{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"FMP\"\n", "\"AudioLabs\"\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\"C6\"\n", "

Adaptive Windowing

\n", "
\n", "\n", "
\n", "\n", "

\n", "Following Section 6.3.3 of [Müller, FMP, Springer 2015], we discuss in this notebook the concept of adaptive windowing.\n", "

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Idea\n", "\n", "One crucial step in practically all music analysis tasks consists of transforming the given audio signal into a suitable feature representation that captures certain musical properties. Since most musical properties vary over time, the given audio signal is typically split up into segments or frames, which are then further processed individually. The underlying assumption is that the signal stays (approximately) stationary within each segment with regard to the property to be captured. In practice, as is the case with the [short-time Fourier transform](../C2/C2_STFT-Basic.html) (see Section 2.5 of [Müller, FMP, Springer 2015]), a predefined window of fixed size is used for the time localization, where the size is determined empirically and optimized for the specific application in mind. Using **fixed-size windowing**, however, may lead to a violation of the homogeneity assumption: the boundaries of the resulting windowed sections often do not coincide with the positions where the changes of the signal occur. As an alternative to fixed-size windowing, one can employ a more musically meaningful **adaptive windowing** strategy, where segment boundaries are induced by previously extracted onset and beat positions. Since musical changes typically occur at onset positions, this often leads to an increased homogeneity within the adaptively determined frames and a significant improvement in the resulting feature quality. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: C-Major Scale\n", "\n", "\n", "\n", "For an illustration we consider an audio recording with a C-major scale played on a piano. \n", "\n", "\"FMP_C6_F24_ScaleMajorC.png\"\n", "\n", "
\n", "\n", "\n", "\n", "The following figure shows a [chroma representation](../C3/C3S1_SpecLogFreq-Chromagram.html) of the audio excerpt using a fixed-size windowing. Let us have a closer look at the frame with index $n=3$ (between $t=1.5~\\mathrm{sec}$ and $t=2~\\mathrm{sec}$). Note that this frame contains note change leading to a rather \"noisy\" chroma feature where the chroma bands contain energy from the two different notes $\\mathrm{D}$ and $\\mathrm{E}$. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:47:27.150363Z", "iopub.status.busy": "2024-02-15T08:47:27.150068Z", "iopub.status.idle": "2024-02-15T08:47:29.827878Z", "shell.execute_reply": "2024-02-15T08:47:29.827291Z" } }, "outputs": [ { "data": { "image/png": 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OhPPmzeOyyy6r8tykSZMA+PWvf01ZWVnNL67ney1RM3TTTTexZs0a1q9fX9lZ9qqrrqJv3758//vfr9c+s5H8Xkp4aHSVjxHWr1/P3Llz6du3b+Vza9eupVmzZjRrVriBw1999VU+++yzyuVEsnLllVcCsHTpUl555RVOPvlkundP+l1XwM+B5E68eZFFWWpqxl2xYgVtUu4u3bBhA/Pnz2fbbbfNJrr6i/FndFOS05oXM9sInAU8C8wAHjGz6ZLOkHRGuNlRwAeS3gNuBI61un5Khr+Qi0LKh3BcrFu3Lm219A8efTSj10+aNIn77rsv12HV6fLLL69x3ZIlS6osp5Zl/vz5eauFyYUbbriBBQsWsL6mmsks32srVqyo3OfMmTOBIDGYNm1aVvurr6lpOugnrs1HH33EqlWr+Mtf/sLf//53vvzyS7744gvWrVvHVVddVev1zqWNGzeyatUq/vvf/9baXPTKK68AQafsL7744ttrNXo0n3/+eWWfj0K6//77KS8vz90Os3if1XT866+/Pm3T1l133cX//vc/5s+fz+uvv15Zy7tu3bp6H7tOMf2Mbmpy/hMlbAqakPLcLUmPxwHVG41rs8UWOYmtUTgs4zvEG5Waqp0/TvrlW5c5c+ZU/grs0KED3/3ud9luu+0oLS3Nae3G2rVrs3pdTWVJ/uXa2G7bvfPOOwEoKSmhpKSksqbp/PPPp2UG77WKiopa1//zn/9kwIBv+9SPHTu28tbuNWvW8Oc//xkz48wzz2TrrYP53xJf0C1atMiqTOkkrk1qp9Q5c+ZUqxF866232G233Vi7di2ffPIJO++8c05iSG5OyyZJuuWW4GOwQ4cOdFq3jg/CDsjt27dn2bJlnHnmmbRv3x5JLF++nC233DLjfdenn9bs2bO57rrrcncrdT0+09auXcvf/va3rJKnl19+mZdffrnKc4karmR77703AwYMyH5up5h+Rjc1OR3nJV98nJfo5b3qOdSvXz922WUX+vXrR0VFBevXr6dZs2b1+iL885//zOrVq/MYZWDLLbfkqKOO4t577037C3DQoEGMGjWK0tJS1q1bl/aDNt+GDh3KoYceCqTvS7J48WLG17ON/8ADD2TIkCFcffXV1dYNGDCA6dOnZ7SfESNGsPPOO9O2bdsqz99+++1VmmIaaujQoYwaNarB+xk3blwkw++PHDmSp59+mpNOOomvv/6alStX8p3vfAdJldd06tSpPPHEE/XabxTjwLz66qv897//Legx27Rpwy9+8Yt6f44Ug2Ie5yUeyYtkU2IQZ0YkiGFZakpexlxyCWMLOJHZT37yE7bffnsqKiows7T9G7JNtApdlnxLV54f/OAHtGvXjh49egBBn5q777678MHVYNiwYbzxxhvVnm/otTnvvPPYsGED7dq1y3ofuUzgG8N77YwzzmDrrbfmhRde4LXXXmPQoEH06dOHHXbYoX41ofX4TLvnnnsKMzVGPfXo0YOysjL69+9P33792LB+faOqYc2WJy8R85qX6BWq5sW5fDv++OOZO3cuu+22Gy+++CJTp06le/fu9OrVq7IzdLL99tuPzp07V94B49I74IADGDx4MJtttlmN28T1c6RVq1YcdthhSKJnz560bt06FiMCe/ISsaG9etmUOXOiDiM3Hnwwdh3Cli1blnZsC4CB06bxQY76FEStmMoCxVWeYioLFFd56irLlltuSb9+/ejcuTOPP/54ASPLTkOvTadOnRg8eDA9e/as7AcGweCLrVu3zkWIGSvm5CXnHXYlHQLcAJQCt5vZlSnrFa4/FFgNnGhm79S60xUrch1mdJ58MnbJS02JC0Dfjz8umg/hYioLFFd5iqksUFzlqassX3/9ddqmwMaqodfm888/Z8KECbVu4/OZNVwUEzMeCvySIHnZA7jBzPaobb+77rqrvfvuuzmL09VPXKt6nXMuDkaPHk3v3r2RxMaNG3N2B6bXvGSucmJGAEmJiRk/TNrmCODecGyXNySVSepsZotr2unGjz7K6Au0X79+leNSJPTu3ZvPP/+cb775ptr22267bZUh23v16sWcpOapFi1aVN722alTpyqTnA0YMIDmzZuTaVI1cOBAmjVrxrDLL+edsWMpKSnJ6NdI27Ztq8TeoUOHyrEhBg4cyKZNmyrHmdh1110pKSmhtLSUzz//vHK+mrKyshoHhRowYAAtW7asvHOhpKSk8nF5eTmLF9d4WQA49sEHqw0gFlfFVBYorvIUU1mguMpTTGWBaMrz4IMPFvR4xSDXyUu6iRlTa1VqmryxyrekpNOB0wG222qrjA6ebsCnWbNm1bh96lwzc1L61SQP/pU6O2t9J7KbMWMGLVu2ZNOAAXwybVrG83WkJl3JZVy8eDGlpaWVy7Nnz6aiooKKiooqk9vVNinh9OnTadu2LWZWeQdP4nEmw8S/M2RIRuWIg2IqCxRXeYqpLFBc5SmmskDxladY5brZ6GjgYDM7NVw+HtjdzH6ZtM1TwJ/M7JVw+QXgPDOrcfKSoYMH25R33qkyEFNDqtTMrCDtjWmPs3JlrKZcN7NaR5ltsW4d61u2LGBE+VNMZYHiKk8xlQWKqzzFVBaIrjzbbLMN5eXlnHjiiZSVlSGJ2bNnU1paylZbbcXXX39Nu3btKCsrw8yYP38+vXr1YtmyZXTs2JGKigpKSr4dNL+iooLS0lJvNspQnRMzZrhNVe+9BzQsYUlWqI5SaY/TpUuQwMREXefqd9dey5UXXFCgaPKrmMoCxVWeYioLFFd5iqksUJjyXHTRRVVqzWuy4447Vj7u0KHq/MWJeb06duwIUCVxSbdcbHJd89KMoMPugQQTM04GRpvZ9KRtRhHMf5TosHujme1e2359nJdovf7661UmMWxstt9+ezZt2sTAgQNZt25dfmfQdc7V22677cYhhxzCH//4x6hDyamDDjqIHj160KVLF4Aqox43Bt5hN0NmtlFSYmLGUuDOxMSM4fpbCOY9OhSYRXCr9El17vjLL3MZZrTGj4fTT486inrZY489akxeBk+ZwjtDC/O3cdZZZ9G+ffs6t8s2eSlkWQqhmMqTj7L07duXsrIyttlmG4ak9HMwM8rLy9kinFetrubT+sq0PMOGDaNbt25MnTqVfffdt8aZlQt1R+AWW2zBpk2bKvvinXHGGei222j/+99nVJMQB8etXk2vcCqPxpSIuKqimJjRgF/Ua6cFmKemYKZMiV3yUlJSwgUXXMAnn3zCP/7xjyrruixaRO2D9DTM4MGDOfzww/N4hG/luyyFVkzlyXVZ/u///o9WrVrVuF5SZeKSWM6l5PLsueeevP7662yzzTYMGjSInXbaqdootcmTY6azxx578Oabb+Ysvv33379y/qQ6k5L58yHDxCX1js6odO3alaOOOoqysrLqK08/PZjywDVq8Rhh15uNGo18/MI76KCD2HPPPSsnU2zRogWSsp5b5Kabbkp751kUfv3rX1NWVsaKFSu4/vrrow6n0erYsSO77ror7dq1q5Yg16Zr1671msQx074G6eTivd+qVSvOOecc1q1bR5s2bRq8v4T333+fxx57rMH7OeGEE9h6662rTZaZK2vWrEk7oWc+7b777gwcOJCuXbsWfT+QVN5sFLWUsVtibfhwmDgx6iiytvnmm7MyqcPxT++6i3tOqrvlL2HEiBH07t2bVq1a0bJlS1om9erP1QfmXnvtxb///e96v66+ZRk1ahQlJSUsXryYd955h4MPPpgNGzYwbNiwal+Q+foyqE268px99tk8++yzjBgxgnbt2lV+IZ9++umYGbfddlve4zr++OPZZpttajwnI0aMqNb0V9O12XvvvXn44YdrPV6mzY35tPXWW/Pll1/y61//mtLSUtocemhOPweyqRm68MILWbt2LQBfffUV3bt3z66GqR6faYUaHn+nnXbi8MMPz+4HUMw/o5uKeCQvYWeoohDzWYt/+9vfVmn7nzh8OKNHj6ZPnz5Mmzatcu6S3Xffnd12240tttiC8vLygn559OrVK6vXTRw+PKPtRo4cydChQ6v8ihs1alStrylEf4Add9yRjz76qHJIgUR5jjvuOLbffvvK7X74wx9WPh4zZkze4hk8eDDDhw9n83oODZCuNjhRlpKSEo488sjKWoZ+/frxwx/+kJ49e3LttdcCcOihh9KzZ09uvvlmgJy995o3b15t7KPUZD5xvGXLllV57sADD6Rfv37fPpHjz4Fsko5mzZpVJpANSq7rWZbDDjuMZ599NqNxpDKx+eabs//++7N27VpatWrFzjvv3LC/t5h/RjcV8Uheammbjp3w9ra4Sv2QPPGKKyqTy5133pmd08wJUuhfve3atauynGnTwrI64jz33HNzWtWfK2eeeSZbbrklzZql/DkvWlTvxH+bbbZhyZIlWcWxyy67sGDBAvbdd1+6du1a7dbOTKVLXhLXplevXuy0006VyYskBg4cCARfwKWlpey2225AcF62ynCAy0xccMEFlTVVhx9+OIMGDaocifp///sfQ4YMoWPHjrRo0YIPP/yQ9957j6OPPprp06dX3tZaKcefA927d697oyQ5/ZusZ1mGDBlCv379KpPNVCeccAL33ntvnftp3749Z511Vr2OnZGYf0Y3FTlLXiRtBTwM9ATmAj8ys6/TbDcXWAlsAjZm1B4XDn9fFIYODb5UYux3v/sdjz32GPvuu2+jLc/RRx9NmzZt6NmzJwAbNmxg4sSJlJeXc8ABB9CyZUvGjRtXORLxmDFjWLn55vzlnHM44IAD6NmzJ8899xzHH388LVq0iLAk1Z177rncfffdDBkyhD32qGVasCyuzUknncRVV11V53ajR4+uMqR5okYkF9IlL6ePH89fzjmncvnnP/95tQ63Z599dpXl5Bl9cy35lth27dpVK3v//v3p378/ECR11eT47yY1YU82ZMgQKioqmDp1KgAHH3xw7e+b+sqiLLXV9Gy33XbVntt777058MAD6x1aVhrpZ5qrKpc1L+cDL5jZlZLOD5f/r4Zt9zezzHtUFsnsq0BR/FFsvvnm/PSnPw0WGml5El8cCc2bN2fEiBFVnjvnnHP44x//yD777ANAi6VLOeXLL+nWrRsAp5xySmGCzdAFF1xAaWkpJSUlnHnmmXW/IItrU9sdOAmJZsIhQ4bw9tvBwNipiUNDpIshkbgkEoZtttkmZ8fLRrVarvoq4N/NYYcdxqJFiyqTl2HDhuX2ADksy49+9KO0zxcscYFG+5nmqspl1+sjgHvCx/cAR+Zsz198kbNdRa6GqtLYinF5SkpKGDNmDAcccAAALceNq0xc8qF37971fs3JJ59MixYtOP/882nevHn97pbI8tok9xlq3bp1tVqFPn36AMGX4h/+8Ad+//vf57Q5LXXMFYA9X3sNgOEZ9kvKt7puXa5Tgf5uzjjjDIDKQdTyIsuypLtNuWvXrgBVaoZOO+20rPaftRh/pjUluax56ZiYGdrMFkuq6aeRAc9JMuBWMxufbqPkiRn7x2guoDoVW1ZfTOXJc1mOOeYYnnnmmcrairqcdtppdOnShd///vfZHTDL8gwaNKhyLI7TTz+dsrIydthhhyp3hiWUlJTkvFmtpKSk2nggm4edYpPHXolSg2+5zcN7beedd2batGmVyz179qwcOj4hL01pWZbl6KOPrnJ328CBAyubv/bff3/mzZvHkUceWa0MeVdMn2lFrF7jvEj6L9ApzaoLgXvMrCxp26/NbMs0++hiZovC5OZ54Jdm9lJtx/VxXlyxeOGFF3jllVeqPHfSSSfRpk0bbrrpJkpLSytnHL/wwgsb3jyRhfLycq677jogv3cj1eWOO+5g4cKFVZ4755xzqg3gVkjTpk1j2rRpHHfccZHFUJPVq1fz5z//uXK5kAM8Zuvpp5/mrbfe4oADDmDvvff2EW1zzMd5CZnZQTWtk/SFpM5hrUtnIO0tC2a2KPx/iaTHgd2BWpOXouqwO2QIZPjLOxaKqTwFKMsOO+xQLXkpKyujXbt2HHfccbRv357Vq1dTUVHR8MQly/K0a9eOiy66iI0bNzbs+Dl02q23ctvPfhZ1GDXeUVdveXivpTbd7bXXXjndf40aUJaRI0cycuTIHAfUQMX0mVbEcvmz7gngp8CV4f/VRgmTtBlQYmYrw8ffBeqeMKRHjxyGGbHxaVvJ4quYylOAsnTt2pUxY8awZMkSSkpKWL16dWVVeWIslrRDlmejAeUpLS2NfK6avn37Vta8PNnIaxDqrQDvtYKNJltMnwFQfOUpUrlMXq4EHpF0CjAfOBqCZiLgdjM7FOgIPB5WDTYDHjSzZ+rcczEN6VxM/XeguMpTwLIU5G6ZmF+bvffemy222CLop/Hxx7yyZEmjHGcnK3m6Nn379uXjjz8GCjiqc8zfZ9UUW3mKVDzmNmrVyqaEw1jHXt++EH64FIViKk8xlQWKqzzFVBbIW3kmTZrExHBo+4L1V/Jr02gVc5+XeFRphCNoFoUi+aOoVEzlKaayQHGVp5jKAnkrz7777gt8e8txQfi1cRGIR/JSTLeuFdu8GcVUnmIqCxRXeYqpLJC38khizJgxnHrqqXnZf1p+bVwEYtFsJGklUERTS9MByHyE4XgotjJ5eRq3YisPFF+ZvDzR62Fm+ZsnI0LxmJgRZhZTu52kKcVUHii+Mnl5GrdiKw8UX5m8PC6f4tFs5JxzzjkX8uTFOeecc7ESl+Sl2EYNKrbyQPGVycvTuBVbeaD4yuTlcXkTiw67zjnnnHMJcal5cc4555wDPHlxzjnnXMw0quRF0iGSZkqaJen8NOsl6cZw/TRJg6OIM1MZlGe4pBWS3g3/XRxFnJmSdKekJZI+qGF93K5PXeWJ2/XpLulFSTMkTZf06zTbxOYaZVie2FwjSa0kvSXpvbA8Y9NsE5vrAxmXKTbXKEFSqaSpkp5Msy5W16homVmj+AeUArOBXkAL4D2gf8o2hwJPAwKGAW9GHXcDyzMceDLqWOtRpn2BwcAHNayPzfXJsDxxuz6dgcHh482Bj2P+N5RJeWJzjcJz3jZ83Bx4ExgW1+tTjzLF5holxfw74MF0ccftGhXrv8ZU87I7MMvM5pjZeuAh4IiUbY4A7rXAG0CZpM6FDjRDmZQnVszsJeCrWjaJ0/XJpDyxYmaLzeyd8PFKYAaQOslNbK5RhuWJjfCcfxMuNg//pd4xEZvrAxmXKVYkdQNGAbfXsEmsrlGxakzJS1dgQdLyQqp/UGWyTWORaax7hlWuT0saUJjQ8iZO1ydTsbw+knoCgwh+CSeL5TWqpTwQo2sUNke8CywBnjez2F+fDMoEMbpGwPXAeUBFDetjd42KUWNKXpTmudQMPpNtGotMYn2HYO6JXYC/Av/Kd1B5Fqfrk4lYXh9JbYF/Ar8xs/LU1Wle0qivUR3lidU1MrNNZrYr0A3YXdLAlE1id30yKFNsrpGkw4AlZvZ2bZulea5RX6Ni1JiSl4VA96TlbkDqdNKZbNNY1BmrmZUnqlzNbALQXFKHwoWYc3G6PnWK4/WR1Jzgi/4BM3sszSaxukZ1lSeO1wjAzJYDE4FDUlbF6vokq6lMMbtGewHfkzSXoKn/AEn3p2wT22tUTBpT8jIZ6CNpO0ktgGOBJ1K2eQI4IeztPQxYYWaLCx1ohuosj6ROkhQ+3p3geiwreKS5E6frU6e4XZ8w1juAGWb2lxo2i801yqQ8cbpGkraWVBY+bg0cBHyUsllsrg9kVqY4XSMz+72ZdTOzngSf2f8zs+NSNovVNSpWjWZWaTPbKOks4FmCO3XuNLPpks4I198CTCDo6T0LWA2cFFW8dcmwPEcBP5e0EVgDHGtmjbb6UdLfCe4c6CBpITCGoINe7K4PZFSeWF0fgl+NxwPvh30QAC4AtoVYXqNMyhOna9QZuEdSKcEX+CNm9mRcP+NCmZQpTtcorZhfo6Lk0wM455xzLlYaU7ORc84551ydPHlxzjnnXKx48uKcc865WPHkxTnnnHOx4smLc84552LFkxfnnHPOxYonL87FgKT2kt4N/30u6bPw8TeSbs7TMX8j6YR87DsbkubWNjKrpIck9SlkTM65aPg4L87FjKRLgG/M7Jo8HqMZwZw0g81sY76OUx/hkO1DzWxpDev3A44zs9MKGphzruC85sW5GJM0XNKT4eNLJN0j6bmwluIHkq6W9L6kZ8J5gpA0RNIkSW9LelZS5zS7PgB4J5G4SPqVpA8lTZP0UPjcZpLulDRZ0lRJR4TPl0q6JjzuNEm/DJ8/MNzu/fB1LcPn50oaK+mdcN0O4fPtw7JMlXQr4YR44XGfUjBL8QeSjgljfhk4KEy8nHNFzJMX54rL9sAo4AjgfuBFM9uJYFj2UWEC81fgKDMbAtwJXJ5mP3sByTPrng8MMrOdgTPC5y4kmPtlN2B/4M+SNgNOB7ZL2v4BSa2Au4FjwniaAT9P2v9SMxsM/A04J3xuDPCKmQ0imE9m2/D5Q4BFZraLmQ0EngEwswqCIdt3qc8Jc87FjycvzhWXp81sA/A+wZxaz4TPvw/0BPoBA4Hnw/mCLiKYFTdVZ+DLpOVpBEnIcUCiGem7wPnhfiYCrQgSjIOAWxK1Nmb2VXjcT83s4/C19wD7Ju0/MWP022GchOvvD/fxFPB1UlkOknSVpH3MbEXSfpYAXdKeGedc0fDqVeeKyzoIaiEkbUiaAK+C4O9dwHQz27OO/awhSEYSRhEkE98D/iBpQLivH5rZzOQXhjMIp3amUyZxA5uo+rlUrVOemX0saQjB5Hh/kvScmV0arm4Vxu6cK2Je8+Jc0zIT2FrSngCSmoeJSKoZQO9wmxKgu5m9CJwHlAFtCWZM/2WYrCBpUPja54AzEn1PJG0FfAT0lNQ73OZ4YFIdsb4E/CTcx0hgy/BxF2C1md0PXAMMTnpNX2B63afBORdnnrw414SY2XrgKOAqSe8B7wLfSbPp03zbrFMK3C/pfWAqcJ2ZLQf+CDQHpkn6IFwGuB2YHz7/HjDazNYCJwH/CPdTAdxSR7hjgX0lvUPQRDU/fH4n4K2wuepC4DIASR2BNWa2OLOz4ZyLK79V2jmXlqTHgfPM7JOoY8mEpN8C5WZ2R9SxOOfyy2tenHM1OZ+g425cLCfoCOycK3Je8+Kcc865WPGaF+ecc87FiicvzjnnnIsVT16cc845FyuevDjnnHMuVjx5cc4551ysePLinHPOuVjx5MU555xzseLJi3POOedixZMX55xzzsWKJy+uUZJ0iaT7o44jTiRdIOn2WtafKOmVeuyvn6SpklZK+pWkWyT9ITfRVjnOXEkHZfG6pyX9NNfxZHjsbyT1yvK1EyWdmsNY9pE0M1f7cy4OmkUdgGu6JI0GfgfsAKwkmOH4cjPL+AvWfcvMrkg8ltQT+BRobmYbs9zlecBEMxuUg/ByzsxGRnjstlEdO5WZvQz0izoO5wrJa15cJCT9DrgeuALoCGwL3AwckcW+Ik3Coz5+HvUApkcdhHPOpfLkxRWcpC2AS4FfmNljZrbKzDaY2X/M7NykTVtIujdstpguaWjSPuZK+j9J04BVkppJ+l643fKwan7HlO3PlTRN0ipJd0jqGDY9rJT0X0lbJm1/gqR5kpZJ+kNy00bYpPWopPsllQMnStpd0uvhsRdLGiepRdL+TNKZkj4Jj/dHSduHrymX9Ejy9inna56kIeHj48J99Q+XT5X0r6S4Ek1tL4X/Lw+bOPZM2t81kr6W9KmktLUXkv4H7A+MC1/fV9Ldki4L1/+fpDcSiZukn4fnvpWkEknnS5odnr9HJG2VtO/jk87themOn7Rtq/A8LwvP7WRJHcN1lc0vkt4L40z8M0nDw3XDJL0Wvv69xPNpjnWSpP8kLc+S9EjS8gJJu4aPTVLv8PHdkm6S9FR4bd+UtH3S60ZI+kjSCknjACWtK5F0UXg+lih4v28RrrtH0tnh466J91C43FvSVwoMl7QwaZ9zJZ2j4L2+QtLDklolrT8vfI8uCt8/lWVxLi48eXFR2BNoBTxex3bfAx4CyoAngHEp638MjArX9wL+DvwG2BqYAPwnJSH4ITAC6AscDjwNXAB0IPhb+BVAmBjcDPwE6AxsAXRNOfYRwKPhsR8ANgG/Dfe1J3AgcGbKaw4BhgDDCJpkxofH6A4MDMuTziRgePh4X2AOsF/S8qQ0r9k3/L/MzNqa2evh8h7AzDDOq4E7JCn1xWZ2APAycFb4+o9TNvkzsB64SFIfghq048xsLcF5PDKMsQvwNXATVJ7bvwHHh+vaA91qKDfATwnOf/dw2zOANWni3SWMsy1BU+RM4B1JXYGngMuArYBzgH9K2jrNsSYB+4QJRWegObBXGHcvoC0wrYY4fwyMBbYEZgGXh6/rAPwTuIjgnM9O7DN0Yvhvf4L3cFu+fZ8nX/f9qH7dXzYzqyGeHxG837YDdg6PgaRDwvNzENA7aX/OxYonLy4K7YGlGfTFeMXMJpjZJuA+YJeU9Tea2QIzWwMcAzxlZs+b2QbgGqA18J2k7f9qZl+Y2WcEX8xvmtlUM1tHkEgl+nYcBfzHzF4xs/XAxUDql8TrZvYvM6swszVm9raZvWFmG81sLnAr1b8YrjKzcjObDnwAPGdmc8xsBUEiVVPfkklJ+9oH+FPS8n6kT15qMs/MbgvP6T0EyVnHerweADOrAE4gSFSeAK42s6nh6p8BF5rZwvDcXgIcFdbSHAU8aWYvhev+AFTUcqgNBO+X3ma2KTzP5TVtLGlvgkTle+F2xwETwvdRhZk9D0wBDk1TpjkEfa92JTivzwKfSdohXH45LHc6j5nZW+F7+oFwH4TH+dDMHg3fl9cDnye97ifAX8L3wTfA74Fjw3NVmUwRJCtX823iU9d1v9HMFpnZV8B/kuL5EXCXmU03s9UECZdzsePJi4vCMqCD6u4rkvwhvxpolfKaBUmPuwDzEgvhl8wCqtaYfJH0eE2a5UQnzC7J+w4/5JelxJZ8bMJmlSclfa6gKekKgl/ayTI9fqrEl1gnoBR4GNhLQafcLQg6Omeq8pyG5aKW49YqTNJeBHoS1qyEegCPh800y4EZBDVTHal+bleRdG5Tmn62JUhanwUeCps5rpbUPF08kroDjwA/Taop6gEcnYgljGdvgqQtnURtR6JGayJBolBXspD6Xq3pvWTU8r4NHzcDOprZbOAbgsRjH+BJYJGkfrmKJ+Wxc7HhyYuLwuvAWoKmhYZIrg1ZRPBFBUDYFNId+CyL/S4mqSlDUmuCX/81HRuCppCPgD5m1o6gOapac0w2zGwWwRfQr4CXzGwlwZfT6QS1U+lqA2pqTsgZSYcSNJG9QNCMlLAAGGlmZUn/WoU1XosJrktiH21IOreJpp/w3/ywL9RYM+tPUIt2GEGNT2osrYF/Adeb2dMpsdyXEstmZnZlDcVKJC/7hI8TtV71reFKSC2vkpdJed8SdFzfyLeJ7SSC2qoW4fmbRFD+Lalf0pocT3IzXfeaNnSuMfPkxRVc2ExyMXCTpCMltZHUXNJISVdnudtHgFGSDgx/mZ8NrANey2JfjwKHS/pO2GdmLHUnIpsD5cA3YTPDz7M4bm0mAWfx7RfoxJTlVF8SNMdkNRZJXcK+HHcApxL0Szk8TGYAbgEul9Qj3HZrSYm7yB4FDpO0d3huL6WWzyFJ+0vaSVIpwfndQFCLk+pO4CMzS33/3B/GdrCkUgUdgIdLqqmfzSSC/ietzWwhQfPiIQQJ1tQaXlObp4ABkn4Q1hr+CuiUtP7vwG8lbSepLUGN3cNJTaqJ657ogD0R+CVB0pruPNTlEeAkSTuGiePFWezDuch58uIiYWZ/Ieg4eBHBF+0Cgg/pf2W5v5kE/Rv+Ciwl6JB7eNhnpb77mk7wBfEQwS/VlcASgmSoJucAo8NtbyNo2smlSQQJ0ks1LFcRNgldDrwaNpcMy3E844F/h31JlgGnALdLag/cQNAP5jlJK4E3CDoKJ87tL4AHCc7t18DCNPtP6ESQ8JQTND9NIkhIUh0LfD+l2WkfM1tA0Ln6Ar59n51LDZ99YXPTNwRJC2G/mTnAq9kkC2a2FDgauJKgeawP8GrSJncSNI29RDAuz1qC915C6nV+BWhDDdc9g3ieBm4kaO6bRVALCrW/t51rdFRzZ3XnHED4i3g5QZPQpxGH41zOKBhO4AOgZQYd6J1rNLzmxbk0JB0eNmdtRnDn0vvA3Gijcq7hJH1fUgsF4xpdRXBnnScuLlY8eXEuvSMIOlMuIqjqP7aWMTWci5OfETShzSboP5Tr/lnO5Z03GznnnHMuVrzmxTnnnHOxEosJ5crKyqxLly5Rh1Fvca7VWr++3jfpNArt2rWLOgQXE4sXL446hKxt3BjPLipx/fts2bJl1CFkZfHixSxfvjyj8aYkZfuF9ayZHZLla7MWi+SlS5cu3HvvvVGHUW8VFbWNet64zZs3r+6NGqGDDjoo6hCanNLS0qhDyMrYsfEdGX/ZstQBn+Mhrn+fvXvHc97Kk046qV7bl5TUvzGmoqIidSTxgohF8uKcc865/JGU1Q+RqH6ke/LinHPOuaySlw0bNuQhkrp58uKcc841cZJo1iw+KUF8InXOOedcXmTbbBSVOpMXSSXALgRTqa8BppvZF7W/yjnnnHNxUhTJi6Ttgf8DDgI+IRiRsRXQV9Jq4FbgHjNL21tH0p0E09cvMbOB4XNbEUxY15NgqPUfmdnXdQVpZrG87VjK6A61Rmm33XaLOoSsxPWWxtatW0cdQtbmz58fdQhZ6d69e9QhZO20006LOoSsdOgQyY0pDRbX2+rrc/dQ3GpeaivZZQSzt25vZgeb2XFmdpSZ7UwwdPoWwPG1vP5ugqnkk50PvGBmfYAXwmXnnHPORay0tLTe/6JSY82Lmf24lnVfANfXtmMze0lSz5SnjwCGh4/vASYS1O4455xzLiLFVPMCgKTZks5Iee7JLI/X0cwWA4T/b5PlfpxzzjmXI4nkJfY1L0k2APtL2gP4mZmtB7rmNyyQdDpwOkCnTp3yfTjnnHOuSSuqmhdgtZkdA8wAXpbUA8i29+wXkjoDhP8vqWlDMxtvZkPNbGhZWVmWh3POOedcXeJW85JJ8iIAM7sauAB4FuiW5fGeAH4aPv4p8O8s9+Occ865HMpX8iLpEEkzJc2SVO1GHUlHSJom6V1JUyTtXdc+M2k2ujjxwMxekHQw3yYgtQX7d4LOuR0kLQTGAFcCj0g6BZgPHJ3B8WPXkagYbLHFFlGHkJWFCxdGHUJWevToEXUIWXv//fejDiEr06dPjzqErP3yl7+MOoSslJeXRx1CVqIaAr+h6jPESL6+ZyWVAjcBI4CFwGRJT5jZh0mbvQA8YWYmaWfgEWCH2vZb2zgvg8OHnyU9Tqizw24tdysdWNdrnXPOOVc4eawk2B2YZWZzwuM8RHDncWXyYmbfJG2/GRl0Tamt5uXapMdDgCmETUjhjg/IKGznnHPONXp5Sl66AguSlhcCe6RuJOn7wJ8I7kIeVddOaxvnZf+knU41M09WnHPOuSLUgJqXDpKmJC2PN7PxybtO85pqNStm9jjwuKR9gT8SjO5fo0wnZozf2PzOOeecy1iWyctSMxtay/qFQPJcHN2ARTVtHA5wu72kDma2tKbtfFZp55xzronLY5+XyUAfSdsBnwHHAqNTjt0bmB122B0MtACW1bbT2jrs/pVva1y6Sboxeb2Z/areRXDOOedco5Ov5MXMNko6i2CYlVLgTjObnhi538xuAX4InCBpA7AGOMbquFWqtpqX5DastxsUfQNJonnz5lGGkJVNmzZFHULW4ni+Ib6jMa9duzbqELIW1xl3TznllKhDyFpcZ6yPa9wtWrSIOoSs1Pd8N2uWn8YYM5sATEh57pakx1cBV9Vnn7V12L2nvgE655xzLn7iNp5abc1G44EbzeyDNOs2A44B1pnZA3mMzznnnHN5VjTJC3AzcLGknYAPgC+BVkAfoB1wJ+CJi3POOVcEiiJ5MbN3gR9JagsMBToTdKSZYWYzCxOec8455/KtmGpegMpheyfmPxTnnHPORaWokhfnnHPOFbeiq3lpLOJ4i12c3gipFi2qcQDERq1Pnz5Rh5CVpUtrHEiy0XvkkUeiDiErxx57bNQhZG3VqlVRh5CV+sxy3JjEdeiI+nxvFl3yImlgujuOnHPOOVc8iip5AW6R1AK4G3jQzJbnNSLnnHPOFVTR1byY2d6S+gAnA1MkvQXcZWbP5z0655xzzuVd0SUvAGb2iaSLCKYMuBEYpKAx7QIzeyyfATrnnHMu/4oqeZG0M3ASMAp4HjjczN6R1AV4HfDkxTnnnIuxYqx5GQfcRlDLsibxpJktCmtjnHPOORdzxZa8HAqsMbNNAJJKgFZmttrM7strdDGXrxk6C6Fr165Rh5CV8vLyqEPIyiuvvBJ1CFk799xzow4hK+vWrYs6hKy1atUq6hCyEtfPxLje4l0fcat5Kclgm/8CrZOW24TPOeecc64IJJKX+v6LSibJS6twigCgcrqANvkLyTnnnHOFlq/kRdIhkmZKmiXp/DTrfyJpWvjvNUm71LXPTOrwVkkabGbvhAcZQjBBo3POOeeKQL6ajSSVAjcBI4CFwGRJT5jZh0mbfQrsZ2ZfSxoJjAf2qG2/mSQvvwH+ISkxXnxn4Jh6xu+cc865RkpSvvok7Q7MMrM54XEeAo4AKpMXM3stafs3gG517TSTQeomS9oB6AcI+MjMNtQvduecc841ZlnWvHSQNCVpebyZjU9a7gosSFpeSO21KqcAT9d10EzTrN2AnuH2gyRhZvdm+FrnnHPONWINaDZaamZDa9t1mufS3r4laX+C5GXvug6aySB19wHbA+8Cm5IOnHXyImkusDLc38Y6Ch5bJSWZ9IdunDbbbLOoQ8jKu+++G3UIWfnb3/4WdQhZu/XWW6MOISubb7551CFkrWXLllGHkJW4zobdunXrujdqhOr7HZSnu4cWAt2TlrsBi1I3CgfEvR0YaWbL6tppJjUvQ4H+lvsb3fc3s6U53qdzzjnn6imP47xMBvpI2g74DDgWGJ1y7G0JRus/3sw+zmSnmSQvHwCdgMX1Ctc555xzsZCv5MXMNko6C3gWKAXuNLPpks4I198CXAy0B24Opk2su0Umk+SlA/BhOJt05ZCUZva9rEoSvhx4TpIBt6Z07gFA0unA6QCdO3duwKGcc845V5d8DTpnZhOACSnP3ZL0+FTg1PrsM5Pk5ZL67DBDe4VzI20DPC/pIzN7KXmDMKEZDzBgwIDiH5vZOeeci0jcpgfI5FbpSZJ6AH3M7L+S2hBU/WTNzBaF/y+R9DjBfeAv1f4q55xzzuVLnJKXOrsiSzoNeBRI3FLQFfhXtgeUtJmkzROPge8S9KtxzjnnXATiNrdRJs1GvyCoGXkTwMw+CZt7stUReDzslNMMeNDMnqntBWbG+vXrG3DIaGzcuDHqELIW1xl3//Of/0QdQlb69+8fdQhZi+tt9StWrIg6hKx16NAh6hCysmnTpro3aoTieot3RUVFxtsWXbMRsM7M1ofJBpKaUcMAM5kIhwiuc9Il55xzzhVOsSUvkyRdALSWNAI4E4jnz1vnnHPOVVOMNS/nEwzX+z7wM4LbnW7PZ1DOOeecK6yiSl7MrAK4LfznnHPOuSJTdDUvkj4lTR8XM+uVl4icc845V1BFl7wQzG2U0Ao4GtgqP+E455xzLgpFlbykmd3xekmvEMxFUBBmFstb7Opzm1pjU15eHnUIWdlmm4bcxR+dQw89NOoQshbXGY7jOlMwBJ+JcZS4azVu4npbfX2+NyXRrFkm9RmNQybNRoOTFksIamLiO5e8c84556ooxmaja5MebwTmAj/KSzTOOeeci0RRJS9mtn8hAnHOOedcNIqu5kXS72pbb2Z/yV04zjnnnCu0fCYvkg4BbiCY1Pl2M7syZf0OwF3AYOBCM7umrn1merfRbsAT4fLhBDNAL8g8dOecc841ZvlIXiSVAjcBI4CFwGRJT5jZh0mbfQX8Cjgy0/1mkrx0AAab2cowkEuAf5jZqZkexDnnnHONVx5rXnYHZoXzGiLpIeAIoDJ5MbMlwBJJozLdaSbJy7ZA8pTO64GemR4gV+J423FJSUnUIWRt5syZUYeQlbfffjvqELLy4x//OOoQsrZ06dKoQ8hKz549ow4ha2vXro06hKzEdbb6Fi1aRB1CVup7a3qWyUsHSVOSlseb2fik5a5UbalZCOyRzYGSZZK83Ae8JelxgpF2vw/c29ADO+ecc65xaEDNy1IzG1rL+nQZVIMHKsrkbqPLJT0N7BM+dZKZTW3ogZ1zzjnXOOSx2Wgh0D1puRuwqKE7zXQ4vTZAuZndJWlrSduZ2acNPbhzzjnnGoc8JS+TgT6StgM+A44FRjd0p5ncKj2G4I6jfgS3MjUH7gf2aujBnXPOORe9fNW8mNlGSWcBzxLcKn2nmU2XdEa4/hZJnYApQDugQtJvgP5mVuM8NZnUvHwfGAS8Ex5okSSfHsA555wrIvka58XMJgATUp67Jenx5wTNSRnLJHlZb2YmyQAkbVafAzjnnHOucSu6EXaBRyTdCpRJOg04Gbgtv2EVh7jO/AowZcqUujdqhE444YSoQ8hKnN8rnTt3jjqErGzYsCHqELL20EMPRR1CVpYvXx51CFkZOrS2m2kar/oMMVJUyYuCm8QfBnYAygn6vVxsZs8XIDbnnHPOFUjRJC9hc9G/zGwI4AmLc845V4SKquYl9Iak3cxsct6jcc4551wkii152R84Q9JcYBXBaHlmZjvnMzDnnHPOFYYkmjXLdOi36NUYqaRtzWw+MLKA8TjnnHOuwIqp2ehfBLNJz5P0TzP7YYFics4551yBFUvykjyZUq9cHlTSIcANBKPt3W5mV9axfaxOasKaNWuiDiFrDzzwQNQhZOXkk0+OOoSsfPppfGfb2HHHHaMOIStvvfVW1CFk7ZRTTok6hKzEqVki2cMPPxx1CFnZuHFjxtvG7Xu2tneS1fC4QSSVAjcBIwgmbJos6Qkz+zBXx3DOOedc5oopedlFUjlBDUzr8DF822G3XZbH3B2YZWZzACQ9BBwBePLinHPORaQokhczy1cpugILkpYXAnukbiTpdOB0gE6dOuUpFOecc84VU81LvijNc9WapcxsPDAeoH///vEdO90555xr5Dx5qdtCoHvScjdgUQRxOOeccy7kyUvtJgN9JG0HfAYcC4yOIA7nnHPOEb+aF0Uxm62kQ4HrCW6VvtPMLq9j+y+BeXkKpwOwNE/7dun5OS8sP9+F5+e88PycV9fDzLbOZENJzxCcw/paamaHZPG6BokkeWlMJE0xs3jOdx5Tfs4Ly8934fk5Lzw/501LSdQBOOecc87VhycvzjnnnIsVT17C27FdQfk5Lyw/34Xn57zw/Jw3IU2+z4tzzjnn4sVrXpxzzjkXK568OOeccy5WmnTyIukQSTMlzZJ0ftTxFDNJ3SW9KGmGpOmSfh11TE2FpFJJUyU9GXUsTYGkMkmPSvoofL/vGXVMxUzSb8PPlA8k/V1Sq6hjcvnXZJMXSaXATcBIoD/wY0n9o42qqG0EzjazHYFhwC/8fBfMr4EZUQfRhNwAPGNmOwC74Oc+byR1BX4FDDWzgQQDnx4bbVSuEJps8gLsDswyszlmth54CDgi4piKlpktNrN3wscrCT7Qu0YbVfGT1A0YBdwedSxNgaR2wL7AHQBmtt7MlkcaVPFrBrSW1Axog8+V1yQ05eSlK7AgaXkh/mVaEJJ6AoOANyMOpSm4HjgPqIg4jqaiF/AlcFfYVHe7pM2iDqpYmdlnwDXAfGAxsMLMnos2KlcITTl5UZrn/L7xPJPUFvgn8BszK486nmIm6TBgiZm9HXUsTUgzYDDwNzMbBKwCvD9dnkjakqDGfDugC7CZpOOijcoVQlNOXhYC3ZOWu+HVjXklqTlB4vKAmT0WdTxNwF7A9yTNJWgWPUDS/dGGVPQWAgvNLFGr+ChBMuPy4yDgUzP70sw2AI8B34k4JlcATTl5mQz0kbSdpBYEnbyeiDimoiVJBP0AZpjZX6KOpykws9+bWTcz60nw/v6fmfmv0jwys8+BBZL6hU8dCHwYYUjFbj4wTFKb8DPmQLyDdJPQLOoAomJmGyWdBTxL0EP9TjObHnFYxWwv4HjgfUnvhs9dYGYTogvJubz4JfBA+KNoDnBSxPEULTN7U9KjwDsEdzROxacJaBJ8egDnnHPOxUpTbjZyzjnnXAx58uKcc865WPHkxTnnnHOx4smLc84552LFkxfnnHPOxYonL87FjKT2kt4N/30u6bPw8TeSbs7TMX8j6YTw8Q7h8aZK2j4fx6tHXA9J6hNlDM65wvNbpZ2LMUmXAN+Y2TV5PEYzgnE0BofjI50PtDazMSnbieAzpWDzKEnaDzjOzE4r1DGdc9HzmhfnioSk4ZKeDB9fIukeSc9JmivpB5KulvS+pGfCqRqQNETSJElvS3pWUuc0uz4AeCdMXA4FfgOcKulFST0lzQhrfN4Bukv6m6QpkqZLGpsU31xJV0h6PVw/ODzmbElnJG13rqTJkqYlXi9pM0lPSXpP0geSjgk3fxk4KEywnHNNhCcvzhWv7YFRBBPX3Q+8aGY7AWuAUWEC81fgKDMbAtwJXJ5mP3sBbwOEIyLfAlxnZvuH6/sB95rZIDObB1xoZkOBnYH9JO2ctK8FZrYnQdJxN3AUMAy4FEDSd4E+wO7ArsAQSfsChwCLzGwXMxsIPBPGUwHMAnZpyIlyzsWL/1pxrng9bWYbJL1PMAXGM+Hz7wM9CZKOgcDzQYsPpcDiNPvpTO3zxcwzszeSln8k6XSCz5fOQH9gWrguMX/Y+0BbM1sJrJS0VlIZ8N3w39Rwu7YEyczLwDWSrgKeNLOXk463hGBGYZ8927kmwpMX54rXOghqJyRtsG87uFUQ/O0LmB7WhNRmDdCqlvWrEg8kbQecA+xmZl9LujvlteuSYliX9HxyTH8ys1tTDyJpCHAo8CdJz5nZpeGqVmGMzrkmwpuNnGu6ZgJbS9oTQFJzSQPSbDcD6J3hPtsRJDMrJHUERtYzpmeBkyW1DWPqKmkbSV2A1WZ2P3ANMDjpNX0Bn1TVuSbEa16ca6LMbL2ko4AbJW1B8HlwPdUTgaeB+zLc53uSpob7mAO8Ws+YnpO0I/B62JT1DXAcQfL0Z0kVwAbg5wBhgrTGzNI1dznnipTfKu2cq5Okx4HzzOyTqGNJJum3QLmZ3RF1LM65wvFmI+dcJs4n6Hzb2CwH7ok6COdcYXnNi3POOedixWtenHPOORcrnrw455xzLlY8eXHOOedcrHjy4pxzzrlY8eTFOeecc7Hy/0YOMRpMl7rIAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import os, sys, librosa\n", "from scipy import signal\n", "from matplotlib import pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import IPython.display as ipd\n", "\n", "sys.path.append('..')\n", "import libfmp.b\n", "import libfmp.c6\n", "%matplotlib inline\n", "\n", "def plot_beat_grid(B_sec, ax, color='r', linestyle=':', linewidth=1):\n", " \"\"\"Plot beat grid (given in seconds) into axis\n", "\n", " Notebook: C6/C6S3_AdaptiveWindowing.ipynb\n", "\n", " Args:\n", " B_sec: Beat grid\n", " ax: Axes for plotting\n", " color: Color of lines (Default value = 'r')\n", " linestyle: Style of lines (Default value = ':')\n", " linewidth: Width of lines (Default value = 1)\n", " \"\"\"\n", " for b in B_sec:\n", " ax.axvline(x=b, color=color, linestyle=linestyle, linewidth=linewidth)\n", "\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F23_PhrygianHalfCadenceC.wav')\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F23_C-major-scale_fast.wav')\n", "fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F24_ScaleMajorC-middle.wav')\n", "\n", "Fs = 22050\n", "x, Fs = librosa.load(fn_wav, Fs) \n", "\n", "N, H = 1024, 512\n", "X = librosa.stft(y=x, n_fft=N, hop_length=H, win_length=N, center=True, window='hanning')\n", "Y = np.abs(X)\n", "C = librosa.feature.chroma_stft(S=Y, sr=Fs, norm=1, hop_length=H, n_fft=N)\n", "\n", "B_num = 10\n", "B_fix_sec = np.linspace(0, x.shape[0], B_num) / Fs\n", "B_fix_frame = (B_fix_sec*Fs/H).astype(int)\n", "C_fix = librosa.util.sync(C, B_fix_frame)\n", "\n", "# Visualization\n", "fig, ax = plt.subplots(2, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1, 2]}, figsize=(8,3.5)) \n", "\n", "libfmp.b.plot_signal(x, Fs, ax=ax[0,0], \n", " title='Signal with fixed-size windowing')\n", "ax[0,1].set_axis_off()\n", "plot_beat_grid(B_fix_sec, ax[0,0])\n", " \n", "libfmp.b.plot_matrix(C_fix, ax=[ax[1,0], ax[1,1]], xlabel='Time (frames)', \n", " title='Chromgram with fixed-size windowing')\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now introduce a function for adaptive windowing, which is informed by note onsets. As is shown by the next figure, adaptive windowing leads to clean chroma features that nicely capture the characteristics of the eight notes. We want to make some notes regarding the following implementation and example:\n", "\n", "* We specify the time grid (in our code denoted by `B_sec`, `B_adapt_sec`, or `B_fix_sec`) in seconds.\n", "* The function `librosa.util.sync` allows for computing a feature representation adapted to a given time grid. In this case, the time grid needs to be converted to frame indices that match the feature representation.\n", "* In our example, we use a chroma representation computed on a fine time grid based on fixed-size windowing, which is then adapted to the coarser grid (specified by `B_adapt_sec`, or `B_fix_sec`). \n", "* When computing the representation on the finer grid, we use a [centered view](../C2/C2_STFT-Conventions.html), which may introduce some \"bleeding\" also on the coarser grid. To avoid this effect, one may use the parameter `center=False`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:47:29.861293Z", "iopub.status.busy": "2024-02-15T08:47:29.860899Z", "iopub.status.idle": "2024-02-15T08:47:30.748781Z", "shell.execute_reply": "2024-02-15T08:47:30.748203Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nov, Fs_nov = libfmp.c6.compute_novelty_spectrum(x, Fs=Fs, N=512, H=256, \n", " gamma=100, M=10, norm=True)\n", "nov, Fs_nov = libfmp.c6.resample_signal(nov, Fs_in=Fs_nov, Fs_out=100)\n", "\n", "peaks, properties = signal.find_peaks(nov, prominence=0.2)\n", "B_adapt_sec = peaks/Fs_nov\n", "B_adapt_frame = (B_adapt_sec*Fs/H).astype(int)\n", "C_adapt = librosa.util.sync(C, B_adapt_frame, aggregate=np.mean)\n", "\n", "# Visualization\n", "fig, ax = plt.subplots(3, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1, 1, 2]}, figsize=(8,5)) \n", "\n", "libfmp.b.plot_signal(nov, Fs_nov, color='k', ax=ax[0,0],\n", " title='Novelty function with estimated boundaries')\n", "ax[0,1].set_axis_off()\n", "plot_beat_grid(B_adapt_sec, ax[0,0])\n", "\n", "libfmp.b.plot_signal(x, Fs, ax=ax[1,0],\n", " title='Signal with adaptive windowing')\n", "ax[1,1].set_axis_off()\n", "plot_beat_grid(B_adapt_sec, ax[1,0])\n", "\n", "libfmp.b.plot_matrix(C_adapt, ax=[ax[2,0], ax[2,1]], xlabel='Time (frames)',\n", " title='Chromgram with adaptive windowing')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Beat-Synchronous Feature Representation\n", "\n", "Adaptive windowing techniques based on beat information are of particular importance for many music analysis and retrieval applications. In this case, a windowed section is determined by two consecutive beat positions, which results in one feature vector per beat. Such **beat-synchronous** feature representations have the advantage of possessing a musical time axis (given in beats) rather than a physical time axis (given in seconds). This makes the feature representation robust to differences in tempo (given in BPM). In the context of [music synchronization](../C3/C3_MusicSynchronization.html) (see Chapter 3 of [Müller, FMP, Springer 2015]), the usage of beat-synchronous features could make DTW-like alignment techniques obsolete—at least in the ideal case of having perfect beat positions. For example, knowing the beat positions already yields a beatwise synchronization of two different performances that follow the same musical score. However, in practice, such strategies have to be treated with caution, in particular when the beat positions are determined automatically. In general, automated beat tracking procedures work well for music with percussive onsets and a steady tempo. However, when dealing with weak note onsets and expressive music with local tempo changes, the automated generation of beat positions becomes an error-prone task, not to mention the problems related to tempo octave confusion. Using corrupt beat information at the feature extraction stage may have immense consequences for the subsequent music processing tasks to be solved. For example, to compensate for beat tracking errors in the music synchronization context, one may have to reintroduce error-tolerant techniques that are similar to DTW. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Shostakovich\n", "\n", "As an example for using beat-beat-synchronous features, we use our Shostakovich excerpt, which was introduced in the [FMP notebook on novelty functions](../C6/C6S1_NoveltyComparison.html).\n", "\n", "\"FMP_C6_F07_Shostakovich_Waltz-02-Section_Score.png\"\n", "\n", "
\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:47:30.751617Z", "iopub.status.busy": "2024-02-15T08:47:30.751402Z", "iopub.status.idle": "2024-02-15T08:47:34.058938Z", "shell.execute_reply": "2024-02-15T08:47:34.058323Z" } }, "outputs": [ { "data": { "image/png": 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KNeNmbNy4kbfeeotjjjmmyDDjx49n0KBBGa+tWLGCtWvXctBBGT0U7pPMnTuXVq1asWHDBtq2bVvoWtu2bdNu6Nu2bWPt2rW0bduWpUuX8uijjxZK86WXXqJGjRp06dKFv/3tb5xxxhmICCNGjODHP/4xderUKVIfe3OSn5/PJ598wsqVKxk3bhwnnnhi2kzltm3b2L59O/Pnz6dPnz7cfffddO3alRNOOIFWrVqlbvb2cFbcuDBq1ChmzJjBJZdcQt26dbnzzjvp3r07LVq0yLge9P777+eXv/xloXEx08P98uXLefDBB9PacPLtzKpVq7jjjju46aabqFmzZqE0iltvC1G73b59Ow8//DAABxxwQLHhjXnz5lGnTp2U4QvRWuPSxi+O6dOT334qzJw5c1iwYAH//Oc/U+fM+N25cye/+c1vMsZ7991305Z3hfF/+tOfFtm+nnjiiZTcJA8++CBt2rRJGYbTpk1j3rx5DBkyhMGDBxeZh0ceeYRvf/vbPP3006lzt956K++//z4DBw4s1VKBUaNGsWrVKr7xjW9Qo0YNZs6cyTvvvMN1112XttTvl7/8ZaG4O3fuZPbs2anjqVOn8u9//ztlLI8bN47//ve/aXFszAeKvLcXNWHx7LPP8v3vf7/IvCTb6siRI/n000/TjNldu3alZr6BjBNv//znP1N9xvpwUkbyvvDwww/TrFkzTj/99GLHmOrAPmn8bt26lZycHDZt2kSNGjXSlhesWbOGp59+miFDhnDQQQcVmhGdMGECb775Jm+++SY9e/Ys1c7hu+66iyuvvJKmTZsWSu+dd97hk08+KTGN0hi0e0K4Hix5k/vyyy+pVasWO3fuZMeOHTRr1oz33nsvYzpm4H355Ze0bt2a3NxcVqxYwYwZM4pd3/v+++8Xee0vf/kLN910E3PmzGHgwIHk5+eTl5fHk08+mfbg8N///peDDjoodfMraTNCGC9THu69914aN27MD37wA8aNG5cyfCEaMMOZo9deew1VZd26dQwdOjR13gbu/Px8Fi5cCMDTTz/N4Ycfzsknn1yibnl5eWXaoPPhhx+ycuVKZs+ezfe+9z3GjRsHlH6py7vvvsv06dOLvfG+/vrr9O/fP23WID8/n3feeSd1Q12/fj19+vShQYMGpdI7Pz+fTz/9lMMPP5xdu3aVyyv/bLBt2zZq1qyZyvvatWvTXm+3bNkyNQO5detWnnnmGTp27Mgll1wCRK/GbTlSy5Yti10H+vzzz3PxxRezYcMGRo4cSd26ddm1a1fq7Up40121ahUtWrRg+fLlPPLII3Tt2pXt27enNt7aEiVDVXnwwQfZsGEDUDB7ZUuYTjvttFKVx+OPP562ZvTDDz9MvdWYPXt2mlGRZP369YWWXhS3veO+++7jvPPOK3bN4htvvFGi54RMbNmyJc34yvQ2LhPh8oqQilxXGRquxqpVq9LWOxuzZs2iU6dOhd4Ghvzud7+jR48edOvWjfHjx3PJJZcUMoSKKuNly5alHiAg6i8jR45k8ODB5OXlpbxmJB/4wrKHgpn15HrnorDJp3nz5tGjR4/UmBkaiECRy0jCDXQ26WLGa0lv1MI+rKq89tprdOjQgaVLl2YMv27dOl566SXOPffcYtM1rAxGjx7N6tWrOfTQQ7O2wdDGs8WLF9OoUSOWL1/O8uXL+eKLLzjppJOoUaMGhx56KJMnTy72Hr0vInvDXrNBgwbpqFGjeOCBBzjnnHPSXqGrKnPnzqVz585s27aNWrVqFXoaPvnkk1m4cCEHHHAAI0eOTLt24403phnHH3zwwR7tNO3fvz9nnnlm6jjTJoTKZtCgQQwZMoQGDRqwefPm1DoiY/jw4YwYMaLEdPr165fxiXRPaNCgQbEbCa+99lpUldzcXJ599tnU7FVZaN++PZdeemlq4Lz99tv573//u1t5adeuXcZBMCcnh0MPPbTQ67yrrrqK1q1bM2bMGN5++20OPvjg1Oa2bHD99dfTtGlTHnroIQ455JC0GdxRo0YV+wYjpGvXrpxzzjnk5eVx3333FRmutDPOn3zySdpu+jPOOIO33nqLq6++mqZNmzJ37lw++OADzj77bD7++GNOPvnkjAZyfn4+EyZMYNCgQbz88svUrl077RXl0qVLmTdvHvn5+eTm5hY7w52JO+64g169ejF48GCaN2/OunXr+Mc//pEWpkGDBlx33XW89tprqbc1AwYMYOLEiWWSBdEa+kzjTaNGjfjGN77BO++8U+RNNkmnTp1Yt25dxhmp0vTVbt26ceqpp7JlyxZGjx7N2WefzR/+8IdC4Zo2bVqqneo/+MEPaNy4MarKiBEjUvsGnD3j9ttvz+p95eSTT6Znz5588cUXRU56lMStt96aMmjLkwsuuIDu3bunllRUFD/5yU/4+OOPy7SB+9xzzy20tGnRokWp2fVscMYZZ/Dqq6/uURpDhgzhgw8+AOBXv/rVBFXN/NpvH2OvMH779u2r3/jGN4DIqNi1axeXX345devW5f77708L27Vr1zIPsrfddltWO9PPfvYznn/+eQ4//PBSGZFO5XLOOeeUek3wnnLAAQeUetZpd/npT3/K7373OwB+9KMfUadOHRYsWLBbG3Q6d+6cWnOciWuuuQZVpV69etSvXz9jmOSscciQIUNo1apV2mvlkGuvvZb333+fb37zm+zatYtx48YVckkVrqVOGgXf/e53qVGjBvPnz6d9+/bUq1ePJk2aFLnZMozfrl07Dj/88EI+W53SccIJJ9CwYUPatWuXmg139pyKMjSrKn379i3RrWVV4eSTT6Zfv36pmfWqOBkW4sZvFaNr16568cUXl1v62TZ+Hacyuemmm4qdrS1PbLbPWLJkCY899liR4Zs1a1am2ftOnTqxYMGCjNcuv/zyYmUZp59+OgMHDmTkyJGMGTOGFi1acOKJJ/LBBx/w5ZdflloXx3Gc0nD99dfTrFkzN36rEHuF8dumTRu96qqrKlsNx3FKyemnn86sWbP8VbfjOM5eQnUyfrP9hTdE5BQRmSUic0XklgzXRUT+Gl+fIiIDsq2D4ziVy+uvv+6Gr+M4jlMlyarxKyK5wP3AqcCBwIUiknRseirQLf67Eii8ZdVxHMdxHMdxyoFsz/weCsxV1fmqugN4Dkg6WT0L+KdGjAEai8j+WdbDcRzHcRzHcQqRbeO3LbAkOF4anytrGMdxHMdxHMfJOtk2fjP5D0ruqCtNGETkShEZLyLjs6KZ4ziO4ziOU+3JtvG7FGgfHLcDkp9PKU0YVPVhVR1UXXYeOo7jOI7jOOVPto3fcUA3EekkIrWAC4BXEmFeAb4Te304HFivqkV/i9FxHMdxHMdxskSNbCamqnkicj3wNpALPKaq00Tk6vj6Q8AbwGnAXGALcFk2dXAcx3Ecx3Gcosiq8Qugqm8QGbjhuYeC3wpct7vpH3zwwUyZMmX3FXScABFhb/jQy55y4IEHcuyxx/LAAw+Um4zmzZtz3HHHsWPHDnr16sXatWt55JFHio2z3377ccABBzBjxoys6XHZZdHz9OOPP54617dvX8444ww2b95M3bp1+c1vfoOIcPXVV/Pgg5G3xdtvv51FixbxxBNPZE0Xx3Gcn//85/zmN7+pbDWcgKwbv+VB48aN6dChQ+qmdswxx7Bq1Sqef/55AA455BDWr1/P7Nmzi02nUaNGrF+/vtD5U089FRHhjTfeyBCrbDRo0ICNGzfucTpO+dKpUyfOPfdc6tWrx/jx43n99dcrRY+6deuydevWckn75z//OTVr1tzt+KeccgpTp07llFNO4dFHHy0y3Mknn8xhhx0GRA8TRps2bYr85PDgwYPp2rUrnTt3BrL7zfs6derQsmVLgLRxA6Bhw4YA3HzzzdSuXZuaNWtyxRVXpMrpgAMOKJTWtm3bsqbb3kJZPzvtOE5hhgwZwkEHHbRH47BTPuwVnzceNGiQjh9f2OlDXl4eOTk55OTksGnTJv74xz9y4IEHMn36dHJycti1a1cq7HXXXUfz5s3Jy8vj2WefpUGDBnz++ed8//vfp2nTpgBs376d3/72twB85zvfoW7duvz9738vtZ7Dhg1j4MCBrFq1KjWbVB60bt2aFStWZCWt3r1788UXX1CrVi127NhRprjXXXcdubm5/PWvf82KLhVFz549Of/889POFWV8XXbZZezatYs1a9akGcjf/e53mThxIpMmTQLgm9/8Jv/6179S1wcMGMCWLVuYOXNmWnpDhw5l5MiRAHTs2JF27drx0UcfZSVfSW6//fZC5+644w6OPvpoPvzww1LH37p1K7///e/Trn3/+99n3rx5dOrUiebNmxebzo4dO/j0009p1KgR//3vfzPqNnfuXJo0acLf/va3jGlccMEFNGzYkFatWrFlyxb++Mc/AnDxxRdzwAEHsGzZMho2bMjSpUs56KCDgPTxoSwsWLCAt99+mwsvvJBGjRoVa5i3bt2a4cOH89prr7Fu3TrWrFlTJlkAxx57LKNGjeKCCy7gueeeKzbsmWeeSd++fbn77rvTxjfT5aqrrmLUqFGMHj26TDoccMABfOMb3yAnJ4fNmzfTsmVL7rzzzkLhunXrxpw5c0o1Xpx00kksWrSoxEkJiB4yLrroomIfspyIiy66iO3bt6OqvPTSS5Wtzl5Hbm4u+fn5ZYpzwgkn0LJlS5599tkyxbvttttSEwJ/+MMf2LJlS6EwTZs25aqrruK9995j7NixaddOPvlkDj/8cJ599tlS9aOycMEFF1CvXj0effRRfvjDH9KgQQNEpNp83nivmPktiho1CtSvX78+t912GwBjxoyhVatWPPXUUwwdOpQ+ffpQv379VJyLL74YgLPPPjstvdq1a6d+d+rUqcz6DBgQfam5ZcuW3HLLLdSuXbvEGa0f//jH/OEPfyiTnEGDBvHaa6+VWb8kt956KzNmzOCLL76gW7duHHLIIUB0I3rooYe48MILi+3sZvSceOKJvPvuu5x33nm88MILu63PjTfeyJYtW1i0aBHvvvtumQeoTNSoUYO8vLy0c+HspNGpUycWLFgARAZqv379qFu3bup6x44d04zfdu3a0a5dO4466ihq1qxJgwYNChm/bdu2RVVTRsSAAQM49NBDOfjggxk/fjwDBw6kbt26pTJ+a9WqxUEHHUSPHj2KNZBOO+00GjVqVGgG0zCjs0+fPqVeAlG3bl1uv/32VFu+8MILadq0aeqhsTS6H3PMMUB046lVq1ahMF27dgXg6quvpmbNmjz77LO0bNmSHTt20KZNG3r06JEKa30ZSM0ct28fOZBp1KhR6lo4PpSFTp06cfXVVxc637NnT+rUqcPkyZNT584//3waNGjAhRdeyIYNG7jvvvuoXbs227dvL5Wsli1bcswxx6TKp0WLFrRp04Z+/frx5JNPps3AnnfeefTq1QuAX/7yl+Tn5/PrX/86ldbRRx8NRMb0Mcccw+bNm6lVqxb33HNPSv9zzjmHzz77jN69e1OrVi3mz5/P0qVLGTp0KLm5uUBB+WZ6VTt8+HAA5syZw4gRI4p8mwZwxBFHcMQRRwCwbNkyGjduzOOPP865555baFLhuuuuo379+vTv3z/1QJlNBg4cyOmnn55m0Hfu3JkTTzyRhx9+uMh4w4YNy8pYuyecdtppiEhq/LG+AqQZv0cddVSpxpLTTz+dsWPHsnr16tS5Dh06sHjx4jLrlnzor2wGDhzIhAkTCp0/99xzU2X105/+NK1df/e73+WJJ55I3W/CyYFjjjmG5cuXc8QRR5CTk0OLFi1YvXo1J598Mm+//Xaxupx00klp95of/ehHGR8or7nmGmrUqMGpp55ayPg9/PDDgait7o7xe91117F58+ZCS7luuOEGmjRpAmSeJKkO7NXGbxJraIMHDwaiim/WrFlGY6e0/OhHP+Lee+8tMVzTpk3T5JghHRoNEDU6u+m0atWK/fbbjz59+jB16tRi0+/cuTM1atTgjDPOYO7cubuZm3REJGXgJQ0m6xCXXHIJr776Kuecc07arMwll1yS+n3kkUdy5JFHlkqmPXUfcsghjBs3LnX+29/+No0aNaJRo0bsv//+HH744Wnl1rt3b2rXrp0a2E455RTeeuutjDLCV+0nnXQSb775Zol6nX322dx3331AQftJ0rFjRxYuXJhmvIUG4HXXXceCBQvo2bMnDRo0ANIN7aOOOorc3Fzq1auXMnYgahPJ2fNWrVqxcuXK1PHPfvaz1G8z1EPj5Be/+AWbN29OvdYviRYtWnDbbbelBuPk8gt7QAzJxiDZp0+fYq+3atUKiMqyOC655JJUGZc3gwcP5tNPP+X8888nLy+PAQMGpIztkIYNG6bKaN26daxdu5bWrVvz3nvv8fnnnxcKn6k8r7322kLXX3zxRaZPn15oHMvNzeXWW2/l7rvvBiLj1hCRlBHbq1cvatWqlXrYNyMZovooqk5q1qzJpZdemrpxtmnTJnWta9euHHfccbRq1arE2eowrtXrmWeeySuvRI6AzjvvvJSuZ5xxBqeffnqaUZ9k+PDhzJ8/nzFjxpQoFyJjb9CgQYXknn/++RkfxEK6du3KCSecwHvvvVcphl6/fv1SkxK9e/cucn/C5ZdfTvv27Us0fq1NtWvXLu0B5LLLLuPBBx9k1apVZdIvbHNl4fzzz08tWywNJ510EvXr12fu3LlF3isbNmzIkCFDMhq/vXv35qWXXqJ3797UrFkzbXxt164dv/jFLxg9ejSjRo1KLZmC6EEy5Morr2TevHn06NGDQw89lLvuuqtInZP3Edtj8NBDqS1QNG3aNO0h3d7cdOnShW7duhVdIEXQpEkTvvrqKy6//HKWLVtG8+bNadasWer6WWedRa9evdIm+qor+5Txm6Sk17GZuPTSS9MaS7169VK/b7rpppSBBKSWVhx66KEMHTq0yDQvvPBCJkyYwAknnJB62gpvON/4xjc455xzMj4VGu3bt091xOJmtGydpxmOvXv3Ztu2bZx55pn86U9/SgsrInTp0oVzzz03NaOUpGPHjnz/+99PO9eyZUs6duyYMXzjxo35+uuvU8fXXHMNLVu2ZPv27bz33nscc8wxTJgwgSOPPJJatWrx8ccfA9ClS5dCaf3whz+kTp06aeul+vbty4gRIzj44INp0KABL774YlqcPn360L59e9q3b8+SJUto06ZN2qxu48aNOemkkwrJMqPRbjSZuOSSS5gzZ05qtjFJ8+bNi21zVvelOX/11Vezbds2fve733HooYemXbvooovIz8+nZs2a3HjjjeTm5pKbm1tqw9cIjamf/OQnrFq1iiZNmuwV69OKan/lwdChQ1P9u0aNGhkN3yThzPjZZ5/N8ccfz9y5c+nduzcLFiwocmY+E6eeeir16tXLeDOsUaMGJ598MmvXri1yecd5551XallJDjjgAK666ir+/ve/p70JERGGDBmSmi1s3749F110UWrZWEm0aNECiIyacOwRkdQMNJBxJrht27Z07NiRLVu2lGrzc+vWrdPS69evH6qasby6du2aNrmQm5vLUUcdRa9evWjWrFnK+LXxZXcpbuP21VdfzciRI1FVzjrrrNT5OnXqFAp7xBFH8Mknn6QZbIYtAcxE69atuf3229m4cWNqxvOaa65h7ty5PPPMM6XOR25ubpne+F1yySXk5+ez3377FRvu3HPPTRn7O3bsSBlr7dq1K9L4PfroozOme+CBBwLRm06r86uvvrrQg8QxxxxD9+7dad26NUuXLs04aVajRo3UW6hM7efcc8+lW7du5OTkZIzfqlUrbr/9dj744ANmzpzJBRdckHa9devWhc6FdO7cmcWLF3PGGWfQqVOnQvf066+/PqWXjVMiwm233caKFSvYf//9i0y7urFPG7+7Q6ab0vDhw1m/fn2acXH77bezfPlyHn74YXr16pU2YCfp3r073bt3L1ZupqfCkNDgPfDAA1m2bBmffvppWpjrr78+Zbhceuml5OTklHijFhF69+5dbJgk11xzTZHXrr32WvLy8lJrRG1Qrl27NqeddhoQbQKw/2b8ZiLTzF779u356U9/CmSuK3vNevnll7Nt2zbq1KnDxRdfnHqw+MEPflCkvNLMbO7O07gtxykrderU4cc//nGhm54Zu5D+mn93OProo1ODeaYbqJMdGjZsmFoWFS7hKA3169dP9Z1M2KvR8qJVq1aceuqpqbXUIdb+evToQe3atQu9sSiKdu3aceONNxb5wGbpnHrqqSnjt1WrVpxwwgkpA+ecc86hUaNGfPjhh1x77bWlXsYjImmGib1Fsn0h999/P2vWrKFXr16pGelwQgTYYw8xZ511Vkbjt0ePHrRq1Srjm5dMnHTSSWkP82eeeSb169dPjVOzZ89OLV075ZRTCsUv7u1JzZo12blzZ5HXrR569erFWWedlVrPXxTHHHNM2kPrVVddRYsWLTLO8tuDo4ikzVI2bdqU22+/nffff58PP/yQ3NxcTj/9dFavXs3AgQMREa6//nr+/ve/c8UVV1C3bt20JY8hmYxTMw4zlVUmfvGLX6Tp36pVq1LNqg4ZMiR1HywN/fv3Z/ny5QwdOjTNwD/yyCP5+OOP+eEPf4iIFPkALCJu+CZw47cUhAbP8ccfz//+9z8g6ijZXC/TqlUrfvKTn/D73/+eb3/72zRu3Di1ASi8weXk5DB06NBCxm84QGcyDG224fLLL2fOnDll3gjUsGFDNmzYUGyYmjVrlnrmsFatWnTo0IG+ffuWSY/iCNeDmtG4J8teskFp5IfLY775zW+mzpc0Q7KnHH/88eWavrP3IyKF3j4YjRo14ic/+Umqr/Xs2ZOVK1emLZEoiuIe3C699FI2bNhAzZo1+cEPfsD06dNTD7Yhxx13HIMGDSr2rUfbtm2L1eOwww5LeSuB4pfcXHvttaxevZr69eunudIrKzb22qyc9f1wpnd36N+/f9pxOCkT5rEobNnRN7/5TdasWcOoUaMKhbHlBeGkSb9+/ejSpQvbt2+nfv365OXlUb9+ff75z3+yYMECLrvsMtq1a5eWjs3I33DDDbz66qupt3PHHXdcie3n+OOPL3LsatasGT//+c9LzGs2yM3N5Uc/+hEbN25k27ZtqTca2SZcthRywgkncPzxx5f5Xu648Vtmjj766LQ1c9nGNhclybTUoV27dixdurTUaZ955pmccMIJNGzYsFSvbpNcffXVGXerZuKmm24q1aaf0A1VWdlvv/048sgjqV27Nu+//z7t27dPM35DrrnmmoyvDasimWbYHKeqEi6HsJlEM6J2lzp16qT6a+PGjTMavhAZj2b4nn322fznP//JGCZbtGjRImXglLRZObnfw/jud78LRJvpbCbU3g5l+0G9Q4cOtG/fntNPP71U4Rs0aJC6/8yaNSvt2nHHHcfBBx9M48aNi4ybnEn+1re+xaJFi+jQoUORMps0acKRRx6ZMn7LMiNaFahXr17a8siKJPkWwyk9WXtcEJGmIvKOiMyJ/2dc4CgiC0VkqohMFpHC/sucNH7wgx9w8803Z7z23e9+N/U0f+GFF5aY1u6sCw2pW7duodd/RdGwYcNyewo2RIQTTzwxtaSkuM0XLVu23KO8O45TMvbKtzI21IRr8W2murTj1e4QvpUpygBJznaKSOrcwIEDU/qVlxFTs2ZNLr/88t16GOnRowc33HBD6njIkCFFGr5FUbdu3VJtiuvSpQvnnHNO2oZPxylPsjnzewvwnqr+VkRuiY9/WkTY41S17A4xqyElDTYnnXQSBx98cKleM+6rtGrVihtuuKHMA7PjONnloIMOYtOmTQwcOLDCZdvbsZ49ezJ06FAaNmxYqlf92eC8885j1apVfPLJJ2zfvj01Hnfp0iXt7ZztV9hbaNKkCUcccQTz588vd1kHH3xwuctwHCNrH7kQkVnAsaq6XET2B0apaqGdHSKyEBhUFuO3qI9cOM6+xKRJk6hdu3Zqd7LjOGVj+fLlNG/evMI8ltiyhnCp2s6dO8nJySE3NxdVZcOGDSxfvpwdO3a4gedUafwjF7tHK1VdDhAbwEVtHVdgpIgo8HdVzehhXESuBK4Eil0v5Dj7CsnNKo7jlI2K3tF+zDHHFPr4QGh4i0jKf7njOFWHMs38isi7QOsMl24FnlTVxkHYr1S10LpfEWmjqsti4/gd4Puq+kFxcn3m13Ecx3Ecp/yoTjO/Fb7sIRHnV8AmVS32E2oishGYVVwYp0JoDvha7aqB10XVwOuhauD1UHXwuqga7E49HKCq5btTvYqQzWUPrwCXAL+N/xfyeC0i9YAcVd0Y/x4KFP1ZswJmVZenkaqMiIz3eqgaeF1UDbweqgZeD1UHr4uqgddD8WTTM/JvgZNEZA5wUnyMiLQRkTfiMK2Aj0Tkc2As8LqqvpVFHRzHcRzHcRynSLI286uqa4ETMpxfBpwW/54PZO9zXo7jOI7jOI5TBvaWb+Jl9AjhVDheD1UHr4uqgddD1cDroergdVE18HoohqxteHMcx3Ecx3Gcqs7eMvPrOI7jOI7jOHuMG7+O4ziO4zhOtaFKG78icoqIzBKRuSJyS2XrU10RkcdEZJWIfFHZulRnRKS9iPxPRGaIyDQR+UFl61RdEZE6IjJWRD6P6+KOytapOiMiuSIySUReq2xdqisislBEporIZBHxr1JVIiLSWET+JSIz4/vF4MrWqapRZdf8ikguMJvIbdpSYBxwoapOr1TFqiEiMgTYBPxTVXtXtj7VlfjjMfur6kQRaQBMAM72PlHxiIgA9VR1k4jUBD4CfqCqYypZtWqJiPwQGAQ0VNVhla1PdUREFgKDVNU/cFHJiMiTwIeq+g8RqQXsp6pfV7JaVYqqPPN7KDBXVeer6g7gOeCsStapWhJ/fnpdZetR3VHV5ao6Mf69EZgBtK1craonGrEpPqwZ/1XNmYR9HBFpB5wO/KOydXGcykZEGgJDgEcBVHWHG76FqcrGb1tgSXC8FL/ROw4AItIR6A98VsmqVFviV+2TgVXAO6rqdVE5/Bn4CbCrkvWo7igwUkQmiMiVla1MNaYzsBp4PF4K9I/4i7pOQFU2fiXDOZ9Zcao9IlIfeAm4UVU3VLY+1RVVzVfVfkA74FAR8SVBFYyIDANWqeqEytbF4UhVHQCcClwXL5dzKp4awADgQVXtD2wGfM9Ugqps/C4F2gfH7YBllaSL41QJ4vWlLwHPqOq/K1sfB+JXiqOAUypXk2rJkcCZ8XrT54DjReTpylWpehJ/zRVVXQW8TLR00al4lgJLgzdR/yIyhp2Aqmz8jgO6iUineMH2BcArlayT41Qa8SarR4EZqvqnytanOiMiLUSkcfy7LnAiMLNSlaqGqOrPVLWdqnYkuke8r6rfrmS1qh0iUi/ehEv8in0o4N6BKgFVXQEsEZEe8akTAN8UnaBGZStQFKqaJyLXA28DucBjqjqtktWqlojIs8CxQHMRWQrcrqqPVq5W1ZIjgYuBqfFaU4Cfq+obladStWV/4MnYK00O8IKqupstp7rSCng5ej6nBjBCVd+qXJWqNd8HnoknDucDl1WyPlWOKuvqzHEcx3Ecx3GyTVVe9uA4juM4juM4WcWNX8dxHMdxHKfa4Mav4ziO4ziOU21w49dxHMdxHMepNrjx6ziO4ziO41Qb3Ph1HGevRUSaicjk+G+FiHwZ/94kIg+Uk8wbReQ75ZH27iAiC0WkeTHXnxORbhWpk+M4TlXGXZ05jrNPICK/Ajap6r3lKKMGMBEYoKp55SWnLMRfNxukqmuKuH4M8G1V/V6FKuY4jlNF8Zlfx3H2OUTkWBF5Lf79KxF5UkRGxrOk3xCR34vIVBF5K/5kNCIyUERGi8gEEXlbRPbPkPTxwEQzfEXkBhGZLiJTROS5+Fw9EXlMRMaJyCQROSs+nysi98Zyp4jI9+PzJ8ThpsbxasfnF4rIHSIyMb7WMz7fLM7LJBH5OyCB3NdF5HMR+UJEzo91/hA4MTbcHcdxqj1u/DqOUx3oApwOnAU8DfxPVfsAW4HTYwP4/4BvqupA4DHg7gzpHAlMCI5vAfqr6sHA1fG5W4k+s3sIcBzwh/iTr1cCnYLwz4hIHeAJ4PxYnxrANUH6a1R1APAg8KP43O3AR6ran+iT7x3i86cAy1S1r6r2Bt4CUNVdwFygb1kKzHEcZ1/FjV/HcaoDb6rqTmAq0efS7dOrU4GOQA+gN/BO/OnoXwDtMqSzP7A6OJ5CZMR+G7BlEEOBW+J0RgF1iAzUE4GHbNZYVdfFcheo6uw47pPAkCD9f8f/J8R6El9/Ok7jdeCrIC8nisjvRORoVV0fpLMKaJOxZBzHcaoZ/hrMcZzqwHaIZkFFZKcWbHbYRTQOCjBNVQeXkM5WImPWOJ3IGD0T+KWIHBSnda6qzgojiogAyU0WUhq9gXzSx+tCmzVUdbaIDAROA+4RkZGqemd8uU6su+M4TrXHZ34dx3FgFtBCRAYDiEjN2JBNMgPoGofJAdqr6v+AnwCNgfrA28D3Y2MXEekfxx0JXG1rb0WkKTAT6CgiXeMwFwOjS9D1A+CiOI1TgSbx7zbAFlV9GrgXGBDE6Q5MK7kYHMdx9n3c+HUcp9qjqjuAbwK/E5HPgcnAERmCvknBsoRc4GkRmQpMAu5T1a+Bu4CawBQR+SI+BvgHsDg+/zkwXFW3AZcBL8bp7AIeKkHdO4AhIjKRaInF4vh8H2BsvNziVuDXACLSCtiqqstLVxqO4zj7Nu7qzHEcpwyIyMvAT1R1TmXrUhpE5CZgg6o+Wtm6OI7jVAV85tdxHKds3EK08W1v4WuijXSO4zgOPvPrOI7jOI7jVCN85tdxHMdxHMepNrjx6ziO4ziO41Qb3Ph1HMdxHMdxqg1u/DqO4ziO4zjVBjd+HcdxHMdxnGqDG7+O4ziO4zhOtcGNX8dxHMdxHKfa4Mav4ziO4ziOU21w49dxHMdxHMepNrjx61RJRORXIvJ0ZeuxNyEiPxeRfxRz/VIR+agM6fUQkUkislFEbhCRh0Tkl9nRNk3OQhE5cTfivSkil2Rbn1LK3iQinXcz7igRuSKLuhwtIrOylV6G9K8SkT8Hx+eIyJK4DPqXl1yn6iEiB4vIJ5Wth+PsKW78OpWGiAwXkfHxTXR5bMwcVdl67a2o6m9U9QoAEekoIioiNfYgyZ8Ao1S1gar+VVWvVtW7sqPtnqOqp6rqk5Uku76qzq8M2UlU9UNV7VEeaYtILeAXwB+C0/cC18dlMKk85FYku/vwlUX5x8R99dfBueNEZKqIfC0ia0XkZRFpW0wad8Xh80TkVyXIyzixEOvQtbi4qjoF+FpEzig5Z45TdXHj16kUROSHwJ+B3wCtgA7AA8BZu5HWnhh4e0xlyy9HDgCmVbYSTqVyFjBTVb8MzhXZLqpaX6hq+iQRkZrAX4DPEpemAyeramOgDTAHeLCYpOYSPay+Xg5qJnkGuKoC5DhOueHGr1PhiEgj4E7gOlX9t6puVtWdqvqqqv44CFpLRP4Zv3afJiKDgjQWishPRWQKsFlEaojImXG4r+NXy70S4X8sIlNEZLOIPCoireLZ5o0i8q6INAnCf0dEFsWzLr8MZ4fimZN/icjTIrIBuFREDhWRT2PZy0Xkb/GsmaWnInKtiMyJ5d0lIl3iOBtE5IUwfKK8FonIwPj3t+O0DoyPrxCR/wR62YzOB/H/r+OZ9cFBeveKyFciskBETi1C5vvAccDf4vjdReQJm52Ky36MGRcick1c9nVEJEdEbhGReXH5vSAiTYO0Lw7K9tZM8oOwdeJyXhuX7TgRaRVfSy0fEJHPYz3tT0Xk2Pja4SLySRz/czufQdZlIvJqcDxXRF4IjpeISL/4d2qWLC6X+0Xk9bhuPxORLkG8k0RkpoisF5G/ARJcyxGRX8TlsUqi9t4ovvakiNwc/25rbSg+7ioi6yTiWBFZGqS5UER+JFFbXy8iz4tIneD6T+I2uixuP8XN+J0KjI7j1RaRTUAu8LmIzAvkJfui1f9GEZkuIucE8i8VkY9F5L64TuaLyBHx+SVxOVwShK8dt9nFIrJSouU3dYuowzDtdcCvJOpn78dtaI2IPCMijePwTxE9eL8at5ufxOdL1WaywM3ASGBmeFJVV6rqsuBUPlDkrKyqPqmqbwIbs6FUnG/rS5vjNtIxvjwKOEFEamdDluNUBm78OpXBYKAO8HIJ4c4EngMaA68Af0tcvxA4Pb7eGXgWuBFoAbxBdEMLDcpzgZOA7sAZwJvAz4HmRH3hBgCJDMsHgIuA/YFGQPKV41nAv2LZzxDdnG6K0xoMnABcm4hzCjAQOJxolubhWEZ7oHecn0yMBo6Nfw8B5gPHBMejM8QZEv9vHL+e/jQ+PgyYFev5e+BREZFkZFU9HviQgtfbsxNB/gDsAH4hIt2IZvC/rarbiMrx7FjHNsBXwP2QKtsHgYvja82AdkXkG+ASovJvH4e9GtiaQd++sZ71gR/GeZwo0avi14FfA02BHwEviUiLDLJGA0fHBun+QE3gyFjvzkB9YEoRel4I3AE0IZqFuzuO1xx4iWjpQHNgnqUZc2n8dxxRG65PQTsP6/0YCtf7h6qqRehzHlF76wQcHMtARE6Jy+dEImPqmCLiG32IyhJV3R6XL0BfVe0ShEv1RVXNi/N5NFHd3QE8HZepcRhRWTYDRhD180Ninb5N9NBlsn5H1Gf7xdfbArcVo/NhRGXVkqgeBLiHqL31ImpLv4rzdDGwGDgjbj+/L2ObQURei43FTH+vFaWkiBwAXE40EZDpegcR+Zqovf+IqL9WCKraOOhPfyEaC76Mr30J7ATKZamN41QEbvw6lUEzYE18kyyOj1T1DVXNB54C+iau/1VVl6jqVuB84HVVfUdVdxKtS6wLHBGE/794RuVLosH8M1WdpKrbiQxx27zzTeBVVf1IVXcQ3WiTRsanqvofVd2lqltVdYKqjlHVPFVdCPydwobF71R1g6pOA74ARqrqfFVdT2SIF7V5aHSQ1tFEN3I7PobMxm9RLFLVR+IyfZLIuG9VhvgAqOou4DtEhu4rwO+D9Z9XAbeq6tK4bH8FfFOiWeJvAq+p6gfxtV8Cu4oRtZOovXRV1fy4nDcUFViiNeO/Bs6Mw30beCNuR7tU9R1gPHBahjzNJ5o560dUrm8DX4pIz/j4wzjfmfi3qo6N2/QzcRrEcqar6r/idvlnYEUQ7yLgT3E72AT8DLggLquUMU5k7P6eAsO5pHr/q6ouU9V1wKuBPucBj6vqNFXdQmSYFkdjSjebGPZFVPXFWP4uVX2e6LX9oUH4Bar6eNwOnycySO+MDeyRRA9WXeMHs+8BN6nqOlXdSPSgdUExuixT1f+L++JWVZ0bjwvbVXU18CeKN/pL3WbivA6LjcVMf8OKKzPgl3G9Z0p3cbzsoTnRw9PMTOF2k/OShnqmQCJyPjAcODduv8ZGorbhOHslbvw6lcFaoLmUvB4vNBK2AHUScZYEv9sAi+wgNlKWkD5juzL4vTXDsc00tQnTjo2EtQndQtlItCzgNRFZIdFSiN8Q3bRCSis/iRlBrYleOT8PHBm/hmwETC4iXiZSZRrni2LkFkts5P8P6Eg8sxtzAPBycFOdQTQz3orCZbuZoGwlfelCB6KHnreB5+LX9L+XaJ1kIUSkPfACcEkwU30A8K3ETf4oIqM/EzbbajPqo4gMpZKMzWRbLaotKcW02/h3DaCVqs4DNhEZrkcDrwHLRKRHtvRJ/M7EV0CDEsIUSkeiZUOTgzLvTXp/SLZ9VDVTf2gB7AdMCNJ6Kz5fWl1aishzIvJl3DefpnDfDClrmykzEm0YaxA/GBRL/ADzJPDfUoyZpeWFpKGeQcf+RG8hzokfGkIaAF9nSRfHqXDc+HUqg0+BbUSvxveEcDZ2GdFNC4B4xqg98au6MrKc4FV8vL6wWTGyIXqVPxPopqoNiZZTFFpOsDuo6lwiA+YG4IN49msFcCXR7Him2ciiXodnDRE5jWiJx3ukewNYApyauLnWiWfclxPVi6WxH0HZ2qvW+G+xRmvB71DVA4lm8YcRzTgndakL/Af4s0ZrH0NdnkroUk9Vf1tEtsz4PTr+bbPuZZ1hN5L5lfCYRLslWn+aR4FxOJpotrxWXH6jifLfhLI99IT6hMtM2hcVMGYK0ZKDkki1t/h1/iPA9UCz2LD6gt3rD2uIDOGDgvprpAXLL4rVJeae+NzBcd/8dkKXZPgytRmJ9g1sKuLvzUxxiJZFDYofllcQvbm6UUT+W0T4GkTLOBoWmessEi/xeJlo2dOkxLU2QC3i5TCOszfixq9T4cSv+W8D7heRs0VkPxGpKSKnisjurmt7AThdRE6IZwZvBrYDu+OT8l/AGRJtwqlF9Gq4pBt3A2ADsCl+TX7NbsgtjtFExoQZYKMSx0lWEy0n2C1ftCURr2V9FLiCaF3uGbExDPAQcHdsBCEiLUTEvHj8CxgmIkfFZXsnxYxDErl86iMiuUTlu5NoFjnJY0ReCZLt5+lYt5NFJFeiDXTHikhR64xHE62/rauqS4mWx5xCZKDvjluv14GDROQb8azdDUDr4PqzwE0i0ile4/ob4HktWBJk9W4bGEcB3yd66MlUDiXxAnCZiPSKHzyKWzsL0dr5ktYFJ6lHZFCuhmgjIdHMb5mJH+weAe4TkZZxem1F5OQyJNOAaAb963g9748T11eS3k/K1GY0crlXv4i/jBtKiZb72DrmfkRLhx4BLovz+A2J/GznxIbon4BJ8SxwIeLxsw5RX6oR65xbUsEUkVYNonXqzxQxM30s8H68bMlx9krc+HUqBVX9E9HGm18Q3SSXEN3k/7Ob6c0imtH5P6LZojOINrHs2I20phEZGM8RzZRtBFYRGdNF8SOitXEbiW5iJb7OLCOjiW7iHxRxnEa8pOFu4OP41e3hWdbnYeC/8brItcB3gX+ISDOiDTKvACNFZCMwhmgTkpXtdUSbnJYTvVZfmiF9ozWRwbyBaPnEaCLjJMkFwDmJWbejVXUJ0ebEn1PQzn5MEWNfvFxiE5HRi0brhucDH++Osamqa4BvAb8lWt7RDfg4CPIY0dKOD4AFRG9Evh9cT9bzR0TLADLWeyn0eZNoren/iDbm2UbIotr2q0DPeLavtDKmA3+M015JtGnu42IjFc9PiXQdEy9beJeybba6AxgArCd6GPl34vo9RBs3vxaRH5W1zewOqrpRVVfYH9Hs9ubAuG1LtLxjIzCV6EE29JjxkIg8FCT5SJzGhcCt8e+Ld1O9dkRvPm7MsAwJonXqDxUd3XGqPqJFbhZ2HAcgnpH7mmhJw4JKVsdxsoZE7gC/AGprERtQReRK4EBVvbEidXOqHiLSB3hYVQeXGNhxqjBu/DpOBuINKe8RLXf4I9HM5QD1DuPs5Ujkc/d1ouUJTwK7VPXsSlXKcRynAvFlD46TmbOINiMtI3pVfYEbvs4+wlVEr/PnEa2fzvb6dMdxnCqNz/w6juM4juM41Qaf+XUcx3Ecx3GqDW78Oo7jOI7jONWGbH0tplxp1qyZtm/fnnCJxq5dkV//4pZtRP7kIScnJ+1/GC8ZPzy2+PbfrtlxpvjFXTP5YZiiwpaUdnHnAPLzC3tlys2N3D7m5RVs6rZyNN1MpoUNZSTLIZOOYRkXpUtpyihTXSV1DtO1PNWoUbhJZwqflJEME16z3zt3Rl/33L498gpVq1atVJgdO3ak6VpcGYXxkpiMTOkk69r0CHW0MHXq1CmUj2T7zdSfLP9WjqHMzZs3A7Bt27a0MGF+TF7NmtFH2DK1tWQbC+Nb/pM6h+GS5ZepD2Sqx6TcTP3Rzll8q9dQLwtveQzrwcIlxx4rs/BavXr1AKhdu3bqmulmYTKNc5amlb8dh2Vt+ifTCc9ZfZrOmeox0zhiets1083KI8yHyc1UR6Z/praeHI/KMs4XR5iO6W+6Fjf2J8+H4ZNhMvX9TOWQHOsy1VVSblgfSbnJOg/zlil+UfeXUH54HwjDFHcvDeUXVTalud9lIlO9lKb8d0dGJqxswjIqqt1lqsdkeYbyrP8m+04mnZL37TCM5TFTHSX7c7Ksli1bxldffVWqj9GISLbWzL6tqqdkKa1Ss1cYv+3bt+edd95JG9xt4LZzmQZrMwD2228/AOrWrZu6ZvFs4LfjUIbFTw4cYQNO3mTDG4DdOA3TI5NhaXLDxhjqAgU3i7BT2LnkjWT9+vWpMKab3bS+/vrr1LWNGzcCBWVj8Rs0KPiiadLICQ0B+20yLJ1Qxw0bNqSFCQ1Ui2/hLX5YV8kBc8uWLYXysWbNGgCaN4++WhoOFhb+q6++StPD6hcKjBNL0+oq/L1s2TIAFiyIvJ21a1fg837p0shVrdVZ2A4tbdPpgAMOSMtP+Ntk2HFoWFnbsnRmz56dumZ1a2EOPPBAIL0cLS3Ld9g+k2XasmVLIL2uxo0bB8C0adMAaNWqFRD1T8MMuf33j74Eu25dgU9+K4etW7cC0KxZ9GG3Nm0KXMiuXh19RdXaQ9jWrLw7dOhASGg8Wjxr//XrF3wIzPLbsGH0kSxr42EerbyszS5ZEn0pd/ny5akwVsYtWkRf2F28eHHqmoWzMFbWs2bNKhT/sMMOA6BLly6pa6av6WT1ErYna49WR6bzqlWrUmEs/ybLyhwKymvMmDEArFwZfUwurAfTw8ohlH/44YenXbM237p160Lxrcwz3aRNf9MtHDOs3Kz+kw9FUDCOWlmF7cDOJQ3CsM1bW2/UqFFaPsK0inrQCMOHYz6k91nLk+UxDJsc60xG+KCUzI+VeZh/Gys2bdpUSIblzcKG8ZOGkIUJ24rVo+U70wOo5TFpvIXhrPytbJL3xjCdTAZ+8kEhHLuTD0rFPQQWV7bJh+NMDzqmv/VLKOiPSfsgDGM6JesjlG/3MBuXwviWX9PD8hHep5IP42EZJcfFTOWRn5/P+eefT1nINNlUVvLy8or71Hi5sVcYv47jOI7jOE7VQEQyzmSXleQkX0VRovErIjlAX6AN0VdjpqnqyuJjOY7jOI7jOPsi2TJ+K4sijV8R6UL0WckTgTlEfiHrAN1FZAvwd+DJ+NvrjuM4juM4TjUhG8seKoviNP818CBwVdK5v4i0IvqG+MVEXwhyHMdxHMdxqgH77Myvql5YzLWVwJ+LS1hEHgOGAatUtXd8rinwPNARWAicp6pflVVpx3Ecx3Ecp3LY243fEr/wJiLzgD+o6kPBuddUdVgJ8YYAm4B/Bsbv74F1qvpbEbkFaKKqPy1JyQEDBuhHH32Utjvedv/aDn6rhEw7fm1BdXgtuUMz3FlpJHfx2nFxLrLCnar223bzWvzwVUHSZUm4+DtZN5ncDhXlNivTrmSLH+7mtV23plOmnaK2M9TKL9ypa7tFLXzS+wQU7GINd6Ym0zbdMrnIMrnJHbdh+Zj84nQ0inPHZvHDsrY6Ms8Flp9wd7ztmDdCbxnJnea24zfceZ30FtG0aVMgvcysrWfy2pFM2zwRhDvordxMbiZXbcn6zLQZwfqKlVXoUcLiZdrVbX022S/CPFrbNC8kmdq35b9x48ZAZhdVFi/TrnLT27w+ZPLeYuW2aNGiQjJMvuV1xYoVqWuhd4kwH6GM5HjUpEmT1DXLk5Wx/TddQ7mWV+troYcX+53Jw4yViXkHsDoP27zVjZVjmP9wHIWCeg3HjOTu9HAstfybZxY7DsdgC58cDzJ580l68YCCPp5052b1Ef62vha2Y8POFeeGLJmvkKSHmbCtW9kmPVOEbTbpBs3Sy6Sv9Z0wfrKPhPpb2di55P0u1Dfpjag4N4nWP8I0TX8bQ8N+YnqYruF4nSwby2NYjpa2jWvmRSSZFhSUeSajLelRJMyHXctkJ9iYm3RzaPkK5Vp+wrJOeoxKupuEwve8THVl5yzPYRhLOznOJ9vsqaeeyueff14qV2c1a9ZU89izJ6xcuXKCqg7a44TKSGkWbOwEjhORw4iWQOwA2pYUSVU/EJGOidNnAcfGv58ERhGtK3Ycx3Ecx3H2AkRkn13za2xR1fNF5CfAhyJyHrC7zo1bqepyAFVdLiItS4rgOI7jOI7jVB329mUPpTF+BUBVfy8iE4C3gablqhUgIlcCV0K6E33HcRzHcRynctnXjd/b7IeqviciJwOX7Ka8lSKyfzzruz+wqqiAqvow8DBEa353U57jOI7jOI6TRfbZmV8RGRD//DL4bby2m/JeITKcfxv//+9upuM4juM4juNUAvvymt8/Br8HAuOJl0AQrfk9vriEReRZos1tzUVkKXA7kdH7goh8F1gMfKs0Subn5/PVV1+l7Y63XZdJbw/hLnvbYZnpO9i2I9R2qq5aFU1Ch5Vpu5qTO84zfZfbdk0uXrw4dc12W9puYtMt3ClraSd3fIaY/uHuUcPSNpLfOQ/P2U7PTLvbbeez7aYNd3FaWVteraygYMe47Ty2HbthOc6bNy8tTLgr28KZt4N27doBsHbt2lQY2w1v+ic9MwC0bt06La+2qziMZ7vDLf/hrmjTw66Fu4Rt965dszyGu4OTO5XDtG03sJVp0jNDGN/ao7WLcHe21bXtQg536ibbqF0L68HqtlWrVoWuWd1YfVqewzxaPVjbyuRJwfQwTxSZ2prFt2smK5P+YTu239aPM9W1pWX5Cdtxcue85T/0fmI6WvsLd44btvPa0uvRo0eh+Nb3re+EdW35sDKyMocCLw3WH82TRNeuXVNhLLy1ZzsOvX8kPSmEXhIs/1aflp9wJ7uNVVbW4c5xKy/bsZ/0IhKey9QOrB1bOVgbC8c3k2vXLGwYJrkDP2xHVjbmhSWTRwOr20weRZI6WV2HZWR1ZedsXAxlJD0hWD+HgjKye5iVXybPHNbnO3funLpmZWK6JT0KhDpm8vqRbKN2HN5DbYy18rD/mfq16RGOy3bOysHaXFhGVleWx9DLgsWzvFo/CuvMrpncMH7SW4SFDcvI8mI62XE49lp4G1fCcamocSy8zyW9OoX3+aQXJ0s7TC+pYybvI0n5YV9JerUy3ULZ27dvL9abSSYqauZXRE4B/gLkAv9Q1d8mrp8F3AXsAvKAG1X1o+LSLM7P73FBwpNUtVhjN0P8ovwEn1CWdBzHcRzHcZyqQ0UtexCRXOB+4CRgKTBORF5R1elBsPeAV1RVReRg4AWgZ3HplnbO2tfcOo7jOI7jOECFzfweCsxV1fkAIvIckdvclPGrqpuC8PUohc269y7YcBzHcRzHcSqcLK75bS4i44Pjh2OHB0ZbYElwvBQ4LIM+5wD3AC2B00sSWtyGt/+jwHpuJyJ/Da+r6g0lJe44juM4juPsW2Rx2cOaEr7wlumLc4VmdlX1ZeDl+OvCdwEnFie0OLM9tMQnFJeI4ziO4ziOU32ooGUPS4HwYw/tgGVFBY6/LtxFRJqr6pqiwhW34e3J3VLTcRzHcRzH2WepQD+/44BuItIJ+BK4ABie0KUrMC/e8DYAqAWsLZRSQHHLHh4G/qqqX2S4Vg84H9iuqs+UNSdlRUSoWbNmmgsTc91iLjvMNVDoXsTckphLmdAViLn0sMozl0jmdgUK3ICsWbMmTaalG6ZpLq7M/Q8UuDNJutEK18mY6xJzIRO6XTK97Zy5vQndkZh7l+T/0CWP6WtyM8U3/a38QlcyVg6WTngtlAMF5Re6u+nQoUNavLCM7Xfohg7SXUMl3cuY/qE7Miur5cuXAwUufjLJtbYSdlxLK+mSJkzb3EZZOmF7SrrvCt2QJV3hJNtlGN/KIXSlk8yH/Q/bYdLFneUnzKPpbW0tLKOkixtLO6wX0z/p0ijMq8nP5LrPfpfGxVnoCsmwNJMu7zK5ADTdzI1UUhcoKMdQf3ONlRwzQvdP9tvaUSa3RUmXdZlcQ2Vy+2S/bRwxt3Rhn0u65rI+G7p1s3qw/hnqaHUbtp8wDhSuh7A/2zhs45LlNezXVkbm2ivMo5WFyU+66grD2DXTJwxj45npE8q3+G3btgUK2kjoji102Qjp/cH6iPUjixe61TPdpk+fnqZj2J4sj5Ze6OrM3MCZOzs7Dl0Imhs7a48//OEPU9esTE3vAw88sFA5mC7WnsL7k5XR6tWr03QL+7zlP+kuMySUF8oMsTaS6R6UTDtsK5Zva392XJxLLuuXUFBv5kozE0k3ZtZnQjePyT4TuhU0naxsrfzC+7y1vyVLlqTlNYxvLgCtXkI3ptbX7L5o+Vq2rGAC1OSZm8ZwPDC51n4HDYpWGdgYZGSq36KoKONXVfNE5HqirwvnAo+p6jQRuTq+/hBwLvAdEdkJbAXO10w30YDilj08ANwmIn2AL4DVQB2gG9AQeAwod8PXcRzHcRzHqVpU1EcuVPUN4I3EuYeC378DfleWNItb9jAZOE9E6gODgP2JLOoZqjqrLEIcx3Ecx3GcfYN99vPGRuw/bVT5q+I4juM4juNUdfZ549dxHMdxHMdxQtz4dRzHcRzHcaoFWfzIRaVQouYi0juTx4eKJD8/n40bN6btQk3uELadluEGPwtjOyTDa7Zb0nam2m7ScBep7fS23ad2HKZj4W2HaLjz1XRK7lIPZdi55C798FyygYU7lk1/2zFqu1FNJhTsWrV8hDKKenILd7omvQyEaSd3gye9aEBB+du5TDt1bce25TnciW75tx2vSQ8ZULBz23ZHh/Vg4W0XrHnvCHfAJ8s/3GlrHiRMx/3337+Q/rZT2vIYesEwnWyHsJVj2J6tTi0d0y2sKyv3TB4IDGsPVlahHkkdzbMBFOwGtt3xSc8OoS5WVlbn4e7qpAeGFi1aFKm/lUO4c9rCZMqblaOFsZ3OYT6aN28OFOxqDtO2Mk56mwi9btjO9xNPjPyjL168GCgoFyhovyY39CiRHCsy1VVyzMrk9cJ0st3YYT0k278dh54QTDcro1C+tZGkN5pQhrWfsP0Zyd3wVtZhOzAvFdb3wzHM4iXTCccVK+Ok14wQ09fihfGT7deO58yZkwpj4a3+zOsCFIw/NlbMnz+/UD6sTG1csLoKxx4bR+xc6Mkg6ZHF2moY3/rsl19+CUCXLl1S1+zekdQjrEcb86ztt27dOnXN5FoerT7C+5tds7QtzMKFC1NhOnXqlKZP6EUj2dZN19CrkdW//Q/HReuP1q+t/MJ+bfmwegzHPBt/rP4sXngPXbBgAVBQV3YctjnLm6V30EEHpa7NnDkTKGjzFjb0pGC/H3/88TR9oKBOjjvuOKCg/sL7m3lMsrzOmzcPgE8//TQVJul1wvQJ85asx9D7R58+fTJ66iiK6rDs4SERqQU8AYxQ1a/LVSPHcRzHcRynSrNPG7+qepSIdAMuB8aLyFjgcVV9p9y1cxzHcRzHcaoU1WHmF1WdIyK/IPrk8V+B/hK9R/q5qv67PBV0HMdxHMdxqg7VYc3vwcBlwOnAO8AZqjpRRNoAnwJu/DqO4ziO41Qj9vWZ378BjxDN8qZWqavqsng22HEcx3Ecx6kmVIdlD6cBW1U1H0BEcoA6qrpFVZ8qV+0cx3Ecx3GcKkV1MH7fBU4ENsXH+wEjgSPKS6kkW7duZcqUKWluOcyFjrlAMVc25g4JClzJ2LnQfdbSpUuBAtc14TXD3DWZyxMLG7qrMZct5pKnTZs2ha6Zm5qkSxkocItiaYZubsxViTUwc30Sumb6/PPP0+JbXkM3K0lXYQcccEDqWtIdm7mvMr2gwD2NuUEJ82/yzIWKuakJ3eVYGNMjdKeSdNFm8cN6tLK1+Ek3UlDQDix+6O4n6QrJ3L6YGyFIL/dk2kkXYaaPuQcL82HlEGLudazeFy1aBKS3FXN3Y27VLL3QjZfpb+26e/fuqWvWtqzerKxCl0CWD2vPofsr083cFM2ePRtIL5ekqzkrj9BtkMk31zyZXBqZ/pZ22B+sHVj9h2lbmVi/MD1atmyZCmP1ZufCfmByky7OQtd7lrbpkcl9lLn2Mt3Ctmo6WdlaeYY3Cas3K5vw2vr169PSsbTDPmO6WT1amYfpWHswPUIdrf1ZX7F0wv5gYUyPnj17pq6Frt2gYCwOdUy62ArHjGbNmgEF9WH1H45rybZm44m5voKCPmMywviWttWf/bfyBWjXrh1QUG6hGzBrm8n7SiZXmpZ2+/btC8mwdmh5DOvB4ifdU4b3IgtvrvZCF2PmWs7ybe0yLKMpU6YABW2kX79+qWs21tv/TC4gk+cyjX0mz8ZVixPm2+4z5hbNZAL07t0bKHD/FbrXM/dz1i+tP5urrzD/1p7DMuratStQ4JrM2rG1TygYh+1+P2zYsEJ6WN3YGBb2h5NPPpkQK4+wHq38DjzwQCB9XDW5VkaZ7iXWVpOuTTO5WbQ2GroeNHlWR5ncyuXn56fVS2nYp9f8Es3ypnqsqm4Skf2Ki+A4juM4juPsm+ztM785JQdhs4gMsAMRGQhsLSa84ziO4ziOs49ixu+e/pVS1ikiMktE5orILRmuXyQiU+K/T0Skb0lplmbm90bgRRFZFh/vD5xfKo0dx3Ecx3GcfY6KmPkVkVzgfuAkYCkwTkReUdXpQbAFwDGq+pWInAo8DBxWXLql+cjFOBHpCfQABJipqjtLiOY4juM4juPsg1Sgn99DgbmqOj+W+xxwFpAyflX1kyD8GKBdSYmWVvNDgI5x+P4igqr+s5RxHcdxHMdxnH2ELK75bS4i44Pjh1X14eC4LbAkOF5K8bO63wXeLEloaT5y8RTQBZgM5MenFdht41dEFgIb4/TyVHXQ7qblOI7jOI7jVCxZMn7XlGADSoZzmuEcInIckfF7VElCSzPzOwg4UEMfL9nhOFVdU3KwyE3L0qVL09w+meuOQw45BChwCRO66rBz5uZl7ty5qWvmfsxc6pgrGXMBAgXuUOyauTsJKzzppiYsJov3ySfRjLy5jwrdi5ibIctPmLa5OjE3XuamJHTp87///S8tHctPx44dU2HM5YmFCd3dmJsXc+tibotCV18WxuSGZWz5b9iwYZp8O4YC9zhJF1dQ2H2a/c/k/ip0SQXpZWX1Zv/DerA0rdxN/zA9K5tMskxfO2f/LU6YprmWCvW3erR0zA1YWI6WF3NXZK5xrA1BgSscc40TlrG5AEq6jgtdjRnWrsNXVqaj5clkhe4Fzd2QucRKurAL851sj6G+yT4TYvKszkL5VrdJ14GdO3dOhbH2ZPFCV29WN1ZG5trKyhMK2o3pankL21OLFi3S/oft0Pqo5T/pXg0K6t36ZTgeJF3+mdwwvrUNK4+kqyoo7I4ubCtJt35W9+aCDArKz8osdNtk4U1/0zF0lWbnrD4y9UcrI2tHof5Wb1ZH06ZNA9JdVNm4aK6lpk8vWAJoLubMraP1j0GDCu6xlqblJ9TRyjR00QbQrVu3QjIsrOXHxtAwvoUJ3cElXdVZmWdqTxY/dENm8axt2LXQxdWRRx4JFLjECt0CJl0F2v01dPGVbH9J92xQUG8WNmzPpqPl2/IR5tHao+maaVywMKFuRtIFYujqzfr2/vvvD2R2y2fxra2bjqEsk295DMclw8rTZIYy7FrSTWEoP1lWYT+1+HbvsfoIx16LZ9cylWNyXA7rYd26dWVaxlCB3h6WAu2D43bAsmSg+GvE/wBOVdW1yetJSpPTL4DWwPLS6ek4juM4juPsq1Tgmt9xQDcR6QR8CVwADE/o0gH4N3Cxqs4uTaKl0bw5MF1ExgKpKUdVPbOUimdCgZEiosDfE+s7ABCRK4ErId1RveM4juM4jlO5VMTMr6rmicj1wNtALvCYqk4Tkavj6w8BtwHNgAfime8Sl9OWxvj91Z4oXgRHquoyEWkJvCMiM1X1gzBAbBA/DNChQ4dsL7lwHMdxHMdxdoOK/MiFqr4BvJE491Dw+wrgirKkWRpXZ6NF5ACgm6q+G3/dbY9yrKrL4v+rRORlIlcWHxQfy3Ecx3Ecx6kK7M1feCuNt4fvES0/aErk9aEt8BBwwu4IFJF6QI6qbox/DwXu3J20HMdxHMdxnIqlAtf8lgul0fw6opnZzwBUdU68XGF3aQW8HK/LqAGMUNW3iouw33770b9//7RztrPYdjTabuZwh6PterUdlj169EhdW7482r9nOzstvXAHvu0qtwq2MOEuTtvZajs8w52+tlu0XbvI33Kmnfe209hkhDt1TRfLm+3KDXewH3ZY5O5uw4YNQMGO0bBRJncT287XME/JnefhTlPb4Wv/w/zbk5/FS4aFgl3xtqs7vGa7T5O748PdxCbDdtra/9Ajg+3UtbyGO3Utb5Z/kxXuLjcyeTAweVZXVi9hW7PduMldzeE1S9PKNmwPplPSo4flJ4yXLAcoKCOTn/T6EOpknkDC8jPPAWHdJOVbGSd3HoflYDpZeqHXC9PRdiNbfCvzTHkMvRTYOWtPltcwH0lPCBY2vGbhrTzCukpes/0GYZu3tC0/4c7tpCcFK//QW4OVl7XL0NuEtTFrv5Z26HXA0grrBtK9NdhYYWHCOrI8mv6mcyZPBNZmwzI2XUxX61dh/OSO87CMk7vhrYxCrx1WVybLxtAwz6aTnTv66KNT16xNmR7WDsz7QxjfZISefqz/JPtsWA9WxqaryQrbgLVZ6zvhuGLyk+NK0sNEmLZ5v4CCOkqOB2EfNhmZnDVZuZl8G8PDsdPCJL1WhONK9+7dgYLyC/Of7CuWXjj2mScKI/QWYeNh0ntI2B6TXibMO1Em+Zk8NlnZWvykx5gwjLF+/fpCMizfVi9hf7Bys3QyebswMrVH8xRlYa09mYcLKFz/oQxrd6FOkD721qtXr9D4XxwVueyhPCiN8btdVXdYpYlIDYrwsVYa4q90lPjdZcdxHMdxHKdqsq8bv6NF5OdAXRE5CbgWeLV81XIcx3Ecx3GqItVh5vcWoi9mTAWuItpx94/yVMpxHMdxHMepmuzza35VdRfwSPznOI7jOI7jVGP2+ZlfEVlAhjW+qto5Q3DHcRzHcRxnH2efNn6B8CsZdYBvEbk9cxzHcRzHcaoZ+/zMr6quTZz6s4h8RPQ5uQohJyeHevXqpblAMTck5pbFXHiELknMhYu5FArdkJmLEHNBkslNTNKVjbkeMbdiYTxzyRK6gDH55lrM3JSELkxMfwsbuoaya9bAbH1N6O7GXPeY6xgLGzZKS8fcvIQy7LfJN0JXNPbb3LuErnisTsxlyrp164D0cmzfvn1amFC+hbN0zBVOqL+5JDO5Vnfm7gUK6tFkhG3F9Lc2YjLDPNs1kxG6hkq6prIwobubpCue0P2VhQ9dWiVJutSyNhbmw+Rb+wndPiVd7q1atQpIdzGVzHdYR0lXQFZHYX9KuqaydhWmYy4EM32SPOl+zVwchf3B5FreQlc+pqOVZ9I9HhTUv8kK9bfwJsPChO3ZysbajJVf6CLI4ls+wnVvlqb9N1dtoQzL29q10dCaydWZlYnJsPTC/Fr5WZhwXLA6Md1CHa1t2blk3wt/m0upsM+GbqYAmjdvXui85cnKMbw2d+7cND3M3aKlAwVjdbLNhuVo5WD3gtDVm7XjpPuu0OWX1Y2VbSb3jtZ+km0+1MXymqnPWHs03ZYuXZq6Zu3ZxjGLF/ZZC2Pyw3oIx78wz2E+kvqHfc3qJunmMKwrc/dp6VgdWZ2F8ZJ1BgX9JhxPIXN7tHtHmLblxeooWVZhWsn7JRT0eZNv6YQ6mrzkfSKUYe3GZNn4CrB69WqgsKu6sM+aqz7LT2iLWPgVK1akyQ3ravr06UDBPcDcVYZtJelWcOXKlYXKwfJt/TvUsXHjxoXqqTj2+TW/IjIgOMwhmgluUERwx3Ecx3EcZx9nn575Bf4Y/M4DFgLnlYs2juM4juM4TpWmOix7OK4iFHEcx3Ecx3H2DvZp41dEfljcdVX9U/bUcRzHcRzHcaoyFbnmV0ROAf4C5AL/UNXfJq73BB4HBgC3quq9JaVZWm8PhwCvxMdnAB8AS0qvuuM4juM4jrMvUFHLHkQkF7gfOAlYCowTkVdUdXoQbB1wA3B2adMtjfHbHBigqhtjRX4FvKiqV5RWyJ6iquzcuTNtB73tdLbdirbzPtzBb7s/k7taAVq0aJFKO0wn3NVtO0vt//z584GC3cpQsMOyQ4cOQPprAPttuy+TO4hD7AnKdi4DLFu2LC2M7YY1WVDgScHStJ2jYTnYDmHbTWo7eAG+/vproKA8TMcPP/wwFcZ2s1oZ205TgM6dI3fPU6dOBWDGjBlA+k7T448/HijY4WoeAaCgbMyzxsyZM4H03bSzZs0CoEuXLkCBB4CDDjqokB5WH+GOYSsTq782bdoUkmHlZ+UWtgPbFW5pW3sIvX4kw4TxrR0kd1WHO2snTZoEFOzKtl3qnTp1SoWxtmrphDvfDesjlv8wjOlh7TfcXW9laju9Q28fyfj2P9OuaJNrbSX0IGDXLP/JnexhHq1dhP3J2v+UKVMA6N69e5ruUOBBIblLPfxtOtkOamsPUNCfrIz69+9fKB3Tzeoh9Mhhfc12ZVuewx34lkfrI2E7WrJkSZo885oxbdq0VBgbF6z9Wto9e/ZMhenbty9QUH7Wv8L8m6cYGyfDPmtt3HS1sQMKPBbYWLV48WIgs0cH0y3s8yZv3rx5aXr37t07FWb27NlAgbcE6wddu3ZNhbF7QNJrBBS0KRvXLGzorcG8NNjYFbaDMBwUeNII27P1taRHEBsLQt0sPdMHCsYau/dYeYZjh5Wf9ePQW4W1P4tnZRWOK6ajhbX+EZ5Leu8I+5yVd9ITQngvtfCZvF1YeSVlhTKtbM0jQhjfxorQK0FSR8uvyQi9fiTbtukYxrdyW7RoUVqY8B6avHeH+ts4auNT0jsRwGeffQYUtPGFCxemrllfs75vZR22ox49eqRds3EhtEXsXm7tOKxrayPmecr6c2hT5ebmFmoLJVFByx4OBeaq6nwAEXkOOAtIGb+qugpYJSKnlzbR0hi/HYAdwfEOoGNpBTiO4ziO4zj7Dlmc+W0uIuOD44dV9eHguC3pKw2WAoftqdDSGL9PAWNF5GWiL72dA/xzTwU7juM4juM4ex9ZXPO7RlUHFXO98KvyDF8dLiul8fZwt4i8CRwdn7pMVSftqWDHcRzHcRxn76SClj0sBdoHx+2AZUWELTWlNdv3Azao6uMi0kJEOqnqgj0V7jiO4ziO4+xdVKCf33FANxHpBHwJXAAM39NES+Pq7HYijw89iFxJ1ASeBo7cU+GO4ziO4zjO3kVFGb+qmici1wNvE7k6e0xVp4nI1fH1h0SkNTAeaAjsEpEbgQNVdUNR6ZZm5vccoD8wMRa0TET888aO4ziO4zjVlIr6yIWqvgG8kTj3UPB7BdFyiFIjoeuUjAFExqrqoSIyUVUHiEg94FNVPbgsgvaE3r1764svvpjmwiTpmsz+h25e7Fwm11Lm+sfckSRd20CBGxBziWNlFVZ40sVYKMNcpoQupcJ0Qh3NNVTo3sTSTLqdCt0m2YJzy6P9D93lmDzLo7lEgQJXNOZuxdy+hGGsrC3f4SJ3c/NjYUxW6BLI6i3priYMb2mbW7XQ5YqVieXJ8hGWg9WVuacJ4zdt2hQocNFkdRS6lbN8mx6h/uaizfJtcotb7B+2J9PN6iZ0E5QMb26zLG2rFyhoD8n6CPVNygjbgeXb0g5djFmeLLyVXyaXbVbG1i7DsjL9rT2HWHkn22NI0qVQmLbpYm3E3P1Y/UJBGVmY0N2QyTe51q/DPFoYc83Vtm3bNH2goNySYw8UlKOVjeUnbCtWNuYGKxwzrN9Zm8/k6inpUsryGrozM/dvpltYDtZnrW2ZDHPVFIZP9jkoKDdrD1b+oY7Wn6w9hG3Vwpm7JXN1FY59JtfK0fpgGMZkWJmHfd7KO3lfCMsh6ZYwHA+sjWaqP8P6tbUHc28Xuo8y/S1+OPbbbwtfXBj7v2BBwWpD09v6gdWf1W+YX6uz0GWe3ZesTK2vWblCgasza5eWTiYXiuE9o6hrmerB9Dc3cGF/MubMmQMUuPkLbQHLr9VDeM3yZDKsjMMySpZ7Jlekds30L87VmbWZUIa5WjM3paEMG6vtv5VZWA/WNi1e0u1kqJPlJ7yW1N/0CXX8+uuvueiii5g+fXqmDWaF6N27t/7rX/8qTdBi6dWr14QSNryVC6WZ+X1BRP4ONBaR7wGXA4+Ur1qO4ziO4zhOVaQC1/yWC8UavxI9ZjwP9AQ2EK37vU1V36kA3RzHcRzHcZwqyD5r/Kqqish/VHUg4Aav4ziO4zhONWefnvmNGSMih6jquHLXxnEcx3Ecx6nSZPEjF5VCaTQ/DrhaRBYCm4m+tqEVueHNcRzHcRzHqTrskzO/ItJBVRcDp2ZbqIicAvyFyGfbP1T1t8WFz8vL46uvvmLu3Lmpc8md5rbTecmSgk9A2w7Pzp07p4UNw9nuVWPt2rWp37bT23Zn265i2+0OBTuXbed0uGPaZLRr1y4tTLjbP7nT2na8QsEOZ9v1aTs2Qy8Hyd2ztht03rx5qTCNGzdOSzvcuW07RE2G7VgOd47bTtNwV7ph9WC6mc62kz3U186FT4u2c93+t28ffcjFdoCHfPXVV0DBzuH58+enri1evDgtj6YHwKpVq4CCnc62Yzj0wmFtw9IxfaBgN3vHjh2BgjrLpKOVke1yh4Ldy7Yb2QaM0IuH5dvK39IJd1BbPEsn3F1vaVldm/7WB6Cg/Lp37w5k9shg5WDtItTRdLG6tvYc7s42+baLONwVbW0y6X0l3HFs4S1MmP+kdwcLY/UbpmX9OKyjZD+2Xe7hzvPwNxSMD9ZmoKA/WFgr1xArI2vr4bhkevTt2zctz1AwtljZWh2Heln+7b+NJ2F7MPmWnnlWgIL2s2jRorT4Yb+0srZd4eZRAgrGRdtV/+WXXwIFO9mhoGxtPArHHMub9VVr12Gfs3PWRqyMwzHc7gfWd8Ld8db/rN4sP+HYabpZ/LAekpg+mcZui5epHqzebHwPPUEY1n+srsJ7UtLDTthXkp5ArIzDctx///2Bgvyb94ZQFytj67thGVma1tYsvUz5ML0ztWfD6igMk8xj2OdNt4MOOgiAAQMGAJk9Wlh9hmnbOUs7eb8O5SfTCe93Se834X3e7mt2zcYcqx8o6I8mI5O3jKSXCLsXQEHbMp2sXqwvQmFvNmEdWf+x+DYGhGPnkiVL0vpXSezLyx7+AwxQ1UUi8pKqnpsNgSKSC9wPnET02bpxIvKKqk7PRvqO4ziO4zhO+bKvGr+hr7fOWZR5KDBXVecDiMhzwFmAG7+O4ziO4zhVnH15za8W8XtPaQssCY6XAoclA4nIlcCVUOCw3XEcx3Ecx6lc9uVlD31FZAPRDHDd+DcUbHhrWHTUYsn09ZBCxrWqPgw8DNCzZ89sGt+O4ziO4zjObrLPGr+qWl65Wgq0D47bAcuKCOs4juM4juNUMfZJ47ccGQd0E5FOwJfABcDwStDDcRzHcRzHKSN7+5pfMRccFSpU5DTgz0Suzh5T1btLCL+ayMfwmvLXzilnmuP1uC/g9bj343W4b+D1uG9QFerxAFVtUXIwEJG3iHTeU9ao6ilZSKdMVIrxuzuIyHhVHVTZejh7htfjvoHX496P1+G+gdfjvoHXY8WSU3IQx3Ecx3Ecx9k3cOPXcRzHcRzHqTbsTcbvw5WtgJMVvB73Dbwe9368DvcNvB73DbweK5C9Zs2v4ziO4ziO4+wpe9PMr+M4juM4juPsEXuF8Ssip4jILBGZKyK3VLY+TukQkYUiMlVEJovI+PhcUxF5R0TmxP+bVLaeTjoi8piIrBKRL4JzRdabiPws7puzROTkytHaSVJEPf5KRL6M++Tk2O2kXfN6rGKISHsR+Z+IzBCRaSLyg/i898e9iGLq0ftjJVHllz2ISC4wGziJ6Otw44ALVXV6pSrmlIiILAQGqeqa4NzvgXWq+tv4QaaJqv60snR0CiMiQ4BNwD9VtXd8LmO9iciBwLPAoUAb4F2gu6rmV5L6TkwR9fgrYJOq3psI6/VYBRGR/YH9VXWiiDQAJgBnA5fi/XGvoZh6PA/vj5XC3jDzeygwV1Xnq+oO4DngrErWydl9zgKejH8/STQAOFUIVf0AWJc4XVS9nQU8p6rbVXUBMJeozzqVTBH1WBRej1UQVV2uqhPj3xuBGUBbvD/uVRRTj0Xh9VjO7A3Gb1tgSXC8lOIbjVN1UGCkiEwQkSvjc61UdTlEAwLQstK0c8pCUfXm/XPv43oRmRIvi7DX5V6PVRwR6Qj0Bz7D++NeS6IewftjpbA3GL+S4VzVXqvhGEeq6gDgVOC6+DWss2/h/XPv4kGgC9APWA78MT7v9ViFEZH6wEvAjaq6obigGc55PVYRMtSj98dKYm8wfpcC7YPjdsCyStLFKQOquiz+vwp4mei1zcp4/ZOtg1pVeRo6ZaCoevP+uRehqitVNV9VdwGPUPAq1euxiiIiNYkMpmdU9d/xae+PexmZ6tH7Y+WxNxi/44BuItJJRGoBFwCvVLJOTgmISL14YT8iUg8YCnxBVHeXxMEuAf5bORo6ZaSoensFuEBEaotIJ6AbMLYS9HNKgRlMMecQ9UnweqySiIgAjwIzVPVPwSXvj3sRRdWj98fKo0ZlK1ASqponItcDbwO5wGOqOq2S1XJKphXwctTnqQGMUNW3RGQc8IKIfBdYDHyrEnV0MiAizwLHAs1FZClwO/BbMtSbqk4TkReA6UAecJ3vSK4aFFGPx4pIP6JXqAuBq8DrsQpzJHAxMFVEJsfnfo73x72NourxQu+PlUOVd3XmOI7jOI7jONlib1j24DiO4ziO4zhZwY1fx3Ecx3Ecp9rgxq/jOI7jOI5TbXDj13Ecx3Ecx6k2uPHrOI7jOI7jVBvc+HUcx3Ecx3GqDW78Oo6zTyAizURkcvy3QkS+jH9vEpEHyknmjSLynfh3z1jeJBHpUh7yyqDXcyLSrTJ1cBzHqaq4n1/HcfY5RORXwCZVvbccZdQAJgID4o/x3ALUVdXbE+GEaKzdVV66ZNDtGODbqvq9ipLpOI6zt+Azv47j7NOIyLEi8lr8+1ci8qSIjBSRhSLyDRH5vYhMFZG3RKRmHG6giIwWkQki8nbiM6TG8cDE2PA9DbgRuEJE/iciHUVkRjzjPBFoLyIPish4EZkmIncE+i0Ukd+IyKfx9QGxzHkicnUQ7sciMk5Eplj8+DPir4vI5yLyhYicHwf/EDgxNtAdx3GcADd+HcepbnQBTgfOAp4G/qeqfYCtwOmxAfx/wDdVdSDwGHB3hnSOBCYAqOobwEPAfap6XHy9B/BPVe2vqouAW1V1EHAwcIyIHByktURVBxMZrU8A3wQOB+4EEJGhQDfgUKAfMFBEhgCnAMtUta+q9gbeivXZBcwF+u5JQTmO4+yL+KyA4zjVjTdVdaeITAVyiQ1GYCrQkcho7Q28E61YIBdYniGd/YEZxchZpKpjguPzRORKonF3f+BAYEp87ZVAh/qquhHYKCLbRKQxMDT+mxSHq09kDH8I3CsivwNeU9UPA3mrgDbEBrrjOI4T4cav4zjVje0QzY6KyE4t2Piwi2hMFGBaPBNbHFuBOsVc32w/RKQT8CPgEFX9SkSeSMTdHuiwPTgf6nSPqv49KUREBgKnAfeIyEhVvTO+VCfW0XEcxwnwZQ+O4zjpzAJaiMhgABGpKSIHZQg3A+hayjQbEhnD60WkFXBqGXV6G7hcROrHOrUVkZYi0gbYoqpPA/cCA4I43YFpZZTjOI6zz+Mzv47jOAGqukNEvgn8VUQaEY2Tf6awIfkm8FQp0/xcRCbFacwHPi6jTiNFpBfwabwUYxPwbSLj+w8isgvYCVwDEBvYW1U103INx3Gcao27OnMcx9lNRORl4CeqOqeydQkRkZuADar6aGXr4jiOU9XwZQ+O4zi7zy1Em9eqGl8DT1a2Eo7jOFURn/l1HMdxHMdxqg0+8+s4juM4juNUG9z4dRzHcRzHcaoNbvw6juM4juM41QY3fh3HcRzHcZxqgxu/juM4juM4TrXBjV/HcRzHcRyn2uDGr+M4juM4jlNtcOPXcRzHcRzHqTa48es4juM4juNUG9z4dZwYEVkoIidWsg6jROSKytQhREQ6ioiKSI0spXekiMwRkU0icnY20iyl3KNFZFZFySur/GyXcxn06hDXRW5Fyi0NJZVJVeiv2UREfi4i/yjm+kUiMrIidXKcfRU3fp29gvhGtzW+Ua8UkcdFpH58LaPBGNw8N8V/C0XklorXft8hC8b5ncDfVLW+qv4nS2oVIq73rnasqh+qao/yklcSSfmVZbgl5arq4rgu8stB1q9E5Olsp1veiMilIvJRRctV1d+o6hWxDoUMf1V9RlWHVrRejrMv4savszdxhqrWBwYAhwC/KGW8xnG8C4HbROSU8lLQKZEDgGmVrYTjOI5TfXHj19nrUNUvgTeB3mWM9ymR4VVcvENEZLqIfBXPLtcBEJEmIvKaiKyOr70mIu0sUjxbNF9ENorIAhG5KLh2uYjMiOO9LSIHBNdOEpGZIrJeRP4GSFGKxTNpL4jIP2M500RkUHC9Vzwz+3V87cz4/OEisiJ8tS0i54jIlPh3jojcIiLzRGRtLKNpBvl3A0cDf4tn0v8mIveLyB8T4V4VkRszxJ8HdAZejePXTs5EhrOFwezXJSKyWETWiMitQdjc+FXxvLg8JohIexH5IA7yeSznfBE5VkSWllRW8bUn4ny9Hqf7mYh0KaJOnhSRm+PfbWN9r42Pu4rIOolIyReRp4AOQTn8JEjyokx5zSC3tojcG4ddKSIPiUjd+FrzuH1+Hcv/MK7jQnIlMcMYl8mvReSTOMyrItJMRJ4RkQ0iMk5EOgZ6/EVElsTXJojI0fH5U4CfA+fH6Xwen28kIo+KyHIR+TKWlRvU571x3ucDpxeV/4CM/TVOb5iITI7L4RMROTi4Zu19Yxz/nPh8L+AhYHCs99dFlP8oEblHRMZK1Hf/K0GfEZEz43b1dRy2V3Dtp3HeN4rILBE5IT4fzpRbG/461mOwJGakReSIuD7Wx/+PSOh3l4h8HMsZKSLN42t1RORpifr613HcVqUoa8fZd1BV//O/Kv8HLAROjH+3JzJi74qPRwFXZIjTEVCgBpFReSSwBTihGBlfxOk3BT4Gfh1fawacC+wHNABeBP4TX6sHbAB6xMf7AwfFv88G5gK9Yj1+AXwSX2sex/smUBO4CcjLlJc4/K+AbcBpQC5wDzAmvlYzlvNzoBZwPLAx0GkecFKQ1ovALfHvG4ExQDugNvB34NlkGWYqa+BQYBmQE+RpC9CqpHos4vhXwNMJ2Y8AdYG+wHagV3z9x8BUoEdcv32BZvE1BboG6R4LLC1lWT0BrIvzVgN4BniuiPxcDrwa/x4el/PzwbX/JuUXke9i85pB7p+BV4jaaQPgVeCe+No9RAZczfjvaEBKkBvW71ygC9AImA7MBk6My+KfwONB/G8T9Y0awM3ACqBOsi6D8P8hal/1gJbAWOCq+NrVwEwK+t//Qt3K2F8HAKuAw4j6yiVx+Nrx9W8BbYgmgM4HNgP7x9cuBT4qYTwaBXxJ9CBdD3iJgnbbPU7vpLj8fxKXaS2itroEaBOUf5di2n6NQGZKrzi/XwEXx2V/YXzcLNBvXqxL3fj4t/G1q4jay35x2QwEGlbkeO5//lfZfz7z6+xN/CeeifkIGA38ppTx1hAZM/8gMvjeKybs31R1iaquA+4muqmgqmtV9SVV3aKqG+NrxwTxdgG9RaSuqi5XVXu1fxWRUTJDVfNinftJNPt7GjBdVf+lqjuJDJoVJeTlI1V9Q6M1mk8RGUkAhwP1iW5wO1T1feA10x941n6LSINY9rOBjreq6lJV3U50E/6mlGLzlaqOBdYDJ8SnLgBGqerKkuKWgTtUdauqfg58TkGerwB+oaqzNOJzVV1bivRKKiuAf6vq2LjOngH6FZHWaOBoEckBhgC/J3rIgqh9jC59NoGi85pCRAT4HnCTqq6L2+NviMoeYCfRA9gBqrpTo/XGWgYdHlfVeaq6nugNyzxVfTcuixeB/hZQVZ+O+0aeqv6R6OEp49rqeHbxVOBGVd2sqquA+wK9zwP+HPS/e0qha8b+SlQ+f1fVz1Q1X1WfJHqYODzW+0VVXaaqu1T1eWAO0cNOWXhKVb9Q1c3AL4Hz4lns84HXVfWduF/fS2SAHgHkE5XRgSJSU1UXquq8MsqFaFZ8jqo+FZf9s0QPDmcEYR5X1dmquhV4gYI2vJPogaVrXDYTVHXDbujgOHstbvw6exNnq2pjVT1AVa+NB/XS0FxVm6hqL1X9K4CIvCkFG+EuCsIuCX4vIpodQkT2E5G/i8giEdlA9FqysYjkxje/84lmrpZL9Lq8Z5zGAcBf4teLXxMZ4QK0jdNOyYsNlFB+JkLjeAtQJzZS2wBLVHVXQv+28e8RwDdEpDbwDWCiqi4KdHw50HEG0U26tK9CnySaAST+/1Qp45WWZJ7rx7/bE81ulZWSyqo4mWnEhssmIsPiaCIjepmI9GD3jN/SyG1BNGs3Iaizt+LzAH8gmmkcKdFSnLJu8gwfXLZmOE7pJCI3S7SkZ32sRyOi2f9MHEA0E7o80PvvRDPAkOgPRHVSEhn7ayzrZpMTy2pPQX/+TrAk4muiGdyi9C6t7JpxGm1C3eN2tgRoq6pzid60/ApYJSLPiUgbyk6ajECH0rThp4C3gedEZJmI/F5Eau6GDo6z1+LGr1MtUdVTNdrlXl9VnwkutQ9+dyB6pQ/RK90ewGGq2pBolg/iNbqq+raqnkQ04zaT6PU1RDe9q2Kj3f7qquonwPJQXjyjF8ovC8uA9vEMZKj/l7F+04lujqcSvZ4fEYRbApya0LGORmurk2SaQXwaOEtE+hIt7/hPGfTeTGTIGa3LEHcJ0ev5slJsWe0Go4mWrtSKy2w08B2gCTC5iDhlmYlNsobICD0oqK9GGm3qRFU3qurNqtqZaCbwh7audA/lpiHR+t6fEs3YNlHVxkRvAWzdelLWEqLZ1+aB3g1V9aD4elp/IKqTkiiqvy4B7k606f1U9dn4rcsjwPVEywQaEy2fKErv0sreSVQ3y4iMbyCtX1tfHKGqR8VhFPhdhrRL0iFNRqBDiW04fhtwh6oeSDQbPYyovTpOtcGNX2dfoUa8kcP+dncm4zoRaRdvXvk58Hx8vgGRwfF1fO12iyAireINLvWIbu6biGZOIVp7+TMROSgO20hEvhVfex04SES+Ec/e3kDZjL+Qz4gMyZ+ISE0ROZbI8HkuCDMiljGE6PW18RBwd2wUICItROSsIuSsJNq0lkJVlwLjiGaUXirDjDxExuEFsc6DiIzI0vIP4C4R6SYRB4tIs6L0DChNWZWF0USGlG1SGgV8n2iJSlEuxIrTr1jimcRHgPtEpCWkNtudHP8eJtFmOyFaU55PQXvcbbkZaEC0Rn01Uf+7DWgYXF8JdLSHDFVdDowE/igiDSXahNdFRGz50AvADXH/awKUZsa6qP76CHC1iBwWt416InJ6vOSnHpFxuRpARC4jfRPsSqCdiNQqQfa3ReRAEdmPyIXfv+L6fgE4XUROiMehm4nGhU9EpIeIHB+/gdlGNKZkaiOriZZSFVVXbwDdRWS4iNQQkfOBA4nePBSLiBwnIn3iJRobiIz2rLu6c5yqjBu/zr7Cg0Q3Evt7fDfTGUF0g54f//06Pv9nonV7a4g2h70VxMkhusEtI1rWcAxwLYCqvkw0s/NcvFziC6LZV1R1DdHGm98Ca4FuRJt2yoyq7gDOjNNeAzwAfEdVZwbBniXaePV+LNv4C9HmqZEisjHO32FFiPoL0Xrgr0Tkr8H5J4E+lH3Jwy+JZm+/Au4gfUa6JP5EZGiMJLqJP0pURxC9Vn4yfq19XhiplGVVFkYTGYJm/H5ENJv9QZExovWsv4j1+9FuyPwp0dKGMXG7epeCtbbd4uNNwKfAA6o6KktyQ94mWhM8m+itwjbSlwLYA9ZaEZkY//4O0cav6UR1/i+ityUQGaxvE611ngj8uxQ6ZOyvqjqeaN3v32I5c4k2jNlbkD8Slc1KonYb9rv3iTbUrhCRsJ8keYpoc+QKoA7RgyWqOoto+c//EbWvM4jcNO4gWu/72/j8CqIlHz9PJqyqW4jWMH8c19XhietriWZsbyYaO34CDEv066JoTVTuG4iWOI0menvjONUG2wHsOI6z24jIEKIbaMfEWlrH2ecQkVFEnhmK/CKb4zhVF5/5dRxnj4hf7f4A+Icbvo7jOE5Vx41fx3F2G4mc939N9Or6z5WqjOM4juOUAl/24DiO4ziO41QbfObXcRzHcRzHqTaU+AWn3UVE2hN9CrM1kcuWh1X1LyLyK6JduKvjoD9X1TeKS6t58+basWPHwhc2boQGDbKodQYqQsa+JsdlVD05LqNqpF8RMjz9yknb06/c9Pdm3StCRinSnjBhwhpVbVFsoH2EcjN+ifw/3qyqE2PfihNE5J342n2qem9pE+rYsSPjx48vFyUdx3Ecx3GqOyJSmq8q7hOU27IHVV2uqhPj3xuJ/Am2LT5WGbnyyqwmV2ky9jU5LqPqyXEZVSP9ipDh6VdO2p5+5aa/N+teETIq6n62l1Aha35FpCPQn+jLSgDXi8gUEXks/pJPpjhXish4ERm/evXqTEFg0KDyULfiZexrclxGlZKzY8eOcpcxceJEFjZvXm7pr1mzhquvvpr5TZuWm4ynn36aRyZNYvPmzeWS/pdffskDDzzAopYtyyV9VWXs2LHk9+9fLumnKO9+UZ7p7826e/qVl3ZFpF/eMirqfra3oKrl+gfUByYA34iPWwG5RIb33cBjJaUxcOBAdZyKZv369eWa/rp16/T666/XBx54oNxkfPzxx1qzZk098sgjdcWKFeUi44477lBA99tvP/3yyy+znv6OHTu0e/fuCmi7du30q6++yrqMZ599Vok+eas33XRT1tPPy8vTzp07K6DdunXTvLy8rMt48MEHFdAhQ4boxo0bs57+559/rhdccIHecsstWU9bVXXLli1622236eTJk8sl/XXr1ukLL7yg+fn55ZL+pk2bdMuWLeWStuNUBMB4LWebsKr8lbfhW5Poc5U/LOJ6R+CLktIp0vitXz/z+WxSETL2NTn7gIy7775bN4DedtttumvXrnKRceSRRyqgG0A//fTTrKefn5+vhx56qLZs2VI3gN56661Zl7F161Zt0qSJHnXUUboB9Lvf/W7WZbz11lsK6C233KIbQH/9619nXcbQoUO1S5cuurVmTc3JydGVK1dmNX3Lw/nnn68bQJ9//vmspr969Wpt1KiRduvWTTeAPvTQQ1lNX1V10KBBKiK6AXTixIlZTTs/P1+HDBmigG4U0U2bNmU1/by8PD3++ON1A+g111yT1bRVVbdv3669e/fWjSL67rvvZj39bdu26V133aU76tQpN+NdVct/7C7P9Pdm3StCRinSduM3O4avEHl7+HPi/P7B75uA50pKq0jjd8OGzOezSUXI2NfklKOMrVu36iGHHKIHd+qkb7zxRrnImDBhgubk5Gj3/fdXQD/55JOsy5g+fboSG9fdWrfWoUOHZl3GBx98oID+4x//0HOHDtX9999fd+7cmVUZzz//vAI6cuRIve4739F69erpjh07sirjiiuu0AYNGujWrVt1SP/+etRRR2U1/TVr1mhubq7ecsstOu799xXQZ599NqsyvvWtb2nz5s1169at2vuAA/S0007LavpPPvmkAjpu3Djt16WLnnTSSVlNf9KkSQronXfeqW0aNNDzzz8/q+mPHTtWAf3Od76j9UF/97vfZTV9K58TDztMAZ0yZUpW0//jH/+ogHZq3lx79uyZ9QfmBx54QAGtX04PNqtWrdLbb79d/3r33VlPO43yvP+U971tL793liZtN36zY/weFb9GnAJMjv9OA54CpsbnXwmN4aL+ijR+X3kl8/lsUhEy9hE5eXl5+u677+rn5TAzZ9x7770K6MWNG2uXLl2ybsypqt5www1ap04dXf3YY1q3bl29+uqrsy7jZz/7mebk5Ojy5cv1kTPP1Fq1amV9tuv222/XnJwc/eqrr/STn/1MAX3nnXeyKuOCCy7Q/fffX/Py8vTjW25RQD/++OOspZ+fn6/NmjXT4cOHq6rqP7/1Lc3Nzc3qkpTQcMx7+WVt1KhRVmewd+3apU2aNNErrrhCVVX/b+hQbdy4cVZn8C655BJt3ry55ufn6xPnnqu5ubm6Zs2arKX/wx/+UGvXrq1r167VB049VevVq5fVvvezn/1Mc3Nzde3atfr9jh31uOOOy1raqtHDR9u2bXXN448roHdn2cjr2rWrHnfccfrGNdcooJ9//nnW0t6xY4cecMABOnjwYP1+x47ar1+/rBvXZ599tgI6DPSzzz7LatrGkiVLdMdLL5VL2qpa/vfQvd3eKEXabvxWsb8ijd9hwzKfzyYVIWMfkXPTTTcpoK+CTpo0Kevp79y5U5s3b65Dhw7VZQMHKuXw+njXrl3aqVMnPf3001WHDdOLLrpIGzdurNu3b8+qnAMPPFBPPPFEVVVdFc9Gvfrqq1mVcdRRR+khhxyiqqo7Tj5ZRUTvuOOOrMro2LGjfutb31JV1e1DhypZXpYwc+ZMBfTRRx9VVdU1gwcroC+//HLWZFx33XVav379yBgdNkzPOecc7dChQ9YMjNmzZ6dm4FVVF/ftq4BOnTo1K+nv2rVL27Vrl6qHtfFymn//+99ZSV9V9dBDD9Vjjz1WVVWXDhiggI4fPz5r6ffs2VOPP/54VVWd1rmz1qpVSzdv3pyVtPPy8rRJkyZ62WWXqQ4bpoMGDdLDDz88K2mrqi5evFgBve+++3Tb0KGam5urP/vZz7KW/qhRoxTQF198URf07q2ATpgwIWvpL1myRHNycvS6667Tt2rWzPpbCVXVv/zlL5qbm6uftWxZbsvIyv0eurfbG6VIuzoZv3v3F95efXXfkFEBclSVzz//nPz//Kdc0l+5ciUPPvgg5557Lt9t2ZLrr78+6zImTJjAmjVruPzyy2k1diytW7fmP1nOz8yZM1mwYAHDhg2DV1/lnHPO4euvv2by5MlZk7FhwwZmzJjB0UcfDUDD0aPZb7/9eOutt7ImY9OmTYwZM4YTTjgBgJpvvcWBBx7Ip59+mjUZK1euZOHChRx22GEA1Hr7bfr27cv777+fNRkTJkwAYODAgQA0GDWKGjVq8NlnnxUXrUxMnDiR/v37k5OTA6++yjHHHMPixYtZsWJFVtI3XQ899FAAdrz0EgAff/xxVtKfN28eS5cu5fjjjwegzsiR5OTkMGnSpKykv337diZPnpzSf1fc57Kl/5o1a5g5cyannHIKAIvvv58dO3bw0UcfZSX9cePG8dVXX3HyySfDq68ybNgwPvvsM7766quspP+///0PgOOOO47ab7/N4MGDU+eywYcffgjA8ccfT913302TmQ2eeOIJVJWbb76Z0T/6EW+99RYbN27MWvorVqzgpptuonPnzhy2ahWPP/541tIO0VdeKZd0U+zt9kZF2TJ7CXu38Tt8+L4ho5zlbNq0iaOPPpp+/foxsWfPcpHx8MMPs337du6++27+16YNH3/8MYsXL86qjHfeib6RcsIJJ5Dz7W9z3HHHMWrUqOgVRpYww+3kk0+G4cM5/PDDARgzZkzWZIwbNw5VTRmNtS+7jMMPP5xx48ZlTcaECRPIy8tjyJAh0Ynhwxk8eDCfffYZu3btyooMM+osHyZjwoQJWauTiRMnUrt2bQ488EAAal16Kb169WLq1KlZST8vL4/JkyenjGuGD+fggw8GyJqMsWPHUq9evVQeOv/yl7Rq1SprDyL2YGb1sN8VV9CrV6+sGb+ff/45O3bsSBm/7X/6U9q3b58149f0HDBgAAAnPvYYOTk5WUv/k08+ASLj1Pq0TQZkg//97380a9aMPn36wPDhDBo0iM8//5y8vLyspP/RRx/Ru3dvmjZtSqubbqJjx45ZHY9GjRpF37596dSpEzeMGcOuXbuyVvYAzz33HLt27eK///0vrzVsyDPPPJO1tI2nnnqKf9epw4MPPpj1tFPs7fZGRdkyewl7t/E7bNi+IaOc5Tz++ON8/PHHnHLKKdw3dy5vvFHs16R3i7feeotDDjmEHj160PiiiwB4/fXXsyrj3XffpX///jRv3hyGDePYY49l+fLlzJ49O2syJk6cSPPmzenYsSMMG0bbtm1p165dVm82yZlAhg2jX79+TJkyJWs3TDPc+vbtm5IxePBgvvrqq6yV19ixY6lRo0bKaGHYMPr06cP69ev58ssvsyJjwoQJHHzwwdSsWTMl4+CDD2bKlClZSX/WrFls3bo1LQ+9e/cG4IsvvsiKjHHjxjFw4EByc3MBkLicpk+fnpX0v/jiC3JycuhpD7bDhtG/f38mTpyYlfTHjh0LpLfXww8/PHV+TzHjt3/sn7jG2WfTtWvXrJX/1KlTad26NS1btkz1NSBrb3PGjRvH4MGDozcHw4YxcOBAtm7dyowZM/Y47fz8fD755JPUWyKGDeOwww7L2puP/Px8xo4dy+DBgwFofPHF1KhRg9GjR2clfYARI0YwYMAAevXqxYZjjuGDDz5g/fr1WUt/xowZfO973+PN3FyuvfbarI7Vaezt9kZF2TJ7CXu38bu3P4nFfPjhh3zUoUO5pJ2fn8+f//xnjjjiCF599VXebdGCp556KqsyNm7cyNixY1OvXfe/+WY6d+6cVeN3586dfPrppykZDB8ezeQQzVxki0mTJjFgwABEJFX3hx9+eFZfs48dO5bu3bvTpEn8fZfhw+nXrx/btm3LmmE6depUmjZtyv7775+SYQZetgzHqVOn0r17d/bbb7+UjGwajqrKpEmTCmZlYxl9+vRhyZIlWXltnTS8GD6cFi1a0KpVq6zM/KoqM2bM4KCDDio4OXw4vXr1YsaMGVmZIf/iiy/o2rUrdevWTaU/YMAAli1bxqpVq/Y4/YkTJ9KyZUvatWuXSr93794sWrSILVu27HH6kydPpkOHDjS1D5jE6Wdr5n3KlCmp2XyGD6dVq1a0bt06K8bvjh07mDVrVjTrG6dv7dWW7OwJM2bMYOPGjRxxxBGp9A8//HCWLFnCsmXL9jj96dOns3HjxtQbrjqXXcYhhxySNeN3/fr1jB8/nrPOOguAdj/+MXl5eYwcOTIr6UO0bCM/P5+fTZ3Kfvvtx6OPPpq1tEN2XXBBVt8yZsRnfiuMvdv4FdnrZfzmN79hyJAhHHX00Tz77LNZT3/s2LHMnz+f6667jho1arBq9WpeeeWVrH7F6qOPPiIvLy+1vlRycjjllFMYNWoU+fn5WZExc+ZMduzYUTBDJ0LXrl1p2rRp1ma4tm/fzhdffJEmA6IZr/nz57N27dqsyJkyZUqBsRXLyfZs1NSpU+nTp09kxMcyevTogYhkZUYKojrp1atXwQmRlPGbDcNlxYoVrF+/PpWmycjmsoSZM2eSm5tL9+7dU+kD9O7dOysG/KpVq1i/fj09evQoOClCr1692LRpE0uXLt1jGdOmTUs3rkVSM/7ZKKNZs2bRq1evtLbUs2dPVJVZs2btcfqTJk1KtX9Lv0+fPsydO5etW7fuUdp5eXlMmzatwPiN89CvX7+sLHuYM2cOeXl5BeUvQvfu3alfv35WjF97O5AyrkVSM/DZSN+W3tjMLyIMHjyYSZMmZWXsHj9+PKqaMq6PHjKE+vXrZ824VlX+/e9/c/zxx9Ola1fOO+88nnvuuax/pfHiiy8mJzeX73znO1lNtxDlaW9UhL20F7F3G7/l/RRWzjJWrVrFXXfdxdlnn80Rgwfzgx/8gO3bt2dVxnvvvQfA0KFDARg9ahRbtmzh7bffzpqMDz74gJo1axbMTqhyyCGHsHnz5qzNZJpRmLpJqiKxIZStmcxp06axc+fOAuM3rnszvrJhNG7dupWFCxemG42q9OzZk1q1amVlneauXbtSxm8oo27dunTs2DEr+dixYwfz5s0reNUey2jatClt2rTJiuFobSdlmMYyLF/ZkDFnzhw6duxIrVq1UulDZGxMmzZtj9dHm3GYLCer/z2ti23btjFnzpz0BwTVVJnNmTNnj9KHKA9pxnsW9d+xYwezZ88uME7j9Hv37o2q7vHSkLlz57J9+/aCvhDXb9++fZk2bVr06e89wNpgqvxVyc3NpXfv3llZ1jJ9+nRycnIK+kBQ9jNnztzj9CdOnEjjxo3p2rVrKv3evXuzbds25s+fv8fp29KYQfZpXVX69euXtfXo06dPZ+7cuZxzzjmgyre+9a3UZt9s8eabb/L0008zcMAAnn766XJZNpiiPG2airCX9iL2buN3xIhyF7H5kUfKLe0HHniAbdu2cc899/DQkCGsXr2al19+Oasy3nvvPfr16xetkwWOXLSI2rVrZ3VDw6RJkzjooIMKXn+PGJHVV38QbbqpXbt2wU0grvuDDz6YqVOnZmUTlxnRqXWysQy72WTjZjZ79mw0NnZTjBhBzZo16dWrV1ZkLF68mE2bNqUbv0FesmH8zps3j/z8/HQjPpaRrVlTM367deuWJqNt27bUrVuXuXPnZkVGmnEd56FHjx5s3bp1j9cum/GbZjyOGJHa/LandTFnzhzy8/PTZ35HjKBNmzbUrVt3j43fNWvWsHbt2kL6d+/enZycnD02wObPn8+uXbsK1YG13T2dubb4qb4Q1++BBx7Izp07Wbhw4R6lP23aNHJycgrKJ06/e/fuWXnwnz59Op07dy5Y0jJiBE2aNKFVq1ZZMX7t7U1qVn/EiKwuXRo7dizdunUrWNISr//9/PPPszKzbEveTjvtNBgxgiOOOAIRyer97Z577qFLly58fP31dO3albvvvjtraReiPG2aCrCX9ib2buP3tdfKLektW7Zw4YUX8t8rr+QHP/hB1l7fhzz33HOcdNJJ9OzZkz6LFtGpU6esuoHZunUrn3zySWo5AkCNt95i0KBBWXV5NXny5PTXlq+9Rq9evahbt27WjN/JkyfTp08fatSokZIBkfG7efNmFixYsMcyZs2aRc2aNencuXOajA4dOrDffvtlxWi0NNKMxlhOt27dsjJTZzfdNAM7ltGrVy9mzZq1x+3ZbryZZHTr1o25c+fu8fq42bNnU7t2bdq3b58mQ0To0qXLHhu/qsqcOXPSjes4D126dAHY49mvWbNmUadOHTqEa/pfe40WLVrQtGnTPW5TVgbJPOTk5NC1a9c9bk8ZjffXXqN27dp07ty53PTv0qULubm5e6y/xU8Z13H92kznnrYhW29dp06dtPS7d+/O0qVL9/j1+/Tp0zOOFT179syK8TtjxoxC6dvxtGnT9jj9cePGccghh6Sl379/fzZv3pyVsW7cuHG0atUqGiNee43GjRvTu3fvrLnJW7t2LR9//DHDhw+nzjvv8O1vf5tPP/2U1atXZyV949FHH43GtD//OavpplGO9tLeyN5t/Jbjk8wf//hHnnvuOZ465RT++te/MiLLshYuXMisWbOiJ1ZAnn2WM888kw8++IBt27ZlRcbkyZPZsWMHRx11VMHJ+Ol4woQJWVlisWLFClauXJlu/I4YQY0aNejbt29WZ35TM7KxDCBrM0QQ3ei7dOlSYGDHMmwnfTaM35kzZ6a/xgzkdO/enQULFuyxxwe7oadeZQYyevXqxfbt2/d4xstuvMkZQYgMxw0bNuzxGunZs2fTtWvXlJeEUEbXrl2ZN2/eHqW/fPlyNm/eXGhmGQqM3z2VMXPmTLp16xZ5AghkmAG/p8a1xU89sMXpA+Vn/AZtaU8NMNMv2VZr1KjBAQccsMflP2/ePFq3bk29evVSaYfy9tT4nTNnTsaysf69J+nn5eUxe/bs1FuCMP1sbJhct24dq1atKvT2pn79+nTq1GmPZ37N60vakpZ45hfIyl6NcePGMWjQoGjmOi6bI488kjFjxmRlwurtt99m165dnH766TBiBMOGDUNVefPNN/c4bWPUqFFcccUVbNq0iW7jxvFaeRmpPvObxt5t/J5xRrkku2HDBu677z7OPPPM/2/vPMPrqI4G/I6q5d673OWCG+50DKaYTuj9CyQYklBCSaixMYEEEiAQCAHTe6+hGQI2hlDcMO5Vlmy5ybZc5aI234+79/pW6bZVnfd59Ohum5nds3t2ds6cc/gkNZUhQ4Zw7733JjX668259Q7szmmnMW7cOPbt25e0fCVv5RLQW/600zj00EMpKSlJSuXj7TQS4Jg65eIdvivRCODWrVvZsmVLYCXt6PA29ybL+Q14kfndX8lKF1iyZAk9e/Y8ECny05OTk0NZWVnCjumqVavIyso6MNKDnw7v+SUjotapUyeaNm0aoiNZjmNISkKQjlWrViWU7hISFfSTn52dTWpqasLnkJubG+jY+eno2bNnwi0Wq1atolWrVrRs2TJEfk5ODrm5uQnVW8uXLyc9Pd0z9F+Q/F69erF69eqEnu8VK1bQsmVL2rRpEyI/GR8Hq1at8t2P/rLbtWtHs2bNEnJOVZXc3Nyw8pORc7169WpKS0sDW1cc+f3792fbtm1s2bIlbvlhW6H86tVEnV/vh1Gw/AEDBpCenp5wX41du3axZMmSA5Flx/ZDDz2UXbt2JaUz5qeffkq7du08Ok47jWHDhtGpU6ekjmR033330b59e1auXMkXWVk89NBDSZMdgEv+Ul2lTjq/FRUVvPvuuyw49FBXhh5588032bZtG7fffjty1VXceuutLFu2jBkzZiRNxxdffEG3bt0OOFsTJnDUUUeRkpKStBmyvGPW+oYocvR4Ox8ko9NBWOd3wgTAU+nt2LGDTZs2JaQjrJPi6GjSpAldu3ZN2JkrLy9n5cqVgc6vowM857JmzZqEmzFXrlwZ6tA5erwRyERzBVeuXEmfPn0O5PH56fBGCBN1KvLz8wMdIj8dyXB+KyoqyM3NDYzK+uno06cP+/fvT2i4p7ARckd+eno63bt3T+g6qSpr1qyhe/fugRscHT179iQ/Pz8h5zQ3Nzcw6usnPycnh5KSkoQmm1m9ejXdu3c/0BriJ79nz54UFxcn5ICtXLmSnJycsPeq9wMnEUKcX0e2OKPFJOL8FhYWsmfPnrDyvfdUIs+y9yM4oHwd+d56KpHIe1jn10/+ypUrE/q49MoPcN4nTCA9PZ3evXsn7Jz+9NNPqNO52isbSOpoMN4xllNSUmDCBFJSUjj66KOTlja4atUqpk6dyvXXX0+zZs3Yfu65TJs2LWljgHvZvn07s4cPT8rQh/WFOuf8qipnnHEGZ599NofdcQd33HFH0nW8//779OjRwzOkzNixnHbaaWRkZPCfJE4P+OOPP3LEEUccqPTHjqVFixaMGDEiaU723LlzD4xZ62XsWLp27UqLFi2S0qFh6dKldOzY8cCYtY4OIGm9ksM2vTo6IDm5svn5+ZSUlETU4X0BJRqVXb16NT179gxc6ehJVg99r/MbTkfHjh1p1KhRwhHHvLy8UKfO0eG9Vok4Lhs3bqSkpCTUwXZ0JMPBXrNmDSISmFPsV+aJOl9FRUUUFxcH5vv66ejZsyelpaUJOfAhkUc/+cnIWw7rvPvZDyR0L61YsSLivdqrVy+KiorYvn17XLL37t1LQUFB4PXxK99EnV/vvRHgnDrymzRpQpcuXZLi/AY8A0HPWCL10fLly8nIyAgsX0d+Mj4uly5dGtiHwk9+v379Ev7I9zrXvs6Mjuz+/fuTmpqa8PutqKiI3NzcA861I3/MmDGsXbuWDRs2JCQf8KVPnH/++QAcNXEiQFI7vm/bto0jjzySY+6+m379+iV95tW6Sp1zft944w0++ugj7r33XgrT0vjb3/6WtKGuwDMV8H//+1/OPPNMj9PYuTNNmzZl3LhxfPjhh0mJNK9fv56CgoIDMyYBdO4MwKhRo5g7d27CoxeEjFnrp0ec8ViT8WUc0mHI0QEHvvgTTRdYvnw5aWlpgU6jowOS4/x6HeyAqKyfjmS86Hfs2MG2bdtCHTpHT9u2bWnRokVC51JRUREa7fLTkZKSQo8ePRJyiCoqKli7dm3E88jKyqJz584JO6ZAqOPo6PCeXyLOy5o1a+jcufOB2eP85Ht1JOMcQpxHR0ei91R5eTl5eXmhkV9HvvfarV27Ni754DmHSGWQqP1lZWWsWbMmov2JOu9euwKca7/y7dOnT0I59mHzrYPqjPz8/Lhkg+eDPDU1lc5+MoPLNhHnNy8vjx49egTm1Add+0Tu/yVLlpCTkxPYauDI90aWE03JycrKokuXLgGyGzVqRE5OTsLvt9mzZwMccH4d+d73djKmo//000/p06eP73q3P/hghg0bxhdffJGwbC933HEHy5YtY2tmJqWlpVx55ZVJk12XqVPOb0VFBXfccQfDhg3jlltuYc/GjTRv3jypQ4989dVX7N+/n9NPP92zYtcuAE477TRWrVqVtB6qQKDz6+gZMWIEu3btSkpHjLKyssDhrvz0DB48mAULFiTszId1fh0dXbt2pUmTJglHfpcvX06vXr0CnRRHB3ic361btyY041fYF6WfDu+LPpGXjfdFGOI0OnpExJdHGS/r1q1j//79oc6v37n06tUrIed3w4YNlJaWhjp1fjqS5TiGOF6OjuzsbEQkoShGWMfO7xx69OhBUVERu/zWxSofIp9Dos5jQUEBpaWloc6j3/MH8Tu/JSUlbNiwwTX7169fT0VFRcT7KNEWBO9xAc9CUPmWlZXFHcFbtWoVIhL4PPvJ79atW0L3Z15eHtnZ2YHOoyO/UaNGdOzYMSHn2uv8BuDIT8bHZcgkOH7y+/btS0lJScKR64DOpH7XfvDgwQlHfr3vaV+fGUf+sGHDSE1NTXh673379jFt2jROOumkAyt37eKEE07gu+++i7ve8Wf79u288MILXHzxxWTs28ekSZP4/PPPkzZ7Yl2mTjm/3377Lbm5udx4442kpqbS5p13uPjii/nggw/ibhoLZsaMGWRkZByY8WbKFADGOk0eyRhCZebMmaSlpQWOkODoSVZP2LBDUfnpGTx4sK83brzs3LmTTZs2hTq/jg5xZoJKRtpDSJ6sowMO5Mom2rmkUaNGdOjQIayO9u3bk5WVlZBjGrYZM0hP9+7dE44WAaEOkZ8Or/Mb74dPNOeRjKgXhHEcHR3p6el06tQp+VFNv3NINHJa1Tl069YNEYn7nor4MeXIz8zMpEOHDnE7YAUFBahqqHPqyG/atClt27aN237vdQ1IO/GT772Hk3p9wpRvvNcnNzeXLl26BHZeDZK/du3auFvxwjqnSawrKpPfrVs30tLS4v7wKC8vZ/Xq1aEpLY58b3pZIqkPIR1i/a7N4MGDyc3NTaiPxpw5c8jJyTnQmdSRn5WVxeDBg32R4Xj56aef2Lt3L8ccc8yBlVOmcPzxx1NaWso333yTkHyAl19+mT179nDttdfClClcfvnlpKenJ3VI1bpKnXJ+X3rpJZo0aeKZzQVg9mwuu+wy9u/fz1tvvZUUHd988w2jR48+UKE5N3i/fv1o1apVUgbPnjNnDoMGDTowcLmfnoEDB5KZmZnwEGFhm/H99CRjlqywY3T66YDEx6NUVVatWlWpjmQ4v94c1oD8aD8d3giPK86vnx7vCy1exzRitNFPR8+ePdm5c2fckXLvCzfEKfLTkZ2dzfr16+NuUl6zZg0tWrSgRYsWEXUkEllTVdauXVvpdfI6ZfE6v2vWrKFRo0a0a9curI7MzEy6dOkSd/QrmrJO5BpFdN6D7qV4n4mq7G/evDktW7aM2/41a9aQmZkZeP2Dro2/HbESNn8/SH5paSkbN26MS35Y5zSoroj33tmzZw+bN2+O+AynpaXRo0ePuJ3f9evXU1paGvH6eJ3feDu9lZaWkpubG/h+C3rvqGpCkeslS5YEzpzoJz8ZM/h5I8djxowJ0HHIIYeQmpqalFGfPvjgAw466CBPUG32bNq2bcvpp5/OK6+8kpSJoeoydcb5LS8v59133+XMM888MGbjlCmMHDmS3r178+GHHyaso7i4mLlz53LkkUceWOl87aWkpHDYYYfx3XffJaxn0aJFoekIfhGtQYMG+abzjZdly5bRtWvXwKGo/PQko3OV99hIkV/wNJ8VFBTEPY3o5s2b2bNnT2glGhTJFJGEKrqqoizgedEn0ky3evVqmjRp4pttL5ye7t27s3v37oQd00jRNEg8oua9BlVFTcvLy+NuUg4blQ3SkZ2dHbdjunnzZvbv31/pdUqG8+uN7lamI175VUVOITHnN6Jz6ie/R48eCTvvbtm/du1aunbtGjjGcpLLN1JUHA58HMZjf0lJCevXr69Ufo8ePVizZk1cTkxVrQaQWOqS956IVG+3bduW5s2bx51+lZeXR1lZWcTIr/edFO87obS0lJUrVwa2nPrJP+igg1i7di07d+6MSz54Or137do1MKd7yhSaNGnCoEGD+PHHH+OWDZ7+S19//bVvLgGv/b/4xS8oLCxMylCndZk64/zOnj2boqIiTj311AMrx45FRDjxxBOZNm1awvO0z5w5k7KyskDn16938OGHH87SpUsTGrx/+/btFBQUBE5HGqTnoIMOSriT2NKlSwNHLgjS0759e5o1a5YU5zdSb23wOFoVFRUJRVcgTCXqp6NRo0Z06dIloTzWsM6vnw4gKZHfHj16hDpDfnq8jka8zZlr1qyhbdu2Bz4Qw+jwvlDj1ZGfn0+7du0q1ZFoVC0/Pz+88+unw+s4xhMlj+jY+cnv7HQOTSTtoapzSNQ5bdOmzYFpxcPIT8Y1CnFOg+yPV/7atWtp2bIlzZo1q1R+ItensvJt1qxZ3JHl8vJyCgoKKpWfyDPgvaaV1Undu3enpKQkrqEkI7ZCBdXd8dZ33uMiyRcRevbsGXed7U2XCHB+g0ZqgfiDO7m5uZSVlQU6v0HvaEhsJKOZM2cG9vvx0zF69GhmzpyZUJ+cL7/8ktLS0gPOryP7+OOPBw7MNdBQqTPO72effYaI+AoOgLvuAuCEE06guLg44ais90vIOw6uvw440Oszkaist6kkoDklSM+AAQNYt25d3F+VqsqyZctC83399IhIwqMkrFixgq5du4a+fP3OJdFxZSNWon46ILEB8Xfv3s2WLVuq1NGzZ0+2b98ed355WAc7SE+ijmnEiKmfjmQ4piERqQg6khpVC6Nj7969cX2MRnR+/eQnmlcczTlkZ2f7cmvjkR9NWRcXF8fVkpCfn0+HDh0Cc1qD5GdnZ7Nv3764y6Aq+xPJaw0rP+iZjte53rhxI+Xl5ZXKT+Q5i+ichqkr4om8Ryu/qKiI3bt3xyx/9erViEjo/R/0bojXuQ7b6ugnu1mzZnTo0CHuyG/YMZD95Hud33hTH4qKili1alWo8+voGDNmDNu3b0/o/fzll1/SuHFjDj/88ADZ7du3Z/jw4Xz22Wdxy64P1Bnnd+rUqYwePTpwJiDnq++YY44hNTU14eFB5s6dS3Z2dmCOmN+XpXfwbO/EDvHgzbENifz66Ul0fNyNGzeyc+fO8JFfPz2JTn8adqSHIB2JOr8RK+mgXOZevXrF3UQXsQkwSEeiIz5EdH799CQjKhvWofDT0bp1axo3bhy38xvNeXijhfHo2LVrF9u2bavyPBJpto7o/AaVebxpCfv372fjxo1RncO+ffvimigibM5ykPxEHLCIzmkSyyAkqhwkv1u3bmzfvj3mQEBZWRnr168PlZ+k8o3m/mnevDktWrSI61n21jEhzqOffO8zGK/8jIwMOnbsGFF+InVRXl4enTt3JjMzM6J8b754PB9+y5cvp1WrVmH9AS+JjOMcdvr2INszMzPjdn69fkDA5FB+OryBtkQ61X333XeMGTOGjIyMANkAxx13HD/88AN79+6NW35dp044vxUVFcyaNYtjjz02cIMToW3evDkHH3xwwrOu/PTTTwwbNiysDvB8MXXs2DEh53fRokU0adIktNL00+N1fuNNffB2Iggb+fXTk5OTQ15eHqWlpXHpiej8+uno1KkTmZmZCUV+27VrF5q77B+dx+P8btiwgT179sSsI6KDHaTDuz2eaIU3YhzWafTT07ZtW7KysuJ64ahqZOfXT4c3IhOPQ+TVETai6acjkSZlrzNS1Xkk6tg1btyY1q1bR5QP8TtH0Z6DK85jEuVXVc6JfOREdN7DlHGs9nuHUausnvXKj/f+gTApIUHy433O8vPzSUlJCZydM0h+os5p9+7dA/Ohg+R766p4PvZXr15dZV3Xq1cv9u7dG1fahnekh4AUsqBrn0jL5tKlS+ncuTPNmzcPKz8tLY1+/fol7PyGtAA7Ovr37096enrcQ5Lt3r2befPmcdhhh4XIBjjssMMoKytr0Hm/dcL5LS4upqys7ED43ovf7DOHHHIIs2bNinvQ7OLiYpYuXRo6KUTQDDdDhw5NOPI7cODA0ErHT0/v3r1JT09P2PkNG/n105OTk+MbkiZWtm/fzpYtW8I7v346Ep1UIWIlGlQu3hyvpDYBBulIZFzTiB1AgvR4HdN4Xmg7duxg9+7d4R2WoHPp1q1bXDoKCwvZt29f1DriffF7j69MR6KOXdjOaEHnEG/ObMQJLoJ0xOs87tixgx07dlR5jeL9QPBOzRyt/FjLoLi4mKKioirlx+vgRewMGOYe9c7EFwsRI79Jegby8vLo2rVr4NjmQfKbNm1K69at467zqqpXE3Guw46EESTfuz2ed0PIMGdBssET+V2/fn1cw50tXbo0NHgUJP+ggw5KyPlt0aLFgQk6gnRkZGQwYMCAuCfw8vpCAT5TkL8EJG2a5rpInXB+vTlHvrF3vTz4oO/nmDFj2L17d9w3488//4yqhjq/fjrA4/wuXrw47s51ixYtCk15CNKTlpZGTk5O3M7v0qVLady4cWjUIEhPIj1iI470EKQDEsvtysvLC1+JhtEB8Q2In5eXFzrGbxgdrVq1onnz5sl1sMPoidf5rdRpDNKRqGMazXkk2qQc1nH009GuXTsyMzOT26Qf5hz27NkTc85sROcoSEe8zmOlkWU/+e3btycjIyPma7Rlyxb27t1bpfx27dqRkZERt/1hI9dhrk+s9ke8/mHK19+eWOS3aNEiMDIYRn4izq+bdUXY6cmD5Hfo0IGMjIyY5ZeWllJQUFCl/fGOOrNnzx7Wrl0b6vwGXRtvR+xYnWtVDe/8Bsk/6KCDyMvLi8u5XrRoEYMGDQr9+PbTMWTIkLidX69T63Vyg2V36NCBnj17JmU4tbpKnXF+Bw4cGNpEGeZLJt7C9Ib/Q9IewkR+S0tL48rH3bJlC5s2bQpt6gijZ8CAAQlFfvv27RsaXQ7S460c4mkaqtT5DTqXeGcUq6ioID8/P6bIbzx6wo7xG0ZHImP9Vur8BumJ94VWqcMVJiJVWFgYc85XxFzECDridUxTU1Pp1KlTpTq8zcKJRH4rkw/xO0fe8gv7AeqnI14HvlLnMQnXKOJYzhHkJ805DZLfsWNH0tLS4pYfTeTXf/9oyc/Pr/Lae+XH02ksonMaJL9Hjx4x1xV79uyhsLCwyrooJSWFbt26xfyx753Yo6rIr1d/rHW2N1hTVeQ33vHfN23axI4dO6KK/Ho7l8eCqrJw4cIq/YDBgwdTUFBAUVFRTPLBkyuck5NDq1atwsoGTzDx+++/T3iW17pKnXB+i4uLQ1MeIOBLpk+fPrRu3TrusfF++ukn2rVrF9oMESbyC/F1elu0aBEQprNbGD0DBgxg1apV7N+/P2Y9Yb9aw+hp164dzZs3j9v59U7HW5kO8Di/27dvjzl6tn79ekpKSqKK/LZp04ZmzZrFHfmNJsoC8Q/qn5eX52umrEpP9+7d2bJlS/KaYiPogPiduqqiRl474nnxr1mzhq5du5KamhqVjlgdl3379rFp06aoI78QX2TQm+9emY54P6hiKet4rlEs8rOzs12Tn5qaSnZ2dlxpD2GHUQtzbfztiZbc3NzQKcSTJL+0tJR169ZFHfnNy8uLyYGJpfUmHuc64vCUQfIbNWpE586dY773ve+rqiK/8U7R7A06RRP59d8/WjZu3EhRUVGVfoC3g308eb9z5sw5MC1zGNngcX7Xr1+f0CyZdZk64fyG5K548StcEWHMmDEJRX6HDx8eGv0LuoH69etHZmZmXM5vxCT3MHoGDBhARUVFzI7pvn37yMvLC5/vG6QnkeHOli5dSo8ePUKHQQrSAfGP+FBpnmyQDq8jHm/kN+yLILjyIP4eyrm5ueHH+A2jJ97B8fPz88nMzKR9+/ZV6vBG/WOdXnTFihW0adMmdOa1MDribdKP2GkvjI54UisKCgoC7KtKPsR+Dnl5eVGfQzy90iuNjocph6Q6p0Hyc3JyYr6P1q5di4gEDvAfQX689kdjezxjOVdUVER2fpPwLK9evZqKiorwgYUw8vfs2RPTUHOVtkIFye/duzfLly+Pqb6LODxlGPnxjPXrvddCxpcPkt2iRQvatWsX87PlbdUNGOYsjPw+ffqQlpYWc6pltH5AvM7v5s2bWbNmTeCQrUGyIfHW8rpOnXB+AY444ojQlUEzcB1yyCEsXrw45mFx9u/fz8KFC0NTHsLoSEtLY+DAgXE7vy1atAhf4QfpiXfEhwULFqCqvgenKj3xOr/Lli2L7GAH6YjX+a20Eg3SAfHNSBRxjN8IOvr378+ePXtibgqcP39++MoujJ54O5p4e/+HTXeJcH95WyOiZcGCBVGfRyJR07CR5TA6unXrxrp162KaRrlSxy5IfocOHUhLS4vpHFSVn3/+OerrlJOTw8qVK2NyMNauXUuXLl3CR8fDlEOsU00vX76cZs2aBQ4lFUH+QQcdxJYtW9i8eXPU8tesWUPnzp1DO3SFkZ9U5zdIdnp6Op07d45J/oYNG9i7d2945zeM7V57osXr7ITMAhpGfjypA5U6v0HyBw0axLZt22KaqTEvL88Xsa9Kfjz9QZYvX07nzp0jzl7qTzwflkuXLqVJkyahrcBh7p2+ffvG7AtU6vz66ejUqRNt2rSJOe93zpw5AKGR3yD7hw4dSlZWVoPt9FYnnN+0tLTw0b+gJq1DDjkEVWXWrFkxyZ83bx5lZWWhnd3C6IADIz7EGv377rvvGDVqVPjoX5Cefv36ISIxO7/e3OWw5xJGT58+fcjPz4+pA19FRUXkSTTC6Ii3V6+3UgzrCIUpF29FGst0n5U2AYbR4f2ajmX8xa1bt5Kfnx/+4yqMnkSc34jRxiAdLVu2pEuXLjFFLby5amFfymF0xPPi37x5M/n5+aFRlwg6srOzqaioiOnl7HUuQiJHYeSnpqbSpUuXmJzfdevWsXXrVg4++ODwO4R5BouLi9m4cWPUOmIpa+9U0+uDcv4q44cffmD06NFR1VXxNP/GYn/37t1j/sBZu3ZteOcrzDMdq3Pt/cCO5v7p1KkTqampMT3L8+fPJyUlxXddK5PvrVNiSfebO3cuzZs3D99qECTf66DF8pG8evVqsrOzSUtLq1J+z549KSgoiOn9E3akhzCyIb7gzsyZMxkyZEjovR9Gvre1ORZfYOHChbRv3z5wPoEwOkQkrk5v3333HSkpKaE+QJD96enpjBw50pzf6kRExovIMhFZKSK3VrV/06ZNw1fC/lMdg6+y/uqrr2Ky56OPPiIlJSV0HOEwOsDjWG7evDmmRPetW7cyf/58xgZNmRtJT+PGjenRo0fMg1zPnTuXVq1aRY6cBenJycnxNeNFy7p169izZ0/kyG+QjmbNmtGuXbu40h46d+4cPrUiTLn07t2b/fv3x+QIeWfrC9vEGEbH4MGDycjIiKlcvDoifpAE6fG+MGONLkccfzeMDvA4LbG81PLz89m9e3dk5zdIR+fOnUlJSYnJsZg2bRoA48aNi0pHPKkVU6dOpW/fvuGdozDXKdbUip9++gkI03k2gg6vExVLhKpS5zFIvrefgvfaVkVxcTHz588PHV0ngnzvh0osH1IRxygOIz9W53337t2Rh1FLQvl6nd+wkd8g+ampqfTp0yempuUFCxbQp08fsrKyqpTfvXt3unfvzowZM6KSrap88cUXHHvsseFbiILke/NSvdHKaIg4PGUY+b169aKioiKmOiKi8xumbPv06UNBQUHUaSHbt29n5syZHHfccVHJP+yww9i6dWtMDnbEzm5hdAwZMoQFCxbEFND59NNPOeSQQ0JHIglj/7HHHsvMmTNjemfWF6rd+RWRVOBfwEnAQcCFIhLmE/cAEV/oQXlmLVu25OSTT2bKlCkx9WJ///33Oeqoo8I38YXJZTvnnHNIT0/n8ccfj1qHt3KK6PyG0XPeeefx8ccfx+Q0zpkzJ3zucgQ93kjmM888E7WOSscRDqMDPJXckiVLYs4di1iJRtABsQ139vjjj9O7d+/QJqIIOjIyMhg6dGhMzm/EkUQi6ElLS2PUqFG88sorUU/asW/fPjZs2BDZIQpzLgMHDmTJkiVRV6yVNseG0RHP9MD//e9/ad68efjyCKPDe77Tp0+PSv6+ffuYPn06J554YlTyAV+Hq2iv008//eSL2kSjI9ZRVyoqKigoKIjsPAbJHz16NL169eLll1+OSv6cOXMoLy8PHCapEvnZ2dk0bdo0aud32rRprFy5MnRq1wjyY21BqHQkjDDl6x3zOtr7dNWqVaSlpYV/L4WRf8kll/DVV19FXS/Nnz8/6nsH4KijjmLGjBlR1a25ubnk5+eHd+7CyG/Xrh3t27eP+iN5yZIlzJkzJ+o6Itax04uKitiyZUt45zfMtfnFL34BwL/+9a+o5E+fPp2Kiorw1yeMfO8HYrTR06KiIt8wZ2EJ0jFkyBD27NkT9cfHpk2bmD17NieddFKVsgEuuOACVJU33ngjKvn1ClWt1j/gUGCq3/JtwG2VHTNixAgNy6RJIau+/vprBbRFixbapk2bKv9at26tgP7jH/+IWoeq6iWXXKKpqalR6WjTpo02btxYs7KydP/+/VHrWbdunaanp2uTJk2i1gPoH/7wh/A6Iui58sorVUSi1tG0aVMFdN26dVHruPHGG2Mql1atWimgl112WdQ6Vq5cqYA2bdo0prJ/+OGHo9ahqvqb3/xGU1JSor5emZmZmp2dHV5HBD0zZsxQQJs3bx6VjiZNmiigr7zyStQ6pkyZooC2atUqJh07duyIWsehhx6q6enpUV+rtLQ0Pf3006O+VhUVFXrqqadGff+2bNlSAf3oo4+iPoe//e1vMZVFZmam5uTkRH0OpaWlmpaWpllZWTHdt//617+iPoeJEycqEFM5b968OWr5o0aNirqcMzMztXv37rpnz56o5C9ZskQBbdasWVTymzdvroDOmDEjKtvnz5+vzZs310aNGkVtf+/evaO+NgUFBZqamhp1vQTo5MmTo5b/1FNPKaCtW7euUnazZs0U0GXLlkUt/9hjj9W0tLSobM/KytLWrVvrxo0bo5K/du3amOps7/P7wQcfRGW7quqpp54ak/2NGzcO/54OI7+8vFxbtGgR9bOblZWl6enpOm3atKiuz8qVK7VRo0aakZER070/e/bsqK/P8OHDffKB2VrNPmFN/YlW8xhvInIOMF5Vf+0sXwqMUdVrgvabAExwFgcB0be7GG7RFthS00YYgJVFbcHKofZgZVE7sHKoHcRTDt1VNUwycv0jTEa664Rrjw/xwFV1CjAFQERmq+rIkKOMasXKofZgZVE7sHKoPVhZ1A6sHGoHVg6VUxMd3goA/2SsrkD03ZANwzAMwzAMI05qwvmdBeSISE8RyQAuAD6sATsMwzAMwzCMBka1pz2oapmIXANMBVKBZ1W1qq6koaNXGzWBlUPtwcqidmDlUHuwsqgdWDnUDqwcKqHaO7wZhmEYhmEYRk1RJ2Z4MwzDMAzDMIxkYM6vYRiGYRiG0WCo1c5vrNMgG+4gIs+KSKGI2FjLNYiIZIvINBFZIiKLROT6mrapoSIijURkpoj87JTF5Jq2qSEjIqki8pOIfFTTtjRkRCRPRBaIyDwRiX4aTCOpiEhLEXlbRJY674sIc5U3XGptzq8zDfJy4Hg8w6PNAi5U1egnkDeSgogcBewGXlTVCPMyGm4jIp2ATqo6V0SaAXOAM+2ZqH7EM394E1XdLSLpwLfA9ar6Qw2b1iARkRuBkUBzVT21pu1pqIhIHjBSVW2SixpERF4AvlHVp51RtRqr6vYaNqtWUZsjv6OBlaqaq6olwOvAGTVsU4NEVWcARTVtR0NHVTeo6lzn9y5gCdClZq1qmDgzg+52FtOdv9oZSajniEhX4BTg6Zq2xTBqGhFpDhwFPAOgqiXm+IZSm53fLsBav+UC7EVvGACISA9gGPBjDZvSYHGa2ucBhcAXqmplUTM8DPwRqKhhOwzPB+DnIjJHRCbUtDENlF7AZuA5JxXoaRFpUtNG1TZqs/Mb1TTIhtHQEJGmwDvA71V1Z03b01BR1XJVPRjPLJWjRcRSgqoZETkVKFTVOTVtiwHA4ao6HDgJ+J2TMmdUL2nAcODfqjoMKAasz1QQtdn5tWmQDSMIJ7/0HeAVVX23pu0xwGlSnA6Mr1lLGiSHA6c7uaavA8eKyMs1a1LDRVXXO/8LgffwpC8a1UsBUODXEvU2HmfY8KM2O782DbJh+OF0snoGWKKqD9W0PQ0ZEWknIi2d31nAccDSGjWqAaKqt6lqV1Xtgecd8ZWqXlLDZjVIRKSJ0xEXp5n9BMBGCKpmVHUjsFZE+jmrxgHWKTqIap/eOFrinAbZcAEReQ0YC7QVkQJgkqo+U7NWNUgOBy4FFji5pgC3q+onNWdSg6UT8IIzKk0K8Kaq2jBbRkOmA/Ce5xudNOBVVf2sZk1qsFwLvOIEDnOBy2vYnlpHrR3qzDAMwzAMwzCSTW1OezAMwzAMwzCMpGLOr2EYhmEYhtFgMOfXMAzDMAzDaDCY82sYhmEYhmE0GMz5NQzDMAzDMBoM5vwahlFnEZE2IjLP+dsoIuuc37tF5HGXdP5eRC5zQ3Y8iEieiLStZPvrIpJTnTYZhmHUZmyoM8Mw6gUichewW1UfcFFHGjAXGK6qZW7piQVndrORqrolwvajgUtU9cpqNcwwDKOWYpFfwzDqHSIyVkQ+cn7fJSIviMjnTpT0LBH5m4gsEJHPnCmjEZERIvK1iMwRkaki0imM6GOBuV7HV0SuE5HFIjJfRF531jURkWdFZJaI/CQiZzjrU0XkAUfvfBG51lk/ztlvgXNcprM+T0Qmi8hcZ1t/Z30b51x+EpEnAfHT+7GI/CwiC0XkfMfmb4DjHMfdMAyjwWPOr2EYDYHewCnAGcDLwDRVHQzsBU5xHOBHgXNUdQTwLHBvGDmHA3P8lm8FhqnqEOBqZ90deKbZHQUcA/zdme51AtDTb/9XRKQR8DxwvmNPGvAbP/lbVHU48G/gZmfdJOBbVR2GZ8r3bs768cB6VR2qqoOAzwBUtQJYCQyN5YIZhmHUV8z5NQyjIfCpqpYCC/BMl+6ddnUB0APoBwwCvnCmjr4T6BpGTidgs9/yfDxO7CWANw3iBOBWR850oBEeB/U44Alv1FhVixy9q1V1uXPsC8BRfvLfdf7PcezE2f6yI+NjYJvfuRwnIveLyJGqusNPTiHQOeyVMQzDaGBYM5hhGA2B/eCJgopIqR7o7FCBpx4UYJGqHlqFnL14nFkvp+BxRk8H/iQiAx1ZZ6vqMv8DRUSA4E4WEo3dQDmB9XVIZw1VXS4iI4CTgb+KyOeqerezuZFju2EYRoPHIr+GYRiwDGgnIocCiEi648gGswTo4+yTAmSr6jTgj0BLoCkwFbjWcXYRkWHOsZ8DV3tzb0WkNbAU6CEifZx9LgW+rsLWGcDFjoyTgFbO787AHlV9GXgAGO53TF9gUdWXwTAMo/5jzq9hGA0eVS0BzgHuF5GfgXnAYWF2/ZQDaQmpwMsisgD4CfiHqm4H/gykA/NFZKGzDPA0sMZZ/zNwkaruAy4H3nLkVABPVGHuZOAoEZmLJ8VijbN+MDDTSbe4A7gHQEQ6AHtVdUN0V8MwDKN+Y0OdGYZhxICIvAf8UVVX1LQt0SAiNwA7VfWZmrbFMAyjNmCRX8MwjNi4FU/Ht7rCdjwd6QzDMAws8msYhmEYhmE0ICzyaxiGYRiGYTQYzPk1DMMwDMMwGgzm/BqGYRiGYRgNBnN+DcMwDMMwjAaDOb+GYRiGYRhGg8GcX8MwDMMwDKPBYM6vYRiGYRiG0WAw59cwDMMwDMNoMJjzaxiGYRiGYTQYzPk1DCMhRORiEfm8GvSMFZGCBGV0E5HdIpJayT4qIn2ilHeXiLyciE21iSivz24R6VWddhmGYSQTc34Nw6gSETlCRL4TkR0iUiQi/xORUQCq+oqqnlDTNkaDqq5R1aaqWg4gItNF5Nc1bVc4YnHCk0U018fZnluddhmGYSSTtJo2wDCM2o2INAc+An4DvAlkAEcC+2vSLsMwDMOIB4v8GoZRFX0BVPU1VS1X1b2q+rmqzgcQkV+KyLfenUXkBBFZ5kSJHxeRr73RQ+++IvKAiGwTkdUicpLfsZeLyBIR2SUiuSJyVTQGishkEXnU+Z0uIsUi8jdnOUtE9olIKxHp4URU00TkXjxO/GNOU/5jfiKPE5EVjo3/EhGpRH0jEXnDsXmuiAz1s6uziLwjIpudc73Ob9toEfleRLaLyAYReUxEMpxtM5zdfnZsOz/MOf/SicA/6lzrpSIyLkj3h06kfqWIXBmke7aI7BSRTSLykLO+yuvjH5EWkRYi8qJzfvkicqeIpPjZV1lZ/9Ip413OtosrLWTDMIwkYc6vYRhVsRwoF5EXROQkEWkVaUcRaQu8DdwGtAGWAYcF7TbGWd8W+BvwjJ9zWQicCjQHLgf+ISLDo7Dxa2Cs83sUsBE42lk+FFimqtv8D1DVO4BvgGucpvxr/Daf6sgZCpwHnFiJ7jOAt4DWwKvA+44DngL8B/gZ6AKMA34vIl5Z5cANznU41Nn+W8e2o5x9hjq2vRFB9xgg15ExCXhXRFo7214DCoDOwDnAX/yc40eAR1S1OdAbT0Q/gCquj5dHgRZALzzX+zI85eZvX0hZi0gT4J/ASaraDM89Mi/CORqGYSQVc34Nw6gUVd0JHAEo8BSw2Ykodgiz+8nAIlV9V1XL8Dg4G4P2yVfVp5y80heATkAHR9fHqrpKPXwNfI4n+lgV3wM5ItIGOAp4BugiIk3xOGVfx3ja96nqdlVdA0wDDq5k3zmq+raqlgIPAY2AQ/A4z+1U9W5VLXHyZJ8CLnDOdY6q/qCqZaqaBzzJAYc9WgqBh1W11HGQlwGniEg2njK7RVX3qeo84GngUue4UqCPiLRV1d2q+kOMehFPp7jzgdtUdZdzDg/66YBKyhqoAAaJSJaqblDVRbHaYBiGEQ/m/BqGUSWqukRVf6mqXYFBeKKJD4fZtTOw1u84xRN99Gej3/Y9zs+mAE5k+QenqX47Hme6bRT27QVm43Eej8Lj7H4HHE58zq+/w77Ha18E/M+3ggPR1u5AZyetYbtzPrfjOH8i0ldEPhKRjSKyE/gLUZxrEOuca+wl39HdGShS1V1B27o4v3+FJ51lqYjMEpFTY9SLY2uGIzecDohQ1qpajMdxvhrYICIfi0j/OGwwDMOIGXN+DcOICVVdCjyPxwkOZgPQ1bvgpDN0DbNfCCKSCbwDPAB0UNWWwCdAZfm2/nwNHAsMA2Y5yycCo4EZEY7RCOtjIdv7w0l16Aqsx+MUr1bVln5/zVT1ZGf3fwNLgRwn/eB2oj9XL12C8pG7ObrXA61FpFnQtnUAqrpCVS8E2gP3A287qQjBVHZ9tuCJIHcPp6MqVHWqqh6PJxq8FE9U3DAMw3XM+TUMo1JEpL+I3CQiXZ3lbOBCIFxT+cfAYBE5U0TSgN8BHaNUlQFkApuBMqdzVCxDqH2NJ+d0saqWANOBX+NxQDdHOGYTnnzVRBghImc55/t7PKNg/ADMBHaKyC3i6XSXKiKDxBkiDmgG7AR2O1HP38RhW3vgOifH+FxgAPCJqq7FE/n+q4g0EpEheKK9rwCIyCUi0s6JVG93ZJWHkR/RBieV4U3gXhFpJiLdgRuBKsc9FpEOInK643DvB3ZH0G8YhpF0zPk1DKMqduHpuPSjiBTjcewWAjcF76iqW4Bz8XRu2gochCcdocph0Zwm+uvwOFTbgIuAD2Ow8zsgiwNR3sXAPiJHfcHT8escZzSCf8agy58P8DThb8OT73qWk4NbDpyGJ194NZ5I6dN4OogB3IznHHfhiXoGd2q7C3jBSZk4L4LuH4EcR/a9wDmqutXZdiHQA08U+D1gkqp+4WwbDywSkd14rsEFqrovjPyqrs+1QDGeTnff4unw92wEW/1JwXP/rAeK8KSm/DaK4wzDMBJGAtPFDMMwkoeTBlAAXKyq02ranvqEiPwS+LWqHlHTthiGYdQlLPJrGEZSEZETRaSlk8PrzWONeTQBwzAMw3ADc34Nw0g2hwKr8DTFnwac6YzGYBiGYRg1jqU9GIZhGIZhGA0Gi/wahmEYhmEYDQZzfg3DMAzDMIwGQ1pNGxANbdu21R49etS0GYZhGIYRNRUVFaSkWIzJqBvMmTNni6q2q2k7qgVVTeofnvEjlwErgVvDbB8L7ADmOX8Tq5I5YsQIDcuVV4Zfn0yqQ0d902M6ap8e01E75Luto67b77b8arT9k08+0bvuukv379/vinxXqMvy67Lt1aEjCtnAbE2yT1hb/5Ia+RWRVOBfwPF4xvacJSIfqurioF2/UdV45pIPZOTIhEXUCh31TY/pqH16TEftkO+2jrpuv9vyq9H2RYsWAVBSUkJGRkbS5btCXZZfl22vDh3V9T6rIyR1tAcRORS4S1VPdJZvA1DVv/rtMxa4ORbnd+TIkTp79uyk2WkYhmEYbvL3v/+dPXv2cPPNN9OkSZOaNscwqkRE5qhqg/CSk52M1AVY67dc4KwL5lAR+VlEPhWRgeEEicgEEZktIrM3b94cXluzZonaWzXVoaO+6TEdtU+P6agd8t3WUdftd1t+kmW//PLLvPrqq2HlV1RUAFBeXp48hXX52rstvy7bXh06qut9VkdItvMrYdYFh5bnAt1VdSjwKPB+OEGqOkVVR6rqyHbtIuRfr18fv6XRUh066pse01H79JiO2iHfbR113X635SdZ9qpVq1ixYkWI/MLCQvbt2wfAP/7xD0pLS5OjsC5fe7fl12Xbq0NHdb3P6gjJdn4LgGy/5a5AwBVX1Z2qutv5/QmQLiJt49I2fXp8VtY2HUF6nnnmGRYsWOC6HteYPp1NmzYlN+IRRoc/M2bMYPHi4NTy5OpwjQh6Nm/e7IseuaUjqdQHHXX9HOq6/QnK37VrF1u2bHFFdjR8/Mc/MnnyZP79738HrJ87d26lx5WUlHD//fezcuXKyhXU4mufDPm7du2q8lrFKzth6vqzVV3vszpCsp3fWUCOiPQUkQzgAuBD/x1EpKOIiPN7tGPD1ri0TZmSmLVx6ti0aRMlJSWu6SkoKODdd99l586dyXcgXb5mqkrpv/7FE088waeffuqeoqDzmDZtGm+99VaVti1ZsoSo89yr4/6KoKeoqIjHH3+c//73v67pSDr1QUddP4c6Yn9JSQlffvkl68NFo6KUv3XrVvbs2ROw7qGHHuJf//pX5IPCyFZVHn/8cV8HtXj4/PPP+f777+nz1Vdht69YsSJivVNQUMAbb7zBvn37eOWVV5g3b15kRdV4/69fv55nn32W3bt3R19nxiA/HC+//DL/+c9/WL58edJlJ0wdebZqRHYdJKnOr6qWAdcAU4ElwJuqukhErhaRq53dzgEWisjPwD+BCzTeJ+s//0mC1bHp2LRpE0888QR//etfIxwQmfLycr755hv+/Oc/s3///kr1gKe57NVXX2Xr1ui+DV588cXA/LNwxHHNZs+eTVFRUZX77d27l7vvvpuPrroKgDlz5pCfnx+zvqiI8zzefPNNfvrpJ9d0xIWfnvLyct566y3WrFkDwNq1a33rE3oBhTkXVaWsrCx+mVHoSDpu66jr51BD9i9cuJDdu3dHLeLRRx/l22+/5amnnqK4uLhK+eF47LHHAiKs/nXqtm3bwh8URnZpaSmbN2/mgw8+YMGCBaxbty4q/f7P4/fff8/nn3/O6xddFHbfVatWcffdd4c4dcuXL+eZZ54hNzfXt+6DDz5g8uTJvPnmm1HZDzBz5kzef//9kPXFxcXs2rUrirMJlf/JJ5+wdu1aHnzwQb7++uvoZUQpP5jS0lIKCwsBeO211wDYsGFD9PVeFfdNWVlZQH23Y8cOXnrpJd99o6ps2rQJ8NxLkydP5oMPPjigP0j+li1b+Pvf/05ubi7z58/n3//+d9Qtdd988w2TJ08OfT8m+PwuXbo08ju3ut5ndYSkj76tqp+oal9V7a2q9zrrnlDVJ5zfj6nqQFUdqqqHqOp3cSuLUNEkFT8d8+bN44knnvAtP/TQQ1E5D2VlZXz44Yfcc889fPXVV1RUVIRWsI6ejz/+OGB1bm4ujz32WFi5kydP5i9/+QuzZ89m5syZrF69mhUrVlQelY7xmpWXl/Pxxx/z6KOP8swzz7Bq1aqI++7cuROAPhMn+tY9//zzbN++PSadUeF3Hu+8847vd6QX1/r16325eVG/pGO8Vrm5udx9993s3bs3YP3u3bsrz/lz9Lz33nvcc889LF68mA8/9DSYFBQUMG3aNO655x7+k0jlFeZcPvnkE+69915f5V5WVpZYi0Y1P491Uf6MGTNYNmJE8tJZgii/4AI+++yz5OWYhiPoGu3cuZN33nmHl156KarDt2/fHvAMhkT5HPm7d+/mnnvu4YUXXghxgObPn+/bx1u/PPnkk77t//znPwGPQzNz5kxf/q2/7fn5+WzcuNH3oVlaWsq7777L008/HdV5hIvQnvX225Ue89prr/HDDz/w/fffk5eX53PywrFkyZLQlRHuz08//ZSff/45ZP0DDzzAQw89VKlNkeT7f5QkLaUsgv0rVqzgL3/5S8C69957jylTpvDwww/HJNs7jmsw9957Lw888IBvefr06eTm5voi/j/++CNPPPEEa9as8d2f8+bN4ytvND/I9lmzZrFnzx5eeukl3nvvPQoLC3n88cejMtUrMyTFo4r6x/seDsfOnTt54403eP7558MfXB31cx2ibk89c2riQwVHo2PVqlWUlJTwwQcfBGzatWsX9957b5Uili5dWnW08dRT2bBhA5GGdJs2bRo//fQTU6dOZe/evXhHwCgtLeXjjz8OSDH461//yuLFi9m7dy+LFy8OrAhiuGbvvPMOr7zyim+5oKAgwNGMxPK+fQOWg6/b22+/zddff826desoKCiI2p4AnPN47rnnWLhwoW/1008/TXFxMWVlZWzcuNG3/qmnngrsmBKljq+//jpATjDl5eW+D4Jvv/0WVWXDhg2+7bt27eLBBx/kL3/5CxFHLTn1VLZv3+57oUNgVGnGjBkA0UesI+gIxnuv3X333bz66qv84x//iKtFozIdScdtHS7K3717N9OmTWNhjx6+iH6ilJeX8/XXX/s+wpf16cOPP/7Ijz/+WOWx77//PpMnT2bbtm0+BzAqgq7Rd9954heFhYV88sknvPfeexE/opYuXcojjzwSsK6goCAw+nvqqbz99ts8+OCDlJeXk5eXFxLVfO+993y/H3nkEfbu3RsS7f2XX/rVRx99BMC+445jzZo17Nq1i+eff54nn3wyoI7zMnnyZCZPnux7Djdt2uRbt3jxYjZu3Oj7QPUnuO4Lx9SpU/n888/Dp3wE4XU6y8rKWLt2LVsPPbTKY8BTf/hfj3379kUVQS0+5hhfnewftPDvf1BRUcHkyZP59ttvK5X11ltvMXnyZHbs2HFgZYTnK1yLpbc+9AZVqsSRfffdd3P33Xfz1FNP8eKLL/K23weJf+uA93o4WZi+Z+CTTz458LGE554NZ3tI6y3hU3GC8b/XQ94tQTq813r27NnMmjWLoqIiCgoKmDx5csAH5LZt2/jHP/4RcFwI1VE/1yHqxPTGEXHpS2bDhg107NgREWHriSfycoTIayK89NJLnHLKKcyfP5+KigouvPBCpvh9lQbjdYAAfvjhhyrlB+e/Tpo0iR07drDzyCMDeiRGIi8vL8Cp9FJZxMpbmSwcMiREVn5+Pi1atKBly5a+L+3pTgL+bbfdFnEQ+NmzZ/ui4ddddx2tWrXybHDKPtxL+5///CdpaWns2bOH4cOHM2bMmErO9IDta9eu5eeff2bBggWMHDmSEy66iOmTJzN9+nSuuuoqOnbsGHLcV199xXfffcfll18e9uUybdo03+/HH3+cSZMmheyzadw4VkaZb7hixQpycnJ8y4sWLWLBggVccMEFlR/oXK/S0lLmzZtH//79Q+R6KSoqonXr1mHFFBcXs2fPHsKOwFJPI7/FxcVkZWVRVlYWcp/u3r2bxo0bB0xh600nSU9PZ8+ePTz55JMMGjTI5yQuHDKEJS+9xDXXXEOLFi2YNm0aw4cPR0T4/vvvOeGEE6KeEnfWrFm+5+joo49m47HHwjffBDynmzdvpm3btnz44Yd06tSJ0aNHA/gihd4oabh708vq1atZs2YNRxxxBCkXXshLL77IyJEj6dSpU0CUedasWYCnDv3tb38bIiecwzd37lzmzp17QP9FF7Fo8uSAfebPn4+qcvrpp5OWFvra+tvf/hayzr/jmzdv9fHt29n13HP07t074rkGn3f37t0DWvzeeustzjrrrLD7B9d9lfHFF19Uuc9bb73FH/7wB/7+97/71nlLSVX5+uuvw6Yk/Pjjj0ydOtW3fP/99wNw8MEHc8YZZ0TU98D69fDMM4wfPz5k25///GfGjRvHqFGjAPjyyy9RVXr27EnXrl1D9vc67g8//DC33XYbb7zxBrm5uQx4803OPfdcNm3aRMeOHaNqGdyxYwfLly9n5MiRnvfy1q0sWbKEI444gnfeeYeFCxfSrFkzbvQ7xv9eO/roo32/CwoKeOaZZ3w2b9++nX//+9++lItNmzYFRP+9znFw3RDJyZ0xY0bY6+fFPxJfWFhIYWEh77zzDhdeeCEt/aLXb7/9tu8aBrcIAzz44IO+Z8b7DHv585//HFrWF13EzJkz6dSpE9nZ0XgB9Zu67fyKQCVfs2VlZYgIqampvnX79+9n9uzZ9O/fn1atWoW8ZJYsWeLLtbrwwgvp268f3HVXpWa88sornHvuuWEduGXLlkWMlvrf0E2aNq1STyLcd9997N+/n0l33RVyzfbs2UNqaiplZWWUl5eTlpbGCy+8EFaO92u3vLyclJQURARVJTc31+egT7rrLiYHnYu3KcZXkfjxwAMP8Ic//IEtW7bQqVMn3/rFixcHXKN//vOfTJw40SOjkrIvKSnxRZ68L9Zw3H///fTt25df/OIXLFq0KKCcvv/+e0448URfmTz55JP86le/ori4mH79+vn28+ZjFxcX+yrbl156iXPOOYeBA8MOYR2AqtKhY0eeiLLsX331VS677DL+97//cfbZZ/uiGjt37qR58+aRD3Su11dffcUPP/zAJ598EnHXRx99NKwjlJeXx+uvv87+/fu56aabaNq0aVgd4VBV1qxZQ7du3cLeA1FTxTOfMEHyd+3aFfCyGjt2rO9Fun//fh588EFGjBjBqU5URVV54YUXyM/P5/jjj/c5OF7HFw48H4888ghXXHEF33zzDStXriQzM5O8vDx69+4d8IETzMsvv0yfPn3YvXs3//vf/wB8kd9jx43jG+de8joFAOPHj2fevHnMmzePTz/9lIsvvjiqy/Hzzz8zZ84cX6RaRDjq6KNZfdddrF69OuJxmzdvDntPVhZ9/N///sfw4cPJatw4bF24YMECOnTowMyZM6Oy3Z/8/HymTp3KjTfdxOS77qo0fcufSKkc7777btj14eq+RAlwfO+6CyZNoqSkJGwrTUVFBSkpKRHzPr33wGmnnUbXrl1ZunQpRx55pO+Z9Nr/2WefhT3+yy+/5Msvv/Qte5vv/euLioqKkACN11av/LvvvjuKMz+AN/WhdevW9O7d25cK+MMPP/giqTfedBNLTjop7PFf+XVE9NrvjXD7B5bC4Wuxc+qG1atX07p164gtiT/++CP9+/enR48eYbcHB5C8eetff/01Z5x5JqhSWlqacKrJvHnzOP300xERdu3aRbPmzfnU79785S9/SWpqKl27dmXTpk0HItwNhKTO8OYWI0eO1FmzZvHll18ycuRIWrZsGbB97969ZGVlAZ7KNfjBuuKKK9i6dSstWrTgxRdfDNjmc6Ycvvnmm4AHJRYaNWrE6aefzoABA3zrJgdFMGoDv/71r+nYsaPvoyDYxmuvvZZHH3004vF9+vRh5cqVjB8/nq1bt/qiPYnQs2dPVq9ezamnnspHH31EWlpa2Hzq3/72t7Ro0QIR4b///W9cL8Jhw4Zx+umn+8570qRJfPzxxxFTToI57LDD+O677zjkkEPYvn07S5cupVGjRgFNZSkpKVx44YW8+uqrAS/8G2+8kWbNmrFmzRrmzZtHz549I75IY2X06NEcf/zxpKWl8eOPP5KTkxMQwV2zZg3PPfdcVLJOOeUURowYQVlZWUgunj+VRQv9WbBgAe+++y59+/Zl1apVXHnllcydO5dx48aRkZHB/v37mTNnDqNGjWLRokUMHTo0rJOsqmzcuJFOnTqxfPlyKioqAqLYO3fuZNOmTYgIKSkp9OrVKyr7vNx3330cddRRjBgxgvT0dDZs2BCSA3r44Yczbtw4Zs2a5Us3uvTSS/nss88ip7ZE4JxzzglolvVyxBFHMGrUKJYvX86uXbuqfEGDxzFfu3Ytq1at8j2jXg4++ODKRxHAU+YjR46kpKSEhQsXMnToUO65556Afdq3b095eXlUnXCvv/56X109Z84cX+qBkRiTJk3i7rvvTtoIDBdeeCF9+vRh7dq1kfNFq2DixIk8/fTTFBYWJrcTbRAXXHAB/fr1q/b36p/+9Cfy8/P58ssvo+4QOWHChIBgDnha1Sp7twK0aNEiMFWkEq699lqeffbZ0E6jMXDhhRf6cs/vuuuuBjPDW51wfocNG6Znnnkm4Pny69ChAyeffDLpb73FG6mprF692ud8HH744b5oSDQ0btyYm2++mbfffpuMjAxWrVoV0Dt20Pz5MTVlgacimD17Nr17967yRk9ETzz46+ncuXNUeWeJ6HCLgxcvZt5BB8V9fO/evX3RnxtuuIHnn38+JGfQjfM4+uijmTFjRsCLK9l6br31Vu677z4A7rzzTlJTU9k9ZQoP+uUiR0OPHj3o1atXpR+Df/zjH0lPTyclJYWU11+PmJbw2WefBeShpqamUl5eztixYxkxYgQPPvhgwP4dOnQgKyuLCy64gE2bNtGtWzcA1tx3H8/t30/jxo19zY7XXHMNbdq0ATytCP4vgptvvpmUlBSKi4vJyMioPDoOvHP22b6yGDZsGMOGDePZZ5+t9JhYcfP5qI5nL1odl156Kd27d6eioqLSD6h45ceD29enOuSf9fbbMUdOY5Ff26/PlVdeyVNPPeWK7MqIR/5ll11Gz549fctPPvlkpX1Iavreb0jOb51Ie/CPqBUVFVFUVMS6des47tlnWX3OOQH7xOL4woG8nUhNDH2XL4/5ZoynYopHTzz463HD8Q3W4Ra9EnR+/Zs9/TsK+OPGeYTLz0u2Hv/Iizdyd9bbb4PzrERLXl4eeXl5le7jzbXMysrirHfeIfvss8nMzPRt37FjB//85z9Dmvq841fn5eX5clb98Q455HXiu3XrRmFhIRd98gmMGxeQb/fYY48xYMAAxo8fHxIBeSBMHv2ll15Kr169KCws5N///jcnnXQSvXr1Yu3atQFl8dNPPyXWyTACbj4f1fHsRasj2tEf4pVf22RXl3y3HF+v/Np+fcI5vsmSXRnxyPe2NN9yyy00atSoUsc3Xh3RUl0+Rl2hTkR+O3furFc5Y8cahlH7ufrqq8nLy2PFihVR51cahmHUVzp16hQwElBtpCFFfuv0UGcXVDWhQx3RUd/0mI7ap6e6dTzxxBN89tlnSXd83T6Pul4Wdd1+t+XXZdtNfs3JTob8aBzfunx96hpJd35FZLyILBORlSJya5jtIiL/dLbPF5Hh8eqaO2JEYsbWEh31TY/pqH16TEftkO+2jrpuv9vy67LtJr/mZFeHfLd1VNf7rK6Q1LQHEUkFlgPHAwXALOBCVV3st8/JwLXAycAY4BFVrXQg1khpDxn791Pil1/oBtWho77pMR21T4/pqB3y3dZR1+13W35dtt3k15zs6pDvto5oZFvaQ/yMBlaqaq6qlgCvA8Ejap8BvKgefgBaikinYEHRcGNQD3E3qA4d9U2P6ah9ekxH7ZDvto66br/b8uuy7Sa/5mRXh3y3dVTX+6yukOzI7znAeFX9tbN8KTBGVa/x2+cj4D5V/dZZ/hK4RVVnB8maAEwA6NSp0wjr8GYYhmEYhuEOFvmNn3BTNwV719Hsg6pOUdWRlRXE8CgnJUiE6tBR3/SYjtqnx3TUDvlu66jr9rstvy7bbvJrTnZ1yHdbR3W9z+oKyXZ+CwD/SaO7AsGDyUazT1R0dmmc2urWUd/0mI7ap8d01A75buuo6/a7Lb8u227ya052dch3W0d1vc/qCslOe0jD0+FtHLAOT4e3i1R1kd8+pwDXcKDD2z9VdXRlcm2cX8MwDMMwDPewtIc4UdUyPI7tVGAJ8KaqLhKRq0Xkame3T4BcYCXwFPDbePX933PPJWhx7dBR3/SYjtqn55cvvMCpp57qqo7/e+45evbsSc+ePfn1r3/NMccc44qOqjjyyCM56aSTQtZfc801nH322VxyySW+dSP8hv+5+uqruf7995NiZ2W4Wd714bmoy9fH5Nec/Nps+w033OC6jpqUXRdJ+vTGqvoJHgfXf90Tfr8V+F0sMhs3bgzAxIkTUVVmz57NihUrmD52LADNmjVj165dcducnZ2NiLBmzZqQbV4dblOf9NQVHb/61a/o2rUrzz//PPn5+a7oiAa39Fx55ZU0btyY9PR0Mg8/nO4jRvDRRx9FfXyvXr3Izc3l0ksvrXSq2o4dO3L++eeTOXo0l/k5nV26dCEzM5PPPvss5JiUlBSGDRvmc8gnT54clU3RXKt+/frRpUsXPv30U9LS0rjtttsQEUSENm3aAJ4pjtu2bUvz5s3p2rUrjRo1okOHDuhDD8GMGVHZEi9u3ld15dmrKfl12XaTX3Oy45XfpEkTxowZQ/PmzV3TES3V9T6rKyTd+XWDli1b8qc//cn3Ahs9ejSjR49m65AhpGZn06xZM7Zv385jjz1Gq1at2LZtW4iMM844g8GDB7N582aefPJJ3/oLL7yQvn37ApCfn8/zzz8PwBFHHEFZWRkLd+6M2s5jjz2WI488ktWrV/vm9I6Wrc5L2W2C9bRs2ZLt27fHJeuUU06hefPmvPbaa5XqcINEdGRnZ3PFFVf4ln/5y1+Gdb62tmnDKaecQmFhIevXr2fdunW+bWeccQYffPCBb3nMmDH8+OOPvuW2bduyZcuWEJlHHHEE3377LeBxGjt27MjKGO6xWOjcufOBhYMO8v30OrVVcfHFF6Oq7N69O2Tb6aefzs8//8zo0aM5yCt76NCQ/caMGUO/fv145JFH6NOnDytXrgTgzjvvRORA39djjz2WTp068corr4S15bDDDqNDhw50TUmhoKKC9957D/BEbo899ljmzJlD06ZN2bRpk++8f/vb35KVlUVKSmgDV69evXy/Dz74YN9v6dePUXv3MmvWLM466yx69+7N3//+94jXqEWLFvziF7/w1RvREHzvNm3alN27dzN06FB+/vnnSo9t2rQpJ598Mm+++WbY7efecQcvffklmzZtitoeL7/61a9ITU1ly5Yt9O/fn7/85S9R2R+JaO8zLyeccAKff/65q/WH23VTdcg/6qijKCoqYvPmzXGVc1Xy3aSulm2HDh3oM2AA+UuWxHTc73//e9LSqnazrrvuOt566y22Br0LunbtyuWXX86f//znmPSGI/j69OnTh0GDBvH+++9z3nnn0b9/f+66666E9dQV6sz0xuFeYG1OPJGWLVuSmppKmzZtmDBhAhMmTKB3796MHz8egOHDh3PRRRdx8MEHk5qaSseOHZk0aRJ/+tOfuPnmm32OL0D37t19v8eNG8eJJ57IhClTorbxkEMOAaBnz55cdtll3H777VUec8455wDEpMd7bvHgr+e3v/0txx57LAAHHXQQo0ePpn///px//vkA5OTkVCpr5MiR9O3blwEDBgAHnIhYzsWfyy+/nJNPPrlKvYnoAI8DEUwbv4qhTZs2XHPNNdzw6quMGDGCk08+mV//+tcB+w8ZMoRJkyZx0UUXccUVV4SUyfHHH8/EiRMDmruaNGnCoYceymWXXUazZs045ZRTOP7442M6l6quTadOnejatSshOfIjPWlcN910ExdeeCHnnntulbpSUlJITU2lRYsWXH/99b71gwYNYtiwYfzyl7884Pj66QimZcuWTJo0iYsvvphBgwYxcuTIAMcXPKkKffr04ZxzzvGlJWRlZQGeCPLxxx/PkCFDaH3CCQwZMsR33CmnnELjxo058sgjGTZsGOPHj/fJbteuXdiyrpSRIzn55JOZNGkSgwcP9rU6eenatWvA8imnnEL37t2ZNGkSl112WVQqgsv7pptu4uabb+bMM88MkBvMsccey0033cSAAQOYOHEil156acD2M844gyZHH81VV13FnXfeyfnnnx8i59prr6Vt27ZcdNFF/OY3v6Ffv36kpKRw880307VrVzp16sTgwYNJT08PKHMvkyZN4sbXXvPVW94yCscll1zCn/70J+68807Gjh3LL37xC5+dwfzf//0fhx56KI0bN07o2Y5Ely5duPLKK0Nkj60iGnbooYfGpMcN29u3b++77yZMmcIxxxzD2WefzdVXX13FkeHJzs6OuC1W+2N9F7lxfaKRPdTvw/yaa64J2OZ9b3sZPTq0C9Lll1/O4b//vW+5W7duVdozbNiwAMc3UurDDTfcQKtWrfjVr34Vcg5XXHEFKSkpcb/zzzjjDA477DAg8PpcfPHFXHzxxQwdOpRbbrmFAQMGhNTJ9Z06EfmNSFDvxU6dPHNlXHLJJagqmZmZDBw4kPT09JBDU1JSaNKkSZUq9qxcCU88UeV+QICenj17AnDHHXdw7733+tZfcMEFNG/enM8//5wBAwYwcOBA3n77bR66+eaodFx11VVRzREeCX89rVu39kUO0tLSAvIk//jHP5KVlcX333/P559/zkknncSnn37q237iiSf6fp999tns27ePJk2aMG/evKjOpVu3bgFpJscddxzdunWjW7dujBo1ipKSEv76179GPH7G669DhKFbjj76aL7++mvgQDSpKk4++WRf076vcqzkOns/xvyd0bPOOovvv/+eI488kpycHEQkoLnr8ssvp3HjxvTs2ZMbb7zRt375nDkQFD0PZtKkSb7f999/P/v27eOaa67hscceAzzltXbt2oCPuQCcZ8XrDB500EHcdNNNPBhh4HPvR5GXli1bctNNN6GqNGvWrFIdlXH22WdXun3gwIGA53xSUlJIS0sLrJQdHePHj6dt27bJr7DDnEN2djZr165l0qRJFBcXs2DBAoYOHUp6enrAy61nz57cfvvtlJSUUFhYSG5uLm3atAloIYADz+C1117rc669dZG3nFWV3r1706pVK9544w2WLl1K27ZtfTJEhF69enH99dfzyCOPANC/f39Yvx4BUlNTPcvAxx9/DHjqorS0NH73uwNZZxdccEHES9GyZUuOPPJIvvnmG+BAS4KsX0+f/fvp1KkTw4cP98kPxttSB55nEvB9uOzYsYPp06cDnme0R48evmuy47LLKq1zjz/+eAoLC6uMknvJycnh7LPPJjMzkzeefhqWLgUO1HFeO8IxZswYli1bRlFRUcC1iES09Xgs/OY3vwFg+fLl7PjVrwj39F188cX07NmTe+65p1JZN998M02aNGH58uUBLXaTJk1i8uTJMdt/8MEHh01rioRX/nHHHcd///vfqI/r2LEjjRo1Ii8vL+I+T999Nzf86lf84x//CNl25pln8vPPP9OmTZuAQAd43mXHHHMMr776Kvn5+bRs2dK3zb/eZf16/rBnD9999x3HHnssJSUl3H///RHtOfnkkwOWmzdvzvnnn88bb7wRsh48z+z8zz6D//6X9PR0zj///Ljrt7Fjx7J8+XIOPvhgVJXvvvuOh26+meHDhzNmzBjat2/v27dRo0Zx6ajr1G3n98EH4aabwm4SkYDmzGgZP358wFddh5df9v0+99xzeeutt0KO6d+/f9gONuBxKg866CAWL17M6aefTr9+/QBPpMPLn/70Jyr+/nfu3bcvol1HH320L0pRWVPXVVddRYsWLfjb3/4WsH7s2LFMnz6dQ7/7ju+dL0GAAQMGMHr0aN/LyYs3onPooYf6oh/+zq//13JqamrAh4S/jvHjxzN48GAKCwt58cUX+b//+z+mT5/OOeecwxdffOF7gR1++OEB+jMyMrjkkkto27Yte/bs8X3YzJ07l//85z8cM3cuaYccwg8//BBwXNu2bRk7dqzP+fWvyLwMGzYsZJ23GTwgshd0f/3pT39i+vTpIZECL4MHD2bw4MFht0WyBaDvf/4Tsu72229nw4YNPPfcc4HpC8Dvfvc7du7cSZs2bbjssstIT08nKysrsuMb5lwgMAJ+5513snDhQrp27UqLFi3CNtVVGUWt5HmMlYgRRUfHmDGVzogeP2HO4dJLL2X//v2Ax0mNVP7g+QBOT0/3dfwDj4OQn5/Pm2++ybnnnsu6G28k54knaN26dUQ5IkKrVq0AzzO4bNmysNGmli1b0rNnT9auXet5iYWxP+AFHiPHHHMMbdu29aWZAPDgg2TedBMTJkzwfcC2cVKEok336tixI+BxqP2vZ6NGjWj0r3/5ljt16hTysT9w4EAGDRrE/PnziWa0oqOOOopMZ1rX8wsKKPzNb9i3b1+lUWt/fve73zF//nyGDh3qc369H0TBBNevkcjIyKCkpCTstlNPPdWXmz9x4kTf+r59+0Z8xtq3b09qamqVer31dN++fbnuuusoLi723ds333wzOyZO5Kko81MBMjMzGTVqFLNmzYpq/8s2bybv7LN99XkkRowYwRFHHEGjRo3YtGmTr1W2oKCAZ555Juwx4xct8pVzOLzvRvDUd/v27fOde0ZGBpdddhmzZ89mxIgRfPfdd6GO54MP0vimmzjuuOOA8E7joYceSq9evWjZsmXYOrR///788Y9/5KmnnmLbtm0B6XcAh//wA4dX8ry2bt2aoqIihg8fzrBhw0Kuxa233kpqaippaWm+d7qIcOedd1Jwww1kn3JK2Fb0hkjddn5dGLcu5KW6fj1jjj+ejRs3BjTx/uEPf6CgoIDXXnuNfv36VZrQXlUTc0pKCimFhZx+ySV8+OGHYffxr+T79OlDVlYWe/fuDdjnwgsv9L1Uxo8fT2pqKiOdpmhVZfr06TTz6xgoIqSkpER03CNx2223Rdx23XXXkbZuHd87y97r2aNHD19F7nX8jz766EqjN7179wbwVVjgcVwHDx5M+q23cuSRR4Y4v96m44kTJ7Jq1Sp69+7NzTffzAMPPABU7ghcf/31gc3cQfdXSkpKSEQ0Gq677jrS09Mjv5zWr4eg+yc9PZ1u3bpx6aWXhji/TZs29TmiXgerSiI8K+np6RxzzDGkpqYGNA3GRXWMI+m2jjDyvQ5tInTv3p0//OEPAPTo2RP8Ii9V0a1btwAnKJiAdIskXx8RYeDAgSxYsIBx48aF6PA6kN26daNnz56kp6dTWlpapdy+fftyzjnn0L9//1AnY/16Ulu1ory8nIsuuiigdaJv376++mDixIk8+uijFBUVcckll/CyX6AiIuvXB0S9AF+u9QUXXEC/fv0C8v+bN28eUyClWZQdr6+77jpfneRPu3btGDFiRMAoJMH2+zNx4kRKSkp8Tl92djYtWrTghBNOICsri6VLl/LOO+/4tvnTqlUr3wcWeBzjku3bQ+qiqjj55JPZvn07K1asqHS/wYMH0/N//6PnMcegqpxyyin07ds3bKS2f//+vmCBfzpi165d+d3vfse//D6Q+vXrx7Jly+jXrBkpmZmceOKJTJ06leOOO4527dr56kjvuxEOBGz8gzYpKSm+lAf/ljkfYZ6t4Nazgw46KCQ1KpisrCyuu+668BsjPL85OTl89tlnnHXWWQdaYER8/ShOO+00WrRoEdH5T01NpXt6Opjj6yOp4/y6xciRI3V2LZmd5LXXXmP58uU+J2rTpk20b98+ac2v27dv55FHHuGEE06gUaNGPmf4hhtuCHGwgztpVRXheeqpp1i/fj3HH38806dP9/WCjxavvmgiSdHuO3nyZIYMGeLLB4yF4uLikBfItddeGzaiFovtNcGOHTt4+OGHAU+e+mmnnVazBhlGlKxevZrs7GzS0tL45JNPmDVrFpmZmdx6661xy9y6dSvr169n8ODBrFq1io8++ojrrrsupL4qLS1l5cqVDBgwIOKIIbfeemulEcHy8nL27t3r+6Dcv38/qamppKamhuhbunQpubm5tGvXjk8++SScuKjwphkA3HLLLb7m8wkTJlQZFY2FVatW+T4Koqn7Nm/ezOOPP+5rZYyUEjJ48GCOOeaYAOd5+fLlbN26lWbNmiEi9O/fn/vvv5/S0lKOPfZYxowZQ0ZGRois4A7iLVu2DFvW/qgq5eXllJeXhy3b0tLShD9Yo2XDhg3k5uZSXl7OkUceWa25syUlJWzbto0OHTokRZ6INJhxfut25HfECJgzp1p1nHfeeb6mEiBpN51XT8uWLbnlllvIzMxERHzOb7RDpVTGxRdfTMro0TSaNMmXBB8Lv/3tb9m6dWvVO44YweXvvUdxcXGVu3pH8YiZESNoNHMm6enp5OTksHjxYtLS0iI2JZ999tkR0w4q0+H6/eXoaeGnxxXHtwaelTqpo66fQw3Y79/64K0PAzpCxiG/zZw5vtzM3r17h+18B56ovLfDrTe9LJgA5yjM9UlNTQ1I6anMUe7fv78vl7pJkyYhaXBXPvkkTzmdTW+44YawUU1vLmhOTg7Dhw+nUaNG3H777Wzbti0kKh1CjOXrlReuk2E42o0fzzWff07r1q2ZE0bPWWedRY8ePcLm/YdLu5owYQLz5s3jiCOO8NTzYezv2bMnZ511Fu+++y7gCWBU9U4QEdLS0gJTC/xku+L4Rrj2nTp1St4HS4zlm5GREb0PUl3vszpC0pxfEWkNvAH0APKA81Q1ZMwxEckDdgHlQFlCXxku9hyNpCM1NTWkB3iy9fjnEl166aURK+NbbrmFN998k9WrV0c1oUDjxo3hhRfiNrFdu3a0a9eu6h2nTImqNyyEH8UjKqZMITU1ldtvv528vDwWL15cadPkoEGD4tJRLdTAfWw6aki+2zpq2H6v05JQi2Kc5zB+/Hif8xvcoTZR2eEI5+B/5Pfh2rx5c9q3b09hYWHAPqNGjQLgoosu8q1LT0+v2vGFmO1v1qxZbK1dU6b4PjpGjBhBRkaGL987nlaztm3b+nJkvfLDMXDgQL7//nuGDx+e0DvBVaxuqFckMwHkVuBLVc0BvnSWI3GMqh6ccHg9Uq/zZFIdOirR06tXL7p06RJ2W6NGjTj33HM56aSTOPLIIxPSk1SqWUf37t05++yzOeGEE1zT4Sr1sEzqrI66fg41bP/AgQPJycmpcgixeOVXhv8Qd+effz49evQIzd106fqMGzeOPn36sD+oWT84KucduSFuqvH+FxGGDBmS1DSMSPanpKQwYcIEXx+VZMpOGlY31CuSlvMrIsuAsaq6QUQ6AdNVtV+Y/fKAkaoaOgNABCLm/PbtC8uXx290NFSHjvqmx3TEpefll1+madOmAeO9JluHq9QHHXX9HOq6/QnKnz17Nn379o2cJpZk2715u3/6059QVaR/f7b8739kZWXRrFkzSkpKWLVqFStWrGDfvn2cd955iSmsgWvvza0Nl6+bDPlJoxbfl7VCRxSyG1LObzKd3+2q2tJveZuqtgqz32pgG6DAk6oaNhYvIhOACQDdunUbEW76WcMwDMOoKZ577jnWrFlTazvSGkYsNCTnN6a0BxH5r4gsDPMXXTa9h8NVdThwEvA7ETkq3E6qOkVVR6rqyIh5ptUxFV91TfdXn/SYjtqnx3TUDvlu66jr9rstP8myQ2byrMvXpq7Lr8u2V4eOBjR1cTRUe9pD0DF3AbtVNXTAw8D9dgHLkmKokQhtgajTVQxXsbKoHVg51A6sHGoPVha1g3jKobuqRtGrve6TzKHOPgT+D7jP+f9B8A4i0gRIUdVdzu8TgLujkL2soYTiazMiMtvKoXZgZVE7sHKoHVg51B6sLGoHVg6Vk8zRHu4DjheRFcDxzjIi0llEvKOBdwC+FZGfgZnAx6oa/cTghmEYhmEYhpEASYv8qupWYFyY9euBk53fuUCCc6gahmEYhmEYRnzUlYmebXTm2oGVQ+3ByqJ2YOVQO7ByqD1YWdQOrBwqIWkd3gzDMAzDMAyjtlNXIr+GYRiGYRiGkTDm/BqGYRiGYRgNhlrt/IrIeBFZJiIrReTWmranoSIiz4pIoYgsrGlbGjIiki0i00RkiYgsEpHra9qmhoqINBKRmSLys1MWk2vapoaMiKSKyE8i8lFN29JQEZE8EVkgIvNEZHZN29OQEZGWIvK2iCx13heH1rRNtY1am/MrIqnAcjzDphUAs4ALVXVxjRrWAHFm4dsNvKiqg2ranoaKM3lMJ1WdKyLNgDnAmfZMVD8iIkATVd0tIunAt8D1qvpDDZvWIBGRG4GRQHNVPbWm7WmIiEgeMFJVbYKLGkZEXgC+UdWnRSQDaKyq22vYrFpFbY78jgZWqmquqpYArwOxTKNsJAlVnQEU1bQdDR1V3aCqc53fu4AlQJeataphoh52O4vpzl/tjCTUc0SkK3AK8HRN22IYNY2INAeOAp4BUNUSc3xDqc3Obxdgrd9yAfaiNwwARKQHMAz4sYZNabA4Te3zgELgC1W1sqgZHgb+CFTUsB0NHQU+F5E5IjKhpo1pwPQCNgPPOalATzsz6hp+1GbnV8Kss8iK0eARkabAO8DvVXVnTdvTUFHVclU9GOgKjBYRSwmqZkTkVKBQVefUtC0Gh6vqcOAk4HdOupxR/aQBw4F/q+owoBiwPlNB1GbntwDI9lvuCqyvIVsMo1bg5Je+A7yiqu/WtD0GOE2K04HxNWtJg+Rw4HQn3/R14FgReblmTWqYOLO5oqqFwHt4UheN6qcAKPBriXobjzNs+FGbnd9ZQI6I9HQSti8APqxhmwyjxnA6WT0DLFHVh2ranoaMiLQTkZbO7yzgOGBpjRrVAFHV21S1q6r2wPOO+EpVL6lhsxocItLE6YSL08R+AmCjA9UAqroRWCsi/ZxV4wDrFB1EWk0bEAlVLRORa4CpQCrwrKouqmGzGiQi8howFmgrIgXAJFV9pmatapAcDlwKLHByTQFuV9VPas6kBksn4AVnVJoU4E1VtWG2jIZKB+A9z/c5acCrqvpZzZrUoLkWeMUJHOYCl9ewPbWOWjvUmWEYhmEYhmEkm9qc9mAYhmEYhmEYScWcX8MwDMMwDKPBYM6vYRiGYRiG0WAw59cwDMMwDMNoMJjzaxiGYRiGYTQYzPk1DKPOIiJtRGSe87dRRNY5v3eLyOMu6fy9iFzmhux4EJE8EWlbyfbXRSSnOm0yDMOozdhQZ4Zh1AtE5C5gt6o+4KKONGAuMFxVy9zSEwvO7GYjVXVLhO1HA5eo6pXVaphhGEYtxSK/hmHUO0RkrIh85Py+S0ReEJHPnSjpWSLyNxFZICKfOVNGIyIjRORrEZkjIlNFpFMY0ccCc72Or4hcJyKLRWS+iLzurGsiIs+KyCwR+UlEznDWp4rIA47e+SJyrbN+nLPfAue4TGd9nohMFpG5zrb+zvo2zrn8JCJPAuKn92MR+VlEForI+Y7N3wDHOY67YRhGg8ecX8MwGgK9gVOAM4CXgWmqOhjYC5ziOMCPAueo6gjgWeDeMHIOB+b4Ld8KDFPVIcDVzro78EyzOwo4Bvi7M+XrBKCn3/6viEgj4HngfMeeNOA3fvK3qOpw4N/Azc66ScC3qjoMz5Tv3Zz144H1qjpUVQcBnwGoagWwEhgaywUzDMOor5jzaxhGQ+BTVS0FFuCZLt079eoCoAfQDxgEfOFMHX0n0DWMnE7AZr/l+Xic2EsAbxrECcCtjpzpQCM8DupxwBPeqLGqFjl6V6vqcufYF4Cj/OS/6/yf49iJs/1lR8bHwDa/czlORO4XkSNVdYefnEKgc9grYxiG0cCwZjDDMBoC+8ETBRWRUj3Q2aECTz0owCJVPbQKOXvxOLNeTsHjjJ4O/ElEBjqyzlbVZf4HiogAwZ0sJBq7gXIC6+uQzhqqulxERgAnA38Vkc9V9W5ncyPHdsMwjAaPRX4NwzBgGdBORA4FEJF0x5ENZgnQx9knBchW1WnAH4GWQFNgKnCt4+wiIsOcYz8Hrvbm3opIa2Ap0ENE+jj7XAp8XYWtM4CLHRknAa2c352BPar6MvAAMNzvmL7Aoqovg2EYRv3HnF/DMBo8qloCnAPcLyI/A/OAw8Ls+ikH0hJSgZdFZAHwE/APVd0O/BlIB+aLyEJnGeBpYI2z/mfgIlXdB1wOvOXIqQCeqMLcycBRIjIXT4rFGmf9YGCmk25xB3APgIh0APaq6oboroZhGEb9xoY6MwzDiAEReQ/4o6quqGlbokFEbgB2quozNW2LYRhGbcAiv4ZhGLFxK56Ob3WF7Xg60hmGYRhY5NcwDMMwDMNoQFjk1zAMwzAMw2gwmPNrGIZhGIZhNBjM+TUMwzAMwzAaDOb8GoZhGIZhGA0Gc34NwzAMwzCMBoM5v4ZhGIZhGEaDwZxfwzAMwzAMo8Fgzq9hGIZhGIbRYDDn1zAMwzAMw2gwmPNrGIZhGIZhNBjM+TUMo14hIioifWraDrcRkR7OuabVtC2GYRh1CXN+DaOeIyJ5IrJXRHaLyDYR+VhEspMke7qI/DoZsgzDMAyjOjDn1zAaBqepalOgE7AJeLSG7akVNKSoaW0+VxFJrWkbDMNoOJjzaxgNCFXdB7wNHORdJyKZIvKAiKwRkU0i8oSIZDnbWonIRyKy2YkafyQiXZ1t9wJHAo85UeXHwukUkZNFZLGI7BKRdSJys7N+oYic5rdfuohsEZGD/Zr0/8+xa4uI3OG3b6qI3C4iqxy5c4Ki2ceJyArH5n+JiDjH/VJE/ici/xCRIuAuEWkhIi8655gvIneKSIrf/t8612ebiKwWkZP87OgsIh+KSJGIrBSRK/22PS8i9/gtjxWRAr/lW5zrsUtElonIuAjXL0tEHnRs2+HYk+W3y8URrtFdIvK2iLwsIjuBX1Zh710i8paz/y4RWSAifUXkNhEpFJG1InKC3/6Xi8gSZ99cEbkqyO4/isgGEVkvIr8Wv3QU59r8W0Q+EZFi4BgROUVEfhKRnY6uu/xkee+Hy51t20TkahEZJSLzRWR7pPvPMAwjGHN+DaMBISKNgfOBH/xW3w/0BQ4G+gBdgInOthTgOaA70A3YCzwGoKp3AN8A16hqU1W9JoLaZ4CrVLUZMAj4yln/InCJ334nAxtUdZ7fuiOAfsA4YKKIDHDW3whc6BzTHLgC2ON33KnAKGAocB5wot+2MUAu0B64F08UvAXQCzgauAy4PGj/ZUBb4G/AM15nGngNKAA6A+cAf4nkxPojIv2Aa4BRznU5EciLsPsDwAjgMKA18Eegwm97pGsEcAaej52WwCtR2Hsa8BLQCvgJmIrnHugC3A086bdvIZ7r3BzP9fqHiAx3zm88njI6Ds89dXSY87oIz/VvBnwLFOO59i2BU4DfiMiZQceMAXLw3MMPA3c4OgYC54lIOD2GYRiBqKr92Z/91eM/PE7VbmA7UAasBwY72wSP09Hbb/9DgdURZB0MbPNbng78ugr9a4CrgOZB6zsDu7zr8Thpf3R+9wAU6Oq3/0zgAuf3MuCMCPoUOMJv+U3gVuf3L4E1fttSgf3AQX7rrgKm++2/0m9bY0d+RyAbKAea+W3/K/C88/t54B6/bWOBAud3HzzO43FAeiXXLgXPB8fQMNuqukZ3ATP8tlVl713AF37bTnPum1RnuZmjr2UEW98Hrnd+Pwv81W9bH+fYPn7X5sUq7puHgX8EnWsXv+1bgfP9lt8Bfl/Tz5v92Z/91f4/i/waRsPgTFVtCWTiiTh+LSIdgXZ4HLo5TtPxduAzZz0i0lhEnnSa3HcCM4CWEiFH00lF2O38PeGsPhtPhDZfRL4WkUMBVHU98D/gbBFpCZyEJzrpz0a/33uAps7vbGBVJecb6TiAtX6/2wIZQL7funw8kc4QWarqjS43xeO8F6nqrkqODYuqrgR+j8fhLBSR10Wkc5hd2wKNSM65RmPvJr/fe4Etqlrut4xXeK1H4AAAG5pJREFUvoicJCI/OCkU2/GUcVs/Xf66/X+HXSciY0RkmpN+sgO42k9eJPuCl5tiGIZRBeb8GkYDQlXLVfVdPBHAI4AteJyGgara0vlroZ7OcQA34WlSH6OqzYGjnPXeZn8Nkv8X9aRANFXVq511s1T1DDxpBu/jicR6eQFP6sO5wPequi7KU1kL9I76xAPxt3kLUIonrcNLNyAaO9YDrUWkWYRji/F8WHjpGGCE6quqeoSjW/GknwSzBdhHcs61KnujRkQy8URaHwA6OB9Wn3DgvtgAdPU7JNzoIhq0/CrwIZCtqi2AJ/zkGYZhJA1zfg2jASEezsCT07lEVSuAp/Dka7Z39ukiIt4c2WZ4nOPtItIamBQkchOeXNlI+jJE5GIRaaGqpcBOPI63l/eB4cD1eHKAo+Vp4M8ikuOc0xARaRPD8YDnYwCPM36viDQTke54clVfjuLYtcB3wF9FpJGIDAF+xYHo9TzgZBFp7UTZf+89VkT6icixjhO5D8819r8uXh0VeFIIHnI6q6WKyKHOcbGea1X2xkIGnlaEzUCZeDoBnuC3/U3gchEZ4OSZTwwjI5hmeCLT+0RkNJ6cYMMwjKRjzq9hNAz+IyK78Tif9wL/p6qLnG23ACuBH5zUhv/iifaCJ+8yC08E8gc8KRH+PAKc4/S+/2cE3ZcCeY7sq/Hr5Kaqe/FEEHsC78ZwPg/hcbA+d87pGcfOeLgWT5Q2F0/Hq1fxOJzRcCGefNT1wHvAJFX9wtn2EvAznpzrz4E3/I7LBO7Dc1034omK3x5Bx83AAmAWUIQnQhxv3V2ZvVHjpE5ch6cMtuFxVD/02/4p8E9gGp5763tn0/5KxP4WuFtEduFxlt+sZF/DMIy4EdXglifDMIzqQ0QmAn1V9ZIqdzbqJM4IFAuBTFUtq2l7DMNo2Fjk1zCMGsNJpfgVMKWmbTGSi4j8wkl7aYUnWv0fc3wNw6gNmPNrGEaNIJ4JFtYCn6rqjJq2x0g6V+HJCV6FJ5/5NzVrjmEYhgdLezAMwzAMwzAaDBb5NQzDMAzDMBoMaTVtQDS0bt1as7PDDROZOBUVFVXvFCcpKe5+W7gp383rcmBm2OSzb98+12QD7NixwzXZbtru5jV3815p1aqVa7IB9u+vbPCBxMjIyHBNdnl5yKhoSaNRo0auyXZbvpv3uZv1rdstsG6/i+oibj5DbuNWnbtu3Tq2bdsW1UMkIsm6aaeq6vgkyYqaOuH8Zmdn8+mnn7oie8+ePVXvFCdNm7o72ZCbL5G9e/dWvVOcZGbGPERp1CxcuNA12QCffPKJa7KXL1/umuzU1LATsiUFN532s88+2zXZALm5ua7JduuDHWDXrl1V7xQnAwYMcE02QL9+/areKU7S09Ndk52VFe9IelVTWlrqmmyoux8cbrJz507XZLt9Tdx6/s8777yY9k/Ge6W8vDx4FsdqoU44v4ZhGIZhGEbtQESS5fwmwZrYMefXMAzDMAzDiBoRIS0tcReypKQkCdbETpWWi0gKMBTojGcKzkWqusltwwzDMAzDMIzaiZvpdG4T0fkVkd54pj09DliBZ7zGRkBfEdkDPAm84Mw9bxiGYRiGYTQAkpX2UFNUFvm9B/g3cJUGdUUVkQ545oi/FHjBPfMMwzAMwzCM2ka9dH5V9cJKtm0CHq5MsIg8C5wKFKrqIGdda+ANoAeQB5ynqtuqMjI1NdW1IY+SkbMSiebNm7smG9ztwVtXh4AbMmSIa7IBWrRo4Zrs/Px812SXlbk3q6ybHRa6d+/ummyAPn36uCa7WbNmrsl2c7SHNm3auCYb3B2+zs38QTd78Ls56pDb8t10gNwcYcPN59PNYQ7BvXd/LP5QsnJ+a4oqvRARWSUiVwet+ygK2c8DwWO33Qp8qao5wJfOsmEYhmEYhlFH8KY9JPpXU0QTgisFjhGR50TE+znTpaqDVHUGUBS0+gwOpEm8AJwZpZ2GYRiGYRhGLaG+O797VPV8YAnwjYh0B+Kd2aODqm4AcP63j7SjiEwQkdkiMnvLli1xqjMMwzAMwzCSSV2P/EaTsCEAqvo3EZkDTAVau2qVR98UYArA8OHD3Z370TAMwzAMw4iKup7zG43lE70/VPVLETkR+L849W0SkU6qukFEOgGFccoxDMMwDMMwaoh6OdqDiAx3fq7z++0lmg5v4fgQj+N8n/P/g2gOKikpYfXq1XGqrJyiouC05OTRrl0712SDu72mN27c6JrswkL3vnl69OjhmmyAzMxM12QPHx78mCWP9PR012Tv27fPNdluV67FxcV1Urab96GboxoAbNtW5QA/cePmSDJTp051TfbAgQNdkw2Qk5Pjmmw3R01w+150i6DRYZOOW6O9xDJyT30e5/dBv98jgNk4KRB4cn6PrUywiLwGjAXaikgBMAmP0/umiPwKWAOcG5/ZhmEYhmEYRk1Qb51fVT3G+1tEflLVSp3dMMdHGid4XCxyDMMwDMMwjNpFfc/5hfhHdzAMwzAMwzDqEfU28msYhmEYhmEYwdRb51dEHuVAxLeriPzTf7uqXuemYYZhGIZhGEbtpF46v3g6uHmZ47YhhmEYhmEYRu2n3o7zq6ovRNpW3aSkpNCkSRNXZLs5BJTbX0VuDjHl5jBqbg6Ns3fvXtdkA2RkZFS9U5w0btzYNdkVFRWuyXbzPt+5c6drssHd69KlS5WzwMfN/v37XZPtVl3rxc1h2j799FPXZN9///2uyf7973/vmmyAbt26uSbbzXpr8eLFrsnevHmza7LdHCoUwK1Zb2MZhrCupz1EHBRRRKaIyKAI25qIyBUicrF7phmGYRiGYRi1keqa3lhExovIMhFZKSK3htl+sYjMd/6+E5GhVcmsLGb9ODBRRAYDC4HNQCMgB2gOPAu8EpXlhmEYhmEYRr2guiK/IpIK/As4HigAZonIh6rq3yywGjhaVbeJyEnAFGBMZXIrS3uYB5wnIk2BkUAnYC+wRFWXJXIyhmEYhmEYRt2lmtIeRgMrVTUXQEReB84AfM6vqn7nt/8PQNeqhFaZrayqu4HpMRprGIZhGIZh1EOS2OGtrYj4D7AwRVWn+C13Adb6LRdQeVT3V0CVyf91t6ueYRiGYRiGUe0kMe1hi6qOrExVmHVhJ14TkWPwOL9HVKW0Tji/qkppaakrst3seVxeXu6abHC3x3dWVpZrst3sHZybm+uabHD3uoiEe8aTg5ujYKi6NwHkt99+65psgA4dOrgmu02bNq7JLi4udk12SUmJa7IBWrdu7ZpsN++Xyy67zDXZLVu2dE02wFdffeWa7LKyMtdkv/HGG67Jnjt3rmuyi4qKXJMNcPTRR7sid8eOHVHvW42jPRQA2X7LXYH1YewZAjwNnKSqW6sSWqXzKyKDVHVhDIYahmEYhmEY9Zhqcn5nATki0hNYB1wAXOS/g4h0A94FLlXV5dEIjSby+4SIZADPA6+q6vYYjDYMwzAMwzDqEdU1yYWqlonINcBUIBV4VlUXicjVzvYngIlAG+BxpwW1rIpUiqg6vB0hIjnAFcBsEZkJPKeqXyR0RoZhGIZhGEadozonuVDVT4BPgtY94ff718CvY5EZlduuqitE5E48Ux7/ExgmHvf6dlV9NxaFhmEYhmEYRt2mLs/wFk3O7xDgcuAU4AvgNFWdKyKdge/x5FkYhmEYhmEYDYC6Pr1xNJHfx4Cn8ER5fV3GVXW9Ew02DMMwDMMwGgjVlfPrFtFYfjKwV1XLAUQkBWikqntU9SVXrXPYtWsXX3/9tSuy3RzqaPPmza7JBigsLHRN9siRleaKJ0TXrlVOvhI3nTt3dk02wKZNm1yT/f3337sm281h99x8hrp06eKabICdO3e6JnvlypWuyXbz2XezPAF++OEH12SPGjXKNdmDBg1yTfYTTzxR9U4J8OGHH7omu2nTpq7J7t69u2uy77nnHtdkuznMIUCLFi1ckXvttdfGtH99j/z+FzgO2O0sNwY+Bw5zyyjDMAzDMAyjdtIQ0h4aOVMcA57pjkXEvVkKDMMwDMMwjFpNfXd+i0VkuKrOBRCREYB700UZhmEYhmEYtZaGkPP7e+AtEfFOJ9cJON81iwzDMAzDMIxaS71Pe1DVWSLSH+gHCLBUVUtdt8wwDMMwDMOoldRr59dhFNDD2X+YiKCqL8arVETygF1AOVFMQ2cYhmEYhmHUDup95FdEXgJ6A/PwOKsACsTt/Doco6pbotnRzaHOMjMzXZELsHDhQtdkA+Tn57sm+7rrrnNNtptDnaWnp7smG6Bfv36uyXZzyKBdu3a5Jrt169auyS4qKnJNNkCvXr1ck922bVvXZLtZbzVu7G5/5o8++sg12cOHD3dN9pgxY1yTfd5557kmG+DEE090TbabDpCbdUv//v1dk+3m8wlQVlbmitwmTZpEvW9DyPkdCRykquq2MYZhGIZhGEbtp15HfoGFQEdgQxL1KvC5iCjwpKpOSaJswzAMwzAMwyXqfdoD0BZYLCIzgf3elap6egJ6D3emR24PfCEiS1V1hv8OIjIBmACxheINwzAMwzAM92gIzu9dyVaqquud/4Ui8h4wGpgRtM8UYApAmzZtLOXCMAzDMAyjllCvc35V9WsR6Q7kqOp/ndnd4nb3RaQJkKKqu5zfJwB3xyvPMAzDMAzDqD7qfeRXRK7Ek37QGs+oD12AJ4BxcersALwnIl79r6rqZ5Ud0Lp1a84999w41VWOmykVmzdvdk02uNuD382esG5e8927d1e9UwJkZGS4Jrt3796uyXaeN1fYt2+fa7LbtWvnmmxw914sKSlxTbab94qbdgNcccUVrsnu3r27a7LbtGnjmuxWrVq5JhugvLy86p3iZP/+/VXvFCduPp9uXpO9e92dBNfN91C01HvnF/gdnrSEHwFUdYWTqxsXqpoLDI33eMMwDMMwDKNmqe/O735VLfFGjkQkDc9oDYZhGIZhGEYDoyGM8/u1iNwOZInI8cBvgf+4a5ZhGIZhGIZRG2kIaQ+3Ar8CFgBXAZ8AT7tplGEYhmEYhlF7qdfOr6pWAE85f4ZhGIZhGEYDpt5HfkVkNWFyfFW1lysWGYZhGIZhGLWW6sz5FZHxwCN4htl9WlXvC9reH3gOGA7coaoPVCUzGstH+v1uBJyLZ9izaiMtLY327eMeYKJSmjdv7opcgE6dOrkmG9wdviovL8812Z9++qlrsocNG+aabICysjLXZLv5Fe3mcESq7vV/zcrKck02QFFRkWuyN2xI5ozwgbhZb61YscI12QBdunRxTXbjxo1dk+3mdXFziDaA4uJi12S7OaxXZmama7JXr17tmmw33xPg3n0e63uiOiK/IpIK/As4HigAZonIh6q62G+3IuA64Mxo5UaT9rA1aNXDIvItMDFaJYZhGIZhGEb9oBrTHkYDK51hchGR14EzAJ/zq6qFQKGInBKt0GjSHob7LabgiQQ3i1aBYRiGYRiGUX9IovPbVkRm+y1PUdUpfstdgLV+ywXAmESVRpP28KDf7zIgDzgvUcWGYRiGYRhG3SRJOb9bVHVkJdvD5XcmnGsXTdrDMYkqMQzDMAzDMOoH1Zj2UABk+y13BdYnKjSatIcbK9uuqg8laoRhGIZhGIZRN6hG53cWkCMiPYF1wAXARYkKjXa0h1HAh87yacAMAnMw6iyFhYWuyW7atKlrssHdnvBTp051TfYbb7zhmuzbbrvNNdkAo0aNck12SkqKa7IzMjJck+2m3T/88INrsgG6du3qmuz33nvPNdlu3oefffaZa7IBxo0b55rskpIS12S7OUqF26MD1NW6ZdmyZa7JXrNmjWuy3RylAqB1a3cG3CovL49p/+pwflW1TESuAabiGersWVVdJCJXO9ufEJGOwGygOVAhIr8HDlLVnZHkRuP8tgWGq+ouABG5C3hLVX+dyAkZhmEYhmEYdY/qHOdXVT/BM7uw/7on/H5vxJMOETXRWN4N8P+cLgF6xKLEMAzDMAzDqB/U+xnegJeAmSLyHp4edr8AXnTVKsMwDMMwDKPWUq+dX1W9V0Q+BY50Vl2uqj+5a5ZhGIZhGIZRG2kIkV+AxsBOVX1ORNqJSE9VdW9uQMMwDMMwDKPWUq+dXxGZhGfEh37Ac0A68DJwuLumGYZhGIZhGLWN6uzw5gbRWP4LYBgwF0BV14tItU5vnJqaSqtWrVyRXVFR4YpcgCZNmrgmG9wdTuXMM890TfbRRx/tmmy3H0Y3hwxya/gagEaNGrkmO9bhcWJh9OjRrskGd4dpOvnkk12T3b59e9dkZ2dnV71TArg5vFyLFi1ck+3WOwjgp5/czSR0c0jPrVu3uiY7JyfHNdmHHHKIa7IbN27smmyA4uJiV+TGUh82hLSHElVVEVEAEXHXozMMwzAMwzBqNfXd+X1TRJ4EWorIlcAVwFPummUYhmEYhmHURup15FdEBHgD6A/sxJP3O1FVv6gG2wzDMAzDMIxaRr3O+XXSHd5X1RGAObyGYRiGYRhG/Y38OvwgIqNUdZbr1hiGYRiGYRi1mnqd9uBwDHC1iOQBxYDgCQoPiVepiIwHHgFSgadV9b7K9t+zZw9z5syJV12llJSUVL1TnLRs2dI12eBuz+a62mv62WefdU02uDvaw5AhcT9SVeJmeaqqa7J79erlmmyA9evXuybbzXtl//79rslu3ry5a7IB1qxZ45rs3r17uyY7PT3dNdlujtwD0K9fP9dkl5WVuSa7S5curskuLS11TbabdSLA9u3bXZEbS1nWW+dXRLqp6hrgpGQqFJFU4F/A8UABMEtEPlTVxcnUYxiGYRiGYbhDfc35fR8Yrqr5IvKOqp6dJJ2jgZWqmgsgIq8DZwDm/BqGYRiGYdRy6m3kF096g5dktj12Adb6LRcAY5Io3zAMwzAMw3CJ+uz8aoTfiSJh1oXIF5EJwASAtm3bJlG9YRiGYRiGkQj11fkdKiI78TirWc5vONDhLd5eEQWA//yZXYGQXieqOgWYAtCrVy93s8cNwzAMwzCMqKi34/yqqlsu/SwgR0R6AuuAC4CLXNJlGIZhGIZhJJG6nvYgbg/JEVapyMnAw3iGOntWVe+tYv/NQH4MKtoCW+I20KhtWHnWL6w86xdWnvUHK8v6Razl2V1V20Wzo4h85shPlC2qOj4JcmKiRpxftxGR2ao6sqbtMJKDlWf9wsqzfmHlWX+wsqxfWHlGxr1R2A3DMAzDMAyjlmHOr2EYhmEYhtFgqK/O75SaNsBIKlae9Qsrz/qFlWf9wcqyfmHlGYF6mfNrGIZhGIZhGOGor5FfwzAMwzAMwwjBnF/DMAzDMAyjwVCvnF8RGS8iy0RkpYjcWtP2GIkhInkiskBE5onI7Jq2x4gNEXlWRApFZKHfutYi8oWIrHD+t6pJG43oiVCed4nIOucZneeM4W7UAUQkW0SmicgSEVkkItc76+0ZrYNUUp72jIah3uT8ikgqsBw4Hs8UyrOAC1V1cY0aZsSNiOQBI1XVBl2vg4jIUcBu4EVVHeSs+xtQpKr3OR+orVT1lpq004iOCOV5F7BbVR+oSduM2BGRTkAnVZ0rIs2AOcCZwC+xZ7TOUUl5noc9oyHUp8jvaGClquaqagnwOnBGDdtkGA0WVZ0BFAWtPgN4wfn9Ap7K2agDRChPo46iqhtUda7zexewBOiCPaN1kkrK0whDfXJ+uwBr/ZYLsIKv6yjwuYjMEZEJNW2MkRQ6qOoG8FTWQPsatsdInGtEZL6TFmFN5HUQEekBDAN+xJ7ROk9QeYI9oyHUJ+dXwqyrHzkdDZfDVXU4cBLwO6fZ1TCM2sO/gd7AwcAG4MEatcaIGRFpCrwD/F5Vd9a0PUZihClPe0bDUJ+c3wIg22+5K7C+hmwxkoCqrnf+FwLv4UltMeo2m5zcNG+OWmEN22MkgKpuUtVyVa0AnsKe0TqFiKTjcZReUdV3ndX2jNZRwpWnPaPhqU/O7ywgR0R6ikgGcAHwYQ3bZMSJiDRxkvYRkSbACcDCyo8y6gAfAv/n/P4/4IMatMVIEK+T5PAL7BmtM4iIAM8AS1T1Ib9N9ozWQSKVpz2j4ak3oz0AOEN4PAykAs+q6r01a5ERLyLSC0+0FyANeNXKs24hIq8BY4G2wCZgEvA+8CbQDVgDnKuq1omqDhChPMfiaU5VIA+4ypsvatRuROQI4BtgAVDhrL4dT56oPaN1jErK80LsGQ2hXjm/hmEYhmEYhlEZ9SntwTAMwzAMwzAqxZxfwzAMwzAMo8Fgzq9hGIZhGIbRYDDn1zAMwzAMw2gwmPNrGIZhGIZhNBjM+TUMo14gIm1EZJ7zt1FE1jm/d4vI4y7p/L2IXOb87u/o+0lEeruhLwa7XheRnJq0wTAMo7ZiQ50ZhlHvEJG7gN2q+oCLOtKAucBwVS0TkVuBLFWdFLSf4KlrK8LJccm2o4FLVPXK6tJpGIZRV7DIr2EY9RoRGSsiHzm/7xKRF0TkcxHJE5GzRORvIrJARD5zpgdFREaIyNciMkdEpgbNkuTlWGCu4/ieDPwe+LWITBORHiKyxIk4zwWyReTfIjJbRBaJyGQ/+/JE5C8i8r2zfbijc5WIXO233x9EZJaIzPce78yE+LGI/CwiC0XkfGf3b4DjHAfdMAzD8MOcX8MwGhq9gVOAM4CXgWmqOhjYC5ziOMCPAueo6gjgWSDc7IKHA3MAVPUT4AngH6p6jLO9H/Ciqg5T1XzgDlUdCQwBjhaRIX6y1qrqoXic1ueBc4BDgLsBROQEIAcYjWe2phEichQwHlivqkNVdRDwmWNPBbASGJrIhTIMw6iPWFTAMIyGxqeqWioiC/BMhf6Zs34B0AOP0zoI+MKTsUAqEG460E7Akkr05KvqD37L54nIBDz1bifgIGC+s+1DPxuaquouYJeI7BORlsAJzt9Pzn5N8TjD3wAPiMj9wEeq+o2fvkKgM46DbhiGYXgw59cwjIbGfvBER0WkVA90fKjAUycKsMiJxFbGXqBRJduLvT9EpCdwMzBKVbeJyPNBx+73s2G/33p/m/6qqk8GKxGREcDJwF9F5HNVvdvZ1Mix0TAMw/DD0h4MwzACWQa0E5FDAUQkXUQGhtlvCdAnSpnN8TjDO0SkA3BSjDZNBa4QkaaOTV1EpL2IdAb2qOrLwAPAcL9j+gKLYtRjGIZR77HIr2EYhh+qWiIi5wD/FJEWeOrJhwl1JD8FXopS5s8i8pMjIxf4X4w2fS4iA4DvnVSM3cAleJzvv4tIBVAK/AbAcbD3qmq4dA3DMIwGjQ11ZhiGESci8h7wR1VdUdO2+CMiNwA7VfWZmrbFMAyjtmFpD4ZhGPFzK57Oa7WN7cALNW2EYRhGbcQiv4ZhGIZhGEaDwSK/hmEYhmEYRoPBnF/DMAzDMAyjwWDOr2EYhmEYhtFgMOfXMAzDMAzDaDCY82sYhmEYhmE0GP4foi4Vb57GA2AAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F07_Shostakovich_Waltz-02-Section_IncreasingTempo.wav')\n", "\n", "Fs = 22050\n", "x, Fs = librosa.load(fn_wav, Fs) \n", "\n", "nov, Fs_nov = libfmp.c6.compute_novelty_spectrum(x, Fs=Fs, N=2048, H=512, \n", " gamma=100, M=10, norm=True)\n", "nov, Fs_nov = libfmp.c6.resample_signal(nov, Fs_in=Fs_nov, Fs_out=100)\n", "\n", "L_nov = len(nov)\n", "N_nov = 500\n", "H_nov = 10\n", "Theta = np.arange(30, 601)\n", "X, T_coef, F_coef_BPM = libfmp.c6.compute_tempogram_fourier(nov, Fs=Fs_nov, \n", " N=500, H=10, Theta=Theta)\n", "nov_PLP = libfmp.c6.compute_plp(X, Fs_nov, L_nov, N_nov, H_nov, Theta)\n", "peaks, properties = signal.find_peaks(nov_PLP, prominence=0.02)\n", "B_adapt_sec = peaks / Fs_nov\n", "\n", "N, H = 1024, 512\n", "X = librosa.stft(y=x, n_fft=N, hop_length=H, win_length=N, center=True, window='hanning')\n", "Y = np.abs(X)\n", "C = librosa.feature.chroma_stft(S=Y, sr=Fs, norm=1, hop_length=H, n_fft=N)\n", "B_fix_frame = (B_adapt_sec*Fs/H).astype(int)\n", "C_adapt = librosa.util.sync(C, B_fix_frame, aggregate=np.mean)\n", "\n", "# Visualization\n", "fig, ax = plt.subplots(5, 2, gridspec_kw={'width_ratios': [1, 0.03], \n", " 'height_ratios': [1, 2, 1, 1, 2]}, figsize=(10,8)) \n", "\n", "libfmp.b.plot_signal(x, Fs, ax=ax[0,0], title='Signal')\n", "ax[0,1].set_axis_off()\n", " \n", "libfmp.b.plot_matrix(C, ax=[ax[1,0], ax[1,1]], xlabel='Time (frames)', \n", " title='Chromgram with fixed-size windowing (frame rate = %0.1f Hz)'%(Fs/H))\n", "\n", "libfmp.b.plot_signal(nov_PLP, Fs_nov, color='k', ax=ax[2,0],\n", " title='PLP-based novelty function with estimated beat positions')\n", "ax[2,1].set_axis_off()\n", "plot_beat_grid(B_adapt_sec, ax[2,0])\n", "\n", "libfmp.b.plot_signal(x, Fs, ax=ax[3,0],\n", " title='Signal with beat positions')\n", "ax[3,1].set_axis_off()\n", "plot_beat_grid(B_adapt_sec, ax[3,0]) \n", "\n", "libfmp.b.plot_matrix(C_adapt, ax=[ax[4,0], ax[4,1]], xlabel='Time (frames)',\n", " title='Beat-synchronous chromagram')\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transient Removal\n", "\n", "Next, we show how the knowledge of onset and beat positions can be used in another way for improving the quality of audio features. Recall that note onsets often go along with **transients** (noise-like energy bursts) spread over the entire spectrum, especially for instruments such as the piano, guitar, or percussion. While transients are useful for the detection of note onset positions, they may cause undesired artifacts in features that capture harmonic or melodic information. To remove these noise-like artifacts while keeping the harmonic information, one idea is to exclude a neighborhood around each note onset position. To this end, we introduce a parameter $\\lambda\\in\\mathbb{R}$, $0<\\lambda<1$, which determines the size of the neighborhoods. Let $s,t\\in[1:N]$ denote the start and end positions of a given adaptive window. Then we define \n", "\n", "\\begin{equation}\n", " s_\\lambda := s + \\left\\lfloor \\frac{1-\\lambda}{2}(t-s)\\right\\rfloor \\,\\,\\,\\mbox{and}\\,\\,\\,\n", " t_\\lambda := t - \\left\\lfloor \\frac{1-\\lambda}{2}(t-s)\\right\\rfloor,\n", "\\end{equation}\n", "\n", "which determine the start and end positions of the shortened window used for the feature computation. With this definition, the center of the adaptive window is preserved, while its size is reduced by a factor $\\lambda$ relative to its original size $(t-s)$. \n", "\n", "In the next code cell, one finds an implementation of this procedure applied to the C-major scale used above. As feature representation, we show a magnitude spectrogam using adaptive windowing once using $\\lambda=1$ and once using $\\lambda=0.5$. In this example, however, the denoising effect by removing signal transients is hardly visible. \n", "\n", "" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2024-02-15T08:47:34.061841Z", "iopub.status.busy": "2024-02-15T08:47:34.061628Z", "iopub.status.idle": "2024-02-15T08:47:35.289817Z", "shell.execute_reply": "2024-02-15T08:47:35.288917Z" } }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def adaptive_windowing(X, B, neigborhood=1, add_start=False, add_end=False):\n", " \"\"\"Apply adaptive windowing [FMP, Section 6.3.3]\n", "\n", " Notebook: C6/C6S3_AdaptiveWindowing.ipynb\n", "\n", " Args:\n", " X (np.ndarray): Feature sequence\n", " B (np.ndarray): Beat sequence (spefied in frames)\n", " neigborhood (float): Parameter specifying relative range considered for windowing (Default value = 1)\n", " add_start (bool): Add first index of X to beat sequence (if not existent) (Default value = False)\n", " add_end (bool): Add last index of X to beat sequence (if not existent) (Default value = False)\n", "\n", " Returns:\n", " X_adapt (np.ndarray): Feature sequence adapted to beat sequence\n", " B_s (np.ndarray): Sequence specifying start (in frames) of window sections\n", " B_t (np.ndarray): Sequence specifying end (in frames) of window sections\n", " \"\"\"\n", " len_X = X.shape[1]\n", " max_B = np.max(B)\n", " if max_B > len_X:\n", " print('Beat exceeds length of features sequence (b=%d, |X|=%d)' % (max_B, len_X))\n", " B = B[B < len_X]\n", " if add_start:\n", " if B[0] > 0:\n", " B = np.insert(B, 0, 0)\n", " if add_end:\n", " if B[-1] < len_X:\n", " B = np.append(B, len_X)\n", " X_adapt = np.zeros((X.shape[0], len(B)-1))\n", " B_s = np.zeros(len(B)-1).astype(int)\n", " B_t = np.zeros(len(B)-1).astype(int)\n", " for b in range(len(B)-1):\n", " s = B[b]\n", " t = B[b+1]\n", " reduce = np.floor((1 - neigborhood)*(t-s+1)/2).astype(int)\n", " s = s + reduce\n", " t = t - reduce\n", " if s == t:\n", " t = t + 1\n", " X_slice = X[:, range(s, t)]\n", " X_adapt[:, b] = np.mean(X_slice, axis=1)\n", " B_s[b] = s\n", " B_t[b] = t\n", " return X_adapt, B_s, B_t\n", "\n", "def compute_plot_adaptive_windowing(x, Fs, H, X, B, neigborhood=1, add_start=False, add_end=False):\n", " \"\"\"Compute and plot process for adaptive windowing [FMP, Section 6.3.3]\n", "\n", " Notebook: C6/C6S3_AdaptiveWindowing.ipynb\n", "\n", " Args:\n", " x (np.ndarray): Signal\n", " Fs (scalar): Sample Rate\n", " H (int): Hop size\n", " X (int): Feature sequence\n", " B (np.ndarray): Beat sequence (spefied in frames)\n", " neigborhood (float): Parameter specifying relative range considered for windowing (Default value = 1)\n", " add_start (bool): Add first index of X to beat sequence (if not existent) (Default value = False)\n", " add_end (bool): Add last index of X to beat sequence (if not existent) (Default value = False)\n", "\n", " Returns:\n", " X_adapt (np.ndarray): Feature sequence adapted to beat sequence\n", " \"\"\"\n", " X_adapt, B_s, B_t = adaptive_windowing(X, B, neigborhood=neigborhood,\n", " add_start=add_start, add_end=add_end)\n", "\n", " fig, ax = plt.subplots(2, 2, gridspec_kw={'width_ratios': [1, 0.03],\n", " 'height_ratios': [1, 3]}, figsize=(10, 4))\n", "\n", " libfmp.b.plot_signal(x, Fs, ax=ax[0, 0], title=r'Adaptive windowing using $\\lambda = %0.2f$' % neigborhood)\n", " ax[0, 1].set_axis_off()\n", " plot_beat_grid(B_s * H / Fs, ax[0, 0], color='b')\n", " plot_beat_grid(B_t * H / Fs, ax[0, 0], color='g')\n", " plot_beat_grid(B * H / Fs, ax[0, 0], color='r')\n", " for k in range(len(B_s)):\n", " ax[0, 0].fill_between([B_s[k] * H / Fs, B_t[k] * H / Fs], -1, 1, facecolor='red', alpha=0.1)\n", "\n", " libfmp.b.plot_matrix(X_adapt, ax=[ax[1, 0], ax[1, 1]], xlabel='Time (frames)', ylabel='Frequency (bins)')\n", " plt.tight_layout()\n", " return X_adapt\n", "\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F23_PhrygianHalfCadenceC.wav')\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F23_C-major-scale_fast.wav')\n", "fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F24_ScaleMajorC-middle.wav')\n", "#fn_wav = os.path.join('..', 'data', 'C6', 'FMP_C6_F24_ChromaticScaleSlow.wav')\n", "\n", "Fs = 22050\n", "x, Fs = librosa.load(fn_wav, Fs) \n", "N, H = 1024, 256\n", "X = librosa.stft(y=x, n_fft=N, hop_length=H, win_length=N, center=True,window='hanning')\n", "X = X[0:100,:]\n", "Y = np.log(1+ 10*np.abs(X))\n", "\n", "nov, Fs_nov = libfmp.c6.compute_novelty_spectrum(x, Fs=Fs, N=512, H=256, \n", " gamma=100, M=10, norm=True)\n", "nov, Fs_nov = libfmp.c6.resample_signal(nov, Fs_in=Fs_nov, Fs_out=100)\n", "\n", "peaks, properties = signal.find_peaks(nov, prominence=0.2)\n", "B_adapt_sec = peaks/Fs_nov\n", "\n", "add_start = False\n", "add_end = False\n", "neigborhood = 1\n", "B_adapt_frame = np.round(B_adapt_sec*Fs/H).astype(int)\n", "Y_adapt = compute_plot_adaptive_windowing(x, Fs, H, Y, B_adapt_frame, \n", " neigborhood=neigborhood, add_start=add_start, add_end=add_end)\n", "neigborhood = 0.5\n", "Y_adapt = compute_plot_adaptive_windowing(x, Fs, H, Y, B_adapt_frame, \n", " neigborhood=neigborhood, add_start=add_start, add_end=add_end)\n", "\n", "add_start = True\n", "add_end = True\n", "neigborhood = 0.5\n", "Y_adapt = compute_plot_adaptive_windowing(x, Fs, H, Y, B_adapt_frame, \n", " neigborhood=neigborhood, add_start=add_start, add_end=add_end)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "Acknowledgment: This notebook was created by Meinard Müller.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
\"C0\"\"C1\"\"C2\"\"C3\"\"C4\"\"C5\"\"C6\"\"C7\"\"C8\"
" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 1 }