{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "from datascience import *\n",
    "\n",
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib.patches as patches\n",
    "plt.style.use('seaborn-muted')\n",
    "mpl.rcParams['figure.dpi'] = 200\n",
    "%matplotlib inline\n",
    "\n",
    "from IPython.display import display\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "solve = lambda x,y: sympy.solve(x-y)[0] if len(sympy.solve(x-y))==1 else \"Not Single Solution\"\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "4f243c01-873e-4eb5-bdfe-451f2a06dfea"
   },
   "source": [
    "# Market Equilibria\n",
    "\n",
    "We will now explore the relationship between price and quantity of oranges produced between 1924 and 1938. Since the data {cite}`01demand-fruits` is from the 1920s and 1930s, it is important to remember that the prices are much lower than what they would be today because of inflation, competition, innovations, and other factors. For example, in 1924, a ton of oranges would have costed \\$6.63; that same amount in 2019 is \\$100.78. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": "5a6c6746-bad6-466e-8c18-bc16f5fad344"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Pear Price</th> <th>Pear Unloads (Tons)</th> <th>Plum Price</th> <th>Plum Unloads</th> <th>Peach Price</th> <th>Peach Unloads</th> <th>Orange Price</th> <th>Orange Unloads</th> <th>NY Factory Wages</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1924</td> <td>8.04      </td> <td>18489              </td> <td>8.86      </td> <td>6582        </td> <td>4.96       </td> <td>41880        </td> <td>6.63        </td> <td>21258         </td> <td>27.22           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1925</td> <td>5.67      </td> <td>21919              </td> <td>7.27      </td> <td>5526        </td> <td>4.87       </td> <td>38772        </td> <td>9.19        </td> <td>15426         </td> <td>28.03           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1926</td> <td>5.44      </td> <td>29328              </td> <td>6.68      </td> <td>5742        </td> <td>3.35       </td> <td>46516        </td> <td>7.2         </td> <td>24762         </td> <td>28.89           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1927</td> <td>7.15      </td> <td>17082              </td> <td>8.09      </td> <td>5758        </td> <td>5.7        </td> <td>32500        </td> <td>8.63        </td> <td>22766         </td> <td>29.14           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1928</td> <td>5.81      </td> <td>20708              </td> <td>7.41      </td> <td>6000        </td> <td>4.13       </td> <td>46820        </td> <td>10.71       </td> <td>18766         </td> <td>29.34           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1929</td> <td>7.6       </td> <td>13071              </td> <td>10.86     </td> <td>3504        </td> <td>6.7        </td> <td>36990        </td> <td>6.36        </td> <td>35702         </td> <td>29.97           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1930</td> <td>5.06      </td> <td>22068              </td> <td>6.23      </td> <td>7998        </td> <td>6.35       </td> <td>29680        </td> <td>10.5        </td> <td>23718         </td> <td>28.68           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1931</td> <td>5.4       </td> <td>19255              </td> <td>6.86      </td> <td>5638        </td> <td>3.91       </td> <td>50940        </td> <td>5.81        </td> <td>39263         </td> <td>26.35           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1932</td> <td>4.06      </td> <td>17293              </td> <td>6.09      </td> <td>7364        </td> <td>4.57       </td> <td>27642        </td> <td>4.71        </td> <td>38553         </td> <td>21.98           </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1933</td> <td>4.78      </td> <td>11063              </td> <td>5.86      </td> <td>8136        </td> <td>3.57       </td> <td>35560        </td> <td>4.6         </td> <td>36540         </td> <td>22.26           </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (5 rows omitted)</p>"
      ],
      "text/plain": [
       "Year | Pear Price | Pear Unloads (Tons) | Plum Price | Plum Unloads | Peach Price | Peach Unloads | Orange Price | Orange Unloads | NY Factory Wages\n",
       "1924 | 8.04       | 18489               | 8.86       | 6582         | 4.96        | 41880         | 6.63         | 21258          | 27.22\n",
       "1925 | 5.67       | 21919               | 7.27       | 5526         | 4.87        | 38772         | 9.19         | 15426          | 28.03\n",
       "1926 | 5.44       | 29328               | 6.68       | 5742         | 3.35        | 46516         | 7.2          | 24762          | 28.89\n",
       "1927 | 7.15       | 17082               | 8.09       | 5758         | 5.7         | 32500         | 8.63         | 22766          | 29.14\n",
       "1928 | 5.81       | 20708               | 7.41       | 6000         | 4.13        | 46820         | 10.71        | 18766          | 29.34\n",
       "1929 | 7.6        | 13071               | 10.86      | 3504         | 6.7         | 36990         | 6.36         | 35702          | 29.97\n",
       "1930 | 5.06       | 22068               | 6.23       | 7998         | 6.35        | 29680         | 10.5         | 23718          | 28.68\n",
       "1931 | 5.4        | 19255               | 6.86       | 5638         | 3.91        | 50940         | 5.81         | 39263          | 26.35\n",
       "1932 | 4.06       | 17293               | 6.09       | 7364         | 4.57        | 27642         | 4.71         | 38553          | 21.98\n",
       "1933 | 4.78       | 11063               | 5.86       | 8136         | 3.57        | 35560         | 4.6          | 36540          | 22.26\n",
       "... (5 rows omitted)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fruitprice = Table.read_table('fruitprice.csv')\n",
    "fruitprice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the Equilibrium\n",
    "\n",
    "An important concept in econmics is the market equilibrium. This is the point at which the demand and supply curves meet and represents the \"optimal\" level of production and price in that market."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{admonition} Definition\n",
    "The **market equilibrium** ...\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's walk through how to the market equilibrium using the market for oranges as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "61b55ebf-36a4-4ce1-89b7-4b860da25de4"
   },
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "Because we are only examining the relationship between prices and quantity for oranges, we can create a new table with the relevant columns: `Year`, `Orange Price`, and `Orange Unloads`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "b75d49b7-7c34-4c8a-a844-e16f26940df7"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Orange Price</th> <th>Orange Unloads</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1924</td> <td>6.63        </td> <td>21258         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1925</td> <td>9.19        </td> <td>15426         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1926</td> <td>7.2         </td> <td>24762         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1927</td> <td>8.63        </td> <td>22766         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1928</td> <td>10.71       </td> <td>18766         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1929</td> <td>6.36        </td> <td>35702         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1930</td> <td>10.5        </td> <td>23718         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1931</td> <td>5.81        </td> <td>39263         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1932</td> <td>4.71        </td> <td>38553         </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1933</td> <td>4.6         </td> <td>36540         </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (5 rows omitted)</p>"
      ],
      "text/plain": [
       "Year | Orange Price | Orange Unloads\n",
       "1924 | 6.63         | 21258\n",
       "1925 | 9.19         | 15426\n",
       "1926 | 7.2          | 24762\n",
       "1927 | 8.63         | 22766\n",
       "1928 | 10.71        | 18766\n",
       "1929 | 6.36         | 35702\n",
       "1930 | 10.5         | 23718\n",
       "1931 | 5.81         | 39263\n",
       "1932 | 4.71         | 38553\n",
       "1933 | 4.6          | 36540\n",
       "... (5 rows omitted)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oranges_raw = fruitprice.select(\"Year\", \"Orange Price\", \"Orange Unloads\")\n",
    "oranges_raw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "8c900ffc-173d-4c91-97c9-e1d8924c8d67"
   },
   "source": [
    "Next, we will rename our columns. In this case, let's rename `Orange Unloads` to `Quantity` and `Orange Price` to `Price` for brevity and understandability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "254b8839-cf4f-460b-a597-c267ad6ebb84"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Year</th> <th>Price</th> <th>Quantity</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>1924</td> <td>6.63 </td> <td>21258   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1925</td> <td>9.19 </td> <td>15426   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1926</td> <td>7.2  </td> <td>24762   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1927</td> <td>8.63 </td> <td>22766   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1928</td> <td>10.71</td> <td>18766   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1929</td> <td>6.36 </td> <td>35702   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1930</td> <td>10.5 </td> <td>23718   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1931</td> <td>5.81 </td> <td>39263   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1932</td> <td>4.71 </td> <td>38553   </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1933</td> <td>4.6  </td> <td>36540   </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (5 rows omitted)</p>"
      ],
      "text/plain": [
       "Year | Price | Quantity\n",
       "1924 | 6.63  | 21258\n",
       "1925 | 9.19  | 15426\n",
       "1926 | 7.2   | 24762\n",
       "1927 | 8.63  | 22766\n",
       "1928 | 10.71 | 18766\n",
       "1929 | 6.36  | 35702\n",
       "1930 | 10.5  | 23718\n",
       "1931 | 5.81  | 39263\n",
       "1932 | 4.71  | 38553\n",
       "1933 | 4.6   | 36540\n",
       "... (5 rows omitted)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oranges = oranges_raw.relabel(\"Orange Unloads\", \"Quantity\").relabel(\"Orange Price\", \"Price\")\n",
    "oranges"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "d3a20db0-45a5-4ca0-8edf-ae8fe730017c"
   },
   "source": [
    "### Visualize the  Relationship\n",
    "\n",
    "To construct the demand curve, let's first see what the relationship between price and quantity is. We would expect to see a downward-sloping line between price and quantity; if a product's price increases, consumers will purchase less, and if a product's price decreases, then consumers will purchase more. \n",
    "\n",
    "To find this, we will create a scatterplot and draw a regression line (by setting `fit_line = True` in the `oranges.scatter` scall) between the points. Regression lines are helpful because they consolidate all the datapoints into a single line, helping us better understand the relationship between the two variables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "cell_id": "b7a2e982-d79e-4094-a295-d8edea5e3c12"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "oranges.scatter(\"Quantity\", \"Price\", fit_line = True, width=7, height=7)\n",
    "plt.title(\"Demand Curve for Oranges\", fontsize = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "46555368-2878-4561-b1bf-34051f611d06"
   },
   "source": [
    "The visualization shows a negative relationship between quantity and price, which is exactly what we expected! As we've discussed, as the price increases, fewer consumers will purchase the oranges, so the quantity demanded will decrease. This corresponds to a leftward movement along the demand curve. Alternatively, as the price decreases, the quantity sold will increase because consumers want to maximize their purchasing power and buy more oranges; this is shown by a rightward movement along the curve.\n",
    "\n",
    "\n",
    "As a quick refresher, scatterplots can show positive, negative, or neutral correlations among two variables: \n",
    "- If two variables have a positive correlation, then as one variable increases, the other increases too. \n",
    "- If two variables have a negative correlation, then as one variable increass, the other decreases. \n",
    "- If two variables have a neutral correlation, then if one varible increases, the other variable stays constant. \n",
    "\n",
    "Note that scatterplots do not show or prove causation between two variables-- it is up to the data scientists to prove any causation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit a Polynomial\n",
    "\n",
    "We will now quantify our demand curve using NumPy's [`np.polyfit` functio](https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html). `np.polyfit` returns an array of size 2, where the first element is the slope and the second is the $y$-intercept.\n",
    "\n",
    "It takes 3 parameters:\n",
    "- array of x-coordinates\n",
    "- array of y-coordinates\n",
    "- degree of polynomial \n",
    "\n",
    "Because we are looking for a **linear** function to serve as the demand curve, we will use 1 for the degree of polynomial. \n",
    "\n",
    "The general template for the demand curve is $y = mx + b$, where $m$ is the slope and $b$ is $y$-intercept. In economic terms, $m$ is the demand curve's slope that shows how the good's price affects the quantity demanded, and $b$ encompasses the effects of all of the exogenous non-price factors that affect demand. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "cell_id": "42bf023c-0b5a-4f12-8158-dcf58f137185"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.14089690e-04,  1.33040264e+01])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.polyfit(oranges.column(\"Quantity\"), oranges.column(\"Price\"), 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "27c4081e-219e-4dba-a16c-44cd17e8fa5c"
   },
   "source": [
    "This shows that the demand curve is $y = -0.000214x+ 13.3$. The slope is -0.000214 and $y$-intercept is 13.3. That means that as quantity increases by 1 unit (in this case, 1 ton), price decreases by 0.000214 units (in this case, \\$0.000214). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the Demand Curve\n",
    "\n",
    "We will now use SymPy to write out this demand curve. To do so, we start by creating a symbol `Q` that we can use to create the equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "cell_id": "d06b28f9-96eb-480c-8a10-3ba4981be472"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.000214*Q + 13.3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q = sympy.Symbol(\"Q\")\n",
    "demand = -0.000214 * Q + 13.3\n",
    "demand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "14d44625-4f20-443d-9ce0-304bd60c6c61"
   },
   "source": [
    "### Create the Supply Curve\n",
    "\n",
    "As we will learn, the supply curve is the relationship between the price of a good or service and the quantity of that good or service that the seller is willing to supply. They show how much of a good suppliers are willing and able to supply at different prices. In this case, as the price of the beef increases, the quantity of beef that beef manufacturers are willing to supply increases. They capture the producer's side of market decisions and are upward-sloping.\n",
    "\n",
    "Let's now assume that the supply curve is given by $P = 0.00023Q + 0.8$. (Note that this supply curve is not based on data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "cell_id": "f9654557-6972-4ba9-948b-f7ff7a48a61f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00023*Q + 0.8"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supply = 0.00023 * Q + 0.8\n",
    "supply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " This means that as the quantity of oranges produced increases by 1, the supply curve increases by 0.00023. It originally starts out with 0.8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "571c23b2-4d84-4173-9ff4-bbe1da039e5f",
    "tags": []
   },
   "source": [
    "### Find the Quantity Equilibrium\n",
    "\n",
    "With the supply and demand curves known, we can solve the for equilibrium. \n",
    "The equilibrium is the point where the supply curve and demand curve intersect, and denotes the price and quantity of the good transacted in the market.\n",
    "At this point, the quantity of the good that consumers desire to purchase is equivalent to the quantity of the good that producers supply; there is no shortage or surplus of the good at this quantity. \n",
    "The equilbrium consists of 2 components: the quantity equilbrium and price equilbrium. \n",
    "The quantity equilibrium is the quantity at which the supply curve and demand curve intersect.\n",
    "\n",
    "Let's find the quantity equilibrium for this exercise. To do this, we will use the provided `solve` function. This is a custom function that leverages some SymPy magic and will be provided to you in assignments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "cell_id": "6e28af89-0a83-465e-bb24-d7e66c9b978e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28153.1531531532"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q_star = solve(demand, supply)\n",
    "Q_star"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "35482561-c903-4cc5-a072-7e7b50e586c8"
   },
   "source": [
    "This means that the number of tons of oranges that consumers want to purchase and producers want to provide in this market is about 28,153 tons of oranges. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "54f6442c-ec0f-430f-bc25-8a37ca24aaa5"
   },
   "source": [
    "### Find the Price Equilibrium\n",
    "\n",
    "Similarly, the price equilibrium is the price at which the supply curve and demand curve intersect. The price of the good that consumers desire to purchase at is equivalent to the price of the good that producers want to sell at. There is no shortage of surplus of the product at this price.\n",
    "\n",
    "Let's find the price equilibrium. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "cell_id": "082dbd17-910a-4073-82ef-75ec53c98ceb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.27522522522523"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demand.subs(Q, Q_star)\n",
    "supply.subs(Q, Q_star)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "6e9a1d0d-3f17-425b-920f-531d4160c2c5"
   },
   "source": [
    "This means that the price of oranges in tons that consumers want to purchase at and producers want to provide is about \\$7.27. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize the Market Equilibrium \n",
    "\n",
    "Now that we have our demand and supply curves and price and quantity equilibria, we can visualize them on a graph to see what they look like. \n",
    "\n",
    "There are 2 pre-made functions we will use: `plot_equation` and `plot_intercept`.\n",
    "- `plot_equation`: It takes in the equation we made previously (either demand or supply) and visualizes the equations between the different prices we give it\n",
    "- `plot_intercept`: It takes in two different equations (demand and supply), finds the point at which the two intersect, and creates a scatter plot of the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_equation(equation, price_start, price_end, label=None):\n",
    "    plot_prices = [price_start, price_end]\n",
    "    plot_quantities = [equation.subs(list(equation.free_symbols)[0], c) for c in plot_prices]\n",
    "    plt.plot(plot_prices, plot_quantities, label=label)\n",
    "    \n",
    "def plot_intercept(eq1, eq2):\n",
    "    ex = sympy.solve(eq1-eq2)[0]\n",
    "    why = eq1.subs(list(eq1.free_symbols)[0], ex)\n",
    "    plt.scatter([ex], [why], zorder=10, color=\"tab:orange\")\n",
    "    return (ex, why)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can leverage these functions and the equations we made earlier to create a graph that shows the market equilibrium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "cell_id": "f5d296fd-a13d-4b0b-a22d-a22806cb498c",
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kVcn9Usz4p/h1qkj3+oKXz3eyhJXg3YTDbx8jr+Fe3AayfKeJMbjBfWP/GbMtSBwF7Z/hlI1NecixIU5Z8+JODa79D9HaaMW1yZEn7XC72tRHTPs/uImMid71ccUoxzGek5KfsuJmL42rYsaf5MV/uEj36isfvpolrNXnLEdBfFYEToA+611+oBBpp0nH3083G72AJ3Gd+ATLcaa+s0le4xOma5priwnMqnBbWaYzpf8+rsGJjYjshfMe2ze41IozIXoF+AD30sPsjRuwREl7N1zHxN/7uzlI+zWS12QOxc2oDIx8A2157YvTvPnloYXA/Bf37H2z2f2A6SIyIG6e2VDV9bgObpgrRKRLIfMpAypwM5Xh+rEKV9bT7cncC7ct8CFxMxKRb+DWyCUc6W4E3sNpUlekifJd3Mel3ajqJtyMb4KeuI5oHE7EmTsn+IeqrmmvbCHStR8rcOX+v8BbuIGdz5eBhwKz7LiMwM3eDA9dm4+zbJpD8tZ44LbF+mewXCgrwVKqJ0leSwmuTZqHaz/mha73Ah4ix/rMckRVlwEvepez1hERqRSRyTjHWL4T6FXA27hntMj7rTNwS7DVdyyCJSt/oM1hYAPu+/Q6bveCMIm6nvi2TCJ5ne5KXDvxDqntxC643Rmi4Jd7xX1X3sZ9N2fhlApJtwL8UkQui5hHcmSRY3Azi9XBpSbcksZXSG33AY4h4trmYHnMFJKXD4CzGn0PN5GyPHR9N9zMbTXlTRVOaTM+dG05ruy8TWq/qBtwv4gMZ8vFd+xXmTZUZqq8817B0quCISLdcabVYd7PEmU47t0kWKeqC6Lmp86vRrg+dgc+FTV+Dm7BfWd9Ev3vRHuQrr9QaCpJXfLegluS9ybum/wuqVvkdgFuE5Ez8slURM4BbqStrDXivsWv4sYTPmcT0S+fiByKGxv5S5zX457rm7ilnwkOxPUPt5ZtjgtdnwveVxGR8bhxTYLVuG2SM2H1ORsF0h6NJ1WbskuB0j4yTdppzWdJnUFYGvr/73jmvLgCmzK7jOv0zcft13soaWZdcZX+M7iOn685PTHCfU314swN/q7CeZ/u54Ufghuk+c8i6+w4rpH2rVMacDPjfb2wO+JMmBPh/GUzE3PkNRi3Vjoc5y3cYLKHF7YrbrbiQy/8Y8Qwh49Yhn6f5rm9gdv7uKDLTyidZUU43xnB7xIK3wP4Du4DFo63hBzbKXlp19K2ldsa3MxBLy/8AaQ3FcuoVc5QfydkCDeKZKuOl2M+Z98kc/8Cl4FHcY32H3CzH2mXXAT17SpcJyYsz08i5DHZi5NoP9YDlwNDvPA1wNWkWsPkXP5C6ixGK2452fZeuJFe2I+9eJML+Zwz1YMCpPkrL80U6zsv/OVe+E0488/RGcruzd57aCJk+RCzDr6HW0LUORS2Ete2+ksxriF5hnkKztdMuJ3ojlsK45eTYyM8twZcZ/wXOIel3dKEqcB14Hxz92Zg7wh5+M8hcY8LcVvZ9fTC74Srj+G8WoAxOfKpwg3cw/ES7V1vL+y+wPNZyv2EIpT7qeE8coSd4MkzN/T/34CxXvguwbP0Z23/Uej7yOde83mewGFeGh/HjP8TL75SeAsAfyvGerIsWyK1bzwnjzzf9dI4POJ7mJol3N5emk2479zwDOEH4ayjbsd9vwr9XPsHcryKW66+J9AlTbhOuLHME578a4FPRcgnHOcT2qzsZuMmIrp64fcktZ+0jhxL73AWtL611zLcErzqULiKoNyH2zG/bRpXyGcd8X349WhEHmlc6aVxW8z4D3nxm9OViXbc49Gk9rUn5Ihj9TnbvRXoxXzfu5ms5icx0x6YpnAfnSHshDRhFfhRzDwPAipjhD+c5D2y/xshztQ0cn4MjMoRzzff/VeO8D/0wteTe59wf31m4piYI56/r/CfyLFGGNfwvujFy6nsifk+R5LZPHspztv3ObiPR+w1zV5e80Jpz4sZd6In27gsYcdluJ/Hst0DrvPuK5T+EOOeEsdqsqxlxg2Y/OUQS8niLyRN/Z2QJeyTXtix2e4hFG8HkgdiMwtZ1oI89ieeqfnuJC//+oQcy8lIVVZokEbWAR9wiRfnjRzh9/OeVyvw9Rxxzs5QNicX4Vn79SBWncuQ5lfSyJ52yR1OMRf2k7CCHO1rEO+rXry3coRPVwenkWX5Es4JWTh8LW2d3OvJbiI60Yv7YJR3EfM5n+HlkXMwnOE5zCDLEkJcW+R/m67Pkc9PvfC52jvBfUfSlfsJRSj3U8N55Ag7IY1MrcC3c8Q7yCujm7I952Id/r3m8zxx7b7/DCLfC85/mR//9ALeYzdSB5KTcsT5hhf+qTzy9RX37b4nnOVSOM2zYsTtV+gyhrN02i9mnEu9e/h1hDjp6v4TpFHaeu/dXzKddbxC6hKGeWTxQYZTPmbymTGukM864rP1ZRiRRxrf9NKI1Y/D9bF8OeL4cfsUThGUOI7ETRD8nNTlspGWOlp9zpFWgQqfX7HnFiLdIG0h1WlW2s4y6T/K9xRKlhxyXuHlmzKr5oWf6oVvJlont4bkmdh1ZLEOwJkRhvM5M+L93JnmWU7MEn4/L+y/iaiwwllkhJ1+TivC+/Ebt0xHI0558ktcZz+W0o3SKisW480sZojra3DX480WZrmnxHFShHy6ktoBy7YG16+/E7KEPckL+7uIz/gqL14sRWaxDuBbnlxpFbKh8JPTvJMTIuTTiVSHehl9EpA6A35TxPv5Sxr5Jhfhufn1YF4R0lQyzMSTPAvXQg4LCS+u3wnIOAORpg6uxrOeyRDPH6Qrbv1yVouyoO7WheKsJ4YCP8YzCH9nmrK1QxmeQz0ZZni8eLt68TI6WMQpN/zObJT2ror0/rEmFOG5TQ3nkSPshDQy3RAxn7978SI7fCvWveb7PEl1DPyDiPF6kN43wHcLeI/+ALQhV/0mdVIpp0IxTRoPF/qegAdD6a0tRrvRQeUu7D9kWYTwfvmYn6s9C+Id5cV7IkvYPiT752klmkVaH9I7XR9XgufqyzAijzR2SpNOJEfAuF3S/LhKjNUAuOU66dLwj2nAwRHTtPqc5SiIzwra/CwkKNjWiOqelr+e3M8vG5cWSpYc+Ot6D4gZ/wFVfS1XIHXbHD4ZutQN1xFLQUT2x1XqBHNwsz9RuJjUdWHZ8Nfa/SR4dzlRtz3aLaFLnxeRQTHyjpLHX3Gaz1xlsyvu3V2M6yR9JCLni4i/Fr0cuVKjbXM0BbdLQIJq4LQY+byqqjl90qjbzs9fi/7NGPlk4yHcLHGCrwdb8mYk8M8wIXRpI26wVA78nWS/EnHbj1dU9cFcgdRtY/ZP7/Je6cIG26GF1yluwM0cROFi3Ezslojv8wHcgCWJwBdQeJvBf6jqSzHy+TVOSZ3gpBhx/6jRtmp7OM21iaqatW0P6m74O1NN21ZvhST83eyEMzmNw83q1upmRVXfxZlDJ9gp8LWVjiNo24YZord3TbhyX+404iZXovAP79z3qbAl4bePPwvauFxcRKpvAEjTJuSDiJyGG6gk5Rmhfvv557N9bmOONPMh7LOllXj9yHLirtD/A4Mt2uNwtUbbJv4pkv1LZKtjp5H8fO9X1VdzZaCqdbgJuK0CVf0At7wmzK9yxQv8gWV6DgWpzyGewS1XeSFieKvPWSiUsqKnd57JcWW++On5+WXiVVX9sMCyZOJj73yPmPH9TkE23vTOMzlROdA7vzuGAmEhbh1uToIG4KjQpVdU9b0ocUM86Z0fFDN+TlT1Ppw56NW4pRBRGIHzXTJLRHynoeVEE27AG5XbvfNxMeJGVXiBcwIV3m/5c4EjsXYRDLr/GrpUgzPfz8YxJDuZfUBVV7dXlkKgqutIdtZXDu3H/jgT0gSPRX1eqroCZ121JZJuf/B0ysoveuexFF+qugq3hCFBnDbvvojhZnrntSQrKrPxjne+fcR4cSjVd7OCZIVEmHHeeZz27jE6xmlge3g6KHtRiNpWbAlcT3JHeyjwQLbvUeBM+n8z/NxuR6oisg/OT1iYf+P8bOXCV87noxz2nUoWwjlsWMnSCzi2AGmWgva0TYrbxjt3QNUWktvaAVkcwI/zzuO0TXeRrBzf0rnWO/+SiFydKXAwTvkDqeOiBIV2jDweZ9n4loh8LkJ4q89ZKJSywp/NbfdgJEd6OWePA15pb8Yiso+IXCUi/xaRj0RktYg0iYiGD1ILSf+YWeW0qgix3DvvnSGcX0HizPrFCT/WkyHOvSTwvd4WZScBVV2tqhfhOiqH47SxL5KqkfTZEXhRRPbLEa5UvB1z4D3VO/d3eogTNyPqdmMJa/4ryTCTnwd/JrnzeXamgBl+/0uB5MiIiIwRkZ+LyEMi8oGIrBSRTX77EbQhYUVKObQffpmYGiOPfMKXC+mU4X77DqnKhfa2e7uKSBRv7U2kKhIy4Q9K34iqsE4TN93scgoiUiEih4rItSLytIjMF5E6EWlJU+Z9pXacct+Ec+AclaKX+0CJ6u8mU24Uo60oe4KJK38wcyhuIuIcERkhIp1FpEZExovIvbjBYCVtS47CpFNqRiaYqX+E5AHFu8A3ItZRf+Y1H+tPf1Ccz2yuz1Pe+V2BdWoUK5aiIiJVInKsiNwkIi+IyCciskZEWtO0TVO86HHapnkxFIKQX9ukRJxQBFDVlaQqr7dkbifVauF/ReQ/IvJlERkYvO/tROSruPHgOUG4dNaTkeuzqt6gqpI4cPVoO5yl5Q1e+mNxY4djciRr9TkLObeti4g/SCrYBy3ovPmdpKiDMl8zGiffg3Da7c/kmUTcF+k3VtnwLU0yac/8maM5MfKIE95XLHxPRL4XMy+fOEt9YhNos58ODkSkErfF7X44jeiXSFWSdcdt4TYmonlfRxLrI6SqC0RkDW11a7iISIROUguuQxVXtvD2jyOJbhqXkeAeHsdZTAB8QUR2VtWU7d5EZBjJ1j8fqOrU9sqQCREZi2s/vpBnEuXQfoz0zuN2dKIOqMuNdN+vdB0Zv91bHk3XkJFKXH3M1basDtqvKPhbhcaZ9ffj5pylEZHjcTPYftmJSpxyH+c5QH7lPp/27h3cDi3lSuS2QlXXeWW63LdlzcVluMFDeHZwOPDHHPF+DhxPsqI93YAnEiIyBGdNGt4efiFwRIxJB79NyroMMgP++2yXAibgPpwj59HBeQ+cdepVIvI8zrJrGvBasNys6ATjiG/ifFYNzBE8E3HapjjfY4jQNgWWAcNDlxZEWfbr8Q7OofcWj6q2BEuoXsDtspXgQDJbT4CzLjkTt5Q4TN71WVU34RzILwWeDCw8/oZzvglO8fB3EfmMqmYal1p9zkKhLCv8xnVgxBmiKAwgVc6ojbnv6yISIvJdnMYyX0UFpGq4stLOl5zpWfuNa9wBdtTK2y9mulHo0BkcVW1R1XdU9S+qeirO6edFpFpcDAV+0JGyRSSOFj9BuB5VEG3mtD6YPYyDL1shNbJ/8s6/nSHcWSTvxX1LhnDtRkS+hJu9zFdRATHbD9qnQY/afsQtY/mUyXIgXWc23frxUrV77XnXxSgn7keRq3B+AfJVVEC8ct/ejlGUcl+I9q7cKFoZKHcC5daJOIVaFEVXE86T/xWkrv/Oa3AjIn1xiopwPVmB22ZwYYyk/IFIPhbNfpx2D24C3y3Hkmo11Zk2a9b/AHUi8pyI/LjQPsrCBIP8O3Df/HwVFVD6tqmXdz2fdqbc26ZYBH5d9iV19j8TK3BKx3RW93krK9LItQw4Dng9dLkHzodFJqw+Z6FQlhX+TfTAOXZMmeHMg3TrxKLO+DfFzUxEDsFp2cONQjNOc/RfnIff5bjGyDcNjlphOgq/cY27Biqd6XM6imEOVChFWl6oagNwtYg8AzxHciPwdcrPWZE/CxoFX5uf8HreEfkUin/jZqQSa6nPFJFLggYW2NxZ+VYoThNuN42CIyI747ZsDZvwKe7j+BLwEU77voHUDs3fcHtUlwv+e4r77gvtu6ij8JcprQ4clPlsde1evojImTjlbphGXCfmFdxyl5W4b0r4OzSIVOfUpSZc7gvR3hllRKCwOE9E/gxcgPM9M8QLtg7nb+l6VU347fCVk7H9oYlIT9wOQmNCl+uBI/Pw8+XP3g+LK55Q4F8AACAASURBVA+p1rdxLQLSoqoficieOMfr30uTD7j+6bjg+LWI/Am4JA9rgVxciuuzhVmDW941A9d/qMN9j8Njhs/iZpDLhfZ+j2ErbJuC5TZHiMiROEe1h5K6lHMx7jtznaouE5Ex3u+rMnzj2yNXo4hcQLKPqJNF5DuBjzIfq89ZKJSy4mWcljo8c/k5CqOs8DuOS7KY0RSCa0lWVDwGnKOqn2SLlMUhTinxB549iKc9jLRGmdRG8x6SnR/mQxRP90VHVV8VkV8Dvwhd3lVEBgROBMuFbnnEyUcL21H5RCIwBbwFuDy4NBCnOb8/FOxwks0nH1bVgjTiabiaZCXhK7jtgnOakgfrZMsJ/4Ma990X2ndRR+H7LHgjQ7j1JLeRX6T9DsyWtjN+hyNup6Rfe5f/Cvw017ptESnGDiPtZR1t77UQ7Z1RhgRt8rdh8zLBgbj3vQRYGJh2E/w+gGSfBeuIuaxWRKqBR0ne8WY9cIyqZmpjsuErN/JxfurHibvkKSOBv6qrApP4z+MGkeNwM+G+uXpn4Ie4QecXCvV9DmZ4fQepvwKuCiakssWtzPZ7CWjv9xi24rYp2OVuirhd34bjrPLBbUO92NsBa7QXPR9/U1F4HmfNklB0VuHGxul8jVh9zkJBlBWq2iAib5G85c5JwN0FSN7fzq1ozquCWdGwJcdM4Mvhj1YWiupjIU/85TJDiKes8GcbMrHSO69T1adj5FPu3EuysgLcs0mnrGjPgDOfj0+CuA4ZIbnMthJt2VRvEakKWy5EwJ+RKqgGG2feeSlt7dnZJCsr/KUhRXGsKW4rxLATpWXAUcF2w1GoKbxU7cJ/T3HLWDGWSRQVERmM+xCHeS5D8JUkKyteL6ISrJwZR7JF0JOq+q0MYX3K8btZR9t7LUR7Z5Q5wWRUtgmpsd75m3H8pQQKvQdIXhq4EThBVfPt087HWS8lBgrdRWR4lK18A5mGk9znWIezMigowSDxP8FxuYgkBmxHAV/DOS9PsAvO6vHoAmV/PMkDqT+ratTthcutbarH9S8Tk6n59Pm2+rYpWLY3Nzgy4dfnoigrVLVVROaR/NwHZwhu9TkLhTQ59fchP1ZE8qlMmxGRT5O65/A/25NmDvydHm6JqKiAZLO+csHfhzjutnBRHfH4li5x96Mud+aluZZJsRA27Y/rjGxA7iAZ+XScwEHDFh5ozY/ogbwS2DVOXqR+GApqGRWsW3w0dOlwERkBICIJS4sE80jdJrdQ7Eny8o97oioqAs/w5Wad9ZF3HquM0T6fP6XiHNzsR5h/ZQi7tbd7UfG/m5NixC3H72a43OfT3m2J5d7Iju/J//GoEYMZ+rtJ3uq4GThVVfNeOhx8r9/2Lh8QIwlfKft2jJ2C8kZVm1R1uqr+HNgZ+D7Ju3p9UUQKtRvcVtM2BYPE8MD1UyIS1fo5gbVNjrzrcwFIq/i2+pydQior/kzyQK0K+L92punPZi8i+v7y+eCvF4+zhvDQQgpSIHwnMl+KGjH4wObaaiecT3gpyAEiko8n23Ilnemcb02SIGydUBNoHaOyd+4gGRkbOO6KysHeeZxtfv24GRGRbiRvoduCWydaaG4OZ0ubj4ozSR583lrEBnxraz9e9c4jv/c8w5eUQIH3I+/yf1TVV/om8C0uyvEddgRW7gMCE2S/02hswQR9oZNDl1qA2yLGFdySqLCFcCtwlqo+WADxHvXOD48R1w/7SDtliY06JpFqhZ1tN4c4bM1tkxDDiXcweVxWCphSICI7kTwJPqcd1k258hJge+/ysixRrD5noGDKimAP3zu8yz8QEd/nRCQCj/onepdvysMzd6xsvfNI+9wG/iq+WXhx2s2TJDvJPF5E0jlGScdJZDZXSiKwPgk7kemO231ha8FXIjST2adGWPNdRcTZ6MCKqD0fkirg1Bjhz/TOI+/XDZwRI+xJJDuGmpHBuVB7eZLkme6zgoFD2Bw9ciczT/JtPwTnGKrceIlkZ4jHRFWIBRYthTLlLTqBcvVeUp1mXpol2hPe+XdiKie3FvIt94OALxdenHYz1TuP094dQ37m2Ub58h2S14L/O7Dmi8LvSS0/31PVQjmVfdg7PzlYjpiVwNHnyd5lfyvHjsQfLBaqDuXbNu0O7F8gGQrJVO88Ttt0OoXzU7gl4+/IUbSd4XD+HMIW082kWryHsfqcgUJ7Hr8Y55woQSXwkIjE2spMRPYg1UP4W7jtpoqJ79wsqjboCsrLiz+wWYEUNmGuBiYFuyNkJHAmFdcL8jXe+eUi4msUS4KIfC9YM5ov53vnL2YZcL/unZ8SMY9C7C7yf0GjlZXAa3J41qAR5xQ1KnuLiO9LJl0+XWlzfJmgvY5X0xJYS/w5dGkoztll2IHfv1V1UTHyD8i3/TiXMtz7PPCOHW4/0r3PTPySiB3DUiMiO+AUM75jzb+qakYlnqrOINm64lNk35psayXfcn8T5bf0CWAKzoozQdT2rgrnvM/YShCRUbj+XYIm4GcR4/4K5zk/zAWq6m+3nTeq+jbJs+09gJ9GiPpTki1GX85iQdYR+IOZqH6echG7bQosaeIsF+lI7iHZgv0rIpLTIldE+gCXFE2qLQQROZHkPvlcivSug3GW/z34T7alwVafM1NQZUXg+ftMkp0MDgWeE5FI5kqBRcWTJO83vwE4PYb/iHx5yTs/J1hLnhER+S5u+6ty5QqSZ0ePA27PNKgN7vdJXMc7srm8qr6A6+QlGAA8KSKR1/uKSIWInBDsvlFI/gB8ECgteucM3SZPpYjcQKp5lW9BFObf3vkPA7OzbPlcgXsv7WU74O/ZZneD9+vLf3se2zb9JbAGyZRPBXAnyXvJL6cwTncz8VeS1wP6SqaiONYMMYPkuvZlEcm65jBo764rqlTt40aS24Hvi8jp2SKIyLdJdWpadojISBG5HqcI9/35vITzZp2LS0len/lTEbkssJaJKscwEbkmSqezTPG/mz/L5a9KRK4kdSaoLAgcJ/7eu5yrvRPcDF2h1tobRUBEtg+WJkYJuxvwDMnO8a6JMggQkf8lValxuapeG1nY6Fzmnf8sW39bRA4mdYeM9i7ZDqd/V5BH1PA1pH4vCrVU1G+brhC3K0smWSpx/YhytKogGOjeHrokwL0iktEZfmD5fT/t84lWlojIzlG/tYGiwp+UO1dV/S3k/XiXiEic5RiJ5c934JxPh4kyCWz1OR2qWvADuBDXwQ0frbjBy3ig0gtfhTOf/GeaeAlHRFHyneDFnZCH7C95aSzGdao6eeE+C/wjFG62F29qjnymhsPHlDHWfeIKrv9cF+Mqzqk4c+0JuMrVGArzRy/OxBz5DAQWeHHWATcEz0vSxKkBDsNZzSTiZn12ebzTsDzrcQPmrwMjM4TvhzOvezvNc3sVqMiR33+9OJ/gnDxWhsIIzvnT46FwH3rxxmXJY5wXdl7o/9dw66wlFL47bpeM1V68pUBNjvsJp11Lm1fqNbgBXS8v/P44MzD/2Z3WAfX3H2nyTbyDyrjp5ZH/3V6+9Tgz4q5euJ1wGv2WINwynB+Uze8zRz6TvXxGxJDRLzu56vVfvPCtwO+AT3nhRuDajNYg3MdevMlFeN7+vSzFtSf+cTxOkf5jXGd0dkhO//i3X6ZzyPCzNGm8hmtbU+oWzuJwt6BcTMEp2HLV93AdzFo20ryTvN5B1PqI+37P88K+h1PyhtsgwTkMeyoUzv9uZpUv3+cQxJ3o5ZXteXcGZnnhM7V3+5D8PffLfdrn1s5yH85PC/Ees8QPx51a6HsJ8tguTZ1NHG95MvwmS9iuOfL5Aa6dvQH3jaxOE2ZskMdGL9+XcqUfxD/Ti6c4z/mZZM52jI74/KZ4+TXi/O90C4Xpjmv/Gr2wjxX4XdbRVrcvx/UHuqUJV43rW7/vyfNmAWXpF9TbcPovA/t64ToBR+La7Uxt08Ri1RNifM9xu5Qs88IvBb4RLp+4yejxXv3x26ZxRarPo7OUab9unJ4h3Ocj5HM/7ltzCa7e+mPLSuCQIJyf73UR7+XBIPxM3OTEPqRpN4KwI4CfkPo9VOC+GM/P6rOffjEKaiD492jrhPnHetzA7BWc5+0NGcI1ACfGyHOCF39CHnIfgJsd9WVZizPxfw3XMPhy7uFdm5ojn6nh8DFljHWfuE6iP+DIdfyO1IHA/0WQbSypCovEUYfrAL6Mq/iLM4TL+uzyeKfZ7rMuqFwvB+/3kyxhPwCGRshvH5ySzY9fi9MwvoHbezn8293E60z77+YXOIuY8LWVQX6zcHXOl2cDcFiE+5kXijMP91H005mNqxv+RzRx3NJB9feQDPlfUcgylSX/UbQpc8JHI67T8ApuO6nwb804T/FJzzlHPpO9NEbEkNEvOxNzhO8V1A3/nlpxZpSJdjz825rgnsLXJhfhefv30p6jFjeYyaqMzCDHdRnSbMF1EF8Ljg9xCtx0YbPV98hlw4s3It93EKc+4jop6e5pdXDf6dq8pcBBceTL9zkEcSdGfd5B+M+QqtxNtHdzSN/evYVb1tWudizCvUwN51Go95ghfjju1ELfSwYZ8z1G5MjnB174Ztx3/RXgHZIVxuHjFaBfxHuZXKB7iVxfccuQ/TZYcd/9mbg+gD+oUVx7NKDA77IuTT7NuLr7Oq6v9S7p+9nrgD0LLE+6ydNE+/MKblLK/2bPwVm7hq9NLFY9SVNmcpXjw0g/bloXlOPXSW1vp+AGm5HbwHY880LUgXkR8vGVEOtwfdGXcUqMhgxp/4WI33nalBXpyvObwPSgjqX7ViSOJ4mg6LT6nPkotM+KzajzCHoA7mX6VOM69XvjzMTTrVt9HthLVTNtG1cUVPUl3Ay0v71MD5xCYi+S/VPUAl9S1Tc6RsL4qCtx38GZCjXmCN4MXKqq/0OyY0RwDXquvN7BPaMpaX7ujdO47otzJrldhmQW5MonJglrmXT0xs1y74t7v5kckN4PHKQRfB6o6is4Myh/H/Y+OC/Eu5O8h/c9uM5ae2jFDRimha71C/IbTeo2qmuBr6jq03EzUtU7aesAgKu/u+He+8A0UW7Flb+io6rP4ZRPSZcDGToi/7m499Dg/dQVN/jZGxgWup5Y4taRW2fFQlXXAEeQuhe5ADvQ1o4nWIPr6M3pEAHbzzu48jxcVX+vbou4WKjqebgZVX85VQVOYbBXcIwi/bbHa9PE3WJQ1ftws1t+O1uDu2+/zVuI63AXfB/4QqFu/fBRuOVrYbrgtjP127s5OAvRXN9Yo3yoxG05vDfOGXY/73fFLe85RN0y57JEVZfhFPVveT9V4/pao3HfoDBv4u5rRfElpBIYjutj7YvzJeUvV12Emzzx/X61C1W9BkjnJ2QQ7r2PJXkb93dwVmFr0sQpC4J+21dw340w3XDleA+S29tpOF8NmfrBWwvdcH3RfXHbaPo7+TUCP1bVs/P5zodIlOfP4iykx+C+dT4bccs6jtEcy03CWH1OpWjKCgBVfRU3WPoyzrQ2l8+J9bhB4WGqOk5V42wzVDBU9XbclkAvZAm2AWdOPEZVp3aEXO1BHb/BFar/w2nDluGUE/W4gcjVwM6qmnAS53v+z6msCPJaoapH4Z7hIzjtWtYouJm3q4HPqGocD8dR5Pk8boB4Lm7rW9/pUiZqcYPcz6vqyaoaNR6qOhl3/9OzBJsLfENVv6YF8MeiqvU4x5kXkXlr1U24XQ9Gq6q/TVKcvH6La0yzbXn6DnCcqn67nR+GuNzmnT+lqvM6KnNVfRLXCcq2dVQzrq37rKr+o0MEawfqnPXuj1N4LskQrIW2e5raQaJFoRXXXq/CDSifwjl3PAsYpaqfUdXfBkqZvFHVO3CKiUtJVZiloxb3vM4ABqtqOsX+FoOqXoUbrPsdrDBrgGuBsao6s0MEaweB4nk33Ezc+gzB1uIcTO+tqp90lGxGXkzFrfnP9S3fCDwA7BcMbIqxg1VBUdX5OKvO/yXzbmUEv/0UtxSiGMrCfYL0nyF33w/cbPClwC6qmq2/lDeqeg6unf0oS7DlgRxbRD0O+m9jcH3aTP3Hlbj+4CFB/3BrYzJuu09/cshnNc5afGdVvTFmHj/CLf97FNeHiMKHOCuWnVX1ClX1J79zYvU5GQnMPDqEwLHNXriZ7AG4GYoNuEHze8AbhRi0FRIRGYHbN307nLx1OFlfUtVMnZetAhH5LclOCsdpFu/4WdKpwhX4kTgvsd1xhb4W16mf3dENaeCQaJdApt6BTBtwnenlONPAj7UAFUREhuO8UA/GaUOX48ynXs83fREZR/JOBJer6sTQ751wg8uxOI3vGtwSl+c0pjNNEZmH06ICzFfVEd7vO+K0y0NxiqcluHubFSefQiEid+CWqiQ4WVXvL5Es2+FM3YfhtP5rcI35S3HfQ7kQOE89EDdzMRDXsf8I5+k6k5JsmyPYJnpv3DPqh1OarMHNOMwB5nawEq/DCJwT7ou79060KYpeLrdvfFREpDtOQTsc16bW4+5pWpxZM6M8CHYB+jSwPW0z66tx/bv/bsn9u6CN3gs385uw/lmOm319vaPancBh5W64Pv9QIOHYfS2uHXxTVT/uCFkCeQRn5bUXri8quOcyE3hNnXPdLY7AqeE4nGP8nrhyPBOYrqrNJRStQwjK2Vhcn34Irj/fhBtbzsSNLQvyboPdLXfGfQd648aF63Dfg4W4+lVQKyyrzx2srDC2LETkDdq2VGwF+qiqb3ZmlIBcyooC5zWPLMqKciLYomsxbctelgPD8tFsG4ZhGIZhGIZROoq6DMTYchGRg2hTVAC8Y4oKYwvgGyT757jNFBWGYRiGYRiGseVhyoptgKj7EIfC98Kt0Q3z18JJZBiFJ1hu9JPQpVbg5hKJYxiGYRiGYRhGOzBlxbbBcBF5XkSOCtYcZUREdgdexK39SrAKuKOYAhpGAbiE5F0p/tmRjjUNwzAMwzAMwygcnUotgNFhfCE4lonIE8AMnEOU9TgnMaOA8bjdJHzO2VKdARpbJ4FztB1wDrKGAMcDJ4aCtOC8MRuGYRiGYRiGsQViyoptj0HAmcGRCwX+t1Q7KRhGFs4Afp7l9+u3hK0RDcMwDMMwDMNIjy0D2TbYQPT9gRPMAY5T1WuKII9hFJOHgYtLLYRhGIZhGIZhGPljlhXbAKq6VEQGAwfj9mLeC7fsYxBuP+Jm3L7MS3D+Kp4C/t1Re/caRjtpBeqAN4DJwF1qezIbhmEYhmEYxhaNWJ/eMAzDMAzDMAzDMIxywpaBGIZhGIZhGIZhGIZRVtgyEGOrRkSWAt2AhaWWxTAMwzAMwzAKyKeA9ao6uNSCGEYxsGUgxlaNiKzp0qVLz1GjRpVaFMMwDMMwDMMoGHPnzmXjxo1rVbVXqWUxjGJglhXG1s7CUaNGjZ41a1ap5TAMwzAMwzCMgjFmzBhmz55t1sPGVov5rDAMwzAMwzAMwzAMo6wwZYVhGIZhGIZhGIZhGGWFKSsMwzAMwzAMwzAMwygrTFlhGIZhGIZhGIZhGEZZYcoKwzAMwzAMwzAMwzDKClNWGIZhGIZhGIZhGIZRVpiywjAMwzAMwzAMwzCMssKUFYZhGIZhGIZhGIZhlBWmrDAMwzAMwzAMwzAMo6zoVGoBDMMwDMMwDANAVVHVUothGEVHRBCRUothGGWNKSsMwzAMwzCMktHY2Eh9fT1r166lubm51OIYRofRqVMnevbsSe/evamuri61OIZRdpiywjAMwzAMwygJa9asYdGiRaUWwzBKQnNzM7W1tdTW1jJ06FB69epVapEMo6wwZYVhGIZhGIbR4TQ2Nm5WVPTo0YOamhq6du1KRYW5VDO2flpbW9mwYQO1tbU0NDSwaNEiqqqqzMLCMEKYssIwDMMwDMPocOrr6wGnqBg2bJit3ze2KSoqKujRowfdu3fnk08+oaGhgfr6elNWGEYIU10bhmEYhmEYHc7atWsBqKmpMUWFsc0iItTU1ABtdcIwDIcpKwzDMAzDMIwORVU3O9Ps2rVriaUxjNKSqAPNzc22G45hhDBlhWEYhmEYhtGhhAdk5qPC2NYJ1wFTVhhGG/Z1MAzDMAzDMAzDMAyjrDBlhWEYhmEYhmEYhmEYZYUpKwzDMAzDMAzDMAzDKCtMWWEYhmEYhmEYhhGTTa2bWNe8rtRiGMZWiykrDMMwDMMwDMMwItLS2sKqjatY2riE1ZtW0dzqdraZPHkyIsKIESNKK6BhbCWYssIwioSq0rixpdRiGIZhGIaxlTNx4kREJOmoqKigV69eDBs2jAMOOIDvf//73H///WzatKnU4m6xtGora5rqWbxhEQ3Na1GUVm2lvqmu1KIZxlaJKSsMo0i8NLues66Zw4MvrmBTc2upxTEMwzAMYxtg0KBBDBo0iIEDByIiLF68mOnTpzNp0iROPvlkhgwZws0331xqMbcoVJX1zetZsmEJtZtqadW2fl1VRRXVld1KKJ1hbL2YssIwikBLq3L7k0uoX9fMnx5dxNnXzuGpGatoabW9sw3DMAzDKB5Lly7dfNTX19PU1MTbb7/Ntddey8iRI1m1ahXnnnsup59+OqrWL8nFppZNLN+4jBUbl9Pc2rT5eoVUUNO5L9t1HUK3TqasMIxiYMoKoyCIyF4i8jMR+aeIfCIiKiJpv4AiUiEiB4nIb0RkhoisFZGNIjJXRG4WkZEdLX+h+XhpI8tr2z5oy+uauO7+hXzvxvd4cVaddQ4MwzAMw+gQKisrGTt2LOeddx4zZ87k1FNPBeDuu+/m6quvLrF05UtzazOrNq5k6YYlbGjZsPm6IPTs1JMh1UPpVdULESmhlIaxdWPKCqNQXAr8CjgRGJoj7A7AC8CFwBDgWeAxoAvwXeAtETmweKIWnx2HdOOvF+7Gsfv3p1Nl20dswfINXPm3efxk0ge8OXdtCSU0DMMwDGNbo1u3btx+++3sscceAFx99dWsXr06JdymTZuYNGkShxxyCP3796dz584MHjyY448/nscffzxj+gl/GVOnTmXVqlWcd955jBo1iurqaoYPH84PfvADVqxYsTn8/PnzOffccxk5ciRdu3Zl++235/zzz2ft2vR9pPXr13PPPfdwxhlnsPvuuzNgwAC6dOnCkCFDOOGEE7LK5ju/nDFjBqeccgrbbbcdXbp0YYcdduC8885j1epV1DfVs2TDYhqaG1DaJpiqK6uZ/9YCvvnVbzFowCCqq6vZZZdduOSSS2hoaMj67A3DiI8pK4xCMR24AjgO2A7YmCWsAk8B44Ehqnq8qn4ZGAVMBnoCd4lIVVElLjJ9e1bxveOG8ZfzdmX8HjWEFe/vfbKei26Zy8W3zuX9T9aXTkjDMAzDMLYpOnfuzMUXXwzAmjVrePDBB5N+nz9/PnvuuSff//73mTp1KqtXr6Zbt24sW7aMhx9+mKOPPppzzz03ax4LFixg99135/rrr2fZsmW0trayYMEC/vCHP3DwwQdTV1fHq6++yl577cXNN9/M6tWraWlpYeHChVx33XV88YtfpKUl1Un5vffey9e+9jXuvPNO3n77bZqamujUqRNLlizhoYce4uijj+aCCy7I+Qzuvvtu9t9/f+677z4aGxtpbm7m448/5vrrr+fzBx7AotWfpPilGNhlII/e/RjjDhrHQw89xKpVq+jSpQvz5s3jqquuYu+996a2tjbKKzAMIyKmrDAKgqr+WlUvU9VHVHVpjrBzVfUIVX1WQ+shVHUj8D2gHtgeOKC4UncMg/t24YJThvOH/9mF/XbrlfTbGx+u5Ud/eJ9f3vUxC5dvyJCCYRiGYRhG4TjqqKOorKwE4Pnnn998fd26dRx11FHMmjWLcePGMXXqVBobG6mrq6Ouro7rrruOHj16cPPNN3PjjTdmTP9HP/oR/fv35+WXX6ahoYGGhgbuueceunXrxpw5c7j00ks5+eST+exnP8vMmTOpr69n7dq13HTTTVRWVvLiiy9y2223paRbU1PDBRdcwLRp02hoaKCuro5169axePFiLr/8cqqqqrj22mt5+OGHM8q2YsUKvvnNb3LmmWeyYMEC6urqWFm3kquuv4qqqirem/M+f7r+TwBUSiV9A78Uc95+l+9+97u0trYybtw45syZQ11d3eZ7W7p0Kb/4xS/yfSWGYaRBbO28UQxEZAPQRVVjL+QTkVeAvYGvqeo97ZRj1ujRo0fPmjWrPckUlNnz13HblMXM/Hhd0vUKgcP36svp4wczoE/nEklnGIZhGMWntbWV9957D4BddtmFior082eqyroNW9824N27VhbU18HEiRO5/PLLASL7xdp555354IMP+PznP8+0adMAuOKKK7jssss4+OCDeeqpp6iqSjVy/de//sWXv/xl+vfvz5IlS+jUqdPm3xL3NGjQIGbNmkW/fv2S4l522WVcccUVAIwZM4YZM2bQpUuXpDBnnHEGd955J+PHj+fpp5+O+AQcv/3tb7nwwgvTxp08eTJnnXUWAGeeeSaTJ0+mubWZuqY61jW7JRxXXnQlt9x0KyNGjWDG7Bn0rupNpTilztFHH83jjz/OzjvvzJtvvkl1dXVS+lOmTOGoo44CYPjw4cybNy+y3FHrg8+YMWOYPXv2bFUdEzkzw9iC6JQ7iGF0HCJSAQwPTrNaaGypjB7end+cvSMz3l/L5CeXMHdxIwCtClNeW82zb9Zy7H79OWXcIHp3typqGIZhbLus29DCyb+YWWoxCs59l32aHtWl/cb37dsXIMlnxa233grAeeedl1ZRAXDCCSfQq1cvVq5cyYwZM9h3331Twpx99tkpigqAI488crOy4rzzzktRVCTCJJZ5xOWYY47hwgsvZPr06bS0tGy2HvG5+JKLqd9Ux5rmNUnLPQ4/5nBuuelW5s2dR9fmrlR2dvHr6uqYMmUKABdeeGGKoiIh9/7778/06dNjy20YRnpsJGSUG6cBA4EVwEsllqVoiAif26UXZq7ECgAAIABJREFUe+7Uk//MrOPOJ5eyaJVz89HUrPxz2goef3UVJx04kBMPGkC3Luk/toZhGIZhGIVg0aJFzJ8/H4BvfetbGQf6wGZnkvPnz0+rrNhnn33Sxhs0aNDm//fee++sYTL5f1i2bBmTJk3iySef5P3336e+vj7Fv8X69eupra2lf//+KfH79q2h+9Bu1DXVJV3vXNGZ0SPaDBRqa2vp1s1tSfr666/T2uqUGoceemhauRK/mbLCMAqHKSuMskFEPgXcEJxeFviwiBo30zqPUe0WrIhUVAgHf6aGz4/pw1MzVnPXM0tZtcZtedq4sZW/PbOUh19ewWmHDOboffvRuZO5mTEMwzAMo/0kLCoSFhCLFy/e/NvKlSsjpbF+fXon4T179kx7PbxkJFeY5ubmlN+mT5/O0UcfTV1dm6KhR48edOvWDRGhpaVls+zr1q1LUlY0tbr+VXWPbjRrW9qVUknvqj706NSDjV02tYVvCm1Bv3z55v+HDs286d2wYcMy/mYYRnxMWWGUBSLSHfgn0B94UFVvLrFIHUqnSuGL+/Tj0D1qeGT6Sv4xdRkNjW6WYM26Fv706CL+NW05p48fzPg9+lJZaXt6G4ZhGFs/3btWct9lny61GAWne9fSWkw2NDTw0UcfATBqlJvXCVsnzJkzh1133bUksmWiubmZ0047jbq6OnbffXeuuuoqDjzwwCSlx9y5c9lxxx2BNt8dzi9FLfVN9UnpCULPqp70rupNhZgFq2GUI6asMEpOsEXpfcDngGnA1+KmkcmxUGBxMbpdAnYgXaoq+MoXBnLU3n154D8r+Ne0FWxscmaHy+uauP6Bhdz/n+Wcefh2HDCmd0GdcxmGYRhGuSEiJfftsDXyxBNPbFZOjBs3DoDBgwdv/n3+/Pllp6yYPn068+fPp7KykkcffTSthcPSpW3uzlq1lbpNdaxpqkdJdjrarbIbfTrXUFWR3i+Hz8CBAzf/v2jRInbYYYe04RYtWhQpPcMwomE25UZJCRxq3g58EXgTOFZVG0srVenpUd2JM4/Yjtsu3I3j9u9Pp5AlxcLlG7nyrnn8ZNIHvPHh2hJKaRiGYRjGlsamTZu46qqrAOjduzcnnHACACNGjNisAHjkkUdKJl8mFi5cCMCAAQMyLsUI7wCybMMy6pvqUhQVlVLJgK4DIysqAPbcc8/NO3Q899xzGcM9++yzkdM0DCM3pqwwSs1NOKea7wNHqmpdjvDbFDU9qzj3uGH85bxdGb9HDWFDivc+Wc/Ft87l4ls/5L2F6deMGoZhGIZhJGhsbGTChAm88cYbAFx00UX06dNn8+9nn3024HYFSYTJRHgXkY6gd+/egHOwuWzZspTfP/nkE373u99tPm/RtmUtlVJJj049ALf8Iy59+vThiCOOANz2qBs2bEgJ8/TTT/PSS1utb3jDKAmmrDBKhohcCXwPWAAcrqrLc0TZZhnctwsXnDKcSf+zC/uP7pX02xsfNvDjSe9z5V0fs3B56sfTMAzDMIxtl9bWVmbOnMl1113HmDFjuOeeewD4xje+wU9/+tOksOeffz5jx45lw4YNHHLIIfz+979n1apVm3+vq6vj8ccf54wzzuCggw7q0Ps48MAD6d69O6rKKaecwvvvvw84XxuPPf4YBx18UIoVhSD0rurNkOqhdK3s2q78r7jiCiorK3n33Xc55phjeO+99wDnS+Pee+/llFNOSVL8GIbRfkxZYZQEEfkJcAmwFDhMVReUWKQtghGDq7nsGztw3Tk7MXZk96TfXpxZzzk3vMv1Dyxged2mDCkYhmEYhrE1M3jw4M1HTU0NVVVVjB07lvPPP5+PP/6Y/v37c/PNN3PHHXek+L7q0aMHTzzxBPvttx/19fX88Ic/ZMCAAdTU1NC7d29qamo4+uijufPOO9m0qWP7Gr179+a3v/0tAC+88AK77LILPXv2pEePHnzp6C+xpn4N19z8m83hqyurGVI9hD6da6iQ9g95Pve5zzFp0iREhGeffZZdd92VPn360KNHD7761a8yaNAgLrvssnbnYxhGG+axyCgIInIMcGnoUufg+suha1eo6mMisjtwbXDtY+CSDI4ib1HVacWQd0tnt+Hd+fXZO/L6B2uZPGUJHy52bj5aFZ58bTXPvlHLl/brz1fHDaJPD6vmhmEYhrGtkFgiISJ0796dwYMHs/3227PHHnswfvx4jj32WDp37pwx/pAhQ5g2bRr33Xcf99xzD6+99horV66koqKCESNGMHbsWMaPH88pp5zSUbe0mXPOOYftt9+ea665htdee42m5iYGDxnMIUeM49zzz2XTprbtRvt16UenGH4povCd73yHsWPH8qtf/YoXX3yR9evXM3z4cE466SQuuugiHnjggYLmZxjbOpLY1scw2oOITABuyxHsLFWdLCLjgMzeibzw7ZRr1ujRo0fPmjWrPcmUNa2tyrSZddzx5FIWrdqY9Ft15wpOOmggJx40gG5dbFsuwzAMozxobW3dbEa/yy67bHZeaBi52NDSSO2mWja1Jlt2VEon+nTuQ/fK7lvcbmn51ocxY8Ywe/bs2Zl2xTOMLR2bcjUKQqBUmBwx7FTIw7uRkZaKCuELn6nhgDF9eGrGau56Zimr1riZhcZNrfztmaU8/PIKTh03iGP27U/nKusQGoZhGIaxZdHU2kTdplrWtyQ7FReEXlW96VXVqyDLPQzDKB9MWWEYWwmdKoUv7tOPQ/eo4ZHpK7l36jLWNjpP2GvWtfDnxxbzr2krOP2wwRy2R18qK01fZBiGYRhGedOqLdQ31bO2aW2KA83unbrTp6qGThU2pDGMrRFTPxrGVkaXqgq+8oWB3PbT0Zx26CC6dm6r5ivqm7jhgYWce+O7TJtZhy0DMwzDMAyjHFFV1jatZXHjYtY0rUlSVHSp6MLgrtvRv8sAU1QYxlaM1W7D2Erp3rWSMw7fjmP3688/pi7jsf+uornFfegXrtjIL++ax87DujHhyO3YY8eeJZbWMAzDMAzD0Rj4pWjy/FJ0kk706VxDt8puW5xfCsMw4mOWFYaxlVPTs4pzjh3GLefvyvg9agh/29//ZD0X3zqXi275kPcWrs+ciGEYhmEYRpFpam1i+YblLN+wLElRUSEV9Knqw3bVQ+jeactzoGkYRn6YssIwthEG1XThglOG88cf7cL+o3sn/fbm3AZ+POl9rvzbxyxYvqFEEhqGYRiGsS3Soi2s3rSaJY2LafQcaHbv1IPtug6hd+c+5kDTMLYxbBmIYWxjDB9UzWXfGMmcBeuYPGUJb3/UsPm3F2fVM312PeP37MvXDxvMwD6Z92E3DMMwDMNoD6pKQ/Na6pvqadGWpN+6VHalpqqGLpVdSiSdYRilxpQVhrGNstv23bn626N4/YO1TJ6yhA8XNwLQqvDUjNU892YtX9qvP18dN4g+PaypMAzDMAyjcDQ2r6e2qZam1qak6+aXwjCMBDYCMYxtGBFhr517sceOPXlxVj23P7mERSs3AtDcojz44gqmvLqKLx80gBMPHEj3rpUlltgwDMMwjC2ZTa2bqNtUS2NLY9L1CqmgV1Vvenbqacs9DMMATFlhGAZQUSEcNLYPB4zuzVOvr+auZ5ayst7NdDRuauWuZ5bxyPSVnHrIII7Ztz+dq6wTYRiGYRhGdFq0hfpNdTQ0NyRtQyoI3Tv1oE9VHyorbFLEMIw2TFlhGMZmKiuFo/buxyG71/Doyyu5d+oy1qx3a0jXrG/hz48t5l/TVnD6YYM5bI++VFaaeaZhGIZhGJlRVdY2r6W+qY5WbU36rWtlV2qq+tK50nxkGYaRik2PGoaRQpeqCk46aCB/vXA0px06iK6d25qKFfVN3PDAQs698V2mzaxDVbOkZBiGYRjGtoiqsr55PUs2LKZ20+okRUWniioGdBnIwC6DTFFhGEZGzLLCMIyMdO9ayRmHb8dx+/fn788t47H/rqK5xSknFq7YyC/vmsdOQ6s566gh7LFjzxJLaxiGYRhGObCpZRO1TavZ0JK8HXqFVNC7qjc9zC+FYRgRsFbCMIyc9OlRxTnHDuOW83flsD1rqAit/vhgUSMX3zqXn93yIe8tXFc6IQ3DMAzDKCktrS2s2riKpRuWJCkqBKFnp54M6TqUXlW9TVFhGEYkrKUwDCMyg2q6cP7Jw5n0o13Yf3TvpN/emtvAjyd9wBV/+5j5yzZkSMEwDMMwjK2NVm1lTVM9izcsoqF5bZIDza6V1Qyu3o6+XfqZA03DMGJhy0AMw4jN8EHVXPaNkcxZsI7bpyzhrY8aNv/20qx6Xp5dz/g9+3L6+MEMqrG1qIZhGIaxNaKqNLasp3ZTLc3anPRbVUUVNVU1dK2sRsQcchuGER9TVhiGkTe7bd+dX317FG982MDkKYv5YJHbM71V4akZq3nuzVqO2bcfpx4yiD49qkosrWEYhmEYhWJjy0bqmmoz+KXoQ89OPU1JYRhGu7BlIIZhtAsRYc+denLj93fm4q+NYNiALpt/a25RHnppJd+8Zg53PrWEdRtaSiipYRiGYRjtpbm1mVUbV7Jsw9JUvxRVvRhSPZReVb06TFExb948RAQRYd68eR2Sp2EYHYNZVhiGURBEhIPG9uGA0b15+vXV/O2ZpaysbwKgcVMrdz+7jEdfXslXxw3iS/v1p3OV6UqN/2fvvuOlLO/8/7+umTm99wqCgPTeBJEiTRQEpNhQMTHRZHe/2U3ZZLPJLxuTb/JN2ai7G5NsNis2VLqiyOEgvVepCqi003tvU+7fH4PA0NFzmHPmvJ+Px3kMXJ+57/kc5cx9n89c1+cSEZHmZFkWS5YsYeHChezbt4+ioiLsdjspKSmkpaUxbNgw7r77bsaPH090dPRNndtjeah2VlHlqvLZhhQgzB5GXHAcQTYt/RSR5qNihYg0K7vdMHloAuMGxPHejhLe3lBIVZ13RkVVnZu/rspj+dZi5o1PZcKgeOx2TREVERH5qioqKpgxYwYbN248P+ZwOAgPD+fMmTN8/vnnbN26leeff56XX36Z+fPn39B5Lcuizl1HxRX7UgQTFxxHmD2sOb8VERFAy0BEpIUEB9l48O5k/vcHvXj0nhTCgi+83ZRUOnlh2VmeffETNh+qwLKsa5xJRERErueJJ55g48aN2O12vve973H8+HEaGxspLS2lvr6eAwcO8Jvf/Ib+/fvf8Dkb3Y0UNhRQ0ljsU6iwGzvxwQmkhaapUCEiLUYzK0SkRUWE2nl8YhrTRiTy1vpC3t9ZisvtLU7kFDfyq4Wn6JYRxpOT0hjUTc24REREbtaJEydYuXIlAL/85S/50Y9+5BN3OBz069ePfv368c///M/U19df83wuj4sKZzm1rlqfcW9fiiiig2KwG21DKiItSzMrROSWiI0M4tlpmfzP93oycXA8totqEidy6/nJy5/zL//zGZ+cqb36SUREROQyH3300fk/T58+/brPDwvznQ3xRYPKdevXUdFUQV597mWFikenPMZtkZ148Vf/cVmh4ovjN2zYQEFBAX//939P586dCQ0NJTU1lccee4xPPvnkirlc2iDzxIkTzJ8/n8zMTEJCQujYsSPPPvsseXl5N/qfA4Af/ehHGGPo3bv3NZ9XVVVFZGQkxhgWLFhwU68hIi1LxQoRuaVS4oL57uyOvPSdHozsHeMTO/B5Df/0pxM899pJThde+1MfERERuVxOTs6XPra0sZRKZwUWF5ZnBtuCSQlNIch2/S3IT548ycCBA/njH/9IYWEhQUFBFBYWsnDhQgYOHMjq1auvefzOnTsZPHgwr7zyCpWVldjtds6ePctf/vIX+vbty759+274e3nmmWcwxnD06FG2bNly1ectXLiQ2tpaYmJieOihh274/CLS8lSsEBG/uC0llJ/O68zz3+pG/y6RPrHtRyv59ovH+PfFpyksb/RThiIi4m+WZVHnqgu4r+bu1TR06NDzyyi/6Fdxoxov2n7UbV3YYtxu7CQEJ5AamkboDfal+Kd/+ieCg4NZs2YNtbW1VFdXs3PnTvr27UtDQwMPPfTQNYspzzzzDJ07d2bnzp1UV1dTW1tLVlYWHTt2pKysjJkzZ1JdXX1DuXTu3JnJkycD8Ne//vWqz/si9vjjj18240RE/Es9K0TEr3p0jOD/Pd2V/Z9W8/LqPE7kemdUeCxYu6+cDQcquH94Ag+PSyE28vqf6oiISOCod9fzgwP/6O80mt3v+r9AuCO82c7XqVMnnn76af76179y6NAhevTowYABAxgxYgSDBw9m2LBh9O7d26cv1LX7UkQTExSDzdzc55r19fVs376dnj17nh8bNmwYa9eupWfPnpSVlfHrX/+aP/7xj1c83uFwkJ2dTXJysjcXY5g0aRKrV69mwIABnDlzhj//+c/84Ac/uKF8nn32WVavXs3ixYt58cUXiY2N9Ynv3bv3/GyNb37zmzf1vYpIy9PMChFpFQZ2jeLFv7uDf32sE5lJIefHXW6Ld7aV8NTvPubV7HxqG9zXOIuIiEj79NJLL/HTn/6UiIgILMti//79vPTSS3z961+nb9++pKam8t3vfpf8gnwqmsqv2Jci1B5CWlg6ccFxN12oAJgzZ45PoeILycnJPPvsswC8/fbbVz3+2WefPV+ouFjPnj2ZPXs2AG+99dYN5zN16lQyMzOpr6/ntddeuyz+xayKESNG0Ldv3xs+r4jcGipWiEirYYxhVJ9Y/vydHvzjrA4kxlyYSdHQ5OHNdYV87XdHWbq5iEanx4+ZioiItC4Oh4PnnnuO3NxcXnvtNZ5++mn69+9PcHAwAEVFRTz//PP06duHjds3+vSl+EJscNwN9aa4mnvuuee6sdLSUk6ePPmljz948CBOp/OG8rHb7XzjG98ALl8KUltby8KFCwHNqhBprbQMRERaHbvdMHlIAuP6x/H+zhLe2lBIVa13RkVVnZv/WZXH8i3FzJuQysRB8djt2u5URCQQhdnD+F3/F/ydRrMLu8EeEF9GTEwM8+bNY968eQA0NDSwbuM6XviPF8helU1ZSRnfeuzbrD+wnoiwcGKD4prttTMyMm4oVlRUROfOnb/U8S6Xi7KyMlJSUm4op6effppf/OIXHDp0iB07dnDnnXcC3hka1dXVxMbGqrGmSCulYoWItFrBQTZmjkpm0pAElm8pYtnmYuqbvDMqSqucvLjsLEs2FfHkpDTu6h2DzaaihYhIIDHGNGtvh/bG6XFSTRW9R/Xir6P+m+89832WvrGU/Nx89qzbwyOzH/lSyz3akvT0dB544AGWLVvGf//3f58vVnwx02LevHlqrCnSSgX2u5OIBISIUDvzJqTxvz/oxYy7knBcNJMit6SRXy08xXf+eJy9x6uavcO6iIhIW+OxPJQ3lZNfn0edu+78+KNPPXL+zzmf55wvVNjtdsA7C+NqKisrr/u6ubm5NxS7Ul+KGz3e4XAQHx9/3Vwu9kW/jEWLFlFVVcWhQ4fYuXMn4N2BRERaJxUrRKTNiI108MzUDP7nez2ZODieiydSfJpXz09e/pwf/c9nfHym9uonERERCVCWZVHtrCavPpcqZ6VPX4oQWwgd4zte+HvIhWbWcXHepSBnz5694nmrq6v5+OOPr/v669evv24sPj7+iktAbvT4fv36ERR0c301JkyYQNeuXamtreWNN97waazZp0+fmzqXiNw6KlaISJuTEhfMd2d35E//2IO7esf4xA5+XsN3/3SC5177nNOF9X7KUERE5NY5efIkBz8+SEFDPmVNpbitCztnOYyDxJBEUkJTefP1CztpDBo06Pyf+/fvD8DSpUuveP7f//73NDY2XjePxYsXc+zYscvGS0pK+Mtf/gJwzf4Qf/7znykpKbls/NixYyxZsuS6x1+NMeb8DIqXXnqJ119/HVBjTZHWTsUKEWmzOiaH8pN5nXnh290Y0CXSJ7b9aBXfevEYv190msLy699giYiItEVOj5Ot+7cysM9AHps5j6ULl3L2tHeJR2xQLImOJI4fOsHXvvY1/vCHPwAwbNgwRo0adf4cjzziXR6SlZXFz372M6qqqgBvkeHHP/4xv/zlL4mNjb1uLqGhodx7772sXbv2/LLM3bt3M2HCBEpKSoiKiuJHP/rR1b8Xp5OJEyeye/duwDtTZO3atUyePJnGxkY6dOhwfknHzXrqqacICQnh8OHDlJeXq7GmSBugBpsi0uZ17xDBr5/uyv5Pq3l5dR4ncr0zKiwLPtxfzsaDFdw3LIGHx6UQF/Xlt2QTERFpLdyWmypnJdXOajx2Nx6Ph/VZ61mf5V0uERwcTGRkJOXl5T79nAYNGsTy5cux2S58Zjl//nzeeOMN1q9fz3PPPccvfvELYmNjqaioAOC3v/0t7733Hhs3brxmTs8//zw//vGPmThxIuHh4dhsNmpqagDvspM333yTjh07XvX4v/zlL3zjG99g2LBhREZG4vF4qKvz9tyIjY1l2bJlREdHf6n/XgkJCcyZM+f8rAo11hRp/TSzQkQCxsCuUbz4d3fwk8c60SHpwlpcl9vi3e0lfO33H/PqmnxqG9zXOIuIiEjr5e1LUUV+fR5VziosLMZMGMOGA+v5xe+fY9bsWfTs2ZOQkBAqKioIDw+nW7duzJ07l7feeovdu3eTnp7uc0673c7777/Pz3/+c3r06EFwcDDGGCZNmkR2djbf//73byi3zp07s3//fv7u7/6OpKQkmpqaSE5O5pFHHmH//v3cf//91zx++PDh7NmzhyeeeIKYmBhcLhcZGRl84xvf4NChQwwZMuRL/3cDmDNnzvk/q7GmSOtn1DlfApkx5kivXr16HTlyxN+pyC3mdlt8uL+M19cWUFzp9IlFhdmZOzaFaSMSCQlSzVZE5FbzeDznext0797d51N+ubp6Vz3lzjKcHt/rmsM4iA2OI9wejjG3fhvvL15z/fr1jB079qaOPXXq1PmGmydPnqRTp07NnN0F//AP/8B//dd/MWLECLZt29Zir3OzvuzPQ+/evTl69OhRy7J6t2R+Iv6iZSAiEpDsdsOkIQmM7R/H+ztLeGtDIVW13hkV1fVu/vZBHiu2FvPY+FQmDY7Hbr/1N3ciIiI3wulporypnHq3b+Nom7ER7YghKijq/DakcmVVVVW8+uqrAHzrW9/yczYiciP0riYiAS04yMbMUcm8/INezBufSljwhbe90ion/7H8LM+88AmbDpbj8WimmYiItB5uy01ZYyn59fk+hQqDIdIRSVpoOjHBMSpUXEdjYyPf+c53qKqqokOHDmqsKdJGaGaFiLQL4SF2HpuQyv13JrJoQyErd5TgcnuLE7kljfz6zdN0SS/iqclpDOoW5ZdptCIiInCuL4WrmkpnBR7L4xMLtYcSFxRHsD3kKkfLF1544QVeeOEFioqKqK/3Fnv+8Ic/EBwc7OfMRORGqAwrIu1KbKSDb07N4G/f78mkIfHYLqpJfJZXz09e/pwf/vVTPj5d678kRUSkXbIsizpXHfkNeZQ3lfkUKhzGQVJIEskhKSpU3KCKigpOnz6NZVkMGDCAt99+m9mzZ/s7LRG5QWqwKQFNDTbles4UNfBqdj5bD1deFruzZzRPTkqjU6q2NhMRaU5qsHm5pnN9KRqu0JciJiiGSIf6UgQqNdgUuTItAxGRdq1jcig/eawzx87W8cqaPPZ/WnM+tuPjKnZ+UsU9A+KYNyGV1Hh9kiUiIs3L7XFT4ayg1lWDxYUPEQ2GCEcksUGx2G12P2YoIuIfKlaIiADdO4Tzq693Zf+n1SzIyud4Th0AlgUf7i9n48EKpgxL4JFxKcRFBfk5WxERaes8locaVzWVzsor9KUIIy44jmCbeiuISPulYoWIyEUGdo1iQJdIth2t5JU1+ZwtagTA5bZYub2E7L1lzLgridmjk4kI1SddIiJycyzLot5dT7mzHJfH6RMLsgURGxRHmD1MjZ5FpN1TsUJE5BLGGO7qHcudPWNYt7+M19cWUFThvaFsaPLw1vpC3t9RwtyxKUwbkUhIkNYQi4jcjIt/Efd4PO2mZ0WTu5FyZzkN7gafcW9filiiHNqNqj3yeC7MrNH/f5EL2seVQUTkS7DbDBMHJ/DX7/XkmakZREdcmElRXe/mbx/k8fXff8yqXRe2QRURkeszxuBweD8za2houM6z2z6Xx0VpYwkFDQU+hQqDIcoRRXpYBtFB0fpFtZ364mfA4XDo34DIRVSsEBG5jmCHjRl3JfHyD3oxb0IqYSEX3jpLq5z85/Icnn3+EzYeLMfjUdFCRORGREVFAVBeXk6g7k7nsTxUNlWS35BHzSUNNMPsYaSGpREfkoDdaFlhe2VZFuXl5cCFnwkR8dIyEBGRGxQeYuex8alMvTORRRsKWbmjBKfLe+OZW9rI/3vzNIvTi5g/KY3Bd2gqr4jItcTExFBeXk5NTQ05OTnExcURGhoaEEtCLMuizl1HZVMFLsvlE/P2pYglzBEO+C4BkPbD4/HQ0NBw/mcAvD8TInKBCdRKtgiAMeZIr169eh05csTfqUgAKq5o4o0PC8jeW8alEyr6dI7gqcnp9Lotwj/JiYi0AVVVVeTm5vo7jWblsTy4LTcWlxYhDHZj1ywKuaKMjAyio6Nv6pjevXtz9OjRo5Zl9W6htET8SsUKCWgqVsitcLaogVez89lyuPKy2J09o3lyUhqdUsP8kJmISOtXX19PZWUl1dXVuFyu6x/QSrktFxVNFdS6ay+LRTmiiAmKxWba/qwRaT4Oh4OoqChiYmIIC7v5+wQVKyTQaRmIiMhX1CE5lH99rDPHc+pYkJXP/k+rz8d2fFzFzk+qGNc/jnkTU0mLD/FjpiIirU9YWBhhYWGkpqZiWVab61/R5GlkXeGHrCvMpslq8on1ju7L9IwHSQ5N9lN20loZY7RcVOQ6VKwQEWkmd2SG86uvd+Gjz6pZsDqfYzl1AFjyYTkuAAAgAElEQVQWrPuonE2HKpgyNIGH70khPirIz9mKiLQ+bekXOI/lYU/Zbt7JXUaFs9wnlh6WwazMOfSI7uWn7ERE2j4VK0REmtmALlE8/+1Ith+tZMGafM4WNQLgclus3FHCmr1lzLgridmjk4gM09uwiEhbc7Lmc5bkvM2p2pM+45GOKKalT2dk4igt+RAR+Yp0lyzNwhgzGJgIDDv3lQFgWdY1Px4xxswHvg30ApqAHcAvLcva1pL5irQ0Ywwje8cyvGcM6/aX8fraAooqnAA0Oj28vaGQ93eWMHdMMtNGJBEarJtaEZHWrqyplHdylrOnfJfPuMM4GJc8nslpUwizh/spOxGRwKIGm9IsjDErgOmXjl+rWGGMeQH4DlAPrAFCgfGAAWZblrWiGfJSg01pFZpcHj7YWcqb6wuprPVtIJcQHcSj96QwaUgCDnvbmP4sItKeNLgbWFuYxdqCNTgtp09sQOxAZmTOIilEfSnk1lKDTQl0KlZIszDG/BCIAHaf+zoFhFytWGGMmQBkA6XACMuyTpwbHwFsAOqAzpZlVXzFvFSskFalrtHNii3FLNlcRH2j77Z26QnBPD4xjdF9Y7HZVLQQEfE3j+VhZ+kOVuYtp9Lpu+NTh7AOzOowl25R3f2UnbR3KlZIoFOxQlqEMaaBaxcrVgFTgH+yLOuFS2IvAv8H+L5lWf/+FfNQsUJapcpaF4s2FrJyewlOl+/78O1pYcyfnMaQO6LaTKM5EZFA82n1CZbmLOJM3Wmf8WhHNNMyZnBnwkj1pRC/UrFCAp2KFdIirlWsMMaEAeVACNDBsqycS+J3A5uAjZZljf2KeahYIa1acUUTC9cVsGZPGZ5L3o77dI7gqcnp9Lotwj/JiYi0QyWNJazIWcL+in0+4w7jYHzKRCalTiHUHuqn7EQuULFCAp0abIo/dMdbqCi+tFBxzhd3B/1uXUoi/pEUG8x3HuzIrLuTeTW7gM2HLqx8Onyylu/9+QTDekQzf1IandPC/JipiEhgq3fXk5X/AeuL1uKyfHsLDY4bwvSMB0kISfRTdiIi7Y+KFeIPHc89XqlQgWVZtcaYCiDOGBNlWVb1rUtNxD8yk0L58aOdOJFbx4KsfPaduPDPftcnVew+VsXY/nE8PjGVtPgQP2YqIhJYPJaH7aVbWZm7gmqX7y3HbeGdmNVhLl0iu/opOxGR9kvFCvGHyHOPddd4Ti0QC0QB1y1WGGOuts6jy82lJuJf3TLC+b9f68KBz6p5OSufY2e9PyaWBes/KmfTwXKmDEvkkXtSiI8K8nO2IiJt2/HqT1hydhG59b6fn8QExTI9YyZD44erL4WIiJ+oWCEi0gr17xLF89+KZPvRSl5ZU8CZogYA3B54b0cJ2XvLmHFXIrNHJxMZprdyEZGbUdRQxIrcJRyo+MhnPMgEMTF1MhNSJhNi1yw2ERF/0h2u+EPNucfwazzni46CN7QE5GqNhc7NuOh146mJtB7GGEb2jmV4zxjWf1TOa9n5FFU4AWh0enh7QxHv7yxlzphkHhiRRGiwPv0TEbmWOlcdqwveZ0PROtyW2yc2NH440zNmEhcc76fsRETkYipWiD+cOfeYeaWgMSYC7xKQcvWrEAG7zTBhUDyj+8Xywa5S3lxXSGWtt/lbTb2bl1fn887WYh4bn8qkIQk47NruVETkYm7LzdaSzbyf9y41rhqfWOeI25nd4SE6RXT2U3YiInIlKlaIPxwDGoEkY0yGZVm5l8QHnXs8eGvTEmndgh02po9MYtLgeFZsLWbJpiLqGj0AlFW7+M8VOSzdXMTjE9IY3S8Wm01FCxGRj6uOsPTsYvIb8nzG44LjmZHxIIPjhmKM3i9FRFobFSvklrMsq94Ysw6YAswBXrjkKbPPPa68pYmJtBFhIXYeuSeV+4cnsmhjIe9uL8HpsgDIK23iN2+fZvGmQp6clM7Q7lG6CReRdqmgIZ/lOUs4XHnIZzzYFsLk1Hu5J2UiwbZgP2UnIiLXYyzL8ncOEoCMMQ1AiGVZV/wtyRgzAcgGSoERlmWdODc+AlgP1AOdLcuq+Ip5HOnVq1evI0eutlmISNtXXNnEwg8LWbO3FI/HN9anUwTzJ6fRu1PklQ8WEQkwta5aVuWvZFPRBjxceFM0GIYnjGBa+gxig2P9mKFI8+jduzdHjx49erXebSJtnYoV0iyMMfcDP71oaBhggJ0Xjf3Csqz3LzrmBeA7eLcwzQaCgYnnjpttWdaKZshLxQppN3KKG3gtu4BNhy6v8Q3rEc38SWl0TgvzQ2YiIi3PbbnYVLyRVXkrqXP77o7eJbIbszPn0jHiNj9lJ9L8VKyQQKdlINJckoDhVxgffslzzrMs6x+NMR8Bf4+3SNEErMVb1NjWUomKBKrMpFD+5dFOzMmtY0FWPntPXOhPu+uTKnYfq2Js/zgen5hKWry25BORwGBZFkeqDrHs7GIKGwt9YgnBiczMnMWA2EFaEici0sZoZoUENM2skPbs4OfVvLw6n0/O+n7CaLfBlGGJPDIuhfjoID9lJyLy1eXV57EsZxEfVx31GQ+1hTI57T7GJY8nyKb3OQlMmlkhgU4zK0REAlS/26P4w7ci2fFxFa+syed0YQMAbg+8t6OE7L2lTB+ZxOwxyUSF6XIgIm1HtbOa9/PfZUvxJiwufPBmMIxMHMXU9OlEB0X7MUMREfmqdHcqIhLAjDGM6BXDsB7RbPionNfWFlBY3gRAo9Ni0cYiVu0sZc7YZB4YkURosM3PGYuIXJ3L42Jj8Xo+yH+Pene9T+yOqO7MypxLZngHP2UnIiLNScUKEZF2wG4zjB8Uz939YvlgVylvrS+kosYFQE2Dm5dX5/PO1mIeHZ/K5CEJOOxa2y0irYdlWRysPMDynCUUNxb5xJJCkpmZOZt+Mf3Vl0JEJICoWCEi0o4EO2xMH5nEpMHxrNhazJJNRdQ1erf2K6t28V8rcli6qYjHJ6Yxpl8sNptu/EXEv3LqzrI0ZxHHq4/5jIfZw5iSNpUxSeNw2HRLKyISaNRgUwKaGmyKXFtVrYvFm4p4d1sxTS7f68HtaaE8OSmNod2j9WmliNxyVc4q3st7h20lW3z6UtiwMSppNPelTSMqKMqPGYr4lxpsSqBTGVpEpB2LjnDw9SnpTB+ZyMJ1hWTtKcXjnWjB5/kN/OyVk/TuFMH8yWn06RTp32RFpF1wepysL/qQrPxVNHgafGI9o3vxYOZc0sPS/ZSdiIjcKppZIQFNMytEbk5OcQOvZRew6VDFZbFh3aN5cnIat6eF+SEzEQl0lmWxv2IfK3KWUtpU4hNLCU3lwcw59I7uo5leIudoZoUEOs2sEBGR8zKTQvmXRzsxJ7eOV9bks+d49fnYrmNV7D5exZh+cTw+MZX0hBA/ZioigeRM7WmW5Czis5oTPuPh9nDuT3+Au5NGYze6bRURaU/0ri8iIpfpmhHOL57qwsHPa1iQlcfHZ+oAsCzYcKCczYfKuXdoAo/ek0p8dJCfsxWRtqqiqYKVeSvYWbr9sr4UY5LHMSVtKhGOCD9mKCIi/qJihYiIXFW/2yP592e7sfPjKl5Zk8+pQu/6cbcH3t9Zytp9ZUwfmcTsMclEhemSIiI3psnTxIeF2awpWE2Tp9En1iemHw9mziYlNNVP2YmISGugO0sREbkmYwx39ophaI9oNnxUzmtrCygsbwKg0WmxaGMRq3aWMmdMMg+MTCI02ObnjEWktbIsi73lu1mRs5RyZ7lPLC00nVkd5tAzWsvvRURExQoREblBdpth/KB4RveL5YNdpby1vpDyGhcANQ1uXs7KZ8W2Yh4dl8rkofEEOVS0EJELTtZ+ztKzizhZ+7nPeKQjkqnp0xmZOAq7sfspOxERaW1UrBARkZsS5LDxwMgkJg6OZ8W2YpZsLKKu0bvfaXm1iz++m8PSLUU8PiGVsf3jsNnUuV+kPStvKuOd3OXsLtvpM243dsYm38O9qfcT7gj3U3YiItJaaetSCWjaulSk5VXXuVi0sYh3txXT5PK9pnRODWX+5DSGdo/WdoMi7Uyju5HswizWFmThtJw+sf6xA5iRMZvk0GQ/ZSfS9mnrUgl0mlkhIiJfSVS4g69PSWf6yEQWriska08pHu9EC04WNPCzV07S67YInpqcRp/Okf5NVkRanMfysLtsJ+/kLqfSWeETywjLZHaHudwR1cNP2YmISFuhmRUS0DSzQuTWyy1p5LXsfDYerLgsNrR7NE9OSqVLuqZ8iwSiz2o+ZcnZtzlTd9pnPMoRzbSM6YxIuAubUT8bkeagmRUS6DSzQkREmlVGYgg/eqQTs8fU8UpWPnuOV5+P7T5Wxe5jVYztH8vjE9JITwzxY6Yi0lxKG0tYkbuMfeV7fMYdxsE9KROYlDqFMHuYn7ITEZG2SMUKERFpEV3Tw/nFU104dLKGBVn5HD1dez624UAFmw9VMHloAo/ek0pCdJAfMxWRL6vB3cCagg/4sDAbl+XyiQ2MG8yMjFkkhiT6KTsREWnLVKwQEZEW1bdzJL9/pis7P6nilax8ThU2AOD2wKqdpXy4r4wHRiYxZ0wyUWG6LIm0BR7Lw47SbazMXUGVq8on1jH8NmZlzqVrVDc/ZSciIoFAd4UiItLijDHc2TOGod2j2XignNeyCygobwKg0WmxeGMRq3aWMGdMCtNHJhIabPdzxiJyNcerj7H07CJy6s/6jMcExfBAxkyGxd+pvhQiIvKVqVghIiK3jN1muGdgPHf3jWX17lLeXFdIeY136nhtg4cFWfm8s7WYR+5J4d6hCQQ59AuPSGtR3FjE8pylHKjY7zMeZIKYkDqJCSmTCbWH+ik7EREJNCpWiIjILRfksDFtRBITB8ezYmsJSzYVUtvg3e+0vMbFS+/msmxzMY9PTGVs/zhsNuPnjEXar3p3HavzV7GhaN1lfSmGxA9jesZM4oMT/JSdiIgEKm1dKgFNW5eKtA3VdS4WbSzi3W3FNLl8r0udUkOZPymNYT2iMUZFC5FbxW252Vayhffy3qXGVe0T6xTRmVmZc7k9soufshMRbV0qgU4zK0RExO+iwh18fUo600cm8ua6QlbvKcXjnWjBqYIG/u3Vk/S6LYL5k9Po2znSv8mKtAOfVB1lac5i8upzfcZjg+KYkfkgg+OGqi+FiIi0KM2skICmmRUibVNeSSOvrc1nw4GKy2JD7ohi/uQ0uqSH+yEzkcBW2FDI8pzFHKo86DMebAtmUuq9jE+ZSLAtxE/ZicjFNLNCAp1mVoiISKuTnhjCDx/uxOzRdbyypoDdxy5sjbjneDV7jlczpl8sT0xMIz1RvziJfFV1rlpW5b/HxqL1ePD4xIbHj+CBjBnEBsf5KTsREWmPVKwQEZFWq0t6OM/Nv51DJ2tYkJXP0dO152MbD1aw5XAFk4ck8Oj4VBKig/yYqUjb5LZcbC7exKq8ldS6a31it0d0YXaHh7gtopN/khMRkXZNxQoREWn1+naO5PfPdGXXJ1UsWJPPqYIGANweWLWrlLX7ynhgZBJzxyQTFa5Lm8iNOFJ5mGU5iyloyPcZjw9OYEbGLAbFDVZTWxER8Rvd0YmISJtgjGF4zxiGdo9mw4FyXssuoKC8CYAml8WSTUV8sKuE2aNTmHFXIqHBdj9nLNI65dfnsSxnMUerfPs5hdhCuDftPsYlTyDIpplKIiLiXypWiIhIm2KzGe4ZGM/dfWPJ2l3GwnUFlNe4AKht8PDKmnze3VbMI/ekcO/QBIIc2rFABKDGVcOqvJVsLt7o05fCYBiRcBdTM6YTExTjxwxFREQuULFCRETapCCHjakjEpkwOI53tpWweGMhtQ3eX8DKa1y89G4uyzYX8/jEVMb0j8Nu03R2aZ9cHhebitezKv996t11PrFukXcwq8NDdAjv4KfsRERErkxbl0pA09alIu1HdZ2LxZuKeHdbMY1O32tbp5RQnpycxvAe0VqDL+2GZVkcrjzIspzFFDUW+cQSQ5J4MHM2/WIG6GdCpI3S1qUS6DSzQkREAkJUuIOv3ZvO9JFJLFxXQNbuUtznZrqfKmzg56+epGfHcJ66N52+nSP9m6xIC8uty2FpziKOVX/iMx5qC2VK2lTGJI9TXwoREWnVNLNCAppmVoi0X3kljby2Np8NByouiw25I4onJ6fRNT3cD5mJtJxqZxUr895hW8kWLC7c4xkMdyWOZmr6NKKCov2YoYg0F82skECnmRUiIhKQ0hND+OHDnZg9uo5X1hSw+1jV+die49XsOV7N6H6xPDExjYzEED9mKvLVOT1ONhStY3X++zR4GnxiPaJ68mCHOWSEZfopOxERkZunYoWIiAS0LunhPDf/dg6frOHlrHyOnq49H9t0sIIthyuYPCSBR+9JITEm2I+Zitw8y7I4ULGf5TlLKGkq8Yklh6TwYOYc+sT0VV8KERFpc1SsEBGRdqFP50h+/0xXdh+rYkFWPicLvJ8+ezzwwa5SPtxXxgMjk5g7JpmocF0epfU7W3eGpWcXcaLmuM94mD2c+9OmcXfSGBw2/VsWEZG2SVcwERFpN4wxDOsRw5A7otl4sIJXs/MpKGsCoMllsWRTER/sKmHW6GRmjEwiLMTu54xFLlfprGBl7jvsKN3m05fCho27k8ZyX/pUIh1qIisiIm2bihUiItLu2GyGcQPiGNUnhqw9ZSxcV0B5tQuA2gYPr64p4N1tJTwyLoUpwxIIctj8nLEINHmaWFe4ljUFH9DoafSJ9Y7uw4OZc0gNS/NTdiIiIs1LxQoREWm3ghw2pt6ZyIRBcby7rYTFG4uoaXADUFHj4k8rc1m2pZjHJ6QydkAcdpvW/cutZ1kW+8r3sCJ3GWVNpT6x1NA0ZmXOpVeMNgMQEZHAoq1LJaBp61IRuRnV9S6WbCzinW3FNDp9r4+3pYQyf1Iaw3tGq1mh3DKna0+x5OzbfF77mc94hD2CqenTuSvpbuxGy5VE2iNtXSqBTjMrREREzokKc/DUvelMH5nEwnUFrN5ditvjjZ0ubODnr52kZ8dw5k9Op9/t6gkgLaeiqZx3cpezq2yHz7gNG2OTxzMl7T7CHRF+yk5ERKTlaWaFBDTNrBCRryKvtJHXsgvYeLCcSy+Xg7tFMX9yGl0zwv2TnASkJk8j2QVrWFuYRZOnySfWN6Y/MzNnkxKa4qfsRKQ10cwKCXSaWSEiInIV6Qkh/PDh25gzJplXsvLZdazqfGzviWr2nqhmdN9YHp+YSmZSqB8zlbbOY3nYU7aLd3KXU+Es94mlh2UwK3MuPaJ7+ik7ERGRW0/FChERkeu4PS2Mn8+/ncOnaliQlc+RU7XnY5sOVbDlSAWTBifw6PgUkmKC/ZiptEWf13zGkrNvc7rulM94pCOKaenTGZk4CpvRjjQiItK+qFghIiJyg/p0iuR33+zK7mPVLMjK42RBAwAeD6zeXcq6/WVMG5HI3DEpREfoEivXVtpYyju5y9hbvttn3GEcjEsez+S0KYTZtcxIRETaJ91JiYiI3ARjDMN6RDPkjig2Hazg1ex88su8vQWaXBZLNxfzwa5SZo9OZsZdSYSFaKcG8dXgbiC7YDUfFmbjtJw+sQGxg5iZOYvEkCQ/ZSciItI6qFghIiLyJdhshrED4hjVN5asPaUs/LCAsmoXAHWNHl7NLuDd7SU8PC6FKcMSCHZoGn9757E87Czdwcq85VQ6K31iHcI6MKvDXLpFdfdTdiIiIq2LihUiIiJfgcNuuH94IuMHxvPu9mIWbyiipsENQEWNiz+vzGXZ5iIen5jGuAFx2G3GzxmLP5yoPs7SnEWcrTvjMx7tiOaBjJkMTxihvhQiIiIXUbFCRESkGYQG25g7xjuLYummIlZsLabR6d3vtKjCyb8vPsOSTUU8OSmNO3tGY4yKFu1BSWMJK3KWsL9in8+4wziYkDKJian3EmrXTjIiIiKXUrFCRESkGUWFOZg/OZ0HRiTx5vpCPthVgtvjjZ0ubOC5107So0M4T92bRr/bo/ybrLSYenc9WfkfsL5oLS7L5RMbHDeU6RkPkhCS4KfsREREWj9jWZa/cxBpMcaYI7169ep15MgRf6ciIu1UXmkjr68tYMOBci695A7qFsX8yWl0y9COD4HCY3nYXrKVlXkrqHZV+8RuC+/E7A4PcXtkFz9lJyKBpHfv3hw9evSoZVm9/Z2LSEvQzAoREZEWlJ4Qwj8/dBtzRiezYE0+uz6pOh/bd6KafSequbtvLE9MTCUzScsB2rJjVZ+wNGcRufU5PuOxQbFMz3iQIfHD1JdCRETkBqlYIX5njBkK/AAYBSQBtcAh4H+BBZam/4hIAOicFsbPn7ydI6dqWJCVz+FTtedjmw9VsPVIBZMGx/Po+FSSYoL9mKncrKKGQpbnLOFg5QGf8SATxMTUe5mQMokQe4ifshMREWmbVKwQvzLGzALeBuzAPmAz3oLF3XiLFxOAx/yWoIhIM+vdKZLffrMre45XsyArj8/zGwDweGD17jI+3F/OtBGJPDQmhegIXaZbszpXHR/kv8/G4nW4LbdPbFj8nTyQMZO44Dg/ZSciItK26S4ogBhjOgNDgG5AGhABOIEK4BTe2Qp7Lcuq91eOFzPGOICX8BYqHrMsa+FFsZ7AFuBRY8z/WJa13k9piog0O2MMQ7tHM7hbFJsOVfDqmnzyy5oAcLoslm0uZvWuUmbdnczMUUmEhdj9nLFczG252Vq8mffy3qHWXesT6xxxO7M7PESniM5+yk5ERCQwqFjRxp1bQvE4MA3oeAOHOI0xm4AlwFuWZVVd74AW1ANIBo5dXKgAsCzrY2PM68D/AYYCKlaISMCx2Qxj+8cxqk8sa/aU8saHBZRVe3eOqGv08NraAlbuKOHhsSlMGZ5AsEP9DvztaOURluYsoqAh32c8LjiemRmzGBQ3RNvSioiINAMVK9ooY8zDwPeBgV8M3eChwcD4c19/MMa8DPy7ZVmnmj3J62u8weeVtmgWIiJ+5rAb7hueyD0D41m5vZhFG4uoqfcuK6iocfHn93JZtqWIeRPSuGdgHHabfhm+1Qoa8ll2djFHqg77jAfbQpicei/3pEwk2KZeIyIiIs1FW5e2McaYKcBvgN5cKFC48S7x2A7sBgrx/oJfBoQBCUA83pkMI4DhQOK5Yy28S0X+AvybZVnlt+QbAYwxduAY0IWrLwMxQFfLssq+5Gto61IRaXNq6l0s2VTMiq3FNDo9PrGOyaE8OSmVEb1i9An+LVDjqmFV3ntsLt6Ahwv/LwyG4QkjeCBjBjFBsX7MUETaK21dKoFOxYo2xBizHHgA7y/wFrAReBVYallW9bWOvcK5BgJPAg/jXYphASXAo5ZlfdiceV8nj7uA94BYvA02T5zL527gKDDfsqz9N3Ceq1UjuvTq1StExQoRaYvKqp28ta6QVbtKcPvWLOjeIZynJqfRv0uUf5ILcG7LxabijazKW0mdu84n1jWyG7M6zKVj+G1+yk5ERMUKCXwqVrQhxhgP0AT8DfiNZVlnmuGcdmAm8G9AL7yzK577que9yRz6AcuB2y8abgL+E/iFZVmVN3AOFStEJGDllzXyenYB6w+Uc+lle1C3KOZPTqNbRrh/kgswlmVxpOoQy84uprCx0CeWEJzIzMxZDIgdpFktIuJ3KlZIoFOxog0xxvwNeM6yrNMtcG4DPArYLct6tbnPf43XfQR4GdgB/DNwBEjH24/jm3hnW4y0LOtG+1tcen4tAxGRgHGyoJ5X1uSz8+PLeyOP6hPDk5PSyEwK9UNmgSGvPpelZxfxSfXHPuOhtlDuTbuPscnjCbIF+Sk7ERFfKlZIoFOxQvzGGNMNb3GiCOhhWVbNJfGVwFTg25Zl/elLvoaKFSIScI6eruXlrDwOn/TdNtNmg4mD4nlsfCpJsWr2eKOqndW8n/8uW4o3YXHhvshgGJk4iqnp04kOivZjhiIil1OxQgKddgMRf3oYCAJWX1qoOGcR3mLFaOBLFStERAJRr9si+O03urL3eDUvZ+XzeX49AB4PZO0pY91H5Uy7M5G5Y1OIidCl/mpcHhcbitexOv996t31PrHuUT2YlTmXjPBMP2UnIiLSvukORvzpizvAq/Wk+GI87hbkIiLSphhjGNI9mkHdoth0qILXsvPJK20CwOmyWLalmA92lzLr7mRmjkoiPMTu54xbD8uyOFj5EctzllLcWOQTSw5JZmbmbPrG9FdfChERET9SsUL8qeDc45CrxIeeezzV8qmIiLRNNpthbP84RvWJZc3eUhZ+WEhplROA+kYPr68tYOX2Eh4el8J9wxMIdtj8nLF/na07y7KcRRyvPuYzHmYP4760qYxOGofDptsjERERf1PPinbCGNMV6A+4gQOWZZ30c0oYYwYBe8/91acvhTHmTmAtEAFMtCxr7Zd8DfWsEJF2pdHpYeX2Et7eUEhNvdsnlhwbxLwJqdwzMB67rX3NGqh0VvJe3jtsL9nq05fCho1RSaO5P30akQ5tAysibYd6VkigU7GiDTPGBAFf3FlVWJblucJzIoHXgWmXhN4H5luWVdayWV6bMeZ3eHf+AG+zzaN4dwMZAdiA/7Ys65mvcH4VK0SkXaqpd7F0czHLtxTT6PS9PHRMDuWJSamM7BUT8EsdnB4n64vWsjp/FY0e342lekb3ZlbmHNLC0v2UnYjIl6dihQQ6FSvaMGPMvwLPAWVAB8uyGq7wnFXAZODSu1EL2IN3W1D3pcfdSsaYmcCzwGAgBqgGPgL+alnWm1/x3CpWiEi7Vlbt5K31hXywqxSX2/ea3z0znPn3pjGgS+DNKLAsi/0Ve1mRs5TSplKfWEpoKrMy59A7pq+fshMR+epUrJBAp2JFG2aMWQNMAP5kWdbfXSE+GfgAb2HiIPAHoAlvYWDMufGvW5a14FblfKupWCEi4lVQ1sjrawtY91E5l176B3aNYv7kNO7IDAgewfMAACAASURBVPdPcs3sTO1pluS8zWc1n/qMR9gjuC99GncnjcZu1JdCRNo2FSsk0OlK3bYNw1twWHWV+NfPPZ4G7rIsqw7AGLME2AUMAOYCC1o2TRER8bfU+BC+P/c2Zo1O5tU1+ez4uOp8bP+n1ez/tJq7+sTw5MQ0OiSH+jHTL6+iqYJ385azs3S7z7gNG2OSxzElbSoRjgg/ZSciIiI3Q8WKNsoYEw5E4y1W7L9C3ACTzsX/9EWhAsCyLLcx5rfAm3gLFiIi0k50Tg3jZ0/czsena/nfrDwOn6w9H9t6uJLtRyqZODiex8ankhQb7MdMb1yTp5EPC7NZU7CaJk+TT6xPTD8ezJxNSmiqn7ITERGRL0PFijbEGNPxor92uOjPYZfEADpxoZhx7ArxU+ce440xHbiop4VlWWeaJWEREWm1et4WwW+/0ZW9x6tZsCafz/LqAfBYkLWnjHUflTPtzkTmjk0hJqJ13i5YlsXe8t2syFlKubPcJ5Yems6DHebSM7qXn7ITERGRr6J13n3I1ZwCLl5pbOEtMhy/xjEGWH6NeBAXChdfsH+J3EREpI0xxjCkezSDukWx+XAFr60pILfUu2OG02WxbEsxH+wuZdaoZGbenUR4SOu5PJys+ZylOYs4Wfu5z3ikI5Kp6dMZmTgKu2k9+YqIiMjNUbGibfkaF4oVacCvAA/wDOC65LmP4F0G8hnwyyucywB/O3f80y2RrIiItA02m2FMvzju6h1L9t4y3viwgNIqJwD1jR5e/7CAd3cU8/DYFO4fnkhwkM1vuZY3lbEidxl7ynb5jNuNnXHJ47k37T7C7IHRKFRERKQ9024gbZQxJgXIx1u8GGZZ1t5L4huAu4GXLcu6rBhhjInAu0VohWVZ8S2fsX9oNxARkZvX6PSwcnsJb28opKbed3fr5NggHhufyviB8djtl+6K3YI5uRvJLsxibUEWTsvpE+sfO5AZGbNIDk2+ZfmIiPibdgORQKeZFW2UZVmFxpgyIA74R+DxL2LGmD7AqHN/zb7KKbqfezzbYkmKiEibFBJkY/boZO4dGs/SzcUs31JMo9MDQFGFk+eXnmXJ5iKenJjGyN4xeHs6twyP5WFX2Q7ezV1BpbPCJ5YZ1oFZHeZyR1T3qxwtIiIibZWKFW3bCrxLQx41xriBZUAy8P8BNqAMePcqx44+93iwpZMUEZG2KTLMwZOT0nhgRCJvrS9k1a5SXG7vjMyzRY388o1TdM8M58nJaQzsGtXsr/9pzQmWnl3EmbrTPuNRjmgeyJjBnQkjsRn/LUkRERGRlqNlIG3YuV08DuLd9cMnhHd5yD9alvWfVzl2L95tS5+2LOvlFk3Uj7QMRESk+RSUNfL62gLWfVTOpbcPA7tG8uSkdLp3+Or9IkoaS1iRu5T95T4rHHEYB/ekTGRy6hRC7aFf+XVERNoyLQORQKeZFW2YZVlnjTETgNe5sKwDwAn86hqFignAQKAJeKfFExURkYCQGh/C9+fexuzRybyanc/2o1XnY/s/rWH/p8e5q08MT05Mo0PyJcWEmiLY9wqc2gpNNRAcCZ1GwaAnINLba6LB3UBWwQesK8zGZfn2jR4UN4QZGQ+SEJLY4t+niIiI+J9mVgQA410sPAS4HagHtlmWVXKN598FdAVKLct679Zk6R+aWSEi0nI+Pl3Ly1l5HDpZ6zNuMzBhcDyPjU8lOcINH/wQPloIHuflJ7EFYQ14lB3DZvBO4SqqXVU+4Y7htzGrw1y6RnZryW9FRKTN0cwKCXQqVkhAU7FCRKRlWZbFvhPVLMjK59O8ep9YuL2J/4z9Mek1u697nuMJSbw0fDROu3fSZ0xQLNMzZjI0frj6UoiIXIGKFRLotAxEREREvjRjDIPviGZg1yi2HqnklTX55JY0AvC0eZH0mt1YFlxrwxALuKO0mNmH97NkwEgmpE5mYspkQuwht+abEBERkVZHxQoRERH5ymw2w919YxnZK4bsfWWszD7ChMbVwLULFeDtCg0wIuc0vWe8SVx8j5ZNVkRERFo9zasUERGRZmO3G+4dmsCLI3YQZFzXP+DiYz1u4g4HdCslERERuUEqVrQhxpiXjTEdW/D884wxT7bU+UVEpP1wnN325Q48taV5ExEREZE2ScWKtuVJ4Lgx5j+bq2hhjLEbY+YYYw4DrwC3Ncd5RUSknWuqubXHiYiISEBRsaJteQ8IBr4NfG6M+dAY87gxJuJmT2SMGWCM+QOQC7wF9ALKga3NmbCIiLQ/da5aCj1fruhQ6wlr5mxERESkLVKDzTbEsqwHjDHTgN8APYCx577+dm5mxA5gD1AAlOEtPoQCCUA80B0Yfu4r8dxpDeAE/gr8zLKs0lv07YiISIBxWy42F29iVd5KRkU5eOBLnGPxmTvIef0kT0xKo2NyaLPnKCIiIm2DihVtjGVZK40x7wOPAt8H+uH9/9j/3NczN3CaLxqv1+Nd+vE7y7JOtkC6IiLSThypPMTSnMUUNhQAsL1jZ+47fhiHZd3wOZyWgyz3/VQcqWT70UrGD4pn3oRUkmODWyptERERaaVUrGiDLMvyAK8DrxtjhgPzgGnAjfSxcAKbgcXAW5ZlVbZYoiIiEvDy6vNYlrOYj6uO+Iw3hsdS0GMcmR+vu+45LLxV9J2h06hoigfAY0H23jLWf1TO1DsTeWhsCrGRum0RERFpL4x1E594SOtmjOkMDAa6AWlAOOACKoBTwGFgn2VZdf7K8VYzxhzp1atXryNHjlz/ySIicsNqXNW8n7eSLcWb8OA5P24wjEi8i6np04khGF6fDadvYIeP20bheWwJW4818sqafHJLGn3CYcE2Hrw7iZmjkokItTf3tyMi0ub07t2bo0ePHrUsq7e/cxFpCSpWSEBTsUJEpHm5PC42Fa9nVf571LvrfWLdIu9gVoeH6BDe4cKgsx5W/wj2vwEe5+UntAXBwMfg3t9AkLdHhdttkb2vjDc+LKCk0veY6HA7D49L4f7hiQQHqU+4iLRfKlZIoFOxQgKaihUiIs3DsiwOVR5gec4SihqLfGKJIUk8mDmbfjEDMMZc+QQ1RbDvVTi1xbs9aXAkdBoFg56AyOQrHtLo9PDejhIWbSikqs7tE0uKCeKx8alMGBSP3X6V1xQRCWAqVkigU7FCApqKFSIiX11uXQ5LcxZxrPoTn/FQWyhT0qcyJmkcQbagFnv92gY3SzcXsXxLMQ1NHp9YZlIIT05K467eMVcvlIiIBCAVKyTQqVOViIiIXFG1s4qVee+wrWQLFhc+3DAYRiWN5v60B4gKimrxPCJC7TwxMY0HRiTy1vpC3t9ZisvtzSenuJH/+8YpumWE8dS96Qzs2vL5iIiISMtTsUJERER8OD1ONhR9yOr8VTR4GnxiPaJ6MqvDXNLDMm55XrGRQTw7LZOZo5J4fW0B6/aX4zlXQzmRW8+P//YZ/btE8tTkNLp3iLjl+YmIiEjz0TIQCWhaBiIicuMsy+Kjin2syFlKSVOJTywlJIUHO8yhd3TfVrPc4nRhA69m57PtyOW7cI/sHcMTE9O4LSXUD5mJiLQ8LQORQKeZFSIiIsKZutMsPbuIT2tO+IyH28O5L30ao5PGYDet67bhtpRQfjqvM5+cqWVBVj4HPq85H9t2pJIdRysZPyiex8ankhIX7MdMRURE5Ga1rrsOERERuaUqnRW8m7uCnaXbffpS2LAxOnks96VNI8LRupdU9OgYwa+f7sL+T2tYkJXHiVzvlqoeC7L3lrH+o3LuH57Aw+NSiI1suUagIiIi0nxUrBAREWmHmjxNrCvMJqtgNU2eRp9Y7+g+PJg5h9SwND9ld/OMMQzqFsXArnew5XAlr2bnk1Ps/b5cbot3tpWwZk8ZM0cl/f/s3Xd01ed+7/n3s7d679KWhOnGljA2pveOwKYLcDm24SQ5J8kkM5mZtJl105ObyU05SSY3mbR7Da7HgKimiN4x3QUJ29gGrN57l/Yzf0j28XalST9p6/Nay+un83x/kj/rGLZ++7ufwppZSYSHuB1OLCIiIt9HzQoREZFBxFrL5ZqL7CzaTk17tU8tJcRDdvp6MqIH7vJnYwyzHothekY0h69U89qRUirrOgBoaffyxtEy3n6nkvVzk1k+NYGgQJfDiUVEROTbqFkhIiIySNxquklOwRY+a/rUZzzcHc6ytJXMSJiF2/jHjAO325A1KZ55T8Ty9juVvHW8jPrmLgDqm7v4z33F7DxTwQsLUlj4ZBxud//YNFRERES6qVkhIiLi52raa9hVtJ2L1ed9xt3GzZzE+Sz1PE1YQJhD6XpXUKCLNbOSyJoUz/ZT5ew4XUFLuxeAyroO/mF7AdtOlfPSIg8zx0b3m5NOREREBjs1K/yU6X7aigPCgGJrbZfDkUREpI+1dbVxuCyXQ6W5dNgOn9q46MdZnb6WpJBkh9L1rfAQNy8u8rB8WgI/P1bG3vNVdHZ1byhaWNHGX75xi9FpoWxY7OHJ0ZFqWoiIiDhMzQo/Y4x5DvgpMBUIAiwwDsj/yj1LgWlAnbX275zIKSIivcdrvVyqvsCuou3UdtT61NJC08lOX8+YqEccSuesmIhAfm15OqtnJvH6kVKOXKnG23MIyo2iFv7g5c94fEQEG7M8PPJQ/z4FRURExJ8Za+0P3yX9njEmEtgCLP5iqOdqgcestV9tVkwCzvfUxlprr/dl1r5kjMnLyMjIyMvLczqKiEif+KzxU7YVvMXt5ls+45EBkSxPXcW0hBm4jDaV/MLtslZeOVTC2by6b9SmZUSzYXEKQ5NDHUgmIvL9MjMzyc/Pz7fWDtxdkUW+h2ZW+I83gKyery8Dx4Hf/rYbrbUXjTF5QAawEvDbZoWIyGBR1VbFrqLtXK656DMeYAKYl7SQLM9SQt160/11Q5ND+MMXhvPh501sOljCe582flk7l1/H+et1zB8fywsLU0iODXYwqYiIyOCiZoUfMMYsA56me6bE71hr/75n/FubFT12AZnAzN5PKCIivaW1q5VDpQc4UnboG/tSjI95klXp2SQEJzqUbuB45KFw/upXRnH1kwZePlDMjaIWALwWDl+p4fh7tTw9JZ5n5yUTExHocFoRERH/p2aFf3ip57rni0bFHbjccx2ci5ZFRAY4r/Vyvuocu4t2UN9Z71MbEvYQ2enrGR35sEPpBq7xoyJ54jce5kxeHa8cLKGgog2Azi7LrrOV5F6qZvXMRLJnJREe4h/HvIqIiPRHalb4hyl0z6p45S6+p7jnOji2gRcR8SM3Gj4mp3ALBc2f+4xHB0azPHU1U+Knal+K+2CMYebYGKY9Gs3hq9W8friUirruWSut7V7ePFrG3ncqWT83mWVTEwgO1P/XIiIiD5qaFf4hqef62V18T1vPNegBZxERkV5S2VbBjsIc3q294jMeaAJZkLyYRSlZhLhDHErnf9xuQ9bEeOY9Hsve85X8/HgZ9U3dJ4HXN3fxn/uK2XG6ghcWprDoyTjcbh13KiIi8qCoWeEf2uhuOsTexfd80eCo/d67RETEcS1dLeSW7ONY+RE6badPbWLsZFamryYuKN6hdP4vKNDF6plJLJ4Yz47T5Ww/VUFLuxeAqvoO/nF7AdtOlrNhsYcZmdG4XGpaiIiI3C81K/xDMTAGGA4cu8Pv+WJjzU97JZGIiNw3r/VytvI0e4p30djZ4FMbGjaMtUOeYUTESIfSDT7hIW5eWOhh2dRE3jpextvvVNLZ1X0EfFFlG3/5xi1GpYayMcvDk6MjMUZNCxERkXulZoV/OEH3RpkvAf/zh242xkQCP6F7n4vjvZpMRETuyYf119leuJWilkKf8ZjAGFamZTMxbpL2pXBITEQAv7osjVUzEnn9SClHrlTj7e5Z8ElxC3/w8meMGxHBxiwPjz4U7mxYERGRAcpYa53OIPfJGDMJOE938+FZa+3WnnFvz9hj1tr8nrEIYCuQBXQBY6y1d7PXxYBijMnLyMjIyMvLczqKiMgdKW8tY3vhNj6oe89nPMgVxMLkLBYmLybYHexQOvk2n5e38srBEs7k1X2jNi0jig2LPQxNDnUgmYj4s8zMTPLz8/OttZlOZxHpDZpZ4QestReNMa/QPbPiDWPMHODNr9wy0hiTCMwGfgqk0t3E+Cd/blSIiAwkzZ1N7C/Zy4mKY3TZLp/a5LiprEhbTWzQ3WxNJH3loaQQ/uCF4XxU0MSm3BLe/bTxy9q5/HreuV7P/CdieXFRCsmxajSJiIjcCc2s8BPGmGBgN7CI7kbEd97ac90LrLb2azu1+RnNrBCR/q7LdnGm4hRvF++iqavJpzYifCTZQ9YzLHy4Q+nkXlz9pIGXDxRzo6jFZzzAbXhqcjzPzksmNjLQoXQi4i80s0L8nWZW+AlrbZsxZinwe8BvA9+1LXwd8DfAX1lrvX2VT0REvim/Lo+cwi2Utpb4jMcFxbMqbQ1Pxk7UJo0D0PhRkTzxGw9zNq+OzQdLKKjoPi28s8uy+1wlBy9Xs3pGItmzkwgPcTucVkREpH/SzAo/ZIwJAeYAk+k+ojQAqAAuA0estY3f8+1+RTMrRKQ/Km0pIadwK/n113zGg13BZKU8xbzkBQS5ghxKJw9SV5flyNVqXjtcSkVdh08tMtTN+rnJLJ+WQHCgNksVkbujmRXi79SsEL+mZoWI9CeNnY3sK36bUxXH8fKLyW0Gw9T46SxPW0l0YIyDCaW3tHd42Xu+kp8fL6O+yXdPkvioQH60IIXFE+JwuzWTRkTujJoV4u+0DERERKSXddlOTpafYF/JHpq7mn1qoyMeJnvIeoaEPeRQOukLQYEuVs9MImtSPDtOVZBzqpyW9u6GVVV9B//vjgJyTpbz0uIUZo6NweVS00JERAY3NSukX+g5reT3geXAQ0ALcIvuZSu/62A0EZF7Zq3lWt0HbC/cSnlbmU8tISiB1elreTxmvPalGETCgt38aGEKT09NYMvxMva8U0lnV/cs16KqNv6fN28zMrWcH2d5eHJ0pP5siIjIoKVlIH7AGDMVOAN0AKOstYU/cP8Q4FPABUy01r7b+ym/N88EIJfuTUHzgGtAFJABpFtr77mppmUgIuKUopZCthds5cOG6z7jIa4QlnieZm7SfAJdOhFisCuvbef1I6UcvlyN92uPZI8ND+fHWak8OjTcmXAi0q9pGYj4O82s8A/r6D6S9OAPNSoArLUFxpj9dM9ieBZwrFnRM6PiABAKrLTW7v5afbIjwURE7lFDRwNvF+/mTOVJ7FdOkjYYZiTMYlnqCiIDoxxMKP1JUkwQ/0f2Q2TPSuKVQyWcuVb3Ze2Dm038n/96g6mPRrFhsYdhKaEOJhUREelbalb4hxmABfbfxffspbtZMatXEt25PwUSgN/4eqMCwFp7oe8jiYjcvQ5vByfKj7G/5G1ava0+tTGRj5A9ZD1poekOpZP+7qGkEP7gR8P5qKCZzQeLufrJLw7ueud6Pec/rGf+E7G8sDCFlLhgB5OKiIj0DTUr/MPonusHd/E9+T3Xhx9wljtmjAkFXgCagJedyiEicj+stbxX+y47irZR2VbhU0sKTmJN+jrGRo/T3gNyR8YMCeMvf3kUVz9pYFNuCR8Xdm/Iai0cuVrDifdrWTo5nufmJRMbqWVEIiLiv9Ss8A+RPdeGu/ieL+6NfsBZ7sZEurOftta2GGOWAouAEOBjYIu1ttjBfCIi36uguYCcgre40fixz3ioO4ynPMuYnTiXAJd+1crdGz8qkidGRnA2v47NB0soKG8DoLPLsudcJYcuV7NqRiJrZycRHuJ2OK2IiMiDpyco/9AAxABxd/E9X9zb/L139a6Mnmu5MWYnsPJr9b80xvyytfbNPs4lIvK96jrqeLtoF+eqzvjsS+HCxczE2TydupyIgMjv+QkiP8wYw4zMGKY+Gs3Rq9W8driU8toOAFrbvfz8WBlvv1PJM3OTWT4tgeBAl8OJRUREHhw1K/xDAd3NiunAsTv8nhk916JeSXRnYnuuK4Au4DeArUAY8JvA7wCbjTHXf+jEEmPMdx33MfIBZRURocPbwdGyw+SW7qPN2+ZTy4jKZE36OjyhqQ6lE3/ldhkWTYhnzuOx7DtfxZvHSqlv6gKgsaWL/7G/mJ1nKnh+QTKLJ8QT4NaSIxERGfjUrPAPJ4FxwP9ijPlHa23j991sjIkEfp3uTTlP9kG+7/LFR0ABwH+x1v7LV2q/a4wZSvdJJ78L/Kivw4mIfMFay9Xay+wszKGqvcqnlhLiYU36WjKjH3MonQwWQQEuVs1IZPHEOHacriDnVDktbV4Aquo7+KcdhWw/WcGLi1OYNTYGl0tNCxERGbiMtfaH75J+zRgzHrhMd/PhKLDeWlvzHffGANuA+T33T7HWXuqrrF/L8r8B/9jzP5OstRVfqy8F9gFF1tp72kLfGJOXkZGRkZf3XRMvRES+3+2mW+QUbuHTxk98xsPd4TyduoKZibNwG/X+pe/VNXWy5XgZe96ppKPT93luZGooGxd7mPBwpDZ3FfFTmZmZ5Ofn51trM53OItIb9HTlB6y1V40xrwIv0t2E+NAY8z/oXhLyxTKPVGAe8Ct0HxVqgTecalT0uN1zbf56o6LHrZ5rUt/EERH5hdr2GnYX7eR89TmfcRcu5ibNZ6nnacICwh1KJwLR4QH85Ok0Vs1I5PUjpRy6XI23p2fxaXELf7jpM8YOD+fHWalkDNWfVRERGVg0s8JPGGNCgP3AHOD7/qN+8fHKCWCptba1t7N9ZxBjHqK7YWGBUGtt29fqM4DTQI219m42D/3qz9DMChG5K+3eNo6UHeJg6QHave0+tceix7E6fR3JIckOpRP5bgXlrbxyqITT1+q+UZvyaBQbFnsYnhLqQDIR6Q2aWSH+TjMr/IS1ttUYswj4Q+C3gKjvuLUO+Bnwl9barr7K922stZ8bY94DHqe7yXLwa7fM6ble7dNgIjIoea2XS9UX2VW0ndoO35V0qSGpZA9ZzyNRGd/x3SLOG5IUwn/50XA+LmxmU24JVz/5xYnm56/Xc+HDeuY9HssLi1LwxAU7mFREROSHaWaFHzLGRNC95GMCkNgzXAFcAo5Za5ucyvZ1xpjngdeBD4Asa21Jz/gTwBG6j1hdb63deo8/XzMrROQH3Wz8jG2Fb3Gr6abPeERAJMtSVzA9YSZu43Yonci9effTBjYdKOGjQt9TygPchqWT4nl2fjJxkYEOpROR+6WZFeLv1KwQxxljNgEbgFrgLBBK9zGswcB/WGt/eh8/W80KEflO1e1V7CrcwaWaCz7jbuNmXtIClnieItQd5lA6kftnreVcfh2bD5byebnvys/gwO7TRdbOTiQiVJNtRQYaNSvE3+k3k/QHPwbOAL8KzKV7D4srwL9Zazc7mEtE/FRrVyuHy3I5XHqQDtvhU3siZjyr0rNJDNbevjLwGWOYnhnDlEejOXq1htcOl1Be2/1nvq3Dy1vHy9h7vpL1c5JYPi2RkCDXD/xEERGRvqGZFeLXNLNCRL7Ka71cqH6H3UU7qOvw3YQwPXQI2UPW83DkGIfSifS+9k4v+89X8eaxMuqaOn1q8VGBPD8/mcUT4wlw67hTkf5OMyvE32lmhYiIDAqfNN4gp2ALnzff9hmPDIhiRdoqpsZPx2X0qbL4t6AAFytnJLJoYhw7T1ew7VQ5LW1eAKrqO/innYXknCrnxUUeZj8Wg8ulpoWIiDhDMysGEGPMF6d3WGttwLeM3wufn+VvNLNCRCrbKtlZlMPVmss+4wEmgAXJi1icspQQd4hD6UScVdfUyZYTZew5V0lHp+8z4QhPKBuzPEx8OBJj1LQQ6W80s0L8nd++SfVT3/WkoCcIEZGvaelq4WDpfo6WHabT+k53nxA7kZVpa4gPTnAonUj/EB0ewE+eSmPV9ETeOFrKwUvVeHt6Fp+VtPBHmz5j7PBwfpyVSsbQcGfDiojIoKJmxcDyXZtNahNKEZEeXuvlXNUZ9hTtoqGz3qc2NGwY2UPWMzJilEPpRPqnxJggfmvNQ2TPSuKVQ6Wc+qD2y9q1m0389r/eYPIjUWxc7GG4J9TBpCIiMlhoGYj4NS0DERlcPm74kG0FWyhqKfQZjw6MYWXaaibFTdG+FCJ34EZRM5tyS7hyo8Fn3BiY+3gsLy5KwRMX7FA6EQEtAxH/p5kVIiIy4JW3lrOzaBvv1b7rMx5oAlmUksXC5CyC3XpjJXKnRqeF8V9/aSTvfdrAy7klfFTQDIC1cOzdGk6+X8PSyQk8Nz+ZuMhAh9OKiIg/UrPCDxhjftbz5Qlr7S5Hw4iI9KHmzmYOlO7lePlRuqzvXsOT4qawMm01sUFxDqUTGfgeHxnJ3/96BOfy69h8sJTPy1sB6PLC2+9UcuhyNatmJLB2dhIRoXqsFBGRB0e/VfzD/w5Y4KjTQURE+kKX7eJM5Sn2Fu+msbPRpzY8fATZQ9YzPHyEQ+lE/IsxhumZMUx5NJpj79bw6qESyms7AGjr8PLW8XL2nq9i3ZwkVkxLJCRIS61EROT+qVnhH6qBWKDwh24UERnortfnkVOwlZLWYp/x2MBYVqVnMyF2ko5ZFOkFbpdh4ZNxzB4Xw/4LVbx5tIy6pu6Tdhpbunj5QAm7zlTw/IIUsibGE+DW30MREbl3alb4h1t0Nyt0Bp+I+K3S1hJ2FG7jWt0HPuNBrmAWpyxhQfIiglxBDqUTGTyCAlysnJ7I4glx7DxTwbaT5TS3eQGobujkv+8sJOdkOS8t8jB7XAwul5oWIiJy99Ss8A+7gSeBpcBhh7OIiDxQTZ1N7CvZw8ny43jx+tSmxk9neeoqYoJiHEonMniFBrt5bn4KT09JYMuJMnafq6Sjs/uUuZLqdv7bW7fZerKMDYtTmTQmUjOeRETkrujoUj9gjIkFrtE9u2KutfaCw5H6DR1dKjJwddlOTlWcZG/xbpq7mn1qIyNGszZ96UpElwAAIABJREFUPQ+FD3UonYh8XUVdO28cKePg5Sq8vn1Fxg4LZ2OWh8xhEc6EE/FDOrpU/J2aFX7CGDMe2En3UpC/BV6x1n7qbCrnqVkhMvBYa8mrv8b2wq2UtZb61OKD4lmVvpbxMU/qU1qRfqqwopVXD5Vy8oPab9Qmj4liY5aH4Z5QB5KJ+Bc1K8TfqVnhB4wxn/V8GUF3s+KL/6hNQA3Q9W3f18Naa0f2YjxHqVkhMrAUtxSzvXAr1+t9/86GuELI8jzFvKQFBLoCHUonInfjk6JmNuWWcPlGg8+4MTD38VheXJSCJy7YoXQiA5+aFeLvtGeFfxhGd4Pii48Zv7hG9PzzfdStEhHHNXY2sLd4D6crTvrsS2EwTE+YybLUlUQFRjmYUETu1qi0MP7il0by/mcNvHyghA8LupdzWQvH3q3h5Ps1LJ2cwHPzkomLUhNSRER8qVnhH06ipoOIDECd3k5OVBxjf8nbtHS1+NQejhxDdvp60sOGOJRORB6EcSMi+dmvR/DO9Xo2HyzhdlkrAF1eePudSg5drmLl9ETWzkkiMlSPpiIi0k2/EfyAtXau0xlERO6GtZb3695jR+E2KtrKfWqJwUmsTl/LuOjHtS+FiJ8wxjAtI5rJj0Rx/N0aXj1cSllNOwBtHZYtJ8rZd76KdXOSWDE9kZAgl8OJRUTEaWpWiIhInypsLiCncCsfN3zoMx7qDmWpZxlzEucR4NKvJxF/5HYZFjwZx6xxMRy4UMWbx8qobewEoLG1i5dzS9h1toLn5qewZFI8AW41LEVEBis9DYqISJ+o76jn7eJdnK08jf3KyjWDYVbiHJ7yLCcyMNLBhCLSV4ICXKyYnsiiCXHsPFPBtpPlNLd171dT3dDJP+8qZPupcl5c5GHOuBhcLjUtREQGG50GMoAZY0YDTwPDATdQBBy21l50NFg/otNARJzX4e3gWPkRckv20ept9ak9GpXBmvR1pIamOZRORPqD+qZOtp4sZ/fZCto7fZ9NR3hC2LDYw6QxUVoaJvIVOg1E/J2aFQOQMcYN/DPwK/zi5I+vOgg8b62t6dNg/ZCaFSLOsdZytfYKOwtzqGqv9KklByezZsh6MqPG6s2HiHypsq6dN46WkXupCq/Xt5Y5LJyNWR7GDvuhg85EBgc1K8TfqVkxABljXgZe4tsbFdB9MshFYLq11vsd9wwKalaIOOPzpttsK9zCp403fMbD3GE8nbqCWYmzcRutRBSRb1dY0cqrh0s5+X7tN2qTx0SxIcvDCE+oA8lE+g81K8Tf6UlxgDHGTAE20N2Q6AS2AWd6vn4CeAEIByYBvwT8pzNJRWQwqm2vZU/xTs5XnfPZl8KFi9lJc3nKs5zwgHAHE4rIQJCeGML//dww1s1uZvPBEi593PBl7cJH9Vz8uJ4542J5cVEKqfHBDiYVEZHeombFwLOh59oOLLHWnvhq0RjzM+AkkNRzr5oVItLr2r3tHCk7xMHSA7R723xqY6MfY3X6WlJCPA6lE5GBalRaGH/+45G8/1kjm3KLuf55MwDWwvH3ajj1QQ1LJsXz/PwU4qICHU4rIiIPkpoVA880umdV/OvXGxUA1tobxpg/Av4NmGCMcVtru/o6pIgMDtZaLtdcZGfRdmraq31qnpBUsoes49EozU4VkfszbkQEf/drozl/vZ7NB0u4Vda9WW+XF/aer+LwlWpWTk9k7ZwkIkP1eCsi4g/0aj7wPNRz3f899+ztuQYDyUBxryYSkUHpZtNn5BRs4WbTZz7jEQERPJ26ghkJs3Abt0PpRMTfGGOYmhHNpEeiOPFeDa8cKqWsph2Atg7LlhPl7Dtfxbo5SayYnkhIkMvhxCIicj/UrBh4onquJd9zT+lXvo7sxSwiMgjVtNewq2g7F6vP+4y7jZu5SfNZkvI0YQFhDqUTEX/ndhnmj49j1mMx7L9Qxc+PlVHT2AlAY2sXL+eWsPNsBc/PSyFrUhyBAWpaiIgMRGpWDDxuupeBfOfSDmut9ytHAepjTRF5INq62jhclsuh0lw6bIdP7fGYJ1iVtpakkCSH0onIYBMY4GLF9EQWTYhj59kKtp0op7mt+xC0moZO/nl3ITmny3lxYQpzH4/F5dIxySIiA4maFSIi8r281svF6vPsLtpBbYfvMYJpoemsHbKehyMfcSidiAx2ocFunpuXwrIpCWw5Uc7usxW0d3afRlRa3c7fbPmcbSfL2ZjlYdKYKL7ygY6IiPRjalaIiMh3+rTxE3IKtnC7+ZbPeGRAJMvTVjEtfgYuoynWIuK8yLAAfnlpKiunJ/Dm0TIOXKrC2z3Rgpulrfzx5ptkDA3nx1kexg6PcDasiIj8IGOtdTqD3AVjjJfuZSCXgKbvuXXuHd5nrbULHljAfsYYk5eRkZGRl5fndBSRAaWqrZKdRdu5UnPJZzzABDA/eSGLU5YS6g51KJ2IyA8rqmzj1UMlnHi/9hu1SWOi2LA4hZGp2l9HBq7MzEzy8/PzrbU6dkv8kmZWDFwTf6D+RRfq++4zX7lPRITWrlYOlu7nSNkhOm2nT2187ARWpWWTEJzgUDoRkTuXlhDM//XcMNbOaWZzbgmXPm74snbxo3ouflTP3MdjeHGhh9SEYAeTiojIt1GzYmDSYksReaC81ss7VWfZU7ST+s56n9qQsIdYm/4MoyJHO5ROROTejUoN489/PJIPbjayKbeE/Nu/mHB6/L1aTn1QS9akeJ6fn0J8VKCDSUVE5KvUrBh4hjsdQET8y42Gj8gp2EJBS4HPeHRgNCvSVjM5bqr2pRCRAe+x4RH87a+O4vyH9WzOLeFWWSsAXV7Yd76KI1eqWTE9kXVzkogM1SOyiIjT9Eo8wFhrbzudQUT8Q0VbOTsLc3i39qrPeKAJZEHyYhalZBHiDnEonYjIg2eMYeqj0UwaE8WJ92p49VAppTXtALR1WLaeKGff+UrWzUlm5fQEQoJ0AryIiFPUrBARGWRaupo5ULKP4+VHv7EvxcTYyaxMX01cULxD6UREep/bZZg/Po5Zj8Vw4GIVbx4to6ax+/WwqdXLptwSdp2p4Ln5ySyZFE9ggGaXiYj0NTUrREQGCa/1cqbyFG8X76axs8GnNix8ONnp6xkRMdKhdCIifS8wwMXyaYksmhDHzjOVbDtZRlNr93mnNY2d/MvuIrafquDFRSnMfTwWl0vbhomI9BUdXSp+TUeXinT7sP46OYVbKG4p8hmPCYxlZdoaJsZN0r4UIjLoNTR3suVEObvPVtDe6fuMPCwlhI2LPUx+JApj1LQQ5+noUvF3mlkhIuLHylrL2FG4lQ/q3vcZD3IFsThlCQuSFxHk0pF9IiIAkWEB/PLSVFZOT+DNo2UcuFSFt3uiBbdKW/mTV26SMTScjVkeHhse4WxYERE/p5kV4tc0s0IGq+bOJvaX7OV4+VG8eH1qU+KmsSJtFTFBsQ6lExEZGIor23j1cAnH36v9Rm3iw5FszPIwMjXMgWQimlkh/k8zK0RE/EiX7eRUxUn2Fe+hqavJpzYifCRrhzzD0PBhzoQTERlgUhOC+f1nh7F2djObD5Zy8aP6L2uXPm7g0scNzBkXw0uLPKQmaJaaiMiDpGaFiIifyKu7xvbCrZS2lviMxwXFsyotmydjJ2idtYjIPRiZGsafbRzBBzcb2ZRbQv7tXzSDT7xfy+lrtWRNjOf5BSnERwU6mFRExH+oWSEiMsCVtBSzvXAr+fW+y52CXcFkpTzF/OSFBLr08Cwicr8eGx7B3/7qKC58WM+mgyXcKm0FoMsL+y5UcfhKNSumJ7J+ThKRYXrMFhG5H3oVFREZoBo7G9lXvIdTFSd89qUwGKbFz2BZ2kqiA6MdTCgi4n+MMUx5NJpJY6I4/l4Nrx4upbS6HYD2Tsu2k+Xsv1DJ2tnJrJqRQEiQ2+HEIiIDk5oVIiIDTKe3k5MVx9lX8jYtXc0+tdERD5M9ZD1Dwh5yKJ2IyODgchnmj49j1mMx5F6s5o1jpdQ0dALQ1Opl88ESdp+t4Ln5ySyZFE9ggI6HFhG5G2pWiIgMENZartW9z/bCrZS3lfvUEoISWJ2+jsdjntC+FCIifSgwwMWyaQksnBDLrrOVbD1RRlNr92y3msZO/mV3EdtPVfDiohTmPB6L26XXaBGRO6GjS8Wv6ehS8RdFLYVsL9jKhw3XfcZDXCEs9SxjTtI87UshItIPNDR3svVkObvPVtDW4fucPSw5hA1ZHqY8EqXGstw3HV0q/k4zK0RE+rGGjnreLt7NmcpTWH7x0GswzEiYxbLUFUQGRjmYUEREvioyLIBfWpLKyumJvHG0lNyLVXT1bCt0q6yVP33lJo8+FMbGrFTGjYhwNqyISD+mmRXi1zSzQgaqDm8Hx8uPcqBkL63eVp/aI5GPsmbIOtJC0x1KJyIid6q4so1XD5dw/L3ab9QmPhzJhiwPo1LDHEgmA51mVoi/08wKEZF+xFrLe7XvsqNwK5XtlT61pOBk1qSvY2z0Y5o+LCIyQKQmBPP7zw5j7exmNh8s5eJH9V/WLn3cwKWPG5g9LoaXFnlISwh2MKmISP+iZoWISD9R0Pw5OQVbuNH4sc94qDuMpz3LmZU4hwCXXrZFRAaikalh/NnGEVy72cjLuSXk3276snby/VpOX6sla2I8z89PJiE6yMGkIiL9g556RUQcVtdRx56inbxTddZnXwoXLmYlzuWp1GVEBGhds4iIPxg7PIK//dVRXPyonk25Jdws7V7q5/XC/gtVHLlSzYrpiaybnURUuB7VRWTw0iugiIhD2r3tHC07zMHS/bR523xqGVFjyU5fR0qox6F0IiLSW4wxTH4kmokPR3Hi/VpeOVRCaXU7AO2dlm0ny9l3vpK1c5JYNT2R0GC3w4lFRPqemhXSbxhj4oHrQCLwqbV2lMORRHqFtZYrNZfYWbSd6vYqn1pKiIc16evIjB7rUDoREekrLpdh3hOxzBwbTe6lat44WkpNQycAzW1eXjlYyu6zlTw3L5mlk+MJDHA5nFhEpO+oWSH9yd8BCU6HEOlNt5tusa3gLT5r+tRnPNwdztOpK5iZOBu30SdoIiKDSWCAi2VTE1j4ZCy7z1ay9UQ5ja1dANQ2dvL/7Sli++kKXlyYwtwnYnG7tMmyiPg/NSukXzDGLAA2AP8O/NThOCIPXG17DbuKdnCh+h2fcRcu5ibNZ6nnacICwh1KJyIi/UFIkJv1c5NZOiWebSfK2XW2graO7r2Mymra+dutn7P1ZDkbF3uY8miUToYSEb9mrLU/fJdILzLGhAIfAG3AKuBjHtAyEGNMXkZGRkZeXt79/iiRe9LubeNw6SEOlR2g3dvuU3ss+nFWp68lOSTZoXQiItKfVdd38MbRUg5crKLL61t79KEwNmalMm6ENmAerDIzM8nPz8+31mY6nUWkN2hmhfQHfwyMAOYAHQ5nEXkgvNbLpeqL7CraTm1HjU8tNTSN7PT1PBL1qEPpRERkIIiLCuQ3Vw1hzawkXj1Uyon3a/jic8brnzfz+//xCRNGR7Ixy8OotDBnw4qIPGBqVoijjDHjgN8GXrbWnjLGDHM2kcj9+6zxU3IKt3Cr6abPeERAJMtTVzI9YSYuo03SRETkzqTGB/P7zw5l3ZwkNueWcOGj+i9rl280cPlGA7Mfi+HFRSmkJ4Y4mFRE5MFRs0IcY4xxAf8J1AK/53AckftW3V7FrsIdXKq54DMeYAKYl7SALM9SQt365EtERO7NCE8of7pxBNduNbIpt4S8W01f1k5+UMvpvFoWT4jn+QXJJEYHOZhUROT+qVkhTvpfgUnAj621VT908/cxxnzXphQj7+fnityJ1q5WDpXmcqTsIB3WdyXTEzFPsip9DYnBSQ6lExERfzN2WAR/89NRXPyogU25xdwsbQXA64UDF6s4erWa5dMSWD8nmahwPe6LyMCkVy9xhDHmIeAvgBPW2k0OxxG5J17r5XzVO+wp3kFdR51PbUjoELKHrGd05BiH0omIiD8zxjD5kSgmPhzJyfdreeVQCSXV3Rs5t3dack5VsP9CFWtnJ7FqRiKhwToWW0QGFjUrxCn/DAQBv/Ygfth37YLcM+Mi40H8O0S+6pOGG+QUbuHz5ts+41EBUaxIW82U+Gnal0JERHqdy2WY+0QsMx+LIfdSFW8cKaW6oROA5jYvrxwqZfe5Sp6dl8zSyfEEBeh3k4gMDDq6VBxhjLF071Xx3tdKIcAUoBU43zP2rLW29B7/PTq6VB6oyrZKdhZu42rtFZ/xABPAwuTFLEpZQohbm5uJiIgzWtu97D5Xwdbj5TS2dvnUkmICeXGRh3lPxOJ2GYcSyoOio0vF36lZIY7oaVbcqeHW2lv3+O9Rs0IeiJauFnJL9nOs/DCdttOnNiF2IivTsokPjnconYiIiK+Glk5yTpaz80wFbR2+j11Dk0PYsNjD1EejMEZNi4FKzQrxd1oGIo6w1n7rb8aeo0tvAp9aa0f1ZSaRb+O1Xs5VnmFP8U4aOht8akPDhpE9ZD0jI/RHVURE+pfI0AA2ZqWyYloibx4rY/+FSrq83bXbZa382as3eWRIGD9e4mHciEhnw4qIfAs1K0REvsNH9R+SU7iFopZCn/GYwBhWpq1hYtxk7UshIiL9WlxUIL+xMp3VMxN57XApx9+r4YuJ1R8WNPP7//EpT46OZGOWh9FpOl5bRPoPNStERL6mvLWMHYXbeL/Od0uVQBPIopQsFiZnEewOdiidiIjI3UuND+b3nhnKutlJbDpYwoUP67+sXbnRwJUbDcx6LIaXFqWQnqi9l0TEeWpWiIj0aO5sZn/JXk5UHKXL+m5KNiluCivT1hAbFOtQOhERkfs33BPKn24YQd6tRjbllnDtVtOXtVMf1HImr5bFE+J4fkEKidFBDiYVkcFOzQrpV3o20tROT9KnumwXZypPsbd4N42djT614eEjWDvkGYaFD3conYiIyIOXOSyCv/7pKC593MCm3GI+K2kFwOuFAxerOXK1huXTEnhmTjJR4XrLICJ9T688IjKoXa/PI6dgKyWtxT7jsUFxrEpbw4TYSdopXURE/JIxhkljopgwOpKTH9TyysESSqrbAejotGw/VcGBC1Vkz0pi9cxEQoPdDicWkcFEzQoRGZRKW0vYUbiNa3Uf+IwHuYLJSlnC/ORFBLk0/VVERPyfy2WY+3gsM8fGcPBSFa8fKaW6ofuY7uY2L68eLmX3uUqem5fM0inxBAVoc2kR6X1qVojIoNLU2cS+kj2cLD+OF++X4wbDlPhprEhbRXRgjIMJRUREnBHgNjw1JYH54+PYc66CLSfKaWzp3sOprqmTf327iO2ny3lhoYf542NxuzTzUER6j5oVIjIodNlOTlacYF/xHpq7mn1qoyJGkz1kPQ+FDXUonYiISP8REuRi3Zxklk6OZ9vJCnaeqaCto7vBX17bwc+2fc62k+VsWJzCtIxoLZcUkV6hZoWI+DVrLXn1H7C9YCtlbWU+tfigBFanZ/NEzJN60BIREfmaiNAANmZ5WDE9gZ8fLWP/xSo6uywAn5e38uev3WLMkDB+nOXh8ZGRDqcVEX9jrLVOZxDpNcaYvIyMjIy8vDyno4gDiluK2F64lev1+T7jIa4QlnieYm7SAgJdgQ6lExERGVhKqtt47VApx96r4etvIcaPiuTHSzyMTgtzJtwglJmZSX5+fr61NtPpLCK9QTMrRMTvNHQ0sLdkN6crTmL5xdOUwTA9YSbLUlcSFRjlYEIREZGBxxMXzO8+M5S1c5LYfLCE89frv6xd/aSBq/+9gZljo9mw2EN6YoiDSUXEH6hZISJ+o9PbyfGKoxwo2UtLV4tP7eHIR8hOX0d62BCH0omIiPiH4Smh/MlLI8i/3cTLucVcu9n0Ze30tTrO5tex6Mk4frQghcQYnawlIvdGzQoRGfCstbxf9y47CnOoaCv3qSUGJ7EmfS2PRT+ufSlEREQeoIyh4fz1T0Zx+eMGXs4t4bOS7g8KvF7IvVTN0XdrWD41gfVzk4kO19sOEbk7etUQkQGtsLmAnMItfNzwkc94qDuUpzzLmJ04jwCXXupERER6gzGGiWOieHJ0JKeu1fLKwRKKq9oB6Oi0bD9dwf6LVWTPSmL1zETCgt0OJxaRgUJP8CIyINV31LOneCfnKs/47EvhwsXMxNk8nbqciADtTC4iItIXXC7DnHGxzMiM4eDlKt44UkZVfQcALW1eXjtcyp5zlTw7L5mnpsQTFOByOLGI9HdqVojIgNLh7eBY+WFyS/bT6m31qT0alcGa9PWkhqY6lE5ERGRwC3AbnpqcwILxcew5V8lbx8tobOkCoK6pk397u4gdp8t5YWEK88fH4XZpiaaIfDs1K0RkQLDWcrX2CjsLt1HVXuVTSw5JYU36OjKjxmpfChERkX4gONDF2tlJLJkUR86pCnacrqCtwwtAeW0HP9tWwLaTFby0OIXpGdH6/S0i36BmhYj0e5833WZb4RY+bbzhMx7uDuep1OXMSpyN2+jlTEREpL+JCA1gw2IPy6cl8PNjZey/UEVnV/fyzc/LW/mL124xJj2MjUs8PDFSyzdF5BeMtfaH7xIZoIwxeRkZGRl5eXlOR5F7UNtey57inZyvOveNfSnmJM1jqWcZ4QHhDiYUERGRu1Fa3cZrh0s5+m4NX38bMn5UJBuzPDycHuZMuAEmMzOT/Pz8fGttptNZRHqDPooUkX6n3dvOkbJDHCw9QLu3zac2Nnoca9LXkhyS4lA6ERERuVcpccH8zvqhZM9O4pWDJbxzvf7L2tVPGrj6SQMzxkazYZGHIUkhDiYVEaepWSEi/Ya1lss1F9lZmENNR41PLTUklTVD1vNoVIZD6URERORBGZ4Syh+/NILrt5v4n7nFXLvZ9GXtzLU6zuXVsWhCHD9akEJiTJCDSUXEKWpWiEi/cLPxM3IKt3Cz6TOf8YiACJalrmR6wkzcRmezi4iI+JNHh4bz1z8ZxeWPG9h0sIRPi1sA8FrIvVTN0XdrWD41gfVzk4kO11sXkcFEf+NFxFE17dXsLNrOpeoLPuNu42Zu0gKWpDxFWIDWroqIiPgrYwwTx0Tx5OhITl2r5dWDpRRVdS8D7ei0bD9dwf6LVWTPTGL1rETCgvXhhchgoGaFiDiirauNQ2W5HC7NpcN2+NQejxnPqrRskkKSHEonIiIifc3lMswZF8uMzBgOXa7m9SOlVNV3PyO0tHl57Ugpu9+p4Nm5yTw9JYGgQJfDiUWkN6lZISJ9ymu9XKw+z66iHdR11PrU0kLTWTtkPQ9HPuJQOhEREXFagNuwdHI888fHsudcJW8dL6OxpQuA+qYu/n1vMTvPVPCjBSksGB+H220cTiwivUHNChHpM582fsK2grf4vPm2z3hkQBQr0lYxNX46LqNPSURERASCA12snZ3Ekklx5JyqYMfpCto6vACU13bw9zkFbDtVzoZFHqZnRmOMmhYi/kTNChHpdVVtlews2s6Vmks+4wEmgPnJC1mcspRQd6hD6URERKQ/iwgNYMNiDyumJfDzY2Xsu1BFZ5cFoKC8jb94/RZj0sPYkOVh/KhIh9OKyINirLVOZxDpNcaYvIyMjIy8vDynowxKrV2t5Jbu52jZITptp09tfOwEVqVlkxCc4FA6ERERGYhKq9t47XApR9+t4etvZZ4YGcHGrFTGDPH/zbkzMzPJz8/Pt9ZmOp1FpDeoWSF+Tc0KZ3itl3eqzrK7aCcNnfU+tYfChpKdvp5RkaMdSiciIiL+4FZpC68cKuFcfv03ajPGRrNhkYchSSEOJOsbalaIv9MyEBF5oD5u+Iicgi0UthT4jEcHxrAybTWT4qZoXwoRERG5b8NSQvmjF0dw/XYTL+cW88HNpi9rZ67VcS6vjoUT4vjRghSSYoIcTCoi90LNChF5ICraytlRmMN7tVd9xgNNIAtTsliUnEWwO9ihdCIiIuKvHh0azn/7ySiu3GhgU24JnxS3AOC1cPBSNUev1rBsagLPzE0mJkJvf0QGCv1tFZH70tLVzIGSfRwvP/qNfSkmxk1mZdpq4oLiHUonIiIig4ExhgkPRzF+VCRn8urYfLCEoso2ADq7LDvPVJB7sYo1sxJZPTOJ8BC3w4lF5IeoWSEi96TLdnG28jRvF++msbPBpzYsfDhr059heMQIh9KJiIjIYORyGWY9FsP0jGgOXanmtcOlVNV3ANDS7uX1I2XseaeSZ+cm8/SUBIICtTRVpL9Ss0JE7tr1+ny2F2yhuLXYZzw2MJaV6WuYGDtZZ52LiIiIY9xuw5JJ8cx7IpY95yrZcryMhpYuAOqbuvj3vcXsOF3BjxamsHB8HG63nltE+hs1K0TkjpW1lrK9cBvX6t73GQ9yBbE4ZQkLkhcR5NK+FCIiItI/BAe6WDs7iaWT48k5Vc6O0xW0tnsBqKjr4B9yCsg5Wc5Liz3MyIzWhy0i/YiaFSLyg5o7m9hX8jYnyo/hxetTmxI/jRWpq4gJinUonYiIiMj3Cw9x89IiD8unJvDW8TL2nq+is8sCUFDRxn99/RYPp4exMcvD+FGRDqcVEQBjrXU6g0ivMcbkZWRkZOTl5TkdZUDqsp2cqjjJvuI9NHU1+dRGRowiO309Q8OHORNORERE5B6V1bTx2uFSjlyt4etvh54YGcHGrFTGDAlzJtwdyszMJD8/P99am+l0FpHeoGaF+DU1K+5dXt0H5BRupay11Gc8Liie1enZjI+ZoKmSIiIiMqDdLmth88FSzuXXfaM2IzOalxZ7eCgpxIFkP0zNCvF3WgYiIj5KWorZXriV/HrfBk+wK5glnqeYl7SQQFegQ+lEREREHpyhyaH80YvDuf55E5tyS3j/s8Yva2fy6jiXX8eCJ+N4YWEKSTFBDiYVGXzUrBARABo7G9hbvIfTFSd99qUwGKYlzGBZ6kqiA6MdTCgiIiLSOx59KJy/+pWFGfA5AAAgAElEQVSRXLnRwOaDJdwoagHAa+HQ5WqOvVvD01PieXZeMjER+tBGpC+oWSEyyHV6OzlZcYx9JXtp6Wr2qY2OeJjsIc8wJGyIQ+lERERE+oYxhgkPR/Hk6EhOX6tj88ESiirbAOjssuw6W8nBS9WsmZXI6plJhIe4HU4s4t/UrBAZpKy1XKt7n+2FWylvK/epJQQnsiZ9LeOin9C+FCIiIjKoGGOY9VgM0zOiOXSlmtePlFJZ1wFAS7uX14+UsedcJc/OS+bpKQkEBbocTizin9SsEBmEipoLySncwkcNH/qMh7hCWJq6jDmJ87QvhYiIiAxqbrdhyaR45j0Ry9vvVLLleBn1zV0A1Dd38e97i9lxuoINiz0seDLO4bQi/kfNCpFBpKGjnj3FuzhbeRrLL04CMhhmJs7mac8KIgN1triIiIjIF4IDXWTPSmLJpHi2nypn++kKWtu79/eqqOvgs5IWFjicUcQfqVkhMgh0eDs4Xn6EAyX7aPW2+tQeiXyU7CHrSQ1NcyidiIiISP8XHuLmxUUelk9L4OfHyth7vorAAMP6uclORxPxS2pWiPgxay3v1l5hZ2EOle2VPrWk4GSyh6wjM+ox7UshIiIicodiIgL5teXprJ6ZxM2SFqLD9ZZKpDfob5aIn/q8+TY5BVv4pPGGz3iYO4ynPMuZnTQHt9FLgIiIiMi9SI4NIjk2yOkYIn5L71RE/ExdRy27i3Zyvuqcz74ULlzMSpzLU6nLiAiIcDChiIiIiIjI91OzQsRPtHvbOVp2mNzS/bR723xqmVFjWZO+jpRQj0PpRERERERE7pyaFSIDnLWWKzWX2FGUQ017tU8tJcRDdvp6MqIzHUonIiIiIiJy99SsEBnAbjXdJKdgC581feozHu4OZ1nqSmYkzsJt3A6lExERERERuTdqVogMQDXtNewu2sGF6nd8xt3GzZzE+Sz1PEVYQLhD6URERERERO6PmhUiA0i7t41DpQc5VHqADtvhUxsX/Tir09eSFKKzvkVEREREZGBTs0JkAPBaL5eqL7CraAe1HTU+tbTQdLLT1zMm6hGH0omIiIiIiDxYalaI9HOfNX7KtoK3uN18y2c8MiCS5amrmJYwA5dxORNORERERESkF6hZIdJPVbVVsatoO5drLvqMB5gA5iUtJMuzlFB3qEPpREREREREeo+aFSL9TGtXK4dKD3Ck7NA39qUYH/Mkq9KzSQhOdCidiIiIiIhI71OzQhxjjAkDFgPLgZnAUKAL+ATIAX5mrW10LmHf8lov56vOsbtoB/Wd9T61IaFDyB7yDKMjH3YonYiIiIiISN9Rs0Kc9DzwHz1fXwd2A1HAdOBPgeeMMXOsteUO5eszNxo+JqdwCwXNn/uMRwVEsSJtDVPip2pfChERERERGTTUrBAndQD/DvyDtfb6F4PGGA+wFxgP/APdTQ2/VNlWwY7CHN6tveIzHmgCWZC8iEUpSwhxhziUTkRERERExBlqVohjrLWbgc3fMl5ijPkN4CywxhgTZK1t7/OAvailq4Xckn0cKz9Cp+30qU2IncTKtDXEB8c7lE5ERERERMRZalZIf/VezzUYiAdKHMzywHitl3OVZ9hTvJOGzgaf2tCwYawd8gwjIkY6lE5ERERERKR/ULNC+qsRPdcOoNrJIA/KR/UfklO4haKWQp/xmMAYVqatYWLcZO1LISIiIiIigpoV0n/9Vs/1gLW27YduNsbkfUfJ8WkK5a1l7Cjcxvt17/mMB7mCWJicxcLkxQS7gx1KJyIiIiIi0v+oWSH9jjHmKeCX6Z5V8YcOx7lnzZ3N7C/Zy4mKo3TZLp/a5LiprEhbTWxQrEPpRERERERE+i81K6RfMcY8ArwGGOB3rbXv/cC3AGCtzfyOn5cHZDy4hHemoPlz/unjv6epq8lnfET4SLKHrGdY+PC+jiQiIiIiIjJgqFkh/YYxJg04AMQCP7PW/qPDke5ZSoiHUHfol82KuKB4VqWt4cnYiRhjHE4nIiIiIiLSv6lZIf2CMSYOOAgMBV4GfsfZRPcn0BXI6vS1bL71MlkpS5mfvJAgV5DTsURERERERAYENSvEccaYCGA/3cs1tgM/sdZaZ1Pdv8djxvNnY0cTGRjpdBQREREREZEBReckiqOMMcHALmAykAs8Z+3XdqMcoIwxalSIiIiIiIjcAzUrxDHGGDfwJjAfOAWssda2O5tKREREREREnKZlIOKk3wRW93xdCfzLd2w++TvW2so+SyUiIiIiIiKOUrNCnBT7la9Xf+dd8Cd0NzNERERERERkENAyEHGMtfZPrLXmDv655XRWERERERER6TtqVoiIiIiIiIhIv6JmhYiIiIiIiIj0K2pWiIiIiIiIiEi/omaFiIiIiIiIiPQralaIiIiIiIiISL+iZoWIiIiIiIiI9CtqVoiIiIiIiIhIv6JmhYiIiIiIiIj0K2pWiIiIiIiIiEi/omaFiIiIiIiIiPQralaIiIiIiIiISL+iZoWIiIiIiIj8/+3debRkVXmw8edVhm4GZcYBpJk1SEsQJ3BAQSUMiooYnMAxohIMqJ/GGJEPTb4EFV2CGBQJDkExRkQRiQOgTCoY5g/pJqAEZGwUgaYZ3vyxd3EPRU339r23zrWf31p7nao9nNp9aq+uW2/ts7fUKgYrJEmSJElSqxiskCRJkiRJrWKwQpIkSZIktYrBCkmSJEmS1CoGKyRJkiRJUqsYrJAkSZIkSa1isEKSJEmSJLWKwQpJkiRJktQqBiskSZIkSVKrGKyQJEmSJEmtYrBCkiRJkiS1isEKSZIkSZLUKgYrJEmSJElSqxiskCRJkiRJrWKwQpIkSZIktYrBCkmSJEmS1CoGKyRJkiRJUqsYrJAkSZIkSa1isEKSJEmSJLWKwQpJkiRJktQqBiskSZIkSVKrGKyQJEmSJEmtYrBCkiRJkiS1isEKSZIkSZLUKgYrJEmSJElSqxiskCRJkiRJrWKwQpIkSZIktYrBCkmSJEmS1CoGKyRJkiRJUqsYrJAkSZIkSa1isEKSJEmSJLWKwQpJkiRJktQqBiskSZIkSVKrGKyQJEmSJEmtYrBCkiRJkiS1isEKSZIkSZLUKgYrJEmSJElSqxiskCRJkiRJrWKwQpIkSZIktYrBCkmSJEmS1CoGKyRJkiRJUqsYrNDYRcT8iDg8In4dEUsj4oaIOD4injjuvkmSJEmSZp/BCo1VRMwDfgx8GFgDOAX4LfAm4FcRsdkYuydJkiRJGgODFRq3vwOeDZwHbJWZr8nMZwGHAusDx4+zc5IkSZKk2WewQmMTEasA765P35WZf+yUZeYngUuAF0TE08fRP0mSJEnSeBis0DjtBDwWWJyZv+pR/s163Gv2uiRJkiRJGjeDFRqnp9XjRX3KO/kLZ6EvkiRJkqSWWGncHdAK7Un1eH2f8k7+JsNOFBGX9yl68uLFi9lmm20m2zdJkiSptRYvXgyw8bj7Ic0UgxUapzXq8e4+5XfV45rL8RoP3nvvvXddccUVv12Oc2j6bV6Pi8faC80ljhlNlmNGk+WY0WSNe8xsTP+/o6U5z2CF/iRkplMn5pDOTBjfN43KMaPJcsxoshwzmizHjDSzXLNC49TZ/WO1PuWr1+Ods9AXSZIkSVJLGKzQOP2mHjfqU97Jv24W+iJJkiRJagmDFRqni+tx+z7lnfxLZqEvkiRJkqSWMFihcToH+D2weURs16N8n3o8dfa6JEmSJEkaN4MVGpvMXAZ8tj49OiI6a1QQEYcAC4GzMvPCcfRPkiRJkjQekZnj7oNWYBExDzgTeBZwI/BTYJP6/Bbg2Zl5zdg6KEmSJEmadQYrNHYRMR/4IPBayn7RtwOnAx/OzOvH2TdJkiRJ0uwzWCFJkiRJklrFNSskSZIkSVKrGKyQJEmSJEmtYrBCkiRJkiS1isEKSZIkSZLUKgYrJEmSJElSqxiskFZgEfH0iPhARHwrIq6PiIyIoVsERcQBEfHziPhjRNweEadFxI5D2uxU691e2/08It44pM1GEfGliLghIpZGxK8j4qMRMW9Am/kRcXitu7S2PT4injjs36XBImK1iNg7Ir4YEVfV63tXRFwcEX8fEWsMaOuYWUFFxCH1/5irI+L3EXFvRFwXESdGxLYD2jlmBEBErBsRN9fPqEVD6jpuVkARcWYdH/3Sbn3aOV6kNstMk8m0gibg20B2pyFtjqr17q7tTwfuA+4H9u7T5lW1/EHgTOCbwJJ6niP7tNkCuKXWuRT4OrC4Pv8ZsGqPNvOA82qdG2qbC+rzm4HNxn3N53IC3toYJ1cA36jv/x9q3pXABo4ZU9c1vhW4p17Xb9V0Vb3Gy4A9HTOmIWPohPq+JrBoQD3HzQqa6nuX9f07oUfa1vFiMs29NPYOmEym8SXg/wCHA3sBjwOWMiBYAexaPyhvBbZs5D8HuLd+YK/V1WYd4Pe13Ssb+RsCV9f8nXu81s9q2acbeStRvugkcFiPNkfUsnOBNRr5h9T8M8d9zedyAvYHPg88pSv/8cBF9Rp/zTFj6rrGOwHzeuS/s17j3wErOWZMfcbPLvW6fp4BwQrHzYqdmAhWLBixvuPFZJoDaewdMJlM7UkMD1acVj8k39Oj7NO17NCu/PfX/G/3aPOKWnZqV/4za/5NdP3iUP8oWAbczsO/4KwC3FHb/XmP17q4lj193Nf5TzHVP/CyjqFVHDOmEcfNonqNFzpmTD2u5/w6Ri4HtmRwsMJxswInJh+scLyYTHMguWaFpJFExHzgRfXpN3tU6eTt1ZW/x4A236N8ud21677NTptTM/PeZoPMvAn4KbA28NxG0U7AY4HFmfmrSfRP0+PielwVWBccMxrJffW4DBwzeoSPAJsB72BirDyC40aT4XiR5g6DFZJGtTXli+gtmXl9j/KL6nFhV/7TusofkpnLgMso92ZuNUqbAa81lTaaPpvV432UX4nAMaMBIuINlDFydU3gmFEVEQuBQ4EvZeZPh1R33KjjLRFxTER8NiL+OiKe1KOO40WaIwxWSBpV5wO/1wc7mXkXZdri2hGxJkBEPIby60Dfdo38TUZ9rWlso+lzcD2e3vj1yDGjh0TE+yLihIg4OSIuA04EbgT2y8wHajXHjIiIRwFfoLzX7x+hieNGHX8HHAi8i3I7x6KI+HBXHceLNEcYrJA0qs62lHcPqHNXPa7Z1WZQu+42o7zWdLXRNIiI3YG3UGZVNP8odMyo6aWURVr3AbYBrqMEKi5s1HHMCOAg4BnA+zLzthHqO250NvAGYHNgNcrsiQ9Rdu44PCIObtR1vEhzhMEKSdKURcSTga8AQflicfGQJlpBZeaumRmU+7OfT7n146yI+NB4e6Y2qdP2jwDOyswTxtwdzRGZ+feZ+ZXMvCYz78nMX2fmx4G9a5XD6loVkuYQgxWSRvXHelxtQJ3V6/HOrjaD2nW3GeW1pquNlkNEPJGyL/3awCcz89NdVRwzeoTMvKOuQbA7cCHwfyPiGbXYMaOjKbshvGMSbRw36ikzzwB+CawFPKtmO16kOcJghaRR/aYeN+pVGBGrU/4YWJKZdwJk5h8oe5L3bdfIv27U15rGNpqiiFgHOINyz+yXgPf2qOaYUV+ZeR/wdcqsnM6q9o4Z7UmZ/n5sRJzZScBJtfyJjfzH1TzHjQbpLOD7+Hp0vEhzhMEKSaO6CrgXWL/+ot5t+3q8pCv/4q7yh0TEysBTKdt9/XqUNgNeayptNAURsQbwfeDPgG8Bb8vM7FHVMaNhbq3H9evRMSMoXxRf0JU6v4rPa+R1toh03GiQteuxs9aD40WaIwxWSBpJZt4D/Lg+fXWPKvvU46ld+d/rKm/ak/LH5g8zc2mPNntFxKrNBhGxIfA8YAlwTqPoHMqvHptHxHaT6J8mob4fpwDPBH7Aw3dyeBjHjEbwgnpcDI4ZQWZGrwRsWqssbuRfW9s4btRTRKxPeU+gbvnpeJHmkMw0mUwmMhPKLwI5oHxXICm/hm7ZyH9ObbsEWKurzTqUD90EXtnI34AyNTOBnXu81s9q2VGNvJWAf6/5h/Voc0QtOwdYvZF/SM0/c9zXeC4n4NGUmRRJWXl9tRHaOGZW4ATsBOwGPKorf2XKjg8PUKb8b+yYMQ0ZSwvq9V3Up9xxs4ImYEfKQpqP7jFmOu/VKY4Xk2nupbF3wGQyjS8BewDnN9KD9UOwmbdHV5ujap27gG8Dp1G2rLwf2LvP67yK8qXkQcqvGSfXPwQS+ESfNlvWPyKSMtXxJMqvr50P71V7tJlX+5zADZT74TvPbwY2G/c1n8sJOLhey6QELU7ok9ZzzJjq9T2gXstbKIuxfpUyI+eGmn8PsG+Pdo4ZU/d1X8CAYIXjZsVNjf9nbqTMZPgqJUBwT82/DNjA8WIyzb009g6YTKbxpcYH/KB0QJ92v6wf8Eso6xfsOOS1dqr1ltR2vwD2H9JmY8rijTdS7i+9GjgcmDegzfxaZ1Ftc2M9x0bjvt5zPQGHjTBeEljgmDHVa7sp8DHKF4cbgGWUle4vAz4DbDGgrWPG1LzmCxgSrHDcrJgJeApwDGV3oZspAYc7gPMoMxHmO15MprmZIjORJEmSJElqCxfYlCRJkiRJrWKwQpIkSZIktYrBCkmSJEmS1CoGKyRJkiRJUqsYrJAkSZIkSa1isEKSJEmSJLWKwQpJkiRJktQqBiskSZIkSVKrGKyQJEmSJEmtYrBCkiRJkiS1isEKSZIkSZLUKgYrJElaAUVE1nTCuPsyVRGxQ0Q8EBH3RcSW4+7PXBARr6zv+y0Rsc64+yNJUj8GKyRJrRcRu0XEMRFxSf2StSwifhcRF0bEkRGx47j7qNkVEQF8jvK3zAmZeXXNP6ARiFmedMI4/30z6D+Ai4D1gI+NuS+SJPVlsEKS1Fr1l/MLgO8DBwLbUr5krQxsCGwPHAqcExHfi4hNx9bZMYuIEzpftKfpfG3/0r4fsANwL3D4mPsyZ2RmAh+uT98aEVuNsz+SJPWz0rg7IElSLxHxCuCrwPyadRlwIuVX4SXAusBzgDcCmwO7A+dHxF6Z+fPZ7/Hckpkx7j5MVUQ8CjisPv1aZv62Ufxt4JcDmv8AeEJ9vO2Aekum3MGWy8zTIuISYCHlOr52vD2SJOmRDFZIklonIp4JfJ0ygyKBDwCfyMwHuqr+Z0T8I/BxygyLDYDvRcR2mfk/s9lnzaqXA501Kv61WZCZdwB39GsYEfc16l42I72bG04EjgReHREfzMzrxt0hSZKavA1EktQqEbEKcBIlUAHw3sz8px6BCgAyc1lmvhc4qmatB5ww4x3VOP1VPV4LnD3GfsxlXwUeoPxw9dYx90WSpEcwWCFJapvXA521J34GfGrEdh8EFtXHu0bEs7srTGZdh4i4ttY9s0/5vIjYuy78+YuIuK3uSvGHiLgyIr5QZ4gMeo0FjbUhDqt520TEv0TENRGxNCJuj4gfR8Rr66KS3ec4rP579m/k9Vow8rCudj3XpOj8uxtZ+/c534KIeFLdjSMj4huDr+hD5z+v1v99RKw+Spuu9hsCu9anJ9c1GGZFRGwQEYc33u9lEXFjRJweEW+PiJUHtF2vce2OrHlb1fGzKCLuiYglEXFWXSR04N9oEfG42pfz6xi5LyLuiIjFEXF2LXtWv/aZ+TsmAj2vn8r1kCRpJnkbiCSpbd7WeHzkqF9GM3NpRHyWiRkW7wDOn+7ONZxEuR2h25rAk2t6S0R8kjI7ZJQAyZuBY4BVG9mrAi+saVfgzcvZ72mTmb+JiNOAPYGXR8T6mXlLv/oRsRDoBJG+kpl3TeFldwceXR+fO4X2UxIR+wBfAtboKnpcTS8FDomIPTNzUXf7HufbD/gCsFojex7w/Jr+IiL+ste4iYgXA/9OGWtNj61pM+B5wMuA7QZ04xzKuFoQEdtm5qXD+i1J0mxxZoUkqTUiYg3KDg8AS4HTJnmKbzUe7zwdfRpgJWAxJTjyOmBHyu4kewJ/C3TWzDikpmFeDBwH/BY4qJ7vmcDBQCcA8KaIeF1Xu2MoC0We0sjbtkc6ZsR/10t4+MKTp/Q5X+ff97l6XAV405Bzv73x+PMj9qfbCxuPZzIY9ZCI2AP4BiVQ8QBwLCU4sQOwL/CjWnVr4OyI2GDIKXeirLVxM/A39fkzgHcCN9Q6+/Lw69Xpy2MpgbI1gWWU9/XlwLPqOXanzDI6E3hwSD/Oazx+0ZC6kiTNqpjF2ZOSJA0UETtSfu0FuCAzH3ErxwjnuImy0CbABs1f+ustD/vD8N0wIuJaYBPgrMzcuUf5lsCifjMmImIe8F1gF8qCjxtn5h+76iwA/ruR9RNgz8y8u6veU4ELKQGBCzNzB7pM5t9W63f6/a+ZecBkyxv1HkUJ2iwArga27jMbYD7li/hawPmZ+ZxhfezzepcDfwb8T2ZuNIX211Le11Gv03zK2hgbUAIVL8vMRwTRIuIoSmAJ4KTM3K+rfD0mgk5QAgW7ZeYfuuptAVxMmXFxVWY+uat8X8riswBvyMyvDOj7upl524Dy9SkBEygzXd7Qr64kSbPNmRWSpDZZr/H4d1M8R7Pd+svRl4Ey8+pBt3Zk5lLKDiVQvqDvMuSU9wKv7w5U1HNdBpxan24fEY+ZQpdnRGY+yMQsiS3pP6PlNZTrAFOcVVHX7Ni8Pp3q+Jis1zER/Dq6V6Cieh9wVX386ojYeMA5H6C813/oLqi3kJxcn24dEY/rqtJ8/pNBHR8UqKhurX2Bid1VJElqBYMVkqQ2ad6Df+cUz9Fst1bfWtMsIh5TF53cJiKeWmdDNH+5337IKX6YmTcMKL+g81JMLEDaFsdTbkmAHrcudOXfwcTMgMlah4n1PG6f4jkm6yWNx8f2q5SZ91Fu44Gypsag4NTPMvOaAeUXNB5v3lV2fePx21gONdi2pD59/PKcS5Kk6WawQpLUJs1AQ/dChqNqtlvWt9Y0iIiFddeP64HfU27puAy4tKZfNaqv1+MUTVcOKW/+St6amRUAmXkzE+uFvKLe8vCQGrjp3Pbx5cy8Z4ov1VyMcknfWtNrYef1MnPYe9Rc8PNpA+otz3v9fcq6JgAfiYjL6s4fL46IdYact5dO0GfSO7NIkjSTDFZIktrk1sbjqf7S22x3a99ayykiDqEEI94CPHGEJqsNKR+2M0ZzscRH9601Pp2FNlelsY1q1Zxt8S/L8Rr3Nx7PW47zTMa69XjTCHVv7NGulym/1zXQsxtwSc3aBvgwcAZwa0RcGhFHDLkNpWl+Pd43Yn1JkmaFwQpJUptcysQX0qdFxMqTaRwRGzGxvsBdwHXT2Lfm6zwP+ATlc/Q24EOUmQMbAvMyM+rijc0vmkMXc5zLMvNs4Ir69K2d/LrQ6Ovr03Pr+htTdUfj8aBgwJ+0zLyCsiXpS4HPUhbkfJAyxp5KGY9XR8Q7Rjhd5zreMbCWJEmzzGCFJKk1MvNOyq4XUH45/4tJnuKVjcdn9VgAs7OYYGcXi0EGTYs/sB4fBHbOzI9n5vmZeXNm3tuoN5Vp+XNZZ3bFkyPi+fXxvsDa9fFUtysFHppV0Nm9YraubeeWjA1HqNtc/HLY4pbLJYszMvOgzNyOsj7LHsCJlHG+KnBM3WGnp4hYlYkZP9fOZH8lSZosgxWSpLb5QuPxoXUHiKHqF693N7JO6lGtuSZG3y+7dUvHQWtMbFuPFw+ZKfDMAWXTrQ17kZ/IxC0Ob+86LgG+MQ2v0bnem0525s0UdW63WDsinjKkbjMwcPEM9aenzLwzM0/LzP2Bg2p2APsNaNbcFvXSGeucJElTYLBCktQ2X2bi9o3nA389YruPMbH94n8D/9ajTnMHhmcMONcbh7zWSvU4bFHCg4aUT6eHFq2sgZvpOt/I56pbcX6tPn1VRDwX2Kk+P7Fu57q8zq/HeQxexHK6nN54fGC/ShGxEhO3vzwA/HAmOzXEDxqPB23f+5zG4/NmqC+SJE2JwQpJUqvU2yj2Y2LBv09FxCH9btuIiFUi4h+BQzunAN6Wmff3qP7jxuP3RcQjFqqMiGcAHx3SzavqccvG7Q7d5/kQZSHE2dLc9nTraTzfZM/V2d5zHg+fSbE8C2s2NYMHz56mcw7yNSYW13xnRPS7Nen/AZ2ZF9/IzOv71FsuddePYVvX7t54vHhAvc71uw/40XJ1TJKkabbS8CqSJM2uzDwvIl5Hua1gHmUxy/0j4svARZRbCtalfNnaH9ii0fzdmdnzi1dmXhYRPwJ2AV4I/CAiPgP8hvIL9O7AOygzO9ah/6/SxwMvp0yzPzUiPgWcTdm+dCvgzcCuNa9nMGMGnN14fHREfIyyxWVnnY5bM3Myu6OcDWwO/HlEfBw4hfLv61icmY/YQSIzL4qIn1NugenszPLTuijkdDiXEkh5AvASygKTMyYzl0bEm4HvUhZM/U5EHAf8B2Vdik2BvwJeXJvcCLxnBrv0cuDAiDiHEri5hIlgyhOAvYAD6vO7gC/2OkkN/u1Sn55RZ8VIktQaBiskSa2UmSdHxHXA0cAOwELgnwc0uQV4a2Z+Z8ip3wKcCSygfFnbpav815SFCvv+0pyZ34mITwMHA48BPtKj2gXAq2q/ZsNPKX3eBXgu8P2u8o8Ch03ifP9MWRxzdeCDNTVtSv9FGT/Hw9frmK5ZFWTmAxFxIvABYLeIWD8zZ/QaZ+ZpEfEa4EuU63EgvW8JuQrYKzNv7lE2nR4FPK+mfm4D9svMa/qUvwjYqD4+fhr7JknStPA2EElSa2Vm5xf63Sk7SVxO+RL2YFfVXwCbjxCoIDOvA54O/ANwJWVthjuB/6Js+bh9Zg6aOt85z3uAl1F+3b6NMpX+JuAnlNkZz53kTIblUnc+2QN4P2X9gSU0dj+ZwvmuBLYHjgP+P3A3oy/i+XUm1ry4HfjmVPvRx9GU670y8NppPndPmXkysBlwBPBLyvXtvOdnUGZXbJuZV89wV0Cdr98AAAHjSURBVP4WeDXlGpxLWZ/lrtqXmynj7/3AFpn5nwPO01mX5VrKrBlJklolHrmrmyRJ7RcRa1NmE2xTsw7OzM+MsUuqImIhE7thfCozD5mB1/gi5XabyylBAv+gGVFErEu51Wl14MDMPHZIE0mSZp0zKyRJc1JmLqEsYNlZyPCoiHjTGLukCZ3tSpMyI2YmHAYspQSr/nKGXuNP1QcogYpF9FnTQpKkcTNYIUmas+qOC7tRpuQHcFxE7DPeXq3YIuLxQCdo9MPMvGpQ/anKzN8C/1SffrRuHaoh6vvzrvr0b3otkipJUhv4wS5JmtMy8/KI2I2J7Rq3ioj5mXnPoHaaPhGxCbAKE2s6rFaLDp/hl/4H4H7KLh0bU9Zv0GALKEGeWzLzu2PuiyRJfblmhSRJWi4RcS2wSVf2sZnZa8cMSZKkoZxZIUmSpsvdlHUQjqNsXypJkjQlzqyQJEmSJEmt4gKbkiRJkiSpVQxWSJIkSZKkVjFYIUmSJEmSWsVghSRJkiRJahWDFZIkSZIkqVUMVkiSJEmSpFYxWCFJkiRJklrFYIUkSZIkSWoVgxWSJEmSJKlVDFZIkiRJkqRWMVghSZIkSZJaxWCFJEmSJElqFYMVkiRJkiSpVQxWSJIkSZKkVvlfqf9hxFcET78AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.dpi'] = 150\n",
    "plot_equation(demand, 5000, 50000, label = \"Demand\")\n",
    "plot_equation(supply, 5000, 50000, label = \"Supply\")\n",
    "plt.ylim(0,13)\n",
    "plt.title(\"Orange Supply and Demand in 1920's and 1930's\", fontsize = 20)\n",
    "plt.xlabel(\"Quantity (Tons)\", fontsize = 14)\n",
    "plt.ylabel(\"Price ($)\", fontsize = 14)\n",
    "plot_intercept(supply, demand)\n",
    "plt.legend(loc = \"upper right\", fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "cell_id": "feb1ae9b-936c-49da-be36-2a6847d8486e"
   },
   "source": [
    "You can also practice on your own and download additional data sets [here](http://users.stat.ufl.edu/~winner/datasets.html), courtesy of the University of Flordia's Statistics Department. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}