├── Combined Lid Driven Cavity (Collocated grid).ipynb ├── Combined backward facing step.ipynb ├── Heated Cavity (4th order).ipynb ├── Mixed convection around confined square cylinder(Square array)-V5-Freestream.ipynb └── Unsteady Lid Driven Cavity- PISO, FE, Multigrid solver.ipynb /Combined Lid Driven Cavity (Collocated grid).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "1e584a55", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "from matplotlib import cm\n", 13 | "from matplotlib.animation import FuncAnimation \n", 14 | "import time\n", 15 | "import scipy as sp\n", 16 | "import pyamg\n", 17 | "from tqdm.notebook import tqdm" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "id": "c3ab445f", 23 | "metadata": {}, 24 | "source": [ 25 | "**A brief rundown on whats going on here The whole code is split into 3 parts; the solvers, initialization and postprocessing**\n", 26 | "\n", 27 | "# Solvers\n", 28 | "**Convective upwind schemes** \n", 29 | "* upwind1: 1st order upwind \n", 30 | "* TVD2: 2nd order total variation diminishing \n", 31 | "* QUICK3: 3rd order deferred QUICK \n", 32 | "\n", 33 | "**Functions** \n", 34 | "* mom: discretise NS eqns and solves them \n", 35 | "* face: calculate face velocities using Rhie & Chow interpolation \n", 36 | "* cont: set ups and solve the continuity eqn to get pressure correction \n", 37 | "* correct: correct the nodal velocities and pressure. (Would be nice if someone explain to me why face velocities need to\n", 38 | "be corrected, even though here it is not done and this code is still able to get decently accurate results) \n", 39 | "\n", 40 | "# Initialization:\n", 41 | "* Here you just set up the domain length, mesh sizes, relaxation factors, inlet velocities, time step size, steady or transient etc \n", 42 | " \n", 43 | "# Solution:\n", 44 | "* Just solves the NS eqns, you can finetune the residual requirement\n", 45 | " \n", 46 | "# Post-Processing:\n", 47 | "* Colorful graphs or animations (reminder to run the %matplotlib notebook cell if want to play animations for transient flow)\n", 48 | "\n", 49 | " \n", 50 | "# A bit more details on the mom solver:\n", 51 | "* **Variables and their meanings:** \n", 52 | " * b: aspect ratio, dy/dx (even though its there, the code can only run on square cells, has not been a major enough problem for me to find the error and solve it)\n", 53 | " \n", 54 | " * a1: eastern face flux from previous iteration\n", 55 | " * a2: western face flux from previous iteration\n", 56 | " * e1: northern face flux from previous iteration\n", 57 | " * e2: southern face flux from previous iteration\n", 58 | " * av1: implicit under-relaxation of systems of equations\n", 59 | " * av2: explicit under-relaxation for the velocity corrections \n", 60 | " \n", 61 | "* **Discretisation methods:**\n", 62 | " * Diffusion: 2nd order central difference\n", 63 | " * Convection: Pick and choose (upwind1, QUICK3 and TVD2 is here, you can just find and replace the functions if you want to convert it)\n", 64 | " *Boundary cells: 1st order forward/backward differencing\n", 65 | " \n", 66 | " * You will notice some lines of code that look like this (UWW=4*UW-6*UP+4*UE-UEE). They are just cubic extrapolations to create a ghost cell. As higher order schemes have a larger stencil, the cells next to boundary cells will need ghost cells to if you want to continue using 4th order CD for example. Moreover, you will notice I updated the source terms for such cells. This is due to the fact those ghost cells are just ficticious and solving them implicitly will result in index error. \n", 67 | " \n", 68 | " * Time: You can choose either explicit 4th order Adams-Bashforth time-stepping or implicit 4th order time stepping. You can set the Explicit=True/False to choose which time-stepping method to use\n", 69 | " \n", 70 | " \n", 71 | "**I hope this is enough to roughly understand what is going on. This code is by no means efficient but it gets the job done decently well.**" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 19, 77 | "id": "afab31bb", 78 | "metadata": {}, 79 | "outputs": [], 80 | "source": [ 81 | "%matplotlib notebook" 82 | ] 83 | }, 84 | { 85 | "cell_type": "code", 86 | "execution_count": 2, 87 | "id": "c8c4613d", 88 | "metadata": {}, 89 | "outputs": [], 90 | "source": [ 91 | "def upwind1(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 92 | " \n", 93 | " AP+=b*(abs(ue)+ue)\n", 94 | " AE-=b*(abs(ue)-ue)\n", 95 | "\n", 96 | " AP+=b*(abs(uw)-uw)\n", 97 | " AW-=b*(abs(uw)+uw)\n", 98 | "\n", 99 | " AP+=(abs(vn)+vn)\n", 100 | " AN-=(abs(vn)-vn)\n", 101 | "\n", 102 | " AP+=(abs(vs)-vs)\n", 103 | " AS-=(abs(vs)+vs)\n", 104 | " \n", 105 | " return AE,AW,AN,AS,AP,uBP,vBP" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 3, 111 | "id": "13eb5b43", 112 | "metadata": {}, 113 | "outputs": [], 114 | "source": [ 115 | "def upwind2(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 116 | " global b\n", 117 | " \n", 118 | " AP+=1.5*b*(abs(ue)+ue)\n", 119 | " defer=0.5*b*(abs(ue)+ue)*UE\n", 120 | " uBP+=defer\n", 121 | " vBP+=defer\n", 122 | " AE-=1.5*b*(abs(ue)-ue)\n", 123 | " defer=0.5*b*(abs(ue)-ue)*UEE\n", 124 | " uBP-=defer\n", 125 | " vBP-=defer\n", 126 | "\n", 127 | " AP+=1.5*b*(abs(uw)-uw)\n", 128 | " uBP+=0.5*b*(abs(uw)-uw)*UE\n", 129 | " uBP+=defer\n", 130 | " vBP+=defer\n", 131 | " AW-=1.5*b*(abs(uw)+uw)\n", 132 | " defer=0.5*b*(abs(uw)+uw)*UWW\n", 133 | " uBP-=defer\n", 134 | " vBP-=defer\n", 135 | "\n", 136 | " AP+=1.5*(abs(vn)+vn)\n", 137 | " defer=0.5*(abs(vn)+vn)*VS\n", 138 | " uBP+=defer\n", 139 | " vBP+=defer\n", 140 | " AN-=1.5*(abs(vn)-vn)\n", 141 | " defer=0.5*(abs(vn)-vn)*VNN\n", 142 | " uBP-=defer\n", 143 | " vBP-=defer\n", 144 | "\n", 145 | " AP+=1.5*(abs(vs)-vs)\n", 146 | " defer=0.5*(abs(vs)-vs)*VN\n", 147 | " uBP+=defer\n", 148 | " vBP+=defer\n", 149 | " AS-=1.5*(abs(vs)+vs)\n", 150 | " defer=0.5*(abs(vs)+vs)*VSS\n", 151 | " uBP-=defer\n", 152 | " vBP-=defer\n", 153 | " \n", 154 | " return AE,AW,AN,AS,AP,uBP,vBP" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 4, 160 | "id": "7a98f516", 161 | "metadata": {}, 162 | "outputs": [], 163 | "source": [ 164 | "def TVD2(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 165 | " global b\n", 166 | " \n", 167 | " uBP0=uBP\n", 168 | " vBP0=vBP\n", 169 | " \n", 170 | " if ue!=0:\n", 171 | " if ue>0:\n", 172 | " AP+=2*ue*b\n", 173 | " \n", 174 | " r=(UP-UW)/(UE-UP)\n", 175 | " limit=(r+r**2)/(1+r**2)\n", 176 | " flux=(UE-UP)\n", 177 | "\n", 178 | " uBP-=ue*b*limit*flux\n", 179 | " vBP-=ue*b*limit*flux\n", 180 | " else:\n", 181 | " AE+=2*ue*b\n", 182 | "\n", 183 | " r=(UE-UEE)/(UP-UE)\n", 184 | " limit=(r+r**2)/(1+r**2)\n", 185 | " flux=(UP-UE)\n", 186 | "\n", 187 | " uBP-=ue*b*limit*flux\n", 188 | " vBP-=ue*b*limit*flux\n", 189 | " if uw!=0:\n", 190 | " if uw<0:\n", 191 | " AP-=2*uw*b\n", 192 | "\n", 193 | " r=(UP-UE)/(UW-UP)\n", 194 | " limit=(r+r**2)/(1+r**2)\n", 195 | " flux=(UW-UP)\n", 196 | "\n", 197 | " uBP+=uw*b*limit*flux\n", 198 | " vBP+=uw*b*limit*flux \n", 199 | " else:\n", 200 | " AW-=2*uw*b\n", 201 | "\n", 202 | " r=(UW-UWW)/(UP-UW)\n", 203 | " limit=(r+r**2)/(1+r**2)\n", 204 | " flux=(UP-UW)\n", 205 | "\n", 206 | " uBP+=uw*b*limit*flux\n", 207 | " vBP+=uw*b*limit*flux\n", 208 | " if vn!=0:\n", 209 | " if vn>0:\n", 210 | " AP+=2*vn\n", 211 | "\n", 212 | " r=(VP-VS)/(VN-VP)\n", 213 | " limit=(r+r**2)/(1+r**2)\n", 214 | " flux=VN-VP\n", 215 | " \n", 216 | " uBP-=vn*limit*flux\n", 217 | " vBP-=vn*limit*flux\n", 218 | " else:\n", 219 | " AN+=2*vn\n", 220 | "\n", 221 | " r=(VN-VNN)/(VP-VN)\n", 222 | " limit=(r+r**2)/(1+r**2)\n", 223 | " flux=VP-VN\n", 224 | "\n", 225 | " uBP-=vn*limit*flux\n", 226 | " vBP-=vn*limit*flux\n", 227 | " if vs!=0:\n", 228 | " if vs<0:\n", 229 | " AP-=2*vs\n", 230 | "\n", 231 | " r=(VP-VN)/(VS-VP)\n", 232 | " limit=(r+r**2)/(1+r**2)\n", 233 | " flux=VS-VP\n", 234 | " \n", 235 | " uBP+=vs*limit*flux\n", 236 | " vBP+=vs*limit*flux\n", 237 | " else:\n", 238 | " AS-=2*vs\n", 239 | "\n", 240 | " r=(VS-VSS)/(VP-VS)\n", 241 | " limit=(r+r**2)/(1+r**2)\n", 242 | " flux=VP-VS\n", 243 | "\n", 244 | " uBP+=vs*limit*flux\n", 245 | " vBP+=vs*limit*flux\n", 246 | "\n", 247 | " if iter_total>1:\n", 248 | " return AE,AW,AN,AS,AP,uBP,vBP\n", 249 | " else:\n", 250 | " return AE,AW,AN,AS,AP,uBP0,vBP0" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": 5, 256 | "id": "1e64b09e", 257 | "metadata": {}, 258 | "outputs": [], 259 | "source": [ 260 | "def QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 261 | " global b\n", 262 | "\n", 263 | " if ue!=0:\n", 264 | " if ue>0:\n", 265 | " AP+=2*ue*b\n", 266 | " uBP-=ue*b*(3*UE-2*UP-UW)/4\n", 267 | " vBP-=ue*b*(3*UE-2*UP-UW)/4\n", 268 | " else:\n", 269 | " AE+=2*ue*b\n", 270 | " uBP-=ue*b*(3*UP-2*UE-UEE)/4\n", 271 | " vBP-=ue*b*(3*UP-2*UE-UEE)/4\n", 272 | " if uw!=0:\n", 273 | " if uw<0:\n", 274 | " AP-=2*uw*b\n", 275 | " uBP+=uw*b*(3*UW-2*UP-UE)/4\n", 276 | " vBP+=uw*b*(3*UW-2*UP-UE)/4\n", 277 | " else:\n", 278 | " AW-=2*uw*b\n", 279 | " uBP+=uw*b*(3*UP-2*UW-UWW)/4\n", 280 | " vBP+=uw*b*(3*UP-2*UW-UWW)/4\n", 281 | " if vn!=0:\n", 282 | " if vn>0:\n", 283 | " AP+=2*vn\n", 284 | " uBP-=vn*(3*VN-2*VP-VS)/4\n", 285 | " vBP-=vn*(3*VN-2*VP-VS)/4\n", 286 | " else:\n", 287 | " AN+=2*vn\n", 288 | " uBP-=vn*(3*VP-2*VN-VNN)/4\n", 289 | " vBP-=vn*(3*VP-2*VN-VNN)/4\n", 290 | " if vs!=0:\n", 291 | " if vs<0:\n", 292 | " AP-=2*vs\n", 293 | " uBP+=vs*(3*VS-2*VP-VN)/4\n", 294 | " vBP+=vs*(3*VS-2*VP-VN)/4\n", 295 | " else:\n", 296 | " AS-=2*vs\n", 297 | " uBP+=vs*(3*VP-2*VS-VSS)/4\n", 298 | " vBP+=vs*(3*VP-2*VS-VSS)/4\n", 299 | "\n", 300 | " return AE,AW,AN,AS,AP,uBP,vBP\n", 301 | " " 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 6, 307 | "id": "2939f5ef", 308 | "metadata": {}, 309 | "outputs": [], 310 | "source": [ 311 | "def upwind4(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UEEE,UWWW,UP,VN,VS,VNN,VSS,VNNN,VSSS,VP):\n", 312 | " global b\n", 313 | "\n", 314 | " if ue!=0:\n", 315 | " if ue>0:\n", 316 | " AP+=2*ue*b\n", 317 | " uBP-=ue*b*(5*UE-UP-5*UW+UWW)/8\n", 318 | " vBP-=ue*b*(5*UE-UP-5*UW+UWW)/8\n", 319 | " else:\n", 320 | " AE+=2*ue*b\n", 321 | " uBP-=ue*b*(5*UP-UE-5*UEE+UEEE)/8\n", 322 | " vBP-=ue*b*(5*UP-UE-5*UEE+UEEE)/8\n", 323 | " if uw!=0:\n", 324 | " if uw<0:\n", 325 | " AP-=2*uw*b\n", 326 | " uBP+=uw*b*(5*UW-UP-5*UE+UEE)/8\n", 327 | " vBP+=uw*b*(5*UW-UP-5*UE+UEE)/8\n", 328 | " else:\n", 329 | " AW-=2*uw*b\n", 330 | " uBP+=uw*b*(5*UP-UW-5*UWW+UWWW)/8\n", 331 | " vBP+=uw*b*(5*UP-UW-5*UWW+UWWW)/8\n", 332 | " if vn!=0:\n", 333 | " if vn>0:\n", 334 | " AP+=2*vn\n", 335 | " uBP-=vn*(5*VN-VP-5*VS+VSS)/8\n", 336 | " vBP-=vn*(5*VN-VP-5*VS+VSS)/8\n", 337 | " else:\n", 338 | " AN+=2*vn\n", 339 | " uBP-=vn*(5*VP-VN-5*VNN+VNNN)/8\n", 340 | " vBP-=vn*(5*VP-VN-5*VNN+VNNN)/8\n", 341 | " if vs!=0:\n", 342 | " if vs<0:\n", 343 | " AP-=2*vs\n", 344 | " uBP+=vs*(5*VS-VP-5*VN+VNN)/8\n", 345 | " vBP+=vs*(5*VS-VP-5*VN+VNN)/8\n", 346 | " else:\n", 347 | " AS-=2*vs\n", 348 | " uBP+=vs*(5*VP-VS-5*VSS+VSSS)/8\n", 349 | " vBP+=vs*(5*VP-VS-5*VSS+VSSS)/8\n", 350 | "\n", 351 | " return AE,AW,AN,AS,AP,uBP,vBP" 352 | ] 353 | }, 354 | { 355 | "cell_type": "code", 356 | "execution_count": 7, 357 | "id": "b7a9e6ed", 358 | "metadata": {}, 359 | "outputs": [], 360 | "source": [ 361 | "def mom(Nx,Ny,U,V,P,U0,V0,time_step,UAB,VAB,U_old,V_old,U_older,V_older,av):\n", 362 | " global dx\n", 363 | " global dy\n", 364 | " global dt\n", 365 | " global v\n", 366 | " global u_wall\n", 367 | " global transient\n", 368 | " global Explicit\n", 369 | " \n", 370 | " N=Nx*Ny\n", 371 | " \n", 372 | " A=np.zeros([N,N])\n", 373 | " uB=np.zeros([N,1])\n", 374 | " vB=np.zeros([N,1])\n", 375 | " DX=np.zeros([N,1])\n", 376 | " DY=np.zeros([N,1])\n", 377 | " \n", 378 | " b=dy/dx\n", 379 | " y=v/dy\n", 380 | " x=v*b/dx\n", 381 | " k=dt/dy\n", 382 | " alpha=(1-av)/av\n", 383 | " \n", 384 | " \n", 385 | " #For interior cells\n", 386 | " for j in range(1,Ny-1):\n", 387 | " for i in range(1,Nx-1):\n", 388 | " ij=i+Nx*j\n", 389 | " \n", 390 | " AEE=0\n", 391 | " AWW=0\n", 392 | " ANN=0\n", 393 | " ASS=0\n", 394 | " uBP=0\n", 395 | " vBP=0\n", 396 | " \n", 397 | " if j==1:\n", 398 | " VNN=V[j+2][i]\n", 399 | " VN=V[j+1][i]\n", 400 | " VP=V[j][i]\n", 401 | " VS=V[j-1][i]\n", 402 | " VSS=4*VS-6*VP+4*VN-VNN\n", 403 | " \n", 404 | "# ASS=(1/12)*y*VSS\n", 405 | "# uBP-=ASS\n", 406 | "# vBP-=ASS\n", 407 | " \n", 408 | " elif j==Ny-2:\n", 409 | " VSS=V[j-2][i]\n", 410 | " VS=V[j-1][i]\n", 411 | " VP=V[j][i]\n", 412 | " VN=V[j+1][i]\n", 413 | " VNN=4*VN-6*VP+4*VS-VSS\n", 414 | " \n", 415 | "# ANN=(1/12)*y*VNN\n", 416 | "# uBP-=ANN\n", 417 | "# vBP-=ANN\n", 418 | " \n", 419 | " else:\n", 420 | " VNN=V[j+2][i]\n", 421 | " VN=V[j+1][i]\n", 422 | " VP=V[j][i]\n", 423 | " VS=V[j-1][i]\n", 424 | " VSS=V[j-2][i]\n", 425 | " \n", 426 | "# ASS=(1/12)*y\n", 427 | "# ANN=(1/12)*y\n", 428 | "# A[ij][ij-2*Nx]=ASS\n", 429 | "# A[ij][ij+2*Nx]=ANN\n", 430 | " \n", 431 | " if i==1:\n", 432 | " UEE=U[j][i+2]\n", 433 | " UE=U[j][i+1]\n", 434 | " UP=U[j][i]\n", 435 | " UW=U[j][i-1]\n", 436 | " UWW=4*UW-6*UP+4*UE-UEE\n", 437 | " \n", 438 | "# AWW=(1/12)*x*UWW\n", 439 | "# uBP-=AWW\n", 440 | "# vBP-=AWW\n", 441 | " \n", 442 | " elif i==Nx-2:\n", 443 | " UWW=U[j][i-2]\n", 444 | " UW=U[j][i-1]\n", 445 | " UP=U[j][i]\n", 446 | " UE=U[j][i+1]\n", 447 | " UEE=4*UE-6*UP+4*UW-UWW\n", 448 | " \n", 449 | "# AEE=(1/12)*x*UEE\n", 450 | "# uBP-=AEE\n", 451 | "# vBP-=AEE\n", 452 | " \n", 453 | " else:\n", 454 | " UEE=U[j][i+2]\n", 455 | " UE=U[j][i+1]\n", 456 | " UP=U[j][i]\n", 457 | " UW=U[j][i-1]\n", 458 | " UWW=U[j][i-2]\n", 459 | " \n", 460 | "# AEE=(1/12)*x\n", 461 | "# AWW=(1/12)*x\n", 462 | "# A[ij][ij+2]=AEE\n", 463 | "# A[ij][ij-2]=AWW\n", 464 | " \n", 465 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 466 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 467 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 468 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 469 | "\n", 470 | " ue=(UP+UE)/2\n", 471 | " uw=(UP+UW)/2\n", 472 | " vn=(VP+VN)/2\n", 473 | " vs=(VP+VS)/2\n", 474 | "\n", 475 | " AE=-2*x\n", 476 | " AW=-2*x\n", 477 | " AN=-2*y\n", 478 | " AS=-2*y\n", 479 | " AP=4*x+4*y\n", 480 | "\n", 481 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 482 | "\n", 483 | " uBP+=b*(P[j][i-1]-P[j][i+1])+alpha*AP*UP\n", 484 | " vBP+=(P[j-1][i]-P[j+1][i])+alpha*AP*VP\n", 485 | " \n", 486 | " Dx=dy/AP\n", 487 | " Dy=dx/AP\n", 488 | " \n", 489 | " A[ij][ij-1]=AW\n", 490 | " A[ij][ij]=AP/av\n", 491 | " A[ij][ij+1]=AE\n", 492 | " A[ij][ij+Nx]=AN\n", 493 | " A[ij][ij-Nx]=AS\n", 494 | " uB[ij]=uBP\n", 495 | " vB[ij]=vBP\n", 496 | " DX[ij]=Dx\n", 497 | " DY[ij]=Dy\n", 498 | " \n", 499 | " #for bottom wall\n", 500 | " for i in range(1,Nx-1):\n", 501 | " j=0\n", 502 | " ij=i+j*Nx\n", 503 | " \n", 504 | " AEE=0\n", 505 | " AWW=0\n", 506 | " ANN=0\n", 507 | " uBP=0\n", 508 | " vBP=0\n", 509 | " \n", 510 | " if i==1:\n", 511 | " UW=U[j][i-1]\n", 512 | " UP=U[j][i]\n", 513 | " UE=U[j][i+1]\n", 514 | " UEE=U[j][i+2]\n", 515 | " UWW=4*UW-6*UP+4*UE-UEE\n", 516 | " \n", 517 | "# AWW=(1/12)*x*UWW\n", 518 | "# uBP-=AWW\n", 519 | "# vBP-=AWW\n", 520 | " \n", 521 | " elif i==Nx-2:\n", 522 | " UW=U[j][i-1]\n", 523 | " UWW=U[j][i-2]\n", 524 | " UP=U[j][i]\n", 525 | " UE=U[j][i+1]\n", 526 | " UEE=4*UE-6*UP+4*UW-UWW\n", 527 | " \n", 528 | "# AEE=(1/12)*x*UEE\n", 529 | "# uBP-=AEE\n", 530 | "# vBP-=AEE\n", 531 | " \n", 532 | " else:\n", 533 | " UWW=U[j][i-2]\n", 534 | " UW=U[j][i-1]\n", 535 | " UP=U[j][i]\n", 536 | " UE=U[j][i+1]\n", 537 | " UEE=U[j][i+2]\n", 538 | " \n", 539 | "# AEE=(1/12)*x\n", 540 | "# AWW=(1/12)*x\n", 541 | " \n", 542 | "# A[ij][ij+2]=AEE\n", 543 | "# A[ij][ij-2]=AWW\n", 544 | " \n", 545 | " VP=V[j][i]\n", 546 | " VN=V[j+1][i]\n", 547 | " VNN=V[j+2][i]\n", 548 | " VNNN=V[j+3][i]\n", 549 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 550 | "\n", 551 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 552 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 553 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 554 | "\n", 555 | " ue=(UP+UE)/2\n", 556 | " uw=(UP+UW)/2\n", 557 | " vn=(VP+VN)/2\n", 558 | "\n", 559 | " AE=-2*x\n", 560 | " AW=-2*x\n", 561 | " AN=-2*y\n", 562 | " AP=6*y+4*x\n", 563 | "\n", 564 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,vn,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 565 | "\n", 566 | " uBP+=b*(P[j][i-1]-P[j][i+1])+alpha*AP*U[j][i]\n", 567 | " vBP+=(P[j][i]-P[j+1][i])+alpha*AP*V[j][i]\n", 568 | " Dx=dy/AP\n", 569 | " Dy=dx/AP\n", 570 | "\n", 571 | " A[ij][ij-1]=AW\n", 572 | " A[ij][ij]=AP/av\n", 573 | " A[ij][ij+1]=AE\n", 574 | " A[ij][ij+Nx]=AN\n", 575 | " uB[ij]=uBP\n", 576 | " vB[ij]=vBP\n", 577 | " DX[ij]=Dx\n", 578 | " DY[ij]=Dy\n", 579 | " \n", 580 | " #for top wall\n", 581 | " for i in range(1,Nx-1):\n", 582 | " j=Ny-1\n", 583 | " ij=i+j*Nx\n", 584 | " \n", 585 | " AEE=0\n", 586 | " AWW=0\n", 587 | " uBP=0\n", 588 | " vBP=0\n", 589 | " \n", 590 | " if i==1:\n", 591 | " UW=U[j][i-1]\n", 592 | " UP=U[j][i]\n", 593 | " UE=U[j][i+1]\n", 594 | " UEE=U[j][i+2]\n", 595 | " UWW=4*UW-6*UP+4*UE-UEE\n", 596 | " \n", 597 | "# AWW=(1/12)*x*UWW\n", 598 | " \n", 599 | "# uBP-=AWW\n", 600 | "# vBP-=AWW\n", 601 | " \n", 602 | " elif i==Nx-2:\n", 603 | " UW=U[j][i-1]\n", 604 | " UWW=U[j][i-2]\n", 605 | " UP=U[j][i]\n", 606 | " UE=U[j][i+1]\n", 607 | " UEE=4*UE-6*UP+4*UW-UWW\n", 608 | " \n", 609 | "# AEE=(1/12)*x*UEE\n", 610 | " \n", 611 | "# uBP-=AEE\n", 612 | "# vBP-=AEE\n", 613 | " \n", 614 | " else:\n", 615 | " UWW=U[j][i-2]\n", 616 | " UW=U[j][i-1]\n", 617 | " UP=U[j][i]\n", 618 | " UE=U[j][i+1]\n", 619 | " UEE=U[j][i+2]\n", 620 | " \n", 621 | "# AEE=(1/12)*x\n", 622 | "# AWW=(1/12)*x\n", 623 | " \n", 624 | "# A[ij][ij+2]=AEE\n", 625 | "# A[ij][ij-2]=AWW\n", 626 | " \n", 627 | " \n", 628 | " VP=V[j][i]\n", 629 | " VS=V[j-1][i]\n", 630 | " VSS=V[j-2][i]\n", 631 | " VSSS=V[j-3][i]\n", 632 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 633 | "\n", 634 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 635 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 636 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 637 | "\n", 638 | " ue=(UP+UE)/2\n", 639 | " uw=(UP+UW)/2\n", 640 | " vs=(VP+VS)/2\n", 641 | "\n", 642 | " AE=-2*x\n", 643 | " AW=-2*x\n", 644 | " AS=-2*y\n", 645 | " AP=6*y+4*x\n", 646 | " \n", 647 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,uw,0,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 648 | " \n", 649 | " uBP+=b*(P[j][i-1]-P[j][i+1])+4*y*u_wall+alpha*AP*UP\n", 650 | " vBP+=(P[j-1][i]-P[j][i])+alpha*AP*VP \n", 651 | " Dx=dy/AP\n", 652 | " Dy=dx/AP\n", 653 | " \n", 654 | " A[ij][ij-1]=AW\n", 655 | " A[ij][ij]=AP/av\n", 656 | " A[ij][ij+1]=AE\n", 657 | " A[ij][ij-Nx]=AS\n", 658 | " uB[ij]=uBP\n", 659 | " vB[ij]=vBP\n", 660 | " DX[ij]=Dx\n", 661 | " DY[ij]=Dy\n", 662 | " \n", 663 | " \n", 664 | " #for left wall:\n", 665 | " for j in range(1,Ny-1):\n", 666 | " i=0\n", 667 | " ij=i+Nx*j\n", 668 | " \n", 669 | " ANN=0\n", 670 | " ASS=0\n", 671 | " uBP=0\n", 672 | " vBP=0\n", 673 | " \n", 674 | " if j==1:\n", 675 | " VNN=V[j+2][i]\n", 676 | " VN=V[j+1][i]\n", 677 | " VP=V[j][i]\n", 678 | " VS=V[j-1][i]\n", 679 | " VSS=4*VS-6*VP+4*VN-VNN\n", 680 | "\n", 681 | "# ASS=(1/12)*y*VSS\n", 682 | "# uBP-=ASS\n", 683 | "# vBP-=ASS\n", 684 | "\n", 685 | " elif j==Ny-2:\n", 686 | " VSS=V[j-2][i]\n", 687 | " VS=V[j-1][i]\n", 688 | " VP=V[j][i]\n", 689 | " VN=V[j+1][i]\n", 690 | " VNN=4*VN-6*VP+4*VS-VSS\n", 691 | "\n", 692 | "# ANN=(1/12)*y*VNN\n", 693 | "# uBP-=ANN\n", 694 | "# vBP-=ANN\n", 695 | " \n", 696 | " else:\n", 697 | " VNN=V[j+2][i]\n", 698 | " VN=V[j+1][i]\n", 699 | " VP=V[j][i]\n", 700 | " VS=V[j-1][i]\n", 701 | " VSS=V[j-2][i]\n", 702 | "\n", 703 | "# ASS=(1/12)*y\n", 704 | "# ANN=(1/12)*y\n", 705 | "# A[ij][ij-2*Nx]=ASS\n", 706 | "# A[ij][ij+2*Nx]=ANN\n", 707 | "\n", 708 | " UP=U[j][i]\n", 709 | " UE=U[j][i+1]\n", 710 | " UEE=U[j][i+2]\n", 711 | " UEEE=U[j][i+3]\n", 712 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 713 | "\n", 714 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 715 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 716 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 717 | "\n", 718 | " ue=(UP+UE)/2\n", 719 | " vn=(VP+VN)/2\n", 720 | " vs=(VP+VS)/2\n", 721 | "\n", 722 | " AE=-2*x\n", 723 | " AN=-2*y\n", 724 | " AS=-2*y\n", 725 | " AP=6*x+4*y\n", 726 | "\n", 727 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,0,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 728 | "\n", 729 | " uBP+=b*(P[j][i]-P[j][i+1])+alpha*AP*UP\n", 730 | " vBP+=(P[j-1][i]-P[j+1][i])+alpha*AP*VP\n", 731 | " Dx=dy/AP\n", 732 | " Dy=dx/AP\n", 733 | "\n", 734 | " A[ij][ij]=AP/av\n", 735 | " A[ij][ij+1]=AE\n", 736 | " A[ij][ij-Nx]=AS\n", 737 | " A[ij][ij+Nx]=AN\n", 738 | " uB[ij]=uBP\n", 739 | " vB[ij]=vBP\n", 740 | " DX[ij]=Dx\n", 741 | " DY[ij]=Dy\n", 742 | " \n", 743 | " #for right wall:\n", 744 | " for j in range(1,Ny-1):\n", 745 | " i=Nx-1\n", 746 | " ij=i+Nx*j\n", 747 | " \n", 748 | " ANN=0\n", 749 | " ASS=0\n", 750 | " uBP=0\n", 751 | " vBP=0\n", 752 | " \n", 753 | " if j==1:\n", 754 | " VNN=V[j+2][i]\n", 755 | " VN=V[j+1][i]\n", 756 | " VP=V[j][i]\n", 757 | " VS=V[j-1][i]\n", 758 | " VSS=4*VS-6*VP+4*VN-VNN\n", 759 | "\n", 760 | "# ASS=(1/12)*y*VSS\n", 761 | "# uBP-=ASS\n", 762 | "# vBP-=ASS\n", 763 | "\n", 764 | " elif j==Ny-2:\n", 765 | " VSS=V[j-2][i]\n", 766 | " VS=V[j-1][i]\n", 767 | " VP=V[j][i]\n", 768 | " VN=V[j+1][i]\n", 769 | " VNN=4*VN-6*VP+4*VS-VSS\n", 770 | "\n", 771 | "# ANN=(1/12)*y*VNN\n", 772 | "# uBP-=ANN\n", 773 | "# vBP-=ANN\n", 774 | "\n", 775 | " else:\n", 776 | " VNN=V[j+2][i]\n", 777 | " VN=V[j+1][i]\n", 778 | " VP=V[j][i]\n", 779 | " VS=V[j-1][i]\n", 780 | " VSS=V[j-2][i]\n", 781 | "\n", 782 | "# ASS=(1/12)*y\n", 783 | "# ANN=(1/12)*y\n", 784 | "# A[ij][ij-2*Nx]=ASS\n", 785 | "# A[ij][ij+2*Nx]=ANN\n", 786 | " \n", 787 | " UP=U[j][i]\n", 788 | " UW=U[j][i-1]\n", 789 | " UWW=U[j][i-2]\n", 790 | " UWWW=U[j][i-3]\n", 791 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 792 | "\n", 793 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 794 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 795 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 796 | " \n", 797 | " uw=(UP+UW)/2\n", 798 | " vn=(VP+VN)/2\n", 799 | " vs=(VP+VS)/2\n", 800 | "\n", 801 | " AW=-2*x\n", 802 | " AN=-2*y\n", 803 | " AS=-2*y\n", 804 | " AP=6*x+4*y\n", 805 | "\n", 806 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,0,uw,vn,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 807 | "\n", 808 | " uBP+=b*(P[j][i-1]-P[j][i])+alpha*AP*UP\n", 809 | " vBP+=(P[j-1][i]-P[j+1][i])+alpha*AP*VP\n", 810 | " Dx=dy/AP\n", 811 | " Dy=dx/AP\n", 812 | "\n", 813 | " A[ij][ij]=AP/av\n", 814 | " A[ij][ij-1]=AW\n", 815 | " A[ij][ij-2]=AWW\n", 816 | " A[ij][ij-Nx]=AS\n", 817 | " A[ij][ij+Nx]=AN\n", 818 | " uB[ij]=uBP\n", 819 | " vB[ij]=vBP\n", 820 | " DX[ij]=Dx\n", 821 | " DY[ij]=Dy\n", 822 | " \n", 823 | " #top right cell\n", 824 | " i=Nx-1\n", 825 | " j=Ny-1\n", 826 | " ij=i+Nx*j\n", 827 | " \n", 828 | " uBP=0\n", 829 | " vBP=0\n", 830 | " \n", 831 | " UP=U[j][i]\n", 832 | " UW=U[j][i-1]\n", 833 | " UWW=U[j][i-2]\n", 834 | " UWWW=U[j][i-3]\n", 835 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 836 | "\n", 837 | " VSSS=V[j-3][i]\n", 838 | " VSS=V[j-2][i]\n", 839 | " VS=V[j-1][i]\n", 840 | " VP=V[j][i]\n", 841 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 842 | "\n", 843 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 844 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 845 | " \n", 846 | " uw=(UP+UW)/2\n", 847 | " vs=(VP+VS)/2\n", 848 | " \n", 849 | " AW=-2*x\n", 850 | " AS=-2*y\n", 851 | " AP=6*x+6*y\n", 852 | " \n", 853 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,0,uw,0,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 854 | " \n", 855 | " uBP+=b*(P[j][i-1]-P[j][i])+4*y*u_wall+alpha*AP*UP\n", 856 | " vBP+=(P[j-1][i]-P[j][i])+alpha*AP*VP\n", 857 | " Dx=dy/AP\n", 858 | " Dy=dx/AP\n", 859 | " \n", 860 | " A[ij][ij]=AP/av\n", 861 | " A[ij][ij-1]=AW\n", 862 | " A[ij][ij-Nx]=AS\n", 863 | " uB[ij]=uBP\n", 864 | " vB[ij]=vBP\n", 865 | " DX[ij]=Dx\n", 866 | " DY[ij]=Dy\n", 867 | " \n", 868 | " #top left cell\n", 869 | " i=0\n", 870 | " j=Ny-1\n", 871 | " ij=i+Nx*j\n", 872 | " \n", 873 | " UP=U[j][i]\n", 874 | " UE=U[j][i+1]\n", 875 | " UEE=U[j][i+2]\n", 876 | " UEEE=U[j][i+3]\n", 877 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 878 | " \n", 879 | " VSSS=V[j-3][i]\n", 880 | " VSS=V[j-2][i]\n", 881 | " VS=V[j-1][i]\n", 882 | " VP=V[j][i]\n", 883 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 884 | "\n", 885 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 886 | "# vs=(27*VP+27*VS-3*VSS-3*VN)/48\n", 887 | "\n", 888 | " ue=(UP+UE)/2\n", 889 | " vs=(VP+VS)/2\n", 890 | " \n", 891 | " AE=-2*x\n", 892 | " AS=-2*y\n", 893 | " AP=6*x+6*y\n", 894 | "\n", 895 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,0,0,vs,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 896 | " \n", 897 | " uBP+=b*(P[j][i]-P[j][i+1])+4*y*u_wall+alpha*AP*UP\n", 898 | " vBP+=(P[j-1][i]-P[j][i])+alpha*AP*VP\n", 899 | " Dx=dy/AP\n", 900 | " Dy=dx/AP\n", 901 | " \n", 902 | " A[ij][ij]=AP/av\n", 903 | " A[ij][ij+1]=AE\n", 904 | " A[ij][ij-Nx]=AS\n", 905 | " uB[ij]=uBP\n", 906 | " vB[ij]=vBP\n", 907 | " DX[ij]=Dx\n", 908 | " DY[ij]=Dy\n", 909 | " \n", 910 | " #bottom right cell\n", 911 | " i=Nx-1\n", 912 | " j=0\n", 913 | " ij=i+Nx*j\n", 914 | " \n", 915 | " uBP=0\n", 916 | " vBP=0\n", 917 | " \n", 918 | " UWWW=U[j][i-3]\n", 919 | " UWW=U[j][i-2]\n", 920 | " UW=U[j][i-1]\n", 921 | " UP=U[j][i]\n", 922 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 923 | "\n", 924 | " VP=V[j][i]\n", 925 | " VN=V[j+1][i]\n", 926 | " VNN=V[j+2][i]\n", 927 | " VNNN=V[j+3][i]\n", 928 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 929 | " \n", 930 | "# uw=(27*UP+27*UW-3*UWW-3*UE)/48\n", 931 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 932 | "\n", 933 | " uw=(UP+UW)/2\n", 934 | " vn=(VP+VN)/2\n", 935 | " \n", 936 | " AW=-2*x\n", 937 | " AN=-2*y\n", 938 | " AP=6*x+6*y\n", 939 | "\n", 940 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,0,uw,vn,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 941 | " \n", 942 | " uBP+=b*(P[j][i-1]-P[j][i])+alpha*AP*UP\n", 943 | " vBP+=(P[j][i]-P[j+1][i])+alpha*AP*VP\n", 944 | " Dx=dy/AP\n", 945 | " Dy=dx/AP\n", 946 | " \n", 947 | " A[ij][ij]=AP/av\n", 948 | " A[ij][ij-1]=AW\n", 949 | " A[ij][ij+Nx]=AN\n", 950 | " uB[ij]=uBP\n", 951 | " vB[ij]=vBP\n", 952 | " DX[ij]=Dx\n", 953 | " DY[ij]=Dy\n", 954 | " \n", 955 | " #bottom left cell\n", 956 | " i=0\n", 957 | " j=0\n", 958 | " ij=i+Nx*j\n", 959 | " \n", 960 | " uBP=0\n", 961 | " vBP=0\n", 962 | " \n", 963 | " UP=U[j][i]\n", 964 | " UE=U[j][i+1]\n", 965 | " UEE=U[j][i+2]\n", 966 | " UEEE=U[j][i+3]\n", 967 | " UW=4*UP-6*UW+4*UWW-UWWW\n", 968 | "\n", 969 | " VP=V[j][i]\n", 970 | " VN=V[j+1][i]\n", 971 | " VNN=V[j+2][i]\n", 972 | " VNNN=V[j+3][i]\n", 973 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 974 | "\n", 975 | "# ue=(27*UP+27*UE-3*UW-3*UEE)/48\n", 976 | "# vn=(27*VP+27*VN-3*VS-3*VNN)/48\n", 977 | "\n", 978 | " ue=(UP+UE)/2\n", 979 | " vn=(VP+VN)/2\n", 980 | " \n", 981 | " AE=-2*x\n", 982 | " AN=-2*y\n", 983 | " AP=6*x+6*y\n", 984 | " \n", 985 | " AE,AW,AN,AS,AP,uBP,vBP=QUICK3(AE,AW,AN,AS,AP,uBP,vBP,ue,0,vn,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 986 | " \n", 987 | " uBP+=b*(P[j][i]-P[j][i+1])+alpha*AP*UP\n", 988 | " vBP+=(P[j][i]-P[j+1][i])+alpha*AP*VP\n", 989 | " Dx=dy/AP\n", 990 | " Dy=dx/AP\n", 991 | " \n", 992 | " A[ij][ij]=AP/av\n", 993 | " A[ij][ij+1]=AE\n", 994 | " A[ij][ij+Nx]=AN\n", 995 | " uB[ij]=uBP\n", 996 | " vB[ij]=vBP\n", 997 | " DX[ij]=Dx\n", 998 | " DY[ij]=Dy \n", 999 | " \n", 1000 | " if transient==True:\n", 1001 | " if Explicit==True:\n", 1002 | " #Explicit\n", 1003 | " At=-k*A\n", 1004 | " uBt=k*uB\n", 1005 | " vBt=k*vB\n", 1006 | "\n", 1007 | " if time_step==1:\n", 1008 | " Un=order_1(At,uBt,U,U0,N,UAB)\n", 1009 | " Vn=order_1(At,vBt,V,V0,N,VAB)\n", 1010 | " elif time_step==2:\n", 1011 | " Un=order_2(At,uBt,U,U0,N,UAB)\n", 1012 | " Vn=order_2(At,vBt,U,V0,N,VAB)\n", 1013 | " elif time_step==3:\n", 1014 | " Un=order_3(At,uBt,U,U0,N,UAB)\n", 1015 | " Vn=order_3(At,vBt,U,V0,N,VAB)\n", 1016 | " else:\n", 1017 | " Un=order_4(At,uBt,U,U0,N,UAB)\n", 1018 | " Vn=order_4(At,vBt,U,V0,N,VAB)\n", 1019 | "\n", 1020 | " else: \n", 1021 | " #Implicit\n", 1022 | " k=dy/dt\n", 1023 | "\n", 1024 | " for j in range(Ny):\n", 1025 | " for i in range(Nx):\n", 1026 | " ij=i+Nx*j\n", 1027 | "\n", 1028 | " if time_step==1:\n", 1029 | " #1st order implicit\n", 1030 | " A[ij][ij]+=k\n", 1031 | " uB[ij]+=k*U0[j][i]\n", 1032 | " vB[ij]+=k*V0[j][i]\n", 1033 | " elif time_step==2:\n", 1034 | " #2nd order implicit\n", 1035 | " A[ij][ij]+=1.5*k\n", 1036 | " uB[ij]+=k*(2*U0[j][i]-0.5*U_old[j][i])\n", 1037 | " vB[ij]+=k*(2*V0[j][i]-0.5*V_old[j][i])\n", 1038 | " else:\n", 1039 | " #3rd order implicit\n", 1040 | " A[ij][ij]+=(11/6)*k\n", 1041 | " uB[ij]+=k*(18*U0[j][i]-9*U_old[j][i]+2*U_older[j][i])/6\n", 1042 | " vB[ij]+=k*(18*V0[j][i]-9*V_old[j][i]+2*V_older[j][i])/6\n", 1043 | "\n", 1044 | " A=sp.sparse.csr_matrix(A)\n", 1045 | " ml=pyamg.ruge_stuben_solver(A)\n", 1046 | " Un=ml.solve(uB,tol=1E-6)\n", 1047 | " Vn=ml.solve(vB,tol=1E-6)\n", 1048 | "\n", 1049 | " At=0\n", 1050 | " uBt=0\n", 1051 | " vBt=0\n", 1052 | " \n", 1053 | " U_mesh=np.reshape(np.array(Un),[Ny,Nx])\n", 1054 | " V_mesh=np.reshape(np.array(Vn),[Ny,Nx])\n", 1055 | " DX_mesh=np.reshape(np.array(DX),[Ny,Nx])\n", 1056 | " DY_mesh=np.reshape(np.array(DY),[Ny,Nx])\n", 1057 | "\n", 1058 | " return U_mesh,V_mesh,DX_mesh,DY_mesh,At,uBt,vBt\n", 1059 | " \n", 1060 | " else:\n", 1061 | " A=sp.sparse.csr_matrix(A)\n", 1062 | " ml=pyamg.ruge_stuben_solver(A)\n", 1063 | " U=ml.solve(uB,tol=1E-6)\n", 1064 | " V=ml.solve(vB,tol=1E-6)\n", 1065 | "\n", 1066 | " U_mesh=np.reshape(np.array(U),[Ny,Nx])\n", 1067 | " V_mesh=np.reshape(np.array(V),[Ny,Nx])\n", 1068 | " DX_mesh=np.reshape(np.array(DX),[Ny,Nx])\n", 1069 | " DY_mesh=np.reshape(np.array(DY),[Ny,Nx])\n", 1070 | "\n", 1071 | " return U_mesh,V_mesh,DX_mesh,DY_mesh" 1072 | ] 1073 | }, 1074 | { 1075 | "cell_type": "code", 1076 | "execution_count": 8, 1077 | "id": "9cdd4400", 1078 | "metadata": {}, 1079 | "outputs": [], 1080 | "source": [ 1081 | "def face(Uf,Vf,U,V,P_mesh,uNx,uNy,vNx,vNy,DX,DY):\n", 1082 | " #U-faces\n", 1083 | " DXf=np.zeros([uNy,uNx])\n", 1084 | " for j in range(uNy):\n", 1085 | " for i in range(2,uNx-2):\n", 1086 | " ij=i+Nx*j\n", 1087 | " \n", 1088 | " UE=U[j][i]\n", 1089 | " UP=U[j][i-1]\n", 1090 | " Ue=(UE+UP)/2\n", 1091 | "\n", 1092 | " DP=DX[j][i-1]\n", 1093 | " DE=DX[j][i]\n", 1094 | " PP=DP*(P_mesh[j][i-2]-P_mesh[j][i])\n", 1095 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1096 | " \n", 1097 | " dp=(DP+DE)/2\n", 1098 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1099 | " Pf=(-(PP+PE)/2)+Pp\n", 1100 | " \n", 1101 | " uf=Ue+Pf\n", 1102 | " Uf[j][i]=uf\n", 1103 | " DXf[j][i]=dp\n", 1104 | " \n", 1105 | " for j in range(uNy):\n", 1106 | " i=1\n", 1107 | " \n", 1108 | " UE=U[j][i]\n", 1109 | " UP=U[j][i-1]\n", 1110 | " Ue=(UE+UP)/2\n", 1111 | "\n", 1112 | " DP=DX[j][i-1]\n", 1113 | " DE=DX[j][i]\n", 1114 | " PP=DP*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1115 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1116 | "\n", 1117 | " dp=(DP+DE)/2\n", 1118 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1119 | " Pf=(-(PP+PE)/2)+Pp\n", 1120 | "\n", 1121 | " uf=Ue+Pf\n", 1122 | " Uf[j][i]=uf\n", 1123 | " DXf[j][i]=dp\n", 1124 | " \n", 1125 | " i=uNx-2\n", 1126 | " \n", 1127 | " UE=U[j][i]\n", 1128 | " UP=U[j][i-1]\n", 1129 | " Ue=(UE+UP)/2\n", 1130 | "\n", 1131 | " DP=DX[j][i-1]\n", 1132 | " DE=DX[j][i]\n", 1133 | " PP=DP*(P_mesh[j][i-2]-P_mesh[j][i])\n", 1134 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1135 | "\n", 1136 | " dp=(DP+DE)/2\n", 1137 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1138 | " Pf=(-(PP+PE)/2)+Pp\n", 1139 | "\n", 1140 | " uf=Ue+Pf\n", 1141 | " Uf[j][i]=uf\n", 1142 | " DXf[j][i]=dp\n", 1143 | " \n", 1144 | " #V-faces \n", 1145 | " DYf=np.zeros([vNy,vNx])\n", 1146 | " for j in range(2,vNy-2):\n", 1147 | " for i in range(vNx):\n", 1148 | " ij=i+Nx*j\n", 1149 | " \n", 1150 | " VN=V[j][i]\n", 1151 | " VP=V[j-1][i]\n", 1152 | " Ve=(VN+VP)/2\n", 1153 | "\n", 1154 | " DP=DX[j-1][i]\n", 1155 | " DN=DX[j][i]\n", 1156 | " PP=DP*(P_mesh[j-2][i]-P_mesh[j][i])\n", 1157 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1158 | " \n", 1159 | " dp=(DP+DN)/2\n", 1160 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1161 | " Pf=(-(PP+PN)/2)+Pp\n", 1162 | " \n", 1163 | " vf=Ve+Pf\n", 1164 | " Vf[j][i]=vf\n", 1165 | " DYf[j][i]=dp\n", 1166 | " \n", 1167 | " for i in range(vNx):\n", 1168 | " j=1\n", 1169 | " \n", 1170 | " VN=V[j][i]\n", 1171 | " VP=V[j-1][i]\n", 1172 | " Vn=(VN+VP)/2\n", 1173 | "\n", 1174 | " DP=DX[j-1][i]\n", 1175 | " DN=DX[j][i]\n", 1176 | " PP=DP*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1177 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1178 | "\n", 1179 | " dp=(DP+DN)/2\n", 1180 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1181 | " Pf=(-(PP+PN)/2)+Pp\n", 1182 | "\n", 1183 | " vf=Vn+Pf\n", 1184 | " Vf[j][i]=vf\n", 1185 | " DYf[j][i]=dp\n", 1186 | " \n", 1187 | " j=vNy-2\n", 1188 | " \n", 1189 | " VN=V[j][i]\n", 1190 | " VP=V[j-1][i]\n", 1191 | " Vn=(VN+VP)/2\n", 1192 | "\n", 1193 | " DP=DX[j-1][i]\n", 1194 | " DN=DX[j][i]\n", 1195 | " PP=DP*(P_mesh[j-2][i]-P_mesh[j][i])\n", 1196 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1197 | "\n", 1198 | " dp=(DP+DN)/2\n", 1199 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1200 | " Pf=(-(PP+PN)/2)+Pp\n", 1201 | "\n", 1202 | " vf=Vn+Pf\n", 1203 | " Vf[j][i]=vf\n", 1204 | " DYf[j][i]=dp\n", 1205 | " \n", 1206 | " return Uf,Vf,DXf,DYf" 1207 | ] 1208 | }, 1209 | { 1210 | "cell_type": "code", 1211 | "execution_count": 9, 1212 | "id": "99621a77", 1213 | "metadata": {}, 1214 | "outputs": [], 1215 | "source": [ 1216 | "def cont(Nx,Ny,U,V,P,DX,DY):\n", 1217 | " global b\n", 1218 | " global u_wall\n", 1219 | "\n", 1220 | " N=Nx*Ny\n", 1221 | " \n", 1222 | " A=np.zeros([N,N])\n", 1223 | " B=np.zeros([N,1])\n", 1224 | " \n", 1225 | " #For interior cells\n", 1226 | " for j in range(1,Ny-1):\n", 1227 | " for i in range(1,Nx-1):\n", 1228 | " ij=i+Nx*j\n", 1229 | " \n", 1230 | " ue=U[j][i+1]\n", 1231 | " uw=U[j][i]\n", 1232 | " vn=V[j+1][i]\n", 1233 | " vs=V[j][i]\n", 1234 | " \n", 1235 | " AE=-b*DX[j][i+1]\n", 1236 | " AW=-b*DX[j][i]\n", 1237 | " AN=-DY[j+1][i]\n", 1238 | " AS=-DY[j][i]\n", 1239 | " AP=-AE-AW-AN-AS\n", 1240 | " BP=b*(uw-ue)+vs-vn\n", 1241 | " \n", 1242 | " A[ij][ij-1]=AW\n", 1243 | " A[ij][ij+1]=AE\n", 1244 | " A[ij][ij]=AP\n", 1245 | " A[ij][ij+Nx]=AN\n", 1246 | " A[ij][ij-Nx]=AS\n", 1247 | " B[ij]=BP\n", 1248 | " \n", 1249 | " #left and right wall\n", 1250 | " for j in range(1,Ny-1):\n", 1251 | " i=0\n", 1252 | " ij=i+Nx*j\n", 1253 | " \n", 1254 | " ue=U[j][i+1]\n", 1255 | " vn=V[j+1][i]\n", 1256 | " vs=V[j][i]\n", 1257 | "\n", 1258 | " AE=-b*DX[j][i+1]\n", 1259 | " AN=-DY[j+1][i]\n", 1260 | " AS=-DY[j][i]\n", 1261 | " AP=-AE-AN-AS\n", 1262 | " BP=b*(-ue)+vs-vn\n", 1263 | "\n", 1264 | " A[ij][ij+1]=AE\n", 1265 | " A[ij][ij]=AP\n", 1266 | " A[ij][ij+Nx]=AN\n", 1267 | " A[ij][ij-Nx]=AS\n", 1268 | " B[ij]=BP\n", 1269 | " \n", 1270 | " i=Nx-1\n", 1271 | " ij=i+Nx*j\n", 1272 | " \n", 1273 | " uw=U[j][i]\n", 1274 | " vn=V[j+1][i]\n", 1275 | " vs=V[j][i]\n", 1276 | "\n", 1277 | " AW=-b*DX[j][i]\n", 1278 | " AN=-DY[j+1][i]\n", 1279 | " AS=-DY[j][i]\n", 1280 | " AP=-AW-AN-AS\n", 1281 | " BP=b*(uw)+vs-vn\n", 1282 | "\n", 1283 | " A[ij][ij-1]=AW\n", 1284 | " A[ij][ij]=AP\n", 1285 | " A[ij][ij+Nx]=AN\n", 1286 | " A[ij][ij-Nx]=AS\n", 1287 | " B[ij]=BP\n", 1288 | " \n", 1289 | " #for top and bottom wall\n", 1290 | " for i in range(1,Nx-1):\n", 1291 | " j=0\n", 1292 | " ij=i+Nx*j\n", 1293 | " \n", 1294 | " ue=U[j][i+1]\n", 1295 | " uw=U[j][i]\n", 1296 | " vn=V[j+1][i]\n", 1297 | "\n", 1298 | " AE=-b*DX[j][i+1]\n", 1299 | " AW=-b*DX[j][i]\n", 1300 | " AN=-DY[j+1][i]\n", 1301 | " AP=-AE-AW-AN\n", 1302 | " BP=b*(uw-ue)-vn\n", 1303 | "\n", 1304 | " A[ij][ij-1]=AW\n", 1305 | " A[ij][ij+1]=AE\n", 1306 | " A[ij][ij]=AP\n", 1307 | " A[ij][ij+Nx]=AN\n", 1308 | " B[ij]=BP\n", 1309 | " \n", 1310 | " j=Ny-1\n", 1311 | " ij=i+Nx*j\n", 1312 | " \n", 1313 | " ue=U[j][i+1]\n", 1314 | " uw=U[j][i]\n", 1315 | " vs=V[j][i]\n", 1316 | "\n", 1317 | " AE=-b*DX[j][i+1]\n", 1318 | " AW=-b*DX[j][i]\n", 1319 | " AS=-DY[j][i]\n", 1320 | " AP=-AE-AW-AS\n", 1321 | " BP=b*(uw-ue)+vs\n", 1322 | "\n", 1323 | " A[ij][ij-1]=AW\n", 1324 | " A[ij][ij+1]=AE\n", 1325 | " A[ij][ij]=AP\n", 1326 | " A[ij][ij-Nx]=AS\n", 1327 | " B[ij]=BP\n", 1328 | " \n", 1329 | " #for top left corner\n", 1330 | " i=0\n", 1331 | " j=Ny-1\n", 1332 | " ij=i+Nx*j\n", 1333 | " \n", 1334 | " ue=U[j][i+1]\n", 1335 | " vs=V[j][i]\n", 1336 | "\n", 1337 | " AE=-b*DX[j][i+1]\n", 1338 | " AS=-DY[j][i]\n", 1339 | " AP=-AE-AS\n", 1340 | " BP=b*(-ue)+vs\n", 1341 | "\n", 1342 | " A[ij][ij+1]=AE\n", 1343 | " A[ij][ij]=AP\n", 1344 | " A[ij][ij-Nx]=AS\n", 1345 | " B[ij]=BP\n", 1346 | " \n", 1347 | " #for top right corner\n", 1348 | " i=Nx-1\n", 1349 | " j=Ny-1\n", 1350 | " ij=i+Nx*j\n", 1351 | " \n", 1352 | " uw=U[j][i]\n", 1353 | " vs=V[j][i]\n", 1354 | "\n", 1355 | " AW=-b*DX[j][i]\n", 1356 | " AS=-DY[j][i]\n", 1357 | " AP=-AW-AS\n", 1358 | " BP=b*(uw)+vs\n", 1359 | "\n", 1360 | " A[ij][ij-1]=AW\n", 1361 | " A[ij][ij]=AP\n", 1362 | " A[ij][ij-Nx]=AS\n", 1363 | " B[ij]=BP\n", 1364 | " \n", 1365 | " #for bottom left corner\n", 1366 | " i=0\n", 1367 | " j=0\n", 1368 | " ij=0\n", 1369 | " \n", 1370 | " ue=U[j][i+1]\n", 1371 | " vn=V[j+1][i]\n", 1372 | "\n", 1373 | " AE=-b*DX[j][i+1]\n", 1374 | " AN=-DY[j+1][i]\n", 1375 | " AP=-AE-AN\n", 1376 | " BP=b*(-ue)-vn\n", 1377 | "\n", 1378 | " A[ij][ij+1]=AE\n", 1379 | " A[ij][ij]=AP\n", 1380 | " A[ij][ij+Nx]=AN\n", 1381 | " B[ij]=BP\n", 1382 | " \n", 1383 | " #for bottom right corner\n", 1384 | " i=Nx-1\n", 1385 | " j=0\n", 1386 | " ij=i+Nx*j\n", 1387 | " \n", 1388 | " uw=U[j][i]\n", 1389 | " vn=V[j+1][i]\n", 1390 | "\n", 1391 | " AW=-b*DX[j][i]\n", 1392 | " AN=-DY[j+1][i]\n", 1393 | " AP=-AW-AN\n", 1394 | " BP=b*(uw)-vn\n", 1395 | "\n", 1396 | " A[ij][ij-1]=AW\n", 1397 | " A[ij][ij]=AP\n", 1398 | " A[ij][ij+Nx]=AN\n", 1399 | " B[ij]=BP\n", 1400 | " \n", 1401 | " A=sp.sparse.csr_matrix(A)\n", 1402 | " ml=pyamg.ruge_stuben_solver(A)\n", 1403 | " PC=ml.solve(B,tol=1E-6)\n", 1404 | " \n", 1405 | " PC_mesh=np.reshape(np.array(PC),[Ny,Nx])\n", 1406 | " \n", 1407 | " return PC_mesh,B" 1408 | ] 1409 | }, 1410 | { 1411 | "cell_type": "code", 1412 | "execution_count": 10, 1413 | "id": "c76d9917", 1414 | "metadata": {}, 1415 | "outputs": [], 1416 | "source": [ 1417 | "def correct(Nx,Ny,U_mesh,V_mesh,P_mesh,PC_mesh,DX,DY,av,ap):\n", 1418 | " global dx\n", 1419 | " \n", 1420 | " #U correction\n", 1421 | " for j in range(Ny):\n", 1422 | " for i in range(1,Nx-1):\n", 1423 | " uc=av*DX[j][i]*(PC_mesh[j][i-1]-PC_mesh[j][i+1])\n", 1424 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1425 | " \n", 1426 | " for j in range(Ny):\n", 1427 | " i=0\n", 1428 | " uc=av*DX[j][i]*(PC_mesh[j][i]-PC_mesh[j][i+1])\n", 1429 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1430 | " \n", 1431 | " i=Nx-1\n", 1432 | " uc=av*DX[j][i]*(PC_mesh[j][i-1]-PC_mesh[j][i])\n", 1433 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1434 | "\n", 1435 | " #V correction\n", 1436 | " for j in range(1,Ny-1):\n", 1437 | " for i in range(Nx):\n", 1438 | " vc=av*DY[j][i]*(PC_mesh[j-1][i]-PC_mesh[j+1][i])\n", 1439 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1440 | " \n", 1441 | " for i in range(Nx):\n", 1442 | " j=0\n", 1443 | " vc=av*DY[j][i]*(PC_mesh[j][i]-PC_mesh[j+1][i])\n", 1444 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1445 | " \n", 1446 | " j=Ny-1\n", 1447 | " vc=av*DY[j][i]*(PC_mesh[j-1][i]-PC_mesh[j][i])\n", 1448 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1449 | " \n", 1450 | " #P correction\n", 1451 | " P_mesh+=(dx**1.2)*ap*PC_mesh\n", 1452 | " \n", 1453 | " return U_mesh,V_mesh,P_mesh" 1454 | ] 1455 | }, 1456 | { 1457 | "cell_type": "code", 1458 | "execution_count": 11, 1459 | "id": "0e2a5cc9", 1460 | "metadata": {}, 1461 | "outputs": [], 1462 | "source": [ 1463 | "def curl(U,V,dx,dy,Nx,Ny):\n", 1464 | " C=np.zeros([Ny,Nx])\n", 1465 | " \n", 1466 | " for j in range(1,Ny-1):\n", 1467 | " for i in range(1,Nx-1):\n", 1468 | " dvdx=(V[j+1][i]-V[j-1][i])/(2*dx)\n", 1469 | " dxdy=(U[j][i+1]-U[j][i-1])/(2*dy) \n", 1470 | " C[j][i]=dvdx-dxdy\n", 1471 | " \n", 1472 | " return C" 1473 | ] 1474 | }, 1475 | { 1476 | "cell_type": "code", 1477 | "execution_count": 12, 1478 | "id": "808d915a", 1479 | "metadata": {}, 1480 | "outputs": [], 1481 | "source": [ 1482 | "def order_1(A0,B0,y,y0,N,AB):\n", 1483 | " y0=np.reshape(y0,[N,1])\n", 1484 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 1485 | " y1=y0+fn\n", 1486 | " return y1" 1487 | ] 1488 | }, 1489 | { 1490 | "cell_type": "code", 1491 | "execution_count": 13, 1492 | "id": "ea7d8853", 1493 | "metadata": {}, 1494 | "outputs": [], 1495 | "source": [ 1496 | "def order_2(A0,B0,y,y0,N,AB):\n", 1497 | " y0=np.reshape(y0,[N,1])\n", 1498 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 1499 | " fn1=AB[-1]\n", 1500 | " y2=y0+1.5*fn1-0.5*fn\n", 1501 | " return y2" 1502 | ] 1503 | }, 1504 | { 1505 | "cell_type": "code", 1506 | "execution_count": 14, 1507 | "id": "64bee86c", 1508 | "metadata": {}, 1509 | "outputs": [], 1510 | "source": [ 1511 | "def order_3(A0,B0,y,y0,N,AB):\n", 1512 | " y0=np.reshape(y0,[N,1])\n", 1513 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 1514 | " fn1=AB[-2]\n", 1515 | " fn2=AB[-1]\n", 1516 | " y3=y0+(23*fn2-16*fn1+5*fn)/12\n", 1517 | " return y3" 1518 | ] 1519 | }, 1520 | { 1521 | "cell_type": "code", 1522 | "execution_count": 15, 1523 | "id": "5a4d3892", 1524 | "metadata": {}, 1525 | "outputs": [], 1526 | "source": [ 1527 | "def order_4(A0,B0,y,y0,N,AB):\n", 1528 | " y0=np.reshape(y0,[N,1])\n", 1529 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 1530 | " fn1=AB[-3]\n", 1531 | " fn2=AB[-2]\n", 1532 | " fn3=AB[-1]\n", 1533 | " y4=y0+(55*fn3-59*fn2+37*fn1-9*fn)/24\n", 1534 | " return y4" 1535 | ] 1536 | }, 1537 | { 1538 | "cell_type": "code", 1539 | "execution_count": 16, 1540 | "id": "11d7737f", 1541 | "metadata": {}, 1542 | "outputs": [], 1543 | "source": [ 1544 | "def curl(U,V,dx,dy,Nx,Ny):\n", 1545 | " C=np.zeros([Ny,Nx])\n", 1546 | " \n", 1547 | " for j in range(1,Ny-1):\n", 1548 | " for i in range(1,Nx-1):\n", 1549 | " dvdx=(V[j+1][i]-V[j-1][i])/(2*dx)\n", 1550 | " dxdy=(U[j][i+1]-U[j][i-1])/(2*dy) \n", 1551 | " C[j][i]=dvdx-dxdy\n", 1552 | " \n", 1553 | " return C" 1554 | ] 1555 | }, 1556 | { 1557 | "cell_type": "code", 1558 | "execution_count": 17, 1559 | "id": "3ece4c59", 1560 | "metadata": {}, 1561 | "outputs": [], 1562 | "source": [ 1563 | "def round_off(x):\n", 1564 | " return float(('{:g}'.format(float('{:.5g}'.format(x)))))" 1565 | ] 1566 | }, 1567 | { 1568 | "cell_type": "markdown", 1569 | "id": "d03eac22", 1570 | "metadata": {}, 1571 | "source": [ 1572 | "# Initialisation" 1573 | ] 1574 | }, 1575 | { 1576 | "cell_type": "code", 1577 | "execution_count": 38, 1578 | "id": "2ff30f96", 1579 | "metadata": {}, 1580 | "outputs": [ 1581 | { 1582 | "name": "stdout", 1583 | "output_type": "stream", 1584 | "text": [ 1585 | "CFL: 5.625\n" 1586 | ] 1587 | } 1588 | ], 1589 | "source": [ 1590 | "transient=False\n", 1591 | "Explicit=False\n", 1592 | "Re=1000\n", 1593 | "u_wall=1\n", 1594 | "L=1\n", 1595 | "H=1\n", 1596 | "v=u_wall/Re\n", 1597 | "av1=0.95 #Implicit under-relaxation\n", 1598 | "av2=0.8 #Explicit under-relaxation\n", 1599 | "ap=0.3\n", 1600 | "\n", 1601 | "Nx=75 #Array size for nodal velocities/pressure\n", 1602 | "Ny=75\n", 1603 | "N=Nx*Ny\n", 1604 | "uNx=Nx+1 #Array size for calculating face velocities\n", 1605 | "uNy=Ny\n", 1606 | "vNx=Nx\n", 1607 | "vNy=Ny+1\n", 1608 | "\n", 1609 | "dx=L/Nx\n", 1610 | "dy=H/Ny\n", 1611 | "b=dy/dx\n", 1612 | "iter_count=1\n", 1613 | "iter_total=1\n", 1614 | "itermax=1000\n", 1615 | "inner_loops=2\n", 1616 | "\n", 1617 | "dt=75E-3\n", 1618 | "end_time=60\n", 1619 | "flow_time=0\n", 1620 | "time_step=0\n", 1621 | "time_steps=end_time/dt\n", 1622 | "\n", 1623 | "U_data=[]\n", 1624 | "V_data=[]\n", 1625 | "U_time_data=[]\n", 1626 | "V_time_data=[]\n", 1627 | "U_res_list=[]\n", 1628 | "V_res_list=[]\n", 1629 | "Cont_res_list=[]\n", 1630 | "t_list=[]\n", 1631 | "UAB=[]\n", 1632 | "VAB=[]\n", 1633 | "\n", 1634 | "U_mesh=np.zeros([Ny,Nx])\n", 1635 | "Uf=np.zeros([uNy,uNx])\n", 1636 | "V_mesh=np.zeros([Ny,Nx])\n", 1637 | "Vf=np.zeros([vNy,vNx])\n", 1638 | "P_mesh=np.zeros([Ny,Nx])\n", 1639 | "U_old=U_mesh\n", 1640 | "V_old=V_mesh\n", 1641 | "U_older=U_mesh\n", 1642 | "V_older=V_mesh\n", 1643 | "\n", 1644 | "print('CFL:',round(u_wall*dt/dx,3))" 1645 | ] 1646 | }, 1647 | { 1648 | "cell_type": "markdown", 1649 | "id": "9d779851", 1650 | "metadata": {}, 1651 | "source": [ 1652 | "# Solution" 1653 | ] 1654 | }, 1655 | { 1656 | "cell_type": "code", 1657 | "execution_count": null, 1658 | "id": "85afe61d", 1659 | "metadata": { 1660 | "scrolled": true 1661 | }, 1662 | "outputs": [ 1663 | { 1664 | "name": "stdout", 1665 | "output_type": "stream", 1666 | "text": [ 1667 | "Steady State Calculation\n", 1668 | "Calculation Data\n", 1669 | "Number of cells: 5625\n", 1670 | "Aspect Ratio: 1.0\n", 1671 | "Explicit relaxation factor: 0.8\n", 1672 | "Implicit relaxation factor: 0.95\n", 1673 | "Pressure relaxation factor: 0.3\n", 1674 | "\n", 1675 | "Reynold's Number: 1000\n", 1676 | "\n", 1677 | " Iteration | U Residual | V Residual | Cont Residual | Time(s) \n", 1678 | "=============================================================================\n", 1679 | " 1 | 0.73201 | 0.3477 | 0.00047406 | 1.1029 \n", 1680 | " 2 | 0.16109 | 0.083849 | 0.0012469 | 1.2411 \n", 1681 | " 3 | 0.085411 | 0.054963 | 0.00068051 | 1.2324 \n", 1682 | " 4 | 0.072777 | 0.029049 | 0.00061714 | 1.1571 \n", 1683 | " 5 | 0.041626 | 0.021183 | 0.00055922 | 1.179 \n", 1684 | " 6 | 0.044576 | 0.021873 | 0.00042898 | 1.1662 \n", 1685 | " 7 | 0.043025 | 0.01848 | 0.00037994 | 1.1891 \n", 1686 | " 8 | 0.040663 | 0.015094 | 0.00035459 | 1.1482 \n", 1687 | " 9 | 0.040626 | 0.014768 | 0.00035019 | 1.168 \n", 1688 | " 10 | 0.039692 | 0.014647 | 0.00034972 | 1.1583 \n", 1689 | " 20 | 0.01872 | 0.012101 | 0.00021359 | 1.1842 \n", 1690 | " 40 | 0.0036766 | 0.0049231 | 7.3721e-05 | 1.2091 \n", 1691 | " 60 | 0.0007175 | 0.0013176 | 2.9806e-05 | 1.1806 \n", 1692 | " 80 | 0.00019617 | 0.00029451 | 1.6835e-05 | 1.1852 \n", 1693 | " 100 | 6.1165e-05 | 7.2037e-05 | 1.2232e-05 | 1.2836 \n" 1694 | ] 1695 | } 1696 | ], 1697 | "source": [ 1698 | "st=time.time()\n", 1699 | "\n", 1700 | "if transient==True:\n", 1701 | " print('Transient Calculation')\n", 1702 | " print('Calculation Data')\n", 1703 | " print('Number of cells:',N)\n", 1704 | " print('Aspect Ratio:', round(b,4))\n", 1705 | " print('Explicit relaxation factor:',av2)\n", 1706 | " print('Implicit relaxation factor:',av1)\n", 1707 | " print('Pressure relaxation factor:',ap)\n", 1708 | " print('Time step size:',dt*1000,'ms')\n", 1709 | " print('Total time steps:',int(time_steps))\n", 1710 | " print()\n", 1711 | " print(\"Reynold's Number:\",Re)\n", 1712 | " print()\n", 1713 | "\n", 1714 | " U0_mesh=U_mesh\n", 1715 | " V0_mesh=V_mesh\n", 1716 | " P0_mesh=P_mesh\n", 1717 | " iter_total=0\n", 1718 | " \n", 1719 | " for flow in tqdm(range(int(time_steps)),desc='Progress bar',unit=' time steps '):\n", 1720 | " if time_step==0:\n", 1721 | " print()\n", 1722 | " print('{:<10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format('Time Step','Iterations','U Residual','V Residual',\n", 1723 | " 'Cont Residual','Time(s)'))\n", 1724 | " print('=========================================================================================')\n", 1725 | "\n", 1726 | " s=time.time()\n", 1727 | " if flow%100==0:\n", 1728 | " U_time_data.append(U0_mesh)\n", 1729 | " V_time_data.append(V0_mesh)\n", 1730 | "\n", 1731 | " iter_count=1\n", 1732 | " time_step+=1\n", 1733 | " flow_time+=dt\n", 1734 | "\n", 1735 | " while iter_count<=itermax:\n", 1736 | " start=time.time()\n", 1737 | " U1_mesh,V1_mesh,DX,DY,A,UB,VB=mom(Nx,Ny,U_mesh,V_mesh,P_mesh,U0_mesh,V0_mesh,time_step,\n", 1738 | " UAB,VAB,U_old,V_old,U_older,V_older,av1)\n", 1739 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U1_mesh,V1_mesh,P_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 1740 | " PC_mesh,cont_A=cont(Nx,Ny,Uf,Vf,P_mesh,DXf,DYf)\n", 1741 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av2,ap)\n", 1742 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U2_mesh,V2_mesh,P1_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 1743 | " PC_mesh,cont_B=cont(Nx,Ny,Uf,Vf,P1_mesh,DXf,DYf)\n", 1744 | " U3_mesh,V3_mesh,P2_mesh=correct(Nx,Ny,U2_mesh,V2_mesh,P1_mesh,PC_mesh,DX,DY,av2,ap)\n", 1745 | "\n", 1746 | " U_residual=abs(max(U3_mesh.flatten()-U_mesh.flatten()))/u_wall\n", 1747 | " U_res_list.append(abs(U_residual))\n", 1748 | " V_residual=abs(max(V3_mesh.flatten()-V_mesh.flatten()))/u_wall\n", 1749 | " V_res_list.append(abs(V_residual))\n", 1750 | " P_residual=np.sum(abs(cont_B))/N\n", 1751 | " Cont_res_list.append(P_residual)\n", 1752 | "\n", 1753 | " U_mesh=U3_mesh\n", 1754 | " V_mesh=V3_mesh\n", 1755 | " P_mesh=P2_mesh\n", 1756 | "\n", 1757 | " end=time.time()\n", 1758 | " t=end-start\n", 1759 | " t_list.append(t)\n", 1760 | "\n", 1761 | " U_residual=round_off(U_residual)\n", 1762 | " V_residual=round_off(V_residual)\n", 1763 | " P_residual=round_off(P_residual)\n", 1764 | "\n", 1765 | " if iter_count==1:\n", 1766 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 1767 | " abs(V_residual),abs(P_residual),\n", 1768 | " str(round(t,4))))\n", 1769 | "\n", 1770 | " if abs(U_residual)<1E-3 and abs(V_residual)<1E-3 and abs(P_residual)<1E-4:\n", 1771 | " if time_step>1:\n", 1772 | " U_older=U_old\n", 1773 | " V_older=V_old\n", 1774 | "\n", 1775 | " U_old=U0_mesh\n", 1776 | " V_old=V0_mesh\n", 1777 | " U0_mesh=U_mesh\n", 1778 | " V0_mesh=V_mesh\n", 1779 | " P0_mesh=P_mesh\n", 1780 | " iter_total+=1\n", 1781 | "\n", 1782 | " if Explicit==True:\n", 1783 | " grad_U=np.matmul(A,np.reshape(U0_mesh,[N,1]))+UB\n", 1784 | " UAB.append(grad_U)\n", 1785 | "\n", 1786 | " grad_V=np.matmul(A,np.reshape(V0_mesh,[N,1]))+VB\n", 1787 | " VAB.append(grad_V)\n", 1788 | "\n", 1789 | " if len(UAB)>3:\n", 1790 | " UAB.pop(0)\n", 1791 | " VAB.pop(0)\n", 1792 | "\n", 1793 | " break\n", 1794 | "\n", 1795 | " if iter_count==itermax:\n", 1796 | " if time_step>1:\n", 1797 | " U_older=U_old\n", 1798 | " V_older=V_old\n", 1799 | "\n", 1800 | " U_old=U0_mesh\n", 1801 | " V_old=V0_mesh\n", 1802 | " U0_mesh=U_mesh\n", 1803 | " V0_mesh=V_mesh\n", 1804 | " P0_mesh=P_mesh\n", 1805 | " iter_total+=1\n", 1806 | "\n", 1807 | " if Explicit==True:\n", 1808 | " grad_U=np.matmul(A,np.reshape(U0_mesh,[N,1]))+UB\n", 1809 | " UAB.append(grad_U)\n", 1810 | "\n", 1811 | " grad_V=np.matmul(A,np.reshape(V0_mesh,[N,1]))+VB\n", 1812 | " VAB.append(grad_V)\n", 1813 | "\n", 1814 | " if len(UAB)>3:\n", 1815 | " UAB.pop(0)\n", 1816 | " VAB.pop(0)\n", 1817 | " break\n", 1818 | "\n", 1819 | " iter_count+=1\n", 1820 | " iter_total+=1\n", 1821 | "\n", 1822 | " e=time.time()\n", 1823 | " elap=e-s\n", 1824 | " if iter_count!=1:\n", 1825 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 1826 | " abs(V_residual),abs(P_residual),\n", 1827 | " str(round(elap,4))))\n", 1828 | " print()\n", 1829 | "\n", 1830 | " if abs(U_residual)<1E-5 and abs(V_residual)<1E-5 and iter_count==1: #check for steady state\n", 1831 | " break\n", 1832 | "\n", 1833 | " U_final=U0_mesh\n", 1834 | " V_final=V0_mesh\n", 1835 | "\n", 1836 | " et=time.time()\n", 1837 | " elapsed=et-st\n", 1838 | " print()\n", 1839 | " print('Execution time:',round(elapsed,3),'seconds')\n", 1840 | " print('Number of iterations:',iter_total)\n", 1841 | " print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')\n", 1842 | " print('Flow time:',round(flow_time,4),'seconds')\n", 1843 | " \n", 1844 | "else:\n", 1845 | " U0_mesh=0\n", 1846 | " V0_mesh=0\n", 1847 | " U_old=0\n", 1848 | " V_old=0\n", 1849 | " U_older=0\n", 1850 | " V_older=0\n", 1851 | " \n", 1852 | " print('Steady State Calculation')\n", 1853 | " print('Calculation Data')\n", 1854 | " print('Number of cells:',N)\n", 1855 | " print('Aspect Ratio:', round(b,4))\n", 1856 | " print('Explicit relaxation factor:',av2)\n", 1857 | " print('Implicit relaxation factor:',av1)\n", 1858 | " print('Pressure relaxation factor:',ap)\n", 1859 | " print()\n", 1860 | " print(\"Reynold's Number:\",Re)\n", 1861 | " print()\n", 1862 | " print('{:<10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(' Iteration','U Residual','V Residual','Cont Residual','Time(s)'))\n", 1863 | " print('=============================================================================')\n", 1864 | " \n", 1865 | " while iter_total<=itermax:\n", 1866 | " start=time.time()\n", 1867 | " U1_mesh,V1_mesh,DX,DY=mom(Nx,Ny,U_mesh,V_mesh,P_mesh,U0_mesh,V0_mesh,time_step,\n", 1868 | " UAB,VAB,U_old,V_old,U_older,V_older,av1)\n", 1869 | " for i in range(inner_loops):\n", 1870 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U1_mesh,V1_mesh,P_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 1871 | " PC_mesh,cont_B=cont(Nx,Ny,Uf,Vf,P_mesh,DXf,DYf)\n", 1872 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av2,ap)\n", 1873 | " U1_mesh=U2_mesh\n", 1874 | " V1_mesh=V2_mesh\n", 1875 | " P_mesh=P1_mesh\n", 1876 | "\n", 1877 | " U_residual=abs(max(U1_mesh.flatten()-U_mesh.flatten()))\n", 1878 | " U_res_list.append(abs(U_residual))\n", 1879 | " V_residual=abs(max(V1_mesh.flatten()-V_mesh.flatten()))\n", 1880 | " V_res_list.append(abs(V_residual))\n", 1881 | " P_residual=np.sum(abs(cont_B))/N\n", 1882 | " Cont_res_list.append(P_residual)\n", 1883 | "\n", 1884 | " if iter_total%10==0:\n", 1885 | " U_data.append(U1_mesh)\n", 1886 | " V_data.append(V1_mesh)\n", 1887 | "\n", 1888 | " U_mesh=U1_mesh\n", 1889 | " V_mesh=V1_mesh\n", 1890 | "\n", 1891 | " end=time.time()\n", 1892 | " t=end-start\n", 1893 | " t_list.append(t)\n", 1894 | "\n", 1895 | " U_residual=round_off(U_residual)\n", 1896 | " V_residual=round_off(V_residual)\n", 1897 | " P_residual=round_off(P_residual)\n", 1898 | "\n", 1899 | " if iter_total%20==0 or iter_total<=10:\n", 1900 | " print('{:^10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(str(iter_total),abs(U_residual),abs(V_residual),\n", 1901 | " abs(P_residual),str(round(t,4))))\n", 1902 | "\n", 1903 | " if abs(U_residual)<1E-5 and abs(V_residual)<1E-5 and abs(P_residual)<1E-5: \n", 1904 | " print('{:^10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(str(iter_total),abs(U_residual),abs(V_residual),\n", 1905 | " abs(P_residual),str(round(t,4))))\n", 1906 | " break\n", 1907 | "\n", 1908 | " if iter_total==itermax:\n", 1909 | " break\n", 1910 | "\n", 1911 | " iter_total+=1\n", 1912 | "\n", 1913 | " U_final=U_mesh\n", 1914 | " V_final=V_mesh\n", 1915 | "\n", 1916 | " et=time.time()\n", 1917 | " elapsed=et-st\n", 1918 | " print()\n", 1919 | " print('Execution time:',round(elapsed,3),'seconds')\n", 1920 | " print('Number of iterations:',iter_total)\n", 1921 | " print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')" 1922 | ] 1923 | }, 1924 | { 1925 | "cell_type": "markdown", 1926 | "id": "5afcd2cd", 1927 | "metadata": {}, 1928 | "source": [ 1929 | "**Number of iterations for LDC Re=1000, 75x75 grid**\n", 1930 | "* 1st order upwind: 563 iterations, 1.216s/iteration (1.191s/iteration)\n", 1931 | "* 2nd order TVD : 690 iterations, 1.273s/iteration\n", 1932 | "* 3rd order QUICK : 682 iterations, 1.259s/iteration (1.238s/iteration)\n", 1933 | "\n", 1934 | "***Number of iterations for LDC Re=1000, 75x75 grid**\n", 1935 | "* 1st order upwind: 563 iterations, 1.216s/iteration (1.191s/iteration)\n", 1936 | "* 2nd order TVD : 690 iterations, 1.273s/iteration\n", 1937 | "* 3rd order QUICK : 682 iterations, 1.259s/iteration (1.238s/iteration)\n", 1938 | "*Number of iterations for LDC Re=1000, 64x64 grid**\n", 1939 | "* 2 inner loops: 437 iterations, 0.742s/iteration, 324.2s total\n", 1940 | "* 4 inner loops: 432 iterations, 1.211s/iteration, 523.2s total\n", 1941 | "\n", 1942 | "**Number of iterations for LDC at different Re's, 64x64 grid**\n", 1943 | "* Re=1 , av=0.95, ap=0.25: 220 iterations\n", 1944 | "* Re=10 , av=0.95, ap=0.25: 218 iterations\n", 1945 | "* Re=100 , av=0.95, ap=0.25: 207 iterations\n", 1946 | "* Re=1000, av=0.95, ap=0.25: 190 iterations\n", 1947 | "\n", 1948 | "**Comparison between steady vs pseudo-transient calculations for LDC Re=5000, 100x100 grid**\n", 1949 | "* Steady State : 615 iterations, 3.3214s/iteration, 2042.7s total\n", 1950 | "* Pseudo Transient : 772 iterations, 3.4100s/iteration, 2633.2s total" 1951 | ] 1952 | }, 1953 | { 1954 | "cell_type": "markdown", 1955 | "id": "8e2533eb", 1956 | "metadata": {}, 1957 | "source": [ 1958 | "# Post-Processing" 1959 | ] 1960 | }, 1961 | { 1962 | "cell_type": "code", 1963 | "execution_count": 30, 1964 | "id": "c3125a76", 1965 | "metadata": {}, 1966 | "outputs": [], 1967 | "source": [ 1968 | "U_final=U_mesh\n", 1969 | "V_final=V_mesh\n", 1970 | "P_final=P_mesh" 1971 | ] 1972 | }, 1973 | { 1974 | "cell_type": "code", 1975 | "execution_count": 30, 1976 | "id": "f0eccf2d", 1977 | "metadata": {}, 1978 | "outputs": [], 1979 | "source": [ 1980 | "U_final=U_data[-2]\n", 1981 | "V_final=V_data[-2]\n", 1982 | "P_final=P_mesh" 1983 | ] 1984 | }, 1985 | { 1986 | "cell_type": "code", 1987 | "execution_count": 31, 1988 | "id": "86ee3b2c", 1989 | "metadata": {}, 1990 | "outputs": [], 1991 | "source": [ 1992 | "V_mag=np.zeros([Ny,Nx])\n", 1993 | "for j in range(Ny):\n", 1994 | " for i in range(Nx):\n", 1995 | " V_mag[j][i]=(np.sqrt(np.power(U_final[j][i],2))+np.sqrt(np.power(V_final[j][i],2)))\n", 1996 | " \n", 1997 | "vort=curl(U_final,V_final,dx,dy,Nx,Ny)\n", 1998 | "\n", 1999 | "x=np.linspace(dx/2,L-dx/2,Nx)\n", 2000 | "y=np.linspace(dy/2,H-dy/2,Ny)\n", 2001 | "xx,yy=np.meshgrid(x,y)\n", 2002 | "\n", 2003 | "xe=np.linspace(1,Nx,Nx)\n", 2004 | "ye=np.linspace(1,Ny,Ny)\n", 2005 | "xxe,yye=np.meshgrid(xe,ye)\n", 2006 | "grid=np.zeros([Ny,Nx])" 2007 | ] 2008 | }, 2009 | { 2010 | "cell_type": "code", 2011 | "execution_count": 32, 2012 | "id": "bf1eae07", 2013 | "metadata": { 2014 | "scrolled": false 2015 | }, 2016 | "outputs": [ 2017 | { 2018 | "name": "stderr", 2019 | "output_type": "stream", 2020 | "text": [ 2021 | "C:\\Users\\Kangluo See\\anaconda3\\lib\\site-packages\\matplotlib\\patches.py:3027: RuntimeWarning: invalid value encountered in double_scalars\n", 2022 | " cos_t, sin_t = head_length / head_dist, head_width / head_dist\n" 2023 | ] 2024 | }, 2025 | { 2026 | "data": { 2027 | "image/png": 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\n", 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\n", 2040 | "text/plain": [ 2041 | "
" 2042 | ] 2043 | }, 2044 | "metadata": { 2045 | "needs_background": "light" 2046 | }, 2047 | "output_type": "display_data" 2048 | } 2049 | ], 2050 | "source": [ 2051 | "fig,ax=plt.subplots(2,2,figsize=(16,12.8))\n", 2052 | "ax[0][0].streamplot(xx,yy,U_final,V_final,density=2.5,color='white',linewidth=0.8)\n", 2053 | "graph=ax[0][0].contourf(xx,yy,V_mag,cmap=cm.jet,levels=255,vmax=u_wall)\n", 2054 | "ax[0][0].set_title('Velocity contour plot @ Re = '+str(Re))\n", 2055 | "fig.colorbar(graph,ax=ax[0][0],label='Velocity (m/s)')\n", 2056 | "ax[0][0].set_xlim(0,L)\n", 2057 | "ax[0][0].set_ylim(0,H)\n", 2058 | "\n", 2059 | "ax[0][1].streamplot(xx,yy,U_final,V_final,density=3,color=V_mag,cmap=cm.jet,linewidth=0.8,arrowsize=0)\n", 2060 | "ax[0][1].set_title('Streamlines @ Re='+str(Re))\n", 2061 | "fig.colorbar(graph,ax=ax[0][1],label='Velocity (m/s)')\n", 2062 | "ax[0][1].set_xlim(0,L)\n", 2063 | "ax[0][1].set_ylim(0,H)\n", 2064 | "\n", 2065 | "graph=ax[1][0].contourf(xx,yy,vort,cmap=cm.seismic,levels=255,vmax=6.5*u_wall,vmin=-6.5*u_wall)\n", 2066 | "ax[1][0].set_title('Vorticity contour plot @ Re = '+str(Re))\n", 2067 | "fig.colorbar(graph,ax=ax[1][0])\n", 2068 | "ax[1][0].set_xlim(0,L)\n", 2069 | "ax[1][0].set_ylim(0,H)\n", 2070 | "\n", 2071 | "graph=ax[1][1].pcolormesh(xxe,yye,grid,cmap=cm.seismic,ec='k',shading='auto')\n", 2072 | "fig.colorbar(graph,ax=ax[1][1])\n", 2073 | "ax[1][1].set_title('Grid Layout')\n", 2074 | "ax[1][1].set_xlim(0.5,Nx+0.5)\n", 2075 | "ax[1][1].set_ylim(0.5,Ny+0.5)\n", 2076 | "\n", 2077 | "plt.show()\n", 2078 | "\n", 2079 | "iterations=np.linspace(0,iter_total,iter_total)\n", 2080 | "fig,ax=plt.subplots(1,1,figsize=(16,6))\n", 2081 | "plt.plot(iterations,U_res_list,label='U residual',linewidth=0.8)\n", 2082 | "plt.plot(iterations,V_res_list,label='V residual',linewidth=0.8)\n", 2083 | "plt.plot(iterations,Cont_res_list,label='Continuity residual',linewidth=0.8)\n", 2084 | "plt.xlabel('Iterations')\n", 2085 | "plt.ylabel('Residual')\n", 2086 | "ax.set_yscale('log')\n", 2087 | "plt.grid()\n", 2088 | "plt.xlim(0)\n", 2089 | "plt.legend()\n", 2090 | "\n", 2091 | "plt.show()" 2092 | ] 2093 | }, 2094 | { 2095 | "cell_type": "code", 2096 | "execution_count": 33, 2097 | "id": "81620a58", 2098 | "metadata": {}, 2099 | "outputs": [], 2100 | "source": [ 2101 | "def column(matrix, i):\n", 2102 | " return np.array([row[i] for row in matrix])" 2103 | ] 2104 | }, 2105 | { 2106 | "cell_type": "code", 2107 | "execution_count": 34, 2108 | "id": "75d1c14c", 2109 | "metadata": {}, 2110 | "outputs": [], 2111 | "source": [ 2112 | "mid_expt_100=[0,-0.03717,-0.04192,-0.04775,-0.06434,-0.10150,-0.15662,\n", 2113 | " -0.2109,-0.20581,-0.13641,0.00332,0.23151,0.68714,\n", 2114 | " 0.73722,0.78871,0.84123,1]\n", 2115 | "\n", 2116 | "mid_expt_400=[0,-0.08186,-0.09266,-0.10338,-0.14612,-0.24299,-0.32726,\n", 2117 | " -0.17119,-0.11477,0.02135,0.16256,0.29093,0.55892,\n", 2118 | " 0.61756,0.68439,0.75837,1]\n", 2119 | "\n", 2120 | "mid_expt_1000=[0.00000,-0.18109,-0.20196,-0.222,-0.2973,-0.38289,\n", 2121 | " -0.27805,-0.10648,-0.0608,0.05702,0.18719,0.33304,\n", 2122 | " 0.46604,0.51117,0.57492,0.65928,1]\n", 2123 | "\n", 2124 | "mid_expt_3200=[0,-0.32407,-0.35344,-0.37827,-0.41933,-0.34323,\n", 2125 | " -0.24427,-.086636,-0.04272,0.07156,0.19791,0.34682,\n", 2126 | " 0.46101,0.46547,0.48296,0.53236,1]\n", 2127 | "\n", 2128 | "mid_expt_5000=[0,-0.41165,-0.42901,-0.43463,-0.40435,-0.33050,\n", 2129 | " -0.22855,-.07404,-0.03039,0.08183,0.20087,0.33556,\n", 2130 | " 0.46036,0.45992,0.4612,0.48223,1]\n", 2131 | "\n", 2132 | "mid_expt_7500=[0,-0.43154,-0.43590,-0.43025,-0.38324,-0.32393,\n", 2133 | " -0.23176,-.07503,-0.038,0.08342,0.20591,0.34228,\n", 2134 | " 0.47167,0.47323,0.47048,0.47244,1]\n", 2135 | "\n", 2136 | "mid_expt_y=[0,0.0547,0.0625,0.0703,0.1016,0.1719,0.2813,0.4531,0.5,\n", 2137 | " 0.6172,0.7344,0.8516,0.9531,0.9609,0.9688,0.9766,1]" 2138 | ] 2139 | }, 2140 | { 2141 | "cell_type": "code", 2142 | "execution_count": 35, 2143 | "id": "49c98e23", 2144 | "metadata": {}, 2145 | "outputs": [ 2146 | { 2147 | "data": { 2148 | "text/plain": [ 2149 | "0.9509723292573328" 2150 | ] 2151 | }, 2152 | "execution_count": 35, 2153 | "metadata": {}, 2154 | "output_type": "execute_result" 2155 | } 2156 | ], 2157 | "source": [ 2158 | "np.max(V_mag)" 2159 | ] 2160 | }, 2161 | { 2162 | "cell_type": "code", 2163 | "execution_count": 37, 2164 | "id": "ef462e38", 2165 | "metadata": { 2166 | "scrolled": false 2167 | }, 2168 | "outputs": [ 2169 | { 2170 | "data": { 2171 | "image/png": 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\n", 2172 | "text/plain": [ 2173 | "
" 2174 | ] 2175 | }, 2176 | "metadata": { 2177 | "needs_background": "light" 2178 | }, 2179 | "output_type": "display_data" 2180 | } 2181 | ], 2182 | "source": [ 2183 | "mid=(column(U_final,31)+column(U_final,32))/2\n", 2184 | "quick32=mid\n", 2185 | "y=np.linspace(dy/2,H-dy/2,Ny)\n", 2186 | "y32=np.linspace(1/64,H-1/64,32)\n", 2187 | "y50=np.linspace(1/100,H-1/100,50)\n", 2188 | "y64=np.linspace(1/128,H-1/128,64)\n", 2189 | "y100=np.linspace(1/200,H-1/200,100)\n", 2190 | "\n", 2191 | "fig,ax=plt.subplots(1,1,figsize=(9,9))\n", 2192 | "\n", 2193 | "\n", 2194 | "#plt.plot(upwind1,y,label='1st order upwind')\n", 2195 | "#plt.plot(quick3,y80,label='80x80')\n", 2196 | "plt.plot(quick32,y,label='3rd order QUICK (32x32)')\n", 2197 | "#plt.plot(quick64,y64,label='3rd order QUICK (64x64)')\n", 2198 | "#plt.plot(mid,y,label='100x100')\n", 2199 | "plt.scatter(mid_expt_100,mid_expt_y,color='k',label='Expt')\n", 2200 | "plt.title('Comparison of various schemes for LDC (Re=1000)')\n", 2201 | "plt.xlabel('Velocity (m/s)')\n", 2202 | "plt.ylabel('Position (m)')\n", 2203 | "plt.xlim(-1,1)\n", 2204 | "plt.ylim(0,H)\n", 2205 | "plt.grid()\n", 2206 | "plt.legend()\n", 2207 | "plt.show()" 2208 | ] 2209 | }, 2210 | { 2211 | "cell_type": "code", 2212 | "execution_count": 103, 2213 | "id": "ab40fae5", 2214 | "metadata": {}, 2215 | "outputs": [ 2216 | { 2217 | "name": "stdout", 2218 | "output_type": "stream", 2219 | "text": [ 2220 | "For 50x50 grid\n" 2221 | ] 2222 | }, 2223 | { 2224 | "ename": "NameError", 2225 | "evalue": "name 'quick3' is not defined", 2226 | "output_type": "error", 2227 | "traceback": [ 2228 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 2229 | "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", 2230 | "\u001b[1;32mC:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_29392/1390803526.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'For 50x50 grid'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'1st order upwind-Mean error:'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mupwind4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mquick3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mquick3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[1;31m#print('3rd order QUICK -Mean error:',round(float(np.mean(abs((quick3-tvd2)/tvd2))),5))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 2231 | "\u001b[1;31mNameError\u001b[0m: name 'quick3' is not defined" 2232 | ] 2233 | } 2234 | ], 2235 | "source": [ 2236 | "print('For 50x50 grid')\n", 2237 | "print('1st order upwind-Mean error:',round(float(np.mean(abs((upwind4-quick3)/quick3))),5)*100)\n", 2238 | "#print('3rd order QUICK -Mean error:',round(float(np.mean(abs((quick3-tvd2)/tvd2))),5))" 2239 | ] 2240 | }, 2241 | { 2242 | "cell_type": "code", 2243 | "execution_count": null, 2244 | "id": "a7d546ab", 2245 | "metadata": {}, 2246 | "outputs": [], 2247 | "source": [ 2248 | "mid_expt_y=[0.0547,0.0625,0.0703,0.1016,0.1719,0.2813,0.4531,0.5,\n", 2249 | " 0.6172,0.7344,0.8516,0.9531,0.9609,0.9688,0.9766]\n", 2250 | "\n", 2251 | "xp=tvd2\n", 2252 | "yp=y\n", 2253 | "error_list=[]\n", 2254 | "\n", 2255 | "for i in range(len(mid_expt_y)):\n", 2256 | " expt=mid_expt_100[i+1]\n", 2257 | " pos=mid_expt_y[i]\n", 2258 | " test=np.interp(pos,yp,xp)\n", 2259 | " error=(test-expt)/expt\n", 2260 | " error_list.append(error)\n", 2261 | " \n", 2262 | "error_list.pop(0)\n", 2263 | "error_sum=0\n", 2264 | "for i in error_list:\n", 2265 | " error_sum+=abs(i)\n", 2266 | "\n", 2267 | "print('Mean error:',round(error_sum/len(mid_expt_y),4))" 2268 | ] 2269 | }, 2270 | { 2271 | "cell_type": "markdown", 2272 | "id": "d7ce5286", 2273 | "metadata": {}, 2274 | "source": [ 2275 | "**For 50x50 grid:** \n", 2276 | "1st order upwind -Mean error: 0.21072 \n", 2277 | "3rd order QUICK -Mean error: 0.00926 \n", 2278 | "\n", 2279 | "**For 75x75 grid:** \n", 2280 | "1st order upwind -Mean error: 0.1737 \n", 2281 | "3rd order QUICK -Mean error: 0.00283" 2282 | ] 2283 | }, 2284 | { 2285 | "cell_type": "code", 2286 | "execution_count": 29, 2287 | "id": "1fdfe3e3", 2288 | "metadata": {}, 2289 | "outputs": [], 2290 | "source": [ 2291 | "np.save('RE5000_LDC_reversal_U.npy',U_time_data)\n", 2292 | "np.save('RE5000_LDC_reversal_V.npy',V_time_data)\n", 2293 | "np.save('RE5000_LDC_reversal_P.npy',P_final)" 2294 | ] 2295 | }, 2296 | { 2297 | "cell_type": "markdown", 2298 | "id": "e79b3ab2", 2299 | "metadata": {}, 2300 | "source": [ 2301 | "# Animation" 2302 | ] 2303 | }, 2304 | { 2305 | "cell_type": "code", 2306 | "execution_count": 22, 2307 | "id": "e0b12a0a", 2308 | "metadata": {}, 2309 | "outputs": [], 2310 | "source": [ 2311 | "U_anim=U_time_data[::]\n", 2312 | "V_anim=V_time_data[::]\n", 2313 | "count=600\n", 2314 | "\n", 2315 | "U_file=[]\n", 2316 | "V_file=[]\n", 2317 | "V_mag_file=[]\n", 2318 | "Vort_file=[]\n", 2319 | "\n", 2320 | "for k in range(count):\n", 2321 | " U_final=U_anim[k]\n", 2322 | " V_final=V_anim[k]\n", 2323 | " \n", 2324 | " V_mag=np.sqrt(np.power(U_final,2))+np.sqrt(np.power(V_final,2)) \n", 2325 | " vort=curl(U_final,V_final,dx,dy,Nx,Ny)\n", 2326 | " \n", 2327 | " U_file.append(U_final)\n", 2328 | " V_file.append(V_final)\n", 2329 | " V_mag_file.append(V_mag)\n", 2330 | " Vort_file.append(vort)" 2331 | ] 2332 | }, 2333 | { 2334 | "cell_type": "code", 2335 | "execution_count": 53, 2336 | "id": "c40fc7bd", 2337 | "metadata": {}, 2338 | "outputs": [], 2339 | "source": [ 2340 | "def animate(k):\n", 2341 | " ax.clear()\n", 2342 | " \n", 2343 | " U_final=U_file[k]\n", 2344 | " V_final=V_file[k]\n", 2345 | " #V_mag=V_mag_file[k]\n", 2346 | " #vort=Vort_file[k]\n", 2347 | " plt.title('Flow time: '+str(round(k*dt*20,3))+' seconds \\n' 'Streamlines')\n", 2348 | " #plt.title('Velocity')\n", 2349 | " plt.xlim(0,L)\n", 2350 | " plt.ylim(0,H)\n", 2351 | " #rect1=patches.Rectangle([0.5,0.4],0.2,0.2,hatch='////',fc='k',ec='k')\n", 2352 | " #ax.add_patch(rect1)\n", 2353 | " #contour=plt.contourf(xx,yy,V_mag,cmap=cm.jet,vmax=5,levels=255)\n", 2354 | " #contour=plt.contourf(xx,yy,vort,cmap=cm.seismic,levels=255,vmax=45,vmin=-45)\n", 2355 | " stream=plt.streamplot(xx,yy,U_final,V_final,linewidth=0.75,density=3,color='k',arrowsize=0)\n", 2356 | " return stream" 2357 | ] 2358 | }, 2359 | { 2360 | "cell_type": "code", 2361 | "execution_count": 54, 2362 | "id": "8d346965", 2363 | "metadata": {}, 2364 | "outputs": [ 2365 | { 2366 | "data": { 2367 | "application/javascript": [ 2368 | "/* Put everything inside the global mpl namespace */\n", 2369 | "/* global mpl */\n", 2370 | "window.mpl = {};\n", 2371 | "\n", 2372 | "mpl.get_websocket_type = function () {\n", 2373 | " if (typeof WebSocket !== 'undefined') {\n", 2374 | " return WebSocket;\n", 2375 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 2376 | " return MozWebSocket;\n", 2377 | " } else {\n", 2378 | " alert(\n", 2379 | " 'Your browser does not have WebSocket support. ' +\n", 2380 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 2381 | " 'Firefox 4 and 5 are also supported but you ' +\n", 2382 | " 'have to enable WebSockets in about:config.'\n", 2383 | " );\n", 2384 | " }\n", 2385 | "};\n", 2386 | "\n", 2387 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 2388 | " this.id = figure_id;\n", 2389 | "\n", 2390 | " this.ws = websocket;\n", 2391 | "\n", 2392 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 2393 | "\n", 2394 | " if (!this.supports_binary) {\n", 2395 | " var warnings = document.getElementById('mpl-warnings');\n", 2396 | " if (warnings) {\n", 2397 | " warnings.style.display = 'block';\n", 2398 | " warnings.textContent =\n", 2399 | " 'This browser does not support binary websocket messages. ' +\n", 2400 | " 'Performance may be slow.';\n", 2401 | " }\n", 2402 | " }\n", 2403 | "\n", 2404 | " this.imageObj = new Image();\n", 2405 | "\n", 2406 | " this.context = undefined;\n", 2407 | " this.message = undefined;\n", 2408 | " this.canvas = undefined;\n", 2409 | " this.rubberband_canvas = undefined;\n", 2410 | " this.rubberband_context = undefined;\n", 2411 | " this.format_dropdown = undefined;\n", 2412 | "\n", 2413 | " this.image_mode = 'full';\n", 2414 | "\n", 2415 | " this.root = document.createElement('div');\n", 2416 | " this.root.setAttribute('style', 'display: inline-block');\n", 2417 | " this._root_extra_style(this.root);\n", 2418 | "\n", 2419 | " parent_element.appendChild(this.root);\n", 2420 | "\n", 2421 | " this._init_header(this);\n", 2422 | " this._init_canvas(this);\n", 2423 | " this._init_toolbar(this);\n", 2424 | "\n", 2425 | " var fig = this;\n", 2426 | "\n", 2427 | " this.waiting = false;\n", 2428 | "\n", 2429 | " this.ws.onopen = function () {\n", 2430 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 2431 | " fig.send_message('send_image_mode', {});\n", 2432 | " if (fig.ratio !== 1) {\n", 2433 | " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", 2434 | " }\n", 2435 | " fig.send_message('refresh', {});\n", 2436 | " };\n", 2437 | "\n", 2438 | " this.imageObj.onload = function () {\n", 2439 | " if (fig.image_mode === 'full') {\n", 2440 | " // Full images could contain transparency (where diff images\n", 2441 | " // almost always do), so we need to clear the canvas so that\n", 2442 | " // there is no ghosting.\n", 2443 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 2444 | " }\n", 2445 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 2446 | " };\n", 2447 | "\n", 2448 | " this.imageObj.onunload = function () {\n", 2449 | " fig.ws.close();\n", 2450 | " };\n", 2451 | "\n", 2452 | " this.ws.onmessage = this._make_on_message_function(this);\n", 2453 | "\n", 2454 | " this.ondownload = ondownload;\n", 2455 | "};\n", 2456 | "\n", 2457 | "mpl.figure.prototype._init_header = function () {\n", 2458 | " var titlebar = document.createElement('div');\n", 2459 | " titlebar.classList =\n", 2460 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 2461 | " var titletext = document.createElement('div');\n", 2462 | " titletext.classList = 'ui-dialog-title';\n", 2463 | " titletext.setAttribute(\n", 2464 | " 'style',\n", 2465 | " 'width: 100%; text-align: center; padding: 3px;'\n", 2466 | " );\n", 2467 | " titlebar.appendChild(titletext);\n", 2468 | " this.root.appendChild(titlebar);\n", 2469 | " this.header = titletext;\n", 2470 | "};\n", 2471 | "\n", 2472 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 2473 | "\n", 2474 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 2475 | "\n", 2476 | "mpl.figure.prototype._init_canvas = function () {\n", 2477 | " var fig = this;\n", 2478 | "\n", 2479 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 2480 | " canvas_div.setAttribute(\n", 2481 | " 'style',\n", 2482 | " 'border: 1px solid #ddd;' +\n", 2483 | " 'box-sizing: content-box;' +\n", 2484 | " 'clear: both;' +\n", 2485 | " 'min-height: 1px;' +\n", 2486 | " 'min-width: 1px;' +\n", 2487 | " 'outline: 0;' +\n", 2488 | " 'overflow: hidden;' +\n", 2489 | " 'position: relative;' +\n", 2490 | " 'resize: both;'\n", 2491 | " );\n", 2492 | "\n", 2493 | " function on_keyboard_event_closure(name) {\n", 2494 | " return function (event) {\n", 2495 | " return fig.key_event(event, name);\n", 2496 | " };\n", 2497 | " }\n", 2498 | "\n", 2499 | " canvas_div.addEventListener(\n", 2500 | " 'keydown',\n", 2501 | " on_keyboard_event_closure('key_press')\n", 2502 | " );\n", 2503 | " canvas_div.addEventListener(\n", 2504 | " 'keyup',\n", 2505 | " on_keyboard_event_closure('key_release')\n", 2506 | " );\n", 2507 | "\n", 2508 | " this._canvas_extra_style(canvas_div);\n", 2509 | " this.root.appendChild(canvas_div);\n", 2510 | "\n", 2511 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 2512 | " canvas.classList.add('mpl-canvas');\n", 2513 | " canvas.setAttribute('style', 'box-sizing: content-box;');\n", 2514 | "\n", 2515 | " this.context = canvas.getContext('2d');\n", 2516 | "\n", 2517 | " var backingStore =\n", 2518 | " this.context.backingStorePixelRatio ||\n", 2519 | " this.context.webkitBackingStorePixelRatio ||\n", 2520 | " this.context.mozBackingStorePixelRatio ||\n", 2521 | " this.context.msBackingStorePixelRatio ||\n", 2522 | " this.context.oBackingStorePixelRatio ||\n", 2523 | " this.context.backingStorePixelRatio ||\n", 2524 | " 1;\n", 2525 | "\n", 2526 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 2527 | "\n", 2528 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 2529 | " 'canvas'\n", 2530 | " ));\n", 2531 | " rubberband_canvas.setAttribute(\n", 2532 | " 'style',\n", 2533 | " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", 2534 | " );\n", 2535 | "\n", 2536 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 2537 | " if (this.ResizeObserver === undefined) {\n", 2538 | " if (window.ResizeObserver !== undefined) {\n", 2539 | " this.ResizeObserver = window.ResizeObserver;\n", 2540 | " } else {\n", 2541 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 2542 | " this.ResizeObserver = obs.ResizeObserver;\n", 2543 | " }\n", 2544 | " }\n", 2545 | "\n", 2546 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 2547 | " var nentries = entries.length;\n", 2548 | " for (var i = 0; i < nentries; i++) {\n", 2549 | " var entry = entries[i];\n", 2550 | " var width, height;\n", 2551 | " if (entry.contentBoxSize) {\n", 2552 | " if (entry.contentBoxSize instanceof Array) {\n", 2553 | " // Chrome 84 implements new version of spec.\n", 2554 | " width = entry.contentBoxSize[0].inlineSize;\n", 2555 | " height = entry.contentBoxSize[0].blockSize;\n", 2556 | " } else {\n", 2557 | " // Firefox implements old version of spec.\n", 2558 | " width = entry.contentBoxSize.inlineSize;\n", 2559 | " height = entry.contentBoxSize.blockSize;\n", 2560 | " }\n", 2561 | " } else {\n", 2562 | " // Chrome <84 implements even older version of spec.\n", 2563 | " width = entry.contentRect.width;\n", 2564 | " height = entry.contentRect.height;\n", 2565 | " }\n", 2566 | "\n", 2567 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 2568 | " // the canvas container.\n", 2569 | " if (entry.devicePixelContentBoxSize) {\n", 2570 | " // Chrome 84 implements new version of spec.\n", 2571 | " canvas.setAttribute(\n", 2572 | " 'width',\n", 2573 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 2574 | " );\n", 2575 | " canvas.setAttribute(\n", 2576 | " 'height',\n", 2577 | " entry.devicePixelContentBoxSize[0].blockSize\n", 2578 | " );\n", 2579 | " } else {\n", 2580 | " canvas.setAttribute('width', width * fig.ratio);\n", 2581 | " canvas.setAttribute('height', height * fig.ratio);\n", 2582 | " }\n", 2583 | " canvas.setAttribute(\n", 2584 | " 'style',\n", 2585 | " 'width: ' + width + 'px; height: ' + height + 'px;'\n", 2586 | " );\n", 2587 | "\n", 2588 | " rubberband_canvas.setAttribute('width', width);\n", 2589 | " rubberband_canvas.setAttribute('height', height);\n", 2590 | "\n", 2591 | " // And update the size in Python. We ignore the initial 0/0 size\n", 2592 | " // that occurs as the element is placed into the DOM, which should\n", 2593 | " // otherwise not happen due to the minimum size styling.\n", 2594 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 2595 | " fig.request_resize(width, height);\n", 2596 | " }\n", 2597 | " }\n", 2598 | " });\n", 2599 | " this.resizeObserverInstance.observe(canvas_div);\n", 2600 | "\n", 2601 | " function on_mouse_event_closure(name) {\n", 2602 | " return function (event) {\n", 2603 | " return fig.mouse_event(event, name);\n", 2604 | " };\n", 2605 | " }\n", 2606 | "\n", 2607 | " rubberband_canvas.addEventListener(\n", 2608 | " 'mousedown',\n", 2609 | " on_mouse_event_closure('button_press')\n", 2610 | " );\n", 2611 | " rubberband_canvas.addEventListener(\n", 2612 | " 'mouseup',\n", 2613 | " on_mouse_event_closure('button_release')\n", 2614 | " );\n", 2615 | " rubberband_canvas.addEventListener(\n", 2616 | " 'dblclick',\n", 2617 | " on_mouse_event_closure('dblclick')\n", 2618 | " );\n", 2619 | " // Throttle sequential mouse events to 1 every 20ms.\n", 2620 | " rubberband_canvas.addEventListener(\n", 2621 | " 'mousemove',\n", 2622 | " on_mouse_event_closure('motion_notify')\n", 2623 | " );\n", 2624 | "\n", 2625 | " rubberband_canvas.addEventListener(\n", 2626 | " 'mouseenter',\n", 2627 | " on_mouse_event_closure('figure_enter')\n", 2628 | " );\n", 2629 | " rubberband_canvas.addEventListener(\n", 2630 | " 'mouseleave',\n", 2631 | " on_mouse_event_closure('figure_leave')\n", 2632 | " );\n", 2633 | "\n", 2634 | " canvas_div.addEventListener('wheel', function (event) {\n", 2635 | " if (event.deltaY < 0) {\n", 2636 | " event.step = 1;\n", 2637 | " } else {\n", 2638 | " event.step = -1;\n", 2639 | " }\n", 2640 | " on_mouse_event_closure('scroll')(event);\n", 2641 | " });\n", 2642 | "\n", 2643 | " canvas_div.appendChild(canvas);\n", 2644 | " canvas_div.appendChild(rubberband_canvas);\n", 2645 | "\n", 2646 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 2647 | " this.rubberband_context.strokeStyle = '#000000';\n", 2648 | "\n", 2649 | " this._resize_canvas = function (width, height, forward) {\n", 2650 | " if (forward) {\n", 2651 | " canvas_div.style.width = width + 'px';\n", 2652 | " canvas_div.style.height = height + 'px';\n", 2653 | " }\n", 2654 | " };\n", 2655 | "\n", 2656 | " // Disable right mouse context menu.\n", 2657 | " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", 2658 | " event.preventDefault();\n", 2659 | " return false;\n", 2660 | " });\n", 2661 | "\n", 2662 | " function set_focus() {\n", 2663 | " canvas.focus();\n", 2664 | " canvas_div.focus();\n", 2665 | " }\n", 2666 | "\n", 2667 | " window.setTimeout(set_focus, 100);\n", 2668 | "};\n", 2669 | "\n", 2670 | "mpl.figure.prototype._init_toolbar = function () {\n", 2671 | " var fig = this;\n", 2672 | "\n", 2673 | " var toolbar = document.createElement('div');\n", 2674 | " toolbar.classList = 'mpl-toolbar';\n", 2675 | " this.root.appendChild(toolbar);\n", 2676 | "\n", 2677 | " function on_click_closure(name) {\n", 2678 | " return function (_event) {\n", 2679 | " return fig.toolbar_button_onclick(name);\n", 2680 | " };\n", 2681 | " }\n", 2682 | "\n", 2683 | " function on_mouseover_closure(tooltip) {\n", 2684 | " return function (event) {\n", 2685 | " if (!event.currentTarget.disabled) {\n", 2686 | " return fig.toolbar_button_onmouseover(tooltip);\n", 2687 | " }\n", 2688 | " };\n", 2689 | " }\n", 2690 | "\n", 2691 | " fig.buttons = {};\n", 2692 | " var buttonGroup = document.createElement('div');\n", 2693 | " buttonGroup.classList = 'mpl-button-group';\n", 2694 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 2695 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 2696 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 2697 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 2698 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 2699 | "\n", 2700 | " if (!name) {\n", 2701 | " /* Instead of a spacer, we start a new button group. */\n", 2702 | " if (buttonGroup.hasChildNodes()) {\n", 2703 | " toolbar.appendChild(buttonGroup);\n", 2704 | " }\n", 2705 | " buttonGroup = document.createElement('div');\n", 2706 | " buttonGroup.classList = 'mpl-button-group';\n", 2707 | " continue;\n", 2708 | " }\n", 2709 | "\n", 2710 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 2711 | " button.classList = 'mpl-widget';\n", 2712 | " button.setAttribute('role', 'button');\n", 2713 | " button.setAttribute('aria-disabled', 'false');\n", 2714 | " button.addEventListener('click', on_click_closure(method_name));\n", 2715 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 2716 | "\n", 2717 | " var icon_img = document.createElement('img');\n", 2718 | " icon_img.src = '_images/' + image + '.png';\n", 2719 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 2720 | " icon_img.alt = tooltip;\n", 2721 | " button.appendChild(icon_img);\n", 2722 | "\n", 2723 | " buttonGroup.appendChild(button);\n", 2724 | " }\n", 2725 | "\n", 2726 | " if (buttonGroup.hasChildNodes()) {\n", 2727 | " toolbar.appendChild(buttonGroup);\n", 2728 | " }\n", 2729 | "\n", 2730 | " var fmt_picker = document.createElement('select');\n", 2731 | " fmt_picker.classList = 'mpl-widget';\n", 2732 | " toolbar.appendChild(fmt_picker);\n", 2733 | " this.format_dropdown = fmt_picker;\n", 2734 | "\n", 2735 | " for (var ind in mpl.extensions) {\n", 2736 | " var fmt = mpl.extensions[ind];\n", 2737 | " var option = document.createElement('option');\n", 2738 | " option.selected = fmt === mpl.default_extension;\n", 2739 | " option.innerHTML = fmt;\n", 2740 | " fmt_picker.appendChild(option);\n", 2741 | " }\n", 2742 | "\n", 2743 | " var status_bar = document.createElement('span');\n", 2744 | " status_bar.classList = 'mpl-message';\n", 2745 | " toolbar.appendChild(status_bar);\n", 2746 | " this.message = status_bar;\n", 2747 | "};\n", 2748 | "\n", 2749 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 2750 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 2751 | " // which will in turn request a refresh of the image.\n", 2752 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 2753 | "};\n", 2754 | "\n", 2755 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 2756 | " properties['type'] = type;\n", 2757 | " properties['figure_id'] = this.id;\n", 2758 | " this.ws.send(JSON.stringify(properties));\n", 2759 | "};\n", 2760 | "\n", 2761 | "mpl.figure.prototype.send_draw_message = function () {\n", 2762 | " if (!this.waiting) {\n", 2763 | " this.waiting = true;\n", 2764 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 2765 | " }\n", 2766 | "};\n", 2767 | "\n", 2768 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 2769 | " var format_dropdown = fig.format_dropdown;\n", 2770 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 2771 | " fig.ondownload(fig, format);\n", 2772 | "};\n", 2773 | "\n", 2774 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 2775 | " var size = msg['size'];\n", 2776 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 2777 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 2778 | " fig.send_message('refresh', {});\n", 2779 | " }\n", 2780 | "};\n", 2781 | "\n", 2782 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 2783 | " var x0 = msg['x0'] / fig.ratio;\n", 2784 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 2785 | " var x1 = msg['x1'] / fig.ratio;\n", 2786 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 2787 | " x0 = Math.floor(x0) + 0.5;\n", 2788 | " y0 = Math.floor(y0) + 0.5;\n", 2789 | " x1 = Math.floor(x1) + 0.5;\n", 2790 | " y1 = Math.floor(y1) + 0.5;\n", 2791 | " var min_x = Math.min(x0, x1);\n", 2792 | " var min_y = Math.min(y0, y1);\n", 2793 | " var width = Math.abs(x1 - x0);\n", 2794 | " var height = Math.abs(y1 - y0);\n", 2795 | "\n", 2796 | " fig.rubberband_context.clearRect(\n", 2797 | " 0,\n", 2798 | " 0,\n", 2799 | " fig.canvas.width / fig.ratio,\n", 2800 | " fig.canvas.height / fig.ratio\n", 2801 | " );\n", 2802 | "\n", 2803 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 2804 | "};\n", 2805 | "\n", 2806 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 2807 | " // Updates the figure title.\n", 2808 | " fig.header.textContent = msg['label'];\n", 2809 | "};\n", 2810 | "\n", 2811 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 2812 | " var cursor = msg['cursor'];\n", 2813 | " switch (cursor) {\n", 2814 | " case 0:\n", 2815 | " cursor = 'pointer';\n", 2816 | " break;\n", 2817 | " case 1:\n", 2818 | " cursor = 'default';\n", 2819 | " break;\n", 2820 | " case 2:\n", 2821 | " cursor = 'crosshair';\n", 2822 | " break;\n", 2823 | " case 3:\n", 2824 | " cursor = 'move';\n", 2825 | " break;\n", 2826 | " }\n", 2827 | " fig.rubberband_canvas.style.cursor = cursor;\n", 2828 | "};\n", 2829 | "\n", 2830 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 2831 | " fig.message.textContent = msg['message'];\n", 2832 | "};\n", 2833 | "\n", 2834 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 2835 | " // Request the server to send over a new figure.\n", 2836 | " fig.send_draw_message();\n", 2837 | "};\n", 2838 | "\n", 2839 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 2840 | " fig.image_mode = msg['mode'];\n", 2841 | "};\n", 2842 | "\n", 2843 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 2844 | " for (var key in msg) {\n", 2845 | " if (!(key in fig.buttons)) {\n", 2846 | " continue;\n", 2847 | " }\n", 2848 | " fig.buttons[key].disabled = !msg[key];\n", 2849 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 2850 | " }\n", 2851 | "};\n", 2852 | "\n", 2853 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 2854 | " if (msg['mode'] === 'PAN') {\n", 2855 | " fig.buttons['Pan'].classList.add('active');\n", 2856 | " fig.buttons['Zoom'].classList.remove('active');\n", 2857 | " } else if (msg['mode'] === 'ZOOM') {\n", 2858 | " fig.buttons['Pan'].classList.remove('active');\n", 2859 | " fig.buttons['Zoom'].classList.add('active');\n", 2860 | " } else {\n", 2861 | " fig.buttons['Pan'].classList.remove('active');\n", 2862 | " fig.buttons['Zoom'].classList.remove('active');\n", 2863 | " }\n", 2864 | "};\n", 2865 | "\n", 2866 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 2867 | " // Called whenever the canvas gets updated.\n", 2868 | " this.send_message('ack', {});\n", 2869 | "};\n", 2870 | "\n", 2871 | "// A function to construct a web socket function for onmessage handling.\n", 2872 | "// Called in the figure constructor.\n", 2873 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 2874 | " return function socket_on_message(evt) {\n", 2875 | " if (evt.data instanceof Blob) {\n", 2876 | " var img = evt.data;\n", 2877 | " if (img.type !== 'image/png') {\n", 2878 | " /* FIXME: We get \"Resource interpreted as Image but\n", 2879 | " * transferred with MIME type text/plain:\" errors on\n", 2880 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 2881 | " * to be part of the websocket stream */\n", 2882 | " img.type = 'image/png';\n", 2883 | " }\n", 2884 | "\n", 2885 | " /* Free the memory for the previous frames */\n", 2886 | " if (fig.imageObj.src) {\n", 2887 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 2888 | " fig.imageObj.src\n", 2889 | " );\n", 2890 | " }\n", 2891 | "\n", 2892 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 2893 | " img\n", 2894 | " );\n", 2895 | " fig.updated_canvas_event();\n", 2896 | " fig.waiting = false;\n", 2897 | " return;\n", 2898 | " } else if (\n", 2899 | " typeof evt.data === 'string' &&\n", 2900 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 2901 | " ) {\n", 2902 | " fig.imageObj.src = evt.data;\n", 2903 | " fig.updated_canvas_event();\n", 2904 | " fig.waiting = false;\n", 2905 | " return;\n", 2906 | " }\n", 2907 | "\n", 2908 | " var msg = JSON.parse(evt.data);\n", 2909 | " var msg_type = msg['type'];\n", 2910 | "\n", 2911 | " // Call the \"handle_{type}\" callback, which takes\n", 2912 | " // the figure and JSON message as its only arguments.\n", 2913 | " try {\n", 2914 | " var callback = fig['handle_' + msg_type];\n", 2915 | " } catch (e) {\n", 2916 | " console.log(\n", 2917 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 2918 | " msg\n", 2919 | " );\n", 2920 | " return;\n", 2921 | " }\n", 2922 | "\n", 2923 | " if (callback) {\n", 2924 | " try {\n", 2925 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 2926 | " callback(fig, msg);\n", 2927 | " } catch (e) {\n", 2928 | " console.log(\n", 2929 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 2930 | " e,\n", 2931 | " e.stack,\n", 2932 | " msg\n", 2933 | " );\n", 2934 | " }\n", 2935 | " }\n", 2936 | " };\n", 2937 | "};\n", 2938 | "\n", 2939 | "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", 2940 | "mpl.findpos = function (e) {\n", 2941 | " //this section is from http://www.quirksmode.org/js/events_properties.html\n", 2942 | " var targ;\n", 2943 | " if (!e) {\n", 2944 | " e = window.event;\n", 2945 | " }\n", 2946 | " if (e.target) {\n", 2947 | " targ = e.target;\n", 2948 | " } else if (e.srcElement) {\n", 2949 | " targ = e.srcElement;\n", 2950 | " }\n", 2951 | " if (targ.nodeType === 3) {\n", 2952 | " // defeat Safari bug\n", 2953 | " targ = targ.parentNode;\n", 2954 | " }\n", 2955 | "\n", 2956 | " // pageX,Y are the mouse positions relative to the document\n", 2957 | " var boundingRect = targ.getBoundingClientRect();\n", 2958 | " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", 2959 | " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", 2960 | "\n", 2961 | " return { x: x, y: y };\n", 2962 | "};\n", 2963 | "\n", 2964 | "/*\n", 2965 | " * return a copy of an object with only non-object keys\n", 2966 | " * we need this to avoid circular references\n", 2967 | " * http://stackoverflow.com/a/24161582/3208463\n", 2968 | " */\n", 2969 | "function simpleKeys(original) {\n", 2970 | " return Object.keys(original).reduce(function (obj, key) {\n", 2971 | " if (typeof original[key] !== 'object') {\n", 2972 | " obj[key] = original[key];\n", 2973 | " }\n", 2974 | " return obj;\n", 2975 | " }, {});\n", 2976 | "}\n", 2977 | "\n", 2978 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 2979 | " var canvas_pos = mpl.findpos(event);\n", 2980 | "\n", 2981 | " if (name === 'button_press') {\n", 2982 | " this.canvas.focus();\n", 2983 | " this.canvas_div.focus();\n", 2984 | " }\n", 2985 | "\n", 2986 | " var x = canvas_pos.x * this.ratio;\n", 2987 | " var y = canvas_pos.y * this.ratio;\n", 2988 | "\n", 2989 | " this.send_message(name, {\n", 2990 | " x: x,\n", 2991 | " y: y,\n", 2992 | " button: event.button,\n", 2993 | " step: event.step,\n", 2994 | " guiEvent: simpleKeys(event),\n", 2995 | " });\n", 2996 | "\n", 2997 | " /* This prevents the web browser from automatically changing to\n", 2998 | " * the text insertion cursor when the button is pressed. We want\n", 2999 | " * to control all of the cursor setting manually through the\n", 3000 | " * 'cursor' event from matplotlib */\n", 3001 | " event.preventDefault();\n", 3002 | " return false;\n", 3003 | "};\n", 3004 | "\n", 3005 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 3006 | " // Handle any extra behaviour associated with a key event\n", 3007 | "};\n", 3008 | "\n", 3009 | "mpl.figure.prototype.key_event = function (event, name) {\n", 3010 | " // Prevent repeat events\n", 3011 | " if (name === 'key_press') {\n", 3012 | " if (event.key === this._key) {\n", 3013 | " return;\n", 3014 | " } else {\n", 3015 | " this._key = event.key;\n", 3016 | " }\n", 3017 | " }\n", 3018 | " if (name === 'key_release') {\n", 3019 | " this._key = null;\n", 3020 | " }\n", 3021 | "\n", 3022 | " var value = '';\n", 3023 | " if (event.ctrlKey && event.key !== 'Control') {\n", 3024 | " value += 'ctrl+';\n", 3025 | " }\n", 3026 | " else if (event.altKey && event.key !== 'Alt') {\n", 3027 | " value += 'alt+';\n", 3028 | " }\n", 3029 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 3030 | " value += 'shift+';\n", 3031 | " }\n", 3032 | "\n", 3033 | " value += 'k' + event.key;\n", 3034 | "\n", 3035 | " this._key_event_extra(event, name);\n", 3036 | "\n", 3037 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 3038 | " return false;\n", 3039 | "};\n", 3040 | "\n", 3041 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 3042 | " if (name === 'download') {\n", 3043 | " this.handle_save(this, null);\n", 3044 | " } else {\n", 3045 | " this.send_message('toolbar_button', { name: name });\n", 3046 | " }\n", 3047 | "};\n", 3048 | "\n", 3049 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 3050 | " this.message.textContent = tooltip;\n", 3051 | "};\n", 3052 | "\n", 3053 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 3054 | "// prettier-ignore\n", 3055 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 3056 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", 3057 | "\n", 3058 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", 3059 | "\n", 3060 | "mpl.default_extension = \"png\";/* global mpl */\n", 3061 | "\n", 3062 | "var comm_websocket_adapter = function (comm) {\n", 3063 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 3064 | " // object with the appropriate methods. Currently this is a non binary\n", 3065 | " // socket, so there is still some room for performance tuning.\n", 3066 | " var ws = {};\n", 3067 | "\n", 3068 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 3069 | " ws.readyState = comm.kernel.ws.readyState;\n", 3070 | " function updateReadyState(_event) {\n", 3071 | " if (comm.kernel.ws) {\n", 3072 | " ws.readyState = comm.kernel.ws.readyState;\n", 3073 | " } else {\n", 3074 | " ws.readyState = 3; // Closed state.\n", 3075 | " }\n", 3076 | " }\n", 3077 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 3078 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 3079 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 3080 | "\n", 3081 | " ws.close = function () {\n", 3082 | " comm.close();\n", 3083 | " };\n", 3084 | " ws.send = function (m) {\n", 3085 | " //console.log('sending', m);\n", 3086 | " comm.send(m);\n", 3087 | " };\n", 3088 | " // Register the callback with on_msg.\n", 3089 | " comm.on_msg(function (msg) {\n", 3090 | " //console.log('receiving', msg['content']['data'], msg);\n", 3091 | " var data = msg['content']['data'];\n", 3092 | " if (data['blob'] !== undefined) {\n", 3093 | " data = {\n", 3094 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 3095 | " };\n", 3096 | " }\n", 3097 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 3098 | " ws.onmessage(data);\n", 3099 | " });\n", 3100 | " return ws;\n", 3101 | "};\n", 3102 | "\n", 3103 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 3104 | " // This is the function which gets called when the mpl process\n", 3105 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 3106 | "\n", 3107 | " var id = msg.content.data.id;\n", 3108 | " // Get hold of the div created by the display call when the Comm\n", 3109 | " // socket was opened in Python.\n", 3110 | " var element = document.getElementById(id);\n", 3111 | " var ws_proxy = comm_websocket_adapter(comm);\n", 3112 | "\n", 3113 | " function ondownload(figure, _format) {\n", 3114 | " window.open(figure.canvas.toDataURL());\n", 3115 | " }\n", 3116 | "\n", 3117 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 3118 | "\n", 3119 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 3120 | " // web socket which is closed, not our websocket->open comm proxy.\n", 3121 | " ws_proxy.onopen();\n", 3122 | "\n", 3123 | " fig.parent_element = element;\n", 3124 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 3125 | " if (!fig.cell_info) {\n", 3126 | " console.error('Failed to find cell for figure', id, fig);\n", 3127 | " return;\n", 3128 | " }\n", 3129 | " fig.cell_info[0].output_area.element.on(\n", 3130 | " 'cleared',\n", 3131 | " { fig: fig },\n", 3132 | " fig._remove_fig_handler\n", 3133 | " );\n", 3134 | "};\n", 3135 | "\n", 3136 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 3137 | " var width = fig.canvas.width / fig.ratio;\n", 3138 | " fig.cell_info[0].output_area.element.off(\n", 3139 | " 'cleared',\n", 3140 | " fig._remove_fig_handler\n", 3141 | " );\n", 3142 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 3143 | "\n", 3144 | " // Update the output cell to use the data from the current canvas.\n", 3145 | " fig.push_to_output();\n", 3146 | " var dataURL = fig.canvas.toDataURL();\n", 3147 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 3148 | " // the notebook keyboard shortcuts fail.\n", 3149 | " IPython.keyboard_manager.enable();\n", 3150 | " fig.parent_element.innerHTML =\n", 3151 | " '';\n", 3152 | " fig.close_ws(fig, msg);\n", 3153 | "};\n", 3154 | "\n", 3155 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 3156 | " fig.send_message('closing', msg);\n", 3157 | " // fig.ws.close()\n", 3158 | "};\n", 3159 | "\n", 3160 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 3161 | " // Turn the data on the canvas into data in the output cell.\n", 3162 | " var width = this.canvas.width / this.ratio;\n", 3163 | " var dataURL = this.canvas.toDataURL();\n", 3164 | " this.cell_info[1]['text/html'] =\n", 3165 | " '';\n", 3166 | "};\n", 3167 | "\n", 3168 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 3169 | " // Tell IPython that the notebook contents must change.\n", 3170 | " IPython.notebook.set_dirty(true);\n", 3171 | " this.send_message('ack', {});\n", 3172 | " var fig = this;\n", 3173 | " // Wait a second, then push the new image to the DOM so\n", 3174 | " // that it is saved nicely (might be nice to debounce this).\n", 3175 | " setTimeout(function () {\n", 3176 | " fig.push_to_output();\n", 3177 | " }, 1000);\n", 3178 | "};\n", 3179 | "\n", 3180 | "mpl.figure.prototype._init_toolbar = function () {\n", 3181 | " var fig = this;\n", 3182 | "\n", 3183 | " var toolbar = document.createElement('div');\n", 3184 | " toolbar.classList = 'btn-toolbar';\n", 3185 | " this.root.appendChild(toolbar);\n", 3186 | "\n", 3187 | " function on_click_closure(name) {\n", 3188 | " return function (_event) {\n", 3189 | " return fig.toolbar_button_onclick(name);\n", 3190 | " };\n", 3191 | " }\n", 3192 | "\n", 3193 | " function on_mouseover_closure(tooltip) {\n", 3194 | " return function (event) {\n", 3195 | " if (!event.currentTarget.disabled) {\n", 3196 | " return fig.toolbar_button_onmouseover(tooltip);\n", 3197 | " }\n", 3198 | " };\n", 3199 | " }\n", 3200 | "\n", 3201 | " fig.buttons = {};\n", 3202 | " var buttonGroup = document.createElement('div');\n", 3203 | " buttonGroup.classList = 'btn-group';\n", 3204 | " var button;\n", 3205 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 3206 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 3207 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 3208 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 3209 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 3210 | "\n", 3211 | " if (!name) {\n", 3212 | " /* Instead of a spacer, we start a new button group. */\n", 3213 | " if (buttonGroup.hasChildNodes()) {\n", 3214 | " toolbar.appendChild(buttonGroup);\n", 3215 | " }\n", 3216 | " buttonGroup = document.createElement('div');\n", 3217 | " buttonGroup.classList = 'btn-group';\n", 3218 | " continue;\n", 3219 | " }\n", 3220 | "\n", 3221 | " button = fig.buttons[name] = document.createElement('button');\n", 3222 | " button.classList = 'btn btn-default';\n", 3223 | " button.href = '#';\n", 3224 | " button.title = name;\n", 3225 | " button.innerHTML = '';\n", 3226 | " button.addEventListener('click', on_click_closure(method_name));\n", 3227 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 3228 | " buttonGroup.appendChild(button);\n", 3229 | " }\n", 3230 | "\n", 3231 | " if (buttonGroup.hasChildNodes()) {\n", 3232 | " toolbar.appendChild(buttonGroup);\n", 3233 | " }\n", 3234 | "\n", 3235 | " // Add the status bar.\n", 3236 | " var status_bar = document.createElement('span');\n", 3237 | " status_bar.classList = 'mpl-message pull-right';\n", 3238 | " toolbar.appendChild(status_bar);\n", 3239 | " this.message = status_bar;\n", 3240 | "\n", 3241 | " // Add the close button to the window.\n", 3242 | " var buttongrp = document.createElement('div');\n", 3243 | " buttongrp.classList = 'btn-group inline pull-right';\n", 3244 | " button = document.createElement('button');\n", 3245 | " button.classList = 'btn btn-mini btn-primary';\n", 3246 | " button.href = '#';\n", 3247 | " button.title = 'Stop Interaction';\n", 3248 | " button.innerHTML = '';\n", 3249 | " button.addEventListener('click', function (_evt) {\n", 3250 | " fig.handle_close(fig, {});\n", 3251 | " });\n", 3252 | " button.addEventListener(\n", 3253 | " 'mouseover',\n", 3254 | " on_mouseover_closure('Stop Interaction')\n", 3255 | " );\n", 3256 | " buttongrp.appendChild(button);\n", 3257 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 3258 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 3259 | "};\n", 3260 | "\n", 3261 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 3262 | " var fig = event.data.fig;\n", 3263 | " if (event.target !== this) {\n", 3264 | " // Ignore bubbled events from children.\n", 3265 | " return;\n", 3266 | " }\n", 3267 | " fig.close_ws(fig, {});\n", 3268 | "};\n", 3269 | "\n", 3270 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 3271 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 3272 | "};\n", 3273 | "\n", 3274 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 3275 | " // this is important to make the div 'focusable\n", 3276 | " el.setAttribute('tabindex', 0);\n", 3277 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 3278 | " // off when our div gets focus\n", 3279 | "\n", 3280 | " // location in version 3\n", 3281 | " if (IPython.notebook.keyboard_manager) {\n", 3282 | " IPython.notebook.keyboard_manager.register_events(el);\n", 3283 | " } else {\n", 3284 | " // location in version 2\n", 3285 | " IPython.keyboard_manager.register_events(el);\n", 3286 | " }\n", 3287 | "};\n", 3288 | "\n", 3289 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 3290 | " var manager = IPython.notebook.keyboard_manager;\n", 3291 | " if (!manager) {\n", 3292 | " manager = IPython.keyboard_manager;\n", 3293 | " }\n", 3294 | "\n", 3295 | " // Check for shift+enter\n", 3296 | " if (event.shiftKey && event.which === 13) {\n", 3297 | " this.canvas_div.blur();\n", 3298 | " // select the cell after this one\n", 3299 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 3300 | " IPython.notebook.select(index + 1);\n", 3301 | " }\n", 3302 | "};\n", 3303 | "\n", 3304 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 3305 | " fig.ondownload(fig, null);\n", 3306 | "};\n", 3307 | "\n", 3308 | "mpl.find_output_cell = function (html_output) {\n", 3309 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 3310 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 3311 | " // IPython event is triggered only after the cells have been serialised, which for\n", 3312 | " // our purposes (turning an active figure into a static one), is too late.\n", 3313 | " var cells = IPython.notebook.get_cells();\n", 3314 | " var ncells = cells.length;\n", 3315 | " for (var i = 0; i < ncells; i++) {\n", 3316 | " var cell = cells[i];\n", 3317 | " if (cell.cell_type === 'code') {\n", 3318 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 3319 | " var data = cell.output_area.outputs[j];\n", 3320 | " if (data.data) {\n", 3321 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 3322 | " data = data.data;\n", 3323 | " }\n", 3324 | " if (data['text/html'] === html_output) {\n", 3325 | " return [cell, data, j];\n", 3326 | " }\n", 3327 | " }\n", 3328 | " }\n", 3329 | " }\n", 3330 | "};\n", 3331 | "\n", 3332 | "// Register the function which deals with the matplotlib target/channel.\n", 3333 | "// The kernel may be null if the page has been refreshed.\n", 3334 | "if (IPython.notebook.kernel !== null) {\n", 3335 | " IPython.notebook.kernel.comm_manager.register_target(\n", 3336 | " 'matplotlib',\n", 3337 | " mpl.mpl_figure_comm\n", 3338 | " );\n", 3339 | "}\n" 3340 | ], 3341 | "text/plain": [ 3342 | "" 3343 | ] 3344 | }, 3345 | "metadata": {}, 3346 | "output_type": "display_data" 3347 | }, 3348 | { 3349 | "data": { 3350 | "text/html": [ 3351 | "" 3352 | ], 3353 | "text/plain": [ 3354 | "" 3355 | ] 3356 | }, 3357 | "metadata": {}, 3358 | "output_type": "display_data" 3359 | } 3360 | ], 3361 | "source": [ 3362 | "fig,ax=plt.subplots(1,1,figsize=(6,6))\n", 3363 | "plt.xlim(0,L)\n", 3364 | "plt.ylim(0,H)\n", 3365 | "x=np.linspace(0,L,Nx)\n", 3366 | "y=np.linspace(0,H,Ny)\n", 3367 | "xx,yy=np.meshgrid(x,y)\n", 3368 | "\n", 3369 | "ani=FuncAnimation(fig,animate,count,interval=2000, blit=True)\n", 3370 | "plt.show()" 3371 | ] 3372 | }, 3373 | { 3374 | "cell_type": "code", 3375 | "execution_count": null, 3376 | "id": "884e9798", 3377 | "metadata": {}, 3378 | "outputs": [], 3379 | "source": [ 3380 | "print('Help')" 3381 | ] 3382 | } 3383 | ], 3384 | "metadata": { 3385 | "kernelspec": { 3386 | "display_name": "Python 3 (ipykernel)", 3387 | "language": "python", 3388 | "name": "python3" 3389 | }, 3390 | "language_info": { 3391 | "codemirror_mode": { 3392 | "name": "ipython", 3393 | "version": 3 3394 | }, 3395 | "file_extension": ".py", 3396 | "mimetype": "text/x-python", 3397 | "name": "python", 3398 | "nbconvert_exporter": "python", 3399 | "pygments_lexer": "ipython3", 3400 | "version": "3.9.7" 3401 | } 3402 | }, 3403 | "nbformat": 4, 3404 | "nbformat_minor": 5 3405 | } 3406 | -------------------------------------------------------------------------------- /Combined backward facing step.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "3ce46a23", 7 | "metadata": {}, 8 | "outputs": [], 9 | "source": [ 10 | "import numpy as np\n", 11 | "import matplotlib.pyplot as plt\n", 12 | "from matplotlib import cm\n", 13 | "import matplotlib.patches as patches\n", 14 | "from matplotlib.animation import FuncAnimation \n", 15 | "import time\n", 16 | "import scipy as sp\n", 17 | "import pyamg\n", 18 | "from tqdm.notebook import tqdm" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 32, 24 | "id": "a64e64eb", 25 | "metadata": {}, 26 | "outputs": [], 27 | "source": [ 28 | "# A brief rundown on whats going on here. The whole code is split into 3 parts; the solvers, initialization and postprocessing\n", 29 | "\n", 30 | "# Solvers:\n", 31 | "# upwind1: 1st order upwind\n", 32 | "# quick3: 3rd order deferred QUICK\n", 33 | "# MUSCL3: 3rd order MUSCL with minmod limiter\n", 34 | "# mom: discretise NS eqns and solves them.\n", 35 | "# face: calculate face velocities using Rhie & Chow interpolation\n", 36 | "# cont: set ups and solve the continuity eqn to get pressure correction\n", 37 | "# correct: correct the nodal velocities and pressure. (Would be nice if someone explain to me why face velocities need to\n", 38 | "# be corrected, even though here it is not done and this code is still\n", 39 | "# able to get decently accurate results)\n", 40 | "# Initialization:\n", 41 | "# Here you just set up the domain length, mesh sizes, relaxation factors, inlet velocities etc \n", 42 | " \n", 43 | "# Solution:\n", 44 | "# Just solves the NS eqns, you can finetune the residual requirement\n", 45 | " \n", 46 | "# Post-Processing:\n", 47 | "# Colorful graphs or animations (reminder to run the %matplotlib notebook cell if want to play animations \n", 48 | "# for transient flow)\n", 49 | "\n", 50 | " \n", 51 | "# A bit more details on the mom solver:\n", 52 | "# Variables and their meanings:\n", 53 | "# b: aspect ratio, dy/dx (even though its there, the code can only run on square cells, has not been a major enough\n", 54 | "# problem for me to find the error and solve it)\n", 55 | " \n", 56 | "# a1: eastern face flux from previous iteration\n", 57 | "# a2: western face flux from previous iteration\n", 58 | "# e1: northern face flux from previous iteration\n", 59 | "# e2: southern face flux from previous iteration\n", 60 | "# Nx_in: index 'length' of the blockage\n", 61 | "# Ny_in: index 'height' of the blockage\n", 62 | " \n", 63 | "# Discretisation methods:\n", 64 | " \n", 65 | "# Diffusion: 4th order central difference\n", 66 | "# Convection: Pick and choose (upwind1,quick3 and MUSCL3 is here, you can just find and replace the functions if you\n", 67 | "# want to convert it)\n", 68 | "# Boundary cells: 3rd order forward/backward differencing\n", 69 | " \n", 70 | "# You will notice some lines of code that look like this (UWW=4*UW-6*UP+4*UE-UEE). They are just cubic extrapolations\n", 71 | "# to create a ghost cell. As higher order schemes have a larger stencil, the cells next to boundary cells will need ghost\n", 72 | "# cells to if you want to continue using 4th order CD for example. \n", 73 | " \n", 74 | "# Moreover, you will notice I updated the source terms for such cells. This is due to the fact those ghost cells are\n", 75 | "# just ficticious and solving them implicitly will result in index error. \n", 76 | " \n", 77 | "# Time: Being lazy here so i just put conditional statements, that goes from 1st order forward euler to 4th order \n", 78 | "# Adams-Bashforth\n", 79 | " \n", 80 | " \n", 81 | "# I hope this is enough to roughly understand what is going on. This code is by no means efficient but it gets the job\n", 82 | "# done decently well.\n", 83 | " " 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 24, 89 | "id": "6e1de091", 90 | "metadata": {}, 91 | "outputs": [], 92 | "source": [ 93 | "%matplotlib notebook" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 33, 99 | "id": "9b67baf9", 100 | "metadata": {}, 101 | "outputs": [], 102 | "source": [ 103 | "def upwind1(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,e2,UE,UW,UEE,UWW,UEEE,UWWW,UP,VN,VS,VNN,VSS,VNNN,VSSS,VP):\n", 104 | " global b\n", 105 | " \n", 106 | " ue=a1\n", 107 | " uw=a2\n", 108 | " vn=e1\n", 109 | " vs=e2\n", 110 | " \n", 111 | " a1b=a1*b\n", 112 | " a2b=a2*b\n", 113 | "\n", 114 | " AP+=a1b*(abs(ue)+ue)\n", 115 | " AE+=a1b*(abs(ue)-ue)\n", 116 | "\n", 117 | " AP-=a2b*(abs(uw)-uw)\n", 118 | " AW-=a2b*(abs(uw)+uw)\n", 119 | "\n", 120 | " AP+=e1*(abs(vn)+vn)\n", 121 | " AN+=e1*(abs(vn)-vn)\n", 122 | "\n", 123 | " AP-=e2*(abs(vs)-vs)\n", 124 | " AS-=e2*(abs(vs)+vs)\n", 125 | " \n", 126 | " return AE,AW,AN,AS,AP,uBP,vBP" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 34, 132 | "id": "329afcaa", 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [ 136 | "def quick3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 137 | " global b\n", 138 | " global defer\n", 139 | " \n", 140 | " uBP0=uBP\n", 141 | " vBP0=vBP\n", 142 | " ue=a1\n", 143 | " uw=a2\n", 144 | " vn=e1\n", 145 | " vs=e2\n", 146 | " \n", 147 | " if ue!=0:\n", 148 | " if ue>0:\n", 149 | " AP+=2*a1*b\n", 150 | " uBP-=a1*b*(3*UE-2*UP-UW)/4\n", 151 | " vBP-=a1*b*(3*UE-2*UP-UW)/4\n", 152 | " else:\n", 153 | " AE+=2*a1*b\n", 154 | " uBP-=a1*b*(3*UP-2*UE-UEE)/4\n", 155 | " vBP-=a1*b*(3*UP-2*UE-UEE)/4\n", 156 | " if uw!=0:\n", 157 | " if uw<0:\n", 158 | " AP-=2*a2*b\n", 159 | " uBP+=a2*b*(3*UW-2*UP-UE)/4\n", 160 | " vBP+=a2*b*(3*UW-2*UP-UE)/4\n", 161 | " else:\n", 162 | " AW-=2*a2*b\n", 163 | " uBP+=a2*b*(3*UP-2*UW-UWW)/4\n", 164 | " vBP+=a2*b*(3*UP-2*UW-UWW)/4\n", 165 | " if vn!=0:\n", 166 | " if vn>0:\n", 167 | " AP+=2*e1\n", 168 | " uBP-=e1*(3*VN-2*VP-VS)/4\n", 169 | " vBP-=e1*(3*VN-2*VP-VS)/4\n", 170 | " else:\n", 171 | " AN+=2*e1\n", 172 | " uBP-=e1*(3*VP-2*VN-VNN)/4\n", 173 | " vBP-=e1*(3*VP-2*VN-VNN)/4\n", 174 | " if vs!=0:\n", 175 | " if vs<0:\n", 176 | " AP-=2*e2\n", 177 | " uBP+=e2*(3*VS-2*VP-VN)/4\n", 178 | " vBP+=e2*(3*VS-2*VP-VN)/4\n", 179 | " else:\n", 180 | " AS-=2*e2\n", 181 | " uBP+=e2*(3*VP-2*VS-VSS)/4\n", 182 | " vBP+=e2*(3*VP-2*VS-VSS)/4\n", 183 | " \n", 184 | " if defer==False:\n", 185 | " return AE,AW,AN,AS,AP,uBP0,vBP0\n", 186 | " else:\n", 187 | " return AE,AW,AN,AS,AP,uBP,vBP" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 35, 193 | "id": "e6a5b8d8", 194 | "metadata": {}, 195 | "outputs": [], 196 | "source": [ 197 | "def MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP):\n", 198 | " global b\n", 199 | " global iter_count\n", 200 | " \n", 201 | " uBP0=uBP\n", 202 | " vBP0=vBP\n", 203 | " ue=a1\n", 204 | " uw=a2\n", 205 | " vn=e1\n", 206 | " vs=e2\n", 207 | " \n", 208 | " if ue!=0:\n", 209 | " if ue>0:\n", 210 | " AP+=2*a1*b\n", 211 | " \n", 212 | " r=(UP-UW)/(UE-UP)\n", 213 | " limit=np.min([r,1])\n", 214 | " flux=(UP-UW)/6+(UE-UP)/3\n", 215 | "\n", 216 | " uBP-=a1*b*limit*flux\n", 217 | " vBP-=a1*b*limit*flux\n", 218 | " else:\n", 219 | " AE+=2*a1*b\n", 220 | "\n", 221 | " r=(UE-UEE)/(UP-UE)\n", 222 | " limit=np.min([r,1])\n", 223 | " flux=(UE-UEE)/6+(UP-UE)/3\n", 224 | "\n", 225 | " uBP-=a1*b*limit*flux\n", 226 | " vBP-=a1*b*limit*flux\n", 227 | " if uw!=0:\n", 228 | " if uw<0:\n", 229 | " AP-=2*a2*b\n", 230 | "\n", 231 | " r=(UP-UE)/(UW-UP)\n", 232 | " limit=np.min([r,1])\n", 233 | " flux=(UP-UE)/6+(UW-UP)/3\n", 234 | "\n", 235 | " uBP+=a2*b*limit*flux\n", 236 | " vBP+=a2*b*limit*flux\n", 237 | " else:\n", 238 | " AW-=2*a2*b\n", 239 | "\n", 240 | " r=(UW-UWW)/(UP-UW)\n", 241 | " limit=np.min([r,1])\n", 242 | " flux=(UW-UWW)/6+(UP-UW)/3\n", 243 | "\n", 244 | " uBP+=a2*b*limit*flux\n", 245 | " vBP+=a2*b*limit*flux\n", 246 | " if vn!=0:\n", 247 | " if vn>0:\n", 248 | " AP+=2*e1\n", 249 | "\n", 250 | " r=(VP-VS)/(VN-VP)\n", 251 | " limit=np.min([r,1])\n", 252 | " flux=(VP-VS)/6+(VN-VP)/3\n", 253 | "\n", 254 | " vBP-=e1*limit*flux\n", 255 | " else:\n", 256 | " AN+=2*e1\n", 257 | "\n", 258 | " r=(VN-VNN)/(VP-VN)\n", 259 | " limit=np.min([r,1])\n", 260 | " flux=(VN-VNN)/6+(VP-VN)/3\n", 261 | " \n", 262 | " uBP-=e1*limit*flux\n", 263 | " vBP-=e1*limit*flux\n", 264 | " if vs!=0:\n", 265 | " if vs<0:\n", 266 | " AP-=2*e2\n", 267 | "\n", 268 | " r=(VP-VN)/(VS-VP)\n", 269 | " limit=np.min([r,1])\n", 270 | " flux=(VP-VN)/6+(VS-VP)/3\n", 271 | " \n", 272 | " uBP-=e2*limit*flux\n", 273 | " vBP-=e2*limit*flux\n", 274 | " else:\n", 275 | " AS-=2*e2\n", 276 | "\n", 277 | " r=(VS-VSS)/(VP-VS)\n", 278 | " limit=np.min([r,1])\n", 279 | " flux=(VS-VSS)/6+(VP-VS)/3\n", 280 | " \n", 281 | " uBP-=e2*limit*flux\n", 282 | " vBP-=e2*limit*flux\n", 283 | "\n", 284 | " if iter_count>1:\n", 285 | " return AE,AW,AN,AS,AP,uBP,vBP\n", 286 | " else:\n", 287 | " return AE,AW,AN,AS,AP,uBP0,vBP0" 288 | ] 289 | }, 290 | { 291 | "cell_type": "code", 292 | "execution_count": 52, 293 | "id": "b7a9e6ed", 294 | "metadata": {}, 295 | "outputs": [], 296 | "source": [ 297 | "def mom(Nx,Ny,U,V,P,U0,V0,time_step,UAB,VAB,av):\n", 298 | " global dx\n", 299 | " global dy\n", 300 | " global dt\n", 301 | " global v\n", 302 | " global u_wall\n", 303 | " global transient\n", 304 | " \n", 305 | " N=Nx*Ny\n", 306 | " \n", 307 | " A=np.zeros([N,N])\n", 308 | " uB=np.zeros([N,1])\n", 309 | " vB=np.zeros([N,1])\n", 310 | " DX=np.zeros([N,1])\n", 311 | " DY=np.zeros([N,1])\n", 312 | " \n", 313 | " b=dy/dx\n", 314 | " y=v/dy\n", 315 | " x=v*b/dx\n", 316 | " k=dt/dy\n", 317 | " alpha=(1-av)/av\n", 318 | " \n", 319 | " UEE=0\n", 320 | " UEEE=0\n", 321 | " UWW=0\n", 322 | " UWWW=0\n", 323 | " VNN=0\n", 324 | " VNNN=0\n", 325 | " VSS=0\n", 326 | " VSSS=0\n", 327 | " \n", 328 | " #For upper interior cells\n", 329 | " for j in range(Ny_in+1,Ny-1):\n", 330 | " for i in range(1,Nx_in):\n", 331 | " ij=i+Nx*j\n", 332 | " \n", 333 | " uBP=0\n", 334 | " vBP=0\n", 335 | " \n", 336 | " if i==1: \n", 337 | " UE=U[j][i+1]\n", 338 | " UW=U[j][i-1]\n", 339 | " UP=U[j][i]\n", 340 | " UEE=U[j][i+2]\n", 341 | " UWW=4*UW-6*UP+4*UE-UEE\n", 342 | " UWWWW=4*UWW-6*UW+4*UP\n", 343 | " \n", 344 | " AWW=(1/12)*x*UWW\n", 345 | " \n", 346 | " uBP-=AWW\n", 347 | " vBP-=AWW\n", 348 | " \n", 349 | " else:\n", 350 | " UE=U[j][i+1]\n", 351 | " UW=U[j][i-1]\n", 352 | " UP=U[j][i]\n", 353 | " UEE=U[j][i+2]\n", 354 | " UWW=U[j][i-2]\n", 355 | " \n", 356 | " AEE=(1/12)*x\n", 357 | " AWW=(1/12)*x\n", 358 | " \n", 359 | " A[ij][ij+2]=AEE\n", 360 | " A[ij][ij-2]=AWW\n", 361 | " \n", 362 | " if j==Ny_in+1:\n", 363 | " VS=V[j-1][i]\n", 364 | " VP=V[j][i]\n", 365 | " VN=V[j+1][i]\n", 366 | " VNN=V[j+2][i]\n", 367 | " VSS=4*VS-6*VP+4*VN-VNN\n", 368 | " \n", 369 | " ASS=(1/12)*y*VSS\n", 370 | " \n", 371 | " uBP-=ASS\n", 372 | " vBP-=ASS\n", 373 | " \n", 374 | " elif j==Ny-2:\n", 375 | " VN=V[j+1][i]\n", 376 | " VP=V[j][i]\n", 377 | " VS=V[j-1][i]\n", 378 | " VSS=V[j-2][i]\n", 379 | " VNN=4*VN-6*VP+4*VS-VSS\n", 380 | " \n", 381 | " ANN=(1/12)*y*VNN\n", 382 | " \n", 383 | " uBP-=ANN\n", 384 | " vBP-=ASS\n", 385 | " \n", 386 | " else:\n", 387 | " VNN=V[j+2][i]\n", 388 | " VN=V[j+1][i]\n", 389 | " VP=V[j][i]\n", 390 | " VS=V[j-1][i]\n", 391 | " VSS=V[j-2][i]\n", 392 | " \n", 393 | " ANN=(1/12)*y\n", 394 | " ASS=(1/12)*y\n", 395 | " \n", 396 | " A[ij][ij+2*Nx]=ANN\n", 397 | " A[ij][ij-2*Nx]=ASS\n", 398 | " \n", 399 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 400 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 401 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 402 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 403 | "\n", 404 | " AE=-(7/3)*x\n", 405 | " AW=-(7/3)*x\n", 406 | " AN=-(7/3)*y\n", 407 | " AS=-(7/3)*y\n", 408 | " AP=4.5*x+4.5*y\n", 409 | " AEE=(1/12)*x\n", 410 | " AWW=(1/12)*x\n", 411 | " ANN=(1/12)*y\n", 412 | " ASS=(1/12)*y\n", 413 | " \n", 414 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 415 | " \n", 416 | " uBP+=b*(P[j][i-1]-P[j][i+1])\n", 417 | " vBP+=(P[j-1][i]-P[j+1][i])\n", 418 | " \n", 419 | " Dx=dy/AP\n", 420 | " Dy=dx/AP\n", 421 | " \n", 422 | " A[ij][ij-1]=AW\n", 423 | " A[ij][ij]=AP/av\n", 424 | " A[ij][ij+1]=AE\n", 425 | " A[ij][ij+Nx]=AN\n", 426 | " A[ij][ij-Nx]=AS\n", 427 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 428 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 429 | " DX[ij]=Dx\n", 430 | " DY[ij]=Dy\n", 431 | " \n", 432 | " #for top wall\n", 433 | " for i in range(1,Nx-1):\n", 434 | " j=Ny-1\n", 435 | " ij=i+j*Nx\n", 436 | " \n", 437 | " AEE=0\n", 438 | " AWW=0\n", 439 | " ANN=0\n", 440 | " ASS=0\n", 441 | " uBP=0\n", 442 | " vBP=0\n", 443 | " \n", 444 | " if i==1:\n", 445 | " UW=U[j][i-1]\n", 446 | " UP=U[j][i]\n", 447 | " UE=U[j][i+1]\n", 448 | " UEE=U[j][i+2]\n", 449 | " UWW=4*UW-6*UP+4*UE-UEE\n", 450 | " \n", 451 | " AWW=(1/12)*x*UWW\n", 452 | " uBP-=AWW\n", 453 | " vBP-=AWW\n", 454 | " \n", 455 | " elif i==Nx-2:\n", 456 | " UWW=U[j][i-2]\n", 457 | " UW=U[j][i-1]\n", 458 | " UP=U[j][i]\n", 459 | " UE=U[j][i+1]\n", 460 | " UEE=4*UE-6*VP+4*UW-UWW\n", 461 | " \n", 462 | " AEE=(1/12)*x*UEE\n", 463 | " \n", 464 | " uBP-=AEE\n", 465 | " vBP-=AEE\n", 466 | " \n", 467 | " else:\n", 468 | " UWW=U[j][i-2]\n", 469 | " UW=U[j][i-1]\n", 470 | " UP=U[j][i]\n", 471 | " UE=U[j][i+1]\n", 472 | " UEE=U[j][i+2]\n", 473 | " \n", 474 | " AEE=(1/12)*x\n", 475 | " AWW=(1/12)*x\n", 476 | " \n", 477 | " A[ij][ij+2]=AEE\n", 478 | " A[ij][ij-2]=AWW\n", 479 | " \n", 480 | " VP=V[j][i]\n", 481 | " VS=V[j-1][i]\n", 482 | " VSS=V[j-2][i]\n", 483 | " VSSS=V[j-3][i]\n", 484 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 485 | " \n", 486 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 487 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 488 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 489 | "\n", 490 | " AE=-(7/3)*x\n", 491 | " AW=-(7/3)*x\n", 492 | " AN=-(7/3)*y\n", 493 | " AS=-4*y\n", 494 | " AP=9.75*y+4.25*x\n", 495 | " ASS=(23/60)*y\n", 496 | " \n", 497 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,0,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 498 | "\n", 499 | " uBP+=b*(P[j][i-1]-P[j][i+1])\n", 500 | " vBP+=(P[j-1][i]-P[j][i])\n", 501 | "\n", 502 | " Dx=dy/AP\n", 503 | " Dy=dx/AP\n", 504 | " \n", 505 | " A[ij][ij-1]=AW\n", 506 | " A[ij][ij]=AP/av\n", 507 | " A[ij][ij+1]=AE\n", 508 | " A[ij][ij-Nx]=AS\n", 509 | " A[ij][ij-2*Nx]=ASS\n", 510 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 511 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 512 | " DX[ij]=Dx\n", 513 | " DY[ij]=Dy\n", 514 | " \n", 515 | " #test[j][i]=2\n", 516 | " \n", 517 | " #for inlet:\n", 518 | " for j in range(Ny_in+1,Ny-1):\n", 519 | " i=0\n", 520 | " ij=i+Nx*j\n", 521 | " \n", 522 | " uBP=0\n", 523 | " vBP=0\n", 524 | " \n", 525 | " if j==Ny_in+1:\n", 526 | " VNN=V[j+2][i]\n", 527 | " VN=V[j+1][i]\n", 528 | " VP=V[j][i]\n", 529 | " VS=V[j-1][i]\n", 530 | " VSS=4*VS-6*VP+4*VN-VNN\n", 531 | " \n", 532 | " ASS=(1/12)*y*VSS\n", 533 | " \n", 534 | " uBP-=ASS\n", 535 | " vBP-=ASS\n", 536 | " \n", 537 | " elif j==Ny-2:\n", 538 | " VSS=V[j-2][i]\n", 539 | " VN=V[j+1][i]\n", 540 | " VP=V[j][i]\n", 541 | " VS=V[j-1][i]\n", 542 | " VNN=4*VN-6*VP+4*VS-VSS\n", 543 | " \n", 544 | " ANN=(1/12)*y*VNN\n", 545 | " \n", 546 | " uBP-=ANN\n", 547 | " vBP-=ANN\n", 548 | " \n", 549 | " else:\n", 550 | " VNN=V[j+2][i]\n", 551 | " VN=V[j+1][i]\n", 552 | " VP=V[j][i]\n", 553 | " VS=V[j-1][i]\n", 554 | " VSS=V[j-2][i]\n", 555 | " \n", 556 | " ASS=(1/12)*y\n", 557 | " ANN=(1/12)*y\n", 558 | " \n", 559 | " A[ij][ij+2]=ANN\n", 560 | " A[ij][ij-2]=ASS\n", 561 | " \n", 562 | " UEE=U[j][i+2] \n", 563 | " UE=U[j][i+1]\n", 564 | " UP=U[j][i]\n", 565 | " UW=4*UP-6*UP+4*UE-UEE\n", 566 | " \n", 567 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 568 | " e1=(27*VN+27*VP-3*VS-3*VNN)/48\n", 569 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 570 | " \n", 571 | " AE=-4*x\n", 572 | " AN=-(7/3)*y\n", 573 | " AS=-(7/3)*x\n", 574 | " AP=9.75*x+4.25*y\n", 575 | " AEE=(23/60)*y\n", 576 | " \n", 577 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,0,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 578 | " \n", 579 | " uBP+=b*(P[j][i]-P[j][i+1])+(2*b*u_inlet**2)+(92/15)*(x*u_inlet)\n", 580 | " vBP+=(P[j-1][i]-P[j+1][i])\n", 581 | " \n", 582 | " Dx=dy/AP\n", 583 | " Dy=dx/AP\n", 584 | "\n", 585 | " A[ij][ij]=AP/av\n", 586 | " A[ij][ij+1]=AE\n", 587 | " A[ij][ij+2]=AEE\n", 588 | " A[ij][ij-Nx]=AS\n", 589 | " A[ij][ij+Nx]=AN\n", 590 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 591 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 592 | " DX[ij]=Dx\n", 593 | " DY[ij]=Dy\n", 594 | " \n", 595 | " #test[j][i]=3\n", 596 | " \n", 597 | " #top left cell\n", 598 | " i=0\n", 599 | " j=Ny-1\n", 600 | " ij=i+Nx*j\n", 601 | " \n", 602 | " uBP=0\n", 603 | " vBP=0\n", 604 | " \n", 605 | " UP=U[j][i]\n", 606 | " UE=U[j][i+1]\n", 607 | " UEE=U[j][i+2]\n", 608 | " UEEE=U[j][i+3]\n", 609 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 610 | " \n", 611 | " VP=V[j][i]\n", 612 | " VS=V[j-1][i]\n", 613 | " VSS=V[j-2][i]\n", 614 | " VSSS=V[j-3][i]\n", 615 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 616 | " \n", 617 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 618 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 619 | " \n", 620 | " AE=-4*x\n", 621 | " AEE=(23/60)*x\n", 622 | " AS=-4*y\n", 623 | " ASS=(23/60)*y\n", 624 | " AP=9.75*x+9.75*y\n", 625 | " \n", 626 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,0,0,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 627 | " \n", 628 | " uBP+=b*(P[j][i]-P[j][i+1])+(2*b*u_inlet**2)+(92/15)*(x*u_inlet)\n", 629 | " vBP+=(P[j-1][i]-P[j][i])\n", 630 | " \n", 631 | " Dx=dy/AP\n", 632 | " Dy=dx/AP\n", 633 | " \n", 634 | " A[ij][ij]=AP/av\n", 635 | " A[ij][ij+1]=AE\n", 636 | " A[ij][ij+2]=AEE\n", 637 | " A[ij][ij-Nx]=AS\n", 638 | " A[ij][ij-2*Nx]=ASS\n", 639 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 640 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 641 | " DX[ij]=Dx\n", 642 | " DY[ij]=Dy\n", 643 | " \n", 644 | " #upper bottom left cell\n", 645 | " i=0\n", 646 | " j=Ny_in\n", 647 | " ij=i+Nx*j\n", 648 | " \n", 649 | " uBP=0\n", 650 | " vBP=0\n", 651 | " \n", 652 | " UP=U[j][i]\n", 653 | " UE=U[j][i+1]\n", 654 | " UEE=U[j][i+2]\n", 655 | " UEEE=U[j][i+3]\n", 656 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 657 | " \n", 658 | " VP=V[j][i]\n", 659 | " VN=V[j+1][i]\n", 660 | " VNN=V[j+2][i]\n", 661 | " VNNN=V[j+3][i]\n", 662 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 663 | " \n", 664 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 665 | " e1=(27*VN+27*VP-3*VS-3*VNN)/48\n", 666 | " \n", 667 | " AE=-4*x\n", 668 | " AEE=(23/60)*x\n", 669 | " AN=-4*y\n", 670 | " ANN=(23/60)*y\n", 671 | " AP=9.75*x+9.75*y\n", 672 | " \n", 673 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,0,e1,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 674 | " \n", 675 | " uBP+=b*(P[j][i]-P[j][i+1])+(2*b*u_inlet**2)+(92/15)*(x*u_inlet)\n", 676 | " vBP+=(P[j][i]-P[j+1][i])\n", 677 | " \n", 678 | " Dx=dy/AP\n", 679 | " Dy=dx/AP\n", 680 | " \n", 681 | " A[ij][ij]=AP/av\n", 682 | " A[ij][ij+1]=AE\n", 683 | " A[ij][ij+2]=AEE\n", 684 | " A[ij][ij+Nx]=AN\n", 685 | " A[ij][ij+2*Nx]=ANN\n", 686 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 687 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 688 | " DX[ij]=Dx\n", 689 | " DY[ij]=Dy\n", 690 | " \n", 691 | "\n", 692 | " #for upper bottom wall\n", 693 | " for i in range(1,Nx_in):\n", 694 | " j=Ny_in\n", 695 | " ij=i+j*Nx\n", 696 | " \n", 697 | " AEE=0\n", 698 | " AWW=0\n", 699 | " ANN=0\n", 700 | " ASS=0\n", 701 | " uBP=0\n", 702 | " vBP=0\n", 703 | " \n", 704 | " if i==1:\n", 705 | " UW=U[j][i-1]\n", 706 | " UE=U[j][i+1]\n", 707 | " UEE=U[j][i+2]\n", 708 | " UP=U[j][i]\n", 709 | " UWW=4*UW-6*UP+4*UE-UEE\n", 710 | " \n", 711 | " AWW=(1/12)*x*UWW\n", 712 | " \n", 713 | " uBP-=AWW\n", 714 | " vBP-=AWW\n", 715 | " \n", 716 | " else:\n", 717 | " UW=U[j][i-1]\n", 718 | " UWW=U[j][i-2]\n", 719 | " UE=U[j][i+1]\n", 720 | " UEE=U[j][i+2]\n", 721 | " UP=U[j][i]\n", 722 | " \n", 723 | " AWW=(1/12)*x\n", 724 | " AEE=(1/12)*x\n", 725 | " \n", 726 | " A[ij][ij-2]=AWW\n", 727 | " A[ij][ij+2]=AEE\n", 728 | " \n", 729 | " VP=V[j][i]\n", 730 | " VN=V[j+1][i]\n", 731 | " VNN=V[j+2][i]\n", 732 | " VNNN=V[j+3][i]\n", 733 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 734 | " \n", 735 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 736 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 737 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 738 | "\n", 739 | " AE=-(7/3)*x\n", 740 | " AW=-(7/3)*x\n", 741 | " AN=-4*y\n", 742 | " ANN=(23/60)*y\n", 743 | " AP=9.75*y+4.5*x\n", 744 | " \n", 745 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 746 | " \n", 747 | " uBP+=b*(P[j][i-1]-P[j][i+1])\n", 748 | " vBP+=(P[j][i]-P[j+1][i])\n", 749 | " \n", 750 | " Dx=dy/AP\n", 751 | " Dy=dx/AP\n", 752 | " \n", 753 | " A[ij][ij-1]=AW\n", 754 | " A[ij][ij]=AP/av\n", 755 | " A[ij][ij+1]=AE\n", 756 | " A[ij][ij+Nx]=AN\n", 757 | " A[ij][ij+2*Nx]=ANN\n", 758 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 759 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 760 | " \n", 761 | " #for lower bottom wall\n", 762 | " for i in range(Nx_in+1,Nx-1):\n", 763 | " j=0\n", 764 | " ij=i+j*Nx\n", 765 | " \n", 766 | " AEE=0\n", 767 | " AWW=0\n", 768 | " ASS=0\n", 769 | " ANN=0\n", 770 | " \n", 771 | " uBP=0\n", 772 | " vBP=0\n", 773 | " \n", 774 | " if i==Nx_in+1:\n", 775 | " UP=U[j][i]\n", 776 | " UE=U[j][i+1]\n", 777 | " UEE=U[j][i+2]\n", 778 | " UW=U[j][i-1]\n", 779 | " UWW=4*UW-6*UP+4*UE-UEE\n", 780 | " \n", 781 | " AWW=(1/12)*x*UWW\n", 782 | " \n", 783 | " uBP-=AWW\n", 784 | " vBP-=AWW\n", 785 | " \n", 786 | " elif i==Nx-2:\n", 787 | " UP=U[j][i]\n", 788 | " UE=U[j][i+1]\n", 789 | " UW=U[j][i-1]\n", 790 | " UWW=U[j][i-2]\n", 791 | " UEE=4*UE-6*UP+4*UW-UWW\n", 792 | " \n", 793 | " AEE=(1/12)*x*UEE\n", 794 | " \n", 795 | " uBP-=AEE\n", 796 | " vBP-=AEE\n", 797 | " \n", 798 | " else:\n", 799 | " UP=U[j][i]\n", 800 | " UE=U[j][i+1]\n", 801 | " UW=U[j][i-1]\n", 802 | " UWW=U[j][i-2]\n", 803 | " UEE=U[j][i+2]\n", 804 | " \n", 805 | " AEE=(1/12)*x\n", 806 | " AWW=(1/12)*x\n", 807 | " \n", 808 | " A[ij][ij-2]=AWW\n", 809 | " A[ij][ij+2]=AEE\n", 810 | " \n", 811 | " \n", 812 | " VP=V[j][i]\n", 813 | " VN=V[j+1][i]\n", 814 | " VNN=V[j+2][i]\n", 815 | " VNNN=V[j+3][i]\n", 816 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 817 | "\n", 818 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 819 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 820 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 821 | "\n", 822 | " AE=-(7/3)*x\n", 823 | " AW=-(7/3)*x\n", 824 | " AN=-4*y\n", 825 | " ANN=(23/60)*y\n", 826 | " AP=9.75*y+4.5*x\n", 827 | "\n", 828 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 829 | "\n", 830 | " uBP+=b*(P[j][i-1]-P[j][i+1])\n", 831 | " vBP+=(P[j][i]-P[j+1][i])\n", 832 | " Dx=dy/AP\n", 833 | " Dy=dx/AP\n", 834 | " \n", 835 | " A[ij][ij-1]=AW\n", 836 | " A[ij][ij]=AP/av\n", 837 | " A[ij][ij+1]=AE\n", 838 | " A[ij][ij+Nx]=AN\n", 839 | " A[ij][ij+2*Nx]=ANN\n", 840 | " \n", 841 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 842 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 843 | " DX[ij]=Dx\n", 844 | " DY[ij]=Dy\n", 845 | " \n", 846 | " #lower left corner\n", 847 | " i=Nx_in\n", 848 | " j=0\n", 849 | " ij=i+j*Nx\n", 850 | " \n", 851 | " uBP=0\n", 852 | " vBP=0\n", 853 | " \n", 854 | " UP=U[j][i]\n", 855 | " UE=U[j][i+1]\n", 856 | " UEE=U[j][i+2]\n", 857 | " UEEE=U[j][i+3]\n", 858 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 859 | " \n", 860 | " VP=V[j][i]\n", 861 | " VN=V[j+1][i]\n", 862 | " VNN=V[j+2][i]\n", 863 | " VNNN=V[j+3][i]\n", 864 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 865 | "\n", 866 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 867 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 868 | "\n", 869 | " AE=-4*x\n", 870 | " AEE=(23/60)*x\n", 871 | " AN=-4*y\n", 872 | " ANN=(23/60)*y\n", 873 | " AP=9.75*y+9.75*x\n", 874 | " \n", 875 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,0,e1,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 876 | " \n", 877 | " uBP=b*(P[j][i]-P[j][i+1])\n", 878 | " vBP=(P[j][i]-P[j+1][i])\n", 879 | " Dx=dy/AP\n", 880 | " Dy=dx/AP\n", 881 | "\n", 882 | " A[ij][ij]=AP/av\n", 883 | " A[ij][ij+1]=AE\n", 884 | " A[ij][ij+2]=AEE\n", 885 | " A[ij][ij+Nx]=AN\n", 886 | " A[ij][ij+2*Nx]=ANN\n", 887 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 888 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 889 | " DX[ij]=Dx\n", 890 | " DY[ij]=Dy\n", 891 | "\n", 892 | " #For lower interior cells (will overlap with left wall)\n", 893 | " for j in range(1,Ny-1):\n", 894 | " for i in range(Nx_in,Nx-1):\n", 895 | " ij=i+Nx*j\n", 896 | " \n", 897 | " AEE=0\n", 898 | " AWW=0\n", 899 | " ANN=0\n", 900 | " ASS=0\n", 901 | " \n", 902 | " uBP=0\n", 903 | " vBP=0\n", 904 | " \n", 905 | " if j==1:\n", 906 | " VN=V[j+1][i]\n", 907 | " VNN=V[j+2][i]\n", 908 | " VS=V[j-1][i]\n", 909 | " VP=V[j][i]\n", 910 | " VSS=4*VS-6*VP+4*VN-VNN\n", 911 | " \n", 912 | " ASS=(1/12)*y*VSS\n", 913 | " \n", 914 | " uBP-=ASS\n", 915 | " vBP-=ASS\n", 916 | " \n", 917 | " elif j==Ny-2:\n", 918 | " VN=V[j+1][i]\n", 919 | " VSS=V[j-2][i]\n", 920 | " VS=V[j-1][i]\n", 921 | " VP=V[j][i]\n", 922 | " VNN=4*VN-6*VP+4*VS-VSS\n", 923 | " \n", 924 | " ANN=(1/12)*y*VNN\n", 925 | " \n", 926 | " uBP-=ANN\n", 927 | " vBP-=ANN\n", 928 | " \n", 929 | " else:\n", 930 | " VN=V[j+1][i]\n", 931 | " VNN=V[j+2][i]\n", 932 | " VS=V[j-1][i]\n", 933 | " VSS=V[j-2][i]\n", 934 | " VP=V[j][i]\n", 935 | " \n", 936 | " ASS=(1/12)*y\n", 937 | " ANN=(1/12)*y\n", 938 | " \n", 939 | " A[ij][ij-2*Nx]=ASS\n", 940 | " A[ij][ij+2*Nx]=ANN\n", 941 | " \n", 942 | " if i==Nx_in:\n", 943 | " UW=U[j][i-1]\n", 944 | " UP=U[j][i]\n", 945 | " UE=U[j][i+1]\n", 946 | " UEE=U[j][i+2]\n", 947 | " UWW=4*UW-6*UP+4*UE-UEE\n", 948 | " \n", 949 | " AWW=(1/12)*x*UWW\n", 950 | " \n", 951 | " uBP-=AWW\n", 952 | " vBP-=AWW\n", 953 | " \n", 954 | " elif i==Nx-2:\n", 955 | " UW=U[j][i-1]\n", 956 | " UWW=U[j][i-2]\n", 957 | " UP=U[j][i]\n", 958 | " UE=U[j][i+1]\n", 959 | " UEE=4*UE-6*UP+4*UW-UWW\n", 960 | " \n", 961 | " AEE=(1/12)*x*UEE\n", 962 | " \n", 963 | " uBP-=AEE\n", 964 | " vBP-=AEE\n", 965 | " \n", 966 | " else:\n", 967 | " UE=U[j][i+1]\n", 968 | " UEE=U[j][i+2]\n", 969 | " UW=U[j][i-1]\n", 970 | " UWW=U[j][i-2]\n", 971 | " UP=U[j][i]\n", 972 | " \n", 973 | " AEE=(1/12)*x\n", 974 | " AWW=(1/12)*x\n", 975 | " \n", 976 | " A[ij][ij-2]=AWW\n", 977 | " A[ij][ij+2]=AEE\n", 978 | " \n", 979 | " \n", 980 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 981 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 982 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 983 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 984 | " \n", 985 | " AE=-(7/3)*x\n", 986 | " AW=-(7/3)*x\n", 987 | " AN=-(7/3)*y\n", 988 | " AS=-(7/3)*y\n", 989 | " AP=4.5*x+4.5*y\n", 990 | "\n", 991 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,a2,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 992 | " \n", 993 | " uBP+=b*(P[j][i-1]-P[j][i+1])\n", 994 | " vBP+=(P[j-1][i]-P[j+1][i])\n", 995 | " \n", 996 | " Dx=dy/AP\n", 997 | " Dy=dx/AP\n", 998 | " \n", 999 | " A[ij][ij-1]=AW\n", 1000 | " A[ij][ij]=AP/av\n", 1001 | " A[ij][ij+1]=AE\n", 1002 | " A[ij][ij+Nx]=AN\n", 1003 | " A[ij][ij-Nx]=AS\n", 1004 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 1005 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 1006 | " DX[ij]=Dx\n", 1007 | " DY[ij]=Dy\n", 1008 | "\n", 1009 | " #for bottom left wall\n", 1010 | " for j in range(1,Ny_in):\n", 1011 | " i=Nx_in\n", 1012 | " ij=i+Nx*j\n", 1013 | " \n", 1014 | " AEE=0\n", 1015 | " AWW=0\n", 1016 | " ANN=0\n", 1017 | " ASS=0\n", 1018 | " \n", 1019 | " uBP=0\n", 1020 | " vBP=0\n", 1021 | " \n", 1022 | " if j==1:\n", 1023 | " VP=V[j][i]\n", 1024 | " VS=V[j-1][i]\n", 1025 | " VN=V[j+1][i]\n", 1026 | " VNN=V[j+2][i]\n", 1027 | " VSS=4*VS-6*VP+4*VN-VNN\n", 1028 | " \n", 1029 | " ASS=(1/12)*y*VSS\n", 1030 | " \n", 1031 | " uBP-=ASS\n", 1032 | " vBP-=ASS\n", 1033 | " \n", 1034 | " else:\n", 1035 | " VP=V[j][i]\n", 1036 | " VS=V[j-1][i]\n", 1037 | " VN=V[j+1][i]\n", 1038 | " VNN=V[j+2][i]\n", 1039 | " VSS=V[j-2][i]\n", 1040 | " \n", 1041 | " ASS=(1/12)*y\n", 1042 | " ANN=(1/12)*y\n", 1043 | " \n", 1044 | " A[ij][ij-2*Nx]=ASS\n", 1045 | " A[ij][ij+2*Nx]=ANN\n", 1046 | " \n", 1047 | " UP=U[j][i]\n", 1048 | " UE=U[j][i+1]\n", 1049 | " UEE=U[j][i+2]\n", 1050 | " UEEE=U[j][i+3]\n", 1051 | " UW=4*UP-6*UE+4*UEE-UEEE\n", 1052 | "\n", 1053 | " a1=(27*UP+27*UE-3*UW-3*UEE)/48\n", 1054 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 1055 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 1056 | "\n", 1057 | " AE=-4*x\n", 1058 | " AEE=0.3*x\n", 1059 | " AN=-(7/3)*y\n", 1060 | " AS=-(7/3)*y\n", 1061 | " AP=a1*b+9.75*x+4.5*y\n", 1062 | " \n", 1063 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,a1,0,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 1064 | " \n", 1065 | " uBP+=b*(P[j][i]-P[j][i+1])\n", 1066 | " vBP+=(P[j-1][i]-P[j+1][i])\n", 1067 | " Dx=dy/AP\n", 1068 | " Dy=dx/AP\n", 1069 | " \n", 1070 | " A[ij][ij]=AP/av\n", 1071 | " A[ij][ij+1]=AE\n", 1072 | " A[ij][ij+2]=AEE\n", 1073 | " A[ij][ij-1]=0 #erase the previous input\n", 1074 | " A[ij][ij-Nx]=AS\n", 1075 | " A[ij][ij+Nx]=AN\n", 1076 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 1077 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 1078 | " DX[ij]=Dx\n", 1079 | " DY[ij]=Dy\n", 1080 | " \n", 1081 | " #for right wall:\n", 1082 | " for j in range(1,Ny-1):\n", 1083 | " i=Nx-1\n", 1084 | " ij=i+Nx*j\n", 1085 | " \n", 1086 | " uBP=0\n", 1087 | " vBP=0\n", 1088 | " \n", 1089 | " if j==1:\n", 1090 | " VNN=V[j+2][i]\n", 1091 | " VN=V[j+1][i]\n", 1092 | " VP=V[j][i]\n", 1093 | " VS=V[j-1][i]\n", 1094 | " VSS=4*VS-6*VP+4*VN-VNN\n", 1095 | " \n", 1096 | " ASS=(1/12)*y*VSS\n", 1097 | " \n", 1098 | " uBP-=ASS\n", 1099 | " vBP-=ASS\n", 1100 | " \n", 1101 | " elif j==Ny-2:\n", 1102 | " VN=V[j+1][i]\n", 1103 | " VP=V[j][i]\n", 1104 | " VS=V[j-1][i]\n", 1105 | " VSS=V[j-2][i]\n", 1106 | " VNN=4*VN-6*VP+4*VS-VSS\n", 1107 | " \n", 1108 | " ANN=(1/12)*y*VNN\n", 1109 | " \n", 1110 | " uBP-=ANN\n", 1111 | " vBP-=ANN\n", 1112 | " \n", 1113 | " else:\n", 1114 | " VNN=V[j+2][i]\n", 1115 | " VN=V[j+1][i]\n", 1116 | " VP=V[j][i]\n", 1117 | " VS=V[j-1][i]\n", 1118 | " VSS=V[j-2][i]\n", 1119 | " \n", 1120 | " ASS=(1/12)*y\n", 1121 | " ANN=(1/12)*y\n", 1122 | " \n", 1123 | " A[ij][ij-2*Nx]=ASS\n", 1124 | " A[ij][ij+2*Nx]=ANN\n", 1125 | " \n", 1126 | " UWWW=U[j][i-3]\n", 1127 | " UWW=U[j][i-2]\n", 1128 | " UW=U[j][i-1]\n", 1129 | " UP=U[j][i]\n", 1130 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 1131 | " \n", 1132 | " a1=1.875*UP-1.25*UW+0.375*UWW\n", 1133 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 1134 | " e1=(27*VP+27*VN-3*VS-3*VNN)/48\n", 1135 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 1136 | " \n", 1137 | " AW=-(7/3)*x\n", 1138 | " AN=-(7/3)*y\n", 1139 | " AS=-(7/3)*y\n", 1140 | " AWW=(1/12)*x\n", 1141 | " AP=2*a1*b+2.25*x+4.5*y\n", 1142 | " \n", 1143 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,0,a2,e1,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 1144 | " \n", 1145 | " uBP+=b*(P[j][i-1]-P[j][i])\n", 1146 | " vBP+=(P[j-1][i]-P[j+1][i])\n", 1147 | " Dx=dy/AP\n", 1148 | " Dy=dx/AP\n", 1149 | " \n", 1150 | " A[ij][ij]=AP/av\n", 1151 | " A[ij][ij-1]=AW\n", 1152 | " A[ij][ij-2]=AWW\n", 1153 | " A[ij][ij-Nx]=AS\n", 1154 | " A[ij][ij+Nx]=AN\n", 1155 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 1156 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 1157 | " DX[ij]=Dx\n", 1158 | " DY[ij]=Dy\n", 1159 | " \n", 1160 | " #top right cell\n", 1161 | " i=Nx-1\n", 1162 | " j=Ny-1\n", 1163 | " ij=i+Nx*j\n", 1164 | " \n", 1165 | " uBP=0\n", 1166 | " vBP=0\n", 1167 | " \n", 1168 | " UWWW=U[j][i-3]\n", 1169 | " UWW=U[j][i-2]\n", 1170 | " UW=U[j][i-1]\n", 1171 | " UP=U[j][i]\n", 1172 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 1173 | "\n", 1174 | " VP=V[j][i]\n", 1175 | " VS=V[j-1][i]\n", 1176 | " VSS=V[j-2][i]\n", 1177 | " VSSS=V[j-3][i]\n", 1178 | " VN=4*VP-6*VS+4*VSS-VSSS\n", 1179 | " \n", 1180 | " a1=1.875*UP-1.25*UW+0.375*UWW\n", 1181 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 1182 | " e2=(27*VP+27*VS-3*VSS-3*VN)/48\n", 1183 | " \n", 1184 | " AW=-(7/3)*x\n", 1185 | " AWW=(1/12)*x\n", 1186 | " AS=-4*y\n", 1187 | " ASS=(23/60)*y\n", 1188 | " AP=2*a1*b+2.25*x+9.75*y\n", 1189 | "\n", 1190 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,0,a2,0,e2,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 1191 | " \n", 1192 | " uBP+=b*(P[j][i-1]-P[j][i])\n", 1193 | " vBP+=(P[j-1][i]-P[j][i])\n", 1194 | " Dx=dy/AP\n", 1195 | " Dy=dx/AP\n", 1196 | " \n", 1197 | " A[ij][ij]=AP/av\n", 1198 | " A[ij][ij-1]=AW\n", 1199 | " A[ij][ij-2]=AWW\n", 1200 | " A[ij][ij-Nx]=AS\n", 1201 | " A[ij][ij-2*Nx]=ASS\n", 1202 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 1203 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 1204 | " DX[ij]=Dx\n", 1205 | " DY[ij]=Dy\n", 1206 | " \n", 1207 | " #bottom right cell\n", 1208 | " i=Nx-1\n", 1209 | " j=0\n", 1210 | " ij=i+Nx*j\n", 1211 | " \n", 1212 | " uBP=0\n", 1213 | " vBP=0\n", 1214 | " \n", 1215 | " UWWW=U[j][i-3]\n", 1216 | " UWW=U[j][i-2]\n", 1217 | " UW=U[j][i-1]\n", 1218 | " UP=U[j][i]\n", 1219 | " UE=4*UP-6*UW+4*UWW-UWWW\n", 1220 | " \n", 1221 | " VP=V[j][i]\n", 1222 | " VN=V[j+1][i]\n", 1223 | " VNN=V[j+2][i]\n", 1224 | " VNNN=V[j+3][i]\n", 1225 | " VS=4*VP-6*VN+4*VNN-VNNN\n", 1226 | "\n", 1227 | " a1=1.875*UP-1.25*UW+0.375*UWW\n", 1228 | " a2=(27*UP+27*UW-3*UWW-3*UE)/48\n", 1229 | " e1=(27*VN+27*VP-3*VS-3*VNN)/48\n", 1230 | " \n", 1231 | " AW=-(7/3)*x\n", 1232 | " AWW=(1/12)*x\n", 1233 | " AN=-4*y\n", 1234 | " ANN=(23/60)*y\n", 1235 | " AP=2*a1*b+2.25*x+9.75*y\n", 1236 | " \n", 1237 | " AE,AW,AN,AS,AP,uBP,vBP=MUSCL3(AE,AW,AN,AS,AP,uBP,vBP,0,a2,e1,0,UE,UW,UEE,UWW,UP,VN,VS,VNN,VSS,VP)\n", 1238 | " \n", 1239 | " uBP+=b*(P[j][i-1]-P[j][i])\n", 1240 | " vBP+=(P[j][i]-P[j+1][i])\n", 1241 | " Dx=dy/AP\n", 1242 | " Dy=dx/AP\n", 1243 | " \n", 1244 | " A[ij][ij]=AP/av\n", 1245 | " A[ij][ij-1]=AW\n", 1246 | " A[ij][ij-2]=AWW\n", 1247 | " A[ij][ij+Nx]=AN\n", 1248 | " A[ij][ij+2*Nx]=ANN\n", 1249 | " uB[ij]=uBP+(alpha*AP*U[j][i])\n", 1250 | " vB[ij]=vBP+(alpha*AP*V[j][i])\n", 1251 | " DX[ij]=Dx\n", 1252 | " DY[ij]=Dy\n", 1253 | " \n", 1254 | " #for step area:\n", 1255 | " for j in range(Ny_in):\n", 1256 | " for i in range(Nx_in):\n", 1257 | " ij=i+Nx*j\n", 1258 | " \n", 1259 | " A[ij][ij]=1\n", 1260 | " uB[ij]=0\n", 1261 | " vB[ij]=0\n", 1262 | " \n", 1263 | " if transient==True: \n", 1264 | " At=-k*A\n", 1265 | " uBt=k*uB\n", 1266 | " vBt=k*vB\n", 1267 | " if time_step==1:\n", 1268 | " Un=order_1(At,uBt,U,U0,N,UAB)\n", 1269 | " Vn=order_1(At,vBt,V,V0,N,VAB)\n", 1270 | " elif time_step==2:\n", 1271 | " Un=order_2(At,uBt,U,U0,N,UAB)\n", 1272 | " Vn=order_2(At,vBt,U,V0,N,VAB)\n", 1273 | " elif time_step==3:\n", 1274 | " Un=order_3(At,uBt,U,U0,N,UAB)\n", 1275 | " Vn=order_3(At,vBt,U,V0,N,VAB)\n", 1276 | " else:\n", 1277 | " Un=order_4(At,uBt,U,U0,N,UAB)\n", 1278 | " Vn=order_4(At,vBt,U,V0,N,VAB)\n", 1279 | " \n", 1280 | " U_mesh=np.reshape(np.array(Un),[Ny,Nx])\n", 1281 | " V_mesh=np.reshape(np.array(Vn),[Ny,Nx])\n", 1282 | " DX_mesh=np.reshape(np.array(DX),[Ny,Nx])\n", 1283 | " DY_mesh=np.reshape(np.array(DY),[Ny,Nx])\n", 1284 | " \n", 1285 | " return U_mesh,V_mesh,DX_mesh,DY_mesh,At,uBt,vBt \n", 1286 | "\n", 1287 | " else:\n", 1288 | " A=sp.sparse.csr_matrix(A)\n", 1289 | " ml=pyamg.ruge_stuben_solver(A)\n", 1290 | " U=ml.solve(uB,tol=1E-6)\n", 1291 | " V=ml.solve(vB,tol=1E-6)\n", 1292 | " \n", 1293 | " U_mesh=np.reshape(np.array(U),[Ny,Nx])\n", 1294 | " V_mesh=np.reshape(np.array(V),[Ny,Nx])\n", 1295 | " DX_mesh=np.reshape(np.array(DX),[Ny,Nx])\n", 1296 | " DY_mesh=np.reshape(np.array(DY),[Ny,Nx])\n", 1297 | "\n", 1298 | " return U_mesh,V_mesh,DX_mesh,DY_mesh " 1299 | ] 1300 | }, 1301 | { 1302 | "cell_type": "code", 1303 | "execution_count": 53, 1304 | "id": "9cdd4400", 1305 | "metadata": {}, 1306 | "outputs": [], 1307 | "source": [ 1308 | "def face(Uf,Vf,U,V,P_mesh,uNx,uNy,vNx,vNy,DX,DY):\n", 1309 | " DXf=np.zeros([uNy,uNx])\n", 1310 | " #U-faces\n", 1311 | " #for upper interior\n", 1312 | " for j in range(Ny_in,uNy):\n", 1313 | " for i in range(2,uNx-2):\n", 1314 | " ij=i+Nx*j\n", 1315 | " \n", 1316 | " UE=U[j][i]\n", 1317 | " UP=U[j][i-1]\n", 1318 | " Ue=(UE+UP)/2\n", 1319 | "\n", 1320 | " DP=DX[j][i-1]\n", 1321 | " DE=DX[j][i]\n", 1322 | " PP=DP*(P_mesh[j][i-2]-P_mesh[j][i])\n", 1323 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1324 | " \n", 1325 | " dp=(DP+DE)/2\n", 1326 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1327 | " Pf=(-(PP+PE)/2)+Pp\n", 1328 | " \n", 1329 | " uf=Ue+Pf\n", 1330 | " Uf[j][i]=uf\n", 1331 | " DXf[j][i]=dp\n", 1332 | " \n", 1333 | " \n", 1334 | " #for lower interior\n", 1335 | " for j in range(Ny_in):\n", 1336 | " for i in range(Nx_in+2,uNx-2):\n", 1337 | " ij=i+Nx*j\n", 1338 | " \n", 1339 | " UE=U[j][i]\n", 1340 | " UP=U[j][i-1]\n", 1341 | " Ue=(UE+UP)/2\n", 1342 | "\n", 1343 | " DP=DX[j][i-1]\n", 1344 | " DE=DX[j][i]\n", 1345 | " PP=DP*(P_mesh[j][i-2]-P_mesh[j][i])\n", 1346 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1347 | " \n", 1348 | " dp=(DP+DE)/2\n", 1349 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1350 | " Pf=(-(PP+PE)/2)+Pp\n", 1351 | " \n", 1352 | " uf=Ue+Pf\n", 1353 | " Uf[j][i]=uf\n", 1354 | " DXf[j][i]=dp\n", 1355 | " \n", 1356 | " \n", 1357 | " #for inlet\n", 1358 | " for j in range(Ny_in,uNy):\n", 1359 | " i=1\n", 1360 | " \n", 1361 | " UE=U[j][i]\n", 1362 | " UP=U[j][i-1]\n", 1363 | " Ue=(UE+UP)/2\n", 1364 | "\n", 1365 | " DP=DX[j][i-1]\n", 1366 | " DE=DX[j][i]\n", 1367 | " PP=DP*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1368 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1369 | "\n", 1370 | " dp=(DP+DE)/2\n", 1371 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1372 | " Pf=(-(PP+PE)/2)+Pp\n", 1373 | "\n", 1374 | " uf=Ue+Pf\n", 1375 | " Uf[j][i]=uf\n", 1376 | " DXf[j][i]=dp\n", 1377 | "\n", 1378 | " \n", 1379 | " #for lower left wall\n", 1380 | " for j in range(Ny_in):\n", 1381 | " i=Nx_in+1\n", 1382 | " \n", 1383 | " UE=U[j][i]\n", 1384 | " UP=U[j][i-1]\n", 1385 | " Ue=(UE+UP)/2\n", 1386 | "\n", 1387 | " DP=DX[j][i-1]\n", 1388 | " DE=DX[j][i]\n", 1389 | " PP=DP*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1390 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i+1])\n", 1391 | "\n", 1392 | " dp=(DP+DE)/2\n", 1393 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1394 | " Pf=(-(PP+PE)/2)+Pp\n", 1395 | "\n", 1396 | " uf=Ue+Pf\n", 1397 | " Uf[j][i]=uf\n", 1398 | " DXf[j][i]=dp\n", 1399 | " \n", 1400 | "\n", 1401 | " #for outlet\n", 1402 | " for j in range(uNy):\n", 1403 | " i=uNx-2\n", 1404 | " \n", 1405 | " UE=U[j][i]\n", 1406 | " UP=U[j][i-1]\n", 1407 | " Ue=(UE+UP)/2\n", 1408 | "\n", 1409 | " DP=DX[j][i-1]\n", 1410 | " DE=DX[j][i]\n", 1411 | " PP=DP*(P_mesh[j][i-2]-P_mesh[j][i])\n", 1412 | " PE=DE*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1413 | "\n", 1414 | " dp=(DP+DE)/2\n", 1415 | " Pp=dp*(P_mesh[j][i-1]-P_mesh[j][i])\n", 1416 | " Pf=(-(PP+PE)/2)+Pp\n", 1417 | "\n", 1418 | " uf=Ue+Pf\n", 1419 | " Uf[j][i]=uf\n", 1420 | " DXf[j][i]=dp\n", 1421 | " \n", 1422 | " \n", 1423 | " #V-faces \n", 1424 | " DYf=np.zeros([vNy,vNx])\n", 1425 | " \n", 1426 | " #for upper interior\n", 1427 | " for j in range(Ny_in+2,vNy-2):\n", 1428 | " for i in range(Nx_in):\n", 1429 | " ij=i+Nx*j\n", 1430 | " \n", 1431 | " VN=V[j][i]\n", 1432 | " VP=V[j-1][i]\n", 1433 | " Ve=(VN+VP)/2\n", 1434 | "\n", 1435 | " DP=DX[j-1][i]\n", 1436 | " DN=DX[j][i]\n", 1437 | " PP=DP*(P_mesh[j-2][i]-P_mesh[j][i])\n", 1438 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1439 | " \n", 1440 | " dp=(DP+DN)/2\n", 1441 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1442 | " Pf=(-(PP+PN)/2)+Pp\n", 1443 | " \n", 1444 | " vf=Ve+Pf\n", 1445 | " Vf[j][i]=vf\n", 1446 | " DYf[j][i]=dp\n", 1447 | " \n", 1448 | " \n", 1449 | " #for lower interior\n", 1450 | " for j in range(2,vNy-2):\n", 1451 | " for i in range(Nx_in,vNx):\n", 1452 | " ij=i+Nx*j\n", 1453 | " \n", 1454 | " VN=V[j][i]\n", 1455 | " VP=V[j-1][i]\n", 1456 | " Ve=(VN+VP)/2\n", 1457 | "\n", 1458 | " DP=DX[j-1][i]\n", 1459 | " DN=DX[j][i]\n", 1460 | " PP=DP*(P_mesh[j-2][i]-P_mesh[j][i])\n", 1461 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1462 | " \n", 1463 | " dp=(DP+DN)/2\n", 1464 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1465 | " Pf=(-(PP+PN)/2)+Pp\n", 1466 | " \n", 1467 | " vf=Ve+Pf\n", 1468 | " Vf[j][i]=vf\n", 1469 | " DYf[j][i]=dp\n", 1470 | "\n", 1471 | " \n", 1472 | " #for upper bottom wall\n", 1473 | " for i in range(Nx_in):\n", 1474 | " j=Ny_in+1\n", 1475 | " \n", 1476 | " VN=V[j][i]\n", 1477 | " VP=V[j-1][i]\n", 1478 | " Vn=(VN+VP)/2\n", 1479 | "\n", 1480 | " DP=DX[j-1][i]\n", 1481 | " DN=DX[j][i]\n", 1482 | " PP=DP*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1483 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1484 | "\n", 1485 | " dp=(DP+DN)/2\n", 1486 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1487 | " Pf=(-(PP+PN)/2)+Pp\n", 1488 | "\n", 1489 | " vf=Vn+Pf\n", 1490 | " Vf[j][i]=vf\n", 1491 | " DYf[j][i]=dp\n", 1492 | " \n", 1493 | "\n", 1494 | " #for lower bottom wall\n", 1495 | " for i in range(Nx_in,vNx):\n", 1496 | " j=1\n", 1497 | " \n", 1498 | " VN=V[j][i]\n", 1499 | " VP=V[j-1][i]\n", 1500 | " Vn=(VN+VP)/2\n", 1501 | "\n", 1502 | " DP=DX[j-1][i]\n", 1503 | " DN=DX[j][i]\n", 1504 | " PP=DP*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1505 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j+1][i])\n", 1506 | "\n", 1507 | " dp=(DP+DN)/2\n", 1508 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1509 | " Pf=(-(PP+PN)/2)+Pp\n", 1510 | "\n", 1511 | " vf=Vn+Pf\n", 1512 | " Vf[j][i]=vf\n", 1513 | " DYf[j][i]=dp\n", 1514 | " \n", 1515 | " \n", 1516 | " #for top wall\n", 1517 | " for i in range(vNx):\n", 1518 | " j=vNy-2\n", 1519 | " \n", 1520 | " VN=V[j][i]\n", 1521 | " VP=V[j-1][i]\n", 1522 | " Vn=(VN+VP)/2\n", 1523 | "\n", 1524 | " DP=DX[j-1][i]\n", 1525 | " DN=DX[j][i]\n", 1526 | " PP=DP*(P_mesh[j-2][i]-P_mesh[j][i])\n", 1527 | " PN=DN*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1528 | "\n", 1529 | " dp=(DP+DN)/2\n", 1530 | " Pp=dp*(P_mesh[j-1][i]-P_mesh[j][i])\n", 1531 | " Pf=(-(PP+PN)/2)+Pp\n", 1532 | "\n", 1533 | " vf=Vn+Pf\n", 1534 | " Vf[j][i]=vf\n", 1535 | " DYf[j][i]=dp\n", 1536 | " \n", 1537 | "\n", 1538 | " for j in range(Ny_in,uNy):\n", 1539 | " Uf[j][0]=u_inlet\n", 1540 | " for j in range(uNy):\n", 1541 | " Uf[j][-1]=U[j][-1]\n", 1542 | " \n", 1543 | " return Uf,Vf,DXf,DYf" 1544 | ] 1545 | }, 1546 | { 1547 | "cell_type": "code", 1548 | "execution_count": 54, 1549 | "id": "99621a77", 1550 | "metadata": {}, 1551 | "outputs": [], 1552 | "source": [ 1553 | "def cont(Nx,Ny,U,V,P,DX,DY):\n", 1554 | " global b\n", 1555 | " global u_inlet\n", 1556 | "\n", 1557 | " N=Nx*Ny\n", 1558 | " \n", 1559 | " A=np.zeros([N,N])\n", 1560 | " B=np.zeros([N,1])\n", 1561 | " #test=np.zeros([Ny,Nx])\n", 1562 | " \n", 1563 | " #For upper interior cells\n", 1564 | " for j in range(Ny_in+1,Ny-1):\n", 1565 | " for i in range(1,Nx_in+1):\n", 1566 | " ij=i+Nx*j\n", 1567 | " \n", 1568 | " ue=U[j][i+1]\n", 1569 | " uw=U[j][i]\n", 1570 | " vn=V[j+1][i]\n", 1571 | " vs=V[j][i]\n", 1572 | " \n", 1573 | " AE=-b*DXf[j][i+1]\n", 1574 | " AW=-b*DXf[j][i]\n", 1575 | " AN=-DYf[j+1][i]\n", 1576 | " AS=-DYf[j][i]\n", 1577 | " AP=-AE-AW-AN-AS\n", 1578 | " BP=b*(uw-ue)+vs-vn\n", 1579 | " \n", 1580 | " A[ij][ij-1]=AW\n", 1581 | " A[ij][ij+1]=AE\n", 1582 | " A[ij][ij]=AP\n", 1583 | " A[ij][ij+Nx]=AN\n", 1584 | " A[ij][ij-Nx]=AS\n", 1585 | " B[ij]=BP\n", 1586 | " \n", 1587 | " #For lower interior cells\n", 1588 | " for j in range(1,Ny-1):\n", 1589 | " for i in range(Nx_in+1,Nx-1):\n", 1590 | " ij=i+Nx*j\n", 1591 | " \n", 1592 | " ue=U[j][i+1]\n", 1593 | " uw=U[j][i]\n", 1594 | " vn=V[j+1][i]\n", 1595 | " vs=V[j][i]\n", 1596 | " \n", 1597 | " AE=-b*DXf[j][i+1]\n", 1598 | " AW=-b*DXf[j][i]\n", 1599 | " AN=-DYf[j+1][i]\n", 1600 | " AS=-DYf[j][i]\n", 1601 | " AP=-AE-AW-AN-AS\n", 1602 | " BP=b*(uw-ue)+vs-vn\n", 1603 | " \n", 1604 | " A[ij][ij-1]=AW\n", 1605 | " A[ij][ij+1]=AE\n", 1606 | " A[ij][ij]=AP\n", 1607 | " A[ij][ij+Nx]=AN\n", 1608 | " A[ij][ij-Nx]=AS\n", 1609 | " B[ij]=BP\n", 1610 | " \n", 1611 | " i=Nx_in\n", 1612 | " j=Ny_in\n", 1613 | " ij=i+Nx*j\n", 1614 | " \n", 1615 | " ue=U[j][i+1]\n", 1616 | " uw=U[j][i]\n", 1617 | " vn=V[j+1][i]\n", 1618 | " vs=V[j][i]\n", 1619 | "\n", 1620 | " AE=-b*DXf[j][i+1]\n", 1621 | " AW=-b*DXf[j][i]\n", 1622 | " AN=-DYf[j+1][i]\n", 1623 | " AS=-DYf[j][i]\n", 1624 | " AP=-AE-AW-AN-AS\n", 1625 | " BP=b*(uw-ue)+vs-vn\n", 1626 | "\n", 1627 | " A[ij][ij-1]=AW\n", 1628 | " A[ij][ij+1]=AE\n", 1629 | " A[ij][ij]=AP\n", 1630 | " A[ij][ij+Nx]=AN\n", 1631 | " A[ij][ij-Nx]=AS\n", 1632 | " B[ij]=BP\n", 1633 | " \n", 1634 | " #inlet (upper left wall)\n", 1635 | " for j in range(Ny_in+1,Ny-1):\n", 1636 | " i=0\n", 1637 | " ij=i+Nx*j\n", 1638 | " \n", 1639 | " ue=U[j][i+1]\n", 1640 | " uw=U[j][i]\n", 1641 | " vn=V[j+1][i]\n", 1642 | " vs=V[j][i]\n", 1643 | " \n", 1644 | " de=DXf[j][i+1]\n", 1645 | " dn=DYf[j+1][i]\n", 1646 | " ds=DYf[j][i]\n", 1647 | " \n", 1648 | " AE=-b*de\n", 1649 | " AN=-dn\n", 1650 | " AS=-ds\n", 1651 | " AP=-AE-AN-AS\n", 1652 | " BP=b*(uw-ue)+vs-vn\n", 1653 | "\n", 1654 | " A[ij][ij+1]=AE\n", 1655 | " A[ij][ij]=AP\n", 1656 | " A[ij][ij+Nx]=AN\n", 1657 | " A[ij][ij-Nx]=AS\n", 1658 | " B[ij]=BP\n", 1659 | " \n", 1660 | " #lower left wall\n", 1661 | " for j in range(1,Ny_in):\n", 1662 | " i=Nx_in\n", 1663 | " ij=i+Nx*j\n", 1664 | " \n", 1665 | " ue=U[j][i+1]\n", 1666 | " vn=V[j+1][i]\n", 1667 | " vs=V[j][i]\n", 1668 | " \n", 1669 | " de=DXf[j][i+1]\n", 1670 | " dn=DYf[j+1][i]\n", 1671 | " ds=DYf[j][i]\n", 1672 | " \n", 1673 | " AE=-b*de\n", 1674 | " AN=-dn\n", 1675 | " AS=-ds\n", 1676 | " AP=-AE-AN-AS\n", 1677 | " BP=b*(-ue)+vs-vn\n", 1678 | "\n", 1679 | " A[ij][ij+1]=AE\n", 1680 | " A[ij][ij]=AP\n", 1681 | " A[ij][ij+Nx]=AN\n", 1682 | " A[ij][ij-Nx]=AS\n", 1683 | " B[ij]=BP\n", 1684 | " \n", 1685 | " #for outlet \n", 1686 | " for j in range(1,Ny-1):\n", 1687 | " i=Nx-1\n", 1688 | " ij=i+Nx*j\n", 1689 | " \n", 1690 | " uw=U[j][i]\n", 1691 | " ue=U[j][i+1]\n", 1692 | " vn=V[j+1][i]\n", 1693 | " vs=V[j][i]\n", 1694 | " \n", 1695 | " dw=DXf[j][i]\n", 1696 | " dn=DYf[j+1][i]\n", 1697 | " ds=DYf[j][i]\n", 1698 | " DP=DX[j][i]\n", 1699 | "\n", 1700 | " AW=b*(0.5*DP-dw)\n", 1701 | " AN=-dn\n", 1702 | " AS=-ds\n", 1703 | " AP=b*(dw+0.5*DP)+dn+ds\n", 1704 | " BP=b*(uw-ue)+vs-vn\n", 1705 | "\n", 1706 | " A[ij][ij-1]=AW\n", 1707 | " A[ij][ij]=AP\n", 1708 | " A[ij][ij+Nx]=AN\n", 1709 | " A[ij][ij-Nx]=AS\n", 1710 | " B[ij]=BP\n", 1711 | " \n", 1712 | " #for upper bottom wall\n", 1713 | " for i in range(1,Nx_in):\n", 1714 | " j=Ny_in\n", 1715 | " ij=i+Nx*j\n", 1716 | " \n", 1717 | " ue=U[j][i+1]\n", 1718 | " uw=U[j][i]\n", 1719 | " vn=V[j+1][i]\n", 1720 | "\n", 1721 | " AE=-b*DXf[j][i+1]\n", 1722 | " AW=-b*DXf[j][i]\n", 1723 | " AN=-DYf[j+1][i]\n", 1724 | " AP=-AE-AW-AN\n", 1725 | " BP=b*(uw-ue)-vn\n", 1726 | "\n", 1727 | " A[ij][ij-1]=AW\n", 1728 | " A[ij][ij+1]=AE\n", 1729 | " A[ij][ij]=AP\n", 1730 | " A[ij][ij+Nx]=AN\n", 1731 | " B[ij]=BP\n", 1732 | " \n", 1733 | " #for lower bottom wall\n", 1734 | " for i in range(Nx_in+1,Nx-1):\n", 1735 | " j=0\n", 1736 | " ij=i+Nx*j\n", 1737 | " \n", 1738 | " ue=U[j][i+1]\n", 1739 | " uw=U[j][i]\n", 1740 | " vn=V[j+1][i]\n", 1741 | "\n", 1742 | " AE=-b*DXf[j][i+1]\n", 1743 | " AW=-b*DXf[j][i]\n", 1744 | " AN=-DYf[j+1][i]\n", 1745 | " AP=-AE-AW-AN\n", 1746 | " BP=b*(uw-ue)-vn\n", 1747 | "\n", 1748 | " A[ij][ij-1]=AW\n", 1749 | " A[ij][ij+1]=AE\n", 1750 | " A[ij][ij]=AP\n", 1751 | " A[ij][ij+Nx]=AN\n", 1752 | " B[ij]=BP\n", 1753 | " \n", 1754 | " #for lower left corner\n", 1755 | " i=Nx_in\n", 1756 | " j=0\n", 1757 | " ij=i+Nx*j\n", 1758 | "\n", 1759 | " ue=U[j][i+1]\n", 1760 | " vn=V[j+1][i]\n", 1761 | "\n", 1762 | " AE=-b*DXf[j][i+1]\n", 1763 | " AN=-DYf[j+1][i]\n", 1764 | " AP=-AE-AN\n", 1765 | " BP=b*(-ue)-vn\n", 1766 | "\n", 1767 | " A[ij][ij+1]=AE\n", 1768 | " A[ij][ij]=AP\n", 1769 | " A[ij][ij+Nx]=AN\n", 1770 | " B[ij]=BP\n", 1771 | " \n", 1772 | " #for top wall\n", 1773 | " for i in range(1,Nx-1):\n", 1774 | " j=Ny-1\n", 1775 | " ij=i+Nx*j\n", 1776 | " \n", 1777 | " ue=U[j][i+1]\n", 1778 | " uw=U[j][i]\n", 1779 | " vs=V[j][i]\n", 1780 | "\n", 1781 | " AE=-b*DXf[j][i+1]\n", 1782 | " AW=-b*DXf[j][i]\n", 1783 | " AS=-DYf[j][i]\n", 1784 | " AP=-AE-AW-AS\n", 1785 | " BP=b*(uw-ue)+vs\n", 1786 | "\n", 1787 | " A[ij][ij-1]=AW\n", 1788 | " A[ij][ij+1]=AE\n", 1789 | " A[ij][ij]=AP\n", 1790 | " A[ij][ij-Nx]=AS\n", 1791 | " B[ij]=BP\n", 1792 | " \n", 1793 | " #for top left corner\n", 1794 | " i=0\n", 1795 | " j=Ny-1\n", 1796 | " ij=i+Nx*j\n", 1797 | " \n", 1798 | " ue=U[j][i+1]\n", 1799 | " uw=U[j][i]\n", 1800 | " vs=V[j][i]\n", 1801 | " \n", 1802 | " de=DXf[j][i+1]\n", 1803 | " ds=DYf[j][i]\n", 1804 | " \n", 1805 | " AE=-b*de\n", 1806 | " AS=-ds\n", 1807 | " AP=b*de+ds\n", 1808 | " BP=b*(uw-ue)+vs\n", 1809 | "\n", 1810 | " A[ij][ij+1]=AE\n", 1811 | " A[ij][ij]=AP\n", 1812 | " A[ij][ij-Nx]=AS\n", 1813 | " B[ij]=BP\n", 1814 | " \n", 1815 | " #for top right corner\n", 1816 | " i=Nx-1\n", 1817 | " j=Ny-1\n", 1818 | " ij=i+Nx*j\n", 1819 | "\n", 1820 | " uw=U[j][i]\n", 1821 | " ue=U[j][i+1]\n", 1822 | " vs=V[j][i]\n", 1823 | " \n", 1824 | " dw=DXf[j][i]\n", 1825 | " ds=DYf[j][i]\n", 1826 | " DP=DX[j][i]\n", 1827 | "\n", 1828 | " AW=-b*dw+0.5*b*DP\n", 1829 | " AS=-ds\n", 1830 | " AP=b*dw+ds+0.5*b*DP\n", 1831 | " BP=b*uw-b*ue+vs\n", 1832 | "\n", 1833 | " A[ij][ij-1]=AW\n", 1834 | " A[ij][ij]=AP\n", 1835 | " A[ij][ij-Nx]=AS\n", 1836 | " B[ij]=BP\n", 1837 | " \n", 1838 | " #for upper bottom left corner\n", 1839 | " i=0\n", 1840 | " j=Ny_in\n", 1841 | " ij=i+Nx*j\n", 1842 | " \n", 1843 | " ue=U[j][i+1]\n", 1844 | " uw=U[j][i]\n", 1845 | " vn=V[j+1][i]\n", 1846 | " \n", 1847 | " de=DXf[j][i+1]\n", 1848 | " dn=DYf[j+1][i]\n", 1849 | "\n", 1850 | " AE=-b*de\n", 1851 | " AN=-dn\n", 1852 | " AP=b*de+dn\n", 1853 | " BP=b*(uw-ue)-vn\n", 1854 | "\n", 1855 | " A[ij][ij+1]=AE\n", 1856 | " A[ij][ij]=AP\n", 1857 | " A[ij][ij+Nx]=AN\n", 1858 | " B[ij]=BP\n", 1859 | " \n", 1860 | " #for bottom right corner\n", 1861 | " i=Nx-1\n", 1862 | " j=0\n", 1863 | " ij=i+Nx*j\n", 1864 | "\n", 1865 | " uw=U[j][i]\n", 1866 | " ue=U[j][i+1]\n", 1867 | " vn=V[j+1][i]\n", 1868 | " \n", 1869 | " dw=DXf[j][i]\n", 1870 | " dn=DYf[j+1][i]\n", 1871 | " DP=DX[j][i]\n", 1872 | "\n", 1873 | " AW=b*(-dw+0.5*DP)\n", 1874 | " AN=-dn\n", 1875 | " AP=b*(dw+0.5*DP)+dn\n", 1876 | " BP=b*uw-b*ue-vn\n", 1877 | "\n", 1878 | " A[ij][ij-1]=AW\n", 1879 | " A[ij][ij]=AP\n", 1880 | " A[ij][ij+Nx]=AN\n", 1881 | " B[ij]=BP\n", 1882 | " \n", 1883 | " #for step cells\n", 1884 | " for j in range(Ny_in):\n", 1885 | " for i in range(Nx_in):\n", 1886 | " ij=i+Nx*j\n", 1887 | " \n", 1888 | " A[ij][ij]=1\n", 1889 | " B[ij]=0\n", 1890 | " \n", 1891 | " A=sp.sparse.csr_matrix(A)\n", 1892 | " ml=pyamg.ruge_stuben_solver(A)\n", 1893 | " PC=ml.solve(B,tol=1E-6)\n", 1894 | " \n", 1895 | " PC_mesh=np.reshape(np.array(PC),[Ny,Nx])\n", 1896 | " \n", 1897 | " return PC_mesh,B" 1898 | ] 1899 | }, 1900 | { 1901 | "cell_type": "code", 1902 | "execution_count": 55, 1903 | "id": "c76d9917", 1904 | "metadata": {}, 1905 | "outputs": [], 1906 | "source": [ 1907 | "def correct(Nx,Ny,U_mesh,V_mesh,P_mesh,PC_mesh,DX,DY,av,ap):\n", 1908 | " global dx\n", 1909 | " global dy\n", 1910 | " \n", 1911 | " #U correction\n", 1912 | " #for upper interior\n", 1913 | " for j in range(Ny_in,Ny):\n", 1914 | " for i in range(1,Nx-1):\n", 1915 | " uc=av*DX[j][i]*(PC_mesh[j][i-1]-PC_mesh[j][i+1])\n", 1916 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1917 | " \n", 1918 | " #for lower interior\n", 1919 | " for j in range(Ny_in):\n", 1920 | " for i in range(Nx_in+1,Nx-1):\n", 1921 | " uc=av*DX[j][i]*(PC_mesh[j][i-1]-PC_mesh[j][i+1])\n", 1922 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1923 | "\n", 1924 | " #upper left wall(Inlet)\n", 1925 | " for j in range(Ny_in,Ny):\n", 1926 | " i=0\n", 1927 | " uc=av*DX[j][i]*(PC_mesh[j][i]-PC_mesh[j][i+1])\n", 1928 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1929 | "\n", 1930 | " #lower left wall\n", 1931 | " for j in range(Ny_in):\n", 1932 | " i=Nx_in\n", 1933 | " uc=av*DX[j][i]*(PC_mesh[j][i]-PC_mesh[j][i+1])\n", 1934 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1935 | " \n", 1936 | " #right wall\n", 1937 | " for j in range(Ny):\n", 1938 | " i=Nx-1\n", 1939 | " uc=av*DX[j][i]*(PC_mesh[j][i-1]-PC_mesh[j][i])\n", 1940 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 1941 | " \n", 1942 | " #V correction\n", 1943 | " #left interior\n", 1944 | " for j in range(Ny_in,Ny-1):\n", 1945 | " for i in range(Nx_in):\n", 1946 | " vc=av*DY[j][i]*(PC_mesh[j-1][i]-PC_mesh[j+1][i])\n", 1947 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1948 | " \n", 1949 | " #right interior\n", 1950 | " for j in range(1,Ny-1):\n", 1951 | " for i in range(Nx_in,Nx):\n", 1952 | " vc=av*DY[j][i]*(PC_mesh[j-1][i]-PC_mesh[j+1][i])\n", 1953 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1954 | " \n", 1955 | " #Upper bottom wall\n", 1956 | " for i in range(Nx_in):\n", 1957 | " j=Ny_in\n", 1958 | " vc=av*DY[j][i]*(PC_mesh[j][i]-PC_mesh[j+1][i])\n", 1959 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1960 | " \n", 1961 | " #Lower bottom wall\n", 1962 | " for i in range(Nx_in,Nx):\n", 1963 | " j=0\n", 1964 | " vc=av*DY[j][i]*(PC_mesh[j][i]-PC_mesh[j+1][i])\n", 1965 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1966 | " \n", 1967 | " #Top wall\n", 1968 | " for i in range(Nx):\n", 1969 | " j=Ny-1\n", 1970 | " vc=av*DY[j][i]*(PC_mesh[j-1][i]-PC_mesh[j][i])\n", 1971 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 1972 | " \n", 1973 | " #P correction\n", 1974 | " P_mesh=P_mesh+ap*np.power(dx*dy,1)*PC_mesh\n", 1975 | " \n", 1976 | " return U_mesh,V_mesh,P_mesh" 1977 | ] 1978 | }, 1979 | { 1980 | "cell_type": "code", 1981 | "execution_count": 56, 1982 | "id": "0e2a5cc9", 1983 | "metadata": {}, 1984 | "outputs": [], 1985 | "source": [ 1986 | "def curl(U,V,dx,dy,Nx,Ny):\n", 1987 | " C=np.zeros([Ny,Nx])\n", 1988 | " \n", 1989 | " for j in range(1,Ny-1):\n", 1990 | " for i in range(1,Nx-1):\n", 1991 | " dvdx=(V[j+1][i]-V[j-1][i])/(2*dx)\n", 1992 | " dxdy=(U[j][i+1]-U[j][i-1])/(2*dy) \n", 1993 | " C[j][i]=dvdx-dxdy\n", 1994 | " \n", 1995 | " return C" 1996 | ] 1997 | }, 1998 | { 1999 | "cell_type": "code", 2000 | "execution_count": 57, 2001 | "id": "808d915a", 2002 | "metadata": {}, 2003 | "outputs": [], 2004 | "source": [ 2005 | "def order_1(A0,B0,y,y0,N,AB):\n", 2006 | " y0=np.reshape(y0,[N,1])\n", 2007 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 2008 | " y1=y0+fn\n", 2009 | " return y1" 2010 | ] 2011 | }, 2012 | { 2013 | "cell_type": "code", 2014 | "execution_count": 58, 2015 | "id": "ea7d8853", 2016 | "metadata": {}, 2017 | "outputs": [], 2018 | "source": [ 2019 | "def order_2(A0,B0,y,y0,N,AB):\n", 2020 | " y0=np.reshape(y0,[N,1])\n", 2021 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 2022 | " fn1=AB[-1]\n", 2023 | " y2=y0+1.5*fn1-0.5*fn\n", 2024 | " return y2" 2025 | ] 2026 | }, 2027 | { 2028 | "cell_type": "code", 2029 | "execution_count": 59, 2030 | "id": "64bee86c", 2031 | "metadata": {}, 2032 | "outputs": [], 2033 | "source": [ 2034 | "def order_3(A0,B0,y,y0,N,AB):\n", 2035 | " y0=np.reshape(y0,[N,1])\n", 2036 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 2037 | " fn1=AB[-2]\n", 2038 | " fn2=AB[-1]\n", 2039 | " y3=y0+(23*fn2-16*fn1+5*fn)/12\n", 2040 | " return y3" 2041 | ] 2042 | }, 2043 | { 2044 | "cell_type": "code", 2045 | "execution_count": 60, 2046 | "id": "5a4d3892", 2047 | "metadata": {}, 2048 | "outputs": [], 2049 | "source": [ 2050 | "def order_4(A0,B0,y,y0,N,AB):\n", 2051 | " y0=np.reshape(y0,[N,1])\n", 2052 | " fn=np.matmul(A0,np.reshape(y,[N,1]))+B0\n", 2053 | " fn1=AB[-3]\n", 2054 | " fn2=AB[-2]\n", 2055 | " fn3=AB[-1]\n", 2056 | " y4=y0+(55*fn3-59*fn2+37*fn1-9*fn)/24\n", 2057 | " return y4" 2058 | ] 2059 | }, 2060 | { 2061 | "cell_type": "code", 2062 | "execution_count": 61, 2063 | "id": "3ece4c59", 2064 | "metadata": {}, 2065 | "outputs": [], 2066 | "source": [ 2067 | "def round_off(x):\n", 2068 | " return float(('{:g}'.format(float('{:.5g}'.format(x)))))" 2069 | ] 2070 | }, 2071 | { 2072 | "cell_type": "markdown", 2073 | "id": "d03eac22", 2074 | "metadata": {}, 2075 | "source": [ 2076 | "# Initialisation" 2077 | ] 2078 | }, 2079 | { 2080 | "cell_type": "code", 2081 | "execution_count": 62, 2082 | "id": "0bec6adb", 2083 | "metadata": { 2084 | "scrolled": true 2085 | }, 2086 | "outputs": [], 2087 | "source": [ 2088 | "transient=False\n", 2089 | "u_inlet=0.2\n", 2090 | "L=5\n", 2091 | "H=1\n", 2092 | "v=0.001\n", 2093 | "Re=u_inlet*H/v \n", 2094 | "av1=0.8 #Explicit under-relaxation\n", 2095 | "av2=0.7 #Implicit under-relaxation\n", 2096 | "ap=1E-2\n", 2097 | "\n", 2098 | "Nx=100\n", 2099 | "Nx_in=int(0.2*Nx)\n", 2100 | "Ny=20\n", 2101 | "Ny_in=int(0.5*Ny)\n", 2102 | "N=Nx*Ny\n", 2103 | "uNx=Nx+1\n", 2104 | "uNy=Ny\n", 2105 | "vNx=Nx\n", 2106 | "vNy=Ny+1\n", 2107 | "\n", 2108 | "dx=L/Nx\n", 2109 | "dy=H/Ny\n", 2110 | "b=dy/dx\n", 2111 | "iter_count=1\n", 2112 | "iter_total=0\n", 2113 | "itermax=500\n", 2114 | "\n", 2115 | "dt=1E-3\n", 2116 | "end_time=12\n", 2117 | "flow_time=0\n", 2118 | "time_step=0\n", 2119 | "time_steps=end_time/dt\n", 2120 | "\n", 2121 | "U_data=[]\n", 2122 | "V_data=[]\n", 2123 | "U_time_data=[]\n", 2124 | "V_time_data=[]\n", 2125 | "U_res_list=[]\n", 2126 | "V_res_list=[]\n", 2127 | "Cont_res_list=[]\n", 2128 | "t_list=[]\n", 2129 | "UAB=[]\n", 2130 | "VAB=[]\n", 2131 | "defer=False\n", 2132 | "stop=False\n", 2133 | "\n", 2134 | "U_mesh=np.full([Ny,Nx],u_inlet)\n", 2135 | "Uf=np.zeros([uNy,uNx])\n", 2136 | "V_mesh=np.zeros([Ny,Nx])\n", 2137 | "Vf=np.zeros([vNy,vNx])\n", 2138 | "P_mesh=np.zeros([Ny,Nx])" 2139 | ] 2140 | }, 2141 | { 2142 | "cell_type": "markdown", 2143 | "id": "9d779851", 2144 | "metadata": {}, 2145 | "source": [ 2146 | "# Solution" 2147 | ] 2148 | }, 2149 | { 2150 | "cell_type": "code", 2151 | "execution_count": 63, 2152 | "id": "85afe61d", 2153 | "metadata": { 2154 | "scrolled": true 2155 | }, 2156 | "outputs": [ 2157 | { 2158 | "name": "stdout", 2159 | "output_type": "stream", 2160 | "text": [ 2161 | "Steady State Calculation\n", 2162 | "\n", 2163 | "Calculation Data\n", 2164 | "Number of cells: 2000\n", 2165 | "Aspect Ratio: 1.0\n", 2166 | "Explicit relaxation factor: 0.8\n", 2167 | "Implicit relaxation factor: 0.7\n", 2168 | "Pressure relaxation factor: 0.01\n", 2169 | "\n", 2170 | "Reynold's Number: 200.0\n", 2171 | "\n", 2172 | " Iteration | U Residual | V Residual | Cont Residual | Time(s) \n", 2173 | "=============================================================================\n" 2174 | ] 2175 | }, 2176 | { 2177 | "name": "stderr", 2178 | "output_type": "stream", 2179 | "text": [ 2180 | "C:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_24416/327126063.py:16: RuntimeWarning: invalid value encountered in double_scalars\n", 2181 | " r=(UP-UW)/(UE-UP)\n", 2182 | "C:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_24416/327126063.py:44: RuntimeWarning: divide by zero encountered in double_scalars\n", 2183 | " r=(UW-UWW)/(UP-UW)\n", 2184 | "C:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_24416/327126063.py:44: RuntimeWarning: invalid value encountered in double_scalars\n", 2185 | " r=(UW-UWW)/(UP-UW)\n", 2186 | "C:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_24416/327126063.py:16: RuntimeWarning: divide by zero encountered in double_scalars\n", 2187 | " r=(UP-UW)/(UE-UP)\n" 2188 | ] 2189 | }, 2190 | { 2191 | "name": "stdout", 2192 | "output_type": "stream", 2193 | "text": [ 2194 | " 1 | 1.0212 | 1.6859 | 0.00017401 | 0.2551 \n", 2195 | " 2 | 2.0253 | 5.035 | 9.8804e-05 | 0.2648 \n", 2196 | " 3 | 0.83537 | 1.8611 | 4.7344e-05 | 0.2759 \n", 2197 | " 4 | 0.19369 | 0.83889 | 2.878e-05 | 0.2717 \n", 2198 | " 5 | 0.099978 | 0.31957 | 2.1964e-05 | 0.3125 \n", 2199 | " 6 | 0.10169 | 0.13959 | 2.144e-05 | 0.2668 \n", 2200 | " 7 | 0.077051 | 0.0651 | 2.0638e-05 | 0.2758 \n", 2201 | " 8 | 0.053622 | 0.05393 | 2.0447e-05 | 0.3054 \n", 2202 | " 9 | 0.036383 | 0.045647 | 2.0315e-05 | 0.2699 \n", 2203 | " 10 | 0.03411 | 0.040485 | 2.0313e-05 | 0.2996 \n", 2204 | " 20 | 0.018789 | 0.015591 | 1.9172e-05 | 0.2642 \n", 2205 | " 40 | 0.0060222 | 0.0040863 | 1.6924e-05 | 0.2971 \n", 2206 | " 60 | 0.0038232 | 0.0016213 | 1.4981e-05 | 0.3136 \n", 2207 | " 80 | 0.0023066 | 0.00070598 | 1.3489e-05 | 0.3203 \n", 2208 | " 100 | 0.0013398 | 0.000322 | 1.2487e-05 | 0.3194 \n", 2209 | "3rd order correction activated\n", 2210 | " 120 | 0.00078725 | 0.00016623 | 1.1728e-05 | 0.2826 \n", 2211 | " 140 | 0.00049938 | 0.00015051 | 1.1119e-05 | 0.2639 \n", 2212 | " 160 | 0.00047035 | 0.00013675 | 1.0592e-05 | 0.3036 \n", 2213 | " 180 | 0.00044347 | 0.00012437 | 1.0117e-05 | 0.281 \n", 2214 | " 200 | 0.00041849 | 0.00011319 | 9.6777e-06 | 0.3225 \n", 2215 | " 220 | 0.00039518 | 0.00010305 | 9.2673e-06 | 0.2728 \n", 2216 | " 240 | 0.00037339 | 9.3814e-05 | 8.8817e-06 | 0.2799 \n", 2217 | " 260 | 0.00035296 | 8.54e-05 | 8.5187e-06 | 0.3226 \n", 2218 | " 280 | 0.00033377 | 7.7714e-05 | 8.1771e-06 | 0.273 \n", 2219 | " 300 | 0.00031576 | 7.0692e-05 | 7.8585e-06 | 0.4865 \n", 2220 | " 320 | 0.00029882 | 6.4265e-05 | 7.5636e-06 | 0.2719 \n", 2221 | " 340 | 0.0002829 | 5.8381e-05 | 7.2935e-06 | 0.3154 \n", 2222 | " 360 | 0.00026791 | 5.2985e-05 | 7.0431e-06 | 0.2767 \n", 2223 | " 380 | 0.00025378 | 4.8036e-05 | 6.8081e-06 | 0.2842 \n", 2224 | " 400 | 0.00024046 | 4.3487e-05 | 6.5868e-06 | 0.312 \n", 2225 | " 420 | 0.00022779 | 4.0246e-05 | 6.377e-06 | 0.279 \n", 2226 | " 440 | 0.00021606 | 3.8297e-05 | 6.1794e-06 | 0.295 \n", 2227 | " 460 | 0.00020645 | 3.6435e-05 | 5.9936e-06 | 0.282 \n", 2228 | " 480 | 0.00019789 | 3.4688e-05 | 5.8205e-06 | 0.337 \n", 2229 | " 500 | 0.00018935 | 3.2985e-05 | 5.659e-06 | 0.3094 \n", 2230 | "\n", 2231 | "Execution time: 147.717 seconds\n", 2232 | "Number of iterations: 500\n", 2233 | "Average time per iteration: 0.2954 +- 0.0294 seconds\n" 2234 | ] 2235 | } 2236 | ], 2237 | "source": [ 2238 | "st=time.time()\n", 2239 | "\n", 2240 | "if transient==True:\n", 2241 | " defer=True\n", 2242 | " print('Transient Calculation')\n", 2243 | " print()\n", 2244 | " print('Calculation Data')\n", 2245 | " print('Number of cells:',N)\n", 2246 | " print('Aspect Ratio:', round(b,4))\n", 2247 | " print('Explicit relaxation factor:',av1)\n", 2248 | " print('Implicit relaxation factor:',av2)\n", 2249 | " print('Pressure relaxation factor:',ap)\n", 2250 | " print('Total time steps:',int(time_steps))\n", 2251 | " print()\n", 2252 | " print(\"Reynold's Number:\",Re)\n", 2253 | " print()\n", 2254 | "\n", 2255 | " U0_mesh=U_mesh\n", 2256 | " V0_mesh=V_mesh\n", 2257 | " P0_mesh=P_mesh\n", 2258 | "\n", 2259 | " for flow in tqdm(range(int(time_steps)),desc='Progress bar',unit='time steps'):\n", 2260 | " if time_step==0:\n", 2261 | " print()\n", 2262 | " print('{:<10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format('Time Step','Iterations','U Residual','V Residual',\n", 2263 | " 'Cont Residual','Time(s)'))\n", 2264 | " print('=========================================================================================')\n", 2265 | "\n", 2266 | " s=time.time()\n", 2267 | " if flow%20==0:\n", 2268 | " U_time_data.append(U0_mesh)\n", 2269 | " V_time_data.append(V0_mesh)\n", 2270 | "\n", 2271 | " iter_count=1\n", 2272 | " time_step+=1\n", 2273 | " flow_time+=dt\n", 2274 | "\n", 2275 | " while iter_count<=itermax:\n", 2276 | " start=time.time()\n", 2277 | " U1_mesh,V1_mesh,DX,DY,A,UB,VB=mom(Nx,Ny,U_mesh,V_mesh,P_mesh,U0_mesh,V0_mesh,time_step,UAB,VAB,av2)\n", 2278 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U1_mesh,V1_mesh,P_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 2279 | " PC_mesh,cont_A=cont(Nx,Ny,Uf,Vf,P_mesh,DXf,DYf)\n", 2280 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av1,ap)\n", 2281 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U2_mesh,V2_mesh,P1_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 2282 | " PC_mesh,cont_B=cont(Nx,Ny,Uf,Vf,P1_mesh,DXf,DYf)\n", 2283 | " U3_mesh,V3_mesh,P2_mesh=correct(Nx,Ny,U2_mesh,V2_mesh,P1_mesh,PC_mesh,DX,DY,av1,ap)\n", 2284 | "\n", 2285 | " U_residual=abs(max(U3_mesh.flatten()-U_mesh.flatten()))/u_inlet\n", 2286 | " U_res_list.append(abs(U_residual))\n", 2287 | " V_residual=abs(max(V3_mesh.flatten()-V_mesh.flatten()))/u_inlet\n", 2288 | " V_res_list.append(abs(V_residual))\n", 2289 | " P_residual=np.sum(abs(cont_B)*dx/N)\n", 2290 | " Cont_res_list.append(P_residual)\n", 2291 | "\n", 2292 | " U_mesh=U3_mesh\n", 2293 | " V_mesh=V3_mesh\n", 2294 | " P_mesh=P2_mesh\n", 2295 | "\n", 2296 | " end=time.time()\n", 2297 | " t=end-start\n", 2298 | " t_list.append(t)\n", 2299 | "\n", 2300 | " U_residual=round_off(U_residual)\n", 2301 | " V_residual=round_off(V_residual)\n", 2302 | " P_residual=round_off(P_residual)\n", 2303 | "\n", 2304 | " if iter_count==1:\n", 2305 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 2306 | " abs(V_residual),abs(P_residual),\n", 2307 | " str(round(t,4))))\n", 2308 | "\n", 2309 | " if abs(U_residual)<1E-3 and abs(V_residual)<1E-3 and abs(P_residual)<1E-4: \n", 2310 | " U0_mesh=U_mesh\n", 2311 | " V0_mesh=V_mesh\n", 2312 | " P0_mesh=P_mesh\n", 2313 | " iter_total+=1\n", 2314 | "\n", 2315 | " grad_U=np.matmul(A,np.reshape(U0_mesh,[N,1]))+UB\n", 2316 | " UAB.append(grad_U)\n", 2317 | "\n", 2318 | " grad_V=np.matmul(A,np.reshape(V0_mesh,[N,1]))+VB\n", 2319 | " VAB.append(grad_V)\n", 2320 | "\n", 2321 | " if len(UAB)>3:\n", 2322 | " UAB.pop(0)\n", 2323 | " VAB.pop(0)\n", 2324 | "\n", 2325 | " break\n", 2326 | "\n", 2327 | " if iter_count==itermax:\n", 2328 | " U0_mesh=U_mesh\n", 2329 | " V0_mesh=V_mesh\n", 2330 | " P0_mesh=P_mesh\n", 2331 | " iter_total+=1\n", 2332 | "\n", 2333 | " grad_U=np.matmul(A,np.reshape(U0_mesh,[N,1]))+UB\n", 2334 | " UAB.append(grad_U)\n", 2335 | "\n", 2336 | " grad_V=np.matmul(A,np.reshape(V0_mesh,[N,1]))+VB\n", 2337 | " VAB.append(grad_V)\n", 2338 | "\n", 2339 | " if len(UAB)>3:\n", 2340 | " UAB.pop(0)\n", 2341 | " VAB.pop(0)\n", 2342 | " break\n", 2343 | "\n", 2344 | " iter_count+=1\n", 2345 | " iter_total+=1\n", 2346 | "\n", 2347 | " e=time.time()\n", 2348 | " elap=e-s\n", 2349 | " if iter_count!=1:\n", 2350 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 2351 | " abs(V_residual),abs(P_residual),\n", 2352 | " str(round(elap,4))))\n", 2353 | " print()\n", 2354 | " \n", 2355 | " print('Execution time:',round(elapsed,3),'seconds')\n", 2356 | " print('Number of iterations:',iter_total)\n", 2357 | " print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')\n", 2358 | " print('Flow time:',round(flow_time,4),'seconds')\n", 2359 | " \n", 2360 | " \n", 2361 | "else:\n", 2362 | " st=time.time()\n", 2363 | " print('Steady State Calculation')\n", 2364 | " print()\n", 2365 | " print('Calculation Data')\n", 2366 | " print('Number of cells:',N)\n", 2367 | " print('Aspect Ratio:', round(b,4))\n", 2368 | " print('Explicit relaxation factor:',av1)\n", 2369 | " print('Implicit relaxation factor:',av2)\n", 2370 | " print('Pressure relaxation factor:',ap)\n", 2371 | " print()\n", 2372 | " print(\"Reynold's Number:\",Re)\n", 2373 | " print()\n", 2374 | " print('{:<10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(' Iteration','U Residual','V Residual','Cont Residual','Time(s)'))\n", 2375 | " print('=============================================================================')\n", 2376 | " \n", 2377 | " U0_mesh=U_mesh\n", 2378 | " V0_mesh=V_mesh\n", 2379 | " iter_total=1\n", 2380 | " \n", 2381 | " while iter_total<=itermax:\n", 2382 | " start=time.time()\n", 2383 | " U1_mesh,V1_mesh,DX,DY=mom(Nx,Ny,U_mesh,V_mesh,P_mesh,U0_mesh,V0_mesh,time_step,UAB,VAB,av2)\n", 2384 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U1_mesh,V1_mesh,P_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 2385 | " PC_mesh,cont_A=cont(Nx,Ny,Uf,Vf,P_mesh,DXf,DYf)\n", 2386 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av1,ap)\n", 2387 | " Uf,Vf,DXf,DYf=face(Uf,Vf,U2_mesh,V2_mesh,P1_mesh,uNx,uNy,vNx,vNy,DX,DY)\n", 2388 | " PC_mesh,cont_B=cont(Nx,Ny,Uf,Vf,P1_mesh,DXf,DYf)\n", 2389 | " U3_mesh,V3_mesh,P2_mesh=correct(Nx,Ny,U2_mesh,V2_mesh,P1_mesh,PC_mesh,DX,DY,av1,ap)\n", 2390 | "\n", 2391 | " U_residual=abs(max(U2_mesh.flatten()-U_mesh.flatten()))/u_inlet\n", 2392 | " U_res_list.append(abs(U_residual))\n", 2393 | " V_residual=abs(max(V2_mesh.flatten()-V_mesh.flatten()))/u_inlet\n", 2394 | " V_res_list.append(abs(V_residual))\n", 2395 | " P_residual=np.sum(abs(cont_B)*dx/N)\n", 2396 | " Cont_res_list.append(P_residual)\n", 2397 | "\n", 2398 | " U_data.append(U2_mesh)\n", 2399 | " V_data.append(V2_mesh)\n", 2400 | "\n", 2401 | " U_mesh=U1_mesh\n", 2402 | " V_mesh=V2_mesh\n", 2403 | " P_mesh=P1_mesh\n", 2404 | "\n", 2405 | " end=time.time()\n", 2406 | " t=end-start\n", 2407 | " t_list.append(t)\n", 2408 | "\n", 2409 | " U_residual=round_off(U_residual)\n", 2410 | " V_residual=round_off(V_residual)\n", 2411 | " P_residual=round_off(P_residual)\n", 2412 | "\n", 2413 | " if iter_total%20==0 or iter_total<=10:\n", 2414 | " print('{:^10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(str(iter_total),abs(U_residual),abs(V_residual),\n", 2415 | " abs(P_residual),str(round(t,4))))\n", 2416 | " \n", 2417 | " if abs(U_residual)<1E-4 and abs(V_residual)<1E-4 and abs(P_residual)<1E-4: \n", 2418 | " print('{:^10} |{:^18}|{:^18}|{:^18}|{:^9}'.format(str(iter_total),abs(U_residual),abs(V_residual),\n", 2419 | " abs(P_residual),str(round(t,4))))\n", 2420 | " break\n", 2421 | " \n", 2422 | " if abs(U_residual)<1E-3 and abs(V_residual)<1E-3 and stop==False:\n", 2423 | " defer=True\n", 2424 | " stop=True\n", 2425 | " print('3rd order correction activated')\n", 2426 | "\n", 2427 | " if iter_total==itermax:\n", 2428 | " break\n", 2429 | "\n", 2430 | " iter_total+=1\n", 2431 | "\n", 2432 | " U_final=U0_mesh\n", 2433 | " V_final=V0_mesh\n", 2434 | "\n", 2435 | " et=time.time()\n", 2436 | " elapsed=et-st\n", 2437 | " \n", 2438 | " print()\n", 2439 | " print('Execution time:',round(elapsed,3),'seconds')\n", 2440 | " print('Number of iterations:',iter_total)\n", 2441 | " print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')\n" 2442 | ] 2443 | }, 2444 | { 2445 | "cell_type": "markdown", 2446 | "id": "8e2533eb", 2447 | "metadata": {}, 2448 | "source": [ 2449 | "# Post-Processing" 2450 | ] 2451 | }, 2452 | { 2453 | "cell_type": "code", 2454 | "execution_count": 64, 2455 | "id": "86ee3b2c", 2456 | "metadata": {}, 2457 | "outputs": [], 2458 | "source": [ 2459 | "U_final=U_mesh\n", 2460 | "V_final=V_mesh\n", 2461 | "P_final=P_mesh\n", 2462 | "\n", 2463 | "V_mag=np.zeros([Ny,Nx])\n", 2464 | "V_mag=(np.sqrt(np.power(U_final,2))+np.sqrt(np.power(V_final,2)))\n", 2465 | " \n", 2466 | "vort=curl(U_final,V_final,dx,dy,Nx,Ny)\n", 2467 | "\n", 2468 | "x=np.linspace(dx/2,L-dx/2,Nx)\n", 2469 | "y=np.linspace(dy/2,H-dy/2,Ny)\n", 2470 | "xx,yy=np.meshgrid(x,y)\n", 2471 | "\n", 2472 | "xe=np.linspace(1,Nx,Nx)\n", 2473 | "ye=np.linspace(1,Ny,Ny)\n", 2474 | "xxe,yye=np.meshgrid(xe,ye)\n", 2475 | "grid=np.zeros([Ny,Nx])" 2476 | ] 2477 | }, 2478 | { 2479 | "cell_type": "code", 2480 | "execution_count": 66, 2481 | "id": "bf1eae07", 2482 | "metadata": { 2483 | "scrolled": false 2484 | }, 2485 | "outputs": [ 2486 | { 2487 | "name": "stderr", 2488 | "output_type": "stream", 2489 | "text": [ 2490 | "C:\\Users\\Kangluo See\\anaconda3\\lib\\site-packages\\matplotlib\\patches.py:3027: RuntimeWarning: invalid value encountered in double_scalars\n", 2491 | " cos_t, sin_t = head_length / head_dist, head_width / head_dist\n" 2492 | ] 2493 | }, 2494 | { 2495 | "data": { 2496 | "image/png": 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\n", 2509 | "text/plain": [ 2510 | "
" 2511 | ] 2512 | }, 2513 | "metadata": { 2514 | "needs_background": "light" 2515 | }, 2516 | "output_type": "display_data" 2517 | } 2518 | ], 2519 | "source": [ 2520 | "fig,ax=plt.subplots(4,1,figsize=(18,12))\n", 2521 | "ax[0].streamplot(xx,yy,U_final,V_final,density=2.5,color='white',linewidth=0.8)\n", 2522 | "graph=ax[0].contourf(xx,yy,V_mag,cmap=cm.jet,levels=255)\n", 2523 | "ax[0].set_title('Velocity contour plot @ Re = '+str(Re))\n", 2524 | "fig.colorbar(graph,ax=ax[0],label='Velocity (m/s)')\n", 2525 | "ax[0].set_xlim(0,L)\n", 2526 | "ax[0].set_ylim(0,H)\n", 2527 | "\n", 2528 | "ax[1].streamplot(xx,yy,U_final,V_final,density=3,color=V_mag,cmap=cm.jet,linewidth=0.8,arrowsize=0)\n", 2529 | "ax[1].set_title('Streamlines @ Re='+str(Re))\n", 2530 | "fig.colorbar(graph,ax=ax[1],label='Velocity (m/s)')\n", 2531 | "ax[1].set_xlim(0,L)\n", 2532 | "ax[1].set_ylim(0,H)\n", 2533 | "\n", 2534 | "graph=ax[2].contourf(xx,yy,vort,cmap=cm.jet,levels=255)\n", 2535 | "ax[2].set_title('Vorticity contour plot @ Re = '+str(Re))\n", 2536 | "fig.colorbar(graph,ax=ax[2])\n", 2537 | "ax[2].set_xlim(0,L)\n", 2538 | "ax[2].set_ylim(0,H)\n", 2539 | "\n", 2540 | "graph=ax[3].pcolormesh(xxe,yye,grid,cmap=cm.seismic,ec='k',shading='auto')\n", 2541 | "fig.colorbar(graph,ax=ax[3])\n", 2542 | "ax[3].set_title('Grid Layout')\n", 2543 | "ax[3].set_xlim(1,Nx)\n", 2544 | "ax[3].set_ylim(1,Ny)\n", 2545 | "\n", 2546 | "rect1=patches.Rectangle([0,0],1,0.5,hatch='////',fc='k',ec='k')\n", 2547 | "rect2=patches.Rectangle([0,0],1,0.5,hatch='////',fc='k',ec='k')\n", 2548 | "rect3=patches.Rectangle([0,0],1,0.5,hatch='////',fc='k',ec='k')\n", 2549 | "\n", 2550 | "ax[0].add_patch(rect1)\n", 2551 | "ax[1].add_patch(rect2)\n", 2552 | "ax[2].add_patch(rect3)\n", 2553 | "\n", 2554 | "plt.show()\n", 2555 | "\n", 2556 | "iterations=np.linspace(0,iter_total,iter_total)\n", 2557 | "fig,ax=plt.subplots(1,1,figsize=(16,6))\n", 2558 | "plt.plot(iterations,U_res_list,label='U residual',linewidth=0.75)\n", 2559 | "plt.plot(iterations,V_res_list,label='V residual',linewidth=0.75)\n", 2560 | "plt.plot(iterations,Cont_res_list,label='Continuity residual',linewidth=0.75)\n", 2561 | "plt.xlabel('Iterations')\n", 2562 | "plt.ylabel('Residual')\n", 2563 | "ax.set_yscale('log')\n", 2564 | "plt.grid()\n", 2565 | "plt.xlim(0)\n", 2566 | "plt.legend()\n", 2567 | "\n", 2568 | "plt.show()" 2569 | ] 2570 | }, 2571 | { 2572 | "cell_type": "code", 2573 | "execution_count": 67, 2574 | "id": "81620a58", 2575 | "metadata": {}, 2576 | "outputs": [], 2577 | "source": [ 2578 | "def column(matrix, i):\n", 2579 | " return np.array([row[i] for row in matrix])" 2580 | ] 2581 | }, 2582 | { 2583 | "cell_type": "markdown", 2584 | "id": "5cfde75d", 2585 | "metadata": {}, 2586 | "source": [ 2587 | "Re=200: RL=2m" 2588 | ] 2589 | }, 2590 | { 2591 | "cell_type": "markdown", 2592 | "id": "e79b3ab2", 2593 | "metadata": {}, 2594 | "source": [ 2595 | "# Animation" 2596 | ] 2597 | }, 2598 | { 2599 | "cell_type": "code", 2600 | "execution_count": 89, 2601 | "id": "0bc30ffa", 2602 | "metadata": {}, 2603 | "outputs": [ 2604 | { 2605 | "data": { 2606 | "text/plain": [ 2607 | "(16, 80)" 2608 | ] 2609 | }, 2610 | "execution_count": 89, 2611 | "metadata": {}, 2612 | "output_type": "execute_result" 2613 | } 2614 | ], 2615 | "source": [ 2616 | "V_mag.shape" 2617 | ] 2618 | }, 2619 | { 2620 | "cell_type": "code", 2621 | "execution_count": 21, 2622 | "id": "d84c45ce", 2623 | "metadata": {}, 2624 | "outputs": [ 2625 | { 2626 | "data": { 2627 | "text/plain": [ 2628 | "546" 2629 | ] 2630 | }, 2631 | "execution_count": 21, 2632 | "metadata": {}, 2633 | "output_type": "execute_result" 2634 | } 2635 | ], 2636 | "source": [ 2637 | "len(U_time_data)" 2638 | ] 2639 | }, 2640 | { 2641 | "cell_type": "code", 2642 | "execution_count": 51, 2643 | "id": "e0b12a0a", 2644 | "metadata": {}, 2645 | "outputs": [], 2646 | "source": [ 2647 | "U_anim=U_time_data[::20]\n", 2648 | "V_anim=V_time_data[::20]\n", 2649 | "count=int(26)\n", 2650 | "\n", 2651 | "U_file=[]\n", 2652 | "V_file=[]\n", 2653 | "V_mag_file=[]\n", 2654 | "\n", 2655 | "for k in range(count):\n", 2656 | " U_final=U_anim[k]\n", 2657 | " V_final=V_anim[k] \n", 2658 | " V_mag=np.sqrt(np.power(U_final,2)+np.power(V_final,2))\n", 2659 | " \n", 2660 | " U_file.append(U_final)\n", 2661 | " V_file.append(V_final)\n", 2662 | " V_mag_file.append(V_mag)" 2663 | ] 2664 | }, 2665 | { 2666 | "cell_type": "code", 2667 | "execution_count": 52, 2668 | "id": "c40fc7bd", 2669 | "metadata": {}, 2670 | "outputs": [], 2671 | "source": [ 2672 | "def animate(k):\n", 2673 | " ax.clear()\n", 2674 | " \n", 2675 | " U_final=U_file[k]\n", 2676 | " V_final=V_file[k]\n", 2677 | " #V_mag=V_mag_file[k]\n", 2678 | " #vort=Vort_file[k]\n", 2679 | " plt.title('1 \\n''2 \\n''Flow time: '+str(round(k*dt*25,3))+' seconds \\n' 'Streamlines')\n", 2680 | " plt.xlim(0,L)\n", 2681 | " plt.ylim(0,H)\n", 2682 | " rect1=patches.Rectangle([0,0],2,0.5,hatch='////',fc='k',ec='k')\n", 2683 | " ax.add_patch(rect1)\n", 2684 | " #contour=plt.contourf(xx,yy,V_mag,cmap=cm.jet,vmax=1.5*u_inlet,levels=255)\n", 2685 | " #contour=plt.contourf(xx,yy,vort,cmap=cm.seismic,levels=30,vmax=160)\n", 2686 | " stream=plt.streamplot(xx,yy,U_final,V_final,linewidth=0.75,density=1.75,color='k',arrowsize=1)\n", 2687 | " return stream" 2688 | ] 2689 | }, 2690 | { 2691 | "cell_type": "code", 2692 | "execution_count": 53, 2693 | "id": "8d346965", 2694 | "metadata": {}, 2695 | "outputs": [ 2696 | { 2697 | "data": { 2698 | "application/javascript": [ 2699 | "/* Put everything inside the global mpl namespace */\n", 2700 | "/* global mpl */\n", 2701 | "window.mpl = {};\n", 2702 | "\n", 2703 | "mpl.get_websocket_type = function () {\n", 2704 | " if (typeof WebSocket !== 'undefined') {\n", 2705 | " return WebSocket;\n", 2706 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 2707 | " return MozWebSocket;\n", 2708 | " } else {\n", 2709 | " alert(\n", 2710 | " 'Your browser does not have WebSocket support. ' +\n", 2711 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 2712 | " 'Firefox 4 and 5 are also supported but you ' +\n", 2713 | " 'have to enable WebSockets in about:config.'\n", 2714 | " );\n", 2715 | " }\n", 2716 | "};\n", 2717 | "\n", 2718 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 2719 | " this.id = figure_id;\n", 2720 | "\n", 2721 | " this.ws = websocket;\n", 2722 | "\n", 2723 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 2724 | "\n", 2725 | " if (!this.supports_binary) {\n", 2726 | " var warnings = document.getElementById('mpl-warnings');\n", 2727 | " if (warnings) {\n", 2728 | " warnings.style.display = 'block';\n", 2729 | " warnings.textContent =\n", 2730 | " 'This browser does not support binary websocket messages. ' +\n", 2731 | " 'Performance may be slow.';\n", 2732 | " }\n", 2733 | " }\n", 2734 | "\n", 2735 | " this.imageObj = new Image();\n", 2736 | "\n", 2737 | " this.context = undefined;\n", 2738 | " this.message = undefined;\n", 2739 | " this.canvas = undefined;\n", 2740 | " this.rubberband_canvas = undefined;\n", 2741 | " this.rubberband_context = undefined;\n", 2742 | " this.format_dropdown = undefined;\n", 2743 | "\n", 2744 | " this.image_mode = 'full';\n", 2745 | "\n", 2746 | " this.root = document.createElement('div');\n", 2747 | " this.root.setAttribute('style', 'display: inline-block');\n", 2748 | " this._root_extra_style(this.root);\n", 2749 | "\n", 2750 | " parent_element.appendChild(this.root);\n", 2751 | "\n", 2752 | " this._init_header(this);\n", 2753 | " this._init_canvas(this);\n", 2754 | " this._init_toolbar(this);\n", 2755 | "\n", 2756 | " var fig = this;\n", 2757 | "\n", 2758 | " this.waiting = false;\n", 2759 | "\n", 2760 | " this.ws.onopen = function () {\n", 2761 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 2762 | " fig.send_message('send_image_mode', {});\n", 2763 | " if (fig.ratio !== 1) {\n", 2764 | " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", 2765 | " }\n", 2766 | " fig.send_message('refresh', {});\n", 2767 | " };\n", 2768 | "\n", 2769 | " this.imageObj.onload = function () {\n", 2770 | " if (fig.image_mode === 'full') {\n", 2771 | " // Full images could contain transparency (where diff images\n", 2772 | " // almost always do), so we need to clear the canvas so that\n", 2773 | " // there is no ghosting.\n", 2774 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 2775 | " }\n", 2776 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 2777 | " };\n", 2778 | "\n", 2779 | " this.imageObj.onunload = function () {\n", 2780 | " fig.ws.close();\n", 2781 | " };\n", 2782 | "\n", 2783 | " this.ws.onmessage = this._make_on_message_function(this);\n", 2784 | "\n", 2785 | " this.ondownload = ondownload;\n", 2786 | "};\n", 2787 | "\n", 2788 | "mpl.figure.prototype._init_header = function () {\n", 2789 | " var titlebar = document.createElement('div');\n", 2790 | " titlebar.classList =\n", 2791 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 2792 | " var titletext = document.createElement('div');\n", 2793 | " titletext.classList = 'ui-dialog-title';\n", 2794 | " titletext.setAttribute(\n", 2795 | " 'style',\n", 2796 | " 'width: 100%; text-align: center; padding: 3px;'\n", 2797 | " );\n", 2798 | " titlebar.appendChild(titletext);\n", 2799 | " this.root.appendChild(titlebar);\n", 2800 | " this.header = titletext;\n", 2801 | "};\n", 2802 | "\n", 2803 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 2804 | "\n", 2805 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 2806 | "\n", 2807 | "mpl.figure.prototype._init_canvas = function () {\n", 2808 | " var fig = this;\n", 2809 | "\n", 2810 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 2811 | " canvas_div.setAttribute(\n", 2812 | " 'style',\n", 2813 | " 'border: 1px solid #ddd;' +\n", 2814 | " 'box-sizing: content-box;' +\n", 2815 | " 'clear: both;' +\n", 2816 | " 'min-height: 1px;' +\n", 2817 | " 'min-width: 1px;' +\n", 2818 | " 'outline: 0;' +\n", 2819 | " 'overflow: hidden;' +\n", 2820 | " 'position: relative;' +\n", 2821 | " 'resize: both;'\n", 2822 | " );\n", 2823 | "\n", 2824 | " function on_keyboard_event_closure(name) {\n", 2825 | " return function (event) {\n", 2826 | " return fig.key_event(event, name);\n", 2827 | " };\n", 2828 | " }\n", 2829 | "\n", 2830 | " canvas_div.addEventListener(\n", 2831 | " 'keydown',\n", 2832 | " on_keyboard_event_closure('key_press')\n", 2833 | " );\n", 2834 | " canvas_div.addEventListener(\n", 2835 | " 'keyup',\n", 2836 | " on_keyboard_event_closure('key_release')\n", 2837 | " );\n", 2838 | "\n", 2839 | " this._canvas_extra_style(canvas_div);\n", 2840 | " this.root.appendChild(canvas_div);\n", 2841 | "\n", 2842 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 2843 | " canvas.classList.add('mpl-canvas');\n", 2844 | " canvas.setAttribute('style', 'box-sizing: content-box;');\n", 2845 | "\n", 2846 | " this.context = canvas.getContext('2d');\n", 2847 | "\n", 2848 | " var backingStore =\n", 2849 | " this.context.backingStorePixelRatio ||\n", 2850 | " this.context.webkitBackingStorePixelRatio ||\n", 2851 | " this.context.mozBackingStorePixelRatio ||\n", 2852 | " this.context.msBackingStorePixelRatio ||\n", 2853 | " this.context.oBackingStorePixelRatio ||\n", 2854 | " this.context.backingStorePixelRatio ||\n", 2855 | " 1;\n", 2856 | "\n", 2857 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 2858 | "\n", 2859 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 2860 | " 'canvas'\n", 2861 | " ));\n", 2862 | " rubberband_canvas.setAttribute(\n", 2863 | " 'style',\n", 2864 | " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", 2865 | " );\n", 2866 | "\n", 2867 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 2868 | " if (this.ResizeObserver === undefined) {\n", 2869 | " if (window.ResizeObserver !== undefined) {\n", 2870 | " this.ResizeObserver = window.ResizeObserver;\n", 2871 | " } else {\n", 2872 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 2873 | " this.ResizeObserver = obs.ResizeObserver;\n", 2874 | " }\n", 2875 | " }\n", 2876 | "\n", 2877 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 2878 | " var nentries = entries.length;\n", 2879 | " for (var i = 0; i < nentries; i++) {\n", 2880 | " var entry = entries[i];\n", 2881 | " var width, height;\n", 2882 | " if (entry.contentBoxSize) {\n", 2883 | " if (entry.contentBoxSize instanceof Array) {\n", 2884 | " // Chrome 84 implements new version of spec.\n", 2885 | " width = entry.contentBoxSize[0].inlineSize;\n", 2886 | " height = entry.contentBoxSize[0].blockSize;\n", 2887 | " } else {\n", 2888 | " // Firefox implements old version of spec.\n", 2889 | " width = entry.contentBoxSize.inlineSize;\n", 2890 | " height = entry.contentBoxSize.blockSize;\n", 2891 | " }\n", 2892 | " } else {\n", 2893 | " // Chrome <84 implements even older version of spec.\n", 2894 | " width = entry.contentRect.width;\n", 2895 | " height = entry.contentRect.height;\n", 2896 | " }\n", 2897 | "\n", 2898 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 2899 | " // the canvas container.\n", 2900 | " if (entry.devicePixelContentBoxSize) {\n", 2901 | " // Chrome 84 implements new version of spec.\n", 2902 | " canvas.setAttribute(\n", 2903 | " 'width',\n", 2904 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 2905 | " );\n", 2906 | " canvas.setAttribute(\n", 2907 | " 'height',\n", 2908 | " entry.devicePixelContentBoxSize[0].blockSize\n", 2909 | " );\n", 2910 | " } else {\n", 2911 | " canvas.setAttribute('width', width * fig.ratio);\n", 2912 | " canvas.setAttribute('height', height * fig.ratio);\n", 2913 | " }\n", 2914 | " canvas.setAttribute(\n", 2915 | " 'style',\n", 2916 | " 'width: ' + width + 'px; height: ' + height + 'px;'\n", 2917 | " );\n", 2918 | "\n", 2919 | " rubberband_canvas.setAttribute('width', width);\n", 2920 | " rubberband_canvas.setAttribute('height', height);\n", 2921 | "\n", 2922 | " // And update the size in Python. We ignore the initial 0/0 size\n", 2923 | " // that occurs as the element is placed into the DOM, which should\n", 2924 | " // otherwise not happen due to the minimum size styling.\n", 2925 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 2926 | " fig.request_resize(width, height);\n", 2927 | " }\n", 2928 | " }\n", 2929 | " });\n", 2930 | " this.resizeObserverInstance.observe(canvas_div);\n", 2931 | "\n", 2932 | " function on_mouse_event_closure(name) {\n", 2933 | " return function (event) {\n", 2934 | " return fig.mouse_event(event, name);\n", 2935 | " };\n", 2936 | " }\n", 2937 | "\n", 2938 | " rubberband_canvas.addEventListener(\n", 2939 | " 'mousedown',\n", 2940 | " on_mouse_event_closure('button_press')\n", 2941 | " );\n", 2942 | " rubberband_canvas.addEventListener(\n", 2943 | " 'mouseup',\n", 2944 | " on_mouse_event_closure('button_release')\n", 2945 | " );\n", 2946 | " rubberband_canvas.addEventListener(\n", 2947 | " 'dblclick',\n", 2948 | " on_mouse_event_closure('dblclick')\n", 2949 | " );\n", 2950 | " // Throttle sequential mouse events to 1 every 20ms.\n", 2951 | " rubberband_canvas.addEventListener(\n", 2952 | " 'mousemove',\n", 2953 | " on_mouse_event_closure('motion_notify')\n", 2954 | " );\n", 2955 | "\n", 2956 | " rubberband_canvas.addEventListener(\n", 2957 | " 'mouseenter',\n", 2958 | " on_mouse_event_closure('figure_enter')\n", 2959 | " );\n", 2960 | " rubberband_canvas.addEventListener(\n", 2961 | " 'mouseleave',\n", 2962 | " on_mouse_event_closure('figure_leave')\n", 2963 | " );\n", 2964 | "\n", 2965 | " canvas_div.addEventListener('wheel', function (event) {\n", 2966 | " if (event.deltaY < 0) {\n", 2967 | " event.step = 1;\n", 2968 | " } else {\n", 2969 | " event.step = -1;\n", 2970 | " }\n", 2971 | " on_mouse_event_closure('scroll')(event);\n", 2972 | " });\n", 2973 | "\n", 2974 | " canvas_div.appendChild(canvas);\n", 2975 | " canvas_div.appendChild(rubberband_canvas);\n", 2976 | "\n", 2977 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 2978 | " this.rubberband_context.strokeStyle = '#000000';\n", 2979 | "\n", 2980 | " this._resize_canvas = function (width, height, forward) {\n", 2981 | " if (forward) {\n", 2982 | " canvas_div.style.width = width + 'px';\n", 2983 | " canvas_div.style.height = height + 'px';\n", 2984 | " }\n", 2985 | " };\n", 2986 | "\n", 2987 | " // Disable right mouse context menu.\n", 2988 | " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", 2989 | " event.preventDefault();\n", 2990 | " return false;\n", 2991 | " });\n", 2992 | "\n", 2993 | " function set_focus() {\n", 2994 | " canvas.focus();\n", 2995 | " canvas_div.focus();\n", 2996 | " }\n", 2997 | "\n", 2998 | " window.setTimeout(set_focus, 100);\n", 2999 | "};\n", 3000 | "\n", 3001 | "mpl.figure.prototype._init_toolbar = function () {\n", 3002 | " var fig = this;\n", 3003 | "\n", 3004 | " var toolbar = document.createElement('div');\n", 3005 | " toolbar.classList = 'mpl-toolbar';\n", 3006 | " this.root.appendChild(toolbar);\n", 3007 | "\n", 3008 | " function on_click_closure(name) {\n", 3009 | " return function (_event) {\n", 3010 | " return fig.toolbar_button_onclick(name);\n", 3011 | " };\n", 3012 | " }\n", 3013 | "\n", 3014 | " function on_mouseover_closure(tooltip) {\n", 3015 | " return function (event) {\n", 3016 | " if (!event.currentTarget.disabled) {\n", 3017 | " return fig.toolbar_button_onmouseover(tooltip);\n", 3018 | " }\n", 3019 | " };\n", 3020 | " }\n", 3021 | "\n", 3022 | " fig.buttons = {};\n", 3023 | " var buttonGroup = document.createElement('div');\n", 3024 | " buttonGroup.classList = 'mpl-button-group';\n", 3025 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 3026 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 3027 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 3028 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 3029 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 3030 | "\n", 3031 | " if (!name) {\n", 3032 | " /* Instead of a spacer, we start a new button group. */\n", 3033 | " if (buttonGroup.hasChildNodes()) {\n", 3034 | " toolbar.appendChild(buttonGroup);\n", 3035 | " }\n", 3036 | " buttonGroup = document.createElement('div');\n", 3037 | " buttonGroup.classList = 'mpl-button-group';\n", 3038 | " continue;\n", 3039 | " }\n", 3040 | "\n", 3041 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 3042 | " button.classList = 'mpl-widget';\n", 3043 | " button.setAttribute('role', 'button');\n", 3044 | " button.setAttribute('aria-disabled', 'false');\n", 3045 | " button.addEventListener('click', on_click_closure(method_name));\n", 3046 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 3047 | "\n", 3048 | " var icon_img = document.createElement('img');\n", 3049 | " icon_img.src = '_images/' + image + '.png';\n", 3050 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 3051 | " icon_img.alt = tooltip;\n", 3052 | " button.appendChild(icon_img);\n", 3053 | "\n", 3054 | " buttonGroup.appendChild(button);\n", 3055 | " }\n", 3056 | "\n", 3057 | " if (buttonGroup.hasChildNodes()) {\n", 3058 | " toolbar.appendChild(buttonGroup);\n", 3059 | " }\n", 3060 | "\n", 3061 | " var fmt_picker = document.createElement('select');\n", 3062 | " fmt_picker.classList = 'mpl-widget';\n", 3063 | " toolbar.appendChild(fmt_picker);\n", 3064 | " this.format_dropdown = fmt_picker;\n", 3065 | "\n", 3066 | " for (var ind in mpl.extensions) {\n", 3067 | " var fmt = mpl.extensions[ind];\n", 3068 | " var option = document.createElement('option');\n", 3069 | " option.selected = fmt === mpl.default_extension;\n", 3070 | " option.innerHTML = fmt;\n", 3071 | " fmt_picker.appendChild(option);\n", 3072 | " }\n", 3073 | "\n", 3074 | " var status_bar = document.createElement('span');\n", 3075 | " status_bar.classList = 'mpl-message';\n", 3076 | " toolbar.appendChild(status_bar);\n", 3077 | " this.message = status_bar;\n", 3078 | "};\n", 3079 | "\n", 3080 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 3081 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 3082 | " // which will in turn request a refresh of the image.\n", 3083 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 3084 | "};\n", 3085 | "\n", 3086 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 3087 | " properties['type'] = type;\n", 3088 | " properties['figure_id'] = this.id;\n", 3089 | " this.ws.send(JSON.stringify(properties));\n", 3090 | "};\n", 3091 | "\n", 3092 | "mpl.figure.prototype.send_draw_message = function () {\n", 3093 | " if (!this.waiting) {\n", 3094 | " this.waiting = true;\n", 3095 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 3096 | " }\n", 3097 | "};\n", 3098 | "\n", 3099 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 3100 | " var format_dropdown = fig.format_dropdown;\n", 3101 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 3102 | " fig.ondownload(fig, format);\n", 3103 | "};\n", 3104 | "\n", 3105 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 3106 | " var size = msg['size'];\n", 3107 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 3108 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 3109 | " fig.send_message('refresh', {});\n", 3110 | " }\n", 3111 | "};\n", 3112 | "\n", 3113 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 3114 | " var x0 = msg['x0'] / fig.ratio;\n", 3115 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 3116 | " var x1 = msg['x1'] / fig.ratio;\n", 3117 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 3118 | " x0 = Math.floor(x0) + 0.5;\n", 3119 | " y0 = Math.floor(y0) + 0.5;\n", 3120 | " x1 = Math.floor(x1) + 0.5;\n", 3121 | " y1 = Math.floor(y1) + 0.5;\n", 3122 | " var min_x = Math.min(x0, x1);\n", 3123 | " var min_y = Math.min(y0, y1);\n", 3124 | " var width = Math.abs(x1 - x0);\n", 3125 | " var height = Math.abs(y1 - y0);\n", 3126 | "\n", 3127 | " fig.rubberband_context.clearRect(\n", 3128 | " 0,\n", 3129 | " 0,\n", 3130 | " fig.canvas.width / fig.ratio,\n", 3131 | " fig.canvas.height / fig.ratio\n", 3132 | " );\n", 3133 | "\n", 3134 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 3135 | "};\n", 3136 | "\n", 3137 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 3138 | " // Updates the figure title.\n", 3139 | " fig.header.textContent = msg['label'];\n", 3140 | "};\n", 3141 | "\n", 3142 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 3143 | " var cursor = msg['cursor'];\n", 3144 | " switch (cursor) {\n", 3145 | " case 0:\n", 3146 | " cursor = 'pointer';\n", 3147 | " break;\n", 3148 | " case 1:\n", 3149 | " cursor = 'default';\n", 3150 | " break;\n", 3151 | " case 2:\n", 3152 | " cursor = 'crosshair';\n", 3153 | " break;\n", 3154 | " case 3:\n", 3155 | " cursor = 'move';\n", 3156 | " break;\n", 3157 | " }\n", 3158 | " fig.rubberband_canvas.style.cursor = cursor;\n", 3159 | "};\n", 3160 | "\n", 3161 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 3162 | " fig.message.textContent = msg['message'];\n", 3163 | "};\n", 3164 | "\n", 3165 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 3166 | " // Request the server to send over a new figure.\n", 3167 | " fig.send_draw_message();\n", 3168 | "};\n", 3169 | "\n", 3170 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 3171 | " fig.image_mode = msg['mode'];\n", 3172 | "};\n", 3173 | "\n", 3174 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 3175 | " for (var key in msg) {\n", 3176 | " if (!(key in fig.buttons)) {\n", 3177 | " continue;\n", 3178 | " }\n", 3179 | " fig.buttons[key].disabled = !msg[key];\n", 3180 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 3181 | " }\n", 3182 | "};\n", 3183 | "\n", 3184 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 3185 | " if (msg['mode'] === 'PAN') {\n", 3186 | " fig.buttons['Pan'].classList.add('active');\n", 3187 | " fig.buttons['Zoom'].classList.remove('active');\n", 3188 | " } else if (msg['mode'] === 'ZOOM') {\n", 3189 | " fig.buttons['Pan'].classList.remove('active');\n", 3190 | " fig.buttons['Zoom'].classList.add('active');\n", 3191 | " } else {\n", 3192 | " fig.buttons['Pan'].classList.remove('active');\n", 3193 | " fig.buttons['Zoom'].classList.remove('active');\n", 3194 | " }\n", 3195 | "};\n", 3196 | "\n", 3197 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 3198 | " // Called whenever the canvas gets updated.\n", 3199 | " this.send_message('ack', {});\n", 3200 | "};\n", 3201 | "\n", 3202 | "// A function to construct a web socket function for onmessage handling.\n", 3203 | "// Called in the figure constructor.\n", 3204 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 3205 | " return function socket_on_message(evt) {\n", 3206 | " if (evt.data instanceof Blob) {\n", 3207 | " var img = evt.data;\n", 3208 | " if (img.type !== 'image/png') {\n", 3209 | " /* FIXME: We get \"Resource interpreted as Image but\n", 3210 | " * transferred with MIME type text/plain:\" errors on\n", 3211 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 3212 | " * to be part of the websocket stream */\n", 3213 | " img.type = 'image/png';\n", 3214 | " }\n", 3215 | "\n", 3216 | " /* Free the memory for the previous frames */\n", 3217 | " if (fig.imageObj.src) {\n", 3218 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 3219 | " fig.imageObj.src\n", 3220 | " );\n", 3221 | " }\n", 3222 | "\n", 3223 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 3224 | " img\n", 3225 | " );\n", 3226 | " fig.updated_canvas_event();\n", 3227 | " fig.waiting = false;\n", 3228 | " return;\n", 3229 | " } else if (\n", 3230 | " typeof evt.data === 'string' &&\n", 3231 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 3232 | " ) {\n", 3233 | " fig.imageObj.src = evt.data;\n", 3234 | " fig.updated_canvas_event();\n", 3235 | " fig.waiting = false;\n", 3236 | " return;\n", 3237 | " }\n", 3238 | "\n", 3239 | " var msg = JSON.parse(evt.data);\n", 3240 | " var msg_type = msg['type'];\n", 3241 | "\n", 3242 | " // Call the \"handle_{type}\" callback, which takes\n", 3243 | " // the figure and JSON message as its only arguments.\n", 3244 | " try {\n", 3245 | " var callback = fig['handle_' + msg_type];\n", 3246 | " } catch (e) {\n", 3247 | " console.log(\n", 3248 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 3249 | " msg\n", 3250 | " );\n", 3251 | " return;\n", 3252 | " }\n", 3253 | "\n", 3254 | " if (callback) {\n", 3255 | " try {\n", 3256 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 3257 | " callback(fig, msg);\n", 3258 | " } catch (e) {\n", 3259 | " console.log(\n", 3260 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 3261 | " e,\n", 3262 | " e.stack,\n", 3263 | " msg\n", 3264 | " );\n", 3265 | " }\n", 3266 | " }\n", 3267 | " };\n", 3268 | "};\n", 3269 | "\n", 3270 | "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", 3271 | "mpl.findpos = function (e) {\n", 3272 | " //this section is from http://www.quirksmode.org/js/events_properties.html\n", 3273 | " var targ;\n", 3274 | " if (!e) {\n", 3275 | " e = window.event;\n", 3276 | " }\n", 3277 | " if (e.target) {\n", 3278 | " targ = e.target;\n", 3279 | " } else if (e.srcElement) {\n", 3280 | " targ = e.srcElement;\n", 3281 | " }\n", 3282 | " if (targ.nodeType === 3) {\n", 3283 | " // defeat Safari bug\n", 3284 | " targ = targ.parentNode;\n", 3285 | " }\n", 3286 | "\n", 3287 | " // pageX,Y are the mouse positions relative to the document\n", 3288 | " var boundingRect = targ.getBoundingClientRect();\n", 3289 | " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", 3290 | " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", 3291 | "\n", 3292 | " return { x: x, y: y };\n", 3293 | "};\n", 3294 | "\n", 3295 | "/*\n", 3296 | " * return a copy of an object with only non-object keys\n", 3297 | " * we need this to avoid circular references\n", 3298 | " * http://stackoverflow.com/a/24161582/3208463\n", 3299 | " */\n", 3300 | "function simpleKeys(original) {\n", 3301 | " return Object.keys(original).reduce(function (obj, key) {\n", 3302 | " if (typeof original[key] !== 'object') {\n", 3303 | " obj[key] = original[key];\n", 3304 | " }\n", 3305 | " return obj;\n", 3306 | " }, {});\n", 3307 | "}\n", 3308 | "\n", 3309 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 3310 | " var canvas_pos = mpl.findpos(event);\n", 3311 | "\n", 3312 | " if (name === 'button_press') {\n", 3313 | " this.canvas.focus();\n", 3314 | " this.canvas_div.focus();\n", 3315 | " }\n", 3316 | "\n", 3317 | " var x = canvas_pos.x * this.ratio;\n", 3318 | " var y = canvas_pos.y * this.ratio;\n", 3319 | "\n", 3320 | " this.send_message(name, {\n", 3321 | " x: x,\n", 3322 | " y: y,\n", 3323 | " button: event.button,\n", 3324 | " step: event.step,\n", 3325 | " guiEvent: simpleKeys(event),\n", 3326 | " });\n", 3327 | "\n", 3328 | " /* This prevents the web browser from automatically changing to\n", 3329 | " * the text insertion cursor when the button is pressed. We want\n", 3330 | " * to control all of the cursor setting manually through the\n", 3331 | " * 'cursor' event from matplotlib */\n", 3332 | " event.preventDefault();\n", 3333 | " return false;\n", 3334 | "};\n", 3335 | "\n", 3336 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 3337 | " // Handle any extra behaviour associated with a key event\n", 3338 | "};\n", 3339 | "\n", 3340 | "mpl.figure.prototype.key_event = function (event, name) {\n", 3341 | " // Prevent repeat events\n", 3342 | " if (name === 'key_press') {\n", 3343 | " if (event.key === this._key) {\n", 3344 | " return;\n", 3345 | " } else {\n", 3346 | " this._key = event.key;\n", 3347 | " }\n", 3348 | " }\n", 3349 | " if (name === 'key_release') {\n", 3350 | " this._key = null;\n", 3351 | " }\n", 3352 | "\n", 3353 | " var value = '';\n", 3354 | " if (event.ctrlKey && event.key !== 'Control') {\n", 3355 | " value += 'ctrl+';\n", 3356 | " }\n", 3357 | " else if (event.altKey && event.key !== 'Alt') {\n", 3358 | " value += 'alt+';\n", 3359 | " }\n", 3360 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 3361 | " value += 'shift+';\n", 3362 | " }\n", 3363 | "\n", 3364 | " value += 'k' + event.key;\n", 3365 | "\n", 3366 | " this._key_event_extra(event, name);\n", 3367 | "\n", 3368 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 3369 | " return false;\n", 3370 | "};\n", 3371 | "\n", 3372 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 3373 | " if (name === 'download') {\n", 3374 | " this.handle_save(this, null);\n", 3375 | " } else {\n", 3376 | " this.send_message('toolbar_button', { name: name });\n", 3377 | " }\n", 3378 | "};\n", 3379 | "\n", 3380 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 3381 | " this.message.textContent = tooltip;\n", 3382 | "};\n", 3383 | "\n", 3384 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 3385 | "// prettier-ignore\n", 3386 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 3387 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", 3388 | "\n", 3389 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", 3390 | "\n", 3391 | "mpl.default_extension = \"png\";/* global mpl */\n", 3392 | "\n", 3393 | "var comm_websocket_adapter = function (comm) {\n", 3394 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 3395 | " // object with the appropriate methods. Currently this is a non binary\n", 3396 | " // socket, so there is still some room for performance tuning.\n", 3397 | " var ws = {};\n", 3398 | "\n", 3399 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 3400 | " ws.readyState = comm.kernel.ws.readyState;\n", 3401 | " function updateReadyState(_event) {\n", 3402 | " if (comm.kernel.ws) {\n", 3403 | " ws.readyState = comm.kernel.ws.readyState;\n", 3404 | " } else {\n", 3405 | " ws.readyState = 3; // Closed state.\n", 3406 | " }\n", 3407 | " }\n", 3408 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 3409 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 3410 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 3411 | "\n", 3412 | " ws.close = function () {\n", 3413 | " comm.close();\n", 3414 | " };\n", 3415 | " ws.send = function (m) {\n", 3416 | " //console.log('sending', m);\n", 3417 | " comm.send(m);\n", 3418 | " };\n", 3419 | " // Register the callback with on_msg.\n", 3420 | " comm.on_msg(function (msg) {\n", 3421 | " //console.log('receiving', msg['content']['data'], msg);\n", 3422 | " var data = msg['content']['data'];\n", 3423 | " if (data['blob'] !== undefined) {\n", 3424 | " data = {\n", 3425 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 3426 | " };\n", 3427 | " }\n", 3428 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 3429 | " ws.onmessage(data);\n", 3430 | " });\n", 3431 | " return ws;\n", 3432 | "};\n", 3433 | "\n", 3434 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 3435 | " // This is the function which gets called when the mpl process\n", 3436 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 3437 | "\n", 3438 | " var id = msg.content.data.id;\n", 3439 | " // Get hold of the div created by the display call when the Comm\n", 3440 | " // socket was opened in Python.\n", 3441 | " var element = document.getElementById(id);\n", 3442 | " var ws_proxy = comm_websocket_adapter(comm);\n", 3443 | "\n", 3444 | " function ondownload(figure, _format) {\n", 3445 | " window.open(figure.canvas.toDataURL());\n", 3446 | " }\n", 3447 | "\n", 3448 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 3449 | "\n", 3450 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 3451 | " // web socket which is closed, not our websocket->open comm proxy.\n", 3452 | " ws_proxy.onopen();\n", 3453 | "\n", 3454 | " fig.parent_element = element;\n", 3455 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 3456 | " if (!fig.cell_info) {\n", 3457 | " console.error('Failed to find cell for figure', id, fig);\n", 3458 | " return;\n", 3459 | " }\n", 3460 | " fig.cell_info[0].output_area.element.on(\n", 3461 | " 'cleared',\n", 3462 | " { fig: fig },\n", 3463 | " fig._remove_fig_handler\n", 3464 | " );\n", 3465 | "};\n", 3466 | "\n", 3467 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 3468 | " var width = fig.canvas.width / fig.ratio;\n", 3469 | " fig.cell_info[0].output_area.element.off(\n", 3470 | " 'cleared',\n", 3471 | " fig._remove_fig_handler\n", 3472 | " );\n", 3473 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 3474 | "\n", 3475 | " // Update the output cell to use the data from the current canvas.\n", 3476 | " fig.push_to_output();\n", 3477 | " var dataURL = fig.canvas.toDataURL();\n", 3478 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 3479 | " // the notebook keyboard shortcuts fail.\n", 3480 | " IPython.keyboard_manager.enable();\n", 3481 | " fig.parent_element.innerHTML =\n", 3482 | " '';\n", 3483 | " fig.close_ws(fig, msg);\n", 3484 | "};\n", 3485 | "\n", 3486 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 3487 | " fig.send_message('closing', msg);\n", 3488 | " // fig.ws.close()\n", 3489 | "};\n", 3490 | "\n", 3491 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 3492 | " // Turn the data on the canvas into data in the output cell.\n", 3493 | " var width = this.canvas.width / this.ratio;\n", 3494 | " var dataURL = this.canvas.toDataURL();\n", 3495 | " this.cell_info[1]['text/html'] =\n", 3496 | " '';\n", 3497 | "};\n", 3498 | "\n", 3499 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 3500 | " // Tell IPython that the notebook contents must change.\n", 3501 | " IPython.notebook.set_dirty(true);\n", 3502 | " this.send_message('ack', {});\n", 3503 | " var fig = this;\n", 3504 | " // Wait a second, then push the new image to the DOM so\n", 3505 | " // that it is saved nicely (might be nice to debounce this).\n", 3506 | " setTimeout(function () {\n", 3507 | " fig.push_to_output();\n", 3508 | " }, 1000);\n", 3509 | "};\n", 3510 | "\n", 3511 | "mpl.figure.prototype._init_toolbar = function () {\n", 3512 | " var fig = this;\n", 3513 | "\n", 3514 | " var toolbar = document.createElement('div');\n", 3515 | " toolbar.classList = 'btn-toolbar';\n", 3516 | " this.root.appendChild(toolbar);\n", 3517 | "\n", 3518 | " function on_click_closure(name) {\n", 3519 | " return function (_event) {\n", 3520 | " return fig.toolbar_button_onclick(name);\n", 3521 | " };\n", 3522 | " }\n", 3523 | "\n", 3524 | " function on_mouseover_closure(tooltip) {\n", 3525 | " return function (event) {\n", 3526 | " if (!event.currentTarget.disabled) {\n", 3527 | " return fig.toolbar_button_onmouseover(tooltip);\n", 3528 | " }\n", 3529 | " };\n", 3530 | " }\n", 3531 | "\n", 3532 | " fig.buttons = {};\n", 3533 | " var buttonGroup = document.createElement('div');\n", 3534 | " buttonGroup.classList = 'btn-group';\n", 3535 | " var button;\n", 3536 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 3537 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 3538 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 3539 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 3540 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 3541 | "\n", 3542 | " if (!name) {\n", 3543 | " /* Instead of a spacer, we start a new button group. */\n", 3544 | " if (buttonGroup.hasChildNodes()) {\n", 3545 | " toolbar.appendChild(buttonGroup);\n", 3546 | " }\n", 3547 | " buttonGroup = document.createElement('div');\n", 3548 | " buttonGroup.classList = 'btn-group';\n", 3549 | " continue;\n", 3550 | " }\n", 3551 | "\n", 3552 | " button = fig.buttons[name] = document.createElement('button');\n", 3553 | " button.classList = 'btn btn-default';\n", 3554 | " button.href = '#';\n", 3555 | " button.title = name;\n", 3556 | " button.innerHTML = '';\n", 3557 | " button.addEventListener('click', on_click_closure(method_name));\n", 3558 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 3559 | " buttonGroup.appendChild(button);\n", 3560 | " }\n", 3561 | "\n", 3562 | " if (buttonGroup.hasChildNodes()) {\n", 3563 | " toolbar.appendChild(buttonGroup);\n", 3564 | " }\n", 3565 | "\n", 3566 | " // Add the status bar.\n", 3567 | " var status_bar = document.createElement('span');\n", 3568 | " status_bar.classList = 'mpl-message pull-right';\n", 3569 | " toolbar.appendChild(status_bar);\n", 3570 | " this.message = status_bar;\n", 3571 | "\n", 3572 | " // Add the close button to the window.\n", 3573 | " var buttongrp = document.createElement('div');\n", 3574 | " buttongrp.classList = 'btn-group inline pull-right';\n", 3575 | " button = document.createElement('button');\n", 3576 | " button.classList = 'btn btn-mini btn-primary';\n", 3577 | " button.href = '#';\n", 3578 | " button.title = 'Stop Interaction';\n", 3579 | " button.innerHTML = '';\n", 3580 | " button.addEventListener('click', function (_evt) {\n", 3581 | " fig.handle_close(fig, {});\n", 3582 | " });\n", 3583 | " button.addEventListener(\n", 3584 | " 'mouseover',\n", 3585 | " on_mouseover_closure('Stop Interaction')\n", 3586 | " );\n", 3587 | " buttongrp.appendChild(button);\n", 3588 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 3589 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 3590 | "};\n", 3591 | "\n", 3592 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 3593 | " var fig = event.data.fig;\n", 3594 | " if (event.target !== this) {\n", 3595 | " // Ignore bubbled events from children.\n", 3596 | " return;\n", 3597 | " }\n", 3598 | " fig.close_ws(fig, {});\n", 3599 | "};\n", 3600 | "\n", 3601 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 3602 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 3603 | "};\n", 3604 | "\n", 3605 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 3606 | " // this is important to make the div 'focusable\n", 3607 | " el.setAttribute('tabindex', 0);\n", 3608 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 3609 | " // off when our div gets focus\n", 3610 | "\n", 3611 | " // location in version 3\n", 3612 | " if (IPython.notebook.keyboard_manager) {\n", 3613 | " IPython.notebook.keyboard_manager.register_events(el);\n", 3614 | " } else {\n", 3615 | " // location in version 2\n", 3616 | " IPython.keyboard_manager.register_events(el);\n", 3617 | " }\n", 3618 | "};\n", 3619 | "\n", 3620 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 3621 | " var manager = IPython.notebook.keyboard_manager;\n", 3622 | " if (!manager) {\n", 3623 | " manager = IPython.keyboard_manager;\n", 3624 | " }\n", 3625 | "\n", 3626 | " // Check for shift+enter\n", 3627 | " if (event.shiftKey && event.which === 13) {\n", 3628 | " this.canvas_div.blur();\n", 3629 | " // select the cell after this one\n", 3630 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 3631 | " IPython.notebook.select(index + 1);\n", 3632 | " }\n", 3633 | "};\n", 3634 | "\n", 3635 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 3636 | " fig.ondownload(fig, null);\n", 3637 | "};\n", 3638 | "\n", 3639 | "mpl.find_output_cell = function (html_output) {\n", 3640 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 3641 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 3642 | " // IPython event is triggered only after the cells have been serialised, which for\n", 3643 | " // our purposes (turning an active figure into a static one), is too late.\n", 3644 | " var cells = IPython.notebook.get_cells();\n", 3645 | " var ncells = cells.length;\n", 3646 | " for (var i = 0; i < ncells; i++) {\n", 3647 | " var cell = cells[i];\n", 3648 | " if (cell.cell_type === 'code') {\n", 3649 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 3650 | " var data = cell.output_area.outputs[j];\n", 3651 | " if (data.data) {\n", 3652 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 3653 | " data = data.data;\n", 3654 | " }\n", 3655 | " if (data['text/html'] === html_output) {\n", 3656 | " return [cell, data, j];\n", 3657 | " }\n", 3658 | " }\n", 3659 | " }\n", 3660 | " }\n", 3661 | "};\n", 3662 | "\n", 3663 | "// Register the function which deals with the matplotlib target/channel.\n", 3664 | "// The kernel may be null if the page has been refreshed.\n", 3665 | "if (IPython.notebook.kernel !== null) {\n", 3666 | " IPython.notebook.kernel.comm_manager.register_target(\n", 3667 | " 'matplotlib',\n", 3668 | " mpl.mpl_figure_comm\n", 3669 | " );\n", 3670 | "}\n" 3671 | ], 3672 | "text/plain": [ 3673 | "" 3674 | ] 3675 | }, 3676 | "metadata": {}, 3677 | "output_type": "display_data" 3678 | }, 3679 | { 3680 | "data": { 3681 | "text/html": [ 3682 | "" 3683 | ], 3684 | "text/plain": [ 3685 | "" 3686 | ] 3687 | }, 3688 | "metadata": {}, 3689 | "output_type": "display_data" 3690 | } 3691 | ], 3692 | "source": [ 3693 | "fig,ax=plt.subplots(1,1,figsize=(11,1.6))\n", 3694 | "plt.xlim(0,L)\n", 3695 | "plt.ylim(0,H)\n", 3696 | "x=np.linspace(0,L,Nx)\n", 3697 | "y=np.linspace(0,H,Ny)\n", 3698 | "xx,yy=np.meshgrid(x,y)\n", 3699 | "\n", 3700 | "ani=FuncAnimation(fig,animate,count,interval=400, blit=True)\n", 3701 | "plt.show()" 3702 | ] 3703 | }, 3704 | { 3705 | "cell_type": "code", 3706 | "execution_count": null, 3707 | "id": "884e9798", 3708 | "metadata": {}, 3709 | "outputs": [], 3710 | "source": [ 3711 | "print('Help')" 3712 | ] 3713 | } 3714 | ], 3715 | "metadata": { 3716 | "kernelspec": { 3717 | "display_name": "Python 3 (ipykernel)", 3718 | "language": "python", 3719 | "name": "python3" 3720 | }, 3721 | "language_info": { 3722 | "codemirror_mode": { 3723 | "name": "ipython", 3724 | "version": 3 3725 | }, 3726 | "file_extension": ".py", 3727 | "mimetype": "text/x-python", 3728 | "name": "python", 3729 | "nbconvert_exporter": "python", 3730 | "pygments_lexer": "ipython3", 3731 | "version": "3.9.7" 3732 | } 3733 | }, 3734 | "nbformat": 4, 3735 | "nbformat_minor": 5 3736 | } 3737 | -------------------------------------------------------------------------------- /Heated Cavity (4th order).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "1e584a55", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "text/html": [ 12 | "" 13 | ], 14 | "text/plain": [ 15 | "" 16 | ] 17 | }, 18 | "metadata": {}, 19 | "output_type": "display_data" 20 | } 21 | ], 22 | "source": [ 23 | "import numpy as np\n", 24 | "import matplotlib.pyplot as plt\n", 25 | "from matplotlib import cm\n", 26 | "import matplotlib.patches as patches\n", 27 | "import time\n", 28 | "from numba import jit,njit,prange\n", 29 | "from IPython.core.display import display, HTML\n", 30 | "from matplotlib.animation import FuncAnimation\n", 31 | "from tqdm.notebook import tqdm\n", 32 | "\n", 33 | "display(HTML(\"\"))" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "id": "57720eb9", 39 | "metadata": {}, 40 | "source": [ 41 | "%matplotlib notebook" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 2, 47 | "id": "842c9912", 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "@njit(fastmath=True)\n", 52 | "def extrapolate_3rd(x1,x2,x3):\n", 53 | " return 3*x1-3*x2+x3" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 3, 59 | "id": "b91cad77", 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "@njit(fastmath=True)\n", 64 | "def extrapolate_4th(x1,x2,x3,x4):\n", 65 | " return 4*x1-6*x2+4*x3-x4" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 4, 71 | "id": "d5aea529", 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "@njit(fastmath=True)\n", 76 | "def face_interpolate_4th(x1,x2,x3,x4):\n", 77 | " return (-x1+9*x2+9*x3-x4)/16" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 5, 83 | "id": "c3b52c6c", 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "@njit(fastmath=True)\n", 88 | "def diff_interpolate_4th(x1,x2,x3,x4,h):\n", 89 | " #h denotes grid spacing\n", 90 | " return (x1-27*x2+27*x3-x4)/(24*h)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 6, 96 | "id": "44447f57", 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "@njit(fastmath=True)\n", 101 | "def p_face(Ny,Nx,P):\n", 102 | " \n", 103 | " Pxf=np.zeros((Ny,Nx+1))\n", 104 | " Pyf=np.zeros((Ny+1,Nx))\n", 105 | " \n", 106 | " #horizontal faces\n", 107 | " for j in range(Ny):\n", 108 | " i=0\n", 109 | " \n", 110 | " PP=P[j][i]\n", 111 | " PE=P[j][i+1]\n", 112 | " Pxf[j][i]=1.5*PP-0.5*PE\n", 113 | " \n", 114 | " for i in range(1,Nx):\n", 115 | " PP=P[j][i-1]\n", 116 | " PE=P[j][i]\n", 117 | " Pxf[j][i]=0.5*(PP+PE)\n", 118 | " \n", 119 | " i=Nx\n", 120 | " \n", 121 | " PP=P[j][i-1]\n", 122 | " PW=P[j][i-2]\n", 123 | " Pxf[j][i]=1.5*PP-0.5*PW\n", 124 | "\n", 125 | " #vertical faces\n", 126 | " for i in range(Nx):\n", 127 | " j=0\n", 128 | " PP=P[j][i]\n", 129 | " PN=P[j+1][i]\n", 130 | " Pyf[j][i]=1.5*PP-0.5*PN\n", 131 | " \n", 132 | " for j in range(1,Ny):\n", 133 | " PP=P[j-1][i]\n", 134 | " PN=P[j][i]\n", 135 | " Pyf[j][i]=0.5*(PP+PN)\n", 136 | " \n", 137 | " j=Ny\n", 138 | " PP=P[j-1][i]\n", 139 | " PS=P[j-2][i]\n", 140 | " Pyf[j][i]=1.5*PP-0.5*PS\n", 141 | " \n", 142 | " return Pxf,Pyf" 143 | ] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "execution_count": 7, 148 | "id": "ebba9dba", 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "@njit(fastmath=True)\n", 153 | "def face_centre(Ny,Nx,phi,scalar):\n", 154 | " \n", 155 | " phi_xf=np.zeros((Ny,Nx+1)) #vertical faces\n", 156 | " phi_yf=np.zeros((Ny+1,Nx)) #horizontal faces\n", 157 | " \n", 158 | " #vertical faces\n", 159 | " for j in range(Ny):\n", 160 | " i=1 #left wall\n", 161 | " \n", 162 | " phi_W=phi[j][i-1]\n", 163 | " phi_P=phi[j][i]\n", 164 | " phi_E=phi[j][i+1]\n", 165 | " phi_EE=phi[j][i+2]\n", 166 | " phi_WW=extrapolate_4th(phi_W,phi_P,phi_E,phi_EE)\n", 167 | " phi_WWW=extrapolate_4th(phi_WW,phi_W,phi_P,phi_E)\n", 168 | " \n", 169 | " phi_xf[j][i]=face_interpolate_4th(phi_WW,phi_W,phi_P,phi_E)\n", 170 | " phi_xf[j][i-1]=face_interpolate_4th(phi_WWW,phi_WW,phi_W,phi_P)\n", 171 | "\n", 172 | " for i in range(2,Nx-1):\n", 173 | " phi_WW=phi[j][i-2]\n", 174 | " phi_W=phi[j][i-1]\n", 175 | " phi_P=phi[j][i]\n", 176 | " phi_E=phi[j][i+1]\n", 177 | "\n", 178 | " phi_xf[j][i]=face_interpolate_4th(phi_WW,phi_W,phi_P,phi_E)\n", 179 | " \n", 180 | " i=Nx-1 #right wall\n", 181 | " \n", 182 | " phi_P=phi[j][i]\n", 183 | " phi_W=phi[j][i-1]\n", 184 | " phi_WW=phi[j][i-2]\n", 185 | " phi_WWW=phi[j][i-3]\n", 186 | " phi_E=extrapolate_4th(phi_P,phi_W,phi_WW,phi_WWW)\n", 187 | " phi_EE=extrapolate_4th(phi_E,phi_P,phi_W,phi_WW)\n", 188 | " \n", 189 | " phi_xf[j][i]=face_interpolate_4th(phi_WW,phi_W,phi_P,phi_E)\n", 190 | " phi_xf[j][i+1]=face_interpolate_4th(phi_W,phi_P,phi_E,phi_EE)\n", 191 | " \n", 192 | " #horizontal faces\n", 193 | " for i in range(Nx):\n", 194 | " j=0 #bottom wall\n", 195 | " \n", 196 | " phi_P=phi[j][i]\n", 197 | " phi_N=phi[j+1][i]\n", 198 | " phi_NN=phi[j+2][i]\n", 199 | " phi_NNN=phi[j+3][i]\n", 200 | " phi_S=extrapolate_4th(phi_P,phi_N,phi_NN,phi_NNN)\n", 201 | " phi_SS=extrapolate_4th(phi_S,phi_P,phi_N,phi_NN)\n", 202 | " \n", 203 | " phi_yf[j][i]=face_interpolate_4th(phi_SS,phi_S,phi_P,phi_N)\n", 204 | " phi_yf[j+1][i]=face_interpolate_4th(phi_S,phi_P,phi_N,phi_NN)\n", 205 | " \n", 206 | " for j in range(2,Ny-1):\n", 207 | " phi_SS=phi[j-2][i]\n", 208 | " phi_S=phi[j-1][i]\n", 209 | " phi_P=phi[j][i]\n", 210 | " phi_N=phi[j+1][i]\n", 211 | "\n", 212 | " phi_yf[j][i]=face_interpolate_4th(phi_SS,phi_S,phi_P,phi_N)\n", 213 | " \n", 214 | " j=Ny #top wall\n", 215 | " \n", 216 | " phi_S=phi[j-1][i]\n", 217 | " phi_SS=phi[j-2][i]\n", 218 | " phi_SSS=phi[j-3][i]\n", 219 | " phi_SSSS=phi[j-4][i]\n", 220 | " phi_P=extrapolate_4th(phi_S,phi_SS,phi_SSS,phi_SSSS)\n", 221 | " phi_N=extrapolate_4th(phi_P,phi_S,phi_SS,phi_SSS)\n", 222 | " \n", 223 | " phi_yf[j][i]=face_interpolate_4th(phi_SS,phi_S,phi_P,phi_N)\n", 224 | " phi_yf[j-1][i]=face_interpolate_4th(phi_SSS,phi_SS,phi_S,phi_P)\n", 225 | " \n", 226 | " #set boundaries for various scalars\n", 227 | " \n", 228 | " if scalar==0: #velocity\n", 229 | " #top and bottom wall\n", 230 | " for i in range(Nx):\n", 231 | " phi_yf[0][i]=0\n", 232 | " phi_yf[-1][i]=0\n", 233 | " #left and right wall\n", 234 | " for j in range(Ny):\n", 235 | " phi_xf[j][0]=0\n", 236 | " phi_xf[j][-1]=0\n", 237 | " \n", 238 | " if scalar==1: #temperature\n", 239 | " for j in range(Ny):\n", 240 | " phi_xf[j][0]=1.0\n", 241 | " phi_xf[j][-1]=0.0\n", 242 | " \n", 243 | " return phi_xf,phi_yf" 244 | ] 245 | }, 246 | { 247 | "cell_type": "code", 248 | "execution_count": 8, 249 | "id": "00c14c37", 250 | "metadata": {}, 251 | "outputs": [], 252 | "source": [ 253 | "@njit(fastmath=True)\n", 254 | "def vertex(Ny,Nx,phi_xf,phi_yf,scalar):\n", 255 | " #only used phi_yf (horizontal faces) to compute vertex values\n", 256 | " vertex_field=np.zeros((Ny+1,Nx+1))\n", 257 | "\n", 258 | " for j in range(Ny+1):\n", 259 | " #inlet\n", 260 | " i=1\n", 261 | " \n", 262 | " phi_w=phi_yf[j][i-1]\n", 263 | " phi_p=phi_yf[j][i]\n", 264 | " phi_e=phi_yf[j][i+1]\n", 265 | " phi_ee=phi_yf[j][i+2]\n", 266 | " phi_ww=extrapolate_4th(phi_w,phi_p,phi_e,phi_ee)\n", 267 | " phi_www=extrapolate_4th(phi_ww,phi_w,phi_p,phi_e)\n", 268 | " \n", 269 | " vertex_field[j][i]=face_interpolate_4th(phi_ww,phi_w,phi_p,phi_e)\n", 270 | " vertex_field[j][i-1]=face_interpolate_4th(phi_www,phi_ww,phi_w,phi_p)\n", 271 | " \n", 272 | " for i in range(2,Nx-1):\n", 273 | " phi_ww=phi_yf[j][i-2]\n", 274 | " phi_w=phi_yf[j][i-1]\n", 275 | " phi_p=phi_yf[j][i]\n", 276 | " phi_e=phi_yf[j][i+1]\n", 277 | " \n", 278 | " vertex_field[j][i]=face_interpolate_4th(phi_ww,phi_w,phi_p,phi_e)\n", 279 | " \n", 280 | " #right wall\n", 281 | " i=Nx-1\n", 282 | " \n", 283 | " phi_p=phi_yf[j][i]\n", 284 | " phi_w=phi_yf[j][i-1]\n", 285 | " phi_ww=phi_yf[j][i-2]\n", 286 | " phi_www=phi_yf[j][i-3]\n", 287 | " phi_e=extrapolate_4th(phi_p,phi_w,phi_ww,phi_www)\n", 288 | " phi_ee=extrapolate_4th(phi_e,phi_p,phi_w,phi_ww)\n", 289 | " \n", 290 | " vertex_field[j][i]=face_interpolate_4th(phi_ww,phi_w,phi_p,phi_e)\n", 291 | " vertex_field[j][i+1]=face_interpolate_4th(phi_w,phi_p,phi_e,phi_ee)\n", 292 | " \n", 293 | " if scalar==0: #velocity\n", 294 | " #left and right wall\n", 295 | " for j in range(Ny+1):\n", 296 | " vertex_field[j][0]=0.0\n", 297 | " vertex_field[j][-1]=0.0\n", 298 | " #top and btm wall\n", 299 | " for i in range(Nx+1):\n", 300 | " vertex_field[0][i]=0.0\n", 301 | " vertex_field[Ny][i]=0.0\n", 302 | " \n", 303 | " elif scalar==1: #temperature\n", 304 | "# #top and btm wall\n", 305 | "# for i in range(Nx+1):\n", 306 | "# vertex_field[0][i]=1.0\n", 307 | "# vertex_field[Ny][i]=0.0\n", 308 | " \n", 309 | " #left and right wall\n", 310 | " for j in range(Nx+1):\n", 311 | " vertex_field[j][0]=1.0\n", 312 | " vertex_field[j][-1]=0.0\n", 313 | " \n", 314 | " elif scalar==2: #pressure\n", 315 | " for i in range(Nx):\n", 316 | " j=0\n", 317 | " phi_p=phi_xf[j][i]\n", 318 | " phi_n=phi_xf[j+1][i]\n", 319 | " phi_nn=phi_xf[j+2][i]\n", 320 | " phi_nnn=phi_xf[j+3][i]\n", 321 | " phi_s=extrapolate_4th(phi_p,phi_n,phi_nn,phi_nnn)\n", 322 | " phi_ss=extrapolate_4th(phi_s,phi_p,phi_n,phi_nn)\n", 323 | " \n", 324 | " vertex_field[j][i]=face_interpolate_4th(phi_ss,phi_s,phi_p,phi_n)\n", 325 | " vertex_field[j+1][i]=face_interpolate_4th(phi_s,phi_p,phi_n,phi_nn)\n", 326 | " \n", 327 | " j=Ny\n", 328 | " phi_s=phi_xf[j-1][i]\n", 329 | " phi_ss=phi_xf[j-2][i]\n", 330 | " phi_sss=phi_xf[j-3][i]\n", 331 | " phi_ssss=phi_xf[j-4][i]\n", 332 | " phi_p=extrapolate_4th(phi_s,phi_ss,phi_sss,phi_ssss)\n", 333 | " phi_n=extrapolate_4th(phi_p,phi_ss,phi_ss,phi_sss)\n", 334 | " \n", 335 | " vertex_field[j][i]=face_interpolate_4th(phi_ss,phi_s,phi_p,phi_n)\n", 336 | " vertex_field[j-1][i]=face_interpolate_4th(phi_sss,phi_ss,phi_s,phi_p)\n", 337 | " \n", 338 | " return vertex_field" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 9, 344 | "id": "e29c3d57", 345 | "metadata": {}, 346 | "outputs": [], 347 | "source": [ 348 | "@njit(fastmath=True)\n", 349 | "def simp_rule(Ny,Nx,phi_xf,phi_yf,vertex_field):\n", 350 | " #use simpson rule to calculate surface integral\n", 351 | " \n", 352 | " phi_xf_simp=np.zeros((Ny,Nx+1)) #vertical faces\n", 353 | " phi_yf_simp=np.zeros((Ny+1,Nx)) #horizontal faces\n", 354 | " \n", 355 | " #vertical faces\n", 356 | " for j in range(Ny):\n", 357 | " for i in range(Nx+1):\n", 358 | " phi_face=phi_xf[j][i]\n", 359 | " phi_vert_top=vertex_field[j+1][i]\n", 360 | " phi_vert_btm=vertex_field[j][i]\n", 361 | " \n", 362 | " phi_xf_simp[j][i]=(phi_vert_top+4*phi_face+phi_vert_btm)/6\n", 363 | " \n", 364 | " #horizontal faces\n", 365 | " for j in range(Ny+1):\n", 366 | " for i in range(Nx):\n", 367 | " phi_face=phi_yf[j][i]\n", 368 | " phi_vert_left=vertex_field[j][i]\n", 369 | " phi_vert_rgt=vertex_field[j][i+1]\n", 370 | " \n", 371 | " phi_yf_simp[j][i]=(phi_vert_left+4*phi_face+phi_vert_rgt)/6\n", 372 | " \n", 373 | " return phi_xf_simp,phi_yf_simp" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 10, 379 | "id": "8a289e2b", 380 | "metadata": {}, 381 | "outputs": [], 382 | "source": [ 383 | "def conv_face(Ny,Nx,phi,scalar):\n", 384 | " phi_xf,phi_yf=face_centre(Ny,Nx,phi,scalar)\n", 385 | " vertex_field=vertex(Ny,Nx,phi_xf,phi_yf,scalar)\n", 386 | " phi_xf,phi_yf=simp_rule(Ny,Nx,phi_xf,phi_yf,vertex_field)\n", 387 | " return phi_xf,phi_yf,vertex_field" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": 11, 393 | "id": "4ab80a6b", 394 | "metadata": {}, 395 | "outputs": [], 396 | "source": [ 397 | "@njit(fastmath=True,parallel=False)\n", 398 | "def diff_face_centre(Ny,Nx,phi_xf,phi_yf,phi,dx,dy,scalar):\n", 399 | " \n", 400 | " #vertical faces\n", 401 | " for j in range(Ny):\n", 402 | " i=0 #left\n", 403 | " phi_P=phi[j][i]\n", 404 | " phi_E=phi[j][i+1]\n", 405 | " phi_EE=phi[j][i+2]\n", 406 | " phi_EEE=phi[j][i+3]\n", 407 | " phi_W=extrapolate_4th(phi_P,phi_E,phi_EE,phi_EEE)\n", 408 | " phi_WW=extrapolate_4th(phi_W,phi_P,phi_E,phi_EE)\n", 409 | " \n", 410 | " phi_xf[j][i]=diff_interpolate_4th(phi_WW,phi_W,phi_P,phi_E,dx)\n", 411 | " phi_xf[j][i+1]=diff_interpolate_4th(phi_W,phi_P,phi_E,phi_EE,dx)\n", 412 | " \n", 413 | " for i in range(2,Nx-1):\n", 414 | " phi_WW=phi[j][i-2]\n", 415 | " phi_W=phi[j][i-1]\n", 416 | " phi_P=phi[j][i]\n", 417 | " phi_E=phi[j][i+1]\n", 418 | "\n", 419 | " phi_xf[j][i]=diff_interpolate_4th(phi_WW,phi_W,phi_P,phi_E,dx)\n", 420 | " \n", 421 | " i=Nx #right\n", 422 | " \n", 423 | " phi_W=phi[j][i-1]\n", 424 | " phi_WW=phi[j][i-2]\n", 425 | " phi_WWW=phi[j][i-3]\n", 426 | " phi_WWWW=phi[j][i-4]\n", 427 | " phi_P=extrapolate_4th(phi_W,phi_WW,phi_WWW,phi_WWWW)\n", 428 | " phi_E=extrapolate_4th(phi_P,phi_W,phi_WW,phi_WWW)\n", 429 | " \n", 430 | " phi_xf[j][i]=diff_interpolate_4th(phi_WW,phi_W,phi_P,phi_E,dx)\n", 431 | " phi_xf[j][i-1]=diff_interpolate_4th(phi_WWW,phi_WW,phi_W,phi_P,dx)\n", 432 | " \n", 433 | " #horizontal faces\n", 434 | " for i in range(Nx):\n", 435 | " j=0 #bottom wall\n", 436 | " \n", 437 | " phi_P=phi[j][i]\n", 438 | " phi_N=phi[j+1][i]\n", 439 | " phi_NN=phi[j+2][i]\n", 440 | " phi_NNN=phi[j+3][i]\n", 441 | " phi_S=extrapolate_4th(phi_P,phi_N,phi_NN,phi_NNN)\n", 442 | " phi_SS=extrapolate_4th(phi_S,phi_P,phi_N,phi_NN)\n", 443 | " \n", 444 | " phi_yf[j][i]=diff_interpolate_4th(phi_SS,phi_S,phi_P,phi_N,dy)\n", 445 | " phi_yf[j+1][i]=diff_interpolate_4th(phi_S,phi_P,phi_N,phi_NN,dy)\n", 446 | " \n", 447 | " j=Ny #top wall\n", 448 | " \n", 449 | " phi_S=phi[j-1][i]\n", 450 | " phi_SS=phi[j-2][i]\n", 451 | " phi_SSS=phi[j-3][i]\n", 452 | " phi_SSSS=phi[j-4][i]\n", 453 | " phi_P=extrapolate_4th(phi_S,phi_SS,phi_SSS,phi_SSSS)\n", 454 | " phi_N=extrapolate_4th(phi_P,phi_S,phi_SS,phi_SSS)\n", 455 | " \n", 456 | " phi_yf[j][i]=diff_interpolate_4th(phi_SS,phi_S,phi_P,phi_N,dy)\n", 457 | " phi_yf[j-1][i]=diff_interpolate_4th(phi_SSS,phi_SS,phi_S,phi_P,dy)\n", 458 | " \n", 459 | " for j in range(2,Ny-1):\n", 460 | " for i in range(Nx):\n", 461 | " phi_SS=phi[j-2][i]\n", 462 | " phi_S=phi[j-1][i]\n", 463 | " phi_P=phi[j][i]\n", 464 | " phi_N=phi[j+1][i]\n", 465 | "\n", 466 | " phi_yf[j][i]=diff_interpolate_4th(phi_SS,phi_S,phi_P,phi_N,dy)\n", 467 | " \n", 468 | " if scalar==1: #temperature\n", 469 | " for i in range(Nx):\n", 470 | " j=0\n", 471 | " phi_yf[j][i]=0.0\n", 472 | " j=Ny\n", 473 | " phi_yf[j][i]=0.0\n", 474 | " \n", 475 | " return phi_xf,phi_yf" 476 | ] 477 | }, 478 | { 479 | "cell_type": "code", 480 | "execution_count": 12, 481 | "id": "5e1f1d22", 482 | "metadata": {}, 483 | "outputs": [], 484 | "source": [ 485 | "@njit(fastmath=True,parallel=False)\n", 486 | "def diff_vertex(Ny,Nx,vertex_field_x,vertex_field_y,phi_xf,phi_yf,dx,dy,scalar):\n", 487 | "\n", 488 | " #horizontal gradients\n", 489 | " for j in range(Ny+1):\n", 490 | " #left wall\n", 491 | " i=0\n", 492 | " \n", 493 | " phi_p=phi_yf[j][i]\n", 494 | " phi_e=phi_yf[j][i+1]\n", 495 | " phi_ee=phi_yf[j][i+2]\n", 496 | " phi_eee=phi_yf[j][i+3]\n", 497 | " phi_w=extrapolate_4th(phi_p,phi_e,phi_ee,phi_eee)\n", 498 | " phi_ww=extrapolate_4th(phi_w,phi_p,phi_e,phi_ee)\n", 499 | " \n", 500 | " vertex_field_x[j][i]=diff_interpolate_4th(phi_ww,phi_w,phi_p,phi_e,dx)\n", 501 | " vertex_field_x[j][i+1]=diff_interpolate_4th(phi_w,phi_p,phi_e,phi_ee,dx)\n", 502 | " \n", 503 | " for i in range(2,Nx-1):\n", 504 | " phi_ww=phi_yf[j][i-2]\n", 505 | " phi_w=phi_yf[j][i-1]\n", 506 | " phi_p=phi_yf[j][i]\n", 507 | " phi_e=phi_yf[j][i+1]\n", 508 | " \n", 509 | " vertex_field_x[j][i]=diff_interpolate_4th(phi_ww,phi_w,phi_p,phi_e,dx)\n", 510 | " \n", 511 | " #outlet\n", 512 | " i=Nx\n", 513 | " \n", 514 | " phi_w=phi_yf[j][i-1]\n", 515 | " phi_ww=phi_yf[j][i-2]\n", 516 | " phi_www=phi_yf[j][i-3]\n", 517 | " phi_wwww=phi_yf[j][i-4]\n", 518 | " phi_p=extrapolate_4th(phi_w,phi_ww,phi_www,phi_wwww)\n", 519 | " phi_e=extrapolate_4th(phi_p,phi_w,phi_ww,phi_www)\n", 520 | " \n", 521 | " vertex_field_x[j][i]=diff_interpolate_4th(phi_ww,phi_w,phi_p,phi_e,dx)\n", 522 | " vertex_field_x[j][i-1]=diff_interpolate_4th(phi_www,phi_ww,phi_w,phi_p,dx)\n", 523 | "\n", 524 | " #vertical gradients\n", 525 | " for i in range(Nx+1): \n", 526 | " j=0\n", 527 | " \n", 528 | " phi_p=phi_xf[j][i]\n", 529 | " phi_n=phi_xf[j+1][i]\n", 530 | " phi_nn=phi_xf[j+2][i]\n", 531 | " phi_nnn=phi_xf[j+3][i]\n", 532 | " phi_s=extrapolate_4th(phi_p,phi_n,phi_nn,phi_nnn)\n", 533 | " phi_ss=extrapolate_4th(phi_s,phi_p,phi_n,phi_nn)\n", 534 | "\n", 535 | " vertex_field_y[j][i]=diff_interpolate_4th(phi_ss,phi_s,phi_p,phi_n,dy)\n", 536 | " vertex_field_y[j+1][i]=diff_interpolate_4th(phi_s,phi_p,phi_n,phi_nn,dy)\n", 537 | " \n", 538 | " for j in range(2,Ny-1):\n", 539 | " phi_ss=phi_xf[j-2][i]\n", 540 | " phi_s=phi_xf[j-1][i]\n", 541 | " phi_p=phi_xf[j][i]\n", 542 | " phi_n=phi_xf[j+1][i]\n", 543 | " \n", 544 | " vertex_field_y[j][i]=diff_interpolate_4th(phi_ss,phi_s,phi_p,phi_n,dy)\n", 545 | "\n", 546 | " j=Ny\n", 547 | " \n", 548 | " phi_s=phi_xf[j-1][i]\n", 549 | " phi_ss=phi_xf[j-2][i]\n", 550 | " phi_sss=phi_xf[j-3][i]\n", 551 | " phi_ssss=phi_xf[j-4][i]\n", 552 | " phi_p=extrapolate_4th(phi_s,phi_ss,phi_sss,phi_ssss)\n", 553 | " phi_n=extrapolate_4th(phi_p,phi_s,phi_ss,phi_sss)\n", 554 | "\n", 555 | " vertex_field_y[j][i]=diff_interpolate_4th(phi_ss,phi_s,phi_p,phi_n,dy)\n", 556 | " vertex_field_y[j-1][i]=diff_interpolate_4th(phi_sss,phi_ss,phi_s,phi_p,dy)\n", 557 | " \n", 558 | " if scalar==1: #temperature\n", 559 | " for i in range(Nx):\n", 560 | " j=0\n", 561 | " vertex_field_y[j][i]=0.0\n", 562 | " j=Ny\n", 563 | " vertex_field_y[j][i]=0.0\n", 564 | " \n", 565 | " return vertex_field_x,vertex_field_y" 566 | ] 567 | }, 568 | { 569 | "cell_type": "code", 570 | "execution_count": 13, 571 | "id": "a715d9eb", 572 | "metadata": {}, 573 | "outputs": [], 574 | "source": [ 575 | "@njit(fastmath=True)\n", 576 | "def diff_simp_rule(Ny,Nx,phi_xf,phi_yf,phi_xf_simp,phi_yf_simp,vertex_field_x,vertex_field_y):\n", 577 | " #use simpson rule to calculate surface integral\n", 578 | " \n", 579 | " #vertical faces\n", 580 | " for j in range(Ny):\n", 581 | " for i in range(Nx+1):\n", 582 | " phi_face=phi_xf[j][i]\n", 583 | " phi_vert_top=vertex_field_x[j+1][i]\n", 584 | " phi_vert_btm=vertex_field_x[j][i]\n", 585 | " \n", 586 | " phi_xf_simp[j][i]=(phi_vert_top+4*phi_face+phi_vert_btm)/6\n", 587 | " \n", 588 | " #horizontal faces\n", 589 | " for j in range(Ny+1):\n", 590 | " for i in range(Nx):\n", 591 | " phi_face=phi_yf[j][i]\n", 592 | " phi_vert_left=vertex_field_y[j][i]\n", 593 | " phi_vert_rgt=vertex_field_y[j][i+1]\n", 594 | " \n", 595 | " phi_yf_simp[j][i]=(phi_vert_left+4*phi_face+phi_vert_rgt)/6\n", 596 | " \n", 597 | " return phi_xf_simp,phi_yf_simp" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": 14, 603 | "id": "916b0870", 604 | "metadata": {}, 605 | "outputs": [], 606 | "source": [ 607 | "def diff_face(Ny,Nx,phi,dx,dy,scalar):\n", 608 | " phi_xf=np.zeros((Ny,Nx+1)) #vertical faces\n", 609 | " phi_yf=np.zeros((Ny+1,Nx)) #horizontal faces \n", 610 | " phi_xf_diff=np.zeros((Ny,Nx+1)) #vertical faces\n", 611 | " phi_yf_diff=np.zeros((Ny+1,Nx)) #horizontal faces \n", 612 | " vertex_field_x=np.zeros((Ny+1,Nx+1)) #vertices\n", 613 | " vertex_field_y=np.zeros((Ny+1,Nx+1)) #vertices\n", 614 | " phi_xf_out=np.zeros((Ny,Nx+1))\n", 615 | " phi_yf_out=np.zeros((Ny+1,Nx))\n", 616 | " \n", 617 | " phi_xf,phi_yf=face_centre(Ny,Nx,phi,0) #Calculate face centre\n", 618 | " phi_xf_diff,phi_yf_diff=diff_face_centre(Ny,Nx,phi_xf_diff,phi_yf_diff,phi,dx,dy,scalar) #Calculate diffusive face centre\n", 619 | " vertex_field_x,vertex_field_y=diff_vertex(Ny,Nx,vertex_field_x,vertex_field_y,phi_xf,phi_yf,dx,dy,scalar) #Calculate vertices\n", 620 | " phi_xf,phi_yf=diff_simp_rule(Ny,Nx,phi_xf_diff,phi_yf_diff,phi_xf_out,phi_yf_out,vertex_field_x,vertex_field_y) #Simpson's rule\n", 621 | " return phi_xf,phi_yf" 622 | ] 623 | }, 624 | { 625 | "cell_type": "code", 626 | "execution_count": 15, 627 | "id": "02102dac", 628 | "metadata": {}, 629 | "outputs": [], 630 | "source": [ 631 | "@njit(fastmath=True)\n", 632 | "def volumetric_source(Ny,Nx,phi,phi_xf,phi_yf,phi_vertex):\n", 633 | " phi_vol=np.zeros((Ny,Nx))\n", 634 | " \n", 635 | " for j in range(Ny):\n", 636 | " for i in range(Nx):\n", 637 | " phi_P=phi[j][i]\n", 638 | " phi_s=phi_yf[j][i]\n", 639 | " phi_w=phi_xf[j][i]\n", 640 | " phi_e=phi_xf[j][i+1]\n", 641 | " phi_n=phi_yf[j+1][i]\n", 642 | " phi_sw=phi_vertex[j][i]\n", 643 | " phi_se=phi_vertex[j][i+1]\n", 644 | " phi_nw=phi_vertex[j+1][i]\n", 645 | " phi_ne=phi_vertex[j+1][i+1]\n", 646 | " \n", 647 | " phi_vol[j][i]=(16*phi_P+4*phi_s+4*phi_w+4*phi_e+4*phi_n+phi_sw+phi_se+phi_nw+phi_ne)/36\n", 648 | "\n", 649 | " return phi_vol" 650 | ] 651 | }, 652 | { 653 | "cell_type": "code", 654 | "execution_count": 16, 655 | "id": "358ed9de", 656 | "metadata": {}, 657 | "outputs": [], 658 | "source": [ 659 | "@njit(fastmath=True)\n", 660 | "def upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_e,phi_w,phi_n,phi_s,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 661 | " BP=0\n", 662 | " \n", 663 | " AP+=dy*(abs(ue)+ue)\n", 664 | " BP-=(abs(ue)+ue)*dy*(phi_e-phi_P)\n", 665 | " \n", 666 | " AE-=dy*(abs(ue)-ue)\n", 667 | " BP+=dy*(abs(ue)-ue)*(phi_e-phi_E)\n", 668 | "\n", 669 | " AP+=dy*(abs(uw)-uw)\n", 670 | " BP-=dy*(abs(uw)-uw)*(phi_w-phi_P)\n", 671 | "\n", 672 | " AW-=dy*(abs(uw)+uw)\n", 673 | " BP+=dy*(abs(uw)+uw)*(phi_w-phi_W)\n", 674 | " \n", 675 | " AP+=dx*(abs(vn)+vn)\n", 676 | " BP-=dx*(abs(vn)+vn)*(phi_n-phi_P)\n", 677 | " \n", 678 | " AN-=dx*(abs(vn)-vn)\n", 679 | " BP+=dx*(abs(vn)-vn)*(phi_n-phi_N)\n", 680 | " \n", 681 | " AP+=dx*(abs(vs)-vs)\n", 682 | " BP-=dx*(abs(vs)-vs)*(phi_s-phi_P)\n", 683 | " \n", 684 | " AS-=dx*(abs(vs)+vs)\n", 685 | " BP+=dx*(abs(vs)+vs)*(phi_s-phi_S)\n", 686 | " \n", 687 | " return AE,AW,AN,AS,AP,BP" 688 | ] 689 | }, 690 | { 691 | "cell_type": "code", 692 | "execution_count": 17, 693 | "id": "a6425cec", 694 | "metadata": {}, 695 | "outputs": [], 696 | "source": [ 697 | "@njit(fastmath=True)\n", 698 | "def upwind_DC_limited(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_e,phi_w,phi_n,phi_s,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 699 | " BP=0\n", 700 | " \n", 701 | " phi_EE=extrapolate_3rd(phi_E,phi_W,phi_P)\n", 702 | " phi_WW=extrapolate_3rd(phi_W,phi_P,phi_E)\n", 703 | " phi_NN=extrapolate_3rd(phi_N,phi_P,phi_S)\n", 704 | " phi_SS=extrapolate_3rd(phi_S,phi_P,phi_N)\n", 705 | " \n", 706 | " limit=limiter(phi_E,phi_P,phi_W)\n", 707 | " ue_abs=dy*(np.abs(ue)+ue)\n", 708 | " AP+=ue_abs\n", 709 | " BP-=ue_abs*limit*(phi_e-phi_P)\n", 710 | " \n", 711 | " limit=limiter(phi_P,phi_E,phi_EE)\n", 712 | " ue_abs=dy*(np.abs(ue)-ue)\n", 713 | " AE-=ue_abs\n", 714 | " BP+=ue_abs*limit*(phi_e-phi_E)\n", 715 | "\n", 716 | " limit=limiter(phi_W,phi_P,phi_E)\n", 717 | " uw_abs=dy*(np.abs(uw)-uw)\n", 718 | " AP+=uw_abs\n", 719 | " BP-=uw_abs*limit*(phi_w-phi_P)\n", 720 | "\n", 721 | " limit=limiter(phi_P,phi_W,phi_WW)\n", 722 | " uw_abs=dy*(np.abs(uw)+uw)\n", 723 | " AW-=uw_abs\n", 724 | " BP+=uw_abs*limit*(phi_w-phi_W)\n", 725 | " \n", 726 | " limit=limiter(phi_N,phi_P,phi_S)\n", 727 | " vn_abs=dx*(np.abs(vn)+vn)\n", 728 | " AP+=vn_abs\n", 729 | " BP-=vn_abs*limit*(phi_n-phi_P)\n", 730 | " \n", 731 | " limit=limiter(phi_P,phi_N,phi_NN)\n", 732 | " vn_abs=dx*(np.abs(vn)-vn)\n", 733 | " AN-=vn_abs\n", 734 | " BP+=vn_abs*limit*(phi_n-phi_N)\n", 735 | " \n", 736 | " limit=limiter(phi_S,phi_P,phi_N)\n", 737 | " vs_abs=dx*(np.abs(vs)-vs)\n", 738 | " AP+=vs_abs\n", 739 | " BP-=vs_abs*limit*(phi_s-phi_P)\n", 740 | " \n", 741 | " limit=limiter(phi_P,phi_S,phi_SS)\n", 742 | " vs_abs=dx*(np.abs(vs)+vs)\n", 743 | " AS-=vs_abs\n", 744 | " BP+=vs_abs*limit*(phi_s-phi_S)\n", 745 | " \n", 746 | " return AE,AW,AN,AS,AP,BP" 747 | ] 748 | }, 749 | { 750 | "cell_type": "code", 751 | "execution_count": 18, 752 | "id": "553c8c6f", 753 | "metadata": {}, 754 | "outputs": [], 755 | "source": [ 756 | "@njit\n", 757 | "def limiter(phi_D,phi_U,phi_UU):\n", 758 | " r=((phi_U-phi_UU)/(phi_D-phi_U+1E-9))\n", 759 | " return (r+np.abs(r))/(1+np.abs(r)) #van leer" 760 | ] 761 | }, 762 | { 763 | "cell_type": "code", 764 | "execution_count": 19, 765 | "id": "b7a9e6ed", 766 | "metadata": {}, 767 | "outputs": [], 768 | "source": [ 769 | "@njit\n", 770 | "def mom_coeff(Nx,Ny,U,V,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx):\n", 771 | " N=Nx*Ny\n", 772 | " y=v*dx/dy\n", 773 | " x=v*dy/dx\n", 774 | " \n", 775 | " uA=np.zeros((Ny,Nx,6))\n", 776 | " vA=np.zeros((Ny,Nx,6))\n", 777 | " DX=np.zeros((Ny,Nx))\n", 778 | " DY=np.zeros((Ny,Nx))\n", 779 | " \n", 780 | " dp=4*x+4*y\n", 781 | " de=-2*x\n", 782 | " dw=-2*x\n", 783 | " dn=-2*y\n", 784 | " ds=-2*y\n", 785 | " gxgbdxdy=gx*gb*dx*dy\n", 786 | " gygbdxdy=gy*gb*dx*dy\n", 787 | " \n", 788 | " Udxf=Udxf*dy*v\n", 789 | " Udyf=Udyf*dx*v\n", 790 | " Vdxf=Vdxf*dy*v\n", 791 | " Vdyf=Vdyf*dx*v\n", 792 | " \n", 793 | " a_s=0\n", 794 | " a_w=1\n", 795 | " a_p=2\n", 796 | " a_e=3\n", 797 | " a_n=4\n", 798 | " b_p=5\n", 799 | " \n", 800 | " #For interior cells\n", 801 | " for j in range(1,Ny-1):\n", 802 | " for i in range(1,Nx-1):\n", 803 | " \n", 804 | " TP=T[j][i]\n", 805 | " \n", 806 | " VS=V[j-1][i]\n", 807 | " VW=V[j][i-1]\n", 808 | " VP=V[j][i]\n", 809 | " VE=V[j][i+1]\n", 810 | " VN=V[j+1][i]\n", 811 | "\n", 812 | " US=U[j-1][i]\n", 813 | " UW=U[j][i-1]\n", 814 | " UP=U[j][i]\n", 815 | " UE=U[j][i+1]\n", 816 | " UN=U[j+1][i]\n", 817 | " \n", 818 | " ue=Uxf[j][i+1]\n", 819 | " uw=Uxf[j][i]\n", 820 | " un=Uyf[j+1][i]\n", 821 | " us=Uyf[j][i]\n", 822 | " \n", 823 | " ve=Vxf[j][i+1]\n", 824 | " vw=Vxf[j][i]\n", 825 | " vn=Vyf[j+1][i]\n", 826 | " vs=Vyf[j][i]\n", 827 | " \n", 828 | " ude=Udxf[j][i+1]\n", 829 | " udw=Udxf[j][i]\n", 830 | " udn=Udyf[j+1][i]\n", 831 | " uds=Udyf[j][i]\n", 832 | "\n", 833 | " vde=Vdxf[j][i+1]\n", 834 | " vdw=Vdxf[j][i]\n", 835 | " vdn=Vdyf[j+1][i]\n", 836 | " vds=Vdyf[j][i]\n", 837 | " \n", 838 | " pw=Pxf[j][i]\n", 839 | " pe=Pxf[j][i+1]\n", 840 | " ps=Pyf[j][i]\n", 841 | " pn=Pyf[j+1][i]\n", 842 | "\n", 843 | " AE=de\n", 844 | " AW=dw\n", 845 | " AN=dn\n", 846 | " AS=ds\n", 847 | " AP=dp\n", 848 | " \n", 849 | " ude_LO=(UE-UP)*x\n", 850 | " udw_LO=(UP-UW)*x\n", 851 | " udn_LO=(UN-UP)*y\n", 852 | " uds_LO=(UP-US)*y\n", 853 | " U_diff_DC=(ude-ude_LO)-(udw-udw_LO)+(udn-udn_LO)-(uds-uds_LO)\n", 854 | "\n", 855 | " vde_LO=(VE-VP)*x\n", 856 | " vdw_LO=(VP-VW)*x\n", 857 | " vdn_LO=(VN-VP)*y\n", 858 | " vds_LO=(VP-VS)*y\n", 859 | " V_diff_DC=(vde-vde_LO)-(vdw-vdw_LO)+(vdn-vdn_LO)-(vds-vds_LO)\n", 860 | " \n", 861 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,vs,ue,uw,un,us,US,UW,UP,UE,UN,dy,dx,v)\n", 862 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,vs,ve,vw,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 863 | " \n", 864 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC\n", 865 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC\n", 866 | "\n", 867 | " Dx=dy/(uAP/av-uAE-uAW-uAN-uAS)\n", 868 | " Dy=dx/(vAP/av-vAE-vAW-vAN-vAS)\n", 869 | "\n", 870 | " uA[j][i][a_w]=uAW\n", 871 | " uA[j][i][a_p]=uAP\n", 872 | " uA[j][i][a_e]=uAE\n", 873 | " uA[j][i][a_n]=uAN\n", 874 | " uA[j][i][a_s]=uAS\n", 875 | " uA[j][i][b_p]=uBP\n", 876 | "\n", 877 | " vA[j][i][a_w]=vAW\n", 878 | " vA[j][i][a_p]=vAP\n", 879 | " vA[j][i][a_e]=vAE\n", 880 | " vA[j][i][a_n]=vAN\n", 881 | " vA[j][i][a_s]=vAS\n", 882 | " vA[j][i][b_p]=vBP\n", 883 | "\n", 884 | " DX[j][i]=Dx\n", 885 | " DY[j][i]=Dy\n", 886 | " \n", 887 | " #for bottom wall\n", 888 | " j=0\n", 889 | " for i in range(1,Nx-1):\n", 890 | " TP=T[j][i]\n", 891 | " \n", 892 | " UW=U[j][i-1]\n", 893 | " UP=U[j][i]\n", 894 | " UE=U[j][i+1]\n", 895 | " UN=U[j+1][i]\n", 896 | " UNN=U[j+2][i]\n", 897 | " UNNN=U[j+3][i]\n", 898 | " \n", 899 | " VW=V[j][i-1]\n", 900 | " VP=V[j][i]\n", 901 | " VE=V[j][i+1]\n", 902 | " VN=V[j+1][i]\n", 903 | " VNN=V[j+2][i]\n", 904 | " VNNN=V[j+3][i]\n", 905 | "\n", 906 | " ue=Uxf[j][i+1]\n", 907 | " uw=Uxf[j][i]\n", 908 | " un=Uyf[j+1][i]\n", 909 | "\n", 910 | " ve=Vxf[j][i+1]\n", 911 | " vw=Vxf[j][i]\n", 912 | " vn=Vyf[j+1][i]\n", 913 | " \n", 914 | " ude=Udxf[j][i+1]\n", 915 | " udw=Udxf[j][i]\n", 916 | " udn=Udyf[j+1][i]\n", 917 | "\n", 918 | " vde=Vdxf[j][i+1]\n", 919 | " vdw=Vdxf[j][i]\n", 920 | " vdn=Vdyf[j+1][i]\n", 921 | " \n", 922 | " pw=Pxf[j][i]\n", 923 | " pe=Pxf[j][i+1]\n", 924 | " ps=Pyf[j][i]\n", 925 | " pn=Pyf[j+1][i]\n", 926 | "\n", 927 | " AE=de\n", 928 | " AW=dw\n", 929 | " AN=-(8/3)*y\n", 930 | " AP=8*y+4*x\n", 931 | " \n", 932 | " ude_LO=(UE-UP)*x\n", 933 | " udw_LO=(UP-UW)*x\n", 934 | " udn_LO=(UN-UP)*y\n", 935 | " U_diff_DC=(ude-ude_LO)-(udw-udw_LO)+(udn-udn_LO)\n", 936 | "\n", 937 | " vde_LO=(VE-VP)*x\n", 938 | " vdw_LO=(VP-VW)*x\n", 939 | " vdn_LO=(VN-VP)*y\n", 940 | " V_diff_DC=(vde-vde_LO)-(vdw-vdw_LO)+(vdn-vdn_LO)\n", 941 | "\n", 942 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,ue,uw,un,1E-12,1E-12,UW,UP,UE,UN,dy,dx,v)\n", 943 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,ve,vw,vn,1E-12,1E-12,VW,VP,VE,VN,dy,dx,v)\n", 944 | "\n", 945 | " uBP+=2*dy*(pw-pw)+2*gxgbdxdy*TP+2*U_diff_DC+y*(-2.75*UP+2.25*UN-1.05*UNN+(5/28)*UNNN)\n", 946 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+y*(-2.75*VP+2.25*VN-1.05*VNN+(5/28)*VNNN)\n", 947 | " Dx=dy/(uAP/av-uAE-uAW-uAN)\n", 948 | " Dy=dx/(vAP/av-vAE-vAW-vAN)\n", 949 | "\n", 950 | " uA[j][i][a_w]=uAW\n", 951 | " uA[j][i][a_p]=uAP\n", 952 | " uA[j][i][a_e]=uAE\n", 953 | " uA[j][i][a_n]=uAN\n", 954 | " uA[j][i][b_p]=uBP\n", 955 | "\n", 956 | " vA[j][i][a_w]=vAW\n", 957 | " vA[j][i][a_p]=vAP\n", 958 | " vA[j][i][a_e]=vAE\n", 959 | " vA[j][i][a_n]=vAN\n", 960 | " vA[j][i][b_p]=vBP\n", 961 | "\n", 962 | " DX[j][i]=Dx\n", 963 | " DY[j][i]=Dy\n", 964 | "\n", 965 | " #for top wall\n", 966 | " j=Ny-1\n", 967 | " for i in range(1,Nx-1):\n", 968 | " TP=T[j][i]\n", 969 | " \n", 970 | " US=U[j-1][i]\n", 971 | " USS=U[j-2][i]\n", 972 | " USSS=U[j-3][i]\n", 973 | " UW=U[j][i-1]\n", 974 | " UP=U[j][i]\n", 975 | " UE=U[j][i+1]\n", 976 | " \n", 977 | " VS=V[j-1][i] \n", 978 | " VSS=V[j-2][i]\n", 979 | " VSSS=V[j-3][i]\n", 980 | " VW=V[j][i-1]\n", 981 | " VP=V[j][i]\n", 982 | " VE=V[j][i+1]\n", 983 | " \n", 984 | " ue=Uxf[j][i+1]\n", 985 | " uw=Uxf[j][i]\n", 986 | " us=Uyf[j][i]\n", 987 | "\n", 988 | " ve=Vxf[j][i+1]\n", 989 | " vw=Vxf[j][i]\n", 990 | " vs=Vyf[j][i]\n", 991 | " \n", 992 | " ude=Udxf[j][i+1]\n", 993 | " udw=Udxf[j][i]\n", 994 | " uds=Udyf[j][i]\n", 995 | "\n", 996 | " vde=Vdxf[j][i+1]\n", 997 | " vdw=Vdxf[j][i]\n", 998 | " vds=Vdyf[j][i]\n", 999 | " \n", 1000 | " pw=Pxf[j][i]\n", 1001 | " pe=Pxf[j][i+1]\n", 1002 | " ps=Pyf[j][i]\n", 1003 | " pn=Pyf[j+1][i]\n", 1004 | "\n", 1005 | " AE=de\n", 1006 | " AW=dw\n", 1007 | " AS=-(8/3)*y\n", 1008 | " AP=8*y+4*x\n", 1009 | " \n", 1010 | " ude_LO=(UE-UP)*x\n", 1011 | " udw_LO=(UP-UW)*x\n", 1012 | " uds_LO=(UP-US)*y\n", 1013 | " U_diff_DC=(ude-ude_LO)-(udw-udw_LO)-(uds-uds_LO)\n", 1014 | "\n", 1015 | " vde_LO=(VE-VP)*x\n", 1016 | " vdw_LO=(VP-VW)*x\n", 1017 | " vds_LO=(VP-VS)*y\n", 1018 | " V_diff_DC=(vde-vde_LO)-(vdw-vdw_LO)-(vds-vds_LO)\n", 1019 | " \n", 1020 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,ue,uw,1E-12,us,US,UW,UP,UE,1E-12,dy,dx,v)\n", 1021 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,ve,vw,1E-12,vs,VS,VW,VP,VE,1E-12,dy,dx,v)\n", 1022 | " \n", 1023 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+y*(-2.75*UP+2.25*US-1.05*USS+(5/28)*USSS)\n", 1024 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+y*(-2.75*VP+2.25*VS-1.05*VSS+(5/28)*VSSS) \n", 1025 | " Dx=dy/(uAP/av-uAE-uAW-uAS)\n", 1026 | " Dy=dx/(vAP/av-vAE-vAW-vAS)\n", 1027 | "\n", 1028 | " uA[j][i][a_w]=uAW\n", 1029 | " uA[j][i][a_p]=uAP\n", 1030 | " uA[j][i][a_e]=uAE\n", 1031 | " uA[j][i][a_s]=uAS\n", 1032 | " uA[j][i][b_p]=uBP\n", 1033 | "\n", 1034 | " vA[j][i][a_w]=vAW\n", 1035 | " vA[j][i][a_p]=vAP\n", 1036 | " vA[j][i][a_e]=vAE\n", 1037 | " vA[j][i][a_s]=vAS\n", 1038 | " vA[j][i][b_p]=vBP\n", 1039 | "\n", 1040 | " DX[j][i]=Dx\n", 1041 | " DY[j][i]=Dy\n", 1042 | "\n", 1043 | " #for left wall:\n", 1044 | " i=0\n", 1045 | " for j in range(1,Ny-1):\n", 1046 | " TP=T[j][i]\n", 1047 | " \n", 1048 | " VN=V[j+1][i]\n", 1049 | " VE=V[j][i+1]\n", 1050 | " VEE=V[j][i+2]\n", 1051 | " VEEE=V[j][i+3]\n", 1052 | " VP=V[j][i]\n", 1053 | " VS=V[j-1][i]\n", 1054 | "\n", 1055 | " UP=U[j][i]\n", 1056 | " UE=U[j][i+1]\n", 1057 | " UEE=U[j][i+2]\n", 1058 | " UEEE=U[j][i+3]\n", 1059 | " UN=U[j+1][i]\n", 1060 | " US=U[j-1][i]\n", 1061 | "\n", 1062 | " ue=Uxf[j][i+1]\n", 1063 | " un=Uyf[j+1][i]\n", 1064 | " us=Uyf[j][i]\n", 1065 | "\n", 1066 | " ve=Vxf[j][i+1]\n", 1067 | " vn=Vyf[j+1][i]\n", 1068 | " vs=Vyf[j][i]\n", 1069 | " \n", 1070 | " ude=Udxf[j][i+1]\n", 1071 | " udn=Udyf[j+1][i]\n", 1072 | " uds=Udyf[j][i]\n", 1073 | "\n", 1074 | " vde=Vdxf[j][i+1]\n", 1075 | " vdn=Vdyf[j+1][i]\n", 1076 | " vds=Vdyf[j][i]\n", 1077 | " \n", 1078 | " pw=Pxf[j][i]\n", 1079 | " pe=Pxf[j][i+1]\n", 1080 | " ps=Pyf[j][i]\n", 1081 | " pn=Pyf[j+1][i]\n", 1082 | "\n", 1083 | " AE=-(8/3)*x\n", 1084 | " AN=dn\n", 1085 | " AS=ds\n", 1086 | " AP=8*x+4*y\n", 1087 | " \n", 1088 | " ude_LO=(UE-UP)*x\n", 1089 | " udn_LO=(UN-UP)*y\n", 1090 | " uds_LO=(UP-US)*y\n", 1091 | " U_diff_DC=(ude-ude_LO)+(udn-udn_LO)-(uds-uds_LO)\n", 1092 | "\n", 1093 | " vde_LO=(VE-VP)*x\n", 1094 | " vdn_LO=(VN-VP)*y\n", 1095 | " vds_LO=(VP-VS)*y\n", 1096 | " V_diff_DC=(vde-vde_LO)+(vdn-vdn_LO)-(vds-vds_LO)\n", 1097 | "\n", 1098 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,ue,1E-12,un,us,US,1E-12,UP,UE,UN,dy,dx,v)\n", 1099 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,ve,1E-12,vn,vs,VS,1E-12,VP,VE,VN,dy,dx,v)\n", 1100 | "\n", 1101 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UE-1.05*UEE+(5/28)*UEEE)\n", 1102 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VE-1.05*VEE+(5/28)*VEEE)\n", 1103 | " Dx=dy/(uAP/av-uAE-uAN-uAS)\n", 1104 | " Dy=dx/(vAP/av-vAE-vAN-vAS)\n", 1105 | "\n", 1106 | " uA[j][i][a_p]=uAP\n", 1107 | " uA[j][i][a_e]=uAE\n", 1108 | " uA[j][i][a_n]=uAN\n", 1109 | " uA[j][i][a_s]=uAS\n", 1110 | " uA[j][i][b_p]=uBP\n", 1111 | "\n", 1112 | " vA[j][i][a_p]=vAP\n", 1113 | " vA[j][i][a_e]=vAE\n", 1114 | " vA[j][i][a_n]=vAN\n", 1115 | " vA[j][i][a_s]=vAS\n", 1116 | " vA[j][i][b_p]=vBP\n", 1117 | "\n", 1118 | " DX[j][i]=Dx\n", 1119 | " DY[j][i]=Dy\n", 1120 | "\n", 1121 | " #for right wall:\n", 1122 | " i=Nx-1\n", 1123 | " for j in range(1,Ny-1):\n", 1124 | " TP=T[j][i]\n", 1125 | " \n", 1126 | " VN=V[j+1][i]\n", 1127 | " VP=V[j][i]\n", 1128 | " VS=V[j-1][i]\n", 1129 | " VW=V[j][i-1]\n", 1130 | " VWW=V[j][i-2]\n", 1131 | " VWWW=V[j][i-3]\n", 1132 | "\n", 1133 | " UP=U[j][i]\n", 1134 | " UW=U[j][i-1]\n", 1135 | " UWW=U[j][i-2]\n", 1136 | " UWWW=U[j][i-3]\n", 1137 | " UN=U[j+1][i]\n", 1138 | " US=U[j-1][i]\n", 1139 | " \n", 1140 | " uw=Uxf[j][i]\n", 1141 | " un=Uyf[j+1][i]\n", 1142 | " us=Uyf[j][i]\n", 1143 | "\n", 1144 | " vw=Vxf[j][i]\n", 1145 | " vn=Vyf[j+1][i]\n", 1146 | " vs=Vyf[j][i]\n", 1147 | " \n", 1148 | " udw=Udxf[j][i]\n", 1149 | " udn=Udyf[j+1][i]\n", 1150 | " uds=Udyf[j][i]\n", 1151 | "\n", 1152 | " vdw=Vdxf[j][i]\n", 1153 | " vdn=Vdyf[j+1][i]\n", 1154 | " vds=Vdyf[j][i]\n", 1155 | " \n", 1156 | " pw=Pxf[j][i]\n", 1157 | " pe=Pxf[j][i+1]\n", 1158 | " ps=Pyf[j][i]\n", 1159 | " pn=Pyf[j+1][i]\n", 1160 | "\n", 1161 | " AW=-(8/3)*x\n", 1162 | " AN=dn\n", 1163 | " AS=ds\n", 1164 | " AP=8*x+4*y\n", 1165 | " \n", 1166 | " udw_LO=(UP-UW)*x\n", 1167 | " udn_LO=(UN-UP)*y\n", 1168 | " uds_LO=(UP-US)*y\n", 1169 | " U_diff_DC=-(udw-udw_LO)+(udn-udn_LO)-(uds-uds_LO)\n", 1170 | "\n", 1171 | " vdw_LO=(VP-VW)*x\n", 1172 | " vdn_LO=(VN-VP)*y\n", 1173 | " vds_LO=(VP-VS)*y\n", 1174 | " V_diff_DC=-(vdw-vdw_LO)+(vdn-vdn_LO)-(vds-vds_LO)\n", 1175 | "\n", 1176 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,1E-12,uw,un,us,US,UW,UP,1E-12,UN,dy,dx,v)\n", 1177 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,1E-12,vw,vn,vs,VS,VW,VP,1E-12,VN,dy,dx,v)\n", 1178 | "\n", 1179 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UW-1.05*UWW+(5/28)*UWWW)\n", 1180 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VW-1.05*VWW+(5/28)*VWWW)\n", 1181 | " Dx=dy/(uAP/av-uAW-uAN-uAS)\n", 1182 | " Dy=dx/(vAP/av-vAW-vAN-vAS)\n", 1183 | "\n", 1184 | " uA[j][i][a_w]=uAW\n", 1185 | " uA[j][i][a_p]=uAP\n", 1186 | " uA[j][i][a_n]=uAN\n", 1187 | " uA[j][i][a_s]=uAS\n", 1188 | " uA[j][i][b_p]=uBP\n", 1189 | "\n", 1190 | " vA[j][i][a_w]=vAW\n", 1191 | " vA[j][i][a_p]=vAP\n", 1192 | " vA[j][i][a_n]=vAN\n", 1193 | " vA[j][i][a_s]=vAS\n", 1194 | " vA[j][i][b_p]=vBP\n", 1195 | "\n", 1196 | " DX[j][i]=Dx\n", 1197 | " DY[j][i]=Dy\n", 1198 | "\n", 1199 | " #top right cell\n", 1200 | " i=Nx-1\n", 1201 | " j=Ny-1\n", 1202 | " \n", 1203 | " TP=T[j][i]\n", 1204 | " \n", 1205 | " UP=U[j][i]\n", 1206 | " UW=U[j][i-1]\n", 1207 | " UWW=U[j][i-2]\n", 1208 | " UWWW=U[j][i-3]\n", 1209 | " US=U[j-1][i]\n", 1210 | " USS=U[j-2][i]\n", 1211 | " USSS=U[j-3][i]\n", 1212 | " \n", 1213 | " VS=V[j-1][i]\n", 1214 | " VSS=V[j-2][i]\n", 1215 | " VSSS=V[j-3][i]\n", 1216 | " VW=V[j][i-1]\n", 1217 | " VWW=V[j][i-2]\n", 1218 | " VWWW=V[j][i-3]\n", 1219 | " VP=V[j][i]\n", 1220 | "\n", 1221 | " uw=Uxf[j][i]\n", 1222 | " us=Uyf[j][i]\n", 1223 | "\n", 1224 | " vw=Vxf[j][i]\n", 1225 | " vs=Vyf[j][i]\n", 1226 | "\n", 1227 | " udw=Udxf[j][i]\n", 1228 | " uds=Udyf[j][i]\n", 1229 | "\n", 1230 | " vdw=Vdxf[j][i]\n", 1231 | " vds=Vdyf[j][i]\n", 1232 | " \n", 1233 | " pw=Pxf[j][i]\n", 1234 | " pe=Pxf[j][i+1]\n", 1235 | " ps=Pyf[j][i]\n", 1236 | " pn=Pyf[j+1][i]\n", 1237 | " \n", 1238 | " AW=-(8/3)*x\n", 1239 | " AS=-(8/3)*y\n", 1240 | " AP=8*x+8*y\n", 1241 | " \n", 1242 | " udw_LO=(UP-UW)*x\n", 1243 | " uds_LO=(UP-US)*y\n", 1244 | " U_diff_DC=-(udw-udw_LO)-(uds-uds_LO)\n", 1245 | "\n", 1246 | " vdw_LO=(VP-VW)*x\n", 1247 | " vds_LO=(VP-VS)*y\n", 1248 | " V_diff_DC=-(vdw-vdw_LO)-(vds-vds_LO)\n", 1249 | " \n", 1250 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,1E-12,vs,1E-12,uw,1E-12,us,US,UW,UP,1E-12,1E-12,dy,dx,v)\n", 1251 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,1E-12,vs,1E-12,vw,1E-12,vs,VS,VW,VP,1E-12,1E-12,dy,dx,v)\n", 1252 | " \n", 1253 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UW-1.05*UWW+(5/28)*UWWW)+y*(-2.75*UP+2.25*US-1.05*USS+(5/28)*USSS)\n", 1254 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VW-1.05*VWW+(5/28)*VWWW)+y*(-2.75*VP+2.25*VS-1.05*VSS+(5/28)*VSSS)\n", 1255 | " Dx=dy/(uAP/av-uAW-uAS)\n", 1256 | " Dy=dx/(vAP/av-vAW-vAS)\n", 1257 | "\n", 1258 | " uA[j][i][a_w]=uAW\n", 1259 | " uA[j][i][a_p]=uAP\n", 1260 | " uA[j][i][a_s]=uAS\n", 1261 | " uA[j][i][b_p]=uBP\n", 1262 | "\n", 1263 | " vA[j][i][a_w]=vAW\n", 1264 | " vA[j][i][a_p]=vAP\n", 1265 | " vA[j][i][a_s]=vAS\n", 1266 | " vA[j][i][b_p]=vBP\n", 1267 | "\n", 1268 | " DX[j][i]=Dx\n", 1269 | " DY[j][i]=Dy\n", 1270 | " \n", 1271 | " #top left cell\n", 1272 | " i=0\n", 1273 | " j=Ny-1\n", 1274 | " \n", 1275 | " TP=T[j][i]\n", 1276 | " \n", 1277 | " UP=U[j][i]\n", 1278 | " UE=U[j][i+1]\n", 1279 | " UEE=U[j][i+2]\n", 1280 | " UEEE=U[j][i+3]\n", 1281 | " US=U[j-1][i]\n", 1282 | " USS=U[j-2][i]\n", 1283 | " USSS=U[j-3][i]\n", 1284 | " \n", 1285 | " VS=V[j-1][i]\n", 1286 | " VSS=V[j-2][i]\n", 1287 | " VSSS=V[j-3][i]\n", 1288 | " VE=V[j][i+1]\n", 1289 | " VEE=V[j][i+2]\n", 1290 | " VEEE=V[j][i+3]\n", 1291 | " VP=V[j][i]\n", 1292 | "\n", 1293 | " ue=Uxf[j][i+1]\n", 1294 | " us=Uyf[j][i]\n", 1295 | "\n", 1296 | " ve=Vxf[j][i+1]\n", 1297 | " vs=Vyf[j][i]\n", 1298 | "\n", 1299 | " ude=Udxf[j][i+1]\n", 1300 | " uds=Udyf[j][i]\n", 1301 | "\n", 1302 | " vde=Vdxf[j][i+1]\n", 1303 | " vds=Vdyf[j][i]\n", 1304 | " \n", 1305 | " pw=Pxf[j][i]\n", 1306 | " pe=Pxf[j][i+1]\n", 1307 | " ps=Pyf[j][i]\n", 1308 | " pn=Pyf[j+1][i]\n", 1309 | " \n", 1310 | " AE=-(8/3)*x\n", 1311 | " AS=-(8/3)*y\n", 1312 | " AP=8*x+8*y\n", 1313 | " \n", 1314 | " ude_LO=(UE-UP)*x\n", 1315 | " uds_LO=(UP-US)*y\n", 1316 | " U_diff_DC=(ude-ude_LO)-(uds-uds_LO)\n", 1317 | "\n", 1318 | " vde_LO=(VE-VP)*x\n", 1319 | " vds_LO=(VP-VS)*y\n", 1320 | " V_diff_DC=(vde-vde_LO)-(vds-vds_LO)\n", 1321 | " \n", 1322 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,1E-12,vs,ue,1E-12,1E-12,us,US,1E-12,UP,UE,1E-12,dy,dx,v)\n", 1323 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,1E-12,vs,ve,1E-12,1E-12,vs,VS,1E-12,VP,VE,1E-12,dy,dx,v)\n", 1324 | " \n", 1325 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UE-1.05*UEE+(5/28)*UEEE)+y*(-2.75*UP+2.25*US-1.05*USS+(5/28)*USSS)\n", 1326 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VE-1.05*VEE+(5/28)*VEEE)+y*(-2.75*VP+2.25*VS-1.05*VSS+(5/28)*VSSS)\n", 1327 | " Dx=dy/(uAP/av-uAE-uAS)\n", 1328 | " Dy=dx/(vAP/av-vAE-vAS)\n", 1329 | " \n", 1330 | " uA[j][i][a_p]=uAP\n", 1331 | " uA[j][i][a_e]=uAE\n", 1332 | " uA[j][i][a_s]=uAS\n", 1333 | " uA[j][i][b_p]=uBP\n", 1334 | "\n", 1335 | " vA[j][i][a_p]=vAP\n", 1336 | " vA[j][i][a_e]=vAE\n", 1337 | " vA[j][i][a_s]=vAS\n", 1338 | " vA[j][i][b_p]=vBP\n", 1339 | "\n", 1340 | " DX[j][i]=Dx\n", 1341 | " DY[j][i]=Dy\n", 1342 | "\n", 1343 | " #bottom right cell\n", 1344 | " i=Nx-1\n", 1345 | " j=0\n", 1346 | " \n", 1347 | " TP=T[j][i]\n", 1348 | " \n", 1349 | " UW=U[j][i-1]\n", 1350 | " UWW=U[j][i-2]\n", 1351 | " UWWW=U[j][i-3]\n", 1352 | " UP=U[j][i]\n", 1353 | " UN=U[j+1][i]\n", 1354 | " UNN=U[j+2][i]\n", 1355 | " UNNN=U[j+3][i]\n", 1356 | "\n", 1357 | " VP=V[j][i]\n", 1358 | " VW=V[j][i-1]\n", 1359 | " VWW=V[j][i-2]\n", 1360 | " VWWW=V[j][i-3]\n", 1361 | " VN=V[j+1][i]\n", 1362 | " VNN=V[j+2][i]\n", 1363 | " VNNN=V[j+3][i]\n", 1364 | "\n", 1365 | " uw=Uxf[j][i]\n", 1366 | " un=Uyf[j+1][i]\n", 1367 | "\n", 1368 | " vw=Vxf[j][i]\n", 1369 | " vn=Vyf[j+1][i]\n", 1370 | " \n", 1371 | " udw=Udxf[j][i]\n", 1372 | " udn=Udyf[j+1][i]\n", 1373 | "\n", 1374 | " vdw=Vdxf[j][i]\n", 1375 | " vdn=Vdyf[j+1][i]\n", 1376 | " \n", 1377 | " pw=Pxf[j][i]\n", 1378 | " pe=Pxf[j][i+1]\n", 1379 | " ps=Pyf[j][i]\n", 1380 | " pn=Pyf[j+1][i]\n", 1381 | " \n", 1382 | " AW=-(8/3)*x\n", 1383 | " AN=-(8/3)*y\n", 1384 | " AP=8*x+8*y\n", 1385 | "\n", 1386 | " udw_LO=(UP-UW)*x\n", 1387 | " udn_LO=(UN-UP)*y\n", 1388 | " U_diff_DC=-(udw-udw_LO)+(udn-udn_LO)\n", 1389 | "\n", 1390 | " vdw_LO=(VP-VW)*x\n", 1391 | " vdn_LO=(VN-VP)*y\n", 1392 | " V_diff_DC=-(vdw-vdw_LO)+(vdn-vdn_LO)\n", 1393 | " \n", 1394 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,1E-12,1E-12,uw,un,1E-12,1E-12,UW,UP,1E-12,UN,dy,dx,v)\n", 1395 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,1E-12,1E-12,vw,vn,1E-12,1E-12,VW,VP,1E-12,VN,dy,dx,v)\n", 1396 | " \n", 1397 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UW-1.05*UWW+(5/28)*UWWW)+y*(-2.75*UP+2.25*UN-1.05*UNN+(5/28)*UNNN)\n", 1398 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VW-1.05*VWW+(5/28)*VWWW)+y*(-2.75*VP+2.25*VN-1.05*VNN+(5/28)*VNNN)\n", 1399 | " Dx=dy/(uAP/av-uAW-uAN)\n", 1400 | " Dy=dx/(vAP/av-vAW-vAN)\n", 1401 | " \n", 1402 | " uA[j][i][a_w]=uAW\n", 1403 | " uA[j][i][a_p]=uAP\n", 1404 | " uA[j][i][a_n]=uAN\n", 1405 | " uA[j][i][b_p]=uBP\n", 1406 | "\n", 1407 | " vA[j][i][a_w]=vAW\n", 1408 | " vA[j][i][a_p]=vAP\n", 1409 | " vA[j][i][a_n]=vAN\n", 1410 | " vA[j][i][b_p]=vBP\n", 1411 | "\n", 1412 | " DX[j][i]=Dx\n", 1413 | " DY[j][i]=Dy\n", 1414 | " \n", 1415 | " #bottom left cell\n", 1416 | " i=0\n", 1417 | " j=0\n", 1418 | " \n", 1419 | " TP=T[j][i]\n", 1420 | " \n", 1421 | " UP=U[j][i]\n", 1422 | " UE=U[j][i+1]\n", 1423 | " UEE=U[j][i+2]\n", 1424 | " UEEE=U[j][i+3]\n", 1425 | " UN=U[j+1][i]\n", 1426 | " UNN=U[j+2][i]\n", 1427 | " UNNN=U[j+3][i]\n", 1428 | "\n", 1429 | " VP=V[j][i]\n", 1430 | " VE=V[j][i+1]\n", 1431 | " VEE=V[j][i+2]\n", 1432 | " VEEE=V[j][i+3]\n", 1433 | " VN=V[j+1][i]\n", 1434 | " VNN=V[j+2][i]\n", 1435 | " VNNN=V[j+3][i]\n", 1436 | "\n", 1437 | " ue=Uxf[j][i+1]\n", 1438 | " un=Uyf[j+1][i]\n", 1439 | "\n", 1440 | " ve=Vxf[j][i+1]\n", 1441 | " vn=Vyf[j+1][i]\n", 1442 | " \n", 1443 | " ude=Udxf[j][i+1]\n", 1444 | " udn=Udyf[j+1][i]\n", 1445 | "\n", 1446 | " vde=Vdxf[j][i+1]\n", 1447 | " vdn=Vdyf[j+1][i]\n", 1448 | " \n", 1449 | " pw=Pxf[j][i]\n", 1450 | " pe=Pxf[j][i+1]\n", 1451 | " ps=Pyf[j][i]\n", 1452 | " pn=Pyf[j+1][i]\n", 1453 | " \n", 1454 | " AE=-(8/3)*x\n", 1455 | " AN=-(8/3)*y\n", 1456 | " AP=8*x+8*y\n", 1457 | " \n", 1458 | " ude_LO=(UE-UP)*x\n", 1459 | " udn_LO=(UN-UP)*y\n", 1460 | " U_diff_DC=(ude-ude_LO)+(udn-udn_LO)\n", 1461 | "\n", 1462 | " vde_LO=(VE-VP)*x\n", 1463 | " vdn_LO=(VN-VP)*y\n", 1464 | " V_diff_DC=(vde-vde_LO)+(vdn-vdn_LO)\n", 1465 | "\n", 1466 | " uAE,uAW,uAN,uAS,uAP,uBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,1E-12,ue,1E-12,un,1E-12,1E-12,1E-12,UP,UE,UN,dy,dx,v)\n", 1467 | " vAE,vAW,vAN,vAS,vAP,vBP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,1E-12,ve,1E-12,vn,1E-12,1E-12,1E-12,VP,VE,VN,dy,dx,v)\n", 1468 | " \n", 1469 | " uBP+=2*dy*(pw-pe)+2*gxgbdxdy*TP+2*U_diff_DC+x*(-2.75*UP+2.25*UE-1.05*UEE+(5/28)*UEEE)+y*(-2.75*UP+2.25*UN-1.05*UNN+(5/28)*UNNN)\n", 1470 | " vBP+=2*dx*(ps-pn)+2*gygbdxdy*TP+2*V_diff_DC+x*(-2.75*VP+2.25*VE-1.05*VEE+(5/28)*VEEE)+y*(-2.75*VP+2.25*VN-1.05*VNN+(5/28)*VNNN)\n", 1471 | " Dx=dy/(uAP/av-uAE-uAN)\n", 1472 | " Dy=dx/(vAP/av-vAE-vAN)\n", 1473 | "\n", 1474 | " uA[j][i][a_e]=uAE\n", 1475 | " uA[j][i][a_p]=uAP\n", 1476 | " uA[j][i][a_n]=uAN\n", 1477 | " uA[j][i][b_p]=uBP\n", 1478 | "\n", 1479 | " vA[j][i][a_e]=vAE\n", 1480 | " vA[j][i][a_p]=vAP\n", 1481 | " vA[j][i][a_n]=vAN\n", 1482 | " vA[j][i][b_p]=vBP\n", 1483 | "\n", 1484 | " DX[j][i]=Dx\n", 1485 | " DY[j][i]=Dy\n", 1486 | "\n", 1487 | " return uA,vA,DX,DY" 1488 | ] 1489 | }, 1490 | { 1491 | "cell_type": "code", 1492 | "execution_count": 20, 1493 | "id": "5be9af42", 1494 | "metadata": {}, 1495 | "outputs": [], 1496 | "source": [ 1497 | "@njit\n", 1498 | "def temp_coeff(Ny,Nx,Uf,Vf,Txf,Tyf,Tdxf,Tdyf,T,a,T_lft,T_rgt,T_top,T_btm):\n", 1499 | " N=Nx*Ny\n", 1500 | " dx=L/Nx\n", 1501 | " dy=H/Ny\n", 1502 | " x=a*dy/dx\n", 1503 | " y=a*dx/dy\n", 1504 | " \n", 1505 | " A=np.zeros((Ny,Nx,6))\n", 1506 | " \n", 1507 | " a_s=0\n", 1508 | " a_w=1\n", 1509 | " a_p=2\n", 1510 | " a_e=3\n", 1511 | " a_n=4\n", 1512 | " b_p=5\n", 1513 | " \n", 1514 | " dp=4*x+4*y\n", 1515 | " de=-2*x\n", 1516 | " dw=-2*x\n", 1517 | " dn=-2*y\n", 1518 | " ds=-2*y\n", 1519 | " \n", 1520 | " Tdxf=Tdxf*dy*a\n", 1521 | " Tdyf=Tdyf*dx*a\n", 1522 | " \n", 1523 | " #For interior cells\n", 1524 | " for j in range(1,Ny-1):\n", 1525 | " for i in range(1,Nx-1):\n", 1526 | " \n", 1527 | " ue=Uf[j][i+1]\n", 1528 | " uw=Uf[j][i]\n", 1529 | " vn=Vf[j+1][i]\n", 1530 | " vs=Vf[j][i]\n", 1531 | " \n", 1532 | " te=Txf[j][i+1]\n", 1533 | " tw=Txf[j][i]\n", 1534 | " tn=Tyf[j+1][i]\n", 1535 | " ts=Tyf[j][i]\n", 1536 | " \n", 1537 | " tde=Tdxf[j][i+1]\n", 1538 | " tdw=Tdxf[j][i]\n", 1539 | " tdn=Tdyf[j+1][i]\n", 1540 | " tds=Tdyf[j][i]\n", 1541 | " \n", 1542 | " TS=T[j-1][i]\n", 1543 | " TW=T[j][i-1]\n", 1544 | " TP=T[j][i]\n", 1545 | " TE=T[j][i+1]\n", 1546 | " TN=T[j+1][i]\n", 1547 | "\n", 1548 | " AE=de\n", 1549 | " AW=dw\n", 1550 | " AN=dn\n", 1551 | " AS=ds\n", 1552 | " AP=dp\n", 1553 | " \n", 1554 | " tde_LO=(TE-TP)*x\n", 1555 | " tdw_LO=(TP-TW)*x\n", 1556 | " tdn_LO=(TN-TP)*y\n", 1557 | " tds_LO=(TP-TS)*y\n", 1558 | " T_diff_DC=(tde-tde_LO)-(tdw-tdw_LO)+(tdn-tdn_LO)-(tds-tds_LO)\n", 1559 | " \n", 1560 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,vs,te,tw,tn,ts,TS,TW,TP,TE,TN,dy,dx,a)\n", 1561 | " \n", 1562 | " BP+=2*T_diff_DC\n", 1563 | "\n", 1564 | " A[j][i][a_w]=AW\n", 1565 | " A[j][i][a_p]=AP\n", 1566 | " A[j][i][a_e]=AE\n", 1567 | " A[j][i][a_n]=AN\n", 1568 | " A[j][i][a_s]=AS\n", 1569 | " A[j][i][b_p]=BP\n", 1570 | "\n", 1571 | " #for bottom wall\n", 1572 | " j=0\n", 1573 | " for i in range(1,Nx-1):\n", 1574 | " \n", 1575 | " ue=Uf[j][i+1]\n", 1576 | " uw=Uf[j][i]\n", 1577 | " vn=Vf[j+1][i]\n", 1578 | " \n", 1579 | " te=Txf[j][i+1]\n", 1580 | " tw=Txf[j][i]\n", 1581 | " tn=Tyf[j+1][i]\n", 1582 | " \n", 1583 | " tde=Tdxf[j][i+1]\n", 1584 | " tdw=Tdxf[j][i]\n", 1585 | " tdn=Tdyf[j+1][i]\n", 1586 | " \n", 1587 | " TW=T[j][i-1]\n", 1588 | " TP=T[j][i]\n", 1589 | " TE=T[j][i+1]\n", 1590 | " TN=T[j+1][i]\n", 1591 | "\n", 1592 | " AE=de\n", 1593 | " AW=dw\n", 1594 | "# AN=-(8/3)*y\n", 1595 | "# AP=8*y+4*x\n", 1596 | " AN=dn\n", 1597 | " AP=2*y+4*x\n", 1598 | " \n", 1599 | " tde_LO=(TE-TP)*x\n", 1600 | " tdw_LO=(TP-TW)*x\n", 1601 | " tdn_LO=(TN-TP)*y\n", 1602 | " T_diff_DC=(tde-tde_LO)-(tdw-tdw_LO)+(tdn-tdn_LO)\n", 1603 | " \n", 1604 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,te,tw,tn,1E-12,1E-12,TW,TP,TE,TN,dy,dx,a)\n", 1605 | " \n", 1606 | " BP+=(16/3)*y*T_btm+2*T_diff_DC\n", 1607 | " \n", 1608 | " A[j][i][a_w]=AW\n", 1609 | " A[j][i][a_p]=AP\n", 1610 | " A[j][i][a_e]=AE\n", 1611 | " A[j][i][a_n]=AN\n", 1612 | " A[j][i][b_p]=BP\n", 1613 | " \n", 1614 | " #for top wall\n", 1615 | " j=Ny-1\n", 1616 | " for i in range(1,Nx-1):\n", 1617 | " \n", 1618 | " ue=Uf[j][i+1]\n", 1619 | " uw=Uf[j][i]\n", 1620 | " vs=Vf[j][i]\n", 1621 | " \n", 1622 | " te=Txf[j][i+1]\n", 1623 | " tw=Txf[j][i]\n", 1624 | " ts=Tyf[j][i]\n", 1625 | " \n", 1626 | " tde=Tdxf[j][i+1]\n", 1627 | " tdw=Tdxf[j][i]\n", 1628 | " tds=Tdyf[j][i]\n", 1629 | " \n", 1630 | " TS=T[j-1][i]\n", 1631 | " TW=T[j][i-1]\n", 1632 | " TP=T[j][i]\n", 1633 | " TE=T[j][i+1]\n", 1634 | " \n", 1635 | " AE=de\n", 1636 | " AW=dw\n", 1637 | "# AS=-(8/3)*y\n", 1638 | "# AP=8*y+4*x\n", 1639 | " AS=ds\n", 1640 | " AP=2*y+4*x\n", 1641 | " \n", 1642 | " tde_LO=(TE-TP)*x\n", 1643 | " tdw_LO=(TP-TW)*x\n", 1644 | " tds_LO=(TP-TS)*y\n", 1645 | " T_diff_DC=(tde-tde_LO)-(tdw-tdw_LO)-(tds-tds_LO)\n", 1646 | " \n", 1647 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,te,tw,1E-12,ts,TS,TW,TP,TE,1E-12,dy,dx,a)\n", 1648 | " \n", 1649 | " BP+=(16/3)*y*T_top+2*T_diff_DC\n", 1650 | " \n", 1651 | " A[j][i][a_w]=AW\n", 1652 | " A[j][i][a_p]=AP\n", 1653 | " A[j][i][a_e]=AE\n", 1654 | " A[j][i][a_s]=AS\n", 1655 | " A[j][i][b_p]=BP\n", 1656 | " \n", 1657 | " #for left wall:\n", 1658 | " i=0\n", 1659 | " for j in range(1,Ny-1):\n", 1660 | " \n", 1661 | " ue=Uf[j][i+1]\n", 1662 | " vn=Vf[j+1][i]\n", 1663 | " vs=Vf[j][i]\n", 1664 | " \n", 1665 | " te=Txf[j][i+1]\n", 1666 | " tn=Tyf[j+1][i]\n", 1667 | " ts=Tyf[j][i]\n", 1668 | " \n", 1669 | " tde=Tdxf[j][i+1]\n", 1670 | " tdn=Tdyf[j+1][i]\n", 1671 | " tds=Tdyf[j][i]\n", 1672 | " \n", 1673 | " TS=T[j-1][i]\n", 1674 | " TP=T[j][i]\n", 1675 | " TE=T[j][i+1]\n", 1676 | " TEE=T[j][i+2]\n", 1677 | " TEEE=T[j][i+3]\n", 1678 | " TN=T[j+1][i]\n", 1679 | "\n", 1680 | " #AE=-2*x\n", 1681 | " AN=dn\n", 1682 | " AS=ds\n", 1683 | "# AP=2*x+4*y\n", 1684 | " AE=-(8/3)*x\n", 1685 | " AP=8*x+4*y\n", 1686 | " \n", 1687 | " tde_LO=(TE-TP)*x\n", 1688 | " tdn_LO=(TN-TP)*y\n", 1689 | " tds_LO=(TP-TS)*y\n", 1690 | " T_diff_DC=(tde-tde_LO)+(tdn-tdn_LO)-(tds-tds_LO)\n", 1691 | " \n", 1692 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,te,1E-12,tn,ts,TS,1E-12,TP,TE,TN,dy,dx,a)\n", 1693 | " \n", 1694 | " BP+=(704/105)*x*T_lft+2*T_diff_DC+x*(-2.75*TP+2.25*TE-1.05*TEE+(5/28)*TEEE)\n", 1695 | "\n", 1696 | " A[j][i][a_p]=AP\n", 1697 | " A[j][i][a_e]=AE\n", 1698 | " A[j][i][a_n]=AN\n", 1699 | " A[j][i][a_s]=AS\n", 1700 | " A[j][i][b_p]=BP\n", 1701 | " \n", 1702 | " #for right wall:\n", 1703 | " i=Nx-1\n", 1704 | " for j in range(1,Ny-1):\n", 1705 | " \n", 1706 | " uw=Uf[j][i]\n", 1707 | " vn=Vf[j+1][i]\n", 1708 | " vs=Vf[j][i]\n", 1709 | " \n", 1710 | " tw=Txf[j][i]\n", 1711 | " tn=Tyf[j+1][i]\n", 1712 | " ts=Tyf[j][i]\n", 1713 | "\n", 1714 | " tdw=Tdxf[j][i]\n", 1715 | " tdn=Tdyf[j+1][i]\n", 1716 | " tds=Tdyf[j][i]\n", 1717 | " \n", 1718 | " TS=T[j-1][i]\n", 1719 | " TW=T[j][i-1]\n", 1720 | " TWW=T[j][i-2]\n", 1721 | " TWWW=T[j][i-3]\n", 1722 | " TP=T[j][i]\n", 1723 | " TN=T[j+1][i]\n", 1724 | "\n", 1725 | " #AW=-2*x\n", 1726 | " AN=dn\n", 1727 | " AS=ds\n", 1728 | " #AP=2*x+4*y\n", 1729 | " AW=-(8/3)*x\n", 1730 | " AP=8*x+4*y\n", 1731 | " \n", 1732 | " tdw_LO=(TP-TW)*x\n", 1733 | " tdn_LO=(TN-TP)*y\n", 1734 | " tds_LO=(TP-TS)*y\n", 1735 | " T_diff_DC=-(tdw-tdw_LO)+(tdn-tdn_LO)-(tds-tds_LO)\n", 1736 | "\n", 1737 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,1E-12,tw,tn,ts,TS,TW,TP,1E-12,TN,dy,dx,a)\n", 1738 | " \n", 1739 | " BP+=(704/105)*x*T_rgt+2*T_diff_DC+x*(-2.75*TP+2.25*TW-1.05*TWW+(5/28)*TWWW)\n", 1740 | "\n", 1741 | " A[j][i][a_w]=AW\n", 1742 | " A[j][i][a_p]=AP\n", 1743 | " A[j][i][a_n]=AN\n", 1744 | " A[j][i][a_s]=AS\n", 1745 | " A[j][i][b_p]=BP\n", 1746 | " \n", 1747 | " #top right cell\n", 1748 | " i=Nx-1\n", 1749 | " j=Ny-1\n", 1750 | " \n", 1751 | " uw=Uf[j][i]\n", 1752 | " vs=Vf[j][i]\n", 1753 | " \n", 1754 | " tw=Txf[j][i]\n", 1755 | " ts=Tyf[j][i]\n", 1756 | "\n", 1757 | " tdw=Tdxf[j][i]\n", 1758 | " tds=Tdyf[j][i]\n", 1759 | " \n", 1760 | " TS=T[j-1][i]\n", 1761 | " TW=T[j][i-1]\n", 1762 | " TWW=T[j][i-2]\n", 1763 | " TWWW=T[j][i-3]\n", 1764 | " TP=T[j][i]\n", 1765 | " \n", 1766 | "# AW=-2*x\n", 1767 | "# AS=-(8/3)*y\n", 1768 | "# AP=2*x+8*y\n", 1769 | " \n", 1770 | " AW=(8/3)*x\n", 1771 | " AS=-2*y\n", 1772 | " AP=8*x+2*y\n", 1773 | " \n", 1774 | " tdw_LO=(TP-TW)*x\n", 1775 | " tds_LO=(TP-TS)*y\n", 1776 | " T_diff_DC=-(tdw-tdw_LO)-(tds-tds_LO)\n", 1777 | " \n", 1778 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,1E-12,vs,1E-12,tw,1E-12,ts,TS,TW,TP,1E-12,1E-12,dy,dx,a)\n", 1779 | " \n", 1780 | " BP+=(16/3)*y*T_top+(704/105)*x*T_rgt+2*T_diff_DC+x*(-2.75*TP+2.25*TW-1.05*TWW+(5/28)*TWWW)\n", 1781 | "\n", 1782 | " A[j][i][a_w]=AW\n", 1783 | " A[j][i][a_p]=AP\n", 1784 | " A[j][i][a_s]=AS\n", 1785 | " A[j][i][b_p]=BP\n", 1786 | "\n", 1787 | " #top left cell\n", 1788 | " i=0\n", 1789 | " j=Ny-1\n", 1790 | " \n", 1791 | " ue=Uf[j][i+1]\n", 1792 | " vs=Vf[j][i]\n", 1793 | " \n", 1794 | " te=Txf[j][i+1]\n", 1795 | " ts=Tyf[j][i]\n", 1796 | " \n", 1797 | " tde=Tdxf[j][i+1]\n", 1798 | " tds=Tdyf[j][i]\n", 1799 | " \n", 1800 | " TS=T[j-1][i]\n", 1801 | " TP=T[j][i]\n", 1802 | " TE=T[j][i+1]\n", 1803 | " TEE=T[j][i+2]\n", 1804 | " TEEE=T[j][i+3]\n", 1805 | " \n", 1806 | "# AE=-2*x\n", 1807 | "# AS=-(8/3)*y\n", 1808 | "# AP=2*x+8*y\n", 1809 | " \n", 1810 | " AE=-(8/3)*x\n", 1811 | " AS=-2*y\n", 1812 | " AP=8*x+2*y\n", 1813 | " \n", 1814 | " tde_LO=(TE-TP)*x\n", 1815 | " tds_LO=(TP-TS)*y\n", 1816 | " T_diff_DC=(tde-tde_LO)-(tds-tds_LO)\n", 1817 | " \n", 1818 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,1E-12,vs,1E-12,1E-12,tn,ts,TS,1E-12,TP,TE,1E-12,dy,dx,a)\n", 1819 | " \n", 1820 | " BP+=(16/3)*y*T_top+(704/105)*x*T_lft+2*T_diff_DC+x*(-2.75*TP+2.25*TE-1.05*TEE+(5/28)*TEEE)\n", 1821 | " \n", 1822 | " A[j][i][a_p]=AP\n", 1823 | " A[j][i][a_e]=AE\n", 1824 | " A[j][i][a_s]=AS\n", 1825 | " A[j][i][b_p]=BP\n", 1826 | "\n", 1827 | " #bottom right cell\n", 1828 | " i=Nx-1\n", 1829 | " j=0\n", 1830 | "\n", 1831 | " uw=Uf[j][i]\n", 1832 | " vn=Vf[j+1][i]\n", 1833 | "\n", 1834 | " tw=Txf[j][i]\n", 1835 | " tn=Tyf[j+1][i]\n", 1836 | "\n", 1837 | " tdw=Tdxf[j][i]\n", 1838 | " tdn=Tdyf[j+1][i]\n", 1839 | " \n", 1840 | " TW=T[j][i-1]\n", 1841 | " TWW=T[j][i-2]\n", 1842 | " TWWW=T[j][i-3]\n", 1843 | " TP=T[j][i]\n", 1844 | " TN=T[j+1][i]\n", 1845 | " \n", 1846 | "# AW=-2*x\n", 1847 | "# AN=-(8/3)*y\n", 1848 | "# AP=2*x+8*y\n", 1849 | "\n", 1850 | " AW=-(8/3)*x\n", 1851 | " AN=-2*y\n", 1852 | " AP=8*x+2*y\n", 1853 | " \n", 1854 | " tdw_LO=(TP-TW)*x\n", 1855 | " tdn_LO=(TN-TP)*y\n", 1856 | " T_diff_DC=-(tdw-tdw_LO)+(tdn-tdn_LO)\n", 1857 | "\n", 1858 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,1E-12,1E-12,tw,tn,1E-12,1E-12,TW,TP,1E-12,TN,dy,dx,a)\n", 1859 | " \n", 1860 | " BP+=(16/3)*y*T_btm+(704/105)*x*T_rgt+2*T_diff_DC+x*(-2.75*TP+2.25*TW-1.05*TWW+(5/28)*TWWW)\n", 1861 | "\n", 1862 | " A[j][i][a_w]=AW\n", 1863 | " A[j][i][a_p]=AP\n", 1864 | " A[j][i][a_n]=AN\n", 1865 | " A[j][i][b_p]=BP\n", 1866 | "\n", 1867 | " #bottom left cell\n", 1868 | " i=0\n", 1869 | " j=0\n", 1870 | " \n", 1871 | " ue=Uf[j][i+1]\n", 1872 | " vn=Vf[j+1][i]\n", 1873 | " \n", 1874 | " te=Txf[j][i+1]\n", 1875 | " tn=Tyf[j+1][i]\n", 1876 | " \n", 1877 | " tde=Tdxf[j][i+1]\n", 1878 | " tdn=Tdyf[j+1][i]\n", 1879 | " \n", 1880 | " TP=T[j][i]\n", 1881 | " TE=T[j][i+1]\n", 1882 | " TEE=T[j][i+2]\n", 1883 | " TEEE=T[j][i+3]\n", 1884 | " TN=T[j+1][i]\n", 1885 | " \n", 1886 | "# AE=-2*x\n", 1887 | "# AN=-(8/3)*y\n", 1888 | "# AP=2*x+8*y\n", 1889 | "\n", 1890 | " AE=-(8/3)*x\n", 1891 | " AN=-2*x\n", 1892 | " AP=8*x+2*y\n", 1893 | " \n", 1894 | " tde_LO=(TE-TP)*x\n", 1895 | " tdn_LO=(TN-TP)*y\n", 1896 | " T_diff_DC=(tde-tde_LO)+(tdn-tdn_LO)\n", 1897 | " \n", 1898 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,1E-12,te,1E-12,tn,1E-12,1E-12,1E-12,TP,TE,TN,dy,dx,a)\n", 1899 | " \n", 1900 | " BP+=(16/3)*y*T_btm+(704/105)*x*T_lft+2*T_diff_DC+x*(-2.75*TP+2.25*TE-1.05*TEE+(5/28)*TEEE)\n", 1901 | " \n", 1902 | " A[j][i][a_e]=AE\n", 1903 | " A[j][i][a_p]=AP\n", 1904 | " A[j][i][a_n]=AN\n", 1905 | " A[j][i][b_p]=BP\n", 1906 | "\n", 1907 | " return A" 1908 | ] 1909 | }, 1910 | { 1911 | "cell_type": "code", 1912 | "execution_count": 21, 1913 | "id": "6b0e2cdb", 1914 | "metadata": {}, 1915 | "outputs": [], 1916 | "source": [ 1917 | "@njit\n", 1918 | "def implicit_relaxation(A,phi,Ny,Nx,av):\n", 1919 | " alpha=(1-av)/av\n", 1920 | " \n", 1921 | " for j in range(Ny):\n", 1922 | " for i in range(Nx):\n", 1923 | " A[j][i][5]+=phi[j][i]*A[j][i][2]*alpha\n", 1924 | " A[j][i][2]=A[j][i][2]/av\n", 1925 | " \n", 1926 | " return A" 1927 | ] 1928 | }, 1929 | { 1930 | "cell_type": "code", 1931 | "execution_count": 22, 1932 | "id": "5f89f11c", 1933 | "metadata": {}, 1934 | "outputs": [], 1935 | "source": [ 1936 | "@njit(fastmath=True)\n", 1937 | "def implicit_time(A,phi_time,Ny,Nx,k):\n", 1938 | " \n", 1939 | " a_p=2\n", 1940 | " b_p=5\n", 1941 | " \n", 1942 | " #2nd order implicit\n", 1943 | " for j in range(Ny):\n", 1944 | " for i in range(Nx):\n", 1945 | " A[j][i][a_p]+=1.5*k\n", 1946 | " A[j][i][b_p]+=k*(2*phi_time[j][i][3]-0.5*phi_time[j][i][2])\n", 1947 | " \n", 1948 | "# #4th order implicit\n", 1949 | "# for j in range(Ny):\n", 1950 | "# for i in range(Nx):\n", 1951 | "# A[j][i][a_p]+=(25/12)*k\n", 1952 | "# A[j][i][b_p]+=k*(-3*phi_time[j][i][0]+16*phi_time[j][i][1]-36*phi_time[j][i][2]+48*phi_time[j][i][3])/12\n", 1953 | " \n", 1954 | " return A" 1955 | ] 1956 | }, 1957 | { 1958 | "cell_type": "code", 1959 | "execution_count": 23, 1960 | "id": "277d1720", 1961 | "metadata": {}, 1962 | "outputs": [], 1963 | "source": [ 1964 | "@njit \n", 1965 | "def timeswap(time_array,phi,Ny,Nx):\n", 1966 | " time_array_new=np.zeros((Ny,Nx,4))\n", 1967 | " for j in range(Ny):\n", 1968 | " for i in range(Nx):\n", 1969 | " time_array_new[j][i][0]=time_array[j][i][1]\n", 1970 | " time_array_new[j][i][1]=time_array[j][i][2]\n", 1971 | " time_array_new[j][i][2]=time_array[j][i][3]\n", 1972 | " time_array_new[j][i][3]=phi[j][i]\n", 1973 | " \n", 1974 | " return time_array_new" 1975 | ] 1976 | }, 1977 | { 1978 | "cell_type": "code", 1979 | "execution_count": 24, 1980 | "id": "9cdd4400", 1981 | "metadata": {}, 1982 | "outputs": [], 1983 | "source": [ 1984 | "@njit(fastmath=True)\n", 1985 | "def face_rc(Nx,Ny,U,V,P,DX,DY):\n", 1986 | " uNx=Nx+1\n", 1987 | " uNy=Ny\n", 1988 | " vNx=Nx\n", 1989 | " vNy=Ny+1 \n", 1990 | " \n", 1991 | " Uf=np.zeros((uNy,uNx))\n", 1992 | " Vf=np.zeros((vNy,vNx))\n", 1993 | " #Height: Ny_btm to Ny_top-1\n", 1994 | " #Width: Nx_lft to Nx_rgt-1\n", 1995 | " \n", 1996 | " #U-faces\n", 1997 | " DXf=np.zeros((uNy,uNx))\n", 1998 | " \n", 1999 | " for j in range(uNy):\n", 2000 | " for i in range(2,uNx-2):\n", 2001 | " ij=i+Nx*j\n", 2002 | " \n", 2003 | " UE=U[j][i]\n", 2004 | " UP=U[j][i-1]\n", 2005 | " Ue=(UE+UP)/2\n", 2006 | "\n", 2007 | " DP=DX[j][i-1]\n", 2008 | " DE=DX[j][i]\n", 2009 | " PP=DP*(P[j][i-2]-P[j][i])\n", 2010 | " PE=DE*(P[j][i-1]-P[j][i+1])\n", 2011 | " \n", 2012 | " dp=(DP+DE)/2\n", 2013 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 2014 | " Pf=(-(PP+PE)/2)+Pp\n", 2015 | " \n", 2016 | " uf=Ue+Pf\n", 2017 | " Uf[j][i]=uf\n", 2018 | " DXf[j][i]=dp\n", 2019 | " \n", 2020 | " for j in range(uNy):\n", 2021 | " i=1\n", 2022 | " \n", 2023 | " UE=U[j][i]\n", 2024 | " UP=U[j][i-1]\n", 2025 | " Ue=(UE+UP)/2\n", 2026 | "\n", 2027 | " DP=DX[j][i-1]\n", 2028 | " DE=DX[j][i]\n", 2029 | " PP=DP*(P[j][i-1]-P[j][i])\n", 2030 | " PE=DE*(P[j][i-1]-P[j][i+1])\n", 2031 | "\n", 2032 | " dp=(DP+DE)/2\n", 2033 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 2034 | " Pf=(-(PP+PE)/2)+Pp\n", 2035 | "\n", 2036 | " uf=Ue+Pf\n", 2037 | " Uf[j][i]=uf\n", 2038 | " DXf[j][i]=dp\n", 2039 | " \n", 2040 | " i=uNx-2\n", 2041 | " \n", 2042 | " UE=U[j][i]\n", 2043 | " UP=U[j][i-1]\n", 2044 | " Ue=(UE+UP)/2\n", 2045 | "\n", 2046 | " DP=DX[j][i-1]\n", 2047 | " DE=DX[j][i]\n", 2048 | " PP=DP*(P[j][i-2]-P[j][i])\n", 2049 | " PE=DE*(P[j][i-1]-P[j][i])\n", 2050 | "\n", 2051 | " dp=(DP+DE)/2\n", 2052 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 2053 | " Pf=(-(PP+PE)/2)+Pp\n", 2054 | "\n", 2055 | " uf=Ue+Pf\n", 2056 | " Uf[j][i]=uf\n", 2057 | " DXf[j][i]=dp\n", 2058 | " \n", 2059 | " for j in range(uNy):\n", 2060 | " #inlet\n", 2061 | " Uf[j][0]=0\n", 2062 | " Uf[j][-1]=0\n", 2063 | " \n", 2064 | " #V-faces \n", 2065 | " DYf=np.zeros((vNy,vNx))\n", 2066 | " \n", 2067 | " for j in range(1,vNy-1):\n", 2068 | " DYf[j]=(DY[j-1]+DY[j])/2\n", 2069 | " \n", 2070 | " for j in range(2,vNy-2):\n", 2071 | " for i in range(vNx):\n", 2072 | " ij=i+Nx*j\n", 2073 | " \n", 2074 | " VN=V[j][i]\n", 2075 | " VP=V[j-1][i]\n", 2076 | " Vn=(VN+VP)/2\n", 2077 | "\n", 2078 | " DP=DX[j-1][i]\n", 2079 | " DN=DX[j][i]\n", 2080 | " PP=DP*(P[j-2][i]-P[j][i])\n", 2081 | " PN=DN*(P[j-1][i]-P[j+1][i])\n", 2082 | " \n", 2083 | " dp=(DP+DN)/2\n", 2084 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2085 | " Pf=(-(PP+PN)/2)+Pp\n", 2086 | " \n", 2087 | " vf=Vn+Pf\n", 2088 | " Vf[j][i]=vf\n", 2089 | " DYf[j][i]=dp\n", 2090 | " \n", 2091 | " for i in range(vNx):\n", 2092 | " j=1\n", 2093 | " \n", 2094 | " VN=V[j][i]\n", 2095 | " VP=V[j-1][i]\n", 2096 | " Vn=(VN+VP)/2\n", 2097 | "\n", 2098 | " DP=DX[j-1][i]\n", 2099 | " DN=DX[j][i]\n", 2100 | " PP=DP*(P[j-1][i]-P[j][i])\n", 2101 | " PN=DN*(P[j-1][i]-P[j+1][i])\n", 2102 | "\n", 2103 | " dp=(DP+DN)/2\n", 2104 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2105 | " Pf=(-(PP+PN)/2)+Pp\n", 2106 | "\n", 2107 | " vf=Vn+Pf\n", 2108 | " Vf[j][i]=vf\n", 2109 | " DYf[j][i]=dp\n", 2110 | " \n", 2111 | " j=vNy-2\n", 2112 | " \n", 2113 | " VN=V[j][i]\n", 2114 | " VP=V[j-1][i]\n", 2115 | " Vn=(VN+VP)/2\n", 2116 | "\n", 2117 | " DP=DX[j-1][i]\n", 2118 | " DN=DX[j][i]\n", 2119 | " PP=DP*(P[j-2][i]-P[j][i])\n", 2120 | " PN=DN*(P[j-1][i]-P[j][i])\n", 2121 | "\n", 2122 | " dp=(DP+DN)/2\n", 2123 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2124 | " Pf=(-(PP+PN)/2)+Pp\n", 2125 | "\n", 2126 | " vf=Vn+Pf\n", 2127 | " Vf[j][i]=vf\n", 2128 | " DYf[j][i]=dp\n", 2129 | " \n", 2130 | " return Uf,Vf,DXf,DYf" 2131 | ] 2132 | }, 2133 | { 2134 | "cell_type": "code", 2135 | "execution_count": 25, 2136 | "id": "35808d27", 2137 | "metadata": {}, 2138 | "outputs": [], 2139 | "source": [ 2140 | "@njit(fastmath=True)\n", 2141 | "def cont(Nx,Ny,U,V,DXf,DYf,dy,dx):\n", 2142 | " N=Nx*Ny\n", 2143 | " \n", 2144 | " A=np.zeros((Ny,Nx,6))\n", 2145 | " \n", 2146 | " a_s=0\n", 2147 | " a_w=1\n", 2148 | " a_p=2\n", 2149 | " a_e=3\n", 2150 | " a_n=4\n", 2151 | " b_p=5\n", 2152 | " \n", 2153 | " #For interior cells\n", 2154 | " for j in range(1,Ny-1):\n", 2155 | " for i in range(1,Nx-1):\n", 2156 | " \n", 2157 | " ue=U[j][i+1]\n", 2158 | " uw=U[j][i]\n", 2159 | " vn=V[j+1][i]\n", 2160 | " vs=V[j][i]\n", 2161 | "\n", 2162 | " de=DXf[j][i+1]\n", 2163 | " dw=DXf[j][i]\n", 2164 | " dn=DYf[j+1][i]\n", 2165 | " ds=DYf[j][i]\n", 2166 | "\n", 2167 | " AE=-dy*de\n", 2168 | " AW=-dy*dw\n", 2169 | " AN=-dx*dn\n", 2170 | " AS=-dx*ds\n", 2171 | " AP=dy*(de+dw)+dx*(dn+ds)\n", 2172 | " BP=dy*(uw-ue)+dx*(vs-vn)\n", 2173 | "\n", 2174 | " A[j][i][a_w]=AW\n", 2175 | " A[j][i][a_e]=AE\n", 2176 | " A[j][i][a_p]=AP\n", 2177 | " A[j][i][a_n]=AN\n", 2178 | " A[j][i][a_s]=AS\n", 2179 | " A[j][i][b_p]=BP\n", 2180 | "\n", 2181 | " #left wall\n", 2182 | " i=0\n", 2183 | " for j in range(1,Ny-1):\n", 2184 | " ue=U[j][i+1]\n", 2185 | " vn=V[j+1][i]\n", 2186 | " vs=V[j][i]\n", 2187 | "\n", 2188 | " de=DXf[j][i+1]\n", 2189 | " dn=DYf[j+1][i]\n", 2190 | " ds=DYf[j][i]\n", 2191 | " \n", 2192 | " AE=-dy*de\n", 2193 | " AN=-dx*dn\n", 2194 | " AS=-dx*ds\n", 2195 | " AP=dy*de+dx*(dn+ds)\n", 2196 | " BP=dy*(-ue)+dx*(vs-vn)\n", 2197 | "\n", 2198 | " A[j][i][a_e]=AE\n", 2199 | " A[j][i][a_p]=AP\n", 2200 | " A[j][i][a_n]=AN\n", 2201 | " A[j][i][a_s]=AS\n", 2202 | " A[j][i][b_p]=BP\n", 2203 | " \n", 2204 | " #right wall\n", 2205 | " i=Nx-1\n", 2206 | " for j in range(1,Ny-1):\n", 2207 | " \n", 2208 | " uw=U[j][i]\n", 2209 | " vn=V[j+1][i]\n", 2210 | " vs=V[j][i]\n", 2211 | " \n", 2212 | " dw=DXf[j][i]\n", 2213 | " dn=DYf[j+1][i]\n", 2214 | " ds=DYf[j][i]\n", 2215 | "\n", 2216 | " AW=-dy*dw\n", 2217 | " AN=-dx*dn\n", 2218 | " AS=-dx*ds\n", 2219 | " AP=dy*dw+dx*(dn+ds)\n", 2220 | " BP=dy*uw+dx*(vs-vn)\n", 2221 | "\n", 2222 | " A[j][i][a_w]=AW\n", 2223 | " A[j][i][a_p]=AP\n", 2224 | " A[j][i][a_n]=AN\n", 2225 | " A[j][i][a_s]=AS\n", 2226 | " A[j][i][b_p]=BP\n", 2227 | " \n", 2228 | " #for bottom wall\n", 2229 | " j=0\n", 2230 | " for i in range(1,Nx-1):\n", 2231 | " \n", 2232 | " ue=U[j][i+1]\n", 2233 | " uw=U[j][i]\n", 2234 | " vn=V[j+1][i]\n", 2235 | " \n", 2236 | " de=DXf[j][i+1]\n", 2237 | " dw=DXf[j][i]\n", 2238 | " dn=DYf[j+1][i]\n", 2239 | "\n", 2240 | " AE=-dy*de\n", 2241 | " AW=-dy*dw\n", 2242 | " AN=-dx*dn\n", 2243 | " AP=dy*(de+dw)+dx*dn\n", 2244 | " BP=dy*(uw-ue)-dx*vn\n", 2245 | "\n", 2246 | " A[j][i][a_w]=AW\n", 2247 | " A[j][i][a_e]=AE\n", 2248 | " A[j][i][a_p]=AP\n", 2249 | " A[j][i][a_n]=AN\n", 2250 | " A[j][i][b_p]=BP\n", 2251 | " \n", 2252 | " #for top wall\n", 2253 | " j=Ny-1\n", 2254 | " for i in range(1,Nx-1):\n", 2255 | " \n", 2256 | " ue=U[j][i+1]\n", 2257 | " uw=U[j][i]\n", 2258 | " vs=V[j][i]\n", 2259 | " \n", 2260 | " de=DXf[j][i+1]\n", 2261 | " dw=DXf[j][i]\n", 2262 | " ds=DYf[j][i]\n", 2263 | "\n", 2264 | " AE=-dy*de\n", 2265 | " AW=-dy*dw\n", 2266 | " AS=-dx*ds\n", 2267 | " AP=dy*(de+dw)+dx*ds\n", 2268 | " BP=dy*(uw-ue)+dx*vs\n", 2269 | "\n", 2270 | " A[j][i][a_w]=AW\n", 2271 | " A[j][i][a_e]=AE\n", 2272 | " A[j][i][a_p]=AP\n", 2273 | " A[j][i][a_s]=AS\n", 2274 | " A[j][i][b_p]=BP\n", 2275 | " \n", 2276 | " #for top left corner\n", 2277 | " i=0\n", 2278 | " j=Ny-1\n", 2279 | " \n", 2280 | " ue=U[j][i+1]\n", 2281 | " vs=V[j][i]\n", 2282 | " \n", 2283 | " de=DXf[j][i+1]\n", 2284 | " ds=DYf[j][i]\n", 2285 | "\n", 2286 | " AE=-dy*de\n", 2287 | " AS=-dx*ds\n", 2288 | " AP=dy*de+dx*ds\n", 2289 | " BP=-dy*ue+dx*vs\n", 2290 | "\n", 2291 | " A[j][i][a_e]=AE\n", 2292 | " A[j][i][a_p]=AP\n", 2293 | " A[j][i][a_s]=AS\n", 2294 | " A[j][i][b_p]=BP\n", 2295 | " \n", 2296 | " #for top right corner\n", 2297 | " i=Nx-1\n", 2298 | " j=Ny-1\n", 2299 | " \n", 2300 | " uw=U[j][i]\n", 2301 | " vs=V[j][i]\n", 2302 | " \n", 2303 | " dw=DXf[j][i]\n", 2304 | " ds=DYf[j][i]\n", 2305 | "\n", 2306 | " AW=-dy*(dw)\n", 2307 | " AS=-dx*ds\n", 2308 | " AP=dy*dw+dx*ds\n", 2309 | " BP=dy*uw+dx*vs\n", 2310 | "\n", 2311 | " A[j][i][a_w]=AW\n", 2312 | " A[j][i][a_p]=AP\n", 2313 | " A[j][i][a_s]=AS\n", 2314 | " A[j][i][b_p]=BP\n", 2315 | " \n", 2316 | " #for bottom left corner\n", 2317 | " i=0\n", 2318 | " j=0\n", 2319 | " \n", 2320 | " ue=U[j][i+1]\n", 2321 | " vn=V[j+1][i]\n", 2322 | " \n", 2323 | " de=DXf[j][i+1]\n", 2324 | " dn=DYf[j+1][i]\n", 2325 | "\n", 2326 | " AE=-dy*de\n", 2327 | " AN=-dx*dn\n", 2328 | " AP=dy*de+dx*dn\n", 2329 | " BP=-dy*ue-dx*vn\n", 2330 | "\n", 2331 | " A[j][i][a_e]=AE\n", 2332 | " A[j][i][a_p]=AP\n", 2333 | " A[j][i][a_n]=AN\n", 2334 | " A[j][i][b_p]=BP\n", 2335 | " \n", 2336 | " #for bottom right corner\n", 2337 | " i=Nx-1\n", 2338 | " j=0\n", 2339 | "\n", 2340 | " uw=U[j][i]\n", 2341 | " vn=V[j+1][i]\n", 2342 | "\n", 2343 | " dw=DXf[j][i]\n", 2344 | " dn=DYf[j+1][i]\n", 2345 | "\n", 2346 | " AW=-dy*dw\n", 2347 | " AN=-dx*dn\n", 2348 | " AP=dy*dw+dx*dn\n", 2349 | " BP=dy*uw-dx*vn\n", 2350 | "\n", 2351 | " A[j][i][a_w]=AW\n", 2352 | " A[j][i][a_p]=AP\n", 2353 | " A[j][i][a_n]=AN\n", 2354 | " A[j][i][b_p]=BP\n", 2355 | " \n", 2356 | " return A" 2357 | ] 2358 | }, 2359 | { 2360 | "cell_type": "code", 2361 | "execution_count": 26, 2362 | "id": "637fd143", 2363 | "metadata": {}, 2364 | "outputs": [], 2365 | "source": [ 2366 | "@njit(fastmath=True)\n", 2367 | "def agglomeration(A_fine,Ny_f,Nx_f,Ny_c,Nx_c):\n", 2368 | " A_coarse=np.zeros((Ny_c,Nx_c,6))\n", 2369 | " \n", 2370 | " a_s=0\n", 2371 | " a_w=1\n", 2372 | " a_p=2\n", 2373 | " a_e=3\n", 2374 | " a_n=4\n", 2375 | " b_p=5\n", 2376 | " \n", 2377 | " #for interior cells\n", 2378 | " for j in range(1,Ny_c-1):\n", 2379 | " for i in range(1,Nx_c-1):\n", 2380 | " j2=2*j\n", 2381 | " i2=2*i\n", 2382 | " \n", 2383 | " #Agglomerate diagonal coefficients\n", 2384 | " #diagonal coefficients\n", 2385 | " AP_sw=A_fine[j2][i2][a_p]\n", 2386 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2387 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2388 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2389 | " \n", 2390 | " #eastern and western link coefficients\n", 2391 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2392 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2393 | " \n", 2394 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2395 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2396 | " \n", 2397 | " #northern and southern link coefficients\n", 2398 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2399 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2400 | " \n", 2401 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2402 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2403 | " \n", 2404 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2405 | " \n", 2406 | " #Agglomerate link coefficient\n", 2407 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2408 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2409 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2410 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2411 | " \n", 2412 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2413 | " \n", 2414 | " A_coarse[j][i][a_s]=AS\n", 2415 | " A_coarse[j][i][a_w]=AW\n", 2416 | " A_coarse[j][i][a_p]=AP\n", 2417 | " A_coarse[j][i][a_e]=AE\n", 2418 | " A_coarse[j][i][a_n]=AN\n", 2419 | " A_coarse[j][i][b_p]=BP\n", 2420 | " \n", 2421 | " for j in range(1,Ny_c-1):\n", 2422 | " #for left wall\n", 2423 | " i=0\n", 2424 | " j2=2*j\n", 2425 | " i2=2*i\n", 2426 | "\n", 2427 | " #Agglomerate diagonal coefficients\n", 2428 | " #diagonal coefficients\n", 2429 | " AP_sw=A_fine[j2][i2][a_p]\n", 2430 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2431 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2432 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2433 | "\n", 2434 | " #eastern and western link coefficients\n", 2435 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2436 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2437 | "\n", 2438 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2439 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2440 | "\n", 2441 | " #northern and southern link coefficients\n", 2442 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2443 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2444 | "\n", 2445 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2446 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2447 | "\n", 2448 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2449 | "\n", 2450 | " #Agglomerate link coefficient\n", 2451 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2452 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2453 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2454 | " \n", 2455 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2456 | "\n", 2457 | " A_coarse[j][i][a_s]=AS\n", 2458 | " A_coarse[j][i][a_p]=AP\n", 2459 | " A_coarse[j][i][a_e]=AE\n", 2460 | " A_coarse[j][i][a_n]=AN\n", 2461 | " A_coarse[j][i][b_p]=BP\n", 2462 | " \n", 2463 | " #for right wall\n", 2464 | " i=Nx_c-1\n", 2465 | " j2=2*j\n", 2466 | " i2=2*i\n", 2467 | "\n", 2468 | " #Agglomerate diagonal coefficients\n", 2469 | " #diagonal coefficients\n", 2470 | " AP_sw=A_fine[j2][i2][a_p]\n", 2471 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2472 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2473 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2474 | "\n", 2475 | " #eastern and western link coefficients\n", 2476 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2477 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2478 | "\n", 2479 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2480 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2481 | "\n", 2482 | " #northern and southern link coefficients\n", 2483 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2484 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2485 | "\n", 2486 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2487 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2488 | "\n", 2489 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2490 | "\n", 2491 | " #Agglomerate link coefficient\n", 2492 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2493 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2494 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2495 | " \n", 2496 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2497 | "\n", 2498 | " A_coarse[j][i][a_s]=AS\n", 2499 | " A_coarse[j][i][a_w]=AW\n", 2500 | " A_coarse[j][i][a_p]=AP\n", 2501 | " A_coarse[j][i][a_n]=AN\n", 2502 | " A_coarse[j][i][b_p]=BP\n", 2503 | " \n", 2504 | " for i in range(1,Nx_c-1):\n", 2505 | " #for bottom wall\n", 2506 | " j=0\n", 2507 | " j2=2*j\n", 2508 | " i2=2*i\n", 2509 | "\n", 2510 | " #Agglomerate diagonal coefficients\n", 2511 | " #diagonal coefficients\n", 2512 | " AP_sw=A_fine[j2][i2][a_p]\n", 2513 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2514 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2515 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2516 | "\n", 2517 | " #eastern and western link coefficients\n", 2518 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2519 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2520 | "\n", 2521 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2522 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2523 | "\n", 2524 | " #northern and southern link coefficients\n", 2525 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2526 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2527 | "\n", 2528 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2529 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2530 | "\n", 2531 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2532 | "\n", 2533 | " #Agglomerate link coefficient\n", 2534 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2535 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2536 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2537 | " \n", 2538 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2539 | " \n", 2540 | " A_coarse[j][i][a_w]=AW\n", 2541 | " A_coarse[j][i][a_p]=AP\n", 2542 | " A_coarse[j][i][a_e]=AE\n", 2543 | " A_coarse[j][i][a_n]=AN\n", 2544 | " A_coarse[j][i][b_p]=BP\n", 2545 | " \n", 2546 | " j=Ny_c-1\n", 2547 | " j2=2*j\n", 2548 | " i2=2*i\n", 2549 | "\n", 2550 | " #Agglomerate diagonal coefficients\n", 2551 | " #diagonal coefficients\n", 2552 | " AP_sw=A_fine[j2][i2][a_p]\n", 2553 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2554 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2555 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2556 | "\n", 2557 | " #eastern and western link coefficients\n", 2558 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2559 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2560 | "\n", 2561 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2562 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2563 | "\n", 2564 | " #northern and southern link coefficients\n", 2565 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2566 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2567 | "\n", 2568 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2569 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2570 | "\n", 2571 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2572 | "\n", 2573 | " #Agglomerate link coefficient\n", 2574 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2575 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2576 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2577 | " \n", 2578 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2579 | "\n", 2580 | " A_coarse[j][i][a_s]=AS\n", 2581 | " A_coarse[j][i][a_w]=AW\n", 2582 | " A_coarse[j][i][a_p]=AP\n", 2583 | " A_coarse[j][i][a_e]=AE\n", 2584 | " A_coarse[j][i][b_p]=BP\n", 2585 | " \n", 2586 | " \n", 2587 | " #for top left cell\n", 2588 | " i=0\n", 2589 | " j=Ny_c-1\n", 2590 | " \n", 2591 | " j2=2*j\n", 2592 | " i2=2*i\n", 2593 | "\n", 2594 | " #Agglomerate diagonal coefficients\n", 2595 | " #diagonal coefficients\n", 2596 | " AP_sw=A_fine[j2][i2][a_p]\n", 2597 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2598 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2599 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2600 | "\n", 2601 | " #eastern and western link coefficients\n", 2602 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2603 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2604 | "\n", 2605 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2606 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2607 | "\n", 2608 | " #northern and southern link coefficients\n", 2609 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2610 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2611 | "\n", 2612 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2613 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2614 | "\n", 2615 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2616 | "\n", 2617 | " #Agglomerate link coefficient\n", 2618 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2619 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2620 | " \n", 2621 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2622 | "\n", 2623 | " A_coarse[j][i][a_s]=AS\n", 2624 | " A_coarse[j][i][a_p]=AP\n", 2625 | " A_coarse[j][i][a_e]=AE\n", 2626 | " A_coarse[j][i][b_p]=BP\n", 2627 | " \n", 2628 | " #for top right cell\n", 2629 | " i=Nx_c-1\n", 2630 | " j=Ny_c-1\n", 2631 | " \n", 2632 | " j2=2*j\n", 2633 | " i2=2*i\n", 2634 | "\n", 2635 | " #Agglomerate diagonal coefficients\n", 2636 | " #diagonal coefficients\n", 2637 | " AP_sw=A_fine[j2][i2][a_p]\n", 2638 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2639 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2640 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2641 | "\n", 2642 | " #eastern and western link coefficients\n", 2643 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2644 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2645 | "\n", 2646 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2647 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2648 | "\n", 2649 | " #northern and southern link coefficients\n", 2650 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2651 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2652 | "\n", 2653 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2654 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2655 | "\n", 2656 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2657 | "\n", 2658 | " #Agglomerate link coefficient\n", 2659 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2660 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2661 | " \n", 2662 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2663 | "\n", 2664 | " A_coarse[j][i][a_s]=AS\n", 2665 | " A_coarse[j][i][a_w]=AW\n", 2666 | " A_coarse[j][i][a_p]=AP\n", 2667 | " A_coarse[j][i][b_p]=BP\n", 2668 | " \n", 2669 | " #for bottom left cell\n", 2670 | " i=0\n", 2671 | " j=0\n", 2672 | " \n", 2673 | " j2=2*j\n", 2674 | " i2=2*i\n", 2675 | "\n", 2676 | " #Agglomerate diagonal coefficients\n", 2677 | " #diagonal coefficients\n", 2678 | " AP_sw=A_fine[j2][i2][a_p]\n", 2679 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2680 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2681 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2682 | "\n", 2683 | " #eastern and western link coefficients\n", 2684 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2685 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2686 | "\n", 2687 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2688 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2689 | "\n", 2690 | " #northern and southern link coefficients\n", 2691 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2692 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2693 | "\n", 2694 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2695 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2696 | "\n", 2697 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2698 | "\n", 2699 | " #Agglomerate link coefficient\n", 2700 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2701 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2702 | "\n", 2703 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2704 | " \n", 2705 | " A_coarse[j][i][a_p]=AP\n", 2706 | " A_coarse[j][i][a_e]=AE\n", 2707 | " A_coarse[j][i][a_n]=AN\n", 2708 | " A_coarse[j][i][b_p]=BP\n", 2709 | " \n", 2710 | " #for bottom right cell\n", 2711 | " i=Nx_c-1\n", 2712 | " j=0\n", 2713 | " \n", 2714 | " j2=2*j\n", 2715 | " i2=2*i\n", 2716 | "\n", 2717 | " #Agglomerate diagonal coefficients\n", 2718 | " #diagonal coefficients\n", 2719 | " AP_sw=A_fine[j2][i2][a_p]\n", 2720 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2721 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2722 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2723 | "\n", 2724 | " #eastern and western link coefficients\n", 2725 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2726 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2727 | "\n", 2728 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2729 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2730 | "\n", 2731 | " #northern and southern link coefficients\n", 2732 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2733 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2734 | "\n", 2735 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2736 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2737 | "\n", 2738 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2739 | "\n", 2740 | " #Agglomerate link coefficient\n", 2741 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2742 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2743 | " \n", 2744 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2745 | "\n", 2746 | " A_coarse[j][i][a_w]=AW\n", 2747 | " A_coarse[j][i][a_p]=AP\n", 2748 | " A_coarse[j][i][a_n]=AN\n", 2749 | " A_coarse[j][i][b_p]=BP\n", 2750 | " \n", 2751 | " return A_coarse" 2752 | ] 2753 | }, 2754 | { 2755 | "cell_type": "code", 2756 | "execution_count": 27, 2757 | "id": "c76d9917", 2758 | "metadata": {}, 2759 | "outputs": [], 2760 | "source": [ 2761 | "@njit \n", 2762 | "def correct(Nx,Ny,U,V,P,PC,DX,DY,av,ap):\n", 2763 | " global dx\n", 2764 | " \n", 2765 | " U_correct=np.zeros((Ny,Nx))\n", 2766 | " V_correct=np.zeros((Ny,Nx))\n", 2767 | " \n", 2768 | " #U correction\n", 2769 | " #interior\n", 2770 | " for j in range(Ny):\n", 2771 | " for i in range(1,Nx-1):\n", 2772 | " uc=av*DX[j][i]*(PC[j][i-1]-PC[j][i+1])\n", 2773 | " U_correct[j][i]=uc\n", 2774 | "\n", 2775 | " #Inlet\n", 2776 | " for j in range(Ny):\n", 2777 | " i=0\n", 2778 | " uc=av*DX[j][i]*(PC[j][i]-PC[j][i+1])*2\n", 2779 | " U_correct[j][i]=uc\n", 2780 | " #Outlet\n", 2781 | " i=Nx-1\n", 2782 | " uc=av*DX[j][i]*(PC[j][i-1]-PC[j][i])*2\n", 2783 | " U_correct[j][i]=uc\n", 2784 | " \n", 2785 | " U+=U_correct\n", 2786 | " \n", 2787 | " #V correction\n", 2788 | " #interior\n", 2789 | " for j in range(1,Ny-1):\n", 2790 | " for i in range(Nx):\n", 2791 | " vc=av*DY[j][i]*(PC[j-1][i]-PC[j+1][i])\n", 2792 | " V_correct[j][i]=vc\n", 2793 | "\n", 2794 | " #bottom wall\n", 2795 | " for i in range(Nx):\n", 2796 | " j=0\n", 2797 | " vc=av*DY[j][i]*(PC[j][i]-PC[j+1][i])*2\n", 2798 | " V_correct[j][i]=vc\n", 2799 | " #Top wall\n", 2800 | " j=Ny-1\n", 2801 | " vc=av*DY[j][i]*(PC[j-1][i]-PC[j][i])*2\n", 2802 | " V_correct[j][i]=vc\n", 2803 | " V+=V_correct\n", 2804 | " \n", 2805 | " #P correction\n", 2806 | " P=P+ap*dx**0.375*PC\n", 2807 | " \n", 2808 | " return U,V,P" 2809 | ] 2810 | }, 2811 | { 2812 | "cell_type": "code", 2813 | "execution_count": 28, 2814 | "id": "90dee63c", 2815 | "metadata": {}, 2816 | "outputs": [], 2817 | "source": [ 2818 | "@njit(fastmath=True)\n", 2819 | "def gauss_seidel(A,phi_old,Nx,Ny):\n", 2820 | " array=np.arange(1,Ny-1,1)\n", 2821 | " array=np.flip(array)\n", 2822 | " \n", 2823 | " a_s=0\n", 2824 | " a_w=1\n", 2825 | " a_p=2\n", 2826 | " a_e=3\n", 2827 | " a_n=4\n", 2828 | " b_p=5\n", 2829 | " \n", 2830 | " phi_new=np.zeros((Ny,Nx))\n", 2831 | " \n", 2832 | " j=Ny-1\n", 2833 | " i=0\n", 2834 | " #top left \n", 2835 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_e]*phi_old[j][i+1])+A[j][i][b_p])/A[j][i][a_p]\n", 2836 | " phi_new[j][i]=phi\n", 2837 | " \n", 2838 | " #top\n", 2839 | " for i in range(1,Nx-1):\n", 2840 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_w]*phi_new[j][i-1]+A[j][i][a_e]*phi_old[j][i+1])+A[j][i][b_p])/A[j][i][a_p]\n", 2841 | " phi_new[j][i]=phi\n", 2842 | " \n", 2843 | " #top right\n", 2844 | " i=Nx-1\n", 2845 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_w]*phi_new[j][i-1])+A[j][i][b_p])/A[j][i][a_p]\n", 2846 | " phi_new[j][i]=phi\n", 2847 | " \n", 2848 | " for j in array:\n", 2849 | " i=0\n", 2850 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_e]*phi_old[j][i+1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2851 | " phi_new[j][i]=phi\n", 2852 | " \n", 2853 | " for i in range(1,Nx-1):\n", 2854 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_w]*phi_new[j][i-1]+A[j][i][a_e]*phi_old[j][i+1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2855 | " phi_new[j][i]=phi \n", 2856 | " \n", 2857 | " i=Nx-1\n", 2858 | " phi=(-(A[j][i][a_s]*phi_old[j-1][i]+A[j][i][a_w]*phi_new[j][i-1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2859 | " phi_new[j][i]=phi\n", 2860 | " \n", 2861 | " #bottom left\n", 2862 | " j=0\n", 2863 | " i=0\n", 2864 | " phi=(-(A[j][i][a_e]*phi_old[j][i+1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2865 | " phi_new[j][i]=phi\n", 2866 | " \n", 2867 | " #bottom\n", 2868 | " for i in range(1,Nx-1):\n", 2869 | " phi=(-(A[j][i][a_w]*phi_new[j][i-1]+A[j][i][a_e]*phi_old[j][i+1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2870 | " phi_new[j][i]=phi\n", 2871 | " \n", 2872 | " #bottom right\n", 2873 | " i=Nx-1\n", 2874 | " phi=(-(A[j][i][a_w]*phi_new[j][i-1]+A[j][i][a_n]*phi_new[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2875 | " phi_new[j][i]=phi\n", 2876 | " \n", 2877 | " return phi_new" 2878 | ] 2879 | }, 2880 | { 2881 | "cell_type": "code", 2882 | "execution_count": 29, 2883 | "id": "381c5846", 2884 | "metadata": {}, 2885 | "outputs": [], 2886 | "source": [ 2887 | "@njit(fastmath=True)\n", 2888 | "def TDMA(A,N,x_array): \n", 2889 | " phi=np.zeros(N)\n", 2890 | " #Forward elimination\n", 2891 | " for i in range(1,N):\n", 2892 | " m=A[i][0]/A[i-1][1]\n", 2893 | " A[i][1]-=m*A[i-1][2]\n", 2894 | " A[i][-1]-=m*A[i-1][-1]\n", 2895 | " \n", 2896 | " #Backward substitution\n", 2897 | " phi[-1]=A[-1][-1]/A[-1][1]\n", 2898 | " for i in x_array:\n", 2899 | " phi[i]=(A[i][-1]-A[i][2]*phi[i+1])/A[i][1]\n", 2900 | " \n", 2901 | " return phi" 2902 | ] 2903 | }, 2904 | { 2905 | "cell_type": "code", 2906 | "execution_count": 30, 2907 | "id": "05e889cf", 2908 | "metadata": {}, 2909 | "outputs": [], 2910 | "source": [ 2911 | "@njit\n", 2912 | "def LGS(A,phi,Ny,Nx):\n", 2913 | " \n", 2914 | " #Row-wise sweep\n", 2915 | " x_array=np.arange(0,Nx-1)\n", 2916 | " x_array=np.flip(x_array)\n", 2917 | " \n", 2918 | " a_s=0\n", 2919 | " a_w=1\n", 2920 | " a_p=2\n", 2921 | " a_e=3\n", 2922 | " a_n=4\n", 2923 | " b_p=5\n", 2924 | " \n", 2925 | " #bottom to top#####################################################################\n", 2926 | " A_TDMA=np.zeros((Nx,4))\n", 2927 | " #bottom left\n", 2928 | " j=0\n", 2929 | " i=0\n", 2930 | " A_TDMA[i][0]=0.0\n", 2931 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2932 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2933 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 2934 | " \n", 2935 | " #bottom\n", 2936 | " for i in range(1,Nx-1):\n", 2937 | " A_TDMA[i][0]=A[j][i][a_w]\n", 2938 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2939 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2940 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 2941 | " \n", 2942 | " #bottom right\n", 2943 | " i=Nx-1\n", 2944 | " A_TDMA[i][0]=A[j][i][a_w]\n", 2945 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2946 | " A_TDMA[i][2]=0.0\n", 2947 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 2948 | " \n", 2949 | " #TDMA solve\n", 2950 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 2951 | " \n", 2952 | " for i in range(Nx):\n", 2953 | " phi[j][i]=phi_row[i]\n", 2954 | " \n", 2955 | " #Interior cells\n", 2956 | " for j in range(1,Ny-1):\n", 2957 | " A_TDMA=np.zeros((Nx,4))\n", 2958 | " #left\n", 2959 | " i=0\n", 2960 | " A_TDMA[i][0]=0.0\n", 2961 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2962 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2963 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 2964 | " \n", 2965 | " for i in range(1,Nx-1):\n", 2966 | " A_TDMA[i][0]=A[j][i][a_w]\n", 2967 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2968 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2969 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 2970 | " \n", 2971 | " #right\n", 2972 | " i=Nx-1\n", 2973 | " A_TDMA[i][0]=A[j][i][a_w]\n", 2974 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2975 | " A_TDMA[i][2]=0.0\n", 2976 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 2977 | " \n", 2978 | " #TDMA solve\n", 2979 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 2980 | "\n", 2981 | " for i in range(Nx):\n", 2982 | " phi[j][i]=phi_row[i]\n", 2983 | " \n", 2984 | " A_TDMA=np.zeros((Nx,4))\n", 2985 | " #top left\n", 2986 | " j=Ny-1\n", 2987 | " i=0\n", 2988 | " \n", 2989 | " A_TDMA[i][0]=0.0\n", 2990 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2991 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2992 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 2993 | " \n", 2994 | " #top\n", 2995 | " for i in range(1,Nx-1):\n", 2996 | " A_TDMA[i][0]=A[j][i][a_w]\n", 2997 | " A_TDMA[i][1]=A[j][i][a_p]\n", 2998 | " A_TDMA[i][2]=A[j][i][a_e]\n", 2999 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3000 | " \n", 3001 | " #top right\n", 3002 | " i=Nx-1\n", 3003 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3004 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3005 | " A_TDMA[i][2]=0.0\n", 3006 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3007 | "\n", 3008 | " #TDMA solve\n", 3009 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3010 | " \n", 3011 | " for i in range(Nx):\n", 3012 | " phi[j][i]=phi_row[i]\n", 3013 | " \n", 3014 | " #top to bottom###############################################################################\n", 3015 | " \n", 3016 | " A_TDMA=np.zeros((Nx,4))\n", 3017 | " #top left\n", 3018 | " j=Ny-1\n", 3019 | " i=0\n", 3020 | " \n", 3021 | " A_TDMA[i][0]=0.0\n", 3022 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3023 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3024 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3025 | " \n", 3026 | " #top\n", 3027 | " for i in range(1,Nx-1):\n", 3028 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3029 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3030 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3031 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3032 | " \n", 3033 | " #top right\n", 3034 | " i=Nx-1\n", 3035 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3036 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3037 | " A_TDMA[i][2]=0.0\n", 3038 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3039 | "\n", 3040 | " #TDMA solve\n", 3041 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3042 | " \n", 3043 | " for i in range(Nx):\n", 3044 | " phi[j][i]=phi_row[i]\n", 3045 | " \n", 3046 | " #Interior cells\n", 3047 | " for j in range(1,Ny-1):\n", 3048 | " A_TDMA=np.zeros((Nx,4))\n", 3049 | " #left\n", 3050 | " i=0\n", 3051 | " A_TDMA[i][0]=0.0\n", 3052 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3053 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3054 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3055 | " \n", 3056 | " for i in range(1,Nx-1):\n", 3057 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3058 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3059 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3060 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3061 | " \n", 3062 | " #right\n", 3063 | " i=Nx-1\n", 3064 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3065 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3066 | " A_TDMA[i][2]=0.0\n", 3067 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3068 | " \n", 3069 | " #TDMA solve\n", 3070 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3071 | "\n", 3072 | " for i in range(Nx):\n", 3073 | " phi[j][i]=phi_row[i]\n", 3074 | " \n", 3075 | " A_TDMA=np.zeros((Nx,4))\n", 3076 | " #bottom left\n", 3077 | " j=0\n", 3078 | " i=0\n", 3079 | " A_TDMA[i][0]=0.0\n", 3080 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3081 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3082 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3083 | " \n", 3084 | " #bottom\n", 3085 | " for i in range(1,Nx-1):\n", 3086 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3087 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3088 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3089 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3090 | " \n", 3091 | " #bottom right\n", 3092 | " i=Nx-1\n", 3093 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3094 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3095 | " A_TDMA[i][2]=0.0\n", 3096 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3097 | " \n", 3098 | " #TDMA solve\n", 3099 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3100 | " \n", 3101 | " for i in range(Nx):\n", 3102 | " phi[j][i]=phi_row[i]\n", 3103 | " \n", 3104 | " return phi" 3105 | ] 3106 | }, 3107 | { 3108 | "cell_type": "code", 3109 | "execution_count": 31, 3110 | "id": "8b3a966d", 3111 | "metadata": {}, 3112 | "outputs": [], 3113 | "source": [ 3114 | "@njit\n", 3115 | "def residual(A,phi,Ny,Nx):\n", 3116 | " res_array=np.zeros((Ny,Nx))\n", 3117 | " \n", 3118 | " a_s=0\n", 3119 | " a_w=1\n", 3120 | " a_p=2\n", 3121 | " a_e=3\n", 3122 | " a_n=4\n", 3123 | " b_p=5\n", 3124 | " \n", 3125 | " for j in range(1,Ny-1):\n", 3126 | " for i in range(1,Nx-1):\n", 3127 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3128 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3129 | " res_array[j][i]=res\n", 3130 | " \n", 3131 | " for j in range(1,Ny-1):\n", 3132 | " i=0\n", 3133 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_p]*phi[j][i]\n", 3134 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3135 | " res_array[j][i]=res\n", 3136 | " \n", 3137 | " i=Nx-1\n", 3138 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3139 | " +A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3140 | " res_array[j][i]=res\n", 3141 | " \n", 3142 | " for i in range(1,Nx-1):\n", 3143 | " j=0\n", 3144 | " res=-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3145 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3146 | " res_array[j][i]=res\n", 3147 | " \n", 3148 | " j=Ny-1\n", 3149 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3150 | " +A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p]\n", 3151 | " res_array[j][i]=res\n", 3152 | " \n", 3153 | " j=0\n", 3154 | " i=0\n", 3155 | " res=-(A[j][i][a_p]*phi[j][i]+A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3156 | " res_array[j][i]=res\n", 3157 | " \n", 3158 | " j=0\n", 3159 | " i=Nx-1\n", 3160 | " res=-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3161 | " res_array[j][i]=res\n", 3162 | " \n", 3163 | " j=Ny-1\n", 3164 | " i=0\n", 3165 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_p]*phi[j][i]+A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p]\n", 3166 | " res_array[j][i]=res\n", 3167 | " \n", 3168 | " j=Ny-1\n", 3169 | " i=Nx-1\n", 3170 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i])+A[j][i][b_p]\n", 3171 | " res_array[j][i]=res\n", 3172 | " \n", 3173 | " return np.reshape(res_array,Ny*Nx)" 3174 | ] 3175 | }, 3176 | { 3177 | "cell_type": "code", 3178 | "execution_count": 32, 3179 | "id": "db16e3b4", 3180 | "metadata": {}, 3181 | "outputs": [], 3182 | "source": [ 3183 | "@njit\n", 3184 | "def restriction(A,res,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3185 | " #need to update the source vector (error vector)\n", 3186 | " res_2D=np.reshape(res,(Ny_f,Nx_f))\n", 3187 | " \n", 3188 | " for j in range(Ny_c):\n", 3189 | " for i in range(Nx_c):\n", 3190 | " res_c=(res_2D[2*j][2*i]+res_2D[2*j+1][2*i]+res_2D[2*j][2*i+1]+res_2D[2*j+1][2*i+1])\n", 3191 | " A[j][i][5]=res_c\n", 3192 | " return A" 3193 | ] 3194 | }, 3195 | { 3196 | "cell_type": "code", 3197 | "execution_count": 33, 3198 | "id": "49da1bc0", 3199 | "metadata": {}, 3200 | "outputs": [], 3201 | "source": [ 3202 | "@njit\n", 3203 | "def prolongation(corr_coarse,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3204 | " \n", 3205 | " corr_fine=np.zeros((Ny_f,Nx_f))\n", 3206 | " \n", 3207 | " for j in range(Ny_c):\n", 3208 | " for i in range(Nx_c):\n", 3209 | " j2=2*j\n", 3210 | " i2=2*i\n", 3211 | " corr=corr_coarse[j][i]\n", 3212 | " corr_fine[j2][i2]=corr\n", 3213 | " corr_fine[j2][i2+1]=corr\n", 3214 | " corr_fine[j2+1][i2]=corr\n", 3215 | " corr_fine[j2+1][i2+1]=corr\n", 3216 | " \n", 3217 | " return corr_fine" 3218 | ] 3219 | }, 3220 | { 3221 | "cell_type": "code", 3222 | "execution_count": 34, 3223 | "id": "9e995db5", 3224 | "metadata": {}, 3225 | "outputs": [], 3226 | "source": [ 3227 | "@njit(fastmath=True)\n", 3228 | "def bilinear_prolongation(phi_coarse,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3229 | " phi_fine=np.zeros((Ny_f,Nx_f))\n", 3230 | " \n", 3231 | " #interior cells\n", 3232 | " for j in prange(1,Ny_c-1):\n", 3233 | " for i in range(1,Nx_c-1):\n", 3234 | " phi=phi_coarse[j][i]\n", 3235 | " \n", 3236 | " phi_w=phi_coarse[j][i-1]\n", 3237 | " phi_e=phi_coarse[j][i+1]\n", 3238 | " phi_n=phi_coarse[j+1][i]\n", 3239 | " phi_s=phi_coarse[j-1][i]\n", 3240 | " \n", 3241 | " phi_sw=phi_coarse[j-1][i-1]\n", 3242 | " phi_se=phi_coarse[j-1][i+1]\n", 3243 | " phi_nw=phi_coarse[j+1][i-1]\n", 3244 | " phi_ne=phi_coarse[j+1][i+1]\n", 3245 | " \n", 3246 | " #top left inner cell\n", 3247 | " phi_fine[2*j+1][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3248 | " #top right inner cell\n", 3249 | " phi_fine[2*j+1][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3250 | " #bottom left inner cell\n", 3251 | " phi_fine[2*j][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3252 | " #bottom right inner cell\n", 3253 | " phi_fine[2*j][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3254 | " \n", 3255 | " #for top wall\n", 3256 | " j=Ny_c-1\n", 3257 | " for i in range(1,Nx_c-1):\n", 3258 | " phi=phi_coarse[j][i]\n", 3259 | "\n", 3260 | " phi_w=phi_coarse[j][i-1]\n", 3261 | " phi_e=phi_coarse[j][i+1]\n", 3262 | " phi_s=phi_coarse[j-1][i]\n", 3263 | "\n", 3264 | " phi_sw=phi_coarse[j-1][i-1]\n", 3265 | " phi_se=phi_coarse[j-1][i+1]\n", 3266 | "\n", 3267 | " #top left inner cell\n", 3268 | " phi_fine[2*j+1][2*i]=0.9375*phi+0.3125*phi_w-0.1875*phi_s-0.0625*phi_sw\n", 3269 | " #top right inner cell\n", 3270 | " phi_fine[2*j+1][2*i+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_s-0.0625*phi_se\n", 3271 | " #bottom left inner cell\n", 3272 | " phi_fine[2*j][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3273 | " #bottom right inner cell\n", 3274 | " phi_fine[2*j][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3275 | "\n", 3276 | " #for bottom wall\n", 3277 | " j=0\n", 3278 | " for i in range(1,Nx_c-1):\n", 3279 | " phi=phi_coarse[j][i]\n", 3280 | "\n", 3281 | " phi_w=phi_coarse[j][i-1]\n", 3282 | " phi_e=phi_coarse[j][i+1]\n", 3283 | " phi_n=phi_coarse[j+1][i]\n", 3284 | "\n", 3285 | " phi_nw=phi_coarse[j+1][i-1]\n", 3286 | " phi_ne=phi_coarse[j+1][i+1]\n", 3287 | "\n", 3288 | " #top left inner cell\n", 3289 | " phi_fine[2*j+1][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3290 | " #top right inner cell\n", 3291 | " phi_fine[2*j+1][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3292 | " #bottom left inner cell\n", 3293 | " phi_fine[2*j][2*i]=0.9375*phi+0.3125*phi_w-0.1875*phi_n-0.0625*phi_nw\n", 3294 | " #bottom right inner cell\n", 3295 | " phi_fine[2*j][2*i+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_n-0.0625*phi_ne\n", 3296 | " \n", 3297 | " #for left wall\n", 3298 | " i=0\n", 3299 | " for j in range(1,Ny_c-1):\n", 3300 | " phi=phi_coarse[j][i]\n", 3301 | "\n", 3302 | " phi_e=phi_coarse[j][i+1]\n", 3303 | " phi_n=phi_coarse[j+1][i]\n", 3304 | " phi_s=phi_coarse[j-1][i]\n", 3305 | "\n", 3306 | " phi_se=phi_coarse[j-1][i+1]\n", 3307 | " phi_ne=phi_coarse[j+1][i+1]\n", 3308 | "\n", 3309 | " #top left inner cell\n", 3310 | " phi_fine[2*j+1][2*i]=0.9375*phi+0.3125*phi_n-0.1875*phi_e-0.0625*phi_ne\n", 3311 | " #top right inner cell\n", 3312 | " phi_fine[2*j+1][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3313 | " #bottom left inner cell\n", 3314 | " phi_fine[2*j][2*i]=0.9375*phi+0.3125*phi_s-0.1875*phi_e-0.0625*phi_se\n", 3315 | " #bottom right inner cell\n", 3316 | " phi_fine[2*j][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3317 | "\n", 3318 | " #for right wall\n", 3319 | " i=Nx_c-1\n", 3320 | " for j in range(1,Ny_c-1):\n", 3321 | " phi=phi_coarse[j][i]\n", 3322 | "\n", 3323 | " phi_w=phi_coarse[j][i-1]\n", 3324 | " phi_n=phi_coarse[j+1][i]\n", 3325 | " phi_s=phi_coarse[j-1][i]\n", 3326 | "\n", 3327 | " phi_sw=phi_coarse[j-1][i-1]\n", 3328 | " phi_nw=phi_coarse[j+1][i-1]\n", 3329 | "\n", 3330 | " #top left inner cell\n", 3331 | " phi_fine[2*j+1][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3332 | " #top right inner cell\n", 3333 | " phi_fine[2*j+1][2*i+1]=0.9375*phi+0.3125*phi_n-0.1875*phi_w-0.0625*phi_nw\n", 3334 | " #bottom left inner cell\n", 3335 | " phi_fine[2*j][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3336 | " #bottom right inner cell\n", 3337 | " phi_fine[2*j][2*i+1]=0.9375*phi+0.3125*phi_s-0.1875*phi_w-0.0625*phi_sw\n", 3338 | "\n", 3339 | " #for top left cell\n", 3340 | " i=0\n", 3341 | " j=Ny_c-1\n", 3342 | "\n", 3343 | " phi=phi_coarse[j][i]\n", 3344 | "\n", 3345 | " phi_e=phi_coarse[j][i+1]\n", 3346 | " phi_s=phi_coarse[j-1][i]\n", 3347 | "\n", 3348 | " phi_se=phi_coarse[j-1][i+1]\n", 3349 | "\n", 3350 | " #top left inner cell\n", 3351 | " phi_fine[2*j+1][2*i]=1.25*phi-0.25*phi_se\n", 3352 | " #top right inner cell\n", 3353 | " phi_fine[2*j+1][2*i+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_s-0.0625*phi_se\n", 3354 | " #bottom left inner cell\n", 3355 | " phi_fine[2*j][2*i]=0.9375*phi+0.3125*phi_s-0.1875*phi_e-0.0625*phi_se\n", 3356 | " #bottom right inner cell\n", 3357 | " phi_fine[2*j][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3358 | "\n", 3359 | " #for top right cell\n", 3360 | " i=Nx_c-1\n", 3361 | " j=Ny_c-1\n", 3362 | "\n", 3363 | " phi=phi_coarse[j][i]\n", 3364 | "\n", 3365 | " phi_w=phi_coarse[j][i-1]\n", 3366 | " phi_s=phi_coarse[j-1][i]\n", 3367 | "\n", 3368 | " phi_sw=phi_coarse[j-1][i-1]\n", 3369 | "\n", 3370 | " #top left inner cell\n", 3371 | " phi_fine[2*j+1][2*i]=0.9375*phi+0.3125*phi_w-0.1875*phi_s-0.0625*phi_sw\n", 3372 | " #top right inner cell\n", 3373 | " phi_fine[2*j+1][2*i+1]=1.25*phi-0.25*phi_sw\n", 3374 | " #bottom left inner cell\n", 3375 | " phi_fine[2*j][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3376 | " #bottom right inner cell\n", 3377 | " phi_fine[2*j][2*i+1]=0.9375*phi+0.3125*phi_s-0.1875*phi_w-0.0625*phi_sw\n", 3378 | "\n", 3379 | " #for bottom left cell\n", 3380 | " i=0\n", 3381 | " j=0\n", 3382 | "\n", 3383 | " phi=phi_coarse[j][i]\n", 3384 | "\n", 3385 | " phi_e=phi_coarse[j][i+1]\n", 3386 | " phi_n=phi_coarse[j+1][i]\n", 3387 | "\n", 3388 | " phi_ne=phi_coarse[j+1][i+1]\n", 3389 | "\n", 3390 | " #top left inner cell\n", 3391 | " phi_fine[2*j+1][2*i]=0.9375*phi+0.3125*phi_n-0.1875*phi_e-0.0625*phi_ne\n", 3392 | " #top right inner cell\n", 3393 | " phi_fine[2*j+1][2*i+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3394 | " #bottom left inner cell\n", 3395 | " phi_fine[2*j][2*i]=1.25*phi-0.25*phi_ne\n", 3396 | " #bottom right inner cell\n", 3397 | " phi_fine[2*j][2*i+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_n-0.0625*phi_ne\n", 3398 | "\n", 3399 | " #for bottom right cell\n", 3400 | " i=Nx_c-1\n", 3401 | " j=0\n", 3402 | "\n", 3403 | " phi=phi_coarse[j][i]\n", 3404 | "\n", 3405 | " phi_w=phi_coarse[j][i-1]\n", 3406 | " phi_n=phi_coarse[j+1][i]\n", 3407 | "\n", 3408 | " phi_nw=phi_coarse[j+1][i-1]\n", 3409 | "\n", 3410 | " #top left inner cell\n", 3411 | " phi_fine[2*j+1][2*i]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3412 | " #top right inner cell\n", 3413 | " phi_fine[2*j+1][2*i+1]=0.9375*phi+0.3125*phi_n-0.1875*phi_w-0.0625*phi_nw\n", 3414 | " #bottom left inner cell\n", 3415 | " phi_fine[2*j][2*i]=0.9375*phi+0.3125*phi_w-0.1875*phi_n-0.0625*phi_nw\n", 3416 | " #bottom right inner cell\n", 3417 | " phi_fine[2*j][2*i+1]=1.25*phi-0.25*phi_nw\n", 3418 | "\n", 3419 | " return phi_fine" 3420 | ] 3421 | }, 3422 | { 3423 | "cell_type": "code", 3424 | "execution_count": 35, 3425 | "id": "ce882958", 3426 | "metadata": {}, 3427 | "outputs": [], 3428 | "source": [ 3429 | "def gmg_solver(A_list,N_list,phi,n_levels,itermax=1):\n", 3430 | "\n", 3431 | " Ny=int(N_list[0][0])\n", 3432 | " Nx=int(N_list[0][1])\n", 3433 | " res=1\n", 3434 | " res_norm=1\n", 3435 | " iter_count=0\n", 3436 | " \n", 3437 | " A_first=A_list[0]\n", 3438 | " \n", 3439 | " N=Nx*Ny\n", 3440 | " \n", 3441 | " for num in range(itermax):\n", 3442 | " corr_list=[]\n", 3443 | "\n", 3444 | " #Calculate residual\n", 3445 | " res_array=residual(A_first,phi,Ny,Nx)\n", 3446 | " res=np.sum(abs(res_array))/N\n", 3447 | " \n", 3448 | " if iter_count==0 and res!=0: #normalize residual\n", 3449 | " res_norm=res\n", 3450 | "\n", 3451 | " res=res/res_norm\n", 3452 | " corr_list.append(phi)\n", 3453 | "\n", 3454 | " if n_levels>1:\n", 3455 | " #GOES COARSER\n", 3456 | " for n in range(n_levels-1):\n", 3457 | " A_coarse=A_list[n+1]\n", 3458 | "\n", 3459 | " Ny_f=int(N_list[n][0])\n", 3460 | " Nx_f=int(N_list[n][1])\n", 3461 | " Ny_c=int(N_list[n+1][0])\n", 3462 | " Nx_c=int(N_list[n+1][1])\n", 3463 | " \n", 3464 | " phi_prime=np.zeros((Ny_c,Nx_c))\n", 3465 | "\n", 3466 | " #RESTRICTION\n", 3467 | " A_coarse=restriction(A_coarse,res_array,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3468 | " \n", 3469 | " #PRE-SMOOTHING\n", 3470 | " for i in range(int(2*n)):\n", 3471 | " #phi1_prime=gauss_seidel(A_coarse,phi_prime,Nx_c,Ny_c)\n", 3472 | " phi1_prime=LGS(A_coarse,phi_prime,Ny_c,Nx_c)\n", 3473 | " phi_prime=phi1_prime\n", 3474 | "\n", 3475 | " #CALCULATE RESIDUAL\n", 3476 | " res_array=residual(A_coarse,phi_prime,Ny_c,Nx_c)\n", 3477 | "\n", 3478 | " #STORE CORRECTION VECTOR\n", 3479 | " corr_list.append(phi_prime)\n", 3480 | " \n", 3481 | " #GOES FINER\n", 3482 | " for n in reversed(range(n_levels-1)):\n", 3483 | " A_fine=A_list[n]\n", 3484 | "\n", 3485 | " phi_prime_coarse=corr_list[n+1]\n", 3486 | " phi_prime_fine=corr_list[n]\n", 3487 | "\n", 3488 | " Ny_f=int(N_list[n][0])\n", 3489 | " Nx_f=int(N_list[n][1])\n", 3490 | " Ny_c=int(N_list[n+1][0])\n", 3491 | " Nx_c=int(N_list[n+1][1])\n", 3492 | "\n", 3493 | " #PROLONGATION \n", 3494 | " phi_prime_corr=bilinear_prolongation(phi_prime_coarse,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3495 | "\n", 3496 | " #CORRECTION\n", 3497 | " phi_prime_fine=phi_prime_fine+phi_prime_corr\n", 3498 | "\n", 3499 | " #SMOOTHING\n", 3500 | " for i in range(int(0.5*(n**2))+1):\n", 3501 | " phi1_prime=gauss_seidel(A_fine,phi_prime_fine,Nx_f,Ny_f)\n", 3502 | " phi_prime_fine=phi1_prime\n", 3503 | " \n", 3504 | " corr_list[n]=phi_prime_fine\n", 3505 | "\n", 3506 | " phi=corr_list[0]\n", 3507 | " iter_count+=1\n", 3508 | " \n", 3509 | " if iter_count>=itermax:\n", 3510 | " break\n", 3511 | " \n", 3512 | " return phi\n", 3513 | "\n", 3514 | "\n", 3515 | "def pg_solver(A_list,N_list,phi,n_levels):\n", 3516 | " \n", 3517 | " Ny=int(N_list[-1][0])\n", 3518 | " Nx=int(N_list[-1][1])\n", 3519 | " \n", 3520 | " A_coarse=A_list[-1]\n", 3521 | " N=Nx*Ny\n", 3522 | " \n", 3523 | " #Iterate on coarsest grid\n", 3524 | " for i in range(100):\n", 3525 | " phi1=gauss_seidel(A_coarse,phi,Nx,Ny)\n", 3526 | " phi=phi1\n", 3527 | " phi_coarse=phi\n", 3528 | " \n", 3529 | " #GOES FINER\n", 3530 | " for n in reversed(range(n_levels-1)):\n", 3531 | " A_fine=A_list[n]\n", 3532 | " \n", 3533 | " Ny_f=int(N_list[n][0])\n", 3534 | " Nx_f=int(N_list[n][1])\n", 3535 | " Ny_c=int(N_list[n+1][0])\n", 3536 | " Nx_c=int(N_list[n+1][1])\n", 3537 | "\n", 3538 | " #PROLONGATION \n", 3539 | " phi_fine=prolongation(phi_coarse,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3540 | "\n", 3541 | " #SMOOTHING \n", 3542 | " for i in range(int(0.9*(n**3))+1):\n", 3543 | " phi1_fine=gauss_seidel(A_fine,phi_fine,Nx_f,Ny_f)\n", 3544 | " phi_fine=phi1_fine\n", 3545 | " \n", 3546 | " phi_coarse=phi_fine\n", 3547 | " \n", 3548 | " phi=phi1_fine\n", 3549 | " \n", 3550 | " return phi" 3551 | ] 3552 | }, 3553 | { 3554 | "cell_type": "code", 3555 | "execution_count": 36, 3556 | "id": "408187ed", 3557 | "metadata": {}, 3558 | "outputs": [], 3559 | "source": [ 3560 | "@njit(fastmath=True)\n", 3561 | "def jacobi_preconditioner(A,Ny,Nx):\n", 3562 | " a_s=0\n", 3563 | " a_w=1\n", 3564 | " a_p=2\n", 3565 | " a_e=3\n", 3566 | " a_n=4\n", 3567 | " b_p=5\n", 3568 | " \n", 3569 | " for j in range(Ny):\n", 3570 | " for i in range(Nx):\n", 3571 | " AP=A[j][i][a_p]\n", 3572 | " A[j][i][a_s]=A[j][i][a_s]/AP\n", 3573 | " A[j][i][a_w]=A[j][i][a_w]/AP\n", 3574 | " A[j][i][a_p]=A[j][i][a_p]/AP\n", 3575 | " A[j][i][a_e]=A[j][i][a_e]/AP\n", 3576 | " A[j][i][a_n]=A[j][i][a_n]/AP\n", 3577 | " A[j][i][b_p]=A[j][i][b_p]/AP\n", 3578 | " \n", 3579 | " return A" 3580 | ] 3581 | }, 3582 | { 3583 | "cell_type": "code", 3584 | "execution_count": 37, 3585 | "id": "3f540f83", 3586 | "metadata": {}, 3587 | "outputs": [], 3588 | "source": [ 3589 | "def steady_solve_mom(N_list,U,V,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T,av,stone_factor,n_levels,v,gb,gx,gy):\n", 3590 | " uA_list=[]\n", 3591 | " vA_list=[]\n", 3592 | " \n", 3593 | " Ny_first=int(N_list[0][0])\n", 3594 | " Nx_first=int(N_list[0][1])\n", 3595 | " #Set up mom coeff\n", 3596 | " uA,vA,DX,DY=mom_coeff(Nx_first,Ny_first,U,V,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx)\n", 3597 | " \n", 3598 | " uA_relaxed=implicit_relaxation(uA,U,Ny_first,Nx_first,av)\n", 3599 | " vA_relaxed=implicit_relaxation(vA,V,Ny_first,Nx_first,av)\n", 3600 | " uA_list.append(uA_relaxed)\n", 3601 | " vA_list.append(vA_relaxed)\n", 3602 | "\n", 3603 | " #Agglomerate mom eqns\n", 3604 | " for n in range(n_levels-1):\n", 3605 | " Ny_f=int(N_list[n][0])\n", 3606 | " Nx_f=int(N_list[n][1])\n", 3607 | " Ny_c=int(N_list[n+1][0])\n", 3608 | " Nx_c=int(N_list[n+1][1])\n", 3609 | "\n", 3610 | " uA_fine=uA_list[n]\n", 3611 | " vA_fine=vA_list[n]\n", 3612 | "\n", 3613 | " uA_coarse=agglomeration(uA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3614 | " vA_coarse=agglomeration(vA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3615 | "\n", 3616 | " uA_list.append(uA_coarse)\n", 3617 | " vA_list.append(vA_coarse)\n", 3618 | " \n", 3619 | " #Solve mom eqns\n", 3620 | " U1=gmg_solver(uA_list,N_list,U,n_levels)\n", 3621 | " V1=gmg_solver(vA_list,N_list,V,n_levels)\n", 3622 | " \n", 3623 | " return U1,V1,uA,vA,DX,DY" 3624 | ] 3625 | }, 3626 | { 3627 | "cell_type": "code", 3628 | "execution_count": 38, 3629 | "id": "e503c172", 3630 | "metadata": {}, 3631 | "outputs": [], 3632 | "source": [ 3633 | "def transient_solve_mom(N_list,U,V,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T,U_time_array,V_time_array,av,stone_factor,dy,dx,dt,n_levels,v,gb,gx,gy):\n", 3634 | " uA_list=[]\n", 3635 | " vA_list=[]\n", 3636 | " \n", 3637 | " Ny_first=int(N_list[0][0])\n", 3638 | " Nx_first=int(N_list[0][1])\n", 3639 | " #Set up mom coeff\n", 3640 | " uA,vA,DX,DY=mom_coeff(Nx_first,Ny_first,U,V,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx)\n", 3641 | " \n", 3642 | " k=2*dy*dx/dt\n", 3643 | " uA_transient=implicit_time(uA,U_time_array,Ny_first,Nx_first,k)\n", 3644 | " vA_transient=implicit_time(vA,V_time_array,Ny_first,Nx_first,k)\n", 3645 | " uA_list.append(uA_transient)\n", 3646 | " vA_list.append(vA_transient)\n", 3647 | "\n", 3648 | " #Agglomerate mom eqns\n", 3649 | " for n in range(n_levels-1):\n", 3650 | " Ny_f=int(N_list[n][0])\n", 3651 | " Nx_f=int(N_list[n][1])\n", 3652 | " Ny_c=int(N_list[n+1][0])\n", 3653 | " Nx_c=int(N_list[n+1][1])\n", 3654 | "\n", 3655 | " uA_fine=uA_list[n]\n", 3656 | " vA_fine=vA_list[n]\n", 3657 | "\n", 3658 | " uA_coarse=agglomeration(uA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3659 | " vA_coarse=agglomeration(vA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3660 | "\n", 3661 | " uA_list.append(uA_coarse)\n", 3662 | " vA_list.append(vA_coarse)\n", 3663 | " \n", 3664 | " #Solve mom eqns\n", 3665 | " U1=gmg_solver(uA_list,N_list,U,n_levels)\n", 3666 | " V1=gmg_solver(vA_list,N_list,V,n_levels)\n", 3667 | "\n", 3668 | " return U1,V1,uA_transient,vA_transient,DX,DY" 3669 | ] 3670 | }, 3671 | { 3672 | "cell_type": "code", 3673 | "execution_count": 39, 3674 | "id": "a312da0c", 3675 | "metadata": {}, 3676 | "outputs": [], 3677 | "source": [ 3678 | "def solve_cont(N_list,Uf,Vf,DXf,DYf,n_levels,stone_factor,dy,dx): \n", 3679 | " #Set up pressure correction coeff\n", 3680 | " pA_list=[]\n", 3681 | " \n", 3682 | " Ny_first=int(N_list[0][0])\n", 3683 | " Nx_first=int(N_list[0][1])\n", 3684 | " \n", 3685 | " pA=cont(Nx_first,Ny_first,Uf,Vf,DXf,DYf,dy,dx)\n", 3686 | " pA_pc=jacobi_preconditioner(pA,Ny,Nx)\n", 3687 | " pA_list.append(pA)\n", 3688 | "\n", 3689 | " #Agglomerate PC coeff\n", 3690 | " for n in range(n_levels-1):\n", 3691 | " Ny_f=int(N_list[n][0])\n", 3692 | " Nx_f=int(N_list[n][1])\n", 3693 | " Ny_c=int(N_list[n+1][0])\n", 3694 | " Nx_c=int(N_list[n+1][1])\n", 3695 | "\n", 3696 | " pA_fine=pA_list[n]\n", 3697 | " pA_coarse=agglomeration(pA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3698 | " pA_list.append(pA_coarse)\n", 3699 | " \n", 3700 | " #Calculate mass imbalance\n", 3701 | " m=m_imbalance(pA,Ny,Nx)\n", 3702 | " dummy=np.zeros([Ny_first,Nx_first])\n", 3703 | " #Solve PC\n", 3704 | " PC_mesh=pg_solver(pA_list,N_list,dummy,n_levels)\n", 3705 | " PC_mesh=gmg_solver(pA_list,N_list,PC_mesh,n_levels,2)\n", 3706 | " \n", 3707 | " return PC_mesh,m" 3708 | ] 3709 | }, 3710 | { 3711 | "cell_type": "code", 3712 | "execution_count": 40, 3713 | "id": "941e6d6f", 3714 | "metadata": {}, 3715 | "outputs": [], 3716 | "source": [ 3717 | "def steady_solve_temp(N_list,Uf,Vf,Txf,Tyf,Tdxf,Tdyf,T,n_levels,stone_factor,at,dy,dx,a,T_lft,T_rgt,T_top,T_btm):\n", 3718 | " #Set up temp coeff\n", 3719 | " tA_list=[]\n", 3720 | " \n", 3721 | " Ny_first=int(N_list[0][0])\n", 3722 | " Nx_first=int(N_list[0][1])\n", 3723 | " \n", 3724 | " tA=temp_coeff(Ny_first,Nx_first,Uf,Vf,Txf,Tyf,Tdxf,Tdyf,T,a,T_lft,T_rgt,T_top,T_btm)\n", 3725 | " tA_relaxed=implicit_relaxation(tA,T,Ny_first,Nx_first,at)\n", 3726 | " tA_list.append(tA)\n", 3727 | " \n", 3728 | " #Agglomerate PC coeff\n", 3729 | " for n in range(n_levels-1):\n", 3730 | " Ny_f=int(N_list[n][0])\n", 3731 | " Nx_f=int(N_list[n][1])\n", 3732 | " Ny_c=int(N_list[n+1][0])\n", 3733 | " Nx_c=int(N_list[n+1][1])\n", 3734 | "\n", 3735 | " tA_fine=tA_list[n]\n", 3736 | "\n", 3737 | " tA_coarse=agglomeration(tA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3738 | "\n", 3739 | " tA_list.append(tA_coarse)\n", 3740 | " \n", 3741 | " #Solve temperature\n", 3742 | " T_mesh=gmg_solver(tA_list,N_list,T,n_levels)\n", 3743 | " \n", 3744 | " return T_mesh,tA" 3745 | ] 3746 | }, 3747 | { 3748 | "cell_type": "code", 3749 | "execution_count": 41, 3750 | "id": "d59fa84b", 3751 | "metadata": {}, 3752 | "outputs": [], 3753 | "source": [ 3754 | "def transient_solve_temp(N_list,Uf,Vf,Txf,Tyf,Tdxf,Tdyf,T,T_time_array,n_levels,stone_factor,at,dy,dx,dt,a,T_top,T_btm):\n", 3755 | " #Set up temp coeff\n", 3756 | " tA_list=[]\n", 3757 | " \n", 3758 | " Ny_first=int(N_list[0][0])\n", 3759 | " Nx_first=int(N_list[0][1])\n", 3760 | " tA=temp_coeff(Ny_first,Nx_first,Uf,Vf,Txf,Tyf,Tdxf,Tdyf,T,a,T_lft,T_rgt,T_top,T_btm)\n", 3761 | " k=dy*dx/dt\n", 3762 | " tA_transient=implicit_time(tA,T_time_array,Ny_first,Nx_first,k)\n", 3763 | " tA_list.append(tA_transient)\n", 3764 | "\n", 3765 | " #Agglomerate PC coeff\n", 3766 | " for n in range(n_levels-1):\n", 3767 | " Ny_f=int(N_list[n][0])\n", 3768 | " Nx_f=int(N_list[n][1])\n", 3769 | " Ny_c=int(N_list[n+1][0])\n", 3770 | " Nx_c=int(N_list[n+1][1])\n", 3771 | "\n", 3772 | " tA_fine=tA_list[n]\n", 3773 | " tA_coarse=agglomeration(tA_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3774 | "\n", 3775 | " tA_list.append(tA_coarse)\n", 3776 | " \n", 3777 | " #Solve temperature\n", 3778 | " T_mesh=gmg_solver(tA_list,N_list,T,n_levels)\n", 3779 | " \n", 3780 | " return T_mesh,tA_transient" 3781 | ] 3782 | }, 3783 | { 3784 | "cell_type": "code", 3785 | "execution_count": 42, 3786 | "id": "bbc6135c", 3787 | "metadata": {}, 3788 | "outputs": [], 3789 | "source": [ 3790 | "@njit\n", 3791 | "def m_imbalance(pA,Ny,Nx):\n", 3792 | " m=0\n", 3793 | " for j in range(Ny):\n", 3794 | " for i in range(Nx):\n", 3795 | " m+=pA[j][i][-1]**2\n", 3796 | " \n", 3797 | " return np.sqrt(m)" 3798 | ] 3799 | }, 3800 | { 3801 | "cell_type": "code", 3802 | "execution_count": 43, 3803 | "id": "a4737878", 3804 | "metadata": {}, 3805 | "outputs": [], 3806 | "source": [ 3807 | "@njit\n", 3808 | "def Nu_calc(T,dy,dx):\n", 3809 | " #heated btm wall\n", 3810 | "# T_top=T[0]\n", 3811 | "# T_top2=T[1]\n", 3812 | "# T_top3=T[2]\n", 3813 | " \n", 3814 | "# heat_btm=np.sum(((46/15)*1.0-(15/4)*T_top+(5/6)*T_top2-(3/20)*T_top3)*dx/dy)\n", 3815 | "# Nu_btm=heat_btm\n", 3816 | "# Nu_mean=Nu_btm\n", 3817 | "\n", 3818 | " #heated left wall\n", 3819 | " \n", 3820 | " T_rgt=T[:,0]\n", 3821 | " T_rgt2=T[:,1]\n", 3822 | " T_rgt3=T[:,2]\n", 3823 | " \n", 3824 | " heat_lft=np.sum(((46/15)*1.0-(15/4)*T_rgt+(5/6)*T_rgt2-(3/20)*T_rgt3)*dx/dy)\n", 3825 | " Nu_lft=heat_lft\n", 3826 | " Nu_mean=Nu_lft\n", 3827 | " \n", 3828 | " return Nu_mean" 3829 | ] 3830 | }, 3831 | { 3832 | "cell_type": "code", 3833 | "execution_count": 44, 3834 | "id": "c55a6da9", 3835 | "metadata": {}, 3836 | "outputs": [], 3837 | "source": [ 3838 | "def local_Nu_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,T,dy,dx,x_size,y_size):\n", 3839 | " Nu_top=np.zeros([x_size])\n", 3840 | " Nu_btm=np.zeros([x_size])\n", 3841 | " Nu_lft=np.zeros([y_size])\n", 3842 | " Nu_rgt=np.zeros([y_size])\n", 3843 | "\n", 3844 | " T_topsquare=T_final[Ny_top][Nx_lft:Nx_rgt]\n", 3845 | " T_btmsquare=T_final[Ny_btm-1][Nx_lft:Nx_rgt]\n", 3846 | " T_lftsquare=T_final[:,Nx_lft-1][Ny_btm:Ny_top]\n", 3847 | " T_rgtsquare=T_final[:,Nx_rgt][Ny_btm:Ny_top]\n", 3848 | "\n", 3849 | " for i in range(x_size):\n", 3850 | " heat_top=(T_square-T_topsquare[i])*dx/(dy/2) #Watts\n", 3851 | " heat_btm=(T_square-T_btmsquare[i])*dx/(dy/2)\n", 3852 | "\n", 3853 | " Nu_top[i]=x_size*heat_top/(T_square-T_inlet)\n", 3854 | " Nu_btm[i]=x_size*heat_btm/(T_square-T_inlet)\n", 3855 | "\n", 3856 | " for j in range(y_size):\n", 3857 | " heat_lft=(T_square-T_lftsquare[j])*dy/(dx/2)\n", 3858 | " heat_rgt=(T_square-T_rgtsquare[j])*dy/(dx/2)\n", 3859 | "\n", 3860 | " Nu_lft[j]=y_size*heat_lft/(T_square-T_inlet)\n", 3861 | " Nu_rgt[j]=y_size*heat_rgt/(T_square-T_inlet)\n", 3862 | "\n", 3863 | " Nu_tot=np.concatenate([Nu_lft,Nu_top,Nu_rgt[::-1],Nu_btm[::-1]])\n", 3864 | " \n", 3865 | " return Nu_tot" 3866 | ] 3867 | }, 3868 | { 3869 | "cell_type": "code", 3870 | "execution_count": 45, 3871 | "id": "29bbd1aa", 3872 | "metadata": {}, 3873 | "outputs": [], 3874 | "source": [ 3875 | "def curl(U,V,dx,dy,Nx,Ny):\n", 3876 | " C=np.zeros((Ny,Nx))\n", 3877 | " \n", 3878 | " for j in range(1,Ny-1):\n", 3879 | " for i in range(1,Nx-1):\n", 3880 | " dvdx=(V[j][i+1]-V[j][i-1])/(2*dx)\n", 3881 | " dxdy=(U[j+1][i]-U[j-1][i])/(2*dy) \n", 3882 | " C[j][i]=dvdx-dxdy\n", 3883 | " return C" 3884 | ] 3885 | }, 3886 | { 3887 | "cell_type": "code", 3888 | "execution_count": 46, 3889 | "id": "08640dd0", 3890 | "metadata": {}, 3891 | "outputs": [], 3892 | "source": [ 3893 | "def round_off(x):\n", 3894 | " return float(('{:g}'.format(float('{:.5g}'.format(x)))))" 3895 | ] 3896 | }, 3897 | { 3898 | "cell_type": "markdown", 3899 | "id": "d03eac22", 3900 | "metadata": {}, 3901 | "source": [ 3902 | "# Initialisation" 3903 | ] 3904 | }, 3905 | { 3906 | "cell_type": "code", 3907 | "execution_count": 69, 3908 | "id": "0f2c1869", 3909 | "metadata": {}, 3910 | "outputs": [], 3911 | "source": [ 3912 | "start_run=True" 3913 | ] 3914 | }, 3915 | { 3916 | "cell_type": "code", 3917 | "execution_count": 70, 3918 | "id": "b8c32461", 3919 | "metadata": {}, 3920 | "outputs": [ 3921 | { 3922 | "name": "stdout", 3923 | "output_type": "stream", 3924 | "text": [ 3925 | "Rayleigh's number 1000.0\n", 3926 | "Grashof number: 1408.4507042253522\n", 3927 | "Kinematic viscosity: 0.01\n", 3928 | "Thermal diffusivity: 0.01408\n" 3929 | ] 3930 | } 3931 | ], 3932 | "source": [ 3933 | "Transient=False\n", 3934 | "#Physical properties\n", 3935 | "L=1 #Length of domain\n", 3936 | "H=1 #Height of domain\n", 3937 | "Ra=1E+3 #Reynold's number\n", 3938 | "Pr=0.71 #Prandtl number\n", 3939 | "Gr=Ra/Pr\n", 3940 | "v=0.01\n", 3941 | "rho=1 #density\n", 3942 | "a=v/Pr\n", 3943 | "\n", 3944 | "T_lft=1\n", 3945 | "T_rgt=0\n", 3946 | "T_top=0\n", 3947 | "T_btm=0\n", 3948 | "gb=(Gr*v**2)/((T_lft-T_rgt)*H**3)\n", 3949 | "gx=0 #gravity directions\n", 3950 | "gy=1\n", 3951 | "\n", 3952 | "av1=0.75 #Implicit under-relaxation\n", 3953 | "av2=0.8 #Explicit under-relaxation\n", 3954 | "ap=1.0\n", 3955 | "at=0.9 #Implicit under-relaxation\n", 3956 | "\n", 3957 | "Nx=256 #Array size for nodal velocities/pressure\n", 3958 | "Ny=256\n", 3959 | "uNx=Nx+1\n", 3960 | "uNy=Ny\n", 3961 | "vNx=Nx\n", 3962 | "vNy=Ny+1\n", 3963 | "n_levels=8 #6 levels for 64\n", 3964 | "N=Nx*Ny\n", 3965 | "\n", 3966 | "dx=L/Nx\n", 3967 | "dy=H/Ny\n", 3968 | "b=dy/dx\n", 3969 | "iter_count=1\n", 3970 | "iter_total=1\n", 3971 | "itermax=5\n", 3972 | "U_res_req=1E-4\n", 3973 | "V_res_req=1E-4\n", 3974 | "P_res_req=1E-4\n", 3975 | "T_res_req=1E-4\n", 3976 | "inner_loops=1\n", 3977 | "\n", 3978 | "dt=0.125E-3\n", 3979 | "end_time=8\n", 3980 | "flow_time=0\n", 3981 | "time_step=0\n", 3982 | "time_steps=int(end_time/dt)\n", 3983 | " \n", 3984 | "U_res_list=np.zeros(itermax)\n", 3985 | "V_res_list=np.zeros(itermax)\n", 3986 | "Cont_res_list=np.zeros(itermax)\n", 3987 | "T_res_list=np.zeros(itermax)\n", 3988 | "t_list=[]\n", 3989 | "U_data=[]\n", 3990 | "V_data=[]\n", 3991 | "T_data=[]\n", 3992 | "\n", 3993 | "U_time_array=np.zeros((Ny,Nx,4))\n", 3994 | "V_time_array=np.zeros((Ny,Nx,4))\n", 3995 | "T_time_array=np.zeros((Ny,Nx,4))\n", 3996 | "\n", 3997 | "# U_mesh=U_data[-1]\n", 3998 | "# V_mesh=V_data[-1]\n", 3999 | "# T_mesh=T_data[-1]\n", 4000 | "\n", 4001 | "stone_factor=0.8\n", 4002 | "\n", 4003 | "dummy=np.zeros([Ny,Nx])\n", 4004 | "pA=dummy\n", 4005 | "\n", 4006 | "if start_run==True:\n", 4007 | " T_mesh=np.full([Ny,Nx],0.5)\n", 4008 | " U_mesh=np.full([Ny,Nx],0)\n", 4009 | " V_mesh=np.full([Ny,Nx],0)\n", 4010 | " P_mesh=np.full([Ny,Nx],0)\n", 4011 | " if Transient==False:\n", 4012 | " itermax=20000\n", 4013 | " U_res_req=1E-7\n", 4014 | " V_res_req=1E-7\n", 4015 | " P_res_req=1E-7\n", 4016 | " T_res_req=1E-7\n", 4017 | " \n", 4018 | " start_run=False\n", 4019 | " \n", 4020 | "U_res_list=np.zeros(itermax)\n", 4021 | "V_res_list=np.zeros(itermax)\n", 4022 | "Cont_res_list=np.zeros(itermax)\n", 4023 | "T_res_list=np.zeros(itermax)\n", 4024 | "t_list=[]\n", 4025 | "U_data=[]\n", 4026 | "V_data=[]\n", 4027 | "T_data=[]\n", 4028 | " \n", 4029 | "if Transient==True:\n", 4030 | " print('CFL:',16.66*dt/dx)\n", 4031 | " \n", 4032 | "nu_mean_array=[]\n", 4033 | "cfl_array=[]\n", 4034 | "print(\"Rayleigh's number\",Ra)\n", 4035 | "print('Grashof number:',Gr)\n", 4036 | "print('Kinematic viscosity:',round(v,5))\n", 4037 | "print('Thermal diffusivity:',round(a,5))" 4038 | ] 4039 | }, 4040 | { 4041 | "cell_type": "markdown", 4042 | "id": "774f34aa", 4043 | "metadata": {}, 4044 | "source": [ 4045 | "# Solution" 4046 | ] 4047 | }, 4048 | { 4049 | "cell_type": "markdown", 4050 | "id": "77121d06", 4051 | "metadata": {}, 4052 | "source": [ 4053 | "## Steady solver" 4054 | ] 4055 | }, 4056 | { 4057 | "cell_type": "code", 4058 | "execution_count": 71, 4059 | "id": "fa41d1c2", 4060 | "metadata": { 4061 | "scrolled": false 4062 | }, 4063 | "outputs": [ 4064 | { 4065 | "name": "stdout", 4066 | "output_type": "stream", 4067 | "text": [ 4068 | "Steady State Calculation\n", 4069 | "\n", 4070 | "Calculation Data\n", 4071 | "Number of cells: 65536\n", 4072 | "Aspect Ratio: 1.0\n", 4073 | "Velocity explicit relaxation factor : 0.8\n", 4074 | "Velocity implicit relaxation factor : 0.75\n", 4075 | "Temperature implicit relaxation factor: 0.9\n", 4076 | "Pressure relaxation factor : 1.0\n", 4077 | "\n", 4078 | "Rayleigh Number : 1000.0\n", 4079 | "Prandtl's Number : 0.71\n", 4080 | "\n", 4081 | " Iteration | U Residual | V Residual | Cont Residual |Energy residual | Nu | Time(s) \n", 4082 | "==================================================================================================\n", 4083 | " 1 | 1.0 | 1.0 | 1.0 | 1.0 |11.2227 | 0.053 \n", 4084 | " 2 | 2.3934 | 0.72032 | 0.67524 | 0.25201 |37.3312 | 0.049 \n", 4085 | " 3 | 1.0822 | 0.64825 | 0.60995 | 0.11079 | 29.033 | 0.042 \n", 4086 | " 4 | 0.91287 | 0.36736 | 0.49706 | 0.088859 |25.9119 | 0.036 \n", 4087 | " 5 | 0.86708 | 0.26631 | 0.41496 | 0.069004 |23.3355 | 0.038 \n", 4088 | " 6 | 0.66465 | 0.38878 | 0.3479 | 0.056098 |21.3667 | 0.046 \n", 4089 | " 7 | 0.58224 | 0.34976 | 0.30201 | 0.046635 |19.7879 | 0.037 \n", 4090 | " 8 | 0.49547 | 0.27177 | 0.2456 | 0.039424 |18.4906 | 0.037 \n", 4091 | " 9 | 0.43178 | 0.12208 | 0.19706 | 0.033762 |17.4018 | 0.037 \n", 4092 | " 10 | 0.34938 | 0.14134 | 0.16976 | 0.029227 |16.4722 | 0.036 \n", 4093 | " 1000 | 0.018873 | 0.00048741 | 0.00021193 | 1.3679e-05 | 1.5256 | 0.031 \n", 4094 | " 2000 | 0.011597 | 0.00027869 | 9.3664e-05 | 3.5632e-06 | 1.1397 | 0.032 \n", 4095 | " 3000 | 0.0065521 | 0.00017132 | 5.3423e-05 | 1.5281e-06 | 1.0585 | 0.032 \n", 4096 | " 4000 | 0.0034905 | 9.8592e-05 | 3.3724e-05 | 1.1272e-06 | 1.0588 | 0.031 \n", 4097 | " 5000 | 0.001755 | 5.2995e-05 | 2.0467e-05 | 7.886e-07 | 1.0768 | 0.031 \n", 4098 | " 6000 | 0.00083493 | 2.6784e-05 | 1.1663e-05 | 4.8767e-07 | 1.0932 | 0.032 \n", 4099 | " 7000 | 0.00037851 | 1.2843e-05 | 6.2804e-06 | 2.7429e-07 | 1.1042 | 0.032 \n", 4100 | " 8000 | 0.00016621 | 5.8984e-06 | 3.2205e-06 | 1.4341e-07 | 1.1105 | 0.036 \n", 4101 | " 9000 | 7.34e-05 | 2.6287e-06 | 1.5796e-06 | 7.0523e-08 | 1.1138 | 0.032 \n", 4102 | " 10000 | 3.4726e-05 | 1.1638e-06 | 7.4206e-07 | 3.2731e-08 | 1.1155 | 0.032 \n", 4103 | " 11000 | 1.8292e-05 | 5.3317e-07 | 3.3344e-07 | 1.4278e-08 | 1.1162 | 0.032 \n", 4104 | " 12000 | 1.0341e-05 | 2.6355e-07 | 1.4293e-07 | 5.7712e-09 | 1.1165 | 0.036 \n", 4105 | " 13000 | 5.9127e-06 | 1.4078e-07 | 5.8465e-08 | 2.0926e-09 | 1.1166 | 0.032 \n", 4106 | " 14000 | 3.3079e-06 | 7.8111e-08 | 2.3305e-08 | 6.3617e-10 | 1.1167 | 0.032 \n", 4107 | " 15000 | 1.7906e-06 | 4.3309e-08 | 9.8766e-09 | 1.7424e-10 | 1.1167 | 0.032 \n", 4108 | " 16000 | 9.3542e-07 | 2.3511e-08 | 5.0685e-09 | 1.2989e-10 | 1.1167 | 0.036 \n" 4109 | ] 4110 | }, 4111 | { 4112 | "ename": "KeyboardInterrupt", 4113 | "evalue": "", 4114 | "output_type": "error", 4115 | "traceback": [ 4116 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 4117 | "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 4118 | "\u001b[1;32mC:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_27972/1493583444.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 58\u001b[0m \u001b[0mPxf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mPyf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mp_face\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mP1_mesh\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 60\u001b[1;33m \u001b[0mT1_mesh\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtA\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteady_solve_temp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mUxf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mVyf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTxf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTyf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTdxf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTdyf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_mesh\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_levels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mstone_factor\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mat\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_lft\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_rgt\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_top\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_btm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[0mTxf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mTyf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT_vertex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconv_face\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT1_mesh\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 4119 | "\u001b[1;32mC:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_27972/3843895423.py\u001b[0m in \u001b[0;36msteady_solve_temp\u001b[1;34m(N_list, Uf, Vf, Txf, Tyf, Tdxf, Tdyf, T, n_levels, stone_factor, at, dy, dx, a, T_lft, T_rgt, T_top, T_btm)\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 25\u001b[0m \u001b[1;31m#Solve temperature\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mT_mesh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgmg_solver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtA_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_levels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 27\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 28\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mT_mesh\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtA\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 4120 | "\u001b[1;32mC:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_27972/1531403146.py\u001b[0m in \u001b[0;36mgmg_solver\u001b[1;34m(A_list, N_list, phi, n_levels, itermax)\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;31m#Calculate residual\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0mres_array\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mresidual\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA_first\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphi\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mNx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mres\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres_array\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0miter_count\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m!=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m#normalize residual\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", 4121 | "\u001b[1;31mKeyboardInterrupt\u001b[0m: " 4122 | ] 4123 | } 4124 | ], 4125 | "source": [ 4126 | "print('Steady State Calculation')\n", 4127 | "print()\n", 4128 | "print('Calculation Data')\n", 4129 | "print('Number of cells:',N)\n", 4130 | "print('Aspect Ratio:', round(b,4))\n", 4131 | "print('Velocity explicit relaxation factor :',av2)\n", 4132 | "print('Velocity implicit relaxation factor :',av1)\n", 4133 | "print('Temperature implicit relaxation factor:',at)\n", 4134 | "print('Pressure relaxation factor :',ap)\n", 4135 | "print()\n", 4136 | "print(\"Rayleigh Number :\",Ra)\n", 4137 | "print(\"Prandtl's Number :\",Pr)\n", 4138 | "print()\n", 4139 | "print('{:<10} |{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^9}'.format(' Iteration','U Residual','V Residual','Cont Residual',\n", 4140 | " 'Energy residual','Nu','Time(s)'))\n", 4141 | "print('='*98)\n", 4142 | "\n", 4143 | "st=time.time()\n", 4144 | "\n", 4145 | "N_list=np.zeros([n_levels,2])\n", 4146 | "Ny1=Ny\n", 4147 | "Nx1=Nx\n", 4148 | "for n in range(n_levels):\n", 4149 | " N_list[n][0]=Ny1\n", 4150 | " N_list[n][1]=Nx1\n", 4151 | "\n", 4152 | " Ny1=int(Ny1/2)\n", 4153 | " Nx1=int(Nx1/2)\n", 4154 | " \n", 4155 | "Uxf,Uyf,_=conv_face(Ny,Nx,U_mesh,0)\n", 4156 | "Vxf,Vyf,_=conv_face(Ny,Nx,V_mesh,0)\n", 4157 | "Udxf,Udyf=diff_face(Ny,Nx,U_mesh,dx,dy,0)\n", 4158 | "Vdxf,Vdyf=diff_face(Ny,Nx,V_mesh,dx,dy,0)\n", 4159 | "#Pxf,Pyf,_=conv_face(Ny,Nx,P_mesh,2)\n", 4160 | "Pxf,Pyf=p_face(Ny,Nx,P_mesh)\n", 4161 | "Txf,Tyf,T_vertex=conv_face(Ny,Nx,T_mesh,1)\n", 4162 | "Tdxf,Tdyf=diff_face(Ny,Nx,T_mesh,dx,dy,1)\n", 4163 | "T_source=volumetric_source(Ny,Nx,T_mesh,Txf,Tyf,T_vertex)\n", 4164 | "\n", 4165 | "for n in range(itermax):\n", 4166 | " start=time.time()\n", 4167 | "\n", 4168 | " U1_mesh,V1_mesh,uA,vA,DX,DY=steady_solve_mom(N_list,U_mesh,V_mesh,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,T_source,av1,stone_factor,n_levels,v,gb,gx,gy)\n", 4169 | " \n", 4170 | " for inner in range(inner_loops):\n", 4171 | " Uf,Vf,DXf,DYf=face_rc(Nx,Ny,U1_mesh,V1_mesh,P_mesh,DX,DY)\n", 4172 | " PC_mesh,mass_imbalance=solve_cont(N_list,Uf,Vf,DXf,DYf,n_levels,stone_factor,dy,dx)\n", 4173 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av2,ap)\n", 4174 | " U1_mesh=U2_mesh\n", 4175 | " V1_mesh=V2_mesh\n", 4176 | " P_mesh=P1_mesh\n", 4177 | " \n", 4178 | " Uxf,Uyf,_=conv_face(Ny,Nx,U1_mesh,0)\n", 4179 | " Vxf,Vyf,_=conv_face(Ny,Nx,V1_mesh,0)\n", 4180 | " Udxf,Udyf=diff_face(Ny,Nx,U_mesh,dx,dy,0)\n", 4181 | " Vdxf,Vdyf=diff_face(Ny,Nx,V_mesh,dx,dy,0)\n", 4182 | " #Pxf,Pyf,_=conv_face(Ny,Nx,P_mesh,2)\n", 4183 | " Pxf,Pyf=p_face(Ny,Nx,P1_mesh)\n", 4184 | " \n", 4185 | " T1_mesh,tA=steady_solve_temp(N_list,Uxf,Vyf,Txf,Tyf,Tdxf,Tdyf,T_mesh,n_levels,stone_factor,at,dy,dx,a,T_lft,T_rgt,T_top,T_btm)\n", 4186 | " \n", 4187 | " Txf,Tyf,T_vertex=conv_face(Ny,Nx,T1_mesh,1)\n", 4188 | " Tdxf,Tdyf=diff_face(Ny,Nx,T1_mesh,dx,dy,1)\n", 4189 | " T_source=volumetric_source(Ny,Nx,T1_mesh,Txf,Tyf,T_vertex)\n", 4190 | "\n", 4191 | " #Compute and store residuals\n", 4192 | " U_res_array=residual(uA,U1_mesh,Ny,Nx)\n", 4193 | " U_residual=np.sqrt(np.sum(U_res_array**2)/(Nx*Ny))\n", 4194 | " V_res_array=residual(vA,V1_mesh,Ny,Nx)\n", 4195 | " V_residual=np.sqrt(np.sum(V_res_array**2)/(Nx*Ny))\n", 4196 | " P_residual=mass_imbalance/(Nx*Ny)\n", 4197 | " T_res_array=residual(tA,T1_mesh,Ny,Nx)\n", 4198 | " T_residual=np.sqrt(np.sum(T_res_array**2)/(Nx*Ny))\n", 4199 | "\n", 4200 | " if iter_total==1:\n", 4201 | " U_res_norm=U_residual\n", 4202 | " V_res_norm=V_residual\n", 4203 | " P_res_norm=P_residual\n", 4204 | " T_res_norm=T_residual\n", 4205 | "\n", 4206 | " U_residual=U_residual/U_res_norm\n", 4207 | " V_residual=V_residual/V_res_norm\n", 4208 | " P_residual=P_residual/P_res_norm\n", 4209 | " T_residual=T_residual/T_res_norm\n", 4210 | "\n", 4211 | " U_residual=round_off(U_residual)\n", 4212 | " V_residual=round_off(V_residual)\n", 4213 | " P_residual=round_off(P_residual)\n", 4214 | " T_residual=round_off(T_residual)\n", 4215 | " \n", 4216 | " U_res_list[n]=U_residual\n", 4217 | " V_res_list[n]=V_residual\n", 4218 | " Cont_res_list[n]=P_residual\n", 4219 | " T_res_list[n]=T_residual\n", 4220 | "\n", 4221 | " #Update values\n", 4222 | " U_mesh=U1_mesh\n", 4223 | " V_mesh=V1_mesh\n", 4224 | " P_mesh=P1_mesh\n", 4225 | " T_mesh=T1_mesh\n", 4226 | "\n", 4227 | " end=time.time()\n", 4228 | " t=end-start\n", 4229 | " t_list.append(t)\n", 4230 | " \n", 4231 | " Nu=Nu_calc(T_mesh,dx,dy)\n", 4232 | " nu_mean_array.append(Nu)\n", 4233 | " \n", 4234 | " #Output\n", 4235 | " if iter_total<=10 or iter_total%1000==0:\n", 4236 | " print('{:^10} |{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^9}'.format(iter_total,U_residual,V_residual,\n", 4237 | " P_residual,T_residual,round(Nu,4),round(t,4)))\n", 4238 | "\n", 4239 | " if U_residualTransient solver" 4285 | ] 4286 | }, 4287 | { 4288 | "cell_type": "code", 4289 | "execution_count": 156, 4290 | "id": "36667d19", 4291 | "metadata": { 4292 | "scrolled": true 4293 | }, 4294 | "outputs": [ 4295 | { 4296 | "name": "stdout", 4297 | "output_type": "stream", 4298 | "text": [ 4299 | "Transient State Calculation\n", 4300 | "\n", 4301 | "Calculation Data\n", 4302 | "Number of cells: 10816\n", 4303 | "Aspect Ratio: 1.0\n", 4304 | "Explicit relaxation factor: 0.7\n", 4305 | "Time step size: 0.125 ms\n", 4306 | "CFL: 3.9\n", 4307 | "Total time steps: 64000\n", 4308 | "\n", 4309 | "Rayleigh's Number: 10000000.0\n", 4310 | "Prandtl's Number : 0.71\n", 4311 | "\n" 4312 | ] 4313 | }, 4314 | { 4315 | "data": { 4316 | "application/vnd.jupyter.widget-view+json": { 4317 | "model_id": "3701e7deaa13495d9e1242b1949d47bc", 4318 | "version_major": 2, 4319 | "version_minor": 0 4320 | }, 4321 | "text/plain": [ 4322 | "Progress bar: 0%| | 0/64000 [00:00\u001b[1;34m\u001b[0m\n\u001b[0;32m 107\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0minner\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minner_loops\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 108\u001b[0m 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n_levels, stone_factor, dy, dx)\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;31m#Solve PC\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[0mPC_mesh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpg_solver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpA_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdummy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_levels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 28\u001b[1;33m \u001b[0mPC_mesh\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgmg_solver\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpA_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mN_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mPC_mesh\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mn_levels\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 29\u001b[0m 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"\u001b[1;31mKeyboardInterrupt\u001b[0m: " 4459 | ] 4460 | } 4461 | ], 4462 | "source": [ 4463 | "print('Transient State Calculation')\n", 4464 | "print()\n", 4465 | "print('Calculation Data')\n", 4466 | "print('Number of cells:',N)\n", 4467 | "print('Aspect Ratio:', round(b,4))\n", 4468 | "print('Explicit relaxation factor:',av2)\n", 4469 | "print('Time step size:',dt*1000,'ms')\n", 4470 | "print('CFL:',round(300*dt/dx,3))\n", 4471 | "print('Total time steps:',int(time_steps))\n", 4472 | "print()\n", 4473 | "print(\"Rayleigh's Number:\",Ra)\n", 4474 | "print(\"Prandtl's Number :\",Pr)\n", 4475 | "print()\n", 4476 | "\n", 4477 | "av1=0.8\n", 4478 | "at=0.8\n", 4479 | "st=time.time()\n", 4480 | "\n", 4481 | "for j in range(Ny):\n", 4482 | " for i in range(Nx):\n", 4483 | " U_time_array[j][i][0]=U_mesh[j][i]\n", 4484 | " V_time_array[j][i][0]=V_mesh[j][i]\n", 4485 | " T_time_array[j][i][0]=T_mesh[j][i]\n", 4486 | " U_time_array[j][i][1]=U_mesh[j][i]\n", 4487 | " V_time_array[j][i][1]=V_mesh[j][i]\n", 4488 | " T_time_array[j][i][1]=T_mesh[j][i]\n", 4489 | "\n", 4490 | "iter_total=0\n", 4491 | "time_step=0\n", 4492 | "\n", 4493 | "U_res_list=[]\n", 4494 | "V_res_list=[]\n", 4495 | "Cont_res_list=[]\n", 4496 | "T_res_list=[]\n", 4497 | "\n", 4498 | "N_list=np.zeros([n_levels,2])\n", 4499 | "Ny1=Ny\n", 4500 | "Nx1=Nx\n", 4501 | "for n in range(n_levels):\n", 4502 | " N_list[n][0]=Ny1\n", 4503 | " N_list[n][1]=Nx1\n", 4504 | "\n", 4505 | " Ny1=int(Ny1/2)\n", 4506 | " Nx1=int(Nx1/2)\n", 4507 | " \n", 4508 | "Uxf,Uyf,_=conv_face(Ny,Nx,U_mesh,0)\n", 4509 | "Vxf,Vyf,_=conv_face(Ny,Nx,V_mesh,0)\n", 4510 | "Udxf,Udyf=diff_face(Ny,Nx,U_mesh,dx,dy,0)\n", 4511 | "Vdxf,Vdyf=diff_face(Ny,Nx,V_mesh,dx,dy,0)\n", 4512 | "Pxf,Pyf=p_face(Ny,Nx,P_mesh)\n", 4513 | "Txf,Tyf,T_vertex=conv_face(Ny,Nx,T_mesh,1)\n", 4514 | "Tdxf,Tdyf=diff_face(Ny,Nx,T_mesh,dx,dy,1)\n", 4515 | "T_source=volumetric_source(Ny,Nx,T_mesh,Txf,Tyf,T_vertex)\n", 4516 | " \n", 4517 | "for flow in tqdm(range(int(time_steps)),desc='Progress bar',unit='time steps'):\n", 4518 | " if time_step==0:\n", 4519 | " print()\n", 4520 | " print('{:<10}|{:<10} |{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^8}|{:^9}'.format('Time step',' Iteration','U Residual','V Residual','Cont Residual',\n", 4521 | " 'Energy residual','Nu','CFL max','Time(s)'))\n", 4522 | " print('='*119)\n", 4523 | " \n", 4524 | "# if time_step%80==0:\n", 4525 | "# Nx_f=Nx\n", 4526 | "# Ny_f=Ny\n", 4527 | " \n", 4528 | "# U_fine=U_mesh\n", 4529 | "# V_fine=V_mesh\n", 4530 | "# T_fine=T_mesh\n", 4531 | " \n", 4532 | "# for n in range(2):\n", 4533 | "# Ny_c=int(Ny_f/2)\n", 4534 | "# Nx_c=int(Nx_f/2)\n", 4535 | "\n", 4536 | "# U_coarse=nodal_restriction(U_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4537 | "# V_coarse=nodal_restriction(V_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4538 | "# T_coarse=nodal_restriction(T_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4539 | "\n", 4540 | "# U_fine=U_coarse\n", 4541 | "# V_fine=V_coarse\n", 4542 | "# T_fine=T_coarse\n", 4543 | "\n", 4544 | "# Ny_f=Ny_c\n", 4545 | "# Nx_f=Nx_c\n", 4546 | " \n", 4547 | "# U_data.append(U_coarse)\n", 4548 | "# V_data.append(V_coarse)\n", 4549 | "# T_data.append(T_coarse)\n", 4550 | " \n", 4551 | "# if time_step%2000==0:\n", 4552 | "# np.save('U_RBC_RA9.npy',np.array(U_data))\n", 4553 | "# np.save('V_RBC_RA9.npy',np.array(V_data))\n", 4554 | "# np.save('T_RBC_RA9.npy',np.array(T_data))\n", 4555 | " \n", 4556 | " s=time.time()\n", 4557 | " \n", 4558 | " iter_count=1\n", 4559 | " time_step+=1\n", 4560 | " flow_time+=dt\n", 4561 | " \n", 4562 | " while iter_count<=itermax:\n", 4563 | " start=time.time()\n", 4564 | " \n", 4565 | " U1_mesh,V1_mesh,uA,vA,DX,DY=transient_solve_mom(N_list,U_mesh,V_mesh,Uxf,Uyf,Udxf,Udyf,Vxf,Vyf,Vdxf,Vdyf,Pxf,Pyf,\n", 4566 | " T_source,U_time_array,V_time_array,av1,stone_factor\n", 4567 | " ,dy,dx,dt,n_levels,v,gb,gx,gy)\n", 4568 | " \n", 4569 | " for inner in range(inner_loops):\n", 4570 | " Uf,Vf,DXf,DYf=face_rc(Nx,Ny,U1_mesh,V1_mesh,P_mesh,DX,DY)\n", 4571 | " PC_mesh,mass_imbalance=solve_cont(N_list,Uf,Vf,DXf,DYf,n_levels,stone_factor,dy,dx)\n", 4572 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av2,ap)\n", 4573 | " U1_mesh=U2_mesh\n", 4574 | " V1_mesh=V2_mesh\n", 4575 | " P_mesh=P1_mesh\n", 4576 | " \n", 4577 | " Uxf,Uyf,_=conv_face(Ny,Nx,U1_mesh,0)\n", 4578 | " Vxf,Vyf,_=conv_face(Ny,Nx,V1_mesh,0)\n", 4579 | " Udxf,Udyf=diff_face(Ny,Nx,U1_mesh,dx,dy,0)\n", 4580 | " Vdxf,Vdyf=diff_face(Ny,Nx,V1_mesh,dx,dy,0)\n", 4581 | " Pxf,Pyf=p_face(Ny,Nx,P1_mesh)\n", 4582 | "\n", 4583 | " T1_mesh,tA=transient_solve_temp(N_list,Uxf,Vyf,Txf,Tyf,Tdxf,Tdyf,T_mesh,T_time_array,n_levels,stone_factor,at,dy,dx,dt,a,T_top,T_btm)\n", 4584 | " \n", 4585 | " Txf,Tyf,T_vertex=conv_face(Ny,Nx,T_mesh,1)\n", 4586 | " Tdxf,Tdyf=diff_face(Ny,Nx,T_mesh,dx,dy,1)\n", 4587 | " T_source=volumetric_source(Ny,Nx,T1_mesh,Txf,Tyf,T_vertex)\n", 4588 | " \n", 4589 | " #Compute and store residuals\n", 4590 | " U_residual=abs(max(U1_mesh.flatten()-U_mesh.flatten()))\n", 4591 | " V_residual=abs(max(V1_mesh.flatten()-V_mesh.flatten()))\n", 4592 | " T_residual=abs(max(T1_mesh.flatten()-T_mesh.flatten()))\n", 4593 | " P_residual=np.sqrt(mass_imbalance)/(Ny*Nx)\n", 4594 | " \n", 4595 | " U_res_list.append(abs(U_residual))\n", 4596 | " V_res_list.append(abs(V_residual))\n", 4597 | " Cont_res_list.append(P_residual)\n", 4598 | " T_res_list.append(T_residual)\n", 4599 | "\n", 4600 | " U_residual=round_off(U_residual)\n", 4601 | " V_residual=round_off(V_residual)\n", 4602 | " P_residual=round_off(P_residual)\n", 4603 | " T_residual=round_off(T_residual)\n", 4604 | "\n", 4605 | " #Update values\n", 4606 | " U_mesh=U1_mesh\n", 4607 | " V_mesh=V1_mesh\n", 4608 | " P_mesh=P1_mesh\n", 4609 | " T_mesh=T1_mesh\n", 4610 | " \n", 4611 | " end=time.time()\n", 4612 | " t=end-start\n", 4613 | " t_list.append(t)\n", 4614 | " \n", 4615 | " if iter_count==1 and (flow+1<=10 or (flow+1)%500==0):\n", 4616 | " Nu=Nu_calc(T_mesh,dx,dy)\n", 4617 | " vmax=np.max(V_mesh)\n", 4618 | " cfl=vmax*dt/dx\n", 4619 | " print('{:^10}|{:^11}|{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^8}|{:^9}'.format(flow+1,iter_count,U_residual,V_residual,\n", 4620 | " P_residual,T_residual,round(Nu,4),round(cfl,4),round(t,4)))\n", 4621 | " #Output\n", 4622 | " if U_residualPost-Processing" 4670 | ] 4671 | }, 4672 | { 4673 | "cell_type": "markdown", 4674 | "id": "ae03a26c", 4675 | "metadata": {}, 4676 | "source": [ 4677 | "n=len(U_data)\n", 4678 | "\n", 4679 | "U_avg=np.zeros((Ny,Nx))\n", 4680 | "V_avg=np.zeros((Ny,Nx))\n", 4681 | "T_avg=np.zeros((Ny,Nx))\n", 4682 | "\n", 4683 | "for i in range(n):\n", 4684 | " U_time=U_data[i]\n", 4685 | " V_time=V_data[i]\n", 4686 | " T_time=T_data[i]\n", 4687 | " \n", 4688 | " U_avg+=(1/n)*U_time\n", 4689 | " V_avg+=(1/n)*V_time\n", 4690 | " T_avg+=(1/n)*T_time\n", 4691 | " \n", 4692 | "U_mesh=U_avg\n", 4693 | "V_mesh=V_avg\n", 4694 | "T_mesh=T_avg" 4695 | ] 4696 | }, 4697 | { 4698 | "cell_type": "code", 4699 | "execution_count": 73, 4700 | "id": "4b694e4e", 4701 | "metadata": { 4702 | "scrolled": true 4703 | }, 4704 | "outputs": [ 4705 | { 4706 | "name": "stdout", 4707 | "output_type": "stream", 4708 | "text": [ 4709 | "0.05214903160895335\n", 4710 | "5.214903160895335\n", 4711 | "-0.0011140143758278963\n", 4712 | "0.998740226383878\n" 4713 | ] 4714 | } 4715 | ], 4716 | "source": [ 4717 | "U_final=U_mesh\n", 4718 | "V_final=V_mesh\n", 4719 | "P_final=P_mesh\n", 4720 | "T_final=T_mesh\n", 4721 | "\n", 4722 | "V_mag=np.sqrt(np.power(U_final,2)+np.power(V_final,2))\n", 4723 | "vort=curl(U_final,V_final,dx,dy,Nx,Ny)\n", 4724 | "\n", 4725 | "x=np.linspace(dx/2,L-dx/2,Nx)\n", 4726 | "y=np.linspace(dy/2,H-dy/2,Ny)\n", 4727 | "xx,yy=np.meshgrid(x,y)\n", 4728 | "\n", 4729 | "# xe=np.linspace(1,Nx,Nx)\n", 4730 | "# ye=np.linspace(1,Ny,Ny)\n", 4731 | "# xxe,yye=np.meshgrid(xe,ye)\n", 4732 | "# grid=np.zeros([Ny,Nx])\n", 4733 | "#print(np.mean(nu_mean_array[time_steps-2000:time_steps-2:]))\n", 4734 | "print(np.max(V_mag))\n", 4735 | "print(np.max(V_mag/v))\n", 4736 | "print(np.min(T_final))\n", 4737 | "print(np.max(T_final))" 4738 | ] 4739 | }, 4740 | { 4741 | "cell_type": "code", 4742 | "execution_count": 74, 4743 | "id": "bf1eae07", 4744 | "metadata": { 4745 | "scrolled": false 4746 | }, 4747 | "outputs": [ 4748 | { 4749 | "data": { 4750 | "image/png": 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\n", 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\n", 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\n", 4775 | "text/plain": [ 4776 | "
" 4777 | ] 4778 | }, 4779 | "metadata": { 4780 | "needs_background": "light" 4781 | }, 4782 | "output_type": "display_data" 4783 | } 4784 | ], 4785 | "source": [ 4786 | "fig,ax=plt.subplots(2,2,figsize=(18.5,15)) #width x height (17 for 5m length, 27 for 8m length)\n", 4787 | "graph=ax[0][0].contourf(xx,yy,V_mag,cmap=cm.jet,levels=255)\n", 4788 | "ax[0][0].set_title('Velocity contour plot')\n", 4789 | "fig.colorbar(graph,ax=ax[0][0],label='Velocity (m/s)')\n", 4790 | "ax[0][0].set_xlim(0,L)\n", 4791 | "ax[0][0].set_ylim(0,H)\n", 4792 | "\n", 4793 | "ax[0][1].streamplot(xx,yy,U_final,V_final,density=1.0,linewidth=0.7,arrowsize=1.0,color='k',broken_streamlines=False)\n", 4794 | "ax[0][1].set_title('Streamlines')\n", 4795 | "fig.colorbar(graph,ax=ax[0][1],label='Velocity (m/s)')\n", 4796 | "ax[0][1].set_xlim(0,L)\n", 4797 | "ax[0][1].set_ylim(0,H)\n", 4798 | "\n", 4799 | "graph=ax[1][0].contourf(xx,yy,vort,cmap=cm.seismic,levels=255)\n", 4800 | "ax[1][0].set_title('Vorticity contour plot')\n", 4801 | "fig.colorbar(graph,ax=ax[1][0])\n", 4802 | "ax[1][0].set_xlim(0,L)\n", 4803 | "ax[1][0].set_ylim(0,H)\n", 4804 | "\n", 4805 | "graph=ax[1][1].contourf(xx,yy,T_final,levels=20,cmap=cm.seismic,vmax=0.95,vmin=0.15)\n", 4806 | "ax[1][1].set_title('Temperature contour plot')\n", 4807 | "fig.colorbar(graph,ax=ax[1][1])\n", 4808 | "ax[1][1].set_xlim(0,L)\n", 4809 | "ax[1][1].set_ylim(0,H)\n", 4810 | " \n", 4811 | "plt.show()\n", 4812 | "\n", 4813 | "iteration_eqn=np.linspace(0,len(U_res_list),len(U_res_list))\n", 4814 | "iterations=iteration_eqn\n", 4815 | "if Transient==True:\n", 4816 | " iterations=np.linspace(0,len(nu_mean_array),len(nu_mean_array))\n", 4817 | " iterations=iterations*dt\n", 4818 | " \n", 4819 | "fig,ax=plt.subplots(1,1,figsize=(12,6))\n", 4820 | "plt.plot(iteration_eqn[:iter_total-2:],U_res_list[:iter_total-2:],label='U residual',linewidth=0.9)\n", 4821 | "plt.plot(iteration_eqn[:iter_total-2:],V_res_list[:iter_total-2:],label='V residual',linewidth=0.7)\n", 4822 | "plt.plot(iteration_eqn[:iter_total-2:],Cont_res_list[:iter_total-2:],label='Continuity residual',linewidth=0.7)\n", 4823 | "plt.plot(iteration_eqn[:iter_total-2:],T_res_list[:iter_total-2:],label='Energy residual',linewidth=0.7)\n", 4824 | "plt.xlabel('Iterations')\n", 4825 | "plt.ylabel('Residual')\n", 4826 | "plt.title('Normalized L2 norm residual')\n", 4827 | "ax.set_yscale('log')\n", 4828 | "plt.grid()\n", 4829 | "plt.xlim(0)\n", 4830 | "plt.legend()\n", 4831 | "\n", 4832 | "plt.show()\n", 4833 | "\n", 4834 | "fig,ax=plt.subplots(2,1,figsize=(12,12))\n", 4835 | "\n", 4836 | "ax[0].plot(iterations[50:iter_total-1:],nu_mean_array[50:iter_total-1:])\n", 4837 | "ax[0].set_xlabel('Iterations')\n", 4838 | "ax[0].set_ylabel('Nu')\n", 4839 | "ax[0].set_title('Nusselt number(Nu) monitor plot')\n", 4840 | "ax[0].grid()\n", 4841 | "\n", 4842 | "if Transient==True:\n", 4843 | " ax[1].plot(iterations[50:iter_total-1:],cfl_array[50:iter_total-1:])\n", 4844 | " ax[1].set_xlabel('Iterations')\n", 4845 | " ax[1].set_ylabel('CFL')\n", 4846 | " ax[1].set_title('CFL plot')\n", 4847 | " ax[1].grid()\n", 4848 | "\n", 4849 | "plt.show()" 4850 | ] 4851 | }, 4852 | { 4853 | "cell_type": "code", 4854 | "execution_count": 75, 4855 | "id": "9000ce4e", 4856 | "metadata": {}, 4857 | "outputs": [ 4858 | { 4859 | "data": { 4860 | "image/png": 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\n", 4861 | "text/plain": [ 4862 | "
" 4863 | ] 4864 | }, 4865 | "metadata": { 4866 | "needs_background": "light" 4867 | }, 4868 | "output_type": "display_data" 4869 | } 4870 | ], 4871 | "source": [ 4872 | "fig,ax=plt.subplots(1,1,figsize=(12,6))\n", 4873 | "plt.plot(iterations[:iter_total-1],1000*np.array(t_list[:iter_total-1]))\n", 4874 | "plt.ylabel('time(ms)')\n", 4875 | "plt.xlabel('Iterations')\n", 4876 | "plt.grid()\n", 4877 | "plt.show()" 4878 | ] 4879 | }, 4880 | { 4881 | "cell_type": "code", 4882 | "execution_count": 57, 4883 | "id": "9367be97", 4884 | "metadata": {}, 4885 | "outputs": [], 4886 | "source": [ 4887 | "@njit\n", 4888 | "def nodal_restriction(phi,Ny_f,Nx_f,Ny_c,Nx_c):\n", 4889 | " phi_c=np.zeros((Ny_c,Nx_c))\n", 4890 | " \n", 4891 | " for j in range(Ny_c):\n", 4892 | " for i in range(Nx_c):\n", 4893 | " j_fine=2*j\n", 4894 | " i_fine=2*i\n", 4895 | " phi_c[j][i]=0.25*(phi[j_fine][i_fine]+phi[j_fine][i_fine+1]+phi[j_fine+1][i_fine]+phi[j_fine+1][i_fine+1])\n", 4896 | " \n", 4897 | " return phi_c" 4898 | ] 4899 | }, 4900 | { 4901 | "cell_type": "markdown", 4902 | "id": "67d681eb", 4903 | "metadata": {}, 4904 | "source": [ 4905 | "U_data=np.load('U_RBC.npy')\n", 4906 | "V_data=np.load('V_RBC.npy')\n", 4907 | "T_data=np.load('T_RBC.npy')" 4908 | ] 4909 | }, 4910 | { 4911 | "cell_type": "code", 4912 | "execution_count": 67, 4913 | "id": "b6344e23", 4914 | "metadata": {}, 4915 | "outputs": [ 4916 | { 4917 | "data": { 4918 | "text/plain": [ 4919 | "1000" 4920 | ] 4921 | }, 4922 | "execution_count": 67, 4923 | "metadata": {}, 4924 | "output_type": "execute_result" 4925 | } 4926 | ], 4927 | "source": [ 4928 | "len(U_data)" 4929 | ] 4930 | }, 4931 | { 4932 | "cell_type": "code", 4933 | "execution_count": 68, 4934 | "id": "a3870f20", 4935 | "metadata": {}, 4936 | "outputs": [ 4937 | { 4938 | "data": { 4939 | "text/plain": [ 4940 | "(192, 192)" 4941 | ] 4942 | }, 4943 | "execution_count": 68, 4944 | "metadata": {}, 4945 | "output_type": "execute_result" 4946 | } 4947 | ], 4948 | "source": [ 4949 | "U_data[0].shape" 4950 | ] 4951 | }, 4952 | { 4953 | "cell_type": "code", 4954 | "execution_count": 70, 4955 | "id": "1f9247b8", 4956 | "metadata": {}, 4957 | "outputs": [ 4958 | { 4959 | "name": "stdout", 4960 | "output_type": "stream", 4961 | "text": [ 4962 | "0\n", 4963 | "50\n", 4964 | "100\n", 4965 | "150\n", 4966 | "200\n", 4967 | "250\n", 4968 | "300\n", 4969 | "350\n", 4970 | "400\n", 4971 | "450\n", 4972 | "500\n", 4973 | "550\n", 4974 | "600\n", 4975 | "650\n", 4976 | "700\n", 4977 | "750\n", 4978 | "800\n", 4979 | "850\n", 4980 | "900\n", 4981 | "950\n" 4982 | ] 4983 | } 4984 | ], 4985 | "source": [ 4986 | "U_anim=U_data\n", 4987 | "V_anim=V_data\n", 4988 | "T_anim=T_data\n", 4989 | "\n", 4990 | "count=len(U_data)\n", 4991 | "\n", 4992 | "U_file=[]\n", 4993 | "V_file=[]\n", 4994 | "T_file=[]\n", 4995 | "V_mag_file=[]\n", 4996 | "Vort_file=[]\n", 4997 | "\n", 4998 | "for k in range(count):\n", 4999 | " Ny_f=int(Ny/2)\n", 5000 | " Nx_f=int(Nx/2)\n", 5001 | " \n", 5002 | " U_fine=U_anim[k]\n", 5003 | " V_fine=V_anim[k]\n", 5004 | " T_fine=T_anim[k]\n", 5005 | "\n", 5006 | "# for n in range(1):\n", 5007 | "# Ny_c=int(Ny_f/2)\n", 5008 | "# Nx_c=int(Nx_f/2)\n", 5009 | " \n", 5010 | "# U_coarse=nodal_restriction(U_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 5011 | "# V_coarse=nodal_restriction(V_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 5012 | "# T_coarse=nodal_restriction(T_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 5013 | "\n", 5014 | "# U_fine=U_coarse\n", 5015 | "# V_fine=V_coarse\n", 5016 | " \n", 5017 | "# Ny_f=Ny_c\n", 5018 | "# Nx_f=Nx_c\n", 5019 | "\n", 5020 | " U_coarse=U_fine\n", 5021 | " V_coarse=V_fine\n", 5022 | " T_coarse=T_fine\n", 5023 | " \n", 5024 | " V_mag=np.sqrt(np.power(U_coarse,2)+np.power(V_coarse,2)) \n", 5025 | " vort=curl(U_coarse,V_coarse,4*dx,4*dy,int(Nx/4),int(Ny/4))\n", 5026 | " \n", 5027 | " U_file.append(U_coarse)\n", 5028 | " V_file.append(V_coarse)\n", 5029 | " T_file.append(T_coarse)\n", 5030 | " V_mag_file.append(V_mag)\n", 5031 | " Vort_file.append(vort)\n", 5032 | " \n", 5033 | " if k%50==0:\n", 5034 | " print(k)\n", 5035 | " if k==999:\n", 5036 | " break" 5037 | ] 5038 | }, 5039 | { 5040 | "cell_type": "code", 5041 | "execution_count": 71, 5042 | "id": "91dad3ff", 5043 | "metadata": {}, 5044 | "outputs": [ 5045 | { 5046 | "data": { 5047 | "text/plain": [ 5048 | "1000" 5049 | ] 5050 | }, 5051 | "execution_count": 71, 5052 | "metadata": {}, 5053 | "output_type": "execute_result" 5054 | } 5055 | ], 5056 | "source": [ 5057 | "len(U_file)" 5058 | ] 5059 | }, 5060 | { 5061 | "cell_type": "code", 5062 | "execution_count": 72, 5063 | "id": "e906fb35", 5064 | "metadata": {}, 5065 | "outputs": [ 5066 | { 5067 | "data": { 5068 | "text/plain": [ 5069 | "1000" 5070 | ] 5071 | }, 5072 | "execution_count": 72, 5073 | "metadata": {}, 5074 | "output_type": "execute_result" 5075 | } 5076 | ], 5077 | "source": [ 5078 | "len(T_file)" 5079 | ] 5080 | }, 5081 | { 5082 | "cell_type": "code", 5083 | "execution_count": 73, 5084 | "id": "d47326f4", 5085 | "metadata": { 5086 | "scrolled": true 5087 | }, 5088 | "outputs": [ 5089 | { 5090 | "data": { 5091 | "text/plain": [ 5092 | "(192, 192)" 5093 | ] 5094 | }, 5095 | "execution_count": 73, 5096 | "metadata": {}, 5097 | "output_type": "execute_result" 5098 | } 5099 | ], 5100 | "source": [ 5101 | "U_file[0].shape" 5102 | ] 5103 | }, 5104 | { 5105 | "cell_type": "code", 5106 | "execution_count": 102, 5107 | "id": "039daf5b", 5108 | "metadata": {}, 5109 | "outputs": [], 5110 | "source": [ 5111 | "def animate(k):\n", 5112 | " ax.clear()\n", 5113 | " \n", 5114 | " U_final=U_file[k]\n", 5115 | " V_final=V_file[k]\n", 5116 | " #T_final=T_file[k]\n", 5117 | " #vort=Vort_file[k]\n", 5118 | " #V_mag=V_mag_file[k]\n", 5119 | " plt.title('Flow time: '+str(round(k*dt*80,5))+' seconds')\n", 5120 | " #plt.title('Streamlines')\n", 5121 | " #plt.title('Flow time: '+str(round(k*dt*2,4))+' seconds')\n", 5122 | " \n", 5123 | " plt.xlim(0,L)\n", 5124 | " plt.ylim(0,H)\n", 5125 | " #contour=plt.contourf(xx,yy,V_mag,cmap=cm.jet,vmax=1,levels=255)\n", 5126 | " #contour=plt.contourf(xx,yy,T_final,cmap=cm.seismic,vmax=0.8,vmin=0.2,levels=50)\n", 5127 | " #contour=plt.contourf(xx,yy,vort,cmap=cm.seismic,vmax=2500,vmin=-2500,levels=255)\n", 5128 | " stream=plt.streamplot(xx,yy,U_final,V_final,linewidth=0.8,density=0.6,color='k',arrowsize=1.0,broken_streamlines=False)\n", 5129 | " return stream" 5130 | ] 5131 | }, 5132 | { 5133 | "cell_type": "code", 5134 | "execution_count": 103, 5135 | "id": "b70c2d04", 5136 | "metadata": {}, 5137 | "outputs": [ 5138 | { 5139 | "data": { 5140 | "application/javascript": [ 5141 | "/* Put everything inside the global mpl namespace */\n", 5142 | "/* global mpl */\n", 5143 | "window.mpl = {};\n", 5144 | "\n", 5145 | "mpl.get_websocket_type = function () {\n", 5146 | " if (typeof WebSocket !== 'undefined') {\n", 5147 | " return WebSocket;\n", 5148 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 5149 | " return MozWebSocket;\n", 5150 | " } else {\n", 5151 | " alert(\n", 5152 | " 'Your browser does not have WebSocket support. ' +\n", 5153 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 5154 | " 'Firefox 4 and 5 are also supported but you ' +\n", 5155 | " 'have to enable WebSockets in about:config.'\n", 5156 | " );\n", 5157 | " }\n", 5158 | "};\n", 5159 | "\n", 5160 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 5161 | " this.id = figure_id;\n", 5162 | "\n", 5163 | " this.ws = websocket;\n", 5164 | "\n", 5165 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 5166 | "\n", 5167 | " if (!this.supports_binary) {\n", 5168 | " var warnings = document.getElementById('mpl-warnings');\n", 5169 | " if (warnings) {\n", 5170 | " warnings.style.display = 'block';\n", 5171 | " warnings.textContent =\n", 5172 | " 'This browser does not support binary websocket messages. ' +\n", 5173 | " 'Performance may be slow.';\n", 5174 | " }\n", 5175 | " }\n", 5176 | "\n", 5177 | " this.imageObj = new Image();\n", 5178 | "\n", 5179 | " this.context = undefined;\n", 5180 | " this.message = undefined;\n", 5181 | " this.canvas = undefined;\n", 5182 | " this.rubberband_canvas = undefined;\n", 5183 | " this.rubberband_context = undefined;\n", 5184 | " this.format_dropdown = undefined;\n", 5185 | "\n", 5186 | " this.image_mode = 'full';\n", 5187 | "\n", 5188 | " this.root = document.createElement('div');\n", 5189 | " this.root.setAttribute('style', 'display: inline-block');\n", 5190 | " this._root_extra_style(this.root);\n", 5191 | "\n", 5192 | " parent_element.appendChild(this.root);\n", 5193 | "\n", 5194 | " this._init_header(this);\n", 5195 | " this._init_canvas(this);\n", 5196 | " this._init_toolbar(this);\n", 5197 | "\n", 5198 | " var fig = this;\n", 5199 | "\n", 5200 | " this.waiting = false;\n", 5201 | "\n", 5202 | " this.ws.onopen = function () {\n", 5203 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 5204 | " fig.send_message('send_image_mode', {});\n", 5205 | " if (fig.ratio !== 1) {\n", 5206 | " fig.send_message('set_device_pixel_ratio', {\n", 5207 | " device_pixel_ratio: fig.ratio,\n", 5208 | " });\n", 5209 | " }\n", 5210 | " fig.send_message('refresh', {});\n", 5211 | " };\n", 5212 | "\n", 5213 | " this.imageObj.onload = function () {\n", 5214 | " if (fig.image_mode === 'full') {\n", 5215 | " // Full images could contain transparency (where diff images\n", 5216 | " // almost always do), so we need to clear the canvas so that\n", 5217 | " // there is no ghosting.\n", 5218 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 5219 | " }\n", 5220 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 5221 | " };\n", 5222 | "\n", 5223 | " this.imageObj.onunload = function () {\n", 5224 | " fig.ws.close();\n", 5225 | " };\n", 5226 | "\n", 5227 | " this.ws.onmessage = this._make_on_message_function(this);\n", 5228 | "\n", 5229 | " this.ondownload = ondownload;\n", 5230 | "};\n", 5231 | "\n", 5232 | "mpl.figure.prototype._init_header = function () {\n", 5233 | " var titlebar = document.createElement('div');\n", 5234 | " titlebar.classList =\n", 5235 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 5236 | " var titletext = document.createElement('div');\n", 5237 | " titletext.classList = 'ui-dialog-title';\n", 5238 | " titletext.setAttribute(\n", 5239 | " 'style',\n", 5240 | " 'width: 100%; text-align: center; padding: 3px;'\n", 5241 | " );\n", 5242 | " titlebar.appendChild(titletext);\n", 5243 | " this.root.appendChild(titlebar);\n", 5244 | " this.header = titletext;\n", 5245 | "};\n", 5246 | "\n", 5247 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 5248 | "\n", 5249 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 5250 | "\n", 5251 | "mpl.figure.prototype._init_canvas = function () {\n", 5252 | " var fig = this;\n", 5253 | "\n", 5254 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 5255 | " canvas_div.setAttribute('tabindex', '0');\n", 5256 | " canvas_div.setAttribute(\n", 5257 | " 'style',\n", 5258 | " 'border: 1px solid #ddd;' +\n", 5259 | " 'box-sizing: content-box;' +\n", 5260 | " 'clear: both;' +\n", 5261 | " 'min-height: 1px;' +\n", 5262 | " 'min-width: 1px;' +\n", 5263 | " 'outline: 0;' +\n", 5264 | " 'overflow: hidden;' +\n", 5265 | " 'position: relative;' +\n", 5266 | " 'resize: both;' +\n", 5267 | " 'z-index: 2;'\n", 5268 | " );\n", 5269 | "\n", 5270 | " function on_keyboard_event_closure(name) {\n", 5271 | " return function (event) {\n", 5272 | " return fig.key_event(event, name);\n", 5273 | " };\n", 5274 | " }\n", 5275 | "\n", 5276 | " canvas_div.addEventListener(\n", 5277 | " 'keydown',\n", 5278 | " on_keyboard_event_closure('key_press')\n", 5279 | " );\n", 5280 | " canvas_div.addEventListener(\n", 5281 | " 'keyup',\n", 5282 | " on_keyboard_event_closure('key_release')\n", 5283 | " );\n", 5284 | "\n", 5285 | " this._canvas_extra_style(canvas_div);\n", 5286 | " this.root.appendChild(canvas_div);\n", 5287 | "\n", 5288 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 5289 | " canvas.classList.add('mpl-canvas');\n", 5290 | " canvas.setAttribute(\n", 5291 | " 'style',\n", 5292 | " 'box-sizing: content-box;' +\n", 5293 | " 'pointer-events: none;' +\n", 5294 | " 'position: relative;' +\n", 5295 | " 'z-index: 0;'\n", 5296 | " );\n", 5297 | "\n", 5298 | " this.context = canvas.getContext('2d');\n", 5299 | "\n", 5300 | " var backingStore =\n", 5301 | " this.context.backingStorePixelRatio ||\n", 5302 | " this.context.webkitBackingStorePixelRatio ||\n", 5303 | " this.context.mozBackingStorePixelRatio ||\n", 5304 | " this.context.msBackingStorePixelRatio ||\n", 5305 | " this.context.oBackingStorePixelRatio ||\n", 5306 | " this.context.backingStorePixelRatio ||\n", 5307 | " 1;\n", 5308 | "\n", 5309 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 5310 | "\n", 5311 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 5312 | " 'canvas'\n", 5313 | " ));\n", 5314 | " rubberband_canvas.setAttribute(\n", 5315 | " 'style',\n", 5316 | " 'box-sizing: content-box;' +\n", 5317 | " 'left: 0;' +\n", 5318 | " 'pointer-events: none;' +\n", 5319 | " 'position: absolute;' +\n", 5320 | " 'top: 0;' +\n", 5321 | " 'z-index: 1;'\n", 5322 | " );\n", 5323 | "\n", 5324 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 5325 | " if (this.ResizeObserver === undefined) {\n", 5326 | " if (window.ResizeObserver !== undefined) {\n", 5327 | " this.ResizeObserver = window.ResizeObserver;\n", 5328 | " } else {\n", 5329 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 5330 | " this.ResizeObserver = obs.ResizeObserver;\n", 5331 | " }\n", 5332 | " }\n", 5333 | "\n", 5334 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 5335 | " var nentries = entries.length;\n", 5336 | " for (var i = 0; i < nentries; i++) {\n", 5337 | " var entry = entries[i];\n", 5338 | " var width, height;\n", 5339 | " if (entry.contentBoxSize) {\n", 5340 | " if (entry.contentBoxSize instanceof Array) {\n", 5341 | " // Chrome 84 implements new version of spec.\n", 5342 | " width = entry.contentBoxSize[0].inlineSize;\n", 5343 | " height = entry.contentBoxSize[0].blockSize;\n", 5344 | " } else {\n", 5345 | " // Firefox implements old version of spec.\n", 5346 | " width = entry.contentBoxSize.inlineSize;\n", 5347 | " height = entry.contentBoxSize.blockSize;\n", 5348 | " }\n", 5349 | " } else {\n", 5350 | " // Chrome <84 implements even older version of spec.\n", 5351 | " width = entry.contentRect.width;\n", 5352 | " height = entry.contentRect.height;\n", 5353 | " }\n", 5354 | "\n", 5355 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 5356 | " // the canvas container.\n", 5357 | " if (entry.devicePixelContentBoxSize) {\n", 5358 | " // Chrome 84 implements new version of spec.\n", 5359 | " canvas.setAttribute(\n", 5360 | " 'width',\n", 5361 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 5362 | " );\n", 5363 | " canvas.setAttribute(\n", 5364 | " 'height',\n", 5365 | " entry.devicePixelContentBoxSize[0].blockSize\n", 5366 | " );\n", 5367 | " } else {\n", 5368 | " canvas.setAttribute('width', width * fig.ratio);\n", 5369 | " canvas.setAttribute('height', height * fig.ratio);\n", 5370 | " }\n", 5371 | " /* This rescales the canvas back to display pixels, so that it\n", 5372 | " * appears correct on HiDPI screens. */\n", 5373 | " canvas.style.width = width + 'px';\n", 5374 | " canvas.style.height = height + 'px';\n", 5375 | "\n", 5376 | " rubberband_canvas.setAttribute('width', width);\n", 5377 | " rubberband_canvas.setAttribute('height', height);\n", 5378 | "\n", 5379 | " // And update the size in Python. We ignore the initial 0/0 size\n", 5380 | " // that occurs as the element is placed into the DOM, which should\n", 5381 | " // otherwise not happen due to the minimum size styling.\n", 5382 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 5383 | " fig.request_resize(width, height);\n", 5384 | " }\n", 5385 | " }\n", 5386 | " });\n", 5387 | " this.resizeObserverInstance.observe(canvas_div);\n", 5388 | "\n", 5389 | " function on_mouse_event_closure(name) {\n", 5390 | " /* User Agent sniffing is bad, but WebKit is busted:\n", 5391 | " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", 5392 | " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", 5393 | " * The worst that happens here is that they get an extra browser\n", 5394 | " * selection when dragging, if this check fails to catch them.\n", 5395 | " */\n", 5396 | " var UA = navigator.userAgent;\n", 5397 | " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", 5398 | " if(isWebKit) {\n", 5399 | " return function (event) {\n", 5400 | " /* This prevents the web browser from automatically changing to\n", 5401 | " * the text insertion cursor when the button is pressed. We\n", 5402 | " * want to control all of the cursor setting manually through\n", 5403 | " * the 'cursor' event from matplotlib */\n", 5404 | " event.preventDefault()\n", 5405 | " return fig.mouse_event(event, name);\n", 5406 | " };\n", 5407 | " } else {\n", 5408 | " return function (event) {\n", 5409 | " return fig.mouse_event(event, name);\n", 5410 | " };\n", 5411 | " }\n", 5412 | " }\n", 5413 | "\n", 5414 | " canvas_div.addEventListener(\n", 5415 | " 'mousedown',\n", 5416 | " on_mouse_event_closure('button_press')\n", 5417 | " );\n", 5418 | " canvas_div.addEventListener(\n", 5419 | " 'mouseup',\n", 5420 | " on_mouse_event_closure('button_release')\n", 5421 | " );\n", 5422 | " canvas_div.addEventListener(\n", 5423 | " 'dblclick',\n", 5424 | " on_mouse_event_closure('dblclick')\n", 5425 | " );\n", 5426 | " // Throttle sequential mouse events to 1 every 20ms.\n", 5427 | " canvas_div.addEventListener(\n", 5428 | " 'mousemove',\n", 5429 | " on_mouse_event_closure('motion_notify')\n", 5430 | " );\n", 5431 | "\n", 5432 | " canvas_div.addEventListener(\n", 5433 | " 'mouseenter',\n", 5434 | " on_mouse_event_closure('figure_enter')\n", 5435 | " );\n", 5436 | " canvas_div.addEventListener(\n", 5437 | " 'mouseleave',\n", 5438 | " on_mouse_event_closure('figure_leave')\n", 5439 | " );\n", 5440 | "\n", 5441 | " canvas_div.addEventListener('wheel', function (event) {\n", 5442 | " if (event.deltaY < 0) {\n", 5443 | " event.step = 1;\n", 5444 | " } else {\n", 5445 | " event.step = -1;\n", 5446 | " }\n", 5447 | " on_mouse_event_closure('scroll')(event);\n", 5448 | " });\n", 5449 | "\n", 5450 | " canvas_div.appendChild(canvas);\n", 5451 | " canvas_div.appendChild(rubberband_canvas);\n", 5452 | "\n", 5453 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 5454 | " this.rubberband_context.strokeStyle = '#000000';\n", 5455 | "\n", 5456 | " this._resize_canvas = function (width, height, forward) {\n", 5457 | " if (forward) {\n", 5458 | " canvas_div.style.width = width + 'px';\n", 5459 | " canvas_div.style.height = height + 'px';\n", 5460 | " }\n", 5461 | " };\n", 5462 | "\n", 5463 | " // Disable right mouse context menu.\n", 5464 | " canvas_div.addEventListener('contextmenu', function (_e) {\n", 5465 | " event.preventDefault();\n", 5466 | " return false;\n", 5467 | " });\n", 5468 | "\n", 5469 | " function set_focus() {\n", 5470 | " canvas.focus();\n", 5471 | " canvas_div.focus();\n", 5472 | " }\n", 5473 | "\n", 5474 | " window.setTimeout(set_focus, 100);\n", 5475 | "};\n", 5476 | "\n", 5477 | "mpl.figure.prototype._init_toolbar = function () {\n", 5478 | " var fig = this;\n", 5479 | "\n", 5480 | " var toolbar = document.createElement('div');\n", 5481 | " toolbar.classList = 'mpl-toolbar';\n", 5482 | " this.root.appendChild(toolbar);\n", 5483 | "\n", 5484 | " function on_click_closure(name) {\n", 5485 | " return function (_event) {\n", 5486 | " return fig.toolbar_button_onclick(name);\n", 5487 | " };\n", 5488 | " }\n", 5489 | "\n", 5490 | " function on_mouseover_closure(tooltip) {\n", 5491 | " return function (event) {\n", 5492 | " if (!event.currentTarget.disabled) {\n", 5493 | " return fig.toolbar_button_onmouseover(tooltip);\n", 5494 | " }\n", 5495 | " };\n", 5496 | " }\n", 5497 | "\n", 5498 | " fig.buttons = {};\n", 5499 | " var buttonGroup = document.createElement('div');\n", 5500 | " buttonGroup.classList = 'mpl-button-group';\n", 5501 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 5502 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 5503 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 5504 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 5505 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 5506 | "\n", 5507 | " if (!name) {\n", 5508 | " /* Instead of a spacer, we start a new button group. */\n", 5509 | " if (buttonGroup.hasChildNodes()) {\n", 5510 | " toolbar.appendChild(buttonGroup);\n", 5511 | " }\n", 5512 | " buttonGroup = document.createElement('div');\n", 5513 | " buttonGroup.classList = 'mpl-button-group';\n", 5514 | " continue;\n", 5515 | " }\n", 5516 | "\n", 5517 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 5518 | " button.classList = 'mpl-widget';\n", 5519 | " button.setAttribute('role', 'button');\n", 5520 | " button.setAttribute('aria-disabled', 'false');\n", 5521 | " button.addEventListener('click', on_click_closure(method_name));\n", 5522 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 5523 | "\n", 5524 | " var icon_img = document.createElement('img');\n", 5525 | " icon_img.src = '_images/' + image + '.png';\n", 5526 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 5527 | " icon_img.alt = tooltip;\n", 5528 | " button.appendChild(icon_img);\n", 5529 | "\n", 5530 | " buttonGroup.appendChild(button);\n", 5531 | " }\n", 5532 | "\n", 5533 | " if (buttonGroup.hasChildNodes()) {\n", 5534 | " toolbar.appendChild(buttonGroup);\n", 5535 | " }\n", 5536 | "\n", 5537 | " var fmt_picker = document.createElement('select');\n", 5538 | " fmt_picker.classList = 'mpl-widget';\n", 5539 | " toolbar.appendChild(fmt_picker);\n", 5540 | " this.format_dropdown = fmt_picker;\n", 5541 | "\n", 5542 | " for (var ind in mpl.extensions) {\n", 5543 | " var fmt = mpl.extensions[ind];\n", 5544 | " var option = document.createElement('option');\n", 5545 | " option.selected = fmt === mpl.default_extension;\n", 5546 | " option.innerHTML = fmt;\n", 5547 | " fmt_picker.appendChild(option);\n", 5548 | " }\n", 5549 | "\n", 5550 | " var status_bar = document.createElement('span');\n", 5551 | " status_bar.classList = 'mpl-message';\n", 5552 | " toolbar.appendChild(status_bar);\n", 5553 | " this.message = status_bar;\n", 5554 | "};\n", 5555 | "\n", 5556 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 5557 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 5558 | " // which will in turn request a refresh of the image.\n", 5559 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 5560 | "};\n", 5561 | "\n", 5562 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 5563 | " properties['type'] = type;\n", 5564 | " properties['figure_id'] = this.id;\n", 5565 | " this.ws.send(JSON.stringify(properties));\n", 5566 | "};\n", 5567 | "\n", 5568 | "mpl.figure.prototype.send_draw_message = function () {\n", 5569 | " if (!this.waiting) {\n", 5570 | " this.waiting = true;\n", 5571 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 5572 | " }\n", 5573 | "};\n", 5574 | "\n", 5575 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 5576 | " var format_dropdown = fig.format_dropdown;\n", 5577 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 5578 | " fig.ondownload(fig, format);\n", 5579 | "};\n", 5580 | "\n", 5581 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 5582 | " var size = msg['size'];\n", 5583 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 5584 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 5585 | " fig.send_message('refresh', {});\n", 5586 | " }\n", 5587 | "};\n", 5588 | "\n", 5589 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 5590 | " var x0 = msg['x0'] / fig.ratio;\n", 5591 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 5592 | " var x1 = msg['x1'] / fig.ratio;\n", 5593 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 5594 | " x0 = Math.floor(x0) + 0.5;\n", 5595 | " y0 = Math.floor(y0) + 0.5;\n", 5596 | " x1 = Math.floor(x1) + 0.5;\n", 5597 | " y1 = Math.floor(y1) + 0.5;\n", 5598 | " var min_x = Math.min(x0, x1);\n", 5599 | " var min_y = Math.min(y0, y1);\n", 5600 | " var width = Math.abs(x1 - x0);\n", 5601 | " var height = Math.abs(y1 - y0);\n", 5602 | "\n", 5603 | " fig.rubberband_context.clearRect(\n", 5604 | " 0,\n", 5605 | " 0,\n", 5606 | " fig.canvas.width / fig.ratio,\n", 5607 | " fig.canvas.height / fig.ratio\n", 5608 | " );\n", 5609 | "\n", 5610 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 5611 | "};\n", 5612 | "\n", 5613 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 5614 | " // Updates the figure title.\n", 5615 | " fig.header.textContent = msg['label'];\n", 5616 | "};\n", 5617 | "\n", 5618 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 5619 | " fig.canvas_div.style.cursor = msg['cursor'];\n", 5620 | "};\n", 5621 | "\n", 5622 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 5623 | " fig.message.textContent = msg['message'];\n", 5624 | "};\n", 5625 | "\n", 5626 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 5627 | " // Request the server to send over a new figure.\n", 5628 | " fig.send_draw_message();\n", 5629 | "};\n", 5630 | "\n", 5631 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 5632 | " fig.image_mode = msg['mode'];\n", 5633 | "};\n", 5634 | "\n", 5635 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 5636 | " for (var key in msg) {\n", 5637 | " if (!(key in fig.buttons)) {\n", 5638 | " continue;\n", 5639 | " }\n", 5640 | " fig.buttons[key].disabled = !msg[key];\n", 5641 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 5642 | " }\n", 5643 | "};\n", 5644 | "\n", 5645 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 5646 | " if (msg['mode'] === 'PAN') {\n", 5647 | " fig.buttons['Pan'].classList.add('active');\n", 5648 | " fig.buttons['Zoom'].classList.remove('active');\n", 5649 | " } else if (msg['mode'] === 'ZOOM') {\n", 5650 | " fig.buttons['Pan'].classList.remove('active');\n", 5651 | " fig.buttons['Zoom'].classList.add('active');\n", 5652 | " } else {\n", 5653 | " fig.buttons['Pan'].classList.remove('active');\n", 5654 | " fig.buttons['Zoom'].classList.remove('active');\n", 5655 | " }\n", 5656 | "};\n", 5657 | "\n", 5658 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 5659 | " // Called whenever the canvas gets updated.\n", 5660 | " this.send_message('ack', {});\n", 5661 | "};\n", 5662 | "\n", 5663 | "// A function to construct a web socket function for onmessage handling.\n", 5664 | "// Called in the figure constructor.\n", 5665 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 5666 | " return function socket_on_message(evt) {\n", 5667 | " if (evt.data instanceof Blob) {\n", 5668 | " var img = evt.data;\n", 5669 | " if (img.type !== 'image/png') {\n", 5670 | " /* FIXME: We get \"Resource interpreted as Image but\n", 5671 | " * transferred with MIME type text/plain:\" errors on\n", 5672 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 5673 | " * to be part of the websocket stream */\n", 5674 | " img.type = 'image/png';\n", 5675 | " }\n", 5676 | "\n", 5677 | " /* Free the memory for the previous frames */\n", 5678 | " if (fig.imageObj.src) {\n", 5679 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 5680 | " fig.imageObj.src\n", 5681 | " );\n", 5682 | " }\n", 5683 | "\n", 5684 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 5685 | " img\n", 5686 | " );\n", 5687 | " fig.updated_canvas_event();\n", 5688 | " fig.waiting = false;\n", 5689 | " return;\n", 5690 | " } else if (\n", 5691 | " typeof evt.data === 'string' &&\n", 5692 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 5693 | " ) {\n", 5694 | " fig.imageObj.src = evt.data;\n", 5695 | " fig.updated_canvas_event();\n", 5696 | " fig.waiting = false;\n", 5697 | " return;\n", 5698 | " }\n", 5699 | "\n", 5700 | " var msg = JSON.parse(evt.data);\n", 5701 | " var msg_type = msg['type'];\n", 5702 | "\n", 5703 | " // Call the \"handle_{type}\" callback, which takes\n", 5704 | " // the figure and JSON message as its only arguments.\n", 5705 | " try {\n", 5706 | " var callback = fig['handle_' + msg_type];\n", 5707 | " } catch (e) {\n", 5708 | " console.log(\n", 5709 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 5710 | " msg\n", 5711 | " );\n", 5712 | " return;\n", 5713 | " }\n", 5714 | "\n", 5715 | " if (callback) {\n", 5716 | " try {\n", 5717 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 5718 | " callback(fig, msg);\n", 5719 | " } catch (e) {\n", 5720 | " console.log(\n", 5721 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 5722 | " e,\n", 5723 | " e.stack,\n", 5724 | " msg\n", 5725 | " );\n", 5726 | " }\n", 5727 | " }\n", 5728 | " };\n", 5729 | "};\n", 5730 | "\n", 5731 | "function getModifiers(event) {\n", 5732 | " var mods = [];\n", 5733 | " if (event.ctrlKey) {\n", 5734 | " mods.push('ctrl');\n", 5735 | " }\n", 5736 | " if (event.altKey) {\n", 5737 | " mods.push('alt');\n", 5738 | " }\n", 5739 | " if (event.shiftKey) {\n", 5740 | " mods.push('shift');\n", 5741 | " }\n", 5742 | " if (event.metaKey) {\n", 5743 | " mods.push('meta');\n", 5744 | " }\n", 5745 | " return mods;\n", 5746 | "}\n", 5747 | "\n", 5748 | "/*\n", 5749 | " * return a copy of an object with only non-object keys\n", 5750 | " * we need this to avoid circular references\n", 5751 | " * https://stackoverflow.com/a/24161582/3208463\n", 5752 | " */\n", 5753 | "function simpleKeys(original) {\n", 5754 | " return Object.keys(original).reduce(function (obj, key) {\n", 5755 | " if (typeof original[key] !== 'object') {\n", 5756 | " obj[key] = original[key];\n", 5757 | " }\n", 5758 | " return obj;\n", 5759 | " }, {});\n", 5760 | "}\n", 5761 | "\n", 5762 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 5763 | " if (name === 'button_press') {\n", 5764 | " this.canvas.focus();\n", 5765 | " this.canvas_div.focus();\n", 5766 | " }\n", 5767 | "\n", 5768 | " // from https://stackoverflow.com/q/1114465\n", 5769 | " var boundingRect = this.canvas.getBoundingClientRect();\n", 5770 | " var x = (event.clientX - boundingRect.left) * this.ratio;\n", 5771 | " var y = (event.clientY - boundingRect.top) * this.ratio;\n", 5772 | "\n", 5773 | " this.send_message(name, {\n", 5774 | " x: x,\n", 5775 | " y: y,\n", 5776 | " button: event.button,\n", 5777 | " step: event.step,\n", 5778 | " modifiers: getModifiers(event),\n", 5779 | " guiEvent: simpleKeys(event),\n", 5780 | " });\n", 5781 | "\n", 5782 | " return false;\n", 5783 | "};\n", 5784 | "\n", 5785 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 5786 | " // Handle any extra behaviour associated with a key event\n", 5787 | "};\n", 5788 | "\n", 5789 | "mpl.figure.prototype.key_event = function (event, name) {\n", 5790 | " // Prevent repeat events\n", 5791 | " if (name === 'key_press') {\n", 5792 | " if (event.key === this._key) {\n", 5793 | " return;\n", 5794 | " } else {\n", 5795 | " this._key = event.key;\n", 5796 | " }\n", 5797 | " }\n", 5798 | " if (name === 'key_release') {\n", 5799 | " this._key = null;\n", 5800 | " }\n", 5801 | "\n", 5802 | " var value = '';\n", 5803 | " if (event.ctrlKey && event.key !== 'Control') {\n", 5804 | " value += 'ctrl+';\n", 5805 | " }\n", 5806 | " else if (event.altKey && event.key !== 'Alt') {\n", 5807 | " value += 'alt+';\n", 5808 | " }\n", 5809 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 5810 | " value += 'shift+';\n", 5811 | " }\n", 5812 | "\n", 5813 | " value += 'k' + event.key;\n", 5814 | "\n", 5815 | " this._key_event_extra(event, name);\n", 5816 | "\n", 5817 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 5818 | " return false;\n", 5819 | "};\n", 5820 | "\n", 5821 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 5822 | " if (name === 'download') {\n", 5823 | " this.handle_save(this, null);\n", 5824 | " } else {\n", 5825 | " this.send_message('toolbar_button', { name: name });\n", 5826 | " }\n", 5827 | "};\n", 5828 | "\n", 5829 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 5830 | " this.message.textContent = tooltip;\n", 5831 | "};\n", 5832 | "\n", 5833 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 5834 | "// prettier-ignore\n", 5835 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 5836 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", 5837 | "\n", 5838 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", 5839 | "\n", 5840 | "mpl.default_extension = \"png\";/* global mpl */\n", 5841 | "\n", 5842 | "var comm_websocket_adapter = function (comm) {\n", 5843 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 5844 | " // object with the appropriate methods. Currently this is a non binary\n", 5845 | " // socket, so there is still some room for performance tuning.\n", 5846 | " var ws = {};\n", 5847 | "\n", 5848 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 5849 | " ws.readyState = comm.kernel.ws.readyState;\n", 5850 | " function updateReadyState(_event) {\n", 5851 | " if (comm.kernel.ws) {\n", 5852 | " ws.readyState = comm.kernel.ws.readyState;\n", 5853 | " } else {\n", 5854 | " ws.readyState = 3; // Closed state.\n", 5855 | " }\n", 5856 | " }\n", 5857 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 5858 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 5859 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 5860 | "\n", 5861 | " ws.close = function () {\n", 5862 | " comm.close();\n", 5863 | " };\n", 5864 | " ws.send = function (m) {\n", 5865 | " //console.log('sending', m);\n", 5866 | " comm.send(m);\n", 5867 | " };\n", 5868 | " // Register the callback with on_msg.\n", 5869 | " comm.on_msg(function (msg) {\n", 5870 | " //console.log('receiving', msg['content']['data'], msg);\n", 5871 | " var data = msg['content']['data'];\n", 5872 | " if (data['blob'] !== undefined) {\n", 5873 | " data = {\n", 5874 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 5875 | " };\n", 5876 | " }\n", 5877 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 5878 | " ws.onmessage(data);\n", 5879 | " });\n", 5880 | " return ws;\n", 5881 | "};\n", 5882 | "\n", 5883 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 5884 | " // This is the function which gets called when the mpl process\n", 5885 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 5886 | "\n", 5887 | " var id = msg.content.data.id;\n", 5888 | " // Get hold of the div created by the display call when the Comm\n", 5889 | " // socket was opened in Python.\n", 5890 | " var element = document.getElementById(id);\n", 5891 | " var ws_proxy = comm_websocket_adapter(comm);\n", 5892 | "\n", 5893 | " function ondownload(figure, _format) {\n", 5894 | " window.open(figure.canvas.toDataURL());\n", 5895 | " }\n", 5896 | "\n", 5897 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 5898 | "\n", 5899 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 5900 | " // web socket which is closed, not our websocket->open comm proxy.\n", 5901 | " ws_proxy.onopen();\n", 5902 | "\n", 5903 | " fig.parent_element = element;\n", 5904 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 5905 | " if (!fig.cell_info) {\n", 5906 | " console.error('Failed to find cell for figure', id, fig);\n", 5907 | " return;\n", 5908 | " }\n", 5909 | " fig.cell_info[0].output_area.element.on(\n", 5910 | " 'cleared',\n", 5911 | " { fig: fig },\n", 5912 | " fig._remove_fig_handler\n", 5913 | " );\n", 5914 | "};\n", 5915 | "\n", 5916 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 5917 | " var width = fig.canvas.width / fig.ratio;\n", 5918 | " fig.cell_info[0].output_area.element.off(\n", 5919 | " 'cleared',\n", 5920 | " fig._remove_fig_handler\n", 5921 | " );\n", 5922 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 5923 | "\n", 5924 | " // Update the output cell to use the data from the current canvas.\n", 5925 | " fig.push_to_output();\n", 5926 | " var dataURL = fig.canvas.toDataURL();\n", 5927 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 5928 | " // the notebook keyboard shortcuts fail.\n", 5929 | " IPython.keyboard_manager.enable();\n", 5930 | " fig.parent_element.innerHTML =\n", 5931 | " '';\n", 5932 | " fig.close_ws(fig, msg);\n", 5933 | "};\n", 5934 | "\n", 5935 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 5936 | " fig.send_message('closing', msg);\n", 5937 | " // fig.ws.close()\n", 5938 | "};\n", 5939 | "\n", 5940 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 5941 | " // Turn the data on the canvas into data in the output cell.\n", 5942 | " var width = this.canvas.width / this.ratio;\n", 5943 | " var dataURL = this.canvas.toDataURL();\n", 5944 | " this.cell_info[1]['text/html'] =\n", 5945 | " '';\n", 5946 | "};\n", 5947 | "\n", 5948 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 5949 | " // Tell IPython that the notebook contents must change.\n", 5950 | " IPython.notebook.set_dirty(true);\n", 5951 | " this.send_message('ack', {});\n", 5952 | " var fig = this;\n", 5953 | " // Wait a second, then push the new image to the DOM so\n", 5954 | " // that it is saved nicely (might be nice to debounce this).\n", 5955 | " setTimeout(function () {\n", 5956 | " fig.push_to_output();\n", 5957 | " }, 1000);\n", 5958 | "};\n", 5959 | "\n", 5960 | "mpl.figure.prototype._init_toolbar = function () {\n", 5961 | " var fig = this;\n", 5962 | "\n", 5963 | " var toolbar = document.createElement('div');\n", 5964 | " toolbar.classList = 'btn-toolbar';\n", 5965 | " this.root.appendChild(toolbar);\n", 5966 | "\n", 5967 | " function on_click_closure(name) {\n", 5968 | " return function (_event) {\n", 5969 | " return fig.toolbar_button_onclick(name);\n", 5970 | " };\n", 5971 | " }\n", 5972 | "\n", 5973 | " function on_mouseover_closure(tooltip) {\n", 5974 | " return function (event) {\n", 5975 | " if (!event.currentTarget.disabled) {\n", 5976 | " return fig.toolbar_button_onmouseover(tooltip);\n", 5977 | " }\n", 5978 | " };\n", 5979 | " }\n", 5980 | "\n", 5981 | " fig.buttons = {};\n", 5982 | " var buttonGroup = document.createElement('div');\n", 5983 | " buttonGroup.classList = 'btn-group';\n", 5984 | " var button;\n", 5985 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 5986 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 5987 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 5988 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 5989 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 5990 | "\n", 5991 | " if (!name) {\n", 5992 | " /* Instead of a spacer, we start a new button group. */\n", 5993 | " if (buttonGroup.hasChildNodes()) {\n", 5994 | " toolbar.appendChild(buttonGroup);\n", 5995 | " }\n", 5996 | " buttonGroup = document.createElement('div');\n", 5997 | " buttonGroup.classList = 'btn-group';\n", 5998 | " continue;\n", 5999 | " }\n", 6000 | "\n", 6001 | " button = fig.buttons[name] = document.createElement('button');\n", 6002 | " button.classList = 'btn btn-default';\n", 6003 | " button.href = '#';\n", 6004 | " button.title = name;\n", 6005 | " button.innerHTML = '';\n", 6006 | " button.addEventListener('click', on_click_closure(method_name));\n", 6007 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 6008 | " buttonGroup.appendChild(button);\n", 6009 | " }\n", 6010 | "\n", 6011 | " if (buttonGroup.hasChildNodes()) {\n", 6012 | " toolbar.appendChild(buttonGroup);\n", 6013 | " }\n", 6014 | "\n", 6015 | " // Add the status bar.\n", 6016 | " var status_bar = document.createElement('span');\n", 6017 | " status_bar.classList = 'mpl-message pull-right';\n", 6018 | " toolbar.appendChild(status_bar);\n", 6019 | " this.message = status_bar;\n", 6020 | "\n", 6021 | " // Add the close button to the window.\n", 6022 | " var buttongrp = document.createElement('div');\n", 6023 | " buttongrp.classList = 'btn-group inline pull-right';\n", 6024 | " button = document.createElement('button');\n", 6025 | " button.classList = 'btn btn-mini btn-primary';\n", 6026 | " button.href = '#';\n", 6027 | " button.title = 'Stop Interaction';\n", 6028 | " button.innerHTML = '';\n", 6029 | " button.addEventListener('click', function (_evt) {\n", 6030 | " fig.handle_close(fig, {});\n", 6031 | " });\n", 6032 | " button.addEventListener(\n", 6033 | " 'mouseover',\n", 6034 | " on_mouseover_closure('Stop Interaction')\n", 6035 | " );\n", 6036 | " buttongrp.appendChild(button);\n", 6037 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 6038 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 6039 | "};\n", 6040 | "\n", 6041 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 6042 | " var fig = event.data.fig;\n", 6043 | " if (event.target !== this) {\n", 6044 | " // Ignore bubbled events from children.\n", 6045 | " return;\n", 6046 | " }\n", 6047 | " fig.close_ws(fig, {});\n", 6048 | "};\n", 6049 | "\n", 6050 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 6051 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 6052 | "};\n", 6053 | "\n", 6054 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 6055 | " // this is important to make the div 'focusable\n", 6056 | " el.setAttribute('tabindex', 0);\n", 6057 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 6058 | " // off when our div gets focus\n", 6059 | "\n", 6060 | " // location in version 3\n", 6061 | " if (IPython.notebook.keyboard_manager) {\n", 6062 | " IPython.notebook.keyboard_manager.register_events(el);\n", 6063 | " } else {\n", 6064 | " // location in version 2\n", 6065 | " IPython.keyboard_manager.register_events(el);\n", 6066 | " }\n", 6067 | "};\n", 6068 | "\n", 6069 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 6070 | " // Check for shift+enter\n", 6071 | " if (event.shiftKey && event.which === 13) {\n", 6072 | " this.canvas_div.blur();\n", 6073 | " // select the cell after this one\n", 6074 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 6075 | " IPython.notebook.select(index + 1);\n", 6076 | " }\n", 6077 | "};\n", 6078 | "\n", 6079 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 6080 | " fig.ondownload(fig, null);\n", 6081 | "};\n", 6082 | "\n", 6083 | "mpl.find_output_cell = function (html_output) {\n", 6084 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 6085 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 6086 | " // IPython event is triggered only after the cells have been serialised, which for\n", 6087 | " // our purposes (turning an active figure into a static one), is too late.\n", 6088 | " var cells = IPython.notebook.get_cells();\n", 6089 | " var ncells = cells.length;\n", 6090 | " for (var i = 0; i < ncells; i++) {\n", 6091 | " var cell = cells[i];\n", 6092 | " if (cell.cell_type === 'code') {\n", 6093 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 6094 | " var data = cell.output_area.outputs[j];\n", 6095 | " if (data.data) {\n", 6096 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 6097 | " data = data.data;\n", 6098 | " }\n", 6099 | " if (data['text/html'] === html_output) {\n", 6100 | " return [cell, data, j];\n", 6101 | " }\n", 6102 | " }\n", 6103 | " }\n", 6104 | " }\n", 6105 | "};\n", 6106 | "\n", 6107 | "// Register the function which deals with the matplotlib target/channel.\n", 6108 | "// The kernel may be null if the page has been refreshed.\n", 6109 | "if (IPython.notebook.kernel !== null) {\n", 6110 | " IPython.notebook.kernel.comm_manager.register_target(\n", 6111 | " 'matplotlib',\n", 6112 | " mpl.mpl_figure_comm\n", 6113 | " );\n", 6114 | "}\n" 6115 | ], 6116 | "text/plain": [ 6117 | "" 6118 | ] 6119 | }, 6120 | "metadata": {}, 6121 | "output_type": "display_data" 6122 | }, 6123 | { 6124 | "data": { 6125 | "text/html": [ 6126 | "" 6127 | ], 6128 | "text/plain": [ 6129 | "" 6130 | ] 6131 | }, 6132 | "metadata": {}, 6133 | "output_type": "display_data" 6134 | } 6135 | ], 6136 | "source": [ 6137 | "fig,ax=plt.subplots(1,1,figsize=(9,9))\n", 6138 | "plt.xlim(0,L)\n", 6139 | "plt.ylim(0,H)\n", 6140 | "x=np.linspace(0,L,192)\n", 6141 | "y=np.linspace(0,H,192)\n", 6142 | "xx,yy=np.meshgrid(x,y)\n", 6143 | "\n", 6144 | "ani=FuncAnimation(fig,animate,count,interval=500, blit=True)\n", 6145 | "plt.show()" 6146 | ] 6147 | }, 6148 | { 6149 | "cell_type": "code", 6150 | "execution_count": null, 6151 | "id": "885cb88f", 6152 | "metadata": {}, 6153 | "outputs": [], 6154 | "source": [] 6155 | } 6156 | ], 6157 | "metadata": { 6158 | "kernelspec": { 6159 | "display_name": "Python 3 (ipykernel)", 6160 | "language": "python", 6161 | "name": "python3" 6162 | }, 6163 | "language_info": { 6164 | "codemirror_mode": { 6165 | "name": "ipython", 6166 | "version": 3 6167 | }, 6168 | "file_extension": ".py", 6169 | "mimetype": "text/x-python", 6170 | "name": "python", 6171 | "nbconvert_exporter": "python", 6172 | "pygments_lexer": "ipython3", 6173 | "version": "3.9.7" 6174 | } 6175 | }, 6176 | "nbformat": 4, 6177 | "nbformat_minor": 5 6178 | } 6179 | -------------------------------------------------------------------------------- /Mixed convection around confined square cylinder(Square array)-V5-Freestream.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "id": "1e584a55", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "text/html": [ 12 | "" 13 | ], 14 | "text/plain": [ 15 | "" 16 | ] 17 | }, 18 | "metadata": {}, 19 | "output_type": "display_data" 20 | } 21 | ], 22 | "source": [ 23 | "import numpy as np\n", 24 | "import matplotlib.pyplot as plt\n", 25 | "from matplotlib import cm\n", 26 | "import matplotlib.patches as patches\n", 27 | "import time\n", 28 | "from numba import jit,njit,prange\n", 29 | "from IPython.core.display import display, HTML\n", 30 | "from matplotlib.animation import FuncAnimation\n", 31 | "from tqdm.notebook import tqdm\n", 32 | "\n", 33 | "display(HTML(\"\"))" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "id": "2ae95a91", 39 | "metadata": {}, 40 | "source": [ 41 | "%matplotlib notebook" 42 | ] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "execution_count": 2, 47 | "id": "b91cad77", 48 | "metadata": {}, 49 | "outputs": [], 50 | "source": [ 51 | "@njit(fastmath=True)\n", 52 | "def extrapolate_4th(x1,x2,x3,x4):\n", 53 | " return 4*x1-6*x2+4*x3-x4" 54 | ] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "execution_count": 3, 59 | "id": "940d44a6", 60 | "metadata": {}, 61 | "outputs": [], 62 | "source": [ 63 | "@njit(fastmath=True)\n", 64 | "def extrapolate_3rd(x1,x2,x3):\n", 65 | " return 3*x1-3*x2+x3" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 4, 71 | "id": "d5aea529", 72 | "metadata": {}, 73 | "outputs": [], 74 | "source": [ 75 | "@njit(fastmath=True)\n", 76 | "def face_interpolate_4th(x1,x2,x3,x4):\n", 77 | " return (-x1+9*x2+9*x3-x4)/16" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 5, 83 | "id": "721f9c1c", 84 | "metadata": {}, 85 | "outputs": [], 86 | "source": [ 87 | "@njit(fastmath=True)\n", 88 | "def diff_interpolate_4th(x1,x2,x3,x4,h):\n", 89 | " #h denotes grid spacing\n", 90 | " return (x1-27*x2+27*x3-x4)/(24*h)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 6, 96 | "id": "44447f57", 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "@njit(fastmath=True)\n", 101 | "def p_face(Ny,Nx,square_coord,P):\n", 102 | " \n", 103 | " N=Nx*Ny\n", 104 | " n_squares=square_coord.shape[1] \n", 105 | " \n", 106 | " Pxf=np.zeros((Ny,Nx+1))\n", 107 | " Pyf=np.zeros((Ny+1,Nx))\n", 108 | " \n", 109 | " #horizontal faces\n", 110 | " for j in range(Ny):\n", 111 | " i=0\n", 112 | " \n", 113 | " PP=P[j][i]\n", 114 | " PE=P[j][i+1]\n", 115 | " Pxf[j][i]=1.5*PP-0.5*PE\n", 116 | " \n", 117 | " for i in range(1,Nx):\n", 118 | " PP=P[j][i-1]\n", 119 | " PE=P[j][i]\n", 120 | " Pxf[j][i]=0.5*(PP+PE)\n", 121 | " \n", 122 | " i=Nx\n", 123 | " \n", 124 | " PP=P[j][i-1]\n", 125 | " PW=P[j][i-2]\n", 126 | " Pxf[j][i]=1.5*PP-0.5*PW\n", 127 | "\n", 128 | " #vertical faces\n", 129 | " for i in range(Nx):\n", 130 | " j=0\n", 131 | " PP=P[j][i]\n", 132 | " PN=P[j+1][i]\n", 133 | " Pyf[j][i]=1.5*PP-0.5*PN\n", 134 | " \n", 135 | " j=Ny\n", 136 | " PP=P[j-1][i]\n", 137 | " PS=P[j-2][i]\n", 138 | " Pyf[j][i]=1.5*PP-0.5*PS\n", 139 | " \n", 140 | " for j in range(1,Ny):\n", 141 | " for i in range(Nx):\n", 142 | " PP=P[j-1][i]\n", 143 | " PN=P[j][i]\n", 144 | " Pyf[j][i]=0.5*(PP+PN)\n", 145 | " \n", 146 | " for n in range(n_squares):\n", 147 | " Nx_lft=int(square_coord[0][n])\n", 148 | " Nx_rgt=int(square_coord[1][n])\n", 149 | " Ny_top=int(square_coord[2][n])\n", 150 | " Ny_btm=int(square_coord[3][n])\n", 151 | " \n", 152 | " #left and right square\n", 153 | " for j in range(Ny_btm,Ny_top):\n", 154 | " i=Nx_lft\n", 155 | " \n", 156 | " PP=P[j][i-1]\n", 157 | " PW=P[j][i-2]\n", 158 | " \n", 159 | " Pxf[j][i]=1.5*PP-0.5*PW\n", 160 | " \n", 161 | " i=Nx_rgt\n", 162 | " \n", 163 | " PP=P[j][i]\n", 164 | " PE=P[j][i+1]\n", 165 | " \n", 166 | " Pxf[j][i]=1.5*PP-0.5*PE\n", 167 | " \n", 168 | " #top and bottom square\n", 169 | " for i in range(Nx_lft,Nx_rgt):\n", 170 | " j=Ny_btm\n", 171 | " \n", 172 | " PP=P[j-1][i]\n", 173 | " PS=P[j-2][i]\n", 174 | " \n", 175 | " Pyf[j][i]=1.5*PP-0.5*PS\n", 176 | " \n", 177 | " j=Ny_top\n", 178 | " PP=P[j][i]\n", 179 | " PN=P[j+1][i]\n", 180 | " \n", 181 | " Pyf[j][i]=1.5*PP-0.5*PN\n", 182 | " \n", 183 | " return Pxf,Pyf" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 7, 189 | "id": "f3ca562b", 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "@njit(fastmath=True)\n", 194 | "def face(Ny,Nx,square_coord,phi,inlet):\n", 195 | " \n", 196 | " N=Nx*Ny\n", 197 | " n_squares=square_coord.shape[1] \n", 198 | " \n", 199 | " phi_xf=np.zeros((Ny,Nx+1))\n", 200 | " phi_yf=np.zeros((Ny+1,Nx))\n", 201 | " \n", 202 | " #horizontal faces\n", 203 | " for j in range(Ny):\n", 204 | " i=0\n", 205 | " \n", 206 | " phi_xf[j][i]=inlet[j]\n", 207 | " \n", 208 | " for i in range(1,Nx):\n", 209 | " phi_P=phi[j][i-1]\n", 210 | " phi_E=phi[j][i]\n", 211 | " phi_xf[j][i]=0.5*(phi_P+phi_E)\n", 212 | " \n", 213 | " i=Nx\n", 214 | " \n", 215 | " phi_P=phi[j][i-1]\n", 216 | " phi_W=phi[j][i-2]\n", 217 | " phi_xf[j][i]=1.5*phi_P-0.5*phi_W\n", 218 | "\n", 219 | " #vertical faces\n", 220 | " for i in range(Nx):\n", 221 | " j=0\n", 222 | " phi_P=phi[j][i]\n", 223 | " phi_N=phi[j+1][i]\n", 224 | " phi_yf[j][i]=1.5*phi_P-0.5*phi_N\n", 225 | " \n", 226 | " j=Ny\n", 227 | " phi_P=phi[j-1][i]\n", 228 | " phi_S=phi[j-2][i]\n", 229 | " phi_yf[j][i]=1.5*phi_P-0.5*phi_S\n", 230 | " \n", 231 | " for j in range(1,Ny):\n", 232 | " for i in range(Nx):\n", 233 | " phi_P=phi[j-1][i]\n", 234 | " phi_N=phi[j][i]\n", 235 | " phi_yf[j][i]=0.5*(phi_P+phi_N)\n", 236 | " \n", 237 | " for n in range(n_squares):\n", 238 | " Nx_lft=int(square_coord[0][n])\n", 239 | " Nx_rgt=int(square_coord[1][n])\n", 240 | " Ny_top=int(square_coord[2][n])\n", 241 | " Ny_btm=int(square_coord[3][n])\n", 242 | " \n", 243 | " #left and right square\n", 244 | " for j in range(Ny_btm,Ny_top):\n", 245 | " i=Nx_lft\n", 246 | " \n", 247 | " phi_P=phi[j][i-1]\n", 248 | " phi_W=phi[j][i-2]\n", 249 | " \n", 250 | " phi_xf[j][i]=1.5*phi_P-0.5*phi_W\n", 251 | " \n", 252 | " i=Nx_rgt\n", 253 | " \n", 254 | " phi_P=phi[j][i]\n", 255 | " phi_E=phi[j][i+1]\n", 256 | " \n", 257 | " phi_xf[j][i]=1.5*phi_P-0.5*phi_E\n", 258 | " \n", 259 | " #top and bottom square\n", 260 | " for i in range(Nx_lft,Nx_rgt):\n", 261 | " j=Ny_btm\n", 262 | " \n", 263 | " phi_P=phi[j-1][i]\n", 264 | " phi_S=phi[j-2][i]\n", 265 | " \n", 266 | " phi_yf[j][i]=1.5*phi_P-0.5*phi_S\n", 267 | " \n", 268 | " j=Ny_top\n", 269 | " phi_P=phi[j][i]\n", 270 | " phi_N=phi[j+1][i]\n", 271 | " \n", 272 | " phi_yf[j][i]=1.5*phi_P-0.5*phi_N\n", 273 | " \n", 274 | " return phi_xf,phi_yf" 275 | ] 276 | }, 277 | { 278 | "cell_type": "code", 279 | "execution_count": 8, 280 | "id": "9e674950", 281 | "metadata": {}, 282 | "outputs": [], 283 | "source": [ 284 | "@njit(fastmath=True)\n", 285 | "def upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_e,phi_w,phi_n,phi_s,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 286 | " BP=0\n", 287 | " \n", 288 | " ue_abs=dy*(np.abs(ue)+ue)\n", 289 | " AP+=ue_abs\n", 290 | " BP-=ue_abs*(phi_e-phi_P)\n", 291 | " \n", 292 | " ue_abs=dy*(np.abs(ue)-ue)\n", 293 | " AE-=ue_abs\n", 294 | " BP+=ue_abs*(phi_e-phi_E)\n", 295 | "\n", 296 | " uw_abs=dy*(np.abs(uw)-uw)\n", 297 | " AP+=uw_abs\n", 298 | " BP-=uw_abs*(phi_w-phi_P)\n", 299 | "\n", 300 | " uw_abs=dy*(np.abs(uw)+uw)\n", 301 | " AW-=uw_abs\n", 302 | " BP+=uw_abs*(phi_w-phi_W)\n", 303 | " \n", 304 | " vn_abs=dx*(np.abs(vn)+vn)\n", 305 | " AP+=vn_abs\n", 306 | " BP-=vn_abs*(phi_n-phi_P)\n", 307 | " \n", 308 | " vn_abs=dx*(np.abs(vn)-vn)\n", 309 | " AN-=vn_abs\n", 310 | " BP+=vn_abs*(phi_n-phi_N)\n", 311 | " \n", 312 | " vs_abs=dx*(np.abs(vs)-vs)\n", 313 | " AP+=vs_abs\n", 314 | " BP-=vs_abs*(phi_s-phi_P)\n", 315 | " \n", 316 | " vs_abs=dx*(np.abs(vs)+vs)\n", 317 | " AS-=vs_abs\n", 318 | " BP+=vs_abs*(phi_s-phi_S)\n", 319 | " \n", 320 | " return AE,AW,AN,AS,AP,BP" 321 | ] 322 | }, 323 | { 324 | "cell_type": "code", 325 | "execution_count": 9, 326 | "id": "7f8d7355", 327 | "metadata": {}, 328 | "outputs": [], 329 | "source": [ 330 | "@njit \n", 331 | "def QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 332 | " BP=0\n", 333 | " phi_EE=extrapolate_3rd(phi_E,phi_P,phi_W)\n", 334 | " phi_WW=extrapolate_3rd(phi_W,phi_P,phi_E)\n", 335 | " phi_NN=extrapolate_3rd(phi_N,phi_P,phi_S)\n", 336 | " phi_SS=extrapolate_3rd(phi_S,phi_P,phi_N)\n", 337 | " \n", 338 | " AP+=dy*(abs(ue)+ue)\n", 339 | " BP-=(abs(ue)+ue)*dy*(3*phi_E-2*phi_P-phi_W)/8\n", 340 | " \n", 341 | " AE-=dy*(abs(ue)-ue)\n", 342 | " BP+=dy*(abs(ue)-ue)*(3*phi_P-2*phi_E-phi_EE)/8\n", 343 | "\n", 344 | " AP+=dy*(abs(uw)-uw)\n", 345 | " BP-=dy*(abs(uw)-uw)*(3*phi_W-2*phi_P-phi_E)/8\n", 346 | "\n", 347 | " AW-=dy*(abs(uw)+uw)\n", 348 | " BP+=dy*(abs(uw)+uw)*(3*phi_P-2*phi_W-phi_WW)/8\n", 349 | " \n", 350 | " AP+=dx*(abs(vn)+vn)\n", 351 | " BP-=dx*(abs(vn)+vn)*(3*phi_N-2*phi_P-phi_S)/8\n", 352 | " \n", 353 | " AN-=dx*(abs(vn)-vn)\n", 354 | " BP+=dx*(abs(vn)-vn)*(3*phi_P-2*phi_N-phi_NN)/8\n", 355 | " \n", 356 | " AP+=dx*(abs(vs)-vs)\n", 357 | " BP-=dx*(abs(vs)-vs)*(3*phi_S-2*phi_P-phi_N)/8\n", 358 | " \n", 359 | " AS-=dx*(abs(vs)+vs)\n", 360 | " BP+=dx*(abs(vs)+vs)*(3*phi_P-2*phi_S-phi_SS)/8\n", 361 | " \n", 362 | " return AE,AW,AN,AS,AP,BP" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 10, 368 | "id": "e7ef1c82", 369 | "metadata": {}, 370 | "outputs": [], 371 | "source": [ 372 | "@njit \n", 373 | "def QUICK3_limited(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 374 | " BP=0\n", 375 | " \n", 376 | " phi_EE=extrapolate_3rd(phi_E,phi_W,phi_P)\n", 377 | " phi_WW=extrapolate_3rd(phi_W,phi_P,phi_E)\n", 378 | " phi_NN=extrapolate_3rd(phi_N,phi_P,phi_S)\n", 379 | " phi_SS=extrapolate_3rd(phi_S,phi_P,phi_N)\n", 380 | " \n", 381 | " limit=limiter(phi_E,phi_P,phi_W)\n", 382 | " AP+=dy*(abs(ue)+ue)\n", 383 | " BP-=(abs(ue)+ue)*limit*dy*(3*phi_E-2*phi_P-phi_W)/8\n", 384 | " \n", 385 | " limit=limiter(phi_P,phi_E,phi_EE)\n", 386 | " AE-=dy*(abs(ue)-ue)\n", 387 | " BP+=dy*(abs(ue)-ue)*limit*(3*phi_P-2*phi_E-phi_EE)/8\n", 388 | "\n", 389 | " limit=limiter(phi_W,phi_P,phi_E)\n", 390 | " AP+=dy*(abs(uw)-uw)\n", 391 | " BP-=dy*(abs(uw)-uw)*limit*(3*phi_W-2*phi_P-phi_E)/8\n", 392 | "\n", 393 | " limit=limiter(phi_P,phi_W,phi_WW)\n", 394 | " AW-=dy*(abs(uw)+uw)\n", 395 | " BP+=dy*(abs(uw)+uw)*limit*(3*phi_P-2*phi_W-phi_WW)/8\n", 396 | " \n", 397 | " limit=limiter(phi_N,phi_P,phi_S)\n", 398 | " AP+=dx*(abs(vn)+vn)\n", 399 | " BP-=dx*(abs(vn)+vn)*limit*(3*phi_N-2*phi_P-phi_S)/8\n", 400 | " \n", 401 | " limit=limiter(phi_P,phi_N,phi_NN)\n", 402 | " AN-=dx*(abs(vn)-vn)\n", 403 | " BP+=dx*(abs(vn)-vn)*limit*(3*phi_P-2*phi_N-phi_NN)/8\n", 404 | " \n", 405 | " limit=limiter(phi_S,phi_P,phi_N)\n", 406 | " AP+=dx*(abs(vs)-vs)\n", 407 | " BP-=dx*(abs(vs)-vs)*limit*(3*phi_S-2*phi_P-phi_N)/8\n", 408 | " \n", 409 | " limit=limiter(phi_P,phi_S,phi_SS)\n", 410 | " AS-=dx*(abs(vs)+vs)\n", 411 | " BP+=dx*(abs(vs)+vs)*limit*(3*phi_P-2*phi_S-phi_SS)/8\n", 412 | " \n", 413 | " return AE,AW,AN,AS,AP,BP" 414 | ] 415 | }, 416 | { 417 | "cell_type": "code", 418 | "execution_count": 11, 419 | "id": "2ecd913e", 420 | "metadata": {}, 421 | "outputs": [], 422 | "source": [ 423 | "@njit(fastmath=True)\n", 424 | "def upwind_DC_limited(AE,AW,AN,AS,AP,ue,uw,vn,vs,phi_e,phi_w,phi_n,phi_s,phi_S,phi_W,phi_P,phi_E,phi_N,dy,dx,v):\n", 425 | " BP=0\n", 426 | " \n", 427 | " phi_EE=extrapolate_3rd(phi_E,phi_W,phi_P)\n", 428 | " phi_WW=extrapolate_3rd(phi_W,phi_P,phi_E)\n", 429 | " phi_NN=extrapolate_3rd(phi_N,phi_P,phi_S)\n", 430 | " phi_SS=extrapolate_3rd(phi_S,phi_P,phi_N)\n", 431 | " \n", 432 | " limit=limiter(phi_E,phi_P,phi_W)\n", 433 | " ue_abs=dy*(np.abs(ue)+ue)\n", 434 | " AP+=ue_abs\n", 435 | " BP-=ue_abs*limit*(phi_e-phi_P)\n", 436 | " \n", 437 | " limit=limiter(phi_P,phi_E,phi_EE)\n", 438 | " ue_abs=dy*(np.abs(ue)-ue)\n", 439 | " AE-=ue_abs\n", 440 | " BP+=ue_abs*limit*(phi_e-phi_E)\n", 441 | "\n", 442 | " limit=limiter(phi_W,phi_P,phi_E)\n", 443 | " uw_abs=dy*(np.abs(uw)-uw)\n", 444 | " AP+=uw_abs\n", 445 | " BP-=uw_abs*limit*(phi_w-phi_P)\n", 446 | "\n", 447 | " limit=limiter(phi_P,phi_W,phi_WW)\n", 448 | " uw_abs=dy*(np.abs(uw)+uw)\n", 449 | " AW-=uw_abs\n", 450 | " BP+=uw_abs*limit*(phi_w-phi_W)\n", 451 | " \n", 452 | " limit=limiter(phi_N,phi_P,phi_S)\n", 453 | " vn_abs=dx*(np.abs(vn)+vn)\n", 454 | " AP+=vn_abs\n", 455 | " BP-=vn_abs*limit*(phi_n-phi_P)\n", 456 | " \n", 457 | " limit=limiter(phi_P,phi_N,phi_NN)\n", 458 | " vn_abs=dx*(np.abs(vn)-vn)\n", 459 | " AN-=vn_abs\n", 460 | " BP+=vn_abs*limit*(phi_n-phi_N)\n", 461 | " \n", 462 | " limit=limiter(phi_S,phi_P,phi_N)\n", 463 | " vs_abs=dx*(np.abs(vs)-vs)\n", 464 | " AP+=vs_abs\n", 465 | " BP-=vs_abs*limit*(phi_s-phi_P)\n", 466 | " \n", 467 | " limit=limiter(phi_P,phi_S,phi_SS)\n", 468 | " vs_abs=dx*(np.abs(vs)+vs)\n", 469 | " AS-=vs_abs\n", 470 | " BP+=vs_abs*limit*(phi_s-phi_S)\n", 471 | " \n", 472 | " return AE,AW,AN,AS,AP,BP" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 12, 478 | "id": "9df8fef1", 479 | "metadata": {}, 480 | "outputs": [], 481 | "source": [ 482 | "@njit\n", 483 | "def limiter(phi_D,phi_U,phi_UU):\n", 484 | " r=((phi_U-phi_UU)/(phi_D-phi_U+1E-9))\n", 485 | " return (r+np.abs(r))/(1+np.abs(r)) #van leer" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 13, 491 | "id": "b7a9e6ed", 492 | "metadata": {}, 493 | "outputs": [], 494 | "source": [ 495 | "@njit\n", 496 | "def mom_coeff(Nx,Ny,square_coord,U,V,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx,uA,vA,DX,DY):\n", 497 | " N=Nx*Ny\n", 498 | " n_squares=square_coord.shape[1] \n", 499 | " #precomputing constant terms\n", 500 | " y=v*dx/dy\n", 501 | " x=v*dy/dx\n", 502 | " de=-2*x\n", 503 | " dw=-2*x\n", 504 | " dp=4*x+4*y\n", 505 | " dn=-2*y\n", 506 | " ds=-2*y\n", 507 | " \n", 508 | " gxgbdxdy=gx*gb*dx*dy\n", 509 | " gygbdxdy=gy*gb*dx*dy\n", 510 | " u_free=0\n", 511 | " \n", 512 | " a_s=0\n", 513 | " a_w=1\n", 514 | " a_p=2\n", 515 | " a_e=3\n", 516 | " a_n=4\n", 517 | " b_p=5\n", 518 | " \n", 519 | " #For interior cells\n", 520 | " for j in range(1,Ny-1):\n", 521 | " for i in range(1,Nx-1):\n", 522 | " \n", 523 | " TP=T[j][i]\n", 524 | " \n", 525 | " VS=V[j-1][i]\n", 526 | " VW=V[j][i-1]\n", 527 | " VP=V[j][i]\n", 528 | " VE=V[j][i+1]\n", 529 | " VN=V[j+1][i]\n", 530 | "\n", 531 | " US=U[j-1][i]\n", 532 | " UW=U[j][i-1]\n", 533 | " UP=U[j][i]\n", 534 | " UE=U[j][i+1]\n", 535 | " UN=U[j+1][i]\n", 536 | " \n", 537 | " ue=Uxf[j][i+1]\n", 538 | " uw=Uxf[j][i]\n", 539 | " un=Uyf[j+1][i]\n", 540 | " us=Uyf[j][i]\n", 541 | " \n", 542 | " ve=Vxf[j][i+1]\n", 543 | " vw=Vxf[j][i]\n", 544 | " vn=Vyf[j+1][i]\n", 545 | " vs=Vyf[j][i]\n", 546 | " \n", 547 | " pw=Pxf[j][i]\n", 548 | " pe=Pxf[j][i+1]\n", 549 | " ps=Pyf[j][i]\n", 550 | " pn=Pyf[j+1][i]\n", 551 | "\n", 552 | " AE=de\n", 553 | " AW=dw\n", 554 | " AN=dn\n", 555 | " AS=ds\n", 556 | " AP=dp\n", 557 | " \n", 558 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 559 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 560 | " \n", 561 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 562 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 563 | "\n", 564 | " Dx=dy/(uAP/av-uAE-uAW-uAN-uAS)\n", 565 | " Dy=dx/(vAP/av-vAE-vAW-vAN-vAS)\n", 566 | "\n", 567 | " uA[j][i][a_w]=uAW\n", 568 | " uA[j][i][a_p]=uAP\n", 569 | " uA[j][i][a_e]=uAE\n", 570 | " uA[j][i][a_n]=uAN\n", 571 | " uA[j][i][a_s]=uAS\n", 572 | " uA[j][i][b_p]=uBP\n", 573 | "\n", 574 | " vA[j][i][a_w]=vAW\n", 575 | " vA[j][i][a_p]=vAP\n", 576 | " vA[j][i][a_e]=vAE\n", 577 | " vA[j][i][a_n]=vAN\n", 578 | " vA[j][i][a_s]=vAS\n", 579 | " vA[j][i][b_p]=vBP\n", 580 | "\n", 581 | " DX[j][i]=Dx\n", 582 | " DY[j][i]=Dy\n", 583 | " \n", 584 | " #for bottom wall\n", 585 | " j=0\n", 586 | " for i in range(1,Nx-1):\n", 587 | " TP=T[j][i]\n", 588 | " \n", 589 | " UW=U[j][i-1]\n", 590 | " UP=U[j][i]\n", 591 | " UE=U[j][i+1]\n", 592 | " UN=U[j+1][i]\n", 593 | " UNN=U[j+2][i]\n", 594 | " US=extrapolate_3rd(UP,UN,UNN)\n", 595 | " \n", 596 | " VW=V[j][i-1]\n", 597 | " VP=V[j][i]\n", 598 | " VE=V[j][i+1]\n", 599 | " VN=V[j+1][i]\n", 600 | " VNN=V[j+2][i]\n", 601 | " VS=extrapolate_3rd(VP,VN,VNN)\n", 602 | "\n", 603 | " ue=Uxf[j][i+1]\n", 604 | " uw=Uxf[j][i]\n", 605 | " un=Uyf[j+1][i]\n", 606 | "\n", 607 | " ve=Vxf[j][i+1]\n", 608 | " vw=Vxf[j][i]\n", 609 | " vn=Vyf[j+1][i]\n", 610 | " \n", 611 | " pw=Pxf[j][i]\n", 612 | " pe=Pxf[j][i+1]\n", 613 | " ps=Pyf[j][i]\n", 614 | " pn=Pyf[j+1][i]\n", 615 | "\n", 616 | " AE=de\n", 617 | " AW=dw\n", 618 | " uAN=-2*y\n", 619 | " uAP=2*y+4*x\n", 620 | " vAN=-(8/3)*y\n", 621 | " vAP=8*y+4*x\n", 622 | "\n", 623 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,uAN,AS,uAP,ue,uw,vn,1E-12,US,UW,UP,UE,UN,dy,dx,v)\n", 624 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,vAN,AS,vAP,ue,uw,vn,1E-12,VS,VW,VP,VE,VN,dy,dx,v)\n", 625 | "\n", 626 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 627 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 628 | " Dx=dy/(uAP/av-uAE-uAW-uAN)\n", 629 | " Dy=dx/(vAP/av-vAE-vAW-vAN)\n", 630 | "\n", 631 | " uA[j][i][a_w]=uAW\n", 632 | " uA[j][i][a_p]=uAP\n", 633 | " uA[j][i][a_e]=uAE\n", 634 | " uA[j][i][a_n]=uAN\n", 635 | " uA[j][i][b_p]=uBP\n", 636 | "\n", 637 | " vA[j][i][a_w]=vAW\n", 638 | " vA[j][i][a_p]=vAP\n", 639 | " vA[j][i][a_e]=vAE\n", 640 | " vA[j][i][a_n]=vAN\n", 641 | " vA[j][i][b_p]=vBP\n", 642 | "\n", 643 | " DX[j][i]=Dx\n", 644 | " DY[j][i]=Dy\n", 645 | "\n", 646 | " #for top wall\n", 647 | " j=Ny-1\n", 648 | " for i in range(1,Nx-1):\n", 649 | " TP=T[j][i]\n", 650 | " \n", 651 | " US=U[j-1][i]\n", 652 | " USS=U[j-2][i]\n", 653 | " UW=U[j][i-1]\n", 654 | " UP=U[j][i]\n", 655 | " UE=U[j][i+1]\n", 656 | " UN=extrapolate_3rd(UP,US,USS)\n", 657 | " \n", 658 | " VS=V[j-1][i]\n", 659 | " VSS=V[j-2][i]\n", 660 | " VW=V[j][i-1]\n", 661 | " VP=V[j][i]\n", 662 | " VE=V[j][i+1]\n", 663 | " VN=extrapolate_3rd(VP,VS,VSS)\n", 664 | " \n", 665 | " ue=Uxf[j][i+1]\n", 666 | " uw=Uxf[j][i]\n", 667 | " us=Uyf[j][i]\n", 668 | "\n", 669 | " ve=Vxf[j][i+1]\n", 670 | " vw=Vxf[j][i]\n", 671 | " vs=Vyf[j][i]\n", 672 | " \n", 673 | " pw=Pxf[j][i]\n", 674 | " pe=Pxf[j][i+1]\n", 675 | " ps=Pyf[j][i]\n", 676 | " pn=Pyf[j+1][i]\n", 677 | "\n", 678 | " AE=de\n", 679 | " AW=dw\n", 680 | " uAS=-2*y\n", 681 | " uAP=2*y+4*x\n", 682 | " vAS=-(8/3)*y\n", 683 | " vAP=8*y+4*x\n", 684 | " \n", 685 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,uAS,uAP,ue,uw,1E-12,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 686 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,vAS,vAP,ue,uw,1E-12,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 687 | " \n", 688 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 689 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP)) \n", 690 | " Dx=dy/(uAP/av-uAE-uAW-uAS)\n", 691 | " Dy=dx/(vAP/av-vAE-vAW-vAS)\n", 692 | "\n", 693 | " uA[j][i][a_w]=uAW\n", 694 | " uA[j][i][a_p]=uAP\n", 695 | " uA[j][i][a_e]=uAE\n", 696 | " uA[j][i][a_s]=uAS\n", 697 | " uA[j][i][b_p]=uBP\n", 698 | "\n", 699 | " vA[j][i][a_w]=vAW\n", 700 | " vA[j][i][a_p]=vAP\n", 701 | " vA[j][i][a_e]=vAE\n", 702 | " vA[j][i][a_s]=vAS\n", 703 | " vA[j][i][b_p]=vBP\n", 704 | "\n", 705 | " DX[j][i]=Dx\n", 706 | " DY[j][i]=Dy\n", 707 | "\n", 708 | " #for inlet:\n", 709 | " i=0\n", 710 | " for j in range(1,Ny-1):\n", 711 | " TP=T[j][i]\n", 712 | " \n", 713 | " VN=V[j+1][i]\n", 714 | " VE=V[j][i+1]\n", 715 | " VEE=V[j][i+2]\n", 716 | " VP=V[j][i]\n", 717 | " VS=V[j-1][i]\n", 718 | " VW=extrapolate_3rd(VP,VE,VEE)\n", 719 | "\n", 720 | " UP=U[j][i]\n", 721 | " UE=U[j][i+1]\n", 722 | " UEE=U[j][i+1]\n", 723 | " UN=U[j+1][i]\n", 724 | " US=U[j-1][i]\n", 725 | " UW=extrapolate_3rd(UP,UE,UEE)\n", 726 | "\n", 727 | " u_in=Uxf[j][i]\n", 728 | " ue=Uxf[j][i+1]\n", 729 | " un=Uyf[j+1][i]\n", 730 | " us=Uyf[j][i]\n", 731 | "\n", 732 | " ve=Vxf[j][i+1]\n", 733 | " vn=Vyf[j+1][i]\n", 734 | " vs=Vyf[j][i]\n", 735 | " \n", 736 | " pw=Pxf[j][i]\n", 737 | " pe=Pxf[j][i+1]\n", 738 | " ps=Pyf[j][i]\n", 739 | " pn=Pyf[j+1][i]\n", 740 | "\n", 741 | " AE=-(8/3)*x\n", 742 | " AN=dn\n", 743 | " AS=ds\n", 744 | " AP=8*x+4*y\n", 745 | "\n", 746 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 747 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 748 | "\n", 749 | " uBP+=2*(dy*(pw-pe)+(dy*u_in**2)+((8/3)*x*u_in)+(gxgbdxdy*TP))\n", 750 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 751 | " Dx=dy/(uAP/av-uAE-uAN-uAS)\n", 752 | " Dy=dx/(vAP/av-vAE-vAN-vAS)\n", 753 | "\n", 754 | " uA[j][i][a_p]=uAP\n", 755 | " uA[j][i][a_e]=uAE\n", 756 | " uA[j][i][a_n]=uAN\n", 757 | " uA[j][i][a_s]=uAS\n", 758 | " uA[j][i][b_p]=uBP\n", 759 | "\n", 760 | " vA[j][i][a_p]=vAP\n", 761 | " vA[j][i][a_e]=vAE\n", 762 | " vA[j][i][a_n]=vAN\n", 763 | " vA[j][i][a_s]=vAS\n", 764 | " vA[j][i][b_p]=vBP\n", 765 | "\n", 766 | " DX[j][i]=Dx\n", 767 | " DY[j][i]=Dy\n", 768 | "\n", 769 | " #for right wall (outlet):\n", 770 | " i=Nx-1\n", 771 | " for j in range(1,Ny-1):\n", 772 | " TP=T[j][i]\n", 773 | " \n", 774 | " VN=V[j+1][i]\n", 775 | " VP=V[j][i]\n", 776 | " VS=V[j-1][i]\n", 777 | " VW=V[j][i-1]\n", 778 | " VWW=V[j][i-2]\n", 779 | " VE=extrapolate_3rd(VP,VW,VWW)\n", 780 | "\n", 781 | " UP=U[j][i]\n", 782 | " UW=U[j][i-1]\n", 783 | " UWW=U[j][i-2]\n", 784 | " UN=U[j+1][i]\n", 785 | " US=U[j-1][i]\n", 786 | " UE=extrapolate_3rd(UP,UW,UWW)\n", 787 | " \n", 788 | " ue=Uxf[j][i+1]\n", 789 | " uw=Uxf[j][i]\n", 790 | " un=Uyf[j+1][i]\n", 791 | " us=Uyf[j][i]\n", 792 | "\n", 793 | " ve=Vxf[j][i+1]\n", 794 | " vw=Vxf[j][i]\n", 795 | " vn=Vyf[j+1][i]\n", 796 | " vs=Vyf[j][i]\n", 797 | " \n", 798 | " pw=Pxf[j][i]\n", 799 | " pe=Pxf[j][i+1]\n", 800 | " ps=Pyf[j][i]\n", 801 | " pn=Pyf[j+1][i]\n", 802 | "\n", 803 | " AW=dw\n", 804 | " AN=dn\n", 805 | " AS=ds\n", 806 | " AP=2*x+4*y+2*dy*ue\n", 807 | "\n", 808 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 809 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 810 | "\n", 811 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 812 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 813 | " Dx=dy/(uAP/av-uAW-uAN-uAS)\n", 814 | " Dy=dx/(vAP/av-vAW-vAN-vAS)\n", 815 | "\n", 816 | " uA[j][i][a_w]=uAW\n", 817 | " uA[j][i][a_p]=uAP\n", 818 | " uA[j][i][a_n]=uAN\n", 819 | " uA[j][i][a_s]=uAS\n", 820 | " uA[j][i][b_p]=uBP\n", 821 | "\n", 822 | " vA[j][i][a_w]=vAW\n", 823 | " vA[j][i][a_p]=vAP\n", 824 | " vA[j][i][a_n]=vAN\n", 825 | " vA[j][i][a_s]=vAS\n", 826 | " vA[j][i][b_p]=vBP\n", 827 | "\n", 828 | " DX[j][i]=Dx\n", 829 | " DY[j][i]=Dy\n", 830 | "\n", 831 | " #top right cell\n", 832 | " i=Nx-1\n", 833 | " j=Ny-1\n", 834 | " \n", 835 | " TP=T[j][i]\n", 836 | " \n", 837 | " UP=U[j][i]\n", 838 | " UW=U[j][i-1]\n", 839 | " UWW=U[j][i-2]\n", 840 | " US=U[j-1][i]\n", 841 | " USS=U[j-2][i]\n", 842 | " UE=extrapolate_3rd(UP,UW,UWW)\n", 843 | " UN=extrapolate_3rd(UP,US,USS)\n", 844 | " \n", 845 | " VS=V[j-1][i]\n", 846 | " VSS=V[j-2][i]\n", 847 | " VW=V[j][i-1]\n", 848 | " VWW=V[j][i-2]\n", 849 | " VP=V[j][i]\n", 850 | " VE=extrapolate_3rd(VP,VW,VWW)\n", 851 | " VN=extrapolate_3rd(VP,VS,VSS)\n", 852 | " \n", 853 | " ue=Uxf[j][i+1]\n", 854 | " uw=Uxf[j][i]\n", 855 | " us=Uyf[j][i]\n", 856 | "\n", 857 | " vw=Vxf[j][i]\n", 858 | " vs=Vyf[j][i]\n", 859 | " \n", 860 | " pw=Pxf[j][i]\n", 861 | " pe=Pxf[j][i+1]\n", 862 | " ps=Pyf[j][i]\n", 863 | " pn=Pyf[j+1][i]\n", 864 | " \n", 865 | " AW=dw\n", 866 | " uAS=-2*y\n", 867 | " uAP=2*x+2*y+2*dy*ue\n", 868 | " vAS=-(8/3)*y\n", 869 | " vAP=2*x+8*y+2*dy*ue\n", 870 | " \n", 871 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,uAS,uAP,1E-12,uw,1E-12,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 872 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,vAS,vAP,1E-12,uw,1E-12,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 873 | " \n", 874 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 875 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 876 | " Dx=dy/(uAP/av-uAW-uAS)\n", 877 | " Dy=dx/(vAP/av-vAW-vAS)\n", 878 | "\n", 879 | " uA[j][i][a_w]=uAW\n", 880 | " uA[j][i][a_p]=uAP\n", 881 | " uA[j][i][a_s]=uAS\n", 882 | " uA[j][i][b_p]=uBP\n", 883 | "\n", 884 | " vA[j][i][a_w]=vAW\n", 885 | " vA[j][i][a_p]=vAP\n", 886 | " vA[j][i][a_s]=vAS\n", 887 | " vA[j][i][b_p]=vBP\n", 888 | "\n", 889 | " DX[j][i]=Dx\n", 890 | " DY[j][i]=Dy\n", 891 | " \n", 892 | " #top left cell\n", 893 | " i=0\n", 894 | " j=Ny-1\n", 895 | " \n", 896 | " TP=T[j][i]\n", 897 | " \n", 898 | " UP=U[j][i]\n", 899 | " UE=U[j][i+1]\n", 900 | " UEE=U[j][i+2]\n", 901 | " US=U[j-1][i]\n", 902 | " USS=U[j-2][i]\n", 903 | " UW=extrapolate_3rd(UP,UE,UEE)\n", 904 | " UN=extrapolate_3rd(UP,US,USS)\n", 905 | " \n", 906 | " VS=V[j-1][i]\n", 907 | " VSS=V[j-2][i]\n", 908 | " VE=V[j][i+1]\n", 909 | " VEE=V[j][i+2]\n", 910 | " VP=V[j][i]\n", 911 | " VW=extrapolate_3rd(VP,VE,VEE)\n", 912 | " VN=extrapolate_3rd(VP,VS,VSS)\n", 913 | "\n", 914 | " u_in=Uxf[j][i]\n", 915 | " ue=Uxf[j][i+1]\n", 916 | " us=Uyf[j][i]\n", 917 | "\n", 918 | " ve=Vxf[j][i+1]\n", 919 | " vs=Vyf[j][i]\n", 920 | " \n", 921 | " pw=Pxf[j][i]\n", 922 | " pe=Pxf[j][i+1]\n", 923 | " ps=Pyf[j][i]\n", 924 | " pn=Pyf[j+1][i]\n", 925 | " \n", 926 | " AE=-(8/3)*x\n", 927 | " uAS=-2*y\n", 928 | " uAP=8*x+2*y\n", 929 | " vAS=-(8/3)*y\n", 930 | " vAP=8*x+8*y\n", 931 | " \n", 932 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,uAS,uAP,ue,1E-12,1E-12,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 933 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,vAS,vAP,ue,1E-12,1E-12,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 934 | " \n", 935 | " uBP+=2*(dy*(pw-pe)+(dy*u_in**2)+((8/3)*x*u_in)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 936 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 937 | " Dx=dy/(uAP/av-uAE-uAS)\n", 938 | " Dy=dx/(vAP/av-vAE-vAS)\n", 939 | " \n", 940 | " uA[j][i][a_p]=uAP\n", 941 | " uA[j][i][a_e]=uAE\n", 942 | " uA[j][i][a_s]=uAS\n", 943 | " uA[j][i][b_p]=uBP\n", 944 | "\n", 945 | " vA[j][i][a_p]=vAP\n", 946 | " vA[j][i][a_e]=vAE\n", 947 | " vA[j][i][a_s]=vAS\n", 948 | " vA[j][i][b_p]=vBP\n", 949 | "\n", 950 | " DX[j][i]=Dx\n", 951 | " DY[j][i]=Dy\n", 952 | "\n", 953 | " #bottom right cell\n", 954 | " i=Nx-1\n", 955 | " j=0\n", 956 | " \n", 957 | " TP=T[j][i]\n", 958 | " \n", 959 | " UW=U[j][i-1]\n", 960 | " UWW=U[j][i-2]\n", 961 | " UP=U[j][i]\n", 962 | " UN=U[j+1][i]\n", 963 | " UNN=U[j+2][i]\n", 964 | " UE=extrapolate_3rd(UP,UW,UWW)\n", 965 | " US=extrapolate_3rd(UP,UN,UNN)\n", 966 | "\n", 967 | " VP=V[j][i]\n", 968 | " VW=V[j][i-1]\n", 969 | " VWW=V[j][i-2]\n", 970 | " VN=V[j+1][i]\n", 971 | " VNN=V[j+2][i]\n", 972 | " VE=extrapolate_3rd(VP,VW,VWW)\n", 973 | " VS=extrapolate_3rd(VP,VN,VNN)\n", 974 | "\n", 975 | " ue=Uxf[j][i+1]\n", 976 | " uw=Uxf[j][i]\n", 977 | " un=Uyf[j+1][i]\n", 978 | "\n", 979 | " vw=Vxf[j][i]\n", 980 | " vn=Vyf[j+1][i]\n", 981 | " \n", 982 | " pw=Pxf[j][i]\n", 983 | " pe=Pxf[j][i+1]\n", 984 | " ps=Pyf[j][i]\n", 985 | " pn=Pyf[j+1][i]\n", 986 | " \n", 987 | " AW=dw\n", 988 | " uAN=-2*y\n", 989 | " uAP=2*x+2*y+2*dy*ue\n", 990 | " vAN=-(8/3)*y\n", 991 | " vAP=2*x+8*y+2*dy*ue\n", 992 | "\n", 993 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,uAN,AS,uAP,1E-12,uw,vn,1E-12,US,UW,UP,UE,UN,dy,dx,v)\n", 994 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,vAN,AS,vAP,1E-12,uw,vn,1E-12,VS,VW,VP,VE,VN,dy,dx,v)\n", 995 | " \n", 996 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 997 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 998 | " Dx=dy/(uAP/av-uAW-uAN)\n", 999 | " Dy=dx/(vAP/av-vAW-vAN)\n", 1000 | " \n", 1001 | " uA[j][i][a_w]=uAW\n", 1002 | " uA[j][i][a_p]=uAP\n", 1003 | " uA[j][i][a_n]=uAN\n", 1004 | " uA[j][i][b_p]=uBP\n", 1005 | "\n", 1006 | " vA[j][i][a_w]=vAW\n", 1007 | " vA[j][i][a_p]=vAP\n", 1008 | " vA[j][i][a_n]=vAN\n", 1009 | " vA[j][i][b_p]=vBP\n", 1010 | "\n", 1011 | " DX[j][i]=Dx\n", 1012 | " DY[j][i]=Dy\n", 1013 | " \n", 1014 | " #bottom left cell\n", 1015 | " i=0\n", 1016 | " j=0\n", 1017 | " \n", 1018 | " TP=T[j][i]\n", 1019 | " \n", 1020 | " UP=U[j][i]\n", 1021 | " UE=U[j][i+1]\n", 1022 | " UEE=U[j][i+2]\n", 1023 | " UN=U[j+1][i]\n", 1024 | " UNN=U[j+2][i]\n", 1025 | " UW=extrapolate_3rd(UP,UE,UEE)\n", 1026 | " US=extrapolate_3rd(UP,UN,UNN)\n", 1027 | "\n", 1028 | " VP=V[j][i]\n", 1029 | " VE=V[j][i+1]\n", 1030 | " VEE=V[j][i+2]\n", 1031 | " VN=V[j+1][i]\n", 1032 | " VNN=V[j+2][i]\n", 1033 | " VW=extrapolate_3rd(VP,VE,VEE)\n", 1034 | " VS=extrapolate_3rd(VP,VN,VNN)\n", 1035 | "\n", 1036 | " u_in=Uxf[j][i]\n", 1037 | " ue=Uxf[j][i+1]\n", 1038 | " un=Uyf[j+1][i]\n", 1039 | "\n", 1040 | " ve=Vxf[j][i+1]\n", 1041 | " vn=Vyf[j+1][i]\n", 1042 | " \n", 1043 | " pw=Pxf[j][i]\n", 1044 | " pe=Pxf[j][i+1]\n", 1045 | " ps=Pyf[j][i]\n", 1046 | " pn=Pyf[j+1][i]\n", 1047 | " \n", 1048 | " AE=-(8/3)*x\n", 1049 | " uAN=-2*y\n", 1050 | " uAP=8*x+2*y\n", 1051 | " vAN=-(8/3)*y\n", 1052 | " vAP=8*x+8*y\n", 1053 | "\n", 1054 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,uAN,AS,uAP,ue,1E-12,vn,1E-12,US,UW,UP,UE,UN,dy,dx,v)\n", 1055 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,vAN,AS,vAP,ue,1E-12,vn,1E-12,VS,VW,VP,VE,VN,dy,dx,v)\n", 1056 | " \n", 1057 | " uBP+=2*(dy*(pw-pe)+(dy*u_in**2)+((8/3)*x*u_in)+(gxgbdxdy*TP))+(16/3)*y*u_free\n", 1058 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 1059 | " Dx=dy/(uAP/av-uAE-uAN)\n", 1060 | " Dy=dx/(vAP/av-vAE-vAN)\n", 1061 | "\n", 1062 | " uA[j][i][a_e]=uAE\n", 1063 | " uA[j][i][a_p]=uAP\n", 1064 | " uA[j][i][a_n]=uAN\n", 1065 | " uA[j][i][b_p]=uBP\n", 1066 | "\n", 1067 | " vA[j][i][a_e]=vAE\n", 1068 | " vA[j][i][a_p]=vAP\n", 1069 | " vA[j][i][a_n]=vAN\n", 1070 | " vA[j][i][b_p]=vBP\n", 1071 | "\n", 1072 | " DX[j][i]=Dx\n", 1073 | " DY[j][i]=Dy\n", 1074 | " \n", 1075 | " for n in range(n_squares):\n", 1076 | " Nx_lft=int(square_coord[0][n])\n", 1077 | " Nx_rgt=int(square_coord[1][n])\n", 1078 | " Ny_top=int(square_coord[2][n])\n", 1079 | " Ny_btm=int(square_coord[3][n])\n", 1080 | " \n", 1081 | " #left square\n", 1082 | " i=Nx_lft-1\n", 1083 | " for j in range(Ny_btm,Ny_top):\n", 1084 | " TP=T[j][i]\n", 1085 | " \n", 1086 | " UP=U[j][i]\n", 1087 | " UN=U[j+1][i]\n", 1088 | " US=U[j-1][i]\n", 1089 | " UW=U[j][i-1]\n", 1090 | " UWW=U[j][i-2]\n", 1091 | " UE=extrapolate_3rd(UP,UW,UWW)\n", 1092 | "\n", 1093 | " VP=V[j][i]\n", 1094 | " VS=V[j-1][i]\n", 1095 | " VN=V[j+1][i]\n", 1096 | " VW=V[j][i-1]\n", 1097 | " VWW=V[j][i-2]\n", 1098 | " VE=extrapolate_3rd(VP,VW,VWW)\n", 1099 | " \n", 1100 | " uw=Uxf[j][i]\n", 1101 | " un=Uyf[j+1][i]\n", 1102 | " us=Uyf[j][i]\n", 1103 | "\n", 1104 | " vw=Vxf[j][i]\n", 1105 | " vn=Vyf[j+1][i]\n", 1106 | " vs=Vyf[j][i]\n", 1107 | " \n", 1108 | " pw=Pxf[j][i]\n", 1109 | " pe=Pxf[j][i+1]\n", 1110 | " ps=Pyf[j][i]\n", 1111 | " pn=Pyf[j+1][i]\n", 1112 | "\n", 1113 | " AW=-(8/3)*x\n", 1114 | " AN=dn\n", 1115 | " AS=ds\n", 1116 | " AP=8*x+4*y\n", 1117 | " \n", 1118 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 1119 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 1120 | "\n", 1121 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 1122 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 1123 | " \n", 1124 | " Dx=dy/(uAP/av-uAW-uAN-uAS)\n", 1125 | " Dy=dx/(vAP/av-vAW-vAN-vAS)\n", 1126 | "\n", 1127 | " uA[j][i][a_w]=uAW\n", 1128 | " uA[j][i][a_p]=uAP\n", 1129 | " uA[j][i][a_e]=0\n", 1130 | " uA[j][i][a_n]=uAN\n", 1131 | " uA[j][i][a_s]=uAS\n", 1132 | " uA[j][i][b_p]=uBP\n", 1133 | "\n", 1134 | " vA[j][i][a_w]=vAW\n", 1135 | " vA[j][i][a_p]=vAP\n", 1136 | " vA[j][i][a_e]=0\n", 1137 | " vA[j][i][a_n]=vAN\n", 1138 | " vA[j][i][a_s]=vAS\n", 1139 | " vA[j][i][b_p]=vBP\n", 1140 | "\n", 1141 | " DX[j][i]=Dx\n", 1142 | " DY[j][i]=Dy\n", 1143 | "\n", 1144 | " #right square\n", 1145 | " i=Nx_rgt\n", 1146 | " for j in range(Ny_btm,Ny_top):\n", 1147 | " TP=T[j][i]\n", 1148 | "\n", 1149 | " UP=U[j][i]\n", 1150 | " UE=U[j][i+1]\n", 1151 | " UEE=U[j][i+2]\n", 1152 | " US=U[j-1][i]\n", 1153 | " UN=U[j+1][i]\n", 1154 | " UW=extrapolate_3rd(UP,UE,UEE)\n", 1155 | "\n", 1156 | " VP=V[j][i]\n", 1157 | " VE=V[j][i+1]\n", 1158 | " VEE=V[j][i+2]\n", 1159 | " VS=V[j-1][i]\n", 1160 | " VN=V[j+1][i]\n", 1161 | " VW=extrapolate_3rd(VP,VE,VEE)\n", 1162 | "\n", 1163 | " ue=Uxf[j][i+1]\n", 1164 | " un=Uyf[j+1][i]\n", 1165 | " us=Uyf[j][i]\n", 1166 | "\n", 1167 | " ve=Vxf[j][i+1]\n", 1168 | " vn=Vyf[j+1][i]\n", 1169 | " vs=Vyf[j][i]\n", 1170 | " \n", 1171 | " pw=Pxf[j][i]\n", 1172 | " pe=Pxf[j][i+1]\n", 1173 | " ps=Pyf[j][i]\n", 1174 | " pn=Pyf[j+1][i]\n", 1175 | "\n", 1176 | " AE=-(8/3)*x\n", 1177 | " AN=dn\n", 1178 | " AS=ds\n", 1179 | " AP=8*x+4*y\n", 1180 | "\n", 1181 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 1182 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 1183 | "\n", 1184 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 1185 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 1186 | "\n", 1187 | " Dx=dy/(uAP/av-uAE-uAN-uAS)\n", 1188 | " Dy=dx/(vAP/av-vAE-vAN-vAS)\n", 1189 | "\n", 1190 | " uA[j][i][a_w]=0\n", 1191 | " uA[j][i][a_p]=uAP\n", 1192 | " uA[j][i][a_e]=uAE\n", 1193 | " uA[j][i][a_n]=uAN\n", 1194 | " uA[j][i][a_s]=uAS\n", 1195 | " uA[j][i][b_p]=uBP\n", 1196 | "\n", 1197 | " vA[j][i][a_w]=0\n", 1198 | " vA[j][i][a_p]=vAP\n", 1199 | " vA[j][i][a_e]=vAE\n", 1200 | " vA[j][i][a_n]=vAN\n", 1201 | " vA[j][i][a_s]=vAS\n", 1202 | " vA[j][i][b_p]=vBP\n", 1203 | "\n", 1204 | " DX[j][i]=Dx\n", 1205 | " DY[j][i]=Dy\n", 1206 | "\n", 1207 | " #top square\n", 1208 | " j=Ny_top\n", 1209 | " for i in range(Nx_lft,Nx_rgt):\n", 1210 | " TP=T[j][i]\n", 1211 | " \n", 1212 | " UN=U[j+1][i]\n", 1213 | " UNN=U[j+2][i]\n", 1214 | " UE=U[j][i+1]\n", 1215 | " UP=U[j][i]\n", 1216 | " UW=U[j][i-1]\n", 1217 | " US=extrapolate_3rd(UP,UN,UNN)\n", 1218 | "\n", 1219 | " VN=V[j+1][i]\n", 1220 | " VNN=V[j+2][i]\n", 1221 | " VE=V[j][i+1]\n", 1222 | " VP=V[j][i]\n", 1223 | " VW=V[j][i-1]\n", 1224 | " VS=extrapolate_3rd(VP,VN,VNN)\n", 1225 | "\n", 1226 | " ue=Uxf[j][i+1]\n", 1227 | " uw=Uxf[j][i]\n", 1228 | " un=Uyf[j+1][i]\n", 1229 | "\n", 1230 | " ve=Vxf[j][i+1]\n", 1231 | " vw=Vxf[j][i]\n", 1232 | " vn=Vyf[j+1][i]\n", 1233 | " \n", 1234 | " pw=Pxf[j][i]\n", 1235 | " pe=Pxf[j][i+1]\n", 1236 | " ps=Pyf[j][i]\n", 1237 | " pn=Pyf[j+1][i]\n", 1238 | "\n", 1239 | " AW=dw\n", 1240 | " AE=de\n", 1241 | " AN=-(8/3)*y\n", 1242 | " AP=4*x+8*y\n", 1243 | "\n", 1244 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,US,UW,UP,UE,UN,dy,dx,v)\n", 1245 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,VS,VW,VP,VE,VN,dy,dx,v)\n", 1246 | "\n", 1247 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 1248 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 1249 | " \n", 1250 | " Dx=dy/(uAP/av-uAW-uAE-uAN)\n", 1251 | " Dy=dx/(vAP/av-vAW-vAE-vAN)\n", 1252 | "\n", 1253 | " uA[j][i][a_w]=uAW\n", 1254 | " uA[j][i][a_p]=uAP\n", 1255 | " uA[j][i][a_e]=uAE\n", 1256 | " uA[j][i][a_n]=uAN\n", 1257 | " uA[j][i][a_s]=0\n", 1258 | " uA[j][i][b_p]=uBP\n", 1259 | "\n", 1260 | " vA[j][i][a_w]=vAW\n", 1261 | " vA[j][i][a_p]=vAP\n", 1262 | " vA[j][i][a_e]=vAE\n", 1263 | " vA[j][i][a_n]=vAN\n", 1264 | " vA[j][i][a_s]=0\n", 1265 | " vA[j][i][b_p]=vBP\n", 1266 | "\n", 1267 | " DX[j][i]=Dx\n", 1268 | " DY[j][i]=Dy\n", 1269 | "\n", 1270 | " #bottom square\n", 1271 | " j=Ny_btm-1\n", 1272 | " for i in range(Nx_lft,Nx_rgt):\n", 1273 | " TP=T[j][i]\n", 1274 | "\n", 1275 | " UE=U[j][i+1]\n", 1276 | " UP=U[j][i]\n", 1277 | " UW=U[j][i-1]\n", 1278 | " US=U[j-1][i]\n", 1279 | " USS=U[j-2][i]\n", 1280 | " UN=extrapolate_3rd(UP,US,USS)\n", 1281 | " \n", 1282 | " VE=V[j][i+1]\n", 1283 | " VP=V[j][i]\n", 1284 | " VW=V[j][i-1]\n", 1285 | " VS=V[j-1][i]\n", 1286 | " VSS=V[j-2][i]\n", 1287 | " VN=extrapolate_3rd(VP,VS,VSS)\n", 1288 | "\n", 1289 | " ue=Uxf[j][i+1]\n", 1290 | " uw=Uxf[j][i]\n", 1291 | " us=Uyf[j][i]\n", 1292 | "\n", 1293 | " ve=Vxf[j][i+1]\n", 1294 | " vw=Vxf[j][i]\n", 1295 | " vs=Vyf[j][i]\n", 1296 | " \n", 1297 | " pw=Pxf[j][i]\n", 1298 | " pe=Pxf[j][i+1]\n", 1299 | " ps=Pyf[j][i]\n", 1300 | " pn=Pyf[j+1][i]\n", 1301 | "\n", 1302 | " AW=dw\n", 1303 | " AE=de\n", 1304 | " AS=-(8/3)*y\n", 1305 | " AP=4*x+8*y\n", 1306 | "\n", 1307 | " uAE,uAW,uAN,uAS,uAP,uBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,US,UW,UP,UE,UN,dy,dx,v)\n", 1308 | " vAE,vAW,vAN,vAS,vAP,vBP=QUICK3(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,VS,VW,VP,VE,VN,dy,dx,v)\n", 1309 | "\n", 1310 | " uBP+=2*(dy*(pw-pe)+(gxgbdxdy*TP))\n", 1311 | " vBP+=2*(dx*(ps-pn)+(gygbdxdy*TP))\n", 1312 | "\n", 1313 | " Dx=dy/(uAP/av-uAW-uAE-uAS)\n", 1314 | " Dy=dx/(vAP/av-vAW-vAE-vAS)\n", 1315 | "\n", 1316 | " uA[j][i][a_w]=uAW\n", 1317 | " uA[j][i][a_p]=uAP\n", 1318 | " uA[j][i][a_e]=uAE\n", 1319 | " uA[j][i][a_n]=0\n", 1320 | " uA[j][i][a_s]=uAS\n", 1321 | " uA[j][i][b_p]=uBP\n", 1322 | "\n", 1323 | " vA[j][i][a_w]=vAW\n", 1324 | " vA[j][i][a_p]=vAP\n", 1325 | " vA[j][i][a_e]=vAE\n", 1326 | " vA[j][i][a_n]=0\n", 1327 | " vA[j][i][a_s]=vAS\n", 1328 | " vA[j][i][b_p]=vBP\n", 1329 | "\n", 1330 | " DX[j][i]=Dx\n", 1331 | " DY[j][i]=Dy\n", 1332 | "\n", 1333 | " #for square area:\n", 1334 | " for j in range(Ny_btm,Ny_top):\n", 1335 | " for i in range(Nx_lft,Nx_rgt):\n", 1336 | " uA[j][i][a_w]=0\n", 1337 | " uA[j][i][a_p]=1\n", 1338 | " uA[j][i][a_e]=0\n", 1339 | " uA[j][i][a_n]=0\n", 1340 | " uA[j][i][a_s]=0\n", 1341 | " uA[j][i][b_p]=0\n", 1342 | "\n", 1343 | " vA[j][i][a_w]=0\n", 1344 | " vA[j][i][a_p]=1\n", 1345 | " vA[j][i][a_e]=0\n", 1346 | " vA[j][i][a_n]=0\n", 1347 | " vA[j][i][a_s]=0\n", 1348 | " vA[j][i][b_p]=0\n", 1349 | " \n", 1350 | " return uA,vA,DX,DY" 1351 | ] 1352 | }, 1353 | { 1354 | "cell_type": "code", 1355 | "execution_count": 14, 1356 | "id": "5be9af42", 1357 | "metadata": {}, 1358 | "outputs": [], 1359 | "source": [ 1360 | "@njit\n", 1361 | "def temp_coeff(Ny,Nx,square_coord,Uf,Vf,Txf,Tyf,T,a,T_inlet,T_square,A):\n", 1362 | " N=Nx*Ny\n", 1363 | " n_squares=square_coord.shape[1] \n", 1364 | " #precomputing frequently used constants\n", 1365 | " dx=L/Nx\n", 1366 | " dy=H/Ny\n", 1367 | " x=a*dy/dx\n", 1368 | " y=a*dx/dy\n", 1369 | " de=-2*x\n", 1370 | " dw=-2*x\n", 1371 | " dp=4*x+4*y\n", 1372 | " dn=-2*y\n", 1373 | " ds=-2*y\n", 1374 | " \n", 1375 | " a_s=0\n", 1376 | " a_w=1\n", 1377 | " a_p=2\n", 1378 | " a_e=3\n", 1379 | " a_n=4\n", 1380 | " b_p=5\n", 1381 | " \n", 1382 | " #For interior cells\n", 1383 | " for j in range(1,Ny-1):\n", 1384 | " for i in range(1,Nx-1):\n", 1385 | " \n", 1386 | " ue=Uf[j][i+1]\n", 1387 | " uw=Uf[j][i]\n", 1388 | " vn=Vf[j+1][i]\n", 1389 | " vs=Vf[j][i]\n", 1390 | " \n", 1391 | " te=Txf[j][i+1]\n", 1392 | " tw=Txf[j][i]\n", 1393 | " tn=Tyf[j+1][i]\n", 1394 | " ts=Tyf[j][i]\n", 1395 | " \n", 1396 | " TS=T[j-1][i]\n", 1397 | " TW=T[j][i-1]\n", 1398 | " TP=T[j][i]\n", 1399 | " TE=T[j][i+1]\n", 1400 | " TN=T[j+1][i]\n", 1401 | "\n", 1402 | " AE=de\n", 1403 | " AW=dw\n", 1404 | " AN=dn\n", 1405 | " AS=ds\n", 1406 | " AP=dp\n", 1407 | " \n", 1408 | " AE,AW,AN,AS,AP,BP=QUICK3(AE,AW,AN,AS,AP,ue,uw,vn,vs,TS,TW,TP,TE,TN,dy,dx,a)\n", 1409 | "\n", 1410 | " A[j][i][a_w]=AW\n", 1411 | " A[j][i][a_p]=AP\n", 1412 | " A[j][i][a_e]=AE\n", 1413 | " A[j][i][a_n]=AN\n", 1414 | " A[j][i][a_s]=AS\n", 1415 | " A[j][i][b_p]=BP\n", 1416 | "\n", 1417 | " #for bottom wall\n", 1418 | " j=0\n", 1419 | " for i in range(1,Nx-1):\n", 1420 | " \n", 1421 | " ue=Uf[j][i+1]\n", 1422 | " uw=Uf[j][i]\n", 1423 | " vn=Vf[j+1][i]\n", 1424 | " \n", 1425 | " te=Txf[j][i+1]\n", 1426 | " tw=Txf[j][i]\n", 1427 | " tn=Tyf[j+1][i]\n", 1428 | " \n", 1429 | " TW=T[j][i-1]\n", 1430 | " TP=T[j][i]\n", 1431 | " TE=T[j][i+1]\n", 1432 | " TN=T[j+1][i]\n", 1433 | "\n", 1434 | " AE=de\n", 1435 | " AW=dw\n", 1436 | " AN=dn\n", 1437 | " AP=2*y+4*x\n", 1438 | " \n", 1439 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,te,tw,tn,1E-12,1E-12,TW,TP,TE,TN,dy,dx,a)\n", 1440 | " \n", 1441 | " A[j][i][a_w]=AW\n", 1442 | " A[j][i][a_p]=AP\n", 1443 | " A[j][i][a_e]=AE\n", 1444 | " A[j][i][a_n]=AN\n", 1445 | " A[j][i][b_p]=BP\n", 1446 | " \n", 1447 | " #for top wall\n", 1448 | " j=Ny-1\n", 1449 | " for i in range(1,Nx-1):\n", 1450 | " \n", 1451 | " ue=Uf[j][i+1]\n", 1452 | " uw=Uf[j][i]\n", 1453 | " vs=Vf[j][i]\n", 1454 | " \n", 1455 | " te=Txf[j][i+1]\n", 1456 | " tw=Txf[j][i]\n", 1457 | " ts=Tyf[j][i]\n", 1458 | " \n", 1459 | " TS=T[j-1][i]\n", 1460 | " TW=T[j][i-1]\n", 1461 | " TP=T[j][i]\n", 1462 | " TE=T[j][i+1]\n", 1463 | " \n", 1464 | " AE=de\n", 1465 | " AW=dw\n", 1466 | " AS=ds\n", 1467 | " AP=2*y+4*x\n", 1468 | " \n", 1469 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,te,tw,1E-12,ts,TS,TW,TP,TE,1E-12,dy,dx,a)\n", 1470 | " \n", 1471 | " A[j][i][a_w]=AW\n", 1472 | " A[j][i][a_p]=AP\n", 1473 | " A[j][i][a_e]=AE\n", 1474 | " A[j][i][a_s]=AS\n", 1475 | " A[j][i][b_p]=BP\n", 1476 | " \n", 1477 | " #for inlet:\n", 1478 | " i=0\n", 1479 | " for j in range(1,Ny-1):\n", 1480 | " \n", 1481 | " ue=Uf[j][i+1]\n", 1482 | " u_in=Uf[j][i]\n", 1483 | " vn=Vf[j+1][i]\n", 1484 | " vs=Vf[j][i]\n", 1485 | " \n", 1486 | " te=Txf[j][i+1]\n", 1487 | " tn=Tyf[j+1][i]\n", 1488 | " ts=Tyf[j][i]\n", 1489 | " \n", 1490 | " TS=T[j-1][i]\n", 1491 | " TP=T[j][i]\n", 1492 | " TE=T[j][i+1]\n", 1493 | " TN=T[j+1][i]\n", 1494 | "\n", 1495 | " AE=-(8/3)*x\n", 1496 | " AN=dn\n", 1497 | " AS=ds\n", 1498 | " AP=8*x+4*y\n", 1499 | " \n", 1500 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,te,1E-12,tn,ts,TS,1E-12,TP,TE,TN,dy,dx,a)\n", 1501 | " \n", 1502 | " BP+=(2*dy*u_in*T_inlet)+((16/3)*x*T_inlet)\n", 1503 | "\n", 1504 | " A[j][i][a_p]=AP\n", 1505 | " A[j][i][a_e]=AE\n", 1506 | " A[j][i][a_n]=AN\n", 1507 | " A[j][i][a_s]=AS\n", 1508 | " A[j][i][b_p]=BP\n", 1509 | " \n", 1510 | " #for outlet:\n", 1511 | " i=Nx-1\n", 1512 | " for j in range(1,Ny-1):\n", 1513 | " \n", 1514 | " ue=Uf[j][i+1]\n", 1515 | " uw=Uf[j][i]\n", 1516 | " vn=Vf[j+1][i]\n", 1517 | " vs=Vf[j][i]\n", 1518 | " \n", 1519 | " tw=Txf[j][i]\n", 1520 | " tn=Tyf[j+1][i]\n", 1521 | " ts=Tyf[j][i]\n", 1522 | " \n", 1523 | " TS=T[j-1][i]\n", 1524 | " TW=T[j][i-1]\n", 1525 | " TP=T[j][i]\n", 1526 | " TN=T[j+1][i]\n", 1527 | "\n", 1528 | " AW=dw\n", 1529 | " AN=dn\n", 1530 | " AS=ds\n", 1531 | " AP=2*x+4*y+2*dy*ue\n", 1532 | "\n", 1533 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,1E-12,tw,tn,ts,TS,TW,TP,1E-12,TN,dy,dx,a)\n", 1534 | "\n", 1535 | " A[j][i][a_w]=AW\n", 1536 | " A[j][i][a_p]=AP\n", 1537 | " A[j][i][a_n]=AN\n", 1538 | " A[j][i][a_s]=AS\n", 1539 | " A[j][i][b_p]=BP\n", 1540 | " \n", 1541 | " #top right cell\n", 1542 | " i=Nx-1\n", 1543 | " j=Ny-1\n", 1544 | " \n", 1545 | " ue=Uf[j][i+1]\n", 1546 | " uw=Uf[j][i]\n", 1547 | " vs=Vf[j][i]\n", 1548 | " \n", 1549 | " tw=Txf[j][i]\n", 1550 | " ts=Tyf[j][i]\n", 1551 | " \n", 1552 | " TS=T[j-1][i]\n", 1553 | " TW=T[j][i-1]\n", 1554 | " TP=T[j][i]\n", 1555 | " \n", 1556 | " AW=dw\n", 1557 | " AS=ds\n", 1558 | " AP=2*x+2*y+2*dy*ue\n", 1559 | " \n", 1560 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,1E-12,vs,1E-12,tw,1E-12,ts,TS,TW,TP,1E-12,1E-12,dy,dx,a)\n", 1561 | "\n", 1562 | " A[j][i][a_w]=AW\n", 1563 | " A[j][i][a_p]=AP\n", 1564 | " A[j][i][a_s]=AS\n", 1565 | " A[j][i][b_p]=BP\n", 1566 | "\n", 1567 | " #top left cell\n", 1568 | " i=0\n", 1569 | " j=Ny-1\n", 1570 | " \n", 1571 | " ue=Uf[j][i+1]\n", 1572 | " u_in=Uf[j][i]\n", 1573 | " vs=Vf[j][i]\n", 1574 | " \n", 1575 | " te=Txf[j][i+1]\n", 1576 | " ts=Tyf[j][i]\n", 1577 | " \n", 1578 | " TS=T[j-1][i]\n", 1579 | " TP=T[j][i]\n", 1580 | " TE=T[j][i+1]\n", 1581 | " \n", 1582 | " AE=-(8/3)*x\n", 1583 | " AS=ds\n", 1584 | " AP=8*x+2*y\n", 1585 | " \n", 1586 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,1E-12,vs,1E-12,1E-12,tn,ts,TS,1E-12,TP,TE,1E-12,dy,dx,a)\n", 1587 | " \n", 1588 | " BP+=(2*dy*u_in*T_inlet)+((16/3)*x*T_inlet)\n", 1589 | " \n", 1590 | " A[j][i][a_p]=AP\n", 1591 | " A[j][i][a_e]=AE\n", 1592 | " A[j][i][a_s]=AS\n", 1593 | " A[j][i][b_p]=BP\n", 1594 | "\n", 1595 | " #bottom right cell\n", 1596 | " i=Nx-1\n", 1597 | " j=0\n", 1598 | " \n", 1599 | " ue=Uf[j][i+1]\n", 1600 | " uw=Uf[j][i]\n", 1601 | " vn=Vf[j+1][i]\n", 1602 | "\n", 1603 | " tw=Txf[j][i]\n", 1604 | " tn=Tyf[j+1][i]\n", 1605 | " \n", 1606 | " TW=T[j][i-1]\n", 1607 | " TP=T[j][i]\n", 1608 | " TN=T[j+1][i]\n", 1609 | " \n", 1610 | " AW=dw\n", 1611 | " AN=dn\n", 1612 | " AP=2*x+2*y+2*dy*ue\n", 1613 | "\n", 1614 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,1E-12,1E-12,tw,tn,1E-12,1E-12,TW,TP,1E-12,TN,dy,dx,a)\n", 1615 | "\n", 1616 | " A[j][i][a_w]=AW\n", 1617 | " A[j][i][a_p]=AP\n", 1618 | " A[j][i][a_n]=AN\n", 1619 | " A[j][i][b_p]=BP\n", 1620 | "\n", 1621 | " #bottom left cell\n", 1622 | " i=0\n", 1623 | " j=0\n", 1624 | " \n", 1625 | " ue=Uf[j][i+1]\n", 1626 | " u_in=Uf[j][i]\n", 1627 | " vn=Vf[j+1][i]\n", 1628 | " \n", 1629 | " te=Txf[j][i+1]\n", 1630 | " tn=Tyf[j+1][i]\n", 1631 | " \n", 1632 | " TP=T[j][i]\n", 1633 | " TE=T[j][i+1]\n", 1634 | " TN=T[j+1][i]\n", 1635 | " \n", 1636 | " AE=-(8/3)*x\n", 1637 | " AN=dn\n", 1638 | " AP=8*x+2*y\n", 1639 | " \n", 1640 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,1E-12,te,1E-12,tn,1E-12,1E-12,1E-12,TP,TE,TN,dy,dx,a)\n", 1641 | " \n", 1642 | " BP+=(2*dy*u_in*T_inlet)+((16/3)*x*T_inlet)\n", 1643 | " \n", 1644 | " A[j][i][a_e]=AE\n", 1645 | " A[j][i][a_p]=AP\n", 1646 | " A[j][i][a_n]=AN\n", 1647 | " A[j][i][b_p]=BP\n", 1648 | " \n", 1649 | " for n in range(n_squares):\n", 1650 | " Nx_lft=int(square_coord[0][n])\n", 1651 | " Nx_rgt=int(square_coord[1][n])\n", 1652 | " Ny_top=int(square_coord[2][n])\n", 1653 | " Ny_btm=int(square_coord[3][n])\n", 1654 | " \n", 1655 | " #left square\n", 1656 | " i=Nx_lft-1\n", 1657 | " for j in range(Ny_btm,Ny_top):\n", 1658 | "\n", 1659 | " uw=Uf[j][i]\n", 1660 | " vn=Vf[j+1][i]\n", 1661 | " vs=Vf[j][i]\n", 1662 | " \n", 1663 | " tw=Txf[j][i]\n", 1664 | " tn=Tyf[j+1][i]\n", 1665 | " ts=Tyf[j][i]\n", 1666 | " \n", 1667 | " TS=T[j-1][i]\n", 1668 | " TW=T[j][i-1]\n", 1669 | " TP=T[j][i]\n", 1670 | " TN=T[j+1][i]\n", 1671 | "\n", 1672 | " AW=-(8/3)*x\n", 1673 | " AN=dn\n", 1674 | " AS=ds\n", 1675 | " AP=8*x+4*y\n", 1676 | " \n", 1677 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,1E-12,uw,vn,vs,1E-12,tw,tn,ts,TS,TW,TP,1E-12,TN,dy,dx,a)\n", 1678 | " \n", 1679 | " BP+=(16/3)*x*T_square\n", 1680 | " \n", 1681 | " A[j][i][a_w]=AW\n", 1682 | " A[j][i][a_p]=AP\n", 1683 | " A[j][i][a_e]=0\n", 1684 | " A[j][i][a_n]=AN\n", 1685 | " A[j][i][a_s]=AS\n", 1686 | " A[j][i][b_p]=BP\n", 1687 | "\n", 1688 | " #right square\n", 1689 | " i=Nx_rgt\n", 1690 | " for j in range(Ny_btm,Ny_top):\n", 1691 | "\n", 1692 | " ue=Uf[j][i+1]\n", 1693 | " vn=Vf[j+1][i]\n", 1694 | " vs=Vf[j][i]\n", 1695 | " \n", 1696 | " te=Txf[j][i+1]\n", 1697 | " tn=Tyf[j+1][i]\n", 1698 | " ts=Tyf[j][i]\n", 1699 | " \n", 1700 | " TS=T[j-1][i]\n", 1701 | " TP=T[j][i]\n", 1702 | " TE=T[j][i+1]\n", 1703 | " TN=T[j+1][i]\n", 1704 | "\n", 1705 | " AE=-(8/3)*x\n", 1706 | " AN=dn\n", 1707 | " AS=ds\n", 1708 | " AP=8*x+4*y\n", 1709 | " \n", 1710 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,1E-12,vn,vs,te,1E-12,tn,ts,TS,1E-12,TP,TE,TN,dy,dx,a)\n", 1711 | " \n", 1712 | " BP+=(16/3)*x*T_square\n", 1713 | "\n", 1714 | " A[j][i][a_w]=0\n", 1715 | " A[j][i][a_p]=AP\n", 1716 | " A[j][i][a_e]=AE\n", 1717 | " A[j][i][a_n]=AN\n", 1718 | " A[j][i][a_s]=AS\n", 1719 | " A[j][i][b_p]=BP\n", 1720 | "\n", 1721 | " #top square\n", 1722 | " j=Ny_top\n", 1723 | " for i in range(Nx_lft,Nx_rgt):\n", 1724 | "\n", 1725 | " ue=Uf[j][i+1]\n", 1726 | " uw=Uf[j][i]\n", 1727 | " vn=Vf[j+1][i]\n", 1728 | " \n", 1729 | " te=Txf[j][i+1]\n", 1730 | " tw=Txf[j][i]\n", 1731 | " tn=Tyf[j+1][i]\n", 1732 | " \n", 1733 | " TW=T[j][i-1]\n", 1734 | " TP=T[j][i]\n", 1735 | " TE=T[j][i+1]\n", 1736 | " TN=T[j+1][i]\n", 1737 | "\n", 1738 | " AW=dw\n", 1739 | " AE=de\n", 1740 | " AN=-(8/3)*y\n", 1741 | " AP=4*x+8*y\n", 1742 | " \n", 1743 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,vn,1E-12,te,tw,tn,1E-12,1E-12,TW,TP,TE,TN,dy,dx,a)\n", 1744 | " \n", 1745 | " BP+=(16/3)*y*T_square\n", 1746 | "\n", 1747 | " A[j][i][a_w]=AW\n", 1748 | " A[j][i][a_p]=AP\n", 1749 | " A[j][i][a_e]=AE\n", 1750 | " A[j][i][a_n]=AN\n", 1751 | " A[j][i][a_s]=0\n", 1752 | " A[j][i][b_p]=BP\n", 1753 | "\n", 1754 | " #bottom square\n", 1755 | " j=Ny_btm-1\n", 1756 | " for i in range(Nx_lft,Nx_rgt):\n", 1757 | "\n", 1758 | " ue=Uf[j][i+1]\n", 1759 | " uw=Uf[j][i]\n", 1760 | " vs=Vf[j][i]\n", 1761 | " \n", 1762 | " te=Txf[j][i+1]\n", 1763 | " tw=Txf[j][i]\n", 1764 | " ts=Tyf[j][i]\n", 1765 | " \n", 1766 | " TS=T[j-1][i]\n", 1767 | " TW=T[j][i-1]\n", 1768 | " TP=T[j][i]\n", 1769 | " TE=T[j][i+1]\n", 1770 | "\n", 1771 | " AW=dw\n", 1772 | " AE=de\n", 1773 | " AS=-(8/3)*y\n", 1774 | " AP=4*x+8*y\n", 1775 | " \n", 1776 | " AE,AW,AN,AS,AP,BP=upwind_DC(AE,AW,AN,AS,AP,ue,uw,1E-12,vs,te,tw,1E-12,ts,TS,TW,TP,TE,1E-12,dy,dx,a)\n", 1777 | " \n", 1778 | " BP+=(16/3)*y*T_square\n", 1779 | "\n", 1780 | " A[j][i][a_w]=AW\n", 1781 | " A[j][i][a_p]=AP\n", 1782 | " A[j][i][a_e]=AE\n", 1783 | " A[j][i][a_n]=0\n", 1784 | " A[j][i][a_s]=AS\n", 1785 | " A[j][i][b_p]=BP\n", 1786 | "\n", 1787 | " #for square area:\n", 1788 | " for j in range(Ny_btm,Ny_top):\n", 1789 | " for i in range(Nx_lft,Nx_rgt):\n", 1790 | " A[j][i][a_w]=0\n", 1791 | " A[j][i][a_p]=1\n", 1792 | " A[j][i][a_e]=0\n", 1793 | " A[j][i][a_n]=0\n", 1794 | " A[j][i][a_s]=0\n", 1795 | " A[j][i][b_p]=T_square\n", 1796 | "\n", 1797 | " return A" 1798 | ] 1799 | }, 1800 | { 1801 | "cell_type": "code", 1802 | "execution_count": 15, 1803 | "id": "6b0e2cdb", 1804 | "metadata": {}, 1805 | "outputs": [], 1806 | "source": [ 1807 | "@njit\n", 1808 | "def implicit_relaxation(A,phi,Ny,Nx,av):\n", 1809 | " alpha=(1-av)/av\n", 1810 | " \n", 1811 | " for j in range(Ny):\n", 1812 | " for i in range(Nx):\n", 1813 | " A[j][i][5]+=phi[j][i]*A[j][i][2]*alpha\n", 1814 | " A[j][i][2]=A[j][i][2]/av\n", 1815 | " \n", 1816 | " return A" 1817 | ] 1818 | }, 1819 | { 1820 | "cell_type": "code", 1821 | "execution_count": 16, 1822 | "id": "a9a913d5", 1823 | "metadata": {}, 1824 | "outputs": [], 1825 | "source": [ 1826 | "@njit(fastmath=True)\n", 1827 | "def implicit_time(A,phi_time,Ny,Nx,k,timestep):\n", 1828 | " \n", 1829 | " a_s=0\n", 1830 | " a_w=1\n", 1831 | " a_p=2\n", 1832 | " a_e=3\n", 1833 | " a_n=4\n", 1834 | " b_p=5\n", 1835 | " \n", 1836 | "# #2nd order implicit\n", 1837 | "# for j in range(Ny):\n", 1838 | "# for i in range(Nx):\n", 1839 | "# A[j][i][a_p]+=1.5*k\n", 1840 | "# A[j][i][b_p]+=k*(2*phi_time[j][i][1]-0.5*phi_time[j][i][0])\n", 1841 | " \n", 1842 | " #3rd order implicit\n", 1843 | " for j in range(Ny):\n", 1844 | " for i in range(Nx):\n", 1845 | " A[j][i][a_p]+=(11/6)*k\n", 1846 | " A[j][i][b_p]+=k*(18*phi_time[j][i][2]-9*phi_time[j][i][1]+2*phi_time[j][i][0])/6\n", 1847 | " return A" 1848 | ] 1849 | }, 1850 | { 1851 | "cell_type": "code", 1852 | "execution_count": 17, 1853 | "id": "0dc5e0b0", 1854 | "metadata": {}, 1855 | "outputs": [], 1856 | "source": [ 1857 | "@njit \n", 1858 | "def timeswap(time_array,phi,Ny,Nx):\n", 1859 | " time_array_new=np.zeros((Ny,Nx,3))\n", 1860 | " for j in range(Ny):\n", 1861 | " for i in range(Nx):\n", 1862 | " time_array_new[j][i][0]=time_array[j][i][1]\n", 1863 | " time_array_new[j][i][1]=time_array[j][i][2]\n", 1864 | " time_array_new[j][i][2]=phi[j][i]\n", 1865 | " \n", 1866 | " return time_array_new" 1867 | ] 1868 | }, 1869 | { 1870 | "cell_type": "code", 1871 | "execution_count": 18, 1872 | "id": "9cdd4400", 1873 | "metadata": {}, 1874 | "outputs": [], 1875 | "source": [ 1876 | "@njit(fastmath=True)\n", 1877 | "def face_rc(Nx,Ny,U,V,P,square_coord,DX,DY,u_inlet):\n", 1878 | " uNx=Nx+1\n", 1879 | " uNy=Ny\n", 1880 | " vNx=Nx\n", 1881 | " vNy=Ny+1\n", 1882 | " \n", 1883 | " n_squares=square_coord.shape[1]\n", 1884 | " \n", 1885 | " Uf=np.zeros((uNy,uNx))\n", 1886 | " Vf=np.zeros((vNy,vNx))\n", 1887 | " #Height: Ny_btm to Ny_top-1\n", 1888 | " #Width: Nx_lft to Nx_rgt-1\n", 1889 | " \n", 1890 | " #U-faces\n", 1891 | " DXf=np.zeros((uNy,uNx))\n", 1892 | " \n", 1893 | " for j in range(uNy):\n", 1894 | " for i in range(2,uNx-2):\n", 1895 | " ij=i+Nx*j\n", 1896 | " \n", 1897 | " UE=U[j][i]\n", 1898 | " UP=U[j][i-1]\n", 1899 | " Ue=(UE+UP)/2\n", 1900 | "\n", 1901 | " DP=DX[j][i-1]\n", 1902 | " DE=DX[j][i]\n", 1903 | " PP=DP*(P[j][i-2]-P[j][i])\n", 1904 | " PE=DE*(P[j][i-1]-P[j][i+1])\n", 1905 | " \n", 1906 | " dp=(DP+DE)/2\n", 1907 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 1908 | " Pf=(-(PP+PE)/2)+Pp\n", 1909 | " \n", 1910 | " uf=Ue+Pf\n", 1911 | " Uf[j][i]=uf\n", 1912 | " DXf[j][i]=dp\n", 1913 | " \n", 1914 | " for j in range(uNy):\n", 1915 | " i=1\n", 1916 | " \n", 1917 | " UE=U[j][i]\n", 1918 | " UP=U[j][i-1]\n", 1919 | " Ue=(UE+UP)/2\n", 1920 | "\n", 1921 | " DP=DX[j][i-1]\n", 1922 | " DE=DX[j][i]\n", 1923 | " PP=DP*(P[j][i-1]-P[j][i])\n", 1924 | " PE=DE*(P[j][i-1]-P[j][i+1])\n", 1925 | "\n", 1926 | " dp=(DP+DE)/2\n", 1927 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 1928 | " Pf=(-(PP+PE)/2)+Pp\n", 1929 | "\n", 1930 | " uf=Ue+Pf\n", 1931 | " Uf[j][i]=uf\n", 1932 | " DXf[j][i]=dp\n", 1933 | " \n", 1934 | " i=uNx-2\n", 1935 | " \n", 1936 | " UE=U[j][i]\n", 1937 | " UP=U[j][i-1]\n", 1938 | " Ue=(UE+UP)/2\n", 1939 | "\n", 1940 | " DP=DX[j][i-1]\n", 1941 | " DE=DX[j][i]\n", 1942 | " PP=DP*(P[j][i-2]-P[j][i])\n", 1943 | " PE=DE*(P[j][i-1]-P[j][i])\n", 1944 | "\n", 1945 | " dp=(DP+DE)/2\n", 1946 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 1947 | " Pf=(-(PP+PE)/2)+Pp\n", 1948 | "\n", 1949 | " uf=Ue+Pf\n", 1950 | " Uf[j][i]=uf\n", 1951 | " DXf[j][i]=dp\n", 1952 | " \n", 1953 | " for n in range(n_squares):\n", 1954 | " Nx_lft=int(square_coord[0][n])\n", 1955 | " Nx_rgt=int(square_coord[1][n])\n", 1956 | " Ny_top=int(square_coord[2][n])\n", 1957 | " Ny_btm=int(square_coord[3][n])\n", 1958 | " \n", 1959 | " #left square\n", 1960 | " i=Nx_lft-1\n", 1961 | " for j in range(Ny_btm,Ny_top):\n", 1962 | "\n", 1963 | " UE=U[j][i]\n", 1964 | " UP=U[j][i-1]\n", 1965 | " Ue=(UE+UP)/2\n", 1966 | "\n", 1967 | " DP=DX[j][i-1]\n", 1968 | " DE=DX[j][i]\n", 1969 | " PP=DP*(P[j][i-2]-P[j][i])\n", 1970 | " PE=DE*(P[j][i-1]-P[j][i])\n", 1971 | "\n", 1972 | " dp=(DP+DE)/2\n", 1973 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 1974 | " Pf=(-(PP+PE)/2)+Pp\n", 1975 | "\n", 1976 | " uf=Ue+Pf\n", 1977 | " Uf[j][i]=uf\n", 1978 | " DXf[j][i]=dp\n", 1979 | "\n", 1980 | " #right square\n", 1981 | " i=Nx_rgt+1\n", 1982 | " for j in range(Ny_btm,Ny_top):\n", 1983 | "\n", 1984 | " UE=U[j][i]\n", 1985 | " UP=U[j][i-1]\n", 1986 | " Ue=(UE+UP)/2\n", 1987 | "\n", 1988 | " DP=DX[j][i-1]\n", 1989 | " DE=DX[j][i]\n", 1990 | " PP=DP*(P[j][i-1]-P[j][i])\n", 1991 | " PE=DE*(P[j][i-1]-P[j][i+1])\n", 1992 | "\n", 1993 | " dp=(DP+DE)/2\n", 1994 | " Pp=dp*(P[j][i-1]-P[j][i])\n", 1995 | " Pf=(-(PP+PE)/2)+Pp\n", 1996 | "\n", 1997 | " uf=Ue+Pf\n", 1998 | " Uf[j][i]=uf\n", 1999 | " DXf[j][i]=dp\n", 2000 | " \n", 2001 | " for j in range(uNy):\n", 2002 | " #inlet\n", 2003 | " u_in=u_inlet[j]\n", 2004 | " Uf[j][0]=u_in\n", 2005 | " \n", 2006 | " #outlet\n", 2007 | " UP=U[j][-1] #2nd order upwind\n", 2008 | " UW=U[j][-2]\n", 2009 | " Uf[j][-1]=1.5*UP-0.5*UW\n", 2010 | " \n", 2011 | " \n", 2012 | " #V-faces \n", 2013 | " DYf=np.zeros((vNy,vNx))\n", 2014 | " \n", 2015 | " for j in range(1,vNy-1):\n", 2016 | " DYf[j]=(DY[j-1]+DY[j])/2\n", 2017 | " \n", 2018 | " for j in range(2,vNy-2):\n", 2019 | " for i in range(vNx):\n", 2020 | " ij=i+Nx*j\n", 2021 | " \n", 2022 | " VN=V[j][i]\n", 2023 | " VP=V[j-1][i]\n", 2024 | " Vn=(VN+VP)/2\n", 2025 | "\n", 2026 | " DP=DX[j-1][i]\n", 2027 | " DN=DX[j][i]\n", 2028 | " PP=DP*(P[j-2][i]-P[j][i])\n", 2029 | " PN=DN*(P[j-1][i]-P[j+1][i])\n", 2030 | " \n", 2031 | " dp=(DP+DN)/2\n", 2032 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2033 | " Pf=(-(PP+PN)/2)+Pp\n", 2034 | " \n", 2035 | " vf=Vn+Pf\n", 2036 | " Vf[j][i]=vf\n", 2037 | " DYf[j][i]=dp\n", 2038 | " \n", 2039 | " for i in range(vNx):\n", 2040 | " j=1\n", 2041 | " \n", 2042 | " VN=V[j][i]\n", 2043 | " VP=V[j-1][i]\n", 2044 | " Vn=(VN+VP)/2\n", 2045 | "\n", 2046 | " DP=DX[j-1][i]\n", 2047 | " DN=DX[j][i]\n", 2048 | " PP=DP*(P[j-1][i]-P[j][i])\n", 2049 | " PN=DN*(P[j-1][i]-P[j+1][i])\n", 2050 | "\n", 2051 | " dp=(DP+DN)/2\n", 2052 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2053 | " Pf=(-(PP+PN)/2)+Pp\n", 2054 | "\n", 2055 | " vf=Vn+Pf\n", 2056 | " Vf[j][i]=vf\n", 2057 | " DYf[j][i]=dp\n", 2058 | " \n", 2059 | " j=vNy-2\n", 2060 | " \n", 2061 | " VN=V[j][i]\n", 2062 | " VP=V[j-1][i]\n", 2063 | " Vn=(VN+VP)/2\n", 2064 | "\n", 2065 | " DP=DX[j-1][i]\n", 2066 | " DN=DX[j][i]\n", 2067 | " PP=DP*(P[j-2][i]-P[j][i])\n", 2068 | " PN=DN*(P[j-1][i]-P[j][i])\n", 2069 | "\n", 2070 | " dp=(DP+DN)/2\n", 2071 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2072 | " Pf=(-(PP+PN)/2)+Pp\n", 2073 | "\n", 2074 | " vf=Vn+Pf\n", 2075 | " Vf[j][i]=vf\n", 2076 | " DYf[j][i]=dp\n", 2077 | " \n", 2078 | " for n in range(n_squares):\n", 2079 | " Nx_lft=int(square_coord[0][n])\n", 2080 | " Nx_rgt=int(square_coord[1][n])\n", 2081 | " Ny_top=int(square_coord[2][n])\n", 2082 | " Ny_btm=int(square_coord[3][n])\n", 2083 | " #top square\n", 2084 | " j=Ny_top+1\n", 2085 | " for i in range(Nx_lft,Nx_rgt):\n", 2086 | "\n", 2087 | " VN=V[j][i]\n", 2088 | " VP=V[j-1][i]\n", 2089 | " Vn=(VN+VP)/2\n", 2090 | "\n", 2091 | " DP=DX[j-1][i]\n", 2092 | " DN=DX[j][i]\n", 2093 | " PP=DP*(P[j-1][i]-P[j][i])\n", 2094 | " PN=DN*(P[j-1][i]-P[j+1][i])\n", 2095 | "\n", 2096 | " dp=(DP+DN)/2\n", 2097 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2098 | " Pf=(-(PP+PN)/2)+Pp\n", 2099 | "\n", 2100 | " vf=Vn+Pf\n", 2101 | " Vf[j][i]=vf\n", 2102 | " DYf[j][i]=dp\n", 2103 | "\n", 2104 | " #btm square\n", 2105 | " j=Ny_btm-1\n", 2106 | " for i in range(Nx_lft,Nx_rgt):\n", 2107 | "\n", 2108 | " VN=V[j][i]\n", 2109 | " VP=V[j-1][i]\n", 2110 | " Vn=(VN+VP)/2\n", 2111 | "\n", 2112 | " DP=DX[j-1][i]\n", 2113 | " DN=DX[j][i]\n", 2114 | " PP=DP*(P[j-2][i]-P[j][i])\n", 2115 | " PN=DN*(P[j-1][i]-P[j][i])\n", 2116 | "\n", 2117 | " dp=(DP+DN)/2\n", 2118 | " Pp=dp*(P[j-1][i]-P[j][i])\n", 2119 | " Pf=(-(PP+PN)/2)+Pp\n", 2120 | "\n", 2121 | " vf=Vn+Pf\n", 2122 | " Vf[j][i]=vf\n", 2123 | " DYf[j][i]=dp\n", 2124 | "\n", 2125 | " #for square faces\n", 2126 | " for j in range(Ny_btm,Ny_top):\n", 2127 | " for i in range(Nx_lft,Nx_rgt):\n", 2128 | " Uf[j][i]=0\n", 2129 | " Vf[j][i]=0\n", 2130 | " \n", 2131 | " return Uf,Vf,DXf,DYf" 2132 | ] 2133 | }, 2134 | { 2135 | "cell_type": "code", 2136 | "execution_count": 19, 2137 | "id": "35808d27", 2138 | "metadata": {}, 2139 | "outputs": [], 2140 | "source": [ 2141 | "@njit\n", 2142 | "def cont(Nx,Ny,square_coord,U,V,DXf,DYf,DX,dy,dx,A):\n", 2143 | " N=Nx*Ny\n", 2144 | " n_square=square_coord.shape[1]\n", 2145 | " \n", 2146 | " a_s=0\n", 2147 | " a_w=1\n", 2148 | " a_p=2\n", 2149 | " a_e=3\n", 2150 | " a_n=4\n", 2151 | " b_p=5\n", 2152 | " \n", 2153 | " #For interior cells\n", 2154 | " for j in range(1,Ny-1):\n", 2155 | " for i in range(1,Nx-1):\n", 2156 | " \n", 2157 | " ue=U[j][i+1]\n", 2158 | " uw=U[j][i]\n", 2159 | " vn=V[j+1][i]\n", 2160 | " vs=V[j][i]\n", 2161 | "\n", 2162 | " de=DXf[j][i+1]\n", 2163 | " dw=DXf[j][i]\n", 2164 | " dn=DYf[j+1][i]\n", 2165 | " ds=DYf[j][i]\n", 2166 | "\n", 2167 | " AE=-dy*de\n", 2168 | " AW=-dy*dw\n", 2169 | " AN=-dx*dn\n", 2170 | " AS=-dx*ds\n", 2171 | " AP=dy*(de+dw)+dx*(dn+ds)\n", 2172 | " BP=dy*(uw-ue)+dx*(vs-vn)\n", 2173 | "\n", 2174 | " A[j][i][a_w]=AW\n", 2175 | " A[j][i][a_e]=AE\n", 2176 | " A[j][i][a_p]=AP\n", 2177 | " A[j][i][a_n]=AN\n", 2178 | " A[j][i][a_s]=AS\n", 2179 | " A[j][i][b_p]=BP\n", 2180 | "\n", 2181 | " #inlet\n", 2182 | " i=0\n", 2183 | " for j in range(1,Ny-1):\n", 2184 | " \n", 2185 | " uw=U[j][i] #inlet\n", 2186 | " ue=U[j][i+1]\n", 2187 | " vn=V[j+1][i]\n", 2188 | " vs=V[j][i]\n", 2189 | "\n", 2190 | " de=DXf[j][i+1]\n", 2191 | " dn=DYf[j+1][i]\n", 2192 | " ds=DYf[j][i]\n", 2193 | " \n", 2194 | " AE=-dy*de\n", 2195 | " AN=-dx*dn\n", 2196 | " AS=-dx*ds\n", 2197 | " AP=dy*de+dx*(dn+ds)\n", 2198 | " BP=dy*(uw-ue)+dx*(vs-vn)\n", 2199 | "\n", 2200 | " A[j][i][a_e]=AE\n", 2201 | " A[j][i][a_p]=AP\n", 2202 | " A[j][i][a_n]=AN\n", 2203 | " A[j][i][a_s]=AS\n", 2204 | " A[j][i][b_p]=BP\n", 2205 | " \n", 2206 | " #outlet\n", 2207 | " i=Nx-1\n", 2208 | " for j in range(1,Ny-1):\n", 2209 | " \n", 2210 | " uw=U[j][i]\n", 2211 | " ue=U[j][i+1] #outlet\n", 2212 | " vn=V[j+1][i]\n", 2213 | " vs=V[j][i]\n", 2214 | " \n", 2215 | " DP=DX[j][i]\n", 2216 | " dw=DXf[j][i]\n", 2217 | " dn=DYf[j+1][i]\n", 2218 | " ds=DYf[j][i]\n", 2219 | "\n", 2220 | " AW=dy*(0.5*DP-dw)\n", 2221 | " AN=-dx*dn\n", 2222 | " AS=-dx*ds\n", 2223 | " AP=dy*(dw+0.5*DP)+dx*(dn+ds)\n", 2224 | " BP=dy*(uw-ue)+dx*(vs-vn)\n", 2225 | "\n", 2226 | " A[j][i][a_w]=AW\n", 2227 | " A[j][i][a_p]=AP\n", 2228 | " A[j][i][a_n]=AN\n", 2229 | " A[j][i][a_s]=AS\n", 2230 | " A[j][i][b_p]=BP\n", 2231 | " \n", 2232 | " #for bottom wall\n", 2233 | " j=0\n", 2234 | " for i in range(1,Nx-1):\n", 2235 | " \n", 2236 | " ue=U[j][i+1]\n", 2237 | " uw=U[j][i]\n", 2238 | " vn=V[j+1][i]\n", 2239 | " \n", 2240 | " de=DXf[j][i+1]\n", 2241 | " dw=DXf[j][i]\n", 2242 | " dn=DYf[j+1][i]\n", 2243 | "\n", 2244 | " AE=-dy*de\n", 2245 | " AW=-dy*dw\n", 2246 | " AN=-dx*dn\n", 2247 | " AP=dy*(de+dw)+dx*dn\n", 2248 | " BP=dy*(uw-ue)-dx*vn\n", 2249 | "\n", 2250 | " A[j][i][a_w]=AW\n", 2251 | " A[j][i][a_e]=AE\n", 2252 | " A[j][i][a_p]=AP\n", 2253 | " A[j][i][a_n]=AN\n", 2254 | " A[j][i][b_p]=BP\n", 2255 | " \n", 2256 | " #for top wall\n", 2257 | " j=Ny-1\n", 2258 | " for i in range(1,Nx-1):\n", 2259 | " \n", 2260 | " ue=U[j][i+1]\n", 2261 | " uw=U[j][i]\n", 2262 | " vs=V[j][i]\n", 2263 | " \n", 2264 | " de=DXf[j][i+1]\n", 2265 | " dw=DXf[j][i]\n", 2266 | " ds=DYf[j][i]\n", 2267 | "\n", 2268 | " AE=-dy*de\n", 2269 | " AW=-dy*dw\n", 2270 | " AS=-dx*ds\n", 2271 | " AP=dy*(de+dw)+dx*ds\n", 2272 | " BP=dy*(uw-ue)+dx*vs\n", 2273 | "\n", 2274 | " A[j][i][a_w]=AW\n", 2275 | " A[j][i][a_e]=AE\n", 2276 | " A[j][i][a_p]=AP\n", 2277 | " A[j][i][a_s]=AS\n", 2278 | " A[j][i][b_p]=BP\n", 2279 | " \n", 2280 | " #for top left corner\n", 2281 | " i=0\n", 2282 | " j=Ny-1\n", 2283 | " \n", 2284 | " uw=U[j][i] #inlet\n", 2285 | " ue=U[j][i+1]\n", 2286 | " vs=V[j][i]\n", 2287 | " \n", 2288 | " de=DXf[j][i+1]\n", 2289 | " ds=DYf[j][i]\n", 2290 | "\n", 2291 | " AE=-dy*de\n", 2292 | " AS=-dx*ds\n", 2293 | " AP=dy*de+dx*ds\n", 2294 | " BP=dy*(uw-ue)+dx*vs\n", 2295 | "\n", 2296 | " A[j][i][a_e]=AE\n", 2297 | " A[j][i][a_p]=AP\n", 2298 | " A[j][i][a_s]=AS\n", 2299 | " A[j][i][b_p]=BP\n", 2300 | " \n", 2301 | " #for top right corner\n", 2302 | " i=Nx-1\n", 2303 | " j=Ny-1\n", 2304 | " \n", 2305 | " ue=U[j][i+1] #outlet\n", 2306 | " uw=U[j][i]\n", 2307 | " vs=V[j][i]\n", 2308 | " \n", 2309 | " DP=DX[j][i]\n", 2310 | " dw=DXf[j][i]\n", 2311 | " ds=DYf[j][i]\n", 2312 | "\n", 2313 | " AW=dy*(0.5*DP-dw)\n", 2314 | " AS=-dx*ds\n", 2315 | " AP=dy*(0.5*DP+dw)+dx*ds\n", 2316 | " BP=dy*(uw-ue)+dx*vs\n", 2317 | "\n", 2318 | " A[j][i][a_w]=AW\n", 2319 | " A[j][i][a_p]=AP\n", 2320 | " A[j][i][a_s]=AS\n", 2321 | " A[j][i][b_p]=BP\n", 2322 | " \n", 2323 | " #for bottom left corner\n", 2324 | " i=0\n", 2325 | " j=0\n", 2326 | " \n", 2327 | " uw=U[j][i] #inlet\n", 2328 | " ue=U[j][i+1]\n", 2329 | " vn=V[j+1][i]\n", 2330 | " \n", 2331 | " de=DXf[j][i+1]\n", 2332 | " dn=DYf[j+1][i]\n", 2333 | "\n", 2334 | " AE=-dy*de\n", 2335 | " AN=-dx*dn\n", 2336 | " AP=dy*de+dx*dn\n", 2337 | " BP=dy*(uw-ue)-dx*vn\n", 2338 | "\n", 2339 | " A[j][i][a_e]=AE\n", 2340 | " A[j][i][a_p]=AP\n", 2341 | " A[j][i][a_n]=AN\n", 2342 | " A[j][i][b_p]=BP\n", 2343 | " \n", 2344 | " #for bottom right corner\n", 2345 | " i=Nx-1\n", 2346 | " j=0\n", 2347 | " \n", 2348 | " ue=U[j][i+1] #outlet\n", 2349 | " uw=U[j][i]\n", 2350 | " vn=V[j+1][i]\n", 2351 | " \n", 2352 | " DP=DX[j][i]\n", 2353 | " dw=DXf[j][i]\n", 2354 | " dn=DYf[j+1][i]\n", 2355 | "\n", 2356 | " AW=dy*(0.5*DP-dw)\n", 2357 | " AN=-dx*dn\n", 2358 | " AP=dy*(0.5*DP+dw)+dx*dn\n", 2359 | " BP=dy*(uw-ue)-dx*vn\n", 2360 | "\n", 2361 | " A[j][i][a_w]=AW\n", 2362 | " A[j][i][a_p]=AP\n", 2363 | " A[j][i][a_n]=AN\n", 2364 | " A[j][i][b_p]=BP\n", 2365 | " \n", 2366 | " for n in range(n_squares):\n", 2367 | " Nx_lft=int(square_coord[0][n])\n", 2368 | " Nx_rgt=int(square_coord[1][n])\n", 2369 | " Ny_top=int(square_coord[2][n])\n", 2370 | " Ny_btm=int(square_coord[3][n])\n", 2371 | " \n", 2372 | " #for btm square\n", 2373 | " j=Ny_btm-1\n", 2374 | " for i in range(Nx_lft,Nx_rgt):\n", 2375 | "\n", 2376 | " ue=U[j][i+1]\n", 2377 | " uw=U[j][i]\n", 2378 | " vs=V[j][i]\n", 2379 | "\n", 2380 | " de=DXf[j][i+1]\n", 2381 | " dw=DXf[j][i]\n", 2382 | " ds=DYf[j][i]\n", 2383 | "\n", 2384 | " AE=-dy*de\n", 2385 | " AW=-dy*dw\n", 2386 | " AS=-dx*ds\n", 2387 | " AP=dy*(de+dw)+dx*ds\n", 2388 | " BP=dy*(uw-ue)+dx*vs\n", 2389 | "\n", 2390 | " A[j][i][a_w]=AW\n", 2391 | " A[j][i][a_e]=AE\n", 2392 | " A[j][i][a_p]=AP\n", 2393 | " A[j][i][a_s]=AS\n", 2394 | " A[j][i][a_n]=0\n", 2395 | " A[j][i][b_p]=BP\n", 2396 | "\n", 2397 | " #for top square\n", 2398 | " j=Ny_top\n", 2399 | " for i in range(Nx_lft,Nx_rgt):\n", 2400 | "\n", 2401 | " ue=U[j][i+1]\n", 2402 | " uw=U[j][i]\n", 2403 | " vn=V[j+1][i]\n", 2404 | "\n", 2405 | " de=DXf[j][i+1]\n", 2406 | " dw=DXf[j][i]\n", 2407 | " dn=DYf[j+1][i]\n", 2408 | "\n", 2409 | " AE=-dy*de\n", 2410 | " AW=-dy*dw\n", 2411 | " AN=-dx*dn\n", 2412 | " AP=dy*(de+dw)+dx*dn\n", 2413 | " BP=dy*(uw-ue)-dx*vn\n", 2414 | "\n", 2415 | " A[j][i][a_w]=AW\n", 2416 | " A[j][i][a_e]=AE\n", 2417 | " A[j][i][a_p]=AP\n", 2418 | " A[j][i][a_s]=0\n", 2419 | " A[j][i][a_n]=AN\n", 2420 | " A[j][i][b_p]=BP\n", 2421 | "\n", 2422 | " #for left square\n", 2423 | " i=Nx_lft-1\n", 2424 | " for j in range(Ny_btm,Ny_top):\n", 2425 | "\n", 2426 | " uw=U[j][i]\n", 2427 | " vn=V[j+1][i]\n", 2428 | " vs=V[j][i]\n", 2429 | "\n", 2430 | " dw=DXf[j][i]\n", 2431 | " dn=DYf[j+1][i]\n", 2432 | " ds=DYf[j][i]\n", 2433 | "\n", 2434 | " AW=-dy*dw\n", 2435 | " AN=-dx*dn\n", 2436 | " AS=-dx*ds\n", 2437 | " AP=dy*dw+dx*(dn+ds)\n", 2438 | " BP=dy*(uw)+dx*(vs-vn)\n", 2439 | "\n", 2440 | " A[j][i][a_w]=AW\n", 2441 | " A[j][i][a_e]=0\n", 2442 | " A[j][i][a_p]=AP\n", 2443 | " A[j][i][a_s]=AS\n", 2444 | " A[j][i][a_n]=AN\n", 2445 | " A[j][i][b_p]=BP\n", 2446 | "\n", 2447 | " #for right square\n", 2448 | " i=Nx_rgt\n", 2449 | " for j in range(Ny_btm,Ny_top):\n", 2450 | "\n", 2451 | " ue=U[j][i+1]\n", 2452 | " vn=V[j+1][i]\n", 2453 | " vs=V[j][i]\n", 2454 | "\n", 2455 | " de=DXf[j][i+1]\n", 2456 | " dn=DYf[j+1][i]\n", 2457 | " ds=DYf[j][i]\n", 2458 | "\n", 2459 | " AE=-dy*de\n", 2460 | " AN=-dx*dn\n", 2461 | " AS=-dx*ds\n", 2462 | " AP=dy*de+dx*(dn+ds)\n", 2463 | " BP=dy*-ue+dx*(vs-vn)\n", 2464 | "\n", 2465 | " A[j][i][a_w]=0\n", 2466 | " A[j][i][a_e]=AE\n", 2467 | " A[j][i][a_p]=AP\n", 2468 | " A[j][i][a_s]=AS\n", 2469 | " A[j][i][a_n]=AN\n", 2470 | " A[j][i][b_p]=BP\n", 2471 | "\n", 2472 | " #for square cells\n", 2473 | " for j in range(Ny_btm,Ny_top):\n", 2474 | " for i in range(Nx_lft,Nx_rgt):\n", 2475 | " A[j][i][a_w]=0\n", 2476 | " A[j][i][a_e]=0\n", 2477 | " A[j][i][a_p]=1\n", 2478 | " A[j][i][a_s]=0\n", 2479 | " A[j][i][a_n]=0\n", 2480 | " A[j][i][b_p]=0 \n", 2481 | "\n", 2482 | " return A" 2483 | ] 2484 | }, 2485 | { 2486 | "cell_type": "code", 2487 | "execution_count": 20, 2488 | "id": "637fd143", 2489 | "metadata": {}, 2490 | "outputs": [], 2491 | "source": [ 2492 | "@njit(fastmath=True)\n", 2493 | "def agglomeration(A_fine,A_coarse,Ny_f,Nx_f,Ny_c,Nx_c):\n", 2494 | " \n", 2495 | " a_s=0\n", 2496 | " a_w=1\n", 2497 | " a_p=2\n", 2498 | " a_e=3\n", 2499 | " a_n=4\n", 2500 | " b_p=5\n", 2501 | " \n", 2502 | " #for interior cells\n", 2503 | " for j in range(1,Ny_c-1):\n", 2504 | " for i in range(1,Nx_c-1):\n", 2505 | " j2=2*j\n", 2506 | " i2=2*i\n", 2507 | " \n", 2508 | " #Agglomerate diagonal coefficients\n", 2509 | " #diagonal coefficients\n", 2510 | " AP_sw=A_fine[j2][i2][a_p]\n", 2511 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2512 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2513 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2514 | " \n", 2515 | " #eastern and western link coefficients\n", 2516 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2517 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2518 | " \n", 2519 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2520 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2521 | " \n", 2522 | " #northern and southern link coefficients\n", 2523 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2524 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2525 | " \n", 2526 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2527 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2528 | " \n", 2529 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2530 | " \n", 2531 | " #Agglomerate link coefficient\n", 2532 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2533 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2534 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2535 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2536 | " \n", 2537 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2538 | " \n", 2539 | " A_coarse[j][i][a_s]=AS\n", 2540 | " A_coarse[j][i][a_w]=AW\n", 2541 | " A_coarse[j][i][a_p]=AP\n", 2542 | " A_coarse[j][i][a_e]=AE\n", 2543 | " A_coarse[j][i][a_n]=AN\n", 2544 | " A_coarse[j][i][b_p]=BP\n", 2545 | " \n", 2546 | " for j in range(1,Ny_c-1):\n", 2547 | " #for left wall\n", 2548 | " i=0\n", 2549 | " j2=2*j\n", 2550 | " i2=2*i\n", 2551 | "\n", 2552 | " #Agglomerate diagonal coefficients\n", 2553 | " #diagonal coefficients\n", 2554 | " AP_sw=A_fine[j2][i2][a_p]\n", 2555 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2556 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2557 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2558 | "\n", 2559 | " #eastern and western link coefficients\n", 2560 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2561 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2562 | "\n", 2563 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2564 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2565 | "\n", 2566 | " #northern and southern link coefficients\n", 2567 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2568 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2569 | "\n", 2570 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2571 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2572 | "\n", 2573 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2574 | "\n", 2575 | " #Agglomerate link coefficient\n", 2576 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2577 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2578 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2579 | " \n", 2580 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2581 | "\n", 2582 | " A_coarse[j][i][a_s]=AS\n", 2583 | " A_coarse[j][i][a_p]=AP\n", 2584 | " A_coarse[j][i][a_e]=AE\n", 2585 | " A_coarse[j][i][a_n]=AN\n", 2586 | " A_coarse[j][i][b_p]=BP\n", 2587 | " \n", 2588 | " #for right wall\n", 2589 | " i=Nx_c-1\n", 2590 | " j2=2*j\n", 2591 | " i2=2*i\n", 2592 | "\n", 2593 | " #Agglomerate diagonal coefficients\n", 2594 | " #diagonal coefficients\n", 2595 | " AP_sw=A_fine[j2][i2][a_p]\n", 2596 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2597 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2598 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2599 | "\n", 2600 | " #eastern and western link coefficients\n", 2601 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2602 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2603 | "\n", 2604 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2605 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2606 | "\n", 2607 | " #northern and southern link coefficients\n", 2608 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2609 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2610 | "\n", 2611 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2612 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2613 | "\n", 2614 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2615 | "\n", 2616 | " #Agglomerate link coefficient\n", 2617 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2618 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2619 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2620 | " \n", 2621 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2622 | "\n", 2623 | " A_coarse[j][i][a_s]=AS\n", 2624 | " A_coarse[j][i][a_w]=AW\n", 2625 | " A_coarse[j][i][a_p]=AP\n", 2626 | " A_coarse[j][i][a_n]=AN\n", 2627 | " A_coarse[j][i][b_p]=BP\n", 2628 | " \n", 2629 | " for i in range(1,Nx_c-1):\n", 2630 | " #for bottom wall\n", 2631 | " j=0\n", 2632 | " j2=2*j\n", 2633 | " i2=2*i\n", 2634 | "\n", 2635 | " #Agglomerate diagonal coefficients\n", 2636 | " #diagonal coefficients\n", 2637 | " AP_sw=A_fine[j2][i2][a_p]\n", 2638 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2639 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2640 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2641 | "\n", 2642 | " #eastern and western link coefficients\n", 2643 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2644 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2645 | "\n", 2646 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2647 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2648 | "\n", 2649 | " #northern and southern link coefficients\n", 2650 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2651 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2652 | "\n", 2653 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2654 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2655 | "\n", 2656 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2657 | "\n", 2658 | " #Agglomerate link coefficient\n", 2659 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2660 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2661 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2662 | " \n", 2663 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2664 | " \n", 2665 | " A_coarse[j][i][a_w]=AW\n", 2666 | " A_coarse[j][i][a_p]=AP\n", 2667 | " A_coarse[j][i][a_e]=AE\n", 2668 | " A_coarse[j][i][a_n]=AN\n", 2669 | " A_coarse[j][i][b_p]=BP\n", 2670 | " \n", 2671 | " j=Ny_c-1\n", 2672 | " j2=2*j\n", 2673 | " i2=2*i\n", 2674 | "\n", 2675 | " #Agglomerate diagonal coefficients\n", 2676 | " #diagonal coefficients\n", 2677 | " AP_sw=A_fine[j2][i2][a_p]\n", 2678 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2679 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2680 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2681 | "\n", 2682 | " #eastern and western link coefficients\n", 2683 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2684 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2685 | "\n", 2686 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2687 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2688 | "\n", 2689 | " #northern and southern link coefficients\n", 2690 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2691 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2692 | "\n", 2693 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2694 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2695 | "\n", 2696 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2697 | "\n", 2698 | " #Agglomerate link coefficient\n", 2699 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2700 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2701 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2702 | " \n", 2703 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2704 | "\n", 2705 | " A_coarse[j][i][a_s]=AS\n", 2706 | " A_coarse[j][i][a_w]=AW\n", 2707 | " A_coarse[j][i][a_p]=AP\n", 2708 | " A_coarse[j][i][a_e]=AE\n", 2709 | " A_coarse[j][i][b_p]=BP\n", 2710 | " \n", 2711 | " \n", 2712 | " #for top left cell\n", 2713 | " i=0\n", 2714 | " j=Ny_c-1\n", 2715 | " \n", 2716 | " j2=2*j\n", 2717 | " i2=2*i\n", 2718 | "\n", 2719 | " #Agglomerate diagonal coefficients\n", 2720 | " #diagonal coefficients\n", 2721 | " AP_sw=A_fine[j2][i2][a_p]\n", 2722 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2723 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2724 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2725 | "\n", 2726 | " #eastern and western link coefficients\n", 2727 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2728 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2729 | "\n", 2730 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2731 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2732 | "\n", 2733 | " #northern and southern link coefficients\n", 2734 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2735 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2736 | "\n", 2737 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2738 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2739 | "\n", 2740 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2741 | "\n", 2742 | " #Agglomerate link coefficient\n", 2743 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2744 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2745 | " \n", 2746 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2747 | "\n", 2748 | " A_coarse[j][i][a_s]=AS\n", 2749 | " A_coarse[j][i][a_p]=AP\n", 2750 | " A_coarse[j][i][a_e]=AE\n", 2751 | " A_coarse[j][i][b_p]=BP\n", 2752 | " \n", 2753 | " #for top right cell\n", 2754 | " i=Nx_c-1\n", 2755 | " j=Ny_c-1\n", 2756 | " \n", 2757 | " j2=2*j\n", 2758 | " i2=2*i\n", 2759 | "\n", 2760 | " #Agglomerate diagonal coefficients\n", 2761 | " #diagonal coefficients\n", 2762 | " AP_sw=A_fine[j2][i2][a_p]\n", 2763 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2764 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2765 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2766 | "\n", 2767 | " #eastern and western link coefficients\n", 2768 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2769 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2770 | "\n", 2771 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2772 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2773 | "\n", 2774 | " #northern and southern link coefficients\n", 2775 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2776 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2777 | "\n", 2778 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2779 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2780 | "\n", 2781 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2782 | "\n", 2783 | " #Agglomerate link coefficient\n", 2784 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2785 | " AS=A_fine[j2][i2][a_s]+A_fine[j2][i2+1][a_s]\n", 2786 | " \n", 2787 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2788 | "\n", 2789 | " A_coarse[j][i][a_s]=AS\n", 2790 | " A_coarse[j][i][a_w]=AW\n", 2791 | " A_coarse[j][i][a_p]=AP\n", 2792 | " A_coarse[j][i][b_p]=BP\n", 2793 | " \n", 2794 | " #for bottom left cell\n", 2795 | " i=0\n", 2796 | " j=0\n", 2797 | " \n", 2798 | " j2=2*j\n", 2799 | " i2=2*i\n", 2800 | "\n", 2801 | " #Agglomerate diagonal coefficients\n", 2802 | " #diagonal coefficients\n", 2803 | " AP_sw=A_fine[j2][i2][a_p]\n", 2804 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2805 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2806 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2807 | "\n", 2808 | " #eastern and western link coefficients\n", 2809 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2810 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2811 | "\n", 2812 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2813 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2814 | "\n", 2815 | " #northern and southern link coefficients\n", 2816 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2817 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2818 | "\n", 2819 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2820 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2821 | "\n", 2822 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2823 | "\n", 2824 | " #Agglomerate link coefficient\n", 2825 | " AE=A_fine[j2][i2+1][a_e]+A_fine[j2+1][i2+1][a_e]\n", 2826 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2827 | "\n", 2828 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2829 | " \n", 2830 | " A_coarse[j][i][a_p]=AP\n", 2831 | " A_coarse[j][i][a_e]=AE\n", 2832 | " A_coarse[j][i][a_n]=AN\n", 2833 | " A_coarse[j][i][b_p]=BP\n", 2834 | " \n", 2835 | " #for bottom right cell\n", 2836 | " i=Nx_c-1\n", 2837 | " j=0\n", 2838 | " \n", 2839 | " j2=2*j\n", 2840 | " i2=2*i\n", 2841 | "\n", 2842 | " #Agglomerate diagonal coefficients\n", 2843 | " #diagonal coefficients\n", 2844 | " AP_sw=A_fine[j2][i2][a_p]\n", 2845 | " AP_se=A_fine[j2][i2+1][a_p]\n", 2846 | " AP_nw=A_fine[j2+1][i2][a_p]\n", 2847 | " AP_ne=A_fine[j2+1][i2+1][a_p]\n", 2848 | "\n", 2849 | " #eastern and western link coefficients\n", 2850 | " AP_sw_se=A_fine[j2][i2][a_e]\n", 2851 | " AP_se_sw=A_fine[j2][i2+1][a_w]\n", 2852 | "\n", 2853 | " AP_nw_ne=A_fine[j2+1][i2][a_e]\n", 2854 | " AP_ne_nw=A_fine[j2+1][i2+1][a_w]\n", 2855 | "\n", 2856 | " #northern and southern link coefficients\n", 2857 | " AP_sw_nw=A_fine[j2][i2][a_n]\n", 2858 | " AP_nw_sw=A_fine[j2+1][i2][a_s]\n", 2859 | "\n", 2860 | " AP_se_ne=A_fine[j2][i2+1][a_n]\n", 2861 | " AP_ne_se=A_fine[j2+1][i2+1][a_s]\n", 2862 | "\n", 2863 | " AP=AP_sw+AP_se+AP_nw+AP_ne+AP_sw_se+AP_se_sw+AP_nw_ne+AP_ne_nw+AP_sw_nw+AP_nw_sw+AP_se_ne+AP_ne_se\n", 2864 | "\n", 2865 | " #Agglomerate link coefficient\n", 2866 | " AW=A_fine[j2][i2][a_w]+A_fine[j2+1][i2][a_w]\n", 2867 | " AN=A_fine[j2+1][i2][a_n]+A_fine[j2+1][i2+1][a_n]\n", 2868 | " \n", 2869 | " BP=A_fine[j2][i2][b_p]+A_fine[j2][i2+1][b_p]+A_fine[j2+1][i2][b_p]+A_fine[j2+1][i2+1][b_p]\n", 2870 | "\n", 2871 | " A_coarse[j][i][a_w]=AW\n", 2872 | " A_coarse[j][i][a_p]=AP\n", 2873 | " A_coarse[j][i][a_n]=AN\n", 2874 | " A_coarse[j][i][b_p]=BP\n", 2875 | " \n", 2876 | " return A_coarse" 2877 | ] 2878 | }, 2879 | { 2880 | "cell_type": "code", 2881 | "execution_count": 21, 2882 | "id": "c76d9917", 2883 | "metadata": {}, 2884 | "outputs": [], 2885 | "source": [ 2886 | "@njit\n", 2887 | "def correct(Nx,Ny,U_correct,V_correct,square_coord,U,V,P,PC,DX,DY,av,ap):\n", 2888 | " global dx\n", 2889 | " \n", 2890 | " n_squares=square_coord.shape[1]\n", 2891 | " \n", 2892 | " #U correction\n", 2893 | " #interior\n", 2894 | " for j in range(Ny):\n", 2895 | " for i in range(1,Nx-1):\n", 2896 | " uc=av*DX[j][i]*(PC[j][i-1]-PC[j][i+1])\n", 2897 | " U_correct[j][i]=uc\n", 2898 | " \n", 2899 | " #Squares\n", 2900 | " for n in range(n_squares):\n", 2901 | " Nx_lft=int(square_coord[0][n])\n", 2902 | " Nx_rgt=int(square_coord[1][n])\n", 2903 | " Ny_top=int(square_coord[2][n])\n", 2904 | " Ny_btm=int(square_coord[3][n])\n", 2905 | " \n", 2906 | " for j in range(Ny_btm,Ny_top):\n", 2907 | " for i in range(Nx_lft-1,Nx_rgt+1):\n", 2908 | " U_correct[j][i]=0 #initialise corrections\n", 2909 | " \n", 2910 | " for j in range(Ny_btm,Ny_top):\n", 2911 | " i=Nx_lft-1\n", 2912 | " uc=av*DX[j][i]*(PC[j][i-1]-PC[j][i])*2\n", 2913 | " U_correct[j][i]=uc\n", 2914 | " \n", 2915 | " i=Nx_rgt\n", 2916 | " uc=av*DX[j][i]*(PC[j][i]-PC[j][i+1])*2\n", 2917 | " U_correct[j][i]=uc\n", 2918 | "\n", 2919 | " #Inlet\n", 2920 | " for j in range(Ny):\n", 2921 | " i=0\n", 2922 | " uc=av*DX[j][i]*(PC[j][i]-PC[j][i+1])*2\n", 2923 | " U_correct[j][i]=uc\n", 2924 | " \n", 2925 | " #Outlet\n", 2926 | " for j in range(Ny):\n", 2927 | " i=Nx-1\n", 2928 | " uc=av*DX[j][i]*(PC[j][i-1]-PC[j][i])\n", 2929 | " U_correct[j][i]=uc\n", 2930 | " \n", 2931 | " U+=U_correct\n", 2932 | " \n", 2933 | " #V correction\n", 2934 | " #interior\n", 2935 | " for j in range(1,Ny-1):\n", 2936 | " for i in range(Nx):\n", 2937 | " vc=av*DY[j][i]*(PC[j-1][i]-PC[j+1][i])\n", 2938 | " V_correct[j][i]=vc\n", 2939 | " \n", 2940 | " #Squares\n", 2941 | " for n in range(n_squares):\n", 2942 | " Nx_lft=int(square_coord[0][n])\n", 2943 | " Nx_rgt=int(square_coord[1][n])\n", 2944 | " Ny_top=int(square_coord[2][n])\n", 2945 | " Ny_btm=int(square_coord[3][n])\n", 2946 | " \n", 2947 | " for j in range(Ny_btm-1,Ny_top+1):\n", 2948 | " for i in range(Nx_lft,Nx_rgt):\n", 2949 | " V_correct[j][i]=0\n", 2950 | "\n", 2951 | " for i in range(Nx_lft,Nx_rgt):\n", 2952 | " j=Ny_top\n", 2953 | " vc=av*DY[j][i]*(PC[j][i]-PC[j+1][i])*2\n", 2954 | " V_correct[j][i]=vc\n", 2955 | "\n", 2956 | " j=Ny_btm-1\n", 2957 | " vc=av*DY[j][i]*(PC[j-1][i]-PC[j][i])*2\n", 2958 | " V_correct[j][i]=vc\n", 2959 | "\n", 2960 | " #bottom wall\n", 2961 | " for i in range(Nx):\n", 2962 | " j=0\n", 2963 | " vc=av*DY[j][i]*(PC[j][i]-PC[j+1][i])*2\n", 2964 | " V_correct[j][i]=vc\n", 2965 | " \n", 2966 | " #Top wall\n", 2967 | " for i in range(Nx):\n", 2968 | " j=Ny-1\n", 2969 | " vc=av*DY[j][i]*(PC[j-1][i]-PC[j][i])*2\n", 2970 | " V_correct[j][i]=vc\n", 2971 | " V+=V_correct\n", 2972 | " \n", 2973 | " #P correction\n", 2974 | " P+=dx**0.38*ap*PC\n", 2975 | " \n", 2976 | " return U,V,P" 2977 | ] 2978 | }, 2979 | { 2980 | "cell_type": "code", 2981 | "execution_count": 22, 2982 | "id": "e4c56093", 2983 | "metadata": {}, 2984 | "outputs": [], 2985 | "source": [ 2986 | "@njit(fastmath=True)\n", 2987 | "def gauss_seidel(A,phi,Nx,Ny): \n", 2988 | " a_s=0\n", 2989 | " a_w=1\n", 2990 | " a_p=2\n", 2991 | " a_e=3\n", 2992 | " a_n=4\n", 2993 | " b_p=5\n", 2994 | " \n", 2995 | " #bottom left\n", 2996 | " j=0\n", 2997 | " i=0\n", 2998 | " phi[j][i]=(-(A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 2999 | " \n", 3000 | " #bottom\n", 3001 | " for i in range(1,Nx-1):\n", 3002 | " phi[j][i]=(-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3003 | " \n", 3004 | " #bottom right\n", 3005 | " i=Nx-1\n", 3006 | " phi[j][i]=(-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3007 | "\n", 3008 | " #interior\n", 3009 | " for j in range(1,Ny-1):\n", 3010 | " i=0\n", 3011 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3012 | " \n", 3013 | " for i in range(1,Nx-1):\n", 3014 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p] \n", 3015 | " \n", 3016 | " i=Nx-1\n", 3017 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3018 | " \n", 3019 | " i=0\n", 3020 | " j=Ny-1\n", 3021 | " #top left \n", 3022 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p])/A[j][i][a_p]\n", 3023 | " \n", 3024 | " #top\n", 3025 | " for i in range(1,Nx-1):\n", 3026 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p])/A[j][i][a_p]\n", 3027 | " \n", 3028 | " #top right\n", 3029 | " i=Nx-1\n", 3030 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1])+A[j][i][b_p])/A[j][i][a_p]\n", 3031 | " \n", 3032 | " return phi" 3033 | ] 3034 | }, 3035 | { 3036 | "cell_type": "code", 3037 | "execution_count": 23, 3038 | "id": "a6e44357", 3039 | "metadata": {}, 3040 | "outputs": [], 3041 | "source": [ 3042 | "@njit(fastmath=True)\n", 3043 | "def gauss_seidel_1(A,phi,Nx,Ny):\n", 3044 | " a_p=2\n", 3045 | " b_p=5\n", 3046 | " \n", 3047 | " a_s=0\n", 3048 | " a_w=1\n", 3049 | " a_p=2\n", 3050 | " a_e=3\n", 3051 | " a_n=4\n", 3052 | " b_p=5\n", 3053 | " \n", 3054 | " #bottom left\n", 3055 | " j=0\n", 3056 | " i=0\n", 3057 | " phi[j][i]=A[j][i][b_p]/A[j][i][a_p]\n", 3058 | " \n", 3059 | " #bottom\n", 3060 | " for i in range(1,Nx-1):\n", 3061 | " phi[j][i]=(-A[j][i][a_w]*phi[j][i-1]+A[j][i][b_p])/A[j][i][a_p]\n", 3062 | " \n", 3063 | " #bottom right\n", 3064 | " i=Nx-1\n", 3065 | " phi[j][i]=(-A[j][i][a_w]*phi[j][i-1]+A[j][i][b_p])/A[j][i][a_p]\n", 3066 | "\n", 3067 | " #interior\n", 3068 | " for j in range(1,Ny-1):\n", 3069 | " i=0\n", 3070 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3071 | " \n", 3072 | " for i in range(1,Nx-1):\n", 3073 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1])+A[j][i][b_p])/A[j][i][a_p] \n", 3074 | " \n", 3075 | " i=Nx-1\n", 3076 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1])+A[j][i][b_p])/A[j][i][a_p]\n", 3077 | " \n", 3078 | " i=0\n", 3079 | " j=Ny-1\n", 3080 | " #top left \n", 3081 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i])+A[j][i][b_p])/A[j][i][a_p]\n", 3082 | " \n", 3083 | " #top\n", 3084 | " for i in range(1,Nx-1):\n", 3085 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1])+A[j][i][b_p])/A[j][i][a_p]\n", 3086 | " \n", 3087 | " #top right\n", 3088 | " i=Nx-1\n", 3089 | " phi[j][i]=(-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1])+A[j][i][b_p])/A[j][i][a_p]\n", 3090 | " \n", 3091 | " return phi\n", 3092 | " \n", 3093 | " return phi_new" 3094 | ] 3095 | }, 3096 | { 3097 | "cell_type": "code", 3098 | "execution_count": 24, 3099 | "id": "fd51aa60", 3100 | "metadata": {}, 3101 | "outputs": [], 3102 | "source": [ 3103 | "@njit(fastmath=True)\n", 3104 | "def TDMA(A,N,x_array): \n", 3105 | " phi=np.zeros(N)\n", 3106 | " #Forward elimination\n", 3107 | " for i in range(1,N):\n", 3108 | " m=A[i][0]/A[i-1][1]\n", 3109 | " A[i][1]-=m*A[i-1][2]\n", 3110 | " A[i][-1]-=m*A[i-1][-1]\n", 3111 | " \n", 3112 | " #Backward substitution\n", 3113 | " phi[-1]=A[-1][-1]/A[-1][1]\n", 3114 | " for i in x_array:\n", 3115 | " phi[i]=(A[i][-1]-A[i][2]*phi[i+1])/A[i][1]\n", 3116 | " \n", 3117 | " return phi" 3118 | ] 3119 | }, 3120 | { 3121 | "cell_type": "code", 3122 | "execution_count": 25, 3123 | "id": "2de48756", 3124 | "metadata": {}, 3125 | "outputs": [], 3126 | "source": [ 3127 | "@njit\n", 3128 | "def LGS(A,phi,Ny,Nx):\n", 3129 | " #Row-wise sweep\n", 3130 | " x_array=np.arange(0,Nx-1)\n", 3131 | " x_array=np.flip(x_array)\n", 3132 | " \n", 3133 | " a_s=0\n", 3134 | " a_w=1\n", 3135 | " a_p=2\n", 3136 | " a_e=3\n", 3137 | " a_n=4\n", 3138 | " b_p=5\n", 3139 | " \n", 3140 | " #bottom to top#########################################################################################\n", 3141 | " A_TDMA=np.zeros((Nx,4))\n", 3142 | " #bottom left\n", 3143 | " j=0\n", 3144 | " i=0\n", 3145 | " A_TDMA[i][0]=0.0\n", 3146 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3147 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3148 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3149 | " \n", 3150 | " #bottom\n", 3151 | " for i in range(1,Nx-1):\n", 3152 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3153 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3154 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3155 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3156 | " \n", 3157 | " #bottom right\n", 3158 | " i=Nx-1\n", 3159 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3160 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3161 | " A_TDMA[i][2]=0.0\n", 3162 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]\n", 3163 | " \n", 3164 | " #TDMA solve\n", 3165 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3166 | " \n", 3167 | " for i in range(Nx):\n", 3168 | " phi[j][i]=phi_row[i]\n", 3169 | " \n", 3170 | " #Interior cells\n", 3171 | " for j in range(1,Ny-1):\n", 3172 | " A_TDMA=np.zeros((Nx,4))\n", 3173 | " #left\n", 3174 | " i=0\n", 3175 | " A_TDMA[i][0]=0.0\n", 3176 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3177 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3178 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3179 | " \n", 3180 | " for i in range(1,Nx-1):\n", 3181 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3182 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3183 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3184 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3185 | " \n", 3186 | " #right\n", 3187 | " i=Nx-1\n", 3188 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3189 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3190 | " A_TDMA[i][2]=0.0\n", 3191 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_n]*phi[j+1][i]-A[j][i][a_s]*phi[j-1][i]\n", 3192 | " \n", 3193 | " #TDMA solve\n", 3194 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3195 | "\n", 3196 | " for i in range(Nx):\n", 3197 | " phi[j][i]=phi_row[i]\n", 3198 | " \n", 3199 | " A_TDMA=np.zeros((Nx,4))\n", 3200 | " #top left\n", 3201 | " j=Ny-1\n", 3202 | " i=0\n", 3203 | " \n", 3204 | " A_TDMA[i][0]=0.0\n", 3205 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3206 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3207 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3208 | " \n", 3209 | " #bottom\n", 3210 | " for i in range(1,Nx-1):\n", 3211 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3212 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3213 | " A_TDMA[i][2]=A[j][i][a_e]\n", 3214 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3215 | " \n", 3216 | " #bottom right\n", 3217 | " i=Nx-1\n", 3218 | " A_TDMA[i][0]=A[j][i][a_w]\n", 3219 | " A_TDMA[i][1]=A[j][i][a_p]\n", 3220 | " A_TDMA[i][2]=0.0\n", 3221 | " A_TDMA[i][3]=A[j][i][b_p]-A[j][i][a_s]*phi[j-1][i]\n", 3222 | "\n", 3223 | " #TDMA solve\n", 3224 | " phi_row=TDMA(A_TDMA,Nx,x_array)\n", 3225 | " \n", 3226 | " for i in range(Nx):\n", 3227 | " phi[j][i]=phi_row[i]\n", 3228 | " \n", 3229 | " \n", 3230 | " #COLUMN-WISE SWEEP LEFT TO RIGHT#########################################################\n", 3231 | "# y_array=np.arange(0,Ny-1)\n", 3232 | "# #left cells\n", 3233 | "# i=0\n", 3234 | "# A_TDMA=np.zeros((Ny,4))\n", 3235 | "\n", 3236 | "# #interior (left wall)\n", 3237 | "# for j in range(1,Ny-1):\n", 3238 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3239 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3240 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3241 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_e]*phi[j][i+1] #BP-AW*phi_W-AE*phi_E\n", 3242 | "\n", 3243 | "# #bottom cell (bottom left corner)\n", 3244 | "# j=0\n", 3245 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3246 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3247 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_e]*phi[j][i+1] #BP-AE*phi_E\n", 3248 | "\n", 3249 | "# #top cell (top left corner)\n", 3250 | "# j=Ny-1\n", 3251 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3252 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3253 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_e]*phi[j][i+1] #BP-AE*phi_E\n", 3254 | "\n", 3255 | "# #TDMA solve\n", 3256 | "# phi_column=TDMA(A_TDMA,Ny,y_array) \n", 3257 | " \n", 3258 | "# #update array \n", 3259 | "# for j in range(Ny):\n", 3260 | "# phi[j][i]=phi_column[j]\n", 3261 | " \n", 3262 | "# #interior cells\n", 3263 | "# for i in range(1,Nx-1):\n", 3264 | "# #convert 5-band form to 3-band form\n", 3265 | "# A_TDMA=np.zeros((Ny,4))\n", 3266 | " \n", 3267 | "# #interior\n", 3268 | "# for j in range(1,Ny-1):\n", 3269 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3270 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3271 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3272 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1]-A[j][i][a_e]*phi[j][i+1] #BP-AW*phi_W-AE*phi_E\n", 3273 | " \n", 3274 | "# #bottom cell\n", 3275 | "# j=0\n", 3276 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3277 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3278 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1]-A[j][i][a_e]*phi[j][i+1] #BP-AW*phi_W-AE*phi_E\n", 3279 | " \n", 3280 | "# #top cell\n", 3281 | "# j=Ny-1\n", 3282 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3283 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3284 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1]-A[j][i][a_e]*phi[j][i+1] #BP-AW*phi_W-AE*phi_E\n", 3285 | "\n", 3286 | "# #TDMA solve\n", 3287 | "# phi_column=TDMA(A_TDMA,Ny,y_array) \n", 3288 | " \n", 3289 | "# #update array \n", 3290 | "# for j in range(Ny):\n", 3291 | "# phi[j][i]=phi_column[j]\n", 3292 | " \n", 3293 | "# #right cells\n", 3294 | "# i=Nx-1\n", 3295 | "# A_TDMA=np.zeros((Ny,4))\n", 3296 | "\n", 3297 | "# #interior (right wall)\n", 3298 | "# for j in range(1,Ny-1):\n", 3299 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3300 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3301 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3302 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1] #BP-AW*phi_W-AW*phi_W\n", 3303 | "\n", 3304 | "# #bottom cell (bottom left corner)\n", 3305 | "# j=0\n", 3306 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3307 | "# A_TDMA[j][2]=A[j][i][a_n] #AN\n", 3308 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1] #BP-AW*phi_W\n", 3309 | "\n", 3310 | "# #top cell (top left corner)\n", 3311 | "# j=Ny-1\n", 3312 | "# A_TDMA[j][0]=A[j][i][a_s] #AS\n", 3313 | "# A_TDMA[j][1]=A[j][i][a_p] #AP\n", 3314 | "# A_TDMA[j][3]=A[j][i][b_p]-A[j][i][a_w]*phi[j][i-1] #BP-AW*phi_W\n", 3315 | " \n", 3316 | "# #TDMA solve\n", 3317 | "# phi_column=TDMA(A_TDMA,Ny,y_array) \n", 3318 | " \n", 3319 | "# #update array \n", 3320 | "# for j in range(Ny):\n", 3321 | "# phi[j][i]=phi_column[j]\n", 3322 | " \n", 3323 | " return phi" 3324 | ] 3325 | }, 3326 | { 3327 | "cell_type": "code", 3328 | "execution_count": 26, 3329 | "id": "8b3a966d", 3330 | "metadata": {}, 3331 | "outputs": [], 3332 | "source": [ 3333 | "@njit\n", 3334 | "def residual(A,phi,res_array,Ny,Nx):\n", 3335 | " #res_array=np.zeros((Ny,Nx))\n", 3336 | " \n", 3337 | " a_s=0\n", 3338 | " a_w=1\n", 3339 | " a_p=2\n", 3340 | " a_e=3\n", 3341 | " a_n=4\n", 3342 | " b_p=5\n", 3343 | " \n", 3344 | " for j in range(1,Ny-1):\n", 3345 | " for i in range(1,Nx-1):\n", 3346 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3347 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3348 | " res_array[j][i]=res\n", 3349 | " \n", 3350 | " for j in range(1,Ny-1):\n", 3351 | " i=0\n", 3352 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_p]*phi[j][i]\n", 3353 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3354 | " res_array[j][i]=res\n", 3355 | " \n", 3356 | " i=Nx-1\n", 3357 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3358 | " +A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3359 | " res_array[j][i]=res\n", 3360 | " \n", 3361 | " for i in range(1,Nx-1):\n", 3362 | " j=0\n", 3363 | " res=-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3364 | " +A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3365 | " res_array[j][i]=res\n", 3366 | " \n", 3367 | " j=Ny-1\n", 3368 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]\n", 3369 | " +A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p]\n", 3370 | " res_array[j][i]=res\n", 3371 | " \n", 3372 | " j=0\n", 3373 | " i=0\n", 3374 | " res=-(A[j][i][a_p]*phi[j][i]+A[j][i][a_e]*phi[j][i+1]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3375 | " res_array[j][i]=res\n", 3376 | " \n", 3377 | " j=0\n", 3378 | " i=Nx-1\n", 3379 | " res=-(A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i]+A[j][i][a_n]*phi[j+1][i])+A[j][i][b_p]\n", 3380 | " res_array[j][i]=res\n", 3381 | " \n", 3382 | " j=Ny-1\n", 3383 | " i=0\n", 3384 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_p]*phi[j][i]+A[j][i][a_e]*phi[j][i+1])+A[j][i][b_p]\n", 3385 | " res_array[j][i]=res\n", 3386 | " \n", 3387 | " j=Ny-1\n", 3388 | " i=Nx-1\n", 3389 | " res=-(A[j][i][a_s]*phi[j-1][i]+A[j][i][a_w]*phi[j][i-1]+A[j][i][a_p]*phi[j][i])+A[j][i][b_p]\n", 3390 | " res_array[j][i]=res\n", 3391 | " \n", 3392 | " return np.reshape(res_array,Ny*Nx)" 3393 | ] 3394 | }, 3395 | { 3396 | "cell_type": "code", 3397 | "execution_count": 27, 3398 | "id": "db16e3b4", 3399 | "metadata": {}, 3400 | "outputs": [], 3401 | "source": [ 3402 | "@njit\n", 3403 | "def restriction(A,res,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3404 | " #need to update the source vector (error vector)\n", 3405 | " res_2D=np.reshape(res,(Ny_f,Nx_f))\n", 3406 | " \n", 3407 | " for j in range(Ny_c):\n", 3408 | " for i in range(Nx_c):\n", 3409 | " res_c=(res_2D[2*j][2*i]+res_2D[2*j+1][2*i]+res_2D[2*j][2*i+1]+res_2D[2*j+1][2*i+1])\n", 3410 | " A[j][i][5]=res_c\n", 3411 | " return A" 3412 | ] 3413 | }, 3414 | { 3415 | "cell_type": "code", 3416 | "execution_count": 28, 3417 | "id": "eab4c0fa", 3418 | "metadata": {}, 3419 | "outputs": [], 3420 | "source": [ 3421 | "@njit\n", 3422 | "def nodal_restriction(phi,Ny_f,Nx_f,Ny_c,Nx_c):\n", 3423 | " phi_c=np.zeros((Ny_c,Nx_c))\n", 3424 | " \n", 3425 | " for j in range(Ny_c):\n", 3426 | " for i in range(Nx_c):\n", 3427 | " j_fine=2*j\n", 3428 | " i_fine=2*i\n", 3429 | " phi_c[j][i]=0.25*(phi[j_fine][i_fine]+phi[j_fine][i_fine+1]+phi[j_fine+1][i_fine]+phi[j_fine+1][i_fine+1])\n", 3430 | " \n", 3431 | " return phi_c" 3432 | ] 3433 | }, 3434 | { 3435 | "cell_type": "code", 3436 | "execution_count": 29, 3437 | "id": "49da1bc0", 3438 | "metadata": {}, 3439 | "outputs": [], 3440 | "source": [ 3441 | "@njit\n", 3442 | "def prolongation(corr_coarse,corr_fine,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3443 | " for j in range(Ny_c):\n", 3444 | " for i in range(Nx_c):\n", 3445 | " corr=corr_coarse[j][i]\n", 3446 | " corr_fine[2*j][2*i]=corr\n", 3447 | " corr_fine[2*j][2*i+1]=corr\n", 3448 | " corr_fine[2*j+1][2*i]=corr\n", 3449 | " corr_fine[2*j+1][2*i+1]=corr\n", 3450 | " \n", 3451 | " return corr_fine" 3452 | ] 3453 | }, 3454 | { 3455 | "cell_type": "code", 3456 | "execution_count": 30, 3457 | "id": "9e995db5", 3458 | "metadata": {}, 3459 | "outputs": [], 3460 | "source": [ 3461 | "@njit(fastmath=True)\n", 3462 | "def bilinear_prolongation(phi_coarse,Ny_c,Nx_c,Ny_f,Nx_f):\n", 3463 | " phi_fine=np.zeros((Ny_f,Nx_f))\n", 3464 | " #interior cells\n", 3465 | " for j in range(1,Ny_c-1):\n", 3466 | " for i in range(1,Nx_c-1):\n", 3467 | " phi=phi_coarse[j][i]\n", 3468 | " \n", 3469 | " phi_w=phi_coarse[j][i-1]\n", 3470 | " phi_e=phi_coarse[j][i+1]\n", 3471 | " phi_n=phi_coarse[j+1][i]\n", 3472 | " phi_s=phi_coarse[j-1][i]\n", 3473 | " \n", 3474 | " phi_sw=phi_coarse[j-1][i-1]\n", 3475 | " phi_se=phi_coarse[j-1][i+1]\n", 3476 | " phi_nw=phi_coarse[j+1][i-1]\n", 3477 | " phi_ne=phi_coarse[j+1][i+1]\n", 3478 | " \n", 3479 | " j2=2*j\n", 3480 | " i2=2*i\n", 3481 | " \n", 3482 | " #top left inner cell\n", 3483 | " phi_fine[j2+1][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3484 | " #top right inner cell\n", 3485 | " phi_fine[j2+1][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3486 | " #bottom left inner cell\n", 3487 | " phi_fine[j2][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3488 | " #bottom right inner cell\n", 3489 | " phi_fine[j2][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3490 | " \n", 3491 | " #for top wall\n", 3492 | " j=Ny_c-1\n", 3493 | " for i in range(1,Nx_c-1):\n", 3494 | " phi=phi_coarse[j][i]\n", 3495 | "\n", 3496 | " phi_w=phi_coarse[j][i-1]\n", 3497 | " phi_e=phi_coarse[j][i+1]\n", 3498 | " phi_s=phi_coarse[j-1][i]\n", 3499 | "\n", 3500 | " phi_sw=phi_coarse[j-1][i-1]\n", 3501 | " phi_se=phi_coarse[j-1][i+1]\n", 3502 | " \n", 3503 | " j2=2*j\n", 3504 | " i2=2*i\n", 3505 | "\n", 3506 | " #top left inner cell\n", 3507 | " phi_fine[j2+1][i2]=0.9375*phi+0.3125*phi_w-0.1875*phi_s-0.0625*phi_sw\n", 3508 | " #top right inner cell\n", 3509 | " phi_fine[j2+1][i2+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_s-0.0625*phi_se\n", 3510 | " #bottom left inner cell\n", 3511 | " phi_fine[j2][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3512 | " #bottom right inner cell\n", 3513 | " phi_fine[j2][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3514 | "\n", 3515 | " #for bottom wall\n", 3516 | " j=0\n", 3517 | " for i in range(1,Nx_c-1):\n", 3518 | " phi=phi_coarse[j][i]\n", 3519 | "\n", 3520 | " phi_w=phi_coarse[j][i-1]\n", 3521 | " phi_e=phi_coarse[j][i+1]\n", 3522 | " phi_n=phi_coarse[j+1][i]\n", 3523 | "\n", 3524 | " phi_nw=phi_coarse[j+1][i-1]\n", 3525 | " phi_ne=phi_coarse[j+1][i+1]\n", 3526 | " \n", 3527 | " j2=2*j\n", 3528 | " i2=2*i\n", 3529 | "\n", 3530 | " #top left inner cell\n", 3531 | " phi_fine[j2+1][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3532 | " #top right inner cell\n", 3533 | " phi_fine[j2+1][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3534 | " #bottom left inner cell\n", 3535 | " phi_fine[j2][i2]=0.9375*phi+0.3125*phi_w-0.1875*phi_n-0.0625*phi_nw\n", 3536 | " #bottom right inner cell\n", 3537 | " phi_fine[j2][i2+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_n-0.0625*phi_ne\n", 3538 | " \n", 3539 | " #for left wall\n", 3540 | " i=0\n", 3541 | " for j in range(1,Ny_c-1):\n", 3542 | " phi=phi_coarse[j][i]\n", 3543 | "\n", 3544 | " phi_e=phi_coarse[j][i+1]\n", 3545 | " phi_n=phi_coarse[j+1][i]\n", 3546 | " phi_s=phi_coarse[j-1][i]\n", 3547 | "\n", 3548 | " phi_se=phi_coarse[j-1][i+1]\n", 3549 | " phi_ne=phi_coarse[j+1][i+1]\n", 3550 | " \n", 3551 | " j2=2*j\n", 3552 | " i2=2*i\n", 3553 | "\n", 3554 | " #top left inner cell\n", 3555 | " phi_fine[j2+1][i2]=0.9375*phi+0.3125*phi_n-0.1875*phi_e-0.0625*phi_ne\n", 3556 | " #top right inner cell\n", 3557 | " phi_fine[j2+1][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3558 | " #bottom left inner cell\n", 3559 | " phi_fine[j2][i2]=0.9375*phi+0.3125*phi_s-0.1875*phi_e-0.0625*phi_se\n", 3560 | " #bottom right inner cell\n", 3561 | " phi_fine[j2][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3562 | "\n", 3563 | " #for right wall\n", 3564 | " i=Nx_c-1\n", 3565 | " for j in range(1,Ny_c-1):\n", 3566 | " phi=phi_coarse[j][i]\n", 3567 | "\n", 3568 | " phi_w=phi_coarse[j][i-1]\n", 3569 | " phi_n=phi_coarse[j+1][i]\n", 3570 | " phi_s=phi_coarse[j-1][i]\n", 3571 | "\n", 3572 | " phi_sw=phi_coarse[j-1][i-1]\n", 3573 | " phi_nw=phi_coarse[j+1][i-1]\n", 3574 | " \n", 3575 | " j2=2*j\n", 3576 | " i2=2*i\n", 3577 | "\n", 3578 | " #top left inner cell\n", 3579 | " phi_fine[j2+1][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3580 | " #top right inner cell\n", 3581 | " phi_fine[j2+1][i2+1]=0.9375*phi+0.3125*phi_n-0.1875*phi_w-0.0625*phi_nw\n", 3582 | " #bottom left inner cell\n", 3583 | " phi_fine[j2][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3584 | " #bottom right inner cell\n", 3585 | " phi_fine[j2][i2+1]=0.9375*phi+0.3125*phi_s-0.1875*phi_w-0.0625*phi_sw\n", 3586 | "\n", 3587 | " #for top left cell\n", 3588 | " i=0\n", 3589 | " j=Ny_c-1\n", 3590 | "\n", 3591 | " phi=phi_coarse[j][i]\n", 3592 | "\n", 3593 | " phi_e=phi_coarse[j][i+1]\n", 3594 | " phi_s=phi_coarse[j-1][i]\n", 3595 | "\n", 3596 | " phi_se=phi_coarse[j-1][i+1]\n", 3597 | " \n", 3598 | " j2=2*j\n", 3599 | " i2=2*i\n", 3600 | "\n", 3601 | " #top left inner cell\n", 3602 | " phi_fine[j2+1][i2]=1.25*phi-0.25*phi_se\n", 3603 | " #top right inner cell\n", 3604 | " phi_fine[j2+1][i2+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_s-0.0625*phi_se\n", 3605 | " #bottom left inner cell\n", 3606 | " phi_fine[j2][i2]=0.9375*phi+0.3125*phi_s-0.1875*phi_e-0.0625*phi_se\n", 3607 | " #bottom right inner cell\n", 3608 | " phi_fine[j2][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_s+0.0625*phi_se\n", 3609 | "\n", 3610 | " #for top right cell\n", 3611 | " i=Nx_c-1\n", 3612 | " j=Ny_c-1\n", 3613 | "\n", 3614 | " phi=phi_coarse[j][i]\n", 3615 | "\n", 3616 | " phi_w=phi_coarse[j][i-1]\n", 3617 | " phi_s=phi_coarse[j-1][i]\n", 3618 | "\n", 3619 | " phi_sw=phi_coarse[j-1][i-1]\n", 3620 | " \n", 3621 | " j2=2*j\n", 3622 | " i2=2*i\n", 3623 | "\n", 3624 | " #top left inner cell\n", 3625 | " phi_fine[j2+1][i2]=0.9375*phi+0.3125*phi_w-0.1875*phi_s-0.0625*phi_sw\n", 3626 | " #top right inner cell\n", 3627 | " phi_fine[j2+1][i2+1]=1.25*phi-0.25*phi_sw\n", 3628 | " #bottom left inner cell\n", 3629 | " phi_fine[j2][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_s+0.0625*phi_sw\n", 3630 | " #bottom right inner cell\n", 3631 | " phi_fine[j2][i2+1]=0.9375*phi+0.3125*phi_s-0.1875*phi_w-0.0625*phi_sw\n", 3632 | "\n", 3633 | " #for bottom left cell\n", 3634 | " i=0\n", 3635 | " j=0\n", 3636 | "\n", 3637 | " phi=phi_coarse[j][i]\n", 3638 | "\n", 3639 | " phi_e=phi_coarse[j][i+1]\n", 3640 | " phi_n=phi_coarse[j+1][i]\n", 3641 | "\n", 3642 | " phi_ne=phi_coarse[j+1][i+1]\n", 3643 | " \n", 3644 | " j2=2*j\n", 3645 | " i2=2*i\n", 3646 | "\n", 3647 | " #top left inner cell\n", 3648 | " phi_fine[j2+1][i2]=0.9375*phi+0.3125*phi_n-0.1875*phi_e-0.0625*phi_ne\n", 3649 | " #top right inner cell\n", 3650 | " phi_fine[j2+1][i2+1]=0.5625*phi+0.1875*phi_e+0.1875*phi_n+0.0625*phi_ne\n", 3651 | " #bottom left inner cell\n", 3652 | " phi_fine[j2][i2]=1.25*phi-0.25*phi_ne\n", 3653 | " #bottom right inner cell\n", 3654 | " phi_fine[j2][i2+1]=0.9375*phi+0.3125*phi_e-0.1875*phi_n-0.0625*phi_ne\n", 3655 | "\n", 3656 | " #for bottom right cell\n", 3657 | " i=Nx_c-1\n", 3658 | " j=0\n", 3659 | "\n", 3660 | " phi=phi_coarse[j][i]\n", 3661 | "\n", 3662 | " phi_w=phi_coarse[j][i-1]\n", 3663 | " phi_n=phi_coarse[j+1][i]\n", 3664 | "\n", 3665 | " phi_nw=phi_coarse[j+1][i-1]\n", 3666 | " \n", 3667 | " j2=2*j\n", 3668 | " i2=2*i\n", 3669 | "\n", 3670 | " #top left inner cell\n", 3671 | " phi_fine[j2+1][i2]=0.5625*phi+0.1875*phi_w+0.1875*phi_n+0.0625*phi_nw\n", 3672 | " #top right inner cell\n", 3673 | " phi_fine[j2+1][i2+1]=0.9375*phi+0.3125*phi_n-0.1875*phi_w-0.0625*phi_nw\n", 3674 | " #bottom left inner cell\n", 3675 | " phi_fine[j2][i2]=0.9375*phi+0.3125*phi_w-0.1875*phi_n-0.0625*phi_nw\n", 3676 | " #bottom right inner cell\n", 3677 | " phi_fine[j2][i2+1]=1.25*phi-0.25*phi_nw\n", 3678 | "\n", 3679 | " return phi_fine" 3680 | ] 3681 | }, 3682 | { 3683 | "cell_type": "code", 3684 | "execution_count": 31, 3685 | "id": "ce882958", 3686 | "metadata": {}, 3687 | "outputs": [], 3688 | "source": [ 3689 | "def V_cycle(A_list,N_list,phi,itermax=1):\n", 3690 | " Ny=int(N_list[0][0])\n", 3691 | " Nx=int(N_list[0][1])\n", 3692 | " n_levels=len(N_list)\n", 3693 | " res=1\n", 3694 | " res_norm=1\n", 3695 | " iter_count=0\n", 3696 | " \n", 3697 | " A_first=A_list[0]\n", 3698 | " \n", 3699 | " N=Nx*Ny\n", 3700 | " \n", 3701 | " for num in range(itermax):\n", 3702 | " corr_list=[]\n", 3703 | "\n", 3704 | " #Calculate residual\n", 3705 | " res_array=np.zeros((Ny,Nx))\n", 3706 | " res_array=residual(A_first,phi,res_array,Ny,Nx)\n", 3707 | "# res=np.sum(np.abs(res_array))/N\n", 3708 | " \n", 3709 | "# if iter_count==0 and res!=0: #normalize residual\n", 3710 | "# res_norm=res\n", 3711 | "\n", 3712 | "# res=res/res_norm\n", 3713 | " corr_list.append(phi)\n", 3714 | "\n", 3715 | " if n_levels>1:\n", 3716 | " #GOES COARSER\n", 3717 | " for n in range(n_levels-1):\n", 3718 | " A_coarse=A_list[n+1]\n", 3719 | "\n", 3720 | " Ny_f=int(N_list[n][0])\n", 3721 | " Nx_f=int(N_list[n][1])\n", 3722 | " Ny_c=int(N_list[n+1][0])\n", 3723 | " Nx_c=int(N_list[n+1][1])\n", 3724 | " \n", 3725 | " phi_prime=np.zeros((Ny_c,Nx_c))\n", 3726 | "\n", 3727 | " #RESTRICTION\n", 3728 | " A_coarse=restriction(A_coarse,res_array,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3729 | " \n", 3730 | " #PRE-SMOOTHING\n", 3731 | " phi_prime=gauss_seidel_1(A_coarse,phi_prime,Nx_c,Ny_c)\n", 3732 | " for i in range(n):\n", 3733 | " #phi1_prime=gauss_seidel(A_coarse,phi_prime,Nx_c,Ny_c)\n", 3734 | " phi1_prime=LGS(A_coarse,phi_prime,Ny_c,Nx_c)\n", 3735 | " phi_prime=phi1_prime\n", 3736 | "\n", 3737 | " #CALCULATE RESIDUAL\n", 3738 | " res_array=np.zeros((Ny_c,Nx_c))\n", 3739 | " res_array=residual(A_coarse,phi_prime,res_array,Ny_c,Nx_c)\n", 3740 | "\n", 3741 | " #STORE CORRECTION VECTOR\n", 3742 | " corr_list.append(phi_prime)\n", 3743 | " \n", 3744 | " #GOES FINER\n", 3745 | " for n in reversed(range(n_levels-1)):\n", 3746 | " A_fine=A_list[n]\n", 3747 | "\n", 3748 | " phi_prime_coarse=corr_list[n+1]\n", 3749 | " phi_prime_fine=corr_list[n]\n", 3750 | "\n", 3751 | " Ny_f=int(N_list[n][0])\n", 3752 | " Nx_f=int(N_list[n][1])\n", 3753 | " Ny_c=int(N_list[n+1][0])\n", 3754 | " Nx_c=int(N_list[n+1][1])\n", 3755 | "\n", 3756 | " #PROLONGATION \n", 3757 | " phi_prime_corr=bilinear_prolongation(phi_prime_coarse,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3758 | "\n", 3759 | " #CORRECTION\n", 3760 | " phi_prime_fine=phi_prime_fine+phi_prime_corr\n", 3761 | "\n", 3762 | " #SMOOTHING\n", 3763 | " for i in range(n+1):\n", 3764 | " phi1_prime=gauss_seidel(A_fine,phi_prime_fine,Nx_f,Ny_f)\n", 3765 | " #phi1_prime=LGS(A_fine,phi_prime_fine,Ny_f,Nx_f)\n", 3766 | " phi_prime_fine=phi1_prime\n", 3767 | " \n", 3768 | " corr_list[n]=phi_prime_fine\n", 3769 | "\n", 3770 | " phi=corr_list[0]\n", 3771 | " iter_count+=1\n", 3772 | " \n", 3773 | "# res_array=np.zeros((Ny,Nx))\n", 3774 | "# res_array=residual(A_first,phi,res_array,Ny,Nx)\n", 3775 | "# res=np.sum(np.abs(res_array))/N\n", 3776 | "# print(res/res_norm)\n", 3777 | " return phi\n", 3778 | "\n", 3779 | "def W_cycle(A_list,N_list,phi,corr_list,first,itermax=1):\n", 3780 | " A_first=A_list[0]\n", 3781 | " \n", 3782 | " Ny_first=int(N_list[0][0])\n", 3783 | " Nx_first=int(N_list[0][1])\n", 3784 | " n_levels=len(N_list)\n", 3785 | " \n", 3786 | " res=1\n", 3787 | " \n", 3788 | " for num in range(itermax):\n", 3789 | " if first==True:\n", 3790 | " for n in range(n_levels):\n", 3791 | " Ny1=int(N_list[n][0])\n", 3792 | " Nx1=int(N_list[n][1])\n", 3793 | " corr_list[n]=np.zeros((Ny1,Nx1))\n", 3794 | " \n", 3795 | " #Calculate residual\n", 3796 | " res_array=np.zeros((Ny,Nx))\n", 3797 | " res_array=residual(A_first,phi,res_array,Ny,Nx)\n", 3798 | "\n", 3799 | " corr_list[0]=phi\n", 3800 | "\n", 3801 | " if n_levels>1:\n", 3802 | " #GOES COARSER\n", 3803 | " for n in range(n_levels-1):\n", 3804 | " A_coarse=A_list[n+1]\n", 3805 | "\n", 3806 | " Ny_f=int(N_list[n][0])\n", 3807 | " Nx_f=int(N_list[n][1])\n", 3808 | " Ny_c=int(N_list[n+1][0])\n", 3809 | " Nx_c=int(N_list[n+1][1])\n", 3810 | " \n", 3811 | " phi_prime=corr_list[n+1]\n", 3812 | "\n", 3813 | " #RESTRICTION\n", 3814 | " A_coarse=restriction(A_coarse,res_array,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3815 | " \n", 3816 | " #PRE-SMOOTHING\n", 3817 | " phi_prime=gauss_seidel_1(A_coarse,phi_prime,Nx_c,Ny_c)\n", 3818 | " for i in range(int(1.5*n)+1):\n", 3819 | " #phi1_prime=gauss_seidel(A_coarse,phi_prime,Nx_c,Ny_c)\n", 3820 | " phi1_prime=LGS(A_coarse,phi_prime,Ny_c,Nx_c)\n", 3821 | " phi_prime=phi1_prime\n", 3822 | "\n", 3823 | " #CALCULATE RESIDUAL\n", 3824 | " res_array=np.zeros((Ny_c,Nx_c))\n", 3825 | " res_array=residual(A_coarse,phi_prime,res_array,Ny_c,Nx_c)\n", 3826 | "\n", 3827 | " corr_list[n+1]=phi_prime\n", 3828 | "\n", 3829 | " #GOES FINER\n", 3830 | " for n in reversed(range(n_levels-1)):\n", 3831 | " A_fine=A_list[n]\n", 3832 | " \n", 3833 | " phi_prime_fine=corr_list[n]\n", 3834 | " phi_prime_coarse=corr_list[n+1]\n", 3835 | "\n", 3836 | " Ny_f=int(N_list[n][0])\n", 3837 | " Nx_f=int(N_list[n][1])\n", 3838 | " Ny_c=int(N_list[n+1][0])\n", 3839 | " Nx_c=int(N_list[n+1][1])\n", 3840 | "\n", 3841 | " #PROLONGATION \n", 3842 | " phi_prime_corr=bilinear_prolongation(phi_prime_coarse,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3843 | " #CORRECTION\n", 3844 | " phi_prime_fine=phi_prime_fine+phi_prime_corr\n", 3845 | "\n", 3846 | " #SMOOTHING\n", 3847 | " for i in range(int(0.5*(n**2))+1):\n", 3848 | " phi1_prime=gauss_seidel(A_fine,phi_prime_fine,Nx_f,Ny_f)\n", 3849 | " #phi1_prime=LGS(A_fine,phi_prime_fine,Ny_f,Nx_f)\n", 3850 | " phi_prime_fine=phi1_prime\n", 3851 | " \n", 3852 | " corr_list[n]=phi_prime_fine\n", 3853 | " \n", 3854 | " w=4\n", 3855 | " if n==w and first==True: \n", 3856 | " phi_prime_fine=V_cycle(A_list[w::],N_list[w::],phi_prime_fine,2)\n", 3857 | " corr_list[n]=phi_prime_fine\n", 3858 | " \n", 3859 | " phi=corr_list[0]\n", 3860 | "\n", 3861 | " return phi\n", 3862 | "\n", 3863 | "def pg_solver(A_list,N_list,phi,n_levels):\n", 3864 | " \n", 3865 | " Ny=int(N_list[-1][0])\n", 3866 | " Nx=int(N_list[-1][1])\n", 3867 | " \n", 3868 | " A_coarse=A_list[-1]\n", 3869 | " N=Nx*Ny\n", 3870 | " \n", 3871 | " #Iterate on coarsest grid\n", 3872 | " for i in range(100):\n", 3873 | " phi1=gauss_seidel(A_coarse,phi,Nx,Ny)\n", 3874 | " phi=phi1\n", 3875 | " phi_coarse=phi\n", 3876 | " \n", 3877 | " #GOES FINER\n", 3878 | " for n in reversed(range(n_levels-1)):\n", 3879 | " A_fine=A_list[n]\n", 3880 | " \n", 3881 | " Ny_f=int(N_list[n][0])\n", 3882 | " Nx_f=int(N_list[n][1])\n", 3883 | " Ny_c=int(N_list[n+1][0])\n", 3884 | " Nx_c=int(N_list[n+1][1])\n", 3885 | "\n", 3886 | " #PROLONGATION \n", 3887 | " dummy=np.zeros((Ny_f,Nx_f))\n", 3888 | " phi_fine=bilinear_prolongation(phi_coarse,Ny_c,Nx_c,Ny_f,Nx_f)\n", 3889 | "\n", 3890 | " #SMOOTHING \n", 3891 | " for i in range(int(0.5*(n**3))+1):\n", 3892 | " #phi1_fine=gauss_seidel(A_fine,phi_fine,Nx_f,Ny_f)\n", 3893 | " phi1_fine=LGS(A_fine,phi_fine,Ny_f,Nx_f)\n", 3894 | " phi_fine=phi1_fine\n", 3895 | " \n", 3896 | " phi_coarse=phi_fine\n", 3897 | " \n", 3898 | " phi=phi1_fine\n", 3899 | " \n", 3900 | " return phi" 3901 | ] 3902 | }, 3903 | { 3904 | "cell_type": "code", 3905 | "execution_count": 32, 3906 | "id": "408187ed", 3907 | "metadata": {}, 3908 | "outputs": [], 3909 | "source": [ 3910 | "@njit(fastmath=True)\n", 3911 | "def jacobi_preconditioner(A,Ny,Nx):\n", 3912 | " a_s=0\n", 3913 | " a_w=1\n", 3914 | " a_p=2\n", 3915 | " a_e=3\n", 3916 | " a_n=4\n", 3917 | " b_p=5\n", 3918 | " \n", 3919 | " for j in range(Ny):\n", 3920 | " for i in range(Nx):\n", 3921 | " AP=A[j][i][a_p]\n", 3922 | " A[j][i][a_s]=A[j][i][a_s]/AP\n", 3923 | " A[j][i][a_w]=A[j][i][a_w]/AP\n", 3924 | " A[j][i][a_p]=A[j][i][a_p]/AP\n", 3925 | " A[j][i][a_e]=A[j][i][a_e]/AP\n", 3926 | " A[j][i][a_n]=A[j][i][a_n]/AP\n", 3927 | " A[j][i][b_p]=A[j][i][b_p]/AP\n", 3928 | " \n", 3929 | " return A" 3930 | ] 3931 | }, 3932 | { 3933 | "cell_type": "code", 3934 | "execution_count": 33, 3935 | "id": "3f540f83", 3936 | "metadata": {}, 3937 | "outputs": [], 3938 | "source": [ 3939 | "def steady_solve_mom(N_list,square_coord,U,V,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T,av,n_levels,v,gb,gx,gy):\n", 3940 | " uA_list=[]\n", 3941 | " vA_list=[]\n", 3942 | "\n", 3943 | " Ny_first=int(N_list[0][0])\n", 3944 | " Nx_first=int(N_list[0][1])\n", 3945 | " \n", 3946 | " #Set up mom coeff\n", 3947 | " uA=np.zeros((Ny_first,Nx_first,6))\n", 3948 | " vA=np.zeros((Ny_first,Nx_first,6))\n", 3949 | " DX=np.zeros((Ny_first,Nx_first))\n", 3950 | " DY=np.zeros((Ny_first,Nx_first))\n", 3951 | " uA,vA,DX,DY=mom_coeff(Nx_first,Ny_first,square_coord,U,V,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx,uA,vA,DX,DY)\n", 3952 | " \n", 3953 | " #Under-relax and store coefficient matrix\n", 3954 | " uA_relaxed=implicit_relaxation(uA,U,Ny_first,Nx_first,av)\n", 3955 | " vA_relaxed=implicit_relaxation(vA,V,Ny_first,Nx_first,av)\n", 3956 | " uA_list.append(uA_relaxed)\n", 3957 | " vA_list.append(vA_relaxed)\n", 3958 | "\n", 3959 | " #Agglomerate mom eqns\n", 3960 | " for n in range(n_levels-1):\n", 3961 | " Ny_f=int(N_list[n][0])\n", 3962 | " Nx_f=int(N_list[n][1])\n", 3963 | " Ny_c=int(N_list[n+1][0])\n", 3964 | " Nx_c=int(N_list[n+1][1])\n", 3965 | "\n", 3966 | " uA_fine=uA_list[n]\n", 3967 | " vA_fine=vA_list[n]\n", 3968 | " \n", 3969 | " uA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 3970 | " vA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 3971 | " uA_coarse=agglomeration(uA_fine,uA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3972 | " vA_coarse=agglomeration(vA_fine,vA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 3973 | "\n", 3974 | " uA_list.append(uA_coarse)\n", 3975 | " vA_list.append(vA_coarse)\n", 3976 | " \n", 3977 | " #Solve mom eqns\n", 3978 | " U1=V_cycle(uA_list,N_list,U)\n", 3979 | " V1=V_cycle(vA_list,N_list,V)\n", 3980 | " \n", 3981 | " return U1,V1,uA,vA,DX,DY" 3982 | ] 3983 | }, 3984 | { 3985 | "cell_type": "code", 3986 | "execution_count": 34, 3987 | "id": "e503c172", 3988 | "metadata": {}, 3989 | "outputs": [], 3990 | "source": [ 3991 | "def transient_solve_mom(N_list,square_coord,U,V,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T,U_time_array,V_time_array,av,dy,dx,dt,n_levels,v,gb,gx,gy,timestep):\n", 3992 | " uA_list=[]\n", 3993 | " vA_list=[]\n", 3994 | " \n", 3995 | " Ny_first=int(N_list[0][0])\n", 3996 | " Nx_first=int(N_list[0][1])\n", 3997 | " #Set up mom coeff\n", 3998 | " uA=np.zeros((Ny_first,Nx_first,6))\n", 3999 | " vA=np.zeros((Ny_first,Nx_first,6))\n", 4000 | " DX=np.zeros((Ny_first,Nx_first))\n", 4001 | " DY=np.zeros((Ny_first,Nx_first))\n", 4002 | " uA,vA,DX,DY=mom_coeff(Nx_first,Ny_first,square_coord,U,V,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T,av,v,gb,gx,gy,dy,dx,uA,vA,DX,DY)\n", 4003 | " \n", 4004 | " k=2*dy*dx/dt\n", 4005 | " uA_transient=implicit_time(uA,U_time_array,Ny_first,Nx_first,k,timestep)\n", 4006 | " vA_transient=implicit_time(vA,V_time_array,Ny_first,Nx_first,k,timestep)\n", 4007 | " uA_list.append(uA_transient)\n", 4008 | " vA_list.append(vA_transient)\n", 4009 | "\n", 4010 | " #Agglomerate mom eqns\n", 4011 | " for n in range(n_levels-1):\n", 4012 | " Ny_f=int(N_list[n][0])\n", 4013 | " Nx_f=int(N_list[n][1])\n", 4014 | " Ny_c=int(N_list[n+1][0])\n", 4015 | " Nx_c=int(N_list[n+1][1])\n", 4016 | "\n", 4017 | " uA_fine=uA_list[n]\n", 4018 | " vA_fine=vA_list[n]\n", 4019 | " \n", 4020 | " uA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 4021 | " vA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 4022 | " uA_coarse=agglomeration(uA_fine,uA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4023 | " vA_coarse=agglomeration(vA_fine,vA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4024 | "\n", 4025 | " uA_list.append(uA_coarse)\n", 4026 | " vA_list.append(vA_coarse)\n", 4027 | " \n", 4028 | " #Solve mom eqns\n", 4029 | " U1=V_cycle(uA_list,N_list,U,1)\n", 4030 | " V1=V_cycle(vA_list,N_list,V,1)\n", 4031 | "\n", 4032 | " return U1,V1,uA_transient,vA_transient,DX,DY" 4033 | ] 4034 | }, 4035 | { 4036 | "cell_type": "code", 4037 | "execution_count": 35, 4038 | "id": "a312da0c", 4039 | "metadata": {}, 4040 | "outputs": [], 4041 | "source": [ 4042 | "def solve_cont(N_list,square_coord,Uf,Vf,DXf,DYf,DX,n_levels,dy,dx,corr_list): \n", 4043 | " #Set up pressure correction coeff\n", 4044 | " pA_list=[]\n", 4045 | " \n", 4046 | " Ny_first=int(N_list[0][0])\n", 4047 | " Nx_first=int(N_list[0][1])\n", 4048 | " pA=np.zeros((Ny_first,Nx_first,6))\n", 4049 | " pA=cont(Nx_first,Ny_first,square_coord,Uf,Vf,DXf,DYf,DX,dy,dx,pA)\n", 4050 | " pA_pc=jacobi_preconditioner(pA,Ny,Nx)\n", 4051 | " pA_list.append(pA)\n", 4052 | "\n", 4053 | " #Agglomerate PC coeff\n", 4054 | " for n in range(n_levels-1):\n", 4055 | " Ny_f=int(N_list[n][0])\n", 4056 | " Nx_f=int(N_list[n][1])\n", 4057 | " Ny_c=int(N_list[n+1][0])\n", 4058 | " Nx_c=int(N_list[n+1][1])\n", 4059 | "\n", 4060 | " pA_fine=pA_list[n]\n", 4061 | " pA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 4062 | " pA_coarse=agglomeration(pA_fine,pA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4063 | "\n", 4064 | " pA_list.append(pA_coarse)\n", 4065 | " \n", 4066 | " #Calculate mass imbalance\n", 4067 | " m=m_imbalance(pA,Ny,Nx)\n", 4068 | " dummy=np.zeros([Ny_first,Nx_first])\n", 4069 | " #Solve PC\n", 4070 | " PC_mesh=pg_solver(pA_list,N_list,dummy,n_levels)\n", 4071 | " #PC_mesh=V_cycle(pA_list,N_list,PC_mesh,2)\n", 4072 | " PC_mesh=W_cycle(pA_list,N_list,PC_mesh,corr_list,True,2)\n", 4073 | " \n", 4074 | " return PC_mesh,m" 4075 | ] 4076 | }, 4077 | { 4078 | "cell_type": "code", 4079 | "execution_count": 36, 4080 | "id": "941e6d6f", 4081 | "metadata": {}, 4082 | "outputs": [], 4083 | "source": [ 4084 | "def steady_solve_temp(N_list,square_coord,Uf,Vf,Txf,Tyf,T,n_levels,at,dy,dx,a,T_inlet,T_square):\n", 4085 | " #Set up temp coeff\n", 4086 | " tA_list=[]\n", 4087 | " \n", 4088 | " Ny_first=int(N_list[0][0])\n", 4089 | " Nx_first=int(N_list[0][1])\n", 4090 | " \n", 4091 | " tA=np.zeros((Ny_first,Nx_first,6))\n", 4092 | " tA=temp_coeff(Ny_first,Nx_first,square_coord,Uf,Vf,Txf,Tyf,T,a,T_inlet,T_square,tA)\n", 4093 | " \n", 4094 | " #Under-relax and store coefficient matrix\n", 4095 | " tA_relaxed=implicit_relaxation(tA,T,Ny_first,Nx_first,at)\n", 4096 | " tA_list.append(tA)\n", 4097 | " \n", 4098 | " #Agglomerate PC coeff\n", 4099 | " for n in range(n_levels-1):\n", 4100 | " Ny_f=int(N_list[n][0])\n", 4101 | " Nx_f=int(N_list[n][1])\n", 4102 | " Ny_c=int(N_list[n+1][0])\n", 4103 | " Nx_c=int(N_list[n+1][1])\n", 4104 | "\n", 4105 | " tA_fine=tA_list[n]\n", 4106 | " tA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 4107 | " tA_coarse=agglomeration(tA_fine,tA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4108 | "\n", 4109 | " tA_list.append(tA_coarse)\n", 4110 | " \n", 4111 | " #Solve temperature\n", 4112 | " T_mesh=V_cycle(tA_list,N_list,T)\n", 4113 | " \n", 4114 | " return T_mesh,tA" 4115 | ] 4116 | }, 4117 | { 4118 | "cell_type": "code", 4119 | "execution_count": 37, 4120 | "id": "d59fa84b", 4121 | "metadata": {}, 4122 | "outputs": [], 4123 | "source": [ 4124 | "def transient_solve_temp(N_list,square_coord,Uf,Vf,Txf,Tyf,T,T_time_array,n_levels,at,dy,dx,dt,a,T_inlet,T_square,timestep):\n", 4125 | " #Set up temp coeff\n", 4126 | " tA_list=[]\n", 4127 | " \n", 4128 | " Ny_first=int(N_list[0][0])\n", 4129 | " Nx_first=int(N_list[0][1])\n", 4130 | " tA=np.zeros((Ny_first,Nx_first,6))\n", 4131 | " tA=temp_coeff(Ny_first,Nx_first,square_coord,Uf,Vf,Txf,Tyf,T,a,T_inlet,T_square,tA)\n", 4132 | " k=dy*dx/dt\n", 4133 | " tA_transient=implicit_time(tA,T_time_array,Ny_first,Nx_first,k,timestep)\n", 4134 | " tA_list.append(tA_transient)\n", 4135 | "\n", 4136 | " #Agglomerate PC coeff\n", 4137 | " for n in range(n_levels-1):\n", 4138 | " Ny_f=int(N_list[n][0])\n", 4139 | " Nx_f=int(N_list[n][1])\n", 4140 | " Ny_c=int(N_list[n+1][0])\n", 4141 | " Nx_c=int(N_list[n+1][1])\n", 4142 | "\n", 4143 | " tA_fine=tA_list[n]\n", 4144 | " tA_coarse=np.zeros((Ny_c,Nx_c,6))\n", 4145 | " tA_coarse=agglomeration(tA_fine,tA_coarse,Ny_f,Nx_f,Ny_c,Nx_c)\n", 4146 | "\n", 4147 | " tA_list.append(tA_coarse)\n", 4148 | " \n", 4149 | " #Solve temperature\n", 4150 | " #T1=V_cycle(tA_list,N_list,T,2)\n", 4151 | " T1=W_cycle(tA_list,N_list,T,corr_list,True,1)\n", 4152 | " \n", 4153 | " return T1,tA_transient" 4154 | ] 4155 | }, 4156 | { 4157 | "cell_type": "code", 4158 | "execution_count": 38, 4159 | "id": "bbc6135c", 4160 | "metadata": {}, 4161 | "outputs": [], 4162 | "source": [ 4163 | "@njit\n", 4164 | "def m_imbalance(pA,Ny,Nx):\n", 4165 | " m=0\n", 4166 | " for j in range(Ny):\n", 4167 | " for i in range(Nx):\n", 4168 | " m+=pA[j][i][-1]**2\n", 4169 | " \n", 4170 | " return np.sqrt(m)" 4171 | ] 4172 | }, 4173 | { 4174 | "cell_type": "code", 4175 | "execution_count": 39, 4176 | "id": "3366d152", 4177 | "metadata": {}, 4178 | "outputs": [], 4179 | "source": [ 4180 | "@njit(fastmath=True)\n", 4181 | "def drag_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,U,V,P,u_inlet,Lc,v,dy,dx):\n", 4182 | " #COEFFICIENT OF DRAG\n", 4183 | " q=0.5*u_inlet**2\n", 4184 | " qs=q*Lc\n", 4185 | "\n", 4186 | " #Viscous drag\n", 4187 | " u_topsquare=U[Ny_top][Nx_lft:Nx_rgt]\n", 4188 | " u_topsquare2=U[Ny_top+1][Nx_lft:Nx_rgt]\n", 4189 | " u_topsquare3=U[Ny_top+2][Nx_lft:Nx_rgt]\n", 4190 | " u_btmsquare=U[Ny_btm-1][Nx_lft:Nx_rgt]\n", 4191 | " u_btmsquare2=U[Ny_btm-2][Nx_lft:Nx_rgt]\n", 4192 | " u_btmsquare3=U[Ny_btm-3][Nx_lft:Nx_rgt]\n", 4193 | "\n", 4194 | " #Pressure drag\n", 4195 | " P_lftsquare=P[:,Nx_lft-1][Ny_btm:Ny_top]\n", 4196 | " P_lftsquare2=P[:,Nx_lft-2][Ny_btm:Ny_top]\n", 4197 | " P_lftsquare3=P[:,Nx_lft-3][Ny_btm:Ny_top]\n", 4198 | " P_rgtsquare=P[:,Nx_rgt][Ny_btm:Ny_top]\n", 4199 | " P_rgtsquare2=P[:,Nx_rgt+1][Ny_btm:Ny_top]\n", 4200 | " P_rgtsquare3=P[:,Nx_rgt+2][Ny_btm:Ny_top]\n", 4201 | " \n", 4202 | " P_lft_face=0.125*(15*P_lftsquare-10*P_lftsquare2+3*P_lftsquare3)\n", 4203 | " P_rgt_face=0.125*(15*P_rgtsquare-10*P_rgtsquare2+3*P_rgtsquare3)\n", 4204 | "\n", 4205 | " shear_top=0\n", 4206 | " shear_btm=0\n", 4207 | " pres_lft=0\n", 4208 | " pres_rgt=0\n", 4209 | "\n", 4210 | " for i in range(Nx_rgt-Nx_lft):\n", 4211 | " shear_top+=v*((15/4)*u_topsquare[i]-(5/6)*u_topsquare2[i]+(3/20)*u_topsquare3[i]*dx/dy)\n", 4212 | " shear_btm+=v*((15/4)*u_btmsquare[i]-(5/6)*u_btmsquare2[i]+(3/20)*u_btmsquare3[i]*dx/dy)\n", 4213 | " for j in range(Ny_top-Ny_btm):\n", 4214 | " pres_lft+=P_lft_face[j]*dy\n", 4215 | " pres_rgt+=P_rgt_face[j]*dy\n", 4216 | " \n", 4217 | " pres=pres_lft-pres_rgt\n", 4218 | " shear=shear_top+shear_btm\n", 4219 | " drag=shear+pres\n", 4220 | " cd=drag/qs\n", 4221 | " cf=shear/qs\n", 4222 | " cp=pres/qs\n", 4223 | " return cd,cp,cf" 4224 | ] 4225 | }, 4226 | { 4227 | "cell_type": "code", 4228 | "execution_count": 40, 4229 | "id": "c4b58a00", 4230 | "metadata": {}, 4231 | "outputs": [], 4232 | "source": [ 4233 | "@njit(fastmath=True)\n", 4234 | "def lift_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,U,V,P,u_inlet,Lc,v,dy,dx):\n", 4235 | " #COEFFICIENT OF DRAG\n", 4236 | " q=0.5*u_inlet**2\n", 4237 | " qs=q*Lc\n", 4238 | "\n", 4239 | " #Viscous lift\n", 4240 | " v_lftsquare=V[:,Nx_lft-1][Ny_btm:Ny_top]\n", 4241 | " v_rgtsquare=V[:,Nx_rgt][Ny_btm:Ny_top]\n", 4242 | "\n", 4243 | " #Pressure lift\n", 4244 | " P_topsquare=P[Ny_top][Nx_lft:Nx_rgt]\n", 4245 | " P_bottomsquare=P[Ny_btm-1][Nx_lft:Nx_rgt]\n", 4246 | "\n", 4247 | " shear_lft=0\n", 4248 | " shear_rgt=0\n", 4249 | " pres_top=0\n", 4250 | " pres_btm=0\n", 4251 | "\n", 4252 | " for i in range(Nx_rgt-Nx_lft):\n", 4253 | " pres_top+=P_topsquare[i]*dx\n", 4254 | " pres_btm+=P_bottomsquare[i]*dx\n", 4255 | " for j in range(Ny_top-Ny_btm):\n", 4256 | " shear_lft+=v*v_lftsquare[j]*dy/(dx/2)\n", 4257 | " shear_rgt+=v*v_rgtsquare[j]*dy/(dx/2)\n", 4258 | "\n", 4259 | " pres=pres_btm-pres_top\n", 4260 | " shear=shear_lft+shear_rgt\n", 4261 | " lift=shear+pres\n", 4262 | " cl=lift/qs\n", 4263 | " \n", 4264 | " return cl" 4265 | ] 4266 | }, 4267 | { 4268 | "cell_type": "code", 4269 | "execution_count": 41, 4270 | "id": "a4737878", 4271 | "metadata": {}, 4272 | "outputs": [], 4273 | "source": [ 4274 | "@njit\n", 4275 | "def Nu_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,T,dy,dx,x_size,y_size):\n", 4276 | " AR=y_size/x_size\n", 4277 | " \n", 4278 | " T_topsquare=T[Ny_top][Nx_lft:Nx_rgt]\n", 4279 | " T_topsquare2=T[Ny_top+1][Nx_lft:Nx_rgt]\n", 4280 | " T_topsquare3=T[Ny_top+2][Nx_lft:Nx_rgt]\n", 4281 | " T_btmsquare=T[Ny_btm-1][Nx_lft:Nx_rgt]\n", 4282 | " T_btmsquare2=T[Ny_btm-2][Nx_lft:Nx_rgt]\n", 4283 | " T_btmsquare3=T[Ny_btm-3][Nx_lft:Nx_rgt]\n", 4284 | " T_lftsquare=T[:,Nx_lft-1][Ny_btm:Ny_top]\n", 4285 | " T_lftsquare2=T[:,Nx_lft-2][Ny_btm:Ny_top]\n", 4286 | " T_lftsquare3=T[:,Nx_lft-3][Ny_btm:Ny_top]\n", 4287 | " T_rgtsquare=T[:,Nx_rgt][Ny_btm:Ny_top]\n", 4288 | " T_rgtsquare2=T[:,Nx_rgt+1][Ny_btm:Ny_top]\n", 4289 | " T_rgtsquare3=T[:,Nx_rgt+2][Ny_btm:Ny_top]\n", 4290 | "\n", 4291 | " heat_top=np.sum(((46/15)*T_square-(15/4)*T_topsquare+(5/6)*T_topsquare2-(3/20)*T_topsquare3)*dx/dy)\n", 4292 | " heat_btm=np.sum(((46/15)*T_square-(15/4)*T_btmsquare+(5/6)*T_btmsquare2-(3/20)*T_btmsquare3)*dx/dy)\n", 4293 | " heat_lft=np.sum(((46/15)*T_square-(15/4)*T_lftsquare+(5/6)*T_lftsquare2-(3/20)*T_lftsquare3)*dy/dx)\n", 4294 | " heat_rgt=np.sum(((46/15)*T_square-(15/4)*T_rgtsquare+(5/6)*T_rgtsquare2-(3/20)*T_rgtsquare3)*dy/dx)\n", 4295 | "\n", 4296 | " Nu_top=heat_top/((T_square-T_inlet))\n", 4297 | " Nu_btm=heat_btm/((T_square-T_inlet))\n", 4298 | " Nu_lft=heat_lft/((T_square-T_inlet))\n", 4299 | " Nu_rgt=heat_rgt/((T_square-T_inlet))\n", 4300 | " \n", 4301 | " Nu_mean=(AR*Nu_top+AR*Nu_btm+Nu_lft+Nu_rgt)/(2+2*AR)\n", 4302 | " \n", 4303 | " return Nu_mean" 4304 | ] 4305 | }, 4306 | { 4307 | "cell_type": "code", 4308 | "execution_count": 42, 4309 | "id": "29bbd1aa", 4310 | "metadata": {}, 4311 | "outputs": [], 4312 | "source": [ 4313 | "@njit\n", 4314 | "def curl(U,V,dx,dy,Ny,Nx):\n", 4315 | " C=np.zeros((Ny,Nx))\n", 4316 | " \n", 4317 | " for j in range(1,Ny-1):\n", 4318 | " for i in range(1,Nx-1):\n", 4319 | " dvdx=(V[j][i+1]-V[j][i-1])/(2*dx)\n", 4320 | " dxdy=(U[j+1][i]-U[j-1][i])/(2*dy) \n", 4321 | " C[j][i]=dvdx-dxdy\n", 4322 | " return C" 4323 | ] 4324 | }, 4325 | { 4326 | "cell_type": "code", 4327 | "execution_count": 43, 4328 | "id": "08640dd0", 4329 | "metadata": {}, 4330 | "outputs": [], 4331 | "source": [ 4332 | "def round_off(x):\n", 4333 | " return float(('{:g}'.format(float('{:.5g}'.format(x)))))\n", 4334 | "\n", 4335 | "@njit\n", 4336 | "def global_mass_imbalance(U,U_in):\n", 4337 | " m_in=np.sum(U_in)\n", 4338 | " m_out=np.sum(U[:,-1])\n", 4339 | " return 100*(m_in-m_out)/m_out" 4340 | ] 4341 | }, 4342 | { 4343 | "cell_type": "markdown", 4344 | "id": "d03eac22", 4345 | "metadata": {}, 4346 | "source": [ 4347 | "# Initialisation" 4348 | ] 4349 | }, 4350 | { 4351 | "cell_type": "code", 4352 | "execution_count": 44, 4353 | "id": "0f2c1869", 4354 | "metadata": {}, 4355 | "outputs": [], 4356 | "source": [ 4357 | "start_run=True" 4358 | ] 4359 | }, 4360 | { 4361 | "cell_type": "code", 4362 | "execution_count": 45, 4363 | "id": "b8c32461", 4364 | "metadata": {}, 4365 | "outputs": [ 4366 | { 4367 | "name": "stdout", 4368 | "output_type": "stream", 4369 | "text": [ 4370 | "Grashof number: 0.0\n", 4371 | "Kinematic viscosity: 0.0025\n", 4372 | "Thermal diffusivity: 0.00357\n" 4373 | ] 4374 | } 4375 | ], 4376 | "source": [ 4377 | "Transient=False\n", 4378 | "#Physical properties\n", 4379 | "L=5 #Length of domain\n", 4380 | "H=1 #Height of domain\n", 4381 | "Lc=0.125 #Characteristic length\n", 4382 | "Re=50 #Reynold's number\n", 4383 | "Ri=0.0 #Richardson number\n", 4384 | "Gr=Ri*Re**2 #Grashof number\n", 4385 | "u_inlet=1.0 #average inlet velocity\n", 4386 | "v=u_inlet*Lc/Re\n", 4387 | "rho=1 #density\n", 4388 | "Pr=0.7 #Prandtl number\n", 4389 | "a=v/Pr\n", 4390 | "\n", 4391 | "T_square=1\n", 4392 | "T_inlet=0\n", 4393 | "gb=(Gr*v**2)/((T_square-T_inlet)*Lc**3)\n", 4394 | "gx=1 #gravity directions\n", 4395 | "gy=0\n", 4396 | "\n", 4397 | "av1=0.75 #Implicit under-relaxation\n", 4398 | "av2=0.7 #Explicit under-relaxation\n", 4399 | "ap=1.0\n", 4400 | "at=0.8 #Implicit under-relaxation\n", 4401 | "\n", 4402 | "Nx=1280 #Array size for nodal velocities/pressure\n", 4403 | "Ny=256\n", 4404 | "uNx=Nx+1\n", 4405 | "uNy=Ny\n", 4406 | "vNx=Nx\n", 4407 | "vNy=Ny+1\n", 4408 | "n_levels=7 #6 levels for 64\n", 4409 | "N=Nx*Ny\n", 4410 | "\n", 4411 | "dx=L/Nx\n", 4412 | "dy=H/Ny\n", 4413 | "b=dy/dx\n", 4414 | "iter_count=1\n", 4415 | "iter_total=1\n", 4416 | "itermax=4\n", 4417 | "U_res_req=1E-5\n", 4418 | "V_res_req=1E-5\n", 4419 | "P_res_req=1E-5\n", 4420 | "T_res_req=1E-5\n", 4421 | "inner_loops=1\n", 4422 | "\n", 4423 | "dt=2.5E-3\n", 4424 | "end_time=15\n", 4425 | "flow_time=0\n", 4426 | "time_step=0\n", 4427 | "time_steps=int(end_time/dt)\n", 4428 | " \n", 4429 | "#Calculate inlet velocity profile\n", 4430 | "u_inlet_array=np.zeros([Ny])\n", 4431 | "v_inlet_array=np.zeros([Ny])\n", 4432 | "t_inlet_array=np.zeros([Ny])\n", 4433 | "p_inlet_array=np.zeros([Ny])\n", 4434 | "\n", 4435 | "y=np.linspace(-H+dy/2,H-dy/2,Ny)\n", 4436 | "for j in range(Ny):\n", 4437 | " u_inlet_array[j]=u_inlet\n", 4438 | " \n", 4439 | "if Transient==True:\n", 4440 | " u_inlet_array=np.zeros([Ny])\n", 4441 | " \n", 4442 | "if start_run==True:\n", 4443 | " T_mesh=np.full([Ny,Nx],0.0)\n", 4444 | " U_mesh=np.full([Ny,Nx],0.0)\n", 4445 | " V_mesh=np.full([Ny,Nx],0.0)\n", 4446 | " P_mesh=np.full([Ny,Nx],0.0)\n", 4447 | " U_time_array=np.full((Ny,Nx,4),0.0)\n", 4448 | " V_time_array=np.zeros((Ny,Nx,4))\n", 4449 | " T_time_array=np.zeros((Ny,Nx,4))\n", 4450 | " U_res_req=1E-5\n", 4451 | " V_res_req=1E-5\n", 4452 | " P_res_req=1E-5\n", 4453 | " T_res_req=1E-5\n", 4454 | " for j in range(Ny):\n", 4455 | " for i in range(Nx):\n", 4456 | " U_mesh[j][i]=u_inlet_array[j]\n", 4457 | " U_time_array[j][i][0]=u_inlet_array[j]\n", 4458 | " U_time_array[j][i][1]=u_inlet_array[j]\n", 4459 | " if Transient==False:\n", 4460 | " itermax=5000\n", 4461 | " start_run=False\n", 4462 | "\n", 4463 | "U_res_list=np.zeros(itermax)\n", 4464 | "V_res_list=np.zeros(itermax)\n", 4465 | "Cont_res_list=np.zeros(itermax)\n", 4466 | "T_res_list=np.zeros(itermax)\n", 4467 | "t_list=[]\n", 4468 | "U_data=[]\n", 4469 | "V_data=[]\n", 4470 | "T_data=[]\n", 4471 | "\n", 4472 | "dummy=np.zeros([Ny,Nx])\n", 4473 | "print('Grashof number:',Gr)\n", 4474 | "print('Kinematic viscosity:',round(v,5))\n", 4475 | "print('Thermal diffusivity:',round(a,5))\n", 4476 | "if Transient==True:\n", 4477 | " CFL=1.2*u_inlet*dt/dx\n", 4478 | " print('CFL:',round(CFL,3))\n", 4479 | " dt_stable=1/((4*v/dx**2)+(2*u_inlet/dx))\n", 4480 | " print('Stable explicit dt:',round(dt_stable*1000,3),'ms')" 4481 | ] 4482 | }, 4483 | { 4484 | "cell_type": "code", 4485 | "execution_count": 46, 4486 | "id": "a04bc2e7", 4487 | "metadata": {}, 4488 | "outputs": [ 4489 | { 4490 | "name": "stdout", 4491 | "output_type": "stream", 4492 | "text": [ 4493 | "[[256.]\n", 4494 | " [272.]\n", 4495 | " [128.]\n", 4496 | " [112.]]\n" 4497 | ] 4498 | } 4499 | ], 4500 | "source": [ 4501 | "#Setting up coordinates for square arrays\n", 4502 | "AR=1\n", 4503 | "y_size=int(Ny/16)\n", 4504 | "x_size=y_size*AR\n", 4505 | "\n", 4506 | "row_square=1 #number of rows and columns of square array\n", 4507 | "column_square=1\n", 4508 | "x_gap=2 #gap between squares in term of diameter of square\n", 4509 | "y_gap=2\n", 4510 | "x_corner=int((1/5)*Nx) #x and y location of first square\n", 4511 | "y_corner=int((7/16)*Ny)\n", 4512 | "n_squares=row_square*column_square #number of squares\n", 4513 | "\n", 4514 | "square_coord=np.zeros([4,n_squares]) #coordinates of cells adjacent to square\n", 4515 | "\n", 4516 | "for j in range(row_square):\n", 4517 | " for i in range(column_square):\n", 4518 | " n=i+j*column_square\n", 4519 | " \n", 4520 | " Nx_lft=x_corner+i*x_gap*x_size+i*x_size\n", 4521 | " Nx_rgt=Nx_lft+x_size\n", 4522 | " Ny_btm=y_corner+(j*y_gap*y_size)+j*y_size\n", 4523 | " Ny_top=Ny_btm+y_size\n", 4524 | "\n", 4525 | " square_coord[0][n]=Nx_lft\n", 4526 | " square_coord[1][n]=Nx_rgt\n", 4527 | " square_coord[2][n]=Ny_top\n", 4528 | " square_coord[3][n]=Ny_btm\n", 4529 | " \n", 4530 | "if Transient==True:\n", 4531 | " cl_array=np.zeros([n_squares,time_steps])\n", 4532 | " cd_array=np.zeros([n_squares,time_steps])\n", 4533 | " cp_array=np.zeros([n_squares,time_steps])\n", 4534 | " cf_array=np.zeros([n_squares,time_steps])\n", 4535 | " Nu_array=np.zeros([n_squares,time_steps])\n", 4536 | "else:\n", 4537 | " cl_array=np.zeros([n_squares,itermax])\n", 4538 | " cd_array=np.zeros([n_squares,itermax])\n", 4539 | " cp_array=np.zeros([n_squares,itermax])\n", 4540 | " cf_array=np.zeros([n_squares,itermax])\n", 4541 | " Nu_array=np.zeros([n_squares,itermax])\n", 4542 | " \n", 4543 | "print(square_coord)" 4544 | ] 4545 | }, 4546 | { 4547 | "cell_type": "markdown", 4548 | "id": "774f34aa", 4549 | "metadata": {}, 4550 | "source": [ 4551 | "# Solution" 4552 | ] 4553 | }, 4554 | { 4555 | "cell_type": "markdown", 4556 | "id": "77121d06", 4557 | "metadata": {}, 4558 | "source": [ 4559 | "## Steady solver" 4560 | ] 4561 | }, 4562 | { 4563 | "cell_type": "code", 4564 | "execution_count": null, 4565 | "id": "fa41d1c2", 4566 | "metadata": { 4567 | "scrolled": false 4568 | }, 4569 | "outputs": [ 4570 | { 4571 | "name": "stdout", 4572 | "output_type": "stream", 4573 | "text": [ 4574 | "Steady State Calculation\n", 4575 | "\n", 4576 | "Calculation Data\n", 4577 | "Number of cells: 327680\n", 4578 | "Aspect Ratio: 1.0\n", 4579 | "Velocity explicit relaxation factor : 0.7\n", 4580 | "Velocity implicit relaxation factor : 0.75\n", 4581 | "Temperature implicit relaxation factor: 0.8\n", 4582 | "Pressure relaxation factor : 1.0\n", 4583 | "\n", 4584 | "Reynold's Number : 50\n", 4585 | "Richardson Number: 0.0\n", 4586 | "Grashof Number : 0.0\n", 4587 | "Prandtl's Number : 0.7\n", 4588 | "\n", 4589 | " Iteration | U Residual | V Residual | Cont Residual |Energy residual | Cl | Cd | Nu |Mass Im| Time(s) \n", 4590 | "===========================================================================================================================\n", 4591 | " 1 | 1.0 | 1.0 | 1.0 | 1.0 |-0.0779 |25.2425 |21.1149 |-0.005 | 48.384 \n", 4592 | " 2 | 0.64933 | 0.60283 | 0.60798 | 0.23462 | 0.4638 |13.9287 |11.4387 |-0.014 | 0.1676 \n", 4593 | " 3 | 0.43461 | 0.41755 | 0.39249 | 0.16243 |-0.1837 | 4.2748 | 7.9082 |-0.004 | 0.1596 \n", 4594 | " 4 | 0.22941 | 0.17065 | 0.20246 | 0.1644 |-0.2717 | 0.5892 | 6.4553 | 0.015 | 0.1666 \n", 4595 | " 5 | 0.12145 | 0.092142 | 0.093005 | 0.14028 |-0.0835 | 0.3841 | 5.6861 | 0.044 | 0.1983 \n", 4596 | " 6 | 0.087197 | 0.070749 | 0.056961 | 0.11235 | 0.0224 | 1.2088 | 5.1822 | 0.079 | 0.2313 \n", 4597 | " 7 | 0.063946 | 0.04677 | 0.040464 | 0.088939 | 0.06 | 1.8425 | 4.8063 | 0.118 | 0.2878 \n", 4598 | " 8 | 0.044575 | 0.030128 | 0.026802 | 0.071278 | 0.043 | 2.036 | 4.5109 | 0.157 | 0.1798 \n", 4599 | " 9 | 0.032998 | 0.021198 | 0.019471 | 0.058298 | 0.015 | 1.9537 | 4.2748 | 0.192 | 0.1626 \n", 4600 | " 10 | 0.026283 | 0.015542 | 0.016322 | 0.048693 |-0.0007 | 1.7896 | 4.0834 | 0.219 | 0.1695 \n" 4601 | ] 4602 | } 4603 | ], 4604 | "source": [ 4605 | "print('Steady State Calculation')\n", 4606 | "print()\n", 4607 | "print('Calculation Data')\n", 4608 | "print('Number of cells:',N)\n", 4609 | "print('Aspect Ratio:', round(b,4))\n", 4610 | "print('Velocity explicit relaxation factor :',av2)\n", 4611 | "print('Velocity implicit relaxation factor :',av1)\n", 4612 | "print('Temperature implicit relaxation factor:',at)\n", 4613 | "print('Pressure relaxation factor :',ap)\n", 4614 | "print()\n", 4615 | "print(\"Reynold's Number :\",Re)\n", 4616 | "print(\"Richardson Number:\",Ri)\n", 4617 | "print(\"Grashof Number :\",Gr)\n", 4618 | "print(\"Prandtl's Number :\",Pr)\n", 4619 | "print()\n", 4620 | "print('{:<10} |{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^8}|{:^8}|{:^7}|{:^9}'.format(' Iteration','U Residual','V Residual','Cont Residual',\n", 4621 | " 'Energy residual','Cl','Cd','Nu','Mass Im','Time(s)'))\n", 4622 | "print('='*123)\n", 4623 | "\n", 4624 | "st=time.time()\n", 4625 | "\n", 4626 | "corr_list=[]\n", 4627 | "N_list=np.zeros([n_levels,2])\n", 4628 | "Ny1=Ny\n", 4629 | "Nx1=Nx\n", 4630 | "for n in range(n_levels):\n", 4631 | " N_list[n][0]=Ny1\n", 4632 | " N_list[n][1]=Nx1\n", 4633 | " corr_list.append(np.zeros((Ny1,Nx1)))\n", 4634 | "\n", 4635 | " Ny1=int(Ny1/2)\n", 4636 | " Nx1=int(Nx1/2)\n", 4637 | "\n", 4638 | "Uxf,Uyf=face(Ny,Nx,square_coord,U_mesh,u_inlet_array)\n", 4639 | "Vxf,Vyf=face(Ny,Nx,square_coord,V_mesh,v_inlet_array)\n", 4640 | "Txf,Tyf=face(Ny,Nx,square_coord,T_mesh,t_inlet_array)\n", 4641 | "Pxf,Pyf=p_face(Ny,Nx,square_coord,P_mesh)\n", 4642 | " \n", 4643 | "for n in range(itermax):\n", 4644 | " if n%100==0:\n", 4645 | " U_backup=U_mesh\n", 4646 | " V_backup=V_mesh\n", 4647 | " T_backup=T_mesh\n", 4648 | " \n", 4649 | " U_data.append(U_mesh)\n", 4650 | " V_data.append(V_mesh)\n", 4651 | " T_data.append(T_mesh)\n", 4652 | " \n", 4653 | " start=time.time()\n", 4654 | "\n", 4655 | " U1_mesh,V1_mesh,uA,vA,DX,DY=steady_solve_mom(N_list,square_coord,U_mesh,V_mesh,Uxf,Uyf,Vxf,Vyf,Pxf,Pyf,T_mesh,av1,n_levels,v,gb,gx,gy)\n", 4656 | " \n", 4657 | " for inner in range(inner_loops):\n", 4658 | " Uf,Vf,DXf,DYf=face_rc(Nx,Ny,U1_mesh,V1_mesh,P_mesh,square_coord,DX,DY,u_inlet_array)\n", 4659 | " PC_mesh,mass_imbalance=solve_cont(N_list,square_coord,Uf,Vf,DXf,DYf,DX,n_levels,dy,dx,corr_list)\n", 4660 | " U2_mesh,V2_mesh,P1_mesh=correct(Nx,Ny,dummy,dummy,square_coord,U1_mesh,V1_mesh,P_mesh,PC_mesh,DX,DY,av2,ap)\n", 4661 | " U1_mesh=U2_mesh\n", 4662 | " V1_mesh=V2_mesh\n", 4663 | " P_mesh=P1_mesh\n", 4664 | " \n", 4665 | " Uxf,Uyf=face(Ny,Nx,square_coord,U1_mesh,u_inlet_array)\n", 4666 | " Vxf,Vyf=face(Ny,Nx,square_coord,V1_mesh,v_inlet_array)\n", 4667 | " Pxf,Pyf=p_face(Ny,Nx,square_coord,P1_mesh)\n", 4668 | " \n", 4669 | " T1_mesh,tA=steady_solve_temp(N_list,square_coord,Uxf,Vyf,Txf,Tyf,T_mesh,n_levels,at,dy,dx,a,T_inlet,T_square)\n", 4670 | " Txf,Tyf=face(Ny,Nx,square_coord,T1_mesh,t_inlet_array)\n", 4671 | "\n", 4672 | " #Compute and store residuals\n", 4673 | " res_array=np.zeros((Ny,Nx))\n", 4674 | " U_res_array=residual(uA,U1_mesh,res_array,Ny,Nx)\n", 4675 | " U_residual=np.sqrt(np.sum(U_res_array**2)/(Nx*Ny))\n", 4676 | " V_res_array=residual(vA,V1_mesh,res_array,Ny,Nx)\n", 4677 | " V_residual=np.sqrt(np.sum(V_res_array**2)/(Nx*Ny))\n", 4678 | " P_residual=mass_imbalance/(Nx*Ny)\n", 4679 | " T_res_array=residual(tA,T1_mesh,res_array,Ny,Nx)\n", 4680 | " T_residual=np.sqrt(np.sum(T_res_array**2)/(Nx*Ny))\n", 4681 | " \n", 4682 | " if iter_total==1:\n", 4683 | " U_res_norm=U_residual\n", 4684 | " V_res_norm=V_residual\n", 4685 | " P_res_norm=P_residual\n", 4686 | " T_res_norm=T_residual\n", 4687 | "\n", 4688 | " U_residual=U_residual/U_res_norm\n", 4689 | " V_residual=V_residual/V_res_norm\n", 4690 | " P_residual=P_residual/P_res_norm\n", 4691 | " T_residual=T_residual/T_res_norm\n", 4692 | "\n", 4693 | " U_residual=round_off(U_residual)\n", 4694 | " V_residual=round_off(V_residual)\n", 4695 | " P_residual=round_off(P_residual)\n", 4696 | " T_residual=round_off(T_residual)\n", 4697 | " \n", 4698 | " U_res_list[n]=U_residual\n", 4699 | " V_res_list[n]=V_residual\n", 4700 | " Cont_res_list[n]=P_residual\n", 4701 | " T_res_list[n]=T_residual\n", 4702 | "\n", 4703 | " #Update values\n", 4704 | " U_mesh=U1_mesh\n", 4705 | " V_mesh=V1_mesh\n", 4706 | " P_mesh=P1_mesh\n", 4707 | " T_mesh=T1_mesh\n", 4708 | " \n", 4709 | " Nu_mean=0\n", 4710 | " cd_mean=0\n", 4711 | " cl_mean=0\n", 4712 | " \n", 4713 | " for i in range(n_squares): \n", 4714 | " Nx_lft=int(square_coord[0][i])\n", 4715 | " Nx_rgt=int(square_coord[1][i])\n", 4716 | " Ny_top=int(square_coord[2][i])\n", 4717 | " Ny_btm=int(square_coord[3][i])\n", 4718 | " \n", 4719 | " cd,cp,cf=drag_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,U_mesh,V_mesh,P_mesh,u_inlet,Lc,v,dy,dx)\n", 4720 | " cd_array[i][n]=cd\n", 4721 | " cp_array[i][n]=cp\n", 4722 | " cf_array[i][n]=cf\n", 4723 | " cl=lift_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,U_mesh,V_mesh,P_mesh,u_inlet,Lc,v,dy,dx)\n", 4724 | " cl_array[i][n]=cl\n", 4725 | " Nu=Nu_calc(Nx_lft,Nx_rgt,Ny_btm,Ny_top,T_mesh,dy,dx,x_size,y_size)\n", 4726 | " Nu_array[i][n]=Nu\n", 4727 | " m_im=round_off(global_mass_imbalance(U_mesh,u_inlet_array))\n", 4728 | " cl_mean+=cl/n_squares\n", 4729 | " cd_mean+=cd/n_squares\n", 4730 | " Nu_mean+=Nu/n_squares\n", 4731 | " \n", 4732 | " end=time.time()\n", 4733 | " t=end-start\n", 4734 | " t_list.append(t)\n", 4735 | "\n", 4736 | " #Output\n", 4737 | " if iter_total<=10 or iter_total%200==0:\n", 4738 | " print('{:^10} |{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^8}|{:^8}|{:^7}|{:^9}'.format(iter_total,U_residual,V_residual,\n", 4739 | " P_residual,T_residual,round(cl_mean,4),round(cd_mean,4),round(Nu_mean,4),round(m_im,3),round(t,4)))\n", 4740 | "\n", 4741 | " if U_residualTransient solver" 4763 | ] 4764 | }, 4765 | { 4766 | "cell_type": "code", 4767 | "execution_count": 81, 4768 | "id": "36667d19", 4769 | "metadata": { 4770 | "scrolled": true 4771 | }, 4772 | "outputs": [ 4773 | { 4774 | "name": "stdout", 4775 | "output_type": "stream", 4776 | "text": [ 4777 | "Transient State Calculation\n", 4778 | "\n", 4779 | "Calculation Data\n", 4780 | "Number of cells: 327680\n", 4781 | "Cell Aspect Ratio: 1.0\n", 4782 | "Body Aspect Ratio: 1\n", 4783 | "Explicit relaxation factor: 0.7\n", 4784 | "Pressure relaxation factor: 1.0\n", 4785 | "Time step size: 2.5 ms\n", 4786 | "CFL: 0.768\n", 4787 | "Total time steps: 6000\n", 4788 | "\n", 4789 | "Reynold's Number : 60\n", 4790 | "Richardson Number: 0.0\n", 4791 | "Grashof Number : 0.0\n", 4792 | "Prandtl's Number : 0.7\n", 4793 | "\n" 4794 | ] 4795 | }, 4796 | { 4797 | "data": { 4798 | "application/vnd.jupyter.widget-view+json": { 4799 | "model_id": "284b5e87091f46ea964e092499111a1c", 4800 | "version_major": 2, 4801 | "version_minor": 0 4802 | }, 4803 | "text/plain": [ 4804 | "Progress bar: 0%| | 0/6000 [00:001.2*CFL:\n", 5163 | " print('Result diverged')\n", 5164 | " break\n", 5165 | " \n", 5166 | " print('{:^10}|{:^11}|{:^16}|{:^16}|{:^16}|{:^16}|{:^8}|{:^8}|{:^8}|{:^7}|{:^7}|{:^9}'.format(flow+1,iter_count,U_residual,V_residual,\n", 5167 | " P_residual,T_residual,round(cl_mean,4),round(cd_mean,4),round(Nu_mean,4),round(m_im,3),round(cfl,3),round(elap,4)))\n", 5168 | " print()\n", 5169 | "\n", 5170 | " U_time_array=timeswap(U_time_array,U_mesh,Ny,Nx)\n", 5171 | " V_time_array=timeswap(V_time_array,V_mesh,Ny,Nx)\n", 5172 | " T_time_array=timeswap(T_time_array,T_mesh,Ny,Nx)\n", 5173 | " \n", 5174 | "U_final=U_mesh\n", 5175 | "V_final=V_mesh\n", 5176 | "T_final=T_mesh\n", 5177 | " \n", 5178 | "et=time.time()\n", 5179 | "elapsed=et-st\n", 5180 | "print()\n", 5181 | "print('Execution time:',round(elapsed,3),'seconds')\n", 5182 | "print('Number of iterations:',iter_total)\n", 5183 | "print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')\n", 5184 | "print('Flow time:',round(flow_time,4),'seconds')" 5185 | ] 5186 | }, 5187 | { 5188 | "cell_type": "code", 5189 | "execution_count": 82, 5190 | "id": "cb4ec0f4", 5191 | "metadata": {}, 5192 | "outputs": [], 5193 | "source": [ 5194 | "np.save('U_Re=50_Ri=-0.5_Pr=0.7.npy',np.array(U_data))\n", 5195 | "np.save('V_Re=50_Ri=-0.5_Pr=0.7.npy',np.array(V_data))\n", 5196 | "np.save('T_Re=50_Ri=-0.5_Pr=0.7.npy',np.array(T_data))" 5197 | ] 5198 | }, 5199 | { 5200 | "cell_type": "markdown", 5201 | "id": "818a3ee9", 5202 | "metadata": {}, 5203 | "source": [ 5204 | "U_data=np.load('U_Re=50_Ri=-0.5_Pr=0.7.npy')\n", 5205 | "V_data=np.load('V_Re=50_Ri=-0.5_Pr=0.7.npy')\n", 5206 | "T_data=np.load('T_Re=50_Ri=-0.5_Pr=0.7.npy')" 5207 | ] 5208 | }, 5209 | { 5210 | "cell_type": "markdown", 5211 | "id": "8e2533eb", 5212 | "metadata": {}, 5213 | "source": [ 5214 | "# Post-Processing" 5215 | ] 5216 | }, 5217 | { 5218 | "cell_type": "markdown", 5219 | "id": "4cad8484", 5220 | "metadata": {}, 5221 | "source": [ 5222 | "n=len(U_data)\n", 5223 | "\n", 5224 | "U_avg=np.zeros((Ny,Nx))\n", 5225 | "V_avg=np.zeros((Ny,Nx))\n", 5226 | "T_avg=np.zeros((Ny,Nx))\n", 5227 | "\n", 5228 | "for i in range(n):\n", 5229 | " U_time=U_data[i]\n", 5230 | " V_time=V_data[i]\n", 5231 | " T_time=T_data[i]\n", 5232 | " \n", 5233 | " U_avg+=(1/n)*U_time\n", 5234 | " V_avg+=(1/n)*V_time\n", 5235 | " T_avg+=(1/n)*T_time\n", 5236 | " \n", 5237 | "U_mesh=U_avg\n", 5238 | "V_mesh=V_avg\n", 5239 | "T_mesh=T_avg" 5240 | ] 5241 | }, 5242 | { 5243 | "cell_type": "code", 5244 | "execution_count": 82, 5245 | "id": "4b694e4e", 5246 | "metadata": { 5247 | "scrolled": true 5248 | }, 5249 | "outputs": [ 5250 | { 5251 | "name": "stdout", 5252 | "output_type": "stream", 5253 | "text": [ 5254 | "Re = 60 Ri = 0.0\n", 5255 | "Square| Cl | Cd | Cp | Cf | Nu \n", 5256 | "===================================================\n", 5257 | " 1 | nan | nan | nan | nan | nan \n", 5258 | " Mean | nan | nan | nan | nan | nan \n" 5259 | ] 5260 | }, 5261 | { 5262 | "name": "stderr", 5263 | "output_type": "stream", 5264 | "text": [ 5265 | "C:\\Users\\Kangluo See\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\core\\fromnumeric.py:3504: RuntimeWarning: Mean of empty slice.\n", 5266 | " return _methods._mean(a, axis=axis, dtype=dtype,\n", 5267 | "C:\\Users\\Kangluo See\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\core\\_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n", 5268 | " ret = ret.dtype.type(ret / rcount)\n" 5269 | ] 5270 | } 5271 | ], 5272 | "source": [ 5273 | "U_final=U_mesh\n", 5274 | "V_final=V_mesh\n", 5275 | "P_final=P_mesh\n", 5276 | "T_final=T_mesh\n", 5277 | "\n", 5278 | "# U_final=U_backup\n", 5279 | "# V_final=V_backup\n", 5280 | "# T_final=T_backup\n", 5281 | "\n", 5282 | "# U_final=U_data[-2]\n", 5283 | "# V_final=V_data[-2]\n", 5284 | "# T_final=T_data[-1]\n", 5285 | "\n", 5286 | "V_mag=np.sqrt(np.power(U_final,2)+np.power(V_final,2))\n", 5287 | "vort=curl(U_final,V_final,dx,dy,Ny,Nx)\n", 5288 | "\n", 5289 | "x=np.linspace(dx/2,L-dx/2,Nx)\n", 5290 | "y=np.linspace(-H/2+dy/2,H/2-dy/2,Ny)\n", 5291 | "xx,yy=np.meshgrid(x,y)\n", 5292 | "\n", 5293 | "xe=np.linspace(1,Nx,Nx)\n", 5294 | "ye=np.linspace(1,Ny,Ny)\n", 5295 | "xxe,yye=np.meshgrid(xe,ye)\n", 5296 | "grid=np.zeros([Ny,Nx])\n", 5297 | "\n", 5298 | "#Crop data\n", 5299 | "#10m\n", 5300 | "x_crop=np.linspace(0.75,2.5,int(0.35*Nx))\n", 5301 | "y_crop=np.linspace(-H/4,H/4,int(0.5*Ny))\n", 5302 | "xx_crop,yy_crop=np.meshgrid(x_crop,y_crop)\n", 5303 | "U_crop=U_final[int(0.25*Ny):int(0.75*Ny),int(0.15*Nx):int(0.5*Nx)]\n", 5304 | "V_crop=V_final[int(0.25*Ny):int(0.75*Ny),int(0.15*Nx):int(0.5*Nx)]\n", 5305 | "T_crop=T_final[int(0.25*Ny):int(0.75*Ny),int(0.15*Nx):int(0.5*Nx)]\n", 5306 | "\n", 5307 | "#For plotting\n", 5308 | "total_size=2*x_size+2*y_size\n", 5309 | "# cd_mean_array=(1/n_squares)*(cd_array[0]+cd_array[1]+cd_array[2]+cd_array[3])\n", 5310 | "# cl_mean_array=(1/n_squares)*(cl_array[0]+cl_array[1]+cl_array[2]+cl_array[3])\n", 5311 | "# nu_mean_array=(1/n_squares)*(Nu_array[0]+Nu_array[1]+Nu_array[2]+Nu_array[3])\n", 5312 | "\n", 5313 | "cd_mean_array=(cd_array[0])\n", 5314 | "cl_mean_array=(cl_array[0])\n", 5315 | "nu_mean_array=(Nu_array[0])\n", 5316 | "\n", 5317 | "print('Re =',Re,'Ri =',Ri)\n", 5318 | "print('{:^6}|{:^8}|{:^8}|{:^8}|{:^8}|{:^8}'.format('Square','Cl','Cd','Cp','Cf','Nu'))\n", 5319 | "print(51*'=')\n", 5320 | "\n", 5321 | "Nu_mean=0\n", 5322 | "cd_mean=0\n", 5323 | "cp_mean=0\n", 5324 | "cf_mean=0\n", 5325 | "cl_mean=0\n", 5326 | "\n", 5327 | "for i in range(n_squares):\n", 5328 | " Nx_lft=int(square_coord[0][i])\n", 5329 | " Nx_rgt=int(square_coord[1][i])\n", 5330 | " Ny_top=int(square_coord[2][i])\n", 5331 | " Ny_btm=int(square_coord[3][i])\n", 5332 | "\n", 5333 | " cl=np.mean(cl_array[i][iter_total-200:iter_total-2:])\n", 5334 | " cp=np.mean(cp_array[i][iter_total-200:iter_total-2:])\n", 5335 | " cf=np.mean(cf_array[i][iter_total-200:iter_total-2:])\n", 5336 | " cd=np.mean(cd_array[i][iter_total-200:iter_total-2:])\n", 5337 | " Nu=np.mean(Nu_array[i][iter_total-200:iter_total-2:])\n", 5338 | "# cl=np.mean(cl_array[i][time_steps-5000:time_steps-4000:])\n", 5339 | "# cp=np.mean(cp_array[i][time_steps-5000:time_steps-4000:])\n", 5340 | "# cf=np.mean(cf_array[i][time_steps-5000:time_steps-4000:])\n", 5341 | "# cd=np.mean(cd_array[i][time_steps-5000:time_steps-4000:])\n", 5342 | "# Nu=np.mean(Nu_array[i][time_steps-5000:time_steps-4000:])\n", 5343 | " cl_mean+=(1/n_squares)*cl\n", 5344 | " cd_mean+=(1/n_squares)*cd\n", 5345 | " cp_mean+=(1/n_squares)*cp\n", 5346 | " cf_mean+=(1/n_squares)*cf\n", 5347 | " Nu_mean+=(1/n_squares)*Nu\n", 5348 | " print('{:^6}|{:^8}|{:^8}|{:^8}|{:^8}|{:^8}'.format(i+1,round(cl,4),round(cd,4),round(cp,4),round(cf,4),round(Nu,4)))\n", 5349 | " \n", 5350 | "print('{:^6}|{:^8}|{:^8}|{:^8}|{:^8}|{:^8}'.format('Mean',round(cl_mean,4),round(cd_mean,4),round(cp_mean,4),round(cf_mean,4),round(Nu_mean,4)))" 5351 | ] 5352 | }, 5353 | { 5354 | "cell_type": "code", 5355 | "execution_count": 83, 5356 | "id": "d0b1efde", 5357 | "metadata": {}, 5358 | "outputs": [ 5359 | { 5360 | "data": { 5361 | "image/png": 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\n", 5381 | "text/plain": [ 5382 | "
" 5383 | ] 5384 | }, 5385 | "metadata": { 5386 | "needs_background": "light" 5387 | }, 5388 | "output_type": "display_data" 5389 | } 5390 | ], 5391 | "source": [ 5392 | "fig,ax=plt.subplots(1,1,figsize=(16,5))\n", 5393 | "plt.streamplot(xx_crop,yy_crop,U_crop,V_crop,density=1.5,color='k',arrowsize=0.2,linewidth=0.7,broken_streamlines=False)\n", 5394 | "plt.xlim(0.75,2.5)\n", 5395 | "plt.ylim(-H/4,H/4)\n", 5396 | "\n", 5397 | "for n in range(n_squares):\n", 5398 | " Nx_lft=int(square_coord[0][n])\n", 5399 | " Nx_rgt=int(square_coord[1][n])\n", 5400 | " Ny_top=int(square_coord[2][n])\n", 5401 | " Ny_btm=int(square_coord[3][n])\n", 5402 | "\n", 5403 | " rect=patches.Rectangle([Nx_lft*dx,Ny_btm*dy-H/2],x_size*dx,y_size*dy,fc='k',ec='k')\n", 5404 | " ax.add_patch(rect)\n", 5405 | " \n", 5406 | "plt.show()\n", 5407 | "print(np.min(T_crop))\n", 5408 | "\n", 5409 | "fig,ax=plt.subplots(1,1,figsize=(16,5))\n", 5410 | "graph=plt.contour(xx_crop,yy_crop,T_crop-np.min(T_crop),colors=['black'],levels=10,vmin=0,vmax=1)\n", 5411 | "plt.xlim(0.75,2.5)\n", 5412 | "plt.ylim(-H/4,H/4)\n", 5413 | "\n", 5414 | "for n in range(n_squares):\n", 5415 | " Nx_lft=int(square_coord[0][n])\n", 5416 | " Nx_rgt=int(square_coord[1][n])\n", 5417 | " Ny_top=int(square_coord[2][n])\n", 5418 | " Ny_btm=int(square_coord[3][n])\n", 5419 | "\n", 5420 | " rect=patches.Rectangle([Nx_lft*dx,Ny_btm*dy-H/2],x_size*dx,y_size*dy,fc='k',ec='k')\n", 5421 | " ax.add_patch(rect)\n", 5422 | " \n", 5423 | "namelist=np.linspace(0,1,11)\n", 5424 | " \n", 5425 | "plt.clabel(graph,namelist,inline=True)\n", 5426 | "\n", 5427 | "plt.show()" 5428 | ] 5429 | }, 5430 | { 5431 | "cell_type": "code", 5432 | "execution_count": 84, 5433 | "id": "bf1eae07", 5434 | "metadata": { 5435 | "scrolled": false 5436 | }, 5437 | "outputs": [ 5438 | { 5439 | "data": { 5440 | "image/png": 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xgIsA7mV/99R/K4B+D2XQpFpHqEaWCul7geuGLssYmuDpWlRUNFn145JFRUVFRUVFu6oCcYqKijahfbTB2gwNWJsjrf8SoiRz4/M4HpGt0RkAB5suxIgqxuCijWs12yUbSVFRUVFRUVFRUVHRZHTZ8lc0qvq43NYayg7kihDRLwpMgMY/6++lO3uqVZxqRUVFRUVF26A+LhDzLW5RUVFRUdFpkOvep6cXh6VIWaDYFBVyss3q+Qu23D4aZ9oC5RnLIgJw1aYLMaKKU62oqKioqKioqKioqKhotyQBHQWGjKaNgzntSPPZimbogla+/H69zKze3tyxzikXoTHy+f52Rbt0LEVFRVNVGaQgTscYZ7CC06IVtt+Wn+NhrbxJLdpmlQ5LUVFRjGLud5dRrjEjSc16Dlrgcpal5FXj0/Vzt/6unxv3UQ1IoCnQqv5/H9Xz+q7RoUw6bTnVyk+gqKioqKioqKioSKICp7dHpa1Or1LavYSDOpXbYdYbrPXRAg0M0wBNEgKq112i+o3wkNCzgfVPoUr4Z1FRkVXLWTldinZEp/F1yjaPypTzAb90FoqKioqKdll9QWoBsaOoF6hzPceazzh8H651+HQznHOGxpWml50b0/dRQbXT+GztUQn/LCraUa3mc8yW5TVCUdGp1S6EgRadLhWnTdG2qAD7oiko1/WyhIOuNWQetF6OtT5hoKZbTRMRPc+2LV0PZ1GFg+rfx+V6/bOJx7GjOgPgcNOFGFEFqhVNQqvZDLPVNltJToFSeWTJp1ZUlK4hHuoLqGmrdJyKioqKtl+572sFrI0iDe2S4NpQYA3su97WOQAX62kcvJnLF611mqrkNB1rUdHoOtkH9qbceS0cs+i0aZvcauVhvqio0tTOhQKmi4qmpaHOx1MO1sYcrTMZrg0B1sCm823x/4u8Om051QpUKyoqKio6XdomsDaUChQoKio6DSrXuqK+KhBlVCXBNdNlptUXrJnb0utdrL/rZfl2iwA0OdVOi07TsRYVFY2tEvqZrmO07eVFeTV1sDbGA3zpbBYVFeVQAQ7T02lqkzHuY6cQrI3pUvPtPxqu2cAa0M6N1ges6VDQJdvmEqXPY+gMgINNF2JEFahWNBmNkVet72AFy9kZzFdXMpaoUdbhpXM/YGzb+A4x5S1XwdOrqYK1U/bgXhRQgZ9FRUVFm9cpAmubBmpc0XBN4lrTx7dCXD9Aj/y5QjNAgZ5+Sn4bUpXwz6KioqIcGvuNTSr4KwlGT7emCtbGUgE2RVPXVDsq5dwpKtq8xj4HTxFYm5pM0BeEbDa4ZnOtATK4ZrrgzqJ5hlxh+wwIA+u0hX+e2XQBioqKinppiTw3slzbmbpO0x1OqinlwSgP6+Oq1HdRUVHRdmpTUHvHYfqUXGo+qVnz59Wc/Wntsz+gAmMzY/lF/addaPr/s+xvwT7fr+cBFTlFRC8mojuI6E8Dy/1tIloS0Zca068motuJ6CeGKmOBakWT0mo2/JV8NS9UYXCN4VIbCoKdFrhW1NYUwNqmAE8BS0VFRanahuvHNpSxaLu0o2BtW4CaKQ7YvMfgA2xAA9dm6MK1s2jAmoZsHK6VPMgt6ZxqoT+hXgLgKb4FiGgG4N8CeJ1l9g8CeKN8d/HKAtWI6ClE9OdEdBsRPdez3JcQkSKiG3Pst6hoE1rO4k6bk6k+zEkhwtQA01jQawrHXZKeFhUVAQUKTFWlXYqKNqMdhVpFeWRCNitoMwHbvvGn4dqCLcfdaXzZ+6E41QzpnGqhP4mUUm8E8MHAYv8cwC8BuKNVDqJPBPBg2GFbNvW27NRU8IUAngzgdgBvIqKblVK3GsudB/BNAH6/7z6LdltjDFhQNKCGAkGbgFwl39rp0ibzq5XOeVFRV+W8KCoqmqp2LL/atrrUpLIdH/FRQE2ZbWvmYePrTOFF/MQUMVDBdUR0C/t+k1Lqpqh9ET0cwBcD+EwAf5tNPwPgRwE8E8DnxGwzVjm6ik8EcJtS6m0AQESvAPBUALcay/0gKkvet2fYZ1FRL/UdBbRoZG26qXzDaxftlk7rwAUl6XpR0XbI985x7GvXDgGFoqIk7QhY23Wg5pL3uGfGYAjn2GezX1KenzoiAAeSvtMSdyql+kYx/gcA36mUukJEfPo3AHi1Uup2Y3p25egmPhzAu9n32wF8El+AiD4BwPVKqV8nogLVioKaulttOTuD+erKposxvGJhVm6X2qZhGlcBa6dHY4O1HXggL9oxuW6/p7TjtXFJH4f4cqWtinZVUwMYOwLWtk1jpdfZs/3ezOtryafWEREgSmOep693I4BX1ODsOgCfR0RLAH8HwKcR0TegwqJ7RHRRKeVMV5aqwbuIte3uxwA8S7DsswE8GwAeccOwNLGoaEy32sm+46K8S9ploKa1CbB2jHw36wIF5TqNjrXiVjvdkoAbc5nTdo7YNOR50+fdol63tFFR0fDaYrC2LS61oSBaKFf28mzM1k6B4UIoIuCqkX5bSqlHNfullwD4NaXUqwC8ik1/FoAbhwBqQJ4u1nsAXM++P6KepnUewOMBvKGmhw8BcDMRfaFSisfPoo6fvQkAPv7GMypD2Yq2WFN3qw0hZVqNh9KUq3WKMI2rONbSZN5Yp/wb1BoDrG3pQ/hOaNvqfih4M1Vws23tk1M5r4+n8QWBTeWlwW6otGFWbQNQ6wvTYgeYW4nsVVU/taujqH3tssRONdG26OUAnoQq/9rtAL4Pdco2pdRP5tlLP+U41DcBeCwRPQoVTHsagGfomUqpe1DZ8AAARPQGAN9mArWiok2oj1tt50NAY6oll0tt6kBNqwxgEFboQY3PnzJgKx3S4VVC1jajAm6mqSGuh8W1Np5C7VfaYHe1ZW61qQO1VJgmhWg+eGYHZsAx9trLdToCBappEQFXZYq0UUo9PWLZZzmmvwTAS/KUqKveXUKl1JKIngPgtahuFS9WSr2ViH4AwC1KqZv77qPo9Oo0utWCmmNa8CkHUJvS8cRoLNdazhDQoZXykDbDtMHaUJriw/fQbo5QO9vmT/zBfys1FLgpbdVPQ18Hc7bRFK9fm1JMu5WXCLutLQFrUwZqKTBNAtJcEC0FoK3YyXtiLFdUi3CqzAdZDlUp9WoArzamPc+x7JNy7LPo9GhosDZWbrWtyqs2JuTaVqCmVcJBK/V9QJsyWCuwoL+mGmJ4GjXkeXZaz5UcMHqs699pbaMh1LfNhrq2bQHUKSqyKRaohWBaDEhLgWjLenrXsVZUoFpRUZFYOx8CGlIfl9q2wzSubQBrQ5UvZ2fgNIG109TpydWmBQb011g5O0s7xWns615po/7KHT4NlDYJaVteTOdwqw34TDlVl1pOoCaFabEgzYRo5vQiJsL2RNlk0NS7gUVFAHbHrTYJ5arGAtTaGhqsTTEEdIhniNME1qaqnCGgudvytHc++7TNmOdV33OlQOeiqao4PYu2WFMFarGKBWopMM3nSDvBXmvaqpy4XRWnWlHRNDXV/Gq53Wq9RwDt2xkeGnjtIlDT2gbHWg6VZ4eikLYpN9Rp0CZunaWNpq0+7bOt0LPvC4Pi9CySaoK51aYM1GJcai6gtkmYxqcX1SpQrahouhoSrI3hVtuqvGo+pbjUdhmmce06WBvjmWHX3WoTe9DOrtOaG8p33m/y+rfJc2kqbeRqm03flzZ9nZtK+2yDtsnpOZQkzzabPqeKrJoyUItRLqCWEubpg2krzMuABadcu9z1K9pRTRGs7UxuNcmhF6AW1q6CtR15KCvaIaV2PnOBTel5zpc7bdfDTUnSNjnbJdYFtWmgNhVtA6g5zU7P2GcZvfym22wKmohbbepATepSGxKoSXKm6WkrzFowTS9XnGqGSk61oqLpa6qhoJPXJqrstD5YDQHWNplXrTwrNJpKZ2dIpYZJnZbOZ59ze8xO5xRuk2O3T2rbnFYYsA3nz2ltm02q7/NL7jbbhSiPDWjqQG0ISYBaTndacao5dMrCP/3j0BYVnUK5RowJKTSss1bs6DaTUqxL7bQ/AC8xjTroe1PbxEPZ1B8EU2HFNp//IU0B4IyhXA+Jp+hhc5Tfxhx56jTXdnya2rkyZnn6Aumxz5tNh09vQjnr+DRd52zaIAzcJaAmdanlAGon2LMCNT7dBtSKU82imeBvR3TaL3VFW6wphoHmUu/BClKU83CnAJKmpG0OB92hG95OyvxdndZzbwy3zRDn8BzDtdnUwI1UKeB529rmNGoIUDN0+0zhHBrTSTjUc8qUzqWYNt3U80+GdtgloJYqV8inlguomdN4uCdfzgRqJ6cp1lGiU+ZUO0WHWrSLmloYqDS32lYOWCB1qU3lwWlqygXWxgwBLQ9lfk0xbGpTkG0Kl+Eh22PIp6VYQNB3BEOfpGVIqY+h2mfotsl9DqWcK7FlGLJOpgI99XbLM0ceDd0j3HRbpZx3ep0texY6LUAt5FJbT4/IoWZOiwFqJfzT0BnsdnSGoRL+WbT1Ws1mzgtpr+0mhoHupApQy6Ntqp9T8lC2NUoNezqNoWy5NdatIOd+YtpkifhQ9V0JbZ/KPlxKreeU9YY6j8eANUNoSte1mLJMCXpuaj9cK/Rvy77bGPEl+i4CNWl6Ha5jC+CSDEpgA2rN+n6gVsI/LTpF4Z8FqhXtjKYC1lIu/qPI90CQo3M0hQ7WNmgb6mmHbnKDa+jODrC5PFFjvGFcWv76KHdHeNffrfSt703CtTHbRrqvXOdMrnrdNPycIqwZyzmR65o2tMa+xo25v9z3gxyAbkDtIlCTSuJS88034RnPu+bLoWZ+LmLS4Z+hP8mmiF5MRHcQ0Z865n8lEf0xEf0JEf0OEX0sm/fNRPRWIvpTIno5EQ1yF5ho77+oKE1TAWsSbc2ABRKXWq6HRlsHW/q3Tepb3tgBI2J0ih/KJqchXGabfuaTnLfbfG73VY72kXT6ctetdFu5OqSb+B2P2Ta5tYnzaJthTWqIruu6leOZZQiYs6n7wRj7HRJ+TRCsnTag5uubxbjUzGkcjEkg2gqzMvqnS2dQpasJ/cn0EgBP8cx/O4DPUEp9DIAfBHATABDRwwF8I4AblVKPR9XLeVrcgci06cfroqLsGiLPWuzABdLcaj55ByvImZvCt52hgdpQnbptuLJt8+AFY2mGPA+vkt/Z1NpiW/JESdsnx3VCWie5cndtssM5JAQZatux7ZSqTZ6r29o2etuhuivnTrxS9zfW+eLSpu952z5Iy4RyRJ42oJZLtjxq1fSZN5yTAzUN0kr4p0UZBypQSr2RiB7pmf877OvvAXgE+z4HcEBE9wE4BPDePKVqqzjVinZSQzjWhtDWuNVc6pPrZejOwza4XKZWvu04beSK+Q1MyUGwa3micjtZx9AUOpxDaIz6m9p1bVu0K22z6XNnLOUM0S3KpzFdZBN0rG2jhhq4TUMzn0vNtrw5zXSpafHpJfzTIVn453VEdAv7e3bPvX4tgN8AAKXUewD8CIB3AXgfgHuUUq/ruX2rSusX7axyO9Y24VbbqEIutdgHwU09OG76bXBIqY61MUcB3TblyBMFbG+IWcy++tTVJkPZJnY+S50CTvdxLg2dOzOHfA6P0IumyHYfpF1Sz5uptE2f8ydz+9jkdelLtI3XNWmbbLuT0NSmRwTdAZ0Gl9p8dWWwfNVmyKY5OIEp7lKrlm9A22rn3kz3lA7/DOtOpdSNOXZJRJ+JCqr93fr7/QE8FcCjANwN4L8S0TOVUj+bY39cU7msFhUNIu1YywXXthasuQ6/b9hC7mWH1AQ74msNWTbpdvs+C9g6WyOOeLVW7pDiqf5mcmmoTs3Q532obfp0PiPaPLZDo5cXQYOxwvy5JCH/kofkDZ87qe0CbKBtYuRrn5iXLL72GSqszSJXO9mmjwI+JRpyu1vgUu7dNqY2kY4gp6Tny2VkHyRjl4Da3uU80Tu2fGo2uUI/+TSJS40vW2QoY/inaHdETwDwIgB/Tyl1Vz35cwC8XSn11/UyvwzgUwBkh2ol/LPoVChnOGjugQt8N5GN3TB9D+6x4XRT0hTLpLXJsqX+zvbZX8r83NokHAo9zEvqYIrPZFMOUd/guaxm/a7PfdfP3i7HkA+AIl02d/sIzo/e9ZppG1EK1dMxZHUuXW4oCa9fun5TwOfGIcIYLwsmJt5ePgg6ifbZdg34QqfILykUM0cG5S61MlDB5kRENwD4ZQBfpZT6CzbrXQA+mYgOiYgAfDaAPxuiDFN8hC8qGkQ5w0FjHGuTcatJ1ReoTfChsKMpO5BiyraJENDUzrxebyj32hjJw4cePCBCkgdqsXsgt6sjhytK+rtOOZd9v+HAtnJ3ZILOtRxt03cwmtC6vrbKda0VArWcCoYhutpmH/LrnASopWjMtolQjjYSuz19505MG42tiYTnprZVtOsTyHOd22R+sxHdncDuArVcbjWfbKN+avnAmDkQAXen6f/LQAUWZbrHENHLATwJVf612wF8H4CrAEAp9ZMAngfgAQD+Y8XOsFRK3aiU+n0ieiWAP0B1lflD1COD5tZUu5VFRYMoZzhoTrB2sj9cok6nhsiJtg1ATWtXwFpfxTwD5HjYGQKujfW7G6pdBuroRHVuYjs1qSHlMW4orRBgc7VL5o7OkB2ZwUZ7dq2X080kATimMrbNxtplSOVqn7FevIwMo/k2R2ufqTzfpJ47EU7CXNrY+bPD2lWgFpKZV222XGaPHNKyhX5ycfCmQVpxqVlEyHefV+rpgflfB+DrHPO+DxWEG1RT7VIWFQ2qXK612BxrKRr1oST1QX4qD5uxKmBNpiHeHOZyCkzptzfg2+ocIW2DX0c26YjKIc/5NkZHpncbSdcdKjzQBXD6Xssm0C6Ao22mDjz19lznTo77zAaAmrn9bEA6xzVyUw7cDNoo/Bw7T2FoX7H13/f+vwE37hTV1622wIk4r1qKbKGfzbypPLRPRGcw7RQimVVav+jUKpdrTQrWJulWk8p3eFOCGimaErxKUR8nguQBbcgb4qZCcMxOT64E3ykasUM6SqihTTldN8Aw4MajmDbwdQYk1/es8NPWln1HdQ7V8Ygh6WO2i97f4GB6bOAZo4R7wVgQYDCnp5ZkfUnbjfWCwNSGwafex2DnT8x2Y/IC57ynbBguxICqyfZFBlBMyKYLmpVRPz06RVWzzV3JoqIsyuFaywXWoiR9ULQdWkxnKydQ6/NgO+TVaqpgbdPlGuMhsA9YyzWiYSxkG6ldhuroDNK5SQE4qRrCEeVYT9IG0s4KX87XabG2z9AujpiOpparrm3tk/mc2US7ODWmw8a3n00Az0wuwq2Bni7FXuskbRF7ziTer8d0PyXnKcyllGfWiTwbprRTquNLr7cpuOZyq5khoJtUyacm0Mijf25ap+hQi4rcyuFaGyMUdHTlAGq5qkTSmeu7/SleETdVrqlYtmdID8mZepLvDYa1OTs3uTo1fR1Ruiy+7UsAQY+QnFAb9AlRCXVaBgEEtjbp09a5B/Cw1WfCtnO0C2Bvm0HBTa5BgmLPmwHuMTnPHXPZaCAN2K9ruZzSfe4zG853lwPUxIKX5HNoU872MZ7BMjoJcyb63zRcC4nnVZutVuu+3BAq0CxBJfyzqOj0qi9ck4A1n1vNFQI6+BvY2M7WpgctGGM0xikp9FCX8nDuez6IvQm6yib9DaQ8LA+Vv8vcTmy9ZsirJn2ADj08hx6ExdcVs33MdYYC7CGQPhIgMLWxTksf4JkbqJnbMet8QGDgOj9yjxoXdT822yYnAEg9bzYEAnzXrxxtNCiQjn2J0/deMzRYywDUQm1mm9/r3jOUW21L33nnaqc+2kR6mjFGAgWAOVYFmA2pU1S10/BQFhVNTH3edkhGo9mofXno3CLLDPuQKvd+pry9qT0Qztlfn2W0cj08DZHkOyU0OlESh4f+C0myrHV/fTvjQwCcIZO6W453LHDj226wM+Uri+Q67Zvn+ovZntk2sb+BibRLUtuENIbTO+c5I6zfoYGadHuDXNeG0lBh8h7lvM/41k8uQ2xbDd1xH/L5awTwmUN9fg+psoE8SQqdBU4AADNLw80TQx9S1zvV0uGfob8d0Q4dSlFRXvVxrfUJBR39jVBMB3iKAxZs2rXWp/MaW+YxwhBCD019c1YN+TsZsnOS21EQWY99H2Z97o6sTtgUkCIN7R7pXE8FN9KXJTFO5U7bpLg4pG3SN8Rw4OvTptpF7yNr25jLDuUkHDF9wNjAk287+bq2qZDCPrLVc0To9CbAZzZX4Ta2FzDYIB9jQy69zymFg/IQUOcyWFkHE9jDSWcEz6KMIow/KMsGVZxqRUUBrWazJOda6CIf61YbLZlsbIdrTGeaT0Pkbgstl8N9k6v+YoCS67fke0DL9UbJtw3pA2LqqIaSP59SO749XR5DhBtK9x2UDwyErhm2Ope0hzm9ryNKIF8bLGdnoq7nvuU30UlKuga51gm1TWa56kvXcUq7xLSN97zZ5VwywnuB5PfM6933l7KfTvuccivBmEDN3Hb0vSd3Ww3d9in30MQBcjbhGjP3P5Zi3GohE8Re7WAD7C62kPg6+rP+vzjZLCpOtWnqeACSvGAnV1FRSCmjhIYca678alN7E9TL2QDEd6xS32yM8VZ+KIAodeGM6DxYa4iH26mGCKfk8cqgPkDN1uEMuW6AwDWmbxtJgEvMdsy2MM+D1HYxtmtrBx+46SO9vtlW5vU/q5PQBjH7bm+I69EG24VvI9Q2HY15bZNqhHtGTPsAaW3E13E9NwEJjkLT/dRncJxNSOhSy/HSxtVu0lHto/MGxwwwMWS79Tl/XPWbuM1NwjSuyfVTMmiGVQ3eKuawB2CJg84yRUKV0T+npSFg2hjbnoIKNMyvlJDQVLAmUuwDfIrDI7Vz3MehwNedknV4jM6SBK4N0UnK8OCnH9pFAMD1200J7xg7dMoEOOZyocEKBHXat0NqLiOB94MNiCINkY4N/RwBFtjaQQoEuFs5dA8A2m3kbRt+7gwZDmUW2TcgyQ62C9+muG0Ad/v4Ov0LjJtbKwZCm3W9YeDJtzPaS8kpwlKXMgI1aXtJ7jfmPoPwc70xyMFaSCntOHJPOZebMPZcS+mHTAWs2UJAFzjBMfYwwxIrzFshoObgBNpltrI0duNAa5jBHk6wxMH6fw3aUtxvOy3CqRqoYPJQrShdU4CGuwr2YuFaSo41UQ6X4I6jdtlVLFAbolOgtyntAPTt0E3lqphyHKlunQSg5nvwM+cNOspXKlCbUB4vaehlnw5pkisqpn14Oywdn33TXNNDDjWzDAvLcsIRWc12iAU3vpB/c57tfmC+YImGnrFOjdjr+5D51CKSzg/ZLoC8bYCmfQYfoXuikgK12GuX9Jkp2e2Zy602NgxN6KCmALVcLlwgPockIBhdF7CDNUm7xeR3lV7TYlNqRIZ9SmFajmcEIA6wjQHWbCOBzldXrMc7W62iUvZI86ppB5sGb3OssMJ8/b8GbEVMhN1Og2BoKt3Hoh1VH7C3DUAuJiTU95DYy62WqpBLLabDNcZDZSxcy61NvIAashMbUmKuD9864rfQPkldT33WkdR3yK3WQzk6pS5JnDdW2TowobrOEWoYcqiNGA5tawPJaM82udxSZvs422Yst5pNsW7aWNjPthsCaq7zIrVd+Lrm/Tp07ojchLzTP1X3Uw+X7dDAE3BDTyDsKAQ8Lw1CYM3VXn3BmuvckPyEM7oIgXz3Gds2e4XsAnnBmt7eupCeeSHFPhNlBmpjt5lNopQSExGHaNrFxmWGgMLox1brzNafsXbDnSJLVpFVBaoVTVYSIDcF8BbjWosFaxuzVk8NqJn7C3XQUjvaU70iuo4n5Tgl9/2MQM22fvBhWQoJUsCwREPn8WJKhQaAHBzEuqIGcd3EXD9sdeoLMYw5D3j99oQ3fcCNbTsmXLOBNbGTMAXYTDW/Wq0x28TclqttdLmygDUTzgwJ3VJDP5k2ATzN9W3g0+coBALn0NhgLeb+Yd5/A0DNpj5huZJ2iw1z5+XKDtYAuXM39ScZO1BJRqA2BEzz7UMC2DYZDspDQLVbLUcIqA2wafBmhoCWwQoMnbKcapMe/XMK4YtF09Yx9rx/Y0o6SmjfB8oso/TFzHPNP0a+ERf5n0RDgLxQs2z6xjBU3QgezNVMPhqVZFSqQUayjQVqkt+cbX7fTq7ndxTTKY25jujlzXVED+Wpv3ufY+0Y/uuHa76k7jNcG0Lt4Kp/fQ+Q/nXWN7bL98vLZD1/9HzbvKHcvQO7rHzXiRig1rdd9LbNthG1Dy8S/125js1sq03fd7gyQOic0NO1TdtooWb5nG0EdO+J0hFEF4g713zLZqgmadh06MVNTLvx5V3ruPZpe3awXgNsm3U9cwxpHhoQqPmeo2JHNc4l6X7HHJk0NcLHHAV0T2jQ2MNJC5zpz/p/6XZOjXROtdCfZFNELyaiO4joTx3zP4qIfpeIjono29j064not4joViJ6KxF9U69j8mhKt+uiouxygbUhHW6SkFCXYy3JrZbyNtvX8ZRABNf6OTpZ0lxXA43COJikdSMNqfJNy6jUpLl8nmi0rz6ujD6hy7Z5qXm8IpULGsTIvPaIHFF9ZANqMfKF2Y4QBmoDaqZi8re41uP3DN5GwfaxnTfaXWObN9WQQy1HG/LriaRNgDztAnTbBoC8fYBwqK7ZJmM61vT2e6iPgzCljWzPVy63J9AOpQY8bQS428nmWOPLc5n3g9iBl2xVFelS65OHcAy3J9+/JB8eYHleALouQ6C/ay2k0E9WCNRi3WkpA7HESJr3WRoaugnXWq4BC4C2Q43/q8M91/ssIaBu5c2p9hIAPwHgZY75HwTwjQC+yJi+BPCtSqk/IKLzAN5MRK9XSt2arWS1slw5iegpAF6A6lLzIqXU84353wLg61Ad2F8D+EdKqXfm2HdRUYpssC0naJOEhMaAtUEVkwNpSJjm2m4qWIvpZA/5eiE1BMtVplh44Fs+cz4Wl1yATTxqnk3S3+KYebx6gJ2hoUEKGFhLt4tuEx+08Uk6qIR5uGY+xcwAjf/OY9rBV/8hp7R5vzHvGbyNeCfGC9Z8wMYWnma2X2rIaN+2sFxTUtsEyNsufHs+8Ak07QNUbdSCAbZrG+/sDwnWYtqHV50gVFri6rQpFXja1vVd23iZXPAT8ABQwD96q6RdEvMJrpUZqPWBaaF2Cz3zAvJchYAQrknCQYF2PaYAthSYBowC1HK9cONKGRnZVOjl6pAKDVhg5lbbA6IHLNCfm2kl/LOljKN/KqXeSESP9My/A8AdRPT5xvT3AXhf/fkCEf0ZgIcDmB5UI6IZgBcCeDKA2wG8iYhuNgjgHwK4USl1iYj+KYB/B+Ar+u67qCinzIftHJAt5FqTgrXoUeBySALUYh7yzfJKL7QhyNRXQ203R34iQAZvcnRsBwJqtm1k/S27oHCMw1LL7Pz46rlnnfs6ply5nFESMGB2Oge9zkidg/zwbRDd1w6+eYG288EBV/3HpBzgy/J7jQ2umW1kghsrWLMBAA1qeEd0Ss61yPNJAjlj00D4ngNMZ6ENTgMB+Al0ASiH1Hp+tfMuWOPzJbLV6SIwHxgEqEmuW772cj2T2RyfNoeUC34CEa41m+sppV1MudphBKDmHSwi8l5jW958BpYO0gIIcuIBftcaEAZsOdQTqKXAtNyh1L7tp+bK49rkYAZDutWqnGoz7AGt7RQhJqfadUR0C/t+k1LqpuzFqaDcxwP4/dzbBvJ0J58I4Dal1NsAgIheAeCpYARQKfVbbPnfA/DMDPstKhpUuSBbyLUmHTo+m/hDuhRIxAI1SUfctozvfuTqIPcJA429Ako7oDmb0wXXBgpJBGShI4A8HEES3iFKvu5T7O/XJluYjln/tnq31fcK8lw8TLxOczmjQtAGaBxRTrDWF76k5jpLcQVG/P4lcNMH1ELQxkx6DKAVOmJuQ7cVb6eQay0I1njbhcBabDunPkV6rvUSl1ro3JDAtO6ob90Ddz0HuNpHl1PkWrOBNcDeXlwSiONqF9950aM/6GsbID+I1go5Pnl5XC8QAEsbAWHXGmCHa1qS88h3/gwM1IZwEYa25wJssXBNHBIKuMNC+8r3ktHRrlKglhOmudpTMriabZ++wdYAOVwD5IDN9+JxvrrSqi/bgAUu+dxqHJpxpKYHJ9DTVpjBzK1WVIsgfRa7Uyl146BFIToH4JcA/Aul1IeH2EcOqPZwAO9m328H8Eme5b8WwG/YZhDRswE8GwAecQNlKFpRUT71hWw+15oNrIXcaqNJmiw8x72Eb8N2H8wJ1qbqUPNt11fmPs4pz3qxD3w22UaQ8joF+ojXfywMNl1RgD/kMINDcEhoYFvWBgX0/lxgraW+IaBIXF5Xh8+tNkD4bSxQs0E0yTL6Ad5sKx9cszlu1k9Oro4kDwX1gTUg37XMd232dJZytQmQ3i5AG7bFtA9gDwntgDXAHg4KNO1V7byt2N+6bxCEHmGfsS8CYq5dlavE/UMMQU8gHFINeOCazbUGdOFaVdi2cgLnCKCW6k4LwbRY16cvlFpL4iwE/M8NWh2nIeB2rwFpkC3k1u8J04D08NwcrkIgDNtCgC1mxNCxBjQA3G41DciWmK3Bmv5uUzMoQfXvqh4JtAkDLVprIqN/EtFVqIDazymlfnmo/Yx6qET0TAA3AvgM2/za6ncTAHz8jWfUiEUrKoqWK3zHJ59rTQLWuKLcPbZ5EpeaBKgN9WLG5vTRZZjARTpJMW4dW0c0Z9in42Em9uFcy/bAlxLa3CmjLZRN1+PS8T9fxpzuk80VyCGOBN4I2sT2gC3tmPo6NjFQwOW4cbmhgiGGNlDDZXPc9NUA1wIJvDHbIARsbMmLZ8aFk2/DBGwueKMrOsm1JgkFDcG1FKdvYF7MuRECajKQFtc21Xx3+7SW8zgLAQZAY1xrWn3vJRsAaqnAMzTfBz3NcvjgJ9ADrlUF4YWOk4uH2O7TjnYB0txpOVyE0vVdzkJAHroLROZc03I52HJoIJiWG6RJJAnhXS/ryJOnJXWvDSmbWy0UBsqdaOZ381++Tgn/NDQBqEZEBOCnAfyZUurHhtxXjkN9D4Dr2fdH1NNaIqLPAfA9AD5DKZX70bqoaKOKBWwu11ooFHQQt5oEqMXAtJxv//Q+zPtU385033VdTeSDNymjHmq5Rj80v4fqJXC/z/G22zff9YDM95891NDcjut3y+vGhGsSd1SCW9L2cJ0bGtiWm2FphWs+N5QTrIFN4+CM143ZBqnuNl97+KSrUde3B+BopQI16ehfruWqh/z2NluXgpnt/lJX5n51PrVca1Kwppe1gTTp9XLu+GzKbA+L9LmRC3LGjMrmW9ZsH34uAXbAxuENb6NOrjWJa63ZUVemu9algYFaLkdns2wNlB0Xb5fzMyWsGrC7C4EAXAPcgC1FAZgGxAE1KUxLCZt2KSac2iyPzV0IdF1QtlBCq3sNSAvP9clTDTEDEQyd5y4lT6Fvn77Rd4H+7rUcso0CCjRuNVM6DFS70PggBHwZG1g7xBEu4WD9uYgpI1QjopcDeBKq/Gu3A/g+AFcBgFLqJ4noIQBuAXA1gCtE9C8APA7AEwB8FYA/IaK31Jv7bqXUq/OUrFGOQ30TgMcS0aNQwbSnAXgGX4CIPh7AfwbwlHp0hqKinZXZWXXJ5VozwVrW0UB9YEcK1GzPtX1BH1/f9jDpcq1xSTvaY781yfEKwTw2CVTMWB/SXCxA2I0J2HMPJcNi06VmutjWOw9sxxZ6bAM5A7uj1sVJcEaFYIC5vgnXXK41s406sMYENXq+CdaGcKv5JGwnG8DRcrWDFKZJRhMD9AN7dzsmxGm4ic0d1cC1NbTRs2zOGjO00HStsU0G5QJqEdcg3RGNgc2+Nqmmuc+J2LYJbc91Pq3n+8J2Y0JCAbcb1CezegYGajEwTQI9JcvMWKe4mRYGn+tlA6G7gAHXADtgA+Kfi1yQ2VJluQeM6BM27VJsODUQdhcCYfcaIABsQPb7uO8lTV+Y1neQj5R1JAODAPKBKLTM4+7bv5GkI+FuNV8YqAnWgCbcE5CBtSImeU61oJRSTw/Mfz8qY5ep3wZ7FBpSvS8pSqklET0HwGtR3aJfrJR6KxH9AIBblFI3A/j3AM4B+K+VCw/vUkp9Yd99FxVNWVL3ms215gNr1lHFuLiDxCWpoyoEJiQPjbGjfuptmg8hJljbhjBQKcS0yTw2X46vGLeaQ7ny5bjmhUadzJ4Yf70z47sv1NRcZwY/WMv8G7Q9UPeBBr7lUmENULvW9CzzeiMFa7nadwDpdpDAG7NepaDGlGs9V44Wv3ttKXOtscWtrjWg+/sOOdhcQE1Pd7gGbdLXnhSglqtdfOvu4cR5/knOJ1FIKNBtLxPqxMosTiJQiwlTT7l25YCeMc7CmLx4Wk7ABvQPLwzANCDOnSaFaalh0y6FwqmrZdoh1YAcsPlyrwF2wKbVJ4+rD6KZZeDKBdP6hueGJAnfBfwutpgBDoC8LjbTrZYC1rhPrflUwJpYEwj/HFNZDrW20L3amPY89vlzcuynqGhbFXKvScDaILLlojLnAXIwkTrqJ9B90L+MMFiLVa6Luw0ISJoqpjldnVdbji/zc0iCsCtAHt7D5YLJtgfjIFjTZZW+8Tddavy3FtqGzS2pf2+hupXUvcON4wMHUpADxEEDEwbwjmcQ1gBrJxRQQwCbC4qv4gNrbLPZ5HszKmwHGyCIhWl58qvsGd8aR5vurK4BTqu9AiAUcJ8TvG3YpgD4f+eueYLrUsx5URWx2ybV93HaZY5VZ9s2ABoLq4NwDUjP3eWDaUAQqOUIxe3r6IxZ13edq777nbqAH64BAcCmleL2ZJLk5Yp1p8WkEogBaLHr25yF1fRw+C7QhTYhwAbY87DlUq7BB/rkuevjLtRyDQ4SGrAt9DK1NU/gYtOywbbYAbN0eVIcazGhoEWGMp9jU9Yp4odFRZuXD66FwFpwVL4czg9b6FwIqOUaqMCWP83lWtPK5RRybSNXXq++2/Ll+OpTBwZgaO2y52h7tnmS/F2d8uljnyH9t2aDBz4HJQe6JljzhYHaQm4j3QoxQM3XEbWBA/2Q6IMBYVhTbX15tnrQ9brWuoXy51/zKTak0FSiayQE1My6tNV7jk5OI/PBvlEIrllHCDVzrZkyAZtENocaEMxtJwnDTXUMukBarrbhkM3mYDPhmm3ACS9cA+zhhSkdFg9MA+xhuLFATQLTXNevMaCnxKnry7um2wpwO6Gso1EKJc3JNQRMk0C0PhA05Cw0yxUK35UANsAN2dbligzXDY1cORZIy3t/8W/ThG0xkC3FxaaVAtD0djswzwHWuJrfqOlVq/6dYbmGcCcMzPU5L4q2X5OFaleQdgIVFfVVzA3KN9S7Ty64ZstH5QJrWtYQUJ9Md5qZi6pVIPZZCtNiqsRW1S64ZkKOWKW4KFKdNDlHTXWFvA6QPN/nSOjzplsymuH6IZjng3K51frk5nLVu/m7k/7mekBNlzvK1jmNBTn2orqW87uhLmFeteGsDQWscO0cqjqJ6aCkgBuwdcz/uSyHLHWpxQA1s25d50P8Q/ee0Qmds/tOA3G4joF1WzVyhISaLigzGXvMiJPmtSYBqHFJzovqs/zc8F2nYsIN/c8JcvBpG3XXCtdsYYa+Tr3N6W3KAdOAfEAth3Mw5ZnM3KYJ2VyALTwoiB+wAf5wwxRJ8nENCdNyg4KYcOqkgUECOdi4zJHI+ygEfnKNvCofoKjfD9A2MIgvdBeIH4hiPS8CsMVKCtYAWF1rJljbAyzXEo7citY6g7yj7E5ck4VqRUVDK8fbHQlI8MkH11xDWGtFJ3eXQohQ6JytWKn3QHM9Xp0myHBBDhfUEIbjiWRz0kidgbEJ802ZsGek5PmAPxSuKpr04c4fcuMFa0Dbrca/A+12yJEIn/+2zI5pIqw0JX14DwG1GGAg2U/TIT2AmZzXBGyLWQCumS4oiSRtJ6134TUhBai52sCs/1xOnG57NPubYbmebwM42UJCTcDmkw2m8X0IHLI5QbPtvIh1ebbVXde2NdO9xuVygdrgGtC+11vDDM37jO0aIwgvHAum9XEO2tqueRFg7/Tz/blchRzgSEJDAXvIYR8nlO/eIMnFFQvTYlMJ5Alpb8vnoJaG72rZXGw2mOUDbbkUO4JnKkjrC89Stmsb+KiZZ4dsKYANyAPZXGBNl0WXLeRam2OFJWbrq4f+zl1rRUwlp1pR0W5qCIt0zP58kO0Ye16wFnKrRcvlUjOBmg+m+e5zEthne3g0wxz75lDTMps+1RUTe2/PMdADB4lAOHm+qxyBDlVqbqnqu6yRbJ2WEFgD6rJzt5rp2NDTbJ9TZANrGX6LvPMqhTmxIMdcNk68o9m0E4c6uqOzdkzNKjCg4VoL2Oh24O2W6yW0y6VmAz/sWuN1RiUCtRBMyxESyh1q7fbg++nCtaSQUK3IcKiWzEP2ADWpYxCIA80SyOmDBBJA08iW0rpSClzTpQO6oMaEM85cXh6lJryPBWqpzsHYa5hvef+W3K5CLZd7LQTYtEJhhy7FhA8OBdNyughtcp1P0pyF5rYkLjbADbxSAI4Pnq2XSRhwoM9IuUBeh6FZ966w3WpeG3xqpQA2IJ+LTa+bC65pV5sJ14rayp23cMoqUK1opzU2SPPJdaPRsrnWQmBNNGJi6B4UC9Rs20vpfJnr2BxBet/SkDyJg8j1k7CtZzpn+kCBmKT5XGZeuVDyfKlrzbNMStgVEOrY2EfKs3UqbWCt5X5yOTU4aAQal1Qo11eqEuo6BuZIQU7uZOzLegSrCty0YcASsxZgm2HVhBvOTrBararr1L4nJFRyHrnmzx2fbcvMLNP4LhyjGJoy28EFbmJchPncBbawky7A6brWAGdIqFYOwMa30QOo5XIMxgJOSTtxMNAGaG3ped6ca0ZYqC71ehsWJ1RsLiitUFjhmDBtqPxqtrDP9gsDW7uF3WuAOzyUywXaYiTNwSUZybNPOG6qi9CmGGch4GrHfi621j4DsC1WOSFaNc9/HgztkAqNTi3JjVdNb6cCAcYHbHw9c3TQdZlm3RdTGqId4Kg+3jZcM8NCiwB1pn9Y9TZpOsShqCiTpgTSXPIBNhOuucBakpaBzz6gZu7W9SCfkiuMb09fgCVgLVa2n4YPwul5HK6FHFExuekkdcWPG2i7pnzJ8yNCQn0JwmPDrqpdxz0QAx5HjRmq5gNk5jEv0QVrMeGIkn24JOgLWh0HDqBmgwYpoaCprsLGHbVXw7R22KENri1ny25IKJAHcNrgGj+X9WEGXGqxTsEQ1HTBHSmANsPUTLndGna4FnKtreZYQ1DACDPkm7Jdc30vRrhYW/mS4OcGarkg5wmrxzgY6sqz04Y3tpxebmBjuf/v93Ou+/Jz9Q31jHXV5hxQwu6CcjsK7cs48qoJXVG5IA3gBjU5XGmxsLkvPPDnVeuCGFf4brOOG7Dpbdhgljmq6BDKHdYprfshQnWB9n3J5SaUDkABTAew8W1KAFuTd62ph9Q827sqRdIQ657RVxlFRDcC+DQADwNwBOBPAbxeKfWh0LrTpw9FRUJtA0yzycxnpMVDQm1gzXSrtRQCD81OeEEqXTa+m+uY200FFK7RFzk84vAqNVl8jDPNJTNXV6rTJhao8eVc4YiZ86nZwj5jOrU2+Ts27iTrq9ms5XwCHGGFQNNGrnYxwZqeNpJsoZ+Ayw0oBzm+TmuqK8GEOc2D8mq9joZpK8xb3zVcax5EjXxruh2AtutQ5+yKyYnHD3dhmbbfnWYdjCMRqMXCtFjHVHtQAi17GCGf73KtaYDjg2u6rQAPYNMSQDQtiTsNCLcFkO4YTDk/QvNcDtw2CAjDNVOShPmN+nfkYnJzxf6+c4VHxwBNF1ixOdOApm38y7jbwXbZkjjZUiR1OqVc/2PDcXOCG1fIpwlifIBNr8Nl+y3obYVG1gS6OY9NSbahlepGy5XnLrWfxO9DKSOLm9DTLIsUsLlyTfNRevsotP4hjti1kQ9woI9pODi7jVJEwhcLw1wrY0RE/xDAPwfwdgBvBvDnqJ5y/i6A7ySiPwXwL5VS73JtYzspRFER06ZgWsrF0xdvb4NrLrBmU9QooC7HmgnUXDAtx8ifQHdgAsDuSNPgSIOklDxXtg64bZ4pngQfiE+EnxJOy+Vz6/FpPPTVB9oszh3ADRn65PZyyZ0fqpsQnzufeKe/A9fM3+QcdvjJgQ7/7fD1fb8pm0NKy/xdGdDB5pAC7I6cEMjpC3H8D/dmJ8aWU23FYBo6cG2FOWazpTvfmgnXTEkBG69zXexZd5orn11foGZrh1z5virtOTrzPrgmDwkFHGGhQAuwAZaXN557ji2PisSdVpVLDtT6OAZTR9Fty+/A1RDAFxZqgk9TcYAtXTF5uYaAaVL3YMipIxlFksvfNt1l9D582w4BmxCoAcKwRgoeUyBmLEQbIreaHZj5JXHFV/tz/x50ebRioJmWpD6GgmhD9Ilc2/SNtgvYRsn0AzZbvcfCtRAYi81JPV+dtJ6PeW62Qxy1ypXyW9l1DekCzaxDAJ+qlDqyzSSijwPwWAAFqhXtnsYfeKD/hcF1c28v0765hEZK7LjVbCDB1jm1udT4Z72N1MEKJDJHcdT7sIG1GM2N/7lsnXDJtswRJnU9m/+HYEAMUOPL+Nx6iWGfQBj0AHHOKbl4SNW8tV0OcPT82WzZONjM0EIO1/Rnm7OQTzOBjuv03g/MD8loCxu8BNoAQQpypBBHGhJnL367LdrgrAvXUC+tl7/E4JrVdWjCtRmac0TiCNXLAe022kcHqJ3s291RfYFayoAGce3gC1nzwzXb8inJ8gFYwwxd+bxS8nX5Qs2l9TkkbDbVBQF+OAaEAE7YFZXiiOId0FAHJyaUUFqPfcJwY+BCyiiSEveaqRgHm01ShxRXrMMp1ZXWNxw39jnZl3tLlkbC7WLTS5nynZ+u8vRVn5xosYOpxGw7Va6ceDbIJgFsEveaFK75wFoIqLnuZ3ssPFHf2+arpjz63rbXe/j53dIVnBFe66wca1QppV4YmP+W0DYKVCvaSo0F1Maw8tre2lTTu3DNB9aCst1jlmg7qVxALQSC+sA1E1ppeAS0XWkut1rMPlxAzdfMtg6+C6yZ+4ytF8ngDbFuPdvgDQHQwyFDaggcX8YUd6m1i9X80PZwgmMs1styBxRmqEANTnC8qEDNfHUFe4sa0mjAdoz2QBwcrunPZ9GFxd0Ct+UDs5ZDNl07IXhZFTcOqMWEikpcCS2gCZ4/retKg2WaOd81mAHA4Joe0ABow86Y9jFCPiV51HidpAK1Pjnw4u5nbiBj7yzKXGsA2u5Qi0N6nX+NyXSzteZZcqmE8nX5AKW0TkMwLc0x2Ja/Q98XrrWn8nbSTl6uUDfOBt1i8zwNCdNCIE3qIJSMIulrE9d6tjxfbbmvn6ZCDqmQYt1NuUBaSjiu71zy5SgMhe4CMheb1P3GyzTGM3/oGtMHokmuX6khu75caoA8Hx5fP8a9FhrMLVh+y33KBdF8UT+Ley3TcOVUjXIZo21LzURE/w7Av0ZF+l4D4AkAvlkp9bOhdbfrSItOvcY4OTcZE++6sYTAGoC1W80bArpEAxXM0M4QUDMBkQsYxYZsmW41mzPLBGsxMoGaD6bZRrtzOcNMsAbYQZoECvD9mdN8YM2UZPTTWjbQY0IGW6c2JQzRlDusp/ndm0ANqB7A+LQTnGA2W60B23J23Hav6RxqGrTp373pKpQAUB88M+fNYA39lMJLG8iJhWkxYYt8flX8JcwRQLv50+xwrVLjkLINZrCYtfPl8ZBezCyADfAPbmDCdQdQW87OWJPh++o0xSUYG7Ib6nzaXDUcljXTm45Kt2PaLG+CUr0O1xqwwci9ZunErOrduJI+mwolv8/hTkvNwWYuZ5N0REnfgAS2bfl+Be1v7baygTYuv3Fa7oDqC9OkrrfQ9qUKD1RgH+WzK7c7yqYQuMmhGGeTBKRJ15O64kKKAVsSEBlysYXkazNXOW2KrYs+oZx94Jxvf65k+67t+Z1obiehy/Hpcq+54Jr9nuQPAzVhmrXfFMqLbMiad/SUS4Gy9amJ6CkAXoDqCftFSqnnG/M/AsCLATwQwAcBPFMpdXs97wYALwJwPQAF4POUUu9w7OpzlVLfQURfDOAdAP4BgDcCKFCtaHc0NFDLdeKnWq1tD8pAN1TBBGsA5G41Uyt0gZoPpknCSiUy17PlAvM50zhU4p9DoZ16ms3ZYsqcbubg0tJQxiy/CWtyjT5plink1vNANhP0AG3IAMghT/W52xnW6vt2VDvXgOo8aEDNcg3Y9hZ7HffaQreBGRqqAbMJ2HwywezcmGcBbz6XGuDujIaAmgTgxAChdpna1x7TqVZtq/2dO6XaIaEn6/nVvyfNg3vtXKteEGAd0gugDdiACrJVO+7KqHcbTAO6DrVcQC0lTFQS/hlKpq7n71nWiRsltD3FB9jqAlhzQklzp7hCDKUgTOpOi4FpEqDgCm2yOZrsudRO6nkuSNBta7vkziiJYtxPQ8K0UBvEQg+fa0aaI80V4uvbT6hkORUTnpkLpOW8v3O5ByrwQ7a4sE/7eS0B6Vy54ZlWKkTLmWdNuqwvn5oPsJnrhdxrNoMBEJdz2pUbtAPTpHmk+48NcyqkQMl9Yi4imgF4IYAnA7gdwJuI6Gal1K1ssR8B8DKl1EuJ6LMA/DCAr6rnvQzADymlXk9E5+AfblSfAJ8P4L8qpe4hkiHTAtWKtkJDArW+MC1XvgLXAx5/C61vKhysAQkj3uiwz8vGNBdQ45u3gYfUGwwHUEAFJkzXl9n0MW417lIzgZplZECvbC62kDhwM0ef5KMegs0bQoFj1C41MxTRBDo2SBAzgEFKWI/ppOFATbvZ9PcF9lrutcPVUZN7TYeGXkS73k3ABvh/zyZQm6MNLiNdakAXkrmAmhT8+KaF2ssMwe1qD+28XBXU8bnW9PLVA3HbTeUa7dWZJL8uqn4YTk2IPyRQiw2VlnT0OCxrQOesM71Ru4PSlc8dZXcPcDdUB7RFKhSiCaS70/psQ1pmwA1pbJ13G1xzbQv1Ut1PTTlco8K6JIFurmeZGNjVF6ZJQJrvviFxzfgcM/EhgD483ZYE3qTK9fsdCqTF5lrzSQpnAH9+PL49adhnCIq6ytZXknpKhWihbffp89hS1DTz/C8dgHAePNt10uwD8X3FgDVTLaDmyyHt6v/4DAdFACqolunceSKA25RSbwMAInoFgKcC4FDtcQC+pf78WwBeVS/7OABzpdTrAUApdTGwr18jov+LKvzznxLRAyHsnRWoVjR5TRWo5YJpoW3bQhQ4WAN0h7HKUbR3ubYh7wMwY//NZO1AdePQQM0F08x1QjcQ133NrG4O0PR+bM6q2FE+XQ4iDjzqaWan3EywzTvzLdeMrjPuPDPdThzeaNnAGurlUgZlkEofu8U9xaXhjemacgGaGIAQCxd4x9F0pgHAAS61ANslHOAQR+tlVrN5NzR0ZuRd4+41wA/X5pbP3L02a8+PCbHVdSEBaiHnoNlWkrxrvEyukVnb820hnlpt11oD09xwzRztVbvX9AsDX+4uU7aE+DGjfMYANYk7TQJxfB3FuBEkbU42HyjYW//fbeP2b6Bayp/fK0YxkMV3PYkdHTQlDJfLB2lscM0/v9suIXeUbYovbMu1DZuksMQHWfrCtJSXL65lYzr1ofxrModUtaZNwz0xNpKCUcDfTrZtSbYRKgeXK9k9EDNQgd/F5tqWbXu28g0dIaPVJ5TTVUZp/ya0b/Oab8o3yEDYjeZ2r9muk760OEAYrFlzqIVS3hwb3139owLWOqrCP0Xn0HVEdAv7fpNS6ib2/eEA3s2+3w7gk4xt/BGqUM0XAPhiAOeJ6AEAPhLA3UT0ywAeBeA3ATxXKdX6oRDRw5RS71VKPbfOq3aPUmpFRJdQAbygClQrmrSGuqGNCdP6hr25HsDXYK3ugAoK0oiHfS6Nz3xZ11sZvp0YmcvrwzHDJhfGNJ9mjuW0g8gEagZMM0PETC3Psk2aoWkcvpijr/oS5HMQ57vJm8ejZbrttIT51IDGPcVdai7IYwM8KQ4dn2Mq5D7Ywwku4RCzGtLMsVo71VogrX7YmqMJQbSFhh7ee6U9augC7XOAuwxtMkONeZuw9Xg9a/lCbM3vPqBm1qsPfPrAZlft8EEOZ9qdFnN0SXsuNb1fG1zjy2lIo0cMBZp8XhqyATJnrm90yVQ4Jgn3lIaASnMOtkNw2x0Ot2vNlNSFs9f6LIFserspkoKZvu40aSipuYwplwsqDNfgna+3Eyfb+esL+42TFJYMBdMkbiqfYl0zknxPkudGvxtNDppCSoUwfUGarR1SXzC7nGO2/cgGKpDnY/O1k2sU2ZDMtkutl1QXWuwgFlK51pUMMlBNk5xbXbjG28HlWrOBNS1fXjUywRkHajZYxvtGfLptoLeiFN2plLqx5za+DcBPENGzUOVAew8a68KnAfh4AO8C8AsAngXgp431X0RE1wJ4A6oBCn4bAJRS96JrUbGqQLWiyWpqQC3mppTD7ioJT+BgTa+F/SvN6DQzdAGN/m4DaqE3L0PeNGLCOm3SAE3/6e8G7FCzcK4ll05Qde6PF03Hfg3aFsaN2kyQr8uwNKbZAJsuBq9vs2j7jum8DhKl4YAPqPngDt8GX8/ctrmcXrY55LqO0R6owAz/tIE0cwRKHhp6OLuE1XzlHjVU1yPgfvvogpwM2rrCPn3AKwaohdbztZNPvpBCG1hr/tWf2uGh1b+u0USbARD0vjuADctWHq912LvjZYJ5HkvcaXx+LFCLAcy+5OxcZifC7PzHh4NWW3VtX5ex7XYKQ7bqGLugTSpfp10C02zL+bYRcstKy2rv8HH42O0Uup5pUgCb25mWzyUldT31gWmxIE1yzpjrSlwzMRCgWUf+nCfN25UCP3K4mGJhWh83oU0ukCVJeK/Xl7aRb+ADaYiu7Xo3RB8hBaL5ypGrnWz7sTk7Ab8bjW9H9oKieTbQ2zavg+Iw0BBQs8E03n8yjQl6etFauXKqoQJk17Pvj6inNftS6r2onGqo86Z9iVLqbiK6HcBbWOjoqwB8MgyoppT6PCLaB/AkVE63HyGid6ECbK9RSr0rVMgC1YomqSkBNekFIcdN0ibfzUXPN6VmANni/Y/R3AxsQM2Eaa4bhCS6PDaU0awWG7TQ07XrbIY2QLMBNQdM46F4Zqe7+mx/GFtPnjV1rx1QALqgBrBDNg3YwKbxnHKhm7Muhy6yLVG+px5cLjUbULNBIIlTypYrjK8vcauZAxWYgxQc4sgJ1RZ1Z3ZZl0Ovs8KsNWooH9igBdiA8G+d/87nbQckh7a+PGrV93nnewhE2r7b1rFBT14OdwhfA8z4CJMmWLM7pJpca2a4JwdFDXxojzCqt2sCNoBd8+q65m+ozST4ui758er60XXCl5G6Kn3LS7ahZesc8uVcnQgfWNP136w/7+xDAnH88KztinJDnrB8HXYbKEtxp0lhnU/tunB1+PydQt9ytmX18n2VE3wCcY6nFAeibTshhYBMjpxPtnXa66ZBi1z51qQQDRgOpEmfcW3nk5YPsoVcbHr92DYKjTLqK28OheotFqJJyhjap3lNd7WTWY6Y0Om0FxTuXNMhtXKphYCazZnmivKxpdc55VLIdq68CcBjiehRqGDa0wA8gy9ARNcB+KBS6gqA70I1Eqhe9xoieqBS6q8BfBYAHmralFepy6ghWr3NRwH4e6gccA9RSj3RV8gC1Yomp10EajHHxG8srnV5ZwqoeQxzq+3hSvfs5m9dzBuD7S0MkJ44PwTUbO4qwD66oukC6gHUfOGOvk6Y/RDa+b4wq51UszqH1GHV2Z+tGLC5bDiifJANsIff2sCZWWeJ9eACai5QE4Jprm251rXJdKXx7wsct4DaAY7WD2AHuLQ+RzRcs61zghPszY6xmJ1gtVp1RqAkXt+BsFwJUPPBRrMOzToPfTfr1ra87TroCzfUD7P8IbcBlGGwpv81HWrV9HbetWaeHbDxdcyk+T75XE362Hhd8WkpQM22DakzytXRkII1wJWDzZcfyA1xzLpud3ZMUJMefujrsOd2p4Vgms1tYS7r6vABfrjmWs5cti1/+JVZ9j557nwK1RWQB6bZnrukHTNbJ9/lduLbDYX28vVcL39cz4sh2BaCOamSgDRA5haVLBtfvu66vkELzPJIQ0X5NlLayBbSOLRSQjlTHIvhcrgdfbZ9unOnzer1uu416QsK3z1RSwrWjIPkG+gCNR9Mu8yWEQUInjaJc6p5pZRaEtFzALwWVa/mxUqptxLRDwC4RSl1MyqH2Q8TkUIV/vnP6nVXRPRtAP47VcN4vhnATwVLTnQ1gHsAvKL+uxhap0C1oklp24BaH5jm2777xt+dYoK12XKJk300IaCmtEtN3xzuRRe2xcoF0WzVI4FCfF0O0/R3G1A7CytEunQ2DJAAf4day3xQaA6zdqmhSaK/DlWcrbA3qwDE7LAakVIE2ZZogzYtG1wz684MfQ0oBoLxupIAs8oltggCOV8CXDPsUz9grTBbgzI9vXKuNY41Dte0S40DOj2fjxqqYehyZoT4es4NX0ixCdS0+gA1Kcg09+u77pguMtOd1gVoZigoOsvqac2/jewDIbgBmwnl+Ho+uTqGIVdTCKjZtuOrc4kzKvbtvAnWzHmAzY3jVyhvmi/8ZqhQ0D7uNKnzzSYfYPOBGZtLKsYh1ewzPkeX7ZzICW5iQgddgMYHQ23bkcqsY3O7UvearTwpbRQK6Yxxt7V/f/K6iQVptnVC164YRyGX+Vu1gU9z/zGhonw7LtBm2xbfpiyXXto1T7JtX92muAVT2iq2nXLAtdD9zPa8si5fHQLqzKtme66WALWL6Pad+P9FLVUDFeRxdSqlXg3g1ca057HPrwTwSse6rwfwBMl+iOgfA/hXqFpdNZtQjw6tW6Ba0c5rE0DNdUNLuTm2ZeuctsHabL7CfGXpaOrQz2N0bwYcpgXcOGtxkMaLzpc1k7nbPvN1jZE514BMfxY6047Ptt1Yl3CYBI4AmXMtlPtrhtUa/Nw9q0DbAY4wO6wAzt7xsRuyAXbnmikTTrL6APwuNX2cUkiWAtOOcNiaX6GTbn1r8fBm/eBk5knj4KEBbfN1XZtwTc+3Odt4m5mAbVXn59CQzSffaJMuWMbrX0sCMvl2JS5DvR9TkjfA/CHXBDk2CAf4IJrtgdkO2Pj+TMDW3qZfUteMDaiF4JkNutnWN/dtkwlrzGOztY3PlSYLLwwrZhRQs4MTI7N+YlyGfJ7kOu5z8rhcYEC7Q2h2KN2dST/EsT0b+N2FPtm3lVMhUBOCaXyZkItTWgeuULVU95qWD7z42qhvMvwuyMgPYHwwLRa8+fbnuka77vu2/U25fXKBg5g65Urpc8RI2k6SvGnVOu5rqet+ZpvvA2tB+QwEPqCm3WnH7PO97HPRWhlzqo2pbwPweKXUnbErFqhWNBmNZX8PqQ9Qi72xpbxlqtbTSb6rZbVDB6g688vZGezNroD20X77chmNU+1eNDcFM+zTVW1mcnwNtfRnm8sMcEM0voy5XTi27QBpNlea6ZC6hIMOhDBhD9B1/+hpWq6HvTZUq7ARH5myKlFVsj0cN7BnEYBsVQHaDwHmM6oDTpoJ822uPV0vqUDNXN8F045wYN2GTSZIscE1/Zs/wkELovH1TbjG24BPMx1vzbrzdf68JsTaniSfJ8i3Jcf3OZ7M5XIBNR9U8MEbvWwnCbB1ms3dBrjgWrXPbn4uF2BzhYg289vAwiZXh9AFW3yQRgLUfNt3df54WV0dCL19sxNhc7bx/fo6pPb7kz+UM8UxKJUkpFACRkPA1LZ92zQb3LA5Lfi+QnDNtg6XzOEU88wkhzc2SZ9hYmCadBmppO4Zc7/DOZziYY5k21JJny9DDsHQtUvyQjgEr1zb7wPZXOV1tbdvm9Jt51ZOiCZ7cW+Xr51sbZQC18wXQd2XfTJoFhUCakaHuNLk2IDaRTT9qALVrFKgXr+7DemvAFxKWbFAtaJJaAiglqKxgFroDWAz3X8x4nDtEg5wiCMcYw+z+Qon+ydYmBd4fTPQNwJ9o+juuA24XCGX3A01M5YD3KGd5mctl7ONQ7q5fdCBS7M2MDs2gBoHOhwCHWMhAhNSxxp3p/EQQ+2M4qDtEEctyLYGcAZkM/OyAUayVUM8txevo5gcaj6gZqszDiz1Z/2/rnsTYPofXBuYppPea53U3y+xESQ5ZDOnm3DthG2bgzYzNNRc5gSLdbuGnhNsjiVADtT4sfYBar7fre0hlgMZLRt0870t5tusZA89tDvO/ICtWm9lWa/Zug0S+DqOvrbi8/sCNd/9xVbvkmV8YE0rLbSwWtP87oZ/csjWACn3viXtleJcsznfQrK510LuPPOcsW/DDgnMYzLlA26xecB42aRKdTylwrTQc5At9M4G2m1wzSxL3/bpC9t82+4j2z4l55hk2T7yATFzv74QREAGQc1tmtvu2za5Qt9tyvXSPkVScBl6meODa67UBnr59nNB2K0mHgXUlJlfmjvWXEDtHjQmhaKWptK/j9B3AfgdIvp9MEyqlPrG0Ipbd6RFRVLFutRyA7UUmCZNLGveRDRQuIQDAFVuteXsDBbzK3zj1QXfdwMwQy3PWv5f1Mst2HQN2xZsO3M73JFKh9GtvzvC6VxQIQTRXGDNhHEnaIcqmp0BsyOpIYx2VGlotsBxC6rZvzfwTa83wxKHs6P1AAiLw5P1g4I1V4SlDl2J8qVAzawTW11pd9px5/89K0wz65LXYfNgxGGaHazxeRymme40n3PNBdpMJ5uZM0/Ll+vG1rn3wRhzOVtdpQI1Xwelj/iDra4PO1yr9tyWLxTUDthc+cW0bCGhPhDgCzEcAqjxspidE7MuXHIdv9SVw8sWJ2noqK/jKrvPAfFhu67pfL9SQGDr4MeCNXM7MU4cm+JBqL2NwtsJKwa8hM4DybOQuxzNdcdVxlB+Lq2U9ukL20JAx9xHSL7thJ5LU2BaaqfZdkypbWMrR652sW3btg/ffmIV24ahffcBG752crVRDFwLgTW9fAis9ZLLpbZk//uA2j3se9FaV3Am+qXNBPSfAfwPAH8C4Epg2ZYKVCvauIag2NsG1Hxvz237XdbwTHcwgQYozLHCJRxiNl9BzU6q0Qt1uCcHavziz11m59AAswWA+9V/Z+tlrq6WOT4LHC/2gjnLfPVku1n73vS5XAe2/brCDm3uKQ6AbCDpqD4+XQbXsXCYZnOkzVC51jhU0zCtWnZlfK/WvVCHiq6hz6z+rEcaDUiDFrOuzO8+sGirSxukvISDdX0dY9GBl/r3rUGl2e78AamaHwfWms9N2KALjtm+8xBQXRZzGf5blPxedRvw+ubTQkCNby8Wiup9murzIOp7k6yPxwyP80kWCtoeEbTrXvMDjpADxuesSQFqIYjAp7nAmmsZm1xAB0gPWbIt2YV9dheifItd+e6VoXayTQ+1s68cfcEa34+tDUPtEQIHXDZQZnO1hUJHbfeUmE5+CIT6lumTmyoWrpnlkLZPbJuY+zH353O1SfYRUsoL3tBzaI7ndr6NEGADwpANyANBc7eL6xktpg5jQVrOfpWvnUL3GR9cs7nWfC/L9LKuUFC+rhkCupydWQ841ZEZ0WML/+QpdFboOtSO688FqnU0hPN2YF2llPqWlBULVCsqEignUJPAtBCM4vP0snrUQ63ZbInF/kkTUbkE8GFUF37+bKKB2v1QAbMH1J+vrT6ra4EL99vDhdl5XMA5XMB5nNSwhAMp7VbSx+K7kPrcPmZ+Mp9458mERhyquUISuavKNf0Ee1hdmWG5nOHk8gKrZQ3X7qs7SlfV5Z6vsLd/jPl8hdkZHvJ5qd7KSQuY2aCaDgHl8/h6PISUQzazvnS9ujqgZuc/NtzTBixNOKmX4f9zkGY+qOmHLL2fPcS9nTcBnOlq4nBNAxqbe810rOl618v4HGv6WHzXBd9vlrdPCHymADUJnLGBNmlIiwnWTHEHm6m22yrkVLO52vy5r1xlSoE0fJrerrmsa9s2SdqFb1MKEiX7tincqXTJDdhSnFExQCYGqMWUIxWsAWEXi6TNJRDUlxNMAtn4NmzbkZYrN0yLOXauFLhmlg2QJ9f3lcn3MtEHdCT7iZXEZdYHpvmAqK0tXNt13fcl7ZMKQaWgTbqP0P588rV7KkSL/S25jicE2MLXyvCoyabj3XyBZ9tnjMvb2EBbGrDxXNPapcZhmgZsGqbVf/cVqNZSNfrn1qGm3yCiZwP4VbTDPz8YWnHrjrRot7QNLrW+lD0GqPk6C76yzNGMeghUN8Q91CGgsyvNhf8DqG4IuhgLAA8G8CAAD2v+3v/A++EuXIe7cQ3uwgPwIVyDiziPCziPSzjsADUeHmkeUxucNQBIP2SZIXd6uVCYnVmPLjDBYQ4HPCYAOsYCR1cOsFzOcHTxECeXF8DlPeAyVTfP+9C8uWI6AdYuv6OrUDn95gpnzl7C/KoVDs5dwmLvxAvTJIDNzMHWOOFW8IE13haAO+TQBtN8DjZbuKcLVJq/ka5zovltcKff3PLZlAng2u41vfV2wntdDy63Gp/HP/Nl+HbadS/rbIZ+t/z4+gI1vr0YgON6MI3NHdUce0xIl92pVu0znH9NyyyHFAa4II3ZXiujbvX0GEeUS1GdA6P8setxhV6I8GOTAbbUUSzdnXzbvcYF1EJtIXHMpORKCrVf32eLUOiaFLK5tiFVrLPQXEYycIRLPsetCTttZezTPrHQzRfmKQVtsZJCmBSYlhKiC+QBbEA/CDoGaMulFJDWtzwnxvXbtX+JK9cH1vQ6Nse7ubwJ0EJutfW0+dyeKsX8uXCH2op9dg30di+aUNB7gKN7gKMyUEFLWzr659Pr/7+LTVMAHh1asUC1oqJESVxqKUBN4pJwPSSYHfLziws4u18lb8cH678lqlDOBwB4FIDHA+pvAX917SPwdjwS78YNeC8eirtwHe7CA3A3rsEFA6itQQlzcNm0t1/dYebz2s11xg1MXDAFaIe5hqz0fsdVFd65hoInezi5vMDx0R6u3HtYAbQLaL+JOkI7/5y5+znagzWcBzAnXDl3Fif7wMm5q4Fzx9jbP8bBuUuYz1c4ONMAtAXawO08LjgB20VjUIMLOO8ET5dwCJdLx3Sq+eotNdyTTzdhmu3BSyoOXIH2A5/toauZZx9RkpfFFvZpGxG07XBrJ+jXZWzq3Z4ryNcGer3cQM0nXqf8d+NygYWU+iDl388euqNS7rF/Xet0JQEBUqBmTnftxwY5+HJp4MYODsZ7OxwH2Jop7nZOcUGlADU7KHC7zPwdxXA79AGdNvk6+rZOsa0sKe45SSd/aJhmW88F8n0Qp0/7xEK37suk/oNWxCj2mdW1Tp8QXXP9XIAN8L90NpUC2iT50/oAUNc2fWXiGgpe+M8v+fWSb8NczwbWfIMX8Ouv794pHqxgafwPtHOpabi2QgPUdF61y5VD7YP3Jg4ZucPaxtE/lVKPSl23QLWindJYLrUxgZpvGt+WBhoV9Kg6fOdxAdcsjkBA5VK7C1Vo58MAfAbw/s+6H34Hn4I/wRPwF/hIvB2PwnvxUNzx4Qfj6M5rgLsJuBvVzeUC3HCJV8d+M+0IqEDTAZoBDTSEmgFgzi7sn+DMbLl2d+kwyhi45qqrE+y1AdpqDlxcVG+ZLqP6f8mO8WL9WTvTjtC8udK71ce8Bmnss85Ld1Af67kFTvYXHcC22Dtfg7NDXKwdaRdwXuRg44DN5+jqvslzQ1sXqHGFezauvzasNB2CNgeWTSGXGgdqrodx/tAF2Dv6rhElbeBMw0IXXKt+Ct38XjbZOppSoGZux2wn234kDzP89+H73bimp3Qi/OWy5X8yR/Zq4Fp7GtCFbm753Bp9gJrPeTxVSe6dNnjMzwHeceKdJl4Psc4oVyff11YhoDZUW8QAnNzyQTIpYLOtH1LoWSgHUEsFznxfIXhjlk+ivpAnBrJJ9meT65g2AdNskrZNtWxanUhhWwi0SfY/BEDoA9Jiy+M7z0JwLUc4qA+s2cTXN7dt5lUTyxzAjedSWxp/NVy7cG/VvTqK39tOa5vCP4no7yqlftsz/2oANyil/tS1zHYcadFOaltOtBS5YZwMqNnCiMwOtq0zN8MSF3Eel3CASzjAHo5x/toLuP/iqIFCNwDv+VcPwIvwdfhVfAH+8AMfhyt/dBa4DcD7UUG0u9EAJ/4HtKDZugn32WcNlw4Cnw1nF/YXuLJY4OQAOJlfXc0/hwa+6X3UAA4AzszsD1RXVnVhLu8BS2puiveh7UK7iC5Q0/8fsf/1zZSLH89daEDavXVZz7H/L9efLwP40AIn5xc42T+PM2cvYXFwHnv7x6IQ0XO4YHWsmaGgUvgI2GGBzRF1gvbADa78aTwkmIeL8v2aIKcNy+RAzXWM5sNZs692Hdhycvngmm06PwY+gIhNtg5mCG6a7eS6brrcgC7xh0+pSy0VqPnKEr4PdB/kTZC2B/OhXuKeahSCAHy+y40zBFDL7WziSukgy9ex139KXrVm334XVG6gZuskTlkuV40JNEOAzVxfsi+tPi8WTZm/E4m7OTUUNFWS31Iod5svhFASohijkAPUtQ/fee+DOvKXG9X2U9rHVycp0DPGXejaf9/rRqidQwC6j/qcZ6lgLbS8+VJN4lbrff3WrjTuYNPONT3vuPp8373Ah5dVV+FC+h6LNq8vIaJ/B+A1AN4M4K9R9dweA+AzAXwEgG/1bWB3qUZRUUBDutSky0vdKvp7KAQMqB5MPoRrcH/cjRXmOMQR7n/ur4CPqvb9vOd/N37w1T8EvBLALQDegfpOcB+qu8URGhOzaUe7GhXhmtf/s1l68oJ95qGRrs/a0cUhm+n+0o6vfVQAbrYADoArWLRhnpZp5eZg7KLxvwnRzPn8ZsqPV5f/XP3/BbRdakv2/7JeRsO1ewGcJVy5fBZH87M4qt1rF+bncXjukhEe2jjXtIuNh4xyh5sGbA2IakCVTbaOqC+80BzAwQXU+CiqrrA4V/ivD6iFYBqX62GuLVcifDtcO8EeDnDUmm4Hc1146HJF+a4Bum1iwj75Nm1yPSy7XGquh99cYWLyee635K4QUBMi+CQJL7Q5hPl6fYCaFFDGhuFypXaOXeUwE0pXn8PuNb2crMxyN6Ht+LbJLajVxxFkG3XXlxhcqw/AkcK06rsfqIWgQB/X2hBgLbxPP3SxOQu1QkBHIim0BPxtJdmma7kYuJazfVy/4T552iRtkvs6k9uNJlFfd2jMtlwAzAfffOsn/45c+dB4fjU90qcepODeKo/ah1F1Ae6K3+vOawiH6xBSSn0zEV0L4EsAfBmAh6LqGf4ZgP/sc7FpbccTRlGRQFM5cSUuNZtM14P5neexcrmH+L7vwnW4E9dVo3V+7B4e+bHvwC34RPwg/TSAfxN5VFcDuKn+/6D+0+TsoAZH9fcLh2guLUIAtwZmaIMq/T+HbQdogziw/80q5iGbmheu2GcO0zRI4zBNfwYq5qgPg5dzica9Z9PKWOaoPhZtHT8L4OICJ+cWwFzh+GgPF6467wwPbdxr3WkXcV40OiiXC+rYoA0fIbUbAto41HxAzZdPjw/C4FpGb8N3TLbjc8/vJr3n0MyEa4Dd0WZzvvl8Ui5HlBSi2yTt7PB6NF1qrrqNqXNA1mmTlNcWkFP9K4drXLr8EuBnh2Rup7BvW1LZjstX5/LQqbgOcsjF0YZlcXCtKk9c3YScZ9KBf6aqXM8vtnxVJuSXhOOGOrMprqe+QI0vl6vDH6uYdgoNklAt4w4flIQnxkpyXa6m9QNqqRoDfMbANl97AHnAp02bAGkxinGrSdZ3hYE28+1uNWm5kqVBG9+VEQK6XDZdihL+2dYVnBn8mpFT9QifP1X/RWvaTxlFRVugXC41Pc323QRqPFm8mduK57+aYYlb8bfw+3gijnCIn/mdrweqDGuR+jCq24W2XGkd1v9ziKa/H7Bp7PvyKmA5r9a9oJc14Jv+eBYNdDNBG4dwPKzUJROqcQeazZ2mP9/HtmHCQFNzth/uXuN3Wg3XDtjyaxdb5V47mQMnlxc4M1ticeB3r53HBcywXE8zR7LkIaE+SWCOOZKq6VzzhXwCcqDGHXcmTDOPw3yAktzAu509+0iTJlzjdWWDbt08X7Jy2K4Jtlxp7XX7udS6IbjhsM+YjuwcK7igRiifD99GA2NM2R+eXev4Rtfzlc/2O7a1izTsVirb78c83txATeIS4uo60eRwTa8TUowLyrb8NijmhVtI5uAJgBuuAe5wr9h69AE1Sf60mPNmTKDWB3ba1u2MTJgI2GIU4xjeFEybgkJO2pCLDRi2nmLOkT5hsRLFgqtUEB6CZL4QUK3VrL7GrYztzABvVZjzlmxa3Y+477gK/fwwml5SUVubBsBjqkC1oo1o6m+NXRqq3OaN2OYWak9vgNoxFjjCwXqEzmMscAHncAHnKwhypbqg3fX+64CfzFFaDtZ0mOghm4d6vu/2womU3hZzvy0Pqr+jQzT06aqGzXGnmgnZ+CZtRdf/8/wIOmfcfWjca7ab7RINK9QhqRrs8VxzYMuY6+vcdCu0YaCepwdxuLjAlXMLHN17iOOjPVw6OKxzr53HeVxYA9RLOFjnXDvGXsutxuEUwCFJ9y0gYB/ggX/m0IzDMz3P17HqA9Rsx8C3y/el5+tj4XDH75SyJb5v4NohjnCMRQuomXDNBuZ8csEbM7zQFertPpaubPXH3XcAHL8VO1CzHVvfjjjQ7vi359vgWgN0zCT6vtBDn6ThhX1darxj4+tsuOb1dXNIgJo0dM0G18xR28zlbWUIyQXUUvOo+TpsofyBtvXSckLlC8nl66TCNaB/rsQhgZpPoettTPsMFfngG/lSCti0JLk73eUYB6jFwpe+1zVpu8WMOlotnwbZ+ioXSDOXm0K+yL5utfY8+7RjYD1YwXJ2Bnu40r/grFjLepf6/XuBam3lHKiAiJ4C4AWoekgvUko935h/A4CXArimXua5SqlX1/O+C8DXomq9b1RKvTZLoQxtJ9koKhpYucm660YvcZjwzjQP+axuGG2gdjeuqf6/ck0F0d6/AO5E9XcbgN/IeVRmmCfQBmQu6dsOT3j2YTS3JW0H00DtfD3tfAXaLlwNXKgBnOlo44Ar5FrjUE3DNO0ya4nFffKwz3NoHHT68zVojwCqXW18cAYeKrqq93eAtmNtzj7X7rWj/UMc7Z9gb/8Yx+f2WqGhR/XQFNqlpkerPMJhC0qFcpHZQkA5MONOSZt7zQz75PuyAbUDXFpP52Ct2noXpklBxArzDlwz1Q6vbnfK24nv97AH4JIl39rh+sfShjta7VDQrnxhbJJQT+l1ygbKXGGfvuVCHaQ9nAQ7YK75NvdgU75ucuL28m44InFH+Tr6tnaRHlOsfCCzDT/jOm+zGvm6FArbc80LjdrW7NsefpgKb3zQJkapYUK5gJpPMeFfPsdMLFwztx2jUE6uHEBNkhPS1Bh51Fzt5SuXBLDF5P+SKAamAeMBtbFgmm956ci9rjbJAdli23SoF/2+svvPNXvd9AGOrpcfPvebK4z4ZB9Y3OvYkc6BbIqPBspfzNehofehAWoFqrWlQFmej4hoBuCFAJ4M4HYAbyKim5VSt7LFvhfALyql/hMRPQ7AqwE8sv78NAAfDeBhAH6TiD5SKZX9xpDlbBTQwwWAlwH4RFR5/L5CKfWOHPsuKkrRUBZtaWiK7w2vy5GiQZpe/xIO1oCjcqedb4DaOx4GvJ+qQQhuQ/X/WwDc+cEeR6dvFxyg8dDOGLCmt6eJ1gH7vEQF2e6ql7kaTS638wA+CODaatryoAon1eGW2sWmwRQvDhe3cTvdaRqmXdUcztn6/3OoAJqGagu0Byk4j7aLTpfBhH26eXXV6irTgK3lXiPg/AIn8wVOLi/WcO1oT7vVmhxrRzhcAyv9vy0nmU0crNlgmSsc1MwDyPfjA2qucrpG/gT8+bRsx6LdajMsW+U0H6z19yMc1OXAGmabOdWANmhrAFx4GHhbGfn+TWDmA2yhh2qX80xPM/Oo2fLwxXSOOFgzQ0C5Qq4jLdOBpvei/20cbe2657lX+LHEwApX3bpcatIOkQ0O++q4D1CzKTZlQWhZW4hnkzvN7loDwmAo5J5z5ejqm0st5FLrLj8OUJOcS4A7952WeX2SAJyQxog+SOmYp7ZNCERrxb5EcJ3nrhEwJTBHIrejdrNAbeyBI3zyQc72crI2GTLsrc/5Fvod5QZqQ8uEbqGQUTUDiAfVuAYoEOg+7lpL38zOSoFynQdPBHCbUuptAEBErwDwVAAcqilUnUUAuB+A99afnwrgFUqpYwBvJ6Lb6u39rm1HRPRmAC8G8PNKqQ/FFLL3XVBID78WwIeUUo8hoqcB+LcAvqLvvouKdkGxOaA4XNOhoZdwgOMasF24+zxwNwHvRgXU/gTAW1GNX4I/6FFSE6ppGYMWdOAa0ISHal1CA9KA5j0Ph2z3oX2bug/NgNUfrOexgROWaKd903nPXHc67k4zd8WPUx8Wd6NpqGaDaTZn2hztQRRM3nhk/G8upyGhDhldtuHa6poZVmc47DpYu9X0/3s4wTGDHe4kylVBOcSxAbUUmFPBs3ZoKg/3tA2y0A1RlDgV9tYQba8uvxTwcHG4prerl2/KdQztZrMBOAlgs4XfAg1kN5f1XTNsD8Q2B1QKUJMeh8Sx1qzjdx2Zjqeua60N1swyA23ow49NSwpvYjqZGuDaprvKwedL6l8Cx23i7RN64JU4cGxhO75wUFvZU0PWcis27HMsKBDTMbE5AbuhVl2IkwJwpLBmyDxqQzkHQ2CtT3gu4Bo50g7XqnnhkETXsvZlNgfUcp83UggqVW7AlqdM/a9/QwC11G1mHVCgljh3m1mV+iW2bZl9eMFbcae5FRH+eR0R3cK+36SUuol9fziqXq3W7QA+ydjG9wN4HRH9c1QWiM9h6/6ese7DPWX5CgD/EBXPugXAfwHwOqWUCh1EjicUCT18KqqDBYBXAvgJIiJJAYuKpqwxk7faQmCWaFxCK8xxdOUAJ5cXwN0A7kEV9vkBAH8JAH+sP/QqRUWgfDYwPt0cyEDrajR3sEtoXG8arvHP/E7Ht3MfW7de37wpVgkV7NJAzVV8c6CEa9CFaXqazoPGwz5naKAYL7b+zPd9nn3nd2j+mYeIcri2v4cP3TfDpYNDHJ67hOMzFUTTcO2gDg515VcDunnJAHd+Nb2c6WRrV193cAEzfxr/3z/656pVxpBWmNX77oI1LRdUcOXz0vBGr21CtvZIoKi3e2Cta5dsQE1LAjFdICsVqElgTreT13RsdHlMl6BLtk64zV0TAmvtENFmHV7uUGc+NbzQBLf8mH2wxhX26WoDyblg1rek0+nPOYjOPEk+HFs4KN9XKrwJjSSpFfrdmctypQK1UHi9uY2h8neZHUyJa62Z7oYFofqUHo/vRQdfxiV3Jz8fsBmyfWztoeWDa80y6V24IY5pEzDN3PYQx7VpwDYGTANSQ6vd240H4eEyxuZVy67h3+vsnITn5J1KqRt77urpAF6ilPpRIvo7AH6GiB4fuxGl1G0AvoeI/iWAv4/KtbYiov8C4AX1CKFW5fh5SOjhehml1JKI7gHwAFRd/rWI6NkAng0AD78hZYTCoqJxFePI6Ks5bGFQlWYar51Z4cxsiSv7iwr0LFBBngcCeN8jAbyzRwm0G43LdwMz3Wmm9KuhQzRwTMtFlmzisZwWsOZbDWhG9+QA7Bz73wfUuFPNDPfULjWfdK4GHu55gOaQ+TPJRTSgjsO1fcKV5VkcreZYLWdYnpvhaH7Qgmtz9hCigYnOv+YSd6lVRWw712wDE7iS3h+03Ghm2Gd3ng362PbBy2d20GxgrYFg/jBKG1zzgTXTJWdDWxKZQE1ybeHQQF+PbE6tdm46O1Drugv9QJNPb+qsf8eGD8yi5QrtlIK1dhll0MMGbmJDGWz7Ghqo8WVDYM0FNiS/PRMKhMBatUxc6OEY7jTb/n1AzdaxloJOwA+j9b5c4ayxsjk3JK41rriQ4Ljfkuv3F3KbpLjTUp1dQ4I1wB+uJoFrscp5LEOEedraSQ7Gh20raXvEuApD6/bV2O60lHM3VaG8akD9bn1W7bM1Aug+gHvRzaXGnWkL2F1qdRNdNQsnujnNypVTDcB7AFzPvj+insb1tQCeAgBKqd8lon0A1wnXbYmInoDKrfZ5AH4JwM8B+LsA/geAj3OtNynmWlv9bgKAj71xXlxsRUUe6U4w/zvAJSwOzuPourMVhLmICrzMAbzlauDdH9Vjj2Z457L+7AJgfGRQTYG4zJtyzBumpfG/JmNXyTaloRdQudH0qhyknYMdqu2z6fdDMwroOVTQSzvb9H58xdcjjq7QhHhywHZUz+PHY8K1/XobRkjo8twMJ3t764cO/T/PWVYV0f0A5II83bxX9g6pb3RPPRiB6VzTwMc1QIGpgxY4aId5crDmktmpa4Octqp5ezjAJfjAmnap2R4lQqGHNpjZzAt3FmwORBtQM+u37S7swjTX70SXjwMD3bEx3Wp6fyEHlGt6H7DGjyt3p0WaR84FalxAzQfTJOetXs92vDlfCLWhmX8Et2qZrjuqL7jxyQWz+DyuGKCW4oyIcc/p8vjysIbKIgFr1bbkuSDtZbGXMfRbi8mVFutOk7aP7QWBuX3z+HKfQ3qbNvVtG74diXKAD0l5U9oHCEPQapnNw7Vm+XG7232cacD4MC3m/iaRc3CD2Rlg/0ozWMEMzfO17hfoPtOMLTND8zyvp7MoGJ4Ap6hRxpxqbwLwWCJ6FCog9jQAzzCWeReAzwbwEiL6W6h6R38N4GYAP09EP4ZqoILHAvg/rh3VOdXuBvDTqEYQ1Vj194noU32FzHGWSwigXuZ2Ipqj6orelWHfRUWTlfTB2Xww0yAA4DfuymmzrDuq+iH5AEfao7b+W109r1P83786w68B8EgADwbwosf2OCITqGloxknQgfFZL6uXs4mDMT4yqBn+qQcO0MtcQGUT+zCa3JS6nKwYfEBRoMm1potkOtM4VFugqj/tTNPT9WcdHnoOwFwB+yc4M1tiflUNJebMkbGsc5XdV/1/ZTUHLu8BS2oGJLgP1Zszfii8SjhL1CGg/Pt+dYAnl/ewvG+Gk4MTrM7NMDvDwxOr3Go8r1roAdo2UmZVfU1nj4M0vc2QO62dV62BPS7oZyajNWXDYfx8Mv/VjjvfcVdQxnT7NBDP7Vgz99gNK+Vyhd6a87h4aKVrfrX/BtJ0AVoXqEmAq7kPXV593TPBmt6XOXhEjEJgDUBnms8l5ZM0vNCmmM5FCKildDb0Mhx4SpyEknuWOGeNIVc47xBy/bZiO5qhcM/QeaflK4urXSTApuusdLv+xgJrQ2komOZbN+Qo1OXKGa0wpGttSMjE1ccxKN+HH4Lqcgx9zFM6X6T1OmWYFlKuum4NVgDYc6rp6eZ3DdzmwHzeBmoDB59unSJyqvm3U0U5PgfAa1HV/ouVUm8loh8AcItS6mYA3wrgp4jom1ENWvCsOs3YW4noF1GlJVsC+GeBkT+/TKc00yKiRyml3q6U+ge+cuaAahJ6eDOAr0E10sKXAvgfJZ9a0SY1VNhmqKNoc25w2cJ1NBRYYY4qjK9xpS0xwwInaBL4V2VYXH2MC1dfwIWHnMfx0R6u3HsIPJ6APwfwv1KPzhygYIkKaFV7rRxpR2gTK9fABaZMsKahGod3qPeh737n0YC1S/U8nYuNlVWPCKrvfDos0/YnBWnnAewrnDl7CfOrVjg4dwnz+QqzM35IdYI9rK7UcG05w8nlRdU+GrBdpqqcl+s/XS0crJlVdgHtAQ3uA3CecAWH65DQg3OXsJrPcHDmqAXXms6DJH+L231jwjkOaszcab7wT59rytQBjtZl6uZMa/5tytVGatIb/UlnazGhoK4ShffnU8gZ5QM0rnBPabitr8xm/jTeqXG51WJdOy6w1p7nB2u8TmzydcT4cdhCb32ywTS9TT5f4hT0XWNCZfU5Cfn2zePRy4QAjSQMtFouT4fUVtYYaGvrFMa400KdShM8m9uSAE/b8bjOHVduISkQHQIU6PqMee7yd8bTQnAlwJNvTwLWJLINHuFSaPTCFLg2NFwaA6SFtu1yGFbzhnWtbQqsxdRrTpjWN8QzBtJJn0H0ctGDFczRDvdcosI2OgyUg7QFW2cOXLWoHtnr4dLKoAWGMoZ/Qin1agCvNqY9j32+FYDVSaaU+iEAPyTc1SsBfIJl2ieGVuwN1YT08KdRJYy7DdWwfU/ru9+ioiElhV5SuR7EfM4N86bA3WoHOMJRnXy+0QXMscIhjnAOF/AgzIGrgdnVlRPorkc/AG++7VN7QDUuHW7JaY+uFz4aqIZgIbDGoZoGaRramc43Dc/09q6qpx00y+jF9Uic3InGQzxnaI/eeX+0QzvPox3+ee4Ye/vHODh3CYu9kzWc0I6ratf+MLn1KJ17MxztHeLw3AyXLh5itX9cDTKh4doFNDnVNEe8yKrrMpow0yO2nNZlAs5VyHV53wyLgxMs92frcvMHD30ckgd/8+HRFsJmhnzyUE9JLjVze666NKFG16vWBWvczWHCOHNwAL7/NkgLD17A68UcEZHP59uzHSOXbSAAXkZz2yZM0/PacM2+TLNP/0P7CvNWh9kG1lIhmu96aFvWBGi+6fbRD+M7Xt3fYHh5rViYJtm+CS+4a01S99L2SXWsmRoarGm5wLNtO1ypIbgumS5CvV0bWBvq5R+XC+CktIsEOPUNK4yBabFOW8AOPQEzzDbeVRj7WwmBtapMcriWGy7lCO3sc/2QuD/b84bPtTY0WEsBk2O60vqANNe2U8po0zH2sJidYLY01jPzqpmbnbPljtEMfqZzVs+B8/vAVZebRDdF2yki+igAHw3gfkTEHWlXI5whG0Aep5qEHl4G8GU59lW0G0qFU1NU6oOvz7lhG61Qd9YP69EcD3DUgjqXcIDzuAgd0jbDCg/AXXgwPoCPxJ/jPC7gR5797XjdP+5ztFfDDcl4NgHuapO0M4dqQEOJHmxsQ+9fA7Sr0XLDHaBykmmQZnOhnTc+82nXwHCmVW60xcEJDs9dwsGZIycYAsLuET6S5gozHOIIJ2f2cHD1EY6uHGC5vISji4cVXJsvmvxp97Iq0lWjL/F8+gGawQ/Wt4AFrsz3cIw6BPUcsJpXIaGNW63KFWaWnz/k8AdGF0gznVC2vGlm6CeHO5K6BBpUZrpsGvdaN9xTb7MKoebT2+eu6d4x4YEZpmkDazaIY9ZpzDXD1+m2L98Fkz53WhfuuB1SXDz0VMM114if+hrmyndnCxd1vdzwudW0bNNc4W4hmdd4s1y+zpy5jF6fb7ua74bJXdBjf3Tn+7fBNV94rsStFnJ/pbjVUuTqIPvcULHOiliYFuPo5NsIheiGzochn6WGAmuxignx7At6XSDWFkVggjVg3BHhU+QejML+Eid+++lhgjEKuT8Be/huNX0YuJYTrPVx9qW6yFLgdF+Q5tt+KPxeKn4vWs2rN9R7uMI33M2rto8mxQp3rxnhnzgLXHVP1YUo+dS6yphTbQz9TVSjfV4D4AvY9AsAvl6ygd2gGkVFGCd/gkTSkBstvQzvZGqw5gphO8RR5XZadypnrZEe93CCQ1zCg3AHHodb8clvfwvwZuDJH/ht0IMeBNxxR+RRXQvgY1BBLA7WOGDrIw3TlpZpQNeZxj5qiObKieaDaPqz4UY7OHepA9EWOOmELvIQyqBDrYZpHKwd19jpGHtYnDnG8d4Ci2tPcHyyh6P9Q5ycWwAXF01IqL6hL9EMYqC1QgPWtJNthgrMnSNcufcQy7MVtJvNV9jbPwb2ql9U89DRDp/TcoV36mkSmLZXH+lhy6HWDf301aVZp64hBWzhnlpNubE+Xpdbje/PBGWhUUH58mb+r9Axhlyy0lCbdhhtF2C6luGyPTT7XFA2sAY0HRm9jplgPUcur+4ABd2ca6lQxwaY9D75cfjUB6ZJOid8GVtYqhSs+dpDL5Ozg5wKb/S6pni9uSBHCEhXn/0wLfZ3ZAMvoecFvV4IrAHhkVTHUncQirjnMt9vIbbjH9NGLqejbTAWXc6hc6yNpVRgkcON1qeNzH1IXnYMBdf6ALW+5+xUQJr0OPqGj5rbcS3vurec7AN7lx151ThE09M5TDuLxt02Bw7OAof3Vl2Joq62xUCjlPoVAL9CRH9HKfW7KdvYjiMtKhpAoYef3CGgtn3bOjF8mgkEtBPnBHutdU6wh8M699geTnAeF3CIS3gY3otH4u3AHQD+J4C/BNRL7sBP/b2vwn/EN+Att34y8NvVdHwA1Xgnd6MCMdoKzXN76Tc4+gYDhEe6BLqe6MvG9GUNyi7z0Tst733M/dtyomm4FgHRdEjn+b2LHYh2iKNOXjATJPlkgrQKZ1TfL+Fg/f8C1QACRzjE3t4C82tWuHRxhePZElfmh01I6D6q9uFwzVXf+lnCAGsAcHJ5gfl8hZMzfsjAj7dqgu53DRn1sj6YxgclMMNmbU4pUxW4abvUKp+d293Z/peHie6tzzMzJ5vp3rGNMqkVA9b08aW8vYt56+uCad1pS+t6rv3xcE+gqScuW/3ZwkB1i6S41ZryuN1qLvUBa3p9LV/4mA/CSGBaSrihDQBIwJpLobDKTSvUQZaF5K6M7+56t21P8ruTuAj1tlLAmrQcMcrnuum3nZiOfx8XpAvG2pyp5guO2HDQXBozl1cf15FWX5equX4IhALjwLWhoKRLfWFlLjid4zch24/7ZUcvmXnV9HO1lnas6ZBPHQJ6lv1/L3DtWeDSvXksBrukKzizNS8ZiOg7lFL/DsAziOjp5nyl1DeGtlGgWlFRBvnCZFwPyeayvHNvA2v2MLdq+SZ/VQUrzuMC7o+78VC8F/e/dE97RMmXA1//gZ/BV3/5z+C1j/tc/M7jPgW34nG4DY/Be08eintufzDw11SNz3sB1Y3jIpqxEPj90fUswpdZWj5zlxUHQytjGVOas+k3Rjxv2hxtgMZzp7H/z9zvXms4pwnR/O40d4eYP8ytatSkfxvamaahmnYXLmroxOHa4sxxFRZ67gCXDk6qAQ32a7g2q+tK51Vz5YG4jOqmr+t5Xo06ugQwm6+wXM6qZc+0V9fQxQYQbZ9nRv3Ywjw1TOO51lxhiTaZHcqD9XnVhmsud6f+d7WGd3s4wCUc4XA9v3F/ds9FU2YokFlWF1jT66a84Q/lhTJhTBtUukdX7QNxbKG4Zv0B+piburfBJ9eoobZyuMCYxK3WVz4nVMjNFAvTJOBAElbIQY7tZY7vRU9uSJAjBNRUbnATbse4jp3LRQi0zxOz7mPB2rYr5bqYwzljAhcb+ATs4brV97zhoCkwuK9SYEUOiJbaRub2pU5iF1yr5uVzVbrXyV/PWlMAaTkgWrOv9N+4+D5j5lWzhYDqPocOB9X9jQWAs8DB5cqtVtTVFCLIhPqz+v9bUjdQoFrRxjRELpDcIaAxbjVfxyMUBtqFaNUWbZ/Mh4W9etgaDjTO4wKuwd24BndjoS/0V6OK4lwC+H1g8T7gCz/qdfjCT34dbnvoI3AbHoN3712Pdz/6enzg0Q/CXbgOd+EBuBvX4BgLXMD5Ou9XNWrlajnD8r6qDFdWdV1crku6pPr/et96VMoV+75kfzaoZrrZtPTNTn/Wgw3oG+FZ9t0YWKANzRp4ZptmhifqaVWdt0O3tPhvz3So6QEmNGQ7xgILHNf/1yAtFq7xer2Mtsy0muznOr+KvfWrRyw1AZdvVE4e4mnWD4dp3KnWBZNdB1VVp3Z3FAfJrnPD/Ldpq/bUYyzW003nGj/nOWjgsiW9N0GaLrf+LgVrvgdSv/OpDWJSYFrIkWPWRSjs0wbWzGPhLsEh7gkpisk7FNspkDgDQ+0QKoMP0pjtpMsYCgOVgDUpKHOBRp9c14VUSc49CUiTdiB9nXufk7BaPg9Yi+nsxnRmhww3jenw9ymHua4NfPLymPkkq23YXWt8fYmGAA052mgod5RUoWuADbDFwLVqXjh0eYz6zwHRfPvNCdJylbW9v+42U176cenBCiotgf0rTQgoD/UEuqOArtC8qF+h6mcsUZkO6n7LtfcCB8cooxUwbVNONaXUr9b/vzR1G5t/ei0q2qCGtOmHQgZ8YM3spHJ3DtCEwOnt6vU1IDrEpSoEdHXUjBj5MFRj72qQ9S4A9wB4O/CYG27HY66/HXg08P4H3g934TrcgQfhTlyHu3ENLuB89XfmHI72DnG8t8ARDtbQ6FJtIbPlDePhjp15V2YdSOcEdJ0KVsD+Cc7MlphftVrnCdOjW5pwzAbQOPyxwSAfkDDFc6cBbaimoQuvLz2Cq3ZLHWAWD9dW8yrnmg7TBZpUdNqHvg6XberLrKsKgHRhmTmYAA9/5Y4z7kKzwTXbtswk+YDbgWOeG3xAAptLLQTWODTj63Lnmm5L3zWCQ7Pub8E+xDvP8WGCtRgXhms900npgml+uBN+4HaFfepPLrCmt+MKA206PN38k30Vyq0WzlXXP8SnC+bd88z5IbkcUL4BJfS0XGAtp0LHPgQoCIE0iTNHmvfJbBPA3WY+sMa36VIcnAqdB/l7jamOp1ho4FIoT50NrsXmWtOy/T6kwCHHqJs+5XUZ5Q0NBNztBIRDrM31Xeu2tz9M6LKpXYJoKS7k2GtO7Isnb3SAflEfcqtpkMYcaut19oGD+wHze1H1q4oAVFBtCi9KY0RErwfwZUqpu+vv9wfwCqXU/xNad7uOtKhIoFi3Wqij0MetJgVrQKiT2v7W7UQ3nedDXMIBLuEcLmBxfNK4w86icqp9kG3yXjRw7V0A/gx4yIPvwUOuvQcf/YC/groWuPvagwaq4Twu4ByOcIhjLHAJBzhhIY1HOGyBJJ0zzIRpx9irPp/Zw3JvhtWeG8ZpmYnNeT3wMMVDw13Gk+abkK2Z3nWkmfXa7DvsTtPlNUGarpfGoVb9rwEaB2krzNZ1XNX3RRzhAMdnFrh09QGOrj7E8ckeTs4tGsBmgsi5qv434OPhuUvr0T/NfHG2cFdzHl/Pli/NzKkWgpTmbxqAFSKbDpumXVxIzQ7WdD3zzy6wxrfiu05wt5r5sK6/NyGJ3eS5vodEyQOlLayQ/4b1suZ327oSuSCAa9AVDtGa9WWjgQLduk95cPetPxRIcLWrxLFm24ZNZucesMMz20sbvV4MWAPC7SGtL4kjoY9S3DWh+g/99lzzJaGEdidhN/+d7VlDy5fXL1RO13kwlNPGphS3U5/92dZ3ATPb85or1Ue1Xry71aac16fYupKUN7T/vu3j2k4IkrngmmTdVOUEaX3AZS6QFnsN8Sl+QJp0d3C09CigtgELNFw7iyaiZgXgfvW6tVnhqjkKVGNSoFFfxGXSAzVQAwCl1IeI6EGSFQtUK9qophLuk6pcYE2vB9jhGoAWYLCFenGnUOW2OsGedi8tUNmWH1B//zDb8ArVTeC4/v8OVDeKBUD3A+5/vyPc/35HwNk7gPsBagFcOnsGJ4tFvacDNorlogXTOFTT86QATh+rrkvz4VTfnLnjwwQ+Zg4vF/Th80yQ5gI/JvA7QTVYhMudptu9AivH69+Jhjh7OMZJDdo06NHwr4FtGsJdwjEurF2DJ1d3XX/reprX9bR/jPl81YFp3J1mg2E20GZCSTtc69bpQT2YRrt+u7/1qmNfiXcwbUGfzW8gL1jjcFe3ccwDpM+t5lPod2cDvDZAY/sdm/UecslJxMFmCKyZ0Iy3B19Hf3ddH/sCNVN9wY6002Fzuoba29yGKVtIrt5eKJea2UZSsBYqk6kYV8FQ0MS37xSI1sdBCNhDdPWyPhjKYY0LcKZ21lOAWv/rhltjdPK5QrkJAX/Irm25Pvm6zPXt8/O5weRuuX4Are/1WwKTXTCUr+9yeZrH5+uj5IbNuwDR+rj7Yn9bvpdRLq1mur1ZCGizg/Ws9Xczt9pCbwjtNDYaur1LVIxToW0K/2RaEdENSql3AQARfQQAJVlxe2lGUZFHY7nVqn31B2tA88AVgms2lwmHFoc4qqDN8TGIrz5DBcv0zcJMqslzmN2L6kbyQVQ3CQ3lZgCdBc6evYKz+0e4/9mj6ipyFsAcOD4LLGcVcNPIxQbRzNBR09XFk/vraS51nTldN5QNtpmuNhtsWHgeII5Z57/6vw3RNDzTx8uh2jEW9T4rkKZ/H0f1NF3WtputBmm1S7DazsWmLs/McbJXO//QhKCanXbTfWZCNF/4ppkbrev4c+ei0/XOy2Kr32PstR6IQ6GeWrnBmv7Xdl1ozl37Q5xkIAK+fvecDodmukCanmb/vvJu2ydfJ7/p4Dd1ZUKbZl9taGZO42UzIUJMeW2SdC5T34yHwIsUpElhh60+pKDGBtaa7frBml7Gp1SXWqxDL83xI+tIxjjvfO4Zc92Q40kCQ6tt2oGzRNLfWCrgjHevpEGaFEebKWlOLsB+TullQxCumiYDbH1DPHO1z6bhmXS7PhdaH/eaVh/nbF83Wmo75wJpYcjX33kmka18rrKZy/LIgCq9iuOY9tH0g3huNZt4GKh2q63QzW1cFHxemKC+B8BvE9H/RMVbPw3AsyUrbt2RFu2epuJWGwOsAe6HLvOBy3+jbXesORyqnFgnmK+uNG9Q9NsWnRMA9bTj+jO/EazQvIG5XC8zQwXY9LDS2g7Nv58FFnNgMb+Cs2ePgPlRNf0coGbAyX4buJlwzQfVeD3Z60MO1vQ8fWOdrVaYLZdVfQGNuw9oQUnFmv5kv6rz5ewMVvMaqs1m6+MynXf696Hhmv5tuODaijnVTOffeQbSNKjkwNIWOqvrRv927C49u2PNNfCAzZWmoZsNYPL6BoDZsvtwtZrPcViPWlG9TTxZw0vuiDL/xXpOf7DWnLN2sMY7VrxeY8TPdRtQs4FzPU+yjgsUu+Cd6zj4b8gViq5lQphqWtd9ZoNoZruZLxRM1xpXyltxG6SsPvvrI6aj4oI0IZAWcgvZ7hfVdHsqgdBgErE51mxlkdaD7zglQK2PiyN227GAL7RsjOMpBEP1PB9wjgmXlgK19I53/3C4WEgglcuVFHI29YFr1fSU+0ZewCLZbo7tS/cRo1gYyssgaWNzXal2GaJJf7M52joW2Kbuczk703WrAQ1M4841MwxU6x72vYwA2tIVnBG/7JmKlFKvIaJPAPDJ9aR/oZS6U7Lu5klGUdFAinWrSZQC1gBZWAHg7jCZ++Hb4p1oDUBmq1ULDtUrVme8dpYB1Q3iIpo8AaY0WNPLHqO6aWgLtN6evtnwzzqx5xygeQ3cFldwdn4E1A43DttW8/kaTOm60O1nXpRtoXQup5kNmpGGhmDHxzfH7891k5KuQwCLGkYu5legZifrY5jNV+tjqNBY26FW7Wa2Bm17OFkDMw3XbM42Hhq6xAzncWEN2CrEdaEDJs16MwGM7Xdjy40mcaW5wmhd9W/TyT4wX9WhkrMz6+mL2UnNdbvOGlcS/BSwpiEnrycTip7U7RqCO5IOKV/G5irj23QBMb9LbWWZ5g4/tMvu0nN19PX2YsBaNW1mabfqky30zaaUh2oJUJM8vIcgja/eQ7+bbpktMBrusECzjVx1KwVrgP3eZZMURPlgZsgBmLJfyTZToJ5NfRxPrraxudaqbXVHnfSpD0xLhVx93E193FkhxTqbJINNhLYr0ViQRbJN33Yl25buwyVXDjQgLszT18Z8vT5l5UqFmpuEaEMOVtKnTlPuBy6t5vPuy13uVgPaJoSz8EuyzCnUFoZ/AsCnAPh09v3XJCsVqFY0CQ3lVssdBgrEgzXXds0bvV4/JFfnWneaFzjp3ig4/OIXfe5U08k5XbqM5i2NhlLcIn3Mvmuw9mH2mecjqMtDc2CxqOAU9FDX+0fr7Wh32Mm+v06ABtas3WUcCPJpx+wzLJ9NzY3P/FhnAO0DixWwN7uCk/2TNVy7NGucZhrMVLuarX+XevoRDq0Qpx1SetSCZhqsmTDNlYNOiw/qwH83tnxzNtjmG6Bg7XY7Pu5CTF89z4HFvcwNuH8F89XJGq5VYK2pQxtYq47NPl8C1vRU3T5HtXuNzz0wACmX6UTicIwvY7rUXEBMb8sHzvgyNleaxP3mU7v8Lq9aF3zlAGu2ZfR0Uz73lw1oudZ3u6riHGwhQBfjjgvJhJpabbfzXmtZV9in2W5cPmBgkw+ODQkzXev7tiWFRrHuIpvjPBSiy5eRhIPqdXn5Qs880txpfQDnJuBZLPQM5eYy4UsImvlCe13bTVE8IMkHbXzbk2y3jyQOUCCcwzBmPb2uVH1ce1OFaEPDUp9igaTvHsLTcqxms3XURMetZgsD1SN9+qDZMUr4p6EtHf3z+QD+NoCfqyd9ExF9ilLqu0PrbteRFu20dgmsAd3OX643Ya6O8l6daWu2WmG+ulKBDH6B16GaKzSgqyl0A56kNwWz2MdoW6b1fvQ87mLT+5wb68zZZwC0aIru/Gnwe6oJzMz/bcv4ZOZV0OBRh84usD5OOleDvf0rQA2aVrMZLuFw3YHlCfCb3097Oj8PeF42M2cbAOvACNWh2UNmXW4m/Z0Ds8qFdimYU80EaSInYLtQlfYBqut27War6xIIgzXAnQTf9m/TMV216q4BaG0gqttnjpXzHLfBGX6emtN8DjMTpvm/S9ZpQJ2WK6cdL2e3o+HOm9YXrDX74cjODhJsigm9CYXU2uaZ2+DL2MoQA9F8nSb/fbEbmmxzrknCPvXWbMuarjVbvdhLZ8KxuLpIAT229Vzrh8oo2aYpV0oHvq+YgQr4dl2uNdd+JcoB02I78bZt9N1ee9vd9c3nQNu2QvAl5CzUy/DtS3N3uRQLW3KBFt+2QuUKbbePYmGoWZYYwGaum6Kcv/OYlwtDALRc4GzIOg2J51Uz3WpqZoA1nlPNBGu6T6P7Lsdw52A7pco5+icRPQXAC1DV+ouUUs+3LPPlAL4f1cACf6SUegabdzWAWwG8Sin1HM+uPg/AxymlrtTrvRTAHwIoUK2oKEVSsAa4H45cHW+9fa3Qflxv7E13CoB1qJ0rxA6z+u8cqrBPDtY0MBK4wjoyQdPSmH4vGqDGARvQhmy6HKi3c9Eon7lPrmPHPBcstD0bmE2py8ZdefqzWZ6LlWttD2jBoMPZJQYqTLxz3II6JlCzAbaq6PN1B4EDOlvYp57ndzi2B26whYGa0M0J0i6zutXtwNtGa8GWmaMVZqxzW3BICTRgrSq/G5y5cnWZjrOua62BGLpebe1jggdev7Y61tNcuf7k89tt1m1Ht+ONAzT9htYmDYOB9qARVb2G86bpaSlgja/HQV0bsVVy5Qw065/PkwK1VOeazY0Vdq2lOX0Adwefz/cNOuAK5Q2F30rvX7FATeoO7ONikzjQUtvE5UQyQ5el+e/4fFsIrwvq6H36JKmzUD3LHX554VlKPrLQeqGBP4C4sN1mu3Pr+dIHTkjrOO4cGM4dmNpeXL7QZl8+NB9gk+Rf65YjnNvXpylBtL4vqCT7zqncwM/lVjM22hbPS613p1PfmBCuKJuIaAbghQCeDOB2AG8iopuVUreyZR4L4LsAfKpS6kNE9CBjMz8I4I3CXV6DKpM40AxFEVSBakWT0lTcakD3xuuSz7VW7dsN1/h+fPJ1QMxE8DrsrnP/0SBIv4Hh//PQTzMMVAKfuDhY484u7pzbR/fGZEI2DmLM/dnKZA62oGW7D5s3Tg7PgObKeNmYLxCt7DAIaH6HpvvJ5piyAbbq0NrgTLvV+DyfuuDFPjqqC6TNVissjk/sIG0Jt1tQy4STpvhvEi6w1j7W5pxo/q3qw5ary0Q0bbCmgZmvfZaYrcGc7ergAt82YOaCaXx917K6zC7YJhkcwif9uzXhmguW2XJySSCc2Q56H91plVwhis167gfwWKAmB21dYBSCaFIowUOctWJCBzlYa7aI1rrukMNu+K0tf5tPLmjVD1qmuKu69R3TaXVt1wdKQmGaPngWyqfmgjqu43Ip9DuMrescIKDaTj/HYEj8HhJyFQLotIHehgts+9xrfLsh9QUuw7vZ5DBD0nb2EH+38zAE2ULhvLZ1tHwj+YaUAiNzvyDwbTe0/dC+cqvPeR0L1GxuNWsYKNBOj6MHIjiLbvocMwKoqA7/zJJT7YkAblNKvQ0AiOgVAJ6Kynmm9fUAXqiU+hAAKKXu0DOI6BMBPBjAawDcGNjXDwP4QyL6LVQ/hU8H8FxJIQtUK5qcpgTWgDyutWr/3RuG6zhDN1Zb51m71BwFbOAGH8HGTMqZqlBzcbAG2OGalr5p+ZrKdv80tyNxprmWNUNjL7NpvmOtlyO4YRDgdlDNsGQd4MaFdmABagtGHSWhJBKn2hxNaGc0SFvCDdK4XDCNi9WjMg5ttlp16pKfn13XlDsJvv6X17UJE3geNQ7XAJlLxzVggA+opYE3+6i2AAPtgHegiEbtBmzgWhiWmVA4BNba7igbWLNN90vqgnKdE+Y093bcbjTf/sz5fFsudTs13VoI5UWrlum6DdtbCwM3PS+Uw8vnAIsBjhIgKanPVKeVaxuuHEyAH8TodV0huubyJkyWQh29HZskx+6rr3wuNvfvPqeThsvnKDTLZIJjSU4833J8PymgIhWkDel4cm0/Vb5tuUBoNU/mNjT3YbadXg8YPtxxbIjW13Uo2ceYiv0N87xqQONW42GgJ/tojwbK+0kapunn2AWaPMs8jU4RgCoGUxjqfh0R3cK+36SUuol9fziAd7PvtwP4JGMbHwkARPS/UbXG99cjeZ4B8KMAngngc4JlVurlRPQGVHnVAOA7lVLvlxxEgWpFk9SQYA0IJ/I1FeNa05JcSGLt4ryjrtfX0ANAOxTPJm1NXqHtDONJOfvcJ2Otzz645pLEeeY6fr4u/3mZbjlzW3x6qJwWsLaa22GQDexUxah+/zqvF9B+OPQBNd8Dtitk0AZmokCarhNfvjrX6cx/c/z34wCUq7mZx6wdBqqvG7weTLAG8Jpvf286rY0zbV7XCx+kQC/j01AwzeVKM0EaB2jOa4I+fn4O7WsI1374XOBEBNY4LIt3rLU/Vd/scG1dNuNBGXBfN22QTDpNb6P6P86N5nK1mcu7j8/e0ZeEB/pyo9ncaSHg5oJrLsnaJt7dF1OnMidb3M3PtXwIsOh1OcSJCQl1tYPEeWNTKkyLd7BtDgAAckchYHcV8v3G5FNzuddMtYGR/7eYC6TFArSUcuWQz4EpyWEIuKGzDa666iU2XYupmN96H7eta5uu7Uq3uWnl/H1ptxoHa+tnHhYK2sqxpnOo8WdgDtemWW0blHiggjuVUiEHWUhzAI8F8CQAjwDwRiL6GFQw7dVKqduJyLkyEX2CMen2+v+HEdHDlFJ/IClAUdEkNRRYq7ad7loDZLZ96Rvj0Hp8v0AXqK2nh0K7bG41DrVyj1rjazoTVHEnmJYEXpnbNBVyTAFduGY608x9JDiZZ8s2DGo6vxVU0B1bDtfMzu4BjmT78nRAfe4mDWScOdJsIM2EaDHPO/wBRFeNL+RXT162nX+u/Gm2B2ibO4rDTJ5rzYRrPCwUbL8yt5osd5r+zEdTlcBPXS8cpFlHXG2Bs3ZZ+aPG3uXqbe18daUazbau89xgzdZWfH3dBmZ9cszDO0WuDqMv5NM8J8zlJNvwTefr8uUA+0ARNtlSEgKmi6YNWnxhnSl51po9dPclVQjOxLr8XPXq25dk+T7y5dTygZjYkFB9ZKF9xigFpvkgTd9w21RAYSrGUQiEXYV8W7H51HzPhSnOvByOwRygxrf9FJn3VWm9xQA2c7shB6O5jZBi27MPREs5h1Kv3VNXqI34SzgbWOOhoABzrZ1D80xsg2sx5oBToowDFbwHwPXs+yPqaVy3A/h9pdR9AN5ORH+BCrL9HQCfRkTfgKoV94joolLKDOn8Uc/+FYDPChWyQLWiSWuKYA2Ig2taKTclX8fMFuYlKERbNrAWqhLfrnxuJC7bPlwgy1zGplig5lKW0P9m/+4wUHMUy+oTjE8uOOSSqzNqc+pkAWm6WBIgywekMJ1pNsCJZh6vx+XsTGt209k0B2TowhrT1aZlutZ4vjQ+EIQt9FPy4GzWv8Sd1uS3c8M03Xao68c66mp18F3pwUO0WHjzOi8gsK5zF1jTxxcD1nSt+wYicLnWeH3qud3w30Y2GOaHZ/5RU13r833bYI8J0HyDRZjS4bdAN78d0A0flAAyLZcL0AXQbFBHy/biyA9d3I4+CUyTONjM5UJl6qO+ie1jQkJd7dB3wILY9uouE+cYNNd3reeS694YglW+HGc2V6FeRzLghF43BNh85XcpBRCnQrSxnU4SeBXKfZYLsLm2maKYeuzTNm7X4WbchZuQqw5WmBm/Az9Yq9S41oD6mWgGEDco6MEJ+LNX0VoKZH0uSNCbADyWiB6FCqY9DcAzjGVeBeDpAP4LEV2HKhz0bUqpr9QLENGzANxoAWpQSn1m30IWqFY0eQ0N1oD4cFAt2425r3w3WrODxjtm3lEYQzIHCdAd6hWaq4QPfLiUs9lyO+l8csE9KXB0AMK2y4q7nrrhiVp98ke5woSjQBqMaSZIC/3GdJ3pOjHdfkvYfycriOtRH6vuhOpj9oM1wAfSNIQ4YNcfHvq5h+P1dNv1wwQrpjsqFOrJc9sFHYW6Hs02iXnI43C9Bms6n512rAHVNUePDmr+fs3wZRdYs/3eXYnxfe7NZplwp94GbcKOtW4+PNc2fSCNX6djB4swZQ4eEXKeVeXT6gKy2HW5+JQQ/JfCND7fdz2TrM/n28rRLN+/U5mSe0sC1yTLSsGOTzlhWnz9y4GOVL71JSF/kkEHXAN2xAK2FPUFaamw07V+SJLIDd82XW7DWMeZLQ+by8GYGzaNAdFS3I6hbY6hPv2n1DK7wBoAL1wDmpeOZPaPilpS8vBP/3aUWhLRcwC8FlUP4sVKqbcS0Q8AuEUpdXM973OJ6FZUT8HfrpS6K3ZfRHQI4FsA3KCUenY9qujfVEr9WnBdpVTs/kbRx944V6+95dymi1E0IQ0F1tr7yGlVypF/IdxRM3MoReW/0pCEd8alTpcUharX51TzQTVX+WJcdQ7XTgs4ztj0Rf2//gObpsNr6+lqVoXTLWdn1jfq1Wy2BkImnLGN9Nctvr9zY3MzWl1NMb8TwA7TbPXM69eEtjN06yxQjxrw8HrUD0DH2MMK81b98RFROQzT06r/2/VtG0VVMs8nH8SxwbQ9tEdh9cI0E6TxNpNIOwX1IfF2Mn67gP/366p7Xk96IIhqnr/updNDkoRp+txpEvDGt+sbcZUPGCEVd2byutcKXUOk9crvfTHngznfpxCckcI037qu+U0Z/HAhRebx2+oScNeZbRk+P3Z75nJS+Rx+MeG3UkDnWt5Wlhyy1Yl5HQnVY2h5V1u51ndtRysGkvQFaTGQZgx3k+8ab6sv12/eth1XfYf6ADmf6Ztp+SGa9AXHaZOk/n2ucvOlmG0QqDMPwJsz5AfbCV114xPUtbcEWRTuoI+YTJ0R0S8AeDOAr1ZKPb6GbL+jlPq40LrFqVa0NTLfEg6zj37ONVMpNy/fRb9zsXckJW/JEVLHNm4P/eSuNT495VlKUp2h0M8USX8qLqBmTovZJtBxYVU34Cq/mh64AOgykO7vxpW4vZKtY2+60oAATNPlXbJp+ndiglcAwb6oy4GWKO6c0nK5pgC0HDcup5OZIFzXs14OaDvYgPZDtTSswhdaaLrWeD41UYguYM9vB+N7zLmgHYIsFFSDNZ4fsAoDbZyC3IEWSn6vJQ0/NL+ZYZ8uhYFNU//6OEx4FnKx+V50rMthuT6bg0eYv28A67fjPARXqwnFlbnWJOGgrtFBq3nu0E+ppEAsBNQkYaJ8fXM/QDinnW5PDjFt4tfvUH40IOw0i8m3ZlMo0XqMAykVpkmdg+ayzTp5oY3NkZQSsukLDeXlluzPth2XpCGE0hF37evGOQaHgjOhcEwAnXaylSclb5rtd8f7A9Jjjh0ptQ9ESw3TzX2OSZTbuJCy/65bsZ3v+Ni4t/H0C9zJBgAr9lx1vB5o6/RCS1MKhNWVzbZ5gv6GUuoriOjpAKCUukS+EQ6YClQr2joNGQ7a7KO56I51Ewi9QWkNSmCEfWqFRvhrycxnxcHaCtXVYYk2UJLkXIuVFKb5BlPQZYp9RjCPxQRqM8eyklFOWegiBxMmWDM7xjZJc9bY8ux1YIwum82dBmOay502ttXdAyeBLtyxgQNfGFsz56T1bb0/A7JxSRwGNhjjC/8U57sD2vDTBdRs0uf3vN6GbWRgVu9mTjsTaHKgw+vMB3NcEIjLbDcb8PRJAmNs7jQJdHOdb+v6qtW5LjvaxXxqUzO0ctsBDVxzXT9SRvg024PXkxuGur+FlBLqKXEcNttsrwvE57Rb5xCs69klXf8AjJB+OVxz5SaUwDXXNjvldICHUCc9BChjQVqfELgUSfJqSXKpmcuHYFwoj5dtOxJJ6q8PSIsPA80DZnzgKiWPmrmdlLxpPtAmVYyTrA9Ec7VRTNn7nHNyt7LnWjpSX2u1bnMbQO0eh9X47y1qgWprKWC53DqodkJEB6gGJwAR/Q0I4z8KVCvaSo3hWmv2NQxgi3mTZQNqvXLz8ESbugpXsIM1oA3etPrkOEt1pZk530zZmmflmWcrjy0ULlYeMGE6fnjHGOgmIrflAQnB1mACewlQA1teHxMwPlAz8qrxOgTscAdod1ClrimuAxwZD/ptuR7SfS7CUPhn1OARQLftuPgjgDnUOwdqHFjyzwYU5vnVTLclBzr6+Polza++cR20tu/uCDeHaG+XkOvM5U7z5Sds6gX+0Vd1vdpkjhw2b7ah4Zo5ImtfsAaE3VEhp1W1TJxyOtNc6wHuF1GA/P7pW44Dtxi4xn+7ffKt8W02soccxoQRVlvpB9P6huBKR8b1yT6oh7/+JG40KYyz7U/vk28nVhII6a9/OXRz7c+3b6naDiG3QyyHA00K6nzb6qMxIFqsOy6nQtuX9NVCLsHcCm27uW67Q7yL/Lpy5QxOLktcCJPS9wN4DYDriejnAHwqgGdJVixQrWirNSZcq/YX95Yl9g1X6OEy1JEDEIYeHKgt0HS+NUgzQ0FtcA0YJlxTqhBc45LCNMAN1GbGfPOzQLbE77xjDLQ7ZoC9U6Al/V14gRqXOa3vC2hX/cQ6/jyyhSLyMFApUADCHU9TBzjqlsfRsQmFf2aBab73aDbAZgI1oVvNFwYqrfuQS8oWkmvKdKn5wI7PNajnS9xpPico4ABp5jXKdV6ZyzHXrL525ARrVVHc7aHnm3AN9Tct33nDE0GbkjjMzDaxrefKaQe0QZovF06KWiPiRsC11JBQcxm+zea7LOTQJh/4CrnOwvP9EM0G0GJGxzWlX7KY91Jz3+Y1h5e5T2ioXt7cn7nPPgo5zPqAtFTnYAiEmmF1vuuGrRzSkTtDYaLmNvh2zG25pPcR4zQcA6LlaKcUmW1rygU7Q3L9BsaQuS9Jzr7quO4YsljbJQWstsSpRkQvBPDzSqnXEdGbAXwyqgCCb1JK3SnZRoFqRTuhseGavQx5LeKucJWsDjXzswnWwKbp5bTGdizZZEKxEGRzgUBbUn2XYkCQZfRKW8cY6HbMql3Jcv4AHqDGy2L+RDmckYr/bnzLrAtW/58BwoZGpHTl+Arlh4oNw5HmmbGBADN8MDrM03QVgn33F7rZDgdr8Hz2uNVi6z4ENV3yuXB4KKj7sN2dfJd7sPlubytdB06YZhvMg89vF7C7LM9rV69DyAfWqqLIIbMv9NCHM+XnSdidJoVpLpAWym8XoyaMPw6uhUJCOYjpG3Jo7kMiaRihD6aFHG6APBQ39llHQ35ze7aXVbxMIbgGyEND9TopudPM59cQIMkF0qRwpi+M8a3PoYwLhIVCamMAm7lt13niAinpeczGgWixbRUKcQ/Jtj8faMsB2XIBNumgLr5BSmIHUDotUoqwvG9r6uQvAPwIET0UwC8CeLlS6g9jNjBZqHYG/d4iFp1OTQGuxUh6Q7Td8JzhRrHSbjUbWAO6rrV1AYztTBGyhWQeg23AAptLzScOJYxpLscJ0Hb+xLylF4WfxTgZJXKBNVd9ZnT8AW7HVLNb+0AEPreOKXt+L1feEplTwxY6KBqAwAXTeBu4fjIWx1lLmdxqze7iXFKAPNTNDMkFGqTDH4x9HRI76Gw7oyTtBVhcoLretGzniG47M6el1hxN3a/zqVXL5QJr+lj7toeuvxT5Qgp97rTU0Va9Oe4ixPPcheAafznicq3p4/LVOa8TachhNS3+QEPXMr5MyJWWIxRX4ijk91Au2/3UhJ28nH3gml6fr2Nbr1N2D/ThkkKcVJAmcQ8C/RyENrleILqchetyGO0ExOVQM7dt24e5zViFXLqh/eSAnTnaK7QNF3Rztal1H4529paLXUNjJIFooRFjXSM2l9DQ7ZVS6gUAXkBEHwHgaQBeXOdWezkqwPYXoW2QUmrgYqbp4288o37jlqs3XYyiHdDUAJvvwSkUBmHrMHjdEuY0W1JzPo9/N11OpvI+W8mUC9zZfhI+oMbXWbDvc888vb45+AEaxw/PDaY7BlomrGgV1RHKJP4tuMIIXb8Bl/PGJ5tDjUM1Vz3y6eY25t26W87OrOtKP9zphzd97vMHHdvDEJfvehF6yyxx0UQPIAE07RQD09oF5wVGXah2vdumG7/fPnXvegjlMh92Y9vHJp+bxh5maA/1tDpAObA2zzlAmNq2FnfAusLQ6+m2dgCQdB50Q1fQWcZczrV8jKTt0p7ehp1AF6aJQFrKfcQ4VD5Sq+06zq/fZnsA7bqTXJ9sdW3rHPZ1SvhBTBxMiwFpIXjG25LXt0uh+ykHAWZn39U2gLzOfeeG1BljShI+mArSYkJwe0VLwP9sYwM0Nhhjq18X0PDV99DOohhHW1+IJgFofdrO126dZYMjJofPAen9RQqyQuddX5Cm538W/e6blVI3igq146KP/USF3/id8IIP359knRHRxwN4MYAnKGUdn72ladGGoqIBlPIGZMgyuNQHqHXkC28Jhe+ZjjXAnsPM5l4bWq7mCx2PT+Zl0vbA7oJmEvEwLmM97vrhjgdA9vAjCmnqGerUUqjNfXWZyaVm3a3AqVMV257PCJCF50g7LGbIoC6jF8xwmKanc+jpg95Au51TQm3575N/doSAauVySQFuV0hTxLi8dxKnjW2wAleopwimxYTkNgVtO9h0/XPHGruG9M2xpusjxh3Vty26h+wOJ5SEekryi3pBWsx1UTCIBOAP63eFg+rjM9uF14Mt1FMvqxVyRMXIdq6Y206BaS4XIRAXkutb1ryfAl0XIS9XX+ca0HWv8W1oSUINJZKED6aCNBuUCT2HxDgJfdt0uQrNcvocbK7QQNt5si6/53yRAreUnIVaqYN2+ABaX+gZs10XaLO1I5d5zlm3YdyP3MuFHWsxYFUC0qr/G5jmelF46nWFgMtpLxE2JSKaA/h7qNxqnw3gDagGLwiqtH7RqZLE7j3EPnySvHXyPYwCnpHmfNLwTP/PBy3gYA2QwTUYy46plCYMwbTYsE+JjE4x0O6QAd3OgClb+1s7krbfQuj3IQkFBrp1ZypUl7a8dMJ6NoGCSyZQqIqxcnaOpJ1RV1LnUDiaE8yYDkLTnQZjug+m8Wm8bjScySRb+LJNoTBcwA4MpOG4zvIJQqJ8MA1AONTTBdPMc0wC/DnENHPeBcBaSCHA7AM4Q8KbUO7Banr43PLCNF73KW5rXb88DJdv13Itd4X1S8JBq88z57VJGnLI5etMSsLZ+f4lLxHW0wLhuEAXjvUNyzW3aXthZYNrJrzh4MYENtJcXoA8tDNWkvDB1Hx2NnDSd1AP1/r83sH3KwFsthxsofxrgP03H5s3zadwTrV0N9pQ0FMi133eFXLdWsbIFcwlhWt9+mmSAQaA/jCNfy+qpQAsadOlEImIngzg6QA+D8D/AfAKAM9WSt0r3UaBakWnXjkfdmIktXC7bpp9ky1bZQNrQNu1BvhH3/TdUzYB3LhcZXNBIK6UkSrNDjGT2Sm2QaKYkBi9TQD+PE+mQs5Fm2ORbxuWeXxdvh+g6/breRdyuXSqXfk7r4D7Idv3cOzrULpgGuAJ9QRkQM2U9BqQ4znP4rRcb97hkKp27XZJAX5gAIShQascAReH1PkkCvX0wTTpLYXBmSiwxhRyq+njcwFmH8CxtUWoHUK57bRCoJMvEwr1FI++Gnv/4cubOe70fiyutRiwpo8zFvz7nLUxrihJ3iffoBL6WADZi0BRbrsYh6dlW74XVqYT3DVAkM21Vn1v3z8AtxPKBmViOt3SgT60XCANyBOKm+OZkz/juF4gmuAT6IIZqXutmm4HbFp9nIM+uXIaDgHRcsEzl2zbt4E2W9ut5/WAa33BmqnYQQdsYZ5LzFowrbjUHNpMFztF3wXg5wF8q1LqQykbKL+AoqKR1SepaLYbp+lS42BFCtYAP1yzKbVznwrjJPvzATXbFbLvVZN1nnlnDJDljbE92PbOFwR0HYtm2wN2sGqTWe8+h1pGxXZetbrJ78P5TnydSpuDJhmocfEiDAHVTVkcbrGhh0AbrFWbdcMcIN3h0Tc8FxBAUKAbpovAZ3th28uaYM0nw63WF6wB4YTs1fJhp7fPJdgcur2dssE0H0iLvT7qw9PbMeGmcT3PBdaqXYZDDqsitE/S2BeFIedTyPUUk6aiA9L6dLjMdQP3VXNgCV3e2JDQ6rvMCRUTbuhSauigz5UWAmkhgCZ1FZpuWnO7Poe+BK4Bfvca4AZszfx+b51CA4K4zsc+IC32hWsfuZ5FzXOJKwTXfIMb5ARroRyIqe40PZ1/L041i65gnGfVDFJKfVbfbRSoVlQ0kkJDXIdupllGMuPAzJQUrAFuuAYMcwEd4j4ldVYB8eGKts6xoyMG2EO5pA9Fzvbv0w4crAHd9pfKVYemSy0xZ50vDNHXeQXCD9o2uTotok6/Dc4A7hxcrnBCiVzAcyy46QFr1ecuzAH6hxZKQqCSHIWA352W6lKz/c71dJdbTagYsAa4AY4vpK1aPxB+5GjLWPeTE1RXharkgmm2IvoGkeDnB3cT6u2arjVLW4bAGtCFNyFXVIwjKlYxzicfTEseKCL1fsWvdUZb2eBayLUGdO8dgBuuVdPiXtCkShI6GBPeKQFpfUJyXeubDn3A7mDLAdcAN2Br5ucNoYiBaEB/kDZI1Ipn+76IClf+vM4o4QHXmmQgg5ByAjWbO838XtxqFikA9226EOOptH5R0cAKwTQgzuadJF+IXyj8z7aMC64B3Q79lN5SuGCDy1mVQ7wzHABrgCxPEl++JVs7Sp4XJY5F1/Zd2+OyAbUUGQ6d1i4Nlw4g6xwB/ryKoVCNZKAmcTv5tA/7uSUJZbbJ1y6WsMPWqpa615K6cYCwy8O1nFasm1CXHbCEegLh9vK1mwvamMDGllfNJ0dutVjAPCbAkQKb6PMK8Ie62yC1RHzQCL4tE6yhO1+aYw3wg38btEnNDcUlDWvX5eDq7UwzzxHXs0HoJ8brnm/DkvNOl6GPaw2QwbVqervwqS4oH+iR3pPW3z2utCiQ1scF79i+K1QX6A/XAD9gA/rlVM4xAFlruhCk5XIRhuR6FrW1lZYrz6rt2QBwu9ZygTWuoYHaCRaia/CpkkK2tD9E9BQAL0B1B3iRUur5juW+BMArAfxtpdQtRHQVgBcB+ARUV6OXKaV+OE+p2urVdSSiawH8AoBHAngHgC8341CJ6OMA/CcAV6Oq2h9SSv1Cn/0WFW2DUmEaEP8QBCAtrMUWPmGDKq7lfXBNy9YZHxu0xYQqmsoFhLRMsGZsN+mBSNpZMZXyG7Dt11cvrvrLBC5tuehcHdeqOP5QEZ9Co9o5O/5AuE1yOtLWBWOfXW7AmHZwgJzWLgVhbkA4jAron/S5F/ysChR2p0lhGp8/sFNQApj7ApxYSUchHAWm+c41fj6YcM0G1iwvSnKBtWo3w+WGinU+ZYFp5nUw5X7H17EBNkdoLiBzrZnQxrx/SMIMm/n9e5RS51MfV5r4JV2K+HYcgG0IuAb4ARsgv/dLlJLapS9IywXQQtu2ATYXXHOFhcaCNZukIaAm3HKFZeYGaiX805BClv4eEc0AvBDAkwHcDuBNRHSzUupWY7nzAL4JwO+zyV8GYKGU+hgiOgRwKxG9XCn1jv4la6tvt+a5AP67Uur5RPTc+vt3GstcAvDVSqm/JKKHAXgzEb1WKXV3z30XFU1OEpCmlQLUTGW7oXKQIoEqKXCNywcD+l6AQ6CBy3b/C4V9+sQ7zCFnA5fUpeJalyu1/ny/AcDeOZXCNHPZWLgWcEqtN+XpuALyt9lcoTfNtrAnoOcorDkUyg2YWTEhuFXx/NAgRhIQkARpYoBajBOKS3Lu62VMkBMBOYG8AMcnaYLumHYCBEDN1x7Sc872osB1XdeyXJ98blrA3y7VLuXJ103p9uob1r6ePyZMk7jmbdsx4ZrQtSYNBwXcrjWtHC4oH+SRuJ5icqVFgbQc4bnm9j0pMFxwLXZAAy7pPV+qlJQuwAggLZOj0LZPF1yTutZiwNoQbjXAfl72Dfks4Z8OKeR63n0igNuUUm8DACJ6BYCnArjVWO4HAfxbAN9ulOIsEc0BHAA4AfDhLKUy1Lf1nwrgSfXnlwJ4AwyoppT6C/b5vUR0B4AHAri7576LiiahGJAGuG+0gP1mm12+vGpaPrBmzufi98RU4BcDxVLl6iP6rog575U2SCRxfdmWN7fLFcpj45PZxj64Zi5j21ZoeiTAlHaOYt9m++TqwIicpEDaOcHbQQKvfaHMkTnrJIoFOYDdJRj7MBoblgsIgZo5TepQG0IJrrZUZxQgD2mTKiZMLbqdqoI2MtvLnB4rE57Z2sIW2m+BbpLrU8gRBcjPlVyJ02NG80yGabHt43I9mXDNvMcarrXQvQOQuda0hnJBpSaz992TRINF5IomsIXn8v0GUmCY9xebC8oH14AwYMupviANSMhtN4SjELA+J7jgms+1JgVrQykU9ukalEDP4+vYINoKsw54K6olh2rXEdEt7PtNSqmb2PeHA3g3+347gE/iGyCiTwBwvVLq14mIQ7VXouJV7wNwCOCblVIfFB9DhPr+qh+slHpf/fn9AB7sW5iInghgD8BfOeY/G8CzAeARN1DPohUVDafUG3IsUIsdkam7Ufg74C53kikbUHG51vi+Edj/2IqBab7ObMqV03SrudxXrgebMTrx5m/A9puI6eTb6ilUdxmetXxgDfC/zTYVk+9QlEA9JF3nrsFCAL/r0dyWlm+wjYQ6T3XiVEVJD8E1JQEBXgiQAtRMpbrUMiqXMwoIh7S5lAvWZHGnpThEbeHttsMPuZANByEgB2tAvCMqVbHOp6hBCHLDNJts7WMDnRb52gaw51kD0sMMJYp1PoWeGbPDNOmznOv+ZIbn8jL0dK0BdrgGhAFbH6W8JM/iShvjedDzotd2vwHsL9mkYC2XW00CtkJAzQzldLnStKOtjP7pkByq3amUujF1N0R0BsCPAXiWZfYTUV29Hgbg/gD+FxH9pna95VTwaYmIfhPAQyyzvod/UUopIlKe7TwUwM8A+BqllBXP11TyJgD4+BvPOLdVVDS2+r7V8t14gbi3WFqD5FIIQbYY15qWeY8ZG7KF7nESoJbqYDMf+m1gDfC78yQ3JNsDma+efcDG9l1aDnMd3/RUl5rAcQD4w3m0QuelTZJOZpJC51tMO5j1L80LmOhkSwEG1e7s0CAkaUiuLo8uo5YXerqAGizL5FBfiBxwRnHFgk4gH+zU++Rl0RJBTyAdqEkPwQXR+D5816pAGGifPF5NEfo5IaQjECbDNMA9Cqu5nFYMlDbr39bxd4E1izMq9f4hDTPsq1hXGpAA01z3rtRnNXM9s4pccC2Daw1w3++Bbn3GQDbp80JfmNYLpKU8h4SiQxzXxSHAWop86QlcLjWfOJRzhXZyoKb3UcI/LVIAjrJs6T0ArmffH1FP0zoP4PEA3kBEQMWtbiaiLwTwDACvUUrdB+AOIvrfAG4EMD5UU0p9jmseEX2AiB6qlHpfDc3ucCx3NYBfB/A9SqnfkxZuqHjqoqKQhn4oas0Xhnx6O+3SjrYtpNMHxWLAmrQctvtfbtCW4uTZpIQ5w5zr5pat3W0uDt9837yB6t3XMQL8D9shSXMdZoHdvvDr2KLnHigiBB4MhYAB0L3emvf9mATQvZ1PLuVyqQ08SIEpX0hbn1yDNkmATRSssUHPoYAaX37u+R6aLp2PPKHqsfkguULARpyPS+JO6wvTzHVscM1V3x6wxiW5fwztgopxPoUgTbIzLffzmM3Nqfed4FqLSYwP+O/3KS/WrNvxPMcPDtP6PgP6QnTNMhhV6QJrqYoZtCBVKWGffN2lZTntVksd7XdnlW/0zzcBeCwRPQoVTHsaKlhW7UapewBcp78T0RsAfFs9+udnA/gsAD9DRGcBfDKA/5ClVIb6dm1uBvA1AJ5f//8r5gJEtAfgv6EawvSVPfdXVDSIhsivILlZx7zREiu3Y0YK1vSysCwfUug+FHrzGSuJW0eybIp8D5iAHK7lzN3lgmi+dpTWS2i5npAh9FDnCxMBAg/cwvPTCrxTDD4up6gkD6KpnANFONQnNxHg7oSmjpycLZQw1jmzifDPRFcUIAOdQL/7YgywmQxQS5Xrml5rKEcUkC+nqzQnVxZ3Wo7zJeQa9LWJw+0M9HsxkwLYUl+6DuJOS32x0CqYY7qZ846XQ5BrLRQOCtjv9UC/l2k+xYI0IDLM01ffQ7xM5duNeMlrewYb0q0mHQGUK7R8atin1pKtX5RfSqklET0HwGtRXUVerJR6KxH9AIBblFI3e1Z/IYD/QkRvBUAA/otS6o+HKGffK8zzAfwiEX0tgHcC+HIAIKIbAfwTpdTX1dM+HcADiOhZ9XrPUkq9pee+i4qiNVRyUlO5gZpo+PM+koQA2sAaEB4VMkfHJufLnxigFru+TaG8L6ZsD0v7juk2pf42UsBaaHsx00PzuCLcBoD7YRuQO0X5tsz9aVnPS0m7uerZBGtark6pZKCIHCG4Ag3h8ugzYrJ3NFaXckMZKeh0Le9SpCsKcINOoJ/jJjWJetD5NFWgZsrhipKCNSC/I6qP+2mSQM0l8zzw5VfrAdaAMKjp64AaDKYB8pFYXev75HA0tfaVAawBca41oFun0TBH8KzgeyGexZ02FEyz7ccG1iJCQV1pCGKVGrVmy2/mcqmZkkA7c3CC4lKzKN/on1BKvRrAq41pz3Ms+yT2+SKAL8tTCr96QTWl1F0APtsy/RYAX1d//lkAP9tnP0VFsRoLnpkaIufCYIp1WLg6/qHtpLrXcivFMZVyhZSO2BdwN7TUF6j1qftYQBoDzTKBnD4hIlJFhdpI61uSo9AHtEPq+9os8hkxl8tDXDwhEPDCzhwvKELXwFzXFoH6whsgDuCkApuoc8e2C199b/peI9DY50pr/YRBkQYHaimu6NjRcT1pFiRgDUgLMZSqbwhhVnda33PIB/qlYM1QDLiR3O9jX6j5FAvTgIHdaa52jbmnR4I1iXwvOLX6hID2cYqZ64ZyqWnxZcpABYauYDwQPAFNJaNQUVG0NgXOXOoL1FzqnQBdKklnPhWs6XW1xur0SK9wQ+c5cj2ExIC1MZQr5DP3ugkKPWwDYcCW8qCcLFfop/4OyM8bW1378tpJBzLQGtjlEVLySMm5rjt9QKde3/U983nSN89gDtip98vLpCWCNbb5kukTU59Q9SGhTZT7NjdQk7RdTCfe51YLLCt9OZPTBRV6HkzOx5UK1HKeSz7XmgSsWdrdBdaAdLjWR9lgGtDfnSa5VPNlMj97puZXyzVggUvS0E/ADcVcI3tyuFZCPx3akvtzDk0eqpXBCk6fpgbLQorpdKTkYDAlsvc7d5JheR9YA2TuN/PKk/OiG3tVk4TMpW6bywfWgH4POCnGBV9evDHaI5S7LlQfwvAQwB2CkOIMjQrhcEkS7mkDaam/Px/EGUG5ct1kgQIxCoHOlPNkhLbYFLxJhp2AHNYMrRzt4QgBNZUCbXKGrPVKdO/r6OcEaiFldKvZlOp67uuCisnJNUmglktCsAb4ww1zwjXJc8OoQC31Gil5qdvTrZYrBDSHhgj9bNbxb/PUKmP45zZoK1q/gLXt1bYBshjFvsHPmdQ0m2wjC0ogmq9DGQPX+PZsCl2M+1zBfA/im7gypsK1ITqducCaFKhJlBgeAuR7sEvq0IQUOgf7tIUEDGdwafZxeAD9OqBiaMOV67oa0zaSa0om96CpVMeNBODE3Nd65QbluxlzYAjpvUDoOt7kucK3byopN5fW2OAzpxJzdwHDuKB65+TqE/I5lGKc+oL8akD6vT7Gqe5azyff/WdSQI2vP3K0hCQEdGy5Qj9NmaGf1bQS+unUFQBHmy7EeNoKqAbI4UyBb/21yyAsl3ICtclJ2lEMLZcC12z7GEJDh3u63p5L3u5J4Zr0J9gXyORw44wo38M2kAbXot86x0oC1rT65LULtYtv/kgOD6miwUAuSGx7weDaR0x+wRT1yBEFhNsj9b7Vq21yuNRyuQhzKTHMEMgftiYNJ2zJB2o2OTDBQIpNup6jrbKEEUp+80PlUBtakWANCN/rc+cwzgrUJJogzE4NAZ2C7BBtbs2tZq5TQj8dUpjk73QobQ1Uk6oAoaIhlSu/DFcvm38OSfOhucLV4JjHtw/BPsZQn9E9x75ajnEjismFl8s1mDNpuwNS+h7s+Hnle+hOcolKXAJShc65lO2Z6gOXN+TwkF4ve10r+XkR4xyUtk3u0HKhfO2RC95sZPTqHNrw0/CQbqhsIxIC25l02nTjDJC7C+jWs6/N+jqfkkb5dGmTQC1x0AKt0L1+jNDD0LNCElDbxvPMoZR26DNYQYz6jNhpA2kFrlk0dWCfUTsH1YqKhlDqaFupQG1QSd7ix4Z8Sra5Sbg21miJEsUkXZ6ScpR5aIcgk+SNaWp4dRYoYJ4zkpFA++zL1EhtIXF4aNk6oX3y1zQbDm7CL5dzMJcbKldI7sRAp94fV+9waalyvAwYIFRaK9UNBcjbKHTuDBLOPoR8bZSrTTKGGGr1eb5Lcj1xTSHskyvHc0+Pl2hDwDXJ80PSs8IYYZ85lPFZNvdgBSUUcyIqOdWKioq4NgHUsuZSmyHfDbgvWAPaD8FDA7Y+I/JJ56VoLLAmbY8xQOdQEMdTl0OEIgwSxqHlAmup24/JaRc7UIRFm+iEDuboleabhGW6qT7gpud1og+8AdwAJwvsNCVdXtI2fF6K+l6fe7ihhg5bS3Y/mev1Pc+kL/gmqCFdUNFAbRc7rZHpBkL3+pxwTXpd896HdrHNBlRsbvU5VklgTTrQgG2QgiKLSk61oqIiIB2mAcPkUBv0YSrGJSPpwEjLNQRgS4E3Q1wJQ6OSbatjLUW5BoVIGIkqJ1jLHrZmO5d8o7Jq9QEIAzvUhshpZ1Nynruph+SOpKHdNqO7oHK5OvW2TI3ost2Eu2awkNzQSxvbdW2M86JnQnxgvGsaL0tHpw3O9LzXS9M/hNYNaZIh7hPSFAcrSNVsErbBiarkVCsqOt3qA9MAGVAbzaXWVylgTTLfJl+nxfZQnquTs8mr4JBgLab+h3SrjdgZdUk/4KbCteADcu6OTUxHNHa7ObeXGI7TpxM6qItjrJDcGAdhrAZI6B2j0aEN15Btk7oc1wahzVa4n2LrdKR7i9QFBQwHaqLOnyk9Q2rlfM7J9BJtqGft0Z8X+qiYrJI0wxIo+dNkmtLvfWAVqFZUxLRpoDaaYhwyMdsw5yOwjFRDPTxLroBDh4VOxbGWG6z1GRQiRYJ65A+7oQdvcSfG9fuWPrC7zqNNtMdISumEbuxlQ86Q3NjcUBnPj6HhzeDQRgI8bcvF7sOmDbk7tYYYuZjvu4gp4UUBl1nftjaLvZYNmnJgyooMAdXa5AiUvc8n6W8jZ1qXVE3huRWVUyxXCOYMS3EIqE17ZbDEtkr4Z1HR6VNfmAbkCfnc6Ft+l/rmUcsZlpNTfYFaTuUGa6n1nQvkbArgRNTjRs6p2AfhHO0RaosNJI/mygXLsrltYq5XWxKSqzUEvMkGO1O2EwqVTmnHodXjfMk9cnEUrOnTzkM4oTdwj0mBNX3Pj61xeI6twL1+E2BN1FZTquctdqn1hV/NdtKB3B5OsMRB53PR6VWBakWnXmMCtUm41HxKefsfk2x4Cg8UY171QnnVuHLlk+lbx31Gac01MIRPobfXYzr/cuZwcm0rNe9gH6dgxhx0Y3RsBu94Ss6JMUJypXXZ8xzI6QrcaGhhjuuA1EFoa5vMyda5tgLWcOUEaxvO0QkMf11LDiHc9dDPCI3VVnxfoyrVrRZTHwMNxmGTOQLobLXCahbXeHs4wUliWKZrcIPKfVZt0wRwc6zWcI9/Lqp1ynKq7UaWwKKiBM1Wq0kBtdHDm3Je+2Py0GzynhOz702Vs0+HM2dndQEZmFlAvuxYGqPTnnsfUkeT9C/H/jJoyM4GrTKERtme2ccKA9yQ22Zr9zX2AAJDjljs0BjtM+iohLEhzrGayH1m6Ova6NpGFiD8rW7sHmRqiOeSGeIg2Ra70xaJoZWzuuJ1aOZcSHnmWK3Xbba16oR48mXM5U+9FKrffehPICJ6ChH9ORHdRkTPtcz/J0T0J0T0FiL6bSJ6XD39yUT05nrem4nos7Icm0XbeBktKuqtHDANGGaUz0EU80YrNVdN7rCpnNq2K12Ka22oehyiEzNGe2xyEIgpugW4cteLIBQHyOsY2FhI/NRCcifothkU2tjUx10b2qapEa5dQzo8N55DLbWtJpqjM3c7bV0IYUh92ySTM31r7kH7SHt+4Me1ckxPKcsWy+U80+IhpNzhNsMSe4DX8daAOb3Opi+sE5VClpxqRDQD8EIATwZwO4A3EdHNSqlb2WI/r5T6yXr5LwTwYwCeAuBOAF+glHovET0ewGsBPLx/qboqTrWiU6Vc7jQgDqj1calNKklt6OEl5QFqzv5yqs92c5alT0cv9CYn8k3P1ij0ICh96MxdL0PX9RiwMbSPAd9k5+iEiJwBOdoo5LhJgc2S9UZ8ATBEp3BwaDOGE2oMN1TgNxrlgBFotPMGiHfd9llmw8rVTuLtbNP9Z4IvM3O0V+5zM7tmiHewbaHaDrGmQWIHC+DrSl1sfF96Hf65qJYO/wz9hfVEALcppd6mlDoB8AoAT23tSqkPs69n671DKfWHSqn31tPfCuCAiAa5q0zwkldUNIxywTQgL1DbqFJGHhxy1E/zihSzjVxXs6leFXcFnI1dv1PJVacVco0OmTR6yLof2DGQpRMT6wAItYXtsew4MD9VKR2kiBFx+zo4Ng4EtLbFCSVUjvYZbETCXCMQ5jhPfG2TWneJA99I22qSYCZX/tsJDP7h0+jttW3PbyGXWob2na+uYDnze3vMvGoLnOA4IWeadqVpR5rNxWbmY+O51Pi/fJ52upWwT4d0+GdY1xHRLez7TUqpm9j3hwN4N/t+O4BPMjdCRP8MwLegaixbmOeXAPgDpVTuoXMATLf7WFSUVZsCaqdaOeDA2Feobci5ts3KWWeRScCT4VrsbzhH6OcQA3tMLOTW7KDYOjZJnZiUOvPBgdjrWB9AsMFrykZhp0uudpG0idkO5iP0FNxPCefLYABgiByR2zQghUs9gU1W9a3PGAia2n6xbTEBN9Uk4eYplTlYQdS6lpE8pSGg5rrS0UV1qKgGbLEuuZ2XHKrdqZS6sffulHohgBcS0TMAfC+Ar9HziOijAfxbAJ/bdz8ulW5h0U4rJ0wD4oGaxKU2+gAFUvVxq8Uut+uKGQW0aFjx36PrDjiV3+zYYHqDHZwsHZuptFuKhsz/F7FtiYMjO+xMuQeOCTv1/lzqc970aJ9sGtIZO/Q5OXTOLmDcEaWnJGnu2ynUzS61UWpetZz7T9QYI61yueCXbRRQqVsN2FtPl7jV9LfiVrNIIddv+T0ArmffH1FPc+kVAP6T/kJEjwDw3wB8tVLqr7KUyKKSU61oZ7VpoJZDo7xBS30Q2ZYRP6XahjJus4ao37436x4jEY2mPnkBN/Gbnlr95dDQdTl0nrvENtG5g8y/sfYf1Fi/7z77mepLs7G0yfNml5Qz/UCK5p6/scvi0lTvPVMtl00SoJbQ5n3MA2ZfzjcKqG3AgFCeMw3DzNxq3ZE/l+u8aTMsO3ncbOuceuUb/fNNAB5LRI8ioj0ATwNwM1+AiB7Lvn4+gL+sp18D4NcBPFcp9b/TDyasAtWKdlK5gVqKJudSS3mAyZlUe8oPwFMu2y7otNSv9HxOORdDHZm+HZ2cHZxNPFfmCI0KaajBVIrSNXT9jRU2vSlt43kTs81c17VtbqOi7dMmRt7c0tE+XSCLh2JqsKaX1fPM6eZ6HKS59qFhWhmowKJMUE0ptQTwHFQjd/4ZgF9USr2ViH6gHukTAJ5DRG8loregyqumQz+fA+AxAJ5HRG+p/x6U5wDbKo9zRTunIYDaEGGfO6OYEI8hckX10dAuPa4SAjqMYnOr7Yo2cfeeal2PeT3ZxkTewObCo8bIAzVUmKGkvnJCm00N4jKGct77N9VzmXobxbzU2XTff6gwwamFgfb5vY8ZBrqh+7ptsAJbXrXQgAU8DJTnR4sJA9VgbYVZKwyU500z/9X76E4tAgBcQbbfsFLq1QBebUx7Hvv8TY71/jWAf52nFH4Vp1rRTmkKQE2qSeVS8z2EDDEq2hQcGpve/2nRGPU8hXMptgwTSNC81hBlGavDvilAn3INS3ESbrODcBPQJte2thFATnVfXH3qdgrPDaehjbZdpd7iFAPUNn3+CWWGZgLtMFCXY42vazrPbK40HQZawj89yhP+uRXaktOjqCisqQC1XC61SY1I5Bu0QCvFLbAp51q58o2jUs+nW0O7BnJfN1IcHNv2Gx/LybGJB+W+95MptOUY7ZPb/dT3vHGVp289bLMbaujzZ5NutTFeKE3NsZaqId1qU3Sd1+rrVnPJNTiBdqwBaI0Gyl1ozeiekoELigDEjP65E9qFS05R0WSA2miaatH6DsM+9ZHC+uo0hYCOXdebDE1Mfejd5TAcLX1O5/49xF4rpuBmjJW0bWJ/+7sOO6WjFprLxmoboc0Unx02fV9OuXfsQhtt4v4zJYf20MrVjvq3mfMelvKstOnz1KKUMNBuuOe8A9a0TLAG2JCaCdaK1soY/rkNKuGfRVuvKQxKoCV1qU0q9FMrdMMcAwYNFd6Rc7sTfLAoqjXF82rKGrODk6uDMXS4wGnp9A1Vh1ODnb5RC8e6lqcc4xDtc5rOm7HcULnrdIrQM5fG/o3sWl3meGm4n2k7GeXqN9mMDbb+njkaKA/BNMNAY0JB+QifPBTUDP90jQhadPpUuodFW62hgNqkXWoh+eziY7yZzJE02rwy9XG/FY2vTdb92I61vlBgU261TXSC+7rWdtnBYSvD0MrpttnlcDWbxmofoH8bbfHjzFYox3m0qTYa67zaFHTd1gFaXOLPNtJnjxzPQxN6njbDQIGwYw1Ax7WmIZl2rfkGL9Dr2RxrGqitMO8MinDqVcI/i4q2Q1MDajlH/JxUPjUuSW41IP9obFO5Uk2lHFPWFOpoLLBWnHFpioEFp+iBbK1NOAj7nLensY3GVGobjd0uUwGfm3xhAMS10xTOHV1fQ7Xdpl2Mu5JfzdTEHGch7V0GThxlto0CCthzqwHxYK36LgsHNbFZ6xjWrrhq3sF6H2X0T6emcI0bSbt4mSk6BZpSyGesJhn6qZUThuUGa7uiXc6rNqU7ytBgLed5PHZndNOdHK0pXx+mAgjGUiy42UTbTaVNNum60bK10xTOp0230RSubdvQTjYN0XZTaA9gXLA21fZNlbDe1EBt3ResAbC61jQg87nWKnVB3V6N5E4MuFacaoYUgPs2XYjxNKUuUFGRSEMCtSm41CYvqVsNKGDtNGmKd5OhwNoQYHzXw3C2UacxkffUr9ebhjZT0RjtNOUXgNuiqZ9PpnK51jZ9HbNpF0Zt3WG53GpAPFgDEOVaA+AYqCAerhUxXYG8v7gDmmI3qKjIqSkCtVOp0wjWytXSrSnXje4Y5oJrQ3Y0hwQGuTo5mxxldRMaE+JMsSNa1FZpo7C2MU/kabuuhZQK16Z+fgw1GjXf9i5p5Ge7VLAGINm1Vk2zO9e4bGGhGq4d4AgrzIpTzVTJqVZUNE1NNeQzxqU2idDP0APvEBBsV8BaLu1yCOhUlaPTNMb5u8shOJvSNgwmcRrbKLVdCrSZvk5rSPuuyaxXs023td5zu9bK861IvrxqErnAGuB3rQGNc40PPuALC61L3NnPwdqlhs6gBmX0T0Ml/LOo6PRpSi61yQ5SYCrGrQZsN1grV0q3tqluUl1rY8PwnB3Sbe30TE1TdxEWaDP8/oriVELad0+7VNdldF2/NvRs53OrAU1/zedaA+zONS0XYDOhGAdsPMxTgzQO2MDmFzHt6vlh0TZ1h4pOsabqUiuKlL7ibNNFtlwl3RqqblYY9uGdQzIXiNi0q7Rvbptd6vxMRbkhQWmjfirQZvoauo1K2xT1VR+4tk3PsjEa8LlX4lYLgTXAD9eAfoDNJR3mCaDlVAOwhmxFp1f+X2xR0QQ0NFDr41Lb2QEKpDfU1BDGAqq2X7vShpcdf1PRjP3lXLYoTbnqt7RRHg1dj6e1nXKPbjyETmvbFA2jJeSQLGbZoiTNV1dEfazZchnsx81Wq9Yf1wInzr8qpLP9t4dj7OEYM6xwgKP132H9VzSMiOgpRPTnRHQbET3XMv/TiegPiGhJRF9qzLuBiF5HRH9GRLcS0SOHKGOvbhERXQvgFwA8EsA7AHy5UupDjmWvBnArgFcppZ7TZ79FRduoSeRTm5K2IRx0SHBU8qoVxap0IqejXU3kvY0ayg2Vu61OW4guV65RJfm2ioqG0NSfS4fWCC9MY3KrSVxrQNcg4XKwAXKzxmJ24pnbnndcQj8typNUjYhmAF4I4MkAbgfwJiK6WSl1K1vsXQCeBeDbLJt4GYAfUkq9nojOoRqXNLv6njrPBfDflVLPr6nhcwF8p2PZHwTwxp77KzplKi61LVBsbjWubQBrRV3tikutKE3bBAeGfpnhS+RdOv9u5W6XnNCGb68or/q009Btsk3XtdOi0h47q1iwBkAE17Ry5MqO2cYejr0g73Qq2/CfTwRwm1LqbQBARK8A8FRUZq1qT0q9o57X6pwT0eMAzJVSr6+Xu5ijQDb1bf2nAnhS/fmlAN4AC1Qjok8E8GAArwFwY899FhUV9ZXkzf5YwGuqYK3cG+0q9VJU5FaBMZtVX9daab9xFAPXSpsUFY2jiT/fcTNDDGDruy+f2lFIPmfbaZTYqXYdEd3Cvt+klLqJfX84gHez77cD+CRhIT4SwN1E9MsAHgXgNwE8VymV3bXT9/R5sFLqffXn96MCZy0R0RkAPwrgmQA+x7cxIno2gGcDwCNuoJ5FK9p2TdmlVmSoj1sNmB5Ym/iDRVFRUVFHxWlTiUMY6WNEATeNxkxVUeq9qGga6vncSytARZ7PMW41U0NHC7lS9pB5T5lS32V7dadSaijT1RzApwH4eFQhor+AKkz0p4fYkVdE9JsAHmKZ9T38i1JKEZGyLPcNAF6tlLqdyA/Kaip5EwB8/I1nbNsqKpqEYi/m0nxqnYv1NikHWAM2f4MqQM2tUjdFRUXbpKlBmwI+i4qKitbqA9Zy7NslLzzj660cyxQhY/jnewBcz74/op4m0e0A3sJCR18F4JOxCaimlHK6y4joA0T0UKXU+4jooQDusCz2dwB8GhF9A4BzAPaI6KJSqjNyQ1GR1tAuta3WmBftTTjINulaK9CoqKioaFiVQXuKivKqnFNFfbThZ18Nt4aEa/8/e+8ZJtdx3vn+q3t6enLOCRiEGQBExpAESDBIIilSpBgk2SYtWRKtsPbKslZrXSXflb2ytdZ6bdm6K2t9uZJs+UpLU6ISSVGkIkmRBAgMQWRgkMPkHHqmp2PdD911WF19YofpnsH7e556Tp2qOt0nn6q33uBIgAZYC9FEfUBZJySiABYy8UMHAaxnjHUiJkx7GMDvO9i2ijFWzzkfA/BWAL0W26REuo/RUwA+AODL8eVP1Aac8/eKPGPsgwB6SKBG5Boy/cwS6WqrCfLNHDRbLKcIoCRwJGSWg8YNDTQJgnDCcnivEcQKJVPCNTvWQZZmnOI39DTRAlJeXmZi/LPiSH8wxzkPM8b+BMDziOmff4tzfoIx9kUAvZzzpxhj1wP4EYBqAO9kjP1Xzvl1nPMIY+xTAH7FYiaTrwP432nvlA7pDpO+DOB7jLEPAbgM4HcBgDHWA+CPOOcfTvP3iWsQ0lIjACy9OSgJjQiCIIhrGRJEE/kCCTeXhjzs+6pCMT0hm123OjIZ00hTBWlhZC7y9IrCdqAC61/i/FkAzyplX5DyBxEzC9Xb9hcAtmZkR0xI61HinE8AeJtOeS+AJIEa5/xfAfxrOv9JELkm284xlwy7UdJyrTW2FP+fh50KgiAIx5CmTf5D14ggiHxgmfR9UxGgCSwFaUD6wrTF+PJasLBxRBSAP9c7sWQsk8eJIDLHUpt+pvMxWJZkygRUJltaa/QGtEcuzlME+edknCCI9LjWvocEQRDXCKlEAM0WSy5MWwRpqyWRsUAFywIaUhJ5BZl+ZogirMzBSya11ujtRxDpkc8aNyvx/UcQBEEQmWAF9oF1BWkCJ9E7VZ9pYaUsAmAeicI06nPokDnzz+XACnykCILIOdnQVhNkQmuN3nwEQRBErshHgTQNCmPk47UhCMIQ28I0ILPaafPKkvRCFMj8kyAIA1aMP7WVQCrCNXrjEQRBEARBENcqK6gvnBOBmg+JgrVAvO7asXS0CZl/EkROWArTz6X2p+YE0w9DrknF7DKb2moy9BbLLnR+CWJ5ka+aNqQJRRCEHfLx/UXYYqn8qqUsTAMyL1DzxfOEApl/EgRBEARBLE9IeEMQBEEQyayAkb8jgZpKuiafehpq80j0uUZIXDsnZQU8WgRBEARB5IR81YoiiHwnn54dEkQTBJHnWFr06Mlv7Jp8yr9hFuEzLKXFeFs5cAEhEQWwkOudWDJIqEYQWaQw3zuqbpBjTYIgCIIgCEE+CTwJIlMs0ag/GyagGROoGW0na6rptVEDFMj5RYvtr1muLZ9qrlzvAEEQKxhvrneAIAiCIAiCIIjlSEo+p40EaqqWmlwmylUtNfEbQotN1M1LZfMgv2rXOKSpRuQFSxGkgCAIgiCIHJDvWtsEQRArmWU64rclUEtVGUo1+zRqsyjlZc021SyUhGoKFKiAIFYk+Rz5c1mQSgRQgiAIYukh87XlAV0ngiBWIJkwAU1JQw0wnsTR01KTMdJSk7cFEk1BA3hTe43GSApRAP5c78SSQUI1giAIQh/6QhDLDdKIIghn0DNDEESeYVugZjfaZ6rby78hC89U7TVVCEeAfKoRBEEQBEEQBEEQBEGsZMzkPosmbeyYj17TCPNPq2QNY+xuxlgfY+wcY+yzOvVextgT8frXGGOrpbrPxcv7GGNvT/uwDCA9BIIgCIIgUodM2AiCIIhMQN8SIhuoQi87QjCzbSJSWQRk/qlLZsw/GWNuAP8E4E4A/QAOMsae4pyflJp9CMAU53wdY+xhAP8dwO8xxjYBeBjAdQBaAPySMdbFOc+4XiFpqhGETQoi0VzvAkEQBEEQBEEQBJELyMzTJsL80ypZcgOAc5zzC5zzIIB/B/CA0uYBAN+O558E8DbGGIuX/zvnPMA5vwjgXPz3Mk7eaqodfp37api/L9f7QRArkDoA47neCYJYYdBzRRCZh54rgsgO9GwRRObpzvUO5A9DzwN/WWejYRFjrFdaf4xz/pi03grgqrTeD+BG5Te0NpzzMGNsBkBtvHy/sm2rzQNwRN4K1QD0cc57cr0TBLHSYIz10rNFEJmFniuCyDz0XBFEdqBniyAyjyIcuqbhnN+d631YSsj8kyAIgiAIgiAIgiAIgsgnBgC0S+tt8TLdNoyxAgCVACZsbpsRSKhGEARBEARBEARBEARB5BMHAaxnjHUyxgoRCzzwlNLmKQAfiOffA+DXnHMeL384Hh20E8B6AAeysZP5bP75mHUTgiBSgJ4tgsg89FwRROah54ogsgM9WwSReei5yjBxH2l/AuB5AG4A3+Kcn2CMfRFAL+f8KQDfBPD/McbOAZhETPCGeLvvATiJWFSEj2Uj8icAsJgQjyAIgiAIgiAIgiAIgiAIu5D5J0EQBEEQBEEQBEEQBEE4hIRqBEEQBEEQBEEQBEEQBOGQvBSqMcbuZoz1McbOMcY+m+v9IYiVAGPsW4yxUcbY8VzvC0GsFBhj7Yyx3zDGTjLGTjDGPpHrfSKI5Q5jrIgxdoAxdiT+XP3XXO8TQawUGGNuxtgbjLFncr0vBLESYIxdYowdY4wdZoz15np/iKUn73yqMcbcAM4AuBNAP2IRHx7hnJ/M6Y4RxDKHMXYrAB+Af+Ocb871/hDESoAx1gygmXN+iDFWDuB1AA/SN4sgUocxxgCUcs59jDEPgJcBfIJzvj/Hu0YQyx7G2H8G0AOggnN+X673hyCWO4yxSwB6OOfjud4XIjfko6baDQDOcc4vcM6DAP4dwAM53ieCWPZwzl9CLCIKQRAZgnM+xDk/FM/PATgFoDW3e0UQyxsewxdf9cRTfs0CE8QyhDHWBuBeAN/I9b4QBEGsFPJRqNYK4Kq03g8aoBAEQRB5DmNsNYAdAF7L8a4QxLInbqJ2GMAogF9wzum5Ioj0+UcAnwYQzfF+EMRKggP4OWPsdcbYR3O9M8TSk49CNYIgCIJYVjDGygD8AMB/4pzP5np/CGK5wzmPcM63A2gDcANjjNwWEEQaMMbuAzDKOX891/tCECuMvZzznQDuAfCxuMsd4hoiH4VqAwDapfW2eBlBEARB5B1xn08/APBdzvkPc70/BLGS4JxPA/gNgLtzvCsEsdy5GcD9cf9P/w7grYyx7+R2lwhi+cM5H4gvRwH8CDF3VsQ1RD4K1Q4CWM8Y62SMFQJ4GMBTOd4ngiAIgkgi7lD9mwBOcc6/kuv9IYiVAGOsnjFWFc8XIxa86nROd4ogljmc889xzts456sRG1/9mnP+vhzvFkEsaxhjpfFAVWCMlQK4C8Dx3O4VsdTknVCNcx4G8CcAnkfM4fP3OOcncrtXBLH8YYw9DmAfgG7GWD9j7EO53ieCWAHcDOAPEJvxPxxP78j1ThHEMqcZwG8YY0cRm2z9Bef8mRzvE0EQBEGoNAJ4mTF2BMABAD/lnD+X430ilhjGOQVTIgiCIAiCIAiCIAiCIAgn5J2mGkEQBEEQBEEQBEEQBEHkOyRUIwiCIAiCIAiCIAiCIAiHkFCNIAiCIAiCIAiCIAiCIBxCQjWCIAiCIAiCIAiCIAiCcAgJ1QiCIAiCIAiCIAiCIAjCISRUIwiCIAiCIAiCIAiCIAiHkFCNIAiCIAiCIAiCIAiCIBxCQjWCIAiCIAiCIAiCIAiCcAgJ1QiCIAiCIAiCIAiCIAjCISRUIwiCIAiCIAiCIAiCIAiHkFCNIAiCIAiCIAiCIAiCIBxCQjWCIAiCWIYwxm5njPVL6ycYY7fnbo8IMxhj72WM/TzX+0EQBEEQBEFkDhKqEQRBEEQcxthextirjLEZxtgkY+wVxtj18boPMsZezvU+GsE5v45z/sJS/y9jrIQx9mnG2Ovxc3aVMfZdxtgOi+0+yBiLMMZ8jLFZxtgRxth9S7C/9Yyx/xO/xlOMse9KdV7G2Lfi+zPMGPvPDn73XxljwfjxTDLGfsEY2yDqOeff5ZzflYXjeRtj7DRjbIEx9hvG2CqTtqvjbRbi29zh4H8uMcb88eMbiR9vWWaOIuF/tsfvpYX4crtJW5+SIoyx/5npfSIIgiAIgjCChGoEQRAEAYAxVgHgGQD/E0ANgFYA/xVAwMFvuLOzd/kJY6wJwD4AawD8IYAmABsB/BDAdxhjj1r8xD7OeRmAKgBfB/DvjLGqrO1wjB8CGAbQAaABwN9JdX8JYD2AVQDeAuDTjLG7Hfz238aPpxXAAIBvZmKHjWCM1SF2PP8FsXu2F8ATJps8DuANALUA/hzAk4yxegd/+c748e0E0APg/9bZpwIHv6duWwjgJwC+A6AawLcB/CRengTnvEwkxO49P4Dvp/r/BEEQBEEQTiGhGkEQBEHE6AIAzvnjnPMI59zPOf855/woY2wjgH8GsCeuETMNaNpJ/4sx9ixjbB7AWxhjLYyxHzDGxhhjFxljfyr+gDF2A2NsH2NsmjE2xBj7miwwYIxxxth/ZIydZYzNMcb+ijG2Nq49N8sY+56RgCGuSXRHPP+X8bb/Fv+dE4yxHqmt1T72xv9vhDH2FZNz9n8AfJVz/kec8yOc8yDn3Mc5/wGAvQD+jDG21urEc86jAP4/AKWICbWE1tjfMcauxPfjnxljxVa/ZQZj7C4A7QD+L875DOc8xDl/Q2ryAQB/xTmf4pyfAvC/AXzQ6f9wzv0Avgdgu/Tf2dB0fBeAE5zz73POFxETCm6TNeSk/+9CTBj2F/F7+wcAjgF4t9M/5ZwPAPgZgM3x3+aMsY8xxs4COJvy0QC3AygA8I+c8wDn/P8BwAC81ca27wYwCuC3afw/QRAEQRCEI0ioRhAEQRAxzgCIMMa+zRi7hzFWLSriApY/QlyzinNeJW33+wC+BKAcwKsAngZwBDFtpbcB+E+MsbfH20YAfBJAHYA98fr/qOzH2wHsArAbwKcBPAbgfYgJgzYDeMTm8dwP4N8R0wJ7CsDXAIAx5rLYx68iJiirALAWMeFQEoyx2wCEOOffYoy1M8Z+zRibYIz9v4yxA5zzKQB/A+CPrXY0ruH3KIAQgMvx4i8jJujcDmBdfF+/YLD93rig0ijtjTfdDaAPwLfj+3owfhyIX+/m+HkRHAFwndX+6+xPKWLX6ZyDbcz2/7MGm10n7y/nfB7AeYN9vg7ABc75nFSW6vG1A3gHYlpvggcB3Ahgk8E2do7vOgBHOedc2vSozX38AIB/U7YlCIIgCILIKiRUIwiCIAgAnPNZxLSrOGIaSmOMsacYY40Wm/6Ec/5KXNtqC4B6zvkX41pbF+K/9XD8P17nnO/nnIc555cA/L8AblN+728557Oc8xMAjgP4Oef8Aud8BjHtIFNfZRIvc86f5ZxHENMC2xYvv95sHxETbK1jjNXFtc72G/z+nYgJ7YCYCeWrABoB/Bgx00AAOAwgSWtKYndc628x/hvv45yPMsYYgI8C+CTnfDIuCPpv0j4mwDl/mXNeZZKEhlgbgLsA/AYxc8G/R8y8sA6A8A82I/30DGLCUrt8Kn48c4jdS39gd0OL/f+ywWZlyv6a7bOTtkb8OH58LwN4EbFrIvib+LXy621o8/hS2kcW8yN3G2LmogRBEARBEEsGCdUIgiAIIg7n/BTn/IOc8zbEtMJaAPyjxWZXpfwqAC2yFg6AzyMmbAJjrIsx9gyLOcGfRUwoUaf83oiU9+us23UOPyzlFwAUsZi/K9N9BPAhxDTETsc1uYyCBzQg5jcMiAkT/09cWPgzAOPx8napjR7741p/1Yhp090SL68HUALgdWkfn4uXp4MfwCXO+Tfjpp//jtj1uxmAL96mQmpfgZiAzC5/Fz+e1fH/6k5zf63wIXF/AeN9dtLWiAfjQrBVnPP/qAjQrhpuZZ9U9/EPEBMiX8zAPhAEQRAEQdiGhGoEQRAEoQPn/DSAf0XcbxRiGmy6TaX8VQAXFS2ccs75O+L1/wvAaQDr4+aVn0fMZ9RSYrqPnPOznPNHEBOa/XfEnNmX6vzOOGLmkkDMN9fvM8YKWMyxfx1jbB1iZrHfsNohzrkPMTPRP2CxqKHjiAmlrpP2sTLukD4JxtgtLDkSpJyEsO4okq8jj+/DFIAhvKnRh3j+hNX+6xzPFQCfAPBVu37gLPb/8wabnZD3N36d1hrs8wkAaxhjstZXSsdngKnZpc3jOwFga1xTUbDVxj6+H6SlRhAEQRBEDiChGkEQBEEAYIxtYIz9GWOsLb7ejphfLGH+OAKgjRkECohzAMAcY+wzjLFixpibMbaZMXZ9vL4cwCwAX9yZvKW/sSxguo+Msfcxxurj5qzT8W2iOr/zawDviec/BeAmxAR2v4eYaeA3AHyac37Qzk5xzifj23wh/t//G8A/MMYa4vvVKvl9U7f9LZciQeok4bz+RwCqGWMfiB/3exAzCX0lXv9vAP5vxlh1/Pp8BDHBKuL7wBljt9s8nl8AGETMjNVOe7P9/28Gm/0IwGbG2LsZY0WI+Zw7GhcIq79/BjFz3L9gjBUxxh5CTGD1g/ix3c4Yy5o/MpvH9wJifgf/lMUCVfxJvPzXRr/LGLsJMX97FPWTIAiCIIglh4RqBEEQBBFjDjFH66+xWCTP/Yj5NPuzeP2vEdOYGWaMjev9QNx/2X2IOde/iJjG1TcAVMabfAqxwAZziAmNnsjGgZhhYx/vBnCCMeZDLGjBw3p+sjjnv0RMQPVezvlVzvlbOefNnPNHOedvAXAH5/xXDnfvHwG8gzG2FcBnEHP0vz9uKvtLpGlOGRfc3Y/YdZgB8FkAD3DOxfX8C8Qc/V9GTDD4PzjnzwGakHUOMa08u/wPAJ9mjHnT2W8jOOdjiEW9/BKAKcTuX83vHItFTP1naZOHEfN3N4VYIIj3xH8DiJnqvpqN/bQL5zyIWMCD9yMm0P1DxExOgwDAGPs8Y+xnymYfAPBDnhiAgSAIgiAIYklgnIIkEQRBEASRAoyxVgA/B/AMYkLCC4iZhP4hgBs550b+2JYdjLH3IWaO+rlc70s2YIx9A8D3OefP53pfCIIgCIIglgskVCMIgiAIImUYYxWIafO9GzFTynHEIoB+WdIAIwiCIAiCIIgVBwnVCIIgCIIgCIIgCIIgCMIh5FONIAiCIAiCIAiCIAiCIBxCQjWCIAiCIAiCIAiCIAiCcEhBrnfAiNraWl5YWJjr3SAIxzDGcr0LabHc959YHpDrASITBAIBBINBw3qfzwfGGIqLi1FZWWnYjiAIgiCI/CXV8Ym6ndl6Om1LS0tx/Pjxcc55fUo7usJYxxhfsNFuCHiec3531ncoy+StUK2zsxO9vb253g2CIAiCIJYp3/rWt/A7v/M7KC8vz/WuEARBEASxgmGMXc71PuQLfgAfs9Hu/wbqsr0vS0HeCtUIgiAIgiDS4Q//8A9zvQsEQRAEQRDXFC4AxbneiSWEhGoEQRAEQRAEQRAEQRBE2jAAnlzvxBJCQjWCIAiCIAiCIAiCIAgibRiuLUHTtXSsBEEQBEEQBEEQBEEQjuGcY3p6GqOjoxgbG9OWe/bsyfWu5RVk/kkQBEEQBLHC+Nu//Vtcf/31eMtb3pLrXSEIgiAIIoeEw2EEAgEEAgEsLi5iYWFBE5KpArOZmZmEbauqqtDQ0ID6+no0NDSgubkZdXUrwt9+xiDzzzyBc46jR48mrOdiH5ZyO6tts123VHl5aZS3qk8lb5Si0WjG69R6kc/2Mt0yguCc2w5bnqv3stH+pVNuJ5/KNlbh2bNdp4fRdctUeTo4PRZ5u4mJCfT19Rn+5uDgIMLhMIqLi/Hggw+a/qfdMiOW8lueifJUrmUmnkOnv5FKu1TuKfV8WK2nsk2+fHNTed85bbvUpHJ90i2zIlvvdyfXJRPrqZIv93sq6N3bVt/8VOqd1GV6uZSoYxCR5PVU6iKRCEKhUNIxiXtPLXe73fB6vVoqLS3VhGT19fW47rrrtPWKioqcv9eWG2T+mSdEIhE88cQTCWW5uJnT6eBn4z+Xom4pB5qMMcO8VX0qeTW5XK6EpV6d2XZ26tX/cLlcumWZWKq/b9bG6DgIYjmSjmDByeA31TqzfclWnahf6m/KUiPOQTQaxcLCQkKZfH7uv/9+lJSU4K//+q+xffv2pHr198zKnAifM9lOr00my+zuazYFeene76nW2e3XWK2n8ztLTTbebek8K5kk1fs9nTIjsvl+X8p1UZaLsVGu0Lu3je73dOqd1GV6mQqpbss5TxoPqeOiVNddLhcKCgqW5X22EnEBKMn1TiwheStUKygowJe+9KVc7wZBEASxDDDqRFHn6tqioqJCy3POcenSJXR2dgIADhw4AJfLlatdIwiCIAgij+Gcw+fzYWZmBtPT05ienk7Iq+tiIg8APv7xj+dwz/OTvBU0ZYFr6VgJIqeo6spGKRKJZKRNtpNqRqqXMl1ndm4zXadipEmZraWeBuFSlC/Fccll6jnOZpmexojVMpNtUtnPVNZV0tHGSvW3lorLly9jYmJC9x6bnZ3Fl7/8ZbS1teFjH/sYdu7cqW1ndg3N8nawcz7snjMrjQejfKrbmbHUmj/ZeC6MrqXd85Luec2H75iat7rv061fatK5Rpl6VgRm5yATdXavx3K+hum2S+X7lMk+jtNt9PppZuuptjU6ZrOydN7vkUgkIYmxjN1yp9uEQiEsLCxgfn5eS/J6JBLR9lW+RxhjKCsrQ2VlJaqqqrRUWVmJNWvWaHlRXlxcnLPnJN9hIJ9qeUE4HMYf//EfL+l/5koNNptlVm2cqB2n214WjBgJU5b7i8nqGFwuF9xud5K6spqctGGM2WrvNLndbng8Ht068VFO1UQ2HdNaI7JRJ66p1dKJqr3dNmbPS7bKsr2MRqMJZXrneSnWM/1us9Nebz3TxyXWsynYMCrLFSdOnMDY2Ji2Lp+PhYUFRKNRDA0N4de//nXS/ZfKYDpb75l0BtFG+VS3U0n3nszWNpkoS+W8pHtec/EdA+y/0zJZv1ToPZtLcW3t7lum2qVyPTJxjXPxzrf7n3baOdn/dPo46W6j10czW0+1rdExm5Wl+651u91JSYw10i2X6zweD9xuNwoKClBaWpqQSkpKtGVBQfoikGAwiL6+PvT19eHMmTO49dZb0/7NlQRDHguaskDeHqvb7caf//mfL/n/pvPhSHXbTA6A9Mqs2hjNntgtc9LeaCbFSmBCEARBEE4IhUL4yU9+ggcffDAjHWiCIAiCIK4dOOcYGRnRhGd9fX04f/48QqEQPB4P1qxZg+7ublx//fXo7u7O9e4SOSRve5mMMbS1teV6NwiCSBExI+bElFRspy4zNUOoN4PmREiciWUmkqrNRxDXGk888QR6e3vxX/7Lf0nwoybj8Xjwnve8Z4n3jCAIgiCIfCMUCmn+0IyWIj8zM4NgMAgAaGpqQnd3N7q6unDLLbdg7dq1KCwsBOccg4ODOH36NE6cOIHy8vIcH2F+4QJQnOudWELyVqhGrGxk9WQ9lWWjJLezCrNslE+1nezPTLbhd1qWym849SkgzJxSuS6ZFNLYMTMVQiIgfT8VTtqI49VbmtWlaxqQqSTuTzNyZbZBEOkSDodx+PBhw3eZz+fDwsIC/v7v/x633nordWaJvMXoPc0Ys3yHLwV6+0HfjZWN3DfIhUm7ui/prqdyv6rb2TWbd7pNrpL8/+q+ZrJenEuz8Vy6ZcJHml4Kh8MJ+6FeA7nc4/Ek+EOT86tXr04oq6iogMfjwczMDK5cuaKl1157DZcvX8bMzAwAoLW1FRs2bMCGDRuwatUqEG/CQD7V8oJQKIR3vvOdud4NwgapftDMPgB2PhRut1sTyKihlY3ydtsZbaPa9BuVCV9kdtvbKXPib0Dscz6gCoLSEX6qv6v3X5ksE9i9VzNVLpKqkSavE8S1zFe+8hU8+eST+NrXvpYQgECmv78ff/M3f4MvfOELaGxsXOI9JAiCyG/M+j2p1tnZVu3DpLueLmYTq1b1ZtvkKsn/r+5rJuvF0k7fNp0yMa7SS2IsaEU0GsX8/Dx8Ph/m5uaS0tmzZzE5OYkrV65gcHBQC2RQWVmJjo4OLe3duxcdHR2orKy0/M9rnUz6VGOMfQvAfQBGOeebTdpdD2AfgIc5509K5RUATgL4Mef8TzK0W4n/nQ+zZHr09PTw3t7eXO8GsYIQHwM1cqa8nom80BYLh8O6GmVG5ZneRk+TLZPn0kmnJhPCTz1hkt4+ZLrMzkxaJsvVpEZCzeR1FPuw1EK6XPxnLtGbPVXPgTrzq1dmt736v6kMFKzqjfZ/qTl58iR8Pl/SfjDGEAwGMTk5CQDYuHEjuru7DWfe1aXZuVdxIqRPtdzJAEfvuuUKxljS/W9036RTn+pv6p0/dWmnjZ22qT4j8jmU91/vvZIJ5H01eibs1qdCOtumex3TvW5G73qBeu3kcr0yPexcE6d5O/V2jz3dNmbtnJQ7/Q2BnmDHaj2VbfR+wypQl902dtrJx6oet1Wd3TL5ftcbs4i8XpndtnKfWH7+9PoopaWlKC8vR1lZGcrLy5NSTU0NOjo60NzcnLIfVsbY65zznpQ2XmFsYYz/0Ea7LsDynDHGbgXgA/BvRkI1xpgbwC8ALAL4liJU+yqAegCT2RKq5bWm2n333Zfr3UhC78Mn18nofUiJpUN+oYprIUfJVCNmZqLO5XKhoKBAV4tMLfd4PCguLrbV1kmZWWQcug8JIr+wElyZLY3qMil0sKrPJ0TnWiyPHDmCe++9F5///Ofx4Q9/GB6PJ2uCEb1yJ23tlBsNhK3a5BPZEvCm+ptOz206y+WG0TNh9cykI8xNdVsjQaC6XE7Pj5mgyMl1SLVeLrN7Luy0S/e3svkOVo/bznoq2+j9hkjqRGqqbczayceqHrdVnd0y9f5Vxyx6+VTqczm2CYVCOH36NI4cOYLDhw/j7rvvzsl+5CsMmTP/5Jy/xBhbbdHs4wB+AOD6hP1gbBeARgDPAciawDMjQjXG2N0AvgrADeAbnPMvG7R7N4AnAVzPOTdVQ/N4PHjmmWcysXsEQRAEkZfkywBuJSB8M4rlrl27MDw8nMtdIhSWg3CWiEHvptziREhEEER2mZqa0oRnR44cwfDwMDweDzZs2IDt27fj0UcfRVdXV653M69wIFSrY4zJcqHHOOePOfovxloBPATgLZCEaowxF4C/B/A+AHc4+U2npC1Ui6va/ROAOwH0AzjIGHuKc35SaVcO4BMAXkv3PwmCWBmImTQjk1rZl1omtBLslAnsqNcbqdOnkkiTkCAyw3e+8x1s3boVW7duzfWuEARBEASRh3DOMTMzg4mJCYyPj2N8fFzLi+XIyAiCwSCqq6uxbds2bNu2Dffeey8mJydx8uRJnDx5Ek888QS+853v4E//9E9zfUh5BQNQbEfSFMY4T99k9h8BfIZzHlXGUv8RwLOc8/5sj7Eyoal2A4BznPMLAMAY+3cADyDmDE7mrwD8dwD/Vwb+kyDyGiEcCofDKaVUt7WznVmbaDRqqhaf6ReSECYZmdiKerm9vDTKp1Ivl9lRqzdTqU8nXcuo91gm80aCT6frTrfJdN6sXpzDpViqeZVs1AkmJiZw8mSsi6F3Hvr6+hAMBlFeXo4PfvCDmiPjVK+pHew+u3bamb1LslWuksp7M516J/d5pp4T9TxkOm9UptYblaUyKaRey3Tea6mWqf+fzrqdNmb3dDrrdp4TPczeGanWiXq9lGqdnW1TIZ1+jNG9bCef6jaCpT6/ss8zub9rtky3rbo/8rpZXapt1edJzxxVz2+wWZtoNIpgMIhAIIBgMKgls3WRn56eRjAYTLjmlZWVqK2tRV1dHerq6lBbW4t169aho6MDPp8P09PTGBgYwOXLl/Hiiy/ixRdfRFlZGTZt2oRNmzbhgx/8INavX4/CwkLd++pahjHAlmu6cEb+rgfAv8fvvzoA72CMhQHsAXALY+w/AigDUMgY83HOP5uRf5XIhFCtFcBVab0fwI1yA8bYTgDtnPOfMsYMhWqMsY8C+CgAtLe348knnzRqakgqL/Nsdf7tbpvNj4VaZ9YRTKXzaKedHMVRfUFaLVNpqxc0QNZ+SqezIJ9TtYMurzPGUFBQkJSEHwC7yai91+uFx+NJaCc+ZmJf1P0zOg71/IkQ1VbJSLPMTOvMbttQKJRU5+TZFufBzro4T3rX1eo/rDpKRh0bs+3SjVCbyjZmncJsJvlcqnn5mTLKO7le6nZm/23222ZtjPZNb//t3s9ye6vfEAJouX2q/6O3zCRmv2l1TUOhEMbHxxGNRjE+Po5QKJSwzde+9jWEQiF0d3fjG9/4Bh544AE88sgj8Hq9uu9Fs//jnCecV7XODKvjM3svG+2T0/eU1f7YHWyq63rPk959ky7q/WeUN9pvuT8il8vvIL16udzsf4yuh95zk+45sbu9fNzqd89sn9V2Rr9rtk9W51YtMzq3Zvut9/92nxe937B6J+jtr533odW1snsvyOdI71416vdbvdf06vW+KVbn1qrMrNwMp30Bq9/RKzca05j9hp17St03+fflZHesI/rD6YyL9I5TL2+17qSt2ucz6m86KXe5XPB6vSgsLITX60VRURHKysq0SKBi3CTGEqFQhlrkhQABAABJREFUCIFAAIuLi4hGo1hcXEyKADo8PIwLFy5o+19RUYFVq1Zh1apV6O7uxl133YWOjg6UlZWZ3GmECmOAx700/8U573zzf9m/AniGc/5jAD+Wyj8IoIdnQaAGLEGgAhazZf0KgA9ateUx+9nHAGDnzp18fn4+1f/M2jbpvMCN2tkZYFrVOdlG/V+r9XTbqhEc5by6n2YfXqNOql4b9WMnlqqgLRspHA4nCKbk/OLiYoJgKhQK2Tp2M8Sgz0gwJwvfUqkvLS3VFfZZBWzI9LqssZZrjDpIeh2lVOrkpd18qu3sOrxN5XjMknwu5aVRPpV6tcyoE6i3b5mqk8v08qnUW21j9F42K3Pa3ug3nJKOkCEajeK3v/1tgtCdc46Kigq4XC6cOXMGPp8PTzzxBObm5tDa2mp5HfXK5TKz72sm1o3aqMlJeSq/IR+zyGdr6bQuE+31nhMn5zDV7Zz24ZzkjertvPPsvhudvEOtsNPOyW9l6to4aWO2r1brqWyTqevndFuj97Sd91cmyu3kM7GN2flJt6+jh965tXvPp4rZ9XSCPG5R89n6T6P90AtuUFxcrBvhs6GhQcvLUUBLSkqyto/XKozZ1FSz9VvscQC3I+Z/rR/AXyDuso1z/s+Z+Zf0yMShDgBol9bb4mWCcgCbAbwQv1mbADzFGLufmwQriEQiKWmqEdYYvdzUF6L8YpdfmHrrqf5nqlo2dvN2I2OaRdf0er2OfkcIqVRhld660DAjlhd6HWqCIPKLI0eO4NChQ3j/+98Pt3uJpksJgiAIgljRcM5x9epVHDlyBEePHsXRo0fx6KOP5nq38grGAI83M7/FOX/EQdsPGpT/K4B/zcweJcPSlYgzxgoAnAHwNsSEaQcB/D7n/IRB+xcAfMpMoAYAPT09vLfXtAlBEARBEARBEARBEETG8fv9OHHiBI4cOYIjR47g/Pnz4Jyjvb0d27Ztw9atW7FlyxZUVlaCMfY6T9/p/oqgp5Dx3nrrdmwQK+Kcpa2pxjkPM8b+BMDzANwAvsU5P8EY+yKAXs75U+n+B0EQqSH7FDBKwgzVbhLBDmRfdXbXnbQV64C5iryRCvpSYmTenEpez+zVKqWzjWzWku66k20ES2UCpe6nUUrVx5zZdnr/r5bZaaN3jE4wMvOxWrdqY/Z/VvuT6rZArKMbDod1/QWePXsWDz74ID75yU/i4x//OIqLi02vz3IgFbMkp3UC9f5LNe90O7vPQC5J1bxOfXaWYgkknr9U3tlWZcuJdM375IjkMup5SOdbZtVupV0TglhKOOeYn5/HxMQEJiYmMDk5qeXlNDMzoz3zRUVFuO6667B161Z8/OMfR0tLC86dO4e+vj6cPn0aX//61zE9PU3RP1UYlsDRWP6QtqZatiBNNSLbiJel8IMmR8VUy+wu9YRQwWDQUlBl1iYSiRh2kqz8FDDG4PF4DJMwQ3WSVDNZq44hY9Z+8OS8OvjQC1Rg9/fSaSvQCyCgdl7F0uVyJVwTPSGIOB92O9FGiN+RB1HyuRb3t16SBZhWAwun61ZthKDU7Nqkmjerd5JS9Z1iJKSwKrPTRu8Y7SDa2xF0WK1b1ak4KXf6G5xzHDlyROv4qucsGAxiZGQEALBp0yZ0d3frXrN8xug9YTWwTrdOPZep5p1uZ/cZyAeMzp3dJH4j20u776lU3uv5dk3s4vR5MGojUM9BOt8yq3b5cE3Ub4q6n6JcLVuqe2W5CBXF+13vfKrtVNRzzznXnTwV963q2zpbSeyb3jcl1f6OUZkYO8njJzvnHABKS0tRW1urm8rLyxEMBjE7O4vBwUFcvXoV/f396O/vx+LiIgCgqKgI69evx4YNG7BhwwZ0d3ejurpaHP+K0LrKBD1exnubrNuxKytDUy1vhWo7duzgDz30UErbpnpMetsZ/Vamy/UGTHbKnLYXeSedWDt1dur1HKRn6+MnPlZWCAf/sk81NfKmk6WeEKqwsFC3XBZI6XVOxPmKRCJJwjd1aSScs7Pu9HlRzy1j5r7rhFZUuvVqoAJ1H4zq0mmrd+/qLe20sdvWTAhmtK5XZve62n1WViri+O1qZ6RSbqfTmYlyUZbJd7tZnd73Re/8ZrpOMD8/j8OHD+uen0gkgnPnziESiaCoqAgf+tCH4PF4DK+VnXW9fctm3uw7mql1vTKB0f1mVpdOWzvl6QzszAZ6RufG7jmzW65eX6dLp9sI0tGgTnUbPfTKnbQ1K3dyrVItk9fN9ku+D83qneatvjmpXEOrOnk/1OM2eodlom0mnhcnS3F+9c6F3TKn22QyGd1zRs+L2tZOnZ3vsryt0XV18u40aqeOEcQ7XfSHFxYWsLCwgPn5eS3prYfD4YR7wePxoLW1FW1tbWhvb0dbW5uWioqK7Bz7ihAQZYKeIsZ7O6zbsbMrQ6iWt0p5LpcL99xzT8rbm70onG6XqQ++VXkqH2Qn9QKzl6L68bRzHu2+ZPW2MxIipJtkTRw52qaqkaa3rpYtLi4atjHrGFidO8ZYkuBNXVrVFRUVoby8XFd4Z3ddFVYRxLVGNgbPekn+r0yXy2WpCBtSrTP71giyUSfO9fDwsO77n3OOz3/+87h06RI+8pGP4JFHHklLAKVOAi1V3u4AOpV1ozIxODG639R1O23s/IZZeTba2TkP2RC2q9fX6dLpNkb3s5O802300Ct30taqPJ17PlWzWL39ku87s/pU8k7fWZm6xkbvKXXdrC6Vtpl6Huy0sToPdsvsbqM3mRoKhbI2LpK/jUudnLolyUQqKSlBSUkJ6urqsGrVKpSWlqKkpASlpaVaKikpgcfjAZElGPJY0pR58vZQw+EwvvjFL6a0rdFHN5OIF7Bapv6/WmY2SFgK9Drq6gcnlWRn22y/QPWSiN6paqGp2mhG62Zt9LSnCIJYfsjvKGJ50dzcnLAejUa16/jcc8+hpKQk599dgiAIgiBWBtPT0zhz5kxCmpubI59qKiRUyw88Hg+efvrpXO8GQSxrIpGI5nMgGAwiEAgkrOuVGa3b8Tlnp04VOhsJnNXZ0aXA7n9mUkiu/qeY1VNNX/XMYdMpk80RVNOEbNXLLMVstxOtg2xo/WRqPZOkqtVj1Fb+XTvLVNsAsevp8/mwuLiYcF+LND09jdtvvx3vf//78dnPfhY1NTVaW/VeXK6CNj0Nh3Tz6vtMb7JML++krdnkm3xN1HdJtp6DdLGrLZRqUv/DyXaAtS9QJ0ujuny7JnawozVktjTD6nyY1ZvV2fnuqt8OgiCcEwqFMDc3h9nZWS2Njo7i7NmzOHPmDIaHhwEAlZWV6OrqQldXFx544AGsX78eFRUVOd77PIQB8OZ6J5aOvBWqEUQ+wjlPMgWVnWVaCazSTaJDaCSMUstcLhcKCwtRWFgIr9er5e2UlZWVaWVqkAIr/3NmZdThs0bPjFnOZ6LMbHBuNRgXz4Hd7SORSMLxqYMTPWFKum31BpzpmP+lsp6p38g0RtrEZoIPs7by79pZptqGc47jx49jfHxc9zwFg0H4fD58/etfx/PPP4+dO3ca3qPLmUwLveXviSrQ0cunUq+W2TE7U9tkGvV7Kf+HfE70yqyEg7lM6QqOrL4Fy+UZ0rtnjIS3ZsJcpwIrO/eq3ftZ71pa5Zcjol+bL8jPeSbPabrHqU6M6k2g6i3NJjDs5J1uZ/asZKJcHouJPq66rpcPh8OYn5/H7OxsUr8UiCn0VFRUJKTa2lrs2bMHH/jAB9DY2JhX92neQ5pq+QHnHAcOHHC8TSr/k255qr+hNzg0K0u3fSaT3d9M1eY/HX8Ber7P0kF9geqZiKrCKT1hlUhFRUWoqKgwrDf6jVT9n3HOEQ6HkwR9dpbz8/O65UYaaXaipKbyQcq3Dle2EMfJOU/SwtHTzMlUuZ1Bhh3NBbtLvQ7XUqyns8/pHqvTjmc+YCTkMxP8qd+9TNTprTPGEAqFEAgENIF9IBDAl7/8ZXzyk59EW1sbNmzYgPvvvx9/9Vd/pUXmWokYDbz1Bt1W62ZCo2ymlaBpo9f3UfN2+lGZLAfeFMLKSX6X261bztfGCLNnx0qIJSPOi3x+1DKzOrvtja7RSrw2RCLyvapOlOqVGUV515vAsJN3up3RMWSiHID23RdJXjerKysrQ3l5OQoK7ItAQqEQ5ufnMTAwAJ/Ph/n5efh8voQ0Pz+P22+/3fZvXjO4c70DS0deR/9817ve5Xi7VD4qmZCap/obmfjIOmmfyWTnN1P1mZaOrzWhUSX7Pct2Z0N8TMQgTxZAmeVTbRcKhcAYsy1skgV/qSzlvKq15iQ66lJci5WA3HFSNc7klKlyo85TupoMZkv1ePXOQSbLzDqJ2VjqDaLtdjzzBbtCQVXYrz7jet8op3XqOuccFy9exJUrVwzP7cjICKLRKKqrq3HzzTendhKk/8vnd5eRMERv8G21bnTfZjstZ00bgXw+1byTflSmyuVzayVstVNnB/lZEddTb12+1lbr2UJ+hxldN706+byL/ZT3Vy0zq3PSXu976+TaOCUX7718f9dmCjvHybmxOxB5gtRundF9bFZmVW+1jfrdVo/PaD3VOiDm+ka2FDJSHlBd3AD676qCggKUlpairKwsIallmzdvRktLy4qIZJkJesoZ791l3Y69SNE/s4rb7cZ/+k//aUn/M9sDO7M2Tj7AmVg66eCm2jHWK49GowgGg7paaU7ydtsK80yrpdW1svOBF8IjVQhllS8pKUF1dbXt9iLwQj50OjiPqWALE9hQKKTN6Ii8XC7nUxHEpCPUkT+QRsKBTNfJQmI7WmRO6r1er2F9PtwbBJFpHn/8cbz97W9HTU2NrfZ/9Ed/hI985CPYtctGr44gCIIg8gwzTTQz7TSjOqP+sV2NtFS2kQX/8lKQ7rpaJvrIdpUHCgsL4Xanp1IljpeQcAEoyvVOLB15K1QLhUL46Ec/mvL2qQ4qrR7UdMvM2pi9dKzapLI0S2aCg1S305tJMcqLF5xZG6t6WWvNaqlqWuQbnMdMOBcXFzE9PY1AIIDFxUUsLi5mJB8MBgGk9ty43e4Ev2tyXl2X807MDsU1Mqq38xti5tuu4DjVOlXzy6nWWLpt0zV3ltGbYdS7R6xmXPU6VUZ5u2Uir5dSrbNTn44mbSrbiX3KxuSK0VLvXs9U59tM4D0wMIALFy4YPtOvvvoqAKC7uxuPPPKI5Tthy5YtOHDgAA4ePGjYNhPJyW8ZaQapk0KZrJOfQ728WZ2dvN662X2fjXXxfje7p1MtM6szu55W1yaVJN6leved2Xq6deozaZbPRDv1G2o2iZbJer13oF7KRDsASecjk98Os2+K3rfO6bqTbay+AU6FN1bt5feR3r6JfCbr5f5BtlKqbmCI1PH7/RgeHsbw8DBGRka0vFiXLYc+8YlP5Hp3849ryPwzb4VqHo8Hjz/+eK53g1jhiA9xIBDQ9ccm+yKzq0LsROVYdAKARJVjkZfxeDwoKiqC1+tFUVGRYV4sKysr0dDQYNpW5D0ej+5/EkQmsRqg2y0T+VQGMekMgNIZDKcyuA6Hwwnnz6wzb9ZGb2nVxmhgbVSWTnvxv7OzsxgaGkoSIovz8cYbb6CyshLvec97cOutt5oOtowG02qdXjKrS3cbJ4NkeRCVzmBb75rbyae6nXxO1Ps+nfVQKGRabzXw1Xt20m0jNNTTFYDY3UY+v3r3ndl6KnVqGzuCKXFd7LTTqxO+V42Ebk7K7E7Iqe8k+fqnkuxur56LTH07hAWBXrnRty6bZU6+Cam2Vcvk/zfKm9Vbba+Wyec4m0n8n/j/pe67G/2nKFe/7Vbrdtuo/+l0nXOujb/kAHPqNVUpLi5GU1MTmpqa0NjYiLa2NvT09KCpqQkNDQ0oLCx0fA6vGRgyJmlijH0LwH0ARjnnm3Xq3wvgM/F/nQPwx5zzI/G6TwL4MAAO4BiARznni5nZszfJW6FaNBrF97//fdM2Rg9ANtqk8lt6+WzUG30klqJcr4OUzbJwOGz6ARH76uQjI/sFk5MoU1WE9dSGvV4vysvLDeuMytJVNyaI5YTRYJwg7HDnnXeiubk517tBEARBEISCHQG+3rqdNvJ/6OWt6gBkLAAcYRMXAG/Gfu1fAXwNwL8Z1F8EcBvnfIoxdg+AxwDcyBhrBfCnADZxzv2Mse8BeDj+exklb4VqQEzlMhOYDd6yWadK1u3O/qZSbzQzlmqZnTbqDJE6M57pcsYY+YtaYQhBqRwhVI3cqq5Ho6n7LEj13tHTKLBaGtXpzbgRBGFNqgK1XMzmEwRBEMS1hDw+JIhMaqpxzl9ijK02qX9VWt0PoE1aLwBQzBgLASgBMJiZvUokb4VqLpcL73//+3O9GwSxbOGcJ/hP00tW9UYpFApp/yMPVlVNQaN1UcYY09UONFtf6o+1PGtm5hRWNdMwq7Pzn3qq64C1YFAIn+2cUzWvJ9C2a2pht05tkwvEOTIzv8r0uhPTllycFz0NYSvTI726XOByuTA8PKz5yRRawGIm+jOf+QxOnjyJf/iHf8Ctt96aYEq3XJGvl933ktOl3mSX0QRYqu2M2i63yQfV9MsoLzsLT9WULpUy4E2tfCdBcfTaL6frYoX6HNl1+C7nAetJ6UwndbKOJuxSR+7jAW9OohIEsWz5EICfAQDnfIAx9ncArgDwA/g55/zn2fjTvBWqEcRKIRqNYnFxEX6/H36/X8sbLe20CQQCSf+jCmIYYwm+06xSaWkpamtrbbUtKKBXR74id/rNNP708qq5tZ6Jd6pl4n/UdukMAlLZVu5A6wmGrNZT2Ub2L2RnqWe2kC0YY9q7w0ooaOT3SR5sL/WgjnOOubk5HDhwwPBajY6Owu/346677sLdd98Nl8uV8jnOhdab0X860Zx1uhSDSiOBqpm7BidJfRbldSPEPauWiXOllsnl2bp2sqBDT2Cl5jNhUeCkjHOekSA4TgTn+aohqt4LegIqJ2XiN81cpWQ66QkCczWpoUcmvmGZuH+MJnhVZGGxeC9lCqv/lo9TTHCKCc9s5q0mRLORNxLmW63no8BYBI+TlRNqa2tzvVv5h73hYh1jrFdaf4xz/lgqf8cYewtiQrW98fVqAA8A6AQwDeD7jLH3cc6/k8rvm0EjYyLnGHX20u0Aqm31HFTKebM6OS+c6OodB5AccMDlcmnCqOLiYtNlWVkZ6urqTNsVFxejsLAw7z4wRH4gOjEejyfXu0IQecG//Mu/wOPx4JFHHiE/lgRBEETeIcZCYrJzKfJGk6F6EyZ22pltY6btaaXhK0/AymMtM+uYdISydrZ1u90JygZerxePPPJISv+3YrHvU22cc96T7t8xxrYC+AaAezjnE/HiOwBc5JyPxdv8EMBNAK4doVooFMI73/nOnP1/vs6wZRq7x5nJWV49jSonJgiptpWdU1ZWVmpmQsJESHVeaZSnQdnSIT7IskaV2jEQyDP0TpdOt1Hvs2vhXUEQy5VHH30017tAEARBEIYw9qY7FIJYEWTQp5rlXzHWAeCHAP6Ac35GqroCYDdjrAQx88+3AejV+Ym0ydsn1+Px4Omnn871bhDEioRzrqkrqyanViapRvlgMGjrf4HUo7Pq5eXZqlSWqWyjakUamRypx62HntmSXhvR2fJ4PLp5s7pUttEzU5JNBYzW7ZbptTG7L8zOT6p1AllAKt9bsnkPsTJ4//vfj3e/+924//77SRhugqwlwDlfsT61rgWsvk8EQRAEkRUyJGlijD0O4HbETEX7AfwFAA8AcM7/GcAXANQC+Hr8OxfmnPdwzl9jjD0J4BCAMIA3EIsMmnHYUvpucUJPTw/v7c2KIJEg8hLOOYLBIBYWFgyT3+83rRcpHA4DMFeJNjMvtcrrlZFJavYQA9xQKKT5PzPKm9U5aaf6AZN9uej5d0mlTK+N3j1kdF9lolxPSCprRGb6Gykfo5EZgzoIthJK2qlTzSzkYzQyac8X7GpTM8YwMzODvr4+w3bj4+OIRqMoLy/HbbfdppXLgTucLFXzFif+xpzWG5m0ZFKTXMAYS5i4SMWnVjrI7hOyFQxBTUIr2iwitV59OsdnVCcwutZ6qO8Oqzqr9kb/KbYTz4FeQBxRttTI7zkz87fljJ17Rzy/6iRROuvi9+Xvtl7ebpndbVIxPZS3M/q+6pkPWmF2rqzKjerMgnNZlaeyDU0WZhfG2OuZMGVcCfQ0Mt5rwyKWfRUr4pzlraYaQSwl0WgUgUAAwWAQgUAgKemVOy2zo83l9XpRUlJimsrLy9HY2GhYX1xcTOrjKwzyk3btYjYQcZKXTeH1tPHyWaiWSXbt2oU777wTn/vc51BZWQkg2ZeNk2WqQhyn9dfK9ZGR799MBEowaxuJRCyFREZRk6/FawMgSQipJ4hc6nOjJ0zSy6/0ayZPFqkTKKmuA2+6azGb2Ek1b1Zm9G40ysu/lY1zanSurMrNzq/6DAUCAczPz9sS8jutk32ULRV6wmBhISGScLNjN1m1l32eyUoAXq93xb8D8gIXgKJc78TSkbcjb845Dh48aFhn9zcy0UavnbxuVuekrV6dbHKWSj7Vbcxmfaxmj1JpozejaOZHSyzVD4OdWVfRTi5jLBYps7CwEF6vV0vqulpWUVFhe5trVZuLc65FxwkGg7qDGbuDolS2kTUe9DpgmV4K4YWRiaVYv9buAyI15EEGkT6vv/56Uhlj5MsmH6F7P79xuVyav1kiv5DfaV6vPU/hhDn0ncgsnHPNMkIkERjObhLt/X6/br1QaJBd2oiInXrjRVVL2CjInJGlz44dO5byFC4PriE35Hn7ZohGo/jZz35mWJ/OgDTVbVUhjJ06O3k77eQZl2zn9QQEVkIEO4IGszbyrK8T0xvqbNuH85gfNZ/Ph/n5efh8Pu0Do35w1GRUJ2ve6T1XQqAlPkIi0IOVGY6dNnaT2A8roW+mliJZmVzaFejL51LvPMsCPCG8U5dmdam2KSgosPUuzeTkhoz87kplabeteD/J50LkrwWNB6eIjrJ8rwOJGg5q0ivPhqYBALzwwgt47LHH8JWvfAVNTU0Z/e18R5iQGw1OhEZjuu/ia037MRNEo1HDwSXnPGsTQXSNMoOY6LuWNUsJwgmMMa0/lY9Eo1EEg0FdP9PquMjv92N2dhZzc3O53u38YgkDFeQDeXuobrcbX/jCF3K9GwSx5ESjUczPz2uCL3mpV6YuA4GA7u8K4VZZWRlKS0tRWlqKkpKSBPVoMdtSWVmphYhm7E1/TEJIJpI8EBCDgHSXwmREnTFaSsx8lsj1dqLOCpNes8i1LpcLBQUFCarscuRZPVX3wsJC04ACIgmBoizkMFqK/OLiomGdKiyxg53r5+Qay9q1VksnbfWWegJS+ZzYwezY5EmFbEzImOU550nHlO6zJh+PPKuvZ5aqpx2tlhkxOTmJSCSC+vr6pDrxfB4+fDjJ5N7v92Nubg6PP/44rr/+ejQ2NoJznmSGYpXcbrdjDVsnpop6SWhop/KsCEGKmQmNy+XS9tFKG9iutrDedXGCiNxt9A5Uy+Troqcln0reqp0sDDO7Z82QB5iqSZN4j9vV/HdqLZDKM68+M+o+632rxHZqMiq3m4y2F/epOrElv8/VayAfn941MpoAEsI0+bxabaP+p9n/GqF3/cS1sfKv5cQXl1wGwPI9oFdut8zO+8XoXaqWZwO9b5ydpFosOG2bahkJeFPD5XJp4yIiRcj8kyAIAec8aaZCLwKmWXRMdV0IvcSHTu1EuVwuTeglBGDqsqamJmFdmJWKDr6RIE5en56exsDAAHw+X4JDdnm/GGMoLi7W/qukpEQzZ9Ub1BQWFqKoqAgVFRWG9VbLXDg3TgXOk/1riI5gKuWik68KGVXVdrncKi+Wep1LuTNupAVnF1VDxWxpV6CSTp3RcRoJPp2uW7XVc0xstJS1RfR8ncm/I4Svar0Y6Oj5ZZHz4l6Qy+VrLgtvjH5DzyxfHgRlcikGh0ePHkU4HIbb7dYGDAMDAxgfH0dJSQn+6I/+CHV1dQmD+oKCArznPe9JEiDt27cPP/zhD/Gnf/qn2L59uzbokO8p+f/lY5SfYc65JsTR87Ml6lRNb1mTRRWWGbk/kIOHRKNR3QkJvfeGmV8fkYLBIGZnZ3XfS3bfB2YCIac+cNTnQ36O1WffaBBtNHhVr5d8TcTzI19jcW3kMr1rJb+HzLQB9Z4fo2UgENCCDolyJ9dEPm+yGaCZNrIdTWX5+qjfD3kpnxORN3sP6jls17suekv5OqjXTj4PYimeX/nbK18bO8JiO4IfO+3Fe199BqyE+mZtxLdCTmbfTfVZks+P3vtQCOfVb5yZxqr6bZb3QV432jd5H9XzqE74ineSPLGoPhN6ebt18r6qfRw9yxwA2r2sTnCJ3xTvDdHX0xMAp1pmJtRNVajuFFUIb/U90CvXE9yXlpaioqIiIXk8+an5ds2yPIZ0GSGvhWpGGjd2sHqJpFLnZFu1k2FVlk69k5m7TLXLZEfDzjbybKPcQdVb6pVZXVeja8kY0/yhqVEv9darq6t12xQVFcHlikUXC4VCWFhYSBBymaWJiQlNA83o4yc+LrIWmpyvqKhAc3Ozbl1paWnefoRkYYC8jETedJorlkZ5q3q7eTE7KXcU5ZnbaxV5MGklJBFCHDvmf+m003tO1H3UE3Zmok4VkqgDZb0BtNng2s4AXPyvnlaBVd5O24KCAhQVFeluZ1eYmsqSMYZwOIzJyUnMzc0lTFB89atfxauvvoq7774b73znO8EYSxJgiPzCwoKWb2howIc//GHMzMzgl7/8ZdJ9avRNcrIu5/XuQ1mIKk8omE022JnAkOvk65hKcuJaQXzX7PrFMar3+/26z41Z3m479VlRr4d4n1sJM+yksrIyQ2GH0fNnZ5mKuwvx3rPSVLaqE0Jd8VuBQMC2gDDVJaCvFWSl8ZNqvXr/p+qewkk958l+pdR1PeGsWia/4/TqsyEQVIWDZmTjuyTyQsBnV6NbXU+1Trz3xPtMuERxMuEpkvrMyv9lR/DFOU+YdBPfFqfvGDUv36tG32k7ZfKxmAlJ1d/R+7YIwWMwGMTCwgJmZ2e1NDMzk3A+5QlVVfgmUmVlJSorK9HU1ISWlhaUlZWZnmvCAdeY+SdzKmxYKrZt28bXr1+f1m9YmdykUudkW3Vgb1WWTr3RoNLu4NNpu3Q7FalsY2cmVW0jtAHEC1iOxml36aSt2cfP6/UmCLKcpmwHOeA8ppVnZV6qpwEXDAaTZspUIXAq+y6uu9oR1tNg0Mtb1TvJi46l1cyfFfLspNzREB0itbMoBssiAIZe3mrdqm0qgzWCyCVzc3NpRzrmnOPixYtYs2ZNBveMIAiCIJYWdUIvVWG2qoVrNlnqtM5q4nVxcRELCwvw+/2apq48dlD73l6vF8XFxSgpKTFcVldXo7a2FqWlpSgqKkI4HE4SxE1PT2N4eBiDg4OYn5/XxnK1tbVoaWlBa2srWltbtXxTU5Nh34Mx9jrnvCerF3uZ0NPBeO9nrNuxP8GKOGd5Kz90uVy4++67U9o21QGvmRqw3TaprlsJAjLR1kjjzCivp45tVmaVl2eWRbJLKteU85i5gRyR02iplgkNALM2ap3H41ky4UQ4HIbP58Pc3Jzu0kwQ5vf7DX9XCP6MzE6rq6vR3t6epO0mNCTkWV8S1DhD7hCJpZjNFIJbkYzW5+bmbLeV16PRpQ+vnm/Igk71fSMmEuzMpqsaXqlov9jdxuVy2eooZ6JONS2xyuuVpXNtZEGz3gSKnUmWwsJCXLlyBbOzsyguLkYgEMDXvvY1rFmzBp/73Oewfft2w/f9UkxqiG+liJasOkI2c5JsthSmg0JTNJOTqXqTJpzHTH2cfD/16oy0iuyWFRSk5k8oEonomtWmmtfTHDLSRpLL5T5SutfN7F1kVi5PvshJnfCxSpnoH+n1JVWtRbtlqqaQkWaR03XZh2OqWqJWKd1rIW+Xb999cY31vj/p5I3qVBcL8nl2sm63bTrvJStcLtc1FYmXc65paQohnJz3+/2Yn5/H2NgYjh8/jpGREYyMjGBhYUH7Da/Xi4aGBjQ2NqKrqwu33HILGhsb0dTUhIaGBgQCAQwODmJgYACDg4M4duwYBgYGMDw8rL2fCwoK0NzcjNbWVjz00EO5Oh35yTWmqZa3h8oY03VA7GT7TGyn9ztO1IqdrFuZrGWirZyXTamc5FPdTq9zkG8f9GzAeSzqppEATG8p8n6/X/ccud1ulJeXo6ysDOXl5Qn50tJStLS0GJqDFhUVXRPnfblxrXWIlhOyCZUd4ZOeuZXRANrn8zk29ZFNeqzMyVKt83q9CWWy0CKVfLrve6FxbNdcTa8sGAxqM9ETExMYGxtDNBrFpUuX8J3vfAenT5821EQOBoNJmsh2zHKcIL6VYvZdDh6jtywpKUFtba1pGzHhI87/Ur37hYmgUw3w+fl5TE5OJmg/y0kW1topM8Po+on7XU9ooQow9Oq8Xi/Ky8tNhVZqmV4b2SdTOggTQzuCPLVMFRKK95U8MaOX9Nqko70utjETXDgpUwMpiLxsUq3WWa0LQbAw69SbSLZKegJBNanndm5uzvCcW6VMIWvd65XLdXrXXX4WUzEBNcvrrRcVFcHtdmvPhjivZiboVut22oqkh9H5EeVOhNvyvann680omdXb3VbuL5hNgskWJ6nAGNOOt6qqKqXfWFxcxOjoqCZwGxkZwenTp7W8z+fT2no8HjQ2NmLVqlW47bbb0NnZidWrV6Ompgajo6MYGBhAZWVlWsdELG/yVqjmcrnwwAMP5Ho3iGWG3JExGgAHg0Fthl9oBBitW7URnRK1Q6Gul5SUaEIvdVleXq7Z8asCMhF9c6UjrpuqRRUMBpM0Gs2E1E4E2mZ1dmYvMzXgIfIfxsh/Xq5xuVyaVlM6vOtd79Lyfr8fzz//PN75znem3cEnEhHPS2lpaa535ZqHsTcDSRQXF+d6d64J6JtBZBIh/LMrwJbLjfyY6fnak62RjJJZvSrAN5vokv0m2j0HQEy7rKqqKilVV1cnlZWWlpr+flFRETo6OtDR0WH5/8FgECMjI7hy5QouXbqEX/3qV7h06RJGRkYQjUbhcrnwZ3/2Z7aO5ZrhGtNUy1ufajt27OAf+chHksozZdqZ6rZOzDvTbWtlummnndNtVPNOPTPQTLUR/2k0awUYm2KZXU8rLQwxMBNaACKpZVbrwnH3ShKsiEhwRsnn8+lqcOitO52Nlk1NZJMr1X+aTDaeTdXExGz2URyfvH0qZjrycyCbRIn7zig5rZfNdMk8l1gpPPXUUzhz5gw+8YlPwOPJz6Aryw3ZBEvWBBNajF6vd8V9/5YLYgIqEolo300SChMEcS2wuLio+UHTS1NTU1pe1jQDYn3t8vJyrF69Gp2dnVizZg06OzvR0dGRtqWIGB8UFRWtCP9gmaCnk/Hev7Buxx4ln2pZhTGGzs5Ow7pUfzOd/TH6nWxqzhiZblrVpbqNnpqvnulnum2W0gxlpcM5T4qAo6a5ubmkdb2ITR6PxzBCTltbG8rKylBUVGTpAD8ffXUsBzjnCeZRIgkNSaMkosNatZdNquxE7LKzv2a+jTweT4LppJG5hNgfp+ZA2b7HVGGpwOVyJQjaU1lamRWJskyayYtrofoE0vMNFAgEEnyTiKVZ2eLiYsJ1yfQ1Er934cIFjI+Pa+UiMuhnP/tZvPvd70ZTU5NlpGZ1Xc+v11L6ydQjGo1iYWHBNEiMWdni4iKA1Po+QrtJNeEVzqQDgQBCoZDt3zZ7HsS1EM6l1bzeerZ93BkRjUY1Z9pmvnz06sV5UyehVGfcMnqTU8J80e12J2l9pIIwo1IdfOs5/bYqywf3EkLoqH4P5e+iWmbm/86uab5Tv5Hi+ym//1NZCj/AepNp6vO2HKwfhLaTaiZuZT4u+hGp+n02q+ecw+Px2PLRbLW8VlzfZAvxzWhsbExp+9nZWVy6dAkXL17EiRMn8PTTT+PKlSta1NCGhoYEgVtnZyeampos+wLCmoWQYACKcr0TS0feaqr19PTw3t7eXO8GQWSNUCikK/AyKpudndUNMsAYQ0lJCcrLy1FZWWkoFJNTWVkZvfyJtJGdCqt+jcRAQ5jBGDn2lc1plwti0KYOzJwszRxjy2WRSCSjjuVVf0JGQj2v12saUUtPCJKrAfWnP/1p/PSnP8WnPvUp3HXXXQkO/EWyWjfzo+YEj8ejRTNLF8ZYQgRoNXCMVVm+DKBF8AU9gYZ8rWQhrZEgV75eKtkS4sowxhLueSPhkt6zIgs25IF2rr/FQtCuXgengkNxbYDsTHqI37TSgpdNxfWESnp5WYvbTpAYPV94qWhuypMcqSxlSwE7wkMRpd7uuTbDSkvfyneaGbJ/QqOgJnqBTeRJfHUiPx0f0QC0ySbVOsPp0q7wVT6XZv7J0s3L63I/TE/pw0gRxKze5XLZ8iWZD5rPnHOMjo7i4sWLuHjxIi5cuICLFy9ieHhYE6x2dXVhy5Yt2LJlCzZs2ICiokSpEaPonxo9axnv/e/W7djvrAxNNRKqEYSEHHnNKFnVy8nn82F2dlYbYMkaHKpWWHl5eZLwSy3Lh1ngTCE0HkSSB6FOTDntrMsakyIJQY6d8pVyzgmCyB5CwEzm1QRBEEQmkH2g6fkoy2Reds0j/79RmZ16vX032g8rl0AAUF5ejurq6oRUU1OTVFZWVpbxvnswGERfXx+OHz+OY8eO4dSpUwgEAigvL8fmzZvxO7/zO9i4ceOKEBBlgp51jPf+D+t27F3WQjXG2LcA3AdglHO+Wad+A4B/AbATwJ9zzv8uXt4O4N8ANALgAB7jnH/V4aHYIm/NPwFgfn5ey6cj/Et126Xy36RXZ+b3LJV6q22s/KFlcilrt+iZhWVjKQu1zGbSGGOWvtbkVF5ejvr6esP2IuBArmeinRCJRHT9FQhNOSMBolyn98yps8tutzvhnKnnyInptNW6uOdEEpokatIrF5pCmTBNlM+BGrVJNaU1KjNrq2dWRb6PiOWG+rwKjcZM4vP5UFZWltHfXI4RfDnn8Pv9mJ+fRyQSseXOwUjTg9w6ZI9QKISFhQVEIhHdSOo0+UMQKxOh7U9BL2Lfq7m5OUxNTSWkyclJnD9/HpOTk1qZ7E8tGo2ipaUF69ev19K6descB24pLCzUtNQeeeQRrXx2dhYnTpxI0lq75sms+ee/AvgaYgIyPSYB/CmAB5XyMIA/45wfYoyVA3idMfYLzvnJjO1ZnIw8oYyxuwF8FYAbwDc4519W6v8zgA8jdmBjAP6Qc37Z7DdDoRA+9KEPqf+Tzj5mZDun6+n8hh0faJnKp9qRdtLhVsvk0NZGQQUytbyWtAYikQhmZ2cNnXjKaWZmJsm8zO12o7KyMimijipAFH6J9ISQy0mImAuEzxC9AA9GgR/ksoWFhaRy1bxK1qo0MzeQBX3CV5ieYE7Ne71euFwu0yhRdgWXatKLZiWbvVgdh1qeicGm/PvCF45smqeXSkpKTOvy9b0ktEjn5+cxNzeHubk5+Hy+hKVRWTAYTNDITeXcq5qlYoJERVwTt9utmdyJcyvnS0tLcfHiRbjdbu2d9ud//ueoqqrCP/zDP2Dv3r3LIsBBOBxO8p1mN8lmeSriHLnd7rQnzlTEt94s+nVZWRnKysp0r2E+PycynHPtmohnQs4LU8n5+Xnd/MLCgqlfSRG5U/i1U5Ndc2P1PcYY086/SPI1kfNiXfZtt5wmbDjnmg9SPV+EcpLNJc18eumddyvzVCPEZJuRSX5hYaHle04tE/fMciYajZr2lez0nYxSNBpN8h1p5CNWz2es3bayD8/l8K3JVxhjmuXOqlWrbG/HOcfQ0BDOnj2LM2fOYP/+/Th//jwWFxfBGENbWxu6uro0gduaNWscTZBVVFRgz549qRzSyoYhJhnKAJzzlxhjq03qRwGMMsbuVcqHAAzF83OMsVMAWgFkXKiWtvknY8wN4AyAOwH0AzgI4BFZAsgYewuA1zjnC4yxPwZwO+f898x+l8w/iWuRaDQKn8+HmZkZzMzMYHZ2NmGp5oVQTMA5h8vlShCKmYWdrqiooNkvQkN2DqyaOuvlxf3mNBmZ3gqBu6yBp2r05csgLhQKaYNidUBmJ4lBtIzZcelpLMpaisKXl1mwCrsIn0Sq4ENPGKLmc6GpFYlEEoQTquBifn4eL7/8MoaGhjRhx4EDBxAOh1FeXo63vOUtpsLa4uLiJKGobIpv5lvGrE4WksnJSIDodrsTBBylpaVJQg+jlEvXAaFQKEkAKwti5+bmTAVOqvsEcT4A6AoaZIECAEPhvaoRqVcXiUTg9/u1fZ6fn08SFot14c9OfV5UYbqeAKS4uDgnwkM5GIaeMFAvL/u2E34zVWGdnBcCQTVAiMgXFhYmCQnNom7rrYsy4a9S/L+4PmLd6/Ua+iBUn289H17Z9IcnJpSMfG2K97r83TET1oql0ThP3LfiO253ktqozuVyGfods/Ippt438v4JDWArDX67mv5yYozpRjm2WnfaVvbhacenmhpIRw3UIgdskbVVVe3VbNblQz8sE0SjUVy9ehVnz57V0oULFxAMBuF2u9Hd3Y23ve1tuPXWW1FaWmr5e4x8qmn0dDPe+7+s27G34TKAcanoMc75Y0ntYkK1Z/TMP6U2fwnAJ8w/dbZ/CcBmzvms9Z45IxNCtT0A/pJz/vb4+ucAgHP+NwbtdwD4Guf8ZrPfJaEasZwQg9n5+XlTIZicF51zGcZYQsCByspKw7xY0qwXQaxsZM1Gsxl5t9ud5IhbrOcqWmK+8vWvfx133HEHurq6TNsJ7RZVm0VE1hRt5KXdvCwkkwf39E63jyzc1tP+UrUe9SKcW9WVlJQkaNMtB825fCIUCiUFB5Hz4t2lCmyM1s3aiHcd4YxMuGSJRCJJAi1ZsCUHFCCMEb6d9QK0qCkYDGqaqqrmql4+U3XpRBsGrLU5xSRSeXm5oYKAnLI1FgqHwzhx4gR+9atf4aWXXkI4HMaePXtw5513YteuXbpCdRKqvUnPBsZ7v2ndju21F6ggHaEaY6wMwIsAvsQ5/6H1XjknE0K19wC4m3P+4fj6HwC4kXP+JwbtvwZgmHP+1zp1HwXwUQDo6OjYdfmyqYUosYJRX95WM5R6dUZaN1ZLu2ZzQGJ0GzEDalcglg0nmrlEDD6NBjdqErPJZgNSszKn7QH9gAWZ0rASSURONJvhps4lQRAEQRAEQSQTiUQwNzdny5XN9PR0wniNc46mpiasXbsWa9euxZo1a7B27VrU1NSktU+BQAD79u3DL3/5S7z++usoLy/HW9/6Vtxxxx1Yu3atcHdEQrU4PRsZ7/1X63Zsd3aFaowxD4BnADzPOf+K9R6lxpKO6Bhj7wPQA+A2vfq4qt9jALB9+3b+6KOPpvQ/qQoKzZyrZ6LMrI3e0qzOSdts+RhygtF+CPT2x2yW0mrWUpTphVQvLy839BGlLvPF1CybcM4xOzuL8fFxTExMaElen52d1QRhZuejqKgoyR+Ommpra9He3g6Px+PIVEqvzGl7M3Mfu/6+xIyu0fay+YOVTxaz2T49cwi9UPLFxcUJZnh6pnkiraTosTLBYFDTIAoGg9o7US/JAVOMkggyIhJpeaWGHOHXTpCTYDCYZPZiZPpidi/Pz8/jq1/9Kj75yU86dkR8LRAKhTA9PY1AIGBoamZkhhaNRjXNLT3fW16vl56VDBCNRpM0VfTyABImbuwk0rTLDqn6UiMIIhnhA7WqqsrxtpxzDA8P4/z58zh//jyeeeYZLZABAJSVlSUJ3Nrb2y3fjV6vF7fffjtuv/12AMDU1BR+85vf4O///u9x/vx5fOpTn3K8rysahpyHxGSxF/I3AZzKpkANWELzT8bYHQD+J4Db4s7kTOnp6eHPPfdcOvuVse0yWWbWRm9pVme3LXHtEA6HMTU1pSsYE+uTk5OalhjwpuPPuro61NbWora2NiFfW1uLyspKlJSUkFlSDhH+VlThnN/vT/JPJCe5TpisqQJt4UjcKIqtnaAFwuxG+BAxG6Sb1QsBmeqrzMjnFRBz4i184QgBmF7Si1So1gPQzP1Ekv2Rqe9VYbZnloRjXaGpWlZWtmycR/v9ft13iZymp6d1/cPJEX6tApwIB85C21U2cdEzgRE+/WSBeTAYxNGjR+FyuTAyMgK3242HHnoI99xzD6qrq5NSSUnJsv5OykFpJicnDa/PzMxMwnYFBQWorq7WfPGpSXWQLieXy5XgY0z1uSWbxcqo/uD0hHJCo1v2+blcnhOZaDSK2dlZTE5OatdF5OUy9bmRJ1LEsyMLlEVeLgOQ5DvRKAktfHkiVqakpEQ7/2ZLoW2/HK+NIBAIaN/F2dnZhKXq/1I1/Vb92Aoy8S5Rr4uYNDN7dvTKS0tLV5TwVLaEkP2SCr9+Zkn0Sey2C4fDCRYHsjsFq7xRmfB/tpy/NyuJubk5XLhwARcuXNAEb5cuXYLX68UNN9yAm2++GTfccIPjSblIJIKCggLSVIvTcx3jvf/Huh3bbq2pxhh7HMDtAOoAjAD4CwAeAOCc/zNjrAlAL4AKAFEAPgCbAGwF8FsAx+LlAPB5zvmzzo/I4jgyIFQrQCxQwdsADCAWqOD3OecnpDY7ADyJmJnoWTu/Sz7ViGsZIRzT65TL+bm5uYTtxGCppqbGVEiWCZ8jnHMsLCzoBk+YnZ3F7Oys5u8hU0k41DXzi5PpMtn0U3bUauTM1Ul9QUFBgrNq2cH2UnaIhSDLLCiBVZ0YuBkNxo0G6kYRzlTH0fko0JWdfBsleeAmJ9n5uvgOFxcXJwjgKioqNDNhkYQWrlleXlcFhKoQRKwvLCwk7Y/YJ/ndoZeqqqryZoAdDofx05/+FO9973uxc+dOvO9970NNTQ2mpqaS0vz8fMK2es7mjZJaX1hYaEsLUk9zcmFhQTNhmZqaSjJrEWXCvEV2Li6EHdXV1YbXp6KiIucD7EgkYnofimdEPu7Z2VlNgKF3zLIATkQR14vCpyajeuEX1SgipFomonqqQirGGCorK1FTU4OamhrU1tZqebmssrIyb54bzjn8fr/2DRfRwcVSLZPfYQJxjWSNdbPgDCIvnh3Zb5PT5cLCgq6QTLzXxDEKhNVCeXk5KioqtKXs41BOctlSuW2Q/TnqvbfNymXhn9Fkvp7jezMNYZfLZeh3y2ipVxYOh5OEY3LwHj0tfXHN9IKRGPU5xLPutM7tdmtBL/SE0k7zQhtbdXmiBmQQ59woCrLZuvjmq65K1DIS6pmzsLCAgwcP4uWXX8bBgwcRCARw3XXX4eabb8bNN9+MhoYGy98g8883yaRQbTmQtlANABhj7wDwj4gFTv0W5/xLjLEvAujlnD/FGPslgC2IhzQFcIVzfr/Zb5JQjViucM41x7zCf5jQJjISjs3OJgYhcbvd2iBJr2Mu8un6ZQsGgxgdHcXIyAhGR0cxNTWlKySbm5tLMlVkjKGkpCTBZ5zqN06OTJSJJM6vWQQ3p+tmbYQgz8qJa6r5cDgMv9+f1Ln0+/2GZuxy50sWxFnlS0tLNUFNvgzk0kEMOIwiCooBL5Cs0Wtn3ePx6N7b2YqYK45HFkrLgmk5spi8bpQXSY7iaSYYKi4uzliHOxqNatpT6vvO5/M5MsV1uVwJzoplQYrIFxUVJf1/OBzWJg/279+P7u5uVFdXW+63mZDUaFArtBmtNCGNNCdLSkqSIjOr0Zoz6YhZROO0c3yLi4soLCw01VjVWxf7n+l3jdDOE8IdkQKBgG4UPjUZ1YdCIW3QbhQRUq+spKQka+9ToX1pFkWYMebI9FN2u5CtfZY1iuxEqRSBCvSEAXaXJSUlCQIykV/u2qjZQpjm6zm/N3KOL97HVhOJRkt5MlGvr3ItBwHhnGNxcVHXH7GdddkliSrIlPOZGPNnAj3BosDj8Rhqtov1mpoatLW1oa2tDa2trUl9gEwRjUZx4sQJvPLKK3jllVcwOjqKdevW4R3veAfe8pa3oKSkJGkbEqq9Sc9mxnuftG7HNpJQLauQUI1IB8656UBTBDEQnQdZ+CV/rIzqzEzRAGjaNvLMU2lpaYJATBaSlZeXZ6zj5/f7MTIykpSE8Mzn82n/5fF40NDQgMbGRjQ0NKC6utpQQLYSBDErAaFRoJpIWuVlbRA9X25CS0ovwqyYCZU1PFRtD6M6YS5mNcgyWi4sLCTsrzyLXVRUlOA/TvYjJyL1yedN9T9pth4MBpOEy0LgpXf+RJAS9fkRghGhQVpdXZ1RAdZSsLCwgNHR0YQ0Njam5aenp5M0daqqqnS1dMrKykyFT2pdNBrVnBWrghSxrpoccs5RWlqKuro6jIyM4Fe/+hUikQje8Y534HOf+xzq6+tRV1cHr9e71KcyK3DOMTU1heHhYd0k/MgIjQiPx2NbE6+oqAjBYNCW5qpY9/v9moadrM3kdru1e0JPi1rk81ErNR38fj/Gxsa0Z0bNT0xMJGl9CWRBn54GL4AkDRmzJLt+EAgXEOp7SizVd9dKRJjspuIUXX6Xq+/BVMZYXq836VqrAijhVmApJnzyFSGIUifUhJDJagxgVR+NRjW/tamkbAuwVypistnIH6vf78fk5CT6+/u1JFx0uN1utLS0aAI3OYn3ZTpwznH27Fk8++yz+PWvfw2Xy4W3ve1tuOeee7Bu3ToAJFST6dnCeO+PrNux9SRUyyo7duzg/+E//AfD+lT3O53jlbdVf8dsPd228qBPrywb9bK2jqzFY1SXbhu5Q6l2SszOnd4shyg3MouSQ6/LQi9V9dqsbqk6/YFAQFfbQ86Pj49rg0rGGIqKitDY2JiUhPCsrKwsrX0Kh8Oaj6WFhYWkD5/RUq8MsB84Q8yS2o3Q6XTpZLZVDZAhB8owK7OzTWFh4ZL73BKdUyFEUpcLCwuG2h6q5oe6HolEEjTrnC6Li4vzXqDLJVNovSTMDScnJzE1NZVgkgTEOoLCXEwewJaUlFgGUxFL1V9LJBLR1TYwSj6fTxvoj4+PJwgOS0pK0NDQgIaGBtTX12t5sV5VVZVXGgbieoyPj+Pw4cP40Ic+hEAggPb2djz00EMYHx/H+Ph4kq88YTZfX1+vTXYYpWxpwKjvfKMkJkfE+7O6uhpNTU1oampCc3Ozlm9qakJ1dXVeDO5kn5+yjz7VX588acU51zT5jJKsvVhaWprxY41Go/D5fIbPt57gXe7TFBUVJTw78rK+vh61tbU5fccJwbX6ntLLqwJsYTKtpyWmlxfrmRBoiwkmPb9oZks9v38ul0ubBLFKmdQa1TsmPb+ial52KSBrN+tFrC8qKtKdKBVBu4TbBeGKQeTVdTlvFsRLHIOVuwgj4fz8/HySoMwoqJOYWJMn1YRJsZHbBLuJMaZNEqSSrCbfAWhCOzlAj1XerP5aDxQTCoUwPDycIHATSbh7cLlc2LhxI/bs2YM9e/agsbEx5f/z+Xz4zW9+g2effRbnzp3DZz7zGdx5550rQkCUCXq2MN77lHU7toaEalll586d/Itf/KJpG7svDjvtUvktdRuz9XTbyuZJemWZrtfzMSVrEegtM9FmpRIMBi39o01MTGBhYSHhPBQWFhqafsoz/umoPkciEUxMTCTMnKuz6XIn1O12axoFpaWlSR96VU3baJlKFDI900296J2pLu20EYIikeSZzlTL5LzwoaLnD0UMYNRBitHgZaVH/dQb7OpplwkzKTP/eeq62+1O0N6QU1VVVUYHVZFIRNPsUYVv8oDDbCkERELI4nK5LCNoyqm0tFQTlNXW1qal8SD7Z9Ib+KmC29nZWfj9/iQTQrNUXl6eMFlgpkFz+vRprF271vSaiWswNjamBXQxCv4h+6BTjzuV50z19aT639JLmRLqCS0qWQtR9jVn1DcReRGhTd2/dF0TCITAX9UUUrUWRfL5fEn7Kn5H3Xez/q/aXg6kYJWyodnNOYfP59OOMxqNOhIQZKuPJUymhdBKFmDpCbVEPhAIpLU/4lkrLi5O8otmtbyWBA+yWwH1vTs3N4dgMKglESjITt5KYCQ763cS+Eh1V1BeXp63PlXTRVwbebI53byIfq73X4JU7/1Uv23ydka+ho2Sx+NJ0p6VUyr+QsPhMI4fP479+/dj3759GB0dRW1tLXbv3o09e/Zg69atKd1vwvWP1+tdEQKiTNCzjfFeGzEnWQsJ1bIKmX8SS4WsauxkaVandjiEcMxKQJYp04pIJILR0VEMDQ1hcHAQQ0NDGBoawsjICMbGxhIGhG63G7W1tdqsud5M+ko1+VhuCCGS3iBfHrSo5bImI/BmB0v4xFOjUxqtC9O9TCAGy0aDY7Vc+OKSTWrUwa7qA00kYQaqCmXN1sPhMGZnZ3Ud3KvmP5xzFBYWoq6uzlAjpa6ubkUMDMS7ZWBgAP39/RgYGNDyExMTWjsRSVC+f8zyqpmhmYmhuG+EEGhkZAR+v1/7b2HWLpu2C82Yrq4ujI+P42Mf+xi++93vYsuWLUt+DrNJKBTC+Pi4oanu2NiY5isSiJl9qxqIsraXlaZ9OBzGzMxMkibd3NxcwnPqcrlQW1urac81NjYmaNNVVFRcM4IOn8+nfZOHh4cxMTGREKBCCNTV81FWVqa5aBDO1O0mPU0fl8tlGuBCpGvx+7+4uIjx8XFMTU3pRtLWKxOCQtVflBWc8yTXCV6vN8mvop6vxeXmSmApEJM6ej4jjaITG51DvfKioiLNv6JsMk/XwhzRv3ISnCwUCiX5Z5XTzMxMksCwtrYW69evR1dXF7q6urB+/XpL08/x8XFNyHbkyBFwzrF58+aUtNnI/PNNerYz3vsL63asgYRqWWXXrl38n//5n5PKc232mY11szZG5pnZaiM7jlbNNK3KUtlG1vwxcrptZ2kXPU0Ct9ttqF5tpnptVmemGp8OoVAIIyMjWodcFZiJc+F2u9HQ0IDm5mY0NzejpaUFzc3NaGxsRH19va5zTeLaQhZqyY7xjbSLhNBOflelopkjb19cXGzLrKuysjKjAr1sEAgEMD4+rqvxKUwqZUGc1+tNErzV1tbqRqPL9nELQYzQTlXzo6OjmiDe5XKhsbERra2taG1t1RwFt7W1oaamJi8GFcFgUDvvwp/koUOHsG/fPkxOTmJ0dFQLDrNz506sX78+QaikmrhWVlbm/LhkYebg4GBCGh0d1d79Ho8nYYJEPaZcCXeFRrTw8zYyMpLg921mZkZr6/F4NLcFTU1NqK2tTdASFYKlfPIfJbSBxGSWnIaHhzE3N6fdQ6Wlpdq3uampCXV1dUnCk6UYoEciEc0c1ygJk0/5vV1QUKCrPalqkuSDoFQIWGSzY5Hkdfn+A2KCE3FdzMzAZT+eqZq0ir6w7DpBaPpaRQXWi9pcWFjoSHNPaIPl4lpFIpGEyLryUs2rwVSERq2eELO4uFg3QI+e1r7R+NBorKZG1FYjAav7wjnXBHFWUaTVstLS0rx3f5FPRKNRjI+P49y5czhz5gzOnDmDs2fPatYKHR0dmrCtq6sLq1ev1v0ehsNhnDhxAvv27cMrr7yCyclJ/N7v/R7e8573WI6fSKj2Jj3bGT/4a+t2rloSqmWVHTt28AcffFC3bilVV422zfS6URs9s02zOrtlRnVGZplWZenUqz4PnC6Xq+mokcmXnBc+04R/loKCAjQ2NmpCMllg1tDQkPNBhqypJ5Lws2W3LBwOw+VyJYQ4F/48nCQReXQ53hvEymdxcTFJ8CY0fGSTKZ/Pp+vEvKCgQBsUFRUVOTIzVn/P4/Ggrq5O8ycmNOvEsqGhYUUJ4n/2s5/hiSeewOc+9zl0dXVhfn4+SZtL1vRSZ8NFNEw7vm7EsrCwUPMXZBTJTQ3OIf+nEGa2tLQkpNbWVtTX16+ogZeISi0EbhMTEwmCBbFUJ9QKCgoSBG8iL/wNGiW9wBmcc8zNzelq0s7NzSXts9frRWVlJerr67VvsxCaNTc3o7y8fKlOX9YJhUJa/0QkdV1ENVeFDEIbyyiZ1QcCAd0IqAsLC9o7zUizqLq6WnvHiSSCY9TV1eWFADBTiKjYTnzNzc/P6wqR5GdCXrebB2LfOiO/yW63O0HrS29pJHgqLi7O64k2gZ4gzk7kZVEWjUZ1NYdVQZ1wqaJnXmkWtdWqLQBTtyipuFsRpp2qvz6rZXV1NRobG1Pqj0SjUfT396Ovr08TuJ0/fx6MMfT09ODWW2/F7t27dTVzfT4fnnjiCTz55JNYs2YNPvKRj2D79u26/0NCtTfZtZPxV1+2bldUSkK1rELmn0Q+Ij6ORg5XzZz9yuZJoiOvFxlQjbiVCyfGnHPMzMwYzl6LWV7h+0sIxIDY4E/1PaYnVBW+q8Q2okz8v/yxFp0KdfCj7rNYytqQQrAmbyuwyssdNr16tUyv3M65BhI7SGpES7OybDlMX0mIAbIQ2BoFp1hO5zEcDmsDJ7/f7yhIxnIYiOQrwm+KE1834juhBuwwyovIcYQzhKmQPFk1PT2t+RkyS7ImvXgnq47rheZspvzFXWtwznUn1Kwm3ESSI6GqkTHpnZYdVCsXp3kg1rdZSUL/fIDzWDAIWQAXDAaThFxWQjA7bUX/2SqAl5PAYNFoVNdPn95S5AOBAKanpzE8PJwwniotLTV0K2BH2SAYDKK3txcvvvgi9u/fj1AohJ07d+LWW2/FTTfdlBTc7dixY/jmN7+JU6dO4V3vehceeeQRVFRUaPUkVHuTnbsY/+2r1u/msqJo3pwzxlgPgFsAtADwAzgO4Bec8ynLbUmoRqSK3c6pXK7ayxvZ19tta1YWDodNBWCqo2+1I2DUaTZzslpWVmYoHMsXvyTRaBQjIyO4cuUKrly5gsuXL+PKlSu4evVqwvlRZ8bEx1XMGIkZ5bKyMstobEJgYaYZoJbJ66IDIa6nXrJTb2W+nGqdGjBBoL5fZRMNvesiaxmFQiFEIhHNv4rH40kQiIgBipj1Ex0H4cBdTaKDkYmoa7kmEAhgcHBQ8+clHMuLJLRY5HMqnFMHAoGETls0Gk3o7AntV1mDtqysTNc0UOTzLfplvuDz+XDu3DlcvXpVN9Lj4uKioS8VANo9L9/7JSUlSdpaIhlpm4h3XnNz81KfgrxHTKDIgWlEfmpqypYzabfbrb13nAxmCHtwzjE/P6+ZYdsdHAsfbCKthHc/QRCEjM/nS3InINLo6ChCoRDcbje6u7uxbds2bNu2DRs2bEBhYaHu74XDYbzxxht48cUX8eqrryIQCOC+++7Dww8/jOrqaq3d4uIifvSjH+Hxxx9HfX09PvzhD2P37t1wuVx5IyDKNTt6XPzF16wnCCsLgjk/Z4yxRwF8HMBFAK8DGAVQBKALwM2ICdf+C+f8iuFv5KtQbdu2bby9vT3l7ZfrLKLeINyqTSr/YXR+9FSMzdo5MacwivZit8zp9kIYYRVxqKioCIWFhStmULywsID+/v4koZn4uCwuLsLr9aKwsFDzqyV8PhQWFqK1tRUdHR1ob29He3s7GhsbNUFZOlFGCeeIwZRqEqy3FP5FwuGwNrsnBKCMMS2yqNvthtfrRUFBAUpLSzWhm2r2V19fj+rq6iV9LgKBgOb7RnWEPzQ0lOBzUY4IJpwSu1wu7dgYYygqKtK0GVSTEpEAJPltUdcXFxc1oW0wGNRMFlwuV4LQzuv1ori4GG1tbdrzI/LNzc0rUsgQCARw/vx5nD17VvNfMjg4iFAoBM45ysvLNU1Bn8+n3ZNerxdut1vT0FJTNBpN8ufn9/sRiUS0iRC/368JPoWQu7i4GF6vF7W1tWhpaUFHRwfOnz+Pxx57DI888gi+9KUvoaWlJdenbcmIRqO4evUqzp07h7Nnz+LcuXO4cOGCdi/7/X7tey8LlcW24rwCyVoooq6oqAgFBQWaoF8EnBBmskVFRWhoaEjQIJAFcDU1NSvm++uEUCiE/v5+XL58WfONJ3wAjo+Pa8KxUCiEgoKCBDNvua8FvKntLfJA7NkU78tgMAgA2vMn+k2ytnxdXV3SNVK1NK5FxHdJzwRZDTChBgWyC+exYAWybz2zJWmoWyO0ioW2sEhW64wxbYwgRzG1U0bXJD8JhULo6+vD0aNHceTIEZw+fRqhUAj19fXYunUrtm3bhl27diUIzQR+vx8/+clP8MQTT6CoqAjvfe97cffddyf0586ePYtvfetb2Lt3L+67776cC4jyhR09Lv7rXutxYw3z5/ycMcY+BuBbnHO/Qf12ALWc818Z/ka+CtVIU40gEuGcY3Z2Fv39/VoSgofh4WFt0B+NRrUBazgcxuLioiYwq62t1QRmHR0dWl5WXbazH7JjVivfQEbrql8nofmm5z9NLZPXhYBD7ejYSZkScqjmqmZmEWZmEqWlpRnTJohEIpicnNQcZctOs4eGhjTT3WAwCJfLpfl9crlcmmagbFYIxMw4ampqkq6H6gdHXWeMYWZmJimKptBIEoPxaDSasD0ATXgiHAyXl5drjvFlJ/lNTU1LYjLHOcf4+Lgm7BOO4wcGBjAyMpLwHAqfL7Iw2+v1oqSkJEHo1tbWpgk3raJULTXRaBSDg4O4ePEiLl68iAsXLuDixYvaoD8YDGrmV0ILUDiJXrNmDdatW4e1a9di7dq1aG1tzajwRFwLoWkrp6mpKfj9fgSDQVy+fBlDQ0MAYoPdzs5OdHd3axMIIq1atQptbW2GM9j5ysLCAi5fvoxLly5p1+jChQuYn5/XzE69Xi+i0SgWFhY0zb+6ujp0dnZizZo16OzsRF1dnWaKWlJSgqKiIsvrJb4Hwj+TSFNTU9r3SUSGFc+GPAAVWrmyNq4IsqP6Dm1sbFxW12ZxcRFXrlzBpUuXcPnyZe0aTU5OAogN9EpLS7V3WyQS0Qb4Ho9HE5TV1NRoUWyFoMvMR5zsisDIJYUQpgqtXcYYysrKtPe16DtEIhFtYqa4uDhB6KaaWS2na8M5x9TUVMI3UV5OT08naJaLAFTCjFGYrwmhjPADq5o5Gk1Uy+UiLwSo4pzL/yf6GOJ54Zxr30qxvbAaMBPG5WOQDyM45/D5fIY+++SyQCCgbSeum+hfCAGy3D9R/TKLpZgok61cxDWSLSdUKwUhvNZzA+LxeBKikYtJaqNEk9dLx+joqCZoO3DgAObm5nDTTTfhnnvuwY4dO5K+f4ODg/jud7+L559/Hlu3bsUHPvABbNu2LaENmX++ybaeAv58r/XETDObWRHnjIRqBJEHiIg18iBECM3GxsY0X25itloM1Ofn5zXtHKEhIwRlq1atQkdHBxobG20PZCORCEZGRhIEBmI/xEAAQII/Lz3fQHbK5c6n6mtFmOYZrev5XDAz/TRKTiLHmqH6nLBy4Gvk0HdhYSGhcyjOjZ6JrZxqamrQ1taWsjN5n8+nK3gTIcuFFpwIcS583KnXlDGWoNElrk84HE7SJJU1ZACgqqpK12y1tbUVdXV1KQtjotGo5nBcRDQVaW5uTjPbNorGlWoUvvn5ee05FoIeWQDu9/u1GXG3260JMgFoAoeCggLU19cnDKhFNEo9x/hi8GuGeG/oOfuemZnRBDP9/f2a4Lu6uhrFxcXaIEeYBZaWlqK7uxvd3d3o6urC2rVrUVtbm1ez9eFwGK+88gq+973v4dlnn0V/fz/cbjduvfVWTcjkcrk0zV3xnpU13uQommpEzaUQJAjhzMWLF3Hp0iXtGvl8Ps0Plbg+wjxQOPju7OxMSB0dHTk1AxTfOvFcCIH04OAgxsbGNEEt5zxBG1UIMITwwuVyadqIIlBPZWUlKioqUFFRoeUzHT13cXFRC2AhtMrk9enpaQBvavKVl5fD4/Fo10YWara2tmL16tXo6OjQNMMaGxuXXEMYiGljiPe+HFV8aGhI64PIZqXivhfCN/EciWPjnKOkpMTQHYbIC4G8mcN0o/eJeB9NT09rASXkwBJqmeyMPxwOa1qUQssyFAphYWEB4XBYEy66XK6kIAdygIO6ujpUV1dnVEgViUSShNTi2zU9Pa1FLB0bG0sIohIOh1FQUKD5ZRSTZLILDXEPqmM/NciHOC+yEEokO2XiGy80v0WS1+V8KBRKEkAKLVgh4BeCMMaYJsgKBAJYWFhI8LElT1KKYyopKUnaT6O8ui4ilNqZNJY1FYUwTrjxEBO+4hkRxyv6VaJfIHy/yscirpHwy5nO0uv16rotkZNVvZrEMcgBx/T8JKr1kUgkQXtdniBXy+Q6r9eLmpqajPvqC4VCePXVV/Gzn/0Mb7zxBpqbm3H33XfjrrvuQk1NTcI9dujQIXz7299GX18f3v/+9+N3f/d3xXtjRQiIMsG2ngL+bG+VZbs2NpE354wx9rcA/hoxf2rPAdgK4JOc8+9YbktCNYJIHWE+KTo8agdILz83N6fNcslCIiGAEiYeQsAiOkaNjY0J2mUdHR1oa2tz5KstGo1iYGBAM9M6c+YMrly5os3Gud1uTZAhtIBEvrq62nKwHA6HMTMzo/sBVT+meku5Q2i0vNbMhIQmiBqBTk4TExPo7+/XOpZutxstLS2aNpSsFZVJTThVSCUGI1YR3jweD8rLy9MeiEQiEfT39+PcuXM4f/68lmSfgEK7TQy05VReXq45+5Wjb8n5hYUFAG92buvr67F69WqsWrVKW7a3t6ekJTc3N6cJF2Th2+joqNaxFUJW0REXPuDkQB+qnz8Zdd3j8WiDFHlwIoTTonMsd/obGhqwYcMGbNiwARs3bkRnZ6ejaxcOh3HhwoUEc97BwUEMDg7aNpcSx1FYWKhpLqlRMNV3lOgUNzU1AQAOHTqE2tparFq1KuG3Z2ZmNDN52Wx+ZmZGEw7LWpdigCqEPEJbVnTwxSSHGgFULN1utzYQk5dicAm8qQkjBEwVFRUoKSmBx+OBz+eD3+/XNCDWr1+fkOTOvxOEo39Zm1ReDwQCCRMlRsv6+npH2s96+P1+DA0NJWiCiuXc3Jym1VNYWIiSkhJNOC3uZ+HYWpi8y/ezmQsMgbjf1IF+QUGBFmlPNnkVAgR5u5aWFqxevRqdnZ1YvXo1Vq9ejcbGxrwSOjslGAxqwjfhI1EksS7eWeJdIrvZEJpVQngaDocNNbpFUiefZM1wYWIstKdlv6PyvSCEFUDs2lRWViZEaZW1IpdzpFa/3590XcbGxjA+Pp7QD1Un78S5EudRvMNk7X9Zi1H2fymEI2qSzb9VzVQx+SYiIgcCgYRrLASa5eXl2uSh7B9Qju5bXV2N8vLyvH6u/H6/aR9ONiMWfQ4g8f0jXNqI740swFQDEIlnR476LZ6DQCCQMPmrBhET/2vkykd19SPay0JwORiUnNTfBaC9q8VS+AlWtQOBN10SBAIBTExMaBN/RUVFCWMWOaUTuXxgYADPPfccfv7zn2Nubg533HEHfvd3fxdtbW1am/n5eXz729/GD37wA3z+85/HHXfckTcColyztcfDn+m17o+sYqN5c84YY4c559sZYw8BuA/AfwbwEud8m8WmJFQjlg55plNd2i1zUicPFOUkazfZKRcDTTEAVWdnCgsLNW0RedAlD4pEh06eaTOL/inMkuwIsvTO89jYmCY0O3PmDM6dO6dptbW2tqKrq0sbgK1evdpQMLe4uKg5rdZL4+PjWqcYiA04KisrEz74spDFrKygoEAbnIiZRzkvlqrZqIrsL89sYGu2VAWeakQyo1k4WYCoChLNyrxeL8rLyy1TRUUFysvLNYGIEaFQCIODg0kmcQMDA5o2VGFhYYLvLyE8bWlpyRuH1pxzjIyMaGZTwoSqv79fm81tb2/H2rVrNTPDNWvWpNWJstqfsbGxhP25dOkS+vv7NZMcIbQRg2ihMZrOOV1cXEwy4VJNYKampuDz+Uy1JOUyYWYnfFvJy3RMuaampnDkyBEtDQ0NoaCgAOvWrUN7e7smAGttbUVLS4tjU5dAIIDh4WFNKCcLXCYnJ7VB+NTUFE6fPo1AIIAPf/jD+Ou//uuU/UMJ03s5yIJeXgyG1G+UmmeMaYNX8a4UAjoxSCksLNTVihEmm/X19Sl9GwYGBtDX16elixcvIhKJaCZQ6uBVXvd6vUnaGbKmo8iPjY1hdnYWQKKAX54Qam1tTfs9wznH9PS0dg+Mj49rEXHVpGrnyHm755HHffapPuEyGQhGvPOERuLExIQ2GSdPzAnhqzzg1MtzzlFYWJgQKbO0tBRVVVWaIEkIprMhTOL8zUjiqgBuamrK8vsq1tXvfnFxsfY9lL+NRmUVFRVZCRYltOWGh4c1zVErTXuxXlhYmPB9F5rSIi2137RoNAqfz5dwn6kTxUK73843Ri5zuVwoKytL0iQVefJHZo3QMjdyt2K0lPNiUk5PmCYLvuwuRV9Cz7+11bpRm2g0qqtNL39r1Hc5j/tvlbVoFxcXMTMzowmQvV4vdu7ciRtvvBE9PT2oqqpydP6DwSB+/vOf43vf+x7Gx8dx77334j3veQ8aGxsBxJ4fv9+PsrKyvBEQ5ZpMCtUYY3cD+CoAN4BvcM6/rNSvAvAtAPUAJgG8j3PeH6/rAPANAO0AOIB3cM4vGfzPcc75ZsbYNwA8yTl/jjF2ZFkL1bZt28Y7OjqW9D/tzF7mEqtrlUq9mVaDWmcW1dBsWzmvN1NhlQDofqAF6jWT26p+D1QhnOwHwegDo87e6PntEuulpaWoqanRNXXIZAdpYWEhQaPi8uXLuHjxIqamYhF/GxoaNMFZV1cX1q1bZ+iraXp6GmfPnk1I09PT2gBCz4G9XJZPflSEMEz4pdFbmtWp/lGsfIWZtbFrLuF2uxEIBAwHg3pJNp2sq6vDqlWrElJ7e7vl4G5xcREDAwMJAjeRhODN7XajqakpQXNRzArLAwGnKvjiOolOj+xvSDjtFu/jhoYGTTglhFWtra2WmmHhcBiDg4MJwTquXLmC8fHxhCh5evsmBs5Cy0le1tfXGx4vj/vpkQVu4r9FhOHa2toEP1Zr1qxBS0tLxs0Yso3f78fp06dx/PhxHD9+HH19fQgGg6iurtYc/27btg1NTU2m7z15IKdq+xYWFiZpJth5j0ajURw+fBj33HMPfD4fysvLcdttt2maRLW1tQm+3tauXZuSkMopsvaA/C0Spo6Z0sKNRqO4ePEiTpw4gePHj+PEiROYmpoCYwytra2ayW53d7djrUOnhMNhDA0N4erVq5rvuytXrqC/v197z5SUlCRofopUWVmZtf3KJdPT05pvQmHSe/XqVa0vIt55q1ev1rT+ZOGQMCm1i2zyLQbYwp+YEEwPDQ1hbm5O26aioiJJG1QI4PLN72M2CAaDhhEFh4eHEzS9ROTb8vJyU/+v6nowGEz6tgtNaT1BMBB7VvTM0EW+qqoqr8cx2UJoLglhoJr0yoULBrtjPxZ3yaCmwsLChIj3RslqMpRInUgkguHhYa0vKfpdAwMDiEQimtBN9IsnJycRDAbR1NSEG2+8ETfeeCO2bNliezzj9/vxs5/9DN///vcxPz+P+++/H+9617tQU1ND5p8SW3oK+Y96Gy3brWf9pueMMeYGcAbAnQD6ARwE8Ajn/KTU5vsAnuGcf5sx9lYAj3LO/yBe9wKAL3HOf8EYKwMQ5ZwvqP8Tb/tlAA8iZv55A4Cq+O/eaHUcJFSzgdGMpnruVDOBTKMKluyWGdUZCa9SSXa2lVWU1WUm6+S8UbQedT2fTArFwFx8FOQPhNCEKC4u1hxry8IGvcg1QEwI19fXp5l9nj17FmNjY2CMOTYfEgK9kZGRhAiJqtmcHDlRfjaMOhWquY0qvCoqKkrooAgtCjXlk5BPDzGQlh0MpwrnMUftckfi8uXLuHr1qtbpLy8vT7hPhDDHjoZQOBzG8PBwgsBteno6aRCgmh3a6aSKa1pRUYGOjo6EAXVzc7NtIVMoFMKpU6dw+PBhHDlyBGfOnNFMmWVH9EJjrLa2NsFkQm8/FxYWtMGTGuhhbGxMO96SkhJ0dXWhq6tL8ytm9uyIZ1sMqMVyYGBAm0Fua2vThG2rV6/W/EUtRRAGPUKhEM6cOaMJaE6ePImFhQUUFxdj48aNuO6667B582Z0dXUZCnIXFxdx5swZnDx5EqdOncKpU6c0rTq3250gMJBTMBhMCm4h/PsB0DS+1qxZg/Xr12PdunVYv369FhDh7NmzWLVqVcI7gXOOyclJnD9/PsFseGxsDMCbZiRyBGQRvTXfBJ+cc1y5ciVBeDY2NgaXy4XOzk5cd911WrK6L0dHRzE0NJR0vlVNSACaH0KjqLrV1dXas2zHDHR+fj7pHXbp0iXNVUJhYWHC9068K/LNf58gEAhoE12y8Ex8v6uqqpJ83bW1tTl+xoVGh5gEEpry6SI0M2WhmxC8DQ4OJjyDXq9X8wUn+4XL18Ar4XA4wW+sbJIuBM9A7PskayOqUVFz5UxeaIIKP35yfnR0VJsQFYjnUUz2yhYR8nqqvkOzgXB5YWYhMTExkTApJgJxyYJnM21/Oxr/KupkvEiBQACzs7OWZp2y3zeB6AOp2ntGKRNuM65FRN/r4sWLOHToEA4ePIj+/n4AsfdxNBrVfMV2d3fjxhtvxN69e9He3m752z6fD08//TR++MMf4tFHH8W9995LQrU4m3u8/MneZst2G9llK6HaHgB/yTl/e3z9cwDAOf8bqc0JAHdzzq+y2IM9wzmvYIxtAvAY53yv2T4wxlo454PxfE18+whjrBRAOed82Oo48laoRuafxEogEAho/qaEw1w1ifLJyUmtkyAGJfJAoqOjw9Kszefz4fTp0zhx4gROnjypaY8UFxdrmglCcFZXV5fUoRCDK1W7R2jaMBZzTi8CIMhO3VUH7yLZcZ6uIvtCEcnv9yc4ITbquIhIZuJ4AGi+2vSCJ6iBFITGiHC2Ly/N8n6/X9MGUAWJMnKEKTvnhfNYlC/ZbE72HWFlsjM7O5swWBUDPbGPra2tmvmk0N5J1Uwum3DOMTQ0hJMnT+LEiRM4fPgwhoeHUVhYiA0bNmD79u3Yvn071q9f77jTubi4iLm5Oe1esIvP50sws+7r68PU1BQ452hsbNQEbUJb1GogFolEMDAwoF2jS5cuYXh4GKOjo5q5l5hxVQevdXV1usELRNQ6mXA4nOTvUV6Oj4/j5MmTmJ2dRUFBAbq6urB582Zs3rwZGzduND1H0WgUp0+fxr59+7Bv3z709/dr75+NGzdi06ZN2LBhQ8ZMzRYXFzUBmZgwGBgY0Jxcr127NmHCoLm5WZtEOXfuHGZmZrBr1y7t9/x+f4J/O5GGh4e193NZWVmCwK2hoSFBKzlT5tOLi4uaaaMabXZubg6MMbS3t2Pz5s2a8Ky+vt7w96LRKC5duqQJNk+dOoWhoSFNO6qlpUXXmbxI4r0g/BDKzsbl5eTkpPYNEWagXq8X7e3tSVqn8Zl90/MQCARw9erVJI3WiYkJrY2IZioH9JDz1dXVaQmdhA9J9VmZmJjApUuXcOHCBU0wW1hYmDCBIQTkdgRMkUgEV69exdmzZzE4OJhkMinMzIGYULmsrCwpKqQesuZ+VVWV5j9MaOCKvFNNJ7/frwVp0EuyAK6wsFC7JiKKqXhPmSXRxu12Y3FxUZuwE/ebnNSyubk5jI6Oav0XI7+xej4ZrdDTNDNLcnAE+bj18uIdXlNTg/r6ei3iqxM45/D7/brRM9X1hYWFhD6L2+3WhG4iWqgaNVNvXc67XC7teshaePJS9e0mKC0tTbKMkFNNTc2yFy7xuM9UNSCFVZKjpgs8Hk+SAE5YERiZd1qZf8p52UelXmAwszLR103VGkzerrS0VBM+Gi2FVq2d/1pYWMDhw4dx8OBBHDx4EOPj43C73Whubta0GR955BE89NBDtgXppKn2Jtf1FPHv9bZZttvMzl8GMC4VPcY5f0ysMMbeg5jA7MPx9T8AcCPn/E+kNv8HwGuc868yxt4F4AcA6gDcAuDDAIIAOgH8EsBnOecJpiqMsWcB1AB4AbEABS9zzsNOjpeEasSKRDXzVE0+VWFJOkufz4eZmRnN4TbwpraVUAs3CqMtlzmJIDUzM4NTp07h5MmTOHnyJM6ePYtQKITS0lJNe2TTpk1Yv3697ocgEong/PnzmnbPiRMnEAwG4XK50NDQkKDZIzQ2lnOYb+GrzSxqk5yPRCKGEYiM8iKYggg2kclZ32AwmDC4lmfXxeAaiDnR7+jo0JzKd3d3WwpAhoaGcO7cOU1r59y5c5pGSl1dnTbwkM2AmpqasqY5FY1GcfnyZe3+PnXqFAYHB8FYzPG3uL+3b9+uOaA3Yn5+HidPntRMFM+fP685qxYIk+6ysjLt+guEKWhtba2WxD50d3cb+ugRwmlZ2Hbu3DnNafrq1as1YdvatWvR0dFh+3wKHz7q4HViYiLJvFnk5WMWA0uzTmltbS02btxoy/xuenoaBw4cwL59+3Do0CGEQiFs2LABe/bswZ49exIc+hoxMzODCxcuYG5uTntHyw6vhf88YYps9/7z+/04f/48zp49qwndhGkxEAtkMD4+jltuuQXf/va30draauu5nZubSxC4jY2NJWh0qQNFERxC/K+8VMsWFxc1VwterzdBmC4nK8G33+/H8ePH8cYbb+Dw4cO4dOkSXC4XVq9ejY0bN2rJyjw3UywuLpoKxoqKirB69WqsWbNGS6tXr7YtoAyHw5iYmEiIyilH55yamtLMTa2OV69fLDQq1WelpqZG0zaza0IsAgbJLhcuXLigORBvb2/H+vXr0dbWlhR1Ml1NbOHjTNbAlTVxRfRSINZ/Uc3fRb6xsdGxUCMQCCQI4MQElEjifaUm+T1WXFycoB2pl9S6+vp6x/2XhYUFQ+G6eD5ln2iqPzS9VFBQoDnnl4/VKL+wsIDJyUmMjo5q32RBdXW1ocCpoaEBFRUVaT3XctCS6elpTStL1dQyW49EIloUYnniVV6mMumqIrTaVA1bOciK0LAHUvOnKNATDIl+gpWGmXhvZCOwghDwysI3YUWgF9FTL29WL4Ik6PWBrcoyJfwUGoxGk4FiOTAwoD2nhYWFWL16teZvd+3atZbflYmJCfzsZz/D008/jenpaVRUVGB4eBg7d+7Ehz70IWzdutV0P0mo9iabeor5d3s7LdvtZKesNNXsCNVaAHwNMcHZSwDeDWAzgDsAfBPADgBXADwB4FnO+Td1/qcIwO0A7gFwc7z9cwCe45xfsTqOvBWqbd++ne/ZsyepPBf7a/SfTspT/Q0932RmZem2t5sy9THI5G/JiIGjXoh2OYJOusvCwkKUlZWhsrIyI0In0eGVo+PJ+WAwiGg0ioqKCmzatElL69atM+xs+3w+HDt2DEeOHMHhw4dx+fJluFwurFu3Dtu2bcP27duxadOmZS00I2IDNWEKevr0aZw6dQp9fX1alMB169Zh48aNWgTH2tpa09/jnGNiYiLh/hNpeHg4we+acAItIrSKJK97PB6t06dGxlUFWatWrcKmTZs0zSY7A/9wOIzDhw/jxRdfxL59+zA/P4/S0lJs2rQJW7ZswebNm7Fu3TrHwsCFhQXNIb04H+LcLi4uwuPxoKurK0FQYeYANxwO4/Lly5rA7fz587hy5QpCoZAWSETuAK5duzZvotH5fD688cYbOHjwIHp7ezE1NYXKykpcf/312LNnD3bu3Gn4HolGozhz5gxee+019PX1aUI0AKisrERnZ6emESE7Lhbr0WhUM0UeGhrSBIUejwfNzc1obW1Fe3u7phVYV1eHYDCIL33pS/jc5z6XtF8+nw9vfetbceLECZSUlODOO+/EzMwMgJgmmjAlFSkdn2uhUEjTBNFzzyDn5SiidpmcnMThw4c1AdrExASKioqwZcsWbN++HTt27MCqVatM959zjsHBwQT3AKoW2vz8fELfgbFYZF010I7QbBNBO+zcv36/H5cuXdK0NIWmphBQNjQ0JAjc1qxZg4aGhrwxWVPx+XwJx3LhwgVcuXJFC3rU0tKi3VtdXV1Ys2aNYw1HPY1u2dG/x+NBTU0NSktLUzpPIiiInvBtdHRUewaF9pfqg62lpcXUB+VSwznH3NxcwvHIaXx8XLu/i4uLEwL5iNTU1JTz4+E8FqBDmH/qJfEuE1RUVCQEPpEnisT6UgdGMCIYDGJ8fDzBrFU2dx0fH08w/SwtLUVdXZ327jEKtpKtCKFONM5mZmYwNTWlmbYLhCsWvXep6qc5XwJJpYM8oSTWrfIAkqKcWhEIBDRNYuHqQXxXCgsLsXPnTuzZswc33nijrqsCv9+PX/3qV/jxj3+MkydPapFJP/CBD+CRRx7R3YaEam+ysaeY/1vvOst2N7DjaZt/Ku3LAJzmnLcxxnYD+O+c89vidX8AYDfn/GNW+8UY60RMwHY3gCbO+Q2m7fNVqLZr1y7+k5/8RLcuFy99o/90Up7qb8j1dsrSbW8nEdYI1XvZHEHkx8fHEwRmsoNgEZVL1Q5qbm42FXwFAgHN99GJEydw6tQpTbAgHIdv374dHR0dGbuG8/PzCZ1sEa7bSLNPLYtEIgnmo+qMr1E+153a5UYwGMT58+cTzL4mJyc18zEhDNqwYQPa2tocdVrC4TB8Pl9S1FZ1PRQKJczWyn5EUvHnEgqF8Prrr2tCtGAwiO3bt+O2227DTTfdtGSCqEAggLNnzyac25mZGTDG0NnZ6UgrSGiwiM6f6AiK90NVVZU20yo0RkTUwUw+E2Iw3d/fjzfeeAO9vb0YGRlBaWkpdu7cieuvvx67du0y9dPl8/k0DbaDBw8iEAigu7sbu3fvxsaNG9HZ2WnL35YVwWAQQ0NDGBgYwJUrV3DmzBk88cQTGBsbQzgcxszMDIqLi/GFL3wBDz/8cML9PTw8rDtImZubSzAnFUImHg8oILQ329ratNTS0pJVf44LCwtJ+zQ8HHPxUVVVpQnPtm/fjrq6OsPfiUajOHfuHHp7e3H06FGcO3dOE/jJgh5hnif8pJWVlaGkpCTh3SCEFKrvNZGfmJjA5cuXtQFkXV2dJigWpuZ6bghUOI9F3ZUFVBcuXMDo6KjWprS01NCxfjYG05FIBIODg0n7JIQZpaWlSULA9vZ2S60NoeXa39+vaSSLpWzuKvpiZgF0QqEQJicnk7ScPB4PamtrUVNTowlWmpqaNK10pyag4XAYo6OjSZMvg4ODGB0d1QQgwu+Vnqa+mq+oqIDb7U6ImqsXSVesB4NBjI6O6grMZM3RioqKBO07OdXW1jr2r6uavwrBz+LiYoK2rZ4GrjiOsrIyS1+x4nykAufJ0YvVCMYTExMJprpATLAotMlUDSQrbaVgMJgUFVgviqMwyxX7CcTuTzn4ghqQQfhFXUlEo1HMzs5aRvgWDvaFOSWQGPW3sLBQm9AUk5pGeY/HY6gZbnXvylqJsma86POJSVczjALOmY1bw+FwgkDO5XKhsrJSEzyK95r8buvo6NC9X/x+Pw4dOoR9+/bhwIEDmJmZwapVqzQt+66uroR9jEQi2LdvH77//e/jl7/8JcLhMLZt24ZPfOITuOmmm+T9JqFanI09Jfxferst2+1hh62EagWIBSp4G4ABxAIV/D7n/ITUpg7AJOc8yhj7EoAI5/wLLBbk4BCAOzjnY4yxfwHQyzn/J7N9YoxVAJBvHB/n3PTGzluhGpl/EuLFqXaonCTZ14heaHO7Sd1O9ushdwpUhKmC6sy5rq4uQXDmRAAQDAZ1hWeFhYXo6urSfOts3LgxZSfBnHNcvXoV586d04RmIgmBgTg+MXhpbGxEcXGxodmknsmk8LlhFP1Kzot12adEYWGhNnBShZB2fPUsJUJ1fXR0FOPj4wiFQpb3rxyBzcjUMB2i0Sj6+/s1YdDp06fR39+vmTMIf1QikuxSmYrpMT09jf379+OVV17B4cOHwTnHrl27cNttt2H37t2OfKEtBZFIBJcvX05wzi+EIM3NzQmapu3t7bYGcyLIgfC1JgaQY2Nj2jPBGENNTY0WhEVo1KrJ6/UiEAhoz/X4eMydBeccXq9XM7Xcvn07enp6tLDxRkxPT+Pll1/Giy++iGPHjqG0tBQ33HADbrrpJvT09GTl/rXixz/+MT74wQ+itbUVt912G6LRKK5evar5lRKC5A0bNhiayqvMz88nmGHLpthisCO0N2XH2OqypKQEi4uLugNNkZ+dndXumeLi4iTtOavnkXOO8+fPo7e3F6+//jpOnjwJzjnWr1+PXbt2Ydu2baaRoTMJtwgQUVpamiBwW7duHVpaWmwLOXw+n+ZIX3WwPzs7q2uyVVRUlGAqWFJSogkD5GshoscBiQO91tZWTaNU+E8z01KVkf3biXfE8PAwWNy/XUdHR5Lw1qnPLyMCgYA2YBcCFTlCqzABZYyhubkZ7e3tSYE7UnmeA4GAZoqn51dWXp+dndWCt8hBqNRgVCLJgQVkgVmqQQW45NhcjtI6NDSkCekYYygqKkrwE9fY2KiZm+pp3KpljDH4fD5dX7Gyz9iZmRnN7JRzDpfLhbq6uqQACiJfWVmZ9r2ysLAAn8/n2IeW0AISPkqFYF5OojwT5p9ATEAi/I+qZp9yXi9YgFOMLGyEvy+7KRuCQaEVLYRbZvlQKGSoGW5WJq8XFBQk+AAUKVPX1YpoNKr5pJ6YmEh4r01OTmJsbAyXL19GJBJBZWVlQvCetWvXJgiqeTzwz/79+7Fv3z709fWhsrISDz74IO69996EcVokEsELL7yAf/qnf8LRo0fh8Xjwvve9Dx/96EfR0NBAQrU4G3pK+Td7N1q228tetzxnjLF3APhHAG4A3+Kcf4kx9kXEBGRPxU1E/wYAR8z882Oc80B82zsB/D0ABuB1AB81EpAxxv4DgP8KYDH+WwDAOedrrI4jb4Vq+RT9U8bMFt/oXMqzCNnGaPYiFb8B8nbqcadyPKmYeup1ppwmeRZXDW9uN6nbFRcXa53xbHw8eNxv0tWrVzXh2cmTJ+Hz+TRzM1l4lo5j+XA4jL6+Ps10qK+vD5FIBB0dHZpjbzml66tDPU4gde1ToaWiZyo7OTmp/X5JSYlh57OhoSElDZNgMJj0ARdpfHwcIyMjGB8f1zrCQMysTMy0FhYWGg4QhIPY2dlZ7ZhEZ5AxliSU3bBhA7q6ujKqrbSwsKD5oxJRY8UAv6ysTPP5I98bmfC1JgYzAwMDOHToEF599VVcvnwZVVVV2L17N26++WZs3749Z9Ew04VzjuHhYc0f4smTJ3HlyhVNk0cI2jZu3Ig1a9Y4vqYiitXi4iKCwWBSEoOfYDComU42Nzc71tKYmJjAb3/7W7zwwgs4deoUqqqqsHfvXtx6663YsmVLXkRUFhMzevsyNTWF06dPa+nMmTPaYHDdunUJQV1aWloca+6IiYDZ2Vnd5fz8vPYdUQeaIpWXl6OhocHWuQwEAjh58iSOHDmCI0eO4OzZs4hGo1i7di127dqFnp4ebNiwIW81PHw+n6aZKXw8Dg4Oan5x1qxZo2m3rVu3zlD7wC6cc92AC0IYIF+PdP5nZmYm4R3a19eH6enpBP924nlvbGzMq4mgSCSCkZERXLlyRRO4Cd9iwodsYWFhgrBNaG02NzfnRJBuF+Fqo7+/P0FwdvnyZU1oVl1dnRChVWgI54OrjEgkgomJCQwPD2NkZCRBM29kZARTU1MAYv2FgoICNDQ0JPR9RHCb2tpaR/58l5JIJILx8XFDc13hT1ZMAjY3N6O+vj7J7FPOZ+ueFJOmaiAyYfKpVy6CDwFvjquEYE71waznkzkd7cVcEYlETK1X1DqXy5UktFPX7d67U1NTWqCrEydO4MKFC4hEIqiurk4QtnV2dmrndWxsDD/+8Y/x05/+FAUFBXjggQfwzne+M2HyxOfz4cknn8TXv/51/N7v/R4+9alPkVAtTndPGf/nXnMfdADwVrYvb84ZY+wsgD2c83HLxuq2+SpUI001YqUQDAa12WB5ZlhdFx9Y0akuKytDa2trgvAsXZM2zjlOnTqFl156CW+88QauXLmCgoICdHd3a6ZD3d3dhh8pv9+fFIlOJHUGUG9wYPd9U1paquujp6amBnV1dVizZo0jzaT5+Xmt4yl3QIXJRigUShIg6wnNVTV7WdVcVj2vra1FY2MjamtrM97piUajmJiYSAhccPLkSZw5cwbRaBRr1qzBjh07sHPnTlx33XVZ8b8xOzuLc+fOaf6tRBLnEnhzFt/r9Rouw+Gw1lmWA33U1NSgubkZ27dvx0033ZRRk2URCOHYsWM4duwYhoaGsLi4qDmQDgQCmjCmpKREGyjKg8Zs+dUZGxtLCEBy8eJFbYZVDL6tfChmg1AohDNnzuDo0aN49dVXcfbsWdTU1OCWW27Bbbfdho0bN+aVMCAdAoEAzp8/j9OnT2tmlkNDQwBiwnlVWywXgpDR0VFNeHbkyBFMTEygsLAQmzZtwrZt27Bt2zasX79+2Q24jAgEArh48WJCQBWhfeB2u7VIm3Igh2yb48rMz89rprlCeCZMUysqKjRNX6H1W11dvST7tRQsLi5qptdXr15N0BCU3+liIkhOtbW1mvZmKi4AzPZJmITLk20DAwMJmvZVVVVobW3F6tWrNcHZqlWr8kJolklCoZBmoir3fYT559TUVIKPMsYYqqqqEvyu1dTUwOv1JkT9NErC/FikSCSiq5moLlXfqm63G/X19UlmuiLlY5TydDASzKmanKJsbm5OCyQgcLlcmp9nIYwTzxZjb0b6VPNmdSL6p5nWmwiwodePlvNut9vUekVdCjc6cpLNTf1+f0J0UUF9fb2m4Sv6bUbavpOTk5qg7cSJEzhz5gza2trw4IMP4s4779TeB5OTk3j66afx1FNPIRqN4r777sMDDzyQ4G4hPomXNwKiXNPVU87/qXe7Zbu72Mt5c84YY88BeBfnfMGysbotCdWIfEM2+5SDI8iRPFNJTrbXU21XkxiAiyRe7OIYBEL4ovowkVM2nY9OTEzgl7/8JX7+85/j6tWr2LRpE26//Xbs3LkT7e3tCR+YaDSK48ePY//+/bh69SoGBgY0X0JAzARJDFjUAUymTIhEx0L10SPWhW8d0QFrbm7WzIXWr1+PtWvXLovOlvCdI7SKjKKd1dTUJPgUsYrmefHiRbzxxhs4dOgQTpw4gUAggJaWFk3Qtm3btiU5P9FoFMFgMElgJS9F6PJszv6Hw2G88soreOaZZ3D8+HEwxrB69Wps2bIFW7ZsQVtbW5KwT2gG+Xw+zcTv6tWr2nJ4eBjhcBherxfbtm3Drl27sHPnTscaTXaZnp5OMBM7d+4cQqEQOOdobGxM0KYQUQhTEShwzjE0NISjR4/i2LFjOHr0KCYmJjTB+5YtW7B7926sX78+o8e5sLCgBW64evWqZm4vL4VmnTCHk7Vi5GM9fvw4PB4Purut/Xg4RRaeiDQyMgLgTVN09f3Y0tLiyEdVKBTC2NhYUnTX4eFhnD9/HsFgEPX19di+fbsmQDPzobbSCYVCuHLlCi5evJgwyTM0NJQQTEVoJZeWlib4FtLzN+T1ejE/P69riicGtNPT05rQSAhbZeFZPgdRWGqi0SgmJyeT/K1NTExoGp2yQEVGaA6Gw2FNi8UMzjkKCwuTnkGxzKSm/UolGo1ieno6aQJYBMGwSmo7IaSTNa7UZWVlZd4ESljORKNR+Hy+BGGc3+9PGkuZjbPUvNAWlt+Ter7alsrk0845GB8f1/pucpqcnAQQE/CJCWgRrE3uR1y6dAk//vGP8Ytf/ALl5eV44IEH8I53vEOLiD47O4tnnnkGP/zhD+FyufC+970P99xzDzweD/lUk+jqKef/T6/1qbiHvZA354wxtgPAvwB4DYDmkJNz/qeW2+arUC1fzT+J7EXslFFnUMRsiWrr7yQ52d7j8WgDbLspX9Tng8EgDhw4gOeffx4HDx5EVVUV7rjjDtx1111QnykhRHvhhRfw29/+FnNzc9iyZQtuuukmrFq1Cq2traivr88LUy49hCmd0GA4e/Yszp07pzncbWlpQVdXF7q7u7UIa0up4QPEBDPC7OfMmTOa+Q8AzRRUqLMXFRVpqbi4GC6XSxMkCgfIYvDBOUdTU5MW2XLLli26fq+EoEQI2o4cOQKfz4fW1lbcdNNNuPnmm9Hd3Z0XnaFMITo8P/3pTzExMYGbb74Z9913H7Zv355xYdCxY8fw+uuv49ChQxgcHERhYSE2b96sCdoyqWWnIhy4iyiDwu+P8Nkn43a7UVVVhaKiIm0gq37/5Xtq69at2LJlS8YFNhMTE3jppZfw0ksvaSaKpaWlmhZPR0cHvF5vgtm9WIZCoQRfZkIzRhyr1+vFqVOncOXKFXzkIx/B3/3d3y2ZtpYcMEHVjpF9VAHJGrvyN7WgoAD19fUJPppEWqr31/T0tGaGefXqVU3Ir5fE/sqOxOV8VVVVzr8f4XBY87cka1moWheyxoXsPF4M/uVUWVm5IqLw5TuBQADz8/PaeyBfBu4EQSxfRMAeETX75MmTCIVCaGpq0oL+bNu2DRUVFRgdHcVTTz2Fn/70p+Cc495778X999+v9bdHRkbw3e9+F88++yw+/elP4+1vf3veCIhyzbqeSv6V3t2W7R5gP8+bc8YYOwDgZQDHAGj+ezjn37bcNl+FaqSpRhD2GBkZwb59+7Bv3z4cO3YMbrcbPT09ePvb347rr78+YVAZjUZx4sQJ/OY3v0kQot1+++3Yu3evNgsjIzTHxsfHTZMwm1Mx8sGnCmcZezOkuGz2WVtbq0ULdDI4E8IkWZh14cIFhEIheDwerFmzRhO2dXd3o7m5Oa3OunBy+sYbb+CNN97A0aNHEQgEUF5ergkMhJ8ms6iJTo/v+PHjOHbsGI4fP47R0VEwxrBp0ybs3bsXN998M2pra3W37+/vx6uvvopXXnkFfX19KCkpwQ033ICbb745Z47l0yEYDOL555/H448/jvn5eTzwwAO49957LR3sc84xPj6OS5cuaYlzrht1try8HM3NzabOyAOBAI4fP45Dhw7h0KFDuHTpErxeL3bt2oUbb7wRN9xwg21n5pkkHA5r2jUVFRUoKytbEmHH5OQkfvOb3+CFF17AmTNnUFtbi1tvvRW33noruru7Myr0WlxcxNatWzE8PAyXy4WbbroJnHO0tbVpWl1bt27Vfc9di0xNTaG3txcHDhzAyZMnNeFfZWWl5r+so6MDJSUlCQJ/OYXDYU3oLwT/8lKYlrlcLqxatQobNmxAd3c3uru7bQfouJaZnJxEX18fLl++nOC3U2hxy2Z7AjPful6vFzU1NbqmUUsVMXmlI/y1jY2NaX0jMVFstSwqKkJ9fT1qamro2SCIJWZ4eFgTtB0+fBjT09PYsmUL7r77buzduxfBYBDPPvssnnrqKUxPT+OOO+7AQw89hM7OTnDOEQqF4PV680ZAlGvW9VTyv+292bLdu9nP8uacMcbe4JzvSGlbEqoRxPIhHA7j+PHjePXVV7Fv3z6MjY2hoaEBN910E/bs2YPNmzfrDlIvXbqEf/mXf8HLL7+MHTt2aEI0eXAfCARw+vRpTUBz+vRphEIhzUdDXV2dYRLaVukghxSXTT/HxsY0/1Kcc3R2dmLbtm3Yvn07Nm/enFLUx2AwiAsXLmjCtjNnzmBwcBCcc1RWVqK9vV0TqMipvLw8ya/c8PCwFh2uvb0dO3fuxI4dO7Bly5acCKYikQhOnjyJ3/72t3jllVcwPj6O1atXY+/evdi7dy9Wr16tKzycn5/HgQMH8Oqrr+LgwYMIBALYtGmTps3W1NS05MdiBecc+/btw3e+8x2cO3cO99xzDx5++GE0NzcbbnPp0iX85Cc/wYsvvqg5pK6vr9f86qxatQoul8sw+mx/fz9mZmbgcrmwZs0aXHfdddi8eTN27NhheL39fj/eeOMNvPbaa3jttdcwMzODpqYm7N69G7t378Z1112XN5qu6RKJRNDb24vnnnsO+/btQ2VlJd7ylrfgLW95C7q6umwJroVJXzgc1g0+4/F4UF9fr/uu6+/vR1NTk3Y+OecYGBjQ/I8dPXoUs7OzKC4uxpYtW7Bjxw7s2LEjyRR+pbG4uIjDhw/jwIEDOHjwIMbGxlBVVYXrr78e119/PTZv3pwRYb8RkUgEV65cQV9fH06fPo2+vj4tEmt5eTm6uroSBG7LwYQ/U0QiEVy4cEELmtHX14ehoSFwzlFdXY3u7m50dnYm+fCsqqpy9N7gnGuRP2WTdpHm5uYAxCwFGhsb0dbWpvmrW7NmjaNorCuRcDiMK1euaAE1BgcHMT4+jrGxMczPzye8PyorK1FfX6/1jfRM6vTM7RYXFzE2NobJycmkIEeqRqi8zAeN0HxCaA5PTk5aRpr1+/2a8DmVoG4ej8fQB7C8rKysXDF+Lq8VOOc4evQonn/+ebz88stwu924/fbb8fa3vx2rVq3Cr3/9a/zoRz/ClStXsHfvXnzgAx/A6tWr80ZAlGuWqVDtvwG4BOBpJJp/TlpuS0I1gsg9wWAQo6OjSdGNRBK+WxhjuO6667Bnzx7s2bMHDQ0Nhr8ZCATw1FNP4bvf/S4qKyvxoQ99CLfccovWafD5fHjmmWfw1FNPYWpqCkVFRdiwYYNmStjd3Z135i3RaBSXLl3SBsjHjh2D3+9HRUUFtm/fjvvvvx+bNm1K6z+mp6cxMDCgRYQTSQhWvF5vgj85eQCfDpFIBJcuXdIcpAeDwSQHwG63G16vF2vXrsWmTZtsadwIDbqXX34Zv/3tb3Hp0iVUV1fjrW99K+6++260t7frbheNRnHq1Cm88sorePXVVzE8PKwJcG+++WZs2rQpJx3EaDSKgwcP4qmnnsL+/fuxe/duvPe97zW87pxzHDp0CD/5yU+wf/9+dHR04IEHHsDb3va2lASygnA4jIsXL+LkyZM4evQoDh06hMXFRaxbtw579uzB7t270dnZadhJHxoawmuvvYb9+/fj+PHjAIDNmzfjxhtvxO7du00Fg/nG8PAwnn/+eTz//POYmJjA9ddfj7vvvhs33nijYZRWYbotBNt9fX04f/48QqEQCgoKsGrVKhQWFiIajSYl8b4UWjq1tbXo6OhAZ2cnNm/ejM2bN1v6eBSmu0Kz9OrVq3C73diwYYMmaMt0RN2lIhwO4+TJkzh48CAOHjyIS5cuoaioCNu3b8cNN9yA66+/HvX19Y5+0+/3awKE8fFxcM61yQYRCbusrAwlJSWOB/ezs7M4c+ZMglBJmPytW7cuISJuLrQ8M4W450WQlGPHjmF0dBQFBQVYs2ZNglAxXc3pdIhGoxgdHcXVq1dx+fLlBCGScCDf0dGBNWvWYO3atVizZg06Ozsz5lc1l8zMzGjm9OfPn8f58+fR398PzjkKCgq0416zZg3a2tq0icVsHruwFlC1QOXl1NRUgkVARUVFkvCtsbFR82Gab307uwh/WXpm9iMjI5og0uPxoKmpCbW1tUm+29RImukGyQgGg0mTwXrLmZmZBEGpeIeKAFz19fXa/STy9fX1Ky5whhGhUMgwIIFwNSD7vjVyw5PN96bP58MLL7yA559/Hn19fVi9ejXe8Y534K1vfSveeOMN1NXVYfPmzXkjIMo1a3uq+Jd7b7Vs97vs6bw5Z4yxizrFnHO+xnJbEqoRROzjJoIUCOeqIm9VFg6HdT8ARmUi+Xw+7f89Ho8W6lwkOfS53cF/NBrFq6++iu9973s4ffo0HnjgAfz+7/++Fm3M7/fj2WefxZNPPon5+Xncd999eOihhxwPsPKN6elpHDp0CD/4wQ9w9uxZ3HXXXXj44YfR1taW610zZHZ2Fk899RSeeeYZzM3NweVyYfXq1diwYQPWr1+PoqKipPssHA5jcXERZ8+excmTJzE7O4uCggKsXbsW1113nTb4tBp4TkxM4Fe/+hWee+459Pf3Y+vWrbj77rtxyy23mHa2hanxK6+8ghMnTsDj8WDXrl24+eabceONN2ZNu2R+fh6/+MUv8PTTT+Pq1au4/vrr8c53vhM33HCD4QC+r68P3/72t3HgwAHs2rULDzzwAG688casCkk45zh37hz279+P/fv348KFCyguLsb111+PvXv34oYbbjA8v+FwGCdOnMD+/fvx2muvYWhoCJWVlbjhhhuwe/duU024pWZqagr79u3DSy+9hEOHDqGxsRFvf/vbcdddd5kK+kdGRvDLX/4Sv/jFLzA4OIi2tjZNiNDV1YW1a9c6HuxxzjE5OakN/oWm7cLCAioqKrB161Zs3boV27ZtQ3t7O4LBIL7xjW/gj//4j5PunVAohNOnT2uCtjNnzmjasULQtnnz5rwakIp7TgjQ+vr64HK5sGnTJk0LbdWqVZYDjfn5eRw5ckQzexkeHk4YpAuzNDHoc7lc8Pl8SZMPCwsLCdo3NTU1mhDCqcZTKBTCuXPntAAdwkTV7Xajs7MzISJuvn3D5ubmcOLECU14duHCBUSjUc1noUhW5ul6RCIRTE9PJ5mDTkxMYHZ2FkVFRYZBGEQSQpdUtJtCoRCuXr2KCxcuaMInOXiQuOadnZ0JUZPTmcTIBCI40OXLlxPS1atXNb+MlZWVWL16NdauXasJDFtbW5eVcJ1zjrm5uSTh28jICAYHBzE8PKxpaQOxSQkhbGtubkZLSwuam5tRU1OD8vLyrGtRh0IhjI+PY2RkRDMjF2lkZAQTExOaMIoxhvr6+oTgE2LZ2Ni47K7TwsICJiYmElypiIkLsZSj6DLGUF1drQncqqurEyY25AkOkRf+eTNJKBTSJppnZ2ctl7Ozs/D5fAnfFNXHqMfjQXFxsZaEb2GRF8FKnASME8LwlpYWzcxdpNbW1rSF4RcvXsQzzzyDn//85ygpKcFnPvMZ7Nq1K28ERLlmTU81/6vet1m2ex/7wYo4Z3krVNu2bRuXo3flYj/T+c9UtzXyPZVKmZPt5A633X038pWVDYx8hJjVqYMIK9Vut9udoBEkL63K1I+A+jHQKysrK8vIjEo4HMZrr72G733vezhx4gRuvvlm/M7v/A6uu+467fej0Si++c1v4vHHH8e73/3/s/fe0XVc57X4novegYvee+8k2DspFlHFkiLZkiPbsuLIcZ6d2PFy7DyvJI5/L4llJ3lWEndbsWTLluRnU42SCImdFAtAohK99457Adxezu8P8Ds6M7gNjQBlfGudNWfmztw7d+o5++xv7z/Bo48+uqTG/N0QFosFlZWVePnllzE9PY2HHnoIf/Inf8KBxbUMm82GP/zhD3jllVdgs9nw4IMP4mMf+9iy0q6sVis6Oztx69YtNDU1oaGhAVqtFtu3b8fx48dRUVHhskFF9PZ3330XFy9ehK+vLw4dOoRjx44hMzPT5W8bjUbcuHEDly9fxrVr16DX65GTk4Ndu3Zh586dSExMXNI1zhhDb28v3nnnHVRWVsJut+Pw4cO4//77kZaW5nQ7jUaDV155Ba+99hpSU1Px1FNPYdu2bS73YWJigjOl2tra0N7ezvUBybSERLKDg4ORm5vLO/KeuH3qdDpUV1fj4sWLuH79Oux2OyoqKrBv3z5s377dJVCm0WhQVVWFq1evciZcRkYGKioqUFFRgfz8/FXv8DDG0NXVhcuXL+Py5cuc6bhjxw7s3r0b5eXlTq8vg8GAixcvorKyEvX19YiJicE999yDw4cPIzExcVX3G5hnnZCLaV1dHfr6+jA0NITGxkYkJibizTffRGlpqcvvYIzJHHUbGxthNpsRGRmJgoICFBYWorCwEOnp6aveobPb7RgcHER1dTWqqqrQ0NAAm82GzMxMDqDl5ua67UBNTU3x/1NbW4upqSkEBQWhtLSUu6GtVKrf9PQ0B1+oDA4Owm63Q61W8/TpwsJCpKWlefSbxOoVwbbx8XEAQEhICAdDiEm1GqAInQvlf9NqtZyBUlBQwMGzjIwMj/fBarWiq6sLzc3N/P9NTEzwZ42XlxdPK6NCqaEhISEwm80y8wVHRaPRcMYhYwzh4eEcACMQLCMjY0nMwKmpKX48xBRTAt28vb2RmJjIfyciIkImt0D1oKAgl8fMbrdDr9dzYFecjo6OctCMUmmB+VT/1NRUWUlOTl6yAYjInCITFXKfNRgMsFgsvDh7V4ht1pCQEISGhsqYVDQfGRnJAa+VYi/RoMTQ0BCGh4cxPDzM69PT05idnYXVapVtI0kSvLy8+L6GhoYiICBA9l8dFdEZVGzDUzo/mZxQiY2NRUxMDNRq9Yrfv2azGTMzMw6dph05T9N/8Pf3dwpg+fv7rzq7lJxZCXCbmpqCTqdzeA/QVK/XuzTHcRWO+lMEVIWGhvJrQLwWlMtoGhQUtCYpyhaLBSMjIw7dQOnYqFQqxMXFISkpCTk5OaioqEBmZuaizufExASsVivi4+M/EgDRSkR6hZp9q/qw2/Wekl5d82MmSdJuxtglF5+HAkhhjDU6XWe9gmoVFRXs4sWLsmVrQYVfzm8udVtH2y11mafbbcTdEVqtlncQa2trMTg4CG9vb5SXl+Oxxx5DUVHRgvNbU1ODb3zjG7j//vvxhS98YdU74aQ7QqPXNDUYDPyF6uy5Q8szMjK4+2BRUdGSGVBzc3N4/fXX8fvf/x5eXl74+Mc/jvvvv/+OM34YYzh58iS+//3v4/7778enP/3pVdUvIqD17bffRnV1NWJjY3H8+HEcPXrULbg4MzODM2fO4N1330VnZyfy8/Nx7Ngx7N+/3y3TgDGGtrY2XL58GVeuXMHQ0BBUKhWysrK4ZpM4EhkYGAiz2SxLAdRoNJAkCUlJSThy5AiOHDniUkDbZrPhvffew69+9SvodDo8/vjj+NjHPub0HJtMJpw/fx5vvPEGWlpakJCQIGNLZWVl8f9ps9lknYLZ2Vm0traiqakJTU1NGBwc5I6ZpD9XUVHhsoNmNptx48YNnD9/HlevXoXFYkF5eTn27duHnTt3uhw5ZYyhp6cH1dXVqK6uRlNTE2w2G7Kzs7F582bk5+cjOzt7ySlyJpOJp3zV1dXh6tWrmJmZQUZGBnbt2oVdu3a5ZD3Z7XbU1dXhvffew8WLFyFJEvbu3YvDhw+juLjYZYPaZrOhv79/AVNhbGwMExMTCAgIkIEIyuKpruO//du/4Vvf+hb8/f1RUlICX19fhISEYPPmzdiyZQsqKio8SquenJxEU1MTB7OJiUQsHSXzIzY21m3HnTGG6elpdHd3y8rAwABsNhskSUJCQgI2bdqELVu2oLi42O13WiwW1NfXc/bk+Pg4IiMjUV5ejk2bNqGsrGxVn0WuYnJyErdu3eKlp6cHdrsdMTExMrBtMXp3MzMzCxhUAwMDsrQrPz8/h86egYGBHKRxVoxGI++EJSYmLmDhLebeMxgMaG1t5eBZS0sL5ubm4OXlhYyMDOTn53M23moy8Rhj0Gg0HADr7+/nTDQCCSMjI5GVlSUrzkxw3AXpXdHvaLVarllJUgsECBDrUQwaPFWpVAgMDJSBHFRiYmKQmpqKtLQ0xMXFLRmU0ev16OrqQkdHBweEBwYGuMO4JEmIiopCYmIiZ78kJiYiPj4egYGBfEDGy8vL7TVst9sxNzcHrVaLmZkZmfbXzMwMJiYmMDQ0hKGhIZjNZn4MYmJiHDK3oqOjV629Z7VaZWwkg8EAb29vPgjlqNDnq5WiZzKZOLttZGQEo6OjsqLMDgkLC+OOso7cppWfeXt7w2QyLWDoUp2eDcCH1yhjjLNCnZUNU4q1D5vNhtHRUfT396O1tRXV1dXo7OyESqVCYWEhH8j0hPUtSdKaA0TrJdIqItk/VB9zu96fSb9Z82MmSdL/BbANwLsAbgAYB+APIAvAAQCpAL7KGKty+h3rGVTbSP/ciI9iMMZgNps5bdloNGJmZoZTwCcnJ3mZmJjgDl92ux0hISHcxY7YBM4e8ENDQ/j2t78NvV6PZ599dlU1moaGhvDKK6/g1KlT8PPzQ0pKCmcLZGZmIj09fVEprF1dXaivr+epXDqdDgEBAXjggQfw5JNPLqmhOD4+jldffRW/+93v8L3vfQ9btmxZ9HcsJS5evIh/+Zd/wY4dO/CVr3xlyQ5rZOSg1WoRERGB0NBQj7cdHBzEO++8g1OnTkGn02HPnj04fvw4SkpKXDYQGGNobW3FO++8g3PnzgEA7rnnHjzyyCMeM41sNhs6OjrQ2NjIRYHF4u3tjezsbA5sedopbWpqwosvvojq6mocPnwYTz75pNN9YozhwoULeP755zE+Po79+/fjgQceQF5e3rIb94wxjI6OorGxEZcvX0ZVVRVUKhW2bduG/fv3Y8uWLS6BD6vVipqaGpw/fx4ffPABd7Dct28fdu3a5fY82+12dHZ2orq6Gq2trWhvb4dGowFjDFFRUYiNjXXYYfDy8sLw8DB6enowMzMDAPD19UVycjLS0tJQUFCA7du3uwWYBgYG8N577+H999/HxMQEysrKcPjwYezevdslyNXf34/r16/j+vXraGxsBGOMd4CVjIWoqCgYjcYFqW7i/NjYGIxGI/r6+pCRkYH9+/ejoqICX/7ylzlzEQA3XxE72TMzM7h58yaqqqpw48YNaLVaREZGcvZXWVmZx8+vyclJ9PT0cNYHldHRUZjNZrf3W0REBNLT03lJS0tDUlKSx8+84eFhXL16FVeuXEFjYyNUKhVKS0u5KYY7cIYxhsnJSZmmVFdXF6anp3mKnCMWODDfUY2JieGyBaKEQVxcnMeDI2NjY7h16xYaGxtx69YtrmkVExPDWaIFBQXcVGSxYTKZuEg5Fa1WC51Ox0EaKkqwxs/Pb9HPDI1Gw4Gz5uZmrpkZEBCAnJwcDp7l5eUt6f1gs9mg1Wq5bpMyjYrY8cvpuE9NTaG9vR0dHR28TE3N6zaTW2xWVhYyMjKQlpZ2V5kaaLVadHZ2oqOjg09HRkYAAIGBgcjMzERWVhbS09M5cLZejDRsNptTjbHx8XGuO0ngX3x8POLi4viU6uvl/yiDAF8CE6nQM5XATWD+/UXPHWVZzPNnpfefUnGdlampKf4MZYwhICAAkZGRvNCgkTivVqvvCnMjq9XK261KkwileYRer4fZbIbJZILdbneZeUThLNPKz8/PYSos1SMiImRMWWffb7FY0NTUxAcye3t74ePjg5KSEg60KfthG6Dah5FWEcm+WX2f2/WekX61Lo6ZJElqAH8CYBeAeAAGAM0ATrpisfHt1yuotmnTJvatb33rjv7mekn3XO31XKWJuksh9XRdR5+JLw1l3d38Uj+z2+1cK43qrpYtdn2qizn8njS4fX19ZWKbRO2PjIzkbppUj4iIWNQoq1arxXe/+13U19fjH/7hHxaAR3FxcRgdHfX4+wAgNjaWNzLFOHHiBJ5//nmEhYXhE5/4BI4ePbrkNAp3odPp8MILL+B3v/sdPve5z+Hxxx9f0ujz1NQUPv/5z2PPnj340pe+tGrMzampKXz1q19FSEgI/v7v/95jpoHFYsHVq1dx6tQp1NTU8P1TqVQ8DWRycpI7tQUFBSE9PR0ZGRnIyclxm1JoNptx6dIlnDx5EvX19UhPT8fx48dx6NAhtx06vV6PyspKnDhxAuPj4zh48CAeeeQRZGS41e9cdjDGUF1djRMnTqCqqgp5eXl48sknsXXrVpdmAC+88AIqKyuxe/dufPazn0V6evqq76vRaMT169dx7tw5XL9+HSqVClu3buUgmytNLpvNhvr6epw/fx6XL1/G3NwcCgsLsW/fPuzevdvjNGYCR8bHxznTTkxpsdlsiI+PR2pqqkfMLIq5uTmcP38elZWVaG5uRmJiIo4cOYJDhw651FObm5vD2bNnucBvamoqtm/fjq1bt6KgoGDFOgjt7e04ceIEZwJOTU1BkiR86lOfwuOPP47y8nKPnGzHx8e5TlldXR0MBgNiYmKwadMmbN68GWVlZWveEZ2enkZ9fT13lp2enkZcXBw3ynDmBk1BhgbkCNrX1weVSoXIyEiZplRmZiYiIiLg4+Pj8nlJBhIiU4Smw8PDnIVFGpBUPL3+xsbGOEu0qamJM9vUarUMbMvIyLjjHU7GGMbHx3m6ZnNzM2cvhoWFyVhnWVlZHunyUZqraOQhPvspvLy8EB4eDrVajdDQ0AWC36TpKraPaLv4+HikpKQgOTmZT5OSkhb1HtdoNBxoI3bl8PAwbxslJCRwgJhclmNjY+9I1gSlN4osvIGBAfT19WFychKSJCE0NFTGwMvMzFzy/tlsNkxPT/MBUnGg1G63eyQnQowuGhSJiYlBUFDQso6X3W7H5OQkhoeHMTIysmAqsrgCAwNlgBtNo6Ki4O/vD19fX/j5+cHX13dR+2Sz2TA3N8fZbUrdrbGxMQ6aUaowAERERCAhIUFWaP+WqollNBqh0Wg4iKOcOlpmsVgQEBAgA9kdleU+ewwGAx80EgfZxfmpqSkOmFJQP4JAuODgYBl7UGQKKpeJdQAL0sUpjdTdvDIoTVRMZ1YaRVChQQsa9FtOmM3mBUxCsT45OcmfB9PT0wA+1OujZyClwqekpMgGN00mExoaGjjQNjg4yI2AHn/8ceTl5a0LgGg9REpFNPt69cfcrvdF6RcfiWO2bkG18vJy9pd/+Zd3/HfXW4qpBzTTFflMgbKvyLqOPqNljuqL+czTdb28vDgrQawrp8tZttajsYzNuzvW1tZy/aWvfe1rOHLkyIqm/yqfFYODg/iLv/gLvPzyy3fU9ctoNOInP/kJXn/9dfzFX/wFHn300UWfA8YY/u///b+4fv06fvzjH6+4o9zvf/97/OAHP8C//Mu/YPv27R5tMzs7i//8z//EmTNnsHfvXhw7dgwVFRVuGxdzc3O8I9PU1IQrV64AAPbs2YMjR46guLjY5Tnv6urCO++8g/fffx82mw0HDhzAY4895tbkwWw248yZM/j973+Pvr4+7NmzBw888IBb9ttiwmq14tKlS/jDH/6ApqYmVFRU4JFHHnGpE2c0GvHOO+/gN7/5Dby8vPCZz3wGR44ccXscGWMYGBjA8PAwBwYo/dDf35+PeItaL5GRkR41/kwmkwxkA4Bt27Zh3759Ls0LgPnO0K1bt3DhwgVcvHgR09PTyM3Nxd69e7Fnz55V10YcGhpCVVUVrl+/jrq6Ovj6+mLfvn04fPgw8vPzXaaCkhU9afXtv21Fn5OTc0fetb/5zW9w8uRJHDt2DPHx8WhubsbNmzcxOjoKX19fFBcXY9OmTSgvL/corWN0dBQ1NTW4ceMGamtrMTc3B7Vajc2bN2PTpk3Izc1FbGzsir8TbDYb2tvbUVdXh/r6ety6dQsmkwkRERHchGHbtm0u0zhJG47Yga2trdxNmhh5KSkpq35eRA1ISp3VarXw9vZGVlYWB9oKCgo8ZuNOTk5yMKupqQmdnZ2w2WwICgrioIBoBET372KAI0pZJO0sUUOLmJ7R0dHcpbSgoABpaWkePx9aWlpQX1+P5uZmtLW1wWAwwMvLC6mpqZzFm5WVhejo6GUDLBRWqxUjIyPo6+tDX18f+vv70dfXh4GBAZjNZgAfMrWys7ORk5ODnJwcxMTEePz7drsdQ0ND6OnpQU9PD7q7u9HT04PR0VEZq4T0mEhDTFl3phXnqHNPenEUkZGRsk5yUlISUlJSFp32bDKZ0NPTI5O26O3t5ccKmB/8IlCDBkijoqJ4ip/S7MqZEZbJZMLk5CR/D4mgl5eXl0yDjK5nqnv6XnIWpEmnBN7Gx8cXgE2u+pDKQWYvLy+umUfnVKzHxsZyzbiltCktFgvGx8cxMjLCCwH7BBzS/lAKOA1si0Chs6mPjw8MBoPL9HCdTrcA7ALmgUHRjVVZDwkJWdY9zRjjGS9U9Hq9Q007d3XGGGfskrGJWHf0Gcl5rPT7g4zgDAYD9Hr9gqndbufnh4qreXcajRMTEzwtnUC3vr4+/owPCwvjA05U4uPjYTAYUFdXh9jYWGRmZn4kAKKViJSKaPbV6kfcrvdl6acfiWO2bkG1jfTPjdiI+bDb7dDpdNxBh1JJ6urq0NHRAcYYUlJSeErotm3bXHbqVgpU+4d/+AfuqrgWodfr8cMf/hB1dXX41a9+taTvuHLlCv73//7f+MlPfoKcnJxl79Po6Ci+/OUvIzMzE3//93/vMRvhueeew9tvv40vfelLePDBB5fdMBEF4hsaGhATE4PDhw/j8OHDLtOADQYDzpw5g9/85jcwmUz49Kc/jePHj7sdeSXwi9hvCQkJOHbsGA4fPryojotOp8O1a9dw6dIl3LhxAxaLBTt37sTDDz+MgoICp8fFarXi7Nmz+O1vf4vh4WEcP34cn/jEJ1wyp4B5VtPp06dx4cIFTE5OIjU1FUlJSbLUw+joaBiNRpkbGU3Hx8dhtVqRlpaGLVu2YOvWrR6ZB5hMJlRVVeHcuXO4du0aAGDr1q3Yt28ftm3b5vK6YYyhvb0d58+fx4ULFzA6OoqkpCSePpabm7skpg45+nV0dODmzZuorq6GTqdDfHw8tm7diq1bt6K4uJiPZDvar+bmZpw5cwYXLlzA7OwsiouLcfToUezevXtRjpkWi2WBI9rExAS8vLwcMnqd7ZOrMBqNaGxs5IL9vb298PLyQl5eHgfacnJy3AJkk5OTuHnzJm7evIn29nYZWBAbGysTRE9ISAAAznJWFo1GIwM3CBggXUIC0AoKCtzqx5HJxbVr13Dz5k0YDAakp6fzc5mbm7uunPLI6VME22ZnZ+Hj44Ps7GyusVZQUOBxh1un0y3oWIvsORJgd8YwFxnoPj4+iI+PX6CdlZiY6DH4xxhDf38/lzYgUxlfX1/k5+ejuLgYBQUFyM7OXnOnTAqdToeuri60tbXxMjY2BgAIDw9HTk4OB9yys7MXxXqlMJlMmJ2d5dphYqFls7Oz8PX1ddjBV3b0ibGz2PeozWbD0NAQ+vr60NvbywE0Yun7+flxZ1BicKampt5xN2Cr1YqJiYkFgz9Un5iY4BqCpOmlBN7Ed9ydHBRdSoiANjHZqE4sI2CeFUXp546A9LViFovmAqIjq1ifnZ1dYFghpnyK7zsqywXiVjMsFgs0Gg2mp6cxPT0tqyuXie6mFKIpCwF2jqYqlYqbSYhgr7JO80rAk9oUrjTvxPtDo9FwWQQqZITi6+uLr371q9i7d+9HAiBaiUiuiGFfqX7M7XpflX7o9phJknQMwHMAvAD8nDH2HcXnKQBeABB+e51vMMbevv3Z3wH4MwA2AH/FGDu1+H/jPtYtqLZ582b23HPPrfVuyGItjtVqpn0udj1HyxezrifLV3rqbh273c6FcB3Vl/qZmHrqzhGJCqWsKvUBVCoVgoODZaO3OTk5KC0tRWZm5qI7RisFqvX29uIzn/kMXnzxRaSkpCzpO1cinnrqKXzzm99Ednb2krYfHBzEpz71Kbz22muL0ikTgzGGX//613jhhRfwve99D+Xl5R5tNzQ0hGeeeQYPPfQQnn76aZfsq8uXL+PUqVPo6urirElKGQkLC5MJeyv/x/DwMN5//3288847MJlMePrpp3Hs2DGX1874+DhefPFFvP3229i5cyf+7M/+zKXzphgDAwM4deoU3n//fWi1WqhUKvj7+8tG7lUqFR8RJF0UPz8/bN26Fbt378bmzZtdAgeMMVy7dg2//e1v0dTUhAMHDuDxxx93m4o6MDCAl19+GadOnUJmZiaOHj2KPXv2ICoqyqP/5mg/enp6eKpgc3MzGGPIz8/HkSNHsHfvXrcAiNls5iDb1atXoVKpcODAAdx7770esbqGh4fR0tKClpYWtLa2oqurC1arFQEBAdx4QWRBUNHpdBgaGgJj806npIVYXl6OzZs3u0wHZoyho6MDZ8+exblz5zA1NYX8/HwcOHAAe/fudcv+bGxs5KxDg8GAjIwMtLS0AJhPjxevFapTCpMyrYq0vhhj3B2VnDAX83y0Wq1obW3lIFlrayu8vLxQXl7OUyo9Tb+12+0YGxvjDoTkQgiA37/KEhYWJkvDi4qK8uh5bbFY0NDQgGvXruHatWsYHR1FWFgYtmzZgm3btmHTpk1LAmn0ej26u7vR2dmJ/v5+GAwGnhqlLKSnptQyiomJQXh4+JLfO2azGe3t7TJDA4PBgKCgIBQXF6OkpAQlJSVL1ldbjWCMYXh4GG1tbZyB1tPTA0mSkJyczE14iouLV5wlfSdjenoa7e3t3DW5ra2NMzri4+O5DllGRgbS09OXBHStZOh0Om7E0tvby8EzShFXqVRISEiQAeHLSQVdD0GaXo4GhWgqpu3RfSyCb+I0IiJiWfcZYwwGg4HraIlTjUaDkZERDA4OYnR0lAMgvr6+HNBWmjEs59myXoPOmcg6c6S5TOxFapv7+vrKADhK/xTTPj2pU9tALM4MXIg1puwf+Pj4IDw8HBEREXxKRTm/Uk62Swmr1cplMhyViYkJmW5faGgoT0Gma5AKMVGDgoI2QLXbkVQRy75Y/Um36/2d9H2Xx0ySJC8AbQAOAxgAUAXgCcZYk7DOTwHUMMZ+JElSAYC3GWNpt+u/BbAVQAKA9wHkMMYWUkqXGSsCqnmAHvoBeBHAZgCTAD7BGOtx9Z3l5eXs8ccfX/a+rXSst/TQ1djO2bYr6TbqatlKT119Ro5MKpUKKpVKVlfOe/qZcj1XbkhiuVMdgpUC1YD51ME/+7M/w3//93+jsLBwubu2pLhx4wZefPFFLAeEv3TpEv7zP/8TL7/88qLOg9lsxssvv4wXX3wRR44cwVe+8hWPWTOVlZX4zne+g//+7/9GQUHBgs8nJyfx4osv4vz587DZbDydMy8vTwaMWK1WTE9Pc2HvxsZGTE9PIy8vD0eOHMH+/ftlI7RjY2P4n//5H5w6dQr33HMPnn76aZcaU4wxXLx4Eb/4xS+g1Wrx5JNP4sEHH1y0dh7phFDj0Gaz8XScxYwg37p1C7/5zW9w9epVbNu2DU888QSKi4tdbjM1NYXf//73eP3116FWq/H444/j8OHDsnNlt9tRU1ODqqoq2UiymHYDgGtOEWiQkpKC8vJypKen83vLZrOhqakJp06dwvnz5+Hj44N77rkHx48f9wiY1Ol0OHfuHN599120trYiKysLx44dw8GDBxd1rPR6PTo6OmAymRYAsTQCHB8f7zHw1NPTg7Nnz+Ls2bMYHR1FdnY2Dhw4gH379nkESjLG0NTUhNOnT6OyshKVlZWQJAnbtm3Dj3/842Wzpxhj3LSBUhwBID8/nwNtGRkZi3oGms1m1NbW4sqVK1y3LD4+Htu3b8eOHTtQUFBwRxlfJpOJg0wE4qpUKhQXF2Pbtm3Ytm2bR5pxYkxPT+PmzZu4ceMGGhsbOUMuMDCQu1qmpKQgMDCQp0spi6inJnbYR0dHuXEGAG6CQWmeS3W1nJ2dxa1bt3hKbE9PDxhjSE1NRV5enswkZzXcnklDrb29XVaoA5aQkMANWJYL+tlsNgwODsrS2sRCg3Lurms6B2q1eoFgPU1DQ0OX3c5ljGFkZIRrrHV1daG7u5tfV8T4Sk9PR1RUlExniaaepLgyxmAymWTpYRqNRgaY9ff3cyZMUFAQUlJSkJKSgtTUVD51JVS+nLBarTI3U3E6OzsLu92+wBhDOb8UY4zlhNlsxsTEhEPwbWxsjIvqL2aflG3HwMBAma6WeO7j4uKQmJiImJiYu0KIfz0FpQxT0el0PLWTUow9qatUqgU6ccrrUlwWEBDwkQM2HQWBnSJTUqwbjUZ86UtfwrFjxzZAtduRWBHHvlD9pNv1/l76d3eg2g4A32KMHb09/3cAwBj7V2GdnwDoYow9e3v9f2eM7VSuK0nSqdvfdcXJb90A8DyA3zDGph2t43Q/lwuqeYge/iWAEsbYX0iS9DiAhxljn3D1vRvpn2sfb7zxBo4dO7ZqwvMbsTaxkqAaMM9Y+9a3vgWz2YxvfOMbbgGO1Yjjx4/j5ZdfXjLTDAB+9KMfoaurC9/97nfdHqPZ2Vn87Gc/w1tvvYVPfOIT+PSnP+1xx21ubg7f+ta3oNVq8dxzzy1gkNjtdjz//PN4+eWX8Vd/9Vc4dOjQotMzGJt37aysrMS5c+dgNptx8OBB/Omf/inX4LLb7aisrMQvfvEL+Pj44M///M+xf/9+l/99amoKv/71r/HGG29g8+bNeOaZZ5CZmbmofVtK9PT04OWXX8bp06eRl5eHT37yk9i+fbvLfTUYDHjrrbfw6quvwm6349FHH8WDDz4oO5YDAwOorKzE6dOnMTk5iU2bNmHHjh2IjY2VUf/F37HZbFzrZnR0FL29vbh58ya6u7uhUqlQUFDAUzkJMJiZmcHp06fx9ttvo7e3F8XFxTh+/LjHaZEdHR149913cebMGVgsFuzduxcHDx5EWVnZqgI6g4ODHEQbGBhAWloaDhw4gP3793sM3PT19eH06dM4c+YMxsbGUFBQgEOHDmHv3r34/Oc/j29/+9vIzs6G2WxGZ2cnWltbOeNudHSUD1Q4CmL3hoeHyzrKAPDnf/7nOHnyJIqLi9HS0oLq6mpUVVWhq6sL3t7eKC4u5kBbYmLiop6LQ0NDuHLlCq5cuYLm5mbY7XYkJyejtLSUM6eWkgYnhk6nQ0tLC9cKa21thcFggJ+fH7Kzs1FQUICKigrk5+cv6hogp9Pq6mrcuHEDGo0G4eHh2LRpEyoqKlBcXOySUWQ0GjE8PAy9Xu9Uu8bb29vh9na7Hb29vTy989atWxgfH4ckSUhNTZXpqblL3XYUpC9Kov4E5hgMBgDgbBtnzp7BwcHw8vKCVquVpScRi2Z6ehpzc3P8XRgdHY3s7Gye/piVlbWsFDOj0YjW1lbuEtrS0oLZ2Vl4eXkhKSlpgWMjOeUuJv2QhPsdidUPDw9Dq9UCmB88SE1N5TpqOTk5SE1NXZHnjdFoRG9vL7q7uzE5OSlzBaS6cjCDQkzJlSQJfn5+MpdT8VmQmpqK5OTkFWXC0PETGagE4plMJqcaYjQV6yqVyiEDSFxGgCBdc/7+/khKSuIpyFRPSEhYU8bPeg273c6dcSnt0FndZDJ5BBhKkrSgPRwcHCxjXymLWq1GRETEuk7ZvJuCMQar1Qqj0QiTyQSj0Sir0xSYv2fIGM5RfaXBW2nD/ZNHQkU8+3z1Z9yu9y3p2V4AE8KinzLGfkozkiQ9CuAYY+xzt+c/BWAbY+yLwjrxACoBRAAIAnAPY+yGJEn/DeAqY+zXt9f7BYB3GGP/z9G+SJKUBeCzAD4BoBrA/wCoZB4AZisBqnmCHnJUUJIkbwAjAKJd7WB5eTn73Oc+t6x9uxtiLdNvOzo6MDg46PTz1157Db6+vtizZw/uv//+Zf3Wek6dXcltldstd97TbZRpXVQKCgrw1FNPydZfaVCNoqOjA9/5zncwNzeHb3zjGygrK1vS7ywlXn31VQwPD+Ov//qvl/U9//Vf/4UzZ87g0UcfxYEDB7gGksFg4J2A8+fPo6amhqdtetrJYIzhxIkT+O///m987Wtfw7333rtgnZqaGvzd3/0djh8/jr/8y79csZe9yWTCqVOn8NJLL8FqteKJJ57AAw88wDtj/f39+PnPf45Lly7hgQcewGc+8xmXaW6MMVy9ehU//elPMT09jaeeegr333//iu2v1WpFbW0tLly4gPfffx+xsbF44okncPDgQZe/YbPZcPbsWfzmN7/ByMgI7r//fnz84x/nLCrGGG7cuMEdRJOSkrhz5VIZM8r9bm5uxtWrV3Hu3DlMTEygsLCQg0ghISFgjKGhoQHvvPMOLl68CH9/fxw+fBj33nuvR2nURqMRFy9exNmzZ1FbW4uAgADs3r0be/bsQW5urlsHV0dBgFZbWxsHtQYHB5GYmIgDBw7gwIEDbo0rKPr7+3Hu3DmcO3cOg4ODSE5OxsGDB3Hw4EEZoNvY2Ijz58/j0qVLXC8pMzMTubm5XBvOU0F00iIjdsqJEydw+vRpAMC9996Lhx56COXl5SgqKkJAQADMZjMaGxs50DYwMMDdu0i0fzHXA7ttckGsqfr6ehkowBhDSEgI1Go1L4wxmb4Mpc3R+gEBAcjLy+OOlrm5uYtmXBmNRm5gU1VVhfHxcYSGhmLz5s3cXEG8zymVuaGhAQMDA3wkfnx8nAOYfn5+SEhIQFBQkFMtG2JOAfNpQCQMT6mt2dnZsuPrCGwbHR2Fn5+fzEwiLS1tWe8v0jAiBzgCLkRXOJvNhvDw8AWpSzRdCZMAvV6PlpYWns7a2toKk8kEf39/5OTkcKfQvLw8hwNFZrMZg4ODXH9Po9EsELtXFm9vb4fu4pGRkQ5T+mw2G/r6+mRaar29vbDZbPDz80NWVpYMcLubUySVIaY9Ezjb19fHNfgiIyNlKaIE5t8JUMtgMMhMMqgQUwaY1xZLSEhYALwlJiYu6f2wXkOv1y8wDqFCx0KSJJcg10qkITLGoNPpZM9zEaxz9Jyn56Ofn9+ClHnSgFtLHbjVCpPJhKmpKe5kSnWxTE5OwmAwyMBLpSwOAD6QowTKxCn9phJ0E+t0X7sKxhi8vb1dgnN+fn44evQoiouLN0C125FQEc8+V/202/X+P+lf3DHVPAHV/gbzuNa/38amfgGgCMB/YhGgmvB9KgD3A/gR5rXY/gfAc4yxKafbrACo5skfbby9zsDt+c7b60wovusZAM8AQEJCwmZPG/HLibUCtdZD44P0UZxFe3s7fH19kZiYuGS9ITHWw3++E7Hc/6kciaW6ch36PCgoiLsnke5abm4uSktLERkZicTExBXZP0/vlZ6eHjz77LPQarX493//d5fC+CsVFosFR44cwZkzZ5Z9/Pv7+3HmzBmcPXsWIyMj8PLygr+/P9LS0pCWlobNmzdjx44di/qdzs5OfO1rX0NJSQm+/vWvL+gcT05O4p/+6Z+g0Wjw7LPPruoxm5iYwG9/+1u89dZbyMrKwlNPPYWKigpIkgSLxYI333wTL7zwAiIjI/H5z38eW7dudcte++Uvf4mTJ0+itLQUx44d80hHTAwyKLh48SKqq6vBGENZWRl2796Nffv2uQQTGGO4efMmXnrpJdTX1+PAgQP45Cc/ifT0dADzQNfFixdx4sQJNDc3Y/PmzW4dRFcqGGO4desWF+/X6/XYvHkz7rnnHuzatQve3t7QaDRc766/vx9lZWW49957sWvXLo9YwrOzs7h8+TIuXbqE9vZ2DubExMQgIyMD/v7+sNlsXO+R6jMzM+ju7obFYoGPjw8HtHJzc5GTk+PxNTgwMMBBtIGBASQlJWH//v3Yv38/B+JsNhvq6+s5iKbT6VBUVIR9+/Zh9+7dK64r9dZbb+GDDz7AY489hrCwMNTV1aGmpgaNjY3Q6/XIyMjAnj17sHv3biQnJwOYfx8SAFVdXc0BqJ07d2Lv3r0oKSlZMkuHOl3UiZicnIRKpZJ16Ii5stSglGNyau3p6VkAFIrsLwKAa2pqUFNTg7a2NgDz6ZklJSVISUnhWjGRkZFL3jcRACLQs729HRMTE1CpVMjMzERRURHXgxQ7/UajEbdu3eIad729vVCpVMjPz+dAW3Z29royXBDDYDBwpuGtW7fQ0tICk8mEgIAA7hBaWFiI3Nxc2fOSUnw7Ojr4cevr64NOp4MkSRyopPR5tVrNU7udFbPZvECbiabT09NcvyokJISnzlLqb3JysuwYG41GDsJTITF/0nzNysriLL7FumyudjDGMDY2xh08CTgj1mRAQAD/7+Twl5yc7FDagcBx0UF1eHh4gSspsdjEdpQrBm5AQICM1UZMKNEog7RJlWGxWDAyMiID3QiEm52d5RIlcXFx/BoSy2qkS3saZMJATHBlofRTYD6NVDQMETXXPNGQpHQ+YqKKxWQyye4f5f3lbN7Pz4+D1p4wSMkESfyPZKQyOjrK3+eSJMHX1xexsbGIioriwD+lzYolLCxsSeY9nhwvvV7PzUOIXSoW5XLRGReYBxFpYCkyMlI20CQuW8o1SAM/VJTLPJ0HINOYo/vUarW6Beiys7ORnJy8AardjviKBPaZ6s+7Xe9Z6Vsrkf55C/NYU//t+S4A2zFvUOBx+uftdUowz1Y7DuAUgJcA7AbwKcZYmdPt1hOoJkZ5eTl74oknlrVvG7G8GBsb48yCpYSzBoOnGmtLXS7qpjmrL2ZdV3VqnHh5eXENtaXUvby8FmjU0MiHj4+P0/9rs9m4a5Yo9nrz5k1cu3YN4eHhePLJJ3Hfffe5PXbuYrHPioaGBvzN3/wNnnzySXz6059edVD1k5/8JH70ox8tO+1qJcNoNOLZZ59FTU0Nvve97y0wUzAYDPiv//ovvP/++/jmN7+Jffv23dH9a2xsxAsvvIDa2lo888wzeOSRR3jnqb29HT/5yU/Q0NCAL33pS7jvvvtcnkMCkN59911cuHABNpuNa3ZRuhIAzn4ZHh7mAvNkULBnzx5s3rzZbWPUYrHg6tWrqKysxJUrV1BWVoY//dM/RVlZGSRJgtFoxHvvvYcTJ05gcHAQe/fuxcMPP4z8/PxlX//LuY6tVitu3ryJyspKXLp0CbGxsXjwwQdx9OhRBAcHgzGG2tpavPPOO7h8+TJCQ0PxwAMP4Pjx44sCnoiV09XVBbPZLHve0DMnKCgI6enpi07vHxwcxLlz53D27Fn09/cjKSmJ66oRQGWz2VBbW4vz58/j8uXL0Ov1KC0txb59+7Br165lpWkvNxhj6OrqwqVLl3Dx4kUMDAwgOjoau3fvxu7du5Gfn887qhqNBpcvX8bFixdRX18PX19fbNu2DXv27MGWLVvuuOuf8j+I5hgENm3duhVbtmxBamoqv1bNZjMaGhp4ymd/fz98fHyQl5eH8vLyNQOnbDYbOjs70djYiIaGBty6dQuzs7Pw8/NDQUEBysrKUFZWJjPksVqtaGlp4a6t7e3tPP2W0kcLCwvvGHOKABoCC0XwzN/fH3l5eXyflOCZXq9Ha2srmpqa0NTUhJaWFhgMBvj6+nJgilhQi9WeXGoQ2E6AU1dXF/r7+2Gz2eDl5cU16wiAT0pKkgE7Go1GpjHX0dGBqan5gf2IiAgOtCUlJXETErVavWIsZ4PB4DCtdWRkBGNjY7DZbJAkCdHR0TIXz8zMTIdpz0ajEQMDAzLQrK+vjzv+AfMadaTVlpKSgvj4eAQHB8ucSV2145TBGIPRaMTc3JxMi21yclLGxiIGKTCvFycCbrQvcXFxDu9rm82GkZER/n/INGhgYICnSxPASIO1olmWcllQUBAsFgs3LiH2qljEZbOzsxw4opRjYD5lNioqagF7i4orowRKTRfbGMS0JaCMgs5FSEiIDJAiVqqvr+8C1qe7eUpHnJqawsTExAJAiVxpRZZodHQ0N4aIjIx0eR+YTCau9+rI5EEsIvvKWUqrO5DX0WeBgYH8GhCLs2WeAotK11/R+ddRoftY+R/FQvvtapmjeQCyc7qYvs8Xv/jFDU01IVYQVPPGvNTYIQCDmJca+yRj7JawzjsAXmGM/VKSpHwApwEkAigA8Bt8aFRwGkA2c2JUIM1rqmkwz3T7PWPMJHz2B8bYI073c72mf27atGnduX/erbGeUi89SW9cznLl6IOj+mLWdVcXnUBFFogjZoirZTQC4qiIznYAeIMwPj4eCQkJiI+PR3x8PHJychAZGSk7JuPj41y3qKioiG+/lFjKdWS1WvFv//ZvuH79Ov7zP//T4xSypcSXvvQlfOUrX3Hr/nin4t1338Wzzz6L//W//hf+5E/+RHbcbTYbfv3rX+OXv/wlvvCFL+Cxxx5bUybn3Nwcfvazn+HNN9/EE088gU9/+tO8MaTRaPBf//VfuHDhAr7whS/goYce8pi1otfrMTo6iuHhYe58SCPJcXFxHgM6jM07TVZWVuLMmTMwGo3Yvn07Dh8+jC1btnAdpJMnT+KNN97A3NwcDh8+jIceegipqakeHweNRoMrV66gp6cHAwMD6O/vx+TkJAA5qC6mH6jVakRHR6OgoACbN29elNj+4OAg3njjDZw6dQoqlQrHjh3DAw88wAHI8fFxvPXWWzh58iTMZjOOHj2Kj33sY6t6HznaR2Ki9ff3IzExEfv378e+fft4uiql6547dw4ffPABjEYjysrKOIi2WDDAZrNxfSdHDWydTofw8HBERUUhOjpaNl0K0DU2NsbZfk1NTQgMDMT27du5Cy1dpwaDAdevX8eFCxdQXV0Nq9WK3NxcDkzl5eWtqD6L3W5HT08P19hqamrC8PAwJElCWloaB9Dy8vJkoFNzczNPbe3u7oaPjw+Ki4uxZcsWbNq0CcnJyeuaOW40GtHU1IS6ujrU1taio6MDdrsdaWlpKCsrQ2lpKYqLi7k+IqXfig6hIyMjkCSJ602JnVcq4eHhC55l7LacgggCzM7OctdIAlfGxsZ4R44MS7Kysjh4JjItZmdn0dLSwsGz1tZWmM1mhym+S3FpvVNhtVrR29sr0z0cGBjg6c1i6nZOTs6CVMOpqSl0dHSgvb0dg4ODmJiYwMTEBKampjhTDpgHVsi9UAQunNXtdjvfNiAgwKEBQ3x8PKKjo2XPZhp8EHXRCGQS9ZiIwUVAVXJyMmJjY9cVQ3Jubk6WFiqCf+RIr1arZf+DirNBDgL3nAEbYpmbm4OPj49sYJjS8xzNBwcHc6AsLCzM5fPIbDZjZGSEg2UiaCYCi5SaLpb4+HjExMQgLCxsTTXnGGOYmZlZwBQlQ6SxsTFMTEzAarXKACx6Vjl7fq2H57jNZuO6dc5SOqempjgYJoZ0WxfRHWgrLgsJCVlX954ypA1NNR5xFYnsyeovuF3v36W/d3vMJEk6DuD7mDfFfJ4x9s+SJH0bQDVj7A1p3uXzZwCCATAAf8sYq7y97TcBPA3ACuDLjLF3XPxOBmOsS7EsnTHW7e5/rASo5gl6+L8AFLMPjQoeYYx93NX3bhgVbMRGOA6bzYaJiQkOVFDjoq6uDpIk4cknn8S9997L6d9DQ0P47Gc/i7fffpu7nS4llvOsaG5uxhe/+EU899xzHNxb6finf/onnla3ltHV1YVvfvObSEpKwj/8wz/IOhaMMVRWVuJ73/seHnzwQfzFX/zFso1AKIVhdHQUs7OzSE9Pd6mH5irMZjNeeukl/OpXv8K9996Lz3/+87zBPTs7ix/+8Ic4deoUnnnmGTz66KOr6s41PT2N06dP47333kNPTw+ys7Nx+PBhHDhwgO/T6OgoXn/9dbz99ttQqVS477778OCDD3qsh2UwGHD58mWcPn0aNTU1CA0Nxe7du5GRkcHTq9RqtdN7xmw2Y2pqCmNjY2hsbMSNGzfQ2toKm82GnJwcrkfmCXtyZmYG7777Lt58801MTExg7969+NjHPsbZdTqdDpWVlXj99dcxPDyMiooK7N+/Hzt37ly0kYWzmJycRENDA+rr61FXV4fBwUEkJCRwEI0ASqvVipqaGg6imUwmlJeXY9++fdi5c+eiQLSZmRn+e7W1tRgYGIC3tzcSExNlI95iCQwMhEaj4Z1y6pxMTEzAaDSCMcaZS5RW+Dd/8ze455578NnPftbtPul0Oly9ehUXL17EzZs3YbfbUV5ejj179mDHjh38nrbZbGhtbeUplC0tLbDZbIiJiZFpV1Gh9BY/Pz8ufO+oDA4OQqPRcLF4ShXMz89HXFwcvx7tdjva2tpQXV2N6upqtLW1cbMMMmFYig4ZOZwpO7BDQ0MyrTh3zNXg4GAOQlBnPjk5GVFRUYveJ8YYent7UVtby7Xr9Ho9wsLCUFpaysG2+Ph42fEh10zquIplenraIYuD0rgIBCDXSGKNpaSkLND7EzXI2tvb0dbWhp6eHlitVoSEhMjOYU5OzoqwHAnsGx0d5e59BDYpASibzeaQKbNSRlRarZZrMlKZm5uDJElISUlBTk4OBzgTExMRHx/v9BhYrVZMTk5ibm7OYbqdsu5skMdut2N0dBQ9PT3o7e3l08HBQQ7ERUdHy5xAU1JSkJSUtKYpkKsRjDGZ9iSBbn19fVzny8vLi6dQqtXqBSmFVF+u26PNZsP09DS/J8V7k+p6vZ6v7+PjwweRlWU5qenrPXQ6ncNjIz6/lCFJEvz9/REQECBjSgYGBi5Y5uPjs2AQn1IZlXVHBgD0eyqVCuHh4QtSOcUSERGBsLCwdQmGMcZgsVhgsVhkOqGLnTebzdi7dy8KCws3QLXbEVuRxD5Z/UW3631f+rt1c8wkSbrJGNukWHaDMbbZ7bYrwWLyAD30B/ArAOUApgA8rkQBlbEBqm3ERiw+RkdH8dJLL+Htt99GWVkZvva1ryE2NhY/+9nPMDc3h6985StrAqoBwI9//GMkJyfLUlFXMjo7O/H5z38eJ06cuONivGNjY3j11Vdx8uRJxMXF4atf/eoC8LCmpgb/+I//iKKiInz9619fUpqqxWLBmTNn8Lvf/Y5r2ADzKQwkatvT08M7i7Gxsdi1axfuu+++Rem02e12vP766/jxj3+MiooK/PVf/zXXY9LpdPjZz36GN954Azt27MDnPvc5rl+2nLBYLLh27RoqKytRVVWF0NBQHDp0CIcPH+bfT+ucOnUKVVVViIyMxIMPPoh7773Xo5RCxhjq6upw6tQpXLp0CZIkYffu3Th06NCKOmkS2HH69GmcPXsWOp0OW7duxT333IPt27e71TuxWCy4cOECXn/9dTQ3N6O0tBQf+9jHsGPHDnh7e8uYYZcvXxa1PLgoNXVg/fz8wBjD3NzcgtSKkZERNDQ0oLW1FVarFZGRkSguLkZJSQmKi4u5HiMJ3l+4cAFXrlyByWTCpk2bsH//fuzYscNjUM9sNuPmzZv44IMPcO3aNczOziIkJAQlJSUcFFmsE6ejEJlLjY2NaGxsxMsvvwyLxYLY2Fi8/PLLqKio8JgVZLFYUFNTg0uXLuHKlSuYnZ1FXl4e12UTZRIYY5x9QxpWymIymRaIZFPqUUREBOLj4xcA49PT07L/093dDcYYcnJysGXLFlRUVCA7O3tRHUyLxYLW1lYOUrW0tMBsNiM0NJQzSpWsj8U8W2dmZnhamZhmNj4+Drvdjri4OBQXF6OoqAjFxcUywNDT0Gg0qK+vR21tLWprazmTTwmWkBPkUsESs9ks0z0aHBxEe3s7Ojs7YTKZoFKpOHiUk5OD7OxspKWlLXngwWAwoKuri2tiUWre2NgYB4RCQkKQlJSEuLg4rv9DgJMj4MkRU4aY8AB4eielQ2ZlZSE9PX1ZAKDdbkdfXx/a29sxNDSEwcFBDtRSepxKpUJMTAwH3AIDA7nrrzgV6wQSKXXiRGY/sQhJDzU1NRVpaWlISEhY0c69Tqfj1/jg4CC0Wi03vhBNMESgSAwxC4HdNgMRXWkDAwMRFBQEtVrNQa+kpCSXAz5LCavViqGhIQwMDDhMKaR0Q+X/cJZe6Og/AvPXWXh4uIyBJU7JdftOhNFoXODwq9FoYLfbF1x7ruZ9fX1lJgjL1chcajDGYDKZuJafwWCQafuJ82az2aHkjCfzd5ohZ7FY+H3lqDhy0RWL0Wh0uM907fr4+HAXa7HubJmz+bKyMqSkpKwbgGitI6YimT1W/RW36/1Q+uqaHzNJkvIAFAL4LoCvCR+FAvgaY6zQ7XesRWqgJ1FWVsaOHTu21rtxR2I5D6elbqvczt38UrZxNr9U/bLF1GlKjcmVnjr7TKmN5u/vvygtjZUKxhiuXLmCb37zm/iP//gPlJWV4f7778fPf/5z7mi5lO9cTvzHf/wH8vLycPz48WV9j6u4fPky/v3f/x2vvPLKqgi1ijEzM4MTJ07gD3/4AwICAvDYY4/h+PHjsk6bXq/HiRMn8OqrryImJgb/+I//uOjUPbvdjvPnz+Pll19GT08PDh06hI9//ONIS0tzuR1p/Zw7dw4nT57E2NgYdu7ciaeeesojl0n6jnPnzuG5555DXFwcvvSlL6GwsJB/dunSJfziF7/A2NgYNm/ejJ07d2L79u0umXIWiwW9vb3o6OjgpaurCzabjad0VlRU8M5od3c3Kisrcfr0aczNzWHbtm04evQoT/t0F+Pj43jvvfdw6tQpDA8Po7S0FEePHsXu3bvvWDqI1WpFVVUV3nvvPVy7dg1+fn6455578NBDD7m9HwkIfP3113H16lWEhobiyJEjOHbsGAe9rFYruru7FzjCDQ4OwmKxQJIkBAcHL2B9xcTEoKioCLm5ufx+sdvtaG9vx7Vr13D9+nV0dHTAz88PZWVl2Lt3L3bs2OExGDU+Po4rV67g8uXLaGxshJeXFzZt2sSvkzulrWYwGHD8+HFs3boVZrMZ0dHRqKqqgtFoRF5eHnbu3Ildu3Z5/GxkjKGlpQWXLl3CpUuXMDY2Bm9vb54CmJ2djaysLGRkZCwqxXlyclJ2/jo6OtDW1gar1Yrw8HAu6F9cXIzU1NRFddrGxsY4I7C+vh5jY2NcV62kpAQlJSXIy8tbMeaSu6DnU0NDAxoaGtDY2MgBsYyMDBQXF8scWxf73RMTE9wYgab9/f2cZUHvcQpl+4ExBrPZzOd9fX0RExPD3fni4+ORnZ2NzMzMZbkGjo6OoqWlhadTdnZ2cj1KMgkg1mxSUhJiYmJWrbNOz+bOzk50dHSgs7MT3d3dHPyKj49HVlYW11PLzMxcEcadzWbD+Pg4B930ej0YYzxtUZxSHQDCw8MXsEFXWufQYrFgcHBwATBMz1ZgPk2PmJiJiYkIDw9HUFAQgoODeQkKCkJAQIDbc0fXnSNgQKmpRlp1jDEOhBPgRoDyal4v6y3m5ua4hh6V0dFRDpjNzs4u2Mbf31+mpUasPG9v7wXXnXj9KeeNRiM0Gg13/ZyZmVnQZiazCbVajaioKP48iY2N5ezm9cjkWukwGo1OTVOortFoZMfP29sbYWFh/F4SwWZlcbR8NYBAs9nMn1nU9tq3bx8qKirWHCBaLxFdkcIeqf6q2/V+Kn15zY+ZJEkfA/AQgAcBvCF8NAvgZcbYB26/Y72Caps3b2YvvvjiWu/Gqsdyjv9St1Vu525+Kds4myc9sqVomC1lG3daZu6mi11XqY1mNBodaqI5OkZiaoajEhMTs2i9lcnJSXzmM5/hQMrXv/51nDt3blHfQbGU681ut+PUqVP42c9+hrCwMHzve99bESdZV/Hmm2/i+9//Pg4ePIhnnnnG41RAT8JgMODtt9/Gq6++CpPJhIcffhgPPfSQjHXGGMPly5fxwgsvYHBwEA8//DAee+yxRTscdnR04IUXXsAHH3yAffv24fHHH0dOTs6S991ms+HixYv4xS9+Ab1ej6effhrHjh3zuBHX0NCAH/zgBxgcHMTTTz+NBx98kG9rsVhQV1eHDz74AFeuXJGliFHQ9ePt7Y3U1FQZ+JCamspBnbGxMVy5cgXvv/8+WlpakJ6ejqNHj+LQoUMeHUOLxYIrV67g1KlTuHHjBtRqNQ4fPowjR44scMJ1FowxDA0Nobm5GW1tbZibm+PpAVarlU9jYmK48HZmZqbH9+fs7Cw3UpiYmMDBgwfx8MMPIysry+22k5OTqKysxLvvvovh4WHO8MrIyEB6errHAu3UoRfTtQjAycnJwbZt27B161ZkZmZ69H12ux3Nzc344IMP8MEHH2B4eBjR0dHYuXMndu7ciaKiokV3GPR6Pe/QTk9PywYvxOLr64vU1FQkJiYuqgNpt9vR2tqKDz74AJcvX8bQ0BDUajW2bduG7du3o6yszONOus1mQ39/Pxdnb29v54CEUgRZDPG9FRUVJWMaZmRkICcnZ1EDBBaLBc3NzRw8IxZiTEwMB89KSkpkTqDrKex2O7q7u1FfX4+amho0NDTAaDQiKiqKa9eVlZUtOcWdgrR9xDaDOAWwYgNiZrMZnZ2dHDxraWnhTpNxcXFchywvLw/p6emrPiC0lGCMYXh4mAO9LS0t6OzshNlsXuAenJube8eMIpYTjDFMTU3JNNUINNPpdADmr4GEhIQFacwJCQl3DID2JGZmZnjnngDA3t5ezmyUJAkxMTEL0piTk5PXVGvMXdA5EoEy0YBCp9PxezYoKIhrDFOJiYnhqazBwcFrdk0SW5xS/CmVk5ivpKkm6gOGhYUtAN5iY2OhVqu5tthamuUYDAanEgako0Zp3GKQQ6pSIkGcd6R3eSf/m3g/0QDl4OAghoeHuQmEr68v4uPjZQYhpaWliI6OXnOAaL1EdEUK+1j1192u9wvpi+vmmEmStIO5cAZ1ue16BdU20j834o8p7HY7T81wVkZHR6HX6xEUFITi4mKUlpaitLRU5vLmKCwWC/7mb/4GJpMJX/rSl7B7926uoeFpxMbGytINlWEymWSpHfQiqqmpweHDh/G5z31uRcEtd2G321FZWYmf/OQn8PPzw5YtW3iHzJPOmM1mQ3d3N5qamnDr1i00NTVhcnIS3t7eOHLkCB577LEFzri9vb148cUXce7cOezcuROf/vSnF7h9uouRkRG8+eabOHHiBOLj4/HUU09h9+7dK94QHBsbw//8z//g1KlTuOeee/D0008jLi7Oo22npqbw/PPP4+TJkzh48CB2796NTZs2LTmdtb6+HleuXMHVq1cxMTGB6Oho7NixAwcPHkRubq5H/72rqwunTp3C6dOnodfrsWPHDhw9ehSbN292C+QQGHTlyhVcuXKFX+eJiYnIy8tDTk4OH7mmFCsfHx94eXlhZGSEAyidnZ0wGAyQJAmJiYnYuXMn7rnnHrdAntlsxtmzZ/GHP/wBXV1d2LlzJx555BGUlJS4/e92ux11dXVoamrijn2jo6MA5sHLuLg4znogRzfgQyAnNjaWi4rn5uYiKyvL446iTqfD9evXcfnyZVRXV8NsNiM/P5+DaJ6mG+v1ejQ2NqK+vh719fXo7e3ljdbAwEAOMKnVaj6IoSwmk4lrJTHGOHArprFRauRLL72ET3ziE07T8qampnDt2jVcvXoVNTU1sFgsyMvLw44dO7B9+/Z1JfKv0Wi4Fl1dXR2Gh4fh6+uLvLw8lJaWoqSkRMZCXErMzMxwwGFkZGSBzo7o6Gez2aBWqxcIanvibOcuxsfHUVtbi5qaGtTW1kKj0SAwMBAlJSUcaEtKSlqzc0P6bSKo2tHRwUGnrKws5OXlcQBtKbpyngZ1eOfm5mA2mxEcHLyq4t4EGooA/ejoKBhjiIqKkjmFxsXFIS4u7o4AOXa7HSMjIxw0I121wcFBDqpGRkby9GARaLpT6Yd3KogZ6oi9Sa6YgYGBSEhIWJCarixLTaG22+08jY80MGkq1kU3UEmSoFarOUhGLFEqd8IRdy2CgB0RdKP61NQUZmdnMTMzs8BhlILueQLfQkND4ePjs8CtVBwkdLbMYrFAp9NxcJYiICCA66WJhZaRhuhagpk2mw0zMzOyFF8xzVfUNhWvu9DQUA6UiQxQSrV3FRtGBR9GZEUau6/6m27X+5X0zJofM0mS/pYx9l1Jkv4L80YHsmCM/ZXb71ivoFp5eTl7+OGH13o3ACwvPXMlf9PZfqzEcnGZMg3C02VLWV8spE/gapkn67haJrIbHOmPeDJVLrvT18fc3BwaGhpQV1eHuro69PX1wc/PDw888AAeeughp6BRdXU1vv3tb6OgoAB/9Vd/hbi4OHR0dODGjRu4ceMGRkdHYTAYZLbjgHu9DMYYfH19ue6O+BLKzs5eVTF7T2JiYoILidfW1mJqasrheaP/abPZ4OXlhfT0dBQUFKCwsBD5+fkLGHYmkwkffPAB3n//fdy8eROxsbH49Kc/jf3793s8wkapfW+++SYuX76MqKgoLrZ/Jxr1BD4+//zzkCQJTz/9NA4fPuzR/hPz7fr167hx4wZmZmagVqtRUVGBtLQ0h2kTExMT6OjoQE9PDz/ORUVFHLDwFHidm5vDuXPn8O6776K1tRUZGRk4evQoDh486JbNNjMzgytXruCDDz5ATU0NrFYr8vPzsWPHDuzYscNjNpuzID2vCxcu4P3338fg4CDy8/Nxzz33YP/+/S41qWw2G65cuYITJ06grq4OZWVlePjhh7Fjx45Fj9qazWaMj4/Dz8+PixQvR0+xo6MD169fx7Vr19De3o7AwEBs3boVO3fuREVFhUcdrfHxcVRXV+PGjRtoaGjA7OwsAgICUFRUxEGg5ehQUYhpbJ2dnWhra+PC8W1tbUhISMCZM2c8YgYSm42A376+PgQHB2Pr1q3Yvn07Nm/evOr3Kongk3aYKNJfVlbGi6fAuBgiK5OcKcXUttDQUA44xMXFISAgwKmjn5eXFzfuEAt1lonVTQNDVNLT05fEStDpdJzRRiYXoo4WaWilpqYiKSlpWdeVzWbjg1ujo6OyFEmTyQRJmncaFRm4WVlZy2aSmM1mDAwMyMCQvr6+BXpoYkiShICAAAQHB8PX15frKYosGIqgoCCEhYVBrVYjPT2dg9DJyckrAsJNTExwoI0ML0ZGRng7gzHGdQQJNKEBGmdZCDSdnZ1dYFZCzFDGGFQqFWJjYzloRiUxMXFV2iUWiwVTU1OYm5vD7Ows11QT6+I8OcA6KvTMphISEsIdTFeLwaPT6TA8PCxjHDkyVBGF6hcTkiTB29sb4eHhC1ybaRoVFYXQ0NA1HbgQs1xcFZVKxY1z1stACzD/ztLr9Vw/lQA4i8XCBwaVxdFyUacxKChoTVNTzWazwzRRqk9NTUGj0Sx4JqpUKoSFhcl0SynNV6xT2u9izuPc3Jws/XNoaAgHDx7Eli1b1hwgWi8RWZHGjlX/g9v1fiP92ZofM0mSHmCMvSlJ0mccfc4Ye8Htd6xXUG3z5s3sRz/60VrvxpJjtdI6V/szRykQnixbyvqOCukTOJtfzjIxRZNeio7csjyZinVqqDoTwaTPyJHH398f/v7+Mmo3jebHxsYiIiJi0Y0mrVaLN954A6+99hrsdjsefPBBPPLIIw7ZQ++//z5efPFFjIyMYMuWLTh+/Di2bdu25uDXeggSp3UUFosFjY2NOH36NC5evAi73Y6dO3fi8OHDKC8v97jBYTQacebMGbz11ltob29HaWkpHnjgAezatWtNz8HQ0BB++ctf4v3338ehQ4fw2c9+dtH6e5OTk7hx4wb6+vpkYr5UIiMjkZWVhZSUlEWxZ6anp3H58mVcuHABDQ0NCAwMxL59+3D06FHk5OS4bAj19fXh0qVLuHz5Mjo6OhAaGoodO3Zg586dKC8vX/X0Ccbm9bfee+89nD9/HgaDAdu2bcPhw4exZcsWp8eBANc//OEPuHbtGmJiYnDkyBEcOXJkAUtypWNsbAzXr1/H9evXUVdXB7PZjKysLGzduhVbt251e8yB+WfSjRs3OIg2MzODqKgoVFRUYPPmzSgpKbljmmoUf/jDH/Cnf/qnUKlUSEtLQ0pKCnJzc7mmmqeA6szMDKqrq3H16lVUV1dDp9MhKysL5eXliIuL489zTwW37XY75ubmoNFoMDg4yFNx29raoNfruQg+sZOLi4sXDeTZ7Xb09/ejqamJl6GhIc6sLCgoQEFBAXJzc5GYmLiqKYgzMzO4desWd5rt7u6G3W5HUlKSDGxbilQAMXKUjo8DAwOwWq1gjCE8PBze3t6ywTdlfXZ2VsZcoOcXpWJlZGQgOzsb6enpy3aKnJ6e5ue7ra0NHR0dnE3u4+PDUw5FNlV0dPSyzxFjDDqdDlqtFpOTk+ju7uZgYX9/P2w2G7y9vZGWloasrCwOuKWlpa3Yc5OxecMBMaVvZmbG6SCsuCwkJIQDMdHR0auiqUb7SK68YgdamQ7m4+ODyMhIhISEyPTUiDWknPfx8YHBYFhQjEbjgmXkyDs+Ps7blf7+/nwAUzmguVipkI9S6HQ6zuiiIqYkUp3OGwWBsQAcygwoB+Ypw8SRAYVKpUJISAjXMRWnSjbvek6/XelgjEGv17sEyCYmJhZk0/j6+jpNFY2KiuIA2UqkZJMpjTL7hnQM6RoJDg7m9x1Ni4qKEBUVteYA0XoJdUU6O1z9LbfrvSo99ZE4ZusWVNtI/9yIj1LY7XausUaNJo1Gg/HxcRm1e2xsTPbQliSJuyTFxcWhrKwM27dvd8l20Wq1eP311/G73/0O0dHR+NznPocdO3Ys6ATbbDbcuHEDb7/9Nq5duwaVSoXo6OgFTllJSUkr1sFizLkILxWDwcD1qxwVq9XKBV+Vrnk0v9QRNepkjI6Oypz2NBoNvL29kZ+fj0OHDmH37t2LarQODw/j5MmTePfdd2EymXDgwAHcf//9i9ZIY4xhZGSEd74mJiYWHD+TyYS0tDQUFRWhsLAQBQUFi+qA2+12vP/++3j++edht9vx2c9+FkeOHLmjo5Sjo6O4ePEiLly4gLa2NoSHh2P37t3Yu3cvioqKnAKfVqsV9fX1uHz5Mj744ANMT08jJSUFu3btwq5duzzWB6PQ6XTo7u7mrnJiWoTFYgFjDImJicjMzPTYudBiseD69evctECtVuPhhx/Gvffe6/I8DQ8P47333kNlZSXGx8dRXl6Ow4cPo6ioCDExMUsaKTeZTGhvb0dTUxNnKs3MzCAmJoYDaCUlJW47qnq9HrW1taiqqkJVVRUmJiYQFhaGzZs3Y8uWLUtKD2ZsXrh/eHh4QcohzZvNZkRERHCWbExMjEtg2mKxwGg08ucnY8yhphqBbMXFxR5d94wxdHV1oa6ubgFDi7SZgA/F8a1Wq6wDJ0kSQkJCEB4ejvj4eJ6Sm52dvWjwzGKxoLOzE83NzbxMTk5CkiSkpKRw8KygoGBJbpurFcSYI6CtoaGBp9zn5OSguLgYZWVlKCgoWNb7iNKqaEBMOQBHg2/UIV6p46PX63laKD2/R0dH+TueXENzcnKQlZW1pHT61Qgl67Ojo0OmFUiMdErxzMjIuOsG6Mi9k/TUaDo2NsbvUdF5U+xEe5IOtlqh1+tlHX6xGAwGMMbg5eWFxMTEBU648fHxd5UovslkwujoKEZGRvhUrIvsucDAQC78r1arZemIVMLDw1f1vNlsNs4Q1Wq1fKrVahewecV99/Hx4U6ojlLo1wszzmq1QqvVukyrJE01nU4n29/AwMAFoJiyvtJsRavVyvtbymtndHRUBuKRKY14n9PUE8fdjfTPDyOiIoMdqv7/3K73e+nJdXPMJEl6D8BjjDHN7fkIzBsVHHW77XoF1UpLS1lBQcFa74bLWOoNv5ppnM6W34lUTkefeZKWuZKfOUrvXMl5SnUhtpmfn9+qimnSKO7o6CiGh4dx48YNXL16FbOzs8jJyeFgQXJyssPt29vb8fzzz6Oqqgr33XcfPvWpTzkd/WeMYXx8XDa639PTw0esxU6goyBLabPZLHNNo6B5X19fh449VAICAuDj4+O0eHt7ywRflakKZIcudliVjMHAwEBuLa78X8HBwYiKikJhYSGKi4tRWFi4aFFso9GIGzdu4MyZM7h8+TKio6Nx33334dixYx4bFkxMTPAU37q6OoyPjwMA73RnZ2cjJiZmgeORn58furu70djYyPXgdDod/Pz8UF5ejvvvvx/l5eUePb9GR0fxy1/+EpWVldi3bx8eeughFBQUrGinaW5uDnV1daipqcHNmzcxODiI2NhY7N27F3v37kV2drbTfZ2dncXVq1dx+fJl3Lx5EzabDSUlJdi1axd27NiByMhIt79vNpvR1taGxsZGNDU1oaOjA1qtFowxBAUFIT09nbNCKCWC6pIkob+/H52dnVxDyN/fHxkZGcjKyuIC/a6O9fDwMF577TW888478Pf3x4MPPoj777/f5XVit9tRU1OD9957D21tbbLOH6U7xcXFwWg08lSj2dlZXiwWC+x2O3x9fZGTk4OCggLk5+cjPz/fLXvMYDCgoaEBN2/eRHV1NQYGBhAYGIiysjJs2bIFFRUVHqfxWiwWdHR08ONOjCLSO4qKikJ8fPyClEMx9VCj0WBoaAhDQ0MYHR3l25KmXFZWFioqKmC1WvHP//zPOHHihMtjOzExwR1LGxoa4O3tjYqKCuzduxdbt25dFiuJgJyV6Mzp9Xq0trZy4KylpQU6nQ7e3t7IzMyUnVNP7gMxGGPQarUygWZi5RiNRv6MN5vNC9gewDyDRuwMimzsmJgYhIeHe9x+slgsaG9v586lTU1NMJvNSEpKwqZNm7Bp0yYUFxevC2bO3Nwcurq6OADV2dmJ/v5+MMYQEBCArKwsGXgWHR29rI6jzWaD0Wh0WWgQz9/fX6Z3FBERsWxWl91ux9DQENra2niKZ1dXF6xWK/z9/ZGdnc015LKzs1dVR85ZWK1WDA0NLQDMROZiUFAQZwOK09VMtbxTQf9f1FHr7e3F8PCw7DmbmprKRf0jIyNl05UEl4H59pHIInNWxPaZn58fd951NF0uW3S9hNlsxsTExIIUejIymJ2dlQ3UUNDAjJIZFxgYKBs4UEpyOFtOA/9U6Fqh8PLycphCqUyvjIqKWpVnM/WNxsfHeaEUcCqig6i3tzeio6P5NaO8fkJCQpZ8jdtsNtTW1uLChQvUVlg3ANFaR3hFJttb/R23670pfXzdHDNJkmoZY2WKZTWMsXK3265XUK2iooIt1aXQ01iL/+7sN1dz+Z1I5XT0mSfpnc6WLXa5+GIQUzSVKZuLWeZonpgOxJQwGo0Oj4Ojh3NoaChPVRCLODrjaSOXMYb29nZcvnwZly9fRn9/P6KiopwyLCwWC06ePImXXnoJOp0OFRUVOHDgALZv374ijRHGGHc+9fX1XVeuWGIwNm99rtfruVbJSjQWJycnOdulvr4ePj4+qKiowL59+7Bz506XIJTNZkN7ezsXG29qaoLFYkFkZCRP9yotLV22W5/RaERVVRXefPNN1NTUIDs7Gw8++CAOHDjg9rqz2+04e/YsKisr0dTUBJvNhpycHFRUVGDLli1cBN5ZWCwWDA8Py1zJ6uvrMT09jaCgIJSWlnIjCVcppwMDAzyVs729HSEhIdi2bRt27dqFTZs2ufwfNpsNXV1dnH3Y2NiI2dlZ+Pj4IDc3F0VFRSgoKEBWVtainVrFMBgM6O7uRltbGy5evIhbt24hMjISBw4cwKFDh5Cenu5028nJSbz55pt48803YbPZcN999+H+++/3WPTfbrdzYHxkZAT+/v4ICQnhhdKOPL0/Z2ZmUFdXh5s3b+LmzZsYGxtDQEAAiouLUV5ejoqKCiQmJrq9h0wmE9ra2njaYXNzMweAsrOzkZ+fj+zs7BXVO7JYLBgdHUVLSwsqKyvxox/9CHNzc/Dy8sK3v/1tPPjgg8jPz3fL1jAajaiursaFCxdw/fp1mM1mlJSUYO/evdi2bduiAavFxvT0tIx11t7eDovFgoCAAOTl5XHgLDc3d1Gi3TabDb29vbK0w97eXv4eCwsLk7maJSYmIj4+HoGBgXwAxdfXF15eXgvOv8FgkLnbKTuHU1NTsNvtiI2NRVFRES+eXEvA/HN8cHAQN2/eRE1NDerq6mAwGKBWq3maIpWVdKGkgScRNOvs7MTExAQkSUJQUJDstzMzM5GYmLhoRpBo/KNMORRTI728vPgAn1JeQlkIyKDUt+npaZjNZlm7hRgaNChAx3IpYvAGgwEdHR1oaWlBa2sr2tvbMTk5KfutuLg4JCQkcD21iIgIp6l24jKdTufU0Glqakqmq0RMLSVotlLunaQVSgL7VERmkli3WCwLBqFdaVsFBwcvAADIAXIlAD9iBPf19fF7UzyWk5OTmJmZ4W1tAHwgW2yDOxpYF39DXObv7y9jjYlFZJStNlBuNBr5YKxer3epo+boM9JUU5agoKA7DsaShABdb3TNkXwAERCUxdFySZLg5+fHwffQ0NBVZ5/abDZMTU3JQDFlmZ2dlZE2wsLCEB0d7bQsZvBmMWGxWHDjxg2cP38eV69ehclkQllZGfbu3Ytdu3YhNDR03QBEax1hFVlsV/V33a73jvQn6+aYSZJ0A8DDjLG+2/OpAE4wxja53XY9g2ob6Z8b8VEJu92+QFSXtAPEomR4hYeHIzk5GSUlJSguLkZOTo5TlsPExAQHdohhsWXLFhw6dAhbt27lL0V6IZw7dw5Xr16F1WpFRUUF9u/fj+3bt/9R6TssJYhdU1VVhUuXLqG3txeRkZGcNegqZUyj0XDGRX19PQYHB6FSqZCdnc3Bs4KCgjtik97W1oY333wTZ86cQVBQEO677z4cP37cI5aR3W5He3s7qqurUVVVhY6ODqeNF7vdDm9vbyQkJCA5OZl3aoqLi6FWq53+xujoKNfjqq2thcFgQGJiIj/Orhhser0eDQ0NqK2tRW1tLXp6eqBSqZCZmYni4mIOoN2pFKuJiQmcPXsWp0+fRk9PD5KTk3Hw4EEcPHjQqT7azMwM3n77bbzzzjsYGRlBSUkJjhw5gt27d6/KqLxo6HHz5k1otVqEhIRwsLO8vNytlhsBlw0NDWhoaMCtW7cwOzsLPz8/5Obm8rTDvLw8lynsqxHPPvssN39ISkrCzZs30dzcDEmSUFRUhK1bt2Lbtm1uddVsNhvq6+tx4cIFVFVVYWpqCiqVCunp6SgsLERhYSHi4uK465oz4N5ut2N6enqBC974+DgXpif9LwLO8vPzF+XQSvpiSr0uo9EIlUqF1NRU7vyak5ODlJSUO5oSNjY2hsbGRjQ0NKCxsRGDg4MAgNTUVBnY5ilwOTU1xYEuYowRg9TLy4uLvwcGBi4o/v7+nAFNTBkCEwhAkCQJkZGRXFuMgLPIyMhFd940Gs0CcG54eBjAPOAkphqK6UerqUNIWkJdXV3o6OjgRgzEjImOjuZgG5WlDkCYTCaMjIxw84Lh4WHOiHEHZijTx8SiVqtXBCwjJgyxYJWFjBYkSeKdeJEl5KgeGhq6YN/o/zlzYSQHSEpTo7ooE0J6f8TAoXcsae+tNLBAg9hixshahsFgWJB2T8L1lMWgBAaBeXCPGFWBgYEOgVxXIC+16wnEojI3N7eA4EBsVbomRC1AKqQN9lFgRzrSsCPQjN5zYtqrSqWCWq12CZKtlYuo0WjE9evXcf78eVRVVcFqtSIzMxOpqakIDAzE0NAQenp6MD09jb/6q7/CsWPH1g1AtNYRVpHFtlf/h9v1KqWPrZtjJknSMQA/BXAegARgD4BnGGOn3G67AaptxEasz7Db7dBqtejp6eE6M21tbbBarYiMjORAW0lJCeLj4xe8bIiZdPr0aVy/fh0BAQE4cOAADh8+LBMat1gsqK6uxtmzZ3H16lXYbDZkZWUtsDCPi4tDVFTUXf/C9zQoLVAU9iZmU3Z2NjZt2oTdu3cjJSVlwbG32+1cY6murg6NjY0wGo0ICwtDSUkJB9ASEhLWvEEKzDOk3n77bZw8eRIajQYVFRU4fPgwduzYcUdYh2NjY1zUvra2FjqdDnFxcdi8eTMqKipQWlrqdNR6ZGSEg2d1dXXQarUIDAxEUVERd0VMTU1dF8eZoq+vD6dPn8aZM2cwNjaGHTt24P7778emTZsc3l+MMTQ0NKCyshIXLlyA1WpFUlIST/ErKChAUlKSy/9oMBh4+pNYxsbGYLPZEBkZydPpysrK3HaWR0dHOXjW0NCA4eFheHl5ISMjgwvNFxYW3nHwbLFhtVrR2NjInU0HBwcREhKCiooKbN++HVu2bHHLmLDZbOjp6cGtW7dw69YtjI6O8lRbg8EAYCGLXKVS8fQYpQNeQkICUlNTPX7W6vV6rtVFaXhTU1MAgJiYGBlwlpmZua5Tpex2O/r6+mSM0snJSfj6+vJ7ury8HBkZGYt6F9ntdp7y76gYDAau1UlMmZCQkCW/7+x2O4aHhxcAZ6TdExYWtoDV5ug9vl5CZOkR4CamyoeHh8vMDLKyslYF0Flu2Gw2jI+Pc1MEKjQvdvRFvUZRWykuLm5dDT6KzrTECKc0V9HYIDo6moNtVJKSku6I0/hiwmq1YnJy0mEKJIFmYtp5QECALL2ctNREnd3Q0NA11ZBjjMFkMmFmZgYajUbmWCumL05PT8tcegMCAvh7wZkJhmh+ERgYuOz/yRiDzWaD2WzmGnCuijId1dvbW/YcFdPORSBxvb2HZmZm0N3djfb2dtTU1KCpqQmdnZ0wGo0ICgriIKyfnx9SUlKQlpaG9PR0rkNNzLgNTbUP424E1QBAkqQoANtvz15ljE14tN0GqLYRG3H3xcTEhEzQmVzcsrOznTrZabVanDt3Du+99x5aW1uRnJyMnTt3cjF7Yu2YzWb09fXJGppUn5iYkLkjxcTEcMAtMDBQlr7gSINKOVVS0Bc7dZZm7Cxd2G63Q6PRyNyGxCl1EBhj8PHx4XpThYWFyMvLc8gSmJ2dRUNDAwfQyPkyIyODA2iFhYUedcz7+/vR0dGBgYEBWaNrcnJS1oh01VGhEeSUlBQZoyA9Pd2jjoDNZkN1dTXef/99XLlyBT4+Pti2bRtPMcvIyFgS0GYwGDgDh1JAGxoaMDc3h5iYGA6glZWVOWzk22w2tLW1cQCtubkZVqsVcXFxKC0tRVlZGUpKShatf7fWYbPZcPXqVbz11lu4ceMGUlJS8MADD+Cee+5x2tmh9DcyFyAnR+U6Yt3f31/WmSLhancdX71ej1u3bvFnTUdHB+x2O2JiYmROjetJ9H65MTs7i+rqaly5cgVVVVUwGo3Iz8/nz1ZP03FXMuj5QKAZpWva7XYEBATIxOJzcnJWPS31TofRaERTUxNqampQW1uLzs5OAEBmZibKy8tRUlKCzMzMO3r/Uyesq6sLXV1d6O7uRl9fH08Ni4+PR0ZGhgw4Wy8GBCsd09PTMjODjo4OjI2NAZgXJqf/n5ycLOtwL8dYSAyj0cgFyEWwbHh4GGNjYxyoIEMmccBQHEBcbx39lQoCRZWDKv39/Rz4Bz40xxKZVDQNCwvjrC1XKYVUSJdLq9XKpsplxPqj8PLyQlRUlEOxfhLsXytziDsder1e1g6cm5tzWXQ6nQyUW2p4eXnB19cX4eHhHqXp3g3vfjJdaWxsRE1NDVpaWtDV1QWNRgODwQC73Y7AwEAEBwcjLS0NBQUFKCsrQ2ZmJtLS0jwyKQA2jArECKnIYRXV/+l2vXPSvevqmEmS9CCAvbdnzzHG3vJou/UKqm3atIn967/+61rvBoCVNSRYyWWu1lmMucBKTJWGAYutL3Yb0Ujgj4U55S4IdKAU0MHBQcTExPB0ucLCQtmx6uvrQ3V1NRezn5mZgZeXF7Kzs3kaU0FBgVOmidVqxdjYGAfcjEajzKHTk6nSfW0xU7vdLrt+HBVH1xhR7x25DoWGhjplCmk0GvT29qK7uxv19fVobGyETqdDcHAwiouLOfvMEXNNPGbd3d2ykf6enh5YrVaoVCokJycjKysLSUlJspSAxTYiRYCOSldXF0wmE1QqFfLz87FlyxZs2bLFLYtrbm4ON27cQGtrK2+EWCwW+Pr6cq0NUQeG6jqdDgMDA7zB7O/vL0v/TE5Odspk0mq1uHXrFurq6lBbW4v+/n54eXkhJyeHA2h5eXnrVrtvOdHd3Y2TJ0/ivffeAwBs374de/bswZYtW1Y1Ldhms6Gjo0OWumkwGBAYGIiCggIOnmVlZa171zidTgeLxbIsbTwx7HY7mpubuYblyMgI1Go1T8nMy8tDTk7Oss+P2WzG5OQkenp6OOusra0NBoMBKpUKKSkpHDTLzc294+ma6y3sdjs6OztRW1uLhoYGdHV1YXp6GsC84QwBWqmpqQgJCZEZutBUqT0qOtiJUyp9fX08vSs0NBQZGRlIT09HRkYGMjIykJSUdNe5X6526HQ6nlI6MDDAjyWl6ClF0MmwiNx+RY00Cuq7iEL2IqteBMuio6M3zomHQRkSYko61bVarUci95TSSsye8PBwhIWFLahTaux6Yv1txN0XjDHMzc1heHgY7e3taG5uRkdHB/r6+jA0NMSldVQqFQICAqBWq5GWloacnByUlJQgOzsbaWlpS0rjp7Db7bh27RrCw8NRUFCwrgCitYyQihxWVv0Dt+tdko6sm2MmSdJ3AGwB8NLtRU8AqGKM/W+3265XUK2oqIitpLjseomV2pfFfI+zdR3l/DsC7hwtFz9f7H65EjR1d85FcVQAfERG+Z1KIMWRECd95mjkTZI+dIkkvRWlELAndTInuBMaWY5ibGyMdwRv3boFPz8/bNmyBZs2bUJhYSGSk5Nlx470wiiNqampCXNzc/yYEH1bqWMiLlur/7qcsNlsGB4eljlk9fX1YXBwkDPEIiIikJqairS0NK7L5UzA2WQyob29XSbMTiLp6enpyM7O5qkyqampdxQcslqtaGpqQlVVFaqqqtDb2wtfX1+UlpaioqICFRUVHqUhmUwmDl6Q/otY9/f3R1JSkssG88zMDJqamvj11tHRAavVirCwMBQWFnIAzV1q450OYqKs9j7p9Xpcu3YNFy9eRHV1NWw2G0pLS5GTk8OZZsnJyR5fP3q9nrNqiFVCzBoxdbOkpAQFBQV3NC3IbDZjeHgYQ0ND0Gg0PH2SnEtnZmYwOTkJvV4Ps9ksE4YOCQmR1d977z28+OKLeOmll3DfffcBcPx+W05MT0+jpaUFLS0t3ECAQGtfX1+H6TmBgYGYmZnhndWpqSkZoODj44OoqCgZeJadnb1m6Vkk5K0ElzQaDWZmZmQda39/f54iI7KTIyIi+HV6Jx06Z2dn+bXe29vLWRx6vV42VR5/0blOTB+jenJy8rpMabbb7RyEIhMlqlutVoSGhnIgYz0DTIwxno5LLr8+Pj7r6vm/ERuxEasfBoMBo6Oj6OnpQWtrK7q7u9Hb28uBMr1eD5PJBJvNBm9vb/j5+SE8PByJiYlITU1FVlYWb0euRiq61WrFtWvX8Pvf/x5XrlyBr68vnn76aTz11FPrBiBa6wiuyGUl1T92u94V6aDbY3Zb6+w5AF4Afs4YW2ArKknSxwF8CwADUMcY+6TwWSiAJgCvMca+6OJ36gGUMcbst+e9ANQwxkrc/Y91C6ptpH+uTYhOlp7WF7OeoxEtV2Up24gjZRaLBWazmReTyeRyXlxGLp/EqFKK5oq/I05F1g5jjI+yih2NqKgo7uSkdAFdrBPoYsJgMKCqqgp1dXW4desWF8NOSEhAUVERZ6g50vqy2WyYnp6WOW1R6qRYTCaTRy8vxhi8vb25i5xYfHx8ZICnCHwql9FUkiTZeVO6tFJd2bm22+08VSc1NZWDFZ44Eep0OrS1tfE0vObmZhgMBvj6+vLUURJmX02B6eWG0WhEfX09qqurUV1djZGREQDzeky5ubnIy8tDbm4uEhISEBQU5BFrjlg3SjOOjo4OtLe3844epdcWFhYiMzNzVdM6GGPQarVc8FlZxsbGYLFYuKaIo2cK3eNeXl6ydObY2FjOkiCmRGFh4ZKc85yF1WpFfX09Ojs70dvbi97eXgwMDPB9Vg4uKN/vAQEBSE9Pl6WjJScnr3on2263Y2BgAO3t7ejv7+dpwF1dXZidnYVer4fVauVukjabDZL0oSuel5cXbzB7e3vDYrHAYDDAbDbDYrFwoW8Cdvv7+zE7OwsAiIqKwqZNm9DQ0IDCwkJ85zvfQX5+/qqyI8xmM3Q6ncPUnNDQUM5AVavVa8I0s9lsGB0d5elfXV1dnIWq1Wr585IYtPTuEgtd9/7+/vD29uYgOzlK0rubzpXRaIS3tzcCAwMRGhqKtLQ0zvKjDtByHY4/CqHT6bhrKpXBwUH09fVhYGAAMzMzsnaLKG8gDhiKIUmSTD6Anl1iIQZ3dHQ0srOzecpTWlraBpPIQSh1vyjtVKvV8vtdBHANBgMMBgOsVqvsXULnzdvbm5toBAUFcSBeHDwIDw/nLqDx8fGIiYlZ1wDpegrGGAwGg8yhVavVYnJycsF7xNFUuYwMmNRqNW+7h4WFydy2SeNsI5vmw/uF2oG9vb3o6elBf38/hoeHMT4+junpaT4QQPeFn58fgoODudRMamoqMjIykJ2djeTkZMTGxt7RQafu7m5UVlbi9OnTGB4ehkqlgslkwqZNm/Dxj38ce/bsgbe39waodjuCKvJYfvUv3K53Q9rt8pjdBrbaABwGMACgCsATjLEmYZ1sAK8COMgYm5YkKYYxNiZ8/hyAaABTHoBq+xljU7fn1ZhPAd0A1TZiI+5kkBsQ6UYo9SNotH98fBxjY2Oc6UNgniRJ8PX15VN/f3+EhIRwvRxK+1lJgXvGGIaHhzlbqLGxkeszkYZFdHT0gvpKCI2SGKqjogQyHAGYyrqfnx9nCfr7+y+Yp2Pr6XGhlM+enh4OYvT29nIx48DAQGRnZ3PwLDc3d92J/i41SIOlpaWFp36OjIzI2B3KFBw6tqRJRyCxCBaTkPVqgWcmkwk9PT0yRlZXVxdmZmZgNpt5hxKYb+iZTCYO2vj5+cnqyuuIlhsMBkxOTmJqagqzs7MylojFYkFYWBhnJZHL4tatW7F161YUFRV9JDtCjDEMDQ2hvb2di+Y3NjZyvRL6z3SfhoWFceH89PR0xMXFcVZQWFjYstib7777LrRaLQICAtDT04OWlhb8/Oc/h9VqhY+PD+Li4hAdHY24uDjs3r0bW7duxebNmz9SeleTk5Po6OhAa2srbty4gVu3bnHHNUrJ9vX15Y506enpyM3NRWZmJpKSkpCUlISYmBin7qWLDavViqGhIXR3d6O1tRUNDQ1obW3F2NgYjEYjDAYDfHx8EBISgtzcXOzcuRNlZWUoLCz8yOjD2e12jIyM8PdIa2srbt26hd7eXuj1ehiNRplmKT1vAgMDkZiYiOTkZH5eYmJioFarOZNL1C9Vgp+kP6rX6x2mtE5PT3MAj0T8CQii1EoSg8/Pz0dRUREyMjKQlpaG5OTkj4y+lU6n47psIyMjGBgYQHd3NwYGBjA8PMzfIfScp04/vR8iIiK4429QUJAMHBNBFvHd4u3tDaPRiJmZGX5uiAmq1Wq5m+Tc3BwMBoOMiWg2mzmw7e/vj6ioKCQlJSEjIwM5OTlISkriAFxISMhdz/aja5gGc+laJWCG2L8ajWaBHAkBYeJ94e3tzZm1ysFbAjpdDeZaLBbOqNbpdAucWwmEI7kasfj6+iIiIgKRkZEcMCIzDHKxVavVCAsLW3egnM1mw9TUFD8Pw8PDHPQfGhrC2NgYpqen+Tkwm8383evj48Pf/7GxsUhMTOSi/xkZGUhISEB0dPS6kfbQarU4e/YsKisr0dHRgbCwMEiShNHRUZSUlOCxxx7Drl27ZINjG5pqH0ZgRT7Lrf4ft+vVSjvcgWo7AHyLMXb09vzfAQBj7F+Fdb4LoI0x9nMH228G8DUA7wKocAOqPQHgOwDOYt79cy+AbzDGXnH3PzZAtY3YiHUUWq0W3d3dvPT09KCnpwdarZY77BCQFBAQgNjYWK4vlZubi+zs7BVLTWGMcS0NclwS3ZeoM0RBDLyQkBCHgJar4mhdPz+/ZTcmaHSSWCLKolw+NDSEgYEBPqofHh7OUz5FFttHVcz4bgmtVssZb2Iao06ng8lkgo+PD2fZBAUFISQkBAkJCTKdHSqxsbEr2im0WCxob2/nzoVNTU2YmprioAGlLhYXF3OgLS0t7a7o8BAo0NnZifb2dm5eoNFooNfrAcwDrMS2yMzMRHZ2NtedSk9PR1RU1B3/r1arFV/+8pdx7NgxFBYWcrDp2rVraGtr4xpZvr6+SE1NxcGDB7F9+3aUlpauW5YOYwxTU1McwKyurkZjYyMfrLFarQgICEBISAgyMzNRUlKC3NxcDpjFxcWtKzDEYDCgpaUFDQ0N+OCDD1BXV4epqSkYjUau31hYWIidO3eipKQEeXl5UKvVa73bsjCbzRgYGOADMc3NzWhpacHg4CBnKokgVWRkJHJyclBYWMiBZRqwWg/Xnd1ux9DQEHp6etDd3Y2WlhY0NjZicHCQg27iYFZsbCyysrJQVFTEGYjJyclrKglhs9kwMTHBtV97e3vR3t6O3t5eDA4OcqF8s9kMm80GX19fDpSFhoYiPj4eycnJSEtLQ0pKCmJiYhAbG4uYmJgVZSEvJex2O8bHxzE8PMyZwMRiHhoa4ulxBKL7+fnB19cXgYGBHNBITU3ljGX6bwQcrFZQu0yn0/G0/omJCQwODqK/vx+Dg4PcGGt2dpYzycxmMx9wpoyG4OBgqNVqzrIkgIqYY5TOHR4ejqCgoDv+7rHb7dDr9Q6lDAYHBzE0NMTB7KmpKQ7cEiBosVgcAuZ0LqmtLMrOBAYGIiAgQMZ8JB1JX19fDtbOzMzIZBaoHUwuyUajUZaZQ+fAZrPBx8eHn4PAwEAZOEjauWQqQQOq6w0cFIMGkUn2pqmpCV1dXQgICEBOTg5mZ2fR0tKCoqIiPPbYY9i5c6fs/9jtdly4cAEREREoKyvbANVuR0BFPsuqftHteo3S1l4AosPmTxljP6UZSZIeBXCMMfa52/OfArBNBMckSXoN82y2XZhPEf0WY+xdSZJUAM4AeBLAPXADqt3+rnjM66oBwHXG2IjbP4ENUG0jNuKuCXroi6Bba2srOjs7MTs7C6PRCEmS4Ofnh5CQEOTl5fHUQ2rgruZLzWKxcHciZQqmsyKmZjoqZEagDJERpVxGy2lebFSIo8fKZUFBQXyk8E52Nm02m6zBJU6Vdbvd7nT/xWWkMXU3ADXOYm5ujrOeCDjo6+uDXq+H3W6Hr68vH6kPDAxESkoK16qjsp60j/R6PW7evIkrV67g2rVrGBkZ4VpBFosFarUamzZtwtatW7FlyxZERUXd8X0kIJ0A/e7ubu7mR41um80GxhiCgoIQHx+P4uJi5ObmctAsKSnprhPPNxqNuHXrFmpra1FVVYWamhoOhAYGBiI9PR07duzA/v37UVpaesccJm02GwYHBznzsqamBvX19RgfH5cBZ8HBwTIXzOzsbKSnp6+b0f7lxNzcHJqamlBXV4crV66goaGBp6hSOmlCQgLXhMzPz0dWVtaK67eR7mZ/fz/6+/s5yEQps5SODIB3bOPi4pCbm4vCwkI+MLPWINNKht1ux9jYGE/nbm1tRXNzM7q7uzExMcHTfolNFR4ejpSUFJ4qHx0dzZlcIqtLXKZSqZymUs/NzUGj0XCG0vT0NE+dFTXlREZZSEgIEhMTkZ6ejqysLKSlpSE2NpZLcawnoHmlglj3dGyGhoY4+DY4OIjR0VHMzc1xFp7NZpMxt5VaxKRfRQAkgTvE/Ka0V2KJien5VAfAGVwE0FBqfFxcHAf8EhMTOWAWGRm5LsDmOxmMMczOzi5glhIA5ijFmO47sdC5tdvtMuDNEaMyNDSUp7KSaUhAQADXVL6b25aMMYyOjsrAs76+Pn5cQkJCYLPZoNFo+IBBfn4+HnjgAWzfvl3Wh2KM4datW3jppZdQVVWFvXv34rOf/SxSUlI2QLXbEVBRwNKrX3K7XrO0yR1TzRNQ7S0AFgAfB5AE4AKAYsyDaYGMse9KkvQUnIBqkiRtcrWPjLGb7v7HBqi2ERvxEQka6ac0TuoYmkwmeHt7w263c20CGkkm17qPSsrieguj0YjBwUEMDAzwaX9/P3p6ejA7O8v1+ry9vXmDVdQDVKYOkDaOCDaKmoWkfwRA1jCOiYnhDJWkpCQkJiZyh9G1HD3U6/Wccdbe3o7W1lZ0dXXBYDDw0VDS2iKbcxE4y8jIuGuvXcYYent7caw+8DIAAQAASURBVOXKFVy5cgUtLS1cj48A8qSkJGzduhX5+flISUlBSkrKsoFCjUYjY8J2d3ejv78fer0eer2ed54MBgO8vLwQFBSEvLw8rn+Vl5e3ounn6zmmp6fR2NiICxcu4OrVq2hvb+cpvWFhYcjPz0dxcTGys7MRExPDO3+uzGkYYzCbzfx46/V6jIyM8DTVW7du8fNhMBgAgJsApKenLwDOPioAzWLDaDSiu7sb7e3tqK2tRU1NDbq7u3maHHX0RV2+0NBQ3jGMiopCcHAwLBaLTGOJBjHoGUQ6faRbSCmzISEhSEtLQ15eHjIyMrircUJCwkcyvXupIQJvBLqRCRCZXTgCXmietFfpHIrO7wTukN5YREQEYmNjkZGRgcTERM5ODg8P/6N4Xq1UmM1mno0wMzMjc2wncIaYTCKAA8wDZWFhYVCr1dw4xpFpy0qllW/ERogxNzeHwcFBXqjtPTQ0xAFjSsW22+3QaDS8j5SdnY3CwkIUFRWhoKDAYVvLZDLh/PnzeOutt9Dc3IyCggI88cQT2LZtG7+eN9I/Pwz/ikKWVP2q2/U6paKVSP/8MYBrjLH/uT1/GsA3AHwZwB4AdgDBAHwB/JAx9g3Fb5x1sYuMMXbQ3f9Yt6BaaWkpS0xMXJPfvpMP+sX81kpqaN3pl5n4m+6E590tW4yAvUiPFosnyxajv7WeQwTbmpqa0NDQgKmpKZ6uRfTywMBA5OTkIC8vD4mJiVxwPS4ubt1Tt+90MMY4fV/5Au/v74dOp+NC3z4+Plzzjej5ZFJBHQFnU2fpCs5MOKanp3maCwknj4yMYHZ2lo9gAvP3IHVgaLQ5ODgYycnJSEhIkKW40JRGJx0JYYthNpsXCG1TId0T2hfSOCPmU2hoKHJycpCVlcWZBBkZGX80o9MWiwUtLS2or69HXV0dGhoaMDk5CUmSEBQUxDugPj4+CAgIQFpaGh9ZdaY7KDJIrVYr12oCwIEzPz8/REREICMjgwNoOTk564rpt55ibGwMNTU1OHfuHOrr67lZhHhPSpK0AAAg4xo6N/SeovSdqKgo3qjPzMzk4MDdxvxb6yBdxaGhIc5sopQr0kKcmprizMugoCBZqpharUZ4eDhCQ0M5eyMuLs6tm/FGrE6Q6cXGfbARG/HHGcSiJ5BMBMzGxsZ41kpQUBBnwBJT22KxYGhoCOPj41CpVEhPT+fv2cLCQrcyAsPDw3j77bfx7rvvwmAwYP/+/bjvvvuQl5fH9YSvXbuGX//617j33ntx//33b4Bqt8OvoojFV/8/t+v1SvnuQDVvzKd2HgIwiHmjgk8yxm4J6xzDvHnBZyRJigJQg3kXz0lhnafgQfrnUmNdg2p5eXkr+p2e/FdPj8dSvsvV/HLWXetYDEjnzh3U1bzyM0mSZGKijoA2R2weaqDRlAoxgGiklDo91Cmi76bfE/8PjZgGBQVBrVYjIiKCF3GeGuvroXFot9sxPDzMRZO7u7vR2NiI3t5ePvpI/58xJtNoILCNnKji4uIQHh7OP6epn5/fXQNMmkwmmcbEzMwM1yqh4zM5Ock7ziRua7VaIUkS/P39ERAQwMWklcwwtVq95seCMYa5uTmHgBfpyc3OznKNGerIEIgnhgiuUV0U2SbWHel/EHAQGxuL5ORkZGRkcOAgPj5+A7R1EWNjY6ivr0dzczMmJyeh0WgwNTWFkZERnnImAp7iufHy8uJAbWhoKJKSkniqZlpa2l3L9FtMWK3WO8oestlsPC1HLHQu6HzcjYwmm83GBw3Ee3s9vNM2YiM2YiM24u4JMp8YHx93yDAjF29JkngbJiAgACqVCjabDUajkZvBURuUNHTFkp+fj5iYGLdtcIPBgKamJtTX1/M2V0xMDI4fP46jR4/KpB/a29vx0ksv4eLFi9i6dSuefPJJFBYWbjDVhPCtKGax1SfcrjcgZbs9ZpIkHQfwfczrpT3PGPtnSZK+DaCaMfaGNH9y/x3AMQA2AP/MGHtZ8R1Pwb1RQSCAvwGQwhh75raraC5j7C13/2Pdgmob6Z8b4S4IACNaurupJ+uIU6K4i5oFNFUCDCJLhNgg5LhF4B0A/rko8kk6aAS6qdVqxMfHIyUlhQM0a5HiQ2wspftlf38/B17EIv5vESAhUFJ0XCLQRaVScX0HElIVRVUdgXKOnlmOQGir1YrZ2Vme1kOAGTH06HyJbqOMMZ56Qs5Q5OpFLC4xxSsxMRGJiYkfWUaPXq/H6OgoJiYmOOisLCJLym63IzQ0lKdXqdXquwY4EMWT7XY7AgMDERgYuAEWrEAQmDs+Ps7NT6gMDw9zoWa6xpRsO5qK7C9xkCMoKIizKunebG5uxtDQEP7u7/4Ojz76KLZt24Yf/OAHf7TpkhSMMUxPT/POy9DQEIaGhqDRaPhgwszMDE+VoXcW1Skdj95vdN/T89zR4JZKpUJ4eDgfYKCODtX/GIBdT4OeuWIZHx93qJNEumF0XpSDHI6CdJQcvXNjY2Nlhi4brLyFwRiDTqfD1NSUjPU4OTnJdQ5Fx0lP6gBkqZFiyqSremBg4JoP1N0NYTab+XNNLGaz2eFgu7N55TKSR3Cl13s3DSyvRNjtdkxPT/P3u/i+p7pGo+Hrkvs6DUr7+Pjwdjil4FMEBAQsAMuoLFbnjTGG/v5+1NXVcQBtdnYW/v7+KCwsRElJCUpLS5GZmSlrA46Pj+OVV17BW2+9heTkZDz55JPYs2ePrM+zAap9GD4VJUxd7RaLwpiUum6OmSRJrwC4AeDTjLGi2yDbB4yxMrfbboBqG7ERKxvEUhABOZ1Oh8nJSe6eOTo6irGxMf5yISYQNXhpFMZgMGB2dhYqlYq7NpGzDoFuycnJiIuLW3OmDwnuix0zZSGQi1IjRY0O0X6bHIYIjHQVzl6kxB4kcIQ6EAEBATy9NyAggHfCRbDsbhZi/WMOs9nM016Hh4cxNDTEU2AJWKWGmiPAgEAbYtmJQI4jwMDf3x9hYWGIjIxEQkICd4hNTU1FUlLSR0Io3lUQ8N7X14e+vj709vZyAezJyUnOeCT3OdqG3Nt8fX0RFhbGC92fogswzfv5+XGWlLLMzMxgenqaP1vMZjM6Ozuh1Wo5Y5nddl285557UFFRgZycHO6YvNYufisVRqORg2QEmpGoPp0P6sRQ2jVjDP7+/vDx8eGdxLCwMAQHBy8AXsRnqZ+fH8xms0vTGVpmMBgwOjoqA+t8fX2hUqlgsVi4kHxwcDDS09ORmZmJrKwsZGZm3vW6cSQ07ogdTO0Aci02mUxgjHGHPwIv7XY7Z6Q6kr0IDAzkrGnRrc/RvojMfAKx6dyS9qrRaIRer+dOi15eXoiKikJ8fPwCF+W4uLgVN4W4U2GxWHhnnwAyJVA2NTXFgS9qn1DbggYIxQFSMdtB1Eal4y0yianNRmL9YlaE0llQHLQVv5+0VklCISYmZkEhV8zVdvVc6SC9Q7ENqZxXLiOAmQZW6dzROfL29uaSF+LAtxI0AyDLaBE/p8wEOufiZyJoajab+WCzeJ4oSCtWac7hybLVAuuoHe/sWDs69vR+J/KCkr1ss9n4u4DOAZEJqN1NbW+xHhUVhYCAgCX9D8YYtFotl2QRpyMj80aOKSkpKCkpQUlJCYqLixEWFubwu8bGxnDmzBm8+uqr8PLywuOPP4777rvP6aDDBqj2YXhXlLLw62+7XW/SK2ndHDNJkqoZYxWSJNUwxspvL6tjjJW63Xa9gmplZWVsx44dTj9fixeDKxfCxayzlGWrNVUuUxbR8Uc5v5KfiSK0og6Nq2WLnReXrceGBYlmKoE3sZB4tdFohK+vL28Em81m3glVq9UccBOnd1uDaiM2gsJoNHKgjMAyAgtGRkZ4B95iscDLy4ubP1DjjrSSwsLCEBoaumB0WcmQtNvtC5y0xHkqOp0OY2NjGBkZ4cvouUZ6dQEBAYiJiUFmZibS0tKQkZGBjIwMpKSkrHvQzWKxYHBwEL29vejr6+OOh5QibjAYeIeCdCkjIiK4axvp8YnlTj2HnnnmGbS3t+PQoUOora1Feno6VCoVWltbuXsmGYX4+PggODgYqamp3Mk0NTUVaWlpSEpKWhdsS6PRyFPRCcTs6enhGo4GgwEWiwWSJHGTEjI4SEpK4oMv5HRIZS3eCyaTiaf4KDs9Wq2Ws8G9vLz4fRQUFISMjAzk5OQgMzOTmwLExsau2fkxm80yAFPU+9FoNDJ9PeqAEwtedFCMjIyUaZjSeSFBbepIr8b/FJkl1M5Q1omBZTAYuDYoaYWSu6a/vz8kSUJoaCgH20TgLT4+ftUlEOx2O+bm5hy2oahO7tHEUqJBNgJYiL1O8hck8wAAoaGhiIyMRGRkJNRqNSIjIxEeHi7T4yXAxlHdmU4vHVtq34lTR8tmZ2cxOTmJ8fFxTE9Pc6BGlKIg0Jp0HA0GA0wmEwc1gPk2f0hIiGxgQyzkAkklICBANiC12DI3N4fp6WloNBpoNBpZXavVyrIFKBvEz8+PnwM6DyLASEA0AZN0fL29vREaGiordE/R9yn7B+KgmatlIqtdOYDual4EuulZQMeGrg8RTBdlZkQDK2I50ud0npcysE7XhyiBI+6H2F8Sj7vZbJb13QAgLCzMJUimVqtXhPnvCjAT9dWIHS1KsiQnJzs16GKMoaenBzU1NaipqUFDQwNMJhOio6Oxe/duPProo27114ANUE0M782lLPjKKbfraf3i180xkyTpA8xrt11mjG2SJCkTwG8ZY1vdbrteQbXNmzez1157bUW/czn/1dm2nqSiLXe5o6mrz5YzdUR39oQWvZx58UHtjEHiaH4p29C8s2NNLw9iSBBzQiyOlrladzUs2u12O0ZHR3lKJqVoDgwM8LRVarRLksTTG8kNLS4ubgHwlpycvJHuscpBjTEaARRd5xwVGoleTAkMDOSaTaLxQXh4+LoCcahhJBoqDA0N8Wt5bGxMllpMYBWZj4SEhCAlJQWpqam8cy12StfiWmaMYWpqCv39/ejr60N/fz/6+/vR0dGB0dFRmVYh3Y8BAQFIT09Hbm4uB9wyMjIQFRW1qh1Qi8XCNQMHBwfR19eHjo4OdHV1YWpqigOVwHyjnYBHcjxMS0vjjqQJCQmr8pxb7ZiZmUF3dzc6OzvR0tKCuro69Pf3cwMHGrQgcCo6OhppaWkccEtNTUVycvKymFRkJEGFxJi7u7vR0dHBzwWBTOSKGxAQgNDQUGRkZCA1NVX2PI+Pj7/r05Z1Oh26u7vR1dWFzs5OdHZ2oq2tjbNNiVlHx8PHx4czucW0oPj4eAQHBzvsHCvnTSYTpqamMD09jenpaV6fnJzE6OgoRkZGMD4+Lku7FDvyIsMyIiIC0dHRsucSlZiYmLvyfgE+ZEMSICo6W9N1arVauVA4gVR6vZ6DOqIUBA2sEgBF4IJYV6lUmJub486sOp2O74/ZbJaxgkR2HbGV7Ha7jD1DQKarEhERcVeeI4PBwAE3SrdTTsmpU2Rz0XGhY0fXNIEuxKYUU7rFIkp6iEU0VaLvE9viNDhM95EI9BEQJk6Vy0JCQu5qJivwoa4YZXpQoWve2TICFEX5jcX8JrHwlenFzqbK+mq8Y1YLMFOG1WpFS0sLB9BaW1tht9uRlpaG8vJylJeXo6ioaElMuQ1Q7cO4S0G1IwC+CaAAQCWAXQCeYoydc7vtegXVNtI/N2KtgkZfxdFBsSx2mVJ/LTg4WDaKo5xGRkYuuzHHbuvmiIAbMRyoQ0ANXEobMRgMPA0nJCSEdxrj4uIQEREhc0cLDw936kz5UQ6bzQatVisbZaUOmEaj4UDZ9PQ0d7kktgJ1NkS2JjWIlJpz1PEQUwtcaXxQY4rq4kgmfYfNZuMji5QC4ePjg5CQEISGhsoaS5TmpUydFXXGlNe7ks1FjntiZ4hYKJQKQO8fMSWDgIu0tLQFaUbh4eF3/TVns9kwMDCAzs5OdHV1oaOjA01NTRgfH4der+eNXWLo0HmiNNOoqCgu2EvnjRxAKR2SjjnpZBGzYXp6Gnq9nnd+AXCQMjAwEImJicjIyEB6ejoHzIjlutohskTEtFFR8zA4OJj/Z0+ekRqNBi0tLdi+ffui92V8fBytra1obW1FS0sLOjs7OfOAnpWMMZ4O5u/vL+uMKq9TAm7of1ksFhlgTMY4BJgFBQVxQw8RMEtKSlrVdDti8YjvCWd1OieOymqyx2w2GwYHB9HV1YX+/n7ORiJwXnzGSJLE98VVihe7rZHk4+PD0ydF7VO1Ws114OLi4jgDg8qdfDaJYIg73S56xoeEhHBpidUOi8XCHaiV70pxOjc3x7ehtD0RIBDPFYHcSvaMyB5zVoKDg+/698ZqBmMMRqORt22UUxKLBxwbFFHd0bLAwECnAFlAQMDGefmIBzFhJyYmnJbJyUnehmWMLRswAz5kESuBOSIeqFQq5ObmcgAtNzd3ySChVqvF66+/jhMnTuBzn/vchvunEKrycuZ//ozb9Qxh6jU/ZpIk/QDAbxhjlyVJigSwHYAE4CpjbMKj71ivoFpZWRkrLXWbvroglvOAXuq27lJAHaVaLnddZy8wdy+45dZFV807UXfExBHBB08/E/P41zIYmxe5dTWKSA6TFN7e3rwzTQ14EshPTExEaGjokvbFZrNhYmKCN35pJH50dBSDg4OcWUOdJ6Lhi0APLaNR/4CAABnwJpawsDC+Pp0PqjtbttjzRZ0NArGoiPPKul6vd9jon56ehsFg4COqNBXp7gRgUcea9t/f35+nu4kj3wRgiYDIao34kTYGsd6okSwWjUbD3SQJLHDECFUWdlsXizo7jjTHKGWL/mdoaCgiIiJ4qhOBZbGxsUvWzXD3/0UQxNVUrKtUKqc6Uo6WBQQErOj5m56eRldXFyYmJjhbhp4NY2NjmJiY4GCMWAg8oPNBqae0n/Hx8VzrjVg8sbGxK8ZeZIzx1CsSJhbr4+PjmJqakm0j3t+UwiOmTYlTSZI4WDgzM8O1cpTfJwJvw8PD+N3vfoeSkhL88Ic/RHFx8YowGO12OyYnJ2UpycPDwzLmgHJK6TV+fn5c50hZVvI+EM+HszIzM7PgGUsDK1TE1DZlXZIkDuIqi9VqdcjC9/b2RkREBDctEKcrzc4k1sPk5OSCfXHW9iX9sJCQkFVrL5jNZhk70VERtbwA8Pcu1Z21e5RtIIvFwoF1nU7n8H97eXlx4I2KWq1ewLD7YzCUYIxhZmaGs0anpqY4O46m4uAF1SnNl74D+PCcLeU6ou8goD0wMNCts3xERMRdz9ryNOi6dqX1Jc7TO1Nsyy2lfeloG0p/Jv1Pd1NXA5br0RyJTL/E40312dnZBaCZ2H9RqVSIiIjgGmmOSkRExKIGYUh2gzRDCTQbHBzk7HofHx/+XiFwjljMK9EG0Ol0eOutt/D73/8eZrMZDz30EB566CEaXFlzgGi9hKqsnPmcOe92PXNk2JofM0mS/hrA4wDiAbyK+ZTPmkV9x3oF1SoqKlhlZeWitlmN9M7FbudqfqXWdZYG6i49dLl1R50ET+pLXU8pzOpsZHYxnyuPi7IRJAY1ZBw5ZXk6daahsZiwWq28gUcdVUq/UFpOq9VqGeBGHZaYmJhlvagtFgvXJREBOBKAJ1CCqP6i4yrN22w2h6k3BKDSfwA+BMeorgxX94cjPT3xmhIbViQi7AjY8/X1XUB7F50tCegk5tB6awitZhCYuBLXt6eh1+udAtE0pRQTYL4xR6w7MT3b3dRutzt1/XVWF0dZXTlU3W26ho7Ao5GREQ6WaTQa2bOUWLhK0Ijmw8PDV5UpY7fbuXHBzMwMfvzjH+NnP/sZAgICcODAATDGeKPbz89vAQOMWEjrNe2LOvykKzg0NMRTEsfGxmQj/gAQEhLiULicyloweCwWC6amphbokA0MDMjAr5CQEP4eIxCepuvNSMZms2F8fFz2XhQHq0SmDzDftlhP6YdWq3VBqtnU1NSCd73ILPP395el28fFxXGtMQJ41vo+stvtmJmZ4WwYR1MapBBD6WAtOjo6q6+WtAK9a3U6HR/sE1OTlXXlf1FqizljjYnplavFMiXNOwJjRGBGOS8CYo4kWxxppjkrISEhfPBLNINYqWeIzWbj5iwia9/Z1J0em8ikVfZRSOJDHNRTZjQsttB5mZ2dlf22eKwJbBePKU3Dw8N5tk1kZOSi7wW9Xi8bhFMOzE1NTcneayTFILLY6P29mrIfJpMJ7777Ll599VVotVo88MADeOSRRxAdHS1bbwNU+zCk0k1MVXnR7Xr2uOB1c8wkSUrFPLj2OIAAAL/FPMDW5nbb9QyqbaR/bsRahsVicdixXsxU2cChCA0NddjpFFNJFtv5ZGzeiY86K2IZHR3lLyV/f3/eWXEkKLpaI/Qklis2JJwVSttZKgtTXEaaESJAJs4HBASsqXMqseVoxNvRtQRABvxRSpKzeeUy0Z1srcNms0Gj0fCOADmtifMTExMywAaYt1N3JIIrpk2vB01AvV4vAz3EQm6/ADhjicT8ldPVSiez2WzcLXN6elrmlkqFBO8BIDIyUub6R6lvi91HYk6JLFCR/UiDKMq6ct7Ly8shE5aYU2KMj4/DbrcjNjYWBoMBLS0tKC8vBzCvPTQwMCDTviO3WAL0vby8EBcXh8TERJ76R9PIyMgVOT92u52n6mq1WkxNTS24frRaLf+tsLCwBQ6MBJItRgiaUr7ENC+qGwwG2XqLqUuSxFOVCWBRq9UIDAxc1PGamZnh7y8RpBoeHoZWq+Xr+fr6ykA3qsfExHBmtJLB5Ww/rFarjOmi1L6k+2ZkZETGulSpVIiOjl4AMlF9Ke9UYrVTqrCyKAeuHH1GbGGliyAN/C3nvUeOruK5oRRzep6LTDvG5t0pxWtCrVY7Zb8565vQ/aJkYM/MzCzYRqVS8fRQcSBMnK7ke4NACvGZJQ4QO5tKksTPzUq7OorXtMjgUk7FupIFvNT9UT4XlExiEahR1kXwZrXBWeX7RmTrKx11KT2ftBPv9GAqSbWIIBwx6JZaVCrVsrMmGPtQL1jMihDrYjaOCJIFBATI+kHKaURExJoMWmu1WtTX16O+vh7Xr1/H+Pg4jh07hkcffRQJCQlOt9sA1T4MqXQzwzsfuF8x0X9dHjNJksoBPA+ghDHm9iLcANU2YiPucBDbQEyLUo7OiIACYwwhISEO04WoLKYzZTAYuGMZ/R697MbHxzE7OytrDIWEhCwAMJRgxnpwx1vNIOcqUR9MKRzrCBBTMpmAhSxJHx8fhyxHMS1AkiSXKa3u5qmuDHFfJElyar4hSZKM4efp1Gq1clFkGk2ldABiM4jMBpoS+8/TTp/JZHJr/S4WAirF8+CsTlOVSuUQLFgqO8NkMi1wphOnIgBHqdXOUm1FwXUyQSCQRBzlpwY06SNGREQgNjZWBtLExcW5HGmmzr7oDkgudGJRPkeAeT1JSleiTr0z0Xhn86KuoVhEIIgiKCiI/8+5uTn87Gc/Q2lpKX7605+isLDQ7bmyWq3cQGNwcFA2pY6BeJ04GuEXXdQoLVIMSZI4U4T2VSm07w6UsdlsXExfWaamppweH0rXV+plKgFKV1IUynm73c7BQbGI4vI02KG8j+Li4mSpyZ68V0wm0wLQjRhWJF6v1BlThsgCERk9jlwQw8PDuc6op4LYIqNIOYBAdY1Gs0BoPCgoiDttupNKcPS52WyWpSmKU+qMi/+fgph0zgb9oqOjFw1EUadb+f/n5uacXtuOlkvSh0L2dO2SYP1SOt5Wq1UmieAMeKI6sXmc7aso3C9OnT2zSZtRr9djbm6Ovy8dfb8IkDoSjlcWupbXk0HRYoKOi1Ks3xGzTSzis0bZ3nIU9DxSZjkoMx5IE5NYaUaj0SGLTvm7lB4qFhGYUy4TP3NUp+lSrndnxnCiQ6sjQExZd/QcFd8n4v1J08jIyEUP/tyJsNvt6OzsRF1dHerq6nDr1i0YjUaEhoaipKQEpaWlKC8vdwmkibEBqn0YUnEFw+tV7lfMVK2bYyZJkjeAezHPVDsE4BzmmWqvu912A1TbiI1Y3yF2YpVAHBVlBy8wMJCzXkQGDBVPtVGIXSKCbsr65OSkw44ijZ6JDQJHGhPOlpE2iJi6qRz1Xcxn4nJqwCqBMep4KhtilEqodERSNmiVgBjR8+8G0NFut8NoNC4w3SAASmxkOps6WuaIQeQsyH2PNIUcTScnJ2VpnsA868vTVJDQ0FAOVLpj3ojLCMwR90dZn56edmhMIgIHSn3E6Ohot/ej3W7nzC5njC6xrlKpeEPWE5DPaDTKUi+UdTH9QpnmGRMTw3VRxLKamlSeBHXISCPxxRdfxPe//334+Phg9+7dCAgI4M8tHx8fxMfHy1Ln4+PjlzVoQIwVArZtNhuCg4M9Oh+MMa5VMzU15RAsEwdeVCoVoqKiZM94KpGRkQgLC1t3ouAWi4UzVomdSgAmpbVSh9XLy4uDvyIATAY6nl7nyw29Xr9A+4zegVSUDFsvLy/Z4IFyMIHmw8LC1k1HkzTfXA38iSx8uv6IVeIKlAwNDV3R/0mgmBJkd1S0Wu0CQIyYr8p9Ve4zTUNCQtaE2S62W5wVasuIbRutVusQAPH19XX6P0nXS+nu6cm8JEmcSaXUnXOmSUf7p9QNBOaBGiWbzVERP18uC3Olw2azceMUSgMVi7jMXfqoWHcG6DkKcfBHBAmVwGFwcLBTUEys341Arch+bm1t5W7fKpUKmZmZKC0tRWlpKQoKCpZkBkTvfB8fn3UDEK11SEUVDH/wAMvJXXsgUpKkwwCeAHAcwHUALwN4nTGmc7mh+B3rFVQrLy9nn/3sZ9fkt52Njq3UMlfruJsuZl1PpjSKoqQCr9a8ctRHWRyNDCnLenpZrtcgEE7shInzNJInSRJ8fHwWpJ+JZTk6UKKTqqgn4ajuaBldM47s25eyjDTcAgMDHdqGr1THU6ntJGqEiI6Gjgoxy5wV8ZlNqZ1LKcRocDVC626Zl5cXB+Kcpa86W+YoNdrX15czvwiAEtlgNL1bxJgJEBcBONLxETurSuZcREQEoqOjeePVVUqvWKfUKGeaNc4E/v39/WUgmZh+QSPLd/tzd3R0FFqtFjk5OQs+M5vNGB4e5vpelHJIAxZi58XHx0fG3CVGpWiw42zKGJMZoThi9dHzJyQkhIMvjsCyj4ITrqdhtVq5liiV4eFhGViiFPUH5mUWiH3n4+Pj1i1TOa9MXwsICHCrhbbauoHrMaxWK6ampjA2NrbAEEdketG8krntyXUsriPeK87SwR0xMFca0Lubg9jdjs4VseudDVS6myf3YlcadGJ9rbX3NuLuDpvNhpGREYfSN2KavqjTmZ2djZKSEiQnJy/rPWo0GnH+/HmcPHkSzc3N+Nu//VscOXJkzQGi9RJSUQXDqx6AaoXrAlQ7A+A3AH7PGJte0nesV1Bt06ZN7Nlnn13y9kv9X862W4nl7tZdyamn64hClSu1zNU67lwFXdGSRedBT8NRg40xBh8fH5mb2UpMia10t3V0zGazLA1NCcSJaWjAh6OJNILoCJxyVV8vI1yUmiiyspTzjpYpHZCU6RoEIJPzpVIw19/f3ynI5QkQRh02xj50Ol1KMZlMLvVDlOmczur+/v4u01edfXYnTQ7upiAL+vHxcczMzLh1rxXrpI3iSKOGykYHZnmhZPEQ+4Xec86cP+m9FRYWtsDFb61ZfR/FELXqNBoNrFarS6dwpYMmDcBsxEZsxEZsxJ0L0t0VHUWVRRzsIiazI5M2tVq94s/xgYEBvP3226isrITRaMS+fftw3333IT8/n9r/aw4QrZeQCioYfu0BqLb5o3HM1i2otpH+uRGrGQSoECV7sVNHy4h2r2Qc+Pn5OR1JVZagoKC7oiFPItfK9ElHKZXKz5VsK0eg50rsn6PvFsPLy2tB+qmyOFouiuiGhobeNaypjdiIP5ZgjKGjowPZ2dlrvSsbsREbsREbsRF/dGG1WmXtf61WK2Pri0VkBlPKPDnwKgtlLNwpWRWDwYCbN2/i5MmTqK6uRmJiIo4fP47Dhw8jPDx8wfoboNqHIeVXMPzSAyxn+0fjmK1/oZ+N2IhVCG9vb84sW+0wGo0LND+mp6cxMDCAxsZG2XLRsh4AZ4SRgLUIwNELhtKQlqIBsNQQRe1jYmLu2O9uxEZ4EmazWQboWq1Wp25s7uqkIeZIGPqPnW1Huljic43qOp3OpWaM0WhcEoBOLGNfX98FU2JBtrS04IUXXkBRURGee+45lJWV3TUDFisR9M7xpMzOzi5I31rKcaJz6ePjs4ChS3pNzsp6YS+vdpAuI90nYlo8FeW8uMxsNjvUnXIWjLEF6fqi1qWoe+as/DGlTDLGuLmDo6LUBFN+RlqfSokVV8scGeIEBAQscGsV644+W286YqsRVqtVdh70ej1MJpPTQox8T9ahwV4xu4Uxxu8ZZwxXT5b5+Pi4NChwtPxOty1ooNzVALmzz5yZa3h7e8syVUJDQ3m/JS0tjdfVavWavwMYY1xrrbW1FS0tLeju7ubZGCUlJXjooYfwf/7P//nI32crGgyA59J/LkOSpGMAngPgBeDnjLHvOFnvTwD8PwBbGGPVkiT5APg5gE2Yx71eZIz968rsleK3l8MKkSRJDeAVAGkAegB8XJmHKklSGYAfAQjF/KH9Z8bYK+6+e4OpthEbMR9Go3GB4C7pM5FZwMTEhEyXCYBD4E2s38mRno3YCGWQ+D4ZI1AHUqyL846cv0wmk0OtHV9fX1nqMXUknbmxuaqTLpooAk3FZDI5ZF06AuKU4t1KkWgyT7hTx56OqSNXW3IAE8EyRyLfPj4+MqCfgP+wsDAEBwe7dDLz8/NbdOOUUp6VnSGazs3NYWBgAK+88gpee+01SJKEsrIypKeny3QkgQ/1+8QiDlyQHtOdBhUWA4hpNJoFph3APDvaE8CErkFvb2+ZLtxyggBtT1x4ScdJ+R98fX05I8HZdC20y2w2GxdhF0FkpUYeLRN1IwlACQsL49cZmduI6fHO5gMDAxfd8aT0Y0dp+55eZ0oNNIqgoCCZeL8r4Xgqfn5+K/6MEwdQFlOU2pLiu4OAKiVw5arQIIvy+zyVZaE6vReVjq3OXFwdubkq/5eXl5fsOiJnb0/nSa/TE6McZ5+L944rEwODwbAANKZ5b29v2fEODAyEn5+f00IDLu4KyWs4ep7Q/eNIf9HTZfSOcmdSIBZHmrOe6g96GvR9dHwJzHUm6+JM5mU17uvVirm5ObS1tXHwrLW1FTMzMwCAxMRE5Obm8pKenr6kPtIGU+3DkHIrGH7kAZZzyPUxkyTJC0AbgMMABgBUAXiCMdakWC8EwEkAvgC+eBtU+ySABxljj0uSFAigCcB+xljP0v6V81guqPZdAFOMse9IkvQNABGMsa8r1skBwBhj7ZIkJQC4ASCfMaZx9d2bNm1i//Zv/7bkfVPsw4qt5850wJGhwGI/c7auqxGupY6MOas7Ell2Vl/s5xux+sEYg1ar5YLooji6I00CYL7BolareWPY0VS5bDHOjn8MwRhbYMzgqSmDOFV2Ytw9LzxZh+7xxRZ32wKQOYWK7qHK94vYWCa2o7PGvLIuds6oUff/s3fn8ZJU5f34P89dZ98XGGYDhkUWQRgRFBUNKLihcQM3iBLiT0W/IioqKmJM1CQGk5goIkHUIEbUoKAIIioGlQEhyj4MA8wMcJn9znbn3rnP74+q03Nu3VpOLd1V1f15v1716lpOVZ+q09Vd9fQ5p6p6Mbdnz57GjYLd9CHYMXSwc+jg8QruW9KDOcwAoPFPflitFpHwh3XY43Yn3+aJfnWpsTI4OIjbbrsNL3vZyyLzPDQ01Hhio/mTwq5pF3xioH3zYZ6uaj+1NermOerGeXBwcFwwSVXHBcTsJ6+FDe3Y/NyUjfmdCr6ap2za35Nh3wOm/1QTxDWDmTY1X+wgvt1vazBg39XV1Qh42eeGGezpGTNmYMKECc0/WCUwfzTYtR2jBvtBKXagICpAYJ9nZtqkD06LeA9ainsqZNTQSX1LmoBW2J9VLuO7d+8e86dU8A8ql3mmu42oYKWZrtpTiqn6TL/QwQeyBfuFVlVMnjwZBx98cCNwdvDBB4c24czCtIqYOXMmg2o+OXi54t8cgmovTwyqnQDgYlV9uT/9MQAI1jgTkUsB3ATgwwAu8INqZwJ4C4DXAZgO4HYAx6vqRhQsbzWV0wGc5I9/E8CtAMYE1VT1IWt8nYgMAJgLYHPchlUV99xzT+zyrLKuG3WDGDaddVlUWpcL9ah/wrKMBztaNq+uHTJHLY869i7/wLg0eQhWwU4a7L6yzM172HRwWdWr/4pI46J/2bJlTusMDw9j48aN4y6Gt27dinXr1uGBBx4YM29wcBA7d+4c857GxIkTxwXjTI2hqAuupPE0aUVkTJ93wfG4ZVHjcZ9de//7+/vH9cdm19Kx582cOXPcvAkTJowJBCR9X7iksc/rNIPLuqa8g8GwCRMmVP48aZbu7u7G574o5ngHm+qFDebisQ7fVc0wdepUnHbaabFp+vv7sWDBAixYsCD19kdGRrB169ZGc/24P6rClpkHSrRjQKwIecrGplbNxrDv/97e3jG1wiZOnMja2w7smrj77bdf2dmhBN3d3S3r7oQoK1XFjh07xtX8tQfzlGH7D8O+vj7Mmzev8WTsefPm4YQTTmhMT58+vfBArarikUcewe23347bb78dq1atwpQpU/ChD32o0Pehhv0APGFNrwHwPDuBiBwDYJGqXi8iH7YWfR9evOpJAJMAfLAZATUgf1Btvqo+6Y8/BWB+XGIROQ5elbxHIpafC+BcAFi8eDE++MEP5swedRJzwxmsih01DA8Pj3vS444dO7Bhw4YxzdKC4zt37hxXcyH472pfX19oYG7q1Knj+karyuPee3t7Gz9Ceagqdu7cOS4AZ/ruCTYNGB4eztykIGpcVSNrJ5jxGTNmhM6PWqcTgxNUHSYow89hOrt27Sq8xpCp1Ttr1qxCt0vFMjWZent7GVAgImoy07VEXGDMDFu3bh33B/CkSZPG1P41w8KFCzFz5kzMmjUL8+bNG1NLvBV27NiBO+64A7fffjvuuOMO7NixAwcccABOOOEEfOhDH8IBBxzAGpZhFMBIYioAmCMidpW2y1T1Mte3EZEuAF8CcHbI4uPgdT+2AMBMAL8RkZtVdZXr9l0lBtVE5GYA+4Qs+oQ9oaoqIpFVwERkXwDfAnCWqo7voMHbxmUALgO8PtWS8kZkE5FGDbQyqWojYBcMzA0ODjb6W3n00UfH9MsS1mdRT0+P85NDp02bVokbbtO0bNKkSdhnn7CvDqLmMedfsDlX0mBqeEUNIoIJEyaMq43XiRdSpv8yuw82M5gaQbt3724M9nTU+O7duzE8PAxgb7M9u0+cNOMTJkzA1KlTsWvXLrz73e/GwoULceWVV+KAAw4o+cg1h/kjI/gnhhk3/f+F9UMXVR4uNfq7urpC+y6KmjblEtavW7ueS6p7+2QMlsv27dtDj3/cPPOaxPQTZQ92/1Fh0xMmTBjTX1KwyX0Vri+aRdXrqD2pb8+w6bCO2vN0/2Jqr4e1loib1461w0dHRxtdatiD3YVG2HRYNxv2bzxQTEsfAKHnUtQ55prWlGNcVxz2dJq0IjKmjzv7YSgur8G+Ig1znRQVGFu8ePGY6arcsxjbtm3D448/jscee2zM64YNGzBx4kQsX74cJ5xwAt73vvdF/knj8rvZUdyDaus1vsnsWgCLrOmF/jxjKoAjANzqfyb3AXCdiLwGXtPPn6nqMIABEfktgOUAWh9UU9WTo5aJyNMisq+qPukHzQYi0k2D13HcJ1T1d5lzS1QDItL4YZw+fXqubQ0PD2Pr1q3jnqy3fv16rFy5ckzfP8F/fVS1EZSz+0ey++mx502fPr2j+hmh5lLVxCdvRTXNNfOCNzJmME/BC74fsPf8Mzcapjlw3GD6e4wbzE1X8GYq7iLKPMkteANkakCGPcEybF7Ust7e3jEdIdsBKtfBHO9gH3Dbtm1rPP3UPr6A1zzPfhCDuRE3TU4nTJjQeKpj0o28Pd3T09M41nv27Al9QlvYk93s5Vu3bsXAwAAGBwfx61//GmvWrMHq1atx1FFH4cUvfvG4MjIdrtvD5MmTI8sgKpBnl0nwszkyMhLZIXVcZ9Wmw3K71q+5gQ/WlDb9DtodxJvxffbZJzS/ceMuAa49e/Yklok9bjrHf/zxx8f1LxgWmOjp6RkTeDPBN3P+pH01ny9TJlmeFjg0NBT54JRg5/fmu8PUUg8LVJkO7tN8D/T29sbejJo/FqKC11HTu3btwsDAAB555JFx+7Zt27bQ7zrTFNTs0+TJkxsBcVNL0IzHzQsu6+npaeTJ7rw9bDxpOqz7BhMACJ5DSZ32T58+Hfvss8+YecG+PeO6Y3DpqsF8xoJ/zAZbUQT/tDWBo7BAR1i5mSdTph3s9QA4B4LtV3vfw37L7UCQ6VLDHuxuM+zfnPnz54emsbvVSApK2XlKSquqsb+tUfOGhoYa/WqGnY9JXXHY03HLoqZFpNHsPfg6e/ZsLFq0aMzDUuw0dXzq+Z49e7B+/fpxAbMnnnii8cCryZMnY/HixViyZAkWL16M448/HkuWLMH06dOhqli/fj3Wrl2LW2+9FWvXrsXatWuxbt06DAwMNCpEnHfeeSXvacUogJ2JqVzcAeAgEdkfXjDtDHjBMu9tVLcAmGOmReRW7O1T7S8AvBTAt0RkMoDjAVxaSK4C8j6o4B8AbNC9DyqYpaofCaTpA/BTAD9W1Utdt33UUUfpwQcfnDlvcaK+DKJ+ePJuw54f9m+Vy7Ks6dPeOOYZgn1fFfGalKanp2fMDZq5ILOHKv0T0mrDw8NjnhRnasTZr/Z42E2BqVkQDMRF3eAEx+v4AxwmrH+eNK/2+MjIiHMz16gmrmWzL3zDLo5NcMvl6VtRzW+DT8AzNzhhgYsqMk/ZC+sAenh42KnWUNI8u0ZX2L/fSf+OmyH4wILJkyeXXuu3CF/84hfxyle+Eocffvi4ZaYPl+DDI7Zv3x55M+hS4yuop6cn9AmowSFs2ZQpU8b0T1nVh3M0gwmS2g/1iOr30uXV/n3r7u5O9d1kD3afoXaArBP/lDIPZTHBt+3bt2N4eLgR1LNf08wbHh4eFxgNXm+ETYctY/cN442OjjaeUmkf86QhmB6AUyA4+McDy4Oy2rVrV+OBNcHBPMxm06ZNY1r+dHV1Yfbs2ViyZEkjaLZkyRIsXLgQQ0NDGBgYwLp16xrBMjOYfqNFBHPmzMF+++03bpg7d+6YrnuET/9skAOWK/7W4UEFb00+ZiLyCnjBsG4AV6jq50TkEgArVPW6QNpbsTeoNgXAfwI4DIAA+E9V/YcMu5Mob1BtNoDvAVgM4DEAb1LVjSKyHMC7VfUcEXkbvJ2511r1bFW9O27bz3nOc/SCCy7InLcocfsb9o+Oy8Wr6zpR/W+ZaZeAnP3vmmtgL6xKczBd3D7EpQvug2lGBSC0OVVcvkwn22EBhahl9kWZ3YTInjYBCJeyNHnp7u6ODNZNmjSpcdNp34BGjdf5xnR0dBTbtm0LDcSZWhNJnfy7NFUBwp/UNmHChMbj3F0CT1HL0t6ERp1f5uY4Sy0Je796e3tzP02rU26siYiIiKi+TG3/rVu3Nv4sMeNR8wYHB8d1i9Pf34/Zs2ePG+bMmYNZs2ZhwoQJGB4exoYNG/DMM89gYGBgzOvmzZvH3AdPmzYNc+fOxYIFC8YEyxYsWODcd9vmzZvx8MMPY5999sHixYsZVPPJ/ssVn3EIqp3VHoHIXHf7qroBwF+EzF8B4Bx//NsAvp122yKSuTZG1kBh2HpFzotLU+SraxoTAAsbj1vmMh62zO6k3h5GRkZiA195ytPUoAurBRM3mKCHCWKISOOfiNHR0ca/dUNDQ9iyZQuefvrpRn8DpgmVGR8ZGRnXzEBV0dXVNSYIFxWYC/ZBM3Xq1Jb9y9fV1dVodrNo0aLkFXIIqwlmgnJRNRXjajHatR2JqHrMb4M9xD3d1NR2ME81HRoawvLly/GpT30Kb3jDGxhozsh899pD1EN/zHwRCa1tV5dapHVimtmG/Xk4PDyM7u7uyKZ6fX19/BOmBOYhTPZg/ji0m7qW+XCqTmO+u4I17qLGATRaxJhryrTjdTrv7Gtwl+45TLcRYYGxwcHB0Iokppav3aTfDPPnz8e0adMa/Teq3w2E+WPfBNzs8bVr12LTpk1j7hOnTJmCefPmYe7cuZg3bx7mzZuHQw45pDFvxowZmcpl+/btWLlyJR566CE8/PDDeOihhzAwMAARwYwZM3DQQQfhjW98YzGF0S5GAYzv2aFt5aqp1kzLly/XFSscoptECey+eaJ+IPIOpmPnsCZx3d3dY5qIBPseMT/A5kfEDMPDw40fLPtHxP6xsgWDb+aHK2wo+0mjRDbTL1hcc8e8zYni5olI41w0Q3A6bF6a6eDNVLBvoajpLOepuRi1bxLibibM6+7duyOfeBz2pGTTjDWqdnTShav50yM42AFxM4gIhoaGcM8992BgYKARkNuwYQNUFfvuuy+OOeaYxra7u7vH9akTNR7VdCxsKKrmscl/MFhlP+TGdA5tD2Hz7PnB8oir1W6Xg/mcmpvCsMFeZjoQD/YDZ34L495TVccca7sc7ObeYUOwj6tJkyY1/bfMPPgkrD+rpNfgPNMENU3N+bhuLnp6ekIDOPb3m/nT0uV63z53k8oiaTDdb9jncHA6q7DPX9p+1oLLgsfO7lMy6hhF6erqGvddLyLjyiZYC8f1PVS18bkI6/MsbIhb5pomuNy0GAn7XXUZd00b7JokTVkEy8Tej7B+4sw4gHF//qcdT+Ly/ZyF+ePeVFCJ66rDnt/b25vYLYc9b/LkyZg2bVqjH1K7ea8JhgXvYezx7du3h1Y4MPcz9n1M1PiMGTNy/3k+OjqKTZs24ZlnnsH69esxMDCAVatW4aGHHsLatV5/+JMnT8ayZctw0EEH4eCDD8ZBBx2EuXPnhrX0aotaV0WQJcsVH3WI5by3PY5ZZYNqxx57rH79618vZFuuX1hZ0iU1/czSxDJq2m4GGjbtMp4mXfAiKOyCyO6njcKNjIyM6fw72Plv1PSOHTvGNPc1uru7G7XYzD88prNjOzhnan0MDw9j165dGBwcHPNjZi7m7LLr7+8f0ylpsJNSl+lO6vMnKNgkOe6iy/XiLE06U8snTS3SrDVW7VpGwU52o24Uopj+jaI6TzcXesGgk+l4PPhdqqpj/jW2bwiC87q7uxvniglwmEBTsLaOuUg2F3DB7z/zavdXE+yDJthvUNg8ezrtsbSPafAmIerVjJtaYCZwEay1GzYEb6ryvIbV5IgLOI6MjOBf/uVf8LrXvQ7Pfe5zxywPfm9u2LCh0az/Xe96F44//vhG7bZgYCgsWGSGsJu7rDdGwUCVKbNgcCL4fWuCSz09PWM+g+ZzGdVxdrBrBJdlpiZUVMfywUCP6zJz3EzzfHPzZx7qEBY4DA4mnX3zGvbHVjDIGBT8rAS309vbG/q0xbBX+0mMpkzs33FVjQwepJ0246oaWwauDwkIfreaJyeb73nzfWY/JTPuCZmmw/WwWqdhrSrSlIndAsGlr7XgPHPumPewfwNMzcDgd3+Rv9l2FyNZzisTuLB/i8xrMEhhpqNqn9q/eXFDMM3w8DC6urpCP1tpxk1Z2IHWYJDE1KKK6+8t7jfWdTCtUpL6dk6zPGle1P1Z3nnmuAXfZ3R0dMzTUaNezbh5sEQc+08s82ruT+KCYpMnTy78fsHutmbjxo1Yv349nnnmmcZgpu0HTXV1dWHmzJmYM2cO5s6di7lz5+KAAw7AQQcdhAULFqQK2jGotpcsXq64wCGo9oH2OGaVDaodffTRevrpp+fejuv+ZUkX9oOfJa3LdoIXIMHxpOm0aYM35vaFUNi4i6Qvzrgbk7Dy6erqcvp3PS5N2EVXsPZCWOfRZdbyMtWh7WBcsA+C4PTg4OC4JrbmBtYOzNlPJrSPW3d3d6O8TZDOvoC2a0+EPcXNZo5hWPDWZV6Wdbq6ukL/zQ+7YYn6R9SF+DWezEWjffyCzQLs6bDaOabpQNi0feEX3Ge7fMNuMIPnuzl/7e+DsGVhwTr7Pcw5YQd17WbfUU+5CgbjXC6wwmpwBDviNzWKggGvsCCY6zwATvuSNhAW9+dF1vnA3qeyBTvTD7thjcqX/e90WG2ZIl9FxtfkcL1hipo/NDSErVu34pe//GWjJtW6deswOjqKGTNm4MQTTwzd966uvU9utYe+vj6nMnW9ptizZ09oTcCwGoBBwRp3UQ+jsG/S0y43tVHCmh0Gg3Eu84LLhoaGxuy3y/dv2NMA7eNgjn/Y91nY/KTB1OC0A0d2X6H2+WTG7c+PHWjLUls1KY05b1xq6Kap0Wt/twWfpBncXzMeLKewhwj09/cDQGigLSoA5zLPtEow33G24PnY09MzJgAQ9hr8U8GluZ9rOnM9kuW8SjrnooJhRdzvqWrjzxgTaI2q0ZeG+UMhqvZYmiHqTxmXwXyu4vpyTrPcZV7UfVmz5ovImO/LsFd7vK+vdQ8bM7/XwZptUbXdtm/fPm4bItJ4sM+sWbMwd+7cMcEyMz5x4sSm7AODanvJwuWK8xyCahe2xzGrbA/qPT09+MxnPlN2NqjCgrVLkoZgWnMRYi4Wt2/fjg0bNoyplWD+qQnOs2+agzeo5scn+M+NGbebgNrjwXnmwjOou7u78W9PXsPDw+OCcFGBuS1btjT+fTb/jtv7C4yvGWACd/ZNuQnWxd3gmXV6enqgqrH9LLkOXV1dmDRpUmTgygQO7WZYrjUKzA2WqjaCGC7MBU4zgipx812fthvsm851vbBlwaeABYNh5ualE4XdJEbdYLqmUdXG8Q32F9nONUl/9atf4YgjjsDs2bMj05xzzjk455xzcPzxx0emiQp2hd2wR3FJFxW8M7W5aCxVHfNbHBzMnzrBWhzBPx7SDOYBRXaQrN3Po7xMMDKq2WXw9ynN71hU2q6urkYtNJZNc5hrJFNLzdSYo84wPDzc+BM9y6t5snaUvr6+yC5s5s+f35KablQgBZCte/xaqmxQjSiJuYiq6s2HaSZhDzt27BhXy+zJJ58MbQJqmkMZJnjX19cXGoSLmmceejBx4sRxPz69vb2YNWsWZs2a1ZRjoJq+41MzbNu2rfHjG1VjzW7+F7xpSqrlFraO/c9oUu2BYFMZXli2hqomNoeKqoVhOlgP1vBL85q0LK4PGrv2on0ums9OGUFFO2htag6F1XALm3ZZFvweK1J3dze2bduGhx9+uPHHxW9/+1sMDw/jyCOPxAc/+MHQJ/Ca+WvWrBkz3w6U2M3sW818b4bViIyaF1ZLxW6ulXfcPuZpmhJmaXbo0sfTlClTMnc4naU8TA3V4eFhbNmyJXXNMNemay7pkphaUcEaP0l9SCUtj6o9lDRv+vTpmDNnTmG/keb8sIehoSGMjIxg06ZNkc0Z7TIqarC/2+JaWxhhf8K6BBjN9UnaWqiu6U3N7rhjnme5ff4Ea9kVPW5qf7Uq2JL0XmG1WNPWeLS7qXCpNZdUMy6qhmkwr8H96unpaXRHEPU6Z86c2DTt8IfE7t27sXHjRmzYsKHxumHDhtg/6jpW9sY/tcOgGlGTmAv/om/KTMApqi+2J598csy0eQrpzp07Iy98TF7tJ5FGjYfNiwpsit8csre3t5Sb0yqya8Ol6dckrPll2HjS8jTjaWqCphmiLqjCanyGCQtsxt28m3FTGzHseKV5jVpm95sTF6TI08S4GeyLeLtWadh4cHrGjBmRy8wNW7MuoEdGRrBlyxY8+OCDje+5O++8E11dXZg+fTpGRkawffv2MTVkwl7NuEvAoghRn3N7vimLsBvgsPnmM26CGeZJnHHBXdfldl+ddrO1pEBS1Kv50yi4zPX70J7frIBtkDkeaYOEdlcApoZb3JDU5C3pDxzzfRQM0tlBpbDxuOXm3AgL+KWZl7ZZvNkfYHwfxC6d6IcFZMPOp8mTJ2PmzJmpg1JF1NKyf4/iulgxTS3TBgVNzaCkYXg4url5EfO7urpij2NwfPr06YlpwsZNjfcqB23sayvXPvhMawvXlgJx86p8bFptz5492Lx587jgWHA82My0r6+vUSFh9uzZjdciWhC1FUVHBdUq26faUUcdpYsXLy47GxTC/NMSNYQ9uS3vEOxvoajXsHmdWOPIVOnetm1bo4q2GXeZZ9+MpvnBtvu5i7rBS1oG7L1IKWoIbs8W9y+fq6T+/4L7GNapbdJ42pp6YeNJ+cwymG0TNcPKlSuxdOnSxJoXREREVB8jI96D3+IGc18SHEyzc2BvsL6ry3tAgQmK2QEye96kSZPSdPvQFv2DFUH2Wa54u0Ofav/YHsessledvb29+PGPf1x2NihCVF8+RfR9FTYE/6EOvpqnskUtj/pXNuzV/ENohAVQVHVMZ8l2555RnX0mpWtlZ6BBvb29mDFjBmbMmNHS9w3r5y7tNIDQfsDyDvb2GAQiqodly5YlphkeHo6sXUtERETuRkdHxz3ALPg0YNdlptZkWI1uuwVQ2DB37lzsv//+ocvKvMeizlDZoBpVm+nsvFM7Fjed8NqdI4eNm+lNmzZFLrM7wDbbBsYG8/r7+xs/DKbZZbA5ZtTysL7UqsJ8hqIeykBUhGCT1jR/CKhqYgC23Wrg2c380vb7FFxmmrq6dgqfp0N5exs9PT147LHHGp2X2w+NOe2003D88cfjC1/4Ag488MDQfu6qKqx5cdpXlzTA2GbBcZ/9opZF9S+U1OSxDGHN+OOa70fVgs4zHbfM9Debpn+0tH2qVfl7L9j3ZlTz1rjpPF0EZHlV1dRNWl36IQybDiu3Zk/bTVjjukcoarn9J3nwqZhpxtOmDesTz/UhHGnXAzDmOyb4nRP2PZRmOliGUf3HiQgmTJiASZMmNQbzUBd7etq0adhnn30il5sm8tQm+KACIkoiImP6FGomE8ALVmu2x9evX4/Vq1eHLt+5c+e47Rk9PT2RNeeyTPf391fu5qeq4pqspm3OGvVEyLg+wIpYnnRD6TrPiOow156X9QbOpYl62HwAzuWQh+u+hQXdg//qFvGecTffafqDMs0wgzePcYNrurj1h4aGcOuttzaeVmx/3jZt2oQbbrgBK1aswAtf+MJG59ZZuDQDDyubqL7Ukt6rGV0hTJw4cdzNNzD2c28HhsxgjlvYsrj14pZF9S2UpT+uZkvTfD+qFrTrdE9PT+P31WVd069aWDBp9+7djRohSUGnpHE7qFAlpvsCl6Bh1DKXB9PELU/7CoQHzc1gyi3N72vUtP19Fyw/ezpuWZq05rsrqjuPrPNMECYsnf3Hv/nDJet4mrRRLXnyDmHbVVVMmTJlTFcq5jsnbtolTVUD5lQzHdSnWmWDasPDw3j9619fyLZcvhRStJUudNp1Hdcv9Sw/GmHTST/+LhcIadPYX+rNfq3Tj4UdwJs9e3ah2x4ZGYmsORecHhgYiF2+c+dODA0Nhf5rFzZtJPXRl3cI6xTWpWPYrFyDI+Zzn6U5atSyqHMr6dwLG7fPk6g0Wf41D86v07lI9fKpT30qdP4nP/lJnH322TjwwANbnCMiIiJqBtMM1QxxtYfjlsUNceu94Q1vKPsQVMsogJ2JqdpGZYNqvb29uPrqq3Nvx+UftDT/EkdNN/OfnqKrJyeN27UE0tRmyZrebn5l/o2zqyEHgx1pX6PmBYMf9rEPzu/t7R3zhLuop+IlDcHqzpMmTUJ/f39pQYWenh5MnToVU6dOLeX9jWb9s2eawQRrC8Q1M2I/akTta3R0FB//+McxceLEsrNCRETUFkztWPupskNDQ2PGzR/xduArbtoeD3sgWvDPeru7B3OPFlWj2OWPX1OL23W9+fPnt/7AVxmbf1ZHX19f2VkggqpiZGQEQ0ND4wbzQxE27NixA5s2bRozb9euXdi+ffuYDjp37doFILwWo6r3QIRgIC7NYJpo9vb2VjZQJNLZffRRseygqksTWwBONfrSzKvquRYUrMkZ95r2Dw3DPhZhTWrCXrOkMd8jjz32GHbt2oW+vr5Gky4z/sgjj+DjH/84Xve61+HCCy/EnDlzYvv6anY5uv6hYKdzaSqepXl53Dr2d3TYcco7HTYvqh8he16rqWro+ZBlOqqZs3kfl/lp05o/mZKahmVdVoXuH0wZRZVTUeNm2jRNDrb2iBvSpg8bomqyR43nSWcHMcK+s4LdUKRdHpXGpduFIocsZVnEYI6t/RlOmpc2vT1uAmB20CsYDDPf+8FzyxyfIBFBX1/fuMEEt/r6+hrdxth9nE6dOnXMfHuZPd3TU+mQBYVRFNb8U0ROBfBlAN0ALlfVzweWvxvAe+GF8bYBOFdV7xORUwB8HkAfgN0APqyqtxSTq7H4CSVKYJq59fb2YsqUKS19b1Wvs107CBc2DA4O4qmnngpdZppm2v/ymP0KMrXpgn2luQx28C6u+W1dgg1ZNPMiMOwGO6wGaJHLo5rOBoek5SbN6OhobPm7Np1NYm44kprXmuXmvdPWsI2bF6eo/Uwj6j1NU9+wczVPM3v7XM97M5DmRmF0dBR/+MMfsGnTptDP5pYtW7Bjxw5cffXVWL16NQ455JDIpuB2YLBZwoJFYcGj4DyXpuEuTcbDAlv9/f3j5tuBCvtY7d69O/LY5Z0O3ryHTZfBtU+iuGn7T6Q0QZew+XFp7SC/iDRupnfu3Bkb/HNdFhYoNMr8ngse66LHe3p6MGHChEYgMSywGTWYfEYFVV3Wt681XMbzprPLMup7KhgIT7s8mCYYOEwa+vr6nH73k64JTPmkKcu8g8ufSmHz0qY348EAmB34MoP5fiLKTFFI808R6QbwFQCnAFgD4A4RuU5V77OS/ZeqftVP/xoAXwJwKoD1AF6tqutE5AgANwLYL3+uxqtsUE1V8Yc//KGw7bn+sGdJF1zHdVmW7QQvoJo9nqfGBuVn//C14oEIQ0NDY/pIixq2bNmCp556atz8Xbt2JdZoqYqoz2jwRi0sXVyAIuvFnMvFnglUuPad1t3d3aihmLZvNfMaVqMkeBOelHeTpgq1GKizrV27Fj/4wQ/w13/915gwYULZ2SEiIiJqP8U1/zwOwEpVXQUAIvJdAKcDaATVVHWrlX6y/+5Q1T9a8+8FMFFE+lV1qJCcWSobVBsdHcWNN95YyLZc/83Mki64juuyLNsJ+2ckajzLOsH1zeBaSyP4GsYORJj3tGsz5A3G2f9QRtW0CM4z/aXZ/9bY/9pkmVfHf3hEpFHVeubMmWVnh4iocPvttx/OO++8srNBRERE2Ntke/fu3Y0nDGcdN6/mPjTqXjtqvK+vD1OnTsWUKVMafU6bcfPa29vbgqPSBtybf84RkRXW9GWqepk1vR+AJ6zpNQCeF9yIiLwXwPnwmnq+NOR9Xg/grmYE1IAKB9W6u7vxyU9+suxsUM3Y1daTHlRgP8nF7lAz2LHmtm3bIpdFzYtrNhQMHpoml1maXYalr2NAj4jag/lTJdhsGYh++rTr/KL98pe/xMUXX4yvfOUrOOKIIwrffpWF/UHmOm1qscY1J2WNdTfBcyVMVK3opHmuaUwtZYpmf6+FNY8MezXXeq6tPFxe7e/EThD87gl2hRH2/dSs+UDyb1ieZWHLkyovBJsRu6aNet2zZ8+YIJUdqAqbDhsfHh5ObAkSrGBhKkSE9YMaNi9qfNKkSZgxY0ajGxqba8uwoaEhDA4OYsOGDVi9ejW2bduGwcHBMa/B7nSM97///Qmf6A7jHlRbr6rLc7+d6lcAfEVE3gLgIgBnmWUicjiALwB4Wd73iVLZoBpRFuZipS7/IoQ1uTRPvAkOW7duxdNPPx3a5NKeDl6Yh9UINP2BmCfk2K9h8+KWBef19/fzAt2BfQHncoEedzHpUpM077ywPt7i+n/LOz/s4jYYpHE9zkByUKaImrJFicpzVNNk11rWJm2z9zMYZAnmM6pGddK8uJrOQWY/77nnHgwNhf8puWvXLmzduhVHHnkkjjnmGCxYsCDXfotII+jUKlnLM+wm33UaQOz5a6bL3sc6sLsMCAtEhp3bLvNc17O/89tZEZ8h+3strJP/4Kt53zwtPsJem7mPVRTWH5t9vrj2Q5lnftxvWNJvl8uyqOVpgq1ZA7XB/e/t7cXkyZMxc+bMRsDKDnCFTdvj9m9+J7HLkXwKYFchW1oLYJE1vdCfF+W7AP7DTIjIQgA/BPAOVX2kkByFYFCNqERlNbkcGRnBrl27Gk8ktcfD5m3fvh0bN26MXB5cv1U3lsGaEVEXrK75cQ3AZE0flOYC3e5TLW3fakUst4+1+RcwrkPgqGVp5odd5Jp978SLNirWrbfeii9+8Yu49NJLcfDBB+faln0TRERERJ2D16Uhinv65x0ADhKR/eEF084A8BY7gYgcpKoP+5OvBPCwP38GgOsBXKiqvy0kNxGkqlHVo48+Wk844YTYNEkf3mYuL2KZXdU3al7a9FHbaPUQ9o9S2M2xyzzX+eaJTL29vePGzStveNqHqapu146I+ueRP3RERERERNQsInJnEU0Z24FMWa549orkhLcnHzMReQWASwF0A7hCVT8nIpcAWKGq14nIlwGcDGAYwCYA71PVe0XkIgAfgx9k871MVQey7FNsHqsaVDv22GP1Rz/6kXP6tPuRlD6pinXeZXZV37BXlzSuadNWQS5qiGqyFjU/TdrgfBNYGR4eHtNXmv0afNx78NjFNSMKLjeBOvNggmYN7B+NiCjZF7/4RbzjHe/APvvsU3ZWiIiIas2+jwv2SR3Xb3Xw3ivuNU1acw/nem8cfBWRMX1Yu/RTPWPGDOy3337o7+93Pm4Mqu0lk5YrDnUIqv2xPY5ZZZt/iggWLVqUnJCoxUwNKfvhBK7D4OBgqvRh/dKE9SvU1dXVtD7S+vr60NPTw9peHcZuOpvUkW9cOte+4uJe064T1a9gs6ZtUbV3sy6LWx7WX0+evvPSpG/GnzBRfwKJCB5//HGsW7cusunyfffdh49//OM48MAD8epXvzq0ZnNS/znBNGFlm2We63rN+AMqLm3wD6Ooz1zcsrRpXY590YOIjPl8ZR3Ps55Lf1l50wCIPQZZz4mkZWk+H1GfiTTrhP2+pPlDNsuypBt213mu6c3+upRDkfPj8hz3mjXQEVeWeafj/ijvhOvZuOsUV6YVUHd3d+M1adxuIRRsLWS/9vX1YfLkyU5pTesjU9HA/k5wfR0dHR3Xh7U97NixAxs2bBgzb9OmTVi3bl2jX9b+/n4sXLgQixYtarwuWrQICxYsqE1f3tQ8lQ2qEVWViDS+4CdNmlR2dgB4nUabQFxSf2dm3tatW536SDP/ELn8QNs3acFx0xdX1A+uPR53U5p1WVLaom9mo+bbx6rVF3Zp3tP1gt7lgj2prziXNGGv5iIrON++ETP7Etw3l2Vp00YFjLIuS1oedszDAk4u81zS2/OLHJK2qarYuXMnBgcHx/0jPjo6ipGREbz85S/HkUceiQ9/+MM4/PDDI2+Yg4GJqPM2KOz7z2VemvWiujkInitFLevq6oq8MQ777LmkS1on6dgXPdhdA4hI43sj+LlLGk+TNix4GHW+uZyTrmnMsc7ymU9aHrbMnCcu31ku81zXc/mMF9ntiDnGRtSNu+s81/RhxzyqjIqanyVYYV6zrht2HZB3utXXVtQZdu3ahbVr1+KJJ57AmjVr8Jvf/AZPPPEE1q1bh+HhYT79M0wxfarVAoNqRG2gu7sbkyZNqkyQL4zdPDdY7Ts4bku6Sc1yYxs27Xqxnaffv2Cwh4jcTJ8+PbZp58DAQKW//4iIiKi+JkyYgAMPPBAHHnhg2Vmph+IeVFALlQ2qDQ8P49WvfnXZ2Rgj6mY4zfyktC7/bmVJG7Usa82BLOslBSGy/luVtCzsAQZJ0+bphgyAFMdUDyciKhoDakREREQVMQpgV9mZaJ3KBtV6e3vx4x//uOxsNEQ1fUszPymt62uWdaL6QMg6mOrjWdZJ6gvBZZlp9pOUNu4BBsHxsOmoMotjr9PT09N4mEFfX9+Y8bB5ecfDmk4SEXWqY489FmeccQbe//73p+psuFPZze5Ys5aIqsxuxhq8zwibl7QsjzzflcHmznHzikgf9fABlyHtOuYBA2F937r0jxucZzePNn/WR/XzFtbf27Rp0zBz5kzMmDGj8WqP9/X1ZS5HCmBNNQqTtpYaEbC3rzP7oQZh42HLduzY4bTO0NAQhoeHG/NUx/ebZd8kBef39PSMCczZQ9R88/CCsB+zqHlFpAnuT3A8bpnLeNiyVvRjEpwfdSEY9lpk2lb12xI1P26Zwe/cdJJuFOzjmfWmIuw7p1XWrVuHtWvXRi5fv3497rrrLnzuc5/DiSeeGLstfrbGdgaf9yYzq6jfK3uZ4fL5NfvTTHH54Oeq+cr8DrLzAIztE81M25+JPJ/HLPuZ9P5h+U3KQ9T7JHHdb9f9zNL/YNy8VrP3M6pfurT92Lmkj7vmjhv6+/sxadIk5/TBfm+z9KcbHLeNjo5GPoU0LAi4e/dubN26FZs2bcLmzZvx8MMPN8Y3b96MTZs2jeuCpre3FwcffDAOO+wwHHbYYTj00EMxZcqUoj4C7Y9BNSIqQtX7OjP/WNmBuqgAXnC++aEaHh6O/Pcq7hHcWdKENWcOjscty7J+WD9pwYuxIudHXQiGvSal6enpcUorIuM6ZW72/trv6bIOURann3463vnOd+I1r3kNP0dERERtoqurq+k1y4aGhvDQQw/hvvvuw49//GN88YtfxPbt29HT04ODDjqoEWx71rOehenTpzc1L7WjAIYTU7UNKeufyCTLly/XFStWlJ0NIiIiIiIiIiIMDw9j5cqVuO+++3Dffffh/vvvx1lnnYVTTz31TlVdXnb+qkC6lysmO8RyBqUtjlllg2rPec5z9Oyzzy7t/dM090zbNDRpfvA1zzyX9GmHrOvZtWVaPZgqyKypQEREREREREUSaY8AURGka7ligkNQbWd7HLPKNv8UERx++OGlvHdUoDFsfpq0LvODr3nmuaSPephA1BC1nssAILIfpWYNdgeXIyNuDbvt/iaSxkVkTIeYZry3t7cxmAcItGKagUMior2efPJJvOQlL8Ell1yCN77xjfx+JCJqM3H3MC7z7Omy8p40niaty3jwgQBRDwgocpnrPWXSfABjHtBm9/dsTwfHJ06ciDlz5mDWrFlj+mmmJmHzz2pg80+qA9Mnmd05pnmKqD3s3r27JdN79uzJtT/m6TguQ7CD02AHo3Gdk8Ytj0sb7KQ0+P2VdzoqTSseEhCVttXTSQ8yaGaaYMCjqr9PRQp2Eh213LDTVSlAFFZ+rfLQQw9h8+bNoctGRkawadMmqCqWLl2KI444orWZK4DrsS3rfLH/cArON1w+t/Z+2n9gxU3HzSv6eITlO2m/O1UZ3wdlfgfZeQDCP/tFfU5d9jPpnAwui/qdKfN4pi1Pu4/YYD+ycfPC0rRasCWQ67w8y8MeCBD3gIC8y4poBWXSqCpGRkYafT3bD2sLTtvjO3bswIYNG7Bx48Yx90t9fX2YO3duY5gzZ86Y6QULFoy7/4gpy7aodVUEkeUKuMRy2uOYVbamGlEdmJpqPT31P5XMj1QwIBg3BGsDmteocfshBElpo+aFXfAE57mkSTMvy0MEsjxkoKenJ/RCL+kiMO+0mReXZ5f9SpMmbDlR0QYGBnD22Wfjc5/7HJ7znOeUnR0iIiKqkF27dmH9+vVYv349nnnmGTzzzDN44IEH8Jvf/AZPP/104wnjBx10EI455hgcc8wxOPTQQ9vi3o+KU+lPQ/Cxtq3g8u9PK5cRtYqINGqhERG1g3nz5uGGG24oOxtERERUQRMmTMDChQuxcOHCyDR79uzBypUrcdddd+Gb3/wmHnjgAezZswdLlixpBNrqWBOeipMrqCYiswBcA2ApgNUA3qSqmyLSTgNwH4Afqer7krY9PDyMN7/5zXmyl1pSFey45c1Y1ixVrpZvVzkPG3dlar7YzROThmBzxiyDqbVmmlEmjbukc61yTEREe91666249tprcckll2DmzJllZ4eIiKgWwroGiRuPWwYgsnloXLPRKlU66e7uxiGHHIJDDjkEZ555JgDvGD322GO466678MMf/hBbt24tOZftS0ROBfBlAN0ALlfVzweWvwjApQCeDeAMVf2+tWwxgMsBLILX09srVHV14XnME9QRkS8C2KiqnxeRCwHMVNWPRqT9MoC5fvrEoNrRRx+txx13XNZ8ZVovad1WLLOnXcazrBPX9j5pOk/aopqPJb1G5cEO7JnXqH6cwvrVCnaUaf/g2M0b7WF4eDj3uPlBiuIasLSDfsHAXlKwLzgd7Ocsy49l2nWyntdZv+PsMg4b4h7q0YxlRaeJSpe1X7esy9LOyzO/TvL8jkV9V4XNs6fznGNh65rt28uC5WB/F2d537vvvjv0ITSqim3btmHnzp0AgOOOOw5z586NfO+w36yw37aofYyaLmJe3vKpurh9M8viri+yjpvtu5wbLtNJ5dMOZRhVHlHnTKvzljS0y/FvxfaD5Rj8zIeJ+64vW97f1KjtxB2LqhyDYLm4Houo+7PgeFI6AGPulVzHswoe90mTJmHatGmxw/Tp07Fo0SJMmzYt8/sC7FPNJnKsAr9zSNkXe8xEpBvAQwBOAbAGwB0AzlTV+6w0SwFMA3ABgOt0bFDtVgCfU9WbRGQKgFFV3ZFhl2Llbf55OoCT/PFvArgVwLigmogcC2A+gJ8BcPqgdXd34zOf+UzqDOX5AotbtxXL7GmX8SzrhP0oul405kkbvLnN29G5+cKN20ZUn11x89KkDX7xF3WxI7K3n7YJEybEBsJch7COQs172eNxN3X2uB28MAHAYFlneVJQ3HgZgjcJYX2RpZ2fdhsuaYL5NeP2/GAa85mwmaB08MYpeEzMazBoEjwH4m6Uw7YZl8ZlO1Hz85ybZV0UF5HfqOOQ9DuS9vs/KR9J89Nuw7jvvvswefLkcX+miAh27NiB6667Dl1dXTj55JPxpje9KTIQYIubDvscRX22wj7jUZ/7uP23t1/073LYdFj+wl7jlmV9jZoX9r0S/O6x50edO3E3/EmfQbPtsPIIlmPcsQ++V9T1mcs8l+X2vof99meZZ+bbx8Wel0eebSQdI3s6aUhKl1fW/XQ5X+LmuS4Hxn63uXxHR33Os7xmKZM85RlXpknlnXXdvMcp67qq2e+9gq9FnPNp2Z/L3t5e9Pf3N57waY+b6d7e3kb6/v7+Rpquri7s2LEDzzzzDLZu3YrNmzfj8ccfx9atWyEimDhxIpYuXYqlS5di//33x/7774+lS5di4sSJLd/n+lIA4//0zOA4ACtVdRUAiMh34cWgGkE19WueiciYaKyIHAagR1Vv8tNtKyJDYfIG1ear6pP++FPwAmdjiEgXgH8C8DYAJ8dtTETOBXAuACxevBj77rtvzuwR1Y9d682uuRY2mCd+xqUJG7KsY7+n+UGNCpYVLe5GqZ3k2c+kgFyaQF2zg4RFrpNmWVat/uxlvWmzPz9hN8f2v8ZRN85x00nLgtLMT7sNs68bNmwYc1NkLvg3bdqE7u5uzJkzB8ceeyxmzZrVWC/uZituOi7fwWVhAYgoccujjn3askszbfY56rXIm+e4baZdHrdO2PpZPvNp1wkbt1+jxl3nuSxPCi40Y14WWdeP+t6LK680y6OWtVqa8yFsXtblUZ9dl89qntcs5Zi1jJPKNO93eNKyLMfSJU1Y2qIePFXWeWCo6pgnew4NDcWO79q1Cxs2bGg8iOCZZ57Bhg0bxvxp39/fj4ULF2Lu3LmYPn06VBWDg4O49957ccstt2D16tXYuXMnRARLlizBEUccgSOOOAKHH3544xqDbArAqX/8OSJiPyb0MlW9zJreD8AT1vQaAM9zzMTBADaLyA8A7A/gZgAXqmrhtTUSg2oicjOAfUIWfcKeUFUVkbBfw/cAuEFV1ySdfP4BvAwAli9fXk71AKKSmR+z3t5e/iNCRJTBnj178Dd/8zc48sgjy84KERERFUhEGjXSirJr164xQbdnnnkGa9euxR//+Eds2rQJU6ZMwfOf/3wcddRRmDdvHp5++mncdttt+OpXv4pNmzbh/e9/f2F5aQ/ONdXWa/OazPYAeCGA5wB4HN6zAM4G8I1mvFEsVY2sXSYiT4vIvqr6pIjsC2AgJNkJAF4oIu8BMAVAn4hsU9ULE94Xq1atSspe4Vyj7kWmC6aJ+rexGWmD/95kneeavir/cBARUfvq7u7GYYcdhsHBQUydOrXs7BAREVGFTZgwAYsWLcKiRYtClw8ODuKee+7BH//4R1x77bV46qmn0N/fjyOPPBLHHHMMli9nV2pjjQIopOuytfAeMmAs9Oe5WAPgbqvp6I8AHI8ygmoJrgNwFoDP+6//E0ygqm814yJyNoDlSQE1wPuX+ctf/nLO7KXjWu28yHTBNPZ03LIi0uatSp42vRla1Wl4VHOdPFw63E/zdNGoJ466pnV5YmlSmqjlDHpS0VyaDuVdFrc8T77rJu33dxFpXPOUN41t7dq12LHDu2gzf9wA3nf1ypUr8clPfhIvetGL8JGPfAT77rtv4h9D5jWuGU3afCftk8u6ecsszavNpamR67y06Vvxnma/iz6Grse42dO2Mv5MbbWw34Cw8TRp47aRR9ZjVMY5aYTtf9h4luVR87KWU9ayDdOq73fXeXnT20OeB1u5pgEw5gFnSfct9pA1/YQJEzBt2jRMmjSpsO+jqVOn4sQTT8SJJ57YmLdr1y78+c9/xl133YXVq1cX8j7tpZA+1e4AcJCI7A8vmHYGgLekWHeGiMxV1WcAvBTAioR1MskbVPs8gO+JyLsAPAbgTQAgIssBvFtVz8mcsZ4e/P3f/33O7IXLc3IV8SOYZl7W9ex5DI4Uw/xgxHW4b6ZNv2X2EJyXNs3u3budtxM3uKY3+2zYnyPVzujjLEzcMbGZZVE3qkVclMe9V9TFcNj8VpalXVM1eBNW1LK45XnyXQd2eaa9OS4iTRKXdGmO9e9//3sMDg6iq6tr3A3E4OAghoaG8Itf/AJ79uzB0Ucf7RT0iFsWl7esy1zXzVtmaV6B5t7UuaZPkybL9lpxTiSdK0Vc5yXNc/2cpz0f4ua1WlRZJJVRnvGssh6jss4de1/jPs8u87Kkb+V4lFZ/v8cty5I2OE9kfOuhZjxIy7xn3APizL3J0NCQ0wPl4gazzq5duzA4OIjt27eHnm/9/f2YNm0apk6d2nidO3culi5diiVLlmDRokVOTUsnTJiA5cuXs5ZaKIVjn2rxW1EdEZH3AbgRQDeAK1T1XhG5BMAKVb1ORJ4L4IcAZgJ4tYh8RlUPV9U9InIBgF+I92G8E8DXc2cqhJT145fkqKOO0kMPPbTw7ebZ37w/gmnnZV3PnlfV8i1KUQEBO+CgqomPiw7+exL8JyVqWRHp0vyDk2d5cN+JwgT/jQz7hzLqn8tmzA/mLc101nVcuZxHac61VgU24saTpvOkNfMefvhh7NmzZ9xFuojgxhtvxLXXXotTTjkFF1xwASZPntxYvmnTJtxyyy04/fTTGxfHaW484riWU9rydL35zfuaJ995l7XiNSwfzfqcJp0nrTgnbc0KbMSlDy4rYl7UZ6mIc6CI8yXpXMqzrlF0OWX5vmvmMYgqk6QyyzMel79WXENkTZN2XtzgkiZtumYSEfT29qK3txc9PT2Ncddh8uTJmD59+pgn3g8NDWHr1q0YHBzE1q1bsXXrVgwMDOCxxx7DY489hscffxzDw15AaPr06ViyZAmWLFnSeCLoAQccEBp0E5E7tXn9g9WKyOEKXO2Q8qi2OGZ5a6o1TW9vL6655pqys0EdSnX8o6bNdPDfk+A/KVHL8qTL8g9OEcvt49CKY94Jwbuk/bSPtQnyRk0X9Z5BJm3wvYN5DN7Ehk1HzWvm/LB9cZ3Ouk4Sl3JzLdtgeWa5eSpquct0nrR79uzBbbfdNu57yIyvWbMGO3bswM9+9jPMnDkTCxYsGJfu2muvTR3EiJOmnFzTtTLglCffRSwrIiji+tqqz6nLeZLnnHM9D10/31Gf+SzLXaazrBM2XeR5kPecSTM/7TaA5gdog/Pi8pJ1mcu6UWWSVGZ5xpPy6FqGWecVPR2VJm4IpomqaeayLZfflTxGR0cxPDyM4eFhjIyMYNu2bY1pl2H79u3YsmXLmCd9Al6sYcaMGZg5c2bjdf78+TjkkEMwc+ZM7LvvvliyZAkGBwcbwbaHH34YP//5z7Fq1SoMDw9jwoQJOPTQQ/GsZz0LL3nJS5p6HOpHUVDzz1qobFCNqEwiMuYfDSIiqp57770Xq1evxite8YqmX9gTERFRe9i9ezc2b96MzZs3Y9OmTY3XJ554An/605+wdu1arF69GqOjo5g6dWojePaiF70IBx98MCZOnIgdO3bgwQcfxP33349NmzaVvUsVoyii+WddVDaoNjo6il/96ldlZyOVrBf0rf5HJDgv7l85l3lp0rv88xG3LO26drNNl1felBER1cfhhx+Oww8/PHTZrl278IUvfAHnnXceZs2a1eKcERERUVX19fVh3rx5mDdvXmLarVu34oEHHsADDzyA7373u3jooYewc+dO9Pb2YtmyZTj00EP5pPFxRgHsLDsTLVPZoJqq4o477ig7G86yNo9rRjv6NGnyVh9Pk96ljX7csqzrBpsxxr3m1YomjFFPHk0a0qRNWi+punizlrU66Bn8XAXnNes1z7witpE0rxnLsxzvLMc0izzrtlrY92+e8bTr5clvFvfddx82bNgQ+t0wMjKCVatW4ZJLLsHhhx+OF7zgBY1lcX8AxY27cNkn1/12PeZ5ytolP81qnhQ1XVRal3wB1TjOzVgWlPbzXtR4s+X9Hsu6vAxFn2uu2zTC9j/pc551Oq4ssqRxSZukDt/vYcvSnI9mXREJHU8r6f1N66CwvqWzzjN9qZlhypQpY6bteRMnTkw8PtOmTcNxxx2H4447bsz83bt3Y9WqVbj//vsbTyInQ8HmnxXQ3d2NCy64oOxsEFWKCQ7YTx7ds2dPY17UYD+l1HVIWsfOixkPDkUvK+N4B2tBRr26pHFJG1bzMm69tO9T1LxmLM9yTLMc9yxaHdDNo+ib4mbfQOdZd9u2bdi2bRsAjPuu2LBhA1772tfi1FNPxYUXXogFCxY00tmvacZd8+qSLsu2sgSTsqxvpLkZblbaIqaj0uQ9TkUd52YuM/IEBPMGE1sh7/dY1uWtlPVznnfa5TOclCbrdFxZZEnjkjZJHb7f0+ShbMH7jqi+ptOkMX2pmWHdunVjprdt29YY37lzZ+i5NGnSpDF9rgX7XzPzDjjgADTj4Yr1p2DzTyKqJBMY6OrqQk8PT18iojibNm1Cf39/Y/qJJ57AwoULa3OzQURE1M5MK5gq3deoKnbu3DmmvzUzvnLlyjF9sG3evBm7d+/GeeedV3a2qUTV+fQGDA8P41WvelXZ2QCQPdLfrPWa/S9nM8ej5hVR28V13B7CmhpGNT9MMz+siWQzXuP6hkuzLG36uCagWcoj6zouml1Vv1VNTVybLTRjG81Kk/c4FTneKq1+z1bVZKgCEcGmTZvw+OOPhy4DgIsvvhi9vb1429vehtNPP33c8rB14tK4yPL9kiZN1nmu64VxOTZJadJOu+Yla5qgomrDpd1GGYoom6LOF1ue41NG7cc88hyvZt8XJCnyeylsXhk1Zov87GVdr6i8u6Zt1TUuMLaSQNw9TZp5XV1d6O/vR39/PyZMmDDuta+vL9XvyqRJkzBp0qRGLXfKgs0/S9fb24uf/OQnZWcjs6K+UNMsL2JZM8ej5rmMp0kbNx42hDU1jGp+mGZ+WBPJuNfgvJGRkcQ0cdsJW540L+/yLOWRZ52sF6FFXLza23CZV7X0abfRrDRhadMszztehlZ/blsR8Cj7mJrvhXXr1uGhhx4KXQYAO3bswJ49e3D99ddjzpw545aHrROXpsjAj2u6qDRFzHfNp8sxC5uXdb00+cizrVYEl6p07iSVT9R1XZa0Zf5eh20nbjrrsqxcP6Ou60Ztr8j5zf4NyTPdrLJOo6jPe7M+p824nurq6ho3Pyqtfe+wZ88ejIyMON0bxaXZs2cPdu/ejV27dmFoaGjc69DQ0JjPdPCYqComTJiAefPmYf78+aGv06ZNK/16pz5GAXROP3OVDarVXRkXD0RERBTtRz/6EV74whdi9uzZZWeFiIiIKmTnzp0YGBjAwMAAnn76aQwMDODBBx9szNuyZUsjbVdXF+bMmdMIup122mkl5ryKFKypRkRERNRmXvva15adBSIiIqqgiRMnYsmSJViyZEli2j179mDDhg2NgNvEiRNbkMM6UfBBBURERERERERENEZ3dzfmzZuHefPmlZ2VihoFsLPsTLQMg2pERERERERERFSAzmr+2ZWchIiIiIiIiIiIKIlp/pk0JBORU0XkQRFZKSIXhizvF5Fr/OW/F5Gl1rKP+fMfFJGX596tCAyq5XTTTTdh5cqVLX/fb37zm/j3f/937N69u6Xv+9BDD+Hv/u7vcOedd7b0fQHgO9/5Dp588smWvy8REdXThz70IaxatarsbFALrF+/Hv/93/+N0dHRsrOS2aZNm3DNNddgz549ZWeFiIgoB9P8M2mIJyLdAL4C4DQAhwE4U0QOCyR7F4BNqroMwD8D+IK/7mEAzgBwOIBTAfy7v73CSZ7HODfTkiVL9PHHHy87G9QEIhL6lFNVdX6suP3Y5jTvm2W9vHp6ekp538mTJ2d6mmyeJ9D29fVh1qxZmddPEpW3+fPnY/78+U173yiPPPIItm/fXug2q/idHJenLVu2YNeuXS3MDdFeO3fuxNDQUORy89ns7+/HzJkzW5WtQlTxu6BZXH93duzYERk027NnD3bu3AkRwdSpUzFp0qQis1iYdtiHqumkc4Uoj6qdKyKCiRMnYtq0aU5pXeZPnz4dixcvRldXV+Oes4jxpOWvfOUrccwxx9ypqssLOTg1J7JAgb9xSHlx7DETkRMAXKyqL/enPwYAqvr3Vpob/TS3i0gPgKcAzAVwoZ3WTpdxtyJVNqgmIoMAHiw7H0RtaA6A9WVngqjN8LwiKh7PK6Lm4LlFVLxDVHVq2ZmoAhH5GbzvmSQTANj/zl+mqpdZ23kDgFNV9Rx/+u0Anqeq77PS/NlPs8affgTA8wBcDOB3qvptf/43APxUVb+fZ9/CVPlBBQ8y0ktUPBFZwXOLqFg8r4iKx/OKqDl4bhEVT0RWlJ2HqlDVU8vOQyuxTzUiIiIiIiIiIqqStQAWWdML/Xmhafzmn9MBbHBctxAMqhERERERERERUZXcAeAgEdlfRPrgPXjgukCa6wCc5Y+/AcAt6vVxdh2AM/yng+4P4CAAf2hGJqvc/POy5CRElAHPLaLi8bwiKh7PK6Lm4LlFVDyeVwVT1REReR+AGwF0A7hCVe8VkUsArFDV6wB8A8C3RGQlgI3wAm/w030PwH0ARgC8V1Wb8njtyj6ogIiIiIiIiIiIqKrY/JOIiIiIiIiIiCglBtWIiIiIiIiIiIhSqmRQTUROFZEHRWSliFxYdn6I2oGIXCEiAyLy57LzQtQuRGSRiPxSRO4TkXtF5ANl54mo7kRkgoj8QUTu8c+rz5SdJ6J2ISLdIvJHEflJ2XkhagcislpE/iQid4vIirLzQ61XuT7VRKQbwEMATgGwBt4TH85U1ftKzRhRzYnIiwBsA3CVqh5Rdn6I2oGI7AtgX1W9S0SmArgTwGv5m0WUnYgIgMmquk1EegHcBuADqvq7krNGVHsicj6A5QCmqeqrys4PUd2JyGoAy1V1fdl5oXJUsabacQBWquoqVd0N4LsATi85T0S1p6q/hvdEFCIqiKo+qap3+eODAO4HsF+5uSKqN/Vs8yd7/aFa/wIT1ZCILATwSgCXl50XIqJ2UcWg2n4AnrCm14A3KEREVHEishTAcwD8vuSsENWe30TtbgADAG5SVZ5XRPldCuAjAEZLzgdRO1EAPxeRO0Xk3LIzQ61XxaAaERFRrYjIFADXAvh/qrq17PwQ1Z2q7lHVowEsBHCciLDbAqIcRORVAAZU9c6y80LUZk5U1WMAnAbgvX6XO9RBqhhUWwtgkTW90J9HRERUOX6fT9cC+I6q/qDs/BC1E1XdDOCXAE4tOStEdfcCAK/x+3/6LoCXisi3y80SUf2p6lr/dQDAD+F1Z0UdpIpBtTsAHCQi+4tIH4AzAFxXcp6IiIjG8TtU/waA+1X1S2Xnh6gdiMhcEZnhj0+E9/CqB0rNFFHNqerHVHWhqi6Fd391i6q+reRsEdWaiEz2H1QFEZkM4GUA/lxurqjVKhdUU9URAO8DcCO8Dp+/p6r3lpsrovoTkasB3A7gEBFZIyLvKjtPRG3gBQDeDu8f/7v94RVlZ4qo5vYF8EsR+T94f7bepKo/KTlPREREQfMB3CYi9wD4A4DrVfVnJeeJWkxU+TAlIiIiIiIiIiKiNCpXU42IiIiIiIiIiKjqGFQjIiIiIiIiIiJKiUE1IiIiIiIiIiKilBhUIyIiIiIiIiIiSolBNSIiIiIiIiIiopQYVCMiIiIiIiIiIkqJQTUiIiIiIiIiIqKUGFQjIiIiIiIiIiJKiUE1IiIiIiIiIiKilBhUIyIiIiIiIiIiSolBNSIiIiIiIiIiopQYVCMioo4kIh8Xkcsd0n1VRD7Zijy1CxFREVlWdj4IEJFtInJA2fkgIiIiakcMqhERUeWIyM9E5JKQ+aeLyFMi0pNyeyeJyBp7nqr+naqek7Suqr5bVT8btZ2yichSP4iV6phUgYicLSK3OaZ9rohcIyJrRGSDiKwQkfNFpC9hvdUistMPLj0lIleKyJRi9sCdiHxARB4Vke0icr+IHGwte4uIPOYv+5GIzHLc5kkiMurv26CIPCgif2WnUdUpqrqq4H2ZJSI/9PP7mIi8JSatiMgX/DLb4I+L4/tcLCLD/v5tFpH/FZETituTxvv0i8gVIrLV/4ycH5P2q35+zDAkIoNF54mIiIjqgUE1IiKqom8CeFvIzffbAXxHVUdcN1THYFOVVOH4ich5AL4F4McAjgEwF8BbASwBcJuIzEjYxKtVdQqAowE8B8DHmpbZECJyDoB3AXglgCkAXgVgvb/scABfg/fZng9gB4B/T7H5df6+TQPwQQBfF5FDist9qK8A2A0vv28F8B/+foQ5F8BrARwF4NkAXg3gb1K81zX+/s0FcBuAH4QF5USkO8U2gy4GcBC8z9NLAHxERE4NS+gH2aeYAcDVAP47x3sTERFRjTGoRkREVfQjALMBvNDMEJGZ8IIRV/k1Sy4VkXX+cKmI9PvpTvJrM31URJ6Cd9P7UwALrNolC/xaMN+2tn+iXxNms4g8ISJn+/OvFJG/FZHJEdvZISKzre0cIyLPiEhvcKdEpFu8ZqeP+DWL7hSRRf6y54vIHSKyxX99vrXerSLyWRH5rb/ez0Vkjr/41/7rZj9PJ4hIl4hc5NciGhCRq0Rkun18AvlaLSIn++MXi8j3ReTbIrIVwNkh+3GlX2PnJj8/vxKRJWEFKSLT/fd/xs/PRX7+ngXgqwBOMDWRItY/CcA7ARyvqt9W1QFVHVXVB1X1A/ACsF8KWzdIVZ8CcCO84JrZ/vFWud/jv19hRKQLwKcBfFBV71PPI6q60U/yVgA/VtVfq+o2AJ8E8JciMjXN+/jbvQHARnjBK/P+hTbF9c+D1wP4pKpuU9XbAFwHLygY5iwA/6Sqa1R1LYB/QshnKomqDsMr630AzPY/g/8hIjeIyHZ4wbCszgLwWVXdpKr3A/i6Sx6tY/HNHO9NRERENcagGhERVY6q7gTwPQDvsGa/CcADqnoPgE8AOB5ecOQoAMcBuMhKuw+AWfBqnrwDwGnwa/T4wzr7/fyA0E8B/Cu8GjFHA7g7kKftEdu51c+b8XYA3/WDAEHnAzgTwCvg1Sx6J4Ad4jX3ux7Av8ALJn4JwPV2sA7AWwD8FYB5APoAXODPf5H/OsPP0+3wAgJnwws0HACvdtS/heQnyukAvg9gBoDvRKR5K4DPApgD71hFpftXANP9fLwYXnn8lR+8eDeA2/18z4hY/9MA3qOqm8VrQrlGvGaOnxWRi+DV6jreBA3jiMhCeGW40p/eD95x/1t4n5cLAFwrInMj1v+JH3wLG34S8bYL/eEIP1j7qIh8xg+2AcDhAO4xiVX1EXi1wA4ev6nYfesSkdfAK4+Vjuv8e8z+/F/EagcDGFHVh6x59/j7EWbM/iWkjctrP7zP9BOqut6f/RYAnwMwFV4ttuA6ifsnXrB+34x5fD2AZ7A3sE1EREQdhkE1IiKqqm8CeIOITPCn34G9NULeCuASv9bSMwA+g7E1ZUYBfFpVh/wAXZK3ALhZVa9W1WFV3aCqd6fI59uARhO0M+E1VQxzDoCL/FpWqqr3qOoGeM0CH1bVb6nqiKpeDeABeE3ljP9U1YesgOPRMXl6K4Avqeoqv/bTxwCcIe5NOW9X1R/5NcKijt/1fu2qIXhBzhNMrTvDPx5nAPiYqg6q6mp4NZWiajWN4Zf9YlW9XUQOA/AReEHE5wJ4HoAeVVUA98JrvhflR+L1e/UEgAF4gTrAK7cbVPUGf19vArACXtBzHFV9larOiBheFfHeC/3XlwE4El6g80x4zUEBL+C5JbDOFniBIhcL/Fp+OwH8EMD5qvpHlxVV9T0x+/PsiNWmANiaIr/B/dsCYIqIW79qAN7k798TAI4F8Dpr2f+o6m/9stsVXNFx/0z/esE8uhz/swBc5X8GiYiIqAMxqEZERJXkNytbD+C1InIgvNpo/+UvXgDgMSv5Y/4845mwm+wYiwA8kjGr/wPgMBHZH8ApALao6h9Svk9wf+BP72dNP2WN78DeYECYsOPTA68PLBdPpEnjB+42YmwZAF6tqd6QvOwHN7MAPO2PHwHgt36gcCu8JsLGIgBrY7bzWlWdCuAkAIf6+QK8moxvtGswATgRXs2lopig5BdVdbMfWPwa9gbutsGrtWibBsC18/t1fi2/afBqOr40V26Tpc1vMP00ANtSBKK+5wfB5qnqS1X1TmuZy+c0yTYrX3YeY4+/iCyG93m6qoA8EBERUU0xqEZERFV2Fbwaam8DcKOqmgDLOngBEWOxP88I3rAn3cA/AeBAh/yM244fvPuen8e3I7qWWtz7BPcH8PYpLlAUmaeQ7S0GMAIvQLUdwCSzwK9NFmzu6BLwaNRKE+9pmrMwtgwALyg6HJIXs19J77MRXnNXAPgzgOeLyAF+f2OvBdAnIu8HMKCqTyZlWFV/BeBKAP/oz3oCwLcCNZgmq+rnw9YXkZ/K2Cc/2sNPI972QXjNOe19tcfvhdeE2bzHAQD6AdjNKxP5NQY/CuBIEXmtyzoy/kmW9nBvxGoPAegREbtm4FH+foQZs38JadOK/fy47J+qbgLwZIY8vh1+kDfPDhAREVG9MahGRERVdhWAkwH8NcZ2Bn41gItEZK54HfZ/CsC3Q9Y3nobXuXlUv1vfAXCyiLxJRHpEZLaIHJ1iO1fB6+/pNYgPql0O4LMicpB4nu33m3YDgINF5C3++78ZwGEAovrpsj0Dr7nrAda8qwF8UET29wNefwfvKYoj8IIiE0TkleI9TOEieEGctF4h3sMd+uD1rfY7VR1Tc0hV98ALOH5ORKb6fdedj71l9TSAhf42xvEDlk+JyLGqeh+AfwDwG3j9Z90Dr0+rpfCb3zq6FMApInKUn49Xi8jLxXuIxATxHuSwMGxFVT3N6k8vOJwWsc4OANfAe6LkVH/b52Jv2X7Hz8MLxev4/hIAP1DVQaDxUIgrXXZMVXfDa177Kcf0747Zn9A+xdTrW/AHAC4Rkcki8gJ4ffBFfe6vAnC+iOwnIgsAfAheYBP+/q0W/6EgRUuxf1fB+z6ZKSKHwvu+uTJ0o3u9wyENERERtTkG1YiIqLL8pnL/C2AyvCcMGn8Lr++r/wPwJwB3+fOitvMAvEDTKr+Z34LA8sfhNcf7ELzaUXdjbM2V2O2o6m/hBbbuUtVgM07bl+AFmX4Or1+qbwCY6Per9ir//TfA6zvsVbq3Q/ZIftDmcwB+6+fpeABXwAty/BrAowB2ATjPT78FwHvgBfjWwqu5tiZk00n+C17fZBvh9XUVFdg6z3+PVfCCYf/l5w8AboFXI+gpEYna188C+JqITFHVL6vqfqp6lKp+GMDhqnq+3xzUiXp98F0F4FN+EPB0AB+HF5x8AsCHUfz10fvgNTNcB+B2WMdAVe+F98CG78Dr720qvPIxFgH4bYr3ugLAYhF5dWLK7N4DYCK8/F4N4P/z9wN+cHCblfZrAH4M7zz9M7wHQ3zNT9sH78Ecv2tiXl18Gl6z7McA/ArAP6jqzwCvmadfs22xSSwiJ8DrK++/y8gsERERVYewb1UiIqL8ROQWAP+lqpeXnZdm82tOrVHVi5LSFvR+H4bX3O4TAH4JrznlC+HVwDvfD2q2HT/odA+AZ2v402RrTUROBPBeVT2z7LwQERERZcGgGhERUU4i8lwANwFYZJrttbNWB9X893wxvJp8xwHog1c78Z9V9fpW5YGIiIiIyNZTdgaIiIjqTES+Ca/T/A90QkCtLP5DBn5Vdj6IiIiIiAzWVCMiIiIiIiIiIkqJDyogIiIiIiIiIiJKqbLNP2fPnqP7Lwp9oj0RFWykul8Fba0HI2VngYiIiIiIcrrznnvWq+rcsvNRBctEdIdDuieBG1X11KZnqMkqeye9/6KFWHHLLWVng4iIiIiIiIgoksye/VjZeaiKHQD+xiHdxcCcJmelJdj8k4iIiIiIiIiIKCUG1YiIiIiIiIiIiFJiUI2IiIiIiIiIiCglBtWIiIiIiIiIiIhSYlCNiIiIiIiIiIgoJQbViIiIiIiIiIiIUmJQjYiIiIiIiIiIKCUG1YiIiIiIiIiIiFJiUI2IiIiIiIiIiCglBtWIiIiIiIiIiIhSYlCNiIiIiIiIiIgoJQbViIiIiIiIiIiIUmJQjYiIiIiIiIiIKCUG1YiIiIiIiIiIqFJE5AoRGRCRP1vzjhKR20XkTyLyYxGZ5s8/RUTu9OffKSIvbUUeGVQjIiIiIiIiIqKquRLAqYF5lwO4UFWPBPBDAB/2568H8Gp//lkAvtWKDDKoRkREREREREQUYSNmlZ2FjqSqvwawMTD7YAC/9sdvAvB6P+0fVXWdP/9eABNFpL/ZeSwkqCYip4rIgyKyUkQujEn3ehFREVlexPsSERERERERERVpI2aNGagp5ojICms413G9ewGc7o+/EcCikDSvB3CXqg4VkdE4PXk3ICLdAL4C4BQAawDcISLXqep9gXRTAXwAwO/zvicRERERERERURFcAmcbMQuzxlWaohzWq2qWClfvBPAvIvJJANcB2G0vFJHDAXwBwMvyZzFZ7qAagOMArFTVVQAgIt+FFzW8L5Dus/B27MMgIiIiIiIiIioBa5/Vl6o+AD9gJiIHA3ilWSYiC+H1s/YOVX2kFfkpovnnfgCesKbX+PMaROQYAItU9fq4DYnIuabq3zMbNhSQNSIiIiIiIiLqRMFmnGzOWX8iMs9/7QJwEYCv+tMzAFwP7yEGv21VfoqoqRbL39EvATg7Ka2qXgbgMgBYfvTR2tycEREREREREVGdtTJIZt6LzUBbQ0SuBnASvP7X1gD4NIApIvJeP8kPAPynP/4+AMsAfEpEPuXPe5mqDjQzj0UE1dZibMdwC/15xlQARwC4VUQAYB8A14nIa1R1RQHvT0RERERERERtirXLOpOqnhmx6Mshaf8WwN82N0fjFRFUuwPAQSKyP7xg2hkA3mIWquoWAHPMtIjcCuACBtSIiIiIiIiIyGDwjOomd1BNVUdE5H0AbgTQDeAKVb1XRC4BsEJVr8v7HkRERERERETNVnRQh80EozGARu2gkD7VVPUGADcE5n0qIu1JRbwnERERERERkasygjiu79nOwTcGz6idNf1BBUREREREREStUscgTlKe6xJ0q+OxJ8qDQTUiIiIiIqIWCAYc6hIoqbJOCeJULejWKcedKAmDakRERERERCkVEVTIso1ODsQxkBMtzbGJ+wzxGBOlw6AaERERERFRhKoFGeLy024Bt6od+3bB40pUHAbViIiIiIiIUP9gQ1T+6xJsq/vxp86xEbNqc15RczGoRkRERERUcUU17aKxOiWIE7afVficdMrxJ6L2xaAaEREREVGJig4suG6vCkGVMjCQ42l1M1IedyJqRwyqERERERG1QNWCCnVvKuiqase9DnjMiJKxCSgBDKoRERERETVFXQMTwXzX8aaxrseeiIjqhUE1IiIiIqKCtGMwp6r9cQW147EnIqJq6yo7A0REREREdbURs8YMnSK432Xse9nvT0REzSUiV4jIgIj8OWTZh0RERWROYP5zRWRERN7QijyyphoRERFRG2tFsKGKtZaaiQGccM2u0cbjTkTUca4E8G8ArrJnisgiAC8D8HhgfjeALwD4eYvyx6AaERERUTsoM+DQCR3eM6CTjctxM58THmMiIrKp6q9FZGnIon8G8BEA/xOYfx6AawE8t8lZa2BQjYiIiKiG6hCAqEtfXFHqcIzbAY8zEVFHmiMiK6zpy1T1sqSVROR0AGtV9R4RsefvB+B1AF4CBtWIiIiIyNYugYc6BNra5VgTERFV2HpVXZ5mBRGZBODj8Jp+Bl0K4KOqOmoH25qNQTUiIiKiCuqkwE5wX1sdZOukY01ERFRjBwLYH4CppbYQwF0ichyA5QC+68+fA+AVIjKiqj9qZoYYVCMiIiKqCAZ3PK2ozcZjTUREVC+q+icA88y0iKwGsFxV18MLtpn5VwL4SbMDagCDakRERESlYnDHTdJxCgbdeFyJiKjZ+FvTXCJyNYCT4PW/tgbAp1X1G+XmaiwG1YiIiKgQzbiwrFpfW0XhRXjxeEyJiIjai6qembB8acT8s5uRnzAMqhEREZGzVgcu4t6vTgE3BnyIiIiI2g+DakRERDROHYJAUXmsSrCtDseQiIiIiLJjUI2IiIjaKgBUVrCtnY4hERERESVjUI2IiKjDdGrwp4impJ167IiIiIhoPAbViIiI2hwDQcl4jIiIiIgoLQbViIiI2giDQ0RERERErcGgGhERUY0xiEZEREREVA4G1YiIiGqCATQiIiIioupgUI2IiKhiGDwjIiIiIqo+BtWIiKhWqh5winuKZNXzTkRERERE7hhUIyKiymiHoFM77AMRERERESVjUI2IiFqCwSYiIiIiImonDKoREVFuDJgREREREVGnYVCNiIhiMWBGREREREStJiIfAPDXAATA11X1Un/+eQDeC2APgOtV9SNl5ZFBNSKiDsaAGRERERERVY2IHAEvoHYcgN0AfiYiPwGwCMDpAI5S1SERmVdiNosJqonIqQC+DKAbwOWq+vnA8vMBnANgBMAzAN6pqo8V8d5ERBSOATMiIiIiIqqpZwH4varuAAAR+RWAvwSwHMDnVXUIAFR1oLwsAl15NyAi3QC+AuA0AIcBOFNEDgsk+yOA5ar6bADfB/DFvO9LRERe4CxqICIiIiIiqqg5IrLCGs4NLP8zgBeKyGwRmQTgFfBqqR3sz/+9iPxKRJ7b6ozbiqipdhyAlaq6CgBE5LvwquLdZxKo6i+t9L8D8LYC3peIqO0xOEZERERERG1ovaouj1qoqveLyBcA/BzAdgB3w+tDrQfALADHA3gugO+JyAGqqs3P8nhFBNX2A/CENb0GwPNi0r8LwE/DFviRyXMBYPHChQVkjYio2hg0IyIiIiIiGk9VvwHgGwAgIn8HL950KIAf+EG0P4jIKIA58Loaa7mWPqhARN4Gr/3ri8OWq+plAC4DgOVHH11KlJGIqCgMmBFR1axenS790qXNyAW5cCkrlg8REbUzEZmnqgMishhef2rHAxgF8BIAvxSRgwH0AVhfVh6LCKqthdeu1VjozxtDRE4G8AkALzYdyhER1QUDZERUV2kDaVHrMoDTfGnLiuVDRERt7loRmQ1gGMB7VXWziFwB4AoR+TO8p4KeVVbTT6CYoNodAA4Skf3hBdPOAPAWO4GIPAfA1wCcWvaTGYiouhi4Iqq/NEEBBgGaL09ALW5bLLtiFVFOLJ/WY21CIqLmUtUXhszbjQr10587qKaqIyLyPgA3AugGcIWq3isilwBYoarXAfgHAFMA/LeIAMDjqvqavO9NRNXHQBlReyuqFhTAm8+iFRlQi9o2yyy/ZpTT6tUsm2ZKU2b8niMiam+F9KmmqjcAuCEw71PW+MlFvA8RVQ+DZkSdp1nBGta0qR8Gb6qLgc/iFVmjkOVCRNQeWvqgAiKqLwbPiKiZNZ+i3os3ntmwrOqhFeXEwGcxii4rlgsRUXtgUI2IxmEAjYiCWhmkCXtf3nyms3Rp68uMQYLqYtnk0+zauSyb5mJfn0TUTF1lZ4CIyrcRs8YMRES2sgJqtirkgZKxnKqLZVNdLJvmWL062xN1WR5ElAZrqhF1KAbPqJ24XgDzH+j0iri5WLkyOc2yZclpWKujHlgrqrpYNum1KsDCsilW3nLj7w0RuWJQjaiDMJBG7aDIp00CvGBuNpeAWjBdUoCNN59u8jQBNeXhEuwMwzKqLpZNdbFsilFkIJRlQkRJGFQj6gAMplHdNbOmAJ84Gc8ckyxl4BpQi1ovLqDDG53mCJZZmmAn1QfPn+pi2eTTjOsF1lojojgMqhG1MQbTqM7SXhgnBXDYvDAf+5i0qjnUypUMrLVS0jmUtvYay4eI2gm/04goDINqRG2IwTSqM5eATZYaUMF1koI1AC+eo+SpvZYWA2utkeacSioTG8unulg21cWyISKqDwbViNoMA2pUV0kBmqxNCV22FxUgYHAtXlKfXcuWFVNuSTWk2u0GdBY2Ri5L+x2fp1+1OGkCa3UXVR7t8HvbbudOO2HZpNeKP3pYLkQUxKAaUZtoh4t76kxFB9Pithd1IZwUYGNwLVqzgjZh4gI5db/RiQukhaWrwne+a2CtjmXjUh5VKgsiap06fqcRUfMwqEbUBnhBT3VUZDDNNagTTBd2UdzOgZtmyRtYSxO0bLfycQ2mha1Xhe/+dquxlqU8GFwjqoZW/slDrcMnt1PVMahGVGO8gK++tBd3nXKRUEZALW7d4HGPa25Yx8BNK2S9mbHX6aQagVmDacFtFPE7kLeJrktgrernTZXKo9WqXjZhimwiXWV1LJtOwbJpjajrik66XiBARLoBrACwVlVfJSJ/AeAfAHQB2AbgbFUtuKMYdwyqEdVUO1001l2R/4pGbatdLhqa0W9aEf9Mpw2utdvFnGtAIcv3TpZ+1ZKOb91rqxURwLG3lVQurL0RrciyoOaqYxNpIsrO5Xer3a7HKNIHANwPYJo//R8ATlfV+0XkPQAuAnB2SXljUI2ojnihWJ48N6ZhgYU0T9Cz1fHioZkPInAJGrg8/TMuuNaOtdbSBhSSblbTBm+S0sYd3zo2O2xWAKcuNaSqdr40ozzSlgUDnsnyNJEGeM1EVFf8fiQAEJGFAF4J4HMAzvdnK/YG2KYDWFdC1hoYVCOqGV4ctk6aH/KsAaG49eICBnbeqnSTGsb1OLoGSKKOWdzFV9g6cQ8nCLv5b6fAWt5gQiuDOFmObxXLpMo1oop4SmvdgpzNLI+6BDmrrqgyylsevLGvF5ZXZ6ri7z4V5lIAHwEw1Zp3DoAbRGQngK0Aji8hXw0MqhHVCC/Sm6foAFqWC7qopodGXHO3sPWroBkXtvZxCB6jrBfTYc082zGw1opmh2FlUETQJkxdAjmtCKgxkOOuygFOqkYNQiKqhjTXdXW5FqMx5ojICmv6MlW9zEyIyKsADKjqnSJykpXugwBeoaq/F5EPA/gSvEBbKRhUI6qJdrgYrOs/h3HBANd9cmn6mfR0o7iaVfb6VbmgaEV5hwVr8vxLHQzShB3TugbW6nyjWufaalUJ4LD2RmvLom5BHJ4vlFfasmvm+cHvu/bC8mxr61V1eczyFwB4jYi8AsAEANNE5HoAh6rq7/001wD4WZPzGYtBNSIqTJ4aQmHKqIGSVJsmTfPCPO9l9j2qmWfS0ynD1mmlImrquW7fHAP7eAYvwNLUlHKp/VSXGlJAOTeprhfARVwoV7ksGCCojiqXBW8YPc0uozyBTpZRNPZ5R63Ac7AzqerHAHwMAPyaahcAeC2Ap0TkYFV9CMAp8B5iUBoG1YhqoGoXHHE/akU0jTTBlWY0F0srbRAt7w9+2L7HBdjigmv2Oq0KrqXd/6z5MutlDZwlCaux5pLXqtT2MBJvdlxPxoT3cPmOyls+VTu2ScoI4pRdO8olwFlGOZYVUCu7POqkVWXEMilOVfq8i8IgTPtJKtM6XSNQdqo6IiJ/DeBaERkFsAnAO8vMk6hqme8fafnRR+uKW24pOxtEpavCxV/wByzsxjjsR66IAIdrDZQifkjTBgvzNP004vYvbJ+C6YNp0m6vSGkuXqPyknSRHnY+xH0+XT67cbIe7ypc2MUeyyIKKyBt2RSZlTI/92HKrBWV9JuR90+RJC7f2a0sk7JrqLn+hlfh5r+TzpWs11ZllFMVfk9szSqzZl3vNqvMqlYuVL7Zs+XOhKaMHWOBiP6NQ7qLgbY4ZqypRkSh7IuQsCBF8OYrb7PIrDWtwmpx5ZW1FlpRTUDT1EwL1vpwaRbajAtB1+Mf9t5pLtDDmosEa63ZNaLyNAMFkmusVbV/tchjmuVEafLOtFttgkw3nAW22S6zJk5Vm+GWybU8yj4POimgRtnxqblEROMxqEZUYa2+uAgGzJKm7XmG6pPO7yeyb+R2XJ+66Cq4bhHNVJvVPDWu6acdXItaFlweVGRwLU8tI6eaVBGZjAquuQTW2l2hATV73YQPTNgNUd6gpquq9KuW6oYzqjya3GY77nxoVvmUhUGb6iq7bLIGcDrt98Soc/PcTi2zKFnKksFOongMqhERVq/2bqTMa3AeYAfL7KDZQMZ3nBfYprE3yPboo95rMPCWJtgW9eTMMEU05Uwj6v3C9g8ID6DlqbUW3FYWWWunjbugi9tQQrQ1eAEeFViztXNttaYE1FqgHW56nG9UXHc0xweINT7KD9rY6lJbrVWqVDZVV4Umhq0urzoE1qpQLmnkLUN7/U7/bSEKwz7ViCqqGT9awQsVEzi7+WZv/OabTaDrSXgBMxP0etraSlxNtH1jls1PkX5ebDo70ObS71hWwcBLlu3GBW+y9BFv58Fenqevtaj3ipK1dlqqYFqaDWP8+eJSqzJNYK0OfauFXjQXfYfusENRZWHL2sQ67u3L7Fet8ICaLWPm435DmlULt5V9YMapYtCm6n2rteq7qmplU/W+1coO3lS5f8gsiii3ssskjWaWH4Nr47FPtb0Wiuh5DukuZJ9qRFQn9g+rHUy7/HJA9S4A9wC4G14wbXuGd5iMvTXXgkGxNPZFfA24fcfUcFu9emzAza7BVYS0wajgBZu9fjBfSf+cJjXrtCuyZK1NFZbvqAvGPE/2HHNhV8RVbWAHo/7ZblYz0KrVVmtJQC2joo57nW5kQlWkPIDm1IiqQvNboHpBG4O11aqJzUCjlX0u1aHGWlW1ouzCuuEgMroBzCg7Ey3EoBpRBRX5AxVWO+2rXwW+/vUnAXwTwP8W8C6TrfH4WmZj2TXgTE22sCahUUG2sQE2kX0T+2ZzFXaD6LKtuIBUWIAt2Ml+1LpFBdaA5JvfvBecqYJpLhHQuParEYE1l/7V2qX/qKoF1FxuhKKOfR1veJxuXkrYqTw3pO1ybpCbTq2lVnVl/ZFQpXJqVmANaGnF4ZYpo+zY3QCF6QE66lPB5p9EFdSMoJp5Xb4cePTR1xSw5ckYG0ALC56FNfnMKrj9+Pe2m4gGuTYhS9uEMkqaJohxF3lx+UnTFDQsTVGcAmpZ7tZTtO0LBpGDb5u1GWie49rMC/FCm9UmcdyRsO+wtMc97QMxy2j+2bKAWhOagALFNQNN833S0nMhrQKfwhqlis1AS6tNWyFVbAbKJ7Lu1aygTZ4nl1dNFcqu04NrbP6518Ei+hWHdC9j808iaoaif5Ds7V1+eZ6A2v7+qwlW2QGzuNpoUdyfEro3vf0+A9gbWDPb2rvc1GALC66FNclzCVBluagK1lCz55vt2zeuaf5BzVpjLSxNEZoWUDPr5ayxZucz7RMpq9K0LailAbUU8tRWM+pwE5Oo5PLIW4OgTjXWMt9QlvQU1k5ShZv9JFVrBsqA2ljNqg3lcs1V9a+AKpUba62R0Q/ggLIz0UIMqhF1iLtWz8JHPxoXULNrntmBs339+WkDZ2mDZknrhr2/HVgz6yXnMyxo5jovajtGUjPO4HbseVmDa/a2ygqshQbUigim2XIG1uL6V6tT8MBIdSEdtXMtjBbWsWlnnMTjX4OddSmTpHOjCgHnTDeVLXgKa5hO61utSjf8zdIOT5asQzk1M2BT9cBZlCqWGwNrBHRe808G1YgqpJnV24899meN6VNOuQ5Ll7o1VbMDPuYpoWY67H32Gl9rLJnpO801gOf+lFAgPnDmGlwD4i9iZi0dW46uF9tJwbUocXEml6BZEYG1TAG1ojq/C24z4eEFRQbW0txrF/3AAud+1JJ2qBlVFn1hx79ZwcxWB3bqFFAr4gYn7LupCsG0zCpUPnFaEVirazChGfKcK+0SBK06Bmz2qmJAzeBDDIgPKiCituPdCP07AGDevD/gppsk1frzp0/HU7fcgpNPPqYRWLMvxO2mjHvfzzb+AQLju3N8EtmfGhrfp1qaYFpwOstTK2ctDcxYOvaiYuXK8bWn7Jpp0ccxWVzwJmpZnrhKbEAtTYdxTW5q5fqAgtoGD7I+BKIC6lhLMFbN7qzTBAOqeC6kvrHMUj4l1Varuyrf9EcpO7DGWmrJOuX8iVOXMmNZdbC+Psh++yWne/TR5uelBRhUI6qIZv7oeBd5+wN4FAMDT8cnDvH0li0AvB/HZcvin64YXGaMv3EOBtr2jajR5toUNDmQZs9ftswhkJa1+WIgStUIsi2NL2M72GbyVXSwocjAmnNArZVBhpz9qxkux6LoAKUrp37Ucnxei+RSW61uYm9mmrVjOYM6vLHx5SmfoqubOmrm+VLph0bUVJ7yYkDNXad+p9WxvDq1rDpef7/btSWDalRXwS+2On5Bt5tm/9h4F2qnwQtQ/biAbSULCwglXWyuXh0WLAsPtoU9gMAlkBY13YqgkGtwzX5bez/C4iRFNu8Mbt/1aYqFHrsiblxz9q/WLEXsWuEBtYqoS221dv29bNU5UHTQIFV5VCySm+ZGs26B6LqfJ3mDAFnKiwE1SlLn8mJz0A7U399R/QsUElQTkVMBfBle89nLVfXzgeX9AK4CcCyADQDerKqri3hv2ivrF1Wa9er8hV5Frfxx2X//U7F6NaD6zlzbmYWNoUEh1yBaMJgBuNTMcutjLW0gDQgEhKKCQWnv9pcti72ijguuRTUJNZstgmtwx+x28H0rWUMtQdr+1WqrgpEp19pqWQNrlWmSWPEPUFKgoG7nQCkBtZJqqwHt0Rl+KlE726KMFxFYA9zKjAG1bDqpBlQ7lBfQWWXW8VxrqrWJ3EE1EekG8BUApwBYA+AOEblOVe+zkr0LwCZVXSYiZwD4AoA3533vTlbWF1LU+7bLl30rlFF2y5YB7363N/7Rjxa3XZcLwajO96M6K8/y1MtgPlIF0uzMZekLLCwjwQ65ItZ3qbkWFlxrpUwBtSrcmcfc/EY1sa16UCGxllqegFor2q06qHKNtVKafVJ+FS6btDeYVf+OAgq4HkzawSb3v1m0uN/vsnahna7ZOyFI007lBXRGmREKramWVIGrCkTH9xaebgMiJwC4WFVf7k9/DABU9e+tNDf6aW4XkR4ATwGYqzFvfvTRy/WWW1Y0ptvtCyWtun35dHp52apWdrNnp3tIgaEbNjinjdvnpDhA1PW0S9PHuAcSpAqkuQYrXNtHBtNGRPs2YtaY7ERVlou75wj7/XJ5ymuUqGPsHFCLi5CkzVieTt989mczy/GN2XRDUjazXGM0NaBmZO1IzlHY90Ka8z2M60eiiOu60oNqBV2cJv0mNXNXigoiOF9j1KQzsjTXCUXsUrOCObmu/bLsWAuiUlW7hsurXa/P262cjHYtL6Pdym32bLlTVZeXnY8qWH7YYbri299OTCfHHht7zPwKXA/BqsAF4MxABa7SFdH8cz8AT1jTawA8LyqNqo6IyBYAswGstxOJyLkAzgWAhQsXj9mAfdK1+xcMUP8vmbD8s9zyy3wMV68Gbr652Cogcc0bYS0LXPSa2ll2H1f25qL6EEsTxMgdSHM9TnGPiAzWlLJrApllgR2Malob1gdYWLaLvr/IFVBzOYbNrh0VUlst6cEFcfMrq6pVuxzEPSiiSrtVekDNvE8LggjN+vy3vFZOrU5id7X7fnKRdYdacE6wVk09tGM5dcJ9UzuWG/n6+or6fj4OwEpVXQUAIvJdAKcDaLugWmFU9TIAlwFeTbWodO0YYOuEL5R2C7S1qswKaUrx1a9i09e/nmtLGzHLy0uWwIlhPxHTBN78L9xGU0iMD7i5GnOs4gJnCc091eFJNLL//mNnhAWIwgJrwNgmoYHg2qyl3v7H7btrsDGPpgfUSpTUvxrQ3CY7ae8DnR5O0Ao5dzztxXNSYK0CrVXdyiJLTdcmqvtNTJ2vG6K0shlo5Wqp5f0+Y2DNWTueO7Z2KSeg/cvKxocYtKcR9LiW6RwRWWFNX+bHhAyXClylKyKothbAImt6oT8vLM0av/nndHgPLMitrgE2fnF46hBoK7OsCjkWK1dCv/51XAZgV/6tNbYJINuTHY1g/2NhATc7net27W2GBXoigj8ugTSbPvpotsBaMF1IrbWwvtZM32r2E0FdYldZ7tvbKqCWcMMV1b+avbxSXJt9ZokGtqBfNdeHFhhRgbVWxqMyfw8nnQsV6ccuTO1rQzU78yU+sMAoov/RopQWUCNnVbu2bpZ2CKx1SlkFtUPZ0V6Dg14jKQfr26HJbBFBtTsAHCQi+8MLnp0B4C2BNNcBOAvA7QDeAOCWuP7UsgqeiFX6UuKXhLu4Y9WsMm338rkZBQXUzBMy456UGSeq1lZwG2HpXPMXtp5jIG1zwuZnBKZDA2uugoE1IFWtNTu4ZjYXlDegluqhBFmDaa6BhYKDD1HNQFvV7ND1njz2Oy9NQC3Nm5YgKbBWSXHfe2makJewgy43L0UG1lral1pNAzVZbyhdy6lyp36R5cTaarGqdD/UCnUtq04rpzB1rbXGshtvaKiwr3mXClylyx1U8/tIex+AG+E9keEKVb1XRC4BsEJVrwPwDQDfEpGVADbCC7w1XVlBtrp9EdQJj20Gq1fjrgI2MwsbxwbUsgRTwpqChj3JK6T2Wqpt29sNLEsbSAumnZEifWp2rTW/iWjSE0LD+qJzkfRAg9wBtSo96ixC2YG1JLHNPl2PeViakssh6oanirWjIq8bigio2ek7ILCWVyfctDQrsNbMUz5TuTTjQ8XAWqimnDdJ5VeB3/q6lVUnfL+lUbfyo/GGhgq7lnapwFW6QvpUU9UbANwQmPcpa3wXgDcW8V55FFkDiic6NVuRP7CF1FK7/PIxQTU7OLU5xWZmmBF//UZNr7BAmksv/GEXdwl9pG1OzmY2YTfGSZ2jRTUXDeyDXWvNfnBB1EMLorg8WDNXQC0uE82+6cmw7aIDa02LjSQVblUiH52uCtHYglUpsJaolRmtQIDaFvZbUKHs7VWbD1O4jr7Zdy27inwA61JWDKiFq0OtNZZdtKJqqkVV4Mq/5WJV6kEFZaryCUuUS0EXNfrRjzaCUcGfkM2INiMwbdZtnHF+wGtGYDq0v7IUXANpUT+HhXwjJB37qAiMfbPmWGstGGBzldh/msmPeZOwaXteMyRFqgq8cK9KjbVUzT6z9G1Y0ZudKgVwMtVSy6rCtdWA8jvGb+dmn0FFBAJadXqnvqFsk77uOi5YU+GnsyapelkxKJOsasE1lpmbAmuqhVbgqhoG1Yja3bJleD6A//UnJwPYnnITk+E9ljfs52xGwrr2T4+9/ubAuhuD23/00dxNLTcn5CfOuPyEGBP4i4pOBbneOEf0swaMf5BB2ocXhGUjtnaaveGiA2quxy1KARfswYvuYGANKLkCUpsEC4LqEFhLpaa11FoRWKN0qh4IoOorPaBmr8/AWigGZ9IpO7jG8kpn9+7OumZgUI2o3S1dilOuuQb/++Y3AwA+nHEzT/mDMcEanxGxTrN/9mbAvSlnET+FM6zx1AG1rLVQImqtYenSvT/wIcE1V8GsRjb3TJrXLC2qvRMXWDPZaFXMpKm11Gqi7ABO6lpqRXw4Kvw0UCNtubTsXrqsD0sFAgZlqVwtNft9Ory2WmUCavZ2GFgbgwGa7FoZXGM55TEM1SfLzkTLMKhGVFEbMau4L/Nly/DpU07BZ266qZjtYWw/bSbYFgy0BWupbc6x3E5nbA5ZbuQ9csGf6hnWeKqAWhE3yTG11sKCa8HVgsKubZ2ae9rz4t6gVQq+SHcJrAHR8ZM0RR2V9VQPJ2jl8W/SDVHcjU5RgbW2ink06eY0zQ1nqwOenXpTU7UggK2yAbUWq3IZ5daGZVZ2TadgPigf+zgWVaYsmyINA2BQjYjaydKlwHe/i/fPno1/aeLbmEDbBOwNeM3wX4PNKTdjfPNPWGk2W8vC0jVTpoBaM4JpQVG11vz3D15gJN17OwVwWhlQS3MMWxTkCQsgVLwyUe20IrCWRim11Cqg6MBa29dSa4G2Dtq0iaqVUSFBgZo+ndVVmWXGoE1zBI+ra/myPJppBMBA2ZloGQbViDrIzDvvxKff8Abc9OijjT7WyrQZyX2ypUmXV1wwDcgZUHO5mHS5kA2rtWYH1/xlqS4UXAJnRQTUigo8NvnC3CWwlkftaqm1QNUCa5TMfI7DyqaoU7QpNzxhQdAKR8prH7Qp4+RtcQCnKmVU2YCave0ODqyVEsBp5hd0hTFYVgW7wZpqRNSeli4Fvv99nHLzzTjlq1/FI48+ilUAVmFsf2lZxfWzlhSwKkvUJdUMa7zpwbSwtEkXtsFaa3YegusG8xC27bigTSuafLoG3Fp0MdiswFqmgFpQs8qg5MBCLQNrbVJLzchyo1nq/VmaD0VcWdl/UFRQVYI2FK3sMqp8QK2C2rZvLpfrR6MDAmxUlhEAT5ediZZhUI2owgrtV81YuhQ45xxg6VIcuHo1Drz5ZmDlSmx69FFshFcrzH61bQ5Mz4h4izRPCU26lIlaL6u49wu+l3PfaXmDaWHiqoEYYRdGwQBb0jZcg2lJ20kSFzRrRdPZDMICa0AJ9x1lB24qctGdJbCWNuupm34WLelhBR3UAXuhv31ln0MFqEK51KKWWomqUEaZdVhZ2ZpZbpUKpkWtU5HfeGo37FONiDrBySd7P6YnnwysXImZq1dj5sqV3ryVK6GPPtoIoplLgs1h28loRkHbyXMZFJaHMYE0IF3wJ+nCJClYFHbj5xrJsZuFxm0vaf2odZsVUMuznRYIu9jOWmuqJbXUavAEyThJNzeVqrHWBoGaKLUODuRR8/OHytcxzQqzqmgQpxm11iofULPXrWCZ1AqPYYghAI+WnYmWYVCNqJMFa1tZARxZuRIz/fGZwJgbSH107Jfk5qZmMp8ZDmliA2lA9mBa2puzuMdLpg2uBddzSRv13kVGMbI+LbXEi5Wwi+00LXXjsp540Z0mcGPS1jww4BJYA4rvKL/0WmoVU/nOvF3LpY2Cn5UvE1vZ502JN7mtLCc2+yxWEcG1lgc5iyg/BoWIcmFQjajimtIENMj8kJpqIMuWNWqwAdhbow0AVq6EmPX8H/KZwe0VeBMTDOBlNS5wZnNpvtmsQFrSNoLHMm1Vnbx9DjWzhloNAmq2qBulsACbS5ZDz+ustdTynnMVO/YuN6Wt6Cg/UhsFauLUvsZalnJKG5TugE7xa1UTqiJa0V8XA2rNE3yaetp1WoblR1QJDKoR0VhhNwd2DSqXKjquNxgOFwMSdXOT5mYp6QYpbp+T0rlsP4+w2mtFdu4VdxwZUBsn6UbJNbtOAbVmBW4qdkyjuAYPitidygQNalzDsKU6/Eay8sHODi8fW7PKqjLfWVnUrFZUZY910edZzcqFqEoYVCOqgZbUVgsT/HEN1pKKqlHl8rTJ4Pph20mTt+D7pL0wSBvUSXPzW0SQcdmy8FprSetFaWXApk0CarY8tRCaHlArKjBTgeNfevCAwYGWl0Flb2CjVLg2Z1HvQ/kUXWutsDLh91t9seyIKoVBNaKaKC2wFhQVzIq7kQ8LCCVtP8sFQ95gQtzNUTOCaHHrhe1/VJ9raZuENiOgliWYFpYmLm0Fpb1ZynwOJ5Wv6+czTRlURLODB5X4Xq240oObWXRAE91WNDFMjTf7kYo4j/h9RURUPQyqEVF+UQGQqFptSYLNTMvQiiCa6zaDNynNbhJqbzNue2mCkG1QOy1O8EbH3Dhl6nC9lcGAmpRDKcGDqM9+WcGaksukMn1EMWgzDpsY1kfhtZyp8/A7kGgcEfkQgH8EMFdV14vIdADfBrAYXszrH1X1P5v1/gyqEdVIZWqruXK5CYyrkeUqy01u1to5ZdzYRgXM4oJrYent9fI0s43i2g9dTQI5eTifp628OG6D41t08KBW36cVUctaax2A50a9pOkIvyllwcBMPbHcqqkNrq/qTEQWAXgZgMet2e8FcJ+qvlpE5gJ4UES+o6q7m5EHBtWIaqZ2gbUkWQNvtmY1X6vaj2Sa4FpcenudsPXSSBMgiyunqh3roOAxzJvfNLWgiriIbqPgZlHBg8p9j1a8Ga6tGYG1ypVHDVWyOSgl4mefiCiXfwbwEQD/Y81TAFNFRABMAbARwEizMsCgGlENtV1gLUnFb/JbLim4BqSrvebyhNVm9dlV17LN+lCMuABZmQ+PqJm8wYPE78+q1Qao4HlSZACnUr9nNT83gBacH1Gqdt4QEVHVzRGRFdb0Zap6mevKInI6gLWqeo8XP2v4NwDXAVgHYCqAN6vqaBEZDsOgGlFNdVxgjcZzqYkWVXstar2wbbjkIe36FQwSxIrrX67KN5Jpj3PNyiVL8KCS35s1DuSwOWh1texBKlVUs++ylqrybxYRdZr1qro8LoGI3Axgn5BFnwDwcXhNP4NeDuBuAC8FcCCAm0TkN6q6NV92wzGoRlRjDKwRALeHGgDxAbYitVMwrVVa9URWoLbNPuO49E/E78rmaVnn61ULBtTknEkqH54b1FI1OW8qp9nffywXqihVPTlsvogcCWB/AKaW2kIAd4nIcQD+CsDnVVUBrBSRRwEcCuAPzcgjg2pENZfqKYPU/lz7UQOKC+TkrdFWF0lPQ82q6IBaltqD7VA+vtzfha0O3CSdPzUqmzTBtUr+ZtW4xqCLSh5zIiKiDFT1TwDmmWkRWQ1guf/0z8cB/AWA34jIfACHAFjVrLwwqEbUJlhrjcZwfQpoK/PRDooOrDGg1tnaNIgT/C2yg2wt+51K85Rjkz4NnjdE1C74fUbt57MArhSRPwEQAB9V1fXNejMG1YjaCGutUajgxVIza+J0woVZUYE11xv+uNqH9vIobI7rjs0Lm6Lyv0ltGtzsWG1y3rQllk12zaotzzKhNqGqS63xdQjva60pGFQjakOstUaxigyyderFWFKgq5nvmQYDatXVRs0+KyvqgS3B5UREnYi/M0SFYFCNqE2x1ho540VVds365zgvBmzSa1U5dkofhFVSdPCM5VNdLJvqYtnkV+Q1B8uDqDBdZWeAiJprI2ZleiIbETlaurT4WmRZLVuWXDuNF9LjVSmgRtXG86e6WDbVxbIpThHHkuVBVCgG1Yg6BINrRE2WNbCWN9BitsHaadmkDahlLS/X9VhO4apYI5Sojlr9HcPvtOJlPab8Y42oKdj8k6jDlPIUNqJOkbWvtWDApcg+oHgBXTzXJ0vyiZLtheVTXSybamK5NI9L/7g8/kQtwaAaUQdjgI2oSfI+yKCIZoK8mE6Wp/YT++nqLCyf6mLZpNeK/kBZLq3F401UGgbViAgAxjUNZZCNqAD2RW6rmq/xwrp+WGbVxvJxU8aDW1g22TWzvFguRNRBGFQjolAMshEVrJkBNt7A1BPLrfpYRtXFssmv6MAay4SIOhCDakTkJOohBwy2EWUQduPhemPDm5bilFGzxn5vcsdaUPXAZoX1k7e7AnsbREQdiEE1Isol7omiDLgRpcCbks7Acq4+llE+bFZYT2lqU7MciIgaGFQjoqaJC7jFYTCOiFqmiFoaad6HsmMtqPpgs8J64/EmInKWK6gmIrMAXANgKYDVAN6kqpsCaY4G8B8ApgHYA+BzqnpNnvclovaWJRjHQBwR5dKM4BpvTIvXrMAay6p4ec8plgkREdVA3ppqFwL4hap+XkQu9Kc/GkizA8A7VPVhEVkA4E4RuVFVN+d8byKihqRAHINuROQkeCOfJiDAIEBrFBUAZXm1hus5xfIgIqIayhtUOx3ASf74NwHcikBQTVUfssbXicgAgLkANud8byIiZwy6EVEmvNGvrrRP1GVZVgPLgYiICiAiRwH4KoAp8FpOvlVVt/rLng3ga/BaTI4CeK6q7mpGPvIG1ear6pP++FMA5sclFpHjAPQBeCRi+bkAzgWAhQsX58waEZE71yanDL4REVUQAzVERESd5nIAF6jqr0TknQA+DOCTItID4NsA3q6q94jIbADDzcpEYlBNRG4GsE/Iok/YE6qqIqIx29kXwLcAnKWqo2FpVPUyAJcBwNFHL4/cFhFRWbI+fCEJg3VERERERETODgbwa3/8JgA3AvgkgJcB+D9VvQcAVHVDMzORGFRT1ZOjlonI0yKyr6o+6QfNBiLSTQNwPYBPqOrvMueWiKhNFRGsY2COiIiIiIhqYo6IrLCmL/MrWrm6F16XZD8C8EYAi/z5BwNQEbkRXtdj31XVLxaQ31B5m39eB+AsAJ/3X/8nmEBE+gD8EMBVqvr9nO9HREQR+NRUIiIiIiKqifWqujwuQULLyXcC+BcR+SS82NRuf1kPgBMBPBfegzN/ISJ3quovCsu5JW9Q7fMAvici7wLwGIA3AYCILAfwblU9x5/3IgCzReRsf72zVfXunO9NREQ5Za0hx2AcERERERE1U1zLSd/LAEBEDgbwSn/eGgC/VtX1/rIbABwDoHpBNb9t6l+EzF8B4Bx//NvwOokjIqI2wb7liIiIiIioLCIyT1UHRKQLwEXwngQKeH2rfUREJsGrvfZiAP/crHzkralGRERUGD6FlYiIiIiIHJwpIu/1x38A4D8BQFU3iciXANwBQAHcoKrXNysTDKoREVHtxAXfGHAjIiIiImpvqvplAF+OWNayFpMMqhERUVuJCrgx2EZEREREREViUI2IiDoCg21ERERERFQkBtWIiKijMdhGRERERERZMKhGREQUgsE2IiIiIiKKw6AaERFRCp0UbHN9GmucdjwuREREREQAg2pERESFqOMTSYsImmV9j6oeEyIiIiIiVwyqERERNZlL8KrIIFMrgmV5BfPIIBsRERER1Q2DakRERBVQh0BYMzHIRkRERER1w6AaERERVQ6DbERERERUdQyqERERUeXZQTYG2NJLqgnJY0pERESUHoNqREREVCusxRYuTxNiPlCCiIiIKD0G1YiIiKjWOjHI1qo++Drx2BIRERG5YlCNiIiI2ko7BoKq8iALNsMlIiIi2qur7AwQERERNdNGzBo3VFld8lv1/BEREVH7EpE3isi9IjIqIstDli8WkW0ickFgfreI/FFEflJEPlhTjYiIiDpO2X2ItVsgijXYmqfszyoREcVrt9/0GvkzgL8E8LWI5V8C8NOQ+R8AcD+AaUVkgkE1IiIiIh8vjPNjgC29LJ87BtuIiKiTqer9ACAi45aJyGsBPApge2D+QgCvBPA5AOcXkQ8G1YiIiIioKRhgG6/Zgdt27FOQiIja0hwRWWFNX6aql+XdqIhMAfBRAKcAuCCw+FIAHwEwNe/7GAyqEREREVHTdWqwp+zajwxsEhFRRa1X1XF9odlE5GYA+4Qs+oSq/k/EahcD+GdV3WbXYhORVwEYUNU7ReSkTDkOwaAaEREREbVcOwZ7yg6gJWnHY05ERO1LVU/OsNrzALxBRL4IYAaAURHZBWA/AK8RkVcAmABgmoh8W1XfliePDKoRERERUanqWIut6gG0JAywNV/YZ4THmoiouVT1hWZcRC4GsE1V/82f9TF//kkALsgbUAMYVCMiIiKiiqlSJ/x1D565YIAtmyIfMAHw2BMRpSEirwPwrwDmArheRO5W1Ze3Oh8MqhERERFRLaQJYsQFKDohUJYVA2zjterzUscam0REZVHVHwL4YUKaiyPm3wrg1iLywaAaEREREbUdBs7y68QgT5U+N514/InqpkrfGVQOBtWIiIiIiChRuwV56nYzzFqERETVw6AaERERERGlVpeO+OsWPHPRbgHOKuIxJiIXDKoREREREVEhyuiIvx2DZmkxAOQmz2fFdV0ee6LOwqAaERERERE1HYNfrdPJQbayP2d1qcFJRMVgUI2IiIiIiKiNtWOgp+zgWRrtePyJyMOgGhERERERUYeJCkpVJdhTp6BZFp1cm5ConTCoRkRERERERACyBbOSAkLtHiArAmuzEdUTg2pERERERESUGYNmzVH12oTN1ozPVaccO2qdXEE1EZkF4BoASwGsBvAmVd0UkXYagPsA/EhV35fnfYmIiIiIiIg6Ud2DbWUGYVkjkIqWt6bahQB+oaqfF5EL/emPRqT9LIBf53w/IiIiIiIiIgpIE6wqMpBU95qKdv4ZYKO08gbVTgdwkj/+TQC3IiSoJiLHApgP4GcAlud8TyIiIiIiIiLKqO6BsGZhgI3S6sq5/nxVfdIffwpe4GwMEekC8E8ALkjamIicKyIrRGTFhg3P5MwaEREREREREVF6GzGLwUdKlBhUE5GbReTPIcPpdjpVVQAason3ALhBVdckvZeqXqaqy1V1+ezZc513goiIiIiIiIioaAyuVZOIvFFE7hWRURFZbs3vE5H/FJE/icg9InKSP3+SiFwvIg/4632+iHwkNv9U1ZNjduJpEdlXVZ8UkX0BDIQkOwHAC0XkPQCmAOgTkW2qemHmXBMRERERERERUaf6M4C/BPC1wPy/BgBVPVJE5gH4qYg811/2j6r6SxHpA/ALETlNVX+aJxN5m39eB+Asf/wsAP8TTKCqb1XVxaq6FF4T0KsYUCMiIiIiIiIioixU9X5VfTBk0WEAbvHTDADYDGC5qu5Q1V/683cDuAvAwrz5yBtU+zyAU0TkYQAn+9MQkeUicnnezBERERERERERlYkPLWiKOaZPfX84t6Dt3gPgNSLSIyL7AzgWwCI7gYjMAPBqAL/I+2a5nv6pqhsA/EXI/BUAzgmZfyWAK/O8JxERERERERER1dp6VV0el0BEbgawT8iiT6jquJaSvisAPAvACgCPAfhfAHusbfYAuBrAv6jqqiwZt+UKqhERERERERERERUtro//mHVGAHzQTIvI/wJ4yEpyGYCHVfXS3BkEg2pERERERERERKHY9LNeRGQSAFHV7SJyCoARVb3PX/a3AKYjpGVlVnn7VCMiIiIiIiIiajsMqFWXiLxORNYAOAHA9SJyo79oHoC7ROR+AB8F8HY//UIAn4D3IIO7RORuEckdXGNNNSIiIiIiIiIiqg1V/SGAH4bMXw3gkJD5awBI0flgTTUiIiIiIiIiIqKUGFQjIiIiIiIiIrKw6Se5YFCNiIiIiIiIiIgoJQbViIiIiIiIiIiIUmJQjYiIiIiIiIiIKCUG1YiIiIiIiIiIiFJiUI2IiIiIiIiIiCglBtWIiIiIiIiIiIhS6ik7A0REREREREREzTALG8vOArUxBtWIqBDt/GO1EbPKzgIREREREWVQ5LV8O9/zUDYMqhGRk07+AQnuO4NsRERERESdx74P6OT7I9qLfaoREREREREREaXAP9oJAERVy85DKBEZBPBg2fkgakNzAKwvOxNEbYbnFVHxeF4RNQfPLaLiHaKqU8vORBWIyM/gfc8kWa+qpzY7P81W5aDaClVdXnY+iNoNzy2i4vG8Iioezyui5uC5RVQ8nledi80/iYiIiIiIiIiIUmJQjYiIiIiIiIiIKKUqB9UuKzsDRG2K5xZR8XheERWP5xVRc/DcIioez6sOVdk+1YiIiIiIiIiIiKqqyjXViIiIiIiIiIiIKqmSQTUROVVEHhSRlSJyYdn5IWoHInKFiAyIyJ/LzgtRuxCRRSLySxG5T0TuFZEPlJ0noroTkQki8gcRucc/rz5Tdp6I2oWIdIvIH0XkJ2XnhagdiMhqEfmTiNwtIivKzg+1XuWaf4pIN4CHAJwCYA2AOwCcqar3lZoxopoTkRcB2AbgKlU9ouz8ELUDEdkXwL6qepeITAVwJ4DX8jeLKDsREQCTVXWbiPQCuA3AB1T1dyVnjaj2ROR8AMsBTFPVV5WdH6K6E5HVAJar6vqy80LlqGJNteMArFTVVaq6G8B3AZxecp6Iak9Vfw1gY9n5IGonqvqkqt7ljw8CuB/AfuXmiqje1LPNn+z1h2r9C0xUQyKyEMArAVxedl6IiNpFFYNq+wF4wppeA96gEBFRxYnIUgDPAfD7krNCVHt+E7W7AQwAuElVeV4R5XcpgI8AGC05H0TtRAH8XETuFJFzy84MtV4Vg2pERES1IiJTAFwL4P+p6tay80NUd6q6R1WPBrAQwHEiwm4LiHIQkVcBGFDVO8vOC1GbOVFVjwFwGoD3+l3uUAepYlBtLYBF1vRCfx4REVHl+H0+XQvgO6r6g7LzQ9ROVHUzgF8COLXkrBDV3QsAvMbv/+m7AF4qIt8uN0tE9aeqa/3XAQA/hNedFXWQKgbV7gBwkIjsLyJ9AM4AcF3JeSIiIhrH71D9GwDuV9UvlZ0fonYgInNFZIY/PhHew6seKDVTRDWnqh9T1YWquhTe/dUtqvq2krNFVGsiMtl/UBVEZDKAlwH4c7m5olarXFBNVUcAvA/AjfA6fP6eqt5bbq6I6k9ErgZwO4BDRGSNiLyr7DwRtYEXAHg7vH/87/aHV5SdKaKa2xfAL0Xk/+D92XqTqv6k5DwREREFzQdwm4jcA+APAK5X1Z+VnCdqMVHlw5SIiIiIiIiIiIjSqFxNNSIiIiIiIiIioqpjUI2IiIiIiIiIiCglBtWIiIiIiIiIiIhSYlCNiIiIiIiIiIgoJQbViIiIiIiIiIiIUmJQjYiIiIiIiIiIKCUG1YiIiIiIiIiIiFJiUI2IiIiIiIiIiCglBtWIiIiIiIiIiIhSYlCNiIiIiIiIiIgoJQbViIiIiIiIiIiIUmJQjYiIiCpBRFRElpWdDwJEZJuIHFB2PoiIiIiqjEE1IiJqKv/m3AyjIrLTmn5r2fnLQkRWi8jJZecjDxFZ6gexesrOS1oicraI3OaY9rkico2IrBGRDSKyQkTOF5G+hPVWW5/Vp0TkShGZUsweuBORD4jIoyKyXUTuF5GDrWVvEZHH/GU/EpFZjts8yT8Xt4nIoIg8KCJ/ZadR1SmquqrgfZklIj/08/uYiLwlJq2IyBf8Mtvgj4vj+1wsIsP+/m0Wkf8VkROK25PG+/SLyBUistX/jJwfk/arge/CIREZLDpPRERE1FoMqhERUVP5N+dTVHUKgMcBvNqa952y8xfUiiBTHQNZaVVhH0XkPADfAvBjAMcAmAvgrQCWALhNRGYkbOLV/uf2aADPAfCxpmU2hIicA+BdAF4JYAqAVwFY7y87HMDXALwdwHwAOwD8e4rNr/P3bRqADwL4uogcUlzuQ30FwG54+X0rgP/w9yPMuQBeC+AoAM8G8GoAf5Piva7x928ugNsA/CAsKCci3Sm2GXQxgIPgfZ5eAuAjInJqWEJVfXfgu/BqAP+d472JiIioAhhUIyKiUohIl4hcKCKP+DVRvmdq2li1qP5KRJ4QkU0i8m6/1tH/+bVP/s3a1tki8lsR+TcR2SIiD4jIX1jLp4vIN0TkSRFZKyJ/a26mrXX/WUQ2ALhYRA4UkVv8fK0Xke+YAIyIfAvAYgA/9mucfMSv+bMmsH+N2mx+zZnvi8i3RWQrgLPj8hRyrLpF5OP+sRoUkTtFZJG/7Pkicoe/33eIyPOt9W4Vkc/6+zcoIj8XkTn+4l/7r5v9/TjBL5OL/FpEAyJylYhM97eVeh9D9uNKv8bOTX5+fiUiSyL2ebr//s/4+bnIz9+zAHwVwAl+vjdHrH8SgHcCOF5Vv62qA6o6qqoPquoHAHwTwJfC1g1S1acA3AgvuGa2f7x4NaA2i8g9/vsVRkS6AHwawAdV9T71PKKqG/0kbwXwY1X9tapuA/BJAH8pIlPTvI+/3RsAbIQXvDLvX2hTXBGZDOD1AD6pqttU9TYA18ELCoY5C8A/qeoaVV0L4J8Q8plKoqrD8Mp6HwCz/c/gf4jIDSKyHV4wLKuzAHxWVTep6v0Avu6SR+tYfDPHexMREVEFMKhGRERlOQ9eTZQXA1gAYBO8miy258GrCfJmAJcC+ASAkwEcDuBNIvLiQNpHAMyBF4z4gextDnclgBEAy+DVOHoZgHMC666CV4PmcwAEwN/7+XoWgEXwaqVAVd+OsTXuvui4v6cD+D6AGQC+45An2/kAzgTwCng1i94JYIe/f9cD+BcAs+EFia4XkdnWum8B8FcA5gHoA3CBP/9F/usMfz9uhxcQOBteoOEAeLWj/g3ugvsY5q0APguvnO6OSfevAKb7+XgxgHcA+Cs/ePFuALf7+Z4Rsf6nAbxHVTeL14RyjXjNHD8rIhfBq9V1vAkaxhGRhQBOA7DSn94P3nH/WwCz4B3Ta0VkbsT6P/GDb2HDTyLedqE/HCFeYPlREfmMH2wDvHPgHpNYVR+BVwvs4PGbit23LhF5DbzyWOm4zr/H7M//Rax2MIARVX3ImnePvx9hxuxfQtq4vPbD+0w/oarr/dlvgXeeT4VXiy24TuL+ichMAPtmzOPrATyDvYFtIiIiqikG1YiIqCzvBvAJvybKELyg1RtkbLPBz6rqLlX9OYDtAK72axytBfAbeMEoYwDApao6rKrXAHgQwCtFZD68YNT/U9XtqjoA4J8BnGGtu05V/1VVR1R1p6quVNWbVHVIVZ+BF6yyA3hZ3K6qP1LVUXiBsaQ82c4BcJFfy0pV9R5V3QCvWeDDqvotP+9XA3gAXlM54z9V9SFV3Qnge7BqW4V4K4Avqeoqv/bTxwCcIe5NORv76L9fmOv92lVD8IKkJ5had4ZfY+8MAB9T1UFVXQ2vplJUraYxRGQCgMWqeruIHAbgI/CCiM+FF0DtUVUFcC+8oG2UH4nX79UT8D5fn/bnvw3ADap6g7+vNwFYAa9Mx1HVV6nqjIjhVRHvvdB/fRmAI+EFOs+E1xwU8AKeWwLrbIEXKHKxwK/ltxPADwGcr6p/dFlRVd8Tsz/PjlhtCoCtKfIb3L8tAKaIuPWrBi/ovhle2R0L4HXWsv9R1d/6ZbcruKLj/pn+9YJ5dDn+ZwG4yv8MEhERUY2V3t8JERF1rCUAfigio9a8PfBqixlPW+M7Q6btjuPXBm5SH4NX02wJgF4AT1r3413wbrYNexx+IO7LAF4I7ya5C15Nujzs93DJk20RvFp4QQvg7aftMQD7WdNPWeM7MPaYJW3vMXjXCvPDk48Tlf/QNKq6TUQ2+u9rrzsH3vEJ5sXerzizsPezcgSA36rf6b6I/AherT3AO65rY7bzWlW92a8R+V9+vjbDK783iogdvOwF8EvH/LkwQckvqupmeM10vwYvcPd1ANvgBWdt0wC4dn6/TlUX+jW5Pg/gpfBqgzZL2vwG008DsC1FIOp7qvq2iGUun9Mk2/zXaQB2WeOxx19EFgM4CcBfF5AHIiIiKhlrqhERUVmeAHBaoBbIBL8WWhb7BWqxLAawzn+fIQBzrPeZpqp2M63gjfrf+fOOVNVp8GomSUz67QAmmQm/plWwKaC9jkueEEh/YMj8dfACPLbFiA8UheUnanuL4TVRfRrp9zFKo1aaeE/TnOW/r209gOGQvJj9SnqfjdgbOPszgOeLyAF+f2OvBdAnIu8HMKCqTyZlWFV/Ba+57j/6s54A8K3AZ3eyqn4+bH0R+amMffKjPfw04m0fhNec095Xe/xeeJ34m/c4AEA/ALt5ZSK/xuBHARwpIq91WUfGP8nSHu6NWO0hAD0iYtcMPMrfjzBj9i8hbVqxnx+X/VPVTQCezJDHt8MK8hIREVG9MahGRERl+SqAz4nfUb2IzBWR03Nsbx6A94tIr4i8EV5faDf4QZOfA/gnEZnm9yF1YKA/tqCp8GqibPH7z/pwYPnT8Pr6Mh4CMEFEXikivQAughfgCJUhT5cD+KyIHCSeZ/v9pt0A4GAReYuI9IjImwEcBiCqny7bMwBGA/txNYAPisj+fsDr7+A9RXEk7T7GeIWInCgiffD6Vvudqo6pOaSqe+A1Vf2ciEz1PyPnA/i2n+RpAAv9bYzjN+l7SkSOVdX7APwDvObCt8Hr9+r1AJbCC5a6uhTAKSJylJ+PV4vIy8V7iMQE8R7ksDBsRVU9Ta0nPwaG0yLW2QHgGnhPlJzqb/tc7C3b7/h5eKF4Hd9fAuAHqjoINB4KcaXLjqnqbnjNaz/lmP7dMfsTGhhW1e0AfgDgEhGZLCIvgNcH37ci3uYqAOeLyH4isgDAh+AFNuHv32oROdslv2ml2L+rAFwkIjNF5FB4tc+uDN3oXu9wSENEREQ1waAaERGV5cvwnv73c7/fqt/B6+8qq9/D6x9rPbxOyN/g9zsGeDeyfQDug9eM8/vwOhmP8hkAx8DrI+l6eMEA29/Du5neLCIXqOoWAO+BF/xaC69W1xrES5OnL8ELMv0cXr9U3wAw0d+/V8ELOGyA13fYq3Rvh+yR/KDN5wD81t+P4wFcAS/I8WsAj8Jr1naenz7LPob5L3h9k22E19dVVGDrPP89VsELhv2Xnz8AuAVejaCnRCRqXz8L4GsiMkVVv6yq+6nqUar6YQCHq+r5qhrs4yuSen3rXQXgU34Q8HQAH4cXnHwCXuC16Ouq98EL7q4DcDusY6Cq98Lrl/A78Pp7mwqvfIxFAH6b4r2uALA40KS1aO8BMBFefq8G8P/5+wE/OLjNSvs1AD8G8Cd4tQ2v9+fBD6bOhvedUaZPw2uW/RiAXwH4B1X9GeA18/Rrti02iUXkBHh95f13GZklIiKi4gn7SCUiorrza6yco6onlp0XiubXnFqjqhe16P0+DK+53Sfg9Xe2G14/eX8Hr2P+NEGn2vCDTvcAeLaqDpedn6KJyIkA3quqZ5adFyIiIupsfFABERERtSVV/QcR+QO8mnxfh1cz8C4Al7RrQA1oNOd8Vtn5aBZVvQ1e7UUiIiKiUjGoRkRERG3Lf8jAr8rOBxERERGlIyJXwOvqZEBVjwhZLvC6lHkFvKfcn62qd7U0j2z+SUREREREREREVSIiL4LXv+xVEUG1V8Drh/cV8Ppm/rKq5umjOTU+qICIiIiIiIiIiCpFVX8N7+FWUU6HF3BTVf0dgBkiEvcwssJVtvmnyCQFZpSdDSIiIiIiIiKiGE+uV9W5ZeeiCpaJ6A6HdE96T3LfZc26TFUvS/l2+8F7Cruxxp/3ZMrtZFbZoJoXUPubsjNBRERERERERBTj4sfKzkFV7ATwXod0FwG7VHV5s/PTbBUOqhERERERERERUV0IgN7Wvd1aAIus6YX+vJZhn2pERERERERERJRbF4CJDkNBrgPwDvEcD2CLqras6SfAmmpERERERERERFQAQXGBJhG5GsBJAOaIyBoAn4ZfEU5VvwrgBnhP/lwJYAeAvyrorZ0xqEZERERERERERLkV2fxTVc9MWK5w68KtaRhUIyIiIiIiIiKi3IqsqVYHnbSvRERERERERETUJC1+UEHpGFQjIiIiIiIiIqLczIMKOgWDakRERERERERElBtrqhEREREREREREWXQSYGmTtpXIiIiIiIiIiJqEtZUIyIiIiIiIiIiSolP/yQiIiIiIiIiIkqJDyogIiIiIiIiIiJKic0/iYiIiIiIiIiIUmLzTyIiIiIiIiIiopRYU42IiIiIiIiIiCilTqup1lXERkTkVBF5UERWisiFMeleLyIqIsuLeF8iIiIiIiIiIqqGLgCTHIZ2kTuAKCLdAL4C4BQAawDcISLXqep9gXRTAXwAwO/zvicREREREREREVUPa6qlcxyAlaq6SlV3A/gugNND0n0WwBcA7CrgPYmIiIiIiIiIqEIEQG9P8tAuigiq7QfgCWt6jT+vQUSOAbBIVa+P25CInCsiK0RkBbCjgKwREREREREREVEriAA9PclDu2j6rohIF4AvATg7Ka2qXgbgMm+9BdrcnBERERERERERUVFEgN7usnPROkUE1dYCWGRNL/TnGVMBHAHgVhEBgH0AXCcir1HVFQW8PxERERERERERlaxLgIkTHBJub3pWWqKIoNodAA4Skf3hBdPOAPAWs1BVtwCYY6ZF5FYAFzCgRkRERERERETURgQAa6q5U9UREXkfgBvhHborVPVeEbkEwApVvS7vexARERERERERUcUJOurxn4XsqqreAOCGwLxPRaQ9qYj3JCIiIiIiIiKiCmFQjYiIiIiIiIiIKIMOijR10K4SEREREREREVHTdAHoLzsTrcOgGhERERERERER5cfmn0RERERERERERBnw6Z9EREREREREREQpsKYaERERERERERFRSgL2qUZERERERERERJQKa6oRERERERERERGlxKAaERERERERERFRSgyqERERERERERERZcCnfxIREREREREREaXQBWBC2ZloHQbViIiIiIiIiIgoPwFrqhEREREREREREaXSYX2qdZWdASIiIiIiIiIi+v/be/cgy477vu/buHdmdhbYBQhAhEgsHEIh4ogWVXogYFyKLVUEuiDHJahKlgRStMkKGZajMCVFkWOqmGIxVCUF2rFiVYlJeUMxelYoibTsLQs29KJKtkuiAImUZEChCEG0sCBNGCBA7mJnZ/be7fxxb8/0/Kbf3edx7/1+qqbmnj59uvt0n0f39/x+3WvCNOEvAaXUA0qpTyulnlJKvdux/y8opT6ulPqkUuoPlVJ/vdUppEJRjRBCCCGEEEIIIYTUY9w/Y3+xZJSaAPgggG8D8DoAb1JKvU5E+58B/ILW+usBPATg/2xyDhlskFEeIYQQQgghhBBCCOmMdgsV3AfgKa310wCglPoIgAcBPGnF0QDOLn/fDOBzTXLOgKIaIYQQQgghhBBCCGlDmtJ0u1LqcWv7vNb6vLV9J4BnrO2LAN4g0ngfgF9RSv33AG4EcH92WSuhqEYIIYQQQgghhBBC6klf/fN5rfW9lbm9CcBPaq3/oVLqLwP4GaXU12itr1emmwxFNUIIIYQQQgghhBBST7vVP58FcJe1fW4ZZvN2AA8AgNb6t5VSpwDcDuC5JiVIgAsVEEIIIYQQQgghhJB6jKhWv/rnYwDuUUrdrZTaxmIhggsizp8D+FYAUEp9NRazuf2H6nPIgJZqhBBCCCGEEEIIIaQeBWCnPhmt9Uwp9S4Aj2LhUPphrfUTSqn3A3hca30BwP8I4P9WSv0PWCxa8Datta7PPR2KaoQQQgghhBBCCCGknnbun9BaPwLgERH2Xuv3kwC+qU1uZVBUI4QQQgghhBBCyAB0JUnMOkqXRGkoqq0CG3SqhBBCCCGEEEIIKWdVJIRW5aQ4V0Ta6p9rwarcEYQQQgghhBBCSCYc8pIaeP1kQ0s1QgghhBBCCCGkSzgUJWQtabRQwarAJxkhhBBCCCGERFnXoVOOe9u61gEhpBm0VCOEEEIIIYSQTWNTh0abet6EkE6gqEYIIYQQQggh6waHPoQQ0jkKXKiAEEIIIYQQQlYLDm0IIWRwaKlGCCGEEEII6RZ2wwmpZ2voAgzAtYJjNrGeciipU+JFATg1dCH6g29zQgghhBBCOoFd7fWna7FiUwf7FIH8sG7a46vTTb3/KqH7JyGEEEII2Vy66B7mrC64arA7vb6MQbyoKcMYBYEx1CkhqcjrdYz31Aih+ychhBBCCFlfhuj+leQ5FiGO3eX1Zd0FnnU/P0L6hiJbMhv06mxyqkqpBwD8GBZGfh/SWj8s9v8ggHdg0Tv6DwD+a631v2uRNyGEEEII8bHKvdpVLjsZFxSXSB/Pk7F8CBgDQzy/h6h/Pluc0P0zD6XUBMAHAbwRwEUAjymlLmitn7SifRLAvVrrK0qp/xbA3wfwPbV5E0IIIYRsJhScVp91GIz1ZaUxdF2N8X5bdwFnjHUeYxXLvE7U1P+63089cwO4UEEm9wF4Smv9NAAopT4C4EEAh6Ka1vrjVvzfAfCWBvkSQgghhGwAfQzUuhAt6BazYGhBqEvW6dxWTRDJKe+YBINVq2eyGcjrckz3zIpCS7Us7gTwjLV9EcAbAvHfDuBfuHYopd4J4J2LrZsbFI0QQggh46TlwGrdOr9dDjr7FEFS81on8a2v+k25RtbtvmjFJoo6m3jOQwq+6/RMa0GXbdFVXVNkq4ILFXSHUuotAO4F8M2u/Vrr8wDOL+K+WvdYNEIIIYQ0pc8uRm5eY+kcd11Hq2JFFCvn2AaoXdZry2tiVe+LFnR1bw1xT43t+h8DY3+2lZZvzG091jpPKVeLerWfKev0rOwIimrZPAvgLmv73DLsGEqp+wG8B8A3a633G+RLCCGEkFGwaj2nLuddGbIuagY9Y7YczDmvFoOnMVmb9UlqecY0oOyiDscmHmyixScwvnbog0085z7w1WvpPWOeO2N6Fo4MimrZPAbgHqXU3ViIaQ8BeLMdQSn19QD+MYAHtNbPNciTEEIIIb0xJnfEoQeOQ/cSc+urpLy1bTLkZNFDD0prr49VcFkb+h4oobZehz7nlPti6Gt/CIZul1rWSZQZU1uk1mutlfSYznlkKAA7QxeiP6qvBK31TCn1LgCPYjEd3Ye11k8opd4P4HGt9QUA/wDATQB+USkFAH+utf722rwJIYQQ0gUtO4qtB3qbZrXRhYjWd5vktIWv/GMcfK6ygOZi1dxwXay6eOZjU+fQG2t7tGJMq1WuU123eo/Yz5NVeP6NCFqq5aO1fgTAIyLsvdbv+1vkQwghhJQzxNt9FQY5retlLELBKgoEpXUXa8PUdLsa4LVwvYmVrct7rcU9UtK2XT+zagaYPrq+r1o+X0rrt49nXEk9hs5nzO+idXTh7eP9skGKRTNcdZZr1TbGvsNI4eqfhBBCyCozltdbTjnGLgr46MMd0UdNnQ0pELQY8NUIaV1cDyWWAS3nuRnLPW/IaeOhyt6FUDm0mOEjt46HPo/WHwWGeBdt8kIsfc8DOSZWwYXdJneVT1qvJUFLNUIIIWSVWJdX2aqcx5jEgq4tM8Y0aKtx62xlyZZC6vxquWLb2AcvXd0XXV2DKfW5Sq64hj7mEKzJK0btXFCtVzkcmjFYJoYY6j3Tx/NwTO+/VEJlLpkzMlVgG/v7aQAoqhFCCCFjos9BUlesWoerlUBQm07pgOWaJ70UhhIMWggtrjRKhbZScgYkMezjW93Tvnux78F7Sn5j7KbX3Ft907UV7ZjeM+Ze6VIgz6WL+hlyvk9XfbS+F1pNsD8EY3ouuOqxxPoztV8yxvYYGAVoLlRACCGEDEkXlhxdvvJy3AV8DCW8tR54tnQ7LJ2cu8bSqYsJwbuc0N6XtuuYHNGtFLueQ241Mt+cAWvXbr+5tBDRUq+RVqvlllp5dmkhVUrX89X1OWAutbTpwmp3SKGg5pnZVbn7sCgrOW8uWHCSWgvplHttzBa7w6MVMF+HSymRDTpVQggh46QLS7QcoSGX3K/Vrb8853bSWw0wci3SumwD4KgeUjvPqzZ/V8k1ntIOXVmuhSxlfG0Vs0TLFYT6GOS0FGO6vkdS0molZrYS+0rTi9FatOzqGdCFoDnWBQzGIpx1uQhKHwvmrNJwvgvBs2bBG1/bpghsFNeCUFQjhBDSP60ex6vwcm+94lqtpU4Os0A5DC3mlArRp/VACyGttYgTc3MKiW25bht9Wd/UDORLhLTWFms+l0BfW6WIbKHjfIyhW9vKqjDluBJqxOaW4kxXz7HWFmh9X1MldVzzwWAM90yMoVfOzU0r517o6mNaH4zF5bHmHZ4yXUFs3jSKay60AmaTGxJiXu+8LH2wCk9SQghZcfp81I7BdcCmT7e3mHDQdQcwxVLHZmyTgNdYcqTUdQshJzSfU0zA8ZWhlfVNV3N1lYrGpfdDyTn7rnmfaNZKZLOPHYIaUaZGbK65Lruy7OzS2ic3Lx+tRLSu3yU5dbxqrro+hl4som9L5rGI0OtOqQV07ANcH/PrrTZaKcynKXVy0HlZ+oCtTwghRXTZqW9BjTl83+RaafjEgq3EsFjerrozHazdQDxXJyxVLBgjqYP+kHjTWuhMtYZyCWmlX6F9pIoQPmrd0VLrWe6L3ROhPA0p1oCu9vDV9RT+QcwYBQCbkjkGc+u95r2Rau2Xeh/42ipEl4sb5NRNzXyQJfmV0uV1n2JtPTS510rt+YSOL30/hGh5P/TVlxi6n9iC1MU8YpbXMo2xv6OGQUPhYLKdEJOiGiGEbABdzPfVIo/aifFX0brDJwZsibAtz/4a66lrjt9ycJkyECqdW8pVjtbkuq2FxBnZJr48hh7clc7pBfQvEqRcqynXfe0ca6FBuc86TQ5AXEJPrlVbSjm7oFSYSX3WtHYFzbVAS7kPurT4bEWLOdRq3LJzyHFLl23R0qKw7489XfevWl53LayWU9LqY2GDIejjGZA7PUDuhzafJVsXwuvqo6Eww2ToYvTGqtyJhBDSIUO7kqSkmTqQlOTOwRLKs4QWVgQugUYKOlvid+iYEjcl29rJ7kDJzlSuFUfL+b+6oETcDIWnWEnFylMy8HMJQblfoyV93R+lbZAievrSd8UrIcUiQ4psrnsid9VQeXzXdCGklcwNVjMPWkhMKxXaXPl0QQsBLba/y+spp+5Sxf8SV+mxDQtTnz81z6mcc26x6FDtAh1jE26G/jBmUyswh+413/Mutb+wmcxH90zpjs05U0LIhtH68VZjPdBCfMsRE2JzSCExrT46CKluUNKdU1qlbXm2fdZrrjxC7pyuDpQU2uwBaKnIZpejbwucUJ6p1k4xSymXW24o3RSXwFgdS2End16vUDm6oKQNYmJa6rGxsuRaqYXiyHZwCTitVw1tRY1VZyis1B03N17qtR56RubU+xDDjZS6qBXRungGlFgF1ghtvrh90/oa7+Kaq/2YCdR/qGl1zbkshvum5bnk5hP7AOG7x1KnLiAaCnNaqhFCyFjo+zFVI4DVWivESDExjw16Q2Wy0+iKHNepVBfP047wkLjmwzdf2gzuzpbpTMk8QiKbTYqrnCyLnY+PlParsexw1aUrLFT3qS6IfXRkSxc4sI8tpcSVMCam5QieKeWw26DmeSytPOU95ar/VOGzZH67lgPY1HdCSr3nvEdySBUiawTMoaxsWggyJfdiSVolUzd0KbT54qYem0KpeNK1gFZSrpoVVWum7Wh973QlqA0h1JXUW8gKDXB/SLWPy5m3dTOhqEYIIZ1Q4srSJbXuIjkD1JwBbCry5R2bND80Z5EkRbwrJXVQk2ORtmv9lmEmbshSzVd316z/MmwWCZfp7OJ4fZuwULuVLnKQ2345A80Ui7SQVaHv2BixBSJkXbosCk247Ez7Otehr9aGmAAaInbusedIrD5TxU9XXinlsXHVq31MqqAWs0D0CdS+8NTyhii1WCoR0Fp9gHGRKtbnuECFjvOVoe/3ik2pRXlLq7WSZ0bKR4Su6jbkAlxKK4GyJL2cdH3H9S2erMLiEj6GlBpq6y00RUTofUeAhai2j5SFCuIopR4A8GMAJgA+pLV+2BHnuwG8D4AG8Ada6zc3yTwRtj4hpCNKO6+xY1tTO7CNDWhleMzyROJbUVIOQmVHT3awUzuCpfNDlHa8Y6KNvW2LY/b/M45wn/Wai12x7RLFjFi2a23bg0nfb1N2ab3mCovRl3ti6jUvr2tfmyGwHcrHkGql1sVgJ9cVt3bgExNVYs+YkjBX2i0JWbhJd1A5UMmx5MyxzKl9x4TqqvYjS+47Ipdc9+dUd9Cun0u51Fie1fRfckm1skn5uJIqnErGtMhEKxGty35kqbV46jsuJ78hraPGLiOUPJN8lrchy7QuP0SvLgtLtfprRCk1AfBBAG8EcBHAY0qpC1rrJ6049wD4YQDfpLV+USn1yuqMMxn73UAIWQlSXV9y43ZFSactNPAJWe3Y+0OCQgy7I2Bb5fhe+mZAmrM6Zcy9q0XHO6X9bfHFrrtdnBTPdq3wXRFeek3JNtwFsIejejdWZ1eW+12imi2YmW1Xm6QScwstGSzFjgXi17FLQJNipowXSjtUvpxr2a7nVOEyZsVmh8uytcZnlSbzDLVP7LlTYq3mIiSGpe6LuYO6BJ8Ut91UwSJE7DmS8p6rFdpyyuMiRfzyXeuhgWmpiGOn0YLUa9dXd2P4CJgrkqUIBqlWcTXvj1Jy6q2LNqi1YAqRW5+lYlsorzHSVz8/1jewSZ07zRXu6mMTm0bun/cBeEpr/TQAKKU+AuBBAE9acf4bAB/UWr8IAFrr51pknMMq3YmEkFGRYumQO4AbwwspVq4UAS1lUFtiBQYct37yhbncqVwWbSliQ5cCQsgCyv4thTJTx8bF86wIFxZRavl/4sjSxpzqfPlf2zt3rf+2pZrPgk1aqdm/Zb3nWKsZQoNgmxaDjdA1HxPQfPdC6NkQKperQ2uHl4qWqXN8wREu95fgSjOlHYa0VvMJXb40QiKaTLdUYIMj/5p5pHykijO1Ylrux4qcQXuKFVTMUiMU35eXL43W1AgxJeJbLSlCTImImTMnXmqZuqaFiNZlfzKWdo54WfOxcmyWUWPowwPlzyHfveL6UOOa4oDYZMypdrtS6nFr+7zW+ry1fSeAZ6ztiwDeINL4TwBAKfVvsOjtv09r/S/zS10ORTVCSCYpliatBnKxL0pdESpjzDIkRWzw5eFDzttlwuS2fLnbllE+4aZUhDC0nLfLZQElRTSXwGZbqy1ROC6iTRGv8lM4qgrXVGnHymbytufzMnXsEtJC1mtAfTuYQuYKPintIuNtOfb5rvdc66kYvnpqUX8lpFpFpRKzeooJ9LXWanK/i5CVUkpb5livyfKkzmWTaj0Qe36VDO5z2tCVRkmb5MRLsTJzDTRrrKa6FgdS7r2uRLSWQkKK+JgrIKda5qSWpzW5wnFtOqnp+UidO1DShZVgyjm3aMOuxLLWMkTKvIM2IVHadZ+liGvEoAHM0kS157XW91ZmNwVwD4BvAXAOwG8ppV6vtX6pMt2sAhBCiIcc0cgV3nJQ7ZtbLHV/Dr5BT0wwmzrCQnXgygtwn5drnq9rYnvmiLcXOF5u74p99n5f+WpeIz4xQNalEdTOWL/Nf+vaM0KaCZaimq/I8hRlFc2tsGNVbMoKHK+7GU62jasdZByZgV0Yn7CQavGW204ugdOkkyIcu+4FebxMO1RO2UjmPG1X6FlCmEzH95U5JCrkznOUQkycyf1okSq++fIL4RuYuOpyK7DfZz1r9sXSk8fKMqa6TvlE2hRSBLBQvadc/60HtK7r2M471R0qZqGR2gYmrZb3josS4axWrMslpR5iz/lWHxdCH2laU9uGuenkpGnoSzypzSdHNK2hq3rILW9pf7SL63tsloPDonEDDrDTIqlnAdxlbZ9bhtlcBPAJrfU1AH+mlPoTLES2x1oUIAWKaoQQgcsCxTVQtgfMpyPx7HRdVi4hQgKPy4woJX6IXEFNnrNPXPOgxLaW8aVoA/iFG5ewZltKwdrvs5wCjnfacwZGkhQrHllHpt5c86cZIc0W1XDcKm26/Dslkjf/XR/NJjhy+ZRFs6tobqVvquqEq6h9Ddh1DZysZxPfZ92Wi7GWk2GpuNrIJ9akiOohsR0iPFSGVFKu1ZQ4ITfFmNttbbcqJLKkCGU5YlqovWP47u0U182pI9wmp/5d1nEhV8QaN8RQ3eSKZSX3QIsue4qVU2p9lloJthL+Y+S0V4vjWuHKIySUxFxAYyJLSXt0LYAaWoloLcSTnL6qi5R2DeWTI1CPka7EvdJ2Sb1PXKKd70NPXyL06tBoTrXHANyjlLobCzHtIQByZc9/CuBNAP4fpdTtWLiDPt0i81TGeucRQnrHNWjeFdtbcFsJCYuhY+EmTZ+oZucpX34usUxaOUjrE9e2K70YrjK6BAMZLleShLtqY8hTOCbemHylRY5tDWXqwQxgfatWwoojrUpyO86pX0hddRsSJ8315RDUgIWAZsQuKa4ZYu/1lPe+qZ6Jlb7RzaLimp2AS3Tz1XVoX8zdziWypZAj3kjV0iWs2+ExUX3M3ZJcAcc+zkXO/eHa56vL1LBQGWL4BuY+wUveAz6hzOcKGkpPlkGmHSp3Da5rNUcsSxHTSkSD0OA7RwBrJbLF8pHUfgjw4avLWhGt9UC6tQtorsjmK0MozRak1mMs76GEjRIxLNY2ten3zRhFpZKPK777y/eO8Y1hNpuMOdXC6Wg9U0q9C8CjWPTAP6y1fkIp9X4Aj2utLyz3/TWl1JNY9Mj/rtb6herMMxhz75UQ0hvSGshlFWSLGjLstOPYJa5J4r1PnmWEQ91suT3HIh/tOMRpnWULbnaCdlgqUiQwYYHztYUdAIfWz6HxlsFVVOl+CABXIazabKHGFtBcK1TuWWG2qOOzWEsR2GICjwuX0GIrYqFrb4myorsENft9ntq3MscYy7QYdvUBoqpswdBuoy0s2sau21ShzUWq4OAjNOCXN65LLHOFy3sGOHG/RIUJoOzaSkFaakpK880VcnLEmRzB0xfHV6bcc40NPFxCmcT1zJHHhPa59ttliAk8OaRcq654KfdWST4+ckSrVLHGd0yquJPTDq0G5qHruUZI61o4iIlkQJ5wnCuyudIIlaWEVgJaTlqp6eWS4q4rybEOLEk/Ja8SWl/7XckQuXUWukdczzjfFARjFBWHQ0MhcU61eFpaPwLgERH2Xuu3BvCDy79BoKhGyMYirYBuxUIcM3NX7QK4AyeEjDNYiES7y/9bAG60kpHChlxx0fWussUjE76Hk2LSZRy3DJoB2NsC5lvAbPf4Pq8AZ2dsh0lCA1URfApH52l+myRyLaZsN0T73GUdXRVhdl1o4MiS64xIRAptJhFbaAOO15W09tvFyXpL7Ti6Bpg+QdcW1naxWOVzicKRhdqOdahJ0lXPNX1L+baUQtoURwsdOK3X5OqhdiJ24fZw0gJRigu2MGeO9QkTvhMIERJlpuK32SfDtxKOKSlPqIx2nbjyMRacrvqEFeZS/11WUzLvUHlT8QkyIZEzFhYSdWTcVFoJna5r2Q5HZJ/Z7xoUxUSb1AdCrgATEo1r2qJFfYfcbkP46tNV97FjUo5tQYmY3eLYlpS2Vyqp93FXokFqPabkW9MmofRbWe2VWmvmnFeOQNqSsckJuXUWEpHHdm6rg4ZqNafaSsArhZCNxBYszi7/Xrn8f+vy/+5i941Y6DKnlr9vwZGgZpK5CW5BzWUtZLAFNFuIMPuuLv/sfS9DCGoA9q395rd9HGB5hLoGmQ53TR/SCs0Wc8z52+KinVVJv8sl2Jg4Zh4w+zynOF5fcwDaZSUlLaLszG3LHZkwPPt9J5IyOHQJaqZCbUHNSsu0w1QcIpNOwTWXWgjXeN6HqaoTIq99kBQt4dguwScC+fAN9n0Cmtwnw0PxWmNfkzYhV8IYofght0QZL0aKqOISCFPDcgSeHELXlhQaZdxUAc2XTsh9FJ44djqGUnFJUiKUpbZF62567DoNWXDErDdix8jjXMf60nGR2341Ilrs+K6I1R8Qf8772k0e6zveVQ4frg8VpbQWNkufdzkCWIicd0TqR8qUPFLzrEkzl74sunLrLPX+8L1/6P5p08r9c1WgqEbIxjHFQiUzYtqty7+vXogVtyz/bsJifRUjqt2EhYh0O44sslyWQjFxwxaBpNWZLYjZgpkJv4Qjwc2IaJc9x9pp2oKbtPhyWrRZuCbBN9s3esLtegDCoqJEalnA8ff8NSzqe9/aZ1tFAcfr1NTFsbnYbHFNCjx2IbbE79QOQ8rXPZcgIy3V5H8cb48SfPVe4hHm0hp9/Qen9aQUP64Ftn2kiEkyLx+ugb7L8knus9sNjv8usac1KSIXRBwpxvmENJdVoGufnTYSymATEl18wliqdVqOmJYyiJf4BhSuB5h9XdcKaCnWgikWH65zTGk7Vz362s2XZiyNnLx95FjahAaVtW6jruNcebrSKSH1HVR6fE5aqdS0lSxHrsAmj08tky/9XFoKm32JNr68SoS22LO2tm1ceXZNn+2Qmn+ozlLvD7p/ptLK/XMVoKhGyEZhrH/OYWGZdufi9y6A/xTAbQC+AkfC2rnl/1NYiGqnrO2pBqZzYDrHDZMZpltutWJ2bfFAvT5fPm6ubi93qCPRyxbGjDh22fpth9kWbHvLckkxTYpvcxwfy0m30pCXohTMXK6eUlizj8/FNY+Xz2jMLh9wZL1m4kgPTq+4ZrDd4+wEtkR4K3yCmvkvLZ7gru+YkAucFNNanUZIWDO6pSzDCau1kICWeiHZ7eMTJAyyYL78pBgmRTT795ZjnyuNPvCJZbWuHCnWbjXuWj7xKyawSbHTlV6pJVSJCJJquRYS10z8kIDm2+fKCyKuq3wxUgWwEqu0WgEo99hUi6QSsSxmypsitMl0fLS2VhpatGnVVjKtlOu+tdWUj5y6G7o9Simps5oPGjn5tGZsdR8jtc5SnoWhDztkYam2OVLT5pwpIRvPFEdunvcBuAO4C8BrsBDP7sXCCu12HIpqu+dexOmbrmD3hj3sYB/bOMAO9jHBHBOPyc98uRcA9rFz+HuOCWaY4AA7OLi+jdlsgvlsivlsgr3Lp4HZBLg6dYtqV3FcVDNWbFexEATNthHYjLhki2pGaJNi2hQLccxFjquna/wrSXnfpnzUsYU0KQqa7R0cFxPt+jgshxF07ITlAHeGk4KN/dvluhjqYMiBfkhQm+JYmZSVhC1gunBdnrI4pf2fmGFHyGINSLBak5RYCIbcP2NCgKuN7DhTnLzgfcLbUB3ukLDoC/NZTvnOISbs2PFc5AgypWJaKM1SYuJ67LpLEddkPinimms/rHiybDV0LaZ1fd+kiI2A/1keOj7V2iM171JS6zAlnyGFg5g4Zgi9d0vE5dR7vDVdCmmthr0lnYfU695QIrK58nGR2qfo47rvSorIaaOc+yNFXCMA3T8JIWvJGSwWHbgfuOsO4GEA/7nGua/6U9yFZ/BqfA6vxVO4BS/hDC5hF3s4g0s4g0vYOfQzXJjx7uE09rGDA2wf/r+EM4eSmwmfY4JLOIMDbB+KafvYxh5OY3bDBAfbO5hvTxZhZ0+fENwuvXgG1/d3lmKbWghCL+G4yCbDjLh2GW6LNtfiB9J1EnBbQ7kWYrDn33QZach54+z9Pssp+VSeiP+yf23KMF+W0c5HCovSTfaYBZtZkMIcPBP/7XB7sCvVvRQxQooCpnJNxZ4WYVYdyCnw7CykAZZPRHO1kSu+ry2kBuBD9iWkFumca20Lx1dvtZFig0vsBI7XfU5nr8RKzSe0ZXTGVWBfzD07ik/I8DW2FItdIrOddshKKlQGV5l8x/gszmKWbL60WxATTgyuB6PPGq0EKezHBL9aUi3+Sq3aSvIO0WLwHLPwrGnD2vZPoWtrp9b3V6mYlfKsj4nLKe7SsbRt+hJGW+XXMp8Sd05DjUVtqYVbC8YkK+SUJfWdXWvtvlloKOxje+hi9AavDELWHrMYweuBv3IH8HeA73zzz+H1+CO8Bn+GO/AcbsPzuBufxe7+FZx++TrUy1iIUy8vk9gBcCOgbwS+cOvNuIwzuILTuIQzuIJdvIDbcQWnsYdd7GMHV7CLA+xggrklvh1gG9uYYo597GCKOWaYHFq8zTHFFHNMbphjvj3B/MwU890D7O9t4/p0B5hOj95nttg1s36bsbyt9+zh6Ek3xfFVM22LLxspnhlrNmkhteU5HjiyVpJznQHHhT2Drz86h9+lVI6v7XG/1L2k5mXXpRHatJ3YliexLeu/b8DmcuOCFSbFAJe1mhBmlDjM9/HLVY8hQXPm+W22W70lZVWac/HO7ReyPArhEzZT3UtzrNRkmMuCKkBITJNxqsU1IGy9FgvfcuxPtZLKLZ/BV58uMU3Gi5XB1UalbkMpQpbvOox9/Zf163quhKzYZJo2KecbupZTRbJSMa3FwLdFO8eeIakWUqlCQY0omnvvDWntlJN+jlDSYs4om5L5wUoZY3uUUmppBuRbtYXyzc07N+11IPcjEVAnQG8GdP8khKwZuwBeC9z+tcBPavzAV30AP/rFH4b6DIDnALwA4MsAvoCFkGavoDnHQlB7JYBXA+oO4Cu/5kvQr/wSXrp1d2nLdgYAsI0z2ME+ruA0JphhbymoAViKZ5OFaLZ0BN1eHgXsH8abYIId7GMfO9g5tY/9qzvY2T3APoDrALDreGTtnww6dOc077w9HL0LjTjm8140VeazSrO1A98T1LyTbUs1KajZ7+2U/tIEx8eYoae3PbeaSd8W0GD9lkLbMesp1yBf+puGcIkMPnHGbNt/lcg6dolpLus1G7vOQyJbyMJdznVnt4PZp+AR1qSwIIWcmGWV6xgXLgFHWpylWq9FSBHTOkVamLnEGVe4TUxgM3Fi5ZD4hLKY+Ok7PpSXK06JsJY7WPSJKz5fat+2FHNCApyrnK2sYHKsA2N5dm215cqjxJqtxH0wNd8uhyarKtqkDvqBsnrOsY5rOWfXqrZHCTltKIndezl5j5XaZ1+r6zJVDE0RsQndPwkha8StAL4B+CHgZ7/qu/C9P/Ix4MeBvZeBL7+8eF1cwUJ3Ak5qDGcBfPU9AF6PxWIGNwJqH3jF/h4mr5wDE2AHt+EAB4fWaSkczbU2xcSyWptjsph97YY5JtNFaaZbcxzM5wtrtS0cCRTyz+yb4EhYM26RcgJ/W+CwcVmp2e6fQHiFR8lM7LfdLl3H2JZpdplMuNRYZDwpGNoiYshSTZbTa7UGLNwzZUfCzjT0anEJMbag5jjWrPg5de92tqNPtPSJaaHL1tUmNqZ9DDLuXMS1y2tbNHqFNVNg2bH2XQjSQi31Ve9TbH2CmwmT4luAwQU1g098NPtCotk1nOxQhyyoYoQszuS2T0xzpZNThhJKrR9CwppMV9ZzyDIwdJwdT1LqttZCTOtDRItRMjhMGeSHLNhkGrn5p1BTt2MeHsXqVZJjwTak62CIMbdHCa0ENmB1RJ2ur5tQ+jV1lGPduSpt0Q+cU40QskZMAdwJvPYs/uO/9wS+990fw00fOPLqTOUVnwG+aISFewDcuPjb2T/A5PTxRQsWW1PMDhcm2MZ8aaM2w2IONXt7fvg3PSa0HaY3sx7Itsjl+m0LYrC2gZNCh2tcZ1uzuYQ1ObaVws0E7neuPafZUUUt8Lkr2kKa/aR2CW4p+CzVri63d0ScqNWaKwMbn5jjs1SzxRnrOCOoxfDVO5A2t13MvXBu/c8R2GKY61C6Cp+IZBNyi4tZqIUmhItZq7naLvECrBHTmrh++pBWa4B/TjqXRVtI5Ckpi+t3THSL7U8ldUDQwpUoNNBPFclKhM2auYpCXzNy4sf2DcVQFmy+/CW+F3YtqzoUyhVmcoTQnHS7oIs2qbleuhRLagQ2YFwi2xifa4Df2jyXHIGNUFQjhKwRWwC+Afgu4DvwS8DT+YIaALwI4It/Dtz6SixcRZeJzCY3HC5UcAln8BJuOfx9Bbt4Ca84nGfNzK12BbuYY7oU2xbC2h5OLwW2IwfRK1dOYz6bYD6b4ODqcsECI4r4xAepyRgrtT2cnMTfVVXmuB2xbYtzMj/guEufbwECe/saTlqsmfLa+ZgymHB721igGSaO32YFUCkm2cLZqeXvHRy5/toioFncQZsC2VZrpsLNSUhrNfvkpWBjb+9a6VqdEltQs9sj9o52iWnAkTlmqpBmY8eV7r12GX0uoqlhyXOs2RePFIakxVUsU5dA5hLSzLZLfPNQKqZ1KqRJfAJlDHmN+yywcvI36dbsLyFU7i4H1iniml2OmMBswrro3q6LeJaKr+yhayVl8Bpqmxw3xBQ2ZZjjM+H2kSoypNZfyjOiy7YYwgqqC/Eqx5rWxyo/c/qk1rJtU54t5SwWKtiJR1wTeEUQstZMAZwFzgG344UyRW3JNVuYmSz+5tMp9rGNK9jFFZw+/DtYrvIpBbUDa4VQW0Qzq4XOMcH8+gSzpZi2v7eN6/PpQlC7Oj1aZGCG40KRQW4by7EtuF3wzG9brJHWaYBbc4jlbedlYwtWPkuqqYgvV7w04a48QsYUUgyysc/RtmLbso5JsloLzd+VYqXmOcz3tvL1fVzWaSa8lWBj0glZsZV41XhdQU0kaW2WMmdaDJdQ5hLQAu0kWQlBTeKzfnK538r9QF07DCGmAf6bqE8rlVQXNd88XS4LNld6rbq96yqkpZDSVq74hhQ3RFJPF5ZsKXn1wbrfY5KcjgSpp+ZeIAZaqhWglHoAwI9hMZz4kNb6YbF/B8BPA/hGLKZF/x6t9Wdb5E0I8bEc/O4CuA24BS+5J/VP5PBVbrlbHkyMSLaDPewuRbNtXMFp7GPn2Eqgxu3TCGxGRDNi25GYNj0uqF3dBmbqpAWVbbUm+xlSNDPltl3spJBmwmxXT/sJmfq0tN8frmnHJHb57f9T8V8iP/4Y6zWJq+95CuE51ex4wbnWbGIT6gPHBRpZ6SKusVIr6Tu76vSEO6sL1+RrCUKGnaYtWBrLNVcyIYJzrNlliFnuuOYF8xVGWqaZeCE3xAAlgtqgYloMl1UaEHY9TElT4rrgU+OV4BowxMqfO8jIKWvM2s83uPQJPS0HR10Iab4HfCmlQlcJpXWbK8qRenKvszGKCmMQ0oaui7G4524SY7wXVgeKahkopSYAPgjgjQAuAnhMKXVBa/2kFe3tAF7UWr9WKfUQgA8A+J7avAkhCZwC8ApgF1favH+nWAg6p7AUzqSIdhr7S3HNWKbtL//bItoxS7WD7UMxbT6bYHZtguv7xuVTHVlNSUHNFoOka6d8uhn3T5fOI63SbOs131NyiqP5yGxsqzhpIWcjBUK77Oa3LcqYxRIMMl1ZTp+Bje/9ZuZUk8iwE8JU7hdU2zLN3hbY5Ux9U/lcb6OCmu2+KsNCCwJ4BDZZjhJxLSqsmcRsl1u7TLHJ9F3n5HL1dG0HWEnrNBeugb/rWg/FK8nPpivLNCDPOq12IJE7F1aq1RqQLq6l5FtCapqha6LWCqWkfVrXT4lQxsFqv5QI/8Aw7TQGAQ0Y/3VZ2qakDD6zctBQmFFUy+I+AE9prZ8GAKXURwA8CMAW1R4E8L7l748C+HGllNJaj64bTch6MV2IaqeAHRxUvXOn9o8poCc4FM2M1ZmZJ80IaUcWacbNc/uYoGYs1E4Iasbl0xbUjNvnVZy0rJJj3H2xfeIkLKRVmhTZZBopY2d7wQKTfmzy+muOOHKhAjM3HHD8nM1+o6O4LOzkdGcSc47S2u0UToqHh3nbYo4UeEJClG0B5SiMsqKm4BLTpKDmxVxY5rdMyBVfukgKpFuoHTV2D9pVGl28wJ67zmclGGoLwH2DhKzVAqyNoGaTK67JuClp23RplRajC0HNRcsJ52Pimi/PEnLK2Yc7XOu28bnQ5lBrvWbDQWt3lFpItm4nimftoMDWL7S4jbFw/9wcl/4WZ3ongGes7YsA3uCLo7WeKaW+BOA2AM/bkZRS7wTwzsXWzQ2KRggxItEE87xVCX1pLZlPgaOVO82qnidX85QrfEpBbTab4ODqthDTJsBVdSSOGGHEFtRMuM810u4zSjdGKTq5XEB9mLR885NN4Z6n38Z2W3W5tUrshQrsebvsxQq2sFiV1aQvLe2kO6l0/7TjTHC0aMEMx91FTXmOuYFuORL2IUU1CyPKxNxvQ4tBuMQ0p2hjLh5ppSY7SC5Ryq6wPRy/eKy42oqu4F6IIoYR1oJuoK5w35xfMdEmZs3mYO1cPV2kWKTlzqvmu0+6GmjGROMSV9AcfOfrG5TkCGMxt6gu6rTlYCGlnvscvLWwZKsVYPoUXLqq2xpX8K5p5Uo4FmEsxiaJHznXEwW4clp+IFo/NBQOsD10MXpjVPKh1vo8gPMAoNSrV67LTcgoWQojc0ziqyZmMrHUjan1e2LJaVVIwclF6CnmmkPNhBuklZrrvymL+W9bhqUQiicFNXvxAp8L5BQLi7WrOC6s2W6nLnwuocDJueBcdTDHkQjpFHpSXimVglqIJHdPW0RzWam5GqvBHFIaR8IasDhP6bEZIugGKq3U7DCfP7TLDzq1MFaZcql+s6e0RZcd29DX6douVV9iGhAX1HwP3BorvFwfaJ9VaEoZuph7qKsucwtBrUQwyL3eWlqyjUngaFWWmmssdGxfQ7VS67WxMqZrbMyUXl/rdK34yL2GeM1JOKdaHs8CuMvaPrcMc8W5qJSaYmGG9kKDvAkhQWYL4eUqsI/tpn2zyfJ9atuhmVnU9rFzKLJNMcf8ME6HL+Fcow/p7inDQhhhbcva9uFy6bTT8cXXOLJwkvtP4cgV1LzDpTUeEJ4PTgprvt/mfThDRJRNHWR5BDW50qoh1Ba++fSSBTU4/tsJlhAYeJoySau11H5YUFgzSMu0ULtUzNnVm6A2hvmiXLQSB4b4su2aO9C1z7U/lZAA4zMbDqXje0Cllm9U35AtYs+a2BeZGmqsLHK+CLgYo7hWQh/CQuqLvQW18/uNgVW/plaFVRNheV30DedUy+cxAPcope7GQjx7CMCbRZwLAN4K4LcB/E0Av8H51AjpiasAXgT2cLrNHb+0qFJzLEW0/cO/CU4vLdQWVmo72Mcck+X2BNOlU+gcU0yWUhsATKZzzK45HryyvK7y+4zhpJWZLy2XkOarJzsd3/RDUuixj7U1Hdt91LZSk09GI67ZLqCuMtr9m9g7TJ6DmbdNGjXNceRy6vIKdIpXmYMsl6AWEzdDUz8FjSNt9c0lLPhcQHOJuJ7JOdea9TnsgWrqzV4wKO5FUEu1nPIhz78rN41ScaAPMS1UhzHrNd/5lFgKhuo+1XrNJ+Ksizgj6VJMS0035RqttV4buv1K8h1aSOhDZFs1cW3d7v9VYEzXBtt/jHBOtUyWc6S9C8CjWAwNPqy1fkIp9X4Aj2utLwD4CQA/o5R6CsAXsRDeCCGdslRp9gC8ALyEWxZzjbVgKQRNMMMODqy//aWt2sGhcDY5FNLsGdcWL+PJDXNgChwAmG7NMQNwHVjMqebC9cSa4KSbpxS2fCKNK1xmHRJp7Hm77P6FEcqMdmO7r/rGrXZaLoy45isncNS+KWKkHe4arxrLNOPuaZdTuoSWfCKR7p6ynCmCWvL8aSaS+W8f6HIBre0sxuYtW+JyCY2RZK2WYslTKOrkCmpVYlpIPXXhEmts7HZp4crmy3tocq3PQmJaC0tBl8gWmqcwlrarrrtYmKBLQs+YUhf0WuHZl2fqtV1jvda3uNanmNa1y3qO5WcuY7dG6uJ6GbOr+BgY+noY+7Od2ND9MxOt9SMAHhFh77V+XwXwXS3yIoTksAfgM8DH78Envu8NwF8oT+nWm7GYCN/6gLmztFQ7jSuHCxWYhQkA4BJuOpbGwmptfugaOlnGnUznmNtC2nQOTJcZ2ZPpm1UozfYMbhGtZtofm2s4uQKjyfcqji+eYP5ftrbNqqVXrf17y+P2rOO9c4A5Bv62Vds+FkKa+Q8cuYRu4XDlVwB+yzY5H5vhlBW+uyyvSd8cb69NEBO1bBHNOp1jZWvq6mkiSes0Ka65hLSQsGOb9EkhwM5rS+yXPsNL7LLbbRDqhyQJa4YGYk+OkFbl4pnaBj5KBpd2e0rGJJTFyBHKQsq+pK8BlMuXPmVhg9BiHX2RM8jLmavOl25qm8TEzdR5vHLdqWvcr+1FVlrTop1q0myRTqweU8q9ipPYt6jnPs+lNK+xiHFDtPsYxbKxXP+ryXXcsJh6aEMYy91LCOmMZ4FP3YM/wuurFtXdeiWAV2IhrJnVNZfYQtlUrP95gOPxspAfSX1Cg61ZSKEtlr7EJdqYcFtQM2KZLajtWdsmzj5OCmq2IOcU1Fwig62AbR2lZ7PvOD/ZT8l56rtcPg2+tnA1sW/Fy5w57Oz/Sa6eLtHsGk7WbcgNNFSYXGyRzTMwMpZrQP1Kva1IFdSq50tzKeIxCysXvokO5c2QIuK0tmbrghyrtFSLtFwLQR85bRFqm9ADwme5NjZqxbRcy7aceKFFJQB3/Zdasfny7nJA3VpMa23llvNSbvFMWiWhoOa6WKXzNLS6Tlrl2TVdC2mreA2sF3T/JISsCTMAzwJPfRl/+r/9Jfzcw9+JOz74MXzhcl4qd0wAfB2Au7EQ1m4GZjcCV3AaB9g5tFCzcZn82mHzpfwG4LiVmiy+xLWip603wdpX660h3TaNOGbEMmltZgS0qyKOLZ7ZglxUUHMNfE1H2iGsudx7pUulq64At0g2c4SbedauOfYB/vGxrzwxXIJakqun7X9rwlyunvbvXFPHHDJdQodmMDEtJPz4wgD/4Nwn0LhEgRSBbUwCTqoFWkhoKxExfQ/WVFFSPoSkMCbrOiauDSms1bhl1ghqLZ9RORZtrYXnMYhpLYW0kk5HqZASa7dVZUghrcX12FU7rINA1Pp+H0udjNHKbngWc6rR/ZMQsjZ8EcDvA//7t+AtD/0ifuDSB/CjX/xhqD8D8Dks1uF9GcBzOBKF9nFklXUKCyHt1cu/u4H9O4DnT9+Gl3ALLuEMruA09nAaV3Aa+9g5dAWVv+eYLB1GF7/n1yfYv7qD+WyCg6s7wGyCw/nUTP7A8fema+40Ka7FxmCu97ArD2mdJv/vW7+lq6fLUk0Kc07XRemu6NovLNaMy+muI7pxAy3B9lY0RTHzrMWOy9ln3rnSOitbUCtx95T1nGKtBoTdBuWqD75Bf4LFmo8sF9AMUsS0qnxT6jvmkuhLV+JSeKVAkCr8yHzGIuLEhDCfUJxqwZYzaHHFlSImEG4HKa6ltIkrbh/Ers0aMa1ESCsZYIaucSBv7ruh7w2bGrGzZRq1uKw8fayCZa2PvoW0IcTcVWuTFrSq5zFRDytJAAAo60lEQVSIZxTOcqGoRghZM/YAPAW8eCvwtq/FP3rHu/HM374Lr7v1SbzmGz+LV+NzuA3P4y48g9PzPZx++QDTl7EQf15eJnEKwI3A/s3A506/Cpdw5tjfC7jtUFxb/N9dimyL//vYwR52lyLbcia2g23MZ1McXN3GweXTS0FNHb07zfxpttUY4H+3yjFaSjx7W1qnyb89HIlo9rxo0mLNN6eatFQ7UT4p/sgwwG2Ns7UQOey8bXIXL/AhFyeQyLnnfKS+X32CmldMM5HN7z3PPp9lVKrIAJycL61kQGwEn4CwBoRFrtbCWmeCWsgyzRcWsqBKISR6AifbUObpEn5s+hYQYhZMuZZpqWJcrAw+Qu61Lks03z6XsGan5SrfECur2vRlmdZioBkTbXx1mtIOruO6pJU1WUnb5qYTIlRnoWdSKO8xCjql9TM2ES0VX19inWhRz10LaF1eC2MQ/8YFRTVCyBqxB+ALAH4N+FdPAf/qq/Gxh78XH/tLAF4P4LUAzgE33fs8bjn9Em45+yLOnL2MM7iE2/E8tpe2ZVNLMZkdzpi2mD3tEs5gH9vL9T+3l2uBbi/Dj0S0g6s72Lt8Gri6fSSg2ZP420KKy0otRiiu/R51LTxg52eLYHYZbeuzGYCXxPZVAJes42yx7Soibp62sOCz5jFCkRFk9pa/p4DeBV7cWjzVL2Mhpp1Z5m8WLDDRTyE8P51r22Qpx77TZXol7eVqEzudZMu0UP3FhLRUYQKOfdLCRlrSSHXXtlqbWfvtShbExDUl4uUQEtGqrdGAMou0mKAZahfX5Oo+Syd7f8zKKSSwdTF4TTnvVBE4RUSrqXMfLtElJqal1F3uXHj2MTW0skorFS9rRc4U5HVu8AnUofiGLqx2cs67VEBrYcWWkmZMlHThm6swlG7sA0NX9GWBlptPn+JHjvgZYizW0TW0qPe+BFIKZLUsFipwzUuznlBUI2QjMArPs4v/f3wr8Md3AJ8AcA7AncDle2/H5a+4HRdvB3DL4m/73Jexe9MV7GwfHJPMJAfYPia0HVzfxmw2wd7l08fdOq+q46KT+TOiGnBcSEl9p6W6ec4cv+1ymHf1y9a2KetlnLQ4u4wj4cxU8csijtcqzQhkdsYuAch3IvbSm2KeNXvOvIkVbXdZLllfPiuzLZxsi5j7Z2wuO9e+kKCW5eaZa5WWUtehQWHIWi1m6SQJuYnieD24xLAcqzWfmNZESAPqrNFShE2faOKyuolZn5k6d8ULWVbZafjKVjuJu8xLxo0JlDJeznWeY20lic1TZ1/nKQK1FEBdebR2i6uxeiq1SCsV3GLH+EgVI1ME5dS5CX15taRGEEv9Mlead278UkvAnHdPC+FzCAulluJql/jyzh2Cp4qtJXRxP9bWeVfPiNbXQl8i32pCSzVCyBoyA/BlLF4AvwvglcAzdwLP3AFMt4A/xUJMuw2HotrBXWdxcNPZxYqfu1hYJN20/O9auVGKIvvWti2g2WKV/SfT8OF6cqWuIGn/luUw+4wbZYqoZouCrmMPhQop+LjcE812zHLEPhEprk0BvXVUDiOMzXBktTY5iupMUiLHUaG6lqJdav/FbgOvoOazQLtiJSLrGeKY0DZEeAgpAAB+iw6f0pgrvC3xzbmmRBxXuCutalLq9Zr479sfCo/lb+OzopH7XfN5XcPJdoUjnilra+EgR2yJiZSp4a58XXFSiIkzPrHT5YYLuMW4HKEzVLZSUkXHWB2niG058XJwidCuPFPcb30fBHzxW9OFkNaFiJZLahsZSlZy9dGHWJBTf32IaDXnnCtqxTpbuQwt7tTcC7Vl70s0HbqOVwvOqUYIWWNmWIg5F7EQ2J4DcCswuxX45GsBbC1cBm/BQjy7E4vt5Zxq2F3uk8KM+XNlJ/8bwUSGp4w17D6L9LJLRZZJCmtGDJS/bfdPKarZYSfcPIFFndsWVSYM1nZM6DH7pOvUNev3l3G4WoHeWmRhR7F1KLsNpS5Q+mH1Go67lRqBzXcqckGIa9ZxXkFNCmuyXl2im6+OfWF2eIjYvF2xY6R4Y0jonLvEMOX5HTuuiFAduuo0JLSFwuW+UlLEz5TjYB2bK+iESBFSSsW00MO1VORJITZXnRTLUsJ9Aqgsbxfd2xzxpUSwbNEWOc+j0CqfgL/9ZNxQPFfaLdom5ZosEdJCx6XeBy0G3qUWaKE2SDm+D7oQ0roS53IIpZsquLUW2rqg9n1cU/9dimetPoIRg4bCjKIaIWR9mWHhg7mHhQjzZSzmXPsygLPApTPApbOL35+BW1TbwfG5uXZx9DSxn5+uefUB/yqPMewnlsnHJ7T5kON9KajZbpv2PltA8wltJ+ZMMwfsOcJKBR45wJSDS5P27uLPlNu2VrMPNYLoBMfrMta/MC6jtmjma2+76Ha9m7A9KyxonWbESRnuEtZCIk6KsOPabxNyJZx54khCF2yBK0cXq4GewFeHrjqNXds5QlquWOBqG9c+1zEut9AcyzWJbOfcAb3vudBSqMwRMlMHjyHhJacd7PAcl1CZdyk5ddGinlPFtFpxQB6fshhBqK5TXEPt+F0Qq5PcemwpYuYQ+6oI+EVQVxoxgc2XRi25bd1aROuqfXJJaU8fpV85a2lxnw4tVHXR/hTRcphvkNS0OWdKCLHYs/5/EYuX+7M4Usd2F2GzWxeT3xuBBlMAZ4/2Y4rDjoHCSbdCI9bgKFqS66ZLmLPjSQEvV1gDjo9T59a2EctCQptXPDP/pchjz59mH2OnIQuWwhTHFy8wg85LOGxHfRbY2wX2to4OuYSj5jSuvEYoNXFsXEXyzXvnEivt8H2crPuoVZqrTmMiWqqoZofHwuR+qUJKl0J7sDkT/+2yuARSW0AYeoLiHAGtVMQsscrxERK/XAKAOcZlIeVzPexiviHfdekTYnLD5b4cgSG1LXxilhRjZnA/rH3hdjo5dR8TtlNJfUbkCJmlcUJxa/DVfW6d26RYhba+l0pF6y7ya0WsHkPXecqxhj7OpaVwliOaDC2GxFx3faRYLfZJ30LZGIWxsYi14+Y6bsABtocuRm9QVCNko3EN4rZwtEyk8R80wpoU2IyCtrtwN5xtATNLaDOuiD6Mi5q0DnaJO1O4xTYptLnScyFdD4HjApstqAEeIc22QHMJQXD8hmMbIjwV12BHCjgmn2W7zbBYtXQfCyHtKo5W77TdeH116BLT7FN0uXT6wrLENMDtMmvvr7GMKukkSUsAe1tarrkGPT5LHRd9CWuhOpTbqfXdynoqh9AgM8WC8BpOinA+66iYxYiPmICSUm8lgmVKPbcYsLkEtlC9+cJzrdfMPhlmh4cIXXcpdZVb36lCWqxNSu6XUhfQUNva6abcGy0GqL66CaWde0zOPVH77ErJu8QyMLX+W7xvcuugpYhW8/zKLXfXrp2pgmhLau/Jkvrv8jlQw1iE9dWE7p+EkA3DHvzagxdbVJPimhTP7HgQcewwqxOgl+EzoZDNgGhnQQpytdZqQMJqkymWUykiWqm1iEF+WZZzq7n+X8OiPXeB2e6RB/AuFsLaTUi3LHSN16W137VAWJVlmqtOS0TLVtYfIXc0VzxE4kkxp9Q6KhdXnV1zbLvi+o5FRrjc54uTgssyI1VIy0lbtqFPSAgRE3ddz4zc8JRnTIkokYJvDi6XSOMTzFxCnK+9Qq65dngOqddl7bWde/23GITGREe7vuUxsm1jbqJ2XJucoUit8JL7vm0p9KSSKnL5xLIagS1UhpakPiNbi2hdnFdOf01Sez903U4xSsWkmnIPJZxJKKTFWCxUsDlS0+acKSEkgquDbwY1xnLNFtqA48KZrcJISzUZH9a2PEbGl/GW23q5bQS4Ju8382J1uWu2Fnl8gkpqOV2uh+a3LaYB7jmillZrl7AQKM0hLhdeHzPxWwpsMixbTJNib6heXQPamGAhT8JVPhehuZvseraFgdQLNPW1XCuwuUSVUB2mCMMpbRC7B2rEnZB1TEiYqQmz8y4dKKTWkSs8JMyE6rpEzPcdJ/EN8AG3C25uGyAx3JU/EL53agWZmFBWKqTF2qb0BRhzSfOJojKuz2Izpf5rX94loljJR5Wc+7vmnFKsyVLET3lca4EzlZqPDDXpDS04GXKfQb7zG3LIXnM9DylYtbwGuv7AsX5w9U9CyIbj6vTLwY1D5PKGS7HMZQLlEtt84pu97QtzuUTaxCyYUoWylHiu9ENlqcEn5Mi5vK4BOI1jrrtGXJOrurpcc+UpSIs0U5TowgPm9xXrYJewWSpYdmWNkzLwdBGzljJpmLZy5eVKM5VUgcZ3P/i2fenJcLnPtd8VJ5VYu/jEtRAp8VLFg9TBv6+OUgSzVJEtVqaa55JLXJH55bh/+nBZs/nSCAl9PlKfF7VCWkvhJ5eYsOISZVwCzjURxxdPpinTDlEruOTWZ0uBJ4cUsSv1HRQTuFPy7pJWdTykcOMiNrxO+WAnyf2gkcqQAlaXgnoprUXgzaaVqKaUegDAj2ExKvmQ1vphT7zvBPBRAP+Z1vrxJpknQlGNEBJAKiXAkbWawed36RPPcuPmHBOKHyJlIBoLS7Ec8eVXgjw3OQ8erG0T36hl9vx4lluv3gJmUyzccaX1YC4u67JrVriMkytk2nFzRBxfvBpsEcwV7hKmfcKniWvvM2GmjU18iDAXoesvJp654tYIPK7ytP76GxrUuwQdl6VTzDIt5oYYE1ljz4QUUS10TFfWUKXY9WvnH/Itj7WLDIMjXJYBIp6vrD5iddZKQF4Vi4jUdnXFk7R4HvdlsZaa57oRa8OuRJVaEbxV+iFqrC9LBP8W+fqoqY8h26C1SJpapk16BuRxHQr7DRYqUEpNAHwQwBsBXATwmFLqgtb6SRHvDIDvB/CJ6kwLoKhGCMlECmyhTqncL491hZt99qqWrmNyLNNsbDdJH6HBUY1VSCzfVtiCgPltRBNTN1LAsX/bgqbPulASEsV8QpodL3S8a7t2MBuKn0LI/SbXGsqHT3hwxTN5yzCbmAicK6yF0pL5pZYrRszawrU/9ZmVQuz41PRznh21Fmqu/a44uftTcD1vQ6JjSDDzhQFp9Z5q/ekrm+vYEuE4R0iLlbWLDzQpIrHPBTQlnoxfQmm9lIpoufdCSbuEnvWx556vbxUSk1Ms4kJlyKWlUDk2a6JYHl25ew4p6JRc40MIdiFKylMi3m8qzeZUuw/AU1rrpwFAKfURAA8CeFLE+xEAHwDwd1tkmgtFNUJIBbHBqvxCbYsEoQGh/WiSlji2hQ5w3BpLHmtj0thz7MvpaJcMmELp9YHL+sa2XLOtoOxw4GhevFz3nJilmUs8K7VISxXRWnZAYwNPlwWNLXDGkIKabDuZtqtMvnCftZmrfn3im4wXyidUllh4DN9xPjHH9cxKnc9LUiPQ5TxHWglpobJ2NThz3Sc+Iaa0nmPCaap7tiT1vVB63ecKnV29P2LiSolwVuKOWEPJM7/WIrB1e4TSSxU+Xen4hM0S909f/6pLC6WhrAZzRccQsfdUav615cih5voubYuhrM1q06CY5iJjTrXblVKPW9vntdbnre07ATxjbV8E8AY7AaXUNwC4S2v9y0opimqEkFXG1wGJCW3m2ND8LL6vsKF8JS4xLYUu3EZKX/SxjnNs0Cyt12zRYBfHhTWTn0/UdJFq5RQT3+z4IaHHla88PkRpR8hVBz7xJtYu0npw1xFmhE4pjpr9slwp16xPWLPDYvUdEnhcx/nCJDUd1NAA0vVcyRHGWlmpmbxtQtd0qoiZkq4vLJRGDT4xQIqeOYKnKz484bWWifDkkytgjuk94qLG8ilVYJNxbXIsckOUiGhdPZNy2ifXPTbHvTmlLUvLkUPps7Emrdx0W6dRMrdaygfMMYg4Q1qddfERKDfNMbTB+EkU1Z7XWt9bmodS6gYAPwrgbaVptICiGiGkQ1K/mMaOD3XqW35NTC1PiK4sPkJph9wMpQAj3T/t443b7Zb4DRyvU1+nr4VFVImFjivvUNxaUgY4pdYxdnoucU0KXrFBkm+wHxJAfWEp4bEw17EtiT1zQpaCLQSYVEot1XL2hcJcx6aSYvHiy8MnuuQInqkuoLXu1zYlAmbL51TX12Wt5VPIMi1FOOhCtCoV0YawjIqlFauv0HsgpS1Ty9Ga1n2qsQkdJSJyzCq3b4awOuvqOuziA0vL9NcLDYVZm4UKngVwl7V9bhlmOAPgawD8plIKAL4SwAWl1Lf3uVgBRTVCSI+EXkgha7TaQWGLR93YOms2IYHFZbnhsz4Lhbk6c7E5vOxyuLZDVjcpYkGoTfrq3PgG7tL6UoZfs47zCQQmLNQ+siyplhq54mWoHWXcULwQLS0H7fxdg8qpY79LZLbDYxZQNYPxHOHMlcaQ90dOWrI9gJN1Ld2lZbwaQa3GIiq1jnPaIlZ3qzJI8z3vQvFtWlmqAWXWainHlsRrQeqUAT5CbeO6H/uipeVaSbpDUPuxbYhzG9qqrMtzbvVhibjQUDjAToukHgNwj1LqbizEtIcAvPkwH62/BOB2s62U+k0AP8TVPwkhJHkwlPp1t2tBbIgXbGw+FJ81wcyKKweyrgGqCbNXFc2llUVHC+GmNSlWASluaz5MO4Xc4GR+ofKlCpnXEuOF8ko5pgWx50XIosYlxnR9PeW2k2u/LyzHOirl2FpCz+2Ue8X3/ArdUyHrqFphpstn1VAWOSnvVt/cjb7nT8gqvWQuLx+1VmerIGqm1KGvTQwlHy1biG259ZdzHbRomxZp5NRTTV23/Ijc+lk/RuGs5TmmlG/MH96HIWNOtXA6Ws+UUu8C8CiACYAPa62fUEq9H8DjWusL1Zk0gKIaIWRFaG1Gv+rEOtoxN5wSl9Hczr2LPgW0Ljs5oS//QLq45hMAfFY6cl+pSBASbbp0Y4sdX0qqe1ToHigRdHxphUitu66sA/vs/IcEz1Kxs8Sts8TKo9ZizRc3ZV8ozdakfMTyiWwpAptMr6v3ct8CWtftk9O3iS1YE/v4UvLRsoY+xLO++n8p+cREs1AaueJmV9dlTX22bIuxfSSnmOZDQ2F+vYn7J7TWjwB4RIS91xP3W5pkmglFNULIihN7oY31Medy2awl9LU6xZJDxo9NOi3TTS2bK6/cY0rK0JKY+1KOYCCPi81Zl+LaluP6V2Il1cqNqhU5A0nXPGsyjkynj0FBijVariVa35YfNr7r2PXs87VFbE68FCu1mrnVcsXj0H3to6RsXVvYlDzfQvdg6Bxr3EZDtLAKHHLAnPMhMSSypexv1T6pafrIva5X5cNpjYVa6jmWWBa2rr/a9Pq431ahjGuCBmazNqLaKjDW0SYhhDRi7C/A0rk1YoQ60TErthC5c+e48pMMOcAJiVg5pLjXhMQ118D/mhUuBQhXmXMHkbVWg7E8c9KpIWY1aPCtVlhqMWWH5Z5bjvjS0lKwz0FojdBp4sbaKmZ929Xqn4YUITQ1rdQ0WpEz0M+xPMuxmnKlnUtKXXVlFdhFO+WuwNlCZAvl29V7t8v4hqH6f6l9pJYWamOyzAsxRku6EEN+mFpttFaYzzZHatqcMyWEkLUh5SWfs6qXT6TxWUMN3aka29fVlEGiq95cbpwynm8AtZdZRkmNUNZKQGpByxUegePtAMSt2WruhxILzRYC5xADzdyVn13I55SvrWDF6/NZMeaPBym0aCMg/uFF3lO5dCHkt7pOStySJTllab3CbR+LFpTUdd/zY+XQco5AoN192DUtr7u+WMUyry76usLB1e2hi9EbFNUIIWQtSfmabZP65bqvAd8qdVpiVgGA35It15WtFSnpjsmqJjfvkoU87ONSXAplvBJqBMyurG+6IGTJCYStBe1jSuLZtHK3WnUBzUVLd8MSa6oaurYm6aqdUtNNtcYF8j6QpaTXJzX1PFTZW7tnlnw0bUnra73rdlkFl9HNRGuF2TW6fxJCCFkrckU2IN6RGMMcHmPENfC3KRHYTNzW9Td2Sw6grKuSM+gPze3li9vFdVxrhTbU3FySWve1kjYIiZw1k+Sn1OkqtY2L3PYC0hcyCKURSzPnWBddtPVQpIpiQHuhrTWrKJy1oKWr59iu1b7fh11Rcx5ja5MxoHB9vjlS0+acKSGEEIsSkU0ytg5uF52a2tdkK4FNxu2CsXYoY2nnCsQpAptvYFozoXdLAXPM87zkWoml3AP28Slic2y1wxK6sD4bwzO0xKovZyGD0jRz6HNux67brMaiqQuhrW+G/IDTgq76Un245rpYF8EMWK9zWQE0AC5UQAghZLPIcf3pmyE7LS3rJUdgs/NoYTE49Dx4XZJrcZErsLmOcx3f1VxzqybS+Khxk7aPLxVFW7MKVp6GVgP91Mnsc8SdrlhVi6gai6ZaoS0lj1pa1O2qvY9sSvtUY362S/psn1V5pmwgWlFUI4QQQhawE+GmtuOcMnBKrfsh22jIjn6t1UvMnTZl4u7YXGG5ZfKVo4ah7+ESgSVkFRiaCL92EvwYrRel6ItWglyqRc3Q52szBlEzl1pRslTs73IeT5uu6nIswlNXFpmSlsP4TZwvd0zPqTVEA7iqhi5Fb1BUI4QQQppT416bYyHSFWPq+PqotbRIEcRSrKtc6ZUwVvfbWkrmbip1m3Yd3xddtEGr86h5ftRYgrYsR4hVF54lNW7mQPmzcWz1YLMK7yRD68ULfIytvYZuo1Wbh23N0RjfJdohFNUIIYSQzqmdw44dt3RyhDDDGN3XXKxLDzXnfsh1m05Js4SWdT+Wee5scgb4pZPc8/6pp+Z6H8rlM4Whr40h6GIxqK5Ylw8VLdjEa7UAimqEEEII6ZYWC0WQNFqIbEC/bdRHT3QMVlCGGlEzdZ6vvljVAVdLy0+bdblv+mjX1haFQDdiZ6icQ13/Yxm9t7zeV/VZEmMsbRVjXeu/JzQ2qgrZiyeEEEIGJ3fem65ZhU5vlxM+j3VlPJux9FZLylHrhpY7z9fYGMO1lHr/5NTvWO+bVblGSsvZ2i29Zk7QFMZwTXRF63Mby1B9zG22Kvf3hqEB7A9diP6oulOVUrcC+HkArwHwWQDfrbV+UcT5OgD/F4CzAOYA/let9c/X5EsIIYRsLmPu3PZJl5YxnNeuW0qsB23GbOm5KvdnF6sUDuXOts73Sgqt57FblWs4RJfXRJ/vgnVoixxW9V7etHZKgO6fWbwbwK9rrR9WSr17uf33RJwrAP621vozSqlXA/g9pdSjWuuXKvMmhBBCCBF0Jbi0mHx6VQcMXVMrYqau9lrKBo0MDulCtF6l63+oNm8tEI95HrVUxnbddGGdu+6MrQ1r2cR3QiYU1bJ4EMC3LH//FIDfhBDVtNZ/Yv3+nFLqOQBfAeClyrwJIYQQQiL0PT/aug0ehqLWmk2yQb37ThnD3Gk1jP066FogNrRevKJ13utGXyuC9sk6t+PYnxMrAEW1LO7QWn9++fvfA7gjFFkpdR+AbQB/6tn/TgDvXGzdXFk0QgghhBAXY3Yf7IOanu5QdTUGl9wWDDUQ7bquQtdUX9fMpozg+hI211k0GSs1dZ57j29S+27Ks2FEUFQ7jlLq1wB8pWPXe+wNrbVWSulAOq8C8DMA3qq1vu6Ko7U+D+D8Iv6rvWkRQgghhLSji8m8axlrb7S2XFwdbxj6HKxLxnotrxtjujdrWadrpq96XZXn4Tq1LfGiAewNXYj+iN7lWuv7ffuUUl9QSr1Ka/35pWj2nCfeWQC/DOA9WuvfKS4tIYQQQsgo4MCgjL7dcUk9Qy1A0CdjEySGqFM+07phbKt7t4bXDXGgsViickOovXMvAHgrgIeX//+ZjKCU2gbwSwB+Wmv90cr8CCGEEELIWrHq83RtOmMU3cYmkuUyxjol3dNKoAo9OymCkR6g+2cWDwP4BaXU2wH8OwDfDQBKqXsB/B2t9TuWYX8VwG1Kqbctj3ub1vpTlXkTQgghhJC1hVZt68GqC1xjhKIbCbFBakav8FmWDEW1dLTWLwD4Vkf44wDesfz9swB+tiYfQgghhBBC4r30VRHdWo42VuWcSX9QdCPkJBTFeoOiGiGEEEIIIavIBvXiD+ninDlEWG8oLoyLdRQ5eY1tNBrA1aEL0R98YxJCCCGEEEIsQkIdhw+EtKWVAFUjzlEEIw2hpRohhBBCCCGEuEgdKa3KMGPokd+q1BMZPxTGyEjQ2KjLkU9xQgghhBBCSGOGFqtWhZx64tCNELICaADzoQvRH3wyE0IIIYQQQsjoSRHgOLwjhIyADfquwqcuIYQQQgghhKwFXY9khxw+dnluHBYT0ozr4EIFhBBCCCGEEELIcdbV/KT1eXGYTTYYzqlGCCGEEEIIIYSQMkpFuk0anq+rQEs4pxohhBBCCCGEEEJ6hkITWRM26FKmqEYIIYQQQgghhBBC6tGgqEYIIYQQQgghhBBCSBZcqIAQQgghhBBCCCGEkEw2zFLthqELQAghhBBCCCGEEELWhFnCXwJKqQeUUp9WSj2llHq3Y/8PKqWeVEr9oVLq15VS/1GrU0iFohohhBBCCCGEEEIIqUcDuJbwF0EpNQHwQQDfBuB1AN6klHqdiPZJAPdqrb8WwEcB/P0m55ABRTVCCCGEEEIIIYQQUo8GME/4i3MfgKe01k9rrQ8AfATAg8ey0vrjWusry83fAXCuwRlkwTnVCCGEEEIIIYQQQkg96QsV3K6UetzaPq+1Pm9t3wngGWv7IoA3BNJ7O4B/kVjKZlBUI4QQQgghhBBCCCH1GPfPOM9rre9tkaVS6i0A7gXwzS3Sy4GiGiGEEEIIIYQQQghpQ5p7Z4xnAdxlbZ9bhh1DKXU/gPcA+Gat9X6TnDOgqEYIIYQQQgghhBBC6tFIXt0zwmMA7lFK3Y2FmPYQgDfbEZRSXw/gHwN4QGv9XJNcM6GoRgghhBBCCCGEEELqaSSqaa1nSql3AXgUwATAh7XWTyil3g/gca31BQD/AMBNAH5RKQUAf661/vb63NOhqEYIIYQQQgghhBBC6klfqCCK1voRAI+IsPdav+9vk1M5FNUIIYQQQgghhBBCSBvauH+uBBTVCCGEEEIIIYQQQkg97eZUWwkoqhFCCCGEEEIIIYSQejSAa0MXoj8oqhFCCCGEEEIIIYSQejSA+dCF6A+KaoQQQgghhBBCCCGkHo1mCxWsAhTVCCGEEEIIIYQQQkg9dP8khBBCCCGEEEIIISQTun8SQgghhBBCCCGEEJIJV/8khBBCCCGEEEIIISQTimqEEEIIIYQQQgghhGTChQoIIYQQQgghhBBCCClAD12A/rhh6AIQQgghhBBCCCGEELJqVIlqSqlblVK/qpT6zPL/KwJxzyqlLiqlfrwmT0IIIYQQQgghhBBChqbWUu3dAH5da30PgF9fbvv4EQC/VZkfIYQQQgghhBBCCCGDUyuqPQjgp5a/fwrAd7giKaW+EcAdAH6lMj9CCCGEEEIIIYQQMkquA9hL+FsPakW1O7TWn1/+/vdYCGfHUErdAOAfAvihyrwIIYQQQgghhBBCyGjRAK4l/K0H0dU/lVK/BuArHbveY29orbVSyrXGw/cBeERrfVEpFcvrnQDeudi6OVY0QgghhBBCCCGEEDIaNIDZ0IXojaioprW+37dPKfUFpdSrtNafV0q9CsBzjmh/GcBfUUp9H4CbAGwrpS5rrU/Mv6a1Pg/g/CLtV2/QIqyEEEIIIYQQQgghq46xVNsMoqJahAsA3grg4eX/fyYjaK2/1/xWSr0NwL0uQY0QQgghhBBCCCGErDKbJarVzqn2MIA3KqU+A+D+5TaUUvcqpT5UWzhCCCGEEEIIIYQQsipobNJCBVWWalrrFwB8qyP8cQDvcIT/JICfrMmTEEIIIYQQQgghhIwRzqlGCCGEEEIIIYQQQkgmm+X+SVGNEEIIIYQQQgghhDSAlmqEEEIIIYQQQgghhGRCSzVCCCGEEEIIIYQQQjK5jnVaiCAGRTVCCCGEEEIIIYQQ0gC6fxJCCCGEEEIIIYQQkgndPwkhhBBCCCGEEEIIKYCWaoQQQgghhBBCCCGEZHAdwJWhC9EbFNUIIYQQQgghhBBCSAM4pxohhBBCCCGEEEIIIZlwTjVCCCGEEEIIIYQQQjKhpRohhBBCCCGEEEIIIZnQUo0QQgghhBBCCCGEkEyuA9gbuhC9ccPQBSCEEEIIIYQQQggh64Bx/4z9xVFKPaCU+rRS6iml1Lsd+3eUUj+/3P8JpdRrGp1EMhTVCCGEEEIIIYQQQkgDjPtn7C+MUmoC4IMAvg3A6wC8SSn1OhHt7QBe1Fq/FsD/AeADbc4hHYpqhBBCCCGEEEIIIaQBzSzV7gPwlNb6aa31AYCPAHhQxHkQwE8tf38UwLcqpVTtGeQw4jnVPn8ZeN+nhy4FIWvI7QCeH7oQhKwZvK8IaQ/vK0K6gfcWIe35i0MXYDx8/lHgfbcnRDyllHrc2j6vtT5vbd8J4Blr+yKAN4g0DuNorWdKqS8BuA09PuNGLKrh01rre4cuBCHrhlLqcd5bhLSF9xUh7eF9RUg38N4ipD1CHNpotNYPDF2GPqH7JyGEEEIIIYQQQggZE88CuMvaPrcMc8ZRSk0B3AzghV5Kt4SiGiGEEEIIIYQQQggZE48BuEcpdbdSahvAQwAuiDgXALx1+ftvAvgNrbXusYyjdv88H49CCCmA9xYh7eF9RUh7eF8R0g28twhpD++rxiznSHsXgEcBTAB8WGv9hFLq/QAe11pfAPATAH5GKfUUgC9iIbz1iupZxCOEEEIIIYQQQgghZOWh+ychhBBCCCGEEEIIIZlQVCOEEEIIIYQQQgghJJNRimpKqQeUUp9WSj2llHr30OUhZB1QSn1YKfWcUurfDl0WQtYFpdRdSqmPK6WeVEo9oZT6/qHLRMiqo5Q6pZT6XaXUHyzvq/9l6DIRsi4opSZKqU8qpf750GUhZB1QSn1WKfVHSqlPKaUeH7o8pH9GN6eaUmoC4E8AvBHARSxWfHiT1vrJQQtGyIqjlPqrAC4D+Gmt9dcMXR5C1gGl1KsAvEpr/ftKqTMAfg/Ad/CdRUg5SikF4Eat9WWl1BaAfw3g+7XWvzNw0QhZeZRSPwjgXgBntdZ/Y+jyELLqKKU+C+BerfXzQ5eFDMMYLdXuA/CU1vpprfUBgI8AeHDgMhGy8mitfwuLFVEIIY3QWn9ea/37y9+XAPwxgDuHLRUhq41ecHm5ubX8G9dXYEJWEKXUOQD/FYAPDV0WQghZF8Yoqt0J4Blr+yI4QCGEEDJylFKvAfD1AD4xcFEIWXmWLmqfAvAcgF/VWvO+IqSefwTgfwJwfeByELJOaAC/opT6PaXUO4cuDOmfMYpqhBBCyEqhlLoJwMcA/IDW+stDl4eQVUdrPddafx2AcwDuU0px2gJCKlBK/Q0Az2mtf2/oshCyZvwXWutvAPBtAP675ZQ7ZIMYo6j2LIC7rO1zyzBCCCFkdCznfPoYgJ/TWv+ToctDyDqhtX4JwMcBPDBwUQhZdb4JwLcv53/6CID/Uin1s8MWiZDVR2v97PL/cwB+CYvprMgGMUZR7TEA9yil7lZKbQN4CMCFgctECCGEnGA5ofpPAPhjrfWPDl0eQtYBpdRXKKVuWf7exWLxqv9v0EIRsuJorX9Ya31Oa/0aLMZXv6G1fsvAxSJkpVFK3bhcqApKqRsB/DUA/3bYUpG+GZ2oprWeAXgXgEexmPD5F7TWTwxbKkJWH6XU/wvgtwH8RaXURaXU24cuEyFrwDcB+FtYfPH/1PLvrw9dKEJWnFcB+LhS6g+x+Nj6q1rrfz5wmQghhBDJHQD+tVLqDwD8LoBf1lr/y4HLRHpGac3FlAghhBBCCCGEEEIIyWF0lmqEEEIIIYQQQgghhIwdimqEEEIIIYQQQgghhGRCUY0QQgghhBBCCCGEkEwoqhFCCCGEEEIIIYQQkglFNUIIIYQQQgghhBBCMqGoRgghhBBCCCGEEEJIJhTVCCGEEEIIIYQQQgjJ5P8HWhxoaHv50qcAAAAASUVORK5CYII=\n", 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\n", 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\n", 5465 | "text/plain": [ 5466 | "
" 5467 | ] 5468 | }, 5469 | "metadata": { 5470 | "needs_background": "light" 5471 | }, 5472 | "output_type": "display_data" 5473 | } 5474 | ], 5475 | "source": [ 5476 | "fig,ax=plt.subplots(4,1,figsize=(24,18)) \n", 5477 | "graph=ax[0].contourf(xx,yy,V_mag,cmap=cm.jet,levels=255)\n", 5478 | "ax[0].set_title('Velocity contour plot @ Re = '+str(Re)+', Ri ='+str(Ri)+', Pr ='+str(Pr))\n", 5479 | "fig.colorbar(graph,ax=ax[0],label='Velocity (m/s)')\n", 5480 | "ax[0].set_xlim(0,L)\n", 5481 | "ax[0].set_ylim(-H/2,H/2)\n", 5482 | "\n", 5483 | "ax[1].streamplot(xx,yy,U_final,V_final,density=1.25,linewidth=0.7,arrowsize=0.5,color='k',broken_streamlines=False)\n", 5484 | "ax[1].set_title('Streamlines @ Re ='+str(Re)+', Ri = '+str(Ri)+', Pr ='+str(Pr))\n", 5485 | "fig.colorbar(graph,ax=ax[1],label='Velocity (m/s)')\n", 5486 | "ax[1].set_xlim(0,L)\n", 5487 | "ax[1].set_ylim(-H/2,H/2)\n", 5488 | "\n", 5489 | "graph=ax[2].contourf(xx,yy,vort,cmap=cm.seismic,levels=255,vmax=30*u_inlet,vmin=-30*u_inlet)\n", 5490 | "ax[2].set_title('Vorticity contour plot @ Re = '+str(Re)+', Ri = '+str(Ri)+', Pr ='+str(Pr))\n", 5491 | "fig.colorbar(graph,ax=ax[2])\n", 5492 | "ax[2].set_xlim(0,L)\n", 5493 | "ax[2].set_ylim(-H/2,H/2)\n", 5494 | "\n", 5495 | "graph=ax[3].pcolormesh(xx,yy,T_final,cmap=cm.jet,vmax=T_square)\n", 5496 | "ax[3].set_title('Temperature contour plot @ Re = '+str(Re)+', Ri = '+str(Ri)+', Pr ='+str(Pr))\n", 5497 | "fig.colorbar(graph,ax=ax[3])\n", 5498 | "ax[3].set_xlim(0,L)\n", 5499 | "ax[3].set_ylim(-H/2,H/2)\n", 5500 | "\n", 5501 | "for i in range(4):\n", 5502 | " for n in range(n_squares):\n", 5503 | " Nx_lft=int(square_coord[0][n])\n", 5504 | " Nx_rgt=int(square_coord[1][n])\n", 5505 | " Ny_top=int(square_coord[2][n])\n", 5506 | " Ny_btm=int(square_coord[3][n])\n", 5507 | " \n", 5508 | " rect=patches.Rectangle([Nx_lft*dx,Ny_btm*dy-H/2],x_size*dx,y_size*dy,fc='k',ec='k')\n", 5509 | " ax[i].add_patch(rect)\n", 5510 | " \n", 5511 | "plt.show()\n", 5512 | "\n", 5513 | "iteration_eqn=np.linspace(0,len(U_res_list),len(U_res_list))\n", 5514 | "iterations=iteration_eqn\n", 5515 | "if Transient==True:\n", 5516 | " iterations=np.linspace(0,len(cl_mean_array),len(cl_mean_array))\n", 5517 | " iterations=iterations*dt\n", 5518 | " \n", 5519 | "fig,ax=plt.subplots(1,1,figsize=(12,6))\n", 5520 | "plt.plot(iteration_eqn[:iter_total-2:],U_res_list[:iter_total-2:],label='U residual',linewidth=0.9)\n", 5521 | "plt.plot(iteration_eqn[:iter_total-2:],V_res_list[:iter_total-2:],label='V residual',linewidth=0.7)\n", 5522 | "plt.plot(iteration_eqn[:iter_total-2:],Cont_res_list[:iter_total-2:],label='Continuity residual',linewidth=0.7)\n", 5523 | "plt.plot(iteration_eqn[:iter_total-2:],T_res_list[:iter_total-2:],label='Energy residual',linewidth=0.7)\n", 5524 | "plt.xlabel('Iterations')\n", 5525 | "plt.ylabel('Residual')\n", 5526 | "plt.title('Normalized L2 norm residual')\n", 5527 | "ax.set_yscale('log')\n", 5528 | "plt.grid()\n", 5529 | "plt.xlim(0)\n", 5530 | "plt.legend()\n", 5531 | "\n", 5532 | "plt.show()\n", 5533 | "\n", 5534 | "fig,ax=plt.subplots(3,1,figsize=(12,18))\n", 5535 | "ax[0].plot(iterations[200:iter_total-1:],cd_mean_array[200:iter_total-1:])\n", 5536 | "ax[0].set_xlabel('Iterations')\n", 5537 | "ax[0].set_ylabel('Cd')\n", 5538 | "ax[0].set_title('Drag coefficient(Cd) monitor plot')\n", 5539 | "ax[0].grid()\n", 5540 | "\n", 5541 | "ax[1].plot(iterations[200:iter_total-1:],cl_mean_array[200:iter_total-1:])\n", 5542 | "ax[1].set_xlabel('Iterations')\n", 5543 | "ax[1].set_ylabel('Cl')\n", 5544 | "ax[1].set_title('Lift coefficient(Cl) monitor plot')\n", 5545 | "ax[1].grid()\n", 5546 | "\n", 5547 | "ax[2].plot(iterations[200:iter_total-1:],nu_mean_array[200:iter_total-1:])\n", 5548 | "ax[2].set_xlabel('Iterations')\n", 5549 | "ax[2].set_ylabel('Nu')\n", 5550 | "ax[2].set_title('Nusselt number(Nu) monitor plot')\n", 5551 | "ax[2].grid()\n", 5552 | "\n", 5553 | "plt.show()" 5554 | ] 5555 | }, 5556 | { 5557 | "cell_type": "code", 5558 | "execution_count": 51, 5559 | "id": "be5c2650", 5560 | "metadata": { 5561 | "scrolled": false 5562 | }, 5563 | "outputs": [ 5564 | { 5565 | "ename": "ValueError", 5566 | "evalue": "x and y must have same first dimension, but have shapes (6000,) and (5470,)", 5567 | "output_type": "error", 5568 | "traceback": [ 5569 | "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", 5570 | "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", 5571 | "\u001b[1;32mC:\\Users\\KANGLU~1\\AppData\\Local\\Temp/ipykernel_2780/3935751390.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m22\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_squares\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterations\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcl_array\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0miter_total\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcl_array\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0miter_total\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Square 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"\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (6000,) and (5470,)" 5577 | ] 5578 | }, 5579 | { 5580 | "data": { 5581 | "image/png": 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\n", 5582 | "text/plain": [ 5583 | "
" 5584 | ] 5585 | }, 5586 | "metadata": { 5587 | "needs_background": "light" 5588 | }, 5589 | "output_type": "display_data" 5590 | } 5591 | ], 5592 | "source": [ 5593 | "fig,ax=plt.subplots(1,1,figsize=(22,5))\n", 5594 | "for i in range(n_squares):\n", 5595 | " plt.plot(iterations[::],cl_array[i][:iter_total:]-np.mean(cl_array[i][:iter_total:]),label='Square '+str(i+1))\n", 5596 | " \n", 5597 | "x1=5.39\n", 5598 | "x2=12.61\n", 5599 | "\n", 5600 | "plt.axvline(x1,color='k')\n", 5601 | "plt.axvline(x2,color='k')\n", 5602 | "plt.xlim(0)\n", 5603 | "plt.ylim(-0.15,0.15)\n", 5604 | "plt.xlabel('Flow time(s)')\n", 5605 | "plt.ylabel('Cl')\n", 5606 | "plt.title('Lift coefficient(Cl) vs time')\n", 5607 | "plt.grid()\n", 5608 | "plt.legend()\n", 5609 | "plt.show()\n", 5610 | "\n", 5611 | "n=15\n", 5612 | "t=(x2-x1)/n\n", 5613 | "f=1/t\n", 5614 | "st=(n/(x2-x1))*Lc/u_inlet\n", 5615 | "\n", 5616 | "print('Freq:',round(f,2),'Hz')\n", 5617 | "print('t :',round(t,2),'s')\n", 5618 | "print(\"St :\",round(st,4))" 5619 | ] 5620 | }, 5621 | { 5622 | "cell_type": "code", 5623 | "execution_count": 77, 5624 | "id": "c2fa80b5", 5625 | "metadata": { 5626 | "scrolled": false 5627 | }, 5628 | "outputs": [ 5629 | { 5630 | "data": { 5631 | "image/png": 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1+rhDd20U6ZQKgogkhaZmH48tK+XcMblMGtrf6zgiMU8FQUSSwl+3HKCy5pQmRhIJkQqCiCSFRcUlFORmcdmkwV5HEYkLKggikvA27a1hdelRbp9TSGqKeR1HJC6oIIhIwltYXELvjFRumFXQ+coiAqggiEiCO1Rbz8sb9nH9zBFkZ6V7HUckbqggiEhCe3JlOQ3NPm4/r9DrKCJxRQVBRBJWQ5OPP6wsY96EfMbm9/U6jkhcUUEQkYT1l42VHKqt16WNIt2ggiAiCck5x6LiUsbm9+HCcQO9jiMSd1QQRCQhrSs/yvsVNdwxdzRmurRRpKtUEEQkIS0sLqVfZhqfOWu411FE4pIKgogknMrqk7y2aT83nV1A74w0r+OIxCUVBBFJOI+vKMM5x21zCr2OIhK3VBBEJKGcbGjmqVXlXD55MAW5vb2OIxK3VBBEJKG8sH4v1ScadWmjSA+pIIhIwvBf2ljCpKH9OWd0rtdxROKaCoKIJIzlu6rYfqCOO+cW6tJGkR5SQRCRhLGwuJS8Phl8ctowr6OIxD0VBBFJCGVVx1m69QCfO2ckmempXscRiXsqCCKSEBYvKyPVjM+fO8rrKCIJQQVBROJeXX0Tz67Zw8enDmVw/0yv44gkBBUEEYl7z63ZQ219ky5tFAkjFQQRiWs+n2Px8jJmjMxhekGO13FEEoYKgojEtaLtByk5fJw7ziv0OopIQlFBEJG4tqi4lMH9e3HNlKFeRxFJKCoIIhK3dhyo5Z0dh7n13FGkp+rHmUg46V+UiMStRctKyUhL4ebZI72OIpJwVBBEJC5Vn2jgT+squG76MPL69vI6jkjCUUEQkbj0zOo9nGr06dJGkQhRQRCRuNPU7OOx5WWcOyaXSUP7ex1HJCGpIIhI3HljywH2Vp/U0QORCFJBEJG4s6i4lILcLC6bNNjrKCIJSwVBROLKpr01rCo9wu1zCklNMa/jiCQsFQQRiSuLikvpnZHKDbMKvI4iktBUEEQkbhyqreelDZVcP3ME2VnpXscRSWgqCCISN55cWU5Ds4/bdd8FkYhTQRCRuNDQ5OMPK8u4aHw+Y/P7eh1HJOGpIIhIXPjLxkoO1dZz59xCr6OIJAUVBBGJec45FhWXMia/DxeOy/c6jkhSUEEQkZi3rvwo71fUcOd5haTo0kaRqFBBEJGYt6i4lH6ZaXz6rBFeRxFJGioIIhLT9tWc5NVN+7np7AL69ErzOo5I0lBBEJGY9vjyMpxz3Dan0OsoIklFBUFEYtapxmaeWlXO5ZMHU5Db2+s4IklFBUFEYtYL7+3l6IlG3bVRxAMqCCISk05f2jhpaH/OGZ3rdRyRpKOCICIxafmuKrYdqOXOuYWY6dJGkWhTQRCRmLSwuJTcPhl8ctowr6OIJCUVBBGJOWVVx1m69QCfmz2SzPRUr+OIJCUVBBGJOYuXlZFqxq1zRnkdRSRpqSCISEypq2/i2TV7uGbKUAb3z/Q6jkjSUkEQkZjy/NoKauubdNdGEY+pIIhIzPD5HI8uK2V6QQ4zRg7wOo5IUlNBEJGY8ffthyg5fFxHD0RigAqCiMSMhcUlDO7fi2umDPU6ikjSU0EQkZiw82At7+w4zK3njiI9VT+aRLymf4UiEhMWFZeSkZbCzbNHeh1FRPC4IJjZVWa2zcx2mtm323i9l5k9E3h9pZkVBpYXmtlJM1sf+Ppt1MOLSNjUnGjkT+v2ct30YeT17eV1HBEB0rz6YDNLBX4NXA5UAKvNbIlzbkuL1e4GjjrnzjCzm4AHgc8GXtvlnJsezcwiEhlPry7nZGOz7tooEkO8PIIwG9jpnNvtnGsAngbmt1pnPrA48Pg54FLTXVtEEkpTs4/Hlpdx7phcJg3t73UcEQnwsiAMB/a0eF4RWNbmOs65JqAGyAu8NtrM3jOzv5vZBZEOKyKR8caWA+ytPskd5+nogUgs8ewUQw/tA0Y656rMbCbwgpl9zDl3rPWKZnYvcC9Afn4+RUVF0U2aZOrq6rSPIyzR9vHPV55kYJaRcegDioq2eh3nQ4m2n2OR9nFs87Ig7AUKWjwfEVjW1joVZpYGZANVzjkH1AM459aa2S5gPLCm9Yc45xYACwAmTJjg5s2bF+a/hrRUVFSE9nFkJdI+3rS3hu2vvcv3rpnEJReO8TrORyTSfo5V2sexzctTDKuBcWY22swygJuAJa3WWQLcHnh8PfCWc86ZWX5gkCNmNgYYB+yOUm4RCZNHl5XSOyOVG88u6HxlEYkqz44gOOeazOwrwOtAKrDQObfZzB4A1jjnlgCPAI+b2U7gCP4SAXAh8ICZNQI+4IvOuSPR/1uISHcdrqtnyfpKPnt2AdlZ6V7HEZFWPB2D4Jx7BXil1bIftHh8Crihje2eB56PeEARiZgnV5bT0OzjDt13QSQmaSZFEYm6hiYfj68o46Lx+YzN7+t1HBFpgwqCiETdKxv3cai2XndtFIlhKggiElXOORYVlzAmvw8Xjsv3Oo6ItEMFQUSial15NRsqarjzvEJSUjQxqkisUkEQkahaVFxCv8w0Pn3WCK+jiEgHVBBEJGr21Zzk1U37uensAvr0iteJXEWSgwqCiETN48vLcM5x25xCr6OISCdUEEQkKk41NvPUqnIumzSYgtzeXscRkU6oIIhIVLy4fi9HTzRy51zdtVEkHqggiEjE+S9tLGXikH6cOybX6zgiEgIVBBGJuOW7qti6v5a75o7GTJc2isQDFQQRibiFxaXk9sngk9OHeR1FREKkgiAiEVVWdZylWw/w+XNGkpme6nUcEQmRCoKIRNSjy0pJSzE+f+4or6OISBeoIIhIxNSeauTZNRVcO3UYg/pneh1HRLpABUFEIuaPayqoq2/SXRtF4pAKgohERLPPsXhZKbNGDWDqiByv44hIF6kgiEhELP3gAOVHTnDX+ZoYSSQeqSCISEQsKi5leE4WV0we7HUUEekGFQQRCbstlcdYvruK2+aMIi1VP2ZE4pH+5YpI2C0qLiErPZWbzh7pdRQR6SYVBBEJq8N19by4oZLPzBxOdu90r+OISDepIIhIWD25spyGJh93nKfBiSLxTAVBRMKmocnH4yvKuGh8PmcM6ut1HBHpARUEEQmbv2ys5FBtvS5tFEkAKggiEhbOORYVlzI2vw8XjhvodRwR6SEVBBEJi7VlR3m/ooY7547GzLyOIyI9pIIgImGxsLiE7Kx0Pn3WcK+jiEgYqCCISI9VHD3Ba5v2c9PsAnpnpHkdR0TCQAVBRHrs8eVlmBm3zSn0OoqIhEmHVd/MRgA3ARcAw4CTwCbgL8CrzjlfxBOKSEw70dDEU6vKuepjQxiek+V1HBEJk3YLgpktAoYDLwMPAgeBTGA8cBXwPTP7tnPu7WgEFZHY9Py6vRw71cRd5xd6HUVEwqijIwj/5Zzb1MbyTcCfzCwD0ETrIknM53MsKi5h2ohszho5wOs4IhJG7Y5BaKscmNkAM5saeL3BObczkuFEJLa9veMQuw8d16WNIgmo00GKZlZkZv3NLBdYB/zezH4e+WgiEusWFpcyqF8vrpky1OsoIhJmoVzFkO2cOwZ8GnjMOXcOcGlkY4lIrNt5sJa3tx/i1nNHkZGmC6JEEk0o/6rTzGwocCP+AYsiIiwqLiUjLYXPnaOhSCKJKJSC8ADwOrDTObfazMYAOyIbS0RiWfWJBp5fV8Gnpg8nr28vr+OISAR0dJnjzcBfnXPPAs+eXu6c2w18JgrZRCRGPb16D6cafdypSxtFElZHlzmOBJ41s3RgKfAqsMo556KSTERiUlOzj8eWlXLe2DwmDunvdRwRiZCOLnN80Dl3CXANsAG4C1hnZk+a2W1mNjhaIUUkdry++QCVNae4c+5or6OISAR1elcV51wt8OfAF2Y2GbgaeAy4MqLpRCTmLCwuYVReby6ZOMjrKCISQSHddi0wOVJhi/VLnHMqByJJ5r3yo6wtO8r9n5hMaoomRhJJZJ0WBDNbCEwFNgOnb87kgD9FMJeIxKBH3i2hX2YaN8wq8DqKiERYKEcQznXOTY54EhGJaRVHT/Dqpv3cc/5o+vYK6eCjiMSxUOZBWB4YdyAiSWzxslIAbj+v0NMcIhIdofwa8Bj+krAfqAcMcM65qRFNJiIxo66+iadX7eGaKUMZlpPldRwRiYJQCsIjwK3ARv4xBkFEksgfV++htr6Ju8/XpY0iySKUgnDIObck4klEJCY1+xwLi0s4u3AA0wtyvI4jIlESSkF4z8yeBF7Cf4oBAOecrmIQSQJ/3byfiqMn+f7HJ3kdRUSiKJSCkIW/GFzRYpkucxRJEo+8W0JBbhaXTx7idRQRiaJQZlK8MxpBRCT2vFd+lDWaGEkkKbV7maOZfd/Mcjt4/RIzuzYysUQkFmhiJJHk1dERhI3AS2Z2ClgHHAIygXHAdOBN4N8iHVBEvKGJkUSSW7v/6p1zLwIvmtk4YC4wFDgG/AG41zl3MjoRRcQLmhhJJLmFMgZhB7AjCllEJEZoYiQRCWWqZRFJMpoYSURUEETkIzQxkohACAXBzPKiEUREYsPpiZF09EAkuYVyBGGFmT1rZteYmS6EFklwD2tiJBEhtIIwHliA/4ZNO8zs38xsfGRjiYgX3is/ytqyo9w1d7QmRhJJcp0WBOf3hnPuZuALwO3AKjP7u5nNiXhCEYkaTYwkIqd1epljYAzC5/EfQTgAfBVYgn+ypGcBnagUSQCaGElEWgrlp8By4HHgOudcRYvla8zst5GJJSLRpomRRKSlUMYgfN8596OW5cDMbgBwzj3Ykw83s6vMbJuZ7TSzb7fxei8zeybw+kozK2zx2ncCy7eZ2ZWhfJ7P9SStSOLSxEgi0looBSHoP27gOz39YDNLBX4NXA1MBm42s8mtVrsbOOqcOwP4OfBgYNvJwE3Ax4CrgN8E3q9D+4/7OHaqsafRRRLOM5oYSURaafcUg5ldDVwDDDezh1q81B9oCsNnzwZ2Oud2Bz7vaWA+sKXFOvOB/xt4/Bzwq8CllvOBp51z9UCJme0MvN/yjj6wrnIn2VkZYYgukphm9OiYoIgkko7GIFQCa4BPAmtbLK8Fvh6Gzx4O7GnxvAI4p711nHNNZlYD5AWWr2i17fC2PsTM7gXuDUNeERGRpNHR3Rw3ABvM7AnnXDiOGHjCObcA/zwOTJgwwa1av4kTDXH71wniHOyrOcm2/XVs2FPNM2v28NNPT+Gm2SM9yVNUVMS8efM8+exkEc597Jzj4w+9S31TM298/SJSNPfBh/S9HHnax5HXk/kNOzrF8Efn3I3Ae2YWNLzPOTe125/qtxdoebH1iMCyttapMLM0IBuoCnHbNmVnpZOdld7dzDFpWE4WM0flcvPsAt7bc5QnV5V7VhAkvhTvrGLLvmM8+JkpKgci8hEdDVL858Cf1wKfaOOrp1YD48xstJll4B90uKTVOkvwT8wEcD3wlnPOBZbfFLjKYTQwDlgVhkxxzcz43OyRvF9Rw6a9NV7HkTjwu7d3MbBvL66b0eYZOhFJYu0WBOfcvhbrHHDOlTnnyoCDQI9/1QictvgK8DrwAfBH59xmM3vAzD4ZWO0RIC8wCPEbBK6ocM5tBv6If0Dja8CXnXPNPc2UCD41YwS90lJ4alW511Ekxn2w7xjv7DjMnXML6ZXW6UVAIpJkQpko6VngvBbPmwPLzu7phzvnXgFeabXsBy0enwJuaGfbnwA/6WmGRJPdO51rpw7jxfWVfPeaSfTRjHjSjt+/vZveGal8/pxRXkcRkRgUyjwIac65htNPAo91rWAM+9w5BdTVN/Hy+5VeR5EYVVl9kiUbKvns2QVk906sMTkiEh6hFIRDLQ75Y2bzgcORiyQ9ddbIAYwf3JcnV+3pfGVJSouKS3CgiZFEpF2hFIQvAt81sz1mtge4D80rENNOD1bcsKeazZUarCgfdexUI0+t2sPHpwxlxIDeXscRkRgVyu2edznnzgUmAZOcc+c553ZFPpr0xOnBis+treh8ZUkqT64sp66+iXsvHON1FBGJYZ0WBDPLNrOfAUVAkZn9l5llRzyZ9Eh273Rmj85lxe4jXkeRGNLQ5GNRcQlzz8jjzOH6Zywi7QvlFMNC/NMr3xj4OgYsimQoCY9Zo3LZuv+YblAlH1qyoZIDx+r5wgU6eiAiHQulIIx1zt3vnNsd+PohoJ8uceDswgE4B++VV3sdRWKAc47fv72biUP6cdH4fK/jiEiMC6UgnDSz808/MbO5wMnIRZJwmT4yh9QUY02pTjMIFG0/xLYDtXzhgjE9mp9dRJJDKLPo/BOwODDuwIAjwB2RDCXh0TsjjY8N689qFQQBFvx9N0P6Z/KJacO8jiIicaDTguCcWw9MM7P+gefHIh1KwmfmqAE8taqcxmYf6amhHDCSRLSxooblu6v47jUTyUjT94GIdK6juzl+o53lADjnfhahTBJGZxfmsqi4lM2Vx5hekON1HPHI797eRb9eadysu3yKSIg6+lWiXydfEgdmjRoAoHEISWzPkRO8snEfnztnJP0yNa2yiISm3SMIgasVJM4N6p/JyNzerCk9yj0XeJ1GvPDIuyWkmHHH3EKvo4hIHAlloqTxZrbUzDYFnk81s+9HPpqEy6zCAawpO4JzzusoEmXVJxp4ZvUePjl9GEOzs7yOIyJxJJTRSr8HvgM0Ajjn3gduimQoCa+zC3M5XNdAWdUJr6NIlP1hRRknG5s1rbKIdFkoBaG3c25Vq2VNkQgjkXF6HIIud0wupxqbeXRZKReNz2fikP5exxGROBNKQThsZmMBB2Bm1wP7IppKwmpsfl9yeqezpvSo11Ekip5dW8Hhugb+ad5Yr6OISBwKZaKkLwMLgIlmthcoAW6JaCoJq5QUY9Yo/zgESQ5NzT4WvL2LGSNzOGd0rtdxRCQOhXIEocw5dxmQD0x0zp3vnCuLcC4Js5mjctl16DhHjjd4HUWi4C8b97HnyEm+NO8MTassIt0SSkEoMbMFwLlAXYTzSITMGJkDwIaKak9zSOQ55/ifol2MG9SXSycO8jqOiMSpUArCROBN/KcaSszsVy1v3iTxYcrwbFIMNuyp9jqKRNhbWw+ydX8t/zRvLCkpOnogIt3TaUFwzp1wzv3ROfdpYAbQH/h7xJNJWPXplca4Qf14v6LG6ygSYf9TtIvhOVm6KZOI9EhId20xs4vM7DfAWiATuDGiqSQiphVks2FPtSZMSmCrSo6wpuwo9144RjfnEpEeCWUmxVLgX4B3gCnOuRudc89HOJdEwLSCHKqON1Bx9KTXUSRC/qdoJ3l9MrhxVoHXUUQkzoVymeNU3eI5MUwbkQP4ByoW5Pb2NoyE3ZbKY/xt2yG+ecV4sjJSvY4jInEulDEIKgcJYsKQfmSkpWigYoL67d930bdXGrfOKfQ6iogkAJ2kTCLpqSmcOaw/G/ZooGKiKas6zsvvV3LLOSPJztItnUWk51QQksy0ghw27q2hqdnndRQJowVv7yYtNYW7zx/tdRQRSRDtjkEws2+0WuSAw8C7zrmSiKaSiJlekMOi4lJ2HKxj0lDdwCcRHKw9xbNrK7h+5ggG9c/0Oo6IJIiOjiD0a/XVH5gFvGpmut1znPpwoKLGISSMhe+W0tTs43/pls4iEkbtHkFwzv2wreVmlot/ZsWnIxVKImdUXm+ys9LZUFHNTbNHeh1Heqj6RAN/WFHGx6cOY1ReH6/jiEgC6fIYBOfcEUDzt8YpM2NaQQ7rNVAxITy6rJS6+ia+fLFu6Swi4dXlgmBmFwNHI5BFomT6iGy2H6jlZEOz11GkB2pPNbLw3RKumDyYiUM0nkREwqujQYob8Q9MbCkXqARui2QoiaypI3Jo9jk2V9YwqzDX6zjSTY+vKOPYqSa+esk4r6OISALqaCbFa1s9d0CVc+54BPNIFEwtyAZg/Z5qFYQ4daKhiYffKWHehHymjMj2Oo6IJKCOBimWRTOIRM+gfpkMz8lig+7sGLeeXFnOkeMNfPWSM7yOIiIJKpR7MUgCOn1nR4k/Dc2OBcW7OW9sHjNH6QiQiESGZlJMUtNG5FB+5ARHjjd4HUW66J29TRysrecrOnogIhGkgpCkphXkAP47O0r8aGjy8cruRmaOGsCcMXlexxGRBKaCkKSmDM8mxTSjYrz583sVVJ1yfPWSMzDTdCQiEjkqCEmqT680xg3qp4IQR5qaffymaBeF/VO4aHy+13FEJMGpICSxqSOy2VBRg3Otp7uQWPTS+5WUVZ3gE2PTdfRARCJOBSGJTSvI4cjxBiqOnvQ6inTC53P86q2dTBzSjxmDUr2OIyJJQAUhiU3XQMW48eqm/ew6dJwvX3wGKTp6ICJRoIKQxCYM6UdGWorGIcQ4n8/x32/tYEx+H66ZMtTrOCKSJFQQklh6agpnDuvPBt3ZMaa9tnk/W/fX8rVLxpGaoqMHIhIdKghJblpBDhv31tDU7PM6irTB53P84s3tjM3vwyemDfM6jogkERWEJDe9IIeTjc3sOFjndRRpw1827mP7gTr++bLxOnogIlGlgpDkpo3IATRhUixq9jl+uXQH4wf35eMaeyAiUaaCkORG5fUmOytdVzLEoJffr2TnwTr++VIdPRCR6FNBSHJmxtQR2azXQMWY0tTs45dv7mDikH5cfeYQr+OISBJSQRCmF+Sw/UAtJxqavI4iAUs2VLL78HH+5bJxpOjogYh4QAVBmDEyh2af4/0KHUWIBU3NPh5auoPJQ/tzxWQdPRARb6ggCDMKBgCwtuyox0kE4M/v7aW06gRfv3y8jh6IiGdUEIQBfTIYm9+HdSoInmts9vHQWzuYMjybyyYN8jqOiCQxFQQBYOaoAawtP6o7O3rsT+sq2HPkJF+/fJzu2CginlJBEMBfEKpPNLL78HGvoySthiYfDy3dybSCHC6eoKMHIuItFQQB/AUBNA7BS8+trWBv9Um+fpmOHoiI91QQBIAxA/uSnZWucQgeOdXYzH+/tYOzRuZw0fh8r+OIiKggiF9KinHWyBwdQfDI48vL2Fdzin+9cqKOHohITFBBkA/NHDWAHQfrqDnR6HWUpHLsVCO/LtrJhePzmTM2z+s4IiKACoK0cFZgHMJ7e3QUIZp+//Zuqk808q0rJ3gdRUTkQyoI8qFpI3JITTGNQ4iiQ7X1PPxOCZ+YNowzh2d7HUdE5EOeFAQzyzWzN8xsR+DPAe2sd3tgnR1mdnuL5UVmts3M1ge+dE1YGPTplcakof1YW66CEC3//dYOGpt9/O/Lx3sdRUTkI7w6gvBtYKlzbhywNPD8I8wsF7gfOAeYDdzfqkjc4pybHvg6GI3QyWDmyAGsL6+mqdnndZSEV151gidXlvPZswsoHNjH6zgiIh/hVUGYDywOPF4MXNfGOlcCbzjnjjjnjgJvAFdFJ17yOmvUAI43NLPtQK3XURLez97YRlqq8bVLx3kdRUQkiFcFYbBzbl/g8X5gcBvrDAf2tHheEVh22qLA6YX/Y7ouLGxOT5ikcQiRtaXyGC9uqOTOuaMZ3D/T6zgiIkHSIvXGZvYm0Na9ar/X8olzzplZV28AcItzbq+Z9QOeB24FHmsnx73AvQD5+fkUFRV18aOSi3OOAb2Ml1ZupaC+tMvb19XVaR+H4GdrT9E7Dc5M3UdR0f4ubat9HB3az5GnfRzbIlYQnHOXtfeamR0ws6HOuX1mNhRoawzBXmBei+cjgKLAe+8N/FlrZk/iH6PQZkFwzi0AFgBMmDDBzZs3r63VpIWLDrzHuzuruOiii7o8aU9RURHaxx1bubuK919bwbevnsjHLxrb5e21j6ND+znytI9jm1enGJYAp69KuB14sY11XgeuMLMBgcGJVwCvm1mamQ0EMLN04FpgUxQyJ405Y/M4XFfPzoN1XkdJOM45/uP1bQzu34vb5xR6HUdEpF1eFYSfApeb2Q7gssBzzGyWmT0M4Jw7AvwIWB34eiCwrBf+ovA+sB7/kYbfR/1vkMDmjBkIwPLdVR4nSTxLPzjI2rKj/POl48nKSPU6johIuyJ2iqEjzrkq4NI2lq8B7mnxfCGwsNU6x4GZkc6YzApysxiek8XyXVXcpt9yw6ap2ceDr21l9MA+3DBrhNdxREQ6pJkUJYiZMWdsHit2V+HzdXX8qLTnqVXl7DhYx31XTSQ9Vf/0RCS26aeUtGnOmDyOnmhk637NhxAONScb+dkb2zlndC5Xfqytq3pFRGKLCoK06fRdBTUOITx+9dYOqk828n+unazbOYtIXFBBkDYNy8liVF5vlu9SQeip0sPHeXRZKdefNUI3ZBKRuKGCIO2aMyaPlSVVNGscQo/8+6sfkJ6awr/qds4iEkdUEKRdc8bmUXuqiS2Vx7yOEreW76ri9c0H+NK8sQzSlMoiEkdUEKRdc8acHodw2OMk8anZ5/jxX7YwPCeLey4Y43UcEZEuUUGQdg3qn8mY/D4ah9BNz6+tYHPlMb511QQy0zUpkojEFxUE6dCcMXmsKjlCY7PP6yhxpa6+if/86zZmjMzhk9OGeR1HRKTLVBCkQxeMy+d4QzNrdfvnLvlt0S4O1dbrskYRiVsqCNKh88cNJD3VeGtrWzfclLbsrT7J79/ZzSenDeOskQO8jiMi0i0qCNKhvr3SOHdMHks/OOB1lLjx01e3AnDf1RM9TiIi0n0qCNKpSyYOYteh45RVHfc6SsxbtuswL22o5H9dOIbhOVlexxER6TYVBOnUJRMHAeg0Qycam33c/+JmRgzI4ksXn+F1HBGRHlFBkE6NyuvD2Pw+KgideLS4lB0H67j/Ex/TZY0iEvdUECQkl0wcxMrdR6irb/I6SkzaX3OKX7y5nUsmDuKySYO8jiMi0mMqCBKSSyYOpqHZx7s7NKtiW37yygc0+hz3f0KXNYpIYlBBkJDMKhxAv8w0/qbTDEGKd/oHJn7xorGMyuvjdRwRkbBQQZCQpKemcOH4fN7adhCf7u74oVONzXzvzxsZldebL80b63UcEZGwUUGQkF0yYRCHauvZVFnjdZSY8au3dlJadYKfXDdFAxNFJKGoIEjI5k3Ixwze3KJJkwC2H6jlt3/fxadmDOf8cQO9jiMiElYqCBKyvL69OHd0Hi+/vw/nkvs0g8/n+M6fNtI3M43vf3yS13FERMJOBUG6ZP70Yew+fJxNe495HcVTT60uZ23ZUb57zSTy+vbyOo6ISNipIEiXXH3mUNJTjRfW7/U6imcqq0/y01e2cu6YXG6YOcLrOCIiEaGCIF2S3TudeRMG8dKGSpqT8GoG5/ynFpp8jgc/M1VzHohIwlJBkC6bP30YB2vrWbm7yusoUffc2gr+vv0Q9101QXMeiEhCU0GQLrt04mD6ZKTy4vpKr6NE1f6aUzzw8hZmF+Zy25xCr+OIiESUCoJ0WVZGKld+bAivbNpHfVOz13GiwjnH9/68kcZmHw9eP5WUFJ1aEJHEpoIg3TJ/xnBqTzVRtO2Q11Gi4tm1FSzdepBvXjGB0QN1akFEEp8KgnTL3LF55PXJYEkSnGYoqzrOD5dsZs6YPO6aO9rrOCIiUaGCIN2SlprCtVOH8uYHB6g+0eB1nIhpavbx9WfWk5Ji/NeN03RqQUSShgqCdNtNs0dS3+TjmdV7vI4SMb/+2y7WlVfzk09NYVhOltdxRESiRgVBum3S0P6cOyaXx5aX0dTs8zpO2L1XfpSH3trBddOH8clpw7yOIyISVSoI0iN3nDeavdUnefODg15HCauak4187en3GNI/kx/OP9PrOCIiUaeCID1y2aRBDM/JYlFxiddRwsY5x7ee28C+6lM8dPMMsrPSvY4kIhJ1KgjSI2mpKdw2ZxQrS46wpTIxbuC0qLiU1zcf4L6rJjJz1ACv44iIeEIFQXrss2cXkJmewuJlpV5H6bH1e6r591c/4LJJg7nnAl3SKCLJSwVBeiyndwafmjGCF9bvpbYhfm/gdPR4A19+Yh2D+mXyXzdM042YRCSpqSBIWNw5t5D6Jh9vlTd6HaVbGpt9fOmJdRyqrefXt5xFdm+NOxCR5KaCIGExfnA/Lp88mFdLGjlyPP4mTvrxy1tYvruKf/v0FKYX5HgdR0TEcyoIEjbfunIC9c3wq7d2eh2lS55aVc7i5WV84YLRXD9zhNdxRERiggqChM24wf24YEQaj68oZc+RE17HCcmqkiP84MVNXDQ+n29fPcnrOCIiMUMFQcLqU2ekk2LGf/11m9dROrXjQC1feGwNBbm9eejmGaTqPgsiIh9SQZCwGpCZwl3nj+aF9ZVs2lvjdZx2VVaf5LaFq8hIS2HxnbM1GZKISCsqCBJ2X7xoLNlZ6Tz42laci73LHqtPNHDbwlXUnWpi8Z2zKcjt7XUkEZGYo4IgYZedlc7XLh3HOzsO8/y6vV7H+Yjj9U3c9ehqyqtOsOC2WUwe1t/rSCIiMUkFQSLijvMKmT06l/tf3ER5VWwMWKw91cjtC1exoaKGX940nTlj87yOJCISs1QQJCJSU4yf3TiNFDO+8cf1nt8OuuZkI7c+sor1e6p56KYZXD1lqKd5RERinQqCRMyIAb350XVnsqbsKL/9+y7PclSfaODWR1ayubKGX99yFh+fqnIgItIZFQSJqPnTh/GJacP4xZs7WFd+NOqfX1Z1nOt/u5yt+2v53a0zufJjQ6KeQUQkHqkgSESZGT+efyZDsjO569HVbNtfG7XPXrG7ivm/LuZwXT2P3TWbSyYOjtpni4jEOxUEibjs3uk8cc859EpL4ZaHV7L7UF3EP/OZ1eXc+shK8vpk8MKX5nLuGA1IFBHpChUEiYpReX144p5zcc5xy8MrIzYV89HjDXz1qfe47/mNnDsmjz99aS6FA/tE5LNERBKZCoJEzRmD+vL43edwoqGZz/5uOatLj4T1/d/aeoArfvE2r23axzevGM+iO87WDIkiIt2kgiBRNXlYf5645xzSUlP47O+W8/9e30ZjDy+B3Hmwji8/uY67Hl3jP6Xw5bl85ZJxpKXq21tEpLvSvA4gyefM4dm88s8X8MBLm/nV33by9o5DfO+aScwenYtZ6DdMKjl8nIeW7uDF9XvJTE/la5eO48sXj6VXWmoE04uIJAcVBPFE315p/Mf107hk4iC+++dNfHbBCs4Y1JebZ49k/vRhDOzbq83tdh+q4/XNB3ht83427KkmMz2FL1wwhnsvHENeO9uIiEjXqSCIp646cygXjR/ES+9X8uTKcn708hZ+9PIWsrPSGZnbm+E5WRxvaGJ/zSn2HztF7akmAKaOyOZfr5zADbNGMKhfpsd/CxGRxKOCIJ7LykjlxlkF3DirgC2VxyjeeZjyIycoP3KCnYfq6NMrjTH5fThvbB5j8vty2eTBDM/J8jq2iEhCU0GQmDJ5WH/dYVFEJAZomLeIiIgEUUEQERGRICoIIiIiEkQFQURERIJ4UhDMLNfM3jCzHYE/B7Sz3mtmVm1mL7daPtrMVprZTjN7xswyopNcREQkOXh1BOHbwFLn3DhgaeB5W/4TuLWN5Q8CP3fOnQEcBe6OSEoREZEk5VVBmA8sDjxeDFzX1krOuaVAbctl5p+L9xLguc62FxERke7xah6Ewc65fYHH+4HBXdg2D6h2zjUFnlcAw9tb2czuBe4FyM/Pp6ioqOtpJWR1dXXaxxGmfRwd2s+Rp30c2yJWEMzsTWBIGy99r+UT55wzMxepHM65BcACgAkTJrh58+ZF6qMEKCoqQvs4srSPo0P7OfK0j2NbxAqCc+6y9l4zswNmNtQ5t8/MhgIHu/DWVUCOmaUFjiKMAPb2MK6IiIi04NUYhCXA7YHHtwMvhrqhc84BfwOu7872IiIi0jmvCsJPgcvNbAdwWeA5ZjbLzB4+vZKZvQM8C1xqZhVmdmXgpfuAb5jZTvxjEh6JanoREZEE58kgRedcFXBpG8vXAPe0eH5BO9vvBmZHLKCIiEiS00yKIiIiEkQFQURERIKoIIiIiEgQFQQREREJooIgIiIiQVQQREREJIgKgoiIiARRQRAREZEgKggiIiISRAVBREREgqggiIiISBAVBBEREQmigiAiIiJBVBBEREQkiAqCiIiIBFFBEBERkSAqCCIiIhJEBUFERESCqCCIiIhIEBUEERERCaKCICIiIkFUEERERCSICoKIiIgEUUEQERGRICoIIiIiEkQFQURERIKoIIiIiEgQFQQREREJooIgIiIiQVQQREREJIgKgoiIiARRQRAREZEgKggiIiISRAVBREREgqggiIiISBAVBBEREQmigiAiIiJBVBBEREQkiAqCiIiIBFFBEBERkSAqCCIiIhJEBUFERESCqCCIiIhIEBUEERERCaKCICIiIkFUEERERCSICoKIiIgEUUEQERGRICoIIiIiEkQFQURERIKoIIiIiEgQFQQREREJooIgIiIiQVQQREREJIgKgoiIiARRQRAREZEgKggiIiISRAVBREREgqggiIiISBAVBBEREQmigiAiIiJBVBBEREQkiAqCiIiIBPGkIJhZrpm9YWY7An8OaGe918ys2sxebrX8UTMrMbP1ga/pUQkuIiKSJLw6gvBtYKlzbhywNPC8Lf8J3NrOa//qnJse+FofgYwiIiJJy6uCMB9YHHi8GLiurZWcc0uB2ihlEhERkQCvCsJg59y+wOP9wOBuvMdPzOx9M/u5mfUKYzYREZGklxapNzazN4Ehbbz0vZZPnHPOzFwX3/47+ItFBrAAuA94oJ0c9wL3Bp7Wm9mmLn6WdM1A4LDXIRKc9nF0aD9HnvZx5E3o7oYRKwjOucvae83MDpjZUOfcPjMbChzs4nufPvpQb2aLgG92sO4C/CUCM1vjnJvVlc+SrtE+jjzt4+jQfo487ePIM7M13d3Wq1MMS4DbA49vB17sysaBUoGZGf7xCzoqICIiEkZeFYSfApeb2Q7gssBzzGyWmT18eiUzewd4FrjUzCrM7MrAS0+Y2UZgI/5DVD+OanoREZEEF7FTDB1xzlUBl7axfA1wT4vnF7Sz/SXd/OgF3dxOQqd9HHnax9Gh/Rx52seR1+19bM51dXygiIiIJDpNtSwiIiJBEq4gmNlCMzvY3uWM5veQme0MzKNwVrQzxrsQ9vFEM1tuZvVm1u4VJtKxEPbzLYHv4Y1mtszMpkU7Y7wLYR/PD+zj9Wa2xszOj3bGeNfZPm6x3tlm1mRm10crW6II4ft4npnVtLg9wQ9Ced+EKwjAo8BVHbx+NTAu8HUv8D9RyJRoHqXjfXwE+Brw/6KSJnE9Ssf7uQS4yDk3BfgROp/bHY/S8T5eCkxzzk0H7gIe7mBdadujdLyPMbNU4EHgr9EIlIAepZN9DLzT4vYEbc4b1FrCFQTn3Nv4/4Nqz3zgMee3Asg5fdmkhKazfeycO+icWw00Ri9V4glhPy9zzh0NPF0BjIhKsAQSwj6uc/8YqNUH0KCtLgrhZzLAV4Hn6eKcOOIX4j7usoQrCCEYDuxp8bwisEwknt0NvOp1iERkZp8ys63AX/AfRZAwMrPhwKfQ0dxIm2NmG8zsVTP7WCgbJGNBEEkoZnYx/oJwn9dZEpFz7s/OuYn4J2X7kcdxEtEvgPuccz6vgySwdcAo59w04L+BF0LZKBkLwl6goMXzEYFlInHHzKbiPy8+PzC/iERI4DDuGDMb6HWWBDMLeNrMSoHrgd+Y2XWeJkowzrljzrm6wONXgPRQvo+TsSAsAW4LXM1wLlDT4t4OInHDzEYCfwJudc5t9zpPIjKzMwJTuhO44qkXoCIWRs650c65QudcIfAc8CXn3AvepkosZjakxffxbPz/93f6fezJTIqRZGZPAfOAgWZWAdwPpAM4534LvAJcA+wETgB3epM0fnW2j81sCLAG6A/4zOxfgMnOuWPeJI5PIXwv/wDIw/8bF0CTbnzTNSHs48/g/4WiETgJfLbFoEUJQQj7WHoohH18PfBPZtaE//v4plC+jzWTooiIiARJxlMMIiIi0gkVBBEREQmigiAiIiJBVBBEREQkiAqCiIiIBFFBEEliZlYX4ff/FzPr3dXPM7PrQr3jXItt3jSzAV3NKCJtU0EQkUj6F6B3Zyu14VvAb7q4zePAl7rxWSLSBhUEEfkIMxtrZq+Z2Voze8fMJgaWP2pmD5nZMjPbbWbXB5anmNlvzGyrmb1hZq+Y2fVm9jVgGPA3M/tbi/f/SeCmMSvMbHAbnz8eqHfOHW7xuf8TWH934N72C83sAzN7tMWmS4CbI7dnRJKLCoKItLYA+KpzbibwTT76m/xQ4HzgWuCngWWfBgqBycCtwBwA59xDQCVwsXPu4sC6fYAVgZvGvA18oY3Pn4v/5jItDQi879fxF4GfAx8DppjZ9MDnHQV6mVled/7SIvJRCTfVsoh0n5n1Bc4Dng1M3wz++w+c9kLgrntbWvz2fz7wbGD5/pZHC9rQALwceLwWuLyNdYYCh1ote8k558xsI3DAObcxkHcz/nKyPrDeQfxHLXS/BJEeUkEQkZZSgGrn3PR2Xq9v8djaWacjjS3mgG+m7Z9BJ4Hsdj7X1yqDr9V7ZAa2F5Ee0ikGEflQ4IZaJWZ2A0DgrqfTOtmsGPhMYCzCYPw3jTmtFujXxRgfAGd0cRsCd6sbApR2dVsRCaaCIJLceptZRYuvbwC3AHeb2QZgMzC/k/d4HqgAtgB/wD9+oCbw2gLgtU5OO7T2NjDDWpzjCNFM/OMbmrq4nYi0QXdzFJEeM7O+zrm6wADBVcBc59z+HrzfL/GPO3izi9sscc4t7e7nisg/aAyCiITDy2aWA2QAP+pJOQj4N+CcLm6zSeVAJHx0BEFERESCaAyCiIiIBFFBEBERkSAqCCIiIhJEBUFERESCqCCIiIhIEBUEERERCfL/AVYjJaUewmN2AAAAAElFTkSuQmCC\n", 5632 | "text/plain": [ 5633 | "
" 5634 | ] 5635 | }, 5636 | "metadata": { 5637 | "needs_background": "light" 5638 | }, 5639 | "output_type": "display_data" 5640 | }, 5641 | { 5642 | "name": "stdout", 5643 | "output_type": "stream", 5644 | "text": [ 5645 | "Expt: 2.5625\n" 5646 | ] 5647 | } 5648 | ], 5649 | "source": [ 5650 | "U_mid=(U_final[int(Ny/2)+1]+U_final[int(Ny/2)])/2\n", 5651 | "\n", 5652 | "fig,ax=plt.subplots(1,1,figsize=(8,8))\n", 5653 | "plt.plot(x,U_mid)\n", 5654 | "plt.axhline(0,color='k',lw=2)\n", 5655 | "plt.title(\"Velocity profile\")\n", 5656 | "plt.ylabel('U velocity (m/s)')\n", 5657 | "plt.xlabel('Length (m)')\n", 5658 | "plt.xlim(1,1.5)\n", 5659 | "plt.ylim(-0.15,0.15)\n", 5660 | "plt.grid()\n", 5661 | "plt.show()\n", 5662 | "\n", 5663 | "for i in range(Nx_rgt,Nx):\n", 5664 | " if U_mid[i]>0:\n", 5665 | " break\n", 5666 | "grad=(U_mid[i]-U_mid[i-1])/dx\n", 5667 | "x_delta=U_mid[i]/grad\n", 5668 | "x0=float(x[i-1]+x_delta)-1.0625\n", 5669 | "x0=0.5*(x[i-1]+x[i])-1.0625\n", 5670 | "\n", 5671 | "print('Expt:',round(x0/0.0625,4))" 5672 | ] 5673 | }, 5674 | { 5675 | "cell_type": "code", 5676 | "execution_count": 65, 5677 | "id": "770e5cba", 5678 | "metadata": {}, 5679 | "outputs": [ 5680 | { 5681 | "data": { 5682 | "image/png": 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\n", 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\n", 5695 | "text/plain": [ 5696 | "
" 5697 | ] 5698 | }, 5699 | "metadata": { 5700 | "needs_background": "light" 5701 | }, 5702 | "output_type": "display_data" 5703 | } 5704 | ], 5705 | "source": [ 5706 | "U_60=U_final[:,int(0.6*Nx)]\n", 5707 | "U_80=U_final[:,int(0.8*Nx)]\n", 5708 | "U_100=U_final[:,-1]\n", 5709 | "\n", 5710 | "T_20=T_final[:,int(0.2*Nx)]\n", 5711 | "T_40=T_final[:,int(0.4*Nx)]\n", 5712 | "T_60=T_final[:,int(0.6*Nx)]\n", 5713 | "T_80=T_final[:,int(0.8*Nx)]\n", 5714 | "T_100=T_final[:,-1]\n", 5715 | "\n", 5716 | "fig,ax=plt.subplots(1,1,figsize=(7,7))\n", 5717 | "plt.plot(U_60,y,label='60% pipe length')\n", 5718 | "plt.plot(U_80,y,label='80% pipe length')\n", 5719 | "plt.plot(U_100,y,label='Outlet')\n", 5720 | "plt.plot(u_inlet_array,y,label='Fully-developed')\n", 5721 | "plt.axhline(0.5,color='k')\n", 5722 | "plt.title(\"Outlet Velocity profile\")\n", 5723 | "plt.xlabel('U velocity (m/s)')\n", 5724 | "plt.ylabel('Height (m)')\n", 5725 | "plt.ylim(-H/2,H/2)\n", 5726 | "plt.legend()\n", 5727 | "plt.grid()\n", 5728 | "plt.show()\n", 5729 | "\n", 5730 | "fig,ax=plt.subplots(1,1,figsize=(7,7))\n", 5731 | "plt.plot(T_20,y,label='20% pipe length')\n", 5732 | "plt.plot(T_40,y,label='40% pipe length')\n", 5733 | "plt.plot(T_60,y,label='60% pipe length')\n", 5734 | "plt.plot(T_80,y,label='80% pipe length')\n", 5735 | "plt.plot(T_100,y,label='Outlet')\n", 5736 | "plt.axhline(0.5,color='k')\n", 5737 | "plt.title(\"Outlet Temperature profile\")\n", 5738 | "plt.xlabel('Temperature')\n", 5739 | "plt.ylabel('Height (m)')\n", 5740 | "plt.xlim(0)\n", 5741 | "plt.ylim(-H/2,H/2)\n", 5742 | "plt.legend()\n", 5743 | "plt.grid()\n", 5744 | "plt.show()" 5745 | ] 5746 | }, 5747 | { 5748 | "cell_type": "code", 5749 | "execution_count": 90, 5750 | "id": "b8a79086", 5751 | "metadata": {}, 5752 | "outputs": [ 5753 | { 5754 | "data": { 5755 | "text/plain": [ 5756 | "1500" 5757 | ] 5758 | }, 5759 | "execution_count": 90, 5760 | "metadata": {}, 5761 | "output_type": "execute_result" 5762 | } 5763 | ], 5764 | "source": [ 5765 | "len(U_data)" 5766 | ] 5767 | }, 5768 | { 5769 | "cell_type": "code", 5770 | "execution_count": 91, 5771 | "id": "93abd0e1", 5772 | "metadata": {}, 5773 | "outputs": [], 5774 | "source": [ 5775 | "U_anim=U_data[::]\n", 5776 | "V_anim=V_data[::]\n", 5777 | "T_anim=T_data[::]\n", 5778 | "\n", 5779 | "count=len(U_data)\n", 5780 | "\n", 5781 | "U_file=[]\n", 5782 | "V_file=[]\n", 5783 | "T_file=[]\n", 5784 | "V_mag_file=[]\n", 5785 | "Vort_file=[]\n", 5786 | "L_crop=10\n", 5787 | "for k in range(count):\n", 5788 | " Ny_f=Ny\n", 5789 | " Nx_f=Nx\n", 5790 | " \n", 5791 | " U_fine=U_anim[k]\n", 5792 | " V_fine=V_anim[k]\n", 5793 | " T_fine=T_anim[k]\n", 5794 | " \n", 5795 | "# x_crop=np.linspace(0,L_crop,int((L_crop/L)*Nx))\n", 5796 | "# y_crop=np.linspace(0,H,Ny)\n", 5797 | "# xx_crop,yy_crop=np.meshgrid(x_crop,y_crop)\n", 5798 | " U_fine_crop=U_fine[0:Ny,0:int((L_crop/L)*Nx)]\n", 5799 | " V_fine_crop=V_fine[0:Ny,0:int((L_crop/L)*Nx)]\n", 5800 | " T_fine_crop=T_fine[0:Ny,0:int((L_crop/L)*Nx)]\n", 5801 | "\n", 5802 | "# V_mag_crop=V_mag[0:Ny,0:int((L_crop/L)*Nx)]\n", 5803 | "# vort_crop=vort[0:Ny,0:int((L_crop/L)*Nx)]\n", 5804 | "\n", 5805 | "# for n in range(2):\n", 5806 | "# Ny_c=int(Ny_f/2)\n", 5807 | "# Nx_c=int(Nx_f/2)\n", 5808 | " \n", 5809 | "# U_coarse=nodal_restriction(U_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 5810 | "# V_coarse=nodal_restriction(V_fine,Ny_f,Nx_f,Ny_c,Nx_c)\n", 5811 | "\n", 5812 | "# U_fine=U_coarse\n", 5813 | "# V_fine=V_coarse\n", 5814 | " \n", 5815 | "# Ny_f=Ny_c\n", 5816 | "# Nx_f=Nx_c\n", 5817 | " \n", 5818 | " V_mag=np.sqrt(np.power(U_fine,2)+np.power(V_fine,2)) \n", 5819 | " vort=curl(U_fine,V_fine,2*dx,2*dy,128,1280)\n", 5820 | " V_mag_crop=V_mag[0:128,0:int((L_crop/L)*1280)]\n", 5821 | " vort_crop=vort[0:128,0:int((L_crop/L)*1280)]\n", 5822 | " \n", 5823 | " U_file.append(U_fine_crop)\n", 5824 | " V_file.append(V_fine_crop)\n", 5825 | " V_mag_file.append(V_mag_crop)\n", 5826 | " T_file.append(T_fine_crop)\n", 5827 | " Vort_file.append(vort_crop)" 5828 | ] 5829 | }, 5830 | { 5831 | "cell_type": "code", 5832 | "execution_count": 92, 5833 | "id": "212f215f", 5834 | "metadata": {}, 5835 | "outputs": [ 5836 | { 5837 | "data": { 5838 | "text/plain": [ 5839 | "(128, 1280)" 5840 | ] 5841 | }, 5842 | "execution_count": 92, 5843 | "metadata": {}, 5844 | "output_type": "execute_result" 5845 | } 5846 | ], 5847 | "source": [ 5848 | "U_file[0].shape" 5849 | ] 5850 | }, 5851 | { 5852 | "cell_type": "code", 5853 | "execution_count": 101, 5854 | "id": "45152c6a", 5855 | "metadata": {}, 5856 | "outputs": [], 5857 | "source": [ 5858 | "def animate(k):\n", 5859 | " ax.clear()\n", 5860 | " \n", 5861 | " #U_final=U_file[k]\n", 5862 | " #V_final=V_file[k]\n", 5863 | " #T_final=T_file[k]\n", 5864 | " vort=Vort_file[k]\n", 5865 | " #V_mag=V_mag_file[k]\n", 5866 | " plt.title('Flow time: '+str(round(k*dt*20,5))+' seconds')\n", 5867 | " #plt.title('Streamlines')\n", 5868 | " #plt.title('Flow time: '+str(round(k*dt*2,4))+' seconds')\n", 5869 | " \n", 5870 | " plt.xlim(0,L_crop)\n", 5871 | " plt.ylim(0,H)\n", 5872 | " \n", 5873 | " #contour=plt.contourf(xx,yy,V_mag,cmap=cm.jet,vmax=2.5,levels=255)\n", 5874 | " contour=plt.contourf(xx,yy,vort,cmap=cm.seismic,vmax=40,vmin=-40,levels=255)\n", 5875 | " #contour=plt.contourf(xx,yy,T_final,cmap=cm.jet,vmax=1,vmin=0,levels=255)\n", 5876 | " #stream=plt.streamplot(xx,yy,U_final,V_final,linewidth=0.8,density=1.25,color='k',arrowsize=0,broken_streamlines=False)\n", 5877 | " \n", 5878 | " for n in range(n_squares):\n", 5879 | " Nx_lft=int(square_coord[0][n])\n", 5880 | " Nx_rgt=int(square_coord[1][n])\n", 5881 | " Ny_top=int(square_coord[2][n])\n", 5882 | " Ny_btm=int(square_coord[3][n])\n", 5883 | "\n", 5884 | " rect=patches.Rectangle([Nx_lft*dx,Ny_btm*dy],x_size*dx,y_size*dy,fc='k',ec='k')\n", 5885 | " ax.add_patch(rect)\n", 5886 | " return contour" 5887 | ] 5888 | }, 5889 | { 5890 | "cell_type": "code", 5891 | "execution_count": 102, 5892 | "id": "8917a4b1", 5893 | "metadata": {}, 5894 | "outputs": [ 5895 | { 5896 | "data": { 5897 | "application/javascript": [ 5898 | "/* Put everything inside the global mpl namespace */\n", 5899 | "/* global mpl */\n", 5900 | "window.mpl = {};\n", 5901 | "\n", 5902 | "mpl.get_websocket_type = function () {\n", 5903 | " if (typeof WebSocket !== 'undefined') {\n", 5904 | " return WebSocket;\n", 5905 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 5906 | " return MozWebSocket;\n", 5907 | " } else {\n", 5908 | " alert(\n", 5909 | " 'Your browser does not have WebSocket support. ' +\n", 5910 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 5911 | " 'Firefox 4 and 5 are also supported but you ' +\n", 5912 | " 'have to enable WebSockets in about:config.'\n", 5913 | " );\n", 5914 | " }\n", 5915 | "};\n", 5916 | "\n", 5917 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 5918 | " this.id = figure_id;\n", 5919 | "\n", 5920 | " this.ws = websocket;\n", 5921 | "\n", 5922 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 5923 | "\n", 5924 | " if (!this.supports_binary) {\n", 5925 | " var warnings = document.getElementById('mpl-warnings');\n", 5926 | " if (warnings) {\n", 5927 | " warnings.style.display = 'block';\n", 5928 | " warnings.textContent =\n", 5929 | " 'This browser does not support binary websocket messages. ' +\n", 5930 | " 'Performance may be slow.';\n", 5931 | " }\n", 5932 | " }\n", 5933 | "\n", 5934 | " this.imageObj = new Image();\n", 5935 | "\n", 5936 | " this.context = undefined;\n", 5937 | " this.message = undefined;\n", 5938 | " this.canvas = undefined;\n", 5939 | " this.rubberband_canvas = undefined;\n", 5940 | " this.rubberband_context = undefined;\n", 5941 | " this.format_dropdown = undefined;\n", 5942 | "\n", 5943 | " this.image_mode = 'full';\n", 5944 | "\n", 5945 | " this.root = document.createElement('div');\n", 5946 | " this.root.setAttribute('style', 'display: inline-block');\n", 5947 | " this._root_extra_style(this.root);\n", 5948 | "\n", 5949 | " parent_element.appendChild(this.root);\n", 5950 | "\n", 5951 | " this._init_header(this);\n", 5952 | " this._init_canvas(this);\n", 5953 | " this._init_toolbar(this);\n", 5954 | "\n", 5955 | " var fig = this;\n", 5956 | "\n", 5957 | " this.waiting = false;\n", 5958 | "\n", 5959 | " this.ws.onopen = function () {\n", 5960 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 5961 | " fig.send_message('send_image_mode', {});\n", 5962 | " if (fig.ratio !== 1) {\n", 5963 | " fig.send_message('set_device_pixel_ratio', {\n", 5964 | " device_pixel_ratio: fig.ratio,\n", 5965 | " });\n", 5966 | " }\n", 5967 | " fig.send_message('refresh', {});\n", 5968 | " };\n", 5969 | "\n", 5970 | " this.imageObj.onload = function () {\n", 5971 | " if (fig.image_mode === 'full') {\n", 5972 | " // Full images could contain transparency (where diff images\n", 5973 | " // almost always do), so we need to clear the canvas so that\n", 5974 | " // there is no ghosting.\n", 5975 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 5976 | " }\n", 5977 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 5978 | " };\n", 5979 | "\n", 5980 | " this.imageObj.onunload = function () {\n", 5981 | " fig.ws.close();\n", 5982 | " };\n", 5983 | "\n", 5984 | " this.ws.onmessage = this._make_on_message_function(this);\n", 5985 | "\n", 5986 | " this.ondownload = ondownload;\n", 5987 | "};\n", 5988 | "\n", 5989 | "mpl.figure.prototype._init_header = function () {\n", 5990 | " var titlebar = document.createElement('div');\n", 5991 | " titlebar.classList =\n", 5992 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 5993 | " var titletext = document.createElement('div');\n", 5994 | " titletext.classList = 'ui-dialog-title';\n", 5995 | " titletext.setAttribute(\n", 5996 | " 'style',\n", 5997 | " 'width: 100%; text-align: center; padding: 3px;'\n", 5998 | " );\n", 5999 | " titlebar.appendChild(titletext);\n", 6000 | " this.root.appendChild(titlebar);\n", 6001 | " this.header = titletext;\n", 6002 | "};\n", 6003 | "\n", 6004 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 6005 | "\n", 6006 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 6007 | "\n", 6008 | "mpl.figure.prototype._init_canvas = function () {\n", 6009 | " var fig = this;\n", 6010 | "\n", 6011 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 6012 | " canvas_div.setAttribute('tabindex', '0');\n", 6013 | " canvas_div.setAttribute(\n", 6014 | " 'style',\n", 6015 | " 'border: 1px solid #ddd;' +\n", 6016 | " 'box-sizing: content-box;' +\n", 6017 | " 'clear: both;' +\n", 6018 | " 'min-height: 1px;' +\n", 6019 | " 'min-width: 1px;' +\n", 6020 | " 'outline: 0;' +\n", 6021 | " 'overflow: hidden;' +\n", 6022 | " 'position: relative;' +\n", 6023 | " 'resize: both;' +\n", 6024 | " 'z-index: 2;'\n", 6025 | " );\n", 6026 | "\n", 6027 | " function on_keyboard_event_closure(name) {\n", 6028 | " return function (event) {\n", 6029 | " return fig.key_event(event, name);\n", 6030 | " };\n", 6031 | " }\n", 6032 | "\n", 6033 | " canvas_div.addEventListener(\n", 6034 | " 'keydown',\n", 6035 | " on_keyboard_event_closure('key_press')\n", 6036 | " );\n", 6037 | " canvas_div.addEventListener(\n", 6038 | " 'keyup',\n", 6039 | " on_keyboard_event_closure('key_release')\n", 6040 | " );\n", 6041 | "\n", 6042 | " this._canvas_extra_style(canvas_div);\n", 6043 | " this.root.appendChild(canvas_div);\n", 6044 | "\n", 6045 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 6046 | " canvas.classList.add('mpl-canvas');\n", 6047 | " canvas.setAttribute(\n", 6048 | " 'style',\n", 6049 | " 'box-sizing: content-box;' +\n", 6050 | " 'pointer-events: none;' +\n", 6051 | " 'position: relative;' +\n", 6052 | " 'z-index: 0;'\n", 6053 | " );\n", 6054 | "\n", 6055 | " this.context = canvas.getContext('2d');\n", 6056 | "\n", 6057 | " var backingStore =\n", 6058 | " this.context.backingStorePixelRatio ||\n", 6059 | " this.context.webkitBackingStorePixelRatio ||\n", 6060 | " this.context.mozBackingStorePixelRatio ||\n", 6061 | " this.context.msBackingStorePixelRatio ||\n", 6062 | " this.context.oBackingStorePixelRatio ||\n", 6063 | " this.context.backingStorePixelRatio ||\n", 6064 | " 1;\n", 6065 | "\n", 6066 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 6067 | "\n", 6068 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 6069 | " 'canvas'\n", 6070 | " ));\n", 6071 | " rubberband_canvas.setAttribute(\n", 6072 | " 'style',\n", 6073 | " 'box-sizing: content-box;' +\n", 6074 | " 'left: 0;' +\n", 6075 | " 'pointer-events: none;' +\n", 6076 | " 'position: absolute;' +\n", 6077 | " 'top: 0;' +\n", 6078 | " 'z-index: 1;'\n", 6079 | " );\n", 6080 | "\n", 6081 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 6082 | " if (this.ResizeObserver === undefined) {\n", 6083 | " if (window.ResizeObserver !== undefined) {\n", 6084 | " this.ResizeObserver = window.ResizeObserver;\n", 6085 | " } else {\n", 6086 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 6087 | " this.ResizeObserver = obs.ResizeObserver;\n", 6088 | " }\n", 6089 | " }\n", 6090 | "\n", 6091 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 6092 | " var nentries = entries.length;\n", 6093 | " for (var i = 0; i < nentries; i++) {\n", 6094 | " var entry = entries[i];\n", 6095 | " var width, height;\n", 6096 | " if (entry.contentBoxSize) {\n", 6097 | " if (entry.contentBoxSize instanceof Array) {\n", 6098 | " // Chrome 84 implements new version of spec.\n", 6099 | " width = entry.contentBoxSize[0].inlineSize;\n", 6100 | " height = entry.contentBoxSize[0].blockSize;\n", 6101 | " } else {\n", 6102 | " // Firefox implements old version of spec.\n", 6103 | " width = entry.contentBoxSize.inlineSize;\n", 6104 | " height = entry.contentBoxSize.blockSize;\n", 6105 | " }\n", 6106 | " } else {\n", 6107 | " // Chrome <84 implements even older version of spec.\n", 6108 | " width = entry.contentRect.width;\n", 6109 | " height = entry.contentRect.height;\n", 6110 | " }\n", 6111 | "\n", 6112 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 6113 | " // the canvas container.\n", 6114 | " if (entry.devicePixelContentBoxSize) {\n", 6115 | " // Chrome 84 implements new version of spec.\n", 6116 | " canvas.setAttribute(\n", 6117 | " 'width',\n", 6118 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 6119 | " );\n", 6120 | " canvas.setAttribute(\n", 6121 | " 'height',\n", 6122 | " entry.devicePixelContentBoxSize[0].blockSize\n", 6123 | " );\n", 6124 | " } else {\n", 6125 | " canvas.setAttribute('width', width * fig.ratio);\n", 6126 | " canvas.setAttribute('height', height * fig.ratio);\n", 6127 | " }\n", 6128 | " /* This rescales the canvas back to display pixels, so that it\n", 6129 | " * appears correct on HiDPI screens. */\n", 6130 | " canvas.style.width = width + 'px';\n", 6131 | " canvas.style.height = height + 'px';\n", 6132 | "\n", 6133 | " rubberband_canvas.setAttribute('width', width);\n", 6134 | " rubberband_canvas.setAttribute('height', height);\n", 6135 | "\n", 6136 | " // And update the size in Python. We ignore the initial 0/0 size\n", 6137 | " // that occurs as the element is placed into the DOM, which should\n", 6138 | " // otherwise not happen due to the minimum size styling.\n", 6139 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 6140 | " fig.request_resize(width, height);\n", 6141 | " }\n", 6142 | " }\n", 6143 | " });\n", 6144 | " this.resizeObserverInstance.observe(canvas_div);\n", 6145 | "\n", 6146 | " function on_mouse_event_closure(name) {\n", 6147 | " /* User Agent sniffing is bad, but WebKit is busted:\n", 6148 | " * https://bugs.webkit.org/show_bug.cgi?id=144526\n", 6149 | " * https://bugs.webkit.org/show_bug.cgi?id=181818\n", 6150 | " * The worst that happens here is that they get an extra browser\n", 6151 | " * selection when dragging, if this check fails to catch them.\n", 6152 | " */\n", 6153 | " var UA = navigator.userAgent;\n", 6154 | " var isWebKit = /AppleWebKit/.test(UA) && !/Chrome/.test(UA);\n", 6155 | " if(isWebKit) {\n", 6156 | " return function (event) {\n", 6157 | " /* This prevents the web browser from automatically changing to\n", 6158 | " * the text insertion cursor when the button is pressed. We\n", 6159 | " * want to control all of the cursor setting manually through\n", 6160 | " * the 'cursor' event from matplotlib */\n", 6161 | " event.preventDefault()\n", 6162 | " return fig.mouse_event(event, name);\n", 6163 | " };\n", 6164 | " } else {\n", 6165 | " return function (event) {\n", 6166 | " return fig.mouse_event(event, name);\n", 6167 | " };\n", 6168 | " }\n", 6169 | " }\n", 6170 | "\n", 6171 | " canvas_div.addEventListener(\n", 6172 | " 'mousedown',\n", 6173 | " on_mouse_event_closure('button_press')\n", 6174 | " );\n", 6175 | " canvas_div.addEventListener(\n", 6176 | " 'mouseup',\n", 6177 | " on_mouse_event_closure('button_release')\n", 6178 | " );\n", 6179 | " canvas_div.addEventListener(\n", 6180 | " 'dblclick',\n", 6181 | " on_mouse_event_closure('dblclick')\n", 6182 | " );\n", 6183 | " // Throttle sequential mouse events to 1 every 20ms.\n", 6184 | " canvas_div.addEventListener(\n", 6185 | " 'mousemove',\n", 6186 | " on_mouse_event_closure('motion_notify')\n", 6187 | " );\n", 6188 | "\n", 6189 | " canvas_div.addEventListener(\n", 6190 | " 'mouseenter',\n", 6191 | " on_mouse_event_closure('figure_enter')\n", 6192 | " );\n", 6193 | " canvas_div.addEventListener(\n", 6194 | " 'mouseleave',\n", 6195 | " on_mouse_event_closure('figure_leave')\n", 6196 | " );\n", 6197 | "\n", 6198 | " canvas_div.addEventListener('wheel', function (event) {\n", 6199 | " if (event.deltaY < 0) {\n", 6200 | " event.step = 1;\n", 6201 | " } else {\n", 6202 | " event.step = -1;\n", 6203 | " }\n", 6204 | " on_mouse_event_closure('scroll')(event);\n", 6205 | " });\n", 6206 | "\n", 6207 | " canvas_div.appendChild(canvas);\n", 6208 | " canvas_div.appendChild(rubberband_canvas);\n", 6209 | "\n", 6210 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 6211 | " this.rubberband_context.strokeStyle = '#000000';\n", 6212 | "\n", 6213 | " this._resize_canvas = function (width, height, forward) {\n", 6214 | " if (forward) {\n", 6215 | " canvas_div.style.width = width + 'px';\n", 6216 | " canvas_div.style.height = height + 'px';\n", 6217 | " }\n", 6218 | " };\n", 6219 | "\n", 6220 | " // Disable right mouse context menu.\n", 6221 | " canvas_div.addEventListener('contextmenu', function (_e) {\n", 6222 | " event.preventDefault();\n", 6223 | " return false;\n", 6224 | " });\n", 6225 | "\n", 6226 | " function set_focus() {\n", 6227 | " canvas.focus();\n", 6228 | " canvas_div.focus();\n", 6229 | " }\n", 6230 | "\n", 6231 | " window.setTimeout(set_focus, 100);\n", 6232 | "};\n", 6233 | "\n", 6234 | "mpl.figure.prototype._init_toolbar = function () {\n", 6235 | " var fig = this;\n", 6236 | "\n", 6237 | " var toolbar = document.createElement('div');\n", 6238 | " toolbar.classList = 'mpl-toolbar';\n", 6239 | " this.root.appendChild(toolbar);\n", 6240 | "\n", 6241 | " function on_click_closure(name) {\n", 6242 | " return function (_event) {\n", 6243 | " return fig.toolbar_button_onclick(name);\n", 6244 | " };\n", 6245 | " }\n", 6246 | "\n", 6247 | " function on_mouseover_closure(tooltip) {\n", 6248 | " return function (event) {\n", 6249 | " if (!event.currentTarget.disabled) {\n", 6250 | " return fig.toolbar_button_onmouseover(tooltip);\n", 6251 | " }\n", 6252 | " };\n", 6253 | " }\n", 6254 | "\n", 6255 | " fig.buttons = {};\n", 6256 | " var buttonGroup = document.createElement('div');\n", 6257 | " buttonGroup.classList = 'mpl-button-group';\n", 6258 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 6259 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 6260 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 6261 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 6262 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 6263 | "\n", 6264 | " if (!name) {\n", 6265 | " /* Instead of a spacer, we start a new button group. */\n", 6266 | " if (buttonGroup.hasChildNodes()) {\n", 6267 | " toolbar.appendChild(buttonGroup);\n", 6268 | " }\n", 6269 | " buttonGroup = document.createElement('div');\n", 6270 | " buttonGroup.classList = 'mpl-button-group';\n", 6271 | " continue;\n", 6272 | " }\n", 6273 | "\n", 6274 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 6275 | " button.classList = 'mpl-widget';\n", 6276 | " button.setAttribute('role', 'button');\n", 6277 | " button.setAttribute('aria-disabled', 'false');\n", 6278 | " button.addEventListener('click', on_click_closure(method_name));\n", 6279 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 6280 | "\n", 6281 | " var icon_img = document.createElement('img');\n", 6282 | " icon_img.src = '_images/' + image + '.png';\n", 6283 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 6284 | " icon_img.alt = tooltip;\n", 6285 | " button.appendChild(icon_img);\n", 6286 | "\n", 6287 | " buttonGroup.appendChild(button);\n", 6288 | " }\n", 6289 | "\n", 6290 | " if (buttonGroup.hasChildNodes()) {\n", 6291 | " toolbar.appendChild(buttonGroup);\n", 6292 | " }\n", 6293 | "\n", 6294 | " var fmt_picker = document.createElement('select');\n", 6295 | " fmt_picker.classList = 'mpl-widget';\n", 6296 | " toolbar.appendChild(fmt_picker);\n", 6297 | " this.format_dropdown = fmt_picker;\n", 6298 | "\n", 6299 | " for (var ind in mpl.extensions) {\n", 6300 | " var fmt = mpl.extensions[ind];\n", 6301 | " var option = document.createElement('option');\n", 6302 | " option.selected = fmt === mpl.default_extension;\n", 6303 | " option.innerHTML = fmt;\n", 6304 | " fmt_picker.appendChild(option);\n", 6305 | " }\n", 6306 | "\n", 6307 | " var status_bar = document.createElement('span');\n", 6308 | " status_bar.classList = 'mpl-message';\n", 6309 | " toolbar.appendChild(status_bar);\n", 6310 | " this.message = status_bar;\n", 6311 | "};\n", 6312 | "\n", 6313 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 6314 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 6315 | " // which will in turn request a refresh of the image.\n", 6316 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 6317 | "};\n", 6318 | "\n", 6319 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 6320 | " properties['type'] = type;\n", 6321 | " properties['figure_id'] = this.id;\n", 6322 | " this.ws.send(JSON.stringify(properties));\n", 6323 | "};\n", 6324 | "\n", 6325 | "mpl.figure.prototype.send_draw_message = function () {\n", 6326 | " if (!this.waiting) {\n", 6327 | " this.waiting = true;\n", 6328 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 6329 | " }\n", 6330 | "};\n", 6331 | "\n", 6332 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 6333 | " var format_dropdown = fig.format_dropdown;\n", 6334 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 6335 | " fig.ondownload(fig, format);\n", 6336 | "};\n", 6337 | "\n", 6338 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 6339 | " var size = msg['size'];\n", 6340 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 6341 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 6342 | " fig.send_message('refresh', {});\n", 6343 | " }\n", 6344 | "};\n", 6345 | "\n", 6346 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 6347 | " var x0 = msg['x0'] / fig.ratio;\n", 6348 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 6349 | " var x1 = msg['x1'] / fig.ratio;\n", 6350 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 6351 | " x0 = Math.floor(x0) + 0.5;\n", 6352 | " y0 = Math.floor(y0) + 0.5;\n", 6353 | " x1 = Math.floor(x1) + 0.5;\n", 6354 | " y1 = Math.floor(y1) + 0.5;\n", 6355 | " var min_x = Math.min(x0, x1);\n", 6356 | " var min_y = Math.min(y0, y1);\n", 6357 | " var width = Math.abs(x1 - x0);\n", 6358 | " var height = Math.abs(y1 - y0);\n", 6359 | "\n", 6360 | " fig.rubberband_context.clearRect(\n", 6361 | " 0,\n", 6362 | " 0,\n", 6363 | " fig.canvas.width / fig.ratio,\n", 6364 | " fig.canvas.height / fig.ratio\n", 6365 | " );\n", 6366 | "\n", 6367 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 6368 | "};\n", 6369 | "\n", 6370 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 6371 | " // Updates the figure title.\n", 6372 | " fig.header.textContent = msg['label'];\n", 6373 | "};\n", 6374 | "\n", 6375 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 6376 | " fig.canvas_div.style.cursor = msg['cursor'];\n", 6377 | "};\n", 6378 | "\n", 6379 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 6380 | " fig.message.textContent = msg['message'];\n", 6381 | "};\n", 6382 | "\n", 6383 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 6384 | " // Request the server to send over a new figure.\n", 6385 | " fig.send_draw_message();\n", 6386 | "};\n", 6387 | "\n", 6388 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 6389 | " fig.image_mode = msg['mode'];\n", 6390 | "};\n", 6391 | "\n", 6392 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 6393 | " for (var key in msg) {\n", 6394 | " if (!(key in fig.buttons)) {\n", 6395 | " continue;\n", 6396 | " }\n", 6397 | " fig.buttons[key].disabled = !msg[key];\n", 6398 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 6399 | " }\n", 6400 | "};\n", 6401 | "\n", 6402 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 6403 | " if (msg['mode'] === 'PAN') {\n", 6404 | " fig.buttons['Pan'].classList.add('active');\n", 6405 | " fig.buttons['Zoom'].classList.remove('active');\n", 6406 | " } else if (msg['mode'] === 'ZOOM') {\n", 6407 | " fig.buttons['Pan'].classList.remove('active');\n", 6408 | " fig.buttons['Zoom'].classList.add('active');\n", 6409 | " } else {\n", 6410 | " fig.buttons['Pan'].classList.remove('active');\n", 6411 | " fig.buttons['Zoom'].classList.remove('active');\n", 6412 | " }\n", 6413 | "};\n", 6414 | "\n", 6415 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 6416 | " // Called whenever the canvas gets updated.\n", 6417 | " this.send_message('ack', {});\n", 6418 | "};\n", 6419 | "\n", 6420 | "// A function to construct a web socket function for onmessage handling.\n", 6421 | "// Called in the figure constructor.\n", 6422 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 6423 | " return function socket_on_message(evt) {\n", 6424 | " if (evt.data instanceof Blob) {\n", 6425 | " var img = evt.data;\n", 6426 | " if (img.type !== 'image/png') {\n", 6427 | " /* FIXME: We get \"Resource interpreted as Image but\n", 6428 | " * transferred with MIME type text/plain:\" errors on\n", 6429 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 6430 | " * to be part of the websocket stream */\n", 6431 | " img.type = 'image/png';\n", 6432 | " }\n", 6433 | "\n", 6434 | " /* Free the memory for the previous frames */\n", 6435 | " if (fig.imageObj.src) {\n", 6436 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 6437 | " fig.imageObj.src\n", 6438 | " );\n", 6439 | " }\n", 6440 | "\n", 6441 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 6442 | " img\n", 6443 | " );\n", 6444 | " fig.updated_canvas_event();\n", 6445 | " fig.waiting = false;\n", 6446 | " return;\n", 6447 | " } else if (\n", 6448 | " typeof evt.data === 'string' &&\n", 6449 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 6450 | " ) {\n", 6451 | " fig.imageObj.src = evt.data;\n", 6452 | " fig.updated_canvas_event();\n", 6453 | " fig.waiting = false;\n", 6454 | " return;\n", 6455 | " }\n", 6456 | "\n", 6457 | " var msg = JSON.parse(evt.data);\n", 6458 | " var msg_type = msg['type'];\n", 6459 | "\n", 6460 | " // Call the \"handle_{type}\" callback, which takes\n", 6461 | " // the figure and JSON message as its only arguments.\n", 6462 | " try {\n", 6463 | " var callback = fig['handle_' + msg_type];\n", 6464 | " } catch (e) {\n", 6465 | " console.log(\n", 6466 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 6467 | " msg\n", 6468 | " );\n", 6469 | " return;\n", 6470 | " }\n", 6471 | "\n", 6472 | " if (callback) {\n", 6473 | " try {\n", 6474 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 6475 | " callback(fig, msg);\n", 6476 | " } catch (e) {\n", 6477 | " console.log(\n", 6478 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 6479 | " e,\n", 6480 | " e.stack,\n", 6481 | " msg\n", 6482 | " );\n", 6483 | " }\n", 6484 | " }\n", 6485 | " };\n", 6486 | "};\n", 6487 | "\n", 6488 | "function getModifiers(event) {\n", 6489 | " var mods = [];\n", 6490 | " if (event.ctrlKey) {\n", 6491 | " mods.push('ctrl');\n", 6492 | " }\n", 6493 | " if (event.altKey) {\n", 6494 | " mods.push('alt');\n", 6495 | " }\n", 6496 | " if (event.shiftKey) {\n", 6497 | " mods.push('shift');\n", 6498 | " }\n", 6499 | " if (event.metaKey) {\n", 6500 | " mods.push('meta');\n", 6501 | " }\n", 6502 | " return mods;\n", 6503 | "}\n", 6504 | "\n", 6505 | "/*\n", 6506 | " * return a copy of an object with only non-object keys\n", 6507 | " * we need this to avoid circular references\n", 6508 | " * https://stackoverflow.com/a/24161582/3208463\n", 6509 | " */\n", 6510 | "function simpleKeys(original) {\n", 6511 | " return Object.keys(original).reduce(function (obj, key) {\n", 6512 | " if (typeof original[key] !== 'object') {\n", 6513 | " obj[key] = original[key];\n", 6514 | " }\n", 6515 | " return obj;\n", 6516 | " }, {});\n", 6517 | "}\n", 6518 | "\n", 6519 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 6520 | " if (name === 'button_press') {\n", 6521 | " this.canvas.focus();\n", 6522 | " this.canvas_div.focus();\n", 6523 | " }\n", 6524 | "\n", 6525 | " // from https://stackoverflow.com/q/1114465\n", 6526 | " var boundingRect = this.canvas.getBoundingClientRect();\n", 6527 | " var x = (event.clientX - boundingRect.left) * this.ratio;\n", 6528 | " var y = (event.clientY - boundingRect.top) * this.ratio;\n", 6529 | "\n", 6530 | " this.send_message(name, {\n", 6531 | " x: x,\n", 6532 | " y: y,\n", 6533 | " button: event.button,\n", 6534 | " step: event.step,\n", 6535 | " modifiers: getModifiers(event),\n", 6536 | " guiEvent: simpleKeys(event),\n", 6537 | " });\n", 6538 | "\n", 6539 | " return false;\n", 6540 | "};\n", 6541 | "\n", 6542 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 6543 | " // Handle any extra behaviour associated with a key event\n", 6544 | "};\n", 6545 | "\n", 6546 | "mpl.figure.prototype.key_event = function (event, name) {\n", 6547 | " // Prevent repeat events\n", 6548 | " if (name === 'key_press') {\n", 6549 | " if (event.key === this._key) {\n", 6550 | " return;\n", 6551 | " } else {\n", 6552 | " this._key = event.key;\n", 6553 | " }\n", 6554 | " }\n", 6555 | " if (name === 'key_release') {\n", 6556 | " this._key = null;\n", 6557 | " }\n", 6558 | "\n", 6559 | " var value = '';\n", 6560 | " if (event.ctrlKey && event.key !== 'Control') {\n", 6561 | " value += 'ctrl+';\n", 6562 | " }\n", 6563 | " else if (event.altKey && event.key !== 'Alt') {\n", 6564 | " value += 'alt+';\n", 6565 | " }\n", 6566 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 6567 | " value += 'shift+';\n", 6568 | " }\n", 6569 | "\n", 6570 | " value += 'k' + event.key;\n", 6571 | "\n", 6572 | " this._key_event_extra(event, name);\n", 6573 | "\n", 6574 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 6575 | " return false;\n", 6576 | "};\n", 6577 | "\n", 6578 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 6579 | " if (name === 'download') {\n", 6580 | " this.handle_save(this, null);\n", 6581 | " } else {\n", 6582 | " this.send_message('toolbar_button', { name: name });\n", 6583 | " }\n", 6584 | "};\n", 6585 | "\n", 6586 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 6587 | " this.message.textContent = tooltip;\n", 6588 | "};\n", 6589 | "\n", 6590 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 6591 | "// prettier-ignore\n", 6592 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 6593 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis\", \"fa fa-square-o\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o\", \"download\"]];\n", 6594 | "\n", 6595 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\", \"webp\"];\n", 6596 | "\n", 6597 | "mpl.default_extension = \"png\";/* global mpl */\n", 6598 | "\n", 6599 | "var comm_websocket_adapter = function (comm) {\n", 6600 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 6601 | " // object with the appropriate methods. Currently this is a non binary\n", 6602 | " // socket, so there is still some room for performance tuning.\n", 6603 | " var ws = {};\n", 6604 | "\n", 6605 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 6606 | " ws.readyState = comm.kernel.ws.readyState;\n", 6607 | " function updateReadyState(_event) {\n", 6608 | " if (comm.kernel.ws) {\n", 6609 | " ws.readyState = comm.kernel.ws.readyState;\n", 6610 | " } else {\n", 6611 | " ws.readyState = 3; // Closed state.\n", 6612 | " }\n", 6613 | " }\n", 6614 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 6615 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 6616 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 6617 | "\n", 6618 | " ws.close = function () {\n", 6619 | " comm.close();\n", 6620 | " };\n", 6621 | " ws.send = function (m) {\n", 6622 | " //console.log('sending', m);\n", 6623 | " comm.send(m);\n", 6624 | " };\n", 6625 | " // Register the callback with on_msg.\n", 6626 | " comm.on_msg(function (msg) {\n", 6627 | " //console.log('receiving', msg['content']['data'], msg);\n", 6628 | " var data = msg['content']['data'];\n", 6629 | " if (data['blob'] !== undefined) {\n", 6630 | " data = {\n", 6631 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 6632 | " };\n", 6633 | " }\n", 6634 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 6635 | " ws.onmessage(data);\n", 6636 | " });\n", 6637 | " return ws;\n", 6638 | "};\n", 6639 | "\n", 6640 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 6641 | " // This is the function which gets called when the mpl process\n", 6642 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 6643 | "\n", 6644 | " var id = msg.content.data.id;\n", 6645 | " // Get hold of the div created by the display call when the Comm\n", 6646 | " // socket was opened in Python.\n", 6647 | " var element = document.getElementById(id);\n", 6648 | " var ws_proxy = comm_websocket_adapter(comm);\n", 6649 | "\n", 6650 | " function ondownload(figure, _format) {\n", 6651 | " window.open(figure.canvas.toDataURL());\n", 6652 | " }\n", 6653 | "\n", 6654 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 6655 | "\n", 6656 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 6657 | " // web socket which is closed, not our websocket->open comm proxy.\n", 6658 | " ws_proxy.onopen();\n", 6659 | "\n", 6660 | " fig.parent_element = element;\n", 6661 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 6662 | " if (!fig.cell_info) {\n", 6663 | " console.error('Failed to find cell for figure', id, fig);\n", 6664 | " return;\n", 6665 | " }\n", 6666 | " fig.cell_info[0].output_area.element.on(\n", 6667 | " 'cleared',\n", 6668 | " { fig: fig },\n", 6669 | " fig._remove_fig_handler\n", 6670 | " );\n", 6671 | "};\n", 6672 | "\n", 6673 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 6674 | " var width = fig.canvas.width / fig.ratio;\n", 6675 | " fig.cell_info[0].output_area.element.off(\n", 6676 | " 'cleared',\n", 6677 | " fig._remove_fig_handler\n", 6678 | " );\n", 6679 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 6680 | "\n", 6681 | " // Update the output cell to use the data from the current canvas.\n", 6682 | " fig.push_to_output();\n", 6683 | " var dataURL = fig.canvas.toDataURL();\n", 6684 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 6685 | " // the notebook keyboard shortcuts fail.\n", 6686 | " IPython.keyboard_manager.enable();\n", 6687 | " fig.parent_element.innerHTML =\n", 6688 | " '';\n", 6689 | " fig.close_ws(fig, msg);\n", 6690 | "};\n", 6691 | "\n", 6692 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 6693 | " fig.send_message('closing', msg);\n", 6694 | " // fig.ws.close()\n", 6695 | "};\n", 6696 | "\n", 6697 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 6698 | " // Turn the data on the canvas into data in the output cell.\n", 6699 | " var width = this.canvas.width / this.ratio;\n", 6700 | " var dataURL = this.canvas.toDataURL();\n", 6701 | " this.cell_info[1]['text/html'] =\n", 6702 | " '';\n", 6703 | "};\n", 6704 | "\n", 6705 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 6706 | " // Tell IPython that the notebook contents must change.\n", 6707 | " IPython.notebook.set_dirty(true);\n", 6708 | " this.send_message('ack', {});\n", 6709 | " var fig = this;\n", 6710 | " // Wait a second, then push the new image to the DOM so\n", 6711 | " // that it is saved nicely (might be nice to debounce this).\n", 6712 | " setTimeout(function () {\n", 6713 | " fig.push_to_output();\n", 6714 | " }, 1000);\n", 6715 | "};\n", 6716 | "\n", 6717 | "mpl.figure.prototype._init_toolbar = function () {\n", 6718 | " var fig = this;\n", 6719 | "\n", 6720 | " var toolbar = document.createElement('div');\n", 6721 | " toolbar.classList = 'btn-toolbar';\n", 6722 | " this.root.appendChild(toolbar);\n", 6723 | "\n", 6724 | " function on_click_closure(name) {\n", 6725 | " return function (_event) {\n", 6726 | " return fig.toolbar_button_onclick(name);\n", 6727 | " };\n", 6728 | " }\n", 6729 | "\n", 6730 | " function on_mouseover_closure(tooltip) {\n", 6731 | " return function (event) {\n", 6732 | " if (!event.currentTarget.disabled) {\n", 6733 | " return fig.toolbar_button_onmouseover(tooltip);\n", 6734 | " }\n", 6735 | " };\n", 6736 | " }\n", 6737 | "\n", 6738 | " fig.buttons = {};\n", 6739 | " var buttonGroup = document.createElement('div');\n", 6740 | " buttonGroup.classList = 'btn-group';\n", 6741 | " var button;\n", 6742 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 6743 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 6744 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 6745 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 6746 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 6747 | "\n", 6748 | " if (!name) {\n", 6749 | " /* Instead of a spacer, we start a new button group. */\n", 6750 | " if (buttonGroup.hasChildNodes()) {\n", 6751 | " toolbar.appendChild(buttonGroup);\n", 6752 | " }\n", 6753 | " buttonGroup = document.createElement('div');\n", 6754 | " buttonGroup.classList = 'btn-group';\n", 6755 | " continue;\n", 6756 | " }\n", 6757 | "\n", 6758 | " button = fig.buttons[name] = document.createElement('button');\n", 6759 | " button.classList = 'btn btn-default';\n", 6760 | " button.href = '#';\n", 6761 | " button.title = name;\n", 6762 | " button.innerHTML = '';\n", 6763 | " button.addEventListener('click', on_click_closure(method_name));\n", 6764 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 6765 | " buttonGroup.appendChild(button);\n", 6766 | " }\n", 6767 | "\n", 6768 | " if (buttonGroup.hasChildNodes()) {\n", 6769 | " toolbar.appendChild(buttonGroup);\n", 6770 | " }\n", 6771 | "\n", 6772 | " // Add the status bar.\n", 6773 | " var status_bar = document.createElement('span');\n", 6774 | " status_bar.classList = 'mpl-message pull-right';\n", 6775 | " toolbar.appendChild(status_bar);\n", 6776 | " this.message = status_bar;\n", 6777 | "\n", 6778 | " // Add the close button to the window.\n", 6779 | " var buttongrp = document.createElement('div');\n", 6780 | " buttongrp.classList = 'btn-group inline pull-right';\n", 6781 | " button = document.createElement('button');\n", 6782 | " button.classList = 'btn btn-mini btn-primary';\n", 6783 | " button.href = '#';\n", 6784 | " button.title = 'Stop Interaction';\n", 6785 | " button.innerHTML = '';\n", 6786 | " button.addEventListener('click', function (_evt) {\n", 6787 | " fig.handle_close(fig, {});\n", 6788 | " });\n", 6789 | " button.addEventListener(\n", 6790 | " 'mouseover',\n", 6791 | " on_mouseover_closure('Stop Interaction')\n", 6792 | " );\n", 6793 | " buttongrp.appendChild(button);\n", 6794 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 6795 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 6796 | "};\n", 6797 | "\n", 6798 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 6799 | " var fig = event.data.fig;\n", 6800 | " if (event.target !== this) {\n", 6801 | " // Ignore bubbled events from children.\n", 6802 | " return;\n", 6803 | " }\n", 6804 | " fig.close_ws(fig, {});\n", 6805 | "};\n", 6806 | "\n", 6807 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 6808 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 6809 | "};\n", 6810 | "\n", 6811 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 6812 | " // this is important to make the div 'focusable\n", 6813 | " el.setAttribute('tabindex', 0);\n", 6814 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 6815 | " // off when our div gets focus\n", 6816 | "\n", 6817 | " // location in version 3\n", 6818 | " if (IPython.notebook.keyboard_manager) {\n", 6819 | " IPython.notebook.keyboard_manager.register_events(el);\n", 6820 | " } else {\n", 6821 | " // location in version 2\n", 6822 | " IPython.keyboard_manager.register_events(el);\n", 6823 | " }\n", 6824 | "};\n", 6825 | "\n", 6826 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 6827 | " // Check for shift+enter\n", 6828 | " if (event.shiftKey && event.which === 13) {\n", 6829 | " this.canvas_div.blur();\n", 6830 | " // select the cell after this one\n", 6831 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 6832 | " IPython.notebook.select(index + 1);\n", 6833 | " }\n", 6834 | "};\n", 6835 | "\n", 6836 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 6837 | " fig.ondownload(fig, null);\n", 6838 | "};\n", 6839 | "\n", 6840 | "mpl.find_output_cell = function (html_output) {\n", 6841 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 6842 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 6843 | " // IPython event is triggered only after the cells have been serialised, which for\n", 6844 | " // our purposes (turning an active figure into a static one), is too late.\n", 6845 | " var cells = IPython.notebook.get_cells();\n", 6846 | " var ncells = cells.length;\n", 6847 | " for (var i = 0; i < ncells; i++) {\n", 6848 | " var cell = cells[i];\n", 6849 | " if (cell.cell_type === 'code') {\n", 6850 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 6851 | " var data = cell.output_area.outputs[j];\n", 6852 | " if (data.data) {\n", 6853 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 6854 | " data = data.data;\n", 6855 | " }\n", 6856 | " if (data['text/html'] === html_output) {\n", 6857 | " return [cell, data, j];\n", 6858 | " }\n", 6859 | " }\n", 6860 | " }\n", 6861 | " }\n", 6862 | "};\n", 6863 | "\n", 6864 | "// Register the function which deals with the matplotlib target/channel.\n", 6865 | "// The kernel may be null if the page has been refreshed.\n", 6866 | "if (IPython.notebook.kernel !== null) {\n", 6867 | " IPython.notebook.kernel.comm_manager.register_target(\n", 6868 | " 'matplotlib',\n", 6869 | " mpl.mpl_figure_comm\n", 6870 | " );\n", 6871 | "}\n" 6872 | ], 6873 | "text/plain": [ 6874 | "" 6875 | ] 6876 | }, 6877 | "metadata": {}, 6878 | "output_type": "display_data" 6879 | }, 6880 | { 6881 | "data": { 6882 | "text/html": [ 6883 | "" 6884 | ], 6885 | "text/plain": [ 6886 | "" 6887 | ] 6888 | }, 6889 | "metadata": {}, 6890 | "output_type": "display_data" 6891 | } 6892 | ], 6893 | "source": [ 6894 | "fig,ax=plt.subplots(1,1,figsize=(20,2))\n", 6895 | "plt.xlim(0,L)\n", 6896 | "plt.ylim(0,H)\n", 6897 | "x=np.linspace(0,L_crop,1280)\n", 6898 | "y=np.linspace(0,H,128)\n", 6899 | "xx,yy=np.meshgrid(x,y)\n", 6900 | "\n", 6901 | "ani=FuncAnimation(fig,animate,count,interval=500, blit=True)\n", 6902 | "plt.show()" 6903 | ] 6904 | }, 6905 | { 6906 | "cell_type": "code", 6907 | "execution_count": null, 6908 | "id": "6ee1bad8", 6909 | "metadata": {}, 6910 | "outputs": [], 6911 | "source": [] 6912 | } 6913 | ], 6914 | "metadata": { 6915 | "kernelspec": { 6916 | "display_name": "Python 3 (ipykernel)", 6917 | "language": "python", 6918 | "name": "python3" 6919 | }, 6920 | "language_info": { 6921 | "codemirror_mode": { 6922 | "name": "ipython", 6923 | "version": 3 6924 | }, 6925 | "file_extension": ".py", 6926 | "mimetype": "text/x-python", 6927 | "name": "python", 6928 | "nbconvert_exporter": "python", 6929 | "pygments_lexer": "ipython3", 6930 | "version": "3.9.7" 6931 | } 6932 | }, 6933 | "nbformat": 4, 6934 | "nbformat_minor": 5 6935 | } 6936 | -------------------------------------------------------------------------------- /Unsteady Lid Driven Cavity- PISO, FE, Multigrid solver.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "c64e18fd", 6 | "metadata": {}, 7 | "source": [ 8 | "## Accurate up to Re 5000" 9 | ] 10 | }, 11 | { 12 | "cell_type": "code", 13 | "execution_count": 1, 14 | "id": "3ce46a23", 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "import numpy as np\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "from matplotlib import cm\n", 21 | "from matplotlib.animation import FuncAnimation \n", 22 | "import time\n", 23 | "import pyamg\n", 24 | "import scipy as sp\n", 25 | "from tqdm.notebook import tqdm" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 18, 31 | "id": "307d9daf", 32 | "metadata": {}, 33 | "outputs": [], 34 | "source": [ 35 | "%matplotlib notebook" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "id": "b7a9e6ed", 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "def u_mom(Nx,Ny,U,V,P,U0):\n", 46 | " global dx\n", 47 | " global dy\n", 48 | " global dt\n", 49 | " global v\n", 50 | " global u_wall\n", 51 | " \n", 52 | " N=Nx*Ny\n", 53 | " \n", 54 | " A=np.zeros([N,N])\n", 55 | " B=np.zeros([N,1])\n", 56 | " DX=np.zeros([N,1])\n", 57 | " \n", 58 | " b=dy/dx\n", 59 | " y=v/dy\n", 60 | " x=v*b/dx\n", 61 | " k=-dt/dy\n", 62 | " \n", 63 | " #For interior cells\n", 64 | " for j in range(2,Ny-2):\n", 65 | " for i in range(1,Nx-1):\n", 66 | " ij=i+Nx*j\n", 67 | " \n", 68 | " a1=(U[j][i]+U[j][i+1])/2\n", 69 | " a2=(U[j][i]+U[j][i-1])/2\n", 70 | " e1=(V[j][i]+V[j][i+1])/2\n", 71 | " e2=(V[j-1][i]+V[j-1][i+1])/2\n", 72 | " \n", 73 | " AE=(a1*b-2*x)*k\n", 74 | " AW=(-a2*b-2*x)*k\n", 75 | " AN=(e1-2*y)*k\n", 76 | " AS=(-e2-2*y)*k\n", 77 | " AP=(a1*b-a2*b+e1-e2+4*x+4*y)*k\n", 78 | " BP=2*b*(P[j][i]-P[j][i+1])*k\n", 79 | " D=dy/AP\n", 80 | " \n", 81 | " A[ij][ij-1]=AW\n", 82 | " A[ij][ij]=AP\n", 83 | " A[ij][ij+1]=AE\n", 84 | " A[ij][ij+Nx]=AN\n", 85 | " A[ij][ij-Nx]=AS\n", 86 | " B[ij]=BP\n", 87 | " DX[ij]=D\n", 88 | " \n", 89 | " #for bottom wall\n", 90 | " for i in range(1,Nx-1):\n", 91 | " j=1\n", 92 | " ij=i+Nx\n", 93 | " \n", 94 | " a1=(U[j][i]+U[j][i+1])/2\n", 95 | " a2=(U[j][i]+U[j][i-1])/2\n", 96 | " e1=(V[j][i]+V[j][i+1])/2\n", 97 | " \n", 98 | " AE=(a1*b-2*x)*k\n", 99 | " AW=(-a2*b-2*x)*k\n", 100 | " AN=(e1-2*y)*k\n", 101 | " AP=(a1*b-a2*b+e1+6*y+4*x)*k\n", 102 | " BP=(2*b*(P[j][i]-P[j][i+1]))*k\n", 103 | " D=dy/AP\n", 104 | " \n", 105 | " A[ij][ij-1]=AW\n", 106 | " A[ij][ij]=AP\n", 107 | " A[ij][ij+1]=AE\n", 108 | " A[ij][ij+Nx]=AN\n", 109 | " B[ij]=BP\n", 110 | " DX[ij]=D\n", 111 | " \n", 112 | " #for top wall\n", 113 | " for i in range(1,Nx-1):\n", 114 | " ij=i+(Ny-2)*Nx\n", 115 | " j=Ny-2\n", 116 | " \n", 117 | " a1=(U[j][i]+U[j][i+1])/2\n", 118 | " a2=(U[j][i]+U[j][i-1])/2\n", 119 | " e2=(V[j-1][i]+V[j-1][i+1])/2\n", 120 | " \n", 121 | " AE=(a1*b-2*x)*k\n", 122 | " AW=(-a2*b-2*x)*k\n", 123 | " AS=(-e2-2*y)*k\n", 124 | " AP=(a1*b-a2*b-e2+6*y+4*x)*k\n", 125 | " BP=(2*b*(P[j][i]-P[j][i+1])-4*y*u_wall)*k\n", 126 | " D=dy/AP\n", 127 | " \n", 128 | " A[ij][ij-1]=AW\n", 129 | " A[ij][ij]=AP\n", 130 | " A[ij][ij+1]=AE\n", 131 | " A[ij][ij-Nx]=AS\n", 132 | " B[ij]=BP\n", 133 | " DX[ij]=D\n", 134 | " \n", 135 | " #for left and right cell\n", 136 | " for j in range(1,Ny-1):\n", 137 | " i=0\n", 138 | " ij=i+Nx*j\n", 139 | " A[ij][ij]=1\n", 140 | " \n", 141 | " i=Nx-1\n", 142 | " ij=i+Nx*j\n", 143 | " A[ij][ij]=1\n", 144 | " \n", 145 | " #for top and bottom cell\n", 146 | " for i in range(Nx):\n", 147 | " j=0\n", 148 | " ij=i+Nx*j\n", 149 | " A[ij][ij]=1\n", 150 | " B[ij]=-U[j+1][i]\n", 151 | " \n", 152 | " j=Ny-1\n", 153 | " ij=i+Nx*j\n", 154 | " A[ij][ij]=1\n", 155 | " B[ij]=-U[j-1][i]+2*u_wall\n", 156 | " \n", 157 | " Un=np.matmul(A,np.reshape(U,[N,1]))+B+np.reshape(U0,[N,1])\n", 158 | " \n", 159 | " U_mesh=[]\n", 160 | " row=[]\n", 161 | " count=1\n", 162 | "\n", 163 | " for i in Un:\n", 164 | " if count%Nx!=0:\n", 165 | " row.append(float(i))\n", 166 | " else:\n", 167 | " row.append(float(i))\n", 168 | " U_mesh.append(row)\n", 169 | " row=[]\n", 170 | " count+=1\n", 171 | " \n", 172 | " U_mesh=np.array(U_mesh)\n", 173 | " \n", 174 | " DX_mesh=[]\n", 175 | " row=[]\n", 176 | " count=1\n", 177 | "\n", 178 | " for i in DX:\n", 179 | " if count%Nx!=0:\n", 180 | " row.append(float(i))\n", 181 | " else:\n", 182 | " row.append(float(i))\n", 183 | " DX_mesh.append(row)\n", 184 | " row=[]\n", 185 | " count+=1\n", 186 | " \n", 187 | " DX_mesh=np.array(DX_mesh)\n", 188 | " \n", 189 | " return U_mesh,DX_mesh " 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 3, 195 | "id": "1d540824", 196 | "metadata": {}, 197 | "outputs": [], 198 | "source": [ 199 | "def v_mom(Nx,Ny,U,V,P,V0):\n", 200 | " global dx\n", 201 | " global dy\n", 202 | " global v\n", 203 | " global dt\n", 204 | " global u_wall\n", 205 | " \n", 206 | " N=Nx*Ny\n", 207 | " \n", 208 | " A=np.zeros([N,N])\n", 209 | " B=np.zeros([N,1])\n", 210 | " DY=np.zeros([N,1])\n", 211 | " \n", 212 | " b=dy/dx\n", 213 | " y=v/dy\n", 214 | " x=v*b/dx\n", 215 | " k=-dt/dy\n", 216 | " \n", 217 | " #For interior cells\n", 218 | " for j in range(1,Ny-1):\n", 219 | " for i in range(2,Nx-2):\n", 220 | " ij=i+Nx*j\n", 221 | " \n", 222 | " a1=(U[j][i]+U[j+1][i])/2\n", 223 | " a2=(U[j][i-1]+U[j+1][i-1])/2\n", 224 | " e1=(V[j][i]+V[j+1][i])/2\n", 225 | " e2=(V[j][i]+V[j-1][i])/2\n", 226 | " \n", 227 | " AE=(a1*b-2*x)*k\n", 228 | " AW=(-a2*b-2*x)*k\n", 229 | " AN=(e1-2*y)*k\n", 230 | " AS=(-e2-2*y)*k\n", 231 | " AP=(a1*b-a2*b+e1-e2+4*x+4*y)*k\n", 232 | " BP=2*k*(P[j][i]-P[j+1][i])\n", 233 | " D=dx/AP\n", 234 | " \n", 235 | " A[ij][ij-1]=AW\n", 236 | " A[ij][ij]=AP\n", 237 | " A[ij][ij+1]=AE\n", 238 | " A[ij][ij+Nx]=AN\n", 239 | " A[ij][ij-Nx]=AS\n", 240 | " B[ij]=BP\n", 241 | " DY[ij]=D\n", 242 | " \n", 243 | " #for left cell\n", 244 | " for j in range(1,Ny-1):\n", 245 | " i=1\n", 246 | " ij=j*Nx+i\n", 247 | " \n", 248 | " a1=(U[j][i]+U[j+1][i])/2\n", 249 | " e1=(V[j][i]+V[j+1][i])/2\n", 250 | " e2=(V[j][i]+V[j-1][i])/2\n", 251 | " \n", 252 | " AE=(a1*b-2*x)*k\n", 253 | " AN=(e1-2*y)*k\n", 254 | " AS=(-e2-2*y)*k\n", 255 | " AP=(a1*b+e1-e2+4*y+6*x)*k\n", 256 | " BP=2*k*(P[j][i]-P[j+1][i])\n", 257 | " D=dx/AP\n", 258 | " \n", 259 | " A[ij][ij-Nx]=AS\n", 260 | " A[ij][ij]=AP\n", 261 | " A[ij][ij+1]=AE\n", 262 | " A[ij][ij+Nx]=AN\n", 263 | " B[ij]=BP\n", 264 | " DY[ij]=D\n", 265 | " \n", 266 | " #for right cell\n", 267 | " for j in range(1,Ny-1):\n", 268 | " i=Nx-2\n", 269 | " ij=j*Nx+Nx-2\n", 270 | " \n", 271 | " a2=(U[j][i-1]+U[j+1][i-1])/2\n", 272 | " e1=(V[j][i]+V[j+1][i])/2\n", 273 | " e2=(V[j][i]+V[j-1][i])/2\n", 274 | " \n", 275 | " AW=(-a2*b-2*x)*k\n", 276 | " AN=(e1-2*y)*k\n", 277 | " AS=(-e2-2*y)*k\n", 278 | " AP=(-a2*b+e1-e2+4*y+6*x)*k\n", 279 | " BP=2*k*(P[j][i]-P[j+1][i])\n", 280 | " D=dx/AP\n", 281 | " \n", 282 | " A[ij][ij-Nx]=AS\n", 283 | " A[ij][ij]=AP\n", 284 | " A[ij][ij-1]=AW\n", 285 | " A[ij][ij+Nx]=AN\n", 286 | " B[ij]=BP\n", 287 | " DY[ij]=D\n", 288 | " \n", 289 | " #for top and bottom cell \n", 290 | " for i in range(Nx):\n", 291 | " j=0\n", 292 | " ij=i+Nx*j\n", 293 | " A[ij][ij]=1\n", 294 | " \n", 295 | " j=Ny-1\n", 296 | " ij=i+Nx*j\n", 297 | " A[ij][ij]=1\n", 298 | " \n", 299 | " #for left and right cell (external)\n", 300 | " \n", 301 | " for j in range(Ny):\n", 302 | " i=0\n", 303 | " ij=i+Nx*j\n", 304 | " A[ij][ij]=1\n", 305 | " B[ij]=-V[j][i+1]\n", 306 | " \n", 307 | " i=Nx-1\n", 308 | " ij=i+Nx*j\n", 309 | " A[ij][ij]=1\n", 310 | " B[ij]=-V[j][i-1]\n", 311 | " \n", 312 | " Vn=np.matmul(A,np.reshape(V,[N,1]))+B+np.reshape(V0,[N,1])\n", 313 | " \n", 314 | " V_mesh=[]\n", 315 | " row=[]\n", 316 | " count=1\n", 317 | "\n", 318 | " for i in Vn:\n", 319 | " if count%Nx!=0:\n", 320 | " row.append(float(i))\n", 321 | " else:\n", 322 | " row.append(float(i))\n", 323 | " V_mesh.append(row)\n", 324 | " row=[]\n", 325 | " count+=1\n", 326 | " \n", 327 | " V_mesh=np.array(V_mesh)\n", 328 | " \n", 329 | " DY_mesh=[]\n", 330 | " row=[]\n", 331 | " count=1\n", 332 | "\n", 333 | " for i in DY:\n", 334 | " if count%Nx!=0:\n", 335 | " row.append(float(i))\n", 336 | " else:\n", 337 | " row.append(float(i))\n", 338 | " DY_mesh.append(row)\n", 339 | " row=[]\n", 340 | " count+=1\n", 341 | " \n", 342 | " DY_mesh=np.array(DY_mesh)\n", 343 | " \n", 344 | " for j in range(Ny):\n", 345 | " i=0\n", 346 | " V_mesh[j][i]=-V_mesh[j][i+1]\n", 347 | " i=Nx-1\n", 348 | " V_mesh[j][i]=-V_mesh[j][i-1]\n", 349 | " \n", 350 | " return V_mesh,DY_mesh" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 4, 356 | "id": "99621a77", 357 | "metadata": {}, 358 | "outputs": [], 359 | "source": [ 360 | "def cont(Nx,Ny,U,V,P,DX,DY):\n", 361 | " global b\n", 362 | " global u_wall\n", 363 | " \n", 364 | " N=Nx*Ny\n", 365 | " \n", 366 | " A=np.zeros([N,N])\n", 367 | " B=np.zeros([N,1])\n", 368 | " \n", 369 | " #For interior cells\n", 370 | " for j in range(1,Ny-1):\n", 371 | " for i in range(1,Nx-1):\n", 372 | " ij=i+Nx*j\n", 373 | " \n", 374 | " ue=U[j][i]\n", 375 | " uw=U[j][i-1]\n", 376 | " vn=V[j][i]\n", 377 | " vs=V[j-1][i]\n", 378 | " \n", 379 | " AE=-b*DX[j][i]\n", 380 | " AW=-b*DX[j][i-1]\n", 381 | " AN=-DY[j][i]\n", 382 | " AS=-DY[j-1][i]\n", 383 | " AP=-AE-AW-AN-AS\n", 384 | " BP=b*(uw-ue)+vs-vn\n", 385 | " \n", 386 | " A[ij][ij-1]=AW\n", 387 | " A[ij][ij+1]=AE\n", 388 | " A[ij][ij]=AP\n", 389 | " A[ij][ij+Nx]=AN\n", 390 | " A[ij][ij-Nx]=AS\n", 391 | " B[ij]=BP\n", 392 | " \n", 393 | " #for top and bottom cells\n", 394 | " for i in range(Nx):\n", 395 | " j=0\n", 396 | " ij=i+Nx*j\n", 397 | " A[ij][ij]=1\n", 398 | " j=Ny-1\n", 399 | " ij=i+Nx*j\n", 400 | " A[ij][ij]=1\n", 401 | " \n", 402 | " #for left and right cell\n", 403 | " for j in range(Ny):\n", 404 | " i=0\n", 405 | " ij=i+Nx*j\n", 406 | " A[ij][ij]=1\n", 407 | " i=Nx-1\n", 408 | " ij=i+Nx*j\n", 409 | " A[ij][ij]=1\n", 410 | "\n", 411 | " A=sp.sparse.csr_matrix(A)\n", 412 | " ml=pyamg.ruge_stuben_solver(A)\n", 413 | " PC=ml.solve(B,tol=1E-4)\n", 414 | " \n", 415 | " PC_mesh=[]\n", 416 | " row=[]\n", 417 | " count=1\n", 418 | " \n", 419 | " for i in PC:\n", 420 | " if count%Nx!=0:\n", 421 | " row.append(float(i))\n", 422 | " else:\n", 423 | " row.append(float(i))\n", 424 | " PC_mesh.append(row)\n", 425 | " row=[]\n", 426 | " count+=1\n", 427 | " \n", 428 | " PC_mesh=np.array(PC_mesh)\n", 429 | " \n", 430 | " return PC_mesh,B" 431 | ] 432 | }, 433 | { 434 | "cell_type": "code", 435 | "execution_count": 5, 436 | "id": "c76d9917", 437 | "metadata": {}, 438 | "outputs": [], 439 | "source": [ 440 | "def correct(uNx,uNy,vNx,vNy,pNx,pNy,U_mesh,V_mesh,P_mesh,PC_mesh,DX,DY,av,ap):\n", 441 | " \n", 442 | " #U correction\n", 443 | " for j in range(uNy):\n", 444 | " for i in range(uNx):\n", 445 | " uc=av*DX[j][i]*(PC_mesh[j][i]-PC_mesh[j][i+1])\n", 446 | " U_mesh[j][i]=uc+U_mesh[j][i]\n", 447 | " \n", 448 | " for i in range(uNx):\n", 449 | " j=0\n", 450 | " U_mesh[j][i]=-U_mesh[j+1][i]\n", 451 | " j=uNy-1\n", 452 | " U_mesh[j][i]=-U_mesh[j-1][i]+2*u_wall\n", 453 | "\n", 454 | " #V correction\n", 455 | " for j in range(vNy):\n", 456 | " for i in range(vNx):\n", 457 | " vc=av*DY[j][i]*(PC_mesh[j][i]-PC_mesh[j+1][i])\n", 458 | " V_mesh[j][i]=vc+V_mesh[j][i]\n", 459 | " \n", 460 | " for j in range(vNy):\n", 461 | " i=0\n", 462 | " V_mesh[j][i]=-V_mesh[j][i+1]\n", 463 | " i=vNx-1\n", 464 | " V_mesh[j][i]=-V_mesh[j][i-1]\n", 465 | " \n", 466 | " \n", 467 | " #P correction\n", 468 | " for j in range(pNy):\n", 469 | " for i in range(pNx):\n", 470 | " P_mesh[j][i]=P_mesh[j][i]+ap*PC_mesh[j][i]\n", 471 | "\n", 472 | " for i in range(pNx):\n", 473 | " j=0\n", 474 | " P_mesh[j][i]=P_mesh[j+1][i]\n", 475 | " j=pNy-1\n", 476 | " P_mesh[j][i]=P_mesh[j-1][i]\n", 477 | " \n", 478 | " for j in range(pNy):\n", 479 | " i=0\n", 480 | " P_mesh[j][i]=P_mesh[j][i+1]\n", 481 | " i=pNx-1\n", 482 | " P_mesh[j][i]=P_mesh[j][i-1]\n", 483 | " \n", 484 | " return U_mesh,V_mesh,P_mesh" 485 | ] 486 | }, 487 | { 488 | "cell_type": "code", 489 | "execution_count": 6, 490 | "id": "a8691572", 491 | "metadata": {}, 492 | "outputs": [], 493 | "source": [ 494 | "def curl(U,V,dx,dy,Nx,Ny):\n", 495 | " C=np.zeros([Ny+1,Nx+1])\n", 496 | " \n", 497 | " for j in range(1,Ny-1):\n", 498 | " for i in range(1,Nx-1):\n", 499 | " dvdx=(V[j+1][i]-V[j-1][i])/(2*dx)\n", 500 | " dudy=(U[j][i+1]-U[j][i-1])/(2*dy) \n", 501 | " C[j][i]=dvdx-dudy\n", 502 | " \n", 503 | " return C" 504 | ] 505 | }, 506 | { 507 | "cell_type": "code", 508 | "execution_count": 7, 509 | "id": "3ece4c59", 510 | "metadata": {}, 511 | "outputs": [], 512 | "source": [ 513 | "def round_off(x):\n", 514 | " return float(('{:g}'.format(float('{:.5g}'.format(x)))))" 515 | ] 516 | }, 517 | { 518 | "cell_type": "markdown", 519 | "id": "d03eac22", 520 | "metadata": {}, 521 | "source": [ 522 | "# Initialisation" 523 | ] 524 | }, 525 | { 526 | "cell_type": "code", 527 | "execution_count": 8, 528 | "id": "0bec6adb", 529 | "metadata": { 530 | "scrolled": false 531 | }, 532 | "outputs": [], 533 | "source": [ 534 | "u_wall=4\n", 535 | "L=1\n", 536 | "H=1\n", 537 | "v=0.001\n", 538 | "Re=u_wall*L/v\n", 539 | "av=0.7\n", 540 | "ap=1E-3\n", 541 | "\n", 542 | "Nx=95\n", 543 | "Ny=95\n", 544 | "N=Nx*Ny\n", 545 | "uNx=Nx+1\n", 546 | "uNy=Ny+2\n", 547 | "vNx=Nx+2\n", 548 | "vNy=Ny+1\n", 549 | "pNx=Nx+2\n", 550 | "pNy=Ny+2\n", 551 | "\n", 552 | "dx=L/Nx\n", 553 | "dy=H/Ny\n", 554 | "b=dy/dx\n", 555 | "iter_count=1\n", 556 | "iter_total=0\n", 557 | "itermax=3\n", 558 | "\n", 559 | "dt=0.32E-3 #MAX dt=0.0045s @ dx=0.02m, u_inlet=2, Re=400\n", 560 | "end_time=4.8\n", 561 | "flow_time=0\n", 562 | "time_step=0\n", 563 | "#time_steps=100\n", 564 | "time_steps=end_time/dt\n", 565 | "\n", 566 | "U_time_data=[]\n", 567 | "V_time_data=[]\n", 568 | "U_res_list=[]\n", 569 | "V_res_list=[]\n", 570 | "Cont_res_list=[]\n", 571 | "t_list=[]\n", 572 | "\n", 573 | "U=[0]*(uNx*uNy)\n", 574 | "V=[0]*(vNx*vNy)\n", 575 | "P=[0]*(pNx*pNy)\n", 576 | "\n", 577 | "#Convert arrays to 2D\n", 578 | "U_mesh=[]\n", 579 | "row=[]\n", 580 | "count=1\n", 581 | "\n", 582 | "for i in U:\n", 583 | " if count%(uNx)!=0:\n", 584 | " row.append(float(i))\n", 585 | " else:\n", 586 | " row.append(float(i))\n", 587 | " U_mesh.append(row)\n", 588 | " row=[]\n", 589 | " count+=1\n", 590 | " \n", 591 | "U_mesh=np.array(U_mesh)\n", 592 | "\n", 593 | "V_mesh=[]\n", 594 | "row=[]\n", 595 | "count=1\n", 596 | "\n", 597 | "for i in V:\n", 598 | " if count%vNx!=0:\n", 599 | " row.append(float(i))\n", 600 | " else:\n", 601 | " row.append(float(i))\n", 602 | " V_mesh.append(row)\n", 603 | " row=[]\n", 604 | " count+=1\n", 605 | " \n", 606 | "V_mesh=np.array(V_mesh)\n", 607 | "\n", 608 | "P_mesh=[]\n", 609 | "row=[]\n", 610 | "count=1\n", 611 | "\n", 612 | "for i in P:\n", 613 | " if count%pNx!=0:\n", 614 | " row.append(float(i))\n", 615 | " else:\n", 616 | " row.append(float(i))\n", 617 | " P_mesh.append(row)\n", 618 | " row=[]\n", 619 | " count+=1\n", 620 | " \n", 621 | "P_mesh=np.array(P_mesh)" 622 | ] 623 | }, 624 | { 625 | "cell_type": "markdown", 626 | "id": "9d779851", 627 | "metadata": {}, 628 | "source": [ 629 | "# Solution" 630 | ] 631 | }, 632 | { 633 | "cell_type": "code", 634 | "execution_count": 9, 635 | "id": "85afe61d", 636 | "metadata": { 637 | "scrolled": true 638 | }, 639 | "outputs": [ 640 | { 641 | "name": "stdout", 642 | "output_type": "stream", 643 | "text": [ 644 | "Calculation Data\n", 645 | "Number of cells: 9025\n", 646 | "Aspect Ratio: 1.0\n", 647 | "Total time steps: 14999\n", 648 | "\n", 649 | "Reynold's Number: 4000.0\n", 650 | "\n" 651 | ] 652 | }, 653 | { 654 | "data": { 655 | "application/vnd.jupyter.widget-view+json": { 656 | "model_id": "fb821fe593e648079d0b2798af842c05", 657 | "version_major": 2, 658 | "version_minor": 0 659 | }, 660 | "text/plain": [ 661 | "Progress bar: 0%| | 0/14999 [00:00=5000:\n", 18149 | " itermax=1\n", 18150 | " \n", 18151 | " if iter_count==1:\n", 18152 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 18153 | " abs(V_residual),abs(P_residual),\n", 18154 | " str(round(t,4))))\n", 18155 | " \n", 18156 | " if abs(U_residual)<1E-3 and abs(V_residual)<1E-3 and abs(P_residual)<1E-4: \n", 18157 | " U0_mesh=U_mesh\n", 18158 | " V0_mesh=V_mesh\n", 18159 | " P0=P_mesh\n", 18160 | " iter_total+=1\n", 18161 | " break\n", 18162 | "\n", 18163 | " if iter_count==itermax:\n", 18164 | " U0_mesh=U_mesh\n", 18165 | " V0_mesh=V_mesh\n", 18166 | " P0=P_mesh\n", 18167 | " iter_total+=1\n", 18168 | " break\n", 18169 | " \n", 18170 | " iter_count+=1\n", 18171 | " iter_total+=1\n", 18172 | "\n", 18173 | " e=time.time()\n", 18174 | " elap=e-s\n", 18175 | " if iter_count!=1:\n", 18176 | " print('{:^10}|{:^12}|{:^18}|{:^18}|{:^17}|{:^9}'.format(str(time_step),str(iter_count),abs(U_residual),\n", 18177 | " abs(V_residual),abs(P_residual),\n", 18178 | " str(round(elap,4))))\n", 18179 | " print()\n", 18180 | " \n", 18181 | "et=time.time()\n", 18182 | "elapsed=et-st\n", 18183 | "print()\n", 18184 | "print('Execution time:',round(elapsed,3),'seconds')\n", 18185 | "print('Number of iterations:',iter_total)\n", 18186 | "print('Average time per iteration:',round(np.mean(t_list),4),'+-',round(np.std(t_list),4),'seconds')\n", 18187 | "print('Flow time:',round(flow_time,4),'seconds')" 18188 | ] 18189 | }, 18190 | { 18191 | "cell_type": "markdown", 18192 | "id": "8e2533eb", 18193 | "metadata": {}, 18194 | "source": [ 18195 | "# Post-Processing" 18196 | ] 18197 | }, 18198 | { 18199 | "cell_type": "code", 18200 | "execution_count": 10, 18201 | "id": "7be5d367", 18202 | "metadata": {}, 18203 | "outputs": [], 18204 | "source": [ 18205 | "U_final=np.zeros([Ny+1,Nx+1])\n", 18206 | "for j in range(Ny+1):\n", 18207 | " for i in range(Nx+1):\n", 18208 | " U_final[j][i]=(U_mesh[j][i]+U_mesh[j+1][i])/2\n", 18209 | " \n", 18210 | "V_final=np.zeros([Ny+1,Nx+1])\n", 18211 | "for j in range(Ny+1):\n", 18212 | " for i in range(Nx+1):\n", 18213 | " V_final[j][i]=(V_mesh[j][i]+V_mesh[j][i+1])/2\n", 18214 | " \n", 18215 | "V_mag=np.zeros([Ny+1,Nx+1])\n", 18216 | "V_mag=(np.sqrt(np.power(U_final,2))+np.sqrt(np.power(V_final,2)))\n", 18217 | " \n", 18218 | "P_final=np.zeros([Ny+1,Nx+1])\n", 18219 | "for j in range(Ny+1):\n", 18220 | " for i in range(Nx+1):\n", 18221 | " P_final[j][i]=(P_mesh[j][i]+P_mesh[j][i+1]+P_mesh[j+1][i]+P_mesh[j+1][i+1])/4\n", 18222 | " \n", 18223 | "vort=curl(U_final,V_final,dx,dy,Nx,Ny)\n", 18224 | " \n", 18225 | "x=np.linspace(0,L,Nx+1)\n", 18226 | "y=np.linspace(0,H,Ny+1)\n", 18227 | "xx,yy=np.meshgrid(x,y)\n", 18228 | "\n", 18229 | "empty=np.zeros([Ny,Nx])\n", 18230 | "xe=np.linspace(0,Nx,Nx)\n", 18231 | "ye=np.linspace(0,Ny,Ny)\n", 18232 | "xxe,yye=np.meshgrid(xe,ye)" 18233 | ] 18234 | }, 18235 | { 18236 | "cell_type": "code", 18237 | "execution_count": 11, 18238 | "id": "abc44c3a", 18239 | "metadata": { 18240 | "scrolled": false 18241 | }, 18242 | "outputs": [ 18243 | { 18244 | "name": "stderr", 18245 | "output_type": "stream", 18246 | "text": [ 18247 | "C:\\Users\\Kangluo See\\anaconda3\\lib\\site-packages\\matplotlib\\patches.py:3027: RuntimeWarning: invalid value encountered in double_scalars\n", 18248 | " cos_t, sin_t = head_length / head_dist, head_width / head_dist\n" 18249 | ] 18250 | }, 18251 | { 18252 | "data": { 18253 | "image/png": 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NgYEHDQ1w48WI1dDwU0MmudaTVZNSNZvItOIiCkumUcMG0mlPFBMwiWJSz2bSyCZAFIggiRLW6zBwIzGRukmUCBpRJBLpVgAXphYDLxKJsNpFqMEgzcozgSgRatFJA6KxPJN6NLyARCcMSCQhBAbSniQx0dQVsSdOidcCNHWmYSJxIxx1QBJFIGIvsN02jKAWg3AsX2IiGgTJTZzUVW+JdjJRwmhJ7dT465LyIg36T9VfHLwEEQyrjj+hTI1f4gRH9QxCqL+wAnT1jNzWNSyQJh2JF7Ug0LAXBgIDE7f13E00stHwoOFF4KGOFWxkAqb1zIO4Ma1Jw36zg3hiv9U+bF/h4hsO4N/40ShmWuyb2F4kaHQq+p+gHba96apVNdx7bz7p6VE++cRR4GBl1xbDu5/DO+PhtodhwVKUMDJZ9GK/3yaxb+6DV+HYY+GwXrBpKpSUhFM0CDuOEQ4bmM8L46t54YVSxwUsoIs9qizrt8tKPsc5tMmDi6+EEYcqzvrbX+HbD+C5TVASAGmDjQ04DhDKy4Pdu8OgbrBLB+iXDWvqBBmawYaNGawPwHo//BFRQafKoirV2MNzgFCuO5uOns508EQpdIfp4oZCHQoNSb4mqBQR+loTnrDAZyl+cjAIECKAST1hSglTSpSIlRcmTAA1kUepI4SJJEyIKJIwYaJECaHjR0LCB6t+z0spJkysQ0J5U3WSJwRPkkA9uTzVBJLcpqm6LSnblnrbh1LdR6q8Pzcm0/mRpuw/vrVMvG5tinrJfdk9NtwKPrHesITz5Pp2mZNTT6Oen3iIfbmCCianHMufIcEOnEz/BrTD7j0SiXD++TP56KN9yc6O8Npr1lDsd8Li3F96CWb9Dv+6ApAw/gWYNgv1Ldijtzl1B9cx8UP48ls49RR49lnIynIxaZKLuXPBMHwMGgS77w4dOkQ5+GCTuroI55/vZ9y4HH78MZ+bb65k2rR6GoJOcmSSCDaQdOpk8MEHXXnuOT/HH+OmtDQOMAjiHJd91KF9dzh+TzjhEFi1GbbUwLwl8O1PsGQzhJ3tHNwOBuTmwW6F0K8Qemer1CkNTAE1JhSFBBsDOmtqdJZH4VcJpTpsiUC5gLBD7AZguNzohonbGwUEhqGjWwtVHQM3QcDt0EGpbUrc1mTi5H5Uudmg3JnvLHOWJ9dJBhf10eq40HEjcaFjoGFYNQ00NDSLhwIdDd3i1zwYaAi8RNHQiGDiw2OtizUMIEiYdHxWnkCgESaMDw86wsoRRJEYVo6GIEwED26rhvrnp4YsMhDWql0gqKSKNuTEzgVQQSVtyLXO7DethmyyEvLChHET3/o8SBBP0kRaRTXZ1nYg9t51W6ggn5yEeqVUxvJsHqmKGrLJtLgulRckhAt3LC9IGBe6xZup3GrqyMBnreUkUSRV1JKBjyiSKFGiSCoI4MNDhKjFf5rUEUCiEyJCiAg6EeoJUU+QaiLUEiC8lcCtY5JJDR6CuC1OsIoQGpIsctR7tx1nv/91HckOC9ooRGcJ9+Pzdea993bnm28ETz5pyVttnYm92Ldozz3gygugZw944S14+1MIBkn9QiSJsjq0hRsvhkvPUSJp3VrQbCqGDn1I4HT69YV774U1a9SxtNTmZvwoQEk+Vxd7/PFOfPRRNT/9FEahm4tEDsb66YP9DoQbrwafD97/Hj74GUoqieuJXCgLJ5vbMGBAL9ivN+zVAwa1h5owTFoLm0OwrBqW18KGiMX12ByKU3Rmi8Gd3Ask6nMgCVysD9MwcXutPMPK0xKBwQkKbkwy0MgF0tHJQicbk3R0Mi1hRh5u0hD40PChU02YrnjxoONFwwWsp47OlqmXsCayzQTIwU0YkzBRwkQJYCKBCFGihKkjjI5mTViSCFH8BDEsgZjiqSR+whhoVo76P4gZAwqVJ4lgWiOQsXwldiMhT/3GMcmqiTZqjT0aayEcQrLEPGLtif0GiCSt3uM1WkYmegpbfRviGp6bGA7Nj4iV2ucRCzQ1q8QWUNl5Eh3dAln7qH4bFvALC/QhHQMdD96YIEunDS6ycJGNgYkkHYMSAqzBzzrqmUsV6wgkGNj4qI8taDKpIY8y0qgnh0ryKGNfDiLABm5hJz6790T4l5glpRyyVQ8yBXUXQt62lW3Ohe1y7b8D7WAguRlog8uVzyuv7MbKlYIHHpD4/dYknAwo1nfUtgtcdBrsMwTKq+Hjb+Grn6AmNfecACo92sIvn0N7a1+6DUVw3yPwzvtQXkSc65cwciTccw/cfjt88klTYKLyZs/emcGDlyClzxq0T91HDFSUfvHWu2HYULjoFlhdRKKhQRKADO4PJ+4Dh+8Kk1fA0lKYvh4WVEBEo6GyPxkwnIDizMPRhvizjelncJQZkkwB7Twm7XRo74lQoAnaagKvHqWbppOnQ44QaEJSb0nFa4RJNZItmIRFlFoktUSpw6QSkxBQT5QwQeqR1BMlQJQwEYJIwo6JMpWIyN2ISEoN/e8oemo5pRL5NEbJANMUhVKIjbbm2k1dK1XfjfVlRpu4RiR1mQC6GtAbgz4Y7Km56I6LKdTzGlsoIxRzJ/BRTy6VMQBpSwmFbGYQbWjPfowhzAf3nr7dgKSHEPKurWxzxj8ISHagWE8pUiFCOFzFWWfN5uKL+zF1agE33FDLd9951dI64lK7NzhApWQ13HWP6mXQIBg9Eq59FZaugV9nwWffQVGJ41KO3cBXrYF9joSfP4XsLLhgDPTuBm++BDNnwIMPQI2li/zyS5gzBxYsACkNvvxS8MsvUebPh/nzI9TGgMtA12HFihBStkE91jTiYKLG37ELvPYa/DgVDr8AogI1advAYelOsrLg4qPghL1hWTG8vwBu/wkCkvjk7yYRQKAhoNjfoy9FHeJ5QocObuieBj080D1dVdkjHXIt/Wh1FGaGleinJOqiVEaYEY5SEoxSo4cpl5JKCZrhEFt5HWIrI3HCtrkZRWpNq8d+x19LGyxSTWBBh5itpfRXAsfWTP5OagkQ/FkAgG2bvEOBFOCQom4k3DAvGkwx5lTXiaTyaU5NlYZkLoD1PqV5gozKMHjB143nqeFLbTP2jr6ZDqMCW19SxTK6cAgQbtoMeSvpf11HsgM5ko4SxqJmuSzr2IYOHfIYP74rmzdrfPJJmO++8xKNOkRDEAcVSFhpd+0Co0bA0SMgIx2++h4++AIWLaWBfrFrZ3jsXjjOsknTQ3DeuXD5JfDyK/DcsxC21CL5+bB+PXi9EA5LgkH46qsgJ51Uis2V9Ojh4oEHOnDiiTWkEmf12RluvwWefQumzCUBOGwrsNwCuPo4OGYPeH4yvPorBGxHqiT9SAOuw1kOjXIhPXJgUCYMSIMB6dDbC5WmUpmujsDqMKwKwaoobIxAheZ4PxxgYIu+IC7+sklPAg0noMTaGKkn9ESASVG+HYBAAG4ErthRJYXNygbLg7BEL8roWGG4iJUbCMKWWE7pYZRYx7S4sbiYB4hqsddVA0JISwOjxiKjghokWSLRcqpKRskw1Ttkf6bVMkqGsO3zwIwK6qXEI5QdXNSqGzSFJVJTKRiVaEIQihiYUn0OfimViEpCxNSUK1FU9RsGwhFBSEJQQkRCMII6j1p5qXxlwknnyX+uVNaTLfW5cU78TvFsBmT4IjyZLxHeIHeLDXipIZ8yOlBEHmV0Zn0s7coELuI33nr0PBi7fTiSXkLIB7eyzfHNcCRCCC/wM3F3yvelTJSgCSHGEfeyMoD+QIGUslwIcQ1qR1SJ2j76XCnl9vJwShzrjgOSDhKuIy76scVAWUAmu+3WhvPPz2PffTN4++0gn38eZeFCexmeBCqQ6Ljkg+xsGHEIHLQf7LwTvP4OvP0+VFWlGIyDY0nzwNVXw2/T4Ycf4vkPPWRy7bUamiYoKYmy225lbNoUnyAPPzydYcMyuO02SNCHZELb9vDlO3DWDbBwDQ04EJcXLj0azjkInvoRXp9tKdkbA5CmRFjW/WNAngv2yoNh+bBHJhS6YVUAfquDlUFYFIblQQh7HP3YZDQPINAQRKAhkNiUClAa1DFM0hFkopGJIA3IQicdQTqCDDSiQFuh4UNYOhZBjYzSVRh4EbFUSpRCi9tJ8ooghCQElGCSgTLMjVh5xdIkU+pEkJbdnqRURskUGhEpMVHzYY2MopsuTGm5ciAJSNAFllExREzHBA9EpaoXNfXYWCQQiko0042TwlGJSxMIMw4wYQkeYXm/CBCmICrBZbmsxLxloup10K28qASvlWcIle8PQ46uzu1UHYZ2btWfS6jXtDQM3bzgluDRwC2gIgydk1b04agqC0ShLgSbAurzrApDZVAdi/1QG4GSOijxK0vGOsttpFEyUvy2DVBs4xWvOt7WIUQkrZ53tI0xAOlAURKQvMalTOX18RfCmL81kAggXUpZK4RwoXYnHWMFx01V/2jgGinlwUKIjlb9AVJKvxBiIvCllPI/WznMFtEO5sbsGbzG+p1lnYeZM8fPFVfU4PW6OOywHG67LY9evQy+/z7CRx9FmT5ds3ybrEnb+SLWQlUtvDtBpYJ2cNYZ8MMnMPVXeOwJpUgHGqyY6uvg3nsajvSuu6Kcd55GWprkgguq2LTJju2gwG/4cDc//6w3cK5MbwsfTICxj8DCLajdwB06kJ16wYuXwSdzYI+HwdRIcmgkNYCk4D7SdTigEA4ugP1yoV7CpxUwtQ7Gl0KJbckMcRByho5oBDxg2wAkCyjUNAqFUrq31zTy0GgjNNogyBYauWhkWKuBUkzaoFNLlGokNVKyQYRxCUGdlNQRpQ5JOSYlUlCHxG/pVuqRBKUkAASQ1Ea0rVRFK4qLchp3l4+LdkRCvVTiHZCpRTywdWKexiba7cEVJPfRWKQBSFh0JfTrqOeVkGYoR94sCdluKxkKiHbOgkNyoK0X2qbBuhronQvLK2BxOSwpgVnFsKaShlyIfQwQ9+eKgG2Q9kCFxu9p6XzuWGw6HXd1TGwfpu1Jgu1vtWX56NkCdNvfoKnX+lTgbce5AfiEEGGUrD1l6KrtQTuYI7mc+OO3zZOyHMfEPK/XzfDhuYwenUHHjgYrV0ree08wZUq0ofirETroIHj4YRg/HiZMaOlolWL9xht1rr3WoLCw0sqP+5CsWAE77QJBNwlAMv4umL4Q3vqBBADRMuHao+H4PeDCt2FBOdvEfWRnwqiOcGIHMKOwNAQ/VCrwqI/G6zUAkG3gPkABiADaC0FXXaOtEPTUNTrq0EHTaC801kVN+ug61VKyWUo2R6OsjUYJu8KUIymXUSqIUi6jVCGp3aYpv+WUSs6filLJ/m1KDRLbQQ/wZwAC/usgkTIWXHPlzV03Am4deuVB/0LoVwA9c2HXDvDLWvhsMUxap97vhO/BTumo7y0HyIez29fTLdPP5yyhG2vozHp6sYLOrKc9RfThIa5mDf8Zf8l240j6CCGf2co2h7ZA2S6E0IFZqGBST0spb2ikXhoq7mEvKWW5lTcGuAf1l/1WSnl6qrbbg/6G+qFqEsHFh3rzMgkEInzxRYgvvihH130ccEA6J5/sY/x4D19/HeHNN+uZN88WDxiOPuL044+w//7w6KMwahRcfDGUltII2V+uevPvv99k9GgDl8tHOBwHrhtugqxsCPqIW19lwpgzwHTBW9NJ4ETS0uDlC2FpOez/HIQtOW9LuQ89DY7uAMe3gz4Ziuu4bg0sC8frYCvyoZFVXfPcRydN0NcQ9NE1+rshWwgG6l6iQHE0ytqoZIkMs0FG+D0iKYpG2SSjjdtShVsm3moptRQgbGoKKGArwQIaAkYqsNganUAqoICt1zM0FUyzMbBw1tsaMEhVvjXAY52HgEVlsGhZPNttwL494cDecN+hcNd38MkC1Ptsx4ZzRsMwgBz4yC/5NjOdz61s2+HUSzrp5OClEwartuvst43K9nwhxEzH+QtWYNsYSSlNYFchRA7wkRBioJRyQYq+jgamOkAkF7VXVHegEnhPCHGGlPKNrR9m8/Q3AZL4ZL/HHoWccUZv8vMz6ds3i/LyMB6PwdKlQZYuDTBnTpDffw9SWxtm0qRqJk1yoWk+9t8/nbvvzsAwNB56KMiPP/qJgxA4b7WuDi6+2MVRR8ETT8Cll0J1zM8w+WtOfOsXLZKkpfmUrsWAYQfCbbfChk0osLCAZMgQOPFwOOgmK9/iRArawYcXw3PT4c2FNAyRYg812frKB21ccEF3OK0jTCqHu4qSwCPZNwRHf7HfDQHEDQx0wZB0k0GGxs6Gl0wB62SUVWaU5dEoH4WiLDOjlDg42MZ0IU3R1k7+TZGG4tfTha0rgXRN4DF1fELgRd1bmhB4BXgMpTj3RDXcQina3QLqo5ICXWD4QI/quIBiU9LNEOgCdKkU7Gsi0NuyStekQBOwNgzdrTybsSo3oY0el0EETaV/iMq48rs4CHmGmssjEkwJ64JQaCg9SNhScG8MQbYGoahSdIeiUBZWf7pAVCm+A2Go9aryehPqQ9bRVPqIuoDqH2ie09gG4Lh9PxjcAYqqYFONSqtLYG0FrN4MoWSAaQr0HBQCJs1S6dnv4fbj4OK94KJ3YIMt2qol/p1Y4FIrlbGE02tGx2Qo95JBezTSeIKeXHsaDNjq4E6paRtDpJS2lBuSUlYKISajdqtNBSSnkCjWOgRYLaXcAiCE+BAYBvyTgUTRFVcMZNiwdrz00mJ++WUToZAHcJGV5aJPn1z69s1hjz2yuPfe7lRVmXz5ZQ1ffFHLihXVTJ7sZ/LkUgYMyGLcuBzuusvD2LG1/PZbhMQ/sc3tRPj8c8jLc3H33YKrrorG8lOT4kB23RWqqoFM2HNv+ORtFQ8sLY0YkGRmwvNj4cTHIWwrAr3Qsx1MPA9u+AG+X0tcSdiM7qNdOlzZFQ4rgJc3wz5zoU5z1HHeXgtEV4Ua7OMRDPNoDPVKfAK+D5psjMLEQITbRZjqZqRN2wIiyaQBbYSIpXyhkY6gUBPkCJWyURZNAzSdDBH38V4cidJH15XuRErqJKyLRkkXAr8mlXePlFRGlDK6XkKFpQwPyighIBByEZKSIAJTWpZKMkokohOWIqYwNyWYkfh5VEI0EreQkgDBuGUVxOMwCkCELfCxleHWURegRy3lt/U83Bq4okrRbQhV16eBO6zKPNbfPUuAV4cMXR2FgFwXpOngs5TiWS5VnuFSfRfVQ8c01b42pBTrS6vUK1MegAq/Om6qg+oAbK6HLRWqLtCQ47COd/0AbV3QPhM6ZED7LBjaAc4dDN1ywWPApipYUQwzVsGMFbBsk/W8UnFhQRpsLbCpGC5+AfbqA59cAEc8D5ttMIk4UlhxkEsJ0BsP0hGcdB0T2YlrEJZf/nPLki/89yIhRAEQtkDEhwKHB1LUywYOAM5wZK8D9rJEXn5gODAzue12G+vfQ0fiYu+923HJJYO44ILJhMNOZViyDkWJuzp2zGDkyHyOPLIAKTUefngLU6cGY3W7d89kwoR83nrLz3PP1ZE42yYCy/ffu7n66jALFqR6FnERltcHkybDsBHQe2eY/RVkWB/m5grodJqytvrP9Up5/tF8YuDQqyvceSg8OB3+KKVxAHGY7WZ64bqecGQh3LMOPrItzrZS7+E2TPb1CA73CQ70CBZGI6w3JdPCUX4Lm1TZDu0pFOiNUVNAoqN0KJ00jQ5Co4N1TBfQU9PJtQAhJKEKSblUepOyqGRT1KQeQYUpqJSSSimpikqqpaSOxjWNjYmloAnRFLRcn9Gc7gEaF03Z1Ngja6n4q6Uiq+RrpeAs0g3Ilgpscg0V2TrXq7heXUCvTChMh8I0BUSXfgm/r03qrymxVpIYK9cLgzrDHt1gj57QpwPMWg7z1sB7U2HT5hT3C4nvtFpXcuDOcO/JcPiLVpy5HNTOHjlAW9C61XF92yAeNrOKKfRkJb1YQReK2JsPMWhHOVUUvp6Nedb20ZEMEGKrZUaDm7faGgRMIL7OmCilvFMIcQnEoq8jhDgHGCGlPCWp/R3Ayai/whzgAinl9pMtO6/1dwGSp58+gIkTV/PTT40ZFqQGFHDRu3caDzzQm2hU54YbNrFypYqb4vG4eeGFdlRWalx9dTlSptKbuBgxwuDgg11cf73za0wMxogBw4bDEYfBv5+C7AK45jy44Vyo9YPPA4NvgKH9YORgOOU/xMRVHdvAZ6fD2Z/D/BoSgzZafTs9+IUBp7WD63vAMyWKC4nYHEgLwSPNZXKUT3CcT9DDEPxmRvgmaDI5ZOLcyW1rwMNJaUAvXaOfCwqFxi66TldNI0cIIsDvkQhCQFFUUiSjbIxG2RiGzZbj4ra8dU2BBWwDYMC26zW2FTS25hqp6jWlIE++7rYoyZsCh6b6aARAYkf7eTmmsXaZMHoYnLgvIOHVb2HiZBV5O0bOT9Uh/j39QOjdGW6fQhxE8okByWlt6zmIaqbwHX1ZGlO6D+Q4sriRCbzLua+dCmdvHyDZSQj5dvPVEmiXVs/27U/DhrVnzJhpTdRwfjF2eBJlz7p8eZjjjpvL/vvn8PrrvXnllTJeemkLwWCEs89ex113teXcc9288koVcb1J/Nb/+MNkzBhPrL9kj3QyAA8cfzx8/xuQA1U6TF8Nz34Bj30Gxx4A++wMz54DXW5TdTAgLxM+Og0unwLzw/H8ZO7DzuuWAy/2hpl1MGwx1EWJs/nNgIdumBzqhbPSNfq5dD4PhbnbH2GRmThtbw14GEBfXWOQrjNI12inCQZaIqXlUaU3WRSOMiUUZk00GuNutpWaAwub/qugAS2zmGp0PE2UtQQ8UuWlABCXBrcNgjI/lAVV2lIPpQHls1FX66i/NeDg/P1nAcTRZ/FmePZjldrnwAn7w69PwB0T4NNf4/eV4CNWC2TA27/BrJGKw6+3r+14JvVEyEBD7eUTIo16dEz8vE8W17CE7SvX2kYdyT+GdiiQtGuXwbHH7kT//vn4fAaffjqCm276jblzy1LUtt/A5C8IbAD4+ecw++1XyYcf7kpVlZ/33lNfzsMPF/PTT314++1q/H6nEt4AwhQXu8jOth044nGxbIdC2zJk+H5w4ytAjuIabj0HTnkCMtvCafvD4C4wrwhKlOszaT4YPwpumQXTakmI+pugZAfwwkUd4aJ2cOUamBa06tn7BjVhbVWgwYUZgpPSBR8EIzwSCDOnNi4e3Brg6KYJ9jR09jJ0Bhs6AQlF0SjzzSg/hCPMM6Ns/hNc7HYBCkgACwGkC0jXIE2ATxOkCeW4l66po1eAx1JqZ+txZzuXqby0s/VEvUU4Cl6LCxSW8tsl4g6EUQnVJqRpaq4zTdV3eUTpNMJSie7CUVVPAIEgBKzr1UWUsrzeZynGg1BnJnFqqQClCQ5EhmFqCeR5lCK/ZzoY+dA7C9p6lIgKCQvKwGfAhmrYUAsbamD1FlhXDSW2+PSvBBEnKFvj31QGT34EE7+B+y6FS46E8+6F4joagkkYoga8NxtOHgSvLrXyHY4ctURJRyeN+hiIKBdTEzOlrrqV/gztUCBp3z6Dxx47EK9XDaNduzS2bGl6ibbvvgUMHlxIUVGAhQvrWLSoCudawDThjDPm8MMPezJv3hKWLg1TVRXmtdfKOOGENF5/vdLRbzwIldcriIGIzYXYrLRHKdb/WGUpz11w3HCYuR7uOB2O2hXyLZb7+3XEvGxfHQlfFsG3FcSdp5Kj8RqQpcMj3aFOwn7LwO8hSfSV2lR3YFqIseku+hsaL/sj7FMeIexSs5bRwuVRJ01wsGEw3KV2J/QJmB4x+TgU4d/1wZRbaDdFLQUKm2zAcAFtdcjToFAX5KWp33ko3UqhoayXcjQ14WdqIKXiMKIoD/1sXSnV603lQ7MhooDEDukRDENFBCIm+C1wCEfAbypltYnleW6BAsJSppsqX7O8xwWWtzjKk9v2FNeFapducQduoYDGkJDnBq9HKcG9GgQi0MGjlON2ikilr7CpzlTXrwxCZUh5hs+vgpeWJz/0+OGrjaTmMhz1PDp09EKnTOiUAd3SYGAv2KM9tE1X/hrrq2D2JlhdBgtKYMl6Cw+aAZFdOqrnvMTWp7QARJz9lATgvHtgv13hy0fgyOtgU61VN+mdfuVXeO8iB5BYZLhMQgTIwB3jSNwE8RBCkEmUdWxv+l8PI79DgWTOnGI++2wVo0f3wjA05s8vp6jInroSl2NpaQZvvHEYtbVBfvxxPd27Z3DQQe3JzPRwxRUzqaqKv6k1NS7GjVvCddd14sIL1Vf3ww8VXHVVewtInJtVufB4sggErOASDlEWmcQcntZVwD0TAZ9yJrzxODjqeThrHxhlrVyDEZhXBeTALUNhfRAmbCBxB0Wncl1Xsa7e6gX3l8MHNTg4EFsD3tBBsLcuuD3DTZ7wcG8gwK9+q04LwEMDhhk6+xs6o9wutkQlkyIRHg4EWWBGt0p30VLQyAl76KJDN0PQSYdOhqCjroJQ7p6rwCAUhc0mLFKb61FmqrTehLKQoCoKVVGojEJt1LFyb6loCraPQ9/WUlNirabKLc4jw4Acl/IMz9EVh9No26bG7igLBmFVEFaVpC7Xo9A5C/rmwsC2cEg36D8KAiFYXwm/roRpq2B+UcPHl5cBlx0KPQpg0lz49Df4ZT4Nfcntho3onqb8AWMeh4/vhxE3QUWNVeaYsYqrYUGxAsQNdqZLiXgjRAggYhyJh5Dl1e7DZFXKzbP+DLWKtnYwXXrp9wwf3oW0NIO7757lKLENw2HQoHxuv31PfvxxLU8++QfqK1OK9pNO6s348YM555xplvmlElf98ksRTzzRH1uEtXFjiE6dbGdFpwgri06dfGzYQKMgQiYUmxabnQMnDoMvl0CJBg/NgIMHwMBCZZmy0oRj+8Pe7WHUL8R1Ik79hmWyOyIf7i2As0tgfggVWwJi4JHsWd7FbXJbmpcemsbd/iA/ReKfcVNWVBqwn24wWvewt6Hze8Tkw1CYRwKhJgKxJ1JzoJEroE/UQz+XoKsOg9yCzrrywyiLSmaFJHURjQ0m/BBU3MKGCNQ4kWt7eH9Dy/UZLQGN5oCgpdRcPw6RVYFHmZOXhaA2qNIGZ91tBJCWOhqawJpSlb5ZHq+rmbBTexjWFa4ZDju3g5lrYfEG+GQ2rCqCSQth0h/gccFBfeCU/eD+s+HtyfDyF1DfnJGCTWEFJne9Ck9fBWfclwKMUHqgQYWwwV5/epXTaxoBsvHhtgDEDvapkUOUDKAqRW9/jnb4ZLoDaYffe1lZgGeemcf11w/m22/Xp6zTo0cW3bpl8uqrC4l/ceo4ceJyDjywI0ceWcjnn9s2hAamGWLp0mo6dNAoKopQWlpDaan9xTgDRPoYMgSm/46a9FMBiY8YV+HLhZtGwYEvqfJRg2BpDRz9M1w8AAI6nN0bTlsAZg4J3AcQi6N1QR4M9cChJZKyKOBtCB6xTaWAK71uTnB7uDfo5xtT3YfezF+vs9A42+3mKMPFV+EwH5pBxoYihJrx8LYpFXjoQD9dsKtLY5Bws5uFzQJYEobFYcmUILxaLdgQsedvgXPrpK0GDNg6c9tmQEMHjBCxQIU6lthKc+5UbvmMuEBG4v4k4agSQUX+jFFBKjWfY+z7toEzuitxWIa1zF1RBXneeKDDYj+sr4HielhfBxvrIZAsh9wGEGlADtFVFMWFzF8Hz09RZe1z4MiB8MQZ0C4LvpkHb0yGJRvh69kqZbrgwhHw6+Pw6TQY/wGUVikx4Q1nwsc/W6KwFH/Pz6fCIXvBsfvAh78mFRqwaDMMaA9friHG8RuGidsKo+khGNORKMpB2rucbq9FQivteCABePbZ+Vx00c401OEqriQ/30durof6+lR/+TDjx8/lsst2cQAJgAspIS1NWWN16ZJPerpOPGS9fYRRx8DDz9M4iGQQ03uMHQEvz4FKA3Jz4M594eBvIOKBV9bD5OFw4WKocgKI02TXgGvyJAd44eQyk6AGmk+Bh1MpbnMYgzWdh71pfBcJc2h9DSGaJgEM13Uud/vwCHgtHOLAupqtktA4ASRfwDC3zt4ujb10FxtMtS/fnJDk65DkgWpJaRSH8jsJJP4kl5GlQb4BuW7I1aGNoXQMbV2QKZR+KVNXCu0+PqVsT9OUvmJJPfRLA2TcWXBtADp5lZ7EBoQqS5ciUfX8puXkh+U8KNTk3cmngMelqeOSWuiXQcJSeUk19MtSfdSbUB+B1XXg06EmopwAayJqJV0bggorlQVhowUwH21QKfkZ5bpVZN62Xmjrg0If7JGv7qdThtJtuHVYUwWrqmFlFSyrgOVlUNyUeW9jv5vi0KyyTZXw0mSV0nU4fBDcfQpk+uCxj+CbOVDjh0c/gic/hFMPgpfHwVvfw8VHw947gctQnAcu4mDikBM9PBEm3gofzmg4nIWb4dCBwBosIJHomonH8lCMcyRq4IJMTGoxYwFitw8J1H1sFf2DgGyHAUlOjo/KSvW7qKie9etrGq1bXFzH2rU1eDw6fr+PRK7ER319hM6dMxq069AhzYrS62PgwHTL4dD2DVG3bnhh4CCYs4pmQaRdexi9K+w1QZ0/eijcvRDKDVX36d3h+SKYBzHnqcRgi5Jb20Tp5xKcVOsn4gG3YytbJ6V7g4zV0hmsubkiUsFyzVRseyPPSAeO07xcqqUzNWxyQ7CeZdGGwoDmAhNmCDjQrXOoR6edJmiDxq9B+LFOcl/QpNIG+z8ZmLAwAp1d0MUDHVzQ0aWOaUB3j5q8JbAlDLUmlIehwlRWUSUhWOuHmpACkBoTqiNKwV5fD/6oIyRII6a0F3ZRdSZsALM+RZ0/QxYQpelWFFwdstyQaSgHwEyX+t09xwJIN2gSzp7aSH/WPVREoKIeFkOjk7xbh66Z0DMbemTAAe3hpsHQPh1qgrCoFH7bCEu3wJxN4I/QPFfS1ITnKKsLqsn+w1+hbwcYMwLuOh0e+QDenaL293ntO1i0FiY/Al436BocMsQCEkipaNiwBRaug8N2h2/nWZnWzLW0QvURN0wx8VFPBgFqqIxxJB5rCSbxEKYck9wmbmrrSdOUL9lWUSuQ/HnKynLHgASgrq6xtbbBr78WI4TA70/95Dt3zmDFimSZp48tWyLU1bkBH716+ZgzJ4KaquLWWQccCj/PoHEQySFmvbVzZ7hlOoSz4PCekOWF94tVu5M6qnqvVJHC4VCCYXJTrklbXeO8gB9c4GlkN8Eu6DxntOFb/JxGKdJI/YeKRHR04Hjh5Qo9nUkyyCmRCjaLKKFoy9/qjqbBcV6dQ9N1cjTB5JDJhzUaU4OSkHO5vRVe4BrQzQV9PdDXDdlRGJquOAlQIe1n1igT2Y1h+KkOiqw9LMojSfLwlirUWzIJWmuQ0W3hgAK4rS/cPg9eW+0An+1AAVOl8lAj42qMtla8l9QmZMLyUpWS22W6oX8+9MqGM3aGRw5VnNvcYvhxNfy6GtZUNHGtFgIKwNIiuOxZ5Ud15ZHw3T1wwROwZgPs3F0p7t0utQDq27WJfq2F2APvwr9Ot4DEBhtDWb/tlIfqyKtEwwYm2YTJIDPGkdik05YwK5Sy/c8aUjhICDD+FvKdHUM77NZ9vsSlh9frwu3WCIUarqLLywNEIlHatMmgvNz2qoob21100UDefHMdTkusAQNyLPNQF+DixBNzOe64ehQ6xE18Tz8VnvuAhiCSQwI3ghe+26yO6TlwyU5w2ULVrk8BjOsJBywmEUQsANE8Qa7P1Bmga1wcrEUY4GtkG9oj8TJWZDJOVjKrGffp4YbBv0QWn5lBjo6UU9WMzZWTG+mqCU5wuzjKcFEjJR8ETc6pClIc8KBgwOqrBRFu20rYxRtP6VHo5Ia1IVgahGUB+LUWnt0Em50TTksU4NDggzcEZAvIMuIpTVfmwekOTiAUUGIgt25tyKSpWFId02CXbFWniwHP7wnP7QHzK2GN9XrVR8DQFLhEoir4oS5UAMZgVB3LQ+o1qouo+nURZaYbMJUIqyqsjrW0wD8kFTU10SX1cVJvWFWu9vNIUJU4+qgJwe/r4HfgrfmqD48Bg9rCXp3gyaOhUxbM2AjfLoLvlqp9fVpEcduYBCqrgdvfgWF94IN/wbOfwktfwYe/wLXHwRXHQkEO9OkMy5JVpDY3r8OKjdC3M6RnK7PoBE5fELOKNFyKI0lDYBJI2IcEIEo+QeaxvWmbRFv/INpht65piRPS2rXVdOmSmYKzUPTaa0u44YbB3HDDTzhBZNy4Pdl773acffZ04nu/+DjppPZMnFgJGPTunUlVFWzZ4iW2nMmAdl2gX1/4fRHxMO+NgAgZxHxL7h4Cn5fDJgHuTPhPP7hwPdSn0wBADJfJeR4Xw1wa58pyXF4a3b/8BrLJQXACJVQJ2egO3N2jHu4QWdQiOVeWs1EziTSxHzcoEPEAo90G53jcrAirWFujagNUSNufw9FHIwDiFbC7B/b2wF4+6OWGDWFYXA9z/fBVGSwJQGQrTW2zdejsUbs4dvJaugA3tPMo35ZOXjWRg5qsI1KJs6ojasLeVAMINaHXh6E0AlVBWFylJv1QFEJhNclLCd32gg5pUBWC8iA8PA9+LFHtQYnWbP8QQ1NPxq0rHwyPBUy6UCKrdEONOy1NKeM7pytz3SzLbHdjHfTLUf2Goup6S6sUQJXUKAV6cT0U1SkHwZpUz64x8LHy23nhkEHQv41S0M8rgbVVMGMTzNykdBmp2gUjCjhmrIUnp6p7HVIAe3eFry+GLTXw7mz4dAHU2KDS0pW8h5gfya+LYL9xcP+58NilMO4FuO11uPdN+OI+ePde2O1C4htWJZMLfl2iAOk724/Gqict3x6pKxGxgUkGYOLHQzCmHwHQySZMndrf/h8kWtrR9LfB0NWrq+nePa1RIHn99aW89dbhDB/ehR9+WIeuC157bSSnnNKHr7/eiFPvAS6OOqqAhx5aDng4/fQs3ngjTCzWiGXdcclF8Nx7KABxirMaAxEvDC2EndrANYtU3j3dYWId/KHTAEB0w+QI3eBkn8bJkXIwTKV/T9qXPI0oj5JPMRH+TTmSREde0zozgCvJortm8Hi0lhnNqt4V9dQ0zvd5ONhl8Fk4wvmVIdZG1Rq5gQe5E0AiKuz6nl44JAsO9imT3SoTpvvh5vXKETBGyROg4zY9Anq7oacPenrVUZgwNFuVV0VgfQAWVSsdx5o6+K0cSuphcxAqww5xVyqrp+b8QZLOS+qVMvqaqfDZmhT9/UXk1lSAxDwvtEtTqa0PBuVB+zTokqF0KKDk/1UhWFGpxmqn+hQT+fg5JEzwHTNh93awR1u4bDfokAk/roFVFTBphdqtM2bc4ng2kXqYvlalxyZBn0I4aTf44XL4ZQVMnA3TF2/DjRvKe/+q5+Da0fDqdXDWg4q7O+Q6eOZquOo4GP9hUjvH6/jzQjhgZwtIjHi/pUHITIfqTEjLqMdNiAzCBAnHOBGdCCYGGtmEqN3+QKLRIGLx/xLtMCAJBCJ4vTqBAEDYApIsYFPK+tGoZMyYn3nnncO46KKdOeSQLmRne9A0wbJlNvjYcbIMTjttDXV1GkL42HtvDw88YIVAsRzY3W3g2ENhz/OJbxlvA4cXsvKg2vYrsfJdmfD4YDhD4RMH50KGB54oBbyRGICAWhkN80W52fBwgtwCbjP2TThltm3QeIG2fEIdr5Pa4EDHpA8uHqSASfi5lnIVxLEZLmQXTec6j5c8NB71B7nZHyTosMhKABEHgORLwdHpMMQLw7zwmx++r4Wny6DMKTdpAjhUn/GfgzLhyraw0g8r6uHXUmVZtLnp7V+2DTRS5SVNvmd8qTiV/zaFgOKQcqZb2EQ9ART4oHuGUpzv3AZG94D5pfDwTEfFRriDjRUqfeaY9HtmwoHd4ab9ld/Tki3wySLlK1JW37AvnwuWbYa7v4G7v4AhXWHscHhgFDz2HXw6J7VvR4L1lYMrsenRj+DuM+CJy+CqpwEdxjwFr14PO3WDhWtIXElZa8QpK+CKUSSACF7FnfrSoNpQizQ3QTJxATXomHgI4SFEPQYaPuqpx2yU399GEvyNluX/fdqhoq38/DQ2bFBf/LJlleyzT3tsB8I4xWeEkpJ6Dj74PaZNO5X0dBe6rlzKQyFwciPgY9kyE8jiuONyWLxYx++3yjMAHc49DV79HEK2TsPBfeQXwldnw9D3IZoez7+hP7xdBislZHvhkS4wskQivYlciNsbpLsBj4k2nC22UC/iMlq346sqxOAu8nieUn6iLqVFVhgPZ5HJCWRwA6XMb0GkwJ1Dafw7zUdYSh70h5luOS5GUoGIBSDtdTjeKxidqZ7gpzXw2Ba4PEzTkW+bmtQd7WYE4KzkNUIqAGgqmm1j7SJKFJVhWFZSJqSlK/GTz1AiKbdURylVnibj5r2RqLL8MaPWXiOWv0goqpTXIVMpdYOmpQ8JQ11YhbTZZgV9M6thCWyuUek367l1zoSNpTQdOrkJUF1ZodLLs1VevwI4og98dIYScX3wh9r6oKRGcYuzb1Zirdu/Uu1nroVTX4FuGTDmUDhlT3htCnw5t4X37NCj3PIGPHM5XH0cPP6hClfz7Odwy5lw6r2ONg5VakUttMl09GXEb89wAd4IPstCKwsXYeot0Vb8JTJoQ4Dwdle2x8b0P0o77NYjkSh5eWls2KCcg9aureHSS3e2SxtpFUZK2Hffd7nwwl255ZY9yM/3EgxGcXIjcV2JwbhxGRx/vO0xrpI7Dy49Dfa/kriC3SHCuvMweGBOIoj0bQNHtoX9FgOZ8FQnuLdKUuKtx+0AEMMwyUTwipbHDWyhmBC+pCWZgUl7DJ6lI3eyidn4Y3+IiGOllIXGw+SziAjHscmavxtfSXVH5w4jk4jQ+FegnnmhuB7KBhEnF+KO6BydDudmQiaCCdVwxgbYZD9+54e2DeDRoCzVeQrg0AQUeqCDT8WjauuFAi/ke5TXty6gR6by5wAQUVhWpURE9Y6Jfm2N0lGEInFAqAiqjZ/swItRqQDCq8d9RjTLV8RtKH2IW1d+J/lpkOaFdJcCrJI66NPGcWtRmLNZWUeV+uNpQ61S8hfVqk2jmuWEGpngbj1YiavCJiwshdnF8EcRzNqkgK4BNaNXWbJFpcemQkcPHDcI7jsS8tJgSTF0yILL94P3Z8ECx+4Oa0rhmrehbRY8ejKcsx+MecPSw9jXbIwrcYDJFc/CD/fCd7MVFzJ1Idx1DvTuCMttabXdxjKnL6+FvDwVPscuMwFDj1tsuQmSTg6SqgQQAYgiYur3Vtp+tEOBJD/fB9hAUk7XrplNN3K0ffbZ+bzyyjLGjNmd2bNtLaATTHwceWQ6c+bAxo1xkRYZcP5p8N6P1pUtHxAbRAZ2gf6FcNkfxEBEeOHZneGKtRDJgONzQOhRPsCfwIUYhomhmTxEG56ikkXU4EYBh5M64uIZOnMbm/gjaSa16/YkjUdoz1OU8TU1hJsQwOYguFbPZLBwcas/yBQzkmChlQwi3XU41atzcgZ8WScYWwJL7cj80CyA9PPBIZlKLDy+iKbBIwVwCKBTGvRIhz5tlfimW7pa6ZbUq7KSABT5rcm3XukJpgWgtFpNzhVBxTGkvEYzYq2tlo238CtxaZDtURNxvk8BT4F1n4d3gvYZKnl0Feo9aCrnwTWVsLoSVlXCsvLGHd8v/FKN3WvAgAJlynvaQHjkMMVpzSyCyWvg17WW8UEyNWERtzEAT05Rv3dvBz+PgXTrlfvoYhhwpwIwJ5VUw+kvwmH94Yux8PT38PIPjQw+GUxQu0xe9zI8dhkc9i+Vd//7cP2pcOETiXXt3yu3QM8CKKsktjCMCNBd4PGFYhxJGm6qqMVtOSPGAUUQxE0Q9/bVkQiaWuP942mHAkmbNrZzoY9IJIphaM01S6Bg0OTBB5cCBm53DprmJhCIcyNnn53GuHEWN6Kr5M6Ei0+B/a4lUbluibbG7Q/XzSJB3HVaN5hdD7M0KEyXXJErOa7Gj8cXigGIrUAfSyalBPmBihgoOFdF7XBxHQXcygYW4E+pVD+YDK6ggDEUsbIZhfox+DhZpvGBDDCuLtRA6uEEkb3cMDZDJ0+HxyrhgbVq+9jmACQTGJEDI3NhtzRY6ocfKuG7YkebRsCjE7BbLnRPh73yoGeGsoZaXw/TSqEqAD9tggm1sLZOhVpPoJaAwNYCR0snkDib2CIKA6UhKK2BhIC0Kb4yTShQ6ZoB3bKhVw6M6gW98xTHs6UePlkKT6fw5g5EVGTe2ZvgTcuMN82l9k3fuS1MOE7Ffft5LXy3EiavUPu6N+woRV4EhnZTP8vqlJ6kRx78Ng6GPtQQTAC+nQ9TlsIto+DlS+DiFxUHl8CVQEN9iaF2SVxTAsfvAx9MhW9nwxkHQV6Wpbex21ncycoK6NkWfq8lZrW1KQRauvLFMix+wyBM2FrI2SRwU8cqTAxCeLa/sj2Vtdn/CO0wIDFNSW5u4pP//fdi8vK8lJWlesOTvwSblQBwcccdfVm+PMorr9QCPkaPzqC8XGftWiPOjWTCZWeq4HE1UVSUFIffx9GDIKrBrCqUCbAX2ubCdR1hn3WAV/JMgeSxSICANxFEdEwOw2Aobi5kjRUdxeFwSJAcDJ6hJ/eylmWWTiTk4DR0TM4kjwPI5mzWUEHjwFqIxn2iDVVEuTBSRRUSafVlcyM2iByIl3PaCNwIHqyCGXXOuFfW0X7k1mPON2BUNhyXBwU6fFkB49fDH2WNRN6NqNhQe+fCHukwLF+Jojb44Y9ymFkG3xTBqloIhxLbJdB/AzhaMoE04hfRaN2mKEU/UeIK8V+T/SdQ3ExmKqVZI2OqD8OUtSo987symd63KxzWG67ZS+3HPnE+fLUM/Kn0Xo6+//Mb/LRCGZJkGGqLhKsPht9vgH9/Bp+nADd/CP71Plw7Aj4dCyeNh9oALQKTm9+AF6+AT2YqAFq4AUYOhdd/JAFEMGDFFuhc4GjvhfY+kCFpxdgK4SFILjlsoDbJ9DcXg0xCFp+y3amVI/nvk2lGycpKFNcIIenePSsFkCSDiMtxNBg6tD177pnNzTcvA7IwDINbbslg5EiHgj0DsrLh3ONhr2tQehEjXuZOh9v3gSMno15eyyLk0e5wSxnUeyQnZ0Wp1U2+iwQbgEh3TK6mLRexDpu3cCrW09B4gl6MZwPzHC5jdp0wHq6jHW1xcRHrCCMxMBN0JjYdgZexMoc7ZTXfRdSHEgo0BJEhhsZdaR62ROHWMsGqCHGHwhRciCsCR+XCuflQFoEF9XDVclgVSKxnty10wyG5sHs2HJSv9s2YVgZTS+GppbAleS8KGjlvIRBkuqGttU9JQRrkeNXKO8etJqDuuWplnm5Auhs2VsFObdXGVDatqVQcgNOLqSqgjCcA0KA+BF6XmnD9YRVGZH2l0plUB5TfSlUASmqVLqa0Hkrr1LG6peGUG6GDe0BxDSwtQ8Uw2xpyPDc/8N0KlQAGtYOTdoZbDoBfVsOH8xWnkrJtGBYVJ+ZPnAUdsuGuo+HYneD696Dcfo0doPvo17ChAr65EUY9ohwScYFuKmMGIG4maz2r0mpYsQlGDlFh5z/7He44E153Bmm0PuWqEOyTS1zZboCmg6mbuDW174iOiQ8PIepiFltKK5JDiGqCeNR3tT05kv9x2qEcSWbSkmv16iq6d89i5kxn8MXGvLsVN+L1enj66Z046aRFSKnCoVxwQRaffiopKSHhhbvuMnj6M/DbmyE6RFpX7QMT10JxlJgu5bBCcLvhi2pJvtvkymw4wV9PWkZ9DEDcBPFi8jjduIeNVFmb6EBc32EguJ3uTKSY36looFjXgXEUEkYwjvUpwQPAg+AOcslAY5QspcbyOEkGkU6mwW1ZLtqgc31llLl+q79GQKSvBpe0hYOz4OtKuH4tLKlJrANqMtgrG44ugAPzlQXTd5vg7ZVw42yINKUbaUYhn+lWoTu6ZipdSedMldqmqVUxqEm6pA5WlSn/g8oAVPphbSmU18M3yyxrqhDU+dUqPWQ5IDYLVklfgiaUWMdO3gyl/8jyKtDJ8iiHw4EFUJCuOIj8dNhUrXRs/jBsrIGiGlhaCpvrlA/H6go17saoTx6cvzv0zVdipNlF8EcxTF0Hi0ute9mGCXDeGpVuAXbvBJfvAw8cBS9Nhzd+a0Qvk3Sdoio4/w04si/8MA4uex2mrWzYbOJvsLkaPrgaxr4JL5wPKzfDSU/QkDux6NWf4d7T4NM/VETfXu0UcIecZrVeBdj5WSR817oGuNV3Z1gbWXlxoyWFihfkEaSWkMW3bFerrb/A/FcI4QV+Jr60fV9KeVtSnWOAu1BMbgS4Wkr5i1WWA7wEDEQJEs6TUk7bvqNUtMOAJBptyJGsWVNNt26ZNA4ekOh4aHDttb15+eUiVq1Sf8mMDBeHH+7lzDMtrsXSgxR2hJH7wV7jiIuzrFTYBvbpDKdMQ4m7MsDrhbPbw5gK5WT4dIHk0XCAcEZdAogYmIylgMlUs4TKlMr1m+jGPGr5hrKEfAMTic699GAJdTxPZaN33Q2D8RTyLnW8GlWffSTJ+9wArnJ5OS5T59oyyS+haNw/JAWIHJQBYwuUFdT4IrhuNZhJIGMIODgbTmgHQ3Pg82KYUg53zVNAAiRacTUGJlZ/7dNgUGfYOVc55e3bQekEaoKwvFJZI5X64Y+NsL5abQcbNGmeq/kzIrIUYqwoFijZr1plivY2pZCN+1zQPhM6Zqljv3wY2Qu6t1EgtOczymIsmZ6bppLdx06FMLQz3LI/9C9QwPT1Mpi+HmZtTN1HjBqZKGdvgPPfVcB3wZ4w9Rp4bw48+bPa+6Q5+mIuzFsPb1wIn/wBj35Dg2e4aCNk+OCX2xR3F9PR2MKEpE980QYVl6ttDpRUwudzYGh/5TsCxECjNKA2z4p9vzpsNEG4IrFQKB6CLOL3BLEWgE469ZRaeyX+Bcr27e+QGAQOllLWCiFcwC9CiK+klNMddX4APpVSSiHEIGAi0M8qewL4Wkp5ghDCjQo0+JfQDuVIgsHEv+SqVVXsumtBIy2SycXRR3dg6NB8jj12MQoBXNx4YzZffQW1tcTNBj1w25Vw91vWfOpUshtw9/4wYS0E04i9sDd1hulBKBImo3NDoBn85KpuACIH4KM/bi5jeQKA2PqR0+gASN5jYwPFug7cSXeW4edVSkh+E23OZF9yuIgsbqSc+YQBPQYiNjeyJ24eyvLykd9kn01SfacpQEQElO7jxi6wxA83rYX59bELKgrA4Cw4vz0MzITZlfDaOrjkD4g6l65mUruk3x3SFPgMLVSgMaRAmb/OL4N5pfDtarh7mpqwt4ueY1uV7E1xJ6naJH81KSZsfwBW1ajNnrZVCesPw8yNKj1tTR0ds2CPTnDJnjCkIyzYrIDlq3lqtd5iiqg9Qe7/Dh6eBOfvBVOvhuemwou/QqSZoAnry+GQh+GWo+GJ02DMWySAyVn7wIAOCkRAhZVPIBcNaMIUOHYveP4HmLcO9u9nAYklZgYoNRX3GeNIMqFnGmjuQOy71DGZyyQ6O2JsARjkEyFCCHejXP8201/AkUgpJSpUG8R9GmRSnVrHabpdLoTIAvYHzrHqhaCFoTC2gXYgRyLp3NneE0BZbq1cWcngwW2baGW/fT66dEnnzjt3ZvjwuUipTH47d87hsMO87L23YVcDH/TuBYP6wuX/IR5Hy1rV7NJNydY/ng/kqvze2XBYDuy7JUJeRj23p6VxktjSAEQKgOPJ5jZWJgSGs2kfctmHXMawqMGdaMCt9GAl9bxMSaN3fAY5jCCbi9hMGQLT4c0eCnhwA7e70sgUgtMqg6yot8SFKUBkpBtu7QqTKuG4ZSrSrl0GkBOB09vDme1hdT28tB4un2u9mS0AkF5ZcHB72CcPdi2AjbXwezH8tB5+L4GyQFKbPwseKdp7DKUkTneDT1PnbgNcOrhNFaU1KcwbEcvHJBSBkK72HPdHlLK4Nqj8Q5oFF2j8a2qEM/C51LNNaVHVCG0MwMbN8PFsdb5zexjRD148CdLc8M4c+HCe0uGkpBRjj5jw/FR4cyaMPRimj4VxH8CPyxrpw6vuKWzCbR/D46fCwyfDde8SA5OHv4LFRfDS+VCYBbnppOT8nPTJLJh4FUxbDoW5MKQnDcx/w6ba/tc+x1BhZ6JGBB9xqy1Pyr0/8whY4VG2u9XWtgFJvhBipuP8BSnlCwndCqEDs4BewNNSyt8aXFqI0cB9QCFwpJXdA9gCvCqE2MXqY4yUcmuWGy2mHQYkUkp8vsRVQVlZwPItSSYFNIoMDEPw+uvDuOyyxZSXS2ywvvfeHG65xcQ0jQRu5J6x8K/XiDseOtIjB8K184hH/9XhyW5wdblEeoJcnWbwtKylQg8ngIiOyd105mNKKE+I6aOOXfAxknxuYRHSsf6xrUVuoCsr8fMam0hl7iGAmyigAA9nUJxgKGxzI701jec86XwQivB4bcRhTeUAkYgKrnhfe1gRhBOWwYYQCWDQ1QPXtlOBEX8ugyNnKD+H2KO3KQlAMl0woh3sXQDDO8DKSpi0UTlzLtriaNcUADQDFm1c0DlHxYpqn6ksdFwG9C2ANmlK2a5psLgE+rdV4cnrrC1qV5VCrgfCEQUUYROq69SEKxxg4nJZQGMo58NgBNplW1ZLPlW2Ygv0KlD9lNcp09glm5UHfFGV0o0UVcGGykbEQym+tF07wqPHqECJC4uVyGn6WphbpMbQEpq/SSWArrlwym7w7SUwrwg+mAffLG25XqU2CHd8BS/8DE+eBMftCuM+ah7orn4bnj0T7hoN//4ofq9fzIX+N8JHY2BY70aegzWuM/aBu06Cjm3g+5sh3QsrbFWp19HOID5p66rMLSRRzbQEVqGYN7sTTEx03ORQZ4m2TPTt79m+9VQqpRzSVAUppQnsauk7PhJCDJRSLkiq85FVtj9KX3II6gntDlwppfxNCPEEcCPw77/gPnYsR5IcSh6gvj5CWprRyG6Iih59dChvvLGBadPqUAhgsPvuWQQCgm+/9cTDSvtg8K7g8cLPy0mM6mvAUX3h9wqYF7TKPDC6PcwISWYY9eyTbrKP28MpWnUDEDmJTCoIMY0yy0Ulzo140bidvtzHcqpTfMHn0BEDYYFIQzKAsbQjgOQK4jOykxs5PpLB5T4vl9QGmBVQs2I06EkAkQIJD3dQIpJL1sFSmwm2hrqLB67vrgDk0VXwaYnFfdhD9ifWJ6KizB7bA47ppCbprzfAG4vhuimWvL4ZHUmDfBQ4DChQOoCuOSqKQIcs9WdcUwmVtWqi3lQDizfDxnJ4o05N6JV+iCZPdC3ldBp7+5v4KtwZCsDy0iE3DTrmQPts2KW9OiLUvuZhE9aWw+oyuOub1OAybQXs/Qi4PDCgLezWEY4eAE8fr8Bwyir4eSVMXaMsyZqjtRXwwCSVdipQXun3HAGvTIcJv7dM/wGwqQpOeBEu3g9+ugYueBPmb0yqZHElNl32Bjx1Opy/H7xsOTZiqEjNB90Hs++CM4bBGym2ywWYtR4yvAq0C7Jg5hooroLcNmoHydg1HYp32/c4JCRRy+nQBpHkhR2ARi4B6gjhpn57qwv+4lhbUspKIcRkYASwoJE6Pwshegoh8oENwAYHB/M+Ckj+EtqhQLIpwf1WcR3LllXQu3cuc+duceTbZHDFFbuRnu7mxRdXE9973cVDDxUwZoxlX6gT40buuw7Gvkzc7sGSt7qy4I694IgpKBWUAWkeuLUQDquux+MyeTjNy9VaaQMQ6YbkNAq5yNrXINlf5GZ24hPWsZbyBr4iR1HIzmRyPUtIxYnoGDxBJ6bh5xUqwF492RTReZAcgrpkeFU91U6vdQtERERwfjZcng23bYRPq4grN03o7YJ7u6nw5c+sV+a6qu+kR27dlk+H0R3gzB7KcmrqZrjkV2UtFR9X0tEmp3lxVAUL3L09DG6rlM47FSoF8uISZa3z8QJYWwlFm5MUyX9Wud6Ust0mI3W+cMUj5YZqobgWbOvYxvQfLh26tIHuec2v6MNBmLtOJYB/f67Aat+eyg/k34cqYPl8AXyxENaUN90fwMJNcNlEyPHBBXsrT/UP58L4n5QJc6PkuPfnp6gF2PiT4KWp8O6spLr2vQfU87nmHfjpBvhtFSxwAo8BRzwGX16j+ginALTFRXDT+/DYqeBzw9vT4dGf4+1jCbVVcexch8Oq/HT2BmP6EZt0TFzkoREAytHIop566snHRMcrtiNT8hco24UQBUDYAhEfitN4IKlOL2ClpWzfHbWJapl1vl4I0VdKuRQYDilk7NuJdhiQGIbO4MHt2WOPDpSX+1m5sgKAn37awIEHdrKAxJ7RFGAcfnhnRo/uxogRU1CsBYDB0Ue3YfVqyYIF3nh1HxxyiArjML+YRN1IBlw2BD7YAJvdxMRcN3aGl+pNqgyTa7LgZxFgjRVQwQYRN0H+RU8eZCUhwrEp3jb5PYL2BInylYPbcBMkhIfB5DCETG5mWYNwVSE8uBE8QWd+ppb/UJUAQGZUJzNi8IqRw7chk0cdHJszflY3BDcXqlDv+y6BuigKREy1O+FtnWFgOvxrGUypoCEAOEBkjzw4rTMc0A4+XgeX/6oUyKm4i4Paw6fHqD3Cv1kJ362G4jq1GdG+nZR57E4FsKAEZhXBG/PUznx19SRSU2KvVHlbCyCpJnWbMXbWtb4Mjws2PwX3fQYPfpEi4HLy9a1XMGyqcB4rt5Camvnyyuvh0/kqAXTJhSN3gqdPVBZuCzbBGzOaB5VKv1KmPzYZzhmqAOXlaUqp3sBLPcXzXlwMo56F9y9UXNgzPzesY99zKADnvwqvngf7358oniupVtzIqUPhtWSuxKKXpsDRu8JRu8Ani61Mx+LPPs90LgpdYLjUHiT2N+rkSAo5HsEconyCixxqCKnSqE6eD5IZrW2mv4YjaQ9MsPQkGjBRSvm5EOISACnlc8DxwFlCiDDq6z3ZUtIDXAm8aVlsrQLO3e4jtEjEr/nfpV133V3OmDGDQCDCt9+u5oQTPgCgsDCP994byQEHvG7VVLqRoUM7ceWVQxgzZjZlZRrKSisTXc9i+vSeHHOMSVGRT/0xc1Sa+gGc8RCsNuN5ZEBuHkw6EYZNA78LyFZmme90luxbVU+ntDAPpbu5VitDUJcAIieSR2+8PI6yS3Q6HXYnnesZwBhmkhysJJ9sHmAAY5jPFiv4th0FSJGXu+nE7/h504o/FrK4ETOq0wOdF8njzkCQzwOqfSSsJ0TxPc0nGJsLl25SIjsAwsr/45oOMCwD3tgMH26wyiIwOB0G50BVnVrp1YVgdCfYvY3abOmJxTDVxsTGJvmIii218CwoTFdZUakmkpfmwGfLYNoaqA2laJvcb6ryFtR3G5CTBpletbFTukclQ1MBFl06GLo6DwfA8EI0qpzkzKgaqz8EflOJkeqDUOWHLB/8dKuyPiqqgFOfgTlrHdduavLYSmutvh2Uj0yqMCROyvDA6EFw5h7qvt6cCe/OgZoWLK/T3HDNgXD8rnDzZ/C1PWE3o0NxG/DmOTB7Pdz3TdN1Lz8AeubCte8k5rdJh6+ugaF3N9LQqwwQKh8Bz1UkcCF44fnjYVQ/tZ9LaQjCArqtAl+nCjpnraczKnVjDb1YQR5l7MIFhPiEKL+QzyTe4V4WMYBXQ+fQ6ZZ2LHxIzGpOT9ESGpIr5MyDt66N+JDtcu2/A+0wjkTTBC6XTlVVkPvuiy9RNm8uIxCI0LlzFuvXqwl133078vjjB3HssT9SViaJu6W7OO20fN59N0xRkWWtZHEjR42EmStgtR3uxLGyuWII3L/EApEMwAUVLsm5tX4MX4h707x8rNUSJkKaA0Q6oHMyhVzMH0Cy57rkJgbwIAuIEsEptvKhczf9uIdllFvmuwnPAniQLkyhlnepBMuqxBZp7YqLR8jlvLp6FssooCeEhM8ydZ7OF0QkHLAGau1VfhiG+ODJ7vBlGZyyGEJ+EriQobnw2MC4GMmtwfo6GP6tUp7HKMnCy/6d74ORfeC4HsrTHBQY/boBRr2Dtd9MirbOPlOVWefZPujeDrplQYccpZPokKMm/106xz3UQybMXgX5mVAXtJIfiivV5BSJQiSoJumgRwU+1HXlzKYJpUfzedREm+ZRlkydC6BLvtKFGDr0bQ8z74Qt1fDWNBXtduUWKKmCVZsVB5awLmuEW2mMTt0NRg2CjZXw7WKVlpY1rFcbhNdnqNQxB04fAp9fBDPXwxOTYV1FwzY21Yfgnm/hhV/hsePglN3hqnfVTpNNUSgCp7wCL56uwqU8Pqnxuk//BK+eCbv0grkb4vnlpoo2vM9OMDWFIyMofd70NdClwHEflm7k69Vw0k7KIKKDD94vVflurx2GsaGS3SAbP5VqB0VH5F9/bRqZ23P2aw3a2DwJIUagnFt04CUp5f1J5dnAG0AXq8+HpZSvNtVnJKJW1cXFdcyalah0fuCBabzwwkjuuWcqe+7ZhV12acfRR3/Jpk0Gcb2ID4/Hxdix2ey/v6nyLEstzQe3Xgyj7qBBUMbO+XBkV7jnVxTA6KqsUjOp1yIc4IuSp8MP1JJGfYJ+ZAxdeZJV+Ik28F6/mP58w0bWWGbfdngTAYylH++wkSX2zmyQoPf4Fz1YjJ93qGxg375X1Med5HBmpJIV0sCM6AmBGPsJnYcL4fVqeLeC2OSVZsKtHWFIBpy9BJb5rbIkUdabK+D+/soCKxKF30ph368dCuwUk32WBqN7wSn9IEOH1+bDdT8o66gjesHzs+G6r1LvwJeKm9CEiui6cwfYuZ3SLQxopyaMqjpYVKREIxsrYcoSFZ9qUyWU1YJMFlUlnyfL41OIrxIoSc593FB462qoqIaKOnjxR5i/Tuk92uepOFQH9YPuBdA+R7WZuVrFtlqwERZuhMWbLD2J895TgMrtX6jUPQ8O7a82kRrcBb5YoHYmdIZyt8e+sRIe/B4e/gGOHQRvnKUsxx74HuY2IbfZUgtnvAan7AqTr4Fr3oefljdeHxR4n/+GEnOVVMPbMxuv+/TPcPfRcPSzifnPTYHL9m8ESKx7mrwCdu0E62pIULB/tAzu96sFS2lIRWAgV8XYskVbNojYoi2DbMLUInGzghdjQBIJ6+Rto39PSvqLle1/d2r21i353NPAoShLgBlCiE+llE7FzeXAIinl0ZaCaKkQ4k3LCSYlbdhQjZSS2277qUHZpElrEAKOPbYvc+eWc+GFPxAK2VyIZaqBi9df786kSWGqqxO5kRNHw88LoNhPLPiiHc33jmFwxyKIelGTRiZgSDRPEJdhco+RyTWUxFY3NogcQjZhTGZQ2QBEBpOHB41PWN/gXk6hKyUE+JaGwvIIOlfSkQCSpxxe7zY3cgherhXZnBSuYH3AlRAaPhr0cIRL5+42cNYmwUKHrmFnA17pA+M3wY2rrMwkEBHAiflwU39YUAV75qnou4d/nwJErHYHdIRTesGQtvDhErjsSxX+PBZnaTp8vBjenJvYztmXECqEyNDOMLQrZLihXzsVQmN+kVrBvj0T1m5MMoHdWt1HUwCSKs/+EpztPIqjef0neOJLWLCOlI50zvZCQMdc2KmjSgcNhz7tVIj5xZvg99UqzVufJMJyTGqry+CFX+AFFIc0cie4dST0yIfXfoMP5igAcVIU+HCWSnv3UCFQ0r1w02eOlX2KZ/DOTOX09/TJsGc3eOi7Ru7PIinh7Akw6WpYUgJzGr7ygNoEK2TCsB7w66p4/rRVCkhyfEp/k2oGml2kgJnFjkxLmnDtr/DxETCzElZHAEe8u6G0pQ1QyxpHQ4FJFRqwmVkEGUA9aUSDnnh8te1Bf41n+/8bagmG7gmskFKuAhBCvAMcQ6IFgAQyhRACtfYvpxmpa0WFn1DI5OOPU3s9/fDDGn74oYR4SJT4FyyEj7vv7sgJJ6TTrl0FkB77Q2o+GHc2jLyVxP3XDWUh1DUDvqolJtJSIi8Tjy/E2Wka34g6Nlh6EfWATDKBi+jMFcxvACLpGFxCX66n4fJsINkMo4CrmQWOgNY2N3Ic+bhwcS9FYOlLbAX7wXg5RWZwfLiCzYF4W1svcl2m4DAPHLpeUG7DdQAuawNn5cOZy5PiZTlAZPdseHyAstY68HsVy2rGCDjkK6jxO9qguI8zB8E5/dQ2r8/MhUu/iPcVH5jSg0xLytcE7J4Pw3tDjzawZxc1Af22VpmkzlmTZNX0Z5XnzQFIY/E/U5kEB6FdOtzzIayxfRqSQ30kXUcasKFcpW/mx4sNHXbuCnv2gMsOUrqcNunww2L4fhHMWmOJF5Mmt/oQvD9HpXQPjNoZ3jpXebE/8xNMWhbn/E7bQ03g01apdGh/eO9c+H6J0mvUNvJFbqyE416Ax06Al86AS96ynDAbodqg2inx/Qvg0CehtDZ1vVu+hKdOgOFPJeYv3QIjdoZ3ZqdoZCjgG7UTcW7EoSv5Yq3ah+YhC9Q1T9zctyOZaFTgj/MduMlDEoyp4mORfyM6ORkprt9K20QtAZKOkLDU3gAMTarzFPApUIRa458spWw2duny5eUJjmGN0aGH9mTAgDY88cQ62rVL55NPBjJokI+6OsmWLZmqUgbgg2NHwLdzYHNQnTu5kTG7w03LiDsregFvBHdGPbneEJfqbTiejQnKdR2Ti+jGRIqod4itbLqCvrzLSqotl0EbJLJwcS39uYk/MFPsjTqYTI4hn3NY00CcNZQ0xskcTpZllnSXmEhLAE/lapRFBCM3iNj8lxGCezooPD1gvvLOBhJAJFfAPYOgjw8umQmLLKutKqDD2476QKEPrt0ZOmXArGI47EOoqHQMsjFuIQKFGcpHZ2Rv6J2vHO1+WAqvTVf+IA3aOK6bsixV3p8FkOT8RgDlkfPUyv6hj+CRr5SCPmW75OsmfVkRE+asUul5q6xdNhzcHy45EIZ0gx+XKNPZz+daivMkUKkLKm7t7ZlK9HPp/jDmIPhoruIqnjoZ1pXDrveq+t8thklL4dy94fnT4I3f4auFqR9DVMKY9+C6Q9RGVqe8oq6X6l5A7Qty0+fw4HFw3lup+1xcDMu3wB5dYMa6eP6n8+HGQx1AktT/ylroWZiiQ4srWV4DK+qBfGWxZetH0sihKikKiK0X0TEx0QniUT4kEbH9OZJW0VaTlGqqT54ZDwf+AA4GegLfCSGmSCmrnZWEEBcBF6mzbDZvriM93U11E7G3s7PTePDBIYwYMYWBAwuYMmVvsrJ0NE2wZUsUKV2xP6Lwwk0Xw8jbaMCN7N4eOqRbG+LkoCYArxJp6YbJNS4fb4oqggTwOERaPfHRCQ8vWAyYc7OqPSggHRc/UtRg3GfTk5dZQQmBGJdhg0xHPFxHdy5gDUHrUdrcyC74uJ1cTpVlbIkIQgFPTKTlBd7L8jErILhtSzz0SdcovNsLHt0EE7cQn8wcIDKyDVzUFd5ZA5etJtY24RhQz2jcIBVM8bE/4F9TrH3JG1G229Q1E47fCUb1B2nCpwvh5i9gWbLP5TYo3BMorCb2TgVQmA1t01WgP59b6b+yvJCVptKmchjQWTWzFyyL10K/LoldrimBrm2hPmBFDg7ApgrwB9Ue4VlpcMepcPHhcOmzaoUf0/9sJaA4y4rL4K3pKgH0bw/HD4HvroMtNfDeDPhodmpQ+WMDXPyW4mrGDlee6OkexQGevRdMsPo0o8oH5JO5SlE+cqcmvNUNeHiyilo8/mS46n31PBqjrxfDGXsos+QvGgGod2bDRfskAsm8IhjQXjliNrBQ8ypfpSynia9t/mv9XlsPERUdFd2hH1H8SC0egg6Fu/pDOXWTphVCPj3Vfi/bSq1A0ixtADo7zjtBg5nzXOB+y355hRBiNSoC5e/OSlYcmRcAhOgglywpJTvb0wiQKKX6o48O44EHFlFSYuL3R3j//XJOOSWftDSQ0podLNA4dgT8usTaByOJG7l9L7hthVXXUrBjmBguk67eMIeINI6nCINE58OxdOd5liJJBBEvOkfRhcdTOJmOpCM6GlMpbVDmxsXldOY2VrPFWiXZHEkvXFxIBmdFyymyQMQF7G/onO5yc3KuwWQ/3OqQeQ/T4elucOEqmFlNIogA6To80lftgX7Bb2rPDLut85gVhVv2Vnuhv7MMrv3RsVpoBEQy3HDSQCX6WrwFZm+Ek16FzbWp6ydQMyKsfB8M6Aj9O0KnPDXxdM5TIqLyWigph81VKlJsSSWs2gi/LoTqeitVQU29Yw+MpkxqdRVmJc2jQCo9Q4FH+zZw8RGqistQVlwf3qTEXKGIinAbjMCSjbC0SKVV5Yr7aHBfjX1pkXjZ4k1w92cq9SxU/hSTb4A56+D5yTAjRSCE8jq16rcp2wePHg8f/pFoDrylFo59Xnmr/3wtnP+60kmlGtvbluPhO+fAsS85nmEKGvsRfHWpErH5U4DT5OXw6GjITFf+LzZ9uVjpyH6x9SdOoDRg4RZwuVUImmRaU6f2IMFQb6itWA8TJkh9jL/X8FDHcoK48RCKhUZJw4dWw/YVbbXqSJqlGUBvIUR3lP/OKcBpSXXWoTwnpwgh2gJ9UQ4wTZKua7Rtmx4z802mww7rQna2j3feWQv4qK4WXHnlRvbZJ4dFiyTDh3viKwEDxl0AJ92P+oM6uJE9Oqh5ZFYdcW7EiHMjN7rSeZByTJQfuU3n0pFKwiyjJgFEAM6mD7+xmYokmUp7fBxDF8YkYmhsRXQz3fmFav5Imtky8PEUhVwWrWAjJqBzj8fLWS4PGoJcTVAblRxVDHYMrdEZcFUuHLUUNjoDAFgTyO5p8OJAGL8aJiyjYbBFy2Lq/O5w+UB4fA7c+EvTFltEYM+OcPlQtRfHewvg9InKE71B3eTfyX1a5b3awh49YI/uaq+P3bupFfmijbBordp+9aVSFRolkuzA2JwIy/mYGzPHNSFqQm0Uav2ABdRVncHrhmUb4YWvYML3DiAGMtOgd3vo100FGDx9PwiFoU8HpUyftRpmrlKRbMMt4FCcZSs3wxPfqXRgP7j2MOiWD098Dx8sSFzJB8NqR8NOOcqKrDADVtwBA+9SAOKk56fA5GXwyPHKgurzxaSkt2dB51x49iS46J3UdQBKapQp8U2Hwq1fOgoc9/L2HDh1d3hhWjzv93VwSG/4JXlJarVLc6v4amvriHMj1rFTJggr9KDb4dHejiyWE8Yd029m4CJXPSNLN2Kicw3D+EZYjo3bi1o5kqZJShkRQlwBfINay78ipVyY5F15F/AfIcR81CO9QUrZcDmeRCUltbRr53Pk2MszH3l5Xh54YB8OO+y7WB74OPXUPJ5+uo6nn/bichHbi/3A/WH2cthgi67sFy8Drty9cW6kvzdCb+HjNqoSuJFR5HIJXTmXaQ1ApBdZ9CGbF0j8CjVgHAN5gsWx2M9O7/RRtCOC5FPKsEOfRNDxIHiBQu6hkiVEiETUZlWfEeUsF+Ra4Wo/qoOwBSIX5MCJGXDUEisMu/34rMnygvZwdgc4YQasdno/OwBhaAHcvzv8Wgz7vge1YRpwNHZdXcDoPnDVXrChCp76FX5dl7puyt9WHV1T+oCD+kCPAgUgKzfDjBXw+Sw1+VbV8+d1IMkcSDKIOPMcgKIGqQ5rN0K/82BNsi+H9dXU1MPslSo5V6NeNwzqosDlskOhXY5y6Ju8CCYvht9XKGe6BtQI2ExeolJBJlywH/w+TnEor8xUnJGtN3HS5QfAt1fC4z/GxVw2LS2BM/8DH1wM7XNV2PhU9OD3ypN+7EHwyI+p6wC8MBW+vUJtS11S07D8xenKiTJGXvh1k4oFhtNo04gfN9Uqf6G1JokiLh0QlrzdMDEM9UdTG8y5iBBHehcZhKnFtDq2dSReDGqDkJXe+D210tZRizBUSvkl8GVS3nOO30XAYVt78eLiWtq2TSeVMPzZZw/ltttmsGVLFHvjZiFcXH55AYceWgNkEA4T24v9hovg6peIcyMZ6u52bQc5HphnmwI7zH11w+TfrnQepiImysrD4EF6M4hMwkRZTV3CQxLAZQxgPAsaKIpG0ZXf2cIiy6nQJhOd3qRxHG05l6UJ4AJwE+35GD8/RMMJm1VNi5gsikh2NyRBCY9Xqfyr28BBXhi1BIJOX40AuASM7wvpAg6dBoG62CBij9kn4e49YedsOO9HB9CkABGPhFN2gav3hO9Xwenvw/qyhvWa0m90SYdRw+CwgdApV4HFj4thwi+wKXm58VcDiLO+y1HHKVpRDCHRKKwpTqrrHEOShRcAHhWB+PcVKvGNats2Gw4coKLc3nKsAoCPZsNnc5Q/TIN7TPFlbqmB+76Ep3+Eqw5RgPLYj0qJnix+evonZSp88+HwzvlwwRuJQRsr6uGIZ5XfScdsuP2rhtcDuPJ9+OB8ZWX3SyoZg6FMj1/+Da7eH276omGVSj9UJlmkba6FvDQVaSCiNWxTVKM4EpJBHCVyFbbFlmbGFno+DEKWsl3HxCADk7qYst0WbbnQCfphSzJ3+2eolSPZcbR6dSX9+uU1yD/99P7U1IT59NMSYs4hGBxxRC6//RamosIyB7bkkjv1U9/+0mIgn/jqJQNu3hPuXUV8NWOZ+xouk929ETKFl9lU4cFkGC4epz8ZGOgISi3uBOLcyAg6M5vSmOOhTYX4OIzOXMFvsTwbMDxoXEJX/s0KAknhUc6igBAwgVps8LEV7Le6vXxdDy9EotyUpTG7XnBlJgx1w/GrICJJAIgCN7zYHyaVwfgVJAZftOrsmwdPDIUXFsJ1P4NMoXAHFVblrF3gqj3hjT9g/1egxgYlR72Eto7fgzrCcQOVmWd5HXw6B66YAOvKGmkH2wVEvG5okwW5bkjzKr2Hxw0eTXEFuh4PjRKNqi17wxEl0qr1Q62pTKAray0LLT3pWsmAkvwFBWkoKw8r7/d3p6kE0LsdHDtEhViPmPDaVPWMypP1Sym+0Gq/0qM88Z2y+PplrHIonL46sV5NAG76BE4dovw+TntFhcO3+w1G4ORX4fYj4NYRcOfXDa8VlXDVB/DJhbD/ExYYpRjTxD9g3IEq8nClU8zahGXUnGLYuSPMsXU/jn431kFBNg2V7T6ojwJuZbFlm/7a32mUgCPybxoBy3/LqWyXKK5+YI/Gx7ZN1OrZvmNo2bIyLrxwdx5/PD75du2azciR3bjkkqkkDs/HmDEFXHllLZCtsjIAH1xzETz6AfHIKZZ+pG++isszvYaUupF/uTJ4iPLYS1iCnzX46Uc6OjpRa9K3X8x0DI6lG1fTUBZwFTvxDEsIWwGtbTLRGUNXplLFCsIJoU8G4+NI0jmBzZhRPSbSMiM613nd5CK4pVpCROeNcsHVWTDYC6evsvQYjtV3V+CD3eD6JTCphAYRfDUBtw6Ejmlw3FewtoaUVlsAR/eAW/eHb1fBQa9Ye4ynEFMltEVZfJ2xJ5y4O/xkmbI+8o2l9G1Kb9JCAGmbC306Qods6NMJOuZBx3xolwsri6BnB8UNVFRBRY2yijIMCAYgGFJlUdMKi2Ip2ENhBTwZGUrnkeFTwLdrL1UuJZTXwOwVqu6qYli9RR03lkG0Ge6kwT1aQLS8GB76XKV2OXD0EPhyrOr7pZ9g0iLHPiKNfKU1AXjoa/h4kbLaWlsON36suA0nvT0TFhXDxAvglk/hy6XxsqhU3MjEc+H8vVVAx2RaX6E4n8eOhwsnph6LGVVbBF86DO6bmrpOjKz7mbkR9rCBxEgs31wPwwpSt0130+CZqNAoiTO5i0xMEkEkiIdi64/hbsy5dFtIY5t3wvwn0A4FklWrKujRIzd27nZ7eOON0YwdO5WamjDOMPH9+2eyfHmUZcuEyrdYyfz20L4AJi0mHuHXWsFcugvc4+RGHLqRIT4TQ8ASamLmvpswuYVlPEU/POgESNz18Cy68wnL8CfJT/anHeUEmWtraYlzI3uQTRd83JcUZzQHN3fSjjMoxR/VE0Rao10G++gGo0qAiA4RwblZMDwdRi9PApEIDDTg9YFwwXyY5VzxW3XydXhjf5i2BS6elLRviANEeuXCU4epleJRb6tVdEIdq16MLGX90QPh4mFKQfrG7zD8QbVqbtA21XkKEMnNgN16qNQxF/YZoKymiitg2XqYtxLmr4avZ6jdAkvKLdFOU2Isx/NIIOfckzyxeJXZcG4mtGsD3TuouF/H7Ak92gNC5S9cC/Msb/VZK5TVmH0vqbiT5OsUV8KL38OLk2Fwd7jgALjpKPj8D3jhR2sF3gQtXwcjnoKTBytfkld+VX47Tpq7AQ4ZD4+foO7nTYdOJSrhjNfhy0uguDqFKa8Br8+BQ/rBQb3gxxWpxzFhPvx0ITzwayN7ySfNNnNL4LRdaAAioLbUzbODWNjciP23Ekq8pRtxXy+VLZPibKUn6Exs/qXQ2h1RNmXJ10pbRTtcqrdxYzVt26ZTUhLk4YeHM3HiMn7/vQR7wypbrHXhhQV8800IFfWXGDBccAp8NJ3EF86rturcux1cvZaU3Mj1RjpPWLoRIKYjOYkOPM5S5lNKW8cSowNp9KENr7AA5+zgQ+cUenEdMwASuBEfLq6gK9egvPdj0XzRGUs+d1JOkS2QR4m0dpUurvF5OLRYxua90RlwRiYctQwizskxAnu44fkBcNIcWG5P/H5ik+ZeOfDsXnDjDBXe3W7nPHrCcMO+cEQPuPob+G1jwzpAghlwphfO3xfO3kOZfl45EVYmh8toARciBAzsDPv3gX36Q6/2SrT0x0qYsxK+mQE3vmoFm3TS1uhCUtV1KtgbE18FQHqhvFqlRWtoIL7wpkP/zjCoO4zcTfmbeN0wYzn8sgimLIINyQroFGACQETpj2atVj4hFx8E026Fd36Dpyc7wLkReneW2h73jXNgn55w91eJE3p5HVzynhJTCaHC0MceSRiOfxleOlVxNgtSiJtu/hI+Ohf2Hp/CJNirRGUzNsAhveBbZ9yuVLOMoSIl7FyYoo5X7dCZn7z3lAUsStEucXuDMQOZ5H1IVJdpBCknaEWVsL+9KAL8sHq7xZCnVUeyowcwefJaTjxxAJpmYBgaTz45h/i2ugAuPJ40hg/PZNy4KmJDzgTNDScfCfuMJb5VriXWumxXeGYF8ZWMlxg3sovXxCdgAdUxbgSgKz4GkMULLMVAUuLQg1xIPyawsIGC/Qx68xlrqXHMlDY3ciXdeYNNbCKaoGA/j1wqEEwmnCDSamMaPJrh5ZTKIDXSDRGdoYbgjHQ4djn47YtbOo+hmfBUDzhqBhTZ+gsHiJzWGUZ1glHfORTkSQAxJBeuGqLiG+37quV82ASIFHjh/L3g5N3hP9Ng/8cs0VVTfiFJINIlG47cXYUMGdYH5q+BKQvhzndgyYaG9bdKod5S89/GwMTuoxlFfKybOgV4c1bG87xuGNJPAeO/ToQ9esMPC+GzmTBtmTUJNwEmGMqr/NGv4ekf4Lz94afr4fVp8PSkRrbhtca5uUZxJ7cfCV9cBmdOSAxhUh+CUS8oMNEEvOawUq+oh1u+gAmnw/7jIZD0sm+sgu+WqcXDK7Y0Okmc85/ZMHZfC0gam11sq7cQZCaLqaz+SsPKlyghTIpVLgRIPW6xpbKDJPtJa2QQYmPMaiuIB4FLqeNN6NGxkfFtK7XqSHYcPffcTCZMOJbSUj9XXPEN8Q2rXNhf2ujRBXz6aQDTtPIEoMOIg2DKXKi3VwPWS+f1wOhusMdUFAOT5MU+1pXGkym4kQvoyOusxEiaqfqTgwtYlGRC0ol0diKX51HLLyc3MpAc2uHla9aAQ0bbkzRGks2xll7EJg34T4aPO2ojrPErEOmM4LlCGLUOauzJxxrabhnwbE8YNROKbG7esWK9sZ+SMZ/6I9QlA4Mlkhq3KxzZC875GFYlB/ZLApEcH1y3v/KMfuB7GHyfY7XbAuut3bvDcXsoy62yWmXq+9BEWN2Y/wn8OYuslpr/NmL62wBMoGXAg9LF/DJPcSSgvO4PHqT8TJ6+AL6dC1/NgR8XqthcDcihFwmG4dkfVNThKw+Daf+Cf3+s9kNvjKISbv0cRgxQcbkueivRhNkfhmNehCeOV+Dx2YJ42eJi+M9MuP8YuPrjhn0/MAl+vgLeXpTaAXHWRhUWJzsDqpKfucVNZHmgynpnZxYrv5ANNSRsXlUdUtv0JrcH2BKNRxawFe1udDZTnsCZCFxEiFuIKEV7GksJQmQ7b7XbqiPZsVRbG+L44ycSF2ORdDQ48MBM7r/ftt8lpmQ/aRQ8+BENHBBP2hleXg0hN/GYWhY30ssboZPw8UcKbqQAD7Mot5gYp2NiX56hYQyIU+nJcyxOWAeF8OBCMJYejGN5LPSJiY4bwQO042pKYxGBbG7kFlcaU4NRvq5Rf5J0Ae+1g0s2wfpaEibJgW54pReMngsbbEW2BSJaFJ7aAzQJo79PCm9iUYcMmHA4TN8AB78JkeQNpxwTuicCF+4LFwyFJ3+GPR921IcmQaRnHpy+j7JOmr5MTaD3faBCkGwvs96MNOjWTsWtKmwD+TmQn6W8lvNy1KLC51J7jVRUQYdCh9WWhOVrVbtqP1TXqbS2RIVHKdqiQtYXbVHK+2aBB1JadflD8MVMlfDA3n3hrP3hsXPg05kwYSqsKKZJikThsa/h7Rnw4InKWuuKN2BtCvNYm75epPw6ProITn0VljhMrf1huOFT+P5y5Rm/pMQqMOCZqfDp+XBYX/g2SddSHYDnZ8BZu8HziT63sfavz4Uj+8Bb8xLzAdplwMujYKSltK8JQa82sCH5722osDuxtnr8dzsXSBlC1+JirTR0MiwRVn8OZyAH4CabfIZQzybm8SgAOm4K8CAiSXvHtNKfoh0OJA3JSDi2bZtF//5prFlTEy8zoF0n6NFJWaMkOyBe0h+Om01cV28Q40Yud/l4wXI+tEnH5HgK+A/LG3Aju5FHMfWsodYRvxcGk4+BxkKUANzJjRxDR76mjCKCOJeul1DIW9SymGiCSOsgXaePpnFqdQjwQETn7hzB+DL4rcJxURM66fBwNzhpHqxxWl4BehQm7A0zNsMT9irTodcA2K8t/Hso3PkzTFlPQ/Bw9HdoF7j/KHhlGgx9xBKpNGO95XHByUNg5K5qd8E3psJ+/7bAw6atBBGXgL7dYWB3pU/JSoOhO4GhQ20drNmkFM51ASgthxVrobQSqsogEFSg4A8oMVQ0qkyANWFtbOVRYJSVAVltlOVWug+6dIADB0OHfAU+NfXQua0KxbJkIyxdD3NXwpL1EBVJY2/KRDgI05aq5HXDqCFw3QgVO+zBz+DnJY20s6i4DM56CfbtDe9cAuO/h7dtMVOyGA4V5v20V+Htc+G8t2HOhnhZRT2c9YbyJRn+FFQ5nvsF78JLJ8OUVUmchxcmzIZfL4GXZ5IYJdga7w+r4N8HWECSdA+baqF9Zvx8RQX0KoTJJYl9AA31DtZvTQPNSDSE8WAQsl6cKjbiJQ8XGXjIwU0bQoRjPiQBwKOrd2O7Uqtoa0eTgceTQShkWqsEVyyddFIBEyf643nW6uSM4+D1SSQq2Q24eBcVFr3YdJZJDJdJoTfEUJHGA1SgQcyLvT0ediKLZ1mSghvpzUOWIt3WcwjgHPpwJ2rJZYNICA/5+BhJAeewKIEb6Ukae5HBcWzC+ca1EYL7POkcXhEgElQgMiZDue6+ZUeOsURaGRLe6w/XLIblzqgyfgUir+8N88rhCXsySgKR8/vBOTvByR9AUS2NgkiHLHhshLrPUS849r5oAkTa58Clw+GY3eDDGXDN62rzqSZNe1MAiBAqqOLeA2DvfoqT6N1JTdwLlsO0+bBwNWzYDJFk0Ulj+pCkMZgh4n+CeqiutUK8rCJxIk6aGFw+JVfv+3/snXW8VNX6/997Zk+dLppDd7cIAioqgoIBKiomdgd2X7vbaxeCYCKKhUpId3ccuk7n9P79sdaeWXvPHMR7uV+93+/v4TWvw6y1ds7e67Oe+jxNoG0LOLWPALfyKli8SXB9zVsHB0v5QzAxExc/myc+nZvAHWfCY+fCC9/DN0tljk8tb+iczXDK8/DPi2BwB7hpgvB/JJP1+wVN/GNnwKM/KdoHsHYfPPaToHu/eFJ8lX6wUvhDxp0Aj/5svS/VIfhxE5zVET5fnXiOaw9Cuzq1cGXpIhGwTor4u6UKTmoS74v99SoeDydxI4UM2zZiQyOS+ddBSGone1lDiIpYZvtqXsIgSgAPTlz4DRFgUq74jv5t+Q842zVNywc+Buoj8j7fNgzjZduYbOB9BFmuH7jcMIw1Sr8TWALsMQzj9KN7hnH5mwAJ3HZbT3bsqGLiRJWZTuecc3IYNaoMJGcOPiAdzhsGJ91L3KSVJkqvvjoAxu8iIQER4DLdywTKiQIuJdFwDPlMxpbNBQwlnz3UsMumpZxII9ZSwn7FKWGCzM0051V24yeerusAHqUe91BICKdFG3nZm8rDlSH218hwYQ+MSocTtyMeCwkiTmBSO3hhOyxQHec1gr7EBJGn1pDg6HZo8EJ/4bw8eQL4Vc3C9ndMN1Hl8P1F8NMa635iokzSLXLg1iHQrQm89Sv0/lJkbQPWCfwwWkjLBjCkGwzoJEBkw05YsA7emQbLt0ieqn/VH1LbOHu0VjLnus0XEqqBjTvEh9/j47LToVdb6NAcbjpLJEHOWClMeXPXyvtRC5iYsnonXPxPUdr3xiFwy1B44HNBD58gcl8VfhjzDlx6nKiFPvZD2FKWZDyiWNbjP8PES2DIG1YOrimr4LhWwnz5zoJ4+z/nwoKb4b0VsNdGh/f6AvjkXPi8Fq6u33fCcU1gRoFsUK59xQHo2hh+2QmbS2BYM6wgImVlCaR5odJEDS/ggp0h0JxhS7SWjpMSSgHwEGArk+jETVSzn/0siWW1O9ApiRp4Ocrhv/+ZqK0wcLthGMs0TUsHlmqaNt1WVPBeYIVhGGdpmtYOUYRwsNJ/M6JEWMZRPztFkpAT/DUyfHgLvv9+h/wm/CX5+Zls2hTi4EGJCPLH6tYJNu2GsiCx1cvQtvDrKFGJbmcApXBVvPrh2ZqPr21Fq7LQ6UIGczhkMWvVw8eNdOQHFP5rQEdjIPUZL4FHNWl1JRsXGgsRb7OpjZxFDvMIskYxaQGMdrkpNwy+CYjjZkWdvJWrcdFuCNkmznsawu9F8Lm5mlQm4mc7w/ISG4jI7d1OmDwEtpTCmK9rAREEWeLE8+DEZnD1538MIk1y4Z2L4cMr4ZtlMOAR+GTOkYGIIwrHd4HHLoblr8ILV4iV8H0fQPdr4PxH4eUvYdGGJCAS4t8HkWRt6lh/LWNqGVdSAdOXwMufw4l3wEl3CQA561j45A744FY4ubtIhLSIHVxDsLMQ7pgAl78Ftw6Fz64XZI2Hkw/nwPUT4PNroXfTWgbpwgx85zfw+eXgsZmMHvxRJBM2yow3h6Pw0G/w+MmJu9tVBmsOQdf6icdBh6mboF9jpU2RZUXQNkf8f08lHNvINk4CdH0fpCYBmBZuA4c3rn7pREjFiVtRA3fzM6CxkueAeFKihg8HGm6HSFI9auJALAz+zOcPxDCMfYZhLJP/r0AAgj3WrAPwqxyzAWgmiXPRNK0xcBrw7r91bUcgfwuNpFmzTEpLA5SWRlDNWmeckcfSpUqbJGi86Gz45DdimexXDYAnT4Ucqf429JEQ8nuGD+ZpfsJKvRGA06nLRHZbHObHUY9xdMWBxhpUJwUMIZ/NlFGuzChBPMIMRD1eYKfFpJWLk0vI5jS7SSvgZYjbxdVlIaLSpPVCjsbDRVBg7lpqI6OzoXMqnLcJMambk3UNPNIBDA2eXU0CiPiAL06D77bCP007epLQ0b4N4Z8j4Inp8Lk9GsgevZUCNwyGYZ3hsW/g+5W2MYcxZfVtC6OPgxO6wLz1MGkW/ONjkTEO/LmoLPt429gUHbJyJE2KG1J84PEIf0skIjaNRCEYFCah0grhjK9W7+GRRnTZwoMra+C7BeKDC/q2gwuOh+eugOkr4cNfYc0Okovc97aDcPaLcEIHmHgtfLYIXvm5lkQ/YM1uOONV+PIGuP9b+Gld8nG/bBQ14Z85A27+Mt5eHYRx38IbI+GM9+Pt0zbCJT2gdS5sVh37OkzbBBd3g9t/jLeZsnAPPDQIHlc0HBD3ans5DJQgE3KIxV/CTKSLaENLFrtcNDodBgbCqmBqJB5chJWiVlFCBCmjgi0EEBFapkZSE3bgQXnu/gtE07RmQHdQOJiErATOBuZomtYHQXLRGDgAvATciUiO+I/K3wJIRo5sx1df2U1LOqefnsWVV1YSi9bygZYBJ/WHuycjrF1p4NChJiLssS4H1EviZL9c9/GAonXoRPAS5TTqcA0LZQiwxm20ZwD1ScNFOUGEGmTIbTRG0DRGEa9qIydSj1Ii7LY52G+jPs9RRrXNpPW0J5WPqiNUyIlhZKo496llWExa7XwwrjGcsAjrhF0DV7SAdhkwepbSLifUDAdMOR0+WA3jk5kf5L6u6SmqGQ5/F3aXWvvU42kBYUK5+WR4+jvo93XimGQgkpMOlw2G4zsLB/ik2TDu3T/wbyjXkWzfTic0qQOt8qF1E5HEmOqDTq1FlBYGVFXDwWIIBUXBquoaKCkBt1toBk6noEkpr4KmDSErE7IyxH7Wb4c2TWH3Adh5CHbsg40FsGknbNsn/QhHmmsSggUbxMfpgBO7whMXiaCEl6bCj8vAOEyBpRnrhLZ339nw691w6Tuwo5CkzvidRSKH5OurwavDN6uS7VGw/b49Gi7qLTLWTfltM5zcRmgle8ri1/ThMrj1OLjuGzlQHnf6VnjyJGE6VSLZAVGcSndAikv4VCC+v00l0CY7vp+gfG9DUet9rIpCmguxGFGu1YmGpjjbRTVTXRIQxVcuJs8WxJMRXTgIRDX0EOy0Ghv+ffnzs2mepmlLlO9vy5pNFtE0LQ34ErjFXiwQeAp4WdO0FcBqYDkQ1jTtdOCgYRhLNU07/k+f2Z+UvwGQ6PTr15Qbb/ydWOoqPtLSnGRlOdm1Sy5XpFnr2F6wZCOEJFCgw5vLYXYlvHeyeKBrwOJkb+8N49ci7JVMvqY2cgp5zKaIIFF0oBlpDKIBbmnxiwp6t5gMIZ/ZHKCKsMXBrqFzMY24jg0WbaQ9qdTHwzSsv/0QXdz2H4IRogEPdaJO7s/UOKEAywSa6oCPW8Glq6HCnGilX+TEunBBExg6XcKcMqGnAB+dBK8sgylbSHC6ExYv+StDxYs+8n0lyS0JiHTKhdfHwJICGPCoUjDpMCDSszHccDp0bALv/wTnPyVrfSQZezgQcbugazPo0U58uraCzbuEE37LLtiyDabOgO274cB+WySOut8kmlhM5FswegT8NEdoJroPGtUVINOkKXRsCWPPFM72miCs2QrzVgrNavMukkZMWe6NS2hA05eLT5umcMsIePRCUcb38yW2Mr6Ksz4ShX9MgelrBMnjc9/DxCScWABFVTD0dfjuWmHGrE0zue1rmH0zzNwOu0rj7TH2XuVaftgEjwyGOqmyaJx5mhGYtwsGtoSZBYn3dOFeOKYhzNhh3V+JP249ANhQInjadoTi2wIUBcCnaivSwl2jRWI/rfkue4kSsD1Yfg7GTFqmONCpiWh4dVFd8qjJv+YjKTQMo9dhd6tpLgSITDAM4yt7vwSWy+RYDdguP6OBEZqmDUPc/QxN0z4xDGPMnz7LI5C/HEg0DVq2zGLv3gDx0AwXgwbVZdq0KuxmrfOGw+TZWHNH0mB4K3h8HXznR3A6ZhNzsl/i8vKl5NxRI7JGUZ+7ib9pW6lgNL/yKv3JxYNPuT06Gj2ow7NKRUTTwX4G9ZhBCYdsmbWXks1DMonR1Eacfg8PpaQwrCQQM2k9m6txXxGUqhNJBJ5pCi/shjVmhJWcDFukwnNd4eTfBHutatJyO+CLU+GTdbWDSKYHPjtXZB8/P4ek4AFipXnXidC3JVz/ic0cUwuIHNcOHjgb1u2C938RCaMW+QMQcYXhmK5wQg/xKS2HojJYugHe+xpWbVbA4jCRWbWCiEoz4lP6dXjjERFJddV98N0M2LFXfFiCZSJMTYNOLaFPR3jyaqEZrSuAnxbCL6tgTyF/mLS4aQ9c90+hsY07E247Cx6aDD+uILmEYf4WGPQ4PHK2+E1umZzc1FUVgFHvwE83iqz2pUlW3pUBUUr3vfNgyNu2nAobIBqGcK5f3w8enmHt+3QtDLEDiXxtftship/NOGDdBh1WHAKvE/yaMGE1SIEdZdYxUQ1SUkH60GOSqWngCMdARFQsdRNVkg/FZQj+FZOsMYAHBy5KowZuEGUojpaYPpKjKBIY3gPWG4bxQi1jsoBqwzCCwBXAbAku98gPUiMZ958CEfgbAEnbtrls2FAsv8Wz2YcNy2HSpDCxTHcZrdW9I9z+CZBDTCPRdDi/JfSZRzyTXTrZPXqEwaTxPIWxSC2dCF3wsYoyyrESIWXgYi9V3Mhc+lOfiASHE2jITqos2ojYl4sepPMPCgAtpo0cTybVaAk5I/e7UnjdH+CAnAGGp4ikwe9tJq1h2VDPDZPUXA/AF4QJx4uyuUUqy6tfUl6cCNMKYOIGkoJI3VSYeiE8/IsI4aytjG7THPjgQpi9CUa+DuHawnblyziwAzwwUpivbn1XAMmRJhlmp8PwY+Hs/iJHZMMO+G0R/PMzsT91rHq99v1mZ0KDXGhQVxB5pvigTpb4m5oi7O3BkNBywmEIR6AmLP0kNcL01KAOTHgBZi+GC28TJprY8eQEW1UJC9eIz6uTxWKoQxs4pgN8MA58PpgyF76cDQWHbOdpgokEr+IKuHc85P8Aj10Ct5wGt38Ma3dhAR5TKvxw20S4/wz48jq44B2hIVnui1doJqPeEcmII9+BbaWJ+5qzE+ZshzE9YfySxH5VJq6FHy8W96hDHdhXIapFztkFr5wK95kAo8woqw7Cpd3gVTVwQ/b7dMjPEvxwB2pEvRbKrNv7I4pGIiO20A1cDjCZ6E4kj7sYhIaBQYQwg5jD08BqDOWhMU1bPhxEozouh/jd/+bSH7gIWC1NVyCitJpArCZUe+BjTdMiwDpg7F9wnn81kOj07duIBQsOJmlP49Zbi1CTELt1hp0HIWw64OQDdmxjWFoCQfWhA3RXhBE+mKnVoMnVi7mKGUl9vme/3HX8gTuD5kxnDzVE+EVh7L2Q1tyUpNbICOqyCT/lCog4gBvI5XLUWQQaaxqnuHT6lYeJBjz4Ik7+kaUxZAeWiTFXhyfy4aTFysbSpPXmMfDOFlh2SGmX297XDdYWw+vLkt/txhkw5QIY9z3M3EatIDKiCzw0VGghC7Ym9sckBK3qwwuXiDrmN70F63clGSfHqpLphdOPhYtPEdXzps6BO18XfojDaRcuHTo0hi5toF0LaNsUmjWC9dugfi7sOyg++w/Anv1w8CBUlwmfSXWNAESnE3Q36Lr4pOdAj04iPwEE8AwdCAcWwPqtIrN9wUrhM1myWfhQIorGYRiwdpv4vP8d5GbCGSfC6zfLiK7lMHmm0HYsovg5dhXCJc9D93bw2GhYth2e/I7E+u9y/GPfwMXHwc+3w9mviaJXdikogsvGw3Nnw4Xjk1OaPD8T5t4E36wRWeu1medObCEKTR28S/hfxk6BSeuFRrS+EDrVhTXF1m22+6G5EgWmzjY7q6BJugCSgyHp17RFaPkjImnTLk7NIIp4b5dTTpgoGfLEKwlQxh5y8BKR6qdq3nKg4484cEahsChx3/+WHOXZ1DCMOWCxricbMx9o/QdjZgIzj9qJJZG/XCNp1iyLn37aj+ofadAgg8LCCMGglVtrxMkwdSFWs5YXRreTK3edhNyRUbqHZ6VubJq10nDShlQep8ySre7BQU/yeJcNsTYNeIReeHFSQjDmAxF9OmdRn8uxGqJPJ5u5BNgDFm3kH55U7q8IEZImrfuy4Z0yOGhOFlIbeao5PLRDENepJq2Lm8K+anh/mxyvTNZXtYNWWXDJD7LBBhIt0uGL0XDtFFi4i0RnNuCIiKS1dvXg+KcPX0ck1Qn3nyuq/t3+McxblXxc7Lqk9O8IVw4ViXzv/QBXPA67DiQfC9AwBwb1hGM6CSJErwdmLoY9B2DuYnh/MhTskUmGyY6vmrLsWo1ievJ5BcCs2wzvfgYTv4EDJULbqF8HWjaHXp3g7ouhfUvhwP9hHizZALOXS3Ob1CCKyuD9H8Snfg5cdTosfFXQ3v/zO1vpXpvTfPl2GPU83HUmzLwPxr4LG/eSVD6eA3vKhf/quk+gMAmYrNgNP6yF58+C6z5L7K8Kwqu/wz2Dk1c3BEE58+V5IvoN4GAVrFWu4cetcGrLRCAB6Q/xQrHtmdhZAU3kO3zAD52ybBs6we8Er2nuNReISmldgGr8rGQXx9GKEEEW8yVRQjjxEsFPQL6rpkbixEnIAK9LLBiOmsg56v+q/OVA0r9/E155ZQNxPd7F8cdn89tvih1AZrYOHQQv3U+8gJUODhfU8cFvFQjfiGLWquuK0gid7QRIUcxaw6jDzxyMPYymDKYBs9hHVJqzfDh5hN50JZslFGIvWHUGdZlBCWXEc0Y0nBxPBnfYDLs9HU4yNJgejAA6LZ3Qw6Xx4D7iJi2EScujwTd7sEyIzR1wUxsYOB1rCLAfBjSAi9rBSZ/H2yA+plEKfHw2XP4FrNiHFUTkmHQdPrlC1K0Y+WoSHiLlXE7vAXcMgwm/w32fQrQm+TgQ1+VxwSWnwOjjYecBkWg4dw2JYBYSQDGwOwzpK/4WlsKMxTDlZ7j/Baisjo+NyZH4Q2rLI5E/6U8/QeuBQosBYm+G4YxrOXMUTS8jT4Dbqf3g8WsFeHw3F76dBzv2EzMx7S8WIc5PTIIz+8P9FwjAengi7LAr4gABiHjgia/g+2Xw8bXwyo8wYW6SscCvq8QcNuUGGPqiEgihyDtz4fOOcGYXkXxolw8WwfyboWk27KhJ7C+shsEfwbcXQF6qAFeVt+unrfDucHhO1aClZrNgH3TKg9n7lT5dhAC3lznGB2rgmCR08jHKFpuWtEcTP7K5MJzCUvrSHD/VbGQe6YADD36Zz2UmIwbwEMagLKShGzJH6WjJfyYh8b9G/vJLz8vzUVwcRsQaCeAYMCCN8eODxJIx06FefVi3HcpDWM1ajUREU8QZbwNh1jrHp/GtVhUDDPNvJ9J4jW3YpR91eVHSnjQkhac4hrp4ceIgLDVMUxtxAL3I5DF2WfZxJplsJUSpzTfyD18KN5YFYw72p+toPFEIKoOEz4DH82Goaq8Og8MP7x8P1y+GahX7/JCfBq8cB6dNhUCSCTXHBV+fDzd/K0GExDH56fDyOaIo1efzEvtNSffBi2Mg0wdnPwdFFRyW8iRNh6vPgItPhskz4OwHRQlb89xNcbtgaG+4YIiIBDpYBD/Og/tekyv9w4QC/2kQMcfoSp8TqqqgajsJDvjaMuDLC2H6AvEBaNwQTuwF79wFTh0++RG+mCFZm4FwEL6YLT5DesGku2HhRnh8Ehyy+QZMWVEAgx6Et6+BXi2EbzCaJOz3l3XiN/nyehj+cmKeI8BVk2D69bCgQBSvUvcRNeC+7+G6QXDXj7YN5bgFu+CCL8SzpGGlPtnvh7ppyUN4t5RCl7oKkMj9HfTDiFzx/7KgWOzERN5vpybDf5VzcXgCNMYdM0e7CXCICoIEWcJszFB9F76kNqFUXOiGPNdkN+r/y78kfymQpKS4qK62Lwt0evZM5bbbiomF/TrhpBNg0wHi2og0a53VBqbsJalZa6TDxzWIZZ+5emmBj3R0SglZzFptyCBIlCI5cwyhKZm40WUosFchAYjgZAB5lBCmnEhMG4ni5GJyOJdDFhAZiJu9YVgfEQ/5sR7xksypxuJgvzcf3tsP+4NYtI7b2sLvB2GhaU6Q7boDPjheVD3ca0a8qOHDLuETefA3mL/T1i/30TIPPr8MrvtU5DrExGbSOq4tvHIJvPAtfDJbttcCIl43XHUyXH6qCP099gZhCoqJ/H9uJjx9naAX+XE+PP4BrFLPwXY9tflOPG5onQ8tm0Kj+tAwFxrWF/6PNi2EX8UwAAM2bYU2LZV9aLB+EzRsAAcOic/BUthcADt2C8d/RSV/mIy4ey98PE18GteDC0+DGa/B9MUwcRas3kYMlH5aAj8vhXNOhHdvhhmr4JXvlfBfhT7FHxTUKQ+cBV/fBhe+DpVJVtJfLoXsVHhyFNw2JbG/pBrungovngXnf5TY/8sm+McwqJ8O+5OYyEDkjTz+O9zZL7FvxX7oXh8W2cxwG8vhPNOCr8w2+yPSLwKURCDHY+3HByFNsGADsfuvuyJomiGb4iuEgxSzWxaQA9BIISwfHlMbEb4SFyFDTBHho62RHOWorf8m+UuBpHnzLObONR3awj+SkpJKNKrh9ythvz44+Th45QfRFCunq8PghnBfAfFoLcSqpakrSoEWoohQzKwFcDq5fG9zggMMpRE/KM71d9nELPbzPL3RABcOS3Gq86nPQ+yw1GA/jXTm4qeYKKrB9D6fhysUbeTJPI2r92IxabXyQrcUOGM9lkm5lRNOrAMjZpFg0nqwD/y6GxbtibcBMX6tZwcLyu8f1yf2A3SoDxMugks+FuVrY2IDkTuHQ6t6cMbTwikM1Aoi5w2Eu8+BD3+C3jckWfUpwFBeBR98A1eukhP9EURm5WRBvx7QqRX06QLNGkMgABu3icl/326YswX27oe9e6C0TE4Y5jmamopqH9chM0NovfXqQr06UKc+nDwA2rWEjHQ4VAwLV8L8FTB3qVJrQ3VQS6DYfQCeHi8+g7rD01cKTeupT2H+WjHGMOCzX2HKPHjgAvjtUbjqDREWnCAhePRrGH0sfHA1XPeBIDy0y7uz4cXz4ZYT4aXfEvt/2QjXHQeDWsGsgsT+V+fDjcfCfT/H74tdnpwLV/VUtA85Zt4e6NcIFtn8JBtLoG124n4O1sQd7CVByE7iVA9GIcsMoJEma1OciqnaSYQaqqiigjjueIkQSMgjcaMRDIpFWOBoaiT/37T110mrVvWorIyivtU9e6ayeLGy7JMPW/d2sPxNIJcYiHTIEw9uQI3Wkg/bKJ+DZUbQEvPgIkJ/snnfRtDoxkFnsnkVawp4C9J5n638ygGyJctABCetSacylsUuwCWMk0Fk8AhlFm2kF242hQ02VYtiVae6NZb4YaPqHI7A0y3h6a2KqUtGab06EB5ehbXErh8GNoR+9eCUKfG22HbAIwNFudIJy2z9Ujo1gA8vhNHvw0bV2a2GGjvgvRtE7fZr31LKqyYBka4t4OWrxcr7pDuhqJw/BIZQCOaaeSa1jM3JhON7wJAB0KuzYGyduwTmLIFJ38KOHYo/54/MWWp/DRYzVlm5+GxSAdXs16FOrjj+wGPg7quEFvTTPPh9KcxYBGH7myQf4VnLxad7Z7j7fLjyNHhtKizbLIYFw/DAx9CjFYy/BT79HV7+FgxFKzFl0nzhUP/mdhj6PJSZYKKYu+78DH67E+ZuhcVJaFhunwKTLoV+r9rK5XoFk++dA+CJmcIJnyDyGFM3wsCm8Kti1Z23Gx47EV5abR1/qEb4MC0zjVeAhFsq+f4IbDdTQOTCEUR+SY2Nn8ypR9iiiZNTfZyVVBKKFdUV0VkBpRCd6WwPoxGIQoYGkf8PJEdN/tJL79WrHgcOmG+30EA6d07j999DIPlx0KFta/h1qXB8xgEDTm8Nc8wIYXPhIbm1bnGmMljbZ/GPdCGNBZQQtCUOHksd5nLQ4q8AGE4+d7GCMkIcoDK2ujmFXD7iQEwbCeOkN5kE0eRe4qugheEIi2qCmLPCQ7kwyoyakiat/unC5zKnFIvWMboJ7KhMNGlluOG+HnDpdIgmCek8qx10qw9nTExy08PCnDV+jMwvKCRpGHB+Jky+Cd7+DT78RdneBiJuHR68ALq3gOtehnXm5HUE2kVMbGNb1BHhs8OPB5cTPv8RPvgSbnzYFvv/Z3wiSZzIdjBJ8ImY/WE4VAQ/zBQfnCLKq3cXGDUMnrsDFq2Dz3+B3xZD2Hy8lPXQ8tVw3mbo2AxevgE27ob7P5QFs1ywbAscdwfcNhK+vBsufgkq1QdS7uuXNYJd+Jtb4LTnRfKhKqEIXPQOfHUjnPgSlNque3uRKHh1fnf4ZKm1LxwVxapGdRI1R2qTqZugb2MrkGwqFrkhMVH+v7tSgEbQ9oKVKs9Bu0xrH7rQGrLMd1uJ2GqF25KM6CRCPeoQJoQvpqmkoskfMqL8TcVBOAy68z9g2vo/DCR2PtL/QdEYM6Y9depYbQzHHJPOxo3yrZb+keP7wZYDKNUOxd8hjeGnUpRkJbGXJ1Jc5OCIQYOpBg8lj/kymkpdzZxKI37CalNoRTqHCFAmGXxMScFNH7JYptRzB7iCDN6hwqKNRMJOwiEnAWnSOt2jsaJGMJ6q8mQTuHsDVm6rGri+FdxjkiIqJq2n+sL4DQo3lgIETTPhvuNgzJcKyCj99dLhs0vh0vG1g0j7evDWWLjtk8ODSNcWMPtZkVQ3/IF/D0Qy0+CaM2DW22JirqiCC26FAefDKx/BgqVHCUQiysfeF1bG2PetHi8iCmXNXgQ3PQzdzoYPv4Rh/WHK8/DgWFF1MeF6/bC2AE4aB7+vghnPwkUnxceEwvD0ZPhsLvzyD/FbJpOpS+HN6fDZDQJo7VJQCP/4Hh4bkXz752aIOiPJtv16HYztReKkqHxfsBtOUzIXWmXD7f2hdz1YeRHsuhLqK87zmogICgEsjvgUXWol8lW3h89GHPHcHlPc3gBajPsu/kO50FEfUF2G/5rSif68yxguJ4+PWghfUW4O/1+OkvxlGJqS4iI/P52WLbMQKCAApX17L+vXB1H9IwP7wFNfEwcLXVSyS3HJHIxYIpPBXdkRrvV48WOQRhRNPmwOoDvpvMBWy3lk4iINF3uVGSWMkyHkM429MRAxVePTqcO3FFm0kQak4sPJ6mg0AURUuScHLtgtv0htZGg6zC+HjaqZokbEnhz/C0RsE1H/+tA8Ha7bAN3qwDWd4JofiE10O4pg6Hgos0dHhSHDC19fAbd9KcJ8k+WSdG8M718Fo1+TdTdMsYHIdafD6IFw6QuwQQ2A+5Mg0rcLXDsKOjaHidNg1C3CH3FEkVmQFER0oFFDyG8kas1nZ0NqqmQCToFIWJh1amqEM7uiQqzcd++BXXugrIpaNRN7tBcI09rcZeLjyYDzT4WpzwnalBcnwuotJGSpT/oNpi2GcaPgiwdh7CvyuAhiy637YMq9cM27sHCzcv/kfibNh4xUeGkMXP+R7dyAKSvh8n4woFViTZMKP0xdAxf2gA8XW/sOVIrExAS2X0WqQuL5THNDZRB61ofH+otExXqpsK8K9is+nB2VomzuVpVyToeSgPCNHKhFM4gawtenErDGNxc/tjPhgUO2ewlSHntv17GF/kTJwEGqS0SNbU0M3PzX5f872/8aadEiG6fTweDB+WjaEgwDdF0sY0Ihq3+kUytYsweLf2RgPvy6n/gVeOG2TLgr1U2KplFClOa4KJDd3UhjNZUJFEjHU4+ZxAP6wzjR0WhJGi/IrVWH3TDqcDkFlrYzSOUdGzGjKaaD/QSXxvwq2FGBZZJ8oCmcs5zECTKizJ+yz+2Al/rDuT+AMwKvD4YrvlO2lX8PJYng0jR493x4+meYtdnaZ27XrwW8egmc9SIUqNE3Coik6vD+HbC3CE68BwuL78WDYcUWWKVmzZtiA5Fhx8Jdl8Ky9fDmROHETjr2MOG9aanQubeoT9Oto6CJ79Je0KDs3Qe7dsC2bWLSq6qAfSVQXSWio7w+QWXi80FuLnSqD/mjRBhvViZsKRCHWbYalq0Sn6IakoOJMsEHyuHDqeIzqCdcMxJyMuCuN2Gnkl8CAsAe+hjOPg5+ewKueAWWbxXHWLwZTn8UPh0H90yCufZoNuDtmYJi/oJjk5M43vwZTBoL/Z9LfLxe/h1+ugo+XpLI1zV+BVzUVUT71SbzpXP95+0weT1c3h0GNxEUKt+pLkhdPPNN00nIBykOQk4KHCiHndXgdYBfmYzDhqAPUkXXI1TKq4lTyAfwJwQ9G4SIo1klFaxmP/1oSmVY45sVIp/nqMn/cdPWX3bpLpfQWd1uB4MGNWTmzAitW6cyc2aQmOFah/zGsHSTzT/ihVObwxdmbLpThHjemolMJtTwotEGKECYsfqRwwwOJaxgTqAuj7HWkmx4DHlsoNwyMoKTdqSzlHLK5AwSxomGlxGk8WL0UII2YoIIYY076sFt+yw75LQMWFkGe8x3QGojMbGZtJplwVtrYHsx3N4Tftoi6Cn+UMLw4FDYfAC+WUlSEOnTQpTKHf6cLDtrivJ+tsiDiXfBa9/KejByws9Kg7duFnXNv5xNrSCiaXDmALjzUli5CS59CLZvTT4WSACRzAw4vi+cdKyIrGpQD1atgRVr4N3xsGa10DKA5OYue5Klmmhg/vzy0XM4oFUb6NEFTh4Ed90IB0sEieS03+C3hcK8ZTlvW1jwrKUwawWcfAx8/ij8sgSeHC+TKpVIr6/mwMptMP5ueHUqfDpLdO0pgtHPwrcPwjXvwOIttuOE4aoPYMY9sGInrFODJvzCH/Ldarh+kAAOVYqq4NfNMKQd/GCNMeGbdTC8PbWLLhh9O9YRQIIXzv8eNl4m8j7eWWsdvrUMWiehStlUDunyWlJ14WPxm/06aG5RmE1l8vYAHjQLiAB4beqAm2wq2B37HsHJJ6yhF005EIJN+/4DeST/hzPb/zIfSTRqEApFcLkcXH11CwA6dvRRWgqqf+TYXrBlP1b/iA5968IC84V0iferaVE1TwRqWGkEqSRKPQUnTyCbJTatIQc3fiIU2lYzJ1KPHxFxjKrmcSZ1mEFNzKQFMIwUfqSmFgVbSHs3GFHYUE7MpEUY7s6Hp7ZjBQzi/TGRE9amQnh3LTTJgFGt4Kn5JGgjKvCY30/rCH2bwAPf2k5Mjm1TH966FO79DPaWKP3KbTm2NTx9OVz3uhVEBnURtv7JM+CK56Cq1HYMOa53B5j9FnRuDSPHwTWP20AkRFIQadoI7r4afp4A0z6EXh3gi6kw5loYMBSuvwPe+QgWL/qTIGJvs/lMolHYtAUmfQV3PggnjYSLr4FJU0Xk1uxJMO19uGiEQrWRJPCBCExfCP3GCiqXD+6D05LkYWzdCyfcDsP7wl3nEPtt9hbDmY/BW1dBt2aJ21X64ZK34d7hghHBLs9MhzO7Qro3se/tBXDjcYnt/rCooNi+jmxIstxcvh9OUs6n2A/X/yoc5IsPWLcr8kO7vMR9eJ2QJcOs/BGhkRxOnHqEFEeUoPR9qg53iIMKiDwSkyLFpEnZQw2VRPjnTnGeR5X99/+4/GUaydq1h5g1ay/33beSqiqBEG3bprBhg4HqH+nTHX5cg8U/UscH5WEl7BdiYb/ZmsYDRhlLtErcMg2pJV524CeMEYOFIB76k8cyWwVEDw6akc4GNluiPUCnO2k8zH7MpUcQD6PJ4OZo6WG1kVvqwMv7LYdhYAYsLBH245jUULspRwGK5wfC3TMgGEjsA6zO9xwYdyKMfFuaMGzO9QZZMOk6GPMG7CoiPhkqIDK4Ezx1GZz5D7FKNsfcdBZ0aQan3yup05NoIvVy4YnroFEduOoxQXhoOUdImIBzM+C80+G80wQ4fPoNnHcdlKj38DAmr4QxyUAEpc+cgE1TVS0+kTAweybMXiiamufD+WfB3EkwZym8NQnWbEI8q7b8kgjwztfw1e/w/r1wyjFw57sQCMWPGwjBRU/Ae3fA45fAfRPE5rsKYeRz8MxFcPMHsFdNGAzDuj2wfAfcOQwetfFlBcLw+TK4oT88+au1b2uh4JxqnAW7bb/dtE1wWhtYn5hyBcDeSmiUbm37bBO8Njhx7IGaePKhKpURQc2DE/xR8KaCYo2KK4w6sYgtDxohohbLgo6TiM14p+OzONvNd3mDEWFuic6xIahOkovzL4uD2uvR/B+Qv0wjcbmcLF16gEWLClm7VmgKbdv62LhRPiDSP9KrIyzZTpwWRYfjm8DMg1j8IyCyXke4XSyWJTdNh9wgsvjdXtQAGEBdfrclJ/akPnOIZ1WZD+AAMvmdKkJSGwnioRFuDhlRtoUh6Leq1qZkOyDHAT+bvhGpbYxrBB+afgibSSvbAT4T8WxmqOMaCcyctROr2EEEYUp673y4ZwoUV5Ew0ad64LWL4KaPYO1ukq6oRxwjaqsPezAOIroT3rhJgMi1L9UOIpcNh9fvhKkz4dTr/xhEOreGD56EVx4StvZzr4VTL4KPvvj3QMT0gzRuDK1bQ4cO0KYNNGgAaWk2elV7NJddU1T6tu+CJ16BniOEuevRW+Dtx2FgryTXKaWoEM64S7Acz3oB2je19keicNmLkJkKz1wcb9++S2S/T7wZXEmA8eWf4fRu0LJuYt9782B0rzjpoiofLYZLeiW2/7AZhrVJbFflQBXUs0U+bS0TmoZlXLUCJMrStSIsaHRARHbFNBLThO0EQ5mh3N4APqIElKgtUR3RRchmVXDisWS2g/B/HiJCkV/4ZrTD8ur+STF9JH/m879I/lIfScRiD/KRmelgyxb56+qCkDHFB6V+LGy/vRrCVNOOr+SPNHZoHCBK1GHNZ+1LBvdjDV1JxUkmLvYps08QDydRh4/YZTFphXHSkyy+smkvI0lluqGsepJoI2MyYLZq0kJksXsdsKaUpCatf/SBn3fBt2a0jhyjAU/1g8umkaiF2CUMNx8PiwpgwXaS+kXevRy+WgRzNmIFEflODu8NNwyFUx+QEUUhUS9k8v3w6zJ4brIcb5swc1Lg7XsF4eLF9yv0KLX4Tk7uD7dfLpzkL7wHMxfwxwBhu/b8POjdC1q3FGDRtIkAj/Jy4e+oqREr0Joa2LMHGjaEjAxITxcRXRs3CnApKIAtW2HzVli1ClZtkSYQu4NdyUGJRuHHWeLTqgU8divcORbufQlWbUQ8t2qURwhe/xJmr4BHroC3vhX3MxYFFoQb3hBayb0j4YkvRfvv6+HbJfDMGLh1gvVWhiNw60R49XwY9rJyv71CK/lwPlzdD16cad3ui5Uw5TJ4fI61/UAlFFZBukepzmmTpfugcx04YEb36bD0ADTLgA2K1uR3QWESUC0OynojiGqI9trtUWQujSRhBaGRHMJvCd/34aBEWfwJ0ahWHhbzfd4fNURJbqcow3w0xfg/7CP5C4HEyf79ftTQ3/x8N36/PCUntGsrKSNs/pFB9eHhnYiS9kr+yGCPg1nRADji9tNUHHhwUEzIAi69yeIXCi05IjoazUhhg7KSCePEi4NepPEoB1FL6Q41UjgtXJLcpCXlokwYskk5cBhubASvqpEtikmrjhf65sFN8+Ljzb8jWorypZsPJvYBFrNVu3owujsMfNF24+XYD68QBZEmzCUpiBzfGe4eCUMekCVyQ6IA1eT74LWvYap5fjbtYnAfeO5mePAt+HaG0pcERI7pBk/fITLVb/6HoDk5kvBeTYOenaFfHzj5RGjSSNTfXrwYli+Hn3+GHQVQnITW/HCi69CsObRqJcDo3HPgrRNF6d3Z82DWfJi3SEk4TGIC27INRt8CPTvBs+Ng9WZ46mNZoMsWMrh6K1z/DEx5RtzbL2ZZ+x8cD1MfgrP3wlfzgQA8/y18fjsM7wrfmqwA8vjzNgun+7DO8P1q67W9NQem3wwvzZJMAHKbqiAcqoKuDWDlPus220uhXz78VJD8fq0vgs658IsSJl4eSgQSgPxU+80WEVnZ8vXzeuKZ7qZ4nIJBQBoY0PUIGYTxSROEM6aRuHHhjn0H8JJJGD8R3DEQCeKhueYkEAKX9v99JEdT/jIg0XUHBw7EjZQZGR7KyuQpSaWkS0dYXYDFP5LqEiU4a9RwO7li6epw8oWjClX6kMpyW/IgQD9ymIrVcdGTLJZKh7yqkfQhl99t++iIi+2EqazNAB/W6K3D5hpRk8HUNtKc0MAD0w6SNErrxk7w2jowbBqEQ4MH+sDQyfE2y18FRDQN7jgBrvlUUHCofX1awDtjoV0DyL2KpCDSpRk8fxkMfdAKIt89Co99Aj8stB0TMeaOi0ShqVNvggNq5JcNRJrVg6fGCW3z2vthvaksHgZEfG44+XgYfqrwmy1fBd98DdddB7t2YdVUDucTiV2wK6EnHIYtm2HLFvjxR2KTer16MHAwnH0avPykAJPxX8CCJST3pwBL18CQS+H0U+CXN+Ce1+GHuSSEDB8qgVNvhi+fEr6ht76Pn2bEBec/Db88AdsPwHKZ93DlmzDjYcEpVmJ93HnzN5h4XSKQVAdh1W44sTX8usnaN3UtjGifCCRzd8JxTW1Aolzj5hI41maa210JjdISbm3MFaX+NIGIyD0BkVWvq6YmXRCbRgxibBUAXjSCRCzJiB5cRCwvkpqQKOx5psPdFXUSMDWSowgkhgaR/2Xmqj8jf5mPRNNg714TSFw0b57C9u1hVEd713awcicWu2LvBrBYTZRSHFwDdJ01hCyOuP74WKyYpEyA6EA6621ldgeSyyxFRTYjs4aQyY+UW7SRU6KpfB4O1OpgJwwjfPCeLTz3nExYXibpwOMnBWHhFzmhHkxcFzuBmIxqJvwih5Knq1hkTC+RVLZiN7FJvH8LWPII/DAOuuTD/M1QqV6+BJHGefDRzXDeU4IF90hAxGXAu/dDi0ZwzZO1g4gzCvddCS/eC29PhhFX1AIiNcRApGt7eONp+O5T6NsD3v8Iuh8Hl14CX0/5V0HE/H8tM4m5D7nfAwfg84lwy13QcwB89R1cczEs+w1uGCvyTizXoJzPdz/DyVfA1WfB63eLpEj76VRUw/D7RLGvy0+x9pdXi99i3FmQlQoEoLQKnvwanjxXGSuPvbNION9P7Szblfv/7ly44tjEy/1xA5zaOrF93k7o3yT5LQJBi9LGRsi4pxIa2bUPBFV8hs3hHoiCR85AYUNohEDsnXbqEFZmKKcjQgoQUMiMnERIRyOYkEfixLCRHkVw4gECVWKKOZqldg0HBDyOP/X53yR/2dX4fC6Ki/2YS5wWLdxs2ybfYOlo79paARL5cHWsB7MKZZtiBmjuibJPC1vmEScRepDOChtgtCCFAosFVTxkjfGxTGbDmhLFQx1crLBRpQzTvPxi1B6I7gJGZMGMEuLhvH64pCF8bLKx2KK0LmwK3+2SrKqKxqEBtx8Dz86Nt1n+KhpHsxx47gx4eJq1/alzoWNjyEkT0Vvfq1xK8jI8Lvj0drjqVdiyF1Foyg0vXQuP1wIimR74/mVYsxWufRzC6supjGvXBGZNEGaVUTfCb6pN3gYibjecfzbM+Q7uHwdfT4OThsO9j8D8RRBVV+F/GkTssdZHBibmuUWj8OuvcOn1cNxQQeUy41N4+DaR55IMTA4Vw5k3iOTGt++H/DokSKACxv1TlB4+qYe1b/t++GY+vDA23vbZPGiSJ8Ky7fLkt3DvaYntS3dC81zhw1KltAaqQ9DAFoVVWC1yO/RaZoniGsi1RSrtqUUjKQ3GQ31N8UeE+QrgoPncmGDihJABZRGR0e6UUVs6UITfYsZy4qTCFkyznq8AK4V8GCdVUeGL87qPMpBoGhFd/1OfPxJN0/I1TZuhadp6TdPWapp2c5Ixx2uaVqZp2gr5efBItz2a8peatg4d8mOiRsOGXjZvNuJn5RQ2/H1VQB1iYHJ8Q7hX9ZvL0MB+upMF0ZDywEXIwck2amJRHqZ0Jovfbcyg+XipIUpQYqupffQllS3KaieCk1ZRD9uJUnEYbeQUN/xSap3b2qQIRlM715Y5+YxtA2dMS2w/pSH8vgv2V3JYEBnUCn64BlbtgTJrXAAnPAlTboKTOwszxwLTvKFg4QuXwaRZsHgTsfn1g3EwfQl8nwREsn3w9bPw7HiYNst2TXKcpsENo2HMCLjqAVi5Jvm1AziDcPEFcNOV8PEkGHWpqL1+RI53QzjV27eHzp2hWTNo2lT8DYejNGigi0FykbBpU5S2bcX/KyujFBY6WLFC1HhfvhxWm8mNGgm+DfO8q6vhowkw8Uu45DyYORk++RpenyALctnMWO99Cat3wNQXYcz9gnfLkhVfAaMehp+egYufsfZ/NhvOGwSn9oAflwEeuPF9ePgcWLDFWtFy+yGYvxX6tYJ51hgTPlwkKiW+v8Da/v1aOLElTFhhbd9cLHJA1hwkqWwuVSjlEaWgzSRDILYAPFCjtMtZJxCJvx+5LsW0JfszveDWjdjv7CRCFg50Zf3rIUAaXjRbGatdzEP90czFYa7mIGwI09bRBBKAyFFNlQfEk3+7YRjLNE1LB5ZqmjbdMIx1tnG/G4Zx+r+47VGRv0wjcTo1iopMIh1o0cLF/v3xtyE9C/LM0EI9/rdVOmypIk7UKKW/rrPAsD4ZPUiVVO9WOY4cVlAV4+EB6EsW86V/RM1yP45MfqPaUndkKF6+jyYJQ1FkdA58WoRFGxmRC+/tkgNs2kjvLCgoE3Ua7MmI446BNxYpbTYQSddhwsUw5QrhS5i8NHGsy4CG2XDrePGjL9+OBUTOP07Y6F//jhiIPDgG9hwStcfV44HQRL594fAg4vPCpGchIw0GXFA7iGgajD4VFv0MLZrCCcPhxTf+GEQ8HjjheHjoQVEqd8kSuPtuaNQItm6F99+Hiy8OMXx4hF69/PTqFaBXr1J69SrlggtC9Ozpp2fPMMOGRbjtthDTp4tzGTtWaB1LlsAzT8PQoTLD2n4O8txCfnj3E+hzMpRVwJzPRCCBReQ9XbQEzrsbxj8K/bta+wAKy+CCx+ClayDHpiFc9xo8ebGInAPYul/4SM7oaT0fgG+XwpUDrb8HwM9rBJDYxy/YAQOakSCrD0DnJCHFpugOaKhoIMV+UXHTLlFE9roqBjLM3Yd4KM0sdgAXuDUIalF0VwS3VzysKTjwE5Y5YmYkl4cwNbXybqnOdvO63f8FznbDMPYZhrFM/r8CWA80+k9v+6/IX6aRGIZBNEby4yM/38vu3c5YRnu7drBhF3Gw0CHHCyUhMNQ4bOlo76C7edDGqd6LVObbwgIdQH087LUBzDFk8zzx8BMTOPqSyvMUYTrtIlEng/Exqrq6Vm3EG4CmOiwpx/KyXtgIjp1NUhqUS1vDm2uxahx+UXPFH4JtJdZ9qbLmHmiYIfI7whE4WKzsQ/69exS8+hN88Au8+aO1znrzPEHAOOZZYpPa8GOhVxs48wE5SJmM0t0CRJ752AYiypiGdeGz5+HdzwUzbm1hu62bw5uPwcy5MORcKDyYfJx5z9xuOGWwiKjq0gU+/xxWroQ33xS+DKuEbDtSb3zcS15VpVNVBTt2hFiwIL460XXo2xfOPBOeeBzWrYPJX8APP0LI/uZExNHefR9+nQvvPg0LV8HDL0PQvHapXWzaCKffDC+OE1rU78ut93DjLnj9G3jtRrjgifh2B0rgqtfFb2yWwnnmG/jsNpiyxHo6czbByxeJjHa1jvu2QmiQIXJKqpV119Jd8MrZ9vsngGRQC/jUvgiQYpqydkjfXU3YRicvpTos2H5ViTqEQz1BJGh7nFCpJIy6CZCOA78NMDx4CGBzRkoxQcR0tpuJKQfLjq5GEsWhVEM5Uqn54yFSNE1rBnQHFibpPlbTtJXAXmCcYRhr/8S2R0X+Mo3E4bA+QY0b6wJIAHzQviWs34PF0d65DsxUJwu5evEAPk0jIs1aZuhvV1JYqURbiaJUqWzGGuriBPxE2U0gZtICqI9OCRHKZQhhJOokK+yiLAKFYUccRGwyOB0WqOYrP/RIgXXlIoNXnkxsfvM5RcjvLDVBUcr13eG1xVgBRu7T/D7ibdh8SEww/pAoq6ruo1EODOsGH0sSvqgNyN66EZ78DCrkZNC0Hjx8EYx5QpZ/VSYiPQqv3A4vfQpTawnv7doOpv0T7n6udhBxOuHOK2D8C3DnI/DoM7WAiHS8t2sHTz0Oi+bBwIHw6qvQrRs8/jhMmfJnQSRka0uO0OEwzJkD48ZB9+7w7HPQrjUsXgC3XCXyT5Jtun0jnHSBCDp45wnIr5c4Zu8uuOVZcS9bN8HqqvHDlDmCpfjs46zbLV4P1eYaKAC7i2D1TvH72i9l8gIYfUzisadvhJPaWtuCEcHkm2NziCdoJLbr3VOR3Cdil+owpNgsPxEjPgHtD0BVBEuSsQMI6sJcreumPwSC+GPJiCL810HwMJOylaFCXEOLeke5QiLIWLIj/wB5mqYtUT5XJduvpmlpwJfALYZh2MNtlgFNDcPoCrwKTPkT2x41+Qu5tsz/+QAX27eHCQRcsbrr9XJgzS4sjvYeDWF7DQl6VDeXg1UR+ypFYydBqolaTFXtSGWhzTHXkgyCGJZxQhvJYIYNdAY53Cy0HUvVRvDD8HT4tpjYd4BzGsDne0lKg3J6fZi2gwSTVpoLWuXAz5viY80+9fvKHYLR9+VfRPLZPjWqLQyPnwsPTrJVxJN9l54sNL8FMlxU1+HLB+GGV0QpXHuI70u3i/yHr36xnqspvdrB6/fB2deLKobJQKRlU5j0MrhcMGA4LF1mG6PM/QOOg2++ghefhV9+gW7d4c47RM5I7VIbiKiRWrWBSe32jhXL4ZlnoX9/8X3uT/DwvZBuTr7KtRoGvPwevP4hTH1bMFjbd7+vEC55ED59TFSCtB/6pteEeTHP3mebzJ/6Gs7uSYJ8NAf6tpBflN/o2xXQxwzbVfb10zro2cjavqdc1EmpTbaWiUJrqmwoTRx3sEZSwisSiUKhBI/nd8FiU0OQSmG6bpgpJDgdYvrNxpnAbJdCakLUlgkzLehBHrmx73vkffS4jrKzHU2BtiP7AIWGYfRSPm/b96tpmgsBBBMMw/gq4biGUW4YRqX8//eAS9O0vCPZ1nacXpqm3app2rOapv1D07RzNU3LOdw2qvxlQBIORzGfGE2Dtm3l0yhfyt6dYLu6QvVCp2xYY74Q5pyvR+ilO1gasT7tHfDiT6h5CH3JZjXVlh+0B+kslpqLGpnVlxTmEY5pI+Gwk+OjPn4ORJKatEw5Nh3mqdgfhj4Z8ONOa5u5zQWtYeJ6pV1e46g2MKPA6khNKEQVhnoZkOmCcZMh7ybBu2T2dckX/T+Y5hOFn6tuFtw8Au59VzS1agRbPxL0JPPXkgAiV54lamC88HGS8wG6t4a3H4MLboPtBSQFkZMHwBf/hCdfhsdfEP4F+xiAY7rAu2/DVVfCgw/C0GECSP4oMsvhCNG1K1x4YZSHHtJ57z0H06ens3BhOosX58hPNosX5/DNNyksXpzBV185ef55FzfcYHDiiZCZeRjjuQFVVfDSC9DnWCjYDLN/gDNPlf3mNUt8WrQCzrsRPn4OBpqTvbl7P6zaDA+8AeMfEaZJta+kAh74AG4fZdtOlYB4T+plQZsG1vt4oAxa14Ncm8awbJdg/bXLzlLo1iCxvVFGLSYooDIETTOsbc0zEpMLU/REH4nTAXl2hT72Xous94Aj7h8BSMNJgKBlSvbiJWRb8JnSgI54SItpI42cGviF2exoO9uPtmiapgHvAesNw3ihljH15Tg0TeuDmNOLjmRbuc2lmqYtA+5BzL4bgYPAccB0TdM+0jTtMEHgQv4yH0lFRXz5kZPjoagoGj8jJzRrCDvKgBxipq0O2bB+E2A+uPLsm7vgS6d4c02HW2d8rFYZ4KQ0xccOW4ZcDzJ4zkY5HcZJRzyspRg1+qOb08mySIDEMB7AD91dsKpKmCXwA2FonwbFIRHuqJq0ADKj0CAVNth9IGG4sDOM/Tr+vTa5sj9Mt+eeyL8PnQ0PTJJtNpLHcWeLiSrVC2/eDMP6iIJhVz1PAoj06QiXnAYnXWO9XlO6tIT3noSRN8DOXcoY5bxvuxKGDYJTzoFDhSQFmrxceOpBUZjq5ttg03plTC0g0rMnnHxyhAEDnDRqpLFqVYS5czWWL/fzzTdRduwIUFJikDgTu9B1aNzYQ/PmDpo399K7t8E//qFjGFFmzHAwfTrMn4+1LKu02wdr4MOP4dtp8NLLMHok3HiPvDYnMTfMps0w7HL48DnQ34bf1Igpv0hU7NQK7r4UHnvPeobfzoNbRkL7JrDevhBR3t4PZ8Il/eG+L6zbf78ChnaGT+ZjoUypCYmiX2op3tX74NSOifd3T7kAk51liX3FfuG7VKUqKBKHg4qSEDZsYOQEh0ckF8esDrY8khSnQY1hDhegkYqDatvL4MNDgJpY3FZAek0A3HgJEAR0wjjFIxQW9XSOpmnL1EiOsvQHLgJWa5q2QrbdCzQBMAzjTWAUcK2maWHEEzfaMAxD07Tjkm0rtRZVUoH+hmEktQ1qmtYNaA3Y2f0sckRAomnaqcDLiNfjXcMwnkoy5njgJYSaUWgYxqDD7fPgwfgkX7++zn41ydwlTCxhk3tHjxPr+R3xMab0d+k8GbU+XF3w8b6tYFUDHBwilDAfBTHYRcASmdUYF7uJivRGqY00CfiY44oQTKaN+IEQDMmD72zUHKfmwA+7bQeVpzu8GUxWM43l5NwoXSwtdpbFxybTRnQHnNMNjnmMBBBpV1ckgS3ZSgKIdGwKPVqKiKzt48HtEivEonJYv1mOlfNuqg/evBvOuk2GtarnAjTMgfuvg3NulJqI7RodDkFomJUBp54nJ+UkIHLJhXDLNfDo4/DV18qYJADSvDlcfLFwhP/8c5iVK+Gaa6Ls2pXMnKX+NQ+oAyHCYRcFBQEKClzMmFEJ+Hj66QhZWTrHHx9l9Gid+++HhQvhn/+Efbbsb4CiIrhoDAw/Gz5/Hx55Fmb8jgVM9u+DC2+Bnz+Ci+6AdVuwPMMvToBZ78BXM2DdNiwMwg9+AI9cAuc+irUeiSkBwcH1wEh44EvJ8iwv8bsVcP+ZEkgUmb8Njm0OP6yLj91wENrmJV7f9hJRCycZkJQkAZLqsACSEhVIojIfRZlxYpnrqsSCawx8GgS0uH8EkM72uEYiSOK9BKghNUnUlgsPFYjFoYY35qNs3QjC4aMXtmWg/QvO9j/Yp2HMwcYpmmTMa8Br/8q2ctzrf9C/4o/2AUdg2tI0zQm8DgwFOgDna5rWwTYmC3gDGGEYRkfgnD8+dPwa8/K8bNkiT8UJefVFfQb1oWuaDfvMyUtpN/MS7VqqG40t+C2mqvakstqWnFifFOmsi48L46Qzafxq02j66TrbEp58q5yYAbNLiGkjhOHUuvDjfpLWGRneFH42/SMKUIxsDxNXcVgQATizA/ywGvxJ/MZ3jICnvyEBRAAeGwP3fwAbdgqtpEpuH4nArgNY5t2nrofXJsOO/fI8FBBxazDpRXjlY9iq8ocpIPLeU8KMcP1dEDbzK8wxYUGe+OVH0Cof+g+Er74gKYhomgCOn34Sk/rmzdCvX4i77jKYODF0BCCiOmDsPhGrz6S0NMyUKQbXXx9i+HDYtAm++AImTIAePZTzUuaub3+AURfBP+6BC0aRYOYqOgQX3gqfPCdD2xUzVjgM1z4Bb90j65QrfXPXCKbmbi2xivJ7BsPw80o4zjZmzW7xftj9E7M3Q1tbWG8oAlsOKmPl/tcdgrq2JEZTipP4PnaUJ0ZoVUYFmAAx5gqcUBIhro04RU2VNtK8/azfz6ZoPIDGTYA3KaDIZlH4kW8T2H/NOiQB+Q+E33StfC+PKvMvpkbyp53tfxvRNO0ZTdMyNE1zaZr2q6ZphZqmjTnS7Y/ER9IH2GIYxjbDMILAJOAM25gLgK8Mw9gJYBhGLelLdhHqRoMGejz5yyfMWsVVliG0zYJNFfbNDdqlBNkkvchOh3jovGg0xJWwPulIBuukLTUszVedSGWFjW4a4Bh8rCcS00aCfg99HS5m+Y1atRF3FHKcsE9BtRQnZDlF/YaYyG1cDnFda5NEK53THr4yzVW1gAh+OK0LvPWrrR1okCZs5r8kcUof00q8SAvWAxFBwrjnEGzdI5h61XSc47tD84bw/jckmLsIwesPwaRpMEcNLJTnoWmCVn3XHrFKT6aFdOkEM76FTyfDAw9BtfobKyBy6qkwdy6cdBJceaX4PnFiSBazUtG5dhBxuaBOnSAZGQExWf8BmJj9wWCITz4RTvbXXoMrroB334XsLDlMAYzCIjj1LAEkt11PgmzYCnc9DZ+/LClBbP6SXxfCxcMSt3v4QxEYYTlNVQLw00o4u6/SJi8vEIYODePHAVi7Dwa0IEEyfVDflgdSFRREjPb9AlSGobHNB5PhTu4j8biIWYQbeCDXHWf/NSU/HR7JBfQIWyMGYW8g9l4D7KUUB/4YTACUUmApaqVKDvWoJiLNXS7q/+9iJjmacoqM6jod2A20Ae440o2PBBYbAarVezdgDypsg4gWmIng5H3ZMIyPOULJy/NSWKjFHrKmjWBHIXHbqRfa1IGNlSTYUds4NRaGwxZ1vzUeNiV5sNqRwmTJ4GtKJ1KZKZ3vQCz8tzNuHqEMFWu7Op2sOIw63Dcd5pdj0UYGZMLPptlOXRADA+rC72bIrzJJ1/OKTOESewa8uQ85vn4mNK8jMpktEoIrB8NbPyTZLgRXDRPaiDkBXnYSvPYFvDMV2jaMb+JxwjWj4OrHSQQR4IIRwhz2hvlL2/w4T9wGBw7Bg0+RFETOOA3uvQ3OHyNW/MnoTrp1gxdfhO3b4fzzYUcs1ccemQUmADRtGmHQIC8tW2r07ZtJbq5YfoZCUZYvr6FdOw9paQ6cTvHbbtsWoaLCYNGiCIsWhVi9WiMUsrMxCpvS/PnCZzJqFMyYIcKPPzc1KGnKqgLOHA3vvAa3XQMvvImF3HH6HGjdDO67Fh55VTl9Pzz9ESz4CCZNl2ZEaeJavBFevkFEcBWWETdxKb6SORvg6TEkmL8WboVjmsGaPfG2HcWCTsfym+iwowSa+GCvEixyqBq61iep+CvjNCemRIxEWhWHFo8abJwNW0+C6ohYTF2dDw/uhpeLIc1BjAjVJGoEYut4u9SWhBg7LjoRokRw4kKnWi7ijMNv9i/Jf8BH8j8p5hMzDPjUMIxi7U+obUcCJMn2Zrfv6EBPYDDidZmvadoCwzAsPKMyTlrGSptsbz7y8pwUFMT31KQR7LBNjm0zYaI56Sq/VzeXk1Wa1bDVDg8b8BPBaXnQ6uLmoG051wgfayRdigkwXjQcaFREHTFtJDuiszQYJVSLNkIEevlguk0XG1AHFh4iwaRFGI5rCFO3kKBxnNYGpq1XxkLCJA1wbm+RK2DpDwlN4Kze0O/zxL62+dAwV3BjAbgjcM2Z0O9qCNXE24nADRfAknWwR3WzyduX3wBuvwyOOy/J+dXA9WMFmeG1tyntypjLxsDos2DwKVCp2t5Nlhwd7r9f5Ixcf71IBrScgLJDXa9h2DAXZ5+dSteuDnbtijJ7dg3Tpvl55ZVSioujxH2J5vbme+PD5YLOnX306ePm+utTaNPGSWEhfPBBgGnTdCIRK5iAMHP9+is8K8OB77tfRHOZYBL2wWXXwOSP4aJRgi1YBZO3PhUZ8JOmSfp8eTo1fpjwPVx5Jrw6WTmkH8ZPh4tOhBfNAAz1NugQqIQDpYKDK+bPCMOCrXDV8fCeydXmB8MrTFkKSzsggSQbFhyIb3+oCuqkcsQSsTvWdZE3FDXE9e/2w+oK6CnJLg+FYLL0K6a5RBKiWX9E1yOWd9gElNoARBizhJlaTOxGLL4rBQfVkscuFDFv3NGR6H/AR/I/LN9qmrYB8ZRep2laHZKWZksuR6Lo7Qbyle+NERmU9jE/GoZRZRhGITAb6Gobg2EYb5sx06JSlZDcXJ3CQj1mN21aX4QiqjVI3tgEK5JcVlvdwcao9aFqg5uNyj0I4iEHnRJlEgriiVU5VGmpgnhojZeZ9sgu3UGxYcdPq5yYBcvMbHYZQ3FcHsw1QVH1gwBDmsCCfUqf/Du8LXy7gcOatADO7QWfL8ICFACD28H8jYKrzN434R749Nf4fi4ZBl/MsJmUIpCVDmOGwWsfKe3KHP7Gw3D7E5KPygYi/foIkLj5bqVdGXPFBXDOCDjj7OQg0rq1WO0HAsKUVRuINGmi8eijsHRpBv3767zySgU9exYzYsQhnnuukkWLaigqSgYi6v9rCIVg2bIa3nyzirFjixgwoIR77y1n0CAXy5a5eOwxnfx8uxkMSkqEmWvhQvjhe1FIy+4XuewauG4s9O5qbY8E4MZHhGnQsls/vPE5jB0hAiBU+fQ3uGCwcowkyvFPS+HETta+lTuF5mqfN1ftgZaqcz0MBcVQz2baOlQli0790bwr+yNRCSTK++tQHes6PLBVlNoNRuHdg4iCDl5I06BKPghOxcnuTAIeaghwbRJV1sEO3BTJ++I66iau/04fiaZpDQAMw7gbOBboZRhGCFH02O7CqFWO5HYuBlprmtZc0zQ3MBqYahvzDTBA0zRd07QUhOlrPX8o4k25994yli4lph+lp8OuUiz60royoQrb6yK3cGoUxLMbAWiLx0K0CNCMDBZTEwMQgEa42CPfONXc1QU3e6OGpQ57V83FkhpnrdoIYWjsht1KOLvHAalOKLZlmhMWNCMQzyY3250OyHDBpsJ4m+WvBJEmmbBqFxSaKKhMHJeeCO9NJwFEHrsMerSCT34R+3E64dqz4LVJ8W3Nd/Luy+Clj5QoLWX/o4fBvoMkrWRYry68/jScf4USp6+MufQcOOsMOGsU+NXQfznJjBgh6E5uuQWefFJNXI2fQKtWEV58Ueejj5ysWxehd+8i7rqrnGXLIkRjNDm1Od5V+2JynwiEWL8+wu23l9G7dzmrVgV44QUXDz0kKFrsM/innwoT1w/fQ34+FjCprobRl8LbL0L9vHg7wJJlsHYTnGvmoMjdVhbDxB/g7EHW9tJCmL0KuqoOddl363Dx9/e1MLCDtS/oF9F7TvVt98P+cmhhi9I6VAlNsrAseMoDsk0V5Tfdp/ozgaAB9sW5wwEBZ3zMj4WCRr46Co8qy9JUl0Fx1IiZtZyOCKegM4ZcC3D0ozm9iccqO4nQhzFkKmteDYd0tXsQBepcoirr0VNEYvIvJiT+HeR9TdMWaJr2FGLxrwFIpWD/4TeNyx8CiWEYYeAG4CcEOHxmGMZaTdOu0TTtGjlmPfAjsApYhAgRXnNkp+CiokKPE6i5RB3rvaXyu277q4gGVESNhOciD50i2yqlJV4O2Ea2IoWNkhYF4vxa7Q03a22TRU/dwbJg7RpJSy9sNYO8pDbSPQN+UHU303cC9K8Lc5TEQbO9ex2ZP6MqRDYQIQzDusI603OlnKrXgOb1YOmGeJ9Lh88fhFtHQpUfdDl+SB/48lcoNbURecvq5ojSteOnYk0Gj4gs7rOHwJ2PkwAiAI/eDbffB7tNe7wy5sS+cOYZcPY5EDDvlUEMRC6/HG66SURmLV2q7FueQEpKmMcfh48/dvPZZzWccEIFn35aJQFLPdHDgQi2/9cOJiAc7Z99FmT06HLKy2HuXCcDB2rYweSnn+CGG2D8xxJMFNmxE25/AJ55mAR55BW4+xoZqaXIx9Pg6pEJt4Ala+C0vmB7vDmjL2Snwdqd0Klx4nF2FELTPOst2FMGDTOtbSWViTTzNf7k/FmmqKSN6JDiEhTw6HBSY9g2Au5oB4+0g697QJsMYVrbWANzK6DGSUx7yXVAtVOckJmI2BAHmnK/nUTIIQ3D9hukU4ew/B1H8jzn8RwNaMTNjOAkWpGKRmUA3AYEgvBnuK7+t4phGEOB44GZwFnAAk3TvtI07aojSUQ05YgUPMMwvjcMo41hGC0Nw3hctr0pE2LMMc8ahtHBMIxOhmG89CeuJS4mWZtL2m3tIGL+dYn/N/JEEmof+tDwK3QnJvK3xMM2ZXaO4KQt3pgJTA3/7YCL1eGopWiVHnWw1V+7NtLZBb8WYnkpe+XCjgoSV0Bh6JgLv9lpUcIwqDnMslOi2LYFOLUT/LDS1heAk7rCHJOyLQRpPlj4qkg2TPGISSFD2ruvGiHyFuQNEeKHq8+AXxZAVFXqZP+NF8OMuVBqmqQkFxbAOcPBiMJvv2Nd+NdA41x4/lm4+mobiEi57TahjZx+OlTEzGxxcBg6NMrcuR527jQ47rhS5s8PE5/ME/0mtYNIMsA5PJhAiEgEXnyxgpEjg4wbp/HAAw4cDutEtmgR3HknfDoRPLp1l7/9JihhThpgbS8uhNmLYPjx1kPu3y2oSZrYQnR/WaLUK1FMXIs2Qu8WIgzYMMCtW/e3ZT+0snF+7T0EDbOsbcVVkG0CiXxdIlFFm6ltNa+G5OviNQFYUyro4+t5oWM6nJALpXIfS6tgSol1N1kOKI0aMbOWoI7XKSUSCwEGyMBNFdUWs5aPVPwyvL+GctLJw4uXNtTHjRNf1EllBHwRpaDaUZT/Uo0EwzD8hmH8aBjGzcLtwO2IX/Q1TdMW/cHmwF9IkSLEhzjfuDFY89k8+YdZCTXXYVvYqiU0x8XWJE97S7xslQ+h+SM2wMV6ZRIyqVAqogYVylmkaZBlD5a3yTFZsNVU8SWfVs8sWGYmJyraCMAp+bBccWiaMqgpzNyitNm1EcQk0SQPtqr5HnLSP6MPfLMg3u5xQVEZ1ASEs9NAAEnddMjLgvXbsYCIywWjToFPpygXJ/vT0+DcYfDeZOU6paSnwL23wt2PkKCpuN0wcbwoixsjV1R+tuuvF6SIo0aBP3ad4gI0TZiNzj3XyUknlfHWW5XS3JUY3psMRDIyHBx/vJexY3N56KG6PPRQI/mpy7XXZjN8eCp166r7qR1MAHburGbEiCDRaJQJE5zoeiKYfPopvPACCf6SO++HJ+5XYkVk+8vvwS2XkeD7+PQ7GH2Ktf1QCfhcYoFgOe4m6NNWnPaandDJFmW1eT+0rmO9tD0lijYh20pqINtmPgZqp6ZRx8ooS5cDUddeh/0hWCoXHTUReLYADsrExGoDtqt5JLpBlm5Q4RQXbCYi5qBRqZiqRQ0SH0HEjuMFrtxE5LjlfBMjciymiq/ZJYAkKJiP6x4xi9SRicn++2c+f0fRNC0DKEOkeVyKoEr5Q/kLgURL/OqD3BxYvBnQ4eqBcFEnEh9WKS10je0KE2Ek6qQhLlYlIXA7QJhirCF67fGxk5Alo70+DiIaFm2kTdTDhiC1aiP4oXMqrC7FMrl2yoI1hSSaqfxQN1VQWdsBoyYUj1h78jQY1JoEp3vvJjBtOQkg4ohCnzawwDRrAUUlcPId8ObXMGOp0EpcBlwwREQHqSACcPZJ8P1MxTeimFBuugjemiB9H6pVICKS8F56E0oKlXY55vbbYPJkETarmrIAzjoLhgyBSy5BoSGJm7I+/9yBYcDll5dTVGRY+pOZslyuGs45J51PPqnPsmWt+fbbpowYkUl1dYilS6tYurQk9tm/P8Qxx3j55JNmrFjRismTG3Duuel4PPYoB/vxanj88TALF4b54gsnHo8VTF5/XTzH55+PBUx27YZpP8FVl1jbt++Eg4XQrQMW0PhqOvQ13QBK+w8LoG9La/vidYJhAMT707oh8UTUEGw7CHmqCSoMB8ttYbphQTlfqb4+8lbsS8jhiv93fYn1+6GgPLR81R7eKIpYlYbhGcXqHohCqc1EF8KgKGLEzFpuAoQxKCVgCQGOEKCSKkv+SLkSA7SbNUQIEyLEP/mdIE4cIRclAUhxCN9V8oScf13+G53tpmiadrWmaQcQ7oml8rNE5g7+ofxNria+vKqXG69r3TgT5pnhtEk0wRa6xtKIYelrgYtDigNdJ4IHjXzbCiCMEzcaFUp7JOqkecTDJhvXYweXxro/eOYaeWGvOf9IgPmqgFjlOHXB3DQTdpRaTka0+xSTSBj6NIPnplnHEIZB7WF1gfyuZK13aQG/LFdMUgpINMyDc+6DEqkhDewGX/wS7zdl8DHw2Ou27QGvEzq1g0uuIR4vKvubNIa6ufDxJ7JdBdP2MOQkOP4EEla1HTrAfffB4MGJTvU6dcJMmeLmjTciTJhQaemz/l/cmEaNQlx9dR3OOCOVb78t55lnDrJ2bZVwribVNsSN/vpr8expmos2bTycc04uCxc245NPynjrLT8VFeZYMw7X/FvDSy/5qKqCt95yMnYsqKTQV1wBb78NP/8sKFTMsOBnX4bp38BbH0LUzD0BPv4CxpwJK5T6dWUVkJ0hmIGLlei2tduhbydh5sIrfoedB6FPa9FfWAKtbX6a4lJoakZuyWesKghNcolRsZjSINM6DpQyvOqMITWJ9jnW720yZWAMgA8WVIjCVo9vjftOcELbVMn1FQV0A/QIHT0wKRR3tAO0x8MU+TubhqGm1OMXKjCjknUMMogzTkZwcIidpNKQrRwiSCtycbK2WiRB1hxFnq3/JTIO6Cijbv+0/A3yPJUYRx3q1pErdS80TIe9h8HDLA2LRgKQj85uxbYSxil5s4KWthycFMmZTdVSWmlOtkQjlqJV7Z0O1tXUro1kANuqSKCIf3IVCSYt/NApD+buSmzv3hiWSwe6ponaEEWVJGgtfVvKUrk26pMBnWBdgWxTQCQ7XTD7miBCBN75CkrM5Ekp9TKhdVNJumhuLwHwrFNFQaagDUQAbr0GPv9ashTbNJUXX4BbbyMBRDIz4eOP4aKLoKwMVN9FamqYb77xcM89lUlAxOrjyMlx8OSTaXz6aSM2bqymd+8t3H//HlatqiISMceajhzV/GU1gxlGiI0bAzz22F6OPXYzfr/B7Nn1uO66dByO2jWTd96JsGUL3HOPdWldWQk//AB3jLPer+oiWLYSTj5BGVwjimGd2Nd2b0MwbwUc287avnojdLJlpRtGnPZj1yHINyd3+YwUViSyAFdWQprH2larD8QkV4QEkkUD6/c0HSpkzogpewMwtRwLEOU6oThibctzaBQZ0ZhZS5TM1imXddpNceIgKtm9PQTwkkbARn9UyEHWsj5Wsz1Dg/IweHXYbU9g+Dflvzhqy5StkITl9gjlbwAkIDKWxP/qZsNBOXc0zFRCC+UwVTq6YU/UOkM1QWcnYYvzvCFedhDGpEUBATiLVee7pEJpHvGw0aZ9NHXC5sMAWptUKFRXOOZ8lcSkBdCtrqhUZ2/v0QCW7xZtrevApgMkznshmXCmZCmbclx7mLOGBHPVoO4w04yCUuc79fxCcOvlMEMl91MmlXuug49M34iyj67t4dILYer3JIDIqFGi5O1ytd6IlIcfFuG969fLg0vR9TDTp7t57bVqZs+2J0xYnepDh/r49de6LFhQw8CBW5gwoZRgMHGyt17MH0d31dSEeO21g/TuvZ3s7AhTp9YlI6O2c6nhySeDnHqqRvfu1gdnwgSRB1O3jqWZdz+CKy6M3ycAfyns3KtoErJ93hLo34P4bxWBrbvFwkBsGG8/VCYSB3cVQr56zAAUVSimLXnZoYjilLfnaJntCkDE2hXtxeGSDL5Kf6pLEDfGxutQFFIWhU7ABVkuKDXXgXoEhydAhgMC0qxlAoeORsTMLSGeN6L+30cmNbY6Q36q2My+2KSdZjgo90O6AzxJwrj/HflfACT3APM0TXtL07RXzM+RbvwXm7ZsGVdOSM+EvTKSo7hG0DNQC2FctkOj1AB1UVVOhIO22Mg66BTYaB0bkEqNmfUajf+odTWNbZGopd5IK5fGLj9JtRHC0NIjCvzE2qV0zIGKGtipRqaEoU0OfLPWOpawiJZZUSC+dsiD2RttFxyCZnVg0z7rdmZfu3zYKLe3mKt6wJe/kRxE5LuUkSZ8IGdebdsvcO1oaNUcthVg0VQ6dYC5P0NBAURNwJf9DgecfZYI57VLt27QsSPcemv8BNzuMBdc4OTll70UFESYODEItZiyUlM1nnsul/r14ZRTdnLoUHKfhsNRQ+/eGQwblkXjxh5atfKRkuJE02Djxiq8Xp0FC8pYsKCSpUsrqK72oHKPhMPw+ONFjBwZ5Jdf6jN69CG2bUMZI/5GIjB2bJDx490MGBASBdoQ5rpnn4W77oLbxxGjUVm5SNDk18mCQ6Xx9m9/gOEnwQvvIN7MCMxfDuPGKr+ZFyJBEUnlMiCkxdsL9kHTurBsi6TXUcxTgRBsMwMdiPetS7IgWW/6MRTAWF+IFUQkQHhTYW91/DvAziowfFhml90hiEiTlik7Q2bZ7PhicJcRN2u5CeAkwn7FRQ2QgocDHLKcs4dUKhEXaE7UBg4qqRa/EU5KQwJImumSheAoimD/tat3/1XyFvAbsBqSFHL6A/kb+Eh8lj+N82CxZP5tlycftD8hrXETRlCjBPHgJkAL3MxRCt+IjHad3cr9MpMPW6bo7Aha9XsdZV5NkkTbMhU2qdTxEmTGdIMZOyWQKBN76xzBsgrW9n7NZCKmHzo3EiVUY/uT0rUZrC2wtYfECnS2GQ5s0zQa5MH8FUqbDUTSUmHeZyJiq1o1xdXANZfCsw/LaCoFREaPgleeEeVmY9Txyr05dYjwDRTaLK5er8haP+00cfDsbHjgAbjgAi8ZGQKALr+8isOByIQJOUydWsH775eRqF2E6NMnlRtvrEOXLqksXlzG998X8c9/bqe8PILfb0Z9+WjZMoW+fXM599w8rr22IcXFYR577AD798fBBODLLyvYti3EJ5804MorC1m71jwnV+z4Gzf6GD8+zNixDt54w+yDzz6Ds88Gn1eEXpug8cl4GDIYPplMDDRmzBF5OKoUl5pJkFYp2AuN6kKBMp/uL4Z62QLAOjRWbosOBKB9Y+W0pbQ33QoKQLSvTwJgtFc1HAU0Ml0yNNh8T53QLF35LsGnmTrHumSbcl3HpoWochg0dDjQtfiDlEeYFGk4Ef7OALn4YpbSFNzUpwE5NCZsC7LJpi4V7CWClzBOurgclFYLGpaqooRb+n9dwoZh3PbHw5LL3wBIIPZkuyA3E4q3ia8Wo5Ut3DFTg6UB6eMImxxZ4DcMS0BYEA/10Nllo4pviIuFVMVMWqbIdzqmjWRGNcoixLURsGgjpkbyg0qNIqVZGhTYtBGkjdavmr7CgkLCAEw2jzZ14csk9Cft6sGG3YntnZpCWRUJIOLzCEd7wFTIbCCS4oPZE6Ftc3EOHVrAnPlADdx/uyAdTE2BasVCdNdtcP8dAoBAgEMMROSP9sorcIaNYKFvX5g6FVwug3nz5PW0i3L11W5SUsSPVlgYZcUK20nKA7vd8PnnuYwfX8annxZZ+gAaNIBnn21Jaio88cQOliwpkdUl7WYu8f+tW2Hr1momTBBL8JEjG/Ddd635+ecKnnlmL6WlcV6Q5cvh0kt388kn+QwcuB+/34l1Vq7hnXd8LFyo89ZbEemfcWEYgnBy8GD47jvEsxkRWe1XjpVAIif77dtETfjYJcmHMRIW5qKqEDHto7hMhG8X7CXGxVXlh9Q/G1VqM0uhEzd4y+9uj4i6UilPQHzPSYVixWTl8CnLWQkibk3GZzjjtyvTIc1a0sk+LctLFIM0TWM99fmaSp6hgvq4KZQRW6bUw0MlpTiJUJcmjOBqIIqBQUfO5CdeZjc7SSGDUplwHMRDuqZRUSEqNVZVmzf56Mh/qLDV/6TMkFyI3xL3vmIYRnHtm8Tlb+IjIfZw5mQIB3OWD0oPE1lRTxdMoaYE/R5yw272J9HK6uLmgPIgCiZQ2Kn4V4J+D+6wk+qoBBEpjZ2wsJqkJi1TslywQ1WVZX/TdNhZYR2f5YV9Kr+UbG+UBrtL4m2t6smaLGAJ823bCDbusLVHoENTWSwJa1/HZrDGbE8yP096ETq1FklkDgf06QbUQNdO8NA4kTsCItEwQ5oYv58KixabIZRitS0GiT9vvCGyu9fKxEivV9CefPedQZ06sGuXgYmq8+dHGTiwnIoK8bsVFERskVZinMMBH3+cw7RplQqIiD5Ng1tvzeW779oyYcJezjprDYsX1wYiIeVj9Y18+eU+jjlmGZs3lzN+fEvy89XCHCE2bQrx/vvFPPFEFsl8Ln5/DdOnRxg+PGrZ79SpCqjKe7R8CXTvhgXbDANCIfCokX7Ajj3QpKFyKX4oLIU6JrW7bK+skPkldneOearqq2EDBPO704GoG6L0p7khFryGdZtcj6j+ac6h2W75XRlbxwcHw9ZtG3hhn1LbZ2owRK7DgUfT0DX4Tfo7GqBzSJq4zE8WaZQhuIV2s5kQflz4cJNCmACFFMhyVz5qlIJ1mgFGWAYDqNRER0n+y30kFyD9JCjhv0e68V8MJOrTDHghNwOKKwVd9urDVDWpq8PBsGZpq69psYgt9cfKxEG5fIvMto6Gh0NELdpIA01jsRmvKzm1GjoOQzkdBmqgWWoSPi3ExOCvtI5vmCJ8PxYwCkPzXNi0N9627YCwa9tzRdo2hE17sIAICFNGLGJL6evSGlatJxFEIuJz48Pwj5fFCq20HDrK/ISVK6F5d5j8NVRUCkBpWF9c7+rVMPhksdL+7DPxF0OA0eefw2WXiYggUx57DC65xIjRuRcXq7pmDXv3RikoiLBjR4Rvvw2QOAPCLbd4WbcuwOuvH7T0aVqIDz7Ix+PR6NdvKT/8UIx1khfHaNvWyznn1OfKK5sxZkw+I0c2pnVrD/GILjE2EgnxwQf7efTRbUyZ0oZGjaxg8uabpbRt6+SEE7wkA5M336zimmt0S9+CBdCnj1JMSQJGZYUw7amXumGT1EoUDXDnXkFkql5S4SGhkajmxMrqeKLiTvXdka/YrmJirBCm7CrD8hq6U2GrmheigycFtpYr+1J8Jzk+CRwAPsjzQaH5XYJNPRccMJU3uW19J+yPEHOyvxkIUh6NEjYMfqaGBdRIjcNFkWKWdhIhm1QqKcVDADc17EDYdINUMYcXicqbGZV+C7N09s6AuJ/pKVB5lIFEJCR6/tTn7ySGYTRP8klSsSa5/H00EikGokRnXurhGTrrOuGgbYKvi5NSw7A4zyM4KZH1CNRVgAuokSBiJh/mGTrOqCRmlFLfgP0BErURCSIJIvsdEaW4j2LyapghMopjItubpMOeUvF/n0PyItlAhLAI46w2J2klOivFIzUVG8B0agarTKe9rQ9EfY/HXhFU5h37iyqGZv/uPbB+A4y5Ao4bAluVHIdm+fDbDDjvPBHCm54Oc+bAsGFCA1FrnN95Z4hbb41QVWUQDhuUloLKrTJ2rM5DD1XSps0+nnyy1HpjqKFFizAjR2bw6KP7bH0hvv++LXv2VPPUU9sIBEy6YzGxd+7s4p//bMvKlSfyxBNdyMnR0TTIzXXTpk0qjz7amQULBnHrra3IzY2gAtiiRRVcf/1Gpk5tQ31zEpfHvPLKfYwd67Odi+jbsiXK7t0hGjaM90Wj8OOPIsAgJgZMnw6dOmIBjaUrId88nmzfWiDYmNXf7UCRAG7l0FTWCIoUgLMfwRrL4oGzXiLBd3HWe8oYL9QYcOEX1jH7quD6mVhBRIJPtgt21xAjqajvgV0BLGDT0As71NukQyO3wQ6FbHWlFiKqQQVR7qUwZsqqh4v91MQc7wCZpFKqRGit4VcMouxiEUVsFtoHDsKEYyASxMPZGwE/pHugskq5wUdJ/hs1Elnb/XD9GZqmdfqj/fyFQGI7tLyvdTJFREp2ChQfxrSV6YB9Ng0gW3NwyBYOrAOptmOpQBP0x1cGdRwah0x1W2ax19PlaqoW8RjSfmwL983xQlES1t9GGbC3zNbuh7oZcLBUfK2XCftNsFFAxOEQxazERVjPo00TqEkCMC3zYctOYnPkuMuhuzmhyXNonC0AZe9+WLbcer79joHF82DZfLGKNjWZ3r1FHXJTmjSB+vXjFCeaBm63mNCjUXj33RDbtxu8806EuXPjKdeaFmLUKC/TpgnyRUHeaQ3LfeqpPO67b580VYXJzXVyyy25lJb2pF07L/fdtx0VXLxeB08+2YJXX+3KxIkF9OjxMyNHzuKttzbz9tvrefnlNTz55FpGj/6doUNnU1MT5MMPezJwYCoqmCxYUM5dd23mmWfyZbs4xt69NTgc0L27ERurhgXv3BnlmGPClr7SUmhn5oNIqa6GfDOMV85pkRDUyVN+wxrhu8rKVDYMifDVVJuHM9WnVCE0QURHhDWqoGLLA0n6PQloWLaX35umQ5HyyuWnwoGgMs4JTTywL2Q9RjMX7ImIuiMm02+pEeVTrZIaaf9zEyAfD4VUxxztTiLUIYsKimPAUsp2okRYzDux8/CQiZ9AbMKO4IwtBtPcUPFf4GzXNC1f07QZmqat1zRtraZpNycZ007TtPmapgU0TRtn6ztV07SNmqZt0TTtbvu2UkZqmjZP07QHNU07TdO0PpqmDdQ07XJN08YD35HgoU6Uv4FGojzhzrj6n5MCJX5qDQdoKJ11qj8jFwfFhjRhRUVYbzYOirG2paJRqTz8ZvJhXsTFwZCDGOOcXzCF7jVz2ezaSATyPLBEjUyS/XkOKKy2toGIZN5ZqLTLibduqqCsIAz1s2B/qW2fQJ0UkStgD+PVNOHDEBcTbwdoUAcO7I/39egkHY3KXN2sicxLsSUhAjSoC/v2xbc3pXs34YA2Ze1aaNYMnntOhANnZhqkpcUvoFcvjalT/Vx3XTlPPBGPXOjf38WSJUGZ6KhGK4iZ9dhjXXg8DmbOLKNTJ53ff2/DunUdePrpfLxeB+eco8ZRh2jVysesWb3ZvbuGE074jd9/PyQd3xCf7OP/Lymp5s03t3LFFYt4/PEOXHJJHVQw+eWXUurXd9K+vQ8VTCZPLuG889KxqBNyuxUrgnTrpqOueLdtC9PCZig4eBDB86WARkWlML2oEg6DbmMUcjhkRKO5JnIJE1TAtvK3gIgKEP8KiNid7U5olC4KVZntjb2wy5Zk2MQLam00dIOmboNdjnhIvtsbYLcW5lOqYtqHToSGeDlko2ZNwUeNvLdOImg4qeAg1UrkQBqZlFNJRGojkagz9nhV+6G8BP4L8kjCwO2GYbQH+gLXa5rWwTamGLgJeE5t1DTNCbwODAU6AOcn2RbDMG4FTgP2AecAjwK3Aa2BtwzDGGgYxuI/OtG/AZAkEV2o8sWH0TyznVBiujNCTiJhJ5kRFyUYFr9HZtRFkc3clRHW2WNEYiYtU7yatNtCbG5q6UFEbR3mPDJl1IyqZdRJEbUd7JpH4yyoDtnaw1IjkfERdXwyl8amkdXPgQOqWUyCRY5PEDPaQQSE8zQSifc1qgt7zNwBeX+bN4ECldJeijtCnN5fjcoyoEcPK5CocsUVYXJzwxQXx4HhmGMibN5sdUJDiPPPdzNxog3ZFCf4mDFZ3HOPSPePRMK0beuhbl0XbreD8vIwS5eWxMZmZupMntyNsWOX8vrrG2MajKbBoEG5jBvXlhde6M6LL/bgwgtb0LChOduFOXDAzymnzGTQoDyGDs1ABZMnntjJPffUjR0Hwvz4YwVDhqRab6TsX7EiRPfuDqUvzPbt0Ly59UE6eFBJVpRdlcXxaDizLRQWpQBi4gLNpdDKSN+D1yv9aqYfpDYQQWmzf69F80hITJSvTWMf7FGc641TYXdYGeOCJm6RMyL2J1ZwTZ2wIxJFd0ViTL+pONhhi9BKQZO1TsXHSxDTiGRKKlmUIVY7pk/ERxYVMtM9gozMlO9o6wZQfpR9JP8JIDEMY59hGMvk/ysQZTwa2cYclBO9HRX7AFsMw9gm+bImUUuhKsMwSgzDeMcwjEsNwxhiGMaZhmHcYxjGnCO9/r8B+2/8v7oznjuR7YNSf/KtwKSbtrZpQJFhbczAwTYjfo/DYScZmgN/JH7pZvJhY4eDShsxY4YTyoIk1UYIiyJUZWquo9w2zWktd2pO7hluKDd9h8r1RSJQItMnctKVB13x52alwsadtm1DomDS/kJbO+KdDYexaBqpKVIjMee+CDRrDAU7sYJbDbRsCVs2YwURKWVlsH8/Nglx7LFR1q83qKy07qxXL52lS1VHejyPZOZMlTbZ7Avh8Wgcc0wK69aJ7davr2HAgOUUSm/urFnxyKycHJ3Zs/vw2GMbWLPGjFgUWoJhhBg2rB7791fz/vtrmDp1Kw0bunjppV7cfHM83ramJsJ9963kkUc6oGlxB/zMmYXk5rqoXz8eDhUIhJg5s5I2bcxVsKm2ws6dAem/iD9327eH8HisbYfMHBDlvpZXKOAt55oavyDzVE1W4YhIdlUd2NGofGdMEAG6NIE62VhAo1cL6XNRtI5jmwnTGBADjYFNpJ9PAZGTGoNmzoE+kbF+KBjfzgnCqa3Mk2EH7FQtznqEEqKUKhUQdT3CDi0UewTdBPARpYCKmIYi6pB42cUueXtMAMqhkAJU8ZHCQUpiwFJTmRJ7DzNToLyMoypmQuKfdLbnaZq2RPlcVdv+NU1rBnQHFh7hKTUCeaOE7MYGQkdT/lYaSXpqnME03QMVtWmeIchyQknQqu/nOxyUyqzicFisQtIjOi5Ni30H8AbdVBhGzKQVO74DKmzglOkU/Dy1SaYTyu00TkCmR75LNs0jwwvlfqyMvmFBnlct59MUB1QnKZOb6ZOUFrYIrAyfCAe1tIcE4d/K9cpxkHUYFBABUdJ1t/nIKQpBg7qwwXTUKxOBpgmQsYo4mQYNYP/+RO2iQwcH69ZZLyglJUKTJrpcWVtDcSHMeeels3u3muFew8aN1axdW8mhQ0Hee28neXkar7zShj17TqRVq1S+/tos8hK2bHfXXQv45JN1rFlTzIwZu3n22SWMGfMLrVr5eOGFbrFj7tvnZ86cg5xzTiPUaK7Fi8vo3Vs1ZYUIBqPk57stxzFvdJ066rNZQzAITZpYn1enU0ZtKc1p2eBRLdI+yMuTkbumduGFRvUELXsMDJzQvCGU+YlTPbjghlMhvy4W0LjrZMjJjn9Hh0dPBLcTC2g81w+invh3dHimu3wUfKDp0D5Nmtjk/vtkwD6bptTJA36DWM6IjiBcBVFO1+0NkIuDbBwWs1YT3DjQLBpKXXKIEI6x/jqJkEE9QjaaqAwaUEaICE6qSRHvuXws0nxmVOFhXuz/GSk0S4/Lz9vJBmmalgZ8CdxiGMaR6lLJ6l4YSdqOivwNgMQVu+S0DBF5ApDhESU+a5OIAWVRLBFWaYaDSgyLAz1N0yiPxO9p0O8hU9MoVQiCzOTDdDQqTO1DOpYzHFKDSKKNEBbldA+oz7DUXFKc0oRltkmprBFU3YDlOfa5wB8CAiKJsMY+r0bA6wZ/dfL2SNDWDnhdkJdtPU6HlvHtTMlvKI+nWmgigljRiJKUcFFEXpkHjE/+Dgc2YACXK8SaNWGFRFGMbdlSZ+tWP8lARNPgyScbUlmpTtKQmmoQCkVp1Og3hg+vy7p1x3Httc3xep1MmbIndrGNG7to2DBF2bcKYmKfwWCQG2+cQ4MGbv7xj07k56cAYZ59dgMnn5ynHDfErl1+8vNdShuUlobJylLvg9kXlr6++LE8HggErDcyLU0Jk3YCPsjOlUXD5Hd0yMtVSip7RV+9OnBA3dYlEk/3mLyF0rzVJE+G/UIMNJrkJIb95nihBCygoTshVu5HF89ZTSR+Xg29sCdIDEScugA8wzy+F3KcUGyCiJSWKUEKotGYSUvXI7R2aGwjZAGNJvjYI/0jpqO9HpkUUxhrA8gkjwri8c4BPGSQTRFVMTNSNOABaWreulMSjB5F+U9xbWma5kKAyATDML76E6e0G5S6w9AYFJ79oyx/AyCJS5oPKk0TkDcJkCir/nyXKIwDAgjCISdpGlTIJ8QEk1Q0qqSz3Wxzhh3sV/m0pCTTSBwoL1MSSddAi5IQteXTJVW1XfPIgaAJTMT/el1QIyeBFBdUq1Yg+W55NfAHE9s9QFABELPP7VLYegE9pEReKefk9YC/zLotCDOYmg9iSnY2FBeDBbUIk5oKZWUqBbCYSFNTNVs+hrjoVq0MNm8OYQcjgDffbES9ei40LYoKBi1b+tizp4ZQyKCiwo/TqaHrDiKRKNOm7Y4d98Yb29O9e5qyT/UY6rmHeeWV1Ywb15ElS07lrLPy2bevku7dsy3jd++uonFjDyoql5WFyMy0TwiqOuHCVCO8Xo1AQEMNLklLk2GoCmhkZcrSxyZXlVMsBg5JRmwTNGL+MtPcpUOjXOlbM30kOtRJh0MhLKDhciI4uuR3p0telQIidbLgkN+6Xcs02KosNlqnw+aq+DbNvFAQtFwirX2wKRbFJXJG2jgdbJIOHrPuSEtcFMjf2dRKmuNin8xgd8qorfoSSFQfSQb1LEACkEkmRVQTI2oNO2PvaKvG5qij52yHox/+q2maBrwHrDcM44U/eTqLgdaapjXXNM0NjAamHuZYSzRNu17TtOzaxhxO/j5Aoos6JHtkWF5ZDVSqvgeb490N2Euo74waVCkaStDvgZDOoaAzBiKRsJMMhwMtqly6TD4kKqlAzMk2rJinkmgj+IWj3a3eRdkXDIlriLVJcRjKAl9ZJG/aF18lBYJKvQTFge5wCBoMu7hdkgLFBjBulwIwNYKzSQUWU7wuRQNS/CGpqcnJ7TIzobhYdRyLC0lN9VMdK0QR94X4fGFqagziFyzUn9xcJ1u2JIa23XFHHueem4vTqdGzZ7qlr0ULN1u3iu3vvnstF120gNLSIOGwwdKlxbHjnnRSfX79tQC7icvuzAdYu7YEh0Ojbl0f7757DO++e0zCNe/bFyAz0+qNLiszcLlMgHQRBxEdoWabM6oPj8dJIGB93dLSJZAo2JOdLaoUmiACUiOpxuIjqZMD+8uwJBimeJUMdNlmz1JPSUdo3Qpo5OfJktDmqXuhRRpsqVDGOaFltgQOub98L6wNxMc098ES8+eUJrKmOqwICZOWKfkug/XREG5vAF2P4HREqAtsUcxawmTlZDcVFtDQ0SmRoGFOyQYGFZLE0Zykd7OXUgRFUhAP+LX4Y2RhPDg68h9KSOwPXAScqGnaCvkZpmnaNZqmXQOgaVp9TdN2IyKt7tc0bbemaRmGYYSBG4CfEE76zwzDWHuYY40GGgKLNU2bpGnaEAlkRyR/HyBBhD265P1tnSe5hQ4nNhNnG4e4HDUSK0vTYswQZrsr6iBE3KQVO74TUXjnT8ipjWFQIxKitjI80lStah5+kRMQTbIwbtcgPs7nAW+SX8bpELxF4mLi7eEwlCXRHJwG7FMYX1268l051/0HbJqKlGAweXSLrocV00AcHIJBcLs1VBAB8Pk0amqisXGmpKRE5UQcR9R+/Tw89lhjsrLE7JSdraPr5j5DtGzpYtu20tj477/fx6OPrmbt2jI2by7GBIpDh6rx+9UkQ3sIYPx7nz65yEcHXdc45ZQGlJZaEbdevRSqq81rEDN/48ZuWUvFmqShaS6psZljdRo2dLFLdX1q0KQZ7D4Y20zkXDSFHfuxhPU2y4cdB7H4SLLSpQ/R1DJSpR9FAY0G9SQAKZFXbevDFtNMJkGjQz3YVBr/DoJ9er3N7NY5A9Yr++udA5uVwIAeGTI6S4kE65VisE3+vGbOyDEuJ1ttQTHH4mGn7TfqRi4HKMNDgIG0pDstaEFTAhy0gEs2jYkQtKzyp/GrdfUv/ZJpugixjq8M/75iGMYcwzA0wzC6GIbRTX6+NwzjTcMw3pRj9huG0dgwjAzDMLLk/8tl3/eGYbQxDKOlYRiP/8GxthiGcR/QBpgIvA/s1DTtEU3Tcg63LfwtgMQXe2l8HqiR72+KC2psDuyYRGB9bLWvQViYqAyIOc8jYRES7Iw6CEQcMRAJh5y4gZqQEssoI7X0KIQVbQS/1BLs2ogUDejTALrXJUEihgANS8ikV5IzqlqtnByiCI0DHdo1VgoXOePb+oPg9dnaEfTc9XLj+zL7/BFoYsZp+ITW0qih/K5MOHXqQUqK3M7cr4bMAk+8tooKnYwMc6B5gT6CQa9kqjVPRJh2dB0OHDBi40wpL9fkfvTY+BUrQlxyyVa++qqYYDBKRoZO27YpsX1WVWl4PA5l/7B8eTm//bafaFSX+/eRnu4hNTVuWjLb4+KL9Z1/flscDgc7dlRy/fVLGDDgN+IahRjXr18W8+ZVKPtw0aNHKsuWhZT96YCLVq08lJYalrbOnWGjGbigifvcuSus20hc+/BB+9awabtyeV4RhBJT9LzC/FVuai0AHmjbBDar1h2viNhafSD+HR26NoRVB7GARtc8WFUS/44O3XJhVWn8vAC6ZsFKRePokgarTNMc0MUHq6LxfaAbdHHDqogwaZnSXnewzV0d00acRGiAzkFJFW+asTxohGX5h1PowsWcRh653MSjDOEieRoZ+G0FrSI4Y9Fa1aSI0P8Q4Icst1Lg7SjLf2NmuyqapnUBngeeRfhlRgHlCHr5w8pfDCQuyzdfWhxIPDoEk+VvSNNOe29iV8wKr0RiuTQtbg2X7aGQTqXpqFf4ulxaoj9kcxLTjgky57QS2cWZbhiQbx0SNcChMrHKl94fIV4nOz5/EgyDSyai9WoDp/QkERgMAbb29spqSE2L78vsK68U1CWxY2uSYFF9hnXh3M1UwVD2l5ahOJPjUl4u92tJUhAajMejLKWllJQ4adVKNQuJSb28PEpGhjXRobo6yqRJFYwcuZm5cyvo3HkxGzYEY/tct66Krl1zUSf0Xbuqyc+33oDvvtvDGWe0wpzIEwFFfPd4PAwenM+bb26gRYupfPLJLs49twmffrrHMq5fvyzmzw8qx9Bp3drL5s1BVMAAF9266axYoSlt0KULrFpFDETwQbs2sGFb/LvDLRzcwQgxEGnSCHaaYdbyt+vcWim1LJ+HLs1g9S7iAKFD58awam/8Ozp0qQcrC7GARte6sFI1benQJQtWlSu3WRflErZWx7fN1CHmWnNBOx9sVExdIJghDkpw0V0R3DJqS3V/ZuCgQmoYplmrEW4OSo4tJxFWsAEdJ06cOHCwk2U4iZBJQyqUKFd1gjYn7OrKFHFAP+Tqpn/v6Goj/+2FrTRNWwq8iPCtdDEM4ybDMBYahvE8sO2Ptv8baCRxUTWSzckoDOzaScyRLbSSUiU6KxwSob16VMMfdsRAJBrwkOoA3eIjEfva4heU3aq0TiWpOHzw9LFCc0pzwwsnYXmBgiqbvdIeiECqLaQSoKhKhPY2bAD1s6F7S0FgqQKDPwheM+tZaa+qgbQU4pOD7CuvFgWr1OMYWnw781kuq5QUHOqi3Skis7Js4akAFRUmkKgnohMO6+zaFUWd5MHFoUMG9eqpGow4mbIynYwMB9aJPo6uBQVBMjNTiUSM2HYrVlTTvbvJFyKOU1AQJCPDRVpa/Aa8+eZGbrutGz165Fn2qR7L6XTxwgsDeOaZNdxwwzKiMmn1rLMa8+WXRbFxDodO3boepU6JTkaGh8rKKIZhBRGA7t09LF9udax36gRr18Xvucm2HDJvlw6tmko6GzM3xAmdWsKa7Vgc7THQ8MROh875NtAAujSAlUVYnr8u9WGVOfvLn6J1Jmwut/w0ZLmhVHlGUz0iYisqb3HTVNih4KrbC9VRCMe2MajjirA3YlhoUFpqDjYTsmgjHYH1+GOFqwCakcZOSmLT7iq2EiRImDAbWco2RNnNXHIpZw+qmNqI+TeiJCPmpZg1cuxZwf++/DcDCXCOYRiDDcOYaBhGAEDTtOYAhmGc/Ucb/62AxOWUSVZAG9WkoibVmd+T/D/ToVmisED4PCK2WD+DJEHWXmjpxUqEZxfTCqPDWU2grlc4LqtC0LEOdDazlHUIRSDFTQLVRFVAhDnH2pS/GRnw8DnipU31whOXyX45H5VUixVrAmDUiP2a12H2+QOypGiiFcpyTcUlIuw0JrK/uFTkOgCWG1ZVZS9YFZ9EmzZ14HKZO4kDSjQKDocVMDZsCNG5cwpW4Imf6PTplQwZYkW48nINXdfQdVPd8xEOG0yZsp+77moX27akxODcc2dx5529uPvu7tStG0M+QOfMM1uzYMEoNmwo5403tqKCzWmnLae0VIuNjUZ9HH/8euKebRfl5VGGDy8kDiLxm9eokZNFi+IPUmqqoI2pDsSG0LUb/PI7lt+iZ1eYtZQ4aACd2sC8jVgc7a0aw6IC8d0nFxb5ebDsAJZnKj0FNir+EIcLKsNQrERjpbtgUwVEFAtjiwyYX2r5KeiWBjMUBuDeGTCvIn5O3XywwXwXZb5IL4/G6mj8BXXqEfr4DNYY1gm8C242KZQnbgK0JY3t0j/iJMJBDuLESQVlzOCzmI8ki6aU2TQS9XMuDXCE4j6SvHQoOgD/Jc72/0n54gjbksrfCkh8HmESikmyBYNs258sx0QJBzZF18BpCz4wkP4Lm4QMYd6KiRcxgZqmd0V+2gMDv4enl8PH62HoVzI0Ur545QGRKBjbj5SqsNRIVIBBsPq2rAcjjxErVacDzj1OJJ4B4BJlWVs2su3TBQeKoVtrLP4Usy8GBPJvwR5JwaHM83sPSvJAm/Vpx05RFhcbmBiGIGlMrNznYtMmaN3absaC3bsNGjVSMuWAPXs0GjbUJXgngsn06dWcckqW0ibGTJtWwpVXNrK0v/deAUOGNKRFi9TYsbdt8zN27FwOHYrw1VdDmDVrFIsXn8fs2WcyeHBTRoyYzauvbsGqUfgoidHQxH/4sjIHVtDwyTbVH+SjZUudunUdFCka9Wmnw6YtyiXoMGIYLDWTRaWP5PQTYZ6iteCC04+D+Rsst4UTusBqmRHw692w9xU4uaMI/zXH1MuDNI81aqtTI8VBL3+efk0ke68pTjiujjRhKaB0XF3YoESTHZsGy5TFS99UWFADar7IsSkRFoWsNCg9NRcrHf6YNuImQBd8bKA0ZtbSidCODLYrYb5OIkSJMpXxuBSzVBZNKJVM3oZCJwABAABJREFUXuoq39RGzqAu/qoUYcnyi5r2hYfAGs33f1ck6eNIIFPTtLOVz6VYCXQOK38rIPG44zTYMVG/Kw98fTOhOEZdkjxSLWpYL9LhCQiNxGEabo3YCxM2QDdX8LJtqzVhNtZXCSwtgiUHRcTLrP1QHVuRiWz4TMXMYLYXFIkwTbsUVUPHRrCjCNbvhgOlsHJ7PNMfYH+FyB+wA0ZVWJq2SNLnV7ibnKLmSOtWyoF9sHELtG1jPU8QpJnZOfFtAQEmGqxbB+3bJ17HunX/j733DJOiWP+/Pz15c4LdBZacs+ScEQEJoogiCiiIqIAZs5hzQsUAimRBghIlJ5Gcc07Lwu6ybE4Tevp5Ud093bOLcs7RR8/vf+7r6mumq6qrq1N9684W6tcPrOa1k2zbBq1bG5bV6u/u3R6aNw/V2wXq7GRkyNjtFqKjjRN2CJ9+eomHHkpSzXFFuSy7GDXqCD/+2I4GDaL0cxcUSHz33Snat19Fp06raNFiBR07rmLs2L1cuaLNjEagMnJFxvEE2oWHh5KQ4CAYRMDG8OF2pk0z3BAJBg2CHxfoTcAKPbvDyo2BfYsTGtSGAycDXUbFiYVApkF/UaMynL2qLrhskJoL5aIhPgI2jYNFDwIu6FgdNl8gABou6JgEmy8bHosL2peDX9MD4wBonwhbjOIvF7SLhN8MivVWEbCjAF2U1sYF2zSTR9VfpJXdwh5DeCKHy81NFhsHLQEbdhsyNXFwRs0/AgI0EgghUzX91dbwueSQrorrNZ7DQTgeQ1BHXZylfqgiFqik60hcVki5xF9C/6WirdpAHyAa6GvYmgIP3mgn/xwgsQodgQYkJuvAINNaUzpZQLMQ0choIeIHHDazqWGxxSe+JYNtOza4IAer/4VtvEX7GIO4EoACC1Q2hvhWJ/Bct3CqDC73KFA+ylyGTXAkGYXQ9AXo/QGs3A+dX4Ndp9A//LwiiDSAgrEPv4JuwmqsO5sM1aqiX9ipC1CzCqZV6fFkqFMDs9WWWnf1GpTVDAkM7/6hw9CwYcn7sWcPVKlinJw1MZWfvn2DuQ4769fLdOtmVBqZRWLffZfJuHGJpuMKChx88kkaL71UHSM3sX9/HkOGHGLatFbcdlslw0UaRWrG8//rIAIhvPtunCFgYwBELBY7ffvCzz+rVRKER0HlynD0HPpkXbmmEBvmFaOLrFo0g91HA/u4oEszWL/fMAQndLsJ1h0JXNo+g3og3w3jV4vyTlVgk1H5DnSsCJuDrLjax8OWawSebQg0i4a9OeZjK7vgvPoNOh3CDD3Pgf7Y6jgVjnvRvykJSLBKXFEUPQyKHXAiUYiicyPhWHAbVNUO3MThJJsiUxiUOCK4pnq0a4ATQjRFwh+/VCV7FHFcRNY92imGWuU1sWxwKIf/jP5ble2KoixWFOV+oI+iKPcbtnGKomy90X7+RiAJXrUKJ7z8YJD4IzJM1pdkxVRls8tkSTJ+RcFml3WFnwcIM165CkJRVggzioJskKcI2/MSpH7ImW4RXiLoUrjmMTgeGlaAV3KhXJS5DOBiFsSGi/Ir2SKlLhCIm6S2P5ZMCastXHA5A8ollqw7c0XkJAFRd/I81Kpuvo5rWSKek04GMDlyAurXxaSExwq7dkGVqiVvy9atMHCgtheYmPfutdK4sQWr1cx1/PKLmy5dXGpIkZIisRkz8unTJ5JKlczHzZiRQUyMk3794gzH2DlxooDu3XfTpk0sW7d25/HHa5GYGG46FqBGjWgee6wO333XlH8FRDp2DKVqVTszZhRjBBGwEx4O778Pbjf66x0RAW98QGDFHwJ2G3zwLSbQKCqGyT8H9rGK/DRzt6CDCHY4mw7LDqJP8ocuC/FVah50+hZOq5xEdCjs0Ky9EPoRnwInstFBJMQKFwtB9y0NgfhQ2JMDXoMurk4ErM0O3BIL8LjB1DjJqbDfA4oKIhanm/pOhW0+WQcRgCY2C1ukIh0wbMjchJMdqve6uPsydQnlIGmmabc85bjESb0dgIdC9jJN39dau3FSSChlCeOA3yckF/lAMZQvC5cvw58t1vo3gzb+7SRJ0nj17z2SJH0WvN1oP/8cjgSRr916PaX6DVCSTdK5EQ00nJJEpEEhYrPLFCkKYWq9kSvJVVS9hmHxmusTEYBFW8S8YVhMZLpFGHeTCMsFqQXQMN5Qrv6m5hmSUxnA5HI21FHBw62oCYq0cRjAJDJUlYUHAcaxS1CnslpmqDt4CqpWQZ8UjpwTwRyDuY+Dx6FsEmbAALZsh7at1TIDs7BrLwy4DV3UpVF+vohqG8i9EbiQ335TaNfOYuooJ8fG4cMyAwaEY57MxQl9Pnjuuat89JFZiaModp566iJPPFGZe+9NwAgm2dk+nn32LH36bKOoCCZPbs7u3b30bc6c9nz4YRMKC2UmTDhHMIhER7uoU0ezmw6ASEiInU8+iWH06FyCQQSEWfScOYb7YRWLgiWrMb0zpy/B+h0EQAM4eBZ2nMCkaN96Eo6kYAoHv+YYXMgOHKdNhx0mw2lVL1M7AcIcasI1FTRalld9UQzi1i4VDEmo1Eu5pSycKcbELfeMhmPuwBiKnLBVkwraFLqFwGGf4Po1C60uditHDWItm02mg8XOsaAJvAVhnFFFU5oPSSNiuaRyGtq0W4UkMkjVle9WZGQ85AaFjzKu+CsTx9liuxBr5YubFR8L6elacM3/6UgQXu8g8rPvKWW7IfrnAImMCBKnOv3VLsXJz0jpmiWmgTTjLA1EAAoVhXCrmVNxW/2ESmYRGDaFXL8I0qiTFVEW0N+ayQZZslk3oivbJVVHgvnYy8XqPBMEMGdyoXrZwH52oQgbHwwmRy9BvUpqmQEwDp0zZD401B08C52aqWUuIdrq1tZ8jSDMTju3UcsMYLJpO/ToGnT9ISIT4oWLUEvTkxjA5Kef4LbbCCI7M2f6GTy4JNfxwQdunn462BEm0Gb9egW/H3r2jDbV5eRYuPXWEwweXI5x4ypgBBOAzEwr33xzjn79dtC8+Rp9u+eePdx223amTEnj0iXZ0GcI/fqVYePGmlSpYlxRhGCx2Hj77Wg+/7yQ5GSVtSjtpTCaVxt0IvrlavvGd9fAmRgV7UZORB+iQdyEDTrWhOl7VRBR6wfUg59OYvIf6VcVlqRgeu/6JMGyy+ZL6RUPvxh9SoBbYmC1MfSJPhbxXXULU1hfrJi+p84OK5tknx4GBaANLnZSqHMjVmSaE8I+VRdiUzmVOsRxnhQdEgCSSCKdc/weaSt9DUiqEsZhrxIQbRXA2bPg9//5Hu3/xaKtperv9NK2G+3nnwMkgNUv0ux2rSdWVEmRlNSHALigbLAyg0B2Rb0/m0yRxU8oElZbwHIk26/gswRWUBpXkucX5pC40D/eDB/EGc8VxJW4/VAzmpIJgq7zPzkH2lQLqrOJ9LsVYgP7p9OhuuaFbgCTo8lQzyhSUuv2nYUmNdRxGKy2zl+GqhUC41WckJwqQnEYgWjDdujSjuA5nmuZYLNDVCQlJE9LlkG/PoG+Ne5k6VKR+CqYtm2zU7eupIagD3SUkqJw+LCPXr2CnWQCJxw9Oos33ihLhw5m2+nCQiu33XaS6OgQNm++iVtuiQ/qwywSu55OJDExgtmzazJ4cAzdu59j5cpivZ3VamP69DiyshSmTTOEvzWdhxsCkXlfQEVtIaACh8Voxad1qYGIRjZoXoOgiRxaVIGX1xhulQ361oSlyZie182VYG1QOJa2ZWBrQaCNxQb1I1RvdbXMZYN4B1w0RPk1ggg2mcZ2iYOWgCTAZpOpbrFwwRGYsB0WmXJYuGoI9x6Kh0is5Bo82u1YcGDBrSrfhRgMnNjwka+XJVKXGENwW+Pk7MaJDyuVCOVQjkvXj7gkEem6ZADP/5z+W4FEI0mS1kiSFG3Yj5EkadWNHv+PApLCYqFs/+xeoV94q/v125aw0bLJHDd4E2qgkRPkQ2K1yeShUM1qMXEu2GTS/QpxQc83zQfltMn5OlyJXzGYExva7LkKMUFciUcCp9EXRC1XFDiaKhwdAfZeVONvBc1Xhy+rllw2c92JZKht9K43gMm5y1ClPAFx1UHo0CxQD7DjELRqHHQNqgRq7Sbo3tV8zQDL10NjDTAM9y39Ktjt0KYNJejtty288EJJruO11xReeslFRITREirQLitLoW/fDD7+OCEITGx4vTZefz2dYcPOc+ediWze3JzbbksgIUFj6UJK2exER7sYPjyJFStuYtasmsydm8XgwZfIyLDo47LZbMyeXYYjR3y8/rrRi91w8+CGQKRdS7A7IPkKOohUqwJz3zIcGwwi6n65MjDxXtNlUz4eLmaDbnxmg0qRcDIHsrTgjUCNeNibqXLO6rgaRsHmTEOoORs0LwursgyXZ4W20bBMizysnzsAIvVssM7rRQHdzLehxcJ6RQCx5njYEAfrVOW2xo04kfiWizo3AlCHSA5wAaO1VgXiOctRXfkOUJOOWEqZjLVJuohQ7FhwF9sER5IPVSIFFy1ARBNv/Xn03wwkQFlFUbK1HUVRsoA/kAsF6B8FJBGh0KoWlIsRE3OvmpB4Hc9yE6kvdpLVQrQRHIACRSHJYbbayrb4iDOwLxpLnuGHRKP4wA6pXtXU2HQ+TFzJ5UIoF0EJrsTnh5rapE/g90oBJBr949Q6qwS1VL+R46nQvLr5OOxw5JLwIzCNBeFQduyi6qGukTqelTuhuZat2Qob90DdWpjAxuuF7QegsrZaNgDiorXQrSMl9CpXs4Vp8U2tAn1r9e+8C88/TwlavRpq1YIqVcxcR0oKfPaZwuTJ2uRvBBTRLjVV1sGkW7cozJwLnDvnZ+TIcwwbdp4aNUKZPr0B+/e3YtmyFrzwQjU++aQu06c3YunS5ixa1JgVKxoSHx/Ko49eonv30yxdWoCRY0lMdDBxYiw7dnh5910v/wmIYIW3n4bnP8Hkpf7KSJi8DBOIdGwCIY7APjZ4qBt8/WtgHxc82ALWqdZgt9WDQw/C291gnVGE5YL7asKGVPO47qkOG4KCN95dFjZoYi11PANjYL3b0M4AIgD9Ivwc8CkmX5E+LguH8OkgAtAdJycp0EHEgRs3XjaRqXu0O3DTlHiSDR7tANWpQhZp6rAEwMRRkQJO60prGSu9GIqMFQ8OEgjnNHIgvUM+VImHc6fAlI/if6SRLEmS9vUjSVJl/oVEWP8oIAkLgbF9IFYFj7gQeKNT6W31oI2GCS9XUYiSJP2FBshzuInFgsPl1q1HMhWFOC3aqwF4UvGTaDffu1QvJGrWqaVxJVZILoJKGuAZ2pzIgTqa5aoBTE5kQO0ylOBK9l2GphVF+d4L0Kyy+TgQesMiD0Qb43GqdWfToetNmCd8F2w5ALd3RZ/7dp+Avh1Vc2EDmOw8CIONoip10jp4DJo0gjKaJZlhMfXxV/DEo5QIr7J/v+i/UWNK0LPPwiefqCc1gMG8eTK5uVZGjgzWlxjBRKJPnwweeiiOyZMTiS1hMmfn3Dk3H36YQc+eB7nppj089NAxNm/OZeHCDN577yKjRp3innuO0bbtcd5/P4Nz57S46oJ7sVhCePjhKFasKMvSpW4++aR0ELHbKRVEemictAFE+t8i4mqduII+SdeuKbIXrt2PDhrRsTDxfvBp/o82sFthwE3w4wH099BmgQH14cfjoizSCXXi4K7a8EZruKemaCfZoF9F+Fk7LyCFQK8EWHE10J8EdIuBtTmB8VmAdhHwq+acGAQiFqebW51WVvsDkZIdLjfdLE42WgIiLCsynXHyqxo7y1hu9B2xIdOQeE6QYlKqV6MqKZzQ27qIwkM+irrvxkkIEYQSTSGhuHFShTIc9suQh26xVSkGzp6BkonU/nP6P+DZ/iKwRZKkmZIkzQQ2A6UsBUunfxSQdGkK+8/Ccz9CSja8uhFWXSdcWO2QkoMXyvIAp+FwuclSFGIli6nM53JzVbVv18jidJMqKyQaJ1EXXJRF7pMSpHElwPFckfQnWB9yPAtqx1ACMA6mQuNgD3Ub7E2GpqrlVa4iPJNNEYTV362noU0tSsjQ1+yDm1sYytRrOZQCdSsLs1PsIj/8VqN4Sx3DovVwR0/zsVrf0xbA8DsxgQVW+HWbcHAspwGmof7t92HEA5SQQ27dCufOwaOPaiUBoHj8cYX+/a20bRusLwmATlqaxKBBGaxZ42X9+kqMHFnWYD5sPEYMJiVFZsuWHLZsyefoUTdXrngoLg7WYgsQadbMwcaN5ahY0UG7dpmsXGmM4hvou3NnWLHCcC/UJo8/DAN6YwKR2Gh4+RF48WtMivWX7oVXZ2ISZz3VFz5fbU481a8l/HRQDXSoPqt+DcS34VbvrcciIk7bLJAQCo/dJI7vmAC7Mg2qxhBoVxZ254DbsJBoFwM788Fr8P1sHwtbC8F/HRBJsEjIksI1g69IHBKKpJCNonuvx2BBAQrw6txIBazcQaKueBc+JRJ2oJhCnfNwAGGE4zVwJLFU5woHMFIstUjmki42qkkUO4ssJout2hWFsv2v0JHAf7doS1GUlQgnxHnq1kxRlP8+HYnDIXJOl42C95bBlrPwxkZYcLz09nmyyB9ipCt+P5r0XOc+8BNbymVWtYov0GoL+JdckcEj+TE6N170Ci9eVL+S0riSUwVQz8ghqB/nsUxV5GUoA9iTDs3LG/pR6/alQmJkoHz1CahvDPuu/m45Ae01Cy0nOqDsOAOta1PSIgj49Qh00EDGDgvWw53dMXEueQXCeuumRoZj1fo5y2Bwf9WgweiYaYWJ38Cw+wxjVOu3boUyZaF/f0qYCT//PNx7L9TTRG7qoIuKYPRoPxMnOujRw+j0V5I7mT+/kPbt06hVy8HChVWZMCGBBg2CuZkQvX2AzIr2pKQQnnkmgZ07yzFiRCQPP5zHCy/kU1RkNPEN9Nm/P7zzDgy5FzMn0hX69oRxb2ASI339Jrz5nXA61U7bvRlggV+PB7qPi4Bbm8GMbYEyayg83xO+3IZp0dG7FnyzX913ifnSIkGuB2achrYrRVXvijD9guGybdC7LMxKNffXMwbmZhpuiwt6hMGCAkoFEYAeIbDA7TX5inSx2ViiFOkgAtAeK2vI0UHEhkxnorChYAyLUo9YXT8CAjQqkMB5jpu4lwo0JgstBICYwMtRjQsqkLhxUIdwdhZadG6EAqhTFY4d04I1/s8hsRRqC3RWt9a/2zKI/jFA8sgQsFqEI16tRKhR9vfb58hmU12L002RRaGsxXxJRTYfVknBZpP1DSAfhXBDO5tdJtfhpokx3aENCh1QvRQudGw1CHUCIXA0F+ppToYGcLhYBE0MJr1an0fSoX5CYF9rn1kIdRKEhz82OJEG3RubjwXYfAo61sEMGE7w+uDXY1BLC0NlsMr6eQt0bIQ+eWw8CLUqiRAcRjD5fhn060IJMMrLh8VroU8PtcwgylqwDG7rDdWrYgaTEBj3BLz2GsSUUctVMHG7YcQIePfd4EjCdlJSoGdPmVdesXHnneGBE5UCJvn5CuPH5zFs2GWOH/fx8suJ7N1bl9deq8DQoQncckscN90UR7lykYSGhlGzZgw9esTy0EPxvPtuEj/+WIVp05LIzPTTo0cGjzxSwJEjWhz3kiAyfDg89hj07AXp1wJNatWAdyfAXWPV5GjqPR12J+QUws+bA8MOi4b3H4Lx3xu6d8LDPeH95YZoPy64rw2sOgHp+fol06GasGo8m42+sKkWKXRsj26DR3bB/j7wVAPonAC/ZQTGGWGDnvGwySAadlmgTwxsKAqc1w70i4SNhrAn/UMkwgx+WkOdDlYrHh1EbDaZQVKYSazlwM1tRPBbkFirA7Hs4IrexopMG8pzissmsVZdaupmvxqXUobaZGBeYYYQyQWu4MZJMaHkopCaF2oSbZUrA6mpmv/In2+1paX1vdHtn0SSJL0LPAYcVbfHJEl650aP/xuBRFv1+3A44JmHRGrYqDD4+J6gpqXoxZI9BkdBldL9fhJtSom2YUGyFZtNJkWRqeISHRtFXF7Jj90mm7iSOBt0DeJKGoRDfRWJkhWDjgT0yUEBrrmhjDYPqpO4bIVCryHciQFMtl2A1qqSfe1puLkuJm4GmwitUeSFpFhKgMmOk3BXZ0OZWr/pMAxorzo6uoR46+BZFTRAn/jW7IB+3SAumhJg9N1CeHlsUJ6VENHXUxPgU+21MwDqtUJ48234+MPAOYzxur7/HpYsERFyjYO+dg169vTz4IM2Ro+2YrFcH0zARl6ek3nzvNx1Vwpt215g06Z8oqPtdOwYzpgx5Zg8uSqff16VN96oRJcuUSgKrFtXzJgx2XTvnsZ33xWTna2JuEqCSHS0GGujRiIIY05+oEnd2vDtZ3Dv45CRG7jOOrXh9lvg8U8wrfTfehC+WCaiHGjPrUlN6NYQ5u0NtHO44LHO8MEGTD4hEzrB61sxccd31IQPD8GsC2CXoHwovNUYaoQLZbuktruvIszOUL8+tWxgWfgpV82wqL5rfaNhRZGqhbDJuCR4LVos7m12mXKShF1SSHOqllg2mSgk4iSJc/h07sOFRCUcnDH4j4SjEIONaxTo3IgVmZqU5ZzqP6KBSVVqkcpBnRsJIQYP+fhNQOCkLElkIuPBQQUicWMVCXjcCNNfDxQXwp/Nifwfot7AzYqiTFUUZSrQE7j1Rg/+R3Akw+4TqUMLiyGvELrXB4eV3zWs8CpQNkjElOZXiJcsphUSQB4K4UFgcsXio4Jk1dtqIq6LskIlXYyk0DBUfHSzakKo4W4dzBPJf7CJ+kPZQXlGAFywMx1aGM14NUuqs9Da6EOn/q6/AF1riP3kLEiIVDmUIDBZsg/6GURV2kS1eB/c1hozd2QX/jk/b4M7OgfGMWkhPHcfJrBQLPDFPBg33HA+tT4lFTbsgHtvC+o/BH47CFnZ0KeXeZwAC1ZAVh7cMxiz7kUSzovffAOLF6tZGg0nzc+307u3TFKSlY0bHTRocH29iXEwxcUK69d7+Oyza7z4YjojR16kb99zjBhxkbvvTub551OZPDmPNWv8pKdr4XGNfZlBpHdv2LABflkJTz6l5sxRmzRvAnOmwEMvqvG01OsrXwHmfAjPTRJBNbV73r6xULBP1XLOOcHqhC/uh0dmq061KkAMbQ1z9kC2ErjfnSpDeqGaBlcdYmIkWKzw7H71MdpFOBSnFWKdMOUm4R+CDe5PgqmpgWOxwgPxMDUo2dX9UQrf56GLswZE+FjqlrGqYuBBIVZ+Un1CtO+sj8XJClW/oVlidSGELeSZnBBbEsFGrujtrMjUJpwUrmGnWOdGYnHgw42s+o84cZNIXVI5qL8lMlYSqEEyyciIjIi1iGebz6BozxcOzlt/g0Aekj/XcktB0vPD3+h2IyRJ0lRJktIlSTr8O206q/ncj0iStMlQ/oRadliSpB8kSXJdrw+Vog3/o67XqDT6RwDJz0ug53D4YTW8/SN0fxs8f8B5pnsh3h5UpigkqMp2m4HLSJV8VLQg5LaqOeJ5xUd1VU+igQnAXq9MVZdX/4BGRokVXhkrfF0NnSs55BGpRsXJIEeBZjGBfe1D3ZUFLUsRY+1JhZtrBJXbYNNZ6GAon74TEmICx2m05CD0a2o4FsAuYpWdTIUmwU6PdvjuFxjRE32yO5cJNSrChAcwcTazV0H/zhAeazinOs+++w08McIQQt5w/vHvw+DbISomcA6t/uU3hXK9U7AZsQRz58L06bBoUTCYgM9n56WX/IwZ4+frr+288UYETqdhQPrgjIMJ5l5sEIysJt1J6SBSu7adDz+EYcOgxy3w44+YdCJdOsDXH0P/B+GYAUSiYmHR5zDmAxG6BsR9TEqEz8fA2ClqmarvH9sL1h6BY4bEVBXi4OG28Plu89Dvawyv78L0zgyvD58YQtJbbeC0CPPzc4XQ4jc4XAQd4mFPHmhWv1ihTrgQEycbnlc5h0KYBU4oAZ3IsBAbc3yBRVcLO/zsL9a/M6tFpiVOlpOvg4gNmerYWRXIowhAb8qwl6t6GysyDUjkAGdN3EgVqpNs4EYAytOMVPab+itHPc5wTqTVxUoDothcYBHpG7OBImhcDVJSEDt/gaL9L9SRTENwB6WS6kT4JdBPUZT6wJ1qeQVgHNBcUZQGiLfz7t85zzvAPkmSpkmSNB0RHuXtGx3kPwJIrl4V1j97j8ORC0KZrBDkqR60eEj3Qrz+MYkX7ZLsJzRIsmW1yBzGQ6LhwVktMufwUUOymwDHapNJlfw0tmu2wTK3RyhYJLBb4PZo6K5q8w8WCJNL7YPekQ2t4yhh7bQ1DZoZ9SSaniMdOlYqWZ6pvuNl4sTvZ78JzsTE0bjg4jXILYZ4g3IeADtM3wi9WxnK1PqLWZCWA3UqBsZ56hK8OBxeHIY+n3p98MV8eKC/4ZwqZRXCpFnw3MOYwcIGqemwYBVM+1x9dob6AjfcPgQ++RjqaHofA5jMnAVffQUbN0J9Y6gX9aIOHoSOHf1cuaKwaVMUb7wRQVKSMbz8H4GJ9v+PQaRDBzuLF9uZOBGWL4e77hbvqAYiFgeMHwdD74be98PFtMC1uMLhx4nwznRhGafdP1cIzHsJHvkSLmeig0jdStCsKry9ApP46rMBMH6Vam2lXtLgRkJSc1wTidmgYhwMqALzL6Ar+MOsEGmH5enQ4Fc4VCT6fqESfK55t6vWY08kwFc5gfNiUxgVBd8XBCI/JFkkslE4b/Fhtck0D5FxShZybapo2CJTDivVsHJR/VA1B8MpXOWUIddIGAoVCeU82ab87K2pyilOm6baujThvGqd5cSNDYVoKpOt6ky0cCihlOUMF3VFuwU7u6+F6cmsKIBG1eDAAQiItv58MPkrdCSKomwGfi/L/D3AIkVRLqrtDeE0sQEhkiTZgFAICkxmPs8PCAX7InVroyjK3BsaJP8QINHIaYcI9Rsv9Ig0tiYAMfgaphmBRKXLikL9UlIcZuGnpmlCgbMWDzWkwMPUuJJDskwjhwj+WMEK0Ra45IVsGebkigUOVvHbOBxsqpnm9lxopfmBaroUVM/4cAgxLohtwsu4wAvxpThcrjgN/Y26EZtQro7qaGjkgnXHYGjHQBut/zXHYGAbEcI8OHrwd6vhBc1D2ipim9lt8Ox9MPd1RAZGO0xfBkP7QIVygfNp4/l+iYjX1bQ+JYDzp5VwOhmeecJwL9Q2aZkwZCRM+hgq1SpZv3iJUGZ//734NZPIsvjllxIdO/o5dkxh9uwIFi0Kp0ePEJWT+T0wCeY4zCDSqFEoTz3lYOFCOyNHwssvQ8+esGGjYZwhULkqrFkEYeHw4IsiQq9GcWVh5bcwZTEs3oRu5osVJj8FU9fAtlPoIBIWBjMegndWgNvANN3WUGQyXKeBA8JZ99l28NIOwyW64K1m8PIB8Bvu41MNYFcO3LYXCiXRrkUEFCpwpBAdRBJscFMYrNYdDhUcwO1hMK8wEDvr4XArK2XhK+JwuRlmCWWmGmhR4/DvIoT5KjeigYjRSksDh85Esp10U3klIskiD68aOt6q6lHCiSLPEHOrDHXI4ITprXAQQgwJXEPCg4MwYojEga9Y0sVaFEPjunDwoBFA/how+RuoFhAjSdJGSZL2SJI0FEBRlBTgQ+AicAXIURRldfDBkiQ11TagHHAJSAbKq2U3RP8MIFEBwu0Fl/ox5LkhojQxogosl4shJgjUZczzmi7Gwk0NrKbVThoyCVixWgKWXA6Xm1OSl7pqWsEUGSIuyVRMVjjghlFXYLdBT7e/EBqrnMOZQvBoYZg0UieBX1OhXSke4/NPQZdSuJKFR2BgQ7VMnbwL/PBgW0MOduCH/XB3S3Of2IQ+ZOYWuF9TpBvAZN1hqFRWzUlCQOEfEQr9OsBLI8W+V4LnJ8FHjwfdVFVRP/IV+Op1QypfA/fx/HvQsxN06WY4Tq0/dgaefgUW/wBNWpesP3pM+Gh07AgffwyJiYY26sTv8cCcORKdOnl47TU/LVrYWLcujm3bYvj001juvDOE+vVdlC9vJSzMKCsLISbGToMGNnr0CGPo0DBmzAjj4MFonnvOwbVrVkaPFqKsgwcxORtawuCeu+GnmfDSuzDhM8MaxwpVq8Kq7+DdGbBgLSYQee8hkfPlu3WY8qxPfgAmroWjmYFnWCYM7mwCT63G9F680hk+PwhZbnQQaRIHZUNgdXrg/jnD4OZ46LEz0A7gxcrw1iUCfizA2HLwRbZ2DsHK3x3lZ1GRgk8FkTC7zK0OK4t8wjorDImWFhsbcevflxWZvoSxiqxSQcRINxHFRjXHusaNtKEq+zhm+j5rUkcXa2mhUcrTnBR26X3JWClPQ85yUtePNKA8m/xeoR/JRnAkeZCeChkZf13UXz+Wf0dHUkaSpN2GbdS/cWob0AyhGL8FeFmSpFqSJMUA/YGqQHkgTJKke0s5/qPf2T680UHcEJBIktRTkqQTkiSdliTpud9p10KSJFmSpIE3OgAjFReBS818mFcAulVosE6sWFht1SlFdZQnycQGJbI6g5fqQW6FVmRO4CEEs07FA/hRcNllUzTTazLUCCEQ0NEKv+WKzHGaSMJihXoRlAg1vzYNupenRMC9dckwuKGhTP09nw3RLojWQEM9bt4BuKtp4PjcYjiSBm1rUAJMvt0A93c1WFgZQPnN+fDiXeI6wkMhI0dYIb02A16fGTjfmr3Ce7tLc0p4y5+6ALOWwUtjTTcVEI9r8OPw6hPQvBUlQtPvOwi3D4Mpn0HPfiXrC4vggRGwdi0sWwavv240EQYj53HggMJbb0m0aZNL9+75LF7so3ZtJ489FslXX5Vh+fJEdu2qxpo1Vdizpzw//hjHM89E0bmzC4sFPvlEoXFjmXvusTBtmirCAh1EJBvccTfs2CBMfDv1h22HDffbCi2aCp3IqLdh5VZMIPLuKMFlv7MIE4g82Fk8v1n7An1Jdpg2BGYeEknOtGfZpJLIeTP1CCZwGF0Xnj1geC4hcF8lmJUi1ANau0aRwjhlb3Hg1oWHQNdwmJsHRj+R+8MkJnuK9fw9d4VYWez1Iqkce1+bnUVKERYDiLTAyQEKcasTtFHZru1bkYnHT20iSeWaiUtpQAWOcVrnRpy4qUVDzrNL149YkYmjBukcMr4IJHETxzmt60eaEcsveVZxA4qBbKgeBy4nQCFmZfufS/+GjiRDUZTmhm3yv3HaS8BKRVEKFEXJQHikNwa6A+cURbmqKIoXIa5qG3ywoihdfmfrGtz+elRSDhREkiRZgUnAzeqgd0mStERRlKOltHsPuGFvyOCVQW4BOFXF8pmrEO4o0USnTB/EBo3eapPZ4fdQ2WLlqBqN1IrMNSAvaGUEUIif+tjZbfEg+60CTFxuTvpDqW+1cAAZH278ONnisdI+ROK0gSP5rQCeL4cueVx9FW5OgKN52oDEz6Y0GKopv13obsYn86B8OEREQl4u4mmogfVm7IdBDWDybrXMBTN3w4JhMEXLW2aDLzbCiA7C211/mj7I9cKyfXBrE1i6V72PTsAtQnKM7in8TV6bDXtOg+yG5wcLqy38gXE+8Rl88CjsOgr5mlhEFvVfLIQ5b8A9A4TDIt7ANadlwH1Pwc9fw4jxsO8wQpqgKfovQI+BsHAalE+EqTPN9ciw4hdYtQruvx9+/RWmTIGZM0XeDzEjeg03LYSCAtiwoYgNGyDw4gS/QMFiLsMSXSNJ+Nf07A0vvQS79kHfeyDV4I8BIlnU+NHQtQ30fxoupmICkbcfFKkAHv4GFENm3gHN4Y6W0G8ypgXE+Jvh0FVYeQp9sRASDpO7w5DVoBhAZEQdKPTDwWx0sVvZUHi0CrTdKdqtbihEq4k2ePaS4TJd8FhZmJkLXgOI9HRJHPfLXLMGzOJvtjp5SVWW22wyD0nhDJGEGF6b5B8knKlB2QvFJQWmTQduelGRtaQQCNropiZxpHMNmWI9ALwLG5FEkc15QlVwiaYaxWQhi+S5aG6MUZTlPOnIlCOPCEJwsivHpYMI+dCqLuzYAQFHRKN4688hTdn+N9Bi4AtVD+IAWgGfAGFAa0mSQhEX2g2Rc6RUUts9CVRSFGWUJEk1gdqKoiy7kUHcCEfSEjitKMpZRVE8wFwEyxRMY4GFQHopdX9MRULJW0Y1OpOAsuEEFg7FJQ9RKBkFOAeFOsETA1AWC2Xw4lBDVgMcwkNj9UvW2HSAPZZi2jnMt+bXYugQpgRWhHY4XgyNw9RUvC5Ykw09yqCquALHFiB0JLVKiY+19Dz0rUaJMCoLTsDwJuayNC8cuAwNKwa62ZUi4nlVSyrZ99cbYcIdwtFTvyWqJ/wHP8FnD8P0tXD4PBy7AqmZ8HBfTJzHxWxYsgUmPWfoQ61XFBj5Foy5B9o2wTwfW+HiZbjjUZj6ITSqa74nWCEzC265U3i3r/zJoDcxnEP2w7ffikjCbrfgUn74AXr3Bpst2CoLSirZS1O2a+1sBINIi5bw6aewbx80bQl3jYBxz5UEkWpVYf0scIZC78fNIGJzwEcPgd1aEkS6NIRnesOgqaBHqLJB+9rQsza8vM5wOS54rwN8exRO5qO/I+UiYExdeOkgJiuyd2vDa2ehSOVCa4fCXXGCa66iSfhcEOOAgeHwbYHZY/2FaPjUIz40ASI2iq0y6fix2WS64eSg5OYafh0wKqsi4oOGnOvBehFtvxuJ/Mo507q8A7XYzWETN1KHhlzikAmUkmhPMltMz6oMNcgmEx8WPDhIoiy5WFDybTqIUASt68P27W4gl78iztZfSZIk/QBsA2pLknRJkqQRkiSNliRpNICiKMeAlcBBYCfwraIohxVF2QEsAPYChxBz/e9xPN8jXkmNa7kEvHmj47wRIKmAUL5odEkt00k1NRsAfH2jJw5QYFWQVwgRLsAN13INjnwaBXGj2/OhXNAi4Ljio7Ykvlrji32UYuobzI8cuDlOPo1wBthnVV+yU/HSymbVfUssTjf7ZZkmRumYCiZ7CqCFKoK6CEgW0DHIIOJacAnu0PQhBtCYdxLalBJ362ohJOdC03KBMoD5x+Dpzpjmzg/Ww9PdMSnEscHlLFh9GIZ1VssMc+aO83DhKtzZKdDPK3PggZ5QJTEwboDZm8Dvh/t6YgYTKxQUwZ3PwaRXoGoSJZwYzyXDwEfhhXHQvQMlgjt6ZHj6ZXjzI/hpNjz4MCVEXUgiFe2330LLliKd7c03i/zwX35pZ9QoO40bg9X6e2BitNgSM68k2alVC4YMEeCxbRuMGwsrV0PTjvDGR3BR0yuok7XTAePuh9kfwTOfwWvfqMnYVBCJiYblb0FaLjwz3QwizWrCB4Phtiki86b2nGpUgA9vhSELDMEaXdCjMlSJhm+Om5/rF63huf1QYAgD1ioayoXAzwaLLodqbRhihbnVoK+6yBkfC5/kKmIqVUHklggfZxWZc/5ADLpnQpx8IhfoYt9HLGF8oyahAvENDSOKWWSWCiJGqks4Z8nCrfqJOHETgkQNEjgbBC71ac4Ztpg83MvRlFT26v2JwIwtOcBxCgmlkFDaEs8yny/gP5IttoQ42LMnh78iNIpGCn+Z1dZgRVHKKYpiVxQlSVGU7xRF+VpRlK8NbT5QFKWeoigNFEX51FA+QVGUOmr5fYqiuEs9iaDqiqK8j4qwiqIUUUq2juvRjQBJaZ0Fu49/CjyrKEpJ+ZGxI0kapSmWxNM295aXFbDaupYPcdoqysiNGN6BYgXqODAkVRBAUqcUid0R3NQLcgI6gRun8Oc1vfinrW7qW823xu90c8ynkORUTALBX3KgVyx62eF86JGAqQ1WWHoJ+lUylKkTw8l8aFgGKkSby7HB5L0wyqhMt8Gmc1CtLFSMDpQtPyIiBSdEluzjveUwrocwPwVME/3zc+GFO4W8HBsUe2Ds1zD5SYP5rnprx0yCcXep6XyDOI+UdBjxOrz3DFQsZziPWn/mEox9FZ5/FJ4arZYHcSdbtkOHPlCzJqxYCDffGqgzAgoIbuGJJ6BJE5g6FWw2eOIJOzt32pg718GcOXa++CKS118P4fHHwxkyJIxx48KZMCGCiRPtzJjh5Pvv7ezZA2++CeXLw8+LoUtXuO9+WLnZrEjXTH6H3QE7foKQMOg8GnYdISAZc0HdqrD2PfhsKbz/M6bshm3qwMu3wcDvIF179W1QNg7mDYaHlsDlInQQqRIJL7eBERvNz3NIDTiRC6vSA/fQJsHDVeDxc4F2Tkk40Pr8cNkL912CpcVi4dXCpTArH4yxs8aFWXmvyK2DSJcQPymKTLJN8E2JFoVUZM4bQr6H46cFoaxVPVOCletGbmQAVdjEeR1ErMg0oxKHOGkyA44iAgsSRWrOdoAYapLBXvwGsRZAJepyUvUfceOgFbEsyzP4j+RBlEXkaS8o8BAIjfLn60eUf0/Z/k8ijyRJIaizsSRJ1YHfAx4T3QiQXAJDKjJIoqQ9cnNgriRJ54GBwJeSJN0W3JGiKJM1xZJBlS5IFgr2cPX+XskWogH9mRufvfr/WDHUDXoeyfhNyKeJsY5QTG0c2JD1zYNCBeyUMSgJrRYZP3BA8VLd5TUFddzhUegVilBOqh/82kK4WfMBtcH8LLjT6ICofuzZftiTCbViCACDOkF8dxxG1CtZvj5ZyL3jjI7WNvj4N3jCqAazwZur4JkehjK1jxwPfLMJRnfBDG52yMyHj5bD+DsD/Ww7BZsOwZjbDW2tUFAM970Ds1+HBC00iwEs9p6Ej2bB4klQvWbgHBpdzYae90PVijDzMwhxUYI7KSyE8RNg7HMweCBs2gg3dw/UA6Zljd8Pu3fDl18Kc+FmzSRGjvTz6qsKM2f62LbNwtWrdiQJsrPt7Nnj58cf4Z13LDz5pMjiOGgQfPAhbNwIxV7MTu1WcETB7bfC9kVQtyZ0eQDemwVuDzqISCHwUH+hY7r3Q1i+F5NxQ5/m8Om98Oh8OH8NnZsMjYBFQ+DZNXAgA/2dCrPD3D7wxFa4aoiJVTMGnqgDbx833DcbvFRLeLqfLAqMvX20CGr6fSbUOg3z1H7ei1f4OAf8BhC5PVQixS/8REBYL95lCeEzSbA3VovMVfw8oToRauAwiBjWkYMPSpj9GkEkDisVCeWECg4BsVZtdrLfVNaQppxgg26pJRwTu3GVPabEVtFU5yqp5OHEgwMX0eQikZ4VITwuVf+Rzg3FsxULV8Pi9X+JrYLpVYSIrKIkSbOBdcD4Gz34RoBkF1BTkqSqkiQ5EN6RS4wNFEWpqihKFUVRqiDkco8oivLzjQ5Co6xM4dOAT0w89bQJ+TpgcqwY6pYS4z0CC9FBl3aWQpNoC4QycA8FtCTUbGVikTkieehqEbOBBiYrZTe9w8zMWLYdLvkgSf2wd+VB40gRN0w9if7Rr78KI6obygFcMP+0iJVklczlCrD0DIxrZT5m8UlIiID4MuiAsfQ4tKgEdSpQApC+3Qx3txXBME3qBDvM2gwNK0Efgz3H+z/D7W1Vay0DWBxPgae+gkXvCWsvrQ+NdhyF+1+FBe9D3bqGerWN1w9jXhcr/o3zoZNm/msUZVnhzDl4YBw88CgMHgJzfhAipwQtEvLvMNz5+XZOnoQdO6z88ouf2bNlZs2yMmOGzLJlFn77zcaxY5CVFdSXQXwFQm/z0duwaxk0qQ+3PQLPfaqa32rt7VClCqx6H2pWgIcmCV2TMbvh8C7wVG+4ZZJIjaAr0SPgy/7w1W5Ya/AXkYBpveCzQ7A7Hd0i0OmAGW1gxC7IN7xbzWOhSyx8mIoJAF+tCO+mwqh0KFDnrHZhCtEWWOEV4lqL002IXeaFECfveIWZmMPlpovkIMQCJ/CZTHwhABgOJAYRzVw1MdX1QMSKTH8qsIozJm6kCjF48FBEhl5uw09NWnBJ1QlbkbFgpwz1uGrwZnfjpBptOMAh3ey3JZVZ5fdAthpfK1ts3VvB2rXGQI1/jfnvf2vQRkmSvpAkqa3qY3I7MBz4AeERv/FG+/lDIFEUxQeMQVhjHQN+VBTliFHh859R4KFm50AlFTxSMqFCFKXnbFfpaB6mCL4a7cNDY4O5rw0ZGchCpkyQ2GsnBbRRZ1yNxQbYZCnkZqvDFD7ltKxQya7g0II6ql2ty4M7y6I7Iq7Nhm5xmDmAEFh6GXqWB3tQeJFiOyw5B7dUNZfjgtmHoX9t4eeh1fkV+Ok4TOgaaAcwfjm8379kH147jJ0NX91viBZgAJMR38Ibd0N5VSzl9cHdH8LHI6FSPCaw2HgIJi6EH98Ce3igD63NgTNwz8vw8ijo3N5w/YY+Zi+FOx+Fp0fDlA8gJooSoi6AMxcFoIx7RkTE/fEHWLkS7rtPxLK6PqAEK+G1raR1lnY+WwS0aQsvPgXTvoJ3X4WNO6FZP3h5ouqN7gq0t4fAgwNh/gSYMAOengpFMjqI2Fzwwb1wUxXo9RVka9yCDcIjYelQWH8e5hzF9KyebQ3Hs2HOKUye7m82hpkX4IDuPChM5b+qCyNPg2wAkVvj4JIHXjBERLDYFD6MgydyAmbtNrvMKKeDZT4PqYqCw+VGAl60hfO2kntdEAG4nUhWk4PXIGs2gohGoXhoRBxbOW/SefSgLjvZZlqj16QmqRzHSq5+zvK05Ao7cWPXPdn92EiiIce5QCGheHDQlbL8kGM16UYohmYNYds2TclutNj688Vb/6V0CvhIlSaNB1IURVmmmhLfMN2QH4miKCsURamlKEp1RVHeUstMCh9D2+GKoiy4sdMbFB4+IS5w2gAvXM2Eskavb+25G7Qw2TI0NjMZAOxVvDQtJR3VXvJphcv08u6liEqqyEsjKzJn8FEZKy4114LGlSwq9tHFqc5AKpj8VASvJuoHMzMD7teEgQYOwOuEX65A/1KCNU49B882LVnudcC3B2B0UN2CI9CwHNTWzuuCHRcg3wM31zP3AbDrCuy/CCO7ljxHZj48PhOmP6rmjHdCWjaM/hpmPK2GyzeAxY+/wqItMG08OI0SSrX+2Dl46lOYMArGP0SJeF0gwor0HQlrtsAvM2HkYLBHUlLRjvBz+XIKdLoVRj8qJuLvvoP9+2DePHj8cWjbFipVMsQAK2G5F9iPiICbmsDAgfDYE7B0KezaBPffA6fPw9PvQL9RsHSdCH6om/QiTH7v7Qs7p0JUKHR4UvVWt6GDSMVEWPcipObB4z+oIjP1PYiKhuXDYPIemHEYE4iMbwV14+AV1XxXO2Z0DYhxwpcphudmg49qwWdX4JQ2N1qF/9GQWHjsqvE5KwwPh2XFfs5ZAyASLUFfh4WJnmJ9wXSXzcEuPFy2eNQuZd3SUftGnMj0Jop5qoTbaKGlkVbWlUoc5yoyHr1NLBJJlOU8p3HgUVXlhTSgEydZq7dz4qY8jTnHWtOTTKQhFzmLB/Covu0yVi5khQdAJBuqR0F6Gng8hQRA5K9Stv81QRv/alIUZaKiKG2ATohQLN9LknRMkqRXJEmq9QeH6/TP8GzXSFajnyJ+vTJm3yGNOzEsJk57oIb6wciq0n0vHm4qBUj2UEAzzDFJivESiYU4rNgMqykrMqukIlqo/Whgssnv495IRVdUAlyVINQKP9UWi9wDhZDkgnhtkjWAyZTLqnMimCb0c3lwoRA6lafERP/tcehcBaKc5rrn1sA73TFNRs8shVd6QEiIuQ+Al3+B7nWhRnzQOWyw6Rgs3gsf3qeWO2HHCRHufOHLavh50Ofjb1cK7mTpWxAeQwmwuJIBN48RFjMLP4TIMoabbhCX/bgSegwT3NCupfDIUHBGB+p1QFHHez4NvvoaevWBJi1gwquCk+3WTVhebdok9Ca7d8PcuXa2brWza5ddL9u+XTg5jh0LtWpDchqMeRaadIJRT8G8VWooeO38KohYLNC/K2yfBo1qQ/en4cOFwuDDeI97t4AlT8NzC+GjVZgAISkaPusDH20LpMjVntvDTaBVOXhgPSiuQF2PcnBXFXhU89pS+xpaXoDGTC0KkzrWbyqJZFTpOuepUM0Gj0TDJ27xAWk6v3fCnHzv9eBTQSTC5qcHLiZK2SW4C6PoaiCx7CeXXAIh4DUyirRcuOlLNX7hmIkb6UhDtrLdJOqyInOIRRRxVu8rjAqEUAYfF/QyN05q0p5D7KGQUPKIoCVV+MnvFWKtbPRAjX3biWdtjvb713Ai/+2JrRRFuaAoynuKojRBxO8agJBA3RD9A4BEXU6pi4SjZ8GqAG4ockOUNiEGg4i6v7sImpdQuMvEYzVJPqzI7KOQyqUAzCby6KoCjBFMdlLIAIvLFNhxpyzTyK4m+FFFXCGSCGHSKxJ+qAmSFaanw7AESoR/P1MgUqE2LRMo037fPQjPNy9ZXuwT4VRe6Giu23oZLuRA12qBshQ3zDoIb/aiBFgUeuDddTDzQXDZMU2A2OCL1RAbCWM0iyknLNgDP2+Huc8j4nCBDgRTVsCUX2DlexCnefgTaOOT4amJMHcNzHgdenXDDDgq5RbC659Bh7vFin3HYhj7iCHOVxCYaEp6RYHjp2DaNHjjTbj9duFv0rw5tGghHBk7dxYmw82bi611G+jUGUaMgrcnwqIVcCGZgI4EwzntUL6iCBuzdw40rA23TYDx38C1AnQvdYDEBJjzBNzaFLp/BNvOYAKRtlVg2YPwzV5YctpwHhfcXQ/6VoXBq0E2LBbqxcE7N8Gde8HtD/TVKBwerQQPanOuOtbhscJTYpGuoFeQgMnxCuPyi/VcIgAdnBIVJAtLbPmiqU3mMSmc3ZZictVvsjQQCUHiHmKZTqoJRIL1Ig7cdKAch0jDq5oMW5EJR6YptTmq+okYweSqqkfRyqpyK+dZpt8qGStWIrATxhkyaEAjQoigA/HMybaYxVrZ0LcbLFtWhLgrufyVviMKErLf+i9t/ySSJMkuSVJfVdH+C3ASuONGj/8HAImBfBATqVoFAafToWYsJZXt2ociw+5caGqX8LvNaHIGDw2CRFZuFKz4qYyis+sO3Gwgl85E6h+FBibbKKY1Tuyqf4nGlfzi9XGrU30RbDJVXQpuRYTu7h0FC2rDD1lwlxbE0bDCxAbvnYbxxoCHat3RfLiQDy0rmsuxwYyT0DEJqsSb6z7cDh/eoga4VMsm74IGFaBdVUqAxb5k+H4HTLzP0I/hPA9Ogf7NoW+rwGHfbIDNR2DGM4aQKyrnMX8zvDYLPh8HDapSAkwA5q+F0e+KIJBz34XEJHMfIO5FXj688yW0GwT5BTDza1i9CIbdDREGyzidSvE30UhRRNpejyfA5V5PsW4CERtEx8CgvrDoM/jhHUi+Cm1Hw5s/wOUMApyKTXAqD/eFFS/CrG3w6Bxhum4EkRGt4IP+0Ge2AH/9OlzwRAu4pzbcsQo8jsCYKkfBhIYwZB9keAJ9xdrh7dpw32nh2a6NpYYDxiTA45pkW/VYfyTazz7Zx3avXwcRl03mfVcI45Vs0dQmU8Mi0VlyMlP1Yi8NRAAeIIZFXMWj5l/X6oJBxI5MJyqynKMmcOhIXfZzAAW3iRsxAor4dVKGm8hAC+MggKQ6HTjLaTw4GEJnvqYHVQklqSgiACJ5EIsweElN1fQjmljrf2QkSZJuliRpKsI6dxSwAuFTcte/YjD1twFJfLxx4vfquo+UK1AhAvDBqctQsyyC+wgGE/V3RwG0DXZcBH6jmI5BcnIHbjaTQ4egnC2XyKUSNpyY/UmsuNmlggmgg8k8uZguTouutKxmh3CLgtcvoovEWYVsfXOeyI8NmMBkex7EO6F6OCXAZOIJeK85JTzd/Qq8sA3ebm+uS8kTq9zXuwfKFAVG/QSv9BRRg4PBYvIWUX63URmu6WQkuPMLuLc9dGiIPtF/shJ+OwZznwaXMQ6iHdbsgTdmw/fPwn03Y3aMVMEi9RoMfgmmLYcVn8HIu9WAj2ACE6xQUAjfL4Kud8ODz0L5JPjqA1j7M7wyHjp1EsEJgd8FExP9DojYI4VS9sXHYNM8WDoZ6lSDF76CTg/B9FUi6Zp+vAoiPZrAt48JA5F2b8KKA2ob9VmHhcLE26Btdeg+Ay4Z4mfhgnc7ilTMt6+CIsNCICkSfuoI7x+H4/mB5+eywMIm8EUanCwOjCXMAjOqwf0pqpOiCiJ1XTJtQvxMyPfqIGK1yYx2OJmvFHABsUCyWmTeIobXuIbM9UEkAZmmhLOI1OuCiHZce8qTSQGFBMRkdhRa05B9bCNUTYBlBo9AgMYKtOciK1Dw60p2gBq0ZyeHKSSUYmQisVMWGxvLSQyLQweT/p2E/iygE9Emjr+IK1HA57P+S9s/hF5AeM7XVRSlr6IosxVFKfhXO/nbgCQ83E4JmWWRCiSxgBdOpagciUbB4k0f5PvFHBGpThSeYjHpb8JDO8SMZwSHX8mhg5oITPMnAdhCDp3UWclqKF9GJp1wmEKonHUU0cAuUcXpF2aUVj8nvPDaNdhdDJ2TodAKk9LgKaPOg8D/d8/C/Zq/hQFMjubAiTwYUJkSQR43XIJCGXpVNddNPgBNEqG5IcLwhXyYvhcmDy55bmzw4DwY0wm6NqYEaGUXwuNz4ON7oUs99Il+0lpYeQhWvgpxRn8YuzB77fwU9GwFXz8RSOmrk9rHyq3Q/kGIjYIdP8KYYSqgGDHfcD8upMI7k+DeJ+Ge0XD0FAzsB78uh1nfwYzJ8MprMOQeaN1OROFNLAfR0YgEWBI4XcLpsEFj6Ngd+vaB55+AOd/CrnWwZSEM6A1nU+D2x6DDffD6VDh+0TAuF/rE3aMFbH4H7uoEby6G5xeoWRNVgMAG7arB5jGwJw1GLIEiJXBN9lD4ohvYLDBsPfgM0tZyEfBzR3h4D+wpDtxjCZjRGGZlwMrswFgAplWDLzLhkB8dRELsMtPKSrxf6MVnAJFmFiv9HTYm+4t0ELmNEA5RzB6VQ+9NBG3VxZPRYusxkviZdBT1QzSCiEYO3FiQuJ06LGWPDiKhFNKauhzkID4VRDQwMW6iX4WqDOAy6/V+ZaxE0oQMUinASyGhOiRk+/1MS4UZe4AMIBvuuRWWLMlGiLQ0k18fkZGl+Av8CeT3W/AUO/+l7Z9AanDGKYqi/F7Okz8k2x83+WvIajUuHX2g2EGGsxfVSQrYeAQOplFS4W6coLywuUjIfNcYii8jU4QfF5JpDZJKIYV4iUDRGV0bMuvIYDhJbOQMHkPYlN0U8xqJfEEOeWr4Cp/PylSvhwecDl73u1nmLWZJmhN8VirYJOo54ShwphgyfNAqHHZo4g51clidAS9Uhzpl4fhVxMQgi7oJ+2FFN1iWDF7tGDFQXtwGK/vDlhTVvapY+JuM+gUm94L+89TgisCcI9CuMozpAl9sC5wbn8j5fvtU+OUhGD4TDqUY6hEOobd+DEufgJd+hDWHxL2eul6Ywq56De58D85pzwcokGHIOyLMyrqP4LkpsOWQ4bmpE19hMbw/C76cD48OEoDy5RyRFCszh0DwR21+Usecng8LlogNIMQB1apAjUpQvSq0bSMAJCoCXC6xFRVBSChkZkJmruj//EU4fQF+2gCnzoGsefGXAhravisEbu8klOluGYZ/BWcN4d81EHG54NXe0KwSDJgDF/MNL58NysXBD7fCnJMw+TglFguP1ILH98GOQnPfH9SDY4XwnSFkPMALSXDeC3MKwRjF97MYC98WeTkuqZO+TSYU+Dw0hPt92UgqiMRjYRyR3M4VHLiJxcpY4hishi0BAQ4NCCMROxtVp8RgEDHud6cyh7hEgZqT3YkbB9CJpnzPFJNIy4GnBFeSSHsyOYiXPJNSuiYd2cQOCgmliBBAohg/EzIlPtuNDiLlneIWXbyYj+BGcnE6PUye3JXoaBcvvriOw4ev8qcq3pWAsc//i/QPAZIApVxRU8iuh+xM4RFONOKZayASxMhsyIOboyz84rbqIR4AzuOlPeFsEOmodLpAMZ2IYSXX9LITFFIJB2FYALcJTH6jkKeI5g2yBA9ng+XksdZahreLxFAsThEleF6RlefKSgy9JMb6/lV4tgIMOkFgxapO2M+fgLfrwe07CUS+lUXE1uln4MG68OURApNNMaR54ON98F5HeGRtoO5MNkw9CN/cCkN+Utv74IlVsG4Y7EmBbRcxgUV6Htw1HeYPh35fq5kYDaCVnivAZOFYYZa9bJ+oWrlfZFr8ZJSw3lq2K3A+7DB1JazbB5PGQspVeHaymhBM69sOeCHfDe/NFDni774FVnwDFy7D1EWwZhPoyQBUgFWD/Oqi7iIPHDkmttL0Z0Bg0v0dncjvgUijejCyD3RsJHLePz9LxEAzcVDqcx3QFF7uCV/8Bs9vAMUotrRBh+rwWRd47FfYnEYJEMEKLx8OGqsNXq0tlO0TUgzXY4d+ZaCeA4alYwKRQSESoVY/0z2qBaJNs9JyMNVfSLLNo3PYHxLDm2oMLIAJlGUi6RQZFO4S8CwVeYNT6u0rGSZeA5EQoCXlmMp6E9fRkQYc4SBeCggtRaRlpKoMZL8hy6tQslcignhSSCOP8hQSShQOnvfm8tmZaB1EyBfv0ty5IJZaQj/y1Vft2b79CllZubRpU0EFkv/Rn0V/m2jL49GmCYNdtw9OnoJaSQT0IkbQMP4vQp+YthRCnWC3AWA5brqroViML/4qrtGLuBIs9XoyuVnVnxjNgNeSzUiiqKp+3VaLjGyTWagU0s9l0eXPAJt8MpXtClXUSWJPIWCBpkZTYLVum7pabVuWQLgQdaL4+iwMrw7VoglMKupxM09DuTDoVNlwsS6YfUSIvkYaogZ7ZLhnITzWHipHY+bmbHD6qhBzTR0KFTUzXsMEl5EH/T6FR2+B5/qp5XbYdw5GfAUP9oKPR6gRCQyirguZ0OclWH8A1nwIQ3uANUi3olGBB777GVoPF4mh+nQSkXU/exlu7QJhxnsHJUKrlKjTyv8NELE7oXMreO8R2Pw1vHgvLN8DTcfBq/OCQES95sY1YM046FkXek6GqQdKgkjrqvBmO7h16XVARKMgEJlQGyo74eVLmECkQyy8UBYezQDZACLNHfBEFIwpEmypBiJ3hAjOdY4l35DV0EU6MtvUhVY3QnAg6QuvQJysGLaTxQU1wq9RhxKsIxlALU6QglttK8yALbSgCTvZovuMBIu0NEApQwMyOUyRIYi4jJX69GInm8kjAg8OylCeoxTz6ZXwQGwtdWt3E/z4Y0DBPnhwBfLyvHz11XbS0wupaZKX/zmkKBI+r/Vf2v4v0d8GJE6nlRKKryK4nAbl4w1lXvTcyyHaB2bkSGWRCEmSoKrK5WiKrH24aUAIwY/sEgXEYCfCUGNF5hfS6EGszq5rH0yResJllKOp4ZgFlgKecjlMUYIB3s+BZ+IC8bjeToW3K2Oe9NSJ5LnT8FAlNRQ96GDikWDcXviqjeEYAsc9/Ct82lH1VTHUjVsN9zSCmyoEjkvOgQ+3wIIhaiDMILDYfRFeWAqLH4ZqZcznwSbidfX5GBJjYOajqv7DDteKof97Ihf8hregRjnMk6Id5m2E7s9CrUqw40sY3BssxvAqRkU7sO84jP0Ibh4pRE8dmsO6GbBuNowdBrd0hfIJhntlOLYEmGiA8zsgElcWureBZx6CSS/ArhkwsAtsOgK3vAB3vQ+r9oDfgikIIzZoUFmkyn2lNzyzBB5aCOnGd9MAEjtTofvPEOoKZNU0gUiwEYANXqkFVV0w4qyaSle9Zw2iYGICDLgMOZYAiJS3wpQ4iaGFhRQQAJG6Lh9PW8N5V8rRQaQ6EoOI4F1SASgDjKYMb3AJCIBIIg4Gk8hMNeXt74FIPE5aUoH1HDaBwy00YSfbsBjMgEsTaQHUYBiXWQ4IAHHjRCGGRGpxiLN4cJBHBL2owTv+Isi3CW5E5UjaVIOCbMjOFtZaZcv6GT++Hi+8sBmAvDwP4eF/gZ5EEZaj/8r2f4n+NtGWogjTSb9fi4GjUhH4vGD1g2yIZtCuEtzTFh7dopZpzooqLc6T6Otw8qUvUKgAq8ilOWHsC1IKLiONDsSyggCLm4YHBaiFk5O48WFV5cbhePATg43ZJPA4GayyeEj1wyHFx60hCsuLZHWobpa7ndwdZqWyS+FCscR+n9CVdIuFdZkERDUuOFkofD9GV4YvL2AS32zPEpFeh9eCaScJiHdckFoIz2yDGbdA78Xg13QfNhixHBbeDn3nQ4oaJmN3Ory4Dn66D3pOhUIvJjHbrsvwwBxYOArumgYnLxvqAdkCj88Wmf0mj4IJP8L5q4AdPl0OG4/CzKdgxS54byF4NO7RDjlueOl7mPgTPHsnPDEQ3psLSzeAx4su6tIBQQavFTbsEBtAQhS0vgk6t4Zxw8ViIysbduwDTxGkZ0B6OlzNgEKV07NYhCe6xQIxMVC+EpSLh3IJwgquTVPIyIZ9J2HfKeGt/+jHlNSR2DCBSNu68FxfsNrgvTWw+RymxYEOEAaA91theH14pD6M3IqIB1UawCGyMr5dB+wSPHDGDCJVwmFGebgrFa5o75FNJkSC+WUlnigq5oIhFHy0y8NkWwyPkUWBGpQxBJnPKMerXKEAPzZk3iSJ77nKtSDdx0tU4RPO4VNz+YjLKwkiVmSG0YiF7FKnfzcOPJTFQR1qM5uPr2ulpYFIPI0pJp1CAuyXjJUa9GAnm3Fjp5BQJGKJwMWybFtApJUNZMDocfD154gPiDxeeqkmr766g4IC4WWan+8hIuKvARL+pyP5/59k2U9MjJ1rqpqiShUv5y8IhfuRU1CnIhy5hvjgQmDnBfh8EDp3YtKVeGFpPsyuIPFlofk8v5LPvcSyD2GUoL20G8jkI+qwSl2RabSAKwwkkfdUD1sfVhJRsKn2o04k3iGOtVwCC0zy5zLRGstyCvSP14ebGUUu3oqxcO8VwAYvX4OvE2B9nhqn2QAmb5yF31rCT6lwBUxg8sJhmN5CZFk8l4MJTNZegg7l4MVW8Mb2QN25Ynh0A2wbCt3mwCkVK1efh/gw+LgPPPYLuLX7qILF/lQYMgMm9IIvNquhPwz6GWwwZQscuAgLn4DPVsP0jYAd9p+DDq/AY71h6/swfhqsP0gA7O0iEOfTU4TCeXRveOU+WP4bTF4M568QWE9oinbD2NJyYPFasWnrgZgoqFYBEstCfKRINtWqmRpZWBFg4fcL4wMPcCUd9hyBK5tFLLcrWlDE0nQkGqkgEhUKgzpAt4ZCd/PyT3BAC0PyByASEyayHGYUQ8eVIuGrTkFckt0O3zeAFB88k2wQkdmhagQsqgjDU9WwKDYRYcEKTCqj8I3bw1afrL+HDpebFy3hfE8BxyzF6ulkXiGWpeRwWAWCgUSTjY915JpApB+xXMbNfrL+EEQaEo0HD0e5oGc1dOKmFz3ZyCokvCaRVktGcJTZaHfDikx1hnKY94EANyIRTg1as5avKSRC5UZq8wsy7oxYHUC4CrEK1K0OW7dmArm0bBlKtWphPPbYef125+e7cbt/J+rnv0sKIiDc/6P0twFJUZGPuDg/164JJPjhByudOomV7KGj0LQ6HElBBwyvG67kQeUwuBBsxeWEVDec9ynESxJ5gOy3YrXI7MbDa7gIxYLHwJHkUEwqxdQhnOMETGt2co1HqUwYVgr0lZIVBThDMWdwM5orOAAPTpItHvb73XQL8bOuSEgKbXaZdd5inrCF0DQU9hZKnFNglxtGxsEUTcevgkkR8PwpmFgfBu3FpFjOL4J3T4jIr13Xg9eDCUxeOwyTWkKf6rDsDPoT3XYF9mfA8dGw4Bi8+RscugKzjggR4c+D4fa5wqrJCBZHM2D8MvhxKEz+DaZr/mCGSX3nRej4Fnx8D/S5CR76VsTr8gEfLYUft8E798CeM5BTgAlM8MKVazBhJrw5Cwa0h29fhNMXYccRWLpRcAkmMNGes5FzkSErB/R8RWD2NzMq24PFW9cDDeO+Hewu6NIUhneGmuVh3k54Yp6waDM6HOoUvO+CQXVhQDWYdRIWXzHUa/oZA4hEumB+I1iWDZ+nG/qxQ3UnLKgID6TBAQ86iEjAt2UVjvsU5np9JhAZbwnDZoX5Up566TI9CSUWWEAhDmQq4aA/UTzMOROIlMdKB2J5mxOlgoiRwvAylJZ8yWqT3qMSCYThIplDOAwirYpUJ4RoJK7qbcvQmkwOUchl3VJLcCN92M92irCSRwQy4XSkLC96ckW4eINYa0gPmDQJBDgV8cEHtRg9+leMogufz0l8fCmOZ/+j/4j+Vs/2hISAnPDcOT9V1ei3ew9Ds7oEIj+repI1x6B7NfUAHyUsdPYUW7jT5ihho72KbHpQUsH2E1e4nXImL3cFWEgKtyGUBVZklpJBbw5yO4eQUdBE3Fq04OmWPF62heN0BRID2ewyLxS6ea+MolvUvJMNjyRAWRtiMjPI9lcVwIViGGTQbQAQArsy4efL8HZjzBORTXVU3AcTWghfEp1s8NQmyPfCoHqwfggcfhgaJcCUA/DDEVg6xBAOHvRJOyUHun8NvevDB3cI8ZBer7YpkOGh72HmVpgyCoZ1VCML2yE5A+79DHJ8gbEYFfF6WHkFftwo4la9vwDiomDhB7DhG3hyCNStYYhW7DIcD6XG4SoR4uRGQcQe2K+cIHKL/DwBdn4MLarDR79Ai1fhw9XXAZHgfRvUTIRf7oCuSUIcWyqIENivEQlf14Vv0g0goo63hgMWVIfhabDPjQ4iAB/FKVz1K3ziLTaByJ0WF02tNl6VApx4fRw8TiQvk8YPVOIHqvApFXiHy8gU6yBiR+Y1ajGPS3hVjiEYRIzcyG00ZAenyCJb5zrCKaYvvdnAQj0wo1C8e2nCEE4wWT9ewkJNhpPMYv2WCG4kjCq0YTsHyCOCIkLoTG3mUsTV1DJwDUgFssGeDyMGwvz5QqQ1eHAE+/dncOyYJnEQCjNJ+gu5Bt+/uP0for8NSHw+P4mJLjRHoTNnvNSoARTB3l3QtB4CIGSEeMsHyw5Bl0qYIwEbOJO5+XCXK7Ak1FY2P5NNk1ICzh8inTAkIgzLSBEy5RJ9SCRENUDNw00WxfiBzWQymjiTvuUSbvZLbgbZHHoYFYCDPj+7fT7uiPCDTaFAgQnX4EPNcTAITN5MgeeqQo1SrJQ+PgF1oqBHIiUmrSwPDNoEU7uKwIBa3al8KFDFRbEhEOEITO4zDgqP+Pl3QWyQwh6X8DO5a6bw3fh5JFTQlPBaG/XcSw7C/VOgURXY+DI0q4pZiW7E9CBFPKBP+Gcvw4eLoNMjcNfLkFsMzw6FfT/AL1/AK6Pg5s5QIQEko4i7NDAxKtt/B0Rio6B7Wxg/HOa+AgsnwKRxQrz01Cxo8gy8tRT2JHN90AjajwyDp9vB9F7w6g4Y/StkGucuI4io4+sXDz82gvcvw6Js83hbhsL31eC+VDWEvAFEHo+WibTAS24ziLSR7IyyhvKwlIGIoiITh4VPieMxLpOEBR8KTQglATsFFGL0B7mfJPaSxXFVf/h7IFIVFw2pwHr2m/QebWnBBY6RS5rJb6Q67bnGEYoN2bsr0Yur7MBNhsmWqxr92cMmirConuxRNCWRKR4fZBiU7BlwVztYugQKC4WSvVo1G6+9to/S8o/oIXP+TIGMn4DY/Ua3GyBJkqZKkpQuSdLh69QPkSTpoLptlSSpsaEuWpKkBZIkHVcj+rYprY8/g/420ZbX6ycxUZNVFHH6tEz16oAMudlCfu2wg8egDzl9RXi6R/ghL1hPAmR6JFK8EnUVO2d8VqwO8XFcxk8kNuoQyilDzmmAvWRyOxWZzWk92UwxfjaSSn/KMN+gjAdYwTVmEU95LFxWUc6Dk0/IZb4lnqW2q+By61zRR4WwJsbKmmKZXKwsKZAYFAGdY2BjFoHJTYYcCd5PgX1toelWOJWHLsZSQmDoDljbGS5th6NZgTpsInrw6G3wVRe4b7Xqt+ETcvxYF2QWw/Y01XxVferzj0JWEawZJvKFH88I3EtNlPXBJthxCZaNgjdXw8L96GI17WPI9cJTc6Buefh0COw6A99uUJXxXgJgoimYg0RdgEmUlV4E3y4RG16RKrV1A2jZAMbeDeVVlvByGuw+JJTpubkiw2ZuDuTmQ2iIcCJ0OsXmCIGalSCpvFDUWy0iPP3BC7D3NCzeDqfSVessCCjXg0RV19sPtcPY1jC4AUzcCx0WgGylVF8RQKTvtcHrtaBxFHQ7DFk+Qxs79IuCl8rDwCtw0YfO2QK8GeunotXCqKIiE4jUl2w8bgvlfuUahZKCCE0CkynDu6STTDHDiSUaKxYk4rAxl3r0YC9WZBoQTkuieEpNLhUc3dcIIiF4uIPWzGETdop1S6yyhNCYxszlY5NeJAqJanRlFy+rj1zGSihJ3MZ2xunnEAmwE6hCKzbwGXkkUEQIXajHNjykpMaZRFpchTGDof+toPmNvPXWYbXy/ydS+Kuir0wDvgBmXKf+HNBJUZQsSZJ6AZOBVmrdRGCloigD1aSEodfp4z+mv40j8Xr9xMU50ORTR464qVZNVpVWkH5V5LkWjdHFW0sPQ99qlPR2F4wNk/P99LOXxMdpZHIP5UqUr+QyXYjHhQgjr22LOU9/kgghkJfBiowFmW+4zDjKoIVSceAmBy8zyONBS6gpwGOuTebjQi/vxqkrSZvC+Az4tDxEG81g1QlmbpZYMe1rCz80Ef4i2iSUZRVB/Ga1hjKGRFfa746r8OVRWNYHotQYW98fg1UXoOo02HkFpvQFp2EVv/Yc3LcY5twJPWpgWl1rk+DmM9DlKxjYGL4dDJHhhnrDRHksHW75ALachpmPwHejoHqS4WY7A21Noi6NgvUVav3lq7BoA7z1PfR7HJrfBy0GwcNvwKrtsPuw0LtYLFCpItSsBrVqiAySLpfwRD+XDLNXw5gPoe0D0OwBuOV5eGYK/LABTqT+DohoyvRS9kPtMLoVbH1AvMmt5sD3J64DIhqpnIhVggtu6HcsCESAh8vAuETocSEIRGwyH8T5ibdIjCoqQoMWh8tNdax8bYvkZSWHqxavKjaCT4llEblsU1PN3qGGCMrEywoyGKUGVozFzgtU520OIqOYQCRYue7ETT8akEoGaappsMZ5dKA9v/IzqPsawNzEEC6yFMWwmKvGYM4yD5kiEzfSiAHsZC05queJm2h6UImJHqUEN9KzEaxZCWlpQskecEQsyY1YLJCdfYPswD+AFEXZDFw3fImiKFsVRclSd7cjUqEjSVIk0BH4Tm3nURQ1SudfQH8bR+Lx+KlWLRzxNng5erSYli1VGUARrF0P3VvDpqMExFshMH8PvHcHzDlPQE9iQ9eTrMm38m6MxBRFothvxWNxYqOQXRTwLAnEYifHsMJS8LGGy/QmiUWGvAcF+FhLCr0pz1JSgjzdM+lODHWROKb35WYeufxMeX6xFHLWJuucyTyfh3tdoTQI9XC40MFln5U3r0lMKg9DLmEKH4IMu/OhSzTcmQjd42BSMryqesYfz4dnj8P8tnDLJtV81sCZ/JIG9kOwrK9wfvvxnNiQ4aMDwlv+l3vgjqXCfBab4G56zIYfB4qos1P2GPpUJ8PsIhg8GwY0hM2PwfvrYM52dcwG7gQbrDwotpsbCDDZdx5+2Ao7jyMmaXegrWYiDJQMjaL1WwrnojghJU1s+rEYjtXa/oFORN/XvoQbBJEqMfBoW7i5Ony1D9rNUFPaBoOGcT/IzNdrNRheqCDikOCzqoI77X0RPFZ0EJFsMm/F+nFKEmOKxQLMqi5aKmJhmj2KR5QsLlg8+rs6gUjS8PGTOhe1wUV1nHxFCj9wmUL86tQt8RY1+ZITpAcFYSwNRKoRTWOS+Jy5GD3Ym9MABxIpHDMFZoynBhGU5Tjr1H58hFCDWG7iFN/r98SNExflKEMNFrKRQqJUbqQBP1LA5WBuJB9eegnuvQs0JXsgZHxJFsHhsBISUor38n9KCuZ37++hEYgQ8ADVgKuIRFWNgT3AY/9OQMYbob/Rs12mSpVQNNaisNCLwwE2FRQ2boYuLcV/o5f7icsitEacpJZrD88n9v0+idnFPgbJESXOOYs0elG2RPlSkmlAFC4Uk9XJMpK5gyRCsJo83a3IzCeV56ikK9xtyEi4eY1rvK0q9o2cyd0FhRyVFeG0aJNZUCxC3w3WvMlB50z2qI/aKgll8yFNxIX4XXMVvr8E3zYXvgxaudZmyWX46DAsuRUijL4QwJRj8OE+WH27ISQ9IlT5LbOgVjwsHqJyPMFiHOCnQ9D5S2hbFdY+BnWTDPXGFTiw5jB0fgd+3A6P9YTt78DDPSAyBjN3olFpinTjvQlyXiwtKKR+7B+BiPG4PwIRrakTejeGBffA1IGwLQWafQ/f7CsFRILFYEEWWiXAxgpJDlhXDw774Nl0M4i47DLzylgoUBSeDgKRJCy8YgvnSSWbk5aAX8ajhBGLjQ9VE/eywPOUZxQn+I5LOogAPEYldpPBHjL/EERCgOG04wd+UTOVi7oYwmhLO35lvs6dhFKIDWjI3Rzhc5OYrC6PcozP0YLhBLiR4axnKR7s5BGBl7I0I4Fv/EG6kavQpw4cPgDnzwslu9iEuLw0ststeDx/0Yz/ryvby0iStNuwjfp3Ty1JUhcEkDyrFtmApsBXarKqAuC5f7f/P6K/DUj8fj9hYWaG6NgxL3XqeEGBnEw4cRbiNH28Qby15TQMb0BJCwh1AfJttpX77E5kn7b2EbPIarLoTVmTch2gEJkz5NKPyqZyNx6WcYnBVNRFWCDA5AS5JFNML2JNYLKfXNLwMcAiZkoNTIpV73dAB5Ox1xT6hEGNEEwT5u5CKPaLIH2X3LBaW0MYJrgZl+BoEXzXQiTSClY6/5wiEmWt7i880o11K87D6I3wVjvoXDNwbq8FnlkjlPBrh0O3apScHF2COxnzE4xfAq/cApMGQ/koQxvMx207B0MmQZ8PIDQUVr0I08fA7R3VNL5/JOq6HmBcr874/3ogogFNMIhopIKIRYJOdeDrwbD7SehWHV7dCF1nwqLTICv8ru5EF1eVBiKGdp0jYGlteDYFvsjC1F8Zh8zKslZWe7184FXfQVUvUhUrc+zRTCGfY5ZiplKGWCzcjZM6OHhJ9VQPw88LlOdTLnJAzTmiTeq3EocLhTmc/0MQsSJzL63ZxGGucc3EjQygHxtYhIVcHIbUuvUZwDV24eaiel4fCXSjkGRyOQkE/EbKchN+LJwiVbfUGkBDFlDIpUvxJpEW1+CFEfDWGyC4EI0bub5JlN1uxev1X7f+36Z/T9meoShKc8M2+d85tSRJjYBvgf6Komg87iXgkqIoqksvCxDA8pfQ32r+6/X6sds1OWYR69cX07hxQLx1+BAM7E4J663ZO2FIA5A0ENHibqlWXNleK794vXQ3mPcIQFGYRxqDSCoRdO4nztGDJMKwmepWcJ52xBOrJsnSzIStyHzFeUaSSCgWE5i8xxWGEkaiGr7CyJkYwSTXKvNBrsLschBmmOQ2FcFzF6HBIXj1EnxVE+zGiUj9ffc0XPLCJE21FgQmK9Ng/F5Y0QNqJxjqbLAnHZ7eCi+3gudagGQAgBXnoOccGNsG3r0Zwg0JlyAwzr1XhLhr9XFY9BB8MhASgjMlGsabUQQfLYc2r8LEFdCkKmx6F35+Ee7pCJXLq23/UzD5T0DEBvGxcE8HmDYUZj8A97SAufugyRfw1Go4bJRYX0d3ou8HmfkGg4hTgg+rwphy0PM0bJWNxyvUcMmsjLfybpGbGbIZRBq6fMywRzFWyeKApZgehNANF2spz+1EMYHLqnTSz2dUZitZ7CJLvQWij+aEM5BEvuLkDYFIO6oRgo39qnJeK29Jc65wlmscMB0TR2XK0ZgLzFfP68NOFJW4k1OIeVM7i5cQGjGUtSyliBDyiMBBeepRhu88UgluZGBzWLkKkpOLEGoEIdKqWdPFHXdUJiTE+NKqj94qkZycV6L8v5UkSaoELALuUxTlpFauKEoqkCxJUm21qBsiKPlfQn8rkOzceZVq1ULRLC127Sri1lvVShnmLYK7ehFAcBU48vJhZzJ0TcQEIIAuq/8238+TUiSy34rHYIO6lAw6EYszKHe7Gz+LOc/dKlcSAIxipnKc0dQChNWKVl+Am2kk87CqxNfApBAvU8jgE2L12EbXA5P9fpnPcxW+TUCfiFJtMDFTLHJ+zoGdBfB9HXWy1yYqdXvpNBT64NWb1NxNRjCxwa/pcO8WmNsZ2lTExDFcKYAeiyEmFH7uDzHRgfuRWgwD5sHJLNgyEvrXoeREqNLiY9DmE9h6ARY8CB/fISykgkVdxv97k+HlhdDiORg/B8JD4IuHYP8kmD0eRveHRrXUuF7/Kpho9+n3QEQbTig0qi78YD64F3a8CnNGQaVY+PxXGDIHHpoPGy+poUquI/YqdT/IzNc0Phs0DoXNDeG8D+48B2nG+2tT6BfhZ14ZK8Nzi1itimM0EGkVIvO2PYJRShbHLQJgHiECFxbisRGJBT/CJ+RDKvIbWaxUAyFqYFEVB09RjZc5gM+Q8fB6IFKBCHrSkHms0ssdeChPPE1pwh7VIVFTrjvx05zRHOZjLHixqh9pbcZxntnIBkdgN06q0IuzHCANN3mqF/tgGvEmeWSnljFxI45ceGEofPUpBLiRIh54oDKfftqchg3jeO01zdo1sLoIDbURHf0XxLlSjYT+bD8SSZJ+QCSeqi1J0iVJkkZIkjRakqTRapNXgDjgS0mS9kuStNtw+FhgtiRJB4GbwBBS+U+mv03ZDpCRUUzdug5OnBAocfhwMfXrgyR5URQ7l5NB8UFSHFzSLLNU8dYXm2BsN1i3BfPD8QI+iXNFDo5FydxscbJJtbzyIBTvs7jMbSQyjzOm8aznDE/SjDKEkGGQsR4khT5UpjnR7FNDz2txuNaRzq3E04YwtlGAQzUH3kouXQjjfkL53lKI7LeK3O8G02Cf14rF6WZWsZMWTitjYuGLTMmk5MYLn6XDm3b4pAY8fhpTjCx88PRJeLcmTG0DI7erFkOge84fzYE+G2ByK1h8ESafCBwv++DZ30TO8Pl94IPdsOq8qFdsMHU/LD0JH94Mw5vAE7/A+WzMYywWoTzm7xPmwbfWF+FsZL94Tiv3ir7UGxdQsgPYRVyvk5dh8kaQPCIYYoc6MKIHtKgFLjtcTIej5+HcFUjLgtRUSMuEtHQodmP2ggewCv1SWCRExEB0GFQqJ7ieKgnC8TDEBRXLwtEU2HtR+MS8sgSK1FQB19V3GEHDdp19jYICMeISCvWx5aB/HAw9AyeCxm6xKbwRA41cEjdnF5KtgM0eCH0yIASeskbxgHKNKxbxbpcDaiM4cD9QEyeDiaQmLk6Sz3wRfEcHizJITKAmb3KYHDVSLwgQGUQN1nGaIkNgxVAk+nITs1iJQoEOIi6s3MYAfuF7nOSagKcut3KZdRRxTgeRMrRFwspVflVfUQFXNspTg05M50uKCCePCCpTHQ8Oluda4YqZG3m4M8xfCFevetG4kTFjytG8eTi33bYMr9fNgQP38eyzvxr8Ruy4XDaKi/8CHYkGJH92t4oy+A/qRwIjr1O3H2j+54+qJP2tQFJcLDN0aBI//3xcLSli3z4vTZpI7N1rhyKYNgf6dYEvl2Gy3jqcDEnh0CgaDmriLe2DVx/ou4UeZkRGsp5CfFj1j2U1V5lKQzaSwjWEAsKBGz+wjos8RAPeY7s+Th9WprCPl2jNOLajafg1MHmHk3xEA0ZwTA2rIsDkQ1L5nipspZgTFrlUMBH9uHkqx8nPcVZSoxQW5ASBCfBSCnxTGR6vCJ8mUwJMnjsF46vA/A4weA+4CwhYQNkgpRDu2CzEYK3LwiPboLgA/Q1Yeg52psFXneHOWvDkRuGbgQ2uemDYYuhYCebeBb9dgLc3w7VcdXCGsfgRJtpLD0OdBBjTHoa1FmmTf9gBRy8TABMIWG2p+i3FAYfOi017jpIESVFQpxJUS4AmNSGhJSRGgdcH1coZPOCBkxehdmXh9V/ggbxCOHpRJLC6kA6bj8KFbSIpVzH8PmgEi6p+DzSM+0EWWtpxXaPh/WowNxO6HAPZyC3ZIMGp8F4MnPX7uS23GAUziIwLtXKLxclArupBGK3ITKc8YUgco4il5LCBHO6nDFcoZBopejuASOAD6jGZ01wkxwQit1GVSkRQRJEOCDZkHuRmDnKCDC6bHAx7cSv72Yib8yZ9SSVqEs9N7GG8DiI2wqnEQA7xOoDas5VCQmnLQ2xkMdmqSKuIKEbQgOFkUZSRFPBiz4BoNwzvB21bgGbq269fNL17J9C//yJdB3LsWCZ16sRz7JhmHSuijhcX/xUzPv9Pp4T/W4FEUbz06lWO0NCjFBYKO9YlSwrp0MHF3r2ADIt+ht/WweSfwVeMyQnxrfXwQke4e6O5XAR1tHLGbWGTR6aHK4RfKdQV71ZkvuY8o6nJW+w3jWkf6fSiMk1IYB/CttSGTBb5bCSZO6jAXJJN1ic55PEDl3iCirypKiwFmMAELvMFFRhIGjmaINGGDiayz4rNLuPDzeBMJ6vLWkmTFX7NN4BJMWCHhy/A1GrwelV45RwlwOT98zBShkkNYfxRkRHQCCbFwIitMLImrO0JQzfD2bxAH2mFcPsKGFwLNg2CF7bAL+cD9ZsvQpvvhNPdmmGw8Ch8slWE8Q82AcYHx9NgzEIIkaBPQ3hzIFSIhIV7YMkeOH450NZk4quZCKtvp+KD5GxI1nxDjQtK7ZzBlp7XE2fZCFiMaXqS0kBEnfglu+oJ/XugEQQ+0S7IDgKfKBt8WQdcVhhwCpI9QWO0Qf9Ihddi4KlsPxvUC9PEoHbguVAblSUb95CB3xLwRB9PDBb8tOY4mfixI/MWSaRTVAJEQlH4kHr8wHkOkWECkbbE05JyvMsGE1jcRlOukclBDurlDjy0oing5Qyb9VhawlExlPqMYi/PYTU8mPo8ySUW4yVbBxE3TpLoSB55HCOVQqLJJpruNOYXCjiQGQWpUoAbuQQTBsKbr0NRkRfIpXFjPy+9VJWuXZerICLOuWnTFZo2LWsCEpvNQlZWMX86/TPMf/82+ht1JH7atk3A4bAwdWpDNAeiVavyGT7cgSQJ662CDFi5Fu7oSgnrra0noUwI1I5ATEbapKiKt/xuJ1973TyjRGGM1SFjZSfZhGClvuqcpZENmW/Zz1AaYg+6PSs4QRvKUZ8QXR+iHbOOZGwo9CRGL3Pg5iyFfEcGE9VEWgBWi2zSmYCYMAocbm6/JjOxDNQLUUqsgP12uP8sJDrhveqGOoPe5NtkWJgOa9pAPc28N8iq69tT8OgO+LELPFibEivsH04K3cnddWD5AKhdLlCv2GDOYWj1nbDeWnEfvNYD4sMNY9H6UqlIgfl74fbJ0GMSXLwGr98B+9+EKQ/AnW0hNpw/Dq0SrBfBcD674fdfARFjPzYIc0DPBvDBANj+MDzTjt8FDW3fKsFd1eG3HjCquvl+AhTZYdpVuOOEAURUCrPDlAQYEg5dr8qsc6tGGiqIJNn9LA8PowCFcVKmPjU7kHmRGKJR6McFMvHjQOZ9KnKZQiZxWb0FsnpL/bxHHX7hMttINYFIbSK4i7p8wq9YDUmpWlCZqpRhOZtMIFKNcjSkGTv4QbfQ0riRVozhJN/jNmQgLcct+CkmnU16mfBeKUMSnVjHcooIIZtonMTTnEQ+8vvwZETqAEIq3BQrEokt/BEgk5iYAqZOrcWQIVvJz9dMfwWlpORTvXo0Rvqrcrb/v05/q7K9TZtELBaJW26J4667RNyLgoJCdu1y06mTaqInw+eTYey9CEtobVMV8K+tgbH1gHwCSnft12flstfCT7KbYUTjI6B4l7EyiZPcTiUsmAO5ZVLMck4ziNr6B2VVTcc+ZzdjuAmHwVILBHB8xXGGUZ6q2HWRgAM3K8niMm7GEfaHYJJmc3NPhszbcQo1XJo3M/rEqNjhoXMQZoM3KorcFYBpAv/lKtx7EGY2gVurUGoCqAPZ0GEVNIiFpd0NJsJq/dUiGLYWXt8F3/aAj26BKIPvh9cPk/bCzbPg9DVYOhS+uU3koC9V9KP+5hTB3IMw6Cto+hp8uwXqlIP3h8Det2H2o/BEf2hfR/U3wXz8DYHJDYKI0w7Na8Co7vDVPbDmUVg3FrrVhXVnoNt0eD8g4SyVY4m0w8O1YHcfaBYHg7bC+xcoATYeK6zJCRqTC3pEwuQE2OxWGJQpk+kXRhgaiHRwSvwUGs67cj5fW4S1kdUiE4PCDBLIxM2rpGFFJgw/n1KZA+TwDVcwWmG58PMOtdlIKmu5ZAKRqoQyjuZ8xGZ8huRTNSlDF+oyk6WEqPnXHXiIw8kt3MFavsaqgocGIvXoRzZHucZWXaQVQnkqMYgzfAwIkZYHB26cNONh9rODDGy6gv1BWvM+2aRdjdbFWWSAlAYTx8C4J0FTrk+fXp5XXz3CqVPZmJUUdoqKfCUst0JD7RQW/kWirb9A2f7fQn+baCsszIHNJibw6GgHU6fWY926/WRkePnuuzzGjIll40YvKHZSLsLe/dC+sfAhMXIlvx6HZ7tAyzjYqZoH6+Itm0h/+Wmhh80RYSyUQihG1sVbZ5C5QBG3U4UlHNfHZkVmE2d5jS7UJJZThggFaeSwhnM8QH2+ZT+aPgTAjZv3OMzr1OchjuDWeV03H3GZl0niVuws11ZNmlI3SGdyEjev5DiZn2hlYKrCmWLJDCbAmPPwcnmYVwuGnoZiDyZR17F86L4LpjeCehHw0WnwF2ESdRUBj+2CbuXg67ZCET/tFCgGcdmOVOi4EO6tCb8MFqKuz7aJ7Kb4BKDMPAIzD4gQK4+3hzplYPpemL9bOI/q4iujXsQF/mLYcVZs+ITPRp14aF4NBrWHx6OETqTYDWdTRay1yxmQmQeZ2UJHk5UHxcVC/OQvEh7vKBAeDlFREBUGUREQHgE1ywlFe5V4wQGdyxAxyPYmw/SdcDAdCo3ixyDQ0MkGHavAiFpQPwa+Pg2d1kKun1KV64YXS6eEMPgkEWwWeCxT0ZNUaVk2LcCToTZaWKzcLWeSbhPvjNUiUx2Jr0hgEldZTT42ZMoAn1OVn0lnCddMIBKCn3epww4yWB0EIuVx8jStmMgW8sjSQaQc4QylI1NYhIVsvTwUmU705Dd+oIgMHCarrrok0Iw9PKODiAs/1bifY3xEIT6TSKsCN1NEMcc5SR4J5BFBC+pzAT9LcyPxp4SJBD2XgAy4rwXs3gWHd4Cw1MrlySdTOH36vLpvpqIimZAQq6ksNNT214m2/o+Bw79CfxuQhITYcLv9HDyYSVGRnw8+SCYnJx+IYMeObJ5+OoYyZXxkZNghH76YDFO+gE6jMSnd8cH4lfDlAOi8GrPS3QV+t5NCu8xbvnyesEfzjmpyKKtgMpNzfEULfiOVa4Ygb34UJrGTp2nDy6zBg4wWTXgNpxhNc5qSyF5S0cDEisx5clhOMqOowJecN1yxm4+5wndUI500dqle9MFgonEnh71uHsizsCDRxR2pCmd9AGYl/BtXYUQ0rKoHdxwXWRiNllRZCgzYCy/VgNVtYNg+NWNikFXXuiuwMwNebQwba8Jj22G/pr9A6ChmnoK5J0SWv433wU/HYeIukf1QA4nVp8VWIRKGNoTNY2HvZZi3GzaeETllgACwGMaKDfw+OJomthm/qnVecDmgWrwIS5IUBzXKQ1xtiA0DnwxJZUFSRBwlSYLzaVA2RuRCyS4SY7yYAUeuwLIjIphktofSOScX1wWRBglwS1UYUhv2XoOvT8E2Tfx+HeW6/l+951Y73JsAj8fBS9dguTsQQ0sDkapOP1PCQ9gp+xhJFlq0X6tFph1OXiea8VzmqGqmWxkLE6nMFyTzG7kmEAlF4T3qsoV0VnBR5SrEecpg43na8DXbuWrIDRKNg9HczCxW4Fa5HQEiRfTlXq5wiDSO6ma+DjxEE0l9HmQP47Go/TvxUI0nKeIYuQYXBs1Kqxb9mM0knRORSOQ2atCLLIoyygpOJA3IgEQZxg6Ezu1BfOSZiGCvaZSuIIPiYh8ul3mKCw21kZLyF3EkfwE+/bfQ3wYkGRmFVKjwDZGRUXz9dTcWLjyHsCcRSvdFiwp4/vkwnnoKUODkcTh8HG7vAIu2Ij5M1Sv66Dk4mgp3JMLCDAIZFFWlu89rZUmRzP12Oy2JYA8yNlX57kXhC44xlga8yhbTGFPJZz1nuJOGzGa/ScE+m4O8RhcukUs6hWgBpDw4WUky46jPvSQxS/UsBiimkMc4zySqMJrLXMQruBkDmAA6d3LACyPyinkjzskbmRLHUTCBSTF8lw0XPbCiHgw9CceLMdXLVnjtFHSMhWWtYMIJWJKK+BYN3EmeAk/thgbR8Fkr2HcN3joAmYasjF6LCLEy7YgAlLplYHtKoF57m1Jy4Z3f4J1N0CoJ7m4M7/eF4+mw+DCsOCTCxOsruCCjATD8twtu6+glsem+olq70iKu/pFOJHiSv47+w2KHllXg9prQvTKczoFFZ6HjcgIeEBpI/F74E5V6x8GrFWBJHrRPhgJrSRAZHCbxtCuUp92F7LYXBLq0yDxOJO1wMoyLXFPX9gDx2HmDcxxVw8EbrbOeozobSdU5ESOIPEMrvmc3l7iig0U4Vu6mFUvYRDYXDCBSSGd6UUAaJ9hoApEQFBrwIMf4BNkQLbsM3XBQVhVpOXSRVhHhNOM+1rKAa9jJVhXsg2nMW2SSkhktFOwGsdaXD8KLr0JBDgT8RozhUDTb/wD5fH6KisygYbNZyMvz8D/6c+lv1ZEA5OZ6CAuz0LBhKMaXYu7cPNq1s5KUpMJ8Ebz2Frz4INgkTJ7u+OCVtTCyLsLNUPN09wLFQuku+6w848/leRKwYdPDpnhwcphsTpJDb6qaxmZFZjWnKEc4zahgqiukiMns4Gla4VIjpULAKXESR2lJJN2I1D9GGzIZ5PMil3iVeBIM7TWdCWByXDwseXmnqJgfEqB1qIzIR4FpMlxTKPwRZtWGO+MwT4yI/c2Z0HUn3BwP398EMZH6RQbIBoezoesa+DVNWHa93BrCg6ybvBaYcgi2XzOfw9iP9rsjFZ5YDk0mwTvroGZZmDgQfnsC3r9DWHNFh/xBP0ZP9+AYXcY64/6/ASIWOzQtB0+2hyWDYc8D0K86bL4CbRfBoNUw91IpIELp/Wn/bwqH1XXhzhgYkAxvZkOB5tugPvNyFomFMQ4aWa30LMwNgIhNpqxFYRZlCQXuIdUEIlZkDpNVAkTisfI59dlMegkQKYudV2jPfA5yTq0T1lYSY+nJYU5ziWMmEGlIU8oQyy7m6yAifEY8NOUpMthJHgfVR+QhmkSSGMZJ3sStgogm0qrFEK5xlRNc1bmRdjTEi4slHgeeVIOC/RIMqgNZGbB6EQS4kVwCgGIEkcALERJiw2o16z/LlAn5n47kL6C/HUjAxxdfHOfRR2tifCkUpYg33shhwgS9GelXYPZPMKI3AU939ZCMXFh4DN5oTCDQo1trJ7iSiz6YTQGPEo8Hpy6q8mFlDqfpQRUqEGEanQJ8wy7uphHlsakeu+KDPMU1fuUs99NEB4pATC4fb7CfoVSjvqp819qcJpvvSWcKlShLSTAJVsKftPjon1PEZzFWbg0XSbIAk9XWcRm6noK7ysLHVcFmN9djE6KusUdheQZsaAuDNOui68TqavULXC2GLXfAk40hJNizuxTAClauG/8fzIQ3NsP9c6HnZFh3EtpUh8UPw6qxMOdheK439G4qxFWmfv4ITIwWX8EgEjQOEDG/mleFka3h8zth80MilP7YNnDNDWNXQ5PZ8MJ2WHYOijWnQeP1/Z7nOtDi/2PvvsOkKu/+8b+mbN+FpfemgDQRFGzYe+89GjWxxZrERBM1MZYYUzQaExVjTTQae++KFRARAUGkd1jKLtvrzM7vjzOzO7O7qPk++n38fZ/nc13nmpn73OfMmTn3ud/3+1MLeXYk5/fh52s5e4OkMy7ppXJ/VBz3Ypcc9zY0+k1TrfokM41G4ybJdpuuHlLhZltENGSASNtiUzBYltuMcrclPkyyjVS/nrL82iQP+cTSpBt7AAhc7GDTzbU4yb5TILKdUbY33hT3JE3krQkZRzpdgxIlnk/emkYRhUa43iK/UZuc4FPxIl3sqIftvOM9jbKVK5avv4Nt4xcaVKzp1QIgNtMzxhl7cfkVgodRqZShPbXwPPDAvk45ZTttVxUdBR8WFmarrv4WGMn/cCD5b1NttUbwZHnzzaVuvnmC4uIs5eVNLe0vvVTtpJMKbL99k88+CwIU/3YnH77OKx+zqi75C6qD1/s+4s0fMqGQmSlbSY4WV+DG+kb3RWs9EC62g0ILNIgkZ4M6IbeY58cmutoUibQ7XaXBP0x1ob3d5BVxiRYwec1iZ9nREbb1oqVJ/W9Al+pxvVl+ZZzrzbcsTTU2W5m/irnHAOdYrTRltO8g1oSA5R9aUevewjz9I80mtwSltKq6Kus5YQWXd+OBYfxqpcBKk6bqEuXJEt4q5U/bcVRvrvmCFaUyVF0EwX53L+GhxYFhedqJPLOUv33G5uq06+zA3tHOuJ6utsoNCpO9tpjXFgRNkXBQtGxsPyYN5dRdGNU3GCabqwIbx+draWygojbYKiuCV7G0gMQIkVy6FFDcKYhoLy6kSxHDejGwO1lRNlWzspw5GwMvsqvepirtt3eYQ0va55R0YFzfoxNXDwrcnq9fw6ymtscHC4Edsvh5ccL65pB9K2s0RFvHR0E07ppQZyNCET+zyfokGGwNRFIyXoGT9XaT+VYmgw1T/frJdabRHjAzg4nkSPie3c21yOdmZoDIYNvYzX5edgvqM0BkoH11Mshc1yAAEcJGusEqD9iSVOum2EhUV+Oc7XF/V5N09a3SxU/s6uc2Wb2hJ5uSMSMlwXbfRfz1bspXEDCRKukpUYiZNm2dv/1tPzNnllqypKLlv8jNzW4XfFhYmKW6+luoQJVK2vg/VEKJ1vwB/3e/ONQ3wUVStVHPP38H2dm57rhjhUC7W4ROxozp4p57uttrr6hYLGje62CuuJgjfinQZRUnuxez3XZB8ab93iWW2leI4oRwQa2iLlW2zY77p27OsESzatktzoiNTjZAjpBnzE/zvw8exIONUiDb022CGCPCfu4QL1jg42QQYyr4sVGOIYpcYZwrzbZWa1qImIg9dfYD/ZxnlTKhDKYUb46IxSItYBKPRUSaIu7qlK0hHnHxxlCw3oslZ9GUtxp2zeZvQ/jbeu7fqHUSTx/sMfbowp9GMGUzN82lKtUv3tonJdnNnLYtF27HrE38eQ4LN7fv1/Id6W0dva/voK2Dfd1yGdgt8HTq04XO+YErcud8qmuCNgLPrUSEkvKgXG55LeX1wVZSwZpaVlfRlAKMrTGptu0d9WtjF8nN5pTenDuAqdU8VMq8YD3U5viE3hFu7JYwKCvhZ1UNFoRjGVUOd4yG3RIq9kSo2kMqJOiQebQFlqN0d6xerjXXlqQ7buq4oQr9xER3mW5tmk0kBxc5yEKLzTRdVLOwmHy1+urrECd5xa0abWlx8c1Xq6cdDHSQBX6vWX0SRBjiUnHVVrm/RaUV2EWK7Ok6H3jdPJuVK1aqmyPs4wvZbm+MqljSmyVatgtHMqyIn5yLWJOAz5UJLPAp9VagwTjvvHFyciLuuGNuy5995JGDDBhQ6M4757SMpb/9bU833DBdSUkqxuWaTxKJxAT/RQn1m5Bw0cz/7KCrQ9/Id38X5L+RkaRLzIMPzvP228eaNq3CzJlxwWojz7x59aZNa/Dcc2GHHx6mjvfe4rRjOXUfHp0uw4Nr4VqeWsi1I/nVQq2spD6kORqwkpXRWn8NV/qV/n5lcQsriYt4xhLX2sWu+pue5ioJr/ncRfa2n6HetbDlmLhmd3jTlQ5V5iNLkzmHAmmwVoM7zHOTHfzMpy3myIi4acrENLvfQBdYoyTNnbgjI3wcP6hsdEle1Bt9s528OW6DSAAmaZPe9Hr2Wswf+nBUN85bwsb0lXGSOXywhd2ncXZf3j+AO1dw38Jkvq7UhJuc3BvDgXvwg4s5pCfX7MSAQh78nCeWJOvDt2E/ydvbsSE91S+9rQOGU1pP6cq0c6UktbBsSGvLkani+iqG8VUgooPPaSCybSfOG8Qh3XmylOMXUdKkXXJI0SDC/+LOnFLEdRUJL9bUt8SLQOfcBj+PFOot5OLQ5pbiz18FIlniLjFQf9l+6hOpBIyp43bQxbnGucX7tiSj2VM2kYsd7DMLfGqGiLgjHaanLt7zrEn294Y7MkAkIq6rbY10jk9dngEivRwuIsdyf2lnFxnjXMvNt8wqVXopV2w7I/VV7NLmukyV1hpG5fD9fdj7oNQ9b2sXyWQVb7+9xiWXjM1o6949TyiUaSPZfvvuKiu/JWP7/2Pqqv9EvgM2kmBQNDTEnX76u+65Z5zi4pTCMaCvV1+9xUEHhUybxqB+wa4rb+L4vemXp5XlVgfbHR+wWzf26KydraShLlssFvGUeg3YV09xrYGKQY6sWU4wup29JCLuPu/a03Db6d3SFhFXo9HfTHGs7fROzkYpmwgsUeIBX7jcdnq0VKULto+Vu91q9xpgcDLQ8cvsJtGsuLtiDW6sq/fnLmF7FaSM8ImMibOmmYvWcucG/rINP+jZcQBjM+5bxx7T6RRlxkF8f3DHRbNSr69u5IwP+d6b9O3E+yfwpz04YAjRsI5VQR3ZULbWT5v9He1Lj4JPbd80iLTx5BKhZw6XDOH9Pbh1JB9VsNNsbljXAYjkkpfFT4qZPjChTNyuJXEvNreCSCQad1hewmvRbtaHYi4PlyZBpL0NZDu5ctNApKuQ6wzVoMG15rYDkb31cZht3OTtDBDJF3aRA3xqXguI5IgZbZT+hjjFxT7yb7U2ZoBId12M9VNzXaNJeQuIdLGbXo6wrA2I1MrX30Gydfa+aS0eWjn6Otn2fqRK2abiVi+tEvIqufYIfvgTGjbQ+oC33VIzd5Y1a6rtuGMqlUNwwzp3zlZRkQkamQGJ3+DM/z/cRvLfDCSZ+o7lyze57rq5HnxwtFYPrjr19TFr1zbbdVdmzuT2m4hV8JcHePDXhBMyPLiamzj7Ve6YSKeYAGDqtXhw1VXnizdHXKPcOXoaorBF1RQXUaPJ7T52mV3limpIy9fRJO4Obzrd7nqkAU1E3GZbvGq+K+yhWxsDe1TcZ9Z6zSp/NE4viVawEDdbuRssd5cBRom2GO1TYIIMMIlE495LNLmytt6vikOu65YIeFUHXl2v13LOMkYVMGU026c8ttIM8aJUx/nTSg6azvAufHwgZw8VVK2kQ0BYG+emT9jpCZ5aztHb8Mnp3H8QR4wip6DNsWnvC1N151PX0fY7pO3f2r62qVHaXN+XgoittHdw7IBOXDCcyRN4ak8SYY6bxdFzeLqSWEpDnObFlZ/HZV2ZNpBwJGG3jXH31RBPi1wflNXs1vxsp4XznGazf4QrNWtvA8kWd7Fefq23LslMDGPluctob9jkIctbxlLquNMMt79B7jFdXdJeEhHXRbafOdx0n/rMJ1IR69sZJEu2LNnCInZycBsmkm87Z5rvRvXWt4BIoREGu8DnrlQn0QIicRFFRutmRy95ssVDq1Y3p5vglzZaWtYlCDxcrcXAftdJvPYm82dIM7Cn20dS1LdV6uvj7aLYi4tz2gHJtyb/w4HkO6LaSimTee655WKxkEikXjyeyuRXafHiJoMGRXTvzvnnBunBz7mcaXvyi+O46RktcSWirF7PTTP41Xb8fKngxlUhGhXLiaitzhfpVOUn1rvdNn5kvsakkbxRjiWqPWWhc030t7RMwFChzj3ecYn93eol5Wk0e6UNHjbDL+zlRu+oljmQ51rrPgl/MM41ZtmQpspapMoVFrvONibb6B11LdfTkRE+Eo3bgEPLE64syPJW3yxnlsat7EDVVV3Pz9YzNos7tuGNLQFT2VKvXRxHaXNggL9lKZcOYer+gRfXPYsDOyjaqaESmLYh2EIxdunNcdty3vZkR/h4TfBd2REOGBxEv/fIZ/CtrE8F9bWNJ0lXjXW0r3VB2ipfBiLavO9IfZWUvAImdOfQgRzQl7JGXlrP75axoimtfzoAJUGkbz4X9eGIQiZXsPu6hNpk4sdUvEgRrizIsm80yw3NFT4MBzq+wIGuVd0VBBxm+72+ptniAsvFcaoeDtbdFT5XqqJl6s7WIFfCJSaoUe8P3pGlvgUMBspyjsM8b4rVFrWASI4GezpYjlyN6sU16qK3qISwuC7yTHC9hW5VbWkLiOTqa5hfWOCXYipJprKPi2i2rYku8LS/2yS/hY380O7e1OTtxvzA1bdES+DhD0cSr+P+ewXPawaApGwi6aqt1psfaLGiGZ/LyjIt4GvXVvtW5H+4sf07ACSpWSE1MOq89NLqtLYAZBYtqnPAAbnq6xPefjvkgnNQyHW38da/eHMWM9YJxlV9sD3xKfsO4JK+3LFRy+TTXJMvnhXX0JhtQXbc3UpdZ6grk9G3KWP3VGsN1skZxnvMNKloeFhnk8dNc6lD3OIl9Wlujout9aywC010j/eVk3Hs59a5W7MfGe5+S6xKA5M1al3hCzcbbpAqDynnK+wm8MfGuHca4+7skuP5+mb3lIeDxVybAMa5TUH68lO6BezkkU3csYb6tnYNgbvwdYv4w1K+15OX92VeBX/5gtlbtJ/Qk+dIRJleEmzQL5fvj+K6vcjPClKhQGltmuE7NRTaAgYZgLVVMPk6KrO2wJLW3r+YnXqwxwB27xOo597dwAel/HZBMu7jK4IOJ+Tz435sk81fy/lNGU0trtpB0GEUZxeEXJiXb3Jjg4Pjm4XSItfTbSBZ4k7TxST5brHSPDVJvAqKp11krpD6DBDpKcv3bW+xTV63MCOL73AFznCwx7xks1UtyRZzNBhvvN4GWGWuhd5QahZJNlIs20TXW+R2Fea3gEi2Hka52UI3qrcuw7jepLu9/NyrHrNaQpUi5YrtaVdbZLmruanFLtKvkrUl7JDDOfuy33ECEtJSOjfd3Te9lG6mu29tbaar78CBhe3sIf36Ffpf+eblO2AjSZemtNfUyiPwypg5s9qSJU0mTarUqVOzcTuging5p1/O5J/TO1+LnSSlzvrxW5w0gN1zkm1ViIU0VudrrM/R2JztRTVmqXOuQRrltHhbBfElCxTJs6/t213tAuu8Zq6LHSyaBJ/UQzvXah9a5OcO0jWZDiVd1bVIiWd84TrjjFaQoeaq0uASC2wv23V6yRX/SrtJJBr3sSYnVdUZltPsjb4JQ6Paq7pyg8SPj1ayy/xARTN9PKf3JtpRYGCUujj3rmfnD/jHCn4wlI8O5qdj6J1yf+3A9tKi/qrndzPoeTfvrglqhEBpHf8+mX+dEGQR/vORXLAL+41mYDFZrRrFjs/f5nu+7BrkBqqmEd05aLugSNdfD+bd7zP7DO7eN9j3xgYOfT2IobliLs+v/RIQyaVnHj/ty8djOac3d29i91X8qzbNHByNi+KHeVEzuuUqCoUcUFPlkUhVC/dIqS8JQGS4iH8abICoqy1NgkjrGHrFxnYgsoMurrWHtyxtAZHAI7HRbvo6xX7u91Q7EBluqB3t7QnXetPvbTRNOoiM8wsLTVZubguIRHU22h8scYsaizTIlmeYmAL1OtnJj031uiW2tIDIEMPtpK8r1Chd04uSkG3rWXESPx/HAz/ktEuoK5FUaVXKNLDX2bpEO1RtlZe3AklWVvjbqdeekv/Bqq3/Zvff82Uu7fK0hiF3SnvNS3stMmBAN88/n+eoo1m9BcXssx/XXsJB19BUqMUdWDED+vP8MRzyMRvytHMHzsluVKDW3fp43yavW5fh6pgj4Sp7eMNn5lmFTPXDnraznb4e8K64zIE6xmBHG+9P3rI5yRFS0wF0VeiXdvOgpT5QlpGdOC7iGL0cqofLrbVerAXkvsxFONYUMTEa9pfCXA/VNLu3PKwx3UWYjDoexRHO6RawjrvX8WAJDalntiO33TqKszi5P98bzMb6IOHjS2spq0nr39YNOBawkfsP4pTtOOvFIIYDeheyXTHDu7Fd14C57NiHrEjg1ltaw7LNwcqnpoGa2iAWpTkexKCEI8FrQ4KeqfiRfLrkUVLLqF7UNrGqkpWVzNvMilrmlVLxZfEjHQQdFuVweF/2Lw7sTv8s5bFSysNtj0/IisZ9vyDk4k68FIu5o75BZSRYABDYvSJptUUKNfqBYococr21Fifzv6WDCJnlcLM1ON42dtHX7T5Qm5ZkMfDEGmuYfv7pOQmVGSAyxhjbm+ht90jYkhEnkq/YRNf5wmSVZiqQI8gOXGCM26z0d+VmtDCRvT2pxmaVtlhliXfNUyfPBr3kG+xYO7lSuXllvTQu7sQKLurBn/cK/q5npnDyyQRl5VNuvpXJ9+nuvh1J1MyZJ5gw4cmWlnfeOdoRR7ysujoYzF275nr00QMdfPDTWhepv/lm3H97Tkg44T90/73rf91/v0UJcm2lq7paqx4FuozVqytdcEGWJx6POvDQoIb7O+8xbntuu4CLJstQca0u4dJ3+ceuHDWHhqjA8C5fXVacQiLZcZdb6x8G2KDOgqTBMnDAjLvFNNfaW6O4RdZmqKret1CeqAsdYLLXxDW3TPTzrBDT7OcOcKu3lKvLMN6XqfYr7/uJnfWU63mrpae7f9YGS9X7u0H+pCTTbsJWVV2fiturvNml2bmm9efasoQXa5FKmZ9SE2VR3sSfNvL3jVzUm4925KES7i2hKgUM6aqkvCAZ4uTlwbZNLicO4rl9aYzz3GqeX5EMhmyjimrGWa/x9mpeXtq6r6Q62N5dkfZ9yeNDIbrl0zWbwlwKo0HdkLxEEFzYnAjK+jZHaYjRsI4tDWypo7yZynjQZ6tG968BIt3zOKoXx/Whew4vlXFLCV+kFrzp8SLolp1wQSdOLIz4Z0Oj/bfEVEdbKx3SHkQOEfVTfTyuzPcsaUl++GUg0k3CZXa2VoUbvSW9lkgOTrenuCb3eUJuMhV8Cih2tb8BBnvbHRJqM0CkUHc7udYCd6gyWw8jbe8v5rrEYOdb5cEMEMm1nZCorobKVelpT6tTYINe6O1Se7jUegsquwd2kQ3YwIkTgnsIR+7J7b/hssvSVVpbN7C3SsfTWNvgw06dslVVfQvBiPyPt5F8BwISW2XMmAHOOWeMrl3zvftuiTff3GzlyriOWAn5TjyxqzPP5OjTiUdRzC2/YUEJ976rhZGktvN2Ya/+nL4QndEFuTF5xVXyC2sVhqv01+wP+rrNYuuT7o2pYMVuslxtbw963/K06nKp1/2NtZ1+LWBCq71lsD7Osoe/esc6FS1g0spOslxgR2Eht1qgLi04MS6ii6jrDTNdrXuVqdeaKywuIt6cTPfSATvpHQ65KS9Xjwg/3RyysG7r7ATyE5zSnYt7M72Sv61gfrWtspPkDwF98jiyNzt0YfcezNzMlFVMWcv6WhnsJPXdQ7uwY28e/6zNvvTv64DdZLRvDRjatn1NECmIsmcv9u/Hnt1YUcfMap7ewLJ0UEq5+ibPOTqfy7omjMtmck2zR2P1AZamlcvNzm1oyasWCccNF3a9YlvE/UmJ0qQaM7iUrYPIBEXON9ZjPvOJVRn2kJ7yne8AH/rU/LRo9Yi4AvUOcoKwmOkeFNakQEhvI1R4X5HedvIrC9ysMhkvNd4NuttHsyZL3WKFl5K3PbCJDPMjIxwtJCQubq31rvecGr1c5QC/V+rFxgIVK3oHK4wlhNdSdS1ZYbZU8Mbr3HAdCxeu0cpCgiy/rcDSNrlaK4i0ZSRvvHGkAw98oWWwjB7d1YUXjnbRRW/7xhlJ9wkJR/2HjOSBr2YkoVDoENwuGGn3JhKJm9vs74yHMVDwZ/wpkUg8kLY/gplYm0gkjvjPLvDry3eEkeTZd98BzjprlEce+UJJSY399hviN78ZIy8vz49/PF9JSUxrzYE8NHniiTo9e+b5y01cdCUauOqPvHo/qzfz2hKtk0WUez5huy78eiDXb5TMvhfVUJctO7dBXXa+NWr91ga3GuUK81TaolGOqLhSTf7gA7+wl7u9rSRZAS7FTt5KJq07ySRPmCqWfHDjIlZY7x5vutgB/ulDC2zWkEw9H5yDv5rpMEPcaoLrzGkJToyIqxR3qQVO1se/DHa19RZrsDWvLlrZyWb8oKbOhESOm7uFlce5fgvL69uzE03UhoJo+Ac2clARv9uOgkgAKK9spq5RBjtpySSM9XXcsxyLg6qBOxWzXx8eGkVuiDU1rKwIJuohRezUK1BJ5UZ4fIFM5tOBIb/dvrZG9f8ERJISzmO7zkzsGWw7dqGyiXm1vLmJa5cG/0nLeduASHGEU7twejHLm3iwhvPKg/saztk6iPQOJ1yuWCchd9hsbpIxtP6cjkEkR7MzbGO0rm70rgrVGSAyVj8n2MXjXrfJ0gwWkifL3o5VrcR8z7eouEY6wmjfs0h3/RxgnuvUWCFHo7BcnYwLfo8sA/zQuqRLcaNstfJt40DN4hrUW22dFyxQrth+xnhQpVebcwPjeomWaod/2S9I//+T3/Lg3dSU0OqllR4vkmIjWwcRWL48syZJcXFmNcSCgrbHf4PyLdQjSYLA33Cg4F/7OBQKPZ9IJD5P63YRPk8kEkeGQqEeWBgKhR5JJBIprnwZFghW4d+afC0g+Rqo+D1cmfxYjR8lEok5vqaEwyE///kE5533hjVrqsHcuZuR55BDhnr55d3cc88KkyeXSiRSnhuQ5W9/i/rLX7JcdwXX3hqorU74Ga/fxbrb+axci4pLNT9/l38cwsmF/Dv4Ks3RVhWXbBaq8hsr3Wy0K8xUrV4qW3CJen8xxUX29XfvKGl1iAVvmWtPo1zmCHd6RV1SHw3rbHGbl1zkYC+Za4bVkKHqet0SK1T6keFets5HSR15SpX1mPVmqHKzwV5R6X5bdKjqQtuCWTObGhxfxQGhXP/sGTa3MeHGzaGgIGv6hJyUBF6r4rWFDIpwRi9+tQ2zq3h4DVNKgxoiLYboFKAkbQ7xGDO2BNvNcwP7yHa5nLhtkP6iICsAEdhYy4925PPNrK9gXRXVtWm/5cu8tr6GsT0UpXsX+hYyvHtgj9muG0OLWV4V/G8fb+HfK/nFZ9R8RUxKXhYHd2WfInYv4l9VHLOOTaEgGSOZlQ7RYhfpLOSycIF95LpDuTds0VHUekcgMlyBnxrtbcvdYK6QWAuIRDU7yGhjDTDZo2IZBalqddfNEc4wxwtWtQQhNsjWZJjDRWTZzjk+dYWatFo6vR0lSydx9eLqNCoV0VWjOrXy7eBCIREv+pdZVtis0Aa9lOrmLlXWNxcpXdE3CDpcgzXs3YXdBjL4cDbOkfQr35pK68uM7K0yeHDRl+7Pz49+O5l/U/LNn3pnLEkkEssgFAo9hqORDiQJFIWCEP5CARLHkv3743D8Fj/9xq8uTb5StZVExUXSUBGnpqNiKBTaHQsSicSWUCh0KH6TSCR2+fLztubamjChlwsv3MEPfvB6m16B4T0vr4tf/3oH225b7Be/WGnZsmzpKq5wuKt//Yv3PuTOR1HMsNE89FtO+CPrmmWouPK788oh3LySV5qS7YUx2YW1ioqrZIcbk/UXIi7R15VmiqlrUXFFxA2W4xL7usc71iWLAqXLjrZxsPH+5mWVbR6ELLnOtLf1KjxljiCeMs2ALqJIvh+baKkq91qiQThD1ZUl5FwD9JBrsjILkqP4qwzxEGuKaG7IcXReyIm5YbUJbikNWZi6zDaqrpaflnxQdsvh9L70zGJjA89t4J2NNDZrp+pKPy5dHZUVZvJeHLMNXXJ5ajGvraBrlB160q+QwuzAojN7fWAjqayjqpHKmsD4nhdJOvcgEQpiVArzA2N4YS5NicB4nzLYb2pg9mYqG1lYyRe1LKkMUr9ksAw6TMZYFOGw7hzfnaG5vFrFv2uY05Ds09bVNy1yPTu3QVch50fz7BXO9rgqT6oWSQMNtg4iuZqdYYgddXWXmdYlQSIFIj3kOcc+llnuLVNlJ2NHUjEiQw23lyO84x4VyTojKYeSgcaZ4CqRJAI3qvChU0SUydXfTh5VZZ7l/q3MLE1iLUykv8MNsLMn/NOWZCLGDckUKBv0tFEvG1f1ZU00UGmVMKqevx/LcT9hw+eCWSXRpDWHVrBlZzfo2rVReXlNmyy+Ha9/01Vb2dlhU6YcbdKkZ1oG3mGH9bfLLn1ce+1U37hqq+uEhP3/Q9XWk6GVZKxE70kkEve0nDMUOgGHJBKJc5Kfz8AuiUTi4rQ+RXgeIwQT4smJROKl5L4n8btk+8/+u1VbX4mKiURialr/6ej/n1zEXnv19/bbq2VS11ajWF1dvV/+cpaddurjyScnuuqqtV59tVHKNbi5uc73v5/n0UcpKePp91g8n5/ewrO/5JCbg9KsKanFMa/z6qGUL2FaVfBXNMpXGw2YSXa4wfsa5Vjr93byC7MEGp1gQK/Q4HZvu8y+HvCOlWk2E5hlmSbVzrSXZ7xnTRqYNKl3j7ccb2c/tp97vCMo1NvKHqrU+o0PHG+Y20z0W59Zk6bqiou40yrDFPmtwWao8VebVX9Vrq5YRDQrLqbBC808tyXHgVkRf+mVUNcc8scyPvyK+zWtgWnLiTawRzFH9+API1hYxZPr+HAL61JBhun5utLUUU34wTu8V8Jde/HwQp5dlDymjT2kMJtOETrlUBQNXrPiAXBoJpSc/BvDQb6vqlgQpV/VSFlT4ADwtSLc24BIJMTOXTmwF/sVB27AM2q4fi3zYm3O0QZEWk4Zjdsmt8lFkSK7h7M8EKpyonLN4hmgwdZBZHuFjjPAKltc4x2hZA6GFIjsbKBjjPekN61Ms5UEINJoV3sbaKCX/UFTMsI9BSI5mox3uZCIRlvUKbHZNM1icgw30q/NdqUywSQZLFQCEOnnUN2N9rh/Kk+CSGor1c1GvWze0I310SByvYRt63nkNE7+BRsWCUrpJkhXaY0ZE3HFFTuKRJp16hSSmxvx979/7vHHF3/puExfFBcURH32WWnG/kgkZMOGGl+X4fxfkM1fAWKhDtrarvwPxmzsh23xRigUeh97YWMikfgkFArt81+/1C+XrwMk/UjqYAJZgy9jGz/EK//JReywQw+33NIWzVOgkoon4ZNPNjnooBkeeWScnXaqddNNW6TGTmMjZ56Z56WXgonjxQ+ZPp1r7+HZn3LoH6ip1qKq2BLluCm8cCBnzGN+Muq9oS7Qq0Y6BQ/ymxplK3GzHV3tY7XIVwtWafRnbzvdRG/6zELrMsBkkdVq1DnHEZ70jsXWtIBFWMyTPrKzbV3pMJNNsVbgIpUOKM/6wmc2u8RI05V6JlBEtYDFYlXO9rmT9fS4If5ogynJmTij+iLtVF3Bv9vgjYYcb2xmfDjitE78qUfIg+U8UpFUL6Wc5tokcozl8M6WYLOY7fPYryv3D6JHdlC+960NTN3IutQCsI2q6sEvAkP8urRKjG2TPlY3BvrSdVVpx5LpJbM1m0hqX9s2bdoigTfWzr3ZpRu7FNOYoCQeZAH420ZKYzpMxtgRiESz4nbN4cRongnRAnepcn2oNCP9SXr697Ygkq1BJ1l+ZIRectxjtg1paU5yNCgQdqIddZHjDo9pTHpepdx7O4s4ytm2WOMNfxPWlJHyJB87u0m9zea7UbVlaJajUVdj9Haaz1yvxgrpaU9q5RvkGL2N86SHM0AkpdLakASR5rUFLR5a/Zt44jTOvJFFnwjW4jFSBvWiogY33dRXr17cdttnpk5diZjCwiz33be/OXM2W7iwSkcSDrNgQXnrcMiN6tu3IKNPUVG2nJyIb0WaZSYP/WZkDQakfe5PcgJolbNxcyJA0SWhUGi5gJ1MwlGhUOgwwRPQKRQKPZxIJE7/xq/S1wtI/DqoGHQMhfYVAMmVW9l/XigUmhkKhWZKTsbQp0+BtWu/7C6kwCRm8+bNDjvsY506Jdx6ay95efVSetTq6iZHH83VP2b/nVDPK69w15M8eRlZTUGb8mBbu46T3ubhMYwIo5rmigINddkaGrPVydcg28saPKDEb02UlwxUTD1UazS533Qn2MVObSoswlqb/c0zjjTJbkZnFMaKiPvEIg+Y4kL72TN5fLrhNCJuqc3+6CMD5fmzcfrLzghgzNLoCetdbKFDFPqn/saItvTJDje2BDFm5zZkFM2KZgWTXzinwafNcT8v46gNCZ2ivDeYyUPYqbPWXFaR5JaasJNBjvBZHbev5JCZ7Po+D5cwqIC/78Lsw3h2P67egZ26yZjkV1YFVReDC2p/3nYgkA4Qbft9HRDpwM33nG14bBJ7dGNWJWfN56jZnLeYJ8o7AJH0c9ACIvk4rzDs/U75fpSd48VwjSNs8mq4pkMQSf+cDhIH6e0vdvSpTa71QQaI5Ks1Tk/XOMJa6/3DixrVylfbAiKD9XKaCyzwjo89Lkt9Boh0UmR3v7Peq6Y6R7UlJD0NOzvQtn5isb9lgEhtMrpkWyfpYbQn2jCRjXo5y44i+iotS4JI0ibSs4pnTuLCW5k7VRC5XkXwXNfaf/8mjz8+wIwZG5x00numTm2tfVBd3eSRRxa64ILM7L7pEgqFDBvWueVzTk5EY2OzdMNFdnaq7VuQbyfX1scYFgqFhoRCoWycQrKCWKuswv4QCoV6YTssSyQSv0wkEv0TicTg5HFvf1sgwtdjJF8HFYVCobG4F4cmEonStvshqf+7J+jftwWMunbNVVq6dSfsHj3y9OpVpKIiZPXqJvF4rSuvXOTMM/t77bX+jj9+nU3J3Ozl5V0ddSQvv8KpF7JkE4++QHY+/zqfUycn72HS+L5kPae9z6N7cvIiFlUHxvcq6FKVSh3kLZWaxfzKTm72qfqkERwq1PqjV1ziAJ3keU/gx9ogR44GlWrc4SlnOFhnhV43Q06aKqvEZr/3vDPsaZS+/mGqtqquuEb3m22krq4z3nNWeckGcYkWdrJFvRstN0q+qw20RswfbLZJ/Guzk2Y5NuGPVRF/qmK/7JCLujOuL8+X82gZC6u1YydtWUQTPqzkwzJuWYU6BhcwsZju+YKJZCvHtvPK+jIDe0fG9v8QRAgi9+9db+suwVsDkWhCKBq3d07IGZ2y5YcTPk/EnVhXrSKnvl2syFeByC7ynW+s6Ur8xDSxZER7ytZRKOxUk3SW5y5PqVGZARARzfaxm+56etVf1dqYURo3Iq6v7WzvAvPdqsLnImmz2iAn62qiT/xMTE0GiMRFDHaiTgZ7yqO26NQStb5JLxebaLlsn5UVaFzRqcVDq2cVz5/CT+9g+gfYJFjMaRKJlLr22iy77JLv+9//1IYNm5OjJ5N5vPjiGr/61c5JMMi0R6YkPWN8dnZEQ0NmvyCyveNj/8uS0Daz/X/9lIlELBQKXYzXBCPw/kQiMT8UCl2Q3H83bsCDoVDoM8Gi/8pEIrF5qyf9luTrAEkLKgoqy5yC09I7hEKhgXgaZyQSiUXtT/Hl8sUXZR22h0Jcc81EI0Z00dTUrLExYdy4ns455xNz51Z66KE11qxJePXVwU47rczChQEz2bQpzwH7U9EsSOQY4aF/B1XyHjmf0+7NtAUvwOkf8O89OXEJS8ozgxVlB7aRKWo1WOtmE/zGp8qT7r/BhN/gVq852QTHm+RpUyXSiFuTmPu95EATXex4D3pZZZKVNcjRpN793rCrUX5kb8+ZY2kQ4tsCKBFxi21yhSmON9qdJrjTF+aoarWLCJI/nucLe+rhBj2t1eRuW6wVxC3ERTp0EyYAEwIFRwJvNUa8tSEkP8SRefxhIFVNLKznhVJmtxLLrVdJTHp2ragJthYA8jWP3RqYaD1PKMohQ3l3bRDB/rVAJCmhPIbkM7YLKxv5tMrXApHx+Qknd0o4JDfio1jMw02NpsbiwknmF00LPvwqENlGtksN1iTmZrOUKk+6+raylF0NdLRxXvGJuebL1ig/DSB6yXa4k22w0nseFFUnO82WEhE30mH62M0nrtSgtAVEQiKGOldUgdmuFBMS2A2zk+Mz13g/Ui/kGU8o19lm3dXJs0kvl9rZIlG/LAu3JmIsoVcdL5zElffw/jsCdVY5EvTtW+6f/+xiypQyhx46S3Pz1lKhRDU3J0ydWmK33Xp5991269h2kpUVsnFjbUZbc3OiXcXEb1S+BYxKJBIv4+U2bXenvV+Hg77iHO/gnW/+6lrlK4Hka6Lir9ENdyYLycT+E0+IESO6dtj+ve9tp1OnHGec8Ybm5mBS7tw5X1NTgQB8m7z11gbf/36TP/1psJtvrvHhh4FrcEVFXqtSrjx4ue0Bss/lH2dzxv1BJLRosH8+zsCdO/PjNXxeH9JYnnQnLCSSHeQ8n6pWtWWus6ObzbHeRi20RYN/meE4413oMPd5Xb3WCovwho+tVOIix3ncFEutzWAn031uiRJn29sCJZ7zqfbshEd95l0FzjXe4RrdaYmNtBji4X2bfGiT/fR0v/6mqzNZaUAnw0E0f7w5slVAaW7ISbqyRtTi37Uh/66lczOHFvLzfozO5cMqnitjWiVVDTJBIV3S3ITf2ZfcMMuqWVTBggrWVLGulnVlSXXzV4EJIs2cPp7f7E3PAiY9xuxNOgSRHoUMKWRIZ4YUBEb7vXuRH2FZHXOqWbZRexBJSk4e+xZxVCGT8hJmNyU8XZfwm7o6zW08tNIDDr8MRAaKOtdgIXGPWmqh0nYspb8sZ9lTlVq3ek6zsnYAMc629nCQdzyhzNyW41N9cmQb7wzEzPSzllLSQVR6ge39RqnpFrs7wx4SeBMW2t1PrbHOW6aqS4JIqlTu940zX9SvyyIBiKxBCb3LeeEELr+b994UgMj64P7tu2+lc8/Nc911a733XpKypqmwM29eILNmbTZ+fPetAkldXStIZGVFFBW1jyP5VnNt/Q+W70Bke56ZM083YcK/2/WZOvUERx75Yhu1V1RrTq68lq1Pn26eeWaom26q9Pzz0DXok/KuLhawk2J+fh6D+3Hp48Q7yXAN3q4nj+3FD5czKwu5CdnFVfIKa+Vn1yWDumoNE/Yjfb1qjQVWZ6gfIuJ2M9jBdjDZq0pVZYAJdFboeHtbbaM3zWxhLynAaBZ1sLF2so0HfGBFh9HwQQqXnQ1yuEHmKPOY1apktesTk+MgXeyrSLWEu1VblgqZ/JKoeASAAkn3YbFQy7MerWdSIfvmc2BnJHi/KogxmVYVuOomLyLztS5wAd4mj+FFDMgJggL75QVp2HPCLNpCn/zAXbeigYr6IE9WYZhEM4dty3bdA++t3GjgsfXsEmrigcfVoE50yUE4SDxZE2d5DcurWV7H0qZATVcT0mEMSiTCTgXs04V9OlMeZ12c5+sTPozFxenQzffrgMgAUefpa4hc/7DULBvbsZQ8HGOYMXp73kyr04ILU+OsWJYjHKlWueleFFHark83g0x0kaUetcmUNuNwoNGusci9NpuRMb4CdVZPOzvbcktMMy8JHoE6q1oPV5noLc1uLsvKAJGBW/jLPtzyGO+/G7TZjCouv7zW4YeHnHrqYhs2lNlppywnn9xN//7ZunfP8uGH69x++7yMhIswdmw3l1++gzPPnKathEJ1Zsw43sSJT4Hx47u78MIxzj33zZY+v/zlOJs31/v732ekDcZvyP23cELC2P/Q/Xfa/+ba+tZl5MguNm6s68B20urF1Xr5WdavL3Xwwc3mzRvpmmsSfvzjOh99JKhpUp3WtYY/3sMV5/PYBZw2OVO1uRDHvhcUL7p0FR+2YSYp8rFYrT9a7Q+GeVOety1q2ZmjwTQrbFLpZHt612fmW50BJhWqPegVh9rVJY73T6/ZkgScBjnCYt4wy1yrHGIH5eo9bY5G8Qx2AjOs9JGNjjTQZBM9YbXnlYqnsRMavK3Ea7aYpLPf6a5es7tU+zDc1Pp3foW6SyyS9FIK6F4sl3djvLuR32ykUzN7FrFbF64ZTH4oiBCfUcaMJPOojiMv+N8X1gRbi1qgDeAUxumcTedI8JorCAbMitGniJ6FdM0L2pvivLqaBWVBfq3NCcobg7T2Lb8v3cU31ZbcP7AzOxUGzgU9s5lYwCd1TGngvHXBHBlkU24fcPhVIJLy5hsj7EyDFQl53nq3KGnZn953bwMca6QPLHS750UzcmgFnlljjbaXvX3gJeuTAYZtQWS4gw22t9luVGNtOsHS15562NMcN6ixumWspOwhUdvY3yXe9aq5VqtKc+9t0t0NJnpEo7vKcjJAZEQdjx7HuX9j5ntBm3IKmrn/3zHr18cddNBysVjgrbXLLkXee2+NqVNXSSQ4+OABnn/+MGee+Zbly1ttJZ9/XmfUqG46kkQis6xuKJTpDgwHHzzI2rU1aUDyDcq3YCP5/5N8JxjJCy8c68gjX8zY/8MfjlJfH/fIIwu3coao1gzBWcn3RQYNyrd06Rj19Qk1NXzxBXfcEfbkU5KBh8mtgIvP5ODdOOFOGgpkMJO+Xbh5Zx6v5MWErTKTTmKuN8Qmdf7pU+lBYKkV43n2t0yJF3wsu4PMbtvq60T7ed0Ms7SamNLZyT5G2dsoT5npE2u3yk6yFDjJML3l+kCpV23RpDXQMT0h5HAFjtfFDgo8pdrjapU1B+5TsST7SMWe0BrIGHxIYyfBlweScqILvlA0xOhsJhYxIBK4BxdGKGng80pmV1BSycpaVlVRH9eevbSJK2mbg+uA4dyxX8BAxj/OwnIde3xFAqYzoCvDCxjeie2K2DafbtmsbeKTGj6pZWZNMP+1ixX5PwSR3RX6oW6aNXvEWrOVtcvgGxU3UrGz7GCVTV4wQ31aMsWUx1YXhfY0SZ6o9z1Bm2y/ORrkyTfGCbLwhbs0J29KRFxI1HbOl6unef6gIfkHp1RZjbJ1Mt6uzvC8x6xUkcFEInr5obEeF/OvsrwMENkpxr0HcdotLPhIYFgvZXgPJk9uNnlypcceWy+IWk9Fr6fSoLTaRnbbrZcrrhjv2GNfTbYEyP/UU/s64YQpOpq2Zs48vCUgcaedejjvvFHOP/8tBMBSWnquiooGQ4b8NW0gfUOMJH9Cwoj/kJF8+r+M5BuVPn0K2rUNHVpsypQ17dr337+/s88eqbo6ZvXqGo8+WmLZstTsVWXlSqZMqXLAAZ0UFFBQkDBhQtyTT0akOVqBvz4U5I26/WR+9iTVafvX4dKpPLUvfSu4pw0ziWen1na1fmGps/R1hd391ccatObfimvwR6853niXOdx93tSoIuM6llrnNo872M52NNzj3laZnEBS7GSK+T6xzMkm2V5/L/vcRlXt2EmTGg9YoIscx9vWqQZ51Hqv2ZhWqzFgGItwo/pkvEEXT+hpYbjRP9T4KBoPlG1tvLsy2Alal/hpBbTSJCaI/J7TEHy4di3q6Z3NqDwG5gaJEQflMzAnyLlVVh8s8DbXs7mBDTXUxKirpz4W3LPqpsA+kkoh/+uPGN6Vffpy+FA6ZQU2kMZmxnWjR/K6GhLMqmBLnEV1PLiORfFk5vL0n5Oezfdrgkhb76yuEo5VbE8FSjX5sxVWqm4HMkH9kSKnG6VZowe8Y5OyDGaRr1aWhD3sZqyx3veMNRa0cxXP0aC/cXbwPQs9YLPWWOFgf3c7uMZGH/rC39rYQoL3fexnuIM85j4lEhlMpLN+rrKDK1V7aUOh5tKCFhA5MIvvj+SYm1j5hRbvrKP35tpfJ5x11hZz526UmQKlPYjAtGkbhELsvHNPM2a0spLc3IiePfNs2NA+qDCRCKW9p7a2lSKccspwublR0WjYrrv2M3362nbH/6/8n8t3AkgyJbikrl1z2qm1jjpqiB/9aIxf/vJ95eVxkyb198tfjhSJRFx99Tzr10OVm29ea8cd8xUWhm3e3OxXv6pEUVDRqU08032PUnkUr13OsXewsbx1XzkOe5OH9gomuWs2hjQqCmwHXarILkrm4Kpyrw0O1slv7eUPZttoI1o9up40y/Z6+4mjPWWqJZZkXEe9Rs/5wGhDXOQ4b/nEjOREkUruWKPa371puD4uspfFNnjaXFvauItE1arR4F6fK5DrRINdb6g5ajxns1qNSRYTgEK1HP+02QO22EmeI3X2+3C+Keo9Fa33aZq6KxKNB/aTDgElLQnkl0lu0qmnQrISngzbSThEl0jAErpHg/TxnaNBSpSicFBMKhanKIt4IplGPsSmOkJZQZbhhXEqm4MSuX9aQmkDibZpT1LX+jVcfNuCSNskjOkgspuw7+ltuGwv2uxai9UkYbwtiGwr21nGyhb2tJlWJ20l6SqsHA0G6+MIh1rmM4+6JTmyMkGkQMhOzpWnk+mu1KgiQ5XVw676O9xi99tsPmkg0pj0AZvgDFmKPeRBVbJaWEiVIv1t5yLDnGOL6Rs6B3EiSdvH2V05awTH/47Ny1FCuJJrL2WnHdlvv3Ll5UHkemsOrfRSEe3lhhvmOe207cyY0brS79Il2667dvfcc6vb9V+5srrlfTgcBCUG70Nuumm3lsJXt99+kF12eaDd8f8lSfgfnUb+OwEk6f7fKamsjKmpyfSwePPN1V59dWmLH/mKFVUeeWSxww8f5plnJrn//lXuuWeDt9/eoKFhW3Pm1HvuuQaTJ+e65JIqNTVFxLLaMZMnnmdzJa/8hBPvYllqfz0NxZz6DtdN5N4+XFwSUq99nEmORq+ptFyN643zuKWmJbOnpuJJPlNijZecbR9jDPKUaUl/qFaZb7ml1jrGnobq53Uf25xmaI+IW2S9mzxjFyP8yqHestA7FqnRVho0afCgL+TKcYgB7jPaDOUes8Galtm7NfXKHHGfqJMtZJJiPw4XKswOm51o8lKoyaeJJg1bZShp7ISOAaWj2JN0yQvC4krrgiDARVuxnbQ7bmvBixGtPhnp+9r2/y+CyBhhxyh0gFzvqvKEzealJWNsCyDjFDjRMHFxL1pghTUZoJCKDekt16EOF9fkRQ+pSgJNWxAZaLgxjrHKFCVpiSWCvGxh2zlHgb7m+pNGFe1YSLNeDnSxJRZ62zuqFKlSpE6eLYrtZKQjDXBCc7lFm9JApITfDGP7ThxyI3WrgrZucf75IK+/zlFHlSVde9OrHaaKVGVm7E0fCJ98Uur++ydltPbvX+DCC0d0CCSDBxd2eKajjx6iZ888NTVNEomEsWN7Gju2p7lzv9qN+GvL/3AbyXcCSFql9XKKirIUFrbmohg/vocVKyrb1GUO7txLLy32xhvr/P73O3vmmR384AefO+igOZYsSaivz3Xyyb298kpnxx5brbS0MGAm5ZmnmfIOP6zirvO56lk+2SKwpQjGyK8/5pxRvLENJ60KWR8rUIXGvEbxwgjhgOosUus8C1ykjx10c79PNSavM0eDLRrd6nUHGeESh3nKNBtk0ux6jR7zlm30dZbDLLDS62ZoEmthJ3ER031hliX2MNL1jvKGBd60SkxbF8dAC/6U5Z6xwu56u8IQcQnP2ugDlVLsJGU/acYUW7ymVr6QPUMFzo3mG6uTmZEmT9fHTY/HWv7GVDBjIF8DUFLyNVyFW6StO3DquK3YQzLOszUQSVdlpa6nDYikpz1JgUhOboMJ0bB9wjmOlG+VmBeVu9s6MbEOU55kidtFF6faRrk6//aZVUrb2UCC14iD7WaYwd72qnXmtzOkB55dOXZ1sjydzHSbOqUZys4C/ezocutMscBdSQAJXNlTMSLFtjPWMT70rrnWqUoykCpFKhU73Tg9FDq6sVpJSY8gd1YpOZu4ZXtqKjjxDzSvRwkTt2XyDVx9Fa+8EkSutwJIuovv1mbevJZ3K1ZUGzSo0MqV1QoLozp3zrLbbj2NHNnZggUVWzk+U95+e40DD3zOqaduq7S03ttvL7F4ccexa//HkvCtxJH8/0W+E0CyYUOdcDikOW3+q6ho1LlzttTM8YMfjPLww1/46KPqNkcHg7GxscZPfjLd0UcP9fbbE/zoRwvU1zcg4d//LrFlS7NXX+3q+ONrrFpV0B5Mypn9Cef/kcev47cv8Vyb0Mp75wbuoi+P49wVzKwoUJcyPhfSGG5MGrRr/cEap+jqd/Zxm4+tV95iN8nR4HVfmG+Ns+xtifVe8HE7drLMOrd6zJ52cLlTvGyauZa2gAk04m2f+dASBxvj9w7yqs+9bbVM58lWoPjAeh9Yb4BODjPQhQZ5T5lnbbKyDaBE1YqJeE3Ca6FqCRE7hvLsmpftKgWam8Pejzd5q6HZtKxaNU2Rbw5QkoGM6fVOOjyuIxZCexbStv+X2UNoByLdo832zks4LJxrfLjQvFCDt9Q4zSb1acbstiDSXbMjDHCgPmZY568+tkllhvoq9T4HexpvG72tsMT9XmrJ5NsWRAbbwQTHW+Apq3zQMiYCCRniSP3tb47bVVrWAiDp7r0jHGQbkzzpCRtVt6ixqhRp1M3VJvhU3GWNdcrX9GJTiA30quHxffjXdCY/p4WdnH0Y5xzPscewcmWd1lwo6XaRlFqrI5VWXsanDz/caNKknlaurHbKKUPk5ETk5EQ8/vg+xo59rkOje0o2bAiep4qKRlOnrjdmTGebNtUl41b+V75J+U4ASTQaUlyco6ys1R2nsrJR586ta6v6+liLzrO9tOpan3tuiU8/3eLGG3cwYkS5Bx8MVh6vv77Rli11nn66nzPOqLFgQRqYxATeWuWs+IIDf8q/r2NoD25JpcJN5sd5fxnH1HHdKN5r4P7qqDpF4rGIvMLa1thEPKLCJ2pca2fPWe5DQfbSlKprrWo3ednBRvql4zzqfUuUZLgJN0t412yfWGhf4+1jvOd9YGHSPJxiJw3qPGOW1823h+38wSGmWO5NS9RkTDCt2YFXq/Q3C2QL211f5+lnoDyvK/OK8jZ5cFoBZma4zkx1bm+u1ikcsmsiz5E5ea7LzxHCJ7FmUxuaTa1meTy4ykBCX8t+gkxA6YidpO5JekqTr8tCUn2/QpXVN7fB3vkhe0ajds7K0RBq9qpa/1Tt56F6oQ6M5sFXBJ/HK3CU7oYr8prVrvSupjTjebpqKk+TSUbb03jzzPG8JzWrzwCO1Psuutnd6RqVmeLXGlVlgEhYPzu7SKXl3nZ10mOrVY3VIEdCJ3v6oVoN7vOgLfJU6aVOnipFCvR1nhEe0ejhymx1m7sF9UQ2M66eB/blp08w5T2UUtjIPb9jw0r225eGhkwQGTs24dhjB9lvv84aG5t06ZLjiy/KvPTSKv/+9xLNzR0PjOnTN9lrr17g0ktHtSRd3HbbIpddNsptt7WW5vjii0qplUdOTkRxcaYjSufOOcrLv/nMivhWClv9/0m+E0BSVtaga9dMIFmzpkZ+fmpmiKqvj8vNTT396ZS4TqpiYurzqlWlzj9/hoce2tXw4QWuvnq1RIKPP+aUU1Z5/PGBLruszvvvC8AkRXKS465iLUdcyd9+yvUHcuMrNKapX1fG+FEFd+7GccWcuTGqNJbp0RUTkaPRF2qd5zMX6O9qe/urmSrUZLCTV31uthUONc5uRnjGdE1tPLuq1XnBVH10c5Q97CvhGdNbkvmlzlev1mvmetcCexrpRgeZZZ0XLbalZXZuq8aKe8car9ukiyz76ulPtlEl5gOV3lCnpI2BPpW7qwavq/V6pFYsFpFVn2N8JGpCVpbduyTslhVWHeezpoS59cxuZFl9yKavGhRbsZ+03PK28lUsJPW5AxYSiTIkizF5CTvmJeyYHdY/mvBJPMsacc/EG1wXrhHbSt6s9PeDZDlCT3vpZokKL1vtdqUtwNE2Ij1Hwp6GG6m/LTa6372akpUS24JItrAdHW6A7c32gE0WtmEhDLK/IQ4wz8NJg7oMAImL6GRb2zvc55b4xOctDKRKkVp5RhnuB7Z1iXIflBVo3NwpCCZczcldOHwgJ9zF0s9RyvY9eOAabvkDjz6cukGVotEqxx+f6+KL+1qwoNKrr67yl7/MtmVLkMq9Z888V1wxzj77DHD++e2DDGHdulpjxnQRjYbk5UWsXFktFkt444115s/fktF3xIjWIoAdJWiMREItGTK+cUn4DmWn/78v3wkgKS2t07VrLmnm4pKSWvvs07flc21ts/z8rZXKbA8mdXWcfPKHfvvbcf797+18//uL1dezZEnM4Yev9PTTA/3pTzWeeioncOdJeXOVIx4sLs6/gUu+z+s/5pS/U1KuhZnU4ex3A/fgjduxpjFkRmMn79TnmNpYb1V+JEPVdau19pDrWnt4wjLT2rCTErUeMNWuBvupo73jM+9bICGRwVDWKzXZcwYZ4jA7SeAlM21SKRVPEgAKb5jrDZ/bxRA/sYtqjV621JwWV6n2aqwaEU9r8rS1usmzl+5+p49CUe+p8bZK8zRqCyjJE0gU1poRi/ggFSFfH5EXixgTDRsTyXZwAbt1CesZIZYIWdnInLqgdsi6piDd/LomNtZTm7KHfB1A6SDAMAUUOVn0yaZvLn3z6JtFr1zG5tIvi3goYWEji5sTPkk0eqC+ydpEosW1V1iLUb0j5jEMB+hpqFxdZXnDWpdaKKZJtoZkeF8mqygQtp/RdjfMHAu84HmNSQDpCES2M8H29rfMh15ynew2s1auviY6X5W13nWjmHraAEhc1AiHGWonT3nGelUZ9pAaRU4y1lDFjmreYvmmzoF772YiJdw8jG1yOeteqlajlDMmceGxfO9UFs6HJuFwpTPOiDj44F5Wrap26qmzrFmzRaurbyAbN9b52c9mu/XWic4/fzuTJ7ePGauoaDJoUKFYLGHYsKftu29v++zT27XXzs7ol5MTlpfXqv/Mzg63S+4YiYTF498ikPyvjeS/V5YuLUvaQ1pl/vwyF100puXzihVVaWmi27ISzj13OxMmDPDOO6s9++wqdXV1Egmuumq200/fxvPPj3LSSfOUl7N+fZWDDlrhyScH6dq1wd//DnmtqtzUgMjljvuYu5hXLuOCh/ioVOvkVszxb7DihNY4iGMSOd5ryHJoeShD1dUox7tqzfWZnxhkjC6e9LktqjPYyXQrfGqNY41zvkO8abYl1rf7zxZZZ5F1RhvobPvbpMJLPrE+Sa9SLIWYjyw21TL99XGQYc4yzmuWmGq9yiQopCd9TIFEKZ60zpPWyZVtF10crtDvFFuh0VQ13ldvmdp2gBItrG1JudIYjfs4FjEtmeeouTIJXLGIQVEGhUL6hxlawF6F9MumOBS4/IYSSLCxKYiSb2gO6s00NrOqNgCHaIhoOHhd3ci4TkGKlJQ34Py6IJ/WuhjrEqyN8Wkdd1axTkKiA6+siI5de7M1yNNsvGx76WIvhTZq8I5Sf7FWXTJbb0e2khwNest3gBG21c1cC/3FP4SSsSUdgcgg/e3ueGVWes2fxVRkLCxCwoY7wkB7mOU+pRYm73umV1ZCTwf5vg1K3eMfypPFd1OqrIQeLjDSGhHHNdbYUtKTzVE2072Kf+3KlEVc8QyJjRTVMflyairZ71DqNkKTAw6oduONxd59t8KFF36hvLxeZrwIrSuAAP2vvnqWp57a12OPLVNRkflcNzcnjBvXmosvPz+aEcGektGjiw0a1Ko2iEZDyspSpSsDTUddXUxDw7ekf/pf1dZ/v9TWxgwa1ElQPyu4pDVravTv3xqoOHVqib/+dQ9/+MOsZEsKTPKMGJFvzJie7rvvE5MmDTFt2gmuu26GZ54JjGoPP7xMeXmT114b59hjP7NuHVVVNY44Yrm//a2fESOa/exndRKJvGAwpNhJYfAV777H0ev460946TMmf5Lcn2QnP5nB/btTnE1TM+etDmuMZKq68pKG9JgG11lpkhxX291b1nrN5+JaI9njGjxmpj4KnGRXBxrvaVNtaOu3jPlWmW+VUQY4yST1mrzuM8uT5X/TWcoa691vvagiuxrgVyYpV+9tq0yzUUxzRgXGdNZRr9G7NnhbxG3WGCzXBF1dqZseojZqNkudGWI+DcfVS7QDlFQMiqxasaYg/9dSLE3m9FITau/em5xXwnFym8kOk91Idois5uTzmyAWDl6bsqkOJdcCX8Mji44DDGllIdnitpNlH7n20F1PUfNU+UCZhy1Tr3mrqq6AB8RN1MNBRsgR9qFPveYtIU0dBhRGxPXQwwR7KlDsXQ+oSnr2pfscdDPMTs6yyjSv+5VmMW0BJC6ir11MdIC3veNTGzSmqbKqFNlWfz8x3A0qPFMZCewhm0KUsit+vSO3vs2r72EzO/Xlnkv545089o/geRk+vMkvftEkOzvLyScvs3JlZfImpozrKckEEairi5s1q9Qee/Ty0kuZQcgnnDBYdnbYcccN9PTTq3TrlqO0tL2d4/rrx8vJiRg2rJPFi+t065YnFstUbXXrlisW++/J5PH/unwnUqQceeQ2dtyxj+uumyEd26ZMOcoRR7yspiaYXWbPPtH48U9IJFKzTccBTZ07d3bnnQeoqGjy4x9P1diYgyyTJvV3xx3jnXzyPIsXN0qlWLn++oFGjsx2xhn16uuLZCR7TCZ6VEROD267jOIunPdPqnKDPqFilp5LcQ63reCYvly8lqkh5MbkFVfJL6xtqQWfMrYWanK2vnbRzWSzrGiTcI8AAIbr5SQTrbLZK2aqaOPdlS7b6OVA4xXK9YbPzLZSLK1+WQpYUqDVVxf7GGyM3pbZ4gPrzbEpmYC8NfUKberBi2S095dnvDzjFOgmoo8cCzWZr8lnzXHzNdoYC2+1fnzwoYO0K20ApYUtpgNOR4Z0tg4gfKlb75Bowo7hqPFy7ChLkZAPVVmm0UcqlareanxI+vu+8uxnoMG6qFDjHXOVWt8h80h97qaLSQ5RqLOZnrHRwpZxkHrNUWiCUxToarp/qAySuWTcm4CFdLa3k5HrGa/aLKRWfgsLqVbkOCNM1MP5yi0oK2q1h2zmx704ri+nP8iq5Vra/3oxf76LpXPIi3HVVXH77x932WWbffzxZu09tFKuvu1BJCUnnjjYsGGd3HTT3Iz2pUuPt802RdasqTV06HN+9KPhNm9u8PDDy1sGxpgxxd5//zDFxdkee2yZU099zU9/uoOSklr/+tfilsHy5z9P8tRTi33wQSoY+BtMkRKakBD9D1OkxP43Rco3KmvWVDvqqPbBRJ9+utmuu/by1lvBamz+/C1Gj+5q3ryNyR6trCQ1SLt3z3f00YP9/OfvOvbYYSZP3suPfzxVRQUffrjGmWfW+d3vxrjuujU++yzw8vj1r1f5wQ+6e/nlrk46qdrmzYWZUfDliNNQz49u5JQjmPIzfng/c8pJxLj0Fbbpy1+WcX9v7t2RL2JctSWqNlbc4tWVYicp28mdSryizI+NVq3RP32mso0xfoHNbvSicQa62OEWKfGK2SqSoJQuy2xwpzf0UORAY00y3DIbve8L5Zpa2EYKqNbZ4l+2aBYxQg+7GuAcYyxR4S0bfKYszesrUwWWzlw2avCCOi8oExcRFjVUtmEKHRzOcZwc47Jy1GUlLG1utrCJL5pjSqKsyamzuiEslpPKNJzm5VXoy1UGW3PnTe2jnUsv9MptsE1OwoBw2PCsqNFZWYaFg1xlc0MN1on5QK17VKpJ/m9tbSMdgUgXEbvqZz+DxTSZapHXfSLeJhK9LYj0VmR7+xtoG9O9ZIO5GT8p+DfChtvfYDv5whTLfZx2J1pBJKinvp29HeMDU023NOnOm93CQrJ1c60RPtbo8MYqW7YUt9hDOm3m/h1Yu5n9/0LTBq11RMq5+LLg9dAD+e1vE+67r8Yee5Sl1ROp0uri+9UgArNnlznxxMEZbXvu2U+XLsF469o121VXjREOhyxcmB7EmOXeeycpLg50yPvv31e3bsW6d881b15mrEh+fjQjbco3Lv9rI/nvlTVrqmRlta/6+9Zbax144IAWIHn66WWOPnpwcoCkZpdMMLnuuj2deeZYkydH3Xzzx555ZpFnnz3YYYe9oq6Ozz4r9YtfzPHEE5P88IfzzZoVDPD77y+xbFmT11/v7fTTK3z+eYF2dpOkquuxZ5i1kKvPZNYa/vIRL07XkvBxTYxDtgQBjO8P4SfrQt7b3Eljdb684irxwojscGMygWKjJWpdZon95fu1Sd633vO+ENeUASifWGuOVXa2jcscYpH1XjVHeZKhpKuyNqnyLx/KlWVXQ13mUJtUm2KBL6xvYROt+bziFthkgU2aRQzT1Wg9nW0XlWI+tsk0pZan14NPSkeqMOIWiflcg+dSbSGyRQ0MZds2O2pQc9R+OWF9m3P0KozIEuTAahK3KRZWGqMkRl08pCZBbYzaZqrS7FiJZhLhIFVKYTZ5YfJyyA8jzMDshO4RukcTuoVDqtE3GrI5kWN1Im5tuNEyce+EGqwINWj09SoZ0lpnvbcse+ltd31kC3vPMnd6W1Wa7aMjEIE++trLngrlmeENH3oh2SdTBhhrJ8dbbY433C6mIQM8UuqsqAL7O06eIvd4xmaNahVngMhYA11giF+r9FJltrrNxZQHrr0TE0zek9+8zfMfygCQVO6sXjn85RFWrYo77LBKJSUVWu0gKRBJRa5vLV4kU5YurdKrVwr5g5XAz342UlZWSG1tTG1tzI9+NMzTT6+2fHlrLNmgQQXGju2qurpJPJ6Qnx9x5pnb6t491+bNmd+bn5+ltvZbNGT8D9aafSdUWzBr1hl23PGx5N4A3woLs7zxxpF22+1pBAnbPvroeDvt9GRS/xkMipycZk8/fZTjjnve6acPdffdh0okEsLhkL/85VOzZ29y4onbOe641zU1BWquwYM7e/rpPfzoRwt89FENApXWyJEFHn54sKuuqvTaa5GW9o5UXdlF3HgJY4fyg38ExlzFgpVwcdB/QB9uGk1lhKu3UJ6dEC6olZPX2KG6K0ez03W3o86mKvGOZcJiGRNYjgYhIbsY4GBjrFfhdbOta0092E7iIrbRw3gDjTPIHKu8Z5n1STfjzMJZkYzXYoV20suO+iqSZaN6n6rwqTLLk1H0W1N5fVlbeh0Ukrm80CkeVRyP6BYO6RzLVhwmPxGWnwjJDwfp4rsGfgQttcvKYuTnJCuAJ6gLJZTGqQk3K21OKIs0qojEVGtvB8FWPbI6YiGFmozTyU666S1HF1HTrTXTahWqt2r3SN2/XI1G2sb2RsqT60Pv2mRxhvoqdZ+76mc3x2tQY7qnVafd47YZoAfY0X4O8KaPTbdErXyNstXKTzKSzn5gW/0Uuri52pJNnTRXBCwkVM5Pe3JMH77/OMsXywCP1Hb2MVx2MVdfHfPSS6Va053UpW1fBiIdMxKYNetIO+74asvn/v3zHXVUf9tsU+BXv5qbtKUcasKEV9u58d5zzwT/+tcyixdXKStr8K9/7eHcc6fYvLnVE/Tf/z7Ij3/8jvXrNydbvmHVlv9QteV/VVvfoASuu42NsaTLXquBrLq6SUNDXLduQU33+vq4115b7eijB3vqqWUt/Roawl59dYUbbpjkj3+c6a67AhAJhfjBD0br1WuyLl1y3HHHJBdc8D5YsaLCUUe95/e/H++OO9aYPr0KUQsWNDnooCWeeGKw/v0b3HdfFfJaXYRTq+E4jfVc8Xv22ZuXLub653hmsQBEkob41THO2MSpo3hnKH/cHPJIMiJ+a+qu+5R51mZn6eMW+3nMEh9Z2TKZ1coXETfdKtOtMlYvJ9tdXLOXzLVISctklJrgYZlNltnkebONM9BpJiiS531LzLXGmrTo7BQTiosoV+01dV6zQkyObXQyRncXGSYirEGzeWp8rsp8de2SQm7NgJ8q+9u2UuOWerZEYpbEImKJZuJtimu1TV1PMJJrtVNhhXMaRLNbDenp3li0AggyWEf652KMVmSsAmMU6CbbPGU+s95TNrcEGW7N7pE6X75cuxtlR9tbaYX3vKvcxgwvrNQ966ab3R0uV74PPGejlBE6007VIEe+Yoc5XKVGd3jaRloM6g1JJjJAb1fZ1mS1LqyMqSvvEXhlVdOjjD+NZmMp+91N01rBWC9v3QYX8KurqKlmjz2aVFeXaQsiWVn1xo6N6NKlu7VrKy1YUKK9pFz1M6V37yIlJen/Q9SaNY3y8rK8/36ZurqQ7t0LlJU1dRgL0q1brk8+KVNVFYzhQYMKbd6c+b8OHhxEtv//SUKh0CG4XaDlvDeRSNy8lX4TMR0nJxKJJ0Oh0AD8A70FGY/uSSQSt39r1/nfz0ggz8MPH+qmm2b5/PMy6fh24YWjxWIJ99wTRLAOGVLk3nv3tf/+zyd7xJLn45VXjvT7339s06YtHn30GKNH91BfH3PAAf8ydeoa999/tH/+c7EpU0pavrd//86ef34fP/zhpz79tEEqw192drZbbx0kJyfLRRfVamxMq33SATsp7sH1F9G3Jxc9xoZm7dhJ50J+O4Ftirh8PQuihDvXyMlrlJ3bICe7UZ5aUfGW1z44Rz/5wqZa7xOrMla36avXgbray7ZG6O19i3xosbovySQXF1EoxwQD7WyIAtk+tt4Mq61OJvZL9UtJW2bRLKKffMN1MVJnw3X2kg2essnZBhoo11L1lqn3hbjVGjVthaFAvDmSwVD4mob5jgAkzROL9gCS+v/SWUcfIaNEDJWnp4hddFYn7nOVvlBmvlKV6tqpvjpiHhFxuRqN0M9E2+mpmznmmONTjcm0KOn3D7roZDeH6KKHD71sdZss0SnwCP77qJ1MsqPxnjPVp9a3MJCUKqtWkTMMsouuLmmu8vGmwoCFbEEpB+Xx++254hXe+FQAHJsFQbrlhCq44Fh+eBqXXsLUqengkclEiovr/P7329uypVrv3nnGj+/hmmtmeOGFFW1GXjqQBGqs3Xfv4dRTB7nkktkZPd97bz/HHvuB0tJGRxzR1667dnPNNZ9JX0V06pTllVf2MWlSUNq8S5dsTz65d3KOaO33yScn2WmnR7SypO82IwmFQhFBtYcDBcn6P8apiUTi8w76vSFwPbk/CSR90CeRSMwKhUJF+ATHtD32m5LvACMJZOHCLbbbrksSSFK5L3jqqWWefvqQFiBZvrzKZ5+VOuigAV5/vTUDaCLBWWe95ZFHDnTbbbPsttuD1q27TCQSts8+g0ydusYf/vCBu+46zJQpzyWPqrNmDccf/56nn97L6ad/Yv78IKFcY2PUxRevcMklvbz2WrGTT95i48ZcW2Mn5fVcegOHHsCrl3Lra/zzMwGQJL2PKqq5+APG9eWuscxr4tryAqU1+RoKalsSQEbCcTERUXExtW6wVl8RP9DLqYZ6xnJTrdAskcFSFqux2FxdzbGXYX7hCAuUmG65FTJXh6mJu1qDdyz2jsXyZRtjiAMNNUovC20yywZzbVCfLMsbXFNEdtLwHha3XpU1ar1tbQtAROT4lzUGyjVIoTHyHCFfoYhi2SrErBazUqP14tYL2yimJJxQmt0o1hzJcB1ukaTrcLt8Xm28sGgPICF0Dyd0EzZAtsES+ss3QBfNmg2XZ5MGy9RaqtYnqky2TCRpF2rrmRV8e3vmAf11soehxhhiubWmmaPE8pZj0u9DRFyR/ibZRxedfORty3zR8vsa2oKtiD4GONBBllnvei+oJ8MWUitfL11cYoDF2K+yQXV5N6qD2JDsSm4awog8Dr6PjWu1gkh5sA3uwt9vZc4s9tyDurqOQKTVJlJeHnP++W+2/Lb8/KjXXz/Cxo11PvpoQ7K1PYgQte22nS1dmumN2KNHjubmhNLSIGvcxIldvfFGScsxKZDYe++e3n23dXyPHl1s3rxSbeXbXTN/K+l/d8aSRCKxDEKh0GM4Gm3B4BI8hYktV5NIrCcIQEskElWhUGgB+nVw7Dci3xkgmT17ox137OOZZ5ZmtG/YUKekpNYOO3QzZ04wOH7/+0+9/PLhpk4tUV1NakCVlNQ64YRXPfroQfbbb4C6upiePQv87Ge7qK+PufXWj+y+ez/vv3+sAw98Wn19kIl0+XJOOeUdkyfv7qyzZlixok7gGlzpjjuazJ9f66WX+jv//DKzZqUGS7Q1JX1MwE7qeeU1PviYmy9l96Hc8jpLNmt1U40xu559NnDScN4cxj9KQ/5aWqAuN19DXbacNoCSo9EqDW60Tk8lTtfHz/Q1R6k3rBJLspfUpLQJz1riOUuMVexgo/S1mxlW+MASZVtxH67V6H0rvG+FiJDt9LCDfk4wyioVNqozzyYLlaoValnJp6uuMoGGZZosTn5fujtxZxE9Fekjqrdcw0V0l6ebLNWaDQwHXjjNWVRmNfs8HtclEdWYoL45pEFcSRNdEmGRSFwY0UhUXSKhTySqIBRSGEkoEJIVKpAdCvKWVWi2SpNqTdZp8IkaL6q3WbV6zRmqrS8zuqc+p7tqD9HdBIOM1d8SJZZb6TUfJCG4vQE9LqK/Xvaytxw5PvCelRYn/6f8jH6p1xxdHWVfRQo9YKrV6jS0sYXEZDvRYAfr4sdNVT4oy2tlIRWMjHPlUD5Zxc/ekaHCsgk1nHMk+03k2muY+iEp8Bg5Mmb58ir19R25+GamhK+tjbn44vede+7IJJCkQCQ9Q0UwBe21V3eTJ6/M2HfYYf29/PJGZMnPb3b00f1cf/38duN2//17e/751viTHXYo8uGHmQunHj3yfPxxR6q277T0EwTXpWQNdknvEAqF+uFY7CcNSNr0GYzx+OhbuUrfISD59NONLrhgh7SWVlby978vcO65o1x8cWDfWL++1u23z/W73+3ikks+yDjPli0NDj/8GZMnH6Bbt+BhTCS44ord3H//HM3NCRMn9vbpp6c75JCnkxlK6yxcWOenP/3Iv/+9u/33f1d1dSoBXMzbb9c58cR6jz66jb//vcL995cKHvQO2EmMqiouuoHdd+Xhi3ltPr97jfqc5M+qRiGPN/D8Un40io8GctuGkIc7AJS6cKuqK5VupRjH6ek2k8y02fOWKVfRwmRSoDJLlTmmyxO1m4HOs48ytRba7FNLbNqKq0lcwuc2+iyZTqWzHKP0sJt+zjJWnZiFtrRs65NBeW3BhEwPr5TUiFus3GJfbpRPhCKKhOSFo7JjUTmhkHAsW3YopLk5LBqKaY5rMfk3JhJi0Sa1iYQGMTWaNYUy7R7Q1hbSti1d5bW1zynJFfVrh1ujzKeWedEsKXffoG8gKWaRr8kYo+xqgo02eM9Ua1psINkt/0X6MWTZ2852NMJTZplpbbK2elGaMT1bL138xiAfJBrtuaVBTXm3wCMrqaq6uDdnDOScZ/lskVYGUo0qekX5+59ZuZRzzqS2Fsr06FHvuuvyjByZ7/vf32j16nQQSam32svq1TWGDi3WHkSiLZ+7d882dmwXM2fOyTh2yZIa77wTGMZPOWWIxx9f2y7FSTgc0qtXrqlTW7M/nHjitk466fWMfmPGdP52Pbb+z0Lbu4dCoXR92D2JROKetM8dVGpq98DehisTiUS8o4j/UChUKGArP04kElsr/vJflv9mIEmBRaBi6t+/bSxJsP/111c777yRxo/v7qCDBvj97z/14IMLnXTSUPvu28+UKWul38R4POqTTzY6+uh6ixZtEYvF5OdHvfrqKWpqGnXrlm/EiK5mzvye449/3nvvrUWWWbPWuu22+R56aKITTnhfItEkGPh5Vqwotd9+9e6+exu77hp2ySWVGhpSD1FRe3YSZ+pbTPqYC89g6pVc9QSvLhTYTOqDvvW5/LmWhxZyxVhmDOB3m0KebgMoDbnZcrIb1cqXr1atuHuUeshG+yv2YzuqUu81a822QbNWY3FcRCPesNIbVuou3276u9QBYpp9YIVPrVPWZjJIt41UaDDNGh8k07UUyjJcN0N1c5DBusv1uQplGixXZYka69RqTFODpRvxW+9w5v5WaQWhalSEE2Q3BTaU7OBeN8YzASo7KwkIkS+3g6TapP1HqbattackmvY+1V4v5lrPaJZIA6LWPGqt3m9FJhplJyPMt8Q/vKS8ZRXfMQNplGNnwx1kvHcsdoU3NQhpUKxOfos9pEmBM/U2NFHgpzWNppXlB2qscoHLbj3378TCDex1Jw3pbr3lqGfiQO65gSuu4I1XoUlWVpWf/CTilFO6+u1v17nwwlVa2Ucq7Um6zaH1n4LGxiyNjXQEICl59tndzJqVdGpJkw8/bFVPnXPOYMccM11bOeaY/tatq1NfH/zvPXsGLsQbN2aO5bFju5s7d5OtAd5/XZr/T869+SvsM2swIO1zf9ok5WYCHkuCSHccFgqFYolE4tlQKJQlAJFHEonE0//pxf0n8t9sbD9f68DK89xzR7v00vetXFmV1jPYf/75o9x66+6amhL69XtITU1Mjx65XnnlCJdd9oEPP1zT9it0lJph5cqLDRzYWWlprVWrKp111gvmzk19XzC4b755D7W1XH/951oM7PKSr1E/+tEgkyYVu+aaUitW5CbbO2l5ENKN8UUopnc/rvoBI4ZwxVPMLpVhiJeMku/dmavGs20RD27hqXqac1tdhlNG+ZS7cLq6ZaRcx+lpnCLvKfGKNbYk1UptJ1KCibCbfOP0sodB8mWZo8Qn1lmsTHMHbCUdXGg1vkMXnW2rsyGKDNFJX/lWqBYWsUatFRqtVmelRuXJSaej6Pm27R0Z5NFiP0m58tLenbej3741sCgSsa1cw2UbotA28r2txGtWbpW1IMM+0va1UNw4Q+1ilIiID33qM0vEMkAz839NxYSMNNBxdvKFMo9boDxpC0sFHabUWSN08utEX881xP1uY5ZYfbRFjWULx3TnomH84T3emCUAjnSvrBpUUxQnUkf5Fqiz88617rij0COPbHb33Rs1NlbIdPNNgUjblXjrM/3DH44wcGCBa69NqebT1Vp5rr12qGuvHWqPPaabOrVcR3LwwUX23beHX/xivrZ2iPfe29fpp0+1alVw7LnnDldQkHDbbakI+eDa7r13X3feOdusWSvTjv4mje07JHj9qztmSO+vMrZHBcb2/bFWYGw/LZFItNfvBf0fxItJY3sID6EskUj8+D+8sP9YvjOqLXj11RV2261HGyCJ2XffQW64YWf5+VmqqxvtuWdfr766yqZN9Y488mXPP3+oyy9/33vvtQXr9OSOQcBiQ0PcBx+s9rOfvemjj9L7t2YPvuqqD9177/5GjSrw+eflWn3iA0C5666lZs7s4plnRrr22o2efz61f+vqrpKqwBi/w/b84VLWVXHNM6wt0erdVU9JNZdW0LeYy7fnFwO4Y2OayqugVl1WXF5hrcZotkia2usTcZ9Zq1DCwbr4vpF6y/KG9T60Pulg3DqJxkSsE7PRKq9bJU/UOL3sb6gjRREy10ZzbbS2TbH7WBtAgQ3qbVBvqg0tE2OWkJ6K9Jevv3w76K1AxED5QqgQs16jTZqUiSvTZKOQUjEVqNasQoMmaSqycAAm0Wi8xWW4I28syNcgX1gXdJWlh7CuorpJ6CNHf1l6yhbGcjVqxK1S4T0bPaJChaZ2vzTdNTqSVGKlu/BmiRijtxH6GaWfWVZ4yPs2t7CP9jE76e+H6ecQY1Vp9HszrdWgTr6YiDr5GmSrky+kwNnNXezWnO/UjSGLq7MDFVVSjVVYze3jg7XMyQ9Rtj5oj9YRq9AOUKqSeJCTU+e665rtvHO+005bbunSCu0DDVOvbSUqtejKzg675JIR9t77ba0Akpe8XyEPPzzW0Uf3UlvbLBzuOLN3UVHUzTePceihU1v+3dRzuvvuXaxeXWvVqtZYkV126e6661KmgNbry8uLJB15tNv3zcg3b2xPJBKxUCh0MV4TkNz7E4nE/FAodEFy/91fcvgknIHPQqHQ7GTbVYlE4uVv9CKT8h1iJOyww0BXXjnRaae9kdF33rzTDBhQpFOnQH98330LnHPOOy37e/XK8/zzh/rVr6Z7/fW21c8yb25Qp6Da1iUY6PvsM9jZZ49y5plTtLIR0hlKly5FHnhgtCVLGv3iFxvEYikX4XwtgYypQ4pksJTDDuaMQ1hbye9fZVNCK6CkMZRuRVw6huGdmVXPvbVsCSM3JpzT0I6ltE3ZMUDU4braT1er1XnXejNsFkuLGWn5b9rEM/RXaAc97aCHXvKtVWWhLT5XZply8SRj6cgG0pHLcNu+cRGdRPWSrWsyBXsn2bommV8/WQpFFQiLCluu0RA5goTAgT54UXOT4aHk/5xUEa/SaJAsIQlxCbXiSjRp1KxMTIV6pZqUabBOgzJ1mmUyleC2tWcwHbUH7DDLWL1N0l9fxeZZ6xPLLLWxQytUWwYCI/RxkjHKNPinZZapa2FkKfaRYiOHNHfy40Rnfy4Peag0IpECkCSI7JLNnXvy5w94+N2gTR0TenLnRRx4NhXrW/sHF9lkzJhat9+e49lny/31r+sEavWOVFnpi6vgXwmklblfdNFQnTvnuummhdJtJKEQ06ZNNG5ckZycsKqqmNNOm+fFF1OBgqSe27vuGm3atHL/+MeSjH3hcMhDD030+9/PN29eBZoceGBfp58+yJlnvp3sF4BFv345HnzwYAce+JT2arhvipGMTfCfztED/p8JSPxOAQl55sw5w447Piwej6b1Zf/9B7jmmokmTuwpkaCw8N6M8/XokeXPf95TQ0PcL34xtU3gUUcrha/SZ+Z5772TnH76m1atSs8TlKWtuuvyy4fYYYfOfv3rEitWRKRiUYJVWZHGxo4BJdyZk4/hZ6fw6lz+9AZbQjpUeeXn8/0RnDOIGTX8pYwvkjmkwgW1okmW0rZuRjqojJTjYF3srosN6n2gxIc2qd+KETldcjQYoMhI3YzQ1RBdrFSlUpMlKixRaZVqcYkvVX+1BZK2779MxdVRe0rNRXs28n8CCF/3uKiQ7XQzXk/b6wk+stI8G634kgwDbX93s4gd9TdJX2FR/7DMUvUt4JFiIanXvo15fqfYmsaIX5SElVbKAJCsRq4dyW49OfvhZLLF+mDfxQdw6v6ccRnL5gvMUC1Es84JJzS44oo8Z5yx3MKF6WqsL2Mh7QEE+vYtctddO/ne9+aorm6b/ijqF78Y4LrrhqitbVZYGHb22Z97+OFMr6r99ivyk58MceSRn8h8XptceeUIhYURv/pVYKAPhZp8+OFhTjvtdStWpH5UcK2XXDJGU1Pc3XfP9e0CyfNf3TFDhvw/AyTfKdUWTJ++3q679vHhh+ukVjuJBG++udqbb67Wp0+BadNOdMcde6R5bMVs2hRz+umvO/bYbb3++tH+/vf57r57XjIKtn39kvREjx1LnZkzN+jXL8eqVVUtba2Rua0rsltuWWTnnbt56qmR/vjHDR57LFi9RSJ5pkwp9MQTNf7612yxdIN8lOYGHn2Ix5/j9KP568msKuf2tygpkcFOagu5u5LJczl4G37Sn20LuK805OmyAg0RGqvzWwLx0lVfAZBkmyVuji2ylRgi14E6u0gPfeT6SJnptlikWlYasKQm01r5FopbaCOCpJmFoobpZKhOTtLTIAVWJOuhrFBrhRor1VinTkziK0Hk/5Y0ymlR7aV7uKVe0/enpLscY/Q2UhcjdLZFrSpNPrXRk5ap1pRmf8pu951twTRH2IEGOtwAc5W7U4lVmsRlicnNAI94c0S4Ot9PIgWGJ7LcsClk6kYBEKQYRT0jw9x3AE9/yoH/oHlLsL+omTsuoqqMfY+nsUwGCwmHq9xwQ9To0RH7779AVVW5rbOQdGlVY7Uurthmm26eeGKcSy5ZlASRzGkmNzds223zHHvsPPX1zX7zm8GqqhLSY0NGjizw618PdeqpmZ5cNNlxxy6OOKKvffd9o6XthBMG+/jjze1ABI47bqhTTnnJt1vC8P/I2P7/jHwHGAnpA+2AA7az22593XDDdJmGuVaJRsOee+5w//jHF/7978Xt9ufnR1111QQjRnQ1f36pJ59c4rPPSn09HWYwGAoLsz399PFOOOFllZWNyX3pND7dnTF4iIqKitx11xiNjVx88Rq1tTlyc7P9/Oe9HHlkoSuvrDNlSvLBCwm0nmkMJVLMSYfx41OZtZw/vc7SCu3YiWjwOrgrP9iOo/rwdiX3buHzEHITLQF6KdVXR0yFYLXdS9zuOtlDJ8Plm6/SfDVmK7M6rTxhW9VXR5ItbIB8gxQYpMAARQpEdJOjUpO16q3WYKNGmzTaIGajRrWaO2Qu3wQjCe7Y19vXTZb+cg2TZxv5tpUnS0iNuM9V+EKZL1SoTpuotsbiOmJmgxU4zEDd5FioxhM22aI1RX86eNRW52uORZyUKHB5YcSdG7h3Dc3VApaRfA2Vc9FIDhrCNU8xd6EWVda2hTx6PTffxdOPCRhIy6XXiUQq3XZbnrKyOr/5zQqJRLotJLVwSi2aaB3v7QGEPKNHF3v44THOPnuh2bPTVcjB81JcHPHUU8M8/HCpBx5IFVzOnIC33TbLk0+Odeqpn/rii5qMPoWFCW+/va/vfe8DixcHoNGjR8Qdd+zi0kvfTfPWCn5k//457rhjP8ce+3yb7/mmGcnoBI/+h0ft8P8MI/nOAUk4nG/mzO/Ze+/HVVU12hqYFBZmuf32vcyZs9lf/tJ21RJIQUGWww4b5IQThho2rNjzzy/32WcbLF9eYcWKSmVlyYjlaFjfvgUGDCiy8869nXbaCL165fvZz970+OMLkmdrH5GbnZ3td7/bw+WXfyz9oTr11G1cdtkQl166xIwZMeQZNCjfH//YR3l5yJ/+1GjRogJfZkM5dF9+eAyhKH99iynLkvvTASU32CJ5HLoNpwxgWCFPlvNoBWtytBRwSmcqHSUqTE2mUXHD5dpdromKDJDtU1WWqfO5SovViCW1/lubQNtKakLtJKqfXH1k6yVPT9l6JLdycX1ki0sol1AuZqkm2UJqNatCrWabhYSFNAqLSWhoDputUWXqmr4ETHpiqJw0g3tUtrhRCnQXlUCZJgtV2aDRUrWWq1W9FQBtC6xbsxXlCTtQd0fopULcUzZ5W73GJHCkQCQFHvFYRGN1vr2iEWflhZTXc/0qyiu0qKlSINI3xH2HM38VVz/KfWdx9WRWLuOAHfjDxfzgEmbPTB6TIKWuCoXq3HdfJ4sW1bn55sW+2iNr6wBClkMO6eY3v9nWmWd+YeHC9vNKv37Znn56mBtuWOvFF8vT9rRO8AMG5HjuuTHOOmu+uXO3ZPQpLIx6/vld3HrrIi++GNhCs7PjXnvtIDfeOMdbb6Xy77Ve8z337OOll5Z57rl5aedK/03/CyTfhPy3AUnPnsMSmzadnvyUSX0vvXR3kUjIn/+cXg2xvWRnh9100+5GjOjihz98y4YNWy/4lJcXtccefUyc2MvgwYUGD+6ke/c8CxaUOuqobeXmRjU3J0QiIYkEJ574gmefTY+yT6m00iWwy3zxRYXJk5e3tBE1ZEhn998/0TvvVLjxxtXi8SC9yp57dvLb3/Y2b16z669vUFJSqB2gpJhKLuN35KLT2GEY973DP2dQk6UdmKQ+F+dxwjBOHRiUnZ1Rz7NVrIskz5800qeKOX1VAsMQhovaSYEd5BspR51mH6qwQaMlqpNG4a8/jjry+EpNwhEUytZFRJGwAtnyheXIViCkGZ1FRYSEEyFZQu5tqrdKvOV3pH5LcL7Wtn1E7K/QFk3KxJSKK0kyo9Lk5NIR64p+TcBM/215wibp7hDFesnyukpPqbY6LUYklVdsQGOexTVRDQ05xCK2T4Tc1J3aJq75gsWp/IgpVVYSTE7Yhqv356cPM2UW2+Tx6V3U1fPQU0wcxcnfZ9MKLWosSUimyoUXFujXr9nVVy/RcXBha1Bwx/ZBgkzahW69dVsNDWGXXbbCxo0xbZ/ZXXbJ98c/9nPlletMm5ZiGZnu+TvtVOTnP+/v1ltXmjEjvbJinU6dol54YVd33LHYk0+uQcygQQWmTz/IU0+tcvHFU9KuOZCxYzu75Za92xjZM/t8c0AyKsHD/+FRO/0vkPxXZdSoHRILFhyX/JQJJIWFnbz//skmTHgkLZK1YzCBAw8c4I9/3MO110733HPLt9ovU1oH8W679fHCC8fo1i1PXV2TO++c62c/e3crx6UPyDx5eVHPPnuMQw55TiKR+dCFw1muvHK0ffbp5aKLFlqypKll37HH9nT11T08+midBx5IKCtL5vGifRxKLl178cMT2HNHSip5YCrTVmtlKVGt4JL83Kczxw3hmH7kRXm+nGerWRTSWuwpja10VGaW9qv7Ygmj5Bgj3yg5tk2CwBy1NolZpt4yDdar0tTGNrI1+bpeXmiXfr6tdAQo6b8j/be0HLMVsPgq1pV+3X1E7aaTUXLsJN8b6jyr3kJN7ZJRxmMRsaaIkY35ZvQL+WkJb23k/B6My+WqL/h4jVbwSGVEqKdTjDuOZsMmfvsIFZtRw/f2495fkZtDQwPXX89NN9EWQKjTs2edl17a3m67vSUWa9RxdHp79W26B2NRUbbLL9/GoYcWu+KKdd59N12VFTwLBQVhN97Yy5gxuc45Z4WVK9uql4PPF1zQxfe/38v3vrcgrd5IsK9r15jnntvNLbd84dln19p99y5uvHEHEyd2lZUVlpubHgweazn21VeP88tffuDTT9NjR9L78M0BycgED/6HR+36v0DyX5URI8Ym1q8/TWVl6oHOBJOf/nQPNTVNJk9OL725dTDp3j3X5ZePd8ABA9x//wL/+McXamq+yibSuv/++w/2ve+NsGlTnWHD7ldX91V+5q0P3d13H+m3v51u9eq2vyV46HbYobu//31nDzywzl13rZdSEUSjWb73ve4uu6yX115rcMst9TZvLpKRZbiNHSWUzV578IPjGT2Ef37AU3NYUylT9ZW0o6Reu+Zz5Lbs3YPxxUyt5tUK3q6jJluHwMLWa3eQOckWajRYlqFybCvbtrL1lqWTkHJxazRZrdEKjUo02yBmg5iGNkymLehkGOU7qF+yNekoSPH/Y++846Qo8jb+rZ48u7M5s4Gcc0ZBEEQU86kn6umZ9dTD4/SMZ05nzid3cuac9RQxK6CIJCVIjsuygc1pYne9f1TPzmyCRZZDfefZT322U1VX98zUU79YYUSTS/RzWIBcrHTFRlesFGDjPqrw7kHaSsHGn0lgDA4qpORL6ePjUJCN6M2yFzdlLg6nwQ9BZgiW9YUudhXDsawGbl8JXxQTsYGEJRCTSMZnw6Onw71vwqsfEwko9MHz98BZp4JhQGUlzJunc9ZZNUQTSJhUTjkllV69LNx996qop4n+vbStvgIrhxySwHnn5TJ8eDyPP76b556raOZlGcaRR8Zzzz3ZPP54Bf/5TziGo/nvKj5eZ/bsbtTXB7jiik34/WF7pOrLuHEuHn54MDfcsJLPPivlnXcOYfLkLBISVL8+/7yYI454t1XbM2Z0Z8yYbGbN+or2pRHoPCLpI+Ff+1jr8BiR7C+ys/vIAQNu4PPPwxJES88OK199dTYXXfQpK1eWt6jdPqGkpDg5//z+nHlmHz78cCufflrI4sWleyAG9YWNi7OxefP5/OUvX/Lqq+s7/Bzdujl44YUTmTjx9RZ5gCKukODC6XRy553D6NcvgQsuWMGuXcK8xoPFYuP009OYNSuDt9/28txzIXbuDDNIlNrLRURKsUBCNhxzOJx3Atjt8PpiePMHKNVpTiTO5ttWFxySCUflQf9ESLLD/Dr4qh4W+cDrIEIs0Ca5QPuDdcuBOgmNfCzkYyUVCz2xkoWVLCw4EWwgSDYWKjGoRKdcSoqkjgHUI6nW1f86aeAN2PFL8IY0/IAv2Ny1VAAum4FDgAOBy6rjQJAgBElC/U81n6O3sJIlNNLRsCLYJENYhWCrDLFDBNmkSxbJQJsuGmGS8AAjsLMwpOOnjVT3JmkAihCCgA6uRlhzCHR1Kff22gAc9Qks2kJEAokiE5uEv06BIwbAefdA4WaaCIQ6wA++3crD8ZlndG6/vZbi4gZaEkhYfXXIIQlcemkPzj//O/x+lalswIBE3njjML7+uoz33y9l4cJG6uoEbredUaMSGDo0mbPOSmfVKi9PP13NggVhFVXz3+PkyW6uuSaVNWv83HdfJcXF0cZ6CL+QM85IYNasLO67r5jXXy9qds5iCXHDDT04/PAkzjnnO7ZvbwRCHHpoGp99dgR2u4ZhSK666lseeWQV0QRx5JE53HTTWI466m0zZ140YkRyIHDQiCQpqbu85prnuf76z6OONieT3r1zePnl6Uya9Dr19S1/zu2TCYDFIpg0KZfjj+/G2LFZZkT7LhYs2EVhYT3bt9eai+BE2nW5rHuVRGw2jd69kxk4MI3hwzM49NAcZs6cx/LlLTOLtpWkzsWkSVlcfHFf3nqriDffDGdEVaSjaS6OOy6Vq67KprxcMH9+gO+/N4iPj8PjsRAfb2XuXCirpJUtJSsXTjkKTjkCCstVZtf/robNlbRWf7WQWFLcMCEfJqXDISmw2guVOixuhO98sNMS9QgtyAVoIhiIkAw0J5q9wYMgBY0UIUhFwxm0kyY04gUkCEGc1DAkpGoCu9RwCLAbGpboPHWGoEJKEjTwSfBLCGg6RbrEJqBGQrUhqdcMdhsGlVJSYkhKpdxrnHMo2FyN1u66KGECgCbSaDoWAqcBMzLgseHg0KAuAEEDnBaYtwlmvERzVZYXeiXBM3+BNz6FR18AWUtE5WV6YV14IVx9tc7w4TXU1UVLINExIOFOKVx8cQ8mTkynujrI2rUNlJR4mTNnJAkJVrxeHU1T9sL1633Mn1/Pl1828tln9dTVGbT8/QkBJ5zg4aqrUti0KcA991Swdm0g6orIfUeNsnP//V344YcGbrllJ1VVYVVWRC313/+OYPHicu6+ez26rtoZPTqRO+8cwn/+s4Frrx1Er14eDjnkHX78sbSp7bFj03jsscM56qi3qahoGc/T1qfcWUTSW8I/97HW1BiR7C8sllw5f/63jB//TNTR1uLxmWcOZOrUnpx77sftrCewZ0IJIzHRzqGH5tC3bzJDh6ZRUODB47ETCuls2lSN3W6hsTFIY2OIhoYgQiipyOVSpb4+yNCh6ei6ZP36SlavrmDhwiK+/TY6zUp7OuboflpJTEzkySfH4vVKrrjiB+rrRdT1ilSOPz6Pl17qisul4ferBxdCMGqUnzVrzLWtW6q+TKLIzoVjJsPxEyEnDeb+CF+sg293QCCaTJw0JxdzP9EFo9NhbBaMSYE8FyypV6s9/uCFHxpguzSvh8jH1mJxKaCJbNpCNAG1HKij0TRoN13cYvCGtvIFtrjZz/ieR7ff8h7RZAERwgifC5nFTJMzIhGOz4ET8+GNDfD0SthVqq6J1yDJAK8XKkqJGNa9cMEUuPh4uOhGWLGCJukj4oEF/fr5uOcewe9/X4LPV0PEXbel3aP19zMhwUr//vH065dIZqad66/visdjRdclZWUhjj56Kz/+6KNljqwwhg2zc/rpcUyZ4uSLL3w89lgtO3bobdwryJgxbi6+OIn0dBtXX13E2rV1NDfsR15ocnKIqio10UtJsXP33QMpKIjj0ksXsWVLHTabn5kzB/HQQ8ubVkwcOTKFf/97Kscf/y47d+5ucf8Q11xzKNu31/Dqq9EeXJ1FJL0kPLqPtabHiGS/byxy5MKF3zF79jI++GAD1dXhX2brUeCKK0Zx1FG9Ofvsj/ayVGbHSCUaFkuIlBQnbreNuDgbbrciDiEEXm8Iny+E16vIpayssVUa67bRVh9bkoqVP/yhN7NmDeWSS75nyZLwypARg2ZGhosffxxFVpYKcNu9O8RRR1WwfDlR10UZ6MOkEvXflQ6HjYFjJ8IhA6GsFj5bDZ+uh9UlYMTRWlIJ/4/a7poIQ1NhaDoMSQSPDY5YFnUtRPKlhz+GMNFA64+1IwN7y4Ec9jyYt4V9/0q0bi9aUxdNFtH7UaQR3u/thjMK4LgCWFcJL6+Aj9dDKGDWD5dw3Qbz3g1KSrzlHLAKuPI68EZJH5FOKgP6K694ePTR3SxaVAHNcqK19WLaeiERFezcuX2ZNMnDihVepk/fSU2NQfR31263MmaMnVGj7Jx5ppsNG0K88koj8+b5CARazvi9WCxw0kkeZs5MZteuIA89VMLixY20nTU44qUVToNyzjn5zJzZhzvv/JE33tgWdT5ST9MEV145mBNP7Mnpp3/Ijh1lrZ5wypQ8rrtuPNOmvdjiN9xZRNJTwr37WOvkGJHsLyyWXLl69Q/06JHMjBlv8s474RXh2ppOwrRp3bnnniOYOfNL5s/f2cG77G0U6fQVzaLQNuF16eIhNTXJtPvY6NYtgXvvHc/nnxcxe/YWWub0Gjw4g88/H43drnHffcUMHBgPCFauDPDGGwE2brTRilTC1cM2lfB/G+R2hyljYUBvmDoCSmtg4Wb4ZhMs3g6NYcN7S2klmliI2rdE3TZ8DFpLKwAthY6ODvJtfUx7Gtyd/Hz4orZDbWyHP9YowgifSzDg8Cw4MhcOyYYF2+GbbfDfNdAYJoH6qDrRtpCocsRIuP9auPEO+O8b5r2aue+GB2Gltnr77Tx+97twLFV04GAYbf+m2lK//v73KRx6qJtZs2oxDOjSxULfvg4OPdTBxIkOEhM1Fi8OMG+ejy++8NPQED1+RF7Y8OF2TjvNQX6+laKiAI89Vm6u/RPuY/sk4nQanHNOVy66qDuvvrqdf/5zDfX1Idoymufne3j66cksWrSL2277jmCwgZbIz4/jvfdmMG3ai5SVtTzfWUTSQ8Jd+1hrRoxI9heDBw+Ty5cvw2rVuPvuhXu0lYTRpYuHF144iU8/3c6zz64xjYltIyHBzuDB6axZU0FVla/d6/aEZ5+dxrx523jttfX7sUxn5Mvfo0cyM2eOYsWKEp59diXhH7LDYeHJJ6dSWenjmmu+N2NOICy5nHJKLv/4xxB69VqAlDYSEhwcd1w2v/99GpmZdt56q465c0OsWRMt0UR53ITdicMDf5TUkpMFhw6HQ0dA33xIS4Q1RbCsUJUfy6A+bLyPJpSW+6j9cdng12G7HyrCY0S0MGZtZ3tf0HLiG70fnRZtb2iL69siqOhtU4IQQG8PjEqFkdmQ5oTuSfDFJvjkJ1i0CYJ+mqu5TGmjyXZS1/y/3Q93XQ/9usN550FpCfTvH8Ri0Vm1qobmRBKxeyxdOpyJE5fR0BDufPSLjbyI/v1d9OnjpLFRw+s1aGw00HWN1FQraWkO0tKcpKVZyMrSGDTIgcslKCrSWbEiwMqVIebP91NWZrTzMtULuuWWJI4/3sm6dX5efbWKjz+uM9ftgeYSR7hO5Fh6uoNLLsnnlFPyeOWVbcyevZHq6oY26qiA5MsuG8D48V34xz++N1Mqtf5A8/Lc3HnnFJ544nsWLy5qdT5GJJ2Dg0Ykw4aNkCtWLANg/vztTJz4bIsr2h5lLBbBaaf157LLRrNjRy2PPfZDCzuFQl6eh6uvHsWAAamkpDgpLW1gwYIi/H6dkpIGSkoaKSlpoKyskfr6YJuuwsXFF+NyWams9HHttQt4440NHSIUq1UjNdVJWpqL3FwP06Z1YeLEAgoLa/ngg43MmfNDG7VcXH21Suty/vlfmvcJixUghC0qTiWi/kpMdHLkkZmcckoaffs6+e47H/PmBfnssyB1dWEyifL+im62JbHYwBoPA/rBiIHQMxfGDwKPG4pqYHURLC+EbXWwoQKqw68silAuGAmjs1X6ljS38iIqaoCyIOxqVNs7fVDuh90h2O2LpC9XH/BeXm5bYR1tDfj7gpbzjCh1VaoTch3QOwV6J0GvFOiRBDYB26phyTZYug2Wb4OmFFXh8dEfta3TXPIInzftISMHwIP3wiuvwOzZQaQMERdXx1dfJXH22YWsXRsmktY5r447Lo3rr+/OueeuY926lp6DERxySByHH56I263hcllwuTQqK3U0zUZ5uU55OZSXGxQWhti4UcfrbY/pW/5WIv2ZPFnw3XdeGhsDLc61LYXExQU44YQczjijC/HxVp59djMvv7yNQCBsW2suhSQm2pk5cwgnn9yDOXNWMWfO6qYls1v26cQT+3LTTYdx2WVzWbSoPS1GZxFJdwm372OtP8SIZH/hdhfITz5ZwPDh2QgBbndbbL7nKevo0TlcfvkYnnjiRxYvLt7jtenpLnr2TCI/P4GsLDdZWXFkZcXh9+v07ZuC221FCGXQLi1tJCPDxeDB6djtamQzDEkwqLN2bSV1dQHKy71kZcVht1sIr3C5cWM1vXolEQwalJd7qajwsXLlblauLGL+/B34/XuPkL7zzkksWVIeFVXfXpru8LFIlLHFYmfMmGSOPjqTRYu8zJ3bSGsJpQ01WEv7Shsk0yUHBvSBgmwY0Qd65UKyB7xBuO4NmL+Z1movp/LmSU+AnGTI8UBOIiQ5oHsapLsg3Q0eO2yoVt5JDUGoCkC1HzbVgNMK9cFIqfaDIZXU4w+CP6S2DUAPqnMhQ93XYhbNLE4ruAzzv1UdS3cr9+ckJyQ7oT4Ao3PAYVEkWNkIKwqhygsbS2FDMWwqA29YVRVNGtC25BHl9tskhZiqLYcdbrkFxo41uOiiABs3RiSPq65Kory8kWefLaT1QNn8ezFoUAp/+UsWXbu6+OijGtau9bFjh8GOHQHTztGyXvi7FP3dag9tJT0No6XOL9jieGsCycgQTJ2axogRbiZOTOO994p4+eUtbNpUR2upRdUbPTqTU0/tycSJOcyZs4pnn/2JQKAtoz44HJIHHphGVlYcF1zw3yj7a1voLCLpJuHmfax1boxI9vvGZq6tCRPy+eCDMzjiiOdZsqS1ZKHQUR1IR/QZHUdR0UVkZrqpqvLxxReF3HzzIrZtq20iDl1X5LLnV9jxjKAOh4WbbhrP1q01UVJL255fkXMtSSX6eESiaR2d3JJYWqjCWhJK9HY4RYsF3Kb7cWPLnH6OFl1rz3bS4nicOagnxUO8uR1vVyXOrrqW4lYDvdMKDiuEdEVGmkkchgSrpv7rIdANRQROqyI+bxB8ISivh1AIqr2qVNVBVSOU1oK/pUdqtBbG32K/JVm0RxxhY7z5fZk6NcStt0pefdXL449XYhjROa68fP75YI4+egmBQEt1UvMYpcgxyMx0csghboYNc5GfbyMvz84LL9Ty7LM1UfXb0zV2BG3pEdsij8j5xEQYMcLF1KlpHH54Mo2NOp98UszcubtYubKizToAAwcmMGNGb6ZP78qqVbt55ZV1fPLJdtNLq/XvymYzOPvsIZxySj/ef38DTz65tAPP01lEUiDh2n2sdelvhkh+rpa607BgwQ6GDp3Nm2/+nkmTnjUTNbZEiI51dW/eUvuG+vogX321nmuvXUhhYd3eK7QDi0Xgctlwu5VXmNttIz3dTV5eAvn5ieTlJWCzaQwfnsVrr/3ESy9Fuyc2d9+0WuMIhVr+cFsOKt6oY83zIkXIxdbGNSaZSBuEXFAXTU60STCNrqhmWpBMM++taI+u8PWOqGPmYzSgSlFbH3d7RvR2Jpxdk+CcCXDru7RN9tFjYvSE29/ifJgUwvdqY9/jMlVbYbJogzjCto0pU4L8/e8Otm0LMmNGsblMQbS7rvrMk5KEuR3tOdU+iYCV0tIQ77xTyzvv1NJc+oi+nqbrO4a2yCP8PNHng8THBxgwII7Bg50cckgegwbF0dCgs9bdIlMAAQAASURBVHBhGd9+W8add/5Afb2vjTa8FBR4mDy5L5MnZzN4cBqffrqDRYuKuOOORab6quX9FZxOOP/8YVx44XDeeWcdZ5zxdiu7qMNhaXLxnzlzDIccksepp77RweePYW846EQCsHVrNbfd9jUvvvg7rr/+c9asaekDDuEvq9vt4uuvz2L16jIWL97F4sW7WLWqjFCoPSNgxyWCCFwkJjooK2vk2WfXtEEi7bf58MNTGT8+r9mx3bsbcTqtZpxKkNpaP7W1AXbsqGHDhgo++2wr27fXUFoa7TzQWtmvaYLPP/8dxx33WlRqe+jb18O//jWVzz4r5OOPd7B0aaU5a3MRGZiiSQPCZHLGGTnMmpXLtm1BNm0KsHFjgPXr69i1C4qKrAQCUZKLpG2CgdYkQ2T/7acgPwe8fqiohd2VauZfXQfVDVBTDxX10GDaS+q9UFejgvW8/igS6KCXV58suO9CGNsf3A64fbaSSprQUsPY1rhmXif8EB8H6R7ISIGMZMhMUckxB/WDHl3B5YDaGjjySGhS7Tc1phglLS3E5MkW/vSnOLZtC3HhhYVs2lRP64y7kc/9P//ZyDPPDOPKK3+gpCQ6aWh4YhU+FjRfTsuJxd68EtubZLX1/Y6QhtUKeXl2une30a2bg7Q0OPTQRHJz7dTVhVizpo7Fi6t59NF1rFpVTigkacna6elORozwMGJEGiNGpBMfb8Xn0/nii53MmfMj8+cXRZF/6/6kpLiYNq2AY47phdWqsXx5CRMmPNPmRHT27GM49tjeeDwOyssbSU93tzNh3R+0fMb/X+iQaksIcRTwCGpomCOl/EeL88I8Px2V2OccKeXyVg01qxOdRl5hyJBMHn98OgsW7OD2279uN8rcatUYPDiTMWO6MGZMHjabRt++aVRWeiksrGXnzjo2barE79epqfFTU+OjpsZPba0fn0/H51PxIe2TD+TnJ/DDDxcipWTnzlouv/xjFiwo3Ou76jyoZ3c6rWRmxpGZGU9ubgLnnDOEO+74lu+/b64GTElxccQRvZk2rSujRmXx+ec7KSvz8uOP5fz4YxVFRQ20HW2vbCsFBW569UqhZ08Pqak2xo5NIj/fhd8vCQZh/Xo/Pp+krExSWiooLdWpqBBUVmpUVhpUVlqor49mkdajvssNqRmQmgLJWZCUCEkp6r/NDtmZ4ElUA7cnDsqqYGBvZe8QAAJ+2qy8mgACQfAHYMtOyEoDqwXGDIF4N1jNbvj88Ppcda3NtItoGlg0qKqF7HRlp3DYwOGAbTsj7SMVia1YCQnxUFYCZWVQVgrbt8GOHbB1qwokjCYNISAnR2fgQJg61c748XYaG3U+/7yWl14qY9u2ciISSLheyzxXCscck8dZZ3XF5bKxYEE5a9f62bEjRGGhj+rqZm+3xdtuj3Xbnju6XIKkJAupqZCSYiU11YrHo5GXZyE72052to3sbAvbt/vo0cPFjh0+tmxpZMuWBtavr2fDhkqKisLPE0ZLVReESeHppydSVuZl2bIyli4tY+vWiqYrCgsvxGYTLF1awiuvrOaTT7aSkOBgyJAMM5tEPhaLYN68zcydu5EffmiZVSKCtDQ3339/AXl5CQghsFhUSp3i4jpych6k81RbeRJm7WOtK/d67/0Ze/dWtzOxVyIRQliADcBUYCewBDhdSvlT1DXTgT+jHmYM8IiUcsye221NJKBm3RdfPIJjj+1NWVkDb7+9lk8/3RIl2raPlBQXubkJ5OUlk5jooEsXD4mJThITHSQmOvD5QuTlJTRFrFutGg0NQeLi1I9OSqir8+PxOLDbLfTrl4rNZjHPSfz+EKtW7SYUMli3roLu3ZMwDIlhSHRdsnlzNQUFCU3HDENSUtJARoa7WT99vhBxcXY0TaBpAr8/RGKiA6fTitOpgiG3b6+ioCAJny9EaWk9JSUNrF5dypIlu/ZgS4LwQNGrVzLDhmUxZEgmQ4Zkk5MTz44dtVRXB9m6tZZt22rZurWBHTvq2bWrgUDAgdtt5dRTuzNrVj/y8tzs2NHIsGFfYrMJsrKSyMiwk5HhJiPDTkqKg6wsOykpTlJSrKSk2KiqMsjPtwECXYfqalizJoDHY6W2FmprJbW1GqWlBqGQnfp6SV2dhbo6SV2dDa8XGhpUCbX1cUfFJwoBdpca/O12RRxWK4waCbOugIEDIDlZJS88bLKqKgEjCLquEhvqOgSDKlOu3weBgNpu+yehBkObLURSEqSnB8jM1MnKspCVJXE4DEaNslJQoNYkLyoKsGBBDStXVrNgQTmNjdFeSNHk0dILq237V9euiYwalczgwank5TnJy3NTURGge3e3+b4l9fU6P/3UQF6eEyk1pEmEu3YF6NbNgdOp4XBoOByC3buDTYGuAA0NOlVVOlVVfiorg1RUhCgpaaSqSqe42E9xcQMlJX58vuiVADtGGs3P08Z16lohIDc3gQ8/PI1Bg9TyxbpuoGmCNWvKeP31n1i2rJhFiwr3yaV/xIhsBg3K4Pjj+zB+fD6JiQ7q6gKkpd1H5xFJFwmX7mOtv+/x3vsz9nakbmeiI0QyDrhFSjnN3L8OQEp5d9Q1/wK+klK+Yu6vByZJKdt1pWqPSCLnYfToLvzud/3o0sVDbm4CS5cWs2RJEUuW7GLLlpZ5dDoXPXum8N135+NwWKmt9fPAA4t48sklBAI6FouGxSKaiEDT1Ewnel/TBEIoYlTPExkFpZTNyMYwJIGAkpQCgb0Z7/cVkRmox2Ona9ckunXLoFu3BLKy4ujdO5mcHA95efFkZ8dhGBKrVc3afvqpkttvX0VRUSNVVX7Ky3UqKgIEgwbNZ7bR6jIFi8VOYqKVhAQLSUlOEhIsJCa6SUiwYLUKMjOdxMdreDwWPB4rPp9BQYEdt1sjLs6C1QoNDQZxcRpCqP5UVxs4HAK/X+L3q/+FhSGSkjRCIQgGJaEQFBfr9O5tZfJkBxkZFh54oI6KCoPUVA0hhOmdZ1Bfb5CaasFmA6tVYLMpuklKUjN0l0sjGNRJSor4JAeDkrVrfVitkpKSAKWlXkpKAhQWNlBY6GfHjjozcjp6sG3LCN2WV1PbEmPbDhfNP1uLRRAXZyUuzoLFYm9yCBFCkUkgYOD3h4uO0aYw3pYqbO8EsC/XO51WCgrs5OZ66NMnlR49kunRI5ncXA/r1lXg8Sj74bhxeYRCOqWlDRx77Ct7lDr2BampLq64YgwzZgykb98nMIybf8lE8rPHXqDr3up2JjpiI+kCROt0dqKYb2/XdAH27JO7B0gJixcXNQURJSU5GTkyh1GjcpgxYyChkEF2djyrVpXxpz99+HNv0y4aG1VsyU03fcWcOctNV0MFXe/4QkcHH5Efdl1diFWrGlm1ahdtffSHHZbHiy+eSHq6G6fTSlFRLbm5gq5d4+nTJ5/0dBdpaS6sVgubNinpq7LSR0WFj2XLynjkkTVNbem6kgYqK9vTw+/J4NH211IIZTRVs2rNNKAK7HYHNpvWRAZCwNdfazz9tCAlxU5trQ9dl1itommWLqUkGDQwDAgGDZOE1EDr9eo0NobwenV8vkBTLqfmaGvQ7egMvL3B19vGeRetr2/97nQdU+pro1udgr3ZW9q2G06e3JXzzhtC9+5Jpp0wwPbtNaxZU0ZpaQP//e86Nm+upKioruk9T5vWg3feOY2lS3dx/PGv7sV9d99QUeHlppu+4qabvuq0NhUOiI1kf8bejtTtNHSESNpIeNRqgYaOXIMQ4iLgInPXD7esbnlNe6iuhs8+U+V/gV27oKDgys5oKg0o74yGDjTmz4f8/J9d/YA/p5Tg86lyEPGr+Tz3E53ynF98ocq+4OOPwe0+a39v3VH06Zxmij+GW9L2sZJTCBHto/xvKWX0Kl37M/Z2aEzuLHSESHYC0W5IuUBLJX1HrsF8Sf8GEEIs/a34UO8Jsef8bSH2nL8ttBjIfzaklEd1RjstsD9jr70DdTsN2t4vYQnQSwjRTQhhB2YA77e45n3gbKEwFqjZk30khhhiiCGGvWJ/xt6O1O007FUikVKGhBCXAx+j3MiellKuEUJcYp6fDcxFeQ1sQrmgnXugOhxDDDHE8P8B+zP2tlf3QPX1IKZIERe10Af+JhF7zt8WYs/528L/l+c80DhoRBJDDDHEEMNvAx2xkcQQQwwxxBBDuzjgRCKEOEoIsV4IsUkI0So9pmkketQ8v1IIMfxA9+lAoAPPeab5fCuFEN8KIYYcjH7uL/b2nFHXjRJC6EKIU/6X/essdOQ5hRCThBA/CCHWCCG+/l/3sTPQge9tohDiv0KIH83n/NXZP4UQTwshyoQQbYYb/FbGoIMKKeUBKygjz2agO8od7Uegf4trpgMfofyexwKLD2SfDuJzHgIkm9tH/1afM+q6L1CGwFMOdr8P0OeZBPwE5Jv7GQe73wfoOa8H7jG304FKwH6w+76Pz3kYMBxY3c75X/0YdLDLgZZIRgObpJRbpJQB4FXghBbXnAA8LxW+A5KEENkHuF+djb0+p5TyWyllOK/Ldyi/7l8bOvJ5gsr98xZQ9r/sXCeiI895BvC2lHIHgJTy1/isHXlOCXjM5IDxKCL5VaW5lVLOR/W7PfwWxqCDigNNJO2F7+/rNb907OsznI+aAf3asNfnFEJ0AU4CZv8P+9XZ6Mjn2RtIFkJ8JYRYJoQ4+3/Wu85DR57zcaAfKphtFXCFlLL9tNm/TvwWxqCDigO9HkmnpVf5haPDzyCEOBxFJOMPaI8ODDrynA8D10gp9ehElb8ydOQ5rcAIYAoqIdYiIcR3UsoNB7pznYiOPOc04AdgMtAD+FQIsUBKecCyeh0E/BbGoIOKA00knZZe5ReODj2DEGIwMAc4WkpZ0fL8rwAdec6RwKsmiaQB04UQISnlu/+THnYOOvq9LZdSNgANQoj5wBBU6u5fCzrynOcC/5DKmLBJCLEV6At8/7/p4v8Ev4Ux6KDiQKu2/r+kV9nrcwoh8oG3gbN+ZbPWaOz1OaWU3aSUXaWUXYE3gUt/ZSQCHfvevgdMEEJYhRBuVGbVtf/jfu4vOvKcO1BSF0KITFSSwy3/014eePwWxqCDigMqkcj/J+lVOvicNwGpwD/N2XpI/sqS4nXwOX/16MhzSinXCiHmAStRi6rPkVJ2OJv1LwEd/DxvB54VQqxCqYCukVL+qrIfCyFeQa3RkSaE2AncjJmL/7cyBh1sxCLbY4ghhhhi2C/EIttjiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIYYYoghhv1CjEhiiCGGGGLYL8SIJIZ9ghBikhBiZ9T+GiHEpIPXoxhiiOFgI0YkvwAIIcYLIb4VQtQIISqFEN8IIUaZ584RQiw82H1sD1LKAVLKr/7X9xVCuIUQVwshlpnvrFAI8ZIQYthe6p0jhNCFEPVCiFohxI9CiGMPYD9vFkJIIcQRUceEEOIeIUSFWe4VQoio812FEF8KIRqFEOui65rnzxBCbBdCNAgh3hVCpOzh/ntsK4YYOgMxIjnIEEIkAB8AjwEpQBfgVsC/D21YDkzvfpkQQmQBi4AewHlAFtAPeBt4UQhx7l6aWCSljAeSgH8Crwohkg5AP3sApwDFLU5dBJwIDAEGA8cCF0edfwVYAaQCNwBvCiHSzTYHAP8CzgIygUbzGdpDu23FEEOnQUoZKwexACOB6nbO9QN8gA7Uh68DngWeBOYCDcARQA7wFrAb2ArMjGpnNGrgrUYNao8D9qjzErgU2AjUAbejBulFQC3wevh6YBKwM6ruNuAIc/sW89rnzXbWACOjrt1bH5ea9ysFHtzDO/sCOK+dc8nAaqBHO+fPARZG7bvN5x9l7juA+4EdZj9mA66f+dl+BEyPfkfm8W+Bi6L2zwe+M7d7oyYRnqjzC4BLzO27gJejzvUAAtHXR53bY1uxEiudVWISycHHBkAXQjwnhDhaCJEcPiGlXAtcgjmDllImRdU7A7gT8KAGpv8CP6IkminAX4QQ08xrdWAWkAaMM89f2qIfRwEjgLHA1cC/gTOBPGAgcHoHn+d44FXUbP99FGkhhND20sdHgEeklAmowfH1thoXQkwEglLKp4UQeUKIL4QQu4QQTwghvpdSVgF3A3/aW0dNSe5cIAhsNw/fgxqAhwI9zb7eZF6fL4So3kM5I6rtU4GAlHJuG7ceYL6HMH40j4XPbZFS1u3hfFNdKeVmFJH0buc+e2orhhg6BTEiOciQUtYC41Gz4qeA3UKI94UQmXup+p6U8hsppQEMAtKllLdJKQNSyi1mWzPMeyyTUn4npQxJKbehVCMTW7R3j5SyVkq5BjWj/0RKuUVKWYOaWe/R9hCFhVLKuVJKHXgBpb4BGLWnPqIG855CiDQpZb2U8rt22p+KIipQksO3QD5KPTjSPP4D0HcPfRwrhKhGSXv3A3+QUpaZdooLgVlSykpzAL6LyHvcIaVM2kN5GUAIEW/W+0s7948HaqL2a4B48/4tz4XPe9qp2/L8nu6zp2tjiOFnI0YkvwBIKddKKc+RUuaiZv85wMN7qVYYtV0A5ETPjoHrUTp0hBC9hRAfCCFKhBC1qEEurUV7pVHb3jb24zv4OCVR242AUwhh3VsfUeqd3sA6IcSSPRjAM4Aic3sQSs0TklJ+BJSbx/OirmkL35nSXTJKappgHk9HqbqWRfVxnnl8X3Ar8IKUcms75+uBhKj9BKBeSinbOBc+X9dO3Zbn93SfPV0bQww/GzEi+YVBSrkOZQMZGD7U3qVR24XA1hazY4+Ucrp5/klgHdDLVB1dDwj+t9hjH6WUG6WUp6OI4h6UUTiujXbKgWxzexVwhhDCKoQ4CkgTQvREqfzm7K1DUsp6lIrvLNPbqxxFmgOi+pgolWE+rNqq30M502x6CjDTJO4SFLG9LoS4xjy/hoikhrm9JupcdyGEZw/nm+oKIbqj7Dob2njEvbUVQwydghiRHGQIIfoKIa4UQuSa+3koe0RYtVMK5Aoh7Hto5nugVghxjRDCJYSwCCEGCtOFGKXKqAXqhRB96YD94ABgj30UQvxBCJFuquqqzTp6G+18gfKEArgKOARFUqcBX6MI5Gop5ZKOdEpKWWHWucm891PAQ0KIDLNfXcJ2HFO1Fb+H8pLZ7BTURGCoWXahvLKeMM8/D/zVbDsHuBI1eUBKuQGlmrtZCOEUQpyE8ux6y6z7EnCcEGKCSbS3AW+3sIPQwbZiiKFTECOSg486YAywWAjRgCKQ1ajBBdTAuQYoEUKUt9WAaY84DjVobUXNrOcAieYlV6GM83WogfK1A/Ege0IH+ngUsEYIUY8yvM+QUvraaOczIFkIcaaUslBKOVlKmS2lPFdKeTjKO+rzfezew8B0IcRg4BpgE/CdqQb8DOizj89aIaUsCRcUIVaZEhAoG9V/URLVauBD81gYM1D2nirgH8ApUsrdZttrUA4YLwFlqElCk+OEEGK2EGJ2R9qKIYbOglBq2Rhi+PVACNEF+ARlYH8K2IJSd/0ZGCKlPPogdi+GGP7fYa8SiRDiaSFEmRBidTvnhRDiUSHEJiHESiHE8M7vZgwxRCClLEK5MfuAd4FKlFrLigrUiyGGGP6H2KtEIoQ4DOX98byUcmAb56ejZoLTUSqaR6SUYw5AX2OIIYYYYvgFYq8SiZRyPmrG1x5OQJGMNH3/k4QQ2Xu4PoYYYoghht8QOsPY3oXmMQ07zWMxxBBDDDH8P4C1E9poKx6hTX2ZEOIiVMI64hyOEakWlWvQ5nQS9EUcdKL3O7L9c+rE7vnbuuevpZ+xe/6y7rmjsbFcSrnfSSx7CiEb97FOMXwspTxqf+/9S0BnEMlOVMBVGLkov/lWkFL+G5XDiZEjR8qlS5cC4PXCi0WwtaCGdZYgTgQva6nt3rDRC1Yr2G2d0PsYOgwDA4EEDKTUERhgFikNhDBAqqLOSZC6+d+8VkpVMK/F3JdEHRORegCGEdWGMOtLdetwm9F1pIi0DVH3JFIXAM2sA2ABI2SeE+Y9zeubTZU0MMw6IlynxTbh+9N6X7NGrtOsoAcj9WVU2IyU0JRZXqMppEbYQIbrWEGabVkc0H1apP6WzyDY2PY9m7a1yHsBEOazt+yPZouqY4k8P6L5uxFa1HPaVN+EMPsZ9c6E+TkKEXlGIVTfkKodzRppU7OBRVPtCAtY7aDZQXOo57bGq3Pt4c3fQ/UWGHsE5M+AhKGRLguxvf2KHYcXuGwf6/y9dXaJXy06g0jeBy4XQryKMrbXSClbps3eIy68EIZfDKfmuykQGsktNG5Swkdfw/sLYfEacNrhqRthYM/m7RgGbNkF60tgcxnsrICAGzZUQGUjXHEYnN6OT5mUUOeDynqo9kF1AGp9EJBQrkNDEIISqu3g1VUhGap08BtwTRc4tGUyij0gZECDhEYJjYbEaw3hReJFYgg/fiHxY2DHTyNBAhi4CdFII0EMXEgC1BJCRyKxUoOOgYFBEiEaqUNHJxMXdRRjoJNCMl52IpG4cWOPym7uQcdAzdISSSVoais95GKYQdN2srGyFbAgsOEwfKhBTsMRSEEYFYCG0LshQjvMc/kQ3IUacRLBX28eF2C4wfCbg0kGBCvUcVs2BErVtiUF/NVmfU2RBKh9kQjBOlXflgb+CrVtTYVAlbpMc0IoYFYRgB2MoHmfJAjUqG17CvjNOtZ4CDREvhhCi9S3xEHIHKBtSeA3U1nZEyBQa97TBqEwqQCW8D0BuwcCZuygPRH8Zh2bOzLwN0G0PudIjLpndFtxUL0dlv9b7Sf3gLT+Zh0P+M3rHAmRe1pdEPJGbqdBE8na4iFohr04kszPALDHR9rSoskTNajr/sh9AuE6iZH6VifojZH7aFETAHu8+XlIcCaDb7c650yE0G6TNAVYG9X3xvBDkhuCOxSBZfcGy0YQqeA6DdznqXuc8rrqZ9VC5Oab2Nx7Mt3iLsXCnmJ89w0a4Oq01n592CuRCCFeQaUOTxNqZbybARuAlHI2KpX5dFQQVyMqm+o+Yf16eGokuNoRMa66C6rqYNa58M/r1UQqGivWwKPPw4pi6JELh46C5CSYOhhy0iE1HlLcYLdCWRnMXwpL10F5Lawsh5AOfXtBsR9S4qB3HgSs4HFAZiJYXJDphng7uOPBZVHFaQe3FRproH4XzF0NxRWw2w4VDapo2bC5HuqC0KcrrDN/d33SoNgJcRpk2w1syY24ABeCTIsXK+BEIxWJHY14rHhwYMWJFQ0HGnYysWLBgoYdsKBhwYIVzC0LGmDBisCC1pnxpwKIXgXl//Ov6JeE4Rcd7B4cXEgDZGULiRAl4aROQqROQmMei+S9jOWGg9PH3yD2SiRm/qM9nZfsu1TXDCeeCG+/DWee2fb5HTvgzquhd4+2z19zE/ztCnjmsD3f5+mnAzz5bxt/vFAwaRT07gp52WDbR7ls/fp6Xn+9lo8/zqG+Hrr2gtRhkJ0K2WnQIxtGFUBqHCQnQJITPHY1+WobFiIB3hDOsyelIrttNVBUCxtqoC4etvpgtx9OyIVzuu9b339tkEgMJDoGIQwMU+4KS1+Rv8gRkObV0vwLITHMfcXkBgYWJDohJBILGiECSAw0LOj4kUgEIAgizVYtCHSUCsyCgyBeQGLDThAlNViwouMzew+a+RTqOicBs46dOILUAhIrbrO+RKAh0ZsUcBbs6OY6Zw7cBFCSgp14Amb+RQuOpmsAhPl0ADbiCNJg1vfgN+vYcOM3pVCBhqkrNNtzNrVnx9N0TxseAuZzWrGjY0p3gMCOgY5AYMNFCD8gzPv7EQisOAkRQqAhEIAVzZzkCByAhoYFK04EFqzYsGHHigsnTuw4sGM36zZHBYV8K14lR/Qhj6FktPOd6s5RvLpxPNudnZduTmDOrv+fojNUW/uNSy+FKVMkAwfCkCGtP9wbZ8I1t8JRk+G8M8HW4hO76mK46s9wwYUw4zRIb8d01qsX+BoqidM8HDrUhsez71+kL7+s5bbb1jFzZgGzZkF8R3Pi7gX+APywUUlKq7fCikrwBSA9AQYMVpJSl0TonQxT4yDdCRmOttsKGrCzEXb5odgHtRK2hqAsoCZmazSoN8Ap4Ntu+9bPEJJ6JLXoeDGox6ARgwBBGjDwYmBBp4YAflMFV4EXPzpOBDV4CWAQRMeGD785sGfhoMIc7PJwU2Km28oijipqsKDhxo7Abw47GolYCRJCQyMFF14aEWgk4sZPIxoaDmxIguawpWEFhLntxIpBCIHAgQOdoDnYKWhmLYupVhOmXBce7DVsSDMjuwUr0hxENfMamu4lm7ZE0znVWnjwFlF1pHk3zDPR/yPthY+1tR1dr/m5MEmFj0dqNPePad6SaCJCabYV6Wlky2iqFaZPvWnbMFWwADqGSex6E+2rPZ0gXgxCgJ1GGggSwIKTCmrx4QMslDYRnI0LOB2HqaJKJY8pXMQu1vM9K6jgW45lGqk0LfHThDNz4zlnaavDPxuKEv//4qClSIk2tgNs3iw59VQ/d95p4+ijW68cW1MLDz4BP22AcWPhj6dCatRK1Q0N8Nzz8MJLMGIMjBkDxx6pVFzRqKoymDPH4KWXJEcdZWHaNI2JE1ury9rDs89W4vPBJZe0u0x2h1FbB+/Pg3fnQn0Q8gbAyH4wsi/07waudogClLSyZTesroJ15bC+AsrtsLMBrBqMzld2yRwXdPVAggMy7JBmg3Q7xAllvi0LwK4AlEgoC8HuEPhsBtsMSYUBmc4Aa4TS7/ewScocXjwI0tFwESQODTca6UisCFxoJCGwInFiwQ3Ym1RxGk40bOa2DYENS5uzy187ZNTQq4ZL0ewYRAbeiOQUHppl09AfXcItt94K1xHNzjV/q6LpjGi2rZlSF83qa2adMKEpS5hokt7CdTSTNlvW/1/ATwAdDXc7Q/gWtjGPL5nC4fSha9Px5exmFw28/mY+L5xqWSalHNlmA/uAbkLIm/exzrnQKff+JeAXQyQA5eWS008PMHWqxsyZVpxtiJ7VNfDiW/DcGzBhDBx+CEw9DJzOyDVr1qnB+YNPYUA/Zf84ejL07x1xEjEMycKF8NJLgkXfwekz4OSToXdb68xFobRU54ILynnuuTRSUn7eUumBADz+OHw4DyYfBScdA/2i+tYWQjp8vwm+3QCfboKSWuieBgP7Q/906JsKPVIUYURjtxc21MGGWtgdgBX1sKMRXDaocUGmHXLs0CUBUq2QYYUMhyTZAqkapGgSlxCIPXXuACGIpBEDPxKvKe0EkPjMY0EMfOYxgaQOnQAGTqCWAAEkNgR1BAliEEICOgEkIQwSsbAbPzqSbOzsoB4dSQZOilFGaCcaQSIG5RQs1KCM97nEUUQjEkkebopM9U8yDqpNVY6GwI6GbpJDBi7K8aEhyMFFKQ1oQCouKvGZKhLN7KsaoONwUk8IAaThpMJUR6k6qp9ubDQSbOqnhpWQKe0k4aDKnMWn46QMHxJIwk6FqbazIMx7KkJzY6fGPJeFk134kEgycFGEHwnEY6WySb4ABza8prSTjZsi854FuNhsbmfipMikTkeTskvDhSAZNzoQj0a2tGFFIw2NTGkhByvZLb6HdYS4mLWkYmMiSRxFBvEt7IANeHmM15jFGU2SyyZqeJctNNw6kFtusceIpBNw0IgkJ6ev3LlzLVoLw0EoZPDoozovvqhz661Wjj3W0uYgJiUsWwmvvw+ffA0Tx8KoIUr9lRblOVxUrM7P+1J5N6YkwHHT4PAJEfLx++HDD2HOf8DngyuvhOlHtz+wv/NOAy+/3MDrr6fv8wC7ZBnMugKmT4dZs8C1ByO1PwgfLYfPV8H8dTCmJ0wdAuP7QXZS6/extQYWl8CmOlhQDGVeGJ0Ddjv0ToA+iVDggXyXchI4EJBSUiMl1UiqpKROSqowqEZ5oVVpIeowqEPiFzq1wqABSRqw0xywe2FlCwGsQA9s1BDEhUY8GnZzAHKgkWDOgcPbErAjiDMVRXYEdgRWBDYEVlO1pf6LZsUS9d9iKpBiOHCQSIJIAkj85uSgHkmDqTptkJJiDCowCIY0lgQlJYakr2bhGXfzZWp24uNrankeH38lhWkt1mBbwApqaeAYxgPgI8R1LGL4C+M4+2xbpwzm3YWQt+9jnT/EiGT/kZ3dV1522XNcf/3oVmQCUFwseeihIAsXSi66yMJpp1lwudr+cUsJq9fCB5/AR59Dfhfo1wuOnQaDB0QIIRiEbxbD+x/Brl2Q4Ibz/ghjRkWu2b4d7r4bNm6Ehx6CwYPb7v/MmRWcfHIcEyc6276gDXw4D265G15/Drp1bf+6LSXw/BfwzhKYNgzOGA9DujUnNilhYyV8vk2VRpTTwJgsGJsDA1Mhw93hru0R0nSBLgpJiqSkWIdSXVIsDcoMKDMkCfYQZaanzBCboBZJkhB00zSEJklEkCIg3gIeNDwI4hHEC404BC7oXK+yAwRDKs1/CAhJA0MIdCS6lOhCRc/oUiJN1aHaboqqwZCyaVtImuqI6OOAQYsP2/zwhYwop4SMXK+Zl4RtKJpJpqbTNBoCi1B1LEK5d1gAi1CWGg2whY8L1V5H1b0HCsU+8BrQ1RVxVJFStjt5q0DnFnaTg41rSWmaDBgYzOEZLuL8pmsn+qq4a5md8ePjO2Uw7yGEvGsf68yIEcn+Y+TIkXLatCf48ccKnn9+MikpbQ/IJSWSp54KsWKFQWamlRkzBOPHCyyW9meM5RXw8RfwwcdKwijIhtNOgbGjo1Vb8PUCePp5FR8141Q4OkoKWbEC7rxTctxxkj/+sfUv6uuv/bz+eiNPPNHakNcW3p0LDz0G77wEKe2YV9bthNtehaIK+OuJcOxIsERpzwwDlhbD22vh4y0wOAtG5cCUrkq11Ypo6uG7ShiRDAP2EuNSp8NGP2wMQGkIVgVga1CputaFIM0CAxySOJtBtkWQpUkyrZBl0cjQwCM4YOovKSV+oF4aeIWkEUmDNGhE4hPgRRKUkjoh8SGxSqgQBn4pcQKl5sxXA6pNtVgAcAmDSmEQRNIVK9tM5U4vbGwyDe+ZaJREra8lDQsWwIogBwu70bEg6IqFInM7B43dGGiAUwoCMmJT8CBoNAf6NMNCuTRVXkI0rRPskYK6KO/VaH8qj7RQZf5mU6VGqRmPmaJBuaGIxQZ4Da2JmOIMjWoJhoQsISgMCXQgV4MtAYFu1i8JKILUJVgMFUMFUGCB7aZDWF8brGtQ7Xa3w1Yv2AUkWoAgODRwapChqXbcFsi2ga5DvBXS7ODSINkOSXZId0CaU8UbtsQX5fD4dtjmhR4OyLfC9ByYkAb2PWiVr6WcYTg4LWpp+md5npM4kUTTI3K8t4p/boQhQ1I6ZTDvKYS8dx/rnLwXIhFCOIH5qBUwrcCbUjbXoAkh/gaE/V2tQD8gXUpZKYSYBVyA+rhWAee2tcZPZ+Cg20jee28rr7yyle7dUzj99AIGDkxqc0DSdcnXX0tefdWgrAy6dBGcdJJg4kSBzdb+ABYMwmdfwGtvQXUVjB0BF18MyVHjf2Eh3HCDijH55z+hu+lS6/VKpk/X+etfNY47rvk33e+XjB1bxooVmewNX30HNzwA854BTxteXjUNcMsbsHwr3H4qHNYix3JpPTy7Al5bBaPyYXovOLK7snOEISX8VANflKkSFOC0wNgUOKULdDW1AUEDfvLC8npY5VPbJUHo5VHSQi8HDHBAFzt0s0GKZc+2m45ASkkNknJDUo1BFZJqw6ASdbxKGuiaQanUqUWShsaGJjWXhY2mx1eBsODXdFwI3KYKy4WGSwiSpYYUKpwlUWoIAU4ELimwCYEDgU2CUwhT5YUqQjPjbmKqrJ8DKVWgrs+AgKH++wxoDKmg3UZdqWirg8qhxDCgqAGqAirUY2MllPsUyS0+CWztSEGNIZi/G+YWw9ZaGBAH1w2BxDZiCusxOJFiXiKTTNMQ/ymfkU8ufegLwLTGGh7bJejTK/GXTCQCiJNS1gshbMBC4AozOW5b1x8HzJJSTjbX7FkI9JdSeoUQrwNzpZTP7mM3O4SDTiQAXm+IuXN38eqr25HSRmqqi4kTUznssFRyc1sbEYJByfz5knfflWzfLklONjj5ZAtHHqm1aaAPo7ERnnsOnnpKxa785S+QEDVT//Zb+POf4aWXoK/6vlFVJTnzzBBPPqlRUNB8GnTiiQHefNOG1dr+PTdsgd//GT56BrLbcGx/fznMnge/Hwdnt/Ae+2k33L0QqhvguL5w+iAVJBlGSIevy+G9Ivi6DEalqDI5E3rGKwIo8sPCOlhUBxu9UBqEfm4YHgeD42CACzJtP48sfFJSZEiKDINdhsEuQ1IiJYYwWGvoVElJH01jg2GQKAR9LAKpQTKCVAQJmvLwShKCBCFIFIIENDwS7Adbr4IiwJCpsgpJmrZ1aaqtUANgkwrLtNNISZM0oNoRTZ5XosXPTWCqpKQZ7y8i6qiwyknDVEeZKierBlZTDWXT1P+D4AvRCvN3KhVUoh2SHJDqApe18/umG/DMRnjsJ7h2MJzeRnzZu9RRSJA/o8T/1SwiiJ9hTAJgVEmINwM+uhZ4OoVIegkhH93HOtP3QbUlhHCjiOFPUsrF7VzzMvCllPIpk0i+A4agltl+F3hUSvnJPnazQ/hFEEkwqLN4cSkbNtRSVhYkKyue4uIQmzcHWLq0kcGD4znyyFTGjk2gRw9HM4nFMCRLlkjeektn7VqD9HSD886zceihbRvpQYnZr7wCDz6ovKcOOSRy7qef4IwzVIBkWDJ54YUAhYWS669v7hI1YgQsW9b+MzZ44YiZMPuvMKRf83P+IFz9GmzbDf85H9KiCG1dOdy/CHbUwHWHwqSukR+jlPBDFTy/FT4rgWk5MC0bJqYrcb8+BF9Xw4dV8F0d9HdD3zgY54HRHvDsg6OZNIliUwg2hSTl0uCHkM4OQ+IGglaDHCHoomnkWwTpQiNLE2QLQYZFkIRA64RRRJfKAFsnDeqBBqlSydRKSSPQIA28oFRdUoKhUW5ekywE2wyV/CVDwCZDEpCq/9V6hKyShKDa/C10t2hs0ZUyqa+wsDmkdAbZmsbukDBtDCrkzyKULSIDQaWhtrtoguKQeVyD8pB6B07AF5VOyyVUmhzMvhWH1OebZYGdAUVGSZoKPg2TljRn/jqQaYHtPghJ6OtUKieA7g7YYm7HW6DezBBj0yBLKCnBqUE3F1T5VZaGLi7whpQqKtmmnjfeCnFW065lU0G1CTZFFPFtBNg+tEw5fNQElHv5ylJoDELfRNhYChnx0DsVEqzQNQW6JkPvNMhJ+nlkUx+EyxbCIZlwcf/m56rQOZ9i3iYXgM2spIxCxnEMoIjkraCXgvyETiGS3kLIf+5jnakdIBIhhAVYBvQEnpBSXtPOdW5U3sOeUspK89gVwJ2oVGCfSCnbCfnefxx0Ilm/vpIbb1xEcrKLCRNy0TQrI0em0rt3IqBUWitX1rN0aQMffFDDtm0Bpk9PYvRoD1OneoiPj4yMUkoWLNB5+ukgVVUGJ5ygcfbZznYlhu3b4bzzJBddpHPaaRE3pm++kcye7eOFF5Q0VFkpOfbYRr79trm3yKGHwjfftP18UsI5d8CkYXDusc3PldTAX1+HsQXw56mRH1G1D25bCN/vgnsPh0OiUmEGdHijEJ7YCH09cHIeHJmtBofKIMwth1fKVFzISelwaBKMTVA6646gNGSwMggrg7DLMPgmaOCXcIhdYBeCnlZBbwt0tQryLALnz/jl1xsG5ShCqkZSJg0qpSKI3aaKq9q0GZRJZW7urlnYLg3iEfTVLNRISZwQZJiqKLdQai434BYaTsBtenU5BbgkODRhenop1ZaD8Cz+FzCN/x9BSkU4Pj1KBaUr8vCax+qDimQCOtT4oCGkJib1XqgNQF0AnBI2VatrZw6Ds/vv9daAkp53N0BJHRRWwbZK2F4J64qgqAYSnDCxK4zuDhP7QEIHU+74QnD0R3DVEDgmv/m5GRTyb3JIwEIRm9jOTxzC8QCcUB7kkUYf3Qo6h0j6CCH/tY91Doft0GQaA/i3mdi2FYQQScA7wJ+llK1WqxVCnAb8QUp5nLmfDLwFnAZUA2+gbCwv7mM3O4SDGoxZXe3jzDM/4plnjmTQoObh6IGAzo4dDZSV+bBaLUyZEs8ZZ6TicFhZurSB//63hrvvLmHoUBeHH+7hpJOScDo1DjvMymGHWams1Ln//kbGjKnk5pvjOP741sb8ggL44AM4/HA//foJBg9WpHTooYJrrjEoKTHIytJISREUFNgJBCR2uxp8tmyD1G7tP9tLX0FKYmsSWVsMZzwDD54Mh/eJHH9/M7y0GqZ3g/unRGZ7Ph1mb4J5Jcpo/sah0MWtBoWPK2DOLhVQeFYmPNYbunfgB+gzJN/7Jd8GJEsCsDEkGWgT5FhgiF1whFPjDpsFVwcH2qBhUIyk0DAolAbFhqG8uwxlcN5kZtIdYtWoEwZpQqNAaDgRpApBL2ElWWgkCqH+S3D8AlRbv3S8UwJ1IWULS7AodZdLUxKEU1PSRJwF4m1qwmET7dsg9hc/FCmy6pIIaXGtPb6sFshOUGVYG6sVVTfC91vgi7Vw14fQKxGOHwEnjlZ124PTCm9NhbPnw9QuzY3wA9DZgpehxJuxPBVN53aJEIZutNHiz4OK/9lnlHeUxKSU1UKIr4CjgLaWPZ8BvBK1fwSwVUq5G0AI8TZwCPDbI5IHH/yeq64a3oxE6uoC3HvvSjZurEPXYeDAZIqLQ1RU+HG5nKxZ42PQIA9TpqRy+eXdqKuTPP98JXffvY5TTknmoovSyMqykZJi4a67PMycqXPbbTV89VUj992X3Mrby+USPPOMk3PP9bJggRub+Us7+2wbX3yhc8YZan/DBluz1Cyffg1HtJPba0sJPPgezL+7+fGlO+HBz+HFc2BAjvm8AfjLl0od8ORUSDdddkMGPL8dHlkPZxTAm4eoAaEsAHfugNd2w0kpcHt3GLiXNC0hKVnsh68a4RMfNBgwxikY74STkwQ9rXufnQelZIthsF432KgbVEuDZTJEpZTKrVdo5GkaeUIjX7MwWgiyNY0sBO59JYVOEBSklPgAryHxI/BJ8EuJXwr8pr3DhzIUSynwmjN2HQgayg4SQtkk/Cg1kxNo0NV2nIB6U01lF2oWD0q61IxwpLca2IOmy22cUNtWodRaUpiDu1CDkF2AXTOdADQlTTqEIgWXRbVljXqVIQlVQfD5VZ0Sv5Io4gRsr1MShU2obAdBc8yMN9TxOCv0S1BZrZMd0NWtbDEpTshwKq+qdDekuVSQ697mFCt3wdebYWeN8sJaU6RIY3gXyPHAgC6qpLfjPZjkhiMHqgKwtRT++THc9iZcdQycdXj7fUhxwuAUeHs7zIjKPZeNnRIzeFSlhmlOHMGAzi8ZQoh0IGiSiAtFDve0cV0iMBH4Q9ThHcBYU+XlBaYAnZgUpkUfDqZqKxS6mOXLL2iKI5FSctZZn3PooTlceGFfrNbWA1AwaLB6dR1LltTy8sulWCyC88/P4YQTMpg3r5Y77yzh1FOTmTUrA6dTa2r3lluqWb06yKuvpjWRRTSuvrqBSZNsTJ9ub6oTPbi2tIdcdj1cfo6KSI+GbsAJd8INp8K4vpHjS3fChe/AO39QumGADdXwhy/gL/3h9D6RH8qiSpi1Bk7OhIu6Kp10sR/u3ancLY9NhTMylD67PdQZkrmN8KUXvvXDGAcc6YTDXZC2B+cAgGJd50fdYKWupItFIR0BTLBa8AhBL4uFXpqgm1Ujmc6LejekpFpKqiTqv2FQLaHGDHL06oJyKak1lLvxBl0FruVpgvW6+h5na4JdIfX5OgXEC+Wq6xSQjUa9VINzjqZykNkFpAiBzxBYhfKzFFI0xVM4UAO+BrhRLrECRQR+c1yyogb1piVPjPAqLUqF5jNdbx1CuVmHzPvW6JF63pBqO2CATUJlSLWfaoEtjUoVNT0Nrt6DFNxRSKm8oGoCqlT5lR2lrBEqfBAIwtYq2N0IdX4oq1H1nFbol6jUUHmJcEI/6NHOskFSquwLW3fDTzth9U61rMPGQujXBQ7pA1MHQd/cPZNUbQNc9wIUV8J/ZkJyO5Om7fUw8zt474jIsY8op44QvyeLCrazlW8YyRkAnNVYx982+RgyJKNTVFv9hdhnndGIvXttDQaeI+J38bqU8jYhxCXQlH0dIcQ5wFFSyhkt6t+KUm2FgBXABVJKPwcAB00iCYUM0tLczYIR587dgdNp4U9/aq54bWwM4nYrccBm0xg2LJFhwxK56KI8Cgt9vP56CYceuoRrr+3KN9/05okndnPWWZt44omuZGTYEUJw663JPPxwFf/4RwU33tg6q+OkSTa++irURCTRg6OuQ1qUZ0hlDSwpUqlXWuLBT2HEoOYksrIM7viqOYl8XAjXfg/PToIh5o+xLgT3bIZlVfDCMOgVD9UhuGWHsoH8LQ8e6N5+FmG/lHzQAB81wo9BOMYFf/LAk2ntSxwBKVka0Fls6CwO6WwyDAZYNLpoGkMtFk6wWbnPrWH9mWRhSEm5abQvNgyKDUmpVEbxjbrBbinJEYL1hrKJ9LdoeCUkaYI0IEHTSBCCHE0jURPEC4FHgEcIPJogznT57Qyj/oHAR5WwuFaplJIsKrbCoSnpxmOJxF24RGTbbUogbovajrMqCaWzIATE2aDKB+UNkBUHA5OVMX1Pr9EbhF21SuoorGmZ5rH1PbITVTkkat0gw4C1RfDtevj7c0ryOHI4nDsF+uS3bichDp64BN5ZBJc+CQ9fAJnJra8riIdSXRFkOGtDArIpbY0ghGEmAgUocdfj83Xe8Cfo/MFUSrkSGNbG8dkt9p8Fnm3juptRy34ccBw0IgkEdHr1ah6ZN2fOTzz00KFN+36/znXXfUcgYPD44xNatfHJJ8UIAX/9awFnn53Dbbdt4bPPynn00X4MG+bk2GPX8sEH/cjIUORw2WVJjB27jfPPTyInp7lGc/x4G88+G2x1D4A16yE9ynX35Y/hzKNa/+h+2gVvLYP5V0eOba+Bsz+EN06IkMicTfBREXx2DKSapptF1XDZWpiZB7f3VgPOP0vgX7vhyixYNLTtoC2A9UHJiw2S9+oF010wKxEGONoeEaSU/Bgy+CKgMy+gU2nACJvGRLvgNreDXpq2zxJGuW6wWRps0SXlhsEq3WC7ofIp1UhIE4Jc07MrVWgMsGhkCUGaQyNDE8TzvzN8S1N95TfUNC0YVmmF3XiNKLfesIQRVTfsniukInQhzOmi+d9lUYkxwyhwQNCj7gFKNRaU6j7VupI0/IZadLDOXDAtXoMdXqWmyrTBqlqlmsp1qKzO0iSjBKGk1UQb5DoVWaXYIcuuvKzSzGC/dGfbAXwlDfDmRvVfN2CzaUJIdkK3OMhNgK5J0CMJeiRDToKSQNqTQgorYMY/YXgBjOymCKRnVvPfiabBgDxVLjwCGn0wbynMfBzcDrjuDBjdr3XbJ42DOCec9RB8dHPzQN0whiXDiio41JwnOtDwm+osgyBa1AI6Eqiv7zzV1s+0kfxmcNCIxDAk9qhvt2FICgtr6No1okR9+OEfSUlxcP31I9pso0ePeG69dRV33bWGxx8fyWOP9eWFF4o48cRlvPvuCO66K5/rrtvKf/6jrNo2m+C661J54YUarrkmrVlbHo9g8+a4tm7DN0th/Gi1LSUs2gAPt1iBJaTDHV/Ak2eqBbQAKrxwzlx46ijolaLq/uMn+L4CXj5U+dhLCfcVwo818NYQ6OZWQYIX74DpHvimvzKetoSUki/8BvfXKhXKpXGCW7qolBct4ZeSL4IhPguE+NwvGWjVONKm8YzHQZc21IftoVTXWWNIVuk6JbrkO12nQUpGWCxoArprGr01jQk2K/maRrLYP4JoNCRVBlQZSg1UaUCtATWGcputNqDOUJ5Z24KCBtN1dnVAqYnyTTdaibJ1GGZXrKhz5aYNoYcVioKKCHKtZrp9Uy3mjbJ9uAQ0muqsdIuKBDdQSS+3+xQB9XbBfVF6+v5xqnQ2fCElwdYEzRKASrMUNcK2Oij3K7vK8t3KhtPVA28fGWljZJYq0ZASKr2ws1atg7O9Gj7eAD8UK2lkQLJatHBgNgztoko471teKnx0FazYDku3wNUvwOZSGNoVThih0v3Et3AGcTvhd+NVWbIOnnwX/vUOPPZXdS4aRw6Db9bCw+/DlSe1fidj0mBdbYRInAgirz5EfHidHyQD0Wlo2Ne3HkN7OGhEIqUkLi7C4bt3e0lPj3zLgkGdl17ayJIlp7SZiwugRw8Pzz9/CD/+WMkf/7iI//xnLGed1QXDkLz/fikzZuTw1FM7+f77GkaPVu7EU6bE8cQTVa2IpKGh/bVF3lsCs69V25/+AK5ESG8hXj/4DfTKgmEFZv8NmPEF/HUcjMpWx25ZrwamN8Yro2mDDudvULPOZweoweux3fByJTyZB0PbyJUlpWSu3+Duep1emuCBJAv929B7GFLyVUjnA3+Q+brO4VYrv7NbuTfO2ibZtESVIfkuGGKJrrNCN9hhGPS3aGRrGoMsGoc5LFxvceDZB6JQCR1hZwh26SpnV7EOjVLFqpTpKjZibVC5APezCmp1jSQNsjQ1KCZokKhBhkWtLhkv1Mzcral9l1D/bfwygvQ6in9sVFJQvBXSbabEYVMxHYlWlU4kyRYxtjutqqR3PNXbHiEl/FACvVIh1a3KkKzW1wVCsHE3rC6G1UXw8CfKFjK2K/TNhMn9YUJvmNgXrpyu1MIrtsIH38Ndb0D3TLj4SJgyrPXnM6ovjLwGnvkQJl4Kz98I/VrYhG78PYy5Fi44EhJbEHSBBz6IWuTbgqTRXIRL0oBmrncTQFJBHY2NzceA/YHG/+9FQg+i15YgGHYlAWpq/PTpExmd58/fxZQpuTgce4+gGzIkhVdfHc8ppyzggw8m8sc/5jadu/zyXB57rJAXXlBEkpxsobq6tUhbUQEDBrY6TGkFNPqhq+my+OC78NAFza/ZUAHv/gRfRR2/4lvljnhcV7V/63rY7oWnhyrCKAnCpdvhhGT4Y5bKb3V2EYx3wfzeaqbcEt8FdK6vCzDEauWFJBvd2jCaFxkGrwQCvOoPMcZq4Uy7lQdszr1KBqWGwfygzvehEAtDOnFCMNpqYbTFwh8ddvK1jhnVa3WDzYYKYCwLwaogbAspEtltKNtHrkXlTUrXBN2tkG0RnGERZGgqAK8j9g5dqsW56nT1vyYIu0w1Ua2hkv0FDEXWPgP8Uunnw6olj4CyoFI5pVmhKKAG8mSLSrcvIbxaE6BUF2k2qAopI3wXB+wOKg+sTBvUBtVnFm9RF4ftHXEWpXJyacpF124ey3OpIMAwRicpD6y6kPIG29Kgnskh1BIAVQEVyLe1TnXLbQGPpjysMpxQ4FZkk+1W7XaJUzaPjsKvw6Pfq+9yQxB6e5Qqa1AWDM1UEojDqqTtAdmqAPztSEVCm8vg63Vw31yorIMMF5w8Vi13PbKnKjefDj9sgdnvwQ1z4C8nw6kTwRo1CgkB5x2rSOUvj8C/r1G58sKwWuHsSfDMF/CX45o/Q7YTdkVlkjKIpL7R8WFBsW49OnFo1NTEVFudhYNGJJomqA+H3AJ2u4XCwvqm/Z9+qmbw4LaVsbt3N7JiRSkjR2aRkqJ+jT17erj55gE88shP3HffqKZrx49P4tZbtzer36dPHIZhoEW5pS5bARnZtMJ/F8EZR6ntJVshqwv0iwoUNCRc9Dk8emJEpTVnM7ic8Lchav+BQqWC+c9QRSIbfHDaVngwFw73wDc+yVXFcHu64Ig2pKJdhsFd9UF26JInEh30a0MdtUQP8ojfT7E0ON9qZ4HHjWsPbrchKVkc0vkoGGJdyKAWySSblRNtVu50O7HvZTCvNSSrgpJVIckOXbI4YFBhwHCTAXtYBX0tgpF2ja5WFeHdESJa74f5jSrbsN+AbX7lwVQZAq+kyTU436ZsDAkW6G2DWl1JIllWZftwCWVrSLRAhk15cDmEGuztZrEIRQQ21OeiCbDIqJmyuR22kRjS9MYyvbCCYduK6W0VMMygPzPYz28oybQ2pAzBVqDEp8hsYiqcFhVPMbmdVT3bQ1BXaqwynyq7vSqH1Y+VYIRgWZkKIuybCDV+pdbqm6wIplcS9ExqnqvNaYVnTojs766H1aWwqhReWQFfv6mkoWm9oH8mTOwFmaYWWgjomanK+RMVYS/ZDG8ugic+gm6p8OdjoX8+DOsB//orFFfA4+/Cv96D2VdC34LmzzeoJ9x2IZx2E3z2CMRHSefnHA5/mtP6nWQ61O8uDA2DFDOjtEYQl5nE0UuQfgjqojNjdgIOaizFQcZBJZJo916320pjY8TYrdxvW9fbtauOP/zhv9jtGk6njSefnEZ2thp9jzwyh7vvXtXseiEEVVWhZsfWr6cZiQAsXATTpzW/l2HAk5/AZ3eq/Vvfg9ta6Gaf+EGlbg/rmpdWwpwt8MXh6gf2Yil8UQPv9lcD149+uLsMniuAwW54pt5gTr3BG3kWclt8GoaUPOUP8h9/gDucDo50NJ/zSCn5ygjyQNBLCnCF3c0oa/vzoqCUfK2HeDsYoNSQZGDhaJuVa10O4vcwyDcakiVBg8UBybKgZH1IUmBRksUgm8ZxDo0r4y2ktr8ofYfRYA7GvezKDpHiUVJCilV5OoU/NkOqa+v0yCy+3hy863QlkXhNO0KZKanYULP+oFTqotKAuleyBcr8amZt18Af9XWJF8robRFqaePqoLltV/exCyUpyCgpxGVRJcWipAKnRams4q3gsSpD+P4GBtos8HkRpDoUOYxMgcQ2VtQ0JJQ2wrZa2FkHq8vhnU1qu6oWunhgQDoMT1PSR59UFQCYHg+Hx8PhUd6KDX5YUQhfboB/LVTeXjkJcMJQGNcz4gyiaTCmlyqGAR+vgKuegWQ3XHEcjO4L2alw5/mwYgOcfSdccAxceHxzddeYAfCnE+EfL8AdF0eOJ8aZdpwKyI2aa3qssDrKuVUngG4uAqZTiQX1grwECeGjujpn/z6EGJpw0IjEatXYtq26aT8hwY7TGZHFU1IczVRfYSxfXsKGDbspKfGRmurmmWdWcf314wCIi7PRpYuburogHk9kQI2OG6mpMUhIaD3glVWpdUmi8ekKGNULkj2wZLsKmhoeNXPaXg/f7YY5h6v9yiA8uAleGqdcED+rhQ8q4fV+auBY4YNzS+HNbLVs7b21OksDgk8zLLhbDMKFhs7tfi8p0sqXCXHEtRjolxoBbgvV0QsHD9jj6KO1/1Gu1EO8EAywXdfJs1g402ZnnMXargqp1pDMD+osCRh84lOz8NE2wRi74HinhV5W0SE7S0chJVTqUBxUKqIEQwXRrQ6p/FMVQXXeLpU6CaCPUxnIPRbo7VCEEWeBdCtg2kxcmhq8U81B3m1KHTYz0E8zpRJb2BMrqoRhGBEpRGJ6eRlq26dH9r2GUg/5DEVWu3yKgKwSir0q1UiyHX6qVc9Y4IItdeoeyXZFYCkOSLVDjlN5YqU7IctUXWW5FQFFo9oPKyvUu9JDsL5aPdO4DPVMPZOgd5KSQMZkwbgWEreUsKse1uyG1SXw3/mwdjf0TYFsDxyaD4cWQKaayBPngPE9VbkR8Abg85/g+W/hsU9UAOJ5k2BwlBuvpsHRI1RZtRWu+hfkpsPd50FGMgzrDQseh6seh5ueUlJI9Ffr7Okw6TIVf9IrShMwsR98vRbOHB85ZtWISvgPAXRspqeWTgM2M4FjA0HisbKtupMj2/d1NA3t/ZJfCw4ikQjKy71N+06nle3bI6qtzEw3n3xS2Kpefn4C1dU+NE2nri7AH/4woNn54uJAs9xaoZAkGIwoo3/4QWf8+Ob6ozXrVbSvx9P8Xq8vhr+eoH5w138EDxwfOSclXPKNSmXtsqn981bDOV2hRzys9cLfiuDjnmqAWxk0uLta8Fa2oMAmubA2QKKAV1JsWFqQyFshPw8GG7nfHs84S/PRY6cM8YDewC5p8A9rAv21tiUQn5S8ofv5LBQgaGj8wWbnSIerzXgQKSUrQgYf+XXWhAwKDcl4m4VJNo2/xVmI309JI2DAtiBs9auyLaAG3uWNSqro51TrxWfblCTiEmodlD5OZZdItSqJJEE7+IstHQj4QkrSqQxAhV8FBZb5YF2NIop1NVDihd8VwBVRX/dLB7RuSzegqB4218CmGlhSCo+tgMI6lVonxaZsHkMyYFimkki6eNSyBGHU+GBxIXy7Hf67WhnWD+sOx/SD8d0jKlyXHY4dqkpIh49Xwq1vqRiV44YrG0n05zWoG8y7G977Fv78CFx8HEweAQ47PPIXOP8uuO1puPn8SB0h4Kbz1PEXoiIiJvSFT9e2fv5+UZHzghAp5hAn0LGZqq0QXrog+FHvvC+TpoGrDYlwj4gRyf5DCEFubvOR22rVCAZ1bDYLffsm8+ijq1rVGzw4k1NP7cfrr69j0qR88vIibUgp8fkMXK7IY61e7WXAgAiRzJunM25c88H3pbfhzN81v8/3m6DSBwPy4ZP1kOWBwVGS8GvblN75MFOl9eROtZLbiRnKrfTiQnixq9LPbwwZnF0V4o00K3kWuKLeT0+rxjVuWzO7QUBKrjeqsUorc52JJIrIFz0oJU9Sz7vSyy1aAodpbbvrVEiDF0NeXgkFOMHq4F5HPJmi9Q/GkJJvggZv+kLs1A1cmmC63colbluTiqpcl/tEIrU6/ORT65ys9cPWgCINu4Cudhhgh0yrsgsV2NSaJ/uSjbglwvaHuqCp2gopKaHOTETo0831MEz7hTDUNUFD3bcioAbeJJsavEEF/zXqkZTu4aSXFqFSiQR0FZORZFdSitOiVCp2i6rrNtVYHptKaRNvbT/+JwynVdl2sjrg9rOkFO5crr573T0qEK9nEvRIVO1YNMhPUOXwvOZ1fUFYWwE/lsHcTXDrV8qOMjAdJuXBIfnQNx0SnXBkL1VAqbQWbIH5G2HWGzAsD04fAVP6RvJgWS1wzDBViirg/vfgnnfgxlPg+KgF5YSAEw+FsX3htFtg3Q6lvtI0mHMd/PlBePdrOHFipN+TR8Aj70BFDaQqnxn6dYFbPmz9ftZGzK748KKZMkqQXVgYA0A9XuxICgv3kltoHyBEc6eB/284qI++eXNVs/0+fRLZvr2Onj2T6NIlThk621ha85lnjueZZ46nJdaurWHChOZWy2XLvEyYEPET/OqrEDfcEJk6BIOwbB3c+Jfmbd39Ptxwohpo5vwA9xwVOVfqg7vXw/zJan95PbxaDZ/2Vzr3M0rg2hy11oeUkr/V6jyfbKXAIvhjoI6JDiuX2JsTQbEMcZGs4hjNxcVaXLNnXkOAR6ijDzY+Euk42pAqygydR40G5ssAFwoXXzsS20x8uFbXedEfZGNIkoTGyQ4Lk+12bEIQkpJv/JIPvAZf+yVJmuDDdA1bG/crD8EyHywx1ziZX68khnFuKLDDsQnQ164GyI5IEdJMC1LsV2qh0oCape/0Q3kQygMqsro8oNQI3dxQ6FX2if5xUB1Qg3aWXX1m4chwh0URhd10DY5WbYHatwqaLOpCRNRY4aBE3VRfGTKSKVeg7lkbVOlby/1KqvXrakZfF1TFZVXSBECfBKXmSnVA3wTVXoZTeVqlmuqrbLcKKmzvnQ1Lh0fGq1xaO+pgxW54fRNsqVHpXFxW6JcCI9KhTwoMSIsY1Z02GJalShghA9aUwcJtcPNnSrU1qQBG58HRfVUCxjgHHNVPlZuOVmreeavgb6/BScPhggkqhiSMLqnw0HlQWg33vAn//hj+eQkURAX1ZqXCJw/A9f+Gh16Hv56mggxvuxCmzIQJQyOkIQRMGKQkmfOOVsdS46G8bs/fKT8BHKYvlU4dVlMiaSRAIi6qqvZUe9/ws1RbvyEc1Ed3Oq14vUFc5jc9NzeejRtr6NkzCU0T2GwaGzZUN3ML3hNmz97E1KnNFcEvvljNc8+pqdnKlZL8fAfx8ZGB8c15MGwguKJmg0u3gd0NI7vDsz9AfrLyPAnjiuVwxyAVVVyvw4Wb4ZXeatCaVSaZ5BJMNyc7QgjeTraiA5cE6hiv2bjE1nzquZYAN1HB1SKZCSJCMDoGT1DHx3h5UKTSpw0Hwzpp8ISs5xPp5XKRwK0WTyv7hV9K3g4G+CwYosoQnOWwcZPLikMI/FLyqc/gtUZYEZBMsMPRLo1bEjXioqSRbQH4phG+aFS2nmEOpW4a5YTTE+GR7PZTt4AiiiI/bPGpUuKHtQ0qkC/HDusaIdUGQ+NUYGCmA7Lt0C9eHU+zq/u5Lb+u+JBoSKnIpdKvSplXeVtV+GFFBRQ3QnED1PqU1BRvg39PUt5WYVg1FS9R4Gm7/ZIGWFupVh6c/QOs3q2kFbsFxuTA2BwYlBGRkqyaihcZkgWXjVVt/LgL5q6Dk56Dbh4Y1xVOHQZp8aYhvZsq1xwNby6F699QJHXD8dA1ah6XmQQPXgBfr4bT7oVZx8Nph0XOO+zwj0vgmKuhew6cOAHSkuCmc+DB1+DOiyLXnnwYXPtUhEgAehco7zVblFTbO/pdEcJjhiTaMbCYRGKlnmTiSI9ECcSwnzioRDJiRBZlZY0UFKipR58+iWzeXNt0fsqUXD79dGeHiKSqys/ChWU8/HAkCn7zZj82myA/Xxnx//Uvg3POiUqTIOGl/8KTtzZv68b34K6ToCEATy6FeVHLwfy3FHLi4DjTdfPGYvhLjopofs2ro1sk16W0fq1XyCpG2excojUnkQV4uZ1K/inS6UnE2WA3IWZSwqF4eJdMbC1S4hpIXqOB96SPI4WTj0VGK8mh3DB4KRjgpWCAY602bnW5yNU0pJR8FzR4plF5Yh3l0Lgs3sJIeySGo86Ad+rgmwb4vBG6WOHYeLg4GYY5245zATXL3uaDVQ2wwQs/1MNGc2Ds4VKk0cMFQ+JgeioUOJUHVWdDSnXP8Pobfl0NOiFTutANJWlICZgSiCbMFQk10zVYU4OUXVMDsV1TM/69qaraw6ub4I1NKsaim0dJUNlx0DMZjstX36toz+7agFKddQRP/aCM70MyYGgGTM6Hi4eqcwEdVpXB4l3wyir44waVdPGonnBYgfLWilY9hSPWr58CxTXw6nI4/t8wJAd+NwSO6Kuuc9rgD+PgzLHw0Uo4czYc0Q/+Oh0So9x1Jw6Ez26Hs+6HtTvg5jMj97NZ4fVb4YJ7oV+Byrf1u8PhwTehsBTyzJWsu+eoQEhdj6RHaQxBWR10SYrca32Utb2OGjJMA3uQrVhJNI/X4iaP7Z0Z2a4B+2oj+Q3hoBKJ36+ze3dDE5H07ZvMM8+sbzp/7LEFXHDBV1x++aC9tnXrrT9wzTX9m0XBv/56NRdfrESJ2lpJYaHkyCMjv9Svl0JyKuRFCTGVDdA/G4blwy0L4exhkGyO/bVBuHEDfGKmS3m3Bgp1eDAN1gUN7q3X+TLV1mzGLJHcKGvIxMIlWnOd7EfU8hENvEwWaVF5gJbi5QZ2cwNpHEbr8Paf8HMD1QzEzlNaCh6aj2y7DJ2HA34WhkJcYrOzIM6DQwgqDIOHGoK87Asx1KpxptPK7EStiTxKQ/B2vZI6tgfhyDg4OQHuzmybOKSE7X6l2ltYB8sblCstBgyKU+WYFEUcHV1gKxp1AeWWW+ZTqwSW+9XsvTKgVpjc5VWpQdLssKZKqRf6JMB6cy6S61aze7tF3d9lPoNVg0ynsoFpQnlGlTQoMkl1KndZ3VCE0mASUUBXRuSiekWWvZJU9maAPsnK+ynBDrkeNaYkO5VNZeaQyIz5xG4wLlNJIpU+5YK7phK+3qlSmhQ3KpLLdSlppGsiDEiBPA/0Slbutu1JY5PyYXExfLIV3lgLS3YpV95JeTAiG4Znq/8A90yFbVWwqBDu+kot6Ty1hypTejWf4WcnwqzDVVm5E56YD9e+q/ZPG6mIQAiYPgSOHgwvLIDDboNbT4YTo7wg413w5vVw5Ry48Xm4/ezIsyR54G+nw8xHYN796vis38PDb8ADl0faGFAAa3bA4G5qPzMeSuubE0n06/Hhw2mO7hIdYQ53jfhw0UkpAaJvHFNtHRxkZMRRVtbYtN+/fwo//VTZtJ+fr0TRzZtr6NEjsd12vvyymPXra3noITXCr1zZgNOp8dprNSxZohSzTz0lmDzZ2izZ211z4OGrm7eVEgcP/B4Ka+HDzfBtVIb/u7fAX7qpeILiIDxQDu8WgBfJXQ0hnkmyktBCvzObWlxCcgPN+/82NbxODf+mCwlRJPIaVXxKHc+SQ3aLjyeI5FGq2USI20lmYJQEA8rQ/mDQy7d6gJkWN3fHubAIwYaQwcONAX4M6pzptPJpspMks5+1huSNOskH9VChC34XD/enK2N4S0ip8oB9WavWf/+yBro64IhEmJ4Mf89VXkEdQcCA7Y0qgru4ETbUQWGjmvUvD5OCR3kDZTjVwJ/qUOQwJNlMHWImLPRYD8za4B2FIdWSsrVmWvZqv0rNXuVrLr24rNA1QZU9oTEI22tV7MeuOnhrPWysUsS0uRr6paq0O72TledVqlvlcuuVAmEnRkOqKPUVJTB7GSwvhkHpMDQdpvdRRvWuyXD6YPWOv9sBb6yEv32ojOynDoYxBc3f6eBc+NcZUFEPj34KY+6Avx0Fp41WpCsEnH0YHD0UbnlTxY88el7EdmCxwMMXw8zZcPsrcNMZkbbH9IeuWfDGl/D7yXDSBHjlK/AHlAoMYMIAWLktQiT5aWpV0TCklPRM8hFOVmJHx20SiTOKOOzUovkcDBgMKz7oyCfcQcSI5OCga9dE6uoibhYJCXY8HlszA/v55/fjtdc2cv31I9tso7i4gSeeWMtTTx2CEALDkFx44SYmTEjl4otTsdkEfj88/zx8+22k3rc/QN9u0L9Hm83y0HK4Y0JkdraoRrmr3tVbDagXl8ENmco19U8NPiY5LQxuEWX2IQ0swMtzIhMRNVd6lyqW4uVpcnGb0oSO5EFK2UmQx8nF2ULK2ISfWVQwFTePkYY1qr2glMzWG/g0FGKG1clttiQsQrAypPMPr4+GkIWL3VYe99jRhFBLEvskL9bBEj/8Ph4eSVfR4i1REYRPamButYqR0AQcngCXZsLDXfc+eFcFYHUd/FQHa2oUYZT6VaxEnBW6m0kNJ6SrtCF5bpVjqr12/brysKryqaVbN/qV+2y5Vy3/ioTSBhWMiFQDeli15ZBqkNcl5MXB1molhfRMhM2VatDPjlfeXDZTvRXOaeWyQkacklTcNsiMU+8iwaEGeI9DSSHpThW7sTf116rd8Pf5yrsqz6PIpUcydE9SEnC/VFVaojEI6yqUDeSjzXDXt8pZwGOFsbkwtguMzFEG9r5pqpxupv7ZUQ2fboIbP1XrjPRLVYQxrgDGd1NFN2DhVnhsIVzxLlw0Fk4fDu6oOUtqPNx6Elw2BR6YB09/DU+cDb1NiSc9AR4/Fx76AI66E16bBWlR5PnQhfC7O+GtBXDyhMjxOy6Ak2+BUyYp0inIgs9XwHTlbEWfApX3Ljy30zQVtR9GENgVtXhVNRU4cSIJYRDRY9VRg6/ORbUkhk7CQTe279rV0vXCYNeuBrp0UWqgk0/uzoQJb3HSSd3p1y+l2ZW7d3s5/viPuP/+Q8jNVUa1l1/ezahRHlas8HPbbeqb/cILcN55EGc6b0kJN82GR/7Wdr8W7oJNdfCg6VsfMGDmBnhlgBrgnq5RNoOj4uHtQIA6KTm3RdT5Srx8RD2zyWhm3/iQat6hitl0xWWSRQCDayikPy6uIrcZ6QC8SQ3PUsm95NC/hSJ2vu7nZqOO6ZqT1x2JOIVgna5zr9dPmZTc4HQwzpwSeg3Jqw06T9ZL+loFl3o0Zqe1Tl1SFIB3quHtarUAVB8X/CUbhrn3bFDf7YflNfBdlfofMCPMB3pUOSVHSRkZbay41xhSAZ5Ld8O2BpXuoz6g1EdlPkUWwvS4SnNCz3g1605yRHJWZccpt9yh6Wrgd1mVjcFuem9ZhZoYWKJsIcL8PoS9tEKm7SRoqASFwXCgYUiV+qCynYUMRV7F9aqNbTWK3HQJO2rVgCxRxOSxK7XUn4argR3UIP7YVCX57qiBTVVKLbWlWj1jXQB6p8CoLKXiGpQJeQmKxIZnqXKauWyPlLCpEr4rgjd+givnKTXcxK5wZA8Ynavcc/OT4PyRqgRC8MUm+M8SuOljGNkFLhgLvdJhYg9VdtfDa8tg7L1w1hj40wSIj9IIZSTAPb9XmX7P+hecdShcOiUinfz1OOjTBc57HJ6+PEImFgu8cCVMuR7G9oMu5jtJT4aB3WDe9zB9LJw8Hp6eFyGS3tmwflfk/gkORYhh+FCpcCL7Ppw40alCI6npuIFBdZ2FlL1IhvsEAVGKhf93OKhEkpLiYt26imbHBg9OZdWqiiYisVo1nnhiIhdf/AWff34SNlNEWL68lAcf/JG77x7LxIkqwKOhQefNN8soKEjkzDPjcLst+Hzw2GMwf37kHp9/r9wPB7QhjYR0uOYbeG5q5Ngju+CP2dDTDdtDko8CBs+kW9hu6DysN/K+O6HZYFxGiGsoMdVWkanpV1TxGTX8k4ImEmlAZybbmUQCZ5HWrC9+DB5kN7vReY0C4qLaqpEGN8kabAiesyaTLyyUGQbX+Lz8ZOjc7nQy1kyXUmNInqyTvNJocH6cxgfpGhktlhyu0eHNKnipEvLsMDoOXuiqYj3agpRq5b6vKmFDA3xartRNR6TAIA/8MU9Fb0cThk9XCQi/LIYdDbCqGjbXqeu210NBnFoy1WODQcmQY7rDZjiVVPBrg5RqoCtthJJ6SI3ys7BGxXsc2ob3UENAqbM2VsA3hfCv5RBvh+I6JXGMy1U2j/xE9Y57papy1mBVf3cDfLUVnlsOl7wHAzPg5P4wrQ/EO1RQ4VF9VfEH4f018Oe31bmzhsNxA1WalMsnwnnj4KmFcOELMLkPnD++uXvyyO6w4O9w1/vw+yfguYuUyzDAMcPV+HriP+CjG8FjvoOEOLjrj/Dn2fD23yNtzfwd3P2KIpKx/eDq5yPrwGQnQ0KUt1p8nJqohOFDp0e8D0xPrTh0rNgIUoWDSMV4aqn2Q892tBE/Cxp0ttnl14SD+vNMS3O1mpmOGJHOli01zY4NH57Buef2Y9y4VzjuuO6sXVtJRYWPRx89nH5R8v8dd2xn6tRkvv46xIMPKk+vOXPgjDMg0TRRSAn3PA9PXtN2n57eAEd2VUFeoDyP3qyEhQOVDvaimiB/T7DiEpI/+eu5xx5HUtSvKoDBzZRwExnkRdkwllPHvyhmNr2JM6cuDehczgZmkMU0mnumlRLkCnZyIolcS0YzKWW+9HKTrGOmiOck4cIAngp6+SYU4jirg4etLoQQ1BuSRxp0VvthvEPju0wLriiRQkpY3AhPVqqMryPd8FxXRSRtoToIn1XAvAolLfgMmJQCM7Lhrj5qph/Gbh98WgJLKuDHathYqxIo5sep5VoHJsHRXaCHJ7KiXUfgN9VYFY1KAqgypZWyRjWLtwkoqlUqIKtQs+qAropVqn7rhlIn7TDjCHqnw5YKpY7KTVRtOyxKPWR3KCnAbYM0t5rZexyQHqcknmQXpLggNQ6SnM2JUwi1LG2CU9kvOorbvlTPMihTRaGf0DsSTV7eqOwd63bD08uUNDM2V9lKpvRUZCKE6t+pA1WRElaWwHur4a4voHsqnDMCjuyjJDSHDU4dqsrm3fDw13D7xzBrEpxmqrWumAznHQK3fwAT7oUnzoChUalQ7Fa45Xfw/EI44h/w1kzIMb/SRw2H6gZFCk9cGCGhqcNh7jL4ZLlaJRGgbz5sKYeyashIUrnWtpdB10z1XBsqIsRis6jPM4xGASERYRY/KoBHpxLN9N4yMBAIyhvpfC+rmERycBAfb2fbtuakMWBAGrff/j2XXjq42fFzzx3A0Ud3ZfXqcmbM6Evv3snNpID16xv4/vta7rhjCJddZrqw1sHTTzeXRt7+UuXs6ZlPK1T54Yk1sNCMdZQSrtsOj3ZT6obvA5JBVo0JDo0njHqmWWytUpjcyS7G4WBc1JI6m2jgYXbyMD1JNF95PTqXs5HTyWxFImto5FZKuJJMxkS140dyN5VsJsBrIp1MYeUnPchfgg2M1Wz80xmP2wwsfKExxMP1Bhe4LTybqjULYgwY8GoNzK1Vg/8lqTDe3bZdYqcP3i1TJdsB3V1wfhcYnRghDt2A1dWwsFx5TH1dqgzj41KVYfyUfKWK2pPdQEo1c99eC1trYGc91PpVTERxgyILI6hSmae5VGyEQNklcjxKfZQTr1Rdh+Saqi2bUm05zMhzC4oILKZKK/y8UkbUWiHdVGeFVCS431Ck1BhQ/2t8SsooqlHLzVZ51X1WlqhzmXHKRpMep/JV5SWqxIZ5icq43TUZ4vaS3v2ikbCyVGXefWklfL5VSTBHdFeEdEg+TO0OM8eqXGDryuHLzXDNPKj2Qu80OL4fTO6h+iYEDMlW5cYjYFUxvLgMrvsQju0PF4+DApPoeqTDY6cot9pnv4Nx98Edx8O0/uBxwr2nqJVAr3gVpvaHq45sLp2cPR66pcPx98PcqyHDnMDNmADLtsAjH8Ks4yLXX34snPcoTB0W+TxOHAfvfQcXHgXj+8HijYpIAJLjoKoBUuLVZxqMsnM0YjTZHMOEobZr0FBON34aSSCZ4gblWBND5+CgEklCgoOaaGsZ0KdPEuvWtR1ympUVR1ZW60/fMCSXXLKO++7riSVqWvzAA3DhhZEFq0IheHEuPHlt2/15bA1cPSSyjsOLlcq3f4wpFY+2a4yyCVbKAB/i5T1rc1XUO1RSj9FMRVVBgL+zmTvpSaYpoXjRuYGNnEkGU2k+Vf2WOu6nhIfIpyBqylRIkDuoZCRObhYp6BIe1Ov5XPfzoC2BgSahfRnQua4uwHEOC9+k2ZqlOGkw4PkqmF0BxyXAwzmQ1YaBvTIIb5TBK6XQwwHDPPDsQLWca1v4uARe2QHj0+DCHvDw8PZtKfVB+KkS1lfBmgr132WFdVWQ6YZR6coIn+eBsVnwxwHKvhDXRj9/qdANJdXsqlXS0c4aWLRDrTS4vFiprQab0eV90pXk0SsVeqYqosvyqHJkz0ibjQH4sUTlv7pmniI+uwWm9FDlsnGqBEPwXSG8vw7unQ+ZLjhjmIpSd5iebYNz4N4cuP1oeGclnPMKdEmEaybDIDMNUIYHrp4KM0bA9e/B+yvhummQmwz9c2DuTLjpfTjjKfjnmWpgD2NCH7jndDj+Afjoakg2z915Bhz2dzhiMAwqUMd6ZEN+ugpanGR6+Z80TqVYARjZB+Yuh9PM5IwjekBxrbqfzQGBSLo+fIRIMRNYhQjgalqLpBzN9JoMUI+bIH5rZGXHTsEBcP8VQjiB+SjZyQq8aa7DHn3NCcDtqNyiIeAvUsqF5rkkYA4wEGWyO09Kuahze6lwkCUSW7NVEgEsFo2hQ1OpqvKSHA7g2Av+/e8iRozwMHJkxMW2tBR++gluuCFy3X/eV+scZKW1bmN1FXxWDH8fpvYrQ3B/mVpkKhp+AbcYNTwikpoFAG7CyxdUcg/dm2ZCfnSuZiOzKKCHGQ8SxOBvbORIUluRyGdU8R8qmEO3pmRzAAto5A7KuZt0huNihwxxfaiOAcLKu7YUbEKw2zC4sdFHdUjjlSQH3aKm/z4DZlfBM9XwpyT4pkfr5XsNCV9Vwb92KUljhAdeGqAWcApDmrO/lpLL9BxVWqLCB0vK1FKvy8pUllqrpmIjRqTCpC5w8UDI9/z8IL+DhScWwn8Wq34PylbSTJYHchJVttzcRMhNgv4ZEbVUNBr8ykC+fjdsqYQXf1D2kIIkpSob0QVG5CgpwmlT6qVx+apcOUF9FmvL4LPNcPdXsLxQpXw/rj9M6KaKlLCyGF5eAfd/raLX/3SIWqQKFLHMGA6nDYP5m+HWj5Vq8I5jlGQCkJ8CL54LX6yHY5+EK6fAH0Yrl967fwcf/AjTHoS3L4e8qK/zlIFqQbhzZsM7f1VSi90G//oTzHwGvrg58j268kS4770IkXTPhm83KOlwYB7c826k3SBQbjpgaUJ9b5veKRKn+dvz0YDLlOZ1qrCiVBB+6nEQT2E99IpK2bLfEByIgEQ/MFlKWS+EsAELhRAfSSm/i7rmc+B9KaUUQgwGXgf6muceAeZJKU8RQtihjaC0TsJBJZK4ODtbtlS3Op6Z6eKbb4o59tjue22jsNDLl1/u5umnm6vCbr0VTjsNbCZP1TfCP9+EhU+1bsMwYNZieHBMZCZ9TxncmKX0+tG4nxqmCQe9RIQAG9H5O1u4g+5NorVEcjcb+R1pjDFnQwaSh9nKBJI4nuY5wd6lnA+pYA49iIv6WF6imrk08CI5pGNlnvTyD1nH/ZZERmoOpJS84Q9wry/AjS4Hx8dH+mVIeLkO3q2F0U74tpta/Cka9To8UwILqtSz/i1fkUi02mdFNbxZBHNL4dXR0Led9Bxb62BRqVon44cKyHbBgCSV9+nUHtAj4edn720MqOCzcKmoV7P+ykYV7byzWqnCMuJgVYkZNJgCm3arviW5oLo+0l6iDWrM2WzPdHUdQN8cWFemtvOSlY3F41R2kdcviLyXy8arEtKhokEZt0vq1PWF1bBou5JEan2qnwlOGJOn1uTok65Kv3RFFABXHKr++wKwugyWFcHLP8Jl7yhHg3EFMKmHIogE0xbTP1MVUGq4LzfDR+vgivdUtt7Th8IhXVVEumHAx+vh+rlq+7zRcOKgiIfVxJ6qfLWR/2PvrcOrOLew79/EFZLgHtzd3a1AgQpSrJQixdrS0lKhAgVKW6BQaAsUh+Lu7u7uHkKIu2x7vj/WJDM7Se0c+va839vFNRfZs2dmz54986xnrfte96LPEmheGj5sBTn0uVyLsnDoXfh0s3RCnPmaOLiOVQUfenE6rHpL2k2nW6eacOQWfLMFxujp4qrBss2GU9BVZ2NVKwF3wiAqAXLp91b14nD+nrRx8DJFwrl85TcHid48TM9nMnb89OcvlST8dKaWCwG4U0T/24UgihKdIvU3z83+hohEKaWA9LvWXV9Upm1MdzW+6e9rmpYDaAK8rm9nASz8TfaPOhJ3d1esVnuW9U2bFmbfvkd/6EiUUgwZcon33y+Jr69xR928CVeuwKxZxraz1sLbPcA/m7zohsdQPTfU0iOVE0lwMRW+dpbt4hxpnCWNtZrzVOYXntCNvJQydW1ewRO8caUD+TLWzeQ+vrjQnfxO+28kgh3EMINSeGf0T1BMIJxY7CygIK7ADBXPCWVhnZaLIM2VOKV4OyWJQporu/x9CTTlk86mKt4O16jpBXMKSJMls0VYYX4Y/BoOffLCnHIQYLobnqTA0hBYHQq1c0CrvPBpOWdgPM4Cu5/A2QjY9giK+kGbwjCsIlTJ9deaN8WnyYz8YSzcioT0VjWnnsj/xXKCi5Jq5ry+gpMUzglVCghOkg5q+3kIY+h5RDhKCVaSkAqJadljSG6u0ikwXw5jpp+dxaVI5HErAq6GwbH7cPCuOLxGxSW1VLMw1CgMtfQl3WJT4PgDOHgfvtoj361TOWhZBmoUEmfg5S7pq/blYGJ7ofauvCBMrF41oV8taF9elgfRMGUfTNwF7zWHbjWM69WsNBx5B1achSbT4Nuu0Lq8vJfDG2Z0gx/3Q9vvYeUgyJ9TmlotHgjdfoatbxsgO8BXr0K/n+HMfahVXNaN6w7vL4XOtY2JRY+G4lwGtJTXrarBuYfiSKKsEsH5ekKhIFFnBnBokGr6nVOwklOvI0kjEQ99eMuRUXkCuSlBbkrgmgNyepkoX/+t/WeOJLemaWdMr+copeY4HVbTXIGzQClgllLqZJaP1rSuwCQgL9BBX10CiAAWaJpWVT/G20qp5ykMk2H/OKkyICBr4r1Ro4LMnn0Jh0M5SZ5ktpUrnxAc7E3z5s65qnHj4OuvjQc/PBpW7oUT2UQjqXYYfwm2t5LXdgVjwmBOYeeBIw3Fz8QxhcCMPtAAe4jiCamM0Gc8AOeJ4Q6JjMHIi23hKVFY+IKyTp+/iXD2EcN0SuOpz6bScPAN4fjhyljykoRiKFFU1NxZQhCumgtn7FaGp6TwnqcXr7ob6G28Q/FJNFyzwC/5FOU8na9fuAWmhMLuOPigAJypbgz4DgW7I2DWfVHPbZUH9jaQ2Xu6PUmGdQ/hUrTIkrQuCC8Wg/G1nHWifsuSLHBBB5KvRIjyrJ+HFJaVySXso+KB0Ly4pHny+v65KCZNB8IjEuB+pEQwaTZJr1hsgh1YbUabXFdNZuYuGnjpKQlPN0khubvJoOXjIXTVHF4yE/4tO3YXhq2EnN4i7lkgEIKDZCmRW/7P6Q3VC8liNptdnMv5J7D1OvxyUiKiagWhYXGJLErnMZwAQEwy7L4J0w+JwGKHCuJAGhWXa+XmKoysNmXFgS07BwNWQh5v+KAVlMkrgPqzeJh3AhpMg0mdoIV+u7q4wGu1xakMWQ5H7sLHbYXdBTC0OZTLD28thRk9JaVVuTDMeA1emgU7RxlaW+5uMLYrDJwPhz6RY+cPhAA/2HYBOupsrZcbwJtzDUdSsQhM2w6DW0GJPPAgEioWEqn2CD0iyVxPmIQDH30ilkoSXvjxW3YzHhxJf8uY+lcsUimVfaW1bkopO1BNxzvWa5pWSSl1JdM26/X3miB4SStkbK8BjFBKndQ0bTowBulJ9tztH3cksWaNA938/T3Jm9eHPXse0qZNcLb7hYQkM3nyTQ4dauK0/tgxSEqCBg2Mdd8sgzF9JEeb2ebdhteKG70gZsdAM3/pume2GcRSHU9KmCi9z0hjLk+YS4UMXCSSNL7nDt9RBXfdMZwkmu2EMY0qTjTeXUSxlUi+p2yGE0nBwXAe0xx/ehNEKDbGE8NL+NAFXxSKudYU1tnSWOHtT7BJ82VnioOf4zQ6+8KMXM6Fhsl2mBYGZxLh5SCYUEyXT0ek0ReGwPYwqTj/qhxUMeAmotOk/8rpSKn/6FoUvqgmLV5/z6x2qeA+ESqigadCJRVRLS/UyAs9KkLFZoaWWWZLSpNB9nGspI0eRcn/YfEyQEYmyUQwOEDEBXN4S6onNkmcQD5/sFgFo/D2AE0ZvdndXcTBOBziSGKT5bW3BzyNFUfk7iafHZcsEYm7u9CI3V1hdm+D/tqgJJz7SM7pSazs8yAa9t2GJafFMdgdUKeopLYqFRDAu1x+OVaF/LL0qmlct4uhcOohjNkKdyNFRPGF8uIcAn2gW3VZbHbYfwcWn4Hh66B7NVlK6ZnTnN4wtKFgI7tuwFurhO48simUzw8ftxGc5MNNsOEijGkNBQNk34IBsHEI/HAAWv8AK94w3mtRXq5Vl1mwaTgUCoTGZeCD9jBkMfw62JiIlS8kUcuCQzCgmawb2Q6GLTAcSdHcwsZKTJWixwqF4ZoejXasamho+XuKThgYVOx0s+Mghz6k2UjDm9+uOIy3gDXhOWZ6/obUltmUUrGaph0A2gFXfmObQ5qmldQ0LTcQAoSYIpg1iCP5W+wfdyS/ZSNGVGPs2KO0aFHUqbc76PUcg84zdWoVp5a6Sil++AEmTjQG0LshcOoafDucLPYsBebdgePt5XWEDebEwNHiztvdJI1jpLAaI3fhQDGO24whOOPmtaOYyR1GUJI8OvL2iCR+5C7TqIqHiWh+klh2EcFUymaks5Kw8xlP6UBOXiKAW1gYRgSTyEUtvEhTik+tSaQoxXovqWIH6an+YayDBzaYm0cjv4m5phSsjYbxT6B/HlhZWvSsQJpCzXsMC0KkFmRRdWn7CjJr3xMGW0PgZAR0D4YJ1aVA8LfM7oDz4bDvIex/KAN2bh+oXxAGV4cf2mSNWtJs0oHvergMuPGpcPSh9B6plE8GyiIBMqsvl09myfn9pVjO3ytruinNIlFPskUcUZpVHER6VKKUOA9XF5nRurpIJOLqIjNuTzfw9ZaIxNfTaNxkNostKytN06QVc4APVMyGeOBwwOMYuPZMvu+FJ7D/tk7rLSPAb4Pi4gDcXaFWEVmGNtKva4jgHzMOyzm+Uhm6VBFwv3VZWdJssPkqDFsn33NkY4liXHUcpG15WY7chSErxXl90V6iptVvwK5r8MKPMLqVRCSaJsvI5lC1EAxfCV90EM0tgPolJSL5cK3UleT0gZdqws4rMP8wDDDN8T59ETpMh94N5TqX0dlad55BKT37+6KOqbSrIs6/SAH5zbrXMY6Tw5uMeo00DZJN91MCqZTWJ3rJxBDo9LxaOM0H1OV7AHLngbhYZ8bof2V/A9iuaVoewKo7EW8k0picaZtSwF0dbK8BeABR+uvHmqaVVUrdBFoC157vGRr2jzuSzPIc6Va5ch5atCjMZ58dYeJE56hj/vy71KmTk+bNnQHrLVsUfn4OKlQwvtbYudK6M7uPGXsBPq0sxWcA06LgszzOgLQDxcdEMD6TvtUawiiJL9VNs57F3KMontTS2VjJ2JjAFcZSgSBTJHODRGbwgB+okFGcmIyDt3hAN4LoSADnSWESsfxIHkrjQZRy0M8WyysuXrzuZozm160OPo6309bDhRmBLk7X81YKDH8AJb1gX3np6wESgcx6DItD4e0icLKBpLJAcI8Vj+Dn21A3FwwuBdNrZ/sTASKat/cxrLsNlyOhVRFpvjSnnUh6mC0xDc6FwukQuBYudFYXDRoVEeCzQj4Bob9sa2g72R0SbTyMhifRIsfxNE7A5XuREJkotRk3w2T78vkhLEacQL4c0rjMw00WLxdxkJomg15MorwO9JVjp9kghw88iBInZLUb+XiloJieQc3jD2ULSGvVggGS2ikcKJIhme+z1+fDswQ5r1rFoWx+GNFEMA2Qz7n8FI7egzGbhf3l4Sr4R4vS4mBcXaBWUVnGtoHQOFh/EV5bJNfv9boi7+7nCa9UleVOBMw8BJ9tFWfUp7aRmmpUEg6MhHUXYeRa+aw360ObCtJn5OONsPcmzOxu/A5Ny8h3ffUXmNcbaurRWMNSkiLrPQ82DJNzndYD+s6D5uUlLQWQ0xfaVoKFR2FwM1nXrAKsOAGfdpbXtUrCvhviSECc+IMoKG3AjHi6Sg8XEL6ruQg2EVvG85RGEp6mGiwLCbiZSs8jLBCb/JwdyfMfTQsAi3ScxAVYpZTaomnaEACl1M/Ay0BfTdOsQArQXQfpAUYAy3TG1j2g/3M/Q93+cUfyezZyZC3sdmdA7NGjJGbOvMXhw62d1tvtinHjHKxfb0whz9+Q8LtlNlnIy7FgR9I0AOdS4FQqTMhECVxGPDXwopLpJgwhhUvE8ykG0f8iMVwghqlIfkKhmMw1XqYopUzyDM9IZTEhTKZshnNJxcFoHvEyQXQkkNOk8BmRzCUfhfHggcNGf3ssn7j608rFmPb8mmxjSpKDhQFuVDYh23YF3z2TXiDji0BdPVWsFPwaBpvCoXZOOFFXGkWBYB/f34JdYTC4JBxsJeKJ2VlMKmy7D0uvy9/dy8D7taBqHueZepIFNt+Eww/h/FNwcUC1AlC7MLzbUJyGe6YZ/6mHMGq9pHM8XSEsVmbdxYKk8LBggKSE8uWUqCSXnzEo/92WmCpd+SIShKkVEiPS6mceyBIeD1WKwMIBxj4L+stAey1UvtOsg3AlVByEjwfUC4Z6xaWK/L0W8hvdeAZ7b8HkvULrbVoKOlaEpqXlehXMCcOayPIoWtJnjb6XY41oAhULSGTz/ctynj8fERzk3ebwWk2DqfVyNel6+OlWaPMj/NITgnPBrB6w4Di0+B6WvwHFdQdaOi+sHwSvL4Zpr0INHRZ8qQacfwRTd4kasI8njGgJ762C9cOMazG8JfSbB282EYfTpQa0/dZwJPVLwcStxvZl8skEwexIvN0lWgXpkpnDNIJ5Q4YjccXFyZFYiccDUcGwOcQBRUf/bxcnKaUuAdWzWf+z6e/JZIpSTO9dAH4Xg3luppT6R5aaNWsqpZRq3XqZslhs6s+Yw+FQb7xxTB04EJblveXLbeqrr5yP0/EdpU5dyf5YL+xX6mRE+nGVanFfqYspztuEK6saqkJVkrJnrLMrhxqqLqpLKi5jXYKyqNHqtApTyRnr1qsHao667XS8ZGVTb6hz6oKKzVhnVQ41XN1Va1SkUkqp0ypJdVCPVKiyKqWUuuKwqKb2Z+qSPS1jH5vDoT6IT1Nvx6apOLvD6TNupSjV5KZSnz1RKs04bXU+TqnGJ5UacV2pKIuxPjRZqZFnlWp7QKnlD5Wy2lW2ZrEptfmeUl23KFVnhVI/nFfqXmz226ZbVJJSY3YqtfWmUsmW39823R5GKXXivlIRCfK7/C/bo0ilHoQrZf1zt6+TpVqUOnlfqen7lHpnlVJVvlLqpdlKzTqo1I0w47unWpTaeU2pDzcoVXm8UgOXKrX3hlK2TJ9pt8t2HX5Sqv1PSu296Xz9IhOVenetUh1/VurInaznc+yuUtUnKrXxorHu5H2lXp6j1IXHztveDVeq6gSlHkUZ62w2pVp8q9TJe8a67rOV2pnp+Ru6VKkdl43XXWcpdfuZ8brXL0qlya2vFp1Q6oeDzvtfjVBq0K6s56+UUh+p8xnP4E41TSXoz5RSSkWpi+qamqGUUioiVakXzir1ww/3FHBGPY/xLBClXv1ry/P67P+F5U9FJJqmtUOKW1yBX5RSX2d6PyewFCiKRDnfKaUW/Jljp6XZSU21ZYgx/p7Nnn0Tf38XHA43Ll6Mp2pVyZ1YrYpvvnGwc6fxdY5fErC0dsWsx9kfJkykOvpMa00C1PCCKpkIZJMIpwv+GbUhAJsIowy+VDaltH7gOu0oRD6d/nuTOPbwlGkYyV0Hih+5z8sUpGpGXYmDL3lMRXx4mVxcJIXPecY8CpMfN84rC6NULPO0IEpq8t0S7A76JFio5ubCpBzuGU2pAJbGwLIYmFIIaumTsSQ7fHYPzifAj+VFhRekSdd3t2D3UxhVFqZVz74a/WE8zL0Km+7Bq6VgXD2olOu3f6NUKxx8AAfuwZ47Aoa+UtHoG/5HVjRIlr9idjvEpwjIHp8sfyemSn49IVlwEodDajTSMRIPN5295QL+eqbQy13EBD3dJcXl7y1aTzl9smeOHb4BK09IWqxkfqkhKZ1fKr9L5BWWUXDu7Pf1dIc6wbKAnNftcDh2TxpH3Y+C5mWgQyVhU7UpDxM6wZE7UmX+7hp4qZoIKhYJks9oU16Wy6Ew+yiM3wrjO0GjUsI6m/qSpLyGrxR22dddBIwHqF8C9oyEfoskYhrTRs7t686Szvq5p6S9QNJVP/WQ9XtGCjju6gqz+0LPOXD4Q7mWX78MgxeLlEr6bfpmY5i4TdJcIOm07ZclggFwcYXrYVC1sKQLt151vm4ertIKIDtLxo6PPqRlTm1Zicddf2bj7ODrChbLbzNC/7L9zWD7/7r94VfX83OzgNYIE+C0pmmblFJm4GYYcE0p1UkHiG5qmrZMSRHM75q3txspKTb8/X8fqbp/P4G5c2+zd29bWrY8zebNNTPeW7rUQYcOLuTJY9wYXy+CCUOzHseh4OOLsFRndaU6YGIk7MykvXWGZOKw09qUlgonjfU85SeM4sdjPMMVjRY6sJeIhZ+4wUcm1hbAEh7iAbxgqiuZQxj5cGcw+XiMhTGEMZfC5MedM6TxMdEs0fJQWHciz+yK1+JSGeztQTdv46dLccA7oZDogFXFwF/3yQdj4buH0DE3fFtKHIVdwfwHMPMejCgBh1tkT9s99Qy+uyCVzh2KwdjaAvJmZzEpsPEm7LoN1yOgWXHoWAbGNnfuY/GfWEwS3I+Ax1FwLwKexEBINETECNMKoFigMKsCfQXETdKZP3lziJPxcNOrwz3lGmiaDLwWq2AwaVbBS1ItMsA/jhYH5OEOt5/JsQFOf+OMgbzWUBbzud55Bg8i4NRdYSmFxICHh6S8ahSDOsXFwWSuc9E0SeWUyQev1xdA/+hdWHNenEaTUlLv0aS04BXJFlhzDkatAaXB+60kRQYiczLzVbj+VAoIFxwXeZNSeSXltX0YLDsFA5fBiGbQWM/QBvkKS+vLbdB7ISzsK/tsHAIfbYT3XKGG/pzULwFvN4fhq2Q7kG3faAw/7IfRbSRNVjQX7LgC7fWq9epFhSUXnSSf17aiOLx0q1YELoSIIykaKAQFs3m4CnMuO8uDW0YfH1+CcDOh3zZS8NALFGOt0lUzNPQ5jvx/A9j+f5P9mStZB7ijlLoHoGnaCqAzzgwABfhrgvT6AdGgi978gRUpkoPkZOvvbuNwKEaOPMzMmXVZvDiULl3yUbCghA9Wq2LmTCt79hgj1qFzAoRWKpn1WKsfQa1cojoLsDAOeueEvKYrYUMxnnBm4Ey/mcJd3iI4Y9YTh4WF3OQ76mVsM4trdKIwhUxqBCeJ4hyxTDE5oG1EcYVEplMaDY1CuLOQwuTDnXOkMYZoFml5KKR/1j2bg25xaXzj70kLUznvEyt0D4FBAdAnQAYkiwM+fwhnE2BeWSiiR1oX4mDYJeiUF440Ebn2zHYgFBbcEHzjvWpQP3/WbUAEDNffgHXX4UkCdC4LnzeHsrmz3/73TCkIjYWLITIIn74vufEigRAeJzn6sgVEG6lKESgcBPlzSNTwT3VFzM4CfaF2CVlerWusT0yFi4+lwG76bjj7UJxc+0pQp4Q4F/dMT6KHGzQvK4vdIZHI4pPwzhroXhNerwd99eXUA/huj4xlgxpBS10go3wBWDsIjt+DXgsluvmwtTjL3nWhWRnos1C2/6iNRBUuLvBlR5ixH7r/AsvekAhxXEd4eS5sHAzF9Ii0Z23YcgVWnpG2uwADGkLdb6BffdHr+rAdfLLBcCQgray3XJZzL55HCBh2nUlXvagQD0CKNEOSna+LmytYM01OWluj2OUWxBMtAXcdI4kl1IlqbyEmw5HEAT7eEBv7Gx7pX/vL9mccSSHgsel1CFA30zYzgU1AKOCPMAf+VNmoxWInMfH3A5dZs65QrlwAFSoEMnr0LXbuNAbuJUvsdOjgSmCgMcWbvR4+ezPrcawO2BQCU3XuerQdfoqDk5mikeXE0ghfipmYVkeJJB8e1DPpY/3AFfpRhhz6dgcIxY6iJUbVWTipzOU+31AFN322dJlEDhHLZEriqt/sLmjkw53rWJhALAvIneFEbtrtjElKY04OT2qYUoCnk2HgU5hZQNR7AR6kwkcPoJYf7KgsM/BUO0y7BzvCYXZVqJQNvf74M/jktNB7x9aAcgHZ/xanQmHheTgWAl3LweRWUOovpqIiEuDEPTh5D04/ELZRqk1opbWKwchWUCa/0dPCbIkp8DhSlGTDYiA6EUKj5f/oRLBZ4GmMRBml8sGdUNmvfCG48Uj+LpwbQp6ZnJApOiiUT46TwwfKF5XZby5/KJJHqL0Fg/QlV/bU4OzMzwsalpYl3Z7GwpHbsPgYDF8mtSjVikrNRGYxQVcXiUKalhGntOkSdJkNZfNJVFAnWORbbj+TaGLyLpjwItQOlv3rl4Aj74lz6LdYopP01NHukfDtbug+D5a8LuQUEMpvkA90+wXWDRLtrTmvwfvrYH4foV4D/NgDhvwqDi+vvzjAMW1gyh6Y3FWiFIsSUkI6bbhrNYlw+uqPcaUCkk6rWliKL6celPXeHnJfmM3dBWyZKhEtSEdVlaVE0TAbifjqRcNxNpEDsmZ3g/2n9m9q6w8tuzlf5l+sLXABaAGUBHZrmnZYKRXvdCBNGwQMAihaVEZvf38Pp3a7me3WrRj27Alh5crWTJhwh379iuDrqxcd2RQ7dtj5+WdjwD98XlR+K5TIeqzF96GEnyiiAkyKhlGBQgtNtzjsnCKJr03RSCp25nCfqZlSWjnxoKEudxJFKgd4yvumbWw4GMd1hlEqg6EViZUvecB0SmcwTNLtETaGEcUcclMECReu2+30TUxhoa83FU0j16YEmBgBG4pAsP71t8XAxw9hdklDsfh8HAy6CK8Xgf0NsuIgd+Lgw5OS8prZECoEZr1uqTZYdR1mnxdV3qE1YOYLciybHXbfhuWXRL3242ZZ949MhD034cBNOP1Q8vKtyshMfFjzrANnbBJcfAR3w+BqiKSLvF3h6mNxLjWKy4CSPwAKBEGp/IJl5PKXOoMcPgbV9a+aUpCSJums2CSIToDIeJFJufYY9lwUyu71UKEH5/CG8kWgRH4oWxAqFobieWV2/3tWIABerS0LwO0w2HIJes+VAb5aUehe21luBMQpvVZHooFDt+GXo/Bws9R31A4WgcVLITDzIPx8WAbz3H7C9nqvlaS7+i+ROpHhTcUZftQO5h+DtjNh7UChN4NELZFJ4nyWvS4O68UqMGwFLH5dtsnpDd1qwkcbYF4fWfdqDZh2UNhq+XJIlDT3KPzQXd4vl1+wkFSrpBxblpO6mqqFRXI/JM74vrn9jP4jYLQ/drom+hAVrGOUCkV1XnT+XXHJwEhSHJDbHY7e/JMzgT9j/zqSP7QQMOl/QGEk8jBbf+BrpZQC7miadh9RoDxl3kiJjswcgFq1aimAokVzkJSUvSOx2RwMGLCXadMaExdn5dKleD7/vEzG+6tW2Sle3IWgIGN0nDAfvn0767FSbfDDTTjQSl4/tsrydaZUzCwiaI6fUzfCpTyiIwXIpSdBk7GxiFt8rYPpCsX3XKEzxfDDGMFW8IgG5KK6HlJbcfA1D3mfohTKlFCNxs5XRDOVIErpx7hjtzMpJZXFft6UN41MC2NhcSxsLwaBrvKgffME7qXCnorykCgF39+Hnc9gSfWsQovJNvjqAtyJhZGVoGk2RXTxafDzBVh6FV6vCGtektaxALcjYd4ZqTLO5QNv1JQ+GSCffe4xbLsK265LXrtVWWnXOqObsxpuSDSsPwPnHsDZBxJJ2ByCI9QMhuYVYGALybVnTv/8HaZp4OMlS4E/EWnFJML9cLjxBM7dg81n4Nx9wZNaVoXgvFC3pFRq/57+V+n88G5+eLcNRMTDmrPQa65QX1uUh67VnZ2jphlRyvnH8MVWoQW/3Vxm/rNfg3UXoOV0kTfpVlP2KV8ADrwLk3ZKWmteHxnM32ggqaReC2Hp6xJdALzTQiKcL7bDuA7yG+69CYtPGBFFl6ow+4ikJqsWlvTYW41g5mEY30EIAItOS51OOs5WICccvy+EgkqFYPZx6FdX9i2aRyYobq6Q5ibNq9IhVDcXiMsckbhbUCieIPNWDY3iOBc/JROGmw6+Rynwc4f4+N+OYP6y/YuR/KGdBkprmlYceAL0AF7LtM0jpHLysKZp+YCySAHMH5qrqwthYdlr3kyefJbWrYtSq1Y+Ro26RM+eBTKq3JVSfP+9jY0bjV/vzDVpWlW5VNZjzb8HfYtLT2+A8dHwWg7ngqYHpHGOZMaYAPEnpHCRWKZRNWPdOu7TlWAC9TvnEE/JhSe1TIq+l4nlNJEZdSUAs/S+7PUySTekoRhCOIPISTX9mI8dDnqnJDHX28fJiUyLgQspsK2oRFIWBwy+K/Ugs0qK7EmMFfpfkiZUm+tmFU/cGQJjzsDAsjC+RtYBLiENvj8DVyOlDezx3tKMye6A9ddg2y1R2x1QE8a1EsegFJx8CBsuw7YrUKUQdK4MI5pKSkh+M7jxFI7egT3XpLaiUICAxHVKwpAWMgP/M7iHUsLQehoNkXEQHiPqsVYbPImEhBR5tp9FC6ieZgUXm1S+K6BwHngWIzP14oUkneXrBQVzAy7SQyPQH3LnhHyBsuTJKVpPZgv0k6VGpgg4KRUuPxbQ/etN8pnRiVKE17yCtKf9LceSJwe81VyWexEw74gwnXrVhd71RI7EbNWLCCB+/B4MWCrX87MX4OXqUvH+/noB7X/uadTdfNkRZh+GNj/A6jclcmhbUSKpTj/BtqGyLQiQ33G24CEdK8HMbtB5LrQqL85L02DlANEkS7eeNaH3EiPqKBIEu25CJ51F2aqsRKnNy4gM/+Wnxr6aJn1cigXKJCUy2XAkLhpkzplraKRhx/N3WhRaScpwJAkOKOAOwcG+HD/+m7v8Nfs3Ivl9U0rZNE0bDuxE6L/zlVJXM1VXjgcWapp2GbmkHyqlIv/MCeTJ40NEugqbyU6cCOXMmSesXv0iT5+mcPhwJN9+ayB2u3bZaNNGo0ABY9SZvBje65X1M9LsMPsOHNKjkTsWuG0R0Nls03jGaPI6iTLO5A79Cc7AN24TxzkimaID7DGk8St3mWKCjZKw8iO3+JzKGRjIIWK4SwrTM4k2KhRTiKEdPrTSAfpIh4NPU5OZ4eVDZVfjJ/o6SnE+TWNpAWFTASQ7oEkOeD2vPIBXE+GrO9CvEHTNBJTHW+Cd0yKRsq1NVrkTix0WXYGZZ2FwNVjcUaKJZCvMOwc/nBAxxTGNoaQOuD6Mhl/PidJs5QLwWg0Y196IOmKSYMUpOHUf9lyVtEaL8jC2k8yOf0+QMc0i8uK3QuG2vjyKkDasBQNksC4QJLN9DciVQwQTi+cXSq+flzC1PNxlNu+qGcV4Ssms12YXR5OcBsmp8n9MoiwRsXDloTicsGiwaRARB37eULe8HL90QShXWBZf00Dq6wX1SsuSbiFRcPA6bDoLg36BCoVEFqRdVQHqs7MSeWBCV/iiE6w/D6/8LNdtTDvBkcxWvwQcHgWLTsJr82F4M2hfEeb0gg0XxMmMaWuwuwY3hpK5ZdvFr4tTb1VOnPHba2FuT8EpXF1gaV8YukqkUooEwuiW8O46WNlfjpVOI043Dzep4t9wBXpUh1erwg9HDEfSpCT8pKtAeblLRGbVdcyCAyXSLRYo+Ft0KuinjIsGeTN9Vj40XNF4jfLO9w+RKOx4kQ93/HHTny8NCHCFmzf/S0rhv5Zhf8qHKqW2AdsyrTNXV4YCbf6TEyhSJAdPniQ4rYuLS+Ott3axbl0X3Nxc+Omnu3z8cVmn7ofTpln4/nvjyb39CKLioF5lstjKh/BqUchpikY+CnKe+V4mhUQUdU2KoeeIIQB3auitcKUW5BojqJjBCPmJa/SlNH4mYP5HbtOZQuTXc7bhWFhNGOMo5eSkAOYRTxwOPtZB/BSl6JWSxCgPL+qYpr/fxCqeOWBZAUNsEUT6vb8eQG2NgLF3YFllKJ9J+PTIMxhxCt6vKCKV5u+ulFSqf3YMepaF432kR3lCGkw7Acsuw+tV4dAbMuu0O2DrNZh5RGacA+rC0REGOB4WB2svSLX3racy0+1VF7579bcdR0winLkr2x++DrefCo6Q0xvKFoIyBaFpRSieTyKDf5KtlZgifcRvPxXndu0xHLgsTqheOSiUG2qXkiXIlFIsnAt6NZK/v+omEcvBG/DCd+JIXqsPL9YweoCYzd0NutWGV2vB3uswapVEEV+86NxQysUF+teHFyqKcu+6C1I/0qWaUHe7z4O3GhtpqVblxVl0+kkYWUWChE32JB4GLIdlfeVa5/KVdNXglbB1MLSrAGsvijBli9JZzxfg9dowdK04ktpFJZ2anrIK8hX8xWITp2NVIrNfNo84z8eJcgzNTRxJxvfTINQUkthQxGLBHVeq4zwzjOAYCjtF6EoC9zMcSajdkAR6bvZvRPLPWsmSgUybZkApNpuDUaP2MHZsfYoXDyA6Oo0tW57w+ecVMra5csWOm5tGuXLG3TB9OXzQO+vxbQ6YesPARm5Z4KEVWmeajS8nmvdNKS0Hip+4xxcYn7uHEGqSmxJ6auoMzwjAPQNwl3VRRJJGWx2sd6D4krv0pSCBJvwE4DgpbCOZFfr+DqV4LzWZ7u4etHU3tv0xTnE8FVblEyfy2AJFMk2mZj2EMwmwuybkMr3nUDDxMux9ChubS88Qsz2Kh0+PynOwuYv0PbfYYeYpWH4FuleEkwOkoDApTWaVC85Ao2CY1lmEFM22+zpM2CHyGxM6G2qxZlMK7oXDoetw+CakpUFIpGgt1SsDX3QTAP3PsqL+T5ufN1QsJovZHA548Ez0wPZcgnl7hTBQuxQ0qQAtKouEOugtb4vKMqIN3H0G2y9Bs4kiyT6gqbSszewwNQ1aVYCW5WH7FegzT3CSD3VpknTLl0PYVivOQIvpAsKXzScFhAOXiV7Xh23keA1Lwqzu8N4G+LGbANxvNhBxyZ+OwNDGcsxmpWHNBVh8SvCMT9pCzyUyichuglAyt3SJDI2TFFguXzj5SOTxQfqvXAqT3isV8or+Wtk80nPmvl4/EuiVtQDRjGykmroiZjYbyXiaWJbpk79EB/i5QNmyrpw7l+2u/5n9j96v/yfsH3ckRYrk4PFjAcmk9/o2SpUK5KWXJAX08893eeutUriaEsrTp6fxzjvGQBsdB8cvwozRWY+/4iG0L2hgI/PiYGymaOQ4SaSgKG/S09rJMyqTk0J6VJGMjbU8yEhpWbCzgBt8YapeT8bGZp4wmvIZN+1GwimLb0aXxHQLx8Y4IlhAQTz0bb+2JpPPFd7wMEaEFYmKSxZYkQ/cNY3tcfBVGBwoI+ktpeDzu3AjCZZUBk/TAx2dBh+dg9xesLu1c+GhQ8FPF2D+VZjSFJoVkWNtvAXfHoU2JWBnb+kVEp8K3x+F5RdhYE3YN1g6DmZnrcsbjZDMlpAC+69JD4oTd6BRGan+fqsVVA/+a07DahVsJCQCHkdI2ik8VlJRmoI7IYKflCkENx7IPiXzw71H8h39fKRjZroFBUK0zi+sWBriEiEoB5QqAp4eUCCXYCqF8kLRvNm3IwAZTEsUkKWbXqiYZoUzd+DYTXhtuhQStq4CnWtJ//H0+7BkPhjeGoa1gmO3YfN5GL1CcKOe9cAr08RB0+CFyhLtzT4IDSfDuM7QqarzNj1rS91Gj3kw8UWJNpa+Lv1TRq2FqS/LdvVLiADnK/Nhx1uSbvq2M7ScJWKRdXSnOakjtPwZXqwsisy1i8CaS9CtWvbXpFohWH8FhjWEdmWlb326I6leWDpB1iossvr6MEAhPzgRIn/n95Oi4YzvBASborxUHBQ3jeARPMMHX3z1zIK7/tz5mfhCAW7gqeD27d/Jq/5VcwHT8PH/nP3jjkTTNBo0KMjEiYfZs+chXbqUYcQIYVykpdlZvfo+x4+3zdg+NlZx9qyD2bONU1++E97smnVWpBRsDzXqRu5Z4FiqM1NLoZhBOBNNdN807OwgjHGmaGQTD+hI0QxW1nHCaEYh8pi6Is7nLjUJIrd+Rz0khfWE8QvO+TY7ird5xifkJr/+E2y0pXHWYWOVpwHE709RzIiDXQXAU9PYFw9fPoWtpQwnMuaORF3LqzgTB67HQa9j8Hkl6FzE6eN5nAD990DLQnCku0Qbd2Pg7d3SeXD1K1DAX9JW3x2GQ/ehbWk4NfSvCSRGJcLmc7DmNIQnSFqmd0P4od+fY2BFxsHVR3D1oYDmx68KRpE/SAQdC+eGInnldZnCkCdAcJIAP6EA/xEFN7MpJThJTIKkSWMTITQKQiOlHuXyA3ik15/4+0G5IlJrUrkkVAmWz81snu7QsLwso7tAXBLsuQwztsOlh9CqCrzWSFrLpsu2NywjS3g8/LQXXv9FyAhvNTfqPNLN1UUaTb1UA77ZAevOwfQezumxqoVh79vw2gJ4FCPYyI89pCr+y23w+Qvyue0rCOY1dgt800V+o4W94NX5cPBtqffx95b+Jl/thimd4eOWMHiNYCDZpRtfqgx9V4gjaV4SZp8x3qtWEBbrEUGRnLD1jvxdIAeE6PwbdzcIMzl9TYP7ptqSNBwkYBQ0n+UU5amIL36kEU5OvX15oqkU7rYVXC3g+xu41L/21+0fdyQAX3zRlHXrbjJ9ehsqVzbynKtWPaRTp8J4mYjja9daeeMN94zOiXY7zFkLRxdkPe6Op1K9nV438k0MfJiJFXSERKrjTXETd28DodQkkJw67hFOCkcJZRqNMrZpSiGnAqhbxHGbBIbqXRHtOPiKO4yhBB44e7jZxNAEHxqZqt/zaS7M8/THVT+56xZxIuvygZ+LxukkGP8UNpWAXG4SUQy7JZL335Vx/k57wmDiVVhcHyoFOF+TzffgsxMwvSk0KSR4x3cn4GI4fFgPGheVAXXVJZh4EHpXg1U9szqQJ7Hyf6FMx0+xwKYLsPS41I/0rQfTe8uM+/csMQVO3IRLD+DQVbj/DOqUEomVisWgbS0Y1kmcxd+Fj2ia3ovEGwrn/f1to+Lh+iNZNp+AsYslCmpSSWpKGlaAaiWzRlo5feHlerKkWWH3Jelpfi8cejWEPo0hSHdIeXPA510lpfjTfqj3FbzTRphbmR1x/pwwpRssOwlNv4U5faB2ceP9IF/YMFikT+JS4IM2MOVlGLEGxu+Az9rLdkMawcDlMPcYDGoovUrebQ5f7ITv9NKMvrUkpXU/UlQH8vnDzhuiJJzZCuUET09pFxzoI3I6KVaZvFTOZ+AfJQPhbrT+vX0gXHckOTzgduxv/w6S2jKerzTS8NCfZRtJuOGHHQsuprRysgNUMpQuCwcP/vax/5L9i5H885Ynjy+DB0vYEBubmtF+d/fup3z9dfWM7ZRS/PyzhV27jKnElsPQvJakKzLb1OswU6eTh9lk6WCahSgUPxLJJFM0koSVI0TyramwcBHX6UPZDOZWuqWnrxwofuA6o6iYAaavJowGBFDBpNUFcI5U9pHMClP1O0A9V1Oqzq7oHWVncS5X8rtp3EqFgY9gYwnI6yED/Wf3pDp3UknngXXZA/jxDqxraDhQEEbM6OPwLBn2vwwBnnArCt7cBk2LwrwOwtC6/AxGboXGxWDvAKFfmu12OHyzV4QBZ7xiOJIrITD3EOy/Af0awHfdRNbktyw5DQ5fg90X4c4TkUKpWwaaVoIu9QRU/28cht0O8Yni2JJTIDVNgF67A9AL3NzdJE3l7SXOw89HXv+Zz82VAxpVkiXdHA64/hiOX4dZW6R2KTENWleDttWhbGHnY3i6Q8easkQlwMrj0PZr6VvyTnspcAQhMbzfDoY0gx/2QIOJMOllaFXR+XiaJk6mQUkpIuxfX+pD0s3LXWi6b6+GiTvg43byG3ZbAEtPQW89Szv9ZWg2QxptVSoo0vNzf4RLoZKCcnGBPrVg8n74+VUY1RQ+2pq9IwGoURD234OuFaFeEelH06S4RDi34uV+DvQ2MB4/D0PkM8BTOlOmm1JQ2vRIWXFQ2JTa8tT/Abjghyve2EjBDwPQKuIOKgWiY7I/3//Y/sVI/jcsPDyJTp3WceJEby5diiUpyU7BgibNqpN2ypZ1JTDQeNK3H4V3Mle1ABdioKA3lNUzRT/Ewsv+zoPESZIphDvBpmhkLaE0Jjde+l1xlzjccaU2vz1F3U4IFQmguO40wkhjM+HMy5TSSsTOJCKZSj6nJllmsynFm9EOvsjpQkUPjSgbjHkKi4pBMf00x9yVvPH3pZ2/zy93Yd0T2NlUCq7S7VkyDDkIDfPDND13v/CyVKr/0ApqFZQ01sQjsP02/NBB+oaY7XEMfLld0mg9asCcHjrb6zJM2y3SFi9UhindfxvveBQBm8/CpjOgOaQvd6uqAq5nNxHIzux2SS/dewoPwyAyFm49grAowUhsqYAmKZ9ShQQL8faE4PyyrYsmUU14lDgWdzcZxBOTISEJPL3lf4CqZSXyK5QXygVD/txQqrCk0zLXk4AMsOkg/JvtZN29MNh9AX7YBsdvQdtq8HJ9qTsx/3a5/GFoGxjSCnZegs/XiFjd512hsp6a9POCjzrCa/VgzBpYeVocSu5MxaYl8sDOEUL3vRMO4180alZcXeD7V6DnfJi+D95uAUv7QLufpN9I3WCJAuf3giFr4MBQ+T1ndIXvDsCinnLeHSpIeutJrBAu0hxwJxJKZSrwBUmLbrsljqRuUTiuOxKAQv6i1VY4h6Sw0jW3IvT0lZe7tHfOME3o++mWioMUk6xfFJEZEUkij3DDFxsp2DCYoVfSwJYmE4jnZv9iJP87NmXKaUaMqIGmacybd5cBA0o6vb9pk4U33zRGyAehQvstVzzzkaSKvZ9eJJZoh8Mp8EUu521mEcVnJqZWEjb28Iy5piLChdzmNUpmRB/3iacofrjq0UkCVs4SzfsY08MlhDCSYhnOKN0mE81L+FM0E3vLbJ/FOajjAZ18XLA4oNsjeD83VNUH2h9D4JkF5pd3Hoi+vQEno2BDQ4ks0u1KFPTZC1MaQIvCkGSF0fslvbCnhxQann4CQzdD32pwoL8zthCfKhXKGy7CZ+2gQ0UZXJefgq2XJBc/8zWpGcjOQqNh5TFYcxzyBkCryjB3sFQv/54pJWD6hTtw4S5ExcCRy/JevQoCPgfnhwrB0KqW4CR5Ap9f9bvDISSOJxGyRMbB7tPw03oZWO89gzJFoHppqFoK6pTPHiMpkR8G604lKRV2XYDlh2Hgj/BKfejTVHS80s3FBdpXk+X4bXh/uQgbjn7BSA8Wyw3Lh8CGc9DyWxjXBTrXcP5cHw/4tb9UsPdeCIv7GU3E3FxhWX9R7l1/EbpWhUW9ocsvsHuoSKRUKgCty4jUyegWUj/i7iJKBR0qyL33QQuYexK+aAv9asGC0zChfdZr0CgYGuvPaO1CsOGG8V69wnAnWhxJfl94liQNzDRN7jMfN0gyYSKZa9FTcTilji2m1JadFFzxIZVoXE1YpgIsFsifjZrDv/af2f+MI4mLS+PRo3gmTGiCxWInPDyFtm2NaXFqqmLLFitffWUCtzfCgC5ZjxWRCo+Tobn+4C1KgI4+RhEfwEVS8MGF0qZoZAtPeYXCGRWyN4hFA8rrdSTxWPiGc0yncYaLWMQ9apMnQxH4CNHEYaOuvk+6HSeF+1gZhzFlO2S30MjF6CmyJcXBRQtsziMPxvhw6OQP7fWoank4bI2BDRWcNbNm3oKz0bC8vnMV+4FQeO8ILG8N5QLhfhz03AojqkOv8jL7++qQyJ0sf1W0stLN4YAlZ2HqAXi7CRwfJWng9eelyrp5WZjaXWimmc1ig41nYP4BcFXQoQZs+EDqP37LbHY4cxuOXoEjV+BuqKS68gVCtVJQpRl8NzT7SODvMBcXyB0oS9UyWd+32uDWY7j+EHadgQnLxFFULwPNqkLzqnqVvMl8vaBrPVkSUmDNMfhitZASRrSHFpWcJwf1S8POD+DQDej1M7SsAB91ksgEpMNgo9IwfgvsuSEYiVl+RtMkfTV1L7w6F1a8YbC/PNwEUG85U6TlKxeESZ1g9GZYoEcdY1pCv18FgC8WBJ+1ge6LoX05uT6dKsIXu2FMC+hcEVZdlvsmM+nF/J1KBYmIaLrl9oUb0dAsWCKpkERxJBVyC4vM113EI9NNKShjcth2HOQ1TdgCyIG7/ix6kQ8X3LGRhqdJdaK0K6SkSsHrc7V/U1v/vC1bdo2aNfPj5ubCtm1PKFjQx4nyu3mzlQ4dnEH2c9fho/5Zj7XgHrxUxJjVzIuDvZny0xuJZ4iJY56CnS085RenaOQmg3TWB8AqbtOVEnjod8xjkrlKXAbAnoaDn3jE95kqbFNwsJBYJpE7I7I5ZLcwzZpCY11AKcSmWJzoYHEuV1w0jfnR8NAC4/S0xpE4mPEEdlV2bk878zYcioBf6zvTe9fcg2mXYXtHAS+PhMCI/TC3NdTKD2EJ0G8j1C4I87o473s7AoavFXrmoeFSFHj4jjRcalwSto7M3oE8iYZZu+DITWhQBmb0g9K/g5PcewpbTsOe80LjrVUaWlaBbwdCyYL/WxLxmc3dDSoWl+WVZrLOaoVzd2D/Rej3DdhdoE5ZeLEu1C3rHOn5e0P/lrJcuA8/bIeF++GFGtCtvvO2TcrB0bEw9wAMmAcDmkAbPWua2x++7yHpxVZTYcXArEKPo1oKXbvXQlEJTn+sAnzg136Ssvypu7C21l2GZWehdy3RxepfBz7dDkt6iTNpXx62XIcXK8p92LUSrLsCr1UXQP/oQyP6yM5cXCDWBnGpkNNLQPbd9+W9XF7wVE9jJSuIs0ph7O1EY38F3DU5gBTsWEyiKVE8Q9MjlGQeo6FhJwWHaZvbaVK75PU8U1F/A9iuaVoRYDGQH1GGmaOUmp5pm0BgPiKWmwq8oZS6YnrfFTgDPFFKdXy+Z2jY/4QjUUqxcOFltm17BYAVKx4yfLjzNHDLFgujRxu//P4zUCR/1jynQ8Hyh4Ycyt4kaO8j4obp9ggL10hzSmttIpR25M+IRq4RgxeulNB56JGkEkka/U2U4DncZjClMwD2FYTShbzkyaTeNpMY6uBNEZ0FFu9w8JEliZWeOdA0DbtSvB7lYGxOV3K5alxIgdlRsF8H0h+lwgf3YXUF8Df9Yr8+hG1PYX1DZ0fw622YdxO2txfWy4obMOM8bOsqoounnsDY/fBefWhjyh7aHTDrCCw9CzNfktqB0Dh4f4Oo4C7oI42XMtvlxzBzp/w/rDV8/nL26rtKCStr7VHYdkqK8zrWge8HSwroP7WkFAgJg6eR8CwKEpPgyTMB29MsEBWl621ZJCpISJTrGlxYUmY+3lBUZ7AF5YS8uSFXkGAjhfJBTr/fl3JJN3d3kU6pWx7G9JAIZc95mL8LPl0MVUvAa83EYZqdZLXiMG+oyN9/t0mWsa9KrUmG6q2L1JR0rgGDF8C6M/BtD3FImgaj2ogEf+dZsLC/iF6a7Y0G8lsOWS5y8OnHLZtP9LOGr4YlfYXS23meqDPnzwFty0l661wI1CgMfWvD6yvFkQC8UQfeWCuO5JVKsPrK7zsSgEp5RMetQWGomlckeAAK+kK4TvUN8JRCxCAv5wjGgbMceSoKT37/x7GRilsmACPNBr7ZTIb+Y/t7WFs24D2l1DlN0/yBs5qm7c7UVPBj4IJSqqumaeWQJoQtTe+/DVwHnue3zWL/E47k0qVn1KqVl9y5ffS0VhK1axt5loQExdWrdipVMk532TYY8krWY+0Lg44FjaZNM6JhUiacfAEx9Delnmw42Es4U03CjLsJoReG9sMK7lKfAhnaWdeIIxUH1fTjRGNhF5EsMLG9AO6QxglSWGViaY23JvGOuw+FXcRpzUlU1POEpl4aCXYY8BhWFAUfF0ixQ88bMKUEFDb5pz3h8GsIrG4gNRXpNv8GrHsAm9vKbG72Rdh4F3a+DP4esOQizDoDK16G4ABjv5BYGSBalYIjIyT18eNhoYFO6iSSGJnt4kMYv0l6ZHzSCRplU4kNArIvOwBrjkLJAtC9MXz4irM21R+Z3Q43H8GVu3D9vhQa2m1w57Eo9darKGmVfLmEulu1LOTwA39fqUT39BBGVrpDSNfaSk0V2fjkVIiOFVwkPhEu3YUn4RAaIXLyrq5QvCDUqAAlC0PV0lCi0O/Xqvh6Qef6slhtsOscTFsP0cnQoTb0bS504HQrEAhT+sGzWJi8GaZuh29fg7qlTNsEwMZ3YNlxwUcWDhDNLoAmZWHpm9BzLkzvDo0zpeQ+aSd1I9/uhQ9aGetfqyUtbbddFWmVkY3hk20wr4e8P/EFmLQPVveF4kGQ108q1OsWFT0shUi/tyhp0HZ/z+oUhHux4kjy+kJn/Tzz+sElXaGveE5ItIkwaV5TKsuhoLiJXKBhJ8DkWoJMqWNvvWupFTuYCoKLeUhE8mcmB3/aXHju6r9KqafAU/3vBE3TriP9ocyOpAIwSd/mhqZpwZqm5VNKPdM0rTDQAZgAjHq+Z+ds/xOOZP36WzRtWhSAw4efUaqUP5ppRNq2zULHjiYeeIoMKHUqZTkUc+7CGH3Qe2AROYRKpgErATthWGll0tTaSzjVCcBPvxy3iSOCVErrN18EKVwnhqGmaGQLTxiM8YSv4ClvUNgJ+FMoPieST8mV4YBO2q3cVg6+cZXo5IpFsTjJwcG8st+IUHg7D5TWz3nsA3gjH9Q3zSeuxMEHl2FHQ/A1/YLLbsPKe7ChDXi7wfgTcCsaNnaWiGXcYYhIgj19hGKZbjtvwIfbYHpnaGqKUIoGwrF3sxbBPYyET9YIUP9eO8nlZza7HbacgZ+3S73E6y3hwESjP/rvmVLCzDp2BU5dh5OXpQ1u2aJQqwxUKQXdWsnA7vWcH97fMotVyB03HsClO7Bqj7S4jU4QJ9a0OtSv/Nvfz90NOtSRJToBFu2DFp8KljL8BZGbT7d8ATC1D1x/Ap+tEcmSST0gQHc6mga9G0jPkj6/wCcd4SU9I1s2P2wdAQOXyKDbtKxxXE0TZ9L+JwHP25oysLN7GBLvXSrDT8fg7GOoWQRqFJGo4WIoVC0IbzeCn0+JIwHoUw0234C36kIfg63/mxYcAJvvQO9Mz28ebwhPkb+tiIq1h2um1JYGj00RShL2jDoShSIa8UQO7KQSrh8r1amO5KFF6ODume7r/2XTNC0YqA6czPTWReAl4IimaXWAYkirj2fA98AHkKkG4W+w/wlHsn//A0aNqgvAzp2hdOzoDGisWWPhs88MkH3zIWjfMOvsNzJVqII1dOhjSRwMD3LeZh3x1MQnY2BXKA4RybuZoo8eGCPqau7Rk5IZKazzRJOMjVL67/OIFM4Rz1sUdfqsnSRRADeq64wRq1L8ZEthqocfmqZhU4rB0XZmB7ni4aKxJlZScH0C9PN/BmFW+NaU9olMgy+uw691IK/JQW56AOvuw/o24OUKX52Ae3GwsJ3MGIdsl/TID22MHLnDAeP2yOxyzyAZsEBm6k/iJOVhtqQ0+HEvrD4F41+Cts7BFyBFeXN3w5KD0LwSzBgIpQtl3S6zhUfDnrOyPA4XULhhJejaGMa9IZHFf2tKiYNL79n+VzAYD3coU0yWF5s6n/eJq3DmJoydL+f9UhNoXgMql8j+M4L84d3OMLIjbD0Lvb8XbbGxr0rElm7lC8GqkbDiODT7Cr56FTqa2FmVCsP+D2DYEqH5jm4nn1cgAH7pCx1/gBk9oIEponF3FVxk8AqokF+UfEFqOtJN0+DbF2HKQVjcU9aNaS4MrZldoWGw1Bql4xydykOX5eJI/oyVCYJb2dRw5PE2uh/6u0OCNev1c+BMNEnDQQ59GLNjw013GMLYkgfERioeuoS8XYkChNX6nzc/+03766Npbk3TTLX+zNF7NjmZpml+wFrgnczNAoGvgemapl0ALgPnAZumaR2BcKXUWU3Tmv3lM/uL9o87koiIJFxcXMiRQ+7kvXtD+eILI8WUlqa4c8dBpUpGDuHgWRjeI+uxVj2Cgfr4b1OwOh5OmfK1CsUG4lho0t25QBweuJFbj0ufkownrlTSgfgY0rhMNEP0aEShWMg9RpkA9Z94xBCKOvWITkOxmUS+MIXaP9lSqODiRgk9pTU93sGrPhpVPDSeWYWldUAffG4nw4+hAq6nP0w2B7x2Gt4t5dyo6mgYfHUedr4gdMmvTsGzVJjXRmalfTdBrQLwbh3jWIlpMHorBHjBljcM5/IgCvotlXTH4IbGZ2w6D59tgLeawfHPsvbSiEmE6dtg/Smphzg8QfL3v2d3nsC6w7DluM7OKgFDXoSaZf+cvInVCo+ewt3HEB4Jt+7D0whQDrh2W5g57m5g18FZTYOCeeDpM539UwJu3pX3SpeCuyGQK1Cwkxz+ULSgLCWLQqlikh7LbHmD4MXGsnzcV2pZDl6Er5bC7SfwahN4sSFUygY3cHWFF+tAp9pw4Ar0nSECj2O7SV1J+jn3bAAtK8K4DbDzCnzX0xgEc3jDggEwZDGMWiF1PC4uUum+cZjoaQX4it5Wxjn7S7X6gF9FVyu7FE/VgtKa+uBdiVIbl4APd8HTeJEw6VUVNl2XCCS3r7AFnyaItM5v2bEQuQ8L+okDymy5veGujpHk9ZFBHyCfaRJhU4oCXpCOlLjiyNCHsJBGLj3VbCMVL52pZcGGhz6Zszggn+5IPJ5nRPKfYSSRSqlav3tYTXNHnMgypdS6zO/rjqW/vq0G3NeXHsCLmqa9gFS45NA0balSqvdfPss/Yf+4Izl2LISGDSUCCQtLJiDAEx8fY6pw5IiwtdJTXQlJcOpq9q10lz6ATU3k752J0MLXuY3ucZIpjyc5TTy9FYTyusmxrOc+DU0g/G5CeIniTtFIafwpps9wbpCIP27UyiTKuJg4yuNBHv0SP3XYWWlLY69XAAAPbIrlKYqj+VxRCj4Ig6/zQ6CbOMH+t2BmKWdw/dvb0Cw3tDdFKHfiYfpVWNcaAj1hxgW4HAW/thXw+LWNkpMeZZothiXAS8tgWF3oZUpFbL4M43ZItXN9feCLTIB3V0iKYccoGaDMlpQKU7fA+fvQvjqcnvTbooYgAovL98PqQyKq2LQyrP1SigR/z55GyO9+7S6cvSqDfvliMiCUKCL3Q+3KUCAv5MsNeYL+etrLZoOYOIiIFrD+0VM4fQlW74JbDyS9VbsKFCkANStA7YryOWbLEwCvNJUlJQ32npVI5VkMDOwg6zOnvzQNmlcW57viCLw1R5SCB7UyBvm8OeGHvjBjF7T4Gn59S+pJQOpC5r4OX2+HIUvh596yX6FAaXDVawHsGmG00AVoUgqqXoFZh6X5WHY2thW8v9lIdw6sDb+cgbEtoHsV6LfOSGV1rwz77kMvPUo9+hhKBBodNQFmnoNPGwi190asUXyYbjk9IFZ3+i4ahOnyKeEmlpbSINIk4piAFXfdlVixYkF2cpCCQy9UtGHFXZ8oWpF0t0OB9x9MdP6S/Q0Yie4Y5gHXlVJTf2ObACBZKWUB3gQO6c7lI31Bj0je/7ucCPwPOJIrVyJp1EgG8mPHImjXzrlKaONGK507G1OHbUfghUZksWtxkNtTlG4B5sXA55lA9uXE8qaJ8htGCn64Ul5PUSVg5TLRDNajjxRs7OEJP5o0tpbykBEYKOYcntAf53OOxc4a4tmAkaKbbE3mC3cfvHSHODHOwZQAVzw1jbVxQkFPrxeZ+AjaB0FN04O/9SkcjYJN9U2fkwY99sPcRlBYf2Cr5oYhOjX0rZ3QuAiMrG3scysCBq6HiW2gme6MHQ5ppXr2Mex8y9B62nIRPlkL47pC50y5b4cDlh2G7zbDwJaw4p3fdiAOB+w5B7O3yqDZohpsGifdB7MzpQSLOHBWlgdPpNivTiWoUwFebS1dDf+qKOMfmZsb5MklS4VscB+l4H6I0M4PnYVVu+HyHahbGdrWh6Y1IcD0m3l7QscGsoTHwNrD0PhdaFUDRnSBYpkYcC4u8FoT6FwHPl8FLcfBzwOlHwvItXu7rbTu7fI9zH1DOi2mvzemPXywBkaugB/0WpCKBWFSZ/hoo3RINKsOjO8AHeZClypGistsFXTG2tknULOQOIsB68UBFM4pEXJYAuT3h0bFYPwhw5EceyKAep/KxvHK54LrUeJICvoJ1bew6Xq5ukBD/Zr4ZipETLf01FS6pZkKEq1YcNeZkXZSM1Jbrrjhke5slNSTpaSC759UU/gHrSHQB7isp65AWFpFIaMnVHlgsaZpdgSEH/APnOc/70gOHnzCwIHVANi3L5xXX3XGGQ4ftvHttyaZlCvQLxs29Ir70FvfNdoOT2xQ1RQ+R2LDgqKKiQa4kWfU1vupA+zkCa0pkoGf7OAxrSiUobF1gzi8caW4DtTfJAkrisqZsKyfiWUgAXjr+1102LivHLTSAfatKQ5SlLC04uww7hns1QeEi4nSe/0XE+MmJAU+uQZ7Ghn5YYeCj8/Cx1WhuqmQsGlhGfAG7YCiOZ2dyJUw6L0aFr8CVfRUR1IafLQZ/L1g0yB5mC02GLNWdKp2vy/igWY7fx9Gzhc59ENfOjOPzJaSBov2wPbTkrf/tBdUL5X9tknJsOcMbD0GF29L5XuzGvDZm1C++F9j2NhswsCKiZMlMUEGjtRUyUBYbXIdvX0k9eXnK6msoECh/nr+xsxS0yT6KVEEXmkj69IscOqKREuTFglDrHsb6NBQ0mjpljcQ3npRopINR0Xk0csTPu0h0vRm8/WC7/pKo693FsIr9eCNFkZasl4pWP82vDoTvugKHaoZ5/fNK/DlZvhqK4zVn5N2FeH0Qxi3DcZ1Mj7Hy12KDkdtgtX9sv/Ob9WF6cdg8atSLZ/DGw49gOYlJL218y70qwYV88D1SLkvXTSoWxBWXXd2JFXywiNdqaRmfniY4OxIAL5vIP/7exjFlZ6m59iGIsBNkV7954oDT/15tZKGj/5s2knFXc8aVOGljP0tdvByyD3g/juR839kz3k0VUodwZntnN02x4Fspj1O2xwADjy3E8vG/nFHEh6eTN688oOfORPF5MnG1PfePTv16rnh6SnX0mKFvadgSiYim1KwJQQ+1LntmxOgd6bZ7kYSqYdfBo5hw8EhopiPfJ4DxS5CmYqMvHYUl4jhPZNe1hIe05PgjNdzCWFgJvHFSGwcJJn39WhEKcV0SwpfefiiaRoWpRgb62BLHnkQpkTAB3kgt57SeusOzC1tdEF0KHjzLMyoKhFXuk24CEGe8FIwWezbU5DTE8Y2MNZdfgp918CK7lBOH7jCE+CleTC4AfTRBfuexAh9tHM1GNXaGexMscDkjSK2+PMg0crKzhJTYO4OWLAbejSFhe9J//PMlpIK247Dyr2ildWgMvRtD/Xe/+MK9rgEuH4Hrt2RtFNiEpy+KANETj9JxQXmhKAA6YGuaTJw58wBSYlGauNZhOzr6gp3HokGl8UCLu6QPw+UKAqVK0DpYKhcVo5nNk8PaFxDlvf6CGV410l47QsZCAe8CC82kugEJCJ4pQm83Bi2nIJXJ0Kn+jC8Q1aJlVolYe378PYCOHgNZr1p4E7BeWDXB9BnrohDvqxn2jVNHEiXWaLF1V2fSHzcDtr+AAduQTPTJKV1WVh1AQ7dlfa3ma1xsDiSZwmi8tu7Ciy8II6kVUkYsV0ciabBzPYSpXi4Qo188MtF52MVywnrdUzKx12cSkOyNw9XCEuTv9NMqSw7GinKEEpJwJpR+2XFyIHZSEEzdS011ktE4+b2nFNbGv9Wtv9TFhWVTPXqMqoppWjbtiC+Jj7rvn1WypY1fp1D56BJjaxMjgvRUCuXyCkALIiF5ZmYQhtIYJEpBXWEaFqQO6OY6QJRVCcoo9/IKSIIxCvj9RNSiMVKJR0LeUAKCdipkika+ZV4hhCQIcq4125Dc2hUdpHvNTvRQX9fFwq6aVxJhYNJ8KUezv/yFFoFQEXTDP+ne1AhBzQxMHv2hcKhMNjRJus1XXAJTj+FlZ2N63Q9HPqsgTU9DVG9+1Hwzlr4sj201Cmip+7DoMUwqxc0zBQ5nLoDb82HAc1h16fZp5VS0uDHrbB4LwxsByenZi+Md+4m/LJVaLSNK8Mn/YTS+1ssqtQ0OHsNjp6HR6Fw5KzUiDSvDblyQrO6AoYXyf/b0cRfNYcDQp9JM6w7j2DdLvhyJuTKpTeCqgaNa0L18s7XolBe6N9JlvuhsO4Q1B0ML9SDtzpDMT0S1DToVFfqSZbsh6Yfw6jO0Ke5c/Tl4wlzh8Cq49B6Eqx+G4roEWigLywbBJ1myOt0Z+LqAr8OhAGLoXQ+abHr6gIL+0LXeXBghNEWGeDDFvD6Cjg8/Dc6MpaEpRfgvcbQsChMOy5q0iUCISxJqME+7tCkmLGfnydcjRLHkl4sWzIA7sbJ30X8RA7lt8zLxWho5WW6vrZMqS2LU2rLakptpeFGVk9hVZDDBeKToNDzrCP5z8D2/9/YP/rVb92Kyeg1omkaX37pzCfdv9/Ghx8aI9HBs9ClWdbjrH4I6dDKI6vM5guYwtZrpFEDL4JMU4YthDEUg0qzgcf0NVF+N/CIYSZ5lF08o4cJ89hMJH0zYSNR2NlKCtt05ohSiglpKcz3Fs8Q61D8kqg4kc8FpeCjpzC1gDysT9JgThgcNQhr3E2Eg5GwyMTriEiB0adha+uszKkjj2H+ZdjVzUiB3Y+GnithWTfDidyOgFcXwS/dpPsdwLqzMHknbBwOxUypModD+mXcCIWVI4WmmtmUkmLDCavhzZZwfIoUCZrNaoM1B2DxDgGbB3SEH97O3iE5HHD+Buw6BntOQGAOEWRsUA26toQZHz/nYrJszMUFCheQpYmJqOBwSAR0/AKs2Q1vfA7lS0CXFtCmPuQKMLYtXhDe6wEjX4F1B+HTBZJK+6QXlCxkfE6/liKdP2kNdBwPs4c6CzmCyKYUzgWdvoNFg6FqsKzP6QObRwprK9AXWuhkQn8vmNBFOiMeeFdeFw6EN+vDZ9tgSlfj2KXyCB6y+Sq8mE1tVo+q0GY+jGqkg/g54OADcTAvlYMjj5wVEtKtTBDcjoby+n3n7wFF9DRpEX+4Gmtsm2qHMefgez2C8nKTVBqgw+diVhx4uFlJn/7bsGV0GLU49SJJQzOlse04mMMFWqga2BxC0vhfbeX8f6P9o47k9u0YSpfOBuVDBmGLRXOi/W47Ap8MyLwd7AiFT3UftDUB+mZKa60ngfqm2UkkaSRjp4SeQ40mjWjSKKWrCDwhCXdcKKrnW5OwcYAI+uo9DaKwcpR4hpscC8Ac4hhIjgyMZZvNSnNXd4qb6L7v+Lvg7aKxJR4KuUFNHf55/x5MCAZvV+N7DbsAn5V3XjfwKHxZHfJnAgofx8Ow3bD9VaOXQ3Qy9FgN816CinrUcysc+v4qcuBV9cFs5j44dAv2jJIBJ+M6xcPrs6FiYZj7ZvbKujdCYOJqmTnvHpdVmDElDeZuhcXboHVtmPtB9k2jbDaJONftk/+bVYc6lWHZ1yLf/r9iLi5QroQsAJPegat34OA56Pi2gO39OkKnptLjBOS6dW8J3VqIwGP/bySN985LoloMgjN93Q8OXoEXv4KPXoFujZ0/u0EZWPsOdJsOs/pDPT0zntMHpvWE9tNg2UCj0r10Xni3Bby3Fub0knUD6wu9+0IIVDPdvp+2hpEbRIgxc1QS6C2yJ5fCoGoB6FIO1l8XR1I1n7C1snMkDQrDNZMjAbgbLzhFXh+4E2es93CBI+HOr5+aPYhudk3DzXSCadgzUls2rKY6EgNsB3FAD4nD6pCJpq/Pc5aR13jurK3/m+xvntf9vkVGplCuXK5s37t/XxxJukjjw1Dh7GemdN6Ih0Z5pX4C4Nc4aG+WVEBxgGSaY+SLdhBJC1N/kf2E0clEAd5CCK1N0cYOwmhDvgwHsYpwupPXqW4kHgf3sNJFdz5KKb61pNJf778eZlNsSdTo4yPh+ZfP4FN9cD8SC7ndhamVbr8+hhK+0MB0eZbchQoB0NGZj0CaDXpuglltRDk1fd0rK2FsM2HcgPQU6b4E5nQTJ6IUTNgqTmTJAGcncjUEus2AQS1gcs+sTsRqg4mr4PXvpbDu56HOTiTVAjPWQd1hAkjvnwGTBmd1IlfuwOjpUKMP7DwOXZrD2WUw4yPo3fF/y4lkZ5oGlUrDsO5wfBHM/FDaAjcYAMMmC6vLvG3b2nDwe6hXHtp/DN+sdFahbVoJDk6Cg9dh+Fy5zmYrmQ82vAfDFsDRW8b6/DnFiUzYCjEmmZLedWXg3nRJXru4wKjm8MFG+f3TrWigiHMeuJv992xYDFZflb8bF4Ozz/S/i8LhR9nvUySHIXmSboX1lFZ+H+cWui6a4Fbp5uliYCP5TM+8TSn8NGNDV8hIbdmxZkQkDuy4mTqQWrDjjgs2BblcITL6OYuCpqe2/sry/yP7R7/OhQvhtGkTnO17R4/aaNTIiEb2noaO2fDdN4ZATX2wCbGChwZ5TN/qDCk0wyeD2QGwm0hmmAoMt/OU73XVXysO7pHAAJ0IoVCcJo6P9NdWHNwhhTdwlrVdRgL18MrARnbZrFRwcaWInoOZHAvvBWi4uWj8GgMv5oDCHnrf9Qcw3wSAxllh1j3YZgLLQ5Nh2hU43CHrNfj0MPStBI30GaZSUjzWrRJ01PGPiATougB+elm63AF8sVXqQJYPck6Tbb8An6yCpUOhgnPQBcDNEPh0qRQQHv7a2ckoBWsOiqz6my/AsR+EyWQ2qw3W7oOf1kijqI6NYcLQ368/+T2z2SAkFO49hLsPICIC7j8UID0wB1zRlYlKB8PtG/J3YJDoaoEMKIWLyiw1fz4oUwby5YFSJaBEsb/W27tkEXj7NRjeHXadgB/XwqNw+KgvNKpqfF6XhtC+NszaBM3ehx9HGoy2HD4wcyBM2wztxsOKUc5Oukgu2Pge9J0jbYzTG19VKASv1IL+C2H9UGOg/KYrtJ4BzUoL66p6ESiQE7ZfE22tdHuvCUzYC82zYda1Lw0TDsH4lkIiyOMrrXFLBgldPMkikjkxqbD8GgytAeWD4NcbzsepkgeeJELxHOCSafTJbYqy3V0EdwHR+kg3m4sCd0MjJVEvIAawkIKfjmFaScITYxZmxU4AXlgVJNvAxf6cWVv/YiT/nD14EEexYjmyfe/iRTsvv2z80ntPw4fZUBS3hcJaPQWQwwVmZMrhbyGJFqaZyT2SKYMvOfUQ+AbxFMU3A1Q/TiTFyJFB+b1EPF64ZvRv30MMxfByUhy14GAjiaw1OZcf0lKZ7iOf+9SqCLPDq75SWfttBOzTUyPrIqG6H5QxPURfXodBxSHARDp5/xRMru3c+RBgxz24EQXfNDPWzT4D8WkwWMdWUq3w8kKY3BHqBcu6r7ZLz+0FfZ2dyOIjsPcK7BoDuTP9NErBgj3w43b4Zbg4ErNdfQBTV0vV9a5vhPJqtsRk+HkDHDgNlUvCknFQVP+9rFbYchA2H4SfPv1tDMRmg0vX4exlOHEert6SGX3pglC0sAz81atAm+biDPLk/mO5cIcDYmOlOj7smTigy9dg/VbQ3ODyTShXChrXgxqVoWblP2aVubqKjE/7hkJnnrQYFu2AIV2kch+E8TXqFXEoA6eJc3n3JdlX02DUi1C1mDiTdR9AMVM0VzgX/PQ6dJ8Fm981APiuNeDQbZhzCAbrE6+8/vB+K5i2Hz5/QdaNewEGrhLp+HSHU6UgRKfAg2gIzlRo6eMBdYrAjUgonwfalIBDj8SRFMsJZ8OgSVG5P+dfFkdSIifEZYqovN3hSbJ8Zmya83uRptfuLhBlzXpdbQrcTZNCOypj8mauI8ms+GvFIYtDjp3T/z+fuGRr/zqSf84KFvTD39+THTse066dM5f08GE748fLjaCUMGAqZcrDRqVCsA/k0e+XHK5Q0QSg2XFwllTGmmRKdhFBbVMV+l7CeMGUxtpOKINMYoybCKOLyUGsJoKvTCA9wFZSaIoPvrpzOWWzEeDiQkl9yvV9PHTyBVdNY140dMlpVLBPeATbTQDnjXh4lAzfmfj3mx8JTtImExMtIhlmnYN57Y3B4MwTWHsVNveSdUrBgJXQqya01KOeHw9CdFJWJzJjF+y4BGtGGP2z0y3VAsN+lgrrgxOdlXtTLTDxV5FMnzVcugaaLTEZ5m6ChdugfwdYNckA4++FSIOyzfuhVT14q5tzykEpuHQDdh6Ca7fg4jWoWh4a1IShfaBCmf8+1+3iAkFBspQrk/V9m02kVK7cgLnLYcjHcg61qkHnVlCkYNZ9zFa1NKwYL4rCo38UXGTSIKPOpHwxODAFpqyFDl/A0veNYs2WVeEnH+j8Nax8zyhOBChbAH7sC6NXwIKBhrjmpK7Q9FtoVtboXNmrNjSeLtI3pfNKb5FigbDjhvQYSbfXa8H80zCubdbvUbMg7LgrjqRpMEw6Bv2rQb1CcCpUHIm7q4ypFrtQeMOT5TdM/00L+kConnpz1Zyr2101ow7FTRPGF0BxpzoSB0GmezYXnk4p5vTUloYbLiZHks7ucijI4wphEc+/oPX/ZftHHcmdOzEkJlr56qsLTo4kOVnuIG9vuUFuPYR8QVlnqXueQqlM3HuzXcBCWTwzZjAKxWFi6KuD5DYcnCaaIXraKpo0/HDLKDhMxMYDkqmqg/B3SSUITwpmQtUWkcgPpjD6V6uFd3RsJNau2JUCE4KEerghBn4Nlu0WPxNcxJwD/vgajCplsK5SbPDZOdieDdV35B54s6pIcYO0xR24EdZ0l2IzgO/2Q5k8UisCsOECrLsAW4c6O5Ep2+BxNGx4x7nLHkBoFHT/Fno2gbfaOw/0l+/D6DnQuiYcmurMhLHZYMFWmLkWhr8MJ+cKxqWUVIZ/t0R6iQztBp8NNLSP7HY4ehbW7BAKrrsrtG0CvUZDoWxYY9mZwwHx8RAVLf1IEhOkINFiMe4jDw9JW+XMKbTe3L9RjOjmBhXLytK9s6x7EAJb98EbH0pKrFk96N0Zcgdl3T/dqpSEHd/B1uPQ+WMY0hne6CDX080VPuwGlYtD20+l9qZysOxXpzQsewd6zYDVo6C4qSK+QRm48gSGL4Z5b8o6Lw+hcA9eAvvek+/r4gITO8LHm2G1TlgZ0xI+3ubsSF6uDL1XZJUvAWhXEgZvhXfrSQHiVR3/qFUAPj9sbNewKNyOgYq5RWkiItm4Rwv6wi29KDGHtwgzBujX3N9T0k5+7jonS7+X7psiE6uLwo6R2orBAISSScBNj0iSiXYC2wUj8SDVAUn2v4G19f842P6PORKr1UGRIr48fpxI0aLO3uDSJahRw4g7D1+Q+pHMtiMU3iqbdX3G+yTTxgSy3yaZGuTER79DLxJDVQIy0lgHCKesqf/LPiJpTK6MGc9aomiXqYXuVSwUx40i+qV8aHdwxaqopVOnfomDkf7gpmksiYJq3hDgJoJ4u6Nhlmn2fixKZmSNTADzz9dgYNmsLK0NOtDa2bT/sC0wuhGU1H3avtuw/QbsGiyvT92HX8/AmoHOyqez9sCxO7BiaFZQ/dpjGDALJveGJqbISSn4cRMs2QPz34cKxZz3O3YZZq2RuolDP0qRoFKw8xhMWiCg+/ihzm1sbz2ABevg9gPIlQNeaSc1Ir+Xy46IlDTUleuSljp9VpyHr4+kMHIFyZI3UD7fwwNy5JBUVloa+PjA3XsQGSnv3QsRZxIcDOXKQbXKshQr6uxAgwvDsL6yRMXAiq3QaTBUKgN9u0KjmtmDuZomkinNqsFHc6D3V9JCuID+m71QG0oVgB7fwdQB0EyPTCsWhdmD4OUpsPUj6V2SbgObCfC+8iR016nKtYKhTQVYdAz6N5J1TUvD7KNw/rHgJCVzi9JzetMqkAlIkB8ceQBNM6Uui+SEqGQj2qiRT9iCpQPhdqyxXaCXqPtWzA2lAuBBguFIgrzgkV4/kq6tle5I3PXaET90hQUT+J5uFhTm28EcjThLpKRlSW25omVIpOQK/De19TztH/vqFoudSpVy8fRpMpUrOw/O5887qFLFmC7cegjdMs3IlYL7iVAje/YwAHex8q5JAmU/MVQwFRDuJoI2GFPcfYTxpakx1VliM2pNLDi4RQrvZqodWUQinU0YzNw0C4N0mVirgiXxGseKioNYHA3LgmW75c+ghCcEmW7maTdggmmwfpoMS+/AiS7O3yshDX48D8tMUjFrrgoA+pp++qFx8O1+WNZbZl7P4mHQclg/SFqsptuSI3D2PizPxokcuQ4jfoFf34XyJtA9MQXe+E6cx8Epzqq4sQkw5id48BRmvSdNoABOX4UPp0PN8jB3LJTWmWd2O2w5AJv2izDjGy/BF8OzV9pVCm7fg2OnYc9BcR41q4qTqlQeXnwBRg0Tx/HfMHJSU+H+A7hxG85fhAVLIT5FjtusIbRuBmVKGp+RKxCG9Zbl3FWYugg+/h7efwNebJ79ufj5wA/viKhjuw9hxghoqoPxZQrDts+hywT4rAe8oGNdNUvCtH4wZA4sf8dIP2oazOwLTSZDg9JQRI+KRrSERpPhpRpCEQYRaJy4G1a/Ia+HNZKumOlNrEA0tVZezupIAKrlh9NPJOoolhNOh0otSSE/uS/9PaFMoCETX9hfKtjr6I9ZXm+phQIo4GOINAIEuEtEgqfQSb3061bGRHZQKIJMqYmCpmfPA68MR+JGDlxMIYIFB964UcQTGuSE5U/B9XmPfv8Pp8r+Mfqv1WrHx8eNR4+S8PFx/kVv33ZQrZrx9O05DdUy5d1vxUMuTyM8vZ4Gn5iohvexYkPhY/qKh4mhke5YbDi4SjxV9ddhpFIQX3LpN18YaYRjI5/++ggJlMHbCehLwsEZ0miiz3zSlOKBTdFVzw1tTIT2vuDrArvioYQH5HUXpzI9BN42wUIHwmVGVtHEzvniDHxaXdabbdwx6FZOHAdAZBKMPwCf6uCqwwFvroT3monkt9UuLVanvATFTdHOnisw54AMQpnTWfsuw5SNsOVjZydy/ym0/AA61YMv+joP+AfOQ/O3pQZk+1RxIk8jYdQU+PxnmD4avn1XnIjFCks2Q+1usO8kfDIYts2GV9o6H9NqhV2H4KOJUK01fPCVOJTRw+DsXpg3HaZOgDd6Q71akDvXf0/r9PKC8uWgayf48hPYuAL2bYAJn0h09PVMqNkWxkyAC1ecabQ1KsLSb2DpZBF3bPw6HDrz25/VsiZsmwSfL4S524z1+QNh+xdSpLj9rLG+aUV4sRYM/Nn5c/29YWp3GLzIWO/vBSNbwNTdxnb1i8vk+ZZOhWpeSooB41KMbZqVkMjDYZImyXgvGM48lb9rFhCQHUTq/Wa0/F06AEJ0am9RP2eab5AnJOrn5+EmDMUMczWiEBcNEvXf8ZZpmyRNYdPkxBwowjHK42OJzFD5TeAJ7qbaMQsONFyp5Acdcst9lV1d1L/2n9k/mtoqWNCPp0+TKVXKmR50/LiD8eNlqh4VCzl8s7JkDj6DFqZ8+a5kKGbaZh/JtDTNVp6SRjC++Otf+Qrx1CMoozbkABFUMIHwu4mktQmk30Q0g03y8gA7VCo98MNFH7nWp9kJdnHFXX89MxYW6btMj4ApOlC6JRI65YK8pgHzh1vwlamw/3YcPEuBLsHO3/tGpCioTm5mrPvmMHzeHIL0rzvzCJTPJ323AT7bDPWKQ0ujUJ+rITB+I6wdmRVY33cZPlwMWz6Rbn3pduamsIt+eQ9qmhy71QaTf4XDF2HLZCiURwah2evg57UwcRhMfU/f1gpLt8L0ZfBSS9g7XzSxzKYUHDsDG3bBzoOCP7zaAcaN/v00l1IQGgr370tEERMDDx9IqsvfD27dks/Plw/Cw8Vh+PlJqitfPsifX1JaJUpAqVLO/So0DcqWkmVwP0hOhl0H4aclcOE69HkJeneFAP27FCsEXw6TWpiPpsOavfBhf5FQyWyF8gjL7c2pcCsEJr8pmEagH2z4GNp/KZIm6anFAS3h8mOYtx/ebGEcp3l52HsdVp2G7nVkXb8G0PA7GNZC2FsAfWrDzMPSLkDToHIBWHtZ+q+DpJW83YW4USeTnlq9wvDuHni7rqS2tul1J+nKvrUKQHBOuKlHJAX9pSVzunm5CS4CevMqE6vLw0VYjebfM7NZUBlUfgt2PExhgNWpsj0Vt0wRiXnb3Lmfc0TiApnawv8/Zf+YI7HZxJGEhCRQqJARuzocipQU8PWVm+X8LWhTL+v+e5/CWNPAuz8Zppke0tvYGGbCOw4RR3kTXrKfKOqbJOUPE8l4Kpr2T2aULtCYiB03NMpl0u5ZppKZrgVkvJ6fYuVnf7l5r6QJHbmYB9xIkSr2Cvru00NgtgnbOREpM7DyJn867iyMqJR1dj16H3za0ADjD9wTafhv2snru5Gw5zas6iuv99yAc49h+zDjGLFJUoOwZFBWZd/jN2H+3qxOZPdZ+GgerPsciptKaJ5FQ88vJe+//VsZAB8+hTEzZdA8Os+o8N57AsbOEnbWvl8gKJMDiYuH5RvhlxVQsQy82RMmf5Q9FVgpePAQjp2EEyfh4mUB1yuUERXf4sWhZHGoXEmA9IAAEelzd5dJic0GKSmQlARxcfDsGYSFweXL8OtyuHsXipcCDy+oWwsaNoAqlYxz8fGBLu1liYyGpeugx0ioXA5GDZC+KACli8GaqbD7hFS+D30V3uya9Xf1cIeFo+Uaj/kFJg0QVlGuHOJMek2BucOhlJ5ZndwLmo6X6vZKpsH+ndbQ6jvoVFUmCG6uMKIZTNkDk7vKNi9UhLHbpLmZnye8Vh36rzIcCUCHsrD1ZlZHEpwT7sfK3wX94bgenVTIDZcj5O9cXsKoBElfmSMSMJQa8nhJNJRuuT0FO0y3vLofGGKSi3HD6L6eio2iplS1PwG46whKcVo5tde148DXNNw9Cv0XI3me9o9iJPny+XH1agi9ext024cPFXXqGCPHiatQNhOQqxRcj4OKAfLapuCxFYrrN0YaijNYKGK6kY4Qywd6K1yF4iyxDNPxjygsuONKkJ5fDSWVKGwE6PvvJZ4SeDsBeyHKhgKKaHIJH9gd5NM0SugKdesSYIh+x88Jh076gH0xAfxdoZQJp/j2JowxRQu3YiVX3CITrXT/A+nFXk+PbKx2eH87rO5pXJe31sCX7QQ0jUmCH/eLWF/6AGi3Q7858EmnrMWGN0Lgrdmw+WNnJ7LlBPy4GXZMcu4hcu4mDP4OJg4S+ROAFbtg8iKY+QE01HP+oeHw2SzBT379GoIz0ZifhMH0hbDrsOAMe341ZvZmi4+Hnfvg0DE4dASKFYFG9eHlLjD+M3EWf8X+aHubDa5egxOnYNlK6DtYgPeunaBNC6NQMXcQvPMmDH8d1myHrm9Bi/rw/puGWnDrenC4Cnw1F14dA3M/kWJJs7m4wOSB8OViGPQ9zH1X1hXMBdPehJ7fwd6vpGDR0x1+GQhvzoFDnxupydz+EoXMPgjv6rhij1qwaT7EJEOgj0QcA+rD+ksSnRQNFP2ssHjIr59Tq5Kw/ErWa6Jp0KWMIdSY0xPi0qBkICy8ZmyjIfdjfh/QMk0E0pVPXFyc60ySbYIrykFA90sMNKVj4zQbOfR0dRo24k1KXNE8Q9PfK0E7p89Mwe4MzNuef2pL/YuR/J83q9VBvny+PHuWSv78xkz/+nVFnjzGD37mBtQq57zvvURR+02f1Z1LhWqmsPI0adQ2hbVpOIjEShE99nxAMtXJmSGtcIQo6piik73E0ML0ejuxtMvUAXGdI5XuptTZshQbrT3lTkpzwJo4aOsLKQ7YEwcddFLArEcwzDSQ3kmAHG5Q2ySFMvkiDKmQtZ5i9nmY2MxYN+cUvFYViuunuvqC9BmpHyyvR62C7rWkijndpu2E2iXgpdrO1zQ8Fj5fAUvfhiKmB3fLCZi4HJZ+6OxEtp+AQd/Bii/EiVisIgly+hocmC1ORClYvAleGAbd28nM3OxEIqNh1Ffw4WTBFs5shIE9nZ1IUhIsWwOvD4c2LwtDq093OH8ENq2ED96BZk1+2ylYrRJpXLsGp0/D8eNw5AgcP664eFFx964iMlJlm0Zxc4OqVWDwm/DtBLhwFEYOkar5Ri9Av6Fw8KiRgnFzgx6d4OgqqFgaWvSBWUvFIYEA7F+/Db3bQ8uhcPZ69uf8WR/p6/7pAuPY1UrA6K7w8VJjXaUi8FoDmLLVef+hLWDRUYjVIwE3V2hcChYeN7bpWgUWnDZeVysIm03nE+gjrXMTMhUNAnzZxKg6/7S+nE8xf3gQb2yT01MKDgM8pemc2dxdhPnl7Sr09ozr7WL0bHeo7BtxpJpSW6nY8PqTCHca9oznHSSd+q9o4/OzfzC1ZSdPHh9y5/bEz1SufeOGg3LljFvIywOCndVIOBoBFUx8/VOp0NyUdTpPGi1MCcvzJFIFg2J8nBhKm9Jcl4inn0lr6xqJvKcLNCZgxxtXSmRKgG5ypLHaTbyDUopdFjtbA2SbTYnQwU8KrDZGQr/cUmAVb4PT8fBzBeM4s+5AZ9Pg+iQJrsVAq0yz9m13hRFTSv/e8akw5zQcG6K/ToEJO+HQ2/J673VpWtWtlnGMcw9g3Rk48LHzsa026DEVPnoJKpmiv4MX4dd9sGU8BJlmz6v2waz1sPM7kXEPj4Yen0gzp1F6IWRMPHw2E9KscGiByL6nW1oa/LIK5q6A0QOh54tZ01eXrsKS1bD7AHR5AT4dJZIl2ZlSgoucPw8PHojDuH9f8uCRkVJomCuX4CGurjLg+/o6ePpUkZgIgYEunD2roRRUrSqRRpUq8nfVqkZlvIsL1Kohy7vD4MRp2LwH3v8Khr0OPbsIddjVFXp1hi6t4cfl0KI//DIeygTLcbo0g8ql4IMZ8HpH6NTE+ftoGnwzEIZMF4r1ML12pVtj2HkJFh+Afs1l3ZDW0OBz6NUQiuoTAE93eKs5zNgDn70o6/rVg6bT4O3murJxgHzOoxiJSDpXhLc3wsC6xnk0KgZHHkL7MsZ1tjmkrifdWhc3/i4VYPxdMqdIoQR6ZcU6gn0h3go53bOmttK3dQDBpkcuVjmIVgpPFwc5TBhJPtNkLhemHBiQRjIpxBNAfjTA25SheBL2fHu2Kw3s/6a2/s+bw6Hw9HTjwYNEp/UxMdC4sdwo0fHS6zpzPvl4JPQ23cA7kmGWCR/ZQyq9TY7jIonUN+Elx4nmUwSkSMPBDVIoqt+QsVgJw0JuPc11iESKZXIidxxW8uJCoB6zn7cqCuKKnw5c7I6HUfo9vSACftQHwA3PoH9BA99ItMK+Z/BdVePYC6/De5WzRiPTTsEvLxjrfj4Jw+qJvhHA+J0wsqkI76Va4f3VsGWEcZxUC0zdLhXQmRlaHy+DDjWhdTVj3eX78M5PsH2isxOZvxVW7Ydt3wj2cecx9PsCvhwErfRB6PQVGDQOPh8i8upm238C3v8aenSAU+udH2alYNcBmDxDwPGRA+Gbz7P+/krB9Ruwbx/s3S+TjdRUqFEdataETp2gWLE/6k2S/XQ0OhquX4eLF2HtWhg6TKKdFs3hhRegWjU9daNB/TqyvB0BPy6Evm9D++bQ5xVxJr4+MHoAtKgHvT6AYa9Bv86yb8nC8Mun0HU0RMZC/xedz0PTYPpQaDNGaNbN9d/m+zeg+VhoVgmK5ZHfclIP+H4HTO1t7N+/EfT7RSYTvp5S/NexEuy/ZZAu+tWCDZdhZBOpKYlNFbFPT/3+aFIMjj4yHMmiiyK9M9LkbMz2IN6U8vKGZ6lQCSidKU2Z6oAUuzwH0Sb6b5wd0gMUu5Iup+l2zm5nn82Kr5eN8nq2IRUrVr04UaGIcVLlgkge8IjL1Kc7cdgINGGczzu1pVwgzfOvJniyocX9X2r/WGrLzc0Fi8WOeyZu69GjGqVKybqLt6Fqqaz7Jtqgpj4zd+j4SFH9pkjCQRqKQNNAcYAEaumOJBU7vrhn0HrPE08Nk5M5QiwNTbUne0igdabmVVtUGi9oxgi4MtVBdx1BjLbB5VQo5yk9RiwOKKn7oTmPoLuJabb6EQwsblQQJ1th5R3oHOz8ffc8gBIBEKyfVlQyLL8E/fUizQdR8DgW+usP+Hc7oXc9KBRoHGPCBqhZTGQ1zLbmqE7RNQ1kYdEwaTms+MSQOQdYuhNW7oP1E8SJnL0Or3wIP35oOJFFG2HaElgzxdmJJCbBkLGwYC2smyWRSLoTUQp27IfGXWDNVpg9BTYugZZNDCeiFJw9D2PGQo36MGeuAOffTIJfl8H6dTB2rAz2Zcr85w2ugoKgYUMYOhS++QbOnpHjlykD30yBGrXhk7Fw+46xT7488OVomD1ZZFwavAhHThnv16wIh5fAmavwxmfSqAsEI9k+XdhwczdmPRcvD1j5CczYJBMqEJrvt/3g7XnGdq0rw/UncOGBsc7DDaoUgWWmdFb7irDEdF6ty8IGEw5StQAcNzGsaheCU0+M1xXywJUI4/X3p52jjcJ+EoUA5DHVizxKlC6O6ebjKnhIZpYWQPrh7MroEgqQqiSllYoDL6fUljz4NlMxYrpZSctgblkypbY8Pf5YL+2vmNI07G5uf2n5I9M0rYimafs1TbuuadpVTdPezmabZpqmxWmadkFfPvuz+z5P+0cjkoiIFMqUcUYcY2IUgYFyo9wKgdrlnfdLtonEQnrXtBsWaOJjwkuUhaZ4ZyRYE3SQzU93LJdIILcJP7lGAg1M1epHSaCfTvO1oQjFRoVMEckORxrLTGmtO1bFl/5y/LVx8LI+A1sRCb31dMP1RAh0d5ZDmX8PVjUyXv96G7qVck4dAHx3Ar5vbbyeegRGNjBmVF9sh9frSMriSQxcfwoL+xvbX34E+6/Bwc+cj/soAr5aBQcmGNfPYoWeE+HT16CsibGz6YhEIhsmStvYM9dg/DxY9w2UKCwDyuc/wuXbsHSizMbT7dRFGPIZjOwL/V5yjjCu3YJ3PoPypWHxDBFdNFtMDCxZIUvTBgKuf/7xb7dJtdslvXXnjjCv4uIc3L3rIDoaAgMVt245UAry5hX8JShIIzAQChVypXhxjVKloFw5jRw5jJPMlw9efVWW5GTYuRuGvy/Xe8RgaNda/g7ICd+MhfuPYOQX8OtG+OZj6Qnv5Qk/fAw/r4JX34N5X0LeXKITNmUkdBwtNPfurZy/T4FcMKg9DPoBNoyVa9e8MszdA1vPQAc9dfnly/D5WlEFTrc3G8ML38PApnoEVQKGroDkNGF0pdcYRSdBkC+0LAXHHkCzkrJ/fn95P10rq0IeeGZKIOy8Dz3KQ349+K+SV4poSweKplaCHm3k9IA4i1B/AQr5CDbi7QZ+phEot4eBi9iAkqZ7yK4pcrtAIwIorKeoShBEAX2Sp+FCAzo53ws48NYniZ644m2aXCYkPn+tLfvzF++yAe8ppc5pmuYPnNU0bbdS6lqm7Q4rpTr+h/s+F/vHHIm7uytRURZcTX0zU1OVUyOrc7dEi8hsF2OgummmfSIVSppofCexUN6UC71EEk1NEccpYp1EGw8QTU+9Wt2B4gbJlNHTXOdJoUQmUbinDjtFcCWXnta6aAUP5YKXPjpuSYAZ+qx/XTRs1NMIG8NggIkldS0OgjyggGlAXHEblmYaSC49k+rg9OZAcSnSJveLlvL65jN4Gg9tdYf7xWboVttwMkrBu0vgx/7O2kkOB3y6BGYOcu4VPn4pdKwLLWsY645fga8Ww84p4kQu3ISBE2D9txBcUAbv974TOuWaKcYDqhT8/KsU5K2eASVNTiIpGb6ZBXuPwLQvoXY15+/98BF8NwOiY6BOTdi5XqKFzJaQAPv3w+HDgo3ExUHdupKOKlkS6tTRaNfOlaAgyJlT4aHn9ZRSJCdrxMQooqMVT57AvXuKgwchIkLj4UPBR1q3hmbNoID+m/r4QNfOsly/KQ5u/BT44kNhcmkaFC8Km+bBwtXQfRh8/i7UqSrvvdVdCjI7DIdNM6BAHnEy6yfB0O8gfy5oWt35O7avDdvOwMpD0KOprPuuH4xeBG2rC2hcpxQUCIBLj6CKrhqQJwfULwkn70E9vRK/S1XYeR266te7ZWnYdwdeqSo92384Bh+3ND47hxc8jIXgQPDzgMdJxnvBOeFBnOFI3Fzgqf6+vwfc08H3CoEQb4F8umNIshm9RsJNqa1IGxmexKog1BStJKBQGlQ2TepymfARN9wpgnPVchpJpCddIkjF3eRI/u4Om8/DlFJPgaf63wmapl0HCgF/6Az+m33/E/vHHEmuXN5ERqaRJ49xYzx+rJGWZgza1x6KMqrZrsVCIxOmdjIVXjcFNWeUhV6aAaSfJIGaJrzkCsn0QZDsGKx44ZIxU7lJMpXwxUW/m4+RTDMTKA+w22GlsimE3pii6KyLS8bYINwGxTzhfor0G8ntLgPqiqdwrIFxnMV34HUTznMxEnJ4QH7nj2PWaZn1pduck9CgqBG1TNgFH7SUQeJmGNx5Bi+aMJfVJ6FMAaiS6Tr+sktqFBoZpTNsOibXfM3nxrqQcJi8FNaMh0B/uBsCkxfDmsniRGw2eF2XPPlsiCmyscDQL8RhLfjauSHZsdMw/BMY8QYcWu/8UD8OgR/nweGjMPpt6NQ+60MfGgpr1sCGDRIhNG8uA/5HH2XnbLTf/NvXFyeGoNksFtF8O3ECevUSgkCv3tClMxTUadnly8LEz+HBI/j8a1ixHj4YCeXLyHXo3w2a1Yeeb8PgntD/VdmvVX1xuh2Hw+YfoGBe8PeFyUPhhfdhy7dZG4BN7AeNRkObGsLoKhgkGMnSg/C6nkLsVg+m74B5g4z9OlSFZSfEkYCkt346bDiSFqVhxXlxJLl9ITZF9LfSGU1V8sHlZ+JIAAK8ICZFuiZWzA2hpgglrw+E6+msAA9D/iQ2TZxHunm4SkorXe033RzKyLVblPQWSrcUpTIma3/WrKThpT/7adidGF7ZVe3/N+bAhbRMqbU/tpQ/3kQ3TdOCgerAyWzerq9p2kUgFHhfKXX1L+z7XOwf88sBAV5ERqY6OZIHD6SyON083CTcN9uxCOfCvWQ7VNcHKYdShGOngGbcMOdJopruDFKwk4g9oxfJBeJpalLtPUZiBpYCsJ9U6plmPQC77BbauBo3zF2Lop3+FXYmQCcdTlkfBa31Q52Lhwp+khuW84TtodDexMxafQfeyJTGi00VLaOWOlhvscHSc9Bfp+4+iJLopIUOho7bBJ93cgbY156Eca84Hzc0Cubvga9M4OzTKBi7COaMMvZPs0DPz2H0a1A0n6gMdPsIPugrYLHDAWOmQ4US8Plbxn6JSSJgWLcqzJtkOBG7Hb79CcZ+A2t/gf49DCeRkABjJ0DXvtC4PhzeCZ07GO9bLLB8uTiM13oJvvLrrzLQT5oEbdpkdSJKKcLCFOfO2di718batVZWrUpl2bIUVq5MZdu2NI4csXL7tp2EBOeRxcMDatWC4cMF1N+8WVJU3XtA15ckCkrHB4KLwqIfYfhA6D8SJk4T2jFA8SKwbxnsPgpfzTL2aVILZoyBru9DXIKsK5gHZo6CEVMlxWg2fx8Y8yqMXWyse68zfL/F6KLYvIIoFkSYaLgty8Ppe8Y2tYvBo2hR9wWoW0zwtXRrUgKumTDravnhQYzxunYBuKdvn9MTHpo+q4CPQecN8BQMBMDXXdfQ0i2vtzwDbpqke9Mtl4eBi1gUBJuidVcXlSEfn4KN/TzNeO8MF1A6uvKYC6SQzjfW8NSfX3/cnXoI/R1mx+0vLUBuTdPOmJZB2R1X0zQ/YC3wjlIqPtPb54BiSqmqwA/Ahr+w73OzfzTAi4pKIzDQmKo+fQrl9cE0MjZ7zaQrsUYhYqoDbqaCl/4t7mEj2BRkWXCQH09y6uuukeREAz5FAmVwjl5q6znXWOy4QMa+IG0+3ZRGWd1RhdkUIVYXAvSc0foY6Kg7kk1RIoMCsPEp9DSB3KcioGleA+exOWDTXWhX1Pm7rr4Cg6sbLK+NV+HlSgZTa9EJGK1HI7efCT7S1FQxP2cfVAvO2qDqw0XwWXejp4hSMGYufDdIopR0m7RE+ow3rCID4wczYNxgqF5W9nl/ijj7jwca+0RGQ7sB8PpLMLC78RtGx0CnfuIwdi6X9E+67dgDXfpAwQJwYhe80MbYLyoKxo2HWrXh6lWYOxcO7IchQ0TSJN1SU6VG5PvvYfBgRa1aqdSokUb//hbmzLGyfbuNGzccREUpwsIcPHxo59YtO6tWWfj00xSGDk2matVE2rVLYuzYNDZscBAba0yXg4Kgb184fAi+/AI2bIImzWH3HsM51KwKhzeLg+01BB7rQLWPNyybBpExMPRzYzbcsDqM7gvdxxiOp1FVqFcRJi4ii/VoKvjGzRB5ncsfXq4Ha3RAXdOgX2NYdtTYx80VagTD4dvyOp36e1E/hqeb1IpE6ymp4EA4ZwLYS+WCs8aYTQ5PU2W7n3NE4u8Bj3Wn6OtupLYK+RmpLBAavEUJZynKlNp6ajUmDqlAtClaiXKx4a632I3DwgHCMt47xsmM9PNdjmDRpeWTiMFVnzSGkJQBzINRKPq8TKFhx/UvLUCkUqqWaZmT+biaprkjjmCZUmpdls9VKl4plaj/vQ1w1zQt95/ZN9Pn1NI07V1N077VNG2cpmndNE3LJpmcvf2jzOf4eCulSxsj1507UFufbd98DGUyyTNYHVDU1xiAr6RBZRMOfllZqWkKL2+QirvJV54jkUomBtZlEhip14/YUHjiQkF9/zOk0jxTNHLObsdbuaDpo9zOFGirb2JTcCMNKntDhAWKekA+/VS2PoMPTeyzlfehvalqfe9jaFpIcsxmW3AeNvQ0Xs89CT+9pF+7FFh/ET5tL68XHYUxLxgDcFIqzDsAR79wPuaJGxCdKHLl6bb+sOzXuqaxbutRYc2tmySvP5kFZYpCB50cMHeN1IrMH2fsE/P/sffeYVIVW9v3r7p7ch6YIQ4MOeecFDEAimAAxZwTZkXM+ehRMaJiRlFRzFlRJAgISM55gAFmmJxD5/r+qOretXtG5Zzj8/qc93vXdc01XXvXznvXqrXWve5VCefcBPdNg3FGbsSeHHjwabj2Ipg41lpeVQUzHoSiUpj3mipzG5Lqanj2efjiS7j1JvhtdcMA+9GjylL49ls1ADVrpuIjN94IXbvG4HL9a66QkAWzfXuQpUslM2cGiYmBsWOdXHCBoLWOcfXuDS88B3v3wiP/gDnvwT8ehA7tFZLsvtthxWqYcAHMegyOH6Hu73P3wr3Pwv3Pwz9uVcsmnwQFpXDfbHhS42puPw9OuBEmjVJKOyRCwMUnwqMfwvt3qGWXjoHzn4PzRqn2ucPg1KfglvHWdhP7wjebYIyepJ3QBRbvhv5amQ/KgrWHYWxXldD6nZGY2CEN9pVZ7dZJlrJokWhPKEyLgXKNSEtwQa1WjnUBBfcNiQOFyooUn4HUcgct9l+AWilJ0C93LQHif2fo8lJPlP5uvbiJ0jEVN4FwSV6A0upGN/9fJUINNG8BO6WUz/5On+ZAoZRSCiEGo25v6bFsq7e/FLgJOACsB3ajWMNGAncKIbYB90spD/3Ruf6tFklRkZv0dGvgP3wYsrTy2JunBi5T9lUpv2pIdnhguDG4/Bb009NQJFupo7ehDLZQS1+tSGoJ0Iq4cHxkF/UkGEH65bjpE8GttTjg4wSX1WdtPWG31poaGJekPvaFZdBFb7q3BrJi1YcFava6ON9Of7L0CJxvDBgA2wpVDYdMbUDlFKtypx100H3uarhosAqgl1TDj9tgbE9r+7eXwLQTIdFQtFLCq9/D0waiq6oWXv4Knr7WWlZSAfe+Dq/fqa7n22Ww84CaPQMsXAUf/Qiv3W8pruoamHQt3HqZXYms2wznXAN332hXIlt3wAmT4KTR8NlcS4lICXPehksuV2Vy16yCSy6xlIjHAx9/DOPHw+23K76sZ59V8ZLXXoPLL4eePcXvKpFAQOIzCZ0MEULQooWDk05y8Y9/OPn1VxeffOKkaVPBRRfBhAnw9deWRdGpE7w3F268DqZeDDOfs7LYRw6FH+bDOx/DR1+F9g+P3w7VtfDoS9Zxp02B7Tnwg7YkXC549Q54/N2GvvzRveFomUI0gmIhaJ8JG/ardnqiquG+/bC1zQldVdGy8D46wf4Sqz08G3ZrWG/vFlBjWAmJMZBlWKltk6FOr28SBzmG26tDCjyiefHiXcrVBcrF5TEUSZMY/R0LlbAYkvRYyx3mBVoabq9YIUnUVkc9PloaQfcMg1w1lqSw8ogihmj9O404G/z3P2WIjpR/0yL5MxkBXASMMeC9pwohrhVChL7YycA2HSOZBUyVUsrf27aRYyQAI6SUZ0spH5dSvimlfElKeZOUcgDwHESgGBqRY7JIhBDjgBdQWVxvSimfaKTPaOB5IAplsh3/Z/stK/OQnm65ttxuwrO+nHwYEhEz2F4JPVOt9tp6mGK85FuljzuF5bo6iI8zdU5IyIfaQiuardSGubUANlBHf0PprMfDnRFFrPb4JZfpfBEp4Zc6wSwd+F9QCYP15gvK4GZ9HT8UwDgDHLC5DE5sATGGW+u7HHjMCMQDfLgVLu1rtd9ZB+f2JnzsH7fBu5er9uu/wFVGzoXPD28sghWP2vf5wzqleLoZlt6T8+Gc0Xb6k9ueg0evhIw0lbX+z7fhq2fVrP9gHtzxLPz0qpUH4vfDTY/AtPPhdAPxs3w1PP4ifDHH7sr6/Bt44gVlhZilbXfugutvVLQkc+dAkpG+U1kJr7wCK1dCz57wwgsqtyNS/H7J1q1Btm71s2GDn5ycIFIGKShQnEwOB7hcXjwe9T5kZcVRWirJznYxaFAsnTu7GDrURWqqGnSaNBFccQVccYWymJ9/Hv7xD5g+HSZPVvsbPhR+XQwvzoZTz4R5cyAjA1o2VxbJGZer3JFLzlHn+Nw9cPYN8NF3cK6OA815CK5+FAZ1h6Zp0KO9Crh/8BNcaNBGCQG3nQkvfAUvayLOC46HtxdBfx1LO6E7fLEOeujnHBOlEhMPl6laJdlNYEWOBevt0Rw+3Ag3jYKMRFh3xH5PD1RYiYqZCbBLK6X0WCizqK6Icak67aCgvju0kkmJiEGXeZVF4gtCjaFgDnmt76I6iMGiBYdFgAStSGrwEdDJfH78lFMR7lfO4XDuSAWFROvJ4GHqwgXspIQ2EflU/6lIxL8RbP+TfUq5gsaZYsw+LwEvNbL8T7fV/V7+k/Wb/mwfcAyKRAjhBF4GTgaOAGuFEF+beGQhRCowGxgnpTwkhMhsdGcN9g1padbN371bUVkAFFdA+4iHvacKehtj+xYPPBL280vSjWxzgLXUcYfOCcnHRwBH2Je6hRr6GPGRI/g4RyuOagK0J8pWy8QnJdv9QZo5tAXjhS7RVvxiSRXc0ly9pG4/9NOD4I+F8HJf65y/y4UBVnyf5UdgVCtrP+pa4Ps9cO9xVvvrbXC3HqTXHlSwzPQEFTRdmwPzDIvii9/g1H4qeS0kwSA8/pHlEgE4XAQ/rYNVxmu4dL0iV5wwUh132j/hnsvV4Ob3w7RH4NX7VA5ESG5/XLHcTjVg/Gs3wS0PwrfvQgvD2njyBVi1VtX3SEy0lr/+Bnz3A8x8QmWnh6SuTimQ996Dq66Cjz5q6OLKzQ3y5ZdBduzws2aNn969nRx3nIMzzoimQwcHrVo5cDhS+D2prlYxk5ycIIsW+XnsMTdt2zpp187JOedE0bOncmd27AgvvaRcaq+/rmDBL78MvXoppXr7LdC9G5w8Ad5+Hfr1URn637yjUGoxMTB1koJHv/80jL0c+nSFrh0gMx0uPA3uehHe1Pk+918Gp0+Hs0fb69KPHwj3vgvVdSoIP6YXTJ9rZWuP7wOnzYT7zrS2Gd0VftkNFw5T3112E8gtU/87NIV9hoUS41TsCKFyza2SIa8K2qcrRVKk4ylRTis3JFLMZEN3wB4jCdVl98uIpMOgFe+skxBvrKsmSKL+Hmvwhb0HbjzERuR5hb5xL/VhRWKKzwcFJQ0W/0eiLJL/Xo4UIcRTwD9QULIFQB9UgP79Y9n+WFxbg4F9Usr9UkovMB+YFNHnfODzkB9NSll0LAfPyakmLS2GsWNVJDA0QwLYvN9OVw6wrRw66QFaSogTkKZnMIcIhOkVANy6tGbInN1BHd0Ni2MXHnra+LfqaK9nMpvwkRzxUmwMBOhnsLwtr4WT9Tta7YfsKEh3wc5aQH8obr8y4bMN5NmPR2CsYREsOwxnRmTvbylQsMt4rWN/y4WTO1vtD36Dy0ao34u2Q8tUe02RF7+H68fadsnPG6B/O8g24hCzPoNHL7Ognn4/vPEFzJquXVq/qFoZp2n/+1NvweBeMNSAF8//VgXS755mLdu1F6bdrZBZphK5/zHIOaBcWSElUl0NF1wEGzbCRx9YSiQYhE8+geHDlatnzRq4/npLidTUSF57LcAll3i55hofMTHwwAPRbNyYyNy5cVxxRQyjR0eRleXE4fjjiVlSkoOePaOYNCmGmTPjWb48mddei6d3byePPOLhwgu9vPJKgPp6bdW2gAcfhBdfVOc0c6YVLB8/Fj79AK69BVboIHh8PLzwKDz/hoI+g0KAvfwgXHaXctcBnH0i5BXBys2qnZ4Mpw6HuT/Yz9fhgMtOhi/1/l1OOGsILNNTu7RE6N4KCgy30/FdYLcRNB/TBbbqoLrTAT0yVfIhwIhsOGC4wvo2VwSOAOlxiv49JKYbzBQhoEuqPl/sGfDJ0UrRBIQK3ockNcaC/HpFkKbRlvZJI0iSVhA+AmFvQj31ZBoEq8kGCjOOJFxa4WQaysbtscPR/58AcIpGdU1AGQydgTv+eBNLjkWRtAIMjytH9DJTOgNpQoilQoj1QoiLj+XgNTV+HA4HNTXqhVmyxFpX71HJb6bk1EAHrUiKAhadAsAO6aeHsAb/3Xhs8ZHDEe0DuGmlX0ZFGe8M549swEP/CDN1lRtGOiyn7Yp6GGzER5roQy8tgxP0e722XOWShKTSA23joanxIX69F0ZG3M0vd8Cphlfyk40wRisbnx8W71IDAcA7y+FyIyax6YBKRGttuY0BePYzuM7IfT14VNVVH2sE3d/5VuWGZLeEereqHXLflWrdtj2KQ+vea6z+u3LgmTfgVSMzvrQMzr8e3n4OsrXClBJuuRP8Pnj9eYuaIi8PTh4HZ54Br71iKYmiIjjjDFi3DhYuhJtvttxo+fmSRx/1M3Kkn8pKmDnTxYIFMVx7rYtWrazXubZWsnKlmzlzqrn//nLuuKOUM8/MZ/z4PCZMyGP8+ENccEEeN95YwAsvlPP997UcOeJD6hEvIUEwZUoUn3wSz0svRVNRAUOG+HnxxSB1deoYffrA0qXq+saOVSzDAB07wBcfwC13wZJlallSInzyGjww00Jz9e0OF0yE5zVCSwh4/nZ48h1r4J12Frz2pYJOmzJlJMxZaLWP66Es0ZC0bQrLdlvtPlnwk5Fd0CYNNhouLLdPwYJBKaZDFdY6h4CCGuv3DoMmJRIgYsouvY9Ypz22WeRR11ofVMokJPu9kKgnNWUR1soevCTp4aocTxjKW089Qc23FcRPHaXhbaooxoETiaTIcJR5vNCx7e+f978r/wMxkv+TEhrcTgU+lFKW/VHnSDkWW6yx6Vwk5sIFDABOBOKAVUKI1VLKPbYdKZz01QBt2rRh8OAEyssDpKermxqiAq93q7rVtgNKFU+I1vd/hxu6G4rmcDBAH2EN9Lvx0NpQBmuoY7x2XZXiI4OosAm8HTfDDeskTwaZRLTtyld6JQ8mWQ8/x2shxpZXwSgdqznDgPUuLVIw35AsOwqtDevkYKVKQIx0DyzYAzcPt6570R54XCuBJbvh+M7KPVJdD7lFCt4Zki9/g2tPse9vR65KYjOTO5+eD3eebymAeje8+DEs0wDEme/ApRMhI12dw42PKN9+qEKhxwOXzYA3/mnRofj9cN40VcmwZ1frWA8+pvIinn/SOt7OnXDehfDiCzDKoIn58UeYMUPlhpxqhAZLSyVPPhlk8WLJPfcI7rnHaWNF8Pslv/ziZ8UKDz/84EUIGDvWRZMmMGRIDG3bOklJSSY2VhAdrfJSysoClJYGOHjQz/Ll9cyeXYHTGU16upMJE2I5+eQYkpOdpKUJ7r7byW23OXjrLWUlPfIITJyorIMZM1TOyfnnwzPPQL9+Csr83Sdw6bWq/G+vHpDVCu67BS6/TUGgHQ649jwYfi5MGQft20CXdoqD6+ffVA2TtGSF3vp+FZxu3KeWTdSgfqRETRpGdoXb3ras+uO7wUerVZIiQFy0cil5fCpm0r0FfLzB2l/7JrC/FDpkQOsUOFJprWsaDyV1VtsMVPuDdk9CY1Lrx7ATVGwkyqHiLrGGIqo3XFvVQWhmBNuDEK5mWo2XRMMiidPuKy91REcgLQF8SFuJbLcHiv+lYfLPJfg/ECP5PyzfCCF2oVxb04QQGdjDVH8ox2KRHAGDYx1aozIoI/sskFLWSilLgGUoH5tNpJSvhzDTGRkZ7N9fSUVFgJQUu3Y+0oj/sqAemhnuzl1u6Ga8aOsCQdoZFslevGGWUICj+Giule5u3LQ3fKdbcduVjvTRNkLHHghIuuopUrEfYoLWjCm3HoZpS6llLKTr8zpSC8cZgfYleXCCYX0sOQind4i4ziro1ARS9elty1ccSDF6n0t3wxTt/vl2I5zSy86T9dmvcGJv+z5f/RYuNILgpZWKE+v0EdayOV/B1WdASqL6yFZshOunqnXzvoaendUMOiRPvgIXTrIve+RpOPUEmHCytezNucr19fIz1nnu2AEXXgrz51lKREp44gmYMwd+/tlSIlLCvHlqkO7RQ/Dbb07OOstSIlu3+rnvvjr69avi00+9jBwZxaJFaaxalc5DDyVz443JTJgQT69eMbRpE0VmpovUVBeZmS66do1hxIh4LrggmX/+synfftuKL75oyrXXJrBpk49LL63iuuuq2LFDOU1jYgTTpgkWLFCZ9ddco1xzAGPGqLjJ5VcoSwqgWaZSnhdfA0V6Fj96OPTrCS+8qdouF8ycAfcYAM37roBXP7Ha552srJJIuXA0LNCusugoGNUV9uovc3AHKCq39x/TBXbq9R2aAoaV0705HNXKo22qvYZ780SlgELSyYhTRjnsVQ1NaZ+q/gel3SKJj1GTLQ+KdiUkaUbM0SuCpDis+aqRMkSQQNj17KGeZB3r9FBDoo6JSiTxmgqpFh8ZEa6tSG/Hfy7i30lI/NtFCNECQEp5FzAMGCil9AF1NAxh/K4cy9WsBToJIdoBecBUVEzElK+Al4QQLiAaGIKCjf2hOByCysoAqan20zhcBFkR4fqcauhuxEv3emGCgdjaKwN0NDLad+DjKq0cqvHTw6hwuBs3XY2Zyw48jNOw4FIZIBURzhUBKA1IEgOOcG32dXUWQktK2FADLSMmI4EgrCm1c2ntLIFHDFfS4ly4bbB9u8U50NWYvi3YAb1aWsf6dhM8eoZqL90ONxmInh83Kip4kzuu3gNLN8NzRjD+rW9g6kkGyssHr38Oy99S7WfehYsmKOvD7YH3v4IPjIFu30H4bjGs+MxatmotLFsFi4y0p5W/wdvvw89fW4lme/fC+RfBh+9D167W8adNU8HoefMs11dBgUo8zMyEjz8WpKRYz2TpUh9PPOHG6YRbbonhoYfiGkB+g0HJvn0+Dhzwcviwj5qaAMXFHoQQREcLkpIctG0bTdu2MXTpEkNiooqnDBkSw5AhMQSDkp9+8jJ9ejWtW8dw++0xdOnipHlzePddlVl/wgnwvr6Wjh0VC/HZk2HOW8r11aUTPPkw3PMwvD5L3YdHZ8C518KUCdC6JRw/BF56H9ZvgwE9oWMbEBI27ID+3VWF0MpaxdLcwnBZHt8bbp4NV+qckXaZ8OtO6NxKMQfnFil2g1j9bqbFw66j0LetCsrvMTLYU+Ngk3a5pcTZXVtxLitGApBTod5vp0NZ1G6f5SkwZb/eJui0U7Yf1eismiAkGFPZMkOxFQYlKfqlkUiKjQhoIbUk62+7hhri9bfsoSb8jfvw4EMFn+rDGC8lbg9kR3g8/lMJwX//C2WOECINWIoKsq8AkFLWArV/sJ1N/tQikVL6gRuAH4GdwMdSyu0mlllKuVOfxBZgDQoivO2P9uv3S3r3TqeqKkBmpl2RFFfamWdBxUdaG1brUR90ig6fI22k08bFU0KADK0nc/DgQhDQHrkCfHQyZii1OGirrZVt0s9waUeBbPJJ+saowkdBCZVBGK09Yfvc0LEhMIQdVdDdUHR1PgWVTAyfM+woUYyppizeZ8VDAH7cAWP1rH/jIejdWn3Abi+s2WNBPAGWbIHzj7Pv74c1yhoJKZdgEH7dAhcZCuirpTBuuCo+VVIGG3fBeXpwen0+HD/Yngn89Gvw3AOWm6u+Hma9Dm/Pso5TUgIvvQrz37ZiH6WlCt77zhyLwcDng/POU3U+XnrJUiKrVsG4cYqi5PXXIUVPInbtCnDaaXV89ZWPZ5+N57vvkjj55OiwEtm508uLL1Ywfnwe/fodYvbscpYsqcPjkbRvH83IkYkMH55A//5xNGniYvt2N6+8UsykSUcYMeIgd99dxKpV9UgpcTgE48bF8P33aVx2WTSXXeZmxgw3dXVqWDr/fHj7beXmWqUD39nZMH8+XHKpqgMPcMqJkJYKs99Q7ZgYuOoCuO8p657efjk88ZrVvnoyvG4o6itOg6+W2Z9t+xZq0hWiUxnRTSmSkPRqA9uM6GaXFvaAe3IsVGqXVfNkRf4JinOr1HBlJUarQmohiXVaeSHV7j+vqlEXsPJDQCUnxjnV8nhjuRmQLw9KQvOGGoJh9m5Qrq0k7W2op85QJNXE6Amhh9owPUotPlu99jq3RUvz/3eRUo4HRqMUyZnAaiHE50KIq4UQbf5oW1OOyb7SqfffRyx7NaI9E5h5rAcOBiVlZR4qKiSxsXZNvr8AOra09z9QDf2MhP3dHmilB7IiKSk33uZagjTDZdAmeOhILC9STG/iWI+bW8PFcYIUI8MY8+3ST6uImcUun2RAlGCvFx4sgA+NWMOmShgVwQcGsKUMTjCUxIYiGGi0j1Qpa8UZocrrPDBQz5bcPuUzDpXKXbnXImRcthOON9xK/gAs2gjPXGHf3/sLFTIrJL9sgGYpquxrSF78AN57TP1+41M46wQ1oNfVwxc/wTcGccPSlVBRDsMHWstmvghD+kM7fV+khOtuhgunQpa+Fr8fzjsfbroR+upr8PkUIeKwYQr9FJL58yWzZgm+/hra6FfZ65U8+6yXb77x8fTTsQwbZr26FRVBPvighrlza2jRwsn55yfw+uuZZGUZvs9jkIqKACtW1PHuu1Vcf30xF1yQxGWXJZOe7mTYMBfLlzt5910vo0fX8f77cXTu7KRXL3jySZg0CWbPhqFDoVNHeOZpxcv1048KKPDIvTBqLEwYB9lt4dQT4Y15KjGzV3cY2k/Rzew7CB2z4aShMOdLFbuKi4WTBsFlj8C1Z6kZ9dPz4b5LFInj+r0wrDv0bQf5hu9/YAfYeUT9B+iQCUsMRdMnC46UQ0q8yh8JhniyYrFph8RoezG0tikqvhEfpazaxsoUB4LQSrt7fUG7IomKVrxz1VLRqoCCAkcbE7Jal48UDbWvwK9rHCpxEQxnqasYiWWRxIbrDtWGKeRrCBBnuK5D9/Svlv9SiwQppRtlCCwA0N6n8SgvU3Mp5eA/2h7+xsz2QCBISko0VVUBkpPtDyC/VAUTTanxQzv9YgZ1cC/kT80JBungsPaRi580I0u9AD8diOEAHtoQhQdJrL70HHx0MPrulH66C/sAtLbeQY8owVY3dIt4ATdUQ+eG8T1WFUMXwyJZUwCDDUWyJh8GRSjLwmooqrbguGsOQFtDeX61HkZptNbavTDByLf4dQcM72YPelbXwaEi6NHOWvb+D3CREcTekaOYZ9u0VIP9h9/DhTof5IOvVXW/RK0opVRUJw9Pt7Y/eAi+/Qmuv9Ja9tmXCqU0ycgreeYZVWVwgoEcu+02GDlSZaiH5MUXA3z4YYCff5ZhJbJ/f5AxY+pwOCTLlsWHlUhJSYB7761k9OhiYmIcLFjQjC+/bMY55yT+y0oEIDXVyYQJSbzySjOWLGlFTAxMmFDEc89V4vVKnE7BZZfF8MYbcZx7bj0//KBpYztZAAEAAElEQVRMgawslVn/7LMKRABw4okwZbICDYCyyp58BO5+SLWFgOnXwpOzreNPPBHe0VaIwwFtWygWAYDWzRQLQVWNgq5++otCcnXJgrW7VJ+YaCiqtAga2zSF3UY0M6sJbDOQWk6h3jdQbq8cHZtMjIEjFVa/aCcU1VjtgloLbdMyyc7gG5L6gMX+Wxu0arwDHPZAchRU+iEUHq0KQpLp5pKSJo4QSitIkjFIe43gTg01JIZjJFXEhNl+68JZ7XUEiDe29/kUff9fKSH233/l73+jCCGSgUpUmselKKqUP5W/TZEEg5L27RPx+SRNmtgVidMBLdLt/TeUQZYesI/6oJcRLDsUCNLdiI8cxGcjb9yOmzbEUEWQprg4xeDb2oefLsZDzfVLOjWwSKBLFOzz2I8LsKUaeiRCfr19+eZy6GMEJTcUQj9DkewohsERimTVQUVXEZJf98EoDQP2+qG0RtWcAPhqDQw3aFUWb4CJEfOGhetg6mir7fEqK2OEAYP4ajFcc7b6vWA5TDlFobCkhNc/hGuNaNji5TCsP3QxXG//eAr+eb/l5qquVq6ep/9p9Vm5En5aqNBNIXnjDaioULxYIXn55QBLl0o++cRJvM5G+/lnP3fe6ebZZ2OZMSMWp9NBMCh57bUapk2roFMnF2vXZnLFFYmkpf11M8KUFCc33JDGokXNqKmRjBtXwG+/KZ97nz5OFi9O4MUXg3z5pRrU2rRRFRovvhhq9KB7zTWw4EdDuYxWwINNW1R7xGBVBCtPu5smnQzfLLJoUc4cA18sts5p3DBYvkn97tUeth2AQV1gjWFldG5pBdw7tYC9hisrNR4qDJdVZjIUa0WSHGsF2BOiodbID4l2WTkmoMCMISvkcE3DQmygkhBDjoZav72AVX0A4hzKKgkBU8oC0MLoEysgWs+KyvGHK576CBJjfNtu3GGLxEtt2LXlppYYrWDq8JNsTBarahqyK/8V8t8YbA+JEOIaIUQhKjyxXv+t07mDfyp/myLx+yUej5/CQj9xcfbTWLfHXuIVFBlcqh7vD3mxVQnZF5RkGkUrigjYrIx8fLTARSFBUnFxI9aInoM/HB8BKJWSRGNfQQlZTohzCDbUQI8Ii6SoTrmgbtliLZMSmjhV4lX4/N3Qzbim5QdgQETC5Y58lQwWkpV7VVU7UK6rZ/WgXl6jUFxmEuLCDTCih31/362Ekb2s9i/rITPNCnxLCfO/h7EavfX2p3CGRnetXK+CwBmGZfj8a3DBWVZ77z7YmwNjjLjM40/ADdMsSvf6erjpZnjzDeu469ernKHXX7csqE8+CfLLL0Hmz3cSrbPSPv3UxwMPeHj55VgGD1YDSW6un5NOKmHPHj9vv53GpZcmEBXVOPa0stLPggUVPP10Hpdcsofx47dz3HFbGDRoDSedtIEpU7bw0EMH+OyzYo4e9TS6j7g4B/ffn8obbzTlllsqeestFX9MSxN8+GE0M2f6WbBAjbK9esFNN8EdOo3L5YKXXlQJi6GB95F7FQAB1LXfdLnFxRUbAxPGwCoNyx3cSynmMLtwV1i1Vf0+rjds2A3d2kCOoSy6tFA8daAskCNGzocQKukvJBmJivwT1OQtOdb6bdYCcWAnUEwy9uELKORWpNT6FTsxqOeeYCgbV6w6lyfaQTc9OSwPKn6u8PYOy7dWjpeMMPTXE85wB+XOitFu6joqwq6tekOpVBEgzvjG6/6HXFv/5TId6CGlzJZSttN/7Y9147/VIklOjqa6OkhSkv006jwQbzxoKZWbKDToHPIqdt2Q5AaCtDMG/x34aaFfHIkkiRgqCNKkkVnAYemnnVRveWkwQIawn8tRP9TrrKn9XmhnfER1+iPaXQNdDV6oI7X2BDJvQLkQzJlbYY1FyBiSFfugVwvrmg+VQhs9kMfHwGgdoF6zByb0t7arrFFuPrN2SzCoaoEPMvI5vlkGE41Bf80W6NtV+fArquBwAfQKJTp+DBNOsPruO6C40HoZcZlX58A9t1vPJS9PEW9ecbnV55ln4PLLoJ12r3k8cN118NBDVhB+/XrJU08FefNNV1gpfPKJhw8/9LFgQTyZmeqZLFniZtKkEh57LJlnnkklIaHh61tZGeCVV4q55JL9nHbabhYtqqJTpzimT2/FO+90YvHiXqxePYiPP+7FE090ZOjQZLZureGSS3YxefJuXnyxgMpKf4P9dugQxaJFTVmyxM1jj1UjpSQlRfD119G8+qqfPXvUwHfRRYrV+Mcf1XYDBqjZ7y+/qPaQQbBxCxzRg/2Ek2HeJ5ayGNwHvtOJuWGkW676P6w3bNRJhp1aK3bmqChlaYasmDYZCq0FCuJaHzGfrHZbtUhiXZYiATvk19xOosAiISmts1Ba2elWUmKNDzZo91i134LHH/bb+bZ8jbjCSgJq8gXglRLTMVmKnyZ6SQVuUn+HEsVNJbEa8ltPTbioVTV+Eo1vPxiEJmn8pfI/RNr4f1JyUJDff0v+NkUipSQjI5a4ONEgRtIpItO7zAMlxoSxPAAdjAG9UgZpbdBgHMFPG/3ilBEkiOAIPnoQ4ZcC9shgOGdkfxBaBOy+9b0+6BSlLJM2UWBOfvfWQK8kOFwHPQ1FsqMcuqVa7X3ldux9UTX0M4HxWvIrrcB6fjl0aQ4VtXA4Iq9m1S7lwgjJbzthWFd7n32HFU2GCQXOK4BRhgJavBrO0VQq3y2xYiNut6qzPspwlc15H669xGqXlsKyZQqRFJLnnoPTT7OOWVgIn38OVxrxkyefhKlTLcLF6mrJY4/5+OgjZ7hO+rJlPp57zsPcuXHhZR98UMv991fxww8ZDBvW8DkWF/t46KF8TjhhD7W1QZ5+OosVK3owc2YbJk1Kp1evBJo1U+gup1OQnh5Fhw7xjBvXhIceasdPP/XhjTc64PNJjj9+B//4x1Fqa+3p5PHxDt59N43DhyWPPqoskyZNBPffH8Wll/rwetUI+cQTcM89KukR4O674HHD1XfFRfDWu+p3QoKqU79Nu6fGDINFK62+I/vD8vXqd0aaeoag4l7b9qvfnVrBYW15tM2Ewgpr+9QEO4NwaryCEoOi3Kk1viszaB5rfAaRWfXegDUp2lFp/c6pgpf0dVT5IEXvo8L47Q9CciNjqBPoqpVNDZIhLmvgrydAU/2NVuImQ/sj/HhJNgrRSUTYIvHjJ1b3S8JFpvHtl5b/9aV2/y9QJHcDK4UQrwkhZoX+jnXjv9W15XI52L/fS2KiYap6VYDYlPx6aGUEtHe5wUQMHwxK0owoc7J0kqEv7TBesojiUISfNCR1SBJ1fOVgIEh7h/0B53mhVzQU+BR3lil7a6FNDGwqh/aGNZBTCd1Trfa+chhoKI5dxZASYRyVVEOndGt2v/UItGsCCzbBvOX2vuv2wUAjTrFml93yAPh1Mwwy2JPzCqG2XiWuheSrhapSHyjf/Ogh6vei5TD1VOtjCwbh+4VwmpExP3ceXHqhdb7l5bBoMZx9ttXntdfUgBqiNzl4UFUbvPlmq89dd/k4/XQH7dtrq2+/n+nT6/jss0QSE9WyefNq+PzzWhYuzKBFC/vz8fsls2YVccop++jZM47167syfXozMjL+9WB7WpqL225rwfr1PcnIcDFy5B4WL7bjRB0OBy+/nMTWrX7mzVNT+AEDHJx1loOXX1YvSFaWUpbz56ttevSAzp1gq3ZLnXMWbN5sDdznTIKfdCwkKRHaNocKnRw4pBfsNPghmqaqAlkZaVCnYzGZKaokMkDzNMg1XF0etz2ZMMFpKY8Yp/rewmK8325jG6+EGANRFXA17s6q8qkgOkBlQKHBQH27of5lfruSCskpCXCR1gnpwsHMWJPeqD6sSMqoC3/HNdTgMAbkWipxhhVOGXHatXU2remBlYRWWw8JjUD2/xNR7L8x/9Lf/zJ5DVgMrMaKkaw/1o3/RtSWJDExipqaoE2RFFdCRoq9b1E9dDOW5fusWgXBYBAH2BIId8tAmDcrjwAtiSIfPy0iXFueYJCWxotYGAzSPmKqstMNmQ444LG7tQD210L7eNhfY1ckeyog27BQdpZAS3N9MXSO4MLaUwgZplWTBz1aqboSPSPQ3E7sXFr78lTQ1ZQte2G4keH+yzo43kB5lZQplE9SolIUu/dDL62Mvv3JDu9duwFGDYVYw6OwcSNceJ7VnjcPrrvWygMpK1NIprOMmMpDD8Fdd1kWy8qVAY4ckVx6qVoQCEguu6yKF1+Mo0UL9Ry+/76O11+vZu7cpsTF2WMhxcU+JkzYR1VVgJUruzB5cprtPfh3xel0cM01GXz9dQc+/LCc554rjlgvmDs3mRdeqGPvXjX63nKLi/ffD1BQoLTDFVfArFmWshgzRiUwgnLpSaliTADDB8HCpdb+W2TCJk3A2K0DrDMysgZ0hT2H1G+PTz275umqOBYoKpyyKqt/YhzUGDkg0VEKuAEqFmIirn7v1rkjBv8gFmy9m/Ee1vghS7/DZV4IlRrKNZRSsR/+VR1frCmNAMqpI02zUlRTTaJWFjKCtamOGuINUI0pUqqcqf8nNvFLKW+TUr4tpZwb+jvWjf82RQKS5GQXzZu7bL7u0qqGrq38eqtOAUCCgBb6ZaxCkBSwtq+RwXDdAoCjBGiGi3okrSIUyVERRBjb7vRBy4gYySEvtI2CAo+d2wsUvLFdPGRGK3qHkORWQwcD+ru/HNobrq3SWugakYi4uxC6GMy8JVXQraVyQ3Qz7kdlLZRX2z/6TXsgO8JVtnIz9O1stddtU6VdQ7JqI5yq4yUOB6z81MoJWLkWhhtura++gzGjrPauXVBTbSUJglIkptJ4803l0rIy2iU+n2SsdqVJKbnrLjdPP+0KD/7PP1/HqFHRDBmibuaBAz5ee62aL77IbBAP2by5jlNO2cdttzXjvvtaNABsRIqUkspKHzt2VLF9ewWVlX8ORsnKimb27CxWrarjiSfsZnJ8vIMXX0zmmmuqCAYlLpfgrruiePxxNWqmpyvOrUWLVP9TT4Xvv7fcTKeOhe8WqN+tWkJBkVUUq39PWK+tl9RkFb8KSZNkOKRRWa0zoLhc/a/U1klaoh2E0bap4mQLSWq8ZaE4HHbLIlVPdoJBaG48W7df55ZoCTH2Sgk7jXMr8lrQ9ZqA+i58QTv5YqkfsvX2u72wMwLjUC4tP1wVAQJI0nCGExIV869SJHXUkq6LWnmoJRUrXT2auLBrK3wd2kVXXKbKH/+V8n+Ba2uJTkJsIYRID/0d68Z/myLx+YLEx0exebO7gSKJ9F8WuqGZ8SJvqbFQIAVBSaaR1VcgA/SV1qheRpDmONlGgMwIRZIvg7Q0YMN5QUkrp31a5pQqsL+vHtIjnv2WUmgVA3vK7QP7oVKrOhwAErKND3PjEUWMZ0pOgVX9EGD1TuiYCat2Q7aBed95WCF1QlJbrwYOG5GeX/2ZVNnrtkFfw2pZvRG6GwzDIRRL7mFVHyPKmDUu/sWOzPriKzjTYOHZuRNatYKm+vylVLTvF19s9XnllSDnnCPD5/nDD346dXLQqZO6qUePBli/3sv996uPPxCQXH55KXffnRIm9bSOV8999+Xz0UftOOWUZH5PCgs9vPzyAU47bTUDBixm0qRVPPbYbl54YTeTJv1C//7fc8kla3n77YPU1jYMsANERQk++KANBw96efddO3nVkCFRjBrl4vPP1Uh99tkOjh71h2u9X3mlihGBsuaGDFF15wFGj4I1q6199eqiKPYBunWEPfusdamJCrYN0LypVUsjMQ7KKtXzL9KJiDHRcMDIHamKAH7UuQ1qnIC95G2oTG6dT9GXhPfhgxjj+6vU29T4INF4T0o80FS/c0d8inK+xAf9U60+eX4rCfGHWlgXoUjG11oJK/dQwEG85FMfDqgfppQmGu5bTlmYsLGeCkwu2XJKcRjDW1DCiDX6vGv/etcW/Nez/56PjpNgwH+PdeO/DcwcDEoSElwEg9hYXEurlHluii8AzX8HrlcYCJJlBNqLkPik1T5EgNNxUUiAzIiHVyqDtDeWxUlBi4jaFZvrINkBeR4YHjFmFbiVJs6MODeJNTMDWHcIMgzFcqgCslLt2xwuh3MNRVJeq3zM6v5Yyw/kQQ+DJ2jvYRje076vnCMw1FgWDCrkUIpxX9dthRsupoGsXgt9DBhxaSl07WS3PrZthauMDPqvvlJMuCHZsEHFRUI1R+rrJYsWBXnqKet1++QTL/ffb924J56o5swzY4iJUff/nXeqOe64GIYOtd/cggIf559/kHnzsuncufGXoqDAwwMP7GHXriomT27BG2/0pWXLxvvm5dXzzju5DB++lPPOa8ttt3UgOto+k3G5BM8+25JRo3IYOTKe9u0tDX311QlMmVLK2WfH4XAIBgxw8vnnPi6/PJpBg1QxrmBQTY769FE5Nb16QYcOsMugeW/XFnbvVdxc2VmQayQOpqdAYQm0y4IeHSFH054kxauk0/gYhXT8PTGD6P6AhbLy+CyXldmn2mOH+Fa4Ldiw2295B0q90MewrP0CmusBusADzaKh0Ac+45M6aril8/z2vKw6KTG9l0fxk4iw1QYqpz7s2qqiikxN51hHGfGa3VvBa+zPsMoPqfq4Xo/FWP1XiUpI/F8X9zhmkVK2+/Nevy9/o2sL4uNddOliv/lVbhVINGVfJYQq8tYHoKcxmygMClINZVBMkEzDPVVCkKY4yZL2ioegYImJQesl3eN2EtWIo1gIcMiGxIwdE6HYDf2M8/X4oXnESyqxWwxtU6wiVeHrrofWej+BAHRvDWU1MDACyb3/KLQ0DM6cPOWuMGXPQWhiDPx5BYo/yZSKYuWLj5RN22Cwgexap3NPQuJ2q0qWTQ2l99NPcIoRiP/0U5gyxWr/8IOfiy4izIe1a1eA4uIg7dur51ZQ4Gf1ag9nn60ebE1NkFmzyrnrLrvZFgxK7rnnCM8804ru3RtOKaWUvPXWYcaNW8vEic1YtmwEN93UPqxEDh6sZuXKItasKWbXrgoCgSCtWsVx771d2bBhDFFRDsaMWcnBgw1RkPHxDl5+uSW33ZYfrlkC0KqVk/btXaxcqUby886L4pNPlO/I4YC+fSWbN6v+w4crSy20rmlTVUIYoHNHOKhhvhlNVP5NSLJaQLGOgQzobiHtMtOtwLFZiqCjkZ/UPNV+HamJRiGzoOXO8vigvbZ8qz2KTj4k9X5VGRGg1A2d9ftX7FaB+JDsrLFq7TgENItRE7CWxideL60aPVJIWrmsHeQFA/QwvAupCKrx2QrStSTRsDQCJOsgupsakrVSqaeWTBthuVJ6bfV5lJb/z8RI/hstEiHEH2auCyGShRA9/6gP/K2orSAJCVHs3m2fSpVWKZPdlBIPNAm9BH4oN4J3JUFoalgR1UFJK2l9VdFSkI6DXTICwwgUShnOG5GNEAbV+qGr3tXGasg0IZFB2FcFhfX22VxxHUQZ7Xov9DI+SilVPCRSco9aCqG0GkoqVYU7d4Qr/3ARtDEUwKFCaBMRH9l/BNoZcZWcQ9Ap22pXVVtIqkhZuw76Gq/NuvUwwFAs69fDICMQX1en4gHNjXP47Tc/J59s3YSvvvJz/PHWqzZ3rodLL7VGl7ffruGaa+LDlQxffrmCK65IaRD3ePvtYtLSHIwZk0x5uY+pU7eHn5uUkunTd7FvXx2//jqUCRMaasnt2yv48MP9zJmzl9de202/ft9w++1r2b69HKfTwe23d+Dpp3swY8ZO9uxpSHw6dGgCqamCZctqbMuvuCKejz5S/du2dVBZ6cftDimPAKtXq3evRw/YYiSutmoOuVp5tGph/RYCqg0vWmyUemaR4vUoF2bAB+UV1vJ9hjWTW2yx/wIcLLe+r1I3ROuBv9wN9fozKnErZFZIDtdZ72ahG5z6OyiotywQgKNuaBnKDaqHFjEqY90kNd3ssRTJag9kGcfJEwHSXMqn5kNSgYd83OGCD/X4KDcIafM5SIpWJNUUhulRaqjCH0ElWeYjzF/hdtu55v63ihAiSwixRAixUwixXQhxcyN9ugohVgkhPEKI6RHrxgkhdgsh9gkh7vqdw5wthFgphHhACHGaEGKwEOI4IcTlQoj3gG+hkXrFEfI3u7aiGgzg5bXQJ2K2EOu0LJJSv1WNEKA+KMkyonn5UtLVsEgO+gV+F40z2wShqd62MijpG5EhXRpQSYeggoeJxiSiXKNSit320qMl9dDUuO2l9VBuTHCr3VYGsSkCy2oprFTEigXlDWeUeYV2+pi6ehgQgdiqqbXHQ44chc7ZVjvnIHTvTKNSWma3NoqLYIoB6d28WREThmTrViuLHVReiNtNGLobDEo2bgwwYID1TDZsqOPBB62NPv+8jp9/VpooEJCsXl3DBx/Yeb4rK/3MmlXA8uUqI/LWW/dx1lkZCCGQUnLPPXuoqwvw9NNdfxe5ddppWZx2mjVT9XoDLFiQx1NP7aBHj1TuuKM7Q4emcdddHbnkki0sXTqYmBj7zPH66zN4+ulCjj/e8hMOHx7D7bdbbIk9erjYvDnAkCEuevQQfPyxeomio62AOijlW1AAvXsrduByQ3mYlxAd3TilRzCo+gWCFloucj5U67YH3yvrIFm/n+V1Kg4HKsmwiR5ci+vsrtjCOmgWoieqg5ahCY/PDipJjYMm+kMLSOVC21UPww2X6mEftNaKqE5CgjEJPCQDtNExywJ8tCCKo7hpoRMQS6ihqVGAzoePqDCdfCmtdQmkGipJwm7NlnihqT636lo7AvGvkP8hGnk/cLuUcoMQIglYL4RYKKXcYfQpA24CzjA3FEI4gZeBk1H1otYKIb6O2BYp5a2aRn4yMAVogSputRN4TUq54lhO9G+E/waJiXHQoYPdtVVZY73oIdlbZdEnVPoh29AKh/2CVENxlMkg6RHIqzIZpHug4ZuzwytIDZXvDAoq/PbtSn2W0uoUbf+4yzzQK1lZHC2MXZe7Idv4uMrqIN34KEtqoGfEZNnvV+ia8D6qFdVFZTW0j+hbWqVyCUKyK0cheWzXtdvujjp4GJoZ+z90BFpqK8kceGproUO2fV8rV1pZ6QDbtkEnI0i/aZPy/Ydk3TrJsGHG+e3yM2RIMGxt5OcHkFISq0vh7drloVevKFJSHPp4dTRpIhpYI2++Wch112WQnOzi11/L8fslU6aoi/jii6N4PAFefrlHo0pESsnatcU88cRGpk1bxvTpK5k7dxe1tX4mTmzDm28O48CBWq64YjWBQJD+/VOYOrUFjz+eE95HSYmX4mIvgwYlIISk3DCLY2IEbdo4yc9XywYPdrJtm9IY3boJiout2XG7dpLqanXTW7dWMGmA1BQ7E3TLZhbCKymhcWLEpERV56NNczhOW43BIFwyzupzxjAVSwlJm2ZWkTQpoKkel8vroZN+16q9qrhVSBwOaKZdW2UeaK/H6H21kBEXusewp165tOoCVoJursdyKQG0i1WM1l4p6RZn9xJUiiDtworESzdiqcVHa61IyqghW8dBfPjIxPLhCQQJug5jCk3oiJ0vqMoPrfR3Gulq/ivkfwK1JaU8KqXcoH9Xowb3VhF9iqSUa4HIqcZgYJ+Ucr/my5rP7xSqklKWSynfkFJeKqUcK6U8Q0p597EqEfibYyTBoJOjR+3XX12vqvSZYs7Wy3z2k64IQqqxIOB3kirVAm8wSEdcVCKpCjZ8cyqlJE0PcBVBiOT8q/RBK71sb4RrocKrErsK6hQBXUjK6lQ8JSRV9dAuxd6uM3D9ADUee32E0ipF6V1cYYdOAiTE2qu7lVba4yGgEtaampn0JZBpcGYVFEEzrUjemAOvv6V+Hz0KzojiEj6fHcG1bx90MaydwsIgvXtbF7xli4+ePYNGOxCOhQCsWOHh+OOtC/jllzoGDLDWf/ZZJVOmpNrOQUrJ/PklnHuuOulnnz3AHXe0RgiB2x3g0Uf3cvfdHcLKypRly44ycuQ3PPfcNnr0SOeSS7pw2mltqa31c8IJX/HPfypiq9mzB9GhQxKzZyu41E03teWbb46G0VwLFpTx1lsq0y8rK4q1a+2ur44dHezYod7l7GzYs0c95CZNRDjXRN3PAFUaMhsVhe13kZFImJ9vvfNVVY1/qEeKFIy1azsYr/nSnE64abLVZ9oES3EA7DVo5neWKOJGgMPV1nu1t8I++VlTCq31N7m7BjL1ukO1kKUVSZnPImA86LaKLzpcKmkXFAvwIf1qHAxIfMKuHbfgpW0oORg36TjZRRUttSIpoJIk/buKcgIGC3AxOeGs9qY0oyVt7ffKDcl6Umh+G3+V/JsJiU2FEOuMv6t/b/9CiGygH/DbMZ5SK+Cw0T5ChBL6K+VvUyROp6CuLkh8vP0UghKSIoyHpoYrq8IPZkHFBATJxviRG5Ak6S+wSkBpQFAlJcmNTEGaCQfJGjJYHYD2EXej0q+S/zwBez0FgGqfImWs9NjJGas99vKhVW67O6OqEddWjRsSjWVVdZAcr/4nRfhyyyLoUjJSIDUC5ZaZbi9E5XTaP57aWivQXlYOqbpvURFkZtj7dYvImC8pgSaGUtq4MUiWEdfcv1/SoYN1s7Zv99G7t9PoX0e/ftb6tWvrGTzYMkHXry/n+OPtyIDt2+vo0yeetDQXRUUe8vM99OmjBo233z7E+ee3IiOjIWLmu+8Oceeda/n44xP54IMTOP30tgwZ0owTTmjFtGk9WbPmbIJBwT33rEUIwc03d+GNN/ZTV+dHCMHppzfnm29UQOv441NZtkxFxvv3j2fDBntAvlUrF/n56kE3aeKgrEy9V5EWUqhefEhCFqHfb0fnmXXQ6+obRxlV1jR89n8kHp+9kmFeJbTUiuRIpQVJP1wNWYaV6wta2x2sgWytVCoEtNHndaAeBmhv5X43dNCPdKvHGsBzfNBeK5scv6RDxCwp16A2OoSHLGIowkOGRkMVUkmmTjIsp5RU1AGljocIft/MKPKqvBYpobTkd7v9n5aSUOlx/fd6Y52EEInAZ8AtUsqqxvo0tlkjyxqxa/8a+VspUurrg7RqFWVbXlBuHzwDQZUlG5J6vz2fY1edg2RjJmoqjRqplEpNEJrLhqbkTp/ApZNWKgICT8B+76sDkORUSqNTxDjl9ik0ikvaYycyaI+R1HrttRjcHrv/GZQf23RhyQCkJwISkv4kzLVtr8odMGX7HvugtP+AVVMEoOCohaOvrYV0rWQqKqFdttWvosKaMYckUh8fPQotDJRQTU2Q7GyrU06Oh+xs6zU7cMBHhw7WCXu9AXr3VjfX75fU1QXDbq+QrFhRzsCB6oQXLSrmoousA773Xg5XXRWR+g9s21bKrFnb+e67U2jVKqHBeoDoaCd3392XbdvKWbQoj8TEKC6+uC2ffqpSxydPbsFvv6kpfFZWLEl60D7uuGT69bM/xNatnVRVKUWSkiKIMhAX2dlWv1atVBwIVKXEEHGlzwetjflixw7W79g45d4COJgP+3Rme0qKQmGFZNEmSzFtzlVJrQBHKxSz9NEqVcs9JM1TVVwDoC4I2fo9OLU9dEhVv2t99iqeUS7I1udyuN6aRO2tt2IQR/3QI0F9PwZpBQd9kgFx6gRzg0F6Gd+FRNJeOInRL5iXAG2J4QY6hlkqRtKZNtp9lUkL+jAovO0QLiRSSnGTizL1BZAZo+KHiY2/Dv+R/E8lJAoholBKZJ6U8vN/4ZSOgA261hrI/52+/7H8bYpECHC7ZbhsaUhq6pX7Jtz2Q5LxwpX7lY81vF7aX1bpd5CgFW+tlGTjojo8Z7GLqZ5rg/b9gFJaCQLcQYhMhK72KvK6wlqIMSykslps1eUCATt1t9tnFR4KiccL5ZVWu6pO+borahpWUIxEaMGf+3vdHntyotttDWBl5Yo4EKC6CgLGuVVWWtYKqHMyA+sA7dvLcO4HwO7dbpo2NRJMSyWZmQYfUq2kbVujVsz2GmJiVP8DB9x062ZpzvfeK2D58gq2b6+hRw91kuvXl9OlixrEq6t9SAmpqRGaFHjppS3cfXcv0tP/OKrqcAieeGIg8+fvBWDUqAxWrVJT1uzsODZssCLg+/apQalVq2jGjk217UfKYNgN5vdDebnldjlwwEIm5uRAQoK6X4WFSpmo+2TV/JDSUhYA2/ZZluJXS2HFJvV77W5oYpzGHW9Z78Kz30N+hfq9aBf8sA0OlFqIK3/Anki7+ihk631N7mplr++twjZKbKqChCiVpGhOoPbWQyetW9fXQXYs7PXAUMNi2uaDlvrRb/UHaWfkjxUTpEJY92wj1bQkmoFYpnR7MsKVDpNJpQnqpjhw0gKDljq8jxLWUhw+7+YxarLU+T/KmPh9+asViVDm7FvATinls//i6awFOgkh2gkhooGpwNd/cKx1QojrdeD9X5a/TZFERTlwuxvOPpunqVlPSKo9KiEwJJHoqY4uOyNvmYRobWXUSqj0C+qRNtqUkJgFrIIBaBLRxR2EGAfUB+3KC5S7K8ah8kbMSsG+oJ12ot6n8Prh9QH79YGyukyF4Q8orH/ovymHIszyjnZXMACdI3JPWre0xzmSkizUSiBgHdvrtffzeu1urEBA1ccwZdcue8DU7ZY2RIzDIUhLsy4uP98ffuZSSluwv6TET6rht9y2rRa3O4jXK+msy1Du3VtDp05qGr51awUnnWTwymjx+4P8+utRRo1q2WBdY9K+fTJ79lQAkJ4eTVmZGvjj4pzU1zeEjTcmNTXBMENDVVUwzFocCEibdVhaat3T4mLI1LP9wiJopi+lugaSjQG4oBiaa0VyuBCymillI6XFAuHx2cvhHiiGdnqbnCJo31RVQAyxJxyuhDZGbK2w1gqom7K7ErrofiYMf3dtBMO1B7pqRbKrHrrGKfqTZsb7tMtnMfzu9gfp4rLei7346Kj5tPxIBOD8A1fVsUgRbjI1crXECxnRCibtrf/j7f4dCSUk/sWkjSOAi4AxQohN+u9UIcS1QohrAYQQzYUQR4DbgPuEEEeEEMlSSj9wA/AjKkj/sZRy+x8cayrQEoXumi+EGCv+BeK6vw3+6/MFcLuDYXdBSI6U2tt1AXuyVb3PHtzeFRG4Nq+8XkIsArffQVwj3sG9HmtH1QGrdG9I/EGF4fcGICVCkTiEonqIdtqVQEI0xBkT5KC071cIC6ppSnNjwI6NVa6p+D+eTAMqIbHBdeXa27n5dirxskqLNiM52XKDRcdAsjG4CNHQtRUJL4181dq3d9mC3kVFwjaQmuLzSXr2tEYvv98+6FZW+klJcZGTU0+U1s7x8dE00RjTuroAHk9DW7O62kv37k1wRppzvyNxcc6wVRMd7QjvPxCQdO5s+Y66dLGPtGvW1NK5cwypqS58PkEzPWpWVkratVP7KC2V9OtnPch27Rzhwl04rPybsgrooBmdD+fDQCN3p0MH650JAG1bQkkFDOpl9TlYZGc4aNXUcovWBqBjM+iAlQibFAO3jrD6PzCyccs2zw29tBW6uwZGaWW3vQa6G261nXVWvsh9rVQcc4cHRhgewJtToHOIbNUVJN14Tw7jpa8ejvJwMwi767CYKurw0JYMfHg5xD46aCvkAL/RDkVdvZcNtKc3Tlx4CdBMK5Iu8eo7LK+AtAhwyv9W0aipPxzMpZQFYJCM2dd9D3x/jMfaB9wrhLgfmADMAYJCiDnAC1LKsj/a/m9FbXk88k/rAtT57ISItQGI/4NtfF5rlPZLaCYE9bKhkmiwnbRbNqCsDidq8IwcrzwB9Vfntz/pkMsrJE6nnTtMCIt9NdzHZbkhQFkobp+yXiL7drIn7B6TREfb3Wkx0RZ9eL3fSHp0QKlxHk6nXQEJoSCrprRrZ49x7d/vDMcAQEFjzeBydrbVPypKsH27dbMcDkFysvX8UlKi8fmk3oc6kbo6KCtT6KjERFejHFlpabHs2lXZaJJpY/Lxxzl07pwKwJIlR2nbVg1ia9eWk6hrxNbU+Dl40D6Vve++/HDi4fr1XtpoeNL27UEydfbqrl2SGA2bqquDgwetl2H5CmivrccN26GNti537oNU7WAoKlUlBkKybAtkt1Cca7FG/GzbYUXwCCoX66ixzdIc6JwJvVtBR22lNE2AUYaL58yIXKSNpTBvP/xaCl3VrWFzFbTQx9xfr0pMA3i1xR6yxE/SA/WeoL2i6IAYQYwQFAWDDebjW3CHy2Pvo5bUiDnuNnI5iAI+lFDAHhSrpR8v2/gu3G8t34ep5XdRSSZxeAKwT+eQ1tRC23/jGzoW+W/MbDdFCNEbeAaYiYrLTAaqUPTyfyh/myKJjXXh8QQbUIN3iIgB1GvkVEiCKJdSSLpGzO4dxrBeL6E6KPBKGqlEokrohiSKhumboXEoyB/cKGmfpTuEHfPvEErhhMTlbJgTEOOyK4xo3Y6OahhP2RNhgWS1aFh4qE1r+zl1ameHHLdoYe03KVF9XKCQQbUGqjUxUQEBwufuUjVFTNm/32k7VlKSCNcsB2jf3kltrdXh6FEn9fWNI5rS06PIyQna2ocPe7n88pbEx6uH1axZDIWFXv07jt97MgMHZrBx45/Dc4JBybPPbuH22/sC8MUXhzjzTBW8/+WXEkaPVr6g9esrGTDAmspKKSks9NO8uXqztm710quXmvFs2eKjd2/1Ym7bJunVS53jzp3QXbvyAwEVYA/FSLbvgh4aIbdtN/TUA/v2HOipLRWvT71PLhdsOwg9s63r2HoIemnMwdYj0EsrfF9ATUxiGvsA/kCWFyk6ov010F4rjM1V0Fsrq1U10Fvfjl1uaNsI5chenyoGFyk7/AF6uezPbRdeumj1so86OkRYJPmU0VIH2ksooAnKNKqmMEyNEiCAwBFGb1XgIZVojnogRLVWYMSl/kr5b2f/FUKsB55DxVZ6SylvklL+JqV8Btj/Z9v/bYrE5wvg8cgGBHk5BfZ+JkkcqBm/Ga/YFcFkYcspkcp35w+Cs5E8ksPGAOymYUZPjNPK45ARdypaWxpOl51BNTXOboFEOe0xkphopeBMiY2y54IkJirK66RExbD6R1JVq4j7TCmpgFpjWU29nYoch6LSBmjRDOp1LDgtDcy6Xk2bqoDwH0lysj1u0ru3Kwx9BRAimvx8q926tZO8POvGd+wYi9+v1rdrF83+/Zb50rZtDAcOeDj77GZhS2XgwBTWrlUXk50dz+rV1VRXN7RKbrqpN1df/StlZe4G60zx+4M89dRQmjePZ926MlJT4+ncOYVAQLJ2bQ0nnqiCGIsWVTN+vAVf2r7dwwknqFG1tjZIx46xYSi72+2ie3d1I3NzHeGs/m3bYbh2J+3eAyecqH4Hg5DRDJqGYifV0FsrlT2HYYiuK7P9IIzR9P4HS6CfkRhaXAt9svW+i2CoVj67imCMAeE+XAXfaWZhKeHVrda6uXsVSgtgYxn0S4c3h1jW/M3toW+K2q7Ip0gZATbUwoCI+EqJX9LMIRt1l20RPvoY5n8QSTqQpIejEtx0ilAkPry01IH3WirJRMW/qiggg/b6dwkt6RjeZ1viEQjy6qCvdqGXlEHTCMDIXyX/zYoEmCKlPFFK+YGU0gMghGgHIKU86882/htRWwKvV1r+YtQsrbE4Rboxg/AFG7qgTKk0BmmnQOWYCPFvFTzyA16UyR45VDkciosr2qlM+/A2EqoMV05ctHJ/hSQxRiUgmpIUrwpZhduxyj2RnKBK7ZrSLdtubaQmQUVEADw9BcoNxdEkTX1AIcloopIUQSmtw/rYzTJh2y6rX0IjwdeuXVXd9ZB07w5HDG6nqCgXOVZCOB06CJuV0bdvNIcOWYrE5XKxa5cavWJjHbRpE0VQm2xDhiTx6692d9K4cRl8/rlC4gghOOOM5nz55VEipU+fJjz8cH9uvHEly5fn/66bKzrayZgxrSkv93LVVWt58EEVeJg79whduiSSkRFDTU2Ar74q5+STrRFo3rwqRo5Uo9P337vJ1nQLublBSksVyaOU8MMPDvr0UZ/ZDz/AYIVYZcly6KgH+x17CPtHpYTlG6Gdti5+Xgv9tCJYuR3aafzA4m3Qu52xzT5op/Xc4r3QR7tvVh+CNsbAuegg7NZxyINV8MNBa92z2yBOW/iFfuiRYuWGAHRPUsCSXA8MMvJMcjzQP+Jd2eC1k6uasikYoL/TciUcwmdzZO2kOpyECEopHKaIBL3sMHtpqi2Scg6QqC2VcgqI1n2KqcetExYP1FllJwJ+Kxn3r5T/oWD7/0n59BiXNSp/myKJiXHi90tbLRK3t2E1QLff7t5JdikFEZIOf/A8fFLBg500HrFKcVkDXIxDUUaYEuVQSsLlaKi8YrQCSY2xu67io1RcJyRJMQp5FpLE2IbnkhynMt7D55UA5TWqSFGktVFQpShUQtImy16jG6B9NhQYiqNDtr1qXpvWitoFFKLrcKhQUis4Yig0IVT2vuk6k2BTFM2aKTbgkHTpIti92xq0e/Z0kpdntdu1i2b1auu+DxgQzfr1luZNTIxizRq37htLQUGAmhrrBNq0iUVKSV6e6nPFFW146608amoaWiWnndaG6dN78cYbOxk16kuuvnop11zzCy+8sMXWr6CgnhkzNvHAAz1o3z4RKSXz5h3l7rtV0sXcuYVceGFGOOAfCEi++66G009X/pyPP67nnHPUDPrrr/1MnBhyd0G3bsoVFQzCjh3QUwfEFy+DMcer37+sgtHD1e+dOdCtvVVkbHcudNaxkxVbYURP9W7ERVvuqgPFirk3NFfaWQDdtYt4dS4MNZB9q/NgqM5XWV0Aw3RKTl6tqqHjEArqXuW3g1xMWVVtubsAFrkbWiRrPDDkd8AiNQTpZNQB2oabnloBVOIjCZfNRV1KFU3NUrlUkaCz2Cs4QqpOlyijgDTt5sqnlla6sFVuHeiwFzt3WZbf/5Mw6ePZQIoQ4izj71Lgd55gQ/kbExKDuN32CLYvAIciXFv+oFU/AaC43k4bkhMxu29lWIwCzavjCiCdDWGclcYs1SHAHTFpTXapfcQ5IQJMRkKUgge7ohUkOSTp8RY9BEBKrN31lR4PuUbOCKi4SRsDxZqRDEXVKmv9aETf5ulQaJD7pSdDfkQoID0VjhguqabpVnlWgKxWsGGn+t02S/nQQaHFWkaQKLRtA4cMooVuXZWvP9zupgbIkPTpI8jPtx5Qv35OfvrJugEjRrhYudIa9EePjmX5cushnn56It98Y2nKM85I4ccf7dCxadOyeOCBgwC0bBnLpZe25uabdxIpixbl06NHOu++eyLffnsqV17Zjauu6sapp6rZSjAY5IMPDjJu3DImT87izDNVYEEIwcKFg0hNjSI318Pbb5cybZoVvJs3r5qpU1PC+S93351Ez54upJT8/LODM89UI3DbtvD442obKeG9d63B/r47FHU8wOknw+TT1O/WzeHxW6xt3n3IUirVPuiRDct2wFiDgXnFPhin+c5yy6B/O8u9WuSF3kbcsdwP/fS7tq0SRmoLZ00pjNVWzG9lMNQYbOsC8LJhda6qtogY64PKCk+M8NTkBoIMjG5oBVbIIDUOPy7DQ7CdOvroGfpuqultEDMC5FFMZ82rVUs1zWgTjoNIJEkoU6yOSprofoXU0S6UBe9WJbFB0QM1b6R8wl8h/6WurS4olFYqcLrx1x+46lh38r/KteX1qQCzrR+QYqC2JH+MhztiZAPGCkmsQ+JCNIh/hPYVkmideGiK0wnlEhJcCi1mSlKU4ttKjra7shKi1ewuJGkJioY7JE0TodjOQg7A/lLLZdUyHQoqoFUTyIsA3fVsp7L/Q9I6A44U2/u0aa4yoEPSrjUcMAaC7FZwQCuHdm1UPfeQVNVBkbG//v1gz16r3a8/5BpKqX9/RS0fkj59BAsWWK9V+/aC/ftl2F3Vpo2TpCQHHo9q9+sXzbp1Fu36yScnsm9fIIz8uvjiJsycWRreHuDMMzMoKPDy669Ky15ySSvi4x08+WRO2IUVDEoWLsxn4MBvmTFjHUuXFujkVwc7d9Zyww3r6d9/IZs2VbB06QmMHWuk56MQZKqG/H5eeCGbhASlHOrqgjzzTDnXXptq3INohBCsXi2Jjydcbz4lRUF3Qb1LJrllvz6WUmnT2polr98F7bNC52CxOG/MgbQkteyHTXCckX/3/TYYruMlP++BnvpScivUJCZkuRTUKor4ON1ecAQG6kH150Lor8/hl3I4zpjYrKqEPcY7XBGAQXqs31APp0RA+INS8lswQLtGfNDrpY+Bwp5Auol6euuYyA6q6BShSA5ylGY6PlLIYeK1peGjnnoqwkWs8skJWyQHqKK57rehHNppi6lJuooF/tXy3xpsl1J+JaW8DJggpbzM+LtJSrnyWPfztymS6GgHTqcg0Ugn9/gaorbcPpU7EpKWEa6syLYpDiGoRKrXqREXuTAYCuNd4I54tolOZW0kOCAlwsxPilIztYxYewwkPU7NgEKSmQBFhuKIjYLmEWy9YHdvpcSrAaNlWkOEV0oi7DVCAu1aQE5EiKBTWxWkDUnHNoo+PCQxMZDVUrmsoqLUgObT97hXN9hqTO67d4dVhqIY0B9+WGS1O3RQRI4hmHBMjMqGL9CWpRCC8eOjWGcoqzZtnHz/vT+8fuzYGBYsUDctMdFBq1YxfPZZne4bzeDBcXz2mWWaCSF48cVOPPdcHnl5HoQQPPtsN/LyPEyduomaGn84Y33dugmMGtWM9etL+eabI7zxxh42by7nrLNas2bNSTz1VN9GM+ODQcnNNx9m4sQ0RoywRsqXX67iyiuTSU9X6DNTwb3+uuSWW9T7vHAhzJyplrvdcMd91r43bbfDqkNSWgF3v2wpGNPS/H4tnDpITTY258IwXQbA74ed+dBLW5I/7YGT9bpF++EkIzl18SEYo13HBXWQFmO5r1aXwBCdrLisFI4z2KJ/qYDjU9Xvcj/s91oxhyV10CsiFrLLL+nqajwuuQcvQx3WbNFHkCCSFD2w5lBDd+wfSC4FtNUKopDDNNOurHJySdPkjEGCBAng0vjMQ9SQpRVSudeKs+7Z17CU918h/yZp498uQogZ+uf5QohZkX/Hup+/1SKpqQna/O+BYEM3TaQFctRj1wn5EZZCR0MZxAjwAE6HpFY08uUaO050QHVEl1SXSjaMdkJuBEVKk1goCqgYSIHhXmsaZ3eRJUZbdR5CklvZMLGvVxYc0taHEIpNNSYKDlTY+3WIUBxd28Kuw/Y+3dvBjgNWOzlRld81By+nC/boPicMh+3a6hg6GNYb+a8jhsCvBt9oixaKuDGkeISAwYPtBZvOPlsFlkNy/PFOPvzQutkXXhjFvHnW7OCqqxJ5/nlf2Jq4/fZknnuuKty+555M5s+vstGOtG8fx623tmbSpJ0UFHiJinIwa1Z3JkzIZN06S+lERzs5/fQsHn20P888M4jXXhvO/ff3ZMyYZkRHNz4r9Psll16aS2qqi5tvtqbmCxe6+fFHH9ddlwrAtGluFi5U5/Tbb1Bc7GDIEPVJ/fOfMGmS2u79j6xM9bIKuOIOS1m8NM+6l58uhikayXWkGC54wjqn7zbD+IGw4aAqWhZiR1iVCyO6W/lJB2ssi+T7AzDeqDuztADGZetrOQoTdbD+SJ2qehjrVIzXyVFWWVqAPT44Tl0yy6rgOGOcX1oHERybrPcFOSW2cb/Bz446BhqKZBf1dNRKRCLJpYrmxiAbJIgLSVK4Tns+zVHasIxcMuiklxeG0VsAPlykEI07oDi2QEHb4/+0RNP/7yQ0bVyHVavd/Dsm+dsUScuWSQQCMlx+FdQMOZISJNqpMshD4hT2mEOk5BiVEGOFBBEkXgjqGumbYrzryQ6JL2J0T3JBiCYp8pAZsVDkVvXaC41AefMExWEUEiHgQLVdcWQkQkkEGis9AQ4YSrR1OhwpU8H5KuPku7SGMuN4zdKsGEdIUpNUgNc8ZvtWVq1vgIE9LYXRvi38qi2GYQNgySrjejQs1UwqnHAqrDKUy4knwZdfW+1TT4UPPrDaJ58MixYZVk8vJ4GAg9xcdeIdO7rIynKwZImyUlq1cnHSSXG8+qq6Sc2bR3HRRWlcemm+zQIYMSKZZ55px+23H+C331Rc5aKLWjF69L8fTT1wwMMFFxygd+9Y/vGPluFZ9f79Pu64o5IPPkjH5RJ8+62P6mrJKac4CQbh9tvh6adV32XLIKsNdO6s3unX34brrlT7f+MDuPI8/V4cgc8WWrQ0q7bB1FPU7/cXwYVaqWzLhRZpiqBx/mqYahQW+3QTTNIZ7ksPwNAste96H+RUQFdtWfiDsLoQ+mlX1rdHYIxWOD8UwmCNzlpUBn0NpFalHw4ZRaHWe+AUHfd2ByXNYiTNI6z177x+hkbSZQNeJJUEyTTcOuupZYC2HA5TRxsSjFK6cJQSErUSkUiKySdFo7SK2Uu6tkiKOEIK6mKr8OIniECwr1p9o6DQif2M7P+/Uv6LXVvf6P9zG/s71v38rZntsbGC+HhDkQShZcQY4Auo3JGQtI2z52UkRbzE5jwoUTgoR5IswNmIb8vtCODVo22KEwoiujRxqYqMoAjpTDdTrFPN2prF2ZVMjMsOBwZIi4UKw901IEv5r01pnwH7iqx2/7awvxiGdYI9BgChTQasNVBTQoDPAWUREOCkVDhgWC7DB8DqbVZ7xEBYo4PkowbBcq1IslqqHBOf4U5s2hSWGcplxDD4/BurffoE+OZbS3FlZ6vtD2vFFRWlarh/9pm1zWWXRfHEE5ZP8N5743jvPW84NnLPPSm8+WYt+/erPmeckUTXrtE880ypDcp7/PEpPPxwG+666yB33nmw0Uz3YxGPJ8izzxYwZcoBbropk+nTLR/r/v1+pk4t4+23U8nMdJKbG2TevACzZ8cihKCqCq64QkGjfT6Yfifcr11ZH3wKJ45RyrjeDRt3wkUalT/7Q7j+PPV73U5VTqBVprqPWw7A5OPUum/Ww4XHqe8jpwTGasURCMKeYhhjuLLO1PWclhyC8R0sy2dVAQxrrkAl3gAcrIUeqWrdz0Vwmr7c70rgNAMe+3MFnGjkOH1TDcN0CGO5GzIivr+glOwKBOnhbGiRbMbDyRFAoEO4GaBjGbuooL9B0ghwgHzahXNGSkkhPRxorySfVM0OUsSRcJ32/dTQTiun3dXQRVuDubmNQ9r/CvlvVSQhEUIsFEKkGu00IcSPx7r936pIKit9NjqNoITiCJSSI8ICKfYqWG9IqiIG/24uQUD7cBKEopKPF4KjIsIHBqQKQbke9Js4oTRCATR1WbGRCqkqJppy1KvKjhoEsYDiM/IZhxvcyo7USk2EPREuvK4t1KAQkjYZsPmICrxvNiwJh6OhlTKwE6wzAuIAw3rASkNxDOsFKzYb2/RU+QoA3TpCZb0F8x3SH1YYMY1J4+HrBVZ7zPGw+BfLVZaYCCOGw4aNVp+rroLXjeoKV1wBzz1nKZvTT3dw8KBk/361k65dnbRs6WTWLDVriIsTvPRSKg8/XBlmiH788Ux27vQyY0aRTZl07BjHokU96dgxluHDN3LrrfvYuTPC5Psdycmp5847DzFw4DaiogQrVnRmxAgr2Lt9u5ezzirl1VfT6NcvhvJyyeTJXm69NZrMTPX5pKbCZZep/i+8CBNPVzkidXXw9Esw4ya1bvb7MKCXojEvrVCWxhna6pj1Cdw0Rf1euFH1SUlQccMPf4PTBsLCHarMcih4vmivyh2Jciry0AUHYVS2WjdvN5xlJCJ+cgjO1CixRYUwvJmGd/tV/ZBOier7qwrAEENxfF8JE7SFss8DWVEWs8SCehgb4bbdGggwLq7x+Mgy6ulmcEwEkGymijbalbWBYnpFlMjdTy4dtLI4Sg6tUf44L7W4iMGhM1CKOGQokmo66jjL9grophXJwVzIblhx4C+T/2ZFAmRIKStCDSllOXDM+La/VZEEAvbAV6gGtSlxUVaZXVAvsYmuiryAQhGgVu8kCagVflKFsEF9Q9LBBeVS7SxdQKRbN8WlKLEBWkQrxWFK0xh1vsURydPNE+CQYSGkxMEeA33VpSnsjlAkPVrANsPy6JOlFEjftrAp1953ZHdYZ1glw7sr7iVTRvSC3Qa6amB32Gv0iY5SLLIHjqhn0Ko5/KbjHGecDF/8ZPU9bhgUlFiKJjoaxoxRSXUhOessePFVqz1lCnz9DWG6lJYtVa33b79VbSEEd98dxQ03BMNK4YEHYvjwQx87dqgDDRsWw4knxnLuuWX4fBKnU/Dmmy0IBGDGjCJqaqwXweEQXHVVc9avH8AJJ6Ty6KMH6dNnOWefvYFHHtnLm28e5vXXD/HKK7nMnJnL2Wdvo0+ftTz9dB6DBiWwfn1PbryxmY2Z+J13apk+Xbmz+vePprJScvPNPh54IIrBgxt+Ott3wMYtcMftqv3SW3DdZboeeyXM/QxuuFitm/kuTB2rXJAHjqrcjVF91brnv4SbdXzl099g0gClPF77Ba4+zjrenLVwhc50/2YPnNpRxfSqvbCrDAbo8I4/CEuPwoka6vvxIZiiB9QfimBspnqPV1ZCSpSVp+WXsLYWBulZ/Pc1cHaqdfzdgSDHx9i/qx8DPvq4Gh9WfsXNSIOIaBd1dNXZ5xJJDlV0MALtEkkBJbTQdPF57KOFzlwvZi9NUZC4IEHc1BGvrZC9VNFJ72dbJfTS53z4iL3Oy/8TmwSEEGE1K4Roy79QCOtvVSSJiQ47jbxUsQJTpAST5aJNvN11JKTdYkkSgkrtg0oVDioFpCBIbCT2FxMVpETDheOcgv1Bu9ViKrUeCVAYoUj6pUFePXSPQFd1SYMcI/WhSzrsNhRJt0yoish/SYxV+TKhwbpHK2XV9GsLg9rb+/ZtB6v2WO1hXeG7iLDY4G7w7RqrHR0F8Qmw30g4HHccfLtC/Z54AnyzVP0ePRRy8yyLIzpaZb3/uNTa9tLzYPZbVvv4UbBrt4XWio6Ga66Gd9+z+tx1FzzwgJqpAxx3nIM2bQTvv69uXkyMYM6cOC68sI5SbR5efHECxx0Xw913V2qST8Ezz2TSs2cMo0YdZPFiu+XhcjmYOLEpH3zQg40bR/Lkk13o0UPF46RU67t0iefJJ9uzceNAXnmlI5MnN7FR9Rw54mPq1FJWrvTyySdN6N49irw8ydixHiZOdHD66Q1nkzU1cMlVcM8MhVxbuxG+WwhXXaLW3/883Hs9xMVCXiGs2wEXnqrWPfoeXDNBvW+bD0CbFtCjrXr3f9oGV4+B3FKVoBrKWC+qUUqov0ZrLcmFizWVyrcH4KJu1vu7rBDGt1YJtp4AlPtgqI6drCiHyTpW8mkRTDbmoKtr4Phki23ii2oYq2f3u30Sp4D4CIblhX4/J7kaEmxVEqA5gnRjJr6FakZpC+QwNfQhzZaIWEI5rckML6uhnGY6JlJGLpkok6uUozTDMjXK8JCtc0jq/NBBG5g7dkH7dg1O7S+R/wsy2+8FVggh3hNCvAcsA+4+1o3/VkVSURGwubYQ9gxs0BQkxvheG7QnACY77WirLi5VWhfAKQRJQBPhYKuI0AJAphAUGZaKhAZUGr0TVZZvSjTsi7A8UmJgb61an28EwNunw27jOro1hQpDcXRIh6URVgZARjrs05mPsVGwr0R9xBePtPcb0cVukbRIV9XwPIbrLToKsjJgn6E4Jo6Er1dY7bNOgM81r+fJw2DBSqU8oqKgVQv4+Ver7xXnwZtGAL1vL6X0crWVIwTccRvMMqySyy6DN95SJXxBIb4uv9xK0gN48kkHX3xhZcf37OnkscdimTy5PpxbcscdSWRnOxk7tpjCwgBCCC65JJVvvsnirbcqOOecg6xZ0xBO4XAIOnZM4Oyzm3PNNW245po2XHVVFhMnZtCxY3yDGu95eX5uvLGUs84q5uqrE3j99TQSEx2sXh3goos8PP10FJMnN0z3Dgbh/kfgpmnQo7tCB02bAW89r/JHVm6AvQfhHJ10eMeLcOuFat2ew1BaBeMVCzoPfQSXn6R+/7RV9clqCs8uggm9rWO++huc0FHd95xy2F0O3XRs45WtMNlAa72xFybrAfTro2ri4xAKobWiGvqkqMlYfhDGGCGKD6tgko5ZFvokAiuw/nVdkDMiCFdLZJCWTklGI/ja5dTTIYI6dTFlDNID/gaKwwmEIdnLAdpo2G8tVfjxhuG9eWwiE3WRR9lPhi5HXo+fanxE46DOD6Ueq8xDXr5igghJVdUf87D9q/Lf7NqSUi5AJSF+pP8GSCn/O2IkwaCd1K0xwGBChGsrwWVXJK2ioNJo+4WkzEhKrI32EicE7kastLbCQbU0eKBioCxCkUgBB73QLhYORLx3nRJhbw10ToY9huLokQbbjbhJp3RYariVnA6VuBj5Hg9tC78ZCmZwO1h7UP3eUwC7dfA8qykcLLczBk8cAqsNqhKAs0bBkk1We8II2GIooOZNVR2UI4WqYuLgXrB0rVp31TnwxsdW3749IeCwEhkBLrsQnjCQ5mdMgEW/WAmLcXFw3z1w7wNWn2nTFDfXbxr1lZIieOghB+efr0r7AowfH8WVV0Zx9tl1VOkg2A03JHHffclcdVU5P/+sblzr1lHMm9eK227L4Kmnipk06SBvvFHCoUMNJw2/J5WVAd57r4qJE/O5555SRo+OZfXqFpx4Yhxer+Tee33cdZefN9+MYuTIhh+/lHDzdGVpXHyBal91B9x6HXRsr8gzZ38Isx9Sg/7Pv6mKlaeNVH1veQXu1CiutftUAH1wJ7Xun9/CPRMVieOKHDhDJzR6/PDpdrhAt1/ZANP6q9+bixU0vbUek0vdkFMNA7VCeCcXLlWTej4thCk6VrK4EppGQcgw80tYVguj9Wz+83qYlGpd99e+AKdHQGm/D3jp52yoaAEWU8MYLHdDNX48BGmiKx6uo4gBES75vRygs46JHGE3WdoCCeDFj5tY7b7KYz+ttJtrL1V01lbOlgrLrRUIQMBjrwV0+HDErPU/kP/2YLuW4cBo/Tf0D3tGyN+qSE47LY0dO6ypvMPRkO462gn5hveiZYy9NojLSThgDtBECEoNZRCPoFYG6dLILKmpU7DPIDQRzgCHIrDF7aLhgBfaxyqGYFO6JKrg5EXtINtIxu2eZi/N69K5KCYH10kdYGMEHcywtrDbCLiP6Qob9MC9vxhe/8Xo2wlWGu6toV3gS8OVBTB2EMxbbLVbZkBuERwy6FNOHgbv6tI3l06C73Tco18PRSufZ/S9/Bx4+g2rPXEcbNoGB7XicDrh4XvgyeetPmedqT7ixUusPg89BNdco6oFAvTuDffeCzNmWG6vCy6I5tJLoxk3ro6CAvWATzoplpdeSmH27BrOP7+Uo0eVJh06NIFPP23LrFktqa0Ncs01hzjxxD2cf/5u7r77IK+9VsAHHxQzd24xr71WyDPPFHH++YcYOHAvN91UQG6un5kzmzJ3bjPOPjsBIeCLL3xccombtDRYtCia9u0bVyJPzFRIrcceVsueeAkym8L5Z6n10x6Fk4ZDh7ZQXQsvfw7P3ab6frUSMlNheA9l1dz+Ljx+oVr3wzbona0KUs1cDHecbMUT526EywYoq7XSA/uqYaK2QN7eAzf1tc5x/mG4tqtSFofqVD2RzlrJrKiC8zVaa24RXGyM40trYVKyRUc0vwam6Hd8v1+SKKBphFvrh4CH050NXTZ+JPtw0xsLx7+eCsZqhJaHAIk4aG4w/gYI4gAyNNS3lCNhRVLKPlrTx+jrpalGdh2ghl6kArCrEoZpF97Bg4qyxpS/WpH4cf5Lf/+bRAjxBHAzsEP/3SyE+Oexbv+3KpIBAxL4/HNr6u50QHVEGcxIEkSHUMitkESirbIcgkojl6QrLooJUCACNusDIEs4OGL0becUHIhQJN3jITcAbWPh1wggUJckWFIKfdPsJHbJ0bCr2p7fcWI72GgoiS4Z8GtEgHxAFvxgKIcRHeGrrer3cZ1hmbHu1L6w0kBqndALlmyz5460aKIGnzwjsH/hKTBvodU+92T4eKEayIb2hpVbIV+f5/jR8Ow7Vt/TT4SDeZCvlYsQ8Mid8Izhzhp7EhSWwI+LrD7/eBhuvd1SHNnZ8I9/wD33WPkpZ50Fw4apJL5QTZQpU6L45z9juPzyen78USmNNm2i+Pzzppx/fjw33FDOtGml7NmjXpC2baO55ZZMfvihI99914H778/iuONSCAZV7ZBKbbp27BjNvfdmsmpVR+bObcV996XTpUs0Ho/kiy+8DB9ex8KFfp5+Oobp06NwNgJlDQTgxtsg7yi8/Ly6zvc+VbkKT2sL7N0v1ft66ZmqfeNzcNZoaNsCKmrg7Z/hSc1m9P5y6JetSEv9AXjwK5gxDvIqYMlemKItDq8fZq+Fy3T7lc1wcraarBTUwaoiGK3jJv4gvJ5jBdZfzQXNis/WGgVtz4qDCj8UB2CI8Q6/WQFnaADVQV+QLJekpdYqP3j8TI2oPV0ug5Q5/HRwNBwg11BHL2JxGsPNYooZoC2KTRSREVEN6BCHSSAmHIg/xKZwfKSAjTTVlkoVpfhxh4tZraWEblqRLCu2LJI9e6BPb9shKCpqLLvs/7dyKnCylHKOlHIOMA447Vg3/lsVSaQ4jWBzSBJcKjcjJGlRUGEoltYuSaUx+Kc6HBw2+bYckgIRoCVOjmLfeWscxBhjRDeXoDhC2bSLgS0eNTMTQpn84X07FRTZb98EUHWuTXdXpyYKyx+SkVkNFUmMC5rGQ56GCjdNVAH3qnqIj4EB7eCQHozH9IBP11mKIyYKxvSCDfvt+7x8PHxkoKvOPh4251iB9IQ4OOME+GGVur4bp8KL89W6yafAkjVQonW9wwFXTYUHXrD2d/JoOJQPSzUrjxDw0lNw58NWmd6WLeGhB+DBR63nO2GCol+55BJr2WWXqfb111txleOPdzFnThwvveTlppvqqaoK6u3j+OSTpkyYEMcNN5Ry0UWFvPBCOXv3Ks0UG+ukW7d4xo9P47rrWnDrrS246abmXHNNMyZNSqVHj1iiogRVVUF++snNNdfUMmhQFevW+Zk/P5bZs+No1arxz6OmBi65EjKawovPKivrwy9g7sdKibhc8PNKmPMFvKTzST5dou75heNU+8ZX4IITVEJpaTX8sBkePlete+83OL2PSkqdvQLuPdny83+wDc7oqure1Prg6xy4XOeOvLoDbuxpBdm/yoMTmyk6n3o/fFsAZ2vk1mt5cK3+/V4JjE+1tisLKBqUATrl4906GKu9UlJK3vN7OCPOrjC+Cbo51dF4AHkltYwz+LN8BMmhji562W8cZSh2rrNd7KWLzlovI480WoSVRRHbyERlFiqXlyIkk0iOUkcLrZS2VEDvVLW/TZssksyQ7A7x6f8FIhF4ifmX/o5FhBBzhBBFQohtf9BntK7nvl0I8Yux/Fa9bJsQ4kMhxJ+x+aYav1N+r1Nj8rcqkg8+KOHEEy24X5SrYUXA5Bg4WGG1m0bbUVspLjhitJsLB0cNZdAKF0cI0FM4yY+gbox3ONhuBOHbuhxsikBudY2GXTpQPjwZ9kT4t07IgD2NpCwc3wK2GEitES1gpZEgmJWi6Ft8EYrzjJ6w7IDVPrM//KhjH73bwJcaohsfo9weWw1ldGJvNbM1ZeJQeP8XS+EkJ6h6JQsMN9iF42GmDqRPOQkO5Kt6Jk4n3HklPDnH6jvpJAUZ3mzwcb3wKDw406pT0qI53HsbTH/IOu6ZZyiyvDsNzqmbb4YuXZRLK6RMLrwQzjsPTjnFiqM0b+7g66/jGD7cyahRdcya5QkjuE49NZ6ffmrOY4+lExvr4PbbS5g06RDjxx/gllvyeeONUt56q4z33y/ns88qmT27gvvuK+GWW4oZPLiQ8eNLWLjQzbnnRrFxYzKPPRZP27ZOSkosN5spGzfB8afApAnw4L1q8J0zH779Gb58G+LjYdNOmPEMfPKccg+u2wVPfQgv3a76f75SKeVzNJT3hnfgnGGQmqAskFlLYfpYWJMLa4/AJD2TrvbAc+vgVk05P2sTnNtVQeRL3fBtHpyrEX5SwssH4Gbt8ppXAJdkqxhIhR9yvDC2ier3fSVcbCQhflQF16Srcw1Kyed1Qc7WgfV1gSBdnA5SIoAKv0kPZzka8o/4kfxCBUON+Mg6yhlFWjix8Fy60BV7JnIl5XTQVkc+u2hPPwDcVJFES6L1/srJDyuSXGrorxMWK73QM0W5lAG2bFUuVFMOHoxIWvsP5H8wRvIOyjpoVHQS4WxgopSyBzBFL28F3AQMlFL2RFXTmPoHx/knsFEI8Y4QYi6KHuXxP+hvk79VkeTkeBg2zJqpxEQ3rFGeFMGumx4FB0xKEiccNQbjlgg8DmtBNk4qCZAmBLmiYdZzMoIKrXi6OB3sjuAbSXKCcCp4b8to2BzhemsVD2sbcbX2a6ISv0LSIUVZUiZMuEMqrMm3bze6A3xu0LKf1AW+0O6tM/vCl0ZS4fnDYIExTzmljyp4ZKvfEg/92sPSrdaym86CWV9Y7Y6tFT398k0KsXXKMHjqXbVu8imwfIPFHiwEPHsXPPaKNfhnZ8GUSXDPU9Y+p5yhBsuX3rSWPfIA5B+Ft96xlj34IKSnwznnQL2+t2PHwpdfwiOPwJNPqhiEEIKpU6NZuTJBWQSX1PHYY/Xk5ann1aZNFNdck8LXX7fkyy+zmDOnNZMmJZOY6MTtDlJU5KeoyE90tGD48DguvTSJX37J5NdfM5k5M5UxY6JxOgWbNkmuvBLGj7eXFfZ64aXZcP2t8OFcmHK2sjDufQK+XwRvzFRJhJt3w+3PwEfPQGYTOFIEVz4FHz2klPiOQ/DYJzDrWrXfz9eoON+ZuuDVjR/Bk2co6/TWr2DWmZal8MQqFVRPiVWQ+I/2wrU6y/3pbXBjd5WcCCpbPTsB2iYoRNbsw3Cpdnm9fhROS1f5Ij9XQ6tYSNdWf1DCG1Vwto6jLPYEmRgP8VpxfO/zcklEIPOw9JPvCJAlGg6Oa6hhAAlEGVCaxRQxwlAczUjAaYP9luDDS6yete9nLa3pBii3VqJGckmCHGIrGToRcTOlZGkFs7pE5XmFJOCHTp3s5yYjy57+h/I/ESORUi4Dyv6gy/nA51LKQ7q/wY+BC4gTQriAeCC/ke1Dx/kQFWD/XP8Nk1LOP6aT5G9WJELYiQSjnBAbQcQa41LIrZBkRkORAaVt6bQPzpnCwR6jnmFrXOzAQxuiONgImfwIRxQ5Ui1PEoIWjoYQ4KZOOOCDfvGwMWKWOjgF1jSiSPqnwzrDchYCWiXCZiNecUp7WBwBA+6WqSDAHn0J/VqpcqluL7RIUZxLeRVq3bjeMG+1FYuJcsGEQfD1Bvs+bzodnvvWandtAxlpsN6Iudx3GfzjPTVDvfhUWPgb5BUrq+QfN8ILBvS3T3dFdf6SkSNy/SWwKwcWLLWWvfAY/LgElqyw7sGcV+Hr72D2a9aye++F006DM86AfP2qZ2fDF18oJTJyJKzUrrOEBME998Tw5pvxtGzp4LzzarjuumpeeqmGnTsV8aMQghYtojjhhETOOy+V669vym23ZXDddU248soUTj01gb59Y4mLU6UMfv01wF13BRgwwMecOZLzzoM1a6z66j8thOGjFKps6Y/QuROUlsHVd6j39+PXlCWydhtcej+89gB0yobiCjjrPnj1dlXZsLwGLn4e3rtVZa3vK4R/fAOzdK7JvDXQIQNO6Q4vroAzekFXnVSYUw6HKuGKvqr94ha4c4D6Pgrq4UANXKCT7aSEdw/D3ZqC/ttiOLEJNNHW/IJyuEwH2Z8tgluNIPvPdZJBsZCqx7mX6oKcrUsIV0vJDwEfo03oE/ARdUwVESnuWlZTzemGl8RLgEPU0iOC4deUHeykB+rm11MFCOJ0/3zW05KBAJRwhCa0DtPIb6SUflpBrSiGkdrKqquDQ4csTrOQ7Nz5R+Pzf410BtKEEEuFEOuFEBcDSCnzgKeBQ8BRoFJK+VPkxkKI/qE/oAVwBDgMtNTLjkn+VkUSH+9ACGvQjo22SsD6A1Cis6JNWvam0eAzrOosF6w1FEuImiGkDNri4iB+BhLDlfqFriKAT8OBMxwOdhqWSp0jSF6EIukfK9nskRyfCHc1V6ixkPLqn9Qw4x2UST2wKeQbiufk1rDYyOs4KRu+jYhpCAFTesHCfVZ7XHf4Qbu3xnSG9zVENzYKjusCCw223itGw+yFtl3Sp51STDsMN9jNZ8LDhiLo2V4F539YrXz8j14Hd76sz3OoKqb11VKr/0M3wRcLYfd+6zznzIQ5H8N+jeKKiYE3n4PbH4B1m/Q5x8LH78NPi+DF2db+Lr9cKZRTT7Wy36Oj4b774P33Yd48FYhfq689MVFw2WUxLFuWzPTpsbhccP/9VZx3XiknnVTAtGmlvPxyFe+8U8X8+dV8/nkNH31Ux8sv1/Dww1Vcd10dgwbVMGJELd99F2T4cMHy5S5mzXJwoqYtWbhQudi+/Aq++FRBmaOjYcmvcOI5MOEk+Oc9yvL6ahE88zZ8+byi7S+rgol3w2NXwdAeKsdnyhPwyHnQvY2qUHnhq/DmFapmzYESeGYR3D9e1VyftxFuGqXOQ0qYtgCuG6CC6tvL4Oc8OFe7re7bCFPbWwXgvi9Sk7JOicoaeSwXbtWIpbklMDJN0cBvqYc20dDD8Ei9UBvk5jT1ch/0BakISvroypAf+7ycExWNw8DsB5Hsw8OERorp1RFgOVX0M9xaqymlD8m2xMNIyWU/XbW76hAb6cggfSw/ftykkg3AYXbTjr6AQnkBtNbHOlADw7Ui2bLFXgsGoKLCTWrqX5cUGMTx78RImgoh1hl/V/8bh3YBA1CB8bHA/UKIzkKINGAS0A5oCSQIIS5sZPtn/uDv6WM9iWNSJEKIcUKI3UKIfUKIu/6g3yAhREAIMflY9uv3SxurbEy0lVR3uByu0bNggZFl7YBiw7BIdUBFRLC7nUNwVKOxUnDSHAfxOGileXkeo5RtKO3TlSh2SmuHvR0utgTsLrCBsbDaC7EO5QK4+hBs1W6YeBeU+FW2cKR0TIaFRoB9bBv41hjMU2IVoWMkVfwpneADwxV1fj+YqzPXp/SFbwx01tXHwwdGvKN9MxWk3XzIvs8ZZ8DcpcY1dVGulrVG7smjV8C7PyrX2PjhUFMPS/RxX7gDHnjFqgUfGwPP3QsX3w11+l40y4AZ18K5N0Kl7te8GXz2Nlx1K2zTcZWYGPhkHuw9ANfeYiG3jjsOfv4Z3nhDKZAQyqtTJ3j5Zbj/fnjsMbj66gAffBCkrk7dhA4dorj22kQ+/bQJH37YhPnzM7joogQyMx3U1AQ5fNjPrl1ecnMDxMUJ+vWL4pproli+PIG1axN5/PEoJk50EB8vKCmBV15RVPjz58OsWTD7JcjKguISdR0ffwXfvQdnjFfv5eOvwSsfwSsPQNuWUFAKk++H+y+GkwcpF+BFz8LkEaqmSDAI170DN5wE/bPVO//IApg9VZXQfXwJvHq2BTL5aAe0T4XhrdVzv+1XeH6EQoRtKlXU8ZN0ol1AwsO74WHNs/VxIYxKVbB5XxBeLoRbdVz78SK40AhNbPGq+9ldF5t7xevh1iRrYvZD0M2FEdP6pdSTJgQJouFQspAKTibFpjQWkM84DdVtTEpRM8l4DQXO4TfaoIIbRWwjgYxwbCWHTWGX124qSSIKgaDOr1CTIdbfHTuUVWvKrl1lDBpkD/D/p/JvxEhKpJQDjb/X/+wYjcgRYIGUslZKWYLKSO8DnAQckFIWSyl9KHfV8MiNpZQn/MHfmGM9iT9VJEIIJ/AyMB7oDpwnhOj+O/2eBI45G9LpFLbMdiFUxT+AtHgo17P59Gh7ZrhLWNnuQgi6R0sChhXRWjjJMUgai/BRaSC2uhHNbtTo1QMnJUbAfbDTwU5pVyRDY2G1cfwRibC8xmqPSoVlFQ2vb1wLWGAE2JvFK9RXiWFhndMFfjpo325AS9hVYtV676lZWwurVQGsri1gqbZYerVWrq4cwzN65Rh44lv7Psf0gjU5dqvkrqkw/S1LKWU1g54d4LlPVfuFW+Ctb6HODZnp8PC1cMNMq3+/7nDVFJj2iLVsYG+4+zqY8aRKvANo1xY+ehMumgarNRlkVBS88BR06QgTplguraZNVXykRw/F5zV7tireBDBwoFo3Y4aDPXskw4f7mT7dy9y5Xg4dUrMJIQRNmzoZNiyWKVMSueGGVO64I4177klnxowkLr88gYkT4+jbN4rYWEEwqGqrv/iiQo1NnKhAA7Nnw1tvKUZftxvefBdOngxjT4DZT6rM/4JimHS9Ugzfzoa0FMVnNn4GPHApnDpMKZFrZ8OwLnDteHUd934KzVLgwhGqfdNn0C8LhraDu3+EXi1hgOIp5HAVPLkGnjhBtd/eDX0zYUCmsopnbIbnBllxlPeOwJktFKzXG4SvyuBObY18UA4XNIU0F+ysV5DfUQYl0Xt1AaZrxVERlCz2BzgtWmmzpUEfzYSDzAh471yqueR33FS/UsEZpIfb5XiIwUH7iAqIpuxgO7204qjX2exJmmvrCGtorfPkaqlEIIjT+1pLMYN0v18NtxaoEgZ9+9qPs317CVlZ9kz6/0T+xoTEr4BRQgiXECIeGIKqMXIIGCqEiBfKTXMiVu2RBqL73SeEeF23OwkhJhzrSRyLRTIY2Cel3C+l9ALzUSZTpNwIfAYUNbKuUYmJwU6RAhzWA0pyrJqxg6oyWGAM3FmxkG9YMnVCcsgIuHcSLls8pDMx7MHaoCvR5On16cLFfuEjqF1d/Zwufg3azYs0pyDNoeovgPr4dhjK4OR0WNQIAKRXKlQH1EwwJGe3g4VHrPbpnWBuxOMVAs7vDZ8YQffx3WGOtg6mjVQ+9JDcdCK8tsxqj+oChZWwx1BiQsA/psJ9RvisZzb0yob5RqLjjKnwyS+wP1/lOxzXD2a8otadcYJCi735pdX/irMV4ePMt61lZ42DAT1h8jQLydW5A3z9PtxwFywwckxuvUHVLz/1TJj/iVJIQijk1q+/Qnk5DBmirIRQML5jR8FDDznZuNHF+ec7KCqSXHNNPVOn1nLqqdXcemstb71Vz/z5dXz3XT3LlnlYvNjH1197mTfPw8yZfq65JsCYMX4mTJA8/rhyWd1/v4rF3HILNG8O1dXwzEsw5GSorIZfv4PJE9U5fL0Ixl0NN1wA912n3IEL18AdL8Pbd8Hofsqyu/BZaJsJt56htnt1ERRXwT810++bK9Wk6MbjlPtybyncrmfPQQmXfw/Pn6i+hbxaeHE73K8912/mQJ806KXy+qjwwXMH4fps1Z6dD70SVVyxLqjiIVfreMjjFfCwVbOLvf4ga/xBRmvm0g+8Xi6PjgrXVp/rd3N9lB2VlYuPJCRdjUTDkORQTy0BWhow14Xk0/sPUKUSyQ4201VbGbmsoQMD1L0gQDk7aarX7WcLHTSSC2ArlfTT9UiWFKpJXEi2b7fiXeH+W4vo2bPp757L/xYRQnwIrAK6CCGOCCGuEEJcK4S4FkBKuRNYAGwB1gBvSim3SSl/Az4FNgBbUWP9H1k8bwNeLKvlCPCPYz3PY1EkrVDBl5Ac0cvCoqFmZwKv8i+Iw+HA67UrktDMyuGAQzoxrk2yqjcdkjaxduRW5yjYY3DLdxEudhhWRn+iOYBlUnQnhlVYmqk9UeRoxdLS4eSoDBKMqIXaKjrIGr3LrrGwwm2RRQ5LgeWNKBIhVK3oXwzVOrEtzDVoSjLioUmcYms15fze8PYmo90H5m9RsaP+rRXk84DeZkIvWLIPCo2g/wNnwKxF9n2O6ArNU2GZoaAevhDmr1AJcqA4ul68CW54Sc2mrzpdZcL/sFqtf2G6GjDXbreu8aFpsGwDzPvO2u/V58Gpo+GGh1QdDoCsVvDjxzDrDXjkactdedxIFcT+cSHccgccOKiWJyaquMnPPyuFMmkS3HmnVY1RCEH//i7uuCOGH35I4MMP43nzzQQmTowmNlZw6FCA1au9LFzo4fvvfWzcGKCgIEjLloLLLxd8+qmT778XfPihyrRv314psl9Xw5U3wMSp6j1cuQBuv17Vsti9H067SuWJLHobxo5U1/HYXHjyfXh9BvTtpNyCV74EgzrDfTo/5LXFqtLhy5eo/X69FT7ZBLOnqPs4oi28M9n6Bp5eq+Jox7dRSuXRDTBzCCRGKW63H/LhoV7WPZ+5H25vpyoclnhhbiHcpr/U5wvhsiaq7s4GN5QEYKRhjTxWHeC+RBdCCGql5B2/h0s08mVr0Ee9w093hz3IPocKJvyOdfEJRZxjUJ5IJD9zlJO1W6seL4XYP5o8DpNJM2J1vGU/q2inLZBittGELji1ezqHDbTX2e1FqMEgSfNwLSyAUfrQhw+riYgzwgDYsqWEvn2b8VeJ5H8MtXWelLKFlDJKStlaSvmWlPJVKeWrRp+ZUsruUsqeUsrnjeUPSim76uUXSSk9jR5ESQcp5VOgBkIpZT2Ns1Y1KseiSBrbmYxoPw/cKaWRJt7YjoS4OhRYKi4uJjpa4PPZd9WxtR3JJaWySI5UW8s6xcMhQ5H0iYJcA7bbjWh2GRZJe6LZgLVBMk7qCYYD7kMj1vePluyOqF9yYqxgkTvkPoEBcbBebxLjgC7xsLeRvIOzWsOXRoC9fTLU+OxVFa/oCV/k2LdrmQTZqbBFQ4gTouGcXvD1LtWe2h9mLlW/nU649USYaQTZR3eDvYWwIQIVNn0iTH/Xggg3SYapo2C6YVEM7QYDO8OTH6lrfetOuOd1ZaXEx8KTN8BVj8EhHf+JioL5T8HCVfDVEms/0y6CEQNg7CVQrOMdTdLhm3nKXXTx9VColWxqKrz9usrPmHoxzLhXKQ9Q+Sf33ANff62y3x95RAXl77lHKZlQvocQgpYtHZxwQhQXXBDLjBlJPPpoCo8+mszTT8fz4INx3H57HBdc4GTIEAfp6dqNUwFffQ3XXQ9jTlYVDS88FxZ9A7dOUwrkyFGY/jhcdS/cfz3Muh+apKqcm1NuUcHtBc8qF+DhYjjpfhjbD27Ttvs7y+GbjfDJDSp59Ncc+MePMP9SFRcBZYWn6Un/okPwYy7cPli1X9iqknNP0nGSq9bC9Z3VMoA1lbCmGi7UiuOew3BfW4hzQr4PfqyDa3XeyEsV8JgxGd/lD+ISQU7S2blvej1cEBVNvNZozwVrucVpVxgVBNiGh5ONQHpIavCzg2pGGC6vrZQyhDQS9WC/gr1swP5y7mUnvVHmVjWFxJNGvKZRKWQDWShTza0ngaGKiGsoYXjI/VWnYL+h+7J0qZogmOLx+Kmq8pCW9mf5eccu8t8Ltv9vEq8QIg49tgshOgB/pHjsIqX8wz9gGPCj0b4buDuizwHgoP6rQbm3zvij/Q4YMEC+806RfP75o9KU46+WsrJa/b50jpRl1VJ+tlPKR5dbfRYUS3nnbqv9mycgryr32vZzgSyUQRmUUkpZLwNyojxgW/+YLJDbZJ2UUsoNsl7eLgvC694O1sg3gtW2/sW+gLysxBduf1Yu5XPF1vo3C6R88ohsID6/lP1/ltIbsJa9tVvKV3dZbY9fyl7vq/+mLD4g5aXfWO3DFVIOfU3KYFD9DX5ByrwKtc4fkHLqW1ZbSik35ko58QXV15SHP5by1Z+sdjAo5eR/Srlwo/28pzwi5Yqtqr12p5SDr5ayRt0yuXKzlCdNk7K8ytqmvFLKkRdL+amxbymlXPCLlMedK+X2PfblPy2Rss/xUr73sf0c/X4p35or5dCRUt5+h5SHDskGUlEh5VdfSXnzzVKedFJQDh7skxdf7JNPPOGX8+d75cKFPrlhg1/u3RuQu3cH5I4dAblxY1AuWCDlnDlSPveclFOmSNm3r5STJkl5971S/rJMSq/9NZI790p57X1SDjlTyk+/lzIQsM5xztdS9r9Uyl+3WP0XbZRy6hNS/rrDurcPfS7l2bOkrPeoZasPSDn+FSkPlzW8LimlPFgh5cmfSnm0RrXXFUk59Asp3fr9eG2vlDeut/p7AlIOWiXlbt3/10opx2237ulFuVJ+U6l+f1st5UX59uOdXV4vl+uXrzoYlBfXV8kavfGegFdO85c3OMcXZbF8TzZ+Ae/LfPm+tB/kEblG7pFqP0EZlA/Iz2W1rLeuQbrl6/IZGZTqBm+UH8kcuUxKKaVfuuXP8vrwuh1yqdwovw9vO12ukoWyVkop5ew9Uj6/0zruZZdJuWaN/fxWrDgkb7ppoZRSSmCd/JMx8Fj+YgZ0lx3ktn/p76869l/xB5wC/AIUA/P0WD76mLc/hgO4gP0oGFk0sBno8Qf93wEm/9l+BwwYID/8sEQ++WSe7SGfMV3Kg/odPP81KbcfkXJdvpQzFll9DtRJecZGq10dCMrhRR7bfi6VBfKgtEaFq2WurJPWaP6hLJXvyBIppZReGZRny8PhdXuCXnlDoFRGyshijywJqA+swi/lkP3WumKvlMO3NNhESinlrZul/Mb4rkrdUg781j543rVCyvm77dsFg1IOfUfKPGOwvuILKb/X/b76/9h77zArqqyN922ioKI45qzjp6OjEhpRGTGjYlZUUDCBIIooIiIYSIIoGFBAAVEyknNqcmwy3XTTxM455z751O/+UdXn1DndqHwfc2fu3Fn97KdPVe2q2lW19157vSslwAcrg8eWxkHXWaHX6Dkd5oYNJIcLbvsEjtnalF0Ezd6HHNvckJ4HzXpARr65PWcDdBxmMhmAZVuhbU8oqwyeU1YBL3wAvy4KveehYxD5BEwP219aBj36Qrf3IP5w6DGvF+bOgwceguc6wqLF4HJRKzmdBgcPGsye7WfUKBcffujk9dcdvP22i44d3XTu7Objj30MHAhjx8Ly5XD0qMkQwqm8AqbOg3ueh0dfhaitod9qwx647RUYMgmqnMG2DpoBDw403yWA2wsfz4e3ppiMHiA6GVp8BWlh3auwylxIlLrgtpmw0xoW+Q5othgSLUYQXwIPbYKq4JqGzxNhXJr52+WHLifghMXwt1XCaxYjdhvQIhUybcxyl8fHU8WOwPaXniomeoLbb/iL2G6EvvQq/DxMMk7beKomLwZ9OUIFwQbmUUVfgivBY2Qzh50h5x1gF9tYD4AfP0vphxdzTKeziQSmB+ou4ysqrLFbjIv32BE49vBGSLHWgIYB7drVXBy8+24UK1eeAE4fI2kQ+Xeu4sgplX8HRiJpnKQ21u+/yDQjflzS+ad0nT95s0clHZeUJOkTa19PST1rqfunGcnSpUWMGhXKSAaOhVhrouw/D6LiocgB988M1vEboYwE4IlCNz7baB9DCUsIShWDyGI3wRkvERe9CC51u5BGtsV4DAzuJRsPoUv5EeVeZlYFZ56HUyHVxr9eOwEpTmrQgWJ4JzZ038vbYEdecDu1DB5dVvPcBUfg8+3B7aQiuGdqUCpp8xMctyQjw4AHxkJMkCdSWAG3DoEyR+h19yXB3UODExzAulh4aGjo5LotHu7vF5RERs6CrqOCE+u8DXBfr6AUCVDlgOf6wsAxwRU8QEUldO4DH46E4tLQ9uzeD/c9bTKUlLSa7+HgQfjwI2h9J3TuDFOnQmYtEuD/hvx+OHIMxv8CDz8Pt7WDbyZCemjXJDoWnvsQOg2EZNu945KhbT8YvSD47nJK4J4R8O3q4LuKOgydptSURIoccPsU2J8DTy+BBVb/9/rhwdWw2vqeFR6IjIJDtne3vRju2wM+6x6fpMPX1gLB5YfI45Bs9dHRJQZjS4J92m8Y3FtWyVFrZZDn93Gnowi31eB4w00Hv03stmgmRcyg5kILYDX5jAmT/n/mINsIvrBxRJFCfkidZI5TgcktM4nhAPMCx/bzAxXW+eUUsYHJgWNRpLHEul+pG57dErxmXBy88EJo+9xuH7fcMgmv1+yY/2Ukek+mMj9VptVt8//Vdf5VDxAZGcnq1SV8/HEobvHR97DWWqz8shVmRpu/m08Oqcbtu8Bhm/A6FXs4bMOPtlHFGFtnX0oJP9o6r4HBe6Tit5jFJAqZa4neAB9SRDShXCHe46d3eXB1Nr0UxtkmhSn5MKKWyc0w4PatkGu73K58eD10UUanKNgeBjv4/BA5FXJtq/6eK2ChtXrflARdFwSPxWXBgz+GTuAzd8GAMEkA4LvVMGhh6L5vlsJH00P3zdwATwwBj9d8lo8mwae/BCfIxVugTQ/Is80tfj8M/B66D4Ei28RnGPDbMri1PcxfGbrSNwxYtQ7ufAg694ADB2u22eeDAwfgq6/goYfgttvg6adh8GBYsAC2bYOkJKioMKWE6uu7XJCfD8ePw4aNMOln+GggPPIMNGsDz78CP0+H1DAYzeuFZRuhfS94vj/E2CDJKieM+A3a9IXYpOD+zQnwwEjYkBDcN2UXtB0DBaGIKWUuuGsarDAXyGTZjju9sCI9+G7ejYHpKcHjJR64fy+kWkx+bwXcnRBkKoOy4Ruryyd5DFplGgEmATDN5ebDqmCnfN9byhJfcPt1I5+YMGmkEh+PcSJEuq8mA4OXiSUfl62+h16sx2fVL6CMkSypca6d1vMVxaRZ5+ewlQGBY3tZwWGbdPMB2ym0xunMFBh2KHidr7+GKVNCr71gwWFGjgxKMKdrMq8feTOXkHxK5d+BkVQXSVdJ+khSjEwz4UGSrv/T5/+rGh4ZGcmWLWW8/35qyIf+ZgbMtOCaFbHwqTXRdV4CVTYRtdsh2GOboL6u8DK1MihOV+DnKRtclYOb7pwIuVd/UkiwsNUjOBlKUF+zjiq+pzSkvmEYtC6tCODHhT64w7Z6LvdBy7iaOgmASanwhU1HYBjQJgrSbQxifz68tqHmub8lwPvrg9vZ5fDCfPBYjLTDTNiQGDz+wVKYGB16r8fHwxrbxAYmk3rwS1gbH9zn90OHr2DaxtC6X82DV782J3LDgHfHQq8xQYa1bo+pLzgRNhEvWg/NnoMdMaH7C4uhzzB4+FWICWuXYcDWaHi9F7R5GMZPhsLaF8D4/ZCYCAsXwrffQu/e0KEDvPoqtG4NLVtCp05w++0m43njDXj/Axg3Hlavgcys2q97NAkGj4VbnoL3voBjKbb35oMpa+DWHjBpVRDqc3mg/2x4cARkmMgLXh/0XwK95kJVKPpKiRPung5LwiDN2mjEEXgvNvQdPRsH8yzVXrkPWsRBosVU9jng2XTwGma/7ZznZ6sj2DGL/QZdKqootaDaeL+HxzxFGFbn3YyTHtSURsaTxzQLVgqn7RQwgeSQfStIZDnBzjmX7cSGSSx2qiCPNQwJbB9iKulsMp8ZgzkMwW0xjhwqGUCwoz+5FY6UBa91992QG1R9YhgGbdpMJT09WOl0Teb1Im/hQtJOqfw7MRJ7kdTCYij+P33Ov6qxkZGR7NtXwYABoTjGvCj4aa75+3CWqScBU+l8wKaXn5wJM2yr9+1uL71KQ1dPT5JOpW3l9CxHcNu2l1LEJIt5GBg8xokAnOXAz4NkBhT21TS4ysl8V3BGeCITElzBOh+mw45yalCFFzruM+GKapqTCoPDVt3tV8L+sPHr88PDCyDFxteGbYFvLYkmtRgix5mYPECFC54OU7znlELkSCgIa1tOCTwzBlJt96x0QufvYGN8aN2v5sMbFvMwDOg/EdbtCx6PPW4yk9VhklZqFnT9DAaMAUcY9LcvDtq9DL0Hm4rtcMrJhW/Hw5MvQrsn4ZuxcOhwqMR1OqiqCjbtgE++hpZPwNNvwoLV4LR1Ka8XZkbBC4Og30Qotr3L6KPw0hj4ZkWwbfnl8NB4GLa6ZnvzKkz9159hIsfK4eFtoX3n6zR4z3buK4kwzZI+HH5olQTHrLZPrvDTqyhUGfRWpYPfXBaUaxj09pQQ6ze3fRh0I59Um54DoBgvr5CE6yTSyBvsI8cmxbvw8g5RuKzrlONgKHPw13J+NR1gBmnsAsCPh+0MwmfpSjI4QjRB8XsBiWyyFovFbuhi63e5uSYjsdO2bRl06bIkZN/pmszrRt7KX/yZp1T+nRiJpPqSnrAU7bky0+0+/afP/1c1PDIykoSEKrp0CZ09NuyCvqPN31Uu6DLB/P31LphuU2bvKoU3bMpZp2FwW2GoImAshWy16Um+JIM9BEd/AR762KSUIWSz3Va/B3kkELqMPOz10bkyWGdppcGAwiAj2VoBL6cE6+8ph1XWavrDI/CbbQXs9UGz1VBgm6z25MHjq6lB61Kh0wrb83qh+UTIsZoyNhq+2ho8vv44PPJz6AS2Mh4eHVdzUtuVCK0Hh+pRckug5Yew1ybpGAZ8Nh1e+bp2JTVAfjE8+C6Mmhlax+eDsbOh5QuwcXfoOYYBG3dAu5fgya6wdXftUl1GJkyeBq++CTc3g0ceg88Gw9y5pmVOQUHt59nJ7Yb0DNi8HX76FXp/BG2fhtvawzsfw4oNUFkVek5xGUxcDM1eh/4/QraN6RaUQfef4JHhcNy2sFl7GB4YAysPUYOOF8E9M2BdUs1jJyNv2DfbWWpaawFMKYAPbOuxwQUwwepzqV6DFtleKvzBF7Pd4+Xx8sqA9DHL56CfN7hKn0IZX9SiAxlOFkstS61C3My2SfBbyGcUR0PqL+U4C237VrOfLdTyQizy4GAlH+DH7DjpbOQQUwPHo5hILuZL82PQk004LSY1MRFG26y1ZsyAMWNCr//YY8uIjQ3Vzfz/nZFIaifpV0l5kpZL6izpzFO+zr/qASIjI0lLc/H006FLsqPJ0PHD4HbzT8zJYX0yjLCZALv80CrMGql9sYMi2yy5iQqGk2fbLuEbQrGXVzhMkaVk30kFY2yDYzUVjK/FxPHeyjLyrPu4DYNbMoLYs2FAqyOQb8FwmS6444C5P8MBd+wInch/TYKPw6SSXtthe6hVtNnW1bDdpoNZmwgvLzV/e33QZhLE2s57fyn8sC30GgMWw+erqEFzd0GncUGYBiA1Hx76HGJTQuuOWQLPfmFKLrWR1wsjp5lK+OQw6Cg9B7oOgifegYTEmufuj4MPh0OL9jByHGRk16wD5vvMzITFS2DECBOyeuBBePJJE86KjISXXjJ/t2wJL3SEFndA67bQ6RV49yOYMAW27ICS0prX93hMXV3nwRD5OoyZazKUaqp0woj50LwvLNwVZGBVLui7wGTY2bVcd2saNPsZYi3IxeODuDCkaOIJiDrJc4fTvipofQQqre+2sByezTLb4zMMni/ysM0Z7HCVhkGbijJSLC5f6PfRxlNAqWHWKcTHA2RSFSY1pOCkJ0kBneIIkllrQVxe/LzBLvLCpJFRbMdhTfRVuBjGTDw2KceNky0ElXcnWEsCSwPb2/iEKkuvWUUp8xgeQAgOkM8YYgN179kAWbaF0EMPQUpKcDs+voBHHllS4/2dNkbS8lbOceecUvk3YSSbJHWXdN7/6Tr/qgeIjIykqMjDG2+ELsuqHNDxg+B2+9GQVQz5lXD/tNBO8GqCqXCspkFVTpa4gzsc+HnChsc68PF82IroF7JZZHVWLwZPchivDd5qR1pg8FTTTy4nE13BQTOwyGBRZbDOlEIYbsNmXzkCGyx+1Oew6QdTTR4/dNoB+bZJ+UgJ/GO5aZ1mp8QSuG1OUDcCpu5oobUSO5QH7aYG/VGcHmj/M+y18U6vD3rMhmW1mCp/FwVdJoUyumNZ0Lwf7AmDnaZvhDYfhpoLh9OOOIh8DSYvqynB7IyFdj2g+2eQUAukVVYOk3+Drn2hzRPw+XdwIO7kklBtdKrwV24BLFwLr3wGtzwPvb+E6DCdV2kljFoIjw+H71eYepFqWhkHzYfCtOiakpFhwI974aWFkGkJxRUeeHQ5/GBbSEw4Do9uAueffM4OSZBoSbSJbmieAiXWucPKvQwqC4Wn+jqrmOwOisA9/UWs9gdn4I/JI8pm3VhNb5HIfktaT6KK1zkUmNRXkMlPhDoJLeIwSwiKCMvZxRZCO91e1rEfUynox8tq+uC27l3EIfYxMlB3P4s5ZlOyf8n+gF/KiXJ43Sbl5uXBXXeFtv+VV9aydWtNS5jTNZlHNG9Oo7LiUyr/DozkdJV/KSPxePy0bh0GxAPNnwn+/nwRRFkD7dYJoQP0oxOwwjYpb/F4eacyFN7qTyZZNnhqCMmkEqyTjoO3CWJkw0hnB8GlZz/yiCb0msV+H/9wFAeggWSvwT25wQHr8MMtx6B6IXioEh61FMqJVXBXGHTzW1qogxlA750wu5YV+4g9Zqmmwip4bC7kW3DM97ugz5rg8cQCaP4tFNngmqJKiBwFB2px9Pt0Ibw9PbR9qflw20ewOQyV2BQHjw6B7WHKcjtVVkH/caZV155alOobdsJDb8CzvWF9dO2Tf1ExzFwIHw6BZm3hoWdh9A+wYAkkHDHhqlMhw4C8Ati8C36Yapojt+wA978Gw36EXXE123EiE4bMgmbvwtjlodLYiVx4bzZ0mgjZJbW8Azd0WQTdl5uQJEBWJTy0DGbb5t+xR+GV6D/PRKqfBaDCD63T4IDVrq0uH48WuvHaPuQGr5tezopAv11lOOhq85faQhVdya6hF9xEKf2tBZmBQW8OEW9BxA68DCaGcpvPViUu3md1QDdShZPJrMBL8ME8uJjBSDyWhVcyGznIjMDx3QyhxGJOPrws4OOAX0khTvoQxHE/ioV5tr78889mqaajR4u5996Fgee202ljJM2a06Co7JTKfxnJaWIkAC1b1lwa3/miKZkAzNoOIy1pt/dqOGKDOFcXQj/batZtGHSsqAzpMNMpDLEyWUIevxC6MnmV+AC8dZAKPrdJMfuo4iNq4gy9XOVs9AVnsGfzfey1Kd1H5MIM22r9uaOw3eJPb4dJJX4D2qyD4zblbaETIpebtvF2cvvg6eWQYINDViTCkwuCviVP/gZLbXD10kPw+vxQ2OpEPrzwK6SGQeGGAcOWwpvTQifT7CJo8wlM3xxaPz0f2n4E3yz+ff3E4RR44wsTKjqRUfN47BHoPshcRHwxEVJqqVNNWdmwYg0M+xJe6gpPdYQWrSCyNbR/HJ7vBF1ega5vwPsfQafX4PHnTSuw5vdCi/ugWx94dyhMngO7Y8FVCzMqrYBZ6+G+fvDwAFgaHfoOc0qg1wxoMwI2Hq55PsDuTGg5EWbbuvnOHGg2B7ZZsJ9hwKex0Gl7aHSDVXlwwiYcHK6CibVAnoYBz2XBDKt/ZfsMmuW5yfAFP0i+4ecORzE5Ngirm7+QAsO8YRV+OpNJdpiC3YGft0gkz5rEN1HIYJv08QvHWURqyDkzOMBGgkjDYrYSHYYE7Gcd+zFDIBj42MnXOC0Jo5x0DjAqUDeRXey3mQzP4zhrrHu6faZvjcv23u64wzT1rqYVK5LZsqV2p6P/MpL/EEby0ks1cY13hpoTC8DhTHjhe/P3dzvhZ9vKvdwLT4UJNB2rKjhqwz+y8PCajTGU4eVlQk+aQw6LrRApBgbPc4hya0AZGDxBCiWELhMP+jz0cwdn/u1OP+8XBwdhkQ+aJ4HHGsvxldDVetQ0p6nfsStQowvgyaB5OwDTEuGdXTVeDzH5cOfcUIjr3XUw1rKgKnJAh3lw3MZsRm6GnotDJ/s9qdDyK8ixYf9gTWwLofNE03ekmiqd8PRXMHppKMTk9sCIufDwIJOxnIwMwzQTbtMDBv4Ix2txPKyohFnL4eHX4fYOMHy8adn1ZyAtn8+01jl+HBISICYGDsTA0eOmor609PeZnWGY5su/LIeHP4Db34QfFkJqbmi9lHzoNQ3aDoe5JzEMcHth9A5oOwVOFAWvP+0w3D4fUq2u4/bBh/vg3b2hUOaiHGgTDUUWg0t2QrMYSAgzBAAYVADDrW/t9vu5J9/FJlewc/kNg9edZURZCx8Dg1fIJ8omaX9KDr/Vog/8mkxmW9CvEx+fcIQCi6lkU0VPogM+IgB5VPARqwP7iijnS2aGWGq5cTKTYXis66SxmVh+CRzfzyjy2R9o6wKGUmWZ4nvw8RbrAtLO3Az4yDacDx6EZ2yIRuB9LjrGs88uYsOGUKZ3uiZz3dqCOrmVp1T+y0hOIyNp0SKuhsj5xU8wx7JQ8vngH0PM3/uy4NUlIVW5/yBk26yeprtdfG3TXwC8xvGAxAEwkkRSbDhwEW66ciCw/SvZzLcp6adQxPRarFie9hRxzAjOtPcWuDlq4w6f5cHPtrHZ5QSsKTF/D0iEn8MU0S/vhaW2fYYBr24P9YCvpq/2wSgbU3V64akFsNNaeMXmQOQkKHUGr9V1AUwIY0xbTsBjEyCvFpPlHzfAY9+FWnP5fDB4Djz8OeSXhtbfFAfN34UZG39fP2EYsHoHPPAWPPUBbNlf+2RcUgbzVsKr/UwHxie7w4gxsGoDZOX8sYXWH1FJGWzeC2NmQO+v4JZOZnsmL4OUMCHUMExz6JfGmM6Gi/ed/Bm3pECrn2BMdHCxUOqCF1ZB9/VBf6gCJ9wfBWMOhz7L3Cy4fyeUWvXSXfDsEdgX5swI8GupqVz3Wf4i3Uo8TKgM5bqjPFUMdgf7+3TK+cjWn7dSSVfSa0Bah6niDU4EdITjSGYewQ46hAPEhI2Lqewl1lZnJqs4ZJNOAGJZx0E2A6ZuJIr3AtJIJdls4/1AW1KJZQtB5eg6UplBECO9dwsk26S2/v1hjQ3aBZg58xDt288nLi6Pp54K9cA9bYzklpYozXtq5b+M5PQxkn/84xBVVaEdf8k6+Oy74Ha7L0yFu8cH904NHXSj0uFXm7if7/PR3Rk64qaTF1hVAawnn/FhjlMfkcBRS5lYiJvOHAx05jK8PMUxfGEDbaPfxTve4Gy6zuXnleIgwyr2wQuZQV1Jqgtax5uDvswLHQ5BiW3Fn+OE53aZPifVlFQBLVdBZSjigM8P7ZZClG1Vn1oKzX4NmgQvOAxvrQxKLh4vPDENftkbeq11R6HVt6F+J9U0Zze0HgZpYZZFqw+YJq8bwpDJ8iroP9kMGbK/FiV6OO0/Ah98C807wec/Q8pJHAQNw4S75iyBASPgkZfggWch8gF4sgt8Ohw+HgqjxsDU2fDzNLNMnQ3fToRBo+D9wfB8b2j9LDR7HJ59D3qPhF8WQdzx2qWepBz4Yh7cORC6jjeNDk7GwNJLTKnvqZmQYltAbEqH55bDXJuB4v5CeHELrA5DXL5OgvZ7TEU8mCF3WsTCrloY/eoKeC4Tqqz+9XWFlx4lnpCF2XqfmyecpQFdSTxuniaXKkvCLsJLV9LJtS20ADz4eZEETlgOu8eooDsxgTEQTR6f26ymABLIYRRBj9p0chnH3BAG5aCcOQzBZ90vmbUcZo7tGmPIx+ygBgYb+YkSG1owmj0UW9ZhB0uhm82PqbISbr019DvGxxfQtu0cqixv0BYtpgTCo8BpZCQ3R6Ik49TKfxnJ6WMkvXqlkJ4e6kiYnAZP9whuD10ICyyrjGfmwhGbfuGYA54Jw6efdJaS4g/2pnw89LMxDhd+XmIfXpu4vZuiEK/cTzjBPpvSfSRZrLaFUAFzBdjVV0SGJZUYhkGnEleIVDKqyCzV9FUWTLKgkum58E7YZPtzKvQOm5wnnoD39lGDcqvg1jmQYZtkNqTAqyvBYc0Lo3bCq8uCk5/DAw9MhgVhkOC2JGj9HSTVdGYm+gTc9xWsD1OWZxRA+xHQ+xfT7NVOccnw5BDoPgbSapGowqmiCqYshVc/hTs6w+cT4eDRP7a88nohLQN27YWVa2D2PJj+m+kj8tOvMGU2zFgAy9fC1l1wLDnUyTCc/H7YcxQ+nwF3D4D2Q2DKelNfcjIqccAnUdBqHKw6GnzX5W54ez20XwhpVlcyDPjpKNy+Ek7YIEWfAZ+fgFdjg5Gij1XB3XFm6JNw2loFt6Wa0RUAFjg8vFnqxmNjIif8Pjo6S8m39CJl+HmAbBJtMeW6kcF6at5gLJn8aukGvfjpTgyJVj0HXgaxnyJbKBQvPgawjDxLCW9gMIuVZBL68bcwi+OYg9mHiyh647auW0k6u3g3wHgyOcRGJgTO3UU2Y23Iwat7YaMNSp0yBYYPD257PH46d44iPj7YqXv3XkdSUklg+7Qxkr9HomOcWvkvIzl9jKRbtyQOHAg1NzQME8qopk0J8JYFof60F8aF+Y9ExpohIqppmtfJN55QMPlNjpFis3P/kUS22aQUPwavsCdgfXKICkbZRPIs3AwgrYb4v8Zw0McfXH5udft4riR4H6cfWqYa5HvN8yp80CzOjBZsGPDgQdhnYwSGAQ9Hw+aC0H2dd8CSWhTQ0TnQZgE4bBLL5DjosMzE3A0D3l8Hw7fbJjgntJsCM2NDr3UgA5qNht2pNe+TUwr3j4IvlocGejQMMyT9c6NhTUzoOYYBy3dB6/eg/y+QVQuTqo2Ky2DmCnhvpBmi5Nl3YdJc2LbXNAs+nVReCVtiYNRseP4zaP4mvPIVzNoAhWW/f25RJXy2ynxnM/cHFfGGAfOPwCPz4Nf44HsvdMJLm+C93aHfq8RjSiHDjgfr7ik3dSIHa1ristcBd6RClnWNTW4fbYoclNmULCWGn7bOEuL8ZiU/Bu+Tz3KC42I2RXxOmAIIiKWcT0kMSB+/kMqvBEXf8RxiBaEKrhXEscKme9zHIeYTFVKnmCyi+DEwhpJYyVGbH0k831BoMQoDgxWMpMRiZgYG/dhMtgVJZzngjk3B92UYZtw1e0iUr76KZdiwIOMBeOON1ezfH4Qw/stI/kMYyYABaaypVhzY6OkekGPN824vNLfitqWXwYNhodKHpcMCG/RS7Pfxgqc4RMSPoohvbc6IKVTSN0w0n0sGv9nqvM1B0mwD72NS2U7o7GIYBk/780k2gtBApxIne2ya8AUVBm/kB9syrxCqQ4wdrYLbDwS9lAHSqqDNjiBGDmbo+eargiGy7fRzAry0NhRy+Ww7DLX8GfwGvLECBtkio1a6TZ+TX8IknZQiE+ZaWtMqG68PRq0ypZOUMKaQWQgvfAMdRkNKmATi98PiHXD7O/DaKIgPRRV/lwzDjHM1axm8PRj+0RGef8eM0dXzE/jmZ5g2z5Q4ovdC7CE4fAyOJZrlwCHYtscMBf/rQhgxAT77Adr1gmYvwmPvQa9vYUYUHE//c74nR3PhnfnQaSr8sivU6CE2Fx6eCz1WQ4FtLbMyHW5dBEvCmHRCOTyxBxba4Nm1xdAypvZI0nuc0DwVUiwl/D63jyeLneTYuLvbMHjXU8Yyb1Bi+JKiEG/1vVTSkeQa4U7K8dKRg2Rai65jlPMZCQHfqjgK6c+ukAVVHuV8whI8FlzmwMkoJlNlW7iZjOFrcq24Wy5KWU+vQPiTUo6x2xacMYODbLdF+Y0ll5+JCWwPPgILbLDg+vVm7pFqSk4uJzJyMS67ORfw/vsb2LQpyARPKyNJ4NTKfxnJ6WMkEybkMHt2Tezj09Gw2KY0e/UnSLaq3TnVtEyqpsMOeCI0OgPdvCXs8wdtOj34+ZATIbG2PuQgGTZGUYGHd22Q106KGE4Q2E7FxcscryGVbDUcdLcFuDvs83FvWSX+gLe7Qbtsg53O6m144gRssFbXo9Phs5TQ9s/Nhk4HQplDdL7pvFi9ms2ugp47TEYxdC8Mtuk+/H7osRY+tULQ+/zwyjIYsT0U5np9EXy+KfQ+JVXw5GQYuCJU+qim7ceh+WCYUYvfx8Z40+ek71QoDJMeDAPW74e3v4d7+sK0tcF8HqdChmEuMqL3w7wV8N0k+Owr6DXQDEPfuSd06g69P4Ou/aH3EBg1Cb6fDvPXmM6QeUWnpqx3uGHOHug+Ex4eD8vjQ99NUrHpHPrgrNCYcLlV8NIGeGc75IWF8p+aBpGbId4Ge43JhPtioTAshwbAboeZTyTVOhbr8dGi0EGyrSF+w6C7t4QffUFRZiWVdCUnoDTPxcPjJAbSJgTeKwbfkMpKm5XW6+wNGKY48dKP7QGpoPqcX9nOYVtEiEWs4YBNIQ6QRiyb+TWwfYBxZFi+IAYGu/iQMovJGPhZxiDKLVjMwOBjNpFpwWZFbmi+KdTq8a23TYutanriiXVs21bTVnr48J2sWRNcyZw2RnJjJNrPqZU/cW8rfEm+pEMnOd5ZZr72OEnRkprZjp0rM2/7USui752n41lrbcc/68J/VKoZyZw5+Xz5pYnZvP++E7fb7OxRW6DvsGAH+HEtfGeF9vh2F/wW5hzX5lAwLAnAJr+LfjZFOMA40llhg7O2kse3Nu9bgImcYJVldWJgMJjDZNtWVqNJZ0stZpKdyWOvDTPuV+lkms054ajb4OU8I4BhZ7ih+WET6vIZ0PUY7AybeN9PgClhToMTE6HzzuAk+MFu+GSfOak9vw5+tI1fvwEvr4JvLKnD54f3ouCD9cHzfX7otRy6LwldWfv9MHQNdJlZ0zwYoNwBfefAvV/BoTCFsd8PM7bAM6Pg45mQV1Lz/LQ8GDIdOg2HV0bC8ujafTn+leTywKpYGDDfzOny6WI4FjY3Hc6H1xbBy4tgnU3S8vph6hFotgCWpoSeU+oxI/m+vD9oWOH0w8Bk6HYsVDqtpqhKaJkKaVYfj/f4uaPQwXHbbGoYBqO9FXzmDXak3Th5jCzKLGnBhZ+OJLOrFu/1+eQy1AbnjuE4i20WWN8Ty+own5HNHGEawYQ5KaQzkZkhiy0PLhbwScCEt5RE9vNNoE4e0Ry26UJS2MtefgtsHySP7wni2Z8fM6NpV1NsLDz6RHB72bJ0unSxid9AWloZmZnlDBq0g+XLg8942hjJ3yLRLk6t/DlGcreklr/DSNpIamr9bi9pt+3YNElvWL8bSDr3dDxrre34Z134j0o1I9m6tZR33zU/bJcuDg4eNDt8eQW89E6wI2QVw72fm7+TiuGh2SH9hJ/zYKJN8eYz/LQzcigLsXF38wrxgQ7sx+B1dlJi83wvxs0H7A/gw9sp4Asbs8nGxYsk1LDgOoabXuQHVn2lfoNOlRUU2pbsI4oNhhcHz5tZBH2sSTjZCc1jodSGnVf54PYdsKck9Fl77YNxlk+Y34BXt8CUE6ZTVsd18JtNge/1Q7d1MGhn0GGx33roujzIOAwDJu6B+36F3DDobN0xUwew7CSx9g6kwjNj4b1ZZhItO3m8MHUjRPaDT2bC0ZPkatl1GD74EToNg6c/hh8Xm06L/1fz3lMlwzBDwkxYC72nQLNP4P2ZsO1YqORlGGbY/l7LTHhwXWJoW9emw23z4eOdpsLdThvzodl6mJUePCfFCW1iYHxW7c+8oAzapEG+1Tf2u/00y3Nz1BPKcUb5KujlKQlIwom4ecDmZGhg8AEZzKzFlP0wFbxMfCDr4UZyGUZcYKzsJYdR7AthEMVU8ikLcVqSjRcv05lPQdj1dzGHBMuay8DPNvpSGgi+6GYH3XFaEr0PD4sZiMNiOgYGH7KBLEsaKXZDy82hDoidOpt5aAAcDh8vvriDnJxQHenPP8cxfnwMQ4dGs3RpMGTEvzsjMZuoq0/GSMLqNZWUZf1uIjMFesTpeL4/vPf/GzeprVQzkuRkBy+/bJpdjR7tYvr04Mi753kzlEU1vfaTGZUWzDwO9hS0xV5oGWaPP4EyJobpNL4mif0266s1ZDInzM79Jw6x3vJ+NzB4i/2k2yCwr0lndS05GYZQyExbdOEFHjfdHcGVn8cwuCPTIN4VhLieTYHF5phhdj68mxz6DElV0Ho75NksjTw+eHQLzEo1t50+eHAtzEk2J657lsE82yP5/Kb/wkfbg05vP+03sXybXQBrT0Cz8bA7TKlfUAEdpsAHS0wLpXDy+80EZM0GwZiomhZcfj+s2g+PDIMHB8OincH85XYyDDiWDmMXQK9v4JYu8PRH8M1sWL0d0rJPH3Px+81Uwmv2wfC58MwIMw/LM6Pgh1WmI2z4vXLLYcJOaDkWui2EA2Gmyjuy4aHF0HNT0OGwmkrc8OZeeHMfZNjmuMX5cHcM7KxF6jMMGFkEHbKg3OIZO91+WuS7SfaGNm6Ct5LXvSWBLKHZeGlHJkdsUvJ48vkuzIoKoBgPXYgh3XJQzKCK19kZMDwpwEEv1lNK6If146fA1t9XsY5tYSl0C0llExOpzreezgbi+DFwPIk5JBFUesaxgtiQwI3p/ETQYeqzI6ZlYzUdPQqvdQtuDxqUwPff17Q7X7LkBEOGRDN8+C6WLg0eP22M5IZItINTK6efkfSTNNn63VzSHpkZa2MkTf7fRPX908//z7rwH5VqRuLx+LntNhN72bzZywcfBGeqL8fD9AXBzjB2DYyx4K3pcfBDmD9E11TYaBvAZfjpTn6I9HCcCvrYrEs8+OnBVipseHEJLt5ka0BXEkMxn9gU86V4eJ6DVIZ5u1fgpx2ZFFavAA2DjlUVbPYGrx3vNmid7cVlDfhCL9yXCOnWxPpmEnwfBp9sKIC2u0MzQlZ4zLAqGywrlXIP3L0aVmVAmRvuXw6LUoL1DQM+3w0vrgaXtbJdfMzMvnjCtoBMLoJ7foEvt9Zchc/aB7eOggWxtU/oTg9M2AC3fgxfLjfhr3BKzIEvF8CtvaHr97A+NtR73k6GAUmZMH8DDPgBnuwDd7wCLZ6HR9+GN4fBFxPg21/h1/mweC0sijL9kFZuhjlr4JfF8NN8GPQzdB8JT3wIt/WCFj3h8U9h2GyYsxVOnEQaKKqEOfvhkYlw1w8weRfYUpxjGLApDTqvhg4rIC7MCMEwYGE63LrazOAXiI3lNdMgdIiDwlqYqtuAnjnwVq6ZnApgqdNHq3w3GWEx5cf6KunpLQnApoV4eYRMdtsg2YWU0IM0vBhsojSgK/Tipyfx7LDgWic+PuMgRyyJwIfBx2wjLiw1bjilks5EpoYEOPXjZQlDKLYWZS5K2MSbeAJmxHnspBc+i0E5KGU9XwViannw8TXRAb+RfBc8vzdoHg3wUhfYuMlqQ2oVt9++McRPpJqio7MYMiT6nwdt3RCJtnBqxUxvu89WetR67T/BSCTdZ+lB/mJtt5Lkk3S7tf29pM9Px7PWev9/1oX/qFQzEoAWLUyOUFFhcMcdwRX8oaPw7mfBzlBQBndY2+UuaP5raFiJmCp4NSzsxhAKWRpmJ9+fQxyzraSWk8bUsFwKc0lktc3EcSAxxNp0IwvJZUwYXgwQRQW9bYrHLL+POx3FlBjBzv19uZ/3i4NcYVsltE0xJw+XH9omwJawFeqkDHghNnRyz3PCbRtgmzV5FbvgH6tgUZrJTB5YBT+HGSGMPwj3L4Tq1C2xuRA5xWQq1eTxwYC18OI8SC8NPb+wEvovMyfWhFriPoGpmP4hCjqOgw9/M0OKhJPfD1sPwaCZcOvb8MIX8NsmyPj9+QowHc6y8mDXQVi+AaYvhrHTzfL5OBj6A3w3Db6aAuPmmBk3l2+HvYchI8/0PTnptf0mXDd+E9z7Lfzja/h+oxn80k4ur5m58o7p8MoKiKul3fuK4N510He/aXVXTRuL4MU4+PUkzCvLA3enwKTi4PFfq7zcV+CmOCwk9HfeCl61MZESfDxPNpttEvRmynmTNKrws5wiepKIFwMDg29JZnaITvAgK216kSnEs8BmcFIbuXAxiV8oDIO04lkTEiNrH1+QY8toeIDPKCBoNridn0i1fEwAlnKUebYYXe8lwCwbPJpwGB58OLjt9xvk2vNZ22j//ly6dYti0KAdrFz5T2Ak/xOJoji1cpokEkm3SkqSLTWupIslpdq220paeTqetdY2/LMu/EfFzkgefjiWvDxzFfKPf1RSUWHOloYBLR8y9SXV9NYvEJNi/Y6CNaGoFPcnBTPDAWTh5TGyQrDdQ5TyoS2ktQc/H7GDEpvoXoWXN9lEpSWppFPJCOIC0o0Pg884TqJtwFZTT3JYa1NmLvC66O4KMi7DMHiryMcymzPBd4XQywrLkemCuxIgKWxMfJkM74XBd5kOaLEedlljuNQNbVfDglTTuuvJdTAuIfScqFRoMRsOWpNjkQMenw+Dt4YGDdyaAi1+hIl7a054u1Oh7VjTjyL3JP4WHi/M2w33jIBeU2HJ3tqlD8MwHRjHL4NHBkKzHvD+jzBuMUQfgspaJJvTRbklsCrGjDLdcwrc+hm8PAlm7zJ9Z8LpcD70jYJbfoJR0aHwajUllkOPaDMcfFxJcH+JB3okwGP7Ie0kz7S9Cpolwmar+3gNgz6lXl4v9uC0fQTDMBjkrmSguyLgtV6KjyfIIsrWJ/dSydMkUoSX3ZTxEscCkvQMMhlms0JcTBpjbPrAHWQxmB0BKcOPwWaO1rBaXMAiDthMcwEKSWE5gwMe7DnsIN4GaeWzgwTGBLbzOMEGRtuiSbjow6pATK2kKiuXj+3WT78I0bXEoquN4uMLeOWV1Xz00VaiolIC+/+/zkgkXSkpUVKbWo5tk3SD9XuIpNGn41lrbcc/68J/VOyM5P33T7DBStjRr18lK1cGZf0hX8OshcEOEXUQ3rTMyw8VwHPLCKGVZfBeGHb9JUWsqyGVxHHIlpN9E5mMC8uXsIpUfrWFmJ/EMRbbpJSjVNCduBr5Sgrw8jbZFNkiqX7qLmeONzjAC/0GzfPcJHmD+pJ3c2CiJfTsq4TIeFP3U02GYeaq/zyMeaZWwUNbYbultil3Q/t18MNhkzH02A7dt4VCAkeLIfI3mG9T2o/dB3dOh8O21XelG95fBa8ugPgw3zXDgAUxZkj6/kvM1LIno7g0+GAm3Nwfhs6HLQknD8To9UHMCZi4HN78Fp7+FJq9Au3ehbdGw5dTYepyWBMN+xNMP5PMXCgqgZJy06GxoBjSc+FQEuw8BMuiYeIKGDwNuv1g5lJp/i68MhY+mg3zdpqSU20SQmoJjNoGt0+EXitg8ZFQC7dqSiqFN6NNqXCdTdrwGzA1A5pvg99OoufxGTAsH+5NgQwLCS3xGzxX5GFYuTfEJ8rr99PLVU5/d0VAsV6Ej+7kssrWzw/h4GkSycFDPBU8zyEKrIl9E4W8T0IAvt1GLv3YE/AFSaeMUeyi3GaIsoT9zLVJDOY94lnMkhDm4sPDUj6lxIK03JSxhe64LN2khwqieRW3JeH78bKMzyi1Rdn+kT3ssI21V+Jgs00tuX2nyUjC6dChQrp1W8czzywlwRYi+8iRQvr120zfvpvZtCloCnnaGMl1kWglp1b+nNXWb5JyJHklZUrqJqmnpJ7W8cmSSiTFWmWf7dzmFmQWJ2lJtXXXP6P8WzCSqVNzGD/e1PBu2OChV6/gaj4xBTq+Gewofj/c9gkUW+Ol/cJQWMFvQJskSLeZAmfg4UnSQyb8E5QzkoQQC65B7CLDBnn5MBjA1sA+Jz7eYDuFNuz5e1JYTE2MZy3l9CAjuMIy/NztKeC4LcjjHrefpwvdVFnLLIcf7kqGDdbjLy2Gl5LM/YE2GdD7CHwTBuFlOiByA6y3dKlunxnLaXCMCdeMjoPOm0IhljIXvLYW3lgfDCQYkwutp8G4vaF+EvuzoO1keG9lqI4AzIl/2m547CfoPR+Sa9ohhNSNioUeE+CWvjBgJszdXtPnJJwMw/T92H4Q5kTBmNkwYKyZlrnjh/B4L+g2BO7vAQ+8Cd2GwlP9ocsQ6Ps9fDYFfloGS3dAbNLJszuCySSiU+DjVdD6e+ixEH7eF5rTxU4H8uHF9dB2KawJszaLLoLn9kGfhFAHUzulueHVDBiUF9SHxLjNFLnL7B8fqDAMnnOW8pPHEWAueXhpTyYbbJLIIRw8TiJpuDhOFR2IJ8uSuPdSyqvEUmEtdI5RSg92BPSE5bh5j7Wk2RZah8hkJCtCIv0WUMCPjMVJ6MvcwzSOsTGwHc9Ysgma4x5mNNkEncTiWEFMSKbEIgazMTB2thbDUweC1zcMMyVAQqjlPlu3ZvHii1HExxeyb18Ob721PnAsIaGQLl1WMWDAVnbsCOJjp42R/DUSLeXUyn8dEk8vI4mLq6BTJ1MacLsNnniiPGQF9sDzkJQa2OTH9fCVFR14Qxq8G+yzACwug7fDord+Sj6rCJ2tvuAQ221KxAQKGcT2kNXVUYr4mG2BfXvIZwhBLb8THy+zjzRqYhUfk8M8m4XYQb+Hrp4SKm36kimVXp4r8gRWljleeCkDDlljc1IBPJUYnGDAlCw6xMHXYcwkzwmd98Acy+rKb8Cwg/DsVlM5vyIdmi+G/WHhVyYfgvsWwD5L4nB64NPN8I9pwZSwYDLxWQfhljEweqtZz04+PyyKhbZjoNsMWH/k9z3FfT6IPgJD5kDbT+Dl7+CdCTBzE5zIPvUMh/9bKiiHqHj4eh20GwctvoJPVsCiODOcTG3k9plJqe5ZDG9vhXDft8Pl8PRueGq3+bs2Mgz4tQiaH4MtFdX7DGZVGNyW4+Woxwipn+nzcp+zhLneYKNS8PB2WPK1IBNxk4yDLhwmyTp+jApe5EBIKPg32E6mBcV68fMDe9lt05PkUspnzKPUxqjcuPmZiWQTKv5ncID1fGmLl7WeGFumwwJ2EcdQG4SVwya+C0BgPvx8z07Sq5X9BjwXA8dtbi+zF0CvD0Pf5Z49ebRsOY/s7GDFZs2mB+aRamire/e17NsX7NSnjZFcG4nmcGrlv4zk9DISv9/gllt2Bj56ly4V7NoVXLnPWwaffBnYpMoFt3xsmpD6/dB2HpwoCR43DHMyTglK5eTj4fEwz/YCnHRjV0CcB/iGvewMGxy/EMcWm5g9khi22MTwBMrpQUwgjESgnfh5kmTibSu2WT4Hr3tLQhhl/zIvn9lSoh5xmTh5qtX+L3PgwwxzUFWTxw+vJ8Do1NAVcLkH2m+H0ceC+2ckw51RkFQOx0uhzXL49VjoeUeL4a758Gl0UE8Sn2cGfHxvbagU4vDAqC3w1HQYFx0MEGmnfWmmF3jzEfBNFKT+jpRSTRUO2BxvWnW9OwGa94I2faHfJBg5C37bADsTTMjK8TuBF8PJ5zOdImOSYcU+GBcF706FR76EB78yY4j1mwuLY2qPgFxNhgH7c6HPJnhupeknEm7me6gUXoyG13YHocbaKMUFnVPg7QzTKRWgxGfQMdega55BZZhSfZfPw0POEnZ4g506FhcPkEmcTbe3nyqetCSRFJx0IJ7jFgNIopLO7CPDYioluOnODo7afDbGsIfFNsMTB26GsIAU24LLwGAh89lvM8sFqKSIDXyN0zK5ryKHrfTAYzEpD+VE8xouy3TewM8ahpFnU+Yv5xgzCLqo/5gOA44H71FVBbfeBYU2vX5aWiWRkatISQn9GO3bLyTTymt88GA+AwZs5+WXV3HoUHAlddoYyTWRaBanVv6DGEmE+S7/36dWrVqxb9++wHb37oc1cODVuvbaxoqK8mj5cq/GjTtTkuTxSK2flLbOl5qcbdb/eZN01/XSjZdJ69OlGUelaQ8Frx9VJc0sl2ZcEtw3XgVqrDp6XX8J7JunZCHUUX+VJBXLqR+1Tx/oDjVSfUlSlbwaqE0aqrvVVGeoXB79oHi9o5t1rhpKkuYoU1XyqpuuCXnOFHn0jrI0W1fqHNWVJA3wlevaiLrqUdd8PgP0VrlHrerXUffG5j13O6Svi6VxF0sX1ZOGZEspHunXq6S6Eea1fYb05jHp3HrS6OukOtZ+ryENPizluaQfW0gN60r7iqTue6Sht0gPXCR9sEcqdEuT/iGdZz6C/Ib0XawUWyD1vEW661LJQJp1SPp6t9TnNqnLzVJ98zFU5pLG75LmxEk9WkldWkjnNgr9zuVOacF+6bc9kscvdYqU2t8qXX3+n+gkkqpc0rEM6USmlJor5RZLyZlSYZnk8kjnNpBKyiWQrr9aSsw2z/vb1dLRPPP3FRdKzjrSZedJlzaVrrpEuuES6fpLpIvOkSIiTn5/kGLzpMXHpBNlEnWlLjdKD18VfA8g7SiQ5qRLxyulT26S7rmw9ut5Den7fGlOqfTtZdLdVn/e5ETjyqROZ0nPnxVsEKDpPpdm+N2a0uBsXVHHvGmUHFqoSn2qprrS6qfRqtAo5esnXSmHfPpemXpHl+l6NVa6qvSxjupz3ahr1FhV8mmYYvW8rlYrmR9jvo6oWC71UHNFKEJ+GZqsdYrU/6iVrg20aZd2Kk+5ekrPBPYZ8mmtRqi5ntfFukmGfIrRYF2rzmqqm4TQCX2vJmqui3WvJOmENqtMmWqlLpKkAlXpS23XF3pADVVP+W7poQPSjtukM+uZ9/n0C+nqK6Q3Xja3HQ6fHnhgg779tqXuvPOCkHc9dOgu3X//FWrb9jLt3p2radOOqk4dv/r0aanrrmsqSYqIiNgPtDp5D/hzFHFtKzR83x9XtFPn03Pvfwv6V3Ewu0QC8OOPGYwZY676vV6Djh3LcTiCq7Ixk2H0BGolw4Bu6yG+MHTfQxmwz3YNB37eIz1ECe7GR0+2kmeDBtaSyC9hq619ZPMl0TaIK4/PbMHrfBj0Joa9tYRPWUc5b5MesPjyGgZFRihu4zQMHihysMQZbNuGKjNUeLVH8+As+CAjFOYyDOh/AvodN02H7fvHJkLbzWakVDD1I09shuHxpmfwohTotAlWhzkgJpbAY0vh1bWQayEFFW74MhpaTIYZYXGmqtwweTe0+A7eWgiHTmIWnFUM4zfAI9/AbcNgyCJYG1/TgfF/Sz7f6XFYLKqC+Yegx1J4di50WgxzE6AsDOaq8sJvSaa012UH7P0DqWt1iWlA8X1u8FtV+A3eLjB4NNsgI8zJsNIweMNRySfOKhzVcdswGE8Zz5NHiU26Xk4pXUmlEC8JVNKB+ACclUYVr7Gf45Yi3omPPuxms023F8UJviM60EcNDGayheU2GBcgmUSWMB9vWErefcwizmbqe5SJIY6GOaziMMEY72Vks5wBgZztBgbD2cghWzTinkdghU3/efQE3PFI0EjDMAy6d9/PjBnJtb7vwYN3sWyZeWzz5kz69NnK448vJjs7aJCg0yWRXB2JpnFq5T9IIvm3YSTZ2S7uuy9oUz58uINJk4Ijt7IKXngLHCfBrHflwmMrQvfFuQweyfWFwEhrKGWgLcIvwD7yGWsLnWJ26i0cswViBPiZ/WywecH/SBwrbGl883HRl1gKqTkzfk8eX9SilLdTid/glRIn623xH9ZWQruMIDP5MgeeTgwmy6qmsRlw734oCHNu25RvZpGrVsL7DRhzBG5fA/ElkFMFz66H17ZCke3dGgYsSYTIGTBily2rX5UZYuWx2TAlJtRc2O+HVUeg5wK4azxM3AmlJzFzLXfA0v3QZya0GgQv/AB9Z8CcaNNp8f8t/YjbCwczYcoe6D4fWo2BznNh8AbYllrTOsswYE8evLXDjOY7PAayTqKEr6a4SuiZCC+dMLMdVtPyCngs0+CXMqNGltBDXi//qChjujt4ggM/H1HEAIrw2PrqrxTQlVQq8bGbcj4kmXQLTk224KwTFrzkwseXxLGa4OphB2kMYiNuG8S7kThmsClEX1hIAT8xhoowXWMK29nODwHv9Tx2EcPQwHYlGezhNbwWxObHxyoGUUAwVMkGEplkswhbUwjP2owoDQPe7Ac7bXxt7NhE3nzTpoUPo1Gj9vPbbyZsFhWVxsiRe7n//nmUlgbf6WljJFdFol84tfJfRnL6GQlA166HyMgwZ56CAj8tW5bit2HFX/8MX51EKgHoFAXrwoIcvl3kY1ZlcFYyMHiDZPaHmQN/zQE2E7TmyKGMD2127AAOPPRlDdnWQHLh5UM2c9wmheyjiHfYiycsPLcfgwFksrAWicVOeX6D1oUOtthm6HWV0CoVMq3JfGIBPJcEpWGT3KpC6BgPcWExr7Id8OA2GHo4GDE1vsT0cxgRZzKDucnwj+Uw7Xjoqt7lhe/2Q7v5MPFgkHHkV8KnG6HZBBizq6YlV3YZfLUROs2Ap38xveJPprgGKKmEdXEwYjG8PRlafAitB8CHU2HQLPh1HWw6CAlppr7jz+RwB9NvJbvY9D1aGwcTNkH/edDpJzNf/W2j4eXpMH477EwNev3byTAgNh8+2QXN58Kr62F1eu2Rke10vAo6H4EHDoaGQEl1Q4dMeCnbNK6wk98wGON0cVdpBYd9wYOpeGlPDtNt/daDwadk8RU5eDBYTTGdOEq+pbg+QjnvEUeSjYn0Zy8rbExkH1l8zQ6qbJEddnKUMSzHa2MsVVQyj1nkhOkPi0hmFf3xWNJPFZlspxvugGe8i930oMymBznAXA6zKrCdTyXfsg2H1YYKL7Q7AFk2pjt1LrzzcXB7584SHn10RyBM/K+/xrNtW2gwt59+imP2bFPfs2DBCUaM2EOXLqtwu4Pv9bQxkisj0XhOrfwHMZJ/Gx2JJM2YkaMTJxwaNszUVwwe7NDNN9fV88+bIL7LLd3+jLT5N6npOTWvmVYhvbBe2vak1MDCr4v9qGuxX9P+UlfnWEqEdLk0QEmaqhvVQHUkSRXyqL+i9YXuVFNL77FOiUpXibrptsA9jqtQa3Rcb+l21VddZatSX2i3RqqtzlYDSdJcpSlbTr2vv4W0zyVD3ZWiN3SB7lGTk76bHL+hvhUevdW4vu62HmS7U3ovX5p3ifTXBtLiEmlknrTgWunKBsFzj1RJLx+W+l0pdboouN+PND5Jmp8l/dJSuv5sU2cxOkFalimNay39z9nS4BgpsVwa1kKKtEHOpS7p+xhpcaLUu7nU+UbpjHpSuUuaHidNOSi1vFj68dGg7qCaDudKCw5KJ/Kl3HKp/Y1m+dvFv6+fcHmkxBwpJU9KzZfKqqSj6VJhueT2ShWlpn5Ckv5yrlRUYf6+7kop0dKP3HilVOyRLj5XuuZC6YJzpWsvMMtVf5Hq16v93uVuKTpLWpYibc+SWlwoPX6d9NhVkqXGOikdrpJGpkqVfuntK6QHzzWfs9IvjSqQVldIoy+V7j0z9LxUv6FhDrcuqhuhIY0aqqH1clbh1FI59GbEWWpp9c0S+dRHmWqrs9RV52mGCnRMTn2sK3SW6ipGpfpOyRqpG3WZGskpn37SUd2gc/WYLpckxSlXMxWnT3WPmljXTVCGVmiv+ugJNbR0Lz55NVO/qo3u0fW2Pu1UqfZqspqrs5roEvnk1F710016V+foBvNdaLTO1v/oCj0tScrVYR3UQrXTJ6qjOjKEPtdGPa9bdJNMxVKvY1LLs6Vul5r3ySuQHn5R2rZEOvssqaDAo3btDmjp0ma66ipTKbdqVbK++Wa/Ro++Wy1bmh3/u+9idMEFjdSly980bdoRlZV5NG1avPbv7xJ4htOmI7mqFfroFHUkvf6rI/mnSCQul59bbtmJ02muMnJyTKnEa8OO566EviNqnBqgEQfgy5jQfdOrfPQsCTUtmkYO3xOqHNhHDsNt5r8GBpPYyb4wKGw5R5hsw453ksU37ArBl7/jMAvCssgBFOPlOY5zMMwj3o/BcduqMNvnp3WhgyjbEjnWCc1SzbwUALsqocVh2BMWEbzMCy8nQN/jZkDHkGcshlYb4OfkoBXYkVLotgPe3AkFTogrgodXQucNtQQfdMKYvXDLLzB8RzDUimHA7lqi+4ZTZglMjoY+C+Dmz6HDJJiwxUzne7p0JadKhgEZpbAwAb7YCm2mmTlvBm2BjWm1Ox/Wdo1NhdA5Bh4+ABuLg5KdxzClyBeSYVxBqI4LTClkXJWHyEIH22yrZaffzwB/CS/7Cym26dQScPIoyWyiHA8Gg0jjM9ICcNd6CviMoxRYEGsFHt5jF1E2iTuOXL4jmhKbReFRMvmceZTb9IV+/CxlHvsIdSH34iKKT8mxnHgNDGIYTBZB341sVnKcsYHx5KSMjXxDpS3g6QqOMNOWPnd9ETwaG5r58Lm3zbQSAD6fQfv2B1i9OixmDbBpUzoDBwZD2o8evZ9Zs0yJ5Kef4pg27TAtWswIOUenSyK5IhJ9z6mV/yCJpM6/mpHZqWHDOura9VJNmpQlSbr44jp6/PH6mjnTE6jz3CPS3ngp/ljt1/jgVulAkZRSEdzXpVEdpfrRVrc/sK+zLlKu3IpXsGKkLlZTnaF1SpUkRShCHdVccxWjIlUF6j2mG1Qih3YrXZJ0hy7VxTpLMxUfOO9t3aCtyle0CkLa11T1NEZXaZAydVTOwP5yoXdUpG1ySZIuqVtHK5qeobEOrxa5zedvdoa05FKpV760qlK6/Uxp0bVS7wxpSlHwHk3qSdNulK5tJN0TY0opgWdsKm27R0p1SPduleLKpL+dI/18p3T/xdKD66TdRdKyh6VXrpc+2yv12i5lVprnn3uG9F4raffL0gWNpXfXSa+vlHZnS7ddWvs3sdNl50rd7pS+6yDFfSKNeFI6q6E0dYd0/2jpqR+kF3+Uhi2R5u2W9idLBeVByeP/Ql6/lFwobTgqTd8j9VkqtZskvfSb9OYyKT7PlKpWvSBFvyoNvVu678qaEpadyr3Sz6nSndHS1Ezpg2ukNS2k+5pKfknzi6XWR6UsjzTpSqnX+VI9mxR20GvopVKf0nyGtp13hu5qYIpIcYZXT/hLdIPqaVrEeWoaYQ7V5SrTAOVqrC7TrTpDHytVV6uhhuoK1VeEZitbi5Wn/vqrzldDlcitb5WgDrpaD+kySVKscjVdB/WaWuhcnSFJSlau5mqHeutRnS1zlY/QGi3WeWqqSN0eaDMyFKfZukZtdbFukSQlabrO0EW6VA9Ikip0VNlarGvVTRGKkCFD2zVe/6P7dKZlNZmkIu1Rpp63rlHqk37IlibeEJRUf1smnX2m9NDd5vbQoVm6554L9cgjNc3+Wra8SNu25QS2fT7pjDPM91lS4lbjxvV1441Nf7eP/K8JmSEST6X8B9G/FbQlSVVVfrVps1dbtkTq3HPrq7IS3XVXldaubawLLzQH04EE6ftp0pQvpTq1sMLtedLncdKaB4MdMtVn6M0qlxY2aaSzrJ1pcmqgEvWLblIjyzTXJZ/Ga5866iZdbsFPh5WrbUpSN92pehbvdcijz7VWPXSnrtFfZAh9q926XZeqra6UJJXJo+91VC/oKv1NoVhcitzqozR9pyt1rTWY8+XXyyrQxzpH91iDucxAnSoderJBfb11holhFfukl3OluxpLA5pKTqQ30qS/NpQ+uVg6w/ZO4iqlUalSq3Okdy8PmghLUnyZNOSIdFUjafBN0jn1pSqvNOaItCBN6vd3qePV0vI06ctY6ZHLTebyV9ujgLQtU5oQY5olN79QevEm6dKzf//7n4w8XikxXzqabcJTx7Ol9CKTmZx/hlThkM47W7rxEqm8UmrUULrkPKnUYbal8RlSkVuqdJmlxCsVW4z0qoukOnWla/4i3Xy5CW39/WLpwrNOrY0G0tYCaUaWdKBMevEy6bUrpAvNzyi3Ic0olH7Mk55uKr11kXRBGBxW4jc0tMJQrM/Q9+fUU7P65kfzgSb6HVqGSz/UO0c3RJgToVOGhihPEZI+00VKk0ufKl3v61K1VRP5hL5VshqojnrpKtVXHWXJoaGK1Zu6XpGWie9eZWmzUtVDkTrH6ncpytVsbVJPPa6/KPjhNmu1XHLpYT2tCAU7ToymqI7qq5lltpurDSrWQd2o9xShuvKoWMf0ta5TLzWymNdBLZIhr1qooyTJIa8Ga536qq0u0dkC6aWj0rPnS89bkGpWrtRnuDT5C+mcJtLKlSUaPz5Py5ffoLp1a8dEmzf/TbGxL0qSPvlkt9q2vUSPPHKlBg6M1n33Xa4BA7bqwIF/ArR1WSvU6xShrU/+C239U6CtapoyJYvPPw+a9C1e7OHll0O1uX2/hJ9+Cz8zSL13w89hQUunuNz0CosAuJA8vrJZXgEkU8yHRIUo2pdxkOlhuRayKeNDllJiwVQOPHzEWuJsJow5VNGN7aSFKfcBTuDkSY6SaIMXCvHyJDkssQV9dBkGr1RU8YXDGcg34TXg3TzongOVfhMCGJ8HdxyF42FKbacPBiTCffvhRJiFkd+AKalw63r4NSWojC9wwpDYoHLdb8DyZLh3MXRYDTtzapraFjlgwn4zV0yPFfDj3tqDGv5fyO2B7CI4nA77jsO2Q7ApDtYeMEPSb4qDHcfgYBok5pqhV06HBZjfgO250PcA3LIa3tgDWwtC30G+G4anQft4GJwBRbUo7j2GwbhyP7dme/mt0heIaAAQ6zc917/3VAWi+QIcxslzpPIbJfjxM5dCPiSVVAu6KsNLLw4xxRaS5wil9CKa47Z8PJtI4WPWU2GLn5VEFp8zi6KwvD3bWU8USwOWV9V0nFXsZEzgPsUcZDc9AhZZPlzspydFtoyG6exjE9/gt65lYDCR3Wy3jbu5BfCGbbz6/fDgK7DJQtQSE128+GIyhbXlILbRI48sCYSRf//97WzdahoHvP32JrZuzeSOO0Iz4ul0QVuXRqIRnFr5D4K2/i0Zid9v8OCDsWzdWhLY17u3kxUrgiOzygEtnoW0rFougOn30HYtJNnmb8MweL2iiihPsDMaGHzKUdaFmfquJZHJNl8SA4Pv2cAOm8kiwCGyGc4q3BbTKcbB+6wm1RYaJZlyhhBDTi1hVE7g5AmOcdR2rAI/z5PHNJuZpd8wGFLl5NnyKsptk8ysMtOiK8HSLxyogoeOw68FNSf6PaVwxx74PDnU5wSgzANfHoHIKFj8B9kJd+dCj03Qej78FA+lteg20krg+11w3zR4bbFpThudZsba+v8KFTlhQRJ03Q43L4XXt8PKrFCTZ8OAHSVm/LOW+2BStpnZMpz8hsGCCoOOeT4GlfiosFkjlhsGI10OHnYUc8Qf7ON+DCZQzJOkcwwXFfjoSxofk47DmpSPU8k7HGKzTe+wmRzeZAdZNj3cao7zJdtw2hZHx8hgDItqMJFdbGEh0/GH5dtJZgNb+QK/dY1K0ojlY5y23OpHGEkmwSirpWSxjP64bAupVRxlos3U95gDWsZCue12X0+GAaPN3xUVPm677Tj79/+BnTXwj3/Mx2FF1e7adSMxMea47tRpNdHRWXTvHhVS/7QykqGcWvkvI/nnMhKAtDQnzZvvpdSKdJeXZ3DrrU7y84MDcPt+eOrdk684dxbA3euCq2yAfL+f1hVlZNpOKsfLi7YMcWAOih/ZyRqb2aIDN8NYygmbxAGwiWN8x4ZAUMhMyujDanJtg+cIpXRnB/nUtIFNxMkrHCfWLoVg0I9ChlAcEmxynsvDKxVVHLfZv8a5TGYyq8yc2Cp88Gaa6W+SF7aA8/hhVCo8HgtRNfWVZDmg5154ZRcsP0m+jGrKd8CoA/DAQnhpDaxJDX3X1VRYCfPiodtieHEuPDoVvtgE21LM6ML/LpRVAQtOwMc74I7FcNdS+Gwv7Mitaeqb4YDvUyByJ3SJgy1FtfdDw4BVFQa3Zxr0KjDIssXP8vv9zPe4aV1RxmS3C7/tAil46EceIynAjUEsVXQgiWU28/GV5PEysaRYDMMMXZJCf/YGAjD6MZjCAb4jOiQUUDwpfMFvlIblbt/FFlazCB8+nFQEJI8MdrGeT/BaUpCTfHbyOhU2v6pkJpPI+MC2iwo28g2FNsnjGAUMZl2gLQ4f3H4QYmzN2BULD78BbqtvjBqVx7Rpv282X0333bco4Cfy7LOrSU4us36vZOfOLJ4LCxd+2hjJJZHoM06t/JeR/PMZCcDixQV06HAs4EuyfLmPt97yhExuA8bA6Cknv8ZXCfDV4dB9W7weHq0sx2sbuIcp5yMO47QNNjc+PiGKBFt60gLK+YQFISlGARYTw1x2BwZeIkX0ZQ2FIRFZi+nPXvJrkUyycPMcR9hmi7jqx2AkJXQjnwpbu2K8PlqXVrDEHeQSFT7omgMvZEGRVXVlKbyQBNMLazKENAe8eNDMjXG4JupGcoWZGvb2tbA4vXYGUU2GAbtzoNcmuH8+9FgLa1NNZ7/aKK0EZsXAZ1Fw51ho+Z3pxDhyLSw5CEdzT37u6aAypxnNeOFh+HgLPDYfHp1vpskdvNNkiLVJWRlVMCkZ7tsF9++CSWlmDvHayGfAb6XQOgn65xqkhgVg3OP18UBZJZ86HBTa+qEPgwmU8giZ7MOJF4OJFNCJZFItSMqJn89JYjQpVFrSgQMvg4lhHIcDPkwuvPzCfmZwMMSxcDdH+YU1VIT1w2g2soBp+PBRQSHzGUQZeWSxjy2MwG2LmbWP9yi2ZQ3NZgVxDMCw+qkfL1EMJ80GcRVRxQesJM+2wOp2Aqba1mXFpdD8GUi2GVSGO2v+HrVrt4ziYnOx9txzaygsNH/ffvtcdu3Kplu3f5JEcnEkGsiplf8gRvJvp2wPpw8/TFeTJnX12Wem0q5PH+nKK6W+fc3jHq/UsZ/0SQ+p1d9rnu83pIe3SgNvNGNMVdMPHoeKhIY2CBrzL1eu9qhEw/S3gHKxSA5NUrS66nZdZCkik5SnpdqvN/WAzrTs7xGaqu1qqjP1tFpKkg4rX7MVq35qq3Mt5fkhFWucEjRYkbpEjUPaWiKfvlSmWutsdbDFA1uqKv2kCk3Q+bpalhWKgfo4nLokIkJDG58R8DlYVCEdcEvDLaOWMr80IMuM0zX+ClMhb6foUmlChqmgH/RX6fIzQo9nOqRxx6U12dLL10jdrpXODbtG+PveniUtPCEdK5TOri+1v1Z66BrpipO4znj90rF8KT7bjI91okBKzDX3N24g/f1CqUEd6YKzpSvONZX6ZzaUzrFeX50Is3iR3D7J5ZUqfVKJ0yweQzpSKOVWSNddYD7TdedJLS41Fe/NLpQuP7umT4vPkPYUSdsKpCX50pl1pScvkTpcJl3WqMZjBN73lGJpY5V0/RlS379Il9oU7Yk+Q+NdHiVj6ItGDfX3ekGTsFi5NVSlulsN9bbOUao8+li5aq+z9JrOUz1FKFlODVKintaF6qALFaEIZahKvyhRd+p8PWwpt4vl1Gjt0AO6Rg9aceQkab0O6LDS1UOP6gzL7wmh7dqgSpXpYT2tKpUqSj+orV4RqlS8ZutufaKGaiKfHIrRh7panXWB2kiSihStPG3QDfpQdS0FfpZiVaJ03awnJUke+TRMG/SSmusmmQPxlzxpa7k09Trz3RuG1GOo9MTd0lMPnLyP/R61bbtYUVGPq3Hj+rrttgXatetZ1a1bRy1bztGYMXdp06YMDR58Z6D+aVO2X9QKdT5FZft3/znK9n97RuL3ox49UvToo+epQ4dz5fFIDz4offGFdNddZp3EdOn5j6SNE6WmtUxWOU7piW3SsrbSpdYEYIA6eSrUuW5DPVUvODN+rUT9VY30jDUgJSlJhZqsXfpUD+lMa/DFKFVrFae+elT1rcndkKGJ2qy/6RLdpxslSYeUqxmK0UDdG2Amx1WmMYrXR2quqxRqMuSUoYFK09VqqHd1sepYVmKxcquvijVY5wYsugCNd3s0z+3TpLPO0N/qntxONbpSej9DaneONPAic1KsJpCWF0jDk6XH/iK9cYV0WRhDqfBKU5OlmSnSTedKb14n3f6XPw54eKRIWpMsxeRK8fmmeW27q6VmF0k3/EWq+wcG6FVuKbtEKqiUCiqkSofpeFjllurVNX8bSGfUlzwy/zesbwb3PKeR1LSxdOHZ0iVNpIvO/n1TXpdP2l8k7ciTjpdLe8ul2/4itbtYaneJdF6Dk58bWyX9ViStdUmvNpVeP086x3avNJ9fw6t8Ou43NPSs+rq3QdATsgRDP6hM8REejdB5ukb1NF2lWq5SjdQluklnyBD6TflarSIN0tW63lqEbFGepilF/XVjwDIwUcVaqMN6TNfrZsvJz5ChBdqmuqqjp9RG9SwrRWRonZbLI48eVQdVqFAbNEF3qYsMlStZG9VS3XSGzpFPTsXqY12uJ3Sx7pcklSleJ/SdmmmM6p/EyRahsYrWLbpY91lMbW+l9H2ONOlaqbH1noZPkiqqpK/eP/l7/iO6//5lWrPmUTVoUE+RkfO1f//zkqTHH1+uHj1uUkxM/n8ZyT+D/lWi0J+BtqqptNRH69bH2b7dFK3T06FNG8i0OcAtXA/P9Ts5pr85H+7eAm4bRFNi+OnmLuOwPwgRefHTh31stcFZALtJZSLbQnDmzRxmBltCQkl48fEdUayz5ZqOI4cv2BgCc6VQTg+2klBLyBQ/Bj+TwzskBtKiAuTj5UVyGU5JSMj6g14fr1RU8YPTFWIFFE5eA8blw2MnYHp+aNpSMLfnZkHr7dAjDhIra17DMGBzLrwaDXeshC/iILUWaKzW+/thd5ZpzfXCfDO8Stcl0HsZTNwNO1Igr+L0BF78PfIbkFYGUcnwc5yZ3Ou2OdBxNby5HaafgKSyP25HvttUrt9xCJ49ZgZlDNelHPUYvF7o44UCLytcoZkOPYbBRKOCu/25LDOqMDDYhYNHSedHigMQVQ5uPiKRr0jDFYCtfPzIMQYRS7nNkXUDSfRnLTk2+MiFhx9ZzjJ2hmUy9BHFEtazAgODIjKZx2cUkEoW+1hLf5y2UCf76Us26wLnV3CcQ3wWULafjBYQH+LAm+2GZnGQbFMXro2GR3qC9/8IabZoMS/wjp95ZjVgQmMtW/7G9OkJfPvtvpD6Ol3Q1oWRqDenVv4Lbf3fKVwiyc+vUkxMgRo0qKc77rhYjRqFxq7IzPTo5Zcz9OOPl+vGGxtq+3bpo4+k9eulRpaUMfhnqUF96ZPXar/nmETpeIUZWr2ajuHTG75SLa53ns6PqA6X4tX72q/+uknX21ZZ63RER5Srd3RPQFJYrQPKVJG66UHVseAwr/waq/W6TVerrRUq4ojy9av2qZ/aBiCyfDk1VPvVT810jWo6XqxSiaYqX6N0la62IAND6HuVK0EeDVZTXWFJQy7Q50639vr8Gt+4kf6n3smX+kVeaWS2tL1CGnq59FBYKHWQVuVL36VKZ9eT+lwt3X1eTemjzCMtSpfmp5rhTB6/Unr2KunqU/AhKaqSEvKlQ3km9LQrRSqoki44U2qIdMk50qVNpIsamxLGuY1Np8gG9UzJo2E90xfMwITVnD7J4ZEqPVKpVyqsMq9X7pOOFUsFDun6CySnId1wntT8YlMyuvk881p/2F6PtKZImlUolfql58+XXrxQutgmrQDa5pLmVEnH/Yb6N4lQuzMiFGG9QEBL/R4tN1z6W/0I9dJZqogw9IVK1ECotxUaHqH5KtIcFWqALlNrq4+kqFIjlaCHdYme1RWKUIQ88mumDqlSTvVQK51h9YtilWuGNugO/U23W1KyJHnk1kLN0DX6H92he5SnZO3TYt2pTqpSujK1Wy3V1YKzXIrTJ7pI9+syPSZJqlKqDmuw/q4RaqzLlacYGfLrEoUusLcrRQkq0BtqpbqqI5chdU6Uel8s3WsNrSMp0kufSmvHSRfU4i/o8fg1b94JzZlzQqWllXr33Ui98ML1tX6fainEMFDr1gu1b99zcjp9atduiV555X/UoEFdvfbazYH6p00iubAVeu4UJZKf/nMkkn8LRrJrV7YGD96uli0vUYMGDbVkSabatLlQ7757s2688dzAOQkJLnXpkq+lSy/RlVfW15Qp0r590tixpmOizyc91k/q/bz0+D9q3hOkjxKka86U3rKlDVlvuLXAcOr7uucEdA3ZcmiwYjRMLUJ0GQt0QBLqoJYBPcoS7RZCT+n2ADPxyKeftE5/06V6WM0kSckq1lIl6Bn9XVfrPElmrpNGqhc4r5o8MtRAdXREDn2pTL2oC/SIgqNsj1waqBK9qbP1vM4MtGWv16d3Kzx6okE99Tuzvhr8DvaU6pbG50p7q6RPL5MeaFKTWRwokyamSwdLpC5XSF0ul86tJdZUnlNali4tTTPjct11kdT+CunOi4Jxz06FvD4pv1LKLpNyys38I8VVpuNhhKS8MlMfcv7Z5vE6EdIFTSSHIZ3ZQGpyhnT22dL5Z5pM6eImpo7m/Ma/D8eFE0hHKqXNhdLCIsllSI+fLz13sfQ/oSouVRnS4nI0rkr6n/rSe+dIrRqG5hbZ7PNqhN+hZnXqqX/9xjonIkIzVa55qlQ/NVU7NVKEIpQql35Wns5UXfXRJWqsujKEFihLR1Wml3SlrrMYS64q9a326B5dqUf110BfOKFMzdEWddb9ulbBxDwVKtNGrdJf9TfdrBbK0CHt0UI9pF4q0EGlarPu0gA10Jnyy6mD+lQX62FdKjPhj0MZStRY/VU9daauVaEOK1aT1VaD1dDmeHtIuZqtg/pM96uR6stAeilReuAcqbuVr6WoVHqotzRtsHTzdTXff3R0nt57b6c6drxGL730VzVuXE/t2i3RokWP6Yoraq5YHn98pVaseEwlJW4991yUNmx4Ujk5lRo8eI+uvfYsXXvtOXrhhRsC9U8bIzm/FXryFBnJlP8cRvIvh7bKy10899xi0tKCtuw+n5+oqAweeWQTH3wQQ2VlUN7dvdtJq1Zp5OSYkM8HH8CnnwYOU1gKLw6G+KBVYgg5fXD3dliXH7p/sr+cbv6iEGjoqGWyW2Jz4DIwmMZ2frPlIjEwmM8OprExxFTXi4+fWMdKDgTqZlNGf5YRZ8uwGE6VeHmdvcRZsEIZXt4jmZFkBKANgHL8vGcU8qFRTK4RhMA8hsFXlW6eLnGyzhaS/mR03AmvJsLLJ2BJUU3IC6DQDd8lwgM74KXdsDrn5JZcFR5YmgI9t8Jdi6DdUhixD7ZlQuXv+5P9y8kw4Fg5TE6CvjFwy0Z4bg/8nAoZtUQvNgwz1lnPbLg1ET7PN8gOs9AyDIOVHjf3VZbxlqOSJJ8XPwbzqOResplMGS6rfzjwM5ZsOnI0xBw8CwfvEsNYTuAKgVfTGM1OjlMUvB8GGzjIz6ylOMy6MIdMJvENqZY/1FG2sYGfcVDOUZYRzXcBE18vleyld0h+9SrS2cMrVGCmLSwikS0MxmHzYwFIoZjhbKDYZhn2WTr0s4Wfc7nhgd6wJrq292rwxRcJvPZaNImJoX4uixYl8s47m2uehAltAaSklNGr11bzGY8W8+KLa/j4422sWhWau0SnC9r6SyR6nVMrf+Lekh6RdExSoqQBtRw/R9JySQclJUh6Pex4XUkxklacjuc8aTv/mRf/vVLNSBYvPs7AgVtq7RR+v8HEiYncd99GtmwJdtRNmxy0bp1LUZEfvx+efx5mBXPocCQVmneF3KJaLgrkueCFvXAozOt6mL+U4UZpyL59FPARuwJ2+WDqMCazhWW2YHOmDX80c9kW8OA16/qZyTamszWwvwQHP7CVTZyovYFADk7eZD8LycSw/hZSwAscDXFeBFhnOLnHn8csf2WoL4LXxwslTp4pcZJ4Ek9Aux4g2Qm9k0znsKl5UFnLKYYBu4ugdyzctRne3gsbc3/fPDizwsxv/sEWuH0W3DYTPt4KY/fBlnTTI/5fQV4/HCuB5Wnw6QFovx6e3QzPbINvj8KeopM/19EqGJptBs3snAxR5TUZsNswmOrw8I8iB+87KknxB1/oAVwMopgiiykYGKyhjMc4wWwKAjowHwZzyaQrB4ixOblW4uFbdvM1u6i09U0nbiYRxXQ2hejzAI4SzzymUEQ+BgZ7WcJqvseNgwP8wi5+CDgbuinmAB+Sw6bA+VWksY/uASZSTDKr6UNFWJ6dHMr5gJVk2RwdJxVAz9TgOzIM6DIExs6v+W7Lyz0888w2+vY9EPBSt5Pfb/DUUytrNQuuZiSxsQW89toGAPbsyaVHjw306bOJLVtCA7WeNkZyXiR6mVMrf3BviwkkSbpWUgOLWdwUVudjSV9Zvy+QVCypge14X0mz/y0YyZ/gip0lxVklWlKzP7pmNSMZNmwHmzbVjJJrp/R0B/fcs4svv0wM+JSsWOGgTZs8Skp8VFXB3ffAypXBc9bvg1eGQ1XYKtLtM53gjlRA822mY1k1+Q2D/kYR48I8fbeSzftE47B5Bfvx8yvbWMDeEMlkJbuZxKoQBbyBwWL2MoF1AQ94F16+YRMz2BvCeELaip8fSaIfhyi1JosTOOjEUWZTEIg2DFBu+PnCX8bT/nyOGqHL/s1uHy8Wu3mv1Eu+LfF7lR/uTIbJxeC2jckCD4zMgFtjoE8yHDuJQ7HbB2tzTH+TNuvgtWiYnwalf+Bk6PCYSvefY6H3Oui2EppPNiPu9lkLn6yDCXtg+VHYlwGpxVDhOjUlvM9v5kg5UQzbMmDBURh/EPpsgaeWQ8coaLkQnl8H38bB0nTIrDr5PfwG7C2DT9MgMhaeOQKzCoP51u2U4zMYVubl3iInn1W4yfmDxCXxOPmADAaSSYGtjx2nkn7EM47kEP+mOPJ5j41sDosunUkhnzOPXTYnWjBzo28hitn8jIMqvHhYzyT2sBg/fopI5CCzAv3YQQ476UahzQekgkRLEjElmVJSWcMHlIdJ1kVU8hGrSbJJSEtL4L5jocnYBk+BIb/WfBeZmU5uv3038+f/fijpxx9fSXp6qJWH2+3lhRdMP5Ft27Lp3XsbAFu2ZDB8+B7eeCOK/ftDnYlPKyN5kVMrf8xI7pQUZdseKGlgWJ2Bkn6UifheY83Rdaxjl0vaIOn+fzYj+UP1YkRERF1J4yW1k5QpaW9ERMQy4LCtWoqke4CSiIiI9pImSbZwob9DO3ZkqUePZiH7YmLy9e23scrIqFTTpmfpqaeu1uLFLTRyZLJ69z6mkSOv02OPNZJhSM88U6rly5tq0cI6eqS9dP75UuvW0gORUmah9OJwaeFQ01RUkrZlSKP3SMs6SONukt48JM1sLjWtL9WJiNBwNdUbKtQ5qlAXC4Nuq0vkkl8/6JDe0y06Q3VVR3X0qtroV23TQu1TB7VShCL0qFprg2I0Wav1uh5WQ9VXhCL0tFopWsf0nZappx7ROWqsPrpHixSncdqurrpdZynUQaOB6ugtXattKlJvxau3rtFtaqqpuk4TlK+XlaRhulzX6QydHVFHAyOaKAaP+lKqB2io7hFn6eyIOrqnQV21rV9Hc52GHiryqkujOup2Zl2dWydCK66UfiiSHkqTHm8sdf+LdH59acDlUr/LpOXFUr9kqdwvvXqR9Nz5pgJeMnUf7S42i88v7S+RVmVLY49LEX6p7YXSfRdLd1wgNbb1tEb1pdaXmsVOVR4ppURKK5UyyqWYbGlZkakwL6yS6iBVuM0Rc21TKckKrPy3i6Wjxabu44pzpQynqTM59wzpnHOkS84ylfVXN5Weula6pol06Zm/bwoMUqpT2lkqrSyR4iul25pIj5wvfXSZdFbYuX7QRqc02+XTMT9648y6WnlWAzWuLaqoReny6BsVqkR+faQL9HfLoKJSPk1Uuo6qSh/qGl1vmYi75NMcHVOSSvSJbtcFlu4OoWgd0ybFq6se0KWW/s08x6n1WqEz1Egd1VVOlWulvtd1ukN/132SpPP0V51nmeVWKEnJmqYb1Vfn6CZJUrmO6ahG6GYNV2NdqRIlaa9+UBv119k23UuZnBqtTXpNrXWt1YZtldL8EmnhtcFgomMXSUczpNmfhL6PuLgKvfJKgsaP/5v+8Y9zT/5xJN10U1MdPlysK64Ims+73YZycx3mc7t8uuIK00esvNwrr9eQ308NI57TRkjynvJZ50dERNgVK5OASbbtyyRl2LYzVXNeHSdpmaRsSWdL6ggY1rExkvpb+/+59Iec9k9wxbD6TSVl/dF1qyWS5s2nhIioBw7k0bLlDLZuzaSy0sOxY6WMGZPALbesZuLERGbPzuG22/aRlGSKEnPmOLn//mIqKw1SU6FZS0hICK44RsyEPuNCV5pj9sKLS81V67J8uHt/KIzjxOB98lkYFmgxigz6sytkdejHYD67mMm2EP3IHo7yFXMpC8s7cph0hjCHDBumvJtU+rOMtN/JnpiHi/eI42tOBO6fgINepPADObhtUo3PMPjNX8nd/lymGpV4bQ/v8vuZUOGlWZ6bwWVeiqzVcrkPvi+A5segfzYcDZPkMlzwRRq03gMvxMPi/Jq5TuxU5ILFaWbwzHtWwB3L4J1omJsE8UVmqJZ/J6r0wPZC+O4E9ImDW7fCE3vh22SIqQW2qqZDThhSZHBLhsFbBQb7XH8sNmXi4SPyeItsttn0IH4MlpJHf46xhNyQ/nSAfN5mM1GkhpjwluHge1bzGztwESqJ5pHNZL4jwfJAzyOJ5Ywmw2aabqci9rGT16m0STrV+6osyaOI46zl3RpwVhlOPmZFiO5vf5UJ/WXZJNR5W6DDYFM/Yqd160pp0+YQx47VYnNeC02efJhJk0Kfo6DAQbt2ZgiUBQsSGT7cNPWdO/c4X321j2eeWUpqaijaoNMlkTSNRM9xauWPJZLnJU22bb8saWxYneckfSdzfXWdzEV9E0mPS/rRqnOv/tXQltXQ8IcZ9zv1+9nrn6xERkbi9xs888yikA/7/POrOHKkpnKjrMzDxx8f5PHH97BgQS4dOyawbZup5Jg928mzz1ZSVWVw+LDJTFJSzPMMAz6YAB//Enq90bvh7XXm8Zk5ZjId+8RYhZ/nyGZpLczkW2JDYC4DgwXsYiLr8dr2J5DK58wiN4xB5FDMUOaw16YjyaSUH9jCRo6HTBR2MjBYRDafcpj9Fl7uweAX8nmG4+wIU6yWG36G+0vp7C1imd8ZwrDdhsGvlV5aZHnoU+wjzcq25DNgVRk8kgz3J8G8ktAAj4YBB8qh/wm4bSc8HwuzsqHoD+CsCg9szoYxcfDSBhNWeinKjNE1bDcsPgF7ciD/d+Cl/ysZBuQ5IDrP9BX5+hA8uxWar4Iu0dDzgBkBOa705IzDMCDOAV/kQ2QSPJMOS8oNnCc7wUbJePjcCsK4lsqQ77yPcl4hnm9IpdzWh4pxMYEEBrG7RmidWNIYzHxiwqJXGxgcYA+L+I0CKy7cEbawkGGUYVqZFHIUv43xZLKCeIbjtvXVPLawmzdxWTBVLvGsoS+VYX4j5bgYwGoO2pjIIaeZDjrJFmpm2S5o2w8qwnRiM2cWcNddCeSFBYb7vdAoS5cmM3jwnpB9mZmVvP22qW+dMeMY33xjMtDffjvG5MmHaN9+Ibm5oYzqtDGScyLRE5xaOT3Q1kpJbW3bGyW1ljRSpgSTKilXkkPSzNPxrLWVPyPn1WYsWavNcERExH2Sukm66yTHe0jqIUlXXnmlysvdKilxBY7n5FSpoMCpv/3tvBrnNmlSXyNG3KrY2HJ1735IHTteqn790tS37yV68cW/yOv16OmnHVqypLGm/Rqhs86qvqc0qrv06ijp63lSvxfM/R/cJr23SRqwTfqyrRkO44UEacHNZjiOxqqjKbpIg1QoCT1pSYcP6XI1UIQ+1S4NUWudrQaKUIQ66HZt1mF9rzV6Ww+pkRroJl2lJmqsyVqu53WvrrfylFyspuqnp/Wr1itPpXpELXWZzlF33amp2qN45egN3aHGCnWljlCEntElukNNNUqJWqN8vaNr1FUXqL3O0WQVaJaK1F+X6Co11NkRdfRJxDnKifDra3+lxlGlD+ucpQfqNFCDiAi9fmY9vdIYLXGi90oMNfBHqPe5EXrkbKl9kwglu6UFZdJtR6V/nCW9ep6ZTKvF2Wbhr9KhSmlxvvRCuhmK5MHzpYfPl1qeI9W3oTpn1ZfuucQs1eT2SYll0tESM3HWqhQprVw6s56UWmhCYFecLZ1dx4KpGpre5XUiTJ+PsxpKbr/pQ1K/nplkyuE10wrne6Vil1mK/KZnviTdepGZKve6JqaH/tNXStecFZqnJZwq/NLWMmlTlbSuUrqhodTxPGnTVdLZdc0v83t0EI/GR5SrQH69pbP1sc4J+CEdlUM/KFONVVfD9FddZUUt8MvQKmVoudL0sv5HPXRjwKy3Sm7N1i455dH7elTn2MzT3XJqpZaqrurqMT2rOpI261c11Jl6Qv1VTw10XEuVrd26UwPUQHWVqElyKEs365NAiJN0zVex9quFvlZ9naUs7VeC5upufapGNjP0Mjn1pbboBd2qWy2Y66hLeildmnOldK2F1m6Kk0bOlVYOlc6y/L4AffllkZKS3Fqz5gY1alRHy5enafXqdEVHZ+qaa87Wddedq6FDb1fjsPzGF1/cWHl5jpB9fr+h7GxzX0SE1LSpOX7Ky83EcE2aNFCDBv+kXH7Via1OL+2V9D8RERHXSMqS1EnSS2F10iU9IGlbRETERZJukJQMDJTJeBQREXGvpH5AF/2T6M8wkkxJV9i2L5eJx4VQRETErZImS2oPFIUflyRM/G+SZPqRlJS41LRpMBbHmjVpeuKJq3+3Mc2bN9HWrbfrwIFy9ehxud58M1XZ2X716WMapsfF+XXHHaGPVaeO9Gs/qfc46afl0ltPmB1tzH1S/63SiD3Sp7dLFYbU8ZA05+9Sw7rSWaqjYTpfbyhHXqEOlnPivbpMZ6iuxipOPfV3nWcN/nt1kxqpgb7RSvXSQ2qqM3W5LtA76qBftELt5NUtFhbdWA31th5VlGL0nZbpDbXTuTpTPfUPRStFwxSl13W7brBCXNjpEp2hb/V3rVW++uuwntBFaq+L9Jku0x5V6kNl6B41UWedp3NVT5dE1NU39c5RiuHVt4ZDo3yVerdOY7WPaKi6deqoQ+MIPdsI7XFL48qlrwzp0cboxTOl/hdG6IMLpE0V0uJSqWeK6cD4XFPptrOkW842i/5qOuutL5RmZkg9D0oXNJQeO1+66Wyp9XnSOWEhRhrWk/7+F7PURk6vlFspFTulMpdZnC4z+VaVR/Ia5u+ICOnshub/8xubv5ufIZ1nlfMbSWf9TniTcCrxSjvLpB1l0h6n2S/ubiI9fp70xWXmQuOPyGMYWhnh0i9U6QLV0Vs6S60jgn39uFz6UXmqJ5/e1qW62RYq56CKNUnHdafO1/dqo0a2YbpfqdqtZLXQlbrD5jMiSZlKV5RWqLXa6BY1V6nytFGT9DfdpRt1r/zyKF7T5JVDd2uoDHkUpy90li7TrRqqCNUR8itRk+VWgW7V56qj+krSBmVoj+7VIDWwtbNETn2n7eqoZmpuMZHjLqlXljTrSulG63E3HJQGTpdWDJKaWmi912vo3XdzBRGaMOFS1asXoQULUrR5c4769r1VP/7YVj6focmTE9St2yb99ttDIe/3/PMbKS/PE7LP7yeQ6K683KN6llOux+PXmWfWV3Z2lRo0+LfSkfz+JcEXERHxjqQomRZcvwIJERERPa3jEyR9LmlqREREvMwVzUdA4eltyZ9r7B9BW/UkJcu0CKg2Qft7WJ0rZVoLtPmzolBkZCTx8fn07Bm0UX/rLTP5TDjt3p3D55/voX//3SxalEJFRVAE9ngMXn45jQ8+yApYdNmp0Gbe7nJD+4Hwy+rgPp8fXo4y4RWAn7PgyfhQOMeBn3fJYYbN/BLgIPn0Yj0ZYZDSEbL4ggWkEXRWceOhqpYQ8mb9DEYwn3hSA/vyqGAoa1jIwRpmnHYqx8s3JPIOB0mw2uHFzwKKeZxEfiKfyjCrsDTDxzBvOXc5ivnF68QRBiHkeQ2+LjGIzDB4K98gqjKYZ9zph6XF8GGqadX1ZiKsKKo9B0emAxZkmMmg2m6AF3fAC9tgxCFYnWmGIfkDg6Z/KhmG2cYN+fB1ErwUA3fsgLv3mdDdknzTgu1UKNHv4zN3JXc4ihnpLyPDCI35EY+DgWTwOknsDQvhnkEV33CIT9lPZtixIir4gXX8xEbKwiAuHz62spEpTKDYgqGOEs08Piffgr3KyWI9H5CEadVUSQY7eJNsNgSu46WKGAaQzMyAyXk8C9nMVwHfkmrKp5IPWUW8LaXCESc0O2ZCf9W0OR5avQ+5NnS3stLPE08U8MMPpX8qsu/TT69m06bQeaG01M29964I2ZeYWEqfPjsA+OGHOKZMOQLAhAlxzJ17nDvvnI3LFfo9dLqgrbMj0X2cWvn/W4iUiIiIR2VaAFRzxRF2rhgRETFZUgdJadYpPv7AY7NVq1b8/PNKjRt3QL/80l6S9OSTKzR+/D0hHqsTJhzU3r35at/+ap15ZkMlJJRpxox0dep0lXr3/pvOOqueAA0cmCuvN0JffnmR6tevDkUhPfqE1PU16fnnzOs53VKfCVKbv0uvPmju8xvSmxul68+TPmwh/ZgjrSySFt4kNbKsc1wy9K7ydLMaqreaBlaCKSrTj4rVa/q7/q5gHuk8lWqS1uoxRaqlLfrqyahMDk3TRl2oc9VBd6i+6smQofU6rs1KVFfdrut0wUnPT1aVvlOqzlV99dJVulgN5ZaheSrVTJXqOZ2jzmqqxgoupwsx9KvPpQN+n25Qfb3RoKGusFkZAdrrlqaXRWirU3rsTLPc2ciMwOtDii6XtpdLSwqls+pIjzSV2jaRIpvUXLn7DOlEhekln1Ep7cyT0iqlK8+U/D7pqrOlq86SLm8kXdRIuvhMM73uuQ1/38KqNgKp0mum3c1zSrlOU9I4UiGlVpnXS3BIl5xh5rG//hypZRPphjOl34kuUysVG2ihx6u5Hq8ure/Xs/Ub6tG6DUKiCuxVlcapQGeojnrqfLWwwVFFcmmaUpSoSr2pv6qZzerKJ0NrdVh7laqndauaWfBoNeUrT0u1RDfrZt2uO+SVR7u0WF45dbc6q4HOULp2KE0bdLM6q6n+qjztUpbW6jq9pCYy3cmdylecPtOVelaX6GEZ8mmPflZDNVEzdVIdBT9Also0U7F6Rn/X9VafT3BJL6dLM6+UbrIkkbWx0pC50qL+0sUWGpab61OHDsV6++2z1LlzaGiAvDyHnE6fLrywUQiUlZRUpi++iNUvv9xj+76oXbvVWr/+UVu9cg0YsFfz5z+giRMTdO65DdSx4/9o9OgDuvzys7RkyXHNmtVe9WwRl0+bZ3uTVqjVKXq2b/rP8Wz/l4ZI+emn5frllzhNmPCwJOmxx5ZpyZLHVN+aNVJTy/Tccyu0bdsLatQo2LHcbr9mzkzVuHGpGj36Fj34oAn/fPttkTZudGju3Mt05plW3KwK6Ymnpbd7Si+YgUDlcEnPDJc63i11tSRmvyH12Cxd0EgaeYc0PU9aVCjN+JvUxJKG/UKfqkB/UYTe1/mqazGTfDk0Urv1lK7TvTYU0CG35mmHztWZelK3BXDxkxFCm3RIycpVOzXXVRbjyFG5ftZO3aCL9aRuUiPVEqPEomiVaLXy9Rc10Gu6XOeqvpwyNEul2qEqtVIjdVFTnWObGDygpT6vfvG49deIOmpfr4EeqV9P9WwToRdpq0OaUybtdkoPnik92Fi69yypsfVYuR5pW6m0rkDaXy5d0EBqcabU+lzptnPMiMK1hSfx+c0IzWmVJoPJKJPyHGbxuKS0MhPGur6JdKLIlN9v+It0zAJQLz5LyvEHFXf1zjCZ1tkNpBv/YuZpv7iRdG0T6cLG0tVnSledacKX/1sqMtBSl6FFTr8i6vl0f4O6eqFhfV1mY8RVMrRcVeqgs7RHVbpI9XWdzcS7WB7NVKZy5dRDOl/3WGHhqylOOVquWN2oi/W4blEDG8Tll197tVdxitWTekoX6xJl6bg2a65uU3tdr1byya1YTZFb5bpNvVRPZ+iEpqtMJ9RcH6mBFc6kRPE6rO91k95XU/1dblVph77TJbpVf9MTIW06oUJN1B69pza6QudKMuG/HtnS3MtN/ZEkLd8rjVwkLRsonW/F1EpIMPT66x6NHh2he+4xK5aUuDVtWpJmzjyu669vIpfLKZ8PXXvtufr66zYBeKpNm2Vas+YRNWkSxChbtlysAweeCWwnJ5erf/89WrDgQX377UFdeGEjdelyvb76ar+uuaaJRo3aoz17XlIdm0LstDGSs1qhW0+Rkez8z2Ek/zJRKDIykj17snnrrWCimZYtQ5OwHzlSRGbmyZN+Z2Y6eOKJHXzySQJVFrYyfXopr7+eTVFREGspL4cXOsMMW7pmhwv+H/bOO7yKom3jvz0tvYeEFAgJvfcOShexoGBX7Fh47VhQ7GIXBeyKiiJ2qdKR3lvoIYWQkN7bSU7f5/tjQwpJKC8ovn65r2uv5Ozu7M7szs4989Q73xf5rIYTo0sV+c8GkZd3av//misyIFaL8noSqqgyXXLlfkkTSw2RUbnY5SXZIvMloZbZpktcskC2ywxZXCeRUEPIkkJ5S36XhbJd7JUWPE5xySpJlCfkD9kmqQ1admnnqrJUcuRmiZW5kiHFldewiEu+k0K5XJJluuTKCakrtznocMqTZov0KC6TaeVWibU764gfHKrIRrPIkxkivRJEbkgReT9TZJ+5trVTrk1kSbbIi/Eio3eKDNikhVh57KDI9ykim3JFciznb6V1IXKyn/kequyzqfJmsUuGZDtkdL5d3i51SKKjduVVUeWAWOVZyZPhki6zpEjKTxEt5ohVvpRUmSB7ZIXk1HIsFdHC6Lwl6+Vd2SjZUrf/p0uGfCqfy5+yTuziELvYZKv8Ib/LB1JaKdoqlBTZJNMlUZaLKqpYpEC2yVOSJL9UJZ/SrrVStsl/xFIphi2VTFkmT0lajWi9NbFGEiW3hiXjBrNIj2MiKTW+ke83iYx9T6SwhsHjqlVO6dbNIkeOVJqbl9rlhRf2SdeuS+XzzxOkvLyGFaSqyrRpu2Xy5OrYKc88s0OWLKntgNm9e22Lz+TkEnnoIU20NX36Pvn+e80xc+bMWPn990Tp0WNunb7MhRJtefYUesm5bf8i0dZFJZI9e7Lk3nurFRbdu1cTSVmZTQYNqh0/weVSZfHiY/L227tl7tx4SU83i6qq8uWXqdK37xZJSNDkykuWlEmvXqmSnl7dOc1mkZFXinz5TfX1LDaRsa+IvPtbzXuITN0ucvMqzXN7daHI+CMiSaeoN36SInlSMiW3hqmmQ1wyWw7Ia7JNyk8ZpA/IcXlZfpKkU+zvG4JLXLJSYmWaLJCEGmWKxSLfyh6ZJmslrUY2xfrgEJcslly5UfbLDEmR3Eo5t1NUWSllcp2kyT2SKVukvA4x2VRVllrtckORRXrlV8h7ZXY5aHfVK9NOsYrMzhG5JVELqX7NEZF300W2lGipVGsi16rpJD5NFHlot8io9SI/nT6wwUWBSxU5bBH5rEjkxkyRrimq3JbnlG/LXJLtrPsMMlSHfKIWyyjJknslT9ZLRa0JhYjIMSmXVyVB7pB9skJy6xBIoVTIZ7JLZsm2Wlk5T8IiVlksy+Vn+U1yKwf+DDkmc+VN2SvrxCVOcYlLjshiWS5TpEhOiIhIruyRDTJR8mRP1bWcYpOD8o4clPfEWdkvsuWQLJXJUlAjfW7t+9d+mb+VigxIFcmq0dU/XCly2ZsiZTW+l08+Ebn+elVyclRRVVV++SVTunTZJHPmJIv9NA5FY8askAMHNGJcseKEvP56bK3j11yzqlYIldTUMrnmmlVaPT7cLz/+qIVyefPNXfLLLwlyyy1L5VRcMCLx6Cl0l3Pb/kVE8heZMJwd9HodBkP1MjMmpjpke1qauZauxOFwccsty4iJ8adHj1AKChzcdtt62rQJ5IknOtKzpx+TJh3mkUeiueqqJvj767n//hzefz+ENm2MeHnBol9g8jMwqwIemQTuJvj1Obj/Q3jzV5hynWbhNa0vzNwPkzbCBwMhsBmMOwqzW0LvyirdiD8RGLmDNKYTRnvcMaDjHjqzjjRmsZfb6EBkpdlwZ1oQRiBfsYbetGYInepE/K0JHTpG0Y1uRPMdm4gkkKvoiR/u3E4PEsjnM3bQiaaMoS2+p3jFAxjQcRVNuJxgVlHA8xwnDHduJ5RReDMKbw5h4w/MvEwRY/HiBrxpggGTojDGzcgYNyNlqrDK5uLlMhfHnMI4d81bvr8bGBWFKDe4J0TbROCYFbaWwbd58NQxzZilixf084JWHtDFD4Y1rO752+GqrPP+CkiywTor5Dqhkzv09YYXAqGDSUEL8lCNTJeLP8TKYrEBcLPBxE80IaCG2NCFyhZK+YEcFOAOQnkev1riojLsrCGZzaQyng70J7LWcUE4SBxr2cgg+tGDrjixsY5fcWDnSu7Fn2DM5LGVzwmjPSN5FQU4zGws5NKHt3Cv1L1UkMlhZhLKQJpxFQBHWEYmBxjK1FrmvSexhTx+JIVZ9EKHwpfF8KsZlkeAr15778/P1xKOLZqshfh3OmHyZCgshO++U3B3h9dfTyI52cLatX0ICjq9Kd3TT3fhnXcOMHfuENq3D+CTT+JqHS8utlNe7sSv0hxQr1dwuTQBp90uOJ21xfbx8UWnvV8j/ntcVCJxd9dTUlJtwnf8eGnV/7m5Ftq3r+7QCxYkERPjx9tvD67aN2lSR7Zty+Ouu7YxZEgoP//cnZtuOkhcXDlPPRXFG28Ec8MNuXz9dTA9erjh4QEz3oMJ90BJCTw/RfM/mP0oPPolPPgpfHw/6PXwaFf4OREuWwK/j4ZF7eD5E3CdHa6pNFcdhBcfEcFb5DAeXy6rlDkPpRnN8eY9tnMd7RhUqTcJxpfJjGUxu5jJEu5iOP5Up/qtDyH4MZkr2EUyb7OY4XRiMO1oQzCvMpJtnOAl1jGI5lxJG9zqeaUGFMYQzOUEsZVS3uEEHhi5hiAG4UsngvgPKgsx8xpFFCJcjxej8MADHT46hfEeBsZ7gEWE9TbhxwqVh4uEfiaFHkYdQ9210OmKotDKQyOM2ystl60qHCyHQ6VaytppyRCkh3QrtPLUfFIC9NDcU0v129QdvA3nFu79dBDRUu9m2CDNBqkWKHJp4fOTK+ug6qGrp6bTubMJhNWjhnKJsNvhYrVqZ43LiScK17vp+dLgR9gpJJOLg/kUsYJiBuDO0zQnhtr5eUuxsZhEdpHJ1bThHUaiP0WPlkE2i1lNFBFM5Ha88OQYh9jKYrozlI70AyCJjcSxlD7cRSjtKCWNg8yiKf3owN0oldfNYhNJfE9nnsCftjixsYNv0aEwlCfR19N/FpLGOnJ5g24gCk/nQ7EKS8LBTaeF/J/4LQR6wfs3aZOxwkK4+Wa46ir4z3+q3+XTT8cAsHZtJitXplNcbCU3t4KuXYO47bY2tb75Sy5pykcfHaa83EGzZl6kpZXXqpePj5GyMkcVkeh0EBysafpr9h2jUcdpItVcGAhgPeNZ/1pcVCLx9DRSUVG/8bXV6sJiUat+z5+fxLPP9qlzXv/+Tdi8eRR79xYSGGhk6dJuPPpoPM88c4xp02JYsCCEm2/O4+23A7j0Ug9MJvhhDjz/CjzxLEx/Q+uAs+6DaT/DE1/BW3eAhxvc2BqaesJNq+HjwfBRDNyUAPEWeDpC66wxmHiPcCaTzmGsPEoIehRaEsDrDOET9pBIIbfSCRN6DOgZRz/iSOdTVjCCrvSmnkQMNaCg0IeWdKYZi9nDV6zjUtrThjAGEkUfIllBEu+yhW6EMYqWmKirSVZQGIgfA/HjGFZ+JI9ZZDGaQK4mgAn4MgFIxMGvlLNULOhEx9WKB8Nwx11R8FAULndXuNxdE4sedQh/WmBKoZaPI1CFAZ4wwAO6uGlOie46bSXX26d2fcxOOFYBJ8rhWDkcyNIcDA8WaBZXJp2mbPcxaj4oESbNidFND2FeUKQtBAhwh2wb2FTtWIZDS7pV5gSTm0ZkPkboEggeJmjurq2QxjaBaPfajpM14RLhkAO22lTWuhwkuYTuBh1XeBh4yM0df6V2QSsqf1LGIopRUBmBH/NoiRenkoyFBRwjnmJGE850RmA4hUCKKWM9u8kmg7GMIpxQisnnV+bhRwDjeAgvfCkll618RRAtGM0rGDCRxCLSWEcPHsEPbeB2YSeJnykjiX68hxEfSshmA5/QkVG0rMeH2InKTOIxouNduuNSFSZkC82NCp+FaE6cxRVw41dwTWd4cIhWLi4Obr0VXnlFI5KqNhXb+eKLY8ybl8oNN0QwenQkffoEU1Hh4vDhQiZM2MCzz3Zh/Hitzoqi0KyZN1u35jByZCQhIZ64XCr6yvzMLVv6Ul5ePX7odEpVrC2dTqkiE4dDRVWhbdt6smZdKPwFfiT/S7ioROLhYcDLq3rq165dAE6nC4NBj8Ggw+GoJhKnU+jSRTM1FBF++uko3357BJ3OwLBhzXjyyR6ANvv45JP2zJyZzhVXHOTXXzuwcGEI116bx1NPqYwb54VeD2+8As+9AhMmwjefgskEL9wEs1fB6Jfh90prk0sj4DNPuHUtTOsNSypXJvckw8fR4KEDX/R8TnNmkcsUMphCU4Iw4IWRJ+nLUo7xHOt5jN5EVjo1tieSZgTzM5vZQxK3cik+p8xY6zwvTNxIf7Ip5ld2sJIDXEdfwvDnKtoyghiWkcjTrOIyWjOMFvWuUABa4s7zNMOMk+WU8Dip+KFnLIEMxYfn8EcQYhUHi8XCArFgVxVG69wZqZgI1ulRFIX2JoX2JnjIT/Mwj7PBFgt8XgS7zdrz6eYBvU3Qzh06ep70BtdWHV19ta0hqJVmvKUOKLNrRGN1alZZlVIM9DptFemm04jExwS+Ri245Jlywte8T4oD9tpgtxVSnMJRXHQ2KgxyU3jNy0hbg64qy+FJWFHZhIWllJGPlR548AxNaVmPqDGeEhZygnxKuYpo7qVjleXfSVRgZQ07iCeV0Qzg6spsnFYs/MEPXMIVNKclKk4OsoIUttGHWwmlDWayOcCvmPDkEt5BXxkZoYxUYplOJMPpwUvo0HGCvezlNwYxkWCi69S1BDsvc5BeBHELUeQ44fpcuM8bJlS+r5R8uH42PHsZjKvMOrpgEbw1HebOhY4dtX0Wi4sZM05w8GAxAwd6s3XrCLy8qvulvz+Eh3vRv39TrrtuDd27B1eJuYcMCWPduixGjoxEr9eTk2MlPFwzGy4psWO1uqquoygKUtknXC6p+v8kjh4tbLgDnC8EcJ3xrH8tLiqR+PiYSE4uqfqdl2ehpMROUJAHXl4GvL2rSeb4cXPVR7xgQSJLliQxe/YoPDwMJCQU17n2o49GEhXlxqRJibz1VgwrVoQyblwu5eXChAneKAq8+TJ8+BncMRE+/xB8feHeURAZBA9+Am/cDq3DoX0ArL4Cblijmae+1R5m58HwOPipFTR3Ax0KjxHKBsq4k1Reoim9KjMXXkkrOhLMz8TRniAur/RI9sadexjBPo7zAxvpSgv60qaWfLw+NMWfh7mMeLL4hk2E4sdYehCMN+PpwGhasYJknmItg4jkclrhQ/3yaG8MXE8Q1xNEElYWUMJH5NEJD8YofvTHix6KCREhTnGyQmy86DITbxeG6IwM15vopdN0KjoFOrpr232Vkz+zC/ZZYX8ZzMmHIxYI1GlhR1q7Q3cPCDJAlDs0c9NMhvU1mq9TwNekbWeQAp4RDlUzUc6wQ5JdE2uVA+stYBfo46WZKF/iAY+7K4Qa6v88cnGxDgursVCMjW64cwf+dMWtjt7LhosN5LKCNHwwMp4oOtOpzju2Ymc9sewlnhH05AoG1xJzuePBrTyMgkIOiWxjHs3pxuVMRY+BRJaQwlq6cx/BlSl1BSGZPyjgIN14HF+icWJnBz9SQRGX8xxueJNCOoH441vptX6MMl7nMPfRin4Es98m3JEH7wfBMA+t3luS4YWl8PGN0CcaXC6NQLZthxWLICBAm/AtXlzISy8lMmFCGN980xU3t4aZ3dvbyBNPdObVV2OZM0fzGenVK5g5c44BEB7uQWZmRRWRmEw67PbqyaaIVIm5FKVavKUZFTXcLxpx/rioROLmZsBur6bx4GAP8vOtBAV5EBjoTlxccb3lfv89gZkzh9Okidah+vevfyZ/zTVNCAtz4+qrD/Ltt+35448QJkzIIznZyYsv+qEoCg8/AKFNYNRVMP9HCA+H0T0hLBCufxs+uAeGdoEgd1g+Gh7bDhPWwZeXQEcPeDQVJoXCyMoMo5fiQ2vcmUUuO6jggUp/k2j8eYhezOUgr7CZh+hJcKVTWjeiaUM4v7ON7SRwC4MJqbTRPx3aEsazXMl+0viOzQThzRV0qySUdlxJazZygmfZRAeCGEsrImqEuDgVrXDnKdyZTAh7sfAHZbxBHh1xY5Tiy2DFkyfwBj0UG1Q2uBz86XTwhNWGn6Iw0mCgq95AH6MOv0pbfW89DPLStpoocECiVdOTJJTD6kJAYG+httIINYHdpqX1DTBCgKKFrffUa7/tLo1kvE1aPnanaKKwPBXKnVrcr1yBfDuUOCHIU5PrNzVCN19txdLZE1q5wyvu1SHO60OZqrIDBxtVO9vFToDBySDFg8n40a4yTcCpSMDMKjLZTQGDaMIzdCa0nhVnBTbWc4gTZNKOSJ7iFowNfJYWStnBb+gxMJQH8SOUYlLZxzcE05phvIW+ciVUQT6xzMSHZvRkMnrcKCaLvfxKU9rRj9sB2MgODhDHbVxb+TuX70jmJToThRe/mIV3i+GXUGhT6eg7Zwd8vgV+uQuaBUBBIdz2IFzaHxb9qq0OExOtPPxwOjExJtas6UFw8NnFqBk5MoKvvoonNbWMqCgfwsI8SUkxA5r+o7DQVnVu06aeVcp10FYhFRVawCuHQ0WvP+mYLBgMCq1b+59VHf4r/DWxtv5ncFGJBCAmxq/q//btA8nPt9K2rbbUzcgwVx1r08YfAFUV4uNLqkjE6VRZtCiR4mI7ffuG0bFjUC3xQ9++vvz6a0cefjiRJ59sxs8/h/Dww4VMmVLE668HYDAo3DAewprC48/A1KehS2foGg0rXoY7Z8CxLLj3MjAZ4JNBMPso3LtBE3V9Fg0TkjQrpanhmld0OEamEc5H5HEHqbxNOBGYcEPPvXTjALm8wVZG05KRtEBBwRM3JjCERDL5ij/pSDNG06OWExpos0yBqpmvgkI3mtOFZsSSysf8SStCGE4HmuLHSKIZTgt2kc2PxFGGnTHE0IumdcQqAH9SSAF2RhDEy4SiIuzDwioqmEURgegZjhcDFQ+uNpgYa1B4wQ1yVJUdDhcr7U6mlauYgHDFQHejQnejjs4GCKwhZwoyahs+UJ/DvkvVdBxFDiiya2KtChdYXNrs1+oCFU2UZdSDQdGiELQ0auTlpdc84oONmkPp6YIy1oRDhDincNjlYrs42Oty4qEo9DGpXKp35ynFCx+lftbJwsZa8lhDHqG4MZYQHqBVHf0HaJEMNnOU3SRwCR25h8sbJBAnDo6yhTjW0otraEF3XNg5xC9kE0tP7iOgUjwlCCdYTxqbaMN4QuiGIBxlHXGsYzB3E0wLLFhZyQYAHmACoPAR8eRjYwY98RADrxer7LAprAkHP52CwwVP/QEIrPkPeLnB7v1w3+Pw2hS4YhQ4HMLbb5eweXMhL77YlAEDtImLiJCRUY7F4sJo1BEV5V1HTAiaeGrgwDBWrcpi4kQfFEXB3V2Py6Xi6WmoIgqAnBxbrWs4nVJFHg6HirFS+aXX69DrFRITi0//8s8HAlj+usv/03HRiaSmaMvd3cCJE2YGDtSWuZGR1bPn+PhiVFWoqLDTuXN1KJK33tpOebmDsDAfPvwwlri4Ut55ZyD9+jWtOqd1a0+++6491157iPx8Bx9/HMI775Ry3XW5/PBDEzw9dQweCKEhcNPtMO0lGDNaC+uwcCrc/zHsPgazJoLJCPe2gy4BMG41vNoLlreDGdlweTx82xLCTZql1GOEsItyHiedmwnkmkqzzy6E8DpD+I6DvMcObqMTYZUrhdaE8xTXsJaDzOIPhtGZ7sRUzXqzKeJbVnMlfelIVNV+HQo9aUEPojhEBl+zFU9MXEEnWhNKX8LoSxgnKGUZx1nOcWIIZBTNaFpDZtQZb1ZTwJMkEIUHrfFiGP5MIZgpQBp21mHhHYpIxsFcmhKOgVCdjqvddFxdqRqwqyqHXRDrEH63uHi5Qk+RKgTroa8BQnQKrUzawN/MBF6njLV6HfibtC36PEVa9aHUBcedkOYUDjiFOIfgUoREHLQzKPQx6rnRZOItdw88TmM+loKVTRSyliLc0XElgcykE74NRB9Io5CVHCSdIkbTialcj6EewwjQSCGJvexiOW3pw7U8jx4jaexhH7/QnssYxrSq8CUVFBLLp7jhR1+exIhn1SrGgMJVTMWAG6mk8zvLGcEgutCePGxMJ47O+PMf2lDogusLVAa7wcJQBZ2ikFsGt/wAI9vA00O0+n38LXy/AH77BmKiYO9eG5MmFTB2rCeLFkWjKMKyZVn8/HMSsbGFDBwYTFGRjcBAEzt35vPYYx255ZZWtTzNAQYPDuWDD44wcWIbQBNhmc1OfHwM2GzVoqyaZAFgsTjxrMygptcruLtr/1dUONDr6+q3LigadSQXFx4eBiwWBx4eRpo39yElpazqWFqambIyOz4+JiIivMjMLCciwov9+/Oqzpk6tX+tDhIXV8jbb+/l11+P8dZb/arCrYSEmFi5sgs33XSE0lIXzzwTzpw5Oq65Jpfvvw8iJMRIm9aw+g94aiokJMGj/9F8TeY8BrOWwAOfwGu3QUQQ9AmFP6+ABzbD9hx4qSd099TI5K1mcLm/Vp/eePE1UbxBNpsxM4VQmmDEAwP3052j5PMe2+lLBNfSBmOlZdcoutGX1ixkJ2s5yHUMoAUhhBHIRC5nCdvZSTwD6EhbIqoIRUGhM5F0JpJj5LGV43zHHkbSlv60oDm+PEBXLDjYSjYz2I8/bnQjhEE0JQQTtxLGrYSRiY31FPMiKQRiJAx3BuHLLfhyO364kAaDvph0OrrroLsRTR5V+TzyXEK8TdNRbLPAn2bYWQ4VKrQ3QZ4FQo1a+HF3AX+DtnmrWpZFD70mhtKjrTJ0ivb9ukSTLFSomvWYVYVCFQqd2lasaMr0PBdEe2hir2gjdHeHVka4ykNHayO4K6f/JCpQ2U05WylhJ2bCMTEaH96nDQENkIcTF7tIZS3xhOFLf1rRgfDT6sLSSGA3a/DHn7E8ghe+lJDJYVbgooKRPIdH5UMVVI7xJwmspAe305QuABxnL7tZSB/GEUW3ytht28gllzu5nkD82UEhn3CMx2hFdwLYY1N5oFDlJX8dV3pob3dHGjywEN69DEa0gVIzvDwDLBWw7idwr4ytVVioMmdOMCEhMH16Ej/+mMkVVzTh4Yfb0717YJW1FUB+vpVp02IpKLDx6KOdarW9S5cAXK5qwvD0NGCxOHE4Kp3/KhEQYMLdvZqEy8ocVUr8ggIrHpWB8ux2FTc3Pc2b/4WJAhtFWxcX3bo1IT/fQrNmRmJifNi/vzoCfadOgRw5UkTfvqEMGhTBvn35REZ6YzTqsNmcuLkZ6swy2rcP5JtvhvPee/sYOfIPvv9+GJGRWgfy9jYwf35H7r47nuRkC2++GUOzZnpGj87i55+b0rq1iaAg+GwWPPQ43PMAfDoL3Nzg0athwyG44tVqvUmgO/w0HD44CEP/gO+Gwpp28FgqLC6C95prIhZv9LxBBJsxczcnuIsgrq1cnbQjmLcZxiISmMFORhBNd7TVlB9e3MFQ0shnLYdYwQGupTeh+HEno8ikkKXsYQm7uZKetDvFka0lTWhJEwopZw0JfMwWgvBmOK0Ix5fhNGM4zcimnHVk8Qw7iMGXrgTTnxDCceMWQrmFUEpxshMziylkLnnYUOiLF/3wpg3up3WurIkmeoUmnjDIs+4xVaDACTkOyHdo+o3iSv+PMgtYVE285YYm7nKJlkI316mJtpq6QYVSKdYyaHlHotw035AQo6YfCTbUVuZrkbsarnsxLmKxsgcreyjBDvTEkyH48TjhuJ0mfloaxawlmQLMhOPJRAYRcoasp5mksoPlGDEyhPEEEIqVMnYwlzyO0ZtbCKVNjfqlc4TFuOHJSF7DiAdWytnCWvkK4AABAABJREFUz7jhwZU8iQe+FFHCjyyjBRHcyJUICt9zgl0U8AFdCRAjn5pVvitX+SlYR0ujDhH4eg98tRsW3KalKo49Ave8AE/dDTdfUbvuffoY+eCDTFatKua223zYunVgLeusmggOduftt/swZMhShg4No0uX6lwCer2Oo0erfcqaNvXAYnHVWnEAHDlSQmBgte7FbHYQGKgtiUtLHfj4mCrLexIY6M6JE9WT1EZcYFwsl/qTqXbvuWeZ7NmjhQApKLDI0KELq8IXfPttnHzxhZY3d8WKVJk8WYuj8+yzm2Xr1upsbA1h/fp0GTVqsRw6VDvjosulyuOPJ8ozzySK3e6Sffus0rVrimzdWh0LS1VFPv5c5KrrRNJrRLDOLBCZMF3k1R9FnDUiRuzMEblljcjXR7UwK1/liFxyWAsTUhOl4pQXJUOmSoYcrxOau1zekW0yTTZL5imZGUVEDkuaTJP58rvskPwax7OkUH6WTfKG/Ca7JFFcUn/YCbs4ZZukymvyp2yQ5HrPOS6lMkcS5QHZKm/IflkkaZJ7Svh7VVRJFav8IvkyTTLkMkmW2+WEfCR5skHKpfA0Ye//6cgQh3wsJXK/5Mn1ki3jJE1elzxZIWVSII4zls+TcvldEuRd2SFvynrZISfE0cD7qIl0OS6/yOfym8yWHEkTERGH2GWfLJdf5UVJlM2i1riOQ6yyR36QpfK85ElS1f7jckB+lJclsTJWliqq7JAj8rH8LkmVIVMypUIelD0yR46Ls/Kac80uua/AKZbKEDilVpGbfxF5bKmIxa59D5/8LHLZRJH447Xrbrer8uGHBTJo0EH55JNMsdlc4nK55PjxUvnzzwxZuTJNUlJK6w2vs29fvtx338Y6+3v2XFyVFmLMmLWSlVUhr74aK3/+Wf3d9+mzpFaIlZ9+SpI339wnIiKPPLJJjh4tPOWa39e5DxcqRAo9BYOc2/YvCpFy0Ynk2Wc3yPLl1bF9unX7per/gwfz5aabtNg5ZrNd+vTRYm8tXHhMnn56sxQXW2X8+MVyOhw9Wijduv0iW7fWjnGlqqq8/fZxueqqWCkvd0paml369k2VH36oHSRv/UaRLn1ENmyq3udwijw/V2TkCxqxnESZXeT+jSLXrhQptokcs4iMOCLyWErdfB17pFzGyTGZJTm1gj+KiBySXHlc1srnsl9KpXYOWy2NarK8JvPlW9kgeTWC+hVImfwsm+VDWS4rZJ+Un0JUp17nTMiQcvlZUuQx2S1PyV75SJJlpxSKtZ6BMVPsslxK5VXJk+skTS6XEzJRcmW6FMtSKZdk1S7OvyqH7gVEktjlAymWj6RI7GdBACIi6WKWX+SYvCOxMkU2yjJJluLTPPuTUEWVJImX7+RLWSELJFu0GYtLnHJINssieU/2yh/iqNEHVFElWXbKH/KGHJHlVZMGs5TIEpkty+VbKRdt9lIq5TJb/pDvZIVUVNZnlWTLc3JQDpwSp81V490cyBbp97nIXG1MlsISkXGTRf7zpojllGatXFkmPXsmy8sv50pJiV3Wrs2WSZN2S9euK2TcuNXyxBPb5I03YuWqq1bK9devkQ0b6k4Au3b9vU4uoVtv3SgVFRpx9+u3QqxWp9x662Y5dKio6pxx49bXKjN9+gH57jstvtbQoQulsLB2ZUeO1MYPm606EOkFJRJFzm37FxHJRRdtxcT4UVxcbdLXvr0/RUU2AgLc6NAhkMOHC1FVFS8vIxER3iQnlzJqVHNefHE7b77Zn/JyB/HxhbRtWzc9L2jerIsXX87TT29Br4c+fTSxkaIoDBjgRZMmRu644xCff96eNWsiueWWbDIyHEyeHICiKFw6GJb+Di+8Art2wROPgUGv6Ur+3A8TP4L7R8NVfbR0sp8NhrUZmiw/xgSr2sEXuXBvMtwdAiMqjdR64MnPRDOPQm7mOJNowgh8UFDoSBPeYwjrSeM9dlea7rbEHQMKCt2Jphst2EcKs9lAIN5cQVciCOAGBmLBziaO8jaL6UIUfWhFM2o/n5oisEPkEE0AXqf4moTjyQ1EcQNRlONgL6VspIBvScOFju740gM/OuJNGEbCMDK6UnTjQiUVF0dwcAg7y8XKEZyIQC+XOwbRE63oiEZPU0VPuE5HgMJfqxCthEOEDFFJExc54iJeZycJJxUIJTobrTHSATcaEnnZcLGPInZRQCFW7NgZQCj30p6AehwRT4UTJwc4SDKJmNBxOWMJpgkqKvHsYg8riaIjI7kPjxqisHzS2c3PeOLPMP6DR6Vzq4rKMr6hB8NoSWcAYkniD3ZwLQPoRDRmnLzMERTgWdrhfcqnr6t05psdC1/uhTnXQocmsGU/zPoJbhsD1w6rPj8tzcnjj5fQvDl8/30Ya9bkMXToXgYONDJuXCQzZ3avCgF/EpmZ5Vx77RreeacPl9bIudy2rR/x8cW1wqPExZmr9B+9ewfh5qbn+HEz0dGaUUpZmYPcXFut65eWOujeXRORFRfb8fev7s8ul0pRZSiEAQPmsHPn3RcsBE8V/h/7qlx0IvHxMXH8eLXlVmioJ/v25TN0aAQ6nUKfPqEcOlRIly7BjBsXw9y58bz0Um+GD2/GihUnuP/+Lsyde4Rp0+pNEw9As2bevPVWf8aOXcpPP11Gu3aBiAg//ngCd3cdt98exujRu5k/vzsLFoQxdWout96ayVdfheHhoSMyEj7/GKY8B9deB9/M1hyuhnfVzITv+wiW7oL37tbyUQ+LqL63osD9oXCFPzySqgUynN4cQiotu+4giDH48h65LKOEB2hC20qdwzCaM4gIlnGcJ1jPlcQwgihM6GsRShyZ/MouAEbSkQ6EM4oujKATB0nnN3ZTgY1htKc7UbifohQ+TiHfE4s/7gwimq40xeeUAdELI4MJYjDah1qAnVhKWU8BMzmBgkIHvOiEL23wJAZ3YjASg5Er8eSkKsElQpreRYqoHBeVgy4XPzpcpKsqfigk2QwYgXAdeLuM+OvAXwfBopGfpw78FM2zXa9oincrJw1mhHIFKkTbzIqLAhGKBTwMLo6oTgToaFSw6l00V3S01enorbhxM140R4+hHvIow0kcZeylhP2UEIKRIPT0JYiuBODegNXVqSiihAMcZj+xdKA9oxiNL764cHKQbexlE+3owjU8gifV7v7llLCTRZSQw0BuoAlRta6rQ8d1PIKCQhkWVrCHUsw8zji88WA3xXxHBlcSxKhK/dsui5Z//rlK0+tSG7y6CUossP4OMCnw6pewbg98+zI0rzaCZPt2G//5TzEvv+zD4cOlPPLIUUaM8Gft2p74+TU8pISHe7Fw4QiuuWYNO3aMrdrfoUMw2dlW2revff7JScWsWb0QESIiPKt0JEePltKuXe2QCDt3FjBxYltEhLZt/WtNSk4a7YDmnHiqpVgjzg8XnUjCwrzZurU6BXyPHsHs3p3L0KHaaDxiRAQLFhynS5dgrr02muuvX4nLpfLww1247bbVbNw4junT97B3bw49eoQ2eJ+oKF/mzRvFK6/s4OOPhxAY6MFHH/Xg8cdj2b49lw8+aMe4cXv57rsuvPVWKLNnF3H11WnMmRNGRIQJkwnefw+W/AHDRsJHM2HgQC2Myu/Pws8b4ZKnYcZ9cEnnuvePdIP5bWBxIVwRD3c0gQdCNSVxE4y8TQSHsDCNbCIw8hghNMWICT3X0IrLaMFqUniMdYwkisuJrlqhdCCCDkRUmZYuJpY+tGQgrehKc7rSnALMbCGRaSwhiiAG0Zq2NEWHjqtoz1W0J5NStpPOG2zCgI4BRNGRJjSrXCnVRBAmRhDMiMoMeRZcHKGcQ1iZTQ7JWGmOO27oaY07bXGnOW6EK0ZaKAZaAENA64GnTOLtIuS7hEIVSiq3CgeYRbPKKlQ1J0SXaIr1ckW7jIcOdAbw00GYAj4GHf46CNBBgE5PsOKG7jTTUEHIw04iVhKwkkMF+yjFAz0D8aU93txEBP4YEYRUSsmhnCh8G7ymExdHSWEre3HgYADdmMT9GGp8evHso4RCrmcSnjUcRm1Y2MNaSsimDd2IYcJpLb22E89KYhlLH7oRgxWVD0gmGQvP04pQ3HCq8F4hLC2DOZUTnh0Z8MAKeLof3NwRUrPhrjdhTG9Y/RGc6uDfubORiRN1PP98KnfdFcTixV1wP51HZw2EhXnh72/ixAkzzZtrbS0sdFRZV4IW+qRmJHCAxEQzTmd12/fsKaRfv+Ba55z0es/MLKe0tHY+97y8Cpo00RxCg4NPH4ronwRFUUYDM9EMFWeLyFsNnNcb2A7cKCK/KYrSDPgOaIrmcvWFiMz8yyp6sWRqJ3Ukx44VyqRJ1cmtjh4tlOuvX1b1u6TEJiNHLqr6PXnyFpk3T0tY8+ij62XBgiSJiyuQPn3mic12ZiXv778nyPXXL6mSkaqqKs88s1cefniXHDpUIl27rpcdOzQl3caNZunaNV62by+vdY2UFJFLh4m8O13EUUP/mpojctnzIq98L1J2mhxW5U6R50+IjIkTWVtbVC2qqPKnlMqjckLelKw6Ct4KcchvEi/TZIfMk3gpOUWHIiJSLBWyWPbLFFkg38l2OS7VihxVVDkqmfKVbJFnZL58J9slUXLr5M4okApZJcdlmmyTSbJG5shhWS/pUnQWsv/qurrkkJTLAimUTyVX7pM0uVKOy38kQyZIlkyRPPlciuV3MctWsUiy2KVErZtI60JBVVUpVV1yTLXLJqmQH6VUPpBCmSxpMl6OyZWSJI9Jsrwt6bJQ8iVeysVR+Vw0A4My+UNOyGdyUB6U1fKGbJfYevKGqKLKMcmSn2SNTJNvZalslWzJO+t62sUusbJFvpXXZZ9sFOcpxgvxklSV8ExEJEdKZKasku9lQ5VeLFZK5WY5IIskp0oflmITGXFM5PVcEbsq4nSJvLlF5NpfRY5V6qV/3yDS9S6RzQfqr9vq1eXSq1eqvPNOvpSU/HdGFTNnHpJffqk29rjzzq0SG1vdR1esyJQnnthbq8wXXyTJjBnxVb/Hj98gcXHVH09OToXceaemyFy58oQ89dTWWuU3bkyTRx5ZKyIiPXrMrtrPhdSRIOe4nf7eleRxDIgBTMB+oEMD560FlgHXVe4LA3pU/u8DJNRX9kJtF31FEhHhw549OVW/27Tx5+jRIkQERVHw9TUREODOrl259O4dwtNPd+Pyy5dy442tmDq1DyNHLmTlyrE8+GBX7rlnJd98M7qObLYmxo1rzfLlx/nll3huvLEdiqLw5pvdeOyxPXz7bRILF/biuuv28M477Rk2rAmLFrXgpptO8Mgjgdx8s6ZniIqCNSvh/Q9g+Aj4+ito2RKah8DyV+HrVTD4KXjzThhdTyJNTz281gyOWWBKOszMgbcjoa2nJr4Zhg+X4s0SSriDVK7Gj+vxxx8DHhgYTxvsuFhDOs+yjX6EMozIqvAnfnhwFV24gk4cIov5HKCQCi6hJf2Ioi1htCUMJy4OkMEa4jnGNtoTSh+iaEsTAvFgJC0YSQucqMRTxD7y+YMUAvHAEyOdCKQjAYTiUe9M2QMdHfGkI7VtfVWEXFyk4SQLJydwsRc72eIky6nDXOm9H2p3p8Cl4KsotEZPoUuHBxCmUyhz6DXRlgI2vQsXWpgUvd5JiSJYBPwMKok4KROVtgYdx/Q2fFBoiwFvRSUCA+0wMRoPojDhUSl/E4RsbCRRxkZyiKMUdwQjQicCGEYzJtKxlsmzisox8thNEnFk0pFwutCK6xl6xhTLJ2HHzj52sI+ddKUXN/Mkxhp6q1zyWcoqPPEgstIPZSWH2Ekyt9CftjTFgosvyWA3JbxLayJwRwR+Koa38+DDCC1cTVop3PUH9AuHn68Fqw3uelvzm1o/E/xPsVJOT3fy7rulZGXZ+O23MKKiGk73fCaoqp6KimqFwvHjjirdB8DmzfkMHFg73MGBA6U88IDmve90ukhNLadt2+pVy/r12VXXSEgooU+fkFrls7MriIz0RlX/KkXGXxL+tw+QJCLJAIqi/ASMBY6cct7DwO9A76raiGQBWZX/lymKEgdE1FP2guCiE8mp8bYURaFbt2AOHy6kUydNHj9pUic+/fQwvXuHEBLiyY03tuKrr+K4776OvPXWAO66aw1LllxJRoaZO+5Ywbffnp5M3nxzEFdc8RM33tiu6p4zZvTkmWd289138Sxf3pdrrtlGfn4rbrghkj//jOa++06wcaOZGTMicXPTYTDA00/BpZfATTfDpAfhzjs1ncg9l2ligSdnayKvN+7UYnedipYe8Gtr2FwGb2Rp3t0vRECYCfQoXIM/V+DHAoqYQCqX4M3dBBGEARN6xhDFZTRnBznM4gBu6LmGGLoRjA4FHTq6EEEXIijFyi5O8C5r8caNQcTQg0h60JweNMeJyhGy2Uk637CHcHzpQzM6EkogHnQkiI4EcSttseMinhIOUchhitlPMU1xpwuBROJDG3wIxtSgGEaHQlMMND21+ylQU30jBqECKBWhVAWLaJtdFewGTS+iAugUDCjoFXDTGfBUFDwU8FLAX1HwRqkj0lIRCnGSjZ0UKthEIenYSKMUK0JT3OiKJ63x5mrCaIJbnfaUYOUo2ewhlRMU0o1IOtKM6+nXoLe6itTxubFiZSc7SCKRtkRzJw9hqiHvM1PBSjbjwMowBhNFMw6SxQ/sYQgxvMDVGNCzg1LeI40baMKntEeHQr4THjyhRQ/Y0FJLQvVrHPx8BF4YCJdGgc0OI56Eh66B20bWzuXhdKp8+GE5c+dW8M47fowYUd2Rs7IsrFqVyv79Bezbl4+Xl5HOnQO59dbWdOwYREPIyrLQoYNmdVJS4sBuV6uCLYoIq1cXM3ly26rzCwps7NxZzKxZWpnVq3Pp3z+klg5k5cpMJk5sDcCyZel8+unAWvdMSiomOtqXsjIbbds2XLd/GCKAtBq/04G+NU9QFCUCuBYYRg0iOeWcFkB3YMdfUkv+AUQCEB3tT3m5HS8vrTMNGRLJsmUpVURyySVhfP75YbKyygkL8+KhhzoxaNBCRo1qxujRURw6VMBDD23g44+HMHPmXsaPX8KMGUOIjvar937BwZ4EBHiQklJMixb+ANjtLt58syc33bSO3347xooVAxk3bgdms4O7745m7twWfPRRHnfdlcrbb4fTrJn2offtC+vXwXPPweVj4PPPtBVLWCDMexqW7oQxL8Ldo+DBKzSLr1MxyAcGesOCIrgqAW4IhHuaaLGojCjcQCDjCOAPSriHdDrhzr0E0qJS7T6ApgygKcmUsJQ0PucoQwlnJBEEobkd++LOcNownDZkUMJe0nmJNTTBi/40pzvhdKncBCGNEvaRzSy2U4ad3kQShT8dCcYXNzoTSOdKSzBByMFKEmUcpJT5ZOACbEA0nsTgRXM8aY4HIZgadF7Mwsld5BCAnkB0BIkJd3R4KwrBigFBy4fipehwKYIe8FBA1QkuNN1KhV4lWwQLgktRKVRclKFiREjCRgFOmqMnHTOBGGiFG8EoNMOdQfgRRTM8G/gsCrFwhEIyKCaWNNwx0JsILqcjLQg6rf6ihDJ2sZ8DxHEPN+FXaY1lx84cvqY7PbiN2zHVWIHYcLCdg+xkH8PpRzfak08F37CHIsqYzBCC8aYYJx+QTjk2PqQVTStJaGkRTM2CV8Phan8os8Gdy7RQ/F+OgYBKVYGbCda8Bz6nOIkePOhk0qQKxoxR2Lo1BHd3BZdLZcmSTGbPTsZsdnLVVUFceWUUU6f2RFFgy5ZsHnxwPd98M4KWLev//srLVWJitNXDunV5XHZZ9eph27YiWrf2qmVxNW9eGrfc0qyKOGbPTuaFFzpUHXc4VMxmB717B+NwqGRmVhAVVXtJFR9fyOWXtyAnp/wvWpX8V67twYqi7K7x+wsR+aLG7/o61KmVnwE8IyKuBmKXeaOtVh4TkdI6J1wo/FUyszNtLVt2rJJT3n77HxIbm131Oze3QgYPrpFIXUR+/jlR/vOfaselTZsyZfz4FeJwaHnEn3xykzz44DpxuVyyeXO69Oz5vcyYsVvM5tq500/i+efXyapVmv+K0+mSXr2+l4oKu1itTrn33vWyfHmqWCxOufXWbfLWW0eqym3eXCJdux6QlSuL6lxzwwaRm24WmTWrtrNihVVk+m8ivSaJrNpdb3Wq4FBF5uSKdDsgMvWESP4p1XeJKmukTO6RNLlf0mRbPfnWy8UhSyRFJss2eV52yWbJEns9ToKqqHJCimSRHJGnZZW8JGtlqcTXcYa0ikP2SbbMlYPyrKyTd2S7zJI9slpSJE1K6+hXTqJY7LJPimWBZMr7ckwelyNyq8TKJDkq90u8vCLH5ZCYa5Vxiir54pQEsckO1SqrVYv8rpbLD84K+cReIdPt5TLLYpEXymzybKlNppmtMtVSLi9YKuRVS4W87SyTmU6zfO40yy9qmfwh5bJBLHJArJIpDrGehf+MKqrkiUV2So7Mk0R5RXbLq7JDpsom+V6OyH7JFutZOCfaxC67JE4+kt/lG/lNdkisWOvRaZ36/uzikD8lVl6RubJO9olDHFIhdpknB+RJWSn7JKuq3CIplKskXpZKUdV1Ch0iE5JErk8Qya3sP5tPiHSbLfLzYc258HSw21V5550KGTCgRA4c0Nrpcqnyww8Z0qPHZpk69YAcOVLSYPmtWzPlwQfX1XssP98qffqsrdKDDR++Q+LiqvvbE08clo0b86ufoc0lQ4dukYIC7bmdOFEuN9ywpdY1FyxIkcmTd4qIyKpVafLii3U/suuvXyxms13Wrj0uTzyxumo/F0xH0lWg4By3M+pI+gMra/x+Fnj2lHOOAymVmxnIBa6pPGYEVgJPXIg2nrauf/UNGtp8fFpUvczXX98qP/54uNaLnzBhpWRkVHcwl0uV/v1/l6Sk4qp9b7+9VyZOXCeqqoqqqvLuu3tkzJhFkpVlFovFIZ9+uk+6dftObr11qcyfHy87d2bKoUO58sUXe2TQoDliNld/1NOmbZMff4wTEY3Iunb9WY4dKxGHwyUTJmyT558/UNX5s7PtcsUVR+X110+I01n7q6yoEJkyRWTQIJH9+2sdkpRskRteE3noQ5EjKXJa2F0iX+eK9Dko8kSKSHrd8UcOikWekAy5TVJlnhSJuR4HuuNSKp/LEXlAtsp0OSSxUtDgwJ8n5bJGjslrskkekVXymeyRzZJexynSJk45LPnyuyTIJ7Jf7pONMlm2yVxJkFWSIcek9LTOfC5RJU/ssl/KJLuegfXvgF1ckilW2SelskJy5CtJkdfkqEySHXKXbJMnZY98KwmyVjIkTcoafGanwip22S1J8rmslFkyX5bIFsmWwjMXFBGbOGSVHJK35A/5Q7aLRWziFJcsl2R5VNbKMkmo8kRPFKvcIqnyvuRISY1JwpICkRviRX6uHIvtTpG3togM/V4kpbi+u9ZGXJxTBgwwywcfWMTh0Nq8dm2hXHvtXnn88SOSk3NmY4vCQqsMG7a03mMzZiTKzJmJIiKydWuhXHddtVJ9z54SGTFiTy1jiw8/TJZXXqlWst977x5ZuDCj1jUvv3yVHD2qNe7WW9fKjh21DSAsFkeVV/tPPx2Szz7bU3XswhFJF4Hsc9zOSCQGIBmIplrZ3vE058+hWtmuoFltzbgQ7TvTplTe9G+Hr2+0lJYeB+CPP5LYsSOL116rzsc+e/Zh8vMtTJlSra3eujWLN9+MZdGiy9HpFESEhx7aRJMmHrz8siYeXLXqBE8/vYUXXujFtde2QlFg165stm7N4MiRfHx9jYSHe3PbbR0JCalW8G3alMZvv2l5TgB2785l1qz9fP31cHQ6heef34fN5uS993qhKAoul/Daa2ls2lTK3LmtCQ+vbcN64AA8/7ymhH/5ZfCrscrfehie/QraNYeXboPw2laMteAS+K0QvsrV5NyPh0GnU0QQ+Tj5lRIWU8pAPBmLH51Okem7EA5QyJ9kcZwy2uDHpYTSiYB6w5w7UImngD3kcoh8nKi0pwntCaQjgQRWisxOogQ7xyglgTKSKaMUB/k4CMODKLyIwIcI3InEg4AGcnhk4+A+TqAD/NATgDsmdPigIxgDLnS4o+Cr6rGrWsxbd8ClV1HRRGxORcWGYKsUNRTiwowLLyCBCsyotMBIFhUEYyQaN5qgVNUtAne8zlHiW4CZfaSzj3SggmhC6ElLmhN8xiRlABU4WEsSO4ijDzGMoAPuGNlMHstIoB0BXEcbvDBSjsq3lPAnpbxICF0rc5wU2OHRFC1B10fRmp/S0Xy4ZzFc1x4e6Vs7W6SqwowvYfwVEBWpTSjnzHHy6ac2vvjCg27d9OTk2HnyyWOYzS5mzGhJVNTZmc2uX5/Nn39m8tprPWrtLylxcM01e5g/vwcBASY2bCggKMhEp04+iAjDhu1l1qy2dO580izYzq237uXXX3vh7W0gLq6UBx7Yx/r1g6vEXLt3FzBzZhxz5w6iqMjKZZetYMeOsbX0J1u2ZPD993F8+ukIpk5dx4ABkVxxhaZPURRlj4jUYxJzblCULqIZTZ0Lmp3x3oqijEETX+mBr0XkdUVRHgAQkc9OOXcO8Ido5r+DgE3AQSpVicBzInKulTwrXDQi8fJqIUVFxzCZ9KSnl3H//StZuvS6quNms51LLpnPnj031uoUTzyxhehoXx5+WHPWcLlUpkzZQXm5g5kzB2I06ikosPDee7GsWZPGQw914eab22Aynd5p7OOP9+J0qjz6aPV7feGFLYSFeTFpUjdEhEcf3YmiwAcf9Ean077KtWuL+fLLLG69tQlXXlmbEVQVvvsOPv0UJk2CCRO0/PCgGf8t3AIz5kOfdvD0DdDEv+H6icCfpfB+luY7cVcwjPavnWdDRdhOBT9QTBoOxuLLFfgSesrA6ERlP4VsIIckygnHk4E0oReB+DQQwbYcB/EUc5gCDlOICx1+mGiHP+3wpwU+eJ9S1olKNlZSKScVG+lYSMcKKOTjJBAjdxJBr1OSeLkQSnFRhEo5KmWolAPlCFYEVB0lLgUnYERwGFzoUNADboqKGwpuKPgAnij4oMcHhUAM+KA7q8H9dCjCylHyOEAOCRQQgYEONKUbkTSlfr1AfcijglXEE0sWQ4lhWGX0gt0U8A3HicaL24kmFA8EYT7lfEoJd+DDLfigR/NG/zUXPsuE/zSD8cFav/tiF8w9DB+PgW5NT7lvAdz5GHRsA69PgYoK4d577TRpojB9ugEPDx2LFxfwzTe53HVXE66++jQznXpw+eVrefPNbnTrVtvCZOLEgwwZEsitt0bUKTNrVjplZQ6mTq3MqyLCjTfu44YbmnLddWG4XMK4cbt4/vlW9O4dWHXOiBF/8sEHPenSJYBXX91LWJgnEye2q3XtmTP3Ehnpzfjxbbj22l95//2RREf7A/98Ivmfwd+x7KlvCwiIkfh4bf2tqqoMHjyvjv/Aiy9ukz/+qB1c0GJxyPjxK2Tnzmqdiqqq8t57sTJ06EJJS6sWh+XklMvzz2+TUaN+k2uuWSgffrhXdu7MlOTkIikoqJD8/HKZPz9e7r9/hVx33YJaoi4RLb7XjTdWB49TVVVeeWWPPPfc9lrn5eba5KqrDsjDDydIRUVdXURhocgTT4j076/pUWrC6RSZu1pk0GMiz30lkltUp3gdHCkX+c8xka6xIh9kiOTVowYqFqf8IsUyXtLkZkmXX6REiurRk7hElTgpltmSJI9LrDwge+RrOS6HpeS0wQZdokqKlMpKSZPZclQelu1yj2yWmXJEvpREWSNZckzK6o3LJaLJ9wvEJmVnoWu4mDCLQ/ZLgSyWVHlHdsp/ZI08KxvlBzkgByVHbOcYoFIVVQ5Irrwu22WyrJctlUEdVVFlqxTI/bJPPpRESZdq36UdLptcZc+Xl9X8Wu/wRLnI9QdE7jwsUlDZB9KLRS77WuSJpSIV9UgNt+wQue5ukaVrqve9/LJdfvlFu67N5pKHHkqSq68+IvmnKujOAj/8cKKOD4iIyIIFOXLHHfvr9RHasqVYBg3aK1ZrdV+ZPfuE3HNPtTPL668nyPPPx9Uqt2jRCfnPf3aIiEh+vkW6dZtfry9Znz7zpKhICzx6003zq75nu915AUVbnQWOn+P274m1ddFuHBHRVlasSKx62WPH/i4JCbWj9B4/XiL9+/9Sp/MdP14iXbv+LEeO1JY9b9iQLt26/SDvvLOnVodSVVXi4wvlk0/2yVNPrZPx4xfI8OE/yT33LJOpUzfKhg0nGnRmHD9+gcTFVSv/XC5Vbr55lcycWVsBoqqqfPZZugwatE1iY+tXRCYkiDzyiMjYsSKHa6uExO4Q+W6VSI8HRZ76QiQtt95L1EKpU+SbHJG++0VuOCqyqkjEWY8o/4TYZY4UydVyQm6VDPlRiiW3gQGwUGyyUrLlfYmX22WnPCH75HfJkANSIrYzBDF0iSppYpaNkiPfSrJ8JAlyl+yV22S3PC9H5A1JkjmSJislV/ZLSaXK+uIHcnSIKulikZ1SJMskW2ZJojwh++RZOSAPy3aZIYdlmaTJMSkS539Z30Kxys+SLPfJFvlMDkp8pZOoQ1RZLoUyVY7Ja3JUTki1J2ui0yWTbCVyo71QjqrVhOtURWYdE+m+XmRtDT/Hn/aK3Py9yJ/VwYCroKois74QGThGJOVE/XUsLXXIqFH75LPPMv4rp9BVq7JlwIB1Ulpam4D+/LNQ+vXbI0VFdYnp2DGLjB9/SE6cqI4wvWFDoYwdGytlZVqb163Lk2uv3Vkr0m9aWrl07bpU8vK0ci+8sEt+/LFuww8fzpdrrlkoIiLJyUVyxRU/VR3btSvjAhJJR4Ej57j9e4jkoom2YmI6yqRJX/LkkwMAmDVrD+7ueu67r1ut8x58cB2XXdaca65pWWv/kSOF3Hffer78cgjt21cvoa1WJzNm7GPv3lwGDYrgttvaEhhYW55/Lhg27Cd++ukqQkKq0/TZ7S4eeGA9t9zSihEjomqdf/SomXvuOcj48aE8+mh0VerPmti6FaZOhX794L77IDq6+pjLBX9sh7d/gVbhMPk66NqyziVqQQT2lsOiQm27PAAmBEPHejILnsDBBipYSAVOhFF4cgkedGzALDcLCwcoYw/FxGOmJd74YKQj3nTAmwjczygqEoQiHGRgIxMr2djIwEEOdvKx4407hbgIwEArPLGj4IeeUEzo0OFVqSdxQ48bCu4o6EXztdGjoBNBFE0Q7EJwKJoIzIlgrtSRVKBShoNiXBThxI6NE9hwILTEhAsn4bjTEg/CMNECr9P6wpwNLLjYTD6rycEL6Io/IwjDGyNluFhBMT+SRz98mEATwipNf7NUlTcsNg66VF7zNDHYUC0yjC2EWUkQ5gMvtNGCgxaVw0MLNDHnh+PA/xRVhs0G9z8KkZHwwlNafp1TUVBgZ+zYQzz8cCQ33hhS94QzYPHibGbPPs7s2d0JCan+3jZsKOHpp5NYsqQzISG1g4IeO2bhuuvi+frrVnTvrulF9u8v4847D7N0aXfCw904cKCUO+6IZenSvoSHa9d1OFxcdtk6Xn65M5dcEsqKFWl8/PERFi8eVSfo55Qpm7jkkkjGjIlm9uxYzGYHjz3WB4A5c/Zx113dL5Boq6PAj+dYqmujaOt8t/btu8hddy2smh0cPJgrjzyySk5FdrZZunX7rmppWhOHDuVL9+4/yvz5dWciBQUWmTlzn/Tq9ZM89tgG+fbbI5KS0rDJYn33ffjh1fL442vqPZ6eXipdu34npaV15Qd2u0veeSdJBg/eLPHxdfOKiGgzxOXLRQYMELn3XpHk5LrH1+0TmTRTZPhkkQWbapsUNwSrS+S3fJGHjon03i8yLU0kobz+cwvEKYukTP4jOTJU0uRRyZWfpUwyTiNuKhG7bJMimS0n5C05JjfIfrlHDsmHckJ+l1zZL2Vi/i/ykWhmv3aJlwrZIWZZJSWyUIrlSymSD6RA3pcCmSIF8rjkyyTJk7sdhXKbvVButhfKo84imeDKlztc+fK8WigPSY5Mllx5RwrkLcmRjyRPvpUCWSiFskFKZb+US7pYzrjCOhVF4pDiM4jiSsUhyyRPnpR4eUYOyZeSLMdrmDjHiVVekAy5UpLkW8mX0hrPKsupyhOFTrmtwC7zbfZaq4Jim8gju0SGrBY5WFR9vzVHRPq9IfJLXWmSiIhk54hcernIx19U73v77VLJzq6+r9OpypgxO+WPP85iGXwKLBanPPzwYRk7drfk5dUId6+q8tFHWTJo0GHJzKxr6XXgQLkMHXpQYmOrv48dO0rlxhsPSWKi1mGPHi2Vnj03SEJCbevNCRO2yaefauHiMzPLpVu33yU7u24nT0srld69v68SZd1660JJTKyWetx4468XcEXSQWD/OW6NK5LzRo8ePaVTp+f57rtrqwite/fZbNt2Jx4etZW2P/10lOXLU/j229F1rlNUZOX221fTq1cIjz3WDT+/2tMtEWH//nxWr05j/fp0PD31lJVZadHCj9at/fH0NGIwKOh0CkVFFpKTi0lIKMRo1HPHHZ244YZ2DUYK/eqrg6SmlvLqqwPrPb5nTzEPPniQe++N5J57WtS7OhGBZcvgs8+0iMJTpkCHDrXPScqAjxdCViF0joa7Rp/e0uskSpzaCmVbGew2w5gAuCYQunnV9l4GTVF/CDsbsbEFKwW46IyJ3rjRFzdaVAaIrPc+OEmigqNYSMJCJjZyEfzR0wJ3YnCnKSaaYSIMI37/DD/YBlGBi1RsJGMnCSv5WDhCOX4YeJBwetcI0uhCOEIFWyhnC2WEIrTFnaEE0rIydEyRuFioVPAr5cRg4HrcGIx31QowzQ7flqosdKo86aPjek8FfeULUgW+T4RPj8ADneH2GO3dWezwymLYnwFf3QHh/nXbkZICd98Dzz4HIzVjRN5+u5SjR5189VVAVb+eMyeVpCQr06a1rXuRBqCqwi+/ZPHLL1kMHx7MpEnNq1YDhYVO3nork8JCJx9/3AI3t9pWgb/8UsTbb2czb14L2rXTlk+LFxfw2mvp/PZbW6Ki3Nm2rYgHHzzMvHld6dhRcy4UESZN2kNQkBvTpnWmpMTO6NEreOed3gweHMapeOyxdVx6aSTXXtualJRi7rrrD9atu63qWl26fMahQ5Mu0Iqkg8D351iq579mRXLRiKRXr16iqhPZuXNiVTiTKVPW0q9fBNdcU7dDT568nrZtA7jvvq51jqmqyrffHmXGjH088EBn7r67PW5u9Q9WIkJubgUpKaVkZpZTUGDB6VRxOlUCAtyIifGjVasAgoM9zpgbw+VS6dXrW2Jj72rwHKvVxauvxrF5cwGff96d9u3rjxQrAuvWwTvvQGAgTJwIQ4bUHvDLLfDLevh6BXRrCSN6wpi+YDyLcbnUCcuLYXsZrC2Bfj5aaPuhfuBTT3k7KgdxsBMbO7BRqIIXCj0VE73EREfFSBPl9JZwxThJwUYqdtKwk44dB5CIHQXohCcV6AhBTzOMeFZ6tPuLDn8M+KLgiw7DBUgcISLYgFJUChQXhaiU4iITB9k4MaNyDDMWBA8UWmMiChMtcaMNbkTghg4FMyoHsbGXcnZjIQsnAzDRDU8G4kNQJUmWqC6Wi50FqhUVYbjRwHi8aFIjdMqhCnivABJs8HiwMM6PKgIB2J0Fr+yDGD94pSf4V86RYlPgvtnwwAi4+9K6kwKAuDi4+Wb46ivo2VPbt2hRCT/9ZGfu3GAMhupCw4ev4ccfB9USSTUEs9nJggU5fPRRKgMGBDBlSgyhoW5Vz3ju3EKmT8/luedCueGGgFrfUHm5yrvv5rB/fwVz5rTAz0+P0ym89loGmZlW3n23Bf7+BhYuzOGNN47x++/dadZMIxqbzcW99+6iQwdfpkxpT0WFkyuvXMXDD3dg3LjoOvVcsyaVmTNjWbxYMwV+8cUNtG8fzM03dwTgwIFs5szZzwcfjL5ARNJeNDeOc0G/RiI5X/Tq1UvatHmWqVMH07GjJpM9cCCHzz+P5eOP6648rFYnV1+9iLvv7shNN7Wrcxw0k+GPPz7A99/Hc9VV0dxzT8cGwzRcCFRUOLj55iUsWjSuwXPy8iz4+5s4eLCUd96Jp1MnL55+usNpzZFjY2HGDEhMFCZOFG68UcHTs/ZocTAZvl0Jq/bAdYPhsj6aGfHZjLlO0VYpm0thUZ6We2JkAAwJgD7e4FbXrQQRIQMXe3FwVBzswE4+LnqLG96ip4NioJ2ip7VixOMsKnEy1lUeKrm4KMFFJiqFuKgQIUOgFKEUFXe7kWLRYqJFqQYSHJqnVjudnmMOzfOqqV4hT3HhBJwi6E1O1MpoElFGIUPnxAOFjooei6IShJ5m6PGDyrhfeiIw4lnpU+MQIV1cHFUcHMXJCXFyRFeBGwqdcaMvRnrgQWSlybOIkOxSWe50sszuxKTAAE9hnM6dVko1UztUWFwEn2RDkAEeiIChp6wQ00vh2Y2QVwHTh0HHytWnywWfLIWf9sLjl8L4IfW/79RUlSeecPLaa6aq1W1hoZOhQxPZuLENfn61+959921l1KhwrruuRb3vKiPDwubNhSxZks+RI2auvTaUe++NJCzMvartf/xRxvvv59GzpxsvvBBW5x5r1pTx1FNZPPBAEBMnBqDT6UhJsXH77ccYPdqPZ54Jx2ZTefzxBIqKHHz5ZXv8/LRnm5dn5frrt3LDDc2YNKk12dkV3HDDeh55pD3XXVeXRPLyKhg58neWL7+WsDBv0tJKGT/+NzZvvqPqu5s69U+6dWvKDTd0ukBE0lbg83MsNbSRSM4XvXr1kvvu+xxvbyO33NKlav+wYd/z6aeX1xtYzWy2c/vtyxkzJoZ77unU4IrBZnOxcOExFi8+zrFjJQwZEsHw4c3o1y8UH58zZ7A7G+zbl8P06bsYObIFt9/eqcHzvvsujvnzj/Hzz5oT5fTpccyfn8asWT3p10+LcLpgQR6LFuUzdWoUrVtXextmZQmzZ6v89pvK8OEK996ro0OH2qO80wVrY+H71XAgGW4aCsN7QK+2Z0cqAPkO2FQCq0tgpxn8DDDQE/r5aSuXwAYCvYoIGeLiKC7ixEm+qrLJ5cImQiedAW9FIVrRMiE2V/Q01+nxu0Bp6Vwi2ESQysx+KoJe0XxJ9IBepMrX51zwq9PGDEcFBkWhu05PqEFopxhoh4HWirEq8ZWIcNwJe+zCCoeL3Xahh0mhn7swxmigmb72vY+WwzcZsLEIBoXAg2EQc8oCoMgCb2+CdDPc0hUuj6l+h8lZcPd7MLgTiBl2H4bf3ofYvdClc7XDa2mpMHKkjZkzTfTrV12HefMyKSpSeeihyDptLimxk5xcRvfuQSQklPHVV8nk59uwWFwcPmwnLMyNAQMCGTMmhJ49/aq+O7NZ5bffSvnxx0Kio01MmRJCixa1FepxcTaefz6P0FCF555rQmRk9fHUVBvZ2Q769vVm06Zi3nsvhauvbsLdd4fX+rYzMy0cOVLCiBFN2bu3gLvv3swHH/Rh6NC64iyz2c5tty3noYe6VRnCjB//Gw8+2JMRI6p9VLp3/5xt2+7B09PUSCQXABeVSD7+eCFffrmX2bOvrtq/YsUxfv01jq++urLecuXlDh56aC0ul8onnwzH29tU73knUVZmZ8OGDDZvzmLLlkzMZgfNmvnQsaMfTZp4Eh7uRWioF15eBry9TVUZ2FRVcDpVysocFBZaKSiwkJRUzMGD+VRUOHBzU3j00Z4MHtzsjG397LODrF+fwXffjcRk0pOYWMorr+zD19fIG2/0wM/PxJo1Rbz+eiq9enlz++1N6dKlOuic0ymsXCl8/rlKYSFMmKBw/fUKgYG1ByuLDdbs0cRf+5Nh/GAY0ElLtOV2+sdUC7l22FaqrVq2lWrJDSPcoJc39PaBTl4a2TQEESFXhGRxcVxcpKkqSS7hhKpSIsLvnt5E/BeD/N8BuwhGaqf8VQVSHLDfqm0FImx0QLQBhntAHw+hp1GpI4JLqYDfMmF5Lni5wx0RcFUTMJ3S9FILfLsHvjoAj/WHCV2rvdBF4OtFsHwP3H8V/LYEmjaFFx+AVavhlddg6WIIqpx3Pf10Ad26+XLLLbXZ/7XXEmnd2pObbqrrDFgTOTlWDhwoJjjYjSZNTISFedbS7dlsKhs2VLBkiZlNmyq47jpf7r7bj/Dw2vc7csTG118Xs2ePlWnTmjBwoOeptwIgK8vGM88kU1TkZMaMlrRsWf95LpfK9OmH2bQph/ff70Pr1nVFxOXlDq66aiH33de5Smrx3XcH2L8/l+nTR1Sdt2jRUXbsyOCNN4ZfQIfENgKfnGOpkY1Ecr7o1auXbNu2gz59ZhMbe3/VfhHhhhvm88QTfenfv+7s6SS+/fYwS5cmc801rbjppoYV4qdCREhLM5OUVERmppnMzHKsVieZmeWYzXaCgjzIyDCj0yk0beqJ1eoiMNCdkBAPmjXzpXPnYFq18j9tmPr68O67O8nLq+Cdd4ZU1WPevGSmTz/Es8925PrrNRvfTZuKeffdVBwO4ZlnohgypLacOTtb+O03lblzhfBwuPlmhSuv1NURfdkdsPkQLNoKGw7AwE7QuQWM7Aktw8+p6jgF4ipgdxnEV8DWUih1wSV+4KPXzIw7eEJrD83r/lyQ64QcJzQxQLBeyxj5d0MECp2Q5YB0B6TYIdUO+Srss2phagZ6a6Kobu7QzV2IMtbNL+90we4iWJYJW/LA4AHXRcC1TSG4noVwoRk+3ATz98MDA+GufuBeYzzOK4KJr0FYMNx7Fdz3JLz4BIy9DNavd/Hs8zr+WKRUkUhiop3778/lzz8j6tRt794iHnwwll9+6UtUVD124Q3A4RD277ewbVsFu3bZOHDAxpAhnlx/vS/9+3vU+u6cTmHNmgpmzChGp4PJkwMYNqx+XWN+voPp0zNITKzgjjtCuOqqhq1H9u8vZPLkXQwaFMLUqV0xGut+e+npZUyZsonRo1tw222aPG/NmuO8/PJGVq68uSqyuNOp0q/fbBYtuomICN8LSCStBWadY6kxjURyvujVq5fs3r2be+9dzLPPDqJly2pfkJSUYsaN+40NGyacVhSVl1fBW2/tYtOmdB5/vCdXX90SL6//PuHOXwlVFe64YwnvvjuMpk2rY3wVFlp5/vmdJCaWMGvWINq3DwDg8GEz33yTxrp1hdx5ZwS33x5RJTM+icOHhcWLVX79VSU6WuHaaxXGjKm7UhGBhDRYtRdW7wGTEfy84NIu2hbVcIbihtsjkGLVCOZwBRQ5YGMpVLigvadGKDHuEO0OLdyguTs0NdUO6QKwuQK+KII8p0YiGVZtv06BIMBTp+XQiDSAXdV+N9GBTdV0Oz46cLg0PYleAZei1c0lgAJmFawqmPSQbgezS7vewQoocUG4CTKdEGCAMCNEukOUG7QwQYwbRLuBsQFyszrhQCFsyoMNOVDm0BTjY8JhRFPwa2AVmJAFM1fB/hMwYRjc2QfcTum2f6yBT+fBpLvBUAHvfgEz34CObSElReWaaypYtcqLkJDqyn33XSYFBS4ef7z+VfL69Xm8+GICOp3CtdeGERnpia+vAR8fAxUVQnGxi9JSJ8ePu0hMtJGUZMVoNNK2rRv9+nkyZIg3rVubahGDiLB3r51588pZs8bCuHEe3HijN+3b19/448etzJ2bx8KFBTz2WDi33NKkluK/JjIzK3jxxQMcP25mxoyedO4cUO95q1en8uSTG/jww2Fccok2+dy4MZXXXtvMvHnX1PIBmz17D6mpJbz22jDgQoZIaSXwzjmWGt9IJOeLk0TyzTex5OSUM2XKoFrHf/rpMGvXpjBr1mW4u5/eLCkry8wXXxxk4cIkOnUK5rrrWjNoUARBQf+s3MyPP76Km27qSN++dcULu3fn8tFHBwkJceeFF3rj46N9iEVFDr75Jp1167QAd3ffHcngwQF1Pua4OFi2TGX+fMFkgtGjFS6/XKFLF6XemfPeRFi/H+LTYXcCtI2EAR2hb3voFgMe56FKyrfDcRsct0KqFZIr4IQVcuwwv7NGKmeCUzTzZbMKZSqUO6FcBYsKLlUjBLuqid0qHBp5KAqIopGQHnA3aCTgrgNvPbjrtRWUrx4CDdr/Z7OQFYGscjhSBIcLIdcKazK1xBCDmkLnJjAkFFp4N6yXstph0U74fiOobvDgMBjTtTr22kmUlcHkaZBfBJ+/CSuWwRdfwIIFEBysveu77y7mgQd86Nu39ncxf34OBw+W8tJLrU/bnuxsK5s2FZGQYKW01ImHh4HycvD3NxAcbKBpUw/atHEjJsatjukuQFGRypYtNhYvtrJ9u51Ro0wMHerGqFEeGOthXadT+PPPEj75JIfSUheTJoVw7bVBDRJIaqqZ998/SkGBjRtuiOKqq+qusAAKCixMnboFEF55ZQChoRphfPllLHPmHODnn68hMrLa2Gb9+hSmTl3L6tUT8PTUmPvCEUlLgTfOsdRNjURyvjhJJMXFVsaMmceWLXfX6SyffrqH+fPj+f338fj6nnlkExF27crmjz+S2bgxA7PZwZAhkTRv7kOHDkG0aRNARIQ3ev3fJ593uVS2b89k0aJEjh7N49dfxzVomqyqwtdfH+HDD/fzwgu9GT++VdUzERG2bi3m66/Tyc210LmzDzfdFEHnzj51nltOjrBqlcqyZcKhQ0KXLgojRigMHKijdeu6IhlVhfg02JUAW47CvmPawNyjJXRoDl1jNLFYUP2WyxcMFU7YkAsBJvA3gq9Bm9V7Gs7ecOBcoAqY7VBkgzwL5FsgpwIyyiGzXFvJxBZoRNI3BDyN0DEQOgdCxyCtXqeD3QHr9sGCLbA3A0Z2gzuHQusGRIvr1sMTT8KUZ+GGcfD113YWLjTy008KXpWT6m3bKvj44yK+/77uZMRsdnLllTu56aYI7rgjEg+Pc5Qz1tcGu3DkiIPYWAfJyU5WrLDi4aFw5ZXu9O/vRv/+pnoJweUStm8v5+efi1i/vozhw324665AunSpX6wmImzenM+iRWls357PE0+0Y+zYyHq/VavVybx5cXz44T6ee64P11/fBkVRKCy08NZbW8nMLOPLL6+o5Y926FAuEyYsYNmyWwgLq9Y/NhLJhcFFJxKASZOWMnp0K66+uq7/yK+/xrF4cQIPPNCDgQPPrNiuCavVyf79eRw5UsDhwwWYzXb27MnF6VTp3DmYwkIrgYHuREX5oijg5WXEy8uIh4cBk0mPm5u+ajWk1yuYTHqcThURQa9XsFhcOBwqLpdKWZmNsjI7ZWV2SkpsnDhRSmammdBQT4KDPbniipaMHh19xtUVaOKuN9/cye7d2cyYMZSuXWvnr7ZYXCxblsNPP2XicAht2nhx9dWh9O8fWMfpUVWFAweEdetUNm3STIr79jXQpo1C376aj4G3N3VgtcPhVNh/XFPcZxZDQiYE+8IlHSDQF9qEa4Ni8yb1Z348VxTaYPpRKLJrm80BeVaNYGK84Fhlfrf2vhBfpImrYrwhu1QTa7npNXI42aMDTJBt0URfUb5wqEDb38YfEoq11Uikt/Y32B2CPSDcC0I9IcIbIr20v+cy70jOhDV7YV2sJsIa3AnGDYIBHcDQwKsvKRE+mCHs3K3ji0+1UCZffWVj0SI7v/3mjclU/U7nzMnE4YCJE+tno/JyJ598ksEPP+TRqpUHY8aEEBZmIjLSRGiooTJNtIJOB1arYDarmM0qeXlCVpaLrCwXGRkqR486SU93ERNjwMtLoXt3I336mOje3YSHR/2snpvrZNOmChYvLmHvXgv9+3swbpwvI0b4Nrj6SE42s2xZJl9/fZyuXf2ZODGG/v2D612BlJTY+PzzA8ybF8ftt3fg/vu74O1tQlVV5s49xPvv72DKlAHcdFOHWuWXLk3g+efX8vPP19GmTW1dzIUjkhiB186x1G2NRHK+qEkkWVllXHHFD2zffm+9/hWJiYVMnrwGb28Tr7wymNatzz/nstXqpKjISmGhleJiG8XFNsxmB+XldqxWFzabC7vdhV6vw2y2o6qCr68bxcU2FAX8/d2oqHBiNOrw9DRgNCr4+Jjw9jbRpIkHYWHehIV51fHSPxPKy+24uRkwGHTs35/HSy9tITrai1deuaTeVVlFhZM//8xnyZIccnNtuLnpGTWqCSNGBBMVVdcCRkRISoIdOxR27IDcXEhKgvbtYfBgiImBzp0hNLT+VUB+qSYOi8/QiMVi01YxLhd0igJ3d2geDFFNIDIYwgMgIgh8LrCUURWwu7S0sS7RxF1OlarkpApa/Y06bTPpNOK5UCsbEcjIg/gTsPMo7IqDvBII8NPMr4f3gA5RdUVXta8h/Pqr8PrrLiZP1jNhgg5FgTVrSpk1y8XPP/vXGbQ3bSrkp58ymTWrY72REmpe+/DhcnbtspKQYCMjw4GHh574eDsul9C8uRtZWSo+PjpCQgx4eJgIC9MTFqYnKspIy5YGIiP1DRqxqKqQkGBn504L8fF2Vq4sx89Px7Bhnowc6UWvXh71llVVYf/+EjZvzmPevBQCA9245ZbmXH11BL6+db8VEWHLlky+/voQmZlmrrhCM/339DTidKr89lsc77+/g9GjW/LEE33x96+Wm9rtLmbM2M7atceZN28cQUF1v4cLRyTRAi+dY6m7GonkfFGTSAA+/ngnx48X8847IxvsvKtXJ/P++zuxWp1MmNCJq69uTXBw3aXyggUJREX50rp1wAXzG/k7kJRUyPTpOxgyJIobb9QsT0SEOXMOMGPGTqZNu5SrrmrTYHkR4cgRM6tW5XL0qJnt24vp2NGHkSOD6dzZl86dfXBzq0vUDgccPQpHjsCuXXDoEPj7Q2YmtG0LXbpAs2Zakq6YGKrELLXvrQ2kJ/K1LaMAUvMgo1Db3N0gvww8TNAjGuxOCPbRCMfNCAFeEOQDvp7gV7l5u4H+Aqx0zhUiUFoO+SWQUwTZhVBshqOpkJINbgY4lAKRTaBnG2jbHHq3g5YRZ09UGzbYePvtMmJi/HnlFT1BQVrB0lIngwfHsXp1O0JC6h9YX3wxnu3bi3juuXYMGuRXrxXThYLLJaSlOUlMdJKe7mDnTiuHDtkIC9MhIvTt60Hfvh707u2Ou3vdeogI8fFmNm8uJCGhjOXLs+na1Y+rr27KyJGhBATUVco7nSqbNmWyaFEycXGFhId7cPfdHRk0SNOVpKaWMG/eIfbsySImJoDHH+9DeHjtHO3r1h3nqadWc+utnXn44b4NWlleOCKJEphyjqUuTHiWfwL+MUQiIjz77J9kZpbx1VdXYzQ2PIKkpZXy44+HWbHiGGVldvr2DeellwbTpIkXqiq8/fYOEhOLSEoqwtfXRFZWOUFBHnTrFoLd7iIgwJ3wcC90Oh3e3kZ8fEy4uRlwd9fEWSaTthmNOgwGXdVfvV6HXq/F5dLpQKfTodMpKAq1yE9VBVUVXC7NF8XhULHZXFitTsxmO6WlmggsK6uU9PQyTpwo4fDhfEJCPBk/vh133lk3DExeXjkvvLABg0HH9OkjqvQsiYmFxMT41ytLVlXh0KEyYmNL2LixgAMHSmnd2hs3NwOdO/vQu7cfgwcH1il3EqWlEB8Px49rJJOcDGlpUFysRZBt1gw++EATxZwtKmyQXQwFZRqxlFkgq0iLYOtU4UQhlFi0/SWl1WKqiECNkEwG6BSpEZW7USOighIw6rVVj6PS012n045bHRoxBHhDbolmaBDsAylZmvjOzwuOZWqxq3QKqE6NDHw8oW0z7bpNAyG6KYT4Q4um0CxEI8Zzhd0urFhh5uuvrXh46HjxRV/at69NFh99lIy7uzv33nt6G+29e0v54osstm0rpWNHL9q08SUqyo2oKBMhISb8/PT4+Ojw8NBV9VcQbDbNF8RiEUpLVUpKVEpLXeTlqeTmusjJcVFY6CIx0UFxsUrLlm44HNC6tZGuXQ20bGmkY0cTvr51v09VFVJSLBw8WMquXcXs2VOMr68BERg4MJDBgwPp1s2vzkTR6VQ5eDCfdesyWLcuHadTJTraj7FjoxkyJBKTSUdCQiHLlx9nz55MsrLKuPXWTowb1xY/v9qWG7t2ZfDRRzsxm+1Mn34ZLVr4n/Y5NhLJhcE/hkhOYvr0rezZk8XEiT0YMqTFGeNdWSwOdu7MpE+f8AbFSKoqFBRYyMkpp7DQSlGRFavVSX6+hfJyBy6XSlGRDavVic3mQlWFigoHDoda6VdShtMpNGvmQ1JSEaoqhId7k5paiggYDAo2m6vqfk2bepGfb0GvV2jVKoCMDDNubnpatPCjrMyOr6+JkBAP/P1NREb6EhnpQ7t2QXh6nt5rUET48MPd7N+fw/TpI/D3d2fq1I0sWXKMYcOac/31bejTJ/y0JGyxOImLK+fAgTIsFhcPPhjV4Lmng82mkUpkpCbO+rvgcEK5TdOf2Bzaysbu0PQgThc4nZV6Eqm05EIjBr1O2wx6bXM3grtJWw15e2j//xUK/dJSF1u3VvDnnxWsXFnOyJFeTJwYQLt29b/rW2/dynvvdScs7OxkgSLC8eNWDh60kJpqJzXVRl6eUFrqorTURWCgibQ0ByLQtq0bKSl23Nx0xMS4YTaDn5+O4GADfn4GQkL0hIbqCQ/XExlpxN9fV+f7ExEKChykplo4dqyCxMRyEhIqyMiwUlBgJzrak4ED/WjTxouePf2rQr+fhNOpkpBQzP79BaSmlrJiRSqlpXYuvTSCzp2DGDo0ghYtfDl2rIjNmzPYvDmd/HwLiqIwZkwMl18eTWRkbasPi8XBsmWJfPHFXry8jDzzzED69j272c2FI5LmAk+dY6lH/n8RiaIoo4GZaFaVs0XkrVOOK5XHxwAVwJ0isvd012yISADi4vJ4443NpKWVcO+9PbjsspY0adKwE1VGRilLliQSHu5DWJgXoaHeBAV54OlpPCMR/S9BExMU8MorG7jppo6MHat57zocLlavTmHFiiQ2bDhBixZ+XH55Kzp0aEK3biENWryJCJ98Ekd4uCfNmnkRHu5JaKjHf23VtmePhW3bLAQE6AkO1mbEfn56/Px0+Prq8PbWnbXj6P8aKipUTpywc/y4jbg4K0ePWjl+3El5uTBwoCdjxvgyeLBXg0rnk3jmmd1ERnrw0EMd6vTdAwdK2LevmJgYL1q08CQ01P28xVqqKpSXuygudlJS4qSoyElpqZPsbAd5eXZUVeXw4XKysmwEBBhITrYQHGyiVSsPmjUz0aaNJ61be9G6tWdVVAgRISGhmNRUM6mpZeTkVLBvXwEpKWW0aeOHqkLXrkH06tWEHj2CCQrywOVSeeyxtRw4kEtJiZ1evUJp3z6IQYMi6dYtpJalo4iQnFzE5s0nWLnyGHFx+Vx+eSvuvLNrHWX6mXDhiKSZwOPnWGry2eRs/6/H3jOVvZA4I5EoiqIHEoCRQDqwC7hZRI7UOGcM8DBaY/oCM0Wk7+muezoiOYnk5CJ+/PEgq1Yl43C4uOSSKDp3DqF79zDatAnEUGkqlJNjZtGiBDIzzWRnm8nOLqewUFtthIZ6kZNTDkDnzk3IzDTj7m6gZUt/CgqsmEw6AgI8sNtdGAyaCOukHsFg0OHra8JicaLX6/Dzc8NicaLTKXh6GnA41CpRl16vq/yrYDLpcLk0cZe7ux67XQXAzU2P3e5CRBO9aBZfJ8VgrsoVkXb93NxyysrsuLsbiI3NJi+vnOhof3Q6HZdc0pyJE3vWawEmIhw7VsS2bZns3JlNbGwOERHemM0OWrcOoEuXEJo29SQqyo/ISG8WLDhBWlo5aWlmrFYXcXHFuFxC+/YB5OfbCQlxp21bTQ7fpIk7ISHuBAa6ERSkbQEBpiriiY+3sWWLhaIiFzabkJnppKTERXGxSkkJlJcLIhAZqSc93YXRCF26uJGX58LbW0dUlOYY5+WlEBCgw2AADw8FDw8dXl4Kbm4K7u46PD21VaDJpO0zGk9uYDTq0Oup3JQaYp3qFYeIZhzgcp0UP2oe3Ha7YLerWK2CxaJisWgWTeXlKna7kJPjoqTEhV6vEBdnJS/Pha+vjtRUOx4eOlq0MNK8uYH27d1p186dDh3c8PY+t5D5NpuThx7aTGJiCdOm9WHgwKZVhBIfX8ayZdkkJ5dTUaFy6FA5Tqeg02km3ADu7jqaNfMiJ8eOokBMjAcJCRU4HEJ0tCfx8VZEIDDQQGGhE0UBb289YWEeld+CgebNNX1HcLCRpk2NhISYCAsz4ednOOuJ2Z13riU01IOoKB9at/YjJsaXqCif00aE2L49k3btAmspzE9FWZmNQYO+ITran4EDmzFyZEu6dg39ryeMF45IIgQmnWOp50977/MZe8+m7IXE2RBJf+BlEbms8vezACLyZo1zPgfWi8iPlb/jgSEiktXQdc+GSGqipMTKjh0ZxMZmERubjdlsJzOzjLAwH3r3Dsfd3UB4uA+RkT4EBnoSGOhRa1UioomrrFYnFosTi8WB3a5it2vWWQ6HWhVO/uTmcqkoiiZTdrlUDAbN5Ff7X4fF4qyhD1FRVW2GZzAoWK2aiMzT04jZbEdE8PIyUl7uADRScTo1M2K9Xoebm4LBoMNk0uPr61b118/PndBQL5o08fqvZ/MiQlaWmYSEIjIyyoiLKyQlpQSXSyU+vhBFUQgP9yYkxJOmTb0IC/OmaVNP3NwM6HSajNtsVsnPt1Fe7iAtrYKCAhsFBZqlm82mjWLR0V4UFzsICDDRpo0vNpsTf38ToaHuuLlppOzra8Tb24Cvrwl3dyMiOnQ6IxaLFmbcbFapqBCsVqGszInFIlgs2iBfXq4N8h4eQna2A5tNCArSk5rqwOEQAgJ0ZGbaNPGWKjidGnGpKoSFGcjKcgLQurUbyck2dDqFmBgTmZkOjEaFgAADDofg7q4RVkCAHqNRwctLR0iIETc3Hf7+egID9QQGGmjSRI+/v/4vWfXGxRUxe/YR1qxJpWPHIIYMCad9e39atvQnLMy73nuqqkaEJ9+HiPbujUYdRqM2yTnX0D7/RIjIBXvm/3Ai+a/HXqDFmcpeSJzNdCkCSKvxOx2N+c50TgTQIJGcK/z83Bk1qiWjRlXnnVVVISurjNTUEtLSSsjMLCMzs4yEhAIKCy2YTDqOHSsGqPL9EAF3dwPR0f4UFlpxc9MTHe1Pbm4FBoOOpk29KC62YjDoGDeuPUOH1g1T/b8GjSh86li2nISqCrm55WRllZOVZSYrq5zk5EIyMszk5JTj4aHnwIE8RIR27YLIzi4lONiT4cPDeOaZ6ogELpdKSYmD4mI7JSV2iopslJTYsVqd5OZWkJpahtXqIj+/nLIyB2VldhTFRVZWOS6X0Lq1L4mJGrG1bx9IWloxHh4GIiN9qKiw4eFhIDDQg5dfHvV3PbqLhvbtA5g+fSCqOoDDhwvYuzeHX36J59ixYoqKLNjtdkwmPcHB2qy/Y8cmPPhgb9zd9bi7XwRTt78R/0xxtQDOC33R8xl7z6bsBcPZEEl9b+3UZczZnIOiKPcB91X+tCmKcugs7v+XYMuWM5/z0UcX5FbBQP4FudI/ALGx1f//+quW0bES593OmtfeexoN23vvnc9dzhv/2Pc56VwnxKfHP7adFxhnnxbytMhaCS+fm4IG3BVFqSmW+UJEvqjx+3zG3rMaky8UzoZI0oGaLuWRQOZ/cQ6VD+kLAEVRdv9bLBZOh8Z2/rvQ2M5/F04ZyP9riEjdbHznj/MZe01nUfaC4WwEpruA1oqiRCuKYgJuAhafcs5i4HZFQz+g5HT6kUY0ohGNaMQZcT5j79mUvWA444pERJyKojwErEQzI/taRA4rivJA5fHPgGVoVgNJaCZod/1VFW5EIxrRiP8POJ+xt6Gyf1VdL5pDoqIo950iD/xXorGd/y40tvPfhf8v7fyrcdGIpBGNaEQjGvHvwP++UXkjGtGIRjTiouIvJxJFUUYrihKvKEqSoih1oppVKolmVR4/oChKj7+6Tn8FzqKdt1a274CiKFsVRakbmfF/AGdqZ43zeiuK4lIU5bq/s34XCmfTTkVRhiiKsk9RlMOKomz4u+t4IXAW/dZPUZQliqLsr2zn/5z+U1GUrxVFyW3I3eDfMgZdVIjIX7ahKXmOATFo5mj7gQ6nnDMGWI5m99wP2PFX1ukitnMAEFD5/+X/1nbWOG8tmiLwuotd77/offoDR4Dmlb9DLna9/6J2Pge8Xfl/E6AQMF3sup9jOy8BegCHGjj+Pz8GXeztr16R9AGSRCRZROzAT8DYU84ZC3wnGrYD/oqihP3F9brQOGM7RWSriBRV/tyOZtf9v4azeZ+gxf75Hcj9Oyt3AXE27bwFmC8iJwBE5H+xrWfTTgF8KoMDeqMRyQV34f4rISIb0erdEP4NY9BFxV9NJA2575/rOf90nGsb7kGbAf2v4YztVBQlArgW+OxvrNeFxtm8zzZAgKIo6xVF2aMoyu1/W+0uHM6mnR8B7dGc2Q4Cj4qI+vdU72/Dv2EMuqg4t9Ck544LFl7lH46zboOiKEPRiGRQfcf/4Tibds4AnhER1z8zJtJZ4WzaaQB6AsMBD2CboijbRSThr67cBcTZtPMyYB8wDGgJrFYUZZOIlP7Fdfs78W8Ygy4q/moiuWDhVf7hOKs2KIrSBZgNXC4iBX9T3S4kzqadvYCfKkkkGBijKIpTRBb+LTW8MDjbfpsvIuVAuaIoG4GuaKG7/1dwNu28C3hLNGVCkqIox4F2wM6/p4p/C/4NY9BFxV8t2vr/El7ljO1UFKU5MB+Y8D82a62JM7ZTRKJFpIWItAB+Ayb9j5EInF2/XQQMVhTFoCiKJ1pk1bi/uZ7ni7Np5wm0VReKooSiBTlM/ltr+dfj3zAGXVT8pSsS+X8SXuUs2/kiEAR8Ujlbd8r/WFC8s2zn/zzOpp0iEqcoygrgAKCiZaC7aNGs/xuc5ft8DZijKMpBNBHQMyLyPxUVWFGUH9FydAQripIOvAQY4d8zBl1sNHq2N6IRjWhEI84LjZ7tjWhEIxrRiPNCI5E0ohGNaEQjzguNRNKIRjSiEY04LzQSSSMa0YhGNOK80EgkjWhEIxrRiPNCI5E0ohGNaEQjzguNRNKIRjSiEY04LzQSSSMa0YhGNOK80EgkjWhEIxrRiPNCI5E0ohGNaEQjzguNRNKIRjSiEY04LzQSSSMa0YhGNOK80EgkjWhEIxrRiPNCI5E0ohGNaEQjzguNRNKIRjSiEY04LzQSSSMa0YhGNOK80EgkjWhEIxrRiPNCI5E0ohGNaEQjzguNRNKIRjSiEY04LzQSSSMa0YhGNOK80EgkfxMURXlOUZTZZ3HeZ4qivPB31OnfAkVRRFGUVhe7Ho1oxP9XNBIJoCjKSkVRXq1n/1hFUbIVRTGc4/WGKIqSXnOfiLwhIveeqayIPCAirzV0nYsNRVFaVA7c5/RM/glQFOVORVE2n+W5vRVF+VlRlHRFUQoURdmtKMoTiqKYzlAuRVEUi6Io5sq+M0dRFO8L04Jzg6Iol1a+q2mn7L9FUZRURVHKFUVZqChKYI1jboqifK0oSmll/Z84pWw3RVH2KIpSUfm322nuf9prNeLfg0Yi0TAHmKAoinLK/gnAPBFxnu2F/hcH2H8S/gnPT1GUh4C5wBKgB9AEuBWIAjYriuJ/hktcJSLeQDegO/DsX1bZBqAoihGYCew4ZX9H4HO0vh0KVPB/7b17/CVFeef/fma+MwwwIOp4ZUAwooa4omaCJmqCQQ0QlWR/muA1kriEjRpzVYyJJmt216y5aOKFnRA0xigxXoniLZugMYoCCgqiZoIExsHgqAMMMAwz8/z+6C5OnfpWdVffu8+p9+t1Xud0d3VVdZ/u+vTzPFXV8BYrye8Dx5Ed65OAl4vIKfm+64EPAe8E7gn8NfChAnEN5pVYMFR16T/AwcDNwI9b6+4J7AFOAA4C3gDsyD9vAA7K050EbAdeAXwb+HvgDuAAsDv/PJDspnqnlf8TgM8Cu4AbgBfm698O/CFwaCCf24F7W/n8MPAdYJ3nuNYCvwP8O3ArcDlwVL7tx4BL8+O+FPgxa7+LgdcC/5rv9wlgU77tekCtOv0o2QPJ7wL/AdwEvAO4h31+nHpdBzw5//37wHvJGqdbgBd5juPtwLnAJ/P6fAp4kLVdgYfkv++Rl/+dvD6/m9fvB/P/c39e712Ba+Ek4EvAEYHtLwbOL7iW7j62fPn/AB+xlh9n/e9XAid1dE2fk5f9duAPrfX/C3iXtfwDwF7gsHz5W8BTre2vBS7Ifz813y7W9uuBUwJ1COaVPov1SRYJoKp3AO8BXmCt/jnga6p6JfAqsgbgUWTCciJZA2W4P3AvsievFwCnAjtUdWP+2WGXJyJHAx8F/oLsafdRwBVOnW4L5HNxXjfD88huzrs8h/YbwLOB04DDgV8Ebs9dGR8B/hy4N/CnwEdE5N7Wvs8BzgTuC6wHfitf/+P59xF5nT4HvDD/PAl4MLAReJOnPiFOJxOTI4C/DaR5LllDtInsXIXS/QWZmDwY+Amy/+NMVb0GOBv4XF7vIwL7vwb4FVXdJSIvy11bXxKR14rI75I9vT9ORO5RdlAispnsP9yWLx9Jdt7/kOx6+S3gfSJyn8D+HxaRXYHPhwvKfRDZf73KXQv8EJmAAaCq/04mJA8VkXuSPaxcaaW/Mt/H7Ptl1UwVcr5sbbfrUJZXYoFIQjLjr4FnicjB+fIL8nWQNWL/Q1VvUtXvAH9A5howHABeo6p35qJUxnOBf1TVd6vqXar6XVW9okI9nwcgImvJhOJvAmlfBPyuqn5dM65U1e8CPw38m6r+jaruU9V3A18Dnm7t+zZV/YYlso8qOZ4/VdVrVXU3mSvnjApuqs+p6gdV9UDB+fuIqn5aVe8kE/YfFZGj7AT5+fh54JWqequqXgf8CfP/VRAR2QAcraqfE5HjgZeTCedPAI8FVvJG9Goyl02ID4rIrWSW5k1k4gTZ/3aRql6UH+sngcvIhH4Vqvo0VT0i8HlaQfl/Dvxe/l+4bCSzQm1uBg7Lt+FsN9vK9vWVU5RXYoFIQpKjqp8hc4ecLiIPBn4EeFe++YFkbhLDf+TrDN9R1T0VijuKzN1Uhw8Bx+d1fApws6p+oWI57vGQLx9pLX/b+n07s4bBh+/8rJD54GO4oUqavIH8HvP/AWTWynpPXY4kjnsB/5n/fgTwr7k43gJ80Ep3FJnbJsTPqOphZG6yh+f1gsxifZZtWZC5OB8QWb9SROTpZG6qvwsk2U1mndocTuYy3G0tu9vK9vWVU5RXYoFIQjLPO8gskecDn1BV06jsIGsEDEfn6wy2qe9bdrmBzDddxqp8csF6D5kV8HzC1khROe7xQHZMRY1jsE6e/I4G9pE1yrcBh5gNudXgunLKzhdkjbfJYyNZo7/DSbMTuMtTF3NcZeV8j8yVB3AV8GMi8mAROQz4GWC9iPwqcJOq3lhWYVX9FFmM4o/zVTcAf+NYFoeq6ut8+4vIR/PeX77PRwPFngxsyXtJfZvMQvs1EflQvv1qMvesKePBZDHAb6jq94Eb7e3576utfR/pdEp5pLXdPvayvBKLxNBBmjF9gGPI/MXbgWdZ6/+QLEB6H7Kny8+QBzDxB5MfThYov4e17vfJg+1kjdutZLGOFbI4xaPybW+38l6VT77+8cwC6McUHM9vk/mwjwOE7Ka/d/7ZRRYHWSFrbHYxC6hfjBX0Jot/fCb/fQhZwPqh1vYXAf8GHEtmubzXOtZ7kFk0Pw2sI3Pz7GM+2P7O0DFY5+QWsqf39cCfAZ+1ttvB9ncCHyBzoTyIzGX3onzbKWTB8PUFZX0G+OH898vIROhK4PXA18niSYcX7H8d88H2+5CJ6aPIxPDbwE+RdYTYkF8/m1u8hg8ji9mZz9/l5+te+fYfys/lE8k6dLwTKwAOvI6sM8M98+vvRvJgOjNr72Vk4vOSfNl7PovySp/F+gxegbF98kb0++S9svJ1G8j8zjfmnz8HNuTbTsIRknz9+cB3yRpoX6+tJ5J1zbyF7En1F/L1b2e+l81cPtb6fwM+VXIsa8k6BXyTTHQuNY0WWaN8OZnf+nLgCc458ApJvvw/yNyAu8g6IawBXp0fx3fyxumezv43ksULfovVvbZihMT02toNfBo41tpuC8k98/K/k9fn1cCafNt6smD394CdgbJ+iixusdGzbSXi+rn72Kx1bwXel/9+LFnj+r28jh8hi8t0dT3PXU/5uueQ9ba6jcxVei9r20H5NXcLmUX5G86+j86vlzuALwKPtrY9F7g6Nq/0WZyP5H94YmKIyD+RdeMsHS0/dUTk7WRi/btlaVsq77fJ3IavAv6ZzEr9CbIn+19W1X/tox6JxFQYfPBXojoi8iNkA+VOH7oui4iqvl5EvgD8JvCXZJbMF4FXJBFJJFZTKiQicj7wNLIA4yM824VsBO1pZL7wF6rqF9uuaCJDRP6aLPD7MlVNPWA6QrNA+aeGrkciMQVKXVsi8uNkful3BITkNOClZELyWOCNqvrYDuqaSCQSiRFS2v1XVT9NFhgMcTqZyKiqXgIcISKt9YtPJBKJxLhpYxzJkcwPKNtO/ACwRCKRSEycNoLt7oy5EBj4JSJnAWdlvw/94YMOengLxScSiUQ99uy5fKeqeuc6q8JDRPT2ivvcCB9X1YWYDbkNIdmONeoY2MzqEccAqOpWYCvAwQdv0WOPvayF4hOJRKIe11wj7lRBtbgD+O8V93n1bOqcydOGkFwIvERELiALtt+sEdNHGDZuhM2bYc+e7LNv3+wbst+JRKKclQl15i+rq7t9w4b59Ssrq39v3Jh9b9iQfTZunH2vrMCbqsxHXYNlnm8qpvvvu8lGb2/K39b3GrKpLlDVc4GLyHpsbSPr/ntmbOH3vz+ccUYmJNu2wfbtsHs3fPvbsGtXsZiYbbFUSV+WdgriZm68urTZKA3ZwE2pcR0rdc9haD/72rTTuMJgPhs2zP92RWLTpuz7/vfPPps2wfqdO+C667LPFVfArl084tytnH12vWMpQ0hCUoiqPrtku5K97KcS69fDi14EzznjAHz729zyhAdyySWZoGzalInKt/P5Z/ftW/2B+Qbd1/i764qW7d+qB/Jf+63Ue/PvA9Y6e3vMclXWVtzmrvNd2r6X2c3vJ7J6v1CjUHV9W9sNVQWzibC0JUpdiFvbjX3dfXz/h08szG9XPGzRMMtGNELCcd9NB7IG42tfg8uug6uugksugSuu4Ia77mIb2fiFp19xBR/84Bf4mZ+pfswxLPMzy2DHvmEDPOQhZMrx7W9z+MNX2LLlvmzcmF0TRxyRfXbtgp07M0tl9+55F9iGDfNuMFdozHcmDKZRdwVhv/Vt/z5gpfet3x/4tt8vVUVYQqLhrl8X2Ob7vSYi7fpVaVVXp73rLl9d1ubr3bLsfH1ltidWPuuwqUiFxMlcTzGNb1Ea94GmSmNeteHvKn2RgLt5hKwO8+0TEJ+IHHHETEg2bcpE5F4b98K267J2xFgfl1yCfuUrfAPu/twOPPLSS3noFe/hyU/+Of7xH+OOMxHHYEJy4EBucTxiM2zYwO0b7wt7MnHZuDG7aO5//yzN7t1+MfHFVcxnfnlN/jGN5H4ygbBFwXwOMBMPs+yKzV3OMp5v23LB2ebiE5GQCOz1rDO/1znL9vcB/BaKyc8WFFNPc65MPvbvIqqJSFHDU7SuznpDmSXTVCyqpKmTtuv0MZZe7LkvE5IqecP8Pb9343rWmwbDNBqbNyPbt3P/73+f28mskdvJ3w+weXNlt3gMybU1EEZIrt95CJs2Hc2ePZmArGcvGzeuZ9OmTDw2b86+bSHZtWsmIkZYzIW1e/dqMZlfl4lKJij2y/jK3FC2iISsGFhtscRgN9xViRERmF3m7vrVFsnqNKF19j7rPWkzqgpHlcalqpslZr+201RJVzVtnfRV3IF1z0EVMfFZIyHce3vPHuaFZNMmOOYY2L2be1x2GcfccQcHyMTk4OOO60xIIAnJIKhmgnDdddnFsGkTrF85kL2pAr+Zu2fPzNx1MdsgEw6TB8y7wMy6zCWzHjC+EfP0bbCtEVdEQm4wX0ylDF/MwqaoMbdFpAsBCbnDzD5x4lFHOKqIRl3BWBQx6TJOVNdNGCMkdk+sULzExr7HzQPihg1w+BEbZiKya1dmleRPnPe86iqOUc0eGR/+cLj//TvpLJMskoE4cGDmsjK+7j171rCysp5du2aWhrE+bHwiA1m6lZVMaHbvnr8wN2yYj52IrEF1P/OunD4pcv+EGn+fgBRtjxWRGAEptz5C4lFHOKqIRhOxaCvo34WYtG05VE3bRESL/s+yrrxF+bju6z174PZ96znkiCOym37z5lmC/Ma/91VXZQm3bOGmXes763WZhGQAVOeFxAiHERX7QjG/XfEw14q7DPPCsbIy22Z/N6dIgNbjt0qKek3FWAdFItKXgJSLh7tcxyoJNaRtx0v6tky6EIghhKTK+fZ1+fV92/dzKH/THmzYMLNKVo44hPWbNs3cG7ZbwjxZPuQh7NzZ1r2/Gt8UH8vCoEJiYhq7ds3cT6aRd3ti2b/rXAj+i9JuQE0g+YC1zhcwd/c3Vs0Ba9lQ5LYKddXty5VVJlR22voWSFsC0qZ49CUsTQLWY0hX9zy5xx36z0PfReW4wwDsTjfrTZcus9Hu8rUn68ljj09rE6FehHNRGExIYCYkMD8CFebHirgXjdsry6QzFF0o5dbIGue3z+pwezG5YkJgP3tfX3lturJiBaQoH3v/Zi6sOgLSlnh07d7qWzD6SlP3uGMfHtzrw/Ym+NK4476M+9p8r6zkLi6TwB58sm8fbN7Mzsu6s0iWmUGFxLi2IGxp2NZISETc7xhWVtyAu2tNGHzPGSGRMGIS2s9O5yujqHHv0pVV3QqpKxpNLJAxWSVj6T7cdR51OjJUsUJdwQiJiI1rkcyJyRGHsH7jvizgbkTECMn973/3g2vbCAM3pgMzuGvLBNQ3bpxdFDBvmXQlJmHWM9812IfPKoF5kTjgWefuH/rdV0B9OCukKwGpu0/M9qYCMubtbYhGzDrf71hr1MRGXCFZWZl5OO51xMZZpmZcwb593LRzTWeuLUjB9kE4cGB2IcDMEgV/tz+TxvdxRcXepz4hC8XdZsdXcPYJWR6+5bH1yOo2FjI2K2TMAtLVvl12ZqhyPfh6cRWVB/42YNcus3VNJiYrK3OurZ0752fCaJskJANh/Jzmt+lRZVsmZpv77YqInTY0h1Y5ReJhWMdsZLstIrGDCkNiYq/vywqx0652ZdXtzptEpL2y2z6msYiHW5fYGBLMGRmrHhiNi+uW3WtYWTmEFauMXdu6m3A1jSMZCOPasp8+7O68Zh34hcE3LUp7gmJwe3GB3xJxpw5xxSjGEoFhrJBx9siq65rqmyFEZCgBqSMevnVls//av30PlKatsH/bQwfsMWQ2ZkxaskjaZ9DbMiQkrqlrcHtoFbm6fPvZv/0Xk88aiRmw6FoyPqskJCa+SRjbEpCivOz9m8VCYpaH7NLbpTVSRBPR61pE6gpF7Lqy/78ofcx5s60S8Lu57IHKxvvRZbA9jSMZEN9gQ/tpw5fe/S7q8VVulcRMZeIKhXFvuQH3mHzsPNz1TQUkJi97//l9h7ZC6qwv27YsNBWR2DybWiCxebiYh0z7t687sLuPKy5ddv2NaQEWlcEtElh9obkXSpmLK+TustP6x5m4c2vhLNvzaLn4YiUu7qVVNgV8VQHxpanfpRfatUKgexEpY0iRsRu/sVNHINq0QorW2dhtg+nBZbcV7qBm83FnDZ8KInIEcB7wCECBXySbzPhcYCNwHfBcVb3Fs+8pwBvJbvbzVPV1XdVz8MvcHlhkzwLqPoH49nO/fTGTUNxk9vIqWC0CRVZKlfEmMC8ebrqqAlKUZvwCEkpbtL6MqTTUQ9A0HlWUZggRMbgCHbJM7JiJPZi5CzocR/JG4GOq+kwRWQ8cAnwS+C1V/ZSI/CLw28DvzdVHZC3wZuApwHbgUhG5UFW/2kUlB7sNVbNvt8uuPWdWFRdX6CIJ9eya4QpK2bQoMBMTWyTuYrVo2OmLfk9TQELr2pxYMbm0+qfqee1LRHwCYs8IbLcZriViv26iK9oOtovI4cCPAy8EUNW9wF4ReRjw6TzZJ4GP4wgJcCKwTVWvzfO6ADgdWCwhAX8vDNs6idnXDryb7yJrZN6ttdf6XRRQD1kgbtzE3e5b9jX403JhhdZVtUKabOsLu6Hy0cR91bXrq6zusdSJadRJ68MnHq7Hwt5msNsFIyRGTLqgo+6/Dwa+A7xNRE4ALgdeBlwFPAP4EPAs4CjPvkcCN1jL24HHtl/FjFH0WPO5o1xz1Nczq6z3lmuNmHUzt5b99kOzXIRPHEKf0PY11sfdtj7/mO1lZdgWzRprf+a2i6yZm9rE3ID275jl0DozpZGLL23MNrO9KWMQorpPwKH9qq5vqz6x1BlzExMXCS2HPA7uy+7cdqQL1lT8AJtE5DLrc5aT5QrwGOCtqvpo4DbgHLI4yYtF5HLgMPy+eF8nMm1yfEWM4FZb/ZThurZ8TxFFgXdXRFbHRnzWiP1O9zILJUTReJEi62K4Xlgxy6F10L4VErM9Nk1fjNkqWRRCFogvVmLHVt0Hzy5jJDWeyneq6paC7duB7ar6+Xz5vcA5qvp7wFMBROShwE8H9rUtlc3AjupVjGM0l7ArHrH72L/LXFqz9eZ97a414gq7z0Kp2s23SBz6GwdSZzm0DuoJSBvb2yamIW/qIqorFqH9quQXU/eQ+6gvYstzO+AUxU7tZdez0RVtjyNR1W+LyA0i8jBV/TpwMvBVEbmvqt4k2c3+u2Q9uFwuBY4TkWOBbwFnAM9puYp3M7iQ+CwLN9Be9Oe71ogh7NLyvULX7Qbc1BrxWR/27/G8YKrKOmj2HvS2GqcxPsGXNYZF29tquOs0yG3m2wchK8TXFdis97m/JsRLgb/Ne2xdC5wJvEBEXpxvfz/wNgAReSBZN9/TVHWfiLyELBC/FjhfVa/uqpIjuTzmiRURO6397YurFLu0jDVSVUBc66TIfQVDTWUSs1y2vg8BabuxarMBbCtwXZU2rJK2KSq7bucE1y0VU36Ru8ttD0yspEsx6WJAoqpeAbjurzfmHzftDuA0a/ki4KIOqrWKUQlJmc/TTWv/tp9MikXEtUZcl5YRk1Dg3Xe5tCUgcT2wxmB9FO1XNU2VdF3Ql0VQ1yqpIia+db7GvcydVcXd1aa15Wvoq/43Ra7tLmMko2pMe2Y0x+5aFj5BKdsnPCDRJyIu9npXRELPGn0IyLisj6L9qqapk3ZI6sQbqtCloLVtUXVhlRSlN/hiJK57y93XDbR3ISZp9t+RErJGQsE089tvjfgsEdelVcWt1VYMpFr8Y0jro2jfuunGRGzjNuXAe9m+fVolbZ0Hd9k3YWsfFgkkIRk1ZdZIuYiEguuhuEjRWJL+BKSPrrvQjvVRJ22TfapQpcEau4uraR2GtEpCYgL1XFexsRTz6SPYnoRkBMQE1mF1jwyzLiwieygXkbK4CLTvxvK7sNqYgTe0ztCm9VE1bRv7Dc0iBd67tEpixMSsh/YEJRQjMXXqguTaGroCNQQEYkXEdlsViUjZiPY6gwmbC0gbriyIb/Sq3shTEYIurJKmcYAxWyVNxKOumJhtRZSdT9862yKZ0qy/U2MSTUHICjHfYREJ9dDqUkTaFZC6gfS2rY429msrjzoN7RhdXG3m2WeQv6rV4hMT6P4/tF1aXQbaDenFViPBZ51MQ0SKrJDmAjIG8Wi67yLSZeB97FZJUzExaaCeiNn5+8r2ube6tEiEbsaRTIVRNw0+EbG3lYuIEY4yEbHfw+7DJyI+V1Y1K6SKgNQVj6aN0VjFI1kl7ZYVs28XYmLSGZr8pyFB6WPCRkgxkkkRviDqWiKhbr9FU7yXiUh9AYmxPpqMMi9jrMLRFm2LySJZJVViI3XLjokt+agSs/F9uo6PpAGJI6As4O66tAzz1gisFhEIi0jRuJFYEYlzZbUhIHUnSSxi6qLRxdN+Yp6mVohvXegV21Xq4a4Lbfd9uiJZJBMgLi7i66FlKBOR0BTwMSLic2UVWyExgfWqbxr0segN7RhcXF2+/KpqnlXKirFKYsquIyamfIgXlLJjtr99gtF1oD0JyYixTVL3AlkdF4HwqPUYSwT8AfZYEVntyooRjjIBiW0YpiQabTauXYtJ1/RRl7bjJ22JCfjdTiFx8QXb3e3muy9LxLDMQjLaYy/641dfGEVxkSoiYvANNDTry0XEvJGwyAqxPwb3TYPudhs3j7E0ilVo8+auk1fsPjHpynzwdY+16waw7KVxVeoRcj2XrQvVq8rYD7eXlsmjTyFZZibR/NgXQrlLC4pFxNdDyzdlyVr8XXzNtrCIwHzjXubGcsXDxxSFIoahLZNFpKvzUNfqCK2DuHq6LrBQbMQnJq6QdHl9LPM4ktFaJD5WP1WEXFpmGed3bIDdnY3XtULqi4i9zbZAfFbFlK2NKgxpmbRplfRNW8caa5XE5lnFomliMdhiEVrfx5sRYTaOpMpnkRh1E+WbaG3eGoHV07/vdZbLRMRnmcB8XMRscwWmuohAsQXSRTxkjI2gyyJYE0PNwdUVTS2OqlaIe53GdFkO7TvEGxEn9VTeMpO4df1PLT5rxHVpxVgiEHZpmWXfOJF5ESmyPHxurCoC0rSBDd3QY6MtMamaT2z6LsVuSkLaREzMeojrHeamM2JSdL7cILstKl2d4zSOZIQUNXR+a6RoupOirr6huAfMXFpumnoiErJChoiJ+HzKY6GtG74rMWnCGMSiqOdUaNR5bDfjqlZIE0FxtxVtN2KycWNxOU3pwiIRkeuAW8kasn2qukVE/g54WJ7kCGCXqj4qZt8OqghEComInEL2juC1ZC+Xf52z/R7AO4Gj8zz/WFXf1qRivm6/s4vGtUbMOtcaieml5f79rksLZoIyb7mE5ssKiUiZgPTdyIxdUPoWk7FS5ziG6sRQRXjsbYbY4LvPKvH11uqLjseRPElVd5oFVf35u8sV+RPg5th9u6L02EVkLfBm4FTgeODZInK8k+zFwFdV9QTgJOBPRGQ9FYnrKlhkjcRMxFhkjYRcWm7sJBwTqSMiyxBQr8MUu21OearyqnUvCqCH1pf9n65bKibfmHymdh3FICIC/Bzw7qHrEiOiJwLbVPVaVd0LXACc7qRR4LD8wDYC3wOi/7qYP3kWZIewNYK1PtYacftP2C4tX3B9tq6qiLiCUSYgvrEiy9KTy6ZJI1C18emyLmOhSQ+pKuubCEpZ/kXvIwp1/e36v1tT8ROJAp8QkctF5Cxn2xOB/1TVf6uxb6vENEdHAjdYy9uBxzpp3gRcCOwADgN+XmetfiWKrRLfuBHXGikSEN+cWGbZ59Ky085Pe1JHRAxtxUV8Zn0dVlbG30A2cdUsiovLR9vHVtYzqo7bqqlLy86j7Hj7fP+IS41xJJtE5DJreauqbnXSPF5Vd4jIfYFPisjXVPXT+bZnU2yNFO3bKjGXoO/8qLP8U8AVwE8CP0BW6X9R1VvmMspU8SyAlZWjgwX6fJzx1oi9bT/FPbZ9Pbpdl9a8uNjB9ew4motIGw3BFMSgKYssCFOnaVA9Jmjuiokter7XTLhWS5fUfB/JzrIAuKruyL9vEpEPkHmIPi0iK8B/BX646r7Vq1lOjIW1HTjKWt5MZnnYnAm8XzO2Ad8EHu5mpKpbVXWLqm5ZWblPdCXng+yw+h3rIWvEN0ZktTjMrJHQcKFZXKTITQXVRKRtF1WTJ/ap0HVdp3Quyig6lqJtRbGSmBhH0bZY92FZjMQOrIfm4+s7NtK2a0tEDhWRw8xv4KnAVfnmJwNfU9XtNfZtnZjjuRQ4TkSOzQPoZ5C5sWyuB04GEJH7kXVNu7ZKRcp9sO6YkNAYkRjXlvldzaVlCLm1QiJSNTaSaJ9FEoihaSImZnvTGElZ2tDvrjDjSKp8Irgf8BkRuRL4AvARVf1Yvu0MHLeWiDxQRC6K2Ld1So9HVfeJyEuAj5O1qOer6tUicna+/VzgtcDbReQrZOf0FXW6nLl/vln2u7XMKHZ7fYjQlCdumnKXVkgQikTEJglIc4Z2cS1DvKbp1PhtuLN8ZRX9Nt/GreW6t7ocS9JF919VvRY4IbDthZ51O4DTyvbtgqhLWlUvAi5y1p1r/d5BZjo1xp3awO/W8k2PUtZTyxcLcV1aLqtdWrDaoogRkbLeWSGqPk0tQ6wEptMgV6HvY4oJXDd9z0rsMdWJkbj7uQF2n4usy3O8zFOkDH7scX5cn1jsXZXeT9lodTftvEvLHXTourWaiEiMi6vrhmUZRMfQ17F2MZZkrP9TlZhHlTx96YvcVT4RSfTH4EISIuzW8v0um5AxNsDuS+cfu+H7XaWXVhWBqJJ2mW6gro51auewblA9hhhRrBpEjyUm4O7r6utzc3WNcW11MI5kEozyeFY/VZS5taBcTMzvogC7we/SgtVxkToiUocuLJOpNZiJ9om5Buq8XKosXdcBd59Lq2uWWUhG4WV2A2RmXUZdt1bdAHu4l5YvLhIiBdkTi0TsFPkxQXZfekPMwEZfGbZw+NuR7lk0cajCRI7d59YqGsnum0/LLJeNGcmwe2m5VkZZXGQKIrIo1kjV41iU465C0+66hiouorpjOIqsFZ+bq8jy6FNMlt21NcImzo2PhNxaNvZy0dxZIVYH2MsGD5aJh5tumYjt1pmYHlVf3lXVQnH3dfcL9bpyYyV9WyTpfSQjw38B1O2tVT/ADuUurbK4SN+NaBc+56ZlJ1Hph6bdWqvsX+dNkHWvg5Abq8gFZuh7JuZFszKqMKpbe3Vj5E4LXzZdfNkU8fEB9iL3VIz10XajOVaXTNVAaBKT+jQ5f22O+YBZI13n1cKxcRF3H1dMQnkOEWhfdkYpovv22d1+ITzY0OfSMt/NAuzgt0Zi4yJjpc2bqq4PvG2GaChS1+OMNp767VhHk67MblffunGauohIpc8iMZrmz57KYIbrxvLN9Av+uIi9rWwEe0ZMgN0s298uQ4hK3w1Qk/KW3TIZ6vjbtkoMTayTUB0MsTGS0P69IVL9xN11Vzd1GYBR3M5FZmr8S6qg+B3sbrrwCHZD0SBEN11ZmjHQ1g3WRj7LLiZd0NY5rZuPbQ20KSo+MfGlG6rb790kIRmOsC8zFB9xfxtsl5ZNKMC+Gp81AsXWSKyI+OYGiqWt7pttMDXXS12WXeiaHr/r8moiLFXrMsigxDoWyQIxuiP3B9xjJ2Y0xATY63X3rYObz1R9+m3Xe9kb6zr02Turzf8nFEuJFZiqdXFjJZ2ThGQ4Qk8L8+NHDO6yL9Beb4p4Q9XuvlXdXu66KT3dT6muY6BOF9k2aDsO0rXY+wSmzqt+fW9V7fWaTUIyHoqtEZcqAXYfYWsEqo0N6TLoPia3VmI5aTKosA5FwXtbTGLGLvWGyDBPDiNhdN1//RdB2YurzGHYLi0bX3ffeULWSFfTnkwprpHEql/6+L/qxur6vBbc1+jG1MMNtvc9KHFZGYVFsvrPDs2jVeXFVWXzacUNPixbn6yRRN8M3TtrCAul7GG/b5FbxZK7tkZjkYQvgtD8WoaQS8ul+eBDO20RY7qekjWSKKLpeKCYgYRtELIsfFbLIF2AjZBU+URnLWtF5Esi8mFr3UtF5OsicrWI/J/AfqfkabaJyDktHGWQETV59oj22Pm0XJeWTXF33yJrxPf0U8UaKWOMPagS/dJ1EHuIgHrTa7KsDkN1YIiiW4vkZcA1wOFZUfIk4HTgkap6p4jcd3V1ZC3wZuApwHbgUhG5UFW/2kUFR2ORrKZsokZbRAxFAfZ4a8Sss7/d9SHaupaS2CSKWMT/Nsa6sa0PnyUy2IDEjiwSEdkM/DRwnrX6vwOvU9U7AVT1Js+uJwLbVPVaVd0LXEAmPp0wuJCU/+G+QLtb7ZgA+4wuYyN90NdNsoiN1VTo+0Fi8BiDQ1F9itxZvnW9HVd1IdkkIpdZn7M8ub4BeDnzDeFDgSeKyOdF5FMi8iOe/Y4EbrCWt+frOmEUrq35Pzw0s68P121VNJ+WfyqUvmMjXfWoSSR81BkVPrYYX8y4GJveByNCXdfWTlXdEs5SngbcpKqXi8hJ1qYV4J7A44AfAd4jIg9WVbV392SpnnWtMJpLptoYEvC7tNzt/sGHTa2RKZDEaPHpqtGfkpgUz9M3eR4PPENETgM2AIeLyDvJrIv358LxBRE5AGwCvmPtux04ylreDOzoqqKDu7bqUfQqXZ9FEm+N2NvctKFtoXUubVgji3SjjKmxWmSmMGakDQYb1Q6wZk3WgFT5lKCqr1TVzap6DHAG8E+q+jzgg8BPAojIQ8kauJ3O7pcCx4nIsSKyPt//wtaO12FiQmKLhPkOBdj9gw/LrJGuGrep3ZSJ5pQNhmvjoSH2uqp7/fXRvTf2OJtYH53ffx12//VwPvBgEbmKLIj+C6qqIvJAEbkIQFX3AS8BPk7W4+s9qnp1w6MMMqpnwlnX39hp44sC7PPr61gjvlhJ1102m2yvmi7RjLG5gMpoY8JHH7Fup9i8i+pYNN+W+7v3+6DDi0FVLwYuzn/vBZ7nSbMDOM1avgi4qLNKWYzmNvDHSELYvbRCAfb+rJGqwcBEu/fclBrzLhh6Esa2r++ykfNVLJORB9sXhlEcefyfXTRRo9keZ43YTMEaGYKVlXHWa5noquGfQptXp56DjyNZUgY/8vhJ1dxeWvZ6XzfgYmvEt9wFbV3Qi9SgL/H91hlT7+YboqyegwbYbZKQjI2yMSSuS8vG7e5bzRoJpanLEDGNsQvOEt9rXoZs0KciJk3p5Z5YciEZWa+tsgkabREx+CyS+alQYq2PIXpzxTCkOKRYxvB0+UAyhW6+ofqFrBHzO00h3x+jubWLL+b1rJ5vqzzAXmSNdC0Si9TDqo1YSVciMnZx6nuiwbpWRlmAu21811Od3loug4nHkr/YatDbsFrjFHJphWb+zSiyRoqC7E2YgjhUpYmYjL2xnwJ9uaG6EpQqY2KKemuVdf1116VeW/0wiiOP/7N9Li1727w1EmOBxLi5qgYx22RMolRVTJb4vqpE2yLRRn7u/1yle3FTyqZEaWPcSuskIZkKoXePrA6w28RYI21R9UIek0jEYp/Dqu6JRDOGHC/S97XaRDAG6QKchGTshEQiHGCvao0UjR2JYYqC0AZD3zdDlz92pt4rq87Mvz5UD6yKl7ZOEpKxUTY9SrlLy9CGNTLk+JJlFagpEdPYxb5zPOZaW9TxIk1xrRD3O5t+qWMxWYYTHWBk3X+LiHdpVYmHtBlkT4yfZfyfp9DFN8RU671sjOy28nXxdfFNEZ8RY40UrUtMh2X+/5p08R3TeYudqLFsf3fcSIqR9M/gRz5vetqsZd7N5U4hX80aMZS5GPrsYplI2PQRTO97vIiv7KJtoe69zeq7H1jb7X1n3keypAwuJHG4Li2DP8BeFFD3rYsd6Z4YB+l/aU4fglJ3pH2TOoWsk85JFsnwzP/ZRij2O8txAXZDHWukDxZpxHsintgR7n138W3qXgrl1TSfNnpOLtL7SMbOyI/cFZFyl1aZNVLEIlwHbUxnMmYW4T9qkzbjHmO+buqMap9nP532LVpyiyTqzIrIKSLydRHZJiLnBNKcJCJXiMjVIvKp9qros0jmXVqGIuEwT4PJrZVYNMYsAHUJCURMnMX+PXvrasd08KpdEdkgIl8QkSvzdvUPnO2/JSIqIpsC+18nIl/J2+XLWjpSL6VHIyJrgTcDTwG2A5eKyIWq+lUrzRHAW4BTVPV6EblvveqsB8ysa764iN+lVSf+4bJIQfZFtUrGKu5jmOJkbD2yuiRWTBaAO4GfVNXdIrIO+IyIfFRVLxGRo8ja5OtL8niSqu7suqIxFsmJwDZVvTZ/V/AFwOlOmucA71fV6wFU9aZ2qwlVXFouIWsksVwM/b93PTPtgjWijeOJg3T/bdEi0Yzd+eK6/KP58p8BL7eWByVGSI4EbrCWt+frbB4K3FNELhaRy0XkBc2qFddLy1B3qpNFdmstwjHYLNrxlFG3ERy7mBj3lPtpii3S8/ntJ3tZXsfurQ6EJMtW1orIFcBNwCdV9fMi8gzgW6p6ZcnuCnwib5PPanR8JcQcjXjWuSq4AvwwcDJwMPA5EblEVb8xl1F2MGcBrKwcXVJseS+tGJeWa40Mxdhv8DEz9H83FFMcK+JSxapo2lPLjZEUbe+E6id8kxO72KqqW+0EqrofeFQePviAiDwSeBXw1Ij8H6+qO/JQwydF5Guq+umqlYwh5si3A0dZy5uBHZ40O1X1NuA2Efk0cAIwJyT5SdoKcPDBWwIm2RrmX7frf3VuXTdVUe+uRWMRYiWL9t/0+aKroQSliTXVTRfkjntsQd0BiTtVdUtMQlXdJSIXk4UVjgWuFBHI2uMvisiJqvptZ58d+fdNIvIBsjBFJ0ISc3YvBY4TkWNFZD1wBnChk+ZDwBNFZEVEDgEeC1xTpSL+2TnNutWvzo35Hos1MiRTPvYp170t2nq/R9cPFG25qapaEb7te/bY641Ly505o2W66bV1n9wSQUQOBp4MfElV76uqx6jqMWQP8Y9xRUREDhWRw8xvMgvmqlaP2aL0aFR1n4i8BPg4WWt+vqpeLSJn59vPVdVrRORjwJfJ/rHzVLVBpW1RKZ5Lq4plsqwN0xQtk7b/qyn/9231yKr7sqqiPMZAqE6rp13qkG7GkTwA+Ou85+wa4D2q+uFwFeSBZG3vacD9yFxhkLXz71LVj7VdQUPUkavqRcBFzrpzneXXA69vVp3QhIyrXVrub3ddkTVSlMeUG5wipiQmi/ofjI2pXA8xhC2iA5i5tjqlAyFR1S8Djy5Jc4z1ewdwWv77WrLwQi+MYhr5+fO/3vku7+obY5UM2TiN5YYdewNdoTPLpKnTDXgs11AftOEey/Ioe7dRoi1Getuun1vyTchY5Nqqa40sA+aYx9YwTf2/6GNA4KIMOuy/E8B+YP8Ye20tDCM/8rWV4yK+jhOx/+8yXQdjEZQ+zvkY/9e6vbemKiZFc2K1dTyrx6bspTerZMnn2hr8yP3nfm0wTVH3XfvGLLNGxk5fMY0hBGVK/8MYGdM4kSK6HkNiPvO9tHzsxwhKZ0KchGQcZA3nGtQaXeK6tGLHgMS4r5b4P/fino82hWXIc73I//MYrZMuHkjqHGfWY8sE2nsgvdhqnMS8NtcXF7GpEg/p6oYc2nVUl6Lz4TumsTVoU6CNwYljsE7aGusSewxFc2uttk7MFCmjHNm+MIzyyO2uvmU9taDcpeUylf97rF12p3T+hth3CPoUlDFek6vrNHNnzZY7JLm2xoNxb5nf9nrft/s0F2O5JBJjou0pU9p846Gb3xRYPZZkP1nQfd0wFVoSRtnEFsVFqopIKO+pMFarZOxM6X/uav6tKV83sW9EtF1Z89vcHlvJIumSQY/cFQf74imySIpcWXVGqi/x/7+QpP9zmoTu/9h9zfdMUEx8pIeAexKS8VFkkTQRkan+z8kqmQZNelH1OStwnxSN4m+ro4Fb1qzHFtiWSba+o8k8kpCMh7IYR9uWSJV0Q5PEJI42/s+hrolFEpOu3wZp43tX++pgew8TOE6lMemA0Ry53VD6LJLQtCexAw+X+D9eGhbhP56ymFQVjzaPdbVLC2ZurR5EJI0jGZYNG+YvQJ+PtCsRmVrDk6ySMFP7L4uYkpj0aXnY+APsPjExlolxcSXXVheM4shNA+nroeUTkUUWizKSmKymzf94LNfLmMVkKPEIEZ5jq+P3tNssuZCMYhp5H7FjQmz3V1EeVbaNnSnXvW0W+VyMqcHes2f2aTvf9vc1riy711aaUr5LRnMbhoLrMSPbi/Kqs30K2F2ml5FF+A9jMI1l39bJmETMxQ2u21OjZD2z9ns+PbAsF6WH0R55qIdWU5FYtP962VxdXf5/Y7427Ia9C1EZg3C04562A+1GVO6i8xHuS+7aGsWRuz227LhI3dhIqJxFZBmsk0X97+rgNvpVhGUMgtEFq3ttmTjJdN9HIiJHAe8A7k+miltV9Y0i8lrg9HzdTcAL89fsuvufAryR7L0c56nq61qtoMXgt2eM66rpwMJlaYQW0Trp679rq5whpnYfqzi412KbY7tWB9jdQLs7PUrHXYC7sUj2Ab+pql8UkcOAy0Xkk8DrVfX3smLlV4FXA2fPV0fWAm8GngJsBy4VkQtV9attVxJGICQublykilvLZVkExMY+5qmKSt//2zJeJ11RdM3VEdkqojL/DhLzMW6t8vo1ogMhUdUbgRvz37eKyDXAkY4YHAqoZ/cTgW2qem1WPbmAzIpZXCEJvUskdt6d1BD4mZLLK/2H06Wr6ytWRObrYKwPX9C9O6tEFfbuq9wJdpOIXGYtb1XVrb6EInIM8Gjg8/ny/wReANwMPMmzy5HADdbyduCxVSsYy+hu3zIrJDU41XHP2dDCMqb/cEx1mRptXEdNOg6Y8meuPXvwIbjWyAjZqapbyhKJyEbgfcCvqeotAKr6KuBVIvJK4CXAa9zdPFn5LJdWGPQ2cn36rhXSxK2VCBM6l20JzFT+q6nUc0w0vUZC7q0Y74P7nna7PqvjI/12AVbt5gFNRNaRicjfqur7PUneBXyE1UKyHTjKWt4MrArIt8Xgt1Looiq6sMb2hL0oLFPD2uWxjvFd6k0Y6/21eur40BgSe4BiN3QhJCIiwF8B16jqn1rrj1PVf8sXnwF8zbP7pcBxInIs8C3gDOA57dZwxqgu9yIrpO4AxLHeBInhWKRGvkuGvHdi/6P5QLsdH9ljLU/WInk88HzgKyJyRb7ud4BfEpGHkR3cf5D32BKRB5J18z1NVfeJyEuAj5N1/z1fVa9uvYY5o7mlXNGIDbRXyTeJSiKJSDFTuUfcEe2rJ2fcz+qYSfd1ahNV/Qz+WMdFgfQ7gNOs5YtCadtmFLdVTFykLCgX05d+Sr2YEu3Tp4hMyb011P1Q9LpsF7uO/nvdJyB30ZeIdBUjmQqjudRDFklsrw47XZmo9DVwb5kvrDExlQa9T4a6NmNinjGCsnpAoi8+spdZfOSuRvUuIwnJ0BUICAgUvxERwn+c2a9IUBZxFHhiNUOKyJiskkW61n1iMh8fuYv5ILuhuzjJgQPjnWGgD0Zyma8WlNDLrEL7+G4U96VZieVhTA34UHWZknhUjYnaYjILtO9l5uIy6xJ9MJLbLcNYJKGXWRXdGKH4R5GYdGmVTOkmXiTGIiA2fYnJUNecr9yy4415UPSVY4Lr/kC7L+C+Nr6AhizzPT+K284IiC/QXtYF2DcxXBUxaZtlvpiGZIwCYtO2mAx9nQ1dvqnD/EDEu5i5tUz3X8jEZJLdfyfD4Lefe3MVvcyqaH93hHysmLRllSzzRTQUYxcPl6qz4Y7tmqpSH59who63jltrfvzIXcxcWXsZ4q2ISUiGLNzp9usLrsd2CXQFxScwbVsmy3zhDMnUBCTEVK6ftutZ1a0VmjJ+3q11h+d3fyQhGQjxDbOh2mDEWGukbZb5ghmKRRGPKdHndW7f82VT0c/HR+x4yB7mrRPTa6v7OEkSkqErYFkjPhGJmWDQN/mjbZm0aZUs88XSJ0k4hqONa7yqW8tdFyMms/EjdmzE/Zip3bsXk2VuG0Z5u7qB91AaCLuyisTEl1fMRbDMF0rXJOEYlr6u7VCPzDLcHluz+Ijd5XcvmVWyl9mo9o7e0Z6YY/AYiWuNxD7JxAhIiDpWSRKR9kniMTxjmpgx1N3fjYvM3FomFmIExLZK7KlRurdG0oDEEVLXIinqCtwkdpJEpB2ScIyHIeIfvuXYa8J+/8hqt5bdS8tYI75ge3eCkmIkI6FoLImPIhdWWwKSaE4Sj3Ex5L1QNOVRTCzUtkzuussdzW5bJaYLsB0f6dbFlYRkyMJX5seN2OtjLJJYMfHtE+veWuaLow5JOMbJkBZI2baygca2FbJ7t93t1xWQUKC9H5a5rRjNbW9bJObCCs38awTAdm8VCUeySrolicd4Gfq6L7qXq06PMu/WMi4sY4HcYX0bUTGurDX57+Ta6oooyRaRU0Tk6yKyTUTOKUj3IyKyX0SeGVuB0LiRounjN2woHrxY9YmobJ+EnxjLMTEM9uC9sVHFfW2C6/Zy5tYyAmKPZnfjJZCJx3q6DrgbIanyWSRKhURE1gJvBk4FjgeeLSLHB9L9EdmrHStji4DdRdD3McSISZG4lL3rZNH+7DYI9a5LjIex9MRyrZE6QXbwNcA+t5ZtlRgR6W8MSVeIyPkicpOIXGWte5aIXC0iB0RkS8G+14nIV0TkChG5rMt6xlgkJwLbVPVaVd0LXACc7kn3UuB9wE2xhbsXmCsiIULur9gR8YlqJPGYBlN40q0zfsS2TDLrxIiHERAzdsSd+RcyEVlz93dX13CHFsnbgVOcdVcB/xX4dMT+T1LVR6lqUHDaIOa0HgncYC1vBx5rJxCRI4GfBX4S+JFKFfA0/rGj2k3so2xCxtB3opgkHNNgLNdyXWvE7cpvfruDEPfscQch2h8jLIa1zqfbwHtX40hU9dMicoyz7hoACc0zNQAxZ9dXW3WW3wC8QlULZ0oTkbNE5DIRuWzfvu/cvd6+6GLjHG46n3VTlk/CT7I+psNYRKSIoodFH76BiLPeWsZ9FXJr2a25iY2Yz/qGR1LMCGMkCnxCRC4XkbO6LCimudgOHGUtbwZ2OGm2ABfkCrkJOE1E9qnqB+1EqroV2ApwyCFbFNqxSFzLpEoPrvQWxRlJPKbD2ASkyBqx11UdP+K6tjLh2MPMnXUb83NtGXw9tkbXa2uTE7vYmreRbfF4Vd0hIvcFPikiX1PVGHdYZWKajkuB40TkWOBbwBnAc+wEqnqs+S0ibwc+7IqIt3CP9VCl55XPXWULQ+yYkmUnCci06Ou6tR/Y6lLlAdGl2K3lWiO+l1kZK2QDXVsjNYVkZ5exC1XdkX/fJCIfIIt3DyMkqrpPRF5C1htrLXC+ql4tImfn289toyJVguYhi6RsYGKZeCybuCy6gMQGc6dEH/V1yzD3UYhYa8T+XXRv+wLsM7eWHWS/zVl2LZJ12DESke7iJGMbRyIihwJrVPXW/PdTgf/RVXlRTYmqXgRc5KzzCoiqvjAmTxMncq2QphaJ6fnluriqCEoMvv2n0jBPpZ6xtPXEDONqDGyGEJCm1O3uC6utkdnYETtG4sZGzCBEu6eWLSbd0ZWQiMi7gZPI3GDbgdcA3wP+ArgP8BERuUJVf0pEHgicp6qnAfcDPpCHG1aAd6nqx9qvYcZompQ2YiS2QPhcXKHfVeIkRRdLG66Arhlz3WLp8hjGaJV2XZ8m+bdljRQFpbN70wiGiZHc5iybF1m5wfWZmEzx2lfVZwc2fcCTdgdwWv77WuCEDqs2x6Cn1hd4a2qRmO1tWh9VGaugjK0+Veiz7mMSky7rEZt3zLmv0t03pl5GQGa9te5iJh53OMv2+0c2kInHwWRWiRGT9Z33RhzLNTMEgzctxhXlrrO/XYosErPddnHZefUpMGW+5b4YQx3qMGS9xyAmYxCRIsr+nyrWiPn2jx0xImHHRWwxMdsNxrV1MJk1cnDVQ6vM2GIkfTOaJia2W6C9LcYiKVsuS2tT50IZq3UyZtK5Go+IhP6LIpdWnbinT0xm1og7kv02ZtaI/RIrOz6ynkxE1ufL3Y1qh/Riq1Hcsu5F5/O12oS69/qsjlirpOvxJENZJ1NrlMdU36GskrGISAxFD4BVe825vbSyILuxPnaTicceVru2DCYecjB9D0ZMFsnQFQg8rZTN/gvzPbNcKwNWi0Ss5dEVfYvJmBrlMqZU1ylS51qPsUYMdbv7mrq5n927ffNq3WEt28H3vayOiZixI7NeW11fY0lIhiq8INgeSmf/WW43X5/F4e5j1g8lKmOJm4yJdD5mdHEddiUisQH2WMvEWCPmM4uNuNaI+W0ExWAsj3X5Z8Pd67ocQwLJIun3FWIBQtaIT1hC7q+Qe8zOM5SfW26oXm2xzBecSxKRGVO6LkL3UWxMBIq7+86sEVswjIDY40cO5LnZIrLe+mzAxEfStdYdg5/a0LTxoWWfJREagGjjs0p86+18u2TZg/BTOe6+rNWuyhizS8sQtkbuYLWAmEC7PZp9DTMLxPTUMkH2WdffLll2i2RUt7PbFTjGzeUTE7PNl7f9tjV3e90LISRSsftOpVFdRqbcOPTp0vIJR9W4iD824rqz7BHtZtr42ViRWbB9A/MB9/I6NSEJyUCsKXCqxf7ZbqDd10PLEPqTQ4H5GMq6Cccex7KJyTIdawyLFhep+iDlWiPhnlq3MR8fMZjguh1gn03WKLJm1UNq2yQhGQFllodve2hQYqiHlsEViiKBaXphVBGIZRGTZTjGKoxdRGL2adpLy9yvxdbIbcxcXWa2X5h1+V3P/ABE01urn/iIahpHMlzhERdg2VOS2wC7lolZF1uXNl1edn6xvuLU0Cb6puiaK7JG7HVVguww78qyrZJZbMS1Rsz8Wva7R2wRMZbIodi9tboeP2JIFsnQFfD0miq6IIu6+Ia2ufjcV75uxW0SKxJtisnYhGlMdRkDY7BG6opIbFdf90Es1Etr9264444DzLuv7gBuYWaNGDEx1oixQjaweiCiEZO1wZ6cbZKEZALECkpoRHvVsnx5+parMoSYJBIhmopIHSvEfNuurN27s8/MbWUEwwiK+exmNiWK6ZVlLBJjicxPjWLiI12ThGSkxF6gIYukTExC27ruDhzr6kpistgM3ei0ISJumqJYpr3sD7AfYCYgdi+t3cyPaLdfYGUskA3Wt91bazbjb5eB9sSIhSSEGw+x1/vcW0X7FJUxhrm32hCTsQjSGOpQlaEb+6rE1reqiLjLZRaJ7x4sCrBn1ohtfdzGvEVyG5mg2CJix0Ts7r5GUFZPi5JcW90x6tu77CJwBcIVE1gtCEXb7Lzs9F0OFkuWyfLR1fVUdq3GWvfu77LXYMf0sHTHihQH2G2r5BZm1ogtIkY4jIgcmn9Mb60Nd7u1+ui1ZY5zWZlEExVzEbjdgKsKhi8/31sWY4QlZA2F0nYtJkmMlpvY+8f3u2jQYVG+vnvEFZFdu3wBdiMmtzAfL3Fn+TVxEWORGGtkFnDv85pfdotkFHNtVcV+yoi5wI0oFK335WeniyXkE66yT900ifaY6vkuu5ZD+/h+xww6jHkI8rm1du0yD2l2jyzTQ+s2a9m4tIxby51Ty1gj9mSNa+eskT7iI+Z9JFU+MYjIKSLydRHZJiLneLaLiPx5vv3LIvKYto8thskJSejCDflvfaLgfkIXmrutqcAUNU5di8lUG8ZFY2z/Q6yIlD2w2XERu3eW+Ta9s2wRmY1gt11YJh5iu7TMdPGuJbKB2eBDY5lkbq3QQ2ZXGIukyqcMEVkLvBk4FTgeeLaIHO8kOxU4Lv+cBby11QOLZFIOjyo+Xp87p2j8SFmPE9ul1SRuUuRmWuSYyZTqPbbGviuqiIibLrQutpfWvEvLWCW2mJiuwHvzdfYARCMgtmtrHfb7SKq64tqgg+vmRGCbql4LICIXAKcDX7XSnA68Q1UVuEREjhCRB6jqja3XpoDJWSQ2IRPevYhc68OXtmia+bZN46Esk2VpIMfKWM6/734xhESk6tO92zvLCMiuXXYvLTsGYmIixr1l9+CCmTWykdXWiN1ja37syMS7/R4J3GAtb8/XVU3TORN5RpynzL3l6wZc5paK6S7s5h8bfPcRYwmVBTPrPGFNyTIYgrE09l1RdB/UFRH3PvD10rJdW7NeWkZAbqM8wG5Gr5sR7G6gfSYmfVsiUDvYvklELrOWt6rqVmtZfEU5yzFpOmchmxS7kTfYf7JvLi5jecQMYGzi2nJp4uqamiiMvb5JROJFxL23fCJii0e8S+uW/GNcWjATCdcaMQMQZxM0DuXWqikkO1V1S8H27cBR1vJmYEeNNJ0zqGsrNuhUxIYNqz8Qdm35XFlFri+XWH9xFZq6utosb1np+pwMec5DLltDkYj40pfhc2vNu7RsC+QW5i2TPcz30oL5aeJta8Tt8ruedev67a1l6CLYDlwKHCcix4rIeuAM4EInzYXAC/LeW48Dbu47PgIjskjMBbdx4/y6ogs4dKGE3uXuS7d7t99y8e3TlVViyq1jmUzNxTVGq2SRhdV3rn3xg5CI+EQI/P+jz51l99JSNT2ydjMvKLuZd3EVWSOHMz8I0bZIVo8dCdW/bboYR6Kq+0TkJcDHyQ7wfFW9WkTOzrefC1wEnAZsA24Hzmy3FnEMdksfODD77YtpQLMLwNwk5qko9mbwubjsdO77TprGSsrq03Rb3fK6ZExisqgiUiQgUE9EiuJ5rhViWyKzrr7GfWWLhz27r+mldZtVgj0hoxGQw5mfDiX7Fllzdz3LPAtt09X7SFT1IjKxsNeda/1W4MXtl1yNkdzOzQkFz+2Gv0w0QoLhEwmf8Nnb+u4eHBOgHxNjEJO+RKRvsRpKRHzWyCwu4rqxbmU+LmJ6adkBduO+MtaH3dXXng6lv+niQyz7yPbBm509e2YXduiPKGt0im6cffuKrQhY/b73srx9r/SNsUpCYhRKF2tFVdneNH2bDCV+i3rDh85jUTzE/h0TWDeUBdd3786skVtvNSLiBtNtd5Y9dsR1aRnxMFaJ3VNr1mPLHsnuHksfLLuQjGIciR18sk3jNoLxtlj4LrSi4HsoIO8znYsClUXHXWd73f3aSt82fZY/9LF2QajRdNfHdESxt7u/DSFLxLZC5mf1NT2zXEGxYyV3MHsPuxkzYgLsh5PFSOzYyGw6FNcaKZtkMtE+g57eOg1e2QXhc3G5lkfsvj7XV2ifGBeXz2IpeyqvEt+psr1p+rZpKzZWlnffdFl2rJUe68py9ysTEfPtC67v3m2C67aI3AF8n3kX125mlojppWXiISbAbgTlMOanj/dbI6HjaOJyLmPZLZLBddq9IIvSVBURe51pKKu87MoWILd3l5u+iovLR53YyKKJiV0PQ536LPoNXcXN26WI2O8UcS2RWXDdDqbvYr7rrx0Xsbv62gF2u7eWWW9eXOW3RoqskKIHv6Ys+nVXxAiajdUNmB03gWIrIxY7LhISE7unl1tuqLyyXlxVRKVObGRRxcQw5Zuz7bpXfZCqMntvrIgYfCJiLJFMROzg+m3MRMSNi9hdfdcyb4Ecni9vZN6lNd9Tq+94iI9kkYwE2yIpu5BjLpqQ+JSJib3NdQ/EWDI+8bCXY4SlqmgsupgsM3Wu9dgXUZWJiO86NbEQNy5iRGQ2ct2OhXyfWXDdTI3iBtfNoEPXpWX31rLfOzJvjZjjHlJUVA+UJ1pQBo+RmIvRHYgYY5W4aUJpQ+t84mBflPaN5Forpg6+fctEoysxsesQu1/V8hP9UEdAIM6VVbYuhBtcd11as26+7qh1Ox5SFhcx4rGRzCLZYH3C1ogvvhlzTO2hzI5l+RhNU+HGSKpYJVUaPdsqgdViYi9v3Gh6nsz29cVF7P189SqzUkLUEY06sZai8kPlJIqp4+aocp5jrBD7d5lwxIqI+651/5sO3TEj7gur3LiI3dXX/tjToRihyWb4da2RmGPoFmV+DMxyMdip13x+SltA7IYuxiqBsFUS2sdt3CFeTNz0vpu5KPheJV7i1rfKtrJ97Po1rUOiGXVifTbutV/VleUuh9xZroj4p4XfzfyIddci8XX1nVkY83ERezoU485ad/dv1xrxxYMS/TGa027cW76gXqxQuA1erIsLwj227N92Gp+ry07jiqJPTGKFJQXhp4fvv21y/mLdWPbvpq4sCIuIGXCYdfO9lfkBhq6YuC+suovMGjEiYuIiRjjsgLsbYF+DyLrCWEj/bi1DipEMgu1vdRvYOnlVdQ24+7hC4bNK3DQ+IehCTIqOsU8xgSQosbRxnsoExE3TtivLjocYQSkWke+TTX9iu7XsHlp2N1+3q+/BzAYf2rP7bmQWYD94zhIx1kiRsPRDipGMAjvoXtUqcbeHGsiYuEpVMXHjKG4+YxATU0aV/MrqUJRnojlFlrMvTRXro8ztWU1E7F5YZg4t2yIxFoqZkNF2adnWx+HMgut2fMQewT6e7r6rSUIyODENqhGLmCcpt+EuEhVfQxojJvY+YxITt25l+5TtV6ceifrECIibLtaVVZR/mYjYgfV5EXFdWLvJxowY8djNrHeWERET67BjIbagbGC++695V/vaueOyrZGy4+uHJCSDYALWdiNbJhh1hKaowYuJmfjExI2pjEVMira17eoy+8DQN/BiUNeNZf8ualSLHj7Mb1dE7Pmz/CJiu7BsMfH10FqXl2S/3dDtqWWLiB1oX19ojQx//S23RRI1aaOInCIiXxeRbSJyjmf7c0Xky/nnsyJyQpVK2D233HX2xzehY9F2H65/1X3CsZ90bN+r/XvjxtXr7PUhH65djvltf7u/65w3e1vVfWK2t71fImw1x1ohMa6sJiKya1eRiLhzZ93mfPYz38AaETHB9Y3Wx57Z1/TSyl6hawfYQ9bI8Byo+FkcSv8GEVkLvBl4Ctn7gS8VkQtV9atWsm8CP6Gq3xeRU4GtwGNjKuB7Gi+Kidjb2rqIbOvAXjYXalFsxATc246ZmO0x1LFAurBOzH6Gcd3k46OJGyv0u6ory/y2H8ZcK8TERopF5Pv459Cyx1bYImJEw+7u6/bYMmJy8JwYho4xXW/DEWORnAhsU9VrVXUvcAFwup1AVT+rqt/PFy8hewF9JepYF7FWSww+S8JnadiWirveTh87Lb0p2/6261Tl/BWdoyr7xOwbW6dkqaymyAJxg+kxVkhdEfHdO74xIrfeeiDQO8u2REzPrFAPLWNpGBFxg+t2V1+TNuvV5RvBPk5rZG/FTzNE5FkicrWIHBCRLdb6e4vIP4vIbhF5U8H+vy8i3xKRK/LPaXXrEvNXHAncYC1vp9ja+CXgozGF2zERKH9CNhRZLFWewH15hN6maHqTudZJUddgk59bZuglWq61Yo6n67iJKaco37I0MXUzjKsBWI17vtusbxdWiC/fqiLiBtbNZzZi3RYRd+S669ayG0r73SLuu9bd4LoRmlkvLXd2X/NAVnacw9B7jOQq4L8C/9dZvwf4PeAR+aeIP1PVP25akZi/QTzr1JtQ5ElkQvKEwPazgLMA1q07etV29waOdXHBfAPs2y/2git7Z4lPOGyRsV1d9psfXfeXvew28k1cXXXdWTGurLruLl8+Ln01CHXjP03r11RA7OU6MbWieIgvJrJnjz13li+w7orInjydGxOxRcTuoWX31DITMm5g1qMrE5N169Z4j3FcAgJDBNtV9RoAEXHX3wZ8RkQe0lddYv6O7cBR1vJmYIebSEQeCZwHnKqq3/VlpKpbyeInHHLIlrvFqIpg+Lb5ntp9vbhin6xD1oRh48bZTWcLh09kfAMYTT18ouXGTdx6tyEmJq8q26qkqcOiur9iBcRNW8cKsXEfRsw68+1aIfa7RGZTwdsvpgqJiHFn7Sbr3mtcWsYSWcdsGnjfOBF7vMgGZl19M5eWOU6fNVJGWw8+8UwygP4SEXkBcBnwm1aIohIxp/lS4DgRORb4FnAG8Bw7gYgcDbwfeL6qfqNKBcpcLlA+ILHoCTfWpeJuM8F244Zyfcm2tWELhk9kzDH4KAvCm7pVtU7KGvy6YlMlzbLTphVSls5eZwuGwRURN6huWyIzETGCYY8X8fXOMgMNXXeWLSL2OJGNZG87tIPrblff8DQo47RQalkkm0TkMmt5a/6wfTci8o/A/T37vkpVP1S1QIe3Aq8lq/xrgT8BfrFORqWnX1X3ichLgI+TXSHnq+rVInJ2vv1c4NXAvYG35GbWPlXdEsrTYDeYMRM0+giJTMjFFZu/HdRzYxquQNjWh3vz2paRrzeXXUaIkHViL8fs69tm51VlXzePonymTtXjGtIKCa0LxUNW98yyx3/Yr8gtEpE9rJ76xBUR487ayGpL5GBmLq35mX2LrJGY/8V3DXdn/VYWkp1l7aSqPrl+fYpR1f80v0XkL4EP180r6hZR1YuAi5x151q/XwS8qE4F7D+6qYvLzdfd5l5QZZaO2acocG4Ewo2N+NbbsRE3LzduYh9HyDqxj6no5igThKbWSZ20U6ErEaljeVStS5GImGvQng5+JiK3WN+7yaY9scXEuLxcETE9rdbgFxHbAjHf9qy+a4CDV7m02qQ7EZnegEQReYCq3pgv/ixZ8L4Wo7nlfQFnlypi4rrGfHN4hS5SX+AbwpZDKNjuxkxCQfiyuImNzzqx6xy6UWLExD32Ktt9aWPTLwJtWyFl28qwrynXleVaIrOeWe68WXcwmzvLtkLMDL6+SRg3MB9cN+4sM2bEiIhxYxkxWd3V1z72JmPHfPGiRUBEfhb4C+A+wEdE5ApV/al823XkL7oXkZ8BnqqqXxWR84BzVfUy4P+IyKPIVPA64Jfr1mWw21ytfl8+F1eoYYwVE3e/0EVUZpW4cQvbOrH9zRCOjxiRgfi4iW+7L47iHluRGMe6qtqMjUxZVGLr26UVUqUeEOfKsi2R+aC6bZEY0QiJyB3MgsvGCjEuqjIRsadIMSKyAZ9LK0Rd66w7lNlcYv2gqh8APhDYdkxg/Yus389vqy6D3tqhhtHnjooNvvtiFaF93PKqXLhFvbFMfX0Bd1cszbJ749vBfpcyd5db56o3UVfB9imLSoixiIgbYLdFwxWQWTzE7ZnlurXs9XZQ3YiImT/LFgP3TYeuO8uIjUlrrJK1d7u0Ys5TnXPULdNzbbXJ4H9BKJBcNE4EysXEF2BzBcoXuI4NwoO/oa8aNwnlY+dnjilEyDrx1bkKVYLtTZ8Qx9EYzIipjy9NFVdW2bYY3IcX+xNyZc3HQ/aw2upwxcUWEdNYrsk/9rxYtrXhvir3UOwpT2ausEw83NHr7n0WOmfjIgnJoPjiI6GG0V5fJCa7d2euJoPbu8rd13WL+S5Wn+C4ri530GEo4G6vty0M9x3xIevE3qfM3dWEWKFoGmgfk7CMSUR859XnsrQfsOxr0XzPXFluPGQPq0XDuLRcEbFHrBsrxFgkJi5SJCImjT3oMMvHJyB1/4dQmm7dW8kiGQVuw+gLTNv40riUiUnI/VV2MbsNOKzO1xYON27ic3UZTHrXVWbKMPu5hATFLDclxjqxy2oqBG1aVm0TIyJlLqo2fPxuQN1nhYRdWfa8WLaQGAslZIkYAbHjIkYgDvX8tt1XtoisvfvbdWn5rJE6/33ROe9GVJKQjIIiMfHFAWLExPf0BvNuJRuf+6vIOrFxrQYjHHYDX+Tqsq2TkGVirBP7GNzj87kKfeeiKlVEoot4SB/iUsW1aYjpQl6ljDJc8XDjbG48ZLUry+6dZYuKKyL7mZ/yxBYQ+zW57utybRGx169blYdvQsaicxR77vp/6FAmOrK9FUYlJOB32RT15CoTk927szTGMrHT+WIpbpqqDYs9HqTM1eUGRn3pzXq3TJ+7y6ZIUOx1dYi1Ttyyuri5+wzg13FnxVJ2TkOxELejhisg866su5gXCp+Law+zbr17me+ZBbO4yAbreyOre2u5MRFbfLI8jIjYVDl3sQ94ie4ZxWkPBb3t9aFAub0c6+ay07nuL7sevrq5aULby1xddn1tK8RnnfgaDENoIKNd/7LjqSMqVcXELadLURlTfKVKQ1nkkrQfOsy37cbyici8K8u1RvY422wR2cO8m8a1QmwRsbvy2jESd6ChPVjR/J6dE581UtWtNbyIJNfWKLAbgj7EZGUlS+uLpcBql1pV66TI1eWKhh2I941RcfOy3V1QLijgt1J89Y4Vljpi4pYxJkEpSl/HpeWjiuVhL/usV/v3aivEFQ9XOOz1toiYeIjdO8sebOjrpeX22LJFxCzbgfm1wYGHRcR2XBgG8z6S5WTw01+E2/D5rIAmYgKztKZhd+MPrkut7AK205gbxXfz+1xXvhiJr2eX6+6yz0PZVCu+4/C5ztx9u6JLK6WJ0NUhtqyiehVZIT4BcUVkZoXstb5tEbmL1QMLbRGxJ14037aIGFeVHQNx1/lEJC64HmONjE9EDClGMip8DZ0rEk3FxBUNd4ChWb9v30x4XEupio82NIAx5NIyx+0brOgTJ/dcxFgo9rm1l2OPqW26sFLaEJOuYzuhbT4Bca0QszxvhRgRMUJxm2edLSL7mVkiM5fTvBVhi4gtHLabq0hEVotHXWskZn3/pO6/gxK6odwGu4qYGMoGLZp9TSNvX5T2fiZgb8QnpnFyxaaKdeLGTtxtbh1jBMU+Zvccm99mX3v7EHTp9qpDrBj50pUdS8iVZf4r9yHCWB52fGTeCjEBdXudz31lxMTtlWUwPaxcATGiciirRWMD9nQn8yIy79IyFLkMyx7UYsWlv+s4Cclo8LlgqoqJ/btMTNzBiW4cwh20aNbbVH1aqmqduMfibrOFMCQopiz3GH2MRVTaEpQ+XVyhsmIsEFs8zLcrIPbvTECMOBjB2MO8eLiCst9ab3pnmTmi1uXftivKFRHXjWVbIraIrA6u2y4tnyCU3UdlIlK2f7fXcLJIRoHrGy4TE6guJmZf+wPzDbH5bVxa9no7L0ORm6soTSiwHlpfNjeXva0tQfH9H+76PmhDULoQk9BDSlF93XNm/weuW9N8bBfWvn22G8uIgRvrsEXE/m1ehWu7sfaTNf4Gu4uumcrEFg3bKgmJiGuJzKhqjcSIyFgs1yQkI6RMTCD77YpJiKIBiEY0zLIRDnsMiis0dcTETW/qE7JOTCMS2yU4VlDMt8/tVcXd1be4dG1ZFOUf2uZzNdr7hNLa233i4XuYyARkP6utENcaMfEQW3CMC8vkYTCNvvltRMGM/bBFw54nq0xEbOpZI3VdWyFCLvBEc0YrJBD2ObsC44spgP/CCT1FunEQV1Dc9bb42FSNnYDfCrEtFJ9g1BWU0M1UZqXEuru6FpcmYhKzb5mYQHh7rMvQ/h1yY7m/Z24sM1DQjnPYPbTc9UZE7Bl7bewxHbb7KuTa2mDtExIRv0vL9gL4BCEUG2ni2upPONLI9kng3uCulRISEx9NgvC2m8sWnypPSna9Q9aJWS4SDN8YE1s03HNhdxv2uQtDVoqPKu6umMa7L2LcZGVp6tQ3JCL2bzsGYrZlAmJ3zbUnTzS/9zjfIRGB1b2y7IGGtjj4XFmu0JSLiCFkhYREpC3XVn9WSIqRjJoyF1eZmLQRN3HLN3mFXF2GGFdXrHXiurtMHX0WiF2Gvb/by6vsJguJik/UfcccewPHCk2bbq0qguJS9cnXZ32Y75C1OS8gxgoxQnHAWTbpXIvF/HbxCYjd+NtWiC0qPtdXSEQy7F5aITGIFZEqAuJL162gKGlA4shpU0xsbIvCxm5oq7i6VlZmjW+sheJzdcFq68SmirvLLsN83DEmofPp1sc+P0UUNdJ1buY2BcQlJIKx+xStD1khoa698wJixML8tmMdrvVhx0324xcRexyHKyD2si0YtoCst9LGiIg/LuK6t+qISJfXQzOSRTJ6YsUEwoF18DeSsa4u+6K2G0vX3dUkEG8TY53ECoorJm7+ZaLruuDs8+NzOdoUNdZ9urXKaFKXkIi416RfPEzswhYQIwi2xeETjDucfXyNme3OcgXE7aVlWyauiKxnXlBcEZmVF4qLlIlIkXD47pOyaWrch57uXF39x0hE5PXA08n+/H8HzlTVXSJyIrDVJAN+P38tr7v/vYC/A44he2f7z6nq9+vUZTJCAuFGym2si6wTm5D4uK4u29pw3V3uDWGnt/OyywxRZp24YlBVUOwy7O2+81AkLFVExT1mX16hbXUJCWHTPGLS+YTDfLu/5wXE7lllC4gtErbrylgproi4AXW7+60rIPPzX60OsvvWF4lIcXC9qoiUucJiKLLs26d3i+STwCtVdZ+I/BHwSuAVwFXAlnz9A4ArReQfVNW9qs8B/p+qvk5EzsmXX1GnIqMWkqIn+SILxV4X6+py0/sw1kaZdWJulKK8Qsfls7SgfMqTooB7maDYy3YcJcbl5dbBpmgkvb0udE6qCEtZ2qYi5dvfZ9na68vFw/x2BcR1TxWtd98ZAjML5GBrXUgAQlaILSDdiUgVl1adSTJturNGhkFVP2EtXgI8M19/u7V+A5m55ON04KT8918DF7NoQlLkh3bFwv4dEhMovhDbtE5ckXHXF5XtrneJtU58+btpfNaJ+R07V1eo7rYomXNa11rp+uavI0Ju54PQt/ldLCC2tWGLhc8KMetgtYDYuDERXyzDFYqQ5dGuiPjEoqqAFFmY7v/Vj1UyeK+tXyRzUwEgIo8FzgceBDzfY40A3E9VbwRQ1RtF5L51Cx+tkBiK3Fmh3+5TttleJRBvpzd5m33tgLstMG7e7nafoMQ8JZVZJyFBsN1dbrqQoLjluQIba53YuJaOKcNss4/TXVe0vg5NRcNOY6eNEw8IWyDGLeXGR1yXlREWG3vZNO7u7L1uzyz7t2uJtCsiZtkVkTIrxBWQIvFw0/UvJrWEZJOIXGYtb1XVrXYCEflH4P6efV+lqh/K07wK2Af87d21Uf088EMi8oPAX4vIR1W1szMwGiEpapiKXFzu9jJhiRkJb+dh9+wyDanboPpuEJ+7q0xQYs6Pm0fo5rDLcwXFl1/RwEXzu8xKiRGXKlaKT1BiXEyx24seIEJpiqwPsMUDwtZHKK7hc1fZriw7b58ry10OCYgtCBvwi40tIHjSQEhE3Hsh5MqKEZBY8XCp8qDTHpWFZKeqbilKoKpPLtouIr8APA04WVVXubBU9RoRuQ14BHCZs/k/ReQBuTXyAOCmatWfMbiQFLl1Yht7d7nstzsliVuWr+yicSdlVsjGjeWCYtczRlQMvp5XrmiEen/Zx+paHu52G3d8SexDgPu7zEoJWS1VLKGYdSGLw/5dtM4vHhAvID73liswdo+gWBGxu+Pabiw3BlLmyoLVQpOtc8eJuALhe8gqExDf9V92T/QvGi7KbPLLfhCRU8hiGj9hx0VE5FjghjzY/iDgYWS9slwuBH4BeF3+/aG6dRlcSGIpc3G5yzG/XeukrJHyjTvxuYB8omHGnRQJig/XaglZFb4pTmxhgdUDEkNuLzedW5Zdh6oTQNrnzPz2CUpd8bDLKFoOWR1lv8PiAfPCAPEC4lohIREJPfH6RqtDNSukyJVl5zsbse6+5dC+rl0rpMgS8QlIVUvEd430a5UMEiN5E3AQ8EkRAbhEVc8GngCcIyJ3kV08v6KqOwFE5DzgXFW9jExA3iMivwRcDzyrbkUmIyQhitxeboMFqxt81zqJKSc0NYpvm+tiihEUnziF6mSwBavM9RQK2NtpfILinrsqglL2IODWvejYq5yXqgJSLh4wP9jPdTfZQlJHQOw8QyOlQ1ZImYC4PbFi4yEm77Ary2eFlFkq5rehSDx81un46FdIVPUhgfV/A/xNYNuLrN/fBU5uoy6jFpK6T6IhK8VdLhKUmLJjenfVFRSTr6lfzHkJiYlrmbh18aVx87OPtw1B8VmJbllF5YQIiUhRLyvfOr94gN91Zf8+YKXzCYhJG7JCzHZbqHzYYjLvbgq7peyxIa6AQDiovtqVFbJCfOuLBKSqFeLey+624YRm8F5bgzJqIYmliniULZe5u3wNYBNBMctuHMXO3735im6WkNURukl9AXSf8LQtKO75dMXbrb+vDF9evt/u+A77d5yAuI16VQGx0/nGfsSKiCsesFpAzO+6VgisFpGwFeJen1UFpMwKGbcFkjCMSkh8F06VJ9EysTD5lS373F2+xs4tq0hQikSjSHDchtTkX4ZPGIpu2qK4iE9QYntuhdxjZd9Fedr41heJSH8CYqcxAuKuw5PeZg3zYmKPDbEFxGzzxTXquLLCVogrEr7r1RWQIvEos0JiHp5ChCzS7kjTyI+aGJPXR5lfPrRsyoyNn7h5+QTF5FkmGq7by97PfEwcxlA2otzk4XMrufgsAXt/g6+3WNF5KevH7xOTpn3/q4hIdRcWzMdAzLYiN5a9v52HW67NGmc5JCBmXawVAsUiEufGihWQkHgU3dvu9eeuK0rfP8m1tTDECIdvnfvE764rEpTQU7Xd4IdM/1i3l7vdzguqN7ZlolKlF1XsCHg337JYiSsmRRaL2df+LhIRvxUS6oEFxUF0sz3kxoJiIYLyBsidLwv8biyz3MQKCXfpLRIQn4Vi9vd9x1D2gFJEmaXaPklIRkWTiydEFYExdfCtK3J5FZVpC4otNLYVUnSj+rabsuvcqL76+kTFZxHYx2v/rho/cYPfRWLiI+Y6CVkg9u/yMSD2b58bC8JWSCgfsw+sbnzW4yckIPZvV0BM+uoiUkVAqlyTsRZI1e2h+BgUDzBtj/Q+kqXFJxyh9U0FxTwJu09uZt+QleJud0XF3m7XOfbJz9fgu/WA+Dm3Yt1RtqUBxWIScreF8JXvs0jiBhKa5aoiYi/j2ScWn4CY7/atkDoCUvXBxp3dwGD//2UUCYdNfzP/QoqRDEyR66QqIf9/UVygaB9TH986+yL1BcBDbi+3TN8NWSQYPlGx87Hr7B5XlZvVpegYbGLFJyadbfHEWDuh/d3f1UVkr7M9ZLmERCTEWme7O0ZkGAHxdQgBv7iY9b5rLqZjSFWKRMRe7ldEkmtroSgznauKidnm5u2uq2KluGWGYinmd4yomDq4N7xb7zJLpexJzxUT91jsNGXWSVE6XxzLpHPFzM6rjNVpbHeELQpm2Z2axN3uc2dBdTFx8QmI+Ta/q4iI/XKq1QISGtdUZn244tFUOELXX5F70v0NfbmzEobRCkkTq6QuIVdX0XbXkqprpdgulxhLxSwXzWXkpvXVqexYi/BZBq7QFFkdselsbDGx628vV+/t5bokXMHY6yy7L48K/Q7hdus162zaEhBjtTQXkJD1YV9TZQ8qEL626grHeEQkWSSD4xOOKm6YmAs4liLrxK1P26Ji9rUtFXv/GCukyN3gTpBY5bzF+qVtQmITyj9GBIrSuHEVe52/7utZPSkirHY5xWL2831j5RkSDvu368Iyv5sJiLlGqgiI75qKfSixaSoisQLi296tqCTX1ugpEpQ2BcSmzDpx07lp64qK+zv0xG1vq+LeCn27v8uIfTo0hEba+4SlKzEBu9w1ZJNuh+IT9nojNm7a/Z7feNLYIuJzY7ll9y8gxjIBv7CY9VDdAoFwo19HQNzlMgEJrWuXJCSjocyd1UQ06u5bZp24aX3lFYkKzG5MXwPrNoA+95adh3vjG6umKL1b3ybnqio+i8XUr2x6+ZA7zJePWTffSBkxOZj5kewGIwT7mYmJvW4N82LhWh13ERYntxzDOmedKx5mnS0gq19WZQTEFo2mLixzPu3lMvoUEF/60LpuSEIyGqq4s6rk14RY68S3j7ufz33XVFhC5RV1D/Z1u7Tr2ZbbomiyxNC3u59bD/e8hHp0mWWzzjfj8b59a/J81qBqRGEvWYPuzta7Dn9X4HXWt92ba731uwifNVIkHGadLTIHdzIGxGDOuTsXXRl1Zlsu+u3m6dtetr4b+n8fyZiIahrzF6i8keyqPU9VX+dsl3z7acDtwAtV9YuNKtZAUIZ2d4X2MzQRFpPGVxefWBSJRNG+u3evTuNbLqLMR10kJD5sESkSEze9+V0kKNnyGktQYCYq9rLdC8vEOWxhMdvWeX6H8AmJEQ6zzjfQsNkYEPu8mN/2d4iYjgxF/32X4lG2rTuSa6sQEVkLvBl4CrAduFRELlTVr1rJTgWOyz+PBd6afzev4OhspuaxmtCFHnLtuXNrGdwnw6JG3/7ts0bc32UWSZP/pcz9UHfKl7LtdkNq0vunUlljfa+b2z6bkwv882P5BqVVsUiycjPm59Uqei+6+R1rYYTWFRHTQNcREN9yrNuqSt0S3RFzKZ0IbFPVawFE5ALgdMAWktOBd+TvDL5ERI4w7wJuvcYjpUospSgPlyLLLCQw4Hc/xIiBa5GE0sVs81GlMYhplMreM2IIHXto/jSXWTlrrHTm97zF0aRRi2nwi0Q+9Nu14Nxpe4r2rUpsnKKos0TV/KvQjegki6SMI4EbrOXtrLY2fGmOBJZGSKAdMfHlGbPdLdcWApfYOnYxKrmMmPeXVFnvo451tXFjfP5jJfQwYtP39ds0fVW6zT9NkVKEeNZpjTSIyFnAWfninddcI1dFlD91NgE7h65ED6TjXCyW5Tgf1k42N38c/mFTxZ0W5vzGCMl24ChreTOwo0YaVHUrsBVARC5T1S2VajtB0nEuFuk4FwsRuayNfFT1lDbymSru8FoflwLHicixIrIeOAO40ElzIfACyXgccPMyxUcSiURimSm1SFR1n4i8BPg4WfeR81X1ahE5O99+LnARWdffbWTdf8/srsqJRCKRGBNRoTVVvYhMLOx151q/FXhxxbK3Vkw/VdJxLhbpOBeLZTnOTpFMAxKJRCKRqEdMjCSRSCQSiSCdC4mInCIiXxeRbSJyjme7iMif59u/LCKP6bpOXRBxnM/Nj+/LIvJZETlhiHo2pew4rXQ/IiL7ReSZfdavLWKOU0ROEpErRORqEflU33Vsg4jr9h4i8g8icmV+nJOLf4rI+SJyk4h/uMGitEGDoqqdfciC8/8OPJhsvocrgeOdNKcBHyUbi/I44PNd1mnA4/wx4J7571MX9TitdP9EFld75tD17uj/PIJsdoej8+X7Dl3vjo7zd4A/yn/fB/gesH7oulc8zh8HHgNcFdg++TZo6E/XFsnd06uo6l7ATK9ic/f0Kqp6CXCEiDyg43q1TelxqupnVfX7+eIlZGNtpkbM/wnwUuB9wE19Vq5FYo7zOcD7VfV6AFWd4rHGHKcCh+UTs24kE5JJzWylqp8mq3eIRWiDBqVrIQlNnVI1zdipegy/RPYENDVKj1NEjgR+FjiX6RLzfz4UuKeIXCwil4vIC3qrXXvEHOebgB8kG2D8FeBlOj9z5SKwCG3QoHQ9t25r06uMnOhjEJEnkQnJEzqtUTfEHOcbgFeo6v7sIXaSxBznCvDDwMlkb8b6nIhcoqrf6LpyLRJznD8FXAH8JPADwCdF5F9U9ZaO69Yni9AGDUrXQtLa9CojJ+oYROSRwHnAqar63Z7q1iYxx7kFuCAXkU3AaSKyT1U/2EsN2yH2ut2pqrcBt4nIp4ETgCkJScxxngm8TrNgwjYR+SbwcOAL/VSxFxahDRqUrl1byzK9SulxisjRwPuB50/sqdWm9DhV9VhVPUZVjwHeC/zKxEQE4q7bDwFPFJEVETmEbEbsa3quZ1NijvN6MqsLEbkf2SSH1/Zay+5ZhDZoUDq1SHRJpleJPM5XA/cG3pI/re/TiU2KF3mckyfmOFX1GhH5GPBlsvnDz1PVSc1mHfl/vhZ4u4h8hcwF9ApVndSstSLybuAkYJOIbAdeQ/4SmUVpg4YmjWxPJBKJRCPSyPZEIpFINCIJSSKRSCQakYQkkUgkEo1IQpJIJBKJRiQhSSQSiUQjkpAkEolEohFJSBKJRCLRiCQkiUQikWhEEpJEIpFINCIJSSKRSCQakYQkkUgkEo1IQpJIJBKJRiQhSSQSiUQjkpAkEolEohFJSBKJRCLRiCQkiUQikWhEEpJEIpFINCIJSSKRSCQakYQkkUgkEo1IQpJIJBKJRoxeSETkaBHZLSJrh65LGSLyURH5haHrkegfEXmhiHxm6HokEkMwCiERketE5MnW8hki8n0R+QlVvV5VN6rq/iHr6CIivy8i77TXqeqpqvrXQ9VpmfCd/6kgIheLyIsi0q2IyC+LyL+KyE4R+ZaIfFBEnlSy30kiciB/ALtVRL4uIme2dwTVEJG3iYiKyEOsdQeJyPkicouIfFtEfsPZ51EicrmI3J5/P8rZ/uv5fjfn+RxUUH5hXonmjEJIbPIn+jcDP62qnxq6PmNDRFaWufw+GMMxisihwCeBJwO/CRwF/ADwFuB1IvLqkix2qOpG4HDg14G/FJGHdVhlLyLyBLJ6u/w+cBzwIOBJwMtF5JR8n/XAh4B3AvcE/hr4UL4eEfkp4BzgZOAY4MHAHwTKL8wr0RKqOvgHuI7shjkL2AlssbYdAyiwki9fDLwW+FfgVuATwCYr/eOAzwK7gCuBk6xtFwN/mG/fDfwDcG/gb4FbgEuBY6z0bwRuyLddDjwxX38KsBe4K8/nSiv/F1n7/zfgmryeXwUeEzh+BX4VuDY//tcDa/JtL8yP9c+A7+X1Pwj4Y+B64D+Bc4GD8/SbgA/nx/894F+svF4BfCuvz9eBk/P1bwf+0KrPScB25/95BfBl4E5gpeg8e47vKOD9wHeA7wJvytevAX4X+A/gJuAdwD2c//0X8uPcCbyq5Pw/ELgwP+5twH+z6lD5GGv8T5+x0v4Y2fV0c/79Y/n6/wnsB/bkdX9T4JydD/yPwLaD8v/1xwPb544tX3cT8CzrvJ8D/Hv+f7wHuFcH9/UK8CXgkfm5e4i17VvAU63l1wIX5L+fmm8Xa/v1wCn573cB/8vadjLw7UAdCvNKn5b+66ErkP+x1wHvI2sUT3C2mQbFFpJ/Bx4KHJwvvy7fdmR+Y5yW3yxPyZfvY+27jewJ6R5kjfs3yERshawhe5tV9vPIhGaF7Knw28CGfNvvA+906noxuZAAz8ov4B8BBHgI8KDA8Svwz8C9gKPzOpl8XgjsA16a1+Ng4A1kDea9gMPIBPF/5+n/N5mwrMs/T8zLfxiZKD7QOq8/kP9+O+WN7BVkgnBw2Xl2jm0tmdD8GXAosAF4Qr7tF/P/48HARjKx+Rvnf//LvMwTyBr4Hyw4/58ie2LfADyKTLiqiOXdx1jzf/pM/vtewPeB5+f/2bPz5Xu710mgnGOBq/JzewTwAbJr74L8v34Q8HjgvYH97z62PI9nAAeAR+frfg24BNhMJkr/F3h3IK+jyR4WQp/nFBzHbwNvtM7dQ/Lf98yX72elfSbwlfz3rwMfdfL6MPCb+e8rgZ+3tm3K87u3pw6FeaVPO58xubaeQnZxfyUi7dtU9RuqegfZ09Sj8vXPAy5S1YtU9YCqfhK4jKzBs/f9d1W9Gfgo8O+q+o+qug/4e+DRJqGqvlNVv6uq+1T1T8huulj3wIuA/6Oql2rGNlX9j4L0f6Sq31PV68mE4tnWth2q+hd5HfeQWTq/nqe/FfhfwBl52ruAB5CJ1l2q+i+a3T378/ofLyLrVPU6Vf33yGMB+HNVvSE/5zHn2XAimaXw26p6m6ruUVUTlH4u8Keqeq2q7gZeCZzhuJb+QFXvUNUryRqQE3yVE5GjgCcAr8jLuAI4j6wxr3OMIYr+J8NPA/+mqn+TXzvvBr4GPD2yHicD71PVA2Tn5BYy8Xg98JNkDwZXAA8vyOOBIrILuINMiH5DVb+Ub/tlMutuu6reSSbKz/S59DSLUR5R8HmXr/D8//hlwOeC25h/32ytu5nsochsv5l5irab34exmrK8Ei0wJiE5m8zKOE9EpCTtt63ftzO7MB8EPEtEdpkPWePyACv9f1q/7/Asm7wQkd8UkWvygN4uMitmU+TxHEVmOcVyg/X7P8gaX9+2+wCHAJdbx/ixfD1kjc024BMicq2InAOgqtvInkR/H7hJRC4QEbuMKvWLOc+Go4D/yEXQ5YH5sRr+g+wJ/n7WutB/7cvLCKud35GB9D5uKE9S+D/ZdXEfGqrU5b5k1izAfwH+TlXvVNXLgavz9UdZaXzsUNUjyGIkf04mQIYHAR+w/rtryB407udm0oA3kLnm3EYcMpceed2wft9qbT+ceYq2m9+3spqyvBItMCYhuYnsSeyJZO6JOtxA5hqxn5gOVdXXVc1IRJ5I5jP/OeCe+U15M9nTIGSmdFldfEHGEEdZv48GdljLdlk7yQTvh6xjvIdmgVVU9VZV/U1VfTDZE/BviMjJ+bZ3qeoTyBoSBf4oz/M2MnEy3N9TP7sOVc7zDcDRgQD2jrwu9nHvY17cQ7jnfwdwLxGxnzSPZtbYVj3GEEX/k12XBznr7LqUlbOTmSh/Bfi5vJfTY4Afyrf9BZlLqpDc4ngF8F9E5Gfy1TcApzr/3wZVXSVMVvf70Oe5gaJPBl6f96wyDwOfE5HnqOr3gRuZty5PYCaSVwOPdB4oH+lsd/f9T1X9rqceZXklWmBMQoKq7iB7cjpFRP6sRhbvBJ4uIj8lImtFZEPeFXJzjbwOI2vUvgOs5L1k7Ceb/wSOEZHQOTwP+C0R+WHJeIiIuI2LzW+LyD1zl8DLgL/zJcrdHX8J/JmI3BdARI7Me7IgIk/LyxIyl8h+YL+IPExEfjLvJrmHTIxMl+orgNNE5F4icn8yy6WIKuf5C2SNxutE5NA87ePzbe8Gfl1EjhWRjWQuur8LWC8uc+dfVW8gC/7/77yMRwK/RNaRos4xhoj5ny4CHioiz5GsC+/PA8eT+eZN3R9cUMY/Az+b/4f/m8wSvp7MTfRxsqf9v1TV98dUWFX3An/CzM10LvA/zfUoIvcRkdMD+5ru96HP3/r2I/MunEDmdn5Uvu7pZG42yOKRv5ufy4eTuWvfnm+7mOza/NVcQF+Sr/8na99fEpHjReSeZB02zL4uZXkl2qAsiNLHh7zXlrV8LNlT0//GH2y3e0a9kPneMo8lC7p+j0wEPgIcHdj3D4G3W8tPBrblv9cCf0XWGN8IvNyuJ1kQ/jNkQdQvBvI/m6x31G6y4OmjA8evzHoDfZfspl/rO7583QayRvfavH7XAL+ab/v1vJ63AduB38vXP5KsUb81PzcfZhZ430DWIN5C1mvp11kdiH6yU4fgefYc39HAB/Nj20kWi4DsQebV+X/9HfIumvm2uf/dPb+B8785P67vkbkVz3bOWaVjbPI/kbn6LiezYi8n72CQb/tRskD998258JT1LuB3AttW9Shztp/E6l5bh+Tn/un5ef8Nsmvz1vxc/a+iPFu4x+8OtufLB5H1TLuFTFh/w0n/6Py83QF8Eefeyev/n/n+bwMOsrZ91D53ZXmlT/OP5Cc6MSAiosBxmsUxEiOlz/9JRA4ni31dQ9bj7Rqy3k5nkgX4t2hmnSYSgzMq11YikchQ1VvIBupdTTaIbifZ0/RDgJ9NIpIYE6UWiYicDzwNuElVH+HZLmQD904j61XzQlX9Ygd1XViSRTIN0v+USPiJsUjeTjaSOMSpZFMdHEc2Mv2tzau1XKiqpMZp/KT/KZHwUyokqvppsuBliNOBd2jGJcARIuIbT5BIJBKJBaSNGMmRzA/S2k61QWCJRCKRmDBtzHLqG4XuDbyIyFlk7i/gkB/O4oZmd/c7lgH7C5RVteqhQPlQtc472bkxXF+B7rpQpWL2jd0Wsz1Em3HputdbzMVQlCa0rcr6mHUt3k/i+W2+3WIk/6zxLJvfa63vlfx7DbCebFa5FWXdur0cxJ2s4y42sIdDuJ2DuYOD7rgr6yh8C3A73LEv6wv8TdipqvehIQ8R0dsr7nMjfFxVi8IGk6ENIdnO/GjfzfhH+6KqW4GtACInaDbuyq3KuhpVOLjGPhXxnamyqtY5u2VD8e6quV807jRTvgLdwnxpQtNVhQ7Al2+VfYsomjqrKnWvtZjruuiCCe0fqo8vfcxF3PBesotY51lvb9/grDevrjs4X96Qf9bm6zbmyxvJprI8lKxD9P3JWp1N+7jn0Ts4ihs4iht4GF/nB/kqx/NVfuQ7V2Wjnv4f2VwB34RrdsDxq6eyqcUdwIsr7vO78dMtjZ42Hj8uBF6Qj95+HHCzqt5Yvpv9uGJfXXUaizYbihap2ri3JgZdMolKJsZKlcsntikwU5nuWWH/gbXcyXpu5xBu5xB2cxi7OIKd99mYTSxzLNmcAsfBD1aZaa6ENWRaV+WzSJQ+M4vIu8lGym4Ske3Aa8ifNVT1XLLpIE4jmyjwdrIBUxFITPEVuIPe/567KH/Q3Ee7hxkqoxWGFOSuBGqkDxmJDPv+2M/MKjHsIbM89pHdbyv57zvIrBOzfk/2uXXXYWy812Hczq5MQNjEvfku23gIR/yXK1j5Xp7fffPP33d8fEtCaROnqr5psu3tSnWrjkxITCtc12XRI00EIWbfmHa099M0pv9lLJbqAA8sUyXmQSuG/Xk+d+bfe8hEZA/ZBC8b8vUbYO/uQ9i18QgOWr+Xg9jLWvZzEHdm+RwOjz75Cg66FvhBstnLWhISuzVbRgZ/pWhGW3/BiG/ykJgsladoTMLUN221qhOl7GHK3W7fFxuYWSPmew+zoHtujbAb2LXCrQcdxkH325s7t27nmxwDwH7Wsv+QtRz1iBs4hNvZ9J3dtEXb/pWpsWDHPoCIVGkfmohG50H2GHyFVQm090mXdRjxA8vQVLHcjWXhurfc02ung5lVYt4XuotZsH4FDqwcyq51++FesDaf4Ho/K9zJQdzOIXyXe3Mwt3PYfXaTTQrdnGSRJBLAuOMjy2zJ9E0PIllknYesEpjFT+xYiTEqvp9/r8u27eVwdgHcK7NEbucQbmUjt7KR/+S+HMReDuNW2hISE2xfVhZISHr4G0M3QNdei0GskbYb7z7FoA9R7KLB7aNnxgTwWSVmPWBCHndju7h2cfcptMXkdg7hTtZzB4dwMLdzCHewflVGibqkq7YtuhKTXtrfmIa3K7dW29bIGNxqXRC6wCbuZnPdW7aW+g7N5+LysS/77N13ODvvWsud91zP7esPYTc72citrLCf9ext7TBSjGQwjDHYxo3f441U9NDYtpg0GbvXWcFjpm8RqdqIL2HA3T5k+94pM77crsD7rd9mvR0rL8jrwL5DufnmQ7n1Hrdxx30O5uA1d7CWfRzUspAs2T87xwhEtC0xGQlttRWDiUgsbY1mr1pGYuGIsUrslsoWlbL75C7ujqUc2HMo373zIFjZz5q1+1hZt79g52okIRkF5qluIoJS9jTVREzK2s7WRaTOlCht0Wa+Q107E3ctjRljlYRcXJC9UNoN0Bt9OMhaNuNOdq/AhhUOrBzE3hZbP/FUcZkYiZBMkBgxgXhBiXn47lxEYmnDGmkzNjKRB5BloejeKHNvuVaJ7eJyL5kN1m93SMgusjm59uf73S0kzObxSq1fa6RT2YSYTjZteWd6cWe1bY304dIag4hUsUrKzNW2em75yhmoV1iMhW7cWzY+F1eMmOyzvg+yvk0ZK6wuqyEp2J5oxqTm0rIZc0+tWMYgIlNn4A4A9v3jG6AIq11cZWJyUP7bHuS4kazb8EF5Hrtp9b5NMZKFYGA/dVdi0pkV4muAY6yAKpZC1y6tsYlIH9fggvT6ip17zufiihETw7o83UYrHzMlfcv3a7JIRsPYGoaKmAu7jTPaqRsrVkRiK1FXlHxMVUQMsWLStiCMMOBf5eHKZ5XY62PFBGbvLzH7bGR1XMQE492ZhhO1GVhI2mwQRnIzNRGUzuMgTUSka5fW1EWkTYYe4d7BveRqZ9EhFsVLYsXEYFxdxjpZ8aRpgTRFymC0+frTEeJe4IPP/NuXiNRxaS2SiAxllcSWMbRIBYiZf6uqmJh0Zj8jKDCb9LEluoqRiMgRwHnAI8jeNf2Lqvo5a/tvA8/NF1fIJsi/D9lbV95B9v7IA8BWVX1jB1W8u+AFYiRWiY9BBxEmEZlRVJe2boc2rsOqE7s1KbMjUXMPocwqKXNx2b+Ne8pcFgc76w225WELiqlPS3QYI3kj8DFVfaaIrAcOsTeq6uuB1wOIyNOBX1fV74nIQcBvquoXReQw4HIR+aSqfrWLSi6YkMCoxWQQ2haREGMWkVgVjzEjY4m5DhckeN4VrpjA6pHvRYMWDbZ1Yka6t0wXFomIHA78OPBCAFXdC4XzujwbeHee9kbgxvz3rSJyDXAk0ImQtPHO9sRo6UJEqgTXm96xdwTKi2Gf9alL0/27oo+pYzpyI7pVd0+vu+xOymiPETG/bevkDmaj2ffn5d2a52PW7cnXj38GngcD3wHeJiJfEpHzRORQX0IROQQ4BXifZ9sxwKOBz3dV0QUVkin40rtmzCIScxc3FZA2qZtnzDG0Oala2z3yWiCmqBgx2RPYbouJLSjmL/MJyh6yqVXam2rr7ilSqnyATSJymfU5y8l2BXgM8FZVfXRe63MCVXg68K+q+r25eolsJBOXX1PVWxoeZpAFdG0lxi8iZdQRkT4axzqB6qm4Wjt0s5XFSnxpfL0fi1xd5rft6oLiU99yjGRd1UtjHztVdUtBiu3AdlU1lsR7CQvJGeRurbvrJLKOTET+VlXfX7F2lVhQiwSW0yoJuYIWWUT6dj/VKa/smOpYJU3jVzHH0OE9FFulWFeX/du1Tsw220Kx07SACKysVPuUoarfBm4QkYflq07GE+MQkXsAPwF8yFonwF8B16jqn7ZwiIUsuEUylafBNmja2A/dO2uoaVbqUtU6KbsW27IIRth7C/ynK7Z3srvOiEmMdQLz1klHl40IrOtmgONLgb/Ne2xdC5wpImcDqOq5eZqfBT6hqrdZ+z0eeD7wFRG5Il/3O6p6UReVXHAhWQbaaOynJCJDC4hNm9MZlJVTpSuwj7oi0eLDWFMxgXYExTCBZ0xVvQJw3V/nOmneDrzdWfcZMo9bLyyBkCyyVVKlsa/iIhmjiIxJQFxirZMmVkkVMYm95mNb9hapIiZ40sYKim+qendqlBY9d2sEDq46Wv628iRTYQmEZBHpygppK++yfcrKqlLGWOhDTJoyAqskRKhqRSPeYbWgFFknZrnF2MjdCEs9d9eSCMmiWCVFDe+URGQoAQnVqa2GO9bVVfd6XGCrxBSLp+ii0+puK3J32ctts+TT/y7xoU+JtgSkKH3VWXzr9ibqS0Sa9mpq0pDGWCdFDX0dF1dsGXXn32r5YaxIHIqsk9A+sYIS2j/RiCU6pVO0SuoICIzTCikqKzb/MtocqmznVUdUuhSTNtLXya+De6iqdWL2MZTFUOzuwj5RaYtkkSTGR91xB21YIXXyL9qnrKzY/OuW2wZFrVoRXYlJUxdXrFXSo5jgKd5UAU81yvYtCsp3gTCbDHIJWTIhGbtV0raAFO0zpnjIWAUkVGYVQelbTGLzH+EU80XFlxmIISulL5dWskiWjbGJSVfzMbUlIHXKKCsrJu865fVFVUHps4GeqIvLENNfoamV0gVLLiQLPEVKEWOYPiVmZtuiyQ2Lpupo0wrpSkSqTDUy1qlaq9Sr7Hi77lAx9NsxKxJzedxF3C3S9yw6S8gSa+gQlknszVd3RtiqAlK0zxhcWWMUDx9VLJQi6yTFS1YR65qK6RvRtZikcSTLStc3QtWntrKGs4510KYrq25ZMflWKacORWW3dRvEupX6EpPY/EcuJoY6ogL9vDtsDa2/B35KLLmQwHzj1+SGaGLq1xWQon37tELKyivLu0o5ZdR57PTtU/fWiLVO+hCTpvGSEYqJwf3LqghLVySLJJHRZ+wk5upu2zIYsxVS927vyl/RtLtPTCPetpjEpo21Sprm2SNVhKULljzYvsSHPhRDCEjZfl0G1MsYm4CUlVXllulSTKrk1Xa8pEqeA5GC672ShKQXqvTsqZvPMlghY2gdYufTMsS4uuqIydDB9yp5LgHJIkl0x5ACUrbfkCIyRQFxqSMoyyQmePJdYNLI9kS7VGkkpyQgZWUui4C4VBGUMYhJbN5NxSSU74KSLJJEO/QlILDYVkgX4lFW/zYau1hBiRmW3aWYVGn0Q2JCxXzx5L1gJCFJNKNNASnLb2xWSFneMflXyauMur3ufPvVbfiqCErVOT5gHGJSlC8V8k6UISLXAbeSvY5rn6pucbbfA3gncDTZH/LHqvq2fNuvAy8CFPgKcKaqdjJ1ZRKSWnThoklWSHW67K7d9AXfMa6luq6uMYtJWd548l8Aun1D4pNUdWdg24uBr6rq00XkPsDXReRvgfsAvwocr6p3iMh7gDNw3u3eFlFzbYnIKSLydRHZJiLneLbfQ0T+QUSuFJGrReTM9qs6BqrO+VRlwqAQRXNyxUw0VFRuUZlFND0mO5+qInIHxeekK+qU28b/H9q/79ctVx3EWnRcQ/x/HSNkI9urfNpBgcNERICNwPeYN2kPFpEV4BBgR2ulOpQKiYisBd4MnAocDzxbRI53khlVPAE4CfgTEVnfcl0HZGwCYvYvKr9JuUX5tuHKqiogQ4lHiK4EpWj/UD2q5NWGmPjSFx1fk2t8QhiLpMoHNonIZdbnLE/OCnxCRC4PbH8T8INkIvEV4GWqekBVvwX8MXA9cCNws6p+oq3DdYlxbZ0IbFPVawFE5ALgdOCrVpoiVZw4i+LGKtu3j1hIHetjzFR11ZS5u8riJl26ufCkb7PbsS9/uxxDX26vUVxbO92Yh4fHq+oOEbkv8EkR+Zqqftra/lPAFcBPAj+Qp/kXMqk6HTgW2AX8vYg8T1Xf2fZBQJxr60jgBmt5e77OxquKrdRwMJIVEpe3yb+MOhbIVKhS3yYuxyLLpC13lC/v2LRFeZt9Yh6iuvr/O8zb9Nqq8olAVXfk3zcBHyB7sLc5E3i/ZmwDvgk8HHgy8E1V/Y6q3gW8H/ix+gdYTIyQiGedOstGFR8IPAp4k4gcviojkbOMGQeh2NHQVBUQ6McK6Sqg3sSVFXOuqrix2rjJ72rhU5eqghKiLPZVVL4vryr5hMSkat5Nrxu73CbXRE9u0Q6EREQOFZHDzG/gqcBVTrLrgZPzNPcDHgZcm69/nIgcknuKTgauaXKIRcTo4nbgKGt5M6uDNmcCr1NVBbaJiFHFL9iJVHUrsBVA5DGuGI2AsQlI2f5j79ZbRUDq0qThj82z6gy6sS6vuq6uqt2DQ66lNgYYFrmtyrpCl7m83LLbpOXrppuR7fcDPpDpACvAu1T1YyJyNoCqngu8Fni7iHwlr8Ur8h5eO0XkvcAXyf6IL5G3vV0QIySXAseJyLHAt8i6kD3HSWNU8V8cVZwQYxORmCf9LsqdgoB0IR6x5VURlRhBiWlsu4ybtDVavU497X0NXb48pMPrpoMBiXlc+gTP+nOt3zvILBXf/q8BXtNurfyUHrqq7hORlwAfJwvgnK+qV0eq4kSoGgtpml9XVkjZvl1bIV0KSN/iEaJOoxcrKEOKCZ586ogJgX2qjvwP5VOHsVw7i0uUhqrqRcBFzrooVRw/YxKRoayQsrxj8o85N1UFZOwNQBXXDJSP7i5qbMsa6SF6dFExf1MGnnJ8NHEx9nztpClSlpk+RSRZIfGMXUBc6vj627ZOuhSTovR1rBNTjqHqlPxN6WhkQnpD4jLSpogkV1b9OlQpr498mrhTqrhlYqyTOmKCZ7+QeLUpJr78zT4E9rPLI1Bmm3Q4tC29s30ZGYuILHpAPVZEmjT8bVsvvvzqiEtMA1pmnfTh6irq0eUru6jcInGsIii+cpvQw9jo5NpaNqYgIstihdQVgan02jL7xlgnUCwoU3J1ESjD7EdgX7dcm6pN1YJMrDERlkxI+hKRZXVldSkgY4mb1BGV2Maz6Il+rGJCYJ8y113VjgptC0MH11OKkSwDUxeRLntl9WWFVL15xyIeIaqKSqy7q66rq+u4SdWyYzoWVBWUpnR0TSXX1jIwBhGZajxkCBEZu4D4qNIglqWt6+rqOm5SVnbd47HLDJXblI6vqfTO9kVnkUVkCq6sIQSkqRukyW1RVVCaWCdTcnWZcgiU5cvH0FZPug5JFskiM3YRWfR4SOz5b3KzdxFU9eVZ9VaJFZQmjW9dV1dbYlJUNoH9TFkEyvNRtSfdFC3aabPAQpJEpHq+ZfvGlB1TRtV0LkP0yKnbNbVNQanj6mpLTEJ1qzu1iymPQJlljEwskkWyiLR5kfUtImOOh7QlInX+nzF152wyKjtGUOpaJ1Wsg6pB+KK6lYmJr3xfmaFyJ0C372wfPQsqJFXoooFaRhHpygoZk4D4qCoqMQ1rXetkrK4usx+Bfd1yDV2JSkcvtlrike0xL7aaGEO7tJKIhNPE/jf7rM+UqFLnmPNR1xoO1SGUXyh90TVeNa+yOoTKb/OlVD284GpJWTCLZGiX1tREZGyurLaEo43GosnTcNUZbsusk7ZdXW1YJmV5EcjP7Edg3xCh/7Tof+pRNFKMZFFo02XSJNDdZlll5Q0pIm3HQpqISBcNhptnHWGJFZSyhrWJq6vLwYtFeRWVb+9rqNvFdyQWRoqRLAJ9+d3rBNeTiBTTxX/RBU2EpYqgNLFO2gjCF+3Txwut+hrh3jIdWSQich1wK7Af2KeqW5ztArwROA24HXihqn7R2r4WuAz4lqo+rf0aZiyAkLQtInXcY13sU1dEho6HdCkgI3n6BOoFhWMa1RjrZIxB+LK6VRUUCvIaId2ObH9SwRtnTwWOyz+PBd6afxteBlwDHN5Z7Zh8sL3tvuR9xkWKWGQRqRpEbzvg2gVV6xdzDorOZVGwe8ggfFFeZXmG8qrSSWMpOR14h2ZcAhwhIg8AEJHNwE8D53VdiYkKSd2Lq+2GvYvg+qKLSCxjFw8fdQSliLLrvEhMQvVoU0zq9OoyeVa9F0cuKsa1VeUThwKfEJHLReQsz/YjgRus5e35OoA3AC8HDsQfSD0m6Nrq6kKq84QXYpFEpK14SGzD0aZ4NLlWmrhVqri9Yt1dbbu62nBzFZVRVjeTLwV5h3D/1xG4wOrFSDaJyGXW8lZV3eqkebyq7hCR+wKfFJGvqeqnnZJdVESeBtykqpeLyEmVa1aRAYVE8d/ovouiDfFo0hBX2advESmjSxHpwgqpS9duzqa9imIEZexiQmCfJmJSlncMse1I2T4Nqd5ra6cbPHdR1R35900i8gHgRMAWku3AUdbyZmAH8EzgGSJyGtlQycNF5J2q+rzKtYxghK6tuzyfpvTp0grRlYh01TurTxGp68bq093R9JqMOb4yl09R2XXiJlXdXEX7lF1LXcTPivC1Ix1eL+ad7VU+JYjIoSJymPkNPBW4ykl2IfACyXgccLOq3qiqr1TVzap6DHAG8E9diQhM0rXVNm1eVG33RFp0EakrHmOgTu+iIa2TtiyTon2KLJOyPN38DUvdRN0P+EDWw5cV4F2q+jERORtAVc8FLiLr+ruNrPvvmUNUdAn+pb6skbZdZ2WMVUS6ioWMRUB8VB0DESMoUxYTAuWYPCnI11cOgbJGRAfjSFT1WuAEz/pzrd8KvLgkn4uBi9ut3TwjdG21Sd0n+r4Cvl0E18cuIlXdWCPuqbOKqnWNeR1A3eugTTdXnYentib5tMsa+dxrayt+FoiRy3yX9BFgH6KHVhFjEJFY2haPmPq1dTtUeepuap30YZkU5VW3R5fJk0C+IUbq9lryubYW2CIZg0urahll+TVpzKciIk0tkH2BT9f7+qhyLDHWSVE5VbdVtUyKaGKZQP3/fAJWypKwpBraVy+fPml6M4foW0Tq0HVj0vQpOPbJu+wJfmjLpE7MxJRDoKzY/Isoiyf1QLdTpIyeBRWSZbNGuvJVNxWRLgVkqCfRJqIS01CWNbpTFZOistz8KSgjxMBB+eTaWjaGtEaGCK4XMUURGZM7o477K9aN08W1MuQ4k7KyfGVM6AGjuylSJsECCsnQ1siYehg1nfokRBuNRdWGYkwC4qOOoJSx7GJiyqnzsNEzSUgWiSYD+LpmTC6tJsH1tkQklrELiEuV+sY0kn327utbTLrsxTe162baLJgu1mVIa2QIl1YRYxORNikru83J/6oEgMtiJ0WxhToxkzpB7S5iJhAXN4kpK0Q/gXgV2DfRYHs+3fwZwBOBB5L9KVcBHwE+qqqlswcvkJCM+elj6Lf52TSJi9Qts2r+bfyXbY4TqiswVQLAYxGTKg17WV5ldatTZt2xJx03dQL7J9iaisjbyKad/zDwR8BNZDOBPRQ4BXiViJzjzDi8igkeel2q+oanao10JSJNG/c+RKQr16Wdb5fdU8cuJnV7jLUtJmXlhepARD3qoQL71laNFHT+mpAY/kRV3YkgIbNI3i8i64GjyzJZECFJ1ki3NHVpdSkifce9uu6e2pWYVC2vjpgU0YWY1KGbtkJF2L9S9bzs7aQuVQiIiL19L9mEkIUsWLA9RFuNzTJaI2MVkaYj4JtyF/XqEHOcXQTgxzAId+zT6CwfInK4iLxORP5GRJ7jbHtLbD4LICRNnjDafN9IlfzrMoRLq4g2RKSrcRh9MjYxqZpnm9OoLKeYKMLetesrfUbC2/Lv9wFniMj7RMR0G3hcbCYL4toqYhwX2jxtd9vsiq5GzMfkXzfPoajq8opx9YxhypC24yVl+5aVO04UYd80p/T9AVX9//LfHxSRVwH/JCLPqJLJxIWkC39nW0H2qVsjfc5fVcaYBcSliqA0FZM68ZI2uwR3TayYjKPu+6fZnB4kImtMF19V/Z8isp3sdb4bYzNZANdWXYYKgk/FGimi7XdN9JVXn/TV1XmRXVxFZY8LRdjP2kqfkfAPwE/aK1T1r4HfpEJvgElKaEYbfuamZY2hkevbGunTpTVkrKoNt0rsk3KZZTIGF1edMtpwcUH8ZI/DWyVTQ1VfHlj/MeC42HyW2CLxMWTDNaSbqU/6EpE7rE/T/bucht/QZHqfIa2SpizGta0Ie1lf6ROLiKwVkS+JyIc9254rIl/OP58VkROsbaeIyNdFZJuInFNSxsvyHlwiIn8lIl8UkafG1jFKSGIqJCInicgVInK1iHwqtgL1GMId0AZ93jRjtEb6EJGmDX8X+fY5or+PPKuUMdYekO1igu1VPhV4GXBNYNs3gZ9Q1UcCrwW2QiY+wJuBU4HjgWeLyPEFZfyiqt4CPBW4D3Am8LrYCpYKSUyFROQI4C3AM1T1h4BnxVagOn27RYbK39CkYe+Tts5H3Xy6EpC2ymnj/CSrZKxkMZKVSp8Y8nmwfho4z1uu6mdV9fv54iXA5vz3icA2Vb02H1R4AXB6UVH592nA21T1SmtdKTEWSUyFngO8X1WvB1DVm2IrMH6qXuRDNTJtlNWlaHUV0+pLQHzlVqWN+FHbg1/bYhE6kYySNwAvJ24+lV8CPpr/PhK4wdq2PV8X4nIR+QSZkHxcRA6LLBOIi3T5KvRYJ81DgXUicjFwGPBGVX1HbCX6petBiCHavpm7etdIHdpwadUVkSGJfYWsTUxQuG6QvI+xF12VMf2xJTV6Ym0Skcus5a2qutUsiMjTgJtU9XIROakoIxF5EpmQPMGs8iRTz34rqrov3/dRwLWqeruI3JvMvRVFzNUaU6EV4IeBk8n+6c+JyCWq+g2n0mcBZ2VLR8XW0WL6JnA1+jzeqbjQDGOqU98NXJ0eSkOPK1nsXlUHEO6sEEDP2amqWwq2Px54hoicRjYj7+Ei8k5VfZ6dSEQeSeb6OlVVv5uv3s58I7sZ2OEp45J83MjHgI+p6i6APJ/vetJ7iXFtxVRoe16J21R1J9lglhOcNKjqVlXdkp28e8fWsQZTio+Mxa3VVZ5dWCNjEhFD29N61H2IGPLcNH3wmfKDYvsxElV9papuVtVjyN4X8k8eETkaeD/wfOfB/VLgOBE5Np/B9wzgQk8ZW8iC+QBvEJFLReTPROSp1lQppcQISUyFPgQ8UURWROQQMtdXqJfBhOgjPlKHMbm1+maMImKY3hxR9RjzfzAMfQ5IFJGzReTsfPHVZE/lb8l7zV4GkLurXgJ8nKwtfo+qXu2tu+p/qOq5qvozwI+RDVJ8MvAvIvKRmDqVyqKq7hMRU6G1wPmqerU5kLwC14jIx4AvkwVoziubnrg6U35aGXvdu3JrtW2NpAZsRnJv1Wd647BV9WLg4vz3udb6FwEvCuxzEXBRxXLuAv4p/yAiRQH6u4k6o74K2QeTL78eeH1MfsMxVKC9Dm2LzxBurTaZioi0+erYMQfdQ/Tzatt6dFuvEU17Eo2IHA68kixk8VFVfZe17S2q+isx+aSR7QvFmASxzylsxsZURG+RKLN0uhWRA6zhTtZX+oyEt5F1qFqGaeQXfabYsTKWBnEs9UjMGLIbcFX6aeYmOvtvmkZ+GPoe9dt1OXVdaE1FcEgR9R1z27dCW1OgN5kUsQ2mHifpp4kzwfYJkqaRT8QydsuniDaFs+htjPtKti8KY57JOjEAyz6NfGK8jLExrlKnMQeMp0Df5y9k4fRXh6laJG1NI5/uliBtNYZ18hljQzxl6v4HTW+PIXtPjXs6kTBF57zK8fTbtCnCnUSP3xsd+cS7LwCOwTp5qvqrMftPREhWSI1rGVNyTcTWtQ23VpPrJlkmiTimapFYXEQ2e/BXqDBZoyHdJYkAizB6Pj18LD4+t9YwzdrEhWSDqv5G3Z1TsD2RSCQSfyMi/01EHiAi9zKf2J0nZJHEurfWMZ0n5kQiEYcvPjIea8S8IXHC7CWbmeRVzGZ3V+DBMTtPSEgS7ZNcP4kuKGpWhp5rq5vys3e2TzfYDvwG8JB89vbKJCEJkgL8iSmzaD22muzfvXgtQLD9auD2ujtPTEhS494ui34+mx5f09tjjI35xG55IN6t5WNoC2gy7AeuEJF/Bu40Kxes+++UWfTGelFZtlsj1OBOpSH2/V/91X0BLJIP5p9aTPBuadIwH0zzsQkpmN/fOWjj/zKXeJVrZgyjsg1jjTfUsbaK6hs6zrrWSP/nZsrB9nxalNpMUEhiGKKxb6PRa0Lbx9zl8Qzx/8Q+gLR1S4zRrbWouP9Z/yJygDWTDLaLyD8AW8lelX6Xs+3BwAuB61T1/KJ8JjqOpG39G5v5vqD6Pjgr+M/tSsG2KTKkiHV9Dsd2r85o+1W7IrJBRL4gIleKyNUi8geeNKeLyJfNa3ZF5AnWtiNE5L0i8jURuUZEftRTzH8Dngh8LX9f+0Ui8k8i8k3g/wKXl4kILM6dkxgVbcaFurCMur7sYxvyvl/GNNbbvYpbq2zf8QpNDe4EflJVd4vIOuAzIvJRVb3ESvP/gAtVVUXkkcB7gIfn295IZmk8U0TWA4e4Bajqt4GXAy8XkWOAB5DdcN9Q1eheXGO9siIoa6xSLGNGOheLR5sNZtVAe9vxkbZos77V6GJAoqoqsDtfXJd/1Emz21o81GzPX6H742SuKVR1LyXTwqvqdcB1deo6UddWE8Y0g2idm2tRfO9Vjn1Kx9yWNTJF6twvTYLsMeX1c+1kvbZWKn1iEJG1InIFcBPwSVX9vCfNz4rI14CPAL+Yr34w8B3gbSLyJRE5T0QObeVgPUxcSIYyqKo2AmM2/OpO2z3sO7LHSZuNVt3zNyXRbRPf9djvuagRI9mUxzXM5yw3T1Xdr6qPAjYDJ4rIIzxpPqCqDwd+BnhtvnoFeAzwVlV9NHAbcE4nB87C3+1VXDptuH8WredW11Sp79DntowqjVZTa6TO/lVHfffhOuvSGulXRLIpUtZX3W2nqm6Jyl91l4hcDJwCXBVI82kR+QER2QRsB7ZbFsx7KRESETkYOFpVvx57AIaJWyRQTwun8NS24Bpfi7H+b23Xa8zWSKiMIa9XV2TGep1UQ0Tuk79wyjTyTwa+5qR5iIhI/vsxwHrgu3kQ/QYReVie9GTgqwVlPR24AvhYvvwoEbkwtq4L0lp1OXq865HpfVkRXZRTlmfMuatar7FZJlUbrTFZI31Qtb5VrZFxiEhHs/8+APhrEVlL9tD/HlX9sIicDaCq5wL/H/ACEbmL7Mb4+TxID/BS4G/zHlvXAmcWlPX7wInAxXneV+S9uKIY8AoTwhdZmw1eV+6tqg3gmLvEFtVtbA03jKdOXYjIGEbV99H7qe5xdhXTa4YJtreap+qXgUd71p9r/f4j4I8C+18BRLnOgH2qenNu3FRmpBaJ/afHNtZVG+quGqM2860jPn3HSYawSmB4MRlCRIryGOMgxC6skar599c7buJzbV0lIs8B1orIccCvAp+N3XkCMZI2LoSmeYxUbytTZ64j6KeRqtsVuu8GtE6ZXV/DRfUZMsgeoi9rZBG7WHfGS4EfIhsE+S7gZuDXYneeSAtpLoiyp9Y23EdD9Hya4hO5TRtWSUw+IUwj0uX56Fqwxn4rVg2yD22N9CsiB1jDndV7bY2CPAZzoao+mewNiZUZ+9XrMLaJCUP1CeXb15TyXYhh03PVx6uS2xaUpuIR25h15dKasjVSpbuvew6GsUTajpH0haruF5HbReQeqnpznTwmeOR1n36bNq5DvVek7XKLzkOTstoUr6Z5uQ1LFWFpy/LoQ0Sa5FuFtqyRJnNqhfIfh4gswPtI9gBfEZFPkg1eBBb+xVZtuVLA/6Sd3FvdlFflf2nzP+g7jtKWiJTR5pxXY4on1JkKxbefi51Puw+FCyAkH8k/tZiokEC9hqbPsRRturfa7r01pFUylJj0RZsi0rZLqw5TskaqlDvhpq8DlvzFVl01iCHG+NrcsVklXYgJEXkOTZUn+qYiUpexWSNtTczYZL92UGSywXaA/P0j6q5X1QfH7D9xIYHqT62+9E3dW20NTmz7CXwIq6Qs77pljNU66WICz7I82wyw1ymnLWukKiFrZFgRgW4GJPaMPXBxA/As4F6xOw945GsIX6hVn7BDjczQVsnQgxO7oq3jqiMmMA5B6Wqqki5EpE55Xc+p1WQqlL7cetWYcoxEVb/rrHqDiHwGeHXM/iOVUPtCadpg+RqrLqyStmg76N6VVdKGiyumnFDeRObfNl32oupKRPpwaTW1RtpwaQ3XnE092J5P+GhYQ2ahHBa7/0iFxCZ2bMDQro+xBt2b0IeLq0k5dabSqUOTBretHkd1e56N0aXVVoB9TD3NJs+fWL/3kb0p8edid56AkBhiBKWKi6uJVTLGGYG7sEqalFk1f3MpNukxZmhDVNpopPoSkT5cWm3RZoA9Zp9+xCYLth/US1ldoKpParL/BObacim70Id8SunD39yF9heds7LyYhqetns0xZTX9NOEFcYhIm26tMYcYPeV1cb/GI9xbVV8Q+JoEJGXicjhknGeiHxRRJ4au/8EhQTqPTWFLraYvH3pujbm2nRJ1M3PMISYTMhYvpuq9R5CROqU16VLq4seV/0/TE5dSIBfVNVbgKcC9yV7d8nrYnee4t2aU9eV0zVDx0q6cnE1Db7HlOErk5Jyx0KbAgLdiciYXVpF22OtkeE8Eh282KpPzItITgPepqpXSoWXk0zUIjFUfQJfBqtkyPJiLZM6Yy/G+MyzQvtWCPQvInXK6sKlFeruGyp3PCKyAFwuIp8gE5KPi8hhwIHYncd4d1ak6hN4Hz2fpmqVNCkzpuym9RiLhdJmcNuli8B6Wfljd2m17TptnwOsYe+Eg+3ALwGPAq5V1dtF5F4Uv5p3jiiLREROEZGvi8g2ETmnIN2PiMh+EXlmbAXaoYvpvqdglXTRqJTdkDFlxv4fdQOidSyBJqzQrMy+RKSPuEgb+Xfh0hqetmMkInKUiPyziFwjIleLyMs8aU4SkZtF5Ir88+rYfR1+FPi6qu4SkecBv0v2cqsoSv+N/KUnbwaeAmwHLhWRC1X1q550fwR8PLbwdgk9CS+yVVJEk9HnTeMlVctvaiXZtHEOu+hxVESTyQdjymozLtK1S6sOVTotdDMvXUcDEvcBv6mqX8xdTZeLyCfdthf4F1V9Ws19DW8FThCRE4CXA38FvAP4iZiKxlgkJwLbVPVaVd0LXACc7kn3UuB9wE0xBXdD6MaIuaEX0Spp0ourT8vElNfW2I2mn6ZUOZYxisiQLq2q1kjR8flei3xwwbZxoao3quoX89+3AtcAR3a07z5VVbK2/Y2q+kYqjGyPEZIjgRus5e1uhUTkSOBngXNjC+6O2IsjNvDehDbHldSt2xjEZAhBGYKqdR9KROqUVzWvrhrpmK7o/QuEkvXaqvKpgogcAzwa+Lxn84+KyJUi8lER+aGK+xpuFZFXAs8HPpJ7mKL/9JiWwNcFzJ1u+A3AK/JXNoYzEjkLOCtbOjqqgvVoc4R636Pd+546pcyt1IabC6q72sw1PIYJGotoe7yPIebWrCsiQw2QbcMaKcpvOAtD6wXbN4nIZdbyVlXd6iYSkY1k3p5fy8d62HwReJCq7haR04APAsdF7mvz88BzyMaTfFtEjgZeH3sgMVfBduAoa3kzsMNJswW4IBeRTcBpIrJPVT9oJ8pP0lYAkS2r5r4fB22PQakaK6mTF9TvxVWWb8z2KmJCSV18ZRvGJCpdCQg0D6rXEZE6+TVxaRVtr+PSGt5NVSNGslNVtxQlEJF1ZELwt6r6fne7LQ6qepGIvEVENqnqzrJ9nXy+LSLvYyZCO4EPxB5IjGvrUuA4ETlWRNYDZwAXOpU4VlWPUdVjgPcCv+KKSP+0Gd9omldfLq6mXSiLiHFzxT6dNpmAcCjX1zqalT92EekzLlK0/zRRpHXXVj4g8K+Aa1T1TwNp7m8GDorIiWRt+ndj9nXy+W9kbff/zVcdSWbdRFH6b6rqPhF5CVlvrLXA+ap6tYicnW8fQVwkRN2p4bsea9FnPjZNx5fEpOnSOnHrYtPFK5TboC0BgfGISJ0yisqbvjXSEY8ni1l8RUSuyNf9DnlcIG97nwn8dxHZR3YznaGqKiJP8O2rqhcFynoxWceqz+d5/5uI3De2olGPBXnhFznrvAKiqi+MLbwfYlxIbcZKQozBxdUk3yppqtShqaAYxhSgr9qwTU1E2hzJH/O/jV9EunhDoqp+Bn+M2k7zJuBNdfZ1uFNV95oYt4is4Hn1bojFsi+jqdtgxjb6fVglQ8VLYtNUHYXelqAMyRACUpamjogU0aVLKybAXlbmMCjC3gm/sx34lIj8DnCwiDwF+BXgH2J3XhIhibEm6jb+XVsldes1BjEpq4eP7gePtUtbM1H7aPo0X1dE2oiLxOzf1piR4VGE/QcmPWnjK4AXAV8BfpnMA3Ve7M5LIiRQz8UVa5VU6Q7cl4urjL7FBKoL4lhFpe6TcJ8uoaFFpEpX3zqMxxoBQGHfvmkKiYisAb6sqo8A/rJOHhOf/bcpTd0GZbSl01304oLymzH2/MSeoyYjx4ccjdx0NHSV4445n2MRkar5hKhqjYxMRCaOqh4ArszHjtRiiSwSmHbgvYt4SVmZJm8K8o+pg69OlNSriFBD0obV0mYj1aYFEpuubRGpWlZMXKRpgH18qAr79026OX0AcLWIfAG4zaxU1WfE7DzpI6+H23AO1R14SmJSlr+dhoh0dr2gvW7OY3lS7eIJvomAQPtWbVsiMt0Au40eEPbumXSw/Q+a7LyEQuKjTuC9qVVSNe+m9CUmVdIZ7Mtw6HeN1KXOrdSHFQL9iEgdFselpSrsu2t6MRIR2QCcDTyELND+V6pa+SZcUiEZe+C9Sr2q5Ne3mBCZ1qZtK6VL6t4+bQkIjEdE2nJpTRXhwP5JNqd/TXaT/gtwKnA8UPbuklVM8sjbYRldXGX7lpVrl0FJOXXS2riX5liEpcktU6UhXXQRWRxrZOIcr6r/BUBE/gr4Qp1MllhIfEzJxdW1mFBQdmw5bloqpHcZQljauj3GJCBlZXQhIkV5TW/MiBcFptn99+4bMp8Oq1YmSy4kbY3d6NLFNZSYlJVtl0NJWU3Shyi7dGOEpsvLv2pDGJt+iiLS1LKYgDVyQGDPJJvTE0TEzCAsZCPbb8l/q6oeHpPJJI+8W2Kskj5dXDB+MTFlUVKeL32Vfaow1KU9lIBANyJSJ6/YdAvm0hqL97UCqtqKGbXkAxKhvZcMVXkHQ52J8opoMj4g9thib+g6x1BlUOPYWOd8qu4XQ4wVUnYN1BWRKg1523GRxFRIFgnQXuC9jXhJneB73TwNsT2lurJO3P0MY3qRlU2Txq7Kvl1bIWX7dxUXiWVC1kj2rt2lJQlJkDqB9yrUya/J5I4xghbr6qKgHm6ZRJRbtr9hKGFp4ym5bQGBcYvIErm0IAnJ0BUYD32PLfHlV5a+rJ59iUlZPXzlElF2bD4ubQlM226Vqvn19RKsutZDXRFZApfWAWDP0JUYjiQkcwzh4hpCTCjI2+RBST6mHhTUJVR2WflVGVsj1JWAwDBWSCjfqiISy8SsEcMSWyQp2F6Ke2N0MSCtTvC96QuLYhq72GOtMytunQD1mKlzPFXOW8wMwn2KSEw6N88FdGklgKGFZIW4+6NX6gQ4fTdaWzdEEzFp2vCYfGKpO836FEWlbm8taF9ATH3K8imqT9V8q/TQcrcvkEvLYGIkVT4RiMj5InKTiFxVkOYkEblCRK4WkU9Z6389X3eViLw7n1erE8ZjkYxSVAwxF3usmFS1SsrK73KAmp1HH4ICzRroLmmjXl0JyNAiUkabY2VGSkdCArwdOCW0UUSOAN4CPENVfwh4Vr7+SOBXgS35C6vWAmdUOqYKjLLZHn7evrrvLekqXlK0T6iM2HxN3hTkb+dDSV5uvaDZTMa+RqjL3ltti1fdF2GV0YY12ZaIVImLLKhLq6Ngu6p+WkSOKUjyHOD9qnp9nv4ma9sK2Uj1u4BDgB3t13BW0Hhpu8dtJWIC700qODYxKcu/al42bQiKzZgsFR91G8XY23FKIrLgLi2b6k3BJhG5zFreqqpbK+bxUGCdiFwMHAa8UVXfoarfEpE/Bq4nu/E+oaqfqFzDSMYtJDCwmLi02YurKL+piAkR+dnYjcyY3sHeBl2LB7QX0xpaRKrUZ6HZqapbGuaxAvwwcDLZifyciFwCfAc4HTgW2AX8vYg8T1Xf2bC8YCXGz2BiEuOWauLiCqUN5Vu2T1E5dr4U5G3yp6CMqvn5WARRadIAti0gMXnW7ZnVVESK9l0QEVGGGi+7nUyQbgNuE5FPAyfk276pqt8BEJH3Az8GdCIk4wm2lzGY5NWdpqKNnlxdBeDL8rbLqNKI1f2TDnY+Y6WNelY9T211hhhSRBY0LmKjwP6Kn3b4EPBEEVkRkUOAxwLXkLm0Hicih0g2N/zJ+fpOmIZFYhiNm6tJvKTqfFxdWSZleceW48uTyHxD+BqYPq2Wthu4OrdZH1ZIWTldisiC0VGwXUTeDZxEFk/ZDryG/ESq6rmqeo2IfAz4cl6L81T1qnzf9wJfJLsZvwRUjb9EMy0hGYy6kzFWaYC7EhNo7uoy5VBQVijfmLxjKGsQY4Wmr6fgLsUjNv+6IlJngGxZPm2MeF8+VPXZEWleD7zes/41ZMLTOdMTklHFS1y6ipeE8o7Zr6i82Pzdsigpz5e3oas/bwwNU93bqW0BgX5EJAXX7yZN2pioT6yLa0piQkEZbnmUlFlURmw5Y6bp7TMmASkqo6mILGhcxGXql3MDpikko7JKpiYmBMqMLcNXJiXlFpVjmMJd2Mbt0sQ9VERMA92HiFQpc4FIFkmiGk1eXjUGMSkq0y2DgnJ85RJRdll5NkPemW3fGl0JCDSzQorKqiMiSxoXOcB0e7G3QBKSWrQZcyjKr4mYULCvKZNAuW45FJQVKrus/BiKLs+mItPHpV/nabxNASmrQ9Xu5UlEEn6SkLRGExcXtC8mZfuWlesri5LyfOUTUYc6jPHSbeLG6VNAisoL7ZdEpDxRN0EAAA8qSURBVBAzjmRJGePdOBHajpeE8ixKH8o/dl+7XAJl+8qjpExfHQxjfQ97XfoSD4hvkLt0ZfnSLVkPrRApRpKox5jEBE8Z9r4U7F9Wdp0yQ7iN0NSEpY3g8dgEJLRvHRFZkh5aLinYPkFG9Yf1LSYE9gmVEVOeWzaB8kNlUlJuEWMVlrZ7G9W51doSkLLyuxCRJSMF2xPN6VNMivYJleHuS8H+dvkU1MFXLiVlx1DUGLUtMl03fE3mHoulbSskVH5VEVkiayQxQSEZlTViMzYxwVNO7P5uHSioR6jssvLrMPYn3ia3U9WGtwsrJFSPJCKlpGD7hBitiBQxlJiEynH3pyQPux4U1CVUvmGSf14ETW+hvgWkKI8kIo1Y1Es8gukIyST+pCqB8qpiQoW8y8rx5UFJPjF1KcK91Cbxhzq0dbt0IR7QrYD48k8icjcp2D4BJvUHVe355BMTaD8Ij6csXz6xcYimr88du7C0fWvUaWTbnIsriUindDSN/FQYt5CMrW2Jpso0KiGLYQhXl8mDknzc+hiadFsJXYpdXwRd3gJ1G9c+BASSiCTaYnxCMlnxcAmJCcS/FKuumPjKsMsiUF6VfHw0tVJ8jO8SDdOkQW17Lq6qAuLbx1dGEhEvS+7ainrVroicIiJfF5FtInKOZ/tzReTL+eezInKCLx8v+5zPQhF7w0K9KSvqvj7VlBfTSK+LyMtlCq/NbYOmr95dR7Xzu0Lc/1bHCkki0hi3LSv7LBClQiIia4E3A6cCxwPPFpHjnWTfBH5CVR8JvJbYVzou2Mn006WYFOVv9msyzqBqXj7aeM/5WGjjWKqKB7Qj+qE6x16HSUQKUTLjvcongoiH+HuIyD+IyJUicrWInOlsXysiXxKRD9c/uHJiLJITgW2qeq2q7gUuAE63E6jqZ1X1+/niJcDmdqs5daqKSehGrhIwLSsnpsxQXnVFBaYjLG49m7qt6pyzLgXE7OcrsyzvMf9vi0PkQ/yLga+q6glk73b/ExFZb21/GXBN13WNEZIjgRus5e35uhC/BHy0SaUWkyo3M3Tj6mpTUGLzLKPNBruNstuqQ1PxaMPtWHTN+VxZMVOeJBHxcgC4s+KnnNKHeDJb6DAREWAj8D1yX4+IbAZ+GjivwZFFEXO1imedehOKPIlMSJ4Q2H4WcFa2dHRUBReLqhMyVg3CF5URs69dLoGyQ3ka2pjGZIqNVRNBrSreZVSNncW4ssryXXK6Cbb7HuIf66R5E3AhsAM4DPh5VT2Qb3sD8PJ8fafEWCTbgaOs5c1klZ5DRB5Jpnynq+p3fRmp6lZV3aKqW+A+deq7AFTxVUOxZdKldWLKrtprqqnrayqso9mxVrE87PKKKHNjJRHpjHoxkk0icpn1OcvJNeYh/qeAK4AHAo8C3iQih4vI04CbVPXyxscWQcxVfClwnIgcC3wLOAN4jp1ARI4G3g88X1W/0XotF5Kq3YOhG+vEV55LG1PGj2VW3zq0JYp1RDmGOr33koC0TvW5tnZmD9VBYh7izwRep6oKbBORbwIPBx4PPENETgM2AIeLyDtV9XmVaxlBqUWiqvuAlwAfJwvavEdVrxaRs0Xk7DzZq4F7A28RkStE5LIuKrt4tBk3qWudlO3vll/HSrHLGLvF0mY9V6h3zmLLrWOBmHr50vvyTwzI3Q/xeQD9DDI3ls31wMkAInI/4GHAtar6SlXdrKrH5Pv9U1ciApFXt6peBFzkrDvX+v0i4EXtVm1ZaCtuUrSPKYdAWfb+FOTh1oOCusTga7z6sly6ErK6AyjbsD7K8qrSgSOJSCU6mCJFVfeJiHmIXwucbx7i8+3nkg23eLuIfIXMFfYKVd3Zbk3Kkcwi6h+RLQrJcJlR1MCHGteiRrysQY4ZfV61UV+KgUEOTUbeVxGzJl286/QAXBbk8hL3Ulwuh25Rjq/Ynl3WTtljYErzTyw4dWb4rWudmPLKxKSKhWLqY1hUUelLOKCZ9QHtvBUxEUV6H0liXNRxdUE4EE9gP1MWgfKq5ONj7LP7xtDG7dGFeMTkm6yQ3pniJd4SSUhGSZ3p4vsUlKK8QoxZWNq8DepOI9NG3m29ETGRqEYSktFSx9UF5e4uCvatMiV80669fU8b39Wl3mSqmLbKqCMgVeuQKOQA7U56PTGSkIyetq2Tsn3tcgmUHcqvLM8Yxn5JNunt1aZ4QBKQEZFiJInxU2adwDgExc6zLN+p0JdwxJbVxXtIEo1Z8veRJCGZFEU9rcrcXdCOoFBQh1C+hrELSxsTUHZRXuwEjiGSgPRCEpLEdKhrnUA7glJWhyKGHIBo09ZgxLovs4qhqXhAEpBEXyQhmSxjEpRQPWIoawyrCk2XU690KRzQjnhAEpAB6GBk+5RIQjJ5ytxd0FxQivKw62HTVheWIefkavIWxFiSeCwEKUaSmD5lrqamghKTR6hORfUaE03fgBhLlWnjy0jiMRqSkCQWhz4FpSifUL1shhKXtt5+GEubwgFJPBJjJAnJQtKHoNj5FOUVoqhBbCoybTe2Vd1rSTyWDvNiqyUlCclC05agQLei4jJkw1n3bYdt5p2EY5Ik11ZisWkqKFBfVMryHZK6gfw+37GeSIyfJCRLRRuCAtVfajWG8SNN33TYdjlJPBKLQxKSpSRWUKA9K6WoDB9VhKbtLsJJOBKJKiQhWWrafP1u29PE9zl+JAlHItGENUNXIDEGDibuVa7mE8OK8xkLbr1iBwSWHfvBziexXJh55Kt8yhGRU0Tk6yKyTUTO8WwXEfnzfPuXReQxsfu2yZju8MTgdPmCK9+l1mU3lz5eiZsEI2Fov/+viKwF3gw8BdgOXCoiF6rqV61kpwLH5Z/HAm8FHhu5b2skIUl46OsFV2O5/JJwJJrSydD2E4FtqnotgIhcAJwO2GJwOvAOVVXgEhE5QkQeABwTsW9rJNdWooQqrpp1VHeBDUGVeiZ3VWIwjgRusJa35+ti0sTs2xpjeSRMjJ6pvo+kqqAlwUjUoZZra5OIXGYtb1XVrdayBAoiIk3Mvq2RhCRRg7oTMoYa9TYEps83GSYSLkqNqX12quqWgu3bgaOs5c3Ajsg06yP2bY0kJImG9PFOkrZJwpFom04m27oUOE5EjgW+BZwBPMdJcyHwkjwG8ljgZlW9UUS+E7FvayQhSbTImKeOH7l4VLkTl3hOp2VCVfeJyEuAjwNrgfNV9WoROTvffi5wEXAasA24HTizaN+u6ipZsL9/RLYoXFaeMLEg9CksIxaNth/dkqg0RC4vcS/F5SKPUPj7insd30rZYyBZJIme6OqdJCMWDUOXd1nVac8SHbHc88gnIUkMyAREoA5D3FUrJDEZFDOyfTlJQpJItEXdu6mtXtJJTAZkud+1m4QkkWhCF/M9NhGWJCaJAUhCkkjUoe2XIsbmESMqSUwGIMVIEolEFcrumi6HxaxjmdurEZNcW4lEIoYhBcQtJ+b1MMvbrg1ArZHtC0MSkkSijLEIiFtmskxGxHK7ttLsv4lEEWMUkVjSY2KiJ9KllkiEaFtEYu62Ku6oZJWMiOW2SJKQJBI+2hSRKndZ2yPVU6ykJ1KwPZFIVKHKa+vrEisAySoZCWlkeyKRsGnjrkh31pKx3K6tFGxPJKoQY420JSJjyyeRCJAusUTCZmx3RIyLK7m3RsByx0iiLBIROUVEvi4i20TkHM92EZE/z7d/WUQe035VE4mB6dMaSUwM49qq8lkcSi97EVkLvBl4Ctn7gS8VkQtV9atWslOB4/LPY4G35t+JRCKxBCx3sD3GIjkR2Kaq16rqXuAC4HQnzenAOzTjEuAIEXlAy3VNJBKJxAiJEZIjgRus5e35uqppEolEYkExMZIqn8UhxqMrnnXui95j0iAiZwFn5Yt3glwVUf7U2QTsHLoSPbAYx1l0f2fbFuM4y1mW43xYO9nc+HH4/U0Vd1qY8xsjJNuBo6zlzcCOGmlQ1a3AVgARuWxRXnxfRDrOxSId52IhIpe1kY+qntJGPlMlxrV1KXCciBwrIuuBM4ALnTQXAi/Ie289DrhZVW9sua6JRCKRGCGlFomq7hORlwAfB9YC56vq1SJydr79XOAi4DRgG3A7cGZ3VU4kEonEmIjq9a6qF5GJhb3uXOu3Ai+uWPbWiumnSjrOxSId52KxLMfZKZJpQCKRSCQS9UhzbSUSiUSiEZ0LybJMrxJxnM/Nj+/LIvJZETlhiHo2pew4rXQ/IiL7ReSZfdavLWKOU0ROEpErRORqEflU33Vsg4jr9h4i8g8icmV+nJOLf4rI+SJyk4h/uMGitEGDoqqdfciC8/8OPBhYD1wJHO+kOQ34KNlYlMcBn++yTgMe548B98x/n7qox2ml+yeyuNozh653R//nEcBXgaPz5fsOXe+OjvN3gD/Kf98H+B6wfui6VzzOHwceA1wV2D75NmjoT9cWybJMr1J6nKr6WVX9fr54CdlYm6kR838CvBR4H3BTn5VrkZjjfA7wflW9HkBVp3isMcepwGEiIsBGMiGZ1LBsVf00Wb1DLEIbNChdC8myTK9S9Rh+iewJaGqUHqeIHAn8LHAu0yXm/3wocE8RuVhELheRF/RWu/aIOc43AT9INsD4K8DLVPVAP9XrjUVogwal60mvW5teZeREH4OIPIlMSJ7QaY26IeY43wC8QlX3Zw+xkyTmOFeAHwZOBg4GPicil6jqN7quXIvEHOdPAVcAPwn8APBJEfkXVb2l47r1ySK0QYPStZC0Nr3KyIk6BhF5JHAecKqqfrenurVJzHFuAS7IRWQTcJqI7FPVD/ZSw3aIvW53quptwG0i8mngBGBKQhJznGcCr9MsmLBNRL4JPBz4Qj9V7IVFaIMGpWvX1rJMr1J6nCJyNPB+4PkTe2q1KT1OVT1WVY9R1WOA9wK/MjERgbjr9kPAE0VkRUQOIXv/zjU917MpMcd5PZnVhYjcj2ySw2t7rWX3LEIbNCidWiS6JNOrRB7nq4F7A2/Jn9b36cQmxYs8zskTc5yqeo2IfAz4Mtlbjc5T1UnNZh35f74WeLuIfIXMBfQKVZ3UrLUi8m7gJGCTiGwHXkP+vstFaYOGJo1sTyQSiUQj0sj2RCKRSDQiCUkikUgkGpGEJJFIJBKNSEKSSCQSiUYkIUkkEolEI5KQJBKJRKIRSUgSiUQi0YgkJIlEIpFoxP8P0+EqW/MaLdIAAAAASUVORK5CYII=\n", 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\n", 18278 | "text/plain": [ 18279 | "
" 18280 | ] 18281 | }, 18282 | "metadata": { 18283 | "needs_background": "light" 18284 | }, 18285 | "output_type": "display_data" 18286 | } 18287 | ], 18288 | "source": [ 18289 | "fig,ax=plt.subplots(4,1,figsize=(6.5,22))\n", 18290 | "ax[0].streamplot(xx,yy,U_final,V_final,density=2,color='white',linewidth=0.8)\n", 18291 | "graph=ax[0].contourf(xx,yy,V_mag,cmap=cm.jet,levels=255,vmax=u_wall)\n", 18292 | "ax[0].set_title('Velocity contour plot @ Re = '+str(Re))\n", 18293 | "fig.colorbar(graph,ax=ax[0],label='Velocity (m/s)')\n", 18294 | "ax[0].set_xlim(0,L)\n", 18295 | "ax[0].set_ylim(0,H)\n", 18296 | "\n", 18297 | "ax[1].streamplot(xx,yy,U_final,V_final,density=3,color=V_mag,cmap=cm.jet,linewidth=0.8,arrowsize=0)\n", 18298 | "ax[1].set_title('Streamlines @ Re='+str(Re))\n", 18299 | "fig.colorbar(graph,ax=ax[1],label='Velocity (m/s)')\n", 18300 | "# ax[1].plot([0.1,0.1],[0,1])\n", 18301 | "# ax[1].plot([0.3,0.3],[0,1])\n", 18302 | "# ax[1].plot([0.62,0.62],[0,1])\n", 18303 | "# ax[1].plot([0,1],[0.4,0.4])\n", 18304 | "# ax[1].plot([0,1],[0.25,0.25])\n", 18305 | "ax[1].set_xlim(0,L)\n", 18306 | "ax[1].set_ylim(0,H)\n", 18307 | "\n", 18308 | "graph=ax[2].contourf(xx,yy,vort,cmap=cm.seismic,levels=255)\n", 18309 | "ax[2].set_title('Vorticity contour plot @ Re = '+str(Re))\n", 18310 | "fig.colorbar(graph,ax=ax[2])\n", 18311 | "ax[2].set_xlim(0,L)\n", 18312 | "ax[2].set_ylim(0,H)\n", 18313 | "\n", 18314 | "graph=ax[3].contourf(xx,yy,-P_final,cmap=cm.jet,levels=255)\n", 18315 | "ax[3].set_title('Kinematic pressure contour plot @ Re = '+str(Re))\n", 18316 | "fig.colorbar(graph,ax=ax[3],label='Pressure (m2/s2)')\n", 18317 | "ax[3].set_xlim(0,L)\n", 18318 | "ax[3].set_ylim(0,H)\n", 18319 | "\n", 18320 | "\n", 18321 | "plt.xlim(0,L)\n", 18322 | "plt.ylim(0,H)\n", 18323 | "plt.show()\n", 18324 | "\n", 18325 | "fig,ax=plt.subplots(1,1,figsize=(6,6))\n", 18326 | "plt.pcolormesh(xxe,yye,empty,cmap=cm.Blues,shading='auto',ec='k')\n", 18327 | "plt.title('Grid layout')\n", 18328 | "plt.xlim(0,Nx)\n", 18329 | "plt.ylim(0,Ny)\n", 18330 | "plt.show()\n", 18331 | "\n", 18332 | "iterations=np.linspace(0,iter_total,iter_total)\n", 18333 | "fig,ax=plt.subplots(1,1,figsize=(16,6))\n", 18334 | "plt.plot(iterations,U_res_list,label='U residual',linewidth=0.8)\n", 18335 | "plt.plot(iterations,V_res_list,label='V residual',linewidth=0.8)\n", 18336 | "plt.plot(iterations,Cont_res_list,label='Continuity residual',linewidth=0.8)\n", 18337 | "plt.xlabel('Iterations')\n", 18338 | "plt.ylabel('Residual')\n", 18339 | "ax.set_yscale('log')\n", 18340 | "plt.grid()\n", 18341 | "plt.xlim(0)\n", 18342 | "plt.legend()\n", 18343 | "\n", 18344 | "plt.show()\n" 18345 | ] 18346 | }, 18347 | { 18348 | "cell_type": "code", 18349 | "execution_count": 12, 18350 | "id": "4ad99aa6", 18351 | "metadata": {}, 18352 | "outputs": [], 18353 | "source": [ 18354 | "np.save('RE4000_U_4.8s.npy',U_final)\n", 18355 | "np.save('RE4000_V_4.8s.npy',V_final)\n", 18356 | "np.save('RE4000_V_mag_4.8s.npy',V_mag)" 18357 | ] 18358 | }, 18359 | { 18360 | "cell_type": "code", 18361 | "execution_count": 15, 18362 | "id": "b75f9462", 18363 | "metadata": {}, 18364 | "outputs": [], 18365 | "source": [ 18366 | "np.save('RE4000_U_time.npy',U_time_data)\n", 18367 | "np.save('RE4000_V_time.npy',V_time_data)\n", 18368 | "np.save('RE4000_V_mag_time.npy',V_mag_file)" 18369 | ] 18370 | }, 18371 | { 18372 | "cell_type": "code", 18373 | "execution_count": 32, 18374 | "id": "8343bef1", 18375 | "metadata": {}, 18376 | "outputs": [], 18377 | "source": [ 18378 | "def column(matrix, i):\n", 18379 | " return [row[i] for row in matrix]" 18380 | ] 18381 | }, 18382 | { 18383 | "cell_type": "code", 18384 | "execution_count": 33, 18385 | "id": "ccb8f920", 18386 | "metadata": { 18387 | "scrolled": true 18388 | }, 18389 | "outputs": [ 18390 | { 18391 | "data": { 18392 | "application/javascript": [ 18393 | "/* Put everything inside the global mpl namespace */\n", 18394 | "/* global mpl */\n", 18395 | "window.mpl = {};\n", 18396 | "\n", 18397 | "mpl.get_websocket_type = function () {\n", 18398 | " if (typeof WebSocket !== 'undefined') {\n", 18399 | " return WebSocket;\n", 18400 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 18401 | " return MozWebSocket;\n", 18402 | " } else {\n", 18403 | " alert(\n", 18404 | " 'Your browser does not have WebSocket support. ' +\n", 18405 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 18406 | " 'Firefox 4 and 5 are also supported but you ' +\n", 18407 | " 'have to enable WebSockets in about:config.'\n", 18408 | " );\n", 18409 | " }\n", 18410 | "};\n", 18411 | "\n", 18412 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 18413 | " this.id = figure_id;\n", 18414 | "\n", 18415 | " this.ws = websocket;\n", 18416 | "\n", 18417 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 18418 | "\n", 18419 | " if (!this.supports_binary) {\n", 18420 | " var warnings = document.getElementById('mpl-warnings');\n", 18421 | " if (warnings) {\n", 18422 | " warnings.style.display = 'block';\n", 18423 | " warnings.textContent =\n", 18424 | " 'This browser does not support binary websocket messages. ' +\n", 18425 | " 'Performance may be slow.';\n", 18426 | " }\n", 18427 | " }\n", 18428 | "\n", 18429 | " this.imageObj = new Image();\n", 18430 | "\n", 18431 | " this.context = undefined;\n", 18432 | " this.message = undefined;\n", 18433 | " this.canvas = undefined;\n", 18434 | " this.rubberband_canvas = undefined;\n", 18435 | " this.rubberband_context = undefined;\n", 18436 | " this.format_dropdown = undefined;\n", 18437 | "\n", 18438 | " this.image_mode = 'full';\n", 18439 | "\n", 18440 | " this.root = document.createElement('div');\n", 18441 | " this.root.setAttribute('style', 'display: inline-block');\n", 18442 | " this._root_extra_style(this.root);\n", 18443 | "\n", 18444 | " parent_element.appendChild(this.root);\n", 18445 | "\n", 18446 | " this._init_header(this);\n", 18447 | " this._init_canvas(this);\n", 18448 | " this._init_toolbar(this);\n", 18449 | "\n", 18450 | " var fig = this;\n", 18451 | "\n", 18452 | " this.waiting = false;\n", 18453 | "\n", 18454 | " this.ws.onopen = function () {\n", 18455 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 18456 | " fig.send_message('send_image_mode', {});\n", 18457 | " if (fig.ratio !== 1) {\n", 18458 | " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", 18459 | " }\n", 18460 | " fig.send_message('refresh', {});\n", 18461 | " };\n", 18462 | "\n", 18463 | " this.imageObj.onload = function () {\n", 18464 | " if (fig.image_mode === 'full') {\n", 18465 | " // Full images could contain transparency (where diff images\n", 18466 | " // almost always do), so we need to clear the canvas so that\n", 18467 | " // there is no ghosting.\n", 18468 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 18469 | " }\n", 18470 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 18471 | " };\n", 18472 | "\n", 18473 | " this.imageObj.onunload = function () {\n", 18474 | " fig.ws.close();\n", 18475 | " };\n", 18476 | "\n", 18477 | " this.ws.onmessage = this._make_on_message_function(this);\n", 18478 | "\n", 18479 | " this.ondownload = ondownload;\n", 18480 | "};\n", 18481 | "\n", 18482 | "mpl.figure.prototype._init_header = function () {\n", 18483 | " var titlebar = document.createElement('div');\n", 18484 | " titlebar.classList =\n", 18485 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 18486 | " var titletext = document.createElement('div');\n", 18487 | " titletext.classList = 'ui-dialog-title';\n", 18488 | " titletext.setAttribute(\n", 18489 | " 'style',\n", 18490 | " 'width: 100%; text-align: center; padding: 3px;'\n", 18491 | " );\n", 18492 | " titlebar.appendChild(titletext);\n", 18493 | " this.root.appendChild(titlebar);\n", 18494 | " this.header = titletext;\n", 18495 | "};\n", 18496 | "\n", 18497 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 18498 | "\n", 18499 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 18500 | "\n", 18501 | "mpl.figure.prototype._init_canvas = function () {\n", 18502 | " var fig = this;\n", 18503 | "\n", 18504 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 18505 | " canvas_div.setAttribute(\n", 18506 | " 'style',\n", 18507 | " 'border: 1px solid #ddd;' +\n", 18508 | " 'box-sizing: content-box;' +\n", 18509 | " 'clear: both;' +\n", 18510 | " 'min-height: 1px;' +\n", 18511 | " 'min-width: 1px;' +\n", 18512 | " 'outline: 0;' +\n", 18513 | " 'overflow: hidden;' +\n", 18514 | " 'position: relative;' +\n", 18515 | " 'resize: both;'\n", 18516 | " );\n", 18517 | "\n", 18518 | " function on_keyboard_event_closure(name) {\n", 18519 | " return function (event) {\n", 18520 | " return fig.key_event(event, name);\n", 18521 | " };\n", 18522 | " }\n", 18523 | "\n", 18524 | " canvas_div.addEventListener(\n", 18525 | " 'keydown',\n", 18526 | " on_keyboard_event_closure('key_press')\n", 18527 | " );\n", 18528 | " canvas_div.addEventListener(\n", 18529 | " 'keyup',\n", 18530 | " on_keyboard_event_closure('key_release')\n", 18531 | " );\n", 18532 | "\n", 18533 | " this._canvas_extra_style(canvas_div);\n", 18534 | " this.root.appendChild(canvas_div);\n", 18535 | "\n", 18536 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 18537 | " canvas.classList.add('mpl-canvas');\n", 18538 | " canvas.setAttribute('style', 'box-sizing: content-box;');\n", 18539 | "\n", 18540 | " this.context = canvas.getContext('2d');\n", 18541 | "\n", 18542 | " var backingStore =\n", 18543 | " this.context.backingStorePixelRatio ||\n", 18544 | " this.context.webkitBackingStorePixelRatio ||\n", 18545 | " this.context.mozBackingStorePixelRatio ||\n", 18546 | " this.context.msBackingStorePixelRatio ||\n", 18547 | " this.context.oBackingStorePixelRatio ||\n", 18548 | " this.context.backingStorePixelRatio ||\n", 18549 | " 1;\n", 18550 | "\n", 18551 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 18552 | "\n", 18553 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 18554 | " 'canvas'\n", 18555 | " ));\n", 18556 | " rubberband_canvas.setAttribute(\n", 18557 | " 'style',\n", 18558 | " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", 18559 | " );\n", 18560 | "\n", 18561 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 18562 | " if (this.ResizeObserver === undefined) {\n", 18563 | " if (window.ResizeObserver !== undefined) {\n", 18564 | " this.ResizeObserver = window.ResizeObserver;\n", 18565 | " } else {\n", 18566 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 18567 | " this.ResizeObserver = obs.ResizeObserver;\n", 18568 | " }\n", 18569 | " }\n", 18570 | "\n", 18571 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 18572 | " var nentries = entries.length;\n", 18573 | " for (var i = 0; i < nentries; i++) {\n", 18574 | " var entry = entries[i];\n", 18575 | " var width, height;\n", 18576 | " if (entry.contentBoxSize) {\n", 18577 | " if (entry.contentBoxSize instanceof Array) {\n", 18578 | " // Chrome 84 implements new version of spec.\n", 18579 | " width = entry.contentBoxSize[0].inlineSize;\n", 18580 | " height = entry.contentBoxSize[0].blockSize;\n", 18581 | " } else {\n", 18582 | " // Firefox implements old version of spec.\n", 18583 | " width = entry.contentBoxSize.inlineSize;\n", 18584 | " height = entry.contentBoxSize.blockSize;\n", 18585 | " }\n", 18586 | " } else {\n", 18587 | " // Chrome <84 implements even older version of spec.\n", 18588 | " width = entry.contentRect.width;\n", 18589 | " height = entry.contentRect.height;\n", 18590 | " }\n", 18591 | "\n", 18592 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 18593 | " // the canvas container.\n", 18594 | " if (entry.devicePixelContentBoxSize) {\n", 18595 | " // Chrome 84 implements new version of spec.\n", 18596 | " canvas.setAttribute(\n", 18597 | " 'width',\n", 18598 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 18599 | " );\n", 18600 | " canvas.setAttribute(\n", 18601 | " 'height',\n", 18602 | " entry.devicePixelContentBoxSize[0].blockSize\n", 18603 | " );\n", 18604 | " } else {\n", 18605 | " canvas.setAttribute('width', width * fig.ratio);\n", 18606 | " canvas.setAttribute('height', height * fig.ratio);\n", 18607 | " }\n", 18608 | " canvas.setAttribute(\n", 18609 | " 'style',\n", 18610 | " 'width: ' + width + 'px; height: ' + height + 'px;'\n", 18611 | " );\n", 18612 | "\n", 18613 | " rubberband_canvas.setAttribute('width', width);\n", 18614 | " rubberband_canvas.setAttribute('height', height);\n", 18615 | "\n", 18616 | " // And update the size in Python. We ignore the initial 0/0 size\n", 18617 | " // that occurs as the element is placed into the DOM, which should\n", 18618 | " // otherwise not happen due to the minimum size styling.\n", 18619 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 18620 | " fig.request_resize(width, height);\n", 18621 | " }\n", 18622 | " }\n", 18623 | " });\n", 18624 | " this.resizeObserverInstance.observe(canvas_div);\n", 18625 | "\n", 18626 | " function on_mouse_event_closure(name) {\n", 18627 | " return function (event) {\n", 18628 | " return fig.mouse_event(event, name);\n", 18629 | " };\n", 18630 | " }\n", 18631 | "\n", 18632 | " rubberband_canvas.addEventListener(\n", 18633 | " 'mousedown',\n", 18634 | " on_mouse_event_closure('button_press')\n", 18635 | " );\n", 18636 | " rubberband_canvas.addEventListener(\n", 18637 | " 'mouseup',\n", 18638 | " on_mouse_event_closure('button_release')\n", 18639 | " );\n", 18640 | " rubberband_canvas.addEventListener(\n", 18641 | " 'dblclick',\n", 18642 | " on_mouse_event_closure('dblclick')\n", 18643 | " );\n", 18644 | " // Throttle sequential mouse events to 1 every 20ms.\n", 18645 | " rubberband_canvas.addEventListener(\n", 18646 | " 'mousemove',\n", 18647 | " on_mouse_event_closure('motion_notify')\n", 18648 | " );\n", 18649 | "\n", 18650 | " rubberband_canvas.addEventListener(\n", 18651 | " 'mouseenter',\n", 18652 | " on_mouse_event_closure('figure_enter')\n", 18653 | " );\n", 18654 | " rubberband_canvas.addEventListener(\n", 18655 | " 'mouseleave',\n", 18656 | " on_mouse_event_closure('figure_leave')\n", 18657 | " );\n", 18658 | "\n", 18659 | " canvas_div.addEventListener('wheel', function (event) {\n", 18660 | " if (event.deltaY < 0) {\n", 18661 | " event.step = 1;\n", 18662 | " } else {\n", 18663 | " event.step = -1;\n", 18664 | " }\n", 18665 | " on_mouse_event_closure('scroll')(event);\n", 18666 | " });\n", 18667 | "\n", 18668 | " canvas_div.appendChild(canvas);\n", 18669 | " canvas_div.appendChild(rubberband_canvas);\n", 18670 | "\n", 18671 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 18672 | " this.rubberband_context.strokeStyle = '#000000';\n", 18673 | "\n", 18674 | " this._resize_canvas = function (width, height, forward) {\n", 18675 | " if (forward) {\n", 18676 | " canvas_div.style.width = width + 'px';\n", 18677 | " canvas_div.style.height = height + 'px';\n", 18678 | " }\n", 18679 | " };\n", 18680 | "\n", 18681 | " // Disable right mouse context menu.\n", 18682 | " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", 18683 | " event.preventDefault();\n", 18684 | " return false;\n", 18685 | " });\n", 18686 | "\n", 18687 | " function set_focus() {\n", 18688 | " canvas.focus();\n", 18689 | " canvas_div.focus();\n", 18690 | " }\n", 18691 | "\n", 18692 | " window.setTimeout(set_focus, 100);\n", 18693 | "};\n", 18694 | "\n", 18695 | "mpl.figure.prototype._init_toolbar = function () {\n", 18696 | " var fig = this;\n", 18697 | "\n", 18698 | " var toolbar = document.createElement('div');\n", 18699 | " toolbar.classList = 'mpl-toolbar';\n", 18700 | " this.root.appendChild(toolbar);\n", 18701 | "\n", 18702 | " function on_click_closure(name) {\n", 18703 | " return function (_event) {\n", 18704 | " return fig.toolbar_button_onclick(name);\n", 18705 | " };\n", 18706 | " }\n", 18707 | "\n", 18708 | " function on_mouseover_closure(tooltip) {\n", 18709 | " return function (event) {\n", 18710 | " if (!event.currentTarget.disabled) {\n", 18711 | " return fig.toolbar_button_onmouseover(tooltip);\n", 18712 | " }\n", 18713 | " };\n", 18714 | " }\n", 18715 | "\n", 18716 | " fig.buttons = {};\n", 18717 | " var buttonGroup = document.createElement('div');\n", 18718 | " buttonGroup.classList = 'mpl-button-group';\n", 18719 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 18720 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 18721 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 18722 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 18723 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 18724 | "\n", 18725 | " if (!name) {\n", 18726 | " /* Instead of a spacer, we start a new button group. */\n", 18727 | " if (buttonGroup.hasChildNodes()) {\n", 18728 | " toolbar.appendChild(buttonGroup);\n", 18729 | " }\n", 18730 | " buttonGroup = document.createElement('div');\n", 18731 | " buttonGroup.classList = 'mpl-button-group';\n", 18732 | " continue;\n", 18733 | " }\n", 18734 | "\n", 18735 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 18736 | " button.classList = 'mpl-widget';\n", 18737 | " button.setAttribute('role', 'button');\n", 18738 | " button.setAttribute('aria-disabled', 'false');\n", 18739 | " button.addEventListener('click', on_click_closure(method_name));\n", 18740 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 18741 | "\n", 18742 | " var icon_img = document.createElement('img');\n", 18743 | " icon_img.src = '_images/' + image + '.png';\n", 18744 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 18745 | " icon_img.alt = tooltip;\n", 18746 | " button.appendChild(icon_img);\n", 18747 | "\n", 18748 | " buttonGroup.appendChild(button);\n", 18749 | " }\n", 18750 | "\n", 18751 | " if (buttonGroup.hasChildNodes()) {\n", 18752 | " toolbar.appendChild(buttonGroup);\n", 18753 | " }\n", 18754 | "\n", 18755 | " var fmt_picker = document.createElement('select');\n", 18756 | " fmt_picker.classList = 'mpl-widget';\n", 18757 | " toolbar.appendChild(fmt_picker);\n", 18758 | " this.format_dropdown = fmt_picker;\n", 18759 | "\n", 18760 | " for (var ind in mpl.extensions) {\n", 18761 | " var fmt = mpl.extensions[ind];\n", 18762 | " var option = document.createElement('option');\n", 18763 | " option.selected = fmt === mpl.default_extension;\n", 18764 | " option.innerHTML = fmt;\n", 18765 | " fmt_picker.appendChild(option);\n", 18766 | " }\n", 18767 | "\n", 18768 | " var status_bar = document.createElement('span');\n", 18769 | " status_bar.classList = 'mpl-message';\n", 18770 | " toolbar.appendChild(status_bar);\n", 18771 | " this.message = status_bar;\n", 18772 | "};\n", 18773 | "\n", 18774 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 18775 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 18776 | " // which will in turn request a refresh of the image.\n", 18777 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 18778 | "};\n", 18779 | "\n", 18780 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 18781 | " properties['type'] = type;\n", 18782 | " properties['figure_id'] = this.id;\n", 18783 | " this.ws.send(JSON.stringify(properties));\n", 18784 | "};\n", 18785 | "\n", 18786 | "mpl.figure.prototype.send_draw_message = function () {\n", 18787 | " if (!this.waiting) {\n", 18788 | " this.waiting = true;\n", 18789 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 18790 | " }\n", 18791 | "};\n", 18792 | "\n", 18793 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 18794 | " var format_dropdown = fig.format_dropdown;\n", 18795 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 18796 | " fig.ondownload(fig, format);\n", 18797 | "};\n", 18798 | "\n", 18799 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 18800 | " var size = msg['size'];\n", 18801 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 18802 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 18803 | " fig.send_message('refresh', {});\n", 18804 | " }\n", 18805 | "};\n", 18806 | "\n", 18807 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 18808 | " var x0 = msg['x0'] / fig.ratio;\n", 18809 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 18810 | " var x1 = msg['x1'] / fig.ratio;\n", 18811 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 18812 | " x0 = Math.floor(x0) + 0.5;\n", 18813 | " y0 = Math.floor(y0) + 0.5;\n", 18814 | " x1 = Math.floor(x1) + 0.5;\n", 18815 | " y1 = Math.floor(y1) + 0.5;\n", 18816 | " var min_x = Math.min(x0, x1);\n", 18817 | " var min_y = Math.min(y0, y1);\n", 18818 | " var width = Math.abs(x1 - x0);\n", 18819 | " var height = Math.abs(y1 - y0);\n", 18820 | "\n", 18821 | " fig.rubberband_context.clearRect(\n", 18822 | " 0,\n", 18823 | " 0,\n", 18824 | " fig.canvas.width / fig.ratio,\n", 18825 | " fig.canvas.height / fig.ratio\n", 18826 | " );\n", 18827 | "\n", 18828 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 18829 | "};\n", 18830 | "\n", 18831 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 18832 | " // Updates the figure title.\n", 18833 | " fig.header.textContent = msg['label'];\n", 18834 | "};\n", 18835 | "\n", 18836 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 18837 | " var cursor = msg['cursor'];\n", 18838 | " switch (cursor) {\n", 18839 | " case 0:\n", 18840 | " cursor = 'pointer';\n", 18841 | " break;\n", 18842 | " case 1:\n", 18843 | " cursor = 'default';\n", 18844 | " break;\n", 18845 | " case 2:\n", 18846 | " cursor = 'crosshair';\n", 18847 | " break;\n", 18848 | " case 3:\n", 18849 | " cursor = 'move';\n", 18850 | " break;\n", 18851 | " }\n", 18852 | " fig.rubberband_canvas.style.cursor = cursor;\n", 18853 | "};\n", 18854 | "\n", 18855 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 18856 | " fig.message.textContent = msg['message'];\n", 18857 | "};\n", 18858 | "\n", 18859 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 18860 | " // Request the server to send over a new figure.\n", 18861 | " fig.send_draw_message();\n", 18862 | "};\n", 18863 | "\n", 18864 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 18865 | " fig.image_mode = msg['mode'];\n", 18866 | "};\n", 18867 | "\n", 18868 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 18869 | " for (var key in msg) {\n", 18870 | " if (!(key in fig.buttons)) {\n", 18871 | " continue;\n", 18872 | " }\n", 18873 | " fig.buttons[key].disabled = !msg[key];\n", 18874 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 18875 | " }\n", 18876 | "};\n", 18877 | "\n", 18878 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 18879 | " if (msg['mode'] === 'PAN') {\n", 18880 | " fig.buttons['Pan'].classList.add('active');\n", 18881 | " fig.buttons['Zoom'].classList.remove('active');\n", 18882 | " } else if (msg['mode'] === 'ZOOM') {\n", 18883 | " fig.buttons['Pan'].classList.remove('active');\n", 18884 | " fig.buttons['Zoom'].classList.add('active');\n", 18885 | " } else {\n", 18886 | " fig.buttons['Pan'].classList.remove('active');\n", 18887 | " fig.buttons['Zoom'].classList.remove('active');\n", 18888 | " }\n", 18889 | "};\n", 18890 | "\n", 18891 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 18892 | " // Called whenever the canvas gets updated.\n", 18893 | " this.send_message('ack', {});\n", 18894 | "};\n", 18895 | "\n", 18896 | "// A function to construct a web socket function for onmessage handling.\n", 18897 | "// Called in the figure constructor.\n", 18898 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 18899 | " return function socket_on_message(evt) {\n", 18900 | " if (evt.data instanceof Blob) {\n", 18901 | " var img = evt.data;\n", 18902 | " if (img.type !== 'image/png') {\n", 18903 | " /* FIXME: We get \"Resource interpreted as Image but\n", 18904 | " * transferred with MIME type text/plain:\" errors on\n", 18905 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 18906 | " * to be part of the websocket stream */\n", 18907 | " img.type = 'image/png';\n", 18908 | " }\n", 18909 | "\n", 18910 | " /* Free the memory for the previous frames */\n", 18911 | " if (fig.imageObj.src) {\n", 18912 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 18913 | " fig.imageObj.src\n", 18914 | " );\n", 18915 | " }\n", 18916 | "\n", 18917 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 18918 | " img\n", 18919 | " );\n", 18920 | " fig.updated_canvas_event();\n", 18921 | " fig.waiting = false;\n", 18922 | " return;\n", 18923 | " } else if (\n", 18924 | " typeof evt.data === 'string' &&\n", 18925 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 18926 | " ) {\n", 18927 | " fig.imageObj.src = evt.data;\n", 18928 | " fig.updated_canvas_event();\n", 18929 | " fig.waiting = false;\n", 18930 | " return;\n", 18931 | " }\n", 18932 | "\n", 18933 | " var msg = JSON.parse(evt.data);\n", 18934 | " var msg_type = msg['type'];\n", 18935 | "\n", 18936 | " // Call the \"handle_{type}\" callback, which takes\n", 18937 | " // the figure and JSON message as its only arguments.\n", 18938 | " try {\n", 18939 | " var callback = fig['handle_' + msg_type];\n", 18940 | " } catch (e) {\n", 18941 | " console.log(\n", 18942 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 18943 | " msg\n", 18944 | " );\n", 18945 | " return;\n", 18946 | " }\n", 18947 | "\n", 18948 | " if (callback) {\n", 18949 | " try {\n", 18950 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 18951 | " callback(fig, msg);\n", 18952 | " } catch (e) {\n", 18953 | " console.log(\n", 18954 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 18955 | " e,\n", 18956 | " e.stack,\n", 18957 | " msg\n", 18958 | " );\n", 18959 | " }\n", 18960 | " }\n", 18961 | " };\n", 18962 | "};\n", 18963 | "\n", 18964 | "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", 18965 | "mpl.findpos = function (e) {\n", 18966 | " //this section is from http://www.quirksmode.org/js/events_properties.html\n", 18967 | " var targ;\n", 18968 | " if (!e) {\n", 18969 | " e = window.event;\n", 18970 | " }\n", 18971 | " if (e.target) {\n", 18972 | " targ = e.target;\n", 18973 | " } else if (e.srcElement) {\n", 18974 | " targ = e.srcElement;\n", 18975 | " }\n", 18976 | " if (targ.nodeType === 3) {\n", 18977 | " // defeat Safari bug\n", 18978 | " targ = targ.parentNode;\n", 18979 | " }\n", 18980 | "\n", 18981 | " // pageX,Y are the mouse positions relative to the document\n", 18982 | " var boundingRect = targ.getBoundingClientRect();\n", 18983 | " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", 18984 | " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", 18985 | "\n", 18986 | " return { x: x, y: y };\n", 18987 | "};\n", 18988 | "\n", 18989 | "/*\n", 18990 | " * return a copy of an object with only non-object keys\n", 18991 | " * we need this to avoid circular references\n", 18992 | " * http://stackoverflow.com/a/24161582/3208463\n", 18993 | " */\n", 18994 | "function simpleKeys(original) {\n", 18995 | " return Object.keys(original).reduce(function (obj, key) {\n", 18996 | " if (typeof original[key] !== 'object') {\n", 18997 | " obj[key] = original[key];\n", 18998 | " }\n", 18999 | " return obj;\n", 19000 | " }, {});\n", 19001 | "}\n", 19002 | "\n", 19003 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 19004 | " var canvas_pos = mpl.findpos(event);\n", 19005 | "\n", 19006 | " if (name === 'button_press') {\n", 19007 | " this.canvas.focus();\n", 19008 | " this.canvas_div.focus();\n", 19009 | " }\n", 19010 | "\n", 19011 | " var x = canvas_pos.x * this.ratio;\n", 19012 | " var y = canvas_pos.y * this.ratio;\n", 19013 | "\n", 19014 | " this.send_message(name, {\n", 19015 | " x: x,\n", 19016 | " y: y,\n", 19017 | " button: event.button,\n", 19018 | " step: event.step,\n", 19019 | " guiEvent: simpleKeys(event),\n", 19020 | " });\n", 19021 | "\n", 19022 | " /* This prevents the web browser from automatically changing to\n", 19023 | " * the text insertion cursor when the button is pressed. We want\n", 19024 | " * to control all of the cursor setting manually through the\n", 19025 | " * 'cursor' event from matplotlib */\n", 19026 | " event.preventDefault();\n", 19027 | " return false;\n", 19028 | "};\n", 19029 | "\n", 19030 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 19031 | " // Handle any extra behaviour associated with a key event\n", 19032 | "};\n", 19033 | "\n", 19034 | "mpl.figure.prototype.key_event = function (event, name) {\n", 19035 | " // Prevent repeat events\n", 19036 | " if (name === 'key_press') {\n", 19037 | " if (event.key === this._key) {\n", 19038 | " return;\n", 19039 | " } else {\n", 19040 | " this._key = event.key;\n", 19041 | " }\n", 19042 | " }\n", 19043 | " if (name === 'key_release') {\n", 19044 | " this._key = null;\n", 19045 | " }\n", 19046 | "\n", 19047 | " var value = '';\n", 19048 | " if (event.ctrlKey && event.key !== 'Control') {\n", 19049 | " value += 'ctrl+';\n", 19050 | " }\n", 19051 | " else if (event.altKey && event.key !== 'Alt') {\n", 19052 | " value += 'alt+';\n", 19053 | " }\n", 19054 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 19055 | " value += 'shift+';\n", 19056 | " }\n", 19057 | "\n", 19058 | " value += 'k' + event.key;\n", 19059 | "\n", 19060 | " this._key_event_extra(event, name);\n", 19061 | "\n", 19062 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 19063 | " return false;\n", 19064 | "};\n", 19065 | "\n", 19066 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 19067 | " if (name === 'download') {\n", 19068 | " this.handle_save(this, null);\n", 19069 | " } else {\n", 19070 | " this.send_message('toolbar_button', { name: name });\n", 19071 | " }\n", 19072 | "};\n", 19073 | "\n", 19074 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 19075 | " this.message.textContent = tooltip;\n", 19076 | "};\n", 19077 | "\n", 19078 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 19079 | "// prettier-ignore\n", 19080 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 19081 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", 19082 | "\n", 19083 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", 19084 | "\n", 19085 | "mpl.default_extension = \"png\";/* global mpl */\n", 19086 | "\n", 19087 | "var comm_websocket_adapter = function (comm) {\n", 19088 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 19089 | " // object with the appropriate methods. Currently this is a non binary\n", 19090 | " // socket, so there is still some room for performance tuning.\n", 19091 | " var ws = {};\n", 19092 | "\n", 19093 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 19094 | " ws.readyState = comm.kernel.ws.readyState;\n", 19095 | " function updateReadyState(_event) {\n", 19096 | " if (comm.kernel.ws) {\n", 19097 | " ws.readyState = comm.kernel.ws.readyState;\n", 19098 | " } else {\n", 19099 | " ws.readyState = 3; // Closed state.\n", 19100 | " }\n", 19101 | " }\n", 19102 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 19103 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 19104 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 19105 | "\n", 19106 | " ws.close = function () {\n", 19107 | " comm.close();\n", 19108 | " };\n", 19109 | " ws.send = function (m) {\n", 19110 | " //console.log('sending', m);\n", 19111 | " comm.send(m);\n", 19112 | " };\n", 19113 | " // Register the callback with on_msg.\n", 19114 | " comm.on_msg(function (msg) {\n", 19115 | " //console.log('receiving', msg['content']['data'], msg);\n", 19116 | " var data = msg['content']['data'];\n", 19117 | " if (data['blob'] !== undefined) {\n", 19118 | " data = {\n", 19119 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 19120 | " };\n", 19121 | " }\n", 19122 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 19123 | " ws.onmessage(data);\n", 19124 | " });\n", 19125 | " return ws;\n", 19126 | "};\n", 19127 | "\n", 19128 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 19129 | " // This is the function which gets called when the mpl process\n", 19130 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 19131 | "\n", 19132 | " var id = msg.content.data.id;\n", 19133 | " // Get hold of the div created by the display call when the Comm\n", 19134 | " // socket was opened in Python.\n", 19135 | " var element = document.getElementById(id);\n", 19136 | " var ws_proxy = comm_websocket_adapter(comm);\n", 19137 | "\n", 19138 | " function ondownload(figure, _format) {\n", 19139 | " window.open(figure.canvas.toDataURL());\n", 19140 | " }\n", 19141 | "\n", 19142 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 19143 | "\n", 19144 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 19145 | " // web socket which is closed, not our websocket->open comm proxy.\n", 19146 | " ws_proxy.onopen();\n", 19147 | "\n", 19148 | " fig.parent_element = element;\n", 19149 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 19150 | " if (!fig.cell_info) {\n", 19151 | " console.error('Failed to find cell for figure', id, fig);\n", 19152 | " return;\n", 19153 | " }\n", 19154 | " fig.cell_info[0].output_area.element.on(\n", 19155 | " 'cleared',\n", 19156 | " { fig: fig },\n", 19157 | " fig._remove_fig_handler\n", 19158 | " );\n", 19159 | "};\n", 19160 | "\n", 19161 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 19162 | " var width = fig.canvas.width / fig.ratio;\n", 19163 | " fig.cell_info[0].output_area.element.off(\n", 19164 | " 'cleared',\n", 19165 | " fig._remove_fig_handler\n", 19166 | " );\n", 19167 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 19168 | "\n", 19169 | " // Update the output cell to use the data from the current canvas.\n", 19170 | " fig.push_to_output();\n", 19171 | " var dataURL = fig.canvas.toDataURL();\n", 19172 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 19173 | " // the notebook keyboard shortcuts fail.\n", 19174 | " IPython.keyboard_manager.enable();\n", 19175 | " fig.parent_element.innerHTML =\n", 19176 | " '';\n", 19177 | " fig.close_ws(fig, msg);\n", 19178 | "};\n", 19179 | "\n", 19180 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 19181 | " fig.send_message('closing', msg);\n", 19182 | " // fig.ws.close()\n", 19183 | "};\n", 19184 | "\n", 19185 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 19186 | " // Turn the data on the canvas into data in the output cell.\n", 19187 | " var width = this.canvas.width / this.ratio;\n", 19188 | " var dataURL = this.canvas.toDataURL();\n", 19189 | " this.cell_info[1]['text/html'] =\n", 19190 | " '';\n", 19191 | "};\n", 19192 | "\n", 19193 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 19194 | " // Tell IPython that the notebook contents must change.\n", 19195 | " IPython.notebook.set_dirty(true);\n", 19196 | " this.send_message('ack', {});\n", 19197 | " var fig = this;\n", 19198 | " // Wait a second, then push the new image to the DOM so\n", 19199 | " // that it is saved nicely (might be nice to debounce this).\n", 19200 | " setTimeout(function () {\n", 19201 | " fig.push_to_output();\n", 19202 | " }, 1000);\n", 19203 | "};\n", 19204 | "\n", 19205 | "mpl.figure.prototype._init_toolbar = function () {\n", 19206 | " var fig = this;\n", 19207 | "\n", 19208 | " var toolbar = document.createElement('div');\n", 19209 | " toolbar.classList = 'btn-toolbar';\n", 19210 | " this.root.appendChild(toolbar);\n", 19211 | "\n", 19212 | " function on_click_closure(name) {\n", 19213 | " return function (_event) {\n", 19214 | " return fig.toolbar_button_onclick(name);\n", 19215 | " };\n", 19216 | " }\n", 19217 | "\n", 19218 | " function on_mouseover_closure(tooltip) {\n", 19219 | " return function (event) {\n", 19220 | " if (!event.currentTarget.disabled) {\n", 19221 | " return fig.toolbar_button_onmouseover(tooltip);\n", 19222 | " }\n", 19223 | " };\n", 19224 | " }\n", 19225 | "\n", 19226 | " fig.buttons = {};\n", 19227 | " var buttonGroup = document.createElement('div');\n", 19228 | " buttonGroup.classList = 'btn-group';\n", 19229 | " var button;\n", 19230 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 19231 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 19232 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 19233 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 19234 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 19235 | "\n", 19236 | " if (!name) {\n", 19237 | " /* Instead of a spacer, we start a new button group. */\n", 19238 | " if (buttonGroup.hasChildNodes()) {\n", 19239 | " toolbar.appendChild(buttonGroup);\n", 19240 | " }\n", 19241 | " buttonGroup = document.createElement('div');\n", 19242 | " buttonGroup.classList = 'btn-group';\n", 19243 | " continue;\n", 19244 | " }\n", 19245 | "\n", 19246 | " button = fig.buttons[name] = document.createElement('button');\n", 19247 | " button.classList = 'btn btn-default';\n", 19248 | " button.href = '#';\n", 19249 | " button.title = name;\n", 19250 | " button.innerHTML = '';\n", 19251 | " button.addEventListener('click', on_click_closure(method_name));\n", 19252 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 19253 | " buttonGroup.appendChild(button);\n", 19254 | " }\n", 19255 | "\n", 19256 | " if (buttonGroup.hasChildNodes()) {\n", 19257 | " toolbar.appendChild(buttonGroup);\n", 19258 | " }\n", 19259 | "\n", 19260 | " // Add the status bar.\n", 19261 | " var status_bar = document.createElement('span');\n", 19262 | " status_bar.classList = 'mpl-message pull-right';\n", 19263 | " toolbar.appendChild(status_bar);\n", 19264 | " this.message = status_bar;\n", 19265 | "\n", 19266 | " // Add the close button to the window.\n", 19267 | " var buttongrp = document.createElement('div');\n", 19268 | " buttongrp.classList = 'btn-group inline pull-right';\n", 19269 | " button = document.createElement('button');\n", 19270 | " button.classList = 'btn btn-mini btn-primary';\n", 19271 | " button.href = '#';\n", 19272 | " button.title = 'Stop Interaction';\n", 19273 | " button.innerHTML = '';\n", 19274 | " button.addEventListener('click', function (_evt) {\n", 19275 | " fig.handle_close(fig, {});\n", 19276 | " });\n", 19277 | " button.addEventListener(\n", 19278 | " 'mouseover',\n", 19279 | " on_mouseover_closure('Stop Interaction')\n", 19280 | " );\n", 19281 | " buttongrp.appendChild(button);\n", 19282 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 19283 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 19284 | "};\n", 19285 | "\n", 19286 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 19287 | " var fig = event.data.fig;\n", 19288 | " if (event.target !== this) {\n", 19289 | " // Ignore bubbled events from children.\n", 19290 | " return;\n", 19291 | " }\n", 19292 | " fig.close_ws(fig, {});\n", 19293 | "};\n", 19294 | "\n", 19295 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 19296 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 19297 | "};\n", 19298 | "\n", 19299 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 19300 | " // this is important to make the div 'focusable\n", 19301 | " el.setAttribute('tabindex', 0);\n", 19302 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 19303 | " // off when our div gets focus\n", 19304 | "\n", 19305 | " // location in version 3\n", 19306 | " if (IPython.notebook.keyboard_manager) {\n", 19307 | " IPython.notebook.keyboard_manager.register_events(el);\n", 19308 | " } else {\n", 19309 | " // location in version 2\n", 19310 | " IPython.keyboard_manager.register_events(el);\n", 19311 | " }\n", 19312 | "};\n", 19313 | "\n", 19314 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 19315 | " var manager = IPython.notebook.keyboard_manager;\n", 19316 | " if (!manager) {\n", 19317 | " manager = IPython.keyboard_manager;\n", 19318 | " }\n", 19319 | "\n", 19320 | " // Check for shift+enter\n", 19321 | " if (event.shiftKey && event.which === 13) {\n", 19322 | " this.canvas_div.blur();\n", 19323 | " // select the cell after this one\n", 19324 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 19325 | " IPython.notebook.select(index + 1);\n", 19326 | " }\n", 19327 | "};\n", 19328 | "\n", 19329 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 19330 | " fig.ondownload(fig, null);\n", 19331 | "};\n", 19332 | "\n", 19333 | "mpl.find_output_cell = function (html_output) {\n", 19334 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 19335 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 19336 | " // IPython event is triggered only after the cells have been serialised, which for\n", 19337 | " // our purposes (turning an active figure into a static one), is too late.\n", 19338 | " var cells = IPython.notebook.get_cells();\n", 19339 | " var ncells = cells.length;\n", 19340 | " for (var i = 0; i < ncells; i++) {\n", 19341 | " var cell = cells[i];\n", 19342 | " if (cell.cell_type === 'code') {\n", 19343 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 19344 | " var data = cell.output_area.outputs[j];\n", 19345 | " if (data.data) {\n", 19346 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 19347 | " data = data.data;\n", 19348 | " }\n", 19349 | " if (data['text/html'] === html_output) {\n", 19350 | " return [cell, data, j];\n", 19351 | " }\n", 19352 | " }\n", 19353 | " }\n", 19354 | " }\n", 19355 | "};\n", 19356 | "\n", 19357 | "// Register the function which deals with the matplotlib target/channel.\n", 19358 | "// The kernel may be null if the page has been refreshed.\n", 19359 | "if (IPython.notebook.kernel !== null) {\n", 19360 | " IPython.notebook.kernel.comm_manager.register_target(\n", 19361 | " 'matplotlib',\n", 19362 | " mpl.mpl_figure_comm\n", 19363 | " );\n", 19364 | "}\n" 19365 | ], 19366 | "text/plain": [ 19367 | "" 19368 | ] 19369 | }, 19370 | "metadata": {}, 19371 | "output_type": "display_data" 19372 | }, 19373 | { 19374 | "data": { 19375 | "text/html": [ 19376 | "" 19377 | ], 19378 | "text/plain": [ 19379 | "" 19380 | ] 19381 | }, 19382 | "metadata": {}, 19383 | "output_type": "display_data" 19384 | }, 19385 | { 19386 | "name": "stdout", 19387 | "output_type": "stream", 19388 | "text": [ 19389 | "-1.0163798199883134\n" 19390 | ] 19391 | } 19392 | ], 19393 | "source": [ 19394 | "mid=column(U_final,20)\n", 19395 | "y=np.linspace(0,H,Ny+1)\n", 19396 | "plt.plot(mid,y)\n", 19397 | "plt.xlabel('Velocity (m/s)')\n", 19398 | "plt.ylabel('Position (m)')\n", 19399 | "plt.xlim(-u_wall,u_wall)\n", 19400 | "plt.ylim(0,H)\n", 19401 | "plt.grid()\n", 19402 | "plt.show()\n", 19403 | "print(np.min(mid))" 19404 | ] 19405 | }, 19406 | { 19407 | "cell_type": "markdown", 19408 | "id": "e79b3ab2", 19409 | "metadata": {}, 19410 | "source": [ 19411 | "# Animation" 19412 | ] 19413 | }, 19414 | { 19415 | "cell_type": "code", 19416 | "execution_count": 14, 19417 | "id": "e0b12a0a", 19418 | "metadata": {}, 19419 | "outputs": [], 19420 | "source": [ 19421 | "U_anim=U_time_data[::1]\n", 19422 | "V_anim=V_time_data[::1]\n", 19423 | "count=int(time_steps/25)\n", 19424 | "\n", 19425 | "U_file=[]\n", 19426 | "V_file=[]\n", 19427 | "V_mag_file=[]\n", 19428 | "Vort_file=[]\n", 19429 | "\n", 19430 | "for k in range(count):\n", 19431 | " U=U_anim[k]\n", 19432 | " V=V_anim[k]\n", 19433 | "\n", 19434 | " U_final=np.zeros([Ny+1,Nx+1])\n", 19435 | " for j in range(Ny+1):\n", 19436 | " for i in range(Nx+1):\n", 19437 | " U_final[j][i]=(U[j][i]+U[j+1][i])/2\n", 19438 | "\n", 19439 | " V_final=np.zeros([Ny+1,Nx+1])\n", 19440 | " for j in range(Ny+1):\n", 19441 | " for i in range(Nx+1):\n", 19442 | " V_final[j][i]=(V[j][i]+V[j][i+1])/2\n", 19443 | " \n", 19444 | " V_mag=np.zeros([Ny+1,Nx+1])\n", 19445 | " for j in range(Ny+1):\n", 19446 | " for i in range(Nx+1):\n", 19447 | " V_mag[j][i]=(np.sqrt(np.power(U_final[j][i],2))+np.sqrt(np.power(V_final[j][i],2)))\n", 19448 | " \n", 19449 | " vort=curl(U,V,dx,dy,Nx,Ny)\n", 19450 | " \n", 19451 | " U_file.append(U_final)\n", 19452 | " V_file.append(V_final)\n", 19453 | " V_mag_file.append(V_mag)\n", 19454 | " Vort_file.append(vort)\n", 19455 | " " 19456 | ] 19457 | }, 19458 | { 19459 | "cell_type": "code", 19460 | "execution_count": 28, 19461 | "id": "c40fc7bd", 19462 | "metadata": {}, 19463 | "outputs": [], 19464 | "source": [ 19465 | "def animate(k):\n", 19466 | " ax.clear()\n", 19467 | " \n", 19468 | " U_final=U_file[k]\n", 19469 | " V_final=V_file[k]\n", 19470 | " #V_mag=V_mag_file[k]\n", 19471 | " #vort=Vort_file[k]\n", 19472 | " plt.title('Flow time: '+str(round(k*dt*25,3))+' seconds \\n' 'Streamlines')\n", 19473 | " \n", 19474 | " plt.xlim(0,L)\n", 19475 | " plt.ylim(0,H)\n", 19476 | " #contour=plt.contourf(xx,yy,V_mag,cmap=cm.jet,vmax=u_wall,levels=255)\n", 19477 | " #contour=plt.contourf(xx,yy,vort,cmap=cm.seismic,levels=30,vmax=80)\n", 19478 | " stream=plt.streamplot(xx,yy,U_final,V_final,linewidth=0.75,density=3,color='k',arrowsize=0)\n", 19479 | " return stream\n" 19480 | ] 19481 | }, 19482 | { 19483 | "cell_type": "code", 19484 | "execution_count": 31, 19485 | "id": "8d346965", 19486 | "metadata": { 19487 | "scrolled": false 19488 | }, 19489 | "outputs": [ 19490 | { 19491 | "data": { 19492 | "application/javascript": [ 19493 | "/* Put everything inside the global mpl namespace */\n", 19494 | "/* global mpl */\n", 19495 | "window.mpl = {};\n", 19496 | "\n", 19497 | "mpl.get_websocket_type = function () {\n", 19498 | " if (typeof WebSocket !== 'undefined') {\n", 19499 | " return WebSocket;\n", 19500 | " } else if (typeof MozWebSocket !== 'undefined') {\n", 19501 | " return MozWebSocket;\n", 19502 | " } else {\n", 19503 | " alert(\n", 19504 | " 'Your browser does not have WebSocket support. ' +\n", 19505 | " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", 19506 | " 'Firefox 4 and 5 are also supported but you ' +\n", 19507 | " 'have to enable WebSockets in about:config.'\n", 19508 | " );\n", 19509 | " }\n", 19510 | "};\n", 19511 | "\n", 19512 | "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n", 19513 | " this.id = figure_id;\n", 19514 | "\n", 19515 | " this.ws = websocket;\n", 19516 | "\n", 19517 | " this.supports_binary = this.ws.binaryType !== undefined;\n", 19518 | "\n", 19519 | " if (!this.supports_binary) {\n", 19520 | " var warnings = document.getElementById('mpl-warnings');\n", 19521 | " if (warnings) {\n", 19522 | " warnings.style.display = 'block';\n", 19523 | " warnings.textContent =\n", 19524 | " 'This browser does not support binary websocket messages. ' +\n", 19525 | " 'Performance may be slow.';\n", 19526 | " }\n", 19527 | " }\n", 19528 | "\n", 19529 | " this.imageObj = new Image();\n", 19530 | "\n", 19531 | " this.context = undefined;\n", 19532 | " this.message = undefined;\n", 19533 | " this.canvas = undefined;\n", 19534 | " this.rubberband_canvas = undefined;\n", 19535 | " this.rubberband_context = undefined;\n", 19536 | " this.format_dropdown = undefined;\n", 19537 | "\n", 19538 | " this.image_mode = 'full';\n", 19539 | "\n", 19540 | " this.root = document.createElement('div');\n", 19541 | " this.root.setAttribute('style', 'display: inline-block');\n", 19542 | " this._root_extra_style(this.root);\n", 19543 | "\n", 19544 | " parent_element.appendChild(this.root);\n", 19545 | "\n", 19546 | " this._init_header(this);\n", 19547 | " this._init_canvas(this);\n", 19548 | " this._init_toolbar(this);\n", 19549 | "\n", 19550 | " var fig = this;\n", 19551 | "\n", 19552 | " this.waiting = false;\n", 19553 | "\n", 19554 | " this.ws.onopen = function () {\n", 19555 | " fig.send_message('supports_binary', { value: fig.supports_binary });\n", 19556 | " fig.send_message('send_image_mode', {});\n", 19557 | " if (fig.ratio !== 1) {\n", 19558 | " fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n", 19559 | " }\n", 19560 | " fig.send_message('refresh', {});\n", 19561 | " };\n", 19562 | "\n", 19563 | " this.imageObj.onload = function () {\n", 19564 | " if (fig.image_mode === 'full') {\n", 19565 | " // Full images could contain transparency (where diff images\n", 19566 | " // almost always do), so we need to clear the canvas so that\n", 19567 | " // there is no ghosting.\n", 19568 | " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", 19569 | " }\n", 19570 | " fig.context.drawImage(fig.imageObj, 0, 0);\n", 19571 | " };\n", 19572 | "\n", 19573 | " this.imageObj.onunload = function () {\n", 19574 | " fig.ws.close();\n", 19575 | " };\n", 19576 | "\n", 19577 | " this.ws.onmessage = this._make_on_message_function(this);\n", 19578 | "\n", 19579 | " this.ondownload = ondownload;\n", 19580 | "};\n", 19581 | "\n", 19582 | "mpl.figure.prototype._init_header = function () {\n", 19583 | " var titlebar = document.createElement('div');\n", 19584 | " titlebar.classList =\n", 19585 | " 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n", 19586 | " var titletext = document.createElement('div');\n", 19587 | " titletext.classList = 'ui-dialog-title';\n", 19588 | " titletext.setAttribute(\n", 19589 | " 'style',\n", 19590 | " 'width: 100%; text-align: center; padding: 3px;'\n", 19591 | " );\n", 19592 | " titlebar.appendChild(titletext);\n", 19593 | " this.root.appendChild(titlebar);\n", 19594 | " this.header = titletext;\n", 19595 | "};\n", 19596 | "\n", 19597 | "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n", 19598 | "\n", 19599 | "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n", 19600 | "\n", 19601 | "mpl.figure.prototype._init_canvas = function () {\n", 19602 | " var fig = this;\n", 19603 | "\n", 19604 | " var canvas_div = (this.canvas_div = document.createElement('div'));\n", 19605 | " canvas_div.setAttribute(\n", 19606 | " 'style',\n", 19607 | " 'border: 1px solid #ddd;' +\n", 19608 | " 'box-sizing: content-box;' +\n", 19609 | " 'clear: both;' +\n", 19610 | " 'min-height: 1px;' +\n", 19611 | " 'min-width: 1px;' +\n", 19612 | " 'outline: 0;' +\n", 19613 | " 'overflow: hidden;' +\n", 19614 | " 'position: relative;' +\n", 19615 | " 'resize: both;'\n", 19616 | " );\n", 19617 | "\n", 19618 | " function on_keyboard_event_closure(name) {\n", 19619 | " return function (event) {\n", 19620 | " return fig.key_event(event, name);\n", 19621 | " };\n", 19622 | " }\n", 19623 | "\n", 19624 | " canvas_div.addEventListener(\n", 19625 | " 'keydown',\n", 19626 | " on_keyboard_event_closure('key_press')\n", 19627 | " );\n", 19628 | " canvas_div.addEventListener(\n", 19629 | " 'keyup',\n", 19630 | " on_keyboard_event_closure('key_release')\n", 19631 | " );\n", 19632 | "\n", 19633 | " this._canvas_extra_style(canvas_div);\n", 19634 | " this.root.appendChild(canvas_div);\n", 19635 | "\n", 19636 | " var canvas = (this.canvas = document.createElement('canvas'));\n", 19637 | " canvas.classList.add('mpl-canvas');\n", 19638 | " canvas.setAttribute('style', 'box-sizing: content-box;');\n", 19639 | "\n", 19640 | " this.context = canvas.getContext('2d');\n", 19641 | "\n", 19642 | " var backingStore =\n", 19643 | " this.context.backingStorePixelRatio ||\n", 19644 | " this.context.webkitBackingStorePixelRatio ||\n", 19645 | " this.context.mozBackingStorePixelRatio ||\n", 19646 | " this.context.msBackingStorePixelRatio ||\n", 19647 | " this.context.oBackingStorePixelRatio ||\n", 19648 | " this.context.backingStorePixelRatio ||\n", 19649 | " 1;\n", 19650 | "\n", 19651 | " this.ratio = (window.devicePixelRatio || 1) / backingStore;\n", 19652 | "\n", 19653 | " var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n", 19654 | " 'canvas'\n", 19655 | " ));\n", 19656 | " rubberband_canvas.setAttribute(\n", 19657 | " 'style',\n", 19658 | " 'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n", 19659 | " );\n", 19660 | "\n", 19661 | " // Apply a ponyfill if ResizeObserver is not implemented by browser.\n", 19662 | " if (this.ResizeObserver === undefined) {\n", 19663 | " if (window.ResizeObserver !== undefined) {\n", 19664 | " this.ResizeObserver = window.ResizeObserver;\n", 19665 | " } else {\n", 19666 | " var obs = _JSXTOOLS_RESIZE_OBSERVER({});\n", 19667 | " this.ResizeObserver = obs.ResizeObserver;\n", 19668 | " }\n", 19669 | " }\n", 19670 | "\n", 19671 | " this.resizeObserverInstance = new this.ResizeObserver(function (entries) {\n", 19672 | " var nentries = entries.length;\n", 19673 | " for (var i = 0; i < nentries; i++) {\n", 19674 | " var entry = entries[i];\n", 19675 | " var width, height;\n", 19676 | " if (entry.contentBoxSize) {\n", 19677 | " if (entry.contentBoxSize instanceof Array) {\n", 19678 | " // Chrome 84 implements new version of spec.\n", 19679 | " width = entry.contentBoxSize[0].inlineSize;\n", 19680 | " height = entry.contentBoxSize[0].blockSize;\n", 19681 | " } else {\n", 19682 | " // Firefox implements old version of spec.\n", 19683 | " width = entry.contentBoxSize.inlineSize;\n", 19684 | " height = entry.contentBoxSize.blockSize;\n", 19685 | " }\n", 19686 | " } else {\n", 19687 | " // Chrome <84 implements even older version of spec.\n", 19688 | " width = entry.contentRect.width;\n", 19689 | " height = entry.contentRect.height;\n", 19690 | " }\n", 19691 | "\n", 19692 | " // Keep the size of the canvas and rubber band canvas in sync with\n", 19693 | " // the canvas container.\n", 19694 | " if (entry.devicePixelContentBoxSize) {\n", 19695 | " // Chrome 84 implements new version of spec.\n", 19696 | " canvas.setAttribute(\n", 19697 | " 'width',\n", 19698 | " entry.devicePixelContentBoxSize[0].inlineSize\n", 19699 | " );\n", 19700 | " canvas.setAttribute(\n", 19701 | " 'height',\n", 19702 | " entry.devicePixelContentBoxSize[0].blockSize\n", 19703 | " );\n", 19704 | " } else {\n", 19705 | " canvas.setAttribute('width', width * fig.ratio);\n", 19706 | " canvas.setAttribute('height', height * fig.ratio);\n", 19707 | " }\n", 19708 | " canvas.setAttribute(\n", 19709 | " 'style',\n", 19710 | " 'width: ' + width + 'px; height: ' + height + 'px;'\n", 19711 | " );\n", 19712 | "\n", 19713 | " rubberband_canvas.setAttribute('width', width);\n", 19714 | " rubberband_canvas.setAttribute('height', height);\n", 19715 | "\n", 19716 | " // And update the size in Python. We ignore the initial 0/0 size\n", 19717 | " // that occurs as the element is placed into the DOM, which should\n", 19718 | " // otherwise not happen due to the minimum size styling.\n", 19719 | " if (fig.ws.readyState == 1 && width != 0 && height != 0) {\n", 19720 | " fig.request_resize(width, height);\n", 19721 | " }\n", 19722 | " }\n", 19723 | " });\n", 19724 | " this.resizeObserverInstance.observe(canvas_div);\n", 19725 | "\n", 19726 | " function on_mouse_event_closure(name) {\n", 19727 | " return function (event) {\n", 19728 | " return fig.mouse_event(event, name);\n", 19729 | " };\n", 19730 | " }\n", 19731 | "\n", 19732 | " rubberband_canvas.addEventListener(\n", 19733 | " 'mousedown',\n", 19734 | " on_mouse_event_closure('button_press')\n", 19735 | " );\n", 19736 | " rubberband_canvas.addEventListener(\n", 19737 | " 'mouseup',\n", 19738 | " on_mouse_event_closure('button_release')\n", 19739 | " );\n", 19740 | " rubberband_canvas.addEventListener(\n", 19741 | " 'dblclick',\n", 19742 | " on_mouse_event_closure('dblclick')\n", 19743 | " );\n", 19744 | " // Throttle sequential mouse events to 1 every 20ms.\n", 19745 | " rubberband_canvas.addEventListener(\n", 19746 | " 'mousemove',\n", 19747 | " on_mouse_event_closure('motion_notify')\n", 19748 | " );\n", 19749 | "\n", 19750 | " rubberband_canvas.addEventListener(\n", 19751 | " 'mouseenter',\n", 19752 | " on_mouse_event_closure('figure_enter')\n", 19753 | " );\n", 19754 | " rubberband_canvas.addEventListener(\n", 19755 | " 'mouseleave',\n", 19756 | " on_mouse_event_closure('figure_leave')\n", 19757 | " );\n", 19758 | "\n", 19759 | " canvas_div.addEventListener('wheel', function (event) {\n", 19760 | " if (event.deltaY < 0) {\n", 19761 | " event.step = 1;\n", 19762 | " } else {\n", 19763 | " event.step = -1;\n", 19764 | " }\n", 19765 | " on_mouse_event_closure('scroll')(event);\n", 19766 | " });\n", 19767 | "\n", 19768 | " canvas_div.appendChild(canvas);\n", 19769 | " canvas_div.appendChild(rubberband_canvas);\n", 19770 | "\n", 19771 | " this.rubberband_context = rubberband_canvas.getContext('2d');\n", 19772 | " this.rubberband_context.strokeStyle = '#000000';\n", 19773 | "\n", 19774 | " this._resize_canvas = function (width, height, forward) {\n", 19775 | " if (forward) {\n", 19776 | " canvas_div.style.width = width + 'px';\n", 19777 | " canvas_div.style.height = height + 'px';\n", 19778 | " }\n", 19779 | " };\n", 19780 | "\n", 19781 | " // Disable right mouse context menu.\n", 19782 | " this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n", 19783 | " event.preventDefault();\n", 19784 | " return false;\n", 19785 | " });\n", 19786 | "\n", 19787 | " function set_focus() {\n", 19788 | " canvas.focus();\n", 19789 | " canvas_div.focus();\n", 19790 | " }\n", 19791 | "\n", 19792 | " window.setTimeout(set_focus, 100);\n", 19793 | "};\n", 19794 | "\n", 19795 | "mpl.figure.prototype._init_toolbar = function () {\n", 19796 | " var fig = this;\n", 19797 | "\n", 19798 | " var toolbar = document.createElement('div');\n", 19799 | " toolbar.classList = 'mpl-toolbar';\n", 19800 | " this.root.appendChild(toolbar);\n", 19801 | "\n", 19802 | " function on_click_closure(name) {\n", 19803 | " return function (_event) {\n", 19804 | " return fig.toolbar_button_onclick(name);\n", 19805 | " };\n", 19806 | " }\n", 19807 | "\n", 19808 | " function on_mouseover_closure(tooltip) {\n", 19809 | " return function (event) {\n", 19810 | " if (!event.currentTarget.disabled) {\n", 19811 | " return fig.toolbar_button_onmouseover(tooltip);\n", 19812 | " }\n", 19813 | " };\n", 19814 | " }\n", 19815 | "\n", 19816 | " fig.buttons = {};\n", 19817 | " var buttonGroup = document.createElement('div');\n", 19818 | " buttonGroup.classList = 'mpl-button-group';\n", 19819 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 19820 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 19821 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 19822 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 19823 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 19824 | "\n", 19825 | " if (!name) {\n", 19826 | " /* Instead of a spacer, we start a new button group. */\n", 19827 | " if (buttonGroup.hasChildNodes()) {\n", 19828 | " toolbar.appendChild(buttonGroup);\n", 19829 | " }\n", 19830 | " buttonGroup = document.createElement('div');\n", 19831 | " buttonGroup.classList = 'mpl-button-group';\n", 19832 | " continue;\n", 19833 | " }\n", 19834 | "\n", 19835 | " var button = (fig.buttons[name] = document.createElement('button'));\n", 19836 | " button.classList = 'mpl-widget';\n", 19837 | " button.setAttribute('role', 'button');\n", 19838 | " button.setAttribute('aria-disabled', 'false');\n", 19839 | " button.addEventListener('click', on_click_closure(method_name));\n", 19840 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 19841 | "\n", 19842 | " var icon_img = document.createElement('img');\n", 19843 | " icon_img.src = '_images/' + image + '.png';\n", 19844 | " icon_img.srcset = '_images/' + image + '_large.png 2x';\n", 19845 | " icon_img.alt = tooltip;\n", 19846 | " button.appendChild(icon_img);\n", 19847 | "\n", 19848 | " buttonGroup.appendChild(button);\n", 19849 | " }\n", 19850 | "\n", 19851 | " if (buttonGroup.hasChildNodes()) {\n", 19852 | " toolbar.appendChild(buttonGroup);\n", 19853 | " }\n", 19854 | "\n", 19855 | " var fmt_picker = document.createElement('select');\n", 19856 | " fmt_picker.classList = 'mpl-widget';\n", 19857 | " toolbar.appendChild(fmt_picker);\n", 19858 | " this.format_dropdown = fmt_picker;\n", 19859 | "\n", 19860 | " for (var ind in mpl.extensions) {\n", 19861 | " var fmt = mpl.extensions[ind];\n", 19862 | " var option = document.createElement('option');\n", 19863 | " option.selected = fmt === mpl.default_extension;\n", 19864 | " option.innerHTML = fmt;\n", 19865 | " fmt_picker.appendChild(option);\n", 19866 | " }\n", 19867 | "\n", 19868 | " var status_bar = document.createElement('span');\n", 19869 | " status_bar.classList = 'mpl-message';\n", 19870 | " toolbar.appendChild(status_bar);\n", 19871 | " this.message = status_bar;\n", 19872 | "};\n", 19873 | "\n", 19874 | "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n", 19875 | " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", 19876 | " // which will in turn request a refresh of the image.\n", 19877 | " this.send_message('resize', { width: x_pixels, height: y_pixels });\n", 19878 | "};\n", 19879 | "\n", 19880 | "mpl.figure.prototype.send_message = function (type, properties) {\n", 19881 | " properties['type'] = type;\n", 19882 | " properties['figure_id'] = this.id;\n", 19883 | " this.ws.send(JSON.stringify(properties));\n", 19884 | "};\n", 19885 | "\n", 19886 | "mpl.figure.prototype.send_draw_message = function () {\n", 19887 | " if (!this.waiting) {\n", 19888 | " this.waiting = true;\n", 19889 | " this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n", 19890 | " }\n", 19891 | "};\n", 19892 | "\n", 19893 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 19894 | " var format_dropdown = fig.format_dropdown;\n", 19895 | " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", 19896 | " fig.ondownload(fig, format);\n", 19897 | "};\n", 19898 | "\n", 19899 | "mpl.figure.prototype.handle_resize = function (fig, msg) {\n", 19900 | " var size = msg['size'];\n", 19901 | " if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n", 19902 | " fig._resize_canvas(size[0], size[1], msg['forward']);\n", 19903 | " fig.send_message('refresh', {});\n", 19904 | " }\n", 19905 | "};\n", 19906 | "\n", 19907 | "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n", 19908 | " var x0 = msg['x0'] / fig.ratio;\n", 19909 | " var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n", 19910 | " var x1 = msg['x1'] / fig.ratio;\n", 19911 | " var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n", 19912 | " x0 = Math.floor(x0) + 0.5;\n", 19913 | " y0 = Math.floor(y0) + 0.5;\n", 19914 | " x1 = Math.floor(x1) + 0.5;\n", 19915 | " y1 = Math.floor(y1) + 0.5;\n", 19916 | " var min_x = Math.min(x0, x1);\n", 19917 | " var min_y = Math.min(y0, y1);\n", 19918 | " var width = Math.abs(x1 - x0);\n", 19919 | " var height = Math.abs(y1 - y0);\n", 19920 | "\n", 19921 | " fig.rubberband_context.clearRect(\n", 19922 | " 0,\n", 19923 | " 0,\n", 19924 | " fig.canvas.width / fig.ratio,\n", 19925 | " fig.canvas.height / fig.ratio\n", 19926 | " );\n", 19927 | "\n", 19928 | " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", 19929 | "};\n", 19930 | "\n", 19931 | "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n", 19932 | " // Updates the figure title.\n", 19933 | " fig.header.textContent = msg['label'];\n", 19934 | "};\n", 19935 | "\n", 19936 | "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n", 19937 | " var cursor = msg['cursor'];\n", 19938 | " switch (cursor) {\n", 19939 | " case 0:\n", 19940 | " cursor = 'pointer';\n", 19941 | " break;\n", 19942 | " case 1:\n", 19943 | " cursor = 'default';\n", 19944 | " break;\n", 19945 | " case 2:\n", 19946 | " cursor = 'crosshair';\n", 19947 | " break;\n", 19948 | " case 3:\n", 19949 | " cursor = 'move';\n", 19950 | " break;\n", 19951 | " }\n", 19952 | " fig.rubberband_canvas.style.cursor = cursor;\n", 19953 | "};\n", 19954 | "\n", 19955 | "mpl.figure.prototype.handle_message = function (fig, msg) {\n", 19956 | " fig.message.textContent = msg['message'];\n", 19957 | "};\n", 19958 | "\n", 19959 | "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n", 19960 | " // Request the server to send over a new figure.\n", 19961 | " fig.send_draw_message();\n", 19962 | "};\n", 19963 | "\n", 19964 | "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n", 19965 | " fig.image_mode = msg['mode'];\n", 19966 | "};\n", 19967 | "\n", 19968 | "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n", 19969 | " for (var key in msg) {\n", 19970 | " if (!(key in fig.buttons)) {\n", 19971 | " continue;\n", 19972 | " }\n", 19973 | " fig.buttons[key].disabled = !msg[key];\n", 19974 | " fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n", 19975 | " }\n", 19976 | "};\n", 19977 | "\n", 19978 | "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n", 19979 | " if (msg['mode'] === 'PAN') {\n", 19980 | " fig.buttons['Pan'].classList.add('active');\n", 19981 | " fig.buttons['Zoom'].classList.remove('active');\n", 19982 | " } else if (msg['mode'] === 'ZOOM') {\n", 19983 | " fig.buttons['Pan'].classList.remove('active');\n", 19984 | " fig.buttons['Zoom'].classList.add('active');\n", 19985 | " } else {\n", 19986 | " fig.buttons['Pan'].classList.remove('active');\n", 19987 | " fig.buttons['Zoom'].classList.remove('active');\n", 19988 | " }\n", 19989 | "};\n", 19990 | "\n", 19991 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 19992 | " // Called whenever the canvas gets updated.\n", 19993 | " this.send_message('ack', {});\n", 19994 | "};\n", 19995 | "\n", 19996 | "// A function to construct a web socket function for onmessage handling.\n", 19997 | "// Called in the figure constructor.\n", 19998 | "mpl.figure.prototype._make_on_message_function = function (fig) {\n", 19999 | " return function socket_on_message(evt) {\n", 20000 | " if (evt.data instanceof Blob) {\n", 20001 | " var img = evt.data;\n", 20002 | " if (img.type !== 'image/png') {\n", 20003 | " /* FIXME: We get \"Resource interpreted as Image but\n", 20004 | " * transferred with MIME type text/plain:\" errors on\n", 20005 | " * Chrome. But how to set the MIME type? It doesn't seem\n", 20006 | " * to be part of the websocket stream */\n", 20007 | " img.type = 'image/png';\n", 20008 | " }\n", 20009 | "\n", 20010 | " /* Free the memory for the previous frames */\n", 20011 | " if (fig.imageObj.src) {\n", 20012 | " (window.URL || window.webkitURL).revokeObjectURL(\n", 20013 | " fig.imageObj.src\n", 20014 | " );\n", 20015 | " }\n", 20016 | "\n", 20017 | " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", 20018 | " img\n", 20019 | " );\n", 20020 | " fig.updated_canvas_event();\n", 20021 | " fig.waiting = false;\n", 20022 | " return;\n", 20023 | " } else if (\n", 20024 | " typeof evt.data === 'string' &&\n", 20025 | " evt.data.slice(0, 21) === 'data:image/png;base64'\n", 20026 | " ) {\n", 20027 | " fig.imageObj.src = evt.data;\n", 20028 | " fig.updated_canvas_event();\n", 20029 | " fig.waiting = false;\n", 20030 | " return;\n", 20031 | " }\n", 20032 | "\n", 20033 | " var msg = JSON.parse(evt.data);\n", 20034 | " var msg_type = msg['type'];\n", 20035 | "\n", 20036 | " // Call the \"handle_{type}\" callback, which takes\n", 20037 | " // the figure and JSON message as its only arguments.\n", 20038 | " try {\n", 20039 | " var callback = fig['handle_' + msg_type];\n", 20040 | " } catch (e) {\n", 20041 | " console.log(\n", 20042 | " \"No handler for the '\" + msg_type + \"' message type: \",\n", 20043 | " msg\n", 20044 | " );\n", 20045 | " return;\n", 20046 | " }\n", 20047 | "\n", 20048 | " if (callback) {\n", 20049 | " try {\n", 20050 | " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", 20051 | " callback(fig, msg);\n", 20052 | " } catch (e) {\n", 20053 | " console.log(\n", 20054 | " \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n", 20055 | " e,\n", 20056 | " e.stack,\n", 20057 | " msg\n", 20058 | " );\n", 20059 | " }\n", 20060 | " }\n", 20061 | " };\n", 20062 | "};\n", 20063 | "\n", 20064 | "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", 20065 | "mpl.findpos = function (e) {\n", 20066 | " //this section is from http://www.quirksmode.org/js/events_properties.html\n", 20067 | " var targ;\n", 20068 | " if (!e) {\n", 20069 | " e = window.event;\n", 20070 | " }\n", 20071 | " if (e.target) {\n", 20072 | " targ = e.target;\n", 20073 | " } else if (e.srcElement) {\n", 20074 | " targ = e.srcElement;\n", 20075 | " }\n", 20076 | " if (targ.nodeType === 3) {\n", 20077 | " // defeat Safari bug\n", 20078 | " targ = targ.parentNode;\n", 20079 | " }\n", 20080 | "\n", 20081 | " // pageX,Y are the mouse positions relative to the document\n", 20082 | " var boundingRect = targ.getBoundingClientRect();\n", 20083 | " var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n", 20084 | " var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n", 20085 | "\n", 20086 | " return { x: x, y: y };\n", 20087 | "};\n", 20088 | "\n", 20089 | "/*\n", 20090 | " * return a copy of an object with only non-object keys\n", 20091 | " * we need this to avoid circular references\n", 20092 | " * http://stackoverflow.com/a/24161582/3208463\n", 20093 | " */\n", 20094 | "function simpleKeys(original) {\n", 20095 | " return Object.keys(original).reduce(function (obj, key) {\n", 20096 | " if (typeof original[key] !== 'object') {\n", 20097 | " obj[key] = original[key];\n", 20098 | " }\n", 20099 | " return obj;\n", 20100 | " }, {});\n", 20101 | "}\n", 20102 | "\n", 20103 | "mpl.figure.prototype.mouse_event = function (event, name) {\n", 20104 | " var canvas_pos = mpl.findpos(event);\n", 20105 | "\n", 20106 | " if (name === 'button_press') {\n", 20107 | " this.canvas.focus();\n", 20108 | " this.canvas_div.focus();\n", 20109 | " }\n", 20110 | "\n", 20111 | " var x = canvas_pos.x * this.ratio;\n", 20112 | " var y = canvas_pos.y * this.ratio;\n", 20113 | "\n", 20114 | " this.send_message(name, {\n", 20115 | " x: x,\n", 20116 | " y: y,\n", 20117 | " button: event.button,\n", 20118 | " step: event.step,\n", 20119 | " guiEvent: simpleKeys(event),\n", 20120 | " });\n", 20121 | "\n", 20122 | " /* This prevents the web browser from automatically changing to\n", 20123 | " * the text insertion cursor when the button is pressed. We want\n", 20124 | " * to control all of the cursor setting manually through the\n", 20125 | " * 'cursor' event from matplotlib */\n", 20126 | " event.preventDefault();\n", 20127 | " return false;\n", 20128 | "};\n", 20129 | "\n", 20130 | "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n", 20131 | " // Handle any extra behaviour associated with a key event\n", 20132 | "};\n", 20133 | "\n", 20134 | "mpl.figure.prototype.key_event = function (event, name) {\n", 20135 | " // Prevent repeat events\n", 20136 | " if (name === 'key_press') {\n", 20137 | " if (event.key === this._key) {\n", 20138 | " return;\n", 20139 | " } else {\n", 20140 | " this._key = event.key;\n", 20141 | " }\n", 20142 | " }\n", 20143 | " if (name === 'key_release') {\n", 20144 | " this._key = null;\n", 20145 | " }\n", 20146 | "\n", 20147 | " var value = '';\n", 20148 | " if (event.ctrlKey && event.key !== 'Control') {\n", 20149 | " value += 'ctrl+';\n", 20150 | " }\n", 20151 | " else if (event.altKey && event.key !== 'Alt') {\n", 20152 | " value += 'alt+';\n", 20153 | " }\n", 20154 | " else if (event.shiftKey && event.key !== 'Shift') {\n", 20155 | " value += 'shift+';\n", 20156 | " }\n", 20157 | "\n", 20158 | " value += 'k' + event.key;\n", 20159 | "\n", 20160 | " this._key_event_extra(event, name);\n", 20161 | "\n", 20162 | " this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n", 20163 | " return false;\n", 20164 | "};\n", 20165 | "\n", 20166 | "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n", 20167 | " if (name === 'download') {\n", 20168 | " this.handle_save(this, null);\n", 20169 | " } else {\n", 20170 | " this.send_message('toolbar_button', { name: name });\n", 20171 | " }\n", 20172 | "};\n", 20173 | "\n", 20174 | "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n", 20175 | " this.message.textContent = tooltip;\n", 20176 | "};\n", 20177 | "\n", 20178 | "///////////////// REMAINING CONTENT GENERATED BY embed_js.py /////////////////\n", 20179 | "// prettier-ignore\n", 20180 | "var _JSXTOOLS_RESIZE_OBSERVER=function(A){var t,i=new WeakMap,n=new WeakMap,a=new WeakMap,r=new WeakMap,o=new Set;function s(e){if(!(this instanceof s))throw new TypeError(\"Constructor requires 'new' operator\");i.set(this,e)}function h(){throw new TypeError(\"Function is not a constructor\")}function c(e,t,i,n){e=0 in arguments?Number(arguments[0]):0,t=1 in arguments?Number(arguments[1]):0,i=2 in arguments?Number(arguments[2]):0,n=3 in arguments?Number(arguments[3]):0,this.right=(this.x=this.left=e)+(this.width=i),this.bottom=(this.y=this.top=t)+(this.height=n),Object.freeze(this)}function d(){t=requestAnimationFrame(d);var s=new WeakMap,p=new Set;o.forEach((function(t){r.get(t).forEach((function(i){var r=t instanceof window.SVGElement,o=a.get(t),d=r?0:parseFloat(o.paddingTop),f=r?0:parseFloat(o.paddingRight),l=r?0:parseFloat(o.paddingBottom),u=r?0:parseFloat(o.paddingLeft),g=r?0:parseFloat(o.borderTopWidth),m=r?0:parseFloat(o.borderRightWidth),w=r?0:parseFloat(o.borderBottomWidth),b=u+f,F=d+l,v=(r?0:parseFloat(o.borderLeftWidth))+m,W=g+w,y=r?0:t.offsetHeight-W-t.clientHeight,E=r?0:t.offsetWidth-v-t.clientWidth,R=b+v,z=F+W,M=r?t.width:parseFloat(o.width)-R-E,O=r?t.height:parseFloat(o.height)-z-y;if(n.has(t)){var k=n.get(t);if(k[0]===M&&k[1]===O)return}n.set(t,[M,O]);var S=Object.create(h.prototype);S.target=t,S.contentRect=new c(u,d,M,O),s.has(i)||(s.set(i,[]),p.add(i)),s.get(i).push(S)}))})),p.forEach((function(e){i.get(e).call(e,s.get(e),e)}))}return s.prototype.observe=function(i){if(i instanceof window.Element){r.has(i)||(r.set(i,new Set),o.add(i),a.set(i,window.getComputedStyle(i)));var n=r.get(i);n.has(this)||n.add(this),cancelAnimationFrame(t),t=requestAnimationFrame(d)}},s.prototype.unobserve=function(i){if(i instanceof window.Element&&r.has(i)){var n=r.get(i);n.has(this)&&(n.delete(this),n.size||(r.delete(i),o.delete(i))),n.size||r.delete(i),o.size||cancelAnimationFrame(t)}},A.DOMRectReadOnly=c,A.ResizeObserver=s,A.ResizeObserverEntry=h,A}; // eslint-disable-line\n", 20181 | "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", 20182 | "\n", 20183 | "mpl.extensions = [\"eps\", \"jpeg\", \"pgf\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", 20184 | "\n", 20185 | "mpl.default_extension = \"png\";/* global mpl */\n", 20186 | "\n", 20187 | "var comm_websocket_adapter = function (comm) {\n", 20188 | " // Create a \"websocket\"-like object which calls the given IPython comm\n", 20189 | " // object with the appropriate methods. Currently this is a non binary\n", 20190 | " // socket, so there is still some room for performance tuning.\n", 20191 | " var ws = {};\n", 20192 | "\n", 20193 | " ws.binaryType = comm.kernel.ws.binaryType;\n", 20194 | " ws.readyState = comm.kernel.ws.readyState;\n", 20195 | " function updateReadyState(_event) {\n", 20196 | " if (comm.kernel.ws) {\n", 20197 | " ws.readyState = comm.kernel.ws.readyState;\n", 20198 | " } else {\n", 20199 | " ws.readyState = 3; // Closed state.\n", 20200 | " }\n", 20201 | " }\n", 20202 | " comm.kernel.ws.addEventListener('open', updateReadyState);\n", 20203 | " comm.kernel.ws.addEventListener('close', updateReadyState);\n", 20204 | " comm.kernel.ws.addEventListener('error', updateReadyState);\n", 20205 | "\n", 20206 | " ws.close = function () {\n", 20207 | " comm.close();\n", 20208 | " };\n", 20209 | " ws.send = function (m) {\n", 20210 | " //console.log('sending', m);\n", 20211 | " comm.send(m);\n", 20212 | " };\n", 20213 | " // Register the callback with on_msg.\n", 20214 | " comm.on_msg(function (msg) {\n", 20215 | " //console.log('receiving', msg['content']['data'], msg);\n", 20216 | " var data = msg['content']['data'];\n", 20217 | " if (data['blob'] !== undefined) {\n", 20218 | " data = {\n", 20219 | " data: new Blob(msg['buffers'], { type: data['blob'] }),\n", 20220 | " };\n", 20221 | " }\n", 20222 | " // Pass the mpl event to the overridden (by mpl) onmessage function.\n", 20223 | " ws.onmessage(data);\n", 20224 | " });\n", 20225 | " return ws;\n", 20226 | "};\n", 20227 | "\n", 20228 | "mpl.mpl_figure_comm = function (comm, msg) {\n", 20229 | " // This is the function which gets called when the mpl process\n", 20230 | " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", 20231 | "\n", 20232 | " var id = msg.content.data.id;\n", 20233 | " // Get hold of the div created by the display call when the Comm\n", 20234 | " // socket was opened in Python.\n", 20235 | " var element = document.getElementById(id);\n", 20236 | " var ws_proxy = comm_websocket_adapter(comm);\n", 20237 | "\n", 20238 | " function ondownload(figure, _format) {\n", 20239 | " window.open(figure.canvas.toDataURL());\n", 20240 | " }\n", 20241 | "\n", 20242 | " var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n", 20243 | "\n", 20244 | " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", 20245 | " // web socket which is closed, not our websocket->open comm proxy.\n", 20246 | " ws_proxy.onopen();\n", 20247 | "\n", 20248 | " fig.parent_element = element;\n", 20249 | " fig.cell_info = mpl.find_output_cell(\"
\");\n", 20250 | " if (!fig.cell_info) {\n", 20251 | " console.error('Failed to find cell for figure', id, fig);\n", 20252 | " return;\n", 20253 | " }\n", 20254 | " fig.cell_info[0].output_area.element.on(\n", 20255 | " 'cleared',\n", 20256 | " { fig: fig },\n", 20257 | " fig._remove_fig_handler\n", 20258 | " );\n", 20259 | "};\n", 20260 | "\n", 20261 | "mpl.figure.prototype.handle_close = function (fig, msg) {\n", 20262 | " var width = fig.canvas.width / fig.ratio;\n", 20263 | " fig.cell_info[0].output_area.element.off(\n", 20264 | " 'cleared',\n", 20265 | " fig._remove_fig_handler\n", 20266 | " );\n", 20267 | " fig.resizeObserverInstance.unobserve(fig.canvas_div);\n", 20268 | "\n", 20269 | " // Update the output cell to use the data from the current canvas.\n", 20270 | " fig.push_to_output();\n", 20271 | " var dataURL = fig.canvas.toDataURL();\n", 20272 | " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", 20273 | " // the notebook keyboard shortcuts fail.\n", 20274 | " IPython.keyboard_manager.enable();\n", 20275 | " fig.parent_element.innerHTML =\n", 20276 | " '';\n", 20277 | " fig.close_ws(fig, msg);\n", 20278 | "};\n", 20279 | "\n", 20280 | "mpl.figure.prototype.close_ws = function (fig, msg) {\n", 20281 | " fig.send_message('closing', msg);\n", 20282 | " // fig.ws.close()\n", 20283 | "};\n", 20284 | "\n", 20285 | "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n", 20286 | " // Turn the data on the canvas into data in the output cell.\n", 20287 | " var width = this.canvas.width / this.ratio;\n", 20288 | " var dataURL = this.canvas.toDataURL();\n", 20289 | " this.cell_info[1]['text/html'] =\n", 20290 | " '';\n", 20291 | "};\n", 20292 | "\n", 20293 | "mpl.figure.prototype.updated_canvas_event = function () {\n", 20294 | " // Tell IPython that the notebook contents must change.\n", 20295 | " IPython.notebook.set_dirty(true);\n", 20296 | " this.send_message('ack', {});\n", 20297 | " var fig = this;\n", 20298 | " // Wait a second, then push the new image to the DOM so\n", 20299 | " // that it is saved nicely (might be nice to debounce this).\n", 20300 | " setTimeout(function () {\n", 20301 | " fig.push_to_output();\n", 20302 | " }, 1000);\n", 20303 | "};\n", 20304 | "\n", 20305 | "mpl.figure.prototype._init_toolbar = function () {\n", 20306 | " var fig = this;\n", 20307 | "\n", 20308 | " var toolbar = document.createElement('div');\n", 20309 | " toolbar.classList = 'btn-toolbar';\n", 20310 | " this.root.appendChild(toolbar);\n", 20311 | "\n", 20312 | " function on_click_closure(name) {\n", 20313 | " return function (_event) {\n", 20314 | " return fig.toolbar_button_onclick(name);\n", 20315 | " };\n", 20316 | " }\n", 20317 | "\n", 20318 | " function on_mouseover_closure(tooltip) {\n", 20319 | " return function (event) {\n", 20320 | " if (!event.currentTarget.disabled) {\n", 20321 | " return fig.toolbar_button_onmouseover(tooltip);\n", 20322 | " }\n", 20323 | " };\n", 20324 | " }\n", 20325 | "\n", 20326 | " fig.buttons = {};\n", 20327 | " var buttonGroup = document.createElement('div');\n", 20328 | " buttonGroup.classList = 'btn-group';\n", 20329 | " var button;\n", 20330 | " for (var toolbar_ind in mpl.toolbar_items) {\n", 20331 | " var name = mpl.toolbar_items[toolbar_ind][0];\n", 20332 | " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", 20333 | " var image = mpl.toolbar_items[toolbar_ind][2];\n", 20334 | " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", 20335 | "\n", 20336 | " if (!name) {\n", 20337 | " /* Instead of a spacer, we start a new button group. */\n", 20338 | " if (buttonGroup.hasChildNodes()) {\n", 20339 | " toolbar.appendChild(buttonGroup);\n", 20340 | " }\n", 20341 | " buttonGroup = document.createElement('div');\n", 20342 | " buttonGroup.classList = 'btn-group';\n", 20343 | " continue;\n", 20344 | " }\n", 20345 | "\n", 20346 | " button = fig.buttons[name] = document.createElement('button');\n", 20347 | " button.classList = 'btn btn-default';\n", 20348 | " button.href = '#';\n", 20349 | " button.title = name;\n", 20350 | " button.innerHTML = '';\n", 20351 | " button.addEventListener('click', on_click_closure(method_name));\n", 20352 | " button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n", 20353 | " buttonGroup.appendChild(button);\n", 20354 | " }\n", 20355 | "\n", 20356 | " if (buttonGroup.hasChildNodes()) {\n", 20357 | " toolbar.appendChild(buttonGroup);\n", 20358 | " }\n", 20359 | "\n", 20360 | " // Add the status bar.\n", 20361 | " var status_bar = document.createElement('span');\n", 20362 | " status_bar.classList = 'mpl-message pull-right';\n", 20363 | " toolbar.appendChild(status_bar);\n", 20364 | " this.message = status_bar;\n", 20365 | "\n", 20366 | " // Add the close button to the window.\n", 20367 | " var buttongrp = document.createElement('div');\n", 20368 | " buttongrp.classList = 'btn-group inline pull-right';\n", 20369 | " button = document.createElement('button');\n", 20370 | " button.classList = 'btn btn-mini btn-primary';\n", 20371 | " button.href = '#';\n", 20372 | " button.title = 'Stop Interaction';\n", 20373 | " button.innerHTML = '';\n", 20374 | " button.addEventListener('click', function (_evt) {\n", 20375 | " fig.handle_close(fig, {});\n", 20376 | " });\n", 20377 | " button.addEventListener(\n", 20378 | " 'mouseover',\n", 20379 | " on_mouseover_closure('Stop Interaction')\n", 20380 | " );\n", 20381 | " buttongrp.appendChild(button);\n", 20382 | " var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n", 20383 | " titlebar.insertBefore(buttongrp, titlebar.firstChild);\n", 20384 | "};\n", 20385 | "\n", 20386 | "mpl.figure.prototype._remove_fig_handler = function (event) {\n", 20387 | " var fig = event.data.fig;\n", 20388 | " if (event.target !== this) {\n", 20389 | " // Ignore bubbled events from children.\n", 20390 | " return;\n", 20391 | " }\n", 20392 | " fig.close_ws(fig, {});\n", 20393 | "};\n", 20394 | "\n", 20395 | "mpl.figure.prototype._root_extra_style = function (el) {\n", 20396 | " el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n", 20397 | "};\n", 20398 | "\n", 20399 | "mpl.figure.prototype._canvas_extra_style = function (el) {\n", 20400 | " // this is important to make the div 'focusable\n", 20401 | " el.setAttribute('tabindex', 0);\n", 20402 | " // reach out to IPython and tell the keyboard manager to turn it's self\n", 20403 | " // off when our div gets focus\n", 20404 | "\n", 20405 | " // location in version 3\n", 20406 | " if (IPython.notebook.keyboard_manager) {\n", 20407 | " IPython.notebook.keyboard_manager.register_events(el);\n", 20408 | " } else {\n", 20409 | " // location in version 2\n", 20410 | " IPython.keyboard_manager.register_events(el);\n", 20411 | " }\n", 20412 | "};\n", 20413 | "\n", 20414 | "mpl.figure.prototype._key_event_extra = function (event, _name) {\n", 20415 | " var manager = IPython.notebook.keyboard_manager;\n", 20416 | " if (!manager) {\n", 20417 | " manager = IPython.keyboard_manager;\n", 20418 | " }\n", 20419 | "\n", 20420 | " // Check for shift+enter\n", 20421 | " if (event.shiftKey && event.which === 13) {\n", 20422 | " this.canvas_div.blur();\n", 20423 | " // select the cell after this one\n", 20424 | " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", 20425 | " IPython.notebook.select(index + 1);\n", 20426 | " }\n", 20427 | "};\n", 20428 | "\n", 20429 | "mpl.figure.prototype.handle_save = function (fig, _msg) {\n", 20430 | " fig.ondownload(fig, null);\n", 20431 | "};\n", 20432 | "\n", 20433 | "mpl.find_output_cell = function (html_output) {\n", 20434 | " // Return the cell and output element which can be found *uniquely* in the notebook.\n", 20435 | " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", 20436 | " // IPython event is triggered only after the cells have been serialised, which for\n", 20437 | " // our purposes (turning an active figure into a static one), is too late.\n", 20438 | " var cells = IPython.notebook.get_cells();\n", 20439 | " var ncells = cells.length;\n", 20440 | " for (var i = 0; i < ncells; i++) {\n", 20441 | " var cell = cells[i];\n", 20442 | " if (cell.cell_type === 'code') {\n", 20443 | " for (var j = 0; j < cell.output_area.outputs.length; j++) {\n", 20444 | " var data = cell.output_area.outputs[j];\n", 20445 | " if (data.data) {\n", 20446 | " // IPython >= 3 moved mimebundle to data attribute of output\n", 20447 | " data = data.data;\n", 20448 | " }\n", 20449 | " if (data['text/html'] === html_output) {\n", 20450 | " return [cell, data, j];\n", 20451 | " }\n", 20452 | " }\n", 20453 | " }\n", 20454 | " }\n", 20455 | "};\n", 20456 | "\n", 20457 | "// Register the function which deals with the matplotlib target/channel.\n", 20458 | "// The kernel may be null if the page has been refreshed.\n", 20459 | "if (IPython.notebook.kernel !== null) {\n", 20460 | " IPython.notebook.kernel.comm_manager.register_target(\n", 20461 | " 'matplotlib',\n", 20462 | " mpl.mpl_figure_comm\n", 20463 | " );\n", 20464 | "}\n" 20465 | ], 20466 | "text/plain": [ 20467 | "" 20468 | ] 20469 | }, 20470 | "metadata": {}, 20471 | "output_type": "display_data" 20472 | }, 20473 | { 20474 | "data": { 20475 | "text/html": [ 20476 | "" 20477 | ], 20478 | "text/plain": [ 20479 | "" 20480 | ] 20481 | }, 20482 | "metadata": {}, 20483 | "output_type": "display_data" 20484 | } 20485 | ], 20486 | "source": [ 20487 | "fig,ax=plt.subplots(1,1,figsize=(6.5,6.5))\n", 20488 | "plt.xlim(0,L)\n", 20489 | "plt.ylim(0,H)\n", 20490 | "x=np.linspace(0,L,Nx+1)\n", 20491 | "y=np.linspace(0,H,Ny+1)\n", 20492 | "xx,yy=np.meshgrid(x,y)\n", 20493 | "\n", 20494 | "ani=FuncAnimation(fig,animate,count,interval=1000, blit=True)\n", 20495 | "plt.show()" 20496 | ] 20497 | }, 20498 | { 20499 | "cell_type": "code", 20500 | "execution_count": null, 20501 | "id": "3eeb9b97", 20502 | "metadata": {}, 20503 | "outputs": [], 20504 | "source": [] 20505 | } 20506 | ], 20507 | "metadata": { 20508 | "kernelspec": { 20509 | "display_name": "Python 3 (ipykernel)", 20510 | "language": "python", 20511 | "name": "python3" 20512 | }, 20513 | "language_info": { 20514 | "codemirror_mode": { 20515 | "name": "ipython", 20516 | "version": 3 20517 | }, 20518 | "file_extension": ".py", 20519 | "mimetype": "text/x-python", 20520 | "name": "python", 20521 | "nbconvert_exporter": "python", 20522 | "pygments_lexer": "ipython3", 20523 | "version": "3.9.7" 20524 | } 20525 | }, 20526 | "nbformat": 4, 20527 | "nbformat_minor": 5 20528 | } 20529 | --------------------------------------------------------------------------------