From e27868ad582c5b36c1e58c9975d1c40667246881 Mon Sep 17 00:00:00 2001 From: kebamba <74548837+kebamba@users.noreply.github.com> Date: Tue, 18 Oct 2022 12:25:40 +0200 Subject: [PATCH 1/2] Create .gitkeep --- workshop_materials/.gitkeep | 1 + 1 file changed, 1 insertion(+) create mode 100644 workshop_materials/.gitkeep diff --git a/workshop_materials/.gitkeep b/workshop_materials/.gitkeep new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/workshop_materials/.gitkeep @@ -0,0 +1 @@ + From 37a8fa9a1b79d41aef4d9610e9d01ae33fb20486 Mon Sep 17 00:00:00 2001 From: kebamba <74548837+kebamba@users.noreply.github.com> Date: Tue, 18 Oct 2022 12:27:53 +0200 Subject: [PATCH 2/2] Add files via upload --- .../Case_study_ECEMP_simple.ipynb | 293 ++++++++++++++++++ workshop_materials/Guidelines_workshop.pdf | Bin 0 -> 327714 bytes 2 files changed, 293 insertions(+) create mode 100644 workshop_materials/Case_study_ECEMP_simple.ipynb create mode 100644 workshop_materials/Guidelines_workshop.pdf diff --git a/workshop_materials/Case_study_ECEMP_simple.ipynb b/workshop_materials/Case_study_ECEMP_simple.ipynb new file mode 100644 index 0000000..d188ff7 --- /dev/null +++ b/workshop_materials/Case_study_ECEMP_simple.ipynb @@ -0,0 +1,293 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3696cfd0", + "metadata": {}, + "source": [ + "# ECEMP 2022. Skill Workshop 01. ENBIOS case study" + ] + }, + { + "cell_type": "markdown", + "id": "418255b7", + "metadata": {}, + "source": [ + "This Jupyter Notebook has been prepared by Miquel Sierra and Alex de Tomas researchers of the LIVEN lab, SosteniPra research group at the Institute of Environmental Science and Technology, Universitat Auronoma de Barcelona. For any questions about the notebook contact us in miquel.sierra@uab.cat and AlexanderDe.Tomas@autonoma.cat. For questions about ENBIOS and the related projects you can reach the director of the LIVEN Lab: cristina.madrid@uab.cat." + ] + }, + { + "cell_type": "markdown", + "id": "a1d93ea5", + "metadata": {}, + "source": [ + "The notebook will will guide you through ENBIOS preparation and use. A simple case study including onshore wind and open ground photovoltaic technologies in Spain, according to PNIEC, was prepared. The materials you need to run ENBIOS in this notebook are here (https://drive.google.com/drive/folders/1YC6dtH-pPvL2UjO-z_udw6YKJ8lLBrZm?hl=es). The URL of the BaseFile you need is here (https://docs.google.com/spreadsheets/d/1uJFMEDOn_HlaJsQ8A1nz7oad0p6WxwJ5rzsQwRA39g8/edit#gid=387453521)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26571376", + "metadata": {}, + "outputs": [], + "source": [ + "from enbios.bin.script import Enbios\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "\n", + "enbios = Enbios()" + ] + }, + { + "cell_type": "markdown", + "id": "4390a220", + "metadata": {}, + "source": [ + "**Before you start with next steps**: please check the BaseFile that has been provided to you. Try to recognise all the elements we commented during the presentation in the sheets \"BareProcessors top levels\", \"BareProcessors simulation\" and \"ScalarIndicators\". It has been filled for you, but in case you don't understand or are curious about something, you can always ask us during the workshop." + ] + }, + { + "cell_type": "markdown", + "id": "a7986a2d", + "metadata": {}, + "source": [ + "### Step 1: Prepare the files you need to run enbios" + ] + }, + { + "cell_type": "markdown", + "id": "20a28df7", + "metadata": {}, + "source": [ + "**To save some time, we have done this step for you. However, here we leave you a brief explanation of the process in case you might want to use ENBIOS on your own in the future.**\n", + "\n", + "ENBIOS needs several input files to be able to do its calculations. These include the following:\n", + "\n", + "1. The BaseFile with all the required information.\n", + "2. The input data file: a csv file containing the data of the energy produced by the technologies that define your system for several scenarios. \n", + "3. The latest version of the Ecoinvent Life Cycle Impact Assessment (LCIA) methods implemented in the ecoinvent database. It can be downlowded from Ecoinvent website: login -> Files -> Supporting documents -> LCIA_implementation.zip.\n", + "4. The spold files (.spold) of all the Ecoinvent processes to be used in your simulation. In our case, the spold files for onshore wind (1-3MW) and openground photovoltaics electicity production in Spain.\n", + "\n", + "ENBIOS has the following intermediate commands for data preparation (already executed as mentioned above): lcia_implementation_to_csv, lci_to_nis and list_lcia_indicators. More information on the commands can be found in ENBIOS User Manual (in the Google Drive Folder -> Documents). Examples of the resulting files from executing these commands can be found in the Google Drive Folder -> Intermediate files examples, where you can also find a more exhaustive Jupyter Notebook in case you want to follow all these intermediate steps to run ENBIOS." + ] + }, + { + "cell_type": "markdown", + "id": "fcd81e5a", + "metadata": {}, + "source": [ + "### Step 2: Run enviro" + ] + }, + { + "attachments": { + "image-2.png": { + "image/png": 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oZi6NnrHfV5SV6Ve1JF3PRmxRS8jZL3loXyWBuLxjOk+J7VGl461+tH7LttvOFiq9LrMqPV6zM11s65+IiIiIiIiIiIxMglfXMXNzruTjw49lNL1/E66UhOmmZYi2L8HVW3GRrWWjyytxt74+O8i+no1bsnf864cZ+2DRjJ7BCIoEF3p241woCGjT7NmILSX6JrLeWMO0SyxHRdSgkb68VqFSe6LMnbGsnUPUIFpe5hQsXvhUS06ZBr9srzO17xX/ME5kLnoWu7AvsqhyLlyr+8PFt4/gzIhuP/KtFG157RLCjqxLm+0yGSd39aoZbGbTGz0D04pnGjv7bVnHS4l+jKp5TJpNv/CIBUo9ioB4lv11PbBJnf/oEewfsdlXlAVOnGuLy/ZhtL2qmYcOBeFNMlD17G8Ph/riqpOe3SdE/1Xb9xsyJys9D8Hm8Wp2M0ONz3dERERERERERFR3pmUDP7g5V/IR+9iFu9v/AtdcDyFyz2cQldOIJeKi1qAd6kXj8JDPNOthdFDrN0Lt28IsM6JnN/YFDZ3GZ/rxMOmnIjAC5PTFMopBQ8mwUdFZhhqA0C7KBgz9M41isGSmRqlpl1gOS8zaMYpB31B+9oNa5i2gH390EL6hsIU+isol7urPW3/qfP3DJzLbYurIoOHO/+xynDC7w97OOps6gjWSD0MYxuS53GXVLuz7DOtxdNCHoXAQoZzUAC3b6CQu6rejlnkyNOLYhXvL7VIDhPqLw2Ic/dTU/Tg8BJ9+Q2jHRI5K9tvs8ZJbik+dpqW2VvOYtLh8BUrtiaDGELK73BSOrAkYLobbWdfahJ8QwYPAkEPBTwPL59oKDGxCEGGEww6WPDThW1lJlqpa1jMTDC/w2VJse1jahxeaHuw+MQx/OJxXys/W+dH0uLFzvIYx5MstJVv98x0RERERERERETUak+DVTVyVkyUf7127hXPvvI9/umcZJpfcgatyErFbV8trxcAxKMokhjEEn5TbJ0ZgJL9Te9NJmGQUDRxToIRWZfu0UfvYWBVSoBjrFI0Ecud70RDgGDgGJRQ09NkRAKwEfEpNu8RyWFh6bFplXHcBXByehJI3nx7sPmdY14GLGJ4so3STDT27z+Wvv8CISdmni7nbq+ByZJmvs7BhOhIk30lsmVTMp9WzG+dM9sHAxWFMmvQhI7KNwkBOkEoEtYDcbLeKWG7XAI5ppe/UcfavnMzvA2bgWLZEnja97cAJJYS87mIq2W8HjkFRQgjmTCMAbBqw3tZqHpOWlm8Ax7Rgj75PrExfbdp7fbi0bxLD/grWNQD9/lNe8NNkn5ek3GCBrXOthenlUQPVjpY8zNfzwCobfRoaiSy50sdose1hcR9eaNSbPsyGWz8/Fj5urB2vQYQmV2J/zr4ZRKja57u8t9orSWh3fCIiIiIiIiIiKk5SlGyRvx/84Af4ifIPiLr/R8k3phIK0r9Poa2rFcmmNABg+m0ZL/2bH+AXv/gFNv3Vpuq12tQoBqUAEKpuEKb6Fsty1BLXWf1xGzhh6sga+IZWGS7UU74pHFnjw6V99ve3qSNr4Du5pWAgNm9cbo+a4fomIiIiIiIiIiJNfubV3A28f3W25OODxBw+bE/ht9cSmWFXb8XqsQwqccf/yH4rJcMa2WJZjlriOqs/boPKWew/iKBlRdnf39SSgZYy27g9iIiIiIiIiIiI6qXZOODG7A289c5HZU0sKV+ruEGV6Nl9DsruujbBEYtlOWqJ66z+GmsbiEywQj3b+IcnG67E19SR7aJfn3PGUMnCW5Za6Nl9AsMnffANPpBfBraA0UG1zzsL66vw9iAiIiIiIiIiIqJqywtefe1PB7HhDwM5wyRIkCTxDAoASVJTtkQfEupfIEbSHkRE9TKAY4qCY/VuhgWiVFoYgLFfH83CWZba6sHucwrsxEsHjilQSoxTensQERERERERERFRteX0efWd73wHyVQSkuQC1Et84rkIR7lc6nOpcHAqmUxi1apV+Ku/+qvqtZqIiIiIiIiIiIiIiIgWpZzgFREREREREREREREREVE9uerdACIiIiIiIiIiIiIiIiINg1dERERERERERERERETUMBi8IiIiIiIiIiIiIiIioobB4BURERERERERERERERE1DAaviIiIiIiIiIiIiIiIqGEweEVEREREREREREREREQNg8ErIiIiIiIiIiIiIiIiahgMXhEREREREREREREREVHDYPCKiIiIiIiIiIiIiIiIGgaDV0RERERERERERERERNQwGLwiIiIiIiIiIiIiIiKihsHgFRERERERERERERERETUMBq8qMopBSYJ/eKreDaEGNTooQfIPg3tIMdaPo9FBCZIkQZIGMVr9hi0Y3M9uXzwmiOh2xs8/IiIiIiKixat08Gp0UL0wlv8YHAWAKQz7F/APx4LLV86FwMZbF1PDfpNl82Mhx9vExdpi20cEQ6TBUTTWNmmktlhTn/3HfD1NDfsRGAkipChQlGMYqMv6tDDPqWH4HQ5qi+3A4ESjqtd5tjGOCSfV5/gq/T1gga5XbbkstFsLgg4uppOMo9/vFr/F+H2RTCz233W1lLcueW4hIiIiInKaxcyrfhyeVKAouY9jA4XGF8GDhXMRxGz5jqHg4tlSr3Uh5uvb8wX1wmb2EQrWui3OGtgUBDCCM4XW6egZjAAIbnJmC1ZXox4rjbb/TOHVk+NAcJNDx2W1TGH4q3uAw5MID/XUuzELXKMeG3r1PE4WyjHhpGoeX9X8HlBn43vwbLHjaGoY3x6pWWtqbCFtV35fvD3VY7vb/V1XSwvhsx8ApjB8pheTmfU3icP9IwgwgEVERERE5KjmyifRg6GwgiH9oKm38EsAX6h84gtMo6yLUQxKAYwEQ1BMfokOHAs36IUbiwY2IYgRjJwZxbGB/CUZPTMCIAgRuzLZJnXTKPtHKfXefwpvs/5en6Xxqqf4PKeGv4o9OIxJBq4q15DHhl69jxOh/seEkxr1+FrI67Uf/f3jBT8vAWDq1ZMY7+9H//h4jdtGOfh98fbUcJ91dT7fNdz6KKQHQ8eGcl+/dBgnfSfx1hQwwK+BRERERESOYJ9Xi9DU8LcxgiBCjXELZRUMQCRfnTG5u3EUInZ1O2UjOGvx7z9V9PBLUMJD4DWLxY/HSR3w+CrLli1BYOTbBcq/jeLZPePo37JlAVwsJqfxPEbkoMkJ8BYAIiIiIiJnORK80neWPDooQfLtwTiAkYBaA3xwNFNLP7cMhL5vIpXan0Wmfrih5npmXrq+HGpDrQFfoh8AK+vCfHqG9VBBO22XkipjnWf/Xmw5qrWMRUoHjp7BCPpx+Ins0pt15q3171GwzXntNBtuf/ks7R/G7aF7+DM7XQPtPzBZnwXLptg/jkSfAj7sGQfG9/hyltW8o3Yn9snC4xXqHH50UILP52ucvg+sHteG8fymV7jVc3XOdhPD/MZ90oFjp/i502p7rK2H8lTvOLG0XWwfE85uv8KfCYv3+DK2y9bxY2EfLPyZ5ICHNyGIcZx81exkewYjCOKbQ58zeaOD27NA/1vWPzuc59T3UnWkAuvA2nG1YL4vwto2y/bTaDj3aO02rlPD8mTfn7/slha9xDartH1W5mHlHFHqs66q54Ui6nW+K7Y+ip5L6n2O1doQGEH/4ZfA5HsiIiIiIuc4nnk1cEyBMnkY/QCCIbUO+LEB9AyFEQoCI9/W/xgKYKT/MCa1Oz6nhuH3if4sMvXDsQc+4w+M8T3wndkkxgnXoLDF1DD8kg97vhDS1Yb/JiZ8ARTrJqLQugAAjD6Lk1smddMLITgSqLwT+qlXcXLcWEqqxLKVsc57tPfmrZcQMl0kVGsZAbV0IDBiiF6NnhkB+rfg4YI/HMWFkMAvD+vq1OvabEeFy1dw/+gZQjinH4JJHO4H0H8YL2m/iBtl/1Ev/OSuTwWh4AgCxsBUmccRBo5l1kG/tp8WukvciX2y1DQqWQe1Yue4/irwktbuUBDje3y5FwenhuHXykrZ2W7FlNgORc+ddtpjdT3YVc3jBCi9XWwfEw5vP62Nxs+E2+X40lg9forugw5+JhU0gCcO92N8z7OGIMMUhr89Ujh44dT2NNtXGmC7Ova91PY+nW9BfF+0vc1GEJDOYJM27uRh9I8E4Pf7Ifkm8E3j8LwJjCAgfRV4KTuvycP9GAmUCGBZPu9X0D6HPmMLb/danBdsqsH5ruhxoLXB7LdIvc+xahvGgyH2d0pERERE5DSllFBQAZD/6D+sTGZGyX2tTB5W+gElGMqbmBLUhk8eVvrRrxzOvkk53A8FxjeFggp044WCUICgkjfpchVaPl078pbPsJz92YWwsS7MmlJgPnbYmF+l67yc9jqyjKrJw/2Gdon9S7898uep2wfNp2q+TgoOz2VcvlKvrWwvsZz6Y8XavMtia/8pNs/89VXRcaROr/i2dWafLDUN6+Nb22esyN/Xi7XJznFt3K8q2W7OHjuF9sWK22NYD2Wp9nFiYbtUfExUsP3sfA4vhOOrnO8B1raTlX2w1GdSpculzsdsn835HmatHWVtT1vfH2q7XR37XlpwHdg4Lzb090V726zQ9xbz4yZ/2uL95m0rvg9a22aVtc/Zz1jz7VDt84K933U1Pd8V++zPO5fU+RxbrA1EREREROQIi5lX/Tg8qc8EUcrsd2IAx0JBjHx7EINf3QPoSyuod4AGNxnu//X1oh/jmJjUN6cXVu8TtcZk+TJ3+ok+lPq3PJy/vD2fq7CPiNxyKIGKboMvZ/aVrHOxXoLfLLUfVG8Zex7egn596UC1ZOCWwmlXAHzo7QdGAk7d2V3lbTg1jK/uGS9QhqTO+0+xYwM9eHhLv65fsmoeR7ntqWyftDqN3PGtrYMasXVcG7MUe/C5LwD45VtqJkKjnf9stMfOeqiqMvaRktvFgXlXuv0Kfg4v1OOr2PeAQm8psZ0s7YNOfyYV0DOEbxqzjJ7dg/HgN0uUuHJgexb4/tAY27XS76V29+lyLaTPe80X8DnDyL7e/sLDxyeQe1oOwrjqC4+rsnXeL7N9jn7GFlLt80IZv+sa4XxnPJc0wjl26lWcHM8tV05ERERERM5xvGxgSQPHEPrCCEagK4Gmk6lxrj3U2ueLjai97sspBRNyooaFekHyl29Z/4VW1jqfegu/LDFK1ZZR0/MwxPUSNTxSsmQgAPRgKKwgFBzHHl9lde+rvnyYwvBX92C8P/9YaaT9p/CknAlJWebEPmlhGnbUfB3oNPK5tPrHTlZV1sNCPk6q6HY6vuwovg8695lUysATh9E/fhKvTgGYGoaoGFj4gmutt6em5tu1ku+lVVoHevy8t68Wn3/VnUftzgtOq8f5rlHOsURERERE5LzaB69GBxEY6Uc/9uBZk98OmRrnhkepG6AXFO2iUUipQm30AWwKAuMnX7V8h35V1nlVl1Gjv9u32F3B+QaO5fZlYN4BexE1WL6p4a9iz3g/Dr9kuBu2wfafQqbeqvYlPZtqsk8aZlnHddCw59Iab4fqrIdFfJyU6zY7vuywsg9W/JlkRc/D2NI/jj3PjmLq1ZMY7z+MgskCddiemVnXers28vdSft6XpRbbrBbzqMl5wWH1ON/V9RzbM4SwEi6RwUpEREREROWqcfBqFIOBEQRDYYS/GcRIYDBbYsSQRdM4RLkJ0x/3o2cq6/A+xxSc+u0v7q7eg6+Wqo9RyTov673OLWO2GWrpwEErJQPNJjCEcCgIaHejFyzpMomJ0ulozi2fWi4wGLL6g7gO+0/RC19TeFXUcoG4dlCD48iJfdL2NOysgxpx9FxqZ7vV4tix0Z4qf6ZU5zhxWq22XyGL8Piyo5x9MO8zydEGYUjUDsRXT45bvtkjq9LtqWmk7Vrh99KS49TguKpAY5/Hfon8pLAS86rFb4l6/F6p6nnBIfU43zXcOZaIiIiIiJxWneBVgVIko4MBjARD4k64gSdwuH8EgUzpBu2iSj9I/2IAACAASURBVAC51RxGMVjXOw3Vdo3vgU/fsKlh+K10OmC2LtRh+h9bo4M+7HGq3oj6w2x8j8/0zsLRQa3ueyXrPPve3PIboxgcHK3+Mmaaof5wHbFSMlDXPv0QQ7nBgU1ie2fvwJ7CsD9guDDu0PKZHitquUDtWCnwnvrvP9kLXz7DeKI9QYQyC1DhcWSt4Q7skyWmYcL6OqgVJ8+l9rabo8eO6bFhpz1V/kypynHitCpsv4Kzul2OLzus7IOlP5McNbAJQYxjfDyIbxa7M6JK2zPTjAbZrpV/Ly29DiwfVw39fbEe22wce3yDOf1olZ5XLX5LODwP08+6Gp8XHOHQ+c5WOctGOMeOYpClCImIiIiIqkcpJRRUgH7l8GSxUaCg/7AymTtQASAewZAYxzidycNKP6D06wfq36c+gqES86qEyfzM5qu1NfP3/sPKpBJSgob2W1kXZtPrPzzp/LIpk8rhfrNlCyr6RatsnYt1YPremiyjokwe7s9M37SFOfPMb69Ze7RpZpdJXZf6FWNh+Uq9VgeaHCvmj8wyNtL+YzZeoXaUfRyJeRi3sfkyO7FPFp6G+fg21kEZjPujcR4l9ysbx7XpcIvbzayt5R47ectQ5P3F2lNqPVTO2ePE+naxcUw4vP0KnmsW6PFl5XuApXN5oeFF90Frn0nlL1f+97fJw/25x5OuHcW+9zi3PTMzqOt2dep7aal1ICZr4bxonFcjfl+0uM3E8hrfa3145rXx3GXy3nI+/yptn5V5lH2OCIaUepwXirWx5uc7k+Og6D5fr3OsfvrOfrEgIiIiIiKVpCiKAiIiIltGMSgFgFAD9KMFoPHa0+i4voioMU0N++Hb8wWElGONWxaUiIiIiIiIqq7GfV4REdGiMCr6mev11bshqkZrT6Pj+iIiIiIiIiIiogbG4BURERU0NeyHf9jQ98ToIKTACPoPv4Ri3ebcDu1pdFxfRERERERERES0EDXXuwFERNS4eh7eAvh8kPboh/bj8KRSl8BHo7Wn0XF9ERERERERERHRQsQ+r4iIiIiIiIiIiIiIiKhhsGwgERERERERERERERERNYzm69ev17sNRERERERERERERERERACA5nQ6Xe82EBEREREREREREREREQFgn1dEREREVCMjv6jfvINfrN+8iYiIiIiIiMie5no3gIiIiIhuH3/0idrP858/qP08iYiIiIiIiKh8rno3gOh2c+jQoXo3gQy4TcxxvZjjejHH9WKO64WI6oHnHiIiIiIiWuhKZl5duHDB9kQ9Hg8AwOv12m9RCfMpBf/fxHW8+/tZJJNJ3fAUPrp2C7NzSUBRgPl5tGMen1gq4dHAF7GkrbXktE+cOIHt27dnnjtNm3YjmpmZAQBEo1Hb761ke0cikcy8i+ns7Cx7HlYdD40DACaibkTisuk42tA+D7B/R3/V2mKVfj91av+yu+9Xc7+20pZGPq6o+o6HJgAApyMdgFsMe21HZx1bRMWMXZjBjHoijcWAuBzLG8eD7Pm3w9OFqJzIvI6p52Y5IaOrTQzr9XoQ6C/92RAajyCizi4aNx9HThjP/er8DIM9bjHAv7LT0ryJjL72tS+X+c51BYafxXe/++OS726U4+Dpp5+2Nb7RU089VdH7iW5Ht9vvWyIiIiJa+AoGr7SgVV9fn+2Jjo+PZ55XI9iQnE/Be3cLPtOxBDdngXQaSCsKZueXYj6dREpJoUlx4er1JN6Lyfjvv/o9HvR60PUHbSWn7VQwoJwfB5X8oKj0h4MWtCpne/94bCzz3O72npmZQX9/8SCQLMsIh8Nlz8Oq02qc9uWdvZmL4EbahZ6Xz8fx2NEIAOCFnYvvwqWV/UnbX/VB30o5ddzwh3QubR0txvWyI9ALADh9dAZuXZCj0YxPiCB9Iic0g8zV4ECfZ1HO2ygSA9raxAm2owPoQFfeOLGYjI6O7ElY3zpPZ/ZzXFuWsfMRSxfOL0QBj6cDANDlUHwzFJ5g8MpB/6rAdvn5TOG/lfLzAvfH1DOI87WvfTk30JS8lj9S87Ls8OZlxccFAAxlAmLFgliNdBw89eSQeKJfPguefvrpTPBrwQexCm5PHZvrx65KA4lGdraJ08EUO99z6jnveq5zIiIiIqKFZEH2eaUoaSxz34H7Otpw9SYwn8r+bV5JIYkklrW24IOPU/hYkfHL965CcsVxV3sT7rSQgUVERERERERERERERET1YSl4JRtvMS3yd4/Hk8mMiUQimeFOZ8sk5oDYTeCjm8CcLnglJ4GkAtx1D7D8zmb47l2C6VbgratXMfHav2DPv3uorPnVOnNBX76w0DyrUfoBsL+9e1euBABMXLqUGV7O9pZlObPPRKPRzHzc7uwd+GNjY+jqEnfq9/b2OrpfZWbjBiLx/NSrWEKGv0sMf2R1J0IQt3M/fnQcB3fWv4SgmUozCQvtY8ZpLebMHjP1zJKshJNZcqUcOvoKutTb+iPRCJ7cuQMAcPSV04jHRLpDZGICLzx3sKL5hMa10rZd6FCzH3Ydn0BMFsfqem8cOwL2M0qdNB6Jw+0W+UOdHcZzi3gdmpAhx0UG7GYHM3nqOW8zsmz+GSMjN2suGsu+Nhs/oUuPjVvMuEvIQGImv0xhJYztLtfjjz+eWU5ZlpFIJDLP43GRFpRIJDLPZVnG5cuXHZm3kZqoh6PnC6Qj6bhl4OBm5zL3CmVJlfpbOeqegaRm3Fx5bxiYVoedBfBodpTp6bMAgPfbz+DtS28CAB5Y+WDmud66LwGFSwpmFToOOjrbEItn9+cOj8hyDF+IIJuOLqPXI8bZv2N9ZtzHLpR5HGgZRVayj3TjP/XkEJ4+MAxAZLAsuKwT3fK+cUXdlu8WHn3tmgezL6qYhVXpeqwkm6jS7yb1/F5Wybzruc6JiIiIiBYCW5lXbrfb9CKScbjWB1JfX19On1lOBhpcLqCpGWiCeABAEoAEwKUomE0mcUezC513NmFZmxsTSTd+fFFxbP7Vpv8hZOVHUTX6PrK7vfv9foxXUN7P7XZnyheuX7++4HjavJ0uJVg8ZCdE1Xl73cAOv1juHfa7hau6agU29dPX72fVnt9i4uSxWkmZxWoHsWQ5BrdbHJfnw+OAGryaOH8e3l4R8O7tXV3xfCai4qKqxw0k1IBVZ4eMqCwuvB4NAzsCFc+mLFq5PrfbA48aOJJlGfpYi1vrt6nLjaPRQwCAUCiWOSG9sPnlmsx7QhYX8U+PTWDz+t6y5mnVzkDxSMGB0xN4crP1Njx2IFJ6JNX+rc4G5+zMu5iDBysL4jpJC1p1dbgzJRvjAGJRsVMe3OzB46dLB7Yq9UedEv55Jvu9rVhJwXI0VDCzW/1/+qwIYAHAumwgau5WNlj19q38wJVdZsfBgVAE+zfnD9+7PnfFPx4S2/6xA+N44Un1xh0rX6BKUYMyV068IdaDaoVWWtBAKzn49IFhyyUEP7xyBXMffQRIUsFxlnz60/Dcc4+tpltiM2Cl6cZZXDmnro/pdVixTQ1kVbmUINXuhp99p8VJbP/mTkvDnVLr8vhEREREROVYkGUDARG8am4Cml1AWgEURfzfLAGSJOHWbBqpNNAkSbjnThfea21G7MaCXVwiIiIiIipT4t13oVy+jNZ77hE/JLQglpINks4tXYrrd9wBJZXKGZ5De58koaW1FW3t7VVuORERERER0e3JUjRHX7ZNe16stJx+/P5+cVemPgPLMRKAFojUqzTQMg+4XC7MSS24dmsec0oas1Ian5GA+WQKLc63oCZqXUJQ234/HhvLDEvIcs42155v3bo1Z3t/Wc2YcmJ7a/OIRCKZjCxZlvPKCAJAV1cXenvFnfpOZGHpi2tpS93R5kZEvfN8AoC/utW1Gp7ZPme1zGC1VSMTsZE4cbxX447ix3Y9nsmmSgCYiIhSopFItqTohO7c4Ha3YbXfDwDw+/14rozsk/Nq5tVENIHz+7MH5Z8fmBDzaHPjlZCY59Yalw+MqXkrvR3ZbFV95pOse+1OyIjLIgtkfddKnJoIoxJ25+1VS6KORfNLpjrJqTJ7eomEE2kf9bVt27bMdorH45mygfF4PDM8kUiYlhasFueKAZZHn3UFOF82EGjATLxvrQO+pWbZrAO6u7Xsq7NobddlIK3MPjUrIVgO/WF0aGymdAlBN7Dv+Bgc0bxMZFwBwNmz2Uy0s2cBs8wrXdbRU089Zbl0WjqZRFNbG5b/6Z/C1dxsGrxKpVKYi8dx/e23kUokcrO0JAmulhZILhcklwstHR2YXb4cbStWFJ+xMetKl3E1Pa29uM/0rdPoQTemAADd3dksrBVrhmqWfTVuyFTrLxTUc4jV7zi1+H5Xk2x19dh7ZTyOrf2e0sMdwuwpIiIiIloIyk5F0gcQ9AEFrT8GbXhnp71SBzc/DEOSWuCSmkz/fk2WkEh0AHfdIYJX6kNyAWgGXCkJrSkArmakUik0p9NoVpogwYWkzWXUq3c/NWbPrYxfSbu/XKB0nz6IVen2Lkbbp6LRaNEygpoxXbDNagArFDoNALhwYQJwm88j29ND9oXbcK30scceAwCs7+/H1h07LM3babUOHFmZrtYmpwMl9Zy3k8ppWyNfbPB6ezPBCTkWx4UJEbTq6OjCrscPAAA6vV7E1JJ+Xq8X6zduAwBMnA/j8cd3AQAOHnzO8jwnRFwbq7tyD0qPV5wPPIhja+DJMpfIOVqgyBhmeTnyuBgux9AJsV4ScgJdauhg46E/z4z72t5/dHTe/a/sF0+icdEZFQBvLAb5kBogiWVLqnnOVxZMK1ckEsHMjIhaxGK5Jd60Gxb0n0O1nrdTXn65vPKQ1aB9vkUTQEdb/dphtWxgKXaDXnL8AuSoLpCec+NO5hm0LwQJ70F4O8sIgpUKPJwFsE4rFwfMqf1ZtbZn+7zq7i7w3grJcsJWCUHMTFQ2w+Q14Oy3xPPuddmygevW5Y9nZCeAowZdXC0tSCUSSN66BaTTkACgSfzuaHK7cceddwKf+QyUZO4vBwWAq6kpE9BKvPceUpIElApewbxU4PvtD6J1ZbY/K30g8oHM8Afxvjbw1pvoVutKXjk3LAJYQNWCWMaglXF4tYNYjaSa3yW1OPH5iIxoTJyw9gY6Cw53UqkbJIsNJyIiIiKqhYaro/e7f3ka7qV/iBa3eb35+Py9mJ1bm/8HCSJ4BcClSFCkJvEjMwW0oAkuNCFVzYYTEREREVFDUhQFSjoNJZVC8vp1zF69CiWVgqQogMsFJZ1Gy7JlaPF40LJsGSRJgiLeqE0AACC1tqKppQXXf/1rpJob7qcUERERERHRouHILy63O1uWyPjctvmbaL/3X6P9E39m+ufZG4DrrSJhqCYAEtCcBKC44HK54CrcL3NRjXhXWS1LCOrLBgKidKCRPgvLSnZUpcxKFwKAx+OBXy0/Fg5nMwRKZWBFo+Ju+kAggPBY8dJTbgBt2p3WhtSrHWq21alTp+A+/QoAYPPmrUWnVwu1LKlSat7VKL1Sr3lbnY6T5xCnz0dObofjr4QAiOMtHB7PDO9b/wgAoKvDA1nN++mK9SEhi3SpyEQEUEvl9fn9OH5QZAJZzbw6HrqAjrYuAIBHd0zuCh3CejUxRpaBA6F9AIAnA/vxyFE1S9K7Fxfi6mdUQoa7TTzv9cjYGXAmqyZ7jnLnZT1lxomL5e/v8kJW63bFE3F0qWW6PJ5OhML2sxqszBtRkTUh+7PLKyfkbHqJDODgcdvzttau4iIRUX5tYiK77LKufK3b7c6Up92/w9r2ki2WF7Qz752BxVc/9uBmkfW3LxTXdhG4PdUtJ2nGWDawZmJjcLu71BddOanWbvV8FY9GAK/IGu3IjAskCh9t1n1LzTb6FjKZR93dAKbF4Pehz8oBHsiUELRWPtDKcaAfZWxCRlv2VIkZ9W9b+9x4ZLXYV8bcvZkSgvt32P8ueOXAsMi4Uq34+zPiiT6jyCzryi41cIVUCsr8PJBMAuk0Uskk0rOzSMkyZj/6CK7mZkitrZBcLiiKAqTTOaUF77j7brR/8pNIJRJIL11acrY5pQLvA9beL27C+28/yy7T26E38S1123evW4dn/lasg7cvvZnZ3mvXPIg3rojxu39yFlcwDABYsfapilaLmUJZV8ZxbrfsK8D579aymh7d5+3A+IQ4xzz+SiTzUWwcftDBcqdWqnuU+v652Et1ExEREVF9OXa7oL4vrLKCVholjaaWdrhcCtI3p8RvRVeT2rFyK+SbLfjt+83ocLdC+uQSNCF3IdISkHYBUjPQBAkuBWhpEpMgewqVDdSzeiHSKYUCpdprQATRxsfFBfRiwatDhw5lAm5erxd9MVEm6OD+1yy1RQvt9QHo7RWlvx555BEcPy4u9tYzeNUIPx6NAdZaB4PrOW9jG2o1fysB7WrsGzMxEYzydHQAuj6NZtR+myKyjAtqUNnb2wuvV1xt7XBnL/VGJrIl2qwaj7gRS4gLOmMRoGuXCDr4+9zwdoir7r2dfQjHxNG68eghACJAdiEKXFIvzHs9bZn+fcaibUBIBC2cDGLllB9ViQvEIkgVunQBHeoV4k5PBybUAEokGsspW+bkvGXts/rUGBBX/xqLAVoZvjr1JRWZiGKmK1uvrqtLBAf6+rJ9lo2Pj2eCTJFIJOdvxew6KoJOng4P4rG46fON3ux+6OS8i/H7/Znyh/r+rGRZxurVoh+5f/zHbNnIRw6M508kQ7y31+vN7EennrQfUNgfyPaxsut0PKcvSCdiNKUYywY63edVXhBHFjOQ4yG4uwJiWOw84F6t/j2GeFzt08q7De6OQP5ELXdDts7aKC+q5fMeXQeo5eKAB3NGm7ulBq2WW523PW1uZIL7gJw5V0ZmZPR3iuH9nR74Hy+jxqQWkJo+mwnO4dFv5Yzyxm9EX1hr71+bHb95We5wG5R0GulkEqm5OSRlGUoqhXQiAUVR0LJsGZra29HsdkPSlQeEogCKAm1vbL7zTrhaWzPTs2LtmgczbTcNxHWLoJV4us68L7PmZVi7QkznjXeBbq28or1VQBVyuoRgTD0VzcRl9HrFcTR2IZb5XWMcvvpx8Tl2/mDlnz1mN0UavzOWCto14s2eRERERLR4NF6tizQAJQ0kf4/Uxz+HkgYUyQUFaaSTTUh82Ia56HIkl3YhHfsDNN1S36NKAZm+rVwp0QdW05LOsrOviIiIiIhogVODUAAgNTWJBwClqQmu5ma0dXaipaMDTW1tIjtLG199j5JOQ1EUuFpagKYmEbiyGLwiIiIiIiIi+xwPXhmzYWybh+gRGS4ALQBSUORbSMZmkPgwCvfv43jY1Yz2d1ogf9SMprQYU9OivR3qkyY30Pp/IJnqKb9NDagWmTVaWSQAiBs6qzfTr5btqzazLD/tjnVAlBC0QpZl9PZm71p85BFR4qxUqUGjiYkJuN1inn19/fB4Ttl6vx1W7m5shKwrTbVKrFidby3mXc1OvO2w0oZqtfO8WiowGomgo0t0Jh6NRrJZkgAO7hfZiUdffhmnXhZlAdva2nQZJqJEnh0v7CyUGbUTR8cOAQDCE2Po6hBZJxPR1ehQx/B2xPCCWm5u3ysTmEl0ZNoadyirxA1x3oxEO9DrFeeImZicSWiSAezoE+vC0wbsOi7a6ekFIhGRzXb+4KWqzfvCzv2ZeYcvifnJE2PYuXNHWfO0wkoyVzQ6g1hMtFmWZdPMpv7+fpw/fx4A0NHRkff3Qp7bWfxO9ZmZGZw/H6vKvIvRl7u14tST/ZbGe+RApJzm5HEDptlW1UzAMpYN/FcWTg+VZGfFo+pntzv7HUCWZ+BWywbGI2Nw9+4So3Rtrn7y2TqI0oGq7m41yyaZzdrqbj6bSVjSDy9HW4GiCe42d+bAdbe50aEu+UQMGIuI5zv63QXfX8yVA6LsHbrXAd3qwBe/hSvTYllWPPVUNrMqeS1bRjB5DdM/EXX41t5vY4ZqoKmprQ1tn/oU3PfeKzKq0mlAkkQ2laIgeeMGkjduQJmbQ1pRgPl5kaGVSkFJJtG8dCncd9+N1OyspeCVli2VWQ4dfYbVo7qsM227dutH1r137ZoH8Yb6fIWVZaeGFVe/K8Xktsx3FDmBTLll4/DeLufKuFopG1jqfURERERE1eR48Co0/grGLomya245e1Gno8NjrbSOAiCVQkqehRz/GMrsdSjyx0h9/HtAaUHbPT3oWtGN1tY70FKi9XMfTSLx3s/hSsfhkuYqWKrGUcsfC1a2V+T0IcyERJm88IEJoFNcXWpb/QjQZ63fmnKNHT8Kd/ioeDETATrFBaeVj+wFulaXfL920VyjlYHyeDw5wbBCtCCZKBuVvZBo5b122f1B2QjBlMXOyjZplB/31d4furrEsbDavxX9aomzmVgM0Yi4khyZCCOgltE8HRrDjl2iD6rVfashq3W2otEZnDru3Dlj5/q9AICNR/8cMkR5LzeASzMiGLRjZfbzSdadCuIy4NRloUCfOCe9MhaBdiuA1+OGRxfviKvzDl+KQjsljYUn4K7wyrjdec/MiPPW5q4ZhA5sFNN40loJVTus9Lfz3LZLuBDNBhBmLhzQT0B7Ard6OS8Ri+PCcXHzQd+OwjcPWLmxJhF5DrLs/LzrxnIZuxKMFfYcmmwxxrKB1STHL0CeEX33ebzrdevNm1nYBACoZQPl+CFk14Ib6BX9X8kOrJlpNWoxDbWvKwDdLwLTWmyq+6woswcA3eugxbSmv2Rt+oWOg1OhGTxp1oeb/pjVPe9wA7B2r1BhZ9XGr1uXfV6Ivv8r/XMbFEWBpAaqJJcLisslgk/q/6lEAun5eaRkGalbt6DMzyOdSgHJJJRkEin1f6m5GWntuYXg1RtX3swNYKk+ufxNQO3PSh/EmkY2aPXAygfx702+jl85N6wLbDlfN7BfUUr2e3U79Xel5/R3qphaN9DtBcbUmye8HcD5qPnwF/Y6U9IYKF02UD9OoWGN8l2XiIiIiBanxiwbmE4jPa9gPiFBuTkLzM4CaTda774f0j1fwHzHQ2hfsgRtreaTkADA1YobE68i8d7P0eRKwtWUNB+ZiIiIiIgWNSWdFtlTqRSU+XkkEwnxXM2oSs/PQ1GDUlpwSkkmATXrKpVMAum0GF/9H7dpAIeIiIiIiKgWHAtehcZfAQBciI5hvVo+ztvRi9Cl0wCA8IXTcJ8Wnc3u3Pxk4QnNA5DuQEvH/VjW9r9BSSWA9BygKJBa2qBISczPvgnXzTnMF7ijWEILmtu8QOo6ACCZugOp5B22l6mSO+sWw11oExMTmefGO3U9kTExPHwKfZtFiSm3txfyBTF8IhxC5LQo2+XdvNfRdo0dVacbOQXvtp358x47Cu/6nerYxUsrTUxkSyNGo6JsVl9fH2IWyiTq36efjpbB5ZRy9iWnyvWxc2ZzxuVvhPVRz4w7rcQd3Nm0nrGxMVELEEBU+zuAS5ci8HRm7+4fC42p7wUmJpw7dh45KrKHYvEEwhdEppecWA+gCwDw3PkAdr4m5uft6sBqr8i3Co/FsLdgOcLybF3vxekxcT4di2RWCxIA2tRMDU9iAkdXqp+dvX5EJkQZudP7/Ni8315JuXLmHWgTZfC6vH7IcsyReZuykJjS4QY2r9eX41Ofu7uyg+JRbOtU95c2D6IONbOe83bKruNiez+3w9n9uBbZVnrGrKtKSgKa0X+vkSNH4faIbSxHs9993J5s5lVX13rEY+fVNwCxqPjc7/LvB9TSweWspBWfGsKV90T5vGlky8WhG5heJ9Ktus8C02rq1bpu3Uhnz2K6u7JygWaKlQB060ZyV1AmfN3ZsyLjChBZV2rJvBXb8jOUzGzfvs3+TLWAUyqF9Pw80nNzohxgMpkJaGmBK0kbPj+fDVRppQPVoJXVzKu1Kx7EG+fUzKr7smUE1654EG9AHb4yu9wP6N6rz7p648qb6H5XS7UDVmwbsr8ObNAyq4wZWMy4cpZWqnjsfBQr1ZKAL+ztxWd3XTAd7qRSZQMb4fstEREREd3eHAtehc+Lqzf+9dkv1b2d6xG6JIJa/j4/Xj4tyssVDV4pACRAcs1DaroGqWkeIh0LAOagzN9ESp5BMjUrKgxCVPxIK0AaKaTSaShKM9xLliF1K4FUSsEvJq/hkvwxmspcNrtf3Mv9cdNoJRh6ewv/QBo/qJZJ2rYTbjWQhbH9cKv9f3i9nZg4JUr6FQtedXZ2Ynx8PPPaSuBHHlOnu6PwvM+fEgGuzs07TacBiAtX+iCVdiHLbtk/43S0IFg1bN++vSb7RiPsf43I6fXi9IUQO+1zct6y2TEjA1D7pItEs8f1TDwKd0d+rSk3gHjcuavTp3ZmS949dlRcjI51dmQuLsflBFZ3qS/cbYipV4+6OqpziX7z+lIXnPowdlR8jsrhELyrReBdTsTxyq6VAICtz5XX/1U9521kZe3KsgzI4jwaj1wQAQQAcvQUZDWY5GkD4jG11Ku3NxuVq7R9dZr3K+O5x5C+qq029Zxgi26WT27Odga16/iELljcC+9Ke304FlKrwFWhfq1+PmOtz6tC7y3F3bUeSKglAd2ebOB94hS6vKIvOlmOA7K6nTr64PUfFO/1lLmO1f6L3nhvOFMqEND1dTSdfT7drQZ71BEyw9etyzyvPISVjVgZSwh62vLHjifk3CCXhZ3kjS9/OVPqrhvILsi6dVix3aT0nb6fK4MTJ14GAGzf9nDpGasURQGSohKDFqzKZFjpMrDyHmrQKpN1lUqJCabTor+sUpqXYe0aEZy6cm4YV9QA1DTWAfeJUcxKA2quvCwCXN3Illac7l6HFWWWT7Sr1sGqRix9Xc02adU4V3vdeE4XnCo03ElWygYSEREREdVT45UNVAAoaSipD5G6EYai6Mr9KQpSSWA2oSCdAtJQkAQwl1Iwn1SQxBzmU/NIJSW0z99A6615pBTg1+/cwIx0E933ONfBLRERERERH+lh7QAAIABJREFULRCKAqgl/9Ja9pUanIIueIVkMlteUPcQd8ulRdaVokBJpyHdpllIREREREREteBY8OqCWjfHjz4Eendkhu9d/wIAIDRxFICFzt/nkE20Mkql4UrNo60pCcWVApCEgiTSzSkorXNII6GWGZTQunwl5m4B1+9w4z8E/Ghb3l1golSIMQtKf9d3Ykb8zd3lBQIm23VfBxIzpTOYvF4vvF57dy2746XnDQvzBnKXya1miYRCocywRCKRycTSZ1jps7N6e3sNd8RX7x71YndDOnXHpJW7S3lXZu018h2xCfWWezeAuNbxuO5eAbfujn5vV7b8miwncsarhkeORuDWyhnKwOoucQw/ubkX+14RJXkuRBOIqod0v7fWxdGy1u98DgAQOrQNibA4D3l7/Tg/ZuGzc4HM2+uW8dgBkTGbABCP6tJiZJFudHwHICe0/cid2UfichwRdTv1eXWl/WRkb1EvNu8OGY8d0jJ9E6aZIvvXV2fepXh0B4IMOXPEyAC0pJeE7jiS3UBkJjvfV8bEeoxGopDVBXvkwFhmWQBnMrCqrViWlNNlA/XcnVuzzwFoO0ds4mXEIyJzMyFH0dG7CwDg6d0Kp0zrX3SLjKvMczXbqlufmdW9DtPdJuNbVOg4sPrVJV6gZLcVOd/Eu9cB02L5Vvz9GfM36DOLCmVh2ck+kiRAUZCenUValsX/yaTIntI/tICUywU0NUFyueBqahKZVooCyeXKlBy0TG3n9H3rgHfFoG6czTy/8pOzBd6Ya/pLIsdu7f0mmWrkqFplgEVfME+7KzTcSaXKBhIRERER1ZtjwauYWkplIjqOC1FxYerJ9S/jwJioSe9GG7KXYIqYA5BMAam0yMLSfkgCQCoJSZlDsysJ0TnWrPqYB5RZIH0TUG5BampBizuJm61AosmF5R0daGprvCSzYhqhhGCxoFK0U9Tvkc+H4D6lBivlOLSQTiwOoNN+jZ9O9T1FSwmq/eUUn3fpC3WyLGeCUB6PJ/O8t7cXHt0F9kLiannAffv2IRAIAKhuyUCjWpUQpCwnAkiNWA6nUrJa42ws9BpCoVPqQLXMFnJLoEWjURzdvx+As2UCjU6PicBUXO7CerU/q1PhGE7p+rMKXRLD169sw9gl0cht1anMY0tg78s4fUCUZh07fRRe/8ZFM++9O4r3QwgAM2O7dK9kyBDbxtPbh17tQrt+35HlTOCr6Ly3Wpn36arMu5SJaPaGCFkG9FO0ElTYul58dkbjCYydF8GWU0+ur7hdBVW+yKaslg10IpBVLJi51StKD6/0dCCsfgU5Fd0F+bwaQJT1+0mWf3Xp7w5G3cl1mFbLwem7s+o+m30xvW5dprTgumlkg1dlKHQcbDswDv8utRQzOkzHEWFVdUW1uSsK3E6fPYu1k2rQqkgA6o3fvAEgN1hz4sTLZfV51XTnnVDcbtz4zW8wG4thPh4XmVaKkvNQ1If2HEA24wrA3EcfITEzAyWdhsvmHRhr718L3K++SOr6wkKB4FX3ukzJQQBVLRX49NNPV23apdTz+2w9513PdU5EREREtBA0XkRnXgHmk0BKLReYSolglvgjAC1wNac+nxWBK2UWSCYAZU7cKan9CCUiIiIiotvaHX/wB0hdvYprExNIRKOYi8VE8MoGLfPK1dyM9vvvR2uHeaCPiIiIiIiIKudY8GqbX2TAjF14Gf4+PwBRKtCtZluFL4Sxbf2Ogu/PmJ8H0gmI4FQzgFaIIBUASOqjRX2kALQB6XkgNQsoy9RhbiDZLAJfDqgkU2KxZsd4H3kcAHAhdBxej/jh7nbHEVPvho5EAe+2XYXeXni6arZXsayvlY/sVed9yHzeEcC7o/S+5vV6cf78eQBqGcA+8Z6dAfMyHfq73xMAuvrEeKFoF3bt2paZ5tatzpUTAqzvf/Xaz+qZSVSveRuP61q2oxGyMo3Oh8OWx7106VIVW5IViYu74d1IQCsE1tXVhs8+rpYdBeDtyt4x39slPqsOjgGAyNraWuBcUAubnzx1W85bkIE2j3ga92QzYtwdcKsv5IQbcKtZLh772S6NNu+9AfuZyqbT2ew1q4ZYsee2enJf7/AUGLMytSwbWCgDSZ4ZQzQsyot6Vu/H5oDIYNvs6NzPAhjKvOq+T5SCQxLovk8deB8AtUScGEf8v2Lbg+i+8mZmfNwHR7z8ZHZ9rHxsLPO8z2vMLHIXeF7aih//OPP8xaefhpXCd2tXqFlHurKB27c9LF7btKy7G/OdnbhT7fcqU9nBBgXilwgASM3NaGlvtz2NjOZlWLtWXQtr61cG8KmnnqrbvG/H75BA/dY5S4MTERER0UJTMHjVp16Yv3DhgqUJ+b3ZsmmnTmt9Br0GrVTgRv9m7Nz8ZMnppNCKxMxbSCdjSM6+JzKwMkGoFLIdYumep5JAOqkOAyAl0Ry7hETsI6TghtX8K+MFaSe+vDsxjVr8uOpSy+RZ3d7wquWI+qK4FH5ZPJ9BplRgR2AbvFtLb28rOjs7c8oIomu1aEJgLy6dOqSbtxr42rHD0rz37t2b8/qxo2Iejx+3uA50Ll++bPs9TihUxq4W+0w1ftxa/VHdCD+sG6EPqsVYgtApezeL+n+R4xM4HhLlPBNwY2WXuLTf783WZnslnL3cv94r1zVoRWqfimrHOgljKEa7iyCRLdeXmLgEOTqx4OftlCc3L4z+rezSygZWs+8rAIDbi6714nuN21Oddfnd7/4YX/val9VX64qOa/SisbycVkJwGvjud525IN7nbcuUEIzLbfC4zetEyup3fDcS8HZZKA1u8PSB4fIbWYYld90F3HVXTedJRERERERE5Wu4soHzrR249qt/QFO60nuH30SyqQ3K0hUAXE40jYiIiIiIiIiIiIiIiKpMUhR2DEVUS4cOHcrL+qL64jYxx/VijuvFHNeLOa6XXCO/AP7oE7Wf7z9/AAS/WPv5EtULzz1ERERERLTQMXhFRERERDUx8ov6zZvBKyIiIiIiIqKFg8ErIiIiIiIiIiIiIiIiahjsDIqIiIiIiIiIiIiIiIgaBoNXRERERERERERERERE1DCaf/Ob39S7DUREREREREREREREREQAACkejysAsGzZsnq3hYgWgGvXrtW7CURERERERERERES0iDUDInD17rvv1rstRLQAPPPMM/jOd75T72YQERERERERERER0SIlKYqivPvuu/jE+L+vd1uIaAF45dZj+OlPf8oAFhERERERERERERFVhaveDSAiIiIiIiIiIiIiIiLSMHhFREREREREREREREREDYPBKyIiIiIiIiIiIiIiImoYDF4RERERERERERERERFRw2DwioiIiIiIiIiIiIiIiBpGs/bk1v/8aj3bQUQLxZkz9W4BERERERERERERES1izLwiIiIiIiIiIiIiIiKihtFc7I//+QcTcEl34Or1JOQUkFYkJJOAUmKiEoDmZqDJlUJ7cxqfvEtBp2cef/GlB51rORERERERERERERERES06RYNXKekO/MkXVwAuF7o6WvCJZc1IpRUkZlNobXahtblw4pYC4J0PZfzL1MeYuPJ7XE/fxF843XoiIiIiIiIiIiIiIiJaVIoGr35/PQVFasKVWBJ3LmmBr70F8Rvz+PGbH2GVdykevH9ZwfcqCnD1egp/sMwN36c78NNf3XK88USU79z33wQApK5+iPStGwAAJZkEkkkxQjoNRUlnhivzc2KwLCP1SS8AYGD3X9a41UREREREREREREREQtE+r27OKkilFHx8I4XErLjYPZ9K490PZXx8c77ohBUFuHojhau3FHjuakXsVsq5VhMREREREREREREREdGiVDTzal6LT6VQuqMrAwXAx7KC332cwqc6UphNJ8tqIBHZk4pfBQBI7XdC0o7bWRlwiWNQSSUhpdU/SK7MsS01p3Dj8ls1bi0RERERERERERERUa6iwatkMj9m1eSS4LmrBe7WphKTVqAoCuaSaVy/lUIqZTP6RURl0UoFutJp/OXef5cZ/v1v/J14kpyDkhSR6c3/aW/m7//v1w8ArqLJmEREREREREREREREVVf0SrWS+QdIpUQgqq21CRv/+F70fGoJ5pPpzGPO7DGXxPzsPObm0kgXrzK4KFx+fgM8niG83mDTAoDXhzzweIo9jPO6jNefH8IG3TgbNgzh9cvlTvcynt+gDtvwPAyTKfB33TDDw6wt5su9ARueN454Ga8/vwEezwbo/3T5+Q3YMOTUGnfQ60N5bSUiIiIiIiIiIiIiWqyKZl4pisi+SqWBN6euYfr9q5DUvzW5RMUxTToFpJXc9/7uWhKpdBpzcxLSzLyqv4eewfnXv47PlhzxMp7fsBrfwKP43vk4NnxWDHv9+UF8ZfUGPHP+dXxdPxHL01X97Pv40eWv507j8o/w/Z8VGP/R7yE+vCG3fUOr8ZXVwPfiw9hQ4G14fQhfefHz+F5cndHl57Fh9TfwMzyEhx76GYCHckb/7Nf/L3ze8xUMbYxjuOBEF4C5OQCAIkk5g//tM/8RAPD9//NQTsZVlgIXM6+IiIiIiIiIiIiIqM6KBq+gAOm0CES999EcLs/OZv7U1KSrMKYAc/NppNO5nWO1NLuw7K4WpNISbHeaRXVz+flBfONnjxoCQ5/Fhq+/ju+97cFXBp/H/2InWKX36DN45lffwPd/dBlf10WvLv/o+/jZQ4/iUbyIF0tO5LP4+t88g++/+A289vowNpgGmi7j+b99EQ89cz67DJ/9Ol6Pf108f30Inq/8yvCeDfibZx7C6r99Hn+zoczlawBaSUC4XPjB//0CAOCv/p/HMn//t4bA1en/+BQAQHK54FJ4nBIRERERERERERFRfZVIs1AApAEoaGtrw9JlyzKPO+9ahvYl4tG2ZBnalyxBe7sb7e0tmUeL2i+WAgWFg1fG8nQbMPR6Ti03DG3YkFsybshQdu7y8xjSl5f7/9l7/9g2rizf88t0z3T3m/feuGcVy/MWb6NxSPdIlnd6DBlPj1x30EQ7aMqCx/Ha+kOAIQMbkJ7IGAleMItxvHLbz/AALaxBNqxEIvyHBQH6g3Erhkcmg7jDINGSUGDBL4BlEWuy/YidYJ8UC92e6fnRPbFd+0cVyapiFVm3yCIp+/sBiMTU5a3749xTt+6pc86B8YoQaw/117ATg63KdR6+ewB7z9wBcA3Htm1TQtXdxnjp/9XV6EMCymHsDqjq/fBB+ZoHTOtwIpTcbfzszB3su/RXhh5NBw6eUDyn7NbvwY/f2Ic7H3yomsOH+PCDO9j3xkHsslutnts/w5k7+/DGj8VMUDt//Ab2mfXPylzUlNeiTNzG7fEDKplX/nagLANWwiISQgghhBBCCCGEEEIIIc8bNY1XkvQERcOTy+UqfQD1p1oNwNcAJDwx/Pvt8b04dgZ4++5jPH78GHff341rx/aWD/P3nsG13W/j7mP574/vvo/d185gbzE3kRIK7v4bd+W/P76L93ffx5m9ZePQ7fFt2HvmPt54Xylz9w3cP7MX20TyG9W4zs63buPupX0ATuD9x49x+y3rRhN5DHaXx+DtXfjgmhJDb+eP8cY+GBp7sO8NCNpmavMwh/sAdntMKj4QwePHurCBglQYiB5+iA/u7MMbP37VfqU6bt+6Zm98dv4Yb+y7gw+MrFc158KCvCrcOfNT3Do4I8/3JeDasQM4cOAWDs7IsnUJ13DsZzbzbz19In+eFD0hJdw4855h0Q9Gz8P1kiR/pGeQlA8hhBBCCCGEEEIIIYQQ0iqqGq+eScDXTyQ8q3KWLUnP8OTrf4H07HcAnlbW8ewZnnz9NZ4ZHYg/fBc/vQaceD+i5FUCdh74K1zaB1y7dRvAAUQeP8bjyIFyCLedcmg3XLslG6d++QB3oPaw2YkDkdt4XAx5p1xj36UZvFW6yFuYubQPuPZT655Lta5jF8MxUNqnXOfHb+wD7jzAL8uNwYM7wL43fiwW2u7OGexVeQRpvIOKA6H0c5eIHclKvWp0BiI5ZKCAoenhbbwbOoM7+y7hr0xCBuZkC5yN0H874dmtN1CV/1Z9LizIq8K+SzOIKBO+07MbAPDGTFEGlOvofmMV6cm/yp9nT+SYn5KEw5f+0rDsG1Pn8I2X5Bx2L+EZvqF8CCGEEEIIIYQQQgghhJBWUd3zSpINWC4X8B9f/j38p13/Bv/pe9pP//f+AP3d/xbenn8P3+5t8O3+Lny7v4v/3PNd/If/4dt48jXw7GulIj2GhpKdeOu2bACwxKu7sA93cGbvARwYfxe39bHWKoxOylV+/Ab24Q4e/BLWqHUdu1gwFsltvYZbJVeyW7hm0Kea7LtU9gjSfUQ8xeqvd6cqdGAxZGAVQ9y1Y1qj2N6f4oPdl3DXNO+WYlASssCVeXWX3kClanmj5oIQQgghhBBCCCGEEEIIIYZUNV4VM17BBXzvf/w29ve6sOfVR9i9c738eXUDf7br1/iz7/196fPnf/qP8P/ZH+DVP/43ePr0GZ49fWaY8eqh7B4Dswh1ciElJ5Yq15ScW0ph51u4ffd9nNgH3Ll2BseO7VXyDN1WXeMOzuzVeQXtPYM7AO7nLBqhalzHLpbGQAlXd02xmNgOiWcFxUhn2ahnk51vvY0Tdz7Ah+9+iA/unMDb1YxnJ97XGcVu43bEzHCF2qEPa7XNsxvAfRiKRq25qCWvzeDJ1/Ln6df4i/8SxF/8l6Dmzzf/+rLm34euXMChKxfgwhO4JPlDCCGEEEIIIYQQQgghhLSKb1b7owSgmPHqpZeAX/327/B//78f4HdPf1u10m9949v4X/7j/4qXXH8ICV/jifQEklRpvlIbCQ4Y2hluY3zvMVzbdwnvzzwulXn47gHsPaOu6AAitw8gAigh5Y7hzLVjGD/4GBElJNulu/Xlaap5HZuxA2uPAVD0VDpz5hZuR4Bb14B9lwRDBlpvEHZDMeoZNej2OLYdu9+A8TyAgyeO4diZO8CJ9+sLvdhUqs2FRXl1mmey8cn17GvN1zfPROXvpWdYfPtvAACDP/3r0t9d0lO4pMrQn4QQQgghhBBCCCGEEEJIM6keNhDQeEx966V/i+3ffhWd33ZX/Wz/9qv4/Zf+QP7RMwn4Ws67U4Ghl89DvHtgG7aN3y6FZLs081YVw46OnQfw1swlFDNGmXoS3R7Htm0HrOe8qnUdu1j0dCqFq3vX6TB1co6mO2d+ZphvqZFeXwcOngAAnDjYYNOV2gBng1recKZzYUdeCSGEEEIIIYQQQgghhBCioabxSs1T6V/xz08e45++/nXVzz8/eYyn0td49gz4+skzfP3sCSQYhCLb+RbePgFcOzaOYgqph7d/hjN3FIOGYtj54MOyEeLh7XGE1GHYikaoUg6qh7j94Qe4gxM4eEC+xsylfbh2TFXm4W2M/1TQCFPrOjAKN3cAB08Adz74sGQIevjuAW37jcbg3QOVoeaK5c44GDKwdKkZXNp3DccOlNsEPMTtdw/g2DXgxNtVQvaJcCCCx4/te62Z8yrktFX2Yh/+Uk6YBdOMWWZzYUVem8Czp/8qf77+HW78H/9X6fPsyb/Kn6dP8OzpUzx7+hR/OzaBvx39a/zt6F/j2bNnePIMePKsqc0lhBBCCCGEEEIIIYQQQjRUNV695JIA6Rm+83sSfv8bAFzAS9+Q8I1vVv+89A0JLpcEF57BhWd4+uwpXC7jE/EDkbt4/xLwUyUn1d5j93Hi/buyQWPnW5h5/wRwZm8pf1Dop8AbJ/apK8DdS7vxwbFimb049sFuXLobKYWi2/nWbW2Zvcdwf/f7uHtbwAhj4To4cBCX9in5tZRcWAcid3Fp9wc4tm0btm07gBDextuX9umqvov3T9zHMWUMQg/ewPuXKn26ip5K+96wGTLwzhns3abL/VX6qL3QduKt23fx/hvledm2bS9++sFuvH/XwNhkud5msROyHTEH8Us/RO6+eoxVnoAqDOfCirw2gz/5U+BP/hSP//lf8Pif/lH5/BP+/p/+Wf7887/gN//yW/zmX36Lf/jtE/z9757i73/3FP/wNfCt7/0ZvvW9P2tuewkhhBBCCCGEEEIIIYQQFS5JkqQvv/wS/+7f/buKP/7v797Hf/7zP8ZL33gJu/7DH+B/2v57ePLsd7oIgJXhAF1w4Ruu38fy2iN8fu+/4+vfPsX/89XfI/5//tC5nrwIPHwXB/Z+gDcakb/recdubi6rY9zGc/GLqwkAwD/ksnB98xvKty64lIXrwjN8o5jb6ukzSE9lr8hnT5/h99z/MwBg4NRfmNZ/48YNfP755/ibv/kbZzpACCGEEEIIIYQQQggh5IXmm9X++O+/JWE1+//h178FfgHgpZck2Vb1BCqbVcUX8reShN/89l/wj//0O/wevonf/xZjkdXLww8/wJ0Tb+N2mxlL2pIDf4VL+/bigw8f4i0B69LDDz/AnX1vYKbGT9p5Ln705oDyfwNVyxFCCCGEEEIIIYQQQggh7UhV49W5/623We1oHQ/fxYG9Z1A9K9E+cQ+eRvPwNn525g5OvK+K2bdV2t4SduKtt0/gzE8/xMO3rIaHLI7x7erljeaCEEIIIYQQQgghhBBCCCENoarx6oVg51u4/fitVreiKrfHt+HYNWDfifdxW20v2QJtbykHInj/1gGE3v0xbluw3j1896e4rx9jHaZzQQghhBBCCCGEEEIIIYSQhlA15xUhhOhhzitCCCGEEEIIIYQQQgghTvJSqxtACCGEEEIIIYQQQgghhBBCSBEarwghhBBCCCGEEEIIIYQQQkjbwJxXhBBb/OEf/mGrm0AIIYQQQgghhBBCCCHkOYQ5rwghtvjNb37T6iYQQgghhBBCCCGEEEIIeQ4peV7duHGjle0ghBBCCCGEEEIIIYQQQgghRBs28PPPP29VOwghhBBCCCGEEEIIIYQQQgipzHn1zjvvtKIdtnny5Imj9X/nO9/B9u3b8dVXXzXsv+91bsfJdUebTQghhBBCCCGEEEIIIYQQsiV5qdUNaHcabbjavn17q7tECCGEEEIIIYQQQgghhBDSttB4VYNGG66++uqrVneJEEIIIYQQQgghhBBCCCGkbaHxqgb0vCKEEEIIIYQQQgghhBBCCGkebWi8eozHhS+wsHAVVz8ptLoxbel5lQqfx44d57FjcAmtHyGZQmwBgzuUdu04jx07pjE4uICUQQOttX8TsUF1feexY8cCUg72oTrt1p7m0Y7y5gipEHbscGHHDhfCKQCFKAaVfw/G8uVyqu/DL4IANJhCzIcdg1FHyhdiPmUOfYg918K69SjPjfIJJ1vdpC2B6Hp5kWjIeqc+1+C0vq1efx6FVBSDg9QPLzov5P2iEEV4sNznwXC0wXvuNllfKp1r+GmTua6t3/JIhVVyOhhy5pmwkEcs7NOMWTiVr/27rUQVmeB9uTkUUlGEB7XyXP2+LsulRv4bqbAaoA+d3D9b2/8kEW4jnfbc0/b7+TaRB6vnXYS0IW1lvPrikwUsLPxXFNAF/84/anVzANDzygqF2DT6J+5hRfPtBlZW7iH5sEWNIsakljC44zzCqc1Wt6T92NmDPgCAF56dALp2oVv5U7fbXS7XNYbFdQkX+sQvkQqLbQxEy7c/eXx0M4Pjp8ccKd8VTGN9PYHj9htIHEKeGwnr6xKWL3hb3Zwtguh6ebFoyHqvQ587TfP1v9P61rz+WNiHwcFJfISDuHLIOf1QYRRRf5RDrlS4ysG67lA1Nlj9kFV9vcFYXnNoYP7RHoapr6EvF46ZH4LIh5Hq/oUQa8Ght3Y8iwf8yiGOicGiWfcLK/Jgu26R8S9EMXhqDYErUqnfVwJrODVYeWBr1uZqh3XNWl+W6NqFbgQxv17uazvuDWrpt1R4BJc9s+W2H1rFZYP5qotUCIP9I8h5zuKKapwm/fIzSYVuKMmsbn216FBSbH0Zy8Skv+nNtoUj+rba/cKkfjv6IRV2of8yELii0rungZv9Zr/LIzbowUR2CMvF8lcOI3mqQQZcAX1ojrP750Y/b4ru9yrkbYfv+TNqi9LG+/m2wup5FyFtyDdb3QA13//hEXxf+f/HX7S0KSWc8byqz4DlnzyH9cnG9K9+spia2ADQiQvzJxG0sMm01v4OBBfPIahcI7wjjrl6m1oX7dYeexTy92UjYx6AxQeC9pK3ZtALdxcAeODpA7Ci3NxJ/aQmMbESxLLVh1HR8oQ8T1D+STNxWt6q1B+cTCv7K6Dg4PlLVzCN9WD537FBF3KntQek/kkJ65NJhHdchGc5jWBXZT2xQRdyAIKLEjDowk14MXc5ikm/+qAsj6mJDACg70IOi0E3gBmsr88gFXbhsqf4nRr5umqCixKCqRB2XO7B+qL2IE5+s/wBlhfH0KX7vn8COD6fw7py0F1IhXBq2IObF4yu6xz+SQnz0Pc3gMl1CaMxH/pzZ7E+GWhae9RYkQc7FMf/gmb8o5i67MFgvnL8U1PjwKEc/KpJ7PLP4HTShanUmKY96janwi4MI4F5DGA4mcSk33gcm7W+rBHA5LpRO+XD5r5Dnqa3SJhUCMPZIaxPluexK5jG6ZwLp2IHG7O+ClEMXgZOL6c1cqEmuJhAbscAMK+XWXl9TaIsI81c80WcWl/thuh6t4y/2v0ij9jgCMJI160f/JMS1nXfdflncOXCKvqNfpeaxAQimvtOV1cAk1ceIBxOwl+nPhfRh+aVtMP+ubwOG0kh5kP/zV7MX8lhskt7fx9s8v2diOCMPNiH511k69FWnlftCD2vrLId7udsM0peMIpvnvT1KHa94uaveHMn9ZFH7HIMfRfCsDacouUJeZ6g/JNm4rS8Pd/y3H1oCH0rcW34oNQk5hDEBaFXs8UO7ruCacx3j+OU+o3tVEg+SF1Olzw0APnwb3E5Akx42jSkznNCIYpTyvgHNeM/hsnFBLoNxj+fNX7jeae5EdTXAAAgAElEQVTHi7mkmXddEsk54HggAH8gCMzd2NqhzAu3cHPFi0Ovt//BayoZM/To8AeCWLl5qyHXiJ2K49CVGVPDlUwAnj4gm2+5RfLFxcZ6bwxuBE/3Yu6ymYeoc/qhkF9F36GDlffyrl0NuY49faip4bnebwAAug/D36W7v883Tv+Q5xied5EtDI1XNXAq59WgKm9SLKXNFzUYzhq4RdvLuVRILWBwUJ+LatrSb6tXvKS0ueiBdA/DO7Tt04ama0bOqE2kwtPa3FuD0winNluWq6li/JX2mJdfQnhwWjNf4VgWqdh0Rc6pQmwB4UHj/lagyFj/xAYAYGViSjcX07qYzQ2SN6P2pJZsyL+6307lNZIfAtG9q/SNuxtAXw/MX0SpjHlv1KZiaI/hOWBlwlMRzkEfKkCkfDkkTwgpfQzyHT4MhpOm45mKhTRx5gcHQ0jFQlXD5dgef+UtuCtW3wirWV4ee037q/QVQDnfQ2m+fNXDehSS2hjwynjGTMI72BlPyxT08eXltlebD9H+CpW30R4x9LLsciCmvtj81hwfddzwcDmkhyyXyXKIDyu5MSysFyvyZk8/qEMOKfOoGSujuRWbr0IqhPCgfv1GUTCdX/H1Lr4erelzO/0VKS96v7DfX/WPG69vxeq3Wo8uVr+CJlSSE/kEUtpxDC7qvAfcYzh9PIOJqfK1i4dntXucRLgUTs+NyXVjby8z/KMRYGKyNBfydWeN6+gaw5UL6gPA8lofjOVRSKllyIfwFglbLK5PxLG6vlJT44DZ+COAyfkg5i5rw1+5u43KAg9zGRwPGHsxFGIXMdcXwagfgD+M44ghWfdDlT15qB5q01ooscJHcaz0DeF1E9kX2Z84fX/JZ03eUN/Zg76VtVJ/be/PC1HcxJCQHpCpzKmSzwJ9nrJRXDNXpTBseW0IQuX3mu8MQrZpwxY6lPOrAvH9iR35dGK9N3w8dfKmpl79UEiFZKPcqKgX1SryFbFOxZ4X7OhDDRb2M6L7NxH9IJKz0c5+ryuYNvZW1stDHfsl554v7OHkfl7sfiGuf6zLg839mPDzuJ3zLkLaAxqvauC859U9TAxr80WtzMXRrzNU2CEVPo/+4XtY0SSj2sDKygaAryo3F1uZwhIGd0xheG5Dm3trZQNzw1MNGU8xZONPxfgr7dEbogAgFZ5G/3AKcysbqm83MDcRx/DEBrDyCOUUYllMTdzD3Ipxfwdjzc5pJd5fGXH5/+hmRvm/DG5+1NiDleCipNkQ+iclrOvC8aiZG/ZoY96f7jGMER5clP8+f1wOH6SP6a538Rcp75+UsL4cQR9WcXnQg5s4W4pBvr6exmnPRcM44amwC8M3e3BlWVXvlcNI3oxVHSN7499orys5XMZlTR6AHE7jIvoHLyJr8ItU2IdTyV24sqgay8VZuJMjxofBqRB29F+E5/SsauxncRoXMWEQM9TueFojifCpEeQCqrZcmQUue3DqJoDjCazrDjxF+ytWXrw9wv3dociyajyXT/fgsmkMfkGE59fC+Cjx1uePAytz48DpHNbXcziUHUD/jgFkDyXkdd0dw3DVA/ba68WqvNnTD3Joi1I8/49CGDx1ozxW872Y6A+pfiM+X107wxi9ksaiSq+d9sTR3290YGNnvYuvR6v6XLy/YuVF7xd2+6sZ3wbrW7H6BfDPYN0gp0ExR1Irc+b4RyPom7uoHGZEBb1IDA78rNJ1EN1YxUcFAEgiV+O6Xa8Poa/0dry81uePA7g5glOXgdOKDC3PDyG7Rby0xPSJONbXVx7JuRrz7j+M4ytxZb6UryYTwGXd4WkhiiSUw2cD5BB7Re8HNwLHYdEroRr25GGnx2ugq3K40Af0XQhbilL+MJcBuncZ6gjR/Yyz95c8cisVXxpid3+Oh2tY6d6lHIrqDlR1Rd3dwEoupwzUDfmF0tL6TiK3ovVi8U/KerKvTx32zY3gYg4X+pR5VJ6Dgouq+5DBs1BwUcL6fBByvqoZq9HoTVhFMmblsFZ0fyIun06t94aP58M1rJS8J7TY0g+qF7D6h1dxyCRsbpe7Fys3b1XKbeoGssggp8l3Lv68YEcflqm13xDdv4nvf0RyNtrZ75khvwCgkgeb+yXHni9U8lX9ozW2OLufF71fiOsf6/Jg5/5r73lc9LyLkHaBxqsaOOV5tbg+VEry2Hd8CMvr57C+fg7ry8r3K/c1DzbFnEvr6+ewrvqtOVkk5wCgExeWi7+T65dvYtvrcw3t2o9FTVv2YH5ddZ31c5j0d9TRfhE2ETuVwgo6cXx+tDyW6+ewvjyK+eOdwEoKp5po0CnErmNiBZBzgY2W2rM875eTJOrbk1rA8JycO+y4qvz6+jmsz+8xuEI3JnXjLdcvl125qfNe8h/B4vo5LF/oBAD0XdBdY/2k7sYmNl9C/fXvtyH/ZV4vJZtufXiRPl1s6S6//q3mZpIBDuWwOBnQbD78wTSuHIprwwoV6d6FLnXhrgAmTwcry6mwNf4N9roqxEZw89AsFoPqvrrhn0xj+RBQca6g5CfQj438m1kcuqnfECYRHl6tCANSvMa82YKwMZ5WSIUHkD00qwkBhS75sKF7JWPwA8H+CpYXbo8ghdhFzB1PyO3RxLwfw+JyBNnhUJ0vIwjOr7D8yLpBHh+3vGaOJ7AYlB8U/IEgkH1g3jyr60VI3mzoB+V3Ezd7cGVxpjxWSh6GYj225qvLrW270pYLfZVvBwuv9yKC69GqPhftr/PyrGBX/zRa3wrW375kMNGvHKYMWzACdo3hUJ/8UkfhozhWjp+tasAvv2U9UGcOVTc8fcqBYeEBsrVCv3TtQreBsWxlpRenF8thyhq9nzF6q7x/ov77BQAhfWIbK+urcKv2+BsSwOhp4HK/8vb3oA+Dp+LwjJocJhkYRxsdGkxEHrqCacN8PDcPWc2/Ug5xVoGN+6+z95echf7oEbv/FvKrAG4g3D+AbHeidCB8JbBWYeza6SkfhqaSMaDPiz7N+q70EutSrtuv8bzyKOOgna+dHm/ZOGZAIb+qCj9VL4fLBoLlWXhyJi+Xlai9PwFsyqdD670x46l4ZQyv4sKVyvCVtvWD8gLW+noO88eBiVMm3kj+MC5gHP0lzxrFy2V4FdAZSuw9LwjqQ80Fa+1nRPdvde5/moFi5O6/OYTlRQN5sIMTzxcl+ar1MTCaOrSfl/9od/9gTf/Ywer91+nncULaDRqvauC859UenJ7sLiu5rpdlV05s6N5cEaVcz0T/NAbDCwiHl5B6+DJeXzyH9fUjDdpotgGpzxTDieJlpQ511z+lGIUMDDqOkcXUhGyIurB8EkGVEa/Lvx+Ly7JBZ2Xis3JoieQ9AMDx+ZM6ox8A/w9kg2Pfy1p33kIWMV2YxP7he851yxTx/pYRl//yGyz1eHY0BqOY3K3D3JjU9foQoIuD7Z+UMO+5oXPD9yGGcEVSeE1dNsZf9K37Wl4AUxMw72vwbIWxNZWMoe/QQZOrycYFzYYwdQNzfeYhW/yTlW/D2R3P2iSRnAvitOGDmBujBm9xifZXrLx4e0T56GaV0CBdYzjUF8NUPSd0gvMrLD91YmW9iMubmH5Q/+7CleoHBs7Ol/h6B+ytR6v6XLS/jssz6tM/jda3YvU3gZTd0K1eXCi+6Ttv7SWE4OkgViZGcGqidnij8lvWiQa/0GUPqx4y9uuvfKu8JZ5yNuTBuft7uU39ycNYXFfe/l5MY3HxLHL9xuF3iyH2NPewhoUOlKlHHlLhEdzsPmvdayB1A3MIImBwQWfvv3b0m1heOhmx++/DXAaYi5UP2Ytl/TO4ciheeb9QXobJZ4G+Q7MlI7pszDamK5jG8qE4+gdDCA96TA05Xe5eIPtAG15QZUAreviUsKNvu8awuJ7GpNpA0OVGcPIsuicmqxhcau9PjKgln06ud+HxVNAa/z04lezBFRPPqPr1Q9E4s4rhU0ZzqXjq4SL6lfYMZ3sxv5zGIU05m88LgvpQTa39hth+rP79j9MUYj7sOHUDgSvG3nx2aN7zhVPtadb5jD39YwVr91/nn8cJaTdovKqBU55XztOB4OIoLhzvBLCBlbl7mJtLYVgx7gyGawd6ee7QhN1zkMKm/LDQt9s4dntXNw71AeXQjZvIZwGg0ziGetELanF/+QZZWMJgfxwT+jCJrUC4v8QZarz9ZxAX3R+c0bwFtVgtjJ5dUiFMrJhtruyUF3/r1Sz5rxmF/KomFrRVHBnPKocPZoj2V6i8jfbYYW7YPKTExEp9CcpF51d0POtCYL2IyZu4fhBBdL4Kqagu35j8du3NihuanbfcZZzUb6L9dVKei9jqrwP6Vqz+5wx/GBf6MljpsxLeSE09ibLzyK0o3hUmXlUarHhnbTGs6xP7WFpfNsc/djlmcKAawOgFGIRnzsvhm1fGdbrEgzk0InRgfaTCLgzjLBaNcrKY/SYZA44fNjyss3P/dfb+ovJ0tIzY/Vf2pvKa5htS3y+63L3AyhoKKm+bUijBh2tYqXLtruAsLiCGOUTMPWN39qCvGOaycEsew1LYy8qwhI2njpCqBliVT8fWu83xVBv/l+eDwFwcHxlesHH6oSt4FsdX4iYGIzeCk+VwaOuLM/B35aHx9LP5vCCmD1VY3G9Y34/Vuf9pFt2H4e9q7Bp05PnCZthA8faI0Yz9gyM06XmckHaCxqsaOO955SQdCE6eLIXPW14exfy8H8f75LxCWyGOvRB9fm3IwIpPk7zNujrQDZiHvitklRuiPnSjVW+7YphEXci99XNYL4bpaya2+0saS40HKEthPdzwT55F98RIzbfbrFJ8C87q2qtdXvytV3e32OFw8c3I+mnAeHbtgknuYvOrCvZXqLyN9tjh+Hz1sBIiceD1iM6v6HjWg+h60VJN3hqhH8wRmq9UCP3DceD0rCo+vpzD5FDFDczOW+5mNE6/icqnk/JsTu3+OqFvxerfIvhnLL5x75ZzCQi9AR3ApCq/SSHmEzuQUUJXyS8PBeDpq56PUg5paGwo2JII6ZNGYrS+AggctzD+Ffq2MrRbkYoQY4VbuLmi8gpUf+YbGzpQlELMh8tIYFnAcAXkZY8hj7GuEb7/NuH+Ytom0xxEYvffLnevcJsePlzDSt8QXu/ShYjr64GxaJVD5y0fihvn3gKAroM4pBjrUlPjWDmewPzxDCamksrhqbnsthv25LNIg9Z7A8azyz+DxeUh3DxlkJOnlfqhcAs3V3rLL7Lafl4Q0IcqrO43rO/HGrn/dIauYFqTu8g5GvB8UU/YQKH2CNCy/UMDaNLzOCHtBI1XNdiynlepJQwqHlaFAoCuDnR1dcDv74ZH0XTZfPNyQDlKMazeSgr9g0u6BJ+bSMUW5NB6g0tNChvYjcBxoBiyMZYqj3MhtYTBftnwhOPdygarA68fknNRzQ1ry8uU+yAbHB8pyYI7cWi0GHJvE4XUEsKXU5Y8sVZyqhCKBX39Tve3Pgoxn+mbOS825g9QhY/igDoMRSGKwR32EpkLjX/Dva4A2RXe/O27QuxiRf4QfyCIFdOwBfnKMBJKgmXT/qVC2KEeP0fHM4DA8RguGx5mJjFlkDNEtL9i5cXbI0rwdND22+OW5FNwfoXlxy5W14steRPQD4KIzlcqGUPfBTlGuz6HSSXi672e9WgF0f7WI8+WsNtfh/StWP3No2K5K54K7XjwWu2ATk/hozigCpNUDF1oqN8KUUshDbcSYvqkjGV5EFxf/tEIYBbqrDj+p7WGUHeVEyi9UccwJFjp4o0NHShCIeZD/80hXKnITVXrh7cq8vOoEb3/On5/KbbJYI2ahzgUvP/6ZzBvYhR5mNN55uzsQR9WcflyTM4LU/ouhsuXVwHAYD6Khis5x1VXMI0rpyvzacm4FWNdtJSXrGQcU2TRKOqG8Poy0lepG5hDr3FUD0Esy6fj693eeFbQNYYrh1ZxOazLydlA/VCIXcSc1fYgj9ipOLrn1fl+7D0viOjDEhb3G2L7sTr2P1uVNnu+cHo/b3f/0B44/zxOSLtB41UNnPK8GtwRV2549zC8YxqxFABkEd4xpeRvAuaGz2MwtglgE6mwKo+T5req78PqnE6PsALZw6q/X/3bKUzMAUAnDr2uy60kQCG1UL0tO6ZVD86i7Rct34HgFcXjaCWF4X5dzquJe7LxxHbYQPHx90+OygY1bGBieEqVk0ox5PT5sTxZ3p11BY8ali/OmdyHTqW02lhULtM/nMJc0XK1kkL/jvMI6wxhXa/vlsdpLlXODaYeo2b0N7UkKP9aPrpZvBlXf9utHdnp8WJlYlL1kJZHKhbC4A6X4ZvWYuWD6M6NYLCUQFcmFfPh1M0hg5AgMQwPhpAqqOvJIxUewFyVRPMi4y9vCq2/dW+1fFdwFodujmAwpu6rnDC4/2ZFvmD5IKA7jsFwVDbmFymU3zyd1FxUDk8x0e9DLJXXlC8mJT4+P6Nrp3Pj6Z9MoPvmCMK6tsQGB5DtM4hpLdpfwfLC7RHFP4N5XMRgOKobT6CgJCbWXFuFNfkUnF9h+bGH2HoRlTdR/SCA4Hzt9HixcvOWaizzStgOV0n/qxFe7wDsrkdLiMqnTXkW0//i/XVM3wrWb4fKPDfy2JwyelgvJpcfVB2QFpIInxrHSr2y0FLyxuvXP4NlRb+p5aqQCmGwfxy40Bh91S6I6hN78iCwvrrGcOXCKoZ3aO8v1cbfP6p4UaiUcyEVxambvbrD2DymDIxfZdwIHG9+6MBCKoRTN4ewXOFxmEe4xotOVQ/bAeH7b1PuL0WdHtPO7+WsWfg98fuvbBTxqNaw3Kbh7JDB+s1gZQVlI17XGA71ASsrGYMQyUmEB0eQOz2r8fjt8s8oBqzKg2L5XjSOOQTlcKiKEWR4IlM2mJUaLri+iuvFSF8Nx3BcYwyxh7h8OrvehcazCl3BWRzKDqhePLWhHwpJDO7wIRxLatZMKuZD/wRMcvrkdWuxLFMVus3G84J1fVjG8n5DcD9md/8jiuj5AAAgFSqF4RuMmet8of0SgLZ6vrDVHusI7x/aDMefxwlpM9rKePXFwlVcvSp/rq/8CvjlL0r/vnr1kyZ5zWjZsp5XeNnkhtqJvuN+zC+f3MIP7AZ07cfi8ijmj3fq+t2Jvr49uHBhCMvNChsIoJhzbHl+D/rUDerrxPH5USyr81eVyp9Tyndqq+orz1lxU+ifHMX8hT2qviplaoUN7NqPxWU5dKSmTRf8mF8+V8eBhmh/7fP6oeLN2PxNTUdJhUpxseeGXarExHLi3f6JDDA3YOj50RVMY/7CKi73l2OQX74JnF42SZQsWD4wmcZpz0WcUsWNvpw7iyuGIYyCmL9yGMlTI6o4zyNIehJVQxBYHv9CFJdNE4nWW96N4OIsTufUffXgMs5iefEsupHBRL8LO1RvJPon07gSAE6dUsW0PjUC6B7gi3QF01ieH0LusipJcv8ILmMIy8tpg7Xi5HgGMHllFp7kiK7tOVw5ZPwL0f6KlRdsj7Jmip/yGil/p/f6LLbn8il1kmofTk09QOCKhEm/8VhZlU/R+bU0Pko89+E5Jbm2+i3muQGtt56Sj6DUb9H1YkPerOqHsvfaAOaKa6nKXKnHx8p8dQVnMd8dR79Kr526vIbAFQkX+vR6FbCz3i2Pj019LiqfduRZTP8LyoPD+lZUnmOD1fRD5WGqnKvlYrnM4Ajy7jCuXPAazJccym/5EMpj2X8ROJQzHJtU2Fj2Kw6Q9LKjWxtyPSp9sCOElPKb8neV+R76dQdKsUEXdgzHDHKYjCCJs1g0uL8X9RtU+q3/MnBoXi87SYT1bQRgZT9jCb1OLNUvX9ewfsH7hT19Yl0eZMTWlzz+vbg5XGv8iz8Yw+KVHiRV95dTSeDKFdVLFKkQSnlrDOStqLeH51Bxv7G+vuzIQx5TwzGsrIyjv0KePTU9Ez66mTHxViojsj9pzv0F8E/O4lBuRDVfPSZ7bRmx/TlkmViOqNawR96f6MOXlkJHab2ainshjadKIYrBHQOYW8lgbli9R1HmdziGlZWYbIhRrfeu14fQB6Cv5OHpVl6iNPKEEV9f1fSVfi8mvj+xI5/OrnfL42lwv9Deh9yyJ9Gwtj9C+qErgMXlWQRyF3FKtWaGb/ZiftkkfFtqUlXWh8GpBxi9kjbZl4s/v1jSh2oE9xti+zFB/WDjeQcQf94XQWy/BDj5fGEP5/bzovcLYf0jJA927r821hchWxiXJEnSl19+iY8//hiff/453nnnnVa3SYgnT544Wv8rr7zScAPWeRdwct3RZhNCXiQKUQz2r+H0usnGvgXISZGrG27qKU9kCjEf+nNn22bc2q09WwVH5b8N9QNpLU7rW+pzQpT74UQv5ql7X1x4/31h4HoXo1HPC9xvtBDqt7aFz+PkeaStPK/aka3reUUIIS2iEMXlOS8ujFrcMImWJ+R5gvJPmonT8kZ5JkROBD+RARDD8A4XdgyGECvI4a8GmbOVkOcLrvfWwP0GIYS8MNDzqgb0vCKEqPnjHa5WN4EQQgghhBBCCCGEEFP++7rU6iYQUjffbHUD2h1nPK+2t7pbhBCbtNvNPxVWYpir6LtQf4xs0n7E9Alk+4K4cGWmZfkL2609pBLqB0IIIaT58P5LiAyfF54/qN/aB64v8qJAz6saPN+eV5uIDU5plZ0F+o4PYXGy25kmOcqL1l9CCCGEEELqpBDFYP84rG+hvbiwnObhCSGEEEIIIaQu6HlVA3peEUIIIYQQQl5YusawuD7W6lYQQgghhBBCXjDoeVUDJzyvCCGEEEIIIYQQQgghhBBCiDEvtboB7Y4znleEEEIIIYQQQgghhBBCCCHECBqvatBowxU9rwghhBBCCCGEEEIIIYQQQsyh8aoG9LwihBBCCCGEEEIIIYQQQghpHm1nvHpc+AILC1dx9ar8WfjkCzx+3Lr2NMXzajMK33QUeQDRaRdCWevtyy/54Drvguu8D9HNhnXbEcptVT4LyVY3iVgiiVCbzVc+G4JPLUvnfaU1VIm19rebfLZbe5pH+8mbI2RDpbkNZSHfB5R/+5ZUkqz6XuTeQGTySz64pqOOlN9K998XjRdXf9aH6Hp5kWjIeqc+1+C0vq1efx75bBS+6QbrB7N7uYMkF5p7vecZsecLh1DJkP7Tcr2xmUd0wadpXyj7PMqd3M/S2E+HkOQ+rxJFVql7iB2c1rfN1OdO7p+t7X9ekPOTdqHt9/NtIg9Wz7ss8s3Gt9A+j79YwPWH30Xf3qM40rUNwGMUPknh+vVf40dv/hBdLWiTE55XFQasR2vIoAdu5AF40fOy9fa596ch7U8idP5iQ/vtBHJb5f/PL/ngedTa9rQjyQUXLr6cQ3q/u9VNaV82oxiJr2JoKId0d+PGqd3ks93aYwfKcxVe7oEXQKao8zt2oRdABkBvh2q8OsaQPjeG6LQLa4KXEB3/52++8rh1P4Pga2lHym+l+++LxvOgP5uP6Hp5sWjIeq9DnztN8/W/0/rWvP7ogg/xr3ox9FoYs7vjjdUPyhwnF1wQkZR8NorJT8cR21C+6Awi8loYY7X2uZtRXLznhbczU7P+kU/HkSnV70Vk9xDiqTWcPTeDgEBb1df2TY2jeOXgkISZbgt/g7i82R4fEWo8X0SnXRhXrq/vD6Dca1Jyj71+pW/ZEFzxmPH1Or2IvDZr0ocgErXmRVe3uk3qtqIzAunkGIA8otOe8vclvIiMpjHWUe1i8vV88VX0+s9i9lwA+lbbGh/IsjBwz+SaexKQjtiSzjpQxgkR5M6l4QaQ30xi8noIOKmak6pzay6fIv1Vj5kao/Gt2h4A3j0JzB5RzVuN8sqvrMmGEXXUb0VfaWQcUMl5EqHzA1Bf2evPId0xKTY+NtZLRZtUvwn6z2Jmv7Esi+g3q/JTKTtFnWIyPvvddbVf+P7i0HmOWP1mc1zEgh5W6nFy/9zo582677/wIjg0ixkn5m2r0Mb7+bbC6nmXRdrIeFXAf10B+o7+EN/fVvxuG7p+eAQ/wlXc/eLP0VX+Q9NohudVfnMV2H4YQA5rG0BP03tJSDUCmDknYabVzVDIZ+PI7DkrsNFpr/aTWrxo89WLXR0A4EFPJ4ANsRcYSBWykxjfCCKnf8BvVHlCnico/6SZOC1vVeofO5LGmPL/+SWHri+AfMgIRIZykJS9rXxY5IFvs/oBU/KzcWT2JJDAgKmxLL/kg+d+LxJHJQSKB66bSYSuDyCDoP2Gqw9vXtMdpneMIT0K+KbqMI6p229zfISuU+P5YuykBEy7EIcXsU+jmOkeU/8akwaGGXTPQDo3Y3hYKBtERhBCutIQYYXuGUijPfBNxTGkO0wfOylh14ILA1AbQ9wYOylhDMWXOs5aNwxtRuH7FDg7mi7LkA5b46NQ+Z18qBx/2WOtfY0kOykbrk6OlQwZ7o4AZo4+QGghiUBxzKrMrdx+87m12l/1izhJZT4TGMBANomZbt3cVWvPZh7R6x54FlTyULX9gGwEqnFYrugAQ2zWb1VfjZ1MYO38AFBhyCs/RxbHTL624PjYWC9jJyWMZUNwfdqjGNK0/XJNP9DIVam/gvrNivy496chdUcNdHBxfOTx71HpDtvtt3F/ET/PEcNa/eU5BmzoxSJtsX925vykPL85zHQU5TOEkbgHPgNdTtqFdjtPa8x5VxuFDezCD988AiP7VNefvIpfPSw0v0locs6rzQdYLU0sIYSQ5xblzRN09igPFMXNH+8BjSGP6KcxeP3hireDG1OekOcJyj9pJk7L2xaS580oRlKQ3+RXHbK5u8cwczKB3pTHPCSN4nUV+UH1gzb5jfAZrdGhI4CZoxF46+8Bxo5GsBoPQRucJo/o9XH0DtVnuKprfByid/cQvBtxbfim7CRiCCKyx3o9skFkCKuf1hHGqmMM6aFejE9pxz+/5MPAVxHkGuS1FL0ex9DRGZaWRC8AACAASURBVFPDlZpGjE9+aQTjiGC2BQej+c1VeHcfrNQdHbuAezdgLQiTG2Ov9SL2qdWwqLX6m8SNe0CwO4BAd1CgHQodboydTCB476JA2FunDYfG9VvXVwH0dAKrmw0IAmdrfMRw708jsX0cI7rQ8I3Qb81YL4bth/P3l/ZnC+037LL9MAIdavmcQXooiMz9Wy1sFNkSNPi8q42MV+YU/tsvge823+sKaI7nVe5RBt7imxKdPVW2CnkkdXGmfQvJKhteXbxmk5jNFbkhSvFU84hO6+Jta+JmWqu/Hkrx6Ev1+xDVxdYuxnp3nQ8hWdEmn3aM1HE2F6IITavHMVn6t2ta/xAm0l8lxqgSy1Mbb9eHkEF8z+I4D9wDMilPRYzz+mJJi7cHsJczJLmkjS3smw4huRRqSAzg4hh5Uhng3oBujCrny/mcJ87Lv3CLLKwXDZtJhKYr10t0oVLm8tkQQtN6/RNF3qC/duTZznxZ6689+a9sV6PzGskPXdi+q/TNru2wdA9Qy5tRm0THX6S8kL7VYUc/2B5/5S04yw9yNcuL3n+dXY+As/q2mNdC3/Zq8yHaX6HyNtojRhP0ueD81hyfuvYzOiysFyvyZk8/lHV0aR41Y2U0t2LzJXL/KtYvut7F16M1fW6nvyLl7e4/69I/DuhbsfqtUc/9To/2eaos08nPxgH/rElYrgBmhoKImRg3oter/dYCHWNIa97It7lf6hjDrH8VA6p9W35pBOPbExVeJ6LyZmd8yvNm9KnUh6LPF+gYw9k9GYx/Vv5L8fByl76sFTbWkLPzuyLdM0jsiZXHv3ggfnSsMYepm1HEMWRdzuodH5P2i55XNHL9llnFA6v7gpd74N1Yq33/tTBf+aWLiHVGEO4G0B1GEDHcEDXabj7Aas1CSYRKMu/GzDldeDxV/hLjuaiz/mpU6Cujul2a58cHX6F8xlYLS+NTH4EfRIDUZEnu6tH/JRq93qugb39VTOZL+DxHcP8mrM/rxcJ+RnT/JrL/ETk/sbPfc+9PG3ui6fWbPreRUftM2ubc84U9nNzPi8mz+PORdXmwud8Tfh63c95VBUmSpL/7u7+Trl27Jv3lX/6l9OWXX7bXZ3VR+tlPfiYtrhr/vVAoOPrZ2NiQJElq+H8lSZIi70HCT8w+QSkhqclJkfe8kvezhJRTfZf4uVfCe17J+xOvFHmkLp+Qgj+B5P15Qsqpvs+tRSTvTyAF16QKIu9B8n6Wq/zDo4jkrWiPeP2a3nzmlfDzRNUyiZ975fq1v5S/17fzUUTy/sQred9DxW8Sn3kl73sR7Xc/l8c5uJaT5LGV/+39LFH+u6Z94v1N/ByS9z2v5H0vKCUe1S5f+o3RHDQAO+0pYm2+IOG9iGZ8pEcJKfie/H2jsNKWen9Tu3x98i9Kw9eLJEnSWlDCT7xSZC1XUV5eC/o1ltPOrSSvrUpdpW6TPXl2or925V+tp51am1Yo6yjVHaDB+sRyeUF9W6zbjn6wN/458/uZrfKi91/n16Oz+jYhBd/zKvfHYt3KGL1nvDbF16NIefH2aGptB30uPL/Wx0d8P6On9noRkjcb+kH5qxT8iVeKfBaUvO8Fy2O1FpTwk6DqNzbmS+j+ZWe9i61HMX0u2l978ixyv6hP/zRe34rVrytdSz/YlGf9eObWggZlc7LMV+1PwriMsi6KLa82f7nPvBJ+IutQ/TowvKKt/ZJq3A2fGw2uUXOO7I1P7jOj+0htubCy94y8p4yBIheRR9r/N+uXaX/XgvJaUn+n1Bf8LCh5S+cCXilYdbyK/VPWf42xFXouWgvKZYtrvLgn+3ml7Nsdn8p+mJcROq+ws36N5kT53mgN1Jrb6ljTV/oy1e7rhu1R5s5ruH9TlzfRN1Wp/hvR+kX0lWYc1oK6MzR5LViaryrjU2qTyHoxnXe1TqtD/6vqqyo/VXVxlXuLpfYr3wjeXzS/szKmNs4fhOqv6ze11q/ofqy+/Y/V9jfivDH3mdd0f2u0TzBrm2PPF48iqvtntY92TJ3dz0s25dnq85EWq+e31vd79T2PN4L29rx6/AUWrq/guz8yDifYDJz0vBo7KUE6JyHSKSfelIaCcrLJcxIk3ZsK+aURxHfPIr1fnUjSjcCRNHK7AX0qz/zSRcT2JJA+EoBb9TaHu3sM6VE5vIPenjr2muz+WeG38Nk4MnsO69ojXr8Q2RAGvhqS69f8wY3AkVkM3Tdyo84Au3MVvwnsT2N2d7zCzdnrzymJBt04uNsL7EkgrSSiDHQHga8e1N3fzEYvzp4su1K7u8cw6/cilm34ex+WcLw923dpxgcdAcy8Vkc8/TbFcfkXRXi9JBGKr1aEKSjqlIRRWI8Ot3ZuIa+tSKeNt//qxZZ+sCf/B3cXgx54MdTixKT6GOet1Sdi+haALf1ga/wb7HUlev9tynoEHNO3yYUBrO7WJeLtcGPsZA69G5WJw4X7K1heuD2COK/PBefXhn4T2c9UYHW9CMmbDf2g/G78fg9mT86Ux0rJm1Gsx9Z8Cdy/hNd7EcH1aFWfi/a3afsTu/qn0fpWsH572JVnmfySDyPZw5jV5QrB5i2b4eLLYYqsBIVz708jN3oW+HQEninVm8FVPGPF90tujB2NAKkR+KYaEC4QsD0+7v1pw/xD8d0NzM/RMYahzgzi2Xwpt4qoB1x+M4lQPIbga2YeE4cxe04+K5BGZ9HzaKRKJA457FlvakBe/w2U//zmKoAbCE0NYHV7AjmlTbPdaxiZNvEKsTk+VsKfiZxXyAiu3+4wIhiHp/Qmv/JWfXwV6KzdB0B5iz2+ishRk3xQxZJWwr1tRhHf0O6Ba4UOrPCqmBpADEGcNQkxWi4/gFit7tlApH4RfeV5uRyYLpmNAZ1eeDXeccZ5VUTHp3G40dOZwdoj1KH/yzQ/vKaq/cVvbNxfhGin8wc9Nfczovu3Ovc/zWBTjuzguT+E3Mnq+s0yTjxfdIwhXbx/Vv0YeH86tJ+X67Irz7Wfj+xidb/n9PO4FdrXeFUyXL2JH3a1rhlNzXkFaFzqyuQxmYLpwZ17/9mKdIi37mcQ1CfyLNIxhqHOGCb1C6Q7jODGuO77JG4YxFO3Vb8AyWwM3t0HTf4qH85UKgTzw0139xBQR1xWu/21+mDZLJxsT+CIhMTLN3Rutj5EEa5I+rnVcVr+RRFeL9kbiHWahwAJHJHaOgGnPf1gT/7d+9Pmm5sm09vRTnMipm/t6gc74y8ae7x6efH7bzPWo3P6Nokb94I4a7j+3Qj7KyPYi/ZXrLx4e0RxXJ8Lzq9d/WYXK+tFXN7s7se8NUPfODtf4usdsLcerepz0f42Y39Sj/5ptL4Vq98uduU5h+i0DyOPzhoYo+tAOTAz1ovGuDsCmDlZvJ/mkDt6GGufeuAyMT7Yel7oGMOsH8jsqQwX2EqSCyOIbz/b8H3t2GtBZFIjGElVWXMq9Afmnus3cHhUqhyrjjGkz6Uxoz7A7HBj7MhZ9KYmzcNeZW/IhgHRfEg1yD3KAPdi5UNY5Xt39wxmd8dN9Yno+FgOfyZwXiEjun7lw7gILsJz3gXXeQ8GvupFYjSNIZMmaefWg5FsD2ZHa+xbLfY3n40jo99D1Agd6PXndAe0OeR2AxenfIYv95XLJ2rqWDuI1i+kr5SXcx58BXh3z5aMptXCAIqOT1vSxHCBtRC9vzwv1NpviO3H6t//OE1+yQfX9Rs4fDQHSf8yjk2a93zhVHuadT5T+/nIds2W9nvOP49b4ZtNuYooj7/AwvWHLTdcAU56XiURUr99Encp/x+D6x5kD6zSArEXCTtWqtMY72Ye0CwBWfA82SRmFEUrx1gegmSw+RKv3zoPvgJ6hb0cary9osRltWu8cbK/zwuB/TMI7J9RfZNHcmEEvs3ZtjaG2KGd5EF0veQ3V4Hth4Wukc9GMflpHDH1mxWdXng3ICdibCL29ANpLOL6tin6IRvC+EYQiZNWva5qlRe//zZjPQIOjafysC/SGtH+CpW30R47OKnPRee3qfpNYL2IyVt77ces37/sZ55xUr+J9rcZ+xNb/XVA34rVbxd78pxJXQQ6gV7Tw6td6EUcDzahTXavZvMBVtGLw6W/F72ucnW8jOaGu8ONmZM54LwHk9mxtjI2lbA1PlqSCy4MIGGcs6NeusOIdHpkzwcL46d/O9seq8bjsRmVPX1GJRzM+uCZjiJn63AxidD5GzisigBT9G4xNgwBq2b6RGh88ghNjaN3SLLwopLYeYW99evG2JE0xo5o2/jAxJNHPbf5bAgj8Thu/WAM5qZ8q/3N49b9DLCRgev8eOWfVWNQHTfc+2eQ7gjB9WkU4e5qslGfN1BtROs311fujl5gYw35onfaUTd2PQIyj3JAxxoy6MVZS9cSGR81leulOnmsbSgyVJd+E1kvjUTVflMae39pp/MHDRb3G9b3Y3Xuf5rF9sMINNhY48jzxWYUvqlxC95qXjkyhqpOJ/fzbSvPtWjS83gt2s94VTJcHWm54QpwxvNKNmAFMHNOwgySCJ2/iJ7RtLzZfHTWYINtK50ZgkMGb3PVwN09BG/qIqI/CGCsQ94wBV9LN6x+q+zaDsSFH65NNvRFOnvq8jpysr/PL24EjpzFjfMjiHa33nOlkbSTPIiuF3dHL3D/ASwfHWZD8MRXERyaRa7brT4SRHTagzXxJteFPf1AGksj9G3j9YPooV7t8uL3X8fXo3lN9Y9nxy70Cq5o0f4KlbfRHjs4qc9F57eZ+q2+Q/Bq8tZG+zGh+5e9/bYxjdNvovLZmv1J7f46oW/F6reLPXn2+meR3p9DaNqHENIGcxLA4T0DuJjNY8ws7JDidVGuP4e1DSCz4YErpS/tgeuRiKHGjZ5ONH0PZx0746P625IPF5FAzgnDFQDZQ0eqYqBoFnlEr48D/py87vbPInLfg5Glg+IHbQYHVO6OXsDUh6Ua1scnueBBbE8CkmaNmBsGRM4rGnY/2ryF+EYvZmvocnf3DNKjUfiuh7DrpLFRw3J/N28hvlF5uAoAyIbgit9A8kjAus57uQfejTXkYLbDCGDmXLm2/JIPI2jkC6j11G+ur3KP1pDpHMJsB+DuDgLxG0h2A+jsEbur1BwfHaIHukqowHAHUI9+syw/1fbQNQz/tdtfiwbcX9rs/EGN1f2G9f1YI/efzuDen4bUnCvV/3zRMYb0uUbdnRu0n29jea5Jk57Ha9FeYQMLn2Dh+kPsPNoehivA2ZxXAEo3jl0dslu+92UjxeVG2A/ZBdqA/NLFCov+2GtBe2FlVDGqi2EpwgYK13b9Fgl0y/GsjcmbuOFmzMcoGwdMw/DUxun+bnk2o/CdDzU0TEU7027yILxeug8juBFHdNPkJ9kQXKr5TGZj8PrlGLf6HCytwJ5+sEd+yae4jfvMx+uFREDf1qEfhMZfeQvOciglS+XF779Or0dnxzOAw3tiuGiYVyOJyVTlO2yi/RUrL94eUerR55bkU3B+m6bfrK4XW/LWPvsxsfuX+Hp3ev8j2l/H9yd2++uQvhWr3y71yHMAM0eHsBo31hGBH0QAs1Bwm1E55Npr6gMY+eVHfd6GxB4lFJbeUJMNwTUdNWmblbfoW4v4+Mjkl3zw3B/CbCPDNTaD4voyul9kbyCGXhzUHZwlF2TvprIBoJj/yjj/qzDdM0jsMV4DuUcNCJmUDWHgXhAJQyOjOn+RCovnFTKNuB/lEb0eR++QxfwiHWOY3b2KiwsGkivQX8OQgUVqhA405NEaMhA7Js88ctYjRFO/qL56uQderOLipzE5T03puxgufiobXIWk08b4iJDPxgFVmDlb+k1ovQTQY5ZL59EaMoIvEunb7/T9pd3OH0pY3G+I7cfq2P9sVdrs+cLp/XzbyrMlnH8et0L7GK8Kn+DqL36NvUeP4PvbDP628EVLmtXMnFcPvjLfALr3z2Lo/gh8S8XkoUAxgajnPlARZbJ7BglchG8hiuSmVsjySqK9kMnCl2NUTyKUrRLHtY76LdE9g8T2OHwLUeTVN+HNctLdyrcYguh9NALfgnqMgOSSDyP3h+pLZul0fxU8L3uRSU2qHlrySC6F4DvvqpKkt12IYWA6pBufPJILA4jZSGTc1jRJHoTaI7ReAgj7gfEpnzah6mY5KXFwSBsyJHP/lqruPPLZKELTLoxvmDfLMXm2pR/scet+8WZsvllqV0THX6y8qL6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+ } + }, + "cell_type": "markdown", + "id": "deb48d3e", + "metadata": {}, + "source": [ + "\"enviro\" is the function that runs ENBIOS calculations. It requires a yaml file that specifies the Base File path and the simulation file path. You have to prepare the yaml file before running enviro, by following these instructions:\n", + "1.\tDownload the yaml file provided in the Google Drive Folder -> files and open it with a text editor software (i.e. Sublime Text, Notepad++, etc).\n", + "![image-2.png](attachment:image-2.png)\n", + "\n", + "2. The .yaml file contains the following lines (**in bold, those lines you have to edit**):\n", + "\n", + "•\tnis_file_location: the BaseFile URL. The same mentioned above but is also written for you.\n", + "\n", + "•\tdevelopment_nis_file_location: leave it as it is. You will not use it\n", + "\n", + "•\tsimulation_type: set to “single_csv”.\n", + "\n", + "**•\tsimulation_files_path: path to the input data file. Copy here the local path where you have saved in your hard drive the file flow_out_sum.csv.**\n", + "\n", + "**•\toutput_directory: path of the local directory where all outputs of the execution will be saved.**\n", + "\n", + "Now you copy and paste the local path to the yaml file you just modified in the variable \"yaml_path\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b8d419c", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "yaml_path = r'your_local_path_to_modified_yaml\\case_study_ECEMP.yaml'" + ] + }, + { + "cell_type": "markdown", + "id": "876e6bfb", + "metadata": {}, + "source": [ + "Now we will start the simulation. NOTE it might take a few minutes and show a lot of messages such as: \n", + "\n", + "\"INFO:sqlalchemy.engine.Engine:PRAGMA main.table_info(\"authenticators\")\" \n", + "\n", + "**It's OK!!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9283648", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "enbios.enviro(yaml_path)" + ] + }, + { + "cell_type": "markdown", + "id": "76b175ec", + "metadata": {}, + "source": [ + "If you get an error such as “ERROR:root:No database connection defined (DB_CONNECTION_STRING), exiting now!”, try closing the BaseFile in your browser and run the cell again. If the problem keeps happening, you will have to run the simulation from the terminal. \n", + "\n", + "In this case, first, open Anaconda Navigator and activate \"enbios\" environment prior to opening the Terminal. \n", + "\n", + "Then, execute the code: enbios enviro yaml_filename (with the local path to your yaml file). For more details you can check the ENBIOS Manual, page 25." + ] + }, + { + "cell_type": "markdown", + "id": "541ff948", + "metadata": {}, + "source": [ + "### Step 3: Results visualisation" + ] + }, + { + "cell_type": "markdown", + "id": "86abba83", + "metadata": {}, + "source": [ + "The results from runing ENBIOS are stored in a file called \"indicators.csv\" (you can find an example file of how these results should look like in the Google Drive Folder -> Files). The function below provides a graphical visualisation of the results.\n", + "\n", + "**Input requirements** (to be specified in the cell below): \n", + "1. results_path: local path where the enbios results were stored. The name of the csv file generated by enbios should be: indicators.csv.\n", + "2. dendrogram_level: number (integer) corresponding to the dendrogram level you want to explore.\n", + "3. plot_output_path: write the local path for the output .png graphs to be saved.\n", + "4. output_path: write the local path to the folder you have downloaded all the Files from Google Drive." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "05e853a9", + "metadata": {}, + "outputs": [], + "source": [ + "results_path = r'your_local_path\\indicators.csv'\n", + "dendrogram_level = 0\n", + "plot_output_path = r'your_local_FOLDER_path_where_plots_should_save'\n", + "output_path = r'your_local_FOLDER_path_where_the_GDrive_Files_Where_Downloaded'\n", + "\n", + "def visualization (dendrogram_level, plot_output_path):\n", + " \"\"\"\n", + " Params:\n", + " ->dendogram_level: integer \n", + " ->plot_output_path: path to save the results\n", + " \"\"\"\n", + " \n", + " df = pd.read_csv(results_path, delimiter=',')\n", + " #rename the columns. Create a standard names\n", + " def rename_columns(df):\n", + " a=df.columns.tolist()\n", + " namecolumns=('Scenario','System','Period','Scope','Processor','Indicator','Value','Unit')\n", + " counter=0\n", + " for i in ddd:\n", + " df.rename(columns={i:namecolumns[counter]},inplace=True)\n", + " counter=counter+1 \n", + " #include the units\n", + " dfunit=pd.read_excel(output_path + '\\lcia_implementation_units_filtered.xlsx')\n", + " mydi=dict(zip(dfunit.Indicator,dfunit.Unit))\n", + " for key in mydi.keys():\n", + " key=str(key)\n", + " df.loc[df.Indicator==key,'Unit']=mydi[key]\n", + " # #Create a dendrogram lvl maker\n", + " df = df.loc[~df[\"Processor\"].str.contains(\"environment\")]\n", + " df[\"Dendrogram level\"] = df[\"Processor\"].str.count(\"\\.\")\n", + " df.drop(df[df['Scope']=='Internal'].index, inplace=True)\n", + " listindicatorr=df['Indicator'].unique().tolist()\n", + " label=df['Unit'].unique().tolist()\n", + " #define a new class to iterate over the dataframes\n", + " class my_dictionary(dict):\n", + " # __init__ function\n", + " def __init__(self):\n", + " self = dict()\n", + " def add(self, key, value):\n", + " self[key] = value\n", + " #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + " #Crea dataframes per cada indicador. Utilitza my dictionary per guardar-los a un diccionari\n", + " dict_obj = my_dictionary()\n", + " for i in df.columns:\n", + " nom_dataframes=[]\n", + " listindicators=df['Indicator'].unique()\n", + " listindicator=listindicators.tolist() \n", + " for i,g in df.groupby('Indicator'):\n", + " globals()['df_'+str(i)]=g\n", + " nom_dataframes.append(i)\n", + " dict_obj.add(i,g)\n", + " \n", + " #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n", + " #visualització\n", + " sns.set()\n", + " sns.set_style({'axes.facecolor':'white', 'grid.color': '.8', 'font.family':'Times New Roman'})\n", + " sns.set_context(\"paper\")\n", + " fig, axes = plt.subplots(3,2,figsize=(10,10),dpi=500, )\n", + " plt.subplots_adjust(left=0.1, \n", + " top=0.9, \n", + " wspace=0.2, \n", + " hspace=0.5)\n", + " \n", + " colors=['r','b','y','mediumaquamarine','navajowhite','pink',] #define the colors\n", + " color= iter(colors)\n", + " for (key, data), ax in zip(dict_obj.items(), axes.ravel()): #iterate plots and axis\n", + " fig.suptitle('Environmental Impacts in level {}'.format(dendrogram_level))\n", + " data=data.loc[data['Dendrogram level'] == dendrogram_level]\n", + " #get the unit label\n", + " unit=data['Unit'].unique().tolist()\n", + " #plot\n", + " sns.barplot(ax=ax,x='Scenario',y='Value',data=data,color=next(color),alpha=0.8,saturation=0.7).set(\n", + " xlabel='',ylabel=str(unit[0]))\n", + " \n", + " title=str(key)\n", + " ax.set_title(title)\n", + " plt.savefig(plot_output_path +'\\Plot_Result'+'.png',facecolor='w', dpi=500)\n", + "\n", + " \n", + "visualization(dendrogram_level,plot_output_path)" + ] + } + ], + "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": "3.9.13" + } 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