# 处理文本(基础) In [1]: ``` import matplotlib.pyplot as plt import numpy as np %matplotlib inline ``` `matplotlib` 对文本的支持十分完善,包括数学公式,`Unicode` 文字,栅格和向量化输出,文字换行,文字旋转等一系列操作。 ## 基础文本函数 在 `matplotlib.pyplot` 中,基础的文本函数如下: * `text()` 在 `Axes` 对象的任意位置添加文本 * `xlabel()` 添加 x 轴标题 * `ylabel()` 添加 y 轴标题 * `title()` 给 `Axes` 对象添加标题 * `figtext()` 在 `Figure` 对象的任意位置添加文本 * `suptitle()` 给 `Figure` 对象添加标题 * `anotate()` 给 `Axes` 对象添加注释(可选择是否添加箭头标记) In [2]: ``` # -*- coding: utf-8 -*- import matplotlib.pyplot as plt %matplotlib inline # plt.figure() 返回一个 Figure() 对象 fig = plt.figure(figsize=(12, 9)) # 设置这个 Figure 对象的标题 # 事实上,如果我们直接调用 plt.suptitle() 函数,它会自动找到当前的 Figure 对象 fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold') # Axes 对象表示 Figure 对象中的子图 # 这里只有一幅图像,所以使用 add_subplot(111) ax = fig.add_subplot(111) fig.subplots_adjust(top=0.85) # 可以直接使用 set_xxx 的方法来设置标题 ax.set_title('axes title') # 也可以直接调用 title(),因为会自动定位到当前的 Axes 对象 # plt.title('axes title') ax.set_xlabel('xlabel') ax.set_ylabel('ylabel') # 添加文本,斜体加文本框 ax.text(3, 8, 'boxed italics text in data coords', style='italic', bbox={'facecolor':'red', 'alpha':0.5, 'pad':10}) # 数学公式,用 $$ 输入 Tex 公式 ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15) # Unicode 支持 ax.text(3, 2, unicode('unicode: Institut f\374r Festk\366rperphysik', 'latin-1')) # 颜色,对齐方式 ax.text(0.95, 0.01, 'colored text in axes coords', verticalalignment='bottom', horizontalalignment='right', transform=ax.transAxes, color='green', fontsize=15) # 注释文本和箭头 ax.plot([2], [1], 'o') ax.annotate('annotate', xy=(2, 1), xytext=(3, 4), arrowprops=dict(facecolor='black', shrink=0.05)) # 设置显示范围 ax.axis([0, 10, 0, 10]) plt.show() ``` ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAs8AAAJVCAYAAAAhjxiSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl4FeX5xvH7yQLZCIvsCIIIFhSQRVBkrTsoBfe2ikDd fmgr4lpFFi1C1bZQFeuCVK0oCCKKoiAQQAREQWRTFgk7JKxhS0KS9/fHOTmeQxaGJTkJfD/X5ZUz 78yZeWaSpnce3pljzjkBAAAAOLaIcBcAAAAAlBaEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhE eAYAAAA8IjwDKHZmlmxmOWY25hTsq5N/Xzlm1vFkt/Nv+wcz+9nMMv3bP2hmg3Pff7I1n6mO5xqa Wd2g79edxVEfAHhBeAYQTqfyQfPO4/4K3c7Mqkr6r6QGknZLWiBpq6RN/tcLTrbQ010hfxzluYYe ArXX7ysAFIuocBcAACVMA/l+NzpJtzvnvgpaN7q4izGzWOfc4eI+blFwzo1WwdewoIBsRVQOAJwQ Os8AwinCzJ42s+1mdtDM3jOzxNyVZhZpZg+b2QozyzCzNDObYWa/PdaOzew+M9toZofM7BNJtTy8 Z7CkubmLkqblTvPIr0NqZmXM7CUz22tmu8xspJk9l892Sf6xWcHHKmw7M3vczLZK2uJfF2Vmj5nZ Sv+12GNm482s7jHOqZqZvWtmW/3vSzGzuWb2R//6fKdHHN09Pmq7/mb2vpkdMLMdZjYweBtJdfy7 uTP4HI8+ZzNLkjTw10OG1JFvmDazZmY2ycx2+s9nhZndV9g1AIBTic4zgHC6UVKWpO2Sqkr6vXy/ l271r39NUh//67WSKkjqLKmjmV3nnPsiv52aWRdJo/yLuyQ1kvSf3NWF1LNJ0ir/9pK0UlKa/7/c MBcc6v4m6X7/62R//bH5bKfjHLtUUjtJPwXt7x1Jt0nKkbRCUg1JN0lqZ2YXOedSCjinUZJ6SDog 6UdJZ0m6RL7zfO+oOrzWN1RSqqS9kmpKGmxmqZImSVooqbmkMpJ2yvd9K2ifKyTV169/2ORO58j3 XMzsIknz5LsmO+W7Pk0kjTKzqs65Z/J7HwCcSnSeAYRTuqQGzrlGkkb6x24ys3pmVl+/BueXnXMN JZ0raY18v7v+Vsh+H/d/TZZ0rnOugXzBrlD+aQV9cxcl9XXOtXXOLdFRodvM4iT92b842Tl3rqR6 8s2PPlnRkro655pIamBmzeQLzk7Sbc65ZvJdiy2SqkvqV8i+Gvq//p9z7mJ/ndUlvXTUdsczPeIb SXXlO99v/WOPO+e2O+cule+PIUma4r9+bfPbiXPufklv/rro29Y5N7WAegbJF5wXS6rtvw4P+dc9 EfyvFgBQVAjPAMIpyTm30/96XND4BZJa+l87SWMlyTl3QNIU/3gzMwsOWMGvm/i/fumc2+9/PT5o f4XxGiLPk1TW/3qcv76DQfWdjJ+dc9P8+3SSWgfVNt4/7SFNv3Zs2xSyr0/8X982s3Vm9rmkeyVt O4n6PnLO5Tjnjkj62D9W28ziT2BfxxPac69DC0mH/dfhX/6xspIuOoHjA8BxYdoGgHDKL/ya8k4h KCgkn8j+i1p+x8k9l8igsfKF7GNHAe+XpO/km+oSLLmgHTnnnjKzeZKuknShpMskXSPpZvmmVwTv 22t94biuwXZIWp/P+JHiLgTAmYfwDCCcOppZZX/3+Wb/mJNvLmyE/7XJN5d4vpmVk3Sdf7sfnHMu qPkcHAKXSWov6SozS/B3rG86xbWvlW/aSYx8c7c/MLOEoPqC5YbhumYWId984GsL2ffR3fFFQa/f cM69kbtgZm2UN0wraH07SbOdc5/7l2+Tr5Pf1MwqKnR+8Xn+bTqp8PDcw8xelS9s/84/ttHfeZek Q/6vCYXsI1futjKzOOfcoUK2/dZ/vF2SrnXO7fO/r6KkLs65+R6OBwAnhWkbAMIpRtIaM/tZv87b neCcS3bO/SLpLf/YA2a2Vr5u43mSsiUNOGpfwR3Q5/1f60pKNrN1km7JZ7vCFLqdP+Tlzhu+0czW S/pFvhvojjbD/7W2pCWSlssfVL3U45xbKul9/+Jr/ukXP5rZXknz5esmF2S4pN1mttbMvtev13Sz c26P/zF4uaHzEf8TQT6R78bEgrSW73ux3v/a6ddrLvluRpSkG8zsezN7SwXL3dYkrTSz+WZWr4Bt h0g6LKmxpC1mtsTMkuW7efHoOdwAUCQIzwDCIXdaxgRJ/5CUKOmgpA8k3R203b2SHpXvqRe15LuR bqakq5xzXwbtK/irnHOfSXpAvhvqYuW7yfD/jt6ukNry2y6/9w2Q9LJ8T50oL9+86tygmB603RhJ /5bvCRFnS/pKv94gGbzfwj4QpKekx/TrkzZqS9rg38/nhZzPB/I9ASNevrnk++Sbpxzc+e4l3yP6 jvj3fb98Tx4pqJYnJSVJKidfcH3WOfdq0PoB8j05I0O+ecgXBp3f0aZIekO+bnJt+cJ4bND64O/r D/I9ieQj+TrWjeQL3V/I93MCAEXOfPeiAACOl/k+jTDdOZfmX46Vb4pFY0nznXOFdYRLFf/zpH/x L/Zyzr0TvmoAIHyY8wwAJ66tpP+Z2SJJ+yVdLKmafB3cp8NZGACgaDBtAwBO3C/yPf3iQvmeYGGS Jkvq4JybGc7CihD/XAngjMa0DQAAAMAjOs8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAA eER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhE eAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgG AAAAPCI8AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAA ADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8 IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8 AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAPA GcTM2pvZT4Wsr2tmOWbG/z8AQD745QgApzF/ED43d9k5N9c595ug9clm9tvwVAcApQ/hGQBOf1bI OneM9QCAIIRnAChCZvaEma01szQzW2Fm3YPWvWpmE4KW/25mX/lflzWzF81sg5lt928b419X2cym mNkeM9tlZnPMLE8ANrM5/pdLzWy/md1sZp3MbJN//buS6kj61L/+kXz2Ud7MRpvZVjPbbGbPMqUD wJmMX4AAULTWSmrnnEuUNETS/8ysun9df0lNzOxOM2svqY+knv51wyWdJ6mZ/2stSQP96x6WtElS ZUlVJf3VOeeOPrBzroP/ZVPnXDnn3IdHrb9D0kZJ1/nXv5hP/f+VlCmpvqTmkq6SdNfxXQIAOH0Q ngGgCDnnJjjntvtfj5e0RlJr//JhSXdI+pekdyU94Jzb6u8i3y2pv3Nur3PugKRhkm7z7zZTUg1J dZ1z2c65eUVRu5lVk3StpIecc4edc6mSRgTVAQBnnKhwFwAApzMz6ynpIUl1/UMJks7KXe+c+9bM fpGvi5zbGa4iKU7S90GzMUy/NjxekDRY0jT/+tedc38vgvLPkRQtaVtQHRHydasB4IxE5xkAioiZ nSPpdUn3S6rknKsoabmCbtAzs/sllZG0VdJj/uGdkg5Lauycq+j/r4J/6oeccwecc4845+pL6iap /0k8MSPPdI8gmyRlSDorqI7yzrkmJ3gsACj1CM8AUHTi5QunOyVFmFlvSRfmrjSzhpKelfRH+eY6 P2ZmzZxzOZLekDTCzKr4t61lZlf5X3c1s/P80zvSJGX7/8vPDvnmKxekwPXOuW2Spkn6p5mVM7MI M6tvZh3y2x4AzgSEZwAoIs65lZL+IWm+pO3yBeevJcnMIuWb5zzcObfMObdW0pOS3jWzaEmPy3ez 4QIz2ydpuqSG/l038C/vl/SNpFecc7MLKGOwpLf9T+a4Sb4wH9xtHiZpgH99/9zSg9b3lK8zvlLS bvmmllQXAJyhLJ8btE/Njs3ektRVUkruP/GZWSVJ4+SbR5cs6Rbn3N4iKQAAAAA4xYqy8zxG0jVH jT0habpzrqGkGf5lAAAAoFQoss6zJJlZXUmfBnWef5LU0Tm3w/+c06Tgj4kFAAAASrLinvNczTm3 w/96h6RqxXx8AAAA4ISF7YZB/6dhFV3bGwAAADjFivtDUnaYWXXn3HYzqyEpJb+NzIxQDQAAgGLh nLNjb+VT3OH5E0l3Svq7/+vHBW1YlHOxUToNHjxYgwcPDncZKGH4uUB++LlAfvi5QH6CPkHVkyKb tmFm78v3/NHzzWyT/8MBhku60sxWS/qtfxkAAAAoFYqs8+yc+30Bq64oqmMCAAAARYlPGESp0alT p3CXgBKInwvkh58L5IefC5wKRfqc5xNlZq4k1gUAAIDTi5kd1w2DdJ4BAAAAjwjPAAAAgEeEZwAA AMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADA I8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPC MwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMA AADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADAI8IzAAAA 4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPCMwAAAOAR 4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMAAADgEeEZ AAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADAI8IzAAAA4BHhGQAA APCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADw iPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMCjqHAXAKBwg/v1k/buDXcZ QOEqVNDgESPCXQUAFDnCM1DS7d2rwXXrhrsKoFCDk5PDXQIAFAumbQAAAAAeEZ4BAAAAjwjPAAAA gEeEZ6CUSkpOVs9Jk4rteJvT0nTxG2/kGZ+/aZMGzZolSfp640Z9u2XLMff19MyZGr14sSTpzo8/ 1rb9+0+4roOZmXr9++9P+P1b0tI0fsWKfNcFn9uJ+N+PP+rRadMK3Waex2sWDg1eekkHMzPDXQYA lCiEZ6CUWrp9u5pXr15sx1uybVu+x7u0dm0N6dxZkjR6yRLtPnz4mPv6MSVFzWvUkCS93b27apQr d8J1fbd1q2auX3/C7//ql1+0eNu2fNcFn9uJ+GH79sB5FuRNj9esqDnnQpbTMjJkkuLLlAlPQQBQ QvG0DaCUWrpjhyrFxuqSN99U6qFDeqtbN3WsW1dHsrP1yLRpStqwQdk5OXr+yivVpUEDzU5O1lMz Z+rrPn20OS1N1773nj6+9VbVrVBBg5KSNHP9eu1JT9eDbdrovlatJEnPzp6t95cvV+W4OF1QpYqa VauWp46bP/xQD7Zpo++2btX7y5ZpybZtevGbb/RVz56665NP9OOOHdqbnq7bLrxQz/iD6IqUFF1Y tapWpKTowS++0Fc9eyojK0tPzZypWcnJOpiZqX6XXKL7WrXSQ198odkbNig9K0tdGzTQC1ddFTj2 L3v26LaJExUdEaHmr72mV7p0UY2EBD305Zfasn+/Isz0bo8eanjWWeozebLa1q6tu1q00OjFizVl zRr1v+QS9Z82TRVjYvTlunX66JZbVK9ixZBz69emjS6rU0dn//Of6nvxxfr4p5906MgRfdWzp6on JIRci/0ZGbp3yhQt3bFDTatV044DB9T7ooskKd9rMWLBAs/XLNhPO3eq3xdfaMfBgzqSna0vbr9d 1eLj8/2+S9Lf5szRhJUrlZmdrUfatlWf5s11IDNTjV55RVfXr6+FW7boo1tu0c+7dumvM2YoOiJC 1zdsqGb+P5amrlmjwbNnKyMrS9nOadHddysmiv/7AHBmCstvPzP7q6TbJeVIWiapt3MuIxy1AKXV D9u3q/tvfqMFd92l6evW6elZszSnd28NnDVLiWXLaul992lLWpoue+stJffrp45166psVJSmrF6t v82Zo1Fduqh+pUp6ZvZs1U5M1Dd/+pPSs7LU9NVXdVeLFnr7hx+0LCVFK/r21db9+3Xuv/+tmT17 5qljRUqKmlWrpra1a2vEggX64b77Auuev/JKVYqNVXZOjhqPGqW/tmunjOxsxUZHq0xkpJb53ytJ /b74QhViYvT9PfdIklIPHtTUNWu0NyNDi++9V5K0Lz095NjnVqyo7uefr+vPP19dGjTQkexsXfPe e3rj+ut1bsWK+nzNGg3/+mu99bvfaUCHDuo6dqzqVqigN5cs0cyePRUbHa3WtWrpH1ddpcZVquR7 bk2rVdOWtDTtPHRIv61XT0+2b69+X3yhaevWqWezZiHb9/38c1169tkae+ONGr9ihe78+GM18u83 v2vxlzZtPF2z2OjowPq96em6/v33Ne6mm9SiRg3tS09XXHR0gd/31777Tmt379aSe+/VoSNH9JtX XtFtF16o5Skp2puerocuuUQXVK2qlampenLGDCXdeafKx8So1euv6+bGjSVJD37xhRbfe68SypRR WkYGwRnAGa3YfwOaWV1Jd0tq5JzLMLNxkm6T9HZx1wKUVkeys7Xr8GE92b69JKlZ9eraeeiQsnNy 9L9ly7TuL3+RJNVKTFRmdnbgfQPat9fV//ufRnfrpvbnnKOsnBy9/O23qpWYqP/45w1nZmcrxzn9 Y/58Tf3jH2VmqpWYqAoxMWp6VOc5PStLmdnZKle2rFbv2qX6lSoF1m1JS9NfZ8zQspQUSdKmffsU HRmpb7dsCexnWVCHduratYG6JalKfLyqxMfrq19+0bC5c3V706aqXb58nmvxY0qKnurQQZL08U8/ aWVqqm4cP16SlJWTow516kjyBe3WtWrp7k8/1Td9+gQC6c87d+o3lSvn2W/wuc3btEldGzbUJWef Hbj+FWNiQrbftn+/5m/apHd79JAkXVClihpXqaIIswKvxdrduz1ds2BvLl6smxs3Vgv/dJDyMTHK KuT7PnLhQs28806ZmeLLlFG1+HjtTU/Xsh079KfmzXVB1aqSpJcWLtTDl16qs+LiJEkNzzor0Hku V7as7v/8c/W56CJ15JnjAM5w4WgfpEk6IinOzLIlxUkqmXfLACXUTzt36rxKlRQV4bttYfG2bbqo enVtSktT9YQElfEHrq3796ta0NSCd378UZXj4lQ1Pl6SlLx3r35TubLm9O4dsv8j2dlKOXhQ51So IEnauG+fEsqUUbmyZUO2W5GSEujY/rhjh5r6g5gk3TFpkh5o3Vrv9OihX/bs0XVjxyoqIkJLd+wI zJ1elpKimxo31jL/HOjIiNDbMFrVrKlFd9+tCStXqu1bb2nK738fCHSSb57u5rQ0nZ2YGKjhud/+ Vr2bN89zzbakpemH7dtVJjJSlf0BceehQyofE6MIszzbB5/b8pQUXVKrVmDdjykpeqRt29DtU1ND avs+aI54QdfC6zULtnTHjkBHONemffvyfN+rJyQoxzntOnw4ML0kPStLuw4fVs1y5fTjjh36bb16 gX0sT03V/a1bB67r4m3b9M+rr5YkLbzrLk1ds0bPzpmjz9es0d+vvDLP9QKAM0Wx3zDonNst6R+S NkraKmmvc+6r4q4DKM1+2L5d6/fsUWZ2tg5kZuqZ2bPV75JLdFZsrHYcOKBDR44oOydHD0+bpr/4 A9HQOXMUExmpCbfcoiGzZ0uSqsTFadXOndrqf9rFvvR0bQzqdm7at085zunxr77SRfncLBg87WLD 3r2qGXTj34rUVF1er54ys7P12PTpgWC5dPv2wHtW7dypC6pWVfWEBK3ZtUtH/N3S1IMHJUmrd+1S 9YQE/V+rVmpQqZJyjrqpbdfhw0oIuqGtRrly+mLdusDNb8t27JAkHcjM1I3jx+vla69Vx3PO0VtL lkjy/fFQs4CbFYPPbVlKSuD8nXNK3rs3ZG60JFWOi9PqXbuUlZOjXYcOadjXXwfeX9C18HrNglWP j9dyf2c6OydHew4fVuW4uDzf9z+3bq0IM8VERQWeZjJw1iz1bNpUki8sB/9LwlmxsYHr9ep332lP errOTkzUut27FWGm688/X39s0kQZQf+SAQBnonBM26gvqZ+kupL2SfrQzP7onHuvuGsBSqsfd+zQ DY0aqe3o0TqclaWBHTqotb8z+nSHDmr1+uuSpDuaNlXv5s01bvlyzd24UZ//8Y+KMFNsdLSmrVun q+rX17DLL1fnt99WbFSU4suU0evXXSdJerZzZ1321luqX6mSLqhSRdX83epgy1NS1MZ/3N/Wq6db J0zQBytWaP6f/qTH2rZV89deU72KFVU7MVHnn3WWr/aUFA2vXl1pGRkqGxmpMpGRurBqVf3u/PN1 4auvKi46Wr87/3w90rat7pg0SQczM1UmMlJ/aNIkz5MrKsfFqU758rpg1Cg927mz+jRvrlnJyWr0 yiuKjY5Wk6pV9Xb37vrDxInqe/HFan/OOTo7MVFXvvuu/tSihRpVrqydhw6pyauv6o3rrw9Myzj6 3JYHhefkvXtVJ5/pIxdVr64WNWqo0SuvqHn16qpXoULgPQVdC6/XLFj/Sy/V7ydO1LgVKxQVEaHX rrtOrWrWzPf7LkkvX3utrnz3XeU4p2vOO0+DOnWSJK3fsydkysgT7drpjx99pBELF+ra884L1P7W kiWasGqVypUpo7MTE/XW736X348kAJwx7OjHExX5Ac1ulXSlc+4u//Idki5xzt0ftI0bNGhQ4D2d OnVSJ/8vfOBMM7hXLw1mnilKuMHJyRr83/+GuwwAOKakpCQlJSUFlocMGSLnXN75ewUIx5znnyQ9 bWaxktIlXSHp26M3Gjx4cDGXBQAAgNPd0U3ZIUOGHNf7wzHneamkdyR9J+lH//DrxV0HAAAAcLzC 8rBO59zzkp4Px7EBAACAE8XHcwMAAAAeEZ4BAAAAjwjPAAAAgEdhmfMM4DhUqKDBycnhrgIonP/T KAHgdFfsz3n2wsxcSawLAAAApxczO67nPDNtAwAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEA AACPCM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAA jwjPAAAAgEeEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8I zwAAAIBHhGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACPCM8A AACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAA gEeEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBH hGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACPCM8AAACAR4Rn AAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAA AMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADA o6hwFwAAxW306NHKzs7WN998o1deeUXx8fHhLgkAUEqYcy7cNeRhZq4k1gWg9JszZ47i4uLUqlUr vfLKK1q9erVGjhwZ7rIAAGFiZnLOmdftmbYBlHDjx4/X22+/nWe8V69euvjii8NQUem2fv16/e9/ /5Mk1a1bV+vXrw9zRQCA0oTOM1DC3XTTTdq1a5dmzZoVMv7LL78oPT1djRs3DlNlv3r66ac1btw4 paSk6M4771RERIScc9qyZYumTJmioUOHqn///uEuU5KUk5OjAwcOKDExUU8//bQqV66sBx98MNxl AQDC5Hg7z8x5Bkqpc889N9wlBDz77LNaunSp2rZtm2cKxJgxY1SmTJkwVZZXRESEEhMTtX37di1b tkwTJkwId0kAgFKEaRsodebPn69u3bqpZs2aSkhIUPPmzTV27NiQbXKnNEyfPl1NmzZVQkKC2rdv r5UrVx5z/3PnzlXHjh0VHx+vypUr65577tGBAwdCthk1apRq166thIQEdevWTdOnT1dERITmzJkT 2KZTp066+eabQ96XlJSkiIiIQB3HOpdevXrpo48+0uzZsxUREaGIiAg988wzIecYbPz48WrSpIli YmJUp04dDRgwQNnZ2afs2hTEOad58+bpkksuybOuadOmOvvss09430UhKytL//znP/XOO+8oKooe AgDAO8IzSp0NGzaobdu2evPNNzVlyhTdeOON6t27tz744IPANmamjRs36rHHHtPTTz+t999/Xykp Kbr11lsL3fe8efN0xRVXqGbNmpo4caJGjBihzz//XL179w5sM3nyZD3wwAPq1q2bJk2apCZNmqhP nz4yC/0XHzPLM3a85zJw4EB17txZLVq00IIFC7RgwQLdddddIcfINW3aNN12221q1aqVPvnkE/35 z3/Wiy8f0hg8AAAgAElEQVS+qAceeCBPXce6NrkhP/iPgcKsXLlSe/bs0aWXXhoY++STTwLHq1ev nqf9FJe33npLTzzxhBITE/XRRx+FuxwAQClCywWlzm233RZ47ZxTu3bttGnTJr3xxhuBdc457d69 W998843q168vyTfXtUePHlq9erUaNmyY776feOIJtWvXTu+//35grFatWrr88su1cuVKNW7cWEOH DtW1116rV155RZJ05ZVXKjU1VW+++WbIvrzM2z/WuZx77rmqWLGinHNq3bp1nvcHHyM3aI8ZM0aS dNVVV0mS/vrXv2rAgAGqVauW52tjZoqKijpm+M/19ddfKy4uTk2aNJEkzZgxQ1lZWZKkFi1aeNpH QXJycjRs2DAtXrxYAwcO1KxZsxQbG6tp06bpueee0+zZs5WTk6O5c+fq4YcfznO8hQsX6oMPPlCD Bg20adMmNW3aVA8//LCeeuopSVLfvn11ww03nFSNAIAzB+EZpc6ePXs0aNAgTZ48WVu3bg1MSzh6 akC9evUC4VCSGjVqJEnavHlzvuH50KFDWrBggV566aVA8JOkyy67TNHR0fr+++/VsGFDLVmyJBCc c/Xo0SNPeD6V53Is2dnZWrJkSZ75xrfccosef/xxLViwQDfeeGNg/FjXpmPHjsrMzPR8/Hnz5qly 5cp66qmnlJqaqvfee0/Jycn5bnvgwAE9+OCDysnJKXSfF1xwgR555BF9+umnuv3227VmzRr169dP U6dOVUxMjNauXas77rhDn332mapUqSLJN786ODzPnj1b/fv317x585SVlaXq1avrgw8+0P79+z2f GwAAwcISns2sgqQ3JV0gyUnq45xbEI5aUPr06tVLCxcu1MCBA9W4cWMlJiZq1KhRmjx5csh2FSpU CFnOvWktPT093/3u2bNH2dnZ6tu3r/r27Ruyzsy0adMm7dy5U9nZ2apatWrI+qOXT/W5HMvOnTt1 5MgRVatWLWQ8d3n37t0h48d7bY7l66+/Vu/evTVo0CBJ0llnnRU4dlZWVsi84oSEBI0ePdrzvqtX r65zzjlHixYt0ssvv6yYmBhJUnJysnr16hUIzhs3blTFihUD78vJyVGfPn304osvBt4zdepUtWvX 7oTOEQAAKXyd55GSPnfO3WRmUZL4eC94kp6ers8++0yjRo3SPffcExg/+qY4ydu0iWAVKlSQmWnI kCHq0qVLnvU1a9ZU5cqVFRkZqZSUlJB1Ry9LUmxsrDIyMkLG9uzZc0LnciyVK1dWdHR0njp27Ngh SapUqVLI+Kl8FOTWrVuVnJysDh06BMa6d+8eOM7IkSP18MMPn/D+27Rpo9TUVK1fvz4k+M6fPz9w 86Qkffnll/rHP/4RWJ43b562bdum6667LjDWvn37E64DAAApDOHZzMpLau+cu1OSnHNZkvYVdx0o nTIyMpSTkxPy6LP9+/frk08+UWRkZMi2Xufr5oqPj9cll1yin376SQMGDChwu+bNm+vjjz8OCbz5 3XR29tln57nhbtq0acd9LmXKlNHhw4cLrT0yMlItW7bU+PHjde+99wbGx48fr4iIiJAb+aTjvzaF mTdvnqKjo0OOkfv6o48+0hVXXBGy/fFO25B8nwrYpk0bRUdHS5LWrl2rjIyMwHST1atXa9OmTerY saO++eYbtW3bVlu2bFGDBg0C7wEA4FQIR+e5nqRUMxsjqZmk7yU96Jw7FIZaUMqUL19eF198sZ55 5hklJibKzDR8+HBVqFBBaWlpIdueSHf1+eef1+WXX66IiAjdeOONKleunDZu3KjPP/9cQ4cOVYMG DfTkk0/qhhtuUN++fdW9e3fNnj1bX375ZZ599ejRQ6NHj1b//v3VpUsXzZo1K2Q7r+fSqFEjffLJ J5o8ebJq1aqlWrVqqUaNGnmON2TIEF199dXq06ePbr31Vi1btkwDBw7UPffco5o1ax7XtZk9e7Yu v/xyzZo165jd2q+//lqtWrUKTI3ItWrVKr3zzjt5pqAc77SN3Ho6duwYWJ4zZ05IXVOnTtU111yj Q4cO6fvvv1fbtm3VokWLPNNQxo0bpzp16uT5YwIAAK/C8ai6KEktJI1yzrWQdFDSE2GoA6XU2LFj de6556pnz5566KGHdPPNN6tnz54h3dSCHhN3rI7rZZddpjlz5ig1NVU9e/ZUt27d9MILL6hOnTqB Obzdu3fXSy+9pE8//VQ9evTQ0qVL8w2DXbp00XPPPacJEybohhtu0KZNmzRy5MiQGrycS9++fXXV VVepT58+at26td544418z/HKK6/UBx98oO+++07dunXTv//9bz3yyCN6+eWX81yDY10b51zgv4Is XbpU9913n9577z3t2bNHDz30kB566CHdf//96tKli5o2bapbbrml0Ovt1S+//KKuXbsGllevXq1u 3boFltu3b68jR45o1KhRgUf5NWzYUIMHD9aTTz6p1157TSNGjNB5551HcAYAnJRi/3huM6suab5z rp5/uZ2kJ5xz1wVt43JvPJJ8HzbRqVOnYq0TOB7Lly9X06ZNlZSUFDL3FwAAlCxJSUlKSkoKLA8Z MuS4Pp672MOzJJnZHEl3OedWm9lgSbHOuceD1rtw1AWcKMIzAAClk5kdV3gO19M2/izpPTMrI2md pN7H2B4o8U7lTXgAAKBkCkvn+VjoPAMAAKA4HG/nORw3DAIAAAClEuEZAAAA8IjwDAAAAHhEeAYA AAA8IjwDAAAAHhGeAQAAAI8IzwAAAIBHhGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAA PCI8AwAAAB4RngEAAACPCM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwi PAMAAAAeEZ4BAAAAjwjPAAAAgEeEZwAAAMAjwjMAAADgEeEZAAAA8IjwDAAAAHhEeAYAAAA8IjwD AAAAHhGeAQAAAI+iClphZi0luYLWO+cWF0lFAAAAQAllzuWfj80sSYWH585FVJPMzBVUFwAAAHCq mJmcc+Z5+5IYUgnPAAAAKA7HG56POefZzOLN7Gkze8O/3MDMrjuZIgEAAIDSyMsNg2MkZUpq61/e KmlokVUEAAAAlFBewnN959zf5QvQcs4dLNqSAAAAgJLJS3jOMLPY3AUzqy8po+hKAgAAAEqmAh9V F2SwpC8knW1mYyVdJqlXEdYEAAAAlEienrZhZpUltZFkkhY453YWaVE8bQMAAADF4HiftnHMzrOZ maSOktrJ99znaEmTTrhCAAAAoJQ6ZufZzF6VVF/S+/J1nm+R9Itzrm+RFUXnGQAAAMXglH9Iipn9 JKmxcy7HvxwhaaVz7jcnVWnhxyQ8AwAAoMid8g9JkbRWUp2g5Tr+MQAAAOCMUuCcZzP71P+ynKRV ZvatfHOeW0taVAy1AQAAACVKYTcM/qOQdcypAAAAwBnH06PqihtzngEAAFAcTvmcZzO71MwWmdkB MztiZjlmlnZyZQIAAAClj5cbBl+W9AdJayTFSPqTpFFFWRQAAABQEnkJz3LOrZEU6ZzLds6NkXRN 0ZYFAAAAlDzH/IRBSQfNrKykpWb2vKTt8n1YCgAAAHBG8dJ57unf7gFJhySdLenGoiwKAAAAKIl4 2gYAAADOWMf7tI3CPiRlWSHvc865psdVGQAAAFDKFTbn+Xr/126Svpa0S8x1BgAAwBmswPDsnEuW JDOrJmm8pMWS3pL0JXMqAAAAcCbyNOfZzCIkXSWpl6RW8oXp0c65dUVSFHOeAQAAUAxO+ScMSpJz Lke+R9TtkJQtqaKkCWb2wglVCQAAAJRCx+w8m9mD8j2ubpekNyVNcs4d8Xej1zjn6p/youg8AwAA oBicsqdtBKkk6Qbn3IbgQedcjpldX8B7AAAAgNMOz3kGAADAGatI5jwDAAAAIDwDAAAAnhGeAQAA AI8IzwAAAIBHhGcAAADAI8IzAAAA4BHhGQAAAPCI8AwAAAB4RHgGAAAAPCI8AwAAAB4RngEAAACP CM8AAACAR4RnAAAAwCPCMwAAAOAR4RkAAADwiPAMAAAAeER4BgAAADwiPAMAAAAeEZ4BAAAAjwjP AAAAgEeEZwAAAMAjwjMAAADgEeEZQB4bNmzQ+++/f8q2AwDgdEF4BpDH+vXrNXbs2FO2HQAApwvC M1BK9ejRQ61atdKFF16oN954Q5KUkJCgAQMG6KKLLtKll16qlJQUSVKvXr304IMP6rLLLlP9+vU1 ceJESZJzTo8++qiaNGmipk2bavz48ZKkJ554QnPnzlXz5s01cuRIbdiwQR06dFDLli3VsmVLzZ8/ P9/tcnJy9Oijj6p169Zq1qyZXn/99TBcGQAAio4558JzYLNISd9J2uycu/6odS5cdQGlxZ49e1Sx YkUdPnxYrVu31uzZs1W5cmV9+umn6tq1qx5//HElJibqqaeeUq9evXT48GGNGzdOq1atUrdu3bRm zRpNnDhRr732mr788kulpqbq4osv1sKFC/Xzzz/rxRdf1KeffipJOnz4sCIiIlS2bFmtWbNGf/jD H7Ro0SLNnj07ZLvXX39dqampeuqpp5SRkaF27drpww8/VN26dcN4pQAAKJiZyTlnXrePKspijuFB SSsllQtjDUCpNXLkSH388ceSpM2bN2vNmjUqU6aMunbtKklq2bKlpk+fLsn3i6F79+6SpEaNGmnH jh2SpK+//lp/+MMfZGaqWrWqOnbsqEWLFikxMTHkWJmZmXrggQe0dOlSRUZGas2aNZJ8netg06ZN 07JlyzRhwgRJUlpamtauXUt4BgCcNsISns3sbEldJA2V1D8cNQClWVJSkmbMmKEFCxYoJiZGnTt3 Vnp6uqKjowPbREREKCsrK7BcpkyZwOvc0Ov/aztk32Z5//j+17/+pRo1aujdd99Vdna2YmJiCqzt 5Zdf1pVXXnnC5wYAQEkWrjnP/5L0qKScMB0fKNXS0tJUsWJFxcTEaNWqVVqwYMEJ7ad9+/YaN26c cnJylJqaqjlz5qh169ZKSEjQ/v37Q45XvXp1SdI777yj7OxsSVK5cuVCtrv66qs1atSoQGhfvXq1 Dh06dKKnCQBAiVPsnWczu05SinNuiZl1Ku7jA6eDa665Rv/5z3/UuHFjnX/++br00kslhXaNzSzP 8tGve/Toofnz56tZs2YyM73wwguqWrWqKlWqpMjISF100UXq3bu3+vbtqxtvvFHvvPOOrrnmGiUk JEiSmjVrFrLdX/7yFyUnJ6tFixZyzqlq1aqaNGlScVwSAACKRbHfMGhmz0m6Q1KWpBhJiZImOud6 Bm3jBg0aFHhPp06d1KlTp2KtEwAAAKefpKQkJSUlBZaHDBlyXDcMhu1pG5JkZh0lPcLTNgAAABAO x/u0jZLwnGdSMgAAAEqFsHaeC0LnGQAAAMWhNHaeAQAAgFKB8AwAAAB4RHgGAAAAPCI8AyXY6NGj NWPGjDyfAggAAMKDGwaBEmr37t2qXbu2zEznnHOOhg8fruuuuy7fj88GAAAnhhsGgdPEP//5T+Xk 5OjgwYNauXKlfv/736t58+bKyeFT7QEACBfCM1AC7d+/XyNGjFB6enpgLDMzU61atVJEBP+zBQAg XPh/YaAEeumll/J0mCMjIzVw4MAwVQQAACTmPAMlzuHDh1WjRg3t27cvMBYZGalbbrlFY8eODWNl AACcfpjzDJRyr732mo4cORIyFh0drSFDhoSpIgAAkIvOM1CCZGZmqmbNmtq1a1dgLCIiQtddd50m T54cxsoAADg90XkGSrG333475CZBSSpbtqyGDh0apooAAEAwOs9ACZGVlaXatWtr+/btgTEz0+WX X67p06eHsTIAAE5fdJ6BUmrcuHE6cOBAyFhsbKyGDRsWpooAAMDR6DwDJUBOTo7OPfdcbdiwIWS8 bdu2mjdvXpiqAgDg9EfnGSiFJk+eHHKToCTFxcVp+PDhYaoIAADkh84zEGbOOTVq1Eg///xzyHjz 5s21ePHiMFUFAMCZgc4zUMp8+eWX2rx5c8hYfHw8XWcAAEogOs9AmF100UVaunRpyNhvfvMbrVy5 Umae/xAGAAAngM4zUIrMmTNHa9euDRlLSEjQsGHDCM4AAJRAdJ6BMGrbtq3mz58fMla3bl2tW7dO ERH8bQsAQFGj8wyUEosWLcozXSMhIUFDhw4lOAMAUELReQbC5IorrtCMGTNCxmrUqKFNmzYpMjIy TFUBAHBmofMMlALLli3TN998EzIWHx+vZ555huAMAEAJRucZCINu3brps88+U05OTmCscuXK2rJl i8qUKRPGygAAOLPQeQZKuNWrV2v69OkhwTk+Pl4DBw4kOAMAUMLReQaK2W233aYJEyYoOzs7MFa+ fHlt27ZNsbGxYawMAIAzD51noATbsGGDJk+eHBKcY2Nj9fjjjxOcAQAoBQjPQDF65plnQoKzJEVG RuqBBx4IU0UAAOB4EJ6BYrJt2zaNHTtWR44cCYzFxMSoX79+KleuXBgrAwAAXhGegWIybNiwkJsE JSkiIkL9+/cPU0UAAOB4EZ6BYrBr1y69+eabyszMDIyVLVtW9913nypWrBjGygAAwPEgPAPF4IUX Xsi36/z444+HqSIAAHAiCM9AEdu3b59efvllZWRkBMaio6PVs2dPVa1aNYyVAQCA40V4BorYv//9 7zxd58jISA0YMCBMFQEAgBPFh6QARejgwYOqUaOG9u/fHxiLiorSbbfdpnfffTeMlQEAAIkPSQFK lP/85z95nuscFRWlIUOGhKkiAABwMug8A0UkIyNDNWrU0J49ewJjERER6t69uyZOnBjGygAAQC46 z0AJMWbMmJBH00m+x9M9++yzYaoIAACcLDrPQBHIyspSrVq1lJKSEhgzM1199dWaOnVqGCsDAADB 6DwDJcDYsWN18ODBkLGYmBg999xzYaoIAACcCnSegVMsJydH55xzjjZv3hwy3qFDB82ePTtMVQEA gPzQeQbCbOLEidq7d2/IWFxcnIYNGxamigAAwKlC5xk4hZxzatiwodauXRsy3qpVKy1atChMVQEA gILQeQbC6PPPP9f27dtDxuLj4zV8+PAwVQQAAE4lOs/AKeKcU9OmTbV8+fKQ8caNG2v58uUy8/xH LQAAKCZ0noEwSUpK0vr160PGcrvOBGcAAE4PdJ6BU6RNmzb69ttvQ8bq16+vNWvWEJ4BACih6DwD YbBgwYI80zUSEhI0dOhQgjMAAKcROs/AKdC5c2clJSWFjNWqVUsbNmxQZGRkeIoCAADHROcZKGY/ /PCDFi5cGDKWkJCgv/3tbwRnAABOM3SegZPUtWtXTZ06VcE/s1WqVNGWLVsUHR0dxsoAAMCx0HkG itGqVas0c+bMkOAcHx+vwYMHE5wBADgN0XkGTsLNN9+sSZMmKTs7OzBWoUIFbdu2TTExMWGsDAAA eEHnGSgm69ev15QpU0KCc1xcnJ588kmCMwAApynCM3CCBg8erKysrJCxyMhI9e3bN0wVAQCAokZ4 Bk7Ali1bNH78+JDwHBsbq4cffljx8fFhrAwAABQlwjNwAp577jnl5OSEjEVERKhfv35hqggAABQH wjNwnFJTUzVmzBhlZmYGxmJiYtS3b1+VL18+jJUBAICiRngGjtPzzz+fp+tsZnrsscfCVBEAACgu hGfgOOzdu1ejRo1SRkZGYKxMmTLq06ePKleuHMbKAABAcSA8A8dhxIgR+c51fvLJJ8NUEQAAKE58 SArg0YEDB1SjRg0dOHAgMBYVFaXbb79dY8aMCWNlAADgRPEhKUARGTVqVJ6uc1RUlAYNGhSmigAA QHGj8wx4kJ6erho1amjv3r2BscjISN1www0aP358GCsDAAAng84zUARGjx6tI0eOhIxFR0fr2Wef DVNFAAAgHOg8A8dw5MgR1axZUzt37gyMmZm6dOmiKVOmhLEyAABwsug8A6fYu+++q8OHD4eMxcTE aOjQoWGqCAAAhAudZ6AQ2dnZqlOnjrZu3Roy3rlzZ82cOTNMVQEAgFOFzjNwCn344YdKS0sLGYuL i9OwYcPCVBEAAAgnOs9AAXJycnTeeedp/fr1IeNt2rTRggULwlQVAAA4leg8A6fIlClTlJqaGjIW Hx+v4cOHh6kiAAAQbnSegXw453TBBRdo1apVIeNNmjTR0qVLZeb5D1QAAFCC0XkGToEZM2Zo48aN IWPx8fH6+9//TnAGAOAMRucZyEfLli21ePHikLEGDRro559/JjwDAHAaofMMnKR58+bpp59+ChlL SEjQc889R3AGAOAMR+cZOEqHDh00d+7ckLHatWsrOTlZERH8vQkAwOmEzjNwEr7//nt99913IWMJ CQkaOnQowRkAANB5BoJdffXVmj59uoJ//qpVq6bNmzcrKioqjJUBAICiQOcZOEErVqzQ3LlzQ4Jz fHy8hgwZQnAGAACS6DwDATfccIMmT56snJycwFilSpW0detWlS1bNoyVAQCAokLnGTgB69at09Sp U0OCc1xcnAYMGEBwBgAAAYRnQNKgQYN05MiRkLGoqCjde++9Yaro1Pv+++/14IMPnvR+/vvf/+rP f/7zCb8/ISHhhN43efLkkE98HDRokGbOnClJGjFihA4fPnzc+wiWmpqqNm3aqGXLlpo3b55WrVql u++++7iuW1JSksqXL6/mzZurefPmuuqqqzy9L9jbb7+tbdu2BZbr1q2r3bt359nu5ptv1rZt29S1 a1elpaUd93FOVq9evTRx4sST3s9ll10myXftrr/++pPeHwAUNSZy4oy3adMmTZw4UdnZ2YGx2NhY Pfroo4qLiwtjZadWy5Yt1bJly3CXccLPyp40aZKuv/56NWrUSJI0ZMiQwLqRI0fqjjvuUGzs/7d3 51FVVXscwL+bK8oQiiNiapgaOQGiAoomJFImpjmlLxUrs0l9avrQciWYqJWVpq8srcAyh3DAISsc wClTUcwh55kUFbSQSYbf++PCiQsXufqUe4HvZy3Wumefffb57cNZ+mPfvc+xvas2Ctu8eTPc3Nyw cOFCrazgc0nXLTc3FzqdzqCsa9euWLt2rWmdMiIiIgKtW7eGs7MzgJKv1w8//AAA2LBhg9H9OTk5 //dc/by8vBKfMnO/nnm+c+fO+9IOEVFZ4cgzVXrTp083SJwBwMrKCmPGjDFTRKU7d+4c2rRpo23P nj1bSyb9/PwwadIkeHt7w9XVFTt27ABgOLJ369YtvPjii3Bzc4O7uztWr14NAFi6dCnc3NzQpk0b TJo0SWv/m2++gaurK7y9vbFr1y6t/Nq1a+jfvz+8vLzg5eVlsK80sbGx8PPzw4ABA9CiRQsMGTJE 2zdp0iS0atUK7u7umDhxIn799VesW7cOEydOhKenJ86cOaONfM6bNw9//vkn/P390a1bNwCGo9tR UVF48cUXDdpo27Ytzpw5o9VJSEhASEgIoqOj4enpiczMTKNtAPoR19deew0+Pj4ICQkp1i9j6zW+ ++47eHt7o23btnjttdeQl5eH3NxcDB8+HG3atIGbmxvmzJmDlStXYt++fXjhhRe0OApkZGSgR48e +Oqrr5CSkoI+ffrA3d0dHTt2xKFDhwAAoaGhGDp0KDp37oxhw4YhMjISvXv3hr+/Px577DFMmzbt jjEVXLsJEybAw8MDv/76K1xcXBASEgI3Nzd4e3vj9OnTWhvbtm2Dr68vmjZtqo1CBwcHIzo6Wqvz wgsvYO3atThy5Ih2Pnd3d60dY99E7N27F56enjh79myxfUREZiciZfoDoBGArQCOADgMYIyROkJU Fq5cuSI2NjYCQPuxsbGRyZMnmzu0Ozp79qy0bt1a2549e7aEhYWJiIifn59MmDBBRER+/PFHCQgI EBGRrVu3SlBQkIiI/Oc//5Fx48Zpx9+4cUMSExOlcePGcv36dcnJyZEnn3xS1qxZI3/++adWfvv2 bfH19ZXRo0eLiMjgwYNlx44dIiJy/vx5adGihYiI7N27V0aMGGE09oceekiLp0aNGpKYmCh5eXnS sWNH2bFjh1y/fl1cXV21+n/99ZeIiAwfPlxWrlyplRfednFxkeTk5GLnEBGJioqS4cOHG22jsIiI CK1fd2ojODhYevXqJXl5ecXaKOiTh4eHeHh4yIwZM+To0aPSq1cvycnJERGRN954QxYvXizx8fHS vXv3Yv308/OT+Ph4rdzFxUXOnTsnAQEB8u2334qIyKhRo2TatGkiIrJlyxbx8PAQEZGpU6dK+/bt JTMzU0REvvnmG3F2dpaUlBTJyMiQ1q1by759+4rF9Prrr8vixYtFREQpJT/88IPB+WfMmCEiIosX L9buoeDgYBk4cKCIiBw9elSaNWsmIiJxcXHSp08fERG5efOmNGnSRHJycmTUqFGyZMkSERHJzs6W jIwMg+tccH/u3LlT2rVrJxcvXjT6eyIiut/y806Tc1lzTNvIBjBORBKUUg8BiFdKxYiI8YmIRA/Q +++/X2ykUCmFt956y0wR3bvC/ejbty8AwNPTE+fOnStWd/PmzVi+fLm27ejoiLi4OPj7+6N27doA 9COG27ZtA6AfzS4of/7553HixAkAwKZNmwzmEKempiI9PR3t27dH+/btS43Zy8sLDRo0AAB4eHjg /Pnz8PHxgY2NDV5++WUEBQUhKCjIaB/vVUltyD9/vN+RUgoDBgwocdpCly5dsG7dOm17/vz5iI+P 165HRkYGnJyc0KtXL5w5cwZjxoxBz549DeZHF45DRNC7d2+EhIRg8ODBAPRTHVatWgUA8Pf3R3Jy MlJTU6GUwrPPPmuwyDUwMBA1a9YEoL8vduzYAZ1OVyym+vXrAwB0Oh369etn0KeC8w4aNAjjxo3T rkOfPn0AAC1atEBSUhIA/Rs633jjDVy/fh1RUVHo378/dDodOnXqhPDwcFy6dAl9+/ZFs2bNil27 P7LIqg4AAB7VSURBVP74A6+++ipiYmK0eIiILE2ZT9sQkSsikpD/+RaAPwA0KOs4iFJSUvDFF18g KytLK6tatSpeeeUVLVG0VFWqVDF4MkhGRoZBMleQPOl0OuTk5Bhtw9gfDUWTtpKOKziXiOC3337D gQMHcODAAVy8ePGu5okXTvJ0Oh2ys7Oh0+mwZ88e9O/fH+vXr8fTTz9tEKMpCtcrupCwpDaKlt+p jbudCx8cHKxdo2PHjuHdd9+Fo6Mjfv/9d/j5+WHBggUYMWKE0XMrpdC5c2ds3LjRoM2Sfj+FYyva p8K/O2MxAYCNjc0dr3PhfVWrVjUaz7Bhw/Dtt98iIiICL730EgB9Ar5u3TrY2trimWeewdatW4u1 7ezsDFtbW+zfv7/E8xMRmZtZ5zwrpVwAtAXwmznjoMrp448/NkhAAf1c58mTJ5spItM5OTnh6tWr SElJQVZWFtavX39Xx3fv3h3//e9/te2bN2/Cy8sLcXFxSE5ORm5uLpYtWwY/Pz94e3sjLi4OKSkp yM7O1haqAfpRzU8//VTbTkhI+L/7lpaWhps3b6JHjx74+OOPcfDgQQCAg4NDiU+VKLrPyckJx44d Q15eHlavXq0lfHdqo2gyWlIbd6tbt26IiorCtWvXAOj/aLtw4QKSk5ORk5ODvn374r333sOBAwdK jHHatGmoWbMm3nzzTQD60e0lS5YA0M8dr1u3LhwcHIr1QUQQExODGzduICMjA9HR0ejcuXOJMZWk 4FuK5cuXo1OnTqX2efjw4ZgzZw6UUnj88ccBAGfPnkWTJk0wevRo9O7dW5unXZijoyPWr1+PyZMn Iy4urtTzEBGZg9mS5/wpG1EA/p0/Ak1UZlJTUzFnzhyDBVnW1tYYMmRIufi62NraGu+++y68vLwQ GBiIli1blli36CgmAEyZMgU3btxAmzZt4OHhgdjYWNSvXx+zZs2Cv78/PDw80L59e/Tq1Qv169dH aGgoOnbsiM6dO6NVq1Zae59++in27dsHd3d3tGrVCl9++SUAYN++fXjllVdMjqfwdmpqKnr16gV3 d3d06dIFn3zyCQD9lIEPP/wQ7dq1M1jsBwAjR47E008/rS0YnDVrFoKCguDr66tNCymtDaWUQTwl tWEs7pLaAPRTGqZPn47AwEC4u7sjMDAQV65cQWJiIvz9/dG2bVsMHToUM2fOBPDPgsSiCwbnzp2L jIwMTJo0CaGhoYiPj4e7uzvefvttREZGGj2/UgpeXl7o168f3N3d0b9/f3h6epYYU0l9u3HjBtzd 3TFv3jzt91G0buHP9erVQ8uWLbVFlgCwYsUKtG7dGm3btsWRI0cwbNgwo23Uq1cP69evx5tvvom9 e/cavc5EROZkljcMKqWsAawHsFFE5hjZL1OnTtW2/fz84OfnV3YBUoU3Y8YMTJ8+3eDreBsbGxw/ fhyNGzc2Y2RE909ERATi4+Mxb968e26jSZMmiI+PR61atUw+Jj09HW5ubjhw4AAcHBzu+dxERA9C bGwsYmNjte2wsLC7esNgmS8YVPphhq8AHDWWOBcIDQ0ts5iocsnIyMAHH3xgkDjrdDo899xzTJyp QjE2En4vbdyNTZs2YcSIERg/fjwTZyKySEUHZQu/N8AUZT7yrJTqDGAbgN+hfzQYAEwWkZ8K1RFz jIhT5TB37ly8/fbbSE9P18psbGzw+++/o3nz5maMjIiIiMpa/oJ5k0cKzDJtozRMnulBuX37Nho0 aIDk5GStzMrKCkFBQQYvdiAiIqLK4W6TZ75hkCqVyMhIg0VYgP5xaeHh4WaKiIiIiMoTjjxTpZGT k4PGjRvj8uXLWplSCt26dUNMTIwZIyMiIiJz4cgzUQmWL1+O1NRUgzJbW1vtEWFEREREpeHIM1UK eXl5ePTRR3H+/HmDcl9fX+zYscNMUREREZG5ceSZyIjo6GiDRYKA/jXGHHUmIiKiu8GRZ6rwRAQt WrTA8ePHDcrbtm2L/fv3mykqIiIisgQceSYq4pdffsGlS5cMyuzt7TFr1iwzRURERETlFUeeqcLz 8PDAwYMHDcoef/xxHD169P9++xoRERGVbxx5Jipk27ZtOHXqlEGZvb09Zs6cycSZiIiI7hpHnqlC 8/X1xa5duwzKXFxccPr0aVhZ8W9HIiKiyo4jz0T59u7di4SEBIMye3t7hIeHM3EmIiKie8KRZ6qw AgICsHnzZoOyBg0a4MKFC9DpdGaKioiIiCwJR56JABw6dKjYdA17e3uEhYUxcSYiIqJ7xpFnqpCe ffZZbNiwAXl5eVpZnTp1kJiYiKpVq5oxMiIiIrIkHHmmSu/EiROIiYkxSJzt7e3x7rvvMnEmIiKi /wtHnqnCGTRoEKKiopCbm6uV1ahRA5cvX4atra0ZIyMiIiJLw5FnqtTOnz+P6Ohog8TZ1tYWISEh TJyJiIjo/8bkmSqU9957zyBxBgCdTodRo0aZKSIiIiKqSJg8U4Vx+fJlLFmyBNnZ2VqZjY0Nxo4d CwcHBzNGRkRERBUFk2cql4zNiZ85c6bBIkEAsLKywvjx48sqLCIiIqrgmDxTuRQeHo6xY8ciKSkJ AJCcnIxFixbh9u3bWp1q1arhtddeQ82aNc0VJhEREVUwVcwdANG9uHDhAr7++mt88cUXGDJkCAAY HXUOCQkxR3hERERUQTF5pnIpNTUVubm5yM3NRWRkJEQEOTk52n5ra2sMGzYM9erVM2OUREREVNEw eaZy6e+//9Y+F14gWECn02HKlCllGRIRERFVApzzTOVSWlpaqXVefvllxMfHl0E0REREVFkweaZy 6datW3fcn5mZiZiYGHTp0gWdO3dmEk1ERET3BZNnKpfS09NLrSMiyMvLw5UrV+Dk5FQGUREREVFF x+SZyiVTkmc7Ozt06NAB+/fvR8OGDcsgKiIiIqromDxTuZSZmXnH/XZ2dujXrx+2bNmC6tWrl1FU REREVNExeaZy6U7Js62tLSZPnozIyEhYW1uXYVRERERU0fFRdVQuZWVlGS23s7PDokWLMHjw4DKO iIiIiCoDJs9ULhV+DTcAKKXg4OCAH3/8Eb6+vmaKioiIiCo6Js9U7hR9KYq1tTXq1q2L2NhYNG/e 3ExRERERUWXAOc9U7qSlpaFKFf3ffTY2NmjZsiUOHjzIxJmIiIgeOCbPVO4UPKbOzs4OAQEB2L17 N+rUqWPmqIiIiKgyYPJM5U5aWhpu376NkSNHIjo6GjY2NuYOiYiIiCoJJSLmjqEYpZRYYlxkGY4c OYKdO3di5MiR5g6FiIiIyjmlFEREmVzfEpNUJs90JyICpUy+x4mIiIhKdLfJM6dtULnDxJmIiIjM hckzEREREZGJ+JxnsngbNmzDp5/+gqysKqhWLQdjxgSiZ88nzB0WERERVUJMnsmibdiwDf/+9884 fTpcKzt9+h0AYAJNREREZY7TNsiiffrpLwaJMwCcPh2OefNizBQRERERVWZMnsmiZWUZ/3IkM1NX xpEQERERMXkmC1etWo7Rchub3DKOhIiIiIjJM1m4MWMC0bTpOwZlTZu+jdGju5spIiIiIqrM+JIU sngbNmzDvHkxyMzUwcYmF6NHd+diQSIiIrov+IZBIiIiIiIT8Q2DREREREQPCJNnIiIiIiITMXkm IiIiIjIRk2ciIiIiIhMxeSYiIiIiMhGTZyIiIiIiEzF5JiIiIiIyEZNnIiIiIiITMXkmIiIiIjIR k2ciIiIiIhMxeSYiIiIiMhGTZyIiIiIiEzF5JiIiIiIyEZNnIiIiIiITMXkmIiIiIjIRk2ciIiIi IhMxeSYiIiIiMhGTZyIiIiIiEzF5JiIiIiIyEZNnIiIiIiITMXkmIiIiIjIRk2ciIiIiIhMxeSYi IiIiMhGTZyIiIgt17uY5WIVZ4ceTP5o1jlu3b8EqzAqLDy4usc7VtKsIjQ3F+ZvnH0gMexL3ICw2 zKS6fhF+GPDDgAcSB5Vu/p75sAqruClmxe0ZERERlZmraVcxLW4azv/1AJPnONOS5wVBCzCr26wH EgdRFXMHQERERA9Wbl4u8iQP1jrrB34uEXng5yjN43UeN3cIFV5GdgZsrW3NHYZZcOSZiIjoAdl2 fhv8I/3hMNMBjrMc4R/pj4QrCdr+hCsJ6La4G+xn2KPW+7UwZNUQXE27esc2c/NyERobisafNIbN dBu0/qw1lh5aalBn+Jrh6LCwA9YcW4NWn7WCbbgt9iTuAQBEH4tG+y/bwzbcFs4fOSMkJgQ5eTkG x688uhKPzXsMduF26BrRFceuH7tjTOdunoPb524AAP9If1iFWRl8bZ+SkYKR60ai/uz6sA23he/X vlo8APDmhjdR78N6uJZ2zSAGqzArbDqzCREJERizcQwAaG0/GflkifEUnbYRGhuKuh/WRcKVBPgs 8oH9DHt4fuGJHRd23LFfADBp0yS4fe4Gh5kOaPRJIwxZNQRJt5K0/bsv7UaVaVXwzYFvtLK/Mv9C o08aYejqoVrZ4auH0fP7nqg+szqqz6yOgT8MNGgnOzcbE36ZgEfmPAKb6TZ4+OOH0Xd5X2TnZt8x vvtxj11Pv47gNcGo80Ed2M+wh3+kP+L/jDeo4zLHBRN+mYD34t5Dw48bosasGgCArJwsjPpxFBxn OaL2B7Ux/ufxxWK+175ZKibPRERED0DsuVh0W9wN1XTVsLjPYqwYsAJPNH4CiX8nAgCupV2DX4Qf MnMysbTfUszrMQ9x5+PQ/dvud0wq3t36LmZsn4HX2r+GdYPXwbeRL15Y9QKWHV6m1VFK4dzNcwjZ FIJ3uryDn4b8BBdHF6w4sgL9VvSDT0MfrBu8DlO7TsWX+7/E5E2TtWP3X96P56OeR1vntlj9/Gr0 eqwXBv4w8I59beDQAEv6LgEAfNbzM+wesRu7R+wGoE+uAhYHYMvZLZgdOBtrnl+DunZ1EbA4QEse Pwz8EDVsauDV9a8C0E8BeX3D63i9/esIeDQAQY8F4a2ObwGA1vZnPT8rMR6lFBSUQVl6djqC1wTj 9favY+XAlahWpRr6Lu+LjOyMO/YtKS0JkzpPwoZ/bcDcp+fizI0zeHLxk9oIu09DH/zH9z8Y9/M4 XPzrIgBgzE/6RH9+j/kAgFMpp+D7tS9u597Gkr5LENEnAkeuHUGvpb2088zcMRPfH/oe0/2nY9Ow TZjz1Bw42jgiV3JLjO1+3WN9lvVBzOkYfBT4EZb3X448yYN/pD9Op5w2uKbfH/oe2y9sx4KgBVgx YAUA/R8XXx34ClO7TsX3fb/H+b/O46NfP4JS/1z/e+mbRRMRi/vRh0VERFR++SzykQ5fdihxf0hM iNScVVNSs1K1st8u/SYqVMnSQ0tFROTsjbOiQpVsOLFBRESS05PFLtxOpsVOM2jrmSXPiOs8V207 eHWwqFAlB68c1Mry8vKk8SeN5aU1Lxkc+/X+r8V2uq2kpKeIiMiAFQOk1X9bGdQJ3xYuKlRJZEJk if05lHRIVKiSuHNxBuWL4hdJ1feqyqnkU1pZTm6ONJ3bVCb+MlEr23lhp+jCdPLtwW/luWXPSbNP m0n67XRt/7zf5okKVSWev7Cu33SVASsGaNtTt04VFapk69mtWlnC5QRRoUp+PvWzSW0WxH3pr0ui QpVsO7dNK7+dc1vcPneTgMUBsuaPNaJClfx08idt/5BVQ+Tx+Y9Ldm62VnYy+aTownTy44kfRUQk 6Psgeevnt0yOReT+3GMbT24s1p+022lS94O68uq6V7WyRz55RBp81ECycrK0sutp18V2uq18sOMD rSwvL09c57mKVZiVVnYvfStL+XmnyXkqR56JiIjus7TbadiTuAfB7sEl1tmTuAeBTQPxUNWHtDKv h73g4uiCnRd2Gj3m8NXDyMjOwIBWhk+SGNhyIE4kn0ByerJW1rB6Q7g5uWnbJ5JP4OJfFzGg1QDk 5OVoP/5N/JGZk4nDVw9rcT3r+qxB+889/pzpnS9i09lNaOfcDi6OLto5BYInHnkC+/7cp9Xr1KgT xnccjxFrR2DdiXWI6B1xX+fUVtVVhZ+Ln7bdom4LAMClvy/d8biNJzei01ed4DjLEdbvWaPRJ40A ACdTTmp1rHXWWNxnMbad34ZBKwfhFc9X8FSzp7T9m85sQh/XPgCgXQMXRxe4OLpg7597AQAeTh6I SIjAhzs/xO9Jv5c6d/x+3WN7EvfA6SEndHmki1bHztoOQY8FGUxrUUqhW5NuqKqrqpUdunoImTmZ 6P14b4N6vV17G8R/t32zdFwwSEREdJ/dyLwBEYGzg3OJda7cuoI29doUK69nXw8pmSlGj7mcehkA 4GTvZFDu9JB+OyUjBbXtahuUFbiefh0A8MySZ4q1q5TCxb/1Uw6S0pJQz75esZju1fX069h9aTes 3yu+WLFZrWYG24NaD8LsXbPhXt8dvo197/mcxjhUczDYLkgCM3MySzxmb+JePLvsWfRr0Q9vd3lb uw4+i3yKHefm5IYWdVrg0NVDeKPDGwb7rqdfx/s738f7O98vdo6C5H3KE1Ngpazw2b7PELIpBA9X fxgTO03EGO8xRmO7X/fY5dTLqGtX13idDMP7sOh9d+XWFa1u0WMLu9u+WTomz0RERPdZTZuasFJW +DP1zxLrODs4IyktqVh5UloSOjToUOIxgH5OcE3bmv8ckz93uJZtrRLPV7BvYa+FaOvcttj+Jo5N AAD1H6pvsJCt4Hz3qrZtbbRv0B4LghYU21dNV037nJOXg5HrRqKNUxscvnoYC+MX4pV2r9zzee+H 1cdWw8neCcv6/zOfvKTnWM/ZPQfHk4+jRZ0WGL1xNOKGx2nzfmvb1kbfFn0xwnNEsePq2NUBAFSr Ug1h/mEI8w/DqZRTWLBvAcb+NBautV0NRrEL3K97zNnB2ejvNyktSftDrEDhecyA/l4B9PeHo42j Vl60vbvtm6XjtA0iIqL7zL6qPbwbet/xpSLeD3vj59M/49btW1rZ3sS9OH/zPDo37mz0mNb1WsPO 2g4rjqwwKF9xdAVc67gaJDtFF8y51nHFw9UfxtmbZ+Hp7FnspyAZ79CgA9aeWGtw7Ko/VpXa55JG crs16YZTKafQqHqjYudsVa+VVm/G9hk4mXISawetRYhvCCbETDBIVAvaz8rJKjWWoknevcrIzkAV K8NxxiWHlhSrd/z6cUzZOgXhT4Zjef/l2JO4B5/s/kTb3+3Rbjh89bDR6964RuNi7TWr1Qwfdv8Q 1apUwx/X/zAa2/26x3wa+uBq2lVsP79dq5OenY4NJzagcyPj92GBNvXawKaKDdYcW6OV5Ukeoo9H l/g7MKVvlo4jz0RERA/ArG6zEPBtAHos6YGRniNhZ22HXy/9ig4NOqDnYz0xvuN4fL7vczz13VMI 8Q1BalYqJm2eBDcnN/Rr2c9om7Vsa2Gsz1hM3z4dVayqoF2Ddlj1xypsPLnRYHQUAASG80qtlBU+ CvwIQ1cPxd9Zf+PpZk+jqq4qztw4g+jj0YgaEAVba1uE+IbAe5E3Bv4wEC+1fQmHrx7G1wlfl9rf xjUaw9baFhEJEXCo6gBrnTXaN2iPYe7DsCB+Afwi/TCh4wQ0qdkEyenJ2JO4B84OzhjrMxYHLh9A +PZwzO8xH484PoKpXadi3Yl1eGntS9g8bDMAoEUd/Rzlub/Nhb+LP6pXqw7XOq5GYxGRYv2/F4FN AzH3t7kY99M4BD0WhF0XdxVLnnPzchG8Jhiezp4Y33E8ACDMLwxTtkxBz+Y94VrHFaFdQ+G1yAs9 v++JFz1eRB27Okj8OxGbzm7CcPfh6OrSFc8tfw7tndvDo74HbK1tEXU0Crl5uXjikSdKjO9+3GOB TQPRqVEnPB/1PGYFzEIt21qYvWs2snKzMNF3osE1Laq2XW2MbDcSU2OnoopVFbSs2xIL9y9EWnaa Qf176Zsl48gzERHRA9DlkS6IGRqD9Ox0DFk9BINWDsL2C9vRqIZ+wVkduzrYGrwVNlVsMHjlYIza OApdH+mKmKExBqOdRUfwpvlPw+TOk/H5vs/Ra2kv7LiwA0v6LsHAVgMNjik68gwAA1sNRPSgaCRc ScDAHwai34p+WLBvAdo5t9NGdts1aIdl/ZfhwJUDeG75c1h7fC2W919ean9tqthgYa+FiL8cD79I P3gv8gag/8p+a/BWdH+0O6bGTsVT3z2FsT+Pxekbp+H9sDeyc7MxPHo4nmzypDZNo2AB3o4LO/Df Pf/VrufEThMx97e58PnKB69veL3EWIr2X8H49ShNj+Y98H7A+1j5x0r0XtYb2y9sx/p/rTeo88HO D3Dk2hFE9I7Qyib6ToRHfQ8Mjx6OPMlD89rNsfvl3bCztsOr61/FM0ueQWhcKGx0NmheuzkAwLeR L9YcX4MXVr2APsv64MCVA1g5cCU8nT1LjO9+3WNrBq1B96bdMfansRj4w0AopbBl2BY8WvNRg2tq zAfdP8BLHi9hWtw0/Gvlv9DQoSHG+4w3qH8vfbNkyhJXPCqlxBLjIiIiIqKKRSkFETH5ryuOPBMR ERERmcgsybNS6mml1DGl1EmlVIg5YiAiIiIiultlnjwrpXQA5gN4GkBLAIOVUi3KOg4qf2JjY80d Alkg3hdkDO8LMob3Bd0P5hh59gJwSkTOiUg2gGUAepdyDBH/0SOjeF+QMbwvyBjeF3Q/mCN5fhjA xULbl/LLiIiIiIgsmjmSZz5Gg4iIiIjKpTJ/VJ1SygdAqIg8nb89GUCeiLxfqA4TbCIiIiIqE3fz qDpzJM9VABwH0A3AnwD2ABgsIuXzHY1EREREVGmU+eu5RSRHKTUKwM8AdAC+YuJMREREROWBRb5h kIiIiIjIElncGwb5AhUqSinVSCm1VSl1RCl1WCk1xtwxkWVQSumUUgeUUuvMHQtZBqWUo1IqSin1 h1LqaP46G6rklFKT8/8POaSU+l4pVc3cMVHZU0p9rZRKUkodKlRWSykVo5Q6oZT6RSnlWFo7FpU8 8wUqVIJsAONEpBUAHwBv8r6gfP8GcBR8ig/9Yy6AH0WkBQA3AJwWWMkppVwAvALAU0TaQD9ldJA5 YyKz+Qb6HLOwSQBiROQxAJvzt+/IopJn8AUqZISIXBGRhPzPt6D/z7CBeaMic1NKNQTwDIBFAExe JU0Vl1KqBoAuIvI1oF9jIyJ/mTksMr+/oR+Esct/aIEdgETzhkTmICLbAdwoUvwsgMj8z5EA+pTW jqUlz3yBCt1R/ghCWwC/mTcSsgCfAJgIIM/cgZDFaALgmlLqG6XUfqXUQqWUnbmDIvMSkRQAHwG4 AP1Tvm6KyCbzRkUWxElEkvI/JwFwKu0AS0ue+dUrlUgp9RCAKAD/zh+BpkpKKRUE4KqIHABHnekf VQB4AvhMRDwBpMGEr2CpYlNKNQUwFoAL9N9aPqSUesGsQZFFEv1TNErNRS0teU4E0KjQdiPoR5+p klNKWQNYCeA7EVlj7njI7DoBeFYpdRbAUgBPKqUWmzkmMr9LAC6JyN787Sjok2mq3NoD2CUiySKS A2AV9P+GEAFAklKqPgAopZwBXC3tAEtLnvcBaK6UclFKVQXwPIC1Zo6JzEwppQB8BeCoiMwxdzxk fiLytog0EpEm0C/82SIiw8wdF5mXiFwBcFEp9Vh+UQCAI2YMiSzDMQA+Sinb/P9PAqBfaEwE6PPM 4PzPwQBKHaAr85ek3AlfoEIl8AUwBMDvSqkD+WWTReQnM8ZEloVTvqjAaABL8gdgTgN40czxkJmJ yMH8b6b2Qb9GYj+AL80bFZmDUmopgK4A6iilLgJ4F8AsACuUUi8DOAdgYKnt8CUpRERERESmsbRp G0REREREFovJMxERERGRiZg8ExERERGZiMkzEREREZGJmDwTEREREZmIyTMRERERkYmYPBMRWbj8 F0cdKqWOn1Jq3V22G6uUavf/RUdEVLkweSYiqrwEfMEMEdFdYfJMRGRBlFIdlFIHlVLVlFL2SqnD AOwL7XdRSm1TSsXn/3QsdHh1pdR6pdQxpdTn+a8ihlIqUCm1K7/+CqWUfdHzEhGRaSzq9dxERJWd iOxVSq0FMB2ALYBvAdwqVCUJQHcRyVJKNQfwPYAO+fu8ALQAcAHATwD6KqXiALwDoJuIZCilQgCM B/BemXSIiKiCYfJMRGR5pgHYByADwGgAjxTaVxXAfKWUO4BcAM0L7dsjIucAQCm1FEBnAJkAWgLY lT8QXRXArgccPxFRhcXkmYjI8tSBfqqGDvrR58LGAbgsIkOVUjrok+MChecvq/xtBSBGRP71AOMl Iqo0OOeZiMjyfAFgCvRTMt4vsq86gCv5n4dBn2AX8MqfE20FYCCA7QB2A/BVSjUFgPx51IVHq4mI 6C5w5JmIyIIopYYByBKRZflJ8C4A/vhnVPkzACvz6/2Ef+ZDC4C9AOYDaAZgi4iszm9zOIClSqlq +XXfAXCyDLpDRFThKBE+pYiIiIiIyBSctkFEREREZCImz0REREREJmLyTERERERkIibPREREREQm YvJMRERERGQiJs9ERERERCZi8kxEREREZCImz0REREREJvofHWbt2xvCuI0AAAAASUVORK5CYII= ) ## 文本属性和布局 我们可以通过下列关键词,在文本函数中设置文本的属性: | 关键词 | 值 | | --- | --- | | alpha | float | | backgroundcolor | any matplotlib color | | bbox | rectangle prop dict plus key `'pad'` which is a pad in points | | clip_box | a matplotlib.transform.Bbox instance | | clip_on | [True , False] | | clip_path | a Path instance and a Transform instance, a Patch | | color | any matplotlib color | | family | [ `'serif'` , `'sans-serif'` , `'cursive'` , `'fantasy'` , `'monospace'` ] | | fontproperties | a matplotlib.font_manager.FontProperties instance | | horizontalalignment or ha | [ `'center'` , `'right'` , `'left'` ] | | label | any string | | linespacing | float | | multialignment | [`'left'` , `'right'` , `'center'` ] | | name or fontname | string e.g., [`'Sans'` , `'Courier'` , `'Helvetica'` ...] | | picker | [None,float,boolean,callable] | | position | (x,y) | | rotation | [ angle in degrees `'vertical'` , `'horizontal'` | | size or fontsize | [ size in points , relative size, e.g., `'smaller'`, `'x-large'` ] | | style or fontstyle | [ `'normal'` , `'italic'` , `'oblique'`] | | text | string or anything printable with '%s' conversion | | transform | a matplotlib.transform transformation instance | | variant | [ `'normal'` , `'small-caps'` ] | | verticalalignment or va | [ `'center'` , `'top'` , `'bottom'` , `'baseline'` ] | | visible | [True , False] | | weight or fontweight | [ `'normal'` , `'bold'` , `'heavy'` , `'light'` , `'ultrabold'` , `'ultralight'`] | | x | float | | y | float | | zorder | any number | 其中 `va`, `ha`, `multialignment` 可以用来控制布局。 * `horizontalalignment` or `ha` :x 位置参数表示的位置 * `verticalalignment` or `va`:y 位置参数表示的位置 * `multialignment`:多行位置控制 In [3]: ``` import matplotlib.pyplot as plt import matplotlib.patches as patches # build a rectangle in axes coords left, width = .25, .5 bottom, height = .25, .5 right = left + width top = bottom + height fig = plt.figure(figsize=(10,7)) ax = fig.add_axes([0,0,1,1]) # axes coordinates are 0,0 is bottom left and 1,1 is upper right p = patches.Rectangle( (left, bottom), width, height, fill=False, transform=ax.transAxes, clip_on=False ) ax.add_patch(p) ax.text(left, bottom, 'left top', horizontalalignment='left', verticalalignment='top', transform=ax.transAxes, size='xx-large') ax.text(left, bottom, 'left bottom', horizontalalignment='left', verticalalignment='bottom', transform=ax.transAxes, size='xx-large') ax.text(right, top, 'right bottom', horizontalalignment='right', verticalalignment='bottom', transform=ax.transAxes, size='xx-large') ax.text(right, top, 'right top', horizontalalignment='right', verticalalignment='top', transform=ax.transAxes, size='xx-large') ax.text(right, bottom, 'center top', horizontalalignment='center', verticalalignment='top', transform=ax.transAxes, size='xx-large') ax.text(left, 0.5*(bottom+top), 'right center', horizontalalignment='right', verticalalignment='center', rotation='vertical', transform=ax.transAxes, size='xx-large') ax.text(left, 0.5*(bottom+top), 'left center', horizontalalignment='left', verticalalignment='center', rotation='vertical', transform=ax.transAxes, size='xx-large') ax.text(0.5*(left+right), 0.5*(bottom+top), 'middle', horizontalalignment='center', verticalalignment='center', fontsize=20, color='red', transform=ax.transAxes) ax.text(right, 0.5*(bottom+top), 'centered', horizontalalignment='center', verticalalignment='center', rotation='vertical', transform=ax.transAxes, size='xx-large') ax.text(left, top, 'rotated\nwith newlines', horizontalalignment='center', verticalalignment='center', rotation=45, transform=ax.transAxes, size='xx-large') ax.set_axis_off() plt.show() ``` ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAvgAAAIZCAYAAADTOkvEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4pVW9t/H7y4AUBRUE7BQbHKz42hUVxd45xyMWsB09 gmBDxQp2ilixHhVFxYaIiKCooKiAooINFAXEDgoovc383j/Ws5lNJskkmczszLPvz3Xl2slTV3LN 7HyznrV+K1WFJEmSpH5YY9QNkCRJkjR/DPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXJEmSesSAL0mS JPWIAV+SJEnqEQO+JEmS1CMGfEmSJKlHDPiSJElSjxjwJUmSpB4x4EuSJEk9YsCXeirJou71BoPP JUlS/6WqRt0GSfMsyaKqWpxkS+CFwD+B91fVlSNumiRJWsnswZd6ZijcbwN8B3gUsMhwL0nSeDDg S/MoyV5J7jPKNgz13H8L+C3w8qrad+JxSfz/L0lSD/kLXponSR4LvB3YK8k9RtSGJFkT2IM2LGdv 4Lhu3/pJtkryuCQbVdWSUbRRkiStXAZ8aZ5U1deBlwIPAvYeRciv5lpgC+AK4JSqqiSPA94P/Bw4 Ejg9yV2h/VGwqtspSZJWHifZSvNgMO69+/xlwBuAE4C3VtVPVmE71qD94X4wsD3wMeBmwNOAvwBf BC4BXg/8CtgBWFy+EUiS1BsGfGmeJFm7qq7qPt8d2As4mRbyT11J97zuD4sJ2zemjcG/FbAOsD/w jao6pdt/MnBlVT1kZbRLkiSNzpqjboDUB13QvqqrXPNyYD1az/mTgBskeUNVnbYS7rk4yW2BhwHb AOcAP62qk5M8ALgtcEFVnd+dE+BetNB/Ylcff4k9+JIk9Yc9+NI8SXIH4ETgdODrwHnAg4FnA0cB e89XT/6EUpjfAG5E+4P9BsC1wMuq6qOTnLc9sCdwD+BBVfX7+WiPJElaOAz40grqxr0vAv4PeDjw 1Ko6cWj/q4C3At8E5q0nv+u5PwH4DfDeqjqmC/BfBa4G7jMI8EnW7dp3R2BT4PFV9Yv5aIckSVpY HKIjraCu3OSSJJvTSlOeBJBkraq6pqr2T7I28CZgcZK3VNVP53q/JOmG1DwKWAy8Ezi+271D97oX 7QnCwMbAXWgTa59RVb+b6/0lSdLCZg++tIIGgTvJ8bRJrfeoqsu68e2DhaduSAv+GwFnAK+oqp+v 4H0/Ajy8qm7XfX0ArUznbsChVXVpkpsCt66qXyZZvzWnLl2R+0qSpIXNOvjSLE2sGz80QfUzwO2B 3bvti1n6f+xy2rCZ84AtgQtX5P5dG64CLuu2vZMW7ncFPj0U4vej1eS/UVVdYriXJKn/DPjSLHST WyvJekk26sbBD3wP+CHw9iQvAaiqa7p9DwSuAXYE7lZVf5rFPZf5g6L7o+IY4M5JjqP9UTHoub+i O++RwH2BX9P+GJAkSWPAMfjSDA1VrtmKVlf+zrQSmGcBe1XVSUleDXwAeHeSuwDfBjYE/hu4JXB1 VV0yh3vekFYh519DTwx+DhxGK8X5FeBj3XwAktwbeFl3zsFDf2hIkqSecwy+NAND4+y3An4A/JnW W38tbbLrpsCuVXVokvsCLwSeDGxA6z3/C/CU2VSumfAHxbuBzYBfAsdW1ce7Y3YAXk1btfbzwCnd cQ8Fbg081Go5kiSNFwO+NEPdJNWvAGvRJsn+pNv+KeCZwFOBI7pQvj5wc+DewN+A31TVX+dwz9vT auv/m1Zf/+60XvlDq+oV3TH3pfXiv5C2gNU/aEH/9VV1xty/Y0mStDpyiI40czcCtgbeNRTu9wN2 Al4AfKubWEs3DOcSYEblKIdKX5Jkzaq6tquvf3/gF8Crq+qnSTYD3gg8J8m6VbVrVZ0MnJzkvbQq Pf8ALqmqy+fvW5ckSasLJ9lKUxiUuRxyc+AWwCDc7w+8nDa59XNVdXGSdZN8eJb32bQb/rMGQBfu twbeC+wMnDWom19V5wJ7A18EnpbkA4PrVNXfqupXVXWe4V5adZJsnmRJkl3meP6zu/O3X4lte8t8 X1vSwmXAl4Z0deOHx79vkeSuXfj+d/fxoCTvok1i3Q347FCg3hnYOcn9Z3i/DwDHJ9liMEG2s0d3 7TsCP+6OXSvJGlX1Z9rKuF8EdkrynhX9viWtsOo+Vqok2ybZp3uaNxvz2rbp2pHkKUn2ns/7SZod A77USXIz4PVJ3tSF+zsDZwFPB9asqrOBI4E302rOvxT4xCDcd2Phd6KtKjvTse+DUL/28MaqehHw MdpE2Zcn2XJQCWdCyP8csEc3VEjSCFTVH4B1aWthrGzb0obpzTbgr8p2PIX2pFHSiDgGX1pqDeCG wMuS3JpWs/6btBC9uDvm7cDGwMOARbQhO39K8gRaj/tWwEOq6qKZ3LCqdk9yq6r6S1dTf40uLFBV L+hq4D8P2CfJG6vqD0nWGIT8bpjQVcAn5uUnIGnGkqwNXFtVi6vq6lV9+1V8v6lM1Q4reEgjZA++ 1Kmq84F9gKOA5wD/pFXL+flg8ixt0uzbgBOA9wCnJfkzLWDfDnhEVf1muvskOaR7OkA39Oev3dOD rwGfSbLlUJv+h9Yr+EzgTUk2G6p1v6gbk/+qqvrtvPwQJE1qaJz8o5O8o/t/fzlwq6nG4Ce5Y5Jj klya5B9JPprkLtOM11+zG/by5yRXJPlBkrsOXW8f4KPdl8d311mSZOeZfQt5dpLfdNf+dZKnTXZQ kpclOT3JlUn+nuTgJLeYQTt2SfJd2lPPDG1fkqFFAZM8K8nPklye5IIkhyW504R2PKQ773lJXpLk rO74E5PcvTtmpyS/7L6f05M8YgY/B2ks2IMvcb0qNpfQHjmfB2wJ7AK8uutJTxeuf5jkibSe9a1p vf4/AL5ZVX9czn22pdXNf0ySB1TVb7ve+H8mOZpWavP9SfaoqrMAqmrndnue1V3jjVV1boYm5c7z j0PS1PanBfsDaL9DLwPW7/Zd12udZBNaR8ANaRPm/0YbuvKpiccOeRvtaeEBtCE/ewJHJLlD18nw ZeBWtPeet7F0KOCJM2j347pzDwIupnViHNq99X1h6Lj3A7sCx3bHbgG8GHhokm2r6sJp2nESbc2P fWgVwJ45dN1/dj+XPWk/wx8Be9Eqf+0OnJTkXoP3vSG70koTHwSsR1v34+juj4zXAh+krRL+KuDL XSfIhTP4eUj9VlV++OFH90F7qvVE4JG0X8RLgP2H9i+ah3s8hlb68kJgqwn7Xg/8Efg6cLsJ+w7p 2nM4cJtR/6z88GOcPoBnd///fg6sNWHf5t2+nYe2Hdhte/jQtjVoc3QmHju49o9ow/QG25/YbX/0 0Lbnd9u2m2G7B227BviPoe03As4B/jR4XwO26Y79yoRrPKHbfsBM2kF76rh4ku0bAVfQCgesNbT9 HrRFA780tO0h3fXPBdYb2r5rt/3fwKZD2x/Tbd991P9W/PBjIXw4REdjbdALPlBVS6rqq1X1TeAd tF9Uew4msVY3VCfJZkm2nHj+TO5VVUfTeq7+CpyYtlLt4P5vpT36vgvwviS3G9q3M/BV2i+yxUga hY9XN+F9OR4LnFFV3x5sqPYE8P3TnPN/df1qWid0r1tOdvAsfbuqTh9qy6W095pb0QI2wOO71wOG T6yqI4Hf0oL+itiBVlDgPcM/w6o6Ffg27cnmxPfUT9f1y/4OnlYcWVXnTbJ9Pn5W0mrPgK+x1Y1h X5JkkyQ7pJV2u/dgf7Wx9PvSQv4ru3G3N+zGz38ceB9tVdkZ6e61Zvf5XEP+k4Etaw6r4kqaFxOH kExlcyZf6O7305xz7vAXtXSy/oYzvOd0Jpunc2b3ukX3unn3OlkVsN8M7Z+r6a5/Bm1Y0qYTtp87 4et/da/XGw5ZVYPt8/GzklZ7jsHXWMrSOvf/AXwBuA1trCxJ9gXeXVUXVtXp3ddLaGM/n0D7w/hW wPZVdeUs7rlGtUWsNq6qf1TVUd3Y+n1pIf/+3R8VVNVbu30vAN6V5JVVdWa3z3Avjc4VMzxuLlVk pnoyt1Aq5oyiMs5UP5OF/rOSRsoefI2dLmgvTnJH4Du0HqGX00pfvg14HbBXkpsDdI+130rrcf83 rQfuvlX1k9nct+vBvznw2yQv6rYdRfvDYaqe/A937XpLkrVW4NuWtGr9gbZQ3USTbZuNuYbsrSbZ Nqhcc86E1/+Y5Nitad/TTNox1b6zl3P9y2gFDiStIAO+xk4XtDekDbH5JfDKqvpEVZ3A0moYe9Jq z2/anfP7qtofeCiw0/BY1lnaEPg78NQkW3fX/jqtAsRkIf/ttIoUr5/huF9JC8PRwFZJdhhsSLKI tl7Giri0e53tUJSHJdlmqC03oj0h/Avws27z17rXVwyfmOTxtD9MjpxhOy5tp+UmE7Z/G7iStjjf dR0WSe5GG59/zIQ5CJLmyCE6GgtdmUsAqqpoPUj3B/asqpO7Y/YF9qCVhNuQtmLtJUkOrKq/d+de RVtYaqb3XVRLa+jTDfl5F22M/aPpxqJW1dFdE/enhfz7VVfbvqreOedvXNKo7Ac8A/hKkvextEzm Bt3+ufbEn9Kd+5okN6UNGTq5ugXypvErWs36g2jlgJ9DG5r4jEGo7t6fPgDsluQbtDVBNqO9J55L Kzwwk3b8GHghcFB3nWtpk2IvTPIG2iTeE5J8nvZeuzvtSepr5vgzkTSBPfjqtSTrdJ8u6oL9xgBV 9QNagP+/7rhX0HrRX0JbtOpLtF6oVwD7JdloDvceDAXaIsmDBtur6mPA54E3JNliaPvRXRvOBc5I cofZ3lPSSjXjUF5t4bwHA9+nva/sTZvU+uLukInzd2Z07ao6m/YUYCNaR8Fnge1mcOrXgFcCO9GC +iLgWVX1+QnH7UF737st8E7a+hufB+4/NOl3ee34NK0+/cNp5YY/C9ysO+9A2voia9PmH70YOA64 Xy1bA9/VcKU5Sss8Uv8k2ZzWY3ZqVR2f5C7AD4EXV9Uhg8Wtuom2XwW+BbyxqgYLsvyQVqv+XsC2 c5nc2o25/xPtl+lrgKOq6tfdgldfB35KG/JzydA5T6IF/eeUK9RKvZLkKcBhtMB88qjbI6mf7MFX n92CNpb+A0meT1tt9od0401r6V+3N6Y9hj6u2oqyayZ5HK23/83A7Vegcs0i2qPsq4A3AQcm2bOq fgZ8DHgA8IQka3Tjc6mqI4BHGO6l1VuSdSd8vSbwUuAilo57l6R5Z8BXb1XVSbRH0pvRHhf/Fnh+ Vf0Krjcu/zLgAuCx3S/gB9Amn10CnNMtCDMjg5A+5O/AF2mP6d9Mm9D24iRH0yasXU57BH6DbjjP Wl3bZ3xPSQvWCUk+kORFSV4NnAw8EHhrVV094rZJ6jGH6KiXuvHvg2o5/6Qt034u8L9Vddwg3HdD dNYF3gvsSFu46gra2M8dquoXc7j35sBawB+r6qokG9CWpz8HeB5wV+ADwDpdu7YG3lFVr1uBb1nS ApNkb+C/aZNZF9Em1R9UVQePtGGSes+Ar17rwvtbaUH6JbRJbq8YLB+f5AZVdXUXwp8I3JPWm3/o JBO+hq/rfxxJWqCqygWvNNYM+OqVQc/9FPv+F3g3bfn4l1XVd7rtawJrV9Vly7vG0LVqsl8g3RCb u9EqUTyV9tTgjcARtOFCTwJeW1XHdsffG3gk8OUVqK0vSYOnh2fRJugfMofzn02rIvbwqjpuOcdu S1vZ++CqOncG174prZPl+Kr63mzbNhtTvT9L48Qx+OqNrub8kiQbJtk2yfZJ7jrYX1UfpoXsOwDv SvKQbtftgEO7+swwi9JsE8fcV9U1VfWTqtqZFvL/DnyONgTo6u5ju8Hku6r6MfA2w72kFdXVoF8X +MwquN22tM6LzWZ4/Ebd8Q9eaS2SdB0XulIvDBaU6kpefhK4PXCTbt+HgM9U1UlVdVA3/H4/4JNJ jgC2Ae5Hq1M9XF1npve8LW3RqrvQxtieUVXHVdVHk3wXeBxtmNBvunZtRVtA5uTufq7cKGnOkqwN XFtVi0cweXe2PeX2rEurgD346oUuaN8e+C5tvP3bgefSerKeD+yf5OHdsQfRFle5hrbgyq1oNalP m8M9t6FVyHkbbTLde4GDk7y5O+bMqnoX8CDao/O/0P7w2LcbGiRJM5bk2UmWJHl0knck+TOtGtet kmze7dtlwjl3THJMkkuT/CPJR5PcZbJjO2sm2SfJn5NckeQHw09Dk+xDW9wK2uq4S7qPnado80No 858A9h46/uChY26V5JNJzktyZZJfJ3npJNf6bpI/JblDkm8muSTJ+UkOSrLeLH6UUq8ZMLTaSzL4 Q3VPutVnBwvIJPkS8G3airUvT/KbqvpzVR2c5Pu0yhb/qqrz5nDf29AWqzqTtuLjt4B7AN8Ddk1y eFWd1o3p/2mSF9OeFPwP8JqqunZFvm9JY21/WrA/gPa7/DJg/W7fdU8hk2wCnADckNYB8TfaAoCf mnjskLcBi7trr0t7bz0iyR2qajHwZVrHyPO6Y8/ozjtxirae3l3jncDh3Qe0Tg/SVgo/EdiEVmHs bODxtKGUt6uq3YeuVcB6tPf179KGXT4A2BXYAnjsFG2QxooBX6utwRAZ4IZVdUmSewJ/Hgr36SbO HtJVyXkf8CjaAlNU1e/neN/BHxRPBK6llbg8rtv3GNofDXsCv+/us6R7PY/2S/Iow72kFbQEeGBV XTPYkGT9SY57NS04P2KoetgHge8s59r3H7x3JTkD+ArwCOCYqvplkh/RAv63quqE6RpaVecnOZIW 8H9RVYdO0sbbADtW1Ve6bR9M8mVgtyQfGaxfQhvic1Pgo1X1mm7bh5OcR+vEedR0bZHGhUN0tFrq esUXJ7kH8OMkd6GtFnvjQY17un/f3dfH0OrhPz7JOkMhfdaGxszfHbiSVuOeJAfQJpG9BDisqi5N sn6Se00433AvaUV9fDjcT+OxtHlB3x5s6N7D3j/NOf83YW7QIMBvOftmzsgTgN8NhfuBA7rXx0/Y XsB7Jmw7sHt93Dy3TVotGfC12snSRaxuSit7eSVtXPvPaBNdd+t67xd3de6rq2l/AUBVXTlPE1uv BdboFst6K20J+l2BTw+tRPte4LmODZU0z6Zcp2OCzWmlgSea7gnm9cpeVtVF3acbzvCes7U5rQjB RGcM7R92ycRhlVX1N9oQzS3mu3HS6siAr9VKrr9C7Ra0R7X7VNVgouv5tOExzwAYVJRI8iDgxsAZ E0tbzuCemfD1Wt2nRwO37R5V70UbW/+lqrqiO2574L4sXUlXkubLFTM8bi6L3SyeYvvKqoDjgjzS PHMMvlYrXbjfmNaz81dgcVV9tdt3XpKnAEcC70tyH9rk2vsC/0X79/6xbtz+jAyVwlyXthjWv6rq mi7z/xT4EfBQ4Oiq+uTQefejjStdk5k/Spek+fYH4I6TbJ9s22zMNpRPd/w5wNaTbN96aP+wDZLc vKr+PtiQ5JbAjSY5VhpL9uBrdXQl8HnaAit3S/LYQS97VZ0IPIT2iHk34DTaMJlb0lZnnPHE2qFw vzWtbv0pSb6R5Kndvf5CG5bzC+AxSY5KsmuS9wMfAu5JmzT2h/n4piVpDo4Gtkqyw2BD9xRztxW8 7mAY4kyH7Ux3/NeA2yd50mBD956+J+0PgyMnOWdiCc1XdK9HzbA9Uq/Zg6/VTlcx53XAv4BX0Ybj /AL4Uzf2/lfd8Jjb03qAzgLOGu7tmeF9FifZklbn/nzgV8CdgA8DJNm4qybxNNqqtY8CdqCtXnsK sFNVnTHpxSVp1diP9h75lSTvY2mZzA26/XMdHnNKd+5ruvlQVwAnT9Wh0T1h/SPwtCRnAhcCZ3er ee9HW0fkc0k+QOuFfyztPfWgSVb6vgjYKcktaE9R79d9j9+sqm9MGFUpjSV78LVaqqp/0+pAv4f2 i+F1STbtJrymqi6qqlOq6pCq+uFswn26Bai6sfaPpf3xsFNVPbmq/oNW5x7gVUluVlW/of2hcQ9a PeZtgV0M95JWkhmH8qo6H3gwraPiJbQVu8+kLfYH7YnorK9dVWfTngJsRFv06rPAdss57VnAn2gV bw4F/re71oXA/YEvADt3+zcDXl5Ve0xyncuAhwGb0v44eBTtqemOM2m7NA5S5dwWrb66+vZvoD2e /Siw96C6Qhf05/QPPMmdgKcBdwb+WVUvGkzw7fYXbfLswcB+VXXBit5TklaVbr7SYbR69yePuj0z leS7wJZVddtpjqmqshtfY80hOlqtVdXFSd7SffkK4Nokb6uqv61AuF+D1tP0Wtq40bd391qSZK2h CbPfAZ4DLE7yzqq6wHAvaaFJsu6gulf39Zq0MewX0coLS+oZA75We0MhfzFtqMxVSV41m2o5E663 JMl7u+u9AdgxyZFVdXpXQWet7rinJfkMrVrO1UneNE/19SVpPp2Q5Me0eUQb0KqKbQu8YlBKeDVj 77y0HAZ89UIX8t8BXA18fq7hfuh6/+iq4dyAFuB3T7J/VZ0zVCaTqnpmkquAQw33khaoo2hzlXYG FtHKDD+vqg4eaavmprBuvrRcjsFXrwyPk5+n621IG6rzclr1nAOq6pxuDP7aq2nvlyT1lmPwJXvw 1TPz3YteVRcmeXv35csBkhzQ7TPcS5KkBceALy3HhJC/O221REmSpAXJgK+xMKHE5TLDeJY3tKcL +W+jhfunrtzWSpIkzZ1j8DU2uuXZByvU3gL4D+Bi4LfdJN1Fy5uc263YeAPg747xlKSFxzH4kgFf PZfki8CvqurNQ9u2Ab4K3Ia2iuM5wH9V1e9mcV1/gUjSAuT7swRrjLoB0sqSZGvgfsBrkrys23Yz Wrj/C22hl/2AdYCTkixvmXVJkqQFz4Cv3qqqM4CdaIu77Jtkd9oCKX8G3lBVH6qqtwPPBU4Hjkjy oJE1WJIkaR44REe9lO4Zbff5g4D3AHcBTqQt9PLg4Um1Se4FHAjcGXhiVX1/Odf3EbAkLUC+P0v2 4KunqqqSrNV9/n1aecuf05Znv65e/tAxpwCvoPX2H5Zk+1G0W5IkaUUZ8NUrSTZJsjZAVV2TZKsk D6uqE2kB/nfAA5K8ZuiY4ZD/cuA84ONJ1h3NdyFJkjR3Bnz1RlfCck/g493XW9PG1j8jyY2q6gRa T/5pwJuSvByWCfk/AZ4NPLSqrlj134UkSdKKcaEr9ckS4FLg6Uk2Be4DHA28F7gMoKpOTLJHt+0d Saiqdw1CflVdU1U/G9U3IEmStKKcZKvVWpI3AF+uqtO7r9cCDgL+hzbU5gnd0BuGF7JK8kCWTrx9 dVW9Z5b3dRKXJC1Avj9LDtHRaizJPYA3AfslWSfJ4N/z1sDZwCbA6wZj6bsVbNfoKuz8gFYH/1Tg XUl2G8G3IEmSNO/swddqK8mawMOAC6rqJ4MhNknuCxTweOA1wFHAM6rq0iRrTCiP+WDaHwkv6urm z/Te9hBJ0gLk+7NkwFdPJLkTcADwsqo6q9u2KbAHsBfwNeBZVXVJt+/2wAZV9bMk6852Qq2/QCRp YfL9WXKSrVZjw4tZAZsBjwPW7obbnFVV5yU5iNabvxfwqSQvBm4O7AdsmuQBg9AvSZLUB/bga7U0 mDCbZANahZwAOwCHAL8AXkgL+ZXkFsCutBKal9Eq7WwAPKIrizmX+9tDJEkLkO/PkpNstZrqwv2t gZ8Cd6+qa4HjaDXs7wp8BLhd18v/N+B9wHOA7wI/AO4/13AvSZK0kNmDr9VWkq2A7wO/BB5dVVd1 ZTIfAXySCT35Q+etU1VXruC97SGSpAXI92fJHnytRpIsmrDpLFrP/N2BF3QVcq4BvkHryb8brSd/ y+GTVjTcS5IkLWT24Gu1MDTm/jbA5cC/uq9vShtycw3wuKr68+B44FHAx4C/AP9VVefMY3vsIZKk Bcj3Z8kefK0mujC/OXAu8BPgRUn+o6ouAl4A3Al4+fDxtJ78XYGbAEsmXlOSJKmP7MHXaqPrvf8t sA7wHWA9WrnLo4B3A88Cnl9Vhw+dswhYt6ounee22EMkSQuQ78+SPfhawJJc799nVf2J1kv/++7j NOAI4O20nv1/Av/Z/SEwOGfxfId7SZKkhcyArwWpK2+5JMktk9x5aNePaOE+tGC/E/DfwINpC1g9 Crj3qm6vJEnSQmHA14LULVC1AW28/ZFJXtdtPxX4Eq1Kzt2r6gvAjsCvgTNp4+3f0pXLlCRJGjuO wdeCluQoSw/eAAAgAElEQVRhwJuAbYGfAa+oqh8lOQh4EnDPqjovyY2BWwFvAPbv/hBYme1yjKck LUC+P0sGfK0Gktwc+E9gD9ownE8CJwHPBH4DvKGqLl/FbfIXiCQtQL4/SwZ8rSa6ajgbAQcCj6QN L7uKttjVHlV12ipuj79AJGkB8v1Zcgy+VhNdNZzzq+pZwEuAbwO3AB4IPGekjZMkSVpA7MHXaqOr rFPd55sAjwXeTFvB9ueruC32EEnSAuT7s2TA12ouybpVdcUI7usvEElagHx/lhyio9VUksGb95Uj bYgkSdICY8DXamkwVKd8BCVJknQ9BnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE gC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE gC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQeMeBLkiRJPWLAlyRJknrE gC9JkiT1iAFfYynJDZOcneQlo26LJEnSfDLgayxV1WXATYErR90WSZKk+WTA1zj7JvCwUTdCkiRp PqWqRt0GaSSS3Bo4Bvgh8EHgLOCKicdV1ZJJzq2qykpvpCRpVnx/lgz4GmNJlgnuQwoIUFW1aJJz /QUiSQuQ788SrDnqBkgjdMgMjvEvYEmStFqxB1+aA3uIJGlh8v1ZcpKttEK6cptLkrx+1G2RJEkC A77GXJKbJXlHkh8nOSfJ/bvtGyXZO8lW053fldv8J3DhqmivJEnS8hjwNbaS3AY4FdgTWB/YDFin 230h8HRgtxlc6nDgSSujjZIkSbPlJFuNs32B9YD/B/wFOH+wo6oqyZHAY2ZwnY8AhyQ5GvgwU5fb PHs+Gi1JkjQdA77G2SOAg6rq50luNsn+c4DbzOA6P+1etwEeNcUxBSxTblOSJGm+GfA1zm4E/Hma /esws1D+5hkcY7kqSZK0ShjwNc7OAe4J/N8U+7cHzljeRapqn3lskyRpEkl2YYadJUl2HnxeVTNZ 80TqFQO+xtkngbckOQo4abAxyZrAXrTx9y8eTdMkSRMcPItjPzn0uQFfY8eFrjS2uiD/JeCJtEm2 twLOBjamVdU5DPjvmuQ/ycSFVJLcEXgT8DBgI2CHqjouycbA/sCHqurHK/lbkqTeSrL5hE0bAJ8C LgXeB5zZbT8N+CGtiMIuVfXLVdREacEw4GusJQnwX8BOwJ2AAL8DPldVn5vmvOsCfpJtgBNpj45/ BOwAPLyqjuv2/ww4taqetzK/F0kaJ0k+Snvf3r6qFg9tL2At4DjgjKp64YiaKI2MQ3Q01rre+S92 H3P1DuBi4D7A1QyV2+x8A9hxBa4vSVrWk4E3D4f7gaq6NsmXgL0BA77GjgtdaWwlOT7Jw6bZ/9Ak x83gUtsBH6iqv06x/1za8B9J0vxZD7jlNPtvydLFC6WxYsDXOHswsOk0+zcFHjKD66wF/Gua/TcB lsy8WZKkGTge2D3JIyfuSPIoYA/ge6u8VdICYMCXpnYr4PIZHPdb4IHT7H8c8PN5aZEkaeCltOGR xyQ5PckRSY7o9h0N/Bt4ychaJ42QY/A1VpI8kVY1Z+AFSR4+yaE3pU2W/ckMLvtB4CNJTgK+MnSv TYC3AQ8Anj7nRkuSllFVv09yV+DVwONpK4kPKoccCOxfVf8YVfukUbKKjsZKkn2AN87g0CuBU4AX VdXpk1xnYpnM99AeB18N3IDW879et/tdVbXnCjZdkjQDE9+fpXFkwNdY6cpirkErh3k1sAswsRxm TVaVYcJ1lvkFkuQ+wNNYWm7z98ChVXXSJJeQJM2TJDcEbgacB1xhwNe4M+BrbHWLppxfVTMZZz/x XHuIJGnEkmwH7Afcu9u0A/AdWpGEzwP7VtWxI2qeNDJOstXYqqo/zCXcT5TknCRPmGb/45OcvaL3 kSQtleQBwLeAmwMH056cAlBV5wOLgOeMpnXSaDnJVmMtyfa0RVBuB2zI0l8Q1X1eVbXlci6zGXCj afbfCNh8xVoqSZrgLbShkPcG1gWeO2H/94BnrOpGSQuBAV9jK8mLgffRVp49GfjVJIfNxxi2OwCX zMN1JElL3Rt4Y1VdlmTdSfb/iekXwpJ6y4CvcbYncALwiKq6erYnT1jl9nVJnj/JYRsCdwGOmVsT JUlTKOCqafZvQquIJo0dA77G2aa0CVizDvedOw59fnPgxhP2F3Ap8BngNXO8hyRpcr+g1b7/wMQd SRYBTwV+vKobJS0EBnyNs1/RVqudk6q6NUCSJcAeVfXZ+WqYJGm53gl8Ock7gUO7bet3r0fRnp6+ chQNk0bNMpkaW90E288Bj6yq02Z5rmUyJWnEkrwU2J/rd1gO1jnZs6reP5KGSSNmwNfYSvJp4G7A 1sCPgHOBZRa4qqqdJznXgC9JC0CSWwM7snSRwf8FNquqP460YdIIGfA1trqhNctVVcusFzEc8LvV cZ/D9cttTnKZWrQCzZUkdZKsRxt7//WqOmzCPjtgNPYcg6+xNVlwn6M3A68Dfg58FrhostvN070k aexV1eVJngr8YNRtkRYiA7604p4PHFlVTxp1QyRpjPwU2GbUjZAWIgO+xl6SrYDtgY2BT1fV2Ulu QCt9eV5VTVdnGWAD4OiV3ExJ0vW9CjgqyY+r6vOjboy0kBjwNba6sfMfAl7QbSrg+8DZwDrAr4F9 gAOXc6lTaJO7JEmrzgG0VcIPTfJ+4A/AFQBJThgcVFXbjaR10gjN1xhkaXX0Slq4PxDYgVZ9AYCq uhg4HHjiDK7zEmCnJE9YGY2UJE3qNt3rH4HLaE9hb9ttu233cZtJzpN6zx58jbPnAZ+vqlcmudkk +38NPHIG1zkIuBw4Islfmbrcpr1IkjRPqmrzybZ3VXQm3SeNCwO+xtlmtJUQp/Iv4KYzuM5taMN7 BjWXJ1sd1yo6kiRplXCIjsbZv4BNptm/NfD35V2kqjavqi2616k+tpi3VktTSfYhWULy4Fmc811m uCbE0DlLSI6f4t4+qdIqleSRSfZPcnCSrbttN0qyXZKZdNJIvWPA1zg7FnhekvUn7khyR1r5y6+v 8lZJc1dDH7M9by73kkYmydpJjgGOAfYEdgZu0e2+ljaPavcRNU8aKQO+xtnewPrAqbQJtwA7Jvlg t+0y4K0zvZi9SFoADqI9eTpl1A2RVoE3AQ+nhfituH6hhCuBw4DHjqZp0mgZ8DW2quoc4H7AmbTe H4AXAS+krY54/6r66/KuYy+SFoyqC6g6k6orRt0UaRV4GvDxqvoAcOEk+88Etly1TZIWBgO+xlpV /b6qHgPcDLgvLfDfvKoeWVVnz/Ay9iJp7pLNu7HrB5PcjuQwkgtILiY5luTO3XEbk3yM5G8kV5Cc QvKQCdeaehx88jSSn5JcTnIeySEkt5ymXTcgeQPJWSRXkpxN8haStefwPW5F8kmSP5FcRfJ3ks/S hsJJc3UL4CfT7L+C9pRWGjtW0ZGAqroI+PEcT7+uF2mKcptnAjvOuXEaF5sDJwOnA58AtgCeDHyX 5IG01ZIvAj4HbET7d3cMyR2p+tO0V05eRlvv4SLgU7QJ5o8Cfgj8e5LjA3wReALwe+D9wNrAc4G7 zuq7Sh5Fe4q1CPhad73bAE8BHkvyUKpOndU1peZ8WjW0qdyDpdXNpLFiwNfYSvI04NFVtcsU+z8F HFVVX1rOpexF0nx4MPA6qt5x3Zbk9cCbacH/UKp2Hdr3LeAQ4GXAy6e8arI5sB9tCMO2VP2x2/5a 4Eu0oD1xwuxOtHB/EvBQqq7uztmb2Yzvb3NPPgdcCmxH1W+G9m3TfV8fA+4542tKSx0JvCDJR2lr kVwn7SnWLsB7RtEwadQcoqNxtgdwzTT7r6KtUrs89iJpPpwD7Dth26e610UsnQg+cChtjsfdlnPd Z9A6c95/XbgHqKrumpNVw3lO9/ra68J9O+ci4C3Lud+wnYEbA3tfL9y3a/2aFu7vQTcpXZqlfWh/ PJ5Gm2AOsFv3ejzt/9TbV32zpNGzB1/jbGvgM9PsPw34zxlcx14kzYfTutA97G/d65lUXXa9PVVL SM4Hbr2c627bvX5vmT1V55D8iTZkZuI5i2mTzSf67nLuN+x+3evdSfaZZP9gDP7WwBmzuK5EVf0j yb1pf3T+d7f5yd3rx4DXVNWyQ9CkMWDA1zhbC1h3mv3rAevM4Dr70MYzn0arrQ+wW5I9gUcCv8Ne JC3fskGk6lqSyfc119L+HU/nxt3reVPs/zvLBvwbAxdQtXiS46e6zmQ26l7/Z5pjCrjhLK4pXaeq LgB2TbIbsDGtyMHfq+qFo22ZNFoO0dE4O4M2zngZaZMMnwD8dnkXqap/APcGvgA8otv8ZOD+tF6k +9uLpBEa/NvbdIr9N5/inA1JFs3w+OXd+65UrTHFxyKqPj2La0oAJNk7XZWpas6vqvOG9m+T5I2j a6E0OgZ8jbMPAQ9K8pm0iYgAJNmCNnTngcBHZnKhqrqg2gTIm9EC0C2ADavqhVU1WX1maVX5aff6 kGX2JFuybO/94JxFwIMm2bfsdaZ2Uve6bNlOacXtzfRVne7SHSONHQO+xlZVfQL4IPB04OwkFye5 GDiLVkXkw1X14Vle87pepKpaMv+tlmbts7TJ5LuTLJ0MnqwBHMDQug1DDu5e33a9uvfJhsDrZ3Hv g2klOfcmudcye5M1lqnlL82f9WnD2KSx4xh8jbWqenGSzwNPBe7Qbf4d8IWq+uFMrpHkxcATquoR k+wL8E3gK1X1oXlqtjRzVeeS7EWrg38qyReAi2nzQzYAfsHEXtCqz5H8N22Y2q9IjqSN9d+Rtl7E zFYHrbqQ5D+BrwAnk3yHVue/aE8O7gfclDbfRVquJHejVY4a/GH6oCTLZJkkL6WtTH7mKmyetGAY 8DX2quoHTF4tZKaeS1swaLJrV5IzgOfRhgRJ82li1Z2aZBtUvZvkb7SymM+mBfxvAq+i1amfrFTm fwF7dcfvBvyVtgDXW4Arp2jLZPc+juSuwGDS+YNoJWj/Cnwb+PJ036A0wZOB4XH1L+w+JnoXrarZ zquiUdJCk2WrsklaniRVVek+vwTYs6omHa+f5IXA/lV148n2S5JmJm3eyOAJ0rG0tSOOm3DYt4D7 Ar+qieVlpTFhD7604pYAG06zfyP8vyZJK6yqzgbOBkjyXOB7VXXO8DFJqKofjaJ90kJhD740BxN6 8E+gTea6d1VdM+G4G9DGLF9eVfdf9S2VpPEy/P4sjSt7FaUV9y7gcODYJG8CftltvytLy7g9dURt k6TeSnITWtWzLWlPUgcdL58YHFNVzx1N66TRsQdfmoOJPUTdqrXvoNUOH7YYeF1V7b8q2ydJfZdW YvWrtCeoFwMXdbs2B/5AC/tVVVuMoHnSSBnwNbaS7AycUFV/mGL/5sB2VXXIJPuWeQTcHf8Ulpbb PBM4vKrOnbdGS5IASHIqrczqE6vq50PbHaKjsWfA19hKsgR4ZlUdOsX+pwGfraqJvfL+ApGkEUty JbBXVb1nwnbfnzX2XMlWmtq6tAo5kqSF5y+0BdgkTeAkW42VJJsBm7F0FcStk2w3yaEbAv8LOLxG khamdwO7JflAVV0+6sZIC4kBX+PmOVx/FcTXdR+TKdoKnpKkhecK4FLgjCSfpnXILIbrauQDUFWf mPx0qb8cg6+xkmRbYNvuy48CHwcmLohStF8ap3SLqkx2Hcd4StIIdfOoJt1Fex+HVkVnmXlUUt/Z g6+xUlU/A34GkOTWwJer6pfTnyVJWoC2n2L78dPsk8aCPfjSHExYyXZv2h8Kv5ri2G2AHavqzauy jZI0jnzCKhnwNeaSLAIewYRVEIdNFswnBPw5l9uUJK2YJAG2AjYBfgFcaMDXuHOIjsZWkrsCR9BW PZzOiva8rw9cu4LXkCRNkGQn4ADglrRx9zt02zcBTgReW1VfHF0LpdEw4GucfRDYAHgybUXbi5Zz /PV0K+EOeokelGSy/08bAi+irWorSZonSZ4AfBb4Ma1owj6DfVV1fpLfAs8ADPgaOw7R0dhKcgXw pqradw7nFkurNCzP5cDOVXX4bO8jSZpckh/RymI+kNaZcj7wcOA7VZUkbwSeW1Wbj66V0mjYg69x dgGtjvJcPaJ7PRbYFzhuwv5Buc1fVdVlK3AfSdKy7gK8qqqWtGH4y/grcPNV2yRpYTDga5x9DNgp yfuraqp6ystIcjxAVX27+/o5tCE+56ycZkqSJnE10+eYWwOXrKK2SAuKQ3Q0NpJMrIu8JvA2YAnw fwytgjisqq7XM59kMbDGTKvoSJLmX5JjgXWqarskN2NoiA6wHnA6cGpVPWWEzZRGwh58jZNvT7Pv XlNsL2Biecu/ALeZlxZJkubqbcB3khwGfLrbdvvu9WTgVsBTR9EwadTswdfYSPLsuZxXVZ+ccJ39 gFcB/wYupj0GvgiYapx92mXqtnO5vyRpckl2BD5Cm2R73WbaHKv/qaqvjKRh0ogZ8KVZ6hbHuhb4 Am1hlYcAv6E9Hp5KVdVDV37rJGm8JFmPVv/+TrRwvy+wflVdOtKGSSNkwJfmYJKVbJ9VVZ8dcbMk aewNvz9L48ox+BpbSXZh+lr2BVwJ/An4WVVdPcVx29Mmc0mSVpEk9wTuU1UfnGL/bsAPq+q0Vdsy afTswdfY6nreZ+oi4K1V9e7u3GV6iJJsRQv7GwOfrqqzk9yAVof5vKq6ap6aLkljL8lRwOKqeuKE 7dUtdHUELec8cfIrSP21xqgbII3Q3YDTgO8B/wncvft4arftVOAB3b5fAwd2Ne+vJ82Hab34BwFv BDbvdq/TnfvilfmNSNIY+n/AD6bZfwJw71XUFmlBMeBrnO0O/At4WFUdXlW/6D4Oo9VSvhh4dlUd TuuZ/xmTB/VXAi8ADqRN9LquZ7+qLgYOB+xBkqT5dRPaauFTuZLrV9eRxoYBX+PsycDhk61iW1WL acF8x+7ra4EvAVtPcp3nAZ+vqlcCP59k/6+BO85XoyVJAPyR9pR1Kg8A/ryK2iItKAZ8jbN1gVtM s/8W3TEDFzPJSrfAZsDx01znX8BNZ906SdJ0vgg8PckLJu5I8kJgJ+CwVd4qaQEw4GucfR/YI8nD J+5IsgOwB20M58CdaRV1JvoXrR7+VLYG/r4C7ZQkLesdwE+BDyc5O8nXknyt2/ch2rDKt4ysddII GfA1zl5CW3322CS/TnJE93E68E3a2M6XAiRZlzZZ6wuTXOdY4HlJ1p+4I8kdgecDX19J34MkjaWq ugzYjlbY4FLa3KmHdbvfADzQxa40riyTqbGWZBPg1cBjaZVvCvgDcDSwX1VNujrthIWutgB+DPwb +DJt0u2HaJNtdwEuAbatqr+uzO9FkuRCVxIY8KU5mfgLJMntgfcBj2RpFZ0Cvg28qKrOXvWtlKTx Y8CXDPjSnEz1CyTJTYE70EL+2VX1j1XeOEkaYwZ8yYCvMZJkF1qv+meqasnQ19OqqkMmuZa/QCRp AfL9WTLga4wkWUIL9OtW1dXd18tVVdebjJ7ktsC5tPKYM1ZVf5zN8ZKk2TPgS7DmqBsgrUJbAlTV 1cNfz8EfJrzORAGL5ng/SZKkGTPga2xU1R8GnydZBCwBLquqC2Z5qecCB3evkiRJC4pDdDSWkqwN XA68qqoOnMP5PgKWpAXI92fJha40pqrqKuBvwLWjboskSdJ8MuBrnH0aeHqStUbdEEmSpPniGHyN sxOAxwE/SfJx4CzgiokHVdVxq7phkiRJc+UYfI2tGZbJrKpapvqNYzwlaWHy/VmyB1/jzSo4kiSp d+zBl+bAHiJJWph8f5acZCtJkiT1igFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC9JkiT1iAFfkiRJ6hEDviRJktQjBnxJkiSpRwz4kiRJUo8Y8CVJkqQe MeBLkiRJPWLAlyRJknrEgC/NUZJnJ1mS5LZzPH9Rkv2S/DHJ4iTHz/E6S5J8ei7nSpKk/jHgS6Pz POCVwNeBnYG3JtkiyT5J7jbLa9V8NizJllO1I8n2SfZOcuP5vKckSZofBnxpdLYHLq6qF1XVZ6vq O8DtgDcCsw34823LadqxPbA3YMCXJGkBMuBLo7MJcPEU+7IqGzKN6dqxUNooSZKGGPCledYNszkk yd+TXJnkt0lelSTd/ockWQI8BLh1N4Z+SZJdgGO7yxw8tH3vGd73sUlOTXJFkrOS7DHFcc9K8rMk lye5IMlhSe40tP/ZU7UjySeB13b7zhnat93Q+Y9O8sMklyb5d5JvJLn3hDZs3p33liQ7Jfl1156f J9m+O2aHJD/qtp+T5Jkz+TlIkvT/27v3YDur8o7j38ciEUTkFqABMSB3aBERWwcvEWSgoFixRSxT oHVkEgsWGmmhVCgdZ+Qi6CARYVAQitBaS0GGcidCy0WlGbmlgECSQklAIZBwN3n6x1qnbl/OZedk J4GV72fmnc1Z79prrX2YWfmd913v2qu7yBzo0l1ptRARCfwZ8B1gcmbOq+VbA7cDi4BvA08CHwEO Bs7LzKkRsTGwN3ACsAlwVG32duBzwHHAucCttfzuzLx3lLEsBe4D3gGcAzwOHAR8ADguM0/rqftF 4DTgTuBSYMOe/nfPzIcjYsuRxgGsA/wN8AngaOAX9dwNmflkRBwEXAbMrr+bCcBUYCKwV2beVscx GXgEmAVsBHwTeJXyTMK6wBHAmcAM4GngSGAbYOfMnD3S70KSIiIz0zuMWq0Z8KVxGCXgXw1sC+ya mYt66p8OTAd2zMz/rmUzga0yc4ueeh+lXD0/PDMv6nMsSykP2e6XmdfWsjUowfx3gc0z85mI2BB4 DLgH2CMzX611dwV+AlyemX881jgi4suUq/j//7l7+pxHCeq/k5nP1fLNKIH/gczcvZZNpgT8xcB2 mflELd8PuApYArwnM++p5TsC9wJfy8zp/fxeJK2eDPiSS3SkgYmI9YF9gB8AEyJio6EDuKZW23MF df/AULgHyMxfAWcBawF71eK9KVfUvz4U7mvdWcANwH4RsTxzwnuBTYFzh8J9bf9x4HvAbhGxaec9 Vw6F++q2+nrHULivbdwPPEt5+FeSJI3CgC8NzjaUB0+PpSzN6T2up1xln7iC+n5wlLIt6+vk+jrc EpfZlD8GNlmOMYzVfu9Yhszt/SEzF9b/nMdrPQtsMN7BSZK0ulhjVQ9AasjQLeFzKFfxhzNn5Qzl DWPJMpZ7212SpDEY8KXBeYRylT4y86ZxtjHeh2K2HaZsaGecRzuvO1Iebu21A/A8sKCPcYx0rrf9 y4dpv7eOJElaQVyiIw1IZj5FWct+WES8JnBHxLoRseYYzSyur8u6FGW7iNi3p683A18AXqxjgrJM 6CXgC/X8UN1dKOvz/z0zl/YxjpHO/RT4X+CIiHhbT/uTgEOAn2bm/GX8XJIkaRl5BV8arGmUB0Xv iojzKWvP1wN2Ag6sr73ry7tLTu4FXgCmRcTzlO0278nM+8bodzZwWUScQwnZBwHvA/52aF17Zj4d EV8CTgduiYjLKCH9KGAhcHyf4/hxrfOViLgUeAW4MTOfiohjKNtk3hERFwBrUrbJ/C3gL8f4DJIk aQC8gi8tn99YrpKZjwDvAS6mBPpvAH9Febj07/n1Epih93bf/zxwKCU0nw1cAnyqj3H8F/AZyi4+ pwGTgGMy85RO+2cAh1F20zmFsr/8TcD7M/PhfsaRmTcD/wDsTNkm9BLqEpzM/D7wMeAZ4GTKXvr3 A1My8/Y+Psdo3NNXkqQ+uA++NA7usyxJr0/Oz5JX8CVJkqSmGPAlSZKkhhjwJUmSpIa4i440ThHh AyySJOl1x4dspXGKiMMpu8hMzsx5Y1Qf7v1HAN8CzgX+A5hP+bKsw4DLM/NnfbQRwEnArMy8YlnH IEmvdxGxFWVXr77mxQH2uz5le9+bM/NHK6tfaRBcoiOtOnsCz2XmtMy8JDNvBN4FnAjs0mcbb6r1 P7GCxihJq9pWLNu8OCgb1n4/vJL7lZabAV9adTYGnhvhXL9bvLkVnKTVxcDnu4hYe1X0K61oBnxp wCJiy4i4KCLmR8RLEfFARPx1XU5DREyJiKXAFGDziFhaj8OA62ozF/SUnzRCP5MpX0QFcHhP/Zt7 6qwXEWdFxGN1LD+PiJMjYs1OWxfW924WEd+PiIX1+MeImDjQX5CkN6SIeGtEfDkiHqzzyfyIuCoi duvU+3BEXFfnkBci4s6IOKBTZ0qdcz4bEUdGxMO1zVkRMaWn3uGMMS9GxMSImBER/xMRL0fEnIg4 JSImdPqcExG3RsTvR8Qt9Vu6Z4zwWacAD9YfT+rp94KeOpvVuXNBHft9EXH0MG3NrGPbJiKujYhF EfFkRJzd5x8Y0jLzIVtpgCJia+B2YBHlW2yfBD5C+dbYrYCplG92/VPgBGAT4Kj69ttrveMo6/Jv reV3j9Ddk5T1+t8FbgHOq+UL6lgmADcC7wbOB2ZRbjV/CdgVOIDXugqYBxwP7FTHu1NEvC8zX+37 FzAz2h4AAAkaSURBVCGpKRGxFjAT2A34HvA1YB3gA8DvAXfVep8C/gm4jfLt3b8C/gT4t4g4JDMv 7TQ9tbbzLeBV4Gjgioh4Z2YuBH7EKPNiRGwI3FHbOA+YC7wXmE5Z0vMHPX0lsDllnruIMnc+O8JH vh/4IvBV4F/rAfBwT7+3Ue7EzqA8P/Vx4MyIeFdmHtXTVgJrAzfU3+GxwB7A5ynfcr7/CGOQxi8z PTw8xnEAhwNLgS16yq4Gfg68rVP39Fp3+56ymcC8Tr2P1nqH9jmGNWr97wxz7vP13DGd8jNr+f49 ZRfWsks7dY+s5VNX9e/bw8Nj1R3A39W54IhR6qwN/AL4l075mygh/DF+vbnHlNreXGDtnrq71PJp PWUjzovAN4FfAu/olP9Ffc8+PWVzatnBfX7mrWv9E4c5d1o998lO+Q9q+c49ZTNr2Vc6db9ay/dd 1f9/Pdo7XKIjDUjdcWEfygQ/ISI2GjqAa2q1PVfikA4AFvPaW9Cn9Zzv+nrn5/NqGx8b7NAkvcEc BMzJzPNGqbM3sAFwcWf+24By8WMSsH3nPRdn5gtDP2TZJec5yh3PUdVlj5+mLOF5odPn9bXaXp23 /TIzLxur7T4cADyUmZd3yk+vrx/vlCevnV/PqK/Orxo4l+hIg7MN5WGsY+vRlcDKXM8+GXg0M1/p LczM+RHxbD3f9UCn7isRMZdyG1nS6msbyhKT0WxXX7uhd0hSlrTM7imbO0y9Zyh/FIxlIrA+JeR/ eoT+unPunD7a7cdk4Nphymf3nO+1KDMX9BZk5hMRsRjnV60ABnxpcIZ2WjiHchV/OHNWzlAkaaD6 +dKcoTlwKmWp4nC6zxQtGaOtfvq7nBEelgWe6Pz8Yh/t9sMvEdLrmgFfGpxHKJN+ZOZN42xjWf/R GK3+o8AeETEhM18eKoyITYG31/Nd21PWyg7VnUC5EuWXvEirt4eAnSMiMnOkeeeh+rpwOebA4YzU 31OU5TxrDbi/sfqFMn/uMEz5Dj3ne60bEZtm5vyhgoiYRHk4eLi5WFoursGXBiQzn6Lcwj4sIrbt no+IdbvbUw5jcX3t5/Y0mbkEeIlym7rrSso/HtM65cf2nO/qbvF2BPBWyq4TklZf/wy8kzInjOQ6 4GnghLrrzm+IiI3H2few82JmLqXs2LNPRHxomP7eEhHrjLPPEfutfghsHRF/2NNfUHbeSfqbX6fX V+dXDZxX8KXBmkbZOu2uiDifsh5zPcqWkwfW13k99bu3oe8FXgCm1T2aFwH3ZOZ9o/T5E2DviJgO PA4syMybgW8DnwXOiIjtgZ8BHwQOBn6YmVcP09a2EXEl5aHgHSm32u+ubUlafZ0BfBI4p4bp/wTe AnwIuD4zZ2Tm4oj4HCV03x8R36XsnDOJspXmdpSdacayLPPi8ZTtf6+v/c0C1gK2Bf6IMu/eMp4P nJkLImIecHBEPEj54+WRzPwxcCpl3f+lETGDchV+f2Bf4OzMvL/T3DPAZyLit4E7gfcDhwDXZuY1 SIO2qrfx8fB4ox6UbTKX0LNNZi2fRNm6bS7wMjCf8g/MdGBCT72b6WyTWcsPBO6p713CMFu0derv SNmGbTFly7Wbes69HTiL8o/sy5R1sScDb+60cWHtaxLlSt3CelwCTFzVv2sPD49Vf1DuCJ5K2Qv+ Zcr69iuAd3fq7U7ZN/4pyh3GObXeQT11ptQ558+H6edROlv/jjYv1nnuVMoXU71U+70TOBFYv9Pu Lcv4mT9IuYjyIp0tiet8eSHlO0leAu4Djh6mjZmUCztbUy6eLKpjnEHPFqEeHoM8hvajlbQai4gL gUOBNbLc9pYkDUBEzAS2yswtVvVYtPpwDb6kIf61L0lSAwz4kob0sy2dJGnZOb9qpTLgS4Jy9d4r +JI0eM6vWulcgy9JkiQ1xCv4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS Qwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD DPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM +JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4 kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiS JElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIk SVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJ UkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS Qwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD DPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM +JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4 kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiS JElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIk SVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJ UkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElS Qwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJDDPiSJElSQwz4kiRJUkMM+JIkSVJD /g8NfX/IA8ubvAAAAABJRU5ErkJggg== ) ## 注释文本 `text()` 函数在 Axes 对象的指定位置添加文本,而 `annotate()` 则是对某一点添加注释文本,需要考虑两个位置:一是注释点的坐标 `xy` ,二是注释文本的位置坐标 `xytext`: In [4]: ``` fig = plt.figure() ax = fig.add_subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = ax.plot(t, s, lw=2) ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05), ) ax.set_ylim(-2,2) plt.show() ``` ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJztnXl8VOXVx38nCQlZ2GWRNS5gICwhICAoxCIK0rqU2kpb Fe1rbRGXt8unr7Z9wbe2fa21dWltFZfaqoivW8GFRTDgxiaEHZFNAdn3kIQkM+f948zlDnEmmczc Pef7+cxn7sw8c++5v7n3zPOc53nOQ8wMRVEUJbikuW2AoiiKYi/q6BVFUQKOOnpFUZSAo45eURQl 4KijVxRFCTjq6BVFUQJOSo6eiLoR0XtEtJ6I1hHRnXHKPUpEnxHRaiIamMoxFUVRlMaRkeL3awD8 JzOXEVEegE+IaD4zbzQKENGVAM5n5p5ENBTA3wAMS/G4iqIoSoKkVKNn5r3MXBbZLgewEUDnOsWu AvBcpMxSAK2JqGMqx1UURVESx7IYPRHlAxgIYGmdj7oA2Bn1eheArlYdV1EURakfSxx9JGzzCoC7 IjX7rxSp81rzLiiKojhEqjF6EFEzAK8CeJ6Z34hRZDeAblGvu0beq7sfdf6KoihJwMx1K9NnkOqo GwLwNIANzPxwnGKzANwYKT8MwFFm3herIDPrgxlTp0513QavPFQL1UK1qP+RCKnW6EcA+D6ANUS0 KvLevQC6Rxz3E8z8NhFdSURbAJwEcHOKx1QURVEaQUqOnpk/QAKtAmaekspxFEVRlOTRmbEepKSk xG0TPINqYaJamKgWjYMSjfHYDRGxV2xRFEXxC0QEtrMzVlEURfE+6ugVRVECjjp6RVGUgKOOXlEU JeCoo1cURQk46ugVRVECjjp6RVGUgKOOXlEUJeCoo1cURQk46ugVRVECjjp6RVGUgKOOXlEUJeCo o1cURQk46ugVRVECjjp6RfEReXl5lu5v2rRpeOihhyzdp+I91NErio+QZZq9uz/Fm6ijVxQfwsz4 +c9/jn79+qF///54+eWXT3/2wAMPoH///igqKsK9994LAJg+fTqGDBmCoqIifOtb30JlZWW9+580 aRImT56Miy66COeddx5KS0tx0003oU+fPrj5ZnPZ58mTJ+PCCy9E3759MW3aNADAsWPHUFBQgM2b NwMAJk6ciKefftpiBZRGYcEK5M8A2AdgbZzPSwAcA7Aq8vhVnHKsKEr95OXlMTPzK6+8wmPGjOFw OMz79u3j7t278549e/jtt9/m4cOHc2VlJTMzHz58mJmZDx06dHofv/rVr/ixxx5jZuZp06bxH//4 x68cZ9KkSTxx4kRmZv73v//NLVq04HXr1nE4HOZBgwZxWVnZGfuvra3lkpISXrNmDTMzz58/ny+6 6CKeMWMGjxs3zg4plAgR31mvn7aiRv8sgLENlFnEzAMjj/stOKaiNGk++OADfPe73wURoUOHDhg1 ahSWL1+OBQsW4JZbbkHz5s0BAG3atAEArF27Fpdccgn69++PF154ARs2bGjwGN/4xjcAAH379kWn Tp1QWFgIIkJhYSF27NgBAJg5cyYGDRqE4uJirF+//vR+L7vsMvTt2xdTpkzBU089ZYMCSmNI2dEz 8/sAjjRQTAOBimIhkXVCY34W6/1Jkybh8ccfx5o1azB16tQGQzcAkJmZCQBIS0tDVlbW6ffT0tIQ CoWwfft2PPTQQ1i4cCFWr16N8ePHo6qqCgAQDoexceNG5Obm4vDhw8mcomIhTsToGcBwIlpNRG8T UR8HjqkogeaSSy7BzJkzEQ6HceDAASxevBhDhw7FmDFj8Oyzz5525EeOSB2svLwcnTp1Qk1NDZ5/ /vnTnbDx/iwagplx4sQJ5ObmomXLlti3bx/eeeed0/v985//jMLCQrzwwgu4+eabUVtba8FZK8mS 4cAxVgLoxswVRDQOwBsAejlwXEUJHIYjvfbaa/Hxxx9jwIABICI8+OCD6NChA6644gqUlZVh8ODB yMzMxPjx43H//ffjN7/5DYYOHYr27dtj6NChKC8vP72/eCNvot+vW4aI0L9/fwwcOBAFBQXo1q0b Lr74YgDA5s2b8fTTT2P58uXIzc3FyJEjcf/995/urFWch5L9Rz9jJ0T5AGYzc78Eym4HMIiZD9d5 n6dOnXr6dUlJCUpKSlK2TVH8BDNj2bJlePnll/GHP/wB6enpbpukeIzS0lKUlpaefn3fffeBmesN j9vu6ImoI4D9zMxENATAy8ycH6McW2GLoviRgwcP4rnnnsNjjz2GgwcPIhQK4bXXXsO4cePcNk3x OJH+GnsdPRHNADAKwFmQYZZTATQDAGZ+gohuB/BjALUAKgD8hJmXxNiPOnqlSREKhTB//nw8+uij WLhwIdLS0s7oJL3iiiswZ84cFy1U/IAjjt4q1NErTYXt27fjySefxPTp01FdXY0TJ07ELJeVlYWd O3eiffv2Dluo+IlEHL3OjFUUB6iqqsKMGTMwdOhQ9OnTB3/6059w6NChuE7e4Pnnn3fIQiXIODHq RlGaLGVlZXj88cfx4osvgohOj3aJR7NmzZCRkYGePXvi7rvvxre//W2HLFWCjDp6RbGYo0eP4oUX XsAjjzyC3bt349SpUwiFQvV+p0WLFiAi3HzzzfjRj36EgoICh6xVmgLq6BXFAsLhMBYtWoTHHnsM 77zzDtLT03Hy5Ml6v9O8eXMwM4YNG4a7774b48ePR7NmzRyyWGlKqKNXlBTYvXs3nn76aTz++OM4 efJkg6EZIkJubi7y8vJw++2345ZbbkHnzp0dslZpqqijV5RGUlNTgzfffBN//vOfsWzZMhDR6Rwv 8cjJyUE4HMb48eNxxx13YOTIkZoLXnEMdfSKkiCbNm3C3/72N/zjH/84neulPtLT05GVlYVu3brh zjvvxPe+9z20atXKIWsVxUQdvaLUQ3l5OWbOnImHH34YW7duRU1NTYMJuvLy8sDM+N73vofbb78d /fv3d8haRYmNOnpFqQMzY8mSJfjLX/6C119/Henp6Q3G3jMzM5GWloaioiLcfffduPrqq0/nhFcU t1FHrygR9u/ffzrfzOHDh1FRUdFgGt+8vDxkZWXhtttuww9/+EP06NHDIWsVJXHU0StNmlAohLlz 5+KRRx7BokWLvpJvJhbZ2dkIh8MYPXo07rrrLowePVqzTCqeRh290iTZtm0bnnjiCUyfPh21tbUN dqympaUhOzsb7du3x5133okbb7wR7dq1c8haRUkNdfSKL2HmRg9PrKysxKuvvopHHnkE69atQzgc RnV1db3fycvLQygUwnXXXYcpU6Zg8ODBOixS8R3q6BVf8txzz6GkpAT5+fkNll25ciX++te/4qWX XkJaWlpC+WbS09NRUFCAu+++G9dddx1ycnIsslxRnEfTFCu+o7a2Fj169MBNN92E3/3udzHLHDly BM8//zweeeQR7NmzJ+F8M2lpafjBD36AH/3oR+jZs6cd5iuKpWg+eiWQ/Otf/8Ktt96K3Nxc7N+/ /3RHaDgcxnvvvYdHH30Uc+fORXp6OioqKurdl5Fv5uKLL8Zdd92FcePGISNDG7qKf1BHrwSOcDiM Hj16YNeuXWjRogVefvllFBYW4qmnnsLf//53VFRUJJxvpmXLlpgyZQpuvvlmdOrUyaEzUBRrScTR a9VF8RWvvPIKjhw5AgA4ceIErr/++tN5Zk6dOlXvd3NzcxEKhXDVVVfhjjvuwIgRI7RjVWkSaI1e 8Q3hcBg9e/bEtm3bEv5ORkYGmjVrhvz8fNx1112YOHEiWrZsaaOViuIsjtToiegZAOMB7GfmfnHK PApgHGRx8EnMvCrV4ypNj9mzZ2P//v0JlW3RogWYGTfccAMmT56Mvn372mydoniXlGv0RHQJgHIA /4zl6InoSgBTmPlKIhoK4BFmHhajnNbolbgwM/r06YNNmzbFLZOVlQUiQnFxMe6++25cddVVyMrK ctBKRXEeR2r0zPw+EeXXU+QqAM9Fyi4lotZE1JGZ96V6bKXpMHfuXHz++ecxP8vMzESLFi0wefJk /Md//Ae6d+/usHWK4m2c6IztAmBn1OtdALoC+Iqj37sXaKqDH0Ih4JVXgI8+Arp2BSZNAtq3d9sq d6ipAV5+GVi2DOjRQ7Q4ceIE2rRpEzOTZG1tLcaNG4f/+Z//ccdgG6muBmbMAFauBM49F7jpJqB1 a7etcoeqKuDFF4GyMqBXL9GiRQu3rXKHigrgySeBhOfxMXPKDwD5ANbG+Ww2gBFRr98FUByjHGdl TeUbbpjKU6dO5ffee4+bCkeOMI8axQyYj7ZtmRcvdtsy5zl4kHnYsDO16NCBeelS5nA4zKWlpXz1 1VdzVlYWZ2dnMwAGwNnZ2VxeXu62+Zaydy9zcfGZWnTuzLxqlduWOc/Oncx9+56pRY8ezOvXu22Z 87z44nvcrt1UBqZyTs5UFjfegI9uqEAijwYc/d8BXB/1ehOAjjHKMcCcm8u8erWtOnmK2lrmyy6T X6JTJ+bf/Ib50kvldcuWzJs2uW2hc1RXM48YIefetSvz/febr9u2Zd62zSx74MABfvDBB7lr166c l5fH6enp/Mwzz7hnvMVUVTEPHiznnp/P/NvfMg8ZIq87dmTetcttC52jvNx08j17Mv/ud+YfYNeu zPv2uW2hcxw7xtyrl5x7797Ms2axZxz9lQDejmwPA7AkTjn+/vfFon79mGtqbNXLM/zpT+bNu327 vFdbyzxhgrw/ZAhzKOSqiY7xm9+YN6/hyGpqmMePl/dHjWIOh8/8Tjgc5sWLF/O1117Lo0ePdtxm u7jnHjnnc881HdmpU8yjR8v748a5a5+T3HWXnPMFFzAfOiTvVVQwDx8u70+Y4K59TnLrrXLO/fuL 02d2yNEDmAHgSwDVkFj8LQBuA3BbVJm/ANgCYHWssE2kDJeXM59zjlgVoMpZXI4cYW7dWs539uwz Pzt6lLlLF/nspZfcsc9J9u2T1hzAvGDBmZ8dPMjcvr18NmtW/H0cOnSIw3X/CXzIzp3MWVlyvh99 dOZne/aY18y777pjn5Ns3cqckcGclsa8cuWZn33+uXnN1NUpiKxfz0zE3KwZ84YN5vuO1eiteESM 5eef59Pxt6oqC1XyIL/6lZzrpZfG/vzJJ+XzXr2C38L5yU/kXMePj/35ww+bNZmgt3Buu03O9dvf jv35734nnw8d+tUWTtC44QY510mTYn/+y1/K51/7mrN2uYHRyp88+cz3fenoa2uZCwvFsunTrZLI exw7xpyXV39tpLqa+bzzpMwLLzhrn5McPMjcvLmcZ7yOxspKCekAzK+/7qx9TrJ7t1mD3bgxdpkT J6SDGmCeN89Z+5xk61azBmuENesS3Sr+4ANHzXOU9evlHJs3l2skmkQcfVqCg3McIz0d+MUvZPvJ J921xU5mzADKy4GRI4GLLopdplkz4Oc/l+0ga/HPf8rQubFjgaKi2GWaNwf+8z9lO8haPPssUFsL XHMNUFAQu0xeHnDHHbIdZC2eekrG11x/PRBv2YHWrYEf/1i2g6yFcW433QR07pzEDhr6J3DqgUiN nlk6Wox/6bpxuaBgjBpoqKZ+7JgZhwziCJxwmLmgILGa+oEDzJmZUsv7/HNn7HOSUEhClgDz3Ln1 l921izk9XWr/e/c6Yp6jVFfLKLREaupbt5q13cOHnbHPSSoqmNu0ie8P4ccaPQBkZwM33CDb06e7 a4sdrFwpj7ZtgW9+s/6yLVtKjQYIphYffghs2iQT5caPr7/sWWcBEyZILe/pp52xz0nefRf4/HOp vV52Wf1lu3QRvWprgX/8wwnrnOWtt2QCZe/ewPDh9Zc991zRq6oKeP55Z+xzktdeA44cAQYNAgYO TG4fnnT0APCDH8jz//2fXMxBYsYMef7udyUk0RCGFjNnAuGwfXa5gaHFjTdKqKohDC1eekkcfpAw tJg0CUhL4M6M1iJoGFrccguQSCbppqJF0jRU5XfqgajQDbM06Xv2lObKwoWpNHy8RThsDiFNdOZr KGQOtVyyxF77nCQUYj77bDmvFSsS+05NDXO7dvKdtWvttc9JqqvN5nn00Ln6qKxkbtFCvrN1q732 OUlFhRmujNcJW5fjx80O/bqdlX7m2DEzXLlnT+wy8GvoBpB/8QkTZPvVV921xUrKyoDt24GOHRtu khqkpZkhniBp8fHHwJ49EqooLk7sOxkZ0lEJBEuL0lJpnvfuLY9EaN4c+PrXZTtIWsybB5w8KaGK BNZ+ByA5b664QrZff9020xznzTcl39HFF6eWB8yzjh4wHf1rrwUnZGHckNdeKyOMEiX6Ty8oIQtD i29+M7HmuUEQKwDGuRjnliiqhYlqER9PrzDFLP/oX3wBrFgh//B+p6gIWL1aai1jxiT+vVAIOPts 4MABYMOGxGt9XqZXL+Czz4APPgBGjEj8e9XVktnz+HFgxw7JcOlnmIFu3YDdu6WTvjEdbhUVQLt2 0hG5bx/QoYN9djpBKCSt3UOHpJP+ggsS/+7Ro3JdMMv3W7Wyz04nqK6WARsnT0onfbzs24nko/d0 jZ5IxlYDwJw57tpiBXv2iJPPyZHx840hPR24/HLZDoIW27aJk2/dGhg6tHHfzcwERo+W7blzrbfN adavFyffqVP8eQTxyMkBRo2S7XnzrLfNaT75RJz0OedIRaAxtG4tc1JCIWDBAnvsc5IPPxQnX1gY 38kniqcdPWDG3YJwQxs3YkkJkMzCR8afXhC0MM7hsssk7t5YjOsiCH96xjlcfnnjQlgGQdTiiitS 0yJI94hx36eC5x396NFSm/3oI+DYMbetSQ3jIk72hzNq9IsWAZWV1tjkFqlqYdzQCxbIQiV+JtUb 2vjevHn+78uySos5c/zfl5XqPRKN5x19q1Zmc2zhQretSZ5wGJg/X7YNJ9VYOnSQ0SlVVcDixdbZ 5jQ1NeZvmawW+fkSvz1+HFiyxDLTHKeiQn5Losb12URTUCAx/gMHgFWrrLXPSY4eld8yIwO49NLk 9jFwoMTpv/gC+PRTa+1zkr17JcybnS0jblLF844eMG+A0lJXzUiJdesk9ti9O9CzZ/L7CYIWn3wi eX4KCmTZxGQJghYffyydbgMHyszfZIj+k/CzFu+/LxWiYcNkRngypKWZ/Td+1mLRInkeOTKxSZUN 4QtHb3Q2+bkWa9g+alRysUeDoGmRCqqFiWpholp8FV84+iFDZKTF6tXSvPMjxg/X2NE2dRk+XGot y5dLs9+PWKXFJZfI80cf+TdOb5UWxveNWrEfsVqLxYv9G6e3SgsDXzj67GzgwgvlR/vwQ7etaTzM 5g9nOKdkadUKGDBAHNvSpanb5jShkIybB1LXomNHidNXVMj4c79x6pTZv5BqHLZHD4nTHzkiwzX9 Rnm5hPTS0+On7U6U3r1lbsHu3TIL3W8cOiSh3qwsYPBga/bpC0cPnPkv7Tc++8yczNLYscGx8LMW a9fK6Kn8fHFMqeJnLVaskI71wsLk4/MGRP7W4uOPpRJQXCzpDFKByKxE+FELoyI0bFhyw7BjoY7e AaKbYanE5w2CooUVqBYmqoWJanEmKTt6IhpLRJuI6DMi+kWMz0uI6BgRrYo8fpXMcYzY9IoVMlvM T1j9wxm1FWPEhp+IHk1gBdGx6VDImn06hZ3OzW+xabu0MK43P2GHo081tXA6gC0A8gE0A1AGoHed MiUAZiWwrwZTdg4aJGlI33030SSf3sBYNaiszLp99u7N9a4360XCYeazzhK7N2+2br926Gs3NTVm iuFdu6zZZzjM3L699fraTWUlc1aW2H3okDX7jNZ3505r9ukEx4/LesEZGczl5Yl9Bw6kKR4CYAsz 72DmGgAvAbg6RjkLAhZmTdZPHbK7d0tColatgL59rduvH7X47DPg4EHpRD3/fOv260ct1q0DTpyQ 1ZG6dLFmn9GxaT9psXKldEz37StJvKwgI8NMA/7RR9bs0wmWLpVRU8XFQG6udftN1dF3AbAz6vWu yHvRMIDhRLSaiN4moj7JHmzYMHn202gTw9YhQxqXlrgh/KzFsGHW9FUY+F0LK1EtTFQLkyTSSZ1B IpHAlQC6MXMFEY0D8AaAmGNPpk2bdnq7pKQEJSUlZ3xuZDlculRikFY6C7swfrjGZmhsiGgt/IJq YaJamKgWJoloUVpaitLGTvttKLZT3wPAMABzol7fA+AXDXxnO4C2Md5vMBYVDjN36CBxty1bEotf uc2oUWLv7NnW7jcUYm7ZUvb95ZfW7tsuBg8WexcssHa/p06ZMd7Dh63dt1306WPP0pDl5czp6fI4 edLafdtFfr5osWaNtfs9eFD227y5LNXodZL1b3AgRr8CQE8iyieiTADfATArugARdSSSujcRDYEs dnI4mYMR+etfOhSSUUKAhG6sJC1NJpEB/tCiqkpmNhNZNwnEIDPTXIpw+XJr920Hx48DGzfKYuiN zT/fELm5EusOhfwxiWz/flk8JjcX6JN0UDc27dpJX1BVlczf8Dqffy56tGsnfTdWkpKjZ+ZaAFMA zAWwAcBMZt5IRLcR0W2RYt8CsJaIygA8DOD6VI5pOEw/OLf162UoaH6+PSv/+EmLVatkNm/v3skn rKoPP2mxfLmEHouKrJsQE42ftDBsHDzY2j4sAz9qMWSI9WHplMfRM/M7zHwBM5/PzL+PvPcEMz8R 2f4rM/dl5iJmHs7MKSWV9VON3q7Yo4FqYaJamKgWJqqF4JuZsQZGuGLVKhmS5WWcuoiXL/f+ZKFl y+TZbi2WLfP+ZCEntfA6Tt0jTV0L3zn61q0lj3l1tcR8vYzdF3GnTpLfvrxcYr5exm4tzjlH8sUc OCAxX6/CbL8WvXsDeXkS8923z55jWEE4bP+fXlGR9OFs2uTtFepqasw+Fav78wAfOnrAH82x8nKJ 0WdkyKISduEHLQ4elMXAc3KsnTQWjV866nftktWD2ra1dtJYNOnp/uio37xZOqa7dLFu0lhdsrLE 2TN7u6N+7VrpNO7Z07pJY9H40tEbF7GXRxWUlcnF1bevpFm2Cz9oYdhWVJTcQuCJ4gctPvlEngcN snceiJ+0sHoUVl1UC586emMonZd/OGPtTsNWu1AtTPyghWGbaqHXRTR2a+FLRz9ggIwjX78eqKx0 25rYOHVDG2Gh1auB2lp7j5Usbjg3r3bIOu3cjJqiF3H6umjKWvjS0efkSIdTKOTdiRBOXcRt20pH ZGWldDh5Eae06NxZEqYdOeLdDlmntDjvPFnA48svpU/AazA7p0VhoXTIbtnizQ7Z2lpzYIld/Xm+ dPSAt5tjVVXS2khLA/r3t/94Xtbi2DG5wTIzrZ/5WBcib2uxd6843pYtrZ/5WJe0NFMLoxXhJbZv l2ujY0fg7LPtPVazZuZ9WFZm77GSYdMm8RnnnAO0aWPPMdTR28DatdLaKCiwNtVoPLyshXFj9e8v N5zdeLmZbjjcgQPFEduNl7WIrs07kZzQy/eIEy0b3zr6QYPk2esXsROoFiaGFk31ho6mqTu3aJr6 PeJbR28kg1q71nvL6Tl9ERtxvVWrZBKKl3DTuXmtQ1YdvYlqYaKOvh5atAB69ZIZZevXu23NmTh9 EXfoAHTtKgnUPvvMmWMmitNadO8uHdQHDsjkJC9haGHnBLpoLrhABi58/jlw6JAzx0yE6I5Yp7To 21fmcGza5K01p8PhM0N6duFbRw94szlWUwOsWSPbVqegrQ8valFRITdWejrQr58zxyTyZvjm8GEZ CZSdLQ7YCdLTzWvQS1rs3i1/xK1bS2ZXJ2jeXJw9s7c6ZLdskVn0XbpIx7Rd+NrRe7E5tnGjhJLO P1/WiXUKL2qxZo3UWAoL5UZzCi9qYdTaBgywd3ZwXbyshVMdsQZe18JO1NFbjNOhCgPVwsSLo03c 0sKLLT23r4umeI8EwtF7aVao2ze0lzohvaCFV1DnZqLXhYk6+gRo3VomnlRVeSdNr9OdTAZnny1p i48dk0yRXsAtLc49V8Jme/bIwwu45dx695YMjlu3AkePOnvseLilRf/+3kqd4mSntK8dPeCtGkso ZHb0OO3cAG9pceoUsG6dxGAHDHD22ESm/l7Q4vhxScnbrJn0VzhJ9KxQL8yQ3b9fRkPl5UlKXifx WuqUL76QTvqzzpJRc3bie0fvpebYZ5/J0K1u3YD27Z0/vpe0WL9eRiD16iVDYZ3GS1oYeUz69ZNU EE7jJS2MP5uiImdmB9fFS1o4OTs4ZamJaCwRbSKiz4joF3HKPBr5fDURWVrX9VLHm1tNUgPVwsRL Wjg1siIeXtJCrwsTJ7VIydETUTqAvwAYC6APgIlE1LtOmSsBnM/MPQH8EMDfUjlmXYwmelmZ+7NC vXIRr1rlfoesl7RwG7f6KgxUCxMvauF5Rw9gCIAtzLyDmWsAvATg6jplrgLwHAAw81IArYnIsqkB 7dtLqMQLs0Lddm7dugHt2snSfW7PCnVbi549JaHcF1+IHm7ithbGrNBPP5XJOW7ithZeSp3iJ0ff BcDOqNe7Iu81VMbSrgcvdEIyu99E90qa3tpac3awWzW36FmhbtbeKiuBDRucS1kdi6wsc1ao0V/g BkePyoiwrCzpFHUDI3VKdbX8Lm6xZ4+krW7Vyv6U1QCQ6hy9RAMEdbsaYn5v2rRpp7dLSkpQUlKS 0M6Li4F//1uc28SJCVpkMTt2yIXsRH7t+iguBubPFy2urtu2cohPPxUHZ2d+7UQoLgY+/FC0GDPG HRuMlNWFhTLqwy2KiyW8uXIlMGKEOzY4nbI6HsXFMgpq5Upn05REE53fprEdsaWlpSgtLW3Ud1J1 9LsBdIt63Q1SY6+vTNfIe18h2tE3Bi/UYp3Orx0Pr2nhJqqFSXEx8MwzqoVx/JdeEntuucUdG1LR om4l+L777mvwO6mGblYA6ElE+USUCeA7AGbVKTMLwI0AQETDABxl5n0pHvcMosdMu9UJ6ZWL2Avj x1ULE69ooX96Jk3xukjJ0TNzLYApAOYC2ABgJjNvJKLbiOi2SJm3AWwjoi0AngAwOUWbv0LnzpKq 9+hR99aAFVdVAAAXIUlEQVQKdXs0gYEX1gr1ihZ9+ri/VqhXtIieFVpV5Y4NXtEieqReKOSODU5r kfI4emZ+h5kvYObzmfn3kfeeYOYnospMiXw+gJkt/x91uxOS2RyXa0zIcIu0tDMXInGa6Pzabtfc omeFutEJWV1tzsB027nl5srSlqGQzFh2mpMnJWV1RoZzKavj0a4d0KOH9CN9+qnzxz90SNYIyMlx LmW172fGGrjp6I382m3ayAXkNm5qsWULcOKE/fm1E8VNLdavF2ffs6csCO42bmpRViYVIqdTVsfD TS2MYxYVyegwJ1BHbwFe6Yg18IoWXkC1MHEzNu01LZradRFIR+90h6zxw7kdtjHwwkWsWqgW0agW Jm5oERhHn58vaYv373c+Na0Rn/dKbeWCC2TJuh07gCNHnD2217To10+axxs3ytKGTuK1WqwxZnzN Gkk45yRe0yI6FYLTqVPcuEcC4+jd7JD12kWckWGmBnayQzY6v7ZXtGjeXOLC4bA5W9cJamvNDmC3 O2INWreWUVmnTjm7fkNlpfRXpKU5n7I6Hp06ycTG48eB7dudO+7Ro7I2gNOzgwPj6AF3HP3evTKU sUULuYm8ghtaGLODO3SQIa9ewQ0tNm0yZwe3bevccRvCDS2M2cG9e7s7O7gubmjh1uzgQDl6Nzqb omuwbuTXjocbWkQPMfVCp7SB29eFl3DDuXlVC7fvESfxkGtKHb2ITVQLE9XCRLUwaUpaBMrRG6lp d+6Uce1O4NWLuLBQmoabN8u4difwqhYDBkgLY90651LTemUCXV3cWL/Bq1q4MVJPHb0FuJGa1qsX sdOpab00O7guRmramhrpFLSb6NnBXumINXB6/Ybo2cFuZYqMR/fu0n/i1PoNJ07ITNxmzeTedJJA OXrA2ebYwYOysEVOjjgSr+GkFrt2iR5t28oN5DWc1MJYO7hrV+mY9hpOauH22sH14fRIvdWrpULU t69UxJxEHX0KRC907NRU5sbgpBZemx1cFye18GrLxkC1MHHrHnEadfQp4LXJQXVRLUyayg2dCKqF SVO5RwLn6Hv3lmbR1q0ypttOvDatuy5GatoNG2RMt514XQsjVr56tUxmshM/OTe7OyH9pIXduHmP BM7RR6emNSYn2IXXL+KcHPnjC4XMDjG78LoWbdrI5CW7U9NGzw726p/e2Wc7s35D9Oxgr14XTq3f UFEhFa70dHfWDg6cowfM2pvRVLIDt6YyNxYntNizRx4tWzqz0HGyOKHFtm2yyInbawfXR3QnpJ1a bNwoi5ycc46kX/AiaWnmaCA7a/Vr1shorIICyUPlNIF09BdeKM/Lltl3DGPfAwe6u9BxQzihxdKl 8jx4sLdmB9fFSS2GDLHvGFagWpg0BS08fFsmz9Ch8myIawfGvo1jeRXVwkS1MFEtTJqCFoF09H36 AHl5slzXPkuXITdx+4dLlKIiWTd10yb71k31ixaDB0vYYvVq+9ZN9YsWRs1yxQr7Oqf9ooVh37Jl 9nVOu61F0o6eiNoS0Xwi2kxE84goZhSOiHYQ0RoiWkVENjaOTNLT5aYG7PmXZnb/h0uUrCxx9szA 8uXW7z8UMvfrdS1atJBKQE2NPTOnT50y92tcf16lfXuJnVdU2DNbuLxc9puR4b3ZwXXp3l36VA4f lqUwrebAAem7yclxfkasQSo1+v8CMJ+ZewFYEHkdCwZQwswDmdmxCJWdzbHt22UW6Flnyc3idezU YuNGuam7d5cc317HTi1Wr5Yp/wUF3u18jMZOLVaskM7H/v3d6XxsDET2amHE/gcNkj8+N0jF0V8F 4LnI9nMArqmnrONzJe384aJr816cBVoXp7TwA6qFiWphEnQtUnH0HZnZiIDvA9AxTjkG8C4RrSCi W1M4XqMwRF2+3PosfcY/tN8uYjtikH7Vws6am9+0sGO0iRecW2OwUwsvXBf1NiSIaD6AWA3yX0a/ YGYmonguZAQz7yGi9gDmE9EmZn4/VsFp06ad3i4pKUFJSUl95tVL586SVGrXLpkgY+VYd79dxOed B7RrJx3TX3wB9Ohh3b79pkVhocRKt2+X2Gn79tbt229aGEOD16+XzIpWJh3zmxYXXiit87Iy6Wux KukYs/WOvrS0FKWlpY01hJN6ANgEoFNk+2wAmxL4zlQAP43zGVvNhAnMAPOzz1q3z1OnmLOyZL9H jli3X7sZN05snjnTun2WlzOnp8vj5Enr9ms3I0eKFrNnW7fPQ4dkn82bM1dXW7dfuxk8WOxeuNC6 fe7cKfts1Yo5FLJuv3bTp4/YvWSJdfv89FPZ59lnM4fD1u03mojvrNf3phK6mQXgpsj2TQDeqFuA iHKIqEVkOxfA5QBsnoxvctFF8vzhh9btc+VK+cf3S4ebgR1aLF0qo24GDPDWWqANYYcWH30kz4MH e3sCXV3s0MLY19Ch3p5AVxc7tRg2zN3+vFR+hv8FMIaINgP4WuQ1iKgzEb0VKdMJwPtEVAZgKYA3 mXleKgY3hpEj5XnxYuv2aexr1Cjr9ukEqoWJamGiWpgEWYukB/sw82EAl8V4/0sA4yPb2wC4tq7M wIGytODmzZKwyIrhf8YPZ1wUfmHIEJk4tXq15OmxojXiVy1GjJDa1fLlMo7citaIX7W45BJ5/ugj mV9gRWvEr1oY9r7/vgzgsKI14hUtfNSwajwZGcDw4bL9fszu38YRCgEffCDbxg3iF7KzpcOJ2Zqm aXU18PHHsn3xxanvz0latZJwU02NNaNvysslOVhamtn89wsdO8rqTydPWjOJ7NAhWZs3K8vMIeMX evSQARyHD0umyVTZtUsmSrVs6U7GymgC7egBa5tja9dKGoH8fFl3029YqcWKFZJGoE8fmTjmN6zU YskSSSNQXOy95fISwUotjIrQ0KHOL5eXKkTWamFULkeMcH8FOnX0jcArzbBkUS1MVAsT1cIkqFoE 3tEbsem1a6VJlgpe+uGSYfhwCS+sWCFN9VTwuxZG6O3jjyUMlQp+16JubDoVgqLF4sWpTy70khaB d/TNm0szMtXYNLO3frhkaNlSOqhrayXckCx+7qsw6NBBhshWVqa2+EZVlaml3/oqDHr0kFxFR4+m thLZiRMy/Dg93X99FQYFBRKK3LNHFhZKlgMHJM7fvLk3EtwF3tED5tCmhQuT38eGDfLjdeoEnH++ NXa5gRVarFwpN/W550rnlV+xQoslS2ReRd++MvvYr1ihhdEiGDRI0oT7ESJrtFi0SJ4vukgiCm7T JBz9mDHyPHdu8vuYM0eeL7/cH4nM4mG1Fn5GtTBRLUyCqEWTcPQXXSSjITZulFwvyWD86FdcYZ1d bjBypIyGWLlSWijJEBQtRo+WMMPHHwPHjye3D0OLsWOts8sNDIe0aJGEs5IhKFoY1/W778oQ3MbC 7L17pEk4+mbN5KYGkvuXrqiQ+DyR+W/vV3JypGnKDMyf3/jvHzkijjEjA/ja16y3z0lat5ap6bW1 yTXT9+6VJFjZ2f7tqzDo2FH6b6qqkptzsn27TExs1co/iczikZ8PXHCB/PknM89i40YZQ9+xo8zX 8AJNwtED5j+r0aRqDIsWSRx20CBrsx26RSpaLFggcdjhw6Vz1+8Ytc9ktJgXSeZRUiKdbn4nFS2M CtRll7m3uIaVpHKPRIdtvJLrxyNm2I9xEc+b1/j1QmfNOnMffmfcOHl+663GN02DqsXs2Y0fWhhU LWbNavzQwiBr0Vg8qUVD6S2desCGNMV1KS6WlKH//nfi36mtZe7QQb63apV9tjlJOMxcUCDnNH9+ 4t87dUpSzwKSfjUIhMPM3bvLOX34YeLfKy9nzs6W733xhX32OUmy1/qRI8zNmjGnpTHv32+ffU5S VZXctb53LzMRc2Ym89Gj9tkXDWxOU+w7JkyQ51deSfw7H34I7N8vQwm9Em9LFSJTi1dfTfx7CxZI Coi+fSU/ShBIVos5c6TTcuhQf6bDiEV6OnDttbLdGC1mz5aW4ahRwQhtAjJg4RvfkO3GaPHGG9Ia GjNG+iu8QpN09LNmJT4b0viRJ0zw97DKuhhavP66TIBKhGgtgkS0o080ZBF0LRpTGQq6Fo1x9J7V oqEqv1MPOBC6YWbu10+aY6+91nDZU6fMpuzSpfbb5iThMPN558m5zZnTcPmTJ5lbt5bya9fab5+T hELMnTvLuS1e3HD5o0eZc3Kk/Nat9tvnJNXVzO3aybmtWNFw+f37JUyRlsa8e7f99jlJRQVzXp5o sWFDw+V37RIdMjKYDx603z4DaOjmq9x8szw/+WTDZWfNkrBNYaH/Uq42BFHjtHjlFZkif+GFEroJ EmlpwKRJsp2IFi++KENuS0okpBckmjUDbrhBthPR4p//lNbx2LGyTnOQyM4GJk6U7enTGy7/zDPS oX/NNR6cJd3QP4FTDzhUoz94UGogRMw7dtRf9vLL5d/8kUccMc1xdu+W9V4zMpj37Km/7MUXixbT pztjm9Ns2ybnl5Ul67/GIxxmLiqSsi++6Jx9TrJ+vZxfXh7ziRPxy4XDzBdcIGXfeMM5+5xk+XI5 v3btmCsr45errWXu0UPKzpvnmHnMnFiN3nUHf9oQhxw9M/N3vytn/rOfxS+zYQOfXuy5vhvf71xz jZznf/93/DKffJLYje93jD/2//3f+GXefz+xG9/vjBgh5/noo/HLzJ3Lpxe+rqlxzjYnCYeZBw6U 83z66fjlXn9dypxzjvMLoqujj4PxL52dzfzll7HLfOtbUubHP3bMLFdYvFjOs0WL+HHFK6+UMj/9 qbO2Oc0778h5tm3LfOzYVz8Ph5lHjZIyv/614+Y5yquvynl26iT9M3UJh5kvvFDK/P73ztvnJP/6 l5xnfr7029UlFDL7/ur7Y7QLWx09gOsArAcQAlBcT7mxADYB+AzAL+opZ7McZ3LttXL2kyd/9bMV K8zafNA6mGJxxRXxWzhGDTYvLzhjpOMRDpshqlgtHKMG26aNjB0PMuGwOe/kgQe++rlRg+3YUeYU BJnaWuY+feR8H3vsq5+/8IJ81q2bjL93GrsdfQGAXgDei+foAaQD2AIgH0AzAGUAescpa7sg0axd Kz3kRMyLFpnvV1SYMdig12ANjBZORgbzkiXm+ydOMPfu3TRqsAaLFsn5ZmYyl5WZ7x89ynzuuU2j BmswZ46cb04O88aN5vsHDjB36cKB7r+qy2uvyfm2bHnmSKs9e8yReU895Y5tjoRuGnD0FwGYE/X6 vwD8V5yytooRi3vu4dNN9blzmXfuZB47Vt4799xgx6Prctddct4dOjC/9550VF96qbxXUCB/gE2F W2+V8+7cmfmDD+TGNmLWRUWxm+9BxejP6tFDhhhv3sw8eLC8N2xYcGPzdQmHzSjA+eczr1wpndb9 +8t7l17qfGzewAuO/lsApke9/j6Ax+KUtVWMWNTUMF91lagQ/WjbNnhjxRvi1CmzMzL60aFDcNId JEplJfPIkV/VoksX5u3b3bbOWcrLmYcO/aoW+flNI6wZzdGjZms/+tGzJ/O+fe7ZlYijrzfPHBHN B9Apxkf3MvPs+r4bIcF5hsK0adNOb5eUlKCkpKQxX280GRkyk+2BB4C//11S8I4ZAzz0UPDGRzdE ZqZMZf/tb2XM8IkTMjb6oYdkmbmmRPPmko3xvvuAZ5+VMfNf/zrwxz8Gb6x4Q+TmSgrnX/8a+Ne/ JIvrNdcADz4oyzE2JVq1knTl994LzJghM8onTAD+8AegbVvn7CgtLUVpaWmjvkPyh5A8RPQegJ8y 88oYnw0DMI2Zx0Ze3wMgzMwPxCjLqdqiKIrS1CAiMHO9CVqsmhkb7yArAPQkonwiygTwHQBJJP5U FEVRkiVpR09E1xLRTgDDALxFRO9E3u9MRG8BADPXApgCYC6ADQBmMvPG1M1WFEVREiXl0I1VaOhG URSl8TgZulEURVE8ijp6RVGUgKOOXlEUJeCoo1cURQk46ugVRVECjjp6RVGUgKOOXlEUJeCoo1cU RQk46ugVRVECjjp6RVGUgKOOXlEUJeCoo1cURQk46ugVRVECjjp6RVGUgKOOXlEUJeCoo1cURQk4 6ugVRVECjjp6RVGUgJPKmrHXEdF6IgoRUXE95XYQ0RoiWkVEy5I9nqIoipIcGSl8dy2AawE80UA5 BlDCzIdTOJaiKIqSJEk7embeBMjCtAmQUCFFURTFepyI0TOAd4loBRHd6sDxFEVRlCjqrdET0XwA nWJ8dC8zz07wGCOYeQ8RtQcwn4g2MfP7jTVUURRFSY56HT0zj0n1AMy8J/J8gIheBzAEQExHP23a tNPbJSUlKCkpSfXwiqIogaK0tBSlpaWN+g4xc0oHJaL3APyMmT+J8VkOgHRmPkFEuQDmAbiPmefF KMup2qIoitLUICIwc739oKkMr7yWiHYCGAbgLSJ6J/J+ZyJ6K1KsE4D3iagMwFIAb8Zy8oqiKIp9 pFyjtwqt0SuKojQeW2v0iqIoij9QR68oihJw1NEriqIEHHX0iqIoAUcdvaIoSsBRR68oihJw1NEr iqIEHHX0iqIoAUcdvaIoSsBRR68oihJw1NEriqIEHHX0iqIoAUcdvaIoSsBRR68oihJw1NEriqIE HHX0iqIoAUcdvaIoSsBRR68oihJw1NEriqIEnFQWB3+QiDYS0Woieo2IWsUpN5aINhHRZ0T0i+RN VRRFUZIhlRr9PACFzDwAwGYA99QtQETpAP4CYCyAPgAmElHvFI7ZJCgtLXXbBM+gWpioFiaqReNI 2tEz83xmDkdeLgXQNUaxIQC2MPMOZq4B8BKAq5M9ZlNBL2IT1cJEtTBRLRqHVTH6WwC8HeP9LgB2 Rr3eFXlPURRFcYiM+j4kovkAOsX46F5mnh0p80sA1cz8YoxynLqJiqIoSioQc/K+mIgmAbgVwGhm rorx+TAA05h5bOT1PQDCzPxAjLL6p6AoipIEzEz1fV5vjb4+iGgsgJ8DGBXLyUdYAaAnEeUD+BLA dwBMTMZQRVEUJTlSidE/BiAPwHwiWkVEjwMAEXUmorcAgJlrAUwBMBfABgAzmXljijYriqIojSCl 0I2iKIrifVyfGasTqkyI6Bki2kdEa922xU2IqBsRvUdE64loHRHd6bZNbkFEzYloKRGVEdEGIvq9 2za5DRGlR6IIs922xU2IaAcRrYlosazesm7W6CMTqj4FcBmA3QCWA5jYVMM7RHQJgHIA/2Tmfm7b 4xZE1AlAJ2YuI6I8AJ8AuKYJXxc5zFxBRBkAPgDwM2b+wG273IKIfgJgEIAWzHyV2/a4BRFtBzCI mQ83VNbtGr1OqIqCmd8HcMRtO9yGmfcyc1lkuxzARgCd3bXKPZi5IrKZCSAdQIM3dlAhoq4ArgTw FAAdwJGgBm47ep1QpdRLZMTWQMjs6yYJEaURURmAfQDeY+YNbtvkIn+GjPYLN1SwCcAA3iWiFUR0 a30F3Xb02hOsxCUStnkFwF2Rmn2ThJnDzFwESTMykohKXDbJFYjo6wD2M/MqaG0eAEYw80AA4wDc Hgn9xsRtR78bQLeo190gtXqliUNEzQC8CuB5Zn7DbXu8ADMfA/AWgMFu2+ISwwFcFYlNzwDwNSL6 p8s2uQYz74k8HwDwOiQUHhO3Hf3pCVVElAmZUDXLZZsUlyEiAvA0gA3M/LDb9rgJEZ1FRK0j29kA xgBY5a5V7sDM9zJzN2Y+B8D1ABYy841u2+UGRJRDRC0i27kALgcQd7Seq45eJ1SdCRHNAPARgF5E tJOIbnbbJpcYAeD7AC6NDB1bFZmJ3RQ5G8DCSIx+KYDZzLzAZZu8QlMO/XYE8H7UdfEmM8+LV1gn TCmKogQct0M3iqIois2oo1cURQk46ugVRVECjjp6RVGUgKOOXlEUJeCoo1cURQk46ugVRVECjjp6 RVGUgPP/Qde6gvF4TtQAAAAASUVORK5CYII= ) 在上面的例子中,两个左边使用的都是原始数据的坐标系,不过我们还可以通过 `xycoords` 和 `textcoords` 来设置坐标系(默认是 `'data'`): | 参数 | 坐标系 | | --- | --- | | ‘figure points’ | points from the lower left corner of the figure | | ‘figure pixels’ | pixels from the lower left corner of the figure | | ‘figure fraction’ | 0,0 is lower left of figure and 1,1 is upper right | | ‘axes points’ | points from lower left corner of axes | | ‘axes pixels’ | pixels from lower left corner of axes | | ‘axes fraction’ | 0,0 is lower left of axes and 1,1 is upper right | | ‘data’ | use the axes data coordinate system | 使用一个不同的坐标系: In [5]: ``` fig = plt.figure() ax = fig.add_subplot(111) t = np.arange(0.0, 5.0, 0.01) s = np.cos(2*np.pi*t) line, = ax.plot(t, s, lw=2) ax.annotate('local max', xy=(3, 1), xycoords='data', xytext=(0.8, 0.95), textcoords='axes fraction', arrowprops=dict(facecolor='black', shrink=0.05), horizontalalignment='right', verticalalignment='top', ) ax.set_ylim(-2,2) plt.show() ``` ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXoAAAEACAYAAAC9Gb03AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJztnXl0HNWV/79XkrV6w6vwKox3eZFl4xVsATaYNRAzSeCE YJhDSPiRyZkkHAaSDM5AEpjEkwQmk7APCcTAsNosXjDINtjIFra8SnjBO7bxJtmyJGvp+/vjdrla cktqddeu+zmnT1dXva669e2q26/ue+8+YmYoiqIowSXJbQMURVEUe1FHryiKEnDU0SuKogQcdfSK oigBRx29oihKwFFHryiKEnAScvRE1J+IPiairUS0hYj+pZlyTxDRDiLaSETjEjmmoiiK0jZSEvx+ HYB/ZeYSIuoI4HMiWsbMpUYBIroWwGBmHkJEkwD8BcDkBI+rKIqixEhCNXpmPszMJeHlSgClAPo0 KXYjgBfDZYoAdCWi3okcV1EURYkdy2L0RJQDYByAoiab+gLYH/H5AIB+Vh1XURRFaRlLHH04bPM6 gB+Ha/bnFWnyWfMuKIqiOESiMXoQUQcAbwB4iZnfjlLkIID+EZ/7hdc13Y86f0VRlDhg5qaV6UYk 2uuGADwHYBsz/7GZYgsBfC9cfjKAcmY+Eq0gM+uLGQ8//LDrNnjlpVqoFqpFy69YSLRGPw3AdwFs IqIN4XUPARgQdtxPMfP7RHQtEe0EcAbAnQkeU1EURWkDCTl6Zv4EMTwVMPN9iRxHURRFiR8dGetB CgoK3DbBM6gWJqqFiWrRNijWGI/dEBF7xRZFURS/QERgOxtjFUVRFO+jjl5RFCXgqKNXFEUJOOro FUVRAo46ekVRlICjjl5R2ikdO3a0dH/z5s3D/PnzLd2nYg3q6BWlnSIZTLy7P8U61NErSjuHmXH/ /fdj9OjRGDNmDF577bVz2x5//HGMGTMGeXl5eOihhwAAzzzzDCZOnIi8vDzccsstqK6ubnH/c+fO xb333ospU6bg4osvRmFhIe644w6MHDkSd95pZkS59957cckll2DUqFGYN28eAKCiogLDhw/H9u3b AQC33nornnvuOYsVaAe4nZAnIjEPK4riHB07dmRm5tdff51nzZrFoVCIjxw5wgMGDOBDhw7x+++/ z1OnTuXq6mpmZj5x4gQzMx8/fvzcPn7xi1/wk08+yczM8+bN49///vfnHWfu3Ll86623MjPzO++8 w506deItW7ZwKBTi8ePHc0lJSaP919fXc0FBAW/atImZmZctW8ZTpkzhBQsW8DXXXGOHFL4m7Dtb 9K9ao1eUds4nn3yC2267DUSEXr16YcaMGVi3bh2WL1+Ou+66C+np6QCACy64AACwefNmXHbZZRgz ZgxefvllbNu2rdVj3HDDDQCAUaNGITs7G7m5uSAi5ObmYs+ePQCAV199FePHj0d+fj62bt16br8z Z87EqFGjcN999+HZZ5+1QYHgk3A+ekVR/E14CH3UbdHWz507FwsXLsTo0aPx4osvorCwsNVjpKam AgCSkpKQlpZ2bn1SUhIaGhqwe/duzJ8/H8XFxejSpQvuvPNO1NTUAABCoRBKS0uRlZWFEydOoE+f prOVKq2hNXpFaedcdtllePXVVxEKhXD06FGsXLkSkyZNwqxZs/DCCy+ci8GfPHkSAFBZWYns7GzU 1dXhpZdeOtcI29yfRWswM06fPo2srCx07twZR44cwQcffHBuv3/4wx+Qm5uLl19+GXfeeSfq6+st OOv2hdboFaWdYjjSm2++GWvWrMHYsWNBRPjd736HXr164eqrr0ZJSQkmTJiA1NRUXHfddXj00Ufx yCOPYNKkSejZsycmTZqEysrKc/trrudN5PqmZYgIY8aMwbhx4zB8+HD0798fl156KQBg+/bteO65 57Bu3TpkZWVh+vTpePTRR8811iqxodkrFUVRfIxmr1QUBQBw9uzZuEMriv9RR68o7YCf/OQnyMvL w+rVq902RXEBDd0oSsD58ssvMWrUKFRXVyMjIwMzZszAk08+icGDB7ttmmIBsYRuEnb0RPQ8gOsA fM3Mo6NsLwDwDoAvw6veYOZHo5RTR68oNnD99ddj8eLFaGhoACBdGjMzM3H48GFkZWW5bJ2SKLE4 eit63bwA4EkAf2uhzApmvtGCYymK0gaKiorw0UcfnXPyAJCWloYf/ehH6uTbEQnH6Jl5FYCTrRTT bEeK4jDMjB/84Afn5aJJTU09l7dGaR840RjLAKYS0UYiep+IRjpwTEVp97z99tvYsWNHo3VZWVl4 7LHHLE9RrHgbSxpjiSgHwKJmYvSdADQwcxURXQPgT8w8NEo5jdErikXU1dUhJycHX331VaP1AwYM wK5du5CSomMlg4JTMfoWYebTEcsfENH/EFE3Zj7RtGzkaLeCggIUFBTYbZ6iBJK//OUvqKioaLQu KysLf/7zn9XJ+5zCwsKY8gtF4kSNvjekRw4T0UQArzFzTpRyWqNXFAuoqKjAgAEDcOrUqUbrJ0yY gLVr1+oEIQHDkRo9ES0AMANADyLaD+BhAB0AgJmfAnALgB8SUT2AKgDfSfSYiqI0zyOPPILa2tpG 6zIyMvDXv/5VnXw7RQdMKUqA2L9/P4YNG9aop01KSgpuuOEGvPnmmy5aptiF5rpRlHbGT3/6U9TV 1TVa16FDB/zhD39wySLFC6ijV5SAUFJSgnfffbdRvvb09HR8//vfx8CBA120THEbDd0oSgBgZkyd OhVFRUWNslR26tQJ+/btQ9euXV20TrETDd0oSjthyZIl2Lx5cyMnn5WVhXnz5qmTV7RGryh+p6Gh AYMHDz43ybZBdnY29u7de26+ViWYaI1eUdoBL7zwAo4ePdpoXVZWFp544gl18goArdEriq85c+YM +vXrh/Ly8kbrR48ejY0bN2q/+XaA1ugVJeA8/vjjOHv2bKN1mZmZOjhKaYTW6BXFpxw+fBiDBg1q NDgqOTkZM2fOxOLFi120THESrdErSoB54IEHGvWZB2Rw1JNPPumSRYpXUUevKD6ktLQUr732WqNR sKmpqbj99tsxZMgQFy1TvIiGbhTFh1xxxRVYsWIFQqHQuXWZmZnYu3cvevTo4aJlitNo6EZRAkhh YSGKiorOc/IPPfSQOnklKlqjVxQfEQqFMHLkSHzxxReN1vfo0QP79u1DRkaGS5YpbqE1ekXxKSdP nsTatWvPW79gwQIcOHCg0bqsrCzMnz9fnbzSLFqjVxSPctttt6G6uhrz58/HoEGDUFNTgwEDBpw3 CnbIkCEoKytDUpLW29ojsdTo1dErikc5cOAABg0ahFAohB/84Afo3Lkz/vSnP6GqqupcmczMTCxa tAhXXHGFi5YqbqKOXlF8zn/8x3/gN7/5DQCgrq6uUQMsEWH69OltnihaCRbq6BXF59TU1CAnJwdH jhw5b1tGRgbWrVuH3NxcFyxTvIIjjbFE9DwRHSGizS2UeYKIdhDRRiIal+gxFaW9kJ6ejqeeegpZ WVnnbUtNTcWxY8dcsErxG1a03rwAYHZzG4noWgCDmXkIgO8D+IsFx1SUdsONN96IcePGndfYWlFR gWuvvRYzZ85EWVmZS9YpfiBhR8/MqwCcbKHIjQBeDJctAtCViHonelxFaS8QEZ599ll06NABHTt2 RGZmJtLS0pCSkoLq6moUFhZiypQpePjhhxs11CqKQYoDx+gLYH/E5wMA+gE4L+h4+DCQne2ARR6k oQF4/XVg9WqgXz9g7lygZ0+3rXKHujrgtdeAtWuBgQNFi27d3LbKHWprgQULgPXrh+H++7fhm99k 9OqVjvR0eRkOvz1QUwP84x9ASQkwdChwxx1Ap05uW+UOVVXA008DmZmxlXfqCmnaUBC11TUnZx6+ 9S1g0CCgoKAABQUF9lvmAcrLgZtuAlasMNc99hjw9tvAZZe5Z5cbHD8OXH898Nln5rrHHwcWLQIm TnTPLjc4cgS49lpg/XpjzSA8/zzw3ntAXp6bljnPgQPANdcAW7aY637/e+D994GRI92zyw0WLCjE j35UiOPHY3f0YOaEXwByAGxuZttfAXwn4nMZgN5RyjHAnJXFvHEjtxvq65lnzmQGmLOzmR95hPny y+Vz587MZWVuW+gctbXM06bJuffrx/zoo+bnbt2Yv/zSbQudo6aGecIEOfecHOZf/5p54kT53Ls3 84EDblvoHJWVzKNGybkPGcL8m98w5+eb18mRI25b6BwVFcxDh8q5jxjBvHAhs7jxVnx0awViebXi 6K8F8H54eTKAz5opx9/9rlg0ejRzXZ2tenmG//ov8+bdvVvW1dczz5kj6ydOZG5ocNVEx3jkEfPm NRxZXR3zddfJ+hkzmEMhV010jAcflHMeNMh0ZGfPMl95pay/5hp37XOSH/9YznnYMObjx2VdVRXz 1Kmyfs4cd+1zkrvvlnMeM0acPrNDjh7AAgBfAaiFxOLvAnAPgHsiyvw3gJ0ANgLIb2Y/XFnJfNFF YtXzz9srmBc4eZK5a1c530WLGm8rL2fu21e2vfKKO/Y5yZEj8jQHMC9f3njbsWPMPXvKtoUL3bHP SfbvZ05Lk/NdvbrxtkOHzGvmww/dsc9Jdu1iTklhTkpiXr++8ba9e81rpqlOQWTrVmYi5g4dmLdt M9c7VqO34hU2ll96SawaOFAeX4PML34h53r55dG3P/20bB86NPhPOD/5iZzrdddF3/7HP5o1maA/ 4dxzj5zrt74VfftvfiPbJ00K/hPO7bfLuc6dG337z38u26+4wlm73MB4yr/33sbrfeno6+uZc3PF smeesUoi71FRwdyxY8u1kdpa5osvljIvv+ysfU5y7Bhzerqc54YN0ctUV0tIB2B+6y1n7XOSgwfN GmxpafQyp08z9+olWixd6qx9TrJrl1mDNcKaTYl8Kv7kE0fNc5StW+Uc09PlGokkFkfvuXR3ycnA Aw/I8tNPu2uLnSxYAFRWAtOnA1OmRC/ToQNw//2yHGQt/vY36To3e3bzvUnS04F//VdZDrIWL7wA 1NdLL6zhw6OX6dgR+NGPZDnIWjz7LMAMfOc7QE5O9DJduwI//KEsB1kL49zuuAPo0yeOHbT2T+DU C+EaPbM0tBj/0k3jckHB6DXQWk29osKMQwaxB04oxDx8eGw19aNHmVNTpZa3d68z9jlJQ4OELAHm JUtaLnvgAHNystT+Dx92xDxHqa2VXmix1NR37TJruydOOGOfk1RVMV9wQfP+EH6s0QNARgZw++2y /Mwz7tpiB+vXy6tbN+Cb32y5bOfOUqMBgqnFp58CZWUyUO6661ou26MHMGeO1PKee84Z+5zkww+B vXul9jpzZstl+/YVverrgf/9Xyesc5b33pMBlCNGAFOntlx20CDRq6YGeOklZ+xzkjffBE6eBMaP B8bFmSnMk44eAP75n+X9//5PLuYgsWCBvN92m4QkWsPQ4tVXgYgstYHA0OJ735NQVWsYWrzyijj8 IGFoMXcuEMscIpFaBA1Di7vuAqjFvIxCe9Eiblqr8jv1QkTohlke6YcMkceVjz5K5MHHW4RCZhfS lStj+05Dg9nV8rPP7LXPSRoamC+8UM6ruDi279TVMXfvLt/ZvNle+5ykttZ8PI/sOtcS1dXMnTrJ d3btstc+J6mqMsOVzTXCNuXUKbNBv2ljpZ+pqDDDlYcORS8Dv4ZuAPkXnzNHlt94w11brKSkBNi9 G+jdu/VHUoOkJDPEEyQt1qwBDh2SUEV+fmzfSUmRhkogWFoUFsrj+YgR8oqF9HRJFwEES4ulS4Ez ZyRU0VwjbFM6dQKuvlqW33rLNtMc5913Jd/RpZcmlgfMs44eMB39m28GJ2Rh3JA33yw9jGIl8k8v KCELQ4tvfjO2x3ODIFYAjHMxzi1WVAsT1aJ5PD3DFLP8o+/bBxQXyz+838nLAzZulFrLrFmxf6+h AbjwQuDoUWDbtthrfV5m6FBgxw7gk0+AadNi/15trWT2PHUK2LNHMlz6GWagf3/g4EFppG9Lg1tV FdC9uzREHjkC9Opln51O0NAgT7vHj0sj/bBhsX+3vFyuC2b5fpcu9tnpBLW10mHjzBlppB8wIHo5 R2aYshMi6VsNAIsXu2uLFRw6JE4+M1P6z7eF5GTgqqtkOQhafPmlOPmuXYFJk9r23dRU4MorZXnJ Euttc5qtW8XJZ2e3PStlZiYwY4YsL11qvW1O8/nn4qQvukgqAm2ha1cZk9LQACxfbo99TvLpp+Lk c3Obd/Kx4mlHD5hxtyDc0MaNWFAApKW1/fvGn14QtDDOYeZMibu3FeO6CMKfnnEOV13VthCWQRC1 uPrqxLQI0j0yu9n5+2LH847+yiulNrt6NVBR4bY1iWFcxPH+cEaNfsUKoLraGpvcIlEtjBt6+XKZ qMTPJHpDG99butT/bVlWabF4sf/bshK9RyLxvKPv0sV8HPvoI7etiZ9QCFi2TJYNJ9VWevWS3ik1 NcDKldbZ5jR1deZvGa8WOTkSvz11qvEkJX6jqkp+S6K2tdlEMny4xPiPHgU2bLDWPicpL5ffMiUF uPzy+PYxbpzE6fftA774wlr7nOTwYQnzZmRIj5tE8byjB8wboLDQVTMSYssWiT0OGAAMGRL/foKg xeefS56f4cNl2sR4CYIWa9ZIo9u4cTLyNx4i/yT8rMWqVVIhmjxZRoTHQ1KS2X7jZy2M2eamT49t UGVr+MLRG41Nfq7FGrbPmBFf7NEgaFokgmpholqYqBbn4wtHP3Gi9LTYuFEe7/yI8cO1tbdNU6ZO lVrLunXy2O9HrNLCmE939Wr/xumt0sL4vlEr9iNWa7FypX/j9FZpYeALR5+RAVxyifxon37qtjVt h9n84RKd7LtLF2DsWHFsRUWJ2+Y0DQ3Sbx5IXIvevSVOX1UVOYG2fzh71mxfSDQOO3CgxOlPnpTu mn6jslJCesnJzaftjpURI2RswcGDMgrdbxw/LqHetDRgwgRr9ukLRw80/pf2Gzt2mINZ2to3OBp+ 1mLzZuk9lZMjjilR/KxFcbE0rOfmxh+fNyDytxZr1kglID9f0hkkApFZifCjFkZFaPLk+LphR0Md vQNEPoYlEp83CIoWVqBamKgWJqpFYxJ29EQ0m4jKiGgHET0QZXsBEVUQ0Ybw6xfxHMeITRcXy2gx P2H1D2fUVoweG34isjeBFUTGphsarNmnU9jp3PwWm7ZLC+N68xN2OPpEUwsnA9gJIAdABwAlAEY0 KVMAYGEM+2o1Zef48ZKG9MMPY03y6Q2MWYNKSqzb54gR3OJ8s14kFGLu0UPs3r7duv3aoa/d1NWZ KYYPHLBmn6EQc8+e1utrN9XVzGlpYvfx49bsM1Lf/fut2acTnDol8wWnpDBXVsb2HTiQpngigJ3M vIeZ6wC8AuAbUcpZELAwa7J+apA9eFASEnXpAowaZd1+/ajFjh3AsWPSiDp4sHX79aMWW7YAp0/L 7Eh9+1qzz8jYtJ+0WL9eGqZHjZIkXlaQkmKmAV+92pp9OkFRkfSays8HsrKs22+ijr4vgP0Rnw+E 10XCAKYS0UYiep+IRsZ7sMmT5d1PvU0MWydObFta4tbwsxaTJ1vTVmHgdy2sRLUwUS1M4kgn1YhY IoHrAfRn5ioiugbA2wCi9j2ZN2/eueWCggIUFBQ02m5kOSwqkhiklc7CLowfrq0ZGlsjUgu/oFqY qBYmqoVJLFoUFhaisK3DfluL7bT0AjAZwOKIzw8CeKCV7+wG0C3K+lZjUaEQc69eEnfbuTO2+JXb zJgh9i5aZO1+GxqYO3eWfX/1lbX7tosJE8Te5cut3e/Zs2aM98QJa/dtFyNH2jM1ZGUlc3KyvM6c sXbfdpGTI1ps2mTtfo8dk/2mp8tUjV4nXv8GB2L0xQCGEFEOEaUC+DaAhZEFiKg3kdS9iWgiZLKT E/EcjMhf/9INDdJLCJDQjZUkJckgMsAfWtTUyMhmIusGgRikpppTEa5bZ+2+7eDUKaC0VCZDb2v+ +dbIypJYd0ODPwaRff21TB6TlQWMjDuoG53u3aUtqKZGxm94nb17RY/u3aXtxkoScvTMXA/gPgBL AGwD8CozlxLRPUR0T7jYLQA2E1EJgD8C+E4ixzQcph+c29at0hU0J8eemX/8pMWGDTKad8SI+BNW tYSftFi3TkKPeXnWDYiJxE9aGDZOmGBtG5aBH7WYONH6sHTC/eiZ+QNmHsbMg5n5t+F1TzHzU+Hl PzPzKGbOY+apzJxQUlk/1ejtij0aqBYmqoWJamGiWgi+GRlrYIQrNmyQLllexqmLeN067w8WWrtW 3u3WYu1a7w8WclILr+PUPdLetfCdo+/aVfKY19ZKzNfL2H0RZ2dLfvvKSon5ehm7tbjoIskXc/So xHy9CrP9WowYAXTsKDHfI0fsOYYVhEL2/+nl5UkbTlmZt2eoq6sz21Ssbs8DfOjoAX88jlVWSow+ JUUmlbALP2hx7JhMBp6Zae2gsUj80lB/4IDMHtStm7WDxiJJTvZHQ/327dIw3bevdYPGmpKWJs6e 2dsN9Zs3S6PxkCHWDRqLxJeO3riIvdyroKRELq5RoyTNsl34QQvDtry8+CYCjxU/aPH55/I+fry9 40D8pIXVvbCaolr41NEbXem8/MMZc3cattqFamHiBy0M21QLvS4isVsLXzr6sWOlH/nWrUB1tdvW RMepG9oIC23cCNTX23useHHDuXm1QdZp52bUFL2I09dFe9bCl44+M1ManBoavDsQwqmLuFs3aYis rpYGJy/ilBZ9+kjCtJMnvdsg65QWF18sE3h89ZW0CXgNZue0yM2VBtmdO73ZIFtfb3Yssas9z5eO HvD241hNjTxtJCUBY8bYfzwva1FRITdYaqr1Ix+bQuRtLQ4fFsfbubP1Ix+bkpRkamE8RXiJ3bvl 2ujdG7jwQnuP1aGDeR+WlNh7rHgoKxOfcdFFwAUX2HMMdfQ2sHmzPG0MH25tqtHm8LIWxo01Zozc cHbj5cd0w+GOGyeO2G68rEVkbd6J5IRevkeceLLxraMfP17evX4RO4FqYWJo0V5v6Ejau3OLpL3f I7519EYyqM2bvTedntMXsRHX27BBBqF4CTedm9caZNXRm6gWJuroW6BTJ2DoUBlRtnWr29Y0xumL uFcvoF8/SaC2Y4czx4wVp7UYMEAaqI8elcFJXsLQws4BdJEMGyYdF/buBY4fd+aYsRDZEOuUFqNG yRiOsjJvzTkdCjUO6dmFbx094M3Hsbo6YNMmWbY6BW1LeFGLqiq5sZKTgdGjnTkmkTfDNydOSE+g jAxxwE6QnGxeg17S4uBB+SPu2lUyuzpBero4e2ZvNcju3Cmj6Pv2lYZpu/C1o/fi41hpqYSSBg+W eWKdwotabNokNZbcXLnRnMKLWhi1trFj7R0d3BQva+FUQ6yB17WwE3X0FuN0qMJAtTDxYm8Tt7Tw 4pOe29dFe7xHAuHovTQq1O0b2kuNkF7QwiuoczPR68JEHX0MdO0qA09qaryTptfpRiaDCy+UtMUV FZIp0gu4pcWgQRI2O3RIXl7ALec2YoRkcNy1Cygvd/bYzeGWFmPGeCt1ipON0r529IC3aiwNDWZD j9PODfCWFmfPAlu2SAx27Fhnj01k6u8FLU6dkpS8HTpIe4WTRI4K9cII2a+/lt5QHTtKSl4n8Vrq lH37pJG+Rw/pNWcnvnf0Xnoc27FDum717w/07On88b2kxdat0gNp6FDpCus0XtLCyGMyerSkgnAa L2lh/Nnk5TkzOrgpXtLCydHBCUtNRLOJqIyIdhDRA82UeSK8fSMRWVrX9VLDm1uPpAaqhYmXtHCq Z0VzeEkLvS5MnNQiIUdPRMkA/hvAbAAjAdxKRCOalLkWwGBmHgLg+wD+ksgxm2I8opeUuD8q1CsX 8YYN7jfIekkLt3GrrcJAtTDxohaed/QAJgLYycx7mLkOwCsAvtGkzI0AXgQAZi4C0JWILBsa0LOn hEq8MCrUbefWvz/QvbtM3ef2qFC3tRgyRBLK7dsneriJ21oYo0K/+EIG57iJ21p4KXWKnxx9XwD7 Iz4fCK9rrYylTQ9eaIRkdv8R3StpeuvrzdHBbtXcIkeFull7q64Gtm1zLmV1NNLSzFGhRnuBG5SX S4+wtDRpFHUDI3VKba38Lm5x6JCkre7Sxf6U1QCQ6Bi9WAMETZsaon5v3rx555YLCgpQUFAQ087z 84F33hHnduutMVpkMXv2yIXsRH7tlsjPB5YtEy2+0fTZyiG++EIcnJ35tWMhPx/49FPRYtYsd2ww Ulbn5kqvD7fIz5fw5vr1wLRp7tjgdMrq5sjPl15Q69c7m6Ykksj8Nm1tiC0sLERhYWGbvpOooz8I oH/E5/6QGntLZfqF151HpKNvC16oxTqdX7s5vKaFm6gWJvn5wPPPqxbG8V95Rey56y53bEhEi6aV 4F/96letfifR0E0xgCFElENEqQC+DWBhkzILAXwPAIhoMoByZj6S4HEbEdln2q1GSK9cxF7oP65a mHhFC/3TM2mP10VCjp6Z6wHcB2AJgG0AXmXmUiK6h4juCZd5H8CXRLQTwFMA7k3Q5vPo00dS9ZaX uzdXqNu9CQy8MFeoV7QYOdL9uUK9okXkqNCaGnds8IoWkT31GhrcscFpLRLuR8/MHzDzMGYezMy/ Da97ipmfiihzX3j7WGa2/H/U7UZIZrNfrjEgwy2SkhpPROI0kfm13a65RY4KdaMRsrbWHIHptnPL ypKpLRsaZMSy05w5IymrU1KcS1ndHN27AwMHSjvSF184f/zjx2WOgMxM51JW+35krIGbjt7Ir33B BXIBuY2bWuzcCZw+bX9+7VhxU4utW8XZDxkiE4K7jZtalJRIhcjplNXN4aYWxjHz8qR3mBOoo7cA rzTEGnhFCy+gWpi4GZv2mhbt7boIpKN3ukHW+OHcDtsYeOEiVi1Ui0hUCxM3tAiMo8/JkbTFX3/t fGpaIz7vldrKsGEyZd2ePcDJk84e22tajB4tj8elpTK1oZN4rRZr9BnftEkSzjmJ17SITIXgdOoU N+6RwDiVYPfDAAAVx0lEQVR6NxtkvXYRp6SYqYGdbJCNzK/tFS3S0yUuHAqZo3WdoL7ebAB2uyHW oGtX6ZV19qyz8zdUV0t7RVKS8ymrmyM7WwY2njoF7N7t3HHLy2VuAKdHBwfG0QPuOPrDh6UrY6dO chN5BTe0MEYH9+olXV69ghtalJWZo4O7dXPuuK3hhhbG6OARI9wdHdwUN7Rwa3RwoBy9G41NkTVY N/JrN4cbWkR2MfVCo7SB29eFl3DDuXlVC7fvESfxkGtKHL2ITVQLE9XCRLUwaU9aBMrRG6lp9++X fu1O4NWLODdXHg23b5d+7U7gVS3GjpUnjC1bnEtN65UBdE1xY/4Gr2rhRk89dfQW4EZqWq9exE6n pvXS6OCmGKlp6+qkUdBuIkcHe6Uh1sDp+RsiRwe7lSmyOQYMkPYTp+ZvOH1aRuJ26CD3ppMEytED zj6OHTsmE1tkZooj8RpOanHggOjRrZvcQF7DSS2MuYP79ZOGaa/hpBZuzx3cEk731Nu4USpEo0ZJ RcxJ1NEnQOREx04NZW4LTmrhtdHBTXFSC68+2RioFiZu3SNOo44+Abw2OKgpqoVJe7mhY0G1MGkv 90jgHP2IEfJYtGuX9Om2E68N626KkZp22zbp020nXtfCiJVv3CiDmezET87N7kZIP2lhN27eI4Fz 9JGpaY3BCXbh9Ys4M1P++BoazAYxu/C6FhdcIIOX7E5NGzk62Kt/ehde6Mz8DZGjg716XTg1f0NV lVS4kpPdmTs4cI4eMGtvxqOSHbg1lLmtOKHFoUPy6tzZmYmO48UJLb78UiY5cXvu4JaIbIS0U4vS Upnk5KKLJP2CF0lKMnsD2Vmr37RJemMNHy55qJwmkI7+kkvkfe1a+45h7HvcOHcnOm4NJ7QoKpL3 CRO8NTq4KU5qMXGifcewAtXCpD1o4eHbMn4mTZJ3Q1w7MPZtHMurqBYmqoWJamHSHrQIpKMfORLo 2FGm6zpi6TTkJm7/cLGSlyfzppaV2Tdvql+0mDBBwhYbN9o3b6pftDBqlsXF9jVO+0ULw761a+1r nHZbi7gdPRF1I6JlRLSdiJYSUdQoHBHtIaJNRLSBiGx8ODJJTpabGrDnX5rZ/R8uVtLSxNkzA+vW Wb//hgZzv17XolMnqQTU1dkzcvrsWXO/xvXnVXr2lNh5VZU9o4UrK2W/KSneGx3clAEDpE3lxAmZ CtNqjh6VtpvMTOdHxBokUqP/NwDLmHkogOXhz9FgAAXMPI6ZHYtQ2fk4tnu3jALt0UNuFq9jpxal pXJTDxggOb69jp1abNwoQ/6HD/du42MkdmpRXCyNj2PGuNP42BaI7NXCiP2PHy9/fG6QiKO/EcCL 4eUXAdzUQlnHx0ra+cNF1ua9OAq0KU5p4QdUCxPVwiToWiTi6HszsxEBPwKgdzPlGMCHRFRMRHcn cLw2YYi6bp31WfqMf2i/XcR2xCD9qoWdNTe/aWFHbxMvOLe2YKcWXrguWnyQIKJlAKI9kP888gMz MxE150KmMfMhIuoJYBkRlTHzqmgF582bd265oKAABQUFLZnXIn36SFKpAwdkgIyVfd39dhFffDHQ vbs0TO/bBwwcaN2+/aZFbq7ESnfvlthpz57W7dtvWhhdg7dulcyKViYd85sWl1wiT+clJdLWYlXS MWbrHX1hYSEKCwvbagjH9QJQBiA7vHwhgLIYvvMwgJ82s42tZs4cZoD5hRes2+fZs8xpabLfkyet 26/dXHON2Pzqq9bts7KSOTlZXmfOWLdfu5k+XbRYtMi6fR4/LvtMT2eurbVuv3YzYYLY/dFH1u1z /37ZZ5cuzA0N1u3XbkaOFLs/+8y6fX7xhezzwguZQyHr9htJ2He26HsTCd0sBHBHePkOAG83LUBE mUTUKbycBeAqADYPxjeZMkXeP/3Uun2uXy//+H5pcDOwQ4uiIul1M3ast+YCbQ07tFi9Wt4nTPD2 ALqm2KGFsa9Jk7w9gK4pdmoxebK77XmJ/AyPAZhFRNsBXBH+DCLqQ0TvhctkA1hFRCUAigC8y8xL EzG4LUyfLu8rV1q3T2NfM2ZYt08nUC1MVAsT1cIkyFrE3dmHmU8AmBll/VcArgsvfwnAtXllxo2T qQW3b5eERVZ0/zN+OOOi8AsTJ8rAqY0bJU+PFU8jftVi2jSpXa1bJ/3IrXga8asWl10m76tXy/gC K55G/KqFYe+qVdKBw4qnEa9o4aMHq7aTkgJMnSrLq6I2/7aNhgbgk09k2bhB/EJGhjQ4MVvzaFpb C6xZI8uXXpr4/pykSxcJN9XVWdP7prJSkoMlJZmP/36hd2+Z/enMGWsGkR0/LnPzpqWZOWT8wsCB 0oHjxAnJNJkoBw7IQKnOnd3JWBlJoB09YO3j2ObNkkYgJ0fm3fQbVmpRXCxpBEaOlIFjfsNKLT77 TNII5Od7b7q8WLBSC6MiNGmS89PlJQqRtVoYlctp09yfgU4dfRvwymNYvKgWJqqFiWphElQtAu/o jdj05s3ySJYIXvrh4mHqVAkvFBfLo3oi+F0LI/S2Zo2EoRLB71o0jU0nQlC0WLky8cGFXtIi8I4+ PV0eIxONTTN764eLh86dpYG6vl7CDfHi57YKg169pItsdXVik2/U1Jha+q2twmDgQMlVVF6e2Exk p09L9+PkZP+1VRgMHy6hyEOHZGKheDl6VOL86eneSHAXeEcPmF2bPvoo/n1s2yY/XnY2MHiwNXa5 gRVarF8vN/WgQdJ45Ves0OKzz2RcxahRMvrYr1ihhfFEMH68pAn3I0TWaLFihbxPmSIRBbdpF45+ 1ix5X7Ik/n0sXizvV13lj0RmzWG1Fn5GtTBRLUyCqEW7cPRTpkhviNJSyfUSD8aPfvXV1tnlBtOn S2+I9evlCSUegqLFlVdKmGHNGuDUqfj2YWgxe7Z1drmB4ZBWrJBwVjwERQvjuv7wQ+mC21aYvXeP tAtH36GD3NRAfP/SVVUSnycy/+39SmamPJoyA8uWtf37J0+KY0xJAa64wnr7nKRrVxmaXl8f32P6 4cOSBCsjw79tFQa9e0v7TU1NfGNOdu+WgYlduvgnkVlz5OQAw4bJn3884yxKS6UPfe/eMl7DC7QL Rw+Y/6zGI1VbWLFC4rDjx1ub7dAtEtFi+XKJw06dKo27fseofcajxdJwMo+CAml08zuJaGFUoGbO dG9yDStJ5B6JDNt4JdePR8ywH+MiXrq07fOFLlzYeB9+55pr5P2999r+aBpULRYtanvXwqBqsXBh 27sWBlmLtuJJLVpLb+nUCzakKW5Kfr6kDH3nndi/U1/P3KuXfG/DBvtsc5JQiHn4cDmnZcti/97Z s5J6FpD0q0EgFGIeMEDO6dNPY/9eZSVzRoZ8b98+++xzkniv9ZMnmTt0YE5KYv76a/vsc5Kamviu 9cOHmYmYU1OZy8vtsy8S2Jym2HfMmSPvr78e+3c+/RT4+mvpSuiVeFuiEJlavPFG7N9bvlxSQIwa JflRgkC8WixeLI2Wkyb5Mx1GNJKTgZtvluW2aLFokTwZzpgRjNAmIB0WbrhBltuixdtvy9PQrFnS XuEV2qWjX7gw9tGQxo88Z46/u1U2xdDirbdkAFQsRGoRJCIdfawhi6Br0ZbKUNC1aIuj96wWrVX5 nXrBgdANM/Po0fI49uabrZc9e9Z8lC0qst82JwmFmC++WM5t8eLWy585w9y1q5TfvNl++5ykoYG5 Tx85t5UrWy9fXs6cmSnld+2y3z4nqa1l7t5dzq24uPXyX38tYYqkJOaDB+23z0mqqpg7dhQttm1r vfyBA6JDSgrzsWP222cADd2cz513yvvTT7deduFCCdvk5vov5WprELVNi9dflyHyl1wioZsgkZQE zJ0ry7Fo8Y9/SJfbggIJ6QWJDh2A22+X5Vi0+Nvf5Ol49myZpzlIZGQAt94qy88803r555+XBv2b bvLgKOnW/gmcesGhGv2xY1IDIWLes6flslddJf/mf/qTI6Y5zsGDMt9rSgrzoUMtl730UtHimWec sc1pvvxSzi8tTeZ/bY5QiDkvT8r+4x/O2eckW7fK+XXsyHz6dPPlQiHmYcOk7NtvO2efk6xbJ+fX vTtzdXXz5errmQcOlLJLlzpmHjPHVqN33cGfM8QhR8/MfNttcuY/+1nzZbZt43OTPbd04/udm26S 8/z3f2++zOefx3bj+x3jj/2xx5ovs2pVbDe+35k2Tc7ziSeaL7NkCZ+b+LquzjnbnCQUYh43Ts7z ueeaL/fWW1LmooucnxBdHX0zGP/SGRnMX30Vvcwtt0iZH/7QMbNcYeVKOc9OnZqPK157rZT56U+d tc1pPvhAzrNbN+aKivO3h0LMM2ZImV/+0nHzHOWNN+Q8s7OlfaYpoRDzJZdImd/+1nn7nOTvf5fz zMmRdrumNDSYbX8t/THaha2OHsA/AdgKoAFAfgvlZgMoA7ADwAMtlLNZjsbcfLOc/b33nr+tuNis zQetgSkaV1/d/BOOUYPt2DE4faSbIxQyQ1TRnnCMGuwFF0jf8SATCpnjTh5//PztRg22d28ZUxBk 6uuZR46U833yyfO3v/yybOvfX/rfO43djn44gKEAPm7O0QNIBrATQA6ADgBKAIxopqztgkSyebO0 kBMxr1hhrq+qMmOwQa/BGhhPOCkpzJ99Zq4/fZp5xIj2UYM1WLFCzjc1lbmkxFxfXs48aFD7qMEa LF4s55uZyVxaaq4/epS5b18OdPtVU958U863c+fGPa0OHTJ75j37rDu2ORK6acXRTwGwOOLzvwH4 t2bK2ipGNB58kM89qi9Zwrx/P/Ps2bJu0KBgx6Ob8uMfy3n36sX88cfSUH355bJu+HD5A2wv3H23 nHefPsyffCI3thGzzsuL/vgeVIz2rIEDpYvx9u3MEybIusmTgxubb0ooZEYBBg9mXr9eGq3HjJF1 l1/ufGzewAuO/hYAz0R8/i6AJ5spa6sY0airY77xRlEh8tWtW/D6irfG2bNmY2Tkq1ev4KQ7iJXq aubp08/Xom9f5t273bbOWSormSdNOl+LnJz2EdaMpLzcfNqPfA0ZwnzkiHt2xeLoW8wzR0TLAGRH 2fQQMy9q6bthYhxnKMybN+/cckFBAQoKCtry9TaTkiIj2R5/HPjrXyUF76xZwPz5wesf3RqpqTKU /de/lj7Dp09L3+j582WaufZEerpkY/zVr4AXXpA+89dfD/z+98HrK94aWVmSwvmXvwT+/nfJ4nrT TcDvfifTMbYnunSRdOUPPQQsWCAjyufMAf7zP4Fu3Zyzo7CwEIWFhW36DskfQvwQ0ccAfsrM66Ns mwxgHjPPDn9+EECImR+PUpYTtUVRFKW9QURg5hYTtFg1Mra5gxQDGEJEOUSUCuDbAOJI/KkoiqLE S9yOnohuJqL9ACYDeI+IPgiv70NE7wEAM9cDuA/AEgDbALzKzKWJm60oiqLESsKhG6vQ0I2iKErb cTJ0oyiKongUdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU 0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuK ogQcdfSKoigBJ5E5Y/+JiLYSUQMR5bdQbg8RbSKiDUS0Nt7jKYqiKPGRksB3NwO4GcBTrZRjAAXM fCKBYymKoihxErejZ+YyQCamjYGYCimKoijW40SMngF8SETFRHS3A8dTFEVRImixRk9EywBkR9n0 EDMvivEY05j5EBH1BLCMiMqYeVVbDVUURVHio0VHz8yzEj0AMx8Kvx8lorcATAQQ1dHPmzfv3HJB QQEKCgoSPbyiKEqgKCwsRGFhYZu+Q8yc0EGJ6GMAP2Pmz6NsywSQzMyniSgLwFIAv2LmpVHKcqK2 KIqitDeICMzcYjtoIt0rbyai/QAmA3iPiD4Ir+9DRO+Fi2UDWEVEJQCKALwbzckriqIo9pFwjd4q tEavKIrSdmyt0SuKoij+QB29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU 0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuKogQcdfSKoigBRx29oihKwFFHryiKEnDU0SuK ogQcdfSKoigBRx29oihKwFFHryiKEnASmRz8d0RUSkQbiehNIurSTLnZRFRGRDuI6IH4TVUURVHi IZEa/VIAucw8FsB2AA82LUBEyQD+G8BsACMB3EpEIxI4ZrugsLDQbRM8g2pholqYqBZtI25Hz8zL mDkU/lgEoF+UYhMB7GTmPcxcB+AVAN+I95jtBb2ITVQLE9XCRLVoG1bF6O8C8H6U9X0B7I/4fCC8 TlEURXGIlJY2EtEyANlRNj3EzIvCZX4OoJaZ/xGlHCduoqIoipIIxBy/LyaiuQDuBnAlM9dE2T4Z wDxmnh3+/CCAEDM/HqWs/ikoiqLEATNTS9tbrNG3BBHNBnA/gBnRnHyYYgBDiCgHwFcAvg3g1ngM VRRFUeIjkRj9kwA6AlhGRBuI6H8AgIj6ENF7AMDM9QDuA7AEwDYArzJzaYI2K4qiKG0godCNoiiK 4n1cHxmrA6pMiOh5IjpCRJvdtsVNiKg/EX1MRFuJaAsR/YvbNrkFEaUTURERlRDRNiL6rds2uQ0R JYejCIvctsVNiGgPEW0Ka7G2xbJu1ujDA6q+ADATwEEA6wDc2l7DO0R0GYBKAH9j5tFu2+MWRJQN IJuZS4ioI4DPAdzUjq+LTGauIqIUAJ8A+Bkzf+K2XW5BRD8BMB5AJ2a+0W173IKIdgMYz8wnWivr do1eB1RFwMyrAJx02w63YebDzFwSXq4EUAqgj7tWuQczV4UXUwEkA2j1xg4qRNQPwLUAngWgHThi 1MBtR68DqpQWCffYGgcZfd0uIaIkIioBcATAx8y8zW2bXOQPkN5+odYKtgMYwIdEVExEd7dU0G1H ry3BSrOEwzavA/hxuGbfLmHmEDPnQdKMTCeiApdNcgUiuh7A18y8AVqbB4BpzDwOwDUA/l849BsV tx39QQD9Iz73h9TqlXYOEXUA8AaAl5j5bbft8QLMXAHgPQAT3LbFJaYCuDEcm14A4Aoi+pvLNrkG Mx8Kvx8F8BYkFB4Vtx39uQFVRJQKGVC10GWbFJchIgLwHIBtzPxHt+1xEyLqQURdw8sZAGYB2OCu Ve7AzA8xc39mvgjAdwB8xMzfc9suNyCiTCLqFF7OAnAVgGZ767nq6HVAVWOIaAGA1QCGEtF+IrrT bZtcYhqA7wK4PNx1bEN4JHZ75EIAH4Vj9EUAFjHzcpdt8grtOfTbG8CqiOviXWZe2lxhHTClKIoS cNwO3SiKoig2o45eURQl4KijVxRFCTjq6BVFUQKOOnpFUZSAo45eURQl4KijVxRFCTjq6BVFUQLO /wdcAmhbrixx1QAAAABJRU5ErkJggg== ) ## 极坐标系注释文本 产生极坐标系需要在 `subplot` 的参数中设置 `polar=True`: In [6]: ``` fig = plt.figure() ax = fig.add_subplot(111, polar=True) r = np.arange(0,1,0.001) theta = 2*2*np.pi*r line, = ax.plot(theta, r, color='#ee8d18', lw=3) ind = 800 thisr, thistheta = r[ind], theta[ind] ax.plot([thistheta], [thisr], 'o') ax.annotate('a polar annotation', xy=(thistheta, thisr), # theta, radius xytext=(0.05, 0.05), # fraction, fraction textcoords='figure fraction', arrowprops=dict(facecolor='black', shrink=0.05), horizontalalignment='left', verticalalignment='bottom', ) plt.show() ``` ![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAVUAAAETCAYAAACGDZVfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8FNX6/z9ne09CAiQEEkroVTpI70VBwYIoghe5/lS8 tmu9eot4bVe/9oKIFZCiVKUJhBqQHiKhBEIgEEp6sn135vz+2M1mN9nNtpndDc779ZpX5syeOefZ 7OyzpzyFUEohICAgIMANomgLICAgIHAzIShVAQEBAQ4RlKqAgIAAhwhKVUBAQIBDBKUqICAgwCGC UhUQEBDgEEGpCgg0ACHkSUJIDiHkD0LIk85rTQghvxFCzhJCthJC4t3qf00IOU4ImRw9qQWiiaBU BQR8QAjpBuBhAP0A9ARwGyGkHYAXAfxGKe0AYLuzXFP/EoA+AB6MitACUUdQqgICvukE4HdKqZlS ygDYBWA6gCkAvnPW+Q7AHc5zOwA1AHmkBRWIHQSlKiDgmz8ADHVO91UAJgFoCaA5pfS6s851AM0B gFJ6GoAEDuX7aRTkFYgBJNEWQEAgVqGUniaEvA1gKwADgOMAmDp1KCGEupWfjqyUArGGMFIVEGgA SunXlNK+lNLhAMoBnAVwnRCSDACEkBQAN6Ipo0BsIShVAYEGIIQ0c/5NAzANwDIA6wHMdlaZDWBt dKQTiEWIEKVKQMA3hJDdABIB2AA8TSnNJIQ0AbASQBqAAgD3UEoroielQCwhKFUBAQEBDhGm/wIC AgIcIihVAQEBAQ4RlKqAgIAAhwhKVUBAQIBDBKUqICAgwCGCUhUQEBDgEMFNVUCgDoQQGYBkAC0A pABoBkAGQArHd6bmABxBVGoOGwArHB5WVwEUAbhGKbVGUn6B6CIoVYE/FYQQAqANHKH8Wkql0lSV StVeLBa3ZFm2udlsThKJREqdTmdOSUmxpaamEpFIJGvevDni4+NFMplMVFpaKkpMTBSpVCrYbDa2 sLCQKhQKRiqVsiaTic3OzoZer7eXlZWJKysrlXK53KxUKkskEslVhmEK9Xr9ebvdfgXAZQDZAC5Q wWD8pkEw/he4aXFToH1kMtlAjUYz1GAwdJXJZKLOnTvbe/bsKWnVqpU8KSmJpKenIzU1FSkpKUhK SoJIxM3KGMuyKCkpwdWrV1FUVISrV6/i6NGjtLq62nLp0iVrdna2RK/XE41Gc9JoNO6yWCy/AzgC QdE2WgSlKnDTQAhpBWCQTCYboFKphptMps4qlQqdOnViBw0apBo1apSoT58+SE5OjraoHly7dg1H jhzB4cOH2b179+oPHDggs1qtrEajOWkwGPY4Fe1+SmlhtGUV8I+gVAUaLYQQEYDeUqn0ToVCcT+l tPnAgQOtffr00QwaNEg0cOBANG/enLf+d+7ciREjRvDS9vXr13HkyBEcPHiQXbVqleXixYtEIpEU GY3G5TabbQ2Ao5RSlpfOBcJCUKoCjQpCiBLAKI1Gcy/LsrcnJiaKp06dqpg2bZp02LBhEIvFEZOF T6VaF4ZhsH//fqxcudK2bNkyu8lksohEonV6vX4lgB2UUnNEBBHwi6BUBWIeZ/i9yQkJCTONRuOQ zp07WydNmqSdPXs26dChQ7TFiwpnz57F+vXr6ffff68/ffq0TKPR7CkvL18G4FdKqRDfNYoISlUg JnFO7cfFx8c/bzKZBnfp0oU+9dRTismTJyMxMTHa4sUUpaWl2LhxI1auXKnfunWrTKlU7q+srHwT juSEwhJBhBGUqkBMQQhJlEgkc2Uy2d+Tk5NVL7/8snrGjBlQq9XRFq0ekZz+B4rBYMCyZcvw7rvv VhcVFRlMJtP7DMMsppSWRlu2PwuCUhWIOk7Tp35arfbvNpvt9jvuuIN96qmnVP3794fjpdgkFpVq DZRSHDx4EC+//LJ53759kMvl66uqqt6jlB6Mtmw3O4JSFYgazgylM+Li4l6QSqUtH374YcWzzz4r SkpKirZoNxUlJSVYvHgx+3//938mo9F4Ta/X/xfACkqpMdqy3YwISlUg4hBCNHK5/O+U0ueHDh3K PPvss5rx48dzZnAv4B2GYbBx40Z88MEH+qysLMKy7P+sVut7lFJ9tGW7mRCUqkDEIITIJBLJX2Uy 2evjxo2Tvv7666quXbtGW6yQieXpvz/y8vLwyCOPmPfv32+12+3/sNvtXwoxCrhBGBoI8A4hREQI malSqS737t37rb1798atWbOmUSvUxk779u2xY8cOxb59+3SDBw9+U6PRXCSE3Oe0uhAIA2GkKsAb zg2oCVqt9qP09PTkjz76SDNy5MhoiyXghczMTMyZM8dUVlZ2Wa/X/w3AFiH2QGgISlWAFwghA3U6 3acymazLwoULFXfeeWdM7+QLOCwG1qxZg6eeespQUVFxurq6ej6l9EC05WpsCEN9AU4hhKTodLqN iYmJ2997771brl69qpg2bdpNqVB37twZbRE4hRCCadOmIT8/X/3qq6/2jouL2xEXF/crISQl2rI1 JgSlKsAJhBAiEokekMvleTNnzhxTWFioevjhh4lEIoTsbWxIJBI899xz5OrVq8rHHntsrEqlOiMS ie4nN+MvIw8I03+BsHGOTr9v2rTpoBUrVqj79OkTbZEEOOTQoUO4/fbbzQaDYa9er3+QUno12jLF MsJIVSBk3Ean5x5//PHhJ0+eFBTqTUi/fv1w8eJFxfz584cLo1b/CCNVgZBwH50uX75c3bdv32iL FHEas51qqBw+fBhTp041VVVVZen1+lnCqLU+wki1EUII+ZoQcp0QkuN2bQEhJJsQcpwQst0ZBR+E kNaEEBMh5Jjz+Mztntud9ywKom/X2mnN6PTPqFD/rPTt2xf5+fnK+fPnD+Ni1EoIETufyw3O8r8J IZfdnteJbnW/dj7fk7l4L7xBKRWORnYAGArgFgA5bte0budPAPjKed7avV6ddpbD8cP6GoCuAfQb r9PptrRp00a/b98+KvDn5tChQzQtLc2g1Wp/AxBPQ3uWnwGwFMB6Z/lfAJ7xUq8bgH8DEMMRtyDq 30NfhzBSbYRQSvcAKK9zrdqtqAFQEkBTIgByACo4Uiv7hBDSUaVS5cycOXP4qVOn1IMHDw5S6tjG ZrPBZrO5yosXL0ZhYW1KqEWLFuHy5csNlq9cueIqL1y4EEVFRa5yUVERGIbhS/yo0LdvX5w9e1Z1 9913D9VoNCcIIUFFDCeEtAQwCcBXAGpGu8Tt3B07ADUcz2tsE22tLhyhHfAyAgXwXwCXAJyGc+Tg rKcHcAzATgBD3OqPAXAYwNt++pqgUCj0b7zxBkNvEjZt2kTPnDnjKi9ZsoQWFha6yizL+m0jMzMz 4P5Wr15Ni4uLXeUPPviAlpeXB3x/rLNw4UJGqVRWAxhPA3+GV8Ex4xoOYIPz2r8AFMCRunsx3EbA AN4HcAjAsED7iMYRdQGEI8QPruFp/YsAvnGeywAkOM97O5WuNsA+iFwufz4uLs64Z88e2phZu3Yt PXLkiKtcWloakOJsiGCUal1YlvXo//XXX6dWqzUseaLNtm3bqFqtNsvl8ufg3AT3dQC4DcCnzvMR bkq1GWpHq68DWNxQO7F4RF0A4Qjxg2tYqaYB+MPHa5kAegfQvkKj0axs27at4eLFi7SxsXv3brpt 2zZXOVwFGklMJhN94403oi1GSBQUFNAOHTrotVrtcgAK6vv5egNAIYALAK4CMAD4ngb4jMfyEXUB hCPED67OAwegvdv5EwB+cJ4nARA7z9sCuAw/mwoAUrRa7YnJkycb9Ho9bQwUFBTQlStXusqNSYn6 o7CwkH755ZfRFiNg9Ho9nTx5slGtVp8EkEL9P8vu0/8Ut+tPA1jm7/5YO6IugHCE8KEBPwIogmNz qRDAXwD8BCAHwHEAPwNo5qw7DcAfzjXVIwAm+2m7n1KpLPn3v/9tjXXFdOnSJde50WikNpstov2H M/0Ph+zsbHr+/Pmo9B0oLMvSF1980apSqUoA9KUNP3MjULv7/wOAE8411bUAmjd0byweURdAOGLn EIlE0xQKhembb76hsY7ZbKbfffddVGWIllKtrKykeXl5rnIs//itXr2aqtVqg0gkmkZj4BmPxCF4 VAkAACQSyQMajebLnTt3Knv16hVtcbyycOFCTJkyBSkpQtAkd77++muMHj0a6enp0RbFKwcPHsTI kSMtFotlrt1uXxptefhGUKoCkEgkD2u12g/37dun6tKlS7TF8cBoNEKlUgEAWJYV8lj5wWaz4cSJ E4i1GAwnT57E0KFDTdXV1U/YbLbF0ZaHT4Qn9E+OXC5/QqfTfZSVlRVzCvXAgQM4eLA2o3KsKdRY jKdKCEFpaWm0xahH165dceDAAWVcXNxHUql0frTl4RNhpPonRqFQPJ2QkPB6VlaWqk2bNtEWBwBw 4cIFtG7dulEEtW4MAVV27dqFli1bol27dtEWBQBw7tw59O/f32w0Gl82m83vR1sePoitn36BiCGV Sh9Tq9X/PXDgQMwoVEop9u7di8byQx/rChUABg4c6Fo+iQUyMjJw7NgxRVxc3OsymezRaMvDB4JS /RMilUr/otVq392/f78yPT0d5eXl2Lp1a1RksVqtOH/+PADH1HXWrFkxN81vzMjlctfGHsMw+Pzz z6P2o7V161aUl5cjPT0d+/btU6nV6vfEYvGcqAjDI8LT+ydDJBLdp9FoPsnKylJ26OCIf5GQkIDU 1NSoyHPixAmwLBuVvsMlFtdUG0IsFmP69OlRW1pJTU1FQkICAMeIddeuXUq1Wv25SCSaERWBeEJY U/0TQQiZpFKpVv/+++/ybt26RU2O8vJy6HQ6iMXiqMnABY1hTbUhtm/fjt69e7sUXTTIycnBkCFD TFVVVdMppZuiJgiHCCPVPwmEkM5KpXLFmjVrGlSoOTk5+O2333iVZfXq1TCbzbz2EQkas0IFgF69 esFkMvHax9atW5GTk+Pz9e7du2Pjxo1KhUKxihDSiVdhIoQwUv0TQAhJUKvVOR9++GHK3Llz/f6Q VlZWIi4uLhKiCcQIFRUVkMvlUCqVnLYb6LP09ttvswsWLCgyGAw9KKXlfm+IYYSR6k0OIUSiVqs3 zpo1KykQhQqAc4VqsVjw5ptvctomV1BKQW1GsIYbYMrzYS8+CfuNP1wHU3oGbPUVsOZKUNYzyHRj W1NtCKPRyMtmZaDP0gsvvCCaPXt2U61Wu44Q0qjzmgsj1ZscjUbzSbdu3R7au3evSiIJ7ln95Zdf kJycDC5yUFFKo7ZBwhpLwJSdA1t2Dkz5ebCVl8AaroPVXwU13ABYe8BtEWUiRJpkEHVz7L3AYuSI ERAnZEDUJAOiuFYgRBinHD58GNeuXcNtt90W1H12ux3Dhw83HD9+/FuDwdBoHQQEpXoTI5FIHkpO Tv7kxIkTqiZNmoTURqiuoWazGXv27MHYsWND6jdUqM0E+9WjsF87AubacdivHQfVRybhJ5HrIG7e E5LmPSFO6QNJy0EQKRrfMorJZMLHH3+M559/PqT7w3EnLisrQ+fOnU2lpaWP2e32b0NqJMoISvUm hRAySKVSbT98+LCyc+fOYbdnNpuhUCgCrn/t2jVYrVakpaWF3XdDUErBFP8B24UdsF/aC3vRIYCx BNeIWAEiU4PItCBSJVAz2qQUlLGAWg2gNj1gNQAI4vtCRBA36wFp2hBI202AOOWWRjOSZRgmaOuM YJ8RX+Tm5mLAgAFGvV4/mlJ6IOwGI4ygVG9CCCEtlUrliW+//Tbhnnvu4aTNxYsX4/bbb0ezZs04 aS8cKGVhv3IQtnMbYTu3CWzV5YZvkCggbtIe4iaOabo4vg2IJhkiTQpE6mYg0sA8jihrBzWWgNVf A6u/hp3bt+LWtmKw5efBlJ4BNZU1eD/RpECWMRHSjEmQtBwIIop9kzKWZVFQUIC2bds2WO/GjRvY sGED5s6dy0m/v/zyC+65554Kk8nUjVJ6xf8dsYOgVG8yCCFKrVZ79KWXXsp46aWXIrrgn5ubi5yc HNx77728tM9UFMCauwrW3JUNKlJRQjtIWg6CJPkWiJN7QZzYAUQU/L/i1KlTSEtLg1qtBgB89tln uPvuu9G0aVMAwJNPPolXXnkFTZs2BaUUSxd/gnG9mkJjOAv75SwYr2RD7qNbkTYVsm4zIe82AyJt i6BlixQMw2D58uW4//77I973/Pnz7d999905vV7fm1LKr+0XhwhK9SZDo9F8NmLEiDkbNmxQ8rUx VFhYiFatWtW7zsdmFGUZ2PK3wnJsMeyF+7zWIXIdpG1GQ9J6BKSthkKkDS3e6qJFizBp0iSXd9mO HTvQv39/aDQaAP6nxCUlJYiLi4NUKgUAfPrhu7hrSBo0JQdgO78ZP+4twh09FVBI3f5HRARp61GQ 954HSdrQRhFIxh1fzwIXUEpxxx13mLZv375Yr9c/wUsnPCAo1ZsIQsgQrVb7W35+viIpKYm3fr77 7jvcf//9qLEmuHHjBufLAtSqhyVnCSzHvgZbVVjvdaJIgLTDbZBlTIKk1WAQsSzoPpYuXYp+/fqh xl2XTyhrR+GRX5BQvh/MuQ1gjGX4YIcBT49WuxSpuHlPKPrNhzRjYkwuDVgsFqxevRr33XcfAMdu /dKlSzF79mze+iwpKUFGRoapsrJyLKXU+69qjCEo1ZsEQohKrVbnLVmypMUdd9wRsX4vX76MI0eO YOrUqZy0Ry3VMB9fDMuRL0HNdWzAiRjSNqMh63I3pG3HgkjkQbW9adMmJCQkYODAgZzIGqqbKrVb YDu3CYbjPwBFWQCAUj2L7WcsuKePEqKEdlAOfh7SDrfH3Mg1Pz/f7/oq13z//fd49NFHrxmNxnaU UmNEOw8BQaneJGg0ms8mTJgw56effuLWJaYB7HY7Dh48iMGDB4fdFrWZYDn2FcyHPgO1VHi8RhQJ kPd4APKec4Jafzx16hRyc3Mxffr0sOXzBhe+/0z5BViOLoLljx9RXm1EgsphHVBuZKFpeQuajP0P JKkDOJCWO7KystC/f38Ea/ccDtOnTzdt2bLla71eH/P2q4JSvQkghAxRq9XbLl68KE9MTIxIn/v3 78egQYOwbds2jBkzJuR2KGVhPb0G5r1vgq323OQVxaVB0W8+ZJ3vcpg6BYD7UoTNZoNEIom50Z43 WGOJQ7lmfwtqqUJBqR3nixmM7iSHNGMiVCMXQKSNTiSxumzbtg29evXCzz//jEceeSQifZaUlKB9 +/amioqKmF8GEJRqIyca036LxYKsrCyMHDkyrHbsN3Jg3PYCmGvHPK6L4lpDMfApyDpNAxFLg5Lr +++/x7x588KSK5qw5kqYD34Ey7HFHva2K46xGHP/39Fu3DMxs95qsVgglwe3BBMOq1evxqxZs2J+ GUBQqo0crVb72fjx4yM67fdGcXExjhw5ggkTJvitS60GmPb/D5ajiwBaG0uVKBOhHPwcZN3vD9gE avfu3UhNTY1KuhA+Q/8xVYUw730L1tOrHWWWgmEBZau+UI97D+LEjrz064vNmzejT58+LnOyaDF1 6lTT9u3bY3oZQFCqjRhCyFCNRvNbQUFBRKb9R44cQadOnVx2m3XJy8tD+/btG2zDdmkvjFuf9rQz Fcuh6D0Piv5/A5Frg5IpmjmtIhFP1X7lIAy//R1sWZ7rmp6RYcnVQXjxvR8j9r4b+mzXr1+PtLQ0 RCK1udsywDhK6V7eOwwBQak2UgghKpVKlffVV1+1qDFx4Zvt27dj1KhRIX2Rqd0C0763YDnyhcd1 SatboRrzNsQJgY00y8vLsX79el7NeGINarfAfOgTmH//EGBtruvSDrdDNeZ/IHJdVNeNKaWw2+0u +1y+WbNmDWbNmlVkMBjax+IygKBUGylKpXLBuHHjnlm3bl3sZHVzcvToUZSVlbk2sJjSMzD8+iiY klOuOkSRAOXwf0PW5e6gFILVaoXNZvM5Wr6ZYUpOQ//r/wNbesZ1TaRrhV/Ze9F72GR06sRtjOdt 27ahSZMm6N27N6ftcsGQIUPMhw4d+p/FYvlntGWpi6BUGyGEkKYKhaLg5MmTKr5tBlmWRU5ODnr2 7BnUfQaDAWq1GtYz62DY8jRgr/UylLQeBfX49yFSB+YwcPz4cYjFYnTv3j0oGfgmGulUqM0E465/ w3ri+9qLUjXUkz+HrC23EcH0er3LmyxQ3n77bTz//PO8j5zPnz+P7t27G00mU2tKaTGvnQVJ4wiZ I+CBSqV6bdKkSZJIGGFfuHAhpC+ISiGDcee/oP/lkVqFKlZAOepNaO5cErBCBQCtVosuXboELcPN CJEqoR7zNtS3LQKkztG6zQDD2tkwH/4C+fn5WLp0aVh91Ay0glWoAPDEE09EZCmiXbt2mDVrlkil Uv2b986CRBipNjIIIa1VKtWp/Px8RfPmzaMtjldYcyUMGx6GvXAvfskxo3WiGD26dIRmymKIkwKb ohYXFyMxMVFIV90ATPEp6Nc96LHpJ+/z/yAZ9BJksuDddgEgOzsb+fn5uPPOO7kSkzeuX7+O9PR0 i8Vi6UgpvRhteWoQnthGhlarfefpp58W861Qi4uLwTCM/4p1YCoLUb1iCuyFjo3Z27or0GvIZOju 3xSwQgUcO8pWqzXo/v9MiJt2hnbmJohb9HNdsxz5ArbdrzjCI9rt2LNnT1Bt9ujRgxOFunPnTuTn 54fdTkM0b94czzzzjEin073Da0dBIijVRgQhpDvDMFOef/553rdZ169fH7RStV8/geofJ4MtPeu6 phj8HDRTvwGR62A0Br5RO3fuXE4CHvNJLOSoEqmSoL1rFaQZE13XrCd+gHHTExCLHK7EgVDz2XA1 dR88eHBEPr8XX3xRCuB2QohHimBCyNeEkOuEEJ+pXAkhHxFC8ggh2YSQW7iSSVCqjQidTvfBP//5 T6lOp+O9r7lz5wY1hbRfOYjqVXeBGp17BmIZ1BM/hXLgM65o90uXLkVZmfdAzpRSvP/++2BZ1uvr Ar4hEjnUt30JWefaGAfW06th/O35gDbSysrKwl6HrYtMJkOLFvzHidXpdPjXv/4l1+l0H9V56RsA Pj1RCCGTAGRQStsD+CuAz7mSSVhTbSQQQoY0bdp0S2FhoSqSroGBYLu0F/q1D7o2pIg8Duqp30Da clBQ7QipscODUhbG7S95WAbI+zwK5bBXAQALFizAq6++GlGb1srKSpSUlPDq8WY2m9GiRQtTeXm5 R1wAQkhrABsopfXMRgghXwDIpJSucJZPAxhOKb0erjzCSLURQAghWq32k3feeUfJp0KllOL1118P 6h7bxV3Qr5lVq1BVSdDes8avQj1//jwAz+mpoFDDgxARVKPfgqzrDNc1y5HPYTn0CQgheOWVVzwU as1nwCdqtRqnTp3yXzEMFAoF3nvvPYVOp/uYBP6LkQrAPVDvZQAtuZBHUKqNg4kajabDrFmzeB1i EELwwgsvBFzffuUg9OvmAIzZcb8mBdp71kDc1H+iwf3794NhGLz99tshbYjFArGwploXQghUY/8H abvama9p7xuwnv3Fw5KiqqoK+/fv510eiUQSdKrqUHjwwQdJYmJiewAT/Vaupe73iZNpu6BUGwHx 8fGvvvvuu8pgs1uGQqCuhvYbOahe8wBgdypUbQuHQm2SEdD9DzzwAMRiMf7xj38EnbVToGGISAL1 5M8haXWr65ph899gL84F4PBKW7x4MR544IGIysXnj6dYLMaLL76o0el0/w7wlisA3PPAtHReCxtB qcY4hJCOLMv25CvQcg1btmwJuC5TUQD9z/cB1moAzin/XSshjk8P6P6qqiqXuZTVag3a7CdWiLQ3 VTAQiQLq27+CKL6N44LdBMO6Odi17RcQQvD0009HVB6WZfHWW2/x2sfs2bNBKe1GCAkkP856AA8C ACFkIIAKLtZTAUGpxjwqleqpOXPmSPhcS7VYLAg0yhVrroB+zQOgplIAjk0pzfQVAQdEAYC1a9e6 THhkMlmj2vHPzs72sJ/dt28fLBZLA3dED5EiHpqp37g8r9iqQhgPfOgRsb+yshJms5l/WUQivPzy y7z2IZfL8eijj0rUavXfCCE/AsgC0JEQUkgI+Qsh5BFCyCMAQCndCCCfEHIOwEIAj3Elh7D7H8MQ QtQKheL66dOn1enpgY0C+YQyNuhXz3QZ9kOsgPbulZC4GZ83dvR6PaRSqSv48gcffIDZs2cjISEB gCPIyJAhQ6BQKLBz507I5XL07t3bVf+dd97BY4895nLxzMrKwsCBA6PqGWY9vwWGdXNcZeXI/0Jx y18AcJ9jLNpcvHgRHTt2NFgslmbRimAljFRjmxlDhw6lfCrUYLyWjJmv1CpUAOoJHwalUEtLSxt8 /erVq9i8eXPA7XEBpdRjpLl27Vro9XpX+amnnnIpVAAYM2aMh1H7oEGDPKLfP//88y6FSimFyVQb SMZqteLIkSO8vI+G2JFHUZZeGx7StPs1MCWnAQAtW7aMqEJlWRYbN26sd33z5s3o1KkT2rdvj7ff frve6yUlJZgwYQJ69eqFbt264dtvv/Xafnp6Orp06SICMMNrhUhAKRWOGDwAEJ1Ol7d582bKF2az mb7zzjuB1T25gpa9l+w6jPv/L6i+zp07R3/55Re/9S5cuBBUu+GyatUqeubMmYj0ZbFY6LZt21xl lmUj0u+FCxcoazPTyu9Huz6/yu9HU9Zu9ahXWVkZEXkOHjzoUbbb7bRdu3b0woUL1Gq10p49e9Lc 3FyPOv/617/oiy++SCmltLi4mDZp0oTabDav7W/atInGxcWdhXMmHulDGKnGLgPEYnHLsWO5Defm jlwux3PPPee3HlNyGsZttaZW0o53QDHgqaD6ateuHSZPnuy3XuvWrYNqN1jOnDmD1atXu8p33XUX OnQIZF8jfGQyGUaPHu0q5+bmYuXKlbz327p1a4fX1aRPAbFjlM0Un3TkwXLjhx9+8BhZ80W/fp6z m4MHDyIjIwOtW7eGVCrFjBkzsG7dOo86KSkpqKqqAuDY6ExMTPSZzXXcuHFQKpUtAPTn5Q34QVCq MYpOp3tX3oCiAAAgAElEQVT2xRdflEU7ShO1GqDf8LDLdErUpD3UY98N2CsnUN/zuhw4cACZmZkh 3dsQ6enpnEVgCtdOtWvXrrjnnntc5by8vJpZSthkZmbiwIEDHtfEiR2hHPSsq2zKescjwtXjjz8O pTJyqc5qNiivXLmCVq1qrZtatmyJK1c8rZvmzZuHkydPokWLFujZsyc+/PBDn+2KRCI8+eSTSq1W 63/EwAOCUo1BCCFJVqv1trlz5/L2+ezatSugesbd/wFb7vS8kSihuX0RiCzwqPvvv/9+SKOfgQMH YuDAgUHfVxdKKRYsWOBS7gqFImZTVhcVFaGwsNB/xQDw9f+T93kEokRntDC7CcbMV+rV4UqxN0RJ SQm++MKRWieQz+ONN95Ar169UFRUhOPHj+Pxxx9HdXW1z/rz5s0TWSyW2wghSZwJHSCCUo1BxGLx w1OmTKF8JvMLxIzJdmE7rCd+cJVVY94KOovnc889F/Lop+a+cL7khBC8+uqrPqeK4cC1nerw4cOR lpYGwJGLq6SkJOg2av5Xvv7nRCyFemxtpDzb+S2wXfb0rDp06BA2bdoUdN/BkJSUhEcffRQAkJqa 6vFjUlhYiJYtPT1Gs7KycPfddwNwLCW1adMGZ86cgS8SExMxffp0RiKRzOVB/AYRlGoMolar582a NYvXedjIkSMbfJ01lcGwtXaqKG1/G2Sd7+ZTJJ+sWrUKubm5AdfPzs7Gzz//zKNE/EMIQVZWVlD3 5ObmYtWqVX7rSVr0g6xL7Wdp2vNfjx+u/v37Y9y4cUH1HQo1I9S+ffsiLy8PBQUFsFqtWLFiBaZM meJRt1OnTti2bRsAR3DqM2fOwF/mi3nz5qk0Gs1f+JG+AaKxOyYcDe76t1Sr1SZfO5uRQr/pCddO cfnn3SljLAn4XpZl6ZdffsmpPMHslEdqVz0zMzMi/VBKKcMwfusE877tlZdo2Qdprs/YkrcxHPFC 5sSJE9RqtdKNGzfSDh060Hbt2tE33niDUkrpF198Qb/44gtKqWPH/7bbbqM9evSg3bp1o0uXLvXb ttVqpXK53AIglUbyOxzJzoQjIKX6/+666y495Ym1a9fSc+fONVjHemmfh/mU5dyWoPpgWZZevnw5 HDF94svs5/z58zQnJ4eXPn0RKaXKMAz9z3/+41NphmoKZch81fUZV3w7ol77ZWVldMeOHV7v3bRp E+3YsSPNyMigb731ltc6mZmZtFevXrRr1650+PDhXuscO3aMVzO6SZMmGQA8QgWl+uc94uPj965Y sYLyRUVFRYMjGtZuoRXfDHV92ao3zONNllD48ssvaUVFRb3r2dnZ1GKxREGi6FJRURHyrIAxltCy j9q6Pmtr/vZ6dY4dO1bvWiB2peXl5bRLly60sLCQUuoYaUaD5cuX04SEhF00gt9hYU01hiCEaIxG Y7/x48fz1kdcXFyDu62WI1+CLctzFKRqqIb/J6j2+c5LNG/ePK9xV3v06BFysrvGBKXUw1QqLi4O 8+bNC6ktkTIR8m4zXWXz4frB73v16lXvWiB2pcuWLcP06dNdG05JSRHfhAcATJgwAQaDYQAhJHCT lTARlGpsMbZv375mvoI1V1RUNPg6ayyB6WCt/Z/y1uch0qYE3D6lNKIRp7744ououH3WEI14qoQQ VFRUcBb4Wd77rwBxhF60F+6F/fqJenUYhoHBYHCVA7ErzcvLQ1lZGUaOHIm+ffvihx9+gC8opfjx xx/DfSteiYuLQ9u2bRkA/HnR1EFQqjGETqe7d8aMGVo+2qaU4ptvvmmwjvnAB4DV4fcuapIBea/g Nk4JIZg9e3bIMgaLTqdD7969I9ZfrDB+/HicOFFf+YWCOK4VpB1ud5UtJ+orP71ej2XLlrnKgdiV 2mw2HD16FBs3bsSWLVuwYMEC5OXlea1LCMEtt3CWd68ef/3rX5VarfZe3jqog6BUYwRCiNhisUye MmUKL5bp/mJoMuUXYDnxnausHPoKiIh7204uoNRh/jNz5syoGvJHK54qIQT33nsvqqursX379rDb U/Sa4zq3nlkHavMM7lR3iSEQu9JWrVrVuIsiMTERw4YNQ3Z2tk8ZOnUKPH15sNx5553EbrdPJoRE JBq6oFRjhwHNmjVDtEL8mfe/C7AOryNJ6gBI2wZnp/jtt9+6lB2fXLx4sV6EIrPZHJOpTbhm586d HrFPtVotmjRpEna74hb9IYp32nxaq2E917DhfyB2pVOnTsXevXvBMAyMRiN+//13dOnSJWxZQ6F1 69ZITk4GIhQLQFCqMYJCoZg2a9YsXhKl22y2BtfgmPILsJ5Z6yorh74S9AhwxIgRERk1pqWlYc6c OR7XFAoFopFhNtKKXC6Xe4QdBMDJtJkQAlm32tmxNdd7kJfDhw+jpKQEEokEn3zyCcaPH48uXbrg 3nvvRefOnbFw4UIsXLgQgGPkOWHCBPTo0QMDBgzAvHnz/CrV9957DzabLez3442BAweq5HL5NF4a r4MQpDpGSEhIOL9x48a2gwYFl9Y5EC5evIji4mL07dvX6+uGrc/C+odjzUySPgza6Ss4l+FmZOfO nTGTUmXPnj0YNGhQyO64bHURKhf1cRREEsT9vxyIFPEedYqLi1FRUYH27duHK65XTCYTb7EZsrKy MHny5PPl5eWBJVELA2GkGgMQQpR6vT6Nr8X69PR0nwqVrb4Ca26ta2OwIf0Yhgk5ElUwfPzxx64U LA1x6dIl3v3Wa4iEQt20aRMuXbrkt17z5s1x/br3FEv+AkADgEjbAsf1bdD0uWvYcFwP24X6a7VN mzblTaECjngFfM12evfuDYPBkEYI4WU26I6gVGODHi1btjTVndpFAvOxrwHWMeWSpPaHtGVwI+W9 e/fi999/50M0D+bOnQuVSuW3XlpaGnr06MG7PJGiR48eriArDdGhQwekpqbWu84wDObPn4/Nmzcj NzcXP/74o9elIIZh8J/1xRjdUQ4KR6CVaGAymXjJuqpQKJCammoCwPvDISjV2KDP0KFDedmZPHv2 LAoKCry+Rm0mWP+otQ+U93086PaHDx+OW2+91X/FMAlEodbgTbnwQSTWVIN9L3q93iM9TCCG+oBj JnDXPfchUeNQCbaCnaCsd+X2/vvvByVTMGzfvr3B6FPh0L59exmAPrw07oagVGOAuLi4oYMGDQpc awSB1WpFfHy899fOrAM1lwMARLpWkLYZ7bVeNMnMzAzZqmDPnj3YvXs3xxLxz+7du0N2orh27Zor mhMQmKH+lStXsG7dOjz293+DSBQgAGCtBlPiPTLYQw89FJJsgXDbbbfxZiVwxx13KOLi4obw0rgb glKNAViWvbVPH35+QLt16+ZTqVqOf+06l/ecAyIKbrDMlVdPQxBCQl5nGzp0KCeBrjMzMz1Mmd54 4w1YrVbXmuqCBQtcu9aU0rBTPg8cOBBDhw4N6d6MjAyPtDWB/O+eeuopvPXWW46Mr8qmqPkJs1/2 vqzj63mKdfr06QNCSPgPhB8EpRplCCFKk8mUEul1QHvxSTA3chwFsQKybsEnn2zImJsrwt0MqokH EEhQ7hoYhvFQjBqNxmNX/eWXX/aIM/Dqq69CKpUCcJivffrppyGNrmtk5DKGQSCG+keOHMGMGTPQ pk0brD9wEc/9XIVNf5hhv+J7rdx9iYFrrl27xsvmZ48ePaDX61vxvVklKNXo06N169ZGPjapTp48 iZMnT3p9zZpbG8RZmjEBImXwRuQzZvCXBZhrU7/ly5f7dJOsy08//YRr1665yv369fNqquRtTVUm k+HZZ591jRBPnjwZkDVCXl4eli9fHpB8gbB//37k5+cHZKifn5+PCxcu4MKFC5g2ZQLena7DxG4K MNd9/2h+++23KCsr40xed06cOMFZWhl3lEolmjZtagXPm1WCUo0+fQYNGsSLP6hWq/VYT6uBsgys p9e4yvLOd/HRfVi8+eabnI5WZs6c2aA5UHl5uev83nvv5Syra9euXQPayGvfvj1mzpzpt16g1ETt CsRQ3x2RPA5wLgOxVYWgzlgQdXnkkUc48ebyxrhx49CmTRte2h49ejQB35tVkYwzKBz1D51Ot3LB ggU0klgLdrlF9e9GWSb4LAO+ghdzhd1u563tsrIyj3J1dTVdtGiRq8yyLGWtRsfBYRYBlmXp0qVL PdqsKwul/gNAL1myhPbo0YN2796dDh48mGZnZ3MmI6WUVnw73PV82IqOcNp2tPn0009pXFzcUsrj dzo2I2b8iSCE9B0zZkxE+7Tm/eo6l3WcGlLgFL5dUsVi/mJf/PTTT5g5cybUajUoy0BRfRb3d62E fs0sMCWnwBpLAMa5ZiiSQKRJgTipEyQt+kHabhxETTqE9P4JIR5OGAaDAT/99JNHsJIau9Jt27Yh NTUV/fr1w5QpU9C5c2dXnbZt22L37t2Ii4vD5s2b8de//rVeOmr39oL9X4qTOoEtdZg1MSWnIUnx Hgns6tWraNq0KS9JFc+cOYOOHYNLMhkIzg1h7t0W3RCm/1GEEEIMBkOrbt26cd62yWTCihX13U0p pbCd3+oqS9tPrlcnEPjyJmIYBhcuXOCl7RrmzZsHqr+Gxa/OQOWivqj+cTLM+9+F7cI2sNVXahUq ALB2sFWFsOX/BtPeN1D13QhUL5sI65n1yMzcEXTfHTrUKmS1Wl0vwHQgdqWDBg1yBeoeMGAALl++ 7LO///7XM6lfIIjjaxPqsVW+1zZzc3MD8vYKhaNHjwa1uRgoXbt2rdms4m1UICjV6BIvkUhYjUbD ecOEEK9mOcyNE6AGxyYMUSRA0qIf532HQ0FBAYqKinhrnzWVwrjznzAsGYkh4h34I8+H0hDLHIcX mOvZMPz6CIyZr4ApCd5Q/cSJE2BZFgsWLKin8AKxK3Vn8eLFmDRpks/XX3311aBH1SJtsuuc1V/z WW/06NF+M5qGyn333ecw8eIYpyUHC4CfSPCAMP2PMikqlYp7nzw43PJatGhR77rHKLXN6JCm/mvX rsUdd9wRlny+aNeuHdq1a8d5u5RS2M6uh3HHy6CmMsgI0EwnxoECG7q2bQ5Zu7GQpA2FpFk3iHSt QKQOXwxqN4MtvwD7jROw5f8GW/4210h2cPwFVC2bCPWEDyFzC/TsT468vDz06NEDL7/8cj2FF4wC zMzMxNdff419+/b5rBPSMoWmNtsDq78a9P2xTmJioqWoqCgFQMOpMEJEUKrRpUXr1q2tAJSR6tB2 sdbDSNo2tAwT0Yr5GirUboZx2/Ow5q7Cxj/MGNFBDpWMQJzcG/fd9iik7caDiKVe7yUSBcRNO0Pc tDPkXe8FayyB+chCWI4sdMRMsJtg+OURYKINss7+I8sRQjB9+nQA3teNA7ErBRyj3Xnz5mHz5s1I SEhosM8rV64gOTk54LVVkcZ9pOo9SEsNubm5vHlAnThxgpc4Dk2aNKFFRUUtAPDivSJM/6NLSnp6 Oi87Ml9++WW9a9RqAHP9uKssSQvNZ5+vaFp5eXm4epXbkRFrLEH1ymmuSFwZTSVQJ6ZCPeVraO/7 BbIOt4GIpTAYDAFF0RepkqAa+g/oHtiKfddqTIooDJv/BnvRIZ/3bd++3SPPkzvr1q1z2cUGYld6 6dIlTJs2DUuWLEFGhv9IdmfOnMHFixf91quByHS1BZt3md3b5ssRIFC74mBJSkoSAwg8+VqQCEo1 uqSkp6fz4t3h7qpYg73okCu6vzipM0TKRD66DpmKioqgAqf4gzWWoHrVXWCuHXNd6zZyJuIe3AlZ xkSPqbFarYZOp/PWjFfESZ2gGv0mRInONCCUgWHTfFCbyWt9nU4Htdp7Qs9Ro0ZBqXRMVgKxK33t tddQXl6ORx99FLfccgv69284oP2oUaOCWvusWfoAUC+1Sl3uvPNO3gKE14zouaZnz54y8KhUhel/ FFEqlW3VajUvn4G36Eb2y1muc0nLwSG1u3btWowfP96lBLikXz/uNs2opRr6n+5xmQaVG4HmE16D qs/DPtcZg+1/1PgpYKt6o+qHMaCWSrCVl2A58T0UfR4Jqm2t1jPX48SJEzFx4kSPa488UtvmV199 ha+++iooWYMhGKXaGGnVqpVUqVTytoYljFSjiEKhSItk3h57UW06Z0nL0OJKdOrUiReFyiWUZWDY 9DiYEueSGRFhE3MHpD1mB7Rxc+7cOWzevDmgvkS6llAMft5VtmR/59rR37x5M86dOxew3Ddu3Ai4 brD88ccfgZsoua8vs/7Tmxw/ftxvnVDIycnhJb1K8+bNoVAouN8NdSIo1ShCCGnpbYc+XC5fvoz1 69d7XKOUhb0mgAoASXKvkNrmK+tlUVERjh075r9iAJgPfQxb/m+usmrc+3j8X58HHKgkIyMjoNTX Nb7/8m73ATKHWRxbcQFsyWkAjmjzgax51rBmzRrecjQVFRWhuro6sMqsm3twANYhvuL1hktJSUlA 2R6CJTU1FZTS+v7bHCEo1Shit9ub8aFUk5KSMGzYMI9rbMVFwOr4UhFlExBtZAI5B0NSUlLYbdiL c2He/3+usrzv45B3vSfodpo1axZwXSJVQtqqdtPPXnwy6DYAxxS/JtoV14wbN87lMOAPylhd58SH ra47fJnXjRw5MmCZg6FFixZgWTb8h80HglKNEk5vqiYpKdyvlysUinoxLxm3Uaq4WfeQ3UyXLFkS lmy+aNGihdfgL8FAKQvj1mddU1ZxSh8oh7wUVrqXHTt2+LQDdfcqE8U5Up4cuGBF5s7QAkzHDIzb aDkEO+ZYJyUlBSaTKYEvrypBqUYPCcMwkrqbFHzhHsVd3Kx7yO0MGDCAC3F4wXZ2Q63JmFgO9fj3 QURilJaWhtzmqFGjAnrP1O6Iv9o3TYoRA7uG3N/Jkyd5WwI4evRoQPWopdJ17mFe5YPjx4/zEv9U r9fj9OnTnLer1Wphs9mkAHgxZxSUagAQQr4mhFwnhOS4XetPCDlICDlGCDlECOnn9tpLhJA8Qshp Qsg4t+u3E0KyCSGLAEic7nKc8/PPP9fLrMmU1/rTi5uEnqWXr2yaddeAg4Wydpj2vuUqy3vPg7iJ Q9aG3DgDoSZgSF3FUbOmSimFpdBhoyoRE4gTQt8DKS8v9whDyCWBxihljcWuc6LyP0suKSnhxVaV ZVmPuLZcQQiBSCRi0YD1EyFkgvP7m0cIecF5ra3zO7+dEOIz/YGgVAPjGwAT6lx7B8CrlNJbAPzT WQYhpAuAewF0cd7zmds0434AtwC4CqCbWCzmRamOGDGiXqxLtiLfdS6O5ydWZTiEm6zPlv8b2MoC AACRx0PRL/gkhv5YtmyZVyP6c3uWYMVvzk02sQLiFO/pwANhyJAhQa/FBsrUqVMDqkeNtSN7kcq/ LfOYMWN82uCGg06n4y1wj3NA41WpEkLEAD6B4/vbBcB9hJDOAB4FcDeA/8LxXfaKoFQDgFK6B0Dd 4cNV1AZliAdQE/ViKoAfKaU2SmkBgHMAauaPIgByACpHs9xGt68hMTHRY8ODUuoxUhUlhBYE48qV K9ixI/jITIEQbo4uy/FvXefyHg9ApHAMJPbu3cvZ1PTBBx/0cNEdPvRWWE+vQVL2fzCjr8PMTNbl LogUvMXqiAjuQVSIqmkUJeEPsVhMAfjaFewP4ByltIBSagOwHI7vtR2Axnn4XKMRlGrovAjgPULI JQD/A/CS83oLAO6x2C4DqBmGfQlgDwAGwCWZTMb9QpQXqKm01t1QpgUJ0ZMqLi7OI65nrMBWX4X9 kjOmARFB1vNB12tWq5Xz2Kym3z/E+Q/74dxbbWHY+Jjrf0vUzaEc8pKfu/1z5MgR/5VC4PDhwwHV Y8vPu84DmdUUFBSguLjYb71QCFTmYCGEUPie/qcCcF8rqfkOf+o8/gLA546toFRDZzGAv1FK0wA8 DeDrBupSAKCUbqOU9qWUvgBA4vy15JzPPvvMo8waao3KRdqUkHf+NRoN+LBWAOA1F32g2PJrI29J Wg6GWFdrRTBq1CjuA2rbDHhz1R948sfadWuiSYH2rhUh5fqqC1f2unXZsGFDQPUYN6UqauJ/fTgn Jyeo2ALBEKjMwWDc9RpEYETwrVS9fi8ppZcppSMopXdQSn0a0N589hKRoz+ltCZk/08AavwGrwBw tw1qidqlAXcYlmVd3/aaTY+aNaRwytOnT/coU8MN7D3n2EgY0aop5/1xUb5+/Tp27twZ0v3W81td 72/syIm8yyvSpqJjMylyi2zYd0WL0XfMhqL/E9i1/yiAq2H38fDDD/PyHoYNGxbQ/7hXmcMLbO85 C1QnSzDaqVd91R88eDDkcjkv//OmTWuXH7hoj1IWvbK/AlhGBMeM0Rt1v8Ot4Dn7bBDC17rezQYh pDWADZTS7s7yUQBPU0p3EUJGA3iLUtrPuVG1DI51mVQA2wBk1F1AJYQ0kcvl18xmMz/W3m5YclfC uPlJAIC0453QTP7Mzx3eKSoqwqlTpzB69GguxQsLSllUftYZ1FIFAND95QDE8bXrnllZWejbty+n aZ9ZYwmoqRS7j+Zj5NiJ/m9oRLCGG6hc2NNRECsQP/+sz7CIjRHWWILKL7oj/ZVSptpka0oprWdq QQiRADgDYDSAIgAHAdxHKQ0oVKAwUg0AQsiPAIYDSCKEFMKx2/9XAJ8SQuQATM4yKKW5hJCVAHLh WNh+zMeOlJ23nao6UEOJ61ykDt2RJCEhgTc31VBhy/NdCpUoE11G+DVIpVJYLBZOlOqmTZvQuXNn R6ZVVRJGjnXkUCooKMDp06cxYUJdA5Hgqaqqgt1u5yVTKaXU71KI/WqtLau4efebSqECtZtwdscs 0eueBqXUTgiZD2ALHLasiwNVqICwphoQlNL7KKUtKKUySmkrSuk3lNLDlNIBlNJelNJBlNJjbvXf oJRmUEo7UUq3+GjWzrIsL///n376ycNOlVprfb6JPPSdaaVSGbbpky/Wrl0b0n3MjT9c5+LkXvWU Rr9+/epFgQqVXr16eU1d3bp1a/Ts2ZOTPk6fPs1bOpnXXnvNbx3mqlvQnQBNw1atWoWqqqqQ5fJF eXl5WN5w3qjJZGCz+1aqAEAp3UQp7ej8Hr8ZTB+CUo0evCnV0aNHe0SDdw/f5h7WLZYINZsAW1W7 1BWO0X0g1N2kq1mz8/ZaqPTv3x98JIIEgFdeecVvHdvl/a5zX1lU6zJ48GBO4+DWQAgB1/nb2IoC AADD0gaVajgISjV62ABQk8l7UONwSEhI8JjuesTElIQXtu/7778P635fhJpNgK2u3QMU6byPon/9 9Vev1wNhz5492LVrV8D1d+3ahT17YtP3359pGWsqBVMz/SciSFoFFnM3NTWVlzTV8fHx6No1dJdf b7DlF2CyUYhFIgqelKqwpholKKVUo9FUXLt2LbFNG549nDgcqd56a2gpWPjCw09d4T1Xk/sOcrAM GDDA53qsN2+f4cOHw2q11q8cAOXl5aisrPS6xBAuDMNAJBI1uKZqL9iFGmsicUofTszDYg2mIh/X qxjEqeWGkkojL3sawkg1isjl8mI+1s8uX76MNWvWuMqU1nrDhpI91R0+Mp0CQHFxMTIzM4O+ryaQ CQAQH6Nwf+lGvMEwDmubUDa4au6paSNQ8vPzeUnLDACrV6/2G5zEmu+eaXdUwG0vWrQoZLkaYv/+ /aio4DbhKVtRgGuVLORyCT8BFiAo1WhzhQ+lmpyc7DGKch+duCvYWCIhIcFrSm2/uL8fETeeU+fP n8fy5cv91nNfU/XG8uXLcf78+QbruNOnTx+kpaX5rxgCd999d4OWG9RSDdu52j1VWdvxAbddN/UL VygUCigU3KVwo4wVbFUhiioZiAjhLfe2oFSjiNVqvcxHaDOJROKZtpi4f8zhKVW+dnolEgk6duwY wo1uyxk+ku4BwKlTpwL2zmnXrh3uv99nvIyAuf/++3kb2YdCQ1N/67lNAOMY9YuTukDcNHB3ZG8p tLnglltu4VSpsuX5AGVRrGdhsbHcf/GcCEo1iuj1+nN6vT64OWIouCvVME1jhw0bxqkhfbgQmVuS OqvvdCGdO3fGyJEjG2yrrKwsqL6DiaDUUNt2ux2LFy8Oqu9gsNlsftOSWHNXus5lnafxJks0sd84 AQC4Vk1QXK4/y1c/glKNLkWXLl0y+68WPB9++GFtQeRmwM2EtolSgzNpWlht+CInJwcHDhwI6h6R urnr3N0SwBsNmecYDAb8/PPPQfUdDD///DMMBoPP172lFOcKf/9XpuQ07IXO7AZEBFmnwNOjLFu2 LKwg4A0RjtWGN5jrjnDIBRUiCoenFC8Iu//R5WpBQQEvZh2zZs1ynRN5rfE7tQSY/C0KdOjQAZWV lf4ruuHuQcVU+g/CfO3aNVRXV9cLtq1WqzFv3ryg+nb3o/dHQ21LJBIkJycH1Xcw+EtiaD5Wm+5a 2m4CREHkLxs7diwv3l9A8Dm+/FGTUqighGHhCN3JC8JINboUnTt3jhc/QPcH3T0lBrWGvx766aef ht2GN+RyedBfJPfQdK6U1A2QlJSEy5drHQays7MRIW9hAA5X0ezsbABAZWUlbxGpAoU1lsB6qnaE Lu/9cFD3N23alPsoYE769evnv1KAOLIJO7zvjDbK60hVUKrR5arRaOT9M+B6pHr33XeH3UZDBKPk xM26udaM2dKzoFZ9g/UlEolrbZVSivz8/JCVQihR6QkhyM/PB6UUBQUFvO321+AvNbX50GeA0yxN 3KwbJKkDeZUnWrDl511xb69XWAmEkepNS4nZbJbykd/HYDDg448/BuDp70/N4Zvn8ZXuA3Bs2rz5 ZuCu1kSqgjixxlSIwl50KOB79+3bx5s5UEPceeedIISgZ8+eSEwMLWB4oBw4cMCntxOrvw7L8W9c ZcWAp4P6gdm5cyf279/vv2IIrF27ltNkgvbLjjVls41Cb7YRAPwsBENQqlGFUsqq1eobZ89yvxGp Vqah70sAACAASURBVKsxd+5cAIBIXasEWf11X7fEBBKJBC+88EJw96TVennZ8rf7rb99+3YYDAak p6eHFbzEn52qN27cuOFyzDAYDNi+3b+84TBt2jQold6dIsy/f1BrRtWsO6QZwf3A3HrrrZxO0d1p 164dp66vtkKH8s8vsUOjVpZTHg22BaUaZUQiUTZfeZ9qglyItLVG9TVResLl9ddf56QdbwSb/kTa dqzr3Ja/xa+DQ3x8PNRqNVq1aoW2bR35ulg2Mk4RMpkMY8c65FWr1YiP95mUk1fsN/6A5URtHAfl 4OeDXgaRSqW8+PwDQPfuoadRrwulFPbLWQCA44U2yKTy4ExMgkRQqlGmqqpq+5kzZ7if/8PxMFFK PZVq9VVONmaee+65sNtoiPPnzwfs5ilJHeBa4mCrLrumer7wlmTw4MGD2LLFV5RG7wS6plpaWura HIuPj/cw7Qo34aEvGIbxcFV2h1IK446XXd5okvRhkLQJLvC4zWYL2g03WrAV+aAGxwwt+4aclpSV B+8PHQSCUo0ylNIj+/bt48VWtSbCEpFpAJlzs4oxOxIBholcLg+7jYYoLy9HXl5eQHWJWAZZpztd Zesfy+rV2bx5c4PtDRw4EOPH17pmNmRTGiw5OTl+bXvz8vKwefNmzvo0m80+Y7xa/1gGpmbtWSSF auTrQY9SN2zYAD6WrQCHz/+ZM2c4a89emOU633eBtQHgJ7OiEyGdSpQhhMRJpdJik8kk5TrrJ8uy IISAEIKqH8aCKXaYlGjvXQdJavBBRupSVVUFnU7nv2IEsF8/geqlTqUoliHuL/s9RujFxcVBRav6 6KOPMG/ePJ/rkYBvO9XCwkJs374dc+bMCbi/UGQMBabiIqp+GO3aCZf3fRyqYf7jrEaSoqIiNGnS hDMnE/26h2A7vxl2hiL15VK7zW5PopQGZxAdBMJINcpQSitlMlk5HzEA3EO9iZrUGrszpdyMML77 7jvYbD7Tn0cUSfMeELdwbpowVpgPf+7xerDK6m9/+5tLoer1evzvf/9zvVZdXe0xtS4tLXVZWgCO gDYPPlibJjtQuFKovmL0UpaBccuTLoUqSmgH5aBnOemTS1q0aMGZQqV2M2wXHfFwz96wQ6VSlvCp UAFBqcYEMpnsIF/5zY1GI2w2G8SJbkq1LLBptT+eeOIJSKX85jBaunRpwD75yv5Pus4tJ5Zg729r sW3btrBl0Gg0HmvICoUCvXv3do1SExMT8cQTT7hel0qlYYXw27ZtG7KysvxX9ILdbscnn3zi9TXz /v/BfsWZnoSIoZ7wMYg0+KDl+fn5EdvYCxf7pX2A3fEjk12WAJFYErjNXYgISjUGqKio2PX777/z sll16NAhHD9+HGIeRqqR4Pbbb4darQ6orqTNKIib93AUGDO6GX/FmDFjGr4pBKRSacjpXwJhzJgx IcWABRwmad42Ea3nNsH8e208CMWApyBJCS3bwt69e3mL+7p7925Ovcys+bWbj7sL1Ux5eXngaRxC RFCqMQCl9MjOnTt5UarDhw9Hv379IE6qjaXJFP/BmWtmcXExCgv9+9yHik6nC3hTjBAC1YjXYGcc 742e/8U19eODUOxUA6XGVIkLA3im5DQMm/9W23b6cCgGPh1ye6EsbQTKLbfcwlkKFUopbOd/c5WP ni+3gOdNKkBQqrHC0by8PBWfJiqihLYgcsemEjWWgK3iRhGqVCq/EeW54NSpUwEtAxQxKVhd1MVV Nmx5GqyJtyDvvLNkyRJcunTJb72ysjKvgbXZqsuoXj0TcLrvinStoJ70GQhHAb25RqvVchZakrl6 GNTgSEnNSONx8fI1KQDegy0ISjUGoJRWqlSq4tzcXF7ar6qqwvXrNyBu3st1jXHL7x4OarXaZczO JykpKTh37pzfemlpaZj3+goQZ34lqr8K47bneQmaEorvf7DMmTMnoPgASqUSkyZN8rjGmspQvXom aI3Dh0wD9dRvQs49ZTQawZejCsDNqNwd90Ax5+X9oVAoeN+kAgSlGjOwLLt18+bNvNi3EULw+++/ e6Qctl/jRqlGivj4+AbXGW/cuOE6F6mbQjX2XVfZlvcLLIe8b940JtzfY12USqWHeRtrLIF+1V1g azYlRVJopnwNSdPQp9ZVVVXIyMgI+X5/fPjhh5zZB1PGCuuZ9a7y8iM2lmXZrQ3cwhmCUo0R9Hr9 yu+++67hEEshotVqMXXqVIjdleqVg5z2sWTJEvARGMYbp055hvgzm831UqXIMiZC1qN27c+0901H yhAO4XNN1RsbNmyA2Wyud62uImINN1C9arpbKEQC9YSPIE0bGlb/ycnJvEbVeuaZZwLelPSHrWCn K3iQSJuKLXuOG/V6/QpOGveDoFRjhx1nz56V8RVFHQAkLfq7wuQx10+ANQWXPqQhxo4dG7G4pDk5 OR4mPQqFwhU8xh3VyAVuoewoDL8+yuvGFd/MnTu3nv1mRkaGhyJiSs+g+sfbwNZYeBARVBM+Ciqa vzci8dlyGZfVfepflTwO+fn5EgC8uqfWICjVGIFSatZoNLs3btzIWx+Z+w5DnFxjRkNrU2hwAJ9p Vupyzz33QCQS4fDhww3aSxKxDOopX0EU5zR/YizQr5sD26W9nMgRiTVVb7Asixq75s6daxP02S7u RvXyKbWbkEQM9cRPIe9yV9j98RlABwAuXrzIWVusqRS287WmVNsvxUGlUu2mlPLiDl4XQanGEOXl 5T+uWLGClyUAwGGmI00b5irzMWorKSnhvE1fFBQU4MiRhi1kRMpEaO5aBVLjsmo3Q7/mflhPew82 0hg4cOAA9uzZ4ypTysL0+4fQr74P1OLM7CBVQT31m7BHqIDDM+8f//hH2O00BJcbYNY/lgOMYylK 3LwnFi5ZaywvL/+Rsw78ICjV2OLXbdu2yfhamxw+fDgk6W5KtSCT82nd8uXLI+ZtM3369Aaj2tcg jmsF7V0/gaideaAYKwwbH4PpwPt+wwQ2RKTXVGto3749nnrqKQAAayiGfs0DMO97yxV1iqiTob13 LWRtubPK4MvYv4aHHnqIk3Yoy8CS/V3thS4P4NixYxIA3GYRbABBqcYQlNIbCoXi3K5d/K37SVL6 gMgdMTxpdRGY69mctj9//nxev4C//fabKzkgIQSjRo0K6D5xQhto71vvEQPBnPUO9GseAGuM3Og6 HGpMjmpiBFhyf0LhF0Pw25ba6FbiFv2gm7kRkmbcxCPNzIzIMiRn2Ap2uJY/iCIBWUU6qFSqM5TS 4kjJICjVGEOv1//w888/87b289v2TFxU1JomWfMi9gPOCcnJyYiLi6t33Wg04v3332/wXrGuFbQz 1kPScpDrmr0gE1U/jAnp/xDJNdUNGzYgJ8eRDZQpPw/9mgdg3PwEdKQKzXWOr7Gi33xo7/4ZIm0K J31SSnlL6ldDfn4+p2H+3NPDyLrdh9XrfjFXVVUt5ayDABBC/8UYhJAuiYmJh4qLi1V8PNAmkwnG s1sg2v4oAEAU3wa6h/Zx/uVZtGgRHnroId4iw3vDarUG5I1DGRtMWe/Us12VthsP5YgFEMe14kvE sGBNZTAfeB+W7G8BttZQXqRrCdXYdyFNHx494UIkJycHrVu3hlar9V/ZD0zpGVR9N8JZItD+ZT+a pHW3VFdX30Ip9Z9qlyOEkWrsccpsNusPHeInmI7y/7d33uFRVOsf/77bWxIgoXdDE0QpBkO50osK AldQ1Ksi6r1YAL128F6xXMX6I1gBBQUBFRCwUUREagISEAQUA0IMBEghye7O9nl/f8xms5teZjfF +TzPPNk5c+acs5vZ7572vq/RiCY9xgBaaRuOmPdHwM+qnIwfP142od6yZQsqY20WLKibNm0qc26X 1FqY/jYHlgmfgExF7vY8JzejYOlACNuegWiveLQY7jnVTz75BNnZ2RDtFyHseAH5HyTAdfCDIEEl 6HtNQ/Sd26FtPxjHjh3Dli3y7G+X00l3efTs2VMWQQUA576iH0lt/Cgc+SMXzHwJQPjtqINQRLWO wczs8/lWLFu2zB2uOkhjgK1Z0YKV+6j8e6KbNWtW5VhTZZGQkIDu3btXnDGI5s2bV2j2qL1sOKKn 7oCu5+1FiaIHrkMfIv/DfrBvfQK+HPmGplWBmTGqd0sYf3oB+R9cA9dP7wIeIXBd0zoRUbdvgmnY /0A66Qeye/fusgTiy8vLw4oVER0x1xhffnrIjg5DwgwsW7bM7fP5VnGEh+PK8L8OQkTxFovll4sX LxrK8zxfE/5v7kzcafkcKhWBDI0R88+DII38IVIOHDiAnj17yuYkozocPXoUjRs3RqtWrcrM4z2b AmHHi/BllvRrq2kzALpuE6DtfD1UxvCFlL506RK+Wb0UN11JcP/2JcSckh0sVWw3GAc8Bm2n68M+ 3xlO7HY7Fi9eHNjFUOPytj4Jtz+QoabtIGjGLkPTpk2ddrv9CmY+KUsllUQR1TpK48aNd86fP3/Q XXfdFZbymUUUfNAPovUsAMA8djF0XcbKXs/Jkyfh9XrRtWvXKt23b98+5OTk4LrrqhY2uTTy8/Nx 5swZXHnlleXmY2Z4/tgK5+5XS58SITU0bRKhaTsQ2rYDoW7RC6Su2Y+FaLsAb+ZP8KbvQkHadriy TyHaWHIAqW7eC4ZrZkEbPwpEFQ8wN27ciNjY2Gr7ZY0ElZ0DrwjRdgH5H/YDfNLgzjJpNZZuOobH H398b35+/oAaV1BFFFGtoxDRuLZt236Wnp4enq4qAMee1+BMfhMAoGk/BFE3RWx/dIWIohi2rVnv vPMO/vGPf5S6iwAoDGm8F66DH8JzclNg/2dxdp0SMbjfFVDHdYWqSSeozM1BpqZQmeIAtRYgNYjU YK8D7CoAuwogWjMg5mfAl3cKvou/4P3NpzG5jxFNzKW8V40Bum5/h/7KO6Bp0avk9Qqo6me4bt06 dO3atcpTLbWN8P1suH6WVv3VLfog6tav0bNnT+vRo0dvY+avI90eRVTrKESkNplM53fs2BEXrjDG nkunsfbxPhjdXRr2R9/1I9SxXcJSl9frxfnz59GmTZty88nVe6moDo1GA5VKBZ/Phz///BMdOnQo Na9oOw/3ia/g/u3LElMDu9JcGNSpalMmZ/N8cHoY8U3L2BWhNkDbcRh0XcZCe9lIKRJuDansZxqJ zx6QpmPkckTtu3QKBR8PDizeWSYsx8+5jTBkyJAsu93ekpkjHkdbWaiqozCzz+PxzE9KSio9ipsM aBt3QOvLBwbOnQc/DFdVICJ8//335ebJzMzEJ598ErY2FKLT6QI9OI/Hg337ijx2CYIQ4gxbZWkB Q5/7EH3rV4i5L1VyTtJjClQx7SolqAUOEWdyihbM8h0iGgUP7zVGqFslwNBvBiyTPkejB47BcuOH 0HWbKIugAtIugszMzArzRWre++jRo7KV5dg9LyComtaJ0HQcjueff97pcrnm14agAkpPtU5DRM0M BsOZc+fOGRo3bhyWOjx/7oFt9U3SicaAmPsOVNuJcUMgOzsbO3fuxMSJEwEAaWlpSEtLw5gxYwAA LpcLoijCaDRCdObDef4orBmHESVmg4UsnEg7haNpGbixbyxY9OH3cwXwQoce8a1A+iioLK2gimkL VXRbqOO6QdWoY6164Xe5XFi0aFFI4ML6gjfzIKyrihxzR936DayGjmjRooXL7Xa3Y+ayHdCGkcjt zFaoMsx8MTo6+rvFixff8MQTT4RlVKFp0x/qplegIOMwzHDCdXgZjNfIsyJbFufOnUOzZs0ChgFn z55F69atw1pnZYmLiwsIKgB06NABwT9oGRkZ+OWXXzB+/HjsSD6I1q1b45S9I0aPng4A6O5yoada HXhvV0e2+RVS/LPW6XSYOnVq7TWomjAzHDuLPGdpO4+FpmUfLH3zTdFgMHzrcrlqRVABZfhf57Fa rfMWLFjgCJeTEiKCtve9+HCPNMvgOrAI7KrYSUlNsNvtgRDMHo9Htg3r4UCj0SA2tmgbVXx8PMaP Hx8479y5M0aPHh041+v1EbUiqypbtmyBx+MJnBORbJvvK2LevHmyleU58SW8Gf4w3qSGcdDTEEUR b775plBQUPCabBVVA2X4X8chIoqJiUlbs2bNZeEItwxIZpsFH/0NYr7k09Iw8Mmw91YVapc33ngD DzzwAMK1D7o0HA6HLPWxy4r8j/4Gtl8AAOh73wPT0BexZs0aTJs27bTVar0s0hv+g1F6qnUcZuaC goKXZs2aFbYFK1JrYQgSUddPC8PeW01OTobP50NKSgrCGUVWoXQeeOAB/PyzvB7KKkIuAXfsfS0g qGRuDuOAJwEAr7/+us1ms71Qm4IKKKJaL2DmZWfOnMnbunVr2OrQdZ8EVUwHfLRXALvy4ExdFLa6 AGlDvlqtRnR0NDIyMsJaV7ioLX+q1cXr9Qb85xqNxoALxXAj53PrvfgLXEG7VEyD54L0Udi6dSuO Hj2az8zLZKusmiiiWg9gZo/dbn941qxZtnD9CJNKA0PiIxjdXY/NRzW4YdoGEBFGj34G33yzQ/b6 CuchL7/8crRv31728hVKsmTJkpCIrMFzweHC6XSGRHmtCSx6IWx9ImCMoWn3N2i7jocoinjwwQdt NpvtEWaWN851dWBm5agHBwBVVFTUrytXruRwIfq8/NkjA7lj7K0McOCIj5/NX3/9Y43L37RpE2dn Z5d5ffny5Xzu3Lka16NQNbKzs3nTpk213YwKEZLnc+4bLaRjfjv25vzOzMyrVq1ii8XyO/xrRLV9 KD3VegIzi1ardcaMGTOcwau3ckIqNT746Sr8kbMSwD4AUq/45Mn/4a23vqtx+e3btw9ZSS/O5MmT 0aTJX3ePbDhITk5GXl5euXliY2NlHy0wM+SMDOzNOgrn3jcC58b+j0HdpBM8Hg8ee+wxu81mu5+Z 68SquyKq9Qhm/s7r9R5asmRJ2B4et6rQv6gIoMiyyOms+Qb1bt26lXtdr9dDr5eslC5cuIA68h0p k/owpyqKYpk+DoKp6H9TVVJTU3H8uDx+odnrgrBxJiBKnQl1y77QXy05Wf/www/ZZrMdZubwLThU EUVU6xn5+fkzn376aacgCBVnrgZ6feGUVCKAol6lwVC9FfrvvvsOhw4dqvJ96enpFUZKVSgdt7vI Fe+AAQOq5CLw0KFD+O67mo9K+vbti0GDBtW4HABw7n0dvmy/k3KNAeYxSSCVBoIg4IknnnDl5+fP lKUimVBEtZ7BzPu9Xu/2//znP2HZh/TQQyPQps09QSkC2ja+CQ/e06da5SUmJqJXr6p7WEpISMDV V9c1e6RQIhmjqrIwM954441q9/J79eqFxMTEatdfkWPwquI5/QOc+98JnBsHzYG6cTwAYP78+V4A 25i5pBPc2qS2J3WVo+oHgK4mk8mRm5vLclFQUMBJSUncsmVLVqvVPHTILAbA13a+hmcMacLW9Xex KIqVLq8qeSsiLS2Nly1bJlt5CpWjOv/D//3vf+zxeGSp31dwli+92z2wOFWw+mYWRR8zM+fk5LDF YhEAdOE68J0MPpSeaj2EmX9Tq9Wrn3/++RqHXPnjjz/w0EMPoUWLFpg9ezYyMzOh1+sx7sb2cP+5 F+unn8Fz43TwnNwM9/G1lSozNTUVX375ZU2bFiA+Ph533HGHbOXJRV2ZUz127Bg++0z+kDhffvkl UlNTq3TP7NmzZTHTZZ8Htm+mgx3SvD6Zm8N83dsBB91z5sxxE9FqZj5R48rkprZVXTmqdwBoYTQa C/bu3ctVRRRF3r59O48cOZINBgNrtVqGtNQfODp06MCiKLLtu8cDPYU/XrmMvXlnKlV+OHn55ZfZ 6XSGtY7K8MMPP9R2E5g5vJ93ZcuWuw327c8VbZ96szW7/9wTuLZ//342GAw2AC24DnwXix9KT7We wsznnU7nA3//+9+dwQsTFfH999/jiiuuwNChQ7F161Y4nU6UtkUrKysL+/fvh+naZ6Fq1BEAsPVw Dk6tuhcslj5v5nBIlrThjp30xBNPBHYJ1Ca1Oae6YMGCwJalcH7ehWUX/m9LY8+ePbIsbhXiOr4W rgPvBc6NA5+Ctk1/6ZrLhVtuucXucrn+xcznZatURhRRrccw8wqbzbZr7ty5ld64OmTIELzyyisY MGAA9Ho9tFptqfmcTifeeecdkM4M83XvAKTGTX2MiBOOwJmSVCJ/VlZWxCJwBocI+fnnn7Fu3bpy cjccgheBZs6cWe6eX7lZsWIFsrJKD9vdv39/jBo1SpZ6vOd+grDl0cC5tuMI6BMeCJzPnTvXk52d vYeZV8pSYRhQvFTVc4iopclk+m3Hjh1RVQ27kpaWhjvuuAP79+8v1amJ0WhEVlYWzGYzHCnz4dz9 SmGtcA55Fy37TJDhHchLZmYmWrZsGZG6tm/fHrHe6sGDB5GRkYFx48ZFpL7KIHccMV/Bn7CuvB4s ZAMAVLFdED3lK5BeMnNNTk7G0KFD7U6ns1Nd7aUCSk+13sPMmQ6H4/6xY8c6XS5Xle7t2LEjTp48 GSKoarU68EVRq9VYs2YNACmOuqbNAH+dIpbPmw5f3hmcPn1anjciE6mpqUhPT6/tZtQYr9eLtWuL FgZ79+5dZwT19OnTcDqdePXVV2Urk9022NbdGRBUMjaBZfyygKC6XC7cdtttdpfL9c+6LKiAIqoN AmZeabPZdldlGgCQwhg7nc6QNK1Wi+uvvx4GgwEOhwNJSdJQn1RqmG94H2RpCSLCfYmE/PV348dt 8s2lycENN9yAdu3aAQB8Ph9eeOEFhGs0Jncv1ev1Bn7gVCpVnY1qumPHDmg0Gjz55JOylMdeJ2wb 7oaY86uUoNbBcuMSqBsVmc4+++yznpycnD3MXHdC/pZFba+UKYc8B4CWJpOp4KeffuLKMnDgwBKr /tdeey0zM1+4cIFfeOEFbtOmDZ84cSJwj+fcAc6d3y6wMnt+9VQ+evSXStcZaYJXpTMyMnjDhg21 2Jryeffdd8t1OFMXkLt9os/D1vVTi1b632jBzl8+C8mze/duNhqNVtTR1f7ih9JTbSCwfxpgwoQJ jspMA6SlpZUwA7VYLIHeR7NmzfDMM8/g9OnTIQsimpZ9cKjRXfD6pN6f5vRGHN/wkozvRF6CV8Zb tWqFhISEwPnx48exY0f13RpWdZ8qM4fstPj8889DIovef//9EV18qirMjJUrVxb+iMPr9WL37t01 KE+EsOVReE5uCqQZBj0NfY+bA+culwu33nqr4HQ66+xqf3EUUW1AMPPK/Pz8Pc8880yF0wBJSUkl FqdMJlMgamgharW6hOcobjsE5r6SKatKRRim3wbnwSU1bX7YIaKQRaxOnTqha9eugfOUlBRs3Lgx cG6321GV7WrFOX78OA4fPhw437RpU8j5zTffjB49elS7/EhDRJgxY0bgh0qj0aCq8/iFMDMc25+F +9jngTR93/thSAiN6vrf//7Xk5eXt4vrw7Dfj7L638AgohYmk+noypUrmwQHqAtGEAQ0a9YMdrs9 kGY0GvHss89Wep6MRR/sX90Dz8nNhTXD1v81pJzVY9KkSTV9G3WC1NRUWK1WDB48GACwc+dOuN1u DB8+vNTzH3/8EV6vN3CemZkJg8GAcIUXjxSbNm3CqFGjZFvplwT1v3Ad/CCQprviNphGvh4ysli2 bBmmT5+e43A4ejDzBVkqjwCKqDZAiCjBbDZv37Vrl6k0ZyZLlizBrFmzYLPZAmkGgwEZGRllDj83 bdqE3r17o3nz5oE09giwrp4E3/mDUoJKC/uAN9GmX8MQVQVJAFNSUip0snLhwgUcPHiwxEinZHki hO+fhvtwUdQTbeex0iKoqsi95JEjR9CvXz+n0+m8lpn31+xdRBZl+N8AYeb9Dodj+siRIx3FHQUz M+bNmxciqCqVCuPHjy93Pq9z584hggoApDXBMmFZwOIKogfmvY/Dk74TzFztoWF9oa7Y/ocTIqqU 16rmzZujc+fO5eZh0Qdhy6MlBfX6d0IENTs7G6NGjRJcLte99U1QAUVUGyw+n2+5IAgLx40bZw9e HElJScG5c+dC8hoMBjz22GPllhcfH19qusoUh6jJq6GKbuuv2Anb+ruQ++v3WLx4cc3ehEKtIIoi nnvuOVR1FFvWMwJIjqbtG2fAffTTQJqu20SYb3gPpNYF0jweD2644QZ7QUHBQlEUI2OiJzPK8L8B Q0TqqKiorZMmTUpcsmSJAQBuuukmrFu3LuQLc/nll+PYsWMl7t+2bRuioqJCVszLwpefDutnE8E2 v2BrjLDc+CG0HYbK9G4UIgkzV9unwP79+2G1WjFs2DAAgOjMh/3LafBm7Ank0fWYIs2hqkIjSkyd OtW1Zs2aFLvdPoyZ62XscqWn2oBhZp/Vap3w6aef5r7//vu+ixcv4ttvvw0RVIvFgqeeeqrU+xMT EyslqACgjmmHqMmrQeZmUoLXAdv6u+D+7UswMxYsWABRFGv8nhTCg9PpxNdffx04r4mTloSEhMCU gViQAetnN4YIqv6qu2Aa9UYJQX3//ffFNWvWXLDb7TfWV0EFlJ7qXwIi6mIymX6aMmVK1MqVK0Os qKKionDx4kUYDIZAWk16Kb5LJ2FbcwtE69nC2mEa8SqEtjfU+1Xw4kTS9j/c5OfnIy8vT9YAgN6L R2D94h+AUBQW2zDoaRgSZpR4vrZt24Zx48ZZBUHoy8y/y9aIWkDpqf4FYOYTgiDcvHTp0hBB1el0 uO+++0IE9ciRI/jiiy+qXZe6cTyipmyAqkmnwtohbH0chmMLwf547QcPHizVgYtCZPH5fMjOlmzt Y2JiZBVU9/EvYP30RmzYm45jmR5ApYX5undg7DezhKCePn0a48aNcwmCMKm+Cyqg9FT/MhDReACf AQg4IjUYDDh+/Dg6dOgQyFeTXmowopAN27rb4btQtNld23kszGOScOiX3xAXF4e2bdvWuB6FPbeu cAAAGQhJREFU6rN9+3Y0bdpUVgME9nng2PkCXKlBi5S6KFjGfwRt2wEl8mdnZ+Oaa66xnz179lmn 0/lGiQz1EEVU/yIQ0R4A/YPThg4dim3btgEAbDYbLBaLrHWyywrbN/+C9/QPgTR18ythGf8xVJYW AIC8vDxotVqYzWZZ61YoHZfLFTYH36I9C/Zv/gVvxt5AmqpxvOQcJbZLiWcsNzcX/fv3t2dkZLwn CMIT3EDESBn+/wUgoi4AQqwAVCoVHnroIQCSsK1aJb8VIOmjYJmwDPreRdFZfRcOo+CTUfCk7wIg fcnl9BofSerjPtW33347LPuHPWd+RMEnI0MEVRs/BtG3bYQ6tgsAYNWqVcjLywMAFBQUoF+/fo6M jIyPG5KgAkpP9S8BEb0L4F4AwW7+Hd27d+d9+/aZItFLdP38MYRtc4DCRV1SwdD/URiueTgQzA2Q 5teCpyPqMvVlocput4dtJMBeFxy7X4brwMKgVIJh0FMwJDwU8r8txGazYfDgwfYTJ058ZrPZ7m1I ggoootrgISIzgIsATEHJAoD/WiyWvj169Bi/detWk9xD/9LwpO+C/dv7A46IAUDTfjDMY96CytwU ALB27VqMGDECMTExYW/PX4HU1FTk5uZixIgRspfty/kN9m8fhC+ryNMWmeJgHrOgzP3JVqsVQ4YM EU6cOLHeZrPdwYWrlw0IRVQbOET0TwBvAAhWTSeAVgAKLBbL0rZt2960d+9eUySETLSdh/2b++E9 m1zURmMTmIa/Al2XsSF5MzIyYLfbQzxJKVRMWloa4uPjwxYQkH0eOA+8B+fe/wN8RbtJNB2Hwzzq /wI/kMXJz8/HgAEDhDNnzqyz2+13NkRBBZQ51QYNSd+qJxEqqD4Aa5j5EjP7bDbb1DNnzqxKTEy0 5+bmhr1NKksLWCavhqFfkYs3duTC/vV9sH/7AETHpUB6bGwsCgoKwt6m6lIX51SZucyYY3LgPX8I 1hVj4Nz1cpGgqvUwDv0fLBOWlymoubm5GDhwoP3MmTMrGrKgAkpPtUFDRIMAbESoqAoABjLzoaB8 ZDab57du3fqeXbt2mZs2Lf2LITeeMz/CvvnfRaatAMjcHKahL0Lb+YYSPa3PP/8cPXr0qDM+SOvK nOqWLVsQFxeHPn36hK0Odtvh2POq5K4vSA/VzXrCPOYtqOPKHk1cuHABCQkJjtzc3EV2u/2RhjaH WhxFVOshRNQWwDIAzSCFQVnEzAuI6DUAYwG4AZyEtCd1DEJHJE4AS5n5AX9Z4wC8CGCfyWTKMhgM j3z//feG0lwGhgPRmQ/H9v+GOCsGAE37ITANewnqxh1D8wdF8Dx58mS5TjwaMpcuXQpYqAmCAJPJ VMEd1YNZhPvYajh2vQS2F1lGQWOAccCT0Pe5F6TSlHn/0aNHMWLECCE/Pz/J4XA8D+BHSM+lDsAG Zn6aiCYDmAugG4AEZk4FACLqAOA4AH/wKuwt7bll5vvkfM81RRn+1088AB5h5h4AEgE8SESXA9gC oAczXwXgLIDRCP0fWwHcW/hg+rkdQG8AmYIgrMjLy5s+aNAg4auvvorIG1EZYmAekwTzjUtBpqIe svfMdhQsGwrHntfBHqEof5Cj5P3799fIM3995ddff0VKSkrgPFyC6slIhnXFGAibHw4RVE37axF9 53YYrp5erqB+9dVXSExMFLKysu4XBGE2MzsBDGXmXgCuBDDUP5o6AmAigNJi26Qxc2//UepzS0R1 Y+jiRxHVeggzny8cvjOzDdKveStm/i5orqq0MTwDWFMsTQWp52AC4Pb5fB/b7fbhU6ZMyX3xxRe9 kRrJ6DqNQfTUndBfdTcA/7Df54Iz+Q3kLxkA1+FPwKI35J4pU6ZAp5PcxmVlZeHtt9+OSFsLidSc qsPhwLx58wLn3bp1q9AZdE3wZf8G21f3wvb5RPguHgmkk7k5TGMWwPL3T0MinRaHmTFnzhzvzTff nGez2YZ5vd5lQdcKfyF1ANQAcpn5V2Y+UcVmhjy3Vbw3rCiiWs/xD5F6A0gJStMA+DukhzYYB4DN /t5BIYsA7ATgK7S7ZuZkQRCunDdv3skJEyY4BEFAJFAZYmAa/hKibt8IdfOi6Qe2X4Cw9XEULBsK d9rGUv18Nm3aNGDMAEjDzs8++ywi7Q4H8+bNC/hpMBqNZXoSkxNfzm+wfTMdBcuGwvP7N0UX1AYY rnkEMXfvhr775HJ3FQiCgEmTJjkWLFhwwul0XsHMKcHXiUhFRIcAXADwAzOX9DkZSkciOkhE2yt6 busKypxqPYaILAC2A3iRmdcHpa8EMBlA8NjMBaALgDgA6yFNE1grKN8YFRW1om3btqM2b95sbtOm jdxvoUxY9MH9yyo49r4OtoeGJ1I3uwKGfrOg7Xx9qZvLS2PXrl0QBAGjRo0KR3NrzHvvvYeJEyei RQvJfFcuHwyVwZd1HI59C+D5bQOkwUwRum4TYRw0G6roiv/36enpGD58uHDhwoWNVqv1DmZ2lJWX iGIAbAbwFDNv96f9AODRoDlVHQAzM18ioj6o5HNb2yiiWk8hIi2ArwFsZOb5QelTAbyF0BV/BrCV mUf584Q8vBXUQwaDYbZWq31mw4YNhqFDI+t0mj0CnKmL4Nz/DuC2hVxTNekEQ8IM6LpNBKm1ZZRQ Ops3b0ZUVBQGDJCcfBw7dgyxsbElQsbIxalTpxAdHY24uDgAwPLly9G/f3906tSpgjvDA7MI7x8/ wJm6CN70klOZ2stGwdD/UWiaX1mp8vbs2YPrr79ecDgc/3O73S9XZoWfiP4DwMHMr/vPy30uq/Lc 1iaKqNZD/PtPPwaQw8yPBKWPAfA2gNYADEG32AH8nZm3ENFlkBYErmDmvCrUOdZoNH769ttvm6ZN mxaZLlQQoiMHzpQkuA4vB7zOkGtkbgH9lf+Avuc/oLJUTxTT0tKg0+nQrl07AMD69evRoUMHFO6C +OKLL9C5c2f07NkTgLSN6fz587jzzjsBSIsy7du3x5VXSiK0du1adOnSJZB/37596NixIyK1Xa0s 2GWF+9cv4ExdDPHSyRLXtZeNhCHx39C0qPzujyVLlvCMGTPsgiBMYeZvyspHRHEAvMycR0RGSD3V 55j5e//1HwA8xswHgvJfYmZfdZ/b2kAR1XqIf25pB4DDKBqvzQawAEALhPZSASAXQCakXQMigP+W 9/CXU+/lZrN5y4gRI2I/+OADY2GvK5KIQjZcqYvhPLQUcBcbBao00Ha6DvqrpkLTJrHSUwOVgZkh iiLUamma2mq1Yu/evYHpBLfbDa1WG7Ehe1VgZnjPJsP9y6dwn/gK8BYblZMK2k7Xw5DwYJXENDs7 G5MmTXLt378/WxCEkcx8vLz8RNQTUmdA5T+WM/NrRDQR0rMbByAfwEFmvo6IbgLwHGr43EYaRVQb EEQUBWkBwBiUbAcwh5mTZKrDZDabX1epVHd//PHHhokTJ8pRbJURnflw/bwUroNLwEJWieuqqNbQ dZsI3eWTyt2Y3pDx5f4O94mv4T62GmLeHyUz6KKg73kb9L3ugTqmar5t161bh2nTpjncbvcSv5ep yKxm1gMUUW1AENH9AF4DEOySyAGgJTPny1zXIIvF8umoUaOaLFy4sFZ6rQDAPjc8aRvhOrQU3rMp peZRN70C2i5jobtsFFRx3epkb1IOmBm+7OPwnPga7rRvIOaUvktJFdsN+p63Q9/jFpA+qkp1ZGdn 46abbnIdOHAg2263T2HmXXK0vSGhiGoDwT/P+geA4A2EXgArmHlqmOo0mc3m1wDcs3z5cn1t9VoL 8WUdh+vwMrh/2wB2Xio1jyq6LbSXjYL2shHQtO4H0lZ/43xdMFMVHZfg/XM3PGd+hPfMjxAL/iw1 H+mjpZ57jylQN7+qWj8sQb3TpYIgPK70TktHEdUGAhENhrQboLidfyIzHyn9LtnqrhO91kLY54bn 9A9wH18Lz8ktgK8Mp8wqLdQtekHbJhGaNv2haZUA0lXeBWJtiKpozYT3fCq8manwZuyF78LPIbb4 IWgM0HYYBl2XsdDGjwFpjaXnq4Ds7GzceuutzuTk5Bybzab0TitAEdUGAhF9DeB6BMyRAACHmLl3 hOo3WSyW15j5noULF+pvv/32SFRbIewqgPvkFnhOfQfP6R9KLm6FQFA1vgzqZj2haXYF1M16Qh3b BWRuHvEpA/Z5IOafhi/nhHRkHYU3MxVsyyz/Rq0Z2stGQtf5Bmg7DqtRTxyQeqd33323w+PxfCQI wmNK77RiFFFtABBRK0gOVIK3UVkB3MfMETUrIqJBZrN5Ve/evRslJSVZwuk5qaqwzw3v2RR4Tm6G J30XxJzfKnejxgh1o45QNe4IVaOOUJmbQ2VuBjLFQWVuCjLGgXSWSu2VZWbAYwe7CsCuAoiOXIjW cxBt58DWcxCtmfDln4F46RQgeipuG6mgbt4L2vbXQtPub9C0uhqk1lXufZVDcnIyZs2aZT927Ngl m812q9I7rTyKqDYAiOhFAI8iVFTzADRn5ojbRRORTqVS3avX61/q27evfsmSJYbOnTtHuhkVIgrZ 8GYk+4+98OX8VhTupRLsSnNhUKegIHoqjdQz1BhBGj3AIlgUpTLZB/i8YHdB2cP1yqA1QdP8Kqhb 9oWmZR9o2vSHytCo+uUV4/fff8fjjz8ubNmyxeN0Omcz82JmroS6KxSiiGo9x29ZdRFA8DfLBeAN Zp5TO62SICKzTqd7VK1WP3HbbbepX3jhBUPLli1rs0nlwh4HfNm/wnfxMLwXj8B38SjEvFNgV+mO skuIqsyQpRXUsZ2hju0CdZMuULfsDXVs13I9Q1WXc+fOYebMma5vv/3WK4riKy6X601mtste0V8A RVTrOUR0M4APAATvjXEC6MTMZ2unVaEQUazZbH7W6/X+c/r06aq5c+dqGzWSr3cVTpgZ7MyFeOk0 fHmnIOanQ7RfBAtZEO1Z0l9HDuARKt8D1RhB+mjpMDSCytICqqhW0mFpBVVUa6ibdKrydqfqkJeX h5deesnz1ltv+VQq1YeCIDzLzDlhr7gBo4hqPYeIUiF5qSqEAWxm5utqqUllQkTtLBbLy0Q0cc6c ObqZM2eqjcbqrUjXNZgZED1gjwPwCGCfCyCVZNVFakAlHaSLkmXOs6Y4HA7MmTPHt3DhQo9arf7C arU+xcyl78dSqBKKqNZj/GZ/yQiNlGoDcCMz/1A7raoYIuoeHR39fz6fb/DDDz+snj59uiaSHrDk oi7sU60qGRkZeO+997zvvPOOh5l3FhQUPFyRealC1VD8qdZvHoHk7DeYXEjuAOsszHwsPz9/tN1u 75uUlPRR586dHUOHDhU2bdpUqq9UhZrBzNi6dSuGDx8uxMfHOxcsWPBRfn5+Qn5+/mhFUOVH6anW U/z+KDNR0s7/KWaOrAv8GkJEUUR0u8ViebJRo0Zx//73v81Tp06l+jLvWlfJy8tDUlKSuHjxYsFq tWZZrdZXmHllXfdHWt9RRLWeQkQzAbyEknb+LZi57sZ1Lge/qe3AmJiYxxwOx3WTJ0/2Pfroo8be vSNiv9BgSE1NRVJSkmP16tWk1Wq3FhQUvAJgd0OPYlpXUES1HuIXn3QAwRORXgAfM/O9tdMqeSGi 5jqd7p9arXZWmzZtdMOGDTM/+OCDqu7du9cZhyh1ZU6VmXHs2DFs2LBBXLRokTMrK8vh9XqT3G73 Ima+UHEJCnKiiGo9hIiGQwotEWyo7oAU3vdo7bQqPBCRGsAws9l8M4CJUVFRhvHjx2snTZqkGzx4 MLTaqnn8l5PaFFWPx4Ndu3Zh6dKl7m+//dbndDoFAF/Y7fbVALYxV8GKQUFWFFGthxDRZgAjEWrn /xMzJ9RSkyKCv4d+pUajmWAyme7wer1tRo8e7Z08ebL5uuuuQ0Ofg83Ly8P69euxfPlyR3Jyskqn 05222WwrvV7vegBHlOF93UAR1XoGEbUFcAIl7fynMXPx8NMNGr/Pg7ExMTF3CoJwdd++fV1jxoyx 9OnTR5WQkBAIoldfOX/+PJKTk7Fx40bx8OHDttTUVL3JZErJy8tbAeBrZj5X221UKIkiqvUMIpoH 4GFIMc8LuQTJzv8va6NNRGYAI3Q63SCTyTRUEITLzWYz+vbt673qqqss1157rapfv36yCq2cw//z 58/jwIEDSElJETds2OD8888/IQgCTCbTLzabbYfH49kFKXijYjpax1FEtR5BRHpIdv7RQclOAK8y 87O106q6iX+qoAOAvlqt9hqLxTJYEIQeJpMJ7dq1w8iRIw3dunVTtWrVCi1btkSrVq0QFxcHlary W7erIqqiKCI7Oxvnzp1Deno6Ll68iPT0dHH37t22lJQUvcfj8UVFRf1SUFDwo8fj2QfgAIDTypC+ /qGIaj2CiG4D8D5C7fxdADoycwWONhWChVatVveOiorqpFar27nd7rYejyfG4/GYoqOjnSaTiTt1 6uSLj4/XtmvXzuDz+ahdu3YwGAzQaDRwOByIjo6GWq2G1+tFTk4ODAYDmBkulwv79u1jr9frPHv2 rOfs2bOckZFhEARBq9frBb1enwMgm5lP2u32NK/XexCKgDYoFFGtRxDRzwCCA7EzgG+YeVwtNalB QUQ6SNFoWwFoCaCVRqNpbTKZOqhUKp1KpdIQkVYURSNJAIBHFEWGtPvCK4qi02azpft8vrMAzkEy 0DgH4HxtuGFUiDzy+xBTCAtEFA1pHtUGydZfBSlcyqu12a6GhF/00v2HgkK1UGz/6wl+K6nLAYwB 8CUAN6Rw1IpHdgWFOoQy/K+nEFFzAK2ZObW226KgoFCEIqoKCgoKMqIM/xUUFBRkpE6LKhHNJaJH a7sd5UFEg4mof1XzEdG/iOiO8LZOQUEh0tRpUYW0ZajS+J1vRJqhAAZUNR8zL2Tm5WFrlUK1IKK2 RPQDER0lol/8LhZBRJ8R0UH/8QcRHQy652ki+p2IfiWiUUHp44joZyJaXBvvRaF2kE1UiWgdEf3k fxDvKyPPaSJ6hYgOE1EKEcX70zsQ0Tb/A7jVb99e/N77iGgfER0iojVEZPSnf0RE7xNRMoBXit3T gYh2ENEB/9Hfnz6EiLYT0WoiOk5EnxRr41x//sNE1NWf3oSI1vvbuJeIehJRBwD/AvCI/8s2iIjG ElEyEaUS0XdE1KyMfIFeOBH18t/zMxF9QUSN/OnbiWie/7P6jYgG1eifpFAZPAAeYeYeABIBPEhE lzPzLczcm5l7A1jrP0BE3QHcAqA7pJ0Z7xZuYAVwO6T4YZlE1CPSb0ShdpCzpzqNma8GkABgJhE1 KSUPA8hj5isBvA1gvj/9LQBLmfkqACsALCjl3rXM3I+ZewE4DuCeoGutAPRn5seK3XMBwEhm7gtg SrFyewGYBenLcBkRFfYiGUCW/573ABSW+RyAA/42zgawjJlPQ7JwetP/hdsFYBczJzJzHwCfAXii jHyMop74MgCP+8s+AqDQ5JQBqJn5Gkj2/oopaphh5vPMfMj/2gbpWWtVeN0vmDcDWOVPGg9gFTN7 /P/nNADX+K+pIO0tNkHaAqfwF0BOUZ1FRIcA7IXkPLlzGfkKH8ZPARTOMSYCWOl//QmA0npkPYlo JxEdhtQD6O5PZwCryzDx0wH4wH/P55D2eRayj5nP+e87BMl8sZAv/H9Tg9IHAlgOAP6gerFEVGgu GuyCry0RbfHX+VhQO4vnkxKkTf0xzLzTn/QxgGsraItCBPCPMHoDSAlK/huAC8x80n/eCkBG0PUM AK39rxcB2AnAx8y/h7WxCnUGWSyqiGgIgOEAEpnZSUQ/INSLUlkEC2FZ7twL83wEKUroESK6C8CQ oDxCGfc+AiCTme/wz7c6g665gl77EPpZuMpIr4zL+bcAvM7MXxPRYABzK3FPMMXrKKstCmGEiCwA 1gCY5e+xFnIrijoAZcEAwMxbAVwdnhYq1FXk6qlGA7jkF9RukHqeZXFL0N89/td7IA3PAakXusP/ mlAkMhYA54lIC+AfqNwiVjSA8/7XdwKoyULWTn/bCn9EsvwB1KwIdXASDcnWGwCmBqUXzwdI+4QL AFwKmi+9A3U8GmpDx/+MrQXwCTOvD0rXAJgIaVqnkLMAgtcA2vjTFP6iyCWqmwBoiOgYgJchTQGU RWOSHIPMgNSThP/13f702yHNdQKh847/gTQM2wVpniuYsgT2XQB3+aclukKym6/onuLlFuabC6Cv v40vAbjLn/4VgImFC1D+fKuJ6CcAWUH3F+ZLDRLQwmt3AXiNihymPF9OexTCiH/O9EMAx5h5frHL IwAcL+Yc+ksAU4hIR0QdIU177YtMaxXqIhG1qCKiPwD0ZebciFWqoFAF/D94OwAcRtGP2NPMvImI lgLYy8yLit0zG8A0SMEXZzHz5ki2WaFuEWlRPQXgakVUFRQUGiqK7b+CgoKCjNR1iyoFBQWFeoUi qgoKCgoyooiqgoKCgowooqqgoKAgI4qoKigoKMiIIqoKCgoKMqKIqoKCgoKMKKKqoKCgICOKqCoo KCjIiCKqCgoKCjKiiKqCgoKCjCiiqqCgoCAj/w/qD62nNqnM/AAAAABJRU5ErkJggg== )