{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section notes: Linear regression\n",
    "Notes by Laila Barakat Norford (2026), Josh Park (2024), Grace H. Zhang (2022), Xinyi Cai (2021), and Jacob Quinn Shenker (2020). These notes were heavily inspired by [Hogg, Bovy & Lang (2010)](https://arxiv.org/abs/1008.4686), which I cannot recommend highly enough."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import scipy.optimize as optimize\n",
    "from scipy.special import logsumexp\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_LABEL = \"Concentration (ng/uL)\"\n",
    "Y_LABEL = \"A260\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is a common occurence in a biology lab to need to measure the concentration of nucleic acids in a sample. Many cloning protocols (e.g., PCR) and sequencing reactions (e.g., RNA-Seq) only work optimally with specific concentrations of nucleic acids, so these protocols all involve measuring the current concentration of your sample and diluting accordingly. In most labs, the instrument used to measure the concentration of nucleic acids is a spectrophotometer. Since nucleic acids absorb ultraviolet light at 280nm, the spectrophotometer can infer the amount of DNA or RNA present by shining 280nm light through the sample and measuring how much is absorbed. The quantity that the instrument outputs is the absorbance:\n",
    "\n",
    "$$\\mathrm{absorbance}=-\\log_{10}\\frac{\\mathrm{intensity}_\\mathrm{sample}}{\\mathrm{intensity}_\\mathrm{blank}}$$ where $\\mathrm{intensity}_\\mathrm{blank}$ is the amount of transmitted light for a sample without any DNA.\n",
    "\n",
    "From Beer's law (remember high school chemistry?), we know that the concentration of the sample should be proportional to the absorbance, so we can say that $\\mathrm{concentration}=\\mathrm{factor}\\cdot\\mathrm{absorbance}$ for some calibration factor. Note that absorbance is unitless and the calibration factor has units of ng/µL. The spectrophotometer manual states that double-stranded DNA should have a calibration factor of 50 ng/µL.\n",
    "\n",
    "Because the ThermoFisher NanoDrop is the “Kleenex” of spectrophotometers, taking a measurement is often called “nanodropping” your samples. The NanoDrop is also known for two other features: being extremely expensive ($10k) and producing absolutely garbage data (biologists often complain that the NanoDrop is nothing more than a “really expensive random number generator”). As a thrifty scientist, you decide to save your lab some money by buying an old NanoDrop off ebay. You don't trust the calibration factor listed in the manual, so you want to measure it yourself and ensure that the instrument can accurately measure DNA concentrations.\n",
    "\n",
    "You take DNA of a known concentration and you make various dilutions of it. You end up with samples of known concentrations spanning a wide range of concentrations. For each sample, you measure absorbance at 260nm (which we will call “A260”). You take a look at your data and it looks... very noisy. You're disheartened, but you don't give up. You email your data to your colleagues Sofía, Li, Fatma, and Nozomi and ask them to try measuring the calibration factor.\n",
    "\n",
    "**Below is the code used to generate your absorbance data. Feel free to read it to understand how these synthetic data are generated, but otherwise you can skip it.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_random_data(num_points, num_replicates, m=1/50, b=0, x_min=5, x_max=800,\n",
    "                         mistake_probabilities=(0.8, 0.0, 0.2),\n",
    "                         multiplicative_error=0, additive_error=1):\n",
    "    # an array of with values 0/1/2\n",
    "    # 0: normal measurement\n",
    "    # 1: erroneous measurement (uniformly random)\n",
    "    # 2: sample is empty\n",
    "    measurement_type = np.random.choice(3, p=mistake_probabilities, size=(num_points, num_replicates))\n",
    "    # normal measurements\n",
    "    xs = np.random.uniform(x_min, x_max, size=num_points)\n",
    "    xs_with_replicates = np.repeat(xs[:,np.newaxis], num_replicates, axis=1)\n",
    "    ys_normal_without_noise = m * xs + b\n",
    "    ys_normal = (ys_normal_without_noise[:,np.newaxis]\n",
    "                    * np.random.normal(1, multiplicative_error, size=(num_points, num_replicates))\n",
    "                    + np.random.normal(0, additive_error, size=(num_points, num_replicates)))\n",
    "    # erroneous measurements\n",
    "    y_min, y_max = m * np.array([x_min, x_max]) + b\n",
    "    ys_erroneous = np.random.uniform(y_min, y_max, size=(num_points, num_replicates))\n",
    "    # add multiplicative and additive error terms in quadrature\n",
    "    empty_error = np.sqrt((multiplicative_error * y_max)**2 + additive_error**2)\n",
    "    # empty sample measurements\n",
    "    ys_empty = np.random.normal(0, empty_error, size=(num_points, num_replicates))\n",
    "    ys = ((measurement_type == 0) * ys_normal\n",
    "                        + (measurement_type == 1) * ys_erroneous\n",
    "                        + (measurement_type == 2) * ys_empty)\n",
    "    # add multiplicative and additive error terms in quadrature\n",
    "    sigma_ys = np.sqrt((multiplicative_error * ys)**2 + additive_error**2)\n",
    "    return xs_with_replicates, ys, sigma_ys\n",
    "\n",
    "def prepare_data(xs, ys, sigma_ys, average_replicates=True):\n",
    "    if average_replicates:\n",
    "        xs = np.mean(xs, axis=1)\n",
    "        sigma_ys = stats.sem(ys, axis=1)\n",
    "        ys = np.mean(ys, axis=1)\n",
    "    else:\n",
    "        # include each replicate as a separate observation\n",
    "        xs = xs.reshape(-1)\n",
    "        ys = ys.reshape(-1)\n",
    "        sigma_ys = sigma_ys.reshape(-1)\n",
    "    data_ary = np.array([xs, ys, sigma_ys]).T\n",
    "    data_df = pd.DataFrame(data_ary, columns=[\"conc\", \"A260\", \"sigma_A260\"])\n",
    "    return data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every time you run `generate_random_data`, it generates new synthetic data (here, we generate 3 replicate measurements for each of 20 DNA concentrations). We then use `prepare_data` to turn the output of `generate_random_data` into a pandas DataFrame. Note that `prepare_data` takes an `average_replicates` flag. When `average_replicates=True`, we take all the replicates for a single DNA concentration and replace them with a single data point (whose mean is the sample mean of the replicates, and whose error bar is the standard error of the mean of the replicates). When `average_replicates=False`, we include each individual replicate as its own data point, and all data points are assigned equal error bars.\n",
    "\n",
    "You can run `generate_random_data` once to randomly generate data, and re-run `prepare_data` as many times as you want for the same synthetic dataset to observe how `average_replicates` affects the results in the rest of this notebook.\n",
    "\n",
    "Note that `generate_random_data` takes many arguments that allow you to change the amount of measurement error, range of the data, and assumed calibration factor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(732) # comment this out to generate new random data!\n",
    "data_replicates = generate_random_data(12, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_no_avg = prepare_data(*data_replicates, average_replicates=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_avg = prepare_data(*data_replicates, average_replicates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 546,
       "width": 1167
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,6))\n",
    "plots = {\"average_replicates=False\": data_no_avg, \"average_replicates=True\": data_avg}\n",
    "for plot_num, (title, d) in enumerate(plots.items()):\n",
    "    ax = fig.add_subplot(1, 2, plot_num + 1)\n",
    "    ax.errorbar(d[\"conc\"], d[\"A260\"], d[\"sigma_A260\"], fmt=\"s\")\n",
    "    ax.set_title(title)\n",
    "    plt.xlabel(X_LABEL)\n",
    "    plt.ylabel(Y_LABEL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the remainder of this notebook, we're going to focus on `average_replicates=True`. After all, in your training as a scientist, you learned to do three replicates of every measurement and average the results! This is an extremely common procedure in science. Is it always the best thing to do? We will return to this issue later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Four possible answers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your colleagues email back, and each of them is convinced that they've correctly measured the calibration factor despite the noise in the data. Oh no, you think, they've all come up with different answers! Which is the “best” line? Which calibration factor should you use? Here are the plots each of them sent you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lines = {\"Sofía\": (0.009, 1.09),\n",
    "         \"Li\": (0.017, -0.25),\n",
    "         \"Fatma\": (0.02, -0.59),\n",
    "         \"Nozomi\": (0.013, 1.45)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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93mqrrXLxxRfnueeey5gxYzJ16tTSHBRFUbqtYLJw83DCCSfkkUceyS677FLrJMX48eNz991355RTTkn//v2zww475KWXXlrg/dSnc+fOjeo3520CF+TLBxMnTmxwvHLuFwAAoKXbdNNNS899r6mpqZVzz8ucuVZj86yv9p1XrjVjxozsvffeeeedd5J8eWX6NddcM9cie3PEVYnzBk2lc+fOefDBB/OHP/whq6yySq117777bq688soceuihWWaZZfLTn/40U6ZMafL9N0ZTngso1+cAgAWjIA8Azeziiy8uPQ99gw02yIsvvpiTTz45W265ZZZZZpkstthitRLXRTUZOv/880vLRx99dIYOHZrvfe97WX/99dOzZ8907NixVv+mnIetttoqd911Vz799NNcf/31Of7447PhhhvWOsHxwAMPZLPNNstjjz3WZPudPHlyo/pNmjSptNy1a9f53k91dXWD45VzvwAAAK3B2WefXcq7r7/++lqP/JqbOXOtxuZZX+07r1zrBz/4QR5++OEkSa9evXLrrbfOc5vmiKu1a9++fU444YS89dZbeemll3LRRRflgAMOyLLLLlvqM3ny5Pz+97/Pdttt16RF+UqcC/A5AGhZFOQBoJndf//9peVf/OIXdYqnX/Xf//633CE1u1mzZpVOMLRp06bWLQMb8v777zd5HL169cree++dP/7xj3nmmWfy6aef5s9//nN69eqVJJkyZUqOOeaYJttfY9/DnP1mxzI/unXrlvbt28/3fuf8rC3IfgEAAFqDddddN/vuu2+SL29h/stf/rJR2/Xu3bu0PGXKlEbf3ruxudbgwYNz6aWXJkk6dOiQG2+8MSuttNJ8xfXBBx+kKIp5blNTU5MPPvigUXEtatZee+388Ic/zJAhQ/Lhhx/mueeey5FHHlla/+STT+aiiy5qsv0117mAOT8Hjd3nqFGjMnXq1IXaLwDzpiAPAM3s448/Li2vtdZac+07a9asRl2h3ZJuBdcYY8aMyfTp05MkSy65ZK2ksT6vvvpqszzHrFevXjnuuONyyy23lNpeeeWV0q0CF9awYcMadWJk2LBhpeUNN9xwvvdTVVWV9ddfv/T68ccfn+c2M2fOzFNPPTXX/ba2zxkAAEBDfvWrX6Vt27ZJkttuuy1PPvnkPLdZdtllaz03vjG51meffZbXX3+99LqhHO+OO+7IT3/609Lrv/3tb9lqq63mOX6SrLfeeqX3MnHixEY9fu2FF14oXRndtm3brLfeeo3a16Jo/fXXzyWXXJKjjz661DbneYGF9cQTT8yzz8SJE/Pyyy+XXi/IuYANNtigtPzaa6/l888/n+c2c55zWnrppdOnT586fZwLAFh4CvIA0MzmvC36vG5bdtNNN+WTTz6Z55idOnUqLc+YMWPBg2smc87BlClT5lmk/stf/lLukGrZYost0qNHj9LrTz/9tEnG/fDDD/PQQw/Ntc8XX3yRm2++ufR6u+22W6B9bb/99qXlK664Yp5zfMstt+Szzz5L8uXnaYsttqjTp7V9zgAAABqy+uqr5+CDDy69/sUvftGo7ebM0S6//PJ59r/iiitSU1OTJOnTp09WX331On1eeeWVHHjggaV+J598cg4//PBGxZN8eavyjTfeeL7iuuyyy0rLm2666Xw9c3xRtfvuu5eWm+o8QJIMGTIkM2fOnGef2Rcu9OrVK2uvvfZ872eNNdbI0ksvneTLCzz++c9/znObOT8HDZ1/cC4AYOEpyANAM1t55ZVLy3MWXr9q9OjROfHEExs1Zs+ePUvLH3300YIH10x69uyZxRdfPEkybty40u3r6/PYY481WUG+sVfZjx07NhMnTiy9ntcV/PPjxz/+ca3bwX3VT3/609JVCssss0x22223BdrPUUcdVfriw7PPPpu///3vDfYdN25cTj755NLrAw88MN26davTr7V9zgAAAObmjDPOKD3u67777ptrbjrbnI81u/HGG3PXXXc12PeDDz6o9Yi2Y445ps7VxmPGjMkee+yR8ePHJ0m+9a1v5be//e18vY+vxnXRRRflxRdfbLDvc889l7/+9a+l19///vfne3+txbRp02rl93Mz523em/I8wDvvvJMLLrigwfVjxozJGWecUXp9+OGHp127dvO9n6qqqlpX+f/qV7+aa+5+++2359Zbby29buhz4FwAwMJTkAeAZjbnN67PPffcer+x/Oyzz2abbbbJBx980Khvqa+zzjql5SeffLIsz1tvSm3atMk3v/nN0usjjjii1u3SZ7vuuuvyzW9+M7NmzWqSb+vvt99+2W233fLvf/+7VPT+qvfffz8HHHBA6Zvpq622WlZdddWF3nfy5TMAn3/++XzrW9/KyJEja62bNm1afvzjH+eSSy4ptZ199tkLlIQnySqrrFLrhMyxxx6biy66qHTFxWxvv/12dt5557z99ttJksUXX7zB5ye2ts8ZAADA3Ky00kq1nh0+5+PDGrLddttl4MCBpdf77rtv/v3vf9fp99xzz2WHHXbI2LFjkyR9+/bN//zP/9TqM2PGjOy999559913k3z5bPt//etfte4q11jf+c53Srednz59enbZZZc8+OCDdfrdf//92XXXXUtXOm+44YY58MAD53t/rcXIkSPTt2/fnHTSSfWed0iSoihy11135fTTTy+1zXnOYmF16NAhp556ai644II6Ofkbb7yRnXbaqXSOoGfPnrUeXTC/TjjhhCy77LJJvnxcwg477JDnn3++Tr9rr702+++/f+n1HnvskW984xv1jjnnuYDrr7++UY/iA6C2BTvDCwAssMMPPzyDBw/OG2+8kWnTpuWQQw7Jb37zm6y33nrp1KlTXn755Tz99NNJvnwO3C677JLf/e53cx1zqaWWyoABA/LYY49l6tSpWW+99bLrrrtmmWWWKSXyq6yySn7wgx+U/f011i9/+cvcfPPNmTJlSt57771svvnm2WKLLdKvX79Mnz49TzzxROmkxFFHHZU33nijUVcrzE1NTU3uuOOO3HHHHWnfvn3WXnvt9OvXL926dcuECRPy3//+N8OGDSslyG3bts2f//znhX6vs/3whz/MzTffnHvvvTcrrbRStt122yy//PL5/PPP8+CDD9Z6vtvee+9d68TQgvj973+fp59+OsOHD8/MmTNz7LHH5txzz81WW22V6urqvP3223nkkUcya9asJEm7du1y6aWXZqWVVqp3vNb4OQMAAJibX/ziF7n88svneiezr7rssssyYMCAvP3225k4cWL222+/rLbaatlss83SoUOHjBgxIsOGDSsVLrt06ZIhQ4ake/futcZ5/PHH88gjj5Rer7jiivn5z3/eqBgOOeSQbLbZZqXXHTp0yJAhQ7LNNttk9OjR+eSTT7L99ttnvfXWy/rrr58kef755/PCCy+UtllyySUzZMiQ0l0CFlVffPFFBg8enMGDB6dHjx7ZYIMNsuyyy6Zjx44ZNWpUXnzxxdL5hyTp169fjj/++Cbb/+9+97uccMIJ+clPfpLBgwfnG9/4Rqqrq/Pmm2/m0UcfLeXkbdu2zcUXX5wll1xygfe1xBJL5Oqrr87AgQMzefLkvP7669lwww2z2WabZc0118z06dPz5JNP5s033yxts9pqq+XSSy9tcMxvf/vbOeWUU1IURW6//fasu+662XLLLdO1a9dSnwMOOKDWYxMAqE1BHgCaWceOHXPrrbdm4MCBeeedd5IkI0aMyIgRI2r1GzBgQK699tpcfPHFjRr3z3/+c7bbbruMHz8+X3zxRa655ppa67fZZpsWVShdc801M2TIkBx00EGZPHlyiqLI448/nscff7xWv6OPPjp//vOfs8suuyz0PudMFmfMmJHnnnsuzz33XL19l1xyyfz973/PrrvuutD7na1bt2654447MmjQoLz22mu5++676+13yCGHzDUZbqzOnTvngQceyJFHHpnrrrsuyZfPsf/qZyP58vb4l156aa0rPerT2j5nAAAAc7Psssvm+9//fv74xz82epullloqjz32WA466KA88MADSZI333yzVpFztlVXXTVXX311Ntlkkzrrvnql8S233NLoGDbeeONaBfnky2eIP/rooznggANKue4LL7xQqwg/24Ybbpjrrrsuq6yySqP32Rq1b98+HTt2zLRp05Ikn3/+ee6///4G+2+77bYZMmRIk9ylb7Y999wziy22WI477rh8/PHH9ebkiy++eC6++OIMGjRooff3jW98I/fff3++853v5J133klRFBk2bFi9d4DYcccdc/XVV8/1Fv2rrbZaTjvttNLjF15++eW8/PLLtfqsvfbaCvIAc6EgDwAV0K9fvzz33HO56KKLcsMNN+T111/P9OnTs/TSS2edddbJQQcdlH333Xe+ble+4YYb5sUXX8yFF16YBx54IO+8804mTpxY+qZ1S7Tnnnvm5ZdfzuDBg3PPPffk/fffT7t27dKnT58MGDAghx9+eIO3TFsQt9xyS5577rncf//9efLJJzNixIh8+OGHmTRpUjp27JjevXtn3XXXzTe/+c0cdNBBpefcN6X+/ftn+PDh+cc//pHrrrsub731VsaOHZsll1wyW265ZY4++ujssMMOTba/6urqXHvttTnhhBNy1VVX5aGHHsrHH3+cKVOmpFevXll77bWz++6757vf/W6jTji0xs8ZAADA3Jxyyim5+OKLG3y0WX2WWmqp3H///bn77rtzzTXX5NFHH80nn3ySGTNmZMkll8wGG2yQvfbaKwcffHCzXoHer1+/PP3007n++uvzn//8J0899VRGjRqV5Msvnm+22WbZZ599svfee9d5nv2iaNlll81nn32WBx54IEOHDs0zzzyTt956K6NHj8706dPTtWvXrLDCCtlkk02y//77Z8cddyxLHEcffXS22mqr/PWvf819992XDz/8MEVRZIUVVsjuu++e4447rnSr+aaw+eabZ8SIEfnnP/+Zm266Kc8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vAp0eAADgxhAZGWkt//LLL0Xq68yZM9ZyXpfJK1u2rKpUqaJjx47lCEG/Fhoa6nFd9mD26zOqs4fMatWqeR3Lrx0/frxA7bNkhX13CrsfhZWfMHn16lX17NnT+tZAXq5cuVLUYXmU/TWQ12vn5ptvdrtddt7qLf2v5lmXYSyo7B+KFKQuhX0dZP1uVa5cOc9vhURFRXldv2zZMqWlpSksLEydOnXKa8h+IXuNQ0JCHBwJAAAA4H/I9bmR690j1/8Pub54kesB+Csm5AEAxSL7faa2bNnis369XQ4uizHGZ8/nS9lD0+TJk3X//ffnaztfXBrQV/JzKb/XX3/dCu2xsbEaPny47rzzTt18880KCQmxguQ999yjNWvW+M3x8odxZA/H3j548keLFi2SJMXFxSk4ONjh0eRP9hrn9cEEAAAAcKMh1+dGrifX54VcX7zI9QD8FRPyAIBicdtttykyMlInT57UmjVrdP78eVWoUKFQfWU/e9vbZd4kKT093fpjPPslvXwl+zcEjh49qltuuSXf22a/V9e1a9e8XkqspDLG6IMPPpAktW/fXt99953HS8Fl/4aEXbK/Bn755RfVqlXLY9vs3/iw47WTH9nDY3HUJ+t36/Tp08rIyPD6wYy3e7FlZmZqyZIlkrxf1i6/3zQo6uUw8yt7jbP/bgMAAAAg17tDrs+JXJ8buf46cj2AGx33kAcAFAuXy6X+/ftLuv5HeFaYK4zg4GDVr19fkvTvf//ba9utW7fq2rVrkmRLML7zzjut5dWrVxdo28aNGysoKEiStHz5cp+OqzDy862Egjp9+rT14cojjzziMbRfvHhRP/74o+1jy/4ayOu1s3HjRknXLxNXkA9kfOnWW2+1LlP3008/2f58jRs3lnT9fo4pKSke22VkZFj3BnRnw4YNOnHihFwul7p16+axXdY9KPP6UMLba8OXsmocExNTYs7+BwAAAIoLuT43cv3/kOvdI9dfR64HcKNjQh4AUGyeffZZK4SMHTtWu3btytd2mZmZ+vTTT3M81rlzZ0nSzp07tWHDBo/bZv+AIGsbX+rUqZN1qblp06YV6B5uoaGhuu+++yRJiYmJVlB0Svb7mqWmpvqkz/T0dGvZ2z3yPvzwQ+sDFjvH1rFjR+vs8A8//NBju4MHD2rFihXWNoGBzlxUKDAwUG3atJEkbdq0yfbny3o9StKsWbM8tlu8eLFOnTrlcf3ChQslSa1bt/Z6T7+sD0S2bNni8VKCO3bs8Pohgi8lJydLkjp06FAszwcAAACUNOT6nMj1/0Oud49cT64HAIkJeQBAMapevbreeecdSdfPpo+NjdWqVau8brNz50517dpVf/rTn3I8PnToUOus7MGDB+vcuXO5tl2+fLkVzlq3bq1WrVr5YjdyiIiI0JAhQyRJmzdv1rPPPusxgFy7dk3Hjx/P8diYMWOss8T79OmjvXv3enyujIwMff755zp06JCPRp9TtWrVrGVv4yiIqKgoRURESJLmzJmjtLS0XG02bdqkl19+2Ws/VapUsb51UJSxRUdHq0ePHpKkZcuW6aOPPsrVJi0tTQMHDrQ+SBgxYkShn88XskLk9u3bffaBiidt27bV7bffLkmaPn261q9fn6vNiRMnNHLkSK/9ZAX3Bx980Gu72NhYSdKRI0c0e/bsXOsvXLiggQMH5mvsRbVv3z6dPHlSEsEdAAAA8IRcT64n1xccuZ5cDwDcQx4AUKwGDBigQ4cOaezYsTp+/Lg6duyoLl26qHv37mrUqJEiIiJ0+vRp/fTTT1q8eLG++eYbZWRk6I477sjRT9OmTfX8889r8uTJSklJ0Z133qk//OEPat68uS5fvqyFCxfqr3/9qzIyMhQUFKS//e1vtu3Tq6++qhUrViglJUXvvPOOkpKSNGTIEDVt2lRBQUE6dOiQ1q5dq88//1yvvfaadYk/Sbr77rs1duxYTZgwQfv371ezZs00aNAgdenSRdWqVVNqaqoOHDigpKQkffnllzpy5IhSUlJUo0YNn+9Hu3btrOWRI0dqzJgxqlatmvXBQp06dQp8RnlAQID69u2r6dOna9u2berQoYNGjhypevXq6dy5c1qyZIneffddhYeHKzo62uPl2wIDA9WqVSutW7dOH330kZo3b65mzZqpbNmykq7fCy6/94ObOnWqVq5cqTNnzuj3v/+91q1bpz59+qhy5cratWuX/vSnP1mXbXvkkUf0wAMPFGiffa1bt26aMGGC0tLStGbNGlu+EZLd9OnTFRsbq7S0NHXu3FnPPfec7r//fgUHBys5OVlvvvmmjh07pmbNmmnbtm25Ljt44MAB/ec//5Hk/T5zktSvXz+NHz9e58+f16BBg7Rnzx517dpVLpdLycnJmjJlig4fPqzmzZtr69ateY794sWLmjlzZp7tbr75Zt1///05Hlu5cqUkqUyZMuratWuefQAAAAA3KnI9uZ5cXzDkenI9AMgAAOCAefPmmTp16hhJef40btzYLFu2LFcfGRkZZtiwYV63rVixotttjTEmISHBapeQkOB1vFntxo0b53b9iRMnzD333JPnvsyYMcPt9lOnTjXBwcF5bh8UFGR2795t23488sgjHp97//79VrvY2FgjycTGxnp9PmOMOXv2rGnWrJnHfitXrmxWrVqVZ5+LFi0yLpfLbR/Z9yc/9diyZYuJjo72WuuePXuaK1euuN0+v/s/btw4q7+iaNiwoZFkBgwY4LHNjBkz3B6rX9u/f3+er8eZM2easmXLuq1LYGCg+cc//mGeeOIJI8k0bNgwx7Z//etfjSRTs2bNfO3b3LlzTZkyZdw+V7ly5czcuXNNfHy8kWRq167tto/atWvn670k68fdcevYsaORZLp27ZqvcQMAAAA3OnJ9TuR6cr035HpyPYAbG5esBwA4omfPnvrxxx/12WefqV+/fmrQoIEqVaqkwMBAVa5cWXfeeaeGDRumlStXKiUlRV26dMnVR0BAgKZPn67Vq1erb9++qlWrloKDg1WhQgU1a9ZMo0eP1u7du91u62uRkZFatWqV5s+fr169eqlGjRoKDg5WpUqV1KRJE/Xt21dff/21Hn/8cbfbP/vss9q7d69eeeUVtWnTRpGRkQoMDFRYWJhiYmL08MMP6/3339fhw4dVr1492/bj008/1aRJk9S6dWtVrFjRunxgUVSsWFHr1q3Tq6++qqZNm6pcuXIKDw9Xo0aN9MILL+j777/XPffck2c/3bp108qVK9W9e3dFR0dbZ9EXRvPmzfXjjz/qzTff1F133aWIiAgFBQUpOjpaPXv21IIFCzRv3rwc97hz0rBhwyRJ8+bN09WrV21/vvj4eCUnJ6tv376Kjo5WUFCQqlevrkceeURr167V73//e50/f17S9eObXdZl7fI6iz5L7969tX79evXo0UNRUVEKCgpSzZo1rTH07t3btzvnxuHDh7V69WpJ/6s1AAAAAO/I9TmR68n13pDr7UWuB+DvXMZ4uCEOAAAA/MLFixdVp04dnTp1Sp988on69evn9JBUr1497d27V/369dMnn3wi6fq94SIjI5WWlqYlS5Y4flnA/Hrttdf0yiuvqEGDBtq5c6dPPrACAAAAACALud5e5HoA/o53JQAAAD8XHh6uUaNGSZJef/11ZWZmOjqeTZs2ae/evZKkNm3aWI8vX75caWlpCgsLU6dOnZwaXoFcvHhRf/nLXyRJ48aNI7QDAAAAAHyOXG8fcj2AkoB3JgAAgBLg6aefVu3atbVr1y7NnTvX1ufas2ePx3WnTp3Sk08+KUkKDg7Wo48+aq0rX768xo0bp2nTpvnNZQHzMn36dJ06dUqtWrVSnz59nB4OAAAAAKCUItfbg1wPoCQIdHoAAAAAyFu5cuX06aef6ttvv1V6erqtzxUXF6dbbrlFPXr00O23366KFSvqzJkzWrdund59910dPXpUkvTyyy8rMjLS2q5Lly7Fcm9HX8r6sKFnz55yuVxODwcAAAAAUEqR6+1BrgdQEnAPeQAAAORQp04d/fzzz17bDBs2TNOmTeNScAAAAAAA+BlyPQD4FybkAQAAkMOqVau0cOFCrVq1SkePHtXJkycVGBiom2++We3bt9fgwYPVrl07p4cJAAAAAADcINcDgH9hQh4AAAAAAAAAAAAAABtwLRIAAAAAAAAAAAAAAGzAhDwAAAAAAAAAAAAAADZgQh4AAAAAAAAAAAAAABswIQ8AAAAAAAAAAAAAgA2YkAcAAAAAAAAAAAAAwAZMyAMAAAAAAAAAAAAAYAMm5AEAAAAAAAAAAAAAsAET8gAAAAAAAAAAAAAA2IAJeQAAAAAAAAAAAAAAbMCEPAAAAAAAAAAAAAAANmBCHgAAAAAAAAAAAAAAGzAhDwAAAAAAAAAAAACADZiQBwAAAAAAAAAAAADABkzIAwAAAAAAAAAAAABgAybkAQAAAAAAAAAAAACwARPyAAAAAAAAAAAAAADYgAl5AAAAAAAAAAAAAABswIQ8AAAAAAAAAAAAAAA2YEIeAAAAAAAAAAAAAAAb/D8RN0heX+033AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1009,
       "width": 1010
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(0, 800, 10)\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "for plot_num, (name, line) in enumerate(lines.items()):\n",
    "    ax = fig.add_subplot(2, 2, plot_num + 1)\n",
    "    ax.errorbar(data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"], fmt=\"s\")\n",
    "    ax.plot(xs, line[0] * xs + line[1])\n",
    "    plt.text(0.05, 0.9, \"Calibration factor = {:.0f}\".format(1/line[0]), fontdict={\"size\": 14}, transform=ax.transAxes)\n",
    "    ax.set_title(f\"{name}'s plot\")\n",
    "    if plot_num >= 2: # only show x-axis label on bottom two plots, otherwise it overlaps with plot title\n",
    "        plt.xlabel(X_LABEL)\n",
    "    plt.ylabel(Y_LABEL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define your objective"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You email all of them back, asking them to argue why their line is the “best” linear model of the data.\n",
    "\n",
    "- Sofía replies, “I used ordinary least squares.”\n",
    "- Li replies, “I used weighted least squares”\n",
    "- Fatma replies, “I saw some obvious outliers, so I threw those out before using ordinary least squares.”\n",
    "- Nozomi replies, “I figured it was better to over-estimate than under-estimate, so I used an asymmetric loss function.”\n",
    "\n",
    "\n",
    "When trying to come up with the best model, it's good to separate this task into two steps: first, define precisely what you mean by *best* (by writing down an objective function); second, design an algorithm to spit out a model that has the highest possible value of this objective function (sometimes called *optimization* or *model-fitting*).\n",
    "\n",
    "As such, when two people disagree and each claim that their model is “better,” there are two possibilities:\n",
    "1. They both wrote down the same objective function, but they ended up with different models. Settling these kinds of disagreements is easy: just evaluate the objective function for each of their models, and whoever has a higher value of the objective function has the better model. It's unlikely that two people could agree about the objective function but end up with different models for simple classes of models, but with extremely complicated models, fitting the model becomes a hard problem and requires fancy algorithms. These algorithms may not always converge, and/or are complex enough that there might be hard-to-catch bugs in their code.\n",
    "2. They each chose a different objective function. Instead of arguing using imprecise, qualitative language (“my model handles outliers better”), you can directly compare the mathematical expressions of the objective functions (“my model penalizes errors linearly instead of quadratically, so it is less sensitive to outliers”). This doesn't make disagreements go away—two competent scientists can legitimately disagree about what the objective function should be—but it does make it extremely clear what they're disagreeing about.\n",
    "\n",
    "Let's return to the problem: whenever you use your spectrophotometer to measure DNA concentration, you're going to need to decide which calibration factor to use to convert absorbance measurement (A260) to DNA concentration. You figure out what objective functions each of them used. \n",
    "1. Sofía and Li don't say what objective function they used, but the methods they use are ordinary least squares and weighted least squares (both assume a specific objective function). \n",
    "2. Fatma also used ordinary least squares but ignored some data points they thought were outliers—is that okay? (Throwing out data points is never okay but there is a principled way to do this, see below!) \n",
    "3. Nozomi said something about a “loss function”—does that have anything to do with the objective function? (Yes, see below!) Nozomi also explicitly mentioned their goal: they noted that it's usually a bigger problem to have too little DNA in an experiment rather than too much. Because they decided it was better to under-estimate the correction factor rather than over-estimate it, they surely wrote down an objective function that expressed that preference mathematically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary Least Squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In lecture, Sean discussed how the formulae for ordinary least squares (OLS) regression follow from minimizing the residual sum of squares: $\\mathrm{RSS}=\\sum_i (\\hat{y}_i - y_i)^2=\\sum_i (m x_i - b - y_i)^2$, where $\\hat{y}_i=m x_i + b$ is the linear model's prediction for data point $i$. For OLS, RSS is the objective function.\n",
    "\n",
    "Sean also discussed how you can arrive at the exact same linear model ($m$ and $b$ values) by specifying a generative probabilistic model, writing down the corresponding likelihood function, and maximizing it. This approach is called *maximum likelihood estimation*. Recall:\n",
    "1. We assumed that there is a mechanism that links some measured variable $x$ to an measured variable $y$ with an exactly linear relationship $y=mx+b$, and that $y$ is measured with Gaussian noise with mean 0 and variance $\\sigma^2$, where $\\sigma^2$ is known. Taken together, we conclude that $y_i\\sim Normal(m x_i + b, \\sigma^2)$.\n",
    "2. We wrote down the likelihood $$P(\\mathrm{data} | OLS, m, b)=\\prod_i \\frac{1}{\\sqrt{2\\sigma^2\\pi}}e^{-\\frac{(m x_i - b - y_i)^2}{2\\sigma^2}}$$.\n",
    "3. By taking the log of both sides, taking the derivative and setting it equal to zero, we maximized the log-likelihood analytically. This isn't too hard for the simple case of a linear model with Gaussian measurement errors of known variance, but quickly becomes confusing for more complicated models.\n",
    "\n",
    "If you assume that each measurement has the same measurement error $\\sigma$, you can get OLS by maximum likelihood estimation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weighted Least Squares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If instead of $y_i\\sim Normal(m x_i + b, \\sigma^2)$, you assume each measurement has a different error $\\sigma_i$, this leads to a generative probabilistic model $y_i\\sim Normal(m x_i + b, \\sigma_i^2)$, and you get weighted least squares (WLS) regression.\n",
    "\n",
    "This is a minor tweak to our generative probabilistic model, with the new likelihood given by\n",
    "$$P(\\mathrm{data} | WLS, m, b)=\\prod_i \\frac{1}{\\sqrt{2\\sigma_i^2\\pi}}e^{-\\frac{(m x_i - b - y_i)^2}{2\\sigma_i^2}}$$,\n",
    "but it requires that we go through the math to maximize the log-likelihood all over again. If we keep making changes to our generative probabilistic model, you worry think that doing all this math over and over again will be slow and tiresome. Let's make the computer do some of this hard work for us, and maximize the likelihood numerically instead of analytically.\n",
    "<br>(We will use the `scipy.optimize.minimize` function.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Least squares with maximum likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because $\\log x$ is a monotonic function (it always goes up, never goes down, for increasing $x$ values), maximizing the log-likelihood is equivalent to maximizing the likelihood. Scipy's optimizers minimize, so instead of maximizing the log-likelihood, we'll minimize the negative log-likelihood (“NLL”). \n",
    "### An aside about vectorization:\n",
    "<br>If we have a set of 2D data (x, y):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we define the negative log-likelihood\n",
    "def nll_slow(params, xs, ys, sigmas):\n",
    "    ll = 0\n",
    "    # params is a numpy array of length two containing m and b\n",
    "    m = params[0]\n",
    "    b = params[1]\n",
    "    # for each data point, add a term to the log-likelihood\n",
    "    for i in range(len(xs)):\n",
    "        y_pred = m * xs[i] + b\n",
    "        residual = ys[i] - y_pred\n",
    "        # this is equivalent to stats.norm.logpdf(ys[i], loc=y_pred, scale=sigmas[i])\n",
    "        ll += stats.norm.logpdf(residual, loc=0, scale=sigmas[i])\n",
    "    return -ll # return negative log-likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of looping over each data point individually (if we have high-dimensional data), we can calculate each term of the log-likelihood all at once by passing in numpy arrays into stats.norm.logpdf, and getting back a numpy array of the same length containing logpdf values (we just need to sum them). Using vectorization often results in more readable code, and for large datasets will be much faster. If you like, you can check that `nll_slow` produces the same answer as `nll` given the same arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nll(params, xs, ys, sigmas):\n",
    "    m, b = params\n",
    "    y_pred = m * xs + b\n",
    "    ll_terms = stats.norm.logpdf(ys - y_pred, loc=0, scale=sigmas)\n",
    "    return -ll_terms.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_runtime = prepare_data(*generate_random_data(1000, 3), average_replicates = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74.9 ms ± 3.55 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit nll_slow(guess, data_runtime[\"conc\"], data_runtime[\"A260\"], data_runtime[\"sigma_A260\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "280 μs ± 31.1 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit nll(guess, data_runtime[\"conc\"], data_runtime[\"A260\"], data_runtime[\"sigma_A260\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.8 s ± 3.76 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit optimize.minimize(nll_slow, guess, (data_runtime[\"conc\"], data_runtime[\"A260\"], data_runtime[\"sigma_A260\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44.1 ms ± 2.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit optimize.minimize(nll, guess, (data_runtime[\"conc\"], data_runtime[\"A260\"], data_runtime[\"sigma_A260\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use `scipy.optimize.minimize` to minimize our log-likelihood. We pass in the function to optimize, an initial guess for the parameter values (`m` and `b`), and a tuple (or list) of arguments to be passed to your function (in this case, the x data, y data, and y error bars). In simple problems, the initial guess often doesn't matter; it often does matter for more complex problems. (Can you think of how you might avoid problems here? Recall how you handled initial parameters in the k-means problem set!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OLS with maximum likelihood: y = 0.009x + 1.09\n"
     ]
    }
   ],
   "source": [
    "guess = np.array([0, 0]) # guess for m and b\n",
    "\n",
    "sigma_y = 1 # assume equal errors for all points\n",
    "\n",
    "minimization_ols = optimize.minimize(nll, guess, (data[\"conc\"], data[\"A260\"], sigma_y))\n",
    "m = minimization_ols.x[0]\n",
    "b = minimization_ols.x[1]\n",
    "print(f\"OLS with maximum likelihood: y = {m:.3f}x + {b:.2f}\")\n",
    "# calculate the y values predicted by our linear model\n",
    "predicted_ols = m * data[\"conc\"] + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WLS with maximum likelihood: y = 0.017x + -0.25\n"
     ]
    }
   ],
   "source": [
    "guess = np.array([0, 0]) # guess for m and b\n",
    "\n",
    "sigma_y = data[\"sigma_A260\"] # use measurement errors given in data file\n",
    "\n",
    "minimization_wls = optimize.minimize(nll, guess, (data[\"conc\"], data[\"A260\"], sigma_y))\n",
    "m = minimization_wls.x[0]\n",
    "b = minimization_wls.x[1]\n",
    "print(f\"WLS with maximum likelihood: y = {m:.3f}x + {b:.2f}\")\n",
    "# calculate the y values predicted by our linear model\n",
    "predicted_wls = m * data[\"conc\"] + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 494
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.errorbar(data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"], fmt=\"s\")\n",
    "plt.plot(data[\"conc\"], predicted_ols, label=\"OLS\")\n",
    "plt.plot(data[\"conc\"], predicted_wls, label=\"WLS\")\n",
    "plt.title(\"Maximum likelihood fit\")\n",
    "plt.legend()\n",
    "plt.xlabel(X_LABEL)\n",
    "plt.ylabel(Y_LABEL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in our generative probabilistic model we have assumed that the two physical quantities $x$ and $y$ are exactly linearly related, and that we measure $x$ perfectly and $y$ with some error. This is not the same as assuming that both quantities are measured perfectly, but that the relationship between $x$ and $y$ is only approximately linear! In many cases, when scientists fit linear models, what they *really* want to assume is the latter, but that's not what least-squares fitting does! (However, this latter assumption is vague. What does “approximately linear” mean? That's why you need to specify your generative probabilistic model, because that's an articulation of what you mean by *approximately* linear.) Least-squares also assumes that you measure one of your variables ($x$) perfectly but the other with significant measurement error ($y$). No measurement is perfect, so ignoring measurement error in $x$ is a big assumption—sometimes a decent one, sometimes a poor one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing fits with likelihood ratios"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe you can say that the WLS fit looks “better” by eye than the OLS fit, but that's a vague statement. What is better? And how much better? Recall that the likelihood is defined as a probability: the probability of the data being generated by a model with specific parameter values: $P(\\mathrm{data}|\\mathrm{model},\\theta=\\theta_0)$. As such, we can define a likelihood ratio which describes how much more likely it is that a model with parameter values $\\theta_1$ will generate the observed data relative to the same model with parameter values $\\theta_2$. In our case, let's compare how likely the WLS best-fit line is relative to the OLS bet-fit line according to the *OLS* likelihood function\n",
    "\n",
    "$$\\mathrm{likelihood~ratio}=\\frac{P(\\mathrm{data}|\\mathrm{OLS~model},m=m_\\mathrm{WLS},b=b_\\mathrm{WLS})}{P(\\mathrm{data}|\\mathrm{OLS~model},m=m_\\mathrm{OLS},b=b_\\mathrm{OLS})}$$\n",
    "\n",
    "Careful, this is a ratio of likelihoods, not log-likelihoods or negative log-likelihoods!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The WLS fit parameters are 8.32e-05 times as likely as the OLS fit parameters according to the OLS likelihood function\n"
     ]
    }
   ],
   "source": [
    "sigma_y = 2 # try changing this!\n",
    "nll_wls = nll(minimization_wls.x, data[\"conc\"], data[\"A260\"], sigma_y)\n",
    "nll_ols = nll(minimization_ols.x, data[\"conc\"], data[\"A260\"], sigma_y)\n",
    "likelihood_ratio = np.exp((-nll_wls) - (-nll_ols))\n",
    "# equivalent to\n",
    "#likelihood_ratio = np.exp(-nll_wls) / np.exp(-nll_ols)\n",
    "print(\"The WLS fit parameters are {:.2e} times as likely as the OLS fit parameters according to the OLS likelihood function\".format(likelihood_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the likelihood ratio depends on what you assume for the measurement error on the y values! This may be slightly surprising because the OLS best-fit $m$ and $b$ parameters do *not* depend on this $\\sigma_y$. To see why this makes sense, consider that in the limit of very large error bars, the likelihood ratio should tell you that it doesn't really prefer one line or the other. Try changing `sigma_y` above and see how the answer changes.\n",
    "\n",
    "Why does this say that the WLS best-fit line is *less likely* than the OLS best-fit line? Isn't WLS supposed to be “better”? Again, the way we specify what we mean by *better* is by writing down an objective function (here, a likelihood) that describes our goal and our assumptions. According to the OLS likelihood function, the OLS best-fit line is 10,000x better than the WLS best-fit line. This shouldn't be surprising, because to get the OLS best-fit line, we maximized the OLS likelihood function. Similarly. Each choice of a likelihood function. There is no way to determine which line is “better” without specifying a specific likelihood function that encodes your hhh.\n",
    "\n",
    "You may wonder how the OLS and WLS best-fit lines compare according to the WLS likelihood function,\n",
    "$$\\mathrm{likelihood~ratio}=\\frac{P(\\mathrm{data}|\\mathrm{WLS~model},m=m_\\mathrm{WLS},b=b_\\mathrm{WLS})}{P(\\mathrm{data}|\\mathrm{WLS~model},m=m_\\mathrm{OLS},b=b_\\mathrm{OLS})}.$$\n",
    "Because the WLS likelihood function is the same as the OLS likelihood function except each data point gets its own $\\sigma$, we can do this calculation by calling the same `nll` function as before but by specifying a vector of $\\sigma$ values from our dataset. As expected, the WLS best-fit line is much more likely than the OLS best-fit line if you assume that the measurement errors given by the data file are correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The WLS fit parameters are 2.03e+59 times as likely as the OLS fit parameters according to the WLS likelihood function\n"
     ]
    }
   ],
   "source": [
    "nll_wls = nll(minimization_wls.x, data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"])\n",
    "nll_ols = nll(minimization_ols.x, data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"])\n",
    "likelihood_ratio = np.exp((-nll_wls) - (-nll_ols))\n",
    "print(\"The WLS fit parameters are {:.2e} times as likely as the OLS fit parameters according to the WLS likelihood function\".format(likelihood_ratio))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fatma looked at some of your data points, decided they were outliers, and threw them out. Then they fit a line with OLS. How do you decide if they did a good job of “throwing out outliers”? If you and Fatma disagree about which outliers should be discarded, how do you decide who is correct? Well, you should resolve this dispute the same way we've discussed that any statistical dispute should be discussed: both you and Fatma should articulate which objective function you used to optimize your selection of outliers, and argue about whose objective function makes most sense in this context. Unfortunately, Fatma probably didn't follow this kind of principled procedure when selecting outliers to discard, they probably just picked by eye—this is subjective and often introduces bias into your results. You could come up with a principled way to select outliers to discard. This is absolutely a good way to solve this problem. Perhaps you could try to come up with a good way to do this.\n",
    "\n",
    "There is an alternative method which we will focus on. We begin by articulating what exactly we mean by *outlier*. Colloquially, an “outlier” refers to a data point that's far away from where a reasonable prediction might guess where it is. Essentially, you're implicitly assuming that sometimes your measurement process will generate values that are much further away than it usually does. We can express that assumption mathematically by saying that our measurement errors follow a probability distribution that has fatter tails than the Gaussian distribution. One such distribution that's commonly used in this context is a Student's t distribution.\n",
    "\n",
    "Using a fat-tailed distribution like Student's t instead of a Gaussian distribution for your measurement errors is sometimes called *robust regression*. Recall that the Student's t distribution has mean $\\mu$ and standard deviation $\\sigma$ parameters, but it also has a third parameter called the degree-of-freedom $\\nu$. You don't need to worry about exactly how to interpret $\\nu$ values, all you need to know is that the tails are fattest when $\\nu$ is close to 1 and thinnest when $\\nu$ is large."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 248,
       "width": 713
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xs = np.linspace(-5, 5, 100)\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(xs, stats.norm.pdf(xs, loc=0, scale=1), label=r\"$\\mathrm{Normal}(0, \\sigma^2)$\")\n",
    "plt.plot(xs, stats.t.pdf(xs, 1, loc=0, scale=1), label=r\"$\\mathrm{StudentT}(0, \\sigma^2, \\nu=1)$\")\n",
    "plt.plot(xs, stats.t.pdf(xs, 4, loc=0, scale=1), label=r\"$\\mathrm{StudentT}(0, \\sigma^2, \\nu=4)$\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the Gaussian curve has “thin tails,” Student's t with $\\nu=4$ has slightly fatter tails, and Student's t with $\\nu=1$ has much fatter tails. (The “fatness” of the tails refers to how much of the probability density lies far away from the mean of the distribution.) To perform robust regression on our data, we need to make a single change to our code: swap the `logpdf` of a Gaussian distribution with the `logpdf` of a Student's t distribution. Remember you also need to pass in the `studentt_dof` parameter into your `nll` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Student's t regression: y = 0.020x + -0.59\n"
     ]
    }
   ],
   "source": [
    "def nll_t(params, xs, ys, sigmas, studentt_dof):\n",
    "    m, b = params\n",
    "    y_pred = m * xs + b\n",
    "    ll_terms = stats.t.logpdf(ys - y_pred, studentt_dof, loc=0, scale=sigmas)\n",
    "    return -ll_terms.sum()\n",
    "\n",
    "# guess for m and b\n",
    "# see what happens if you guess [0, 0]\n",
    "guess = np.array([0.02, 0])\n",
    "\n",
    "sigma_y = data[\"sigma_A260\"] # use measurement errors given in data file\n",
    "studentt_dof = 4 # see how the results change if you change this\n",
    "\n",
    "minimization_t = optimize.minimize(nll_t, guess, (data[\"conc\"], data[\"A260\"], sigma_y, studentt_dof))\n",
    "m = minimization_t.x[0]\n",
    "b = minimization_t.x[1]\n",
    "print(f\"Student's t regression: y = {m:.3f}x + {b:.2f}\")\n",
    "# calculate the y values predicted by our linear model\n",
    "predicted_t = m * data[\"conc\"] + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 494,
       "width": 494
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.errorbar(data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"], fmt=\"s\")\n",
    "plt.plot(data[\"conc\"], predicted_ols, label=\"OLS\")\n",
    "plt.plot(data[\"conc\"], predicted_wls, label=\"WLS\")\n",
    "plt.plot(data[\"conc\"], predicted_t, label=\"Student's t\")\n",
    "plt.title(\"Maximum likelihood fit\")\n",
    "plt.legend()\n",
    "plt.xlabel(X_LABEL)\n",
    "plt.ylabel(Y_LABEL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see three data points that seem to be outliers: at ~550, ~675, and ~750 ng/µL. All three points are dragging the OLS line down, causing it to under-estimate the slope, and hence over-estimate the correction factor. The outliers at ~675, and ~750 ng/µL have larger error bars, so they receive a much smaller weighting in the WLS fit. However, the WLS line is still dragged down somewhat by the outlier at ~550 ng/µL, which has a small error bar, and hence still is given significant weight in the WLS fit. Because the Student's t distribution has fatter tails, it is more forgiving of outliers, so it is dragged down even less—which allows the Student's t best-fit line to accurately infer the slope, and hence the correction factor. Note that robust regression with the Student's t distribution isn't perfect—it's still biased by outliers, just to a lesser degree than a model that assumes Gaussian errors.\n",
    "\n",
    "Note that robust regression with the Student's t distribution is a way to fit a linear model in the presence of outliers without manually picking specific outliers to throw out! You never made any subjective and unprincipled choices! You specified an objective function and then maximized it. Another scientist can argue with *why* you used a Student's t distribution as the error distribution in your generative probabilistic model, and maybe that was a good choice and maybe that was a bad choice, but that depends on how your data were collected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Asymmetric loss function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Will you always be optimizing a likelihood? No! It depends on what your goal is—what *objective function* you pick. Sometimes you only care about the *predictions* that your model makes. The primary goal of generative probabilistic modeling is accurately inferring the parameters of your generative model; if you care about making accurate predictions, you can just define what you mean by “prediction accuracy” and use that as your objective function. As a warm up, we start by minimizing the residual sum of squares between our model predictions and our data. Note that we have made no assumptions about how our data was generated (e.g., we have not defined a generative probabilistic model of our data, nor have we written down a likelihood function)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimizing RSS: y = 0.009x + 1.09\n"
     ]
    }
   ],
   "source": [
    "def residual_sum_of_squares(params, xs, ys):\n",
    "    m, b = params\n",
    "    y_pred = m * xs + b\n",
    "    return ((ys - y_pred)**2).sum()\n",
    "\n",
    "guess = np.array([0.02, 0]) # guess for m and b\n",
    "\n",
    "minimization_rss = optimize.minimize(residual_sum_of_squares, guess, (data[\"conc\"], data[\"A260\"]))\n",
    "m = minimization_rss.x[0]\n",
    "b = minimization_rss.x[1]\n",
    "print(f\"Minimizing RSS: y = {m:.3f}x + {b:.2f}\")\n",
    "# calculate the y values predicted by our linear model\n",
    "predicted_rss = m * data[\"conc\"] + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that these are the same best-fit parameters as OLS. That makes sense, since (as Sean discussed in lecture), maximizing the OLS likelihood function ended up being equivalent to minimizing the RSS. RSS (sometimes called “quadratic loss” or “L2 norm”) is an extremely common way to value how good or poor predictions are, but it's not the only way! Recall that you are trying to calibrate your NanoDrop machine by measuring the 260nm absorbance (A260) of various solutions with known DNA concentration. In your experiments, when you measure the A260 of an unknown sample, you use this linear model to estimate DNA concentration from that A260 value. Since $\\mathrm{A260}=m \\cdot \\mathrm{concentration} + b$, we can solve for concentration: $\\mathrm{concentration}=\\frac{1}{m}\\cdot\\mathrm{A260}_{read} - \\frac{b}{m}$. If we overestimate A260, the actual A260 read will be lower, giving a lower concentration. Thus overestimating A260 means we underestimate concentration. You might want to make the assumption that underestimating concentration may be better than overestimating concentration, because often in cloning experiments having a little extra DNA in your reactions won't matter, but having too little DNA in your reactions will cause them to fail. You can modify your `residual_sum_of_squares` function to express this mathematically: that overestimating A260 is penalized four times more harshly than underestimating A260. This is an example of an *asymmetric loss function*. (In prediction-oriented fields like machine learning, the term *loss function* is often used interchangeably with *objective function*. Think of it as “how much money will I *lose* if I make a prediction error?”)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimizing asymmetric loss: y = 0.013x + 1.45\n"
     ]
    }
   ],
   "source": [
    "def asymmetric_rss(params, xs, ys):\n",
    "    m, b = params\n",
    "    y_pred = m * xs + b\n",
    "    # if we overestimate, penalize at 1x weight; if we underestimate, penalize at 4x weight\n",
    "    return np.where(y_pred - ys > 0, (ys - y_pred)**2, 4 * (ys - y_pred)**2).sum()\n",
    "\n",
    "guess = np.array([0.02, 0]) # guess for m and b\n",
    "\n",
    "minimization_asym = optimize.minimize(asymmetric_rss, guess, (data[\"conc\"], data[\"A260\"]))\n",
    "m = minimization_asym.x[0]\n",
    "b = minimization_asym.x[1]\n",
    "print(f\"Minimizing asymmetric loss: y = {m:.3f}x + {b:.2f}\")\n",
    "# calculate the y values predicted by our linear model\n",
    "predicted_asym = m * data[\"conc\"] + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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AFUC+wU5XAAAAAACQY2yNE5jbfq6aVWjmoG4AwDEIXQEAAAAAgN2dizqnLmu6WK05OvioXEy8CRdA/sNvNgAAAAAAYFcvhr5oNXANLhesiJAIAlcA+RY7XQEAAAAAgF0YhqG6S+parfmq61e6v8T9DuoIAJyD0BUAAAAAAGTb0WtH9cSmJ6zWRIREOKgbAHAuQlcAAAAAAJAtj33zmH65+YvF9d41emtSs0mOawgAnIzQFQAAAAAAZElCUoIClwZardnae6vKe5d3UEcAkDswsRoAAAAAAGTatvPbbAauESERBK4ACiR2ugIAAAAAgEwJXBKoBCPB4vrzDZ7XkwFPOrAjAMhdCF0BAAAAAECGxNyNUeMvGlutCesXJh8PHwd1BAC5E+MFAAAAAACATV+e/NJm4BoREkHgCgBipysAAAAAALAhYHGA1fV3Wryjrvd1dVA3AJD7EboCAAAAAIB0XY+9rjYr21itCX8iXB6uHg7qCADyBsYLAAAAAACANGYcmmE1cC3tVVoRIREErgCQDna6AgAAAACAVGyNE5jXfp6aVmjqoG4AIO8hdAUAAAAAAJKks7fOquta67NZjw4+KhcTb5wFAGv4LQkAAAAAAPRC6AtWA9fgcsGKCIkgcAWADGCnKwAAAAAABZhhGKq7pK7Vmq+6fqX7S9zvoI4AIO8jdAUAAAAAoIA6cvWIBn07yGpNREiEg7oBgPyD0BUAAAAAgAKo1ze99NvN3yyu967RW5OaTXJcQwCQjxC6AgAAAABQgCQkJShwaaDVmq29t6q8d3kHdQQA+Q/TrwEAAAAAKCC2nd9mM3CNCIkgcAWAbGKnKwAAAAAABUDgkkAlGAkW159v8LyeDHjSgR0BQP5F6AoAAAAAQD4WczdGjb9obLUmrF+YfDx8HNQRAOR/jBcAAAAAACCf+vLklzYD14iQCAJXALAzdroCAAAAAJAPBSwOsLr+Tot31PW+rg7qBgAKFkJXAAAAAADykQvRF9RpdSerNYeeOCR3V3cHdQQABQ+hKwAAAAAA+UTf9X114sYJi+ulvUpre9/tDuwIAAomQlcAAAAAAPIBW+MEPu3wqZqUb+KgbgCgYCN0BQAAAAAgDzty9YgGfTvIas3RwUflYuJa2gDgKISuAAAAAADkUbZ2t0pSREiEAzoBAKRE6AoAAAAAQB5jGIbqLqlrtWbuw3PVzK+ZgzoCAKRE6AoAAAAAQB6y+exmjds5zmoNu1sBwLkIXQEAAAAAyCNsjRMo41VG2/puc1A3gGNF3r6jhlO+T3XfodceVklvDyd1BFhG6AoAAAAAQC4Xnxivhp83tFqzuttq1Shew0EdAQCsIXQFAAAAACAXezXsVX1z+hurNYwTAIDchdAVAAAAAIBcytY4gZZ+LfXxwx87qBsAQEYRugIAAAAAkMtcj72uNivbWK3Z3me7Shcu7aCOAACZQegKAAAAwGm4KAqQVre13XTm1hmrNYwTAIDczcXZDQAAAAAAgHsCFgdYDVyb+zUncAWAPICdrgAAAAAAONkvN37RY+sfs1qzr/8+ebt7O6gjAEB2ELoCAAAAAOBEti6WJTFOAADyGsYLAAAAAADgJLYC1+F1hhO4AkAexE5XAAAAAAAcLPRCqEZvH2215sdBP8rNhf9tB4C8iN/eAAAAAAA4EOMEACD/Y7wAAAAAAAAOYBiGzcD1nRbvELgCQD7ATlcAAAAAAHLY4p8X6/3w963WELYCQP5B6AoAAAAAQA5inAAAFDyErgAAAAAA5IC4hDgFLQuyWrO041LVL1PfMQ0BAByG0BUAAAAAADt7NexVfXP6G6s17G4FgPyL0BUAAAC5SuTtO2o45ftU9x167WGV9PZwUkcAkDm2xgkULVRUewfsdVA3AABnIHQFAAAAAMAOrsdeV5uVbazWbO69WX7efg7qCADgLISuAAAAAABkU7e13XTm1hmrNYwTAICCw8XZDQAAAAAAkJcFLA6wGri28GtB4AoABQw7XQEAAAAAyIJfbvyix9Y/ZrVmX/998nb3dlBHAIDcgtAVAAAAAIBMsnWxLIlxAgBQkDFeAAAAAACATLAVuD4V8BSBKwAUcOx0BQAAAAAgA0IvhGr09tFWa44MOiJXF1fHNAQAyLUIXQEAAAAAsIFxAgCAzGC8AAAAAAAAFhiGYTNwfafFOwSuAIBU2OkKAAAAAEA6Fh1bpA8OfWC1hrAVAJAeQlcAAAAAAP6BcQIAgOwgdAUAAAAA4P/FJcQpaFmQ1ZqlHZeqfpn6jmkIAJAnEboCAAAAACDp37v/rfW/r7daw+5WAEBGELoCAAAAAAo8W+MEiroX1d7+ex3UDQAgr3NxdgP5RXx8vD777DM9+uijKl++vDw8POTt7a37779fw4YN0/79+53dIgAAAADgH67HXrcZuG7uvZnAFQCQKex0tYMLFy6oc+fOiohI/TaT+Ph4/frrr/r111+1cOFCjR07Vh988IFMJpOTOgUAAAAAJOu6pqvORp21WsM4AQBAVrDTNZsSEhJSBa5169bVokWLtG/fPm3dulUTJ05UkSJFJEkzZszQ+++/78x2AQAAAAC6N07AWuDawq8FgSsAIMvY6ZpN69atMweuTZs21e7du+Xq6mpeb9++vbp166amTZvq7t27+s9//qOxY8fKzY0vPQAAAAA42i83ftFj6x+zWrOv/z55u3s7qCMAQH7ETtds2rNnj/n2K6+8kipwTdawYUN16dJFknTz5k2dPHnSYf0BAAAAAO4JWBxgM3CNCIkgcAUAZBuhazbFx8ebb1erVs1i3X333We+fefOnRztCQAAAACQmq2LZT0V8BTjBAAAdsN73LOpZs2a5tu///67ateunW7d6dOnJUkmk0k1atRwSG8AAAAAUNDtOL9Dz+14zmrNkUFH5OqS9l2LAABkFaFrNvXv31+vv/66oqKiNG3aNHXq1CnNiIEff/xRGzdulCT169dPxYoVc0arAAAAgNN0nR2ma9Fp3/GVZBhp7us4a7dcTKZ0j1O6qIfWj25h9/6QP9na3SqJ3a0AgBxB6JpNpUuX1qJFizRw4EDt2bNHQUFBGjNmjGrWrKnbt29rz549+uCDDxQfH6/69etr+vTpmTr+xYsXra5fvnw5O+0DAAAADnEt+o6uRMVlqPZqOuEskBmGYajukrpWa/7T8j/qUq2LgzoCABQ0hK520LNnT4WHh2v69OlasGCBQkJCUq2XLVtWkydP1ogRI1SkSJFMHbtSpUr2bBUAAAAA8rVFxxbpg0MfWK1hdysAIKcRutrB3bt39cUXX2j9+vUy0nl71J9//qnly5erZs2a6ty5sxM6BAAAAID8j3ECAIDcgtA1m/7++2916tRJu3btkqurq8aPH6+hQ4eqWrVqiouL0w8//KA333xTYWFh6tq1q2bMmKHnn38+w8e/cOGC1fXLly8rODg4u08DAAAAAPKsuIQ4BS0LslqztONS1S9T3zENAQAKPELXbHrjjTe0a9cuSdJnn32WarSAu7u72rdvrzZt2qhDhw7asWOHXnjhBbVp00Z161qfL5SsYsWKOdI3AAAAAOQHr+x+RRt+32C1ht2tAABHc3F2A3mZYRhauHChJKlmzZppZrkmc3Nz01tvvSVJSkpKMj8GAAAAAJB1AYsDrAauxdyLEbgCAJyCna7Z8Oeff+rGjRuSpMDAQKu1DRs2NN8+efJkjvYFAAAAAPnZ9djrarOyjdWazb03y8/bz0EdAQCQGqFrNri5/e/Ll5CQYLX27t276T4OAAAAAJBxXdd01dmos1Zr2N0KAHA2xgtkQ4kSJVSsWDFJ0r59+6wGrzt37jTfrlq1ao73BgAAAAD5TcDiAKuBa0u/lgSuAIBcgdA1G1xcXNS5c2dJ0h9//KG333473bqbN29qwoQJ5s+7dOnikP4AAAAAID84eeOkAhYHWK3Z13+fPn74Ywd1BACAdbzPPZsmTpyodevWKSYmRpMmTdKhQ4cUEhKiatWqKS4uTvv379fMmTN1/vx5SVK7du3UoUMHJ3cNAAAAAHmDrbBVYpwAACD3IXTNpgceeEDr1q1T//79df36da1fv17r169Pt7Zt27ZatWqVgzsEAAAAgLzJVuA6ou4IjQ4c7aBuAADIOEJXO3j44Yd18uRJffbZZ/r222/1888/66+//pKbm5vKlSunoKAgDRgwQN26dZPJZHJ2uwAAAACQq20/v13P73jeas2RQUfk6uLqoI4AAMgcQlc7KVmypMaPH6/x48c7uxUAAAAAyLMYJwAAyA+4kBYAAAAAwOkMw7AZuP6n5X8IXAEAeQI7XQEAAAAATrXw2EJNPzTdag1hKwAgLyF0BQAAAAA4DeMEAAD5EaErAAAAgBxXuqhHuvcnGYauRt9JdV+Zoh5ysXABWkvHQd4TlxCnoGVBVmuWdlyq+mXqO6YhAADsiNAVAAAAQI5bP7pFuvdH3r6jhlO+T3Xft8+3VElvwtX87OXdL2vj7xut1rC7FQCQlxG6AgAAAAAcxtY4AV8PX+3ut9tB3QAAkDMIXQEAAAAAOe5azDW1XdXWas2W3ltUwbuCgzoCACDnELoCAAAAAHJUlzVddC7qnNUaxgkAAPITF2c3AAAAAADIvwIWB1gNXB+q+BCBKwAg32GnKwAAAADA7k7eOKk+6/tYrdnXf5+83b0d1BEAAI5D6AoAAAAAsCtbF8uSGCcAAHmHoRGuG9THdZdeTxgq6WFnN5QnELoCAAAAAOzGVuA6ou4IjQ4c7aBuAADZ4XnwI531nGz+/Ev3KYo6V0+q3cGJXeUNhK4AAAAAgGzbfn67nt/xvNWaI4OOyNXF1UEdAQCy7OiX0pqnVSSdpSKbx0q1f3Z4S3kNoSsAAAAAIFsYJwAA+cSp76XPe1stiWswPN0wFqkRugIAAAAAsiTJSFK9JfWs1kxtOVWdq3V2UEcAgCy5dEj6tK3NslUJD6lt0LOErhlA6AoAAAAAyLQFxxZoxqEZVmvY3QoAudz1U9KchjbLziWVUef4d3RbhXXIAW3lB4SuAAAAAIBMYZwAAORx0VekmQFSYrzVMsPNU8G3p+uafB3TVz5C6AoAAAAAyJC4hDgFLQuyWrO041LVL1PfMQ0BADIn7pb0SQvpr/O2a0cf1g2Piro25fuc7ysfInQFAAAAANj08u6XtfH3jVZr2N0KALlUwh1pSXfp/D7btU9tl/z+f+TA7Ts521c+RugKAAAAALDK1jgBXw9f7e6320HdAAAyLClJWv2kdOxr27VPfC1VfzjneyogCF0BAAAAAOm6FnNNbVdZv5r1lt5bVMG7goM6AgBkiGFIW1+T9s2xXdtznlTv8ZzvqYAhdAUAAAAApNF5dWedj7Y+849xAgCQC+2dI2191XZdhylSs9E5308BRegKAAAAAEjF1jiBVhVbaU67DOyeAgA4zk8rpdVP2a5rOupe4Goy5XxPBRihKwAAAABAknQi8oT6buhrtWb/gP0qUqiIgzoCANh0apv0eS/bdbV7Sr0/k1xcc74nELoCAAAAAGzvbpUYJwAAucofP0rzWtuuq9REGrxOKuSZ4y3hfwhdAQAAAKCAsxW4Pl33aY0KHOWgbgAAVkWelmY3sF3nW1kaGSZ5+uR8T0iD0BUAAAAACqht57dpzI4xVmuODDoiV96KCgDOF/2nNKuulBBnvc7VXRoTIRUt55i+kC5CVwAAAAAogBgnAAB5RFyUNPch6eYZ27WjDkmlqud8T7CJ0BUAAAAACpAkI0n1ltSzWjOt5TR1qtbJQR0BANKVcEda2lM6t8d27ZPbpYoNc74nZBihKwAAAAAUEJ9FfKaZh2darWF3KwA4WVKStGaEFLHKdu3Ar6Qa7XO+J2QaoSsAAAAAFACMEwCAXM4wpO8mSns/tF3b4xOpfv+c7wlZRugKAAAAAPlYbEKsgpcFW635vNPnqlfa+sgBAEAO2vextOUV23UPT5JajM3xdpB9hK4AAAAAkE9N2DVBm85sslrD7lYAcKKIr6Svh9uua/Iv6ZF3JJMp53uCXRC6AgAAAEA+ZGucQHGP4trVb5eDugEApHJ6h7S0h+26Wt2lxxZKLq453hLsi9AVAAAAAPKRazHX1HZVW6s1W3pvUQXvCg7qCABg9scRaV4r23UVg6WQ9VIhzxxvCTmD0BUAAAAA8olOqzvpQvQFqzWMEwAAJ7jxu/RhoO06n0rSyDDJyzfHW0LOInQFAAAAgHzA1jiB1hVba3a72Q7qBgAgSbp9VZpVT7obY73O5CKN/VkqxrsQ8gtCVwAAAADIw05EnlDfDX2t1uwfsF9FChVxUEcAAN2Jlua1liJP2a599qBUumaOtwTHInQFAAAA4DQlvT10dmpnZ7eRZ9na3SoxTgAAHCohXvq8l3R2t+3a4d9LlYJyvic4BaErAAAAAORBtgLXp+s+rVGBoxzUDQAUcElJ0tqR0k8rbNcOWCnVfCTne4JTEboCAAAAQB6y7dw2jQkdY7XmyKAjcnVxdUxDAFDQfT9JCpthu677x1LgwBxvB7kDoSsAAAAA5BGMEwCAXGT/J9LmCbbr2k2UWr6Y8/0gVyF0BQAAAIBcLslIUr0l9azWvPvQu+pYtaODOgKAAuzY19JXw2zXBT8tdZwmmUw53xNyHUJXAAAAAMjF5kfM16zDs6zWsLsVABzg953Skm626x7sKvVZLDHmpUAjdAUAAACAXIpxAgCQC1w+Ks19yHadX0NpyEapkFfO94Rcj9AVAAAAAHKZ2IRYBS8LtlrzeafPVa+09ZEDAIBsuHFG+rC+7bpiftIzeyUv35zuCHkIoSsAAAAA5CLjd47Xt2e/tVrD7lYAyEG3r90LW+Nv26594YRUrEKOt4S8h9AVAAAAAHIJW+MESniW0M7HdzqoGwAoYO7clj5tI13/1Xbtswek0vfnfE/IswhdAQAAAMDJrsVcU9tVba3WbO29VeW9yzuoIwAoQBLvSssek34PtV07/DupkvXxL4BE6AoAAAAATtVpdSddiL5gtYZxAgCQA5KSpHX/ko4ut13bf4V0/6M53xPyDUJXAAAAAHASW+MEWldqrdltZzuoGwAoQLa9Ke3+wHZdtzlSg0E53w/yHUJXAAAAAHCwE5En1HdDX6s1+wfsV5FCRRzUEQAUED/Mk74dZ7uu7evSQy/lfD/ItwhdAQAAAMCBbO1ulRgnAAB29/MaadUQ23VBT0qd3pdMphxvCfkboSsAAAAAOIitwHVkvZF6tv6zDuoGAAqAM7ulxV1s193fWeq7RHIlKoN98EoCAAAAgBy27dw2jQkdY7XmyKAjcnVxdUxDAJDfXYmQPmlhu65CoDT0W6mQV873hAKF0BUAAAAAchDjBADAgW6elWbVs11XtLz0r32SV/EcbwkFE6ErAAAAAOSAJCNJ9ZZY/x//dx96Vx2rdnRQRwCQj/19XfqwgXTnlu3ascclH7+c7wkFGqErAAAAANjZ/Ij5mnV4ltUadrcCgB3E35Y+eki6dtJ27b9+kMo8kPM9ASJ0BQAAAAC7YpwAAOQ8NyVoQaH3VPLDDPw+HbZFqtwk55sCUiB0BQAAAAA7iE2IVfCyYKs1yzotU93SdR3UEQDkPyYl6SePp1TUFGu7uN9y6YFOOd8UkA5CVwAAAPwfe3ceZnPd+H/8dWaMIcvIXtmVX9Q0IpM1YyeUPVkLLZSkzVIxSpaUte1WyCBkCUOyhYx9CaMUkZ3sw1hmxpzz+8P3dt9uzjmfMefzOdvzcV1dl8775czruusWLx/vAyCT3ln9jhYfWOwyw9OtAJA5+T4pqL+zGQg2HStV7Gx6H8AVRlcAAAAAyAR31wnkzZZXq59ZbVEbAAhAX1SRTv7uPlfrPanm2+b3AQxgdAUAAACAO3Dy8knVmVXHZWZpy6W6J+c9FjUCgAAzp5uUOMt97rEuUuORks1mfifAIEZXAAAAAMigRnMa6UjyEZcZrhMAgDu0+mNp5UeGomfeOKZ8uXOYXAjIOEZXAAAAAMgAd9cJxBSN0bja4yxqAwABZOf30twXDEXLX/2XziuXtoYwbcE38W8mAAAAABiw+8xutVnYxmVmY7uNuivsLosaAUCAOLBW+vZJQ9GaKSN10FHY5EJA5jG6AgAAAIAb7p5ulbhOAAAy7PRe6bPHjGW7LNGZvI/q4ODl5nYCPITRFQAAAABccDe4do/qrh7le1jUBgACwKXT0ojSxrKtJkoPt7z+7eQU8zoBHsboCgAAAAC3sfzgcvVe1dtlZnvH7QoNCbWoEQD4ubQr0kcGrwaoM0Cq8aa5fQATMboCAAAAwP/gOgEA8CC7XfrgbmPZ8u2lZl+Y2wewAKMrAAAAAPwfu8OuqLgol5mPn/hYjUo2sqgRAPi52AhjuXvKSy+tNrUKYCVGVwAAAHhF03EJOnXx1rvZ7A7HLa81GrNGITbbbd+nQK5wxfes7vF+CD5f7/xaY38d6zLD060AYNBXNaQTOw0EbVLsebPbAJZjdAUAAIBXnLqYohMXrhrKnrzNOAt4EtcJAICH/PCytGO6seyAc1JIiLl9AC9hdAUAAAAQtK5cu6LoadEuM989+Z0iC7gfZQEgqP3yifTzh8ay7/4jhWUztw/gZYyuAAAAAILS26vf1k8HfnKZ4elWAHAjcbY0p6ux7Nv7pBz5ze0D+AhGVwAAAABBx911Avmz59fKNistagMAfujgemlSQ2PZV7dK+e83tw/gYxhdAQAAAASNk5dPqs6sOi4zy1otU+EchS1qBAB+5sw+aVwFY9nnF0vFq5rbB/BRjK4AAAAAgkKD2Q107NIxlxmuEwAAJy6dkUaUMpZtOUGKbGVuH8DHMboCAAAACHjurhOoVbSWxtYea1EbAPAjaVeljwoZy9Z6T6r5trl9AD/B6AoAAAAgYP1+5nc9s/AZl5mN7TbqrrC7LGoEAH7Cbpc+uNtY9pFnpBbjze0D+BlGVwAAAAAByd3TrRLXCQDAbQ3KKznS3ecKRUrdE8zvA/ghRlcAAAAAAcfd4Nojqoe6l+9uURsA8BPjY6RjvxrLxiaZWgXwd4yuAAAAAALG8oPL1XtVb5eZ7R23KzQk1KJGAOAH5r8i/TrVWHbAOSkkxNw+QABgdAUAAAAQELhOAAAyaM1IacUgY9l3T0hh2c3tAwQQRlcAAAAAfs3usCsqLsplZsQTI9SwZEOLGgGAj9s1V5r9vLHsW39JOQuY2wcIQIyuAAAAAPzW1zu/1thfx7rM8HQrAPyfQxulifWNZV/ZLBUoY24fIIAxugIAAADwS1wnAAAGnd0vjX3UWPa5RVKJ6ub2AYIAoysAAAAAv3Ll2hVFT4t2mfnuye8UWcD9KAsAAe3yWenjksayzf8lRbU1tw8QRBhdAQAAAPiNN1e9qaUHl7rM8HQrgKB3LUUaXNBYNqafFNPX3D5AEGJ0BQAAgFcUyBV+29ftDodOXky56bWCucIVYrNl6H0QeNxdJ5A/e36tbLPSojYA4IMcDmlQHmPZh1tKrSaaWgcIZoyuAAAA8Ir4nre/L+5McooqDl5+02uLe9VQvpyMq8Hqn0v/qO7sui4zy1otU+EchS1qBAA+aHAh6dpV97kCZaVXNpjfBwhyjK4AAAAAfFaD2Q107NIxlxmuEwAQ1L6pJx3ZZCwbm2RuFwA3MLoCAAAA8EnurhOoXbS2xtQeY1EbAPAxC16Ttk02lh1wVgoJNbcPgJswugIAAADwKb+d+U1tF7r+BO2N7TbqrrC7LGoEAD5k3Thp6XvGsv2PS1n5sRLwBkZXAAAAAD7D3dOtEtcJAAhSv8+Xvu9kLPvWXilnQXP7AHCJ0RUAAACAT3A3uPaI6qHu5btb1AYAfMSRLdI3dYxle2yUCj5obh8AhjC6AgAAAPCqhKMJ6r7c9Zi6veN2hXIfIYBgcvZvaWx5Y9lOC6RSNU2tAyBjGF0BAAAAeA3XCQDA/7hyThpewlj26S+kR9ubWgfAnWF0BQAAAGA5u8OuqLgol5kRNUeoYYmGFjUCAC+7liINNngP6xPvSLXfNbcPgExhdAUAAABgqfh98eqf0N9lhqdbAQQNh0MalMdYttzTUps4U+sA8AxGVwAAAACW4ToBAPgvQ+6TUpPd5/LdL/Xcan4fAB7D6AoAAADAdCnpKXps6mMuM/ObzVepiFIWNQIAL5rYSDq0zlg2NsncLgBMwegKAAAAwFRfbP9CX+740mWGp1sBBIWFvaUtE41lB5yVQkLN7QPANIyuAAAAAEzj7jqBesXraWTMSIvaAICXrP9CWtLPWLb/MSlrDnP7ADAdoysAAAAAjzt79axqzqzpMrP6mdXKmy2vRY0AwAt2x0szOxjLvvmnlKuwuX0AWIbRFQAAAIBHvb36bf104CeXGa4TABDQjm6Vvq5tLNt9vVSonLl9AFiO0RUAAACAx7i7TuClR17Sq4++alEbALDYuYPSmEeMZTvOk0rXMrUOAO9hdAUAAACQaX8n/a2n5j3lMrO5/WZly5LNokYAYKEr56XhxY1ln/pMqtDR1DoAvI/RFQAAAECmNP2hqQ5cOOAyw3UCAALStVRpcAFj2RpvSnUGmNsHgM9gdAUAAABwx9xdJ/BR9Y/0VGnXT8ACgN9xOKRBeYxlH2witZ1mah0AvofRFQAAAECGbTmxRc8ved5lZkenHQqxhVjUCAAsMqyYdDXJfe7uElKvHabXAeCbGF0BAAAAZIi7p1slrhMAEIAmN5X+/sVYduB5yWYztQ4A38boCgAAAMAQu8OuqLgol5kJ9Sco+p5oixoBgAUWvSVt/tpY9v0zUihTCwBGVwAAAAAGxO+LV/+E/i4zPN0KIKBs+Er6qY+xbL+jUnhOc/sA8CuMrgAAAABccnedQLFcxbSoxSKL2gCAyf74UZrxrLHsG39Iue8xtw8Av8ToCgAAAOC2rl67qkrTKrnMzG82X6UiSlnUCABMdHSb9HUtY9mX10qFHza3DwC/xugKAAAA4BZfbP9CX+740mWG6wQABITzh6XRBgfUDnOk++ua2wdAQGB0BQAAAHATd9cJ1CteTyNjRlrUBgBMcjVJGlbMWLbpGKnic6bWARBYGF0BAAAASJLOXj2rmjNrusysfma18mbLa1EjADBBepr0YX5j2Wq9pHofmNsHQEBidAUAAACgt1a/pSUHlrjMcJ0AAL/mcEiD8hjLlmkktZthah0AgY3RFQAAAAhy7q4TeOmRl/Tqo69a1AYATPBxaenyafe53EWkN34zvw+AgMfoCgAAAASpv5P+1lPznnKZ2dJhi8JDwy1qBAAeFtdM2r/SWHbgeclmM7MNgCDC6AoAAAAEoaY/NNWBCwdcZrhOAIDf+qmftOELY9n3z0ihzCMAPIsfVQAAAIAg4+46gY+qf6SnSrt+AhYAfNKmr6Uf3zKW7XdECs9lbh8AQYvRFQAAAAgSm09sVpclXVxmdnTaoRBbiEWNAMBD9iyRvmtjLNv7dyniPnP7AAh6jK4AAABAEHD3dKvEdQIA/NCx7dL4msayLydIhd3/WAgAnsDoCgAAAAQwu8OuqLgol5mJDSaqUuFKFjUCAA9IOiKNeshYtv1s6YF65vYBgP/B6AoAAAAEqPh98eqf0N9lhqdbAfiVqxekYUWNZRuPlCp1NbcPADjB6AoAAAAEIHfXCZTIXULxzeMtagMAmZSeJn2Y31i2yqtSg4/M7QMAbjC6AgAAAAHk6rWrqjTN9VUBC5otUMmIkhY1AoBMcDikQXmMZe+vJ3WYbWodADCK0RUAAAAIEJ9v/1xf7fjKZYbrBAD4jU/+n5R8wn0uZ2HprT/N7wMAGcDoCgAAAAQAd9cJ1CteTyNjRlrUBgAyYWor6a9lxrIDz0s2m6l1AOBOMLoCAAAAfuzs1bOqObOmy8zqZ1Yrb7a8FjUCgDu05F1p/WfGsu+fkUKZNAD4Ln6EAgAAAPzUm6ve1NKDS11muE4AgM/bPEFa9IaxbN/DUrbc5vYBAA9gdAUAAAD8kLvrBF6OelmvlH/FojYAcAf2LpOmtTKW7f2bFFHE3D4A4EGMrgAAAIAf2Z+0X0/Pe9plZkuHLQoPDbeoEQBk0IlE6avqxrIv/SLdE2VuHwAwAaMrAAAA4Cea/tBUBy4ccJnhOgEAPivpqDSqnLHsszOl/9fQ3D4AYCJGVwAAAMAPuLtOYEj1IWpauqlFbQAgA1IuSkMNXg3w5CdS9Avm9gEACzC6AgAAAD5s84nN6rKki8vMjk47FGILsagRABiUfk36MJ+x7OPdpUbDzO0DABZidAUAAAB8lLunWyWuEwDggxwOaVAeY9lStaRO88xsAwBewegKAAAA+Bi7w66oONcfHDOxwURVKlzJokYAYNCoh6Wkw+5zd+WX3tlnfh8A8BJGVwAAAMCHxO+LV/+E/i4zPN0KwOd894y05ydj2YHnJZvN1DoA4G2MrgAAAICPcHedQIncJRTfPN6iNgBgwLIB0toxxrLvn5ZCw8ztAwA+gtEVAAAA8LKr166q0jTXVwUsaLZAJSNKWtQIANzY+q0U38tYtu8hKVuEqXUAwNcwugIAAABe9Pn2z/XVjq9cZrhOAIDP+Gu5NLWlsezriVKeYub2AQAfxegKAAAAeIm76wQalGigT2p+YlEbAHDhn9+kL6say764Srr3UVPrAICvY3QFAAAALHbmyhnFfB/jMrP6mdXKmy2vNYUAwJkLx6WRDxrLtp0uPfikuX0AwE8wugIAAAAWemPVG1p2cJnLDNcJAPC6lGRp6H3Gso0+lh5/ydw+AOBnGF0BAAAAi7i7TqB7VHf1KN/DojYAcBvp16QP8xnLRr8oPTnC3D4A4KcYXQEAAACT7U/ar6fnPe0ys6XDFoWHhlvUCAD+h8MhDcpjLFuihvTcQlPrAIC/Y3QFAAAATNTkhyY6eOGgywzXCQDwqjFR0rkD7nPZ8kh9Xf94BgC4jtEVAAAAMIm76wSGVB+ipqWbWtQGAP7HzA7S7nhj2YHnJZvN1DoAEEgYXQEAAAAP23xis7os6eIys6PTDoXYQixqBAD/ZfkgKWGksex7p6QsWc3tAwABiNEVAAAA8CB3T7dKXCcAwEu2TZEWvGos2+eglD2PqXUAIJAxugIAAAAeYHfYFRUX5TIzscFEVSpcyaJGAPB/9q2UpjQzlu21U7q7uKl1ACAYMLoCAAAAmbRg3wK9m/CuywxPtwKw3Mnd0heVjWW7/SwVqWhuHwAIIoyuAAAAQCa4u06gRO4Sim9u8INqAMATzuyTxlUwln1mmlS2ibl9ACAIMboCAAAAd+DqtauqNM31VQELmi1QyYiSFjUCEPSunJeGG7waoMEQqcorptYBgGDG6AoAAABk0Lhfx2n8zvEuM1wnAMAy6dekD/MZyz7WVWoy0tw+AABGVwAAACAj3F0n0LBEQ42oOcKiNgCCXmyEsVyxqlKXxeZ2AQDcwOgKAAAAGHDmyhnFfB/jMrP6mdXKmy2vNYUABDejY6skxSaZ1wMAcFuMrh50+vRpTZw4UfPnz9e+fft07tw55cuXT0WLFtUTTzyhFi1aqEqVKt6uCQAAgAx6Y9UbWnZwmcsM1wkAsERGxtaB5yWbzbQqAADnGF09ZNasWerevbvOnDlz0+vHjx/X8ePHtWnTJu3du1fz5s3zTkEAAADcEXfXCfSI6qHu5btb1AZA0PqyuvSPwd/c6X9cynqXuX0AAC4xunpAXFycnn/+edntdhUsWFDdu3dX9erVlTdvXp04cUL79u1TfHy8wsLCvF0VAAAABu1P2q+n5z3tMrOlwxaFh4Zb1AhAUPq2iXRgjbHsG39Iue8xtw8AwBBG10zavXu3XnzxRdntdtWoUUPx8fGKiLj1j3v07NlTqampXmgIAACAjGo8t7EOXTzkMsN1AgBM9fNg6ReDH8r34mrp3vKm1gEAZAyjayb17NlTKSkpyp8/v+bOnXvbwfXfsmbNamEzAAAA3Al31wkMqT5ETUs3tagNgKDz+3zp+07Gsq0nSw81M7UOAODOMLpmwh9//KEVK1ZIkl599VXlz5/fy40AAABwpzYd36SuS7u6zOzotEMhthCLGgEIKv/8Jn1Z1Vi21ntSzbfN7QMAyBRG10yYNWvWjW+3bt36xrfPnTun06dPK2/evMqXL583qgEAACAD3D3dKnGdAACTXDojjShlLJuzsPTWn+b2AQB4BL9NnwkbNmyQJEVERKhs2bKaNm2aoqKilDdvXpUpU0b58+dXqVKlNGjQICUnJ3u5LQAAAP6X3WF3O7hOajCJwRWA56WnSbERxgfX2CQGVwDwIzzpmgm///67JKlEiRLq2bOnPv/881syf//9t2JjYzV79mwtWbJE9957b4a+xpEjR1yeHz9+PEPvBwAAgOvm/zVf7619z2WGsRWAKWKdfxbIrdkk83oAAEzD6JoJZ8+elXT9btcdO3YoT548GjZsmFq0aKHcuXMrMTFRAwYM0OLFi7Vr1y61bt1aa9asUUiI8QeMixYtalZ9AACAoOXu6daSESW1oNkCi9oACBqMrQAQNBhdM+HSpUuSpJSUFIWGhmrx4sWqXLnyjfPHHntMCxcuVJMmTbR48WKtW7dOc+fOVatWrbxVGQAAIKhdvXZVlaZVcpmJbxavEhElrCkEIDhkZGwdcE7KwIM6AADfxOiaCdmyZbsxvLZu3fqmwfXfQkJCNGLECC1evFiSNH369AyNrocPH3Z5fvz4cUVHR2egNQAAQHAa9+s4jd853mWG6wQAeNSguyWH3Vi272EpW25z+wAALMPomgm5cuW6Mbo2atTIae6hhx7Sfffdp6NHj2rz5s0Z+hpFihTJVEcAAAC4v06gYYmGGlFzhEVtAAS8KS2kfSuMZXtuk/KVNrcPAMByjK6ZULRoUZ04cUKS+3G0aNGiOnr0qE6ePGlFNQAAAEg6c+WMYr6PcZlZ/cxq5c2W15pCAALbquHSqiHGsh3mSvfXMbcPAMBrGF0z4aGHHrrx5Gp6errL7L/Ps2Thf3IAAAAr9F7ZW8sPLXeZ4ToBAB7xxyJpRjtj2fqDpao9ze0DAPA6bufOhCeeeOLGt/ft2+cyu3//fknSfffdZ2onAAAAXL9OwNXg2iOqB4MrgMw7ufv6h2QZGVzLNZNikxhcASBI8NhlJjz11FMKCwtTWlqa5s6dq5dffvm2udWrV+vMmTOSpBo1alhZEQAAIKjsP79fT89/2mVmS4ctCg8Nt6gRgIB0+az0cUlj2ex3S30OmFoHAOB7eNI1E/Lly6du3bpJkpYtW6YZM2bckrl48aJef/31G3//0ksvWVUPAAAgqDSa08jt4JrYOZHBFcCdS792/clWo4NrbBKDKwAEKZ50zaRBgwZp0aJFOnTokDp27Ki1a9eqRYsWyp07txITEzV8+HD98ccfkqTu3burUqVKXm4MAAAQeCInR7o8H1pjqJqUamJRGwABKTYiA9kk83oAAPwCo2smFShQQD/99JOeeuop/fXXX/rss8/02Wef3ZLr0qWLxowZ44WGAAAAgWvT8U3qurSry8yOTjsUYuMPeAG4Q4ytAIA7wOjqAWXLltX27dv15Zdfavbs2dq7d6+Sk5NVsGBBVatWTS+99JJq1arl7ZoAAAABxd3TrZL4sCwAdy4jY+uAs1JIqHldAAB+h9HVQ3LkyKG33npLb731lrerAAAABDS7w66ouCiXmW8bfquKhSpa1AhAQBlcSLp21Vi27yEpWwbGWQBA0GB0BQAAgN+Y/9d8vbf2PZcZnm4FcEe+ayvtWWws++oWKf8D5vYBAPg1RlcAAAD4BXfXCZSKKKX5zeZb1AZAwFjzqbTiA2PZdrOkMvXN7QMAXtB0XIJOXUy55XW7w3HLa43GrFGIzXbb9ymQK1zxPat7vJ8/YnQFAACAb7OlKWbOYy4j8c3iVSKihDV9AASGP3+Spj9jLFs3Vqre29Q6AOBNpy6m6MQFY1ernLzNOItbMboCAADAZ2UtsETh+Ve6zHCdAIAMObVH+rySseyDTaS208ztAwAISIyuAAAA8Em5yvZ1ed6oRCN9XPNji9oA8HtXzkvDixvLZs0p9T9qah0AQGBjdAUAAIBPSU5Ldju4/vLML7o7290WNQLg1+zp0gd5jedjk8zrAgAIGoyuAAAA8Bnf7vpWn2791GWG6wQAGBYbkYEsYysAwHMYXQEAAOATIidHujzvUb6Hukd1t6gNAL/G2AoA8DJGVwAAAHjViUsnVG92PZeZJc3W6t6I3BY1AuC3MjK2vn9GCuWXxAAAc/BfGAAAAHjNRxs+0ow/Zzg9t6fm0aV9fRXeMtzCVgD8ztCiUsoFY9k+B6Ts3AkNADAXoysAAAC8wt11ApcPvKT0KyUtagPAL83sIO2ON5btsVEq+KC5fQAA+D+MrgAAALDU3nN71WJBC5eZi7uHSrJZUwiA/1k7Rlo2wFj22RnS/2tkbh8AAP5HiLcLAAAAIHi8vPxll4NrTJEYrWq5RQyuAG7rr+XX7201MrjWfu/6h2QxuAIAvIAnXQEAAGA6u8OuqLgol5n5T89XqTylJEkHhjW2ohYAf3H6L+mzisayD9SX2s8ytw8AAG4wugIAAMBUm09sVpclXVxmEjsnWtQGgF+5miQNK2YsGxImDThtbh8AAAxidAUAAIBpGs5pqKPJR52edyjbQX2i+1jYCIBfsNulD+42no9NMq8LAAB3gNEVAAAAHpeWnqYKUyu4zPzc+mcVuKuARY0A+I3YiAxkGVsBAL6J0RUAAAAetfjvxXrnl3dcZrhOAMAtGFsBAAGE0RUAAAAeEzk50uV5n0p91KFcB4vaAPALGRlb3z8jhfLLWACA7+O/VgAAAMi0i6kXVXV6VZeZDe02KEdYDosaAfB5I+6XLp0yln3nb+muvOb2AYAgViBX+G1ftzscOnkx5abXCuYKV4jNlqH3CUaMrgAAAMiUSbsmaeTWkS4zXCcA4IZZz0u/zTWW7b5eKlTO3D4AAMX3rH7b188kp6ji4OU3vba4Vw3ly8m46g6jKwAAAO6Yu+sEPq35qeqXqG9RGwA+bf3n0pL+xrLPTJXKNjW3DwAAJmJ0BQAAQIaduHRC9WbXc5nZ1nGbwkLCLGoEwGftWylNaWYsW7OvVKufqXUAALACoysAAAAy5KMNH2nGnzOcnpfIXULxzeMtbATAJ53dL4191Fi2VIzUab6pdQAAsBKjKwAAAAxzd53Atw2/VcVCFS1qA8AnpVyUhhYxno9NMq8LAABewugKAAAAt/ae26sWC1q4zOzstFM2J59kCyAI2O3SB3cbzzO2AgACGKMrAAAAXHp52ctae2yt0/OYojEaV3uchY0A+JzYiAxkGVsBAIGP0RUAAAC3ZXfYFRUX5TIz/+n5KpWnlEWNAPgcxlYAAG6L0RUAAAC32HR8k7ou7eoyk9g50aI2AHxORsbW909LoWHmdQEAwAcxugIAAOAmDec01NHko07PO5brqHcqvWNhIwA+Y2Q56YLzHx9u8vY+KUd+c/sAAOCjGF0BAAAgSUpLT1OFqRVcZn5u/bMK3FXAokYAfMbcl6SdM4xlX06QCkea2wcAAB/H6AoAAAD9uP9H9VnTx2WG6wSAILTxX9Jig0+2t54sPdTM1DoAAPgLRlcAAIAgFznZ9RNpfSr1UYdyHSxqA8An/P2LNLmpsWyNt6Q675vbBwAAP8PoCgAAEKQupl5U1elVXWY2tNugHGE5LGoEwOvOHZDGRBnLFq8uPb/I1DoAAPgrRlcAAIAgNGnXJI3cOtJlhusEgCCSkiwNvc94PjbJvC4AAAQARlcAAIAg4+46gU9rfqr6Jepb1AaAVzkc0qA8xvOMrQAAGMLoCgAAECROXDqherPrucxs67hNYSFhFjUC4FWxERnIMrYCAJARjK4AAABBYPCGwZr550yn5yUjSmpBswUWNgLgNYytAACYjtEVAAAgwLm7TmByw8mqUKiCRW0AeE1Gxtb3TklZsprXBQCAAMfoCgAAEKD2nNujlgtauszs7LRTNpvNokYAvGJMlHTugLHsW39JOQuYWgcAgGBg+eialpam5ORkXblyRdmzZ1fOnDkVFsa9YQAAAJ700rKXtO7YOqfnMUVjNK72OAsbAbDc/FekX6cay764Wrq3vKl1AAAIJqaPrr/++qvmzp2rhIQE7d69W6dOnbolU6BAAZUtW1bVqlVT8+bNVbFiRbNrAQAABCS7w66ouCiXmfnN5qtURCmLGgGw3OYJ0qI3jGVbTpAiW5nbBwCAIGTa6Lpr1y69/vrrWrly5Y3XHA7HbbMnT57UqVOn9Msvv2jo0KGKiYnR6NGjFRnp+v4xAAAA/Mem45vUdWlXl5nEzokWtQFguQNrpW+fNJat1kuq94G5fQAACGKmjK6LFy9WmzZtdPny5RtDa44cOVS6dGkVLVpUOXLkUHh4uFJSUnTp0iUdPnxY+/bt06VLlyRJq1atUpUqVTRr1iw1atTIjIoAAAABpcHsBjp26ZjT807lOuntSm9b2AiAZc4fkkYbfGClSLTUbZm5fQAAgOdH18OHD6tdu3a6dOmSsmTJoq5du+q5557TY489ptDQUKffLz09XVu2bNGkSZM0ceJEXb58We3atdPOnTtVtGhRT9cEAAAICKnpqao41fXVTD+3/lkF7uKDcYCAk3pZGnKP8XxsknldAADATTw+uo4dO1ZJSUnKlSuXlixZosqVKxv6fqGhoXr88cf1+OOPq3PnzmrQoIEuXLigsWPHasSIEZ6uCQAA4PcW7V+kvmv6usxwnQAQgBwOaVAe43nGVgAALBfi6TdctGiRbDab+vXrZ3hw/V9VqlRRv3795HA4tGjRIg83BAAA8H+RkyNdDq59o/syuAKBKDbC+OAam8TgCgCAl5hyvYAk1apVK1PvU7t27ZveDwAAANKF1AuqNr2ay8yGdhuUIyyHRY0AWCI2wnh24HnJZjOtCgAAcM/jo2vWrFl1+fJlXblyJVPv8+/vnzVrVk/UAgAA8HuTdk3SyK0jXWZ4uhUIMBkZW987KWUJN68LAAAwzOOj6/33368tW7Zo5syZiomJueP3mTFjxo33AwAACHaRk11/MvnImJGqV7yeRW0AmG7cY9KZvcayb+6RchUytw8AAMgQj4+urVq10ubNmzV+/Hg98MADeuONNzL8Hp9++qnGjx8vm82m1q1be7oiAACA3zhx6YTqzXY9pm7ruE1hIWEWNQJgqvhe0tZvjWVf+Fm6r6KpdQAAwJ3x+AdpvfrqqypTpowcDofefvttPfzwwxoxYoQ2bdqkCxcu3Pb7XLhwQZs2bdKIESP08MMP65133pEkPfDAA3rllVc8XREAAMAvDN4w2OXgWjKipBI7JzK4AoFg6+TrVwkYGVybj7/+AVkMrgAA+CyPP+maPXt2LVq0SE2aNNGff/6p3bt3q2/f/3yybo4cOZQzZ05lzZpVqampSk5O1qVLl256D4fDoTJlymjRokXKnj27pysCAAD4PHfXCUxuOFkVClWwqA0A0xzaIE1sYCxbuYfUcKi5fQAAgEd4fHSVpNKlS2vLli0aMWKExo4dq/Pnz984S05OVnJystPvGxERoddee01vv/22cubMaUY9AAAAn7Xn3B61XNDSZWZnp52y8cnkgH9LOiKNeshY9p7y0kurTa0DAAA8y5TRVbr+RGtsbKzee+89rVq1SmvWrNHu3bt1+PBhXbx4UVevXlW2bNmUK1cuFSlSROXKlVP16tUVExOjsDD+iBwAAAg+Ly17SeuOrXN6XqtoLY2tPdbCRgA8Lu2K9FFh4/nYJPO6AAAA05g2ut74AlmyqG7duqpbt67ZXwoAAMAv2R12RcVFuczMbzZfpSJKWdQIgMc5HNKgPMbzjK0AAPg100dXAAAAOLfp+CZ1XdrVZSaxc6JFbQCYIjYiA1nGVgAAAgGjKwAAgJfUn11fxy8dd3reqVwnvV3pbQsbAfCojIytA89L3NUMAEDA8NrompKSouXLl2vz5s06efKksmTJovvuu081atRQ1apVvVULAADAdKnpqao4taLLzMo2K5U/e36LGgHwqIyMre/+I4VlM68LAADwCo+PrmfOnNHatWslSY0bN1ZoaOgtmfHjx2vAgAE6derUbd8jMjJSX331lSpXruzpegAAAF61aP8i9V3T12WG6wQAP/VFVenkb8ayb/wh5b7H3D4AAMBrPD66xsXF6a233tIDDzygp5566pbzt956S6NGjZIkORyO277Hzp07VbNmTc2ZM0dNmjTxdEUAAACviJwc6fK8b3RftS/b3qI2ADxm0VvS5q+NZbsul4pWMrcPAADwOo+PritWrJAktW7d+pazH3/8USNHjpQk5ciRQ927d1eDBg1UtGhRpaena9++fZozZ46mTp2qtLQ0tW/fXnv27FGhQoU8XRMAAMAyF1IvqNr0ai4zG9ptUI6wHBY1AuARv06T5vcwln36C+lRflMFAIBg4fHR9Y8//pAkRUVF3XL28ccfS5LuvfderVq1Svfff/9N52XLllWTJk3UuXNnNW7cWMnJyRozZoyGDBni6ZoAAACWmLhrokZtHeUyw3UCgJ85vFmaUNdYNvpF6ckR5vYBAAA+J8TTb/jPP/9IkooXL37T61evXtW6detks9n0ySef3DK4/reYmBi9/vrrcjgcWrx4sacrAgAAWCJycqTLwXVkzEgGV8CfXDh+/UOyjAyuBR+SYpMYXAEACFIef9I1JOT6jnvt2rWbXj9x4oSuXbsmm82mRo0auX2fJ598UkOHDtX+/fs9XREAAMBUJy6dUL3Z9VxmtnXcprCQMIsaAciUtKvSRxm48iw2ybwuAADAL3j8Sdf77rtPkrR79+6bXg8L+88vKrJly+b2ff6dSU9P92A7AAAAcw3eMNjl4FoqopQSOycyuAL+wOG4/mSr0cE1NonBFQAASDJhdK1Vq5YcDoemTJly0+v33nuv7r77bknSli1b3L7Ppk2bJP1nxAUAAPB1kZMjNfPPmU7PJzecrPnN5lvYCMAdi42QBuUxmGVsBQAAN/P46Prcc8/JZrPpl19+0ejRo2+8brPZ1LFjRzkcDvXr10+pqalO3+PkyZMaNmyYbDab6tVz/UfzAAAAvG3PuT2KnBzpMrOz005VKFTBokYA7lhsxPW/jBh4nrEVAADclsdH10qVKqlLly5yOBx688031a1bNx08eFCS9OGHH6p06dJau3atatSooeXLl9909+uVK1c0Y8YMVa5cWYcPH1bWrFn1yiuveLoiAACAx7yw9AW1XNDS6XntorWV2DlRNpvNwlYAMiwjY+u7J66Prfz/GgAAOOHxD9KSpM8//1xHjx7VTz/9pEmTJunbb7/VY489pipVqqhVq1YaPXq0tmzZogYNGihr1qwqVKiQ0tPT9c8//yg9PV0Oh0M2m00jR45U2bJlzagIAACQKXaHXVFxUS4z85vNV6mIUhY1AnBH/lVTOr7dWLb3b1JEEVPrAACAwGDK6Jo1a1bNnz9fH374oYYPH660tDRt3rxZmzdvvinncDiUkpKiw4cP3/h7SSpYsKDGjh2rNm3amFEPAAAgUzYe36huS7u5zCR2TrSoDYA78lM/acMXxrJdlkjFKpvbBwAABBRTRldJCgsL0wcffKDnn39e//rXvzR79mzt37//tlmHw6GwsDBVqVJFLVu2VJcuXZQjRw6zqgEAANyxerPr6cSlE07PO5XrpLcrvW1hIwAZsmOm9MOLxrJPjZMqdDK3DwAACEimja7/VrJkSQ0bNkzDhg3T6dOn9fvvv+vs2bO6dOmSsmXLply5cqlo0aJ64IEHlCWL6XUAAADuSGp6qipOregys7LNSuXPnt+iRgAy5OhW6evaxrIVn5eajja1DgAACGyWrpz58+fXE088YeWXBAAAyLRF+xep75q+LjNcJwD4qIv/SJ+WMZbN94DUc4u5fQAAQFDg0VIAAAAXIidHujzvG91X7cu2t6gNAMOupUiDCxrPxyaZ1wUAAAQdRlcAAIDbuJB6QdWmV3OZ2dBug3KEcQ894FMcDmlQHuN5xlYAAGACRlcAAID/MSFxgkZvG+0yw3UCgA+KjchAlrEVAACYx9TRde3atZo9e7b27dunkJAQPfjgg2rTpo0qVKjg9vvu3btXDRo0kM1m0759+8ysCQAAcIO76wRGxYxS3eJ1LWoDwJCMjK0Dz0s2m2lVAAAAJJNG17S0ND3//POaPn36Ta/Hx8drxIgRatGihb744gsVKFDA6XukpqbqwIEDsvETIgAAYIETl06o3ux6LjPbOm5TWEiYRY0AuJWRsbX/cSnrXeZ1AQAA+C8hZrxpt27d9N1338nhcNz2r7lz5+qRRx5RQkKCGV8eAAAgQz5c/6HLwbV0RGkldk5kcAV8xTf1jA+urydev0qAwRUAAFjI46NrQkKCpkyZIpvNpjJlyig+Pl4XL17UmTNnNGfOHEVHR8vhcOiff/5R/fr1tWDBAk9XAAAAMCxycqS+3/O90/O4RnGa12yedYUAOLdswPWx9cgm99nnfrw+tuYpZn4vAACA/+Hx0XXChAmSpPvuu0/r1q1T48aNlSNHDt19991q3ry51q9frxEjRihLliy6evWqWrVqpWnTpnm6BgAAgEt7zu1xe3/rzk479WjBRy1qBMCpxNnXx9a1Y9xnG4+8PraWqGZ+LwAAACc8fqfrunXrZLPZ9Oabbypv3ry3nP/77LHHHlOLFi107tw5de7cWRcvXtTLL7/s6ToAAAC3eGHpC9pwfIPT89pFa2tMbQPjDgBzHdsuja9pLPtoB+npz02tAwAAYJTHR9djx45JkqpUqeIyV7NmTf3yyy9q0KCBjh07pldeeUXJycl66623PF0JAABAkmR32BUVF+UyM7/ZfJWKKGVRIwC3lXxS+uQBY9m7S0i9dphaBwAAIKM8PrqmpaVJkkJDQ91mH3roIa1Zs0Z169bV33//rT59+ujixYsaNGiQp2sBAIAgt/H4RnVb2s1lJrFzokVtANzWtVRpcAHj+dgk87oAAABkgsfvdC1YsKAk6dChQ4byJUuW1Jo1a1S2bFk5HA4NHjxYb775pqdrAQCAIFZ3Vl2Xg2vncp0ZXAFvi40wPrjGJjG4AgAAn+bxJ10ffvhhHT16VGvWrFHz5s0NfZ97771Xv/zyi+rXr69ff/1Vo0eP1s6dOz1dDQAABJnU9FRVnFrRZWZlm5XKnz2/RY0A3CI2IgNZhlYAAOAfPP6ka40aNeRwODRr1iw5HA7D3y9fvnxauXKlqlatKofDoZ9//tnT1QAAQBBZuH+h28E1sXMigyvgLbERxgfXgecZXAEAgF/x+OjasGFDSdc/UGvu3LkZ+r65c+fWsmXLVK9evQwNtgAAAP8tcnKk+q3p5/S8b3RfrhMAvGVCfeNja7+j18dWm83cTgAAAB7m8dH10UcfVY0aNVSqVClNnjw5w98/e/bsWrhw4Y2rCRhfAQCAURdSLyhycqTLzMZ2G9W+bHuLGgG4YXGf62Pr4Y3us712XB9bw3Oa3wsAAMAEHr/TVZJWr16dqe8fFhamOXPmeKgNAAAIBhMSJ2j0ttEuMzzdCnjB1slS/GvGsi/9It0TZW4fAAAAC5gyunrK5s2bFRcXp3Hjxnm7CgAA8GHunm4dFTNKdYvXtagNAEnS32ukyU2MZdtMkco9ZW4fAAAAC/nc6HrkyBFNmTJFU6ZM0Z9//ilJjK4AAOC2Tlw6oXqz67nMbOu4TWEhYRY1AqCz+6WxjxrL1n5feuItc/sAAAB4gU+MrpcuXdLs2bMVFxen1atX37jH1eFwyMal+QAA4DY+XP+hvt/zvdPz+/Pcrx+e/sHCRkCQu5okDStmLFuumdQm45//AAAA4C+8Nro6HA4tX75ccXFxmjdvni5fvnzjdUkqUqSIWrRooZYtW3qrIgAA8FHurhOIaxSnRwsafNIOQOakX5M+zGcsm+te6c3d5vYBAADwAZaPrr/99psmT56s7777TsePH5f0n6G1WLFiat26tVq1aqXHH3/c6moAAMDH7Tm3Ry0XuP4N2Z2ddvInZQCrxEZkIJtkXg8AAAAfY8noeurUKU2bNk1xcXHasWOHpP8MrXny5NH58+dls9n08ccfq02bNlZUAgAAfqbb0m7aeHyj0/M6xepodK3R1hUCghljKwAAgEumja4pKSmaP3++4uLitHTpUqWnp98YWsPDw/Xkk0+qQ4cOevLJJ5U9e3azagAAAD9nd9gVFRflMrOg2QKVjChpUSMgiA26W3LYjWUHnJNCQsztAwAISE3HJejUxZRbXrf/36703xqNWaMQJ3/KqUCucMX3rO7xfoARHh9dExISFBcXp1mzZunChQuS/vOBWDVq1FCHDh3Upk0bRURk4HfHAQBAUNpwfINeWPqCy0xi50SL2gBBbFJj6WCCsWz/41LWu8ztAwAIaKcupujEhauGsidvM84CvsDjo+sTTzwhm81246nWcuXKqX379mrfvr2KFTP4aaYAACDo1ZlVRycvn3R63rlcZ71V6S0LGwFBaMm70vrPjGXf2C3lvtfcPgAAAH7CtOsFcuXKpTFjxui5554z60sAAIAAlJqeqopTK7rMrGyzUvmz57eoERCEfp0mze9hLPviKuneR02tAwAA4G9MuWTJ4XAoOTlZXbt21SOPPKKPP/5YR44cMeNLAQCAALJw/0K3g2ti50QGV8AsB9dd/5AsI4Nr62+vf0gWgysAAMAtPD66rlmzRt26dVNERIQcDod27dqlfv36qUSJEqpVq5YmTpx4465XAACAf4ucHKl+a/o5Pe8b3Zf7WwGznDtwfWyd1Mh9Nqb/9bH1oeam1wIAAPBXHh9dq1WrpvHjx+vEiROaOXOmnnzySYWGhsput+uXX37RCy+8oMKFC6t169ZasGCBrl275ukKAADAj1xIvaDIyZEuMxvbbVT7su0tagQEkasXro+tY6LcZ/9f4+tja0wf83sBAAD4OdPudM2aNatat26t1q1b69SpU5o6dari4uK0Y8cOXb16VXPnztXcuXOVJ08esyoAAAAfNyFxgkZvG+303CabdnbeaV0hIFjY06UP8hrL5iggvf2XuX0AAAACjGmj638rUKCAevfurd69e2vXrl369ttvNX36dB0/flznzp2TzWaTJL3xxhtau3atWrVqpRo1alhRDQAAeIm7p1tHx4xWneJ1LGoDBJHYiAxkk8zrAQAAEMBM+SAtVx5++GF98sknOnz4sH788Ue1bdtW2bJlk8Ph0LFjx/TZZ58pJiZG99xzj3r06KEVK1ZYXREAAJjoxKUTbgfXbR23MbgCnhYbYXxwjU1icAUAAMgEy0fXG184JEQNGzbUd999pxMnTmj8+PE3nm51OBz6559/9K9//UsNGjTwVkUAAOBhH6z/QPVm13N6fn+e+5XYOVFhIWEWtgIC3OBCxsfWAecYWwEAADzAkusF3MmVK5e6deumbt266cCBA4qLi9PUqVP111/cHQUAQKBw93TrlEZTVL5geWvKAMEg7mlp/ypj2f7HpKw5TK0DAAAQTHxidP1vJUqU0IABAzRgwACtXbtWU6ZM8XYlAACQCX+e/VOt4lu5zOzstPPGHe8AMml5rJQwyli2929SRBFT6wAAAAQjnxtd/1u1atVUrVo1b9cAAAB3qNvSbtp4fKPT8zrF6mh0rdHWFQIC2Y4Z0g8vGct2+1kqUtHcPgAAAEHMp0dXAADgn+wOu6LiolxmFjRboJIRJS1qBASwQxulifWNZVtOkCJdP3kOAACAzGN0BQAAHrXh+Aa9sPQFl5nEzokWtQEC2PlD0mjXdyXf8MQ7Uu13ze0DAACAGxhdAQCAx9SZVUcnL590ev7cQ8/pzcfetLAREIBSLkpDDd7D+kB9qf0sc/sAAADgFoyuAAAg01LTU1Vxquv7IVe2Wan82fNb1AgIQPZ06YO8xrLZIqS+h8ztAwAAAKcYXQEAQKbE74tX/4T+LjNcJwBkUmxEBrJJ5vUAAMACBXKF3/Z1u8OhkxdTbnqtYK5whdhsGXofwAqMrgAA4I5FTnZ9n2S/6H5qV7adRW2AAMTYCgAIQvE9q9/29TPJKao4ePlNry3uVUP5cjKuwvcwugIAgAy7kHpB1aZXc5nZ2G6j7gq7y6JGQIAZUkRKvWgsO+CsFBJqbh8AAABkCKMrAADIkG8Sv9GYbWOcnofYQrSj0w4LGwEBZGpL6a/l7nOS1O+oFJ7T3D4AAAC4I4yuAADAMHfXCYyOGa06xetY1AYIICs+lNZ8Yiz7+i4pT1Fz+wAAACBTGF0BAIBbx5OPq/6c+i4z2zpuU1hImEWNgACxc5Y0t5uxbNdlUtFoc/sAAADAIxhdAQCAS4PWD9LsPbOdnt+f53798PQPFjYCAsCRLdI3Bp8Kbz5einrG3D4AAADwKEZXAADglLvrBKY0mqLyBctbUwYIBOcPS6MfNpat8aZUZ4C5fQAAAGAKRlcAAHCLP8/+qVbxrVxmdnbaKZvNZlEjwM+lJEtD7zOWLV1b6sjT4wAAAP6M0RUAANyk25Ju2nhio9PzusXqalStURY2AvyY3S59cLexbFgO6d1j5vYBAACAJRhdAQCAJMnusCsqLsplZkGzBSoZUdKiRoCfi43IQDbJvB4AAACwHKMrAADQhuMb9MLSF1xmEjsnWtQG8HOMrQAAAEGP0RUAgCBX5/s6OnnlpNPz5x96Xm889oaFjQA/NbykdOWsseyAs1JIqLl9AAAA4DWMrgAABKnU9FRVnFrRZWZlm5XKnz2/RY0AP/XdM9Ken4xl+x6WsuU2tw8AAAC8jtEVAIAgFL8vXv0T+rvMcJ0A4MbKodLqYcayvXZKdxc3tw8AAAB8BqMrAABBJnJypMvzftH91K5sO4vaAH5o1xxpdhdj2ed/kopXMbcPAAAAfA6jKwAAQSIpJUnVZ1R3mdnYbqPuCrvLokaAnzm6Vfq6trFssy+l8vzmBQAAQLBidAUAIAh8k/iNxmwb4/Q8xBaiHZ12WNgI8CMXjkkjyxrLVu0p1R9sbh8AAAD4PEZXAAACnLvrBEbHjFad4nUsagP4kdRL0pB7jWVL1JCeW2huHwAAAPgNRlcAAALU8eTjqj+nvsvMrx1/VZYQfjoA3MRulz6421g2NKv0/ilz+wAAAMDv8KssAAAC0KD1gzR7z2yn5w/c/YDmPjXXwkaAn4iNyEA2ybweAAAA8GuMrgAABBh31wlMaTRF5QuWt6YM4C8YWwEAAOBBjK4AAASIP8/+qVbxrVxmdnbaKZvNZlEjwA98UkZK/sdY9v0zUig/fQYAAIB7/KwRAIAA0HVJV206scnpeb3i9TQyZqSFjQAfN7OjtHuBsWzfQ1K2DDwJCwAAgKDH6AoAgB+zO+yKiotymVnQbIFKRpS0qBHg41aPkFYONpZ9bbuUl//vAAAAIOMYXQEA8FPrj63Xi8tedJlJ7JxoURvAx/0+X/q+k7Hscz9KJaqZ2wcAAAABjdEVAAA/VPv72jp15ZTT8+cfel5vPPaGhY0AH3VsuzS+prHsU59JFTqaWgcAAADBgdEVAAA/kpKeosemPuYys7LNSuXPnt+iRoCPunBcGvmgsWzlHlLDoeb2AQAAQFBhdAUAwE/E74tX/4T+LjNcJ4Cgl3pZGnKPsWyxKlKXn8ztAwAAgKDE6AoAgB+InBzp8rz/4/317IPPWtQG8EEOhzQoj/F8bJJpVQAAAABGVwAAfFhSSpKqz6juMrOx3UbdFXaXRY0AHxQbkYEsYysAAADMx+gKAICP+ibxG43ZNsbpeagtVNs7bbeuEOBrGFsBAADgoxhdAQDwQe6uExhda7TqFKtjURvAx4x6WEo6bCz7/hkplJ/yAgAAwFr8DBQAAB9yPPm46s+p7zLza8dflSWE/4QjCM16XvptrrFsn4NS9jym1gEAAACc4VdsAAD4iNh1sZqzd47T8zJ3l9Gcp5yfAwFrzUhpxSBj2Z7bpHylze0DAAAAuMHoCgCAD3B3ncCURlNUvmB5a8oAvmL3Qmlme2PZzvFSySfM7QMAAAAYxOgKAIAX/XH2D7WOb+0ys7PTTtlsNosaAT7g3EFpzCPGsk3HSBWfM7UOAAAAkFGMrgAAeEnXJV216cQmp+f1itfTyJiRFjYCvOzKeWl4cWPZ6BelJ0eYWgcAAAC4U4yuAABYzO6wKyouymUmvlm8SkSUsKYQ4G3XUqXBBYxl73tMemGFuX0AAACATGJ0BQDAQuuPrdeLy150mUnsnGhRG8DLHA5pUB7j+dgk06oAAAAAnsToCgCARWp/X1unrpxyev78w8/rjYpvWNgI8KLhJaQr59znQrJIA86YXgcAAADwJEZXAABMlpKeosemPuYys7LNSuXPnt+iRoAXTW4q/f2LsezA8xIfIgcAAAA/xOgKAICJ4vfFq39Cf5cZrhNAUPjxbWnTeGPZ989Iofw0FQAAAP6Ln80CAGCSyMmRLs/fffxdtX2wrUVtAC/Z+C9p8TvGsv2OSuE5ze0DAAAAWIDRFQAAD0tKSVL1GdVdZja226i7wu6yqBHgBX8ulqYb/E2FN/6Qct9jbh8AAADAQoyuAAB40Nc7v9bYX8c6PQ+1hWp7p+3WFQKsduxXaXyMsWz3dVKhh0ytAwAAAHgDo6tJ3nnnHY0YMeLG369cuVIxMTHeKwQAMJ276wRG1xqtOsXqWNQGsNj5w9Loh41lO8yV7uf/CwAAAAhcjK4m2LFjh0aNGuXtGgAAixxIOqCm85q6zPza8VdlCeE/uwhAV5OkYcWMZZuOkSo+Z2odAAAAwBfwqz8Ps9vteuGFF3Tt2jUVLFhQJ0+e9HYlAICJnvvpOW39Z6vT8zJ3l9Gcp+ZY2AiwSHqa9GF+Y9lqvaR6H5jbBwAAAPAhjK4eNnbsWG3evFkPPvigmjdvrqFDh3q7EgDAJO6uE5jSaIrKFyxvTRnAKg6HNCiPsWyZhlK7mabWAQAAAHwRo6sHHT58WO+//74k6csvv9SqVau8WwgAYIpt/2xT5586u8zs7LRTNpvNokaARUbcL1065T4XUVTqvcv8PgAAAICPYnT1oB49eig5OVmdO3dWTEwMoysABKCq06vqYupFp+elI0prXrN51hUCrBDXTNq/0lh24HmJ33AAAABAkGN09ZDvv/9eCxcuVN68eTVixAhv1wEAeFi6PV3lp5R3mZnddLb+X97/Z00hwAo/9ZM2fGEs+/4ZKZSfWgIAAAASo6tHnD9/Xr169ZIkDR8+XAUKFPDYex85csTl+fHjxz32tQAAt/fbmd/UdmFbl5nEzokWtQEssOlr6ce3jGX7HZHCc5nbBwAAAPAzjK4e8M477+jEiROqWrWqunbt6tH3Llq0qEffDwCQMa+vfF0rDq1wel6/eH19GvOphY0AE+1ZIn3Xxli29+9SxH3m9gEAAAD8FKNrJiUkJOibb75RlixZ9NVXX/GhKQAQINLS01RhagWXmWWtlqlwjsIWNQJMdHyH9K8njGVfWiPd84i5fQAAAAA/x+iaCampqXrxxRflcDjUu3dvRUZGevxrHD582OX58ePHFR0d7fGvCwDBbN3RdXpp+UsuM1wngICQdEQa9ZCxbPvZ0gP1zO0DAAAABAhG10wYMmSIdu/erWLFimngwIGmfI0iRYqY8r4AgNtrE99Gu8/udno+utZo1SlWx8JGgAmuXpCGGbzCqPFIqZJnr08CAAAAAh2j6x36448/NHToUEnSuHHjlCNHDi83AgBkxqW0S6r8XWWXmc3tNytblmwWNQJMkJ4mfZjfWLbKq1KDj8ztAwAAAAQoRtc7NGrUKKWmpqpUqVK6fPmyZsyYcUtm165dN779888/68SJE5Kkpk2bMtICgA9ZuH+h+q3p5/T88Xse1zf1v7GwEeBhDoc0KI+x7P31pA6zTa0DAAAABDpG1zuUkpIiSdq/f7+effZZt/kPP/zwxrf//vtvRlcA8BGPT3tcl69ddno+ueFkVSjk+gO1AJ/26YPSxePuczkLS2/9aX4fAAAAIAgwugIAgtLpK6dV6/taLjPbO25XaEioy8yZ5BRVHLz8pte2vldX+XKGZ7ojkClTW0l/LTOWHXhestlMrQMAAAAEkxBvF/BX3377rRwOh8u//vvDtVauXHnj9RIlSnivOABA3+761uXg2uKBFkrsnOh2cAV80tL3pNgIY4Pr+6el2CQGVwAAAMDDeNIVABBUIidHujyf9/Q8lc5T2qI2gAdtmSgt7G0s2/ewlC23uX0AAACAIMboCgAICocuHFLjHxq7zOzstFM2nviDv9m7TJrWyli2929SRBFz+wAAAABgdAUABL7hm4Zr6u6pTs+7R3VXj/I9LGwEeMCJROmr6sayL/0i3RNlbh8AAAAANzC6AgAClt1hV1Sc66FpWatlKpyjsEWNAA+4cEwaWdZYtt33UpkG5vYBAAAAcAtGVxPFxsYqNjbW2zUAICj9dvo3tV3U1mUmsXOiRW0AD0i5KA01eDXAk59I0S+Y2wcAAACAU4yuAICA8/rK17Xi0Aqn5wOqDFDrMq0tbARkQvo16cN8xrKVe0gNh5rbBwAAAIBbjK4AgICRlp6mClMruMwktE1QRHiERY2ATHA4pEF5jGVL1ZI6zTOzDQAAAIAMYHQFAASEdUfX6aXlLzk9L5KziBa3XGxhIyATRj0sJR12n8tRQHr7L/P7AAAAAMgQRlcAgN9rHd9af5z9w+n5mFpjVLtYbQsbAXfou7bSHoO/OTDwvGSzmVoHAAAAwJ1hdAUA+K1LaZdU+bvKLjOb229WtizZLGoE3KFlA6W1o41l3z8thYaZWgcAAABA5jC6AgD8Uvy+ePVP6O/0vPI9lfV1/a8tbATcga2TpfjXjGX7HpKycR8xAAAA4A8YXQEAfid6WrSuXLvi9DyuUZweLfiohY2ADPprhTS1hbHs64lSnmLm9gEAAADgUYyuAAC/cfrKadX6vpbLzPaO2xUaEmpRIyCD/vlN+rKqseyLq6R7+c0DAAAAwB8xugIA/MKkXZM0cutIp+ctHmihQVUHWdgIyIALx6WRDxrLtp0uPfikuX0AAAAAmIrRFQDg8yInR7o8n/f0PJXOU9qiNkAGpCRLQ+8zlm04XKr8srl9AAAAAFiC0RUA4LMOXTikxj80dpnZ2WmnbDabRY0Ag+zp0gd5jWUrvSA1/sTcPgAAAAAsxegKAPBJwzcN19TdU52e94jqoe7lu1vYCDDA4ZAG5TGWLVFDem6hqXUAAAAAeAejKwDAp9gddkXFRbnMLGu1TIVzFLaoEWDQmPLSub/d57LlkfoeNLsNAAAAAC9idAUA+IzfTv+mtovauswkdk60qA1g0MwO0u54Y9mB5yWuwwAAAAACHqMrAMAnvL7yda04tMLp+cAqA9WqTCsLGwFurPhAWvOpsex7p6QsWc3tAwAAAMBnMLoCALwqLT1NFaZWcJlJaJugiPAIixoBbmybIi141Vi2z0Epex5T6wAAAADwPYyuAACvWXt0rV5e/rLT8yI5i2hxy8UWNgJc2L9KinvaWLbXTunu4qbWAQAAAOC7GF0BAF7RakEr/XnuT6fnY2qNUe1itS1sBDhxcrf0RWVj2W4/S0UqmtsHAAAAgM9jdAUAWOpS2iVV/s71gLW5/WZly5LNokaAExf/kT4tYyz7zDSpbBNz+wAAAADwG4yuAADLxO+LV/+E/k7PK99TWV/X/9rCRsBtpF6ShtxrLNtgqFSlh7l9AAAAAPgdRlcAgCWip0XryrUrTs/jGsXp0YKPWtgI+B/2dOmDvMayj3WRmowytw8AAAAAv8XoCgAw1ekrp1Xr+1ouM9s7bldoSKhFjYDbiI0wlitWVerCh7sBAAAAcI3RFQBgmkm7Jmnk1pFOz1s+0FKxVWOtKwT8r3GPSWf2us9lzSn1P2p+HwAAAAABgdEVAGCKyMmRLs/nPT1PpfOUtqgN8D++7yz9Ps9YduB5yWYzsw0AAACAAMPoCgDwqEMXDqnxD41dZnZ22ikbIxa84eePpF8+NpZ976SUJdzcPgAAAAACEqMrAMBjhm0apmm7pzk97xHVQ93Ld7ewEfB/tn8nzTP4716fA1L2u02tAwAAACCwMboCADLN7rArKi7KZWZZq2UqnKOwRY2A//P3L9Lkpsayr22X8pY0tQ4AAACA4MDoCgDIlN9O/6a2i9q6zCR2TrSoDfB/Tv0pfR5tLNt1uVS0krl9AAAAAAQVRlcAwB3r9XMv/Xz4Z6fnA6sMVKsyrSxshKCXfFL65AFj2TZxUrmnze0DAAAAICgxugIAMiwtPU0VplZwmUlom6CI8AiLGiHopV6WhtxjLFvvQ6naa+b2AQAAABDUGF0BABmScDRB3Zc7/0CiIjmLaHHLxRY2QlCzp0sf5DWWrdBZemqsuX0AAAAAQIyuAIAMaLWglf4896fT87G1xqpWsVoWNkJQizX4JHWRaKnbMnO7AAAAAMB/YXQFALh1Ke2SKn9X2WVmS4ctCg8Nt6gRgtrnlaVTu93nQsOl90+a3wcAAAAA/gejKwDApfh98eqf0N/peZV7qmh8/fEWNrLemeQUVRy8/KbXtr5XV/lyMjJbanZXaddsY9mB5yWbzdQ6AAAAAOAMoysAwKnoadG6cu2K0/O4RnF6tOCjFjZCUFo1XFo1xFj2vZNSFsZwAAAAAN7F6AoAuMXpK6dV63vXd7Nu77hdoSGhFjVCUNoxU/rhRWPZd/6W7jL4gVoAAAAAYDJGVwDATSbtmqSRW0c6PW/5QEvFVo21rhCCz4EE6dvGxrKv/SrlLWVuHwAAAADIIEZXAMANkZMjXZ7Pe3qeSucpbVEbBJ1Te6TPKxnLdlkqFXvc3D4AAAAAcIcYXQEAOnjhoJr80MRlZmennbLxwUQww6XT0giDY36rSdLDLcztAwAAAACZxOgKAEFu2KZhmrZ7mtPzHuV7qHtUdwsbIWikXZE+KmwsW2egVOMNc/sAAAAAgIcwugJAkLI77IqKi3KZWdZqmQrnMDiKAUbZ7dIHdxvLlu8gNfvc3D4AAAAA4GGMrgAQhHad3qVnFz3rMpPYOdGiNggqsRHGcvdVlF742dwuAAAAAGASRlcACDKv/fyaVh5e6fR8YJWBalWmlYWNEBS+qi6dMDDk20KlgWfN7wMAAAAAJmJ0BYAgkZaepgpTK7jMJLRNUES4wScRASPmvijtnGksO+CcFBJibh8AAAAAsACjKwAEgYSjCeq+3PmHYRXNVVQ/tvjRwkYIeL+MkH4ebCz77j9SWDZz+wAAAACAhRhdASDAtVzQUnvO7XF6PrbWWNUqVsvCRghoibOlOV2NZd/eL+XIZ24fAAAAAPACRlcACFDJqcmqMr2Ky8yWDlsUHhpuUSMEtIPrpEmNjGV7bpPylTa3DwAAAAB4EaMrAASg+H3x6p/Q3+l5lXuqaHz98RY2QsA6/Zf0WUVj2ed/koq7/o0AAAAAAAgEjK4AEGAqTa2kq+lXnZ5PaTRF5QuWt64QAtOlM9KIUsayLSdIka3M7QMAAAAAPoTRFQACxOkrp1Xre9d3s27vuF2hIaEWNUJASrsqfVTIWLb2+9ITb5nbBwAAAAB8EKMrAASAibsmatTWUU7PWz7QUrFVY60rhMBjt0sf3G0s+0hbqcW/zO0DAAAAAD6M0RUA/Fzk5EiX5/OenqfSefjQImRCbB5JDve5wo9IL68xuw0AAAAA+DxGVwDwUwcvHFSTH5q4zOzstFM2m82iRgg442OkY78ay8YmmVoFAAAAAPwJoysA+KGhG4fquz++c3reo3wPdY/qbmEjBJR5PaTt04xlB5yTQkLM7QMAAAAAfobRFQD8iN1hV1RclMvM8lbLVSiHwQ86Av7bmpHSikHGsu+ekMKym9sHAAAAAPwUoysA+Ildp3fp2UXPuswkdk60qA0Cyq650uznjWXf3iflyG9uHwAAAADwc4yuAOAHXvv5Na08vNLpeWyVWLUs09LCRggIhzZIExsYy766Rcr/gLl9AAAAACBAMLoCgA9LTU9VxakVXWbWPrtWubPmtqgRAsKZfdK4Csayzy2SSlQ3tw8AAAAABBhGVwDwUQlHE9R9ufMPwyqaq6h+bPGjhY3g9y6flT4uaSzb4mvpkTbm9gEAAACAAMXoCgA+qMWCFtp7bq/T87G1xqpWsVoWNoJfS7sqfWTww9VqvSvVfMfcPgAAAAAQ4BhdAcCHJKcmq8r0Ki4zWzpsUXhouEWN4NccDmlQHmPZyNZSy29MrQMAAAAAwYLRFQB8xIJ9C/RuwrtOz6vcU0Xj64+3sBH82ocFpPRU97mC5aQe683vAwAAAABBhNEVAHxApamVdDX9qtPzKY2mqHzB8tYVgv/6pq50ZLOxbGySuV0AAAAAIEgxugKAF52+clq1vnd9N+v2jtsVGhJqUSP4rQU9pW1xxrIDzkr8OwUAAAAApmF0BQAvmbhrokZtHeX0vOUDLRVbNda6QvBPa8dKy943lu1/XMp6l7l9AAAAAACMrgBgNYfDoUfiHnGZmf/0fJXKU8qiRvBLv82TZnU2ln3rLylnAVPrAAAAAAD+g9EVACx08MJBNfmhicvMzk47ZbPZLGoEv3N4szShrrHsK5ukAv/P3D4AAAAAgFswugKARYZsHKLpf0x3et6jfA91j+puYSP4lbN/S2PLG8t2jpdKPmFqHQAAAACAc4yuAGAyu8OuqLgol5nlrZarUI5CFjWCX7l8Vvq4pLFss6+k8s+a2wcAAAAA4BajKwCYaNfpXXp2kesRLLFzokVt4FeupUiDCxrL1uwj1epvbh8AAAAAgGGMrgBgkp4/99Sqw6ucnsdWiVXLMi0t6wM/4XBIg/IYyz7UQmo9ydQ6AAAAAICMY3QFAA9LTU9VxakVXWbWPrtWubPmtqgR/MZH90ppl9zn8peRXt1sfh8AAAAAwB1hdAUAD0o4mqDuy51/GFaxXMW0qMUiCxvBL0xsKB1abywbm2RuFwAAAABApjG6AoCHtFjQQnvP7XV6Pq72OMUUjbGuEHxf/OvSVoPXAww4K4WEmloHAAAAAOAZjK4AkEnJqcmqMr2Ky8yWDlsUHhpuUSP4vPWfS0sMfvBV/2NS1hzm9gEAAAAAeBSjKwBkwoJ9C/RuwrtOz6vdW01f1fvKwkbwabvjpZkdjGXf3CPlKmRuHwAAAACAKRhdAeAOVZpaSVfTrzo9n9JoisoXLG9dIfiuI1ulb2oby/bYIBUsa24fAAAAAICpGF0BIINOXzmtWt/XcpnZ3nG7Qrl/E+cOSGOijGU7zZdKxZjZBgAAAABgEUZXAMiACYkTNHrbaKfnLR9oqdiqsZb1gW+yXT0vfVLGWPjpz6VHDV45AAAAAADwC4yuAGCAw+HQI3GPuMzMf3q+SuUpZVEj+KKsStOebJ2lzwyEa7wl1Xnf9E4AAAAAAOsxugIIameSU1Rx8PKbXtv6Xl3lyxl+4+8PXjioJj80cfk+OzvtlM1mM6Uj/IFDB7K1NxYt21R6Zqq5dQAAAAAAXsXoCgAuDNk4RNP/mO70/JXyr+jlqJctbARfk++TgjqQzUAwbynptV9N7wMAAAAA8D5GVwC4DbvDrqg41x+AtLzVchXKUciiRvA1d497QEpJMhaONZgDAAAAAAQERlcA+B+7z+5S9znPucwkdk60pgx8TlzYUD0RmiilGAgPOCuFhJreCQAAAADgWxhdAeC/ZCsyWd1X7nZ6PqjqILV4oIWFjeAreoXOUe+wOYayZ3vuU958+U1uBAAAAADwVYyuACBJtmvK9eB7LiNrn12r3FlzW1QIPuPQBuWb2EC9w9xHq6eM0RFHAW0Nz2V+LwAAAACAz2J0BRD0QnP8qbuKTXJ6Xjx3cS1svtDCRvAJp/dKnz1mKNo8ZZB+dTxgciEAAAAAgL9gdAUQ1J5f9ozuKrbP6fm42uMUUzTGukLwvuST0icGB9RWk3SmRGP9Oni5uZ0AAAAAAH6F0RVAULpy7Yqip0W7zGzpsEXhoeEWNYLXpSRLQ+8zFP0krbWe7/e58uUMl5KNfKIWAAAAACCYMLoCCDqbT2xWlyVdnJ5XKlRFExuOt7ARvCr9mvRhPkPRfmldNT29jiTpeTM7AQAAAAD8GqMrgKDS55c++vHvH52eXzrQXSNadrWwEbzG4ZDGVZTOOr9e4t8uP/66yq12/WQ0AAAAAAD/xugKIChcSL2gatOrOT13OEKU/MdgSSHWlYL3TG8n/bnIfe6hFlLrSbqSnCKt5t5WAAAAAIAxjK4AAt6KQyv0+srXnZ5fPd5Caed5ijEoLHlXWv+Z+1zhSOmlNZLNZn4nAAAAAEDAYXQFELAcDoe6Lu2qzSc2O83MevJHNfx0p4Wt4BUb/yUtfsd9Lks2qf8xKSTU/E4AAAAAgIDF6AogIJ26fEq1Z9V2el6hYAV92/Bbnb2UamErWG53vDSzg7HsuyeksOzm9gEAAAAABAVGVwAB54e9P2jAugFOz0fGjFS94vUsbATLHdooTaxvLPv2filHPnP7AAAAAACCCqMrgIBhd9jV9IemOnTxkNNMQtsERYRHWNgKljr9l/RZRWPZ136V8pYytw8AAAAAICgxugIICIcuHFLjHxo7Pa9fvL4+jfnUwkawVPIp6ZP7jWW7/SwVMTjMAgAAAABwBxhdAfi9CYkTNHrbaKfn39T/Ro/f87h1hWCd1EvSkHuNZdtOlx580mNfutGYNQqx2WR3OJye3U6BXOGK71ndYz0AAAAAAL6H0RWA30qzp6nqd1V1Nf2q08zGdht1V9hdFraCJdKvSR/ml3Tr4HmLxiOlSl09XuHkxZQ7OgMAAAAABD5GVwB+afeZ3WqzsI3T8/Zl26tvdF8LG8ESDof0ebR0eo/7bLXXpXqDTK8EAAAAAMD/YnQF4Hc+3vyxpvw+xen5jCYz9FC+hyxsBEvMaC/9sdB9rtzTUps48/sAAAAAAOAEoysAv3Hl2hVFT4t2eh4WEqaN7TcqLCTMwlYw3dL3pXVj3ecKlpO6r5Oc3KUKAAAAAIBVGF0B+IXNJzary5IuTs9fe/Q1vfDICxY2guk2fS39+Jb7XEiY9N4/Ukio+Z0AAAAAADCA0RWAz+vzSx/9+PePTs/jm8WrREQJ6wrBXH/8KM141li2/3EpKx+UBgAAAADwLYyuAHxWUkqSqs+o7vS8SM4iWtRikUJsIRa2gmmObJG+qWMs+/Y+KUd+c/sAAAAAAHCHGF0B+KQVh1bo9ZWvOz0fWGWgWpVpZV0hmOfMPmlcBWPZntukfKXN7QMAAAAAQCYxugLwKQ6HQ12WdNGWf7Y4zSxvtVyFchSysBVMcem0NMLggNp1uVS0krl9AAAAAADwEEZXAD7j5OWTqjPL+R8vr1Cwgr5t+K1sfDq9f0u9LA25x1j2mWlS2Sbm9gEAAAAAwMMYXQH4hLl752rguoFOz0fGjFS94vUsbASPs6dLgwtK9mvus09+IkW/YH4nAAAAAABMwOgKwKvsDrua/tBUhy4ecppJaJugiPAIC1vBoxwO6cuq0snf3WervibV/9D8TgAAAAAAmIjRFYDXHLpwSI1/aOz0vEGJBvqk5icWNoLHfd9J+n2++9yDTaS208zvAwAAAACABRhdAXjFhMQJGr1ttPPz+hMUfU+0dYXgWctjpYRR7nP5/5/0ykaJe3oBAAAAAAGE0RWApdLsaar6XVVdTb/qNLOx3UbdFXaXha3gMVsmSgt7GwjapPdPS6H8ZwgAAAAAEHj41S4Ay+w+s1ttFrZxet6hbAf1ie5jYSN4zJ+LpeltjWX7H5Oy5jC3DwAAAAAAXsToCsASH2/+WFN+n+L0fEaTGXoo30MWNoJHHN0qfV3bWPatvVLOgub2AQAAAADABzC6AjDVlWtXFD3N+d2sYSFh2th+o8JCwixshUw7u18a+6ix7Ktbpfz3m9vHCwrmCleIzSa7w6GTF1Nue3Y7BXKFW1EPAAAAAOBFjK4ATLP5xGZ1WdLF6flrj76mFx55wcJGyLRLZ6QRpYxluyyVij1ubh8vWtyrhvLlDNeZ5BRVHLz8tmcAAAAAgODE6ArAFO/88o4W/73Y6Xl8s3iViChhXSFkTtoV6aPCxrJtpkjlnjK3DwAAAAAAPozRFYBHJaUkqfqM6k7Pi+QsokUtFinEFmJhK9wxe7r00T1Seor7bMPhUuWXze8EAAAAAICPY3QF4DErDq3Q6ytfd3o+sMpAtSrTyrpCuHMOh/SvJ6QTO91nK78iNRxificAAAAAAPwEoyuATHM4HHp+yfPa+s9Wp5nlrZarUI5CFrbCHZvdVdo1232uTCOp3Qzz+wAAAAAA4GcYXQFkysnLJ1VnVh2n5xUKVtC3Db+VzcknucOHrPhQWvOJ+1y++6VXt0j8MwUAAAAA4LYYXQHcsbl752rguoFOz0fFjFLd4nUtbIQ7svVbKb6Xsez7Z6RQ/tMBAAAAAIAr/MoZQIbZHXY1/aGpDl085DST0DZBEeERFrZChu1ZIn3Xxli231EpPKe5fQAAAAAACBCMrgAy5NCFQ2r8Q2On5w1KNNAnNQ38EXV4z9Ft0te1jGXf3CPl4i5eAAAAAAAygtEVgGETEido9LbRzs/rT1D0PdHWFULGnDsgjYkyln1ls1SgjKl1AAAAAAAIVIyuANxKs6epyndVlJKe4jSzqf0mZc+S3cJWMOzyWenjksayz/8kFa9ibh8AAAAAAAIcoysAl3af2a02C53f+9mhbAf1ie5jYSMYlnZF+qiwsWybOKnc0+b2AQAAAAAgSDC6AnDq480fa8rvU5yez2gyQw/le8jCRjDEni4NLSKlXXafbTBUqtLD/E4AAAAAAAQRRlcAt7hy7Yqipzm/mzVrSFZtaL9BYSFhFraCIeNrSce2uc89/rLUaLj5fQAAAAAACEKMrgBusvnEZnVZ0sXpea8KvdQtspuFjWDInBekxO/d5+6vJ3WYbX4fAAAAAACCGKMrgBveWf2OFh9Y7PQ8vlm8SkSUsK4Q3Fs5VFo9zH3u7pLSa79KNpv5nQAAAAAACHKMrgCUlJKk6jOqOz0vkrOIFrVYpBBbiIWt4NK2OGlBT2PZ909LoVwFAQAAAACAVRhdgSC34tAKvb7ydafnA6sMVKsyrawrBNf2LpOmGfzn0e+IFJ7L3D4AAAAAAOAWjK5AkHI4HHp+yfPa+s9Wp5nlrZarUI5CFraCU8e2S+NrGsu++aeUq7CpdQAAAAAAgHOMrkAQOnn5pOrMquP0vGKhiprUYJJs3P/pfecOSmMeMZbtsVEq+KC5fQAAAAAAgFuMrkCQmbNnjmLXxzo9HxUzSnWL17WuEG7v8lnp45LGss/9KJWoZm4fAAAAAABgGKMrECTsDrua/NBEhy8edppJaJugiPAIC1vhFmlXpY8MXunQaqL0cEtz+wAAAAAAgAxjdAWCwMELB9XkhyZOzxuWaKgRNUdY2Ai3sNul4cWllAvus/UHS1V7mt8JAAAAAADcEUZXIMB9k/iNxmwb4/R8Qv0Jir4n2sJGuMU3daUjm93nol+UnmQcBwAAAADA1zG6AgEqzZ6mKt9VUUp6itPMpvablD1Ldgtb4SY/vCztmO4+V7q21PEH8/sAAAAAAACPYHQFAtDvZ37XMwufcXreoWwH9YnuY2Ej3GTVcGnVEPe5iGLS6zslm838TgAAAAAAwGMYXYEAM3zTcE3dPdXp+cwmM1UuXzkLG+GGX6dJ83sYy753SsqS1dw+AAAAAADAFIyuQIC4cu2Koqc5v5s1a0hWbWi/QWEhYRa2giTprxXS1BbGsn0PS9lym9sHAAAAAACYitEVCACbT2xWlyVdnJ73qtBL3SK7WdgIkqTjO6R/PWEs+8YfUu57zO0DAAAAAAAswegK+Ll3Vr+jxQcWOz1f2HyhiucubmEj6PwhaXSksWyPDVLBsub2AQAAAAAAlmJ0BfxUUkqSqs+o7vS8SM4iWtRikUJsIRa2CnJXzknDSxjLdl4olaxhah0AAAAAAOAdjK6AH1pxcIVeX/W60/PYKrFqWaaldYWC3bUUaXBBY9mWE6TIVub2AQA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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 700,
       "width": 686
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,8))\n",
    "plt.errorbar(data[\"conc\"], data[\"A260\"], data[\"sigma_A260\"], fmt=\"s\")\n",
    "plt.plot(data[\"conc\"], predicted_rss, label=\"Minimizing RSS\")\n",
    "plt.plot(data[\"conc\"], predicted_asym, label=\"Minimizing asymmetric loss\")\n",
    "plt.title(\"Maximum likelihood fit\")\n",
    "plt.legend()\n",
    "plt.xlabel(X_LABEL)\n",
    "plt.ylabel(Y_LABEL);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction vs. inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prediction and parameter inference are related, but fundamentally different tasks. Often the same statistical methods can be used to do both, but in many circumstances, you will approach your problem differently depending on whether prediction or parameter inference is your primary goal. The approach of *generative probabilistic modeling* is focused on writing down your best assumptions about the physical and biological process which generates the data you have collected in terms of a set of unknown parameters, and the machinery of Bayesian probability gives you tools for inferring those parameters. If your generative model is a good approximation of reality, then your model should also be good at predicting future data given the inferred parameters. However, for complicated physical and biological processes, writing down a model that accurately reflects reality often yields extremely complicated models with a large number of unknown parameters. Since it may be difficult to obtain precise parameter estimates for large numbers of unknown parameters with limited data, these models may offer poor predictive performance. A simpler approach, if you only care about predictive performance, is a simplified model with fewer parameters which doesn't attempt to faithfully describe the process of generating the data, but merely tries to predict measurements.\n",
    "\n",
    "Further reading:\n",
    "1. [Döring](https://www.datascienceblog.net/post/commentary/inference-vs-prediction/) provides a very readable blog post describing some differences between inference and prediction.\n",
    "2. [Wu, Harris, and Mcauley (2008)](https://onlinelibrary.wiley.com/doi/abs/10.1002/cjce.5450850401) uses multiple regression as a case study to look at where simplified models may be worse at parameter inference but better at prediction.\n",
    "3. [Breiman (2001)](https://projecteuclid.org/euclid.ss/1009213726) was one of the first to clearly address the distinction between statisticians and machine learning/data science practicioners in this highly-cited, influencial paper.\n",
    "4. [Shmueli (2010)](https://projecteuclid.org/euclid.ss/1294167961) gives a more recent comparison between explanatory and predictive modeling.\n",
    "5. [Sanders](https://hdsr.mitpress.mit.edu/pub/a7gxkn0a/release/5) uses publishing trends to investigate this dichotomy, focusing on industry.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "ms = np.linspace(0.01, 0.03, 100)\n",
    "bs = np.linspace(-1.5, 1.5, 100)\n",
    "sigmas = data[\"sigma_A260\"].values[np.newaxis,np.newaxis,:]\n",
    "predictions = ms[:,np.newaxis,np.newaxis] * data[\"conc\"].values[np.newaxis,np.newaxis,:] + bs[np.newaxis,:,np.newaxis]\n",
    "residuals = predictions - data[\"A260\"].values[np.newaxis,np.newaxis,:]\n",
    "rss = residuals.sum(axis=-1)\n",
    "ll_terms = stats.norm.logpdf(residuals, loc=0, scale=sigmas)\n",
    "ll = ll_terms.sum(axis=-1)\n",
    "m_prior = stats.norm.logpdf(ms, loc=0.02, scale=0.01)\n",
    "b_prior = stats.norm.logpdf(bs, loc=0, scale=1)\n",
    "log_posterior = ll + m_prior[:,np.newaxis] + b_prior[np.newaxis,:]\n",
    "log_posterior -= logsumexp(log_posterior)\n",
    "posterior = np.exp(log_posterior)\n",
    "likelihood = np.exp(ll)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is the joint likelihood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 388
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extent=[left x-value, right x-value, top y-value, bottom y-value]\n",
    "plt.imshow(likelihood, extent=[bs[0], bs[-1], ms[-1], ms[0]], aspect=\"auto\")\n",
    "plt.colorbar();\n",
    "plt.xlabel(\"b\")\n",
    "plt.ylabel(\"m\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here's the posterior, which is just the product of the likelihood and the prior probabilities of each of the parameters. Try adjusting the prior distributions to make them narrower, see if you can make the posterior look qualitatively different than the likelihood."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 265,
       "width": 400
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(posterior, extent=[bs[0], bs[-1], ms[-1], ms[0]], aspect=\"auto\")\n",
    "plt.colorbar();\n",
    "plt.xlabel(\"b\")\n",
    "plt.ylabel(\"m\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also marginalize by summing our two-dimensional array of probabilities to get one-dimensional probabilities of each parameter separately."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 380
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ms, posterior.sum(axis=1))\n",
    "plt.xlabel(\"m\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 384
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bs, posterior.sum(axis=0))\n",
    "plt.xlabel(\"b\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction uncertainty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if we make an A260 measurement and we want to know what our uncertainty in DNA concentration should be?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_A260 = 11\n",
    "new_predictions = (new_A260 - bs[np.newaxis,:,np.newaxis]) / ms[:,np.newaxis,np.newaxis]\n",
    "new_predictions = new_predictions.reshape((-1,))\n",
    "order = new_predictions.argsort()\n",
    "sorted_predictions = new_predictions[order]\n",
    "prediction_posterior = posterior.reshape((-1,))[order]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 384
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(sorted_predictions, prediction_posterior)\n",
    "plt.xlabel(\"Predicted DNA concentration\");"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:mcbenv]",
   "language": "python",
   "name": "conda-env-mcbenv-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}