{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Projects\n", "\n", "Wells are one of the fundamental objects in welly.\n", "\n", "Well objects include collections of Curve objects. Multiple Well objects can be stored in a Project.\n", "\n", "On this page, we take a closer look at the `Project` class. It lets us handle groups of wells. It is really just a list of `Well` objects, with a few extra powers.\n", "\n", "First, some preliminaries\u2026"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"text/plain": ["'0.5.1.dev15+gbf10f3b.d20220223'"]}, "execution_count": 1, "metadata": {}, "output_type": "execute_result"}], "source": ["import welly\n", "\n", "welly.__version__"]}, {"cell_type": "markdown", "metadata": {}, "source": ["---\n", "\n", "## Make a project\n", "\n", "We have a few LAS files in a folder; we can load them all at once with standard POSIX file globbing syntax:"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["2it [00:00, 19.00it/s]\n"]}], "source": ["p = welly.read_las(\"../../tests/assets/example_*.las\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we have a project, containing two files:"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><th>Index</th><th>UWI</th><th>Data</th><th>Curves</th></tr><tr><td>0</td><td><strong>3-2-B</strong></td><td>12&nbsp;curves</td><td>SP, SN, ILD, LLS, LLD, MLL, NPHI, RHOZ, CAL1, GRC, DTP, CAL2</td></tr><tr><td>1</td><td><strong>3-2-A</strong></td><td>12&nbsp;curves</td><td>SP, SN, ILD, LLS, LLD, MLL, NPHI, RHOB, CAL1, GR, DT, CAL2</td></tr></table>"], "text/plain": ["Project(2 wells: 3-2-B, 3-2-A)"]}, "execution_count": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["p"]}, {"cell_type": "markdown", "metadata": {}, "source": ["You can pass in a list of files or URLs:"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["0it [00:00, ?it/s]Only engine='normal' can read wrapped files\n", "3it [00:06,  2.09s/it]\n"]}], "source": ["p = welly.read_las(['../../tests/assets/P-129_out.LAS',\n", "                    'https://geocomp.s3.amazonaws.com/data/P-130.LAS',\n", "                    'https://geocomp.s3.amazonaws.com/data/R-39.las',\n", "                   ])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["This project has three wells:"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><th>Index</th><th>UWI</th><th>Data</th><th>Curves</th></tr><tr><td>0</td><td><strong>Long = 63* 45'24.460  W</strong></td><td>24&nbsp;curves</td><td>CALI, HCAL, PEF, DT, DTS, DPHI_SAN, DPHI_LIM, DPHI_DOL, NPHI_SAN, NPHI_LIM, NPHI_DOL, RLA5, RLA3, RLA4, RLA1, RLA2, RXOZ, RXO_HRLT, RT_HRLT, RM_HRLT, DRHO, RHOB, GR, SP</td></tr><tr><td>1</td><td><strong>100/N14A/11E05</strong></td><td>18&nbsp;curves</td><td>CALI, DT, NPHI_SAN, NPHI_LIM, NPHI_DOL, DPHI_LIM, DPHI_SAN, DPHI_DOL, M2R9, M2R6, M2R3, M2R2, M2R1, GR, SP, PEF, DRHO, RHOB</td></tr><tr><td>2</td><td><strong>303N764340060300</strong></td><td>22&nbsp;curves</td><td>BS, CALI, CHR1, CHR2, CHRP, CHRS, DRHO, DT1R, DT2, DT2R, DT4P, DT4S, GR, HD1, HD2, HD3, NPOR, PEF, RHOB, SPR1, TENS, VPVS</td></tr></table>"], "text/plain": ["Project(3 wells: Long = 63* 45'24.460  W, 100/N14A/11E05, 303N764340060300)"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["p"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Typical, the UWIs are a disaster. Let's ignore this for now.\n", "\n", "The `Project` is really just a list-like thing, so you can index into it to get at a single well. Each well is represented by a `welly.Well` object."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Long = 63* 45'24.460  W</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>location</strong></td><td>Lat = 45* 12' 34.237\" N</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td></td></tr><tr><td><strong>longitude</strong></td><td></td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>section</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>township</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>"], "text/plain": ["Well(uwi: 'Long = 63* 45'24.460  W', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["p[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Some of the fields of this LAS file are messed up; see the [Well notebook](Wells.ipynb) for more on how to fix this. "]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Plot curves from several wells\n", "\n", "The DT log is called DT4P in one of the wells. We can deal with this sort of issue with aliases. Let's set up an alias dictionary, then plot the DT log from each well:"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": ["alias = {'Sonic': ['DT', 'DT4P'],\n", "         'Caliper': ['HCAL', 'CALI'],\n", "        }"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"image/png": 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Xp8Xm/Sf39JrWq0BrXYaRnB6liu+pmdjbYPTg762m79QJwBCv7+N5wE1KqW98iG0cRmWO7ea6t2FcUfjTnF/5fXfCqLixwXzYlFJdvcr2/i5XjtkTt3nQtYeKHUHX9DtgwRhotW1VcVVadzXGQbD337dfDWWbkQWu5tJWjGHco7weNoxT+d8xzshSMO61PGauMw7YWUU5483nn2ActcZg1IbaThW1zBoYtx3jHtb9ZrzHYnxpe5jzrwRam897mX/g5+u4jZYYp/fuxw6MH644c/7ZGD/qFowzx3xgXDWxfoeR4GxVzG9daTsa4wzpqCHkMWo4eS/7O8b1+0Rz/vEYFRsGmNv9J8YlF/f6CzD6s4sCzsDHWpRe63+Ican3ea9pt5rTfvPj5/sYXrUoMRLob16vYzGuGrg/793AiV7zH8GscYbZk7zXvAr7L8aZ+rd41aL0mmejUs1F4HSMexN7zcdhjHsjezFqnV1ornPAa5kSYKnX/pANJFTxvmuq3XdULOb02XjVpK00r8a/WxXLv0odvyd++rxbY1T6ijP/hhMx7uOfivEbdBjj1kcUxkHJAnM9C8YBYwuMpDUM48f7BnN+pvk3s5mvrRj39w5W/jsDr2NULqkcW7XfKYwzYe/v4yfm/GRz/kVAe/N5B4xL+F+ar2MqrfssRnJ110rujXEZcrT5uX1AxVqUH2NcKo3F+P3z1KLEqFB0CONKghXjtyqHIzU6nzJjaYHxG72HI7UoTwQGmuslYJzl7waizPnXYFSsaotxQLCao2tR3oiRjK/Hh1qUgdyxtnKkxpL78Zi5I71ovvE95nP3GxxHzQmuFUZtsDyMG+P/wLj/4e/Ye2OcPRRiVFU9w2ve2xg/uoVmbM+442/g38q7mcBcc6fKw7jPcX416401/65FVKyOPbqa5TVezQSAH4F7qll2NpV+3DAu6+wyd/DvgAyveZnmOsUYN6Hr1EQE48dEY1bDNqcNM6c96YfP1Gbue09i3EtxH3BV+yNnrlfTF/Zh4AGvZSvsvxhf4myMH73LvKZHYfx4aIwar+79P5KKP0w3AguBNHP+NOCtSssMxqjd1xejlufMSu/bbm7vQ458/6y1xVLdPuA1r7a/m08HaYF+mHH+inHAlYdRu/f/vOaPx0hMxeb7zTSnWzDOzHMwvlMbMJoBuav4Z+KV4Mxp55rTHqr0980FTqgmvmq/U5WWe4eKzQQex7hqU2j++zrQspp1H8KrmYA57UKME4RC4BvMxGnOS8Y4aC40l7mw0rqjzb9jAUbt2dFe87yb1ezDq1kNxkH8OnO9Axi/5f285iuMWqk55uNp99/bnD8QWGJ+Vn/i1Tyh2s8/2Ducn3fefwDvhjoOeTT+h/klr3zA9ZA5r8ofOXNeTV/YxcAQr9fjOPoA7Q5zW5d5Tasch64m5sswr1BgHNU6gL5VLDcV4yj9WeC2SvPeqWJ7PsVC1e3glnjNr+nv5tNBmjzkEchHk+ps2ayCHIFx9DAU44t9pTaqfwsRNEopd03FtrqRfImUUmuAs7VRw1iIsGerfZFGJR7j2nAbjKPp5zBOr4UItkTg1kaU3CIw7vNIchPC1KTO4IQQQghfyWgCQgghmiVJcEIIIZqlRn0PLiUlRWdmZja4HKfT6EXJarU2uKzmKtB/oyVLlmRrrVsFouyUlBSdkWG0+Q/Xz7i57OOB3k/88XtSnebyGdSksbxHX/eTRp3gMjMzWbx4cYPLycszulZLSDhquCRhCvTfSCm1rfal6iczM5OZM2cC4fsZN5d9PND7iT9+T6rTXD6DmjSW9+jrfiKXKIUQQjRLkuCEEEI0S5LghBBCNEuS4IQQQjRLkuCEEEI0S5LghBBCNEuNupmAEE2B06V54ZcNzN5wAKdL43Rpyp0u0hOjmdg7FZcGl9a4zNE73M9dWqM1xERYufiYDtitcrwphD9JghNhTWvN/M0Hcbg0UXbrUfO05zloNC4XXPzWQp/K3nygkHmbsn1a1uHU/N+YTnUJXQhRC0lwIqx9tmQnd3y+IqDbGN+zNe1axKAUWJTCokAp5XkeH2Xn0pGZAY1BiHAkCU6EtVP6tWHZjly2HyyipNzJzkPF7M0r8es2flm7v8LrMd1a8d7lw/y6DSHE0STBiWZp9NMz2ZFTXO18pbyeV5pntRyZ4nT5fzipsd0C0tWiEKISSXCi2dl7uKTG5AbGPTXP85pm1uCSER248YSuRNqtRFgt2K0KpSqnSyFEqEiCE81OWmIUn149gq0HC0mIshMdYcVl1m50eP514dIah9N47dTm9Cper9lzmGmr9x21nffmb+O9+Uf6fFUKIqwWIm0WImxWIm0WoiOsxERYibYb/8ZE2CpMi7BZsFvdD0WkzYLVYmHV7sMMyEji1P5tjqr8IoTwjSQ40SwN65jMsI7Jfitv5c7DzNl4gEibhYRoOzaLoszhotThMv91el4feTgpKXdSVGY8sgvKKCororjMSVG5k+IyJ2VOV7UnjB8u3M4dn69gbLdWXhVTABRKGZdW3SeMjnIHKLDb7FR3EunSmmi7lYdO7U1STITf/jZCNFaS4ITwQd92ifRtl1jn9bQ+cuboMp+7XODU5lmkC0rKnYx7dna1Zfy64UADIj9aZkosN43v5tcyhWiMJMEJ4SfX/e9Pfli5J+jbffLMvkTgACA6OrrKZbQGi4IIm4Vx3VsHMzwhQkYSnBB+sHr34aAmt7tO6kGLGDs90xPo1y6p0QxEKURjIglOCGDFzlxO/fdvoQ7DZ0/9uK7G+V1bx/HzLWODFI0QjZMkOBHWflq1hxs+XkaZwxXqUPyqb9u63y8UormRBCfC2r9nbWpyye3nm8fQNTW+wjS5RCnE0STBibD23fWjKHUYVfVd2mj/pl1Hnrt7/D9YUMYl/11EdkFpUOOLsFmYftMYMlNig7pdIZoDSXAirCmlfGpIvfNQcUCT2/lDM7h9YndsVguJ0faAbUeIcCIDUAnhg8EdWrD5icl8ctUxASn/4z92MPixX+j/8HRmrdtf+wpCiFrJGZwIW7tziznlpXkcLCwLdSgAJMXY6ZYaz6AOLUIdihDNgiQ4EbY27S8IWHJLiYvklhO7YbMqbBaF1XwYzy3YLEZ3WzaLBatFMbB9kvQ5KYSfSYITYWtMt1ZsfWqKpzstpza60XJ3oeXucHnJthwe+W4Nuw/7Pk5cdkEp93y1skHxbX1qSoPWFyLcSYITYU8pZZxpeaZUPJMa2L4FVmtwh8G5dESHoG5PiOZIEpwQNfhiyU5u/Wx50LZ35aiO3Hdyr6BtT4jmTBKcEJX8uuEAD3+7mqzswqBv+815W3hz3pZal1v50ATio6Q5gRA1kQQnhJcN+/K59L+LQh1GjZJi7Ngs0sJHiNpIghPCS9fWcTx3Tn9W787DblVoIDHaTmyElTKni3KnpszhotzpwqXhtV83By222Agrqx+ZFLTtCdHUSYITwotSirMGt+Oswb4tf9dJPdiXV8I/f97A9DX7yAlAs4P0xChOG9CWc4a083vZQjRnkuCEqMbvm7OZtmqv0W7NbM9ms1rMfxV28zLhW/O2sDfP9yYEddE9NZ63LhtCuxYxASlfiOZMEpwQ1bjwjYUB38ZdJ/XgmrGdA74dIcKRJDghqvHTTaOZv/kgTpempNzJoaJy1uzOY37WwQaX/fpfBjOhd5ofohRCVEcSnBDV6JGWQI80Y3y1nvf/RHG5029l55c4/FaWEKJqkuCEqMaNHy/lm2W7/VLW0MwWvH/FcM99PCFE4EmCE6IK5U6X35IbwB9bD9Hj/p+qnPfTTaM9Z4pCCP+RQ0khqvCvXzYEbVsLs3KCti0hwomcwQlRhavGdCansIzconJ+XLW3wjyrReF0ab9t68FvV/Pgt6uPmv7C+QPIbBnrea3M/p4ViugIC11ax/stBiGaI0lwIizlFJahtUYphUtrXOZwOU6X9gyVc8WoTri05oYTurJsRy53f2kMf+PP5FaTGz9eVuP8F84fwGkD2gYlFiGaIklwImzsyCli9NOzQrb92Agr3dLiUYA7RWp95Ll7gve8ytxz0xOjOa5H68AFK0QzIAlOhI1ypyuk2y8sc7J0e26Faf86bwDH92yNRSksCizKGOnbohSKI6+VCu54dEI0B5LgRNjo1Cqu2lGy9+eVMOyJGUGOCG76ZFmty5w5sC3Pnzcg4LEI0dxIghMCaJ0QxZpHJrJ0ey4vzdxISbmLZTtyQx0WAGcNlk6WhagPSXBCmGIibNz5xQp2HioOdSgeC+85gdSEqFCHIUSTJO3ghPDy9XXH8vCpvUMdhhDCD+QMTggvQx77JeDbuO64zpwxsC1gtKcrc7goc7pIS4yibVJ0wLcvRLiQBCeElyn90vlhxZ6AbuPlWZt5eVbVI4H/ef+JJMdGBHT7QoQLSXBCeHn5wkHcPqGQi99aGJJ7cYMe/bnG+W9eMoTxvVKDFI0QTZskOCG8HCosY9yzs0MdRrUKy2SYHSF8JQlOCC9JMXZun9idZ6at92u57ZNjapx/XPdWnDHoSHOAhCgbnVrF+TUGIcKNJDghvCilmLZ6b+0L1tH2nKIa5787fxvvzt921PTUhMgKXXZpvLvwOjLDZfaPabG4ezypvucT705R7j+5F6f2b1NjbEI0VZLghPCitWZHLckomPbllQa0/Hu/WikJTjRbkuCE8KKU4qULBnHxWwtDHUpQ5Jc4yLzrh2rnf3r1CIZ1TA5iREL4jzT0FsLLnsPFYZPcfBEbaQ11CELUm5zBCeElPTG8G1oP6dCCz68dGeowhPALOYMTopKpN4wOdQghc/pAGUBVNB9yBidEJYu35YQ6BL87vkdrXr14EJE2ueQowockOCEqKS0P7cCo/jTj1rF0lvZ0IkxJgguBHTlFHCgopW/bROxWuUrc2Dw+dW2oQ6jSaxcPJtLutb94tY8rKjaaNsTGGA3KlVL0bpNASlxkMEMUolGRBBcAWms++WMHOw4VoVD8uuEAK3cdrnLZ6kaYFqHzwvkDuPHjZSHb/pWjOnLpyEysFoVFKawWRVKMvcaDoby8PAASEhKCFaYQjZ4kOD969/etPDd9PXklvvUXeOWojgGOSNTHaQPactqAtjW2D6sri4JuqfGAcXbl7kzE6JnEOBXTGlxaM3vDAWat32+coJnT3Mu6tEbrI72ZOF0al9bcO7Ezx3Vr6bd4hWgOJMH50YPfrq5x/qdXj6BbahxJMTIcSlPwxBl9ueerlX4py6Vh3d58v5RVldu+Wst31wwh32UHjI66lDISoa5mHe3VB5hSqsJrt7SEKGxyGV00UWGd4ErKnfS4/6egbe/c/8znb+M6c8ekHkHbpqi/C4e3x2ZV3PH5ilCHUqtyp2bSy3/4vdxRXVL44Mrhfi9XiGAI6wSX7+OlRH96ZfZmXpldcbDL1/8ymAm904Iei6jduUMyGNO1FeVOF0odubxoUcrrDEl7zpS01jicmmmr91JQ6uClmZtC/RZ81iYxCqUqdtL89+O7hCgaIRourBNcq/hI1j06iVs/Xc6cjQfMexsap/kj5dIaV3XXdyoZ3TUFq0XhdBk/dvM2Zfscx2dLdkqCa2TW7snjlJfm4fB1B2gkurSOIyUuwthvvZKvS2vzHh7gea5RKLqnxXP+0AxPwm7XIoa0xKjQvhEh/CCsExxAlN3KyxcN8nu5b87N4rEffKtuPjSzhd+3Lxpm8bZDTS65AWzaX8Cm/XVbZ+Wuw3y+ZGeFaQ+c3IuYiKMbhbtP8I6csZqv0RWTp8v496zB7UiIstf1bQjhF2Gf4Pxtf34JF7y+gM0HCquc365FNEkxdlwu4wciPtLGKTJcSaPzl2M6cNGw9pQ5Xbi0Nmoruo6cCWnz7N5zeVJXnFfVNJdZLdJlzvtq6S4WbTmIS0NhqYON+wtC+p69PfL9Gr+UM2fDAd7+6zC/lCVEXUmC8xOnS3P1+4v5ZW31h8+3nNiNG07oGsSoRH1prTlQUIrVonC5jMvWLm2emZiJy30J231Z25MAq5jv0piXr43new4X89qvm2sPJETOGdwOi3m6pr3qYbrP2JQChTLvS7qnVbw/aVGKv43rHOTIhThCEpxpR04R13ywhFLHkW6aisucRNotxEYYfyZ3myOn+YOnzR+tvJJycovKayx/St90ThsgZ2pNxVM/ruM/c7JCHUbIPHVWP6yW6kcFF6IpaLYJbs3uPCa/ODfUYXj8sHIPP6zcIz2XNBFnDGrbJBPcX47pwD2Te6IUWC0Vz6gq15AUorlrtgkuyh6axqmRNgvje6aCcje2NX5UFHCGDEXS6O05XMyIJ2eGOox6uXBIG+6e3IPoKiqHCBGOmm2CS4mP5OzB7di4v4DlO3KDtt0HTunFRcM7BG17wn82HyjghOd+DVj595/cC5tFYbEobObD6vWwmX1P2qwKq8VS4bXFvKfl7pvSohQWizEtymalsLAApeBQ0ZHL5e77ZHB0W72UuEii7JIIRfPWbBPce79vParqcyA9cUZfLhzePmjbE/5nC/A9p0f9VDPRX+RyuWjumm2C+9u4LnRPS8DhdFFWWsLi7Yd5f9GugG3vnq9W8tB3q7GZjb29K6t4+/X2cXRoGRuwOET9dWgZy8qHJtD3oemhDsVvUhMi0bpSrUeQ2rwiLDTbBGexKE7slQoYQ4mM65rM2B7p5BSWVd07O0f36l7tdHM9p9OoTfmvXzYCUOZwUVZDTDaLIi6y2f7Jm4WvlwbuICgU9uWVep6P7ppC37aJ3HBCV7k8KcJC2PzaKqUYbyY8f/t980EWbcmpcRm5HNQ0XHxMB/JKHDwzbX2oQ/G7uRuzmbsxm/TEKP4yIjPU4QgRcGGT4ALp06tHAEa7uZ4PVBydoEvrOL64dmQowhL1oJTizEFtm0WCe+z0PhVeKwVxkTZO6SftMUV4kATnR1VVz960v4C84nISo6U/vqZiyovzQh2CX9z39aoqp7tHK3/t4sFM6iOdfIvmSxKcn313/ShO+XfFH8jRT8/i7b8O9VT77p+RRIzdikV6imhU3p+/lfu/qXnQ2ubkmg+WMLB9kuf1mYPacfHw9tIgXDQbkuD8rG+7RP5+fJejxgH769tVD0bZpXUcx3VvRblTU+504TD/LXO6mNw3ncl904MRtgBiw7AC0NLtuRWeXzSsPZLfRHMRft/oIOjSOo7TBrSh3Omi3Kn5ec2+apc1hjepuhf571fsYcuTk+WIOkjOHNSOMwe146dVe7nmgyWhDqeCq8d04rgerT2v3XuEUgqtNYVFRSggNjb2qARV1d5z9C6lyEiOlqsKolmRBOdnu3OLPfc4qnLtuM5E2ixE2qxYFDhc2tOjRaTNgs1qwW61YLcqBrVvIcktBBpbckuKsdOpVSy7c4uBisnJ3VNJcUkpCoh2WMxllKd5i9uxXVJIiYsMSsxCNAaS4PxgX14J13ywpMLlHm/piVHMvn0ckTZpe9TYrd+bH+oQjpJbVM6dX6z0a5lSwUSEg9D0SNyMbDtYyPAnZlSb3ADm332CJLcmYNrqvUz815xQhxEUv6yt/rK5EM2FnME1kN1a+zFC5l0/AJAYbWf5gxMCHZKop2M6tgx1CPUypEMLxnROQimIjorydNxstViwWsBqsTC2WytaxcvlSRFeJME1UIuYCJ+XHdm5af6Ahot5m7JDHUK9dE2N47Jj2gGQkJAQ4miEaDwkwTVQlN3C7RO7V9vzRedWsXRpHUdGixhundA9yNGJujihZ2vOH5rBx3/sCNg2LApsFosxJI4yhs4xhr8BzM6QKztSV+RIn6g3je/KwPYt6JoaR6TNSl5eXsBiFqKpkgTXQEoprjuuC1eO7si9X606aoiezQcK2XygEIA3522RPikbsSi7lafO6se5QzO4+v0lHMgvrX2lOnJpKHO6KHM2rBx3g/SB7ZP46m/H+iEyIZofSXANNGPtPq75YIln5IGa/PXYzGCEJBpoUPsWJEbbA5Lg/KVtUjQJ0Xbum9Iz1KEI0WhJgvOB1hqnS+NwaVza/Nd8/fjUtZQ7a0tthrd/28rbv20lLtLG4vvGy5Aljdi1Yztz62fLG1zOl38bicvlNTyTS5tDMJnDMCmIj7TTt11ig7clhKhIElwNXvhlI//8ZYPfy23XIlq6Q2rk+mf4J+Fc8PoClAKLMppkK3Vk0FGljHtvSilyCmsaSbCiE3q0NmpKWo2+TW0WhcvpwGpRREdFYlXGfT33Y1KfNDq3iquwXeNfozzlde+v8sCoVFpeOh4QTYkkOIwj6eJyJ7lF5RwuLie3qJz/LdzG9yv2+KX8k/qk0Ss9gf8b00nO2pqILq3jPfdLyxwuPly4jYJSBy5tnH2VO11YFCRE2yl1uChzuDyD5LoHxXWfpbkHzXWvq9EV5rm05ocVezhUVO5TbDPW7a/Te3l9Tlbd3nw9/XTTaHqkSS1O0XiEXYJ7b/5WvvhzF6XlTtaZvVbYrcrny4xXjenkOWq2eNobmQ+lyC4sJaegjBKHi+IyB6kJUdw3pVeVQ+mIpiHCZuGyYzsGdBuPnd63wuudh4o45aV5Pie9xkBV2eulEKETVglOa80DlYZDGdKhBUMyk0mKsZMUbScx2k5ijJ2k6AgSY+wkRNmIjbBJJ7QiqFbtOtxkklvbpGgArni34ogZ3pdA3a/dl0htFsXrlwyhY0psMEMVYSasEtxXS3dVeC1V9kVjUOZw8fgPa1i+8zBlDhdr9lTdpm1Ep5b0TE846hKn1lBSatT4tNntnsukYDRLcLk0TrOi1Jo9eWSZzVb8ZZfZCXRdfbhwG/dO6eXXWITwFjYJzuHS3PLpkVpxY7q14v35W80b/QqrBYZ3bEmmHFGGNadL8+uG/Tic2lOhwvvc3aU1V70fmtEG5mcdZH7WwTqtE2mzGBVc3BVdGsGFiKk3jCbSbqGTfNdEgIVNgssvcVR4PWfDAeZsOHDUcvdO7kmk3eLpbcJmUcYQNua/NrP2ms1iDGljtSjsVguHisoY3rElETbpv7ope276el6ZvTnUYdRL5ZqOd5/UgytHdwptUEKEUNgkuBYxdr7/+yhOfmlejcs9PnWtX7drUdAzPYG3/zqU1vFRfi1b+N+Vozux7WARP6z0Tw3a+mifHENGcnSl6vteVfWpXNVf4XQ6UIDdbvOs9+f2Q1z3vz/BXEdj/E+jOblfGxktXjR7YZPgAPq0Tazyvpu70bbDZYzA7XC6cLg05U4XDueReQ6nMa3M4eKV2ZtZuv0QeZXODI8qW8Pq3Xls2FsgCa4JSI6N4OWLBjF+6U5u/qThDb3rY3tOEdtzigK6jakr98o9aNHshVWCq47FooiwKCJ8HB5v0/4Cfq3i8mZ1Ztw6lpRYGaqkKfFlGKSm4sULBqK1rnDW16eN9Jwimj9JcPXQpXUcP9wwiqwDhZQ7XeZD8/Ef21m16+gacCc89ysgDWGbiiXbcrj+w6VB2Vb/jCQyWhjV7N0JyHh+5FIkmBVdzJnlTu255Gg0LteUlZWjNVhsNk+jcpfWnDskQy5FirAlCa6eerdJpLd5FHyosIw35mZVmdy87TpULAmukSssdbBubz490uLZkl1IlN3K4eLAtUdbviOX5TtyG1zOO3/px6CMRBkPTggvkuDqadP+fL5fsQet4YUZG31aZ+Wuw5zQMzXAkYmGOOWleWRlH2knVupwhTAa3132/gpizd5y3H3yuLsFi7Ba+OLakXRNjQ9dgEKEgCS4ehr//Byfl/33hQOZ0jddOqptAv5xdj/OeW1+qMOol8JqBpkrKXfx06q9kuBE2GlQglNKbQXyASfg0FoPUUolA58AmcBW4Fyt9SFl/Lq/AEwGioDLtNZ/NmT7oTTtpjH8sGI3mw4UMHXl3hqXvf7DpVzPUmbcOpbOreKCFKGoj6GZyUHdXlykDbvVaGMZYbWY7diMSi42i7sTAuNf98gD7hHALUpx+sC2nNgrlaKCAiwKEhMTqriXp6QvVBGW/HEGd5zWOtvr9V3ADK31U0qpu8zXdwInAV3Nx3DgVfPfJql7Wjzd07rjcLqYuvJHn9bZl1ciCa4JWPvIJP71ywY+W7KzTsPY1EdBac3NTGqzcEsOFwxrT57L6KorPsruj7CEaBYCcYnyNGCc+fxdYDZGgjsNeE9rrYEFSqkkpVS61jp0LWr9wKIUE3qlMn3NvhqXO3dIOw4WlDFt9V5cLs2Ybq2IjZQrxI3NK7M38fRP60MdhsfTZ/eregw389/+7ZJCFJkQjV9Df2E1MF0ppYH/aK1fB1K9ktZewF2roi2ww2vdnea0ahOc0+kkL6/mmom+8EcZNXn29G4UTe7M8S8upKia+yCfLt7Jp4t3Vph25cgMbhiXGdDYfBXov1Eg+Ws/AViz85BfynEbnJFQoap/uUuzbOfRsY7pkkxitI0l2w+z+3ApvdLiePyUbnRuVVt/jS7y8vKa9OcXLP7cT6oSDp9BU3uPDU1wo7TWu5RSrYGflVLrvGdqrbWZ/HymlLoKuAogIyOjgeEFhsOlKS5zUlTupLjMSYnDhdOl+c8FfXC6NA6nZv3+Qp75peaBJh2uOv1phJdA7SdPnNqd71fVbUDRmizZkceYLsmcO6hiW7ThmUlESr+lAdcUfk9E4DQowWmtd5n/7ldKfQUMA/a5Lz0qpdIB96/FLsB7D2tnTqtc5uvA6wBDhgzR/mzX44+yth8sYswzs+q17rDMZIZ3SmZc91YM7hDcygy+airtqKrbTxoSv9aaxdv8ewYHMGdTDnM25fi93Mrat4hizp0nBHw7TUkgf0+q01S+Qw3RVN5jvROcUioWsGit883nE4BHgG+BS4GnzH+/MVf5FrheKfUxRuWSw03l/tum/fnc8ulyLErhcNW/XdSirTks2prDSzM3kfXEZBlEtZF5Y24WT0xdV/uCjdT2QyVk3vVDlfOePac/Zw9uF+SIhAithpzBpQJfmfcXbMCHWuuflFJ/AJ8qpa4AtgHnmstPxWgisAmjmcBfG7DtoHpp5iZW7DzcoDJun9gdMAa3HNu9lSS3RujEXmlNOsHV5LbPlnPbZ8tZ/9gkIm3SZECEh3onOK11FtC/iukHgaOuk5i1J6+r7/ZCJb+knH15JQ0u57yhGaTESYfLjdkNHwWn/8lQ6dM2Aat0NiDCSNjVU88tKqOk3Ogg2eHSOD1D5GjKzSFxyhwuLn5rYZ3Lvv/kXhzbpSURVgsRNguxETaiI6xE2izSi0kT8O8LB/LijE188efO2hcOoi//NpJB7VvUuIy7dltTuTciRDCEVYI745XfWLo9t0FlKGWMlGy1GD1NtIyLYHKfdLnk2Ax0aBnL42f04dvluyh3Np4arme+8nuF1x1axnDt2M4M65js6XeyZYTGIgdRQlQQFgkuu6CMSS8vosyHH61/ntef9MRoo/skiwWbVRFhtWC3Woi0W0hLiJKzsWYsym5l4+OTK0w7kF9Kfkk5GqMD49yiMq75YAnZBUd6OUmJi8TpcnGoKHAjD7htO1jEXV+urDDtjP6pPDylW8C3LURTEhYJrqTc6VNyA2odxfmm8V05sVeqVx+BZv+Ayuwv0GL0buLuA9At0mahRWxEg96HCJ5Z6/dzsKCMw8XlPPr9mlqXzy4oDUJU1ftq+T6+Wn6kN50vrh3J4A41X9YUorkLiwQX48eOZv/1y0b+9Ytvw+NU9uONo+mZLvdIGqO3f9vCw9/VnsiagoQoG+3MQVSFCGdhkeAayyXFORsOsGhLDif1SaN1QlSowxFe6nvQ0lh8esVA2iREEp+Q4LmqUFjqQAM2iyLKLk0DRPgJiwTXIsbOintG17uGmculcWqN06Vxmf86XZr/LdzOluxCisocFJU5aZ8cQ4TVQnZBKV8v231UOU/+aLSxevDb1Tx3Tn+O69GaZLls2Sgsf3ACYPRm4tJ4PmutYfqavdz48bLQBliLc99qWBOHXukJTL1xtJ+iEaJxCIsE11D5pQ5en7MZrcFljpLschk/hAlRduIibbi0pqDEQaTdSlJM7Unr1s+W0zElllm3jQv8GxA+M+6ngtWrVuxpA9oyvGNLisudnruq456dHZL4AiXSLv1iiuZHEpwPHvluTYPbRsVH2ujYKtbTI0rvNgk8f+4AP0QngiEtseIl5fl3H8+35lm6+8y8KVn50ATPEDzuSlHFZU6UQi5nimZDEpwPThvQpsEJLr/UwbfXj/JTRCKU3pu/lQe+WR3qMBqk70PT67T8bRO6cf3xXQMUjRCBIQnOB+mJvlUI6ZEWT2pCFGkJUThcmlKHkzKHCw2cIx3dNhtZBwpDHULQPTt9gyQ40eRIgqvEu0KJ03yeEhfJ4vvGsyDrINd/WP3N/HV781m3N5/TBrThhfMHBjFqEUwPndqbeyb3RKPZnVvChwu3AfDG3C0NLrtTSiyRditr9wR+YMlx3VsZbTfNS5SJ0Xbun9KLuChbpdHDG0ctZCHqShKc6ar3FjN9zb7aF/TBuUNkYMXm7Lvlu/l7gDpmzsoOztnh1qemBGU7QoRSWCe4A/mlDH38F7+Xe9GbtXfUHB9lY8HdJxAbGdYfQZMxb2N2vTrgboweOa13qEMQIijC+tc1JsJKZssYth4sCvq280scOBpRh76iZhv354c6BL9pFRfJn9sPoYAIm4XebRJDHZIQARHWCS420sbs249j0r/msG5v8H/A+j9SfU225NgI/rz/xCBGI2ry12M7cumITA4VleHUGpcL81/N6KdnhTq8Orn2f39WOf2ykZk8dKqc3YnmI6wTnFukrfE1cpW+BBufffkljHhyZqjDCJgv/9wpCU40K5LggG/q0T7tgtcXMD/rYJXzrh3XmeuO60KkzRgzTmqhNU05hWVc8/4SFm3NCXUoQXHvlJ6hDkEIv5IEV4v8knIKSh1YLQqXCxwuF3/735+eHkmq8urszfz12EziIqVD5cauuMzJzHX7WbEz1+jZQ8GuQ8V8u/zovkSbsvTEKKLsVqwWhc2isFstnudWi6JDyxhO7d821GEK4VeS4GpQUu6sc48Pbt8t38MVozr6OSLhbz0f+CnUIQRMr/QEXr5oEB1TYkMdihAh0awS3OGict76zWhsq4DPl+xkV25x0OOwWxWfL9nJl3/uRCkqNKa1mIOjYvxnDppqDpCqzAFUvcqyWS149fuL1nD12E4M7pAc7LclmphtBwsluYmw1qwS3D1fr+SHFXtCHQblTh3QnijmbcpmzSOTAlZ+OPni2pGc9ervoQ4jIDq1iuNLsw/V1vFRjOzcEotF7geL8NGsEtwTp/ela+s4tAYNoDUaKC0tBSAiIpJ/z9oUyhDrrHOrWG6b0B3veirDOrYMXUDNzOAOLars1WPnoSIibBb2Hi7hoW9X8+f23OAH10Ardx3mlk+Xe14/dnofLj6mQwgjEiK4mlWCS4yxc9P4bkdNz8szzqYSEhK4bWL3OpWZX1LOXV+sZHtOEWUOF/vyS8gtKvd5/fgoGysfmlinbYrQa9ciBjDOfL7827FkF5SSU1h21HJVnQ+5m+9/sGAb783fFrggq9GuRTSDO7QAjsSXEhfJGQOlEokIL80qwQXCT6v28sPK+l/2vLmKhCuanpS4SFLiIqucl3WggFNemkdhmTPIUVUtNsLGc+f0x2ZtfO07hQimZp3gHE4XL87cxIY9uaA1VrsdPCNyG/+6L2dq45qm57VLa7YfLKpz57erH54o/UuGmUVbchpNcgNYvy8f6QROiGae4OZtyubFGRuDus3eD06r8zpTbxhNrzYJAYhG+Mrl0ny5dBeHCstQ6kjtVotZu9Wo+Gpc8DMrwXqmWZTi6bP7oYBnp69nX15pSN/L6K4pXP7OH5zSv42MbCHCWpNLcJl3/RDqEPzuuenr6dUmwTx7rHhWqdGcMziDLq3jQh1ms/bdit3c9tny2hdsAuZuzPb8Gx9pM3vS0ThdxhhwcoVBhIsmt6dfMKw9Hy3aHuow/GrGuv3MWLe/2vlvz9vKhsdPCmJE4WdK33Tu+2oV+aWOUIfiV5U7Vh7VJYUPrhweomiECK4ml+CePLMvT57Zl+0Hi8gvLa942ci8ZKQUXPe/P9m4vyDE0R7RLTWOCb3SPJe3UKrCZS7vy17G7CPv6+S+bUIUdfiwWS10S4tnybZDoQ4lICb2TsWiFHed1CPUoQgRNE0uwbm1bxlT4/xbJ3Tnmg+WVDu/Z3oCEdaKlbzdlUsAnC7QWvttGJ2OKbF1bqIgguuLa0fWOH9/fgkfmNX+3fuKS8OOnCK+bwQdDNRk2mpjtPofV+2tcv6c24+r9TslRFPTZBNcbSb1SfM04PVuB1cXm/YXMP75X+u1/cX3jSfSZiHSZsVulREFmqID+aXszi32nGWf/drvlDpcoQ4rIGT3FM1Rs01w/pDZMoY7JnXn7d+2ciDf95pxJ/dLr7bNlGgaSh1Ohj7+S6jDCKiLhrfn8TP6hjoMIQImbBKcw6WZunIP5U5XhdqKTpf5r9b8ue0Qny3ZWeeyM1vG8M/zBpCeGO05Et6fV2I88Toy9r5f6D3L++zuyLSK61Qop5r1vQ/CK69f1RG69zLlTuPMpNzpqlROxbKVOnpecxRhtXDflJ58s2y3Z18BPP+6L2XX9DdwuTTr9wV/pHiAtknRRJgD+TpcLs4f2p6/jevcrD8zISprdglu+Y5csgtK0dr4EdJAUVERL8zaytacwIwssPVgEWe80jw77K3O1WM7cfdJzXeATKUUV47uxJWjO1W7zNyNB/jXLxvRWrMlu5BDdejCLdAqj6LxzLT1XDu2s1yKFGGlWSW4BVkHOf/1BaEOIyxM7pMe6hAa5HBROdd8sITNBwqIMEdet1ktnsFAbVZjMFD3GbBG43BpnC5NuVOH9Oysrl67eBCjuraSkQRE2GlWCW5whxZcNaYTm/YXeFW5VzidDiyA3W4HwGIxL915quYbP2MWc7w297hsFqX4ZPGOBsd164nd+PsJXRtcTiDVtyJOU7Vhfz7zsw6GOowadWgZ4+kg2X1pVCmjBvDE3mkVlg23z08IXzSrBGe3Wrhn8tGXzer75S9zuBjaMbnBPVw89/MG/vvbFlITohjfM5VbTuwmR9MhNjQzmbWPTCK3uIxyh6bM6aLM4aLc6aLM6aLc4eLCNxeGNMZtB4v41y/VdzUXZbfQOj6K7TlFVc6/aXzXKkfXECJcNKsE54uScifP/7yB1+dkBXW7h4rKOVRUzrq9+UEbk+7Tq0cwrKOM/F2d6Agr0RHR1c5/569DeX1OFg6nRqNZt8e8JOk16rr7QMU9hFJitN2zvktruqfGM7JLCgfyS/3eA09Juava5Abwr182SoITYS2sElxeSTn9Hpoe6jCC5tz/zA/Jdi8bmclDp/YOybb9aVz31ozr3rre62ut+eSPHWzJLmTN7sN+jMw3M24dG/RtCtGYhE2CW7Yzj0vemxvqMMJCi5iIUIfgV021g+8Tnvu1ytHKhQgXYZHgyp0u3l7gW/u2W07sxg2NvEJIIEglhebnxhO6smrXYfq0TQx1KEKERFgkuAveXsaG/dUPXHrZyEy6tI4jwmrhjEFtgxiZCLU1u/M4UHCkl5oKHV6bnWDfNqEbz07fEJoAG+CFGRt5wWs8xBfOH8BpA2T/FuEjLBLc2QPSeGL65mrnXzuuM6kJUUGMSDQGHyzYxn1frwp1GEFz48fLuPHjZVw6ogPXjutCWqLs86J5s4Q6gGDYkVtS7bxVD0+U5Bam+obppbt352/jwjekQwTR/IXFGdxxXZN5f9Guo6af2CuVaLs1BBGJxqB/RpJPlTAOFZYx8NGfgxBR8HRPi0drLX1Timat2Se47QeLuPx/Kz2vXzh/AMd2SZHe/kWNftuUzWqzav/GfY1n4Fx/+XHVXjrePVVqWYpmrdknuJhIKzaLwuEy+jq68eNlAPLFFtWau/EAf3lrUajDCLirx1TfkbQQzUGzT3ApcZH8edcotNb0f3IeAON7poY4KtGY/bH1UKhDCAg5qBPhptknOLcKY67JbQdRgz25gRlWKZhum9CNswdnSE1JEdbCohalW6eU2FCHIJqAswa3a/IHQc9O38CFb0pNSRHewirBZWUbjb1/XrOP7IJSDhWWkVdSTmGpg5JyJ+VOFy7zXp0IT1prBndowdpHJjGqS0qow6m3v43rzHuXDwt1GEKEVNhcoqxsyGO/+LzsxsdPwm4Nq2OBsLF2Tx4nvdD0+ij96P+OYUTnlp7X0tWaEEcLm1/tcqeLFjH22hes5PQBbbA29etVolrpTfQeVbnTFeoQhGj0wuIMLq+4nFH/rHg/wqLg8mM7cs/knjL4aBhzX7YOhZGdW9I+OabK+30XDGtPv3ZJQY9JiOYkLBJceRX31Vwa3py3hTsm9SBCElzYGpiRxOXHduS/v20J+rZ/33yQ3zcfrHLeR4t2sPmJyZ7On6XHESHqLiwSXMvYCF48pxc3fLamwvRbTuxGhC1srtKKKiileOCUXlwwLIMzX/2d/BJHqEPy6HzP1GrnxUZYefuvw+jbNpHoCOluToiqhM2v+7iuLY+a1jNdbsgLw68bDjSq5FabwjIn5/5nPme9+nuoQxGi0QqLMziAaWsOeJ7/87z+nDGwXQijEY3NZSMzeeyHtaEOo1ZKwZAOLTyv753SK4TRCNG4hU2CO1BQ5nl+8yfLKXO4mNQnncToutesFM2PRSkm901j6sq9Ad9Wy9gIEqLtxr01jMukWhv3iY+6W6xhZJeWPHhKb2mqIkQdhU2Cu2hoG57+Jcvz+s4vVnLnFyt585IhjOveCpv8eIQ1i0XxykWD2ZFTxOinZwV0WwcLyzhYWFb7gqas7EI+WLAdgA+uGM6ork23AboQwRQ2v+qzN+ZUOf3K9xbzzPT1QY5GNEYz1u4LeHJrqOU7c0MdghBNRrM7gztcXM5TP64lr8TBjyv3UFPPW5E2CylxkQxq34Il2w5hMatjW5RxyQqMfy0WUCjPfGXOV+Z8oxq3uWyl1+5lLOb1qMrbiJIBVxuNojJnqEOo0k3ju3LT+G6hDkOIJqfZJbiXZ23io0U7fFq21OFiV24xV7+/JMBR1c0TZ/TlwuHtQx1G2Dmlfxv+/tHSUIfB2G6tPAdCMRFWLhuZGeqQhGiSml2Cu21Cd3qlJ2C1KCJtFqLsVpxlJVgUxMTG4nJpNODSGq1Ba+O5SxvTtda4XFRaRuPSoDH/Nae7Kv3rnu8yl5+1bj9rdufhcGnKnS5KHU5KymvvYmnJtkOS4EKkdXwk+/NLA7qNeyf3xKU1XVrHcYKMTShEwDS7BBdhs3D6wLYVpgW6I9o352bVq4r542f0oaDEgdWiGJqZTP+MJP8HJ6o0c90+Ln9ncVC32TYpmqk3jCaxHn2iCiHqrtkluFBIqEdTg+6p8fRvl0Sv9ATpCzMEisuC31nxrtxi+j8yPejbdTtrUDueO7d/yLYvRLBJgvODc4dkcO6QjArT9ueX8OKMjZ7q3ZWt35fPyS/NA6B3mwRev2QIreMjzQop0vdgoE3pl86UflMAOJBfyuQX53IgwJcmQ21/fkmoQxAiqCTBAQ6ni8PF5UYNSfDci3N63WNzuio+d2n4fsVu/vXLxqPKO7V/G9okRRMTYeOKUR1xujTv/L612u2v3p3HsU/NrDDt82tGMCQz2b9vVFSpVXwkf9w7nsNF5VzwxgLW7MkLdUh+17tNAu9fMTzUYQgRVJLggEkvzGXT/gK/lfft8t0NWr9negJdWsf5KRrhq8QYO1NvHA3A8c/ODulQOv62enceb87Nol0LY3iebqnxdEyJDXVYQgSUJDjgjondufOLFYD77M2Yrs2ale4ZutK0knJnje3sfPHHveNpFR/ZsEKE38VENr/2iTVVhBrTrRXvXT4siNEIEXiS4IAJvdOY0DutQWWUOpxc/OZC/th6yOd1OqbE4nRp8kvKibZbpbuwRuT7v4/mjTlZPD618XfA7A8ZLaJDHYIQficJzk8ibVYGd0iuU4Lbkl3IMU/O8Ly2WxXlTk231Dh+uGG0dK4bYr9vzg51CAG39akpoQ5BiICRX1A/yC0q45r3lzT4B7HcaVzv3LCvgD25UuMt1NokNe+zmlbxkZz16u/sOVwc6lCECAg5g/ODr5fu4qfVDRtm5d3Lh9GnjdEDi81qIS5SPppgcLk06/bmM3PdPp6dviHU4QTVgfxSDuSX8skfO6SvS9Esya+oD3bnFnPXlytxuozGwRaliLBasFmNZGSzKLqlxnEgv5RDReX12sal/11U7byJvVP5z1+G1KtcUbN/TFvHf37Nqn1BP2sdH0m6eYbobvGolNEBwCUjMj0dfLuney9DhbUANPn5BWggNjbOnHKkKzp3tagjlaeOlGNRip7p8YF4i0KEnCQ4H/x71ibmbDhQ+4IBkpoQFbJtN2eZd/0Qsm3vzy+lV5sEnC7NuO6tufzYzAY17s+LMrJXQoIkKyHcJMH54OFTezOxd5rRwwgKp9aUO1w4XC7KnZo/tubw3vxtPpU1947jyEiOCXDEwheXjcyssQF+oM1ebxw0zd2YzaPfr6lXGaf0b8M/zurrz7CEaDYkwfnAbrUwtlsrAMqdLv47bwsH8ktxao3LZYxE0CLGXu3lydm3jSMuykZcpA2bRaG1lq64QuiteVvqnVAam++W7+Y7HzsWOKlPGv++cBBW6ftUhAlJcHX0xZKdPPnjujqtM+7Z2dXO69Ayhl9vP66BUYm6GNm5ZahDCIkfV+2l3OnCaml+jdiFqIokuDo6d0gGShkjh1uUqtcwOd7OHyrjvgVbz/QEn9t/Lcw6yHmvLwhwRP6x4p7RARsSSoimSBJcHVksivO8ktLlx3ak/8PTyS911Ku8Ldn+6wNT+N/wTi2Jj7TV+/MNpn5PzGVg+yQUR0ajWLLN944HfNUtNY4ou9Uz0G/31HgeP6Mv0RFyZigaF0lwDZRf6iA1MYr8enbWvNGPnTwL/ykuc9LzgZ9CHUadLd2eG/BtbNhXcZ9dvTuPc4ZkMCJML/2KxksSXAOd//qCeo1EMPu2cWRKb+6NTlGZg4e/XcMni3eEOpQa3Telp+e5UorSkhKUguioKM80pYwzuMVm93HutnXu8QbdQ0OB0Ym4ez2XPtJurnJdKK3hzkk9aNci2myfp0iIttGhpezLovGRBNdAD5zciwveqPs9mnHPzubr645lQEaS/4MSNco6UMDf/ven5wd83d78UId0lJP7pZOeGOW51OhOJgAn9mrN4A4VxwrMyzPGsKt8D+6SEZmBD1aIRkoSXAO1Tqh5qJux3VpR6nCyICunwvTk2AjaNvO+Dhur9+ZvC1pSG98zFZvFOJtSXmdOR/41lrtkRAfSE6Obff+XQgSTJLgG6tgylnWPTmJB1kEue/uPo+Y7XC7GdGvF2G6tAeifkcjIzinBDlN4eeDkXlwwrD0ajcsFb87N4suluwKyrV/W7vNpuW+W1X2Q3Em903jhggFE2qRyhxBVkQTXAL509fTbpoP8tulghWkf/t9wSXIhZLEouqcd6dJqdLeUgCW4QPpp9V4OF5fTOl4SnBBVkQQXAhe+sRCAObcfR/uW0m1XMJU7XeSaPc64Lw+u3RPce3AjOrWkVXykpxNli1KcNbgd/dolei5duuNTHLm8aVEKi9elTekNR4iaSYJrAO/Gwi/P2sQz09bXaf0xz8wC4IcbRtG7TaJfYxNVG/Toz+SXhLZN2/ysg0dNq3wGed+Unlw5ulOwQhKiWZIE5yfXHdeFv4zowOh/zOJwcd2GzCksdQYoKlHZg6f05smpazlYWBbqUGr02A9ra+0lRzruFqJmkuD8KCHKXufkBvDjqj0M65hc+4Kiwc4e3I6zB7c7avrOQ0UcKiz3jKPm0sYoasaYakeeu7Tmjs9XsD2nKOixV/bxH9vplBKHU2sKi4rRWhMReQiXpxNwI96e6Qkc20Xu+YrwIwnOR06X5ompa/l8yU5sFoXForBZFFb3Q6l699J+cr82fo5W1FW7FjG0a2E811qzeNshHE5NUowdq0WZScNIGNcd15k7v1gZ2oCBl2dt9nnZ3+46XpqliLAjCc5HJeVO3pq3xa9lxkZY+fJvx1ao0SdCo7DUwetzslAKvl22m6zswlCHVKP0xCiGdUz2VDxxOctRShEVGYHFq0KKRSkGtk+S5CbCkiQ4H8VG2lj/2CSKSp0MfPRnv5RZWOYku6CU7kiCC5WsAwWc89r8kN6TG98zlcdO7+PpQst9RUBZ3IkKrBZVY3u36noyESKcSYLzQZnDZVyi0hq7zcLz5/bnlk+XA5AYbVzCyqnHD+RLFwyUeyMhtHhrDme/Nj/o271zUg+uHdc56NsVItxIgqvF7PX7q+yhxK0+lUrcZq7bzyn95f5bqLSKr7mbtUD5x0/r+MdPxqC5n18zgiGZUsFIiECwhDqAxq5fuyR6pfv/sk9itJ2Vuw7z2PdrcDhdfi9fVG9HThEPf7ear5fWvXssf4uPsoc6BCGaLTmDq0VybARTbxxdYdol/13EnA0HGlTu4eJyDheXs2l/AZeOzJT2TEHiS/dqDfX930fRp6003Bci1CTB1cPVYzo1OMHddVIPjuvemhYxdlonRPkpMlGbc4e049PFOwO6jZNfmlftPEl+QgSPXKKsh4LShnf11DEllu5p8ZLcguzps/tX6GIt2E5+aR5TXpzLXV+soKgstF2GCdHcyRlcPXRu5fvoxVP6pvPyRYMCGI2oi+FP/MK+vNKQxrB6dx6rd+cxtlsrTuqbHtJYhGjOJMHVQd8Hp5Ffx7O3f184MEDRiPqY3Dedt3/b6peyIqwWUBjt11AYHXoZtK647IOn9Ka9eZ9Vo4mPssto7kIEmCS4OpjcN51PFu+o0zoPf7eGzJYxnDGoHYnRUmMu1B48pTcPntLb83rDvnwWZh3k/m9W16mcYZnJnDc0gyn90omyy3hsQjRGtd6DU0r9Vym1Xym1ymtaslLqZ6XURvPfFuZ0pZR6USm1SSm1Qik1yGudS83lNyqlLg3M2wmsf5zdjy1PTqZTHS5RvvP7Vh76bg39H54ewMhEfXVtHcepA9oy/+7j67Teoq053PrZcnrc/xMPf7eab5bt4tvlu5m1fj+68umbECIkfDmDewf4N/Ce17S7gBla66eUUneZr+8ETgK6mo/hwKvAcKVUMvAgMATQwBKl1Lda60P+eiPBopTi55vHsutQMaUOJy/N3MS3y2tvTzVMGvM2CluyCznu2dl+LbPyJc+nz+7HuUMy/LoNIUTd1ZrgtNZzlFKZlSafBowzn78LzMZIcKcB72njEHaBUipJKZVuLvuz1joHQCn1MzAJ+KimbTudTk8few3hjzK83frlWn5el12ndV46p4ff4/Cnxhxbbeqyn2zcFfhjqjs+X8Edn6+oct4VIysmPgVUPt/zHpPCPS8xysZFQ9tgt1Z90aUpf37B4q/fk+qEw2fQ1N5jfe/BpWqt95jP9wKp5vO2gPdNqp3mtOqmH0UpdRVwFUBGRuM8Cr7smHZ1TnDDn/mdtklR/HDtECyqfsPqiCPqu58c07EFK+4xGu4fLCjjuBcXBiS+6rz1e93u4Xp7fuYWzhuUjjJHC/D+d3LXeNolhabrscasKfyeiMBpcCUTrbVWSvntpoPW+nXgdYAhQ4Zof/aO7q+yjk1I4LYJRTw7fUOd1jtvaHuSEht3I9+m0ht9dftJXeJPSKBCm7iScievzNrEizM3+TdYP/rkzz1VTv9xdQQzbhjeZD6/YAnk70l1wuEzaCrvsb4Jbp9SKl1rvce8BLnfnL4L8D5MamdO28WRS5ru6bPrue1GwZfkNrprCu9fMTwI0YiG2JJdyI0fL2XFzsOhDqVKZwxsy+AOLcxRxY3RujXGILxaG80OxnVqGj84QgRTfRPct8ClwFPmv994Tb9eKfUxRiWTw2YSnAY84a5tCUwA7q5/2KHlcmmW3n8iBwpKmfDPOdUuN3dj9lF9H657dJJUK29kXpm1KaTJrUdaPMM7HqmEZCQyI3G5tPF89e7DaA12q4XbJnY/qslJU7s3IkQw1JrglFIfYZx9pSildmLUhnwK+FQpdQWwDTjXXHwqMBnYBBQBfwXQWucopR4F3OPOPOKucNKUnPbybyzfkdugMg7kl0rHyiGmtXHmY4zxB7dP7M5nSwLbP2VN1u3NZ93efJ+Xj46wcs/kngGMSIjmwZdalBdUM+uEKpbVwHXVlPNf4L91iq4RcA+tojUNTm5Pn92PhVtyWJB10PMDqzEuQclZXfD89Z0/mL2+YZ1lX35sR5SqWONx3qbsoxJVm8QorhrTqc7lK7PyiFIKBea2FDar4vQBVdbPEkJUIj2Z1OKJqWv5Ze3+2hf0QXVVx9fszuPR0/v4ZRuidhcN71CvBDewfRIRVgsTe6fRKj6SVbsOc0LPVIZ1lDaOQjRGkuBq8c/zBnDGwAMopXh22nrW7/P9UpLbqf3bGP0Veo7GlefoP8pu5bYJ3f0et6jeib1SPbUnP/1jB1/8uZOFW2q/Yr50ey5AhWX/MyeLb647lv7Sr6QQjY4kuFpE2a1M6J0GGJcUr35/ic/rzr5tHB1axqCk3Vujs+dwMSOenOmXsv71ywbiouw8cmpvWsRG+KVMIUTDSYLzQanDyf68UqaurLoNUmVtk6KZedtYIm1yX62x2n6wyG9lzTIvd7ZrEc2dk3r4rVwhRMNIgvPBiCdnklNY5vPyu3KL6X7fT57XP988hq6p8YEITdTT8E4t+eLaERwuLufHlXv9Uovy1dmbeXX2Zs/rq8d04m6p7ShEyEiC88ETZ/Tlmg98vzRZWWGZ04/RCH/4bvlu/v7R0oBu45hOLQNavhCiZpLgfDCpTxqrH57Ihn35WJRi3d487vxiZY3reHcBJRqf8T1TuWBYBnsPl1To13FXbjGrdzes0XTL2AguPqYDy3bksnRHLmkJUZw/NAOLRe7FChFMkuBqsD+/hNP+/Rt7DpfUab2WUtGg0YuOsPLkmf2Omr5mdx6TX5zboLIPFpbxwoyNFabd81XNB0T+0K11LN9cP5roCLn3KwRIggPA4XTx7PQNbNiXj8OlcThdOFyaRT5UHa/KE2f2ZdrqvRjjXrr7CzS4u2CyKsWJvVKxVTP8iQieaav31ql2bGO1YX8hRWUOSXBCmCTBAYu25PDar5trX9BHvv5YXj22E3efJJUQQi0Uye37v4+iT1v/jSzh7osyIU6GzBHCTRIcMLJLCl9cO5LcojKsFoXNYsFmVdgsCqv5yCks47K3/6i9sCpM7J2KwrjPA0a3S5E2K9eM6ezHdyHqa9XDE7nxo6XMWOefHmuqkpEcbZ7RQ/fUeHbkFLEjpwirRXF8j9ZyJi9EAEiCMw3u0KLG+f9pwBnetNX7jpr2/Ln9pVFwIxEXaeOty4ZS7nTx+pwsnpm23u/b2JFT7Hm+81BxhWQqZ/JCBIYkOB9F2vx7hJ2WEOXX8kTD2a0WzhzUlq+W7mLXoWIcLhflTr+N5VtBSlwEQzokY7dZuFrO5IUICElwPrrs2I5cdEwHisqcvPDLRv7725Yal3/h/AGcJr2+NylXvbeY6WuOPtsOhOyCMv4yogPHdkkJyvaECEeS4OrAZlHc9tlyfvbhR/CHFXsYkJFEhM1CpM1KpM1ChM2CzaKkb8pGatb6+t+Dc99ndVMKzz23qnRsFSujEAgRYJLg6sDp0j4lN4Dpa/bV6WzgmE7JvHf5cCL8fClU1MxfTQSqus9anb8emyl9VgoRBJLgarE7t5iRT/mn1/maLMjKodzpkgQXZO1DMLr6279t5e3ftta4zOkD2tAxJY62LaI5a1BbOesXoh4kwdXC4qcflncvH8aAjCRP0wP3v/LDFVo90xOq7Fbtho+W8u3y3SGIyPD1siPbbhUfydhurUIWixBNlSS4WqQlRlX5A7gw6yDnvb6g2vW6e40eoNE89eM6IqwKu9W4F2e3WujSOo47J/WQs7ZGqHOruFCH4DE0s+YmLEKIqkmCq4bWGodL4zQf+/JKOP65X31e35eRv3/dcIARnVoyvldqQ0IVflZS7uQvIzqwdMchZptjvYVSrwem8epFgzipb3qoQxGiSZEE52XzgQJOqEMSa4gXzh9Am6RohmZKTbrGZEdOEaOfnhXqMI6ydk+eJDgh6kgSnJfUhCgGZCSxbEduQLcTF2ljfM9UYiPlz9/YpCZE0S01jg37CkIdSgUvztzEizM3BWVbH/7fcEZ2lvZ5oumTX1gvcZE2vr7u2CrnvTxrk89dOJ0zuB3piUZPJVP6taF7mozm3VRE2CxMv3ksWQcK+Nv//sSljdEglKJSf6JGqzfv/kVX7WrYOHKNRV6xI9QhCOEXkuBqsSOniKveX8LaPb7/eH22ZKfn+YszN7Hq4YnYrQq7xSKDXjYRnVrF8dNNY+q83vq9+bwxN4vPvfaBxkwG5hXNmSS4WnywcFudkltV+jw4rdZlWsZGMO3mMaTIcCeNzqHCMgY++nOowxBC1JHUT6/FnROD0+PEwcIysgtKg7ItUbvCUgdXv7+Y45+bzbhnZ4c6nIA59d/zQh2CEAEjZ3C1eP7nDXVa/rWLB3FirzQsCmnE3YQt25Fbp+63mqqt2YV8sGAbmS1jGdVVKpaI5kXO4Gpx+sC2ZCRH+7z8NR/8KT2UNAPHdknhjkndQx1GwOWVOLjv61Vc/NZCMu/6gcy7fuDR79eEOiwh/ELO4GrRpXUcc+84vsY2chcMy+C8oe3RWtMppfH0gCHqrqDUwbUfLOGPrTmUlLtCHU5IDJdRDkQzIQmuCiXlTu7/ehXr9+XjdGkcTk25q/ofu48W7eDeKb2Ik3ZtTc78zQe54I3qu1zzp66t47AohUYfNZSO90ttztSVFnBqY190uty97Lg8ve04nMb+eenITO6Z3FOuIAiBJLgqLdySU6Gqvy+k9n/TlF9SHrRtpcRFkp4UVWHcODjSlg5AAelJ0VwwLAOL2dbOvbi7HZ7R/k55llcKCvKNruHiExLIL3VUuYx7uy6tPX2iCtGcSYKrwthurZh+8xiWbc/lji9W+LTOhn0FDMhICmxgwu8m9E7ztAV7Y04Wj09dG7Btzc866POyL87YGLA43NY9OokouzXg2xEiVOQQrhrdUuM5e3A7n5c//eXfmLsx9B3zivr7vzGdwqLhs92quGJURyKs8vUXzZucwVVj0/4Cxj9ft46X/9iSw+iuMm5XU7Art5h//bwBDbjc97S0xuXSWC0Kp0vXWkZT8fV1x8rVBRGWJMFV4Z3ftvDQd75Vlb5kRAei7Va6psZz1qC2AY5MNNRb87Y0imrwvdITSE+MQimOqnBSmcWiOLFnKucOzah2mbw8o7edhIQEf4YpRJMmCa4KWdmFPi/73vxtnue3fba81uU3PHaS3NwPodSEwHSF1qlVLFkHat9vou1WTu6XzuNn9JX9QIgAkwRXye+bsiskLX+a3DcNm1S3DKmT+7Xh5H5tANiXV8LTP63niz8b3jFy1oFCqbQhRCMjCa4SZ23Xixpg6sq9dFo5lVcuGsRkGbwy5GwW5Zfk5nb75yuwWYyq/BalsCg8vdq4j2uuHtOZjOQYv21TCFE9SXCVjO7aylOT7vMlO/lo0XaWbDvk1238uGqvJLhGwN+HMt8t313rMku25fLjjaP9vGUhRFUkwdXg7MHt6N8ukQveWEB2QVmd14+LtGGzKuxWC+/+dRg90uJlPLhGJCUuskKzgMy7fgj4NtfvzWPIYz/jbr3tGTDVfB5hs/De5cPpmBIb8FiEaO4kwdXg7d+28LCPtSm9DchI4tKRHThjoO/t6ERobTvoe8WihnBpaj1Y+mrpLm45sVtQ4hGiOZMEV4M/tubUa71lO3JZ9kkuN39Sc63KP+4dT6t4GeA02PbnlXD2a/PZnlMU6lAAOG1AG+4+qafndaBqegoRbiTBAeVOF7d+upxvfbiH4k97DhdLgguB56ZvaDTJDeCbZbv5Zllg9r34SBuvXjxYxnoTYUkSHMbozYFMbhcMa8+TZ/YNWPmibh45vTejuxk/+DaLwmqxoIAv/tzJur35WMxakFnZhU2+R5P8UgdLtx+SBCfCkiQ4ICkmgq1PTcHlMobFuf/rVXy62H/Vxz9atJ24SCu3Tugu7aQagUib1dMWzm3T/gJ+XLU3RBH5x2vn92HSgA6hDkOIRkMSnBeLRRFpsfL02f15+uz+lDqcTH5hLpt96KGiNm/M3UKX1nGcN7S9HyIV/taldRyfXTOCTfsLmL/5YNAvV9fXmG6teO3iQeTl5RMTIQdPQniTBFeFMoeLe75ayec+jgnXIy2e7IJSkmIiSIq2kxBtp2tqHBktjAa9ShlNBib0SqOk3InWEC0/Ro3O0MxkhmYmc+agtrRPjuHd37eSX+oISSwjO7fkxQsGYlXmGHBmY3H3vxalKlwNcMj+JMRRJMFVYcehIp+TG8C6vcZgk97Vv2eu21/jOn8/vgu3TuhevwBFg+UWlTHs8RmUOasfqT1QUuIi+Pb6UbRJig76toUIJ5LgqtC5VRzLH5jAj6v2cNeXK6tdLjbCSlyUjX15pTWW5z7idh+JR1gtTOknPZmEUnG5MyTJDYwDoZFPzQSMs/svrh1Jj7T4IyN2qyOjd9vMrr6EEHUnCa4aiTF2fllb81lYYZmTwjInAJktY7AoxQvnD6Rvu8RghCgaID0xusrBTa9+fzHTVu8LWhxaw5mv/F7t/GO7tOR/Vx4TtHiEaE5kvI4qOJwuDuSXsnF/vs/rbD1YRFZ2If+etTGAkYlAe/qs/kHfZoTNQoTVeNitCrtVYbMYj8uP7Rj0eIRoLuQMrgpd7v2xXut1ahXLyxcO8nM0IpgSY+zMvHUsxz9Xt9HcKztncDuuHN2J7mnxfopMCFFXkuCq8Pfju/DSzE0+L58SF8m8O4+TNm5N3LIduZz+8m/1Xr9bahwOp6Zz6zguHZkpyU2IEJMEV4W3f9vq87JXj+nEdcd3keTWDKQlRBFps1DqqF/lkw37CgBjRPiUuEjpvUaIEJMEV4UHTunFHZ+v8GnZ/8zJItJm4Rap8t/kpSVGsf6xkypMe+rHdbz26+Za133yzL4kRNlJS4yiZWwE7VpIEwAhQk0SXCUOp4sZa6uvRTehVyojOrf0jNgcabdyxsC2QYxQBFphqYNrPljC/M0HsVgU0XYrFoWnxmxV7q6iOcmYbq147/JhgQxVCFEDSXCVOFy6xmri09fsw2pRvHrx4CBGJYLpsR/WMHdjtvGiAZ0tR1il/ZoQoSQJrpIou5UtT07mincXV9sbyY+r9pJ51w+cNySDf5zdL8gRikApdTg56V9zycquW9+jLWLsnDmoHWcMbEufttIGUojGQhKcl63ZhYx7drbPy3+yeAcb9+fz/hXDiY2UP2VTtz+vtM7JDeBQUTlvzdvCW/O2VNl4XAgRGvKr7MVRj8tRf27P5cWZGyuMyCyanlKHk9FPz2pwOZl3/eB5PiwzmZZxEZ5u2qwWRXJsBHdM7CGdbQsRBJLgvHRpHceaRyZy1xcrycouYNWuvGqXPaFHa0Z2ScFuVZw3NCOIUYpAiLBauG9KT774cxcKqHyoc7CglOJyJ2ijr8i8ktpHGVi0NafK6RktYrh8lPRQIkSgSYKrJCbCxr/OG8CWg4WcUENvFjPW7WeGeY/ugW9WA/Do6X34yzEy4GRTpJTikhGZbNpfwOz1BwBwaY1Lg9Yal9aexOfrKN83je9K19bxZjnGI8pmZWLvtMC9ESGEhyS4Sm78eCnfLKvfYJftZPiTJm1fXgkf/7GjQWX0z0gixm7lwVN70SMtwU+RCSHqQxJcJRN6pfmU4I7r3ooJvdOwmx3kRlgtOFyaGWv3ER1hZUSnljLMSROTkRzDiocm8MqszT417nZrHR9Jq/hIrhzdkSl92xBhkz7MhWgMJMFVMqVfOlP6HakJd/tny/msisFPZ60/wCzzUlZVHj61N5eOzAxEiCIACkodPPD1KjZnF2K3KLq0jqPc6WLbwaJa192fX8r+/FJu/mQ5N3+ynMRoO8sfnBCEqIUQNZEEV8mCrINc/f4SDheX17hc6/hIjuvemnKnizKni3KnC4dTo4GkaDtnDJLeTZqSpdsP8eXSXX4p63BxeYXalFWJsFpYcM8JWC2qwoC4Ff6l4kC5Qoi6kQRXyUszN9aa3MA4av9k8Q56pScw9cbRQYhMBNLorq2Ydds4DhWV8ch3a1i2Izeg2ytzuhj06M9+L1fa4QlxhNwsqOTdvw7jn+f1p2VshE/LF5bVXl1cNA0dU2IZ1L4FbZtoR8kXDmkT6hCEaFTkDM5UUu6k38PTKavjUCnbDhaxNbuQzJTYAEUmgu1f5w2gZ1o8z07fEOpQajU0swUf/d8xFBUWhDoUIRodSXBerPW4zzGhVyptpHlAs9K1niO6h8IfWw/h1PXvEFqI5kwSnCnKbmXto5NwujSd75la5TItYuz8/fiuRNgsOJwuhnZMpncb6Vy3ufn3hQO5/sOlQd/uPZN7MLlvOu1axNR53dIAxCNEUycJDpi+ei9Xvb+k1uUOFZWz7WAhD5/WJwhRiVA5uV8bTu5X/f2s2ev38+z09TV25VYfT0xdxxNT11U7f96dx9Ur+QkRriTBYXTJ5Kt352/j3fnb6JEWz083jQlgVKIxWb4jl9Ne/i2kMZTW8f6wEOEurBPczHX7uPydxfVad2D7JP8GIxoFrTXLdx7G6dIoBZv3F3D75ytCGtPMW8fSqVVcSGMQoikK6wSXFONbU4CqfL10N18v3Y1LaxwuXWMHvAMyknj/imHER9nrvT0RHC/N3MTzPze89uSFw9tzQo/WWCwKm0Vhs1jwrsNU00UD7TWWQWpClCQ3IeoprBPcoPYtSImLJLug7rfoi8udPi+7bEcuew+XSIJrAs4flsGiLTnszy9hw776V73/cOF2Ply43adlO7WKRWtjCKa7J/fEapFeS4Twh7BOcACL7xuP06X5YslOvlq6i/lZB31a7+Jj2nPtuC5YlNHtks1iwWLB869VKawWJV0sNTHRdivzNmUHdZtZB4xRxN+ct4W3fttS49mdt86tYhncoQXHdknhuE7xAYxQiKYp7BMcGCMtnzmoLfvzS3xOcMM6tqSttH9rVkY8OYM9h0tCGkNdmrRtPlDI5gOFfLr4SGfgWU9MxiJngEIAkuAAuOb9Jfy0em+d1rnho6WkxEYwsktKgKISwaS1bvLD3FwwOB25YCDEEZLggLMHt6tzggO48M2FFV5veOykJv8jGa6UUvx6+3EUljro/eA0v5V7Yq9ULAqMsQEwRwYwXytwOjUOl4tSh4sLhrVnSIcWYM63mKMIKCqu4z3agHt6fn4eSinKnC6veV7LSOYTYUgSHDC+Vyr/PK8/N3+yvN5lnNQnDbtVfkSauthIW5U98tc2/E11fl6zz+dl524M3L2/1Q9PJDZSvu4ivMgebyp31r8/vyX3jadlXKQfoxGNweGicjYdyEcpxf+uHE5OYRkWpbBaABSg2XygkGemrQ9xpDU7d0g7ouzWUIchRNBJgjOdOySDc4dk4HJpyl0uut/3k8/r1tQGTjQ9+SXlDH7slzqPLBFKQ9on8p8L+tCyRVKoQxGi0ZAE52XG2n1c8W7dezYZ9sQMXjh/AKcNkFG8m6K9h0s4/eXf2Jvn/xqUFw1vz67cYsCoIamB9IQoLhnZwdP4u/J9Mq2NSi/eh03Gutrz3C0+yka7FjHk5fm3X0whmgNJcF76tk0ks2UMWw8W1XndhvSKIkJHa81rv24OSHLLSI7mgVN6EWmTy4NChIIkOC+tE6KYfftxACzZlsNZr86vdtkBGUl8fd2xwQpNBMgnf+zgnd+3BqTsHTnFvPv7VhKj7SiU5yxtYPskOkv3W0IEnCS4ahSW1twV17IduZ6adVP6pZMSG4FLGyMTuDSM7daKSX3SghGqaIDTB7Zl7Z48duWWoLXGqTV5xeX8uT3XL+VXN/xNVTU1hRD+JQmuEq01xz07u06XKX9YseeoaR8t2s6aRyYSEyF/4sYsym6tcXy/fXkljH1mFiXl/q1wcuYrv3FCz1SuHdtZeh4RIkDk19fL75uzufCNhdXOP2NgWzKSY7BZjH4mrRajMa5FuZ8rLBajce2AjCRJbs3AgfxSvyc3gD+35/Ln9lzOH5ohTUyECBD5BfbSpXXN90W+WrqLq8Z04p7JPYMUkQi1Pm0TWfPIRG76eBl/bj9EdkGZ38q+YFh7SW5CBJD0K+WldXwUH191TI3L5JeUByka0Vhs2l/A9DX7/JrcAMqdTaednRBNkZzBVXL+6wuOmta1dRzREVYUsGZPPme88hsK49JkdISVuRuzGd01hfbJMZQ7XZw2oC3HSifMzcK3y3fz5twsOqbEcri4nJxC/yY5IUTgSIKr5LNrRnDOaxWbB2zcX/vAl979CH66eKfUkmuiCksdPDt9PcVlTpSCjxbtCNi2Pl+yk1nr9vPHveOlookQARD2Ce7JqWv5z5ysBpVxYq9URndNQQE2q4VRcvbWZL01bwtv/7Y1aNs7WFhGYZlDRnsXIgDCPsE1JLn1Sk/gH2f1o2+7RD9GJELpb+M6k5EcTUm5y9OmEbPbLJfZ52hhmbPBHSzfOakHMRFWou1W5mzIrtANl7snrvbJMQzISGrQdoQIZ2Gf4M4c1JYv/9xVr3XX7MnjlH/PY3jHZO48qQeD2rfwc3Qi2GxWC2cMbOd57XRpyp0u86FxujTFfkhw//ip6gbglc24daz0eiJEPYV9gnv+3AE8f+4ASsqd3PH5Cr5dvrvOZSzcksPZr/5O1pNy362p2nyggBOe+zXUYQBwfI/WxERYGdi+BZ1SYkMdjhBNVtgnOLesA4X1Sm5u314/yo/RiGBbvbvx9MY/c91+AL5fsYfuqfGM6ir3dIWoD0lwpl5tEhq0/gcLtvHUWf38FI0IlD2Hixnx5MxQh+GzPYeLQx2CEE2WJDhgyotz63wEP7prCumJURzXvTVKKY7v0TpA0Ql/agxj0144vP2RMeDMUQbAPS6c8cLp0sREWjm5X5uQxSlEUycJDrh0ZCZ3fL6iTuu4272dOySDIZnJgQhLBEDbpOij2igWlTl4ccYmSsqdARs6x9uHC7f7vGxyTARXj+0cwGiEaL4kwWEkqXOHZADw+pzN1Q5xUpWzKzUK75Yax3PnDPAclVdHa+PyU3G5E60hwmYhym7xzPNersJ6VZalq51fcf2j13bPLywyRk+IiSk8al5aYiSDOzTfJB4TYeOuk3oAkJ4YxZM/+v7516RnegKXjeyA02WMxu3+e3o+BXczBI58hmarBGxWRWyEjdMHyijxQtSXJLhKXpm9uUHrb9hXwCn/nuenaBqPz68ZERZnqleP7XzUGdP+/BIOFZbj0pr/LdzGBwt8OwNbuyePHHM9N601Fw7vQHKsjAAvRKDVmuCUUv8FTgb2a637mNMeAv4POGAudo/Weqo5727gCsAJ3KC1nmZOnwS8AFiBN7XWT/n3rfhHblH9O1PObBmDUsZwOZjD6LjvtVjMUzr3/NyiMnYfLqmw/vCOycREWD3LVeaeUvXZ4ZGJledXtbj3MgpFucN433abveI8BV1bxzOwmbfx01rz7u9b+XqZUZO24omvZvnOw/Uqt6r2bguycvjgyuH1Kk8I4TtfzuDeAf4NvFdp+j+11s96T1BK9QLOB3oDbYBflFLdzNkvAycCO4E/lFLfaq3XNCD2gFj18ET6PDitXuu6B0n94tqRDO7QtBJCXp5RySYhoWG1SZuSLdmFXP3+YlwaHE5XnQa5bYh5m7I9o8F7k/5LhfCvWhOc1nqOUirTx/JOAz7WWpcCW5RSm4Bh5rxNWussAKXUx+ayIU1wDqeLbTlFaK0Z//wcv5X7y9p9TS7BhaP/LdjGhn21d6QdDCf2SkVrXeWZuxCifhpyD+56pdQlwGLgVq31IaAt4D3ezE5zGsCOStNrvUbjdDo9ZxYNUV0Z93y7nu9X7W9w+QB3ntiJ0/qlYrUoou1Wv8QdTE0tXm/13U/+Protp/RK9vQ5aVTbNy4Pbz1YxObsIrSGH9ccYHO2f8/uMpOj+faaIRWm5efn17u8pvz5BYu/fk+qEw6fQVN7j/VNcK8Cj2LcqngUeA643B8BKaWuAq4CyMjI8EeR1brsmHas3J3vuU+25WD9G9V+vnQvFw2VGm/B4o/9RClFx5SYo6bnlTg4/fUlDYqvNltziun3xFwAWsTY+eivA2iTGBXQbYajYP6eiManXglOa73P/Vwp9QbwvflyF+C9F7Uzp1HD9Mplvw68DjBkyBDtz3tClcsakpDAr3eke17vPVzCDR8vZdGWnDqVe/Ex7Xns9L5+iTHUmso9uOr2E3/EHx+veeKMvsxctw+twaU1ucXllDlcpCZEebrS8pdDReVsPeyiR0bDY28qn1+wBPL3pDrh8Bk0lfdYrwSnlErXWu8xX54BrDKffwt8qJR6HqOSSVdgEcaVn65KqY4Yie184MKGBO5PQx77heyCUp+XH9g+ibcvG4rNasFmUUTaLAGMTgSbUooLh7fnwuHtAbjy3cUs3Z4LBK7Pyms+qP2M8dT+bXj2nP6eS6nuWBXI/TshquBLM4GPgHFAilJqJ/AgME4pNQDjEuVW4GoArfVqpdSnGJVHHMB1WmunWc71wDSMZgL/1Vqv9vebqa/hnZL5YcWe2hc0je7aiqQYacfUFK3efZgpL/reTjEu0kZBqSOAEfnu2+W769Qh+JpHJhITIU1dRfjypRblBVVMfquG5R8HHq9i+lRgap2iC7BN+wsoLnNyzZjOnDckg0v+u8in9V6csZEXZ2w8avqTZ/blgmHt/R2m8KMt2YW1L+SlpuTWpXUcKXER7MgpZldu4+oU+cyBbYm0WUMdhhAhFbaHdz+s2MN1H/7p1zK7tJaBKRu7k/u18XRgPGfDAZ8PaqpisygKSh0kx0bQMs44o/dcJDQvF1oUnD80g3MGZ2CxBO4SYji2YxSiNmGb4EZ3S2Fi71R25RajzSriq3b5dn9l1m3j6JAcY9wLkfseTVZDu8tat9e3av1Lt+dyQs9UUuIiG7Q9IUTdhG2CS4iy85+/VGyH9OniHTWOKpAYbefjq46ho4yy3Cz0aZvo6T2kpNxJj/t/qnMZx3RK5rYJ3bFbLZ7uvSp3nJyaECnJTYgQCNsEV9n8zQd5a+6WGpc5XFzOSS/M5cP/G87IzjLKcnMSZbd6kt3xz80m64Bv9+oWZOUQF2WjR5pcGhSisZEEZ7rozQU+D4Z54RsLAYi0WVj18ETsVmkm0Jw8cmofHvh2FU6XxuHUtVYgmfQvo8H2TzeNlkQnRCMS1gnuxo+X8s0y36tdV1bqcPH6nCwsSuHSGq212TDYGP/LpfGapnG6NKUOF6UOJ2cNahcWw880RaO6pjDz1nH8uHIP1/7P94pIW7OLJMEJ0YiEdYI7vkfrBiU4gGemra/Xeh8t2iG9xzdyG/f71hFz26Ro7jqpBzERVmlwLUQjEtYJbmLvNH6763gAPly4jZdnNWywU283HN8FpRQWpbAoo5am1WKM2h1pszK6q9zDa0z+2JrDOZVGZ/fVrtxi/v7RUp+WXfvIJKIjpH2aEMEQ1glu1D9mkl1Q5peyLj+2I6cOaINLa/q1TcQm9+WalKRoe8C3Mal3GnarnN0JESxhneAeP6MvD36zGm1W8N6X53t/lJUt23GIOyZ1x2ZRWAPYoFcERtfUeLY+NYV5G7P54s+dJMdGEBtpq7LHmtqc0KM1b102NABRCiHqIqwT3MTeaUzsncbnS3Zy22fLG1TWn9tzq2xH9Y+z+nLeUOm+qykoLnNy8VsLfV7+2nGdSYy2kxBlJybC6mn4f0KP1gGMUgjhK7mOBmytY/+EdTGwvYzs3VRER1h569IhtS8IxEfaeHX2ZqLtVnq3SaBnegIDMpIYlplMudNFSbkTl6/tToQQARHWZ3But03szoXD2zPyqZl1Wq9zq1juO7kXx3WXI/bm4oSeqZ7arVNX7mHVrsMUlztZuj2XdXvz6JGWgM2iWLztEAAPfuvboBhSY1aI4JMEZyosdfDAyb2IibBSWObk0e/X1LrO5gOF/PXtPwDISI7ml1vGSg/uzcjkvulM7pte5bwL31jA75sP+lTO/43u6M+whBA+kgRnOvGfcxq0/o6cYv7zaxbXHddFKpk0U1prTn/5N5bvPFztMluenCzt4IRoJML6Htzbv20h864fyLzrB7+U9/zPG/iuDgNSiqblhRkba0xuAH0fms7q3TUvI4QIjrA+gxuQkdTgMib1TmNox2S01iTFRHBq/zYND0w0Sv/6pfYmAwWlDj75YwePnJYYhIiEEDUJuwS353AxI56sW2WSmpw+sC2T+qT5rTwRGg6nixW7DmM1Ly8u3nbIp/uwVXlv/jbem7+twrRF955A6/ioBscphPBd2CW4FjERjOnWijkbDjS4LOk9vvl4+Ls1vL9gW+0L1tOPK/dy6cjMgJUvhDha2CW4KLuV9y4fdtR0rTXPTd/Av2dt8rks9zApbt9cdyz9/XDZUwTfVWM6BTTBWRS8P38rlVvGaV1xgFTv6S6tzVEq8MzX5kgVWsNpA9rQrkVMwGIWoqkLuwRXnXKnrlNyq6xdi2gykuXHpqnKSI7xtFXzV6Ujb/d/41t7ubp4Y24Wyx6Y4PdyhWguJMGZImwWsp6YzK8bDvDgt6vZnlNU6zr3TO7BVWM6ByE6ESyf/rGjTsvHRlhJjotAoYyuusDTTMDTWEABGpzmmIBDOrQgPupI587u9fBaF6gwEoV3mUoZ2zp3SEa93qMQ4UISnGlh1kHOe31BndY5WOifkQhE43Fcj9YMyEhi2Y5cn5YvLHNSmFPziN+V7TxUzBfXjiQhykbX1Ph6RCmE8IUkOKCk3Fnn5LbyoQkVjsJF83C4uIycIBy4nPXq7/VeVyo3CeGbsE9wh4vL+bYejbP7PjQdgO//Poo+baXNU1O27WAhJ70wl6IyZ6hD8UlpuSvUIQjRJIR9grv+wz+ZuzG73usnBmGgTBEY578+nwVZOSGN4Y5J3QE8NSVdLn1UbUnjtfF8SGYLqakrhI/COsHtzyupV3J79LTe/GVEpv8DEkEV6uQG8PRP6+u8zvIHJ8iBlRA+COsEl11Qv3st93+zWhJcM7Dp8ZMod2o0GpeGSf+aw85DdaswEkg90xM4sVeqUZPSrKU5pEMLSW5C+ChsE9z6vflMfnFu7QtW4dfbx/k3GBESNqsF9+hGBwtKGdm5JZ8u3hnaoLzcOak742SsQSHqLWwTXJfWcfz12Eze/m1rndc99d+/sfxBaWDbXOQUljH4sV8Cuo1B7ZO4cXw3z2ujPZtxZgYwqEMSMRFh+3UUIiDC9htltSjumNijXgnun+f1939AImSSou3ccEJXpq7cw6b9BQHZxp/bcxma2UKSmBBBFNbftugIq6d7JoDPl+zkts+W17reuG5y2ag52XyggJ9W7cEW4IFqez0wzfP8jIFt+ed5AwK6PSHCXVgnOIBN+/P558/GOF+FZQ6f1vn4jx1cOLx9IMMSQfSfOVls2BeYM7fqfLV0lyQ4IQIsbBPc1uxCxj07u17rHtejlX+DEUF3uLic/g9PD1j55w/N4Kmz+gWsfCFE7SyhDiBUyp317w0ixh62xwXNxtbswoCWP6F3akDLF0LULmx/qbumxle4/zZ34wH+8tYin9bt/8j0CuuKpqd/RhKbn5iMw+VCazj2qZl+7Tz78ncWc8mIDjxyWh+/lSmEqJuwPYOrrGd6At197Nn98TPkR6s5sFoUkTYrUXZrQMbyi7Jb/V6mEMJ3YXsGB7Alu5D1e/M4XFzOh4t2MKlPGuv35de4TmK0nYuGdwhShCJYnjm7H0/+uI7ducWs21vzPuCr1+dk8fqcLABO6pPGqxcP9ku5QgjfhG2CK3U4Oa5SJZPlNYwBFhthJTUxitJy11EjPv9yy1i6tI4LQJQiWLqmxnPflJ5c8Ebdhk3y1Z7DJQEpVwhRvbBNcJE2K8+d059bfWj3BsbAllkHqq6Y8MKMjbx0wUB/hidCYNrqfezLK21QGVeO6sjNJ3YjNjJsv1pCNBph/S08a3A7cgrLeHzq2jqtN6JTSyLtFmwWC2cNasuE3mkBilAE07XjOrMlu6BB/VG+OW8Lb87bwgvnD8CilPkAZf5rUQqrxeg4uar5xrwjy1rUkWWtFveyR2+3vKSEtklRDXj3QjQ/YZ3gANIS6/ajEBdp45pxnbEocGkoc7iYtnovLnO8LpfWnufJsRGM7dYKVdUvkmiUnj67P0+f3Z9N+wsY//yv9S7nxo+X+S8oHz00uSuXjZGRvoVwC/sE9/ePltZp+YJSB5f+17fmBABvXDKEE3tJm6jG6Oc1+/jnzxuwWo6cHbnPoFbtOhyQbSbF2D2DmgLgPaApeA6OtGdexUFPXbpieVaLokPLGFrH2jm+W8uAxCxEUxX2CW7ZAyfy64YDPh9xx0ZY6ZIaT6TVQoTNQqTNgt1qwWJxX2Y6cnkpo0U0x3WXXk8aq2s+WIKzcsYIkPhIGz/cMJr2Lf3fHAEgLy8vIOUK0ZSFfYKLslu5+8uVPi9fWObkv5cOoWVcZACjEsGw4sEJbMkuxOnSrNh1mPu/XlXvsp48sy/je6YSabcQYTUOfOTStBChFfYJDqCozOnTcqkJkVw5qhPJsREBjkgEQ2ykjT5tEwGjZ5O0hCjW7M5j4/58vl+xp05l3f3lSu7G9wOlhrp9YneuO65L0LYnRFMU9gkuym4MmTPwkekcKiqvcdl9eaU8PnVthVqXSTF2lj0gg582dZv2F/B/7y0OdRg+69xK2l0KUZuwT3BuPdISmJ91sM7r5RaVexp+f/h/wxnZOcXfoYkg6NI6jpcvHMSni3fw64YDQd/+JSM60DElluO6tyYzJTbo2xeiOQr7BOdwunhx5qZ6JbfKLnxjYbXzvv/7KM/lMNH47M8vYdrqvbSMjWBEp5ZsO1jI7iD2PvLe/G0APPzdGunIWwg/CesEt2TbIc569fegbCuvuObLnyK0XpqxiW+X7w7Jtif3TSMlLhKLUpw5qG1IYhCiOQrrBFfXtk5xkTZWPTwxQNGIULr/5F4c06klucVl3PtV/WtTunl6IrEo7Baj+QgAFf9BKcVvmw56eif5Ztkuz3T3ci6z/ZtLayKsFj6+6hi6+jjyhRDhLKwT3KUjMzljUFuu/WAJv22q/RJlQanjqI6W3W49sRt/P6Grv0MUQRJhszClXzol5U6em76BnAaODedOSLg0/htlzjB15V5ulAQnRK3COsEBJETZeeOSIbw1dwt5JeUcKirn8yV174vwuZ83SIJrBqLsVv68/8RqD2T86ZWLBtErPcHT1ySAxaJQ4OmDUoGnpxV3H5XxUfaAxyZEcxD2Ce6JqWs9Y3Y1VE0/it1S41AcafhbuQ3wI6f1YVjHZL/EIepn5rp9XP5OcJoKvHTBQLqlxnlGFFdK0blVrDQOF8KPwj7BTeydxutzsrBZFBE2i8+Nvutqw76CGuc/8M0qfrppTEC2LXzTrkVgutGqSlV9oMplbiH8K+wT3OAOLdj61BSu+9+f/LCybr1X1Nfblw0lLTEK7dUNYqdW0vYp1LqlxrP1qSnklZTT76HpQdlmpM1CuxbRxETYOHNwu6BsU4hwEfYJzq0uw+Ysvf9EWkh3Xc1WQpSdj/7vmDqP7n3ZyEweOrV3gKISQtSVJDjT/Sf34v6Te1HudPHE1LW8/dvWapc945XfaBUfWWEwyq6t47hnSk8ibdbgBS0arKDUQZ8Hp/mlrHd+38rxPVozppuMICFEYxD2Ce675bt5/Aejb8n9+SVHjbdVla0Hi9h6sKjCtN83H2R8r1RGd5Uft6bEZlG0SYzyW68ll/x3EYvvG0+KjDYhRMiFfYK74/MVFJfXrWLJib1S+b/RnTw14LQ2Ol2Wrrianii7ld/vPoFvlu3y2yjcQx77pcLrGbeOlc6RhQgBS6gDCLVXLx5U53XGdE1hWMdkRnZO4dguKYzqmiLJrYk7bUBbvvzbyICUfcJzv3L3lysCUrYQonphfwa3YmfduusCuP+b1dz/zWrP6wibhWk3jaGj9ALfpOkADu790aIdPHlmv8BtQAhxlLBPcCM6t4SfG1ZGmcPFsh2HJME1ce4mI1Bzo/2aXDWmE63iIrFaFDar0Qel1WL0QPLp4h10bhXH4A4t/Bm2EKIaYZ/ghmYmM+PWsXz15y7+PWtTndd/7PQ+XHxMhwBEJkLpl1vGMv75X+u8ni+94sy6bZwcDAkRBGGf4AA6toytc3K7akwnkmLsnDc0I0BRiWB4Zto6Xp61OeDbOXdIO1wa+mckkdkyeD2mCBHOJMFhdHA7LDOZRVtzfFr+iTP6cuHw9gGOSgSa1pofV+0N6DYuGJbBw6f2IcIW9vW5hAg6SXAmX5Jbq/hIfr55DEkx0otJU1ZY6qC3nxp3V0dGcBci9CTBmX64YRRTXpxX4zJ/3Ds+SNGIQLIoRXykjfxSh9/K7Ns2kTsn9cCpNSlxEfRuI8lNiFCTBGfq3SaRFQ9NqLGT3apq1v16+zg6tJQKA01JdISVlV4js7+/YBv3f92wUbxX7jrMxW8tpGvrODTmYKcaYiNttE+OQWO0QXA3RTBmGy8yU2K59cTuchlTCD+TBGdasu0QuUVlXHxMez5YsN2ndfq0TZAumZqBvxzTgTFdUxj7zOwGl7Vx/9HDIq3cVXtby84pcYzt3soYMdAcEk5xZNBTpZT5r/m80vTicicKiCh3opSRQKPs0i+qCG+S4ICfVu3hmg/+rNM6STF2DuSXctyzs3G3D9YaSh1O8kuMS19TbxhNrzYJfo5WBEJspI2YCGvAxgOszR1f+L+nk5vHd+PG8TK+nAhfYX9NZNvBwjonN4DconL25ZWyP7+UA+Yju6DUk9wAXpq50Z+higBKiYtkzSOT+Oa6Y48abT2YjDM0sNTwUJUeFvNszqKMzqMjrBbiIm1M6pMWujciRCMQ9mdwuw4VN2j91y4eTPe0eKxKYbGAzWKhuNyJVSnaS3unJuerpbsC2mVXTabdNIbuafH1WjcvLw+AhAS5YiCEW9gnuJFdUtj61BQ27c9n/PNz6rz+m3Oz+PzawHTSK4Lv/pN74dKa9+Zv82u5Izu35J2/DpOKJEIEUdgnuE/+2M6dX6wEYFD7JErKXazZk1frel1bx3FslxTuOqlHoEMUQbQ/v8TvyQ2M8QJLHU5JcEIEUdgnOHdyA/hze261y502oA0TeqURE2FlbLdWWCwhvFEjAibC6v8E9Nk1I+iYEku5M0TXPoUIU2F/ODntpjF08OFe2TfLdpMcG8FxPVpLcmvGWsZFcvvE7n4t85zX5jPksV8Y9OjPvFyPDr2FEPUT9mdwX/y5k20Hi3xa9oI3FvDcOf05uX86NovFrNEmya45eXnWJp6Ztj5g5T8zbX2Dy3/6rH6cK518C1GrsE9wbZOi67T8rZ8t59bPljdomw+f2ptLR2Y2qAzhfyXlzoAmN39pU8d9VohwFfYJ7uJjOmCzKnKLynG5NB//sYNducX0Sk/g4mM6cM9XK2svpI7W7M7jp1V7cHdZ4T4J9D4XdJ8ZKu/5Xj1cHFmw5nWPTKu4brnLRUmZk46tYumRJlXLATbsyw9o+R1TYpl127iAbkMIcUTYJzirRXHR8CMDlv79hCM9Pzz+w5qAbPOTxTv4ZPGOgJRdH8seOFFuxgKHisrrve45g9tx+sC2WFTFkbxtFlVh2tbsQmOEb3OeAjRGLzgpcRHYAlDJRYhwFbYJrszhwqV1lb1WuBv6zlp/ILhB1VFMhJW4SOMj1OD5sfTWKSUWu/mj6d3hr81qIdpu4YSeqSTFRJCXVxK8wBupLQeO7kfSV58t2clnS3b6MRrj8517x3G0lP5OhaiXsExwu3OLGfnUzFCH0WBFZc5a+07smBLLB1cOD1JETdtlx3ZkfK9URv1jVqhDAYzPN6/EIQlOiHoKywTXMi6CKf3S+WXNPlxa+6V9UnJsBJ1SYlm87RBgjA8GR86aKqvcHdRRr6vZjvaxHyn3YndOkobodXHLJw2rQFSTMwa25Z/nDQhY+UKIisIywUXarLx84SAADhWWMfDRnxtcZk5hGTmFZZ7XvgyR4qtRXVK4cHh7zxhi5lBjnmTXKi6SkV1S/La9cPXn9kM+jexenfhIG3ee1AOX1rhcGpfX55QUE8GZA9v6L1ghRK3CMsF5S4i2c/XYTkxbtZetXu3h+mckERthxWpRzN2YHcIIYd6mbOZtqjmG968YxuiurYIUUfP09m9bG7R+fqmDGWv38eSZ/UhLjPJPUEKIegvLBLcjp4i3f9tKVnYBs6upSLJ8Ry4ZydHsyKn/aAMjOrXEYjkycKVbhcErK732bjpwZBmvae7XXut2SollRKeW9Y4znOUUljHiyRmUOlx+KW/W+gMc8+QMQEZ7FyLUwi7BzduYzcVvLfRp2dqS20f/dwyZKTEkRNmJjQy7P2WzEB9l4/gerflx1V6/l70jp1gSnBAhFHa/yoM6JDGlXzo/rNjj8zodWsbw881jpSf4ZshutXDXST1Ytftwg87WvW19aopfyhFCNEzYJbiYCBsvXziIly80Xn+2eAe3f77CM79fu0S6tIrjjkk95D5KmFiYleO35HbtuM5+KUcI0XBhl+AqaxVvtDFqmxTNjzeNJiHKHuKIRLCdOzSDKf3SufXT5fy0un6XKuMibVwzthPXH9+19oWFEEEh19xML104UJJbGIuNtPHyRYN4/Iw+9Vr/9UsGS3ITopGRBCeEKbeojH/PrN94bV1bx/s5GiFEQ4V9gouNtNE9NZ4omzXUoYgQ25tXwp7D9euTc+jjv5B51w98tdS//VEKIeov7O/BDc1MZtrNY0Idhgihw0Xl3PzpMrYeLKRr6zg0sGl//TpeTk+UsdqEaCzCPsEJ8cfWHGau21/n9VY+NIF4uW8rRKMlCU6EvfG9Uvnnef25uY4dLfd9aDoA7ZNjcLo0Lq25YlRHhnVMPmpZhUKjibBZZIBZIYJEEpwQgKWqgQF9tD3nSB+mj/2wtl5lDOnQgs+vHVnvGIQQRwv7SiZC/G/hNm78eFlIY/AeiUII4R9yBifC3sTeadz71aqgbGt8z1QAhmS24MpRHbFZ5RhTiECRb5cIeylxkVx8TPugbOuXtfv4Ze0+nvpxHbtz69ckQQjhGzmDEwJ47PS+PHJqH56etp7Xft0csO0M7tCCC4a1p2NKLO1bxgRsO0IISXBCeMzZeMDvye3Lv41kUPsWfi1TCOGbWhOcUioDeA9IBTTwutb6BaVUMvAJkAlsBc7VWh9SSingBWAyUARcprX+0yzrUuA+s+jHtNbv+vftCFE/l/x3EXM2VD34rS/+Nq4z2nyuNWjz1bTVe1m8NYfLj5X7bUIEmy9ncA7gVq31n0qpeGCJUupn4DJghtb6KaXUXcBdwJ3ASUBX8zEceBUYbibEB4EhGIlyiVLqW631IX+/KSF89dGi7dz95coGl/PK7JrP/J6Yus4zcoU3rQE0LWMjeefyodITihB+VOshpdZ6j/sMTGudD6wF2gKnAe4zsHeB083npwHvacMCIEkplQ5MBH7WWueYSe1nYJI/34wQddU9LXidJB/ILz3qkV1QSnZBGev35fPr+vqfQQohjlane3BKqUxgILAQSNVau4fF3otxCROM5LfDa7Wd5rTqplfL6XSSl5dXlxCr5I8ymrum/DdqyH7SJcnKintGV5j25LRNfLTE9xHfa5OWEInVorBZFFYFVovyPJRSWBSc2T+NyT2S6v0+mvLnFyz++j2pTjh8Bk3tPfqc4JRSccAXwE1a6zzl1fOD1lorpXS1K9eBUuoq4CqAjIwMfxQpmiF/7Sd5JQ5em7sNl4YIm4UIq/JrcgPYm1da6zIrduVz5oA0v25XyO9JuPMpwSml7BjJ7X9a6y/NyfuUUula6z3mJUh3b7W7AO89qZ05bRcwrtL02ZW3pbV+HXgdYMiQITohwX/99vmzrOaqqfyNqttP6hr/s7NW8cEfu+sVw+0TuzOofQtPhRI0uMwKJkZFE9Bae/414qbCPJe5ar92iSQkNPz+W1P5/IIlkL8n1QmHz6CpvEdfalEq4C1grdb6ea9Z3wKXAk+Z/37jNf16pdTHGJVMDptJcBrwhFLKXWd6AnC3f96GEPVzz+SedGkdR5nDVed+JJ+Ztp67TurBNWM7Byg6IURD+HIGdyzwF2ClUmqZOe0ejMT2qVLqCmAbcK45bypGE4FNGM0E/gqgtc5RSj0K/GEu94jWOscfb0KI+oqyW7lkRCYAZwxsy+DHfqnT+k/9uI4yhwsABSgF7sv37qv4CuM+m1LGc+9+nZVSKKBdi2gm9JZLlEL4U60JTms9D+O7W5UTqlheA9dVU9Z/gf/WJUAhAm1rdiF7DpcQH1W/fg+e/3mDX+L46P+OYUTnln4pSwghPZmIMLf3cAnjnp1dr3WvGdsZu9U49tNH3XszltEYEzTgcumj5rmfZ7aMYXgV48gJIepPEpwIa6kJkdwxqTuz1x+goMTBmj2+V4OurluvpBg7310/ioxk6WtSiFCSvoNEWFNK8bdxXfjgiuEM7uCfPiNzi8pZuiPXL2UJIepPzuCEALZkF/L+gm0+LXtMp2TOHNSOMwe29TTUVg0YEVwIERiS4ITA6LLr97uOJ6ewjJNfmlfjsguycliQlcMdn68AYFjHZAa2TzLbuHm1d+PIvbkIm4Ubju9KbKR85YQIFvm2CWFqkxTNu/O31nm9RVtyWLSl9hYvFqW4c1KPekQmhKgPSXBCmLTW/OfXrICVHxdp4825WZ6ak3BkWJ2q4/Fe7sg073U8tTATbYzuIrUwhfAmCU4I0+Hi8oCW/8y09QEt/4drh9C7iXShJEQwSIITwpQUE8Gie07ggjcWsPlAYZ3Xt1uVp1cUb1pD5Too3i9rq5/i6RnFa2XlVYJS0LmFnYwWMpacEN4kwQlhKix1MOyJGfVev9ypeWveljqtc3K/dM9z7fmf+7WueJnSu/F4pWnrdjv4YfUBrFabZ22XBpfZoXOv9ARum9BNRhUXYUUSnBCmwjJH0Lf5/Qr/Ds1TnTkbDjCxdyoD2/unrZ8QTYEkOBH2Xvt1M0/9uC7UYRzljknd6ZAcW7FzZve/FS5rKoqLi1BAdIzRe4rFbJ9nMXp4plVcJH3aJgYpciEaB0lwIuyN7dYqYAnunsk9OLZLCr3bBDa5uEdabirjdAkRDJLgRNjrmZ7A1qemAPDm3Kw6jwtX2cD2SUzolcbZg9vRKj7SHyEKIepBEpwQXj5ctN3nZWMirIB7HDijXmOHlBjundyTKLuVfXkl7MsrqbGMqmpQeiqTeFUqibZb6Zoa73NsQghJcEJUMPPWcdzyyTK+XLqr1mWLypxHTVu1K4+zX5sfiNB46YKBnNK/TUDKFqI5kjrDQlTy2+bsUIdwlIm9UxnbvVWowxCiSZEzOCG8zFq/n315pXVa55T+bXjm7H6ey40KhVl50XPpUsmIA0IEnSQ4IbyM7NzS52VbxUfyx73jAxiNEKIh5BKlEF4ibVYePb2PT8u+dvHgAEcjhGgIOYMTopK/HNOBpdsO1VrR5KxXf/c8v//kXlwxqmOgQxNC1IGcwQlRSV5JOd+vrFsXWha5vSZEoyNncEIA+/NLGPZ4/Ttafvi7NTz83Zoal7l3ck/+b0ynem9DCFE3kuBEWDtYUMrcjdkUlAa+o+XHp67l8akVe0l56sy+nD+sfcC3LUQ4kgQnwtqEf87hYGFZyLb/6eIdkuCECBBJcCKsvXfFMKa8OC8gZbdrEY3dasFqUdgsCqtFYVFH2sihFBo445XfgCMjBcRE2Hj+3P60TogKSFxChAtJcCKs9W6TyJmD2vLln7V3zVVXOw8V13vdDxZu55YTu/kxGiHCjyQ4EfaeP3cAz587wPP6iyU7ufWz5X7dxsdXHUNMhBWtjUG7tdbmv+AextvdubLFohiYkeTX7QsRjiTBCVHJ6t15dV7HuxNk7c5UuFMXfLBgG/3bJXHl6I7SZZcQQSIJTohKsrIL6rzOd8t317rM9yv2MLZ7K7rJsDdCBIUkOCEq+csxHchsGcs7v2+t03qju6Zw9uB2QNUdK2e0iJbkJkQQSYITwsv6vflc8e7iOq3z2sWD6JYaT5Tdis2isFkt2KxGzUmbxYLNorBIVydCBJ0kOBG2Hvt+DW/O29Lgcq754M86r5P1xGRJekIEmPRFKcLSnsPFfklu9XHagDZIPRMhAk/O4ERYahUXyV+PzeTbZbtplxyDRYFFKZZsO+S3bSy5bzwt4yL9Vp4Qom4kwYmwZLNaePCU3jx4Su8K0x/6dnWdK5cA3HBCV24e31WaAAjRiMglSiG8PHRqb7Y+NYVNj5/EsI7JPq/34oyNdLx7Kpl3/cD8zQcDGKEQwldyBifC3tyNBziQX4rWkF1QyrfLd9ersbdbTITVj9EJ0fgt25HL7txiJvdND3UoFUiCE2Ft3sZs/vLWIr+Weetny/nllrF+LVOIxmrjvnxOf9noMLxzq1g2HyisdZ2+bRPp1y4Ru9VCblEZXy8zOkq4YFh7WsdHcuXojsRH2RscmyQ4EdZGdG7JLSd2I+tAgedL1hAWBdcf18UPkQnReC3emsPq3XmUO11szynyTPcluQGs3HWYlbsOHzX9o0XbAWMA4ifP7NfgOCXBibBmtShuOKErAD3SE3jqx3X1KmfB3SeQlijD24SrUoeT2RsPYrMoYmJKgCP9kFagvZ9qPly4nV/W7q/3dtsmRTM0s4VXx90VN+PuF1UDkTYL0XZrpflHXrnXNzoErzTNXL6srIy9eaUs2nZ0cvLVZSMzeef3rfRKT+CcIe3QGmZvOIDdoujTNhG7VXHZsR3rXb43SXBCmK4Z25nTB7TlcHE55U4XRWVOFm05yLPTN9S67jFPzqjwevZt48hMiQ1UqMIPduQUMfrpWaEOo0F25Raza1n9h2UKpvhIG9/+fRQdU2J56NSKtZcvH+WfhFaZJDghvKQlRnnOxA4WlDJ15Z56lWOVXkoaPZs1/D6jtknRRNqMyvNVneUBbDtYdPSKfpBf6uC4Z2d7Xt87uSf/N6ZTQLblJglOiGo8/sNavlx69ECod53UA5fWxqUcrXFpcLo0l47MJDk2IgSRivpIT4xm61NT/FZeXp5R8zYhIcFvZdbXip25lJS7sCij4293RwaeEeWVeSlSg0tr82Hsz9kFpXy99Mj9aKfWOF2acqeLRVtyKHW4/BLjZ0t2SIITIhRenb25yuQ2umsKl43MNH4kUBV+QKSRt2gMPlu8g9s/XxHqMDym9E0nKcaO06VxuDRXjekUtFE1JMEJUcmW7EL+8VPVlU3mbsymx/0/+W1bv9wyli6t4/xWnhCT+qQxe8MB9h0uQUOFszOn68jzdXvzAx7L42f04aLhHQK+nepIghOikgVZwemJJCM5mlbSV6Xws/goOy9fOKjGZT5cuJ17vloZ0DjuP7kXFw5rH9Bt1EYSnBCVDGrfIijb2ZFTTP9HphMXaWPJ/eOJtEkPKKKicqeLzxbvpNThNO6hWczL4aiAJ6iGiI+yMaFXasgv20uCE6KS9skxTL95DBP+OSco23O6qmwxJRqRvg9NI7/EEeowGp3xPVt7Kq/YbRa+W25UTskvcVTZBOM/fxnMxN5pQYtPEpwQXpbvyOU0s9shf4q2W1n76CS/lyuC46xB7eo1ykRzV9dG6le/v4R3/jqUcd1bByiiiiTBCeHF4Wp4FegeafGe3iDcPUH0a5fEQ9+upl2LaC4/tqOM5t3EPHRq76MaJ1fWmJoJ+Ju7Ocyh3MM880vW/7d33/FtVWcDx3/nSvKMR5w4iZM4e5CEDMgmBAJhQ9lltAVaoMBbKNBCWyiFAi1taEsptNCWVaCsUKBAWYGEsMmeZO8dx4njvSTd8/5xJVlesSxLupL8fD8fsO7QuY8sxY/uvec8h9nLwhsfCrCvrDaCkR2ZJDjRaUXrbK2l3mmbDlQGHg/u0YWTYvQNVohIOPdvX7ZYOzIcd76xmlNG9CQ/K/odrCTBiU4rGsktFD/41+IOPf+9m6czsnfynSWI+HXt9IHc8sqKDrfzoxmDmT40PybJDSTBiU7s+asn8dj8zSzcVmJ3KO2SLvPNiTDNX3+Au9/6JuT9/Z0gFYr+3TLQpolSCodh4DF1o5kEQvH4J1t4/JMtzdY/fdUE0l0OhvXKonsEh85IghOd1gnD8jlhWH6jdRv2V3D6X8LvPXnHmUdxw4mDOxqaEFHx67fXsPtw/BVnvua5JYHHkSyfJglOCKz5p7YfrEYp+NO3x3L7f1aG1c6s99ezP+gmevA34IbHNNoePFZIB8150tL0J8Hru2elcN30QTgdRlixis5n7k9PpKi84fP55Odbef7rHVE51gnD8nH4amAqfw1MrKlx6pvUs5wyKA+Aq6YOiGgMkuCEACY9MK/tnUIUy+7k+V1S+faEwpgdTyS2FKdBYV5GYPn+847m/vOObnX/TzcWc9Uz4c14/9nG4mbrnr5qAk9cOSGs9sIhCU4I4A8XjeHVJbsA62xp6Y7D9gYEXHfCoEalvJoWhcjNSOHCY/rEOCqR6KrrPdz88nKW7SzFGq1ifbCCrzD4Hx+qrI/osa95bklEL0G2RRKcEMAlEwu5ZGLDmdCxv/mIkqrI/uP2W3b3qTKtjrDN1uKqDs0i3hE/P2N4TI8nCU6IFkQruQH8cc56fn/hmKi1LxJDncfL4m2HA/em/Pep/Brfq204y2p6D7fp3v51DfdwfXMX0jD/25NXTqC63hOYXcA0NV5tTWfj9Zp4TE1ptZu/zd9Mn9x0pg/tTo3by1srGuaJO5Krpw3kppOH2P5FThKcEC04aXg+8zc0v4cQCSt2lfHuqn2BSifWHx7rr1HfrhmM7x+bYs/CXre8vIIP1uy3O4w27Smt4ZXFu0Lef/Z1U5g8qFsUIwqdJDghWvDwpeMYd/9HUWl73b5ybnxpWavbP7h1Okf1koHcyeqRuZt4eO5Gu8OIGrtnEAgmCU4In3qPyeVPLuhwB5OxhblkpzlxGFaV9eDLTwp/t2kCs4L7hwooYEzfHIbHaLZjYY+F22Iz36BdLvnn183WfWtsb66c2j/QqcXwfebrPSZur4mpdeDyafDPVJfB8UO6h500JcGJTmf5zsNc8PhXUWt/5a7SsJ97xtG94uobsIi8l344xe4QWlVV5+E7Ty5g5e6GupNpLoNad8eKkP9v5d7AVDrtdfc5I7nm+IFhPVcSnOh00lzxW+rqqc+3ctboArvDEAnqxpeW8e6q8Cv9t6Qjya1PbjrKN9jbUPgmbPU9Vor0FAfpLkdgH6DR9pwMF5dM6Bv28SXBiU5nREF2m2NxnvliG/e/szaix7Uu0SichsLpMHAYML5/V04c1iMw9kjO3kRHTB6YF/EEF65tvz/L9s+zJDghmthfVhvx5Hbvt0by/WnhXWYRIlRXTh3AlREqd/XU51v57bvrwn7+wDvfa7zcPZMnrxzPkB6xu8csCU6IJv42f1PE27z3f2u5939W0hzeM4s5Pzkh4scQ8elgZR2PzN3UYi1S3WTfxvVHm25tvN3//KbrCJpo1z/xbtM2dNDjZsfybf/v8j0tvp5wbTtYxZvL93L76bEb7C0JTogmXliwM6rtbyiqYMAd7zKyIJu3b5omxZKT3K/fXhM3lw1j5bUbppLiNHA5DFwOhcthkOI06JWdFtM4JMEJ0cTTV01oNH1HtKzdV86OkmoG53eJ+rGEfR68aAyTBljV8v0D+jUNZ3Kt3adqaXWzVb7hJU2fEzx7hXVcX9d7XwxaWz/NoEIDq/eUhVyppC0X/6NhqMCsC0dz1pgCstNcEWm7PSTBCdHEzBE9I9LO1dMGcs5Yq0dk8B8hh6FQKPK6pNAnNz0ixxLxq0uqk6uOGxCRtj7bWMyPX17uK7GlA5cgTY3v0qIOlN+KF3e8sZo73lgd0yLLfpLghGjimz1lbe8Ugme+3MadZx2FSy5Bigh56KONlNW47Q6j3f7+3WNtOa4kOCGaWL4zclPlDL3r/bCed/PJQ/jpabGtvC7i3+s3TKWqzttwScB31hZc09Tf8eS/y3fzu/fW2xitpV9eBqeMjMxVkfaSBCdEE3e/tcbuEHj2q+2S4EQzTodBToZ1ReDzTcXc+cbqQMk3IFACC6xei3a6ZeZQbj1lqK1j4STBCdHE0B5d2HSgMurHWX3vaWTZcONddFyt28us99ezt7QGsM6ePB4PCnA6rT+rTTt8NJ4KxypMGtzRxKpT2vAYX+LyV/VoaMN6/NLC6Pb27ahH5m1i2pDuTBqYZ1sMkuBEp1RT7+W3767lQEVdowLIAAO6Z8YkwY2+90P++6PjOKafTI+TaD5aW8SzX223O4y49tvzj2biAHs/25LgRKf07up9vBgH34A3FlVIgktA54wpoFtmCuW1nsC6mppqANLTG3rGBk80GlgXdM/M2kcH7asxzab303zd+ZvcbyOwvnF3/6b7mUHDAoLXmRo27q+I2Jx0Z43uxePfHR+RtiJFEpzolC46tg8FOWl896mFMT3uL886iutOGBzTY4rIU0px3JDujdaVl5cDkJ0d2bn8Fmw9xOzFu3A5FA6j6f0sRUOqbPtel1INyVYpIjrh6s0zh0asrUiRBCc6JaUU04Z059/XTOLml5fj9mo8ponX1Li9kR9E9OK1k5nW5A+iEKG47IkFdocQkjP+8nmr235yyjBuOSX2CVASnOjUpg/NZ/k9pzVaV1Pv5cxHPmP7oeqIHeeVxbuoqmu4nBVcC9Bravp3y+DoPjkRO55IHl/ecTILthzC7TXxNi882Ujw2Vkbu7KvrAaP16pg8tWW6E7Ceri6Pqrtt0YSnBBNrNxdGtHkBqFN+PjFL06ib9eMiB5XJL4+uelcNL7lOdHKqt3sKKnCYSichhG4hOm/Lxdc8cSrrcemqXl50S5eX7Y7ajHfeeZRXH+i/ZfiJcGJTumPc9bz2PwttsbgcihmDO+B01BMGdRNynaJdht7/4d2h9DIRcf25ZdnHUW3Lql2hwJIghOd1Edri2w9/sDumcy+fgo9smJbXV0kl0cuG8dTn2/DY2o8XusecjNH6IfSUrcU/8DszWEMlfnW2IK4SW4gCU50Ql9vOcTtpw0P6r6t8Ziaeo9Jncdka3ElT36+LeLHnXfbiTJzgIio88b14bxxfaLS9uLtJbzcZCjNG0eYI+7Kqf2ZMbxHVGIJlyQ40al8trGYK59ZZMuxZz70abufc+7Y3jx6+TFRiEaII5s4II+JAxpXIbn3zEHsLq3lhy99w+HqeqrrvYFtz3+9g+e/3gHAC9dMZmxhDhkpzhaGNsSOJDjRqUwb0p1bZg5lzd5yX1kka72/ksl7qyM3LigS3l65VxKciCtfbilhj69EWWu+93TD+NKXfziFqYO7RTusFkmCE52Kw1D85NRhrW6/4/VVvLJ4V0xiOXtMAekuR5v73f6flc3WBXcHB6ivt7phu1wp1nYa7sUoFBpN364Z/PjkITJ9T5LQ2pr7zWM2/mnqhp6T/oolphm8rPl4/QHeXGFdbvTXuzx+aHf6d8v0teuvgNIwv5ypNdU1tZhac9NJQ6j3mry5fA8HKupajXFU72yG98qK1a+kGUlwQgSZddEY3lm1j8qgMWvR8u6qfVE/RlNH987mtFG9Yn5cEbpat5dlOw5T5zUD6x6Zu4kVu0qjetyVuyMzD6LfS9dOblbtJdYkwQnRRO/cNDYWhVdsuSAnDUVDTzR/BXgjqJS8Ak4d2ZNB+V2sy6TBZeX9Wur1pmm1GlNtTQ1KKdLS0gK7KaUCdQ4BundJZcbw/LBelwhddb2HO99YzbaDVayKcNKIhBOH5TOqd3aj2JSCi8f35dh+XXEYCkP5ZjHwPXYohTKgqqICw1DkZGc32s/OKXGORBKcED5biivD6ggSbF9ZbUj7Pfn5NrbPOrtDxwoWrTqIov0WbSvhrRVHHtRvp083FvPpxuJm6z/fdDAi7Q/Oz2TebTMi0lZHSYITwic/K5URBdms21ce9WNNG9KN3723LjDvl/Uj6CwvsK7xcvB+KujEr7y6llV7yhnaM4fvTxvAiAJJdHaZMbwHH992IqU17mYVRaDh/fPPFuC/T6ab3DfTwevb2E9rKK6s449zNtj3wn22FFcx4I53G60b1Tubd2+eHvNYJMGJTu+WV5bH/Bv3l5sP8eXmyNf/W7arnLnrilh696kRb1uEbpAN4x3//om9lXmOxK7ZxSXBiU7Pjs4e7TGweyYXHWsN5m08r1jD8qHyKlbuKefyyQOYOaJn7IPsZNbtK+e8v31JfVBHkFAYyurJqzWkuxycObpXoJdrYM63oDM2/5la8FxvDesBGp/tZaU5qan34vXtEytbf3cWho3j3VojCU50ept/d1ajZY/XpNZjcvSv59gUUWPbDlbx7QmF9MxuvayX3IOLrXnritqd3MBKSqZvOqaKOg+vLoleweNYeuqLrXhN8JomtW6Ts0YXMLK3/Z9FSXBCNOF0GPzy1eZjz8KVkeKwBpID+HpU+r/Jg/L1siSwj1LK10PN6sU2Y1gPFFBcURfosebvfakM63nV9V6UghS3VVkiLYTxdSJ8N508lBtPGhIYH7avtJYrnl5ASbUbrQl5bsFvj+9LqsvAa/rPwnTgsTdoDJsZNB4teNtnLXQWscPv3lvfaPlv8zdHtBNVuCTBCdGCg5WtD15tr+ByRuHYWryNZ75sX23M35w3iiumDujQccWRKaVwKHCg6Nctg//dMAFo+Sz6zx9t5NF5m5qt/8/S+DyDmzigKylOg1Sng3SXA6dvNnGvx4OhID0tFUfgi5jCYVhfxpyGItXp4Myj42OspSQ4IVrw0g+nsHTHYS76+1cxO2ZGSvOzLv99lODKJC1ttx5rUIq8jJS4K3rb2cXLmVaoFm8/3Gj5ySsncOrIngl3KVwSnBBNaK357bvrePqLyM8ocCTtOdM7fkh3Xrh2cmA50f7wdDZ/vfwYpv9hvt1hhGV4zyyG9UzMWTAkwQnRxJbiypgnt/aqqo9+KTEROYV5Gay573TqPGaz4jQt1Y5sOtbNXxfS//ijNUU89NHGmMS+oaiCE//4SWD5svEF9M/PCdxHNvyXKn33jR1Ksae0hi82H6RHViqpTgc/PnkIQ3vGvialJDghglTUujnlz5/F7HjjCnObdRzRNO0e7qvc5Vv+7flHM6ZvbsxiFOHRWlNW4240WNt6L6031PQ99r+//qSmW1jvp7A6FZ02qhenjuqJ1lBV5+GGF5ZF9L7xkbyydB/QvqE1G4sq+ODWE6IT0BFIghMiSGm1O6bHC6eA7m/fWcerN0yNfDAiYq58ZlHC3XeLlptnDuWaaQNtObYkOCHixMQBXbnttOHWt3TDP2QA/EMJ/EMIjrJx+hERmnNGF0iC8/ne5H7kZLhsObYkOCGChDN4t6PuO3cUA7tnYiiFqXVg5gGl/F3RVWBCVoeh2H6oyhoDF3Qzp6rSKoXUpcZa37drhoyFs9ElEwu5ZGJhTI9ZWl3PjD99EvOrEH7P/mAiLoeBqbX1mTUUuRkuehyhQEG0SYITAvjL3I38ZW7zcUqx8Ou310S8zfysVBbfdUrE2xXxKzcjhRX3nNZsvembDNXtNXF7TarqvbywYAcrdpaiFLgcBkpZBaE9Xs3qPWVhzYf4/X8tbnH9c1dP4sRh9kzT1GaCU0oVAs8DPbHueT6htX5EKXUv8EPAfx7+S631e77n3AlcA3iBm7XWc3zrzwAeARzAU1rrWZF9OUKExzRjWLgvCgZ1S0cphTKs2bp/fvpwmyMS8cIwFCmGIsVpfTa2FB+OeGHm7DQnvXPTSXEauBxGoMPU4B6ZTB6YF9FjtUcoZ3Ae4Dat9TKlVBawVCn1kW/bw1rrPwXvrJQaCVwGjAJ6A3OVUsN8mx8DTgV2A4uVUm9rrddG4oUI0RE/PW04Pz1tONc8u5h56w/YHU67vXl961U0RPwor3Uz86FPKa6ITY/HWBjUPZN3bj6ejJT4uyDYZkRa60CfUK11hVJqHdDnCE85D3hFa10HbFNKbQYm+bZt1lpvBVBKveLbt9UE5/V6AwNYOyISbSS7RP4dRepzAsQ8uV0xqU+je2kh0da3coehcCr41uieCf3+xUokPyctCaXt/eV1CZ3cMlMMumW4+MfloynISfPVUwVPbTXloc31G1PtSrlKqQHAMcBCYBpwk1LqSmAJ1lneYazktyDoabtpSIi7mqyfTBNKqeuA6wAKC2N7k1Ykjmh9TmYO78a8DZGfp601/160x/p51VjG9gn/7Ku83J6OBfEu3v6e9MpO5bsTe/Pi4vid8ftInr1sOAXZKWRnp9sdSkhCTnBKqS7A68CtWutypdTfgd9g3Zf7DfAQcHVHA9JaPwE8ATBhwgQdyUsucvmmbYnyO2rtc9Le+N9dtY9564pQSvH6MvsK324r9TB9RMd/94ny/sVKNP+etKatY3y4PnZfoCLtlre24vZqat0mFXUexvfvyuzrpuB0GHaH1qKQEpxSyoWV3F7UWr8BoLUuCtr+JPCOb3EPEPxVqa9vHUdYL0TM1Xm83PjSMrvDAOCet9Zwz1uh96Zcc9/pZKbG3z0P0bb29G5tNMkpDZOdQuMJUZfuONxqL8ZI2lvW+PLq0h2HqXF7yUrUBKeUUsDTwDqt9Z+D1hf47s8BXAB843v8NvCSUurPWJ1MhgKLsMapDlVKDcRKbJcB34nUCxGivVKdDubcegLf7Clj/oYDvBPnM3sHc8Th7Mki8pRv/KPBkd/vGcN7hDT/2rNfbuPe/0WuX9/Xd55MVpo9g7hDEcpXwGnAFcBqpdQK37pfApcrpcZhXaLcDlwPoLVeo5R6FavziAe4UWvtBVBK3QTMwRom8IzWOvIDgIRoh+G9shjeK4tzx/WmICeNb/aUU17rprLOQ2m1m7Ka+Lu39bsLRssgbhGS5TsPc81zS3D65mrbWxbZniBTf/8xi+6aSY8s+wZzH0kovSi/gBa/Prx3hOc8ADzQwvr3jvQ8IezichjcdfZIAB78YH3ExwmFanB+Jj8+eSjnH3OkjspChOb5r3dQUlUf1WNMemBes3VH9cpi9vVTyUm39+xOLuIL0URFrX1nbVuKq7h19gpunb2i0fo/XDyGSybY3wtQtF+9x+S4WR/HrNp/PFi/v4J9ZTWS4ISIJ9/sKeOFBTvtDqOZcYW5docgwqTRCZ/cHv/usfTMTqWupganQ5GbnUWKwwhULml4bI3PVO0e3BkdkuCECJKeEpt7W10zXHzys5Ns/4Yroi/V6QipA0hHPffV9qjUNQX445wNjCvMpbq2jlEFWVx9Yg+6JEAv3viPUIgYOVBRy2VPLGh7xwg4XO3mjWW7+YFN82SJ6CmpqmfmQ59wOMJV/TNSHAzp0QWvqRv+0xrT99Pr1fTMTsVrWkMHPF4TUxPYz/+ccGw7WMW2g9aMFXPWHWTzoToevfyYSL68qJAEJ4RPcUX0yyidM6YAQym6d0nl8kn9onosYY+qOk/EkxtAdb2XVbvLIt5ue11ybAG/OGuk3WGERBKcED6jeucELiVFa0bm4LF2z3y57Yj7nj26gMe+e2zEYxDRVZiX0eIlSf+gba/WLNtxmK+3HqKy1kO124vbY+IxNfVeE4/XZNXuMvY16dJ/+aRCMlKcgYHegXZpvKLp9pZo307Pfb2jfS8OeHXZPl5dZn2OF991CvlZqe1uI1YkwQkRpKSqnuv/vYTF2w/bHQrlNvbmFJEXPGh78qBuTB7Uze6QuO+8ozFNjds08XitOeP+s2Q3D7y3LqTnT3xgLmAl35+eOhyXQ5GV5oqbQgSS4IQIUlnrCTm5/ev7E8lKc/pmA1D075ZB9y7x+21WiJYYhiLVcODvM3LJhEIWby/hw7VFR35ikJcX7eLlRVYt/bGFubx147RohNpukuBEp7S3tIYLHv+SovLw77n94Nno1f5bec9p5GRID0sRezkZLp64ckJgedXuUs7925chPTcrzcktM4dEK7R2kwQnOqXPNhZ3KLlFU0FOGi5nfFziEZ3XY/M388c5G464z8Dumfzq7BEMyu/CwO6ZMYosdJLgRKd0yYRCPlpbFJezd+8rq2XkPXPa3O/Pl4zlwmP7xiAi0dlsPlDRZnID+MUZRzFzRM8YRBQeSXCiU/KYOi6TW3uM6ZtrdwgiCoor6gKdN+LZKSN60rdrOhv2VwBQWedhUPdMumam2BxZA0lwolNKcRps+/1Z1HtN6jwmdW6TOo8Xj1fjMTXFFXVc/mR0Bn3f+62RfF8GeItWOOOkB2Jb5q4rYu661juixMN9ZElwotNSSpHqdJDqdECT2T6y0iL7T+PEYflccEwf0lwOThsZv5d0hP26Zqa0u7TXxAfmRr1IQXvd9eZq/vYde8dxSoITogU9s9PYPutsdpVU89HaIkytKamq5/Ewp9H5dGMxz/5gYtwUoRXJ5fxxvXny8yMXDoi1d1btY0C3Ddx++nDbYpAEJ8QRFOZlcPXxDZcTf37GUZTVuLnu+SWs2VuO22tS7zXbrB4x68LRktxE1Nx19sjAfIZteWXRTu54Y3WUI7K8vGinJDghEklOuovZ108Nad81e8v428ebGdk7O8pRCdEyrTXbD1X7Ci1rwqy33MisC0dz8fi+cTU1TkskwQkRRaN65/D37423OwzRic16fz3//GxrRNv815fbuSwBioVLghNCiCR22aR+zFt/ANPUbPVNeROuS48t4OenDqJb19zIBBdlkuCEECKJDeyeydyfnghYM9af89cvwm5r9rJ9DM7P4LqTciMUXXRJghNCiE7i0whMAfW7OVv43Zy2exOPKMjm/Vumd/h4HSEJTgghktDhqnpmvb8ej6lRChSgFJw3rjdvrdhLmsugd266VdzAa+L2zfjt8ZrWT/9ymL1S4qHriSQ4IYRIQo/M28TsJbta3V7rNtla3LF7cq2Zd9uJDM7vEpW220MSnBBCJKFfnjWC0X1y8PoHaWowdcP839q37NsEvm3+9aa2hhj89t3QJj+dfd2UuJjENZgkOCGESEIpToOLxjeebWLzgUp++PwStrXRm3Jg90y8vkuUobjppCFxl9xAEpwQQiSVt1fu5eaXl3eojbYSYFN9u6azclcpYwtzO3TcSDPsDkAIIUTkdLNhupo73ljNeY99yQff7I/5sY9EzuCEECKJTBvSvcXZCPaU1jBt1sdROeZxg7vRLy+DGcPzo9J+uCTBCSFEJ9AnN51/fG88z361jQVbSzrU1qc/m0GvnDQcSuF0xO+FQElwQgjRSZxxdC+ADie47DSXNY9inJMEJ4QQEVJe62HiH96nzmPaHUrEfXt8X+4+3Zo6KtuG+3zhkAQnhBAR4vaaSZfcJg7oitMwuGHGYGrcbgylqPeYGAoMpTCMeKhZ0jJJcEIIESHdMlOadfBYvvMwFzz+lU0Rddzi7YcBmPnQpy1u//RnM+jfLTOWIYUsfu8OCiFEEnhrxV67Q4iak4bn0zM7ze4wWiVncEIIESVfbj7Is19ttzuMsHTvksrXd56MK6iXZHl5OQDZ2YkxQ70kOCGEiJLRfXM4fkh3NhZVBOo8gka3UAGr6Srt2+lwtTvKUbbsYGUdQ+96n/m3z2Bg9/i8BNkWSXBCCBEl2WkuXrh2ctSPs2hbCZf88+uItztlUB49s1Mj3m6sSIITQogEV5ATnftgC7aWMPKeOYHl35wzjPPG9IzKsaJBOpkIIUSCC7Xqf0cN6p4Rk+NEipzBCSFEghvQPZO5Pz2BA+V1eLU1zY3WUF7r5pZXVoTVplJwyoieeE3NicPyuXJqfyoqKiIbeJRJghNCiCQwpEcWQ3pkNVt/3rg+zdYNuOPdNtvTGj5aWwTAx+sPcPJRPehiaBxxPLC7KUlwQgiRpD7dWMy24koAlFIYyvr5uwtG4zBAobjvf2uoqve22db0P8wH4IKxPXn48glRjTtSJMEJIUQSWrO3jKueWRTxdv+7soj/rmw4A7x62kBM35AGrTWmBlPrwLCIY/rlcsmEwojHEQpJcEIIkYRGFmTzuwtGs2F/eSDZaHwJyNSYvmT02tLdgedkpjg4qiA7kKhW7Cpt8zjPfLntiNtfXrSTSQPyGGDDWDpJcEIIEWW7D1fzg38tprLOg6EUSlmdOAylUFiXDZUCBYHthrLudQUv+/dRwet8+6DAf3tMa+s/K4lZyUprTWaqk8KuGWjfYHOt4YJj+gT2MbW2kptpPT5lRE+01sxbf6Bdr/dXZ48g1eXAUDC0R5YtyQ0kwQkhRNR9sekgmw5U2h2Gz6EW17ocCqdh4HIoMlKcZKQ4SHU5aGs+05tOGsLEgXkYCiprPZw6smfcTIIqCU4IIaLsskn9OPPoAirrPZi+MWvW2RKNzrBMbY1p8595fffJhVTUeWISo9urcXu91Litee2CnTgsn+euniS1KIUQQjSXk+EiJ8PVruesvu90PF6TWo+J16vxao3HNDFNrPFuvnVe0+SFBTujVtj5043F3PrKcn4xsz+ZqYmTNhInUiGE6IScDoMuIVzyu/fcUdx77ii01lz97GLmbyiOaBxvrtjLWSPymDKwa0TbjSZJcEIIEcc2FlVw2sOf2R0GAKv2ViRUgouPO4FCCCGaKamq57tPLbQ7jICnvtpldwjtImdwQggRRy75x9cs2l5idxgtmtAvh7v+twGX00X/bpnceNLguOkx2RJJcEIIYTOP1+Sx+VvYdrAybpMbwBdbDjdaHtcvlxOH5dsUTdskwQkhhM0Wbivh4bkb7Q6jXS6f1C+ukxtIghNCCNtNG9KdF6+dzIGKWnQrU7upNor4/2T2ysgHdgTH9MuN6fHCIQlOCCFsNumBuRyoqLM7jIDzx/Xml2eNwGEostNduHz32WSgtxBCiHbpkZ0aVwnuzRV7eXPF3pD2XfjLmfTMTotyROGRBCeEEDZ758fTY3Ic7Zvt2+srE+Y1NRuLKrjg8a8C+4ztm8PK3WUht7n7cLUkOCGEEPZSSuF0KJzA60t3c9t/mt+3a09yS3EYLc4iHi8kwQkhRJIzTc1j8zfzyUarfFdZjZvNEZjdoN5rMva+D9vc77UbpjJhQF6Hj9dekuCEECKJmKbmnL9+wdp9VoeQ88b15lBlPV9sPmhbTCt2lUqCE0II0TFu0wwkN4C3QuwsEg2njezJ3783HofRxhiHKJEEJ4QQNth5qJrvPLWACt/cazpoAJwO/I9G6/z7WI/963XQY3vMv30GA22atftIJMEJIYQNPly7n92Ha+wOo8MG52fSrUuK3WG0SBKcEELY4Nrpg7h4fF+0bqhSogi6lKeaVy9RND5LC5z0Ba289vnFLN7euGZkR311x8n0zk2Xgd5CCCFCk5thnfm8t3ofP3pxmc3RNJbqNHjqqgn0yU2nd2663eGERRKcEELYbPuhKrtDAOCH0wcGHiul+HLzIS6dWGhjRB0jCU4IIWz2oxlD+NGMIW3uV+fx8ocPNrD7cDXQcInS1GBq7fvPGirgNXWjdV7fusB/WlNcUUdZjTvQ/pOfb2t2zH98uoXZ101heK8slNaotqo+xxFJcEIIkSBSnQ7uPmdku5+3Zm8ZLy3ciaFU0P0+S0m1m/+tPPJQgkufWBB4PKJXF9675YSESHSS4IQQIsl9669fYEZoDMG6/ZUs2lbC5EHdItNgFEmCE0KIJPfFL05m3roivKZ1ibGl8XT3v7M25PZmL9klCU4IIYT9euemc8XUAQD84F+LmL+huEPtvbFsD28s2wPAzTOH8tNTh3U0xKiQBCeEEJ3IjpLqiLb36LxNPDpvU2D55R9OYerg+Di7kwQnhBCdyMe3zWi2btnOw8xbVxTobTmiIIstB6rYV1ZLncdLrdvkYGUdK3aVttn+5U8u4C+XjuP8Y/pEPvh2kgQnhBCd3LH9unJsv66B5U1FFfxkdvO54kJVUeeJRFgdJglOCCFEI4PyuzCuMDekMza/x75zLGeN7hVXwwcMuwMQQggRXxyG4rUbpnLrKUO5MMRLja8t3cWts1cwf/2BKEcXOjmDE0KITkZrzX+W7uaDb/ZjKIXhK+xsPVYE13wuqawPqU1/z8y3Vuxl+6yzoxF2u0mCE0KIBKK15vmvd7Bw2yFq3WagFNeUQXmkOh2YZlDJLq19y+DVGu0r3VXnNnnqi+ZluTrqL5eOY2xhbsTbDZckOCGEiCN1Hi9/mrOBvaW1GIY1gY51hmWV2Vqzp5wNRRXNnvfZxo6NbeuoW08ZGhc9J4NJghNCiDgyf31xi0WPo+39W6YzouDI87zJfHBCCCHCdsbRvXjph5M5WFmP1hqtQaMxTau0lum71Ki1NYuAxroEefeb37T7WJ/97CScDuu+W8/s1Mi/GJtJghNCiDhz3ODu7X6OQyn+8ekWdrajUskJf5zfaPnf10xi+tD8dh87XkmCE0KIJPCdyf34zuR+ADz04Qb++vHmdrdxxdOL6JLqJNVpkOI0KKtxU13vbbbfx7edyKD8Lh2OOdokwQkhRBz6aG0RP3x+SaD7vsLqyu/2Nsx7k5vhok9ueqPnaQ1V9eFXEqms81BZd+R96jxm2O3HkiQ4IYSIQ//60upoon1d/FtSWu2mtNrd4jaAMX1zrN6XWD0x/ePcAmPeDF/y9I2FS3EYpLocvp9WHZD56w+wr6wWgBe/P47RvbOkk4kQQojwvXjtZN5bvZ8bX1oWdhurdpc1Wr71lKHcMnNo2OW0/L0oE4UkOCGEiENKKc4eU8CJw0/ncFU9XlNz9bOL2XqwKuw2/zJ3E3+Zu4lHLz+GVKeB22tiahicn8mo3jkRjD4+SIITQog41iXVSZdU60/1x7fPaHP/t1fu5R+fbGHtvtbPtm5+eXmzdT85ZRjnjC0g3eUg1WngdBikOAzSUxxhx243SXBCCJFEzh3bm3PH9g4sbymuZOZDn7b5vIfnbuThuRubrf/nFeM5fVSviMYYK5LghBAiSdV7TC58/Kt2Py/d5cBpKArzMhrNE5doJMEJIUSC+t/KvcxbV4ShFP5+lsHdRz7ZWExZTeu9LJs6e3QBj3332IjGaCdJcEIIkYBq3V5+3MK9tHDddNIQbj99eMTaiweS4IQQIgGluRx8+JMTWLO3DG/QuGsFzF68i0XbS9rVXl5mSmQDjAOS4IQQIsHMXVvEtc8viVh7sy4czWWT+kWsvXghCU4IIRLMwm2HItrer99ew6/e/AaP2bxiymc/O4l+3TIierxYkQQnhBAJ5q6zR3LX2SNb3Ka15uevreI/S3eH3N6RakvOXrKTc8b0xuUwqK+pwekwqKEWBeRnpYZdFSUWJMEJIUQSUUpx66nD2HGomvqgm3MaOFxVz4GKWmrdoRdLfmz+Fh6bv6XFbdefMIg7zxrR0ZCjRhKcEEIkmT656bx6w9RG62rqvYy454MOtZud5sTlUChl4DQUF4/v26H2ok0SnBBCdAJpLoObTx7Co2HMEwfwv5uOp3+2dTkyUWYTMOwOQAghRPQppbjuxMFhP7/O03zi03gnZ3BCCJFkdpVUM/0P8yPW3i/OOIpeOWnsL6+i1u3lqIwupDjj//xIEpwQQiSZyrrwZ/RuyYMfrOfBD9YHlvMyU1h296kRPUY0tJnglFJpwGdAqm//17TWv1ZKDQReAboBS4ErtNb1SqlU4HlgPHAIuFRrvd3X1p3ANYAXuFlrPSfyL0kIIRLPf5bs4mevrbI7jJCUVNXbHUJIQjnHrANO1lqPBcYBZyilpgAPAg9rrYcAh7ESF76fh33rH/bth1JqJHAZMAo4A3hcKZW4Ew0JIUQE6eZjrONCj6zUZuvu/VbLY/DiTZtncFprDVT6Fl2+/zRwMvAd3/rngHuBvwPn+R4DvAb8TVkjAc8DXtFa1wHblFKbgUnA15F4IUIIkcgumVjIJRMLw36+1hqvqfH4//OaeExr3fEPfozbG14GdTkMts86G4DycmsS1UTpRRnSPTjfmdZSYAjwGLAFKNVa+y/07gb6+B73AXYBaK09SqkyrMuYfYAFQc0GP6dFXq838AvtiEi0kewS+XcUqc9JIuvsrz8U0f6cRPs98Jiah+Zt5cXFe6N2jBlD83B7dSAxek3Ndyf2ZunmfTgMRXlFBfVezTEDTVyOJOlkorX2AuOUUrnAf4GjohWQUuo64DqAwsLwv82I5CafExGKZPqcHKysj2pyA/hkU/MZCJbvbilxb2LqwFz6dU3n0vEFpLkcFGSn4jDiq2xXu3pRaq1LlVLzgalArlLK6TuL6wvs8e22BygEdiulnEAOVmcT/3q/4OcEH+MJ4AmACRMm6EieCifKabWdEuV31NrnJFHij5bO/vqbiubfk9ZE6xjZ2bD0V6dwuNqN1hqlwGEYOJRCKTC1Rmvr/pHWGqtusrXO1KB9j61l63Kl9q03NYEzNv9/C7Ye4m/zWx8U/vW2Ur7eVsrsZfsAuHh8X/707bFRee3hCqUXZT7g9iW3dOBUrI4j84GLsXpSXgW85XvK277lr33bP9Zaa6XU28BLSqk/A72BocCiCL8eIYRIGqap8frurX3wzX4WbiuhIWlZP/333bymZsKAruRnpVoJTTfez9Q6kPz8Sa/5uoafOeku7jzzKLxaY5pWEjxQWskLrZxFnjayZyx/NSEJ5QyuAHjOdx/OAF7VWr+jlFoLvKKU+i2wHHjat//TwL99nUhKsHpOorVeo5R6FVgLeIAbfZc+hRCiU9lSXMnMhz6NeLvvrt4X8TZDFY9T6oTSi3IVcEwL67di9YJsur4W+HYrbT0APND+MIUQInl0zUihW2YKh6Iwnqxrhovg/pKl1e52t5HqNFqdQueeM4dw2phCslJdpKc44rqiiVQyEUKIGMvLTGGpDZVAvKamtLqeFxfuJD8rlQ37K3B7TV5cuBOAFIfByN7ZVNV52HSgssU27n9/M/e/3/je3OWTCvn9hWOiHn97SYITQohOwmEounVJ5eaZQ7nzjdW8vGhno+31XpMVu0rb3e64wtzIBBhh8XtuKYQQImp+NGMwkwfmMXFA1w639YvXV7P9YFUEooosOYMTQohOqDAvg9nXT2VPaQ3TZn3c4fZm/OkTAMb378rr/3dch9uLBElwQgjRifXJTeefV4xn1e5SvCa8tHAH5bXhz0Zw1uiCCEbXMZLghBAiQX20togfPr/E7jB47+bpjOwdf0UGJMEJIUSCym+h0n80Dc3PwGEoXE4nhoJUl4O/XDqO3rnpMY0jVJLghBAiQY0rzA1U+g/XgDvebXOfZXefSl5mSnLOJiCEECL+vLtqH49/shmnw8BpKByGavbTX5/SX4bLaShG9ckBX5muk4/qwcfrDxzxOMf+5iMAfnPOMM4bE38luVojCU4IIRLUjS8tC+t589pIaK25+52N3P3Oxkbr0lwG82+fQUFO/F2mlAQnhBBx7Pb/rOS1pbvtDqNVtW6THYeqJcEJIYQIXU29l26ZKTE73gvXTCbFaWAoMAyFoRQOpdhbVkOdx2RgjoM+uWl0zclGqfia+60lkuCEECJOjbjng6i1PbIgm0kD8zC1Nd1O9y6pTBqY12Lx5NF9c4CGWcsTIbmBJDghhIhbsy4czeOfbMFpKAzDOpvaUFQBQG6Gi+OHdEcphQJq3V4WbS8JefaAtfvKWbuv8Wzdj87bxKkje/LXy48hzeWI9MuJOUlwQggRpy6b1I/LJvULad/Ln1gQ1tQ4TX20toi9pTUMyu/S4bbsJsWWhRAiCfzm/FFMGpgXWM7LTKF7l1QyU9p/Jnb2o19EMjTbyBmcEEIkgSE9snj1+qktbps262P2lNaE3FaN28sx93+IUoqSoElZF9x+HBlhJEy7SIITQogkVV7rZv2+Cu751kie+nxro0Hf1XXewP28lhxu4XLnit3lHDeo49PrxIokOCGESFIn/+lTDlbWRay9G175JvD4nR8fz9F9ciLWdjRIghNCiAS1saiCFxbswFC+MWtGQxd+BfTvlhHRBBesawzH54VLEpwQQiSoCx//isq68OduC8fXt02lID+v7R3jgCQ4IYRIELVuL0fdHb3B36H4/YdbOHtcPaapMTVMHNCVHtlptsbUGklwQgiRIByGYnz/rizdcdi2GN5efYC3Vzcu1tzRKXuiRRKcEEIkCJfD4PX/O67FbbVuL6a2zqq8pvadYWm8vmlyvL7lu/77DZ9uLG71GJMG5lm1KH339ZSy7usZCrweD0opUlNcGMpKuFdOHRClV9txkuCEECLBLdlewjd7ynAYCnyluz5ef6DNed5a0jXDxT+vmNDiNpnwVAghRExorVm7r5yL//F12G0Mys+kX14Gn2ywzuqumDIgQtHZTxKcEEIkoAF3vBuRdrYWV7G1uCqw/L2nFzba/sp1U5gyqFtEjhVrkuCEEMImByvr+GLTQfyzz/hnBggs47sHFlinAtuG9ujCpgOVUY9x04FKSXBCCCHa5/gHP6bWbUb1GAU5aZhaU+cx8Xq1tVJZCfKRy44hJ8OF01A4DIXTsCY7TZT53toiswkIIYRNXrx2ctSPsa+slqLyOkqr3VTUeaz/aj0s21nK1oNVZKe5yEhxkup04DBU0iQ3kDM4IYSwzfj+eS2OIdtVUs3Mhz6l3huds7vfnDeKLmlOMlIcLN1RAqjA0AAV9FOhMAzfMlBdVY1SkFXvQGEVbtbaOiu0HkNWmpOecTLwWxKcEELEmcK8DDY+cGbI+3+2sZi73/qGHYeqQ9r/7rfWhBtaSN740XEc28/+WQckwQkhRBQt33mYCx7/yu4w2qUgJw3/hcpA8WbVcLbmMIygjjCNCzxPHpTHiF7xMU5OEpwQQkRYvcfki83FOA2Dp77YZnc4reqVncacW08gJ8MV0v4y0FsIITq5n7+2kjdX7LU7jIQewxYJkuCEECLCbjttOKU1bjxejcaqBWnNpm09XritJCZx5KSHdmaWrCTBCSFEhBXmZfDsDya1uv3pL7bxyqKdmFqjgeCO+VuCqop01JmPfN7qtmd/MJEZw3tE7FjxSBKcEEJEyT8/3cLv319vdxgt+umrK1l296l2hxFVkuCEECJK/r1gR8yOdebRvUh3OUhPcTT8THGQ6rTGrNW4vQCYpiY3M4WLju0Ts9jsIglOCCGi5ItfnAzAjS8t491V+6J6rPe/2d+u/Usq67nllKFRiiY+SIITQogoe+w7x/LYdzrWRlmNm7H3fdjhWLLSnPTtmsHlkws73Fa8kwQnhBAJICfd1WJZL4CSqnqu//cSFm8/3GY7FbUe1u0rJ83liHSIcUeKLQshRILLy0zhPzccx/u3TA9p/9tOHUZWavKf3yT/KxRCiCRkmhqv1nhNjen7eaRhAX7HD+nOuv3l3PTSclANhZT9xZYBTF9Jrium9md8/7xovoyokgQnhBBxYMn2Ei7+x9dRP84Xmw+GvO+bK/aSk+4K1J3UWuPxajJTncy+fioDu2dGKcrIkAQnhBBxID8r1e4QWlRW4262rqrey5ebD0qCE0II0bb+3TJb7UTSHn//ZAsPftDxweW3nzaMc8b0bpgjzlBUVVaS5jTo1b0rKc7478IhCU4IIeLM55uKueLpRYFla/JRgpaVb5oa3zKNd0hzGUzon0eK0wjco6vzmCxqRw3MP324kT99uJHX/+84xve35nYrN6yzuURIbiAJTggh4s6byxvPRGAVam6yovEezdoIvtc2rGcXXA6DcYW5pDgMnA5FWY2bNXvL24ylV058zM4dDklwQggRAdpXONnjNTG11RNR+35a/1n7mEHrGrb7nu9bvunkIfzfjMHUebzUuk08XhOPqXF7Tdxejcdr4jY1bo+Jx7TWub0mHq/mgffWNYttY1Fl2K9r2qyPj7g9I8XB5z8/iW5d4u8eoiQ4IYQIUVWdh1G/nmN3GHGlut5LZZ1HEpwQQiSyVKfB6aN6MmdNkd2htOnSCYX0zk3H5VS4DAOHoXA5DfrnZdAzOw1DWffy/OPfgjuT+Nep4G2Au7aKVKdBTk6O3S8vJJLghBAiRE6HwT+vmNDitvJy635WdnZ22O1/veUQf5izPnDPTWvNqt1lYbU1e8muVretu/8M0lPaX6qr3FsbVix2kQQnhBBxYurgbgCs2FXa4bYGdMugMC8Dr6nxmBrT9/OcMQWkuRKjF2RHSYITQog4snxnaUTaue+8ozlxWH5E2kpUkuCEECKObJ91NntKa9rsvdiWn7+2EkMpstNcPP39CfTtmhGhCBOHJDghhIgzvXPSePCi0Szefti6H6c12w5Vtevsrqi8DoB9ZbUc/+B8Jg7oyjljenPu2N44HFbHE6dD4TQUSqk2WktMkuCEECLOKKW4dGI/Lp3Yr9m2t1bs4ZZXVrS7zcXbD7N4+2F+/faaVvdZ/5szkmqeOElwQgiRQM4b14fzxvVpdfstryznrRV7W93emokDulJSVU+ay0GK08DlO8szjMQ9u5MEJ4QQNiuvdVNUVovLYeByGiisYQKm2VDdxKutnpCmJlBf0gyaD86//tIJhVw8vi+vL93Nm+1IdIu3H+a4EO77/eyUQdx4SvhDIWJJEpwQQnRQvcfkpEcWcKiq+dQyyebhj7dx4ykj7A4jJJLghBCig0yt4za5vXTtZLLTXRhK4fBXKTEUDl+FEsPAt14deR+lqKysCMz6nQgkwQkhRAeluRwsu+N4tNY88OF23li+J+YxjO/fFa01LocRKLN1xtG9mDq4W8R6SSZScgNJcEII0WGPztvEnz/aaMuxu3dJZcGdJ+N0dI7qJO0hCU4IITro1JE9eXz+Zuo8Zgszs0XXwco6htz1fmD5rrOs+2OXTCwkJ90V42jiiyQ4IYTooBEF2Sz6+TReXbaPfy3YE7hn5b9UCAQuEza9yKeBzQfCn6+tKf98cA+8t44lvzqF7nE4jU2sSIITQogI+e0Hm+0OoZEJv53LuWN7c6CilrNGF3DFlP5JW7WkJZLghBAiDPUekzeW7cbUkOI08NTX8sC3huHGicNhoLXG7bXGrnl949X8/5naquzvNRtm4vaY1kzdXlPjNjVes2H27vkbisOO8+2V1li4BVtLuOetlquYvH/LdEYUJMbYtvaQBCeEEGEY9qv3294pipSiWTf+qnpvWG1V13siHF18kAQnhBBhePjSsfxk9spWt6c6re76praqkdR7zYgc97jB3XA6DMYV5nLLzKE4EriUVrRJghNCiDBccExfLjimb2A5lBm9tda8t3o/N760LOzjfrXlEACfbSzm0XmbWt3v7ZumMaZvbtjHSQaS4IQQIopq3V6ufnZxIDG1JMVh4DFNTN8Yg4KcNOo9Joeq6sM+7sKtJZLg7A5ACCGSWUWt54jJDZpfvtxXVhvWsUYWZHP5pEIuGt+XjBT58y6/ASGEiKL8rFS2zzobgDMf+Zx1+8qjdqy1+8q5YuqAqLWfaCTBCSFEhByqqufLHft8g7utQd7+LiCrdpdFNbn5fbn5INOGdI/6cRKBJDghhIiQkx5ZaNuxh/Xswpi+uUwY0NW2GOKNVOcUQogIefaKMZw+qqctx95YVMmynYfZfbjGluPHIzmDE0KICDm2MIcZowrb/bz9ZbVM+f28Dh9/a3EVMx/6lN+efzRH9coCYGTv7E7b4aRzvmohhIgjvXLSAh1Rgj3+yWb+8MGGdrf3qze/CTwe0C2DT352UofiS1SS4IQQIk79aMYQfjRjCHtKa9hWXIWhrNm3HYaipKqe6/69NLDvsJ5dMJTC6VA4DAPD18HlTt/0OZ2RJDghhIhzfXLT6ZObHlgura5n3P0fBZZvOHEwd5x5lB2hxTXpZCKEEAnG6TDIz2qY5y3NJX/KW6K0jvX8s6FTShUDOyLUXHfgYITaSlbR/B3111rnR6PhoM9JZ3+Pk+H1x+JzEk3J8B60JR5eY0ifk7hOcJGklFqitZ5gdxzxLNF/R4kef0d19tcfDzrDe5BIr1HOa4UQQiQlSXBCCCGSUmdKcE/YHUACSPTfUaLH31Gd/fXHg87wHiTMa+w09+CEEEJ0Lp3pDE4IIUQnIglOCCFEUpIEJ4QQIilJqS4hhAiTUqon0Me3uEdrXWRnPNGklMoD0FqX2B1LqJKyk4lSKge4Ezgf6AFo4ADwFjBLa11qW3BxJlH/gcp7bEnU9y/RKaXGAf8AcoA9vtV9gVLgR1rrZfZEFllKqX7AH4CZWK9NAdnAx8AdWuvttgUXgmRNcHOw3oDntNb7fet6AVcBM7XWp9kZXzxI9H+gnf09TvT3L9EppVYA12utFzZZPwX4p9Z6rC2BRZhS6mvgL8BrWmuvb50D+DZwq9Z6io3htSlZE9wGrfXw9m7rTBL9H2hnf48T/f1LdEqpTVrroa1s26y1HhLrmKKhjdfZ6rZ4kaz34HYopX6O9e2+CAKXcr4P7LIzsDiS2fSPI4DWeoFSKtOOgNqps7/Hif7+Jbr3lVLvAs/T8HkrBK4EPrAtqshbqpR6HHiOxq/zKmC5bVGFKFnP4LoCdwDnAT2x7s8UAW8DDybSTdJoUUo9Cgym5X+g27TWN9kVWyg6+3uc6O9fMlBKnYn1+QvcAwXe1lq/Z19UkaWUSgGuoYXXCTytta6zK7ZQJGWCa0opNR2YBKzWWn9odzzxIpn+gXbG9ziZ3j8hoiEpE5xSapHWepLv8bXAjcCbwGnA/7TWs2wMT0SAvMfCTkG9eIOvICRdL16llBPrDO58Gn+RegvrDM5tU2ghSdYEt1xrfYzv8WLgLK11se/exAKt9Wh7I7Rfov8D7ezvcaK/f4nuCL14vw+cnCy9eJVSL2P1zH0O2O1b3RfrHlye1vpSm0ILSbJWMjGUUl2VUt2wkngxgNa6CvDYG1rceBU4DJyktc7TWncDTsL6ML9qZ2Ah6uzvcaK/f4lugNb6QX9yA9Ba7/ddOehvY1yRNl5r/X9a6wVa692+/xZorf8POMbu4NqSrAkuB1gKLAHylFIFAEqpLlgDFUXi/wPt7O9xor9/iW6HUurnvp67gNWLVyn1C5KrF2+JUurbSqlArlBKGUqpS7G+YMW1pBwmoLUe0MomE7gghqHEs4TuZi/vcWK/f0ngUqxevJ/6fu/BvXgvsTOwCLsMeBB4TClV6luXC8z3bYtrSXkPTrStSTf7Hr7V/n+gs7TWcf/trDOT989+SqmjsO5HLdBaVwatP0NrnTRj4ZRSk7ES+BbgKGAqsDYReutKghPNKKV+oLX+l91xiPDI+xd9SqmbsXrurgPGAbdord/ybVumtT7WxvAiRin1a+BMrKt9H2ENxfkEOBWYo7V+wL7o2iYJTjSjlNqpte5ndxwiPPL+RZ9SajUwVWtdqZQaALwG/Ftr/UhwD99E53ud44BUYD/QV2tdrpRKBxZqrcfYGV9bkvIenGibUmpVa5uwup2LOCbvn+0M/2VJrfV2pdQM4DWlVH+Sq5OTx1dkuVoptUVrXQ6gta5RSpk2x9YmSXCdV0/gdJr3hFLAV7EPR7STvH/2KlJKjdNarwDwncmdAzwDJNMYzHqlVIbWuhoY71/pG4cpCU7ErXeALv5/oMGUUp/EPBrRXvL+2etKmoy31Fp7gCuVUv+0J6SoOMFfb1JrHZzQXFiDveOa3IMTQgiRlJJ1oLcQQohOThKcEEKIpCQJLo4opVxKqWW+xzcrpdYppV5USp2vlBppd3zCfvIZ6XyUUgOUUt80WXevUup2pdQUpdRCpdQK32fh3qB9gj8rWin1QtA2p1KqWCn1TsxeiA2kk0l8OR740vf4R8ApWuvdSqlnsToVrLUrMBE35DMigj0HXKK1XqmUcgDDg7YFf1aqgKOVUula6xqsgdp7Yhtq7MkZXBQ1/ebl+8Z1r++b91ql1Cql1CtBTzkDeF8p9Q9gkO/xXcC5wB9939IGx/ZViGiSz4jooB7APgCttVdrHfwF5wzg/aDl94CzfY8vB16OSYQ2kjM4e9wBDNRa1ymlcoPWnwTcp7V+Tyl1BtZUKAeVUkOBd7TWr9kRrLCFfEZEKB4GNviGhnyAVXy71rftJOC+oH1fAe7xXZYcgzVmb3oMY405OYOzxyrgRaXU9/CNpVFK9QFKfAMqhZDPiPBrbSyX1lrfD0wAPgS+g5XkWvysaK1XAQOwzt7ivlByJEiCiy4PjX/Hab6fZwOPAccCi5U1LfwZwJzYhifigHxGRFsOAV2brMsDDgJorbdorf8OzATG+iYBbu2z8jbwJzrB5UmQBBdtRUAPpVQ3pVQqcA7W77xQaz0f+AXWxJ1daH69PFgFkBWDeEXsyWdEHJGv5uU+pdTJAEqpPKzPwhdKqbOVUv7al0MBL9as7q19Vp7BusS9OuqBxwG5BxdFWmu3Uup+YBFWj6X1gAN4wVfLTQGPYv1xGqK1Xt9KU68AT/qm6LhYa70l+tGLWJDPiAjRlViTjv7Zt3yf1nqLUuoB4GGlVDXW1YDv+ra3+FnRWu/G+jx1ClKqKw4opY4Hvqe1vsHuWER8ks+ICJV8VhpIghNCCJGU5B6cEEKIpCQJTgghRFKSBCeEECIpSYITQgiRlCTBCSGESEqS4IQQQiQlSXBCCCGS0v8DEwvTyJjBGsIAAAAASUVORK5CYII=\n", "text/plain": ["<Figure size 504x1008 with 3 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "\n", "fig, axs = plt.subplots(figsize=(7, 14),\n", "                        ncols=len(p),\n", "                        sharey=True,\n", "                        )\n", "\n", "for i, (ax, w) in enumerate(zip(axs, p)):\n", "    log = w.get_curve('Sonic', alias=alias)\n", "    if log is not None:\n", "        ax = log.plot(ax=ax)\n", "    ax.set_title(\"Sonic log for\\n{}\".format(w.uwi))\n", "\n", "min_z, max_z = p.basis_range\n", "    \n", "plt.ylim(max_z, min_z)\n", "plt.show()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Get a `pandas.DataFrame`\n", "\n", "The `df()` method makes a DataFrame using a dual index of UWI and Depth.\n", "\n", "Before we export our wells, let's give Kennetcook #2 a better UWI:"]}, {"cell_type": "code", "execution_count": 11, "metadata": {"scrolled": false}, "outputs": [{"data": {"text/html": ["<table><tr><th style=\"text-align:center;\" colspan=\"2\">Kennetcook #2<br><small>Kennetcook #2</small></th></tr><tr><td><strong>crs</strong></td><td>CRS({})</td></tr><tr><td><strong>location</strong></td><td>Lat = 45* 12' 34.237\" N</td></tr><tr><td><strong>country</strong></td><td>CA</td></tr><tr><td><strong>province</strong></td><td>Nova Scotia</td></tr><tr><td><strong>latitude</strong></td><td></td></tr><tr><td><strong>longitude</strong></td><td></td></tr><tr><td><strong>datum</strong></td><td></td></tr><tr><td><strong>section</strong></td><td>45.20 Deg N</td></tr><tr><td><strong>range</strong></td><td>PD 176</td></tr><tr><td><strong>township</strong></td><td>63.75 Deg W</td></tr><tr><td><strong>ekb</strong></td><td>94.8</td></tr><tr><td><strong>egl</strong></td><td>90.3</td></tr><tr><td><strong>gl</strong></td><td>90.3</td></tr><tr><td><strong>tdd</strong></td><td>1935.0</td></tr><tr><td><strong>tdl</strong></td><td>1935.0</td></tr><tr><td><strong>td</strong></td><td>None</td></tr><tr><td><strong>data</strong></td><td>CALI, DPHI_DOL, DPHI_LIM, DPHI_SAN, DRHO, DT, DTS, GR, HCAL, NPHI_DOL, NPHI_LIM, NPHI_SAN, PEF, RHOB, RLA1, RLA2, RLA3, RLA4, RLA5, RM_HRLT, RT_HRLT, RXOZ, RXO_HRLT, SP</td></tr></table>"], "text/plain": ["Well(uwi: 'Kennetcook #2', name: 'Kennetcook #2', 24 curves: ['CALI', 'HCAL', 'PEF', 'DT', 'DTS', 'DPHI_SAN', 'DPHI_LIM', 'DPHI_DOL', 'NPHI_SAN', 'NPHI_LIM', 'NPHI_DOL', 'RLA5', 'RLA3', 'RLA4', 'RLA1', 'RLA2', 'RXOZ', 'RXO_HRLT', 'RT_HRLT', 'RM_HRLT', 'DRHO', 'RHOB', 'GR', 'SP'])"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["p[0].uwi = p[0].name\n", "p[0]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["That's better.\n", "\n", "When creating the DataFrame, you can pass a list of the keys (mnemonics) you want, and use aliases as usual."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'Sonic': ['DT', 'DT4P'], 'Caliper': ['HCAL', 'CALI']}"]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["alias"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th></th>\n", "      <th>Caliper</th>\n", "      <th>GR</th>\n", "      <th>Sonic</th>\n", "    </tr>\n", "    <tr>\n", "      <th>UWI</th>\n", "      <th>DEPT</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th rowspan=\"5\" valign=\"top\">Kennetcook #2</th>\n", "      <th>1.0668000000</th>\n", "      <td>4.3912849426</td>\n", "      <td>46.6986503600</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1.2192000000</th>\n", "      <td>4.3912849426</td>\n", "      <td>46.6986503600</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1.3716000000</th>\n", "      <td>4.3912849426</td>\n", "      <td>46.6986503600</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1.5240000000</th>\n", "      <td>4.3912849426</td>\n", "      <td>46.6986503600</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1.6764000000</th>\n", "      <td>4.3912849426</td>\n", "      <td>46.6986503600</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th rowspan=\"5\" valign=\"top\">303N764340060300</th>\n", "      <th>3387.5471999996</th>\n", "      <td>303.7090000000</td>\n", "      <td>32.0276000039</td>\n", "      <td>252.4951</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3387.6995999996</th>\n", "      <td>303.7090000000</td>\n", "      <td>32.0276000000</td>\n", "      <td>252.4951</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3387.8519999996</th>\n", "      <td>303.7090000000</td>\n", "      <td>32.0276000000</td>\n", "      <td>252.4951</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3388.0043999996</th>\n", "      <td>303.7090000000</td>\n", "      <td>32.0276000000</td>\n", "      <td>252.4951</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3388.1567999996</th>\n", "      <td>303.7090000000</td>\n", "      <td>32.0276000000</td>\n", "      <td>252.4951</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>46594 rows \u00d7 3 columns</p>\n", "</div>"], "text/plain": ["                                         Caliper             GR     Sonic\n", "UWI              DEPT                                                    \n", "Kennetcook #2    1.0668000000       4.3912849426  46.6986503600       NaN\n", "                 1.2192000000       4.3912849426  46.6986503600       NaN\n", "                 1.3716000000       4.3912849426  46.6986503600       NaN\n", "                 1.5240000000       4.3912849426  46.6986503600       NaN\n", "                 1.6764000000       4.3912849426  46.6986503600       NaN\n", "...                                          ...            ...       ...\n", "303N764340060300 3387.5471999996  303.7090000000  32.0276000039  252.4951\n", "                 3387.6995999996  303.7090000000  32.0276000000  252.4951\n", "                 3387.8519999996  303.7090000000  32.0276000000  252.4951\n", "                 3388.0043999996  303.7090000000  32.0276000000  252.4951\n", "                 3388.1567999996  303.7090000000  32.0276000000  252.4951\n", "\n", "[46594 rows x 3 columns]"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["keys = ['Caliper', 'GR', 'Sonic']\n", "\n", "df = p.df(keys=keys, alias=alias, rename_aliased=True)\n", "df"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Quality\n", "\n", "Welly can run quality tests on the curves in your project. Some of the tests take arguments. You can test for things like this:\n", "\n", "- `all_positive`: Passes if all the values are greater than zero.\n", "- `all_above(50)`: Passes if all the values are greater than 50.\n", "- `mean_below(100)`: Passes if the mean of the log is less than 100.\n", "- `no_nans`: Passes if there are no NaNs in the log.\n", "- `no_flat`: Passes if there are no sections of well log with the same values (e.g. because a gap was interpolated across with a constant value).\n", "- `no_monotonic`: Passes if there are no monotonic ramps in the log (e.g. because a gap was linearly interpolated across).\n", "\n", "Insert lists of tests into a dictionary with any of the following key examples:\n", "\n", "- `'GR'`: The test(s) will run against the GR log.\n", "- `'Gamma'`: The test(s) will run against the log matching according to the alias dictionary.\n", "- `'Each'`: The test(s) will run against *every log* in a well.\n", "- `'All'`: Some tests take multiple logs as input, for example `quality.no_similarities`. These test(s) will run against all the logs as a group. Could be quite slow, because there may be a lot of pairwise comparisons to do.\n", "\n", "The tests are run against all wells in the project. If you only want to run against a subset of the wells, make a new project for them."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": ["import welly.quality as q\n", "\n", "tests = {\n", "    'All': [q.no_similarities],\n", "    'Each': [q.no_gaps, q.no_monotonic, q.no_flat],\n", "    'GR': [q.all_positive],\n", "    'Sonic': [q.all_positive, q.all_between(50, 200)],\n", "}"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Let's add our own test for units:"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": ["def has_si_units(curve):\n", "    return curve.units.lower() in ['mm', 'gapi', 'us/m', 'k/m3']\n", "\n", "tests['Each'].append(has_si_units)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["We'll use the same alias dictionary as before:"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["{'Sonic': ['DT', 'DT4P'], 'Caliper': ['HCAL', 'CALI']}"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["alias"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now we can run the tests and look at the results, which are in an HTML table:"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/html": ["<table><tr><th>Idx</th><th>UWI</th><th>Data</th><th>Passing</th><th>Caliper*</th><th>GR</th><th>Sonic*</th><th>SP</th><th>RHOB</th></tr><tr><td></td><td></td><td></td><td>%</td><td>3/3&nbsp;wells</td><td>3/3&nbsp;wells</td><td>3/3&nbsp;wells</td><td>2/3&nbsp;wells</td><td>3/3&nbsp;wells</td></tr><tr><td>0</td><td><span style=\"font-weight:bold;\">Kennetcook #2</span></td><td>5/24&nbsp;curves</td><td>54</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">HCAL<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">4.39 in</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"4/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">78.99 gAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT<div title=\"5/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">63.08 us/ft</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">SP<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">52.47 mV</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2.61 g/cm3</span></td></tr><tr><td>1</td><td><span style=\"font-weight:bold;\">100/N14A/11E05</span></td><td>5/18&nbsp;curves</td><td>79</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">CALI<div title=\"2/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">8.90 in</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"6/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#33EE33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">103.74 gAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT<div title=\"6/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">74.90 us/ft</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">SP<div title=\"4/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">101.60 mV</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"4/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2.62 g/cm3</span></td></tr><tr><td>2</td><td><span style=\"font-weight:bold;\">303N764340060300</span></td><td>4/22&nbsp;curves</td><td>78</td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">CALI<div title=\"3/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">311.97 MM</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">GR<div title=\"6/6\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#33EE33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">67.49 GAPI</span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">DT4P<div title=\"6/7\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">279.84 US/M</span></td><td style=\"background-color:#CCCCCC; line-height:80%; padding:5px 4px 2px 4px;\"><div title=\"\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#CCCCCC;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\"> </span></td><td style=\"background-color:#CCEECC; line-height:80%; padding:5px 4px 2px 4px;\">RHOB<div title=\"3/5\" style=\"font-size:80%; float:right; cursor: default; padding:4px 0px 4px 6px; color:#FFCC33;\">&#x2b24;</div><br /><span style=\"font-size:70%; color:#33AA33\">2493.56 K/M3</span></td></tr></table>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["from IPython.display import HTML\n", "\n", "HTML(p.curve_table_html(keys=['Caliper', 'GR', 'Sonic', 'SP', 'RHOB'],\n", "                        tests=tests, alias=alias)\n", "    )"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Here's how to interpret the result:\n", "\n", "- Green background: the log is present. You can see the mean value and the units (check them!!).\n", "- Grey background: the log is not present.\n", "\n", "And the traffic light dots (hover to see how many tests passed): \n", "\n", "- Green dot: all the tests passed.\n", "- Orange dot: some tests failed.\n", "- Red dot: all tests failed.\n", "- Grey dot: no tests ran.\n", "\n", "The **Passing** percentage shows how many tests passed for that well."]}, {"cell_type": "markdown", "metadata": {}, "source": ["---\n", "\n", "&copy; 2022 Agile Scientific, CC BY"]}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}}, "nbformat": 4, "nbformat_minor": 2}