{ "cells": [ { "cell_type": "markdown", "id": "83d68c5f-79b2-4947-920f-9fd1fbdd392f", "metadata": {}, "source": [ "## One-bin `2A + 5B <-> 4C + 3D`, with 1st-order kinetics for each species, taken to equilibrium\n", "\n", "Diffusion not applicable (just 1 bin)\n", "\n", "LAST REVISED: June 4, 2023" ] }, { "cell_type": "code", "execution_count": 1, "id": "9e3ee9ae-4017-43c9-83fd-d32801a05b9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Added 'D:\\Docs\\- MY CODE\\BioSimulations\\life123-Win7' to sys.path\n" ] } ], "source": [ "import set_path # Importing this module will add the project's home directory to sys.path" ] }, { "cell_type": "code", "execution_count": 2, "id": "82fd4f08-1a4c-48d4-aac3-711aa72df099", "metadata": {}, "outputs": [], "source": [ "from experiments.get_notebook_info import get_notebook_basename\n", "\n", "from src.modules.reactions.reaction_data import ChemData as chem\n", "from src.modules.reactions.reaction_dynamics import ReactionDynamics\n", "from src.life_1D.bio_sim_1d import BioSim1D\n", "\n", "import plotly.express as px\n", "from src.modules.html_log.html_log import HtmlLog as log\n", "from src.modules.visualization.graphic_log import GraphicLog" ] }, { "cell_type": "code", "execution_count": 3, "id": "6635becc-7040-420b-b631-e89a23e80909", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-> Output will be LOGGED into the file 'reaction_6.log.htm'\n" ] } ], "source": [ "# Initialize the HTML logging\n", "log_file = get_notebook_basename() + \".log.htm\" # Use the notebook base filename for the log file\n", "\n", "# Set up the use of some specified graphic (Vue) components\n", "GraphicLog.config(filename=log_file,\n", " components=[\"vue_cytoscape_1\"],\n", " extra_js=\"https://cdnjs.cloudflare.com/ajax/libs/cytoscape/3.21.2/cytoscape.umd.js\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "8acfd3e5-effa-4d10-b400-47777be200fc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0:\n", "1 bins and 4 species:\n", " Species 0 (A). Diff rate: None. Conc: [4.]\n", " Species 1 (B). Diff rate: None. Conc: [7.]\n", " Species 2 (C). Diff rate: None. Conc: [5.]\n", " Species 3 (D). Diff rate: None. Conc: [2.]\n" ] } ], "source": [ "# Initialize the system\n", "chem_data = chem(names=[\"A\", \"B\", \"C\", \"D\"]) # NOTE: Diffusion not applicable (just 1 bin)\n", "\n", "# Specify the reaction\n", "\n", "# Reaction 2A + 5B <-> 4C + 3D , with 1st-order kinetics for each species\n", "chem_data.add_reaction(reactants=[(2,\"A\") , (5,\"B\")], products=[(4,\"C\") , (3,\"D\")],\n", " forward_rate=5., reverse_rate=2.)\n", "\n", "bio = BioSim1D(n_bins=1, chem_data=chem_data)\n", "\n", "bio.set_all_uniform_concentrations( [4., 7., 5., 2.] )\n", "\n", "bio.describe_state()" ] }, { "cell_type": "code", "execution_count": 5, "id": "c9eaf7cf-04b7-49f7-ad99-e4b273a39574", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEABCDcaption
004.07.05.02.0Initial state
\n", "
" ], "text/plain": [ " SYSTEM TIME A B C D caption\n", "0 0 4.0 7.0 5.0 2.0 Initial state" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0), caption=\"Initial state\")\n", "bio.get_history()" ] }, { "cell_type": "code", "execution_count": 6, "id": "5429cc81-fe31-4a1e-87c3-7c42dbd9e06e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of reactions: 1 (at temp. 25 C)\n", "0: 2 A + 5 B <-> 4 C + 3 D (kF = 5 / kR = 2 / Delta_G = -2,271.45 / K = 2.5) | 1st order in all reactants & products\n" ] } ], "source": [ "chem_data.describe_reactions()" ] }, { "cell_type": "code", "execution_count": 7, "id": "e95f3814-f76b-476a-963d-ae440e87b2e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reaction 2 A + 5 B 4 C + 3 D\n", "[GRAPHIC ELEMENT SENT TO LOG FILE `reaction_6.log.htm`]\n" ] } ], "source": [ "# Send a header and a plot to the HTML log file\n", "log.write(\"Reaction 2 A + 5 B <-> 4 C + 3 D\",\n", " style=log.h2)\n", "graph_data = chem_data.prepare_graph_network()\n", "GraphicLog.export_plot(graph_data, \"vue_cytoscape_1\")" ] }, { "cell_type": "markdown", "id": "a1bdfcec-2945-4ace-97c0-739e9716edb2", "metadata": { "tags": [] }, "source": [ "### First step" ] }, { "cell_type": "code", "execution_count": 8, "id": "177f241c-6ec9-4a3a-863e-685e031b4feb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.001:\n", "1 bins and 4 species:\n", " Species 0 (A). Diff rate: None. Conc: [3.76]\n", " Species 1 (B). Diff rate: None. Conc: [6.4]\n", " Species 2 (C). Diff rate: None. Conc: [5.48]\n", " Species 3 (D). Diff rate: None. Conc: [2.36]\n" ] } ], "source": [ "# First step\n", "bio.react(time_step=0.001, n_steps=1)\n", "bio.describe_state()" ] }, { "cell_type": "markdown", "id": "bb01e107-69e1-4ffc-939f-ead43bc358a1", "metadata": {}, "source": [ "_Early in the reaction :_\n", "[A] = 3.76 , [B] = 6.4 , [C] = 5.48 , [D] = 2.36" ] }, { "cell_type": "code", "execution_count": 9, "id": "ffc297af-487e-4020-a406-959f0a6fab98", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SYSTEM TIMEABCDcaption
00.0004.007.05.002.00Initial state
10.0013.766.45.482.36
\n", "
" ], "text/plain": [ " SYSTEM TIME A B C D caption\n", "0 0.000 4.00 7.0 5.00 2.00 Initial state\n", "1 0.001 3.76 6.4 5.48 2.36 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save the state of the concentrations of all species at bin 0\n", "bio.add_snapshot(bio.bin_snapshot(bin_address = 0))\n", "bio.get_history()" ] }, { "cell_type": "markdown", "id": "265fcc89-7cfd-44c0-ac69-36846c3f93de", "metadata": {}, "source": [ "### Numerous more steps" ] }, { "cell_type": "code", "execution_count": 10, "id": "d547bdba-3819-40bb-b965-28d06516619c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SYSTEM STATE at Time t = 0.041:\n", "1 bins and 4 species:\n", " Species 0 (A). Diff rate: None. Conc: [2.80284552]\n", " Species 1 (B). Diff rate: None. Conc: [4.00711381]\n", " Species 2 (C). Diff rate: None. Conc: [7.39430896]\n", " Species 3 (D). Diff rate: None. Conc: [3.79573172]\n" ] } ], "source": [ "# Numerous more steps\n", "bio.react(time_step=0.001, n_steps=40, snapshots={\"sample_bin\": 0})\n", "\n", "bio.describe_state()" ] }, { "cell_type": "markdown", "id": "e9db30bd-ed51-491b-b11f-651094524269", "metadata": { "tags": [] }, "source": [ "### Equilibrium" ] }, { "cell_type": "markdown", "id": "a7b6400e-696a-4b2f-98c6-a8dea6515160", "metadata": {}, "source": [ "Consistent with the 5/2 ratio of forward/reverse rates (and the 1st order reactions),\n", "the systems settles in the following equilibrium: \n", "[A] = 2.80284552 , [B] = 4.00711381 , [C] = 7.39430896 , [D] = 3.79573172" ] }, { "cell_type": "code", "execution_count": 11, "id": "f888886f-a56d-4f6b-9c28-5a58797b5924", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2 A + 5 B <-> 4 C + 3 D\n", "Final concentrations: [C] = 7.394 ; [D] = 3.796 ; [A] = 2.803 ; [B] = 4.007\n", "1. Ratio of reactant/product concentrations, adjusted for reaction orders: 2.49898\n", " Formula used: ([C][D]) / ([A][B])\n", "2. Ratio of forward/reverse reaction rates: 2.5\n", "Discrepancy between the two values: 0.04092 %\n", "Reaction IS in equilibrium (within 1% tolerance)\n", "\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Verify that the reaction has reached equilibrium\n", "bio.reaction_dynamics.is_in_equilibrium(rxn_index=0, conc=bio.bin_snapshot(bin_address = 0))" ] }, { "cell_type": "code", "execution_count": 12, "id": "747a27b9-06c9-4097-90ab-827266ae5c6f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SYSTEM TIME A B C D caption\n", "0 0.000 4.000000 7.000000 5.000000 2.000000 Initial state\n", "1 0.001 3.760000 6.400000 5.480000 2.360000 \n", "2 0.002 3.571091 5.927728 5.857818 2.643363 \n", "3 0.003 3.421344 5.553360 6.157312 2.867984 \n", "4 0.004 3.301981 5.254952 6.396039 3.047029 \n", "5 0.005 3.206419 5.016047 6.587162 3.190372 \n", "6 0.006 3.129645 4.824113 6.740709 3.305532 \n", "7 0.007 3.067794 4.669486 6.864411 3.398309 \n", "8 0.008 3.017854 4.544634 6.964293 3.473220 \n", "9 0.009 2.977457 4.443643 7.045085 3.533814 \n", "10 0.010 2.944734 4.361834 7.110532 3.582899 \n", "11 0.011 2.918195 4.295487 7.163611 3.622708 \n", "12 0.012 2.896651 4.241627 7.206699 3.655024 \n", "13 0.013 2.879148 4.197870 7.241704 3.681278 \n", "14 0.014 2.864920 4.162300 7.270160 3.702620 \n", "15 0.015 2.853348 4.133370 7.293304 3.719978 \n", "16 0.016 2.843932 4.109831 7.312135 3.734101 \n", "17 0.017 2.836269 4.090672 7.327463 3.745597 \n", "18 0.018 2.830029 4.075073 7.339942 3.754956 \n", "19 0.019 2.824948 4.062370 7.350104 3.762578 \n", "20 0.020 2.820809 4.052024 7.358381 3.768786 \n", "21 0.021 2.817438 4.043596 7.365123 3.773843 \n", "22 0.022 2.814692 4.036729 7.370617 3.777962 \n", "23 0.023 2.812454 4.031135 7.375092 3.781319 \n", "24 0.024 2.810630 4.026576 7.378739 3.784054 \n", "25 0.025 2.809144 4.022861 7.381711 3.786283 \n", "26 0.026 2.807933 4.019834 7.384133 3.788100 \n", "27 0.027 2.806947 4.017366 7.386107 3.789580 \n", "28 0.028 2.806142 4.015355 7.387716 3.790787 \n", "29 0.029 2.805487 4.013717 7.389027 3.791770 \n", "30 0.030 2.804952 4.012381 7.390095 3.792572 \n", "31 0.031 2.804517 4.011292 7.390966 3.793225 \n", "32 0.032 2.804162 4.010405 7.391676 3.793757 \n", "33 0.033 2.803872 4.009681 7.392255 3.794191 \n", "34 0.034 2.803637 4.009092 7.392727 3.794545 \n", "35 0.035 2.803444 4.008611 7.393111 3.794833 \n", "36 0.036 2.803288 4.008219 7.393424 3.795068 \n", "37 0.037 2.803160 4.007900 7.393680 3.795260 \n", "38 0.038 2.803056 4.007640 7.393888 3.795416 \n", "39 0.039 2.802971 4.007428 7.394058 3.795543 \n", "40 0.040 2.802902 4.007255 7.394196 3.795647 \n", "41 0.041 2.802846 4.007114 7.394309 3.795732 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = bio.get_history()\n", "df" ] }, { "cell_type": "markdown", "id": "f4ef60c7-9d9e-4324-9a93-525dd426bc6e", "metadata": {}, "source": [ "A and B get depleted, while C and D get produced.\n", "\n", "**2A + 5B <-> 4C + 3D**" ] }, { "cell_type": "markdown", "id": "a1525fa9-e852-4942-aef9-26b4ddcbab4e", "metadata": {}, "source": [ "#### Let's verify that the stoichiometry is being respected" ] }, { "cell_type": "code", "execution_count": 13, "id": "ea6a6286-aa49-4b4f-adff-2a3d3ff729d0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([4., 7., 5., 2.], dtype=float32),\n", " array([3.76, 6.4 , 5.48, 2.36], dtype=float32))" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We'll check the first two arrays of concentrations, from the run's history\n", "arr0 = bio.reaction_dynamics.get_historical_concentrations(row=0, df=df)\n", "arr1 = bio.reaction_dynamics.get_historical_concentrations(row=1, df=df)\n", "arr0, arr1" ] }, { "cell_type": "code", "execution_count": 14, "id": "8cd8dfee-21c5-4dd6-8bf4-155d275797a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bio.reaction_dynamics.stoichiometry_checker(rxn_index=0, \n", " conc_arr_before = arr0, \n", " conc_arr_after = arr1)" ] }, { "cell_type": "markdown", "id": "b0e3663b-3c12-4790-b948-79df3e6f740e", "metadata": {}, "source": [ "Indeed, the change in [A] is -2 x 0.12, and the change in [B] is -5 X 0.12, \n", " while the change in [C] is 4 x 0.12, and the change in [D] is 3 X 0.12" ] }, { "cell_type": "code", "execution_count": 15, "id": "72e2ec04-2d7d-4c43-941e-11a613d784e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-2.0000002, -4.9999995, 4.0000005, 2.9999993], dtype=float32)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(arr1 - arr0) / 0.12" ] }, { "cell_type": "markdown", "id": "058add5c-cb7b-4ed1-a3ad-87e32670ed39", "metadata": { "tags": [] }, "source": [ "# Plots of changes of concentration with time" ] }, { "cell_type": "code", "execution_count": 16, "id": "68ab68a0-2b22-4f91-b2fc-978401b7dc73", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "Chemical=A
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gAAEIQAACEIAABCAAAQhAAAIQgAAEIIDkZA5AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiSaA5Ex0+QgeAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQHIyByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFEE0ByJrp8BA8BCEAAAhCAAAQgAAEIQAACEIAABCAAAQggOZkDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQKIJIDkTXT6ChwAEIAABCEAAAhCAAAQgAAEIQAACEIAABJCczAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAg0QSQnIkuH8FDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACSE7mAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJBoAkjORJeP4CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEkJ3MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSDQBJGeiy0fwEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAJKTOQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgkmgCSM9HlI3gIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAyckcgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBJNAMmZ6PIRPAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIDkZA5AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiSaA5Ex0+QgeAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQHIyByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFEE0ByJrp8BA8BCEAAAhCAAAQgAAEIQAACEIAABCAAAQggOZkDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQKIJIDkTXT6ChwAEIAABCEAAAhCAAAQgAAEIQAACEIAABJCczAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAg0QSQnIkuH8FDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACSE7mAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJBoAkjORJeP4CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEkJ3MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSDQBJGeiy0fwEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAJKTOQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQgkmgCSM9HlI3gIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAyckcgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABBJNAMmZ6PIRPAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIDkZA5AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACiSaA5Ex0+QgeAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQQHIyByAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAIFEE0ByJrp8BA8BCEAAAhCAAAQgAAEIQAACEIAABCAAAQggOZkDEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQKIJIDkTXT6ChwAEIAABCEAAAhCAAAQgAAEIQAACEIAABJCczAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAg0QSQnIkuH8FDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACSE7mAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIJBoAkjORJeP4CEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEkJ3MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQSDQBJGcI5du2eziEXuhCBwJzB3tk/+GCFIplHdIlxzYJGCKycE6fvMmfMW2S1Ofy3nxG+nsysufgmD5Jk2lbBAYHclIsWXJ4pNhWP1ysD4H5M3tlz8FRe97wgMBUBDKmIerfv2/tHZmqKZ9DwCag/h2Tz2Vk3yH+LcOUaI7ArOl5GRktyfBYqbkLQmi1aE5fCL3QRScIIDlDoIrkDAGiJl0gOTUpdEhpIjlDAqlRN0hOjYodUqpIzpBAatQNklOjYoeQKpIzBIiadYHk1KzgIaSL5AwBYoq6QHKGUEwkZwgQNekCyalJoUNKE8kZEkiNukFyalTskFJFcoYEUqNukJwaFTuEVJGcIUDUrAskp2YFDyFdJGcIEFPUBZIzhGIiOUOAqEkXSE5NCh1SmkjOkEBq1A2SU6Nih5QqkjMkkBp1g+TUqNghpIrkDAGiZl0gOTUreAjpIjlDgJiiLpCcIRQTyRkCRE26QHJqUuiQ0kRyhgRSo26QnBoVO6RUkZwhgdSoGySnRsUOIVUkZwgQNesCyalZwUNIF8kZAsQUdYHkDKGYSM4QIGrSBZJTk0KHlCaSMySQGnWD5NSo2CGliuQMCaRG3SA5NSp2CKkiOUOAqFkXSE7NCh5CukjOECCmqAskZwjFRHKGAFGTLpCcmhQ6pDSRnCGB1KgbJKdGxQ4pVSRnSCA16gbJqVGxQ0gVyRkCRM26QHJqVvAQ0kVyhgAxRV0gOUMoJpIzBIiadIHk1KTQIaWJ5AwJpEbdIDk1KnZIqSI5QwKpUTdITo2KHUKqSM4QIGrWBZJTs4KHkK7uknPv/oNy7U13yg3XXCbnnLE8BKLd62L9Q4/KE089L7euvEr6evMtBYLkbAlb9UVIzhAgatIFklOTQoeUJpIzJJAadYPk1KjYIaXabclpjI6IlEoiZUuMclnE/lG/l53f7c/c962yGJXfLbeN7/NSabwPy7nW7sMSpw9LvRDn2f/abiBi1L7fqL3vffuaqj4bjFHVzrlk0vHcmGpjbSVGB0Cz+bl83JwmjCeWDPRmZXikIOVyg1wV63qc6zD38pvIUcVRHUu9+jnXNcO8uuZ1x1P9TFFzh4f75as3X7zra/qqn1+T49XmVzfGKerWIC5/voFj9OXYeF46887MGFIqVn8HK+NNxb1Ovg3Hm6Sv6vxqauj/Tqs/k3hAAAIQmIpA5c/VqRqG8/kX1q6Tr/7TQ1WdffzDK+TT11wm3ZSc3tiXXXKRXLriwraTRXK2jTCcDpCc4XDUoRckpw5VDi9HJGd4LHXpCcnZQqWVCLPFmSvLSn6R5n5WEWve7450q76uuh/7s5Ir6Tw55/ZTuc6Te974npSzry2J+K9TcXkCUH1WLFaEYEX8ueMZlVxqc1O/j4tEdV1WymKVylIqFMW+zh+TG4MtC72YbPnotnP7Gr/OZeLjaY/n/l6RlhH/h0ELs4JLIAABCEAAAhCAQGMCEf1bxpOISxbNr1rdODwyJresuUc+dMlFctzRR7KS01cpVnKG8MVFcoYAUZMukJyaFDqkNJGcIYHUqBu/5DSGDotRKIqUimIUCrYUM4rus3q/8rrgtFEyamxsvE1NW0NJtUJBjJJ6Vn2N9+EIN3fVXEVq+WWYt8LOkWOOlLMmyi/vM3cVnpKE4yv3vOt80syTj5V2nqj0tVVxecJRCbqRYY1mRDJStXr7RExTxDTEUs+ZjIihfjfHf7c/V79n3LZue19bu49MxrlG/ag+vN8VCsMQUX+wVl67v9jv+16LIZb/9wCv7etq+mo4XqWdNDeeF7xvjAnj1cZaL/am85vISo030JuT4bGSOOs1a/KtN55di0naNZXX1HVzatagplPUsCHHevm5fTnXTB1XvZhaqVvw/Nw5rxZ51uU/OaspY6z9HvlY+a/NZAyZNb1Hdh0cm/S7VzVeM/Oq3veg2e/eBCbjrGrnqtXTm4w/SFMUJdvVU1TMiFJJ83Z1tYJz+449k27f9kTodR/9gHznP38iD37/CZu8t9LTXwb/itAjFsyRtbffIMcuXSSeND3t5GPlF8+/UtXHxy5/ry1Rn3vhVburz914VWXVZr1VpN57Xnv/OLUrUk896Ri567brZdbgdLtvVnJG9KWZahgk51SE+NwjgORkLgQhgOQMQitmbS1LjLFRMYZHxN6OOzpSea1+9943RkbE/lFt/K+HhyvvN2rjf995PSzG6KgtInkEIKAkmCfK1HPGL9bczzzxZvp/N2uu833mtcu4sq3qOq+dK+vUePa4jtirvLZFndtWfaYEoP3stvWucaWfc50r9Vy5Zws++xqnL0f4Gc5Ybt8D/XkpGaaMqIWjnkC0rxuPvaofuw83Brev6uvGc3Guc6WlJyjtPD0zFKBONI0NAe7JGZtSJCIQ7smZiDLFKkgkZ6zKkYhg0io5m90K7rXbtWd/RVq+snmbXHPjHbJ61dWV+3Qqwageaou7ejy5YaOsWn23fc2iBXPtlaFP//LlCX2otp4MVf1+ZvXd8lerrrblaK3krBezGkc91P1C/+4bD8i7LzjbvlY9aiUukjMmXzkkZ0wKkYAwkJwJKFKMQkRyhlMM88ABZ/Wekosjo44MtMXi+GvxvbYl5IhPOlbaV4vIiZLRkZlqvDg87NVx2YxYuZxINidWNiuSy4mVUc9Z99l9v/J5Vqysap91rsuo56zzu/v+eF/+97IVkVaRbUpqeVLOW2Hn+90v5WrlXpXMq5F7lZV+VVLQv4LPE3C+8b3VfJ4EZGVOZYp2+56ccfiuEEMwAkjOYLx0b43k1H0GBM8fyRmcme5XpFVy1hOV9WpdbzWltzLz3LNPtlddqr7WfPk+WX3z1ZVVk/42733Xubbk9NqrcWr7qPde7dhBJWVtXEGvr8eD7eoh/ImA5AwBoiZdIDk1KXRIaaZRcpr79om9jXpoSIzDh8QcOuysYFQrF5VcVLLRez0yKjI85LyvXtsrFX2i0m4/JobbRjyB6WsTUila7qY8Y4ZIT69YfX1i9fSI2vZW+1qq3u+120l/f037XrHb2f04bSqve3vEFpo9vZKfO0vUfxzs8bYFthw5F+pCAMmpS6XDyxPJGR5LHXpCcupQ5XBzRHKGy1OH3pCcE09XrxWUajXllZ+6re50UNvPw5KcamXmssULGx5C5MXlbalXAfm3syM5Y/KNRXLGpBAJCAPJmYAixSjEbklOWxoeViLysJjq2X7tSEn1rMSk91qUqKzXxm5bfZ0x5t6PK0LG9mpEJQR7+8Tq7XV+lCS0XzviUXyvq95XQnGyNkou2n2pdj2OzHRfW/mermzJ5eChCCdXSoZCcqakkBGmgeSMEHYKhkJypqCIEaeA5IwYeAqGS6vkDLpd/YZrLqtsTa8nOe9Yu67q/pf+0jezalO1r21Xu5JzMsnprUxd8a63V7bM125/R3LG5AuJ5IxJIRIQBpIzAUWKUYitSk57teSBfWLu329vnTb37RXjwH4x9+8TY/9++8d0PzcOHhBz714xD6j393V8q7U1fbqU+wfEUj8DA2JNmy5WT96ViUoSukJSbSf2Vih64tAWjtVtrD61wtGTlz6ZqcTmwLQYVTOaUJCc0XBO0yhIzjRVM5pckJzRcE7LKEjOtFQyujyQnNGxTstIaZWcqj6THTzk3euy3unq9bar+++lWVv7sCTnZJJSxXv/A49UHaKE5IzptxDJGdPCxDAsJGcMixK3kCxLbEl5cL9k9u+Xudaw7N26Y1xSqs/8ktJ+7fzYkvLgQRHLaisrezWi2i49bZqU1bMnJO3n6vdkYJpYA/1SVs+qrf2sJKbvPfX7tAGxVzfy6CgBJGdH8aaycyRnKsva0aSQnB3Fm7rOkZypK2nHE0Jydhxx6gZIs+T0VkouWTS/Sg4qmfjnt98jX/viTdKM5PQk5pZtOyacZr540Xx52/JjQrknp7daU530ru4Fqh6ejFWvvYOO/Ce6+w87YiVnTL6eSM6YFCIBYSA5E1CkdkMsl21JqVZK2jLS/lHS8oDzrD5Tqym9lZb2ykrV3m13+HB7ktIwHDk5OFPKMwbtZ2tQPQ9KecZMsWbMEGvmTOd993P1md1mxkwpz5zZlW3W7WLneocAkpOZEJQAkjMoMdojOZkDQQggOYPQoq0igORkHgQlkGbJ6bFQKzq/+k8PVdD472PZzMFDjfo59aRjbOnZ29MTiuRU43ieGGXIAAAgAElEQVSi8823dtvD1t5zU8lZ7/2V1/6h/P19362c1o7kDDr7O9QeydkhsCnsFsmZrKKau3dJZscOMXftFHPnW5LZuUOMPXvE3LvHEZZqK3itsFQH6bT5sLd0u5IyN3e2jPSr311JOdOTlq6ktEWlT1LOmtXm6FyeZAJIziRXrzuxIzm7wz3JoyI5k1y96GNHckbPPOkjIjmTXsHo49dBckZPNbkjcrp6CLVDcoYAUZMukJzdL3TmzW1i7twhGSUulcDcuUPMHW+JuWuHLTHt93btlMyOt1oO1pGO7krKGa6A9CTl7Nnil5h2O3elpaVWUg4OVsZt9Z6cLQfOhYkngORMfAkjTwDJGTnyxA+I5Ex8CSNNAMkZKe5UDIbkTEUZI00CyRkp7tgPhuRss0S/IyKXHCrIh0aLbfbE5ToQQHJ2psqZLa87Ky49eakE5lvbHWFpr8bc7sjMffsCBVCeMUPK8xZIed58Kc2bZz+X5y+wt3zbW7uVzFRbv9VqSltmqtczAo0xWWMkZ2gotekIyalNqUNLFMkZGkptOkJyalPqUBJFcoaCUatOkJxalTuUZJGcoWBMTSdIzjZLqSSEery9UJYvHh6TJaX2DvxoMxwujzkBJGfwAik5md26RTJvqJ+tktnie/3GlmDi0jSlPHuOlJSsdKVlad4CseYvqEhM57P5Upq/QCSbDR5wiFcgOUOEqUlXSE5NCh1imkjOEGFq0hWSU5NCh5QmkjMkkBp1g+TUqNghpYrkDAlkSrpBcrZZyG+JyNVlS3aZhvRaIjcMF+Wa4YJk2uyXy9NJAMlZU9diUbK/3iqZrVsdgalE5pbNkvG9Z4yNTjoZrHxeynOdVZYl9Txfrbqcb4vL8lzntfeeEpximomZXEjOxJQqNoEiOWNTisQEguRMTKliEyiSMzalSEQgSM5ElClWQSI5Y1WORASD5ExEmSILEskZAuoX94zIZ/uz8k+9WVHrOJcXy/KlwwU5pVgOoXe6SBMB3SSnMTwk2ddfcwSmWo25dbNk3njDkZnqd3Xfy/Ik3xPTtCVl6ajFUlp0lP1cXuy8Lqr3jjzKlptpfSA501rZzuWF5Owc27T2jORMa2U7lxeSs3Ns09gzkjONVe1sTkjOzvJNY+9IzjRWtfWckJyts6tc6R089NOcKZ8ayMuWjGGv5PzEcFFuHC7YKzx5QEARSJXktCz7oB5bVtpbyd9wJKYtM7fYqzHVyeOTPdT9K0tHOrLSFphHLXFfHyVF9d6io7q+ZbybMxfJ2U36yRwbyZnMunUzaiRnN+knc2wkZzLr1q2okZzdIp/ccZGcya1dtyJHcnaLfDzHRXKGUBf/6epjhsiavqz8XV9O1FFER5UtufNQQd5RKIUwEl0knUCSJKdRKIxvG7e3j9dsJf/1GzLZVnIr3yOlRUdWpKWSmWW1+tJdlVlculSsvv6kl7Sj8SM5O4o3lZ0jOVNZ1o4mheTsKN5Udo7kTGVZO5YUkrNjaFPbMZIztaXtWGJIzo6hTWTHSM4QyuaXnF53G7OmfHIgJ7/KOvf/U6ev33q4KIMWyzpDQJ7YLuIkOc0DB8YP82m0lbzRfDUM+2AeewWmWolpr8J0Xx/pbCVP8zbyqCYgkjMq0ukZB8mZnlpGlQmSMyrS6RkHyZmeWkaRCZIzCsrpGgPJma56RpENkjMKyskZA8kZQq3qSU7VrbrT4Nq+nPxNX1ZGDJE5ZUs+O1SQD46yqjME7InsIkrJaRw6JLmXNkpm82uS3bpVTO8+mG9skewbW8U4eLAhQ2v6dGe7uLuVvLxYbSNfLCX7fphHSnHp0Ynkn7SgkZxJq1j340Vydr8GSYsAyZm0inU/XiRn92uQpAiQnEmqVjxiRXLGow5JigLJmaRqdT5WJGcIjBtJTq9rdY9Oda9Odc9O9XhnoSR3HCrIEWVWdYaAP1FddEJyZt7aLtmXX5Lsiy9I9uUXnZ8XN0pm+5sN2ZSWLJOiEpeLFtnyUknM4pKlUl64SOxt5P0DieKa1mCRnGmtbOfyQnJ2jm1ae0ZyprWyncsLydk5tmnsGcmZxqp2NickZ2f5prF3JGd3qjo8Mia3rLlHtmzbIXfddr3MGpzenUBqRkVyhlCGqSSnN8R9vVn7FPb9hiEDlshNwwX52HBRlMjgoQeBdiRn9rVXJPvSi5Ld9LJkNz4v2U0vSW7j83VXZJZnzpTiiSdLYflJUjr+hPFt5Wp15oKFesBOQZZIzhQUMeIUkJwRA0/BcEjOFBQx4hSQnBEDT/hwSM6EF7AL4SM5uwA94UMiObtTwFc2b5O1X/83OXBoSD7+4RVyzhnLuxMIkjN87s1KTjXyLtOQm/tz8mCPOn9d5PRiWf6fQwU5vqQ2t/NIO4GpJKc6yMeWmC9tdFdnbnRev7JJjNGRCXjU6eQVmXnSyVI48WQpnnSyfb9MHskngORMfg2jzgDJGTXx5I+H5Ex+DaPOAMkZNfFkj4fkTHb9uhE9krMb1JM9JpKzO/Vb/9CjlYFf37pdPn3NZd0JBMkZPvcgktMb/fv5jPyfAznZYRqSFZE/GyrIp4aLkgs/PHqMEQFPcpb27HO2l29yt5mrFZrqZ8vrIuWJwtuaNk0KJ54kRfWjZObyk6W4/GQpHbEoRtkRStgEkJxhE01/f0jO9Nc47AyRnGETTX9/SM701zjMDJGcYdLUoy8kpx51DjNLHSTnww+/ImNj0Z/tcvHFJ9Qtldqqvuau++Qjl/6O/fmaL98nq2++OhZb1tmuHsK3qxXJqYY9ZBjyuf6s3NubFXV3zqNLlnzp0JicVWRVZwhliU0XmS2vS/6ZpyT77DMy8NwzIr/6lZg7dtSNz+rtk+KJy51t5mq7+Slvk8IJJ0pp8dLY5EMg0RFAckbHOi0jITnTUsno8kByRsc6LSMhOdNSyWjyQHJGwzlNoyA501TNaHLRQXLOn79Gdu4cigaob5SdO1fK3Ln9E8ZVW9XvXf89WXnt5fZn6t6c5559sly64sLIY6wdEMkZQglalZze0E9nTfnTaXlRBxQpqXHFSFH+76GiTLM4mCiE8kTaRWbbryX3iw2Se+pJyW94SnJP/1zM/fvrxlA49XQpnLhcSstPkcLJp0jxhBOluOyYSONlsHgTQHLGuz5xjA7JGceqxDsmJGe86xPH6JCccaxKfGNCcsa3NnGNDMkZ18rENy4dJOcVV6yXAwdGIy/CN75xqcyY0TNh3C+sXSfLFi+sSE21df2Jp56XW1deJX29+cjj9A+I5AwBf7uSU4UwZoh8oTcrd/XnpCgi88uW/M3hgry7C0uSQ0CiRRfmnj2Sf+pnktvwtCM1ldDcs3tC7mqr+dhpZ0rhrLOl98Lz5MAxy2Vk2bFaMCLJ9gggOdvjp+PVSE4dq95ezkjO9vjpeDWSU8eqt54zkrN1drpeieTUtfKt562D5GydTvhX7t1/UK696U557oVXqzo/YsEcWXv7DXLs0u7eUg/JGULNw5CcXhgbs6Z8ciAnv8qa9lsrxkqy+nBB5pZZ1RlCqVruwjxwwF6Vmd/wtGSfcZ4zv35jotDs6xe1QnPsjLOkeObZMnbm2VI89jgRQ+kqkakOHmo5QC5MJQEkZyrL2tGkkJwdxZvKzpGcqSxrR5NCcnYUb+o6R3KmrqQdTwjJ2XHEqRsAyRltSZ/csFHuWLtO7rrt+qp7cNau7ow2qvHRkJwhkA9Tcqpw1B05v9KXldv7cjJsiMywLPmLoaJ8eESt8eTRaQLG4UOS3/CMs0Lz6Z/bz9nNr00Umvke556ZZ57t/Jxxln04kJiOoK73QHJ2unrp6h/Jma56RpENkjMKyukaA8mZrnpGkQ2SMwrK6RkDyZmeWkaVCZIzKtLpGQfJGW0tlcxUj9rT1JX8vP+BR7q+ZR3JGcJ8CFtyeiG9YRpy/bScPJ7L2G+9vVCWLx4ekyUlVnWGUDa7C2Nk2L6HprNC8ynngKBXNonU3g81m7VPNK9aoXnSKWLlcoFCQXIGwqV9YySn9lMgMAAkZ2Bk2l+A5NR+CgQGgOQMjEzrC5CcWpe/peSRnC1h0/oiJKfW5Z+QPJIzhPnQKcnphbauNyv/uz8r+w3DvlfnpaNF+dORksxmC3vg6hmjI5L/6ROSf/S/pOexH9pyU4o1K2RNU4rHn2ALzcKZv+Gs0DztdLHyE2+4GzQAJGdQYnq3R3LqXf9WskdytkJN72uQnHrXv5XskZytUNP3GiSnvrVvNXMkZ6vk9L0Oyalv7etljuQMYT50WnKqEHeZhvx5f07+rcdZ1dlriXx0pCj/a7gosziFvWEVjUJBck8/KT0/+qHk1c+TPxVjzHcqmWFI8ehjHZF55ln2PTQLp58pVl9/CDNjYhdIzo5gTW2nSM7UlrZjiSE5O4Y2tR0jOVNb2o4lhuTsGNpUdozkTGVZO5oUkrOjeFPZOZIzlWVtOSkkZ8voxi+MQnJ6o6mDidb0ZeU/8hlRm9anWZZcNVyUa0dK9r07tX+Uy5J/9hnJP/ao9Dz6X5L/6U/EGDo8jsUwpHDichk770IZO+8CGb3gt6U8a1Zk2JCckaFOxUBIzlSUMdIkkJyR4k7FYEjOVJQx0iSQnJHiTvxgSM7ElzDyBJCckSNP/IBIzsSXMNQEkJwh4IxScvpl51/3ZeXhvLOyc7plySeGi3LNSMl+rc3DsiS38Xnp+dEjkn/0Eck//iNRJ6FXHkpqqntpnneBLTZHz39npFKztg5ITm1mZiiJIjlDwahVJ0hOrcodSrJIzlAwatUJklOrcredLJKzbYTadYDk1K7kbSeM5GwbYao6QHKGUM5uSE4v7F+6Kzu/58pOtZpTic6PDxdTKzuzr7zsbD9/9BHp+fGjYu7eVS01TzpFRs+7UApqpaaSmjNnhlDlcLpAcobDUZdekJy6VDq8PJGc4bHUpSckpy6VDi9PJGd4LHXoCcmpQ5XDzRHJGS5PHXpDcupQ5eZzjLXk3Lv/oFx7053y3AuvTsjo1JOOkbtuu15mDU5vPtsOteym5PRSetaVnf/lys6ZliX/c6QkVw0XZCDhCzszb2x1Vmr+6BFbbmbe3DZeSdOUgis1x86/UMaU1Jwxo0OVbr9bJGf7DHXqAcmpU7XDyRXJGQ5HnXpBcupU7XByRXKGw1GXXpCculQ6vDyRnOGx1KUnJKculW4uz1hLzi+sXWdn8elrLmsumy61ioPk9MvOv+7Pyg9zzjZ2dSjRdcMl+dhIQfoSIjvNXTud+2mqlZo/+qFkN79WLTVPOVVG33GBKKmpVmpa07svupudekjOZknRThFAcjIPghJAcgYlRnskJ3MgKAEkZ1BierdHcupd/1ayR3K2Qk3va5Ccete/NvvYSk61inPV5++WldddLscuXRTrqsVJcnqgns6acnt/Vn7kys45ZUuuGynJlSMF+2T2uD3Uqee9D/2b9D78Xcm+uLEqvMKpp8vohRfJ2DsusOVmkqRmLWckZ9xmXrzjQXLGuz5xjA7JGceqxDsmJGe86xPH6JCccaxKfGNCcsa3NnGNDMkZ18rENy4kZ3xr043IkJxNUB8eGZNb1twjD37/Cbv15268Si5dcWHlyjhKTi+4n+Wc09gfd2XnvLIlfzZSlD8eKUq+y7Kz54f/Jb0Pflt6H3pAMtvfrPAcO/NsGXvH+fbW89HfOl+sadOaqFIymiA5k1GnuESJ5IxLJZITB5IzObWKS6RIzrhUIjlxIDmTU6s4RIrkjEMVkhUDkjNZ9YpDtEjOOFQhPjHEVnIqRGq7+rLFC6uEYtToPMF57tknN4wjzpLTLztX9+VESU/1mF+25H+NFOWKCGWnMTYqPT/4niM2//1BMffutWOx8nl72/noJR+U4fe9X8qz50Rd5sjGQ3JGhjoVAyE5U1HGSJNAckaKOxWDITlTUcZIk0ByRoo78YMhORNfwsgTQHJGjjzxAyI5oy1h7SJANfoRC+bI2ttviMUu7FhLzlc2b5N7139PVl57ufT15qOtnDva+ocelde3bp/0vqBJkJwevB/nMvI3fdmK7FxYtuSTw0X5o5Gi5DpA2Dh8SHr/4yHp/c6/Se/3Hxb1uy02+/pl5N2/K6MXf1CG37Mi0VvQg2BDcgahRVskJ3MgKAEkZ1BitEdyMgeCEkByBiWmd3skp971byV7JGcr1PS+BskZbf3rLQRU3uyJp56XW1de1TV351GIreSc7GR1FXxUp6ur1aRf/aeHKrOmnqFOkuT0ElH36lSy8+fuys5FZUv+j+GiXD5SlGyb3xFzz27pe+gB6fnOt+0DhIyxMbvH8uCgjLxnhYxc/EEZfffvitXT2+ZIybscyZm8mnUzYiRnN+knc2wkZzLr1s2okZzdpJ/MsZGcyaxbt6JGcnaLfHLHRXImt3bdihzJGS35epLzyQ0b5Y616+Su266XWYPdPRg6tpIz2jLVH80r3ocuuUjOOWO53UgZ6nUPPFJVvKHRYhzCbSmG75mG/GXGlCdNpVNEllqW/F8lSz5SKgeSncavfy3Zb64X89vfkszjPxYplez+rAULpXTJJVL64O9L6aLfFsm2q1BbSjM2F/XkMlIolqVsdfmGqLEhkuxAnG9NZx+9PVkZafBnTMkSyRhRRNHZHOk9PAKmaUg2Y8hYoRxep/SUagK5rCHqr6Si+gOFBwSaINCTN+0/Y/inTBOwaCLqnyk9OVNGxsL9e6lULosSqDzCJRCHvwmypiHq3zNjxXDnTLik6C1OBPJZU0ply/6J6tHfE63XeFhEnKVj0T4urjNco5WcU+2AjipyJOckpOtJTm+F6Q3XXFYRn3sPdmO6hTtFvpc1ZXU+I89knH8sLCtbsnKsLJcVSpJpMFTmlU2S/9a/Svbfvi3ZZ56qtCovXSZjl3xACu//fSm+/Vyx/3XDwyYwvT8rQ6MlKfEfk6mYEZ3+a1R9c2ZOz8tkf8bw9UrFVAotCfWPPPVzaCS5//MtNBh01BQB9Y/0ctmSkYLzPyd5QGAqAoP9eTk0Uoj0PyanionP40vANAyZ3p+T/YfD/e8lS/kv/hMj9MLHAWk+Z0o2Y8oQ/5YJvb5p7XCgLyuFQjlSMa5Wj0b5mC8iO6Mc0B1LjTm3Ztx69+RUTT7+4RWT3uYxqvBjLznVstcrP3VbFY+vffGmimDsNKjaw4+U5Fz1+btl5XWXV26qmsTt6o24/Wc+I3f0ZeW5rHNAUb8l8u6xknxktCQXFEqSe/YZ6X3gW9L34Lcl+/JLlW4Ky0+SkRXvl5FLPiiFU0/vdFkS2z/b1RNbuq4Eznb1rmBP9KBsV090+boSPNvVu4I90YOyXT3R5Ys8eLarR4488QOyXT3xJYw8AR22q18hIgciJyvyDRGZ0UBy+g/nbubA7qjCj7XkrLevXx1GdM2Nd8h1H/1AJKeuqxhWrb67clJUvRuqpklyehPvYVd2Pm+KnP/YY3Lp+vXyoW9+UxZt2VKZm2Nnnu0cHHTJB6R4zHFRzdlEj4PkTHT5Ig8eyRk58sQPiORMfAkjTwDJGTnyxA+I5Ex8CSNNAMkZKe5UDIbkTEUZI01CB8kZKdApBmskNJs5tDuKPGIrOettFfeAKPF4/wOPRHZykyrWn99+jz18vQOP0ig5s5teloGv3CW59eskv2dPZS7+8J3vlG9deqnse9/75b3zFsl5hRI7RQJ8U5GcAWDR1P5uLZzTJ2/uHoYGBJoigORsChONfASQnEyHoASQnEGJ6d0eyal3/VvJHsnZCjW9r0FyRlv/epKTlZxN1KDetnDvMrWac82X75PVN1/d9ZObVExpkZzGyLD0fWu99H/9Hsn/7CeVKo38zntk3/t/X/7h0g/KVxbOk00ZZyu7eiwtWXLFaEkuHy3K7Ahv9NvEFIplEyRnLMsS26CQnLEtTWwDQ3LGtjSxDQzJGdvSxDYwJGdsSxPLwJCcsSxLrINCcsa6PLEMDskZbVka3ZPzczdeFclu66myZSXnVISa+DzpkjP74kYZ+Pu/k751/yjmAedOD6UlS2Xojz8uh6/4qJTnzqui8NOcKf9fT0Yeymdl1L07tTpb7L+NlmzheT6rOxvOGiRnE18omlQIIDmZDEEJIDmDEqM9kpM5EJQAkjMoMb3bIzn1rn8r2SM5W6Gm9zVITr3rX5t9bCWnClRtE1/3wCNy123XV1ZsRn1PzmamSxIlp71q85v/Iv3/8FXJ//xnTpqmKWrV5tDHrrafpzoV/YBhyLrejNzbk5GXWN3ZzFQRJGdTmGjkEkByMhWCEkByBiVGeyQncyAoASRnUGJ6t0dy6l3/VrJHcrZCTe9rkJx61z9RklMF2+3T1ZuZLkmSnPaqza/+rfT9y32VVZvlefPl8BVXytCVn5DSkUc1k/KENj/LmfKNnox8h9Wdk/JDcrY0vbS9CMmpbelbThzJ2TI6bS9Ecmpb+pYTR3K2jE7LC5GcWpa9raSRnG3h0/JiJKeWZW+YdKxXcialVHGXnMbwkPStv99Ztfn0zytYR8+/0Fm1ueL9YuVyoeA+aBhyf2/G3s7O6s6JSJGcoUwzbTpBcmpT6tASRXKGhlKbjpCc2pQ6tESRnKGh1KIjJKcWZQ41SSRnqDi16AzJqUWZm04Sydk0qsYN4yo5cy/8Svq/ulb6//WfxTh40E6gPDgoQ394hQxd9SdSPO74ELJv3MXP3dWdD+SzMuK7d+d7RkvyPzS9dyeSs6NTLnWdIzlTV9KOJ4Tk7Dji1A2A5ExdSTueEJKz44hTNQCSM1XljCQZJGckmFM1CJIzVeVsOxkkZ9sI43W6ulq12f+v66T/a1+R3IanK9mNnXm2LTaHL/2QWD29IWTdfBfe6s578xnZmNX7ZHYkZ/PzhpYiSE5mQVACSM6gxGiP5GQOBCWA5AxKTO/2SE69699K9kjOVqjpfQ2SU+/612YfO8m5d/9BufamO+Vjf/jf5O//+d/luRderVuxU086pupAom6WNQ4rOe1Vm1/5W2fV5qFDNg6rf0CGLv2QDH3if0rhbad1E1Fl7KeypnyjNyNqdeewhqs7kZyxmIaJCQLJmZhSxSZQJGdsSpGYQJCciSlVbAJFcsamFIkIBMmZiDLFKkgkZ6zKkYhgkJyJKFNkQcZOcnqZK9m56vN3y8rrLpdjly6qAqIOI7r/gUfk1pVXSV9vPjJYjQbqluQ0Dh9yVm3+w1cl9+wzlfAKy0+SoSuvlqE//IhY06d3nU+9AA4ZhvxrT0a+3lO9uvOUYlk+MFaWdxZK8rZiOZaxtxMUkrMdevpdi+TUr+btZozkbJegftcjOfWrebsZIznbJajX9UhOveodRrZIzjAo6tUHklOvek+VbSIl5yubt8maL98nq2++WmYNdl/iRS051TZ0JTb7198vSnSqh5XPy8jFH5TDV10tY+eeN1XdY/X501lT/rEnI+t7sjLqru5UAS4pWfK+saK8p1CWcwrpEJ5IzlhNvdgHg+SMfYliFyCSM3YliX1ASM7Ylyh2ASI5Y1eSWAeE5Ix1eWIZHJIzlmWJdVBIzliXJ/LgEik51z/0qDzx1PPareTs/8evy8BdX5LcC89XJkrpqMUy9LE/kcP/40opz54T+QQKc0C1uvOhnow8mDPle/lMVdfzy5asGCvJe8bKcmGhFOawkfaF5IwUd+IHQ3ImvoSRJ4DkjBx54gdEcia+hJEngOSMHHmiB0RyJrp8XQkeydkV7IkeFMmZ6PKFHnzsJKdapXnNjXfIm2/tbpjsEQvmyNrbb5iwjT10Ok122OmVnP3/8s8y7bbPSfb18fuTjrz3Yhm68hMy8u7fazLKZDUbMkR+kM/IQ7mMfD9vihKg3mPQsuR3x0qyYqwsv10oSd5KTm5IzuTUKg6RIjnjUIVkxYDkTFa94hAtkjMOVUhWDEjOZNWr29EiObtdgeSNj+RMXs26HTGSszsVqOfuvvbFm+ScM5Z3JyB31NhJTo/GZPfk7CqxOoN3SnL2fetfZPqaz0v2xY32qOpU9MOfuEYOX/NnUlp0ZNwwdCyegoj8yBaepjycz8huc1x49lsiv62EZ6Fki8+BmAtPJGfHpkkqO0ZyprKsHU0KydlRvKnsHMmZyrJ2NCkkZ0fxpq5zJGfqStrxhJCcHUecugGQnNGXVJ2Tc+WnbhO/1FQO7+/v+65c+9EPdvXsnNhKzujL1PqIYUvO3oe/K9NXf1Zyzz07Ljc/+nE59OkbpTx3XuuBpuBKtVH9yZwpD+Yy8u89GdnmE545EbnAFp5lec9YSWaX42c8kZwpmIQRpoDkjBB2SoZCcqakkBGmgeSMEHZKhkJypqSQEaWB5IwIdIqGQXKmqJgRpYLkjAi0O8zwyJjcsuYeOffsk+XSFRdGO3gToyE5m4A0VZOwJGfPIz+Q6atvlfxTTzpyM98jQ3/8MTl0/Y1SWrBwqjC0/PzZrCkP5U35bj4jr2TMCgN1R091WNH7CiV572hJjoiJ8ERyajlNW04aydkyOm0vRHJqW/qWE0dytoxO2wuRnNqWvqXEkZwtYdP6IiSn1uVvKXktJOfDD4uMjbXEp62LLr54wuVqm/pnVt8tf7Xq6tjcQtIfZKwl52T35zz1pGPkrtuuT8Xp6vmfPi4zPvsXop4duZmXoT/6Yzl0w01SOmJRW3NSp4tfypj26k61rf257LjwVAxOL5blfWNlWTFWlKNL3VvhieTUaUa2nyuSs32GuvWA5NSt4u3ni+Rsn6FuPSA5dat4e/kiOdvjp+PVSE4dq95ezlpIzvnzRXbubA9UK1erMefOrbpSebo1X75PVt98dSx8XG1asZWc/iWwp59ynNy7/nuy8trL7b39X1i7Ti54+2ldv6GpB7PVlZy5DU/LjL/8C1ErOG25mcvJ0OVXOHLzqMWtTEGucQn82jTku7bwzNjb28s+MieUyrJi1NnWfkrR/0nn8SE5O884TSMgOdNUzWhyQXJGwzlNoyA501TNaEeqeKsAACAASURBVHJBckbDOS2jIDnTUsno8kByRsc6LSNpITmvuELkwIHoS/aNb4jMmDFBcrKSs4VS+A8eUpf7TbG6yen9Dzwit668qqs3NG1VcuZe+JVM/9xfiLr3pv3IZmXosj+Sgzd+BrnZwlyZ6pI9piH/kc/Id3KmPJJXG9nHH0p4XjJWlpOLZXlHoSwzrM6u8kRyTlUtPvcTQHIyH4ISQHIGJUZ7JCdzICgBJGdQYnq3R3LqXf9WskdytkJN72u0kJwxKjH35GyxGH7JOXvmdFn9pXtl1Sc/Yi+Hjdvy2GZXcqpT0qff9lnp+863RZRMy2Rk6EOXO3JzybIWSXFZEAIHDUP+U53Unjflv3IZGRk/qN3u5tRiWc4vlOS3ipacWwj/tHYkZ5Bq0RbJyRwISgDJGZQY7ZGczIGgBJCcQYnp3R7JqXf9W8keydkKNb2vQXJGX39OV2+Bea0dVlvUly1eaJ/etP6hR+WJp55PzErO7GuvyPTbPid93/wXkXJZxDRl6A8uk0M3fkaKRx/bAh0uCYPAqCHyo1xGfpw15Uc5U16ouY+nGuMsJT3HSnJe0ZJziiXpaXOhJ5IzjMrp0weSU59ah5UpkjMskvr0g+TUp9ZhZYrkDIukHv0gOfWoc5hZIjnDpKlHX0jO7tS53hk6X/viTV2/rWRs78lZWya1svPam+6U5154VY5YMEfW3n5DbE5yarSSM/PGVpn+138p/ev+UaRUsuXm8Af/u71ys3jc8d2ZiYzakMA+w5DHc6Y8ljPlxzlTNvlOa1cX5S2R3yiW5PyiJecVSnJGoSzZgDyRnAGBad4cyan5BGghfSRnC9A0vwTJqfkEaCF9JGcL0DS+BMmpcfFbTB3J2SI4jS9Dcmpc/DqpJ0ZyxrlstZIz89Z2mbbm89J/7z+IUSiIGIYMv/9SObjqL5CbcS5kTWw7TUMes1d6GvLjXEa2ZKr3tg9YIr9ZGJee6hCj6jPdJyaL5EzQBIhBqEjOGBQhYSEgORNWsBiEi+SMQRESFgKSM2EF63K4SM4uFyCBwyM5E1i0LoeM5OxyAWI2fGwlp/+enMcuXRQzbNXheJLT3LVTpn3hdhn42lfEGBt15ObFH5CDN/2FFE9cHuscCG5qAurE9h/lM/JYVq34zMhbZrX0HLQs+a1CWc6zt7iXRR1qVPtAck7NmRbjBJCczIagBJCcQYnRHsnJHAhKAMkZlJje7ZGcete/leyRnK1Q0/saJKfe9a/NHskZwnzYvmmbTPvS38jAV9aKMTxk9zjy3ovl4M23SOGkU0IYgS7iSEBtZ7e3t2dNeTxvyl6jWnrOK1v2ie3nF8v29valJUuQnHGsZHxjQnLGtzZxjQzJGdfKxDcuJGd8axPXyJCcca1MPONCcsazLnGOCskZ5+rEMzYkZzzr0q2oYis5FRB12NAFbz+t6zcunbQ4t9wi1t/cIcbQYUdu/t577W3phVNP71ZNGbdLBH6lZKcrPX+Sy8jhmpPbF5YtWSEi7x8uykCxLCcXJ6707FLoDBtTAkjOmBYmxmEhOWNcnJiGhuSMaWFiHBaSM8bFiWFoSM4YFiXmISE5Y16gGIaH5IxhUboYUqwlpzqt6d7135OV114ufb35LmKaZGh39d7Iu3/PkZtnnBXPOIkqcgLPZNUBRhl5LGfIk9mMjNRITxXQbxTKckaxLKeVLDmtWJbj62xxjzxwBowNASRnbEqRmECQnIkpVWwCRXLGphSJCQTJmZhSxSJQJGcsypCoIJCciSpXLIJFcsaiDLEJIraS03+aej1ap550jNx12/Uya3B6d2HecovsuuB3ZezMs7sbB6PHnsATOVN+MS0vPy5ZsiFjyK6ae3qqBNRhRqcWy3JWsSRvK1m2AFXb3HnoSQDJqWfd28kaydkOPT2vRXLqWfd2skZytkNPv2uRnPrVvN2MkZztEtTveiSnfjWfLOPYSs4klan2dPUkxU6s0RLw35PzTdOQZ7OmPJsx5FklQLMT7+upoptpOas8T1erPt0Vn4vKiM9oK9ed0ZCc3eGe5FGRnEmuXndiR3J2h3uSR0VyJrl60ceO5IyeedJHRHImvYLRx4/kjJ55nEeMreSc7HT1JzdslPsfeERuXXlVLLaxIznjPMXjFdtUBw9tyRi27FQrPZUAfS5rysGaA41URupQI1t8Fh3xqVZ8zkF8xqvYIUSD5AwBomZdIDk1K3gI6SI5Q4CoWRdITs0K3ma6SM42AWp4OZJTw6K3mTKSs02AKbs8kZJT3atzzZfvk9U3X9397eoiguRM2beig+lMJTlrh1brNV+zhWfGFp9KgP4ya8pQnft7HlG2KtLzNPden4MWKz47WM6Od43k7Dji1A2A5ExdSTueEJKz44hTNwCSM3Ul7WhCSM6O4k1l50jOVJa1o0khOTuKN3GdJ1Jyrn/oUXniqedZyZm46UbAQSVnI2IvZJ3t7b80DXk6Z8qGrFm36ZKSJb8/WpS8YYh6fVzJOdyoD/eZiMmI5ExEmWIVJJIzVuVIRDBIzkSUKVZBIjljVY7YB4PkjH2JYhcgkjN2JYl9QEjOaEs0PDImt6y5Rx78/hNVA3/tizfJOWcsjzaYOqPFTnKqVZrX3HiHvPnW7oZwjlgwR9befoMcu3RR1wGqAFjJGYsyJCKIsCRnvWTV1nZbfGYMedpd8dkIypFlS463T3O35PiyIz9PLFr2/T95xIcAkjM+tUhKJEjOpFQqPnEiOeNTi6REguRMSqXiESeSMx51SFIUSM4kVSsesSI5o62DJznPPftkuXTFhfbgnse77qMfqLwXbVTjo8VOcnqhTXZPzm7BajQukjNuFYlvPJ2UnPWyVis8X8yasskUeSljyssZUzZn6ux1dy+ereSnu+LzBFt+WnJcsSxHcb/PrkwqJGdXsCd6UCRnosvXleCRnF3BnuhBkZyJLl/kwSM5I0ee+AGRnIkvYeQJIDmjRV5Pcnqi8zOr75a/WnV1VxckxlZyRlum9kZDcrbHT6ero5ac9diOGSKv2sLTsKXny6YhmzKGvJIxZbSB/+y3xF7tqX5OKIu9ClQJ0GWlsmR1KmDEuSI5IwaeguGQnCkoYsQpIDkjBp6C4ZCcKShihCkgOSOEnZKhkJwpKWSEaWghOd98WKQ8FiFVd6gjL54wZiPJ2ej9qINGcoZAHMkZAkRNuoiD5JwMtVrlaYtPW36KLT5fzBp1T3j3+nHkp7P9XQnQ944VuednSPMZyRkSSI26QXJqVOyQUkVyhgRSo26QnBoVO4RUkZwhQNSsCySnZgUPIV0tJOe/zhcZ3RkCrYBd/MFOkZ65VRdNJjO/sHadLFu8sKtb1mMtOdWW9WtvulOee+HVCZU49aRj5K7brud09YBzlObdJRB3ydmIzg57taez+nOTacjLWcPe/v6W2Xjr+3TLkqNLlr3VXa34XFIWWVayZHG5bD/zmJoAknNqRrSoJoDkZEYEJYDkDEqM9khO5kAQAkjOILRoqwggOZkHQQloITkfv0KkcCAomvbbv+MbIrkZTUlOVnI2gVtZYPX49DWXNdG6e01Yydk99kkbOamSsxHnQ4Za+amkp7PyU4nQ1zKGbDEbb333+lpctmRpqSyL1bb3ssiSclmWliz7FPhZHIBkY0JyJu0b3v14kZzdr0HSIkByJq1i3Y8Xydn9GiQpAiRnkqoVj1iRnPGoQ5Ki0EJyxqgg3JOzxWJw8FCL4Lgs1gTSJjkbwVbrNHeahn3IkRKe6nmzKfbr1zOG/dlkaznVKlC12lMJz6VKfioJ6r4+smRpcx9QJGesv86xDA7JGcuyxDooJGesyxPL4JCcsSxLbINCcsa2NLENDMkZ29LENjAkZ7Sl4XT1FnkjOVsEx2WxJqCL5JyqCOqAo82u/NyiRKhh2CtAt7pSdKTxLni7a7UFfknJWfn5rkJZZrnGdIZlyfyyJfNScho8knOqmcTntQSQnMyJoASQnEGJ0R7JyRwIQgDJGYQWbRUBJCfzICgBJGdQYu219yTng99/oqqjr33xJjnnjOXtdR7C1bG+J2ccblraDGO2qzdDiTaKAJKzuXmg7gGq5KcnQl93V4GqFaHqs2YeC8qWLCw70lP9LLSk8lp9pt47IuYyFMnZTKVp4yeA5GQ+BCWA5AxKjPZITuZAEAJIziC0aIvkZA60QgDJ2Qq19F4Ta8n5yuZtcu/678nKay+Xvt58bKuA5IxtaWIXGJIznJKoQ4/sFaAZQ7YZIttNwz4ESQlQ9XzQaE6EqmjmuMLTFp/uStAFlognQtWz+unpwllJSM5w5otOvSA5dap2OLkiOcPhqFMvSE6dqt1+rkjO9hnq1gMrOXWrePv5IjnbZ5imHmIrOSc7WV0VgNPV0zQN9ckFyRlNrcdc8amk5w7DEZ9vGVKRoPb7piF7TEPKTYaktsJ74lOtArXlp7s6dEF5fJWoup9oWA8kZ1gk9ekHyalPrcPKFMkZFkl9+kFy6lPrMDJFcoZBUa8+kJx61TuMbJGcYVBMTx+xlZxJQsxKziRVq7uxIjm7y7929JJ7QJK3CtRbCVorRNVBScUmQ++riM8GW+Xd1aKzm9gqj+RsEjrNKgSQnEyGoASQnEGJ0R7JyRwIQgDJGYQWbRUBJCfzICgBJGdQYuluj+QMob5IzhAgatIFkjOZhVZrM9WqT0+CVlaIGlK1TV69P9WhSR6BrEy8R6h/m7x9H9GyJW+b1Ss7dg8nExxRR04AyRk58sQPiORMfAkjTwDJGTnyRA+I5Ex0+boSPJKzK9gTPSiSM9HlCz34WEtO/6lNRyyYI2tvv0EWLZgrt6y5R849+2S5dMWFoQNppUMkZyvU9LwGyZn+uh9wt8er1Z/2NnnTkJ2GJdtNU3aazv1DlQxt9r6hi0XkyML4pvpey5KZlshMy5JZZUtmicjMsvP7zLLz2SzLkmZWiqa/GvpliOTUr+btZozkbJegftcjOfWreTsZIznboafntUhOPeveTtZIznbope/aWEtO73T1977rXFlz133ykUt/R45dukie3LBR7n/gEbl15VWxOJAIyZm+L0anMkJydops8voddVeB+g9M8rbJe/cR9e4b2spdPtVWd3V/UCVAlfR0pOi4DPXkqP2ZK0c9UZpJHk4idgkgOZkKQQkgOYMSoz2SkzkQhACSMwgt2ioCSE7mQVACSM6gxNLdPraSUx08tOrzd8vK6y63V2/6Jac6dX3Nl++T1TdfLbMGp3e9QkjOrpcgMQEgORNTqlgEqkTlvDl9snHviOwzRPYZhuw1Dft5nymyV0T22b+L7K35TK0obUWO2v+4tEQGLEumW44o9V5PsyyZZomo5xmWMeF9p61zjWqjXvOIlgCSM1reaRgNyZmGKkabA5IzWt5JHw3JmfQKRh8/kjN65kkfEcmZ9AqGG38iJScrOcOdBPQWHQEkZ3Ss0zBSOwcPqQ3u+10Z6khRR47u9eSo+9r7TElSJU6VHG32xPmpGJviyFJPjKrnKhFadoTpdDFcMer87rX3t+1FmE6F2/4cydkUJhr5CCA5mQ5BCSA5gxLTuz2SU+/6t5I9krMVanpfg+TUu/612cdWcqpA1z/0qDzx1POy6pMfkf/3nm/a29Vnz5wu1950p1x2yUXck5O5nDgCSM7ElayrAbcjOdsJ/JBhyGFD7PuGHjJE1O/e80H1u7qnqFj2++r3w+6z0855Tz0PqwRCeqiDmiorSst+ETouRu0VpPY2/WpZWpGmZZEZliVp3o6P5AxpwmnUDZJTo2KHlCqSMySQmnSD5NSk0CGmieQMEaYmXSE5NSl0k2nGWnKqHNSqzSs/dVtVOl/74k1yzhnLm0yx883Yrt55xmkZAcmZlkpGk0e3JGdY2anFl35R6snPQ6bzvidSPWHqF6R+uarajoUsTPssS9Tq0F61+tF7bf/uvm+J9PleqzZ9hjHe1nddX811VX1YlihBG9UDyRkV6fSMg+RMTy2jygTJGRXpdIyD5ExHHaPMAskZJe10jIXkTEcdw8oi9pIzrEQ72Q+Ss5N009U3kjNd9ex0NkmXnGHyKYmzlf6gWbty1Fk1agvUOlK1ssrUHF+NWgwzsCb6Utv285ZIXizpsZ9FcjW/q897LEtyDdr2iGX30WMYlb6cPlXf4/0OZE2ZkTNl9HDB/UxEXeuM58Th9dVE6DTRgACSU4Mih5wikjNkoCnvDsmZ8gJ3ID0kZwegprxLJGfKCxwwvVhLTnW6+vYde6pOUR8eGZNb1twj5559cuTb1b1VpbUrSZGcAWedxs2RnBoXv4XUkZwtQGviEiU5hw1DRgz1LDIizmv7x/9atbHbqs8MGbYs+7luW/c6r626zmlnSNRStQkEdhO/UHUkqyNi1Wv1mf93T5B6n/d4MtayJG8YzbV1ZatzrSttfcJXjRvlqtdmOaW9HZIz7RUOPz8kZ/hM09wjkjPN1e1MbkjOznBNc69IzjRXN3husZWcnsz80CUXTdia3o2Dh/zb5pGcwScaVzgEkJzMhCAEkJxBaMW77ZAhUhBn2/2oiP08Zhj264L72aj/M7ftmNdWDFGfj1mWfV1VP762RdMQyzTkQNmSQu216npxbhMQ54cnV9WzJ2K91aeebM2KJVlXiioxql7nxLnfqlq1aj+77/vb5gzDvc65jYC/D+c6Z9Wrd33GXQFriCVqRa79436uMHrvqXbjvztx2L+7bb126j3Vtup3t2+VbzceSM5uUE/2mEjOZNcv6uiRnFETT/54SM7k1zDqDJCcUROP93ixlZx79x+UVZ+/W1Zed7kcu3RRFcVXNm+TNV++T1bffLXMGpzeccLeeDf+6Yfl5tV3yw3XXFYlXlnJ2fESpGYAJGdqShlJIkjOSDCnapAg9+RUEnTUFaZ+mWoLWPuzcTHr/a4kqfd5RdRaliNgxbCFbcO2nuT1ta0IX/czdVsCHn5h6ojSimD1ydZqseqIWdXWk61K2Krr6ovV8X5zGUNMyxKr7IrcqvF8Y1eErTU+htt2fEynvVEld532frlrGoYTrxuzk1+1SB7PeWIMznhOe6/fpmLwjWfH5BPV45zrx1ER1xPGdKS2n72KK80PJGeaqxt+bkjO8JmmvUckZ9orHH5+SM7wmSa5x9hKzris5FSC8zOr75a/WnV15WR3JGeSp3x3Y0dydpd/0kZHciatYt2PN4jk7H609SNQW/49mepJULUK1haprnhVgrZoiH0rAPvHfu1IViVK1eclu6363Xm/aDmrW533jTrXN+rDkLKI82O4z97vE9437PHVokyvrfpdXWu/547v/a5iU69VbjzSS0Ct0u2kqPZLVW8qqdXH3mP8vXHGk75XZ1Wxv31PzpSxYtmZ1K5IDzrWpO2b6Nf/lamXc23//jjrX+vm4su9HqNm+p2s/4ZxTMG8Oc5OJ/XGnzTuKXKedK4YzqeT5WwaIgM9WTk0ov6U9s3BmjrX+2Nwqjo3M7fr9ttOzm4Kk3Gecr41McdruY7XsHGdm/1eNzO3W5rHddhU4g6Qs/ozpidryqHhuN7wJ71/XyU1s+n9WRkrlGW0oP5VFc3jD2ao/UU84kggtpJTwVJbxFetvlvW3n5DZTWnko7X3HiHXPfRD3T8npy1q0nV79fedGfVSs4zz1wrS5YOyjsvOlreedEyOfHEOXGsMzHFhEBfT8b+w7dc7tK+xJhwIIzmCKh/YPb3ZeVwg3/kqVmEG2mOpS6tshlD1M/IWHT/yNOFbbfyHHYFqZKzFdlqy1vvd5+ErRGpXvuKePU+90naTNaUolgyVrJsZ+Xvd/y1b2xfHP7PJ8jcGilc1daqk0sDgVwbg/274ZPGvlw8aezn5LVV19XG64lnWz7XCGt/PrX91rZVNeIBAQhAAAIQgIA+BPiv+fjWOtaSU2HzpOabb+2uUKy9J2an8NYb2xvLi8Ewbq0afuHCAbngwmW28Lzwnctk6bLBToVHvwkkMNCbteVDqYyASGD5uhLyjP6cHBhS688mPiy1NRXL2ZW6xHXQbMaUfNaUoVFWP8S1RnGLS63+VX8ljRW5WUAnaqMOIau3stcRq856S7+M9q8W9svperLXqohhR3Srh/cfXf7/+Gr1vXp9qDF61f+wHStLWf0l5Buz3vgqRu/Rahx1+/UVq16/tWO20kfT8bo5jrcfT3qyPvzzrbZdGPULL+cm6jxJPQwxJJtVq39LvvXFE+dqOzk3z9KpTdVYNfULj1v1d3KyGNv57k7JrYn8aplM9b1uNt4pY6v8uVX9nVGrfw3DkBKLQjrx11Iq+1S3xbAsS6KaMuqWQD/MpP3mNMmdKrGXnHFCW28l5+uv75NvPfiyPP7YZvnJj7fKG1sPVIW8eMmgnHf+Ejn/wmVy/oVLZN78gTilRCwRE2C7esTAEz6c+iffwjl98uZu1gklvJSRhZ+G7eqRwWIgmwAHDzERghLgnpxBiendnnty6l3/VrLnnpytUNP7Gu7JqXf9a7NHcgaYD/Ukp7rcf/DQ1q375fHHtlak56/fqJaexx0/W867YKmcf8FSecf5S2TmrN4AEdA06QSQnEmvYLTxIzmj5Z2G0ZCcaahitDkgOaPlnYbRkJxpqGJ0OSA5o2OdlpGQnGmpZHR5IDmjY52EkWItOT2p+NwLr05geepJx8hdt10fyenq3uDNSM7aQLds2W+v8FQrPR9/bIts+/XBShO1zfTkU+bb0vO8C5bIb523WAYGuIFtEr44rcaI5GyVnJ7XITn1rHs7WSM526Gn57VITj3r3k7WSM526Ol3LZJTv5q3mzGSs12C+l2P5NSv5pNlHGvJ+YW16+zYP33NZbGumn8l51SBbtm8Tx53paeSn37pmckYctoZC23hef4Fy+Sctx8pvb3Zqbrk8wQRQHImqFgxCBXJGYMiJCwEJGfCChaDcJGcMShCwkJAciasYF0OF8nZ5QIkcHgkZwKL1uWQkZxdLkDMho+t5Kw92Txm3KrCCSI5a/PY/Hq19Hxz2/hKz3w+I2f9xiI5/0JnpedZZy2SbI4b3MZ5LkwVG5JzKkJ87ieA5GQ+BCWA5AxKjPa1ktMoD4tY6hgbSwz1bLlH3lhlMXyvVRtDnUlut1XtVPvxtt51Th/u+eX2Ne7v7vuGURYp+8fwnXVuj+lrb7gx+N+zj++p6VN9rvpV8VTGj7jW7qE8NUecjAdR+Vy95Tuew/C9btDGaHRt1XEqNaer1D3yRbVpdLzQ+PtV4xmW9OUzMlIo2Yc8VB6N+ql6vyZXf0yNcm2ijTTBrIrxJDFVs/XPmSZybRSrP74GbZwjqEIarwlm1eNNPQfsyJqYK9VpOP2qnWu5jCFjRed4rIbzt5nvRNNx+OZvG3O/Yc61c6hBjZv6rk6Yj948CDbnJszdJuZd4+9FgznRzvfUmUS+Sd74tTqsyim1/6DWBn8mTfjuuEM0M19r5pNhqaPieECgSQJ/VPvndpPX0azjBJCcISBuR3LWDv/6a3vt7e2PPbpZHv/xFtnx1uFKE7Wq8/f/+8ly5FEzZPnyuXLy2+bL0mUzQ8iALqIigOSMinQ6xkFypqOOUWbRNcnpiq6K9PIElifAbAlVcoSVJ6SUnKqSVa7Qctt68myiaPP1MUG0+caojGn5RJdfiI0LOkeSqd+dtk6cPmFXLjp92OLME21uv+KTfa4UtK932zp5j7e1z9Su5O3E4JyN7YtB9ekTg5UxK+x8sk/9B56fgxeDr+04e4+Fk1sl7ygnKWNBAAIQgAAEIACBpBNAcsa2grGVnIqY2q6+bPFCuXTFhbEFqAILU3LWJvrqK3ts6fmTH2+xt7m/tf1QVZPp0/Ny6ukL5LTTF8rbTlsop562QNThRjziSQDJGc+6xDUqJGdcK9NcXEbpkBilMTGsMZGy82yUCyL2s/teuVD/89Ko+773eUGkPCJGqeBc6/Xh9mmPURqTjBQkY5SkWCj4JFvNCjy/ZLMFWe1KON/vVXLSbatW+vFINQHL7BMR016CZRkZEUPtIlG/Z8Syn70f93ffe5b6zDJFzIx7jSn2e6of9b7h/e72IaZY4o7h9muP6bataq/GkYzbnxuTGseq06cds6/fblXMjsP/cFYo2Q+1xG38l/qv/W2sBu19bSx3BdTk/deGFCyOaX15GRopStmy1HrfqXNoJqYqFjacKXk0HLsRVz+/oOydtc31y9VErA3zaYKf1QKbZvhVtWk0h5piVlOvGramKTI40CN7D4658772yzjOtan5G/A70YhFNVf/fJs8n7qToJn55Kt1U3k6X+JQ5lzD8Zr4M6WaX/A/t1r5nvb1ZCSfM2X/4WITf75Ux9SYbet/DlsmBwV366/QZsdlu3qzpPRoF2vJ+crmbXLv+u/Jymsvl77e+B7I00nJWTsN1Wntv9iwXTZseNN+fnbDdtm/b7SqmTq86JS3zZe3nebITyVBjz9hjqh7fvLoLgEkZ3f5J210JOfEihnWqIgSg1PKwxq5WBqXi450dOVhadQRhjXy0Ci74zQQlI6kbCAo1WfaPVx55Uml2AgyJcSMcUk3qSBTQkx961xhViXe1Gd+aTfe1pZwVTJNXV8jBt1+HVnoSrqKGPT/7o2h5KJP0nkx+fq1x3A5OyKwVgy6Mdiy0BODLgu3rRpjcFqvFEuWHB7x/8ekdhOYhAMQ4J6cAWDRVLgnJ5MgKAHuyRmUGO2RnMyBqv+NYVXdUCc+cCY7WV1F2Y3T1RvRiVJy1otB3ddTyc5nn3lTNjyzXX75i+1y6FChqmlPT1ZOOmWevdLTWfW5QE46aa7k8uo/knhERQDJGRXpdIwTR8mp7tdnlEbEeR4Ww15dqJ7d10Xv9bCIet9uOyJSOuy89rf1XtcIS1selkd9KxbV78lbPWiZ/WKZOREjL5apfnpEzJxY7u/2+xn/53m3rdNGMnm3bU5ErSKw+8hP7MM3Rq6nV3ryPXJwuORIL1ek+V9PegLbnQAAIABJREFUFG1KfLkSzLcCz1l954q2imRz29or/XikgQAHD6WhitHmgOSMlnfSR0NyJr2C0ceP5IyeedJHRHImvYLhxh/rlZzhptq53rotOWszU7f+emXTHnul5zNPOys+f/XLHTI8XC0+1SFGJ5441xGfZzhb3dV9PjnRvXNzBcnZObZp7LkdyWkW9om9Xbp4SEz3Wf1uFtUWavdHvS4eHG9jtzssRnlIDFtWujJTCUz1e0xuyG5lptny0BaBSvzZzzlXJNaIQv/ntjTsGZeOrmCUTM94HzUC0j+GIxhd0Wi3q47BUv1npnV1KnbtnpxdzZrB2yGA5GyHnp7XIjn1rHurWSM5WyWn73VITn1r32rmSM5WyaXzOiRnCHWNm+RslNLzv9pZWfH53C/ekg1Pv1m36YknzbW3u6sVn+9573GyZCmHG4UwTewukJxhkUxXP45UnCgjlZyc2TMqBw7stT+X4sE6wnJYjMJ+Mcuu0Czs7jiccnaG2PfsM/vEyvSKlemzf7efvddm9fuSVW163Xb9znX2NTWrHT1hWbOSsdvysONQQxoAyRkSSI26QXJqVOyQUkVyhgRSk26QnJoUOsQ0kZwhwtSkKySnJoVuMs3YS84nN2yUKz91W1U6X/viTXLOGcubTLHzzZIiOeuRePaZ7bLhmTfluWe3yy+f2yFKftZ7DM7skRNPnCfHHj9bTjhxjpxw4lz79eLFg50HnKIRkJzpKKZZ2C9G6aAYpcNiqpWQaiu2LSCdZ+93JSYr7/vbu6spnVWVBzsCRUnBcna6vbLQyk6Tsvtsv++9Vp/bbQbs57L9rARmT7WszCoRqaTlQEdipdPwCCA5w2OpS09ITl0qHV6eSM7wWOrQE5JThyqHmyOSM1yeOvSG5NShys3nGGvJqQTnHWvXyV23XS+zBqfbWanDiK658Q657qMfiM2p60mWnPWmylNPbpNnn3Xu8fnCr3baW90bPdTWdnWokTrR/fgT59rb3485zhGhPCYSQHLGYFZYZTGLe0Rt57ZlZXG/mO6PWhFpjO0Vs7RfDPdzR2g6be3X5cOhJ2EZvWJlB8TKeKJRyUdHPPb0D8pQqc8WkJKbYYtGR176xGTGaV92n9W9IHnoSQDJqWfd28kaydkOPT2vRXLqWfdWs0ZytkpO3+uQnPrWvtXMkZytkkvndbGVnMMjY3LLmnvkQ5dcNGHVppKf9z/wiNy68qpYnLqeNslZO9XVPT63bt0vm17aLS+rnxd3ycsv7ZFNm3bLvr0jdb8Z2awpy46eKcedMEeOP95Z+aleKxna359L57epiayQnE1AarJJZuTXYo7tcOXjLkdUFvaLFPY67xWVqNxvr640C7uc1+q9EA6wGV8N6chGe/u22nZdWQ3pSkhXStry0paWTjtbRnqSMtf4dhDt3JOzSYw0SxkBJGfKChpBOkjOCCCnbAgkZ8oK2uF0kJwdBpzC7pGcKSxqh1NCcnYYcMK6j63kVKerr/r83bLyusvl2KWLqrCq1ZxrvnyfrL756soKz25yT7vknIztrl1DFfn50ou7ZdPLjgB9c1v9LbiGIXLEounu6k9Hfh5/wmw57oS5MmdO+k/rRXJO/k3NDr8m5ugOW15mlMAc2ynm6FtijiiZuUsyo9sdsVnc39ZXvpybJeXsTLGyg1LODUo5O+i+nilWTv2o92ba75e93zOD7vsz2ho7yMVIziC0aKsIIDmZB0EJIDmDEqM9kpM5EIQAkjMILdoqAkhO5kFQAkjOoMTS3T62kpOVnMmeeIcPj8mml/Y4Kz+V+HxRrQLdI6++sqdhYuq+nxdcuMze6p7LZ+Woo2bIkmWDsnTpTJk3Px33AtRNcppqVeXYTldYvuUIzNFdYo5ud9/bI5nRN21xqQ7fCfIo5+dLKTdPyj3zHBGphKQSl3klJ2eIpSSlkpWVz9zXXT79OkiOSM4gtGiL5GQOtEIAydkKNb2vQXLqXf+g2SM5gxKjPZKTORCUAJIzKLF0t4+t5FTY1z/0qKx74BHuyZmyOfjSxt227Hzppd3y0sadtgjd9PIeGRkpNsy0ry8ni5fOkCVLZsrRx8ySJUsH7VPfly6bZa8ETcoj8ZJT3c9Srai0V1h6qy3fEsN7PeJbgVnYKUZ5rOnSWEaPIyyVvMzPd5575onVs0BKPer3BVLOz3Ne59Q9X5UCTPcDyZnu+nYiO1ZydoJquvtEcqa7vp3IDsnZCarp7RPJmd7adiozJGenyKa3XyRnemvbSmaxlpwqIU5Xb6WsybxG3ffz1U175fXX9srWLfvktdf2yZbX98mWzfvk0KHCpEktWDhNli2bKYuXDsqyo2fZMlStAlXvxWkVaCwlp1WsbAO35aXaKj6yQwz1rLaK29vG1eudYhZ2i0i56Qmm7l05LiwdcalEZblngZR7lMxUr12BmY1uG3jTCXS5IZKzywVI4PBIzgQWrcshIzm7XIAEDo/kTGDRuhgykrOL8BM6NJIzoYXrYthIzi7Cj+HQsZecMWQ2ISSd78kZVX327hmxZefmzUp87pfXX99r/75l837Z9usDUipZDUNRq0C9be/26s+jZ9pb4J2VoIOSz2eiSkOilJyZsbckM7xFMiPOdnD7x73fpTnmv7/lgUD5q+3fzirLhVL2tosraemtwHTlZalvWaB+aTyRAJKTWRGUAJIzKDHaIzmZA0EJIDmDEtO7PZJT7/q3kj2SsxVqel+D5NS7/rXZx1pyfmHtOtm+Y0/VKerevTrPPftkuXTFhbGoJpKz+2V47VW1+vNA1SrQN7bsl9de3TPpKtBZs3vlxOXz7ARMU2T27H575ef8BdNk7tx+mTO3X+bN65e58wdkyZLBthMNU3Kqe1lmhrdKZuR1yQ5tEXP4dckqqTm82X7fsOqffF8viWpB6a649MRl7xFSzs12Vl72VB8C1jYQOpiUAJKTCRKUAJIzKDHaIzmZA0EJIDmDEtO7PZJT7/q3kj2SsxVqel+D5NS7/omRnBw8xEQNi8C+vSPOCtCaVaCbX596FWhtDAMDeZk7T4nPAZk7v98Woeq1EqPqeY79mfqZJtNn5CekEERyZka3SWZki2SHNtvP5uHXJDuy1ZaY2aFNU+JRh++U+o+VUuVelkeI1TPXvb/lQinn3de5uVP2RYPuEEBydod7kkdFcia5et2JHcnZHe5JHhXJmeTqRR87kjN65kkfEcmZ9ApGHz+SM3rmcR4xtis59+4/KKs+f7esvO5yOXZp9eqxVzZvkzVfvk9W33y1zBqc3nW+rOTseglaDqBctmT37mHZvWtIdu44LLt2DsnOnYdk5073tfverl1Don4KY6Wmx1Lb4CtCVK0GndsvSxYPyuCsPpk1p1eWzDkkiwZ3y/z+nTIjs10yaiXmyGbJDG2RzOjWKQ/t+f/bu9cgucr7zuP/vnePZjQaCXQBZIKEWZkNXvm2RezYZs2LlMGOU9kyRTZbZYOL1dr7xpiCQkm5HJcrESUKhzcxq1AmcsVeu+QKiWMjeyu2l2WBVYIdIMTAYkNhc7EkpJmR5tbT163nnD493U3fTvdzTp/zPN+uUs1MzznP5fN/Zk7rN+ecVpeNV/OXSqVwqVQLl0pt6i1SybufV6b2SD1lxjvSDw1u4IaEnAYWNeApEXIGDGxg84ScBhY14CkRcgYMbFjzhJyGFTSE6RByhoBsWBeEnIYVdMzpRDbk5EzOMSvL7oEInFtcd8LOM04Q6gWjK87nZ97wPrqfr66WZe+OBbli51n5N7vOyBW7zspbd56VvdsX5LLtCwPHt1KekfPVnbKavFjWc3skObNbMtv2yaYde6Q+s1fqyamBbbBBvAUIOeNdv0mMnpBzEurx7pOQM971m8ToCTknoR7fPgk541u7SY2ckHNS8vHtl5AzvrULYuSRDTnVZNU7qx88dL8cOXxb82xOdRbngTvukc984mPckzOIFUGbvgXUO46nV34h6dUXJLXyc8ksPSvp1ZckvfJ837bOrU3Ly2e3yS/fmJHnX52VV+dn5Rentsqr85udj2uldM/9t8zlnTNDL1CXyjv/3PuGOpfRNy6nV5fO79w5HeobK/nGY4e+AoScLBC/AoScfsXYnpCTNeBXgJDTr5jd2xNy2l3/UWZPyDmKmt37EHLaXf/O2Uc65FSD9ULNX5862xz70XvvlPfs3xeZSnK5emRKEehAMsvPSnrlBSfQTK38P0mvvCSZ5X+VRHWpZ7+1zAVS2XSFVDa9Vaqb3iqF7W+XpdTFUsxeJvVkvm2/119bcs4SXZhfk5Mnl+XsG6ty+vRy4wxR9/J559+ZVV/zvPq9u5vbqzdX2jybl7m5gmzdVpAtswVRgemWLXmZ27rxuXpOvSs9j8kKEHJO1j+OvRNyxrFqkx0zIedk/ePYOyFnHKs2uTETck7OPq49E3LGtXKTGzch5+Tso9hz5EPOKKJ1jomQMw5VGmaMdUkVX5P0ys+df6mVFyTjhZrFX4lIrWsj9WRBKlN7W8JMN9QsT++Temq6bR8/bzzUa8T1usjZs6tOCOpcMu/cP1RdKr8iZ067l8w79xht3Fe05OM+oq195nJpUe8+rwLQLXNuADrnfJ6XrVs3NcPR5vdUWEo4OsxCG3obQs6hqdiwIUDIyVLwK0DI6VeM7Qk5WQN+BAg5/WixrRIg5GQd+BUg5PQrZvb2hJwa6kvIqQEx5CbUm/xkzz8l6fPPSNo5K/MFSa++KInaWs+RVAtvkcrU5VLedKVUN+2RytRb3TM0C28ZevQ6Qs6hO2tsuLJSEvUO8wsLRefj4uKa+3GhKPPzq7K4sCaLi+7XC4trsjCvtin6epOl1jGlM0nZtCkj09M55+Om6Wzz6ynn+cbXM/nG81lxn++2vbttQqV9Fj4IOS0s+phTJuQcE9DC3Qk5LSz6mFMm5BwT0LLdCTktK7iG6RJyakC0rAlCTssKPmC6hJwa1gMhpwbEAJtQZ2Vmlp6UzLmnJXP+nyWz9LQky4tde6ylZxtnZF4u1el9TqjpnpX5m1pGOImQc9SBqzdOckPRonMJfWs4urCgLqtvfK8jOF1fr4zaZdf9VMCpLp13w9KsTE9nnI+tX085z6t/OdnU+L762n1+Y3svYM1kU1rHGFRjhJxByZrbLiGnubUNamaEnEHJmtsuIae5tQ1iZoScQaia3SYhp9n1DWJ2hJxBqMa3TUJODbUj5NSAqKmJzPLPJHNOnaH5tGTPPymZ809JorbyptbrqRkpbd4v5c3vlMrMPjfYnLpcatkLNY2kezNxCjnHgZifXxN1BunKcllWlkuytlaRpfNFWVZfO8+XZHl53fl6dUV9XnKeX12tyPKSer7UeL4sa2vlcYbSc9/ZLbmRzzbN59OSL2Qkn09JPq8+qq/TzkedD0JOnZp2tEXIaUeddc6SkFOnph1tEXLaUWddsyTk1CVpTzuEnPbUWtdMCTl1SZrRDiGnhjoScmpA9NlEolaS9PLP3EvOzz0pmaWnJHP+XyVRL76ppVpmm5Q373dCzcrsO5zPK4U9PnvUs7ktIacerY1WlpY2Qk8nDG0Eo24YWnYC0xX1canxcXm98bwKT1XQ6j1fkvPn1nUPr6291sDTDUBTTiCqzkbN5dTnaSk0QlL3oxuOFgpZyRdaQlPnubRcvGOTnFurSCadlHQmJdlMStKZRMvnScmkU5LJJmVqijeLCrS4MWickDMGRYrYEAk5I1aQGAyHkDMGRYrQEAk5I1SMmAyFkDMmhYrQMAk5I1SMCAyFkFNDEQg5NSD2aULdJzNz/l+cszIzjUAzvfycJOpvPsOvmtvlhJjlmf1SbgSa1fwlwQ7QR+uEnD6wAtxUXVLvhKItIWlxrSrnzxUbz7sh6VIjNG0NSddW3bNLi8WKFNcqsqY+FsuydL4U4Ij9N62C00xGBaNJyWRSzr90OinZbFLSKhRVz2e95xMb26jnG6GpF6y6+7ttqZDV2S/thq3Nthvfc/pLu22rUDaXTUkqnWhsvzGWjApqnX5UW26Aq24twGM8AULO8fxs3JuQ08aqjzdnQs7x/Gzbm5DTtoqPP19CzvENbWuBkNO2ivefLyGnhvVAyKkBsdFEorLknJ2p7qGZPveUe6bmygtd39lcveFPaWbj7MzS5ncGfrn5uDMl5BxXMPr7q3uZqsvzi04QWnUCUBWGOqFoIxBVgarzfLEia6tuSNoanDa3XStLvVaX5dWy80ZQ5XJNKhX1sS7lsvpYlUq51vi85rRnyiOXUyGte8ZqMzR1wtnBwaobmrqBbFvIm0lIPpeWZMoNclMqjM0kJKW+zjS+buyrAuHmv0ZYm0yKu4+3byOcTadVYNvYXvWZUv27z6l5hPkg5AxT24y+CDnNqGOYsyDkDFM7/n0Rcsa/hmHPgJAzbPH490fIGf8a6pwBIacGTULO0RFTpVOSnX9EsvOPSnbhMcksPyci9Y4GE1KZ2tt2dmZ58zukltkyescT2pOQc0LwMe121HtyOsFnRQWgdSk5QagbkLrBqApKa25o6mxTk1LJC0/dj87XjUC1VKq627fsXy5VnOda99sIXVsCWG8b1W9FtVltjKfe8rk71nJJfXS3qXf+Cohp/VqHnUwmRP1LqI8J93P3n4gkZOPrxvec7Vr/qXfg6tive3sqXE1Ird5os629jX4SiYSof219dIxPden20X98Xjvt7Xn7uvP12nL6SyXb5rsxVzXFXk4NOzUeNa4e83L68qxb5tfNXT3XHFcfd3e7jXr1cndK5NWs3/jaxjj5xU3IOfkaxG0EhJxxq9hkx0vIOVn/OPZOyBnHqk12zISck/WPWu+EnBoqQsg5PGJ69eeSXXhcsvP/R3Lzj0uq+HL7zomUlDdd4d43c7N7/0x1L816anr4TiK8JSFnhIsTwaGNGnJGcCq+h6TObFUhrRuAbpzF6garXmDrncnqPucGso1AtyW4bYa7jfDXC3irFRXIusGu2r/5Tz1X9YLelu83xlNthL7Vqhv+Ol8392+0Va4H9qZZvjHZIVYC6lYTTpDqBKW9gtNGGOwF5C3BqdqnM4htDaA7g1h1CwuVtNel7uynAonWkF2d6eyFxKmUF0K7fajvtQbL3vedELoZZjdCcrVvS/DcrSgJJ/Xv/lAhctd9enzD7/aq7Z77qL9EdHmM0kePppx6++vDn5M7P799dN9+Ziojq8WK1Lr9RapXHz3r6m9MOufRq36j9NFr8ejsI+FzHY4yj15rpNe67ftz0yit+rlXa+bcintbH7/rcJR9dFrp+pkdaR6afmb71ann2vX5M9t/Lfj7OVdXpWQzSVktVd0/inrHjsYxqPvxp/3Y5xyfGscz59jU8sdH71il+w1DY/Viw7DBEnIaVtAxp0PIOSag2p2QswdivSaZ5Wckp87QnH9UcguPS7J0um3jeiIn5dl3y/rce6W07f1S2nK1MYFmNxVCTg0/cBY1YXPIaVqZa7W6qH/q9gMqGPC+rtXEeU5lBc5zje8527X+q7v71pv7utt3tpdJJSSXTsnicqlLe40+Gv2oTtv66NKeGle91r5fs9+W8alxbYxNtfvm8TXnWK1t9NuYlzuOxj7ecx3ja5o4fbW03zG+VjtvTL3cnbmotsZ090y617Hdz7Hy1oOBZy2b9rPLfBBAAAEEEEAAgU6Bev0LoERUgJBTQ2EIOV1E9Y7nmfM/cc7QdELNxf8riepSe6iZmnaCTBVorm95r5S3vFtU0GnLg5DTlkrrmSchpx5Hm1rhnpzxrra6r64X+LaGob1CWC+g3Qh2u4ffbWFvR5heyKakXKm7Z+Z1huvqaxVKN0LgalUFtBuhbbVWawa2al/1fbWtGo8607k17K62huI1N+ju9uh1uwo1Bz/bq23V2al+9unVR49mnKb9jqv3/HqvXf99+Jt3YyLdrbo8q9bMWqnS9dYi/W43EsY8/PbR6/4oI82jRwl9j2mkddXjLyZ9/pDidy36nYe3vToZMZdJSrFUawr17FvXz6z7C6DHz7+/n4+evxecOk2uj55r1+c6dH+PhTGP7mdzdvNNqasYEglZX6+2/2FYHWMax5L24484x5/WP7aqY5B3bPSOP+7Xaluz7mMf71c+8R09IWd0a0fIqaE2toacieqyZBdPSPbso5JdfFyyiz+RRL3YJlrLXCDrW98n5bn3yfrcb0t55u3q2jsN6vFsgpAznnWb1KgJOSclH99+CTnjW7tJjZx7ck5KPr79ck/O+NZuEiPnnpyTUI93n9yTM971m8TouVx9EurR7ZOQU0NtbAk5k+UFyc0/Ipn5xyS3+Jhklv5FpF5tE1TveO6cobn1/bI+9z6pbLpCg7A5TRBymlPLMGZCyBmGsll9EHKaVc8wZkPIGYayWX0QcppVz6BnQ8gZtLB57RNymlfToGdEyBm0cLzaJ+TUUC9TQ85k+YzkzvxYsmcfltziCUmvPP8mrfL0lY3Lzz8opbnflmpulwZRc5sg5DS3tkHMjJAzCFWz2yTkNLu+QcyOkDMIVbPbJOQ0u766Z0fIqVvU/PYIOc2vse4ZEnLqFo13e4ScGupnUsiZWXpa8qePS+6N70v23E/epFOafbeUZtU9NVWo+V6pZeY0CNrTBCGnPbXWMVNCTh2KdrVByGlXvXXMlpBTh6JdbRBy2lXvcWdLyDmuoH37E3LaV/NxZ0zIOa6gWfsTcmqoZ9xDztzZHzrBZv709yRVfLVNRAWa69uukdLc+6S09beknpzSIGZvE4Sc9tZ+lJkTco6iZvc+hJx213+U2RNyjqJm9z6EnHbX3+/sCTn9irE9ISdrwK8AIadfMbO3J+TUUN+4hZzJ8lnJv/F9yZ36nuTP/ljUGwh5j3pqWorbPiTrOz4ixQs/LLXMNg1CNOEJEHKyFvwIEHL60WJbJUDIyTrwK0DI6VeM7Qk5WQN+BAg5/WixrRIg5GQd+BUg5PQrZvb2hJwa6huHkDOz/GzjMvSHJLv4hIjUmjOv5i9xAk0VbK7PfVDqyawGFZroJkDIybrwI0DI6UeLbQk5WQOjCBByjqJm9z6EnHbX3+/sCTn9irE9ISdrwK8AIadfMbO3J+TUUN8ohpyJWlmyC49K7vRDUjh9XFLFl1tmmpDy5ndI0Tlb8zopz7xdgwJNDCNAyDmMEtt4AoScrAW/ApzJ6VeM7Qk5WQN+BQg5/YrZvT0hp931H2X2hJyjqNm9DyGn3fXvnD0hp4b1EJWQM1ledC9DP/2Q5M/8gySqS83Z1ZMF596a69s/Imvbr5dadruGmdOEXwFCTr9idm9PyGl3/UeZPSHnKGp270PIaXf9R5k9IecoavbuQ8hpb+1HnTkh56hy9u5HyGlv7bvNnJBTw3qYZMiZXnlB8m8cl/yp70n23D+K1KvNGVWzOzYuQ9/2H0QFnTwmK0DIOVn/uPVOyBm3ik1+vISck69B3EZAyBm3ik1+vISck69BnEZAyBmnakVjrISc0ahDnEZByBmnagU/VkJODcahhpz1iuQWHnfP1jx9XNJrL7bNoDx9lRS3X+ecsVmafaeIqJiER1QECDmjUol4jIOQMx51itIoCTmjVI14jIWQMx51itIoCTmjVI3oj4WQM/o1itoICTmjVpHoj4eQM/o1CnOEhJwatMMIOXMLj0rh1b+Wwqm/bXs3dDX89W3XOsFm8cKPSLWwW8OMaCIoAULOoGTNbJeQ08y6BjkrQs4gdc1sm5DTzLoGOStCziB1zWubkNO8mgY9I0LOoIXNa5+Q07yajjMjQs5x9Br7BhVypkqnZOrVr0vhta9JevUXzZHWMlukeOH1Utzxu7K+7UNST23SMAuaCEOAkDMMZXP6IOQ0p5ZhzYSQMyxpc/oh5DSnlmHNhJAzLGkz+iHkNKOOYc6CkDNMbTP6IuQ0o466ZkHIqUFSd8hZOPV3Unjtr503EfIepdl3y9olN0l55ipRn/OIpwAhZzzrNqlRE3JOSj6+/RJyxrd2kxo5Ieek5OPbLyFnfGs3iZETck5CPd59EnLGu36TGD0h5yTUo9snIaeG2ugIOdOrL0rh1b+Sqde+IeoMTvWoZeZkddd/ktXdn5LK9D4NI6WJSQsQck66AvHqn5AzXvWKwmgJOaNQhXiNgZAzXvWKwmgJOaNQhfiMgZAzPrWKykgJOaNSifiMg5AzPrUKY6SEnBqURw05E7VVKZx8UKZe+ZpkFx9rjCQh61s/KKu7b5Li9o9JPZnVMEKaiIoAIWdUKhGPcRByxqNOURolIWeUqhGPsRByxqNOURolIWeUqhH9sRByRr9GURshIWfUKhL98RByRr9GYY6QkFODtt+QM3vuCSm8clSmTn67+SZC1dwuWb3oPztnbVYLb9EwKpqIogAhZxSrEt0xEXJGtzZRHRkhZ1QrE91xEXJGtzZRHRkhZ1QrE81xEXJGsy5RHhUhZ5SrE82xEXJGsy6TGhUhpwb5YULOZHlBpl7/uhRePSqZ5efcXhNpKV7wO+5Zmxf8jkgipWE0NBFlAULOKFcnemMj5IxeTaI+IkLOqFcoeuMj5IxeTaI+IkLOqFcoWuMj5IxWPeIwGkLOOFQpWmMk5IxWPSY9GkLOARV44qnn5ZOfvau51fXXXi1fvP1mKeQ3LiPvGXLWa5Kf/5EUfnVU8mcekkSt5LRTKeyRtUs+KSuXfEJq2QsnvQboP0QBQs4QsQ3oipDTgCKGPAVCzpDBDeiOkNOAIoY8BULOkMFj3h0hZ8wLOIHhE3JOAD3mXRJyxryAmodPyDkA9MHjj8jui7bLe/bvk7ViSb5w9wOyc/tW+dyBG5p7doacqbVfyZTzJkJfl9T6a8529UReijt+V1Z33yzrWz+guYw0FxcBQs64VCoa4yTkjEYd4jQKQs44VSsaYyXkjEYd4jQKQs44VWvyYyXknHwN4jYCQs64VWzy4yXknHwNojQCQk6f1VCh54mfPtt2NqcKORP1dcmxQB5EAAAWQUlEQVSf/I4TbubmH1GxptNyefo3ncvR1y76Q6mlN/vsjc1NEyDkNK2iwc6HkDNYXxNbJ+Q0sarBzomQM1hfE1sn5DSxqsHNiZAzOFtTWybkNLWywc2LkDM42zi2TMjps2pfPnLM2aN5JufCU7Lys/8uhde/JcnKOed79dS0rO66QdYuuUlKs+/y2QObmyxAyGlydfXPjZBTv6npLRJyml5h/fMj5NRvanqLhJymV1jv/Ag59Xra0Bohpw1V1jtHQk69nnFvjZDTRwXV/TnvOXJM7rvrVpmbnXH3/B8qhnAf1a2/JeXLPiWViz8u9dSUj5bZ1BYBddBeL9ekWnPP9OWBQD8B9dtlupCWpbVK183q9bokEhu/g9BEIJ1KSCaVlLVSFQwEhhLIZ5KiDkmlSm2o7dkIgU35tKytV5x1wwOBQQLJhMhULi3Lxe6vZQbt3+v7vAYaVS76+2VSCUmlklLktUz0ixWRERayKalUa1KuhndgmimkIzJ7htEpQMg55JpQAefBQ/fLkcO3yd5LL9rY64fXSGlmv5Qvu0Vq01cM2Rqb2SowlU87B+wa/zOwdQn4nvf0VEaWV8vdQ05JSKJxawzfDbODkQLpVFIy6YSsrRNyGlngACaVy6acY1KZkDMAXTObdEJOXsuYWdwAZqX+GDuVT8lKjz/YjtplnddAo9JFfr9MOinqDGBCzsiXKjIDzOdSUqnUnaAzrMfMVCasrujHpwAh5xBgPQPOxr493119iLbZxC4BLle3q97jzpbL1ccVtG9/Lle3r+bjzpjL1ccVtG9/Lle3r+bjzJjL1cfRs3NfLle3s+7jzJrL1cfRM29fQs4BNe16iXrHPoSc5v1gBDUjQs6gZM1sl5DTzLoGOStCziB1zWybkNPMugY5K0LOIHXNa5uQ07yaBj0jQs6ghc1rn5DTvJqOMyNCzgF66o2GvvrN421b7dqxre2ydULOcZagXfsSctpV73FnS8g5rqB9+xNy2lfzcWdMyDmuoH37E3LaV/NxZkzIOY6enfsSctpZ93FmTcg5jp55+xJyaqgpIacGREuaIOS0pNCapknIqQnSomYIOS0qtqapEnJqgrSoGUJOi4qtYaqEnBoQLWuCkNOygmuYLiGnBkSDmiDk1FBMQk4NiJY0QchpSaE1TZOQUxOkRc0QclpUbE1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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.line(data_frame=bio.get_history(), x=\"SYSTEM TIME\", y=[\"A\", \"B\", \"C\", \"D\"], \n", " title=\"Changes in concentrations with time\",\n", " color_discrete_sequence = ['navy', 'cyan', 'red', 'orange'],\n", " labels={\"value\":\"concentration\", \"variable\":\"Chemical\"})\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "0500edb0-a509-453d-b3b4-8fbaae42925d", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }