZlnwl57\nNTua1jRokEeRmeXlBNMHzJkDP/gB3HwzTJwIn/xksyNqXYMHwwsvNDsKs9bgBNPmJk+GMWNSHa2/\n/a2Pd+bXgJvIzPIrWYssIvq5/6W1XXMNbLwx7LwzXH+9k0tJFXbyv59g3MlvVlZFtchaRV8vFTNj\nRupr6ehI/S6f+lSzI+rlKij5cvfdcMQRMGlSZfuZtYJmleu3FvHXv8JGG6W/e4884uRSa24iM8vP\nfTBt4u230yfr669P5fZ3373ZEbUnJxiz/HwH0wZuuAHWXRfmzoUnn3RyqScnGLP8fAfTwv7znzT8\n+PHH4dJLYZtcD5u2nvjoR1OZnfnzoX+zgzHr5XwH04LeeQeOPDL1r2y2GTzxhJNLj1RQU6x/fxg4\nMP0MXIvMrDyPImsh8+bBJZfAMcfAdtvBySfDCis0O6q+Z8SINIpsxRWbHYlZbTXygWPWSyxYAFdd\nlT4wDx8O117r0WHN5H4Ys3ycYHqxBQtSB/4xx8CAAfDb38K22zY7Khs0yAnGLA8nmF5ozhy44gr4\n1a9SKf3gCELyAAAQwElEQVSf/Qz+539c+bi38B2MWT5OML3IO+/AhRfCaafBmmumr9tt58TS2zjB\nmOXjUWS9wBNPwNixsNJKcN99MG4cTJgA22/v5NIQFdYUez/BuBaZWVlOME0yZ07quN96a9hxR1h2\n2TSf5aqrYJNNmh1dH3P88RVt/n6CqXA/s77GTWRN8uyzcN55cMghsNtusMgizY7I8nITmVk+TjBN\nsvbacPvtzY7CqjF4cHrOjpmV5yYyswr5DsYsHycYswo5wZjl4wRjVmFNscGD0+MRXIvMrDzXIjOr\n0N//Dl/7Gjz6aLMjMastP9HSrMlcKsYsHycYswq5D8YsHzeRmVVo7lxYfPH0+ARXWrB24iYysyZb\nZJGUYGbMaHYkZr1b0xKMpIMkPSvpXUkPStqqm+3Xk9QhaZakFyX9pFGxWpuroqbY4MEQx1a+n1lf\n0pQmMkljgD8ABwCTgLHAfsA6EfFSF9t/FHgG6ABOANYBLgaOjYjTutjeTWSWnwQV/r6ssw489c/K\n9zPrzdqliex7wO8j4vcR8XREHApMAQ4ssf1XgCWAr0XEUxFxLfAL4P81JtzepaOjo9kh1FUrXN+g\nQdXv2wrX1xPtfH3tfG310PAEI2kRYBNgfNGq24BPl9htc+DuiJhTsOxWYAVJK9U+yt6t3X/JW+H6\nnGBKa+fra+drq4dm3MEsA/QHphYtnwosV2Kf5UpsrzL7mNXN4MHNjsCs93M1ZbMqdCaYXXapfN+n\nn4aHHqptPL1JO19fO19bPTS8kz9rIpsF7BUR1xQsPwtYNyK26WKfS4CPRcQuBctGAvcDq0bEC0Xb\nu+fVzKwKtezkb/gdTETMlfQQsD1wTcGq7YE/ldjtXuDnkhYt6IfZAXi5OLlk5/D0NzOzJmvWKLJT\nga9L+qaktSWdDiwPnAsg6WRJEwq2v5x013OxpHUlfRE4HDil0YGbmVk+TemDiYirJX0MOJqUWJ4A\ndiqYA7McsErB9m9L2h44G/gbMB34VUT8prGRm5lZXm1Zi8zMzJqvbWqRSVpK0hmSnsrKyfxX0jnZ\nnVLhdkMk/UHSm9nrUkktM+i00hI7vZGkIyU9IOktSdMk3SBp3S62O07S5OzneYekTzQj3p7KrneB\npDOKlrfs9UlaTtLF2c/vXUlPSPpM0TYteX2S+ks6qeD/2bOSfiqpX9F2vf76JH1G0vWSXsp+B/ft\nYpuy1yFpUUlnSnpV0ozseB/Pc/62STDACtnrB8B6wJeBUaT+m0JXABuSBgnsCGwMXNq4MKuXldj5\nDXAi6RruAW6WNLypgVVuFHAWsAWwDTAPmCBpSOcGkg4nVXwYC4wEpgHjJQ1sfLjVk7Q58C3g0aLl\nLXt92QeySUAAOwFrA4eQrqFzm5a9PlLT/beBg4G1gEOBg4AjOzdooetbEnicdA2zilfmvI7Tgd2B\nMcBWwCDgL1KOWuIR0bYv0i//PGDJ7P3awAJg84JttsyWrdHseHNcz33AuUXLngF+1uzYenhdA7Of\n0+cLlr0MHFHwfnHgbeBbzY63gusaDPwb2Bq4AzijHa4POIlUWaPcNq18fX8GLipadjFwQytfH/AO\nsG8lP6csmcwmTSvp3GY4MB/YvrtzttMdTFcGk745nZl7C+CdiLivc4OImATMpHSZml6hyhI7rWIQ\n6W56OoCkVUgDPd6/1oh4D7iL1rrW84GrI+LOwoVtcH27AfdLulLSVEmPSBrbubINru9mYBtJawFk\nTUafBW7M3rf69QG5r2MkaTBY4TYvAU+R41rbdiZ/1txyAnB+RCzIFi8HvNrF5tPo/SVnypXY2bbx\n4dTU6cDDpPlOkH4WQdfXukID46qapG8BqwJ7d7G61a9vVVKT0WnAyaTm2rOyKubn0OLXFxHnZH0M\nT0maR/p/97OIOC/bpKWvr0Ce6xgGzI+I17vYptu/mb3+DibrXFtQ5jVf0qiifQaSbnNfJM2XsV5K\n0qmkT0Jfiuz+u9VJWhP4GbBPwYebdtIPeCgijo6IRyPiEuAMUjt+y5N0KOnxIWOAjYB9gbGS9mtq\nYC2oFe5gTiM9O6ac/3b+I0suN5PaCHeJD1dgfgVYtov9h2brerPXSNc0rGj5MHp/7F2SdBqwJzA6\nPlyR4RVSIdNhQOHzgVrlWrcAlgb+UdAP2h8YJekA0iCUVr6+KaQmkkJPkTqSofV/fkcBJ0ZEZ2WR\nJyWtTOrkv4jWv75Oea7jFaC/pKWL7mKGkZrSyur1dzAR8UZEPNPN6z0ASUsCt2S77hwRxaMm7gWW\nzEb2kO3zaWAAaURWrxURc4HOEjuFtieN6GkpStUbxgDbRMS/CtdFxHOkX+ztC7ZfHPgMrXGt44D1\ngQ0KXg+SRjBuEBHP0NrXN4k0uqrQWsAL0BY/v36kgT+FFmTL2+H6gNzX8RBpAE7hNsNJD33s/lqb\nPbKhhiMkliQlkMeB1UgZtvO1SMF2N5GGjG5O+qT5GHBds+PPeY17Au8B3ySNiDudNOJjRLNjq/A6\nzgbeAkYX/ZwGFmzzI1Kn/+6kT/xXkj5lDWxW3D285uJRZC17faSO39mkT/qrAXsAbwIHtMn1nU9q\nFdkZWCm7hmnAL1vt+kgjNDcg9ZPNBH6cvR+R9zqAc7Lvx7akJsPbSYlH3Z6/2d+AGn4jtyY1IRW+\nFmRfRxVsN5g07+XN7HUJMKjZ8VdwnQcAzwLvksrmbNnsmKq4hgVd/KzmA8cUbXcMMJk0CvAO4BPN\njr0H13x7YYJp9esjTQH4exb7P4GxXWzTktdHatH4Vfb/bCZpqPlPgUVb7fqyv4td/X/7fd7rABYh\nfZh9FZgBXAd8PM/5XSrGzMzqotf3wZiZWWtygjEzs7pwgjEzs7pwgjEzs7pwgjEzs7pwgjEzs7pw\ngjEzs7pwgrH3dVNUtPP1bLPjbCZJ20r6SbPjKCV7guHbkpYuWr6opEMl3aP0JNf3JP1H0vmS1qvi\nPK9IOqeC7dfKfn/2rPRcOY49XNJMSRvW+tjWM61Q7NIaZ/Oi99eRZmsfSyqKB6lESF+2HfB90szu\n3uhU4DdRUJhQ0iDgVlIpkHNIj7GYCawJfDVbl+sRuAUqnaH9POn361/dbFexiHhJ0u9IhXG3qfXx\nrXpOMPa+iHig8L2k2cBrEfG3JoVUd9lz1hUR8/PuUqc4FolU0LQnx9iOVHPqC0WrzgE+SSor9PeC\n5XcDv5O0a0/Om0dEzAYe6HbD6v2WVPV403b+fW01biKzqknaTtIdkt7JXn+RtHbRNvdJGi9pF0mP\nSZol6QFJG2fNNqdlT0V8NWuuWaxg385mlW9KOiPbZoak67KKrsXxjM3O8W52zHOzT++d6xfLjvcT\nST+W9Dzpjmx1SQMknS7pyewckyWNk7R6wf4nk4oD9i9oMpyVrftc9n6zopgOyJYPLVg2RdIFkr4j\n6WlJc0hPTETSkpJOkfS8pNmS/i3phzl/JPuTHmU8ueBcKwF7AWcVJZf3RcQNRTGPkXR/1uz0hqQr\nJJV9kJakFSRdJunlrPltcvZzGpytX6iJLPvduKmLY32o+a27Y2fX8BSpiO23u/keWQP5DsaqIumL\nwNXANaSnNvYnVde9S9L6EdH5lLwAPkFqljme9Af9FOB60ifot4GvkD5h/5z0jPDjik53LOnT71dJ\nT9r7OXCTpA0iK6Yn6TekQqCnkgpLjiA9O34dUsG/Qt8hFWj8Lqk69TRSgcPFsxinkJ4gejBwr6Q1\nI2I6cFZ2/r2BLUl3M51l3YOum41KLd+JVJX4x8DrwL+VHos9kVTB94Qsxi2BEyUNioiSfT+SRCqp\nfnbRqs9mcf651L5FxzmM9PM5j1QEcQipOfB2SRtG9miMLlwFLAUcRvoZLpfFszipcnZXSjWzFS/P\ne+y7SY9ztt6i2dU+/eq9L+A54NIulotUvvvPRcuHkEp/n1Sw7F5SldYVCpbtQfrDfEPR/jcCTxa8\nXyvb7sGi7T6bLd87e78mqULs94u22ybbbofs/WLZ+2eB/t1cez/SIyDeBb5TsPxkYE4X2++YxbBZ\n0fLvZMuHFiybQvrDuFTRtt8iPXtjZNHyzj6TklW/gTWya9uzaPkx2fm7faQDqdL4DODMouWrA3OB\nbxddwzkF72cD+5c59lrF8WW/Gzd1sW1Fxy7Ybv/sWoc1+/+OX+nlJjKrxrrAcOAySf07X6Q/Tn8D\nRhVt/2REvFzw/p/Z11uLtvsn6c6j2P8VvomI20lP+NwiW7Rj9vXyonj+SvrjVBzPTdFFn4ukL2fN\nd2+S/tC/DSzKwg/XqoW7I90VFdoReAZ4pOg6xgNLAJsVH6RA55NOi5+dXonPZOcp/j4+T0rKxd/H\nQg8BR2XNlJ/oQQw9OfZr2ddunxVvjeEEY9Xo7E+4jPTJtvM1h/RQoqWLti/+QzqnzPLFuzjf1BLL\nOkc+LUu6q5pcFM9sUoIojmdK8cEk7UF6NPfDpCdtbkZqwnq7REw9tVAMpO/r2nz4GuYCd5KajYqv\nI48Xs68r5dh2KOn7OImFf66rd3P+L5CeJnsU8LikFyUdUUW8jT621ZH7YKwanZ+Sv0/Xz+Uu1U5f\nrWEllt1REE+Q+lqKH5MN6UFJhbpq+x8DPB4RB3QukLQEqdkoj85rXrRoeak/yl3F8DrpLu7LdD1a\nrdwcpM4kXHy+27Nz7UK6oyun8+e6F+khW8XeLrVjREwDDgIOygZ67AecJGlKRFxSYrf3KPp+ZX1J\nQ6o89jLZ11ewXsEJxqrxOKmzdZ2I+E0DzrcHqWMfSJMdSX9M7skW3Ub6IzoiIq6o8hwDSM1ihfbr\nYrvZpFFk/Yua2V4gJYX1+PAf8s9XEMMtpGayNyM9L70S/yY9ofWTpMEXAETEC5KuAA6WdFVEPFy8\no6TdIuJ60oeFd4HVIuLq4u3yioh/AodLGkv6fpTyArCdJEXWiULqvF+s1A7dHPuTwOT4YICJNZkT\njFUsIhZIOhi4WtJA0kiy10lt31sCT0dE7lneOSwj6RrgQtIorpNISe6qLJ6nJJ0OnC9pfdJootmk\nZqHtSY8qvq+bc9wCnCbp56SE9SnSqLR3irb7R/b1R5ImAPMi4pGIeF7S/cAxkt4G3gC+nsWb10XA\nvkCHpFOAJ0h/bNcg3YHsUPCH+EMiIiRNJPWjFBsLrJod91zSXc0sYDXSyLy1gOsjYnrW9PRrSR8n\n9ZG9Q2qK3IbUd3Vt8cGzIdjXA5cDT5M62vcgNS3eVuZ6r8zO/ztJl5Ga4Q4l9eVVc+ytSP1V1ls0\ne5SBX733RWqSuaTM+i1JI79eJ/3B+g/wRwpGQZFGCt1atN9apD8U+xQtPxmYXbTdAuAbfPiZ4OPo\n4pngpD/o92fbvEVKQr8hG1VE+mM9Hziyi337ZeefTPqjOp40mOFl4OyC7foD55KGNs8DZhWsGwH8\nhdS3NJk0vPpAFh5F9jJwXonv6eJ8MET53eya7wWOzvHz2iGLaXgX6xYBDsmO9Rapeepf2bWsU7Tt\nLqTmx7ey7+XTwPnAGkXXcHb27wGkYc1PkJrRppPuLr/Yxc+8eJTb2CyOmaS+pvUrPXa23TrZ78qn\nmv3/xq8PXsp+OGa9jqS1gKeAr0TE5c2OpxVIepCU0I9udiyNlN3BbhgRxXOerIk8isysvXyP1N9S\nzYizlqRU1eGbpImz1ou4D8Z6O99iVyAi7ib/yLe2EBEvkSbFWi/jJjIzM6sLN5GZmVldOMGYmVld\nOMGYmVldOMGYmVldOMGYmVldOMGYmVld/H9zZQ3T5KeXJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f438d7f9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T,R)\n",
    "plt.plot((35,35),(0,2.5),'r--')\n",
    "plt.plot((35-5,35-5),(0,2.5),'r--')\n",
    "plt.ylabel('Temperature Factor')\n",
    "plt.xlabel('Temperature (Celsius)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.0 18.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f438d6ac9e8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vX0737t1zxJ+amkqtWrn3UIT4+Pgc79u3b8+HH37I5s2b6dOnDzVr1qRr166s\nXLkyIPGWZW7HYI4BHlVVEdkGJOBMVQbYA9hqyqbEysjIYOHChYwZM6bQdPYsjG/i4uKIj49n7Nix\nmT0HOTRt2hSAqlWrkpSUxPz584mJicmq6Dt37sy9995LSkoKGRkZ+a6w4I8vv/yS/fv38+GHH1Kn\nTp2s44cP51ztqk6dOvzyyy958m/bti3PsT59+tCnTx8OHz5MSkoKDz30ED169LDlhYrgtgXzM3Cq\n9//fAg+JyJki0hx4FFgTjOCMCYQVK1ZQvXp16tevX2g6a8H4pnv37qxevZr69evTqlWrPK/o6Ois\ntJ07d2b+/PmkpKRktVQ6derEn3/+ydixY6lfvz6NGjUKSFyZFUm5cn/9/bxmzRoWL16cI13btm3Z\nuHEjy5cvz3H8448/LvDalSpVomfPntx+++2kpqbaAqlFcNuCeZO/9oN5HKe77H/e90eBPgGOy5iA\nmTNnTtbMocJYC8Y3DzzwAB988AEXXnghDzzwAE2bNuXQoUOsXr2ar7/+mmnTpmWl7dSpE6NHjyY1\nNTWrpVKjRg1atGjBvHnzsmamBUKXLl2IiIigf//+PPjgg2zdupXk5GQaNmxIRkZGVrobb7yRZ555\nht69e/Pkk09Ss2ZNXn/9dfbt2weQtTrAsGHD2L59O506daJu3bps2rSJsWPHcvbZZxMXFxewuMsi\nt0/yT1bVZO//fwZaAFcB/YGmqjo7aBEaU0xz5sxx9WCetWD+UtCT8dmPx8TEsGTJEnr16sWzzz5L\n9+7dufnmm5k+fXqe8ZSLLrqIcuXKUadOHZo1a5Z1vHPnzoiI6/EXEck3tuzHTj/9dKZOncrGjRu5\n4oorGD16NM888wwXXXRRjnTly5dnzpw5tGzZkjvvvJMbb7yRhg0bctdddwEQGxsLwHnnnceGDRsY\nPHgw3bp14x//+AedOnVixowZrmI+mbnacKy0sQ3HQiQ5uXgD9cXN78KRI0eoVasWW7ZsydoiuSAz\nZsxg/PjxzJw506cybMOxsuXSSy/l119/5bfffgt3KAFXYjYcE5EJIpKQ61hdESn48VlzcikFa5F9\n/fXXtGzZssjKBawFczIaM2YMr732GikpKUyfPp3+/fsza9YsHnrooXCHViYUNgZzC86ClusBvBXL\nJqAN8EPQIzMmANyOv4CNwZyMIiMj+fe//83GjRtJT0+nadOmvPHGG9x4443hDq1MKLCLTEQygLaq\nusz7PgJa0xjuAAAgAElEQVTnaf7WqlqiKxjrIgsRESjO51zc/C4kJSUxfvx42rVrV2Ta9PR0oqKi\nOHLkSI4ZSEWxLjJTWpSYLjJjSrvt27ezYcMG2rRp4yp9REQENWvWZPv27UGOzJiTg1UwpsyaO3cu\nHTt29Kk1YuMwxgROUT95N4hIF+//PTjLxNwkIt1zpVNVfSrg0ZmSbdiw8OYvgi/jL5lsVWVjAqeo\nMRi3VFVLzOwyG4Mxqkq9evVYuHAhp556atEZvG699VZat27N7bff7jqPjcGY0iLUYzCFtWAqBqoQ\nY0Jt5cqVREZG0rhxY5/yxcfH+zwG07BhQ9uy15QKDRs2DGl5BVYwqnoslIEYE0hffPEFvXr18vkX\nf61atVizxrel9davX+9TemNOFmEZ5BeRi0TkMxHZLCIZIjKgiPQNvemyv9JFxLcOdnPSmDlzJr16\n9fI5nz8tGGNM/txPrwmsysBKYDIwxWUeBS4Bfsx2bHeA4zJlwO7du/nxxx+L3P8lP1bBGBM4YWnB\nqOosVX1UVT/BqTjcEGC3qu7I9joRxDBNUUrohmOzZ8+mQ4cOREVF+Zw3Pj6eHTt2BCEqY04+pe05\nmE9EZLuIfCMiV4U7mJNeCV2LbObMmfTs2dOvvLVq1bIWjDEBUloqmIPAg0BfoAcwD3hfRPqFNSpT\n4qSnp/Pll1/6XcFUq1aNQ4cOceyYzXExprh8GoMRkRjgXCAOmK2qeyUED52o6i4g+363P4hIHPAQ\nMDWYZZvSZdmyZdStW5cGDfzbxdvj8VCzZk127NhR5A6YxpjCua5gROQJYDDO8zHKX6sqfyUiKar6\nZHBCLNAyYGBBJ5Oz9e937NjRrwFfU/r4O3ssu8xxGKtgTFmXkpJCSkpK0K7vqoIRkf/DaS08A8wB\nFmY7/TlwLRDqCuZsoMC11ZODvJGVKZlmzpzJuHHjinUNG4cxJ4vcf3wPD/C4qNsWzO3ASFV9Ip8N\nx34D3K/FAYhItDeP4IwDNRCRljizxDaJyFNAG1Xt4k0/AGergP8CGcDlwJ04lZ4JlxK2FtmWLVvY\nuHEjbdu2LdZ1bKqyMYHhtoKpDywu4NwxnOdafNEaWMBfU5SHe1+TgZuA2kBirjyPAg2AdGANMFBV\n3/WxXBNIJWya8rRp0+jVq5dPqyfnxyoYYwLD7U9iKtAMmJ/PuTOADb4UqqoLKWQGm6oOzPV+Cu4f\nyDQnqU8//ZS777672NeJj49ny5YtAYjImJOb22nKHwOPi0jrbMdURBJxpg9/EPDIjPHB7t27WbZs\nmc/L8+fHxmCMCQy3FcwwnFbKUpwlXgDeAX4CNgOjAh+aMe7NnDmTzp07Ex0dXexrWReZMYHhqotM\nVQ+KyIXAjTjrge0EdgEvABNVNS1oERrjwqeffkrv3r0Dci2rYIwJjCJbMCJSXkQuAeqo6muq+jdV\nba+qvVX1VatcTmIlZJD/8OHDzJs3j8suuywg17P1yIwJjAJ3tMyRSCQN6K6q+Q3ylzi2o2WIiEBx\nPufi5veaNm0a48aNY968ecW+FsCJEyeoWLEiR48eJSKixGzUakzQBXpHS7djMOtwlocxpsQJZPcY\nQLly5ahatSo7d+4M2DWNORm5rWCeB/4pItWCGYwxvjp+/DgzZszgiiuuCOh1rZvMmOJz+xxMa6Am\nsF5EvsZ5LiZ734aq6u2BDs6YoqSkpNC4ceOArxuWOdB/5plnBvS6xpxM3FYwl3r/PQyck895xVlO\nxpiQev/997n66qsDft2aNWvy559/Bvy6xpxM3E5TrhPsQEwpFOa1yNLS0pg2bRrDArymGVgFY0wg\nlJYNx0xJFOZpynPnzqVp06ZBWVa/Ro0aNshvTDG5Xa6/VlFpVNVGRE1Ivffee1xzzTVBuXaNGjVY\ntWpVUK5tzMnC7RjMNnIO6ufHHhgwIXP06FE+//xznnnmmaBcv2bNmtaCMaaY3FYwd5G3gokDegF1\ngWcDGZQxRfnyyy9JSkqiTp3gDA9aF5kxxed2kP+VAk6NEpH3gCK70IwJpGB2j4FTwdggvzHFE4hB\n/knAbQG4jiltwjTIf+jQIb788kuuuuqq4pVfCOsiM6b4XK1FVugFRPoBL6lqiXnK39YiC5EwrUX2\n3nvvMWnSJL788kv/yy5CWloa0dHRpKWlIRKwpZmMKdECvRaZ21lk5+ZzuALObpaPU/B2ysYE3OTJ\nkxkwYEBQy6hQoQKVKlVi7969VKtWYv52MqZUcTvIv5T8Z5EJ8B/gzoBFZEwhUlNTWbp0KR9//HHQ\ny8rsJrMKxhj/uK1gepK3gjkKbFDV9QGNyJhCTJ06ld69e1OpUqWgl5U5k6xJkyZBL8uYssjtLLLg\ndXYb44MpU6bwwgsvhKQsm0lmTPG4mkUmIodFpHUB584WkcOBDcuUCiFei2zFihXs3buX9u3bF69c\nl2wmmTHF43aaclQhacvhDPibk02IpylPmTKF/v374/GEZgk9a8EYUzxux2Agn0F+EYkAugC7AxaR\nMfk4ceIE77zzDosWLQpZmfY0vzHFU+CfgiLymIikiUgaTuWyOPN9tuNpwEjgkxDFa05Ss2fPJiEh\ngdNOOy1kZVoXmTHFU1gLZgnwHM5U5IeAd4GtudIcA34Ggj9n1JzUXnvtNW655ZaQlmldZMYUT4EV\njKrOA+aB82Q88KKqbglVYMZk2rJlCwsXLuTtt98OabnWRWZM8bgaLVXVf1jlYvII0SD/xIkTufrq\nq6lcuXLxyvORdZEZUzyu1yITkWpAX6Apzqyy7FRV7w5wbH6ztchCJARrkWVkZNCoUSM+/vhjzjnn\nHP/L8sOePXtISEhg3759IS3XmHAJ11pkpwLfApVxpiTvB2K9pw8Ah4ASU8GYsmPOnDnExcWFvHIB\niI2N5fDhw6SlpVGhgs3EN8ZXbh8o+BfwI1ATZ9D/YiAauAfYC3QNSnTmpDdhwgRuuy08u0F4PB7i\n4uLYtWtXWMo3prRzW8GcC7wIZD6x71HVI6r6MvAK8O9gBGdObqmpqcyfP59rr702bDHYTDJj/Oe2\ngokFdqpqBk73WFy2c/8B2gY6MGPeeOMN/va3vxETExO2GGwmmTH+c/sk/wYgc/PzNUBvYLb3fTec\nSsecbIK4Ftnx48cZP348X3zxRfHKKKa4uDh277aFKozxh9sWzFyccReAF4DbRGSFiHwP/B8wxZdC\nReQiEflMRDaLSIaIFLl7lIicISIp3oU3N4nIY76UaYIgiNOUP/nkE5o0aULLli2LV0YxVa9e3cZg\njPGT2xbMP4BKAKr6jneZmKu9xx7AGZ/xRWVgJTAZF5WTiFQB5gApwDlAc2CSiBxU1TE+lm1KgbFj\nx/Lggw+GOwyqV69uLRhj/FRkBeNd0LI+sCPzmKp+CHzob6GqOguY5b3+ZBdZrgcqAjeoahrwi4g0\nBwYDVsGUMcuXL2fz5s1cfvnl4Q6FuLg4G+Q3xk9uusgUWIUzkyxc2gJfeyuXTLOBuiLSMEwxmSAZ\nN24cd999N+XK+bLYd3BYF5kx/iuygvHOHNtC3qf3Q6k2sD3Xse04z+TUDn04Jli2b9/O9OnTQ76w\nZUGsi8wY/7kd5H8duNfbXWaMIwiD/K+88gp///vfqV69evGuHSA2i8wY/7ntgxCgGfCbiHwBpJJz\nAzJV1acCHVw224D4XMfivTFsyy9DcrZfXh07dqRjx45BCu0kNnx48SqZXPkPHz7MSy+9FNJNxYpi\nXWSmLEtJSSElJSVo13e12KWIZBSRRFXVr9aNiBwA7lbVAmeTicgdwNNArcxxGBH5J3CnqtbPJ70t\ndhkKAV7s8sUXX2TevHl8+umnAQguMLZs2ULr1q1JTU0NdyjGBF2gF7t020VWsYhXJV8KFZFoEWkp\nIkneGBp439f3nn9KROZmyzIVZ5maSSLSQkT6AA/jbIhmyoATJ07w3HPP8fDDD4c7lBwyx2DsDxZj\nfOd2P5hjRb18LLc18F/ge5zJA8OBH7z/gjNwn5it/P04C2rWBb4DxgH/UlVbA62M+OCDD2jQoAFt\n25asVYcqVqxIREQEhw8fLjqxMSYH1/vBAIhIN6A9zlpko1R1k4i0Bdapau5ZXmFjXWQhEqAuMlXl\n7LPPZtSoUfTs2TNw8QVIvXr1WLJkCQ0aNAh3KMYEVVi6yEQkRkQWAl8CDwK34SzdD3AfzpP+5mQT\noLXIvvrqK9LT0+nRo0cAggo8m6psjH/cjsE8C5yGsx5ZDM6sskxz+GudMnMyCcA0ZVVl5MiRDB06\nFJGA/eEUUDZV2Rj/uK1gegP/VNUFQO4ZZRsA6zswfpk3bx47duzgmmuuCXcoBbKpysb4x20FEwNs\nKuBcJGAPYBqfqSrJyck8/vjjRESU3G8h6yIzxj9uK5jfgM4FnLsI+Ckw4ZiTybx589i5c2eJbr2A\ndZEZ4y+3T/K/CjwnIrtwnkkBqCQi1wL3el/GuFZaWi/gtGBsRWVjfOf2OZiXgFdwBvs3ew8vBN4C\nJqjqpKBEZ0q2Ygzyz5s3j2tWr+bqq68OXDxBYmMwxvjHbRcZqjoIZ6OvQcBInL1YzlTV8O8KZcJj\n+PCi0+QjIyODAYsWcc+uXTlaL8v37+fe334j9Zivz+0Gl3WRGeMfnzbcUNU1wJogxWJOEv83YwZ7\nTzstz/GGUVFUEOHM775jeGIid9ati6cETF22QX5j/OO6BSOOa0VkrIh87P33GimpDy+YEunwsWOM\nO3KEx2vn3canZoUKPHfqqXxz9tm8s3073X/8kV3Hj4chypysi8wY/7h9kr8esAJ4B7gG56HLa3AG\n/P8nIqcELUJTptz8ySdUOXSIoV26FJimWXQ0i5KSaFm5Ml1XrCAjzMv+WAvGGP+47SJ7EWcByi6q\nOj/zoIhcjFPJvIjzMKYxBdq2bx8fREXxVpMmRaYt5/Hwr8aN2XX8eNi7ybKvqGwNdmPcc9tF1gUY\nmr1yAVDVecA/cVY6NicbH9cie2jSJBJ27KBf69au88eVL+9PZAGVuaLyoUOHwh2KMaWK2w3H/gT6\nq+qX+ZzrAUxR1Zp5c4aHraZc8mzatImkpCSWL19OYmJi0RlKGFtR2ZwMwrXh2LvALQWcu4W/Hr40\nJl8PPfQQ99xzT0Aql3D88RAXF2cD/cb4yO0YzArgShFZDnwEbAfigb8DNYCZItIvM7GqWoVjsixa\ntIglS5bwxhtvFPtaM3bu5JOdO3mjadOQjodUq1aNvXv3hqw8Y8oCtxXMa95/6wGt8jn/erb/K9ai\nMV7p6encd999PPvss1Sq5NPO2vnqVK0a/1i3jinbt3NDPlOdg6Vq1ars2bMnZOUZUxa4rWCaBzUK\nU2b9a+JEYmNj6du3b0CuFx0RwVvNmtHtxx+5uGpV6kVFBeS6RbEWjDG+c7sW2a++vIIdtCkhiliL\nbNWWLfyzZk0eGjMm/+4sP9cyS6pShftOOYWbf/01ZOMx1oIxxneun+Q3Jo8i1iLrNWsWbXbtoler\n/HpVi85fmKENGrDz+HGm7tjh9zV8YS0YY3znqotMRCKBf+AM6tcHcvdLqKpGBjg2U4oN//JLtsbE\n8H2PHkG5fjmPh7eaNycmREv9V61alTVrbBk+Y3zhdgxmHDAQ+Ar4EkgLWkSm1Nu2bx9PHDzIyOrV\nqVGlStDKOT06OmjXzs1aMMb4zm0F0xvnSf7nghmMKRuuevddGpYrx9C//S3coQSMjcEY4zu3FUwG\n8GMwAzFlw9KlS/l91CgWL1sW7lACylowxvjO7SD/VOCqYAZiSqFca4kdOnSIAQMG8PKYMZzq5hkV\nH9cyCydrwRjjO7drkVUAJgGVgdlAnp+0kvT0vq1FFh733HMP+/bt46233gp52euPHOGzXbu4v169\noFx/y5YttGnThq1btwbl+saUBIFei8xtF9kZwEXAKcCl+Zy3p/dPcl999RXTp0/nxx/D05NatVw5\nnli/nsvj4kisWDHw17cWjDE+c9uCWQbEAo8Cq8lnFllJesDSWjChtWHnTs5v25bJr7xCl0I2Egu2\nh//4gzRVxpx6asCvrapERkayf/9+okK0eoAxoRboFozbCuYw0FdVZwSq4GCyCiZ0MjIyaPDmm9RL\nS2PpXXeFNZbNR49y1vLlrGvblthybhvn7tWqVYsff/yR2iFcA82YUArXcv2/A/Ygpcnj6vfeY3dk\nJF/eeGO4Q6FeVBQ9qlfn9dTUoFzfZpIZ4xu3FcwjwKMiUjeYwZjSZcUtt/BxdDQzzj2Xqv6slOzn\nWmSFeaBePSZv2xaUNcpsHMYY37itYO4DagG/i8h3IvJVrtfsIMZoSqANu3bR8o03GAR0btrUv4sU\nYy2ygrSOiWFZq1ZB2SvGWjDG+MZtR3UVYKP3lfnenKRUlZueeYZ5wPNz58LKldC8OZx+OjRtCp7w\nrqEaFaT1yawFY4xvXFUwqto22IGY0mPkyJFEzpvnvDntNNiyBaZMgVWroE0beO+98AYYJNaCMcY3\ngZ9q45KI3AUMAeoAPwGDVPWbAtI2BNblOqxAD1X9KqiBmhw+++wzJkyYwLJly6BuXbj33pwJjh4N\nT2AhYC0YY3zjui9DROJFZJSIfCMiv4jI6d7jd4lIa18KFZGrgX8DI4EkYAkwS0QKewxbgW5Abe+r\nDjDfl3JN8axatYpbb72VTz75hDp16uSfqKBnRCZNglL+FLy1YIzxjasKRkSaASuBO4HDwGn8tSdM\nU2CQj+U+ALypqm96d8G8D0j1Xr/AMIDdqroj2+uEj+UaP+3cuZMrr7yS559/njZt2jgHfVlLbP16\nOOsseOEFyMjwPb8fPt+5k1UHDwbsetaCMcY3blswo3G6qBKBnji/7DMtBtq5LVBEygPnAHNynfoK\nOL+I7J+IyHZvK8oW3wyRnTt28Pfx4/l7795cf/31f53wZZpxcjIsXgwffgg9esD27UGZppzdfw8e\nZEIAn4mxFowxvnFbwXQARqnqXpyuquy24XRXuVUDiAC25zq+HafrKz8HgQeBvkAPYB7wvoj086Fc\n44e0Y8e4avJkKpQrx8innirexZo2hZQUOO88OOccZ/ZZEF1bqxYf7NjBicwWUzFZC8YY3/gyyJ9e\nwPE44EgAYimQqu4CxmQ79IOIxAEPUcAim8nZ/jru2LEjHTt2DGKEZVNGejp9n3mGo1WrMvP224kI\nxPIr5crBiBHQtq0zSSCImlSqRP2oKBbs3UvX6tWLfT1rwZiyJiUlhZSUlKBd3+1aZAuAHap6tYhE\nAMeB1qr6g4i8DVRT1V6uCnS6yA4D16jqx9mOvwi0UNVOLq8zABivqnn2zbW1yALjpuRkljRowJzO\nnamfkBDucPzy/KZNrDp0iDebNSv2tX7//Xe6devG2rVrAxCZMSVPuNYiexK4SkQ+B/6O003WXkRe\nxem2GuW2QFU9DnwPdM11qivOeI5bZ+NMDDBBMHzYMBaceiofJCWV2soF4OpatZi2cydpAegmsxaM\nMb5xVcGo6lyciqQlTpeUAM8DvXBWWfalYsCb90YRuVlEmonICzjjOK8AiMhTIjI3M7GIDBCRa71p\nTxORITgzzsb6WK5x4cUXX2TyW28x+5xzOKtVq4ITFneQPsiD/ACnREby+Zlnup+PX4jY2Fj27dtH\nRoDGdIwp61x1kWUldhZ4aoGzLtkuYKWq+vXTJiJ34Iyh1AFW4Txoudh7biLQXlUbe98PAB4GGuCM\nBa0BxqjquwVc27rI/PT666/zxBNPsHDhQhKKarmIQHE+5+z5VWHIELjrLmjc2P9rBllMTAybNm0i\nNjY23KEYE3Ah2w9GRNYCvVV1RaAKCxWrYPwzYcIERowYwYIFC2jSpEnRGQJZwQC8/DK89BIsWwbR\neYbWSoQGDRqwaNGioitfY0qhUI7BJGB7wJw0nhk1iqeffpqFCxe6q1yC4c47nenLuZefKUGqVq1q\n4zDGuBTeZW9N2GWkpzMgOZmPKlVi0aJFNA5n95SI04r59lt4663wxVGIzHEYY0zRiqpgrJ+pDEs7\ndoy/jxzJ8vr1mXzxxdSrV9hScCFSubKzGvPgwc4qzQG070TxVxayCsYY94p6cm64iOx0cR1V1RsC\nEZAJjR3btvH3t97icPXqzOrUiYaNGvl+keKuJVZQ/pYtYfJkiIkp3vWzyVClyX/+w39bt+aUSP97\nfq2CMca9oiqYJOCYi+tYS6cU2bB+PZfOnk39tDRm3n47lf39RR7Maco9exbv2rl4ROhUtSqzd+/m\npoJWgnYhNjaW/fv3BzAyY8quorrIrlTVRBcvP/78NeHw448/0qFjRy7fsIEZQ4f6X7mUQpdUr87s\n3buLdQ1rwRjjng3yn0TeeecdLr74Yp5++mmeHDUKT5C2Fi6pulWrxtw9e0gvxtTqmJgYq2CMcSls\nO1qa0ElLS2PIkCHMmjWLefPmcdZZZ4U7pLCoFxVFnQoVWH7gAOf52XKLjY1lw4YNAY7MmLLJWjBl\n3A/LlvH3Pn1Yt24d3333XemsXNauhaFDA3Kp6+Pj2ZaW5nd+6yIzxr0CKxhV9ajqslAGYwJr1HPP\nccnmzdQ7+2w+++wzqlatGtgCQrUWWe3aMGUKfP998coDhjZsyBU1avid3yoYY9zzaS2y0uJkXypm\n29at3PLmm6xKTGSUKv2y70IZSIFeKqYwL74Is2bBzJn+lxcAX3/9NUOHDmXxYl/XdzWm5AvXcv2m\nlJgyaRLtFixAgSUdOgSvcgm1W2+FVatgyZKwhmEtGGPcswqmjNi/fz933XUXb06ezO3btzPz0Uep\nWxKezA+UyEj45z/hySfDGoZVMMa4ZxVMGTB9+nRatGjB8ePHmfbppwwdPDjcIQXHDTfA7t0Qxgcd\nrYIxxj0bgynF/vjjD4YMGcLKlSt57bXX6NTJ1W7TgRPKMZgAe3nLFm6oXZtoH58FSk9Pp0KFCqSl\npRFxkj1HZMq+QI/B2HMwpdDWzZv5v9dfZ8f69XQ+91zeffddoqKiQh9IsNYiC4F3d+ygScWKdK1e\n3ad8ERERREdHc+DAgcDPyjOmjLEWTCly9MgRhj3/PFOaNiXp11956pJLSGrdOtxhlUqPrVtHhipP\n+rHIZ/369Vm8eDENGjQIQmTGhI+1YE5CGRkZPPGvfzHplFOoERPDc0eP0u+RR8IdVqnWITaW5PXr\n/cpr4zDGuGOD/CVYRkYG06ZNIykpie+2bePhgwf57t57y87U4zBqGxPD/w4e5FhGhs95bT0yY9yx\nFkwJlJaWxrvvvsu//vUvKlSowJNPPsmll16KSMBarqXfH3/A44/DO+/4lb1yuXI0qVSJ/x44QNvY\nWJ/yWgvGGHesgilBtm7ezPjXX2fym29y2mmnMWbMGLp06WIVS34aNoSFC2HFCmeDMj+MSkykdoUK\nPuezCsYYd6yLrASY+dln9B4xghY//MB/KlTgk48/Zu7cuXTt2rVkVy6hWossP+XKwS23wOuv+32J\nHnFxJFSs6HM+q2CMccdmkYVJWloao194gY8rVmRr7dpcsmoV9/XsSatzzw13aO6F+zmYdevg3HNh\nyxbwoyXir4ceeojq1aszNEArPBtTUtgssjJi7dq1fL92LX0aN+aB7t2p9Le/hTuk0icxEU4/3VkA\ns3fvkBVrLRhj3LEKJkyaNWvGx+PHhzuM0m/AAPj665BXMFu2bAlZecaUVlbBmNJt4EDwhHYo0Vow\nxrhjg/ymdCtm5fLHkSMM+OUXn/JYBWOMO1bBGP+V4rXIMsWXL89Hf/5Jmg8PXMbGxrI/jCs6G1Na\nWAVj/BfOacoBUrlcORKjovjp0CHXeawFY4w7VsGYk17rKlVYfuCA6/S2VIwx7lgFY8qGvXvBz1l5\nvlYw1oIxxh2rYEzZEBXlbKmcmupzVn9aMPv376ekP8xrTLhZBWPKhqgo6NULpk3zOes5Vaow7Ywz\nXKcvX748UVFRHDx40OeyjDmZhK2CEZG7RGStiBwRkeUicmER6c8QkRQROSwim0TksVDFagpQ0gb5\nL78cPv/c52wVPB7q+7gjqHWTGVO0sFQwInI18G9gJJAELAFmiUi9AtJXAeYAqcA5wP3A/4nIA6GJ\n2ORr+PDw5s/tkkvgm28gBC0Lm6psTNHC1YJ5AHhTVd9U1V9V9T6cyuPOAtJfD1QEblDVX1T1E+AZ\nYHBowi1ZUlJSwh1CUPl9f7GxcN55MGdOQOPJT+Y4jD/s61d6leV7C4aQVzAiUh6nFZL7t8BXwPkF\nZGsLfK2qadmOzQbqikjDwEdZspX1b/Ji3d+zzzorLAeZVTAFK8v3V5bvLRjC0YKpAUQA23Md3w7U\nLiBP7QLSSyF5zMno7LPhlFP8ypquSobLmWHWRWZM0WyxS1P23XknbN6c9/jLL0P9+llv2/7wAzER\nEVT66Sc4ehSASidO8P6MGXnSV4qL4+Zt27j7jTdIOHCACunpAEQdO8aTL7wAwPgzz2Snd0OzE+XK\n8cOllwJQbulS5j33nOv0vl4/3Ol//fVX/rNiRYmJJ5Dpt3z9NT3j40tMPIFM/9+ePQm0kG845u0i\nOwxco6ofZzv+ItBCVTvlk2cyUF1VL8t2rDXwH6CRqm7Ild4eUDDGGD+U6g3HVPW4iHwPdAU+znaq\nK/BhAdm+BZ4WkQrZxmG6AVtzVy7eMkrwPsPGGHNyCNcssueBG0XkZhFpJiIvAHWAVwBE5CkRmZst\n/VScVs8kEWkhIn2Ah4HnQh24McYYd8IyBqOqH4hIdeARnIplFdBDVTM7ymsDidnS7xeRrsBLwHfA\nHuBfqvrv0EZujDHGrZCPwRhjjDk5lJm1yESkmoiMFZFfvMvJbBSRl70tpezpqorIWyKy1/uaIiKx\n4YrbV74usVMSicg/RGSZiOwTkR0iMl1EWuSTLllEtni/ngtE5PRwxFtc3vvNEJGxuY6X2vsTkdoi\nMsn79TsiIqtE5KJcaUrl/YlIhIiMyvZztlZEnhART650Jf7+ROQiEflMRDZ7vwcH5JOm0PsQkQoi\nMkvFhNEAAA1ZSURBVE5E/hSRg97ruXoWoMxUMEBd72sIcAZwHdAeZ/wmu3dxlqfpBlwCtAKmhC5M\n//m6xE4J1h54EWgHdAJOAHNFpGpmAhF5GGfFh7uB1sAOYI6IRIc+XP+JSFvgVmBFruOl9v68f5At\nBhToATQD7sW5h8w0pfb+cLrubwPuAZoC9wF3Af/ITFCK7q8ysBLnHg7nPunyPl4AegNXAxcCMcAM\nESl6MpWqltkXzjf/CaCy930zIANomy3NBd5jTcIdr4v7WQq8kuvYGuDJcMdWzPuK9n6demU7thUY\nmu19FLAfuDXc8fpwX7HA70AHYAEwtizcHzAKZ2WNwtKU5vv7HJiY69gkYHppvj/gADDAl6+TtzI5\nhvNYSWaaekA60LWoMstSCyY/sTgfTmbN3Q44oKpLMxOo6mLgEAUvU1Mi+LnETmkRg9Oa3gMgIok4\nEz2y7lVVjwKLKF33OgH4QFUXZj9YBu7vCuA/IvKeiGwXkf+KyN2ZJ8vA/c0COolIUwBvl1FnYKb3\nfWm/P8D1fbTGmQyWPc1m4Bdc3GuZfZLf290yApigqhnew7WBP/NJvoOSv+RMYUvsXBz6cALqBeAH\nnOedwPlaKPnfa90QxuU3EbkVaARcm8/p0n5/jXC6jMYAT+F0174oIqqqL1PK709VX/aOMfwiIidw\nfu6eVNVXvUlK9f1l4+Y+4oF0Vd2VT5oif2eW+BaMd3Ato5BXuoi0z5UnGqeZuwnneRlTQonI8zh/\nCV2l3vZ3aScipwFPAv2y/XFTlniA71X1EVVdoaqTgbE4/filnojcBwzEGXM4GxgA3C0iA8MaWClU\nGlowY4C3ikizMfM/3splFk4f4WWacwXmbUDNfPLX8p4ryXbi3FN8ruPxlPzY8yUiY4C+QEfNuSLD\nNpyFTOOB7IuIlZZ7bQfEAT9nGweNANqLyB04k1BK8/2l4nSRZPcLzkAylP6v3z+BkaqaubLITyKS\ngDPIP5HSf3+Z3NzHNiBCROJytWLicbrSClXiWzCqultV1xTxOgogIpWBL71Ze6pq7lkT3wKVvTN7\n8OY5H6iEMyOrxFLV40DmEjvZdcWZ0VOqiLN6w9VAJ1X9Lfs5VV2H843dNVv6KOAiSse9fgqcCbTM\n9lqOM4OxpaquoXTf32Kc2VXZNQU2QJn4+nlwJv5kl+E9XhbuD3B9H9/jTMDJnqYe0Bw39xrumQ0B\nnCFRGacCWQk0xqlhM1/ls6X7AmfKaFucvzR/BKaFO36X99gXOArcjDMj7gWcGR/1wx2bj/fxErAP\n6Jjr6xSdLc1DOIP+vXH+4n8P56+s6HDFXcx7zj2LrNTeH87A7zGcv/QbA38H9gJ3lJH7m4DTK9IT\naOi9hx3As6Xt/nBmaLbEGSc7BDzqfV/f7X0AL3s/j4txugzn41Q8UmT54f4AAvhBdsDpQsr+yvD+\n2z5bulic5172el+TgZhwx+/Dfd4BrAWO4Cybc0G4Y/LjHjLy+VqlA4/nSvc4sAVnFuAC4PRwx16M\ne/7/9s492KuqiuOfr3d4SI3haNgojDMJXDEhpimsoZrBCGoaynEGH6lNJokJmQ3Ty2evAWYKxVJE\nihqbgGAyQY0RSawoETR1QgISDUYvmIrI5eXFx+qPtX9w2Px+9/7u7/Lj3kvrM3Pmd88+++y99jnn\n7n322uustaI4wHT39uGfADydZN8ATCqTp1u2D9do/CT9n+3BTc1/BPTsbu1L/WK5/7dfVdsOoAf+\nMvsKsBtYDJxWTf3hKiYIgiCoC11+DSYIgiDonsQAEwRBENSFGGCCIAiCuhADTBAEQVAXYoAJgiAI\n6kIMMEEQBEFdiAEmCIIgqAsxwAQHaMOpaGl7vrPl7EwkfUrSjZ0tRyVSBMNmSSdl6T0lXSPpUXkk\n1zckPSdpjqSza6jnJUmz2pG/MT0/F7S3rirK7i9pj6ThR7rsoGN0B2eXwdHjo9n+Yvxr7Ztxp3jg\nLkL+nxkNTMG/7O6K3ALMtIJjQkknAMtwVyCz8DAWe4DBwGXpWFUhcAu09wvtzfjz9Wwb+dqNmb0o\naS7uGHfUkS4/qJ0YYIIDmNma4r6kFuBVM3u8k0SqOynOuszs7WpPqZMcPcwdmnakjNG4z6nzskOz\ngGG4W6GnC+krgbmSPt+ReqvBzFqANW1mrJ07ca/HHzmWn9fuRqjIgpqRNFrSI5J2pe0BSWdmeR6T\ntFzSOEn/lLRX0hpJH0pqm1tTVMRXkrqmV+HcklrlCkk/S3l2S1qcPLrm8kxKdexLZc5Ob++l471S\neTdKukHSZnxGNlBSH0m3SVqX6miSdK+kgYXzp+HOARsKKsO96dhn0v6ITKarUnq/Qto2Sb+QNFHS\nRkn78YiJSHq3pBmSNktqkbRJ0reqvCUT8FDGTYW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f438d602ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#R3: match Khangaonkar max temps for diatoms (12 C) and dinoflagellates (18 C):\n",
    "tr1=14 # 12 C\n",
    "tr2=13 # 18 C\n",
    "tm1=26 \n",
    "tm2=31\n",
    "R3_1=[1.0*np.exp(0.07 * (TT - 20)) * min(max((tm1 - TT), 0.0),tr1) / (tr1 + 1e-10) for TT in T]\n",
    "R3_2=[1.0*np.exp(0.07 * (TT - 20)) * min(max((tm2 - TT), 0.0),tr2) / (tr2 + 1e-10) for TT in T]\n",
    "plt.plot(T,R,'k-',label='current model')\n",
    "plt.plot(T,R3_1,'r--',label='new diatoms')\n",
    "plt.plot(T,R3_2,'c--',label='new flags')\n",
    "plt.plot((tm1-tr1,tm1-tr1),(0,2.5),'r--')\n",
    "plt.plot((tm2-tr2,tm2-tr2),(0,2.5),'r--')\n",
    "plt.legend()\n",
    "ii1=np.argmax(R3_1)\n",
    "ii2=np.argmax(R3_2)\n",
    "print(T[ii1],T[ii2])\n",
    "plt.ylabel('Temperature Factor')\n",
    "plt.xlabel('Temperature (Celsius)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.0 18.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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c4f60qUzhDxw40GG/devWDk+uLV++nC5dutCoUSNycnLsW9++fUlNTeXXX38F\nID4+nv3795OcnExaWhrbtm1j5MiRNGvWjMRE23KxiYmJxMbGFhnPunXraNCgAZ06dbKXGWO49dZb\nS/R6Xn/9ddq3b09gYCCenp40bNgQYwy7du0q9tzVq1fTtWtXGjdubC+zWCzcfvvtbNmyhbNnzzrU\n79+/v/37kJAQIiMj6dq1KwEBf0wd0qJFCwAOHjwIQKdOnUhPT2fkyJEsW7aM06dPl+h1uUqpxjiJ\nyA4RuRuIAgYDM4BPcrcZuWVRInKXiPzq6mCVUqowJxad4OcBP5N9NtvdoRRp25+2kXUoy91hKBcK\nDQ112Pfx8SEr649/4+PHj7Nq1Sq8vLwctsuJTGqqbXLUXr16YYxh5cqVJCUlUbt2bdq2bUtcXBwr\nV64kIyODTZs2ER8fX2Q8KSkpREVF5SsvqOxKr7zyCpMmTaJv374sWrSIn376ifXr1yMiDq+pMGlp\nadStWzdfeZ06dRAR0tPTHcpr167tsO/t7V1gWd779+rVi4ULF3Lo0CGGDRtGREQEffr0Ydu2bcXG\n5wrOTkdwDvgyd6swxhgPbMu93AbUBVKA/wDTRAenK1Vjnd12lt3jdtPmizZ41nL6YeEKYfG1kP5N\nOnXH5P/PRVVPYWFhREVFMXv27Mtdlg5iYmIAW4tL+/btSUxMJCgoyN6yFB8fz/33309SUhJWq5W4\nuLgi71e3bl17K1ZeR48eLTbWTz75hBtvvJHnn3/eXrZ///5iz7ssNDS0wPukpKRgjMmXFDlr2LBh\nDBs2jPPnz5OUlMTf/vY3BgwYwKFDh1xy/aK4/xnd0nkcGAfcB8QADwATsc0lpZSqgS6evMgvf/qF\npv9qSlCn0s9HU9FC+4aS/nV68RVVtdG/f3927txJgwYN6NChQ74tb7dUfHw8iYmJJCUl2VuW4uLi\nOHHiBLNnz6ZBgwYOT7AVpFu3bhw8eJAff/zRXiYiLFiwoNhYz58/j5eXl0PZu+++m28QuY+PD5mZ\nmfnO7927N+vWrXPoqrRarXzyySd06NCBWrVqFRtDQa68/2X+/v4MHDiQe++9l5SUFHvrXXmqaolT\nJ+BzEflCRA6IyFLgc6CLm+NSSrmB9ZKVX2/5lYhbI4gaUXw3RGVQu29t0r5JQ3J0+ZWaYsqUKURG\nRtKjRw/efPNNkpKSWLZsGS+++CJDhgxxqBsXF8eRI0fYsWOHvWUpPDycVq1asWLFimJbmwBGjRpF\n48aNGTa5H5HwAAAgAElEQVRsGHPnzuXLL79kyJAhZGRkFHtu//79Wb58Oc899xwrVqzg8ccf55NP\nPslXr2XLlqSlpfHGG2+wYcMGfvnlF/trDQkJoU+fPnz00UcsXbqUwYMHs3fvXp555pmS/LgKlLel\nbtq0aYwfP55PPvmE1atXM3/+fGbPns21115LWFiY0/coqcrdpp3fl8DfjDExIrLLGNMSiAec/9dQ\nSlVZF49cxL+FP02ece/M4KXh28AX70hvMjZnEHRd5W8hU0UrrCUkb3lQUBBr1qxhxowZPP/88xw+\nfJiQkBBiYmK4+eabHc7r2bMnnp6eREZG2gdFg60lavv27cWObwLw8vLi22+/5b777mPSpEkEBAQw\nYsQIBg8ezPjx4/PFmTfWp556itOnT/Pyyy+TlZVFbGwsX3/9NU2aNHGod/fdd7N+/Xoef/xxTp06\nRaNGjdi3bx9169bl+++/5+9//zsTJ07kwoULtG/fni+++II+ffoU+7MraHbzK+t26dKFV155halT\np5KWlkZkZCT9+vVjxowZxf5sXMEU1N9amRljnsHWNZcNeADPiMhThdSVqvb6lFLV357Je/C5yoeG\nDzd0dygFMsYgIgVnBGW7rn4mqyqhqPdAlWpxMsY8AIwBhgO/Au2B2caY30XkvYLOSUhIsH8fGxtb\n7GOcSilV3po80wSLX+UZKZGUlERSUpK7w1CqSqhSLU7GmKPA0yLyap6yx4FRItK8gPr6141SSpWS\ntjipmq6o94DTf/IYY641xnxmjDlpjMk2xnTILX/WGNO/uPOdZAGunHbAStUb5K6UckJln6NJKVX9\nOZVwGGN6AGuBFsD8K65jBcYXdJ4LLAYeMcYMNMY0MsYMBaYA+RfgUUpVK9mns9ncbTPpSfoov1LK\nfZzqqjPGfA+kAkOwDdC+CFwnIpuMMcOAl0XE5aMejTH+wHTgZmyzl6cAHwEzReRiAfW1WVipasB6\nycq2Qdvwa+5Hs1eaFfokk3IN7apTNV15DA7vAAwTETHGXPkuOAlEOHndIonIeeDh3E0pVQOICHsm\n7sF4GZq+3LRaJU3n957Hp64PHgEe7g5FKVVCzo4NygL8CzlWF6jYFfeUUtVW8oxkMjZm0PLjllg8\nq9dwxj2T9pD+rXY9KlWVOPsp9D0wOXftuMsutzyNBRLLFJVSSmEbDH5m/RnaftkWz8AqNXtKidSO\nr016oiZOSlUlzo5xagf8AOwHPgWeBF4B2gEdgU4isst1YTpH+9OVUpXZmZ/OsOuuXXTa1sndoTjQ\nMU6qpnP5dAQishXoBRzDtvCuwbbwLkDvypA0KaVUZVfr2lpkHczi4rF8z7YopSoppwcMiMgmEbkB\nCATqA0EiEicim10WnVJKVWMWTwshvUI4lXTK3aEo5VaxsbH06tXLJdc6ffo006dPZ8uWLS653pWc\nncdpX253HSKSJSJHcp94wxjT2hizz5VBKqVqhkvpl6hpXTlRd0RhvKrPk4JKOcOVT8ueOnWK6dOn\ns2nTJpddMy9nW5yiAZ9CjvkCjZy8rlKqhrp44iKbu2/mVGLNan2JHB5JxLBymcFFqRqpvP/4Ksuz\nvYVFdh1Qsz75lFJlcunUJX7u9zPhw8KpfUNtd4ejVIkkJCRgsVjYu3cvgwcPJjAwkOjoaGbOnJmv\n7smTJxk/fjz169fH19eXa665hrfeest+PDU1FQ8PD+bPn28vW7p0KRaLhTvvvNNelpmZiY+PD6+/\n/nqhceXk5PDkk0/StGlT/Pz8iIiIoFevXqxZs8Zep3HjxowcOZIPPviAmJgY/P396dWrF3v37uXs\n2bPcfffdhIWFUadOHR5++GGs1itXOysZi8XCE088wbPPPkuDBg3w9/end+/ebN26tcD6K1asoGPH\njgQEBNCmTRsWL15sP/bZZ59hsVjYtm1bvvNiY2Pp3r07ycnJNGnSBGMMd999NxaLBQ8PD+bNm+dU\n/AUSkRJt2JY2OZC75QBH8+xf3k7kHvtPSa9bnpvt5SmlKrNLGZdkY/eNsvuB3WK1Wt0djhKR3M9O\n/UwuRkJCghhjpE2bNvLPf/5TVqxYIZMnTxZjjLz//vv2emfOnJHmzZtLo0aN5J133pEVK1bI3/72\nN/Hw8JBXX33VXq9t27YyduxY+/6UKVMkICBA6tevby/76quvxGKxyK5duwqN6+mnn5bAwEB55ZVX\nZNWqVbJ06VJJSEiQzz//3F4nOjpaGjZsKN27d5clS5bIwoULpV69etKmTRsZMGCAPPzww/Ltt9/K\nU089JcYYef311536GRljpEGDBtKjRw9ZsmSJLFiwQGJiYiQ8PFzS09Pt9WJjY6Vu3brSunVrmT9/\nvixfvlz69OkjXl5e8ttvv4mISHZ2tlx11VUyadIkh3vs2LFDjDEyb948uXjxoixatEiMMfLEE0/I\n+vXrZf369XLy5MlSxV3Ue6A0v/B/At7L3azA0jz7l7c3gAcA/5Jetzy36vYmVaq6yc7Mls3xm2XH\nXTvEmqNJU2VR0YkTuH9zRkJCglgsFpk7d65DeZs2baRfv372/RkzZoifn589AbjsnnvukYiICMnJ\nyRERkQcffFCaNGliP96+fXv561//KhaLRXbv3i0iIo888ojUq1evyLgGDx4sN998c5F1oqOjJSws\nTDIyMuxls2fPFmOMjBs3zqFuhw4dJD4+vsjrFcYYIxEREZKZmWkv279/v3h5eclTTz1lL4uNjRVv\nb2+Hn9Hx48fFw8NDnnvuOXtZQkKChISEyPnz5+1lU6ZMkdDQUMnKyrJf3xgj77zzjlMxixT9Hihx\nV52ILBGRMSIyBpgL3H95P882XkRmS+5AcaWUKkpORg5BXYKImRODsegA6ZrK/WlT2eIfOHCgw37r\n1q05cOCAfX/58uV06dKFRo0akZOTY9/69u1Lamoqv/76KwDx8fHs37+f5ORk0tLS2LZtGyNHjqRZ\ns2YkJtrmlU5MTCQ2NrbIeDp16sQXX3zBE088wQ8//MClS5cKrNetWzdq1apl32/RogUAffv2dajX\nokULDh48WLIfRgEGDhyIr6+vfb9Ro0Z07dqVtWvXOtRr1qwZTZo0se9HREQQGRnp8LMcN24c586d\n46OPPgLgwoULzJs3j1GjRuHjU9jQa9dydh6nMSLyu6uDUUrVLN4R3jR5tgnGQ5Om5P+XzIWjF9wd\nhnJCaGiow76Pjw9ZWVn2/ePHj7Nq1Sq8vLwctltvvRWwjW8C6NWrF8YYVq5cSVJSErVr16Zt27bE\nxcWxcuVKMjIy2LRpE/Hx8UXG8/jjjzN9+nQ+//xzevXqRVhYGHfddZf9PpfVru04ntDb27vQ8ryv\np7SioqIKLDt8+LBD2ZU/R8j/s6xbty5/+tOfeOONNwBYsGAB6enpjBs3zun4SqtMaxjkTkkQg+1J\nOgci4sKRWEopVb2dWXcGv8Z+RA6PdHcoysXCwsKIiopi9uzZl7ssHcTExAAQEhJC+/btSUxMJCgo\nyN6yFB8fz/33309SUhJWq5W4uLgi7+fh4cHDDz/Mww8/zPHjx1m6dClTpkwhMzPT3lJTkY4dO1Zg\n2VVXXeXU9SZMmECfPn3YtGkTc+bMoWfPnvbWsorgVOJkjAkBlgFdLxflfs37G6GJk1JKlVBIrxBO\nrTqliVM11L9/f1599VUaNGhAeHh4kXXj4+OZP38+ISEhTJo0CYC4uDhOnDjB7NmzadCggUN3VnEi\nIyO56667WLZsGb/88kux9V05n9JlX3zxBZmZmfj5+QGwf/9+1q1bx2OPPebU9eLj42nevDlTp05l\nzZo1Dk8iAvYuu8zMzLIFXghnpyN4FgjDtuyKAYYC8cB/gH1AZ5dEp5SqNkSE4wuOI9aaNcFlSQX3\nCub0qtPuDkOVgylTphAZGUmPHj148803SUpKYtmyZbz44osMGTLEoW5cXBxHjhxhx44d9pal8PBw\nWrVqxYoVK4ptbQIYMmQI06ZNY8mSJaxatYqXX36Zr776in79+hV7bkEtYgXx9PTknnvuKVFdPz8/\n+vbty5IlS/jkk0/o378/ISEhTJ48uUTnF2TChAmsWrWK8PBwhg0b5nAsKiqKsLAwPv74Y1atWsXG\njRtJS0sDYMaMGXh5eZVpzJaziVM/bMnTutz9QyKSJCJ3At8CDzodkVKq2hER9j64l4MvHiTnfI67\nw6mUarWvRVZyFpdSCx7Iqyqnwlpo8pYHBQWxZs0aBg0axPPPP0///v0ZO3Ys//vf//KNV+rZsyee\nnp7UrVvXofspPj4eY0yx45sAevfuzTfffMPdd9/NgAEDePPNN3nkkUf4xz/+4RBfQbGX5PWA7T1d\n0rmd7rzzTgYNGsR9993HmDFjqFOnDitWrCAkJKTYexcW5y233ALAmDFj8PLyynfOO++8Q3p6On36\n9KFz584sXbrUHvflzVnGmZONMeeBfiKyOvf7gSKSlHusD/CxiIQ5HZWL6ErcSrnf5aTpzPoztPu6\nHZ7BZRpaWa1t7beVqyZeRfifiu7OKW9FrQxfxuvqZ3INc3kCzBkzZrj0um+99RYTJkxg9+7dpeq6\nLKmi3gPOfoIdBS4Pf08GugFJuftNnbymUqqaERH2Tt7LmXVnaPt1W02aitHkuSZ4R3m7OwylKq0d\nO3awd+9eEhISGDp0aLkkTcVx9lPse2wDw5cAHwDTjDHRQDYwCvifK4JTSlVth/55iDNrbUmTV4hX\n8SfUcIEdAt0dglIuVVhXm7MmTpzI2rVruf7663nllVdcdt3ScLar7mqgXm5XnRfw/4DhgD/wFbbJ\nMVOLukZF0GZhpdzrUuol8ECTpipGu+pUTVfUe8CpxKmq0DepUkqVniZOqqYr6j3g7FN1Rd3Myxhz\nn6uvq5RSSinlbk4lTsaYsNwuurxlFmPM3cAe4F+uCE4pVXVYL1iRHG1NUEpVbyVOnIwxnsaYZ4wx\np4DjwFljzLvGGB9jTAdgGzAHOAb0L59wlVKVUc75HLbdtI2Ut1PcHUqVZ71kZV3TdVgvlGyOHKVU\nxSrNU3WPAY9im+ByE9AYGAn4AIOBFOBPIvK5q4NUSlVe2Wey2TZ4G77RvtQZW8fd4VR5Fi8LnsGe\nZGzMILh7sLvDcSlfX99jxpj8K74qVcn4+vrmX2AvV2kSp78Ar4mIffySMeYu4G3gG+AmEbnodJRK\nqSrn4omLbBu4jVoda9H8teYYi+vXuaqJgrsHc3rN6WqXOGVmZmpmraq80oxxagQsuqLss9yv/6yo\npMkYU8cY874x5rgxJtMY84sxpmdF3Fsp9Yesg1ls7rGZ0P6hNH9dkyZXCuoexJk1Z9wdhlKqAKVJ\nnLyAjCvKLu+fcE04RTPGBAM/AAIMAFoA92Mbc6WUqkCeQZ5EPxVN45mNy2VF9ZrscouTPrqvVOVT\n2pnDrzLG5J3f3CNP+am8FUVkX5kiK9jfgSMiMiZPWXI53EcpVQzPYE+i7tDhKuXBp6EPFl8LFw5f\nwLe+r7vDUUrlUeIJMI0xVmwtPfkOFVQuIh4F1C0TY8x24EugPhAHHAHeFpF/F1JfJ1tTSlVJ1mwr\nFk+XT7VXIuU1AaZS1UFpWpzGFF+l3DUBJgIvAc8B7YFXcxOk19wamVLVnFhFxzFVIHclTUqpopU4\ncRKRueUZSAlZgB9F5PHc/a3GmObAJKDAxCkhIcH+fWxsLLGxseUcolLVi4iw/6n95GTm0HRWU3eH\no8pBUlISSUlJ7g5DqSqhSq1VZ4zZD3wtIuPylP0FeF1E8i0rrl11SpWN9YKVnWN3kvVbFq3/1xrv\nCG93h6QqgHbVKVW4qtYW/AMQc0VZDDpAXCmXu5R2ia19t2LNstIusZ0mTUopRdVLnF4CuhpjHjPG\nXG2MuQXbdASvujkupaqVrINZbOq+iaDOQbRa0AoPP5c/66FKIDsjm8zfMt0dhlIqjyrVVQdgjBmA\nbWB4c+AA8Io+VaeUa+WczyH181Qih0e6O5Qa7eTnJzn8ymHafd2uQu+rXXVKFa7KJU6loYmTUqoq\nu3jyIuuvXk+PtB4Yj4rLYzRxUqpwVa2rTimlagzvcG+863hzbvs5d4eilMpV2pnD7Ywxo4DbgYbA\nlVPbiohcXZbAlFIVI/tMNljAs5bTHweqHAVfH8zpH05Tq20td4eilMLJFidjzJPAe0A9YAvw3RXb\nKlcFqJQqP5m/ZbKp2yaOz9flHiuroK5BnFmvC/4qVVk4+yfmWOBfIjLFlcEopSpOemI6v474lein\noql7T113h6MKEdwjmMx9+mSdUpWFU4PDjTEZwJ9EJNH1IbmODg5XKj8R4cjrR9g/Yz8t57ekdnxt\nd4ekKhkdHK5U4ZwdHP4dULHPxyqlXOLYvGMcef0IHX7ooEmTUkqVUolbnIwxeZOsJsBnwCzgCyDt\nyvoiYnVFgGWhLU5K5We9YEWyBY8AndRSFUxbnJQqXGkSJyuQt7K5Yj8vERG3P6KjiZNSSpWeJk5K\nFa40yc0MCk+UlFJKKaWqPZ05XKlq6uKJi+x7dB9NX2yKZ7DbG4BVGaW8n0Jo/1B86viU+720xUmp\nwjk7j9M+Y0yBg8ONMa2NMfvKFpZSqixO/3CajddtxDvCG0uALhBQHZxcfJLTq067OwylajxnP1Gj\ngcL+7PEFGjl5XaVUGUiOkPxMMr/c/AvNXm1Gk+eaYPHUxKk6COqsE2EqVRmUpf2+sD6w64BTZbiu\nUsoJ1otWfh74M3JJ6LihI771r1wJSVVlQV2C2J+w391hKFXjlThxMsZMAS7PFC7A58aYi1dU8wNC\ngY9dE55SqqQs3hbqT65P2IAwjIcOT6luAjsFkrE5A+slKxYvbUVUyl1K0+K0D1iR+/0oYANw4oo6\nF4BfgbfLHppSqrTCB4e7OwRVTjyDPPFt5Mu5X84ReG2gu8NRqsZydsmV94AZIvK760NyHX2qTilV\nnZxccpJa19bCt2H5dsPqU3VKFU6nI1CqihERUuakEHhdIIEdteVBuZ4mTkoVztnpCAYYYwJcHYxS\nqmgXjl5g2+BtHJlzRKcZUEopN3D2k3cZkGaMWWOMecYYc4MxRh/hUaocnfjsBBvabyCwQyAd1nYg\noIX+7aKUUhXN2TFOTYEbgFggDojENjD8RyARWCkiq1wXpnO0q05VF3sm7yF1aSrXfHANwd2C3R2O\nqua0q06pwrlkjJMxpiUQDwzFlkzpIr9KudDpdacJaB2AZy23v61UDaCJk1KFK9OnsDHGH+iJrdXp\nBuBa4AzwXdlDU0pdFtxVW5mUzem1p0n7Io3GMxu7OxSlaiRnB4fPMMZ8D6QDnwLtgAVAFyBMRIa4\nLkSlahZtJVVF8Qj04PiC4+4OQ6kay9kWpyeA88Bs4HkRuXIiTKVUKV1Ku8TeKXsJ7hlMvbvruTsc\nVUn5t/DnwuELZJ/OxjNYu26VqmjOPlX3IPA1cBeQYozZaIx5IXeaglquC0+pmuHE4hP81OYnPIM9\nibwt0t3hqBLakpHBJau1Qu9p8bRQq10tMjZnVOh9lVI2ZRocbowx2MY1xWIbHN4D8Ad+EpHrXRFg\nWejgcFXZZR3KYu+Dezm37Rwx78QQ0jPE3SGpEjiTnc2Tv//OJ8eP8227drSuVbF/L+55YA8+DX1o\n+NeG5XJ9HRyuVOHKNINeblbyC7AJ2AzsxNb917XsoRXPGPOoMcZqjJldEfdTytX2TNpDQOsArvv5\nOk2aqgARYdGJE7T66SfO5uSwvXPnCk+aAAKvC+TsxrMVfl+llPPzOHXH1sIUB3QDfIBUIAlYiW0e\np52uC7PAGLoC84HTwGoReaCAOtripCo1sQrGon/YVwVns7O5Y8cOdmdm8kbz5vQOcV+ie+nUJXIy\ncvBtUD7zDmuLk1KFczZxsgKngFX8MeHlNhfHVtT9g4GNwFggAdimiZNSqjyJCHOPHuX2qCh8LNV7\nuRtNnJQqnLPv/uvInXZARGZXZNKUaw6wQER0vihV6YkIxz4+xoWjF9wdiioDYwyj69YtMmn65exZ\nHtyzR6eUUKoacypxEpFNQB1jzCxjzE/GmN9yvz5vjKnj4hgdGGPuAZpgmxJBqUrt7NazbOm9hYP/\nOEh2ara7w1EllJWT49R5Tfz8SDx1ijkpKS6OSClVWTg1CYgxpjnwPRAC/ADsBepgm6bgTmNMTxHZ\n47IoHe/7DHC9iJToGeCEhAT797GxscTGxro6LKXyuZR6id+f/J0Tn54gekY09e6ph/HQno/K7oLV\nyksHD/LGkSPs6NwZPw+PUp3v7+HBp61a0WPzZroEBtI+MLCcInWtpKQkkpKS3B2GUlWCs2OcFgGt\ngT4isj9PeSNs8zttF5Fhrgoyz/VHAe8CeZMmD0CAHCBARC7lqa9jnFSFy87I5seYHwkfFk7jGY3x\nCvVyd0iqBL5MTeXBvXtp4e/PS02bcrWfn9PX+ujYMZ7av5+NHTsS5Fl+k1SW18MFOsZJqcI5mzid\nAsaLyMcFHLsdeE1EarsgviuvHQTUv6L4fWA38IyI7LiiviZOyi0uHL2ATx0fd4ehSmDv+fM8sHcv\nezIz+VfTpgwMC3PJdcfv2sXpnBw+atnSJde7koiw9qq1dNreCa/ark3ONXFSqnDO/inkDRQ2bW1G\n7nGXE5EzwK95y4wx54C0K5MmpdxJk6aq44II/UJDWVyvHt4ufFru5aZN+Smj/Gb3Nsbg19SPjI0Z\nhN4YWm73UUo5cvZTYgtwvzHG4fzcmcQn5h6vKNqkpNzi4rGLpLyjg4CrulYBATxYv75LkyYAXw8P\nepbzXE+BHQPJ2KBLryhVkZxtcZoBLAV2GGM+AVKwDQ6/BWgGDHJNeMUTkfiKupdSADnnczj4z4Mc\nevkQdUbV0UksqwirCGdzcsp1zFFFq9WhFqlLU90dhlI1irPTEXwFDMbWLfc48G9s0wOcBQaLyNcu\ni1CpSkJyhJT3U/gx5kfObTtHx/UdafpiU02aKjkR4eu0NK7buJFZBw+6OxyXqnVtLc5u1qVXlKpI\nTv/plZs8fWWM8QdqA+kict5lkSlVyRx88SAn/3eSlgtaEtwt2N3hqBL44fRpHt+3j6MXLzKzcWP+\nHBHh1nguWK0unXXcP8af7NPZ5GTl4OFbuqkTlFLOKfVTdcYYb+AT4CURWVUuUbmIPlWnXMl60Yrx\nMtiG8qnKTET48/btbMjIICE6mpFRUXi6eZmUrWfP8pcdO/ipQwd8Szk/VFFExOW/k/pUnVKFc3Y6\nggzgJhFJcnlELqSJk1I11+pTp+gcFFRp1pUTEW7evp0W/v4826SJu8MpkiZOShXO2U+UH4CurgxE\nqcrgzE9n2NpvK+kr090diiqjniEhlSZpAlsy8lqzZryTksLGcpymQClVvpz9VHkIGGuMuc8YU98Y\n42GMseTdXBmkUuXtzPoz/Dz4Z34Z+gvhQ8MJvl7HMFUFe86fZ9aBA+4Oo8Tq+Pjw4tVXM2bnTi5a\nS7RqlFKqknG2q+7yO76wk0VE3P7Mr3bVqeJkHchi19hdnN99noaPNKTOmDo6yLYK2HnuHM8cOMCX\nqancX78+TzZqhKWKjD0TEf7vl1/oGhTE440auTucAmlXnVKFK8s8TpqRqCrPK9yLyBGRRN0RhcVb\nG0oru1/OnuXp5GQST51icv36vNqsGcFVbF4mYwxvNW/u0kQv62AWHoEeeIXouohKlTenWpyqCm1x\nUqp6eeXQITKtVibUq0dgFUuYytOvt/9K6IBQ6txZxyXX0xYnpQpX5k+eAsYziWYrqjKxXrRy/OPj\neEV4ETbANQu4Kve4v/6Va3wryDMR5p3ujkSp6q/EfRPGmDrGmGXGmDvzlHkAl67YThljolweqVKl\nlH0mm4MvHmT91es5OvconkHaQlEVXLJaWXTiBPr3V8nVal+Ls1t0BnGlKkJp/icZD3QA/nxFuQHe\nAo7kfj88t+50VwSoVGnlnMth/8z9pLyVQmjfUFovaU1gh0B3h6WKcTY7m7dTUnjp0CGu9vOjV0gI\nYV41Y8xOWSexvJw4lcdkmEopR6VJnAYCb4lI5hXlArwpIpsAjDEnsDUYa+Kk3MLia8HibaHjho74\nNfZzdziqGAeysnj18GHeO3qUuJAQPm3Vik5BQe4Oq0Ldsn07D9avT8+QEKfO9470xuJvIWt/lv7O\nK1XOSpM4xQBPFVB+5Z83u3PrKlX+cnJg2TJ4+21ITwdvb4yPD429veHv3uCdZwsMhKCgP7a8++Hh\nEBlpK9O/2CvUqlOnyBFhfYcONPGrmf/p3xIZyf179rDxuuvwcPL3L3J4JNmnsl0cmVLqSiV+qs4Y\ncwG4QUS+v6I8CjgpIjm5+z2Bb0TE19XBlpY+VVd9Xdp7jLMPzibwxw/wbFIXJk6Exo3h4sWCtwsX\n4OxZOHPGtmVk/PH96dNw8iQcPw7Z2bYEKu9Wvz40bAiNGtm2hg2hhv4Hr8qHiNB7yxb+EhXFuHr1\n3B2OPlWnVBFK0+J0HGgCOCROInLsinqNgRNljEupAp3/73ouPv4itXZ/iaVxHBdfmofnX2JdeIPz\ntgTq8nbsGBw8CGvXwscfQ3KybT84GJo2hZgYaNHij69Nmthat5SDQ1lZvHf0KH9r2LBSLYNSWRhj\n+FfTpgz4+WduiYigdg0Z26VUVVSaFqePgHAR6VNMvW+xtUDd5oL4ykRbnKqJrCxy5n/K+akv4Z1x\ngMy4O/F/eTLerRu4Jx6rFY4ehb17Ydcu27Zzp+3rwYO25Kl9e7j22j+2sJo3DUKOCF+npfHmkSN8\nd/o0f4mKYkZ0tCYFRbh31y78LBZebtbMrXFoi5NShStN4tQNW2vTS8AjIpJ9xXFP4HngAaCniKx1\ncaylpolTFbdzJ7z1Fsybh1x7LWc73U7A4yOw+Pu4O7LCXbhgi3vz5j+2rVttLVQdO0L37tCjB3To\nAD6V+HWU0SfHj/P3334j3MuLe+vV4/bISGrphJXFOnHxIktOnuRuN3fXaeKkVOFKNXO4MeYhbMnR\nCYliKmoAACAASURBVOAb4PLqmg2BPkA48KiIvODiOJ2iiVPVk3PqHLLwv3j+511bAjJmDNx9N1x9\ntbtDc57VCr//Dj/9BD/8YNt277a1RPXoYdt694ZatdwdqctsOHMGYwwdA3UaiKpIEyelClfqJVeM\nMXHA34FewOUB4FnAKuB5EUl0aYRloIlT1XF+6UYuTvs3AZsXkdOiLb4zH4Cbbqq+44UyMmDdOlsS\ntWqVLanq0AH69rVtHTqAR+VfbPhMdjZB2pJUaZzddhZrlpWgTmWbzkETJ6UK5/Radbmzhl8euJF6\n+am6ykQTp8ote/9xzj7yNl5L5+OVeZRznW/B77n78Y1t6e7QKt65c7YE6uuvbdvRozBwIAwZAv37\nQ0CAuyO0y8zJYfHJk7x/9Ci/nj/P71264KkDviuFw68fJmNjBi3eblGm62jipFThdJFfVbEuXYLl\ny2HuXGT5N5wJ6YYZN4Zafx2Cxbeati454+BB+PxzWLzY1jL1/9u78/ioyquB478zE5IQdggk7FBA\ndtmUioCCgGsVawVFxaXyilu1vi6ttlKtVltrtbauba2vtIgbtuAGiIiyhj0CsoYdQyAJkH2bed4/\nnhsdhpkwmSTMTDjfz+d+hrn3zr1nnkmYk2cdNcomUVdcYeecioC0vDz+mZnJe4cPc3aTJtySmsr4\n5GQaxkDN2Oni2NJj7LhvB0NWDqnRdTRxUio4TZzUqfH11/DmmzBjhh11dvPNMHEihDlT8mnl6FH4\n5BP4z39sbdR558H119skKinplIXxy4wMmsXFMTklhQ6JEZ+mrd4zxjA9K4uJrVuHnJxWHKtgWftl\njDw2EnGHn/do4qRUcJo4qTpT8tVmip/6PxK+/IDEZiW4br0RbroJzjgj0qHFrvx8m0DNmAFpaTZ5\nmjwZxoyBWmou8xqDS2dPjwpXbdzIsKZNebBTp5Bfs6LrCs6cfyZJPcJPqjVxUio4TZxUrSpN303x\n428S99ksEgr3Udj7YtxTb6DxnRcjcdqkU6sOHoR33oE33rAJ1W232VGIbdpU+1L5FRXMzs7mrUOH\naBYXx8w+p2E/syi0taiIEevWsWXo0JAXPN4wfgOpN6bS+ietw76vJk5KBaeJk6q53FyYNQvefhvv\n8jXktR2F6+braHz/+Oiec6m+MAZWroTXXoMPPoCLLoLbb7f9oqqoOSr3evkkN5e3srKYm5vLyGbN\nuC4lhStatdI5l6LI7Vu30iQujj+GOCXH4Q8OE58aT7Nzm4V9T02clAouphInEXkY+DF2EeFSYAV2\n3qhNQc7XxKmOeA9m45r3Mbz3HixebIfQT5oEl1yi67hF0tGj8O9/wyuvQIMG8MADcM019t9+Sr1e\nrty4kSuTk7m6deuQazTUqXWgtJT+q1ax6eyzaXuKJk3VxEmp4MJKnETkDmPMK0GOJQDPGWPuqmlw\nAa79KTATWA0I8AQwDOhtjDka4HxNnGpR6frdFP9hJu75s2l4dCPu8RciV10F48eDTnQYXYyBuXPh\n2WfxbttGxc9+RvzUqXYGcxVz7tuxg9YNGvBI586n5H6aOCkVXLiJkweYDUwxxuT67O8HvA10NsbU\n+TepiDQCjgHjjTEfBziuiVMNmZ07OXrf/9HgizkkFmRQ0HkUMuEqGj/wE9xtajbJnqo7ueXlzM7O\nZtbhw+StXMk7c+fS9osvYOpUuP/+03LtvFhW7PGQ4HKdsk77mjgpFVy4w3Auwdb0pIvIKAARuQdY\niW1Cq9kkIqFrin0PR07R/eo/Y2D9enjiCRg8GBk2jLjMHXgfegRX3mGa7/qQZs/coklTlFqQm8uo\ndevosmIFH+bkMCklhQ9vvpm2778Pa9ZAdrYd1ThtGhzRX5tY0dDt1pGOSkWJmswcngK8CYwF0oEB\nwAvYBYDLay3CqmN4F/gBcHagqiWtcQqNJzufgr98SNKWz2mwbJ7to/SjH8GPfwzDh8fE0h/KSi8o\nYG9JCWNatCAp2Oe2axc8+STMng333AP33qtNeOo4WuOkVHA16hwuImOAj4AEYA0w1hhzrJZiO9m9\nnwMmAsONMXuCnKOJUxAly7dR8pdZuD7/lKTDayhu3hf3pCtI+vlEnWcpih0tL2fekSNkFBfXvL/L\njh3w29/amdynTbPTGWgH8Xohb3UeRZuKSL0pNazXa+KkVHBhjTl21ql7AngQmA/8C1vblC4iNxhj\nltReiAHv/zw2aRoVLGmq9Nhjj33371GjRjFq1Ki6DC16lZbCsmUwfz7lb83Bve8AdDkfc91kzH2z\naNI5/DlfVN3xGsOq/Hzm5eYyLzeXrwsLOa9ZM65qXQufV/fuMH06pKfb0Xd//Ss8+yxcdlmV0xio\n6OfJ85D5embIidOiRYtYtGhR3QalVD0RbufwNGzT3C+NMX929qVim+4uAJ4yxvymNgP1ufcLwARs\n0rTtJOeetjVOxuulfMlG4tcvsst0fPUV9O4NF12Ed/Q4ZPgwJF7n6olGWVmwYAEsWWKX3fio8DAd\nmsbRp2UifVom0qyxi6Qku9pKo0Z21ZoWLezWvDmENQWTMfDppzaBatvWJlE6CWZU+vLoUT7OyeGZ\nKuZ1KjtcRlqPNEYcGYGEkQRrjZNSwYWbOG0GJhlj1gc4dj/wpDGm1ifzEZGXgBuA8cBmn0MFxpjC\nAOefVolT+Y4sil6ag/lkHg0zloALEm663M6xNGYMtGwZ6RBVANn5Hv4+v5D9yxqx9HM3u3fD6NF2\n/sqEBCgshKKi47fKfQUFduqmI0fsduyYTaYqE6kWLewAutRUu7Vt+/2/U1MhJcUv0aqogFdfhccf\nh1tvhUcftRdUUSOnvJweaWmsP+ssOlWxZuCydssYvGIwiZ2qv66gJk5KBRdu4pRkjCmq4vggY8y6\nGkUW+LpeIFDAjxtjfhvg/PqdOBUUwNKlmIULKXr5YxIKdlPUZgiekWNJvPVyEi86E6ml9ctU7ckr\n9vLmwkI+WFDBuq/iOLY5iSZnFHPtJQ24+fIEhg4Ns9YI8HohL+/7ROrIETuQLivLrtCSmWkfK7fs\nbEhOhi5d/LamuXSZ+TSd188m8S/P2Lm6tPkuajyUkUGx18tfe/QIek76Rem0/1l7kn+UXO3ra+Kk\nVHAxNXN4ddW3xMlzKA9ZuQzXsq/giy9gwwYYMgRGj6ao/VASrxmNq6nO2h1tKipg9WpYuNB+bF8t\n9xLXuZi+w8v40Rg3Uy5Mol2LyDSbejw2mdqzB3bv/n7btcs+7tvrpbU5RK8WWfS+vDu9BjeiVy/o\n1QvatdNcKlIOlpbSe9UqNp99NqlBZhPPeDCDuJZxdH64+oMINHFSKriajqobgF3+5IS6YGPM9BrE\nVStiPXHyHM6j8I0FeGYvoMGGZTTM34Y5cyBx48fatpxzztHlTaJQQYmXmUuKWLvMzd60hixZAl27\n2o9s9GgYOdLQokVsfCd5PLBvRylbHnubzR9lsOXsyWzxdGfLFqG42HaDGjTIbgMHQv/++iN5qvxs\n+3YaulxB+zoVbirEW+6lycDqz0WsiZNSwYXbVNcc+Bg4p3KX8/jdxYwxEZ/8J+YSp8xMWLoUli6l\n9N2FxH27jeImPSk/czhx48eRdMsY3Mm6tEm0OZDj4fUFhcz70sOmNNv01rBTCWcNM9z7o8acf75t\nDot5a9bAzTfbLPDVV8lNbMfGjbBund3Wr4etW6FbN5tIDRkC555rE6r4+EgHX//sLSnhgYwM3unT\nJ6wO4FXRxEmp4MJNnF7Gjp67FViMXXj3GPBT7Izi1xpj1tRinGGJ5sTJVHgonrMa19oVJO5ZbROm\no0ftN82IEZT3Hopr5FDcLRtHOlTlZ+9eO+Jt6VL7uGOnIb53Af3PqeCS891MHp1Eh5b1dMRiWZmd\nPPO11+xCwlddddzh0lLYtMkmUqtWwfLlkJFhE6lhw+yP97nnQps2EYpfhUQTJ6WCCzdxygAeB2YA\n5diZu9c4x14BGhljbqzNQMMRTYmT50AOhTO+xDN/Ke70NJJy0qmIa45n0Dk0mnKhnaG7Vy/QztxR\nJTvfw9uLi5i33MPG1S48m5pQWiqMGMF328CBp+G8kWlpcN11MHYsPPdclSPv8vJg5UqbRC1bZh87\ndLADPS+4AM4/306joKKHJk5KBRdu4lQEXGSMWez8+1JjzCLn2DjgbWNMxFcRjVjiVFJiJxVcudL+\n2b1yJWbvAQob9KC8x2BcY0bQcPIY4vt1PPWxqaA8Hti8GdLSDM9/lkfG2gaU7E2gUbcSfjC4nHOH\nws8vakbPHqKdosFmRHfdZXu+z5xpM8gQVFTYGqnPP7cd5pcvt1OMXXCBnXtz2LDwRxWq2qGJk1LB\nhZs47QTuM8bMduZ0mm6Medo5dgd2HqfTInEyFR6K566nbPZSWJFGwoF0GpbshJ49YejQ77fevfXb\nIIp4vYZl20rZt7EB6avdrFxpv/9TUuzH5e6Tx/nD3Fx9TkOaJWktYJX+/W+47z67fMvtt1d7qF1p\nKaxYAZ99Bp98Ykf4XXyxXS7x4ovtXFTq1NLESangwk2cpgMHjDEPi8gjwDTsrOEVwE3AHGPMdbUa\naRhqPXEqK/uuA4d31VqKZiwhMX87FXHNKW03AO/As2lw8bk0vmmkndZZRQWPBxZuKGHOilJWrDFk\nfO3m6OZEJMHLuUPcjB0Wxw9/CGefbSeLVGHYvh1+8hPbmemVV2r0879/P3z8MXz0EXz5JQwebC99\n9dV2Ak8VmNcYXH5J67Hlx8ienU233wefZTwQTZyUCi7cxKkb0M5pqmsA/B64BkgC5gI/M8bk1Gqk\nYahJ4lRx8Cgl/0mjfOEqmjfaiaSvs0OGunb9bvx1gbsHCZf/kAbdUmo5chWu8nL45htYu9Y2B61d\na1tN3S3KadyriD4DvYwc4ubKYYn076RDvWpVYSFMnWrnF/vgAzu8roaKimyT3vvvw5w5MGAATJxo\nE6kU/bX7TnpBAfdu384XAwceN8KuIL2AbyZ9w9Bvhlbrepo4KRWcToDp9drZ/jZu5Ng/lkL6BuIP\nfkN8eRYlSd0o79SPJlPOwz3iLDtJjdYkRQWv17BmVxlzV5eyPN3Dlo0uMrc0wLMvke5dXQwaZGsq\nBg+2XW+0uecUMQZeftku2fLGG7bTUi0pKYF58+Ddd22N1JAhcMMNMGECND7NB596jKH3ypW83rMn\nI3162ntKPCxpvoSRx0biSgi9yVkTJ6WCCzlxEpELqnNhY8zCsCKqRb6Jk/F6Kdu4j9JPVpNUtpO4\n3Zth40ZbPdGqFfTrR35FFzxdexE/biiJlwzElRR4Rl51auXl2Y9qw4bvt1XpHkpcHpr3KKVznwoG\n9hfOHxTHpUMa0qZpxKcQU8uW2Yzm/vtt/6da7k1fXGz7Q02fbtevvvJKuOUWGDny9J3N/NUDB/gk\nN5c5/fsftz+tVxp93+tL4/6hZ5eaOCkVXHUSJ9914oL9QhnnmImWCTBz+99A3N7NJObtAKCkWQ8S\nLhxM/Jgh0K8f9O0LzZpFOFIFkJFVzvz1pazYWMGmzYa929yU7WxI+ZEG9OljK/wqt959vbRvq522\no9qePXD55XaY3Isv1tmcDQcPwowZtoKruNiuTTxlyuk3V1Sxx0PnFStYMmgQZ/jUjG/8yUZaT2hN\nyrWht21q4qRUcNVNnPKAWc5WWNX5xpgvaxxdDYmIyb1yGu5hA0i4eAjx/TrqorcRZgzs2wdbttih\n/5Xb15u9HC3y0qhLKak9yunRyzCoj5sLByUwsncC7oin4Sos+fkwaZLNaN5/v07bTI2xIyNfew1m\nzbKj8u6+246SPF1qoX61cyf5Hg9/8Vn8d9e0XWCg6xNdQ76OJk5KBVedxOk87Ii5q7G1Sv8B3oyG\nJrlgomkCzNPNgUMe5m4sYfU35Wzebtid4eLwzjhK9yTSppmb3r3tfJ+9e9vtjF6G9m3B5dL/q+sd\njwcefNB2TJo71w6wqGO5ubYG6uWXba529902fwuyHm69sb+khKf37uWlM874bl/pwVJEhPiU0AdD\naOKkVHDV7hwuIonAVcBkYCyQiZ1BfLoxZnOtR1gDmjjVre0Hy1m5uRxzIIkdO+yI9O3bYccOKPcY\nvO2LSO5SQaduXnr3EIb2iWPcgAQ6tz7dptlWALz0Ejz9tE2gBgw4Jbf0em2u9sILtp/cz38Ot92m\nrfMno4mTUsHVaFSdiLQFrgNuBPoBrxhj7q6l2GpME6eaKSuz67Lt3g1bM7y8lX6MA3tcZO+Oo2hf\nPMYrNOlUxmUDkujeHXr04LvH5OTTp3lEVcO779rqn/ffh/POO6W3Xr8e/vhHm0hNmQL33gvt2p3S\nEGKGJk5KBVfTqaxzgN3O1hfQQd8xpKzCsCqjlFXbK9i4o4LtOw0H9rpol9uUXbuEQ4fsF0uXLtCl\ni5DYAi6+2DCop+G8voaebeNwuXR6BlUNEyfaUaxXXw1/+5sdDneKDBxoO5Hv2gXPP2/HhkyaBI88\nAu3bn7IwlFIxLtwJMIdjm+omAAnAbGA68JkxxlurEdbA6Vzj5PUadh2uYO3OchKyE/n2gIt9+2zH\n7P37Yfduw679Blfzchq3L6dVpwo6dDZ07ypMOrMpPbq56NBBV4lRdWTNGjvi7ne/s/MIRMChQ7YG\n6vXXYfJkePhhSE2NSChRR2uclAquOp3Du2OTpRuALsBX2GTpPWNMQV0FWBP1OXE6duz7JKgyIZq1\nKY/M/UJhVhxlWfHgNiSklDG8eyLdOrvo2NGuSt+xI3TuDG07eGncUEcZqgjZtg3GjIFHH7UdjyLk\n4EH4wx/gzTfhpz+FX/wCWreOWDi1KtAyLKHQxEmp4MKZjuAD4F/AnqrON8bsrHF0NRRriZMxNiHK\nzLTbR1vz2bqvgoMHheyDwrEsF0WH42iQm4ggdOzId1uHDnC4eT7tO0L/Lm4G/yCeDi21ukhFuYwM\nuOACeOghuOuuiIZy4AA89RS8844dBHjvvZCYGNGQauTfBw+yKj+fF3r0IHdBLpn/yKTv231Deq0m\nTkoFV93EqdJJXxQtE2BGQ+JUVgaHD0PanmI2f1vBnm897PvWkJkJOVku2uQ15miWm8xMcLttv6K2\nbSG7SQGJbcpJTYVO7V107+CiVwc3Z3dNJLm5Sztfq/ph1y6bPN17rx32FmHbttlap3Xr7CDAa6+N\nzYEO+0pKGLh6NXuHDUN2lPL1pV9zTsY5Ib1WEyelgqtO4nRTdS5sjHkzrIhqUV0lTl4vHDkCa/eW\n8M235ezJ9LI/y5CVZcg+LLQrakxJbhyHDtl+FAUFtuq/rFkJpnk5TZM9tE6Fdu0MndoJY7o1ok/n\nBrRtq2tuqdPU3r02ebr9dnjggUhHA8CXX9oVY9xueO45GD480hFV3/gNG7giOZlbWqewpMkShmcP\nx93o5H/TauKkVHCn/SK/Xq9tHsvJgTX7i9lysJzMw16ysg3ZuXAkR2hX2gjvsQYcPmwToexsaNIE\nTPMyvM3LaJLsoXmyoVVrQ2qKMLpLEn06xNOmjV32oXlz0AnLlTqJ/fth1Chb63R3dMxq4vXCzJm2\nBmrcOHjmmdjq//RpTg7Tdu9m1ZAhrBqwil7/7EWTIU1O+jpNnJQKrt53grnhmaMczjbk5MLRHKHg\niIvk4iQqjsWRkwNHj0KjRnaEdEljF2VNoHELaNYSWrSCPn0Mwzsaerflu0QoORni4wHinU0pVWMd\nOsCCBXZ+p8aN4eabIx0RLhdcfz1ccQU89phd2vLJJ+08ULHwx9CFLVty5/btrM7LI6l3EoWbC0NK\nnJRSwdX7GqeeE47QvIWhVStokyy0be1iaPtEeraNp1UraNlSh9wrFVW2bLHNdi+8ABMmRDqa43z9\nNdxxB1RUwKuvwqBBkY7o5F7cv5828fEMfbUIU25CWrNOa5yUCq7eJ071+f0pVW+lp8OFF9oF5y69\nNNLRHMfrtVMX/PKXdgqqxx6LjdF33lIvEi9ICD3dNXFSKrgYqGxWSp12BgyA2bNtc93ixZGO5jgu\nl02YNmyw6zIOGQIrV0Y6qpNzJbhCSpqUUlWLycRJRO4UkZ0iUiwiq0VkRKRjUkrVsnPOgbfessuz\nfPNNpKM5QZs28N57MG2a7QP18MNQUhLpqJRSdS3mEicRuQb4M/AkMBBYBnwqIh0iGphSqvaNHQvP\nPmub6779NtLRnEAErrnGtixu22Zrn9asiXRUSqm6FHOJE3Af8E9jzD+NMVuNMfcAmcAddXXDRYsW\n1dWl64TGW7c03rp1QryTJ9slWS67DPLzIxJTVRYtWkRKCrz/Pvz613DJJXbeJ2/UrNp5vFj7eVAq\n2sRU4iQiDYAhwGd+h+YD59bVfWPtPxqNt25pvHUrYLwPPww//KFttisvP+UxVaUyXhGYNAnS0mwT\n3mWXQVZWZGPzVeH1cuH6dD79dEGkQ1EqpsVU4gQkA27A/7+jLEDXNVeqvhKBF1+0E6hNnWoXdoxS\nXbvCV1/ZZrtBg2D+/EhHZMW5XLgzy0lLOxTpUJSKaTqDkVIqNsTFwdtv2wkyR42Cpk0jHZG1desJ\nHZsaYDthjuncnxvH30evxvtJdEW+pqyi3MOOogpGNF0CQJxXaOR240qMtb+hlYqcmJrHyWmqKwKu\nNcbM8tn/ItDXGDPa7/zYeXNKKRVFdB4npQKLqRonY0y5iKwBxgGzfA6NA94LcL7+4iullFKq1sRU\n4uR4DpguIquApdjRdG2B1yIalVJKKaXqvZhLnIwx74pIS+BX2IRpI3CJMWZfZCNTSimlVH0XU32c\nlFJKKaUiSYdSVCGWlnYRkd+IiNdvi5qplkVkpIjMFpH9Tmw3BjjnMRE5ICJFIvKFiPSJRKxOLFXG\nKyJvBCjvZRGK9WERWSkix0TkkIjMEZG+Ac6LivINJd4oK987RSTdifeYiCwTkUv9zomKsvWJp8qY\no6l8lYo1mjgFEaNLu2wBUrBzWqUC/SMbznEaAxuAe7AjI48jIr/Azgp/F3AWcAj4TEQancogfVQZ\nr+Mzji/vS4OcV9fOA14EhgGjgQpggYg0rzwhysr3pPE6oqV89wEPAYOwE/AuBP4rImdC1JVtpSpj\ndkRL+SoVW4wxugXYgBXAq377tgG/i3RsQeL9DfB1pOMIMdZ84Ea/fd8Cv/R5ngjkAf8TpfG+AcyJ\ndGxB4m2ETUYui5HyDRRv1JavE19OZdlFc9lWEXNUl69uukXzpjVOAURqaZda8AOnuWCniMwUka6R\nDigUTpyp+JS3MaYE+IroLu8RIpIlIltF5G8i0jrSATmaYmuTj0BMlO9x8fqIuvIVEZeIXItNjr6M\ngbI9IWafQ1FXvkrFAk2cAovFpV1WADcDFwFTsHEuE5EWkQwqRKmAIbbK+1PgRuAC4H+BocDnTtId\naS8Aa4HlzvNoL1//eCHKyldE+olIPlCKnfpkojFmG1FctlXEDFFWvkrFkpibjkAFZoyZ5/tcRFYA\nu4CbsH21VC0yxrzr83STiKwF9gCXAf+NTFQgIs9hazqGG2OifshssHijsHy3AAOAZsDVwNsiMioC\ncVRHwJiNMWuisHyVihla4xRYNuDBdpz0lQIcPPXhVJ8xpgjYBPSIdCwhOAgIsV3emcB+IljeIvI8\ncA0w2hizx+dQVJZvFfGeINLla4ypMMbsNMasM8b8ClvDexdRWrZQZcyBzo34z69SsUITpwCMMeVA\n5dIuvsZhZyuPeiKSCPQCMiMdy8kYY3Zhv2S+K28n/pHETnm3BtoTofIWkRf4PgnZ7nssGsu3qniD\nnB/R8g3ABbijsWyr4MJ2QThBFJavUlFLm+qCi6mlXUTkj8CHwF7sX7uPAknAm5GMq5IzNLs79q9z\nF9BJRAYAucbO+v5n4GER2QpsB36NHc02M9ridbbHsOslZgJdgaewX6D/iUCsLwE3AOOBYyJSWftR\nYIwpdP4dNeV7snidsn+M6Cnfp4GPsUP8mwDXA+cDFzunRE3ZVqoq5mgrX6ViTqSH9UXzBtwO7ASK\ngVXYfhgRjytIrDOxVe0l2P8s3wN6RToun/jOB7zYJlDf7Z8+50wDDmDnTfoC6BON8WJHJ83FftGU\nYPuSvQ60j1CsgeL0ANP8zouK8j1ZvFFYvm84MRQ7Mc0HxkZj2YYSc7SVr266xdqmS64opZRSSoVI\n+zgppZRSSoVIEyellFJKqRBp4qSUUkopFSJNnJRSSimlQqSJk1JKKaVUiDRxUkoppZQKkSZOSiml\nlFIh0sRJnUBEvCFsOyMdZySJyBgReTTScQQjIiNFJE9EWvntjxeRe0RkmYgcFZESEckQkb+JSL8w\n7nNQRF6uxvk9nZ+fidW9VwjX7iAihSIysLavrZRSlXTJFRXIOX7P/wusB36DXYIEoPSURhR9xgL3\nA09EOpAgngP+bIzJqdwhIk2BeUA/4GXgt0AhcAYw2TnWvpr3qe4MuruxP18nXZ+uuowx+0XkdeB5\nYHRtX18ppUATJxWAMWal73MRKQWyjTGrIhRSnRMRFyDGGE+oL6mjOBoYu8h0Ta4xFhgIXOl36GXg\nTOzSQet99i8GXheRK2py31AYY0qBlSc9MXyvAJtE5Oz6/POqlIocbapTNSYiY0XkCxHJd7aPRKSX\n3zkrROQzEblcRL4WkSIRWSkig53mo+dFJEtEDjvNRgk+r61s3rlVRP7inFMgIv8VkQ4B4rnLuUex\nc81XndqWyuMJzvUeFZFfi8hubA1adxFJEpEXRGSTc48DIvIfEenu8/qngYcAt0/TZZFz7GLn+VC/\nmG539rfx2ZcpIn8XkakislVEyoALnGONReRPIrJbREpFZIeIPBjiRzIFWGyMOeBzr87AtcCLfknT\nd4wxc/xivkZE0pzmr1wRmSki7aq6sYi0E5EZIvKt0wx4wPmcmjnHT2iqc342PglwreOaAU92bec9\nbAbSgdtOUkZKKRUWrXFSNSIiVwHvYldanwS4gUeAr0SkvzEmyznVAH2wzUOPYxOVPwGzsTUey254\nlAAABllJREFUecAN2BqR3wPfYldw9/UbbG3FZKCdc94nIjLAOIsuisifsYszPwcsBDpiV37vjV24\n19dUYAtwL3ax00NAEnYR1MexK8cnA3cDy0XkDGPMEeBF5/6TgOHY2ievz/sM1HwVbP8lwFnAr4Ec\nYIeINAA+Bzo75bXFuc+TItLUGBO0b5WICDAOeMnv0AVOnB8Ge63fdX6O/Xxewy5g2xzbLLlQRAYa\nY0qCvPQdoAXwc+xnmOrEkwgcC/KaYM19/vtDvfZiYHyw96aUUjUS6VWGdYv+Dbt6+vQA+wXYC3zo\nt785cAR4ymffcuzK8e189k3AJhxz/F7/MbDJ53lP57zVfudd4Oyf5Dw/A/AA9/udN9o570LneYLz\nfCfgPsl7dwGNsavMT/XZ/zRQFuD8i5wYhvrtn+rsb+OzLxP7hd/C79z/ASqAs/z2V/ZJalpFvD2c\n9zbRb/805/4dQ/i8mwEFwF/99ncHyoHb/N7Dyz7PS4EpVVy7p398zs/GJwHOrda1fc6b4rzXlEj/\n7uimm271b9OmOlUTfYEOwAwRcVdu2C/dVcB5fudvMsZ86/N8i/M4z++8LdiaIn/v+z4xxiwEsoFh\nzq6LnMe3/OJZgv3S9Y/nExOgT5OIXO80Ix7FJjB5QDz2S7+2LTa2FsvXRcA2YJ3f+/gMaAgM9b+I\njxTnMaeKc05mpHMf/3LcjU02/cvR1xrgEae5tE8NYqjJtbOdx9Ravr9SSmnipGqksr/ODGxNROVW\nBowBWvmd758glFWxPzHA/bKC7KscCdYaWwt2wC+eUmzi4x9Ppv/FRGQC8C9gLXANNkk5C5s8BYqp\npk6IAVuuvTj+PZQDX2Kbr/zfRyj2OY+dQzi3DbYcl3Li59r9JPe/EpiLba7dICL7ROSXYcR7qq+t\nlFIh0T5OqiYqazXuB74KcDxYP5hwpQTZ94VPPAbbl6kowLmH/Z4H6ltzDbDBGHN75Q4RaYhtvgpF\n5XuO99sfLNkIFEMOttbtegKP3qtqDq3K5NL/fgude12OrYGrSuXnei2wI8DxvGAvNMYcAu4E7nQG\nCNwCPCUimcaYN4O8rAS/8nL6ajUP89rJzuPBYHEqpVS4NHFSNbEB20m3tzHmz6fgfhOwHcIBOwkl\n9ktymbNrPjY56GiMmRnmPZKwzXO+bglwXil2VJ3br7lvDzbZ6cfxCcpl1YhhLra57qgxZlc1Xgc2\n0TmK7WT/buVOY8weEZkJ3C0i7xhj1vq/UETGG2NmY5PgYqCbMeZd//NCZYzZAvxCRO7Clkcwe4Cx\nIiLGmMpEchy2L1o41z4TOGC+H5iglFK1RhMnFTZjjFdE7gbeFZFG2JF1Odi+JcOBrcaYkGeVDkGy\niMwC/oEd1fYUNnl7x4lns4i8APxNRPpjR1eVYpunxgF/McasOMk95gLPi8jvsYnYD7Gj9PL9zvvG\neXxIRBYAFcaYdcaY3SKSBkwTkTwgF7jZiTdUbwA3AotE5E/ARmwS0QNbY3ShT4JxHGOMEZHPsf2U\n/N0F/MC57qvYWqgioBt2pGJPYLYx5ojTBPasiLTH9kHLxzaJjsb2DfvA/+LOVAuzgbeArdgO2hOw\nTZzzq3i/bzv3f11EZmCbA+/B9pUL59ojsP3BlFKq1mnipEIRbCg9xpj/ishobL+Tf2A7FWdiR0r5\nT3QYbJh+sHv6exwYAEx37vMZcLdvEmGMuV9ENgB3YL98PdiRf59jRwee7D29CLTFJi53ASuAS517\n+Z4/C/g7cB92mH4ZtrYKYCJ2IsaXsInJ37D9k14M8B5PiMEYU+bUpj2CbZrqjE0idgAfBUuafPwd\nO01DB2PMfp/r5onIKGwieB12pF8Ctv/T5877rTz3r2Lnt/pfbFLjxvYd+xKbrAZ6DwXA1851O2HL\nfjN2BN1nfq/xfb9zReQe7DQD1wCrsc2E86p7bRHpjZ38846TlJFSSoVFTv5/sFKRJSI9sV+SNxhj\n3op0PLFARFYD84wxv4p0LKeSU+M40BjjP2eXUkrVCh1Vp1T9dB+2P1M4I/BikthZ5G/FTmiqlFJ1\nQpvqVKzQqtFqMMYsJvSRgPWC0yzZONJxKKXqN22qU0oppZQKkTbVKaWUUkqFSBMnpZRSSqkQaeKk\nlFJKKRUiTZyUUkoppUKkiZNSSimlVIg0cVJKKaWUCtH/A9pNR1pyObxxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0545f5c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#R3: match Khangaonkar max temps for diatoms (12 C) and dinoflagellates (18 C):\n",
    "# simultaneously nudge down the flagellate max growth rate? not in first test\n",
    "R1=0.600E-04\n",
    "R2=0.250E-04\n",
    "R2b=0.20E-04\n",
    "tr1=14 # 12 C\n",
    "tr2=13 # 18 C\n",
    "tm1=26 \n",
    "tm2=31\n",
    "R3_1=[R1*1.0*np.exp(0.07 * (TT - 20)) * min(max((tm1 - TT), 0.0),tr1) / (tr1 + 1e-10) for TT in T]\n",
    "R3_2=[R2*1.0*np.exp(0.07 * (TT - 20)) * min(max((tm2 - TT), 0.0),tr2) / (tr2 + 1e-10) for TT in T]\n",
    "plt.plot(T,R1*np.array(R)*24*3600,'m--',label='old diatoms')\n",
    "plt.plot(T,R2*np.array(R)*24*3600,'c--',label='old sm. phyt.')\n",
    "plt.plot(T,np.array(R3_1)*24*3600,'r-',label='new diatoms')\n",
    "plt.plot(T,np.array(R3_2)*24*3600,'b-',label='new sm. phyt.')\n",
    "#plt.plot((tm1-tr1,tm1-tr1),(0,.0001),'r--')\n",
    "#plt.plot((tm2-tr2,tm2-tr2),(0,.0001),'b--')\n",
    "#plt.plot((30,30),(0,.00013),'k--')\n",
    "plt.ylabel('Max Growth Rate (day$^{-1}$)')\n",
    "plt.xlabel('Temperature (Celsius)')\n",
    "plt.legend(bbox_to_anchor=(1.6,1))\n",
    "plt.xlim((-2,36))\n",
    "ii1=np.argmax(R3_1)\n",
    "ii2=np.argmax(R3_2)\n",
    "print(T[ii1],T[ii2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# plt.plot(T,R)\n",
    "plt.xlim(20,40)\n",
    "plt.plot(T,R1)\n",
    "plt.ylim(0,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 40)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x/wVwZVXd1nnuA8CrqupFXX9//Qov8+WqeuEKr20LSGoBvydgtgzbAvJuoJI2xEzA7PBu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\nrHLiigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0545f34e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(T,[ min(max((35 - TT), 0.0),5) / (5 + 1e-10) for TT in T])\n",
    "plt.xlim(20,40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Light Response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "I=np.arange(0,200)\n",
    "Iopt=42\n",
    "L42=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06\n",
    "Iopt=37.000\n",
    "L37=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06\n",
    "Iopt=10.000\n",
    "L10=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06\n",
    "Iopt=47.000\n",
    "L47=(1-np.exp(-I/(.33*Iopt)))*np.exp(-I/(30*Iopt))*1.06"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f59618dfdd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BY8YUFwdef90acFbUdj15stW0EBHh3suqsxcfHMyLwcFMadOGlUePMicjg4s3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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5960ef4160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(I,L42,'b-',label='diatoms')\n",
    "plt.plot(I,L37,'r-',label='M.rubrum')\n",
    "plt.plot(I,L10,'c-',label='flags')\n",
    "plt.plot(I,L47,'b--',label='47')\n",
    "plt.legend()\n",
    "plt.xlabel('W/m$^2$')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "                  ! LIGHT\n",
    "                  zz_plank_growth_light(ji,jj,jk) = \n",
    "                       (1.0 - np.exp(-I / (0.33_Iopt)) ) *(np.exp(-I / (30. * Iopt))) * 1.06_wp\n",
    "                  zz_Uc(ji,jj,jk) = (1.0_wp - zz_rate_gamma) * zz_plank_growth_light(ji,jj,jk)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mesozooplankton\n",
    "      REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakval !uM N magnitude of mesozooplankton summer concentration peaks\n",
    "      REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakpos ! year-day times of mesozooplankton summer concentration peaks\n",
    "      REAL(wp), dimension (1:3) :: zz_rate_mesozoo_sumpeakwid ! year-days widths of mesozooplankton summer concentration peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#&nampismezo     !   parameters for microzooplankton\n",
    "zz_rate_mesozoo_winterconc       = 0.369       #uM N mesozooplankton background concentration\n",
    "zz_rate_mesozoo_winterconc2       = 0.38       #uM N mesozooplankton background concentration\n",
    "zz_rate_mesozoo_summerconc       = 1.0         # uM N mesozooplankton relative summer concentration\n",
    "zz_rate_mesozoo_sumpeakval = np.expand_dims(np.array((0.339,   0.0,     0.0)) ,0)     #uM N magnitude of mesozooplankton summer concentration peaks\n",
    "zz_rate_mesozoo_sumpeakval2 = np.expand_dims(np.array((0.45,   0.3,     0.0)) ,0)     #uM N magnitude of mesozooplankton summer concentration peaks\n",
    "zz_rate_mesozoo_sumpeakpos = np.expand_dims(np.array((217.000, 120.000, 300.000)),0)  # year-day times of mesozooplankton summer concentration peaks\n",
    "zz_rate_mesozoo_sumpeakwid = np.expand_dims(np.array((114.0,   51.600,  40.000)),0)   # year-days widths of mesozooplankton summer concentration peaks,0)\n",
    "zz_rate_mesozoo_sumpeakwid2 = np.expand_dims(np.array((100.0,   10.00,  40.000)),0)   # year-days widths of mesozooplankton summer concentration peaks,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zz_day=np.arange(1,367) # in model, nday_year starts at 1 on jan 1\n",
    "dts=[dt.datetime(2014,12,31)+dt.timedelta(days=float(ii)) for ii in zz_day]\n",
    "zz_day=np.expand_dims(zz_day,1)\n",
    "zz_MesZoBar = zz_rate_mesozoo_winterconc + \\\n",
    "        zz_rate_mesozoo_summerconc*(np.sum ( zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid**2),1) \\\n",
    "                    + np.sum ( zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) \\\n",
    "                    + np.sum ( zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) )\n",
    "zz_MesZoBar2 = zz_rate_mesozoo_winterconc + \\\n",
    "        zz_rate_mesozoo_summerconc*(np.sum ( 2*zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid**2),1) \\\n",
    "                    + np.sum ( 2*zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) \\\n",
    "                    + np.sum ( 2*zz_rate_mesozoo_sumpeakval * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid**2),1) )\n",
    "zz_MesZoBar3 = zz_rate_mesozoo_winterconc2 + \\\n",
    "        zz_rate_mesozoo_summerconc*(np.sum ( zz_rate_mesozoo_sumpeakval2 * \\\n",
    "                                            np.exp(-(zz_day-zz_rate_mesozoo_sumpeakpos)**2/zz_rate_mesozoo_sumpeakwid2**2),1) \\\n",
    "                    + np.sum ( zz_rate_mesozoo_sumpeakval2 * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos-365.25)**2/zz_rate_mesozoo_sumpeakwid2**2),1) \\\n",
    "                    + np.sum ( zz_rate_mesozoo_sumpeakval2 * \\\n",
    "                                            np.exp( -(zz_day-zz_rate_mesozoo_sumpeakpos+365.25)**2/zz_rate_mesozoo_sumpeakwid2**2),1) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f36170a4cf8>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJEmqaAsWhOPoTzsNhg2Dd9+1nJIy0cCB8OmnYdXkIYfABRfA8uVJp5Ik/QQLKkmSVPUV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0VkLDBURC7EdJU7sJWP30u0tbu3szPwjYSsv6OqX4vIOZgXzJuY5+PJ2Pj2ylKfTVCVNGh7i+8b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\nywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f36176a7c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(1,1,figsize=(20,6))\n",
    "ax.plot(dts,zz_MesZoBar,'b-')\n",
    "ax.plot(dts,zz_MesZoBar2,'r-')\n",
    "ax.plot(dts,zz_MesZoBar3,'c--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.43942235,  0.43834659,  0.43730266,  0.43629065,  0.43531065,\n",
       "        0.43436275,  0.43344703,  0.43256358,  0.43171247,  0.43089376,\n",
       "        0.43010753,  0.42935383,  0.42863271,  0.42794425,  0.42728847,\n",
       "        0.42666543,  0.42607518,  0.42551775,  0.42499317,  0.42450149,\n",
       "        0.42404273,  0.42361693,  0.4232241 ,  0.42286428,  0.42253747,\n",
       "        0.42224372,  0.42198301,  0.42175539,  0.42156085,  0.4213994 ,\n",
       "        0.42127107,  0.42117584,  0.42111374,  0.42108476,  0.4210889 ,\n",
       "        0.42112616,  0.42119655,  0.42130005,  0.42143666,  0.42160638,\n",
       "        0.42180919,  0.42204509,  0.42231406,  0.42261608,  0.42295114,\n",
       "        0.42331922,  0.42372029,  0.42415434,  0.42462133,  0.42512123,\n",
       "        0.42565403,  0.42621967,  0.42681812,  0.42744935,  0.4281133 ,\n",
       "        0.42880993,  0.4295392 ,  0.43030104,  0.4310954 ,  0.43192221,\n",
       "        0.43278142,  0.43367294,  0.43459671,  0.43555264,  0.43654065,\n",
       "        0.43756066,  0.43861255,  0.43969625,  0.44081163,  0.44195859,\n",
       "        0.443137  ,  0.44434675,  0.44558771,  0.44685972,  0.44816266,\n",
       "        0.44949636,  0.45086067,  0.45225542,  0.45368044,  0.45513554,\n",
       "        0.45662053,  0.4581352 ,  0.45967936,  0.46125278,  0.46285524,\n",
       "        0.46448649,  0.4661463 ,  0.46783441,  0.46955054,  0.47129443,\n",
       "        0.47306579,  0.47486432,  0.47668972,  0.47854166,  0.48041981,\n",
       "        0.48232384,  0.48425339,  0.4862081 ,  0.48818759,  0.49019147,\n",
       "        0.49221935,  0.49427081,  0.49634543,  0.49844278,  0.5005624 ,\n",
       "        0.50270384,  0.50486662,  0.50705026,  0.50925426,  0.5114781 ,\n",
       "        0.51372127,  0.51598323,  0.51826343,  0.5205613 ,  0.52287629,\n",
       "        0.52520779,  0.52755521,  0.52991794,  0.53229535,  0.53468681,\n",
       "        0.53709167,  0.53950927,  0.54193894,  0.54437999,  0.54683172,\n",
       "        0.54929344,  0.55176441,  0.55424392,  0.55673121,  0.55922554,\n",
       "        0.56172615,  0.56423226,  0.56674308,  0.56925784,  0.57177572,\n",
       "        0.57429592,  0.57681761,  0.57933997,  0.58186215,  0.58438333,\n",
       "        0.58690263,  0.58941922,  0.59193221,  0.59444073,  0.59694392,\n",
       "        0.59944088,  0.60193072,  0.60441256,  0.60688548,  0.60934861,\n",
       "        0.61180102,  0.6142418 ,  0.61667006,  0.61908488,  0.62148533,\n",
       "        0.62387052,  0.62623952,  0.62859142,  0.63092531,  0.63324027,\n",
       "        0.63553539,  0.63780976,  0.64006248,  0.64229264,  0.64449935,\n",
       "        0.6466817 ,  0.64883882,  0.65096982,  0.65307382,  0.65514994,\n",
       "        0.65719734,  0.65921514,  0.6612025 ,  0.66315858,  0.66508256,\n",
       "        0.66697362,  0.66883094,  0.67065372,  0.67244119,  0.67419258,\n",
       "        0.6759071 ,  0.67758403,  0.67922263,  0.68082217,  0.68238196,\n",
       "        0.68390129,  0.68537951,  0.68681595,  0.68820996,  0.68956094,\n",
       "        0.69086826,  0.69213134,  0.69334962,  0.69452253,  0.69564956,\n",
       "        0.69673018,  0.69776391,  0.69875027,  0.69968881,  0.70057911,\n",
       "        0.70142075,  0.70221335,  0.70295655,  0.70364999,  0.70429337,\n",
       "        0.70488637,  0.70542874,  0.70592022,  0.70636058,  0.70674961,\n",
       "        0.70708714,  0.707373  ,  0.70760708,  0.70778925,  0.70791943,\n",
       "        0.70799756,  0.70802361,  0.70799756,  0.70791943,  0.70778925,\n",
       "        0.70760708,  0.707373  ,  0.70708714,  0.70674961,  0.70636058,\n",
       "        0.70592022,  0.70542874,  0.70488637,  0.70429337,  0.70364999,\n",
       "        0.70295655,  0.70221335,  0.70142075,  0.70057911,  0.69968881,\n",
       "        0.69875027,  0.69776391,  0.69673018,  0.69564956,  0.69452253,\n",
       "        0.69334962,  0.69213134,  0.69086826,  0.68956094,  0.68820996,\n",
       "        0.68681595,  0.68537951,  0.68390129,  0.68238196,  0.68082217,\n",
       "        0.67922263,  0.67758403,  0.6759071 ,  0.67419258,  0.67244119,\n",
       "        0.67065372,  0.66883094,  0.66697362,  0.66508256,  0.66315858,\n",
       "        0.6612025 ,  0.65921514,  0.65719734,  0.65514994,  0.65307382,\n",
       "        0.65096982,  0.64883882,  0.6466817 ,  0.64449935,  0.64229264,\n",
       "        0.64006248,  0.63780976,  0.63553539,  0.63324027,  0.63092531,\n",
       "        0.62859142,  0.62623952,  0.62387052,  0.62148533,  0.61908488,\n",
       "        0.61667006,  0.6142418 ,  0.61180102,  0.60934861,  0.60688548,\n",
       "        0.60441256,  0.60193072,  0.59944088,  0.59694392,  0.59444073,\n",
       "        0.59193221,  0.58941922,  0.58690263,  0.58438333,  0.58186215,\n",
       "        0.57933997,  0.57681761,  0.57429592,  0.57177572,  0.56925784,\n",
       "        0.56674308,  0.56423226,  0.56172615,  0.55922554,  0.55673121,\n",
       "        0.55424392,  0.55176441,  0.54929344,  0.54683172,  0.54437999,\n",
       "        0.54193894,  0.53950927,  0.53709167,  0.53468681,  0.53229535,\n",
       "        0.52991794,  0.52755521,  0.52520779,  0.52287629,  0.5205613 ,\n",
       "        0.51826343,  0.51598323,  0.51372127,  0.5114781 ,  0.50925426,\n",
       "        0.50705026,  0.50486662,  0.50270384,  0.5005624 ,  0.49844278,\n",
       "        0.49634543,  0.49427081,  0.49221935,  0.49019147,  0.48818759,\n",
       "        0.4862081 ,  0.48425339,  0.48232384,  0.48041981,  0.47854166,\n",
       "        0.47668972,  0.47486432,  0.47306579,  0.47129443,  0.46955054,\n",
       "        0.46783441,  0.4661463 ,  0.46448649,  0.46285524,  0.46125278,\n",
       "        0.45967936,  0.4581352 ,  0.45662053,  0.45513554,  0.45368044,\n",
       "        0.45225542,  0.45086067,  0.44949636,  0.44816266,  0.44685972,\n",
       "        0.44558771,  0.44434675,  0.443137  ,  0.44195859,  0.44081163,\n",
       "        0.43969625])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zz_MesZoBar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zz_rate_kapa_diat         = 1.0\n",
    "zz_rate_kapa_myri         = 0.5\n",
    "zz_rate_kapa_nano         = 0.300\n",
    "zz_rate_k_diat            = 2.0  \n",
    "zz_rate_k_myri            = 0.5 \n",
    "zz_rate_k_nano            = 0.1   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def OUP(zz_NO,zz_NH,zz_rate_kapa,zz_rate_k):\n",
    "    Oup_cell = zz_NO * zz_rate_kapa  / (zz_rate_k + zz_NO * zz_rate_kapa + zz_NH)\n",
    "    return Oup_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def HUP(zz_NO,zz_NH,zz_rate_kapa,zz_rate_k):\n",
    "    Hup_cell = zz_NH / (zz_rate_k + zz_NO * zz_rate_kapa + zz_NH) \n",
    "    return Hup_cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22ba55fe48>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GwFjy8qTo/+9/MponKgpUQt1pastqKd5cTOGfhRRtKqIyuZL+0/vjOtsVvztlGKYqX6zo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\npZLU/wOHL+365+RwSAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22ba5b8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NH=np.arange(0,20,.1)\n",
    "NO=5\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b-')\n",
    "plt.plot(NH,HUP(NO,NH,.9,zz_rate_k_diat),'-',color='purple')\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m-')\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r-')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b--')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m--')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f22ba56c160>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q8RJgXrgLtNZ7GmMA/wPuBWzAGmBBZzsaE9xuM8P3qqssqfx1QLNv8T6+ueEbkvOTmfzM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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f22ba642e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NH=np.arange(0,20,.1)\n",
    "NO=1\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b-')\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m-')\n",
    "plt.plot(NH,HUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r-')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_diat,zz_rate_k_diat),'b--')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_myri,zz_rate_k_myri),'m--')\n",
    "plt.plot(NH,OUP(NO,NH,zz_rate_kapa_nano,zz_rate_k_nano),'r--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}