evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2bfa2100",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy import signal\n",
    "import seaborn as sns\n",
    "import librosa\n",
    "import madmom\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as ptc\n",
    "from matplotlib import ticker\n",
    "import pandas as pd\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d85b23cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib notebook\n",
    "%matplotlib inline\n",
    "sns.set_theme(context='paper', font=\"Times New Roman\", font_scale=.9)\n",
    "#sns.set_theme(context='talk')\n",
    "\n",
    "colors = sns.color_palette(\"Blues\", as_cmap=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e29b67eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################################\n",
    "# Data section\n",
    "########################################################\n",
    "\n",
    "\n",
    "# F-Scores (Global Topology)\n",
    "glb_f_scores_1x1 =   [0.8303218520609824, 0.7650876054510058, 0.7429479539133889, 0.8157758173326414, 0.8090386624869383, 0.8159371492704826, 0.8285317598056231, 0.7262816735415439]\n",
    "glb_f_scores_1x3 =   [0.8592280701754387, 0.8426623897353649, 0.8213323065075087, 0.8572384937238493, 0.8527427771414026, 0.8761834044922963, 0.8814789219073946, 0.7921071176885129]\n",
    "glb_f_scores_1x5 =   [0.8686440677966102, 0.8362810319649521, 0.8291716210860087, 0.8688692098092643, 0.8543987706492508, 0.8692123224497326, 0.8862731521550977, 0.7998306041784303]\n",
    "glb_f_scores_1x7 =   [0.8695041438404271, 0.8331459771960816, 0.8291239147592739, 0.8721652948093398, 0.8490661282180716, 0.8710980536173338, 0.8785947993800586, 0.8040370058873003]\n",
    "glb_f_scores_1x9 =   [0.8715194346289752, 0.8282315622521809, 0.8316911321134124, 0.8724307796840497, 0.8538952745849298, 0.8716626772233474, 0.8779318609827085, 0.8061884669479608]\n",
    "glb_f_scores_1x11 =  [0.8673412029229906, 0.8276305220883535, 0.8332679482149862, 0.8713579826205486, 0.8502781992918562, 0.8629238060517682, 0.8831213475421108, 0.8034429236630449]\n",
    "glb_f_scores_1x13 =  [0.8671841919548341, 0.8230174081237912, 0.8263327948303716, 0.8749152542372882, 0.8427640822582705, 0.8683379758588673, 0.8788395904436861, 0.8090297251822771]\n",
    "\n",
    "glb_f_scores_3x1 =   [0.8314257453723329, 0.7641185136683583, 0.7429367290091525, 0.8153659802734037, 0.8087659796504043, 0.8166697813492806, 0.8294882914137033, 0.7262536873156341]\n",
    "glb_f_scores_3x3 =   [0.863802302723954, 0.8444300097434232, 0.8252714708785784, 0.8613778002358093, 0.8583979328165374, 0.8795811518324607, 0.8813559322033898, 0.7896311066799601]\n",
    "glb_f_scores_3x5 =   [0.8692101928762496, 0.8408908828713347, 0.8315830416180475, 0.872739563968227, 0.858898847631242, 0.8724782528225061, 0.886254295532646, 0.8101018557276406]\n",
    "glb_f_scores_3x7 =   [0.8703756001129626, 0.834875791524598, 0.8330051201260339, 0.8772819472616634, 0.8591188524590164, 0.8640000000000001, 0.8830721542965387, 0.8103954170741932]\n",
    "glb_f_scores_3x9 =   [0.869283804699592, 0.8237956676365987, 0.8300846623351053, 0.8809563899646404, 0.8572153826627744, 0.8672760511882998, 0.8842608398770913, 0.8097109177659276]\n",
    "glb_f_scores_3x11 =  [0.8742921857304642, 0.8276530447423679, 0.8229001584786054, 0.8802852776362711, 0.8534493792397286, 0.8569335043048177, 0.8781985670419651, 0.8115007012622723]\n",
    "glb_f_scores_3x13 =  [0.8661882650907555, 0.8201923076923077, 0.8213031603589543, 0.8765537204154606, 0.8553104155977425, 0.8571952337305225, 0.882687338501292, 0.8059325591157129]\n",
    "\n",
    "glb_f_scores_5x1 =   [0.829579260518487, 0.7657291497317509, 0.7436765584544911, 0.8160581415469804, 0.8092998955067922, 0.8163799551234106, 0.8297428769979153, 0.7257446181067533]\n",
    "glb_f_scores_5x3 =   [0.8597501052927138, 0.8376012965964343, 0.8258492776259273, 0.8574770258980786, 0.8605588044184534, 0.877271875585535, 0.882668049075514, 0.7933276788238621]\n",
    "glb_f_scores_5x5 =   [0.8671507902605724, 0.8361747036856633, 0.8319591676482136, 0.8725933366817344, 0.8559429880376688, 0.8680632120543919, 0.8852010265183917, 0.7993230856014667]\n",
    "glb_f_scores_5x7 =   [0.8684800897363992, 0.8381954407779372, 0.8335949764521193, 0.8751478290251732, 0.8594725442731559, 0.8664830119375574, 0.8842820730671198, 0.8079954954954954]\n",
    "glb_f_scores_5x9 =   [0.8697836706210748, 0.827430779137154, 0.8312512393416617, 0.875753516409913, 0.8575472912109123, 0.874837269853078, 0.8882040746447527, 0.8102149037119731]\n",
    "glb_f_scores_5x11 =  [0.870289343683839, 0.8296343605300973, 0.8248874975542947, 0.8739495798319328, 0.8465828037867981, 0.864954128440367, 0.885161731986993, 0.8186972756188632]\n",
    "glb_f_scores_5x13 =  [0.8681863230921704, 0.8188204541741545, 0.8302554027504911, 0.8810975609756098, 0.8529753452949528, 0.8562031792435593, 0.8861283643892339, 0.8158404873996123]\n",
    "\n",
    "glb_f_scores_7x1 =   [0.8310954063604241, 0.7644927536231885, 0.7415953849214244, 0.814236111111111, 0.8085161964472309, 0.8159222575219585, 0.8287849817486529, 0.7251548215865526]\n",
    "glb_f_scores_7x3 =   [0.8604324627913507, 0.8424967658473481, 0.8240906380441265, 0.8571428571428572, 0.8631660631143301, 0.8754646840148699, 0.8820459290187891, 0.797358646306232]\n",
    "glb_f_scores_7x5 =   [0.8681843732433953, 0.8408650742414461, 0.8288006261005675, 0.8711573996304385, 0.8599021375225342, 0.8627665390924003, 0.8838886980419101, 0.8054260279779568]\n",
    "glb_f_scores_7x7 =   [0.8691261046430074, 0.8355726167626361, 0.8255137650252036, 0.8739071956960323, 0.8523627075351213, 0.8601297764960347, 0.8789464798244133, 0.8075903446351334]\n",
    "glb_f_scores_7x9 =   [0.8651513038627806, 0.836352081347315, 0.8283227848101267, 0.8753315649867374, 0.8557937723328227, 0.8609174311926605, 0.8797108931337119, 0.8120050020842017]\n",
    "glb_f_scores_7x11 =  [0.8684771790132234, 0.8284345047923323, 0.8248496993987976, 0.8725687458081823, 0.8448407643312102, 0.8581844695588771, 0.8878969957081546, 0.8164164715603912]\n",
    "glb_f_scores_7x13 =  [0.8713903366671363, 0.8242093433938031, 0.8126225009800078, 0.862882973380211, 0.8347128166541259, 0.8431124751760246, 0.8731573534453206, 0.7985659274996681]\n",
    "\n",
    "glb_f_scores_9x1 =   [0.8304869442484122, 0.7637666992505702, 0.7446424994992992, 0.8139575056140957, 0.8072669826224328, 0.8150224215246638, 0.8300561797752809, 0.7232909144717372]\n",
    "glb_f_scores_9x3 =   [0.8606384474757417, 0.8365400161681487, 0.8243797616722016, 0.8509529239289786, 0.8566666666666667, 0.8747433264887065, 0.8833216538192012, 0.7913729912602199]\n",
    "glb_f_scores_9x5 =   [0.8613016095171449, 0.8382141142582814, 0.8017107309486781, 0.8673858921161824, 0.8534005037783375, 0.8667287977632806, 0.8853083853083853, 0.8110061145080601]\n",
    "glb_f_scores_9x7 =   [0.8636490639843531, 0.8281049935979513, 0.8217023786121486, 0.8769801145938658, 0.8532892225473979, 0.8573494847224732, 0.8825659596482153, 0.8106081643987781]\n",
    "glb_f_scores_9x9 =   [0.8453461975028377, 0.8089708923174805, 0.8037937166567872, 0.863788487282463, 0.8374634642267125, 0.8343579834884947, 0.8710006807351941, 0.8129458388375166]\n",
    "glb_f_scores_9x11 =  [0.8623259738433414, 0.8281025967819021, 0.791993720565149, 0.8625608323544219, 0.8346296060181053, 0.8309859154929576, 0.8736499228527345, 0.8164556962025318]\n",
    "glb_f_scores_9x13 =  [0.8575795470334862, 0.8297972654408298, 0.8046995377503852, 0.8656864364777795, 0.8471559396362698, 0.8561631474463093, 0.8717948717948719, 0.8106995884773662]\n",
    "\n",
    "glb_f_scores_11x1 =  [0.8294694922486133, 0.7644356955380577, 0.7414614773629865, 0.8152777777777778, 0.8079820272234703, 0.8191307648510153, 0.8271562175268445, 0.7280971186192774]\n",
    "glb_f_scores_11x3 =  [0.8578644590992002, 0.8429912593072191, 0.820763956904995, 0.8507560815253122, 0.8608978529603123, 0.8735760971055089, 0.8796521739130436, 0.7924421082540135]\n",
    "glb_f_scores_11x5 =  [0.8601240834743373, 0.8354349561053471, 0.8191072826938137, 0.8654459936166639, 0.8473976755937344, 0.8480085729594571, 0.8867859600825878, 0.8014705882352942]\n",
    "glb_f_scores_11x7 =  [0.8627780109106169, 0.8216908892014376, 0.830009871668312, 0.8719594594594593, 0.8425368353619475, 0.8598749540272159, 0.8824636441402909, 0.8102592388306673]\n",
    "glb_f_scores_11x9 =  [0.8635602678571429, 0.811700767263427, 0.8044151820294345, 0.8615791212421539, 0.8463870641738253, 0.8644098810612992, 0.8718909710391822, 0.81793254729915]\n",
    "glb_f_scores_11x11 = [0.8457335014712064, 0.8162102263732784, 0.7913725490196079, 0.8689905831818933, 0.8422649140546006, 0.8166786484543493, 0.8671729957805907, 0.8162387417663665]\n",
    "glb_f_scores_11x13 = [0.8591133004926109, 0.8155884645362432, 0.8175210405508799, 0.8761776581426649, 0.8403511003689098, 0.8519195612431444, 0.8626187245590231, 0.8124145474432596]\n",
    "\n",
    "\n",
    "\n",
    "# F-Scores (Ring Topology, k=3 neighbours)\n",
    "loc_f_scores_1x1 =   [0.8302046577275934, 0.7650876054510058, 0.7430955692429962, 0.8157758173326414, 0.8088830829523187, 0.8159371492704826, 0.8285317598056231, 0.7262816735415439]\n",
    "loc_f_scores_1x3 =   [0.8580931263858093, 0.8443202367253001, 0.823529411764706, 0.8533916849015316, 0.8495803744351194, 0.8761061946902655, 0.8758526603001364, 0.7923681257014591]\n",
    "loc_f_scores_1x5 =   [0.8676182479708929, 0.8285396472270777, 0.8310648518815051, 0.8690890990542559, 0.8432835820895522, 0.8655034532897128, 0.8767860144562111, 0.8002820874471086]\n",
    "loc_f_scores_1x7 =   [0.8723495090365733, 0.8317911434236616, 0.8235064427957829, 0.8714670255720055, 0.8503507404520655, 0.8708898383800855, 0.8817278025368529, 0.7975442604226156]\n",
    "loc_f_scores_1x9 =   [0.8678062678062679, 0.8257600523046748, 0.8280131553491971, 0.8720989327460613, 0.8443993917891537, 0.8638772154211584, 0.8798901853122856, 0.7962589928057553]\n",
    "loc_f_scores_1x11 =  [0.8669744020142677, 0.8254428341384862, 0.8283303904257406, 0.8725642352129677, 0.8504158669225848, 0.8715852072105557, 0.8783309957924265, 0.8120405049243999]\n",
    "loc_f_scores_1x13 =  [0.8625523919641567, 0.8198051948051949, 0.815914489311164, 0.8698214595250476, 0.8356077030990946, 0.8493899107630667, 0.8745337402509326, 0.8086350974930363]\n",
    "\n",
    "loc_f_scores_3x1 =   [0.8295983086680762, 0.7607790910604294, 0.7434014685453463, 0.8124560168895144, 0.8091443500979753, 0.8166542195668409, 0.8289542369932139, 0.728338953232664]\n",
    "loc_f_scores_3x3 =   [0.8446002805049088, 0.8429319371727749, 0.7977505523197428, 0.8544474393530999, 0.8528330586893141, 0.868222753170373, 0.8704663212435234, 0.7905202941977663]\n",
    "loc_f_scores_3x5 =   [0.8620353612696645, 0.8242258652094717, 0.7849366410851328, 0.8135979180221211, 0.8458999746128459, 0.8642517380168313, 0.8663173905695455, 0.7740645529848614]\n",
    "loc_f_scores_3x7 =   [0.8626567122129877, 0.840470588235294, 0.8232530827950676, 0.8743188010899182, 0.838424640242363, 0.8554975706316358, 0.8745091343691309, 0.7782963827304551]\n",
    "loc_f_scores_3x9 =   [0.831647498960787, 0.8148271276595744, 0.78878303073881, 0.8471691559569295, 0.8426421831165518, 0.870324662996013, 0.8581780538302277, 0.7954450770726288]\n",
    "loc_f_scores_3x11 =  [0.8682499293186317, 0.8045335085413928, 0.8172942817294282, 0.8598130841121495, 0.8338275106355549, 0.8218988218988218, 0.8603863097255168, 0.8193418401611821]\n",
    "loc_f_scores_3x13 =  [0.8523823443437593, 0.8210967935271202, 0.7595396729254997, 0.8511551155115512, 0.7844595460956533, 0.8707216898102875, 0.8676268861454046, 0.8021538036725113]\n",
    "\n",
    "loc_f_scores_5x1 =   [0.828684136376444, 0.7636304276852759, 0.7413385826771653, 0.8128096249115356, 0.8078226857887875, 0.8166167664670658, 0.8287944492627928, 0.7248560035445281]\n",
    "loc_f_scores_5x3 =   [0.8481631512781114, 0.843508997429306, 0.8033718878651245, 0.8505403158769742, 0.846047505341209, 0.8358371040723982, 0.8736806264896152, 0.7929428369795343]\n",
    "loc_f_scores_5x5 =   [0.8530555555555555, 0.8245529241179315, 0.7680440771349862, 0.7780265711300194, 0.8326769152585439, 0.8695019992729917, 0.8230622617534944, 0.7826795167639473]\n",
    "loc_f_scores_5x7 =   [0.8452461684524617, 0.7972387221062771, 0.7783515012430675, 0.8380952380952381, 0.8081796512357297, 0.7926527728717768, 0.8238866396761133, 0.7939101373932417]\n",
    "loc_f_scores_5x9 =   [0.8271186440677967, 0.7701883179061604, 0.747385358004827, 0.8308970099667775, 0.7968000000000001, 0.7938504542278128, 0.8303201194227898, 0.7664092664092664]\n",
    "loc_f_scores_5x11 =  [0.8132605958875366, 0.7837998808814771, 0.738355376653249, 0.8203059337703815, 0.7944996180290298, 0.7774891774891776, 0.816072629455279, 0.7899758300470678]\n",
    "loc_f_scores_5x13 =  [0.8243757934828608, 0.7916227211089347, 0.7700492610837439, 0.8317093311312964, 0.7973540262053174, 0.7916303400174368, 0.8154821488154821, 0.7860330795484378]\n",
    "\n",
    "loc_f_scores_7x1 =   [0.8291166077738515, 0.7654759980604494, 0.7426076602500497, 0.8130939809926083, 0.8092561119100536, 0.8159371492704826, 0.8293273387515132, 0.7285505124450952]\n",
    "loc_f_scores_7x3 =   [0.8404771267531786, 0.7963816956367507, 0.6996799096215403, 0.8331765443712763, 0.8500390523301223, 0.8713724021718778, 0.8373806732540613, 0.7660297239915074]\n",
    "loc_f_scores_7x5 =   [0.8200598375837015, 0.8003152088258472, 0.6998401278976819, 0.7894060995184591, 0.7931982995748937, 0.7614957953122206, 0.7557692307692307, 0.756313460036449]\n",
    "loc_f_scores_7x7 =   [0.7976802425201002, 0.7865305500533293, 0.7400932400932401, 0.7938009787928222, 0.8094519859225742, 0.7751229796205199, 0.8027947598253274, 0.7776736715299156]\n",
    "loc_f_scores_7x9 =   [0.8108258928571428, 0.7704758821625122, 0.7589823266653719, 0.819509064655864, 0.7956005931784478, 0.7781175671083927, 0.8227264971847807, 0.7821461009471908]\n",
    "loc_f_scores_7x11 =  [0.817647901446784, 0.7770460959548448, 0.7412474849094567, 0.8308264323136725, 0.8003116073747079, 0.7695231513476157, 0.8525663107130554, 0.7738079771339482]\n",
    "loc_f_scores_7x13 =  [0.8106439282563952, 0.7516178736517719, 0.7244618395303327, 0.8224115334207077, 0.7929194325133411, 0.7532510836945648, 0.8098846787479407, 0.7770374167093796]\n",
    "\n",
    "loc_f_scores_9x1 =   [0.8288930047015244, 0.7644951140065147, 0.744408945686901, 0.8147120055517002, 0.8092319728778199, 0.8147179678744864, 0.8294722173837311, 0.7274331079094384]\n",
    "loc_f_scores_9x3 =   [0.8440570084405701, 0.7857705021249802, 0.7731182795698924, 0.8383838383838383, 0.8507206135131561, 0.8798486281929989, 0.8624070317782286, 0.7671194114318053]\n",
    "loc_f_scores_9x5 =   [0.8017904602042243, 0.7630683575626354, 0.7067669172932332, 0.8130641720006565, 0.7975227502527804, 0.767719298245614, 0.7998672419515434, 0.7726743433820674]\n",
    "loc_f_scores_9x7 =   [0.7991002389990158, 0.7602184345523747, 0.7069359756097561, 0.8135818908122504, 0.7878787878787878, 0.7904109589041096, 0.795677136102668, 0.7608171551833525]\n",
    "loc_f_scores_9x9 =   [0.811586051743532, 0.7525067642845774, 0.7040063783137332, 0.8197664579455068, 0.7859169460923394, 0.7844535567837405, 0.807495741056218, 0.779625507398193]\n",
    "loc_f_scores_9x11 =  [0.8215955788578717, 0.7361312108055957, 0.7343597739232117, 0.8285055957867019, 0.7891891891891892, 0.7747716698259522, 0.8177356020942409, 0.7772486772486773]\n",
    "loc_f_scores_9x13 =  [0.8070725794952232, 0.752733801207769, 0.6937073540561031, 0.828671914252219, 0.7884565160616349, 0.7489148580968281, 0.8268879668049792, 0.7759436980166348]\n",
    "\n",
    "loc_f_scores_11x1 =  [0.8301136363636364, 0.7631360848665671, 0.7422845691382766, 0.816637781629116, 0.8098078197481775, 0.8153558052434458, 0.8307692307692307, 0.7208197208197208]\n",
    "loc_f_scores_11x3 =  [0.7801398601398601, 0.8132372214941023, 0.6916437098255279, 0.7875150060024009, 0.7986939595629787, 0.7599584343609284, 0.7802369077306733, 0.748024355486462]\n",
    "loc_f_scores_11x5 =  [0.8050296693981351, 0.7606918238993712, 0.7208255159474671, 0.8078994614003592, 0.8004529441368899, 0.7944963838419474, 0.8230201987816608, 0.761904761904762]\n",
    "loc_f_scores_11x7 =  [0.7946301056840903, 0.7406179351921627, 0.7044917257683216, 0.8087248322147652, 0.7820625549554077, 0.7530562347188264, 0.805477643953143, 0.7736749560411957]\n",
    "loc_f_scores_11x9 =  [0.8145092460881935, 0.7475965455434251, 0.6688646198227901, 0.8264656616415411, 0.7916879143294238, 0.7678571428571429, 0.8333035554761481, 0.7754695857693743]\n",
    "loc_f_scores_11x11 = [0.8093519988454323, 0.7753984865561102, 0.7142857142857143, 0.844406779661017, 0.7928616953473551, 0.7545980031529164, 0.8096774193548387, 0.7630319818685469]\n",
    "loc_f_scores_11x13 = [0.8042895442359249, 0.7657770800627944, 0.6933696286214271, 0.8289164709828918, 0.784149892391442, 0.7610619469026548, 0.8112244897959182, 0.768460881240083]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f158ca60",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            1          3          5          7          9         11\n",
      "3   84.787169  85.048096  84.931301  85.027476  84.732697  84.736800\n",
      "5   85.158510  85.526962  85.205104  85.262386  84.813202  84.547189\n",
      "7   85.084191  85.401561  85.420643  85.039362  84.928117  84.769661\n",
      "9   85.169390  85.282296  85.437784  85.169810  83.470841  84.273448\n",
      "11  84.992049  85.065160  85.176959  85.020860  83.758803  83.308277\n",
      "13  84.880263  84.817038  85.118839  84.008172  84.294704  84.196305\n"
     ]
    }
   ],
   "source": [
    "# Calculate means (Global data)\n",
    "g_mean_1x1   = 100*np.mean(glb_f_scores_1x1)\n",
    "g_mean_1x3   = 100*np.mean(glb_f_scores_1x3)\n",
    "g_mean_1x5   = 100*np.mean(glb_f_scores_1x5)\n",
    "g_mean_1x7   = 100*np.mean(glb_f_scores_1x7)\n",
    "g_mean_1x9   = 100*np.mean(glb_f_scores_1x9)\n",
    "g_mean_1x11  = 100*np.mean(glb_f_scores_1x11)\n",
    "g_mean_1x13  = 100*np.mean(glb_f_scores_1x13)\n",
    "\n",
    "g_mean_3x1   = 100*np.mean(glb_f_scores_3x1)\n",
    "g_mean_3x3   = 100*np.mean(glb_f_scores_3x3)\n",
    "g_mean_3x5   = 100*np.mean(glb_f_scores_3x5)\n",
    "g_mean_3x7   = 100*np.mean(glb_f_scores_3x7)\n",
    "g_mean_3x9   = 100*np.mean(glb_f_scores_3x9)\n",
    "g_mean_3x11  = 100*np.mean(glb_f_scores_3x11)\n",
    "g_mean_3x13  = 100*np.mean(glb_f_scores_3x13)\n",
    "\n",
    "g_mean_5x1   = 100*np.mean(glb_f_scores_5x1)\n",
    "g_mean_5x3   = 100*np.mean(glb_f_scores_5x3)\n",
    "g_mean_5x5   = 100*np.mean(glb_f_scores_5x5)\n",
    "g_mean_5x7   = 100*np.mean(glb_f_scores_5x7)\n",
    "g_mean_5x9   = 100*np.mean(glb_f_scores_5x9)\n",
    "g_mean_5x11  = 100*np.mean(glb_f_scores_5x11)\n",
    "g_mean_5x13  = 100*np.mean(glb_f_scores_5x13)\n",
    "\n",
    "g_mean_7x1   = 100*np.mean(glb_f_scores_7x1)\n",
    "g_mean_7x3   = 100*np.mean(glb_f_scores_7x3)\n",
    "g_mean_7x5   = 100*np.mean(glb_f_scores_7x5)\n",
    "g_mean_7x7   = 100*np.mean(glb_f_scores_7x7)\n",
    "g_mean_7x9   = 100*np.mean(glb_f_scores_7x9)\n",
    "g_mean_7x11  = 100*np.mean(glb_f_scores_7x11)\n",
    "g_mean_7x13  = 100*np.mean(glb_f_scores_7x13)\n",
    "\n",
    "g_mean_9x1   = 100*np.mean(glb_f_scores_9x1) \n",
    "g_mean_9x3   = 100*np.mean(glb_f_scores_9x3) \n",
    "g_mean_9x5   = 100*np.mean(glb_f_scores_9x5)\n",
    "g_mean_9x7   = 100*np.mean(glb_f_scores_9x7)\n",
    "g_mean_9x9   = 100*np.mean(glb_f_scores_9x9)\n",
    "g_mean_9x11  = 100*np.mean(glb_f_scores_9x11)\n",
    "g_mean_9x13  = 100*np.mean(glb_f_scores_9x13)\n",
    "\n",
    "g_mean_11x1  = 100*np.mean(glb_f_scores_11x1)\n",
    "g_mean_11x3  = 100*np.mean(glb_f_scores_11x3)\n",
    "g_mean_11x5  = 100*np.mean(glb_f_scores_11x5)\n",
    "g_mean_11x7  = 100*np.mean(glb_f_scores_11x7)\n",
    "g_mean_11x9  = 100*np.mean(glb_f_scores_11x9)\n",
    "g_mean_11x11 = 100*np.mean(glb_f_scores_11x11)\n",
    "g_mean_11x13 = 100*np.mean(glb_f_scores_11x13)\n",
    "\n",
    "g_data = {\n",
    "    \"1\":  [g_mean_1x3, g_mean_1x5, g_mean_1x7, g_mean_1x9, g_mean_1x11, g_mean_1x13],\n",
    "    \"3\":  [g_mean_3x3, g_mean_3x5, g_mean_3x7, g_mean_3x9, g_mean_3x11, g_mean_3x13],\n",
    "    \"5\":  [g_mean_5x3, g_mean_5x5, g_mean_5x7, g_mean_5x9, g_mean_5x11, g_mean_5x13],\n",
    "    \"7\":  [g_mean_7x3, g_mean_7x5, g_mean_7x7, g_mean_7x9, g_mean_7x11, g_mean_7x13],\n",
    "    \"9\":  [g_mean_9x3, g_mean_9x5, g_mean_9x7, g_mean_9x9, g_mean_9x11, g_mean_9x13],\n",
    "    \"11\": [g_mean_11x3, g_mean_11x5, g_mean_11x7, g_mean_11x9, g_mean_11x11, g_mean_11x13],\n",
    "}\n",
    "\n",
    "g_df = pd.DataFrame(g_data, index = [\"3\", \"5\", \"7\", \"9\", \"11\", \"13\"])\n",
    "\n",
    "print(g_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0a5d346a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            1          3          5          7          9         11\n",
      "3   84.665523  84.022158  83.676155  81.181714  82.517816  76.993118\n",
      "5   84.777087  82.941618  81.644998  77.204976  77.780919  78.429009\n",
      "7   84.995335  84.342836  80.969510  78.539355  77.682757  77.034200\n",
      "9   84.726302  83.112710  79.537115  79.229799  78.066968  77.821928\n",
      "11  85.071055  83.566816  79.171988  79.537212  78.494216  78.295151\n",
      "13  84.203250  82.614198  80.103209  78.027847  77.779859  77.715624\n"
     ]
    }
   ],
   "source": [
    "# Calculate means\n",
    "l_mean_1x1   = 100*np.mean(loc_f_scores_1x1)\n",
    "l_mean_1x3   = 100*np.mean(loc_f_scores_1x3)\n",
    "l_mean_1x5   = 100*np.mean(loc_f_scores_1x5)\n",
    "l_mean_1x7   = 100*np.mean(loc_f_scores_1x7)\n",
    "l_mean_1x9   = 100*np.mean(loc_f_scores_1x9)\n",
    "l_mean_1x11  = 100*np.mean(loc_f_scores_1x11)\n",
    "l_mean_1x13  = 100*np.mean(loc_f_scores_1x13)\n",
    "\n",
    "l_mean_3x1   = 100*np.mean(loc_f_scores_3x1)\n",
    "l_mean_3x3   = 100*np.mean(loc_f_scores_3x3)\n",
    "l_mean_3x5   = 100*np.mean(loc_f_scores_3x5)\n",
    "l_mean_3x7   = 100*np.mean(loc_f_scores_3x7)\n",
    "l_mean_3x9   = 100*np.mean(loc_f_scores_3x9)\n",
    "l_mean_3x11  = 100*np.mean(loc_f_scores_3x11)\n",
    "l_mean_3x13  = 100*np.mean(loc_f_scores_3x13)\n",
    "\n",
    "l_mean_5x1   = 100*np.mean(loc_f_scores_5x1)\n",
    "l_mean_5x3   = 100*np.mean(loc_f_scores_5x3)\n",
    "l_mean_5x5   = 100*np.mean(loc_f_scores_5x5)\n",
    "l_mean_5x7   = 100*np.mean(loc_f_scores_5x7)\n",
    "l_mean_5x9   = 100*np.mean(loc_f_scores_5x9)\n",
    "l_mean_5x11  = 100*np.mean(loc_f_scores_5x11)\n",
    "l_mean_5x13  = 100*np.mean(loc_f_scores_5x13)\n",
    "\n",
    "l_mean_7x1   = 100*np.mean(loc_f_scores_7x1)\n",
    "l_mean_7x3   = 100*np.mean(loc_f_scores_7x3)\n",
    "l_mean_7x5   = 100*np.mean(loc_f_scores_7x5)\n",
    "l_mean_7x7   = 100*np.mean(loc_f_scores_7x7)\n",
    "l_mean_7x9   = 100*np.mean(loc_f_scores_7x9)\n",
    "l_mean_7x11  = 100*np.mean(loc_f_scores_7x11)\n",
    "l_mean_7x13  = 100*np.mean(loc_f_scores_7x13)\n",
    "\n",
    "l_mean_9x1   = 100*np.mean(loc_f_scores_9x1) \n",
    "l_mean_9x3   = 100*np.mean(loc_f_scores_9x3) \n",
    "l_mean_9x5   = 100*np.mean(loc_f_scores_9x5)\n",
    "l_mean_9x7   = 100*np.mean(loc_f_scores_9x7)\n",
    "l_mean_9x9   = 100*np.mean(loc_f_scores_9x9)\n",
    "l_mean_9x11  = 100*np.mean(loc_f_scores_9x11)\n",
    "l_mean_9x13  = 100*np.mean(loc_f_scores_9x13)\n",
    "\n",
    "l_mean_11x1  = 100*np.mean(loc_f_scores_11x1)\n",
    "l_mean_11x3  = 100*np.mean(loc_f_scores_11x3)\n",
    "l_mean_11x5  = 100*np.mean(loc_f_scores_11x5)\n",
    "l_mean_11x7  = 100*np.mean(loc_f_scores_11x7)\n",
    "l_mean_11x9  = 100*np.mean(loc_f_scores_11x9)\n",
    "l_mean_11x11 = 100*np.mean(loc_f_scores_11x11)\n",
    "l_mean_11x13 = 100*np.mean(loc_f_scores_11x13)\n",
    "\n",
    "l_data = {\n",
    "    \"1\":  [l_mean_1x3,  l_mean_1x5,  l_mean_1x7,  l_mean_1x9,  l_mean_1x11,  l_mean_1x13],\n",
    "    \"3\":  [l_mean_3x3,  l_mean_3x5,  l_mean_3x7,  l_mean_3x9,  l_mean_3x11,  l_mean_3x13],\n",
    "    \"5\":  [l_mean_5x3,  l_mean_5x5,  l_mean_5x7,  l_mean_5x9,  l_mean_5x11,  l_mean_5x13],\n",
    "    \"7\":  [l_mean_7x3,  l_mean_7x5,  l_mean_7x7,  l_mean_7x9,  l_mean_7x11,  l_mean_7x13],\n",
    "    \"9\":  [l_mean_9x3,  l_mean_9x5,  l_mean_9x7,  l_mean_9x9,  l_mean_9x11,  l_mean_9x13],\n",
    "    \"11\": [l_mean_11x3, l_mean_11x5, l_mean_11x7, l_mean_11x9, l_mean_11x11, l_mean_11x13],\n",
    "}\n",
    "\n",
    "l_df = pd.DataFrame(l_data, index = [\"3\", \"5\", \"7\", \"9\", \"11\", \"13\"])\n",
    "\n",
    "print(l_df) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8da85f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 325x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Global Heatmap\n",
    "fig, axs = plt.subplots(figsize=(3.25, 2))\n",
    "#fig, axs = plt.subplots()\n",
    "#sns.heatmap(data=g_df.T, ax=axs, rasterized=True, annot=True, cmap=colors, fmt=\".2f\", cbar_kws={'label': 'Mean F-score'}, xticklabels=1, yticklabels=1, vmin=83., vmax=85.6)\n",
    "sns.heatmap(data=g_df.T, ax=axs, rasterized=True, annot=True, cmap=colors, fmt=\".2f\", cbar_kws={'label': 'Mean F-score'}, xticklabels=1, yticklabels=1)#, vmin=79., vmax=85.6)\n",
    "axs.invert_yaxis()\n",
    "axs.set_xlabel(\"Filter size (time)\")\n",
    "axs.set_ylabel(\"Filter size (frequency)\")\n",
    "axs.set_yticklabels(axs.get_yticklabels(), rotation=0)\n",
    "\n",
    "#axs.set_title(\"F-Scores Heatmap (Global Topology)\")\n",
    "\n",
    "plt.savefig(\"heatmap_global.pdf\", bbox_inches=\"tight\")\n",
    "plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "510893f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RjrECRkRERFrDCph6TMCIiIhIe5h/qcUpSCIiIiIdYwWMiIiItIZTkOqxAkZERESkY6yAERERkdawAqYeEzAiIiLSGiZg6nEKkoiIiEjHWAEjIiIi7WEBTC1WwIiIiIh0jBUwIiIi0hoeA6YeEzAiIiLSGiZg6jEB01DLqnawMTcGFArkyeS4GJ6Cdp72+MDHBTkSGTbdjMHj+Cy1bb9oXQVNK9sCAH48HoaX6XnFblteSSQSdGzXBqmpqcpl3/0wBWvXrFJZNmXqdAwc9PFb2/39mmNHDyMpMQkCgQAWFhboN2CgDnqiGyUdr78lJSVhz64dqFq1Gpq3bAUHBweDHi+g6H3Fytr6nf2OCA/H/n17YG5mhibNmqPVe63fuu8Zgn8zXn87ceworl4JwOy5vwAArl8LxM0b1yGXy1Gjhhe693xf293QKX6PlX9yuRxr1qyBp6cnIiIi4O/vD29vbwDAkSNHcPXqVcybN0+nMTEB04CJUIAOXvb4+UwEAGBaJ0+EJ2ZDaCTAxEMh6FDTEWPeq4KvD4ZA8Y+2jiIT3I/NwJbbL6FQKJCRJ4ODyKRYbcuzO0G3sXDJctSpWxcCCLBwwXxYW9sUWtbWr90727X1a4fc3Fzs2rEdm7ftBAAMHtgP3Xr0gEhkpYfelb6SjheQn3x9983XWLB4Gezt7QHA4McLKHpf+eHbb97ab4VCgRnTpmDNug0QiawwYtjnaNK0Ge7eCSrWeJdXJR2vv2VlZWLTxg2oWTP/j1dOTg6WLV6Ibbv2QiAQYPDAfmjUuAkqubrqtF/axO+xf6csVMBCQkKQnJyMsWPHIiwsDLt378bUqVMREhKCR48eQaHQ/V/eMpGAZWZmYt26dfD29kZwcDA+//xzVKpUSd9hFWJsJICrjTnsLIyRliuFqdAIUrkCZ58kAQBOhSaiV11nWJsZIz1PqtK2U01HGBsJ8Cw5B1GpucrlxWlbnjVq1BgmpqbK58lJiejWvUehZa6ubu9s5+rqhoyMDESEP0VcXBwcHR2Rm5sLExNTGIqSjhcAzJ45DZ8NGaZMvoD8X+CGPF6A+jGztrZ5Z7/vB9+DUChU/tGrXKUqTp86ic6duxZrvMurko7X33Zu344ePXvhSVgoAOD2rRuwFImUf2Tr+dTHieNHMWz4SO13Rkf4PfbvaCMBi4+PR0JCgtp1Tk5OcHZ2Vlnm4eGBoKAg3Lt3D2FhYRg8eDBSUlIQHByMdu3a4cCBA6Ue47uUiQRs06ZNcHZ2Rvfu3eHt7Y1Zs2Zh9erV+g6rkByJHJefpWBSOw/cjErDltsxeJUhVq43EgAZuVK1CVRilhjeTiJM6+SJDdejcf1FGpKyJMVqW54V/PJ5+vQJatTwUrusOO0AwNraGr379MXoEcPQuUtX/DhtBkxNDeeLq6TjFfksAtevBeK9Nm3xw7ffwK+9P7p172Hw4wWoH7Pi9PvRw4ewt3dQPnd0dMSz8PBijXd5VtLx+vv1LhVdIJPKlMtyc3KRlJSkfO7o6IjYly+12wkd4/dY2bNr1y6sWLFC7bpx48Zh/PjxKsssLS0xc+ZMrF+/HiKRCL1798a2bdvw+eef4+bNm7oIuZAycRkKiUSCBw8eAABcXFxw48YNPUdUtL33XkEmV6BVVTuk5qgmSw1crfFXaKLadqfDkrDiygusvPICnzRxhalQUOy2huLShfPwbe//zmXvavfVxEkwMTbGsSOH4eTk/JaW5Zsm43Xz5g3U86mPD/sNwPivJ2Dm9B8RFvoYwH9nvADV8XlXvzMzM2Bja6N8bmxsjOTk5CK3Z4g0GS8g/9ivHj17qSyr37AhYqKj8PDBfQBA1IsXsLWz02rc+sTvsRIQlP5j4MCB2L9/v9rHwIGFj6eLj4/H/v37sXz5cnh4eOD999/HiRMn8Pnnn2Pu3Lm4fPky1q9fr+WBUFUmErARI0bA2toaq1evxoYNGyASifQdUpE+buKKhReeISQuE5PaeSgTKaGRADWdRLgQnvzW9vdeZiA0PguuNubKZcVtW949evgAPj7137nsXe1++Xk21qzfiOYtW2HUiKHIycnRSrz6psl4paakwN29MoRCIdzc3NG4cRNcv3YNwH9nvADV8XlXv21sbZGXl6d8npubCytrqyK3Z4g0Ga+TJ46jc5duhaaTnJ1dsGjpb/hj/Tps+XMT7t4JQvMWLXXWB13j91jZ4OzsjLp166p9/HP6EQCCg4OVVcYRI0bA2dkZu3fvxpYtWzBlyhS0bdsWw4cP12kfykQCZmVlhSlTpmDMmDGIjo5Gjx499B2SWm62ZrAzN0ZGngybb71EWq4U9SpaAwA6eTng2CP189H/FJ8pRkaBqUZN2pZXaampsLWzU/nyVrfsXe2ePAlDQkIC7O3tMXX6TDg6OuHqlQCtx69rmo6Xs7MLEhPfVFCdnJ1hZm72nxkvQHV8itNvb+9aSIiPVz6Pi3uF2rXrqt2eIdJ0vE6eOIavx49Ft07+WLxwPk6f+guzZkwDALT19cOipcvRslUr2NjaolnzFvroktbxe6xkBAJBqT805evrC5lMhpMnT+Lo0aMYO3asFnqqmTJxDNjfbt++jfDwcMycOVPfoaiVLZbB5HXFSwEgIikbydkStPGogODYDGSKZTAVClDV3gJPErJR21mEJ4nZAIBKNmaISs2FqVCAHKkcSdn5x38V1dbQBARcQpu2vu9edvkiWrfxVX7A/vkaG2sbiMX5VQsjIyP4+NRHxYoVtRy97mk6Xu3a+2P92jWQiMUwMTVFXFwcvhjvDygU/4nxAlTH5237yfVrgWjUuAnqN2iIzMxM5OTkwMLCAjHR0fBr117t9gyRpuO1dPlKZdtDB/bj1s0bmP7TbOUysViMlSuW4+d58w02aeX3WMmUhf3B1NQU06ZNU7uuRYsWaNFC9z8aykQFLC4uDn/99RcCAwOxdevWMjsFmZIjxfUXaehU0wGtqtrhVUYeKtqY4vOmrpjWyROrP6yDVR/WhVye//rBjV3hYm0KazMhvmxbFePaVEH7Gg44+Ti/2tWyqm2RbQ3N9WuBaNmq9VuX5eTkYPbMGUhPSyvyNS4VK6JLt+7YtuVPHD1yCNU8PFC3no/2O6Bjmo6XrZ0dfvhxGpYvW4KtmzehzwcfwsXF5T8zXoDq+Lyt37/+MhfPn0dCIBBg+sxZWL50EX5fvRLjvvwalgW+e9T9PzAkmo5XUbKzshBw+SK2bfkT3/0wBdU8qusifL3g9xiVJoFCHxe/0IKhO+/rO4RyZXU/ftiJiP5rzPUw71Vj0olS3+bThd1KfZu6ViYqYERERET/JWXqGDAiIiIyLGXhGLCyiAkYERERaQ3zL/U4BUlERESkY6yAERERkdZwClI9JmBERESkNcy/1OMUJBEREZGOsQJGREREWmNkxBKYOqyAEREREekYK2BERESkNTwGTD0mYERERKQ1PAtSPU5BEhEREekYK2BERESkNSyAqccEjIiIiLSGU5DqcQqSiIiISMdYASMiIiKtYQVMPVbAiIiIiHSMFTAiIiLSGhbA1GMCRkRERFrDKUj1OAVJREREpGOsgBEREZHWsACmHitgRERERDrGChgRERFpDY8BU48JGBEREWkN8y/1OAVJREREpGOsgBEREZHWcApSvRIlYKmpqYiLi4OlpSUqVqwIExOT0o6LiIiIDADzL/WKnYApFArs3r0b27ZtQ3h4OGxsbAAAGRkZaNOmDT7//HO0atVKa4ESERERGYpiJWDp6elYunQpmjdvjo0bN8LBwUG5TiqVIjY2FgEBAQgPD8cnn3yitWCJiIiofOEUpHrFSsDOnz+PH3/8EUKhsPAGjI1RuXJlDBo0CJmZmbh79y4aNmxY2nESERERGYxiJWC9e/dW/vvChQto166dyvrIyEjcvHkT9evX11vy9X5tR728LxGpJ5bK9R1CuZMtluk7hHLHzpLHIJd1LICpp/FlKPbs2YOPP/4YvXr1wq+//gqJRIJRo0ahXbt2yM7Oxvnz57URJxEREZVDAoGg1B+GQOOzIBUKBQYMGAA3NzfEx8fj0KFDiImJgZOTE5ycnPD111+jffv22oiViIiIyCBonIB17txZOSUpFouxd+9eGBu/2Ux0dHTpRUdERETlmoEUrEqdxgnYkydP8NFHH8HExASenp4ICwuDvb09goKCoFAoVJIxIiIiIipM42zp22+/xY0bN5CTk4O2bdsiPT0dubm5ePXqFdatW4cpU6ZoI04iIiIqhwzlmK3SVqJyVfPmzZX/trOzw8WLF+Hn54eVK1eWWmBERERU/jH/Uk/jBGzRokXYvn07srOzAeQflC8QCBASElLqwREREREZIo0TsPPnz+PEiRNwdnZWLrt06VKpBkVERESGgVOQ6mmcgH3++eeQy1UvsGhvb19qAREREZHhYAKmnsYJ2O7du7F48WJYWloCyJ+CTE1NRVBQUKkHR0RERGSINE7A+vfvj6ZNm8LMzAxAfgJ28eLFUg+MiIiIyj8WwNTTOAEbMGCAynOxWMwpSCIiIiINaJyA1apVq9B8bqVKldCtW7dSC4qIiIgMA48BU0/jBOz333+Hn5+f8nloaChvP0RERERqMf9Sz0jTBgWTLwCoXr06li9fXmoBERERERk6jStg/v7+KuXEtLQ0+Pv7l2pQREREZBg4Baleia4D1rFjRwD5gyoSiWBra1vqgREREREZKo2nIC0tLeHm5gY3Nze4urrCyMgImzZt0kJoREREVN4JBKX/MATFroDt3bsXt2/fxtOnT1UuuioWi3Hnzh0MGTJEG/ERERFROWZkKBlTKSt2AtavXz8A+QlX8+bNlcuNjIwwfvz40o+MiIiIyEBpdAxYv3790KVLF1hbW6ssf/78eakGRURERIaBBTD1ND4I//bt29i3bx8yMjKUN+WOiorC+fPnSz04IiIiKt/KwlmQcrkca9asgaenJyIiIuDv74+MjAxs3rwZoaGhmDRpEjp16qTTmDROwGbOnImJEyfCxcVFOahXr14t9cCIiIiISkNISAiSk5MxduxYhIWFYffu3ejfvz+WL1+OW7duYfPmzWU/AXv//ffRrVs3mJiYKJdVqlSpVIMiIiIiw2CkhQJYfHw8EhIS1K5zcnKCs7OzyjIPDw8EBQXh3r17CAsLw+DBg1G9enXltkaMGFH6Qb5DsRKwGTNmQCwWAwCkUimGDRsGd3d35fonT55g79692omQiIiIqIBdu3ZhxYoVateNGzeu0MmBlpaWmDlzJtavXw+RSITevXsDAK5fv45Vq1ahUaNG8PHx0XrcBRUrATMxMUHt2rVhZmamdr1CoSjVoIiIiMgwaOMYsIEDBxZ5Fx4nJ6dCy+Lj47F//34sX74ca9euxZw5czBr1iy0aNECBw8exPvvv4/k5GTY29uXeqxFKVYC9tVXX6mc+ZiTkwMLCwukpKTAwsICXbp00VqAREREVH5p4xh8Z2fnQtOMbxMcHAxTU1MAwIgRI/DZZ58p1xkbG6Nhw4Y6v6tPsRKwgsnX3Llz8eDBA2zfvh12dnbYtm0bqlWrhjZt2mgtSCIiIqKS8vX1RWBgIE6ePAmJRIKxY8di/PjxqFq1Kjw9PTF48GAIhUKdxqTxQfgymQx//vkngPyyYr9+/dCtW7f/zGUo7lw+jcy0FAgggKm5OZp3fB8ymRQ3Th/Bs0f3MHjiTLXtFAoF7l05ixtnjmDkzGXK5Snxsbh26hCc3Koi9vlTdB08Eiam6qd6yyOJRIKO7dogNTVVuey7H6Zg7ZpVKsumTJ2OgYM+Vmm78rdl2Ll9GypVqoQ58+ajpnctAEDQ7VsIuHwJUqkE3Xv0Qq3atXXRFZ1QN15Tpk6HlbU1khKTIBAIYGFhgX4DBqptP/Gr8Th75hQAYN+ho6hRw0u57sSxo7h6JQCz5/6i1T7omlQiQdeOvkgruI9NmQZraxvEvoxBcnISfP380axFy0Jt16xcjj07t8OlUiX8NOcXeNX0BgBs2rAOhw7shUgkwq+Lf4Orm5uuuqN1UqkEfbu2R1paqnLZ199NhZW1NV7FxiA1ORmtfdujcbMWatuP/GwAQkMewdTMDHuPnoWtnZ1y3daN6yCRiDF05Bda7oVu/ZvPZUR4OPbv2wNzMzM0adYcrd5rDQDYsG4tDuzbA5GVFRYv+w1ubu6F2hoKAfR/GQpTU1NMmzZNZVmrVq30FE0+je8FaW9vDyOj/GYKhQIbN27UedaoLxJxHq79dRBtew5Am579cfPsMeTlZCM3OwtCY2NkpqcU2TYvJxtmFpZISXilsvz07k1o2LYTmrbvBntnV9y+cFLb3dCpO0G3sXDJcly9cRuBN4LwYf+BsLa2KbSsrV87lXZ37wTBq2ZNnL0YgGbNW2DGtCkAgOzsbCxe+Cu+GP8Vxn85AQt/naeHXmmPuvFq69cOu3Zsx2dDhuLTz4dg/749yMrKLNQ2OjoKbdr64uyFAJy7dFUl+crKysSmjRt02RWduXsnCPMXLsOFq7dwMfAWPvhwANq09cPRQwcwdPgojP96Ehb8MgfZ2Vkq7e7dvYMaXjVx4uwlNG3WHLNn/AgAiAh/ivfatMX+IydRzaM6dm7frI9uaU3w3Tv4af5inLhwHScu3sD7H/RHqza+OHn0ID4dOhKjxk/EsgVzkZ2dXajt3ds3MWzUOOw/caFQ8hX/KhbHDu3TYU90p6SfS4VCgRnTpmDMF+Mw7qsJ+GP9WojFYoQ/fYo2bX1x5MQpeHhUx/YthrWP/ZORoPQfhkDjClinTp3w8cf5lYqoqCgIBAIsWLDgXwURFRWFfv36wcbGBkD+BdPOnj37r7apDTKpBPHRkUhLToC1nT0k4lwIjY1hZmEJh4pv//VibimCS2WPQsvFuTmIeHgXlap6Ij05Ae41amkrfL1o1KgxTF7PuwNAclIiunXvUWiZq6tqhcHJ2RkNGzUGAIwa8wU6tm8LALh44TyqVK0KoVAIoVAIY2MT3Am6jUaNm+igN9qnbrysrW0QEf4UcXFxcHR0RG5uLkxMTAu13b5lMyQSCerUrVeoKrhz+3b06NkLT8JCtd4HXWvQqJHKeCQnJ+FKwCU4u1QEkP/L183NHQGXLqJz1+7K1zk5OaNBw0YAgOGjxqJ7Rz8AQHXPGsrX1PWpD6GRYf3A9GnQSOUyQinJSbh25RKcnF0A5I9XJTc3XAu4CP/O3VTa7t+9He5VqqJy1Wpwc6+isu7wgT2FXm8oSvq5vB98D0KhECKRFQCgcpWqOH3qJHr07KV8jU/9+jAysH2MikfjBKxmzZrYunUrnj17BplMhurVq6t8mEsiLi4OFy9ehLm5OeLj47F169Z/tT1tMbe0QpN23fDHnG/h09IPvf83AcavP3DFOchQ3Ut8e3+EP+ZMgjg3ByZm5vBp2a5UY9a3gl9aT58+QY0aXmqX/VPBcnx2djbq1ss/PTjk0QPY2zso1zk6OuJZRITBJGDqxsba2hq9+/TF6BHD0LlLV/w4bYbyYNKC3NzdcevWTXw6eABmzZmHbj16KrfjUtEFMqlMZ/3QpYJ/9MKfPoFnjRrIzclFcnKicrmDoyNexb5UaVdwWjE7Oxt16tZTWS8WixETFYWvJ32vpcj1o+D39bPwp/DwrIG83FykJCcpl9s7OOLVq1iVdjKZDNVr1ETw3ds4sGcHfl74Gxo3zb8v8M1rV9C4aQvcu3NLN53QsZJ+Lh89fFj4+yo8XPlcLBYjKioKk777Qfud0KOycCX8sqhYU5APHjyATJb/5X3z5k3cuXMHqampyMjIwL1797B06dJ/FUTTpk1hbm4OADh+/Di6d+/+jhb60/XjkRAKhbhz+TSsC3ywSqqKVx206TkA5w9sha29YylEWHZdunAevu3937nsn86eOYXhI0cDADIzMpWVUgAwNjFGcoE/HIak4Nh8NXESTIyNcezIYTg5qT/z5+NPP8eSZSuwcMlyzPt5tnIK6cSxoyq/uA1ZwKULaOvbHk2aNcetmzcQF/cKMpkMMTExsLW1K7Ld+bOnMXT4aOXz3Jwc/LZ0Ifbt2YU//1ing8j1IzDgIt5r2w4NmzTDnVs3Ef96vGJjYgqdESYUCjFkxBgsXrkeo8ZNwMKfZwDIPz7qQfDdIo8ZMzSafC4zMzNgY1vg+8rYGMnJyQDyryawdNEC7Nm1E3+sX6ub4KlMKVYC9ttvv+Hly/xfj4sWLcKCBQuwfPly5ePQoUOlFlBoaChq1Sq703CH/1iOYdMWoYZPE/wxexLEebn/ansht66igpMLPvt2Do5sWoHHQYGlFGnZ8+jhA/j41H/nsoLSUlORkpKC1m3ypyBtbG0hzstTrs/NyYWVlXVRzcu1gmPzy8+zsWb9RjRv2QqjRgxFTk5Oke382rVHk2bN8CwiHCdPHEfnLt3+M79AQx49RF2f+qhdpy5++HEGli9egH17diI05BGaNFefIKSlpSI1JQWtWr85k9vcwgLffDcFS1esxvYtf+oqfJ17HPIQtev6wLt2XUz8YRpWL1+Ew/t2Iyw0BI2aNC+yXZ9+H8FSZIXU1BQcObAHvfoO0GHU+qXJ59LG1hZ5Bb+vcnNhZZ0/HWlhYYHvJv+IFat/x5Y/N+ksfn0QCEr/YQiKNQX5+++/K//97bffokkT1emeyMjIUgnm2bNn8PAofJxUWfHqRQQyUpJgZWOH3sMnYM20cXhy7ybqNm9b4m2e3bsJQ39cAJG1Lbp9Mho3zhxFrcb6PTNDG9JSU2FrZ6eSCKhbVpBcLsfOHdswYtQY5TJv71oIDLyifB4X9wq169TRXuB6UnBsnjwJQ0JCAuzt7TF1+kx8/skgXL0SgA4di75vWeXKlVHB3h7r1q5BaEgIACA7Owt5eWKYmJhg+k+zddUVnUlLS4Wt7Zv9qWevPujZqw8CLl1AsxYt4e5euVAbuVyOPTu3Y9iI0YXWAUCLVq1hV+BAc0OSnpYGW1tb5Xh17dkbXXv2RmDARTRp1gKuasaroGrVPWFpKcLJo4ewa+smAEBmRgYAQCAwwpARY97SunzS9HPp7V0Lp06eUD6Pi3uFNm18VbbZ6r3WsKtQQWd90AcjQ8mYSpnGZ0FOnDgR4QXmsAGgWrVqpRLMsWPH0LNnz1LZljZYiKwgleTfksnIyAiVvWrD1iH/irvqbgbwPPQBcrIylM/V3S/A0toWktdVtKredZXbMzQBAZfQpq3vu5ddvqi8s8KWzZvwYb8BMDMzQ2pqCh4+uA+/9v54GhYGuVwOsVgMmUwGn/oNdNYPXSk4NjbWNhCL839FGxkZwcenPipWzD/A/Pq1QIjFYojFYjx+nWjl5ORAJLKCq6sbli5fiROnz+HE6XOYOOl7dOrcxSCTLwC4GnAZ77VR/TGUlpaKvbt34Psp05XLrgRcUu5j27f8iT4f9lfuY48ePkBeXh5yc/M/k3Fxr9DGt53O+qBL169eRov3VMcrPS0NB/fuxITvpyqXXbtyGQqFAlmZmYh8lv/dn5qSDC/v2jA1NcXazbuw6/Ap7Dp8Cv0GfYJ+gz4xyOQL0PxzWb9BQ2RmZiorYzHR0fBr1151H3v1Cr7/OAucyraEhASEhYVBIpEgNjb23Q2KoHECNm7cOMjlcpVl+/aVzqnHsbGxcHV1LZVtaYOtgzPqt/bHleN7cefSKTi5VoG7Zy3kZGXgyb0bSI57iZiIN2eZndu3GS+ePAKQfwmLkFtXkJWehvAHd5SveX/olwg8eQD3rpzF49uB6Nh/iK67pRPXrwWiZavWb12Wk5OD2TNnID0tDRvWrcXyJYvRs1tntGzaCB382sDRyRmWlpYYO/5LLF+6GGtXr8T0n2YrL4tiSAqOjUvFiujSrTu2bfkTR48cQjUPD+VJCb/+MhfPn0ciJTkZX48fi6+//AK7d+3A50P/p8/w9eLm9Wto0fI9AEBKcjIuXTiPQ/v3YuacX2DvkH+8Zm5ODubNnon09DRs2rAOK5cvQd+eXeHbsgm6dvCFo6MTzp89jf59umP5kgU4eewIxn45QZ/d0prbN6+haYv8antqSjKuXLqAo4f2YcrMeajw+vjW3NwcLJr3EzLS0xEd9RwTxvwPc6b/gIvnTqPfR5/oM3y90PRzKRAIMH3mLCxfugi/r16JcV9+DUuRCGdPn0Kfnt2wZOGvOHb0ML78eqI+u6V1hjQFuW3bNnTu3Blr1qyBiYkJbt++jf3795doWwKFhjdy9Pf3x8uXL5Vla4VCAYFAgJDXv771Zf+9kmeh/0Xd61bSdwhk4MRS+btfRCqyxYZ5pqo22Vn+u7Pw/2vMNb72wb/34R+3S32b+4bp58z3iRMnYtasWTh9+jQ++OADpKamonv37rh69arG29L4f8WECRPQqVMn5VmLAHDmzBmN35iIiIgMnyGdBOTt7Q0rKytln/bv369yu0ZNaJyAPXr0SOXm248fP8bt27fRsWPHEgVAREREhsuA8i+0aNECI0eORFpaGnbu3Ino6GgsXry4RNvSOAE7ffo0zp07h3nz5qFx48aoVatWiUpvREREROXN4sWLERsbq7wYvboLYxeHxgnY4MGD4eXlhUmTJqFLly6YMGECzMwM5+bRREREVHoM6TIUEydOxJYtW+Dl9eYOLjk5ObCwsNB4WxqfPiaXy9G2bVscOnQI6enp6NWrF+7fv6/xGxMREZHhE2jhoS+DBg3ChQsXcPPmTdy8eRPXr1/Hr7/+WqJtaVwBCw8Px5MnT+Dl5YWff/4Zly5dwvTp09/dkIiIiKgcO3v2LGQymcqJiNHR0ZgxY4bG29I4AZs3b57Kc19fX2zYsEHjNyYiIiLDZ0hnQU6dOhX16tVTWRYaGlrEq99O4wTs3Llz2Lt3L7KysqBQKKBQKBAdHY3z58+XKAAiIiKi8qB27dpYu3Ytzp07BxMTE3Tr1g2DBg0q0bY0TsBmz56Nb775Bs7Ob+78fuXKlbe0ICIiov8qI8MpgGHevHlISUnBJ598ggoVKuD58+f4/fffMXq0+vvJvo3GCVj37t3RuXNnldMuy/Ltg4iIiEh/DGkK0tbWFlOnvrlXauvWrbFx48YSbUvjBCw1NRXDhg1D5cqVlcuePHmCvXv3ligAIiIiovJAKBSqPI+MjERAQACGDh2q8bY0TsAqVKgAHx8fXvuLiIiI3smACmBo1qwZBgwYAJFIhMTERMTHx2PBggUl2pbGCdjo0aORk5ODlJQUVKtWDUlJSejatWuJ3pyIiIiovGjWrBk2bdqE69evQ6FQoFmzZiW+F6TGF2I9dOgQOnfujDVr1sDU1BS3b9/GiRMnSvTmREREZNgEAkGpP/QlMDAQy5YtQ/v27dGgQQOcPHkS6enpJdqWxgnY7du3cfnyZbRt2xYA0KZNGyxcuLBEb05ERESGzUhQ+g992bhxozL/cXBwgL+/PyZMmFCibWmcgHl7e8PKykqZge7fv7/E5TciIiKi8qJNmzZo06aN8nlsbGyJb8eo8TFgLVq0wMiRI5GWloadO3ciOjoaixcvLtGbExERkWEzpMtQmJmZYd68efD09MSzZ8+wf/9+fPjhhyXalkChUCg0bSSRSBAZGQmZTIbq1atDLBbDysqqRAGUlv33YvX6/uVN97qV9B0CGTixVK7vEMqdbLFM3yGUO3aWJvoOoVwx17js8u8N21myCtHb/PGRT6lvs7iuX7+OCxcuQC6Xo1mzZujYsWOJtqPx/4qDBw+qPL979y6ePXuGyZMnlygAIiIiovKiRYsWqFOnDu7du4fq1auXeDsaJ2CbN2+Gt7e38nlUVBSqVatW4gCIiIjIcBmV8ynIfv36oWLFiujVqxc6d+6MsLAwDBs2DHK5HBUqVMAXX3yB7t27a7xdjROwlStXolKlN9NXubm5WLp0qcZvTERERFTWJScnY82aNXB0dIRcLsd3330Hd3d3bNiwAebm5pg+fbpuErDo6GhER0crn8fHx+PYsWP44YcfNH5zIiIiMmzlvACG7t27w9HREQCwfft2hIeH49ChQxCJRADyL0dREhonYJMmTUKVKlUA5J/ZIBKJMGfOnBK9ORERERm28n4WZE5ODiQSCcLDw7F48WKMGTNGeeyXTCbD5cuXMXHiRI23q3ECtnbtWpVjwP4pOTkZ9vb2GgdCREREVNb0798fQ4cORUREBAYMGIAxY8YAAM6fP499+/YhNrZkV2HQ+DIU69atQ15eXpHrHzx4gDVr1pQomH+Dl6HQDC9DQdrGy1Bojpeh0BwvQ6EZfVyGYtTeh6W+zd/71S31beqaxlfCv3fvHrKyspTPIyMjVe6DJJFISicyIiIiojJq6NCh/6q9xrlwvXr1MHr0aJVlX3zxBaZMmQIASEpK+lcBERERkeEo75ehKEpQUNC/aq9xAiYWi5GdnQ1LS0sAwOXLl1WCKOnZAERERGR4DDT/QgluJKRC4wRswIABGD9+PDIzM5GZmYlXr15hxowZ/yoIIiIiovJk3bp1/6q9xglYxYoVsWHDBkRFRSE1NRUeHh56vw8kERERlU3l/TIU48ePR6dOndCjRw8IhULl8hYtWvyr7WqcgAUFBWHGjBnw8PDA4sWLsXXrVjRp0gQ+Pvq7MSYAzNj7QK/vX97EZhZ9Jiup52kn0ncI5UrDynb6DqHcEZkJ3/0iUvEsIevdLyKl2pV0/z2m8dl+ZYxIJEKvXr0AACdOnMBff/2FAQMG4L333vtX29V4XH777TdMmjQJzZs3h7GxMXr37o3vv//+XwVBREREVBZVrVpV+e9u3brBzs5OJfkq6dUfNE7A/Pz84Ofnp5x2DA4ORkpKSonenIiIiAybQCAo9Yeu4y/IxsZG5fmxY8dKtF2NpyBtbW0xd+5cZGRk4NmzZ9i+fTvGjRtXojcnIiIiKstWrFiB1atXK59LpVL8+eefAAC5XA6pVIo+ffpovF2NE7AePXrAx8cH169fVwZRp04djd+YiIiIDJ9R+T4GH6NHj0afPn3UVt7kcjmOHj1aou1qnIB16tQJf/zxBz7++OMSvSERERH9d5T3BGzIkCFvvdrDp59+WqLtanwM2Lhx4yCXq97jbd++fSV6cyIiIqKy7F2X2irppbg0roCtXr0aL1++VJbiFAoFBAIBPvzwwxIFQERERIarvF8HTFs0TsAmTJiATp06wdzcXLnszJkzpRoUERERUXnw6tUrVKxYUeN2xUrANm/eDCcnJ3Tu3Bnvv/9+ofUdO3bU+I2JiIjI8JX3Y8AKun//Pg4fPoysrCzlvSDDwsJKdChWsRKw06dPY9OmTRAKhfjrr78QFxcHLy8vtGrVSuM3JCIiov8OQ5qBHDduHAYPHqxy9Qcjo5Jd679YCVjLli2V9z/q3Lkzxo0bh88++6xEb0hERERUHvn5+WHUqFEqy5o3b16ibRUrASt480mBQABPT0+V9Tdu3ChxAERERGS4jAyoBGZtbY2JEyeievXqymUPHjzAmjVrNN5WsRKwAwcO4Pbt28rnkZGRCAkJAZB/FuSTJ09w8eJFjd+ciIiIDFt5vxl3QampqXBzc1NZJpVKS7StYiVgNWvWRPv27Yu8CqylpWWJ3pyIiIiovJgwYQIcHR2Vz7Ozs5GZmVmibRUrAfv+++/h7u5e5PqWLVuW6M2JiIjIsBnQDCRycnKwdu1aiMViAPlFqGvXrmH79u0ab6tYlcFnz569df3f5biMjAwEBARoHAQRERGRtsjlcqxatQp//fUXVq9ejdDQUBw5cgQDBw5Ex44dcerUqWJt58cff0R6erryMKzExES0bt26RDEVqwJWt25dLF++HA0bNkTTpk0LTTkmJibi/PnzSExMxJgxY0oUCBERERmesnAQfkhICJKTkzF27FiEhYVhx44daNasGXbt2oXQ0FAMHToUnTt3fud2OnfujE8++QQHDx5E9+7dYWpqikmTJpUopmIlYPb29hg7diy2bt2KX375Ba9evYKDgwMAIC0tDV5eXhg+fDj69+9foiCIiIjIMGkj/4qPj0dCQoLadU5OTnB2dlZZ5uHhgaCgINy7dw9hYWH49NNPlWcyent7FzqwvihhYWH48ssv8fPPP2PSpEmoUKECrl27VqI+CBR/X8pVA0lJSYiNjYVAIECVKlVgbW1dojcvTT7TTus7hHJldFcvfYdQ7njaifQdQrnSsLKdvkMod0Rmwne/iFREJ+foO4RypXYl3X+PTf/rSalv0ynsJFasWKF23bhx4zB+/PhCy4ODg7F+/XqIRCL89NNPMDU1BQBcu3YNaWlp6NKlyzvfVyaTITIyEp6enoiKisKZM2fQqlUr1KpVS+M+aHwvSABwcHBQVsCIiIiIiqKNWxENHDgQ/v7+atc5OTkVWhYfH4/9+/dj+fLlWLt2LebMmYNZs2YhNTUVr169Qp8+fYr1vqmpqdi+fTusrKwwbtw4WFtbl+g+kEAJEzAiIiKi4tDGMWDOzs6FphnfJjg4WFnxGjFiBD777DOkpqbiypUryuQrLi4OLi4ub93Od999h1q1asHExAQmJiZo06YNJkyYgI0bN2rcByZgREREZNB8fX0RGBiIkydPQiKRoH///vj0009hYmKCDRs2IC0tDZs2bXrndlq1aoXhw4fjwIEDAPKPg79//36JYtI4AXv27Bl++eUXODs7Y/r06di9ezd8fX1RuXLlEgVAREREhqsMnAQJU1NTTJs2TWVZr169NN6OVCrFqVOnkJiYiCtXrmD+/Pno1KlTiWLS+A4BM2fOhJ+fH6pUqQITExN07doVEyZMKNGbExEREZUXI0aMQFJSEu7fv4/t27ejb9+++Omnn0q0LY0rYO3bt8fgwYNx8OBBAMDz588RFRVVojcnIiIiw6aNg/B1qeCxYUKhEIMGDcKgQYPUrtdEiY4B27hxIxISErB7926sWrWK1/8iIiIitQQo3xnYr7/+ig4dOqhdp1AocPHiRfz6668ab1fjBGzIkCG4dOkS4uPjER4ejqlTp6Jjx44avzERERFRWRcQEICAgAB4e3vjn5dOVSgU77xdY1FKVAGrUqUKnj17BoVCofZ6G0RERERA+Z+CvHz5Mk6cOIH79++jZs2aeP/992FhYaFcf/PmzRJtt1gJ2NGjRzF79mxUrFgRo0aNwty5c/Hee++hYsWKOHLkCK5fv46RI0eWKIDypkf9inCwMoVCAeRIZNh7Kwa9G1XC562rwdTYCN/tDsajlxlq21qYGOGT96riVVoubj1LQWxaLlp52sPH3Rap2RJYWxhjw6VI3XZIBx4HnkN2egoEAgFMTM1Rr113yGUy3L9wDDGhweg+dmqRbeUyGe6ePgBTSxFca9SFvWsV5brYpw8RuP9P9P1O89JvWXbr4ilkpCUDAgHMzMzxXufeeBUdicDTR2BqZgbPOo1Qq2Gzwu0unUJy/CtkpqWgXvM2qOnTBDKpFNP+1xtZGWnK1/UbMRFtu/XVZZe0SiqVoG/X9khLS1Uu+/q7qbCytsar2BikJiejtW97NG7WQm37kZ8NQGjII5iamWHv0bOwtbNTrtu6cR0kEjGGjvxCy73QHalEgi4dfJGWmqpc9v2UabC2tsHLlzFISU6Cbzt/NGvRslDb3JwcbN28EcnJyfhu8pvP7Ucf9sbTJ2HK558OGYavJn6r1X7o2sXTx5GakgyBADAzt0CX9z9E1PMInD52EGamZqjbsAkaNi08Znm5OTi4eyvSUpIx8qvvlcuL05bKBlNTU/Tu3Ru9e/fGkydPsGbNGgBAz5494eXlhWbNCn8fF0exErD79+/j/PnzSE5OxqRJk3D48GHY29sDAKKiorBr164SvXl5Y2ZshI9aVMan6/Kz3e2jmuPKkyS8TM1F3xWB+KpTDYz0q46vd9xT23bZ4IaYc+QxXiRnK5dP6OKFAauuAwCWDKoPDycRniVk6aZDOiAV5yH43GEM+HEpAGDX7PGo2aIdZDIphEJj5BRIDP5JIZfjxJq5aNS5L1y96qqsk8tluH54W6FycHknzsvD5ZP7MWFe/gd80Xcj0LhtJ+xYMQ9jZiyBuYUlVsz4CjXqNoCxiamyXWpiPG6cP4GxM5ZAKhFjwaRhmPjLWrx4+hhDv52Nyp61IABwcNMK1G3ynp56px3Bd+/gp/mL4V2rLiAQYNXSBWjVxhcLfp6BRSvWQSwWY8Qn/bF60w5YWlqqtL17+yaGjRoHL+86MDY2Vkm+4l/F4tihfejc/X0d90i77t4JwvxFy1C7Tl0IBMCShb+ija8f5vw0HSt/3wCxWIxPBvbFpm27YGmpetuazKxMSKVSZGe9+Y4Kf/oEw0eNRcv3WsPISIAdW7egYePGuu6WVuXl5eL4wd2Yv3ITAODb0Z/Ct0NXrJj/E2YuXAULSxGmTRyFuvUbw8TUVKVtdlYWZFIpcrLffO8rFIpitTUU5b0CVpCXlxcmTJiAnJwcLF++HMHBwfjoo4/w/vuaf08U6zIUFhYWsLS0hLu7O1atWqVMvgDg5cuXqFu37ltaF09iYiI6dOgAX19fzJ49+19vTxtMhEao7iSCs7UZjASAuYkQiZl5uPksBQAQHJWGqALJVUFfdaqB848TVJIvAHCxMYd3RSsAgL3IFJm5Uu12QsdkUimSX75AZkoi5HIZJOI8GBkbw8LKBrYurm9te/fMQdg4uhRKvgDg0eW/UKNpW22FrTcyqQRxUZFITUqAXCaDOC8X0RGhMBIKYW6Rnzw4VXTD3asXVNo9uHUFdg75hwMYm5jC3tkVD28HwqOWD7zqNYa5hSXMLCyRkZYCe+eS3TajrPJp0AiNmjSHpUgES0tLpCQn4dqVS3Byzj8rydTUFJXc3HAt4GKhtvt3b0fw3SDk5marJF8AcPjAHvh37qaLLuhUg0aN0LRZc4hEIlhaipCclIQrly/B2SV/vzA1NYWrmzsCLhUeL0dHJ7i6uassq1K1Kjp27gIrKytYWooQ+jgEDRs10UlfdEUqkSAqMgJJCfGQyWTIy81FeNhjGAmFsHidpFZyq4yrF88UalvBwREulVS/68Ie3S9WW0MhEAhK/aEvMpkMJ0+exKhRo7Bt2zZUqlSp2Dfy/qdiVcCqV6+uvOfSPzuuUCggEAjQrdu/+6LavXs3jhw5UugXalmSmSfFoTsv8fuQxjj1IA4/HwmBRJZfgREIgCbVKmD1+fBC7SxNhejX1B3LTj/B7A/qIORlBrZfz790x9oLEVj5aSPsuB6FbYEvkJCRp9M+aZuZpQh12nTGwUWTUaOpL9p/Ml5ZuXnbh0ihUCDo5F7U9e2G85uXw8KmApr3GgwjIyGy01MhycuFUxVPXXVDZyxEVmju3x2rZ01Aw1bt0X/kN4h9EQ4r2wrK11hXcEBcTKRKO3FeLjJSU5TPbezskZLwCsYmJsplsS8iUKmKh9b7oGsmBfr4LPwpPDxrIC83FynJScrl9g6OePUqVqWdTCZD9Ro1EXz3Ng7s2YGfF/6Gxk2bAwBuXruCxk1b4N6dW7rphA6ZFKichj99As8aNZCbm4vkpETlcgcHR8S+fKm2/T8/twW3l5mRAZFIBKHQsG4qLrKyRoduvTDz27F4z68jRk34Ac8jnsK2wptiRAV7B0Q/L+pgbNUxCw8L0aAtlQVxcXHYuXMn9uzZAwsLCwwcOBBLly5VKUhpqlgVsF69euHcuXPYu3cvNm/erPLYsmULPvrooxIHAAC5ubm4ceMGOnXqhIULF0IqLbtVoCWnnkAqU6Bng0qIf50sGQmAUe2qo39TN3zb1btQm4ZV7BCbloM9t2Lw64kwDPfzQNuajgCAbdei8ORVJj5tVRUvU3N12hddea/fMBgJjRF67SxEdsXbWVNfRUMqzkOjzh/Ad/BYRARdwf3zxwAA9y8chU/7ntoMWa96fToaQqExbl06BVt7R+RkZcFSZK1cLxQKkZGWotLGq14jPHkQhNTEeMhlMiTFx8LS2lblNQ9vXUXdpq110gd9CQy4iPfatkPDJs1w59ZNxMe9gkwmQ2xMDGxtVcdDKBRiyIgxWLxyPUaNm4CFP88AAEgkEjwIvlvkMWOG5PKlC/D1a48mzZrj1s0biHuVP14vY2IKVQSL4+qVy2jVuk3pB1oGfDbySwiFxrh4+jjsHZyQlZUJKysb5Xqh0ASpqcnF2ta/aVseGQlK/6FLX3zxBTp37ownT55g/vz5OH36NIYPH65Mvs6fP1+i7Wp0JXx7e3u4ublBIBDgypUruH79OiwsLDBixIgSvfnfzM3NsWnTJpw8eRLh4eFYv379v9qeNk3pUQsjN93G9YhkrB3SBOYmRpArgDXnIzDo9xt4v2ElWJmpFhYrWJogLi0PYqkcGblSnA9JwHs1HADkT03+cjwU2669wO+fN4aztZk+uqVVF7etQp9v5qFy7UY4uGgyJHnvTjRzMtNhbmUDM0srCI2NUbNFe7x4eBsvwx7ApVpNleOfDM3e9UswdsYS1PRpglU/TYCFyAoSiVi5XpyXBwtLK5U2lT1rYcCoSTi0eSWunDqI6IhQeNVrpPKaqPDHqOpVRyd90JfHIQ9Ru64PvGvXxcQfpmH18kU4vG83wkJD0KhJ8yLb9en3ESxFVkhNTcGRA3vQq+8AHUatPyEPH6KuT33UrlMXk3+cgWWLF2Dfnp14HPIITZtrnoBeC7yC91ob3qEBALB2+Xz8tGg16jdpjhmTxkJkZQ2J+M2MRV5eDkQiq7ds4Q1ra9sSty2PBILSf+hSaGgo+vXrh1q1auHOnTtYsWKF8rF06VIsXbq0RNst1hTk/v370a5dO9jb2+PcuXOYPHky6tWrBzs7Oxw+fBhDhw6Fn59fiQIoyNraGrNmzcKPP/74r7elDTWcRXC0NkNKtgSzj4Rgy/BmeK+GA86FJAAAIhKyEJ6QCZlcrtIuLj0PjtZvEoaEjDyYmxjByswYrb0csOz0U6y7+AzeFa3QpZ4LtgS+0Gm/tCkpOhJZacmwtLFD+0/HY8+8iXjx4DY8m7y9EmNVwQHZ6anK5yI7exibmiHs5kU8u5t/0oJMIkZediYOLZmK3hPmaLMbOvPyeQTSU5JgZVsB/UdNwrIpY5CbnYX05DfTQ2lJ8ajduPAZU83bd0Pz9t3w8NZV1PRpCseKb45LyMpIh6W1rV6PndC29LQ02Nq+6WPXnr3RtWdvBAZcRJNmLeDq/vb71Var7glLSxFOHj2EXVs3AcifUgMAgcAIQ0aM0Wr8upaWlgpbOzvlePXs3Qc9e/dBwKULaNaiJdzfMV7/JJfLkZOTAytr63e/uJx5HvEUKUmJsLWrgNETpmDyuKHIzspEclKC8jVJCfFo3KJ4FeZqNbxw5cKpErUl3Vu8eDHq169f5PrOnTuXaLvFSsAePHiAhg0bwt7eHqdOncKpU6dUyvnr1q371wnY38eSJSUloX379v9qW9qSkSuFmXF+0VChAIKj05CQkQdLUyGyxTJYmArx+GUGciT5CVhzjwq48yIVd1+kwlhohAqWJkjJlsC9ggV23IhCnlSmcoXguy/SEJduWNOQppYiyF5XbwRGRqjo4Q0r+/yDxdWdwRj79CHsXavCxrEiHN09kPAiHE5VPJGW8Ao1mrSBVzNftPs4/5IA0Y/v4fqhrQaTfAGApcgKEnH+eBkZGaGqVx3UrN8UdwPPQ5yXC1MzcyTFx6Le66nER0GBqN2opfKPaFZGOgJOHsDgcVNUthsSdA111CRthuT61cto8Z5q9SU9LQ0H9+7ED9Pf7CPXrlxGi/faIDsrCwkJcajm4YnUlGR4edeGqakp1m5+c1b3xrUrAcDgki8AuBpwGe+1UR2vtLRU7Nm1AzNmzVUuu3L5Et5r01a5jxV15nHwvbuoX7+h1uLVp/xq15vPpVftemjQpAWuXjyDvNwcmJlbIC42Bs1a+QIAbl+/gsbN3yvwg0d1zLzr1EdWVqbatobIqJz/8Htb8gUAdeqUbGahWAlYq1at4OiYf8xSrVq1Ch1LUdKrwP7typUrWLhwITp37gw3NzeVeyyVJXHpeThx/xU+blkZaTkSRCZmw8xYiMNfvoczj+KRlCXGor+eKF//Qw9vfLf7Pp7GZ+GHPfcx1t8TYXGZeBCThocx6QCAFWefYoSfB6KTc2BqbITTD+P11T2tsLZ3Qs3m7XD39AGYW9nArpI7XDxqIi87Ey8e3EZa/EvERz6BczUvAMCNw9vRoFMfVPNphs4jvsOtYzvhUt0bRkIhvJoZ7hfU3+wcndG4TUdcOLobImtbOLtVQVWv2vhozHc4snUNrKzt0GPwCJhZWEKcl4tdaxbgu0WboJDLEBn2EHHRz/HJV1Mh+sfxX2H3b6HfiIl66pVu3L55DV9/l189T01JxsP7wXgeGYEpM+cpj2fKzc3Bonk/YcO2fYh9GY0fJnyBJs1bwqdBI/T76BM9Rq97N65fw/evr+WVkpyM+8H38CwiHD/9/Avs7PJP+sjNycHc2TOxfc9+2NraITExAUG3buLpkzBEPotANY/qyu1duXQRvT/sp5e+aJujswva+HfBkb3bYW1jC7cq1eBVqy7GfjMVW9atgLWtHT7+3xewsLREXm4OVi/6GUvW74C1jS1SkhLx4G4Qnj97gujnz+Be1QMCgUBtW/pvESiKcSGlb775BqNGjULNmjWxYcMG5OXlwd3dHUlJSTh9+jTq1KmDqVOLvpimLvhMO63X9y9vRnf10ncI5Y6nnejdLyKlhpXt9B1CuSMyM6yzB3UhOjlH3yGUK7Ur6f57bHlA6Z/h+WWb8n9Gd7EqYIsWLVL++3//+x+OHTuGv/76CwqFAv369cMHH3ygtQCJiIio/CrnM5Bao/G9IPv06YNFixahR48e2oiHiIiIyOBpdBkKAPD394eVlerpsgEBAaUWEBERERkOIwhK/WEINK6AXb58GVu3boX161ON5XI5EhIS8ODBg1IPjoiIiMgQaZyA9ezZE23atIG5ubly2enTPACeiIiICuMxYOppnIDZ2trC01P1Hnw+Pj7YsmULvL290bx50VebJiIiov8WXd86qLzQ+BiwY8eOoV27dmjSpAm++uorZGZmYuLEiejduzccHR1x4MABbcRJREREZDA0roBVqFABCxcuhJubG+Lj43HixAkkJSXBxsYGNjY2WLRoES9LQURERADK/5XwtUXjBKxBgwZo2rRpfmNjY9y5cwcmJibK9S9fviy96IiIiKhcY/6lnsYJmFgshq+vL4yNjdGjRw/cu3cP7u7uOH36NHJycmD3+pYfRERERKSexglYo0aN0LZt/o1ZPT09IZVKIRaLkZSUhC1btmD69OnaiJOIiIjKIU5BqqfxQfgTJ06EhYWF8kxIY2NjCAQCVK5cGVOmTIGHR/m/PxMRERGRNmlcARs0aBAuXLiAmjVrAsi/EOvJkycxY8aMUg+OiIiIyjcWwNTTOAE7e/YsZDKZyoVYo6OjmYARERFRIRpPtf1HaJyATZ06FfXq1VNZFhoaWmoBERERERk6jRNTkUiE0aNHY/r06ZBIJNi+fTssLS21ERsRERGVcwKBoNQfhkDjBGzmzJnw9fVF5cqVYWJigs6dO2PChAnaiI2IiIjIIGmcgLVv3x6DBw+Gk5MTAODFixd48eJFqQdGRERE5Z9ACw9DoPExYACwceNGJCQkYPfu3Vi1ahX69+9f2nERERGRAeB1wNTTOAEbMmQILl26hPj4eDx9+hRTp05Fx44dtREbERERkUHSOAE7ceIEunXrBl9fXwBAWloajh49ip49e5Z6cERERFS+sf6lXrETsAMHDiAmJgZ37txBeHi4crlEImECRkRERGpxBlK9Yh+E37t3b8hkskLLzczMMH/+/FINioiIiMiQFbsCZmRkhK+++gqxsbGoVKmSyrrMzMxSD4yIiIjKP0O5bldpK1YCdvDgwbeuv3nzJn7++efSiIeIiIjI4BUrATt58iQsLCxU7v9YEG9FREREROrwXpDqFSsB+/HHH1G5cuUi18fFxZVaQERERGQ4OAWpXrESUzc3t7euZwWMiIiIqPiKVQHr3Lkz1q5di+rVq8Pf3x+xsbHKdQqFAgKBACEhIVoLkoiIiMon1r/UK1YCdvz4cZiamgIAJkyYgM6dO8PMzEy5/uLFi9qJjoiIiMo1TkGqV6wELCIiAgDg5eWF999/v9B6Pz+/0o2KiIiIyIAVKwH78ssvsXLlSgiFQjx+/BgAYGJiAk9PT60Gp4mM9Dx9h1CunHiQoO8Qyp0b12/oO4RyxadR0SfukHrJyTn6DqHcqevpoO8QypWtnzTQ+XvyLEj1ijUuPXv2hJeXFwDA3NwcU6ZMgbW1tVYDIyIiIjJUxUrA/j7+CwCqVauG1q1bw9nZWbns7ylKIiIiooIEAkGpPwxBsaYgb926hXXr1imf37t3T/lcoVAgMDAQGzdu1E6EREREVG4ZRrpU+oqVgMXExCA8PFyZdbq5uSmrXnK5nBdiJSIiItJAsRKw+fPno379+kWuf/jwYakFRERERIbDQGYMS12xjgF7W/IFAHXr1i2VYIiIiIj+C4pVASMiIiIqCSMeBaYWEzAiIiLSGk5BqscEjIiIiAyaXC7HmjVr4OnpiYiICPj7+8PDwwP79u1DYGAgli9frvOYeIFaIiIi0hqBFv7TVEhICJKTk9GlSxd06NABe/bsgampKVq2bImUlBQt9PrdWAEjIiIirdHGFGR8fDwSEtTfUs/JyUnlYvEA4OHhgaCgINy7dw9hYWEYPHgwgPzbKuoLEzAiIiIqV3bt2oUVK1aoXTdu3DiMHz9eZZmlpSVmzpyJ9evXQyQSoXfv3roI862YgBEREZHWaOMsyIEDB8Lf31/tOicnp0LL4uPjsX//fixfvhxr167FnDlzMGvWrFKPSxNMwIiIiKhccXZ2LjTN+DbBwcHK+1qPGDECn332mbZCKzYehE9ERERaIxCU/kNTvr6+kMlkOHnyJI4ePYqxY8dCJpMhMDAQsbGxytsr6hIrYERERKQ1ZeE6YKamppg2bVqh5f3790f//v31EBErYEREREQ6xwoYERERaU1Jrtv1X8AKGBEREZGOsQJGREREWmPEAphaTMCIiIhIazgFqR6nIImIiIh0jBUwIiIi0pqycBmKsogJGBEREWkNpyDV4xQkERERkY6xAkZERERaw7Mg1WMFjIiIiEjHWAHTUO8mbnC0zr+jenaeDDsCXyjX9WrsCt9aTpi0/V6hdp4uVvioVWXkiuW4Hp6EgNBEAMCEbjXxedtqeJmSg4nb7uLxywzddESH2tWwh52FCRQAcqVy/BWSgK61nfBJMzdki2X47VIk7qvpdwM3a/hUsoGREfA8OQcXnyarrPf1tEfjyrZYeuGZjnqiGx+2rAInG3MoFEC2WIotF9/cJPaDFpXRvm5FfPnHzULtBrauhrFdvGFmYoSRa64h+HmKyvq3tS3vOtR0RAXL1/uYRIZjD+PRpZYT+jd2hYlQgDknn+BJQlahdkYC4P16LqjvZoPZJ58olzuKTDGsVWUERaXhzOvPqqHp5uMCB5EpFAByxDLsD3qJ9xtUxGfvVYGp0Ag/7HuIkFj130cL+9dDhzrOAIAPV11HREIWdo1ujpouVsrXbLryHMvOhOuiKzrxXjU72FoYQ6EA8qRynH+aDH8ve3zYoCJyJXKsvxaFkLjC+xgAmAmN0KW2I5KzJAiJz0RSlgRGAsDfywG1Xazw2+XnOu6NbvEYMPVYAdOAmYkRPmtbFRsuPMOGC88wsFUViMyEAACRmRAj/T2LbLtgUAMsPRGGRcdDMaZjDZgKjdC4WgWExmag+bQzCHyahF8HNdBVV3TGVChAj7ouOHg/Dofux6FLLUdUtbeAsZEAn2+9h+MP4/F9R89CH08zYyN83rwytt6KweYbMejt4wInK1PlegsTI/RtUFG3ndEBcxMhhvnXwJpTYfj9dBg+busBkXn+7ySRuTG+6FpLbTsnGzNEJ2XBb/pfOHIrGhPfr62y/m1tyztToRF616+IvXdjse9uLLrXdYGztSniMvIwfPs9XHqajE+auatta2VmDKlcATsLE5XlAgHgZGUGoYGevmVmbISBzdyx9VoUtl2LwgeNXVHJ1hyxabnov/oGzoTEY3jbamrbutqZ48rTJHRcGAD/BZcRkZCF6k4irLv4DG3mXcR7cy9ixblwBDxJ0m2ntMhEKEBHb0ecCEnEyceJaFfDAe525hAaGeHLfY9wJiwR49pWVZtmmAgF+LpdNVx/noqAZylIypIAACxNhJDJFbAxN/w6iEBQ+g9DUCYSMKlUivnz52PQoEEYPnw48vLy9B2SWqZCI3i5WMPF1hxGAsDC1AgSqQIA8FnbajhwK1ptu0ZV7SCVy5GVJwMAPE/IQreGlRCfnovjd2Mhlsmx7OQT1KxorbO+6Iqx0AiVK5jDwdIERoL8L36pTIGjD+Mhkytw8H4cjI0EsLVQ/RKqV8kaORKZ8nlYfBZ8Pe2Vz3vUdcYFA/qC/5uJsRFqutqgop0FjAQCWJgaQyKVAwCG+dfA3kD1v5QT0vNw5XECAOB2eBIi41V/ib+tbXlnIhSgqr0FHEWmyn0sJUuCuzHpAICQVxl4mZartm16rhQxqYXXJWSKkZhZNr+HSoOJUIDqTpZwss4fM3MTIyRm5uFWZCoA4H50OqJTctS2HdyiMmpVsoaDlSlSsvOTiRdJ2TgTkoAssQw5EhlqVbTGnRepOuqN9hkbCeBma44KFsYQCABTYwFkcgVOhyZCpgBOhCTCWCCAtZpkamDDSgiKSkNchlhleaZYhlcZhruP0buViQTs1atXGDduHHbs2AGpVIrnz8vmH4qMXCn23IjCljEt8GWXmpi25wHEMjm8KlohNjUXaa+/jP6pXmVbJGW++fAlZOShhosVopPffMGJzIS4Z0BfWH/LFstwJjQRs3p446PGrlgV8BwxBf4YGgmAtBwpUnOkKu3MjI1UqhIp2RI4W5sBAKpUMEdilgQZeaptDEFGjgQ7AiKx+xtffNOrDr7fehtiqRzerjaITc5GSpb4re0FAqCVtxMWHn6oXFbctuVVlliGk4/iMb93bXzSzB3LLkRAIs//YSQAUN/NBltuqP9xBAAKDZcbgsw8GQ7djcXqTxpihK8H5h0Lg0T2ZswaV7XD2ovqp/ZjUnPgYGWKzcOboGs9FwCAVP5mtKzMhMjKk0FuQAOYI5HjUngyvu/giQ98XLDpRgxi098kT0YCID1PhvRc1e8kc2MjtPdygJFAgJGtKqOTt6OuQy8TBFp4GIIykYC5u7tDJBIhJycHTZs2Rc2aNfUdUpHmH34MqUyOPk3dEPc6kejdxA0Hb8UU2cbawkQlOZPI5HCwNlV5TdcGlbDy9FPtBK1nm65HQyZXoJ2XA5KzVJPUZlXscCD4VaE2j+MyUdHGDDUcLQEAlWzNkfH6y82vhoNBVr/+NmdvMKQyOfq1rIK419WZvi2rYO+1F29tZyQQ4Jv36+Azv+qY9VFD5fLitC3v1l99AalcgY7eTsopHiMB8Elzd/Ss64IxbavqOcKyZ/mZcEjlCnSv74KE15UYIwEwwq8aPmziim+6eKltt+N6NCbtfoBvdz/A991qwtxE9c/IezUccDXc8D6fu+7EQiZXoLVHBaT+48d2QzcbnAiJL9TGy0mExCwxzj1JwtZbMehdzxkN3QxvpoNKpkwkYACQmZmJ5cuXY9euXXj8+LG+wynSzH518cmq67galoitY1viw2buOHbn5VvbpGWLYVbgS8rCVIiMAhUfW0sTVBCZ4NLrKSRDM7pNFUw9Fop7MemY3cMbZsb5Y2FsJEC9StY4GVK438nZEsw79RT9G1VCbx8X1KlohXsv09HW0x4B4cmFXm9I5n7cCP0XXcLlkHjs/sYXA9+risM3o97ZTq5QYOHhR+gy5yz6t6oKawsT9G5WuVhty7txfh747uAj3IlOw6+9a8PM2AhyBbDlRjS+2H0fHWs5QWQq1HeYZcr33WpizJa7uPEsBas/bQjz12O29mIkPl13Cz3qV4SVWdFjdvlJEm4/T0F1R5HK8lae9rj61PASsM+aueGXs+F4+CoT33esDlNhfh1GaCRALWcRzj0p/L1kbSZEcrYEErkC2RI5bkelwafSfy8BMxIISv1hCMrM0X9WVlb4/vvvUatWLezevRvTp0/Xd0iF1KxkDWcbcyRnifHjnvvY91VrLPqkIaKTsgEAlmbGMDMxglgmx5Rd95XtQmLS0aOhq/J5RVsLXHz9a0kgAD5rUw0rTxlm9atqBQvYW5oiPVeKVZefY0Gf2mjsboPAyFS8X88Fe+7GFtn2VlQabkWloaq9BXw97XH/ZQZ+7FwD1V9XxcyNjWBqbASpXI4Vl8rmtLWmarnZwMXWAkkZefh2y20cm+yP34a3wIvE/GO6RGbGMH+9j03687babYS9TEfYy3TIZHJ80KIy6lWpUOy25VE1ews4iEyQlivF0vMRWN6vHppWscOViPw/iM9TcvA8OQcyQ5oT+5c8nURwtDJDSrYEc4+GYtOwJmhVwx7nH+ef8RmRmI2IhCyVqUV1olNylMeBAflTQxYmQmTmyYpuVA6525rDzsIYGXkybLwejeldaqC+qzVuRaWji7cjDj8sXP0CgJQcCewKHBeWkiOFmbDM1D10xjDSpdJXZhKwv3l4eCAzM1PfYaiVni1RVrIUCuDu8xTM2PcA96PSAAD9mrujZQ0HZfLVyssBtyNSEBSZCmuL/D9+uRI5KjtY4MyDOADA/9pVx87AF8iTymFnaYLKDpbK7RmCTLEUJq9/KSoAhMZnIiFTjI7ejrj1IhXpuVKYGRuhhqMlHr7KRH1Xazx6lan84jc2EuCTpm5YfD7/eJSfCySqHWo6wMfVxmCSLwBIy5Yop3QUCuD2s2RM3n4H9yLzLykxsHU1tPZ2UiZQbWo548bTREhlCliYCZGVK4XIzBj3X6QiWyzDkBVXldv+Z1tDkZkng+nrP2oKACFxmUjKEsPCxAg5EjnMTYzwNCELua9PZmjoboOHLzNUjhNTx5D/aGS8/twB+WN2PyYdCRliWJoKkS2WwcJEiMevMpAryR+zZtUq4G5UKgDAw1GEsLhMmBsbIStPhtgCx3TWr2yLe9GG8/31t2yJDCYF9rHwxGwkZUngW70C7r5Mf70PCuBhb4nQhCzUcbFCWEIWwuKzYCwUwNpMiIw8GZytTHEmzDAva0KaKxMJ2JUrV/DHH3+gW7duEAgEGDBggL5DUutVWi6OBL3EUD8PpGSJERGf9dZkaUbfuvhy8x2ExWZg8s77+P792kjOFGPRsVBki2UY09ETE7p5Y0K3/GPeTI2N0Oanc7rqjk4kZUlw6WkyetVzQXqeFNGpuXCzM8cXbasqkyxToRG+PxwCABj5XhUsOBuBuIw81K1kjWr2Flh79QUSMg3zAPJ/ik3JwYEbURjR0QspmXkIj81QJl/qzBncEKN/v4YKIjOsHtkCx4JikJCei592F74WnaFKzBLj/JMk9G1QEem5UkSl5MBUaISNnzTE5fBkpGRLsPbKmyT9i7Ye+PmvMEQm50BkKkTTqnZwtTWHl5NIea2wKhUs4OFgCbkCuPE8FSk56k+wKa/iM/Lw18M4DGrhjrRsCZ4nZcPM2Aj7v2iBcyEJSMoSY2mBY1K/7eqFyfseIiNXiqUf1UdIbAbuRqVi81XVYwvbejngQNDbD8koj5KzJbgWmYou3o7IFEsRm56HijZmGNbCXfk9ZiI0wuzXPxA/beqKlQEvEJ2Wi5UBL9C3fkVEpeYiIikbEUn5J19ZmhihfiUbOFuZopq9BSKT1Z91ahAM+dfMvyBQKBQGUZev9tVRfYdQrtSr46LvEMqdG9cN64Kv2ubTqLK+Qyh3kg35j7CW1PV00HcI5crWT3R/vcnr4aVfFW3haVvq29S1/95kNBEREZGelYkpSCIiIjJMBnLSYqljBYyIiIhIx1gBIyIiIq1hAUw9JmBERESkPczA1OIUJBEREZGOsQJGREREWiNgCUwtJmBERESkNTwLUj1OQRIRERHpGCtgREREpDUsgKnHChgRERGRjrECRkRERNrDEphaTMCIiIhIa3gWpHqcgiQiIiLSMVbAiIiISGt4GQr1WAEjIiIi0jFWwIiIiEhrWABTjwkYERERaQ8zMLU4BUlERESkY6yAERERkdbwMhTqMQEjIiIireFZkOpxCpKIiIhIx1gBIyIiIq1hAUw9VsCIiIiIdIwVMCIiItIelsDUYgJGREREWsOzINXjFCQRERGRjrECRkRERFrDy1CoxwoYERERkY6xAkZERERawwKYekzAiIiISHuYgallMAlYYmyivkMoV86+eKXvEMqf3Ax9R1CuBKZl6juEckeRkaTvEMqduFhPfYdQvnzSQN8R0GsGk4ARERFR2cPLUKjHBIyIiIi0hmdBqscEjIiIiAyaXC7HmjVr4OnpiYiICPj7+8PW1hbbt2+Hq6srbGxs0L17d53GxASMiIiItKYsFMBCQkKQnJyMsWPHIiwsDLt370ZMTAwmT56MKlWqYMyYMWjatCmcnZ11FhMTMCIiIipX4uPjkZCQoHadk5NToUTKw8MDQUFBuHfvHsLCwjB48GD069cPVapUAQBUq1YNV65cwQcffKD12P/GBIyIiIi0RwslsF27dmHFihVq140bNw7jx49XWWZpaYmZM2di/fr1EIlEaN26NUQikXK9qakp4uPjSz/Qt2ACRkRERFqjjbMgBw4cCH9/f7XrnJycCi2Lj4/H/v37sXz5cqxduxa//fYbxGKxcn1WVhbc3d1LPc63YQJGRERE5Yqzs7NGx2sFBwfD1NQUADBixAh89tlnaNy4MaKjo+Hu7o7IyEgMHz5cW+GqxQSMiIiItKYsXIbC19cXgYGBOHnyJCQSCcaOHQt3d3ds374dVapUQe/evVGxYkWdxiRQKBQKnb6jllgN2KTvEMoVmUSq7xDKH14JXyMCS1t9h1Du8Er4mqtQnVfC18TLNX11/p5P43NKfZs1nC1KfZu6xgoYERERaU0ZKICVSUzAiIiISHuYgallpO8AiIiIiP5rWAEjIiIireHNuNVjAkZERERaUxbOgiyLOAVJREREpGOsgBEREZHWsACmHitgRERERDrGChgRERFpD0tgajEBIyIiIq3hWZDqcQqSiIiISMdYASMiIiKt4WUo1GMCRkRERFrD/Es9TkESERER6RgrYERERKQ1nIJUjxUwIiIiIh1jBYyIiIi0iCUwdZiAaWhAm+pwtjWHAkB2rhQbz4Yp1/Vv7YEODdwwelWA2raWZsb4onsdRCdlIeDRK0QlZuHagl6oV9Ve+Zolh+5j2rbb2u6GTn3k6wlnOwsoFEBWrgR/nA5VrhvQpjo6NnLHyN8uFWp3Y0lf+FR7MzaLDwTjx8034N/ADc28nJCckQdbkSkW7r+nk37oykfta8HZzhIKhQJZeVL8ceI+AGDHjz3Rp40XAKDxqM0IeZGk0s67sj2GdqmHHLEUl+9H49ydFwCA1nXd0LlpNZgYG2Hn+ccIjkjQbYd0YGDb6sp9LDtPqrKP9W9THR0bumHUisuF2l1f3Ac+BT5/iw8GY+qWW/ikfQ181csHZiZG+HzxBdyJSCrUtrz7yL82nCuI8vezXAn+OB4MANgxrTf6tK0JAGg84g+EPFezn3Wrn7+fBUfhXNBzGAuN8GznGDjaWipf99Vvp7H2yF2d9UfbPmheGU7WZvnf/XlSbAuIVK7r09QdfnVdMOHPwt/dn7T1wHfv10ZGrhTfbg3C1bBEAECNitYY3LoaciUyXHuSiEsh8Trqie5xClI9JmAaMDcRYmSXWug47TgA4MLcHthzJQKZuVJYmRvjq/fr4cGLlCLb7vzWHxPWByL8VQYAoLa7HX7Zew9ng19CLldgbPc6CHwcp7P+6IK5qRCjutVB+8lHAACXf+2F3ZcjkJkrgZW5Cb7uUx8PnicXale7sh3m7Q7CmbsxkCsU+KJnPVwNeQUA+PmzZmj1zUEAwM7vO8Lb3Q6h0am66pJWmZsKMapnA7T/ZhcA4PLSQdh94TEcbCzw161IfL3qHORyBRLScgq1/X1CJ/T8cT8ycyQ4Pu9DBDyIgbGRAHOHt0X7b3ZBaCTA4dkfoNvkfbrullaZmwoxqmtt+P94DABw6Zf3sftyuPJzOaF3Pdx/Xvhzmb+P3cXZe6/3sR51cTUkDs625niRkIlmEw7gp4+b4Pt+DfHRr2d13S2tMjc1xqj3G6H9hO0AgMvLP8Hu8yFwsLXEXzcj8PWKM5ArFEhIzS7U9vdvuqHn5N35+9n8AQi4H42WdVwxePZh3HnyCgoF8Muo9jh5PULX3dIacxMjDPWrjl4LLgIAjv3QDgdvRiMrTwqRmTHGdK6JRzFphdq5O1jCVChA4x9OYFh7T6wZ3hwNvj8OhQJY/FljDFp2BVl5Uuz6ug2uPUmEWCrXdddIj8rMMWDh4eEYPXo0bty4oe9QimRqbIRa7naoVMESRgIBLE2NlR+YkV1qY+fl8CLb/jS4CY7dfKFMvgDgaWw6Dl5/jowcCbLypGjgYY+rjw3rV5CpsRFqVbaDq70ljIwEsDAzhlgqAwCM7l4bOy89VdvuaWw6DgRG5o9NrhQNPRxwNSQ/OXVzEKH+68qYk6050rPEuumMDpgaC1Grij1cHUQFxkuOcX0aoWENZ7hUEKlNvprXqgipTIHMHAkAIPxlKvq28UKPltXxNCYVcrkCEqkcEpkcreq46rpbWqV+H8v/XI7qVgc7Lqn/XD6NTcfBa2/2sfzPXxzi03Jx6UF+sn8zLAERr9J11hddMTU2Qq2qDnB1sMofM/O/97PGaOjlkr+fqUm+mteqBKlMrrqfta2JwIcxuBwchcwcCbJyJXCpYIkX8YYzbiZCI3hVskZFO3MYCQALEyEksvx9bGi76th3/UWRbf+4EAGpXIG1Z5/CWGgEByszNPaoAJksv8INAJHxWejR2E0nfdEHgRYehqDMJGCenp4QifLL4WVVeo4EWy88weGpnfBDvwaYsOEaxFI5arvbISY5CymZ6hMBK3NjDO1YE0IjAVaPaY3RXWsBgPIDDAA2FibIyJFAXob7XxLp2RJsORuGIzO6YXL/Rvjq96v5Y1bZDtFJ2UjOyFPbTlLgl6CN5euxkeePzfy9d3FgahdM6tsAK48+RGxK4T8U5VV6thhbTj/CkZ8/xORBLfDVynMQS2SIfJWOihUscXHJRxjg512oXaMaLip/MONSsuFd2b7I5YYkPVuCLeee4PD0LpjcryG+XvdmH4tJykJKZvH2scwC+xiQP23Suk5FzNtzV9td0Ln0bDG2nHqAI/P6Y/LHrfDVb2de72dpqFhBhIvLP8aAdrUKtWvkVVF1f0rOgncVB5WxrF3VAQ8jE3XSD13JyJViV+Bz7PiyDSb0qI3JO+5CLJWjZiVrvEzJQWq2+u/+6KQ3YyU0EiApMw+JGXmoX6UCEgt89yWk58KrorXW+0FlS5lJwADAxMRE3yG80/RttyGRKTCwbXXEJud/uAa08cCuy0WX21t4OyMqIRMbzoThhz9vYtIH9dGlkbvKazo1dMOZuzFajV1fpm65CYlMjkF+nsoxG+hbAzsvqq9+/VPnRpVx+k608vnKow9x/3kyxveqhxcJmVqJWZ+mbgyARCrDIP/aiE3K79/KQ3fw0ZyjGPzzUSwe2x6WZqpHD9iITJGSmat8LpHK4GRnCRuRmZrlFrrpiA5N23oLUqkcH/kW2MfaemJnEdWvf+rU0B2n7rz5/BkZCTC5X0P8r5M35g9prpWY9W3qhkv5n0v/Om/2s4NB+GjWIQyefQiLx3WEpbnqd3Kh/UyWv58V1L2FJ45fK964lydzDzyEVCZH3+aVEZeWPwYfNK+M/TeiitW+o09F/H4m/zvP2sIEadkS5TqJTA5Ha7PSD7qMEAhK/2EIylQCVh4sGtYCvWb/hYv3Y3F4WmcM9vPEvsDIt7ZxsDZHTHI28iQypGWLcexWFDo0UJ0G8m/gitMGmoAtHvEees48gfPBL3FsZjd83N4L+64U//iQDg3dcOrOmy+5WZ80xaQNgVh59CEOT+8KV3vLt7QufxaPaY+eP+7H+bsvcGzuh7AokGyduPEMAfejUauKg0qblIw8mJu+eZ2lmQnSs/KQkpGrZrnhTNn+bfHwVug56y9cuP8SR6d3xcftamDflWfFbt+xoRtO332T5MvlCszdcxe+PxzBIL8asLEs+z8ONbX4iw7o+cPu/P3sl/6q+9n1CAQER6HWP6ql6vcn1Qpj45oVcfNxrHaD14M5Axtg4LIABDxOwM6v2qB/yyo4cqt439kmQgFa1HDE1sv5+2RqlhhmJm/+/FqYGiMjR1JU83JPoIX/DAEPwtdAncp2cKlgicSMPHy1PhBnZnfH2i/a4nl8/q9HK3NjmJkKIZHKMH5toLJdbHI2XApUHWKTs1W+7AQCQGRmgnQD/ADWqVIBFStYIjE9F1/+fgXn572P9V/64Xl8/rFwInMTmJsI848/WV347NH8sTFG+utfizaWJujUyB3Tt97Cr3vvon41e3zYujp+O/JAp/3SljpVHVDRXoTEtBx8ueIszi8aiE5NquLw1TcVhYjYNCSmqU673o9IwIdtvZTP3RytcOpWJADAv3EVleV3nxrWcYZ1KtuhYgWL/H1s7VWc+7kn1o33LbSPSaRyjFtzpVB7gSD/DOX07MKfv8fRqXgclQqpzLAODahTzfHNfrb8FM4v+Ridmnrg8JUnytdExKYi8R/HG96PSMCHBabA3Rytcermmx9TFazNkZRe+BjF8s7b1QYutuZIzhTjhx13cOhbPywb0hRRSVkA8r+jzIzz97Hvtt0p1P5//jWw4uSbM3MfRafh/SZvjvmqVMEc5x8Y1glY9G5MwDSQmiWGuYkQAKBQALeeJGLSH9eVp6h/7FcDbetWVCZffnUrIjA0HoGhcTAxNoKjtRkSM/JQzcUaa/8KUW63RU1n3AgzrD+Kf0vLylMZs5thCZi4LhBB4fnHiHzS3gu+9Sopky8/n0oIDIlTHkTd0tsF1wuMTa5YBkGB+vO1x3GIef0laAgKjVfoK8QmZaF+dScERyTAwswY6dlivHidXPg1qIzARy9x/XEsbERmsDAzRk6eFNUq2uLY9QgoFAp82bcxBALAxFgIY6ERboQaVnUiLUsMs4Jj9iQB32wIRFB4/ufyk/Y10LZuJWXy5VevEgIfv9nHWtR0VtnHjIwEsDQVIjNXCpG5Me49S0L264OlDUVa5ptKlkIB3Hz8ErFJmahf3RnBEfEF9rP8A+n9GlZB4MMYXA95WXg/KzDd2KWZB07dLH7lsbxIz5YoK1YKBXDnWQqm7ryH4BepAIABraqgVU0nZfLV2tsJN8OTIJbKMbBVVZx98ArJWWJYmAjhU9UON54mwcbCBBYmQuRIZKjiIMKp+4b1uVRhGAWrUldmErCYmBhERkYiKCgIDRo0gLm5ub5DKuRlcjb2XnmGsd1qIykzD09epr31+kC/Dm2Bocsu4lFUKoYtu4QpAxri4YtUBIUnKv84AECXxu74s8D1xAxJTFI29gSE44uedZGUkYewmDRl8qXOwv+1wueLz+PR68t5dG1SGRvPvPnlKJbKMWv7bXzXryEiX6XDzESIA++YAi5PYhIzsediKL7o3QhJ6TkIi07By6RMnFs4EHfD43H1YQyW7rulfP3CUe3w+fzjePQ8CeOWn8HsIW2QmJ6DmZuvICs3v6Iza0sgZg1pA6lMjrHLzsDAzvNATHI29l6JwNgedZCckf+5LPj5+qcFw1pgyJILeBSVCgDo1qSyyvX83qvlgo1f++HQtUjEp+Viyuab2u6CzsUkZmLPhcf4ok9jJGXk5u9niRk4t2Qw7j6Nx9UH0Vi6502/F47xx+fzjuJRZCLGLT2F2cN88/ezTZeV+xkAtGtYFRNWntFHl7QqNjUHh25G43/+nkjJFCM8LkOZfKkza0B9jN1wE3XcbDD/40bKkxTMTIzwwcL8ax5+u+0OJn9QF8mZYsw//AjZeTJddEUvmH+pJ1CU5dMONWA1YJO+QyhXZBLD+kWvE7kZ734NKQksbfUdQrmjyDC8C75qW4XqnvoOoVx5uaavzt8zLr30D69xsSn/x2WWmQoYERERGR5DOWuxtPEsSCIiIiIdYwWMiIiItMZQLhtR2piAERERkfYw/1KLU5BEREREOsYKGBEREWkNC2DqsQJGREREpGOsgBEREZHW8DIU6jEBIyIiIq3hWZDqcQqSiIiISMdYASMiIiKt4RSkeqyAEREREekYEzAiIiIiHeMUJBEREWkNpyDVYwWMiIiISMdYASMiIiKt4WUo1GMCRkRERFrDKUj1OAVJREREpGOsgBEREZHWsACmHitgRERERDrGChgRERFpD0tgajEBIyIiIq3hWZDqcQqSiIiISMdYASMiIiKt4WUo1GMCRkRERFrD/Es9TkESERER6RgrYERERKQ9LIGpxQoYERERkY6xAkZERERaUxYuQxEVFYV+/frBxsYGACCXyzF58mTExsbC2NgYEokEn332mU5jYgJGREREWlMWzoKMi4vDxYsXYW5ujvj4eKxfvx5z587FuXPnAAATJ05EkyZNULduXZ3FxASMiIiIypX4+HgkJCSoXefk5ARnZ2eVZU2bNlX++/jx4+jbty92796NqKgoVK5cGc7Ozrh58yYTsJLI3D1E3yEQERHRP5hrIdNYt2sXVqxYoXbduHHjMH78+CLbhoaGYsiQIVi2bBl+//13tGjRAufOncOIESNKP9C3MJgEjIiIiP4bBg4cCH9/f7XrnJycimz37NkzeHh4AAD8/Pzg5+eHyMhIJCQkoEOHDlqJtShMwIiIiKhccXZ2LjTNWBzHjh1D3759lc8VCgXmzp2LGTNmwN7evjRDfCcmYERERPSfEBsbC1dXVwDArVu3cPv2bQwdOhStWrXSeSwChUKh0Pm7EhEREf2H8UKsRERERDrGBIyIiIhIx5iAEREREekYEzAiIiIiHWMCRkRERKRjTMCIiIiIdIwJGBEREZGOMQHTEqlUivnz52PQoEEYPnw48vLy9B1SmZeYmIgOHTrA19cXs2fP1nc4ZV5UVBRatGiBTp06oVOnTjq/jUZ5k5mZiSVLluD48eP45ZdfEBsbq++Qyqzw8HCMHj0aN27cAADExMTghx9+wMGDB/UbWBn2zzG7desWvvzyS3Tp0gWnT5/Wc3RUFvFK+Fry6tUrjBs3DiKRCEOGDMHz589Rs2ZNfYdVpu3evRtHjhyBpaWlvkMpF+Li4nDx4kWYm5sjPj4eW7du1XdIZdqmTZvg7OyM7t27w9vbG7NmzcLq1av1HVaZ5OnpCZFIhL+v0+3m5gZnZ2fI5XI9R1Z2/XPMrK2tsXz5cty6dQubN29Gp06d9BwhlTWsgGmJu7s7RCIRcnJy0LRpUyZf75Cbm4sbN26gU6dOWLhwIaRSqb5DKvOaNm0Kc3NzAMDx48fRvXt3PUdUtkkkEjx48AAA4OLioqxUkHomJiZvfU6FFRwjb29vAEB8fDxGjBihr5CoDGMCpkWZmZlYvnw5du3ahcePH+s7nDLN3NwcmzZtwsmTJxEeHo7169frO6RyJTQ0FLVq1dJ3GGXaiBEjYG1tjdWrV2PDhg0QiUT6DokM3PXr17Fq1Srs3r1b36FQGcQETIusrKzw/fffY9KkSfwAFpO1tTVmzZqFoKAgfYdSbjx79gweHh76DqPMs7KywpQpUzBmzBhER0ejR48e+g6JDFyLFi1w8OBB3Lp1C8nJyfoOh8oYJmA64OHhAU9PT32HUeb9fexEUlIS2rdvr+doyo9jx46hZ8+e+g6j3Lh9+zbCw8Mxbtw4fYdC/wHGxsZo2LAhbG1t9R0KlTE8CF9Lrly5gj/++APdunWDQCDAgAED9B1SmXblyhUsXLgQnTt3hpubGwYNGqTvkMqN2NhYuLq66juMMi8uLg53797FkydPsHXrVp7s8RYxMTGIjIxEUFAQGjRogMzMTISGhiIlJQWdOnWCtbW1vkMsc/45Zt9++y2qVq0KT09PDB48GEKhUN8hUhkjUPxddiAiIiIineAUJBEREZGOMQEjIiIi0jEmYEREREQ6xgSMiIiISMeYgBERERHpGBMwIiIiIh1jAkZERESkY0zAiHQgODgYXbt2xf/+9z+sWrUKy5YtQ8eOHRESEoK8vDx06NABUqkUL168wKhRo3DgwAGtx/TNN9/gr7/+wk8//YS9e/cqlx86dAhLlizBzz//jCNHjmi0zaSkJFy8eBEA8Nlnn+Hnn3/GkiVLsGTJEnTt2hV9+/aFTCYrsv3IkSMRHBz8zvcpOGbqPH/+HMOHD9fJOKalpeHw4cOQy+Vafy8iMiAKItKJ7777TvH7778rnz9+/Fjx6NEjhUKhUDx9+lS5/KuvvlLs27dPoVAoFFKpVPnv0hYSEqJISUlRPH36VJGQkKBQKBQKuVyuaNeunUIqlSoyMzMVzZs3L/b2xGKxYunSpcrnR48eVf776dOnCh8fH8W9e/feuo3nz58rxGKxQqFQKNLT0xUnTpwo8rUFx0ydr7/+Wu3YvWu7JREdHa347bffSnWbRGTYWAEj0hGBQKDy3NvbGzVq1AAAlXuFmpubK//922+/ISYmRivx1KpVC3Z2dvD09ISjoyMAKG8YLBQKkZKSAmdn52Jvb+vWrWjYsKHyuZ+fn/LfM2fORN++fVG/fv23bqNKlSowMTGBQqHAnDlzkJ2dXeRr33V/VTMzs0LLirPdknBzc0NycjLCw8NLdbtEZLh4L0giPXj27Blyc3NRu3ZtHD58GEuWLMH58+dVXvPy5UvldNzx48fRvXt3bNq0CQBw7tw5zJgxA9nZ2fj555/h7++P7du3Y/Hixbh79y7MzMxw9+5dpKSkoEePHkhLS8PRo0fRtm1bnD17FocPH8b69ethbGyMkJAQdO7cGR06dEBOTg4EAgESExPxzTffYNSoUcp4/vne/0yAdu/ejT179iifW1lZAQD279+PiIgIrFy5UrkuMzNTub0HDx5g8eLFCA8Px5w5czBx4kRUqFABT58+hVgshqOjI+7evYtHjx7Bzs4Oqamp6N69u8qYnTx5ErGxsbh16xa6dOmCXr16AQDi4+MxadIkBAUFYevWrcjMzFRu19TUFAEBAQCAOXPmYPny5bhz5w5WrVqFNWvWICUlBXZ2djh+/DimTp2Kjh07Ij09HQsWLECzZs2wdetWVKtWDaNGjYKnpyeaNm2KHTt2YOrUqf9m1yCi/whWwIh06PLly1i4cCEmTpyoXNa5c2e8fPmy0GtdXV3RqFEjNGrUCN27d8fFixchk8kwZMgQdOnSBfPnz4ePjw+kUiksLCywZcsWWFtbY+PGjfj4448xcOBAhIWF4YMPPkCbNm0QGRmJAQMGYPHixTh69Chyc3MxZMgQTJ48GZMnT0Z0dLTyvR0dHbFq1SrMmzcPERERat+7oMTERLx69UqZdP0tNTUVv/76K77//nvY2Ngol69evRp+fn4YN24czMzMsH37dvj4+CiPo6pZsya8vLzQtm1b+Pr6okGDBggLC8PkyZPx3XffqYzZs2fPcPz4cQwdOhTjx49XSWQzMjKwcOFCtGrVCqdPn1bZbs+ePdG8eXMAgLGxMVq3bg0AsLa2hru7OyIjI/HVV1/hm2++UR4j9+eff8LOzg69evVCjRo1UKlSJWUi6uTkhOvXr2u4RxDRfxUrYEQ61LZtW4wcORKffvop0tPTAahOOb7NtWvXAORXlLKysuDk5AQAsLCwQM2aNVG5cmU8ffoUxsb5H2tXV1dkZGQo38PGxgaVKlUCACxbtgwdOnQAANjZ2aFevXoICAhAmzZtlO/n4OCAVq1a4dKlS4iLi1P73n+LjY2Fvb19oZgXLFgAb29vZUXqb4GBgahWrRqePHkCV1dXZcxFjYW5uTnc3Nxga2sLW1tblXVXrlxB1apVAeRPqy5ZskS57u/kyMXFRTkWxWFmZoYqVarAzMwMzs7OyMrKAgDk5OQoY3R1dUVKSoqyjZ2dHV69elXs9yCi/zYmYER64OLiAhcXF43aSKVSeHp6om/fvgAAsVhc6DU1atRA9+7dERcXh/j4eLRt21btthQKBZKSkpTPK1SooEyCCqpUqRIUCsU73/ufx7cBwO3bt3HkyBEcPHhQpQ+ZmZmQyWRo0qQJqlevXmRfiksmk+H58+fK50lJSXBwcCgUn0KhKNRWXdxvM3z4cKxZswZA/vRm+/btVdZbWFhotD0i+u/iFCSRjsjlcrVJwNsYGRlBIpEgNTUVzZs3x+rVq3H79m0kJiaqHG/199SdRCJBcHAwjh8/jrCwMMydO7fQawCgffv2OH36tPJ5QkIC2rVrp3weGBiIFy9eIDs7Gw0bNnzrewP5B8+npaUpn0ulUsycORP/+9//lEkWkH/8mFwuR7NmzTBz5ky8ePEC4eHhOHPmzFv7/s/4C2rWrBnOnTuHS5cuISMjA8eOHXvbkKpsVyQSIT4+HgAQFhaG3Nzct7YNDw9HaGgoDh8+jFatWqkkYAkJCXBzc3treyKiv7ECRqQDDx48QHBwMOLi4tCxY0eVA9j/vm7WxYsXUatWLYSGhkIgEKBTp05o3rw5vv/+e9SoUQO9evXCnTt3MHr0aFSuXBlLlizB48eP8eLFC5w8eVJ5hmFKSgqWLVsGiUSC3377DUuWLMGDBw+QkJCAS5cuwdfXFx988AEePXqEyZMnw8PDA0OHDoWjoyPWrl2r3EZAQACcnJzQqFEjACj03gXZ2NjAy8sL8fHxcHZ2xtatW/HkyRO0adNG+dqsrCxcunQJp06dwvjx4/H999+jd+/eaNu2LRYsWIBnz57h+fPnuHLlCpo2bYpWrVph5cqVcHJyws2bN/H06VMEBwejfv36KmPm5+eHiRMn4ttvv1XG9vLlS4SGhsLIyAjNmjXDgwcPIJfLkZKSotxupUqV0Lp1ayxbtgzDhg1Dx44dAeRX7m7cuIGwsDBERUXh6tWrykQxOzsbDx48wO3btwEAO3bswPr162FqaoqYmBjlNoiI3kWg0PQnORGVWVFRUXj06BG6dOkCIP8g+EOHDuHzzz/X+nufPXsWycnJ6N+/v9bfS192796Nbt26wdraGjKZDAEBAbC3t4ePjw+mT5+Or7/+Wu2xcERE/8QpSCIDsnnzZpVptKSkJHh7e+vkvTt06ID4+HiVqUhDs2nTJuXxakKhEMnJyahRowYeP36Mdu3aMfkiomJjBYzIgERERGDWrFlISUmBq6srOnbsiA8//FBn7y+XyxEQEABfX1+dvacuHThwAH/88QdEIhGqVauG//3vf6hUqRJevnyJmjVr6js8IipHmIARERER6RinIImIiIh0jAkYERERkY4xASMiIiLSMSZgRERERDrGBIyIiIhIx5iAEREREekYEzAiIiIiHWMCRkRERKRjTMCIiIiIdOz/bVlfl3qxE/0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Local Heatmap\n",
    "#fig, axs = plt.subplots(figsize=(6, 4))\n",
    "fig, axs = plt.subplots()\n",
    "sns.heatmap(data=l_df.T, ax=axs, cmap=colors, annot=True, fmt=\".2f\", cbar_kws={'label': 'Mean F-Score'}, xticklabels=1, yticklabels=1, vmin=79., vmax=85.6)\n",
    "axs.invert_yaxis()\n",
    "axs.set_xlabel(\"Filtergröße (Zeitrichtung)\")\n",
    "axs.set_ylabel(\"Filtergröße (Frequenzrichtung)\")\n",
    "axs.set_yticklabels(axs.get_yticklabels(), rotation=0)\n",
    "\n",
    "#axs.set_title(\"F-Scores Heatmap (Ring Topology)\")\n",
    "\n",
    "#plt.savefig(\"heatmap_local_gleiche_skala.png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "999201b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Auswertung des besten Ergebnisses:\n",
    "# Filtertyp: Global 3x5, Länge Zeitrichtung: 5, Länge Frequenzrichtung: 3\n",
    "\n",
    "filter1 = [ 1.4514875 ,  0.67962813,  1.3267739 ,  1.076459  ,  1.1447549 , -1.4494876 , 0.4485518 , -1.3983144,  12.780292 ]\n",
    "filter2 = [ 0.4388686 ,  0.26955575,  0.44204888,  3.9786017 ,  1.6983291 , -2.8763483 , 1.0405272 , -4.2872205,  13.682718 ]\n",
    "filter3 = [ 0.90074927,  2.0068014 ,  1.384222  ,  0.90909255,  1.429568  , -1.5369593 , 0.37383863, -1.3617892,  15.807245 ]\n",
    "filter4 = [-0.5914803 , -0.35116303, -0.5254847 , -3.965828  ,  2.2166693 , -1.5370568 , 0.5555174 ,  3.1105368,  11.309557 ]\n",
    "filter5 = [ 0.45256743,  1.0780698 ,  0.77992415,  2.171062  ,  0.98768353, -1.6954808 , 0.8178946 , -2.5986037,  14.437158 ]\n",
    "filter6 = [-0.6369055 , -0.83030605, -0.52570707, -3.5917513 ,  2.562756  , -1.8627614 , 1.2491975 ,  1.9648478,  12.008744 ]\n",
    "filter7 = [-0.34577784, -0.22609143, -0.32085896, -2.1855235 , -4.527265  , -0.41339868, 2.7202175 ,  4.980925 ,  14.5411005]\n",
    "filter8 = [ 0.7999404 ,  1.8004252 ,  3.543838  ,  0.8375645 ,  0.12475213, -0.34295174, 0.23983702, -0.9645398,  14.424377 ]\n",
    "\n",
    "def split_list(a_list, freqlength):\n",
    "    return a_list[:freqlength], a_list[freqlength:-1]\n",
    "\n",
    "freq1, time1 = split_list(filter1, 3)\n",
    "freq2, time2 = split_list(filter2, 3)\n",
    "freq3, time3 = split_list(filter3, 3)\n",
    "freq4, time4 = split_list(filter4, 3)\n",
    "freq5, time5 = split_list(filter5, 3)\n",
    "freq6, time6 = split_list(filter6, 3)\n",
    "freq7, time7 = split_list(filter7, 3)\n",
    "freq8, time8 = split_list(filter8, 3)\n",
    "thrshlds = [filter1[-1], filter2[-1], filter3[-1], filter4[-1], filter5[-1], filter6[-1], filter7[-1], filter8[-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ef25bff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot frequency filter kernels\n",
    "fig, axs = plt.subplots(4, 2, sharex=\"all\", sharey=\"all\", figsize=(6, 4))\n",
    "#fig.suptitle(\"Frequency Filters for (3x5)\\nMean F1: \"+str(np.round(g_mean_3x5,4)), fontsize=\"medium\")\n",
    "axs[0, 0].stem([x+1 for x in range(len(freq1))], freq1)\n",
    "axs[0, 1].stem([x+1 for x in range(len(freq2))], freq2)\n",
    "axs[1, 0].stem([x+1 for x in range(len(freq3))], freq3)\n",
    "axs[1, 1].stem([x+1 for x in range(len(freq4))], freq4)\n",
    "axs[2, 0].stem([x+1 for x in range(len(freq5))], freq5)\n",
    "axs[2, 1].stem([x+1 for x in range(len(freq6))], freq6)\n",
    "axs[3, 0].stem([x+1 for x in range(len(freq7))], freq7)\n",
    "axs[3, 1].stem([x+1 for x in range(len(freq8))], freq8)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.set(xlabel='Filter coefficient', ylabel='Value')\n",
    "    ax.label_outer()\n",
    "\n",
    "#plt.savefig(\"3x5_freq_filters.png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b19366c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot time filter kernels\n",
    "fig, axs = plt.subplots(4, 2, sharex=\"all\", sharey=\"all\", figsize=(6, 4))\n",
    "#fig.suptitle(\"Time Filters for (3x5)\\nMean F1: \"+str(np.round(g_mean_3x5,4)), fontsize=\"medium\")\n",
    "axs[0, 0].stem([x+1 for x in range(len(time1))], time1)\n",
    "axs[0, 1].stem([x+1 for x in range(len(time2))], time2)\n",
    "axs[1, 0].stem([x+1 for x in range(len(time3))], time3)\n",
    "axs[1, 1].stem([x+1 for x in range(len(time4))], time4)\n",
    "axs[2, 0].stem([x+1 for x in range(len(time5))], time5)\n",
    "axs[2, 1].stem([x+1 for x in range(len(time6))], time6)\n",
    "axs[3, 0].stem([x+1 for x in range(len(time7))], time7)\n",
    "axs[3, 1].stem([x+1 for x in range(len(time8))], time8)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "    ax.set(xlabel='Filter coefficient', ylabel='Value')\n",
    "    ax.label_outer()\n",
    "#plt.savefig(\"3x5_time_filters.png\", dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ecae51cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 325x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, sharex=\"all\", sharey=\"all\", figsize=(3.25, 1.75))\n",
    "axs[0].stem([x+1 for x in range(len(time3))], time3)\n",
    "axs[1].stem([x+1 for x in range(len(time6))], time6)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylim=(-4.5, 4))\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(2))\n",
    "    ax.set(xlabel='Filter coefficient', ylabel='Value')\n",
    "    ax.label_outer()\n",
    "plt.savefig(\"time_filters.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "88d83a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 325x175 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, sharex=\"all\", sharey=\"all\", figsize=(3.25, 1.75))\n",
    "axs[0].stem([x+1 for x in range(len(freq3))], freq3)\n",
    "axs[1].stem([x+1 for x in range(len(freq6))], freq6)\n",
    "\n",
    "for ax in axs.flat:\n",
    "    ax.set(ylim=(-1.5, 2.5))\n",
    "    ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "    ax.set(xlabel='Filter coefficient', ylabel='Value')\n",
    "    ax.label_outer()\n",
    "plt.savefig(\"freq_filters.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89c39cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create 10 example spectrograms\n",
    "ex_specs = []\n",
    "\n",
    "def get_spectrogram(title):\n",
    "    # Feature extraction parameters\n",
    "    sr = 44100          #Sampling rate\n",
    "    fs = 2048           #Frame size\n",
    "    fps = 200           #Frames per second\n",
    "    window = np.hanning #Window for STFT\n",
    "    num_bands = 24      #Number of bands per octave\n",
    "    fmin = 27.5         #Minimum frequency\n",
    "    fmax = 16000        #Maximum frequency\n",
    "    diff_max_bins = 3\n",
    "    \n",
    "    # File parameters\n",
    "    audiofile = 'data/audio/' + title\n",
    "    annotationsfile = 'data/annotations/onsets/' + re.sub('\\.flac$', '.onsets', title)\n",
    "    annotations = madmom.io.load_events(annotationsfile)\n",
    "    \n",
    "    # Get spectrogram\n",
    "    signal, sr = librosa.load(audiofile, sr=sr, mono=True)\n",
    "    signal /= np.max(np.abs(signal))\n",
    "    signal = madmom.audio.signal.Signal(signal, sample_rate=sr, num_channels=1)\n",
    "    frames = madmom.audio.stft.FramedSignal(signal, frame_size=fs, fps=fps)\n",
    "    stft = madmom.audio.stft.ShortTimeFourierTransform(frames, window=window)\n",
    "    y, sr = librosa.load(audiofile, mono=True)\n",
    "    simplespec = madmom.audio.spectrogram.Spectrogram(stft)\n",
    "    filtered_spec = madmom.audio.spectrogram.FilteredSpectrogram(simplespec, num_bands=num_bands, fmin=fmin, fmax=fmax)\n",
    "    log_filtered_spec = madmom.audio.spectrogram.LogarithmicSpectrogram(filtered_spec)\n",
    "    return log_filtered_spec\n",
    "\n",
    "ex_specs.append(get_spectrogram(title='ah_test_guitar_guitar2.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ah_test_clarinet_44361_debudding_Clarinet_ORTF_Stereo_Pair_NT_5_s_01.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ah_test_guitar_guitar3.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ah_test_kemence_01_sultaniyghtaksim_Kemence.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ah_test_mixtures_classic2.flac'))\n",
    "ex_specs.append(get_spectrogram(title='al_Albums-Cafe_Paradiso-16(17.0-27.0).flac'))\n",
    "ex_specs.append(get_spectrogram(title='api_RM-C036.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ff123_BachS1007.flac'))\n",
    "ex_specs.append(get_spectrogram(title='ff123_Hongroise.flac'))\n",
    "ex_specs.append(get_spectrogram(title='vorbis_bassdrum.flac'))\n",
    "#example_spectrogram1 = np.random.randn(1200, 80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd63d61a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "       [ 3.51075276e-11, -1.55975802e-12, -2.56494102e-12, ...,\n",
       "         5.61768323e-14, -8.85705105e-14, -9.49332017e-14],\n",
       "       [-7.07838128e-11,  4.65388446e-11, -1.24055810e-10, ...,\n",
       "         1.21129522e-13, -2.09679032e-13,  1.00929386e-12],\n",
       "       ...,\n",
       "       [-3.51952276e-08, -8.51763048e-07,  1.71137494e-07, ...,\n",
       "        -1.98493411e-09,  5.96073478e-09,  7.23943657e-09],\n",
       "       [ 2.04351857e-07,  3.36253461e-08,  4.04092246e-07, ...,\n",
       "        -2.86660917e-10,  1.39779618e-09,  9.73603238e-09],\n",
       "       [ 5.35186255e-08, -7.58418333e-08,  1.42077994e-07, ...,\n",
       "         2.89736694e-09, -1.76353395e-08,  3.20148300e-08]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test if sepfirs and convolved filter are equal (result should be matrix of zeros)\n",
    "freq3, time3 = split_list(filter3, 3)\n",
    "combined_filter = np.multiply(np.asarray(freq3).reshape(-1, 1), np.asarray(time3).reshape(1, -1))\n",
    "signal.sepfir2d(ex_specs[0], time3, freq3) - signal.convolve2d(ex_specs[0], combined_filter, mode=\"same\", boundary=\"symm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2accd372",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "64.3 ms ± 1.2 ms per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1000\n",
    "\n",
    "for spec in ex_specs:\n",
    "    signal.sepfir2d(spec, time3, freq3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6ae38da9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "257 ms ± 2.25 ms per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 1000\n",
    "\n",
    "for spec in ex_specs:\n",
    "    signal.convolve2d(spec, combined_filter, mode=\"same\", boundary=\"symm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cde7ead7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157.73569160997732\n"
     ]
    }
   ],
   "source": [
    "# Calculate total length of above audio files\n",
    "def get_duration(audiofile):\n",
    "    signal, s = librosa.load(audiofile, mono=True)\n",
    "    return librosa.get_duration(y=signal, sr=s)\n",
    "\n",
    "durations = [get_duration(\"data/audio/ah_test_guitar_guitar2.flac\"), get_duration(\"data/audio/ah_test_clarinet_44361_debudding_Clarinet_ORTF_Stereo_Pair_NT_5_s_01.flac\"), get_duration(\"data/audio/ah_test_guitar_guitar3.flac\"), get_duration(\"data/audio/ah_test_kemence_01_sultaniyghtaksim_Kemence.flac\"), get_duration(\"data/audio/ah_test_mixtures_classic2.flac\"), get_duration(\"data/audio/al_Albums-Cafe_Paradiso-16(17.0-27.0).flac\"), get_duration(\"data/audio/api_RM-C036.flac\"), get_duration(\"data/audio/ff123_BachS1007.flac\"), get_duration(\"data/audio/ff123_Hongroise.flac\"), get_duration(\"data/audio/vorbis_bassdrum.flac\")]\n",
    "total = np.sum(durations)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cc1316cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.56  1.66 -2.1   0.65 -2.03]\n",
      " [ 0.73  0.78 -0.99  0.3  -0.95]\n",
      " [ 1.43  1.52 -1.92  0.6  -1.86]]\n",
      "[[ 1.75  0.75 -1.26  0.46 -1.88]\n",
      " [ 1.07  0.46 -0.78  0.28 -1.16]\n",
      " [ 1.76  0.75 -1.27  0.46 -1.9 ]]\n",
      "[[ 0.82  1.29 -1.38  0.34 -1.23]\n",
      " [ 1.82  2.87 -3.08  0.75 -2.73]\n",
      " [ 1.26  1.98 -2.13  0.52 -1.89]]\n",
      "[[ 2.35 -1.31  0.91 -0.33 -1.84]\n",
      " [ 1.39 -0.78  0.54 -0.2  -1.09]\n",
      " [ 2.08 -1.16  0.81 -0.29 -1.63]]\n",
      "[[ 0.98  0.45 -0.77  0.37 -1.18]\n",
      " [ 2.34  1.06 -1.83  0.88 -2.8 ]\n",
      " [ 1.69  0.77 -1.32  0.64 -2.03]]\n",
      "[[ 2.29 -1.63  1.19 -0.8  -1.25]\n",
      " [ 2.98 -2.13  1.55 -1.04 -1.63]\n",
      " [ 1.89 -1.35  0.98 -0.66 -1.03]]\n",
      "[[ 0.76  1.57  0.14 -0.94 -1.72]\n",
      " [ 0.49  1.02  0.09 -0.62 -1.13]\n",
      " [ 0.7   1.45  0.13 -0.87 -1.6 ]]\n",
      "[[ 0.67  0.1  -0.27  0.19 -0.77]\n",
      " [ 1.51  0.22 -0.62  0.43 -1.74]\n",
      " [ 2.97  0.44 -1.22  0.85 -3.42]]\n"
     ]
    }
   ],
   "source": [
    "# Compute 2D-Filter-Kernels\n",
    "A1 = np.array(np.hsplit(np.array(freq1),len(freq1)))\n",
    "A2 = np.array(np.hsplit(np.array(freq2),len(freq2)))\n",
    "A3 = np.array(np.hsplit(np.array(freq3),len(freq3)))\n",
    "A4 = np.array(np.hsplit(np.array(freq4),len(freq4)))\n",
    "A5 = np.array(np.hsplit(np.array(freq5),len(freq5)))\n",
    "A6 = np.array(np.hsplit(np.array(freq6),len(freq6)))\n",
    "A7 = np.array(np.hsplit(np.array(freq7),len(freq7)))\n",
    "A8 = np.array(np.hsplit(np.array(freq8),len(freq8)))\n",
    "B1 = np.array([time1])\n",
    "B2 = np.array([time2])\n",
    "B3 = np.array([time3])\n",
    "B4 = np.array([time4])\n",
    "B5 = np.array([time5])\n",
    "B6 = np.array([time6])\n",
    "B7 = np.array([time7])\n",
    "B8 = np.array([time8])\n",
    "list_of_2d_kernels = []\n",
    "list_of_2d_kernels.append(A1.dot(B1))\n",
    "list_of_2d_kernels.append(A2.dot(B2))\n",
    "list_of_2d_kernels.append(A3.dot(B3))\n",
    "list_of_2d_kernels.append(A4.dot(B4))\n",
    "list_of_2d_kernels.append(A5.dot(B5))\n",
    "list_of_2d_kernels.append(A6.dot(B6))\n",
    "list_of_2d_kernels.append(A7.dot(B7))\n",
    "list_of_2d_kernels.append(A8.dot(B8))\n",
    "\n",
    "# Print Filter Kernels (matrizes)\n",
    "for fil in list_of_2d_kernels:\n",
    "    print(np.round(fil,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9081fb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AYpgPqQU1eGmU1Jot5FeaKK42o1EpCTRoqKhrJDbIm0Vbslu+cXvjOi2w4d2C2J1Rya6MCjQqBUEGLeV1DcQGebPw92yO5FW7nd9e19/6F66/9S/O6dVfLeSySVPRanXUVFVSXJhPYo9e7Nm+mf5DRjjHPtlPG8TVvWdf6utqMZtM6Ly8KC7MZ/BFo9sd37asSq7uG4ECUCkVKBUK0kvr6R/tx968ajYeL+fRcU3J1Euj5GBBDQHeajQqJSW1DUT7e7Ezu7LdsQBcd8tfuO6Wpu317dcLufTU9qo+ub2SmrbXoOFj+O6bxdhsNqwWC1ablW7JfTAZjag1GtRqNfk5WYwYc7kk8Z1ypu71sLAwly70P7Jp0yaSkpKIioqiqKiI8HDp787qSB2VZ7ZlVTK1X9O+qVIoOF7SbN9ML+evzfZNb42Sg/k1zukhcQFsz6qUPK59+TVc1j2k6ZhRKsiuNNEzzMCRZrfZK3AdB+VuvYzylo9laKteE2bQa8IMwDGmacM7f8Nus2GzWbHZrATFJWGzWig5foDwHgOw22yk/bqcpEumolAoqCsrwmZpxDfccVm24NB2IntLcys+wJ68ai7v0fR3q5SQVWGkV7gPh4v+eL8xaFWM7R7Ep9uk6X0aMe0WRky7BXDchbf0X49jt9mwWi3YbDYiuyZjtVjISd1HfJ9BzvVO7N1G1wEtHx3T2vz2kkOO6XQF1ObNm/n000+ZOHEiCoWCG25oeYYhlRAfLUO6BpEU7kNCqIGMkjqeuTKZvy7ZT7WxkXdvHcDh/Gp2Z1bw6cZMwDGO4bEv9jPnsm4cLazhQE4VB3OlHeQZ4qtlaNcgekT60jXUwImSOp67uhcPL9rLscJaHlm0jwcv70bayc8/cPL5MK3Nl8qKJfNYOv99vpz/AQCWxgbeXbQas8nEh/95hf/7YDE+fv5UlpdyeP9usjOOk5edSXSXeBQKBfc88gyLP3kHX/8Abpx1L17e+j/4xD9mtthYtCuPW4bGYLXZeee3DEIMWmZfHMfspQfIqzLx3cEiZgyKwmK18/n2XBqsdvqFGnhgdALbsio4XFDLL8fK2h3L6VZ+MY8vP3+frz5v2l5vL3Bsr4//+wqvvufYXtffeg9ffPYuKqWKvzz8DEqlkh9XLWX9j6sYPno8fv6BTLzm/I1PaIt58+Yxf/58AgMDsdls9OvXjxdecP94i87kfOQZs8XGop153Do0BqvdztsbMwjx0XLfqDj+8sUBcitNrD5YxE2Do2i02pm3Ldflbrv4IG++3CPt+CeABqudVYeLmdonDJsdFuzKJ8BbzYwBkTz/43EAgvUa4oO8sdshSK+hvL7R7XoSP/kDtc6LXhNv5uB3n6NQqhg0fQ4KpZK60iL2fPUeVzzzAXu+fo/M7T+T9vM3ACiVKqa8tMD5HlUFWfS8XJrxT+DYXisOFnFtv3Csdpi3PY9AvYZbBkfxt+8c4yaDDRq6Bumx49h2ZfWN+OpUdA3WE+mn4+OtuR1yp69G583Iabfy29efoVQquXzWwyiUSmpKCvn587e549XPnCe1pbkZbsc5tTa/veSQYxT207sKLgDJT/3o6RDcslikHS8glW8eutjTIbj13Br3Dzf1tH9e4dkxea0ZEHf2t0gnPNL6c6Uy/iPdWKsL1ZQPdng6BLeig9t/UtIRQiS++0wqp8agdTbD46V7aK6U/jL87O/ulkOO6XQ9UIIgdLwL6WF2giB0PnLIMaKAEgQZUsrgZxYEQfAcOeQYUUAJggzJIbkJguA5csgxooASBBlSqS785CYIgufIIceIAkoQZEgOZ4eCIHiOHHKMKKAEQYbkkNwEQfAcOeQYUUAJggzJIbkJguA5csgxooASBBmSQ3ITBMFz5JBjRAElCDIkh+QmCILnyCHHiAJKEGRIDslNEATPkUOOEQWUIMiQHJKbIAieI4ccIwooQZAhOSQ3QRA8Rw45RhRQgiBDckhugiB4jhxyjCigBEGGFIoLP7kJguA5csgxooASBBmSw9mhIAieI4ccIwooQZAhOSQ3QRA8Rw45RhRQgiBDckhugiB4jhxyzAVZQHWND/R0CG5ZbXZPh+DWgeIqT4fgVoCPztMhuHWgpNLTIbg1IM73rNuqVcoOjOTCl5Fd4ekQ3Ar19/J0CG6F+mg8HYJb6cV1ng7Brb4h/p4Ood3kkGMuyAJKEIQzU8ng7FAQBM+RQ44RBZQgyJBSBnfICILgOXLIMaKAEgQZksHJoSAIHiSHHCMKKEGQITkM8BQEwXPkkGNEASUIMiSH8QmCIHiOHHKMKKAEQYbkcHYoCILnyCHHiAJKEGRIJYMBnoIgeI4ccowooARBhjpz97rdbpfF72gJwoVMDjnmwn/SlSAILSiVilZfnrJ7926mTJnCgw8+iMViYd68eRw4cMBj8QiCcO7kkGNEASUIMqRSKlp9ecrbb7/N448/zrBhw1Cr1UydOpUnn3zSY/EIgnDu5JBjRAElCDKkVChafXnKmDFjGDNmDD4+PgDs27ePiorO+ZMpgiCcmRxyjBgDJQgyJOVZ4KJFi9BoNBQXF3Prrbfi5+d3Tu/j7+/PK6+8Qk1NDRkZGSxevJg5c+ZIFqcgCOePHHKM6IESBBmSanzCoUOH2LdvHzfccAPDhw/nnXfeOeeYpk2bxg033ECfPn0ICgpi/vz53Hbbbef8foIgeI4ccoyse6B6R/gwuEsAKqWC9cfKOFFW71zm76VmwS39XX5R+vnv09iZXcXdI7owMiGQgmozL/94jNoGq6Rx9Yn0ZUgXf1RKBb8cPS0ubzWLbx3gEtez36ayI7uKqf3CsdrAR6cis8zIlkzpL39kp+7n+N7t2KxW+oy8lIj4bi3abP9hGVaLhYriAgZdNoXwLolnnN9eSaF6+kX5oVYo+D2zguxKk8vymwZGMjjWn+LaBuZuzKK+0fHvNSTWnyh/HQatih9TSymrb5QknuayUw+Qvm87NquF3iPcb68dPyzHam2koriAgZdeSXiXrgBYLY2889BMaivL8AkM5oH/LkapUkkS15nODouLiykpKXG7LDQ0lLCwMOf0unXr6NbN8Tf17NmTJ554gqeffvqcYsrPz0ev1zN27FgATCYTCxcuZObMmef0fp2Bl0bJ7RfHE2TQ8sq3qW7bvDUjhfF9wgG46r+bSS+uQ6VUcMOQGAYnBPLXL/ZLHlf3UD39In1RKRRsyaokp9kxowBuGxLNkFh/cqtMfLAlh/L6xlbnSy3/6EGyD+7AZrWQNGwcIa3kieqSAtK2/ExgZBe69B2MRuvFhgVzSd+5kaCYBC6760l8g8PcrttWyWEG+kf7oVIq2HSigqwKo9t2dw2P5VhJHRvSywG4dUg0Q7oEUFxj5s31GdRJ/F0BYDaZ+GH5IqorK7jl3sfctnn2/pvJzTzunJ547Uym3/GAc3rr+h85sHsbdz/6vGRxySHHyLaA0qmV3HlRFx5bcRilQsGLk3vwt9VNCa5PpC8PLztMfpUjsfx1XFf251XTK8KHFfsL+XRrDq9M6cGlPUJYeaBI0rjuuqgLjy4/hFKh4OUpPXhyVVNcfSN9efCbQ+SdTHiPX5rIvrxqIvx09I3048UfjwHw0Yx+khdQDSYj6xZ/yO3/+C92m43F/36KW559w6VN3vFUCjKPcfXsJ6mtKmfxv5/i7pffJ//EUbfzFcr2dYJqVQpuHBDJi2vTUSoUPH5JAv/+5YRzefcQPT+mlrJ0TwFPjuvKxV0D+CmtjEBvDZd0C+L/fs3Az0vN7UOimftbVrtiOV2DycjPiz/gtpPba8mrf2PmM6+7tMlLT6Uw8xhXzn6C2qoKlvz7Ke56+T0USiVHtm1k+uMv4RMQjFqjlax4gjM/o2Xp0qWtnuXNmTOHBx5oSrylpaX07dsXAJ1OR3Fx8TnHNHnyZIKCgrDb7QDU19cTExPzpy6gfHRq1EoFBp37f7voQG9+O1bKS6uPYLNDeV2Dcz2LzUaQQSt5TFqVgukpkbz8s+OY+euYeF77NcO5fGCMH79nVrB4dz53DYvlxgGR/G9zdqvzpdRoNrHl64+Z9tQb2G02vn3rWa5+7NUW7SoKc9i85H2uuO85NDovAE7s2kTSRZcy8sbZ/PLp6/z+5Ydcce+z7Y5Jp1Zy8+Bonl9zFKVCwd8uS+Sln463aNc1WM/AWH+OldQBkBRq4LvDxSzamcczl3djdGIQa464Lxraw1hfh9ViwWSsd7s8L+sEV8+4gz4Dh6FQKPlp5VKSeqe4rL9m2SJi3JzctYccckynK6Bqa2v529/+xrFjx4iPj+fNN99Er9dL/jnD4gLIrzJhs4PNbsdis9MrwofDhbUAbMuqxGJzbGSNSkGj1U6D1e5cDnC8tJ6CKrOkcQ2PDyDvDHFtzTwtLpuNBqsdL7WSpDADXmolSoWCRqtN0rgAju7eQlBENEqlCpQqlCo1OWkHie3Rx9nmyI6NBIZFAuDjH4TNaiX/RBpHdvzmdn50t57timlAjB9FNQ3Y7WC127Ha7XQP0XOs1JFMTv0XILPCSHGN4wtqZEKAs2ev2mQhyk9HoLeGCqN0Z9TH9mwlKCLGub1UKlWL7ZW2/TcCnNslELutabvs+eU7EvoMpP/YSXgZfCSLC858djh9+nTGjRvndlloaKjLdHBwMEaj42y8rq6O4ODgc47p888/dyZKgNzcXLZs2XLO79ea85VjAEprG8itMBIZ4OV2+S0XdUGjVnIor5rUghrn/CpjI1ml7r8Q26t/tB9FteamY8Zmp1uInuMnPy+zwkhZneM4+PZwMfdcFHvG+VLK3LeVgLAol2Om4NghIrv3drax2+2s+/DfjLvjMWfxBBAS1w2/kAgABk6+kXUftiy8zsXAGD8Kq123V1KogaMnCyVw9NoNjfPnQH61c17z5ZnlRopqpP2uOCUgKJjQiCjKStyfyIdHxRId19U5nX0ijSnX3+qc/vnbr7lo7ARyMloWhe0hhxzT6cZA7dixg1deeYU1a9ag1+tZtmxZh3xOt1ADlc2+LCvqG4kN8HZOnypSwJFw9uVVu6yv16rADtuzKyWNq3uogarT4uoS6D6uATH+7M11xJVZbuRYSR0vTu7B9EFRvLo2XdK4AAozjmHwC3BO+wQEUprv2mtjMZupq2rq+fLxD6KytKjV+e2VEKin2mRxTlcZG4nyb/ll5a1x7Op78x1fUvFB3i7rnSqipFSYcdRlexkCgijNdz1jb2wwuWwXg38gVaVFmOpqiU3uy7E9W/n4mdmU5GZKGtuZbjEOCwujd+/ebl/Nu9YBLr/8co4cOQLA0aNHW02KZ6N5YgPH2eYnn3xyzu/XmvOVY5zsrS/KrTAS7KNl8T1DmdQv4mxXa5f4QNd9v+q0ff9UkQSg0yjJKDeecb6USrKO4e0b4Jz29guiosD1mMk9vBtzXQ0FRw/w0/svk3t4N4CzeAJoNJkIS0iSJKauwXqqmm2vSmMj0acVxKMTg9hwvNzt+t4aJXY77M6tdrtcGq0XK2qNxvn/9XW1eHnrnb3ZeVknCAoJw+BzboOyz0QOOabTFVBjx47F19cXhUJBSkoK/v7+HfI5Bq2KWnPT9WirzYa/t/sOuSFdAlwKJT8vNfddHMdVfcMZ1/3cq2H3campMTcdrBabnQBvjdu2Q7oEsD2rKa7/bsggwk/HgBg/Kk3Sj00w1dfhZfB1TitVauqqKl3axPXqT+qOzZiN9TQ2mKmuKEXv49fq/Pby1ipdxhVYbOB32uUSH52KWwdHc3lSCCPiAxzraVQu61ntdvy8pO2QdWyvpp4jpUpNfXWlS5u4Xv1J29m0XWoqyvD28cPL4MMl189i1gvv0O/i8fz0+buSxqZUtP5qi169etGtWze++uorfv/9dx5++OFzjik5OZmePXvSs2dPkpOTmThxIhMmTDjn92vN+coxZ2PhlmweXryPR5bs45krk/HWSHeZtjXeWhX1zfd9mx3fVvb9AVF+fO/mslNr89urwViPzuWYUWGsqXJpk5e6j6geKfQZdxUpl1/LmndfoL7KdbhC5r4tDJhwvSQx6bUq6hqacrLVZse/2fby0anw0akpqG7Zw+SrU3HHsFgm9gxlVNdASeJpjwO7ttB30EXO6S3rf+SisdIfYyCPHNPpLuE1l5uby80339wh711jtqBVNf1L6tSuSaW5AG8NFc0GS1abLLz+ywkOFNQwtV8EvxwrkziuprrWS61sdeBhoF7jHMSpVMDdF3XhnqUHePKyRP45sQcPLzskWVwA3j6+WBobnNONDWZ0eoNLm55DR1FZXMAP8+YSEd+d2ooyorv1ROvl7XZ+e9WZrWhc/h0V1De6Xr6sNVv5YEsOacV1TEgO5ffMSuoarGiabWetSukcXC6V07eXxWxC5+26vZKHOLbXj/PfJiKuG7UVpS22y5jrZ/H+47MkjU3KW4zvuusuSd7n7bffZvz48ZK819nqyBzTFhvSStmRUUHXMAOH8jqypwLqzBbXfV+txOgmx4T6aKk0Wcg/rTBobb4UvPSnHTMNZnTerpdXTbXV+IdFARDeNRmfwBAK0w/TdeBIAKqK8zH4BxEUHS9JTLVm11yhUytdvisuSwrh+8Pux+XUmK28uymLI0W1TOoVxm8nPPtcs4O7tzHj7ocB2LZxLUNHXdphP5skhxzTaQuotWvXMmvWLNTqjgkxo6yeATFNZ54hBo1zDEBzCcHeLnfBNbcpvZwJPUPdLjtXJ0rrGRjb1DMT4qPleLNr6ad0DdaTXto0f0iXAOobrNQ3WHnlp+N8eGM/EkP0pLdzHMWGbz5n86rFAIy86iaqml12qykvITK+e4t1LppyAwA7flrBgHGT0Hp5n3F+e2RXmugT0XTGGqjXtHqHzPbsSsZ0C3KsV2EkoFmPY6BeQ6YElyQ2fvM5v69eAsCIK2dQVda0varLS4lIaLm9hk92bJedP62k/9jJLbaLSq0m0s167aHqmJzZLu4S2/r167nkkks65PM6Ose0VU650TmIvEM/p9JE7+bHjLeGrArXO1c1KgXDu/iz+nDJWc1vjx2rFrLn+6UADJg0ndqypmKkrqKUkDjXwc2GgGDqq5sKEX1AMGqt4xKkpcHM8e3rGTh5hmTxZVUY6RvZ1PMeZNCSUd7091+RHMqlSY4rEb46NQNi/FEoYH2zS3pbMysZlyTN1YoViz/m2y/nAzDlhtuYetPZFRc2mw2z2Yj+ZA/fto1ryUo/CoDZVE9jQwNqtZpZD57bHW6nk0OO6RyZ4zSbNm0iKSmJqKgoioqKCA8Pl/wztmZWMq1fBAoclbJSqeB4SR0DYvzY0+xa9dC4ALZlNR2sOrUSi80xkDAmwIuNrVz3PldbMiu4pr8jLvXJ68XHS+oYGOPncg19WHwA2zIrndPVJgteJ8f5mC02jhbXuvSanasx197KmGsdAw4bTEY+f+mv2G02rFYLNquV6G49sVosZB3ZS9e+g53rVRTlk75/J9c++JzL+7U2/1ztzq1iQnKI899RpVCQWW6kd4QPhwpr0aoUWG12rHaI8NOx7eQlzy2ZldwxLAZwPLIir8rkMs7hXI2+9lZGN9teC19utr1sVqITk09ur3107TvIuV5FcT4nDuxk2gOOu4ZqK8uxNjbiHxpOaX423foPb3dszWk6SXYbN24cBQUFzulTd8coFArnD36eGv8gpfORY5xO29TDugax+2ROSQzzIbWgBi+NkjqzhYLTHifQEfbkVXNFj2bHjNJRJPQK9+FwUS0KYEJyKD+mOoqEYIMGjVJBUU2D2/mFNe0r+oZcNZMhVznugmo0m1j5f49jt9mwnTxmwhMcx0x+2n5iew+k29BL+Pnj1wDH/mIxGYlK6ovdZmPvj1+Tcvm1KBQKqksLsVoaCYxo32D3XTlVTO4V2izHQEaZo6g6UFDDvV8ddLa9d2QXDhfWsiG93PFdYbVhtUOUv47fM6TpfZp6011uiqaWI+YO79tJ91790Ggcd3Kmpx6gW3LTGKAHn33N+f+/rf2W1AO7JSueQB45ptMVUPPmzWP+/PkEBgZis9no168fL7zwguSfY7bYWLgjj9uGxWCz2Zm7IZMQHy33jYrn7iVNz12JD9KzdHfTxr+yTziXJ4fwW3o5VUYLK/YXSh7X59tzmTU8FqvNzlu/ZhDio2XO6ATuWLzPJa4lu/Kd00eKaukb5cvEXqFYbXZ+z6iQ/BktWi9vLrnuNn5Z+glKlYrJd/8VhVJJVUkhaz6dy31vzqe6rJj8E0epLCng2gefQ3PyzLCqtMjt/PZqsNpZtr+I61MisNrtfLo9lyC9htuGRPPE6jTGJ4UwOjGI7dmVVJss/JhaCkBZfSNbsyq5uk8YOpWST7blShJPc1ovb0Zfezu/fvkJSqWKyXc9ikKppLqkkB8+m8u9b8yjuqyYgoyjVBYXMu2BZ53bpeDEUb77+A2SBo0gtkdf+o2+XNLYOssvpT/xxBOMHTsWnc79/rBx40bJP/N85Rhw9CAPSQike7gvCSF6MkrreWpyDx5feoAaUyNv39yfwwXV7M6s5LPfMp3r+XqpGdk9hNggPb2ifDmcX9P6h7RRg9XO8oNFXNsvHJsdPtueR6Bew62Do3jqu6PcMjiKixMCmZQcAoDNDg+tONLqfClpdF4MufpWti2bh0Kl5JJbH0KhVFJbWshvi95hxsufEBgZS59xV7F95QJUag3Drr0DtVbHhgVzSdu8lj0/fAWAUqni9jeXtDsms8XGV3sLuXFgFFa7nQ+35BBs0HDH8BgeWd76339FcgiXdAtma6Yj96yRsNeuucryMlIP7CE38zj5OZlExcYDsPiDN5n9xIvExDueo7Vvx2ZGX351h8TgjhxyjMJ+qhy7gEx6f7unQ3DLauucm/qWYdGeDsGtn9Kk7d2TymVJnh8M6s6tg8/+TPuVn1u/S/PpS6V5wGl7GY1Gjh49SkpKyh83Ps96P/OTp0Nwa2jfSE+H4FZKtLSP4ZDK1hOVng7BrYdGJng6BLcu6hZw1m3lkGM6XQ+UIAgdr5OcHLpYu3YtX3zxBY2Njp5Tq9VKUVER69at83BkgiC0lRxyjCigBEGGOkv3enOrV6/mtttuY8eOHYwaNYri4mLq6zvmYZKCIHQsOeSYTvccKEEQOp5K2frrfGo+eLNnz56MHj2afv360aVLF6ZMmcLq1avPb0CCIEhCDjlGFFCCIENqpaLV1/l0//33s2LFCsxmM97e3kyaNIlx48bxj3/8g8mTJ1NbW/vHbyIIQqcjhxwjLuEJggyd6Yc+z6dZs2bRtWtXPvjgAywWC//85z9RqVS88847pKWl0bVr1z9+E0EQOh055BhRQAmCDJ3vbvTW3HLLLQD069cPq9XKpk2bePPNN/H19WXixIl4e7f/YauCIJx/csgxneRPFAThfDrTD316LCaVijFjxjh/PHT8+PG8+OKLHotHEIRzJ4ccI3qgBEGGOssdMiUlJYSGhlJbW8vq1av56quvyMrKYtKkSSxdupR+/fp5OkRBEM6BHHKMKKAEQYY6y/iEp556ioCAAH755Re6d+/OjBkzmDx5Mnq9/o9XFgSh05JDjhEFlCDIUGdJbnv27OGaa67hiy++oEePHp4ORxAEicghx4gCShBkqJP0rvOvf/2LK664wtNhCIIgMTnkGFFACYIMdZazQ1E8CcKFSQ45RhRQgiBDyk6S3ARBuDDJIceIAkoQZKiznB0KgnBhkkOOEQWUIMiQHM4OBUHwHDnkGFFACYIMySC3CYLgQXLIMRdkAZWbX+3pENyyWm2eDsGt5KCeng7Bra/qCj0dgls9g/w8HUK7yaF7vSOd2L7H0yG41Scp1NMhuBXv3zl/kmd1defMMSlx/p4Ood3kkGMuyAJKEIQzk0P3uiAIniOHHCMKKEGQIXVneUiLIAgXJDnkGFFACYIMyaF7XRAEz5FDjhEFlCDIkBy61wVB8Bw55BhRQAmCDMnh7FAQBM+RQ44RBZQgyJCCCz+5CYLgOXLIMaKAEgQZksH4TkEQPEgOOUYUUIIgQ3LoXhcEwXPkkGNEASUIMqSQQXITBMFz5JBjlJ4OQBCE80+paP0lhddee42pU6cybdo00tPTpXlTQRD+NOSQY0QBJQgypFAoWn21V2pqKlOnTmXFihVcf/31/Pe//5UgYkEQ/kzkkGPEJTxBkKEznQUWFxdTUlLidlloaChhYWFnfO/k5GTn/w8cOJAjR46cU4yCIPx5ySHHiAJKEGToTGeBS5cu5Z133nG7bM6cOTzwwAPO6bfffpusrKwW7V5//XUANm/ezD333NPOaAVB+LORQ44RBZQgyNCZzg6nT5/OuHHj3C4LDQ11mW6e6E6XmppKnz59iImJOacYBUH485JDjpF1AeWlUXLriDiCDFr+/X1aq+0m9g1nRLdgnlt+GICr+kdy+8g4NGolT3x5gCMFNZLHdfvF8QQZtLzybarbNm/NSGF8n3BHPP/dTHpxHdcPieGBy7pRZ7bw9+WH2J5RIWlcAGaTie+/WUh1VQW33fe42zYH92znyP5d2Gw2YuITGTl2AgA/f7eMrz9/H2+9gTsffobeKYMlialXhA8DY/xRKxVsOF5GRrmxRZtwXy2XdA8mt9LE7pwqjI02RiQEEhvghUGnYtWBYkrrGiSJ5xSzycT3yxZRXVne6rY6sHsb6WkH8fH1p76ulqum3+5cZrVa+HHFUrwNBpJ6pRDdJUGy2M50dhgWFvaHXeh/5OjRo9TU1DBs2DBqampQKpUYDIZ2veef0XO3XczsqwaSU1LNXa9+x8EM18sW08f2pEuEP2EBBr7dcowNe7NRq5RkLL2fEH+9s91Dc3/iw9V7JIsrOcxA/2g/VEoFm05UkFXR8pgBuHt4LEdL6tiQXn5W86XQYDaxcfVS6qoquPrOh9222bp2FT998Qle3gaunf0YiX0GnnF+e/WN8mVoXAAqpZKf00pIL613WX5NSgRWmx0fnZqMsnp+b5Z7/3dDX3qE+2C22Ljxs11UmyySxHTKpx9/yIpvvsbg48Mbb80lKrr1YuKTjz6gsaGB2fc7ipL/vTOXpYsXEREZyYuvvEpSjx6SxSWHHCPrQeQGnRq1SoFBp2q1jV6r4vaRcc7pIIOWgioT17y7lbWHivjLGOm+1E7x0alRK1uPKzrQm9+OlTLmX+sZ9cp60ovriArwQqNSMO7VDXyxLYfXb0yhI+4iNdbXYrVaMNbXuV1uNplY+uk7XHfrbKbPup8fli+htLiQksJ8LJZG3l70PZdNuY53Xnkam83W7nh0aiW3D41h8a48Fu7M446LYlu0ifbXMXtkHMv3FbH5RAXGRhvBBg2XJ4ewdE8B3+wr5C4367WXsb4Wq8WCyVjfapsvPnmbqTPu5LIp13Hi6GHysjMAsNlsvPOvZ+mW3IexE6ZKWjxBx94hs2PHDmbPns2//vUvrrnmGm6//Xb0ev0fr3iBGd4rmkMZJSTc+C4b92bz/mMTXZZHh/gy84q+/N+SrTzz8XrevH88Bi8NI/rEcNMLKwi96j+EXPkmH3+7lx+2SXeXkU6tZObgaJbuLeCLPQXcOiTabbvEYD0DY/3Per5UzPV12CwWzEb3RV15cQFWSyPPfPANw6+4mkX/+Sc2m63V+e3lpVbyl5FxfLY1h0+3ZHPvxfEuyyP9dPSN8mPlgSIW7czjrhFdnMv6Rfkxb1sO132ys0OKp/T044y8eDQrv/+R+IQEFi9c0GrbwoICVnzztXN6757ddO+exNr1vzFk6DD+8dzTksYmhxzTKQuo9PR0Zs+ezfbt2zv0c8pqG8hr5czrlBuHxfDt/kLndHldAztOnl3sz60m9w/WPxeltQ1nfN9bLupCryg/Qnx1lDfrNVm8NQeLzc78zVmoVQqC9FrJYwsICiE0IqrV5UcO7MJLb3CefST26M2W9T8CcMXV01Gr1Uy85iYsVgs1Ve3vIRvaxZ/8ajM2O1hsdqw2O8nhPi5t/jquK/O25dJgbUqml3QL5mixowisMlqICfAi2KBpdzzN/dG2AigvLSYr/SgA1ZUV6A2O2H9auZTQ8EiSeqdIGtMpHXmHzJAhQ/jll19YtmwZy5Yt45tvvul0z4Q5Hzkmv6yGZRvTaGi08srCzfSKC3FZPml4Inkljt7rhkYrmYWVTByWyJZDufy2P4daYwN1pkbCA/VkF1dLFtegGD8Kqs3Y7WA9ecz0CHU9c1cAQ+P82Z9ffVbzpeQbGExgeOQZ24yceC0qtZpRU27AZrVQV115xvntcVFCIHmVJmeOsdjs9I70dS730qjoEe6Dl0aJXqOi0Wp3LpuWEkHfKF+81ErJiyeAxMRu9EhORqFQ0LdvCjGxrZ8IfvPVUq6YOMk5HRYWxvgrJqDVavnL7Ps4kX5c0tjkkGM6ZQGVmJiIwWDAbrf/ceN2OtNHJIYaKKoyU1Xf2GKZQgGD4gJ4f/2JDgqs9UW5FUaCfbQsvmcok/pFAJBfaXIuVykVVNQ1UCbxJakmre+oDWYzVRVlzmn/wGBKiwtcCgmbzYqffyB+AUHtjiQxxECVsenfp6K+kdgAL+d0/2g/fHRqekf68PilXUmJ8j25np6qZgmtymQhxr9pPan80UF99Yw7eP3vj7Bq6TyumDqdwOBQ7HY7332zEJ23N5+9/W++WfghNqtV0rg6+hktnd35yDHZRU1Fho+3ll1phS7L9V4awgKbCpeiijq6hPvRaGkq9HvGhXAos1TSuBKC9S5f5pXGRqIDXPf90YlBrD/e8vJca/OldqbfUQsKayqubDYrBr8AfPwDW53fXt3DfKh0yTENxAV6O6czyuo5VlzHK1f2ZMbgaF756RjgOJZOlNbTI8yHD2b0o3+0X7tjaU1DQwM5Odlcd8ONbpdv2byJIcOGo1Y3jdppfqmvvr6eXn36ShqTHHJMpyygADQaaXsDzsWkfhF8t7+wxXylAu4Zk8D1g6N5fELSeY9r4ZZsHl68j0eW7OOZK5Px1rhe6hvdI4R5m1retXA+dO/Zl+LCfE4cdYwXK8rPwcfXtbt/z/bNTLrmZknOGPRaFbUNTcWF1WbH37spSfSN8uVgfg3fHy5h5f4i/nZ5N/y91Y71zJbT1jv/+9wVV08nNqEbPyxfQsjJL4CCvGzMZhMTps7glnsfY9fv6/n5+2WSfm5Hnh3+WZzPHHP1xUm8umSLy7yN+7IZk9KF6BBflEoF8RH+lFWbXNpMGp7I91ul7RnQa1XUNbju+35eTceMj06Fr05NQbXZZb3W5ntS6u6tjLpyeov9trX558KgVVHTrOC02OwE6F33nf/8mk6En46Bsf7OEzqbHRbsyOWJlUf4aHM2j45LbHcs7hiNRv775ut8/eVSPvvkoxbLGxsb2Ld3D0OHDW/1PX5Zt5a7/iLtnWxyyDGdtoDytCv6hPPjwSK3y2x2eH99Bjd9uIMpKZH4nGEMVUfakFbKjowKuoY1ncVqVAoGxwfy1Y5cj8QUGBzKw8++yqov57Nm2WKOHt5Pr/5DnMstjY2kHtjNuEnTJPm8WrMFrarpgNSqldQ3K6j8vJoS/tGSOsrqGugZ7kOt2YpW1bT769RK6hqk7eU5G1/O+x+33PMoV1w9nVeffZDy0mJqqyrx9QvA4OOLWq1m+CVXcGD3Nkk/V6Fo/SVIK9DXi2A/Pet2ZrjM33OsiAfn/sQrf7mEu6f0p3+3CDbsdT3xGZgUwY7UAknjqTVb0TTb908/ZsYnhfBTWstn9LQ231MsjY1kHN7HsMuuPKv556rGbEGrbp4rVNQ1O/lSKuCekXHcvXgfFfUNvDglucV7rD5YRH2DxaVQlYq3tzePP/U0b//vfRbOn9di+Tdffcm1N0xvdf2qqkoqKisYMXKUpHHJIcfI+i68M5nQJ5zkk9e59VoVOrWSRquNF1Y13RV3oqSO9JI6rLaOv9TYmpxyo8s4qJsv6sJHGzLOsEbbfLPwI1YvnQfAldNv59qZd//hOv2HjqT/0JFkZxxny4af6NVvkHPZjyu/4OrpsyQ7C8koN7p0jYcYtC53yJTVNRKgb9rNy+sbMVtsZJTVE9TsLDJIr+FEaeuDvc/GskUfsXrpfACunH4b19x85m1VX1fL/p1bmD7rfq6ecQdZGcfY9ts6Bo+4hKqKpsskgUEhaLW6dsV2ugspiXVmCgXcc9VAXl38u9vli9YeZNHag0wY2pX1e7LILKxyLgv09aKsWvoxllkVRvo1G8MTbNCSUd5UGF2RHMq4pGAA/HRqBsb4o1C0Pr+9l/TWfjWP9csXAXDJtJsZf/3tZ7Xe5jXfMHZay57s1uafq/SSOgZ1CXBOh/poOVbSdBPN0LgA6hqs1DVYefGHY3x6cwrdQvQcPy2fZJYbMUpwkvbB/97h048/BOCOu/7CPffNAWD4iJEEBAa0aL965Qo+n/cpADU1jjF3CqWSe+69H5vNxtIli7nrL7PbHdfp5JBjZF9Anf6PPDQhkD3ZlTzyxX7nvKv6RzIkIZAXVqWiVDgGDdY3WPHWqkgtqMHY2P47PVoG5jo5rGsQu7Mcg64Tw3xILajBS6Okzmyh4OT4p6kDo/gtrZTK+ka8NEp6RfmxO6uyXWFcO/NuN0VTy4Lx0N4dJPVKQaN1DFxvbGjgmwUfMPuxfzgT2YafVpEyZCS+/gGYTSYyjh8huc+AdsW3PauSq/uGo8Ax9kulVJBeWk9KtB/78qr5Lb2cR8c23cHmpVZxqKCG3EoT949y3F0Z6K0hu8JEhbHlWLe2uObmu1sUTe7G2Di3lUaLvdm2TOrZj6CQMELDo+iS0I2s9KPEJSZRXJjHkIvHtiu20ynlkN06gQevHcJn3+/D3GglyNeL+MgAQvz1/LSjaexkoK8Xf7lqIPe8/r3LulcM6cpP26UfY7krp4opvUKdx4xSARllRvpG+nKgoIbZXx10tr13ZBcOF9ayIb3cpVBqPr+9xl9/e4uiye4mxxw/sJv45D6oNVp2/Po9yQOGY/ALoMFsIu9EGgk9U1qd3x5bMiq4bkAUCkB9MsccK65jUKw/u3KqqDZZ8Do5jMJssZFWVEd5fSN6jYoQHy3ZFUb8vdQcL6mjUYKT7Xvum+MsmsxmMyaTCS8vL4oKCxk15hJnu02/bWTkxaNYtLTpzrv3333b8R733g/Aws/ncc1116PT6aisrCAvN5feEo2FkkOO6ZQFVF5eHpmZmezevZuUlBS8vKQf3AsQ7KNlcHwg3cN9iA/Rk1laz5OTevDkVwc4Xuz+Nv0BcQH8+9o+rDtSTHltA2/+eEzyuEJ8tAxJCKR7uC8JIXoySut5anIPHl96gBpTI2/f3J/DBdXszqzks98yAZjcL4J/XN2LxpN3mmnVSm79aIfksVWWl3Jk/25yMtPJz8kkKjYegM/ff4M5T71EaHgUqQf3kJOZzsx7HiX05N00m3/9gU/m/gu12tHr09jQwPOvf9jueMwWG4t35nHLkGisdjvv/JZJiEHL7JFduPfLg+RVmfj2UDEzBkbRaLPx+Y5cGqx2Smob+C29nBsGROKlVvLuye0opcryUlIPtNxWCz54k/uffInY+ESuu2U2K5d8SmhkNI2NDQy52PFwudmP/4PVS+fTtUcvVCoVw0ZdJmlsckhuZ3I+csxj04fx3G2jePbWiwHQaVQkz3yfL/4+jWGzP0OlVDC0ZxTJXYK569VvKa9xHf90yYA4HnlnreRxmS02vtxbyI0Do7DZ7Xy4JYdgg4Y7h8fw8HLP/+xOTUUZGYf2UZh9guK8LMKiHSc6qz+by4yHn6cgK53lH7yB6mQusTQ2MPuFuez5ba3b+e1lstiYvy2HO0d0wWqz88Yv6YT4aHnwkgRuW7CXw4W19IvyY3LvMCw2O5tOlFNe30j3UAMvX5nM7pwqDhbUsGyftJdiAX5Z9xNz3/oPV0yYiH9AAA889CjgGBf18j//zhffLMffP8Dtup9+/CHvvfM277/reCJ4Y2MD3/34s2SxySHHKOzn41a386zf8+s8HYJbVmsH9FRJYP5drQ8u9KQX1x31dAhuPXtpd0+H4NaQrmf/bJ7MUlOry+JDOuaE5ULiPf5VT4fg1lUzL/d0CG7NGHDmxxJ4ytsSDneQ0urZnTMn6zVnXxTJIcd0yh4oQRA61oV0J4wgCJ2PHHKMKKAEQYYupGexCILQ+cghx4gCShBkSAYnh4IgeJAccowooARBhuTQvS4IgufIIceIAkoQZEgO3euCIHiOHHKMKKAEQYbkcHYoCILnyCHHiAJKEGRIDmeHgiB4jhxyjCigBEGG5HB2KAiC58ghx4gCShBkSCWD5CYIgufIIceIAkoQZEgGuU0QBA+SQ44RBZQgyJAckpsgCJ4jhxwjCihBkCE5/NCnIAieI4ccIwooQZAhGeQ2QRA8SA45RhRQgiBDcjg7FATBc+SQY0QBJQgyJIPcJgiCB8khx4gCShBkSA5nh4IgeI4ccozCbrfbPR2EIAiCIAjCn4nS0wEIgiAIgiD82YgCShAEQRAEoY1EASUIgiAIgtBGooASBEEQBEFoI1FACYIgCIIgtJEooARBEARBENpIFFCCIAiCIAhtJAooQRAEQRCENhIFlCAIgiAIQhuJAkoQBEEQBKGNRAElCIIgCILQRqKAEgRBEARBaCO1pwPoCAmPfOfpENxKTgrxdAhu3TMyztMhuLVkT4GnQ3DrsdFdPR2CW0MS/M+6rfdl/251mXHdU1KEc0Gb+N42T4fgVkZWpadDcGvUwGhPh+DWqK5nf8ycT4n+Pp4Owa2R3QPPuq0ccswFWUAJgvAHlCpPRyAIwoVMBjlGFFCCIEcySG6CIHiQDHKMKKAEQY5kkNwEQfAgGeQYUUAJghzJILkJguBBMsgxooASBDmSQXITBMGDZJBjRAElCHIkg+QmCIIHySDHiAJKEORIBslNEAQPkkGOEQWUIMiQUimeoSsIQseRQ44RBZQgyJHC0wEIgnBBk0GOEQWUIMiQHM4OBUHwHDnkGFFACYIMySG5CYLgOXLIMaKAEgQZUqou/OQmCILnyCHHiAJKEGRIDmeHgiB4jhxyjKwLKC+Nkrsv6Uqwr45/LDvUYvmax0eRHOXnnP7gl3T+vToVgP/dPpCJKZEAXP7qBo4V1koWV+9IXwZ38UetVPDr0TJOlNU7l/l7q1l4ywDUzar7579LZUd2FWqlgnkz+xNs0FJa28BtC/dgs0sWFgANZhMbVy+lrqqCq+982G2brWtX8dMXn+DlbeDa2Y+R2Gdg0/omI5u++xr/kFC69upPYGhEu2NKDjPQP9oPlVLBphMVZFUY3ba7e3gsR0vq2JBe7pwX5qNldGIQuZUm9uZVY7LY2h3PKWaTie+/WUR1VTm33fe42zYHdm8jPfUgPn7+1NfWctWNtwOQeTyNHZt+ISQ8kqKCXG64/T5JE5JCKYMRnp3AuR7LABG+Osb1CCGnwsjO7EqMjdLtm14aJbNGxRPso+WlValu28yd2Z/L+4QDMOXNTRwvrmPlQyPoEenrbPPxhgxeX3NUsri6h+jpE+mLSgHbsqvIqTS5LNdrVUzpFUqNycKa1NIW609MDkGtUrL6ULFkMZ2SnXqA9H3bsVkt9B5xKRHx3Vq02fHDcqzWRiqKCxh46ZWEd+nqsvz7T/5DdLdepIy5QrK4zCYTPy5fTE1VOTfPfsxtm+fn3ExuZrpzesI1N3PDHQ+wfs1yli/8EG+9gdse+Bs9+w2SLC455BhZF1A+XhrUKiUGXcvnVXSP8GHuT8f4LbUUm93OrNEJ7MxwfPHGBHmz4UgJz39zCLvdTlltg2Qx6dRK7rqoC39dfgilQsGLU3rwt2YJrk+kLw8vO0TeycTy10sT2ZdXDcCoxCD+/n0a5XWNNFhtkhdPAOb6OmwWC2aj+yKlvLgAq6WRZz74hs1rvmHRf/7Jsx8tR6lU0mg2M/+1Z5h296OERMZIEo9OrWTm4GieW3MUpULB05cl8uJPx1u0SwzWMzDWn6Mldc55kX46bh8awxu/nqDBKv3GMtbXYrVaMNXXt9rmi0/e5uV3FwLw1gtPkJedQXSXBBZ+8CaP/uMN9AYfFn7wHw7s2krKkBGSxSaHs0NPa8+xHB3gxeyRcbz04zHMEhb1p/jo1Cdzn/uvgOhAbzamlfDCisPY7FBe10C3MAP/+zmdTcdKsdvhlpFx7M6skCwmrUrBdSkR/PuXEygVCh4eFccbGzJbtNFrVG6LyUBvDRd3DWJrVqVkMZ3SYDLy8+IPuO0f/8Vus7Hk1b8x85nXXdrkpadSmHmMK2c/QW1VBUv+/RR3vfweipPHWn56Ksd2byW6Wy9JYzMZ6xx5xug+z+RlneCqG++k98BhKBQK1q5aSlKvFEqL8rFYGnlj/mp+Xv0l77/6LP9Z8J1kuUEOOabT/YUWi4VXX32VGTNmcNddd2E2mzvss0przOSUu9/pMkvqWLOvkFqzhfoGK71j/NhxwlFAzRqdQO8Yf0J9dZIWTwDD4wPIqzRhs4PFZsdqtdMrwse5fFtmJeml9ZgsNqx2OxarzfnlP7FXGEO6BABQ12CVNK5TfAODCQyPPGObkROvRaVWM2rKDdisFuqqKwFYs/hDeg+5WLLiCWBQjB8F1WbsdrDa7FhtdnqEGlzaKIChcf7sz692mf/Q6HgW7szrkOIJICAohNCIqDO2KS8tJivdcQZfXVmB3uD4tzYZjRzZvxuAirISfPwCpA1OcYZXG9TW1vLAAw8wYcIEZs+eTf0ZisXO5HzkmfYcy09d1o1PtmR3SPEEUFrbQF4ruQ/g1pFx9IryI8RXR3mdI8dlldXz48Ei6sxW6hus9IryZZeEBVRKlB9FNQ1Nx7LdTmKw3qVNpdFCeX2j2/XHJAayI7tSsniaO7ZnK0ERMSiVKlRqDSqVipy0gy5t0rb/RkCYIzf6+Adit1nJP5EGgN1mI23HJrr2la6H5xT/wGBCwlvPM+FRsQy+eBzeegNe3nqy04/SvVcKAJddeQNqtZrLp87AarFQUyXdv6ccckynK6AKCwuZM2cOS5YswWKxkJWV1aGfZ2/lu7Ox2Zeqr5eaWpPF2aOTU15PqJ+O5Q+P4MoBZ/6CbKtuoQYqjU0JosLYSGygt3Pa0qxbqX+0P3tPnrEatCoO5NcwLD6Qd67vQ5dm60hNcYYjICisqbiy2awY/ALw8Q/EbKxn+7rV2Gw2vnz3X2xe840ksSQE66k2WZzTlcZGogO8XNqMTgxi/fFyl3n9onwx6FT0DPfh4THx9G12WUJKZ9pWAFfPuIPXn3+EVV/M44qp0wkMDj05fxYfvPFPVn85n+Q+A0jsIe1Zq1KpbPXVFjt27OCVV15hzZo16PV6li1bJmmcHeV85JlzPZYHxvjjq1PTJ9KPv43vRv/opmEEUjrTaUNuRT0hvjqW3jeMySmOy+zNc6KPTk2d2SppL3dcoBc15qZjucpkIdJP16Kdu4/sFe5DanEdHXQuRGHGUQzNTmIMAUGU5me7tGlsMFHXrAAx+AdSVVoEwP7f1tJvtHSX7U6nOEOaUWs0zv+vr6vFS69HqVK5FF02mxVf/wD8AoIki0kOOabTFVAxMTEYDAaMRiODBw8mKSnJ0yExOjmUDaklzul5GzO597Nd3DdvN/+8tjfeWukeWW/QqqltlkQsNjsB3hq3bYfEBbD9ZHd1XYOVBTtyeWTZIX4+Wsrsi+Mki+lcpe7eyqgrp6NQKMhMO0hAaDjDxl/JlbfP4ddlCzmya0u7P0OvVVHX0LS9rDY7fl5NlyV8dCp8dWoKql17GHpH+HC4sJaf0kr59lAxj41NwN/r/F/RvuLq6cQmdOOH5UsIadazN2TkWIaNupSVSz4jJLz948ROd6bkVlxczKFDh9y+iotdx5aMHTsWX19fFAoFKSkp+Pv7Sx5rRzgfeeZcj+V+0X7sy6/m20NFLNtXwPMTkwjwPr/75oLN2Ty4cC8PLdrHc1f1xFvjmuNG9Qjht6MtxyC1h7dGRX2znvPTj+XWqJQKEkP0pBbX/WHbc2Wqr8PL0NR7qFSpqT/Zs35KXK/+pO3cjNlYT2ODmZqKMrx9/KivqcJYW01wVGyHxXe2Du7aQt+Bw1vM37/jd66YdhOKM1VibSSHHNPpCihwdNnNnTuXpUuXkprqfoDj+TSqRygbmxVQp/xyuJht6eV0C/dxs9a5qTFZ0Kqb/ll0aiV1ZveX4wK9NVS46c6evz2XCN+WZ27nk6WxkYzD+xh22ZUA1NdU4R8Uikarw9vgS68hF3Ns3452f06t2Yqm2SBcrVrpkoTHJ4XwU1rLfztfnZrCk0XV8dJ6yuobSQoztGjX0b787H/cMvtRrpg6nVefeZDyUkfyWLv6a4aPGc/t9z/B2688TebxNEk/V6FUtPpaunQp11xzjdvX0qVLW33P3NxcJk6cKGmcHamj88y5Hst+XmoKqhzjotKK6yitbaBXRMf0kP6R9aklbM+oIPG0Y2Nk92B+S5O2gKprsKJRNX2Ba1VK6hv/eCjC6K6BbEwv/8N27eHt44ulsWm4hsVsQuftuk2Sh4xi2KRr+XH+2+z5+VtqK0qJ7taT3T9/y6CTedDTDu7ZTp9BF7nMszQ2knZwD2MmTJX0s+SQYzrlIHIfHx+efPJJkpOT+fLLL3n++ec9FotC4ejlqGl2mai57NI6yiUcB3WirJ6BMU1d9iEGLcdLW55ZJQTrSXczHxxnbsdKpDkbW/vVPNYvXwTAJdNuZvz1t5/VepvXfMPYaTc7z2j8gkKorWxKcn6BwTRKMO4kq8JIv2aX34INWjLKmwqmK5JDGZcU7PhMnZqBMf4oFFBe3+jSG1BR30hDO8ebLFv4EauXzgfgyum3cc3Mu8/Yvr6ulv27tjD9jvu5esYdZJ04xraN65h4zU2sWbaINz5dhkKhICfzOBvXfkt8tx7tiq+5M3WjT58+nXHjxrldFhoa6nb+2rVrmTVrFmp1p0wpbnV0njnXY7msroFAfdO+WVbf0GFjoc5GTlm9cxwUOHKit1bl0rsmhdxKEz2bjREL1GvIPu7+ZpXmRsQHcHmPEAD0J3vK7HY73x5ueeLUFhu/+ZzfVy9xfMaVM6gqK3Iuqy4vJSKhe4t1hk++AYCdP62k/9jJaL282bl2JXt//R5wnEge27MVu91G/0vOrRBYufhjvvvqcwAmX38rV99011mtZ7PZaDAZneMsT1m3+ksmX3+bpL1PII8c03kicSMhIYHaWukeD+DO6fvMRd2C2ZVRQYPVkbAGxgeyO6vpurZWpSQx3Icj+dV4aZTUmizktXLb/LnYmlnBNSkRKAC1UoFKqSC9pI4BMX7syW0aBD0sLoBtze42CfTWoFEpKK5tICbAS7LBlOOvv71F0WR3Mwrh+IHdxCf3Qa3RsuPX70keMByDXwANZhN5J9KI79EXi8VCbVUlPv4BlBcVcJEEZzy7cqqY0isUBY6ufKUCMsqM9I305UBBDbO/ahroee/ILhwurGVDejlRfjrmjIp3LvPSKDlc1L597ZqZd7comtxtq0N7d5DUKwWNRou92SC8pF79CAoJA8Bbb6CxsQGtVkf3Xv0oys9tV2ynO9MtxmFhYYSFhZ31e23atImkpCSioqIoKioiPDxcihDPm47KM+d6LG84XsbjlyY6p73VKg6cdgOEFE7fA4YlBjnvqksM8yG1oMaR48wW8ps9TqB/lwD2dsBg7b351YzvEdzsWFaQXWGiZ7iBI0VNBaYC13FQL6874fz/K3s79tv2Fk8Ao6+9ldHX3go47sJb+PJfsdtsWK0WbDYr0YnJWC0Wso7scxkcXlGcz4kDO5n2wLMAPPzul85lq99/jS49U9r1GIOrb7qrRdHkbizvkX076darHxqNFoD01AMkJvd1afPb2m/pO3gEvv4BmE0mso6nktSn/znH1pwcckynK6A2b97Mp59+ysSJE1EoFNxwww0d9lkhvjqGJQbRI9KPrmEGThTX8fy0Xjy0YA9HTz7XaWzPMJZuaxosGOSj5aM7B3Ewt5qdGeV8uP5Ea29/TswWGwt25HL7sFisdjv/XZ9BiI+W+0clcNeSfc528UF6vtid75zuHmbgoUsS2JpRwcGCGtZJ3L1+Sk1FGRmH9lGYfYLivCzCoh1jrVZ/NpcZDz9PQVY6yz94A5XacQZtaWxg9gtzUanVzHjoOdZ99RkRXboS0y2Z2G492x2P2WLjy72F3DgwCpvdzodbcgg2aLhzeAwPLz/S6nr51WZ+SC3h+pQIGm12Fu/KdxkkK4XK8lJS9+8mJzOd/JxMomLjAVjw/pvc/9RLxMYnct2ts1m55FNCI6JpbGxgyMWOs7JZc55k5eJPiU3oRmFeNpOumylpbFLdYjxv3jzmz59PYGAgNpuNfv368cILL0jy3h3pfOSZcz2WcytNrD5YxMwh0TRa7Xy2LUfyO0VDfLQM6RpEUrgPCaEGMkrqeObKZP66ZD/VxkbevXUAh/Or2Z1ZwacbM13WvSQ5lK92SFvQAzRY7aw8WMy0vuHY7HYW7Mwj0FvDzQOjeHbNMQCC9RrigxwD8YP0mlbvyJOa1sub0dfezq9ffoJSqWLyXY+iUCqpLinkh8/mcu8b86guK6Yg4yiVxYVMe+BZNNrzM4yiqqKMowd3k5uZTkFOJpEn88ziD//DPY+/QEy8oxjfv+N3Rl1xlXO9ret/5PN3/436ZK5ubGjgqVfflywuOeQYhd3e2n1of14Jj3zn6RDcSk4K8XQIbt0z0vMDzt1ZsqfA0yG49djorn/cyAOGJJz94MozHSMZ/5ksRTgXtInvbfN0CG5ldMAzkKQwamC0p0Nwa1TXzjMgublEf+nG1UppZPfAs24rhxzT6XqgBEHoeHJ4yJ0gCJ4jhxwjCihBkCGlDH5mQRAEz5FDjhEFlCDIkBySmyAIniOHHCMKKEGQIZXqwk9ugiB4jhxyjCigBEGG5HB2KAiC58ghx4gCShBkSA7JTRAEz5FDjhEFlCDIkBySmyAIniOHHCMKKEGQIal/tkEQBKE5OeQYUUAJggzJ4exQEATPkUOOEQWUIMiQHO6QEQTBc+SQY0QBJQgyJIezQ0EQPEcOOUYUUIIgQ3JIboIgeI4ccowooARBhuSQ3ARB8Bw55BhRQAmCDMnhDhlBEDxHDjlGFFCCIENyODsUBMFz5JBjRAElCDIkh+QmCILnyCHHiAJKEGRIDslNEATPkUOOuSALqKgoP0+H4JafXuvpENwqrjd7OgS3vLUqT4fgVlZ1vadDcGsI/mfdViWD5NaRDF4aT4fgVmiowdMhuNVZv0zLjRZPh+CWt9ro6RBaEXjWLeWQYy7IAkoQhDOTQ3ITBMFz5JBjRAElCDLUWXsEBEG4MMghx4gCShBkSCWDW4wFQfAcOeQYUUAJggzJ4exQEATPkUOOEQWUIMiQHMYnCILgOXLIMaKAEgQZksPZoSAIniOHHCMKKEGQITmMTxAEwXPkkGNEASUIMtSZu9ftdrssfkdLEC5kcsgxSgliEQThT0apVLT68pTdu3czZcoUHnzwQSwWC/PmzePAgQMei0cQhHMnhxwjCihBkCGVUtHqy1PefvttHn/8cYYNG4ZarWbq1Kk8+eSTHotHEIRzJ4ccIwooQZAhhULR6stTxowZw5gxY/Dx8QFg3759VFRUeCweQRDOnRxyjBgDJQgy1BkHePr7+/PKK69QU1NDRkYGixcvZs6cOZ4OSxCEcyCHHCMKKEGQIZVKuuS2aNEiNBoNxcXF3Hrrrfj5nduPeU+bNo2+ffuybds2LBYL8+fPp1evXpLFKQjC+SOHHCPrAkqnVjJjSDSBei3/+Tm9xfK4IG+u7BeB2WJjb04VO7IqUSkVrLp3GAH6pl9jf33tcZbvLZAsruQwA/2j/VApFWw6UUFWhftf5r57eCxHS+rYkF7unBfmo2V0YhC5lSb25lVjstgkiwsgN+0AGft3YLNa6HnRpYTFJbosr6+u4IOHb8JmtTrnTXv0JbqmDGX3T8tRKFWY62oIiU2g28ARksSUFKqnb6QfaqWCLZkVZFeanMsUwO1DohnSJYCcShMfbMmmvL4RgPFJwdjsoNeqyK00sSevWpJ4Tmkwm9i0+kvqqiu48o6H3LaxWi3sWLuajMP7mPHoP1os2/L9N+j0BuJ69CUsJk6y2M40DqG4uJiSkhK3y0JDQwkLC3NOHzp0iH379vHaa6+xc+dO3nnnHZ5++ulziik/Px+9Xs/YsWMBMJlMLFy4kJkzZ57T+3UGPcMNDIj2R6VU8NuJcjLL3R/LfxkRy9HiOtYfdxzLk3qGolYpCPPRsTattNUccK50aiU3Do4mUK/hrV9OtFgeF+TNlL4RmC1W9uZWszOrEoA7R3Thmv6RFNWYeXnNUdJL6yWNq1uInj4RPqgUCrZlV5FbZXJZPjDaj0g/HXqNknXHyqkwOo7lKT1DqW+0Eumn4/sjpc75Uso/epDsg47clzRsHCFdEt22qy4pIG3LzwRGdqFL38FotF5sWDCX9J0bCYpJ4LK7nsQ3OMztuueiwWxi87dfUldVyZQ7HnTbxmq1smPdKjIP7+PGR/7hnJ+2Zys/f/kZtZXlTL3ncZL6D5UsLjnkGFmPgTLoVKiVSvRaldvlT09M4tPN2Xy0KYuZw2LRqBSkRPvx7KojjH/rdy57azMr9hXw+4lyt+ufC51ayczB0SzdW8AXewq4dUi023aJwXoGxvq7zIv003Hn8FhWHSxia1al5MVTo9nIxqUfMfLa27j4ulmsX/J+iza5aQe56e9v88AHK3ngg5V0H3wxsT1TqCwuIDftAAMuu4phV93Eb19+IklMWpWC6f0jWXagkK/3F3LjwCiX5QNj/NicWcnDKw5TbWpkxoBIAEINWnqE+fDzsTJWHyrm+pQISeJpzmysw2a1YDa2/iVjrq9DqVZTV+16Hd5ms7H0rReI7d6LIZdOkbR4Akf3emuvpUuXcs0117h9LV261OV91q1bR7du3QDo2bMn69atO+eYJk+ezC233MLMmTOZOXMmN910EytWrGjPn+lROrWSWwbH8MWefJbszue2oTFu2yWG6Bnc7FjuFqInIVjPqoPFfLW3gDmj4pD6YohBq0KtVKDXuM99T13RnU9/z+bjzdnMHBKDRqWgT5Qv6aV1TP1gO3tyqnjqiu6SxqRVKbi2bzgrDxaz4mAx16eEuywP8FYzqmsg3x0p4Ye0Uqb3dxyzg2L8UCjgl+PlbDxRwcxBkZLGBdBoNrHl648ZOvVWhk27nc1ffui2XUVhDhsXvkP/K66j25DRaL30ZOz5naSLLuXW1xej9wvk91bWPVdmYz1WqwWz6Ux5phaVSk1tdaVznqWxAWtjI/f96wMuueYW1i75SNK45JBjOl0PVG1tLX/72984duwY8fHxvPnmm+j1+g75rPK6RvKrTIT76Vos6x3pi9Vmp77R0ZOSW2lkbFIIv6SVYrHZne2C9BqKqs2SxTQoxo+CajN2O1jtdqw2Oz1CDaSV1DnbKIChcf7sz3ftMXlodDzvbsqiwWqnI6Tv2UpAeDRKpQqUKpQqNXlHDxGd1NvZJnHAcFRqR++cpaEBpUqNRquj0Wyi8EQaDSYjdrsNlUbT2se0yYBoP4pqGly2V7cQPcdPnhlnVRgprXOcja4+XMzsi7oAji+3hCBvdGolCnD5N5WKb0AwgWGRVJYWtdpG7+tPSETLL9Yta74hMDSCuOS+kscFZz47nD59OuPGjXO7LDQ01GW6tLSUvn0dMep0OoqLi885ps8//9z5XgC5ubls2bLlnN+vNecrxwyO9aeg2oTNDrZTx3KYgbRi12N5WFwA+/JqnPOGxwdQVOPIKVUmCyqlgm4heo5J2NtTXt9IQZWJcN+Wua/XydxnbJb7LukewoH8ag7mO+L8bEs2K+6RrrcCoF+kL8W1Ddg5dSxDYrA36WWO3rdhXQLIONmDV2O2EuGrI8BbzcBoPw4UOOLKqTTRLViPr05Fjdna2ke1Wea+rQSERTlzn0qlouDYISK7N+U+u93Oug//zbg7HkOj83LOD4nrhl+Io9gbOPlG1n34qmRxAfgGBBEYGklVaevHnt7Xn+DT8oxao6XX0FEAxHbvRfqBXZLGJYcc0+l6oHbs2MErr7zCmjVr0Ov1LFu2rGM/0O7+i7NHhA8V9U3dwOV1DcQF612+aBOC9ZyQuAs7IVhPtcninK40NhId4OXSZnRikLOr/5R+Ub4YdCp6hvvw8Jh4+kb6ShoXQFHmMfR+Ac5pg38gZfnZLm1OFU8A2Yf30KXXAABCYxMIj+/O8jefZfvqL5h0z1OSxBQfpKfa3LS9qkyNRPk1ba9TxROAl1rpTMC5VSYyy408MiaeKb3DeP93179DKmc1jvK0Nna7nd9WLUXr5c3Kj97k5y8/w2aT7ssAznyLcVhYGL1793b7at61DhAcHIzR6NimdXV1BAcHn3NMzRMbOJLlJ59I01PZ3PnKMV2D9VSddizH+Lsey2O6BbH+WJnLPK1KSYC3xmW9EB+t5PG1dsrQI/y03FffSFywN4XNThT1WhVHimoljScu0JuaZsdytclCRLMCLy7Qi5pm27PG7FiuVSnx82rqC6g2WwjWS7u9SrKO4e0b4Jz29guiosA1Z+Qe3o25roaCowf46f2XyT28G8BZPAE0mkyEJSRJGhtwdne2naHNkR2bmXDLvRJGJI8c0+kKqLFjx+Lr64tCoSAlJQV/f/8/XqkD+GjVLgerxWonUO/aazIiMYjN6dJdvgNHYqpraPpcq83ukhx8dCp8dWoKTuv16h3hw+HCWn5KK+XbQ8U8NjYBfy9pOxjN9XV46X2c00qVGmNNZavtT+zfTtdm19THz3qIqpJCsg7vQe8nzb+rt0ZJXUNTcWG1gZ+X+8sSA6L9+e5w09nLvB25hBq09Ar3cUncnlZakENjg4kRk69jyqwHObxjE9t/WiXpZygVrb/a4vLLL+fIkSMAHD16tNWzyrORnJxMz5496dmzJ8nJyUycOJEJEyac8/u15nzlGMex3LRvWmx2/L1bHsv5px3LhwprGRrnj5daiValIEivpVbC3pQ/4qNVUWs+Pfe5FiSju4WwYGuOpJ/rrVFS3/xYtrvmPm+NynlFABy50VenJq2kjiGx/igVoNcoMWjV1DZIezw3GOvRGZrnPhXGmiqXNnmp+4jqkUKfcVeRcvm1rHn3BeqrXC/NZ+7bwoAJ10saW3tt+3EFW39cxooPXpf0feWQYzrdJbzmcnNzufnmmz3y2dWmRuLVTd36XholdaclseQIHxZtz5X0c2vNVjSqprpWq3ZNKuOTQlyKgFN8dWrnGeLx0nrK6htJCjOwI7uqRdtz5eXjh6WxwTltaTCj9Ta02r6+qgKfAMfZgs1qZcMXH3Hbyx/y/Qf/ZsVbf2fGc/9t9zNB6hqsaJodkVqVAmNjy7FfoT5aKo2Nzi8rpQKmD4jk2TVHueeiLjw8Op4X17a8kcAT6qurMPj6421w9CL2GzmOY/t3MnzCNMk+Q6qH2fXq1Ytu3brx1VdfkZ+fz8MPP3zO7/X2228zfvx4SeI6Wx2ZY2rNFjTN7kTSqVyP5ct7hPLtoZaXd7dlVRLuq+Wui2LJKKsnUK/hWLNL+B2t2mQhvlkOcuS+poLE10uNv7ea7ScHlkulruG03HfasVzXYEXd/FhXKzE2WtmVW4WPTsVNAyKpMFowNlpdep6l4KX3bZH7dN6ul31NtdX4hznGYIZ3TcYnMITC9MN0HTgSgKrifAz+QQRFx0saW3sNu2IqvYeN5r+P3k5hVjoRce4Hx7eVHHJMmwuoI0eOsG3bNgoLC/H29iY6OpqxY8e2q1vNnbVr1zJr1izUas/UeMdK6hjXo+labKivjq0nms4mfL3UVBul77XIqjDSr9nlt2CDlozyprsVrkgOZVySY1v76dQMjPFHoXB0szfv9q+ob6RBgkHkvy9fwPbvvgBg6OQbqS5rKt5qyksIj+/mdr3i7HTCmt2lknFgB1pvAzq9gSn3PcO8p++mODud8Dj365+t7EoTvSOazgyD9JoWdzppVAqGxwWw+lBT7H0jfTE12jA22vjf5ixemdSDLoFeZFe43vXTFj9/NY+NKxYBMHrqzVx6/e3n9D5+QSHUNjtz9QsMQaOV9pKEhHcYc9ddd0nyPu4S2/r167nkkkskef/TdXSOySw3khLV/FjWcKKsad+c2DOUy04ey75eagbF+qNUKPjlWBmrDjr21QnJIfx8tFTyG0LO5HhJHWN7hDinQ310bM109LQrgGv6R7Jgm7S9T+C4rN4zrOlYDvTWkF3R1MOfW2lyyXEBXmqyKx1jzFac3F7X9g1n7VHXS6Lnaseqhez53jGgecCk6dQ2y311FaWEnJa7DAHB1De7GUQfEIxa67gEaWkwc3z7egZOntHuuH75aj4bV57MM1ffzLjrb2v3e/oEBBHfsx9Klfve+3Mhhxxz1pkjNTWVN954A3AM8goICMBsNrNt2zZWr15NbGwsTz75JL6+7R97s2nTJpKSkoiKiqKoqIjw8PA/XulcndYDMrCLPwfyqjmUX4NBp0KnVmK22Ijy92JzetOBeVFCIFsypL18B7Arp4opvUJR4KjglQrIKDPSN9KXAwU1zP7qoLPtvSO7cLiwlg3p5UT56ZgzKt65zEuj5LAEYxRGTLuFEdNuARx34S391+PYbTasVgs2m43IrslYLRZyUvcR32eQc70Te7fRdcBw57S3jx+NZkdxotF5EZGQhME/sN3x7cmt4ooeIc22l4KsCiO9I3w4VFiLApiYHMoPqY4iNMSgQa1UUmu2olU7znYbrHYyyuupamdBfOn1t7comtwNsUs/uJu4Hn1Qa04WRae1CQyLJKJLV/IzjxEV353yonx6DxvTrthO11l+6HPcuHEUFDQ9AsR+coMpFArnD36e6r6X0vnIMTtzqriyd5hz31QpFZwoq6dflC/782u4e2nTb3Ddf3EchwprXMY2hvloSYn24z/rMySPDVoMvWNgrD8H8qs5VFCDj07tzH2R/jp+PzlU4YZB0azeX0iD1XF5LdLfizSJxkLty6/hsu7NjmWlguxKEz3DDBwprmN7ThUzT95l6+fluPTZfLxofJA3/t5qvjnQ+k0bbTHkqpkMucpxe3uj2cTK/3PkPpvVgs1mJTzBkfvy0/YT23sg3YZews8fvwY49mOLyUhUUl/sNht7f/yalMuvRaFQUF1aiNXSSGBE7DnFNe7621oUTXY3iaZlnrG3WMdUX4e3wQebzYpG50VIVJdziskdOeSYsyqgdu3aRWpqKu+++y7aVs6Ea2pqWLVqFZMnTyYgIOCcggGYN28e8+fPJzAwEJvNRr9+/XjhhRfO+f3OJMigYUCsP11D9HQJ8ia73MjD4xL5+7epZJTW89pPx5k9Op4qYyMf/pbl0p08KC6AN9dJf8nHbLHx5V7H7fg2u50Pt+QQbNBw5/AYHl7e+j9yfrWZH1JLuD4lgkabncW78mmU+G48jc6bkdNu5bevP0OpVHL5rIdRKJXUlBTy8+dvc8ernzkvyZXmZjD8qpuc60Z160Vu2gH2//odSpWaboNGOi/vtUeD1c7yA0VclxKB1Wbns+25BOo13DI4mqe+TePWwdFc3DWQST0dvYk2Ozy4/DCFNY47osYkBmG12dmVW+0y4FcKNZVlZBzeS1F2BiV5WYRGOx5F8N1nbzP9oecI79IVY10Nx/Ztp7won7wTaUR37QHAdQ88w4bli4jploxSpaLvRZdIGpu6kyS3J554grFjx6LTtbwbDGDjxo2Sf+b5yjFmi42lewq4aVAUVrud9zZnE2zQcPdFsTzwzeFW1wsxaEgMMRDmo+U/6zM65K7aIL2G/jH+dA01OHPfg2O78s/v0sgoq+e1n45xzyhH7vt4syP33Tw0hjtHdOGOEY4vWa1KwfUf75QspgarnVWHi5naJwybHRbsyifAW82MAZE8/+Nxyusb2ZlTxaSeoehUChbsygcchWaXAC98dGo+254nWTzNaXReDLn6VrYtm4dCpeSSWx9CoVRSW1rIb4veYcbLnxAYGUufcVexfeUCVGoNw669A7VWx4YFc0nbvJY9P3wFgFKp4vY3l0gWW01lOZlH9lGUfYKSvGxCox3/Pt/Pe4cbHnyO8C4JGOtqObZ/h0ueKSvM49MXHqF7ylACQsMZf+NdKJXSDYuWQ45R2N2Vrqc5fPjwWT+tMyMjg4SEhHMOSAoj/+83j35+a2KadU93Jpf2CPJ0CG79niHd+C0pTekV+seNPOC6lLN//s3LPx9vddkzl7bvsqpUjEYjR48eJSUlxdOhtHDdZ7s9HYJbReXS3hUslV5x7e9t7gg9wzvmETntFe3m8RKdwfX9o/640UlyyDFn1QPVvHiaO3cu06ZNo6amhu7du6PRaFiwYAEmk4nIyMgOuWtGEARpdcbfqVq7di1ffPEFjY2OAcBWq5WioqJ2PThPEATPkEOOafPoyU2bNvHZZ58RHByMTqfj7bffZu7cuWzevJmGhgb+97//8eCD7h8nLwhC59BZxic0t3r1am677TZ27NjBqFGjKC4upr6+c/aoCIJwZnLIMW2+4Dly5Ei2bdvGunXrWLFiBXv37sVoNKLVavHx8WHfvn3nHIwgCOeHStn663xqPnizZ8+ejB49mn79+tGlSxemTJnC6tWrz29AgiBIQg45ps09UDabjYKCAtRqNQcOHOD48ePOn0Gw2+0UFUlzB4QgCB2nswzwvP/++3nwwQeZOHEi3t7eTJo0idWrV3P//feTk5PT6k0rgiB0bnLIMW0uoIYPH86sWbMwm8289NJL1NfXM2rUKD7//HOOHz9Ov379zjkYQRDOj84yPmHWrFl07dqVDz74AIvFwj//+U9UKhXvvPMOaWlpdO3a1dMhCoJwDuSQY9pcQF100UX88ssvzumxY8diNBrRaDSsX7+eESNGnHMwgiCcH+e7G701t9zieMZYv379sFqtbNq0iTfffBNfX1/nGaMgCH8+csgxbf4Ta2trneOfTr1efPFF1Go1l112WYf8qrkgCNI60w99eiwmlYoxY8Y4f/tq/PjxvPjiix6LRxCEcyeHHNPmHqgbb7yR8PBwl19MPn689ec9CILQ+XSWO2RKSkoIDQ2ltraW1atX89VXX5GVlcWkSZNYunSpGBIgCH9ScsgxbS6gYmNjee+991zmZWdnn3MAgiCcf52kd52nnnqKgIAAfvnlF7p3786MGTOYPHmy6MkWhD85OeSYNhdQo0aNYtGiRSQlJTnn/f777zz00EPtDkYQhPND2UkGeO7Zs4drrrmGL774gh49eng6HEEQJCKHHNPmAmr16tXYbDaXW/9ycnJEASUIfyKd5Rbjf/3rX1xxxRWeDkMQBInJIce0uYB64YUX6N69u8u8/fv3SxaQIAgdr7PcYiyKJ0G4MMkhx7S5gDp06BCHDh1yTptMJjIyMsRgT0H4E+ks3euCIFyY5JBj2lxAff755y7XEXNycoiPj5cyJkEQOlhnOTsUBOHCJIcc0+YC6t133yUyMtI5bTKZeOutt6SMSRCEDiaHs0NBEDxHDjmmzQVUbm4uubm5zuni4mK+++47nnrqKUkDEwSh48ggtwmC4EFyyDFtLqAee+wxunTpAoBCocDHx4eXXnpJ8sAEQeg4cuheFwTBc+SQY9pcQL3++usMGTLEOV1VVYXVapU0qPbKz6/2dAhu+fl0zl+WDzfoPB2CW8aGzrVfnRLn9+d/yKMcutc7krHB4ukQ3KqsNHo6BLdssQGeDsEtPy+Vp0NwK1zv5ekQ2k0OOabNDws9ePCgy7TBYOC5556TLCBBEDqeSqFo9SUIgtBecsgxZ90D9fXXX/PWW29RWlrKa6+9BoDdbkelUjFq1KgOC1AQBOnJ4exQEATPkUOOOesC6rrrrmPUqFHs2rWLSZMmdWRMgiB0sAvpLFAQhM5HDjmmTWOgwsPDmTRpErW1tdTW1mKz2QDYsGEDM2bM6JAABUGQXmf5mQVBEC5McsgxbR5E/sQTT7Bu3ToCAgKc86qqqkQBJQh/Igou/OQmCILnyCHHtLmAysjIYOvWrS4/Jix+C08Q/lxkcHIoCIIHySHHtPkuvPvuu4+8vDyXeRUVFZIFJAhCx5PDHTKCIHiOHHLMWfVAXXLJJRQVFQGOO+/A8RDNU9MKhYIjR450UIiCIEhNcQElMUEQOh855JizKqCeeOIJLrvsMpfLds39+uuvkgYlCELH6uju9ddee43ff/8dhULB66+/TmJiYsd+oCAInYoccsxZXcKbNGmSs3gqKiriu+++A6CsrIyNGzcyduzYjotQEATJKRSKVl/tlZqaytSpU1mxYgXXX389//3vfyWIWBCEPxM55Jg2DyJ/6qmnGDp0KADBwcGEhITw0ksv8eyzz0oenCAIHeNMZ4fFxcWUlJS4XRYaGkpYWNgZ3zs5Odn5/wMHDhSX9wVBhuSQY9pcQI0ZM4bbb7/dOR0eHs7KlStFASUIfyZnSG5Lly7lnXfecbtszpw5PPDAA87pt99+m6ysrBbtXn/9dQA2b97MPffc075YBUH485FBjmlzAVVfX8/PP/9MYmIiWVlZvP322wwcOLAjYhMEoYOc6WcWpk+fzrhx49wuCw0NdZlunuhOl5qaSp8+fYiJiTm3IAVB+NOSQ45pcwF111138cEHH/C///0Pm83GkCFDmDNnTkfE1uG8NEruvqQrwb46/rHsUIvlax4fRXKUn3P6g1/S+ffqVAC8tSruGJ1AQaWRbenl5FVI9yvovSN9GdzFH7VSwa9HyzhRVu9c5u+tZuEtA1CrmoavPf9dKjuyq1ArFcyb2Z9gg5bS2gZuW7gHm12ysABoMJvYuGoptdUVTL3zYbdttv60ih+/+AQvvYFrZz9Gtz6OAvvNR2dRmH3C2W7M1Tcy+ZZ72x1TcpiB/tF+qJQKNp2oIKuVf4u7h8dytKSODenlznlhPlpGJwaRW2lib141Jout3fE0ZzaZ+P6bRVRXlXPbfY+7bXNwz3aO7N+NzWYjNr4rI8ZOACAvO4P1P6xEq9WR3G8gfQcOkyyuMyW3sLCwP+xC/yNHjx6lpqaGYcOGUVNTg1KpxGAwtOs9/4x6R/gwqEsAaqWC9cdOO5a91Hw+s7/Lsfz379PYmVPFyIRAugR6Y9CpWXmgkJLaBknj8tIouW1kHEEGLf/6Lq3VdhP7RjCyWzDPLm/Kj2/e2I/xvcMBmPr276QX10kWV/cQPX0ifVEpYFt2FTmVJpfleq2KKb1CqTFZWJNa6pw/ITmEUQmBGC023tucTVl9o2QxnZKTeoAT+7Zjs1rpNXIc4XHdWrTZ+cMylCoVprpaQmMT6D5oBFaLhXfnTMdYW+1sN/62Bxg4/ipJ4jKbTKxdsZjqqgpuuuevZ2hnZN3KpQSGhtGjzwCCwyIpyMnkt59WodHq6NFnAL0GDJUkJpBHjmlzAaXVannggQdcqsKioiL8/PzOsFbn5OOlQa1SYtCpWizrHuHD3J+O8VtqKTa7nVmjE9iZ4fji1WmUfHjHIJ77+iCZpfUt1m0PnVrJXRd14a/LD6FUKHhxSg/+tirVubxPpC8PLztE3snE8tdLE9mX5zgwRyUG8ffv0yiva6TBapO8eAIw19dhtVowG90XKeXFBVgtjTz74TdsXvMNi978J899vJzivCwuu/42klKGoFAo2PT9N8Qn9213PDq1kpmDo3luzVGUCgVPX5bIiz8db9EuMVjPwFh/jpY0JftIPx23D43hjV9P0GDtgI0FGOtrsVotmOrd7ydmk4mln77LC3PnoVAoeP7B20nq3Z/g0HA+evNFnnzlbbz1Bl556j6S+wxA08qdsG3VkXfI7NixgyeffNL5awUKhYKvv/664z6wk9KpldwxvAuPrzyMUqHghUk9ePrbpmO5d6Qvjyw/TH6V41h+dGxX9udXE2zQMKFnGM99n0aAt5r7R8Xzspt9uj0MOjVqpRKDtvWvAL1Wxe0Xx3GssNY5LzrAi03Hynj521TsdjvlddIVKlqVgutSIvj3LydQKhQ8PCqONzZktmij16gwNjad6ET66ThYUMsPqaXcNSyGS5OC+XJvoWRxATSYjPy65ENm/v0t7DYbX772NDOe/j+XNpXFBeSkHWDaQ3/Hbrfz8ZN30n3QCHKPHmTqg88TntAdhULBL4s+IHGAdCdDJmMdVkvrOQYcJ77vvvwkN9/7OOFRsYDjEUTz/vsyj7zwFl56A288M4fuffqj0Ygcc7bOqoD617/+xZ133klYWBh///vfaWhwPRs6duyYpMGnp6fzf//3f9xxxx3OAesdobTGTE55PVGBXi2WZZbUuSSO3jF+vPezI4k9MTmZtQeLJC+eAIbHB5BXacJmB5vdjtVqp1eED4dPxrItsxLLycpIo1JgsdqcX/4Te4WxO6eKH4+UUNdglTw2AN/AYILCIqksLW61zchJ1wIwasoN/Pz159RVVxISEUNEbIKzTX7GMcZOvand8QyK8aOg2ozdDla7HavNTo9QA2nNCiUFMDTOn/351S7rPjQ6nnc3ZXVY8QQQEBRCaEQUZcXuE3rqgd146/XOO1MSe/Rmy/of6dl3IEqVCm+944wqPDKW7Zt+YeS4CZLE1ZHPaBkyZAi//PJLh72/FM5HjhkWF0B+VdOxbLHZ6Rnuw5Eix7G8Peu0Y9lmp8FqZ1z3ENKKHW0qjRZiA7wJNmgok7BYKattIK/CSKR/y9x3yoxhsXy3r4CkcF/nvJsv6oJGpeRQXhVpzfKjFFKi/CiqaWg6lu12EoP1pDfrtas0Wig/rXepoNrs/P+MciM2u/TH8/E9WwmKiEapVIFShVKlIvfoQWKS+jjbNJpNFJxIo8FkxG6zoVZrAIhJ6o3q5P8D1FdX4B8SLlls/oHBhEREUVZS1GqbZZ+/R/9ho5zFE8CJtEMoVSq8TuaYsMgYdm3+leGXXCFJXHLIMWf1GIO+ffvi4+MDgEajoV+/fgwdOtT56t69u6RBJSYmYjAYnA/t7EitfURjsy9VXy81tSYLNjsYdCpmXNQFpVLBazf247ZR8ZLG0y3UQKWxKUFUGBuJDfR2TluadSv1j/Zn78neJ4NWxYH8GobFB/LO9X3o0mwdqZ3pwAgKi3T+v81mxeAXgI9/IGpNUwIx1tWi8/ZGqWrZ89dWCcF6qk0W53SlsZHoANcvhdGJQaw/Xu4yr1+ULwadip7hPjw8Jp6+kb50lDP9JpTZbKKqoik2/8AgSosLyTiWin9AkHN+QFAw+dkZksWkVLT+koPzkWNaHMv1ZzqW/ZzHcrcQA1VG1306NkD649lO6397YpiBomoTVUbXYiWvwkiIr5ZFfxnKxL4RksYTF+hFjbnp764yWYj007Vo11rUaqWCUB+tyyV6qRRlHEPvF+Cc9vEPoiwv26VNaGwCEfFJfP36M2xd/QVT7vsbgEvxVJKbSUhMvOTxnWnEtqm+jt9+WInNZuOzt17i59VfAZCdnoqff6CznV9gMAU5mZJFJIccc1Y9UFOmTHH+/6BBgxgxYgT+/v7OeRMmSHNW3Jym2Reup41ODmVDquOWy0HxgeRVGFn8ezY6tZJ1fxtDdlk9vx5uvUemLQxaNcU1TWdUFpudAG/322JIXABLdjl+VqeuwcqCHbks2JHLHcNjmX1xHE+vTnW73vmSumsro6+c3qLgStu7naT+0nRh67UqyuqaekStNjt+Xk27tY9Oha9O7XKWCo6xKYcLa/kprZQTZfU8d3k3Hlx2mKpmxdj50L1nX4oL8zlx9DBdk3pRlJ9LUGg49fW16H2aijqVSkVleekZ3qlt5PCU4D/S0TnGoFVRXNN837QR4O0+5Q7uEsAXu/MBxz5d22Bptp4d/1ZyQEeZ1C+Ct9elc/WASJf5i7bmsGhrDqOTQnj52t6sTy12uZzWHt4alUvv0unH8ploVQqm9Q1nTNdAakwWvjvi/hb5c2U21uEX3DRmR6lSUV9d2aLdFXc8xPzn76fRbGbwhGtaLE/fs5VuA4ZLGtsfOZ56gKCwCMZMmEpjQwPP3zeD0Igo6utOyzFqNVVn6MVqKznkmDb/Ft7cuXOxWFy/ZEwmUyutLwyjeoSy8WQBFWjQUlhppMFio8ZkYe3BIkb1CJHss2pMFrTqpn8WnVpJndn95bhAbw0VbgZLzt+eS4RvyzO388nS2MiJI/sYNv7KFsuO7dtBj/7SXDapNVvRNBuEq1UrqW92+XJ8Ugg/pbVMpr46NYUni6rjpfWU1TeSFHb+BzkHBofy0LP/ZvWXn7Nm2WKOHt5P7/6D8fH1o7HZpXKz2Yy33keyz1UoWn8J0nAcy00bVKdWtXppPaDZsVxjtrjs0zq1kvqG81fYX9EnnB8PnvmLdOPRUnZmVpAQKt0+WddgRaNq2l5alZL6xrMbitBgtbN0byFzN2UxPilYsphO8TL4YmlsOh4bG8zo9K75wma1sn7JR9z5r48wBATxzZvPt+jhLMw4SmRiMudTbXUVgSFhaLQ69D6+pAwbxeE92zH4+tHY7G9qMJucQwakIIcc0+ZB5BMmTODjjz+mR48eANhsNtavX8/cuXMlD64zUCgcZ4Q1J3smCqtMhDQrToqqTHhr238p6pQTZfUMjGkakB9i0HK8tOVdLgnBetLdzAfHmduxEmnujFn75Tx+Xb4IgLHTbmb8Dbef1Xqbv/+GcdNubnEWYrPZaDCZ8DZIk3izKoz0a3b5LdigJaO8qWC6IjmUcScTqp9OzcAYfxQKKK9vdOnZq6hvpEGCO/CWLfyI1UvnA3Dl9Nu4Zubdf7hO/6Ej6T90JDkZx9m6YS09+w1Co9GybeO6pvhKi+g3eES74zvlQkpinVVGWT39Y5p66kN8NKQfajluMiHImxPNxlNmlNUTrG/aN4MNWo53wHjL1kzsG0HyyWNKr1WhUytptNr45yrXhxXmlBupqJPu7sDcShM9I5ryQqBeQ/bxtt3dfKSojlqJxn9uWraArauXADD8yhlUlzYVlTXlpYTHuw5dObF/B1pvPTq9gavnPMPHT95FcVY64fGOu/WMtdV4+/i1u2dm1ZJPWPPV5wBMvP5Wrppx5xnbBwaHUl1R5pwOCAqhwWwmNqE7Ozc1jSOqKC2mz6CL2hVbc3LIMW0uoA4dOoRer6eystI5LycnR8qYzqvT/5Ev6hbMrowKGqyOL9OB8YHszqpwLt+VUYFWrSTIoKW8roEuwXoWbGr5kK9ztTWzgmtSIlDguKavUipIL6ljQIwfe3KbBkEPiwtgW1alczrQW4NGpaC4toGYAC92ZFe2eO9zMf6G21sUTe7GjRw/sJv45D6oNVp2/PI9yQOHY/ALoMFsIi89jYReKQBkHz1EXI/eksQGsCuniim9QlEAKqUCpQIyyoz0jfTlQEENs7866Gx778guHC6sZUN6OVF+OuY0G7/mpVFyuKj9g2KvmXl3i6LJ3ViTQ3t3kNQrxXlXXWNDA18v+IB7HvsHCoWCbj37Ul9Xh9lkQuflRXFhPgOHj2p3fKec6RZjQRpbsyqZ2i/CuW+qFAqOl9S5jHcCGBoXwPZmOWb98TIeGO244SJQryGrwui2p7m9Th+bNzQhkD3ZlTy8ZJ9z3tUDIhkSH8Q/Vx1Bo1LQNdRAWmEtXholdWYLBVXSXX3Ym1/N+B7BzY5lBdkVJnqGGzhS5HpTSPMjSq1UoFA4xq0GeqvZn18jSTwXX3MLF19zC+C4C2/Jy49ht9mwWi3YbFaiEpOxWixkH9lHQt9BePv40Wh2bA+NzovIrkkYmo0xOrFvB11T2t/zftWMO90UTS1zzJF9O+nWqx+JPfthsVioqarA1z+QksJ8xk6+lvjuPTHW1TpzTGlRASlDL253fKfIIce0uYB67LHHSEpKcpkndQGVl5dHZmYmu3fvJiUlBS+v1u8UaY8QXx3DEoPoEelH1zADJ4rreH5aLx5asIejJ+8wGdszjKXbmgYLWmx2Hlqwh4cndCe1oIb92VXsz6mSLCazxcaCHbncPiwWq93Of9dnEOKj5f5RCdzVLLHFB+mdYyYAuocZeOiSBLZmVHCwoIZ1adKNl2mupqKME4f3UZh9guK8LMKi4wBY9dlcbnr4eQqy0ln24RvOgZOWhgbufbGpd/LIri0MvWyK2/c+F2aLjS/3FnLjwChsdjsfbskh2KDhzuExPLy89cf751eb+SG1hOtTImi02Vm8K9/lxgGpVJaXkrp/NzmZ6eTnZBIVGw/Agvff5P6nXiI0PJK0g3vJyTzOLfc8Ski4Y8yJQqHgzof+xtLP3sXXL4Drb7sXL2+9ZHHJILed0fnIMWaLjYU787htaAxWu525GzMJ8dFy36h4/vLFfme7uCA9S/cUOKdLahvYmF7GjQOj8FIrmbtBupsHTgn20TI4PpDu4T4khOjJKK3nqck9eOLLAxxv5blOQQYtc2/uz5H8GnZnVTJPwhNHcFyGW3mwmGl9w7HZ7SzYmUegt4abB0bx7Jpjjrj1GuKDHAPqg/QayusbGRjjxzV9w9mRU0Vdg5VlB6Qbx3OK1subi6+7jQ1ffopSpWLCnY+gUCqpLilk7by53P36PKK79yIndT97f/kOpUpF0uCL8QlsupyYdWgP42+T/pmJVRVlpB3YQ15WOgU5mUSezDFLP/oPdz/+AtFxidz92D9ZtfhjouMTSUjqSUJSLwBunfMUyz9/Dx8/f6bO/IvIMW2ksLfxNpSxY8fy8ssvM2KE43JCamoqr776Kp999lmHBHguEh75ztMhuJWcJN1YKSnNvjjO0yG4tXh3wR838oDHRnf1dAhuDUnw/+NGJ2WUtt5zkBDSMScsF5LJH2z3dAhuZedKdzInpeF9I/+4kQdclNA5n1/YzV+6sWVSGp0U9MeNTpJDjmnzIPKUlBT++c9/8vzzz1NfX09ycjKXXnppR8QmCEIHkcMAT0EQPEcOOeacCqiVK1ei1Wq58sor2bJliyxuVxSEC4lKoWj1JQiC0F5yyDFtHgNVWlqKl5cXzz77LJdffjnPPvssKpWKm2++uSPiEwShA1xAOUwQhE5IDjmmzT1Q3bt359gxx4C+oUOHsmLFCsaMGSN5YIIgdByFQtHqSxAEob3kkGPa3AM1depUqqurKSgocN7OHhsb+wdrCYLQmVxIP6cgCELnI4ccc06PMfj5558JCgpyFlBVVVXiEp4g/IlcSGeBgiB0PnLIMW0uoDIzM9m+fbvL70jt37//DGsIgtDZyOHsUBAEz5FDjmnzGKh7772X7GzXX6Fu/lRyQRA6PzmMTxAEwXPkkGPa3AP18ssvk5/veAK2QqHAbrejUCg4cqT1pz4LgtC5yOHsUBAEz5FDjmlzAfX0008zevRotCd/wwtgw4YNkgYlCELHOv130ARBEKQkhxzT5gLq4osvZuHChajVambMmMG6devEk8gF4U/mAupFFwShE5JDjmnzGKhHHnmE/fv3U1RUhEajoUePHjz++OMdEZsgCB1EpVS0+hIEQWgvOeSYNhdQffr04a233qJbt24AaDQaNm3aJHlggiB0HDn8TpUgCJ4jhxzT5kt4drudQ4cOYbVayczM5O9//ztDhw7tiNgEQeggygspiwmC0OnIIce0uYCaNWsW7733Hlu2bGHhwoUMGTKEBx54oCNiEwShg8ggtwmC4EFyyDFtLqAMBgOPPfaYy7zU1FT8/PwkC0oQhI4lh7NDQRA8Rw45RmE/9XssZ+mWW25xeRCW2WwGYOnSpdJGJgiCIAiC0Em1uQdq4MCBjBw50jl99OhR1Oo2v40gCIIgCMKfVpt7oGw2G0ql6817d955J5988omkgQmCIAiCIHRWbe46euaZZ1ymi4uLKS4uliwgQRAEQRCEzu6crr2demyBQqHAYDC4XNITBEEQBEG40LX5El5NTQ2+vr6tLt+/fz/9+vVrd2CCIAiCIAidVZsLqDvuuIOsrCy3y+x2OxUVFezZs0eS4ARBEARBEDqjNl/CCw8P56mnnsJgMACQkZFBQUEBI0aMwG63s27dOsmDFARBEARB6Eza3AM1b948br/9due0zWZj4sSJ/Pjjj1LHJgiCIAiC0Cmd02/hrVy5kr59+1JXV8f8+fOdvVGCIAiCIAhy0OYeKLvdzhdffMG3335LVVUVffr04aGHHiIyMrKjYhQEQRAEQehU2lxAARw8eJCamhoGDRrE0aNH6dOnT0fEJgiCIAiC0Ckp/7iJqzfffJP77ruP1atXo9Vqqays5L333uuI2ARBEARBEDqlNo+Bqqys5Ndff2XVqlUA9OrVi0cffZR7771X8uDO1fh3tno6BLfKK4yeDsGtF6/p7ekQ3Fq8p8DTIbh1Xb9wT4fg1tR+EWfd1vuyf7e6zLjuKSnCuaAF37rE0yG4tfzZKzwdgluPf3PA0yG4dde4eE+H4Faot87TIbglcoyrNhdQYWFh2Gw2FAoFAB9++CFRUVGSByYIQgdSqjwdgSAIFzIZ5Jg2F1CTJ09mzpw5ZGZm8tZbbxEQEMBrr73WEbEJgtBRZJDcBEHwIBnkmDYXUGlpabz66qtotVqsVusZf9ZFEIRO6mQPsiAIQoeQQY5p8yDy//73v9hsNvR6vbN4qqiokDwwQRA6kFLV+ksQBKG9ZJBj2twDNWHCBD766CN69OgBOJ5Evn79eubOnSt5cIIgdBCVxtMRCIJwIZNBjmlzAXXo0CH0ej2VlZXOeTk5OVLGJAhCR7uAzgIFQeiEZJBj2lxAPf3008THx7vMEwWUIPzJyCC5CYLgQTLIMWc1Buq6665j1apVWK3WFsUTQGxsrNRxCYLQgZRKZasvQRCE9pJDjjmrvyQlJYWrrroKlUrFmjVrmDlzJr///ntHxyYIQkdRnOElCILQXjLIMWd1CS8kJMT5/xMnTmTPnj2MGDHCOa+2thYfHx/poxMEoUNcSGeBgiB0PnLIMWdVQBmNRsrLyzn1u8M2m805bbfbWb58OXfffXeHBioIgnTkkNwEQfAcOeSYsyqgPvzwQz766CNnAQWwcOFC5/8rFApRQAnCn4hCeQH1owuC0OnIIcecVQH1wgsvcMMNN7hdZrPZ+OabbyQNShCEjiWHs0NBEDxHDjnmrP7CMWPGtP4GSiVXX321c7q8vLz9UQmC0KEUSkWrL0EQhPaSQ445qx6otLQ0LBYL0dHRbpdrtVoANmzYQEhICEFBQdJF2IH6RPoyNC4AlVLBL0dLSS+td1k+rV8EVrsdH52ajLJ6tmQ4frLmtqExXNUvgpIaM6+uSyejrN7d258zL7WSmRfFEqjX8n8/Hmux/JqBUdx7SQJ1ZgsvfZvGzqxKAIbEBzI4PgClQkF6SS0/HCyWNC6ABrOJ9Su/oKaqgmvvfsRtm+2/fM+vK7/AYmnk1kf/QWyi46n1aXt3kHXsMAZfP4x1tVx27S2SxNQj1EBKtC8qpYLNGRVkV5hclt88KIohsf4U1zbw1sZM6husLsvvGBbDsZI6fjsh/U8SNZhNbFy9lLqqCq6+82G3bbauXcVPX3yCl7eBa2c/RmKfgQDs/HUNG1cvxdLYwE0P/52Yk9tRCirVhf+Mls7gqWv6ctdl3cktq+e+D7dyOKfyrJZfe1EcXUIMhPh58cPuPH47UiR5bGaTibUrFlNdVcFN9/z1DO2MrFu5lMDQMHr0GUBwWCSb133HT8sXY2ls4C9PvEhct2RJYtKpldw8LIZAvZY31h5vsVylgGkDohjQxZ9nVhxxzu8eZmBC73CqTRYarTYWb8+VJJ7mctMOkLF/B1aLhV4jLiUsLrFFm10/LkepUmGqqyE0JoFug0ac9brnqj05BmDB689xcNtGAB55cx4RsQmSxCWHHHNWPVCjR49m8+bNPPXUUyxatIiffvqJnTt3snPnTtatW8eHH37Io48+ipeXF7179+7omCXhpVbyl5FdmLcth8+25nDPxXEuyyP8dPSN9mXVgSIW78zjrou6ANArwoeMsnpu/HQXe/OqeezSrpLHZtCpUSuVGHQtd8BIfy80KgUT3/qdr3bm869re6M4+fc8cGlX3lufwbu/nmDG0Fgi/HSSx2aqr8NqtWA2uS8aayrLCQyN4Mn/fk7KRWNY+9V857LVC97j8utvY+SEaWQfT6UoN7Pd8ehUSmYMjOSrfYV8ubeQmwdFuSzvHqrnhyMl/HXlERQK+H/27js8iupr4Ph3e3rvISEhnRJ6kw6CoICAIIKAgKCo2HtvP/W1N+yFIkgVQVRQEKlK7zWQhEAK6X2zm2x5/4imkA0mZMJG53549nmyc+/OHnZ3zp65c2e2X7hnrfY23o50DnZrchz1MepLsZhMGMvKbLbnZWVgNlXw9Gff0eu6G1ny7otYLJa/Xkd/HnpnAR16DWTzd4ukDUyiU4xLSkq49957GT58OHPmzEGvl3Zn4t+se6QPJy4U0Pa+New4mcmHs3o0qD3I05FJfcN5d90JXlx+iP+b2hVnXaOvefyPDGWlmE0mDJd5z8qNBj565XG69RvCNYOvx9svkKL8PLz9AnjxoyV07TOIH5fPlywmZ50KtVKJk9b2l6+zgxqTxYqnk7bW8idHRPPFjnMs/PM8nUPcaRck7Y/clxvL2LrsC/rcdBv9Jszg928/rdOnICuD1NNH6XztaHqNnsy2FV81+LFNcaU5BiAvM4OYzj155vPVPPvlGsmKJ0AWOabBBylvvvlmHn74YfR6PStXruTFF1/kxRdfZO3atXh5efHaa6/Rs2fPJgdkMpl4/fXXmTRpErNmzcJoNDZ5nbb0CvckrcCAxQomixWzxUq7gOpLMTholMT4ueCgUeKkUVFhrvzA5ZZWsC0xjwqLlcV7U2nt5SR5bLml5aQV2N4YAJbvTcNksbJk9wXUSgWezhq6tPZAb6weWTmeXsR17f0lj83N0xsvv8B62109vIjqULl30zq6Hd4B1aOWBblZpCVXjqiVFObj4NT0S190buXGxWIjViuYLVbMlsqi6W9nsvXk6iswW+FcXhmZJdWfJwXQLcSdoxnFTY6jPq6e3nj61/96AfQZcRMqtZp+I2/GYjZRWlSAq4dX1V5iaHRbvAJsj/5eKakucrd3715effVV1q9fj5OTE6tXr5Y0zuZyNfLMxYIyfth7gXKThTfXHCMm2L1B7dd1DiY9v3L7LzdZSMkpYWinoDrrbyp3T298Ai6/3tWLPqFTz374B1VfLNnN04vY+K4AtIlpj5+En8280grSL5P7ispMXMir3R7m7YSHowZDRWWOPnShkBs6BEgWE0DSwV14+gejVKpQqTWoVGrSEo7X6lNhNHAx6TTlhjLKDXpUGk2DH9sUV5pjAHb8vIq0pASKC/Jwcfe87DoaSw45plG7Nb6+vsyePbtZz7i7ePEic+fOxdnZmenTp5OSkkJ0dLTkzxPl60xBWUXV/Tx9BaFejhy/WALAudwyErJKeWVkLMcvFvPaX8PJmcXVidZRo+J0ZonksQFgtb04o7D68JRKoSBfX0FeaQUOGhVeLtV7Zbkl5QS6OzRLaArFP+9CWCwWko4f5rqJM6qWDR1/G1+88hh9R4yj/8gJuHv5XGYNDRPu7UiRwVR1v7CsgmB3B85k195LcdRUbrSH0qqLpb5tPNmelM+odn5NjuNyFJfZ5apZjFosZpzdPGolMovFQvKJw1w7YbqkMV0uiWVlZZGdnW2zzdfXFz+/6tdr0KBBVX937NgRd3d3Ww9rca5GnrmQU1r1t7ODhoNJeQ1qd9Sq8a0xepxVYCDE21nS2KrV/9k06EvZvmEtY2+7i/nv/Y/QiBiGjJpQ1W6xWEg4fohRk26XNKJ6Ul+N9to9HDRKPJw0KP56bG5pOd3CPCSN6WLyGRzdqtfp5O5Jbvp5gqOrj7j4hoTjHx7F6refISiqLTfMeaLBj22qK80xXn6BJJ04xEdP382Eux+nU99rJYtJDjlG+nHhJmrVqhVQee2pbt26NUvxBJVDxVnF5VX3zWYrHo61fz36/S1JfHRzB3RqJasPZdRZR982Xizdn9Ys8TVE3yhvFu+q/B3CI6mFBHs4EBfoysmMYkK8HMksap7Ru39iMZv5ddUi/tj4A2X6Em65pzKR9L9hPKcO7mbruhXMevp1SZ7LUaMit7TG+2i14nbJ4Q4XnYopXYO4JsyTc3ll7EzOx0WrwkWnJsNOr5Etpw7sot+oiVUFqsVs5rfV37B74zoM+lLG3/WYZM91uYmcy5cvZ968eTbb5s6dy7333muzLTU1lVtvvVWS+Jrb1cozfxvZrRXvrKt/1KFm+85TmTx5UweCPB25WGCgta8ze8/mNGt8tpw9dRQvvwAGDB9DRXk5z909Cd+AIOK798FiNvPjigVs27CGstISbrvvqase39+Sc/QYTRYGxvjw++kcQrwcKSwz/fMDG6G8rBRX7+ovdZVajf6vUZyahs64n8XPzaWi3EjX4eMa9dir4dIc0/eG8fS9YTwn9//Binmv0bZbH7QOjpI8lxxyTIs8z7CkpIQPPviA5cuXc+rUqWZ5jmKDCa2q+g3WaZSU1phcrFTA7D6tmb30CHn6Cl66ofYEXledCjdHNfvOFzZLfP9ErVTQJdSd1QfSAcgpKeexVceY3ieUyT1b0bGVO3uTpZ8U3RBKlYrhE2fw4OufsW/rr5SVVo7S/bTkM8befh/9R07gs5cepiDX9h5IY5QazWhU1R9jrUqJvqL2JPESo5lP/7jA13tSGRFXOeo1ONqb3xKu/pdSfUwVFSSfOEzPa0dVLVOqVAydMJ25r33KgW3Vr6MULje8PnHiRFavXm3zNnHiRJvr27hxIzNmzECtbnH7ZPW6GnkGwMNZi7erjt+PXmxQ++Fz+Ty6cB8v3NKZGYMjiQ/zYkczTCL/JyVFhXj6+KHR6nBycaVjz36cOLgHqPxsjp50O0+9/RW7ft+AXsLPZmMZTRYeWXWM69r6MbVXCD3Dvdh3Ttrc5+DshrmieketwmhE51R7VNBiNrN16RdMf/VznN09WfPu81it1gY99mqwlWP+Ftf1GsLbdiQrLUWy55NDjpEkkuTkZMLDpZt85uLiwuOPP05sbCwrVqzgueeek2zdf0vK0dMlpHoo0MdZy5ns6iH17q090Jeb0ZebeeWXM3w5uSMRPk4k5uhRAKM7BPDtPvuNPk3q0Yr5O8/XWrbzbB47z+YR6efMde38q87Oa4oNy+fz2+rKi6YOGTeF4TUOyf2TgJBwAkLCUKpUlJWWcOrgHkZOmcPQ8dNIO3eWQ39sZuAo2xtLQ50vKKN9QPWEUS8nDefybM+h2JNSwKBIbwCGxfhU/e2qU9E52A2FQsG2xKZdhmPjygVs+X4JAAPH3srQBh5627n+OwaNvdXm4VH/kDD8W7WW9KyWy+0d+vn51RpC/yc7duwgOjqaoKAgMjMz8feXfu5dc7gaeUahgNuvjeKdH2yPPtXXvmxHMst2JDO0YxDbjl8kpUZuulI/LP2K9SsrT0YYMWEao//h0Junty9F+blV9z28fCi/ZK5YUGg4gaFhdj/j6nh6MU98fwIvJw3Xt/dn06mm75z98f037P5xGQA9R95CUU71Wc0l+dn4t46s1T/5yF50js7onJwZdc/TzH9yNlkpifiGtiHl2IHLPrYxmiPHAHj7B+Nc41BjU8khxzS6gNq8eTOrVq2itLS06qdcUlNT+f333yUPLjw8nJKS5tmz+TM5n5s6BaKgcjRHpVRwNquULiHuHLhQSJHBhIO6cmTDaLKQkFVCvr5yztRNnQL5+UQWFWYrrg5qAt10JGQ1PcHVcslnr3uYB4cuFFJhtjKqYwA7zuZSUFaBg1pJbKArhy5UjoRpVArmDAjnubUnJAlj+MQZdYqmmlek/9uZo/sJj+2AUqmivNyIg6MTxjI9weFR6BwcMVWU13pceEx73L19mxzfgQtFjIjzRQGolAqUSgXn8spoH+DCsYsl6FRKTBYLZisEujmwK6Vyz3Tud9Wvzx29QziZWSLJZQyGTpheJ6FdOmcD4OzRA4TFtket0bL395+J7dwLZzcPyo0G0pJO0zq6PRXlRnR/vY5B4VGSDa2DdBe5W7BgAQsXLsTT0xOLxUJ8fDwvvfSSJOu+Wpozz9x1XSzfbEnEWGHB00VLa18XvF11/HYko972Q8mVRbyHs5aZQyK594vdksQyetLtNoqmup/Nk4f3Edk2noi4eEwmE8WF+bi6e5J9MZ1BN9yExWym3GjAwckZQ5me0DbR6CT8bF76tdu1tQdHUitzX2V7/V/Mcwe34f82JFRNKG+Ka8ZO5ZqxlZdaKTeWsfzVR7FaLJjNJixmC4ERsZhNJi6cOkxY+644urhRYayco6rRORAQHo2zhyeegcHs2/BdncdeKalyTEhkHFmpKQSFR1FuNKBzcsLTV7rJ93LIMY0uoF5++WUefvjhWtXjzp07JQto586dfP3114wYMQKFQlHvFdCbymCysHB3KjN7h2C2WHn39yR8XLTcNyCM6YsPc/JiCfFBblzfzg+zxcqOpDzy9BVM7BLEbT1bMa1H5RwKjUrBlEUHJY3N21lL19YeRPq5EObtxLlcPY9cF8VTq08Q5e/M0zfEVJ0VqFUrmbXgII4aFZ1D3Yn0c+GtX89wsbB55vYU5eeSeOIQGSlJZKam4N+q8vIP33/1AVMffJ7SkkIWv/sy8b364+rhxejb7gZArdEyYtIsNq5ahLd/EKaKCjr2HtjkeIxmC6sOX+TmToGYrVa+2nUBLycN03u04pEfTjE0xpsBkV7sTqksijecvLqH7Yrzc0k+fpiL55PISkvBL7jy9Vo3/wMmPfAcGSmJfP/Z26jUlfPvTBXlzHnpA86dOsrSD/5H+579cHX34vqpd0kal1QXs5s+fTrTp0+XZF1X09XIM/ePjOOJcR14fFx7AHRqFZ0f/oGF9/VlwDMbuG1QhM12b1cd3SK8iQ5y5+7Pd5FfUn65p7lihfm5nD56kLSURDIunCMwJAyA5V+8y+xHXyK4dQSzH3mRH779kuCwCMKj4wiPbsvpowf48u0X6NJ7IG4eXoyfYXu+ypXwctbQOdSDSF9nWns5kpJXxkPXRvDs2pMk5ehx0ano2caTVp4OxAa4cOpiCSoFdAvzJMLXmW93p3JWgtG6S2l1jvQZN43tK+ejVCkZNvMBFEolxdkX2bTwQ25/Yz5BUW25cPooh3//CaVKTVTXPrh4VI5y23qsVK40x5QUFbDwjacJDo8iLC6eAaNukSwmkEeOUVhtDSdcxptvvsn9999fdfFMqJzY9fekzJZg6Lxd9g7Bprz8+k/PtaeXx7XMa3d9e7DuxP2WYHx8yxg+vtSY+IbvPbZ56Od625LeuV6KcP7TvKcttXcINn3/zHX2DsGmR787au8QbJo1OMzeIdjk6yj9NfykIHJMbY0egSooKGDmzJmEhFRfF+TMmTOsWrVK0sAEQWg+yv/QzykIgtDyyCHHNLqA8vT0pEOHDuh0LbNCFgThn6lULfIEXEEQ/iPkkGMaXUDNmTOHsrIy8vPzCQsLIzc3l+HDhzdHbIIgNBOV6r+/dygIgv3IIcc0ukRcu3Ytw4YN49NPP0Wr1bJ//37Wr1/fHLEJgtBMlH+dsWjrJgiC0FRyyDGNLqD279/P9u3b6devHwB9+/blrbfekjwwQRCajxySmyAI9iOHHNPoQ3gxMTG4uLhUXYxr9erVuLpK+8vXgiA0r/9SEhMEoeWRQ45pdAHVs2dP7rjjDgoLC1m2bBmpqam88847zRGbIAjNpCE/CC0IgnCl5JBjGl1ARUVF8dFHH3Hu3DnMZjOtWrWqdU0oQRBaPjnsHQqCYD9yyDGNngO1YsUKNBoNUVFRxMbG4uLiwosvvtgcsQmC0EzkMD9BEAT7kUOOafAI1KpVq1iwYAHZ2dksXry4anl5eTm+vk3/TTNBEK6e/1ISEwSh5ZFDjmlwATV+/Hg6dOjA1q1bueGGG6qWq1QqUUAJwr+MHJKbIAj2I4cc06g5UDExMcTExNRZvnXrVgYMGCBZUIIgNC85XOROEAT7kUOOafQk8nfeeYclS5ag1+sBsFqtKBQKTp48KXlwgiA0DzmcISMIgv3IIcc0uoDavHkz69evx8/Pr2rZtm3bJA1KEITmJYfhdUEQ7EcOOabRBdRtt92GxWKptczLy0uygARBaH5ySG6CINiPHHJMowuoFStW8M477+Dk5ARUHsIrKCjgwIEDkgcnCELzkENyEwTBfuSQYxpdQE2YMIFu3bqh0+mqlm3ZskXKmJrszJkce4dgU+bpBHuHYFPPh/rZOwSbPtyWbO8QbOraytPeITSZSgbJrTnp01PsHYJN7o4ae4dgU3Gx0d4h2OSibfRX4FXh5fDvvzi1HHJMoy+kefPNN3PmzBkOHTqEn58fycnJ3Hrrrc0RmyAIzUSlVNR7EwRBaCo55JhGF1BPPvkkX375JQcOHECj0eDm5iauRC4I/zJyuEqwIAj2I4cc0+gCysPDg5UrV9K+fXsA/P39WbduneSBCYLQfFQKRb03QRCEppJDjmn0AWAnJyfy8vJQKBSUlZXx+uuvExsb2xyxCYLQTORwjRZBEOxHDjmm0SNQt9xyC6+88goff/wxgwcPxmAw8OabbzZHbIIgNBM5zE8QBMF+5JBjGj0C5evry9tvv11rmcFgkCwgQRCanxx+ZkEQBPuRQ45pUAG1adMm+vbti4ODA2vWrKnTvnfvXl555RWpYxMEoZn8l+YhCILQ8sghxzSogNq+fTsdOnTAwcGBn3/+GWdnZxwcHKraT58+3WwBCoIgvZY8jP7372sKgvDvJYcc06ACquZlCp588knCw8NrtWdmZjY5EEEQrp6WeCrxgQMHeO655wgPD+fdd99l8eLFdO3alQ4dOtg7NEEQGkkOOabRk8jfffdd8vLyqu6fOnWKDz744IqeXBAE+2iJEzw//PBDHn30UXr27IlarWbMmDE8/vjjdotHEIQrJ4cc0+gCKjU1lRtvvJGffvoJgNjYWKKioq44AEEQrj6FQlHvzV4GDBjAgAEDcHFxAeDw4cPk5+fbLR5BEK6cHHJMo8/CGzlyJH379uWJJ55gw4YNvPjii2g0LfP3lwRBsK0lTvB0d3fn1Vdfpbi4mOTkZL799lvmzp1r77AEQbgCcsgxjS6gTCYT0dHRrFy5kk8//ZQbb7yRdu3aid/DE4R/ESnnJyxZsgSNRkNWVhbTpk3Dzc3titYzduxYOnTowO7duzGZTCxcuJC2bdtKFqcgCFePHHJMowuo/Px8zpw5Q1RUFPfccw9DhgzhqaeeuuIA7MlBo2T2oAi8XbW88N3xOu19Y3zo1NqDgtIK3BzVfLwpEYCh7f15cXx7lAoF/1tznB8PZkga17NTr2HO6E5cyCpi1lsbOJacU6v9lkGx+Hk6YbVCqaGCr9cfBWDPJ1PpEO5b1e+dlXt5+qvtksa2aP4XrPv+O5xdXHjtrfcJDAqu0+fQgf3s2rkdk8nEsOtvIDomDoDl336DWqWiqLiIiMho+g8cLElM7QJd6Rbqjlqp4PeEXJJy9VVt7o5qFk/tjFpVfbT6uZ9Osfd8IWqlggVTOuHtrCWnpJzbFh/EYpUkpCoLPv+ItauW4hcQyOPPvUKbyOha7SZTBTePHEJRYUHVsnsfeYobb7qFpYu+Yv0Pq3FyduaF/3uXgMC6r/WVkmoewvHjxzl8+DBvvPEG+/btY968eVecD9LT03FycmLQoEFA5fXlFi9ezJQpUySJ1R6eva0fc27sUrktv/4jx5Kza7VPHNyWUH93/Dyc+PHPs2w9lALALYPb4ufpjBUoNZTz9U+HJY9t+YJP2bB2BT5+Acx9/EVat6k7FcNoMLBu1WKK8vOYee9jVcu3/LKOdSu/oaKiggeefpU20XGSxOSgUTK9bxjeLlpeWXfKZp/3JndkWHt/AEa/t5OzWaWXXS6VcqOBP39cSWlRPtfPuM9mn+y0FA5s/hm1VktYXEci4rvVat++5ltMFeUMmjBdsriMBgMb13xLUWE+k+98+DL9yti0djmevn7EtO+Mt18gOzf9xK/fV8Z0x2Mv0zpSul8VkUOOafQcqMcff7zWnKfY2Fg+/vjjK3pye3N10KBWKXDR2a4jnxwdx7xfz7J4ZwrxoR5E+LvgqFXRtpUbA1/+nRdXH+etyZ3wdJbuEGavuECOn8smfPJnbDt8gU8fHFar3UGr5s5Rnfhg9QE+/P4AM4Z3wMVRQ1xrb15bsgu/cfPwGfMhzy/Ywfo9yZLFBZCcdJbeffqxYu16WoeFs+Lbb+r0KSvT89F7bzH77nu5c+59fPD2GwCkpV7g8IF93DRxMtNvv5NPPnhXkph0aiWzeofyzZ5UFu5OZXaf0Frt7QNdeWD1ccZ+sZexX+xlR1Ieh9OKAOgX4cXzP59m8oIDzFl+RPLi6fiRQ4RHRLH8x8107NKdt155rk6fY4cP8tyrb/PDpj9Z99subrhxPL2u6U9KciI9evdl4cofCWkdzurlSySN7XI/9JmVlcXx48dt3rKysmqtZ9OmTURGRgIQFxfHpk2brjimG264galTpzJlyhSmTJnC5MmTbV537t+iV9tgjidnET5xHtsOn+fTR66v1R7s48qUYR14c+mfPP3lFt6Zey3ODprKbfzGLnzw3V4+/G4vM0Z0xMVRK2lsp48fJjQ8ks+Xb6Bdx6588tZLNvuV6Uswm0yUlVXvlBTk5+LjH8jbX66gV78hfLfkK8nictGpUasUOGlVNtuDPR3ZnpBD/1e30PeVLVVFUn3LpWQs02M2mzCWldlst1qtrP30TQaOv40hE29nx9qlmCrKq9oLc7I4sPknyeMylJViNpkw6PX19ik3Gvjolcfp1m8I1wy+Hm+/QIry8/D2C+DFj5bQtc8gflw+X9K45JBjGjQC9dprr3H77bfj5+fHCy+8gNForNV+5swZVq1adcVB1FRSUsKTTz7JmTNnCAsL45133sHJyUmSdV8qu9hIap6eYE9Hm+0BHg60DXbjRFoR3q46issqcHFQ88nGRMrNFtYfzuC+66Jo7eNMfmmBJDGl55Wy62TliNar3+4iacmdtdq1aiWxoV4EebtwMb8UR52acpOFs2n5nEzJrerXKcKPt1bslSSmv4W3iaz6u237eFTKuvX3jm1baBXaGpVKhUqlQq1Wc+TQAZydXTh54hh6fSlWixWtVpqis1eYB2kFBixWsFitmM1W2ga4cOJiCQC7zxVg+qsy0qgUmMwWys2V90e09ePAhUJ+OZlNablZknhq8vb1pV18JwCmzpzDxFFD6vRpF9+51hzC/Pxc/AODavWJa9cBpdL2F8qV0lxm73D58uXMmzfPZtvcuXO59957q+7n5ORUnQKs0+nqJL/GWLRoUa3TiVNTU/nzzz+veH31uVo5Jj23hF0n0gB49ZsdJC2vPdfi+t6RpOUUA1BeYebcxUJG9Irg173JxLb2qbGNayg3Sfv59PT2JaZdRwDGT53NnRNH2Ozn4eWDX0AQOVkXq5d5euPh6Q1AVFx7TGaTZHHllJSTlldGoLuDzfap14SiUSk5nlbEqYzif1wuJVcPLzz9AijMsf0ZTz17EqVShc6x8rPkFRDMid3biO97LQD7fltH+2ukGXWvyd3TG5+AIHKz67+c0OpFn9CpZz/8g0Kqlrl5euHm6QVAm5j2mE3SvY8gjxzToAIqLi6OEydO4Ofnh1qtJiYmptaFNKW0d+9eXn31VVxcXHj44YdZvXp1sw7hWy8z6jDv17N8fUcPFm1PZv7WZLKKjHU7KeDMRek22POZRVV/uzho2J9wsVZ7kb6cb349zrpXx7F6+xnun/cb5RW1k6ubk5bisnIsUg+p/KW8vJy01Avc99BjddpOnziBp5dX1X1vHx/OJScxeux4YuLa8ch9d9OhYyeef+UNSWKJ9HWmoKyi6n5+WQUhno5VBZSpxmvQKdidQ3+NPjlrVRxNL6ZnmCej2vvz5LpTnM+3vWd5pWoecisr0xPTtl2dPjWLp3NJZwmrUaRC5WudnpbKXfc9ImlslxtenzhxIoMH2070vr6+te57e3tT9tceeWlpKd7e3lcc06XXYtHpdHz11VdMmDDhitdpy9XKMeczC6v+dnHUsv907W3ZSafBz7O6cMvMKyXUz52iUiPf/HKUda9PZPW209z/wa91tvGm8guoLtINZXoiY+qfB1LfWVMWi4WTRw8yfupsSWO7XNZKzS+je7gnS+f04OnvjvPzkYuXXS69+rebjKQEnN09qu67eHiRnVZ5SPbs4b2Et+tMyskjVz0ug76U7RvWMva2u5j/3v8IjYhhyKjqbcpisZBw/BCjJt0uaURyyDENKqAWL17MW2+9BcDMmTMJCqq9hzx06NArenJb/j42CdCxY0fc3d0lW3djzd+azIBYX2YObMPtn9cdzWkb7Mbm45mUGqUfvQC4sU8Ury/bXWf5M/N30L9jCJMGx7H0t5N12od1C2PjvnPNEpOhrIxP573HmlXL8fTyYsasObXaS0qK8Q8MrLqvVmvI/+u6YY89/Ty3T7kZg6GMibdOkyQeZ62arOLqwtZkseLhaHt0q3trD5burxwRKC03883eVL7Zm8rMXiHM6duap+qZcyGFHVs2Mfm2y3/R7Nq5jd59B1TdNxjK+PqTD/jx+xV4eHoxZcYdksVzuekJfn5++Pn5NWg9w4YNY9GiRQAkJCTUmxQbIjY2turL2mq14uLi0iyFjT1yzI19Y3j92z9qLdt2OIXnpvcj2MeVjLwSwgLc2f3XiNUzX26hf8dpTBrSjqWbjjVrbHt2/M7YyTMb9RiL2cx3S75i44+r0ZeWMOfhZ5sputoW/3GexX+cZ0CMD69NaM/vJ7MpqzDXu/xqMuhLcXB2rbqvVKkozs/FZKrgQsJxBk2Y3owFVP3OnjqKl18AA4aPoaK8nOfunoRvQBDx3ftgMZv5ccUCtm1YQ1lpCbfdJ918ZjnkmAbNgRo+fDhhYWEAVdd/qmnPnj1XHMDlpKamMmKE7aHlq+GxkbG8uPo487cms+iuHvhfMqw8plswH2w40yzP7enigLe7I5v2p9Rpe+euQYx86jt+P3Sen167CcdL5nAN6RrGr81UQDk4OvLAo0/y1gefsGzxwjrtbu7ulNc4xGswlOHi4oLJZOKj997im+Vr8Pb24fEH5mK93PBfAxUbTGjV1R9jnVpZb0Hr6aghX19RZ/nCPakEuOqaHEt9igoLKSwooHuvPpftl3DyOLFtq/eQHBwcufvBx3nl7Y/4bukiSWOS6iJ3bdu2JTIykpUrV/LHH3/wwAMPXHFMH374ISdPnuTkyZOcOnWKffv2NWl9DXE1coyn61/b8r7acxIPnsnkvvd/4dU7BjF7ZGc6RQWw9XDl9v7O3KGMfHwZvx88x0+v31JnG5dKSVEhRYUFdOreu1GPU6pUTJh2B699tJBtm35GX1LSLPHVZ+vpHPYm59PGz7lBy68GRxfXWnOeKoxGHJyc2b/pR7pdO+qqx/O3kqJCPH380Gh1OLm40rFnP04crPzOVqpUjJ50O0+9/RW7ft+AvlS691EOOaZBW2W7du0YPXo0paWllJSUsGzZsqo2i8VCdnY2x45Ju5e0ceNGZsyYgVrdPInjn7g6qBkQ58sbP57io41niQt2Y2TnQL7aUpkEb+4VwpKdKRhNFsmfW6GAO0d35PWldUef2rb2JsDLmZzCMu77cBO/v3MLQ7uG8cMfZ6se66xTU6Qvr/PYK/HVZx/zzfwvAJg6Yza333k3AD16XYOHh2ed/pHRMezdVX1MOSszk+jYtuz+YwfOzi64uLry8v+9zeTxo0k4fYqY2KadvZOUq6dLq+pTWn2ctZzNqTuBNNzbiUQbywHMFitnsqWZdLroq09YuqhyUu2kabczZcad/PDdMm6dfvnRp6LCQtzcPWweLunaozduNl7rppDyh9JnzZolyXpsjWRv2bKFgQMHSrL+S12NHFO5LXfh9SV/2GxfsvEYSzYeY3jPCLYcPMe5jELahvkQ4OVSuY2//wu/vz+Vod3a8MPOhCbFsnLR56xZugCAMZOmc9OUWWz4YSXjbm3c6FNNrVq3oVVoOEqVtHP0GuJCbhn5pXXzXH3LG2vLqoVsX/stAP1unMzA8bddtn9A6wiO79pSdb8oL5uoTj3486dV/PHjCgAM+soCRaFUMvCmKxuF/2HpV6xfWblDNWLCNEb/w6E3T29fivKr58d6ePnU2skFCAoNJzA0DJWE76McckyDMsfevXuZOXMmvXv35ptvvmHSpElVbRaLhXXr1l3Rk9dnx44dREdHExQURGZmJv7+/pKuv6ZLv696R3mzPykfY4Wl1lHlA8n5ZBQYABjSzo9z2aWk5OhRKxV0j/DizzO5SOW+sV2Zv/4oxgozXq4OhAW44+PuyK/7zlFYasRBW/m2Wa2w99RFUrOr52D1igti90npLqtw+513VxVNRqMRo8GAzsGBrMyLXNOv+nDTnzu30+uavvQbMIhlixdisVgwmUyYzWbadYjn+NEjVcexHRwdiWvXHm9vnybHt+tcPuM6BqAA1H/t3SRml9K5lRsHU6vnk/Vs7cHulIKq+56OGjQqBVkl5bTycGDv+YI6674S026/i2m331V1f+W3C7n+xpvQ6nQUFhZwMT2NmLh27PlzO9179a0qmPbu2kH33n2rHlduNGK1WtE5OJCddZFeffpLEt/fWsoPfQ4ePJiMjOrP69+jkgqFouoHP0+erHuYuqmuVo6576YezP/5cOW27OZAWIAHPu5O/Lo3qaqPp6sDd4zuzJ1v/gxAYYkRh7/OQrNaYe/JdFKzi2yuvzEmTLuDCdOqDwOvW7mYa68fg1aro7iwgKyL6UTEtOXgnp106n5NrUMdNVnMZoxGA45OzhjK9IRFxuDgaPtEnCtx6SezZxsvDqRUXi06ws+FUxnFOGiUlBhNpBcY0KgUNpc31cDxt9komuqOmicdO0BoTHtaRbXFqC+l3GhAq3OgIOsi0V2voUOf6pNHfl+5oHLdV1g8AYyedLuNoqluXCcP7yOybTwRcfGYTCaKC/Nxdfck+2I6g264CYvZTLnRgMNf72Nom2h0DtK9j3LIMQ0qoI4ePcodd9yBg4MDgwYNIji49vVobrnllit6clsWLFjAwoUL8fT0xGKxEB8fz0sv2T7Ftql8XXX0jPAmJsiVCD9nErNKeX5cO+5bdJCEjGLe/vk09wyN5EKuHq1ayfrDGfSM8GLe9K5YrH+f2aXkkSWHJIvpkZu78+zUa3hmauWQuk6jIva2r1j27Ch63r2YtJwSVm49zT03dia32EBCah4HzlSffTG8RzjzNzTPnImtmzfyyYfvce2w4bh5eDBn7gNA5byoN155gYVLv8PN3YPZd93Lp/PeQ6VS8cSzL6JUKunQsROHDu5jzXcrUKs1DBg0BJ9LJgteCaPJwjd7U5neMwSz1cr7W5LxcdFyT79wZi2tvnZOmJcTyw6kV92P8nPm/oHh7ErO51hGMZtO59hafZMsXfQVC7/4iIVfVl7mo6K8giWr12MwlPHu6y/z2cKVuP01/+bA3t3c+8iTVY/dvmUTX336AQOHXIebuwe3z7F93ZkrpW4hye2xxx5j0KBB6HS2D6Fu27ZN8ue8WjnmkVt68ext/XjmtsrCWKdREXvrJyx7YSw975yPSqmgR1wwsaHezHr9R/KKKr/003KKWfn7Se4Z243corLKbTxB2knRa5YuYPnCT1mx8DMATBXlfLRkHUaDgc/ffZU3P/sWFzd3CvJyOHHkAOeTz5J2/hzBoWGcOnaID157lh59B+Hh6cXUO+6XLC4fFy3dwj2JDnAl3NeJ5Gw9T46M4ZHlRykuq2DelE6cSC/iQEoB87efA8DLWWtzudSKC/I4d+IIWReSyE47j29w5SVTNiz8iJvuewb/kHBGzX6Y35Z+iZOrO4MnzpS0IKlPYX4up48eJC0lkYwL5wgMCQNg+RfvMvvRlwhuHcHsR17kh2+/JDgsgvDoOMKj23L66AG+fPsFuvQeiJuHF+Nn3Hv5J2okOeQYhbUBE1E++eQTrrvuOhwcHFi6dGmdEaiffvqJO++88zJruLrC7v/R3iHYlHm6aUPwzSX1O2m/nKUyZdF+e4dg0+cTO9k7BJtCvBo+j+uV387W2/b0kMh6266msrIyEhIS6Nixo71DqcPx2v+zdwg27Zp/j71DsGnSJ9JfjkIKz06oe2ZsSxDs3PyF15XoH+31z53+Iocc06ARqDFjxvB///d/nDp1ivz8/DoTybOzs1tUASUIwuW1xN+p2rhxI8uWLaOionKiv9lsJjMzs0kXzhMEwT7kkGMaVEAFBgby/vvvA/DNN98wderUWu1bt269oicXBME+Wsjoei3r1q3jtttuY+/evfTr14+srCz0l7m6siAILZccckyjf8pl6tSpZGdnk5CQQEVFBRkZGQwYMOCfHygIQosh1SnGTVVz8mZcXBz9+/cnPj6e0NBQRo4cKfkJKoIgXB1yyDGNLqCWLFnCsGHD+PTTT9FoNOzfv5/Vq1dfcQCCIFx9GpWi3tvVdM8997BmzRqMRiOOjo5cf/31DB48mBdeeIEbbriBkqt8fSFBEKQhhxzT6Aug7N+/n+3bt7Nx40YA+vbty/XXX8+4ceOuOAhBEK6uljI/YcaMGbRp04bPPvsMk8nEiy++iEqlYt68eZw+fZo2bdrYO0RBEK6AHHJMowuomJgYXFxcqq4Rsnr1alxdXf/hUYIgtCSqRo89N4+/51PGx8djNpvZsWMH77zzDq6urowYMQJHCa8vJAjC1SOHHNPo/2LPnj254447WLp0Kbfccgtff/01L7/88hUHIAjC1ddS5ifUikmlYsCAAQwbNoyTJ08ydOhQkVsE4V9KDjmm0SNQnTp14qOPPuLcuXOYzWbatGmDVqu94gAEQbj6WspVgrOzs/H19aWkpIR169axcuVKUlJSuP7661m+fDnx8fH2DlEQhCsghxzT6ALKbDYzf/58Nm/ejEajYcSIEUyaNMnmb3gJgtAytZDRdZ544gk8PDzYvHkzUVFRTJo0iRtuuAEnJyd7hyYIQhPIIcc0uoB67bXXyM/PZ8qUKXh6epKSksJnn33GnDlzmhyMIAhXh7KF7PAcPHiQcePGsWzZMmJiYuwdjiAIEpFDjml0AeXu7s4zzzxTdb9Pnz7Mnz9f0qAEQWheLeUMmddee43rrrvO3mEIgiAxOeSYRo+yqVSqWvfPnTvHjh07JAtIEITmp1Qq6r1dTaJ4EoT/JjnkmEaPQHXv3p2bb74ZZ2dncnJyyMrK4s0332yO2ARBaCYtZe9QEIT/JjnkmEYXUF27dmXBggXs3r0bq9VK9+7dq36YTxCEfwe1DJKbIAj2I4cc0+hDeOvXr8fJyYlBgwYxePBgXF1dq35oWBCEfwelQlHvTRAEoankkGMaXUB9+umnZGRkAJCTk8PMmTNZu3at5IEJgtB8FIr6b4IgCE0lhxzT6ALqm2++YfXq1Sxbtoxx48bRu3dvfvnll+aITRCEZqJSKOq9CYIgNJUcckyD5kDt3bu31v1u3brx8ccfM2vWLOLi4li6dCkPPPBAc8R3RXx8ne0dgk0V5eH2DsGm1Lwye4dgk5Ou0VP0roqLhQZ7h2BTiJeuwX3/S8Po9uATG2fvEGxKLWyZ27KPT8vMyTn6cnuHYJPVarV3CPXwanBPOeSYBn1DvfHGG6hUKjQaTa3lGzduZOPGjVy4cKFFFVCCIFzef2kvUBCElkcOOaZBBdSrr75KVFQUAIcOHSI2NhYHB4eq9lOnTjVPdIIgNAs57B0KgmA/csgxDZoD9XfxBPDQQw+Rm5tbq71169bSRiUIQrOSw/wEQRDsRw45ptGTTCZNmsSWLVuIjo4GwGKxsGHDBp5//nnJgxMEoXnIYe9QEAT7kUOOaXQB9dtvv2E2m2sdwktNTRUFlCD8i1zlX1MQBEFm5JBjGl1APfPMM7Rv377WstOnT0sWkCAIzU8Oe4eCINiPHHJMo68D5ezszJw5c3juueeoqKjg22+/xcnJqTliEwShmagVinpvgiAITSWHHNPoAuqFF16gf//+hISEoNFoGDZsGA8++GBzxCYIQjNRKBT13gRBEJpKDjmm0QXUoEGDmDx5Mr6+vgCcP3+e8+fPSx6YIAjNR6mo/yaFN954gzFjxjB27FgSExOlWakgCP8acsgxjS6gAObPn09CQgIrVqzgoYceYsKECVLHJQhCM2rOvcNTp04xZswY1qxZw4QJE8SPjQuCDMkhxzR6Evn06dPZtm0bWVlZnD17lmeeeYZrr722OWITBKGZXG4vMCsri+zsbJttvr6++Pn5XXbdsbGxVX936dKFkydPXlGMgiD8e8khxzS6gFq/fj0jRoygf//+ABQWFvLjjz8ycuRIyYMTBKGZXCa5LV++nHnz5tlsmzt3Lvfee2/V/Q8//JCUlJQ6/d566y0Adu7cyZ133tm0WAVB+PeRQY5pcAH1/fffk5aWxsGDB2sdb6yoqBAFlCD8y1zuFOOJEycyePBgm21/z338W81Ed6lTp07Rvn17WrVqdWVBCoLwryWHHNPgAurGG2/kww8/rLNcp9Px+uuvSxrU1aJTK5nSKwQvZy1v/nKmTvu4LkHMGRBOqdHE/346zf6Ugqo2B42SyT1CyCwysP98ARcLjZLF5aBRMefaSLxddTy74kid9r4xvvSO9kGlVHAqvYg1e1MB6BfrS5dwL/JLy3Fz1DDvlwTJYvrb8gWfsmHtCnz8Apj7+Iu0bhNVp4/RYGDdqsUU5ecx897HqpZv+WUd61Z+Q0VFBQ88/SptoqX5Rfs4fxc6t3JDrVSwLTGPc3m1f5FerVTwyc3t8XLSkltazpwVR7Fa4c4+oVwT7kVKnp73tyaTU1ohSTx/MxoM/PzdEooK87jt7kdt9jl6YDeJp47h4uaOvqSE0bdMByAlKYGdv23A2dUVjUbL9TfdKmlsl5uG4Ofn949D6P8kISGB4uJievbsSXFxMUqlEmdn5yat89/IQaPiziEReLvqeG7l0Trt/WJ96dzas3KbddLw0a+VeSgu2I1x3VtRqK+g3GTh883ST5ItNxrY8sMySgrzGTfL9pnUezb/zJYflmGqqGDqQy8QEhFT1Tb/jWc4unsrAI+9u4iA0PAmxxQf7EbPMA/UKiUbT2ZzNru0VvtNnQPRKBUEujuw9shFknL0AIzvHIjZYsXVQU1Sjp4diXlNjuVS6QnHSDm6F4vZRHSvwfiGRtjsV5iVwek/f8MzMJTWHbqhdXS67PKmqjAa+OOnlZQWFnD9DNvFRnZaCgc2/4xGq6N1XDwR8d0A2LF2KQd+/xmdoxMTH3oRD98ASWICeeSYBk8iVyqV3H///fzvf/9j7ty5Vbe777671vHIfxMXnRq1SomTVlWnLdDdAY1SwYj3/2DV/nReG9euakRSp1by9oQObDyRxU9HMyUtngBcHSvjcnGoW986aFQ8OaYdb647yf+tPcGswREEeToC8My49ry//jSLtiXTsbUnkQGuksZ1+vhhQsMj+Xz5Btp17Monb71ks1+ZvgSzyURZmb5qWUF+Lj7+gbz95Qp69RvCd0u+kiQmnVrJtO7BLDuQzrf705neo+6eSO8wT1799Sy3Lz3MA9+fwGKFHq092HY2j1lLD1NQZmJ6jxBJ4qmpTF+C2WzCoNfX22fZVx8yZvLtXDtyPEkJJ0g7n4zVauWr915l3NTZ3HjLDE4dPcjZU8ckjU2pUNR7a6q9e/cyZ84cXnvtNcaNG8f06dNle604V4e/tmWd7X3Vp25sywe/JPDNjnN0DPUg0t8FgDcmdeKdn08z79cz9Iz0plNrT8ljM+hLMZtMGMtsfz6LC/Lw9A3gsfcWEd97ABtXLaxqy81MJ65LT174cg0vfv2DJMWTg1rJXf3D+OqP83yxI4W5A8JqtccFuBDt58yy/eks2HWBp4dHoQAC3XV0bOXG94cvsmh3Knf2lf63WSuMBnau+JKeY6fRa9x0diz73Ga//IwLbF08j87DxxPVo39VkVTfcikYy/RYzCbKDbbfR6vVyg+fvcnA8bcxeOJMdv6wDFNFOdmp54js1IN7312ET1Aou9avliwmkEeOadAI1Jo1ay7bvnfvXl555RUp4gEgMTGRN998k5kzZ9KjRw/J1nup3NJy0gvKCHDT2Wxfvi8NgCW7L3B739Z4OmvIK63gnkFt2JqQw4X8MpuPa6rsIiPnc0sJ9nKs09Y72odSo6nq/qFz+Yzp3oqPfz1DoIcj7Vq5czy1EB9XHcVl0o6oeHr7EtOuIwDjp87mzokjbPbz8PLBLyCInKyL1cs8vfHw9AYgKq49JrPJ5mMbq1uIOxlFRixWsFitmC1WYvycOZ1Vvec6NMaHw+lF/JaQg77cDEBSrp7sknIAVh/O4IGBTf8CuJSHlw++AUHk1ngdLpWXk0VKYgKtI6IpKsjHydmF9AvnKC4qwMGh8v2Pad+J7Rt/JDK2fb3raazmvBRL9+7d2bx5c/M9gQSuVo7JLjZyIVdPsGfdbRkg0MORtsFunEgrwttVR7HBRKS/C54uWsr++qzuSczl5l4hHErJlzQ2N09vvP0DKcjJtNnu6uGFq4cXAGHR7bCYzVVt235ahdlUQUhELMHhdUehr8Q1EV6k5pdVbcsmi5X2Qa4cSy8GYECUN+l/7azm6ytQKRXEBrhgNFmI9XfFUaNEoVBQbrZIEk9NyYd24eEfhFKpAqUKpUpFxpnjBEa1q+pjtVr59bP/Y8isR9DoHP5xuVRcPLzw8A2kMCfLZnva2ZMolSp0fxVtngFBnNi9jfi+1Sd/BUfGVv7fJCSHHNOgEagNGzawdetWdu/ebfMm9Qz4iIgInJ2dsVqtkq7XlvqeIqPQUPW3SqEgX19BXmkFTloV47oEoVQoeG5ULBO7BTdTYLYXO2pV+LhWF3zZRUZaeVVuGO+vP83Ce3oz97povvr9LJk1/g9S8AsIqvrbUKYnMqZtvX3rO1XVYrFw8uhBxk+ZJUlMET5OFBqqi7H8MhOtPKqTlJNWxYnMErqFuPPWjW0J+avt7+IJKkf1zubUP0rUFIrLzaQEbpw0k7eee5Afli3gujET8fT2pdxooLiwAIul8ovA3dO7VjEqBTn8UvrlXN0cU/9zfPBLAgvv6sU9w6L4eksSmYUGHLUqvF10VV9AWUVGgr2aa+/6n99vi8VC4onDDJswvWqZt18gxQV5vP/kHA5s3yRJJDH+LhToq3f68koraF3j/61TK/Fy0tRqD3DTkZSj53RWCa+PbcuUHq14eb30UxeyU87g6OpRdd/J3Yu8jNrXP7xw/ACG0mLSTx9lw8evcOH4gcsul9LlNtv05ASc3atHMF3cvchJq47dVFFOfmYG3a4dJWlMcsgxDRqBevrppwkJqf8QR2am7T2YptBoNP/c6SrpG+XNkt0XAIhv5U5GoYHVB9LRqpWsmtOD1AIDO8/mXpVY9iflEerjTHyoB0fOFxDm60z6XyNhX/2eyMB2fsweEsltH/3ZrHHs2fE7YyfPbNRjLGYz3y35io0/rkZfWsKch59tchxOWlWtYshsseLuUP3Z0ZebWXYgnWUHYEq3YGb2CuHFDbXnu3UPdWfNEWkLlIa67saJHNn3Jxu+X8rDL70DQHBoOFqdA/v+2EKPvoPJTL+Ai6u7pM/7X7oa8JVqCTnm6y1JDIzzY9agCGZ8uguAMxeLKSs3MbxjIOsPZRDu60x+afk/rKl5WMxmNn63iD83/oBBX8LEu58AoP/ICfQfOYHj+3ay9MNXade9DzoH26NsDeWsVZFVXD0dwmSx4lmjYDp4oZAHBkfw6fYUzFYrvi5aCssqd57e2pjIF7d2RKdWsmJ/epPisMWo1+PqXT25WaVSUVZUWKtP6qnDBMd2JH7IaPzColn71hNM/b/59S53cpf+sKzt2EtxcHapEbuawoLK0aoKo4Hfln3Fvk3rcHbzoP+4KZI9rxxyTINGoIKDLz/K8l/+MWG1UkHnUHdWH6jcKD2cNGQVGSk3WygxmtiakEOvNldnQwDILDRwx+e7mXtdNLMHR9A9wpudpyuvp/HEjW15fsVRvtp8liX3XUOAu/TDxQAlRYUUFRbQqXvvRj1OqVIxYdodvPbRQrZt+hl9SUnTYzGa0aqqP8Y6tRJ9hdlm36X70/BzqX241t9VS76+ggsF0o7WNdSK+R8zdc5DXDdmIq8/fR95OVlodQ489MJb/Pn7L6xbsZCj+3fRtlM3SZ9Xoaj/Jlw9j4+O4/nvjvL1liS+uac3Ae4OGCoszPxsD2O6teKuayPpH+fHHwk5dolPqVJx3c0zeOD/PmPf1l8pK629zbbr1oeIdp3ISq17mnljFRtMtbdljbLWdIVtZ/NYsT+NBwe34cYO/ni7aDl5sRiVAu7uH8b0RQfJK63gtTHSnJxSk4OzK+aK6iK2otxYZx6ToaQID7/KUfqAiFicPX3IOHui3uVXi6OzG6bymrEb0DlVTrbW6BwYfts9TH7sFf78eZWkzyuHHNOgAmrYsGEkJSUBMHjwYOLi4qpusbGx/+nrvNzSoxXzd1YPd2YXG/F20VbfLynHWCH9MffL2Xw8kzu+2MP2U9nkl5bzR0IOrg5qBrXzJymrhA82JLD9ZBajujb98OLKRZ9z64hruHXENaxc9DkWi4UNP6xk3K2NG32qqVXrNrQKDUepavox93N5+lp7qd7Omqozcy5ltkJSbvXcKK1KQd82Xqw7bnvuQGOtXvwFM0b1Zcaovqxe/MU/9teXlnBk/58EtmrNjZNm0r5zD3ZvqzwcEhnbnvuffZ3+Q0dSpi+lZ39pL1Yrh+TW0rk6qBkY50dyVikf/pLA9lPZjOxS+UV7KCWfO7/cy8rdF3BxULPuQFqTn++XFfN57JYhPHbLEH5ZMb9Rjw0ICScgJMzmNuvtH4Szm0eT4zubXVort/q5aEnIqn0W3rL96bz6yxlMFis/Hs2krMJCjzBPSstNlJabeeGn03g5aYjybfrZWHvWLuaTO0bxyR2jUGu1lBZUn9lXmp+Db+vIWv2dPbwpLcqvdV+t1dW7/EptWbWI/00bzv+mDWfLqkX/2N+/dRuK86uPkBTl5RAYVnveWkR8N5xc3a44JlvkkGMadAjv559/Rqut/GA/+OCDDBs2DJ2u+gOwdevW5onuKrj0zewW5sHhC4VUmK2M6hjAzrO5FJZVoFMriQt05dCFQjQqJR5OGgr0FQR7OLByX9OTW93Aat/tE+PD3sQ8yk2VxZpWreSRUXE8sHA/AEaTpdZD9iXlkSHBJPcJ0+5gwrQ7qu6vW7mYa68fg1aro7iwgKyL6UTEtOXgnp106n5N1bDtpfM+LGYzRqMBRydnDGV6wiJjcHBs2pA/wN7zhYxq548CUCkrz/BIytUTH+TKkfRiPBzVaJRKskvLCXbXsf9C5bC7Ahjd3r+qePJ10aJRKkgvuvIzKsdNmc24KbNrLbPamMx2/NBeott2RKPR1nqdotvG4+VTfWqv1Wpl6VcfMvO+J6smlEtFijNhhIa59FDGNdE+7EvKq9xma7TtT8ojo6D2NvvUmLY8ufRw1YTyprju5hlcd/OMS5bW/XyeObqf8NgOKJUqysuNODg6YSzTExwehc7BEVNFOZmpKQSHR1FuNODg5IyXX9NPf9+ZmMfNXYNQAGqVApVSQUJWCd1C3dl3vvpwWaC7jh5hnjz/U+WRjyKDCQdNZWFnNFk4lVlCnr7phzx73DiFHjdWHtKqMBr4/vVHsVosWMwmLGYzAW1iMZtMpJ0+Qmi7LkT3HMivn78BVG67FcYygmM64Objb3P5lRo4fhoDx0+rtczWNLvkYwcJiWlHq6i2GMtKqTAa0OgcKMjKIKZrb0zl5VixotHqKMrNJrpLryuOyRY55JgGFVB/jz5FRUUxalTdiWYDBgyQNKi0tDTOnTvHgQMH6NixIw4OzXMoyttZS5dQDyL9XGjt7URKrp5HhkXx9PcniPRz5qnrY6gwVxcssxcexGyx8vT3x7mjfxiJWaWcSC/mREaxpHH5uunoHeVDbLAbEf4uJGaW8OKEeO75ei8XcvX0jPQhNsiN51ceIe2vax6Vmyy8ue4k9w2PJiVHj1at5KeD0s4FWLN0AcsXfsqKhZ8BlZMPP1qyDqPBwOfvvsqbn32Li5s7BXk5nDhygPPJZ0k7f47g0DBOHTvEB689S4++g/Dw9GLqHfdLEpPRZGHZwXRu7RaM2WLl050peDtruKN3KHO/O06EjzN39WnN3vMFnMwsYcvZyr3I2deEMijSmzHxlYnfYrEyc2nda241RUFeDqeOHODCuUTSL5wjKCQMgG8+fYd7nvgfIWERjJ82h7VLv8Y3IJiKinK69x2M2WzixKF9XDiXyPXjbiXUxrW2mkoGue2yrlaO8XXV0SvSm5igGtvy+A7Mnb+P0xnFvPXjSe69LprzOaVo1Up+PpSBSqmgT3TlNv75b4mcSi9qltiK8nNJPH6IjPNJZKam4N+q8vT/77/+gKkPPk9pcSFL3nuZDr364+ruxahpdwNQUlTA1//3JMFtomkTF8/A0ZMkicdgsvD1H+e5o29rzBYrb2w8i6+LjoeGRDB5/gH8XLXE+rsQ6O7A8z+drtqZPJ5RTMdWbozq4I/JYmX72VxyJb6mm0bnQM8x0/jzuwUoVUoGTb8fhVJJSc5Ftn4zjymvfYVnYAjx145m95pvUKk1XDN+Jmqtrt7lUikpyCPl5GGyLiSTk3Yen+BQADYs+oib7n0av5BwRs56iE3LvsTJ1Z1BN89E6+DI0Z2/8duyL2nXayCOrm4MmSjNiT1/k0OOUVgbcBrKsGHD+Oijj4iKiuLUqVNA5QTMiAjbFxKzt27/+93eIdiUkVZg7xBs+vmplvlbhi9tlP5sGik8MSjynzvZQffwhk80T86pf85XuE/zFBP/JSFz19o7BJs+ndO4eYlXy+u/1r1QcUswoWfQP3eyA29H+5/gYMvkLg2/4rccckyD5kCNHDmSqKjKvWAHBweeeuopXF2lvUijIAhXjxzmJwiCYD9yyDENKqD+nv8EEBYWRp8+fWpdhv3vQ3yCIPw7NOdVggVBEOSQYxo0B2rfvn188UX1WUWHDx+uum+1Wvnzzz+ZP79xZ3UIgmA//50UJghCSySHHNOgAiotLY3ExMSqM0aCg4OrRp0sFkuzXEhTEITmI4eL3AmCYD9yyDENKqBef/114uPj620/fvy4ZAEJgtD8VA3+GXFBEITGk0OOaVABdbniCaBdu3aXbRcEoWWRw96hIAj2I4cc06ACShCE/xblfz+3CYJgR3LIMaKAEgQZksPeoSAI9iOHHCMKKEGQITnsHQqCYD9yyDGigBIEGVLI4iRjQRDsRQ45RhRQgiBDMhhdFwTBjuSQY0QBJQgy9F+6GrAgCC2PHHKMKKAEQYbkMD9BEAT7kUOOEQWUIMiQHM6QEQTBfuSQY0QBJQgypJTBVYIFQbAfOeQYUUAJggzJYX6CIAj2I4cco7BarVZ7ByEIgiAIgvBvIoNBNkEQBEEQBGmJAkoQBEEQBKGRRAElCIIgCILQSKKAEgRBEARBaCRRQAmCIAiCIDSSKKAEQRAEQRAaSRRQgiAIgiAIjSQKKEEQBEEQhEYSBZQgCIIgCEIjiQJKEARBEAShkUQBJQiCIAiC0EiigBIEQRAEQWgktb0DaA4ety62dwg2ffPEUHuHYNM7mxPtHYJNA+J87R2CTddH+tk7BJt6tHFvcF/Ha/+v3rayTU9IEc5/Wv93dto7BJsCvZ3sHYJNQ2K87B2CTVvP5Ns7BJtuive3dwg2jesY2OC+csgx/8kCShCEf6BU2TsCQRD+y2SQY0QBJQhyJIPkJgiCHckgx4gCShDkSKGwdwSCIPyXySDHiAJKEORIBnuHgiDYkQxyjCigBEGOlGLTFwShGckgx/z3/4eCINQlg71DQRDsSAY5RhRQgiBHMkhugiDYkQxyjCigBEGGlKr/fnITBMF+5JBjRAElCHL03z9BRhAEe5JBjhEFlCDIkFIpfsVJEITmI4ccIwooQZAhOSQ3QRDsRw45RhRQgiBDCqUMxtcFQbAbOeQYUUAJggzJYe9QEAT7kUOOEQWUIMiQHPYOBUGwHznkGFkXUE/dFM/sYTGk5pYy55M/OH6hoFb7A6PaMW1gJMVlFUx9byvnc0qr2n5/eQSd23hTVm6i7b2ryS8plzS2cqOBrT8sp6Qon7G3P2Czz97f17Plh2WYK8qZ8uALtIqIAcBsNrHr1x9IPHGYaQ+/KFlMHYJc6RnmiUqpYNOpbBJz9LXax3UMQK1SEuTuwA9HL5L0V7urTs3Unq0o0Ffw7b40yeKpKevsMTJO7MdiMRPWbSCerdrUardarRxc/SUXDv+Bi08A/WY9jdbJpd7lUjAaDKxfvYSigjym3f2ozT5HD+wm8fQxXFzd0ZeWMHri9FrtCSeOsGrRJzz1f59IEtPf5LB32BLEB7vRM8wDtUrJxpPZnM0urdV+U+dANEoFge4OrD1Svc2M7xyI2WLF1UFNUo6eHYl5ksYV6+dMp2A3VEoFO5LySckvs9lvdq8QErJL2frX86uUCubd1A4vJw15+nLuWXUci1XS0Eg9fZTkI3uxmE3E9R6CX+uIWu36onw+e2AyFrO5atnYh/5HQHiUzeVtOvZockwxfs50DKp8vf5Irvt6TekWRPcQD7JKjLy79Rz6cjNuDmo+vKkd6hqFxBu/JXI4vbjJ8fyt3Ghg+7oVlBblM3rm/Tb7mM0m9mxcR/KJw0x+6IWq5enJZzi2eyuevgHkXkxj2KTZkuYFOeSY//7/sB49onw4fqGAmHu+Y/uJTObd0btWe0ywO5sOp9Hl4bUkZBQyZ3hsVVufWD9eXXWY6LtXNUvxBGDQl2I2mzCW6W22Fxfk4enrz6PvLiS+90A2fbeo1mNVajUlhfmSxeOgVjKnbxhf/3mer/44z939w2u1x/q7EOXnwooD6SzcfYEnh0VVncWqUytx0anRqJrn42YyGji0dj4dRk4hfuRUDqz+sk6fnOSTxAy6kVHPf4HVaiV5z2+XXS6FMn0JZpOJsnreQ4DlX33ImEm3c+3I8SQnnCDtfHJVm8VsZs23X2KV+huKyr3D+m6NUVJSwr333svw4cOZM2cOen39/1e5cVAruat/GF/9cZ4vdqQwd0BYrfa4ABei/ZxZtj+dBbsu8PTwym0m0F1Hx1ZufH/4Iot2p3Jn39aSxqVTK5nSLZjlhzJYdjCDad2DbfaL8HaiS4h7rWW9W3vw+m+J3LH8KA+vOSV58VRhLGPb8i/oc9Nt9B0/gy1LP63TJ/X0MSY//yH3fraWez9bS1S3voTEdax3eVPp1Eomdwli5eEMVhzK4NZuQbXao32dWX8im4fWnEChUNCvjSdQWXQ993MCM5ceYebSI+xJKeBEZkmT46nJWFaK5TLfE1Dj+6Co9vfBjwvm0X/0JLoPGYnJZOLM4b2SxiaHHNPiCiiTycTrr7/OpEmTmDVrFkajsVme52J+GWv3nKfcZOH11UeJbVU7UZxOK+TY+QIA9p/NITmzeq/hjuti6BXjh5NO3SzFE4CbpzdefoH1trt6eBHZvgsAoVFt8fav3qidXd3xCWwlaTy923iSWlCGxQomixWzxUL7QNeq9v6R3mQUGgDI11egUiiI9a8cyckpLSeruHneR4C0Y7tx9Q1CqVShUmtQqlRkJ52o1ce3TVucvfxQqtR4tYrAxSfwssul4OHlg29A0GX75OVkkZKYAEBRQT5OztWjX1t/XUePvoMli6cmhUJR760x9u7dy6uvvsr69etxcnJi9erVzRKv1K5GnrkmwovU/OptxmSx0j6oepsZEOVNemHl8+brK1ApFcQGuOCoURHr74qjRomTVkW52SJpXF1buZFRZMRqBbPFitliJcbXuVYfBdCjtTtH0otqLR8S7U2XVm4A6CvMSC3x4C48/INrbMtq0hKO1+oT0bkX/q0j0To4olSqUKrUaLS6epc3VedWblwsrv16Rdd4vRKyS8nVV2C2wrk8PVnFld8JB1KLSMkvw2iyYPnr/a8wS1txunp443mZ7wmo/D7wDqj7fWA0lJF04hAARXk5OLm61+nTFHLIMS3uEN7FixeZO3cuzs7OTJ8+nZSUFKKjoyV/npqH41wc1BxMyrXZT6tWEu7vylOL9wOgVCg4fr6A3jF+zB4Wza3vbGX7iUzJ4wMa9EGzWCwknTzMsAkzLn20pLFE+7lQUGaqup9XWkGolyPHMioLS51aiaOmuh7P05fj76bj5F97XFbpB1Gqn+tCIjqX6o3fwc2ToosX8G3Ttk7f8rJSUEBw+x4NWt5k//Ae3jhpJm8//yDXjprAsDET8fT2BaCwIA+DQU/YX4dlpaa6zFWCs7KyyM7Ottnm6+uLn59f1f1BgwZV/d2xY0fc3aVNws3lauSZGH8XCvQVVffzSito7eXEsfSa24yqVnuAm47fE3I5nVXC62Pbciy9mJfXJ0gaV7i3E0WG6m25oKyCYA8HTtc4vNg/wostZ/MY08G/apmTRsXJzFK6tHJnWIwv/9t4ltQCg6SxZZ47g5ObR9V9Z3dPctPPExzdrmqZSq2p+vv8iYOEtu182eVNFe516etlIshdR8Ilh2MdNUqsVjiYVll0mmsMz7ULdOX4RekO3dXUkHrEVp/BN01l5UevMfDGyYTHxRMSGVu3UxPIIce0uBGoVq1a4ezsTFlZGd26dWuW4ulSo7qH8NaaY3WWO2pVvHhLZ2YMieLB0ZUbsMVq5Y3vjzL2/37jhaUHef/2Xs0eX30sZjObVi1k18Z1/LBwXrM+l4tWRXGNJGKyWPF0qk5Yh1IL6RvhjaNGiValxMdFWyvpNKeKstJa85aUKjWG4sI6/Ywlhexf+SkJW9aRvGfzPy6/GobdOJGQ8Eg2fL8Unxp7kpt/Ws2QG25qtue93PD68uXLGTdunM3b8uXL611namoqI0aMaLaYpXQ18oyzVkWxsf5t5uCFQvpFeuOoUaFVK/F10VL4107KWxsTCXRzoGuoOwV6abcjJ62K0vLqdZotVtwcqvelXXQqXHVqMopqj8rpK8ysOJTBMz8nsC0xj+ndpR3lBjDqS3G4ZFsuKy6ot3/SkT206VR3p6e+5VfCSauktLx6tO3S1wsqX7PpPVoxPNaXvuGeddbRKdiVQ2lFdZbbU7se/ejQayCbV3+Dh6//Pz+gkeSQY1rcCBRUHvP86KOP+PHHH7n22muJjZW2Mq7Jw1mLt6sDm4+ertNWVm7mycX7+eVQGvPv7VenyPr6tzPcNigKLxcdeSXNd4iqPkqVimE3zyC+9yDeffR2Rk27B0dnaSZAX6rIaEKrrt6NcdAoKTVWJ5XtiXkEujtw/8A2nMkuxdtZy4lm2uO6lNbJFYupek/fXG5E4+hUp5/OxZ3e0x7GN6Idp39fS3iPwZddfjWsWPAxU+58iD3bf+ONZ+7jtU+Wkn0xnfCoOLQSHH6oz+UmeE6cOJHBg22/Br6+vjaXb9y4kRkzZqBWt8iUYlNz55ligwltjXl/Oo2S0hoF1bazeQS5O/Dg4DacySrB20XLyYvFqBRwd/8wpi86yDMjonltTBx3LT0iWVwlRnOt+YhatRJ9jQJhaLQPP53Iuuw6lh9M552xdUd4m8rBxQ1TRfW0CFO5Ea2jc7399YX5uHh4N3j5lah8vapzn06tpKzcUqfPJzvPcyqzhOFxvuxIrj3fyN1BU2sEvyX485c1dOwzmLC4eJa++yJzXp5HUHiUZOuXQ45pOZHU4OLiwuOPP05sbCwrVqzgueeea5bnUShg9tAY3lp79LL9thy7SG49c3hOpRVQYqiw2dYYv66Yz+bvlwAweOytDLv50kNy9QsICcO/VViz/nhjYraebqHVQ6c+LloSsmpPiFxxIB2AMfEB/HQsE0OFtHM3ajr687ec2LgSgLZDJ1CaX53w9QU5eIVE1PdQQjv3JfGPXxq8vKFWL/mCH5cvBGDkxNsYd+vsy/bXl5ZwdN+fTJxxDzdOmsn55DPs3r6J7Iw0Du7ewcKPoKLCiL6khDeffYBHX37vimO71OUmcvr5+dUaQv8nO3bsIDo6mqCgIDIzM/H3l35vtjk0d545m11Kt9YeVff9XLQkZNU+7LNsf+U2M7ZjAD8ezaSswkLvcE9Ky02Ulpt54afTLLqtM1G+zpy55JDRlUrJLyO+xvxFb2ctyXnVh1Oui/VlcHRl8eGmU9OllTsKBWw5W30moNkKybnSTOb94/tv2PPTMgB63HALRbnV23JxXjb+YZE2H5d1PhG/0LrbeX3Lr9T5/LJa8z29nDQk59n+v+9OKWRQVO3CLdTTod6zHBtr08oFbF1T+T0xYMytXDth+hWva8ePK3jkgyUoFAoyzyexf8sGSQsoOeSYFllA/S08PJySEmnPWqjpnhFxLPz9DMYKC54uWlr7uuDj5sCmw+noNEoUKDBUmAnycuKXg5Wn37s6agj0dCQhvQhvVx1HzuVTbmp6oTDs5hl1iiarjYlDZ47uJzy2A0qlivJyIw6OThjL9ASHR6FzcKz56CbHVNMfSXlM6BKEAlCrFKgUCs5kl9I11J3956sPlwW66ejW2oOXfq49b0OhkHYeVIfrJ9Ph+slA5Vl4v33wJFaLBYvZjNVixrt1DBazicwzRwmM7YzJaECpVqNUqSnKSqN11/5Vj7W1/EqMu3V23aLJxn/6+KG9RLftiEajxVrjfYqKi8fLx48RYydXXfbg5JH9rF78haTFE0h3ivGCBQtYuHAhnp6eWCwW4uPjeemllyRZ99XSXHlmZ2IeN3etsc0oFSRkldAt1J19NbcZdx09wjx5/qfKUfAigwmHv+ZGGU0WTmWWkKeX7mSV/RcKGdnWFwWVlyVQKiA5t4wOga4czShmzsrqkfa7+oRy4mIJWxPzcHdQo1EpyCmtIMhNx4FUaQ5JXTN2KteMnQpUnoW3/LVHsVosmM0mLBYLgW1iMZtMXDh1mLD2Xasel3RoN206151CUd/yK3UgtYgRcX41Xi8F5/LKaB/owrGMEnRqJSazBbO18r3881xBrcd3DnbnQGrdKQVX4toJ0+sUTbbyauKxA7SOaY9ao623j87RCVNFORqtjtDoduRelPYSM3LIMS2ugNq5cydff/01I0aMQKFQcPPNNzfL8zwwqh1PjY/niZviAdCpVcQ/8D3fPDCAfk/9xLUdg3h+YidW70ohr9jISysOAtDG35Xljwxi6/EMdiVk88mGU80SX1F+LkknDpFxPomstBT8gitPZV47/wNufeB59MVFfPv+y3To2R8XD09GTrur6rFlpcWcPrSH3Mw0UhNPV10fqikMJgvz/zzPrD6hmC1W3v4tEV8XLQ8MasPUhQfxc9US4+dCgJsDL/2cUOvMIX9XHbH+Llj/+jtT4jPy1DoHOlx/K4fXLUKpUtFj0r0olEpKsjPZt+JjRj77OQnbfiRp10ZCO/dF5+JOzMAbAepdLoWCvBxOHj1A6rlE0i+cIygkDIDFn73D3Y//j5CwCG6aOoe1S7/GLzAYU0U53ZvprLtLSXWRu+nTpzN9+nRJ1nU1XY08YzBZ+PqP89zRtzVmi5U3Np7F10XHQ0MimDz/AH6uWmL9XQh0d+D5n05X7YgdzyimYys3RnXwx2Sxsv1sLrmlTR/l/pvRZGHFoYvc0iUIi9XK539ewNtZw+29WvHA9yfrfVyEjxN3XhPKvvOFnMwqYZvE16YC0Ogc6TN2GttXzUepVDJsxgMolEqKsy/y26IPmfn6/KqTa3JSk+k1enKdddS3/EoZTRa+O5zBzZ0DsVitfLnrAl5OGmb0COHhtScZGuPDgAgvdqcUUGw0seFk7cnRIZ4OrD3WPCcaFRfkknziEBfPJ9f6nlg3/0Nuuf9ZAkLbUFZazJnDe8jLTCct6TTBbSq/D8bMepDfV39DQGgbcjJS6TdqoqSxySHHKKy2hjn+5TxuXWzvEGz65omh9g7Bpnc2J9o7BJsGxNk+Fm5v10c2fOj5aurRpuFnp7R56Od625LeuV6KcP7T+r+z094h2BToXXfuX0swJMbL3iHYtPWMdNfKk9JN8S3jENWlxnVs+GVe5JBjWtwIlCAIzU8pg59ZEATBfuSQY0QBJQgyJIfkJgiC/cghx4gCShBkSA7JTRAE+5FDjhEFlCDIkEr1309ugiDYjxxyjCigBEGGVM30w86CIAggjxwjCihBkCE5DK8LgmA/csgxooASBBlq7C+iC4IgNIYccowooARBhuSwdygIgv3IIceIAkoQZEgOyU0QBPuRQ44RBZQgyJAckpsgCPYjhxwjCihBkCE5JDdBEOxHDjlGFFCCIENySG6CINiPHHKMKKAEQYbkkNwEQbAfOeQYUUAJggzJ4RRjQRDsRw45RhRQgiBDcviZBUEQ7EcOOUYUUIIgQ3IYXhcEwX7kkGP+kwVUWFSgvUOwKaPUYO8QbPJ00dk7BJv0FRZ7h2DTuaJSe4dgUw/cG9xXJYPk1pyO7E+2dwg2WTq3tncINnXq18beIdi0bE+6vUOwqUeot71DaDI55Jj/ZAElCMLlySG5CYJgP3LIMaKAEgQZksPwuiAI9iOHHCMKKEGQIZUMzpARBMF+5JBjRAElCDIkh1OMBUGwHznkGFFACYIMyWF+giAI9iOHHCMKKEGQITnMTxAEwX7kkGNEASUIMiSHvUNBEOxHDjlGFFCCIEPKFjw/wWq1ymL+hCD8l8khxygliEUQhH8ZtUpR781eDhw4wMiRI7nvvvswmUwsWLCAo0eP2i0eQRCunBxyjCigBEGGVEpFvTd7+fDDD3n00Ufp2bMnarWaMWPG8Pjjj9stHkEQrpwccowooARBhhQKRb03exkwYAADBgzAxcUFgMOHD5Ofn2+3eARBuHJyyDFiDpQgyFBLvMidu7s7r776KsXFxSQnJ/Ptt98yd+5ce4clCMIVkEOOEQWUIMiQlKcYL1myBI1GQ1ZWFtOmTcPNze2K1jN27Fg6dOjA7t27MZlMLFy4kLZt20oWpyAIV48ccoysCygHjZKpvUPxctby+vqEOu2jOwUy7ZpQtColj686xsmMYgB6hHvSLcwTlVLB2awS1h/NlDy21NNHST6yF4vZRFzvIfi1jqjVri/K57MHJmMxm6uWjX3of7Tp2IMDv36PQqnCWFqMT0g4kV2ukSSmOH8XurRyQ6VUsC0xj3N5ZbXa1UoFn97cHi9nLbml5dy5/CgWa3X7nD6hnM4q5fczuZLEU1Nu0nEyT+7HajbTqusA3INr//q71WLh8MqPSTu8A7fAMLpOeRgnT18A0g/vpPjiBSoMpbTpP7pquRTKjQZ2/riC0sICRs68z2afrNQU9m/+CY1WR1jbjkTGdwPg8I5NFGRfpLSwgNhu19CmfRfJ4pJqHsLx48c5fPgwb7zxBvv27WPevHk89dRTV7Su9PR0nJycGDRoEAAGg4HFixczZcoUSWK1h6duimf2sBhSc0uZ88kfHL9QUKv9gVHtmDYwkuKyCqa+t5XzOaW12h8c3Q6dWsX/rT4iaVw6tZJbugXj6aTlvc2JddpbezkyKj4AY4WFg6mF7EupjtvDScPYjoFcyC9j3/kCCvQVksZmNBhYv3oJRQV5TLv7UZt9jh7YTeLpY7i4uqMvLWH0xOkAHNqzk9WLP6MgL49ZDzxNfLfeksTUIciVHq09UCuVbDqdTWKOvlb7uI4BmK1WXHVqknL1/JFUeVhoSIwPnk4asEJZhZmfjmdJEk9N8z+fx9pVS/HzD+Tx514hIiqmTp9NG34kPy8XhUKBg6MjI8dMACAlOYmff/gOnU5HfOdudOspzXcFyCPHyHoOlLNOjVqlxFmnqtPm5awlvcDA+I93s/FEFrP7hwGVRdf910by8e9JfPhbIrf2CiHAXSdpXBXGMrYt/4I+N91G3/Ez2LL00zp9Uk8fY/LzH3LvZ2u597O1RHXrS0hcRwqyMkg9fZTO146m5+jJbF/xlSQx6dRKbusRzNID6Xy7P50ZPVvV6XNNuCevbDzLzG8Pc//qE7WKp0gfJ7qFuEsSy6VMRgPHf5hP3Ihbibt+CsfW1v0/ZxzbRatuA7nuhYU4uHlU9SkryOHcn78Qc90tRA4ex7E1X0gam7FMj8Vsotygt9lutVpZ8+kbDJ4wnWtvuZ3ta7/FVFFOYW42B7f+woCxUxh26x38OP8DjIYym+u4Ekqlot5bVlYWx48ft3nLyqr9BbBp0yYiIyMBiIuLY9OmTVcc0w033MDUqVOZMmUKU6ZMYfLkyaxZs6Yp/0276hHlw/ELBcTc8x3bT2Qy747aX+Yxwe5sOpxGl4fXkpBRyJzhsbXag72cmDYwsllic9aqUKuUOGnr5j6AJ4dH89XO83yxM4UpPULQ/HXmlIeThpdGxvL9oQw2ncqWvHgCKNOXYDaZKCuzvc0ALP/qQ8ZMup1rR44nOeEEaeeTqSgvx1RRzksfLGLM5JmsmP+RJPE4qJXc2bc183dd4Ks/z3N3v7Ba7YFuOuKD3Vh7JJPFe9OYfU0oAFqVkjHxAaw6mMGqQxnc0M4fR420X7nHjxyiTUQUK378nU5du/PWK8/V6WM0GFj73TImTL6N8ZOm8fMPq9GXlmK1WnnzlWe5bdbdzJxzH8u++Zry8nLJYpNDjmlxI1AlJSU8+eSTnDlzhrCwMN555x2cnJya5blyS8pJzy8j0EYBlFdaTl5p5YfpaGph1amXXVt7Ulpuqup3LK2I4e0DWLAzRbK4Eg/uwsM/GKVSBUoVSpWatITjBEe3q+oT0bkXKrUGAFN5OUqVGo1WR4XRwMWk05QbyrBaLag0Gkli6hbqTnqREYsVLFYrJouVWD9nTmVV7zEPjfHhcHoRm07noC+vHhlTAD3DPDiUViRJLJe6eGIPzr5BKJSqyptKTW7yCbzDq4dmPVpF4OTlD0DUkAnsX/w2ABf2b8GzdTQADq6eFGemUlaQg6OHjySxuXp44ekbSGGO7T3P1DMnUapU6BwrP+Ne/sEc37UVY5ked6/KGNQaLZ5+gSQc+JMO1wyWJK7LzU9Yvnw58+bNs9k2d+5c7r333qr7OTk5dOjQAQCdTlcn+TXGokWLqtYFkJqayp9//nnF66vP1coxF/PL2HMmB4DXVx/l1EfjarWfTius+nv/2RzMNfc4gOlDovjuz3OSxwWQp68go9CAv2vd3Ncu0BWzxUpZReU2nFZQxsBoHzaezOaxoZEs25dGQZn0hdPfPLx88A0IIif7Yv3x52SRkphA64hoigrycXJ2QaPV0q1P5chCRGx7jh3YI0k8vcM9SSsw1Mp97QJdOf7XEQkHjYoYfxccNEqUCgUV5sr3UaNS0NrLER9nLXn6cnQaJaZL3uOm8vbxpV18JwCmzpzDzaOG1OlTYaogJTmJ7KxMvLx9MBoMqDUaTh4/gkqlxsnZGYCgViFs2/wr1w4fKUlscsgxLa6A2rt3L6+++iouLi48/PDDrF69ulmH8P/p46xQQJfWHny2NRmoHIHydtZWteeUlBPk4SBpTJnnzuDk5lF139ndk9z087UKqL+LJ4DzJw4S2rYzAL4h4fiHRfH9O88QFNmW6+98QpKYIrydKCqrLhwLyky08nCoKqCctCpOXCyhW4g7I+L8eGF9AhcKDAAMjPLm9zO5jIsPkCSWSxVeSETnUj265eDqQUlmaq0C6u/iCcBcbsAjNKrysaln8W7TvqpN5+JOcVaqZAUUUPkhqkd68mmca7zXLh5eZKedx9HZlZLC6rNDXN29KMiW7lCx+jI7whMnTmTwYNuFmq9v7cOb3t7elJVVjoyVlpbi7e19xTHVTGxQmSy/+uorJkyYcMXrtOVq5Ziah+NcHNQcTLJ96FqrVhLu78pTi/dXLRvUIZDtxy9yTay/zcdIwWq1nf1i/F3IrzGylFtaTpiXEyGejnRr7cHuc/k8f0MMO87m8dvp7OYJ7h8mIN84aSZvP/8g146awLAxE/H0rv253P/nVibPvl+SUKL9ar8eefpyWns5VhVQybl6zmSV8troOI5nFPPqL2cAKC03s+FEFm+MiWPr2Vze/z2pqriSSkBQcNXfZWVlxMS1q9PHxcWV4SNv5PH772TA4GHc/+jTaLVaEk6dwNPTq6qfl7cP588lSRabHHJMizuEN2jQIFxdXVEoFHTs2BF39+Y57NMQSgXc0T+c8d2Cefi6yi/cI6mFBHs60jbIFYBQL0fp5wDoS3FwcqmOQ6WmrLig3v5JR/bQplOPqvtDZ9xPYfZFUk4cxMlNmtfPSauipMbIm9lixd2xuojTl5tZeiCdJ9adZsvZXGb2CgHARafCVacivdAoSRy2VBhK0dR4vRQqNcaSgnr7Xzy2h6jBN1U+tkyPxtG51mPLiwvre6jkDPpSHJ1dq+6rVGpKi/IJa9eR5OOHKMzNxmIxk5+dgZPrlU2ctOVypxj7+fnRrl07mzc/P79a6xk2bBgnT54EICEhod6k2BCxsbHExcURFxdHbGwsI0aMYPjw4U36f9pijxwzqnsIb605Vme5o1bFi7d0ZsaQKB4cXfnlp1Ep6Rnly7YT0s+tbAhnnZpiQ+1t3dNJQ5cQd05klLDuyEU+236OJ4ZHEeHTPEcH/smwGycSEh7Jhu+X4uMXWKtt04+r2PjDCr58/xVJnstZp6LEWP16mCxWPBxrj+y/szmRADcdXULca43OffHHeUwWK9fG+JJT2nyjdgDbt2zi1ul32GybdfeDqNVqNv3yI94+ldtwaUkJLjVyilqlpiA/T7J45JBjWlwBVVNqaiojRoyw2/NbrPDZ1mSmfLGPkfGBuOhUZBeX88iKo8zoG8aUXiF0CvFgT7K016pxcHHDVFF9LNpUbkRb40v+UvrCfFw8Kqtyi9nM1mVfcNsrn+Ps7sma956vd0+zMUqMZjSq6o+LVq2ktMZhupq+3ZdWdWjgulhfNpxspr3Uv2icXLFUVCcnc7kRtYPt16s0JwOdmyduAX/NU3ByxWKq8dgKI2rHq/el4OjiRkWN97qi3IDOyYXgNjGMmvUAvyz+lL0bfyA9+Qzh7TpL9rwalaLeW2O0bduWyMhIVq5cyR9//MEDDzxwxTF9+OGHnDx5kpMnT3Lq1Cn27dvXpPU1xNXIMR7OWrxdHdh8NKNOW1m5mScX7+fmN3/nnhFxAEwfHMn8zWeaNabLKTJUoK0xfKBTKykpN+PuqCG9sAyLFS4WGTmSWkjX1h52iXHFgo+ZcudDXHfjRN545j7yahwiv3bkeP7v02WcO3OK88lNfx2LDSa0NXKfg1pVaxqHUgF39m3NrCWHydeX879R1XPZ7h0QzqNrTnDgQiFvjolDd7lhmSYoKiykqCCf7r362Gyf9/arvPHB53Tu1pPH7rsDg6EMNzd3ysurd2wNRgPOLq42H38l5JBjWtwhvL9t3LiRGTNmoFbbP8Sk7FKSskurjl/vOJPLjjO5RPo5c117f/ada3oB9cf337Dnp2UA9LjhFopyqxNCcV42/mG2J5NmnU/EL7T6DL3ko3vROjqjc3Jm5N1Ps+Cp2WSdT8S/ddMmo57L09MxuHpvxdtZQ1Ku7UmeZisk/nX44vq2fgyNqTwc5uqgpluoBwpgcxPPxDv1y1LO/LYKgKgh4ynLr369DIU5eLSKqPMYc7mR1IPbiL725qplbkFhGArzajw21+ZjG2rzyoVsX7sEgH433srgCbddtn9A6wiO/bml6n5hbjbRnXsC0HnAcDoPGM7pA3/Spn0XvPyDrjiuS0n5awqzZs2SZD1Dhw6ts2zLli0MHDhQkvVf6mrkGIUCZg+N4a21l/+5iC3HLpJbXPllNqlfBPfeUHn42d2pcrqAxWrlje+vzs/anM0qZXBM9WEUP1cdu5Lz0agUtAuq/oLNKSnHaLI0+flWL/mCH5cvBGDkxNsYd+vsy/bXl5ZwdN+fTJxxDzdOmsn55DPs3r6JEWMnV/Vx9/QmpkMnVErbk+QbIzGnlK4hHlX3fV20nKkx97NHaw/05WZKy828tP4M86d0JNLXCZPZirezhsIyE+/9nsQHE9rTPdSDHUlNG+VZ9OUnfLvoSwAmT5vFlJl3sva7pfWOPiUnniE3JwcPTy8eeOxZ7rtjKvt2/UGbqGi2/PZLVb+crEx69O7bpNhqkkOOsX91YsOOHTuIjo4mKCiIzMxM/P2bbx7Ape9x93BPDp0vwGyx4qBRoS8346hVcepiMYaK6mShUSm4e1Abnvn+hCRxXDN2KteMnQpUnoW3/LVHsVosmM0mLBYLgW1iMZtMXDh1mLD2Xasel3RoN20696q67+jiRoWxcu6RRudAQHg0zu6eTY5v7/lCRrf3R8Ffl+hXKEjK0dMxyJXD6cV4OKrRqJRkl5QT7K5j/4XKw2C3L60+/Xpuv9Ycv1giyWUMYq+bROx1k4DKs/B2fvwUVosFi8WMxWLBMzQai9lEztmj+MV0xmqxcHbL90QMGINCoUCfl4nZVEGrLgM4tLLybB1DUR6u/qE4uHld7qkva/CE2+oWTTZGAJOOHSA0pj0hUW0x6ksoNxrQ6hwoyL5ITNfqU4n1JUXs+XUtY++S9idNWsovpQ8ePJiMjOqRmb9HSxUKRdUPfv49fC+lq5Vj7hkRx8Lfz2CssODpoqW1rws+bg5sOpyOTqNEgQJDhZkgLyd+OZgGwODn1lc9/olx8QDNUjxdekXoLiHuHE0v4nhGMS5aFTq1EqPJQqC7AzsTc1GrlEzrFYpaqcBkseLrqmOnBCfPjLt1dt2iycY2c/zQXqLbdkSj0WKtMXs1Ki4eLx8/rFYr+tISnF1csZjN6HSOBIaENTm+P5LyGd85CAWVv++mUio4k11K1xB39l8opMhgwkFdWagZTRZOZ5aSV1qBUqmoGrW3AqculpBV0vSpDNNm3cW0WXdV3V+xZCE33DgerU5HYWEBF9PTiIlrx+4/ttOjd19cXF2rRpqUSiVx7eLx9Q8gOrYtpSXFGAxlODg4kpGeRu++A5sc39/kkGNaXAG1YMECFi5ciKenJxaLhfj4eF566aVmeS5vFy1dwzyI9HMhzMeJczl6HhsezRPfHcPDUcOrN7Xjt5PZ5JWU8+6vlUPBjloVXUI9iPJ35s0NZ8goNEgel0bnSJ+x09i+aj5KpZJhMx5AoVRSnH2R3xZ9yMzX51clv5zUZHqNrt7zCopsS+rpoxz5/SeUKjWRXftUHd5rCqPJwtID6dzaLRiz1conO1LwdtYw+5pQ5q46ToSPM3f3bc3e8wWcuFjClrPSHUv/J2qdA7HDb+XEz9+gVKrodPNcFEol+pxMjnz3KUOe/JQj333K+b2/cWbzagAUShXDX1qESq0huHM/Tv+6DFO5kU4Tpb3ydXFBHudOHibzfBLZaefxDa48dPjTgnncfN+z+IeGM/qOh9m49AucXN0ZMnEmOgdHSosKuHDmBNmpKdx0z5M4uUo7T0fdQpLbY489xqBBg9DpbF8KZNu2bZI/59XKMQ+MasdT4+N54qbKIkinVhH/wPd888AA+j31E9d2DOL5iZ1YvSuFvGIjL604KHkM9fFy0tCplTsRPk6EejlyPq+M+wdH8MJPp0jO0fPGxrPM6RdGQVkFX+xIoazCAhUW3v3tLHf2CyOr2MhPRzPJKZHutPe/FeTlcPLoAVLPJZJ+4RxBfxVBiz97h7sf/x8hYRHcNHUOa5d+jV9gMKaKcrr3HUxm+gVeffxu4rv1wscvkAnT70KpbPohM4PJwoJdF5h1TShmi5W3f0vE10XL/YPCmbboECculhAf7MYN7fwwW6xsT8wj7695sb8n5DKuYwBFBhMXCspIyCr9h2drnKWLvmTB5x+x8MvKncCK8gqWfL8Bg6GM915/ic8WrcLXL4BBQ0fw3fLFuLm5ExIaVjXZ/KEnn+fLj9/H3cODmXfOxVHCs1HlkGMUVikmyLQwnV74zd4h2HT38Oa5pktTbTieY+8QbIoMcPnnTnbQLVi6eQJSurlTww/xvfLb2Xrbnh7SMj6nZWVlJCQk0LFjR3uHUofHrYvtHYJN7Tu3tncINr3z10haS/PEj9IcQZDaoqld/7mTHbTy1P5zp7/IIce0uBEoQRCaX0v8naqNGzeybNkyKv46IcBsNpOZmdmkC+cJgmAfcsgxooASBBlqIaPrtaxbt47bbruNvXv30q9fP7KystDr678atSAILZccckyLvoyBIAjNQ6VU1Hu7mmpO3oyLi6N///7Ex8cTGhrKyJEjWbdu3VWNRxAEacghx4gCShBkqKUkt3vuuYc1a9ZgNBpxdHTk+uuvZ/DgwbzwwgvccMMNlJSUXNV4BEGQhhxyjDiEJwgypGohu04zZsygTZs2fPbZZ5hMJl588UVUKhXz5s3j9OnTtGnTxt4hCoJwBeSQY0QBJQgy1FImeE6dWnnts/j4eMxmMzt27OCdd97B1dWVESNG4OjoaOcIBUG4EnLIMS2kRhQE4WqS6mcWpKRSqRgwYEDVb18NHTqUl19+2W7xCIJw5eSQY8QIlCDIUEu5SnB2dja+vr6UlJSwbt06Vq5cSUpKCtdffz3Lly8nPr5lXj9IEITLk0OOEQWUIMhQSxl6fuKJJ/Dw8GDz5s1ERUUxadIkbrjhBpwkvCKyIAhXnxxyjCigBEGGlC1kfsLBgwcZN24cy5YtIyYmxt7hCIIgETnkGFFACYIMtZQJnq+99hrXXXedvcMQBEFicsgxooASBBlStpD5CaJ4EoT/JjnkGFFACYIMtZS9Q0EQ/pvkkGNEASUIMtRS5icIgvDfJIccIwooQZAhOewdCoJgP3LIMaKAEgQZ+u+nNkEQ7EkOOUYUUIIgQ+oWMsFTEIT/JjnkmP9kAXUxLd/eIdjk46i1dwg2lRgq7B2CTbqW8muUlwhwcrB3CE0mh/kJzck7wNveIdjk7NQyc0yW3mDvEGxycWiZX4FZRS3z9Wrl2fDPlxxyTMv89AiC0KzkMD9BEAT7kUOOEQWUIMiQHPYOBUGwHznkGFFACYIMyWHvUBAE+5FDjhEFlCDIkBz2DgVBsB855BhRQAmCDMngBBlBEOxIDjlGFFCCIENy2DsUBMF+5JBjRAElCDIkh/kJgiDYjxxyjCigBEGGZJDbBEGwIznkmJZ5pUJBEJqVUqGo9yaFN954gzFjxjB27FgSExMlWacgCP8ecsgxooASBBlqzuR26tQpxowZw5o1a5gwYQLvv/++BBELgvBvIoccIw7hCYIMXe4MmaysLLKzs222+fr64ufnd9l1x8bGVv3dpUsXTp48eUUxCoLw7yWHHCMKKEGQo8skt+XLlzNv3jybbXPnzuXee++tuv/hhx+SkpJSp99bb70FwM6dO7nzzjubFqsgCP8+MsgxooASBBm63DD6xIkTGTx4sM02X1/fWvdrJrpLnTp1ivbt29OqVasrC1IQhH8tOeQYWRdQj41uy4zBkaTl6bn3q72cTCus1d4zyofB7QPQqBR8t/s8xy9Uts8aEonZbMXdWcPJ1EJ+OZwheWzlRgPb162gtCif0TPvt9nHbDaxZ+M6kk8cZvJDL9Rp++Pn73BwcqZ1TAf8WrVuckztA13p3toDtVLBbwk5JOXoa7WPiQ/AbLHiqlOTnKfnz+R8AHRqJeM7BeLuqOHj7eeaHIct2YnHyDixH6vFTGjXgXi2alOr3WqxsG/FPFIP7sA9KIyeUx/B2atymDhh6w9YzCZKcy4S0WcEHsHhksVlNBjYuOZbigrzmXznw5fpV8amtcvx9PUjpn1nvP0C2bnpJ379/ltMFeXc8djLtI6MrffxjXW5aQh+fn7/OIT+TxISEiguLqZnz54UFxejVCpxdnZu0jr/jRw0SmYNCMfbVcuL39s+zOCoVTGjXxgZBWXsTsojPd/ANVHe9IzwQqlQkHCxmHUHpc0x7QNd6R7qjkqpYHNCLkm51duyu6Oab6d1Rq2qniL7zI+nSMgutbl87/naebOpyo0Gtv6wnOLCfMbNesBmnz2b17P1h2WYKsqZ8tALhETEAPDnr2vZsPQrdE7O3DznUSI7dJEkprYBLnQNqXy9tp7NJTm3rE4ff1cdg6K8uVBQxoELhZRVWKra7u7XmlOZJWxOyJUknppWLvyUX35YiY+vP3c99iKt20TV6XPq6EEO7f0Ds8lEnyHDCYuIwWq18s2n77J35+/4BQbz4HNv4OLqJllccsgxsp1E3i3Cm5NphXR85Ed2nsrmvelda7U7aVU8N74Dr685xmvfH+PFmzsCEOrjTK8oH+ZvSeS9n07x9E0dmiU+Y1kpFrMJY5m+3j4GfSkqtZqSovxayy0WC8vee4mQqLZ0HzJSkuLJQa1k9jWhLNx9gfm7LjCnT+11Brjp6BDkyrpjmXy7P43be4VWtTlpVaiUCpw0qibHYYvJaODw2vm0v2EK7W+YyuE1X9bpk3Z0F2HdBjPq5UU4uHlW9clNOU1BaiKxg8fRbvgk9ix5B6vFUufxV8pQVorZZMKgr/99LDca+OiVx+nWbwjXDL4eb79AivLz8PYL4MWPltC1zyB+XD5fspigeSd47t27lzlz5vDaa68xbtw4pk+fjpOTkwRRbi7vywAAHoFJREFU//u4OKhRq5Q462zvq+rUSj6d0YWfD2fw/f500vMNOGiUPHp9NO9uOMPb6xOY0T+MQA8HyWLSqZXM6h3Kwj2pLNidyp19Qmu1dwh05b7vjnPj53u58fO97EjM43BaUb3LpWbQV24z9eW+4oI8vHz9efS9hcT3HsimVYsAyMvKwGQy8dwXq+lz3Ri+eecFLBJsyzq1khk9Q1iyL43Fe2vntr8FuztwV9/WrD58kZ1J+bWKp0hfZ7qHejQ5DlsSjh8mJDyST5aup23Hbnz29st1+hjKyljyxQfcfNscJs64m28+fa/ysSeOcP1Nk3l3wfdYLBa2b/pJ0tjkkGNaZAGVmJjInDlz2LNnT7M9R2ZBGev2p1FusvDOjyeIDnKv1T60YyBJmSVYrFBhtlJhttA9whsnnYpOYV446VS4OKgpN0n3ZVuTq4c3nn6Bl+3j7OqOd0Ddocs/1n+Hp28AYbHSFXe9wjxJKzRgsYLJYsVksdI2wKWq3UGtJMbPBQeNEieNiooaiStfX8HFIqNksVwq/fhuXH2DUCpVqNQaFCoVOUknavXxbBWBb2R71FoH4obeTOHF8wCkHv4DZ++Ayv+DmycWs5m882cki83d0xufgKDL9lm96BM69eyHf1BI1TI3Ty9i4yuL+jYx7fELCJYsJqjcO6zv1lTdu3dn8+bNrF69mtWrV/Pdd9+haGEXhbkaOQYgp7icC3n1F8+P3hDDpmOZnKsxmtszwotSo7nq/uHzhYzqfPnPUGP0CvO47La861wBiTl6DCYLZquVCouFcrO13uVSc/P0xsu//tzn6uFVNbLUOrot3jW2r37X34RKrab/qJsxm02UFhU0OZ4erT1IL6r9esX5u9Tq88iQNszfdYFyc+3vAwVwTZgnh1KlLzQBPL196dX/WjRaLeOmzCI1JalOnwO7thMQHIJSpUKt0aBWqzl9/DAx7Tri4xeAWq0mPDIW/0BpD4PJIce0yAIqIiICZ2dnrFbpN86/XagxZO2sU3PoXF6t9o6tPcktrv7Szy40EBXoyqm0Io6cz2fJvX25b0Qs93zZfAm4IZ+HS/tYrVa2/7AcrYMja754h00r5mOxmG0/uBGi/JwpKKuoup+vryDU07Hq/rm8Ms5kl/K/G2K5pWsQ/7fxbO24mhxB/fIvJKJzqS6AHVw9Kcq8UKuPs7d/1d8mYxleoZXD3OZyI4bigurHunmiz8+SOML630iDvpTtG9ZisViY/97/+G3dylrtFouFhOOHGDnpdkkjau5rtLR0VyPH/BNnnYpbeoWgUip4fWIHpvWtHNV10KjwdtVW9cspNhLsKd0IVJSvM4WX2ZZNlurXpHMr96ov//qWN49//hxaLBaSThxm2ITpAHjV2OG0WMw4u3ng4u7Z5EgifJwoLDNV3S8oq6BVjRHBTsFuuOjUtAt05bEhEXQMrj4MNijam98ScpocQ318axSPxjI9EdFt6/RJPnMSN4/q18Hd05u088lV9/WlJSgUCrr06idpbHLIMS2ygALQaDRX7bmu7xLM+z+fqrXM1VFDgb686n6F2YqPW+VG89g3BwjxcaZ/W79aRVZLkJNxgfJyA31uGM+oGfdxfO8Odv/6Q5PX66xVUWKoLsRMFiseTrXfo/e3JBHgpqNzK/daCbq5VZSVonWq3iNUqtQYiuufl5F+bA+xQ8YD4BcVT9qRP6kw6DGVGykrzEXr5NrsMf/t7KmjePkFMGD4GCbOup/1KxdyZO9OACxmMz8un8+2DWtY8aW01zlpzr3Df4urmWNs6RLmSVp+Gd/+eYH/rT3JXUPaMDDOl4MpBYR6O9GhVeUXcWsfJwr00m1Pzlo1xcbqgsBkseLhaPu16B7qwZ6UggYvv1osZjMbVy3kz43rWLug7tlcJ/f/ycDREyUZlXDWqii5zOsVH+TG0fQifj6RxZojF3lqWCQejmpcdSrcdGrSCg1NjqEh9uz8nTGTZ9RZri8twdmluqhTq9UUFVRO+ygqLGD+vDdYv2YZ2zf9LGk8csgxLbaAulo8nDR4uejYcjyz1vKC0nJ0NebsOGpVFOsrUCkVPDe+A4Ne/JWsQgML515ztUO+rNKiQpxd3XF0dkWlVtOxz2DOHNnX5PUWGUxo1dWffJ1aWeswg1IBs69pzR3LjpCvr+DF62Oa/JwNpXVyxWyq/oIxlxvRONo+Hl6Sk4GDmyfugZV7+606XkP0oDEcWPUpSX9swFCYh1fY1Yu9pKgQTx8/NFodTi6udOzZjxMHK0c1lSoVoyfdzlNvf8Wu3zegLy2R7HkVl/knXB2ezlouFhooN1koNpjYdCyLftE+ZBUZuWfBQe4cHMGM/mF0DfPkzzPSTT4uNprQ1pgI7qBWUlpue5Ta00lDno3irb7lV4tSpeK6m2dw//99xv6tv1JWY9swVVSQdOIwvYaOluS5io1mNDVeL90lr5erg5qMv6YoJGSXkltaTqy/C8Pb+vHzCdvXOpJaSVEhxYUFdOzWu06bi5sbFeXVgwFGowGnvyZbu7l7cM/jL3H7fU/w8+pvJY1JDjlG1gWUQgEzBkXy/k91z445nlpIgHv1MG2gpyNHzuczuH0AxQYTxWUm7vx8N35uDrQP8WhyLJtWLuDZW4fy7K1D2bRywRWvx93Lh5LC6knlbp4+aLTayzyiYZJy9Xg5V6/H10XL2ezSqvvdQz0oLTejLzfzyq9n8HTSEuHTfJP6jq//llUPj2XVw2NRabSUFVZ/wegLc/BsFVHnMaZyI+f3byV64Jhay2MHj6PnlIdQqFSE974Ojc6xzmMb6oelX3HXuAHcNW4APyz96h/7e3r7UpRfHbuHlw8ara5Wn6DQcAJDw1CppJuEr1TWfxOujsxCA76u1e91ZpERg6nyi3nLqWzmLjrIzoQcCvQV7ErMq281jZaUo8fbuXoExeeSbflvbbydSMxp+PIr9cuK+Tx+y7U8fsu1/LKicSdLBISE4R8ShrLGtrH951UMHjdFsjkx53Iveb2ctbX+/3n6cjxrjEjllVZQbrJwQzs/Prq5PV9MiueaNp7M7BXCtTE+TY5n1TdfMO2GPky7oQ+rvvkCi8XCrz+sZMzkmTb7h7aJJj+vupDLy84i7JIzenv1v1bS/ALyyDGyvozBnddGsXh7EkaTBU9nLaE+zni76th87CK/HkpnztAoFArQqJSolUoOJOfRJdwLJ23lB62s3Myhc/lkFTV9iPbaCdO59q9j+X+zNT0j8dgBWse0R63R2uzj6RdIQGgb0s+dISgsirzMdNr3HNDk+HYl53NTx0AUgFpZeRz7bHYpXVq5cyC1kCKDCQdN5ZZhNFlIyCohv8YeqtT7HO1GTKbdiMlA5Vl4v3/4JFaLBYvFjNVsxrt1DBaziawzRwmI7YzVYuH076uJGTgWhUJBaW4mZlMFbv6VEydLci6SeeoAvW57vElxjZ50O6PrzFeq+0aePLyPyLbxRMTFYzKZKC7Mx9Xdk+yL6Qy64SYsZjPlRgMOTs4YyvSEtolG53Dlhd2l/kvzEP5tekd6sT+5gP3J+WhUCrycteSVlhPq7cg3O89X9dOqlDw4PIpHlh6R9Pn/PJfPuE4BVduySvn3tuzGgRrzmnqGebD7XEGdx9e3/Epdd/MMrrv50kNPdbeZM0f3Ex7bAaVSRXm5EQdHJ4xleoLDo6q2jT2bfyauS29c3DwoNxpITTxNm7YdmxTf7pQCbuxQ+Xqp/sp9iTl6OgW7cSitiG1n83hocPVlUxw0So5lFDN98eGqZfcPCOdoRpEklzEYP3U246fOrrr/46rFDL5+DFqtjuKiArIuphMR3ZZDe3bSsfs1dO3Vn5+/W4LFYsFsMmG2mIiMbY+hrKxqUnnGhRR6DxjW5NhqkkOOaZEFVFpaGufOnePAgQN07NgRBwfpJlD+be7wGB67sR2Pjm4HgFatpPuTP/PVXb259qWNFOgreGPtCZ4a2x6Txcoj3+zHaoX9SXn0jvZlSr9wTGYLPx9MI6sZjnEXF+SSfOIQF88nk5WWgl9w5SGndfM/5Jb7nyUgtA1lpcWcObyHvMx00pJOE9ym8tDTzfc+zZbvlxASGYtSpaJD74FNjsdgsrBoTyozeoVgsVp5b0sSPi5a7h0QxowlhzmZWUKHIDeub+uHyWJlZ1Je1RC/p5OG+CA3wr0dCfFw4EKBtK+XWudA++tv5eiPi1CoVHS75V4USiWlOZkcWPkxI575nP0rP+bcnt84vek7ABQqFaP/t5jSvCzyL5ylNPcivW57HPUloz9NVZify+mjB0lLSSTjwjkCQ8IAWP7Fu8x+9CWCW0cw+5EX+eHbLwkOiyA8Oo7w6LacPnqAL99+gS69B+Lm4cX4GfVfTO5KyCC3XdbVyDEAPq5aekZ4ERPgShs/Z5KySnl2TFseWHyIhIslPLjkMPddF8npjGKOXCjk6IVCnLQqurfxJCbQlZfXniQ9X9rtxVhjWzZbrLz3ezI+Llrm9g9n5rfVX/phXk4s3Z9e5/H1LZdKUX4uiccPcfF8EpmpKfj/dRmWNV9/wJQHn0dfXMSS916mQ6/+uLp7MmraXQAc2L6RVZ+9hUpdORpkKi/nnv992OR4jCYLS/anMbVHK8wWK/O2J+PjrGVO39bMWX6UtEIDPx3LZFLXIExmK4v2pDbL2Ym2rF22gJWLPmPVos8BMFWU88E3P2A0GPjy/df4v0+W4OLmzoRpd7J8/kcolWpmP/A0SqWSX39YwZZffqBX/2txc/dkxLhJksYmhxyjsNrzNJRmEjB7lb1DsOnjuX3sHYJNn+6oe5n8lqBnhLe9Q7BpaJuWGVf/aK8G903Oqf9LOdyneYqJ/5KIh9fbOwSbIiMa/hm4mu7tH2bvEGz6/I/z/9zJDl647urNw2yMLq0bfqFNOeSYFjkCJQhC85LD3qEgCPYjhxwjCihBkCE5zE8QBMF+5JBjRAElCDL0309tgiDYkxxyjCigBEGGWtpPqwiC8N8ihxwjCihBkCHlfz+3CYJgR3LIMaKAEgQZksP8BEEQ7EcOOUYUUIIgQ/+lqwELgtDyyCHHiAJKEGRIDvMTBEGwHznkGFFACYIMyWF+giAI9iOHHCMKKEGQof/SL6ILgtDyyCHHiAJKEGRIBqPrgiDYkRxyjCigBEGG5HCGjCAI9iOHHCMKKEGQITnMTxAEwX7kkGNEASUIMiSHM2QEQbAfOeQYUUAJggzJYe9QEAT7kUOOEQWUIMiQHPYOBUGwHznkGFFACYIMySC3CYJgR3LIMQqr1Wq1dxCCIAiCIAj/JjL4tRpBEARBEARpiQJKEARBEAShkUQBJQiCIAiC0EiigBIEQRAEQWgkUUAJgiAIgiA0kiigBEEQBEEQGkkUUIIgCIIgCI0kCihBEARBEIRGEgWUIAiCIAhCI4kCShAEQRAEoZFEASUIgiAIgtBIooC6jMTERObMmcOePXvsHQoAJSUl3HvvvQwfPpw5c+ag1+vtHVIVk8nE66+/zqRJk5g1axZGo9HeIVUpKyvjxhtvJDU11d6hVMnJyWHIkCH079+fl19+2d7hCHYickzDiRzTOCLHND9RQF1GREQEzs7OtJTfW967dy+vvvoq69evx8nJidWrV9s7pCoXL15k7ty5LF26FJPJREpKir1DqrJ06VJ0Op29w6hlxYoVrFu3jm3btvHss8/aOxzBTkSOaTiRYxpH5JjmJwqof6DRaOwdQpVBgwbh6uqKQqGgY8eOuLu72zukKq1atcLZ2ZmysjK6detGdHS0vUMCYM2aNYwYMaJFJTeDwcCePXsYOnQob731FiaTyd4hCXYkckzDiBzTcCLHXB2igPqXSk1NZcSIEfYOo5aS/2/nXmOaOv84gH9RJ0zFDDckgFOnDuPUqUs02ZTNKtOhZi7ovCzAZrzgixkv2wR2cRN1mxODQmToxFUCMjUwBXXGYRQiiCKalEWBWC/cRsulYlsFWvj9XxDOrFWw6uz++v28oT2n53l+z2k5fPM8p5hMiI2Nxd69e1FcXOzsclBQUID+/fvD29vb2aXYcHNzg1qtxtGjR6HVarFz505nl0Rkh9eYzvEa82xjgPo/9Oeff2LBggXo1q2bs0ux0atXL4SHh+Pzzz/Hvn37nF0OkpOTERMTg5CQEFy6dAmrVq1CfX29s8tSuLu7IyoqCufPn3d2KUQ2eI15MLzGPNv+W78d1KlTp07Bz88PPj4+0Ol08PLycnZJdl555RWYTCZnl4GtW7cqj0NCQvDDDz+gT58+TqzoHyICFxcX1NXVQaVSObscIgWvMQ+O15hnGwNUByorK3Ht2jWcP38eo0aNgpubm1PrUavV2L17Nzw8PNDa2orXX38dUVFRTq2pXW5uLnbt2oXAwEC4uLhgzpw5zi7pPys3NxfR0dGYMmUKfH19MX/+fGeXRE7Ca8yD4zXmwfEa82S4yH/l6x9ERERE/yd4DxQRERGRgxigiIiIiBzEAEVERETkIAYoIiIiIgcxQBERERE5iAGKiIiIyEEMUEREREQOYoAiIiIichADFBEREZGDGKCIiIiIHMQARUREROQgBigiIiIiBzFAERERETmIAYqIiIjIQQxQRERERA5igCIiIiJyEAMUERERkYMYoIiIiIgcxABFRERE5CAGKCIiIiIHMUAREREROYgBiugJ0Gg0eO+997Bw4ULEx8dj69atCAgIwKVLl9DU1ITJkyfDarWirKwMYWFh+P333x+qn9LSUsyePRtnzpx55JqrqqoQHx+PRYsWYcuWLTh9+jR++eUXBAUF4cCBA9iwYUOHx69ZswbHjh175DoeVXZ2Nurr65GSkoKlS5ciJiYGMTExWL58OYYNG4aioqL7HpuZmdnpONt1NN7m5mbExcUhLCzsocbgqKysLFRVVT2RvoieWUJET8Tq1atl+/btyvPi4mK5ePGiiIhcvnxZ2b58+XJJS0sTERGr1ao8flBz5syR/Pz8R6532bJlcvnyZTGbzZKeni4zZswQERGtVivHjx+X6urqDo+vqqoSs9n8UH3v27fvoY6722+//SZlZWUiInL48GFle2trq3z00UcSFRXV4fFGo9FmnB3V1dl4s7OzJTg4+J77Htd477RhwwYxmUyPvV0iasMZKKInxMXFxeb50KFDMWTIEADA4MGDle1ubm7K47i4OFRWVjrUT/fu3R+hyn8UFxfD1dUVPXr0wFtvvQWz2QwAGDRoECZNmgQvL68Oj/f29kaPHj0c7rekpARqtfphSrah1+tRWFiIl19+GQDw9ttvK/vS0tJw/fp1rFixosM2evXqpYwzNzcXhw4duu9rOxuvq6vrPbd31u7DmjFjBhISEh57u0TUhgGKyAmuXr2KS5cu4bnnnkNGRgZUKpXda6qqqqDRaHDhwgUcOXIEAKBWq6FWqxEaGgqtVouioiLMmzcPO3bswMSJE3H+/HnleIPBgBkzZiAzMxNWqxVnz57Ftm3bEBsbi3Xr1sFisQAALly4gISEBKxYsQLJyckAgKSkJBgMBqSmpuLcuXNISkqC0WjEjh07UFlZifXr12PNmjUAgB9//BEJCQlITExEUFAQli1bhitXrmDx4sXKUqRer0dsbCw2btyIiIgItLa2Yu/evZg9ezb279+P6dOnIzo6GkDbkltNTQ0SExNhMpns6jMajdi0aRO+/PJL/PTTT5g4cSKysrLszt+BAwcwcuRI5XmvXr2U87Jp0yZERkbC3d1d2Z+eng61Wo1PPvkEhYWFaGhoUMbZ3NyMEydOoLKyEomJiTh16hSmT5+OlJQUjB8/3m68VVVViI2NRUxMDFauXKmcawDYsWMHAgICkJqaatfusWPHMHToUABty76jR49GRUUFsrKyoFKpkJWVhVmzZuGzzz5T2tu1axdSUlLwxRdfYNGiRUoNI0aMQEZGBlpaWh7gE0lEDnP2FBjRsyI8PFyCg4Nl06ZN8sEHHyjLd7dv3xY/Pz+b17Uv28XGxkpsbKyIiJw8eVJ27twpIiLJycmyePFiERGZNWuWJCUlSVlZmdy6dUuCg4Pl9OnTEhcXJxUVFSIiYjKZZPbs2dLa2ioiIitXrpSEhARpamqSRYsWiYjIzZs3ZdSoUcqSlUqlkvLychERKS8vF5VKpdSYkpIi4eHhItK2FNn+c/z48aLX60XEdily1apVUltbq9R75MgRuXz5sowbN04qKiqkurpaxowZY9fX/erbs2ePzJ8/XxobGyUjI0PCwsLszvfChQvljz/+sNseEREhCxYssNlWWloq69atU87ztGnT7MaZn59vswT35ptvyvHjx0Wr1YrFYrEZb2hoqNTV1YmISFhYmFy7dk3y8/NlypQp0tDQIAUFBTJz5sx7tnvnZ6H9PWhsbBQ/Pz8pKSmRpqYmGT16tBgMBikrK5MJEyaIiEhBQYH4+/vbjGvSpElSVFRkdw6I6NF1c3aAI3qW+Pv7Y8mSJQgJCcHNmzcB2C7ZdSQ/Px9A20yJ2WyGp6cnAOD555+Hn5+fslQFANu2bUNgYCB8fX0BAGfOnEGPHj2UZUSVSoXk5GSoVCo0NDQgPT0dADBx4kQYDIZOl+furHno0KGwWq2IjIzE6tWrlbrufI1Go0F2djYAYOTIkbBarXB1dUXPnj2VGtuXCO907dq1e9bn6uqK/v37w9XVFX379r3nsdXV1XjxxRdttp07dw6HDx9GRkaGzfYzZ87AZDIp53bgwIFoaWnp8L1xc3ODn58f+vXrZzNek8kErVaLPn36AICyjFZdXY2+ffuid+/e8PLygtFovG/bd2tf/vPz8wMAeHh4wGw2o6mpCd26tV3GfXx87Np84YUXUF1djREjRjxwX0T0YBigiJzAy8ur05ByN6vVisGDByMoKAhA2ze77mfy5MmIj49HQEAA+vbtCxFBXV2dst/DwwPdunVDS0sLXFxclDaDgoI6bPd+tm/fDm9vb7z//vv33G80GjF16lT07NlT6UOv13fa7v3qu3jxYqfH3n3PmcViwXfffYfFixdj4MCByvba2lpYrVZ4enoq/cydOxddujzcHQ4igvr6ety8eRO9e/dW+ri7NhF5qPbv7GfIkCGYNm0adDod9Ho9/P397V73MPehEVHneA8U0RPS2trq8B/NLl26wGKx4MaNGxg3bhx+/vlnFBYWora2Fvv377dp+07Dhg1DaGgowsPDISIYO3YsdDodrly5AgC4fv06pk2bhkGDBqGiogKJiYkwGAzIzMxETU0NgLY/0O31iohdH+2Ki4uRmpqKtWvXKs/vNmbMGHz99dfQ6XTQaDTKbNq9dO3aFc3NzWhtbUW/fv3uW19nBgwYAIPBoDz/9ddfYbFYbP6VwO3bt5GXl4exY8ciNTUV2dnZMBgM2LNnj10A69q1KywWC27fvq2EzHu9n+7u7nj11VcRHR0Ns9mMzMxMNDY2djjeO9vt2bMndDodKioqlFmm+7FYLNBoNDhy5AhKS0vx/fff2+yvqalRZviI6PFigCJ6Av766y9oNBrk5eVBq9Xa7Gtf2srOzoZOp0NJSQkKCgpgNBoxbtw4HDp0CDk5OXj33Xcxffp0LF26FEuWLMGECRNQXFyMsrIyHD16FGazGcXFxbh+/Try8vIwc+ZMFBYW4ttvv0VzczOio6Oxfv16JCYmoqGhAfPmzYOrqys2b96MvXv3IjAwEE1NTfD19VVu5D506BBqa2uRkZGBmpoaZGVlwWg0oqCgAKWlpfj7778RGRmJ4cOH4+jRo1Cr1di9ezeqq6uVcdy6dQvffPMN9Ho9AgMDkZaWBn9/fyWs3Lm8d+LECXh6euKll17CV199he7du9vV5+HhgbNnz6K0tBTl5eXIy8tDWVmZ3Xl95513cPXqVQCATqdDfHw8+vXrh23btiEmJgabN29GcHAw3N3dMXz4cHz66acIDw/H/Pnz8cYbb+DWrVs24xwyZAjq6uoQFxcHjUaD+vp6HDx4EC0tLXbj3bhxIy5cuKD8fy9vb2/k5OSgvLwcWq0WOTk5qK+vR1FRkU273bt3R0hICEJDQ5GbmwtfX1/k5ubi5MmTymdEo9HAYDDg5MmTaG5uhsFgwNatW7F27VoEBgbi3LlzANpmKPv06YMBAwb8mx9tomeWizzqPDIR0X9QY2MjIiIisGXLFmeX8q8pLy/HxYsXMXXqVADAjRs3cPDgQXz88cfIzs6GXq/Hhx9+6OQqiZ5OnIEioqeSm5sb5s6dq8zePI2SkpJslgfr6uqUm/pLSkoYnoj+RZyBIqKnWlFREXx8fOy+kfc0uHLlCqKiomAwGODj44OAgADMmjULGo0Gr732mvINPSJ6/BigiIiIiBzEJTwiIiIiBzFAERERETmIAYqIiIjIQQxQRERERA5igCIiIiJyEAMUERERkYMYoIiIiIgcxABFRERE5CAGKCIiIiIH/Q9PcJcldnCBfgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot 2D-Filter-Kernels as Heatmaps\n",
    "fig, axs = plt.subplots(4, 2, figsize=(6, 6), sharex=True, sharey=False)\n",
    "#fig.suptitle(\"2D-Filter-Kernels\", fontsize=\"medium\")\n",
    "fig.supxlabel(\"Filterkoeffizienten (Zeitrichtung)\", fontsize=\"medium\")\n",
    "fig.supylabel(\"Filterkoeffizienten (Frequenzrichtung)\", fontsize=\"medium\")\n",
    "i = 0\n",
    "for fil in list_of_2d_kernels:\n",
    "    data = {\n",
    "        \"1\":  fil[2],\n",
    "        \"2\":  fil[1],\n",
    "        \"3\":  fil[0],\n",
    "    }\n",
    "    df = pd.DataFrame(data, index = [\"1\", \"2\", \"3\", \"4\", \"5\"])\n",
    "   \n",
    "    if i < 4:\n",
    "        sns.heatmap(data=df.T, ax=axs[i,0], cmap=colors, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, xticklabels=1, yticklabels=1)\n",
    "        axs[i,0].invert_yaxis()\n",
    "        axs[i,0].set_yticklabels(axs[i,0].get_yticklabels(), rotation=0)\n",
    "    else:\n",
    "        sns.heatmap(data=df.T, ax=axs[7-i,1], cmap=colors, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, xticklabels=1, yticklabels=1)\n",
    "        axs[7-i,1].invert_yaxis()\n",
    "        axs[7-i,1].set_yticklabels(axs[7-i,1].get_yticklabels(), rotation=0)\n",
    "    i = i+1\n",
    "    \n",
    "\n",
    "#plt.savefig(\"2D-Filter-Kernels_(5x3).png\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f26dcc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 375x200 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fold3 = [[ 0.98,  0.45, -0.77,  0.37, -1.18],\n",
    "         [ 2.34,  1.06, -1.83,  0.88, -2.8 ],\n",
    "         [ 1.69,  0.77, -1.32,  0.64, -2.03]]\n",
    "\n",
    "fold6 = [[ 2.35, -1.31,  0.91, -0.33, -1.84],\n",
    "         [ 1.39, -0.78,  0.54, -0.2 , -1.09],\n",
    "         [ 2.08, -1.16,  0.81, -0.29, -1.63]]\n",
    "\n",
    "fold3_data = {\n",
    "    \"1\": fold3[2],\n",
    "    \"2\": fold3[1],\n",
    "    \"3\": fold3[0]\n",
    "}\n",
    "df3 = pd.DataFrame(fold3_data, index = [\"1\", \"2\", \"3\", \"4\", \"5\"])\n",
    "\n",
    "fold6_data = {\n",
    "    \"1\": fold6[2],\n",
    "    \"2\": fold6[1],\n",
    "    \"3\": fold6[0]\n",
    "}\n",
    "df6 = pd.DataFrame(fold6_data, index = [\"1\", \"2\", \"3\", \"4\", \"5\"])\n",
    "\n",
    "# Plot 2D-Filter-Kernels as Heatmaps\n",
    "fig, axs = plt.subplots(1, 2, figsize=(3.75, 2), sharex=True, sharey=False, rasterized=True)\n",
    "#fig.suptitle(\"2D-Filter-Kernels\", fontsize=\"medium\")\n",
    "fig.supxlabel(\"Filter coefficient (time)\")#, fontsize=\"medium\")\n",
    "fig.supylabel(\"Filter coefficient (frequency)\")#, fontsize=\"medium\")\n",
    "\n",
    "sns.heatmap(data=df3.T, ax=axs[0], cmap=colors, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, cbar=False, xticklabels=1, yticklabels=1)\n",
    "axs[0].invert_yaxis()\n",
    "axs[0].set_yticklabels(axs[0].get_yticklabels(), rotation=0)\n",
    "  \n",
    "sns.heatmap(data=df6.T, ax=axs[1], cmap=colors, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, xticklabels=1, yticklabels=1)\n",
    "axs[1].invert_yaxis()\n",
    "axs[1].set_yticklabels(axs[1].get_yticklabels(), rotation=0)\n",
    "\n",
    "#plt.savefig(\"2D-Filter-Kernels.pdf\", dpi=300, bbox_inches=\"tight\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9a61de05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 325x100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(3.25, 1))\n",
    "sns.heatmap(data=df3.T, ax=axs, cmap=colors, rasterized=True, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, xticklabels=1, yticklabels=1)\n",
    "axs.invert_yaxis()\n",
    "axs.set_yticklabels(axs.get_yticklabels(), rotation=0)\n",
    "axs.set_xlabel(\"Time direction\")\n",
    "axs.set_ylabel(\"Frequency direction\")\n",
    "plt.savefig(\"2D-Filter_Fold3.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7c3ee86a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 325x100 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(3.25, 1))\n",
    "sns.heatmap(data=df6.T, ax=axs, cmap=colors, rasterized=True, vmin=-3.5, vmax=3., center=0., annot=True, fmt=\".2f\", cbar_kws={'label': 'Value'}, xticklabels=1, yticklabels=1)\n",
    "axs.invert_yaxis()\n",
    "axs.set_yticklabels(axs.get_yticklabels(), rotation=0)\n",
    "axs.set_xlabel(\"Time direction\")\n",
    "axs.set_ylabel(\"Frequency direction\")\n",
    "plt.savefig(\"2D-Filter_Fold6.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fbc88bb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform Onset detection on all data with all given filter kernels\n",
    "\n",
    "#import numpy as np\n",
    "#import os\n",
    "#import librosa\n",
    "#import madmom\n",
    "#from tqdm import tqdm\n",
    "#from madmom.audio.signal import SignalProcessor, FramedSignalProcessor\n",
    "#from madmom.audio.stft import ShortTimeFourierTransformProcessor\n",
    "#from madmom.audio.spectrogram import (FilteredSpectrogramProcessor,\n",
    "#                                      LogarithmicSpectrogramProcessor,\n",
    "#                                      SpectrogramDifferenceProcessor)\n",
    "#from madmom.features.onsets import OnsetPeakPickingProcessor\n",
    "#from madmom.evaluation.onsets import OnsetEvaluation, OnsetSumEvaluation\n",
    "#from madmom.processors import SequentialProcessor\n",
    "#from sepfir.sep_fir_processor import SepFirProcessor\n",
    "#import matplotlib.pyplot as plt\n",
    "#import seaborn as sns\n",
    "#\n",
    "#\n",
    "## Feature extraction parameters\n",
    "#sr = 44100               # Sampling rate\n",
    "#fs = 2048                # Frame size\n",
    "#fps = 200                # Frames per second\n",
    "#window = np.hanning      # Hann-Window for STFT\n",
    "#num_bands = 24           # Number of bands per octave\n",
    "#f_min = 27.5             # Minimum frequency\n",
    "#f_max = 16000            # Maximum frequency\n",
    "#diff_max_bins = 3        # Number of bins used for maximum filter\n",
    "#\n",
    "## Peak picking and evaluation parameters\n",
    "#pre_max = 0.03      # Use pre_max seconds past information for moving maximum\n",
    "#post_max = 0.03     # Use post_max seconds future information for moving maximum\n",
    "#pre_avg = 0.1       # Use pre_avg seconds past information for moving average\n",
    "#post_avg = 0.07     # Use post_avg seconds future information for moving average\n",
    "#eval_window = 0.05  # Time window around a reference onset\n",
    "#combine = 0.03      # Only report one onset within combine seconds\n",
    "#delay = 0           # Report the detected onsets delay seconds delayed\n",
    "#\n",
    "## Instantiate Processors with above parameters\n",
    "#signal_proc = SignalProcessor(sample_rate=sr, num_channels=1, norm=True)\n",
    "#frames_proc = FramedSignalProcessor(frame_size=fs, fps=fps)\n",
    "#stft_proc = ShortTimeFourierTransformProcessor(window=window)\n",
    "#fil_spec_proc = FilteredSpectrogramProcessor(num_bands=num_bands, fmin=f_min, fmax=f_max, norm_filters=False)\n",
    "#logfil_spec_proc = LogarithmicSpectrogramProcessor()\n",
    "#\n",
    "#preprocessor = SequentialProcessor([signal_proc, frames_proc, stft_proc, fil_spec_proc, logfil_spec_proc])\n",
    "#\n",
    "#def preprocessing(audiofile, sr, preprocessor):\n",
    "#    # Load and normalize audio file\n",
    "#    signal, sr = librosa.load(audiofile, sr=sr, mono=True)\n",
    "#\n",
    "#    # Calculate preprocessed spectrogram\n",
    "#    log_filtered_spec = preprocessor.process(signal)\n",
    "#    return log_filtered_spec\n",
    "#\n",
    "#def reduction_filtering(log_filtered_spec):\n",
    "#    deriv = filter_proc.process(log_filtered_spec)\n",
    "#    odf = deriv.sum(axis=1)\n",
    "#    return odf\n",
    "#\n",
    "#def peak_picking(odf, peakprocessor):\n",
    "#    # Peak-Picking\n",
    "#    onsets = peakprocessor.process(odf)\n",
    "#    return onsets\n",
    "#\n",
    "#def evaluation(onsets, annotationsfile):\n",
    "#    # Evaluation\n",
    "#    # Load annotations\n",
    "#    annotations = madmom.io.load_events(annotationsfile)\n",
    "#    evl = OnsetEvaluation(detections=onsets,\n",
    "#                          annotations=annotations,\n",
    "#                          window=eval_window,\n",
    "#                          combine=combine,\n",
    "#                          delay=delay)\n",
    "#    return evl\n",
    "#\n",
    "#\n",
    "#freq_filters = [freq1, freq2, freq3, freq4, freq5, freq6, freq7, freq8]\n",
    "#time_filters = [time1, time2, time3, time4, time5, time6, time7, time8]\n",
    "#df_list = []\n",
    "#\n",
    "#for i in range(0,8,1):\n",
    "#    freq_filter = freq_filters[i]\n",
    "#    time_filter = time_filters[i]\n",
    "#    filter_proc = SepFirProcessor(hrow=np.array(freq_filter, dtype=np.float32),\n",
    "#                                  hcol=np.array(time_filter, dtype=np.float32),\n",
    "#                                  pos_diff=True)\n",
    "#    peak_proc = OnsetPeakPickingProcessor(threshold=thrshlds[i],\n",
    "#                                         pre_max=pre_max,\n",
    "#                                         post_max=post_max,\n",
    "#                                         pre_avg=pre_avg,\n",
    "#                                         post_avg=post_avg,\n",
    "#                                         combine=combine,\n",
    "#                                         delay=delay,\n",
    "#                                         fps=fps)\n",
    "#    \n",
    "#    \n",
    "#    \n",
    "#    \n",
    "#    # Perform Onset Detection on all given data and print average measure\n",
    "#    str_audiodir = 'data/audio' # Relative path to directory containing music files\n",
    "#    str_annodir = 'data/annotations/onsets' # Relative path to directory containing .onset files\n",
    "#    audiodir = os.fsencode(str_audiodir)\n",
    "#    list_evals = [] # List containing all evaluation objects\n",
    "#    f = open('log.txt', 'a')\n",
    "#    dict_fscores = {}\n",
    "#    for file in tqdm(os.listdir(audiodir), desc=\"Progress\"):\n",
    "#        filename = os.fsdecode(file)\n",
    "#        audiofile = os.path.join(str_audiodir, filename)\n",
    "#        filename = re.sub('\\.flac$', '.onsets', filename)\n",
    "#        annotationsfile = os.path.join(str_annodir, filename)\n",
    "#    \n",
    "#        # Pipeline:\n",
    "#        spec = preprocessing(audiofile=audiofile, sr=sr, preprocessor=preprocessor)\n",
    "#        odf = reduction_filtering(log_filtered_spec=spec)\n",
    "#        onsets = peak_picking(odf=odf, peakprocessor=peak_proc)\n",
    "#        eval_obj = evaluation(onsets=onsets, annotationsfile=annotationsfile)\n",
    "#    \n",
    "#        # Write evaluation measures to log file\n",
    "#        dict_fscores[re.sub('\\.onsets$', '', filename)] = round(eval_obj.fmeasure, 2)\n",
    "#        f.write(re.sub('\\.onsets$', '', filename) + \": \" + str(round(eval_obj.fmeasure, 2)) + \"\\n\")\n",
    "#        #f.write(\"Precision: \" + str(round(eval_obj.precision, 2)) + \"\\n\")\n",
    "#        #f.write(\"Recall:    \" + str(round(eval_obj.recall, 2)) + \"\\n\")\n",
    "#        #f.write(\"F-Measure: \" + str(round(eval_obj.fmeasure, 2)) + \"\\n\\n\")\n",
    "#        list_evals.append(eval_obj)\n",
    "#    f.close()\n",
    "#    \n",
    "#    # Evaluation over complete dataset\n",
    "#    sum_eval = OnsetSumEvaluation(list_evals)\n",
    "#    print(sum_eval)\n",
    "#    \n",
    "#    # Sort results\n",
    "#    results_dict = dict(sorted(dict_fscores.items(), key=lambda x:x[1], reverse=True))\n",
    "#    df = pd.DataFrame.from_dict(data=results_dict, orient='index', columns=['F-Score'])\n",
    "#    df_list.append(df)\n",
    "#    print(df)\n",
    "#\n",
    "#\n",
    "#\n",
    "#for i in range(8):\n",
    "#    print(str(i+1) + \". Filter Kernel: \")\n",
    "#    print(df_list[i].head(10))\n",
    "#    print(\"...\")\n",
    "#    print(df_list[i].tail(10))\n",
    "#    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aa88d5a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cost functions\n",
    "import numpy as np\n",
    "cost1 = [0.258, 0.25, 0.221, 0.173, 0.16, 0.156, 0.153, 0.152, 0.148, 0.148, 0.148, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146, 0.146]\n",
    "cost2 = [0.25, 0.222, 0.188, 0.188, 0.164, 0.161, 0.161, 0.148, 0.148, 0.148, 0.146, 0.144, 0.143, 0.143, 0.142, 0.142, 0.142, 0.142, 0.142, 0.142, 0.141, 0.141]\n",
    "cost3 = [0.245, 0.20, 0.193, 0.173, 0.161, 0.149, 0.149, 0.149, 0.147, 0.146, 0.145, 0.144, 0.143, 0.143, 0.142, 0.142, 0.142, 0.142, 0.142, 0.142, 0.141, 0.141]\n",
    "cost4 = [0.254, 0.208, 0.18, 0.18, 0.17, 0.158, 0.149, 0.149, 0.149, 0.149, 0.148, 0.148, 0.147, 0.147, 0.147, 0.147, 0.147, 0.147, 0.147, 0.146, 0.146, 0.146, 0.146]\n",
    "cost5 = [0.256, 0.229, 0.219, 0.199, 0.175, 0.175, 0.162, 0.15, 0.15, 0.15, 0.147, 0.147, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.145, 0.144, 0.144, 0.144]\n",
    "cost6 = [0.254, 0.215, 0.183, 0.181, 0.179, 0.164, 0.157, 0.156, 0.15, 0.149, 0.149, 0.148, 0.148, 0.147, 0.147, 0.147, 0.147, 0.147, 0.147, 0.147]\n",
    "cost7 = [0.254, 0.18, 0.18, 0.18, 0.164, 0.154, 0.154, 0.151, 0.151, 0.151, 0.151, 0.151, 0.15, 0.15, 0.15, 0.15, 0.15, 0.15]\n",
    "cost8 = [0.247, 0.197, 0.178, 0.164, 0.163, 0.15,  0.149, 0.147, 0.142, 0.142, 0.14,  0.137, 0.137, 0.137, 0.137, 0.137, 0.137, 0.137, 0.136, 0.136, 0.136, 0.136]\n",
    "len(cost1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b7fc74b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24ab08794b0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,4))\n",
    "#fig.suptitle(\"Time Filters for (3x5)\\nMean F1: \"+str(np.round(g_mean_3x5,4)), fontsize=\"x-large\")\n",
    "filter6 = sns.lineplot(data=cost6, label=\"Fold 0\")\n",
    "filter2 = sns.lineplot(data=cost2, label=\"Fold 1\", linestyle='dotted')\n",
    "filter8 = sns.lineplot(data=cost8, label=\"Fold 2\", linestyle='dashed')\n",
    "filter4 = sns.lineplot(data=cost4, label=\"Fold 3\", linestyle='dashdot')\n",
    "filter3 = sns.lineplot(data=cost3, label=\"Fold 4\")\n",
    "filter7 = sns.lineplot(data=cost7, label=\"Fold 5\", linestyle='dotted')\n",
    "filter5 = sns.lineplot(data=cost5, label=\"Fold 6\", linestyle='dashed')\n",
    "filter1 = sns.lineplot(data=cost1, label=\"Fold 7\", linestyle='dashdot')\n",
    "\n",
    "plt.xlabel(\"Iterationen\")\n",
    "plt.ylabel(\"Kosten\")\n",
    "#plt.title(\"Kostenverläufe auf allen Test-Folds\")\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "#plt.savefig(\"Kostenverläufe.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff43411f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 40 files\n",
      "  Onsets:  3559 TP:  1133 FP:   920 FN:  2426 Precision: 0.552 Recall: 0.318 F-measure: 0.404 mean:  11.6 ms std:  29.2 ms\n",
      "Mean Eval: mean for 40 files\n",
      "  Onsets: 88.97 TP: 28.32 FP: 23.00 FN: 60.65 Precision: 0.508 Recall: 0.326 F-measure: 0.373 mean:  12.3 ms std:  24.3 ms\n",
      "Frequency-Filter: [1.4514875, 0.67962813, 1.3267739]\n",
      "Time-Filter: [1.076459, 1.1447549, -1.4494876, 0.4485518, -1.3983144]\n",
      "Threshold: 12.780292\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 41/41 [00:08<00:00,  4.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 41 files\n",
      "  Onsets:  3220 TP:   789 FP:   650 FN:  2431 Precision: 0.548 Recall: 0.245 F-measure: 0.339 mean:  10.8 ms std:  27.0 ms\n",
      "Mean Eval: mean for 41 files\n",
      "  Onsets: 78.54 TP: 19.24 FP: 15.85 FN: 59.29 Precision: 0.584 Recall: 0.340 F-measure: 0.401 mean:  10.9 ms std:  21.5 ms\n",
      "Frequency-Filter: [0.4388686, 0.26955575, 0.44204888]\n",
      "Time-Filter: [3.9786017, 1.6983291, -2.8763483, 1.0405272, -4.2872205]\n",
      "Threshold: 13.682718\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 40 files\n",
      "  Onsets:  2542 TP:   799 FP:   786 FN:  1743 Precision: 0.504 Recall: 0.314 F-measure: 0.387 mean:   9.1 ms std:  30.5 ms\n",
      "Mean Eval: mean for 40 files\n",
      "  Onsets: 63.55 TP: 19.98 FP: 19.65 FN: 43.58 Precision: 0.491 Recall: 0.314 F-measure: 0.372 mean:  11.7 ms std:  23.2 ms\n",
      "Frequency-Filter: [0.90074927, 2.0068014, 1.384222]\n",
      "Time-Filter: [0.90909255, 1.429568, -1.5369593, 0.37383863, -1.3617892]\n",
      "Threshold: 15.807245\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 40 files\n",
      "  Onsets:  2995 TP:  1065 FP:   643 FN:  1930 Precision: 0.624 Recall: 0.356 F-measure: 0.453 mean:  12.1 ms std:  25.4 ms\n",
      "Mean Eval: mean for 40 files\n",
      "  Onsets: 74.88 TP: 26.62 FP: 16.07 FN: 48.25 Precision: 0.552 Recall: 0.343 F-measure: 0.407 mean:  14.1 ms std:  19.8 ms\n",
      "Frequency-Filter: [-0.5914803, -0.35116303, -0.5254847]\n",
      "Time-Filter: [-3.965828, 2.2166693, -1.5370568, 0.5555174, 3.1105368]\n",
      "Threshold: 11.309557\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 40 files\n",
      "  Onsets:  3915 TP:  1114 FP:   984 FN:  2801 Precision: 0.531 Recall: 0.285 F-measure: 0.371 mean:   5.9 ms std:  30.0 ms\n",
      "Mean Eval: mean for 40 files\n",
      "  Onsets: 97.88 TP: 27.85 FP: 24.60 FN: 70.03 Precision: 0.513 Recall: 0.320 F-measure: 0.374 mean:   6.4 ms std:  25.7 ms\n",
      "Frequency-Filter: [0.45256743, 1.0780698, 0.77992415]\n",
      "Time-Filter: [2.171062, 0.98768353, -1.6954808, 0.8178946, -2.5986037]\n",
      "Threshold: 14.437158\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 41/41 [00:07<00:00,  5.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 41 files\n",
      "  Onsets:  2696 TP:   878 FP:   893 FN:  1818 Precision: 0.496 Recall: 0.326 F-measure: 0.393 mean:  12.2 ms std:  28.7 ms\n",
      "Mean Eval: mean for 41 files\n",
      "  Onsets: 65.76 TP: 21.41 FP: 21.78 FN: 44.34 Precision: 0.536 Recall: 0.352 F-measure: 0.412 mean:  15.0 ms std:  21.6 ms\n",
      "Frequency-Filter: [-0.6369055, -0.83030605, -0.52570707]\n",
      "Time-Filter: [-3.5917513, 2.562756, -1.8627614, 1.2491975, 1.9648478]\n",
      "Threshold: 12.008744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 40/40 [00:08<00:00,  4.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 40 files\n",
      "  Onsets:  2920 TP:   948 FP:   575 FN:  1972 Precision: 0.622 Recall: 0.325 F-measure: 0.427 mean:  14.9 ms std:  24.1 ms\n",
      "Mean Eval: mean for 40 files\n",
      "  Onsets: 73.00 TP: 23.70 FP: 14.38 FN: 49.30 Precision: 0.539 Recall: 0.318 F-measure: 0.385 mean:  17.4 ms std:  21.4 ms\n",
      "Frequency-Filter: [-0.34577784, -0.22609143, -0.32085896]\n",
      "Time-Filter: [-2.1855235, -4.527265, -0.41339868, 2.7202175, 4.980925]\n",
      "Threshold: 14.5411005\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing...: 100%|███████████████████████████████████████████████████████████████████| 39/39 [00:08<00:00,  4.71it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum Eval: sum for 39 files\n",
      "  Onsets:  3980 TP:   923 FP:   666 FN:  3057 Precision: 0.581 Recall: 0.232 F-measure: 0.331 mean:  11.9 ms std:  29.4 ms\n",
      "Mean Eval: mean for 39 files\n",
      "  Onsets: 102.05 TP: 23.67 FP: 17.08 FN: 78.38 Precision: 0.540 Recall: 0.280 F-measure: 0.349 mean:  10.3 ms std:  25.4 ms\n",
      "Frequency-Filter: [0.7999404, 1.8004252, 3.543838]\n",
      "Time-Filter: [0.8375645, 0.12475213, -0.34295174, 0.23983702, -0.9645398]\n",
      "Threshold: 14.424377\n",
      "Total time: 7.116091966629028\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Perform Onset detection for each filter kernel on its corresponding test fold only!\n",
    "import numpy as np\n",
    "import os\n",
    "import re\n",
    "import librosa\n",
    "import madmom\n",
    "from tqdm import tqdm\n",
    "from madmom.audio.signal import SignalProcessor, FramedSignalProcessor\n",
    "from madmom.audio.stft import ShortTimeFourierTransformProcessor\n",
    "from madmom.audio.spectrogram import (FilteredSpectrogramProcessor,\n",
    "                                      LogarithmicSpectrogramProcessor,\n",
    "                                      SpectrogramDifferenceProcessor)\n",
    "from madmom.features.onsets import OnsetPeakPickingProcessor\n",
    "from madmom.evaluation.onsets import OnsetEvaluation, OnsetSumEvaluation, OnsetMeanEvaluation\n",
    "from madmom.processors import SequentialProcessor\n",
    "from sepfir.sep_fir_processor import SepFirProcessor, SepFirProcessor2d\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from time import time\n",
    "\n",
    "\n",
    "# Feature extraction parameters\n",
    "sr = 44100               # Sampling rate\n",
    "fs = 2048                # Frame size\n",
    "fps = 200                # Frames per second\n",
    "window = np.hanning      # Hann-Window for STFT\n",
    "num_bands = 24           # Number of bands per octave\n",
    "f_min = 27.5             # Minimum frequency\n",
    "f_max = 16000            # Maximum frequency\n",
    "\n",
    "# Peak picking and evaluation parameters\n",
    "pre_max = 0.03      # Use pre_max seconds past information for moving maximum\n",
    "post_max = 0.03     # Use post_max seconds future information for moving maximum\n",
    "pre_avg = 0.1       # Use pre_avg seconds past information for moving average\n",
    "post_avg = 0.07     # Use post_avg seconds future information for moving average\n",
    "eval_window = 0.05  # Time window around a reference onset\n",
    "combine = 0.03      # Only report one onset within combine seconds\n",
    "delay = 0           # Report the detected onsets delay seconds delayed\n",
    "\n",
    "# Instantiate Processors with above parameters\n",
    "signal_proc = SignalProcessor(sample_rate=sr, num_channels=1, norm=True)\n",
    "frames_proc = FramedSignalProcessor(frame_size=fs, fps=fps)\n",
    "stft_proc = ShortTimeFourierTransformProcessor(window=window)\n",
    "fil_spec_proc = FilteredSpectrogramProcessor(num_bands=num_bands, fmin=f_min, fmax=f_max, norm_filters=False)\n",
    "logfil_spec_proc = LogarithmicSpectrogramProcessor()\n",
    "\n",
    "preprocessor = SequentialProcessor([signal_proc, frames_proc, stft_proc, fil_spec_proc, logfil_spec_proc])\n",
    "\n",
    "total_time = []\n",
    "\n",
    "def preprocessing(audiofile, sr, preprocessor):\n",
    "    # Load and normalize audio file\n",
    "    signal, sr = librosa.load(audiofile, sr=sr, mono=True)\n",
    "\n",
    "    # Calculate preprocessed spectrogram\n",
    "    log_filtered_spec = preprocessor.process(signal)\n",
    "    return log_filtered_spec\n",
    "\n",
    "def reduction_filtering(log_filtered_spec):\n",
    "    t1 = time()\n",
    "    deriv = filter_proc.process(log_filtered_spec)\n",
    "    #deriv = filter_proc_2d.process(log_filtered_spec)\n",
    "    odf = deriv.sum(axis=1)\n",
    "    t2 = time()\n",
    "    t = t2 - t1\n",
    "    total_time.append(t)\n",
    "    return odf\n",
    "\n",
    "def peak_picking(odf, peakprocessor):\n",
    "    # Peak-Picking\n",
    "    onsets = peakprocessor.process(odf)\n",
    "    return onsets\n",
    "\n",
    "def evaluation(onsets, annotationsfile):\n",
    "    # Evaluation\n",
    "    # Load annotations\n",
    "    annotations = madmom.io.load_events(annotationsfile)\n",
    "    evl = OnsetEvaluation(detections=onsets,\n",
    "                          annotations=annotations,\n",
    "                          window=eval_window,\n",
    "                          combine=combine,\n",
    "                          delay=delay)\n",
    "    return evl\n",
    "\n",
    "def get_filenames_from_fold(fold):\n",
    "    path_to_folds = \"data/splits\"\n",
    "    with open(os.path.join(path_to_folds, fold)) as fd:\n",
    "            track_names = [line.rstrip() for line in fd]  # List containing all track names for a specific fold\n",
    "            fd.close()\n",
    "            return track_names\n",
    "\n",
    "freq_filters = [freq1, freq2, freq3, freq4, freq5, freq6, freq7, freq8]\n",
    "time_filters = [time1, time2, time3, time4, time5, time6, time7, time8]\n",
    "fold0 = get_filenames_from_fold('8-fold_cv_random_0.fold')\n",
    "fold1 = get_filenames_from_fold('8-fold_cv_random_1.fold')\n",
    "fold2 = get_filenames_from_fold('8-fold_cv_random_2.fold')\n",
    "fold3 = get_filenames_from_fold('8-fold_cv_random_3.fold')\n",
    "fold4 = get_filenames_from_fold('8-fold_cv_random_4.fold')\n",
    "fold5 = get_filenames_from_fold('8-fold_cv_random_5.fold')\n",
    "fold6 = get_filenames_from_fold('8-fold_cv_random_6.fold')\n",
    "fold7 = get_filenames_from_fold('8-fold_cv_random_7.fold')\n",
    "\n",
    "test_folds = [ fold7, fold1, fold4, fold3, fold6, fold0, fold5, fold2 ] # Sort folds in correct order!!!\n",
    "df_list = []\n",
    "dict_fscores = []\n",
    "total_sum_evals = []\n",
    "total_mean_evals = []\n",
    "# Iterate over all filter kernels and their corresponding test folds\n",
    "for i in range(0,8,1):\n",
    "    freq_filter = freq_filters[i]\n",
    "    time_filter = time_filters[i]\n",
    "    filter_proc = SepFirProcessor(hrow=np.array(freq_filter, dtype=np.float32),\n",
    "                                  hcol=np.array(time_filter, dtype=np.float32),\n",
    "                                  pos_diff=True)\n",
    "    filter_proc_2d = SepFirProcessor2d(hrow=np.array(freq_filter, dtype=np.float32),\n",
    "                                  hcol=np.array(time_filter, dtype=np.float32),\n",
    "                                  pos_diff=True)\n",
    "    peak_proc = OnsetPeakPickingProcessor(threshold=thrshlds[i],\n",
    "                                      pre_max=pre_max,\n",
    "                                      post_max=post_max,\n",
    "                                      pre_avg=pre_avg,\n",
    "                                      post_avg=post_avg,\n",
    "                                      combine=combine,\n",
    "                                      delay=delay,\n",
    "                                      fps=fps)\n",
    "    track_names = test_folds[i]\n",
    "    list_evals = []\n",
    "    \n",
    "    dict_fscores = {}\n",
    "    # Iterate over all tracks in this fold and perform Onset detection with evaluations\n",
    "    for file in tqdm(track_names, desc=\"Processing...\"):\n",
    "        str_audiodir = 'data/audio'\n",
    "        str_annodir = 'data/annotations/onsets'\n",
    "        audiodir = os.fsencode(str_audiodir)\n",
    "        refdir = os.fsencode(str_annodir)\n",
    "        audiofile = os.path.join(str_audiodir, file+\".flac\")\n",
    "        annotationsfile = os.path.join(str_annodir, file+\".onsets\")\n",
    "        \n",
    "        # Pipeline:\n",
    "        spec = preprocessing(audiofile=audiofile, sr=sr, preprocessor=preprocessor)\n",
    "        odf = reduction_filtering(log_filtered_spec=spec)\n",
    "        onsets = peak_picking(odf=odf, peakprocessor=peak_proc)\n",
    "        eval_obj = evaluation(onsets=onsets, annotationsfile=annotationsfile)\n",
    "        \n",
    "        # Write evaluation measures to log file\n",
    "        dict_fscores[re.sub('\\.onsets$', '', file)] = round(eval_obj.fmeasure, 2)\n",
    "        #f.write(re.sub('\\.onsets$', '', filename) + \": \" + str(round(eval_obj.fmeasure, 2)) + \"\\n\")\n",
    "        #f.write(\"Precision: \" + str(round(eval_obj.precision, 2)) + \"\\n\")\n",
    "        #f.write(\"Recall:    \" + str(round(eval_obj.recall, 2)) + \"\\n\")\n",
    "        #f.write(\"F-Measure: \" + str(round(eval_obj.fmeasure, 2)) + \"\\n\\n\")\n",
    "        list_evals.append(eval_obj)\n",
    "        \n",
    "    # Evaluation over complete dataset\n",
    "    sum_eval = OnsetSumEvaluation(list_evals)\n",
    "    mean_eval = OnsetMeanEvaluation(list_evals)\n",
    "    print(\"Sum Eval: \"+str(sum_eval))\n",
    "    print(\"Mean Eval: \"+str(mean_eval))\n",
    "    print(\"Frequency-Filter: \" + str(freq_filter))\n",
    "    print(\"Time-Filter: \" + str(time_filter))\n",
    "    print(\"Threshold: \" + str(thrshlds[i]))\n",
    "    total_sum_evals.append(sum_eval)\n",
    "    total_mean_evals.append(mean_eval)\n",
    "        \n",
    "    # Sort results\n",
    "    results_dict = dict(sorted(dict_fscores.items(), key=lambda x:x[1], reverse=True))\n",
    "    df = pd.DataFrame.from_dict(data=results_dict, orient='index', columns=['F-Score'])\n",
    "    df_list.append(df)\n",
    "print(\"Total time: \" + str(np.sum(total_time)))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2c989f8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum for 8 files\n",
      "  Onsets: 25827 TP: 21624 FP:  3152 FN:  4203 Precision: 0.873 Recall: 0.837 F-measure: 0.855 mean: -11.1 ms std:  10.8 ms\n",
      "mean for 8 files\n",
      "  Onsets: 80.58 TP: 67.43 FP:  9.82 FN: 13.15 Precision: 0.850 Recall: 0.861 F-measure: 0.844 mean: -11.6 ms std:   9.4 ms\n"
     ]
    }
   ],
   "source": [
    "# Total F-measure, Precision and Recall over all folds\n",
    "print(OnsetSumEvaluation(total_sum_evals))\n",
    "print(OnsetMeanEvaluation(total_mean_evals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a0a5af7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.012269496917725"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from time import time, sleep\n",
    "\n",
    "\n",
    "t1 = time()\n",
    "sleep(10)\n",
    "t2 = time()\n",
    "t2 - t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9fe7b34d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. Filter Kernel: \n",
      "                                                    F-Score\n",
      "ah_test_trumpet_Miles_DAVIS__Michel_LEGRAND_DIN...     1.00\n",
      "jpb_Jaillet64                                          1.00\n",
      "SoundCheck2_65_Instrumental_Acoustic_steel_stru...     0.98\n",
      "ff123_rushing                                          0.97\n",
      "jpb_arab60s                                            0.97\n",
      "ff123_41_30sec                                         0.95\n",
      "jpb_jaxx                                               0.95\n",
      "jpb_violin                                             0.95\n",
      "al_Media-103611(8.2-18.2)                              0.94\n",
      "al_Albums-Latin_Jam5-01(7.9-17.9)                      0.93\n",
      "api_2-artificial                                       0.93\n",
      "api_8-ambrielb                                         0.93\n",
      "al_Media-105905(7.8-17.8)                              0.93\n",
      "ah_development_violin_violin2                          0.92\n",
      "sb_Media-103302(15.0-25.0)                             0.92\n",
      "ah_test_clarinet_my_clarinet1                          0.91\n",
      "al_Albums-Cafe_Paradiso-16(17.0-27.0)                  0.90\n",
      "ah_test_cello_03_CelloTaksimi_pt1                      0.89\n",
      "lame_youcantdothat                                     0.89\n",
      "ah_development_piano_autumn                            0.88\n",
      "lame_else3                                             0.88\n",
      "...\n",
      "                                                    F-Score\n",
      "ah_development_piano_autumn                            0.88\n",
      "lame_else3                                             0.88\n",
      "SoundCheck2_83_The_Alan_Parsons_Project_-_Limel...     0.87\n",
      "mck_train13                                            0.87\n",
      "ff123_BlueEyesExcerpt                                  0.86\n",
      "ah_development_oud_8                                   0.83\n",
      "al_Media-105320(2.5-12.5)                              0.83\n",
      "al_Media-105615(12.4-22.4)                             0.83\n",
      "ah_development_violin_01__hicaz__keman                 0.82\n",
      "ff123_drone_short                                      0.81\n",
      "sb_Media-104210(2.0-12.0)                              0.81\n",
      "al_Albums-Latino_Latino-05(3.4-13.4)                   0.80\n",
      "al_Media-105808(15.5-25.5)                             0.76\n",
      "sb_Albums-Ballroom_Magic-09(4.0-14.0)                  0.74\n",
      "ff123_ExitMusic                                        0.73\n",
      "ah_test_sax_sax1                                       0.69\n",
      "sb_Media-103307(4.0-14.0)                              0.69\n",
      "ah_development_guitar_Guitar_Licks_07-06               0.67\n",
      "ah_test_clarinet_SL1_pt1                               0.64\n",
      "ff123_bloodline                                        0.46\n",
      "api_RM-C002                                            0.33\n",
      "\n",
      "\n",
      "\n",
      "2. Filter Kernel: \n",
      "                                        F-Score\n",
      "ah_development_percussion_bongo1           1.00\n",
      "lame_hihat                                 1.00\n",
      "SoundCheck2_75_Instrumental_Snare_drum     1.00\n",
      "SoundCheck2_77_Instrumental_Toms           1.00\n",
      "ah_test_guitar_guitar3                     0.99\n",
      "ah_test_oud_Trio_Joubran-04-Safarcut       0.97\n",
      "ff123_Enchantment                          0.97\n",
      "jpb_Jaillet34                              0.97\n",
      "sb_Media-103416(12.0-22.0)                 0.97\n",
      "ff123_BigYellow                            0.95\n",
      "api_6-three                                0.94\n",
      "ff123_cymbals                              0.94\n",
      "ff123_dogies                               0.94\n",
      "ah_development_piano_p5                    0.93\n",
      "al_Media-104002(11.3-21.3)                 0.93\n",
      "mck_train15                                0.93\n",
      "jpb_Jaillet66                              0.92\n",
      "api_3-long_gone                            0.91\n",
      "ah_test_tanburpluck_ta_1eb03               0.90\n",
      "al_Media-103714(4.2-14.2)                  0.89\n",
      "al_Media-104016(5.1-15.1)                  0.89\n",
      "...\n",
      "                                                 F-Score\n",
      "al_Media-104016(5.1-15.1)                           0.89\n",
      "vorbis_11                                           0.89\n",
      "al_Media-103505(1.9-11.9)                           0.88\n",
      "mck_train16                                         0.88\n",
      "al_Albums-Fire-03(12.4-22.4)                        0.87\n",
      "al_Media-104506(3.8-13.8)                           0.86\n",
      "sb_Albums-Chrisanne1-08(9.0-19.0)                   0.85\n",
      "sb_Albums-Fire-13(15.0-25.0)                        0.84\n",
      "ah_test_violin_42954_FreqMan_hoochie_violin_pt1     0.82\n",
      "al_Media-104807(16.5-26.5)                          0.82\n",
      "sb_Media-105810(5.0-15.0)                           0.82\n",
      "SoundCheck2_79_Instrumental_Violins                 0.82\n",
      "vorbis_dr4                                          0.80\n",
      "ah_test_guitar_Summer_Together_110_pt1              0.76\n",
      "ff123_Blackwater                                    0.74\n",
      "mit_track6                                          0.74\n",
      "ah_development_guitar_Guitar_Licks_06-11            0.73\n",
      "ah_test_violin_my_violin2                           0.63\n",
      "ah_test_ney_ne_icm13                                0.60\n",
      "ah_test_ney_ne_icm09                                0.53\n",
      "SoundCheck2_70_Instrumental_Flute                   0.48\n",
      "\n",
      "\n",
      "\n",
      "3. Filter Kernel: \n",
      "                                                    F-Score\n",
      "jpb_tabla                                              1.00\n",
      "vorbis_lalaw                                           0.99\n",
      "sb_Albums-Chrisanne3-02(12.0-22.0)                     0.97\n",
      "api_RM-C026                                            0.96\n",
      "ah_test_piano_Chick_Corea_-_03_-_Folk_Song             0.95\n",
      "ff123_DaFunk                                           0.95\n",
      "al_Media-106109(12.4-22.4)                             0.95\n",
      "jpb_fiona                                              0.94\n",
      "sb_Albums-Latin_Jam-13(6.0-16.0)                       0.93\n",
      "jpb_Jaillet29                                          0.93\n",
      "lame_castanets                                         0.93\n",
      "ah_development_guitar_my_guitar1                       0.91\n",
      "sb_Media-100608(3.0-13.0)                              0.91\n",
      "mck_train11                                            0.90\n",
      "al_Media-105614(20.9-30.9)                             0.89\n",
      "mck_train19                                            0.89\n",
      "ah_test_clarinet_44361_debudding_Clarinet_ORTF_...     0.88\n",
      "jpb_PianoDebussy                                       0.88\n",
      "api_RM-C036                                            0.87\n",
      "lame_iron                                              0.87\n",
      "al_Media-105016(4.1-14.1)                              0.87\n",
      "...\n",
      "                                               F-Score\n",
      "lame_iron                                         0.87\n",
      "al_Media-105016(4.1-14.1)                         0.87\n",
      "ah_test_mixtures_classic2                         0.86\n",
      "ah_test_tanburpluck_ta_1eb06                      0.86\n",
      "al_Albums-Cafe_Paradiso-14(15.9-25.9)             0.86\n",
      "mit_track1                                        0.86\n",
      "al_Media-104717(18.2-28.2)                        0.85\n",
      "ah_test_ney_ne_se04                               0.80\n",
      "ah_test_trumpet_1_-_Lost_In_Madrid_Part_1         0.79\n",
      "sb_Albums-Ballroom_Classics4-11(7.0-17.0)         0.79\n",
      "jpb_Jaillet67                                     0.77\n",
      "al_Albums-Cafe_Paradiso-09(14.9-24.9)             0.76\n",
      "al_Media-105913(10.6-20.6)                        0.76\n",
      "ff123_2nd_vent_clip                               0.71\n",
      "ah_test_sax_george_russel2_-_dimensions_pt1       0.70\n",
      "ah_test_clarinet_clarinet1                        0.67\n",
      "unknown_violin                                    0.61\n",
      "ah_test_ney_ne_icm05                              0.60\n",
      "SoundCheck2_62_Vocal_Male_Rock_Vocal              0.57\n",
      "SoundCheck2_80_Instrumental_Cellos_and_violas     0.32\n",
      "ff123_duel                                        0.27\n",
      "\n",
      "\n",
      "\n",
      "4. Filter Kernel: \n",
      "                                             F-Score\n",
      "jpb_Jaillet21                                   1.00\n",
      "sb_Media-106003(0.2-10.2)                       1.00\n",
      "SoundCheck2_72_Instrumental_Bongos              1.00\n",
      "SoundCheck2_74_Instrumental_Kick_drum           1.00\n",
      "ah_test_piano_Chick_Corea_-_08_-_So_in_Love     0.98\n",
      "al_Media-104105(15.6-25.6)                      0.96\n",
      "jpb_Jaillet17                                   0.95\n",
      "al_Media-103611(0.2-10.2)                       0.95\n",
      "SoundCheck2_84_Robin_S_-_Luv_4_Luv              0.94\n",
      "sb_Albums-Cafe_Paradiso-07(13.1-23.1)           0.93\n",
      "sb_Albums-Cafe_Paradiso-05(4.3-14.3)            0.92\n",
      "api_tiersen11                                   0.92\n",
      "ff123_deerhunter                                0.92\n",
      "ah_development_oud_rast_taksim1                 0.90\n",
      "api_2-uncle_mean                                0.90\n",
      "ff123_charlies                                  0.90\n",
      "ff123_Hongroise                                 0.90\n",
      "ff123_Waiting                                   0.89\n",
      "al_Media-103814(13.8-23.8)                      0.89\n",
      "ah_test_mixtures_pop1                           0.88\n",
      "ff123_grace1                                    0.87\n",
      "...\n",
      "                                                    F-Score\n",
      "ah_test_mixtures_pop1                                  0.88\n",
      "ff123_grace1                                           0.87\n",
      "al_Media-105402(9.7-19.7)                              0.87\n",
      "ff123_freeup                                           0.86\n",
      "lame_fatboy                                            0.86\n",
      "al_Media-106011(14.8-24.8)                             0.86\n",
      "vorbis_bassdrum                                        0.86\n",
      "api_25-rujero                                          0.85\n",
      "al_Media-103515(19.7-29.7)                             0.85\n",
      "ah_test_guitar_Guitar_Licks_15-06                      0.84\n",
      "al_Media-104317(12.3-22.3)                             0.83\n",
      "al_Media-105009(20.3-30.3)                             0.83\n",
      "mck_train18                                            0.83\n",
      "sb_Albums-Chrisanne1-01(3.0-13.0)                      0.81\n",
      "ah_test_cello_08_-_Bach_(JS)-_Cello_Suite_-4_In...     0.77\n",
      "ah_test_tanburpluck_ta_1eb07                           0.73\n",
      "ah_test_sax_20928_stephenchai_Gypsy_1_Alto_Sax_...     0.71\n",
      "ah_test_kemence_22_NevaTaksim_Kemence                  0.67\n",
      "api_realorgan3                                         0.58\n",
      "mck_train7                                             0.58\n",
      "mit_track5                                             0.56\n",
      "\n",
      "\n",
      "\n",
      "5. Filter Kernel: \n",
      "                                                    F-Score\n",
      "ah_development_percussion_conga1                       1.00\n",
      "ff123_FloorEssence                                     1.00\n",
      "ah_development_percussion_castagnet1                   0.99\n",
      "ff123_BeautySlept                                      0.99\n",
      "ff123_eb_andul_short                                   0.97\n",
      "al_Media-106001(9.7-19.7)                              0.97\n",
      "mck_train8                                             0.97\n",
      "ah_test_piano_Oscar_Peterson_-_02_-_Love_Ballade       0.96\n",
      "jpb_Jaillet27                                          0.95\n",
      "ah_test_piano_Oscar_Peterson_-_08_-_I_Love_You_...     0.94\n",
      "api_its_alright_for_you_o                              0.94\n",
      "lame_main_theme                                        0.94\n",
      "SoundCheck2_82_Yello_-_The_race                        0.94\n",
      "jpb_dido                                               0.93\n",
      "mck_train1                                             0.93\n",
      "lame_ftb_samp                                          0.92\n",
      "al_Albums-Step_By_Step-07(1.1-11.1)                    0.91\n",
      "al_Albums-Latin_Jam-02(6.8-16.8)                       0.90\n",
      "sb_Media-106103(4.0-14.0)                              0.89\n",
      "SoundCheck2_64_Instrumental_Acoustic_steel_stru...     0.89\n",
      "ah_test_clarinet_44784_alikirodgers_AB_Clarinet...     0.88\n",
      "...\n",
      "                                                    F-Score\n",
      "SoundCheck2_64_Instrumental_Acoustic_steel_stru...     0.89\n",
      "ah_test_clarinet_44784_alikirodgers_AB_Clarinet...     0.88\n",
      "al_Media-103515(9.1-19.1)                              0.88\n",
      "ah_test_violin_03__kurdilihicazkar__keman              0.87\n",
      "api_15-tamerlano_act_i_track_15b                       0.85\n",
      "mck_train3                                             0.85\n",
      "al_Albums-Latin_Jam3-11(11.5-21.5)                     0.84\n",
      "sb_Media-105215(12.0-22.0)                             0.84\n",
      "ah_test_trumpet_trumpet1                               0.83\n",
      "al_Albums-Ballroom_Classics4-07(9.8-19.8)              0.81\n",
      "ah_development_guitar_Guitar_Licks_15-05               0.79\n",
      "al_Albums-Latino_Latino-05(7.8-17.8)                   0.79\n",
      "ah_test_cello_cello1                                   0.77\n",
      "ah_test_violin_dark_eyes_viol                          0.76\n",
      "ff123_beo1test                                         0.76\n",
      "sb_Media-105907(0.0-10.0)                              0.76\n",
      "mit_track4                                             0.76\n",
      "sb_Media-106117(7.0-17.0)                              0.69\n",
      "ah_development_guitar_Guitar_Licks_06-12               0.58\n",
      "ah_test_kemence_01_sultaniyghtaksim_Kemence            0.52\n",
      "SoundCheck2_60_Vocal_Tenor_opera                       0.40\n",
      "\n",
      "\n",
      "\n",
      "6. Filter Kernel: \n",
      "                                                    F-Score\n",
      "jpb_Jaillet65                                          1.00\n",
      "lame_velvet                                            1.00\n",
      "SoundCheck2_73_Instrumental_Tambourine                 1.00\n",
      "ah_test_piano_PianoMic_full_stick                      0.99\n",
      "ah_development_piano_piano1                            0.98\n",
      "gs_mix1_0dB                                            0.98\n",
      "al_Media-103919(8.0-18.0)                              0.96\n",
      "al_Albums-Cafe_Paradiso-10(1.5-11.5)                   0.95\n",
      "api_RM-J001                                            0.95\n",
      "lame_BlackBird                                         0.94\n",
      "ff123_ATrain                                           0.93\n",
      "ff123_TheSource                                        0.93\n",
      "sb_Media-104111(5.0-15.0)                              0.93\n",
      "al_Media-105819(8.1-18.1)                              0.93\n",
      "jpb_Jaillet74                                          0.92\n",
      "ah_development_oud_ud_taksimleri_-_02_-_huezzam...     0.91\n",
      "api_RM-G008                                            0.91\n",
      "ah_test_mixtures_jazz3                                 0.90\n",
      "ah_test_mixtures_rock1                                 0.90\n",
      "al_Albums-Latin_Jam2-09(5.3-15.3)                      0.90\n",
      "jpb_Jaillet15                                          0.89\n",
      "...\n",
      "                                              F-Score\n",
      "jpb_Jaillet15                                    0.89\n",
      "sb_Media-105407(6.0-16.0)                        0.89\n",
      "mck_train2                                       0.89\n",
      "mck_train4                                       0.89\n",
      "ah_test_trumpet_chetbaker                        0.88\n",
      "al_Media-103605(10.0-20.0)                       0.88\n",
      "ah_test_oud_Diverse_-_02_-_Mahur_Pesrev_(Ud)     0.86\n",
      "al_Media-104917(15.6-25.6)                       0.85\n",
      "lame_testsignal2                                 0.84\n",
      "mck_train6                                       0.84\n",
      "ff123_BachS1007                                  0.83\n",
      "al_Media-105020(11.1-21.1)                       0.80\n",
      "SoundCheck2_71_Instrumental_Saxophone            0.80\n",
      "ah_development_piano_p3                          0.77\n",
      "sb_Albums-Chrisanne2-01(16.0-26.0)               0.77\n",
      "ah_development_percussion_triangle               0.75\n",
      "ah_test_tanburpluck_ta_1mc08                     0.74\n",
      "ah_test_sax_herbieHanckockTrack06_pt1            0.72\n",
      "sb_Albums-Ballroom_Classics4-01(3.0-13.0)        0.67\n",
      "mit_track7                                       0.66\n",
      "ah_test_mixtures_classic3                        0.32\n",
      "\n",
      "\n",
      "\n",
      "7. Filter Kernel: \n",
      "                                                 F-Score\n",
      "SoundCheck2_78_Instrumental_Whole_drum_kit          0.99\n",
      "ah_development_piano_MOON                           0.98\n",
      "ff123_wait                                          0.98\n",
      "ah_development_percussion_cajon116_13               0.97\n",
      "api_RM-C027                                         0.97\n",
      "SoundCheck2_63_Instrumental_Piano                   0.97\n",
      "ah_development_piano_mussorgsky                     0.96\n",
      "lame_spahm                                          0.96\n",
      "ah_test_oud_ud_taksimleri_-_06_-_segah_taksim       0.95\n",
      "api_RM-C038                                         0.95\n",
      "ff123_fossiles                                      0.94\n",
      "sb_Albums-Latin_Jam3-02(3.0-13.0)                   0.91\n",
      "lame_Fools                                          0.91\n",
      "al_Media-106015(16.4-26.4)                          0.91\n",
      "mck_train9                                          0.91\n",
      "ah_development_oud_1                                0.89\n",
      "gs_mix2_0dB                                         0.88\n",
      "al_Media-104306(5.0-15.0)                           0.88\n",
      "al_Media-104415(10.7-20.7)                          0.88\n",
      "al_Media-106113(16.4-26.4)                          0.88\n",
      "ah_test_oud_Diverse_-_03_-_Muayyer_Kurdi_Taksim     0.86\n",
      "...\n",
      "                                                 F-Score\n",
      "al_Media-106113(16.4-26.4)                          0.88\n",
      "ah_test_oud_Diverse_-_03_-_Muayyer_Kurdi_Taksim     0.86\n",
      "ah_test_mixtures_jazz2                              0.85\n",
      "al_Media-105306(21.1-31.1)                          0.83\n",
      "mck_train5                                          0.83\n",
      "ah_development_violin_my_violin1                    0.82\n",
      "sb_Media-105801(11.0-21.0)                          0.82\n",
      "lame_pipes                                          0.81\n",
      "ff123_gekkou-intro                                  0.80\n",
      "al_Media-106110(6.4-16.4)                           0.74\n",
      "jpb_Jaillet73                                       0.73\n",
      "ah_test_violin_my_violin3                           0.72\n",
      "ah_test_tanburpluck_ta_1eb08                        0.71\n",
      "sb_Albums-Ballroom_Magic-04(6.0-16.0)               0.70\n",
      "ah_test_ney_ne_se03                                 0.69\n",
      "ah_development_percussion_tambourine                0.68\n",
      "al_Media-105506(11.4-21.4)                          0.67\n",
      "ah_development_guitar_Guitar_Licks_07-11            0.65\n",
      "ah_test_ney_ne_tmu2n06                              0.58\n",
      "sb_Media-105213(13.0-23.0)                          0.58\n",
      "ff123_Debussy                                       0.50\n",
      "\n",
      "\n",
      "\n",
      "8. Filter Kernel: \n",
      "                                                    F-Score\n",
      "ah_test_oud_ud_taksimleri_-_17_-_ussak_taksim          0.98\n",
      "ff123_kraftwerk                                        0.98\n",
      "al_Media-105404(8.0-18.0)                              0.98\n",
      "sb_Albums-I_Like_It2-01(13.1-23.1)                     0.96\n",
      "jpb_Jaillet75                                          0.96\n",
      "al_Albums-Latin_Jam2-03(6.1-16.1)                      0.94\n",
      "lame_t1                                                0.94\n",
      "mck_train14                                            0.93\n",
      "lame_vbrtest                                           0.92\n",
      "mck_train20                                            0.92\n",
      "ah_test_sax_Tubby_Hayes_-_The_Eighth_Wonder_-_1...     0.91\n",
      "al_Media-103917(13.5-23.5)                             0.91\n",
      "al_Media-104218(9.3-19.3)                              0.91\n",
      "mit_karaoke_tempo                                      0.91\n",
      "sb_Albums-Chrisanne3-07(3.0-13.0)                      0.90\n",
      "ff123_ItCouldBeSweet                                   0.90\n",
      "al_Albums-Latino_Latino-09(8.8-18.8)                   0.88\n",
      "api_3-you_think_too_muchb                              0.88\n",
      "jpb_metheny                                            0.88\n",
      "ah_development_guitar_2684_TexasMusicForge_Dand...     0.87\n",
      "jpb_Jaillet70                                          0.86\n",
      "...\n",
      "                                                    F-Score\n",
      "jpb_metheny                                            0.88\n",
      "ah_development_guitar_2684_TexasMusicForge_Dand...     0.87\n",
      "jpb_Jaillet70                                          0.86\n",
      "jpb_wilco                                              0.85\n",
      "al_Media-106115(15.9-25.9)                             0.85\n",
      "ah_development_oud_Diverse_-_01_-_Taksim_pt1           0.83\n",
      "ah_test_guitar_guitar2                                 0.83\n",
      "al_Albums-AnaBelen_Veneo-13(1.8-11.8)                  0.83\n",
      "ah_test_trumpet_waldhorn33_-_Paloseco_pt1              0.81\n",
      "ah_test_kemence_08_-_HicazTaksim_cut                   0.79\n",
      "SoundCheck2_61_Vocal_Soprano_opera                     0.79\n",
      "sb_Albums-Step_By_Step-09(2.0-12.0)                    0.76\n",
      "ah_test_guitar_Guitar_Licks_51-10                      0.73\n",
      "ah_test_kemence_11_RastTaksim_Kemence                  0.72\n",
      "ah_test_kemence_10_huseyni_taksim_ve_cecenkizi_cut     0.69\n",
      "ah_test_cello_14_VioloncelloTaksim_pt1                 0.67\n",
      "mck_train10                                            0.64\n",
      "ah_test_ney_ne_se01                                    0.62\n",
      "mit_track3                                             0.61\n",
      "api_RM-C003                                            0.54\n",
      "ah_test_cello_03-Cello_Sonata_3__I_Allegro_ma_n...     0.17\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(8):\n",
    "    print(str(i+1) + \". Filter Kernel: \")\n",
    "    print(df_list[i].head(21))\n",
    "    print(\"...\")\n",
    "    print(df_list[i].tail(21))\n",
    "    print(\"\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dcd2df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 321/321 [00:05<00:00, 55.98it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "321 files.\n",
      "Total time: 101.8384761904763\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute total lenght of complete dataset\n",
    "\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "import datetime\n",
    "import librosa\n",
    "\n",
    "str_audiodir = 'data/audio'\n",
    "audiodir = os.fsencode(str_audiodir)\n",
    "\n",
    "def time_converter(seconds):\n",
    "    return str(datetime.timedelta(seconds = seconds))\n",
    "\n",
    "\n",
    "def get_duration(audiofile):\n",
    "    signal, s = librosa.load(audiofile, mono=True)\n",
    "    return librosa.get_duration(y=signal, sr=s)\n",
    "\n",
    "\n",
    "durations_list = []\n",
    "for file in tqdm(os.listdir(audiodir)):\n",
    "    filename = os.fsdecode(file)\n",
    "    audiofile = os.path.join(str_audiodir, filename)\n",
    "    durations_list.append(get_duration(audiofile=audiofile))\n",
    "\n",
    "print(str(len(durations_list)) + \" files.\")\n",
    "#print(\"Total time: \" + str(time_converter(sum(durations_list))))\n",
    "print(\"Total time: \" + str(sum(durations_list)/60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7793860e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Duration of global optimization runs\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "duration_1x1   = datetime.datetime.strptime(\"2:33:18\", \"%H:%M:%S\")\n",
    "duration_3x1   = datetime.datetime.strptime(\"3:11:00\", \"%H:%M:%S\")\n",
    "duration_5x1   = datetime.datetime.strptime(\"2:52:57\", \"%H:%M:%S\")\n",
    "duration_7x1   = datetime.datetime.strptime(\"3:35:51\", \"%H:%M:%S\")\n",
    "duration_9x1   = datetime.datetime.strptime(\"2:59:53\", \"%H:%M:%S\")\n",
    "duration_11x1  = datetime.datetime.strptime(\"3:45:19\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x3   = datetime.datetime.strptime(\"3:43:10\", \"%H:%M:%S\")\n",
    "duration_3x3   = datetime.datetime.strptime(\"4:32:09\", \"%H:%M:%S\")\n",
    "duration_5x3   = datetime.datetime.strptime(\"5:44:55\", \"%H:%M:%S\")\n",
    "duration_7x3   = datetime.datetime.strptime(\"6:00:43\", \"%H:%M:%S\")\n",
    "duration_9x3   = datetime.datetime.strptime(\"6:27:39\", \"%H:%M:%S\")\n",
    "duration_11x3  = datetime.datetime.strptime(\"6:32:31\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x5   = datetime.datetime.strptime(\"3:57:37\", \"%H:%M:%S\")\n",
    "duration_3x5   = datetime.datetime.strptime(\"9:56:42\", \"%H:%M:%S\")\n",
    "duration_5x5   = datetime.datetime.strptime(\"6:23:43\", \"%H:%M:%S\")\n",
    "duration_7x5   = datetime.datetime.strptime(\"7:42:16\", \"%H:%M:%S\")\n",
    "duration_9x5   = datetime.datetime.strptime(\"7:37:55\", \"%H:%M:%S\")\n",
    "duration_11x5  = datetime.datetime.strptime(\"9:14:31\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x7   = datetime.datetime.strptime(\"4:37:16\", \"%H:%M:%S\")\n",
    "duration_3x7   = datetime.datetime.strptime(\"5:29:43\", \"%H:%M:%S\")\n",
    "duration_5x7   = datetime.datetime.strptime(\"7:09:37\", \"%H:%M:%S\")\n",
    "duration_7x7   = datetime.datetime.strptime(\"8:14:00\", \"%H:%M:%S\")\n",
    "duration_9x7   = datetime.datetime.strptime(\"10:33:11\", \"%H:%M:%S\")\n",
    "duration_11x7  = datetime.datetime.strptime(\"11:28:19\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x9   = datetime.datetime.strptime(\"5:00:42\", \"%H:%M:%S\")\n",
    "duration_3x9   = datetime.datetime.strptime(\"6:15:31\", \"%H:%M:%S\")\n",
    "duration_5x9   = datetime.datetime.strptime(\"8:09:03\", \"%H:%M:%S\")\n",
    "duration_7x9   = datetime.datetime.strptime(\"9:49:35\", \"%H:%M:%S\")\n",
    "duration_9x9   = datetime.datetime.strptime(\"12:01:30\", \"%H:%M:%S\")\n",
    "duration_11x9  = datetime.datetime.strptime(\"12:18:10\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x11  = datetime.datetime.strptime(\"5:45:59\", \"%H:%M:%S\")\n",
    "duration_3x11  = datetime.datetime.strptime(\"7:37:26\", \"%H:%M:%S\")\n",
    "duration_5x11  = datetime.datetime.strptime(\"9:43:14\", \"%H:%M:%S\")\n",
    "duration_7x11  = datetime.datetime.strptime(\"12:03:40\", \"%H:%M:%S\")\n",
    "duration_9x11  = datetime.datetime.strptime(\"13:41:23\", \"%H:%M:%S\")\n",
    "duration_11x11 = datetime.datetime.strptime(\"13:37:40\", \"%H:%M:%S\")\n",
    "\n",
    "duration_1x13  = datetime.datetime.strptime(\"6:27:09\", \"%H:%M:%S\")\n",
    "duration_3x13  = datetime.datetime.strptime(\"8:05:50\", \"%H:%M:%S\")\n",
    "duration_5x13  = datetime.datetime.strptime(\"12:01:07\", \"%H:%M:%S\")\n",
    "duration_7x13  = datetime.datetime.strptime(\"13:00:39\", \"%H:%M:%S\")\n",
    "duration_9x13  = datetime.datetime.strptime(\"16:00:59\", \"%H:%M:%S\")\n",
    "duration_11x13 = datetime.datetime.strptime(\"18:04:13\", \"%H:%M:%S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "960a42f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean duration: 7:57:20\n"
     ]
    }
   ],
   "source": [
    "def to_seconds(datetime_object):\n",
    "    a_timedelta = datetime_object - datetime.datetime(1900, 1, 1)\n",
    "    seconds = a_timedelta.total_seconds()\n",
    "    return(seconds)\n",
    "durations_list = []\n",
    "durations_list.append(to_seconds(duration_1x1))\n",
    "durations_list.append(to_seconds(duration_3x1)) \n",
    "durations_list.append(to_seconds(duration_5x1))  \n",
    "durations_list.append(to_seconds(duration_7x1))  \n",
    "durations_list.append(to_seconds(duration_9x1))  \n",
    "durations_list.append(to_seconds(duration_11x1)) \n",
    "durations_list.append(to_seconds(duration_1x3)) \n",
    "durations_list.append(to_seconds(duration_3x3)) \n",
    "durations_list.append(to_seconds(duration_5x3)) \n",
    "durations_list.append(to_seconds(duration_7x3)) \n",
    "durations_list.append(to_seconds(duration_9x3)) \n",
    "durations_list.append(to_seconds(duration_11x3))\n",
    "durations_list.append(to_seconds(duration_1x5))  \n",
    "durations_list.append(to_seconds(duration_3x5))  \n",
    "durations_list.append(to_seconds(duration_5x5))  \n",
    "durations_list.append(to_seconds(duration_7x5))  \n",
    "durations_list.append(to_seconds(duration_9x5))  \n",
    "durations_list.append(to_seconds(duration_11x5))\n",
    "durations_list.append(to_seconds(duration_1x7))  \n",
    "durations_list.append(to_seconds(duration_3x7))  \n",
    "durations_list.append(to_seconds(duration_5x7))  \n",
    "durations_list.append(to_seconds(duration_7x7))  \n",
    "durations_list.append(to_seconds(duration_9x7))  \n",
    "durations_list.append(to_seconds(duration_11x7)) \n",
    "durations_list.append(to_seconds(duration_1x9))  \n",
    "durations_list.append(to_seconds(duration_3x9))  \n",
    "durations_list.append(to_seconds(duration_5x9))  \n",
    "durations_list.append(to_seconds(duration_7x9))  \n",
    "durations_list.append(to_seconds(duration_9x9))  \n",
    "durations_list.append(to_seconds(duration_11x9)) \n",
    "durations_list.append(to_seconds(duration_1x11)) \n",
    "durations_list.append(to_seconds(duration_3x11)) \n",
    "durations_list.append(to_seconds(duration_5x11)) \n",
    "durations_list.append(to_seconds(duration_7x11)) \n",
    "durations_list.append(to_seconds(duration_9x11)) \n",
    "durations_list.append(to_seconds(duration_11x11))\n",
    "durations_list.append(to_seconds(duration_1x13)) \n",
    "durations_list.append(to_seconds(duration_3x13)) \n",
    "durations_list.append(to_seconds(duration_5x13)) \n",
    "durations_list.append(to_seconds(duration_7x13)) \n",
    "durations_list.append(to_seconds(duration_9x13)) \n",
    "durations_list.append(to_seconds(duration_11x13))\n",
    "\n",
    "mean = np.mean(durations_list)\n",
    "std = np.std(durations_list)\n",
    "\n",
    "seconds = mean % (24 * 3600)\n",
    "hour = seconds // 3600\n",
    "seconds %= 3600\n",
    "minutes = seconds // 60\n",
    "seconds %= 60\n",
    "\n",
    "print(\"Mean duration: %d:%02d:%02d\" % (hour, minutes, seconds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e0c5d454",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.868\n",
      "0.8425\n"
     ]
    }
   ],
   "source": [
    "# Filter types A (große Zeit-, kleine Freq.Koeffizienten) and B (kleine Zeit-, große Freq.Koeffizienten) mean F-Scores\n",
    "\n",
    "a_scores = [0.841, 0.873, 0.872, 0.886]\n",
    "b_scores = [0.869, 0.832, 0.859, 0.810]\n",
    "\n",
    "a_mean = np.mean(a_scores)\n",
    "b_mean = np.mean(b_scores)\n",
    "print(a_mean)\n",
    "print(b_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "985861a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum for 8 files\n",
      "  Onsets: 25827 TP: 21479 FP:  2911 FN:  4348 Precision: 0.881 Recall: 0.832 F-measure: 0.855 mean:  -6.4 ms std:  10.3 ms\n"
     ]
    }
   ],
   "source": [
    "# Total F-measure, Precision and Recall over all folds\n",
    "print(OnsetSumEvaluation(total_sum_evals))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fe0a933d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.875, 18.875, 20.5, 20.25, 21.375, 24.125, 22.25]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e9af221400>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i3x1 = np.mean([12, 11, 11, 11, 13, 12, 12, 13])\n",
    "i3x3 = np.mean([16, 20, 18, 21, 17, 21, 19, 19])\n",
    "i3x5 = np.mean([21, 21, 21, 22, 22, 19, 17, 21])\n",
    "i3x7 = np.mean([18, 21, 19, 15, 25, 21, 22, 21])\n",
    "i3x9 = np.mean([23, 23, 19, 22, 20, 19, 22, 23])\n",
    "i3x11 = np.mean([25, 30, 24, 23, 21, 21, 23, 26])\n",
    "i3x13 = np.mean([23, 26, 17, 17, 23, 23, 25, 24])\n",
    "verlauf = [i3x1, i3x3, i3x5, i3x7, i3x9, i3x11, i3x13]\n",
    "print(verlauf)\n",
    "fig = plt.figure(figsize=(6,4))\n",
    "filter6 = sns.lineplot(data=verlauf, label=\"1x3\")\n",
    "\n",
    "plt.xlabel(\"Filterdimension (Zeitrichtung)\")\n",
    "plt.ylabel(\"Durchschnittlich benötigte Iterationen\")\n",
    "#plt.title(\"Kostenverläufe auf allen Test-Folds\")\n",
    "plt.legend()\n",
    "#plt.show()\n",
    "#plt.savefig(\"Kostenverläufe.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85330f04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}