[Tutorial] Alignment.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# String-to-String Alignment Problems and Algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t1QCihHpNez6vbrVJSafyhXydD6kU4kT9uQAAwCOeCjqVeCmJmH9kE+75B1PQKfFSErXUgoj5oyn0g1010ZcDgObkOmPOVniH3B99p++fLPaCDRar4/pz2+e31p8HILa0sRdemmgrmCBf+2t99gU296Uuy4W4z+vA/ZsVQpygrwdAdPKcHbtA5qBJUbdVPj5qihCn6M8DACDuxVtBpxIPJRHzj2zCPf9gCzolHkqillwQMX8ESz/QVRN9OQBoFnmVxyVaC6+V+6Ed+n7JYit4z2J15Kpl9KcBiH2WVMfvEq0Foy11P7euuuyYY9fXHLBXB/P15wGIfuqStfL1PV6+zr81Keq2lQtxPWfUAQDgJR4LOpVYL4mYf2QT7vk3paBTYr0kaukFEfNHMHwPgFPQAYgMi63gKovd8ZG+P5L3vX2hfaJNXx5A/FKfW3dJh/sG9+h905Ly1H+tW5b5q2cWpyVWbnzgn1dtmZLM2TRAjCoVonW5EDc7vS5r65VN8rE++nMAAIhL8VrQqcRyScT8I5twz7+pBZ0SyyVRNBREzB+B8jkQTkEHoJlZUqf8wmIveEzfD8mstKQ40vTlAcS3qqLklOqipKdl9soYJvlpa1HSC1uK2nXQnwsgNqhL2arPoXMKsdmkqHt7uhD/1J9jplIIzsoHAMSmeC7oVGK1JGL+kU245x+Kgk6J1ZIoWgoi5o9AmBwQp6AD0GwS7QVZCTbHdm0fVJVgL+gnhHGMvjyA+LXjoX/+proo6TWTQs5/itu9va243cX6ugDEBnVZS6cQN8hs10q6IzLTK4Q4Q39ODflYP6frpDwAAGJQvBd0KrFYEjH/yCbc8w9VQafEYkkUTQUR80dj6cUcBR2A5mBJnnJKgq1wlrbvOZhocxS06Zx/qr48gPhWPbWdrboo6VufAq5x2b21KDlbXyeA2KEufen5jLr9WlG3Rd7f0XtZ+e9T5f2zPY+v8X4MAICYQUHnTqyVRMw/sgn3/ENZ0CmxVhJFW0HE/NEYejFHQQcg3BKveeC3FlvhKm2/syIhtfCv+rIAUD0t6bLqoqR9JsVbIDm8tSiZS+YCMW6GEL92CvG0VtKplOcLcay8vVjmE6/7D6vCTl8PAABRj4KuNrFUEjH/yCbc8w91QafEUkkUjQUR80dD9GKOgg5AOLWxFVwm9zHfee1vDifYHPeJvEo+AwaAj83TLmtTXZT0tUnhFkx+2FLU7vf6NgDEnnIhsp1CbNNKuqUye/XybroQl+rPBwAg6lHQ1U2slETMP7IJ9/zDUdApsVISRWtBxPxRH72Yo6ADEC4J1sKOcv/yo9e+ZmtC6oSr9eUAoEZ1UdIsk6KtCUl+Vt8GgNhUJsRZTiEe1ws5k9ygPxcAgKhHQeebWCiJmH9kE+75h6ugU2KhJIrmgoj5wx+9mKOgAxAOlhRHmty3HPTaz6xpYy28UF8OAGpUT73sD9VFSUd8S7amZUtx8r/0bQGITdOFuKRciO9MSrmfIx+foz8PAICoR0FnnmgviZh/ZBPu+YezoFOivSSK9oKI+cOMXsxR0AEItUTbxP+T+5U9XvuYpb9Lzz9dXw4AvFUVJ92ll2uhyNaipCn6tgDEngoh+jlNLmlpkjX6cwEAiHoUdP4TzSUR849swj3/cBd0SjSXRLFQEDF/6PRijoIOQCglpBScLfcp1V77l3fPyZh4mr4cAOiqi5Le1cu1EGWdvi0AsaNCiFOdQsw2KeL85bB6jr4eAACiGgVd/YnWkoj5Rzbhnn9zFHRKtJZEsVIQMX9404s5CjoAoZRoc1R67Vu+vLCj4yx9GQAwU12U9I1JuRaKHDIq847TtwcgNjiFeNikhKs304W4VF8PAABRjYKu4URjScT8I5twz7+5CjolGkuiWCqImD9q6MUcBR2AULHYCjp47VcOJKY4/qkvAwBmPs2/5MTqoqSjJuVaSPJ16T8v0LcJIDZUCnGiU4hZegnXQG7Q1wMAQFSjoGtcoq0kYv6RTbjn35wFnRJtJVGsFUTMH4pezFHQAQgVi63gvZp9SqLNUaA/DgD+GPni2OqipIN6sRaqfFnR9gx9mwBii1OIkTKHTMo4n5QLMUd/PgAAUY2CrvGJppKI+Uc24Z5/cxd0SjSVRLFYEDF/6MUcBR2AUGhjL7y0dp9S8O1vrIUcDAcQkOqipE16sRai7NG3BSA2TRfC6hTiW72QM8ka/bkAAEQ1CrrAEi0lEfOPbMI9/0gUdEq0lESxWhAx//imF3MUdABCwWIvcNTsTxKsjlL9cQBoSHVxu2dMyrUQJHmZvi0AsUv+R8jvnEJ8alLKeedwhRCn6s8FACBqUdAFnmgoiZh/ZBPu+UeqoFOioSSK5YKI+ccvvZijoAMQCt6Xt2xjdVj1xwGgIVuL2vXxLdeanq3FyTfp2wIQ22YKcZpTiIUmxdzPmS7EpfrzAACIWhR0waWll0TMP7IJ9/wjWdApLb0kivWCiPnHJ72Yo6ADEApyP1L98z7lmvEJ+uMA0JBvJl52WvXUpB16wdbE7N5eful5+rYAxL58IY51ClGgF3NeuUF/DgAAUYuCLvi05JKI+Uc24Z5/pAs6pSWXRPFQEDH/+KMXcxR0AEIh0V54l8XqGKci8iqP0x8HgMaoKkoablKyBR25vjv1bQCIL+VCdHcK8aNe0Mn75+jLAgAQtSjompaWWhIx/8gm3PNvCQWd0lJLongpiJh/fNGLOQo6AMG4MKXgYoutcKTcf8yQeV1msUqCreAVi9VRoR5LtE/4k/48AKiPUZl3XHVR0ot60RZklq8vTj9J3waA+FMhxD+cQmzRSro1+nKVQhwn779SLn9HuRCPya8Xe0VdMrNY3t9/hvyfVfpzAQCIKAq6pqcllkTMP7IJ9/xbSkGntMSSKJ4KIuYfP/RijoIOQCAS7AVd5D7jQ30fUk/WJNgK+ghhHKOvCwDMfFvc7vTqonarTAq3xqc4af224ivO1dcNIH6VCnFBuRDLvQq6wxVCnKoeKxbiJPn1KJMSz1+OyrxcJsQV+nYAAIgICrrQpKWVRMw/sgn3/FtSQae0tJIo3goi5h8fTA6eU9ABaNBF7fPPtFgdC/V9RwBZ/OuUCefr6wUAM1+Xtm9dNTXpKZ/irXF5ZcuU5F/o6wSASiFOdAoxq6Zomy7EpfL2YplPTUq4xqa0QogT9G0BANCsKOhCl5ZUEjF/38ebM+Gef0sr6JSWVBLFY0HE/GOfyUFzV/TlAKBGQkrB2XI/8bG+3wgi6y9q/8AF+voBwIxhiGO2Tm3Xu6oo+UuTEs4sVVuL2g1Vl8nU1wUA3pxCjJQ5JPOAzHcmpVugWURJBwCIKAq60KallETMP7bn3xILOqWllETxWhAx/9hmcsDcFX05AHDJqzzO4v58OZ/9RjBJsDneFPn5x+qbAQB/Vla0PaF6alJGdVHSDJkvZH7wFHJ71aUstxYlzamampzH580BCESF/K8cmb0mZVuweUDfBgAAzYaCLvRpCSUR84/t+bfUgk5pCSVRPBdEzD926QfLa6IvBwBKoq1whL6/aGoS7Y6B+nYAAACaU4UQ/zYp2ZoSdUbexfp2AABoFhR04UmkSyLmH9vzb8kFnRLpkijeCyLmH5v0A+U10ZcDgEvy8k+U+4et+v4iBFmrbwsAAKC5qCJN5qhJydaklAsxQ98WAADNgoIufIlkScT8IzP/w5vLyjetKFig5v/q4ze9/9SMYRseKx20deaUvntUnBN7HS4t6GE0NZ6CrkXnrLPOMq644grXrf5YuOMpiOI6gc7/mGOOOXD8ccdtUZH/XiHzhIxDpr9MO5lGX3qIgi48TA6Uu6IvBwAJ9oJO+r4iZLEW/lnfHgAAQHNwClGol2shyreVQvA5mACA5kdBF95EoiRSYf7uNMf8VSG39KlRyxc4h252Tux1aF7JkB/V/N985r49ny4uObjxvdnG1x8vcGXPuueMn75c1OSo1+3XH21o8Vn69ItGdmaW61Z/LJxR89fvi8cEMv+qlWuND15c4sor858xyh1Tdo3oP/iry/+VtO6X512w5bhjj/vxmGOOWSzfNsbJXClzjPZ28jMKuvDwOUjuib4cAMh9w1R9XxGqJNgcw/XtAQAANAenEO+blGshSYUQ/J+QAADNj4Iu/GmOkkgP869NOOb/zZrJc19bcNOKWVP67X2kaNC+1xbcuXftsvIjuz5/xlWgqfnrpVooEy0FnUogJVGoQkFXm1DNf+M7HxvzS2bu65PTbeP5555Xffxxx20X7g/T/pv+vkJBFx76QfKa6MsBQKLd8aq+rwhVEq2OKfr2AAAAwk3+D59jnEL8pBdrIUyOvk0AAMKOgq55Eo6SqL4w/7oJ1fw/fv3uF/5dNnjbjMl9D75RefdPVSvn+ZRnKhR0dROqkqixoaCrm3DMf/mzLxuDe/bdcvppp+084fjj18i3k+7Cc0kQCrrw0A+S10RfDgDkvmGNvq8IYebq2wMAMxtntz95c/Glv93yUFL76qlJGVVFSf30uB4rTv77jqntztefDwDeKoQ4w6RUC1nk+ofq2wQAIOwo6JovoSqJGhPm75umzP/DV+58aW7xwO/mlw7bt3Zp+dG96xf6lGbeoaDzTThKIn+hoPNNOOc/f9rDB35/0W+rTzzhhK/k28pQCrrwMDlI7oq+HABYrI7P9X1FCDNf3x4AVBW3s1QXJ/fYWpQ0pboo6dXqouSt8tYIMLuri9qtkrcPV09td9224nYX69sBEL9mCXGuXqqFMhVCjNC3CQBA2FHQNW+aUhIFEuZvnkDnv+6t+56eWzxw1+Pl1+5b99b0o3pR5i8UdOYJZ0nkHQo684R7/oseqTz010v+XOUp6Kz6+w2axuQguSv6cgAg9w0f6vuKkMVeOF3fHoD4Y+SLY6uK2l1dVZQ8dWtR0gaTsi1UqZbbqKia9q9rjMo819UaAMSnCiFO1Uu1UEauv5++TQAAwo6CrvkTaEkUTJi//zRm/rvXFc18bs6IL2Y/NODg2qXORhdzNaGg859wl0QqFHT+01zzP+vMs747/rjjn5ZvM4n6+w6C43OQ3BN9OQBItDme1/cVoUqCrfAOfXsA4oe6ZGV1UfIkVZyZlGnhTXHS5qqipDu3l196nv59AYgPTiG+04u1UGW6EFfp2wMAIOwo6CKTxpRETQnzrz/1zf+Dl+98ecakPgeWPnnvwd1fPOtTjjUmFHT1J9wlEQVd/WmO+Ve9/5kxctCw70888cRd8q2mi/7eg8DpB8lroi8HAAl2x3h9XxGqXGifaNO3ByD2bS1qd/nWouRF1UVJR32Ks+bPT+pSmhR1QPxxCrFYL9ZClEMzhThN3x4AAGFHQRe51FcSNTXMv+Ho8z+4qdT5wrwbPp1bNOhA1cp5PqVYIKGgazjhLIko6BpOc83/tcefM875xdm7TjjhhAr5lnOy/h6ExtMPktdEXw4ALLaCq/R9RWhS+P3v0otP0rcHIHZtKU7+19aipDdMSrKWkN1bi9qN/TT/khP17xtAbJL/o/IOk3ItFHlF3xYAAM2Cgi6y0UuiUIX5Ny41839uzqhX5k0b9N0L82498MPnwZ015x0KusYlXCURBV3j0lzz/+/bHxn2K6/57qSTTvpMvu0k6O9DaBzfA+Xu6MsBgMjPP9ZiL9ig7y+aHKvjAX1TAGLTjqntzq8uSnq0umWcMVd/ipM/2Tb10n/qPwOA2FMhxIVOIQ6ZFGxNSpkQGfq2AABoFhR0kU84Sjrm3/g8MfOmJR3SbUefmHHnEb0ICzYUdI1POEoivSAi/tOc879r5Jj9p5x8ynb51vO/+nsRGuZzoNwTfTkAUCy2wt76/qIpSbA5tl/Y0XGWvh0Asae6qN2A6qKk73yKsJadQ+psOsMQx+g/D4DY4hSiXC/YmpjX9W0AANBsKOhaRkJd0jH/xmXt0vznZkzue/CFRycczcnqbKx4scKnDAsmFHSBJdQlkb+CiJinOec/bfykQyeddNL38u2nnf5+hPrpB8troi8HADUsdseT+j4jyBxMsE1I0dcPILZsKrv8rKqipCdNyq8oSvKz2yf9pZX+swGIHcVCnO4U4guToi3gVAhRXc5VXgAAkURB13ISypKO+Tec1a/e9eLDD/Y7+NV7s12llyrnQlXSUdAFnlCWRPUVRMQ8zTn/x8tmHT3l5FP2yLcgLkUUAJMD5q7oywH+WKyFY+XfzOKmJNHmeF5fL1ouS/KUUyx2x4sWk31HAPnJYi3I0dcNILZsmZb856qi5C99C6/oS1VR0optxVecq/+MiF1OIR6VWdzEFOjrRctVLsRFFUKsd5qUbgFks8zF+roBAGhWFHQtK6Eq6Zh//VFnzqlyrvqDR+sUX6Eq6SjogkuoSqKGCiJinuac//ySh41TTzl1l3wb+r3+vgRzJgfNXdGXA/xJtDnm6H8/QeQ7fb1o4fIqj0u0F95lUUWb7++zoaxMSC38q75KALGlqvhfHauLkvboRVc0p6ooac3WyW3P0X9WxKYQFDUqC/X1omUrE+Is+Xt7ROaoye+zoVSWC3Gevk4AAJodBV3LSyhKOubvP/99d8ITD0/ue6DmzDk9oSjpKOiCTyhKosYURMQ8zTn/qfcWHml1aiv1mXTn6+9N8GVy4NwVfTnAHwq6+PbrlAnny7+Buy12x0cmv1fv/CiXeTbRWpgphMFnOQExrqooqV91UdIhveCKjbRb9c3Ey07Tf2bEHgq6+CZ//3+WmSZ/h5tMfq/e2SHzsMz/6esAACBiQlnQbV75wOxUu+2QOjhbk1S79ZC6X1+W1J9gSjr3/K3a/G3M3ys7Pp40b8bkvvvXvVlxVC+9vBNMSVf94QIjPS3159lbrSnG92uf9VkuFInlgk4lmJJo3ZsfGGmp3vO3GlUrP/NZjjScYOefGsT8b7vupkNnnnb6p/Lt6AT9/Ql1mRxEd0VfDvCHgg41fpeef3pCSkFygr2gi0w/V6yFHS0pE/4i8vOP1ZcHEJuqpyYPqi5KOuJbbMVOthYlvWBU5h2n/+yILRR0qDFDiPPLhWgv00v+XfRTkb/bFHn/r/VlAQBoEUJZ0Kncc2uf1d27dN6pDtDm5XT8Xv1bX4Y0LsGUdGreuZkdflDz79Gt89fMvzYHN5U65xYP/PaDlx88pBdeZgmmpCt2jDFyszu7CooeXbN9Hg9VYr2gUwmmJJp8b4HRqWMn1/x7devh8zhpfIKZ/6T8CUZaappr/v169fZ53F9SLrvywLlnnz1Nf39CXSZFiSv6coA/FHQAgBpbi5N6Vcd4OVeb5Pv0nx+xhYIOAABErVAXdOpsrQ7p9gPqAG16mv0AZ281LXpJV71q8ix9Ge+oeael2g6q+XdIT2X+Xlk09/pPXpg7Zr9edtUXvaTbtrrSZxnvqLPoOnZwFxRTJ4z2eTxUiYeCTkUviTYsX+WzjHfUWVzpaemGzWYzHho/0edxElj0+W967xOfZbzjPosxzUhLSzOKHZN9HveXtUtWGGefedb+k48/+Rr9PQq1TIoSV/TlAH8o6AAASlVRckp1UdIB3yIrZnNky0NJ7fU5IHZQ0AEAgKgV6oJO5e5b+6y22axH7hnT5yP9MRJ4akq60knXvtmxQ+r+hkq3O0f3+cg1f86e+zkfvHzny48UDd7/w+eBX3KypqRbMGO8kdm5o6uE05fxzuT7bnYVdO+8UO7zWKgSLwWdSk1JNLdkhpHRubOrBNKX8c59Y+92zX/xk4t8HiOBp2b+E8eNl/PPaHD+d426Laj5P+GcY5xx2um75NvSGfr7FNxMihJX9OUAfyjoAABVJf/6n+qidt+blFgxna1FSRu2VrQ9VZ8HYgMFHQAAiFrhKOg8Z9E1WCSRxkeVc3a79bDVmnL09lt6rdEf9w7zr5tdn0+dpT53rmrlPJ+iq7FR5Vxqqt1Ik5laMNrnce+oAq9zp/Swff6cSjwVdCqqnLPb7UbH9A6uy1jqj3tHFUjqMpeN+fwz0nDWvP6OMaB3P9fsczKzjAfvq3/+XyxbGfT8+3bpfvQ3v7roMf19Cm4mRYkr+nKAPxR0ABDftk/6S6uqoqQ1enkVP0l26DNBbKCgAwAAUSscBZ3KipfyF+j3keCybfWUWTWXrVRJtdsONVS+Mf/aPDf7us+XPJm/Ty+5GpvtHz1hdEh3X7ZSRV3CsqGz6F574iGf+0KZeCrovnx7tZGeVjv/Th06NngW16J5lT73kcDz4cvLjMzOGa7LhtbMv3PHTmGbv7rU5Zmnn3HotNNO+1/9vQoUdGg6CjoAiG/VRe1m+pZW4U7yVpllMi/Lfz+9tThpsfvfSdt8lw179m0tuvxCfS6IfhR0AAAgaoWioDtSVV66cP6dL919981bevbudSArO+fIzwfTO3U60qdPj92jb+y97vm5YxYe3FRWrj+fNJzP33LMG9A3q9pqtR5VZ9HdNar3xzWPqfk//9i4l++5e3R1rz69D5rNf9QNvb+Ix/l/8eZ9z8x6aMD+3V807Wy2L96aa1w3uLch5+/6fLPigjE/P/bjhoXGsicfMCbf2tvo3yXVyO3onr1KRgebMbRPtjHhruuNN5+bZuxZ/7zPuoNJPBV0KitfWmoM6T/QNX91Jp33WVw7Vq83XqpcaEy4f6LRp+8AIzunS22Z1DnDGDRgsHHv2LuMVx5/1ti2ap3Pukn9+e87HxmlEx9yFXVq/h3SO/jO/4lFpvPPzMwyrr/2euPR0oeNLe837oy6e0ffYfz2ol9/pL9XgYIOTUdBBwDxa+tDyVkmhVU48lP11Hbzt05tl/t16T8v0L8Pb98Wtzu9elrSZXL5e6unJr9vsq5w5GH9+0D0o6ADAABRq6kF3ZOzRr3UrVuXn/oPHnZ06vR5xqK3PjKWrdlsrNq8x5Xla7cZC5etMkrnPH5owIA+u/NyO33/7n/yK/X1kMblv+9NnDOkf84mdUadOouucuZoOf+8fQ3Nv2z2/IP9+/f6Id7mP7eo/3drl5Yf1guuYLPxvceMkdf2Mzp3THedRffagslGny4djKFdrjYevvEK4537LjU+m5isPuPAlfWTkozl915mPHqr1RjRP8fo1yvPWLN4ls96A028FXQ1+fj1t40Rg4f9fBbXwkefNHr27GMMGDLcqO/v3zlngTFMLtOnRy/jvRfe8FkvaVxe/vczxoA+8u+/k2f+jz1l9Ozlf/7LPtli/HvREmPUbXcb3bt1N1a+uMRnnXqqVq41LkywHP7j//xPB/39Kt6ZFCWu6MsB/lDQAUB8+rKi7RnV4T9j7ZuqoqRb1Lb07TdWdXHy3+X/hpoj13XYZP2hyv6vHmr7S33biG4UdAAAIGoFW9Dt31haPvrG3l9079Hj4FOvvvvzAdmGMuOROd927pT2E5dgbFpUUTdiaNdN3Xr0OBTQ/Gc/vD1e5v/hK3e+9HjZtXv1cisU2bzy38btNw0w+mRbjTfuueznQq6hPOHobeRkdTLWvx385+GpxGtBV5O1S983xtw0yujRq68RyN//I3MWGNmZWcZHry33WSdpfNa/tcoYO2psQPOf+/QrRlZWtvH+fxb7rE/Pg/dMMC7+/R/W6u9X8c6kKHFFXw7wh4IOAOJTdVFSqUlRFaocrC5Kvu+biZedpm83WFsfSvqHXO+7JtsKUZLv07eJ6EZBBwAAolYwBd3hzeVlNwzruuHGm2/+ccWGnT4HYhvK3MrKb7MzO/zw9ZopD+vrJg1Hzf/G4d2/HDlq9P5g5j9vwfxt2ZnpMT//ucUDdq5703lIL7eamr3rFxr3jB5qjO1vN76aUnu2XGNTWdDbGDawp2s9+robm3gu6LavXmfcfsutxs1j7jCC+fufM3u+63KZaj36uknDcc1/1K3GTWPuDHj+jzz9stGta3fXZTP19Xpny4pPjXN+cfaRS/7nkr/q71nxzKQocUVfDvCHgg4A4s/2okv/VB2mM9Lk/7b5uOqhS/+mbzMUjPz2x1eHr1j8yjDEMfo2Eb0o6AAAQNQKpqCbWjD07SFD+v+wcuP3PgdgG5t7xo3dcdfoPh/p6yYNxzX/oYP3NGn+d4+ujuX5f/z63S88Vjr0R73YCkUeLc83buzb0dgyNfByribD++YYyxeW+qy7sYnngm7W1DLj2hE3Gk35+x8ycIjxeuXzPusmDWdmUZkxfMTIoOc/zvGgMfE+h8969Yy59kbjn3/7x3L9PSuemRQlrujLAf5Q0AFA/KmamvQfk4Kq6ZmaNPvT/EtO1LcXalVF7e722XYIsrX4X1fo20L0oqADAABRK9CCbvPKybM7dUzbt/iDdQf1A6+BZPlnVYfUeja8W/iIvg3iP675d0rf//qqDUf0mQaSdz7btL9Tx9SYnf/80kHVny0pOaAXW02N+ty57IwOxkeOxl/W0ixzb001Jo670Wf9jU28FnTr3vrAyMrMMl7/8Eufv+lAUj7zMWPCneN81k/qTyjmv/yzrUamXMdnS1b4rN87a+Xjp7VqfeTcc89trb9vxSuTosQVfTnAHwo6AIgv1dOSLtOLqRDkyNai5Nv1bYWT/N9P5SbfR1MzUd8OohcFHQAAiFqtc0uPHqnyLTj8pdgx7K2xt9+8XT/oGkzuvGvMjqkThizXt0H8R83/9jvG7NRnGUzuuP2m6lic/46PJ82bManPgaZcQtJf5jvvMybckOVTuAWaZfmXGsP75fqsvzH5ccMi47QupT6FRjxkdrHTuDt/gs/fcqB5dvEHrrPo9PWT+qPmf1d+gc88A81d4ycZM+S69PXrsV7R3uhwjX2c/r4Vr0yKElf05QB/KOgAIL6E4ey5o9VTkwfp2wm39cXpJ8ltf2by/TQlH+nbQfSioAMAAJFyrMxVMu098b7ExNle9//e6/46Ts8r+fHb9c7pesnhLwN6Z1U/uXBRSAqiJxa99L1an74N4izd92WJU79PRc2rctGre/RZBpMnFj2/PRbn/9qCm1a8vuDOXXqxFUh++OI5n/tURg7vZ7ySf7VP4RZovpiUbGR3svusvzH5Zu1C47yeZT5lRixl6wef+9yncv3Qa40FL77p87ccaN76tNp1Fpe+fuKOv/mPCNH81TquHT7CZ/16SsZPMtr+5W8b9feteGVSlLiiLwf4Q0EHAPFjy7TkP1erQq3Ip5hqQtqN0LfTXMJwNuCRb4vbna5vB9GJgg4AAESS/G8RoQ7QqYz1ur/Sc99BmX943V/Hmd1Kvv7iA+dsveTwl6yM9D1LV29o0uUta7L0o41HsjLS9ujbIM5Su812pHOntJ+uH9p1g3PydUvfXnRPpSrtsjI67F28eqPPLIPJslXrfozF+c+a0m/3lpVzj+jFViCx221Gl5wMY9xt1xpPzpxgfPzGTFdp1y03w/ikMPjPnqvJ5oeSjDS71We7jcnGVc8Zvx1Q4VNmxFLsNruRm51j3D16rPFY2Uzj3UWvu0qjvNw8IxR//+//9zsjLS3NZ7vEHbn/kX//ucY9t94elvmrdXTp0tVnu3o2LF+lLnN5tHP79ufo713xyKQocUVfDvCHgg4A4kd1cdJ0k1KqKYn4VQ3k9/CKyfcVdKqKklP0bSA6UdABAIBIUv+vr00y6iDdHhmLzJWef6vcX7uor3O7l6x+5oXyJ/WSw1/sduthdXBbP+AaTNR6bDbrkZSUFIP4j5z5IXWblmY/qOb1/+ydCXwU5fnHX/FGrVq1anYWqdrTHv+WKhutbSS7GyJyClqPqvXEW6vWeq8KOxtAYENCNkGkHnjE+6qi4NGKKOKBt1YFJRvAE2/lmv/zJlkNz0w2O7PvzLzvzPP9fL6fQDJ5j+fd2cD7y8wsfOtDUy2d+OySj9ZR/Xv3wNqajo8HDak1kom4sXSyOXCza9vUmBG36Is0e2BtbcfHg4YMgfeLhCHi9f/c0k+NeDxu6ovcUF6jIQce2PFnkfVftOQTo6amxhTIWclvczn6oBEp/LMrjFgEJR3i4wiiJyigIwiCCAf8yrB8NvYlDqTK8A7DYBvhfrymfWpljcXYyvF03AehJhTQEQRBEAThNzXgepBv1PEr557v+vNLbMPbXprY8bDGW8Zf3TQXB3E9OWJY7RePLV66Hm+4OhHaMfgVYbgPMtdY2CDnAdqQA2u+vvyCo597/6XJV48YPuRLXjdcSyc+vvidNfyKSNy3yj5++znzH77pnx/iq87sWqh/TTJhjBx+kJGb9E/j/RdvMw49eKjxSl35V9B13OLywGpTv6U469ZbjL9ecYspyAiS39W/pgbqP8KYXjfFeGfB4o4ruES8/jtucTlilKlfstNC/QfXDDZGjRgJr/9sZ/1Hi6v/CGgX92vlpWf9w6je/89P459dYcQiKOkQH0cQPUEBHUEQRDjgz4mzCKOc+sr7jVVb4z78wEhVbQLj+dhijA6tbMB9EGpCAR1BEARBEDIwEyxcNcddA/5hgyMs2Gb0tHNHXtr4LA46evLoI0a03zbn8W/whqsTb3vwiTW8PdwHmWusSSbWHn/0qLZ5t114Z/fPH3PkyOW3PTRfSEB624OPfhG0+t+SO+Hd1x5r+ByHWnYdXJM0zj39WGPRnJYNPn/6CUcYcy/ZzxS42XX+5fsax4529gy6MydfZ+jND5iCjCA5uKbG+PtpZxj/vevBDT5/8gljjVvnPGl6Ldv13v88bxx1zLGmfslOeTB63ulnm+o/VmD9jz66tPr/+7pbjZ/uvie/Ojz0WAQlHeLjCKInKKAjCIIIB/nswMfNYZQjV+frK3+H2/cTGNNsi3E6tPIu3D6hJhTQEQRBEAQhAzuwzufNFQK66Rt+2Zq+oxr37ndUwzIcdPTkpCuOeyp15RWr8IarEy8bN+4z3h7ug8w18qvl8Oe4HfUfn/4S19KJqStTHwep/mvfm96Uqzti9arX7jSFWnblV8vhz3H/Ne1SY+LJSVPgZterz9jfmHjOoab2S3Hv01qMhx9YZAoyguQ7CxabPsdtmTzNuDwzxfRatuvUluuN8VdmTO2TnfZU/2aB9U9fUVr988++bmy91VbG8YcdtjP++RU2LIKSDvFxYadfTWavaCI9LJrUT4km9HQkoc/iaonM7VCvR+HjrYXPgVdE45mT4fMj+fexMa0b4/aCBAV0/rDroHG7wetseCSRuRBem7mO12NSfwBqeUfHn+N6M/z9PK3wOiSkp98QffuKRHo//v7B30e+f5/R54CP8nON/z0a1ydHk5mL4e+HaTV1++w4rG4b3BbRC/C+3K86/Ut+fkQTmVO1RPryQr2hrq283uD9361BXL+MrwtY079qwi64uTCwdMqAXfPZ2DpzGOVI3587h2nPDjzTYpxOXYDbl5VZjG3RzNjvmxg7BDw7x9hV4Kwu7wcfBW/o+nsLHHsZHHdsC2O1DYzthtsLGhTQeU+KsT7wGvsV1O0w/nrLff96fAT+fhP/M3w9Cx9P56/DRvinKG6DkA9Yu135evH3GfhzXdeaXpvrfI+Z022d0+C54Cjwt3DsprgtojhQs778fb3rHDoHPma61feerprfwf8OX88V3tenMxYDt8ftEQQhB+eB3a+gWwr2/p9A+E/PdmMaPln8zPRrceBh5SuPj7th+PCh3z715vumTVc78u/n7fD2cB9kz/J6jRgxbLWQ+g876Jsg1f/dhelbrq8/9lMcaIn0rQU3GAcPG2y8fVV5t7k8aXSV8djsy0zt9+abz95l9DsqZyx/4X+mICMMvjj3SWPkqIM7Xr/4NW3Hv514svFA6z2m9sniiqr/sVD/ObfcbWq/Jwf+7g/GUaMPOwX/+AobFkFJh/i4MKFVTt6Sb8JGEpmrtET6aajHF7g+Nv1WS+qL4eOMSDxz1G7JK3+M+1QZCui8Yc/a+s21ZKZWS2SuhnqttKhhb36qxfV7tWT6uD1rU/xZ04TP7FSV2jqaTI/gYWokoS+xWDM7vgLtzIwmM3/hQR/uK+xEE3UVWjxzeCSuN0KtXgDXWNTQjh9Ae3dGE+m/R6rTP8X9BZG2bGysRRDlxMWLmgdIt+GanzowYTFWhw58HbcvC82M7di1aTsTXAx/XpszB0p2/AxcAF4F7Y24mrEf4j5VhgI6b5jF2HZNjP0VanU7+KVFDYsK65TPdYY9w1OMbYLbJ7yHB3KwpsfAmtwMfozXzIbfQFuLoK1JYE09Y5vjvsIO1Ofn4MlQqxsEvWe9BV7LQ7tGxnbB/REE4T0/Ab8C+UbdI10fubnuB/XEDw9tmHHaVdOfwIFHT55/1pGvXlk3+Vu86WrHKydMWX3+2X99DbdN9i7U7XWo3xpcUzteWTfp6/PPOuJV3LbKLnrwwofumXXm+zjUEm3dJacYk8dWmUK3Un3k0v2Mv45KGl++da+p7d6smznbGKvfZgowwuSVF6eMKydOM72mS/X2h58yDj/ir8bKkIac5XrFJZcbV04qs/6H26v/ESPHGIcMHXkN/tkVNiw2HTvExwUdHn5EEvqYaEK/B+b/Na6HC/5Pi+sT+sUzf4R/Wm2Ex+M2Wjzzax4IiBDm8rnF/Oy6DrdbrloyPRvP2w78yiTcphNhHGfgtu2wc3Lij6CdKzQeCJjr5tDMkbgfu8CYJuG52hXG8gJu1y0qkuOiuH9HJtNH47btEkmMr44m0rdoPLw3rY0Qv+0IY+PpUUG/grcY/Qfr/aNx/SKoxwKLGon2f5F4+kotMYH//9kx/Bc4TK85B4o4xzHLp+67d76+8mVzGGXT+lgcty0D79Xvu4dprM5dgtv3kwbGdsgxdkoTY/MtNmLd8Mlmxi7km8Z4LF4A/f8NXCLI1Tnz/OzKA6clIuVXIeF526FrM9/UrgMrcdt2gO//JQ+L4eM3OXPdHDmDsbJ/ES7XeYXRkjK9GrfrFrnOq81w/7ZtYaysOy7w8AzaOQx8PGexNoJcBV4Dr5uBuP8wAe8B+/DQMtcZpuEaifZJ8PRrGNsJj8MO0MbonMXrzq5+/WwhCL/gm0WPgXyTLg/yBzjz3/zhf18P4n9Ybwf273Ir/oltRjfuW3Fkw4q1bU3TcehhZduzk2aNHDn06xvunmfafC1F/n38+3k7uG2ydzvrP+wbp/Wffffc9SNHDP0qaPV/+OaznvnPHZeuxKGWaNufbzUOGVFr3HeB/WfRLZ1caRw5/ADj4esuN7Xbm1+8dZ/xm7EtxoP3PWMKMMLk/554zhg9ekzH+wh+bffmwrc+NA474ijj3tm3m9olS7Oz/od4Wv+LzzzXqD0grswtiNzCYpOxQ3xcUNmtZvyuWjIzUUtkPsE18NB3+e3TeICAx+cW0UTdHyzGETTn4XnbIZrM/MmiTdtGE/oluO1SiFSnd9AS6QbNhcCY3x4T92cX1a6c7BfXd7fo375x/TTcdinsNSa1Gf9eaONNU5su2hHWJPUTWVUqHFcUwDyjSf1Q8GGY/3pcD498lN9+lqVSffDweoNfjWfRnm357Thx2yLgV761T41dmM/GvrIIpXq1bWrs37hNWVhev/9OeLxlyPdPfCfXeXs4fkvAb3PmDVevfAYcW8+YZ1duQ39nWowjaF6O520HeF3806JN20I7B+C2S4FvsMP334vbE+Ay3JcTBF2FdA9u1y26rj7E/dsW5v173HYpwPft2Nx528oPcJsu+xS/qg6PJ6jw91Go81kw75csauGF/BcWbgT/gMdWCl1XVOI2bTudsd/gtgkiyIwFC1fMHdP1uT3Ar7s+t5R9f6tLfhsV/ltiheP/2vV5tsOhDc9Omz393zj06Mkn77u0ddiwg1bb3aTlxx94YO26GVNOfRS3SZZuZ/2HrnFS/9rawWuDWP/brx77v5fmTf0QB1tu+OIjVxsjD0rYCumWTa00/nFElXHSX0caCx9oNrXZm7fd02r86axZpvAijC64/xFj+IiRtkKiRUs+Mc4870Lj+GOPNx6/4wFTm2TpdtZ/lKP6X37RZab2evOaqxqM2O/3aSv8vAoreFOvID4uaPCrkvhzu2Cu3+C5++g6/ky7fkl9AB6vaCig6x3fArqq1CadQU76I9yWIJfiLp1AAV1pDBjQvKkWT5+g8SAet+Wtb0SS+iA8vqDAr4LmzwiF99B3LObul69FEvoYPNZiyB7QFei62uwhi2CqmOuXTR0o7abakllVW1iM2alLcftewjcvmxj7N97U9Fl+FdnUGfBPTzxe0eQooOsVvwI6OH5b+L4pOTFXJpqE131Zd08oQAFdacxibDv4nnG5zlvdmtrz0LmN8E8BPL6gAOv7o1zns/k+sZi7L8KYHuZX8eGxFoMCOoKwD78VSpZ1vN+yqWD33/47ruvz3CGs80q7u8FvwYfADQK6vqOmDdvt6IZlpV5Fx+Uh0ahRI77htztb8MZK04Zsd/nX+XGjRo38lodDQw8a/NXc2y68E7dJlm5n/Ud+W2r9x02atn7UyOHfBLX+10/728p3F85chYMtt3xx3gzj0BE1xuST/mS8Nan4M+le0Pc1Tjv0AOOSM44wFtw/3Rg1YqitkI5fPbfPaTOMG1ufMIUXYXXBfY8YY8Yc2vG+0tvrf+6zbxknnnKmccG5/zQeue0+Y+TwERTSlSkP6UbbqP9Jp5zRUf/2594wtdWbD990l/HT3ff8vPDzKqzgTb2C+LjAUJXaJJLUz4Y5rsJzlsw5FTXjbf0n2Q4U0PWOHwFdZ4jU8dxDUzsCvR736wQK6HonUjP+APieN0xt+CisW1P/qtQWeKwqoyX1IzT/A9BiLozE62J43FaoEtAVaK+PHZGfGltpEVCZlPnqOc77jVVb4zGX4Wu4fS+Yztj2OcYawDV4Q1Miv21ibMY0xirw+EWRo4CuV/wI6GDdBzV3PivO1I4ooY+TcL9OoICuOPAfxY3g2BNz5T1bTrRfQR1K/jeaCkxmbEuY18U5/wPQYt4Cr4V+eOxWUEBHEO4yGuQbeWeyzn8kbBDQcbY/pOGJCxsb5+Lgo5j55ydd889zjn97+IgRay/Tpxi3PbTAePSFJR2bsvwj/zv//IiRI9edf+4J7/Dj+ffxcCiIIZHXdtb/pCW8vsXqz9fn/HOOfyvI9f/X1KM/XvHCDZ/jcMtNl7/QamTO/5sxakjcqDvhT8a8S/czXq7btyOU4x8fvng/44q//dkYMSRpXJ893/jyrc7v4+GcnZAud+NNRvyc60zBRdh9a/7zxuWXXAHvLwd3vM7x67/1wSeMCy/XjeHDRxhXZ3PfPfeMh3MU0pWvrfpPnW7ruXPdffreeUa0IvJN959XYQRv6hXExwUB/jwizZtnEYmSP5dtFr8NJ55LuVBA1zteB3RdIcOn+PuFG0+fgPt2AgV0PcNvT8rPXdP3SmI0nnlWGzQugsetGpGazG9hPk/h+UnqenjtzOxfleKPhegR1QI6zrvT/7h9PhubAa63CKq+V9JnzxVoqx+omcbs1KmxJ3D7btPC2J9zjC3DG5kS+3kzY5fwDWg8l3LJUUDXK14GdCnGNsl1Xv2zDn+/C/4S9+8ECuh6Bo77JfgE/l5Z5FdR8mfh4XGrBsxjJMznPTw/SeVXSJ+fgnMdz6M7FNARhLvwK+gO7vqzZUC31Zhpv97+0MaPFz8z/VocfvTm60/o108cd+rzxx571KejRo1cXV1dza+WW83/PnHc6S/wr+PvCWJI5Jed9T/jBXP9j1w1cdxpz4Wh/ldP+usXH758y9c43PLCd56ebVwz8WzjtKNHGIcMrzF4/Q8dMdg449iDjdkNFxn5524xfU+pId2bz95l9D8mZ/z34edNwQXZ6cuPPGVMn9xgjD3plI7no/H6jxlziHHK2FONa+pzxpv/fdb0PRTSibNY/Wf1UH87vjxvgbHjD3dY2/3nVRjBm3oF8XGqE02mR2jyXzXXk6u0ZJrfwUAYFND1jncBnbFR53MQzd/rhhXJtJAHq1NAZ03HVXNJfbnp++RzqZaY8BM8fhXgz/OLxNNXwhxWW8xLdtsjyfRBeE4FVAzoCrTX77N/Pht71RRWdboYHy8by+sr/2wxbqfegdt3k2bGLssxthZvYiriW9MZ2x/PqRxyFND1ilcB3UzGtuG3wsPf55If8Cu78BicQAGdNXDMKTl/n2lZqo80MrY1Hr8KNHXezrLVYk7SC6+fRS3wzzQ8pwIU0BGEd1gGdJytDm4472fHN7yx+r3mkm91aSXfoMWfszJoIZEshrH+zXVHfPvZG3euxQGXH/L6489Z2VtI9/n/7jMS5840xuUeMoUWZM/y+uPPWUkhnTuWWv9SXbLgRWOrvn3X459XYQNv6hXEx6mMFs+cDnNai+eonukH+1dN2AXPzwkU0PWOFwFdR9CQSN+Ev8dFV+IxOIUCOoyxUSSpX6Cp9V7zXkU8U9JtiWQhOmjCHh1XAJrnoprT+HPz8PxUDug4i5oHbNpWH7s4n4193T2wasvG/o6PlY381MrTLII2R7ZnY5Nx+26Q6rwy6Vq8eamg/Mqqqa2MbYbn6IQcBXS94kVAx29jCse8gL/HRYUF4xTQbQgPu+DrN+LjJXeealfSwZirwRUWc1HJr8CxeG4cCugIwjt6DOhYKtXnB2Om/bvm/MancABix1IDIm6QQiJZDGP9p+uHrf+q6xaSfltqQMctFtKdOvE6Y+gFNxkrHN4aMKzaCYgopBOvnfqXIr81Zp8+fQIVRDkBb+oVxMepihbXL8NzU9x8RTKzL56nXSig6x23AzoezsHX5+DjXTWp34bH4RQK6L6HBy28tqZj1fCVHYfVbYPnJCNaPD1KU/dKaCsXRmrrtO5zVD2gK7AsO/An+amV87oCq7VLpwwQfqtm0bRlY7fhoM2xUweegtsXDQ+zcozdjzcuFfepJjgN8FztkqOArlfcDuhmwFs2fP0dfLybwljOwuNwCgV0G7Qdga+9jI9VQZjPTXg+MsKv/IQ6p3LqXglt5bWzGNvgmccU0BGEd/Qc0AE7jWncepvRDYsPv2L6PByClKqdgIgblJBIFsNY/8b0YaaAyy/tBHRcq5Au1TTb2OfUfxlLn3nDFFiQxbUbEFFIJ1a79S9F1vkzK9TgTb2C+DgV0RKZM/G8AuK30Xj6b3i+dqCArndcDejGtG4Mr8/b8bGum0yfgYfiFAroOtmpKrU1fH6e6TiFjCbSt3Sfk4zAOM8H1+GxK29SX94vqQ8ozDMoAV2BtqmVR+WnDrwRf142jFTVJvnswE9MQZtD+e0+cR8iSTHWJ8fYzXjTMiCuaGZsIJ6zHXIU0PWKmwHdNfCjEb72Gj7Wbacz9t17ablQQNdJC2M/hc8txcepJIxf2L993YCHWDDOW/C4A+LT/P2gMFcK6AjCfeaAS8BPQL6p90HX300nzdYjm360zZhpL428pMFRSGc3IOIGISSSxTDWX+WAjts9pLugYbbxh1NmGa89+aopqCB710lARCGdOJ3UvzdZ58+sUIM39Qri41RDi+sHa0HczP3e9ZGkfjaed6lQQNc7bgZ0kUTmGnycF1bE9d/hsTiFAjpoc4i+PXxuoekYBY0k0pa/YOk7qVSfaCKdw+MNmJ9HEuOr+XSDFtCpQn5qbBgO2cpw3Uf1A3+A+xBJjrGr8IZlwPzcKvgplRwFdL3iVkDHnzkHn38WH+eBn7YytnH3sZQDBXSM8RAEPrcSH6Og3zQy9ovu9ZKFWYxtB2s332LMgZGfSzDH/ny+FNARhPuMYx3vLSZ/3P2gAtuMbNhh69ENT/7qpIanP/pfrgUHIsV0EhBxVQ+JZDGM9Vc9oOP+596cER88xPjzCZOMNxe8ZgopyNJ0GhBRSCdGp/UvJvxIUj6IKhe8qVcQH6cSXRvxQboNWs/GM//E8y8FCuh6x62AzscrO1fxsKP7WMoh7AFdxdBUXy2pP2n6urp+uEtt+rvfcpaCjnBOyOtMBb+M1Iw/gAI6f8jXD7zTImhz6mLcvkhyjA0H1+MNywDKn2OUwPMvhRwFdL3iVkAHn2vFx3jkA93HUS5hD+gaGdszp/6z0L6Th2Dwn9uNcN385GrGfgj1XoTHGlDfgddUlAI6gpCRE5s33XpUQ277Qxrevfb2putwKNKTTgMirsohkSyGsf6qB3RzH77T2OuEq42jzptOIVGZlhMQUUhXvuXUvyfhp5HSQZQI8KZeQXycMqRSfbSAXNFSouujCf0YXIbeoICud9wI6PjzA+Fz3+JjPPL+7vMrF0HBiaIBnbER/PkO09fUdxqum38YG0WT+rUWYwyucf2zSDI92vR5B1JAVzorp+y9ez4bW2sRtDm0sgH3IYpmxnbNMfYx3qwMsJ81MWb7yu8cBXS94kZAB38+C3/dK6HvC7vPr1zCHNBNZ2x7+POb+GuqCzX6C66bX0Cdt4UxPYfHGHBfEfUeQQEdQbhA39ENB209umHZr09qmPvUf5t6vZqunICIq2pIJIthrL+qAd2rT99lHD1utvGTY2cYN932ZEcYQSFReZYbEFH9y7Pc+lsJP4bUDaIEgTf1CuLjVIFvSuK5uOTaSEJfAh9fAB/t8k3wY4tj3Xb1bskrLe9a0BMU0PWO6IBu93hmW/j7Mvx1Dz0fz7EcwhzQ8TU1fT4YruZ1wrXzA6jzBIvxBd+4/pnpcw6kgK50eKBmDtmc2za18iDchyiaGJuNNypd8gtwCfgU+Cj4eNff+VVt+Fi3fdvurQtzFND1iuiAjj//Df6+Gn/dK2Ecf8RzLIewBnRd6/gA/nxAfCvF2Ca4dl4D72ebwVgesRhfGPzU4nO2pYCOIFxix2Ezt+k7quHirQ5ueP+nxzXOnXb99GYckhQsNyDiqhgSyWIY669aQPfAA3cah6duNKJ/bTEumTbHWLLwjQ0CCQqJnCsiIKL6O1dE/bHwI0jZIEoUeFOvID5OBSLV6R20RPojPBdBvqwlMnXRZHpEdNCEPXDf3amomrRjRTIzlB8P3/eGRVtCjSb0NB5Db1BA1zuiAzr48zT8NS/lV+/hOZZDWAO6SCJ9E3xciz8fHNOuXf1TKtFE5lTzuEg7UkBXGm31A7V8NvY1DtnK8Mv25gF9cT8imM7Y/jl3bm25tomxx/gVSPDnxAzGdsZ9dwfGsTscezh8z4yc+7fAWwP9DcNj6I0cBXS9IjKgSzHWp9nfW/V9Vc/Y5niO5RDWgM7DXwLwRRmuooNx3IjHRdqTAjqCcJntxzRvu/XoxlO2OrjhWfj4wU+ObXjwiHFNM5tvbpq+8Ilcw0f/a2oRERBxVQuJZDGM9Zc1oPvgtXuM1xbeZzz80L1G/XV3GkdecVNHKDfwtOuMyTMfNd5e+LopjChIIZEzRQVEVH9niqp/d+FHj5JBlEjwpl5BfJwKuHBVy5pIInONFs/8Gvdlh341mb20ZGZiJKGvsOijPOP6TNxfKVBA1zsiA7p+SX2A5m+o8+VeY1Kb4TmWQ1gDOmEm9cVaMj07mkiPh78fH0mmj4bz+WCw6juT+hH8mYWRuN4IxywC15jaEe+X/atS2+H6eUW/eOaPMIbVFuNyW97ncx0BbFy/DN77TyqsCazRMP5nfjth/jX4ew7+/AQc/6lFO1JIAV1ptGUrmy1CtnK8Bfchihxjc/AmZZl+Al4+jbEK3Fep8Gc6NTFWBe1cmxN/dd16aPtY3Gcp5Cig6xWRAR2s02n4817KA2Y8v3IJa0AnyG/Ap6CGM8FLwKO7HMLfL7ocxD8Hx50PXg++btGOcKHfhbh2XgJj+Dsek0d+Di6AmufAy6AOxxTWBBzTtRZj+dfg443NnYH7txbtSCEFdAThIVsOb4z2HTXtuK0Pbmzqe3DDE1sd3LAEPn7ON2jhz0LcdXjK+FP8wI6P+GuktVR/f+1e/50OazJ+dtxM409nzTZOTN9jTPvXY8YL/3nZFED0JIVE9hUZEFH97Suy/gXhx42SQZRI8KZeQXyc7PSvSm0hOAB7odxgDrNnbf3m0O7xWlx/3aI/20YT+j1sTKut2z4VoICud0QGdPBxPv68p8Yzj+D5lQsFdLb9GN6jZvGra/esTf0Aj68U+g3Rt4/G03+Dtp6yaF+YfoU7P64evzP0n8fjcdGl0bg+OZLUB1UMTTm46snYKFKT+a2W1M+Dth63aN83/VpDlViR3fdX+WxsjUXI5tj2KZUjcD8iaGbs1zmBV89Be7ddw9hOuJ9ygDZ3hLYvBT/E/TmxuYxniuUooOuVZkEBHTgKXGXxec9sYuwKPL9yaaaAzq5LwIkwjv34LRzx+EoBvrd/rjOw423h9oXpV7jTdRW0l7eBfRa8mN+21O5tgjmzGNuCjxnWZXzOowC1VP1aQ4IgusE3aPHnyiGRSPwZ2lzJP+KvEWZCWn/Tpr5fig4oKCSyJ9XfX0XXn8vPb3zChw28qVcQHyc7kXjmEDwH56YfdLZZWyJjWjfWkunjoK82c9+l2XH1RuXkLXHTpUIBXe+ICuh4OGb6nNcm9RSeX7lQQFeyL4HH818iwGMqh37JukQkob9q0Z8A00/j/rwA+r7PPBYXTOp38frBPwE2wmMoB/4sUHhv1zV/nkW6gRTQ9U57NvYIDtjKdMWi5gGb4n5E0MRYFm9QOhXa0nH7ImlmbFvo50rwS9y3Detxu3bIUUDXKwIDunkWn/PaBJ5fuVBAV7Jz+FVY/GpaPCanpBjbBNo8MSco7LfwKtyn28Ab2g+g33ctxiLar8FG/ksdeAzlAm0OzHVeLe1lyGgpBXQEIQGiAyKOIiGRFIS0/qZNfb90I6CgkKh0qf7+6kb9+fmNT/iwgTf1CuLjZEeL6zfgOTj0JVfDuW7sVJXaOpLQJ2n2b1v3Er+SBrdnhx2H1W2zwa30yjGRftBijPaM65+Z2i1TfmULnrcdhAV0Qsy8A17NN90jNeMP6D9Y768NGhfh49x10LjdeDAQiWeGwLHng3eAq7p/fyQxvhrPr1wooOvVZfy2iCyV6oPHIgp+VS6/3aJF3+W6viI5Lor7c5OO20eaxyHWpP6A6Cujreh4f01kLoU+vzCNwSMpoCtOe33sCIuArSzbsgNtPw+2VHLirii5FrftFg2M7dbM2N0WY+jNm1OMlfW+OQP+WdjtVnplCeNpsxijXZ/E7QqwP563HQQGdCJ8DsYzGT4eBfPah8+NX5HJr8Tif25k7Bfw8WD4ejrXGQjyWygWvnc1fH1rPL9yoYCuuNDfwhbGXN2/47ffhb4ex30LcAnuy22gz6stxiHSNWBjb88QFQGcG/2gr1ngOotxeCIFdAQhAW4ERBwFQiIpCGn9TZv6fulGQMGlkKg0qf7+6kb9+fmNT/iwgTf1CuLjpCaV6qOJuWJhXbRa3xs37zZdzyd72WI8Vr5bCGZkQbWgplR8D+iS+nItkanjzzDEY+uVMa0bVyTS+2nJdD201bZzcuJW+JByUW3dvQ3oMlc7vY2lE+C1erF5DOUZTeqn4H7couvWlhuEyoLN81uL4n7dhr9X86v1LMbjuhTQ9czy+v13ymdjH+CArUxX56/a25VQu4mxX+HNSYeu4Fe34fbdBsZ/SK7EK2Hg2Ied3h7PLQQFNffidv1GgoDuZRjDOY2M7YLH1hvwfX2nMzYMPt7U5MLz5ziC1j2IAd3XfN1SZYbopVLP2ObQ510W4yjXsn6Jzw4tjMUt+hcmD0v5zwncr9vAvPaG18JiPB4vpICOICTArYCII3lIJAUhrb9pU98v3QgoClJI1LtUf391o/78/MYnfNjAm3oF8XEyo9Xoe+LxOzKpP4Db9oqu59NNM41pQz+sSKZ/jr/Xb1QLakrFx4CuTYvrp/HXBB6TM8Texq+AauvuUUD3Fbxu/oL79oKuMBaPx7nxzJ24D7fgz+Yz9S/OO8q94rhc+JWUmsdX01FA1zPt2cpbLQK2smzLxq7H/YiiibFj8OakE6Gdi3DbXtHM2K65Xm6FCMcscuNKqHIRFNRQQPe9/LlYw/F4nGIIvLVidwSte9ACuvdaGPs/3LfbdIV0T1qMpxzPxf24AbyONoW+XrHoX4T8CrbLU4xtgvv1iq75TcgJfEZqKVJARxAS4GZAxJE4JJKCkNbftKnvl24EFN2lkKi4VH9/daP+/PzGJ3zYwJt6BfFxMgPjHYnH78RIPHMUbttrup6lZ7WR+4WWTA/Ex8uAakFNqfgQ0K3W4voEfutTPBYZUW3d3Q/o0h9pNXX74H69YsCA5k1hHIvM43Lsh8ylcLc7vGbQ1zqL/st1nRbP/BP35xf8StjOW9WaxumKFNBZ01YfOwaHawJctyK7r2tXLzQxNglvTjqxmbE9cNte0srYxjCGcTmLjVwehlzD2E74e2RAUFBDAR1jH0GfJ6Y8uvKqXASte5ACuuedXO0oCug7CmNYZTEup3pyTsLr6CyLvkX4KTgY9+cXMM8RMJ4vLMbpihTQEYQEuB0QcSQNiaQgpPU3ber7pRsBBZZCop6l+vurG/Xn5zc+4cMG3tQriI+TGS2R/gcevxOjgyb4unlVoCKu/07jV1F9P7bVWjJTi4+TBdWCmlLxOKBbKmsA2xOqrbu7AV36o4qa8b/HfXoNH4MmMOzqV53+Je5DNNDPPNyvAFf7dSVjMbpu5fmcxXiFSwGdmZVT9t49n419ZhGwlWd97Gbcl0ianT3HDbsct+sXMJ/DYTxfdx/bDDg98HGyICio8SQMsIPHAd3j/HlieAwyI2jdgxLQvdDA2A64T6+BOZ5mMTanfoTbFw2/Ihj6+cCi73JdnvPwFp2lwm95mXNnviYpoCMICfAiIOJIGBJJQUjrb9rU90s3AgorKSSylurvr27Un5/f+IQPG3hTryA+TmaiifR4PH4HrufP7cJt+0Wktk7T4vrrfFwyXNlXDNWCmlLxMKB71O9b8DlBtXV3MaDjVz5W4f78IhLXb7YYozOT+hG4fZG4dI6tAUfivmRBq5n8Q82DkI4Cug0xWsdsnM/G5pvCtfJd0z4l9gvcn0j4M7bw5qRdoY35uF0/gTH9Kdd5BcgqP26ZZwdBQU1oAzp47WX51ZO4f9kRtO7KB3RQh7ws4Wpz5+0Ul+AxOhVq1h/3IRLo4wLcpwD5s0Sle9xBARjfb3MlPnO0HCmgIwgJ8Cog4kgWEklBSOtv2tT3SzcCip6kkMgs1d9f3ag/P7/xCR828KZeQXyczGi9P7utFL/B7frNzsmJP4ok0n/Fn5cN1YKaUnEpPMDeIe5Zc96i2rq7FtAl02fgvvyk65aR5nE6MZmZiNsXSTSpP2zqs0z5895wP7LB39vdvt0lBXQbks/GUhbhWtm2ZSubcV+iyTH2PN6cdOA83K7fwJj+0MKY3//P7xVBQU0oAzro40LcryoIWnfVA7rVUAep7u4A8zzbYpyOhLmNwO2L4jrGtsqJD6o+k/0XGjgwzkrwK4vxC5MCOoKQAC8DIo5EIZEUhLT+pk19v3QjoCgmhUQbSvX3Vzfqz89vfMKHDbypVxAfJzPRhN6Ex+/EXWrTUj7/RHZUC2pKxf2ALv0gf24Y7lcVVFt3lwK6ecyD57TZBcb1ksVY7RvXXdtU7nwmm77e1GcZwmsyjfuRFX77UBjz53gOoqSA7nvaspXV+WxsLQ7XBPjZ+41774L7E02OsRfx5qQDX8ftEqUhKKhx7b3UKW4HdE2MXYH7VAlB6650QCfjGsK67AhjW43H6tDzcfuigHGeaNFfOa6D9TgI9yMrMN7DLOYgTAroCEICvA6IOJKERFIQ0vqbNvX90o2AojcpJPpeqr+/ulF/fn7jEz5s4E29gvg4mYkmM1Pw+B0p8XPeZEa1oKZUXA7oXtg5OXEr3KdKqLbuLgR03/I2cT8yoMX1yyzG68TXcNuiiCTT0y36K8fHZLpNcSnwW4hazEOIFNB18t60/Sry2dgKi3BNhP/A/blBE2ML8eakA9c1M7YtbpvoHUFBTdgCulm4P9UQtO4qB3RvtjK2Ge5HBmBsD1mM17ZQsxm4bVHA62cx7q8cYaw67kN2YNxNeB6ipICOICTAj4CII0FIJAUhrb9pU98v3QgoSpFCok6p/v7qRv35+Y1P+LCBN/UK4uNkRkukL8fjd2IkrjfitoneUS2oKRUXA7pPtcSEn+D+VEO1dRcf0KUbcB+yIPA2l98wF64QrBia6qvF9c8s+nPqpxXxTD/cjwpoicytFvMpWwroWNdz5wY+bhGsifC1Rc0DPLkCOsfYPLw56cRmxg7HbRO9IyioCU1Ax4OJyYxtiftTDUHrrmxAB+39BfchCzC+v+PxOtSVW/9OZyxm0Vc5Pg+vR09+3ogExtwXxv6WxXzKlgI6gpAAvwIijs8hkRSEtP6mTX2/dCOgKFUKiaj+futG/fn5jU/4sIE39Qri42QmktDH4vE7M/PJTlWprXH7RHFUC2pKxa2ADup1DO5LRVRbd8EB3Rpt0LgI7kMaqlKbaIJunxipTu+Amy+XSDxzCO6nHKNJ/RTchyr8uHr8zvxnD55TuVJA1/HcuTqLYE2E65dNiVXh/twix9j1eHPSiU2MPYbbJnpHUFATloDuW/CXuC8VEbTuqgZ0b8J/BIX/co4oYHx/sBizbWGNX8VtiwDWIov7KsO10N7vcB+qAGMfZDGnsqWAjiAkwM+AiONjSCQFIa2/aVPfL90IKOwY9pCI6u+vbtSfn9/4hA8beFOvID5OZiLxzBA8fqdG4ukrcftEcVQLakrFpYDuUdyPqqi27kIDuqR+F25fNmCc803jdiB/Vhxuu1yg3TtwP45N6otVu7UlBs6lc0zzKtOwB3RtUyvH8CDNIlwToWu3RbMix1gab06W4WDcPlEcQUFNKAI6aHMc7kdVBK27kgEdzP083L5M8Ftv5jrDYNPYbfoJbrtcUoz1gXbbLfpyZHPHjwC1gQncgedVrhTQEYQE+B0QcXwKiaQgpPU3ber7pRsBhV3DHBJR/f3Vjfrz8xuf8GEDb+oVxMfJzK6Dxu2Gx1+GqysS6f1wH0TPqBbUlIoLAd26SE3mt7gfVVFt3UUGdPwKMNy+bEQT6RwetyPjehVuuxz6V6W2gHa/MvXj1Gpd+cBhrzGpzWAuy0xzK8MwB3TLpg78TT4b+8IiWBNhfsmUqu1wn27SzNjheHOyDJfNYGxn3AfRM4KCmjAEdMv5Le1wP6oiaN1VDOjWQ1vy3iGgCxjnyxZjt22z4FtHQu32w32U4ZdBeL+GGv8c5rLOYn6OpYCOICRAhoCI40NIJAUhrb9pU98v3QgonBjWkIjq769u1J+f3/iEDxt4U68gPk52YMwf4jmU4YfRan1v3AdhjWpBTakID+iS+m24D5VRbd0FBnRrtZrJP8Ttywa83s6zGLtto4n0MNx2OfRL1iVwH2W4ELevKlo8c7rF/Bwb1oBu5dSBO+ezsaUWwZoI1y+fFqvFfbpNI2O/wJuTZfp8EDZ9vUJQUBP4gA7aOwv3oTKC1l3FgO4l3LaMwDjvshi7bRsYE3obb2jzctxHGU7B7asKzOVmi/k5lgI6gpAAWQIijschkRSEtP6mTX2/dCOgcGoYQyKqv7+6UX9+fuMTPmzgTb2C+DjZ0RKZ2/EcyvQrvsE9YECz0N+sDCKqBTWlIj6gSw/EfaiMausuMKB7EbctI/D6/YvF2B2YORK3XQ5aMjPR3Icz+Rxx+6rCn38Kc/oUz9GpYQzolk2u3DJfP/BJi2BNiG3ZymbcpxfAP8g2yjG2HG9QliNMJA+OwH0RZgQFNUEP6FYF6eo5jqB1Vy6gg3am47ZlBMbagMfuxBmM/Ri3XQ7Q5pO4D4fyZ8/1x+2rCsyn0mKOjqWAjiAkQKaAiONhSCQFIa2/aVPfL90IKMoxbCER1d9f3ag/P7/xCR828KZeQXyc7GhJ/UQ8B0G+qcUzh6v+jCM3US2oKRXBAd3LuH3VUW3dRQV00UT6Fty2jERqxh+Ax+5IeG/FbZeDlkg/berDmSv5rSFx+yoTieuNFvN0ZNgCOiPF+rRNjd2OQzVxDnx9xcTfbIX79YocY9fiDUpBPgr+CfdHfI+goCboAZ0v4bWbCFp35QI68HTctozA+lxiMXbbtjAm7Dm7PKSGNtfgPhx6H25fdaA+iy3m6UgK6AhCAmQLiDgehURSENL6mzb1/dKNgKJcwxQSUf391Y368/Mbn/BhA2/qFcTHyQ6/5RyM+xs8D2Em02+BZ+w4rG4b3HfYUS2oKRWRAV00rl+E21cd1dZdVEAH7wM6bltGKuL670xjd2A0kTkVt+2UrmetCXqfTjfg9lWnXzzzR/M8nRm2gC6fjTWaQzVhfpOvr/wd7tNLmhirwRuUgn0CHJ1ibBPcd9gRFNQEOqCD12cVbl91BK27cgEdtHMQbltGYKyn47E7VNizoVsY29eifUfCOvwFt686MK/z8TydSgEdQUiAjAERx4OQSApCWn/Tpr5fuhFQiDAsIRHV31/dqD8/v/EJHzbwpl5BfJwKRBLpm/A8XPBL8LqKuB5nVSnayGLqBTWlIjKgC9rtLTmqrbuogC6S1C/AbcuIlpjwEzx2Z2bOxG07pV9SH2Bu36lpN/7N7zPGRjC3dvNc7RumgC6fjV1mEaqJsz52Bu7Ta1oZ2zjH2BK8SemC/FaaE5sY8zWQlAlBQU2QA7ov4PUZqKuZOYLWXbmALqfIFbUwzr9ZjN2Jf8BtOwXaOtOifSd+1cjY1rh91YFzag+LuTqSAjqCkABZAyKOyyGRFIS0/qZNfb90I6AQZRhCIqq/v7pRf35+4xM+bOBNvYL4OBWIJMb/H4x9PZ6Li34YSeizIvHMIbvHM9vi8YQF1YKaUhEY0H0RxFukqrbuogI6fiUtbltG+g/W+5vG7sBoIv133LZTokn9WNy+I+P6Z0H9BQmo0bWm+TowLAFdezZ2vClQE2h7fawV9+kXzbHnSKQAAGZBSURBVIydijcpXfadJsYmtTAWr2dsczyesCAoqAlyQDcXtx0EBK27cgHddMYG4LZlBMZ6JB67E2Gdhf0CHbQ1E7fvRFjLh3HbQQHm9xaerxMpoCMICZA5IOK4GBJJQUjrb9rU90s3AgqRBj0kovr7qxv15+c3PuHDBt7UK4iPUwUtkbkVz8Uj14IL+O3vtGp9cMXQVF88tqCiWlBTKqICumg88yxuOwiotu7CArq4fhpuW0YqkuOiprE7UGhAl9DTuH2HBu7ZLAUiyfTRFvO1bRgCurb6fYbks7E1OFQT6Bsf1O0nzW2tZzG2RY6xd/BGpUd+AT4IXgBW8iv68PiCiqCgJsgBXT1uOwgIWnflAjqY9+9x2zICYz0Mj92JIgM6aO8/uH0n8nMTtx0U4HU6A8/XiRTQEYQEyB4QcVwKiaQgpPU3ber7pRsBhWiDHBJR/f3Vjfrz8xuf8GEDb+oVxMepQkU800/rvA2laU4e+42WyMzVEul/aPHMr/E4g4RqQU2piAro+K1XcdtBQLV1p4DOmSIDOlG/QAGvvUtw20GhX01mLzxfJwY9oFtWX7lPPhv7wiJUE+Vny+sH/hL36zfNjI3AG5U+uQpsBY9rhB8HeJxBQlBQE9iArokxJX4m2kXQulNA5xL8GW147E4UHNC14/adCHMbhNsOClDvE/F8nUgBHUFIgAoBEceFkEgKQlp/06a+X7oRULhhUEMiqr+/ulF/fn7jEz5s4E29gvg4lYgk9LF4Pn4LY1oSSWSu6hfP/BFedhvhMauMakFNqYgK6MBpuO0goNq6U0DnTLEBXfoZ3L4TI/HMENx2YBjTujHM8Ss8Z7sGOaBbNrlyz3w29r5FqCbK9e3ZypG4X1nIMXYD3qz022bGFjUxdtF0xn6Gx6s6goKawAZ04KG47SAgaN0poHMJ2QK6riuc1+P2nQhtbYfbDwotjO2N5+tECugIQgJUCYg4gkMiKQhp/U2b+n7pRkDhlkEMiaj+/upG/fn5jU/4sIE39Qri41Qjmkjfguckke1aMjMxmhz/KzxuFVEtqCkVUQEdv60fbjsIqLbuFNA5U2xAp7+L23fmhJ/gtoMEzPFl85ztGdSAbnn9/ju1Z2NvWYRqIk3hfmWinrEf5Bh7DW9YSuRLzYydB+6Kx64igoKawAZ00E4gf2FC0LpTQOcSsgV0/Epi3LZDV+K2gwQPHy3mbFsK6AhCAlQKiDgCQyIpCGn9TZv6fulGQOGmQQuJqP7+6kb9+fmNT/iwgTf1CuLjVGPP2vrNYR5z8Lzks+OKkuP7V6W2wHNQBdWCmlIRFdBpicyluO0goNq6U0DnTMEBXdlXhoHrBgxo3hS3HSTg3LrHYt62DGJAt2Lib7Zqy8YWWgRqwmyvj7UaBpP+KvcZcDrlGFuCNy0lcw14P3ggHr9KCApqAhvQBfV2fILWnQI6l5AtoON1w2079EncdtCAOX5kMW9bUkBHEBKgWkDEERQSSUFI62/a1PdLNwIKtw1SSET191c36s/Pb3zChw28qVcQH6ciFUNTfWEu8/DcJPX9SEK/oqJq0o54HrKjWlBTKqICuqA+L0u1daeAzpmiArqu92NT+w58H7cdNGCOMyzmbcugBXRG65iN27Ox+3GgJthFyyZXbon7lpVmxvbIyR/SFeRX/I1tZWwzPA/ZERTUBDagg3YOwG0HAUHrTgGdS0gY0CVx2w69E7cdNHICrgCngI4gJEDFgIgjICSSgpDW37Sp75duBBReGJSQiOrvr27Un5/f+IQPG3hTryA+TlmqUptoAjY7PfRTLZG+fPd4Zls8FVlRLagpFQroiqPaulNA50xRAV2kOr0DbtuJkYT+Km47aGjJtI7nbdegBXT5bOxqi0BNnPWx996btl8F7ld2mhj7ETgfb17KajNjS2G8x6YY2wTPRVYEBTUU0CmGoHWngM4lZAvooK3huG0nNnVcIB1sYJ7/xfO2KwV0BCEBqgZEnDJDIikIaf1Nm/p+6UZA4ZVBCImo/v7qRv35+Y1P+LCBN/UK4uNUh2+qa2Jur+aVH0TimaPwPGREtaCmVCigK45q604BnTNFBXTRRF0Fbtuhi3DbQYPfFtdi3rYMUkCXz8YuMwVqYv102bTKX+N+VaGesc2bGcvhDUzJfamFsX3xXGREUFBDAZ1iCFp3CuhcQraADtoZg9t2Iswri9sOGjDPOXjedqWAjiAkQOWAiFNGSCQFIa2/aVPfL90IKLxU9ZCI6u+vbtSfn9/4hA8beFOvID4uCFQk0z+HuS3Ec5XceXyjHc9FJlQLakqFArriqLbuFNA5U1RAt1vyyh/jth2Z1AP/fBaY5/mmeds0KAFdfmrlcRaBmkhX5+sHJnG/KtLCWG0zlAxvZErsuibGpk9mTOrbigoKaiigUwxB604BnUvIFtBBW0fith06AbcdNGCO91rM25YU0BGEBKgeEHEchkRSENL6mzb1/dKNgMJrVQ6JqP7+6kb9+fmNT/iwgTf1CuLjAkNVapNIPH0WzPFjPGeJ/UBLZmrxVGRBtaCmVCigK45q604BnTNFBXS7Dhq3G27boU/htoNGJKlfYDFvWwYhoFuWHXhgPhtbYxGqiXJ9WzZ2NO5XZaYztn2OsXpwNd7QlNgXYdw/w3ORBUFBDQV0iiFo3SmgcwnZAjpo53DcthNhXpNw20ED5nk/nrddKaAjCAkIQkDEcRASSUFI62/a1PdLNwIKP1Q1JKL6+6sb9efnNz7hwwbe1CuIjwsaWs3kH8I8s+C3eO6Suk7WYEC1oKZUKKArjmrrTgGdM0UFdP2rJuyC23boc7jtoKEl9ZTFvG2pekC3fOq+e+ezsS8sQjVhtk+NXYj7DQo88Moxdg/e1JTYT8E/4XnIgKCghgI6xRC07hTQuYRsAR2M52DcthNhPNNw20EDavUwnrddKaAjCAkISkDEsRkSSUFI62/a1PdLNwIKv1QxJKL6+6sb9efnNz7hwwbe1CuIjwsqFfFMPy2RbtAUeT5dNK5fhOfgN6oFNaVCAV1xVFt3CuicKSqg6zdE3x637dC3cdtBI5LIXGUxb1uqHNC9V7/vHvmpsZU4UBNrZQPuN4g0MbZPM2N35xhbjzc4JfQrfptOPAe/ERTUUECnGILWnQI6l5AwoDsIt+3Qa3HbQQPm+LTFvG1JAR1BSECQAiKOjZBICkJaf9Omvl+6EVD4qWohEdXfX92oPz+/8QkfNvCmXkF8XNCJVKd36Lqt2Lu4FrIZTejn4PH7iWpBTalQQFcc1dadAjpnigroBgxo3hTaW4/bd+CnuO2gEU3q11rM25aqBnTL6/ffKV8f+585UBNofexmI8X64L6DTAtjP+VXZuQYW4U3OiXzqybGqvD4/URQUEMBnWIIWncK6FxCwoCuCrftRGjn37jtoAHzfBvP264U0BGEBAQtIOKUGBJJQUjrb9rU90s3Agq/VSkkovr7qxv15+c3PuHDBt7UK4iPCw2pVB/+vLdIXL8Z6vA1roskro8m9MPw0P1CtaCmVCigK45q604BnTNFBXQcTdCzP/esTf0Atx0kokn9YTxnu6oY0LU3D+jbno09bQrUhFo555XUXpvhvsNCM2N9waNzjD2KNzwl8tMWxvbCY/cLQUENBXSKIWjdKaBzCdkCOv6ehdt2IoxnMW47SBiMbQTz/BLP264U0BGEBAQxIOKUEBJJQUjrb9rU90s3AgoZVCUkovr7qxv15+c3PuHDBt7UK4iPCyM7DqvbJpJMHw31uB/8BtfIV+P6Z1qNvicesx+oFtSUCgV0xVFt3Smgc6bggO4N3L4TK2rGK7GB6BQtkXkHz9muqgV0RuuYjfPZ2L3mQE2oT62Y+JutcN9hZQa81JoZOyfH2JN481MCX5rF2BZ4zH4gKKihgE4xBK07BXQuIVtAdw1jO+G2HfoFD7Fw+0FhGvzz1GLOtqWAjiAkIKgBEaeXkEgKQlp/06a+X7oRUMiiCiER1d9f3ag/P7/R+R468KZeQXxc2NmpKrV1NKkf2nXrsZW4Xn4YSej/xeP0A9WCmlKhgK44qq07BXTOFBzQPYbbd2bmSNx2UKgYmuoLc1xrnrM9VQvo8vWxFotATaSvtDXsswPul+iEb5o2M3ZijrF7cwKubhBkBo/TDwQFNRTQKYagdaeAziVkC+hSjPWB9r7F7TsR5tYftx8UYG6D8HydSAEdQUhAkAMiTpGQSApCWn/Tpr5fuhFQyKTsIRHV31/dqD8/v7ud66EEb+oVxMcRG9IvqQ/Q4pl/aonMXM3Hq+uiyfQIPDavUS2oKRUK6Iqj2rpTQOdMoQFdXJ+J23cinJtTcNtBIVKdrsTzdaJKAV2+vvJSi0BNpEvb6gdquF/CmnrGNuebqKDezNgivDHqoV9B//3w+LxGUFBDAZ1iCFp3CuhcQraAjgPtvY7bd+ho3HZQgLmdazFf21JARxASEPSAiNNDSCQFIa2/aVPfL90IKGRT5pCI6u+vbtSfn9/dz/8wgjf1CuLjiJ7puOIhmanVEumGSEJfgmvpss/h8XiNakFNqVBAVxzV1p0COmeKDej4LzWY+3DgQtx2UIgk9bMt5mtbVQK6fHbg3ywCNZG+n5+6389wv0Tp8Fu3NTN2eI6xG8CP8UapmzZ3XNznL4KCGgroFEPQulNA5xKSBnT8CmRTH3aFMU3GbQcFmNtteL5OpICOICQgDAERxyIkkoKQ1t+0qe+XbgQUMiprSET191c36s/Pb3z+hw28qVcQH0eUjlY9/jdaIn051PE1XFc35Ffz4TF4iWpBTalQQFcc1dadAjpnCg3oEvpI3L5D1/Ybom+P2w8C8Pq612K+tlUhoFs+LVabz8bWWIRqovysfdo+f8D9Es5pZWzjJsaqwOk5WEK8aeqCH8/y+Vl0goIaCugUQ9C6U0DnEjIGdDCmSbh9h76I2w4Cqc7bgH5oMV/bUkBHEBIQloCII2NIF8b6b7/99qZNfb90I6CQVRlDIqq/v7pRfzjVQ/Oe1hN4U68gPo5wRsetMJPpei2R+QTXWJTRuO7rb1qqFtSUCgV0xVFt3Smgc6bIgK4inumH23du8J5Dp1VO3hLm9rl5rvaVPaDjwVk+G/vcIlQT5Tdt0/YZhPslxMHDuhbGanPwx5ygZy9Z2cTYwbhvLxEU1FBApxiC1p0COpeQMaCD9g7D7Tt0/QzGfozbVx34ebGvxVwdSQEdQUhAmAIijmwhXRjrv//++0sTUrgRUMisbCER1d9f3ag/nOahek+zAm/qFcTHEeXBN14jicxJUNs3cK0F+Aruz0tUC2pKhQK64qi27hTQOVNkQMeJJPQVuA8nwuvPs41Pr9Di6VF4nk6VOaBbOWXv3fPZ2AqLUE2Ua9unDvQ11AkbjYztkmPscvADvJEqwGbcn5cICmoooFMMQevu2c8pCuicKTKgg/fBPXH7ToX5/QO3rzpQ68l4nk6lgI4gJCBsARFHppAujPXnV9DJElK4EVDIrkwhEdXf/HUvdaP+cIqH7j0Ngzf1CuLjCEGkUn34lR9Q46W45mW4vn/VhF1wV16hWlBTKhTQFUe1daeAzpkuBHR34z4cunq3mvG74vZVRmBtpA3o2icN2DGfjb1pEaoJs31q7ATcL+EN1zG2VRNjF+UY+xxvqDqVByW4Hy8RFNRQQKcYgtadAjqXkDGgg/88b5QTdAtH8DXeHu5DVaDOm+YE3haZAjqCkIAwBkQcWUK6kNZfmpDCjYBCBan+/hrk+vPzG5/wYQNv6hXExxFi2Tk5cSstkbkV192xyUwt7sMrVAtqSoUCuuKotu4U0DlTdECnxTOn4z6cm7kUt68qXeu1xjxHZ8oY0LU3D+ibz8aewoGaSNuzlRfgfgnvaYa33BxjL+FNVYeuh/a2xX14haCghgI6xRC07hTQuYSMAR0H2rwZ9+FUmGNgbtMMdR6D51eOFNARhASENCDqQIaQLqT179jIlyGkcCOgUEWqv78Gtf78/MYnfNjAm3oF8XGEC4xp3VhLpB/EtXdkPPNP3LxXqBbUlAoFdMVRbd0poHOm6IAuUp3+Ke6jDD/gv+yA+1AReL+ZYjE/x8oW0BmtYzbOZ2P34EBNpO3ZmK/PYyU2pImxHzUzthRvrDoR2vkjbt8rBAU1FNAphqB1p4DOJWQN6GBcx+I+yvAh3L6qQF0WWszPsRTQEYQEhDQg+g6/Q7qQ1v+7zXy/Qwo3AgqVpPr7axDrz89vfMKHDbypVxAfR7jDzsmJP9IS6Y9w/e0aTaRzuG2vUC2oKRUK6Iqj2rpTQOdM0QEdB9p9G/dThufj9lWD36IY5vGlxdwcK1tA15atbMaBmkjb6wdeaxjBuR1YUGhh7M94Y9WhR+K2vUJQUEMBnWIIWncK6FxC4oAuAu2uw/04FdrbD/ehGjCPA/G8ypUCOoKQgJAGRBvgZ0gX0vpvsKHvZ0jhRkChmlR/fw1a/fn5jU/4sIE39Qri4wj3iCYzF+P62zau+7b5o1pQUyoU0BVHtXWngM6ZbgR08NpJ437KcBX/RQfch0poiczVFvMqS5kCurb62MU4UBPsvUaqahPcLyEHOcYexJurDvQtiBcU1Pj2b7SeoICuOILWnQI6l5A1oONAu4/jfpzKrzwzFH4WXStjG+fE3e74OymgIwgJCGlAZMKvkC6k9Tdt6vsVUrgRUKgo1d9fg1R/fn7jEz5s4E29gvg4wj0q4pl+uP4OfAq36xWqBTWlQgFdcVRbdwronOlGQKfFM7/G/ZTpdbgPVdCS6YEw/nUWcypLWQK6tvrYMflsbL1FqCbK/y6bXLkl7peQhxxjh+HNVbs2MTYJt+sVgoIaCugUQ9C6U0DnEpIHdGNxP+UIcz0J96EKUN+z8HxESAEdQUhASAMiS/wI6UJaf9OmPtePkMKNgEJVqf7+GpT68/Mbn/BhA2/qFcTHEe6iJTLv4DWw6Qu4Ta9QLagpFQroiqPaulNA50w3AjoOvOc9j/sqx0gyfRDuQ3b2rK3fHMb+Cp6LCGUI6NqzlYPz2dhqi1BNlIuXTKnaDvdLyEXXLd9MG6w2bcTteoWgoIYCOsUQtO4U0LmEzAFdA2M7QNtf477K8FMYZz/cj+zAmPeAsX9hMZ+ypYCOICQgpAFRj3gd0oW0/qZN/YJehxRuBBQqS/X31yDUn5/f+IQPG3hTryA+jnAXLZGZi9fApm/gNr1CtaCmVCigK45q604BnTPdC+j043FfZfpBpLZOw/3IDH92qMU8hOh3QNeeHTggn419bhGqCbEtW/n2+41774L7JeQkV+ZmdTNjM3GbXiEoqKGATjEErTsFdC4hc0DHgbZn4b7K9EkY66a4H1mpZ2xzGO8ii3kIkQI6gpCAkAZERfEypAtp/U2b+t31MqRwI6BQXaq/v6pef35+4xM+bOBNvYL4ONXZqSq1df+qCbvgz8tCNKHfg9fApq/gNr1CtaCmVCigK45q604BnTNdC+gqJ2+pJdIf4f7K9CneLu5LRrRk+jiL8QvTz4BuxbR9fpzPxlbgUE2gy9+r33cP3G+Y4VdXyLx5m2PsI7zBatNm3KZXCApqKKBTDEHrTgGdS8ge0PE64r4E2IT7kRUY6zUW4xcmBXQEIQEhDYh6xauQLqT1N23qY70KKdwIKIIg1d9fVa4/P7/xCR828KZeQXycymiDxkVgTs+BL/avSkl5OywtnnkEr4FNF+I2vUK1oKZUKKArjmrrTgGdM90K6DhaIn057q9s45k72ZjWjXFfMqFV64NhrKtNYxeoXwFd+6QBO+azsTcsQjVBDvykrX6f3+J+w0wLY/s2MfY+OBv+8bYR/roM5Bj7Bm+w2nQKbtMrBAU1FNAphqB1p4DOJWQP6DjQ/jzcnwDPx/3IBtT0MotxC5UCOoKQgJAGRCXhRUgX0vqbNvWt9CKkcCOgCIpUf39Vtf78/MYnfNjAm3oF8XGqEkmM/z+YT1u3uc3fcVjdNvg4v4kk9CV4DezIr8DDbXqFakFNqVBAVxzV1p0COme6GdDxX5iAPj7GfZZrJK7fLGtIF41namCMX+Exi9aPgG7Z5Mot89nYAnOoJsyv2uv32R/3G2a6Nqm/u30k/H06PsZvZsA/NfHmqgN925QWFNRQQKcYgtbds3+bU0DnTDcDOhjjfrg/EcKYz8J9yQKM7UI8XjekgI4gJCCkAVHJuB3ShbT+pk39nnQ7pHAjoAiSVH9/VbH+/PzGJ3zYwJt6BfFxKhKJZ4bAXD7Hc4sm9CdkupKOPzcJxrUej9Oe6QbcrleoFtSUCgV0xVFt3Smgc6abAR0HzrOLcZ+CvG/n5MStcH9+oiX1I2Bc31qMVbheB3RG65iN8/Wxuy1CNVGugfaH4n7DTBNjF+UYW483LsGmFGN98PF+AeM51GKMtuSb8bhdrxAU1FBApxiC1p0COpdQIaDjQB8P4j5FCOMeh/vykxT8zIExTcbjdEsK6AhCAkIaENnCzZAupPU3beoX082Qwo2AImhS/f1Vtfrz8xuf8GEDb+oVxMepBt9Eh3msxfPq5ov81pf4+/yAb4BbjM+eSf1E3K5XqBbUlAoFdMVRbd0poHOm2wFdxdBUX+hnKe5XkC9oNfqeuE/PqUptAq+bCVrZv4hRul4HdO3ZWJNFqCbK9W3Z2NG4z7DSzNimOcZm4Q1LZOssxrbA3+sHMJbbLcZnyxbG9sLteoWgoIYCOsUQtO4U0LmEKgEdf++CflbjfgV560zGfL8rTANjO8BY7rcYn2tSQEcQEhDSgMg2boV0Ia2/aVO/N90KKdwIKIIo1d9fVao/P7/xCR828KZeQXycMoxp3RjGn8XzsTSpL69IZvbFTXjJnrX1m2sCNqf5rTxx216hWlBTKhTQFUe1daeAzpluB3QcqOnBuF+BrorG03/DfXpFRTL9cxjDAotxuaqXAV1bNnaRRagmTGjf9degKkxnbPscY4/gzUormxhbyG8vidvwkmbGfg5jWYPHZtNVrYz5dstaQUENBXSKIWjdKaBzCVUCOg70MwX3K0r+OuW30sR9egWMYTD4Hh6X21JARxASENKAyBFuhHQhrb9pU78U3Qgp3AgogirV319VqT8/v/EJHzbwpl5BfJwK8NuZaXH9XjyXXlwTSeoX+PW8okg8faXFmOy6El7KG+G2vUK1oKZUKKArjmrrTgGdM70I6DjQ179x3yKNJvWH+1Wnf4n7dYudqlJbd72/f4PH4oVeBXT8yrZ8NrYeh2qihPavXz4l1l956/ffCdfOLtMZ2z3H2Gt4o7IXPwSH47a8IMVYH+h7rsWY7HonbttLBAU1FNAphqB1p4DOJVQK6OoZ+wH0tQz3LdB1YBPMZUfct1s0MLYbrMFsi7F4IgV0BCEBIQ2IHCM6pAtp/U2b+qUqOqRwI6AIslR/f1Wh/vz8xid82MCbegXxcbITTdRVwLifw/Ow4cKKuP473K6bRJPpEVrx23CW6gzctpeoFtSUCgV0xVFt3Smgc6ZXAV3Xe/jHuH/BruGv293idb/A/Yti93hmW+jn/EhCX2HRv2d6EdDl6wcm89nYahyqkWbb62OtuH52aGFs3ybG3seblKXazNhNjYztgtt1E+izDo/DidDO0bhtLxEU1FBApxiC1p0COpdQKaDjQF+JnPUzQ0W6CuYz7hrGyv6FkJ6AuveHfurBry3690wK6AhCAkIaEJWFyJAupPU3berbUWRI4UZAEXSp/v4qe/35+Y1P+LCBN/UK4uNkJlKT+S2MeRmegwPXRRL6rOigCXvgPkSjJfUjNEFXVkRr0vvj9r1EtaCmVCigK45q604BnTO9Cug4WjxzOO7fRedFEum/9q9KbYfHYZcBA5o3jSTGV/OfH9DulxZ9ea7bAV37lNjv89nYZziIIq0tJ6DLMXZoTsxm6OdNjKX4bTJxHyLht6OEvq6y6N+JX/j9jCVBQQ0FdIohaN0poHMJ1QI6DvTXiPt3ya/5L2XAx8H1jG2Ox2EX/h4MbR0GNf83fFxr0Z/nUkBHEBIQ0oCobESFdCGtv2lT366iQgo3AoowSPX3V5nrz89vfMKHDbypVxAfJyuReGYIjPdzPP4y5VdZtFbE9TgTfOvISHV6By2uz7To06nP4T68RrWgplQooCuOautOAZ0zvQzoOFDfZjwGl10NPg6v5zS/qrnj6rpUqg8eV3cq4pl+WjJTqyXS/9CS+l0w5s8s2vVVNwO6FdP2+XE+G1uOQyiyZ50GdE2MXZQTf9XFp9BuFvwV7q9cGhn7BbT/X4s+HdnM2DTch9cICmoooFMMQetOAZ1LqBjQzWJsC+hvER6Dy34O3gOe38JYLbxHR/G4utP1Cxa/hHGOgBqPhz8/Cn5r0a6vUkBHED5RU1Pzw+rq6q/55mw3v+afx8cSPeM0pKP6lx/QcZ2GFO8sWGwMHjz4u9rXJJNG+7Ovm44ji0v191dZ68/Pb3zChw28qVcQHycjWlI/URNzi8hi5iNxvVFLpA/ccVid49/i1mr0PbW4PkFLZD6x6MOxkYQ+BvflNaoFNaVCAV1xVFt3Cuic6XVAt2dt/ebwfvs0HocPfgjvr0u6C59rtzhOtEJu8+lWQGe0jtk4nx34Og6gyOI6CehyjM3Cm5Iu+BJ4eTNjf4QBbobHUCr8FpzQzvXgGos+nPotv50a7strBAU1FNAphqB1p4DOJVQM6Dj8PS3X+WxQ01i8FOadh49LkB/j41zwI4vP2ZYCOoLwkerq6hsGDRr0edcm7TfxePxGfAzRO05DOviem8DVXfX/BL7/enxMgDFt6jvVaUhxwdnnfhdQ1A4ebPo6WZpO63/u6Wd9V/+DDhxi+jpZmk7rf8bYU7+r//Chw0xfL0d+fuMTPmzgTb2C+DgZiSTTB8FY1+Gxu2pSXxyJ6zfz0AX6P1qL61XRRN0f+g/W+xesGFz3M/75SEIfC98zDXzF1I4YFzDBV/g5QbWgplQooCuOautOAZ0zvQ7oOF3Po3sXjyUErufBmsXnbetWQMdpy8YewwEUWVwnAV3X1QumjUkX/Rr6XAgfW5oZO49vgINV8Odf803lgvD138LHGvh4JnztOvi4zKItEV6Fa+IHgoIaCugUQ9C6U0DnEqoGdJwWxv6cE3PbYtX8RNT7DgV0BOEj1dXV+4MfFQIi/nd8DFEaTkK6rvp/2lX/D0NWf9Omfjk6CSn+c+cDxoG1tR0BxdhjTzB9nSxdp/UfXFPTUf/TTzrF9HWydJ3U/7Hb/20kE4mO+p9z2pmmr5cjP7/xCR828KZeQXycrGhJ/Tw89pC4uiKu/w7Xww9UC2pKhQK64qi27hTQOdOPgI7Trzr9Sy2R/giPJ8jyq7Uj1emf4s870c2ALj+18nAcQJHFdRLQpRjrk2PsTrwxGRLfrWfsB7gmfiAoqKGATjEErTsFdC6hckDHgX7HQP/r8HiCLMz5cFi3Y/DnnUgBHUH4THV19TtdAdFS/DXCHjikSyaTP8LHYOD497rq/xb+WsAxbeqXKw4p3pr/vOkY7OiRozoCiqsnN5i+RtrTSf1HjRhhxONx49ppLaavkfbE9X/36ZdNx2D5lXM8pJs9fabpa+XIz298wocNvKlXEB8nM5GEPgmPP/DG9ctwHfxCtaCmVCigK45q604BnTP9Cug4WjI9EMawCo8pmKaf6V+V2kKFgO5/9bWb57OxD3AIRfask4COM4uxLXKdzwAybVAGWL5pncC18AtBQQ0FdIohaN0poHMJ1QM6DszhhFx4QrqmrjlTQEcQQaC6uvrMQYMGrY/H42fhrxH2KYR0yWTyDPj4SVVV1S74mO7AMX/n9efrgL8WcEyb+iIshBTXNcwwhh001Hjzv8+ajunujCkNHQHdgvvmmr5G2ve7+k9rKan+0ydMpfoLtFD/zKVXllT/KVdmXKk/P7/xCR828KZeQXyc7EQTehOeQ1CNJNIPsVSqD66BX6gW1JQKBXTFUW3dKaBzpp8BHSdare+tCXoum8Qu1QaNi/D5qhDQcfLZyok4hCJ71mlAx5nJ2DZNjM3HG5QB9nJcAz8RFNRQQKcYgtadAjqXCEJAx4F5HJsLfkj3ENR50675UkBHEEGgpqbmh/F4fBX/iL9GOKMrnPsWXAvm8Ne7E+L6mzb1RcnDOVgDowaccNk409e7+86CxcaQAw802p993fQ10pk8nONXZfHn+k28PG36enep/mJ9ad4C45gjj4La13ZcHXfVFf7Un5/f+IQPG3hTryA+TgW0uD4BzyNoRhL6q1rNZKl+DqsW1JQKBXTFUW3dKaBzpt8BHUerHv8bGMsyPLaA2M5DucJcVQno2hr2+Wk+G1uPgyjS2nICOs51jG3VxNjDeJMygN6aYkyaX0DiCApqKKBTDEHrTgGdSwQloOPAXA7JBfSZdPyXSxoZ27rbXCmgIwhVicfjuw8aNOii6urq/3S7xeK74KPw+fPB3fD3EKVRW1u7E9Txi66acr/CV9FR/TswbeqL8O0nX+gIJwr158+Yw1cRvfzIU0bzpHrj1BPGGoccPLrjuENHjzFOP/HkjltdvvrY06Z2ydLk9R88ePB39efhD9XfG5+b85+OUK57/Q8aMqSk+v9lzKHGP84427j3ulZjxQtvmtq2Kz+/8QkfNvCmXkF8nCpoyfQZMP41eD4Bcemug8ZJ93NXtaCmVCigK45q604BnTNlCOg40URdBYznOTw+xX2vezjHUSWg4+SnVs7DQRRpbbkBHQca2CzH2Cy8URkg5/JbeuJ5+42goIYCOsUQtO4U0LlEkAI6Tgtj+8Kc3sfjU1mYz2PdwzkOBXQEoSD8mWiDBg1qSSQS7489/ez5s+955OVHnn975fPvfW7Axw+uu2veayeddvbT8UTiQzhuelVV1Y64DaJ3amtrNajfnHg8zq+g47evbOCfL9Qf6vvBKWee89RN9z32xiPPv/NhV/1XXHfnQy+deOqZT8L3fRCC+ps29UX5ymNPG6efdIoBr/MOJ6bGd3yePxNt/EWXGaNGjDTGj5tg3PngE8bjz79jPLtklfGf59827rz/MePKy/WOWwTql1zRETbhtsne5VdxnXriWCMRT3RcSTep6yq67vUfN26icdccVP/7HjXGwbFUf+e+s2Cx0XLVtI4a8tc+D6sLV9EV6j+yq/53PjjfeKyr/o89+5Yx+5Z7jJNPPMU46dgTjBfnPWlq2478/MYnfNjAm3oF8XEq0S9Zl4A5fIDnpLhv9x+s98dzlQHVgppSoYCuOKqtOwV0zpQloONUDE31jST0WXiMapp5noeOeI4qBXRt2dghOIgirRUR0BVoYuzsHGOr8Yal4s6ZzNiWeK4yICiooYBOMQStOwV0LhG0gI4DY+kHY1qAx6iiMJeb6hnbHM+RAjqCUIwDDjhgQDwef++Mc/65YMEbK77moVBPzn+t/dvTz/nnIn58IpH4P9wWURr8yrlBgwbdDX4GJnk9z/rHxc8ueH3lalzzDer/6vIvTznzvAX8qroA19+0qS/a1/+zyDjn1DM7riJ6+Ja7jUNGjzEydVOMBW+sNNW8uwtebTf08RM7rup65oHHTO2Spfna488YZ518mjF0yEHf1V+vm9p7/V/JG5nxE6j+ZfrIbfcZJx5znDH0oO/rn56Q7bX+uX/dYowaOcqYf/dDpjZLlZ/f+IQPG3hTryA+TjU6r7bIzMXzUtSndk5O/BGeoyyoFtSUCgV0xVFt3Smgc6ZMAV0BeO0dA2P7HI9VGZPp2TxsxPPiqBTQvZLaa7N8NrYCh1GkWZEBHaeFsb1zjL2FNy0V9ZoUY5vgOcqCoKCGAjrFELTuFNC5RBADOg6MZ1OY26Scus+lWwOej+dVgAI6glCIrnDugxk33fMy3owtZstN97zJvw++/7e4TaJ0qqurE1DHT2a23vcOrnExm2ffsRi+7/2A1t+0qe+Wc1vvMYYNHWbceOfDphoX85bb7u+42mvx3PmmNsnSdVr/1lup/iJ8FF7HI4YNN2bfNddU4568+d//MUZA7Z/596Om9kqRn9/4hA8beFOvID5OTYyNIgl9rJbIfILnp4qRZHr6nrX1pt9AlAnVgppSoYCuOKqtOwV0zpQxoOPwK4ojifRDeLxSG9c/4+Einkt3RAV0/GcfbtsN8lNjGRxGkWZFB3Qc/ly6HGNX5To3RE0bmAr4TTNjrgfJ5SIoqKGATjEErTsFdC4R1ICuAMxvPxjfG3i8kvsOWInn0h1RAR208yvcNkEQAqmurt4ZXGY3nCvYfOOdb8H3L6mpqfkhbpvonUL9Z7bevxTXthSbrrvtBfj+dwJYf9Omvhv+74nnjDEHj7YdDhW85l+3GCccc6yQ53KF0UL9Zzus/6xZN1P9y7BQ/xtshHMFr71jjnHoIX/puG0mbrc3+fmNT/iwgTf1CuLjVKZ/1YRdtLg+E+a1Fs9TYtujifQwPBcZUS2oKRUK6Iqj2rpTQOdMWQO6ApF45igYZxset3TG9Xv5muDxY0QFdFoyfRxu2w1WTtl793w2tg4HUuSGuhHQFWhi7Hc5xubhDUyZbWZsEfhrPBcZERTUUECnGILWnQI6lwh6QMfhz+SEeaZgnF/gcUvmGqjjZP5LI3gOGFEBHfT3c9w2QRACqa6unsFva4k3X+0I378Y2snitone4fU/54LUi7imdhx7+t/57S6DVn/Tpr4b8mdu6XVZU03tePxxJxjzWu8xtU32Lq//+Anl1f+EY6n+Th13UQrqX2+qaalepk82MlfopnZ7k5/f+IQPG6ZNvS7xcUGgX01mr2hCb9XkDupWR5OZKf2rUtvh8cuKakFNqVBAVxzV1p0COmfKHtBx+O0i+XkG412Fx++3kYT+qpZIH4jH3BMVg+t+httwYiSZPhq37Rb5bOUcHEiRG+pmQFcgx9hg8Em8kSmZH4GnpyS+pSVGUFBDAZ1iCFp3CuhcIgwBXYFp8E8DGOs1OTmvlp7TwtheeMw9Aet2rEUbtoV12wO3TRCEIKqqqvZMJBIrF7y58iu88WrH+a/mV8cTiY/i8Xg/3AfRMx31r6n5YMGb76/FNbXjE6+2fwa1/zBg9Tdt6ov2xblPdjxL66k33zfV1I6NM2/sCDpw+2RxRdV/ekf9LzO1Txa3s/4Hl1X/+a+2G8OHjzRefWyhqf1i8vMbn/BhA2/qFcTHBQktMeEnWiLdoMm1mbsmmtSv5WPD45Ud1YKaUqGArjiqrTsFdM5UIaArsHs8sy28t/9DS+rL8Tx88DV+dR8b07oxHmcxtHjm1xZt2TeuH4zbdov2+tgoHEiRG+pFQFcgx9ifmhm7DT6uxpuaPvopeOXVjCl3px1BQQ0FdIohaN0poHOJMAV0BRoY2w3GXA9+hefhg/NaGPszHmNvwPeNtWjLto2M7YLbJghCENXV1ReefMbf5+NNVyeefMY5zw0aNOhc3AfRM7z+Z5x7wfO4lk48buxpTwSs/qZNfdG2XDXNGJeeZKqlXe969NmOq+hw+2Rxef2v1K8y1dOuvP78KjrcPllcXv8r9Mmmetr14nETjRn1OVP7xeTnNz7hw4ZpU69LfFwQ0Sonb8mvMtAS6QdhzqtxDTzyfS2uT6iIZ5T9xRbVgppSoYCuOKqtOwV0zlQpoCuw15jUZlo8PQrGf5/m7RXTqyMJ/e7O2xMbG+FxlUK0Wt/bol3b9kvWJXDbbmGkqjbJZyvbcShFfq+XAV0BvoHZzNh5TYwtxJubHvo6jOEscFs8PlUQFNRQQKcYgtadAjqXCGNAV2AWY9vBuE+G8T+D5+Oyn4Et4B/wmEqF/zywaNe2kxnbErdNEEHmj2BVl327ff5H3T6/e7fPl0V1dfV/b7h73it4w9WJN9w1b8mgQYMew30QjNXW1m6OP8fh9b/p/sfbcS2deO3tD/KA9DHch8KYNvWd2v7s66bPcU85/iTj9jnzTbW06xOv5I3hw0eY2ic7LVb/26j+rttT/U8+4STj1jlPmupp11se+K9x8thTTe0Xk5/f+IQPG3hTryA+LuhoNZN/qMUzh3eFDstwPQS7MpLQZ0WS6YMGDGjeFI9FNWA+x3fMpxzjeiNu12/4s6BM43SgKs8StItq675LbXonU/8OrIjrcdy2jPD3NDx2J0bjmRrctkrwdYf39WO0ROZ2eM1+avF+XK48AFwQjafP/XH1+J1x/3aBsSYt+rBtNFHneAPNCW1TK4/KT43NInty4Cm4Zl4yg7Ef8w1d8O4cY6vwZqdg32xibJKKG99WwDzqYE6zypFvSuN2/QbGNASP04nwQ/wXuO0gIGLdwTNxu27Rwti+Fv3bll+lhduWEf7+gsfuxOkC97T9AM6/PWEef///9u4EPKrqfuP4EbXWHVBrNQkg4lI3VNRuaiGzZCUkLNrqv9W6YNEqqOACImHLBAWqYCERqStaqbu4i+AKrai4ixvWtbhbq6AC53/OJNHwO0OSmczNnZn7/TzP+yTM3NwJv3Ay7Xm9MyaLTNbUub+P25uvTO61BXA6SjFzrvEJHiPZrJHnBXJdrUnT5qX9vMndjbetNtmr2e3tEgqF3ln87Mr/yg3XVLJo+cq15nxvy8dAfM7fmXxissDktH79+vWxpV04HH538bNvObNMJYueeePDHJu/s6mfaqLRqK6sGKDPOeNMPW/WXL10wcJ4aTFk0GCdjvk/+eanuri42Hlc0pBoJKqrBlTqc4efLeY/hPl3QCKRiB5YWaVHnznKk/mb3/168OCjnMdtKXZ9ywUfNHJTrynyuKDJK5mSb6/AyIvGJpl53JIXfx+h2P/knNoQ+zVP241ue+VNXlFtb/lYAICOYX8HF4RrhxVEYrPN7+VHze/njxL83m4pH5o8nB+tmWGfI3qVVG8nH6M97H8okuAxk459DpPnBprY9wyardTxdUpNM7nL5I261N7f6GOTR+uVmmk3cE16yMcCAHjvSqV+bH4H/9r8Pj7bfJxnPi4zv5+/TPB7e6MxX/OW+XiX+frJ5vN+M5RKeIFHqsx5Z8nHTDb2e5TnBXLdj01eNrGbdN+Y7Gli3+C6aUNz1A+Htl8oFFqzbOVnzoZrKrGb5OZ8tqSzH8nG823jxy/D4fDaJ9/4xJllKjHzt7O3RaB8PNIsRdGi+Mfy0rJ4cZGO+T/91hfa/CydxyJuiosa51/G/P2ILTLtx3TO3z6HFBUlV5CqhuezQJObek2Rx6FB/Eq7oliv/HCsr73Swb5EZvOY235n77ObwPbKDfn1AIDMYl/uuEdxrEdBUc0Rjb/bf9f0Oz0/UlNqb8svmnKY/f0vvzbd8qKx8+XzcQpZlwtXZ6PjzVRq18byru8spSrqlTquecztVfY+8/nec5XaVn49ACCzXKlU5zql9rC/uxt/fzf/nV5obzP3975Gqa3l16abeZwFsnBLIUvkeYEg+IXJWhO7UfeAySuNny81SeoNr1sTslfQLX9rrdxwTSX2SoxIJPKefAzE59y0Qb7GZFVhYeG4oqKirpFo9P10XMFiY36OX9ufp3zsLOZs6qeapvmXFBfrQZVVun7qpfrNJc+m7Qqi+EssVg50Hpc0pGn+pSUlenDVQD1n+mXMvwPzw/xL9eCBg/TfLpnVMP/B6Zt/pVlX8nFbil3fcsEHTYKNvXjkcQAAwFvm+XeOfD5OIbn0SiYAACAH1Cn1coLCLanUK3WDPC8QFFNNmjYxbdaY7LPBEWkQCoUe/fvdj3wkN1xTyfx7Hv/Knk8+BuJzXh0Oh+8yse8x2Pz2R/9x3+PfyVmmkhsWLH43x+bvbOqnGnvV1qgzztSP3nbvBrefmqb34LrzkWf0H44/0Xlc0hA7//NGnO3MP13vgRaf/3EnOI9LGmLnf/6Zo5z5/+nkP6Vt/sclOX+7vuWCD5oEG3vxyOMAAIC38iM1/5TPx0knXPuQPC8AAIBf7HvY1aX2UsobZLZSE+S5gaDorBpe4rJpI3PGhnenRygUGn3meRe+IjdcU8nIMRPetOeTjwGl7NVy8jbLzmvU2Ilvy1mmkhHnXPBCjs3f2dRPNfZqIXmbzeXTZuoJsenOLJPNJZdfqydNnOKcnzSkpfmPr/2LM89kY+c/eUKtc37SkI3Of3r65l+T5Pzt+pYLPmicjb3GyOMAAIB39h1S/SPz/Pu1fD5ONvb99eS5AQAA/DJLqV/Isi3F/J88NxAUE02aX0G3yiTt76nSt2/fXkUlpZ8tffVDZ9M1mdivLy4p/dyeTz4GNs7Oq6S07Iu0zL+45NMcm7+zqZ/uPPfgE3pg1aD4/ORMk8kfhw7Td89f4JyftJz4/Ae2f/4nnDxM3zv/duf8pOXY+Velaf733Zjc/O36lgs+aOTGXlPkcQAAwDvdwrWHy+fiVJIXqT1FnhsAAMAvdUqdm6BsSzqzldpPnhsIgt4m35rYjbp/mKxv/PzG5gelSyQSuWLspIvb9TKXF9ZM/zwajc6V50brioqLrxwX+8uXcqbJZMyEKR+EQqE58txZztnU9yKTxlTrSRfPdGba1tz8wFJ9zLG/16uWv+acm7SeSRdU64lTmb9fmXjB+PbP/5jk52/Xt1zwQSM39poijwMAAN7Jj9SMl8/FKaVoymHy3AAAAH6pU2qhLNtSyNfVSm0mzw3kuk1NnjSxm3Rvmmxhcl3jn20G/3BoXE+TASY/Vw1fm7RQKLRzUVHxqmtve3Ct3HxtS667feH64pLSj+x55LnROjs3Oz87RznbtsT83L6LRov+k4Pzdzb1vchrjz2tBw8eYv8dO7NtLf96/WP9u2P/oG+fd7NzXtK2pGP+dzL/lJOW+V93k3Pe1mLXt1zwQeNs7DVGHgcAALxjnntfkM/FKeSrPn3qN5fnBgAA8EO9UjvWpeH950wWyXMDQTDKRJZxu5j8t/G25i91ObXxtqY83+y+pPTr169PcUnpf5PdpLXHR4uKvgmHwyfLc6Lt7PxLSkv/l8L815v5r8nR+Tub+l5l6YKFurKyKqmSYtnKz/TwUaP1SSecpB++5R7nnKTtac/8T2b+7c6SBQ/pASnMf4SZ//jR45zztSV2fcsFHzQJNvfikccBAABvdIvG+sjn4ZQSrn1InhsAAMAvdUoNT1C2JZ3ZSk2Q5wZyXSeTc0zGmZwt7uvfeLtNX5MiE7uR95jJIJMHG/8cazw+afGSqKz84wkXzfhuyYpVzoZs89j7J1w8c11pWdknthwKhUKrIpHIb+Q50XZ2/mVl5Z/aubZp/hfN+LaktOyjHJ6/s6nvZWxJNGTIUfGX+2tt/g8+9boeeupwff7I8/RDNy3QVQMqKYnamSV3JTf/U049g/mnMbakGzLk6OTmf/Z5+v2nVzjnakvs+pYLPmiczb3GyOMAAIA38sOxufJ5OJXkRWPny3MDAAD4QSu1SZ1SL8uyLcUcKc8PYEN9TXo0fv5bE7uxd03TnamIRqM/qagYcENZef/V42LT1910/xK9aPnK+Kas/Wj/bG8v71+xpqKy6kZ7vP06Ww7laEnUoew8qwYO/oed74W1f1mfaP4XxqavtT+f/hWV1+f4/J1Nfa/z+uPP6AkXjNeVVQPNv/O/xOfdfP7z731Mj66O6QEDKvUVl87+/n23bDlESdT+NMx/Qtvmf8ks5p/mxOc/1s5/UKvzn9ts/qnErm+54INGbu41RR4HAADSb9dwbTfzvPuNfB5OKaHJB8jzAwAA+KFOqcEJirZU8mk17z8HJOVWE7uxd4y8IxXhcLhnaXn5xMqBg14qLev/eSgU0vZj1aDBL/cfMGCyvV9+TY6WRL6w862orKyx824+f/vzKCsrmxCQ+Tub+h2VFx5aquumzdTDhp4af38uO397ddepfzpNXzmjTr/66FPO11ASpS8bm/9pzL9DYuc/28z/T0nMP9nY9S0XfNA4m3uNkccBAID0K4jGrpbPwSklWvO6PDcAAIAf6pXavC59V89dLc8PIDHbZF9hYjf15plssuHd6WE3aOVtieRgSZQRAjp/Z1Pfr9j5y9sShZLImzB/f9PW+ScTu77lgg8aZ4OvMfI4AACQXgVFNUeY59z18jk4leSFaybK8wMAAPihTqlzExRtKWV2w9trAWjFdib3m9gNvYtUw3vYeaKtBZGVYyVRRgjo/J1Nfb+STEFBSZT+MH9/k8z82xq7vuWCDxq5wdcUeRwAAEifHSumbGueb9+Qz78pZn1eqGZP+RgAAAAdbZZSB9QptUYWbSnm3Wpe3hJo1S4mL5isNTlHNbwXnU3eD4ekTzIFkZVDJVFGCOj8nU19v5JsQUFJlN4wf3+T7PzbEru+5YIPmgSbfPHI4wAA8FtBZMohPfpW/1jennWqqzvlRWK3y+fe1FP7oHwIAACQPeqVOlzelo2uUKqr+bu8lqBoSymzzf9qko8BwFVm0rTJ2TyvNj8oXZItiKwcKYkyQkDn72zq+5VUCgpKovSF+fubVObfWuz6lgs+aNxNvobI4wAA8NWQ+Zva91kzz1Ev7Fo0+WB5dzbJj9RcJp9325OCSE2FfAwAAJAd5ii1W51S35nc+VelfirvzxbXKLW1+Ts8Jku2dmTNTKV2lY8DwGVfSmNcgpze/KB0SaUgsnKgJMoIAZ2/s6nvV1ItKCiJ0hPm729SnX9LsetbLvigkZt8TZHHAQDgp/xw7THNnqfW5kdrZvQM124vj8to1dWd0l3Ombxg/ueMJ+//DgAAvDdbqVnNSqnP65U6ozrLXtbxSqU6m+/94QQlW3tyuXwcABkg1YLIyvKSKCMEdP7Opr5faU9BQUnU/jB/f9Oe+W8sdn3LBR80CTb64pHHAQDgH72JeW56Tj5XmXycH6k5J/+X07eUX5FpevSt7lwQid2R4O/QrhREY0fLxwIAANnBXjFXp9TqBOXUK7OVOkoen4nqldrbfL8vJvg7tCffzFKqp3wsABmgPQWRlcUlUUYI6PydTX2/0t6CgpKofWH+/qa9808Uu77lgg8audHXFHkcAAB+2TVa218+T4msKojWXpAXqtlBfm0myCua3C8vEluZ4Ptub5Yprp4DACBr1Ss1JUE51TwvzFbqhPlK/Uh+rd+0UpuY7+0U8z1+meD7bm9myMcDkCHaWxBZWVoSZYSAzt/Z1Pcr6SgoKIlSD/P3N+mYv4xd33LBB02Czb545HEAAPglPxp7Qj5PbSRfF0RjV+8arf2VPIcfukcn7pYfjl2X4PtMR9Z3C9ceLh8TAABkhysbXhbyiwTlVKKssmXeX5XqJc/jh9lK/drk8QTfZzry0WVKZeR/dAVApacgsrKwJMoIAZ2/s6nvV9JVUFASpRbm72/SNf/msetbLvigSbD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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='fables/string2string-logo.png')\n",
    "# Image(url='https://raw.githubusercontent.com/stanfordnlp/string2string/main/fables/string2string-logo.png?token=GHSAT0AAAAAAB5KHQETSF3UOOWXYPJHECZAZAXW5HA')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial provides an introduction to the **alignment** module in the **string2string** package, and presents a few examples to showcase its usage. While it is not an exhaustive overview of the module, it offers insights into its key functionalities. For a more comprehensive understanding of the module, please consult the official documentation."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairwise string alignment is the problem of identifying an optimal alignment between two strings, such as nucleotide sequences in DNA or paragraphs in a text. This task involves aligning them in such a way that maximizes the number of matching symbols while allowing for gaps or mismatches where necessary. Pairwise string alignment is a widely-used technique that plays a crucial role in tasks such as DNA sequence alignment, database searching, and phylogenetic analysis. \n",
    "\n",
    "The string2string package currently offers the following alignment functionalities:\n",
    "\n",
    "* Needleman-Wunsch for global alignment\n",
    "* Smith-Waterman for local alignment\n",
    "* Hirchberg's algorithm for linear space global alignment\n",
    "* Longest common subsequence\n",
    "* Longest common substring\n",
    "* Dynamic time warping (DTW) for time series alignment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we begin, please make sure that you have installed the string2string packag via *pip*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install string2string"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Needleman-Wunsch Algorithm for Global Alignment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Needleman-Wunsch algorithm is a dynamic programming algorithm—commonly used in bioinformatics—to perform global alignment between two sequences, such as DNA or protein sequences. \n",
    "\n",
    "We can compute the global alignment between two strings using the Needleman-Wunsch algorithm in our library as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the alignment between \"GATTACA\" and \"GCATGCU\", as computed by the Needleman-Wunsch algorithm:\n",
      "G | - | A | T | T | A | C | A\n",
      "G | C | A | - | T | G | C | U\n"
     ]
    }
   ],
   "source": [
    "# Import the NeedlemanWunsch class from the alignment module\n",
    "from string2string.alignment import NeedlemanWunsch\n",
    "\n",
    "# Create an instance of the NeedlemanWunsch class\n",
    "nw = NeedlemanWunsch()\n",
    "\n",
    "# Define two strings (e.g., DNA sequences)\n",
    "seq1 = 'GATTACA'\n",
    "seq2 = 'GCATGCU'\n",
    "\n",
    "# Compute the alignment between two strings\n",
    "aligned_seq1, aligned_seq2, score_matrix = nw.get_alignment(seq1, seq2, return_score_matrix=True)\n",
    "\n",
    "# Print the alignment\n",
    "print(f'Here is the alignment between \"{seq1}\" and \"{seq2}\", as computed by the Needleman-Wunsch algorithm:')\n",
    "nw.print_alignment(aligned_seq1, aligned_seq2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualization of the Alignment Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **misc** module in the string2string package includes a **plot_pairwise_alignment** function, which can be used to generate a visualization of pairwise sequence alignments.\n",
    "\n",
    "To use the function, provide a pair of sequence lists along with their alignments as inputs, and the function will produce a clear and informative plot of the alignment.\n",
    "\n",
    "For an example of how to use the plot_pairwise_alignment function, let's look at the code snippet below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need to import the necessary functions from the misc module for visualizing the pairwise alignment\n",
    "from string2string.misc.plotting_functions import plot_pairwise_alignment\n",
    "\n",
    "# Then let's the alignment path and indidivual aligned parts\n",
    "alignment_path, aligned_seq1_parts, aligned_seq2_parts = nw.get_alignment_strings_and_indices(aligned_seq1, aligned_seq2)\n",
    "\n",
    "# Finally, let's visualize the pairwise alignment\n",
    "plot_pairwise_alignment(\n",
    "    seq1_pieces = aligned_seq1_parts,\n",
    "    seq2_pieces = aligned_seq2_parts,\n",
    "    alignment = alignment_path,\n",
    "    str2colordict = {'-': 'lightgrey'}, # color the gaps in the second string as light grey\n",
    "    title = 'Global Alignment Between \"GATTACA\" and \"GCATGCU\"',\n",
    "    seq1_name = 'Seq 1',\n",
    "    seq2_name = 'Seq 2',\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add color to a plot alignment based on the type of the element (e.g., 'A' is colored crimson, 'G' is green, etc.), a dictionary can be provided to the str2colordict parameter of the plot_pairwise_alignment function.\n",
    "\n",
    "This feature is useful when you want to visually distinguish between different types of elements in the aligned sequences. Simply provide a dictionary mapping each element type to a specific color and the function will generate a colorful and informative plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's specify the color dictionary for the characters in the alignment\n",
    "color_dict = {\n",
    "    'A': 'crimson',\n",
    "    'C': 'lightblue',\n",
    "    'G': 'green',\n",
    "    'T': 'orange',\n",
    "    '-': 'lightgray',\n",
    "}\n",
    "\n",
    "# Then let's visualize the pairwise alignment again, but this time with the color dictionary\n",
    "plot_pairwise_alignment(\n",
    "    seq1_pieces = aligned_seq1_parts,\n",
    "    seq2_pieces = aligned_seq2_parts,\n",
    "    alignment = alignment_path,\n",
    "    str2colordict = color_dict,\n",
    "    title = 'Colorful Global Alignment Between \"GATTACA\" and \"GCATGCU\"',\n",
    "    seq1_name = 'Seq 1',\n",
    "    seq2_name = 'Seq 2',\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also visualize the score matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from string2string.misc.plotting_functions import plot_heatmap\n",
    "\n",
    "plot_heatmap(\n",
    "    score_matrix, \n",
    "    title='Score Matrix', \n",
    "    x_ticks= ' ' + seq1,  # Note that ' ' (space) is added to denote the insertion/deletion at the beginning\n",
    "    y_ticks= ' ' + seq2,  # Note that ' ' (space) is added to denote the insertion/deletion at the beginning\n",
    "    x_label='Sequence 1',\n",
    "    y_label='Sequence 2',\n",
    "    cmap=\"Blues\",\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alignment at More Complex Levels"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the key advantages of our string2string library is its ability to compute alignment not only at the character level but also at more complex levels by passing *lists of strings* (say, tokens or DNA sequence chunks) to the function as inputs.\n",
    "\n",
    "This is a powerful feature that allows for more sophisticated text analysis, particularly when dealing with tokenized or segmented data. To implement this, simply pass lists of strings instead of individual strings to the alignment functions, and our library will handle the rest.\n",
    "\n",
    "To better understand this functionality, let us look at the example below, which demonstrates how to compute alignment at a non-character level using our library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the alignment between \"['X', 'ATT', 'GC', 'GC', 'A', 'A', 'G']\" and \"['ATT', 'G', 'GC', 'GC', 'A', 'C', 'G']\", as computed by the Needleman-Wunsch algorithm:\n",
      "X | ATT | - | GC | GC | A | A | G\n",
      "- | ATT | G | GC | GC | A | C | G\n"
     ]
    }
   ],
   "source": [
    "# Let's create a list of strings (resembling DNA sequences), but they can be any strings (e.g., words), of course.\n",
    "seq1 = ['X', 'ATT', 'GC', 'GC', 'A', 'A', 'G']\n",
    "seq2 = ['ATT', 'G', 'GC', 'GC', 'A', 'C', 'G']\n",
    "\n",
    "# Compute the alignment between two strings\n",
    "aligned_seq1, aligned_seq2 = nw.get_alignment(seq1, seq2)\n",
    "\n",
    "# Print the alignment\n",
    "print(f'Here is the alignment between \"{seq1}\" and \"{seq2}\", as computed by the Needleman-Wunsch algorithm:')\n",
    "nw.print_alignment(aligned_seq1, aligned_seq2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then let's the alignment path and indidivual aligned parts\n",
    "alignment_path, aligned_seq1_parts, aligned_seq2_parts = nw.get_alignment_strings_and_indices(aligned_seq1, aligned_seq2)\n",
    "\n",
    "# Finally, let's visualize the pairwise alignment\n",
    "plot_pairwise_alignment(\n",
    "    seq1_pieces = aligned_seq1_parts,\n",
    "    seq2_pieces = aligned_seq2_parts,\n",
    "    alignment = alignment_path,\n",
    "    str2colordict = {'-': 'lightgray', 'ATT': 'indianred', 'GC': 'darkseagreen', 'A': 'skyblue', 'G': 'palevioletred', 'C': 'steelblue'},\n",
    "    title = 'Global Alignment Between Two Sequences of Strings',\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smith-Waterman for Local Alignment"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Smith-Waterman algorithm, known to be a close friend of the Needleman-Wunsch algorithm, is too a dynamic programming algorithm commonly used in bioinformatics. But it is used to perform *local* alignment between two sequences.\n",
    "\n",
    "We can compute the local alignment between two strings using the Smith-Waterman algorithm in our library as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the local alignment between \"GAGTCGCTACTATGGGCCCGCTAC\" and \"TTAGCCTATGCACCTATCGGTCAAC\", as computed by the Smith-Waterman algorithm:\n",
      "G | C | T | A | - | C | T | A | T | - | G | G\n",
      "G | C | - | A | C | C | T | A | T | C | G | G\n"
     ]
    }
   ],
   "source": [
    "# Import the SmithWaterman class from the alignment module\n",
    "from string2string.alignment import SmithWaterman\n",
    "\n",
    "# Create an instance of the SmithWaterman class\n",
    "sw = SmithWaterman(\n",
    "    match_weight=1, # weight for a match\n",
    "    mismatch_weight=-1, # weight for a mismatch\n",
    "    gap_weight=-1, # weight for a gap\n",
    "    gap_char='-' # character to use for a gap\n",
    ")\n",
    "\n",
    "# Define two strings (e.g., DNA sequences)\n",
    "seq1 = 'GAGTCGCTACTATGGGCCCGCTAC'\n",
    "seq2 = 'TTAGCCTATGCACCTATCGGTCAAC'\n",
    "\n",
    "# Compute the local alignment between them using the Smith-Waterman algorithm\n",
    "aligned_seq1, aligned_seq2, score_matrix = sw.get_alignment(seq1, seq2, return_score_matrix=True)\n",
    "\n",
    "# Print the alignment\n",
    "print(f'Here is the local alignment between \"{seq1}\" and \"{seq2}\", as computed by the Smith-Waterman algorithm:')\n",
    "sw.print_alignment(aligned_seq1, aligned_seq2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize the local alignment as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then let's the alignment path and indidivual aligned parts\n",
    "alignment_path, aligned_seq1_parts, aligned_seq2_parts = sw.get_alignment_strings_and_indices(aligned_seq1, aligned_seq2)\n",
    "\n",
    "# Finally, let's visualize the pairwise alignment\n",
    "plot_pairwise_alignment(\n",
    "    seq1_pieces = aligned_seq1_parts,\n",
    "    seq2_pieces = aligned_seq2_parts,\n",
    "    alignment = alignment_path,\n",
    "    str2colordict = {'-': 'lightgray', 'A': 'skyblue', 'G': 'palevioletred', 'C': 'steelblue', 'T': 'orange'},\n",
    "    title = f'Local Alignment Between \"{seq1}\" and \"{seq2}\"',\n",
    "    seq1_name = 'Seq #1',\n",
    "    seq2_name = 'Seq #2',\n",
    ")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Longest Common Subsequence (LCSubsequence)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Longest common subsequence (LCSubsequence) of two strings is a subsequence of maximal length that appears in both of them.\n",
    "\n",
    "The following recurrence relation can be used to solve the LCSubsequence problem:\n",
    "\n",
    "$$\n",
    "    \\begin{align}\n",
    "        L[i,j] =\n",
    "        \\begin{cases}\n",
    "            0 &\\text{ if } i=0 \\text{ or } j=0\\\\\n",
    "            L[i-1,j-1]+1 &\\text{ if } i,j>0 \\text{ and } str1[i]=str2[j]\\\\\n",
    "            \\max(L[i-1,j],L[i,j-1]) &\\text{ if } i,j>0 \\text{ and } str1[i]\\neq str2[j]\\\\\n",
    "        \\end{cases}\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "where $L[i,j]$ denotes the length of the LCSubsequence of the prefixes $\\texttt{str1}[0:i]$ and $\\texttt{str2}[0:j]$.  The solution to the problem is then given by $L[n,m]$, assuming that $\\texttt{str1}$ and $\\texttt{str2}$ have lengths $n$ and $m$, respectively."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The traditional dynamic programming solution to the longest common subsequence problem admits a quadratic time and space complexity. Our string2string library offers both the traditional dynamic programming solution, as well as a space-optimized version. Additionally, we provide recursive and recursive-memoized solutions to the problem.\n",
    "\n",
    "The user can choose and use any of these methods by providing the name of the desired method as an argument to the compute function. By default, though, the space-optimized version of the dynamic programming solution to the LCSubsequence problem is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the longest common subsequence is 8.0.\n",
      "The candidates are ['abcfglmu', 'abcfglqu', 'abcfhlru', 'abcfhlnu', 'abcfglpu', 'abcfhlmu', 'abcfglnu', 'abcfhlpu', 'abcfhlou', 'abcfglou', 'abcfglru', 'abcfhlqu'].\n"
     ]
    }
   ],
   "source": [
    "from string2string.alignment import LongestCommonSubsequence\n",
    "\n",
    "# Create an instance of the LongestCommonSubsequence class\n",
    "lcsubsequence = LongestCommonSubsequence()\n",
    "\n",
    "length, candidates = lcsubsequence.compute(\n",
    "    str1=\"abcdefghijklmnopqrstuvwxyz\",\n",
    "    str2=\"xzytawbvckjfhgslrqponmedui\", \n",
    "    returnCandidates=True\n",
    ")\n",
    "\n",
    "# Print the length of the longest common subsequence\n",
    "print(f'The length of the longest common subsequence is {length}.')\n",
    "\n",
    "# Print the candidates\n",
    "print(f'The candidates are {candidates}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the longest common subsequence is 19.0.\n",
      "The candidates are ['The i bon  jump ly.'].\n"
     ]
    }
   ],
   "source": [
    "length, candidates = lcsubsequence.compute(\n",
    "    str1=\"The quick brown fox jumps over the lazy dog.\",\n",
    "    str2=\"The five boxing wizards jump quickly.\",\n",
    "    returnCandidates=True\n",
    ")\n",
    "\n",
    "# Print the length of the longest common subsequence\n",
    "print(f'The length of the longest common subsequence is {length}.')\n",
    "\n",
    "# Print the candidates\n",
    "print(f'The candidates are {candidates}.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like before, we can also make the comparisons at the word level:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the longest common subsequence is 74.0.\n",
      "The candidates are None.\n"
     ]
    }
   ],
   "source": [
    "from string2string.misc.default_tokenizer import Tokenizer\n",
    "\n",
    "# Construct a default tokenizer, which splits a string into words by spaces\n",
    "tokenizer = Tokenizer(word_delimiter=' ')\n",
    "\n",
    "# The following texts are taken and modified from the Wikipedia page of John von Neumann\n",
    "neumann_str1 = \"Von Neumann made major contributions to many fields, including mathematics (mathematical logic, measure theory, functional analysis, ergodic theory, group theory, lattice theory, representation theory, operator algebras, matrix theory, geometry, and numerical analysis), physics (quantum mechanics, hydrodynamics & ballistics, nuclear physics and quantum statistical mechanics), economics (game theory and general equilibrium theory), computing (Von Neumann architecture, linear programming, numerical meteorology, scientific computing, self-replicating machines, stochastic computing), and statistics. He was a pioneer of the application of operator theory to quantum mechanics in the development of functional analysis, and a key figure in the development of game theory and the concepts of cellular automata, the universal constructor and the digital computer.\"\n",
    "\n",
    "neumann_str2 = \"Von Neumann made major contributions to many fields, including mathematics (logic, measure theory, group theory, lattice theory, representation theory, operator algebras, matrix theory), physics (quantum mechanics, nuclear physics and quantum statistical mechanics), economics (game theory and general equilibrium theory), computing (computer architecture, linear programming,  self-replicating machines, stochastic computing), and statistics, among many others. He was a pioneer of the application of operator theory to quantum mechanics, and a key figure in the development of game theory, the universal constructor and the digital computer.\"\n",
    "\n",
    "tokenized_nemannn_str1 = tokenizer.tokenize(neumann_str1)\n",
    "tokenized_neumann_str2 = tokenizer.tokenize(neumann_str2)\n",
    "\n",
    "length, candidates = lcsubsequence.compute(\n",
    "    str1=tokenized_nemannn_str1,\n",
    "    str2=tokenized_neumann_str2,\n",
    "    returnCandidates=False,\n",
    ")\n",
    "\n",
    "# Print the length of the longest common subsequence\n",
    "print(f'The length of the longest common subsequence is {length}.')\n",
    "\n",
    "# Print the candidates\n",
    "print(f'The candidates are {candidates}.')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Longest Common Substring (LCSubstring)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Longest Common Substring (LCSubstring) of two strings is a longest substring that appears in both of them.\n",
    "\n",
    "The following recurrence relation can be used to solve the LCSubstring problem:\n",
    "\n",
    "$$\n",
    "    \\begin{align}\n",
    "        L[i,j] =\n",
    "        \\begin{cases}\n",
    "            0 &\\text{ if } i=0 \\text{ or } j=0\\\\\n",
    "            L[i-1,j-1]+1 &\\text{ if } i,j>0 \\text{ and } \\texttt{str1}[i]=\\texttt{str2}[j]\\\\\n",
    "            0 &\\text{ if } i,j>0 \\text{ and } \\texttt{str1}[i]\\neq \\texttt{str2}[j]\\\\\n",
    "        \\end{cases}\n",
    "    \\end{align}\n",
    "$$\n",
    "\n",
    "where $L[i,j]$ denotes the length of the LCSubstring of the prefixes $\\texttt{str1}[0:i]$ and $\\texttt{str2}[0:j]$. The solution to the problem is equal to the maximum $L[i',j']$, for $i' \\in [0,n]$ and $j' \\in [0,m]$, assuming that $\\texttt{str1}$ and $\\texttt{str2}$ have lengths $n$ and $m$, respectively."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dynamic programming solution for the longest common substring problem is similar to the longest common subsequence problem and can also be optimized. The compute method in our implementation uses the optimized version of the algorithm by default. However, users can specify other (less efficient) versions of the algorithm (e.g., \"recursive\") by setting the method parameter.\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the longest common substring is 8.\n",
      "The candidates are [' dreams '].\n"
     ]
    }
   ],
   "source": [
    "from string2string.alignment import LongestCommonSubstring\n",
    "\n",
    "# Create an instance of the LongestCommonSubstring class\n",
    "lcsubstring = LongestCommonSubstring()\n",
    "\n",
    "# Compute the length of the longest common substring\n",
    "length, candidates = lcsubstring.compute(\n",
    "    str1=\"we are the dreamers of the dreams and we are the makers of the makers\",\n",
    "    str2=\"only in their dreams do people only see it was thus and thus always will be\",\n",
    "    returnCandidates=True\n",
    ")\n",
    "\n",
    "# Print the length of the longest common substring\n",
    "print(f'The length of the longest common substring is {length}.')\n",
    "\n",
    "# Print the candidates\n",
    "print(f'The candidates are {candidates}.')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The length of the longest common substring is 14.\n",
      "The candidates are:\n",
      "- \"nuclear physics and quantum statistical mechanics), economics (game theory and general equilibrium theory), computing\"\n"
     ]
    }
   ],
   "source": [
    "# Compute the length of the longest common substring between two tokenized strings (see the previous example)\n",
    "length, candidates = lcsubstring.compute(\n",
    "    str1=tokenized_nemannn_str1,\n",
    "    str2=tokenized_neumann_str2,\n",
    "    returnCandidates=True,\n",
    "    boolListOfList=True,\n",
    ")\n",
    "\n",
    "# Print the length of the longest common substring\n",
    "print(f'The length of the longest common substring is {length}.')\n",
    "\n",
    "# Print the candidates\n",
    "print(f'The candidates are:')\n",
    "for candidate in candidates:\n",
    "    print(f'- \"{tokenizer.detokenize(candidate)}\"')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  With this example, we have reached the end of the alignment tutorial. We hope that you have found it useful. If you have any questions or suggestions, please feel free to contact us."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### $\\square$ Q.E.D."
   ]
  }
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