diff --git a/BINF_TP5.ipynb b/BINF_TP5.ipynb index 9a6c7bf..f541f7d 100644 --- a/BINF_TP5.ipynb +++ b/BINF_TP5.ipynb @@ -1,26 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "authorship_tag": "ABX9TyO0SUJI6kaczFcOh8NoKcqb", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "view-in-github", - "colab_type": "text" + "colab_type": "text", + "id": "view-in-github" }, "source": [ "\"Open" @@ -28,6 +12,9 @@ }, { "cell_type": "markdown", + "metadata": { + "id": "BoOWxsamkRHR" + }, "source": [ "# *TP5 Reconstruction de génome par alignement sur une référence - Algorithme bowtie et transformée de Burrows-Wheeler*\n", "\n", @@ -40,13 +27,13 @@ "La méthode bowtie propose une solution à ce problème basé sur le calcul de la tranformée de Burrows-Wheeler du génome de référence. La transformée de Burrow-Wheeler est une structure basée sur l'ordonancement des préfixes / suffixes d'une séquence permettant une recherche efficace de sous-séquence. Pour pouvoir faire le lien entre transformée et séquence d’origine, on va aussi calculer un index faisant la correspondance entre un caractère de la séquence transformée et la position d’apparition du préfixe correspondant.\n", "\n", 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05RqHQl8CL8exul/6PQXZBbUajp16fz1BRg0YAAEIZxl+KiRQqorSTPv+ZDBCOPq3hGkkXv378nAwUOAR38uq1euUj23xEmSKlV08aJFcvjAIY0YGjRppL+nNBWx9VWqVZGCYLfNhPINefHk6BMLTzZZdGD6SbW9DS47Q1cy2EiB5bG7duxQXnqJUiU1Cvl28B6XQ7EnDF7w4MC1U5AjX8H8IAINk6vAv9tD/aZmHQhSgtk3FBPWQ7D7iKsPGdIB8lkD5AMmmv5wJjoEJ6gmUK/hBDcURKLbN2/Ji2fPxTGUoyrpnoYTxIfTU1LL2fmJjB01BnTaBzqhNIQCThYoAnnXoNM3wYoZKlRo6QHBD9rzPqS7eJ/UDxg/bjy4/XHlHuyV44+cIOs0V1IStQ74/YLhe5Pp54DvOXfOHKU2pwQd+cULF8iVLVOufk0KhODh0cGnTp6ik3O6jBmkEliKFDG5ef2m2NrZSOu2bVXFZypUiQ5DJ4EjLjQD2rVrD2GSWzIQ74yNja3akpyLMRBS+Rnpybvs8zvnMU7/AytduXxF+oLfnhSrQrgIEVT95dCBg2DG9cSqF0WGDR4KRy8jsWPHVicqC7EJzv7UjatctaoyzSpUrihRIkWRyVC1IftuzerVCD2PypgJ48CiOwzq6hJVpiEBZTm08Xr366OMNYcQDrqCTRo/QV9MKt+cxopOVt4RfO7ho4e6epJx1hF005eur6AoMwiT0QhJggmGg3tdsuEqQW+PxBYq4mTJllXCgmW3Hfz7rj26qW7fPrDiOnTqIJ07dlQBi2LFiktX8OlzQdDC1cVVpa8pYbVrxy5EGselZZtWynennBa/E5VyyKuvXKWy0n+74LzU/6NMNh2Hkl/HEFXwe/5IQPN3XtAfHcPIgpEVQS2x8AzI1CtavKjs2LZdJ7CBQ4foREchETL0aGuSojjJLZy3ADoBD6FFWBn8/4763xIlS6ouwVYo+QwYNAB18wh6WeZ0GkMaLWLESNKpa2elRDNUp93/hg7iaTAm+w3or8+WAigkP03Ac6Mq8iIIpJJURbGS6VOnqo7C7HnzVMPAr4dx+h88AToiV3auUEwSUZRi/br1mNmp1vpJxoInnidvXtWMJxOuEF58ilAyEsiA1XAeHi4/SwGJ2xC8KFS4CDTntogjVGxagv7arUsXcNNvS7ESxTXcZ9jZCIo0G6FKmyxpMikAsckVoLpyheHqVAhKtMWKF/v8IiJMp2bd7p27pGKVSrqaHkLEUAW0VIu8Frn78/Fyk3rLkJ77X1eIZNyFCAZD9ixY3Rl6PsI+lpPUwnnzoc1XU8NhSn3ZQlnnDaivxYoVkzRp00Kn7wmijYG6J+WEVrJ0SUQIOaQeOPK1QKn9I9cfkBBrpNHGlk2boOX3Bp9Lo9LgZNY1xiTmXSsct1rPnkFyHFumS9Am5AS9fet2nYgSgPu/e9dOVRjipDVi+HC9t6V/LlHqMuXJODH3w4RYBGKifCY9IBZCduGGDet1chuICdQymDPoDK2CUhBM4fMuX6ashvhZYb+HDx7qs23VtrUcwSp///4DvaejEButAHEVqiNZIgFO8mOwPaQeQoQvE4pfOr5x+h9Y//y58xred0MYGBr0VY5uEIuIHTuW7tnmzZ4jTRA2hg0XVk5iBeTedsP6DepIlIbmKsB8ADP+3NtzHzkHnwmLPXlzSE1Rleb582fSGRJSTzBRnDt7RhVw/1y0WJInT45Qv5BuB67iheY504JrXrNWLd0fXgRX/zxyDlxR+2KV4X5y7549+nmGtRwUu+DE0KlrF+whIb6BkJR7b/Lh7SBlRXVcvqQMe1Pj+0ycMFGFLhiqUr+P1FU6LLUBisLxb9yALt3Y8SpQOR2y2cVLlNTr1alZS2pCPCNP3jyqklsTEwf3xZGiRIa6bllV2iHqktJaFslrr77sVOGh9j6FPNJiQuJYs2q1rFixXIoWLaZiJQMwcXECHgMdwqKYLCk0SsfNmy+v5jOmYeWtUKGiPidu4dhkY+2aNeDy71NtQsvgfp6RT3FMzvny5YdISDVJjcmsFiKGm0gachINHz6Cio9WrlpNex+MHztObUlBT04sxYqXkM2QJKNK8ORp03C876LvfmRv4/Q/cXru5bp0g9MgKccxsP8ALbfwxR+JlSRO3Ph4WSLKOkhet2nXTl/2e3fuSovWraDoOlQSJUkskRAWboeUc4uWLWTe3HnKQ2+LY7mXH4lVqGKlyroP5erTBiq2MyE+mTJFStWX536TTpoC/+bfy1cor+F9kmTJtDowDC92XjgTV/D9+/bLYOy1ufXg4EQwGnLWn5AbiB07zueXvGIFKNckgWzXFIhVVJELUOxh4qlu/foQ8egvdRCGcnXu3aOXxIz9OYlIZ+E+9jBWLybzatSqId06d1Hl2z+gqUdBzzqQ6MoHqavamADYBIM6A7RFIbzw+zAZ2SKsboucBvMC3jGYfBs8YKDeU3k4Lr/j3+v+1q1JxIjhZSOUhEaOGa0qwhT3bAjBESYjN0I+u0iRothKLdNVO3OWzNAtHC89evWCTHhk3faMhUJvtx7dJUPGjHqrTKp2gBgpVYKoxsvoi7mYStjO7EKkxSiQEuCzEP1QxOQekocrIEPeAROFi8sLRBhLsepXkLXIrzBaogQaowq/Hsbpf/AEGFLzRcmQKdPXFerkiROaMGLTB2rPLYeAJUNXrgBcQfh7hs7ZoUN37ux5XXkYHvJ3eSEFdWAfknZYCagQw8HVmVsGp1AoBUJmKhaiiENI4nFljoNM+TlEG69euUrGjJkQRayXndgfx40XV8NxlqiYzedektiBspgQkmEysAw6xgmUELdu3qwTFfehvC5lq7lvpaw1X74qOBcrDbt37dbQni/xfkhpMzmWAtHLqhUr5eChg8ghxMaxVVS0g3r0rAaw+w3vKxkiE+YY6FSpoIfPSZL6/UePHkEoHENFOTk5eud4B828lctXysmTJ3QyyYjnVASr/B0kz1iBoIY+V2lm5FMi7Oe2is7OVT5FylTqtBQy4b+ZD2C+gRqD1OVnYpLbLg5WTXbv3q3PwxJF/bV2reZCYuA7V0NeIzQanKyAnY4j8kqbLq0KmVDeK8cffyCRimsiEqQtObGwwmARH/FOe3j0XMbpPWoxDxwPHSCAdXyfR0i9eCrqurhSINNJLpy7INXRMILbBI8Oz34HTjy+jSn36HcLqscbpw+kT5776V1IarG4zpXw2/p+IP3K5mv9pgWM0/+moX50WFBezZg38G0euBcelfnoNxYwTu/J1+HihfOqGZ8Te7df4akJ8mDiiTDPOHHiasLsV6Ev95NsVUUJaibRfmdwEmJtnFLTRIz55Lh48TxKfo/RSecPn7yMObcPWMA4vSeMyh5wo9CbLlfe3JIz53+/9HRESlJfh7ory32EpxYBkIQdZb4fH6DzbmPzuSc8u8AwwZYIDvx/uOf/f2/57MePxKh9VhAmQpClM5aUmMz6fvBeKEVLGelvx49W7W/vxXIsJyKL6AlLZ6xSsL2VT+DVPfFYzEd+0wLG6X/TUN8edujgQVmDum5PlHuY9aUzMAv+o0GJaHanKQmobLr0GZSUQ+16lv4sEYJ2dIX2/GGcNzWyzcx250ZZ6fjR46oZz/ZWEwDhfIT+eGkwcYSPGEFbW+1BZnk1atR0WgJmcqN9NjvJsNzGiSkKMsZsBMn2UTt37MSxq3Qy+VyzzocecTcBtpktJJvkK5BPe8gxZF+CvnE8XrvU4vPMbq9cvgI/26HYA9ahiT1gd1mWqWgHP8hXeuLJmY/QAsbpPfEeEG31Bsgw1qUJZ/0TTsL2zUTEsU3Tt4Orq4XcwSaU69etU3BL3vz5vh5GmOhSlHdqYdVk37qTKMdNRq+7aZOnSqmypbSGTORZqTKl1UlJiOkHxOBkgGqYkX8J9NlGNLAkGYiNONgOqgIwABtRUkubLp0CZcYCqJIyTSrF9LO81rtPH9Tvp6iTs8ZMh2YDTpaYWJYqh1bcBBzFjhtHsmXLhmrALG2XffPGLbAP3+lkQmISm3oMRdQTARORGQHDAsbpPfGcCKyJiTpthYoVlalF0gax6cyYs500wSuszX47GBGwk8wBdHDJjdp9DaDXuNITRsvVmai2cmjdTOjtKCDo+gEZx6ggefIU8uefi7V/G8+5GOfYs2e39snbjUnEHc0mnzo/AyBmCxpLtlbyB6MKAkoI1V2Ec/TARMDOs26A1jLKID6Aba/24jxE+TG7HyVKVOUadEajykjoaV8K7aWJPWC3nfqY3IgJIPOPYB9iCohCJACmA8BG7YFhTwHSihkBwwLG6T3xnNhIMS5WdKLkLIPtmFajFfVdNK5kCyr2mucg+eUUaJc50APeHjRaAjvmoE0zW0gRyEGnbwVcOFdbht1c0Yej/RWRYUTEkQH2Jxpg0umTJU8mf6/9C/TRrUokWQAwSTqs5ATrkFHHdk/sZc/GmjkBk92DvnmMIrjHXwkkWSbwApwAsV0IMkgjNKJkD7rbmKiuXLksZ0+f1UhiOiaNZHBosu9IByalNBMmhScALN0G9PT0qdMKiCHykA0o27Vuo05PCK8ZAcMCxuk98Zy4lyWfmxDUK+hpN2PaDBBh3sofyOQzm/0tuYRMtW5dumHlLaxoPm4NbmB17dO379dWVewBvx0MsZ5g9m1ESL0BW4BxaB/NJo+lEZr/BUdnkq4BIKXDwCKzsraRMmD2rf/7b2V87QdmfCJ6tvfp11dZf2yjRTovWWZR0b6aE8d8kGoYPTAPMHnSRPy+vSLqCqMbbVaE7+QDFAWU9RSw649Bj20Piikx7WyoSUjxRaAQidknw43NLhk9PEPrrr7YJhACHAXRgRkBwwLG6T3xnAih3YR9Mfncly9fRjPLq8iW5/rac+37U24G84ydbIOHsFfRjProzpo4yWfhDA4KWEyeNBkO9lChvc/BVR8LCu6mjZu0vTRpoSRycP99AzBTEkR69+0D7Pg4JOEeSswYMdEwksy2tEjO3VK4MBOIbCFNTD/ZfhQFYYkxXoJ4IPs8VzYg8/7UBSA0lFDbZiARvXN/K6NHjJIHyM7bopLQEOy/GCj/jQGW3xlOzkECCrcPzGVQPIL5hV+VLT1hZvMRH7KAcXpPGJZh7Sg4Bju3Elv/O4OO/AQrKGvu33PLD4KayYoAMd7E/BM33h3cfYbRDP/XrlkLUs49rNrRFYMfP2E8iFo00ww9uf4s6bHZI2mnZLOxxfPde3clAhhdIfBvjtc4lq2hLT3viR3gsU+fOiujjtGBRfGF9F0qz5AHYLlX/uwOqKQ8H78D2X8kvpRD/oJUUzMCjgWM03vyWVEu6yoko0id/L7u7dFTcmVmP3iy7SIiC14amXNOAJbBkJqhNp2cXVyp1OPXWmtsPU01n2JgBJpV3qNP3G+PN07vBfuzvs6Q27te+hcIu6mk+6OuPyTRsDLg185uMRfvxxb9580IeBYIVE5PtRRnhKsMUy3QVTom6+QUp/Qu5wx4j9ncsbHA/y0QaJz+MzptDfagIbB/fQ2RizhaX74E6atNGzZAsaaFilD+1+A5qLjC0pUZxgKB1QKBwukvXbwkXaBlVh7qMIWgKvsYcNVxY8dAu64w1GqcVNet38D+RJ0jYRXjH8+SJTWG01sgaURNtXyApw78RjIpsD54872CrgUChdOPBAyUWnTTZs74+iSvQi2G6qT3AJYZNmQIlGKTIKN9TwoWLKAKpeugj7YXQBki5Qh0OY2s+bQp0yQLpKioPWcZF+66yPk7LmJLkorv62EEnjeT9UHYL1Ws0BI7kukA5JcPNlA4fYtmzbRW3REih98PotLYHIFNDJh4IlZ8NHDoi1Fjvn79BmrTzUBqSaYJOSrgsrQ2ZPiwr6eZuvaiTFl9UULYW5ucgBfeVJYeOdqWSypl88b/5ZlIAmIJklUM6s2dhyCoHVR6uW17BLzBc/QW4BxMiW8Hh5AqLkqmIXUAHR1DyhnAjl2BVYgD5CTzO1SupV5/GIiZknr80n0yS5oAACAASURBVPWl6gTa2toofZn5oPsoi/I+2YyEOSBKYBN5+Q6a9RcvXFT9P5Y2eR0SjT6B4UiZ6yjQJqQqMvkYMaCW+wHQ7IcQHqXqEHUSiYN4iSQs1Xo5CHiizj4ly4jx+AidP5ZGEyVKrJJqPj0ChdN3QKMJik72AjrMMlhnZr37xrXrQKPNk+mzZspbPJSmjRprQ4O9gM3GxQtTGMqzltEfKDk+/G+dfseRe7J5z02xs/5onN4rbyOcCcRgKZwztmRN+2t9ANKR3d66KUdhJfQII0f+jPhzcAgB/EEEOQBxUToo5cF3bN8G/b6wWjoNHy68Mglv3Lgp0YFreP7imVJ/90E8lB13KF5KhOEDaPNTslp7FWL2YCl088aNgB6/UQVdtiljB6EuUESmlh4nDd7D3bt3lHewC3gJdg9iQw2+YzxPGGjaE7/ASUR7FKDhRyX0BqBE+SWAuCigSt17SnIf0fZb9SGSelAnAVK0U4EQZcFKeMXUv/psoHB6SkcvBcZ8BvbulpZMI4FAI7iEMy913ydOmazqpm1atkKDh06yG6g6diMpDM16yyBDjSvA0BHDv/7sxLWnIME8xOvKl8PE9796oX7++0+6AqZLHU2Sx/9vRViiHIkUJEqQg6t+C0iHc3C1Jb13//59EAW1ATApKmS5pyOS66u/f/LkscKPK0DFlysr+QNENrKKQ64ECUrcylHmvDS4DufOn9NeAmRIUj/fBa3KSH7aAlHRq7iPVKlSq+Iwu/uQQemCRYGRBDkQjhDFzIifUwCzWIli0LSPqIhKB5RdCY0mdbl4yRI4dq2qHqdOkxb06vQKn3727KlqJcTARHQFyWY23PCtESicni2NqEtPtBiJK1dBIGFHEUJV7wBAQjlr7vf5ArRo2hR6892gH7dLQzeCSyyjd4+e6D+HJgkgw1jG2u03ZDX+BA/GerxvPZbAdx1G9x8xcVYskkByp/9xE1DLt54HfsJRrITRsFJTS56todhph+QfRnDMz5AhaG1ti4khpsp69wJvgZEdyUMb1m1QOnFirOgM4c8AKszOw0QP8n04g840z9EwoxCiPMp1k+TUGFtEdiB6hsiA4T8JTCz18tpkH7IHAFdwThaMGLaB9OSAkDxrlqyybOkSKQzJb0YCTCozWXz06DFtrcV7oNMz9A8bNhwUljMCVbld36XgwUNoizKCs0qC1ehbI1A4PY3FLDzDpnuAn1IBltjw1OgzR+roGTDDCoFYwodIWejMkCJmhxlGBfER4lsGMfU8hg/CMhZsvSYLt1wVB3V64/WefTE/ftnT1y+SWApn+bmUFzkDZAamh/Y8t2NkLXJS57PiKh0Oe/L8EBDhpMCQmnwDcgDYRJSRBHkJTlhpL8A5ud9+gXxAdsClmbTlv9kIhIzAp1hpeZwNyEvnzp3VNl106KfgF7D9F2nI3B7o1gHOig285g7uwpErQQ6c8tp2wey0uQm5Fc5gITphi0lIM9ucUSad7x7JV7xP0pLZ9ILhOwVIIkQID+j1Id2Whg4dRrkbvjUCjdNbDEanZTcX7xrOL9/KE5d3JnHvDQblnBkxdHAJ4/Dz58PkHSsq1ODnIP/fBsk2Eo2CwWGoP8/nS0gyE2WOIR014Ue2I7kKDMFZgmWCjJEd1X/IMaA+Ias33PKRf8DJ5T7+zXMR5cj+dTyebck5ubMfAXsKkEnIf3My4H2xdx673vLzTOhxgeH2gIm8d5iU2N+QExTPxftigpjH8FhiQMh5iIampBwkWr11e4sINYTSrH1rBDqn9y3DmesYCwRUCwQ6p+e+jjO+kWcOqK+kuW+ftkCgcfpb2KuxIeFraMBxpEyTGkCcgrqvopQVGzH+V9jPhpBUuGHrpjzY01saV/r0AzDnNxbwbQsECqenJFV3ZOQJnMiP7D0pquSg12tQT/dos9AYkko03Df9SG+e/duWI3mUFuWU86DMMlJoB6047+yp7tsP1lzPYxZgSW/eHAieAkRTD5wN36iXe+wOve/oQOH0rMOv/3udzERXUDYP5NgB+Sk6OMtEUwD0YANHCldWRPNCZnOPoPnERZRzbqBdMx94kiRJtbEhlWfYP56gDDaFfH/mvHy4dF6s7G1wVn+avUdmGWGM2KZOL9ZGtspT3vHu7Tspi7o9E3Yb0XqaybfAOgKF07dt1UZLOd179fzXc2LPcSq2shUzx7YtW2QMNOTnQpySJZOKlSupw8eOE1tXdoJCqCXff9BAVaR5txhIvgWTRUICHmn1uRGFvxuYtKwcnMS+WQ+xy5TD391eQLghZuCrQjb8Ger3q/9a66vZdN+2T6BweqrJ0kE7YYW2DJZ0KE9Nx54xbZrMnjdXZaQaQDG2IxB51HmPDhAFQRwcFH5cvGiRPIOkFKWo2a6K48OJHfL+8GaBHhWc3p+u9MBuSzAHsctVXqxjJ/LtdyhQXI9OXw2QWTr9qrVrjNP796dKJViG64ThWgA0BGwQFMF6L/uOT5g8SXXi2kIb3oK9j58wgRQoUEBhkOOx+mcFaKcYcNek41rGh8un5MP5Y6pA61+je8xuCO/txSbtH2Id+b/Rbv79WfrV/Rmn9yvLe/K6FGxs16atdo0pATjjJezVSZNt3ba1QjOnQGmWhBs6ffMmzaRLt66yE22cCassCuTeyGHDoPd2F5TbOsqsIgiEUEwyrdw3r5J365YJEB/+N7wHwUQY3ldrKrZJknvSikH7Y8bpA+DzP37sKMQy5qDlkrtSHvNADKMCwnR2dmFziMZNGqtDz5g+Q8qgPdNZQC+jgHKZFjDOKdCXP3X8pCKjiJiiVnwDaLwzSnD/a5G8XbUYTk/Ko38N77GnDwGnr9dKbFOlC4BPz+9v2Ti93z8DT90BWXSPHz/RWnsoMKA42NWVYhoWsA6bPTKr/5lSaaV/CN1lBp97fksHWDZ91LZTL5zl03MX/7vKWyyF72QFyqnVl+qFpwwYhD9knD4IP3zz1YOmBYzTB83nbr51ELaAcfoA+PAfQmjhAJB1lCGKCvki0mfZ2JEAHCbkYn/TPOJHX+/atWugRzqDnx3rp73mA6BZzC3/pgWM0/+mofzLYdQvo/hlPNAo48aNh/7uxzTpRs28SSjFxYACS63atX56u+uA5qMoAjnOVDghkId93c0IOhYwTh+AnjU7rbRt20ZFFAbB8YmZfoJk3tSpU6QQCDdrIWtEok0C1OQ/oZ5dBi2jguHf165fU5UTajuwjzslkgpCSWXM6FHo9/4UHWD7qRU2Pzwt2x6dVmkmr+buCQl2tA4mVWPlkDiOkQKQlQP/rRqnD0DPmHj6OhBPZAfZP3L9X32ED5EZ+iGDBsmunTuhYVYCYoWHVFGnIvTTOgOVd/vmbakMFZT0GdOjV3tYrPL3tb87+8DXqlNHrTDgzDLpf26FhALiDbl+L1nG/dMHcYLTL8jSUrJHTualc5kPe68FjNN7rz199Gznz5+H7l0zGTVmDGSQUv7jWoThdu/aFSU6G+k/cIAsX7ZM9lISu1cvGTRwoOqS5cj5GatOCi7ZeAf3H5JGTRtJKZAvWLLb/wAr/Z1j2o/eq07/gZJLtvZSLl4uielk+rn76IvhwZMbp/egwfzycEoQ1axeTRo0bqzqppaxYN58lUpaB+XRGDFjScNGDUG3XQPM/U4IY3aR6dOmY8WvKNEA0DmDrrCUNWYegOy8FcuXy9DhQ1Ur7dKzm3LyEVh2+luvrfTUibMHyCdr9LQSMURYvzSbufZ3FjBOH8BeCXa4YeZ+2MgRqoG2G0q3M+DUvaFjPx9Em8hRo0nLli1kOZx5F5y+e88eQl11hvaRQUXtAAgvNcgzZsqkGuvrkAcYMWa0aqeturlH5l7dIU429qqZ5pXhDmKMPRRcO6QqL0nCfJZ3NsN/WMA4vf94Dr99F9RCHzZkGIQP7yp0llj8sujhXhaSxyOAq48Cp6+GaGATmhlQIadRkyaqoFoQmvcUUpw3Z44cRnOCqGDqUXO9apUqkr9QQb3++EvrZMzVrRIc3VW8utJzTx9WbGV8ujqSLkLC3/5+5kCft4Bxep+3sbdfgTBaOjRVSkmWoWophzM6jth+UTx9Bd30z/rjn9VMqaRDlRxCdU9CUsv56RNtLWT5LD//1M1FHr5z/QK799pKrxBfVAtiOISXkLbBvd0G5oSet4Bxes/bznzSWCBAWsA4fYB8bOamjQU8bwHj9J63nZ99krx5qt+4oz5PamwMdLFlnZ4NBal3RijurwZLfHz4v3Psr85lfh+wLGCcPmA9L62xU/ySFTUnx1Dy4OF9yZuvANpT5ZJRaGRZvmIFSaEluZ8PUm4nArLr6Ogkdep91tMzI+hYwDh9AHrWbB3UqkVLCQkBjK49umNVDw0E3g5ZvXKV1KxTW2aidFe6bBktv0VDOyO2HSJ/np+7hXbG1Ldnw8HtqM93aNcWrauLqCjm1+FyAK1Sd3zh03s1kedFwyL7LzZofxShGkQ9TJ3fi9b8x8eN03unNX34XKdPnpJmTZvIZIhfUrLaMgjP/Yi6eE90oiU+Pyyc+xU0zfv076dOP3LYcAhuPJa27dtLkmRJta5/C3LYrPNz8vg6Hs5Dd8zZEMZk/zU/VsP95P7Z2eOhlXawWD5s2aB1euP0Aeh5b1y/QYYOHixLViyX8OhK+u1ga+PaNWoAb19M6tWvr1LYWbJl1dbDQwcPkWYtW0IPP63MnTUXXUVj6DaBrYzZ9vjrcAVj7wVWe5W/9g8rPXT9I6Ktse1/93gPQI/QX9yqcXp/8Rh+7yYOgUTTqX0HmY1GFxbOPBNyO7YDRYfOo9OnTJXGiARIle3Xu48kSpxIkiZPri2wugCXTynsgf0GSHno6R09eljlttp36qjKuDpen0V4f+qLw/u100POywYJyXD58N/PcmBmeI8FjNN7jx195Sxs91u3Vm3JkjWLtO/YUa+5CzDc6VOmSItWLSGEORP8+DqSOUsW6dW9hzLokiZLDhz+ag3tb1y/gf38VlByg8tO5ALYmrhj5y7Qvc/5+f6fbhR5shKRvT8QxtQ9vQPQPa0R3hupa+98wYzTe6c1feFc+/bulSEI8dOkSQu1HCfZu2evlAMEt0SpkiqN3aBhA8mUObP06dkLDp/0s9OjoUGTpk0VnWcZlMpmH7xeffv8/64fQQnXGX9s2C7Lr1d6SF3bQJM/Vl84fXRfsGzQuYRx+gD4rCmLxQw84bgZMqTH3j2b/v34sePKtgsHxhzlsJ0goRUaf+5DIYe69992sr1y5Ypm9f9R3vvwApK6L/3e4b8+EzTdsEXuwgo6/GZ4mwWM03ubKc2JjAUChgWM0weM5xTk75IJS44ftd8O8sbxoAGM03vQYOZw37cAty5zZs3WJh5sx2WG1yxgnN5r9jOf9gULsJ96fTi7nV0w7dNnadzpC5cOlJcwTh8oH2vg+lJ8SVug+mALcY/xkyYap/fi4zVO70UDmo/7vAWM03uvjY3Te689zdl8wALG6b3XqMbpvdeePz3bfejM37lzG7p2kSRO3Di+dFXfvcxrdNL9CNquIzj93jmM03unNUV1FKpVriLPnj2TVQBuUUotsA4rCE1Atc33x5zZc+SvtWtBkgknL9AKOlPmTNKkWVMfEbDgVyQwh0y76DFiePnLXrt6TUE8hPT+alB99+VLVylavPgPDz196rQ4gBZMoJBHhnF6j1jr18cap/+1jbx0xLq//pZ+kKfuBgprViDnrl27Lt3Rd64mMPTValSXd2/fyq1bt7XPfISIEfRa796xh7y7wmQpWx0yZEj9OZ2P6rehQoWWSJE/t4pyRznr9s1bmK1D4meRhavtuDFjJQGQeVTIZab70aNHcMaXEgeNLVnnZgmMP79/955Y29pINCjjWgb72711eytx4sVVIC7Vc+/ff6DY/hDoB8/zEOEXEbRc3vO3g401eT9Roa//Ec0uXF1cxdXVVXn9HGQIhg4dRuoDKszy2wMc/wb3GycurvUfktvG6b30Cv7rw8bpvdee/zgbnYsdadhbrl2HDl9/d+TIEUhduYMpF1tbSz0F8cU+mD2ctAIYcqmhUz9ZFWz5cxs4ZatWrfW/s2fOUgfmal6hYkU9LxtZuDx/rj8rgS42MWJEV6ENnnvAwEFyDqv+qpUroYz7BhNBAhXb2LNzt5w8eRJc+3dQ0HVWtZ1s2bPL6lWrZP++/Xounpstsdgs8+SJk9IPXXJiRI8mjFoeP36kyrrVqlUDP///EcDBgwdUwitS5CiycMFCTBLB5SZIPpmyZJZixYtJl46ddVIagR561zH58b7eg+/Pe2Vrre8nEYvBjNN770tqnN577fmPs9Fp6fRsLpE3Hyii343RI0fqylitenXg5o/JwQMHdUVtCyfPkCmjlCtXXvqgLRUJNFxl79+/h+ighpw+dUpevXqtghh70Lsue44ccNjVWDnvyQD0sxs9cjTkrRNpZNG7Z08piW44CeHE/Hn+gvnlDiKDO/fuSPsOHbVxBkN4ds3hFiQmtgTvMBkMGzJUnfME5LKvXb0ubdA4c8b0aYgSPqg6z5ZNm/Hzq5gMBnzdpiz5c7G8eeOm0trdwN4bPGyIfPjwEQShQdK3X3/ZsnmzBEfTTYb/1OjnxEWK8Fi06SIH4GfSXcbpvfclNU7vvfb8x9moXtO4fiNdMUuUKvH1dzchXXX58iV0p9mNPnKlIG6RXld2NqCsVbuOTJwwQRtW0NkHDxgIBflPWqNm08rMWDUtg+Exw29X15dy+9bnEH8kutXMxWqcDE7ELjUtmjWXIsWKQVrLUS6cvyCZMJlwtY0RM6by6k+eOAEH3iT1GzUCL3+7rvTsh7d3z26ZOHmy3ITCzsOHD6UG2l/Xq11b7HAfiZMkURFOJEmkC7YtlOfiWLl8GSIKbA3gyLMQwUxDvzx+r6aNGoMK3AGT1WkJjxbZkSJFBsJultbcOf5asxbX3Sd9B/TXsP/7YZzee19S4/Tea89/na0HxCtc4JQjRo2E49rqvrxd67bYE0fUEJmOnb9AfjjXTYT1E9BQsqmustWx3+dKTTGM4FjlrW2sJQUEMQoVKaL7ekpkbYeTcrWlEAaddy8mkaEjh8vM6TMkRcqUiA4clFffuVtXCYckIkNthv9b0Z8+WrToUqlKZTl08BD+HJDUadLIsKFDpTkUdpLAqduDptutRw9w8K/D6R8gWmkgrZq3kJQ47x+5c8kTZ2d5jy1KDnDxLey95UuXaIONuHHj6VZk0tRp2KI80c91QOdcRjMR4PSJkySFiOcIRALooQe6L+W77qFjD6/3I2y9cXrvfUmN03uvPf91tsuXLsuA/v0lPlpKpUyZQk5jj83EG3vCnzx5QjZhlc2H0J+rMFfjKtVrSNeOnTTJR7GMQVjpSZVNkjSJNqjM+ccfSqHl/vzZs6dyAytxqdJlZDPaWF24cF6Gwpkoq8UEWVWcY+L4CZpY48q+bcsWqYw2VocPH8aKGwHhNRR0Dh+VI0cOScrUqWXa5Cma/HODxPYUrPIM6W0BfWWLLP79yOEjcu7cOWwnssshbEXCI/FYp27dr0m41Zo7cNOQfe6sOTIek5jzU2dMcm3g9B3l7Jmz+vnm6LU3G1h6B0xm0aJHxf1uxJ6+tm5TfjSM03vvS2qc3nvt+cOz3UcYvgcKN2w7FQUOmCNnTqx4EbDf/YAweo9cunBRnTBf/nzihFCZveXjYpLgMWdOn4HSjb3uyXdg/34JevfsQ5c/f379/MYNGzR64Op8D9n+lClS6naAZTsew1Cee2lm0lOmSiUZMmaQK+hhx+ghVqxY4owV+/Gjx8rD52TCfAEnmE9gtVkj1E6EtlkU6uCem/e0besWuY7Vn6KauXLn/kfyjRHIe9wTqw3ME7A0+RbVCbbgYqdc3gvbZ+fBJGdtbYWJahNyEy8lddq0kg4SXz8bxum99yU1Tu+99jRn8wELGKf3XqMap/dee5qz+YAFjNN7r1GN03uvPc3ZfMACxum916jG6b3Xnn52NirLrFy+Qstl6bFv/93BnnguKKulz5DBw6o0BPG8evVKcw7f6u/x2rt27tLz5cj54+Tc794fjzNO7xFr/fpY4/S/tlGAOIIO+OfiPyUekm1MoP3uWLVqpdy5dUf18llS9Mgg4pCquizhsZT37dgB4U6WGYkt8OowTu9VC/7z88bpvdeevnY2tqli9pv4/LTp00k2lLtYBQiHzjc2cDZm2C9fvASMgKtCYOMC387mFtTADw7Ib6zYsbTf3QUo694G9r9Rk8YK8SVXgHj5goUKKgjn23ELpcYtmzcpNyBP3rwS0tEB9fx2iBLSS1101bmFrjmXLl2SePHja5mQ1QVyC4hMvITSJSG3lOpmVYL4fSIAnZxCKc4/XNhw2pzjR8M4vfe+VsbpvdeevnI2OhPBLR8AiSVcl46bJ08ewGRnSHZg6Nl+eumypVKqZCm5hPIcwPRaBx8DGC474bDVFRFxhYoUVsbb3Tt3pWDhwjJu9BjF3IcKHUrRgqzNs0zHwXJbz27dtYQXGr+/c/euNsAcNGCApEIpkJiA3tDaZymvJtB7e/B58gXYoIPw4nLly8lRcA5IvqlRqxYgwSPBuAup25GFC+bjvnNIK1zPOL3Pv0LG6X3ext5+BTr9mNGj5eTxk5ILLarTpUuPOn5C6dq5s4J3gmGPffvWTWnWogVq8leEITwdffGCRTJm/FiNBrp06gwGX2RJDpSfszMIP/bBZPSIUcqAI796KrrmELFXG0QYDmIBuuIzb9+9VR5BGiD4YqLOT5hwsRLFUdtPKt06dZXmrZpj4kiorbCDBw+hjTO5tyc4h3iBPbt3SznAf6cC/NMPoKUwQOQNAC6fW4tOYB8ap/f21+VfJzRO7/M29vYrsEPtwwePFK9OJ6IYQkOE5yuXLQejLYty1p8glOeKewkh/vr163WvvxKNL0ePHQtqbiiluTIiSAqW3NOnz+HUrxUOWxP4ehs4KRN0GZEbyAyYMAf378ThM0HHVlq2WMXZH49suiLFiio4aEDf/tAJaKKTweSJEzXB9/TpM40M6jdsqE5Pgk+JkiVl5oyZIAcN/DLBTAV4yAVMxPbG6b39bfn3CY3T+4KRvfsSJLEsWrhIMiFLHyt2HCXoJEyUUA6ALEO8vkNIB0wKD6VBo4Zy/tx5WbF8uRJ7xsHhCwO7z8igR7du6tDxgMR7gGMTJ0kkYxHeEyMfHrDfpUuW6gpuaYnNHMGCufOkUuXKYAa6Sycw9NqCLrxj+zZJj/NlR5aezTGbt2yuW4KxiESCo2eeM/bzbL/FqGPDunU6aZBJOH7cOMkNRB/zBtwWJEPEQVKOWem9+20xTu8nyjne/RgZ3m/A6n0CsFnCablPL1K0qOxE91ruyUnkcXnhIrnz5gGR5Z7sx+pauFAhxb2zHRbD+/V//wXYbwHJmesPeQQW3R9wwI04J4k71BeKBEJQ5apVNSqwhPdLUB24gWRdMDtbtLuOJaXLlYUjr9de92UrlMcqfgChf15IgkWUjcDr2yFkZy6AEUX+AgWUw38e91ARlNqjIN/swv2GDhtatm/djgkoi7Rs08o4vXe/LD84n1npfcHIPnUJ0ltfQcmGKjYODg4QxXDX/TMVbzi7kabKUh7/UPFmOlhv8ePH0+O5qjOUL1K0iHLeGa5zPEYGnxz/b9V0vr3/+6gWfPr4CZn/z2o7DPsZmlMXj9fW64PS+/4DGlBy4Eas8D8m9T4AS8B740TASCVa1Cg6AU2eOEnKlC2rPH39CO73KtiDFNOJH/+ztFZjsPwsEtj89/nz5zG52ev3McNjFjBO7zF7BdijtY6/aBEIOht1MiAYpzYYclS38e1BINFy5B82IkrgLEAyTsPGjb7KgvF+tm7ZKsNB9SU5qTi2GdzCMEdA6u8aCIaQVUg1H49gEnz7e/rX6xmn969Pxofu6x5KbW6onTOx59fjFoQ/uFUhhuD7wbxFTdCMSQdm5YGRC6MRaytrOQUxDtKCJ6AC4BeTll/bzavXN07vVQuaz/uYBRbMm68JQ0YGwYLZ4TpWEO18o4nK4RAlKVO2nI9dOzCf2Dh9YH66Afy7kedfCTX9C9i/k6PPLcobCHxw5Z81d46WBs3wuAWM03vcZuYTvmgBIg8H9h+gOnwWgk99JPX6o8ZvhucsYJzec3Yzn/IlCzCLXx0SX7egIWhrZ6dgntnz5krGTL9PKvKlWw0wlzFOH2AeVdC90YGA644fO05LktQOHAngz48ENIOuhTz2zY3Te8xe5mg/sAAFRGtUraYtwRZDcTdrtqx+cBeB55LG6QPPswy034TJuxropkPN/Tnz5yni0AzPW8A4vedtZz7pixZYtJBtskJI6TKfUXtmeN4Cxuk9bzvzSV+0AHX8Ce8l18AMr1nAOL3X7Gc+bSwQ4CxgnD7APTJzw8YCXrOAcXqv2c/Tn3aHEMaHTx889PlPIKdYfxSxe/dB2Wr+YuBGrIIplc5fDLf39oLePLidQMGi9pRN+c3RQEiC2YL1+IPnYpzeU2b14ofwVCZe/FvmPTgqtta2v+0vr22tpPANV2l/+LXYf7CSD9ZevA+vfhxzlnUYKwlR1kWswjmIeGwO8+rV//l5K1KIraXHxiJy9Wlosbf9Qu313qsEiLO9c/8o0SI6SvsqqSRKKHIW/jmM0/vFY4TTDzy5SGbf3i8hbe1/+w5eY+YudO2x9Dn1UkKKrbz369UVUYc4fZQQ5bGyhkUZzR84ffPFReT0g6Dt9G8RCcaO7iRDmmWRaOH/nfg0Tv/bLud9B7LB5JY7R7EiXRd763/PxD+70nvw4KM7P5Jcj15IcCtbhLF+PJTvaiW2KRzFygEzkF9G1FzpP1rLnN2J5bGLA2i4fm4dP3s4799/hKR4CClTKL6Ec/o3psE4vR89mn0PTsv5J1eUH/67C7Y7SlYJUbrKev+Z2HzAS/67H/Sp7wgn/wSnkCO5EgAAIABJREFUt0vhBKeH8g4UdfxswOkFTr/8SCJ58tIR2ya/DDv8zAp64fd4N8KECiaFc8eU0I7/FkkxTu8XzwcU0TFnVsiqB8clmI3dbzv9azSxKPTktbQ8cVvs3rrjHfdjr+diao89fZkIYhUG3XH82Om50vdam1cuPwqNJFbQ3tNHj+ggHWunkUgRkGsxe3q/8PJ/XvPTp4/S5fg8+ROJPHub34eUvrazkjI33KT3lpti7/YOiTw/zuRhMbUKayUO9ZElDo/v6JeLq9VHiG3YSMNV1eTMwwgS3Nbd7x+0H92BmzvUiCI7yohGGZHQM07vh/HnP9+AR24v5IX7Z5TZ746PONTpzSeJ+OKtWGPi+PTbMcLvXsHjx1nZffjs8J91Nf100B63XoaXtx/ssG3yN4/a121C3QE75H+ihQ2uZTuz0vv6IzAXNBbwXxYwe3r/9TzM3RgL+LgFjNP7uIn/fQEKPTKs90ho7we3aS4ZCCzAUJ/j23fNOL0vP1i2g965Y4cULFhQQocJ48tXN5cLShagw69FO/A0adNKrJgxv3514/S+/BZcRp92Nm7oiJ5xUaNF9eWrm8sFJQswouyFPoFl0X4sLRzfMozT+/JbcOXKFZk2eYq0addOoqCtkxnGAj5lATp9f7QBL1mqpLYWN07vU5b+xXkvX74sUydNRi/2LhIhYgQ/ugtz2aBigX59+kpJdCxOnTq1cXq/eujPnz+Xfr37Snl0ec2aPZtf3Ya5bhCwAJt8zp4xU1q3aytRo/5/K2nCez94+Dt37JTlS5dKYXSMzZY9u4QxCT0/eAqB95JPnz5F++9taFa6QcqVLy+FihT+x5c1Tu9Hz/7woUPCfu/v0cAxTuzYEjN2LAkfPoKEQC/3kI4hg1Q5j1lmtrMOFSa0tsj2qKb9h/cf5N69e+Li8iJI2Y2v7ueuP6+13Rdbl99GU9Dr169LcLxH5dHkM2PGjP96w43T+5HT87Jv0T321KlTcunCJTywx/L61Stxf+eunVxAvtMHGmQGvir719vbB5OYKC+lQyvtxIkT//Trv3//Hj3uLsjp06fl9u3b4g5bWoOQFJRsxtr7J9iMdrOzC6aLRdgwYdH6O7mkRrbe+icQb+P0/sirOAnwZWb75qA4XoM2/OD+fWErK1Y5IkaMKJUrV9Eutd+OJ4+fyJ+LFomrq6skwsQQP2EC7FmjYHULmkq5NsDZ22Gh+N1+AMbpg6J3BYDv/PDhQ5kza5bQwbt06yphw4XTu3Z2dpahg4dIsqRJpHS5ctrY0gyPWcA4vcfsZY72RQvw5Rw6aLAK8nTv2UP71Pfq3kMiRookLVu3CnL7d+8yvXF677KkOY+PWIB5jqFDhkqePHnkPpJ1J0+dlF69e5umF16wtnF6LxjPfNR3LHDp4mV0rR2jSbqOnTtJzFixfOfCgfQqxukD6YMNTF+Lzt6udRuJHj2atOvYMTB9NT/5Lsbp/cTs5qIetcCli5ckhEMILeeZ4TULGKf3mv3Mp33BAlzpnz17plcK9yWL7wuXDbSXME7vQ4+Wmea7d+/KU5SYPkIllm2W4yeMrz3W/Xq4u7vrvUWKFFkcsHp+RrTdlQgRgAh0cFCswJ3bdwAPDq0Zc6dQobQO7JlBSCjPwXN7dvB+mbW3tbORPmCNGfERz1ry8+eM03vNfj/9NIk1fXr1khvXb0jYsGHlxfMXkilLZmnesqWE/A5s4kO38NPTEgTTtlUrYP+Lar/3S5cuSd2aNaVthw7gXpcD0u289O3dRxo2aqS/I0srShTP0YCnT5umENEW+N6eHXxJmzRoCKe3lck4n3F6z1rSOL3XLPeLTz948EBrzGXLl5MUKVOi3HRfhg8dJs1btZRkyZIKHY+TAVex55gQQocOpX9/88ZNHj9+LDFiRFdk3r279yRe/PhYfd3VGdmfPVmyZPp5/p7glM9/fy9OTqHUwegkIUOGFO6DX758KQmAWPs2LGa43KFde8W4Dx0+TFavWiW9unWXwsWKyeChQ2TZsmWyeMFC6du/v6xeuVJy/JETEYED7imGRP7i/K4uLnLhwkV8hzA4f8Kv1riMSYIAmgQJEoA6HFFGDB8u7C3fvWdP/PeNkPlFp02aLIki6FxevIBc/idxfuIs0ZCoY0T0/eD3adG0KbrW2Mn4SRON03vxzTUrvRcN+LOPE1FGJ8+fPx+w0CnlwYP7smbNGmmAFevataty/Nhxad22jeLGJ02YJPUb1Jd16/6We3fuSoxYMSU6iCf79u5Tzn2x4sVl+7btSqhwA1Q3OZyePz9/7rye48/Fi+X6teuKXFuzerW4urjCQWzl8uVLiskmnr1GrVog9FCr+vNYtWKlLAaUdfrMGTJj+nR1wNPgAXTu3k3mAglHwkbNWrV1xQ8VJpQ4wBlfoWbevGULTE4fZNaMGQr7fPXyleTJm0fyFcgvmzZulIP7DwhhoR8h0V2nbl1Zi+/MiadF69Y472xMCE+UcxA1ajSpXrOGzJw2Xa7fuCHxMUlUrVpFwmAiNE7vQy/ll9Map/ch+3Iv36VTZ90fx0Jdmc5tF8wOK+tw2bZli+zatUtGjBoFx7wsPbv1kG49uslUKOqkTZdWKlaqJAsXLpQ7d+5IGzj1zu075OTJk1DbaY+o4JmMHzNWkqVIgUlhr/To1VPhqvz91BnTZQQmmpSpUsEBN0nSpEmlHHj7z5EEixY9uty//wBO+lLixI2jivltUQarU6+uHDl8WGrDQWdMnSYJEyeSQwcOSZlyZTRC6QsgDO8nbbp0MrD/AMmCLcrx4yfExdVFGjdpIseOHpUN69dLz169ZfSokZK/QAFJnjy53n8oRyclwbx9+06y58ghixcvksaNG6vTz5w+A9coK0v/XKJagW3A+Wbk86NhVnrvfUmN03uvPb+ejeH96JGjpHTZMpISzsMXfeL4CRr2Ojk5yb59e2XAoEFy8+ZN6d2jl7Tr0E6WLV2m4hp0ttlYFR3BmipfoYJMmTQFK7aN1K1fX88/ZtRoiQU67tEjhyUJHPsRogpi1LNAlGMHIoIOnTrI1StXZfrU6VidX0ph8KnzQ4hz0ICBcvHCBamHqKJ6jRrSEZMIQ+3YceJKoyaNZM3KVbJjx3YJZm+vcl5csWdiD10LEwIpr6NHjkSEEQlc7a2gsbogMZlQSS/2WPErVqoITPxQiRQ5koRC4o+/z5o1q9JdOaJFiy7TcC5+NzLD3sEelapUxtZiNSaE7Jpb+NkwTu+9L6lxeu+159ez3QdbbOSw4VK5WlVJh1WSTj929GhksSMiax5Jtm7dIqPGjJE9e3Zj7z9EBg0ZLIsWLtIcAI+fgbCXIXa1GtXBKFusk0OHTh1Bv32NBGFvdZjz585qiF6kaDEJFz6cLF+2XDJlyqhOTbpu7Hhx5RZC58kTJ0nN2rUkVarU8sbtjeYB6JgL5s+XYYC4dujYAdepoRNCg3r1VdFn0ODBiAzu4z7g9LVrY2KII0MwScWNF18jDH4HfoYTDjP08RPE13NVrFxZ/34F+YSImAB2bNsm7oh2UqdJLZvWb5JGTZtoNv/G9WtKnR0OG+VAFMDvY5zeh17G705rnN6H7ExHGIjyEvs3M7TmfpjGrt+woU4AdJAUKVPIMxx3F/t4OjT32UWKF9PIYDEmgGDYDpSF8sntW7d13x0+QnitApAn3bZDe7kJh27SsJF+lnviRg0aSE+E49lz5tCJhPv66EgI3oCoAkP0xEmS/OPbnjlzBln81tDr6yz58ufH3vuVNEX4nTNnTmnQqCGSiHcRki+WihUrSgyAYsZiW5EcSUhHJAyZR0iSJLE8fPhI4mBCqFq9mk4iTFhGjBRRcwxlEOWcOnlKXr1+pZHFJKgAhwHfm1seNyQcq6NiwJ+lB3eeFQLj9D70Mhqn9x3DMrPOEPsJMvF0fIbMzMJbsuhnz57F769g351MV3Q6NLPe4bGvdXB0lMfQx7dGV9rwiAw47mJ/f/TYMc2iZ86cWbcIvAYltWPEjKHnuHTxIvbrcfWYp85P5dixoxp+p8QKnwCr7/eDk9ANTBzRIMXtiP03s/q3MMFwW8Gk31v8nvdBXrs97p+1fWbX+R3o1KdBfgkXLrykz5hBf84k46GDBzXhyHwC/zxAHuHjxw9fcgr3sSU5IjbWNpIufTqtBHBCYtWB3984ve+8m2al9x07m6t4wQJmT+8F4/3go8bpvdee5mw+YAHj9N5rVOP03mvPf5xNBR890IraB28lQJ/aOL33Pr4g6/R0SO49mcXmftU7x4Z161GH3yktAHX9Vm/cO6/hkXNRfGL3rt1SHJ1OCLLhuAYdug3rN6A+X0dzAP81iJZbvWqlFCtRQiJHjvzLS9O2q1H+ixQlsmTL5nVtf+P0vzS5hw4Isk7P7HEnYM1rAHWWAYkoy7Cszh+QfKJOE2vV3w6Wm76XaGZCjSs6f84sfUOUvTJlzgSnb4mf2Wi2mpn078e35/r271Q3tcG5fjZ+dr4f/ZwQ13PI0k9C2W7AoIFfNfZJqGHprRSy5vZIAnL87LzXr12TTu07SN+BA5Cx/1wB+Nmx/B2/yw406QyPJB9LdRyszVt9951oa0ph/Uy11fL9jdN7yKd/eXCQdfpd23dK2zZttH5NGOmdO7eVWcZMN52YWWi+lClTpwKUNj8y1afl7NkzCjtlOa4EVk06wP79++Uwjn3v/l5BJqxJkxFWBvX2UqVKo0Ptds1gM+NdCgCU4CGCy2bAVV9icogbNx6u8QFYe2d5jHq3NSYY1sNZLyfgpRRKXt+urAT8rPvrL3HG9elQvAc2ytgBxN79+/f0OpFRP6+ImvcH3MdSNNR49vQZrvER0N/riqtn1p+D5+J3Sp8hPVB1x7TKcPv2LQhVRAdSrpxOdsvweYJ+OAERTzAYaD/e/8rlK8QVmPk4wAFkR3lvy+YtiiRktp5Y/XTp06Nq8FJVbImtv3Xzlp7/JYBCtEFcVBgIISaSj5wDQm9jxYr9j8n32zfXOP0v/dhDBwRZpz97+oy0a9sWSLQmWhojCKVa9RqSDBDSPbt36wvo5vZWu4Q0Qu16K1Bo7EpDgUaukDdR2qqPuvj8uXOlYKFC2qTi1q2bipQbPmSYlChdUj5i8iAyLVfuPLJr504lwRCj3rxxU8mSNYvUBYhmwrhxcIqbykKbjzo3mz6w5r0VjkR55wpAulkGQ+abuAax+PPnzFWiS6kypaV9m7a472SSPXsOrecXBXGG5bX7cOwSJUsobv8EHHvy9Kkoxznq6Q4cOCATce1uPXqouiw77RA3QPJNyVKlFB589PARxcdvB8CGsN6x48crYo9w2XTp0qtt4saNA4DOI9lNmwEY9ByTTGMAcKYDXESiz80bN7VM1wJClrthAxsbW91SjAAoh3gElhhHAZqcMVMmaQ+8wY+GcXoP+fQvDw6yTu/m5iZNQR1thxCfuPitW7bKsBHD1WAHDxyUC+fOyVWEtewW0rtvHw1Xr6GuTugsfz9+7Djp2aeXIueeYs/LVT5/oYKaI+jcoSOcraQyyooWK6qgmDu4xkIw1/7I9Qew8+OkOzDzSSDj3BX4/OhgrzVr0VwdgfVxOgXBL2SnERNvGSTxcMKhk61ft04KFCygABey+SqDrELM/djRYzT0JrCGP8uADicnT5wEJHeq9Ed4b5GMPgSnZ8hPYM+c2XOkdZvWaJ0dTWaCSBM5chTZBIfOC7JQqdKlEXlclF49eiopaCVWcrL8SPg5cuiw6tWRhNOjazfYcIvMWzAf+P3EMghbgcSJkqj9uLVphfNvw8S5G5wDnnM6cP6MPBwReYwaMVLvmfdinP6XPuvlA4Ks05P51bRRY2WmEeK6b89e6Tugv1y5fAXIszGSAl1CIuHlPwjnaAh02naE0HcR/vYA4m0vjiW0dejwobqHP4wV8fDBA4KNvdQDPn7KpElY/QvLRXLREYInxIrMZNq8ufMkB8JhOmDXHt0Vh98N3WvjxUsA6Gw9ZeWRt87VdREIK+x2w+0HB7ccjAouAoBD2O1eJOYoylEBSLuRWCkr4b8k4dDpSabhRFYeUUImrKCcfCZPnKhU2a9Ojy0Jv0N74PQXL1wsTZs1RevsqBrxcBLavGkLVv+s2guNAB46dTVEIKvB4kuTOo2Ccp67PEd0kEoyYItAZB/JN5Pw3VJhSzQYk2OihInUtoxwGjZuhHNuUnuq02Oy7Ad7c5KcOGEiKMGvpV379sbpvezSvz5BkHV6N/DWawHXXgEQUwjbKHllyLBhsn/fPoTs86RxsyYKKV2E1blxs2ZyAlTYWzdvyIDBgxCq75LJcOzmWJ0PHTwkufLkVngsQ2MST7j6165TV0PxhwixGdKTKUfUHKWcu3XtqhMMcwIdsMWIFz+BNIHTkcVGp69bvx4miLkgpbxTHD0HobutWrSUJFhFi5YoDvjqRHGEMzXBvY0bO1aqVKkiqZA046pPHjv35EToEca7c+cOpbzOmDVTV1YOfk9Obt0w+cydPVdXYu7nJwMWGxNbFF5v6+atUqtOLY0uSMYZMWY0tjnbJAZ474wqDmLiiIeJ6xDOzUgmY6bMcvL4MWkLLP/c2bMxmcXXlZ5Oz2hgPasaiJja4/e0H/MXiRIlwqQ1QhOfbY3T/9pjveGIIOv0zBqvBEGFCackyRJj7/1SCsA5uXIvX7YUsFeKV8STt9gGJEaCihnmFy9cQB3Nrysfw2Pu5ffu2SMnTpzA760V884ebBvxcmdFqcohZAhZumSptmoi0YarJuWp1q5ZqxRUYtQ3IIxmko8rMrvZOjk5aiLsBOirXN2ZaLOMo0eOalgfEaF12LDhcD8vtCMpoxNy7COjtRNzB06A1CYGLn4Jrk34LqWqCKXl/t4OCUIONjrcv28/Jqw8OqFlzZZVQiGpxkksVCgnbRW1Cgm7S4D5ciIKAccthq0K4b1r16xWWzC05158PxiDBfB9+G8SgFIjEqByUGjw8JlIDAZOf2bkMCiwcf3qNd0GcctA0lHIkI46AaVALqUlJh4T3nuDV//iFEHW6S12YQhNnvu3g1l7KsNwVWQZ7r/KU/wc8e0MqS2r6Lfnoj4e6aUMYz3ajfVHz47KOsSyc/XkffGc/3VeJhJ57Pelx999tV7iu4VE8u9bkBEVfjg5hg4dhjuafw1OVv91PRdMGLNmzgRjL55ORpOhhsOIqzhwAF+fC65h0eV7D1WgZtiKUTlnwuRJX48hPZecBjM8ZoEg7/QeM5c52jsswGa8LNdRKYj1e0Y2NcDCs0QhvAYjqFNIQObTCCKm5gyCQZxz9PixSvb5C4o8rFakR8XADI9ZwDi9x+xljvZGC1DWmkCeb2W8LKdn+F+/Th0tm3LbRH1AOj1FOLahhBgGmgDTkKMwOvgefyDG6T1uM/MJX7JA3159IME1SvMgFqi0G0RA3AGE6gGlYSIezfC4BYzTe9xm5hO+ZAGWAKtXrqpIPkvvdVYVWJ2YBhASwUBmeNwCxuk9bjPzCV+yABOGzRs3UUAQ4brMBbyCXBhBSROnTP6KLvSl2wk0lzFOH2geZeD8IjtR169fpx5wA25fpLU/yYiRI6Q8sv1meM4Cxuk9ZzfzKV+yAMuSjeo3UP18wnnTQDR08ZI/tQRqhucsYJzec3Yzn/JFC6wASKgdmmUQD0HUpAWl6Iu3EKguZZw+UD3OwPllSDSqVL6CoiOXr16lGvxmeN4Cxuk9bzvzSV+0QD+012Ifv58x8XzxVgL8pYzTB/hHGDS+AFF4tgDnxITctxles4Bxeq/Zz3zaWCDAWcA4fYB7ZEHnhj/iq75680GsqUtoC2KRjZV8fP8RUmBoHmJnDbLNP/ULg45lvPZNjdN7zX4++ukP7z8oL/0CNPOUCQh0SlbIWlEOyzPS2ux7b2trA5LLJ1m7ei3UbYspuu2/BqXDbPCZrFl/rWpLbb97EAvJkzevt9jFxe2DdJ93WoK9fCup44eVSOFCyL0Hr+TkreeSLV1UqZg7trdcJ6idxDi9P37iFPqoUaWqxAO3PR+kq9gCasniJSpCUbhoEQ/f+ZRJk7V1dA1IbO3Yvl05/xYlnZ+d7BRaYLMNVXLo5/1qUA6L3Hz22fOO8cT1neTtvEPsX36UIukjS4xIDnL59kvZfOaxlC8SW7pXTuodlwly5zBO748fOTvU1oMCT81aNaQIVneO1i1bKQ+9FYQmt0HFZi9UakM4hJTKUMAlv5wKt2wGSZVeNsekiOYldJC9B7XcSRC2tLcPIV26d1VxizRp04gtFHZWQfHnDIRCY8WOJeWghBsWoh6WQamt4OCsO4CTf/jQIdXnu3njura+zvnHH5DluqOCoe+gS+Du/k5CQYOAIpgUzFgF+Ox7RCsU2qQUF+myBQsXkvc4dhMUgdmzPjK08X823N66yro10C18F0KixUyu4iCuLs5y+955iZc4jaRPX9wfPz3/e2vG6f3vs0FnVzepizbRbPSYA/RS6s8fgDMPgCTW02dPNfSneg+bTFKWm3LUk8ZPgBRXP5XTYsfYZSuXy8L5C6GeE14VfYPZBpMq0LqbBQFMMtWOQ97qJJR/cubKpTqBVPGhnp1FmGPalCkqsx0XzTc7d+woVapW1X4A1Lvr3a+vrEV/eSLlkkM+azW67saKHRMTUFXV7WPLayLqKNVVv2EDWYnfkxv/4tlzlfHuCn1CCnT8dLx3hsQPNAKt0IwjZC40IcBW5O1VkTe7RUIVFYnQ3B8/Pf97a8bp/e+zUaevB6enumyefHlVO34bFGfTQn6aTh4PK375ihWwwrp/1tovV1bFKSJC+56KQMfR5bZM2bJyA+KUlaGht2zJEs0LlMZx/fv0hW5dS5kJthodk6H+xYsXVDqbqDdL2D9rxkyV0YoVM5bq2o0eN1YdtUHtulK4WBHVr28CyWte8y/IgN2EjiA74M6bM0dFPXlvCxcskA6YMFKnTStVIeAZPnwEmQ4uPCeT/xzvn4pcpUYgnN4pp4htBDj9dZGXe0XCFRSJ3MQfPz3/e2vG6f3vsxEN72vXkVqQxC4MLTyOzZs3yzxIVlNUklr1RbC3Z0ecnl26KgmF6rNsUkE9PlJS90A1NyNaW9eBlPY4CGGyiQadfmC//tIAKzpXfOrtpUiREs7vrtLWVOy1UFlnz5ylTh8zRkwV66QwKBVuWjVrgVC9oIpbNICUeCQ4PTXvrkAv//2792iAsVlq1qkN7f+PCP3faiTBxheNGzSCfJeDzJk/D2rDv2iRZXF6K7TicvxDxA5O73bts9OHLSQSxTi9Z15f4/SesZovfebN6zdSDaFyRvDGixQtChUZN3StWSJRo0RVBd0DEJSsW78uxC/vykas8JTVvo8uN43QhKM99PwTJUmkn28MemoDyHhTV//pU2epXqOmht9s3MEwnZ11yleooHtuRhNVqlX72oZr2pSpuiLHguglG2kMGzlSKwkkwdSrVw+KuAdU9TZTlswaNYQIHgJRST7h5zp16SyfoJe3bNkyRAVFUTFYrcdewn6fL14fbA+oEPyz8drttcxbMkXswbCLGz+1hHUMKw+f3UVC85wkTJlFcmfHam/Gb1ngJroMfYDWIKNDDsKan0K5aMOmjcpevHrlqmoSMq8TmIYVBC8pghtgBsNuNp+4jW46jo4htWNORGjxl0fLLLaMWjBvgTzCfp7lO+75ufJTz3/xosWSG7LcMaBfPxGS1rlz51YdOibitiNrz5LalYuXtWT3Cg01li9dJm5IAvL8jChSpf7cf46DE0EIwF+Z3Nu3d5+UZcsr1MwXzJknBZBPoEAmpb+DBbPXl4rJwXxoA/b3X39rroAJgCRQE06FPT+76pRHBPICYp1LsdUoXbqMxPgPhN2Tl++kYA8kKlGyK5w2CrL3IeXqHVfZefGJFMsbRzpX+NxXz4xfW4Cy7UOQC0qfIZ0qII8fM1bFX7t064aWZXug7nxQOiPBmxNRXmAaAc7paXzOU++xL9amlii32UFO+ttBnTkm0iw96n71wLjCWqGNl50N6v5flGwZ1j9/BsVe6M4F+4EyMCVvrTBdst2kBR/wbRtuKvS+hvOHDRf2H8q8VAHmRUiDtcy3336e+nj/pZrrDKcv3meHhHj1XgqliSLRIoaU63ddZP8VZymYK7a0KWNKdr963pbfM7dSs2p1TMZ/qbw5cz60f/Dg9ljlr2DLV06mz5zxVYH4d8/r348LkE7v343qk/dncXqHV+5SME3Ub5z+CZw+jnF6DxqfzUYaYkvGCfhzzsbqqzjJ9FmzUAkKfNsl4/QefEn8+nCG94V6YnvB/6aJJDEjOyK8d5HdF50R3seWDuVNeO+RZ+Ti6iL1ataRXegnyKYqHNwOcms4DVUaRmqBbQQYp2fCjl1j2fLJogL7/cN4iZbZlIl2cgqp3W1/ZzDTzlk+Kbrh/O5gS2u21/qdBA+bXLAT0Pd96H92LYb/ly9fBcgm3Q8PcUUis/+kuWL79pOkShxfIoYPAxjuYzl99aZkzZxKSufP/rtfwxz3xQLz582X7qj0WJqU8H1g52ImegPjCDBOvw2lr8toVcVmmNyvfz+YYZ8AEM4TtJhyQfsolsNqo+fcj4799rPMohN3XwG1/d8dRNs54zoE7PzX4P5wHfaLbPUdD0Ce3xmvX7/SikJeJP6yIwn5/fj0/pm8O1cHyQQHsQ6bR6yCRZCPr6/LR5cDwOkUELvoDX7nMuaYbyzA1uH1gfI8h67MbNHO0u80JIuTJguc+ZEA4fRvsMr3REa1FDLbDLt+NFYsX65tojugJMa6ODOxndEU89sH99TZWbvrhgsfTuKjySSRdkz6cWZnbzqi+44dPy4J8Tsi/iKi391H/O4oZKfv370n6TNm0EYSdPo7KAny3y5o0pkDrbZfIyRkYpENLzmJ3L5zG11n3wAHMFZyAFrLFtlvER3sBkaAGf2cf+SEdr0DwEZvNIP/FBBgttBbDaXwAAAgAElEQVSOBXTeX2vXoqa/BW2rByOphHr8t4N1+mtfwDmOsIXW6QHOefWlTm/AOR6e0Dg5d0Ir9YXz52sir1SZ0jIW8GwLLsPDJ/TnHwgQTr8PzSCnTJwC5NsYgGZ+vMfaiy6yIeFEaYBwu4lmmv2ArmvRqpWkRtdajidPnshEtLWm44UNG0abUHbEBMHmlgzBiXmfOnmyAmqYxX2Ist+gIYMVUHPgwH6JFjU6VoKz0rFjJ7mCzO5coOtY8nuOSSNOvDjqnCzlTQCBh5PHCNT8CwKLP2fWbEmLUL1xkyZof71QniEKYdmR+8dGjRsrYu/K1ataaeD+slnz5potbtmsubRu2wblpAzfOT1guDex0ktI/P8LOOctwDmv9oiEAVgpYjN//sr5z9tjA9U26ID8DlyJ0ePHSVmgNgPrCBBOPwU948+cOS1jUV/nOHb0GJz4MaC36bSOzpU66heNuNUgtLBGnjFjJqmD9tYWoMs61MiPYxXv0rULHqy79EDkUKNWLbSJ3ql94AmgcYQj1gZKj4Sa0SNGSp/+/RTNR5IOZ/3hQ4YA+FMfZB4H7W5LIM1r1PTHjh6NySUNoLULAa3toNsQMvEI9Bk/dpx2470PKO/EiRNQA+6uJBxOCvXq1Vf03mus9sXQapvY/oQJEyrct03L1jph1Qby8Nvh/vaFnNraCznm4BImaioJ7hBO3ry4K88fnZJIcXKIS0R04bUWSRTx5wCfwPoye+V7saMwwTl8n5auWqktwgLrCBBOP2zoMO1pPxza7tewKs4F9BWpMcXa22APRkflvnksQunTJ08AXVdDmzx+O5ahRfXzF8+lPpB5HONGj9F9M1d6dqHl3j9FqpRoGlFQyLHn9oB7dvae34cVnGAa8uirgZhD0A0jgbr16mo4OGbUaEUIclJ5hTBfxFqyZMsiuQAAGon9eeEiRQD93YU22X8qGYiaAIQDl0EdOHLkSDIHEOIL5y9gixEZq3tb3UJ0at9RQTrft6l6ClBO1WHrxO61u+RMEkPiREXUcvux7Lp0X0rmTiJRYseW3ZeeSfIoISRfYmxjIpoOtr/rvIz0iMS0vCO/+7mAdlyAcPoZ02fI4YMHZdLUKVpOYZvmMGgJzWw+Q/OkcMj1f6+T1YC0dsZKzn01HYvQWAvQZTfQV1uQDGzfvoNYQW2mf59+Ojls37ZVKbDc39MRmzZrpqvvTFyTZB06Lc+ZFJNK4/oNpUSpkorGI02XKz3zBFMnT5FaaCz5Ekm47l26SHzAarthlQ8dKrQMRXTAieQ+aLyceMYgdGRksn7dOkmcJKmyATlhEKAzANj/LNmySoX/sXcVgFFcXffG3YgTQhI8uLu7FXeKVigtFNpSF0q91PWvf3UXaItTijvFCRqFBIi7J/85b7M0pAkJ2U1Yknnfl4YkuzOz8+a8d+Xcc0H/XXj3PapCkOXBxUcC+tb1/OMNRO/zZHLddtLIzUu2R5+SdfHnZFKLzjLUta9sDU2WhIwc0H0LpaWvnXQKdJYmXnZijc+tjbLvQEoK2qvjFlWU1HWz3subAvSHDh6SN157DZVuy1UNemnjfUTuD+zbB259MyE/38XVWVXR6c1+UmNZHcddneY54wRLoSZ7YP9+1dt+8NCh8slHHymmHINq4YgLPLX0Kfnxhx8U+8/Ht66i7DK117RZU1kLUkdgUBCOlyJ+2JlZvEMwz7vjTgkAJ/+Jp5eq6r0P3n9fxQimTJ+GKrsvVXAvH+fLh19/x53z5I8/fpdYyFl7eHpJMopvps+YjgIgG+z0D8ojcEFKRpATc9Kl69ZHxRKL2ly/rhJg7yG7E87KHwknZEpQPxlkPkL2RKVCOstCMnLy5XIKuuDAGmpYx1pa13WQRgC/pyOYh9qotXfgpgA9qwMIeqa9xiCyWtpg7jwhLl5ZAXp2VSBSLwQ4RwwkqxjsY5EMK/V+QaqOgbT6MIcZWDuPaDtN7GZYNBiw279vvzy17Gm1A584fgKlr3XEFUHEZETr3VGHnwvqLvn/BaDh0tJwgKAGg4QvvfCC4vuztxxHPCyBeFxX4yaN8XcIehw9qqL7BDN3FFoulNRi++mAwABVG/DNV1/BjQlFbf9TIIhdvTsT9L3XvyAWufkyrV47aeDkJQcvRcmK+GMyoXE3GWg/SPZEpEElF/p5Re/NxgKRkZ2vxDtsLM2kQR0b6RzkJI08SmQGai0MatcHvylAzylhxJx187dC1oqprusdNKNfx8KRlpqmFgVyre+4884rDR8PoRCGKjqse2dgbwh2/lugbqMXzijvfLHIDrA0lwE/gtXxWkIY1zgY4xQfIHA5DZYBLYmSIz4jS4a8vU5sQcMd0txPAj3cJDQiVdYg1zymZ0Pp3qSR7CXooeGnH/+uG2aSA+AnQHKLsYiWvvbSjeD3vDnBz8/A+Slew6D/HT978X+X9TNJU8WX1eLv0ddSFK+pKD4fdC317mNpry3v76U9BsWPw79XRvexvGf1pgE9bwaj6E4wv5nSqsxghJY7OHu7M/JP37z4uHD+vNqJGRNo2arVdd1wsgH3w71oCkvB19enMpen3kPzn62qmDosbaTCXH/+0R/wp3xp1a6ReAKwkRFJcuJolPQe1FTcOrYG6NPFClV/pQ3u/qhRkuzcArmUnKWA0dzbTroGuUgDDxtYAqW/r9IfyEhv5NzQ4urXv5+Qt8FUKueTKU26SocQRCXIeP9bISC7bcs2Feht376dNINLthtKRadPn4KlF6C4HpHIyjADY4Pimt6QOGNw9u+/NynXsC3eY40iLrp+qWmpEgSLkZvE/r0HVBUmN52/N/0tkXAB/WEpenl5KmIPrUD2IGiPazqG5ygqMlIaotzbBWXYJ44fx7ymqDLqprDydsHqHIqNhdYj08nr165Tqd2TOA6rRFnoRQ0HBnWNPW4a0Bv7g9+sxytISJL022+VArccsUF2wNLHT3JREpy9c4849B8vO/rNUD69DZF9jUE/3waBvSyAPzGdO3+h+LtaSxCAH+xtL3Vdrq5cvJH3iwv+6lV/yt49+xQ1llqC1AZsBJeJWZ2WLVuqeAuJVsyObEZXX4K4KajYDKDa2UE89Mxp6Q4lJBK3qGh0BjwNlkunZ6ShnDZVEuMTpW69uqpcOhrp1RYI3FJ0df36DbAuZ6hFgsHicSjhNjMzl3M4DqXPmNL1AImL10H+/uQpUxQVnJWbDNBGwgWs61dXLl+8JJs2bZJpkFaLiIqUDevWw5ocooK8W5A2/g3ksuEjdQvKBpyTQjAt8LmqIqiogf5GPs2VOHdBfJKkzbxVLF3SxXJQfzH3rS/5J7DLbd8r1oOnyvZBcwH6NAXoaw2a/HwNaaf8X45S84Hvjy8GNht52ErPhi7i73bjU36Mi7z1BjIWllawwFqqHf82cDAYpCVPgkBhxiUZKdmO4GcwrjJn7m0KbIzXfP/td8pyogApxz9gWFIYlSxJDgqW7kF2iLoIlFM7h7QwBVoYUP3l558A9AlKTYlp3bjYOGka3ExZgq3wxUFriQvE3wD1eGReqMXQCyxN1onwbzTRuXtTto27+zdff6N+T9HW+QvuESoyOzk7KRm10WPGYBHYDFr4JBUnqoqhgb4q7moVHrMwLVmyXpoBKkCmWHYeKGbu9aQg/LjkhRwW665jZHvTqbInMl0F7CoCer3PyEWApj931WwECSm1Tf+/iac9wO8Mv//GkX3WY1ekPDmJS9zNCY7OXTpLMFKef4CyPAW7Z3h4qAJkX4hhMJNDK6ANREr+/ONPcYK0GTMoFENZt2Yt+v/ZSjpiO9QzPB95Xvbu3aNiMf0hpGJlY42MzY9qB2ZT0J/w746gW1NlmQKmx2Gm14GUGmncffv2we6+DYuPLxSPnRWo52Ix+mPl70rQpQ/qP7b8vVmCW7ZQAF6Fa23cuIk6Xz1IrdFloTwaOR90C8gkJcuTmSOCviz2qaGPlwZ6Q+/gdb6fOw8ZeIzgXxVBKnYcffbBvkTMQb0kP1EkHKCnMKYDGmhYUQ03TArTdkEjb7BsS5kuuwF66+sEffGPwQXAEhZAHjrnxCRlSy6sgMYAfQd/B5j/tuJgXX1ddHgvKClGU5dAo1lMxiIDr/xbM+y6JEGxaCYFqjf05yPhS5MHUcjrbtJUOnbuKOvWrlU+PgOsDNKGALw0wxnIowAqszEhISfgY+vqMQZAdJUS57t37VGZGxdXF/jnDeUwmJasw+BrMmFl2GCOuJhwXln7MQwSaJdgyq9CKpbHptbiINTkM96wBzUWdDU8obnI1C+l2anXOHzkcFXkc/z4MbVw5ebmSdduXSsdDC7vkdRAX94dMvLfCfioqAuSi4dEn1IreQo+IKwPqAfz9D+DEtjhZBVi56UEthUCflln8QXQu0KS69J02RuVLnUcLdXx87m4lDL05n1p0WG+wwIvcMDCwe8pSPddSs2GUlChuNtbKr+fkX8/1+ox/VnGbI0dmIO7PAO5THXStNcHPGkuM7VLJSUO8jF4r/V/Z7qUbgILqSwY8cd9YZqXOzBFTjkYUOO9Z4cj9RockD9T3pwdjXiv9NF1lnqzspPxAZZ68/x0iywsLNXruCBQ0twbOzmv999jFeJYutfwPbwufUD5ymt4/8EMrIrIPT+nBnojg7q8w/FBjIiIUqAva1L5oLkD9KV1o83MSpdvV6IGITNHgvzaw3QFqSc+UiIvhyIq3EKyXHrKhpAEycIu52JnJW4O1spvp6xXcTXEioDeriguoF+cuONnY/dPSM/G90IJAuGnK1J+TdBlRxs3zx3QQF/Nc0XQR8KPrAjo69Xz+8/VxcPXHv/EJnGCj9qntY/4QCMvNCpZdh27KEMGNpRFU1pLdEqOHLmQJkfOZ0gsOPqW2P1c7K1g8lsodp4S9YDAnxWDeCXIPzyhfqfXg15/EYwSKN8f72PALyohU7Ig7d0Cu36fJrBMsPNrRN9qfqAqcToN9JW4aYa8xVDQ5yUky3kUAlk5Z4l9n15i6e0n2aeOwbo/DBd/jLghkKQfmUjHhcVnScjFDIlIyJI0qO3AvlC5eFtrc3FEh1uasdQMKG4FlAX64p+bPr8FLIH0rDyJQb6fbkRAHVu1ADSE+e9i91+hE0Pum/Ze490BDfTGu5cVOpKhoC9Eyi579mQxd80Ui0EDxMynvhSGHJKCrbvFbOBUsZy/uNTryMgpkKjELIlMzJGLsASi8D0lu0DsbBBscrASW3D1mbqjG8AwAH35kjt9aQe2AvBtgO+0LJj9mbnK/HdAnK8+uf71nKS+CaT8KjQxtehFGuirebINBX0eWGjh9y0QSwc0xkSE1w6BqeyzYZIIpqFbn9HiOmN2uZ+Iu3JsWq6cuZwpBy+kywVE6Gm313G0QaDOWsmBI4ylqvLK64rAuBlfp0/5keSTit3/MoQ7U1EJ2NDdVvo1RmMQ8P21YRp3QAN9Nc+DoaDPz0mS6P0PIfJrJo51uoqNo5ekx0dIVsouca43SNwazryuT8RdPTopR/ZFpio3gAE68tldEaV3trVCLwBdDIAWQNH/rzq+HvTF/X4d4UckA/n+KLgVXARa+TpI+/pI+WERYFxBGzfuDmigr+Z7byjohRp54fTbKZfFBpbuaFtNjbydSNlBo92r8r3s6AKEIwZw+nKGXEiG+Q+TvaAQGj3IyzvZWog9XAA9YGkt0PcnVb+sOn0y/uj7J2WiihHWRA5M/3qg+gbD7yfjzw0Lizaq/w5ooK/me24U0CthTICewpiqa22RRp7bMHStvcsonygdufmLqbnK9D8blyUXEAPIzC0UJwTovJ2sVRBQxwAoVMAu1d9XmQId0YdZgnSY+4mZUA3Cd476btbSqb6T1MN3bWDtBkHoGAg7OqGYfLD2/Cos5X49908D/fXcLSO81mDQ5yZK/vG7YD6jXt4Dwpg2IOdkRElW3CGx9uoi5v6V3+nL+niM7l9KzpWTsABOXUqX8IRcpP/MxcfVVupgt9bz/LkIFI8BMCVYfD3gv5ktoLOQBqviQhJN/3xV3dctyBnR/9rl9+dB2SgPN8y2qLKRRJ096J+3Ehp9AQGBiubbGtqLxh4a6I19R8s5nqGgT8pMl/nfvSvOSL/1DWovXnVc0MvuomyKCpVBHVrKnB5XawMa/ePhIWUa8DACgCGXs1GllwcOgLW42FoiA2AudniA6QJwoWAGoKQRwL+pdJ9aAMwkGf7+6dgsSQHZqCmq+7oGOqqIv1U5VYJG/1zVeEAujGdiM2RtSJIMaOwiLaBoVHx8/eVXqpV606ZNquSqNNBXyW0t+6CGgj4hO13arn9WrJGDn1OvkzRwrCO7YkPlt/iTMq1xb3m51ehq+0RZCNSFJ+TI2dhM5OpzJQM+ez52L1oBdZAGdIUrQFKs3v/nhelBr79InekvkqTy/Tmq2MfbyUpV9zWGtBf/XZMGP+MBVEGuPgZKMD7b3b39xLPEZ/z0409QeNMRmg/G3+V5LzXQV/MTZSjoKZfVbetjSiNvdt0uEujgKbvjz8pKaORNDuwjzzWbXM2fSHc6EoFiwRaMSsyWUFgCEYm6DrAeSAPWBVOPFoCOt66r5is5lO+P3Z1U3zikExPAJOS/6wH83QOdxNeE6vsrc4PzEPjcfi5ZwuJwjxAncQA5alYXT9yf/8YzqKXYBv0bWqN4qCqGBvqquKvXOKahoE/IzpRev74uNjCrpwS2kQAnd/knFlVll87J+OBOsrQjgnk3eDBXzzz9iYtZchxpwAT828HWWryw+3tAlJNuAEdJJiAXA1srXZFPPhaIZAT9TsMMToDIR1Pw+1niezP6/eexu/9+JF6VO7uDDn0xJVcmtPPAvSg9e0GBVPZrKKtno6HTq4He0Dt4ne83GPSQwB7y6bdilmQmE5oHSoC7oxyNvSgbIy7I6Pat5b4e3RQrTs+x130nw07Hma/uwWq2UwgAHo3OwA4HWW5cAEtznVEM5AoqnxUr+VgQRCsA/+PPiuNf5ArQKEhByu/UpQxJBd+/KaS92sAH9kPqT794VPdnquj5OA//oOJxLzQMW/jYi7eztewPT5UBwSAr3UCmogb6is6gkV5nKOiz0lLk27fulVyzXGnftitqvb0l4nyInDpxSFp2vEVyGk2Sg5EpAI+uOMYGRTb0mxlht8MOy/gYdxx+WeMHK/6dVFp8MYpMIClqLXZcFXArCrxxFzZ0zUgHaCMTsmH6Z0OaO1fRgAl2F5CAXJAFIF/fFtdFS6F4RTCvh7t/Imi+0fT72dMA1x3oaiWt/ByU9WBq49D5dDkSrRMz6dHABS5LjuwKS5O+CNwFoxHJjRwa6Kv57hsKemrkZc+dJhauGWLev4h7f/KwFGzfI1YDp8j6vnNkVzhAX7R78uNxd9VXyClevSqz1X3lg3yjq7nX5dv1OXe9kAZByOo8LgxwQ8Ueu7Q9FgR7Ruq5MGDhsAFphwU8XGD4ensuKmqBKduy4DkTsYOfg/l+GlmAGCwCvMh6LjYg8NjiPObIVeP6ikhA/Bxq8cKxKerJ915IzlZ5/wDk+VnieyN3T/1jRH7D78cS5VJKtvSGO9Iai9LhCxny15lkGdPKHWXIN06BSH+NNRL0VEn9G/JGXSFc6AtuemUGxQrZ+KIdlFGNOYwB+pS5M8XCOVOsB/SFRp6/5IcckVxo5NkMmiw7B8yWveeLaeQVaWgUl9LQldYyoKYrrNEP/WsKC+gKYBkA4NiJNwcgy8X3XICQxjf9bQIUcTZFIuHxCEim2SjYY24GaWr8jsCn9eAMIg8j+qzKs7PGjg52H018B2szMP1gWeBcl9Ly5URMupxC+i4TOXwPB0uY8HaKB8Dr1J0Z0iFcaIqunb8j+Gn6x2fkKUmvng2cxAdm9I0Ypy5nyXpoGbjBbx8a7KI+A+Maf55IkHFtPMFCNA0eQo0EPeWSnkX3mgX3LlSNJYsPRpQT0FveBSIVVsX63JMFRfEKSjFxvPj889A+80OP+9nqZ71iyxWAqEi0DjDFddf5M1VbCKDSlEwNBX1GSrp88wx6rqEJZvP2jcXN00Viwy9J6LFzEtS3ixS27yF7YFrSdL/WAF6VNVAahZYfi5/NBl98DdGmsxR0wGMAju9XH58WA7/RJMcXySZcHBit5s8EJum3StlG93LF4+eiwL/zJ5b6OiDPz+8Z4P5HxmUojT57LBbc9T0QAKRFwYWFMmAEPQRp1TFIDabbwmj/aRQQkfXHEt+OAY7qOy2Gqh5xaXmyMyxFzoG52BptxLoi28DzMoux7mSydG/gLG2x45vKqJGgPx8VJS+/+BLaQ98lLYoUS3nDqUP+/XffAcD50B9zkEmTJ6k2WTuhQU4FUgpbBEM3fQJECd9As8x69epDoHCiam3VCPps+rbR1KZn37xuXdFWKjBQSR872DuovverIcRojgf0IlpVdevWTTWvLD4MBX12dqrs2PyC2BSCDuvTGrrtbhB5vCCXY4+JV0BXuWQzGGq41Mi79sOu482XDnpeL0Fty7/TTSjjaVWYZ6qNC4QKROsWQbVoFL2Ha4ayLAh/Fu4ol0L3R70QJ3/mIsEFga/nYsD307rggoH/q/exDoAVgIX4PS0Q6MxeWYi4ePG9STD3I1FCTKujMUxpL+bAcVwuFKwhoLwfXQRbNgPh7xD8oPVAi4SLB//GW1dRqaq9kWlyAkFKd+zqbetB/w4LFK8jBNbH1rPJYBq6YAFyKhfv1O/jOfmd2vuFMLfYX9EPHY+MPWos6Je/9DLaVs27Anru1OxZT5XSCZA0XvnbCnGASCIbUj73zLMyCt1svNBB9q3X3pC5d9wm59BRxwZpE2qix8fHyXw0tnR3R3ELRgpSKg+i1xy71nYB8N9DZxz3Ou5YQHwgv/yiPP/ii6pD7icffohuN0tVR139MBT0quAmbA6eSkhUORbrT5+2A8KYQ2Vz8kydGi4e5GuN8kDPv9uUA3oeXx0HDyus9lIHFwCl0FPir/w9rRG9d1G0XOheh/8QfHrX44rbQa9Cb12oX4L1B7BzkVCuBr4IWMYfMuCS6CwMWB60QLCy0LBg7h9rvrJIsvE7nafCv9EE0Vkn1ipmYaEahjBOQYFLxjEcsXszpuECl4SS4RtPJoFRWIDuwGgQigIiFXDEZ+WOvw5su+Et6khzFBeVNyiE+dLzL4hPXR/VIo1qutTy69uvr1LHNfao0aC/C6BvXrTTU2745ZdeksX33ad64rF//I8//iitW6EjDDTPX33jdXVv2a2WGuU+uOlf/O9z1eHkq2++Vuql+kHV1ccffkRmwvTv1LmzfARwu7m6ocddHVn15yp5DRrtHA8tWSLtQbKYMn26cUEfyio7gh5VdlYouMkKQ5UdQF8HjR5S0OziJgA9b0hx0Jd8sHVmfOlWhsI9FxsAnIFD3fjXyijySHS/xa8JVL1roXM5yAzUuRoMFDJGkQu3ghYHwU9WIIuLcvHvnHwUCkESLAe/0y0MqG/CYpKKgF0i4ghkHjL9SEUirnvMgpy+nK4yCn0QyCsk9wAXygWB6UgWK5GYU3wcOXxEfl+xQolszpw9S7775lt02Wkvbdu2qXBbtetZGGos6F95ebksWLhAGqPLCUdMzEX46c/JwnvvhSxyEzkMCeWfAPq2bdsp8/71t95Ur/sYbacTkxJV+yy2GHJGS2wHtMF64KGHrrTTIugffvBB1QCzLUD9DnrZU9bYG/3l16xaI8tfe1Uda/G9i7Ba90HTzXFGBX3GibulIM9erOv0AHI8pTDjnBSkoNmFzwDZljGlxoOeN1NnYegsgmsNLgAE/b/LwpX14erfAbR0UYrHOLgw6OMYPJ9+MaFVQfdH9QjAYpGFRQCGBRaOfFgX/F0+FgrEjjLQNxALQ0I6qwuxLBTkyezuftKq7r+7P4/x0QcfwJpMUM002OyC3XpatW6DeFTr68FyhV9bM0GPXX3JfffDrG6udnWa9sHBzWUbfG/KII+8ZSRaB62XBkENZBhaCT0L874j+o9RCvkX9JBfuHgR+pztEH80JBiI7rMP3r9ERmEy+D4OVkM9+MAS1c6o/4AB8i5APwpuQkBQoDyz9GlZdP/9kFeOkZ3bd6CH/bPwy/4VuDTUvE9Fc81lH34rFukinYIbwsJwlZjLl+REeKR079pSnP1bKdCX1ctO/2TcaPOe11HZnb6yoL8WKng/mEWg5VAyhlG0wau/kcfAzIZeVYguRR7iC6wWjAe4EzL57zxlLVhCxtoSu7wjYwZMaWLtaYjeg84IWupHFOJPP+OZ6w+d/XBo99uhEUdMTAw2k/bSpo0G+gqvZGwmyVZG4RFhqnNJHnymXmgG2AGdSr5FS6FTp06pPmfsUeYK0LBpJV9PrXI2K2B3k61I2XG3Z8pu545daG6QIMPxN32Ahw0TyZF2dHJUTQnZ1+wC2l2z4ywbJubjnJOnTlXNF4oPQ0FPksfQp7aJHfjpQ9v7iB860Jw7j+De2XgZ0S9QOrXxBejTFHmlvIf8WoG8qvbpTRX0dgR9sUAk51sf2ONOn4dtnrt3DLQGEjAHKVmF+HcOAJ+jegB6OVuJj5MtegHC7IfvT44DXZWy7BFq7TNuFNQgSLVQj0P8iHX13FD4bFbFqJE7fXk3in3ai7dyLu/1V3ZHfSCp2BuKtzZe+dtvWCB2ykvLX1bBn9KGoaCPB+hvIejRqnpwOx+pC9CHXUiV3WfiZUj/QGnXWgN98fte3LwvOR8Eoo6voOMtcJ1ktoIpR5rr9OHZ4JP030upearhRwZ2cQYErRG59AZ3noVErJpjmm5wsNuVwGRFn6kb8bpaCfqqutEH0Rjx5MmTMrVY4K7kuQwFfVx6lgx94zdxzBC5pXmQ1PN0krPnE2RdWJSM6hksXdDvbTf07rSdXnfn9aDXZwVUq27u5OQJKJ+8QCn5pIESzICe4hrgi1JhEaiGi4bQR303W7T1sgdvAEQjmPfUD2SxDE37vxavvGgAACAASURBVMG0cwN9eGAz15sC8LwnGuiNuAKoqjH4d2xJVNYwFPQJKK3tvP55XT29X0cJdPKQfbFh8mtciExp0lOG2vYH6NPEsqiSrazrqMk+ffEdnKY1YwcEtC7qTvMcZjkCa8n4Yq6f0l+MB+4GwSYH89fe3xlFPfaYR4hdgGXHIJwn8vBt/BzFC1F5WgXU/fsTdFs2+BjWwq3c3oFGfMwMPlSNBD19JCZmSKOt6GB74jwE/JrA1z+N3uW26E9GR4ydQ6/nOCXPx8Ah2WoeiO5zGAr6pOw0Gbz+UcnH55vl30WC7AH6hHOy+uIJmdign/SwH652+toCehJr1P+K/HB+15F5wIxUOzgCbCDspCDQloWuQFzsnFDg44U6dn9XS/FBnb49TPWDMNH/OpUIKq8dgC+SiuKeBu528NNtlfl/Amy/UygT9sOuH4xKv/2onPNBncCY1nVumh1e/2zWONCnIxjy3TffqGgoI/cVGbHIyz8NEs3gwUNU73FSeOeAeHNg/z8q5UYCTmXHd998B9pvnNwDSrAxQJ+DdOLux24TKxcz8evWU2xRZZcSflJi9x2SgE7j5EyXSWDkpRjMyDOlQJ7eNNft4EWlt/hB0Z/xfxJlmFdPg++dCaCTdJOGn2mq21qy6aaVeDrbAOTWUheBNjuk8PSkIMp1/XUqWakA9wB7zh/FO/TjWQPPvgDMxVO1tyHIN6QJR6PI51hMhlpUZnT2Vvp++mNV9hmp7vfVONDHoE0w02b3L7kfuXNP1decKbYOYDYFNWiAdsQhoNfWU5HRsNBQ9Cq3kyPI2X8FXbLF998nW7dsUeKEL72yXM5HnUdP8nqSlZWtKJHsgBoWGgZfsADChQGyfds2uYC2xc0RrdcX5jCqfwL5/Xr+/qDhdpWDBw9LInb7IWhnbAzQs8ON2YxpkLtOExk0UMS3vgjKamXbbpHBU2Xz4NsB+mIFN2U8UaZo3vOaCGz64SzWYVCNQTN+MfedBSBnYhtnOowMOzbppMymrhIQX3iPA5h0rnbwveFnkzjDopfSBqvhdoalQqsuC1F2C2lb11HJfNMFUIQeWBCkMjMVF4lS4DiAPxOLCrn9tAxSYQ6QPETfngsCJb0d8f6KDn33W/3rFXkIrgU78ap4A+pALNHtVt/RtqLHrcjrahzos9HWmMCjOf3rz78ocFtZWymwzp47R15d/orMnDVT7d6vLl8O6q231EEP8U8/+UTmog/c3j175fixY/L4k0/IL3g/03TMm4aFhctUpOBI5Z126zR1PC4gbVHQcxy9zpn+I53ys08+li7g3O/Ytl169e6liDnsXe4A+q8xQJ+bmCQn75sltk754tqzLxRw/SXz7CFJ3rNf3PuOl+PdZsjumwj0qvoWX6TcMoWoqLL4D0GeTGILfOdM8mbxe3M03WQjToLZC2Am643+OItbWObLnbis9t/FwcAdfCN2dzssEDTVPXEcgo4BPD3bjyERRQVWuXvSbgvl2wNxiujTCeKd57BYRKKRRwYWIm77DgB8CzT06OiPGowK6Pm/98676HffQIYOH64slv999pnUAavTydUZfJLtqlirR88e0tUAK7OsBaDGgV7/QeOR/3wYLLp+/ftLn759JR1VdM6ooHsETLo7waTr0bOnPAcAE/RDhw2Xb0G1XfLQg8oy4G6/BO99AASfqdOnSTuw7p56/Ak5c/oUduxhMnb8eFgTS+XO+fOlRcuW8tH/fQAKpScYeb7yCdhVS59Zpnj7tDBa4u/Fh6E+fWJWioz58xGxRDJ5cv1ukMvykYNxJ2XdhaMyJri/dHAcaXI+vWp7VbSL6+4FimPIdsO/yG4joKixR0VcSmRlAXzMjTsDzNxJvUBd9YZ57oviGRd7NNyopD3Nc6wHJ/5QVKq0RbCOajZcdIrX7PPq9EQdgl7f3YfKtXk474yOnkjT6QqH4kDGCbkEXx+LSCj49uwo7GJnIb0aukqvRk6qxLa0ER4epqjbPj515W7UdERHR4OV93/i61tXbrvjdrUBcTPpDEVc9rY39qixoGeZ7MYNG+R3FC8wQNcd5JlBQ4bIE489rqrveENffO558QToB4F1981XX8tDjzyM96xXKy3//fCDD6kquwEDB2KBeE7efect2bBpk2JNPQAOf8dOnZWWWfSFC8pyILHnf5gw7vw87rhx41RlXvGKLYNBn5MhnbYuZa2v3O7bRRo4uMuueKjhJp6QaUG9ZZjlMJBzblwgTx9QUzuuftssKrfVFbjohDG4mxOE5Ezoi2+4g/sA2D7wu6mk446fy+qec71A4O6+BVVvSRkFqHxzUnJbzMfTNy859KC3R70/P8T20BRlcUzv5AX34b/8C1Jxz6Oyj1Le/8DKYnyAn2N+L18lClpyfPXll4qIw2rN4SNGSMiJ4xIKy9HaxloVfm3bslXRcFu1vnrDuN7PXOt2+ssobf1r41/Su09vVUDzyUcfy8Qpk1URzTjs1P1hAdx1550wwXvLkKFD5Euw6x557FHQc9ehzHarPPbE4wrY06bfqqrnWIjDqrxElD7OnDlT3n/3PTDupoDl10m9JygoCL5/liqN7AdFm+WotLt86aJ8BLOteArPYNBnZsuQz74Rq8wsGdughfi5O0kIdO/XXAiVcZ3bSc96bXWgN7C0tqKBPAVYbNuU5yKACGZWsrGFNUHNnwkdW1ag4csJ5jh9X7LVXEFHpUy2G3ZvJ/y7KnrcUV5rD3Tp2Km3LqLtzSCwSf+fgC9rqM9O+TC4DDsAeMpzj23jUSGznS5CWHy2ivR3rO+osgPFxyU8l9989ZU0RKl2bGycsiRiYy8jLtRCYi5eRPC5gUSEhyNG1EHaG1nARX8dNXanT09Pl7feeFMiIyJQTgs/C6IZ96DYZgVYcytR0RTcLFhiMAEjRo5UAbdff/5Z5i9YgF0egbyde2TBooXyPDj5/bHLn4Lv7l/fH2W4o+XJJ56UJiDAeCBIyPfQ97JAocSi+xYLC3Fef+U1cUWaLw3VeWPGjlHHN6Z5Hw/657CntoOGmyOD24KG6+0gYYjW7wAjb/iAQOmI3xmbhltkkavUmDLOizZxpaoD8Kj8N4BBDTxKWdnBLHdHSswbPatpnrMVdh2AmwEz/q08gQ9j7WhbQJzZdi4FEXlbaQ6KrAPINax2K213L35OfSBxE1J4yUjdTe/sBavDOGo8F85fUKXardu0Uc/Lzp07VdyJvjspuZcAfNbT+yEQ7IvNpipGjQU9bxZ3XpbQUhWHdcm8uQUF+XII0Xqmc5qg2s7ayloF+si7dwCAc+CH88sR/+akWMOnysSuymIcVkHRJONxmb9nPp/CHM2bB0Nlp66anxj4ZyEhJ6V+QH0lvFFyGLrTU0565ovrsdPnSs9gX6nv6yxh55Nk+8kYcO+bopQ4UHYjh1xZRl7x9JgNdnAaDNwUCe5cgJs16pkwyTMQxeZuaYNIl6ONGXY0CFYgIOYHzjmbONijZI3BtRsxUrAArTqaIGFoxNEl0BnXZK0WI+7g5SnpKKsEJvyeiBQVqJvd1QuBQ+MA/kbci9LOWaNBbyo32Zg7fV5Oipza9TANUHHx6CI29u6SkXJeUhMPiYd/DzlVOAKBvPJFNLhp61Ry2Yle59sycq4UavAPlosWUHiCkTbFSy9KjdFEh6/rDoC7o5EFAU4OenlVfdUxF1nwrQ/Dp94PRiKvmc0xKeDJBUulKGGi0NIoaxDwFB85gTx8aHymjG+LcukbpLdXlfdLA31V3t1Sjm3oTi/Z8SJbJ2H7RU7Y9xYRF1+oSl4AL3Qb2sB2lc1Wdym5rJJ15lcKS4rMc1aLkbnGnZsCEdy1GVGHBQy/W+drezjC/8YvyGBzQ1SagpYUtqSva2qDdNltocmwQMChQCPMQOjjqbr3Ilu+PNDz/jDldyRaF4gbBx++LoJ9NXHUWtAzup8DWiYj8cYcepKFeRVV2RUgT59293SxcMoUqz4DxcwHargnj0sB1H+s+o2VHb3BNWCeXolL6MCpIuZAAH1vmr4EPG12muBOCC5TeNKL0tPY1bwROadSLXe86vK9Dbn/Kg0H2So21GziYafq1fnZVRquWKzuWqBXOzwAvx8lysexy8/o5CmB7sZ9Lgz5jMZ+b60EfRqCfBS+aAoRzLHjxhr1njIYs3b1ahmOAB5ZfCWHoTu90r2fPVXM3TLEAgIe5mDkFYQclIJte8Ry4DTZ1H+ubIfuvaVe9gkmLw147uBUnKVwox+aRKjcNxDvhd3cFEzzykwCu+f+cSxB6dR1h/gkG0GWbJShP+61QE/AU8jyAPL3Uzt4glpbcwHP+1ErQE9J7FTIUjdG1J2iGps3/y1ffPa5irhT7kq/G+aAzUeKLYtkSNVl0O4sgnUuri4IzAVceS5DUZzDvzXC8fT9xmJjY4UcfgYNmR24G5kAP79/FXP0bzYU9DlJKXJw0cNibgVqcI8u4uTrIZmnz0n8vsPiPmi4hHQZhWKQFHEHFZUmeR1E0Ql0byfozYMsUlKfrTJgu9HvSQJTj347O8gwlx/s46AabNB3L2uUBnru8KTahiJgdwzHGtbcTWnn1/RR40H/w3ffy+7du8UFFXdu7m5QsL1d5eopYc1cfL9+/dQcMyL/MXL54WFhSlWHLL4ffvgeuyQ6qeblYuceIV1A6KGE9rGjx7B4QIvd0koWLrpXkXM+//xzpYhL8DNdR5ksqp8Ye6dPTMuWmcvWiQ1ENHq3rSf10M45IjJZdp++JP0GNpapQ1sqXjp55MYitpgKCGiu74Tfvh+BSlJjA1EFx9p22vFMxV1rlAS9AjyCmKfBpDsSnSojoVzLPnnFx3ZQqdesXiUDBw1SzM6aMmo06MmNfwp59QegSsv02VNPPqny5u4wu3//faXcs3Dhld2YO/ecmbMUaYKFN1+BQMHU3V2g2u6CGs6hw4fkznl3yY/ffyfdQeHlDv8IZLDvB633BBh4VmCnUB5r3Zq1snrVKlWww4IfY4OeyjlDl20Ve4B+KHLyvp7I00M5Z8fZOBnWJ0AeGNuspjybV30OlsiuhCkfCtZbS19HEG2sr5CBKiKQWRL0XCTOAPDbziapKH3belc3o6DFNhlS6avXrZPxY8bKp5//70ojlJv9Btdo0FPtdi+URV95VadOq+StQcghXXY9JvOuu+eDXKOruWd102IsArfdcYei1C4EJ5rEm5aQyOZOTibVAwB4TPQF2YeinEsw5beBo/8g6LohJ0JUcU07yBaz0Qa5+PMX3HMld1/8ITHUvFdyWcs26+Sy2oLmqUCfIrsA+sF9AuW+scE3+zN51fVT/OIYesFtOp2k+u518Nd1j6E1o4Km+Lpe0HOHj4QqztZzSTKoqSty+f9tRsEF/yEs6j9+/71SQnr2hefF3r58Dfub4ebXaNBvWLceMtc/yNvvvac48ux64+zsJF1RBUd9+gX3LgBhx03NExl8iwD62XPmSE+IaN63aBEIOHUURTc6OkaVOjZu2kReQ5UelXG9UGDDLjiLFt+vqvIYLxg0ZDB2/ROqYOJBFO+we05V7PS1BfRR8LW3ofw1EiSbuii40fnbOpkr/bhe0JMVeAGU3IPo99cD7abaoyqurMHYDl05unukYteUUaNBT0bdMlTDWVhYghsfiJLbQyikeUTS0tNkBYJt94KWS2YdByP69959D8puZ0nf/v3kT7SnopneF759aOg5FbRj8cxjDz0sA+DjMRtGDvXs2XNA0a0vP4OS26VbFzAAj0gcONWvoXlGVfj0pOGOepI03FwZ1N5bmfcRMO/3gIY7sF99WTyhyU3/bFLEYsvZFKTh0hT9NQi+Owtd2JWmJIX2ekDPIGYCGmVuDU2Sftzh0e+uNo4aDXpOKEtsf/rpJ0hYJ6o0WpvWrVV0PioySuniM5rPwZr3XeBBE9y+RTv0elgKO+DPU0iDFXNMwVHtltH/Ll26qhx/cnIKAj0DUZ23UXXKadKsqTRt0hQlty2uRPaNat4j9jDw4x/FOitXxgYHQA3GVY6xuCgiWka1byVP9Ot2Uz/H4djdfz0Ur4QqW1GTDtF5EmdUr8tSRkVBz9x9MgQx1iGn37exq/RrXH5/uZv6Rl7j4ms86E1t4gz16ZNy02ToxqeQUciGRl4nCXL0kr1xZ2XV5ZMyHhp5j7ecaGofuULXw92dqcat51JRdWeNPu52VwQxrhWZLw/0/DuzGOlYRLbAh29Vz1GGB1eNnnyFPqgJvEgDfTVPgqGgz4HLsvkZtLWyyZAGnXqLk6evxIaGyIVDB6R+t9HSYsLt1fyJDDsdzXWKUu7DF3d3yk17g1vAjrVMu7P5pSGgp+wWZa72ogiJzST7Nta1Iq/NQwN9Nc++oaAvgEZezqypYukKLTW4FaThFoYcksKtu/HzNLGYf281f6LKn44KtauQhmP9ub8bi3dsVGEPKbQc/K8hoGdBUTosiG1nE6U1+sMPAfmm7F4zlf8cN9s7NdBX84wZCvpCxCZy7p4h5i65YtGrD0DvJwVnT0oh+uZJnwliefvNAfoj59NlzYkEKTAzl9YQpWRhDzvHFnfdDQE9BTlYKbghJAGkG1sZ19azSkQ6qvnxMcrpNNAb5TZW/CCGgj4nLVn2vvc0tkBbcW/aVuxcvSUt4YLEHdsnfm17SONhpu3TxyHrQGGLfwB6PxT5UFueTSVgzf9nVBb0CvDY4fciRuDvZiWjUTGn7fD/3l4N9BXHq1FeaSjoE0DDnfrKWrFGG+Q+LeuLn4eLhEXHgZwTI0N6NZYFI69umGmUizbCQdjCmcIU/6Dslx0i66P0ldV8VN+xpORsKaMyoOehqFm3H5JhgSixpUlfWRku1mwc2LcPLaPbVriHghFuVZUfQgN9ld/iq09gKOgVDXcpu9biOxpY+kHzLbSoa+3QvgGyZJzp0XDps684Eqci6A1Q/uqKwh+oWatWU5SmsjIS6AluMvU2g1rr4wxadHsvFSOozMjJyZa7581XkmhTpk2V5a+9Jo7QSKwJQwN9Nc+iMUB/szDyGJDbA0bd6pBEVQ3XBEQi4pvikfphLNCTeMMqu63nqGdvju4znkoTv7IjKytTpk+ZJn+gMGsyQP/e//2fQe3NKnsdVfE+DfRVcVevcczaAvpzcZmyHTn30LhsCYAgBf33PJjyJRl1xgA9vASV02e5LXX5JreHNr2SrzZsHD1yVDb9tVF6QVG5PdRpa8rQQF/NM2kU0C/VVdkNUv3pdQU37E8/COb9fTfYvGezig1gvZ2NzVQ943wBdl3nGb2Oz9U33FDQq844FgVy4HwGtPvM0VDSXaneaqPsO6CBvpqfDmOAfuTzG8UOLauHtwoQXzcXORudAH32WBncu4EsuaVqGiRU5DYdi8lE++Z4FYkP8LAXR4BQ3z1G3+Wm5HEMAT3fS5+d6r9UB5rdxcsgk74in7EmvEYDfTXPoqGgT8xOlx7rnxMrCHvMqNdJAh09QMMNlT9jT8nExj1lWasx1fyJRLWBXoe2T3sg01XX1VbJTRUiQl+8Gs7YoCcfn+q2B1Atl4I6hNu6+aAnXenNKqv9hpj4CTXQV/MEGQr6BOzwHf5+UiyhPT+3bmdp4OgpO8G9/zUeba0a9JaXm0Mpt5oGA3LHojPgu+vaPgUhMu8Msc2SveF4OcYEPQFPs/7kpXQl9jm+nUeZ3Wmr6VbcVKfRQF/N02Uo6JNy0mTC5qfFGlp+Y33aST0HTzmUGCob4k7I8IA+sqR59ZBz2LBxHaLysWl5qpsNv9gKqvjuXvzWGgv0aoeHXX88Jh3R+nwVtGP/eG1U/A5ooK/4vTLKKw0FfUFuosQfuUdsUBJs59ZNzKw8pCArTDIS94iF6yBxCrzTKNdZ1kEYfd+GtNja4wni7ADZbJjz+jZXJbX2jQ16nofaeCcupsuFpByZ1cUTgcKaqU1flZOogb4q724px86EAGdERBR45nlXdbMt/lJq8nug955fvf+q6Uoeml1EzMHLofji0BvbHnT4skOh97VdxHWIiOc9VfaJYtAM8q9TSXIU2vBBCNS5g2STU6QvrwNk2WkyQ3d6HtkcO/yZyxko0MmUmV28Ia7x346wVfbha9CBNdBXw2RSoCMDTQnZsjo7O0ti4xOKWjSXDpIChL+dHR3E3b2OalRBqS+92Ecu2lqF7HoC7WHNxdmzq1jbe0t2cqikJx0VN//O4td0mtE/USoCdTuoQgsKLftk+NexQ7soi6tINlUJei4YZNudRe4/Lj1HRrWsUy3a9BRLZcNJHx8f9DasGWw8Phwa6I0Okf8ekKCPwsOTnpYu5qguKcvvLf5OLgcEvI2tjdSHBr8NepdzJOChn/zSRkhg50j/VvXEx8MZclmJsi88Vvr3CJQFRk7ZnbyYiWq4REnNKUD3Gxvw5S2Rc0ePuxJKNlUFegKeC8zp2Ay5mJwl0zp6ltrz3djTyDlbDk3FH374QSZPmQJR1CWlKiEZ+7zVcTwN9NVxl3EO7vQREZHoHZePqrLyySMEPOtM/fx8VZtt/SD3fsrSjeKIarU+7euCnKPj3h84fVn692sg88cbJ0/POnT67TvAqvN2tQGjjko2//aGK3nbqgL0PAfptREJ2dCmT5FbO3lJC9+yhSyNOZXsXTD2ltGyEdJoo0aMkC++/lp1Pa4JQwN9Nc5iYlKSnIeyLsFMU/9agya+Gx4yv7o+8GX/XSSyklLl5KIHIRSZJfa9uomdl7uknAmX7L0Hpc7AoeI5e4bBn+gM2HSrAPgLiTkSBMafG3x3tosqu3+Mrme9sX16SlXHpGQjcJcmY1vXkXaQv66uwUV35W+/ya+//CqjoX48bsL4cuesuq7N0PNooDf0Dl7H+/kgxVy8KPHoR0dSalm4ZyDP0cEBZr2fWKGTTvFRGJ8oBXMniplbppgPhCqvT32RkH+gnLNLCgdOF/N591/HFV390kjsqDtCU+QsquJY+eYJJRtGy0mYvxbgeRRjg54SWZdTcyQqIVOGtHBDg4vq2eFL3rzc3Fw0Mrl6Dip9g03kjRroq3kicvAQRUWdl3SY+6V1tmV3WVtbawnw98f3/0an8xKS5fz8eWLtkCG2KAQx96knuaePS/refeLYe5x43FG5lN3mM8kqMm8GJRtvpOFsKTmN3d0O4Gd1anWCnoBPQG3B6cvpqmV0kxLtpqp5ymrc6TTQ34ApTUEzzcioCypQV9zM1/2M6Dh2ePbeK20kpWbJoqUrxBGLRsf2QeLm7SIXwuNkz9ko6d+7pcye2Pm6PhGbQa6GTt0hMOu8AHZn9KInxPUgr27Q08JIzMiVveHJMhiqtf2a1Aw/+rompYpfrIG+im9wWYe/DD3+SxcvK7tYD3yC3gNpOl8f7zKvKg4ps55P7BI7NL24pYOv1IeIxukoKOSeipPRg4Pk8aH1KvSJ0vCq/ZFZshVNJZLRJy4AaTju7v/67rpAog70WIyuqonV/e3K4GKFX9iirLWsQbZeaUNXcMO690Iw7dCMAoA/AbZdtyAnGdjsX8DTzGbrMRcXnZptdHS06krkD4uIv09MSKCTgdZTdqqd2KVLFyUFPQn8kPlgO6okxFMuwrVif0F3d3ekTrMlEoFVK5Cc6tf3V1ZXeHi46l9YD8fMQ8A1NvayKtm1hHnP9/A87H3A17J3wmW0NvNEk1Jr9DVMwM9cKi3MLdQ5E3A97Lng5e195fWcZ2scy8vLS5KhasxmKPwd3Tm+3t7OXuLiYpWaEAdfVxXCHRroKwQR479I79/HIWdvDpOaPzshF+znVxf94suglSJvnLD/kDz53m6xhqxzh4bO4u5sLRfRp/0kdur2rb1kXP+GePbz8IVjWP/bhdXMyhq/whcezCT0c990Hu9JzBNnOytxBYU2z9Ze8syhaFOEZjNcgxnASDiaAZkFeJiJczN+x0NfUCwBYY7XWuBhZl07h1og+KXyjrqfiWt+KvW2YgsIX8K1ArdAUjJzJSw2XfoEOUoX/6s7yP6z/4Bs3rJZ7rrrLtU67PDhw8idOykXKDAwUPbv3y/pqWnSvVdPlR1hNyMnKN0QVB07dULTkh1IudlKakqydOvRE5mUcEmiyGh2LkDup7gQYWHh6jvjKIEBgapd2alTp9DBuI/YYeH4+Ycf5R50RbLE593011+q12FmZoZqjsJ+h2fPnpXu3buLE6y0kydPwVpzUiQstiyPijyP34WgY1If9feff/xJZs2do1pmnTlzWj54//9k0uQpcjEmWo6gjj+4eTDaq/UGR6DsDaCyT6UG+sreOSO8j7ngCHTaSU1F62U+vNhxbNE1p6yRfzREMr56T/IcDgEk1ni4mwNsblKQmyC5ZsfEIhctug5lSUFKlpgj4i6FAL8CH+BmYwcQc0dG3z4UqcRnY9fBIsBa9wL8NsvGXgosdKa9Ai5BbwmkMoiHxSDH0gnSdrBKzC0pcacDfVFWwQxtw8yRv9fv/un2Ljgezsf3Au2F9g7qO5cNcwCv0I6A1lkL+SjQyYNbUZhnJqei86QNFrI+zdxFnLDLY6Hi4C7/y08/ywUAa9iwYfL7yhUyBN9btmwp1LFjW7GQkBPYbROkZ+/e8vabb0k/tCNr3bYNyDXnZf++A/iMBTJh4kSJuRCNmEqknAIoJ02ehGPnyenTp+TokWM45lDcUwsJCw1T3Y/S4Ib99tuvMgfg/GvjJjlz+rQ6FxcFZwCXbc4vxsRgp7eG1XYJC8HfaHY5VantNGrcRHr06CHRADF36+SkZPRP/FOm3Tpd/kI3pIjICEX6mYJOxyt+W4Fzhqpeia3xmdag8/H4iRNKbYtmhMdOI+cY4yYacowkPAzRMDvr4gFwdb12I4ZcpOXyD28W2347lRquOI4QsW+ALfIgNKgQvU9uImnvhIpZzgXs8uZiVpBXJF6B/ZsmY2E+4ax+Z4EFBzQ/MQMgib4ru7P+w+TiH7QyrYp27HwsAPk4DlcNWByF2Xg/mjzoKAAAIABJREFUwUtgY4ctzMlRiwJHfp4NTFQsDghKFkLCqgCLTGEBt3NAD9dghqIhXpvKYGDRycPiko/X5eIcDiDi2DSpL3bPvSLmvgHqeOHhEfLWG2+It5c3TOe6MMsjZMbs2TDL68MEj1Umd8iJE2gxloxdvbO8+sorMmPWDKnvX1/Yz3DlipUKQMOGD8OlZ8pR9Bs8d+4cAKpjLx4+dFiOHTuqUnMHYDEcP3Zc/Ztuwc8//ygjbhkpX37+JVqcu6sFohU6GXNBaQvBzCzQqmmlsVvxXxv/kolYSH779Ve14zdv0UK5ILQM4mDqr1+zTqnw/AjCDxeC+IR4mTFzpnz15ZeI49RTn2XU6DGye/cutUDpXRlDnq/S3qvt9Ma+o9d5PPpz2XhwaD6Wl7vPw26U/cMXaHRxVAotsTvaBEqBtYtYZkdjO4RPmestOXsvirkVRCUsdZF/1rrnwna2xA6rHwRngTX+TrdCv7PTvlZ2gG4UFOD4BdibbRhcLJBcC1vJs8B7lCHARpIUyChyQ2hN0OSHT8pD6BeQQlgOhTwuBTC50uRkinNEiKQ0aCx5eOitIWmVhgUCJfHSvb69eMFVyc2HhQEyknU/aPo76KivP2OXZ4tx7r407WkNOSCl6Q2fl7vyFIA39Fwo/Ow46d2nj6z49TdJBbmGZj//HtQgSO3SLVq2AsAPSROY1PTvaXbTl07PSIfVY6m6DHMB2fz33zJg4EDx9vbBTv+LBAc3l7NnzkqjRo3k4D8HVIbDGbGFZsHN5GTISUhptVd+/bq1a2X23LmyC65EJKwJLg6nYNLTTCejcvWq1aqeIgZcDXY5PnXqpPLtvTy90GA1SELwWh8fdCxCrGD02DFVRgbSQH+dIL2RLy+E/5i7eZtkvvoSdugCMQ9uKmZePmIWc1Fyj50WyzbdxXpwP7FCfj0Puyb7wlFf3tXVUWyKcccJ+jyY3+YIruldc3P46leBHmYuf8W6dSrXFlpZSkGxWAN9e7oAVwZeZ0UTv9goZH6bYMf7rfB+65QEqbdhhVwYOEryvOtKNsz205ezpX19J+nrbyFlOTbcxesHBCig0wymL82dOiszSxo3aSJ1EQeh+ZwNBVsGv7j7cvfmYtqwUUMVWKN/fvlSrHh6ewLEwQp4PAZrGlq0bC45MPPPnjmj4ik2AHCDhg1Vfv483AMbLJA8pxN8dC4SyQgKpsL057/dsUgEY0fPzspWpnxDvI8L+SEsLhmgXXt5e6lFhi4K/X7SsN3QHt0FVh2Df2cQB2iJxcjBAcFGxBiSYK1wkeSCxCBjVQwN9FVxV6vwmIUpqCOfPglltODxDx4kZnWx2x8/LAWbd4j5ELS1mnePJANkvx5MkhOXMsUV5a+2jLCVCJ5zw2ZdujVBWerQ+f+M3quXlFS0VEvEvyF8Rc4pKT5bZBXw8ASQXUq8+G74TWL7j5L0Ol5yICxR2vnZy+hWunbh2qieO6CBvnrus9HOkpWaLBuW3iEWLmZoYNldHOt4S0LEKYk8ckgadLtFcttPkxVH44T597pudsIGjkzDlRxXQF+OLryx8vTc6W2x0/v9tVKieo+UXZl24u9kITM6eSKYeG1KstFunnYgdQc00N9kD0IigmB9N6C0FjvvbL/OEujgIfvjz8mq+JMyoVEfaZbZHyWwqeKLDjJs5VRWRd+NAL19aoK4rf1NNgf3kzoN/GVya1exR8BRG9V7BzTQV+/9NvhsidDI67TtcZ1Gnm9XCbL3kJ0JZ2VlYohMC+ojQ82Hy16A3gJR8WtRZ6sb9Nbw9+2SE8Ty1x+kYNRo6d2jKYQtDb4d2gEqcQdqJOhjkDslycLNTecrkiDBlIkHWFVxl+MU2cTH16dCt4tBGUZ6eSymcPQjNTUFRI80xf4qp2Cu1PMwYMOAUAACVBRqiEPkuVWrVqXy8YsfICEzW8Z++JuYp2fIsKaNxd/DSU5HxsrfkRdlRLdg6RLYVPag6YMVc+zXGNUJeiVVDZ8+8nSk9DqwRlovRCUgItbauDF3oMaBnjnTxx55RJo0aSpzbpur7uolgPbNN96Uu++5W7Zu3apypB07dVQ0zG7Ip15rkHrJvGqvXr0QCW505aUbN6yXP3//U1578w1F6KjIyMrOUXngjh07yPGjx2Tv3r0qNbPsqaUSEBQojz72WLmHYT39mKd2iG16tgxsi2YXXg4Sjnp6NrsY0j9A2rbxNSnQk8bKgN/p+ByxTYqVaVHbxWPqOA305c501b2gRoL+3nvukdbt2sodRRVniUiFEFj3Ll4EAkeKAumuXTtlE1hWz7/4gsrjbtywAVzqWGnTpo20btP6yh0nF/uff/5RaRdX7PYb129AqihDLoD7fRhUzw8+/khxsLdu2QJKZ4706NlDApAfDkU6iCKYZ06dVqSbPv37KdbXC889J9PBymqH3C4JHfFx8fIB+qTNm3+XWlS8wA33wBdJJRGghTZp1vQqxRaCvqxedkP7Bkm7Vn4mA3qd6IZA1y5d3JFGnOAv4rxmpZiNvAXVgcanl1YdTGrWkWsk6B9YvFjN0tgJE5RpryiSAPXSZcsUW4qmf14+qsv+XCXLnn1GEThOnz4DU7u+bN60SRYuWiStioBPKubHH34kI0fdoqyEQ/8clO6gV/6Cbqa+YNEte/459XfSMmnCnof1MA8Wxf+9966EIqc8ZOhQ2b51G65lHIozvOTpJ5+UmbNnKRLGFiwUDRo0kK+/+kpmzZ6DxeOSYo6Rqvnn73/INpyP12eraKu6cbOAnu46d/lzkLnydLSQiR29xC49WbK+/0msR43SQH8D15EaB3reyyX33y+7d+xUNMg8+ORkPfEhfPf99+WTjz4SdwCLBQ37YF4veegheRpWAPPIt91+m6rO8iTdE+QIDpIw3n37bcW6IjgX4dgdO3aU10D1ZFXW1GnTQfNcIQtRiEFZ6g8//EDatmsva9esBqAbyv1LHlDFFHGgWLJY4+MPP1T87DOnzsjq1avB/56s3IcnnnxCdu/aJUePHpXFOMeypU8rbvaCexde9Xgo0D+9GQDKlcHtfK/0stt1Nk5MZadXgMc2HwrAs2Ls9u4+Spu+EBZXNth1GuhvIOJx6hoHevr092Onb9Sosdx5151SAP43A3tvwae/74EH5BsAt46Hu6pu+ufAAYByiarY+varrxXAWfQwadIkxeLSg/5DmN+BgUGy6o8/5aVXl4Oe6S1rANgNMPX7Dxgo337zlXRCJZeu5DJBeqOSai0olz0RBxgzbqx8/r//gY0VLfPuni+ffPyx3DpjhpxBwceaNWtk3Pjx8uP338vjAD2ZZPx7HxSLfPrJp/LIo4+ohav4IOhHPb0V/elzZSD700POij49QT+kX6C0a133hpr3BDzpxBGQqU7PyZNpHTwhl63j2mmgv7Fg15+9RoJ+wd13S0tEwufjOwd97ueWPaMUTb/4/AuY0AB9s2bKfH7k0Ufl9xW/g3bZAHxqZ1n61FPSB/ztu4reS9/6rdffkP4DB8h333yL7wNl5MgR8tILL6rFZMasWQq08+6+S/neWzZvVYG6Tz76GP59T5k0ZbJ8hN39Iqiy9yxcIO+8/Y7y6Vk0Qi42Cyu+hejiE089Kd7Y2V9/9VVUhe1TMQS6IyX5+Anomz70o+/FJitPRjRpIIHISJw5Hy9rz5+TWzq2AIe9jexF9N6SMlfXGFUVvef1hkGq2sq8QMa19UQzjH+ppBroNdBXyR3gTv/k40+gtLGxzJ4zW52D0ft333lX7kKw7Jeff0FAzlU6d+4sjz36mMyH/52WkgoArlJ86AykwmiqB6O4gyMtLU0+/uBDGYWqqwRURbFsk4E2VkQ1wTlovn/26WeqoINMVdY/c0d/D+Du2LmTjLzlFlgC36i4wt0L7kEg73nlt7ds2QLBxN3K518BAUaa8bQg1qGs8tGHH5Z3P3gflVo9/nOPmKfvsekp5OkLZKYfGliirRUbWP6RcEImNewtA62GqJ3eEky86ga9TrkWZb1ouTUNTDt3hxL6fpp5XyXP/PUetMbt9LwBVDWhj87gGgdz7Sy7dHZ2QaljGsxwC6TtHOQkqpxcXdwgguAD0YWDchGVXG1btxGfur5X7iNVabnbs9iDx6RQwnnk1ZvCDbBHgM0FirUs1+TuTLUVpgJZnMFiCgbgmNtPRelpHrrMMtdP8LPckjwBZgZYXcefWUbJrAJrrX/95Rd57a03xRaWQ8lB0HfbAnIOPtNs3y6Kkbcb5Jw/E07KlAZ9ZID5sBsCeuraxULIMiE9S2Z39pL67leLYGjm/fVCs+peXyNBX3W3q2qPzDQfg34s66SgQ2kjEeZ931/fhuhEjsxs0FIC0Kr6QHyYrIkKl3EtOsmAOr1ld2RqtZr3LNyJgXBHCqSuxrRxlzZ+pSvXauZ91T4/FT26BvqK3qlqeB3LOKkO0759B1WKWtpQ0fsX/hKrjGwZ0dZP/Nwd5Ux0ouwEOWdY7wbSoUX9ag3kWWKHj0bnmZzcfJmIltENi4J2pV27BvpqeIgqcAoN9GXcJJr1dAuKa56zWi0f+X0qoZQneFGBe69ewvPwi8esyFCgX7pV7JGyG1TEyAsjI48pu36BcE+qL3pPk5695ShXPaOThwSiyOdaQwN9RWa46l+jgb6Me0yBBbLvZoBIk45gHn16iias+G0lJI5uNZqUkZJqOn5cJiNfb1kOX56XairkHO7wKcgghCJSP6mdu7SvV34zCg30VQ/oipyhxoKeopOM5F9CcC4xMUHl7e2KmG0pKJYhxZW5eMojcTCYdu7sOZW2o8wSi2z+Afuua9cu8tnHn6j8fc/evWTHtu0yeMgQxeoLDwtT0X3SZ3lsiizyvJcuxqjvDXFOFuOQFXgOckuFiGrztfoOtDzvOcg8hYeHQeapN+SpIG+VnKQ08wKg/1aaRpopgJ4dZOPhv7Oh5KBmoBg3vLa2n/5B1EBfEUhW/WtqLOjPIcr+3bffIiJuKZkAtBvy2UzPMX33Dhh2BG0mGkbcBn5+YFAAyDCfKMmlpKRElSpr3bq1bAIltxeA/ujDj0DTrJHMvf0O2YBCm1kQZVy7eo3sgYChIzIEdRCVvxPSzKfAs//qiy9UZD4CYo5dunSVcRPHQ+PtJ7XI0F2oi8zATLxfX7F3AAQh6r6NhPjiF//7XBKQ1uJCQTnne+9brMQfiw+CfuQyHSNvSNvqZ+SRTx8LYlByVo6MRdCulc+/lYflPa4a6Mu7Q9Xz9xoLeqbQ3nz9dZBelipd8rdRDdd3QH9Zv3a9AvzCRfeCVvu1nIauORl4TJM9+PBDqgSXabk6bnUU7XbZc88qFl8T7PTkyb/x6uvK5P/hu+9Vbp3WwisvvywDBw1SO/2br70mn37xPyWY+CEi8aTeknwz9/bbUdjTQHZu34HKvm7I9XuoGV4LVt6ObdvkngULwBt4FFbEULUAPPzQw9IBdN+5RZWC+schDqAf+uR2HegVI89ezqJ+fsvJWBmLZhedsBBUVWktd/hkmPSRcRkyqYO7tPW7vp7tGuirB9TlnaUGg36/UjVdfP99Kkj2E1hzSci37965SxFqWkBfnNz5xwCuu8GU24j8eDy6i7AxAll3tAg+hVn/AgD96y8/K0VUb4gcvvHa69IJxB4uDmT4cXwBmi3pp+yMsm7tOnn51VdUEc9SFNc8/NijsgG/O3zkiKrg690HeuxtWl0J3G1Yv14tBLffeae8/+67ciskkVkXsPyl5ar5Ba2T4iM9K13e//xTkWxbCW7cUNycHSQuMUmOnz0jHdo3F9t6nQF6qrsal5xDLn0mIvRnUTHXt5ETWk7p3KLrGRror+duVd1razTo//zjD3n08ccUPfajDz5QhTbrAUAWuQwYNFAV3LwJii2LbgoK8pVfv3nT35IMn38I/PbfoZe+DKWwP/7wvbRu1QY0WS8U2rwKvv0AOXL0iCx9+mkF3nfBvnMHtZdKrOvXAfQoxgkJCZFnn16mFgaSbvJB3DkIhdQD+/ehxHexUmTl4OsZJ6B7QBbftBm3gkLcUp5/9gUw9+qokturRl6iSOgsyNQiUu7cF989IS0dhsogaOG7DpbNyTMV6G2u0WKKx7seGi6j9EmZeWhdnSHd0W5qaHM3VUF3vUMD/fXesap5fY0FPYtolj35lGpz5Aj9dPr4Sx56UHai+o5U3L7QVd8NGmz//v2lPVh0H4Fqy0YEZ0GnJXuPwP4JbYy4a3/+2WcI2KXLUFBmP/zg/2QJaLKfffqpYsxxdz+KNkT3ohyXjRBYl/8q3AqC/vlnnlVgXrN6lTQD198CCwTbM5GDrxfkoFb6doB+Hl7HWMOtM2coBZ1nn3lGKf3MhzZA8VGYliwZr80SS+sMsWg3EBrxflIQEykF5w6IZasBsq3hrQB9RrlikxUFvSPovOnodReVkCFD0VCyRwWDdqU9rhroqwbE13vUGgv6fw78g1ZIP4k7fOd0AHbUmNHKvOZYgyDcrp07pF27djIWVW4cGzdulK2bt6BRYR2ZPGWK2NjaqcYIvdAmiX7/oUMHVQFNJAJ0XeGTs4XS9999C63yZBk2Ypgy+SPgLrAIp2u3bqphIlVyqMxzGgE+tjSiNXELasnb4rz6QUIOWyMFN28ux1BWSx13ZhRYAcjYQ8kquwL0tk+fPV0snDPEcmB/dIGpL/khhyV/xz6xHjhJtg+cK3ui0qSshpH681YE9OQiWCADcu5ymgxGM8m+BnaQ1UB/vfCsmtfXWNDTdN+8ebMsefBBoxFpqmYKru+oBH3y7TOge58tNv36iwXEOPJPHpfsnXvEFqDf2W+2CuQZCnr68Pko6glHTXyrunYyGSWypNsaMjTQG3L3jPfeGgt67rgRKF/t0IGU1qurvYx3+6r/SKlodvHmIx+LneRLi47BUsfLUeIiLkvIsTBp3b+D2HRAIC8izaBAHqP02eiKGxWPTrj+DjKqlcc121BX9C5ooK/onara19VY0FftbbtxR49Djrz/c7vEHv3ph6E1dT2k7M6gGdzmc/EydmB96dbc26CUHTfzPDSTjECUvnsDJxnRsg56rhu2w19xKbTS2hv34BQ7swZ6I08DFXKSwABsjpRgVYy05Ax5+7FP0EcuU1q2aSounpD1DotGyu6sdOzbVezbdIaIRjoksK8/ZceAPN8VBdWbFt62MrG9Z6Xkvcv63NpOXxVPxPUf0+RBTzpracUoVJ5lX/CSIw+NAilQWXxko26dabv/vLbYscmWKyllXdb7rnWbNyAFdxJSWHfMm6e49CWvPRctnUtrTJgPWS8L9I3jyMnFZyvqzV7yXPTpM25HzzrXTLHsN0DMfPyl4ORhyYNPb91/imzvX7lAHoN2+SgoikZarh66x07t6CnO/2lOd/0PWPF3aKA37P4Z690mDfq9e/ZCSWaNaijhDUUaEmooFvndd99JZFiE+Af4y5RpU8FZL0RvcJJr4lQv805dOssoRMlZIPMTRCcZHWfX0wlg3hGEzI0zLcf2xu07tBfq0R9H2m3AoAGq1TFFM36BgGO44uf7KckrV4hlcLD76FYECNmplB1Lf0NbZJ6PQhx///W3aiNFgUtPyljjHDxeX8hfUw6bslo8ZyN0UqWgZg66rO5CCvEIovadoLJDiayff/xJiXPys1I/r2Q8ohCgz7p9KnrZZYp5f4Ae0fvCkENSsH2PWA6YIlsH3Hbd0XvVRRpBuygUzwT72IJe62F0wPPeaaA3FmwNO47Jgj4K/b1fB+V1AIQnbWxt5LXly5VuHQthKBU9cdJkSFj/Cd58oHTu0kXm3zlPgdq/vr8Sonj0icdVHv4i9OknTJoIxdqVaCPcSIYNHybTp02T4SNGiD9y7C89/4LMnDtb3EG7Xb9urSyHRh0FK+PQ1ng46LBUqiWNl40z1G4Ii2D5iy9Jd6TvvLw8ZNaMWUpnz9+/npLNGjNunHz5+ecyaw4INOhjvhpimiQIfQfabjwktiZjkfr+2+8Ut5+Lwb33LEBRT1e5ZfRo9FX/VdywkDD99/FHH8qgwUOQ4rvlqhlORyDv4yc/EnMsVM06BIuDv5ukhMZI9KEz0nBAZzHr0E32wMe3LrIayjS18QdG49k8kotmaGy6BHvbyzTs8NblNLWs7COngb6yd8647zNZ0G9Yt17WYUdeDnIMVWYfe+RRJUt9BKSbbj26S/Pm0JjbsUNiQZ2de9vtYLA9K8++8DwKWurK3XfNV3Raik/OmTtHNZ8gF3818vMToIVP8cknlz6lZK3moeDmlddfQ0Wbszz60COKAUfOfWN0yKHsNXP1Z9C3nPrz+iq9b8DZZ6WcOcz3X0H0GQVgXgag6Vq0R7aA56LKLq2CF7Go9ARB6N133pERWGjatG0nf2/6S4llLFh4r3yJAp3HHn8cVkkiVHwXyXgsUA2CguQP6N6zV/oTuM7iIz05XT5Z8pZYm2VL064txQULT3xYpIQfiZDgft2kAEU+FaXhkmlng8XhfEKm+DhYyKyu3lXaUFIDvXHBW9mjmS7owUnftPEveXH5y+qzUX2WmnT7QXgJREeaQJjrNN99fetiEegBies3lIw0iS0P3He/YrVFA1jk1VOIkgUwJNOMhsDl56hmexSceO7aDy55UF7COejzk0E3HYw4CmGyUo4FNjT13WAFTJ0+7UpJLEk3v6/8Xb2fUtqZ6Hhz8J9Dcvu8O1X3m0Ookb8L4phZaInFdlpt0W3nnbfeUjt6/YBAZal4QVufixepvovvu08iz0fJgvnzFUPQE3Reuipk7dHEv8ovjkcV3twJIm4ZIgMHQ4kzQOTEAZHte0UGTJfNA++okHnPoJ0F/nMpJVucbcxkZicv8Xaq2tSmBvrKwtS47zNZ0Eeh5ROlp0dAbtoW4pEvQUV22q23yokTJwBQW5jRY2TH9u3iAwXZFi1bqXZRz6DbDPnvd6IE9hao0LLenT42WXBkxLGclWb9kzD9n3v+ecU/Xwjz+g2IUNrZ2cqjDz4sj2Lh+AOAtra2kkGDBstff228Anq9Wg5r6G+bMwc+ea6893/vK4VcWhWffv4/5c8f2P+PLH7gPgX6Z5c9C5N+CnT1v0JcwE0xA1mUQ8ltVtGxqOehRx5WC8jSJ55UwppslvEbqv46Y5EYWkIrrwCgz0aVn5VLNhh5PQF69Io6eVJy6dMPmShb+s8qF/R6wF9IzBJfZyslguFTxYDXfHrjAteQo5ks6PmhtoAW+x3ko+nnsmptBnZh9oAjRz06+oIy5e9ZuFAB9OcffpKp0JOnAu7/vfeeqmbz9PKU11EgQyEN+vr3LlqM+ICtaj5xKwpbCGLq08+GC2BjbQNT+3OlY0+lHEpmx8BS8MKiwkq3Bg0bXnWf2ZSC5jzjDN9CD59jGqwBuh8MJo5APCAXWYMfvv9BiVwS1G+/+abqUMtOtQthyjNbwCq70fDnHdBU8xB6432Gun723qMuP60FWinFR1pGijz+ycPwu/NlQLMO4uHqJWejI2RP+DHpAS6+l+/Iojx92Sk7FstcTMoUb0dLuQ3dZxyrqUe8ttMbAlXjvddkQU+gbkGUnPXq1gAk20HNBCDpH+cg7UUpafLk7SFjRYUcfWqPQKYvTUAxFsC0G5tdeLh7QG7a7qrX8jZSmtrSUmfWFj8GK+4opU3LgRz4koO6dhw8BwHN7zy30rzD9VgWdbLlMXkt/Buv+/Kly2oxojvB69anCvVWBLMKqXAp6iJ+wWOWHAm56dJq59NiBitjnk8XaWjvKTsTz8qPicdlZmBvGYP+9LsjoIZbhu496bVxkKq2M0N/+27e4u3831Sm8R6vq4+kgb6q7uz1HddkQa9aRGOXZJuoXACTKbEpU6eWmm+/vo98c786IStDBn3zGSSw0aAzqIk0cqkjx2LiZHX0aRmPppt9fDsqGm7JDjeKU4f/XEyC1r5FIYQs2W7qv9r0VXl3NNBX5d2t+LFNFvT8CNw1Y6JjsGtZqu4v2hDJSkqRjfeijt++QLy6tRMnCHskno2V6IMnpGm/HnK5e3+Y96n/SdnRpGeU3smqUGZ28ZaAcpRrq+Jea6Cvirt6/cc0adBf/8ep3DtoUtPc1pvxNMn5OwpTlidNnQNX4iIWJkb7iwteVu5K/n0Xm2ky4Obo6HTVoUjOyZ0zWcxdUU8PhR/xqS8FIUekkIG8gVNkcynkHHLnY1OzxArKm3d29xVvl/8yGQ293oq8XwN9Re5S1b9GAz3u8fvvvS9t27ZRfec5KIW14rcVSO+NVsy6a41vIb558kSI0tdj3t9Y4wew99guazayBMVHQUKyxKNHn4VLltj27wsaLkQ0Th6VrB0HxG7QBNk7YMZV0XuGBRKhp5eTlSu3Ii0X7FtxIUtjfRb9cTTQG/uOVu54NzXomUNnw8mzZ05LG6S5KAK1e+dOlYrr3LmLalRJF+EgpKwZ7Se1lbl3jmgw9di/zgd5/p/BuuvTr69qJsnBlBz16Nu1b6csgL2796h8PRtSukPNRj8YIFyy+H5pByovtfiiIiNVf3l/KNiSccdAHRmEzD5QZYeLCc1sMyCRwcZTEOdgAI/NNGklMHhIDT83BCjZ045++BIsJsVHJlRy17z4Mrj5tuLfKlhs3Bwk/XycRJwOlQZdOklyi54qek+NPJ7rEtpNFcBymYgOsu3rX5+QZeUeqbLfpYHe2He0cse7qUHP1tFr0eU1qGEQ0mXT5c/ff0cdeI5kZ2Yp9tw9UKv96ccfwJA7oBpOUuV2EYgwJPBQG88OUXm+nmSbB6GTR7ksDgL1qy++VGw+tpOmDBaPx4zBU8uWIo2m2/2pbkMi0LDhw5VCzueffSpNwOSLiIyQsePGq4Xk9eWvKJZdY3S4/fyz/ylKMXdwavGR938Wevhsg00uASW7SD/29vZRKrmjkMqjtFbxkZ+bJJeP3Y8FwUHsXDqJhbWr5GZckOzU/WLv1UcOZo9DL7s0sbed0/6dAAAgAElEQVSxkEvQpc/GDj8TTLtWdctvRlG5R6ji79JAX/F7VZWvvKlBT+BSwfYx9Hbfs2uP0qJ7FJTWAlSsvQ4p6iFDhyBFdgn8/CCV974fefqx4yegiMVCiVEufWaZ2n0XzL9b7rjzDhTcDFL3+nzUeZj870Gh9g6IWz6tcv4TJ09CP/ttINR0UGk8Du7kz+Dv5MpTTbdZs6ZFmnhrwPVfIU9D5+49cAqmgT9AOSyq3dpChovpSDbEeAN/I2lo/7796vjvvvW2Ihixu+3ts+eo8lwSd64aeQkQxrwNv4KZ7tQLBHrk8bNDYZ7sEKkzRDanzJR9UemShQKazMxsZdI3v4EmffFr10BflVCu+LFvatB/8P4HIOj4qN7x36GI5Y+VK6VXr17KvL8IUA0fPkKlqXaCo5+dlS07dmyHOu3DqHePl+iYi9Ca14lOLlv6NPz57ihwAa0VgwSa9wDQRWg2cQzVdyy6IRC5m1Mem9LU+kF6MCvxtm3ZKg88tAQWRTO4DjGqxzzfvwoFN5NRpccKOnINGBgkz97VzUVuA3PwL1CN9+zereIJf/y+Uolq0uQnwSgdrgvFPP8D+jD4+WYAvQMsEyuAPgugT9eBflvKbNl4OgkWTK6MQzOKzoHGizNU/LEq/ZUa6A29g8Z5/00N+vfeeQ+7rqfaJX+Hac8inYVoLsF69MPw18lzJ7NuytTJqgDnycefkOlg4lmj3p4KtI+Af08/ejHeQw4A21X9u9PjfajGoygmxSmTwJJ7BosDGYC3jB515e4/jWYa5NZv/nuz8s25q3MB+BoNLh574gkF3tnoaNMEVsD9i+9Tvj6tC2dnJ8Xmoxou/fjxEydCafcDeRzvYSZg0YKFqqSY11h85OQkytFtD4pNvpO4+HQQa3sPyUyJkPTEfeIe2E/2ZIyQLWcSZXQrd+ndyNmoIhiGPnIa6A29g8Z5/00N+o8//Ehp2Y8bPw418xdVJRv99KwspKcgsDEDjSPeQEUdU3H+oL5u2rhJ2sM8nzfvLgXG7Gy0WIZPTy29+9AAozt2crXTg37L7jRz7rhdvv7yK9X4wh+7OWMCD8D3p5y1flDbnr67FUQvWFTTEHRdBgnH4ppGjByput9EwV3wQhaAdQMs842KjFKgZ9cbSmZvR7OLRx59RL4Ad//o4WNg7Hmo3X/M2HFwF+ZdNdPp6KLz29aj4pAtsDDqiKOtNRh2GRIXm4AefN5yLssehTM20ruh6ezw+g+ggd44oDX0KDc16AlOgo27PQfFMLnbk/Y6aMhg5XtTQINBMfr1LLFlIU+PHj2VUMV67LKk+dLfZ05e3zCS1F369QGBAbAacpXoRgIoubQE6uH1xQeLelxcXBGhd0MlXwiKgHZI0+Bm0hP19hzc1Xl+0oBbgzFH1ZwsBPJ4jaTaslKQXHsuFgzwrV61CvRbC1gPbRA8tFeiISUHCcDFVevoziifBqMAuXjLUui7hj4oxni/Bnpj3EXDj3FTg97wj68doTrvgAb66rzbZZ9LA71pzEOtuAoN9KYxzRroTWMeasVVaKA3jWnWQG8a81ArrkIDvWlMswZ605iHWnEVGuhNY5o10JvGPNSKq9BAbxrTrIHeNOahVlyFBnrTmGYN9KYxD7XiKjTQm8Y0a6A3jXmoFVehgd40plkDvWnMQ624Cg30pjHNGuhNYx5qxVVooDeNadZAbxrzUCuuQgO9aUyzBnrTmIdacRUa6E1jmjXQm8Y81Iqr0EBvGtOsgd405qFWXIUGetOYZg30pjEPteIqNNCbxjRroDeNeagVV6GB3jSmWQO9acxDrbgKDfSmMc0a6E1jHmrFVWigN41p1kBvGvNQK65CA71pTLMGetOYh1pxFRroTWOaNdCbxjzUiqvQQG8a06yB3jTmoVZchQZ605hmDfSmMQ+14io00JvGNGugN415qBVXoYHeNKZZA71pzEOtuAoN9KYxzRroTWMeasVVaKA3jWnWQG8a81ArrkIDvWlMswZ605iHWnEVGuhNY5o10JvGPNSKq9BAbxrTrIHeNOahVlyFBnrTmGYN9KYxD7XiKjTQm8Y0a6A3jXmoFVehgd40plkDvWnMQ624Cg30pjHNGuhNYx5qxVVooDeNadZAbxrzUCuuQgO9aUyzBnrTmIdacRUa6E1jmjXQm8Y81Iqr0EBvGtOsgd405qFWXIUGetOYZg30pjEPteIqNNCbxjRroDeNeagVV6GB3jSmWQO9acxDrbgKDfSmMc0a6E1jHmrFVWigN41p1kBvGvNQK65CA71pTLMGetOYh1pxFRroTWOaNdCbxjzUiqvQQG8a06yB3jTmoVZchQZ605hmDfSmMQ+14io00JvGNGugN415qBVXoYHeNKZZA71pzEOtuAoN9KYxzRroTWMeasVVaKA3jWnWQG8a81ArrkIDvWlMswZ605iHWnEVGuhNY5o10JvGPNSKq9BAbxrTrIHeNOahVlyFBnrTmGYN9KYxD7XiKjTQm8Y0a6A3jXmoFVehgd40plkDvWnMQ624Cg30pjHNGuhNYx5qxVVooDeNadZAbxrzUCuuQgO9aUyzBnrTmIdacRUa6E1jmjXQm8Y81Iqr0ED//+1dBXhVx/M9ccGCBwharMHdirRIcFqcYsW9uEuhtBR3KQ5FCsUJXrxYcXeHhELRBKIvyf/Mpsmf9hdCQgK5j7fLl498L/fu3Tvzzs7s7JlZY6hZg94YerCIUWjQG0PNGvTG0INFjEKD3hhq1qA3hh4sYhQa9MZQswa9MfRgEaPQoDeGmjXojaEHixiFBr0x1KxBbww9WMQoNOiNoWYNemPowSJGoUFvDDVr0BtDDxYxCg16Y6hZg94YerCIUWjQG0PNGvTG0INFjEKD3hhq1qA3hh4sYhQa9MZQswa9MfRgEaPQoDeGmjXojaEHixiFBr0x1KxBbww9WMQoNOiNoWYNemPowSJGoUFvDDVr0BtDDxYxCg16Y6hZg94YerCIUWjQG0PNGvTG0INFjEKD3hhq1qA3hh4sYhQa9MZQswa9MfRgEaPQoDeGmjXojaEHixhFTEB/9uxZ3LxxE2nTpkXBQgXh5ORktrIJCgrCs2fPkCJFCtjZ2RnmPTToDaOKj38g0YE+OCgYC+YvwOFDB5EyVSq8ePEcGTK4oVPnTnBNl+69C+f48eOwsbZBocKF4u1Z9+7exfJly9Gy1TdwdXWNt37j2pEGfVwlqO+PsQSiA/36deuw7JelGP79COTJlxeP//4bCxcsQMHChVG5cmWcP38BRw4dUhOAR7WqBKg1bt++jfv37uHqlav4JHt25MmbB7du3kSBggXh6OiI69euIyQkBLly58L+fftw8cJFXpMXZcuVxePHT/D0yRNcv34dyZIlxdYtW/Hwr78wbMRwZMyYMfKdXvm9wo6t2/HkyWOUq1ABOXPmhFhw6e8mn5XeNR0+r/gFEiVOjKdPn2LH9h0IDAhAzVq1eF0gpk6eik/dc8PEcbi750HJUiVjLK/3daEG/fuSrO73fyTwJtALMLt07Ihsn2RHvwH9I+8LDg5GWFgoLpy/iAnjxqFEyZK4eFGAmwetWrXG98OHIyAwEPny5cPZs2dQvHhxbN68GX369kORokUwbMhQZbmdHJ2xZZMn3Hnfn0f+RJt27eDgYI+xP42BWyY3VK9eA9u3bcOdO3fw/Q8/EKSfqjEEBQZh8uRJeP70GVKnSc1nX8LIH0bi1KlT2L51K4oVL4FTJ0+hdOlSqORRBd/xeUmSJkFoaCieP3uObj26Y+yoMUidNhWyZcuGQ5y0evbqzUmpQIJ+OzToE1T8lvXwN4E+MCAQLZs1owWvhlZtWv+PUPr27o10tKi9+vbBtatXMXjQYAK7DzZ7bkaVah4oU6YMFs6fD1tbO3oE5+FGS/11068xcfwEfFX3K8yZPZfeQiU0aNQQC+bNp3W/hs+/+AK/rViJ0WPHqOWEfG5jY6Nc8Yh24vgJDB/2HcZNGI/sObKje9duajIp/dln8Pf3R5YsWdjHClhZWSNd+vT4ZdEiLFm+TPWzetUq5KBXIH9v3bYtcufOjZEjRiBXrtxo2LhRgipegz5BxW9ZD38T6MUyCqDEdR88dEikUE7Tir7w9cUSgql+w4aoSrc+kJa9c/sOqPVlHXjd98YXdK3FMi9euAguyZPDxSUZli9dxmurKZe8StWq6NyhowoKZsqcCX8/eowUvC5z1qw4e+YM+vTry8nCFnN+nq3+b922TeTzN3l6Ysyo0ajXoD4cnRxx5/YdFCboxWpv27oNzlxC3L51G4WKFMIrPz9cuXQZU2dMV/cHcwkgnsMyjqVNu7Zwc3PDjyN/IOhzqndJyKZBn5DSt7BnR7em37F9O36eOYugH6qs6ZVLlzBp4iTUJeDOnDrNwN4LjBj5PQ4cOIC5s+egNy397p27CGoP5d7PnzuXkX5n1KtfH7179lTXt+vQHsVLlEDXTp1pncugcZMmWLNqNRIlSoTESRLj0MFDGDh4EF19B8yaMQNcSaBzty6RWjlz+hS9iiEYNGQwsnPpsXTJEjU2WSK4uWUgeBth+rSpcOT9MhHMmjELs+fNwcuXL7Fi+XJUq1EDnhs3olXr1shMr2AEvQaZoBo2bpygmtegT1DxW9bDowO9WPslixdj44aNSMrAmq+PD13wiujQqSMt9hOMHD5CWXk/BtaatWiJcuXL0bovxBeVKinX+VeCTLbF6jdooFzqNWvWYPLUqUhH7+HAH38oT0DA7ePzghNGP/j5++EwQd+lW1f1uSefKy7+oGFDUKxYMaUYiTUs+WUJ9u7eAwdHByTmZDFg0CDs27uHgb8tdOkz4CU9EQda/J69emLFrytw/PgxTh5hqMjlRJUqHvzsVzT5ugky0NJPnjgR2RlwrFm7doIqXoM+QcVvWQ+PyT79ubPncOPGdWTnejhvnjyRAnr+/DmO/nlUWVh3fh4WFgb5TKy2vb09fAg+K16dJEkSSADwOffHZa1uzSi/tKvXruHq5ct08wspV1vW5PKTnK6+lZWVishfYpAwfYYMSJ069b8UI9t5zxiZL1W6NBIzSi/XyoRhz2Bg/gIF8OjhI4I6gxrH4YMHYc01vQQdVUCPY0yaNKlaOjzlmOw5MUkfCdk06BNS+hb27JiA3sJEkiCvq0GfIGK3zIdq0BtD7xr0xtCDRYxCg94YatagN4YeLGIU0YFeAmO2XAtX9vCIlMXtW7dwiVH8ihUrwpcRcQnUyfo4ou3bsxd29nYozX36d2kbN2xQMQDZs4+q/fXggQryJSd3/mNqGvQfkzYN/i7RgX4UmXBCamnStCkcGBBLS666t5cX98FvIV/+/JgyabKi2sq2m5W1hOyAmdNnwNnZGRUZwQ8NDVHbYhHt0aNHihabLdsnDLCFJ7uYTCZSc68R6ElV4O07bg9KsO/b7t3JvguEP0lCQsmVJsHAoYOHwI2BPdnGsyYvX2i/sv8u44gIEBpc5FEOT4PeHLVmpmOODvRTCeq9e/eiMLn2kqjSqk0bpE3nivOM5gsFdkDf/opDP3rsWFre5EoCi7hlt3vnTuTIkZPR9WeoWKUyatSsgT3cYtuwfh0j5fbkxCdCN4JaQPrzzJnk2z/ltp+fmjwOMbknDbP5GjZqRHLOzyhRqjRK/cONv0wPo2O79qTVJsW4iRNw9vQZ/LF/v4rY58yZSzEHJVpvjk2D3hy1ZqZjjg70E8eNx8kTJzCBXPd169bjIum09Rs2wKEDB9GC1NgpkyYpbn5rgi0iTXX2rFk4cuQIRv74I7y9vRXxplefPmrPPlfOHNyeK4yeBHyzFi0Uh/8wue89evXCaZJ9rpLO+/zZU+W6S4af7Pe179BBeQ7S/F69Qr8+fZX3UIkJP3PnzkGXLl3U32fOmIk6ZAR+VrasWWpCg94s1Waeg44O9OPHjIWNrQ16kme/Z/durP5tlQL9yRMnSdDpRFd+uuK/161XL/LlBfRO3KdvQVALcUeWCI2bNKX7D1rm02offvVvq9GsZXNm7T3GJ59kQ9Xq1dX9/v4BzICbTFLOBjizj/EkzuQvkP9fgh1Kjn/OXLm4d58eV65cQWeCXtrcOXPVEqTFNy3NUhEa9GapNvMcdHSgHzd6DGztbBXoJePNc6Mn2XX1cfzYcdJpO2DalCnIy5TbL7/6KvLlZ5G2izCgU5dOivo6cfx4cu6rk+++BEWLFkVeptGOGztOJbg8YFAuTZo0iqYrFF1Jx92yZTN8Xvgoltxz5u/34rMlcBfR+vfti/z5CyjO/kF6HAMGDVR/mj5lKlKlToXGX39tlorQoDdLtZnnoKMD/Q/fj1TrbuG5b960CevXrkMjctTFfe/Zu5fKmHvp64NhTKeV3HVpwtXfuH49WjGL7S6TW2AVxqy6+pCsvIqVKiJN6jRcFkxUtNcinARWrfyNlr4aTpBhlylTJnh5eSMjAd2Yz+nZrTs8qldVNN6INnjgQDzwfoAefP6cWT+TCeiuGHzC2f+WabOSRWeOTYPeHLVmpmOODvSSa27Ff6WYm36DhS2uXL2mklMe3PdCSX52/tw5BtL+4ETQiFY2nCZ78cIFXL58hTntJ9XSQNb7mTJlJtf+ANauXs2ofz6W3XLFfe4CNGRmmxS+2LFjB1NiM/PaNnTZrzIw54AiRQoTyAcVZbYa3X+h5UoTWu7aNWtp0ZvAxGi+cP2Dgk1cz3+pCnGYa9OgN1fNmeG4jUDOET58dNttpPQT9GYo3FgMWYM+FsLSl8ZNAkYAfdze4OO4W4P+49CjWbyFBr0x1KRBbww9WMQozBH0shyQYpvC3HNxcYmTnkJCWKWDS4cQMgOFk/CpuzsZgMni1Oe73KxB/y5S0/e8kwRiAnrJk48IpL3+kLetxV+/Vog4UrcuPprU7xtHFqAHK/QUY+FNaVGNJarP/vsushMheflly5VnpZ853Gmoy+3AzKpPoQhLzv2HaBr0H0LK+hlKAtGBXr70K1l5RqyqcO2dnJ1QuUoVRucvYyMZegEsK13hi89RnmWoZe/e2+s+i23cQCgnieYk58ge/GkScrayGq4pxISyZcuhwuefq8j+XdJ6pV6dMPkKsjz2oUOHVTGLJtxnl/32y6xtt560XX8/f9SqU1vt8Uc0Af0E7v/XqFmTW3Q5VHWfeyy7XZTVdSI4A/v27GE1nf2qeEb9BvVIF86tSmEfP/qnKtb5JcGdLn06DOzXH3/99RA/jh6l6umV4Q6ADaOG8t7eD7yRg3yBRhyTr48vDh8+pCrqXiUpqDIr7ZYtVy7evkUa9PEmSt3R2yQQHehX/roSu3b+rrbDtm/fpsA/edo01sObrdxgOfFm967dqgruLwTe79x6+7ZHD4J8C9IzeaZL166sQTec1NsCLLeVjASf7ejdpzdLX2/CmtVr8W2vHqpstbfXA7Rt3w4HWUKrBHn2lSpV5n3DWPCyqEr42c5afVLmOsICC+inkLknST3bWBv/0aOH6vfffluJFi1bInPmLJjBcUp2oABUSEBSSHP+3HlqAvD2/gt3mDTUrfu3mMbyXQ8fPkTXbt0wgaShnnyXg9xefMTPPuMEIOMrV74CsmTLio5t2ymegpTU3uS5CePJN5Dqu/HRNOjjQ4q6jxhJ4I0lsEmh7UaKa3UWkhTrKTRcKX5Zr349/MkSWUJ3leOtlvIwDNlH388adVLjTog6m1mxVg6qaNq8mQJQn/791FhUTTzWtfPmPv9jVsUdNXq0ytTzZVmtIcOGqsKVAQS0K5N6Fi9YqA65kDJWI1iLr1btmpx8wpl/AnpJ1HFliesNa9di5KhRzLL7RPH8hVsgrD9J4JFae+LOC23YJXkyPCDxJyXJQSdYM2//vv2YMWumKuR55+4dleDTncBv2aoVjpAf0LR5c8XxP3L4sGL+lSpTWrEL5/GwDykH1qJpM9V/fHH9Nehj9HXVF8WHBN4EeuHISwnseuTae9BinqGbPn3qNEWAOUAQlGBFWyuy9YQ+KyfHbKQrLpNAZ1r3TaTryiRRo1ZN5aZ37hrOj1/LwphCsZXsu1D+681EnEnk1wsBqAeLWC5bupQgBey4jl68aCGB2Fitqb3oustJNgK8SNCT4y95/FIQcyLpwHI2nRB9pOhl9hw54ELPQmrbS5N0XqnNL1Ti1AT9K9KDZWkxaeoUNVZJDGrCmvxSsVdovGd4cEbLb75R2X7neI6feChSwXfJL4sxlfkGksrbrUtXdOzckcSld6sb8F/dadDHx7dZ9xEjCUTn3gulVsDRvWcPWvQltHgHmDHXm8y6NcoiSnrtOv7+Vb26BKlUtnVUZbDXkTEn62epXz+TNedbfNOKVFkX5XLXrlOHR2Edplfgj6HfDcMYWvswRtAHsOz1IlpRE3+XktaS4debHoIza9svmLeAIGyplhQRoJ84YQKBWByreDhG4WJFlTcy5qefVDKOuNy7aMFlKXGS8YNjR4+plFs5uHLY8O+UJ7KOlOI58+epcV7k8Vyt27VBH2b79eTPzt93qvLaQgGW95b6+vlZ0nvyxMmYs2AumYAmtGvTVtF+taWP0ddMX2QkCUQHel/flyqpRjj0Um1WrP9cAtPTcyO2cE0rLDo5sKITlwFiYe3tHdQpNmJxxa3vz9LUUvpajrUSE16seLHwa+nGm0whKv9drHsoy1M3b9FcrfWlaIacgCOn0WzhfeLKl6aFb9u+feRpuTKW5cuWoRwDaXJGnUwA4s5LcY5+AweoBJ1p9ErOEPASVJSS3RlYeGPcmDGcVMLotmdSSwypdS9LiamTpqhc/v3796pli88LX3o1U9RSQ4KKMnn58rNlfGb/gf0RyonpJy4p6jdoqN4/Ppq29PEhRd1HjCQQHeiPHTumDpDMmjWbCujduH5DpbtKOay7XAf7+QWwWEYOBtus1dpZmmztye/yE0GtvcNIvbjEkjkn7b/Xvn5fxO/yv5xeI/1JRt1/2+tbbxITkDJaWXhCTkRev9THl4o8KVOmjMwLEDf/Iav3yCEYYfy7TAiSiy/ZgFLcQ94rYmtSOP9ezA/Iyj4j8vlff+br7xAjQb/lIg36+JCi7iNGEgjjmjyQATB7rsut06b51z1yKORmrnkl8v6KBSykAo7UlNct/iWgQR//MtU9vkECYTy1JoDrVgdmrVlHUWxSAnVymo3suSf0gRAfsxI16D9m7Rrk3cJIMgnitpXp3HmYblyFLc9pt2Nuuh2r2FrFkdpqkFc0q2Fo0JuVusxvsMGnziJo6xZYk2RincsdNq5pEcrSVaYTPPMtwAcOrDVnm6+w+b2YGY9Ygz62ylNBJGZNxHPOdZhJkjH+6Tu2Y3oP18u+ePg7ypjCA2exa1YE9in4eW6BUx0P2BUqwttf48OHhcB07CCCft8H+6/q0frLFhllEG2TATF4p/7FswJi93JmdXW41P6/adDHUn3rvI5izo09cLIJr6X+ri2UX1t7G1t0yVkV5ZPlRMCsCTBdvgYrbkUlbCPAyV23LVcGDvWakW3iCbzYwiHFIhnEhqD0dYTf9KuwI8fF3iMHK1Ga1Faa+pFvIOvIw8EaQdtYS/5CEji340GS9iFvwP0/E0+KOkCyKvhl1y3sOHEfTg62Gvpv+bJIYp+Tgw0618oF94zhGX0a9LFE2LRr29DvzHIksXWM5Z3/vtzETLBEnDimFGmFummKwf+HLgg5e4lAcIpTv3G+WbbAgoNgV9EDjl1IaX24EHi0jCCNxfs68xTYHQEwnXKAczeWlLbx40QSxciIe5PJCS9nPYB95URwduczgqO6kKAPZZnqNJyE0rbAhOUXsHjLJaRM7Pi67xDnV/8YOwghL8GWk+uoTsVR3D28zJgGfSw1ffLRFey6f5RWOm6WXvZe7a3sUCVLSWRPnAHBOxYj7IkXDWrCH6AQFhgM68wMtlWgZX15nJb+6D8OYkxcal7jZIOAxYd5jwscm+YjsgPevEIgP95/zklYu7rAoQ4LTfq//I8z+o+COEkiGZcISUpg2x93ceTkA+6Tc9aIyZBiqeOP6XIhI4mlr181O7K4hZ/eo0EfSw1feXwLfz48B1txT+PQBPR21nYo5VYQGZ3SwHRwHcJePKRVjNtkEochRaCL1pb56BnzwrYwud7+FwGfszEHvRw5ZeNMIO+DtZsrHGoSyEG09FGGBfihUxIEzvsDVimdubZnSqu/T9Sgl/V+Uu7bO32K/cce4PK1p6w9b/NO0Ya4y8h8eggj6O0pp8/LZED61Ik06N9FdVu9jmHB1d/hbBu3tXcoLZcDJ472uaujeIrcCFz4I0K9rnEBHLd+3+Wd/n0PgcjcdptCZWFfoxXwbAd/dtG9Dz/55e2NoHe2ReDyy/TIk8OpSTq69oFvAL0VQjnHvZx5iYE8FzhXcOOEw7V/VC2M7n2KyrT25bFy203sO3KPFFht6d+mD7H0zo52aFX3U+TMotf0b5NXlH+ffXMnBpxbgSR2cVt7hwjorWwwvmBz1E1bFK+Gdobp9ClYOcZi7fxOb/CWm/glQUgQHGrUhkNHHu7waDHX9YsIepmMYuJL836uUEIu2yDQ8zmc2ibiXjzvjQrMBG3w3TD4rXoJp6a2sE8lN0Y1Plp5CQDKmj5NC4xbcRFLd1yBE7/MMRnR+xCTufQZQv5/4kR2GN2+KIrnTqUt/bso7s7Lv3HJ14sVT+JWjkk2nWwJeneXjHC1T8bI/UWEPXxMcMWt33d5p/+x9FImmskhNswiQ+A9utw3/rkkhhCTy7gzEbh0H8HuDYeWDTkRENDkxIdH73mBIydNHl7xasEG2GZ05yTDtX+oRPijNPP8UJYCWRjozIrLXj649/gVl1gJLau4S/t99yDitrWxQr4sLkieKDxepNf071vqFtx/GMs+BSxfgbAgE9f2tWGTLUOkNAJ4UEXIti2w4Qm09g1Jy2WxCN0+jAQ06D+MnC32KWFMnhH6beDGzdyHt6iaut8AAABySURBVIZ1EgeE+gQg+PkzOH5WEo61uEPgqAH/Ib8gGvQfUtoW/KyQv/9GKCvKwO8RkCglbHPlY8Q+/Jx53T6sBDToP6y89dO0BBJcAhr0Ca4CPQAtgQ8rAQ36Dytv/TQtgQSXgAZ9gqtAD0BL4MNK4P8A3KqcCRkKwm0AAAAASUVORK5CYII=)" - ], - "metadata": { - "id": "BoOWxsamkRHR" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "Wc6rm1agmoCb" + }, "source": [ "## Exercice 1 : Transformée de Burrows-Wheeler\n", "\n", @@ -60,24 +47,44 @@ "3. Générer un tableau d’indice qui contient l’ordre de chaque permutation.\n", "4. Trier le tableau d’indices et perm par ordre lexicographique de perm.\n", "5. Retourner la séquence contenant le dernier caractère de chaque permutation dans l’ordre lexicographique ainsi que le tableau d’indices.\n" - ], - "metadata": { - "id": "Wc6rm1agmoCb" - } + ] }, { "cell_type": "code", - "source": [ - "print(\"votre fonction ici !!\")" - ], + "execution_count": 2, "metadata": { "id": "D5zJsnmQnajj" }, - "execution_count": null, - "outputs": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('nnb$aa', [5, 3, 1, 0, 4, 2])\n" + ] + } + ], + "source": [ + "def BWT(s):\n", + " s = s + '$'\n", + " perm = []\n", + " for i in range(len(s)):\n", + " perm.append((s[i:]+s[:i], i))\n", + " perm.sort(key=lambda x: x[0])\n", + " perm = [p for p in perm if p[1] != len(s)-1]\n", + " bwt = ''.join(p[0][-1] for p in perm)\n", + " sorted_indices = [p[1] for p in perm]\n", + " return bwt, sorted_indices\n", + "\n", + "print(BWT(\"banana\"))\n", + "# ('annb$aa', [5, 3, 1, 0, 4, 2])\n" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "bG6dMzI1ndK-" + }, "source": [ "### Exemple\n", "\n", @@ -109,13 +116,13 @@ "\\end{array}\n", "$$\n", "* La transformée est la dernière colonne de ce tableau (le dernier caractère pour chaque ligne) : n$iche\n" - ], - "metadata": { - "id": "bG6dMzI1ndK-" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "JgSWho3GorwJ" + }, "source": [ "## Exercice 2 : Transformée inverse\n", "\n", @@ -127,24 +134,47 @@ "Le résultat est la séquence de table qui se termine par le caractère $. Notez que c’est pour ça qu’on a besoin d’ajouter ce caractère au moment de la transformée.\n", "\n", "Ecrivez la fonction iBWT(t) -> str qui retourne la transformée inverse de la transformée BW t.\n" - ], - "metadata": { - "id": "JgSWho3GorwJ" - } + ] }, { "cell_type": "code", - "source": [ - "print(\"Votre code ici !\")" - ], + "execution_count": 3, "metadata": { "id": "oH-bAVbgpOUe" }, - "execution_count": null, - "outputs": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "banana$\n" + ] + } + ], + "source": [ + "def iBWT(t):\n", + " n = len(t)\n", + " table = [\"\"] * n\n", + " \n", + " for _ in range(n):\n", + " for i in range(n):\n", + " table[i] = t[i] + table[i]\n", + " \n", + " table.sort()\n", + " \n", + " for seq in table:\n", + " if seq.endswith('$'):\n", + " return seq \n", + "\n", + "print (iBWT(\"annb$aa\"))\n", + "# 'banana$'" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "MMRvEACHpVDs" + }, "source": [ "### Exemple :\n", "Transformée inverse de n$iche :\n", @@ -160,13 +190,13 @@ "e & n & en & n$ & en$ & n$c & en$c & n$ch & en$ch & n$chi & en$chi\\\\\n", "\\end{array}\n", "$$\n" - ], - "metadata": { - "id": "MMRvEACHpVDs" - } + ] }, { "cell_type": "markdown", + "metadata": { + "id": "ZveHazSWsOJ_" + }, "source": [ "### Exercice 3 : Recherche d’un mot dans la transformée\n", "\n", @@ -177,24 +207,73 @@ "Ou P est la séquence recherchée de taille p, C est un tableau qui donne pour chaque caractère le nombre d’occurrences dans la transformée de caractères qui lui sont lexicographiquement inferieurs et Occ(c,k) est une fonction qui retourne le nombre d’occurrences du caractère c dans la transformée jusqu’à la position k (non-inclue). Les indices sp et ep indiquent le début et la fin respectivement des lignes contenant un fragment de P dans la table. Si P est présente dans la transformée, alors elle sera présente dans toutes les séquences entre les indices sp et ep. Le tableau C peut être précalculé. Attention dans l’algorithme ci-dessus les indices commencent à 1.\n", "\n", "Ecrivez la fonction exactmatch(p, bwt) -> (int, int) qui retourne la paire d'indices (sp, ep) (avec sp < ep) de toutes les séquences dans la transformée bwt qui contiennent la sous-séquence s." - ], - "metadata": { - "id": "ZveHazSWsOJ_" - } + ] }, { "cell_type": "code", - "source": [ - "print(\"Votre code ici !\")" - ], + "execution_count": 5, "metadata": { "id": "C63yJjfjtUSQ" }, - "execution_count": null, - "outputs": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 4)\n" + ] + } + ], + "source": [ + "def exactmatch(p, bwt):\n", + " C = compute_C(bwt)\n", + " \n", + " m = len(bwt)\n", + " sp = 1\n", + " ep = m\n", + " \n", + " for i in range(len(p) - 1, -1, -1):\n", + " c = p[i]\n", + " \n", + " if c not in C:\n", + " return (1, 0)\n", + " \n", + " sp = C[c] + Occ(bwt, c, sp - 1)\n", + " ep = C[c] + Occ(bwt, c, ep)\n", + " \n", + " if sp > ep:\n", + " return (1, 0)\n", + " \n", + " return (sp, ep)\n", + "\n", + "def compute_C(bwt):\n", + " char_count = {}\n", + " for char in bwt:\n", + " char_count[char] = char_count.get(char, 0) + 1\n", + " \n", + " sorted_chars = sorted(char_count.keys())\n", + " \n", + " C = {}\n", + " current_sum = 0\n", + " for char in sorted_chars:\n", + " C[char] = current_sum\n", + " current_sum += char_count[char]\n", + " \n", + " return C\n", + "\n", + "def Occ(bwt, c, k):\n", + " k_adjusted = max(0, k)\n", + " return bwt[:k_adjusted].count(c)\n", + "\n", + "print(exactmatch(\"ana\", \"annb$aa\"))\n", + "# (2, 4)" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "uRHVKu1s9n1l" + }, "source": [ "## Exercice 4: retrouver les indices dans la séquence d'origine\n", "\n", @@ -209,48 +288,70 @@ "Notez que le modulo apparait car on peut se retrouver à dépasser la taille de la transformée initiale dans la recherche de l’indice le plus proche.\n", "\n", "Ecrivez la fonction index_from_match(k, idxs) -> int qui retourne l'indice dans la séquence d'origine à partir de k l'indice dans la transformée et idxs le tableau des correspondances des positions." - ], - "metadata": { - "id": "uRHVKu1s9n1l" - } + ] }, { "cell_type": "code", - "source": [ - "print(\"Votre code ici !\")" - ], + "execution_count": 6, "metadata": { "id": "3RzUM2FJ91u_" }, - "execution_count": null, - "outputs": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "def index_from_match(k, idxs):\n", + " n = len(idxs)\n", + " return stepleft(k, idxs, n)\n", + "\n", + "def stepleft(k, idxs, n, count=0):\n", + " if count >= n:\n", + " return -1\n", + " \n", + " if k in idxs:\n", + " return idxs.index(k)\n", + " \n", + " return stepleft((k - 1) % n, idxs, n, count + 1)\n", + "\n", + "print(index_from_match(2, [0, 1, 2, 3, 4, 5]))\n", + "# 2" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "PtBlbmeNtaA2" + }, "source": [ "## Exercice 5 : Transformée progressive\n", "\n", "Le calcul de la transformée BW a un énorme coût en mémoire (il faut déterminer l’ordre des préfixes pour chaque position) ce qui la rend inutilisable pour des génomes. Une version légèrement modifiée permet d’échanger de la consommation mémoire contre du temps de calcul en séparant l’ensemble des suffixes à trier en k sous-ensembles, générer la transformée pour chaque sous-ensemble et les concaténer. Pour générer les sous-ensembles, on va simplement tirer k positions aléatoires le long de la séquence.\n", "\n", "Ecrivez la fonction BWT_prog(s, k) -> str qui retourne la transformée de la séquence s en utilisant k graînes.\n" - ], - "metadata": { - "id": "PtBlbmeNtaA2" - } + ] }, { "cell_type": "code", - "source": [ - "print(\"Votre code ici !\")" - ], + "execution_count": null, "metadata": { "id": "aoWF2thh8zEn" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "print(\"Votre code ici !\")" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "RsY4KN-x83cM" + }, "source": [ "## Exercice 6: reconstruction de génome\n", "\n", @@ -260,10 +361,23 @@ "1. Calculez la transformée BW du génome\n", "2. Alignez chaque read sur le génome\n", "3. Calculez la séquence concessus" - ], - "metadata": { - "id": "RsY4KN-x83cM" - } + ] } - ] -} \ No newline at end of file + ], + "metadata": { + "colab": { + "authorship_tag": "ABX9TyO0SUJI6kaczFcOh8NoKcqb", + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +}