diff --git a/beaker-notebooks/errorbars_html.bkr b/beaker-notebooks/errorbars_html.bkr
index 10f1b267faf054e9f3a572846b288accba7d36b7..8167c0709bd314dcfc8d75f11a51c01cf9d86e6f 100644
--- a/beaker-notebooks/errorbars_html.bkr
+++ b/beaker-notebooks/errorbars_html.bkr
@@ -38,9 +38,16 @@
"type": "markdown",
"body": [
"<p style=\"color: #20335d;;font-weight: 900; font-size: 22pt;\"> NOMAD analytics toolkit</p>",
- "<label style=\"text-align: center; color: #20335d; font-weight: 900; font-size: 18pt;\">Error bars from Elemental Solids:</label> <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Using <a href=\"https://nomad-coe.eu/\">NoMaD</a> for data retrival.</label>",
+ "<label style=\"text-align: center; color: #20335d; font-weight: 900; font-size: 18pt;\">Analyzing and Estimating Error Bars from High-Accuracy References:</label> <label style=\"color: #20335d;font-weight: 900; font-size: 15pt;\"> Using <a href=\"https://nomad-coe.eu/\">NoMaD</a> for data retrival.</label>",
" </p>",
" <p style=\"font-size: 15px;\"> Developed by Björn Bieniek and Mikkel Strange</a>, 2017.</p>",
+ " <p style=\"font-size: 15px; \"> <br> ",
+ " Beaker Notebook: Björn Bieniek, Mikkel Strange, Christian Carbogno. <br> <br> ",
+ " Curated VASP data: Elisabeth Wruss, Oliver T. Hofmann, Institute of Solid State Physics, Graz University of Technology, NAWI Graz, Petergasse 16, 8010 Graz, Austria<br>",
+ " Curated GPAW data: Mikkel Strange, Kristian Sommer Thygesen, CAMD, Department of Physics, Technical University of Denmark. Fysikvej 1 2800 Kgs. Lyngby, Denmark<br>",
+ " Curated exciting data: Sven Lubeck, Andris Gulans, Humboldt-Universität zu Berlin, Department of Physics, Zum Grossen Windkanal 6, D-12489 Berlin<br>",
+ " Curated FHI-aims data: Björn Bieniek, Christian Carbogno, Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, D-14195 Berlin, Germany</p>",
+ "",
"",
""
],
@@ -68,7 +75,7 @@
"from ase.db import connect",
"from ase.atoms import string2symbols",
"from bokeh.sampledata.periodic_table import elements # Periodic table data",
- "#import sys",
+ "import sys",
"",
"# Plotly",
"#import plotly.graph_objs as go",
@@ -77,15 +84,14 @@
"#ply.offline.init_notebook_mode() # allows output in notebook",
"",
"# Path to files",
- "base_path='/home/beaker/test/errorbars/'",
+ "base_path='/home/beaker/'",
"",
- "#sys.path.append(base_path)",
+ "sys.path.append(base_path)",
"from errorbar_base import *",
"",
"# Set label sizes globally",
"params = { 'axes.labelsize': 15,",
" 'legend.fontsize': 15,",
- " 'text.fontsize': 15,",
" 'font.size': 15,",
" 'xtick.labelsize': 15,",
" 'ytick.labelsize': 15}",
@@ -207,6 +213,8 @@
"ref_xc['GPAW']=['pbe','lda']",
"",
"xylist={}",
+ "xylist_bins={}",
+ "xylist_bins_two={}",
"xylist_pred={}",
"",
"# Init periodic table",
@@ -246,13 +254,12 @@
"state": {},
"selectedType": "Hidden",
"pluginName": "IPython",
- "shellId": "301E1EAB870E42619DB2FA8F3E4314AE",
- "elapsedTime": 695,
- "height": 51,
- "hidden": true
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 626,
+ "height": 51
},
"evaluatorReader": true,
- "lineCount": 179,
+ "lineCount": 180,
"initialization": true
},
{
@@ -298,8 +305,8 @@
},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "301E1EAB870E42619DB2FA8F3E4314AE",
- "elapsedTime": 154358,
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 126505,
"height": 55
},
"evaluatorReader": true,
@@ -322,16 +329,15 @@
"",
"",
"<div style=\"max-width: 800px;\">Electronic-structure theory has become an invaluable tool in materials science. Still, the precision of different approaches has only recently been scrutinized thoroughly (for the PBE functional) using extremely accurate numerical settings [1]. A synergistic effort showed that \"most recent codes and methods converge toward a single value\", if extremely accurate and computationally expensive numerical settings ",
- "are employed. Little is known, however, about code- and method-specific deviances and error bars that arise under numerical settings commonly used in actual calculations. We shed light on this issue by systematically investigating the deviances in total and relative energies as function of typical settings for basis sets, k-grids, etc. for 71 elemental [1] and 81 binary solids in four different electronic-structure codes.<br><br>",
- "The following function calculates the error for the binaries from the elemental solids. For now only one function is implemented. <br><br>",
+ "are employed. Little is known, however, about code- and method-specific deviances and error bars that arise under numerical settings commonly used in actual calculations. <br><br>",
+ "In this notebook, we use the NOMAD infrastructure to shed light on this issue by systematically investigating and analyzing the deviances in total and relative energies as function of typical settings for basis sets, k-grids, etc. For this purpose, the NOMAD team has systematically computed the properties of 71 elemental [1] and 81 binary solids in four different electronic-structure codes using various different computational settings, including extremely accurate ones that constitute a fully converged reference.<br><br>",
+ "One the one hand, this allows to analyze and compare the convergence behavior of different codes with respect to different settings. On the other hand, this allows to develop models ",
+ "to estimate the errors in calculations for which no highly converged reference is available. As an example, we have here discuss the following function",
"<center>",
- "$\\hat{\\Delta}E_{tot}=\\frac{N_{A}\\Delta E_A+N_{B}\\Delta E_B}{N_A+N_B}.$",
- "</center>",
- "<br><br>",
- "With $N_A / N_B$ the number of atoms $A$/$B$ and $\\Delta E_{A} / \\Delta E_{B}$ the respectice error of the elemental solid per atom. $N=N_A+N_B$ is tthe number of atoms of the (binary) system.",
- "<br><br>",
- "Feel free to add more.",
- "</div>",
+ "$\\hat{\\Delta}E_{tot}=\\frac{N_{A}\\Delta E_A+N_{B}\\Delta E_B}{N_A+N_B}$.",
+ "</center><br><br>",
+ "that is used to estimate and predict the (total energy and relative energy) erros in binary systems with $N_A$/$N_B$ atoms of species A/B",
+ "from the errors $\\Delta E_{A} $/$ \\Delta E_{B}$ occuring in the respective elemental solids.</div>",
"<br><br>",
"<div style=\"max-width: 800px;\">",
"[1] K. Lejaeghere et al., Science 351 (2016).<br>",
@@ -348,7 +354,7 @@
"id": "markdownIvKOSd",
"type": "markdown",
"body": [
- "<div style=\"font-size: 150%; font-weight: bold;\">Data Browser</div>"
+ "<div style=\"font-size: 150%; font-weight: bold;\">Data Browser: Analyzing the Curated Reference Data Set</div>"
],
"evaluatorReader": false
},
@@ -399,10 +405,10 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n .phasediagram_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#phasediagram-motivation-modal\">\n Explanation\n</button>\n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"phasediagram-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"phasediagram-motivation-modal-label\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"phasediagram-motivation-modal-label\">Explanation</h4>\n <div style=\"max-width: 800px;\">\nThe interface below allows you to explore the error due to numerical settings for the 71 elementary solids and the 82 binary systems (Drop down menu \"Systems\"). The left plot (button \"Add plot\") shows the error with respect to highly converged settings for each electronic structure. The last data set is plotted as periodic table below. The color of the elements relates to the error. By chosing the code (Drop down menu \"Code\") the number of possible numerical settings avaiable in the interface is in- or decresed.<br>\nIn the right plot (button \"Predict binaries\") the error for the binary systems is calculated from the error of the elementary systems for the selected code and numerical settings. It is plotted against the error obtained directly from the DFT calculations of the binary systems. Points on the black diagonal line indicate agreement between anlytical prediction and the DFT calulation. For points to the right/below of the diagonal the predicted error is larger than the error obtained from the DFT calulation. You can select between the error in the total energy or in the total energy relative to a calculation with cell volume increased by 5% (Drop down menu \"Quantity\"). This allows to explore the effect of error cancellation. Additionally you can explore the error in the cohesive energy for the binary systems. We define the cohesive energy as the total energy of the binary systems minus the the total energy of its constituents in their elemental solid structure, devided by the number of atoms in the binary cell.<br><br>\n</div> \n </div>\n <div class=\"modal-body phasediagram_instructions\">\n\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n\n<div style=\"height: 3em;\"></div></div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n .phasediagram_instructions{\n font-size: 15px;\n } \n</style>\n<!-- Button trigger modal -->\n<button type=\"button\" class=\"btn btn-default\" data-toggle=\"modal\" data-target=\"#phasediagram-motivation-modal\">\n Explanation\n</button>\n\n<!-- Modal -->\n<div class=\"modal fade\" id=\"phasediagram-motivation-modal\" tabindex=\"-1\" role=\"dialog\" aria-labelledby=\"phasediagram-motivation-modal-label\" style=\"display: none;\">\n <div class=\"modal-dialog modal-lg\" role=\"document\">\n <div class=\"modal-content\">\n <div class=\"modal-header\">\n <button type=\"button\" class=\"close\" data-dismiss=\"modal\" aria-label=\"Close\"><span aria-hidden=\"true\">×</span></button>\n <h4 class=\"modal-title\" id=\"phasediagram-motivation-modal-label\">Explanation</h4>\n <div style=\"max-width: 800px;\">\nThe interface below allows you to explore the error due to numerical settings for the 71 elementary solids and the 82 binary systems (Drop down menu \"Systems\"). The left plot (button \"Add plot\") shows the error with respect to highly converged settings for each electronic structure. The last data set is plotted as periodic table below. The color of the elements relates to the error. By chosing the code (Drop down menu \"Code\") the number of possible numerical settings avaiable in the interface is in- or decresed.<br>\nIn the right plot (button \"Predict binaries\") the error for the binary systems is calculated from the error of the elementary systems for the selected code and numerical settings. It is plotted against the error obtained directly from the DFT calculations of the binary systems. Points on the black diagonal line indicate agreement between anlytical prediction and the DFT calulation. For points to the right/below of the diagonal the predicted error is larger than the error obtained from the DFT calulation. You can select between the error in the total energy or in the total energy relative to a calculation with cell volume increased by 5% (Drop down menu \"Quantity\"). This allows to explore the effect of error cancellation. Additionally you can explore the error in the cohesive energy for the binary systems. We define the cohesive energy as the total energy of the binary systems minus the the total energy of its constituents in their elemental solid structure, devided by the number of atoms in the binary cell.<br><br>\n</div> \n </div>\n <div class=\"modal-body phasediagram_instructions\">\n\n </div>\n <div class=\"modal-footer\">\n <button type=\"button\" class=\"btn btn-default\" data-dismiss=\"modal\">Close</button>\n<!-- <button type=\"button\" class=\"btn btn-primary\">Save changes</button> -->\n </div>\n </div>\n </div>\n\n<div style=\"height: 3em;\"></div></div>"
},
"selectedType": "BeakerDisplay",
- "elapsedTime": 1,
+ "elapsedTime": 0,
"height": 72
},
"evaluatorReader": true,
@@ -424,28 +430,6 @@
" dropdown.appendChild(el);",
" }",
" ",
- " function errorUpdateForm_quant() {",
- " var system = document.getElementById(\"errorbar_systems\").value;",
- " ",
- " var pquant = document.getElementById(\"errorbar_quantity\"); pquant.innerHTML = '';",
- "",
- " switch(system) {",
- " case \"monomers\": ",
- "",
- " addDropdownChoice(pquant, \"E_tot\", \"Total Energy\");",
- " addDropdownChoice(pquant, \"relR\", \"relative Energy\");",
- " break;",
- " ",
- " case \"binaries\": ",
- " ",
- " addDropdownChoice(pquant, \"E_tot\", \"Total Energy\");",
- " addDropdownChoice(pquant, \"relR\", \"relative Energy\");",
- " addDropdownChoice(pquant, \"E_coh\", \"Cohesive Energy\");",
- " break;",
- "",
- "",
- " }",
- " }",
" ",
" function errorUpdateForm() {",
" var code = document.getElementById(\"errorbar_code\").value;",
@@ -458,7 +442,7 @@
"",
" switch(code) {",
" case \"VASP\": ",
- " dprec.innerHTML = 'Precision';",
+ " dprec.innerHTML = 'Precision:';",
"",
" addDropdownChoice(pprec, \"Low\", \"Low\");",
" addDropdownChoice(pprec, \"Normal\", \"Normal\");",
@@ -468,7 +452,7 @@
" break;",
" ",
" case \"FHI-aims\": ",
- " dprec.innerHTML = 'Basis set';",
+ " dprec.innerHTML = 'Integration grid';",
"",
" addDropdownChoice(pprec, \"light\", \"light\");",
" addDropdownChoice(pprec, \"tight\", \"tight\");",
@@ -492,7 +476,7 @@
" break;",
" ",
" case \"GPAW\": ",
- " dprec.innerHTML = '$E_{cut}$';",
+ " dprec.innerHTML = '$E_{cut}$:';",
"",
" addDropdownChoice(pprec, \"300\", \"300\");",
" addDropdownChoice(pprec, \"400\", \"400\");",
@@ -519,15 +503,13 @@
"",
"",
" ",
- " function add_plot() {",
+ " function add_monomers() {",
" beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;",
" beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;",
" beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;",
" beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;",
" beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;",
- " beaker.ctrl_pred = document.getElementById(\"errorbar_formula\").value;",
" beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;",
- " beaker.ctrl_sys = document.getElementById(\"errorbar_systems\").value;",
" beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;",
" beaker.ctrl_button = 1",
" beaker.evaluate(\"exe_cell\");",
@@ -541,24 +523,22 @@
" beaker.ctrl_button = 3",
" beaker.evaluate(\"exe_cell\");",
" }",
- " function predict_plot() {",
+ " function add_binaries() {",
" beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;",
" beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;",
" beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;",
" beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;",
" beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;",
- " beaker.ctrl_pred = document.getElementById(\"errorbar_formula\").value;",
" beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;",
- " beaker.ctrl_sys = document.getElementById(\"errorbar_systems\").value;",
" beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;",
" beaker.ctrl_button = 4",
" beaker.evaluate(\"exe_cell\");",
" }",
- " function clear_last_predicted() {",
+ " function clear_last_bins() {",
" beaker.ctrl_button = 5",
" beaker.evaluate(\"exe_cell\");",
" }",
- " function clear_plot_predicted() {",
+ " function clear_plot_bins() {",
" beaker.ctrl_button = 6",
" beaker.evaluate(\"exe_cell\");",
" }",
@@ -605,21 +585,20 @@
" <td id=\"errorbar_precision_description\" style=\"white-space: pre;\"></td>",
" </tr>",
" <tr>",
- " <th>tiers:</th>",
+ " <th>Tiers:</th>",
" <td><select id=\"errorbar_tiers\" ><!-- content inserted programmatically --></select></td>",
" <td id=\"errorbar_tiers_description\" style=\"white-space: pre;\"></td>",
" <th>relativity treatment:</th>",
" <td><select id=\"errorbar_relativity\" ><!-- content inserted programmatically --></select></td>",
" <td id=\"errorbar_relativity_description\" style=\"white-space: pre;\"></td>",
- " <th>Prediction formula:</th>",
- " <td>",
- " <select id=\"errorbar_formula\" >",
- " <option value=\"1\" selected>1</option>",
- " <option value=\"2\">2</option> ",
- " </select>",
- " </td>",
- " <td id=\"errorbar_formula\" style=\"white-space: pre;\"></td>",
- "",
+ " <th>Code:</th>",
+ " <td><select id=\"errorbar_code\" onchange=\"errorUpdateForm()\">",
+ " <option value=\"VASP\">VASP</option>",
+ " <option value=\"FHI-aims\">FHI-aims</option>",
+ " <option value=\"GPAW\">GPAW</option> ",
+ " <option value=\"exciting\">exciting</option> ",
+ " </select></td>",
+ " <td id=\"errorbar_code_description\" style=\"white-space: pre;\"></td>",
" </tr> ",
" ",
" <tr>",
@@ -627,24 +606,12 @@
" <td><select id=\"errorbar_quantity\">",
" <option value=\"E_tot\">Total Energy</option>",
" <option value=\"relR\">relative Energy</option>",
+ " <option value=\"E_coh\">Cohesive Energy (binaries)</option>",
" </select></td>",
- " <td id=\"errorbar_quantity_description\" style=\"white-space: pre;\"></td>",
- " <th>Systems:</th>",
- " <td><select id=\"errorbar_systems\" onchange=\"errorUpdateForm_quant()\">",
- " <option value=\"monomers\">Elemental solids</option>",
- " <option value=\"binaries\">Binary systems</option>",
- " </select></td>",
- " <td id=\"errorbar_systems_description\" style=\"white-space: pre;\"></td> ",
+ " <td id=\"errorbar_quantity_description\" style=\"white-space: pre;\"></td> ",
" ",
"",
- " <th>Code:</th>",
- " <td><select id=\"errorbar_code\" onchange=\"errorUpdateForm()\">",
- " <option value=\"VASP\">VASP</option>",
- " <option value=\"FHI-aims\">FHI-aims</option>",
- " <option value=\"GPAW\">GPAW</option> ",
- " <option value=\"exciting\">exciting</option> ",
- " </select></td>",
- " <td id=\"errorbar_code_description\" style=\"white-space: pre;\"></td>",
+ "",
" </tr>",
" ",
" </table>",
@@ -652,12 +619,12 @@
" <table class=\"error_table\">",
" ",
" <tr>",
- " <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_plot();\">Add plot</button></th>",
- " <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot();\">Clear plot</button></th>",
- " <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_last();\">Clear last</button></th>",
- " <th><button type=\"button\" class=\"btn btn-secondary\" style=\"margin-top: 2ex;\" onclick=\"predict_plot();\">Predict binaries</button></th>",
- " <th><button type=\"button\" class=\"btn clear-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot_predicted();\">Clear prediction plot</button></th>",
- " <th><button type=\"button\" class=\"btn clearlast-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_last_predicted();\">Clear last prediction</button></th>",
+ " <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_monomers();\">Add el. solids</button></th>",
+ " <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot();\">Clear el. solids </button></th>",
+ " <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_last();\">Clear last el. solids</button></th>",
+ " <th><button type=\"button\" class=\"btn btn-secondary\" style=\"margin-top: 2ex;\" onclick=\"add_binaries();\">Add binaries</button></th>",
+ " <th><button type=\"button\" class=\"btn clear-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot_bins();\">Clear binaries</button></th>",
+ " <th><button type=\"button\" class=\"btn clearlast-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_last_bins();\">Clear last binaries</button></th>",
" </tr>",
" </table>",
"</div>"
@@ -669,14 +636,14 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n // Adds an option to a dropdown menu\n function addDropdownChoice(dropdown, value, content) {\n var el = document.createElement('option');\n el.value = value;\n el.innerHTML = content\n dropdown.appendChild(el);\n }\n \n function errorUpdateForm_quant() {\n var system = document.getElementById(\"errorbar_systems\").value;\n \n var pquant = document.getElementById(\"errorbar_quantity\"); pquant.innerHTML = '';\n\n switch(system) {\n case \"monomers\": \n\n addDropdownChoice(pquant, \"E_tot\", \"Total Energy\");\n addDropdownChoice(pquant, \"relR\", \"relative Energy\");\n break;\n \n case \"binaries\": \n \n addDropdownChoice(pquant, \"E_tot\", \"Total Energy\");\n addDropdownChoice(pquant, \"relR\", \"relative Energy\");\n addDropdownChoice(pquant, \"E_coh\", \"Cohesive Energy\");\n break;\n\n\n }\n }\n \n function errorUpdateForm() {\n var code = document.getElementById(\"errorbar_code\").value;\n \n var dprec = document.getElementById(\"errorbar_precision_name\");\n var pprec = document.getElementById(\"errorbar_precision\"); pprec.innerHTML = '';\n var prel = document.getElementById(\"errorbar_relativity\"); prel.innerHTML = '';\n var ptiers = document.getElementById(\"errorbar_tiers\"); ptiers.innerHTML = '';\n var pxc = document.getElementById(\"errorbar_xcfunctional\"); pxc.innerHTML = '';\n\n switch(code) {\n case \"VASP\": \n dprec.innerHTML = 'Precision';\n\n addDropdownChoice(pprec, \"Low\", \"Low\");\n addDropdownChoice(pprec, \"Normal\", \"Normal\");\n addDropdownChoice(pprec, \"Accurate\", \"Accurate\");\n addDropdownChoice(pxc, \"PBE\", \"PBE\");\n addDropdownChoice(pxc, \"LDA\", \"LDA\");\n break;\n \n case \"FHI-aims\": \n dprec.innerHTML = 'Basis set';\n\n addDropdownChoice(pprec, \"light\", \"light\");\n addDropdownChoice(pprec, \"tight\", \"tight\");\n addDropdownChoice(pprec, \"really_tight\", \"really_tight\");\n addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");\n addDropdownChoice(prel, \"zora\", \"zora\");\n addDropdownChoice(ptiers, \"minimal\", \"minimal\");\n addDropdownChoice(ptiers, \"standard\", \"standard\");\n addDropdownChoice(ptiers, \"tier1\", \"tier1\");\n addDropdownChoice(ptiers, \"tier2\", \"tier2\");\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n case \"exciting\": \n dprec.innerHTML = '';\n\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n\n break;\n \n case \"GPAW\": \n dprec.innerHTML = '$E_{cut}$';\n\n addDropdownChoice(pprec, \"300\", \"300\");\n addDropdownChoice(pprec, \"400\", \"400\");\n addDropdownChoice(pprec, \"500\", \"500\");\n addDropdownChoice(pprec, \"600\", \"600\");\n addDropdownChoice(pprec, \"700\", \"700\");\n addDropdownChoice(pprec, \"800\", \"800\");\n addDropdownChoice(pprec, \"900\", \"900\");\n addDropdownChoice(pprec, \"1000\", \"1000\");\n addDropdownChoice(pprec, \"1100\", \"1100\");\n addDropdownChoice(pprec, \"1200\", \"1100\");\n addDropdownChoice(pprec, \"1300\", \"1100\");\n addDropdownChoice(pprec, \"1400\", \"1100\");\n addDropdownChoice(pprec, \"1500\", \"1100\");\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n\n }\n }\n\n\n\n \n function add_plot() {\n beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;\n beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;\n beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;\n beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;\n beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;\n beaker.ctrl_pred = document.getElementById(\"errorbar_formula\").value;\n beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;\n beaker.ctrl_sys = document.getElementById(\"errorbar_systems\").value;\n beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;\n beaker.ctrl_button = 1\n beaker.evaluate(\"exe_cell\");\n beaker.evaluate(\"ptablecell\");\n }\n function clear_last() {\n beaker.ctrl_button = 2\n beaker.evaluate(\"exe_cell\");\n }\n function clear_plot() {\n beaker.ctrl_button = 3\n beaker.evaluate(\"exe_cell\");\n }\n function predict_plot() {\n beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;\n beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;\n beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;\n beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;\n beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;\n beaker.ctrl_pred = document.getElementById(\"errorbar_formula\").value;\n beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;\n beaker.ctrl_sys = document.getElementById(\"errorbar_systems\").value;\n beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;\n beaker.ctrl_button = 4\n beaker.evaluate(\"exe_cell\");\n }\n function clear_last_predicted() {\n beaker.ctrl_button = 5\n beaker.evaluate(\"exe_cell\");\n }\n function clear_plot_predicted() {\n beaker.ctrl_button = 6\n beaker.evaluate(\"exe_cell\");\n }\n</script>\n\n<style type=\"text/css\">\n \n .error_table th { font-weight: bold; padding-right: 2ex; }\n .error_table td input { margin-right: 1ex; }\n \n</style>\n\n<!-- Controls area -->\n\n<div class=\"error_control\">\n <table class=\"error_table\">\n \n <tbody><tr>\n <th>XC-Functional:</th>\n <td>\n <select id=\"errorbar_xcfunctional\">\n <option value=\"PBE\" selected=\"\">PBE</option>\n <option value=\"LDA\">LDA</option>\n </select>\n </td>\n <td id=\"errorbar_xcfunctional_description\" style=\"white-space: pre;\"></td>\n <th>k-point density:</th>\n <td>\n <select id=\"errorbar_kdensity\">\n <option value=\"2\" selected=\"\">2</option>\n <option value=\"4\">4</option> \n <option value=\"8\">8</option> \n </select>\n </td>\n <td id=\"errorbar_kdensity_description\" style=\"white-space: pre;\"></td>\n <th id=\"errorbar_precision_name\">Precision:</th>\n <td>\n <select id=\"errorbar_precision\">\n <option value=\"Low\" selected=\"\">Low</option>\n <option value=\"Normal\">Normal</option> \n <option value=\"Accurate\">Acurate</option> \n </select>\n </td>\n <td id=\"errorbar_precision_description\" style=\"white-space: pre;\"></td>\n </tr>\n <tr>\n <th>tiers:</th>\n <td><select id=\"errorbar_tiers\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_tiers_description\" style=\"white-space: pre;\"></td>\n <th>relativity treatment:</th>\n <td><select id=\"errorbar_relativity\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_relativity_description\" style=\"white-space: pre;\"></td>\n <th>Prediction formula:</th>\n <td>\n <select id=\"errorbar_formula\">\n <option value=\"1\" selected=\"\">1</option>\n <option value=\"2\">2</option> \n </select>\n </td>\n <td id=\"errorbar_formula\" style=\"white-space: pre;\"></td>\n\n </tr> \n \n <tr>\n <th>Quantity:</th>\n <td><select id=\"errorbar_quantity\">\n <option value=\"E_tot\">Total Energy</option>\n <option value=\"relR\">relative Energy</option>\n </select></td>\n <td id=\"errorbar_quantity_description\" style=\"white-space: pre;\"></td>\n <th>Systems:</th>\n <td><select id=\"errorbar_systems\" onchange=\"errorUpdateForm_quant()\">\n <option value=\"monomers\">Elemental solids</option>\n <option value=\"binaries\">Binary systems</option>\n </select></td>\n <td id=\"errorbar_systems_description\" style=\"white-space: pre;\"></td> \n \n\n <th>Code:</th>\n <td><select id=\"errorbar_code\" onchange=\"errorUpdateForm()\">\n <option value=\"VASP\">VASP</option>\n <option value=\"FHI-aims\">FHI-aims</option>\n <option value=\"GPAW\">GPAW</option> \n <option value=\"exciting\">exciting</option> \n </select></td>\n <td id=\"errorbar_code_description\" style=\"white-space: pre;\"></td>\n </tr>\n \n </tbody></table>\n \n <table class=\"error_table\">\n \n <tbody><tr>\n <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_plot();\">Add plot</button></th>\n <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot();\">Clear plot</button></th>\n <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_last();\">Clear last</button></th>\n <th><button type=\"button\" class=\"btn btn-secondary\" style=\"margin-top: 2ex;\" onclick=\"predict_plot();\">Predict binaries</button></th>\n <th><button type=\"button\" class=\"btn clear-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot_predicted();\">Clear prediction plot</button></th>\n <th><button type=\"button\" class=\"btn clearlast-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_last_predicted();\">Clear last prediction</button></th>\n </tr>\n </tbody></table>\n</div>"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n // Adds an option to a dropdown menu\n function addDropdownChoice(dropdown, value, content) {\n var el = document.createElement('option');\n el.value = value;\n el.innerHTML = content\n dropdown.appendChild(el);\n }\n \n \n function errorUpdateForm() {\n var code = document.getElementById(\"errorbar_code\").value;\n \n var dprec = document.getElementById(\"errorbar_precision_name\");\n var pprec = document.getElementById(\"errorbar_precision\"); pprec.innerHTML = '';\n var prel = document.getElementById(\"errorbar_relativity\"); prel.innerHTML = '';\n var ptiers = document.getElementById(\"errorbar_tiers\"); ptiers.innerHTML = '';\n var pxc = document.getElementById(\"errorbar_xcfunctional\"); pxc.innerHTML = '';\n\n switch(code) {\n case \"VASP\": \n dprec.innerHTML = 'Precision:';\n\n addDropdownChoice(pprec, \"Low\", \"Low\");\n addDropdownChoice(pprec, \"Normal\", \"Normal\");\n addDropdownChoice(pprec, \"Accurate\", \"Accurate\");\n addDropdownChoice(pxc, \"PBE\", \"PBE\");\n addDropdownChoice(pxc, \"LDA\", \"LDA\");\n break;\n \n case \"FHI-aims\": \n dprec.innerHTML = 'Integration grid';\n\n addDropdownChoice(pprec, \"light\", \"light\");\n addDropdownChoice(pprec, \"tight\", \"tight\");\n addDropdownChoice(pprec, \"really_tight\", \"really_tight\");\n addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");\n addDropdownChoice(prel, \"zora\", \"zora\");\n addDropdownChoice(ptiers, \"minimal\", \"minimal\");\n addDropdownChoice(ptiers, \"standard\", \"standard\");\n addDropdownChoice(ptiers, \"tier1\", \"tier1\");\n addDropdownChoice(ptiers, \"tier2\", \"tier2\");\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n case \"exciting\": \n dprec.innerHTML = '';\n\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n\n break;\n \n case \"GPAW\": \n dprec.innerHTML = '$E_{cut}$:';\n\n addDropdownChoice(pprec, \"300\", \"300\");\n addDropdownChoice(pprec, \"400\", \"400\");\n addDropdownChoice(pprec, \"500\", \"500\");\n addDropdownChoice(pprec, \"600\", \"600\");\n addDropdownChoice(pprec, \"700\", \"700\");\n addDropdownChoice(pprec, \"800\", \"800\");\n addDropdownChoice(pprec, \"900\", \"900\");\n addDropdownChoice(pprec, \"1000\", \"1000\");\n addDropdownChoice(pprec, \"1100\", \"1100\");\n addDropdownChoice(pprec, \"1200\", \"1100\");\n addDropdownChoice(pprec, \"1300\", \"1100\");\n addDropdownChoice(pprec, \"1400\", \"1100\");\n addDropdownChoice(pprec, \"1500\", \"1100\");\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n\n }\n }\n\n\n\n \n function add_monomers() {\n beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;\n beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;\n beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;\n beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;\n beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;\n beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;\n beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;\n beaker.ctrl_button = 1\n beaker.evaluate(\"exe_cell\");\n beaker.evaluate(\"ptablecell\");\n }\n function clear_last() {\n beaker.ctrl_button = 2\n beaker.evaluate(\"exe_cell\");\n }\n function clear_plot() {\n beaker.ctrl_button = 3\n beaker.evaluate(\"exe_cell\");\n }\n function add_binaries() {\n beaker.ctrl_xc = document.getElementById(\"errorbar_xcfunctional\").value;\n beaker.ctrl_kpt = document.getElementById(\"errorbar_kdensity\").value;\n beaker.ctrl_prec = document.getElementById(\"errorbar_precision\").value;\n beaker.ctrl_tiers = document.getElementById(\"errorbar_tiers\").value;\n beaker.ctrl_rel = document.getElementById(\"errorbar_relativity\").value;\n beaker.ctrl_quant = document.getElementById(\"errorbar_quantity\").value;\n beaker.ctrl_code = document.getElementById(\"errorbar_code\").value;\n beaker.ctrl_button = 4\n beaker.evaluate(\"exe_cell\");\n }\n function clear_last_bins() {\n beaker.ctrl_button = 5\n beaker.evaluate(\"exe_cell\");\n }\n function clear_plot_bins() {\n beaker.ctrl_button = 6\n beaker.evaluate(\"exe_cell\");\n }\n</script>\n\n<style type=\"text/css\">\n \n .error_table th { font-weight: bold; padding-right: 2ex; }\n .error_table td input { margin-right: 1ex; }\n \n</style>\n\n<!-- Controls area -->\n\n<div class=\"error_control\">\n <table class=\"error_table\">\n \n <tbody><tr>\n <th>XC-Functional:</th>\n <td>\n <select id=\"errorbar_xcfunctional\">\n <option value=\"PBE\" selected=\"\">PBE</option>\n <option value=\"LDA\">LDA</option>\n </select>\n </td>\n <td id=\"errorbar_xcfunctional_description\" style=\"white-space: pre;\"></td>\n <th>k-point density:</th>\n <td>\n <select id=\"errorbar_kdensity\">\n <option value=\"2\" selected=\"\">2</option>\n <option value=\"4\">4</option> \n <option value=\"8\">8</option> \n </select>\n </td>\n <td id=\"errorbar_kdensity_description\" style=\"white-space: pre;\"></td>\n <th id=\"errorbar_precision_name\">Precision:</th>\n <td>\n <select id=\"errorbar_precision\">\n <option value=\"Low\" selected=\"\">Low</option>\n <option value=\"Normal\">Normal</option> \n <option value=\"Accurate\">Acurate</option> \n </select>\n </td>\n <td id=\"errorbar_precision_description\" style=\"white-space: pre;\"></td>\n </tr>\n <tr>\n <th>Tiers:</th>\n <td><select id=\"errorbar_tiers\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_tiers_description\" style=\"white-space: pre;\"></td>\n <th>relativity treatment:</th>\n <td><select id=\"errorbar_relativity\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_relativity_description\" style=\"white-space: pre;\"></td>\n <th>Code:</th>\n <td><select id=\"errorbar_code\" onchange=\"errorUpdateForm()\">\n <option value=\"VASP\">VASP</option>\n <option value=\"FHI-aims\">FHI-aims</option>\n <option value=\"GPAW\">GPAW</option> \n <option value=\"exciting\">exciting</option> \n </select></td>\n <td id=\"errorbar_code_description\" style=\"white-space: pre;\"></td>\n </tr> \n \n <tr>\n <th>Quantity:</th>\n <td><select id=\"errorbar_quantity\">\n <option value=\"E_tot\">Total Energy</option>\n <option value=\"relR\">relative Energy</option>\n <option value=\"E_coh\">Cohesive Energy (binaries)</option>\n </select></td>\n <td id=\"errorbar_quantity_description\" style=\"white-space: pre;\"></td> \n \n\n\n </tr>\n \n </tbody></table>\n \n <table class=\"error_table\">\n \n <tbody><tr>\n <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_monomers();\">Add el. solids</button></th>\n <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot();\">Clear el. solids </button></th>\n <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_last();\">Clear last el. solids</button></th>\n <th><button type=\"button\" class=\"btn btn-secondary\" style=\"margin-top: 2ex;\" onclick=\"add_binaries();\">Add binaries</button></th>\n <th><button type=\"button\" class=\"btn clear-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_plot_bins();\">Clear binaries</button></th>\n <th><button type=\"button\" class=\"btn clearlast-secondary\" style=\"margin-top: 2ex;\" onclick=\"clear_last_bins();\">Clear last binaries</button></th>\n </tr>\n </tbody></table>\n</div>"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 179
},
"evaluatorReader": true,
- "lineCount": 246,
+ "lineCount": 207,
"initialization": true
},
{
@@ -690,6 +657,7 @@
"keys, ref_keys = get_keys(beaker.ctrl_code,beaker.ctrl_prec,beaker.ctrl_kpt,beaker.ctrl_xc,beaker.ctrl_tiers,beaker.ctrl_rel)",
"",
"if beaker.ctrl_button==1:",
+ " beaker.ctrl_sys = 'monomers'",
" # Database for code",
" db_con = con_code[beaker.ctrl_code,beaker.ctrl_sys]",
" # el. solids or binaries",
@@ -697,30 +665,15 @@
" # The plot label generated from the settings of the drop down menus",
" lab=beaker.ctrl_quant+', '+beaker.ctrl_code+', '+beaker.ctrl_sys+', '+', '.join(array(keys).tolist())",
" # Error:",
- " if beaker.ctrl_quant=='E_tot':",
+ " if beaker.ctrl_quant=='E_tot'or beaker.ctrl_quant=='E_coh':",
+ " lab='E_tot, '+beaker.ctrl_code+', '+beaker.ctrl_sys+', '+', '.join(array(keys).tolist())",
" if beaker.ctrl_code=='FHI-aims':",
" ref_data=data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]]",
" else:",
" ref_data=data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0]]",
" data=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code,",
" beaker.ctrl_sys+name_base[beaker.ctrl_code],keys, recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " xylist[len(xylist)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
- " elif beaker.ctrl_quant=='E_coh':",
- " db_con_mono = con_code[beaker.ctrl_code,'monomers']",
- " db_con_bins = con_code[beaker.ctrl_code,'binaries']",
- " if beaker.ctrl_code=='FHI-aims':",
- " ref_data_mono=data_ref[beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]]",
- " ref_data_bins=data_ref[beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]] ",
- " else: ",
- " ref_data_mono=data_ref[beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code],ref_keys[0]]",
- " ref_data_bins=data_ref[beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code],ref_keys[0]] ",
- " data_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
- " beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code], keys, recommended,",
- " name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " data_bins=get_data(db_con_bins, name_dict[ref_dict_binaries['binaries']], Z[ref_dict_binaries['binaries']],",
- " beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code], keys, recommended,",
- " name_dict_monos_gpaw,name_dict_bins_gpaw) ",
- " xylist[len(xylist)]=get_xy_Ecoh(Z[mono_or_bin], (ref_data_mono-data_mono)/N[ref_dict_binaries['monomers']],ref_data_bins-data_bins,zeroinds),lab ",
+ " xylist[len(xylist)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab ",
" # Relative error",
" else:",
" if beaker.ctrl_code=='FHI-aims':",
@@ -739,14 +692,25 @@
" xylist[len(xylist)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
"",
"elif beaker.ctrl_button==4:",
- " # Database for el. solids:",
- " db_con_mono = con_code[beaker.ctrl_code,'monomers']",
- " # Database for binaries:",
- " db_con_bins = con_code[beaker.ctrl_code,'binaries']",
+ " beaker.ctrl_sys = 'binaries'",
+ " # Database for code",
+ " db_con = con_code[beaker.ctrl_code,beaker.ctrl_sys]",
+ " # el. solids or binaries",
+ " mono_or_bin=ref_dict_binaries[beaker.ctrl_sys]",
+ " # The plot label generated from the settings of the drop down menus",
+ " lab=beaker.ctrl_quant+', '+beaker.ctrl_code+', '+beaker.ctrl_sys+', '+', '.join(array(keys).tolist())",
" # Error:",
- " if beaker.ctrl_quant=='E_tot' or beaker.ctrl_quant=='E_coh':",
- " # The plot label generated from the settings of the drop down menus",
- " lab='Binaries pred., E_tot, '+beaker.ctrl_code+', '+', '.join(array(keys).tolist())",
+ " if beaker.ctrl_quant=='E_tot':",
+ " if beaker.ctrl_code=='FHI-aims':",
+ " ref_data=data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]]",
+ " else:",
+ " ref_data=data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0]]",
+ " data=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code,",
+ " beaker.ctrl_sys+name_base[beaker.ctrl_code],keys, recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " xylist_bins[len(xylist_bins)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
+ " elif beaker.ctrl_quant=='E_coh':",
+ " db_con_mono = con_code[beaker.ctrl_code,'monomers']",
+ " db_con_bins = con_code[beaker.ctrl_code,'binaries']",
" if beaker.ctrl_code=='FHI-aims':",
" ref_data_mono=data_ref[beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]]",
" ref_data_bins=data_ref[beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]] ",
@@ -758,49 +722,34 @@
" name_dict_monos_gpaw,name_dict_bins_gpaw)",
" data_bins=get_data(db_con_bins, name_dict[ref_dict_binaries['binaries']], Z[ref_dict_binaries['binaries']],",
" beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code], keys, recommended,",
- " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw) ",
+ " xylist_bins[len(xylist_bins)]=get_xy_Ecoh(Z[mono_or_bin], (ref_data_mono-data_mono)/N[ref_dict_binaries['monomers']],ref_data_bins-data_bins,zeroinds,binaries_to_monos_min,binaries_to_monos_max),lab ",
" # Relative error",
" else:",
- " # The plot label generated from the settings of the drop down menus",
- " lab='Binaries pred., '+beaker.ctrl_quant+', '+beaker.ctrl_code+', '+', '.join(array(keys).tolist())",
" if beaker.ctrl_code=='FHI-aims':",
- " ref_data_mono=(data_ref[beaker.ctrl_code,'monomers'+name_base_expanded[beaker.ctrl_code],ref_keys[0],ref_keys[1]]-",
- " data_ref[beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]])",
- " ref_data_bins=(data_ref[beaker.ctrl_code,'binaries'+name_base_expanded[beaker.ctrl_code],ref_keys[0],ref_keys[1]]-",
- " data_ref[beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]]) ",
- " else:",
- " ref_data_mono=(data_ref[beaker.ctrl_code,'monomers'+name_base_expanded[beaker.ctrl_code],ref_keys[0]]-",
- " data_ref[beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code],ref_keys[0]])",
- " ref_data_bins=(data_ref[beaker.ctrl_code,'binaries'+name_base_expanded[beaker.ctrl_code],ref_keys[0]]-",
- " data_ref[beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code],ref_keys[0]]) ",
- " data_one_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
- " beaker.ctrl_code,'monomers'+name_base[beaker.ctrl_code], keys, recommended,",
- " name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " data_two_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
- " beaker.ctrl_code,'monomers'+name_base_expanded[beaker.ctrl_code], keys, ",
- " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " data_mono=data_two_mono-data_one_mono",
- " data_one_bins=get_data(db_con_bins, name_dict[ref_dict_binaries['binaries']], Z[ref_dict_binaries['binaries']],",
- " beaker.ctrl_code,'binaries'+name_base[beaker.ctrl_code], keys, ",
- " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " data_two_bins=get_data(db_con_bins, name_dict[ref_dict_binaries['binaries']], Z[ref_dict_binaries['binaries']],",
- " beaker.ctrl_code,'binaries'+name_base_expanded[beaker.ctrl_code], keys, ",
- " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
- " data_bins=data_two_bins-data_one_bins",
- " # Get the predicted error",
- " data_pred=get_binary_error_from_solids((ref_data_mono-data_mono)/N[ref_dict_binaries['monomers']],binaries_to_monos_min,",
- " N_bins_min,binaries_to_monos_max,N_bins_max,beaker.ctrl_pred) ",
- " xylist_pred[len(xylist_pred)]=get_xy_predict(N_bins[zeroinds], (ref_data_bins-data_bins)[zeroinds], data_pred[zeroinds]),lab",
+ " ref_data=(data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base_expanded[beaker.ctrl_code],ref_keys[0],ref_keys[1]]-",
+ " data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0],ref_keys[1]])",
+ " else: ",
+ " ref_data=(data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base_expanded[beaker.ctrl_code],ref_keys[0]]-",
+ " data_ref[beaker.ctrl_code,beaker.ctrl_sys+name_base[beaker.ctrl_code],ref_keys[0]])",
+ " data_one=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code, ",
+ " beaker.ctrl_sys+name_base[beaker.ctrl_code],keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_two=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code, ",
+ " beaker.ctrl_sys+name_base_expanded[beaker.ctrl_code],keys,",
+ " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data=data_two-data_one",
+ " xylist_bins[len(xylist_bins)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
"elif beaker.ctrl_button==2:",
" if len(xylist)>=1:",
" del xylist[len(xylist)-1]",
"elif beaker.ctrl_button==3:",
" xylist={}",
"elif beaker.ctrl_button==5:",
- " if len(xylist_pred)>=1:",
- " del xylist_pred[len(xylist_pred)-1]",
+ " if len(xylist_bins)>=1:",
+ " del xylist_bins[len(xylist_bins)-1]",
"elif beaker.ctrl_button==6:",
- " xylist_pred={}",
+ " xylist_bins={}",
" ",
"# Matplotlib figure",
"fig=figure(0,(15,10))",
@@ -813,58 +762,41 @@
"# Labels",
"ax.set_ylabel('$\\Delta$E per atom [eV]')",
"ax.set_xlabel('Z [#]')",
- "ax2.set_xlabel('$\\Delta$E (predicted) per atom [eV]')",
"ax2.set_ylabel('$\\Delta$E per atom [eV]')",
+ "ax2.set_xlabel('Z [#]')",
"",
"#Plot",
"for i in arange(len(xylist)):",
" ax.semilogy(xylist[i][0][0],xylist[i][0][1],'o',label=xylist[i][1])",
- "for i in arange(len(xylist_pred)):",
- " ax2.loglog(xylist_pred[i][0][0],xylist_pred[i][0][1],'o',label=xylist_pred[i][1]) ",
+ "for i in arange(len(xylist_bins)):",
+ " ax2.semilogy(xylist_bins[i][0][0],xylist_bins[i][0][1],'o',label=xylist_bins[i][1]) ",
"",
"# Diagonal line for right plot",
"ax2.set_ylim(ax.get_ylim()[0],ax.get_ylim()[1])",
- "ax2.plot(ax2.get_xlim(),ax2.get_xlim(),'-k')",
"# Legend",
"ax.legend(numpoints=1,loc=4)",
"ax2.legend(numpoints=1,loc=4)",
"# Figure title",
- "fig.suptitle('Error bars - VASP/FHI-aims/exciting/GPAW')",
+ "ax.set_title('El. solids')",
+ "ax2.set_title('Binaries')",
+ "fig.suptitle('Numerical errors - VASP/FHI-aims/exciting/GPAW')",
"# Show",
"fig.show()",
- "",
- "\"\"\"",
- "# Plotly figure",
- "fig = tools.make_subplots(rows=1, cols=2,shared_yaxes=True, subplot_titles=('Error from Data','Predicted error'))",
- "fig['layout'].update(height=600, width=1200)",
- "# Labels",
- "layout = go.Layout(xaxis1=dict(title='Z [#]',range=[0,90]),",
- " xaxis2=dict(type='log',title='\\Delta E (predicted) per atom [eV]',autorange=True,exponentformat='e'),",
- " yaxis1=dict(type='log',autorange=True,exponentformat='e',showexponent='All',title='\\Delta E per atom [eV]')",
- " )",
- "fig['layout'].update(layout)",
- "for i in arange(len(xylist)):",
- " fig.append_trace(go.Scatter(x=xylist[i][0][0],y=xylist[i][0][1],mode='markers',name=xylist[i][1]),1,1)",
- "for i in arange(len(xylist_pred)):",
- " fig.append_trace(go.Scatter(x=xylist_pred[i][0][0],y=xylist_pred[i][0][1],mode='markers',name=xylist_pred[i][1]),1,2)",
- "# Diagonal line for right plot",
- "#fig.append_trace(go.Scatter(x=[1e-5,1],y=[1e-5,1],mode='lines',showlegend=False,line = dict(color = ('black'),width = 2)), 1, 2)",
- "ply.offline.iplot(fig, filename='Errorbars data viewer', show_link=False)",
- "\"\"\""
+ ""
],
"hidden": true
},
"output": {
"state": {},
- "result": "<div class=\"output_subarea output_png\"><img 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CF8fFx1dbW\nqrW1VRUVFVGHEwusmQQAFB3WTPqhPgLA1LZt26bHHntMR48eLehZyWzWSM7mCgAAAAAX0d3drY6O\nDvX39xf0QDLbaHNFRvTr+yFffsiXH/IFxAvHpB/y5Yd8+cl1vsbGxrR27Vq1tbVp7ty5OX2vQsNg\nEgAAAAAy2L59u6qrq1VTUxN1KLHDmkkAQNFhzaQf6iMApNfV1aUNGzaov79f5eXlUYeTFayZBAAA\nAIAcGhkZUX19vdrb24tmIJlttLkiI/r1/ZAvP+TLD/kC4oVj0g/58kO+/OQqXw0NDVq5cqWWL1+e\nk9cvBsxMAgAAAECSzs5OPfLIIzpz5kzUocQaayYBAEWHNZN+qI8A8Lzh4WG96U1vUmdnp6qqqqIO\nJ+uyWSMZTAIAig6DST/URwAIOOe0atUqLViwQLt37446nJzIZo1kzSQyol/fD/nyQ778kC8gXjgm\n/ZAvP+TLTzbzdeTIEZ09e1Y7duzI2msWM9ZMAgAAACh5Q0ND2rp1q7q6ulRWVhZ1OAWBNlcAQNGh\nzdUP9RFAqXPOacWKFaqurlZzc3PU4eQUba4AAAAAkCVtbW0aHR1VY2Nj1KEUFAaTyIh+fT/kyw/5\n8kO+gHjhmPRDvvyQLz+Xmq/BwUHdfvvtOnTokGbPZhWgDwaTAAAAAErS5OSk6urq1NTUpMrKyqjD\nKTismQQAFB3WTPqhPgIoVfv27dOxY8d04sQJzZpVGvNsXGdyhiiWAFAaGEz6oT4CKEUDAwNatmyZ\nenp6NH/+/KjDyRtOwIO8oF/fD/nyQ778kC8gXjgm/ZAvP+TLz0zyNTExodraWu3ataukBpLZxmAS\nAAAAQEnZuXOn5s2bp/r6+qhDKWi0uQIAig5trn6ojwBKSW9vr2644QadOXNGFRUVUYeTd7S5AgAA\nAICn8fFx1dbWqrW1tSQHktnGYBIZ0a/vh3z5IV9+yBcQLxyTfsiXH/Llxydfzc3Nuvrqq7V69erc\nBVRCuConAAAAgKLX3d2tjo4O9ff3y4yVENnAmkkAQNFhzaQf6iOAYjc2NqaFCxeqpaVFNTU1UYcT\nKa4zOUMUSwAoDQwm/VAfARS7zZs3a3x8XO3t7VGHEjlOwIO8oF/fD/nyQ778kC8gXjgm/ZAvP+TL\nz1T56urq0vHjx9Xa2pqfgEoIayYBAAAAFKWRkRHV19ervb1d5eXlUYdTdGhzBQAUHdpc/VAfARSr\nNWvWqLy8XPv37486lNjIZo1kZhIAAABA0ens7NSpU6fU19cXdShFizWTyIh+fT/kyw/58kO+gHjh\nmPRDvvwfdyqAAAAgAElEQVSQLz/p8jU8PKzbbrtNd999t+bMmZP/oEoEg0kAAAAARcM5p40bN6qu\nrk5VVVVRh1PUWDMJACg6rJn0Q30EUEwOHz6svXv3qre3V2VlZVGHEztFd2kQM3u9mf2zmT1lZt8x\nsx1mNuUHNLO3mtkXzez74e1BM3trPmIGACAfqJEAMH1DQ0PaunWrDh06xEAyDyIfTJrZ5ZK+JOln\nkmok7ZC0NfzvxZ73KkkPKvgM75P0ewpOKPSgmb0ylzGXCvr1/ZAvP+TLD/kqTdTI+OKY9EO+/JAv\nP4l8Oee0bt06NTQ0aNGiRdEGVSLicDbXzZJeKGmVc+4pSf9sZuWSPmJme51zYxmed4OkF0t6d2If\nM/uqpP+RdL2kv8t96AAA5BQ1EgCmqa2tTaOjo2psbIw6lJIR+ZpJM3tI0necc7cmbXulpMck/Y5z\n7oEMz/ugpI9KmuOcezbc9gJJP5b0B865tjTPYU0IAJSAYlkzma8aSX0EUOgGBwe1ZMkSnTx5UpWV\nlVGHE2vFtmayUtKjyRucc0OSng4fy+Szkn4o6aNm9otm9jJJLZJ+IOkzOYoVAIB8okYCwBQmJydV\nV1enpqYmBpJ5FofB5BWSRtNsHwkfS8s5NyxphaT3SvqepCclvVvSCufc93MQZ8mhX98P+fJDvvyQ\nr5JFjYwpjkk/5MsP+fLzoQ99SLNnz9aWLVuiDqXkxGEwOSNm9mpJD0h6REHBvE7SaUnHzewVUcYG\nAECUqJEASsXAwIA6OjrU3t6uWbMKdmhTsOJwAp4RSeVptl8RPpbJNknPSPpd59ykJJnZlyX9Z/jY\nH6R70tq1a3XllVdKki6//HK9+c1v1rJlyyQ9/1cg7gf3E9viEk/c7ye2xSWeuN9PbItLPHG/n9gW\nl3jidr+1tVV9fX3P/fteRPJWI6mP/vcT4hJP3O8nxCWeuN9PiEs8cbw/MTGhG2+8UevXr9f8+fMj\njyeu9/v6+jQ6GjS5nDt3TtkUlxPwPOGce1/StldIelwXP7nAA5Kcc+6302x/1jn3O2mewwkGAKAE\nFNkJeHJeI6mPAArRHXfcoZ6eHj3wwAOaxuV3ESq2E/B8XtIKM5uTtG21gpMLPHSR552TdLWZXZbY\nYGZlkt4QPoZLlPqXMVwc+fJDvvyQr5JFjYwpjkk/5MsP+Zpab2+vDhw4oIMHD+qhhy72zyFyKQ6D\nyTZJP5X0D2b2DjPbIOkjkj6afP0sMztrZncmPe8Tkiok/aOZXW9mN0j6nKSXh48BAFDoqJEAkGJ8\nfFy1tbVqbW1VRUVF1OGUtMjbXCXJzCol7Zd0rYKz1t0paUdyz42Z/ZekLzvn1iVtq5a0Q9Ibw03/\nV9KHnXPdGd6HNh4AKAHF0uYq5adGUh8BFJJt27bpscce09GjR2lvnYFs1shYDCbzhWIJAKWhmAaT\n+UB9BFAouru7dfPNN6u/v19z586NOpyCVGxrJhFT9Ov7IV9+yJcf8gXEC8ekH/Llh3ylNzY2prVr\n16qtre28gST5ig6DSQAAAACxt337dlVXV6umpibqUBCizRUAUHRoc/VDfQQQd11dXdqwYYP6+/tV\nXp7u8ruYrmzWyNnZeBEAAAAAyIWRkRHV19ervb2dgWTM0OaKjOg/90O+/JAvP+QLiBeOST/kyw/5\nOl9DQ4NWrlyp5cuXp32cfEWHmUkAAAAAsdTZ2alTp06pr68v6lCQBmsmAQBFhzWTfqiPAOJoeHhY\nCxcu1H333aeqqqqowykaXGdyhiiWAFAaGEz6oT4CiBvnnFatWqUFCxZo9+7dUYdTVLjOJPKC/nM/\n5MsP+fJDvoB44Zj0Q778kC/pyJEjOnv2rHbs2DHlvuQrOqyZBAAAABAbQ0ND2rp1q7q6ulRWVhZ1\nOLgI2lwBAEWHNlc/1EcAceGc04oVK1RdXa3m5uaowylKtLkCAAAAKDptbW0aHR1VY2Nj1KFgGhhM\nIiP6z/2QLz/kyw/5AuKFY9IP+fJTqvkaHBzU7bffrkOHDmn27OmvxivVfMUBg0kAAAAAkZqcnFRd\nXZ2amppUWVkZdTiYJtZMAgCKDmsm/VAfAURt3759OnbsmE6cOKFZs5jvyiWuMzlDFEsAKA0MJv1Q\nHwFEaWBgQMuWLVNPT4/mz58fdThFjxPwIC/oP/dDvvyQLz/kC4gXjkk/5MtPKeVrYmJCtbW12rVr\n14wHkqWUr7hhMAkAAAAgEjt37tS8efNUX18fdSiYAdpcAQBFhzZXP9RHAFHo7e3VDTfcoDNnzqii\noiLqcEoGba4AAAAACtb4+Lhqa2vV2trKQLKAMZhERvSf+yFffsiXH/IFxAvHpB/y5acU8tXc3Kyr\nr75aq1evvuTXKoV8xdX0rwYKAAAAAJeou7tbHR0d6u/vlxkrEgoZayYBAEWHNZN+qI8A8mVsbEwL\nFy5US0uLampqog6nJHGdyRmiWAJAaWAw6Yf6CCBfNm/erPHxcbW3t0cdSsniBDzIC/rP/ZAvP+TL\nD/kC4oVj0g/58lOs+erq6tLx48fV2tqa1dct1nwVAtZMAgAAAMipkZER1dfXq729XeXl5VGHgyyh\nzRUAUHRoc/VDfQSQa2vWrFF5ebn2798fdSglL5s1kplJAAAAADnT2dmpU6dOqa+vL+pQkGWsmURG\n9J/7IV9+yJcf8gXEC8ekH/Llp5jyNTw8rNtuu01333235syZk5P3KKZ8FRoGkwAAAACyzjmnjRs3\nqq6uTlVVVVGHgxxgzSQAoOiwZtIP9RFALhw+fFh79+5Vb2+vysrKog4HIa4zOUMUSwAoDQwm/VAf\nAWTb0NCQFi9erK6uLi1atCjqcJCE60wiL+g/90O+/JAvP+QLiBeOST/ky0+h58s5p3Xr1qmhoSEv\nA8lCz1chYzAJAAAAIGva2to0OjqqxsbGqENBjtHmCgAoOrS5+qE+AsiWwcFBLVmyRCdPnlRlZWXU\n4SAN2lwBAAAAxMrk5KTq6urU1NTEQLJEMJhERvSf+yFffsiXH/IFxAvHpB/y5adQ89XS0qLZs2dr\ny5YteX3fQs1XMZgddQAAAAAACtvAwID27Nmjnp4ezZrFfFWpYM0kAKDosGbSD/URwKWYmJjQ0qVL\ntWnTJq1fvz7qcDAF1kwCAAAAiIWdO3dq3rx5qq+vjzoU5BmDSWRE/7kf8uWHfPkhX0C8cEz6IV9+\nCilfvb29OnDggA4ePCizaBpCCilfxYbBJAAAAABv4+Pjqq2tVWtrqyoqKqIOBxFgzSQAoOiwZtIP\n9RHATGzbtk2PPfaYjh49GtmsJPxls0ZyNlcAAAAAXrq7u9XR0aH+/n4GkiWMNldkRP+5H/Llh3z5\nIV9AvHBM+iFffuKer7GxMa1du1ZtbW2aO3du1OHEPl/FjMEkAAAAgGnbvn27qqurVVNTE3UoiBhr\nJgEARYc1k36ojwCmq6urSxs2bFB/f7/Ky8ujDgczwJpJAAAAAHk1MjKi+vp6tbe3M5CEJNpccRH0\nn/shX37Ilx/yBcQLx6Qf8uUnrvlqaGjQypUrtXz58qhDOU9c81UKmJkEAAAAcFGdnZ06deqU+v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XXfccYd6enr0wAMPyIx/ihAveT0BD0oX/ed+yJcf8uWHfAHxwjHpp5Tydfr0aX384x/XwYMH\nZzyQLKV8ZQP5ig6DSQAAACALxsfHVVtbq9bWVlVUVEQdDpBztLkCAIoOba5+qI9Admzfvl3nzp3T\n0aNHaW9FbGWzRk7nbK4AAAAALqK7u1v33HOP+vv7GUiiZNDmiozoP/dDvvyQLz/kC4gXjkk/xZ6v\nsbExrV27Vm1tbZo7d+4lv16x5yvbyFd0GEwCAAAAl2D79u2qrq5WTU1N1KEAeTWjNZNmtkDSL0t6\nYepjzrnjWYgrJ1gTAgClIco1k4VYI6mPwMx1dXVpw4YN6u/vV3l5edThAFOKbM2kmb1R0qclvV5S\nugCcpMuyEBcAAAWFGgmUnpGREdXX16u9vZ2BJEqSb5vrJyVNSPptSQskzU+5vSar0SFS9J/7IV9+\nyJcf8lUQqJElhGPST7Hmq6GhQStXrtTy5cuz+rrFmq9cIV/R8T2b6+sl3eSc68pFMAAAFDBqJFBC\nOjs7derUKfX19UUdChAZrzWTZvZlSR3OuTtzF1LusCYEAEpDFGsmC7lGUh8BP8PDw1q4cKHuu+8+\nVVVVRR0O4CXK60xulnSvmT0t6cuSRlN3cM49nY3AAAAoMNRIoAQ457Rx40bV1dUxkETJ810z+aSk\nb0s6JGlI0o/T3FAk6D/3Q778kC8/5KsgUCNLCMekn2LK15EjR3T27Fnt2LEjZ+9RTPnKB/IVHd+Z\nyXskLZW0T9JZSc9kPSIAAAoTNRIock888YS2bt2qrq4ulZWVRR0OEDnfNZNPSVrvnOvIXUi5w5oQ\nACgNEa2ZLNgaSX0Epuac03XXXae3ve1tam5ujjocYMayWSN921zPSWK9BwAAFzonaiRQtNra2jQy\nMqLGxsaoQwFiw3cwuV1Sk5ldmf1QEDf0n/shX37Ilx/yVRCokSWEY9JPoedrcHBQt99+uw4dOqTZ\ns31Xifkr9HzlG/mKju/RsEPSqyT9h5mdU/oz1b01C3EBAFBoqJFAEZqcnFRdXZ2amppUWVkZdThA\nrPiumWyfah/n3PsvKaIcYk0IAJSGiNZMFmyNpD4Cme3bt0/Hjh3TiRMnNGuWb1MfED/ZrJFeg8lC\nR7EEgNIQxWCykFEfgfQGBga0bNky9fT0aP78+VGHA2RFlCfgSQRQYWY3mdl6M1tlZhXZCAbxQv+5\nH/Llh3z5IV+FgxpZGjgm/RRiviYmJlRbW6tdu3blfSBZiPmKEvmKjteaSTO7TNLHJK2XdFnSQ5Nm\n9glJv++cezaL8QEAUBCokUBx2blzp+bNm6f6+vqoQwFiy3fN5J9L2ibpdkl/L+l7kuZJulnSn0r6\nS+fch3MQZ1bQxgMApSGiNZMFWyOpj8D5Tp8+rXe9613q6+tTRQXNBSguka2ZNLPHJf2Nc25fmse2\nSWpwzr0qG4HlAsUSAEpDRIPJgq2R1EfgeePj41q8eLGampp06623Rh0OkHVRrpl8maT+DI/1h4+j\nSNB/7od8+SFffshXQaBGlhCOST+FlK/bb79dV111lW655ZbIYiikfMUB+YqO73Um/0PSaklfTPPY\naknfuuSIAAAoTNRIoMB1d3frnnvuUX9/v8w4ITQwFd821/dKulfSCUmfVbAe5GWSflfS2yWtds59\nJgdxZgVtPABQGiJqcy3YGkl9BKSxsTEtXLhQLS0tqqmpiTocIGcivc6kmb1T0g5Jb5H0AkkTkk5L\n+ohz7sFsBJUrFEsAKA1RXWeyUGsk9RGQNm/erPHxcbW3t0cdCpBTkV5n0jn3RefctZJeJOnlkl7k\nnKuKc5HEzNB/7od8+SFffshXYaBGlg6OST9xz1dXV5eOHz+u1tbWqEORFP98xQ35io7XYNLMPpy4\n+LJz7lnn3HDimllm9ktmFstTngMAkGvUSKAwjYyMqL6+XnfddZfKy8ujDgcoKL5rJiclXeuc60nz\n2GJJPc65yy58ZjzQxgMApSGiNZMFWyOpjyhla9asUXl5ufbv3x91KEBeZLNG+p7N1SRlqjavkDRy\naeEAAFCwqJFAgens7NSpU6fU19cXdShAQZqyzdXM6szshJmdUFAkDyTuJ90elnRE0kO5Dhj5Q/+5\nH/Llh3z5IV/xRI0sXRyTfuKYr+HhYd122226++67NWfOnKjDOU8c8xVn5Cs605mZfFrS98P/N0k/\nlPSDlH2ekfR5SR/PXmgAAMQeNRIoQM45bdy4UXV1daqqqoo6HKBg+a6ZbJf0Z865/8pdSLnDmhAA\nKA0RrZks2BpJfUSpOXz4sPbu3ave3l6VlZVFHQ6QV5FeZ7KQUSwBoDREdZ3JQkV9RCl54okn9Ja3\nvEVdXV1atGhR1OEAeRfpdSbN7GYz+5KZPW5mw6m3bASFeKD/3A/58kO+/JCvwkCNLB0ck37iki/n\nnNatW6eGhoZYDyTjkq9CQb6i43udyVsl3S3prIIz090v6Vj4Oj+SxDmVAQAliRoJxF9bW5tGRkbU\n2IQ46KcAACAASURBVNgYdShAUfBdM3lG0mcl7ZY0Ieka59zXzewlkh6U9Fnn3L6cRJoFtPEAQGmI\naM1kwdZI6iNKweDgoJYsWaKTJ0+qsrIy6nCAyETZ5vorkv7VOTcpaVLSz0uSc+7HkvZI+lA2ggIA\noABRI4GYmpycVF1dnZqamhhIAlnkO5j8kaQXhf//HUmvT3rMJL00G0EhHug/90O+/JAvP+SrIFAj\nSwjHpJ+o89XS0qLZs2dry5YtkcYxXVHnq9CQr+hM5zqTyb4maaGC62XdL+nDZvYzBdfQ+rCkU9kN\nDwCAgkGNBGJoYGBAe/bsUU9Pj2bN8j73JICL8F0zuVTSlc65e83scgUnGrhBwQzn1yTdEufra7Em\nBABKQ0RrJgu2RlIfUawmJia0dOlSbdq0SevXr486HCAWYnWdSTMrk1TmnPtRNgLKJYolAJSGuFxn\nslBqJPURxeqOO+5QT0+PHnjgAZlF/k8CEAuRXmcylXPup3EvkpgZ+s/9kC8/5MsP+SpM1MjixTHp\nJ4p8nT59Wh//+Md18ODBghtI8v3yQ76iQ+M4AAAAisr4+Lhqa2vV2tqqioqKqMMBitYlt7kWEtp4\nAKA0xKXNtVBQH1Fstm/frnPnzuno0aMFNysJ5Fo2a6Tv2VwBAACA2Oru7tY999yj/v5+BpJAjk27\nzdXMXmBmv25m9AqUCPrP/ZAvP+TLD/mKN2pk6eGY9JOvfI2NjWnt2rVqa2vT3Llz8/KeucD3yw/5\nio7PmslJSSckVeYoFgAAChU1EoiB7du3q7q6WjU1NVGHApQE3+tMfkPSLudcR+5Cyh3WhABAaYjo\nOpMFWyOpjygGXV1d2rBhg/r7+1VeXh51OEBsRXlpkCZJHzazN2bjzQEAKCLUSCAiIyMjqq+v1113\n3cVAEsgj38Fks6SXSuozs8fN7Gtm1pN8y0GMiAj9537Ilx/y5Yd8FQRqZAnhmPST63w1NDRo5cqV\nWr58eU7fJ1/4fvkhX9HxPZvrN8IbAAA4HzUSiEBnZ6dOnTqlvr6+qEMBSg7XmQQAFB2uM+mH+ohC\nNTw8rIULF+q+++5TVVVV1OEABSGbNXJGg0kzu0rSYkmvlPRJ59yTZvY6Sd9zzv04G4HlAsUSAEpD\nlIPJQqyR1EcUIuecVq1apQULFmj37t1RhwMUjMhOwGNmP2dmRxW08RyU9GeSEtfU2iXpI9kICvFA\n/7kf8uWHfPkhX/H3/9u7+/i4rvre99+f7VaAIUpogkE8xXBaC3NAsUlBFYkxJL3YHCq3pjQYbmS1\nlp04IfYB417biRLcIp/YN0Fq6+bYYFD9VCA0DuUYgwhx4OhCjGKBbBBweqziS0UAXcgYEKmKUNb9\nY4+SiTIjzRrNzNqz5/N+vfQKs+fppy/a8/Oavdbe9Mjqwj7ppxR5HTlyROfOndPOnTuL/tqh8ffl\nh7zC8T0Bz4clNUm6RtLzJGWOaE9IWlGkugAAqDT0SKBMhoeHtWXLFh06dEg1NTWhywGqlu91Jn8q\nabNz7qiZzZU0LulK59w3zOzNkj7rnHteiWqdNabxAEB1CHSdyYrtkfRHVBLnnFasWKGrr75at912\nW+hygIoT8jqTz5b0sxz3PU/SxOzKAQCgYtEjgTLYt2+fUqmUtm3bFroUoOr5DiYfkdSS474/lfS1\n2ZWDOGH+uR/y8kNefsirItAjqwj7pJ9i5TU0NKT29nYdOnRI8+b5XuGucvD35Ye8wvHdC9slPWBm\nX5L0aUlO0tvM7H2KGuWyItcHAECloEcCJTQxMaG1a9fq1ltvVX19fehyAKiAS4OY2Rsl3SmpUdJc\nRc3ylKS/dM59tegVFhFrQgCgOoS6NEil9kj6IyrBXXfdpePHj+vkyZOaM8d3ch2AScGvM5ku4tmS\nLpF0wTn3eDGKKTWaJQBUh5DXmUy/f0X1SPoj4m5wcFDLly9XX1+fFi5cGLocoKKFPAHPk5xz/+6c\ne7QSmiQKw/xzP+Tlh7z8kFdloUcmH/ukn9nkNT4+rpaWFu3atatqBpL8ffkhr3C8Vy6b2W9LapX0\nekkvkvQjSV+XdNA59+uiVgcAQAWhRwLF19HRoQULFqitrS10KQCm8L3O5KskfUFSnaR+SSOSXiBp\nqaQfS1rhnPtOCeosCqbxAEB1CHSdyYrtkfRHxFV/f79WrlypgYEB1dXVhS4HSIRgaybNrFdSraS3\nO+d+kLH9ZZKOK1obEtuz1dEsAaA6BBpMVmyPpD8ijsbGxvS6171Ot956q9797neHLgdIjJBrJq+U\ndHtmk5Sk9O07JP1+MYpCPDD/3A95+SEvP+RVEeiRVYR90k8hebW3t2vx4sVas2ZN8QuKOf6+/JBX\nOL5rJs9LelaO+54l6Qc57gMAIOnOix4JFEVvb6+OHj2qs2fPyizYiZkBzMB3musqSXdLeo9z7usZ\n2xslHZH0AefcZ4peZZEwjQcAqkOgaa4V2yPpj4iT0dFRNTQ0qLOzU83NzaHLARIn5JrJRyS9XNLv\nKDqxwOTJBV4g6WeKvpV9knPu9cUoslholgBQHQINJiu2R9IfEScbN27U2NiYuru7Q5cCJFLINZPf\nlvQ5SYcUnbHuG+n/HkpvH5zygwrG/HM/5OWHvPyQV0WgR1YR9kk/+ebV09OjEydOqKurq7QFxRx/\nX37IKxyvNZPOuT8vVSEAAFQyeiQwO6lUSm1tberu7lZtbW3ocgDkwWuaa6VjGg8AVIcQ01wrGf0R\ncXD99dertrZWe/fuDV0KkGjF7JG+Z3MFAAAAiurYsWM6deqUBgYGQpcCwIPvmklUEeaf+yEvP+Tl\nh7yAeGGf9DNdXiMjI7r55pt18OBBzZ8/v3xFxRh/X37IKxwGkwAAAAjCOacbbrhBa9euVVNTU+hy\nAHhizSQAIHFYM+mH/ohQDh8+rD179uj06dOqqakJXQ5QFcp6aRAze7eZPX/KtpeZ2bwp2+rMbEcx\nigIAoBLQI4HCDQ8Pa8uWLTp06BADSaBC5TPN9bCk/zR5w8zmSvq+pNdOedxLJf118UpDaMw/90Ne\nfsjLD3nFFj2ySrFP+pmal3NO69at06ZNm7RkyZIwRcUYf19+yCucfAaT2Q6BMnUIAAB6JFCQ/fv3\nK5VKadu2baFLATALM66ZNLMnJDU65/rSt+dKGpd0pXPuGxmPe4Okrznn5noXYfYqSXslNUq6IOmA\npA/mu4DDzEzSI5KWSnq7c+5EjsexJgQAqkC51kwmpUfSH1FOQ0NDamxsVG9vr+rr60OXA1SdRF1n\n0swulvQlSd+W1CzplZI+rOib3dvzfJn1kl4siU4IAEgMeiSSZmJiQmvXrtWOHTsYSAIJkO+lQbI1\noGI1pY2SniVptXPuQefcRyTtlPR+M3vuTE9ON9oPSdohphYVFfPP/ZCXH/LyQ16xRo+sQuyTfibz\n6uzs1Lx587R58+awBcUcf19+yCucfI9M9pjZb6Zse3DKtkKPcq6Q1OOc+1XGtk9K2i3pTZI+N8Pz\nPySpV9LJAt8fAIDZoEcCeRgcHNTu3bvV19enOXO41DmQBPmsmbzD5wWdczu9CjD7iaS/d8791ZTt\no5LucM7dPc1zXyvpa5JeI+kJRWfQY80kAFS5Mq6ZTESPpD+i1MbHx9XY2Kgbb7xR69evD10OUNXK\numbSt/EV4BJFJxSYKpW+bzp/K+nvnHPfN7OXF70yAACmQY8E8tPR0aEFCxaora0tdCkAiqhocwzM\n7DVmdrRYr5fH+71L0u8pmsKDEmD+uR/y8kNefsirstEjk4d9Mn/9/f3q6urSgQMHFJ1cGDPh78sP\neYUz68GkmV1pZp+RNCDpXQW8REpSbZbtl6Tvy/ae8yTtUbRmZJ6Z1Wa8xvx8TkoAAECp0SNR7cbG\nxtTS0qL3vve9qqurC10OgCIr+NIgZvZGSe2S/lDSvyi6BtZ7C3ip70l62rmhzewlkp6Tvi+b+ZJe\nouj06J0Z252kT0k6p+gb2WdobW3V5ZdfLkm6+OKLdcUVV2j58uWSnvpWg9vR7cltcakn7rcnt8Wl\nnrjfntwWl3rifntyW1zqidvtrq4uDQwMPPn5Hlol9kj6o//tSXGpJ46329vbdemll+qaa67RpDjV\nF+fbk+JST9xvT4pLPXG6PTAwoAsXohUT58+fVzHNeAKeZzzB7BpJt0lapui6Vx2SPp2+fdL3gsxm\ntk3SByS9fPJsdWb2AUkflPRC59xolufMlfTGKZtfqOgMd9skPeSceyTL8zjBAABUgXKdgCfL+1Zk\nj6Q/ohR6e3t13XXX6ezZs7r00ktDlwMgrZg9co7Hm77NzL4m6YuKvhH9E+dcg3Pu3ll2oH2S/kPS\n/WZ2jZltkHSHpLszm6SZnTOzj0qSc27COfc/M38kfT390G9nG0jC39RvejA98vJDXn7IK97okdWH\nfXJ6o6Ojam1t1b59+3TppZeSlyfy8kNe4eQ1zdXMHpb0ekXXqlrhnHugWAU45y6kv8ndK+mzis5a\nd7eiizJnmqOZB798rQoAKCt6JPBMW7du1bJly9Tc3By6FAAllNc01/Taj5sk9Ug64px7Istj3qQC\npvCUE9N4AKA6lHOaaxJ6JP0RxdTT06MNGzbo7Nmzqq3Ndv4oACGVfZqrc+6rzrn3SPqupLvNbF36\nbHEAAFQ1eiTwlFQqpba2Nn3sYx9jIAlUgbzXTEqSc+4R59z7JPVJ2m1mG83st0tTGkJj/rkf8vJD\nXn7IK/7okdWFfTK7TZs2adWqVbr22muftp28/JCXH/IKp6BvTp1z35K0xczqJf21mQ1LGipqZQAA\nVCB6JKrVsWPHdOrUKQ0MDIQuBUCZeF8aJOuLmC2UtEXSeudczaxfsERYEwIA1SHUpUGyqYQeSX/E\nbI2MjKihoUH33XefmpqaQpcDYBrF7JFFGUw++WJmL3LO/ahoL1hkNEsAqA5xGkxOinOPpD9iNpxz\nWr16tRYtWqQ777wzdDkAZhDkOpP5iGuTRGGYf+6HvPyQlx/yqnz0yGRhn3zKkSNHdO7cOe3cOfWK\nNU8hLz/k5Ye8wuFscwAAACjI8PCwtmzZop6eHtXUxHIWN4ASKuo017hjGg8AVIc4TnONM/ojCuGc\n04oVK3T11VfrtttuC10OgDzFdporAAAAqsP+/fuVSqW0bdu20KUACITBJHJi/rkf8vJDXn7IC4iX\nat8nh4aG1N7erkOHDmnevJlXTVV7Xr7Iyw95hcNgEgAAAHmbmJjQ2rVrtWPHDtXX14cuB0BArJkE\nACQOayb90B/h46677tLx48d18uRJzZnDcQmg0sT2OpNxR7MEgOrAYNIP/RH5Ghwc1PLly9XX16eF\nCxeGLgdAATgBD8qC+ed+yMsPefkhLyBeqnGfHB8fV0tLi3bt2uU9kKzGvGaDvPyQVzgMJgEAADCj\njo4OLViwQG1tbaFLARATTHMFACQO01z90B8xk/7+fq1cuVIDAwOqq6sLXQ6AWWCaKwAAAMpibGxM\nLS0t6urqYiAJ4GkYTCIn5p/7IS8/5OWHvIB4qaZ9sr29XYsXL9aaNWsKfo1qyqsYyMsPeYUz81Vm\nAQAAUJV6e3t19OhRnT17VmbMHAfwdKyZBAAkDmsm/dAfkc3o6KgaGhrU2dmp5ubm0OUAKBKuM1kg\nmiUAVAcGk37oj8hm48aNGhsbU3d3d+hSABQRJ+BBWTD/3A95+SEvP+QFxEvS98menh6dOHFCXV1d\nRXm9pOdVbOTlh7zCYc0kAAAAnpRKpdTW1qbu7m7V1taGLgdAjDHNFQCQOExz9UN/RKbrr79etbW1\n2rt3b+hSAJRAMXskRyYBAAAgSTp27JhOnTqlgYGB0KUAqACsmUROzD/3Q15+yMsPeQHxksR9cmRk\nRDfffLMOHjyo+fPnF/W1k5hXKZGXH/IKh8EkAABAlXPO6YYbbtDatWvV1NQUuhwAFYI1kwCAxGHN\npB/6Iw4fPqw9e/bo9OnTqqmpCV0OgBLiOpMFolkCQHVgMOmH/ljdhoeHtXTpUvX09GjJkiWhywFQ\nYlxnEmXB/HM/5OWHvPyQFxAvSdknnXNat26dNm3aVNKBZFLyKhfy8kNe4TCYBAAAqFL79+9XKpXS\ntm3bQpcCoAIxzRUAkDhMc/VDf6xOQ0NDamxsVG9vr+rr60OXA6BMmOYKAACAgk1MTGjt2rXasWMH\nA0kABWMwiZyYf+6HvPyQlx/yAuKl0vfJzs5OzZs3T5s3by7L+1V6XuVGXn7IK5x5oQsAAABA+QwO\nDmr37t3q6+vTnDkcVwBQONZMAgAShzWTfuiP1WN8fFyNjY268cYbtX79+tDlAAiANZMAAADw1tHR\noQULFqitrS10KQASgMEkcmL+uR/y8kNefsgLiJdK3Cf7+/t1zz336MCBAzIr74H7SswrJPLyQ17h\nMJgEAABIuLGxMbW0tKirq0t1dXWhywGQEKyZBAAkDmsm/dAfk2/r1q06f/687r333rIflQQQL8Xs\nkZzNFQAAIMF6e3t19OhRnT17loEkgKJimityYv65H/LyQ15+yAuIl0rZJ0dHR9Xa2qp9+/bp0ksv\nDVZHpeQVF+Tlh7zCYTAJAACQUFu3btWyZcvU3NwcuhQACcSaSQBA4rBm0g/9MZl6enq0YcMGnT17\nVrW1taHLARATrJkEAABATqlUSm1tberu7mYgCaBkmOaKnJh/7oe8/JCXH/IC4iXu++SmTZu0atUq\nXXvttaFLkRT/vOKGvPyQVzgcmQQAAEiQY8eO6dSpUxoYGAhdCoCEY80kACBxWDPph/6YHCMjI2po\naNB9992npqam0OUAiKFi9kgGkwCAxGEw6Yf+mAzOOa1evVqLFi3SnXfeGbocADFVzB7JmknkxPxz\nP+Tlh7z8kBcQL3HcJ48cOaJz585p586doUt5hjjmFWfk5Ye8wmHNJAAAQIUbHh7Wli1b1NPTo5qa\nmtDlAKgSTHMFACQO01z90B8rm3NOK1as0NVXX63bbrstdDkAYo5prgAAAJAk7d+/X6lUStu2bQtd\nCoAqw2ASOTH/3A95+SEvP+QFxEtc9smhoSG1t7fr0KFDmjcvvquX4pJXpSAvP+QVDoNJAACACjQx\nMaG1a9dqx44dqq+vD10OgCrEmkkAQOKwZtIP/bEy3XXXXTp+/LhOnjypOXM4PgAgP1xnskA0SwCo\nDgwm/dAfK8/g4KCWL1+uvr4+LVy4MHQ5ACoIJ+BBWTD/3A95+SEvP+QFxEvIfXJ8fFwtLS3atWtX\nxQwk+QzzQ15+yCscBpMAAAAVpKOjQwsWLFBbW1voUgBUOaa5AgASh2mufuiPlaO/v18rV67UwMCA\n6urqQpcDoAIxzRUAAKDKjI2NqaWlRV1dXQwkAcQCg0nkxPxzP+Tlh7z8kBcQLyH2yfb2di1evFhr\n1qwp+3vPFp9hfsjLD3mFE9+r2wIAAECS1Nvbq6NHj+rMmTMyYwY3gHhgzSQAIHFYM+mH/hhvo6Oj\namhoUGdnp5qbm0OXA6DCcZ3JAtEsAaA6MJj0Q3+Mt40bN2psbEzd3d2hSwGQAJyAB2XB/HM/5OWH\nvPyQFxAv5done3p6dOLECXV1dZXl/UqFzzA/5OWHvMJhzSQAAEAMpVIptbW1qbu7W7W1taHLAYBn\nYJorACBxmObqh/4YT9dff71qa2u1d+/e0KUASJBi9kiOTAIAAMTMsWPHdOrUKQ0MDIQuBQByYs0k\ncmL+uR/y8kNefsgLiJdS7pMjIyO6+eabdfDgQc2fP79k71NOfIb5IS8/5BUOg0kAAICYcM7phhtu\n0Nq1a9XU1BS6HACYFmsmAQCJw5pJP/TH+Dh8+LD27Nmj06dPq6amJnQ5ABKI60wWiGYJANWBwaQf\n+mM8DA8Pa+nSperp6dGSJUtClwMgobjOJMqC+ed+yMsPefkhLyBeir1POue0bt063XLLLYkcSPIZ\n5oe8/JBXOAwmAQAAAtu/f79SqZS2b98euhQAyBvTXAEAicM0Vz/0x7CGhobU2Nio3t5e1dfXhy4H\nQMIxzRUAACABJiYm1Nraqh07djCQBFBxGEwiJ+af+yEvP+Tlh7yAeCnWPtnZ2am5c+dq8+bNRXm9\nuOIzzA95+SGvcOaFLgAAAKAaDQ4Oavfu3err69OcOXy/D6DysGYSAJA4rJn0Q38sv/HxcTU2NurG\nG2/U+vXrQ5cDoIqwZhIAAKCCdXR0aMGCBWprawtdCgAUjMEkcmL+uR/y8kNefsgLiJfZ7JP9/f26\n5557dODAAZlVxwF0PsP8kJcf8gqHwSQAAECZjI2NqaWlRV1dXaqrqwtdDgDMCmsmAQCJw5pJP/TH\n8tm6davOnz+ve++9t2qOSgKIl2L2SM7mCgAAUAa9vb06evSozpw5w0ASQCIwzRU5Mf/cD3n5IS8/\n5AXEi+8+OTo6qtbWVu3bt0+XXXZZaYqKMT7D/JCXH/IKh8EkAABAiW3dulXLli1Tc3Nz6FIAoGhY\nMwkASBzWTPqhP5ZWT0+PNmzYoLNnz6q2tjZ0OQCqHGsmAQAAKkAqlVJbW5u6u7sZSAJIHKa5Iifm\nn/shLz/k5Ye8gHjJd5/ctGmTVq1apWuvvba0BcUcn2F+yMsPeYXDkUkAAIASOHbsmE6dOqWBgYHQ\npQBASbBmEgCQOKyZ9EN/LL6RkRE1NDTovvvuU1NTU+hyAOBJxeyRDCYBAInDYNIP/bG4nHNavXq1\nFi1apDvvvDN0OQDwNMXskayZRE7MP/dDXn7Iyw95AfEy3T555MgRnTt3Tjt37ixfQTHHZ5gf8vJD\nXuGwZhIAAKBIhoeHtWXLFvX09KimpiZ0OQBQUkxzBQAkDtNc/dAfi8M5pxUrVuiqq65Se3t76HIA\nICumuQIAAMTM/v37lUqltH379tClAEBZMJhETsw/90NefsjLD3kB8TJ1nxwaGlJ7e7sOHTqkefNY\nRTQVn2F+yMsPeYXDYBIAAGAWJiYm1Nraqh07dqi+vj50OQBQNrFYM2lmr5K0V1KjpAuSDkj64HQL\nOMzsSknvlbRM0gsl/UDSP0ra7Zz7jxzPYU0IAFSBJK2ZLEePpD/Ozl133aXjx4/r5MmTmjOH7+kB\nxFsxe2TweRhmdrGkL0n6tqRmSa+U9GFJJun2aZ56naTLJX1I0jlJr03/79dIemfpKgYAoDzokfE3\nODio3bt3q6+vj4EkgKoTh0+9jZKeJWm1c+5B59xHJO2U9H4ze+40z/tvzrnlzrmPO+f+p3Nur6St\nklab2UvLUHfiMf/cD3n5IS8/5FW16JEx9eUvf1nj4+NqaWnRrl27tHDhwtAlxRqfYX7Iyw95hROH\nweQKST3OuV9lbPukpOdIelOuJznnHsuy+Zvp/9YVrzwAAIKhR8ZYR0eHFixYoLa2ttClAEAQwddM\nmtlPJP29c+6vpmwflXSHc+5uj9faJOluSS9yzv00y/2sCQGAKpCUNZPl6pH0R3/9/f1auXKlBgYG\nVFfH+BxA5UjadSYvUXRCgalS6fvyYmYvlHSrpEPZBpIAAFQgemQMjY2NqaWlRV1dXQwkAVS1OAwm\nZ83MfkvSvZJ+Ien9gctJDOaf+yEvP+Tlh7xQKHpk8bW3t+vSSy/VmjVrQpdSMfgM80NefsgrnOBn\nc1X07Wptlu2XpO/Lx2FJr5LU5Jz7+XQPbG1t1eWXXy5Juvjii3XFFVdo+fLlkp76Q+R2dHtgYCBW\n9cT9Nnn53SYvv9vkNf3trq4uDQwMPPn5niBl65H0x/xu9/b26uMf/7je9773ycyC11MptwcGBmJV\nT9xvkxd5FTufCxeiSS7nz59XMcVhzeRXJA07596Tse0liq6J9UfOuc/N8Py/kdQm6Vrn3MMzPJY1\nIQBQBRK0ZrIsPZL+mJ/R0VE1NDSos7NTzc3NocsBgIIkbc3k5yW91czmZ2x7l6THJX1luiea2XZJ\nN0l6z0wDSQAAKhA9Mka2bt2qZcuWMZAEgLQ4DCb3SfoPSfeb2TVmtkHSHZLuds6NTj7IzM6Z2Ucz\nbr9bUoekQ5J+ZGZvyPi5tMy/QyJNHiZHfsjLD3n5Ia+qRY+MiZ6eHp04cUJdXV2S2Cd9kZcf8vJD\nXuEEXzPpnLtgZtdI2ivps4rOWne3oosyZ5qjpw9+/1CSk9Sa/sn054oaKAAAFYseGQ+pVEptbW3q\n7u5WbW22JawAUJ2Cr5ksJ9aEAEB1SMqayXKhP07v+uuvV21trfbu3Ru6FACYtWL2yOBHJgEAAOLq\n2LFjOnXq1JNnVAYAPCUOayYRU8w/90NefsjLD3kB5TcyMqKbb75ZBw8e1Pz58592H/ukH/LyQ15+\nyCscBpMAAABTOOd0ww03aO3atWpqagpdDgDEEmsmAQCJw5pJP/THZzp8+LD27Nmj06dPq6amJnQ5\nAFA0xeyRDCYBAInDYNIP/fHphoeHtXTpUvX09GjJkiWhywGAoipmj2SaK3Ji/rkf8vJDXn7ICygP\n55zWrVunW265ZdqBJPukH/LyQ15+yCscBpMAAABp+/fvVyqV0vbt20OXAgCxxzRXAEDiMM3VD/0x\nMjQ0pMbGRvX29qq+vj50OQBQEkxzBQAAKKKJiQm1trZqx44dDCQBIE8MJpET88/9kJcf8vJDXkBp\ndXZ2au7cudq8eXNej2ef9ENefsjLD3mFMy90AQAAACENDg5q9+7d6uvr05w5fM8OAPlizSQAIHFY\nM+mnmvvj+Pi4GhsbdeONN2r9+vWhywGAkmPNJAAAQBF0dHRowYIFamtrC10KAFQcBpPIifnnfsjL\nD3n5IS+g+Pr7+3XPPffowIEDMvP7kp590g95+SEvP+QVDoNJAABQdcbGxtTS0qKuri7V1dWFLgcA\nKhJrJgEAicOaST/V2B+3bt2q8+fP69577/U+KgkAlayYPZKzuQIAgKrS29uro0eP6syZMwwkAWAW\nmOaKnJh/7oe8/JCXH/ICimN0dFStra3at2+fLrvssoJfh33SD3n5IS8/5BUOg0kAAFA1tm7diYYh\nLwAAHvBJREFUqmXLlqm5uTl0KQBQ8VgzCQBIHNZM+qmW/tjT06MNGzbo7Nmzqq2tDV0OAATBmkkA\nAAAPqVRKbW1t6u7uZiAJAEXCNFfkxPxzP+Tlh7z8kBcwO5s2bdKqVat07bXXFuX12Cf9kJcf8vJD\nXuFwZBIAACTasWPHdOrUKQ0MDIQuBQAShTWTAIDEYc2knyT3x5GRETU0NOi+++5TU1NT6HIAILhi\n9kgGkwCAxGEw6Sep/dE5p9WrV2vRokW68847Q5cDALFQzB7JmknkxPxzP+Tlh7z8kBfg78iRIzp3\n7px27txZ9Ndmn/RDXn7Iyw95hcOaSQAAkDjDw8PasmWLenp6VFNTE7ocAEgkprkCABKHaa5+ktYf\nnXNasWKFrrrqKrW3t4cuBwBihWmuAAAAOezfv1+pVErbt28PXQoAJBqDSeTE/HM/5OWHvPyQF5Cf\noaEhtbe369ChQ5o3r3Sredgn/ZCXH/LyQ17hMJgEAACJMDExodbWVu3YsUP19fWhywGAxGPNJAAg\ncVgz6Scp/fGuu+7S8ePHdfLkSc2Zw/flAJAN15ksUFKaJQBgegwm/SShPw4ODmr58uXq6+vTwoUL\nQ5cDALHFCXhQFsw/90NefsjLD3kBuY2Pj6ulpUW7du0q20CSfdIPefkhLz/kFQ6DSQAAUNE6Ojq0\nYMECtbW1hS4FAKoK01wBAInDNFc/ldwf+/v7tXLlSg0MDKiuri50OQAQe0xzBQAAVW9sbEwtLS3q\n6upiIAkAATCYRE7MP/dDXn7Iyw95Ac/U3t6uxYsXa82aNWV/b/ZJP+Tlh7z8kFc4pbuaLwAAQIn0\n9vbq6NGjOnPmjMyY0QwAIbBmEgCQOKyZ9FNp/XF0dFQNDQ3q7OxUc3Nz6HIAoKJwnckCVVqzBAAU\nhsGkn0rrjxs3btTY2Ji6u7tDlwIAFYcT8KAsmH/uh7z8kJcf8gIiPT09OnHihLq6uoLWwT7ph7z8\nkJcf8gqHNZMAAKAipFIptbW1qbu7W7W1taHLAYCqxzRXAEDiMM3VT6X0x5aWFl100UXau3dv6FIA\noGIVs0dyZBIAAMTe/fffr4cfflgDAwOhSwEApLFmEjkx/9wPefkhLz/khWo2MjKim266SQcPHtT8\n+fNDlyOJfdIXefkhLz/kFQ6DSQAAEFvOOd1www1au3atmpqaQpcDAMjAmkkAQOKwZtJPnPvj4cOH\ntWfPHp0+fVo1NTWhywGAisd1JgsU52YJACgeBpN+4tofh4eHtXTpUvX09GjJkiWhywGAROA6kygL\n5p/7IS8/5OWHvFBtnHNat26dbrnlllgOJNkn/ZCXH/LyQ17hMJgEAACxs3//fqVSKW3fvj10KQCA\nHJjmCgBIHKa5+olbfxwaGlJjY6N6e3tVX18fuhwASBSmuQIAgESamJhQa2urduzYwUASAGKOwSRy\nYv65H/LyQ15+yAvVorOzU3PnztXmzZtDlzIt9kk/5OWHvPyQVzjzQhcAAAAgSYODg9q9e7f6+vo0\nZw7fdwNA3LFmEgCQOKyZ9BOH/jg+Pq7GxkbdeOONWr9+fdBaACDJWDMJAAASpaOjQwsWLFBbW1vo\nUgAAeWIwiZyYf+6HvPyQlx/yQpL19/frnnvu0YEDB2RWGQeU2Sf9kJcf8vJDXuEwmAQAAMGMjY2p\npaVFXV1dqqurC10OAMADayYBAInDmkk/Ifvj1q1bdf78ed17770Vc1QSACpZMXskZ3MFAABB9Pb2\n6ujRozpz5gwDSQCoQExzRU7MP/dDXn7Iyw95IWlGR0fV2tqqffv26bLLLgtdjjf2ST/k5Ye8/JBX\nOAwmAQBA2W3dulXLli1Tc3Nz6FIAAAVizSQAIHFYM+mn3P2xp6dHGzZs0NmzZ1VbW1u29wUAsGYS\nAABUqFQqpba2NnV3dzOQBIAKxzRX5MT8cz/k5Ye8/JAXkmLz5s1atWqVrr322tClzAr7pB/y8kNe\nfsgrHI5MAgCAsrj//vv18MMPa2BgIHQpAIAiYM0kACBxWDPppxz9cWRkRA0NDbrvvvvU1NRU0vcC\nAORWzB7JYBIAkDgMJv2Uuj8657R69WotWrRId955Z8neBwAws2L2SNZMIifmn/shLz/k5Ye8UMmO\nHDmic+fOaefOnaFLKRr2ST/k5Ye8/JBXOKyZBAAAJTM8PKwtW7aop6dHNTU1ocsBABQR01wBAInD\nNFc/peqPzjmtWLFCV111ldrb24v++gAAf0xzBQAAsbd//36lUilt3749dCkAgBJgMImcmH/uh7z8\nkJcf8kKlGRoaUnt7uw4dOqR585K3qoZ90g95+SEvP+QVDoNJAABQVBMTE2ptbdWOHTtUX18fuhwA\nQImwZhIAkDismfRT7P5411136fjx4zp58qTmzOF7awCIE64zWSAGkwBQHRhM+ilmfxwcHNTy5cvV\n19enhQsXFuU1AQDFwwl4UBbMP/dDXn7Iyw95oRKMj4+rpaVFu3btSvxAkn3SD3n5IS8/5BUOg0kA\nAFAUHR0dWrBggdra2kKXAgAoA6a5AgASh2muforRH/v7+7Vy5UoNDAyorq6uSJUBAIqNaa4AACA2\nxsbG1NLSoq6uLgaSAFBFGEwiJ+af+yEvP+Tlh7wQZ+3t7Vq8eLHWrFkTupSyYZ/0Q15+yMsPeYWT\nvKsIAwCAsunt7dXRo0d15swZmTGzGACqCWsmAQCJw5pJP4X2x9HRUTU0NKizs1PNzc0lqAwAUGxc\nZ7JADCYBoDowmPRTaH/cuHGjxsbG1N3dXYKqAAClwAl4UBbMP/dDXn7Iyw95IW56enp04sQJdXV1\nhS4lCPZJP+Tlh7z8kFc4rJkEAABeUqmU2tra1N3drdra2tDlAAACYZorACBxmObqx7c/trS06KKL\nLtLevXtLWBUAoBSK2SM5MgkAAPJ2//336+GHH9bAwEDoUgAAgbFmEjkx/9wPefkhLz/khTgYGRnR\nTTfdpIMHD2r+/PmhywmKfdIPefkhLz/kFQ6DSQAAMCPnnG644QatXbtWTU1NocsBAMQAayYBAInD\nmkk/+fTHw4cPa8+ePTp9+rRqamrKVBkAoNi4zmSBGEwCQHVgMOlnpv44PDyspUuXqqenR0uWLClj\nZQCAYuM6kygL5p/7IS8/5OWHvBCKc07r1q3TLbfcwkAyA/ukH/LyQ15+yCscBpMAACCn/fv3K5VK\nafv27aFLAQDEDNNcAQCJwzRXP7n649DQkBobG9Xb26v6+voAlQEAio1prgAAoKQmJibU2tqqHTt2\nMJAEAGTFYBI5Mf/cD3n5IS8/5IVy6+zs1Ny5c7V58+bQpcQS+6Qf8vJDXn7IK5xYDCbN7FVm9qCZ\n/crMfmhmO81sxkOvZnaRmXWb2WNmdsHMjpjZ88tRMwAA5RCiRw4ODmr37t3q7u7WnDmx+KcCACCG\ngq+ZNLOLJQ1K+rakPZJeKenDkj7snLt9huf2SPpPkrZIcunn/9g596Ycj2fNJABUgaSsmSxXj8zs\nj+Pj42psbNSNN96o9evXF/PXAQDEQDF75LxivMgsbZT0LEmrnXO/kvSgmdVKusPM9jjnRrM9ycz+\nQNIfSrraOffV9LZHJX3dzN7inDtZpvoBACiVsvfIjo4OLViwQG1tbUX/ZQAAyRKHuSsrJPWkm+Sk\nT0p6jqSsRxgznvfjySYpSc65RyR9X9LKUhRabZh/7oe8/JCXH/KqWmXtkf39/brnnnt04MAB5TGT\ntqqxT/ohLz/k5Ye8wonDYLJe0vcyNzjn/k3S4+n78n5e2ndneB7yNDAwELqEikJefsjLD3lVrbL1\nyLGxMbW0tKirq0t1dXUFlls92Cf9kJcf8vJDXuHEYTB5iaQLWban0vcV+3nI04UL2eJFLuTlh7z8\nkFfVKluPbG9v1+LFi7VmzRrvIqsR+6Qf8vJDXn7IK5w4rJkEAACBHT16VGfOnGF6KwAgb3E4MpmS\nVJtl+yXp+4r9POTp/PnzoUuoKOTlh7z8kFfVKluP3Ldvny677DLvAqsV+6Qf8vJDXn7IK5w4XBrk\nK5KGnXPvydj2Ekk/kPRHzrnP5XjeTkltzrkXT9l+TtL9zrmtWZ7DdUEAoEok5NIgZemR9EcAqC5J\nujTI5yV9wMzmZ5yt7l2KTi7wlRmed5uZNTnnviZJZnalpFdIOpHtCUn4hwUAoKqUpUfSHwEAhYjD\nkcnJCzIPStqt6ILMdyu6IPMdGY87J+kh59z6jG1fUHRB5q2KLsh8p6JToS8v2y8AAECJ0CMBAHEW\nfM2kc+6CpGvStXxW0h2KGuUHpzx0jp5Z758p+mb2Y5L+QdIjklaXrloAAMqHHgkAiLPgg0lJcs59\nzzl3rXNuvnPuxc65D7oph0ydc69wzq2bsu0X6W1vlNSvqEl+y8x2Wh6nozOzi8ys28weM7MLZnbE\nzJ5fzN8tjszsVWb2oJn9ysx+mE9eZnalmf2Dmf2rmT1uZt8zs9vNrKZcdYdSSF5Tnm9mdtrMnjCz\nt5Wy1jiYTV5mttrM+tJ/Yz81sxNm9uxS1xxSoXmZ2evN7Itm9rP0zwNm9vpy1BySmb3SzPab2Rkz\n+42ZnczzeRX7eU+PLC96pB96pB96pB96pJ8QPTIOayZnxaIpQF+S9G1JzYqmAH1Ykkm6fYanf1rR\nFKC/UDQFaI+k+yW9qVT1hjaLvK6TdLmkD0k6J+m16f/9GknvLF3FYc3y72vSekkvVvQ3lmizycvM\n2iT9naKpeB9QdNbJtygBn1O5FJqXmb1M0gOKjjS9J/34v5T0gJn95/RF7ZPq1ZJWSDolv7+Nqvu8\nl+iRvuiRfuiRfuiRfuiRBSl/j3TOVfSPpO2SfiZpfsa2rZJGJT13muf9gaQnJL0xY9vvp7e9JfTv\nFcO8np9l23pJE5JeGvr3ilteGY+9WNKIpD9P/229LfTvFMe8JP2OpF9I+ovQv0OF5LVR0njmY9J/\na7+RdEPo36uM+X1a0sk8HleVn/fp35MeWZ686JEeeWU8lh5JjyxFXvRIV74eGYtprrO0QlKPe+os\nd5L0SUnP0fSj6RWKTkTw1ckNzrlHJH1f0spSFBoTBeXlnHssy+Zvpv9bV7zyYqfQv69JH5LUKymv\naQYJUGhe1yn6JuxQCWuLo0LzMkVN8fGMbb9Kb+OsnM9UrZ/3Ej3SFz3SDz3SDz3SDz2yPGb1eZ+E\nwWS9pO9lbnDR4evH0/fl/by0787wvEpXaF7ZNCn61mKoOKXFUsF5mdlrJbUqmo5SLQrN6/WS/pek\nNjP7NzP7tZmdMrM/KF2psVBoXv8k6eeS7jazy8zsBZI6JT2m6JtIPF21ft5L9Ehf9Eg/9Eg/9Eg/\n9MjymNXnfRIGk5dIupBleyp9X7GfV+mK8nub2Qsl3SrpkHPup0WqLY5mk9ffSvo759z3i15VfBWa\n1wsVfWDdqmgKy9sVfYv4eTO7rNhFxkhBeTnnRiS9VdHZOn8i6ceS/ljSW51zPytBnZWuWj/vJXqk\nL3qkH3qkH3qkH3pkeczqcy8Jg0mUmZn9lqR7Fc3ff3/gcmLJzN4l6fcUTeHBzEzSfEXrQT7pnPui\nog/+JyTdHLSyGDKzl0v6nKSvK2qYKxSdrfOEmb0kZG1AtaNHzowe6Y0e6YEeWV5JGEymJNVm2X5J\n+r5iP6/SFeP3PizpVYoWyv+8WIXFlHdeZjZP0VmwdkuaZ2a1Ga8x38yeW4pCY2I2+6NTdE08SZJz\n7peKPvxfXcwCY6bQvD4g6deS3umceyD9D4s/VXSyj2qaMpavav28l+iRvuiRfuiRfuiRfuiR5TGr\nz70kDCa/pynzedPfOjxH2ef/5nxeWq55w0lRaF6Tj/0bSX8kqdk5979LUmG8FJLXfEkvUXT66lT6\nZ0BRI/iUpG+UqtgYKPTv67uKvnmdujDelOzTxRea1yskfcc5NzG5wTk3LmlQ0anT8XTV+nkv0SN9\n0SP90CP90CP90CPLY1af90kYTH5e0lvNbH7GtncpWpz7lexPefJ5LzSzpskNZnaloj/AE6UoNCYK\nzUtmtl3STZLe45x7uHQlxkoheY1KWi7pzen/Lk8/xyRtU3TNo6Qq9O/rePq/b57ckP62+nV66oyI\nSVRoXuclvdrM5k5uSF8c/T+n78PTVevnvUSP9EWP9EOP9EOP9EOPLI/Zfd6HvgZKEa6hcrGkH0r6\noqRrJG2Q9EtJO6c87pykj07Z9oX09j9RNPf8e5K+HPp3imNekt6taG7+xyS9YcrPpaF/r7jlleV1\nXq7quIbWbPbH+9PPbZH0XxQ1ip9Iqg39e8UtL0kNkv5D0T8w3pbO6/Ppba8J/XuVOLNnS3qHoilL\nX5P0rfTtd0h61jR/X1X3eT+bv7FqzYweWb6/ryn30yNnyIseSY/MM7Oy98jgv3SRgquX9CVFZ7b6\noaQPSrIpj/lXSR+bsu2i9Af/Y4rOYnRYWS48nLSfQvKS1K1ornm2n5bQv1Pc8sryGi9PZ5XoRjmb\nvBRNW/l7Sf9f+rk9kl4d+veJcV7LJD0k6afpn4ckXR369ylDXpP/6Mz2WfSyafKqys/7Wf6NVWVm\n9Mjy/H1NuZ8eOUNe9Eh6ZJ55lb1HWvoFAAAAAADIWxLWTAIAAAAAyozBJAAAAADAG4NJAAAAAIA3\nBpMAAAAAAG8MJgEAAAAA3hhMAgAAAAC8MZgEAAAAAHhjMAnEkJm9ycyemOGnZZrnn08/ZsLMmnM8\nZo6ZPW5mr03fvt/M/nKaOkaK+1sCAACgks0LXQCArPolNea4b7+kV0jqneb5TtJRSX8r6V9yPGaR\noi+UvpO+vUTSvhx1rJe0asaqAQAAUDU4MgnEkHNu1DnXN/VH0hWSXivpvc6578/wMj9yzj3inPt5\njvsbJH3HOfcbM7tE0kslDWSrQ9LwLH8lAEAWZnZHjtknE2b27hmea2Z2xszeU656p6nl1em6l2Vs\n6zazPo/XeKeZrS1xTX9jZt3Feo8Qip3TbJjZlzP+Xjd5PO+zZnZ2mvv3mtljZvZbU/aRe4tTOYqF\nwSRQIcxskaQPS/qkc+5wEV6yQU8NHpdK+qlz7idFeF0AQJqZvdHMHp7hYRckvUHRTJDJnz+Q9IUZ\nnne9pOdI+sfZ1lkkbsrtv5LU6vH8P5NU7EHS1Jr2SLrOzOqL/D7lVIqcCuUknVT0N/tJj+d9QtKr\ns/3/YGZzJL1D0n3OuXFJH02//jdnXy6KjWmuQAUws3mK/rEwImnjLF7nTZIeytjkzKw1438/oagx\nLHTO/aDQ9wEAPOkmSa83s7c5507keMxvnHOPFPDa/1VSt3Nu6oDJW/of8HPT/3gv+GUyb+Qxg6Yc\nptb0QzN7UNItkm4OUlBxso6Txwr4+/1nSf8uaY2kO6bc9xZJL1A04JRz7lFJj5rZL2ZbKIqPI5NA\nZeiQ9BpJ73HOzebD9BFFU2WvlDQu6U/Tt78qqSv9v5dIenRW1QIAZGa/L6lH0Rr324r82g2KPrP/\nacr2bjN7xMxWmdl3zezfzazXzF41zeO+regf9q9P33d1evrir8zsp2b2ETN77pTn32RmPzCzUTP7\nZ0kvylLjP5jZI1O2LTOzk2b2SzO7kP7fV6Snnr5D0uSJ3ybM7PaM5xWlprR/krQm/UVtrnzzyjGf\n2qbLOstrNZrZP5vZo+nf45uZ053zyOnPzOysmY2ls/iQmc3NUsvbzGwwXfPnzOxiM6s3s4fS7/uI\nmb0mVz4zmSkT59zjkv6HpOuyPP1dir48fyjLfYgZBpNAzJnZmyVtkfQh59xMU6Wm5Zx73Dl3VtHR\nx19L+kz69islfdY5dzb985tZFw4AeLekI4q+EHyDmV2T64FmNnfqzwyv/WZFR4SynWTt5ZLulrRT\n0ZGfWklfMLPfnvK4yyXtlrRL0kpJ3zezN0p6QNGXiu+QtFnS2yR9PKPWVZL2SvqspD+R9K30/VOP\nkLrMbWa2XNKXJP2HpBZF0zV7JdUpmhL7kKKpjG9QNM33QPp5xaxJkh6WdLGk12W5L9OMOeZTW9rl\nmpJ1jve8PF3fOklvVzTw/biZTQ66/lq5c/o/FE01PS2pWdFJ+D4g6e+mvMfL0r/TrYpOsNcoqTv9\n3H9M/x7zlD4y6Msjk09I+l0zW5Lx3HmK/v/7VDGOuKP0mOYKxJiZXSzpkKLG8tdFeL3Jf5w0SfqG\npDlm9jJF00kGzGyuc25itu8DANUufVTn2865JyQ9YGanFR2dfDDLwy9VNFskkzOz6ZYcXCHpuznu\n+x1Jf+Sc+3q6lm9IGlK0fvEjGY97vqS3OOe+lVH3pyT9P865zKNhj0p60MwWO+e+I2mHpBPOufem\nH/KAmb1A0QBoOv9N0jedcysztn0x430ek2RZpkzeWcyanHP/Ymbjis4X8PVp6s0nx3xqk7JknY1z\n7mnrDs2sV9EJ8tYrGmD96zQ57ZR00jn3F+nbXzQzk7TLzD6Uni4qSZdIeoNz7nz6PRoUDTpbnHNH\n09vmSDpuZoucc/9rupqzyJXJl6Zk8nlJP1d0JHJyPeQKRQP9ggayKD+OTALxdkDScxVNb53VN3Rm\n9nJF/1j5taJvKa9K3x5StKYkJenXlnHWOwBAwVolHcy43SFpWfqozVQXFB0luzLj5/c1/ZKDyyQ9\nluO+kckBkCSlB6T9eubUyh9OGUg+W9FRqk9POUL6VUX94nXp20sVHQHMdGyaWmVmz0m//8HpHpfl\neaWq6TFFX6ROZ9oc86kt47V+ONNAMv2aF5vZ31p0vejx9OtskPR7MzxvjqIM/mnKXZ+SNFfREcxJ\n5ycHkmnn0v99aMo2k/TimWqeUsd0mfxGGZmk14weU3SEetJ1kv7fzNwRbwwmgZgys3WKpnpsLNLJ\ncB7VU/9A+Zmik0JcKekziqZhTd7XX4T3AoCqZdEZKs9lLhlwzn1W0rcltWd5ym+cc990zn1jys9M\nSw4sx/aRHNumriGcegbvSxQNPO5RNIiZ/BlTNJvtpYqOos7N8h4j09Qz+dom6cfTPCbX80pR03S1\nZj4/27bJHPOpbVK+Z0s/KOmdiqbE/qGi3vxxSc+a4XmXSvqtLO8zefv5GdsuTHnMr7Nsn9w20/tO\n5ZOJFB2BfFl6rWiNoum5HJWsIExzBWLIzF6h6IQ4pxStYXlDlocNO+d+mO9rpr8B/IaZ/a6k50k6\n5Jz7dzNbLOkDzrlvFKN2AID+XNkHjR2SPmFmVzrnTs/yPUaU+2hVtiNuL1A0mM00dcbLhfS2OyRl\nO/Pso5J+Kmkiy3u8IMvrZUpJekK5T4qTS6lqukTZB4tTn59t22SO+dQ2acbZRenB1H9R9CXyRzO2\n53Pw56eKBm1Ta16Q/u/P8niNYvDJRIqOho4omupap2g2ls8lRhAYg0kgnq5WdO2wRklfy/GYnYpO\nWODrWkmn0gPJl0paKOnLhRQJAHg6M1uo6Mu+X2e5+9OKPrtvk/THs3yrb0paleO+F5hZo3PuVLqm\nlymaAvmx6V7QOfe4mZ2StMg596FcjzOzyffOXH/5jjxe++uKTrzz9zke9mtNORJWiprM7PcUHcWb\n6bqF0+aYb20eahTNGnzyb8fMnqfoaN0TGY/LltMTZtav6Kjm/oy7rlM00D5VhPpm5JtJuu57FU11\nfYmk7+YzHRjxwWASiCHn3EF5rivJwrKdUMc5998l/ff0//43Rc1ruheZq2jKCgBgZlsknbDour7Z\nfFHSe83sNRn/aJ6XYwbKv2WcNGWqByV1TjmhyaSfSTpiZu2KphfuVDS9NJ++8peKTpTiFK2/+6Wi\ns5q+TdIO59w5RWckPWZm90i6X9KbJL01j9fepujEOJ9XNOj7laK1fI+kr8H5PUnN6TOzDkt61Dn3\noxLU1KToxC8zHR3OJ8d8asuLc+4XFl1K5XYz+6WiI3z/l6KjfRdlPDRXTncoOtvsxxUd3Xutoi+d\nPzLN31Ep+GbyCUXX/fxjSbcLFYU1k0ByvV/RCXWaC32B9D+GxhWdPpxTdAPANMzsRYrOuvk/JJ3M\n8TN5ttFtGU+tVTQLZepPa673cs4NKjqy9qdZ7j6v6OycH1R0qYcLklbkOFo69XW/KmmZojV4hxSd\n1OYDkn6g9Po759xn0r/H2xUN3K6Q9BfZXm/Ka/cqWgf4bEmHFQ14likaEEnROrsvKjry16coy1LU\n9A5Jn8hjTep5zZBjPrV5WiPpXxUNWDsVDcYOTXlMrpweUDRd9HXpOjZJ+r8VDdQKUVDf980kfeT3\nfPomU1wrjHEJFyB5zOzVeuqI4znn3C8KfJ35khalb44z9QQA4sPM/k9FR8t+N30JksmL2r/aOTf1\nzK2QZGYvlvS/JS11zn1vmseRYx7M7CFF6zXfVapLi6UvbzJX0TVKR5xzfzbDU1BGHJkEEsg5N5hx\nNsCCBpLp1/lVxuswkASAeDmqaArhu2d6IJ60VdFRyZwDSXhbrWgm1KYSvf7titaJXl2i18cssGYS\nAACgAqWvP3xF6DoqiXPuv4auIWE2KDpDvBRNYy2F/Yqmjku5r62KQJjmCgAAAADwxjRXAAAAAIA3\nBpMAAAAAAG8MJgEAAAAA3hhMAgAAAAC8MZgEAAAAAHhjMAkAAAAA8MZgEgAAAADgjcEkAAAAAMDb\n/w+0j3paIJKA6AAAAABJRU5ErkJggg==\n\"></div>",
+ "result": "<div class=\"output_subarea output_png\"><img 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ArF+MAVfWn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\n5Dh65Dh65Dh6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"selectedType": "Html",
"pluginName": "IPython",
- "shellId": "7D657CFFBE3442BB86E5CDE1D2A08A35",
- "elapsedTime": 1032,
- "height": 709
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 1305,
+ "height": 706
},
"evaluatorReader": true,
- "lineCount": 166
+ "lineCount": 131
},
{
"id": "ptablecell",
@@ -877,13 +809,8 @@
"ax=fig.add_subplot(111,aspect=7./18.)",
"",
"# Set data (atomic numbers and error)",
- "if beaker.ctrl_sys=='binaries':",
- " atnum=Z_bins",
- "else:",
- " atnum=xylist[len(xylist)-1][0][0]",
+ "atnum=xylist[len(xylist)-1][0][0]",
"dE=abs(xylist[len(xylist)-1][0][1])",
- "if beaker.ctrl_quant=='E_coh':",
- " atnum=atnum[zeroinds]",
"# Logarithmic colormap",
"cmap = mpl.cm.cool",
"norm = mpl.colors.LogNorm(vmin=min(dE), vmax=max(dE))",
@@ -923,10 +850,9 @@
" # Text for Tectangle",
" ax.text(0.5*(left+(int32(source[\"group\"][i])-1)*width+left+(int32(source[\"group\"][i]))*width), ",
" 0.5*(bottom+(int(source[\"period\"][i])-1)*height+bottom+(int(source[\"period\"][i]))*height),",
- " '${'+str(int32(array(source['atomic_number'])[i]))+'}$\\n'+array(source[\"sym\"])[i]+'\\n'+fstring,",
+ " '${'+str(int32(array(source['atomic_number'])[i]))+'}$\\n'+array(source[\"sym\"])[i],#+'\\n',+fstring,",
" horizontalalignment='center',",
- " verticalalignment='center',",
- " fontsize=14, color='k',",
+ " verticalalignment='center', color='k',",
" transform=ax.transAxes)",
" ax.add_patch(p)",
"",
@@ -934,21 +860,22 @@
"ax.axison=False",
" ",
"# Heading",
- "ax.text(0.5,1.3,xylist[len(xylist)-1][1],size=20,horizontalalignment='center',",
+ "ax.text(0.5,1.3,'Last data set of elemental solids: '+xylist[len(xylist)-1][1],size=20,horizontalalignment='center',",
" verticalalignment='center',",
" fontsize=20, color='k',",
" transform=ax.transAxes)",
+ "",
"fig.show()"
],
"hidden": true
},
"output": {
"state": {},
- "result": "<div class=\"output_subarea output_png\"><img 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qzjuENKntIEdtc7px7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XWi0QENCGjfBfNAtsRZ/Cv5wz7J9/ls70j6316N59M62j/vYX3Ukww3OWz/WXt+9yin1z3\nessDtt2Ue0oYFHAjpe5ijtoPEKWI9atkw+89fFH7PrfoBA/p9kO0VLUnWiH0mVzXOdSoaaf2v1fj\n3aG+y2f7E8f7TFBNpq+y4Tl1pTKxmay5y2f7M/v7jFdPpo9qAIWeAh4MmIdGpgEgzbWPHso+tFS0\nFkHp5bk+6S6f7dTs9+mbMJk2UcL33TZyIGfK99ExZhhGWxl2l5nuTcaKoPTKDI+7y2d7/cX3GRQ7\nmY6h9efNBVMGWoX/xu+xIXf5bH9T/T43Bk3mOt0Asmwn2GX6XfzGQ8cA/4vf/Vrc5bO9+/T79Eye\nTKsYoS3mfdOM2YNWkhzVi91nPmBg6/sI1vlf/I5WNWF6+DzUcqEPH7Xuo3NAH5I0rUlQt6DaXU6x\n8yIxqiYcNO8iRdOOBI1/xW9NZBPiJs9DrhY81KbvI7BDHwIS/Wsc+iPkcU0IeGAeMo3gw/nzPpTX\n9UHZQvDh3LcbT1kJ6mE34ikpxnn4APLoWL9KNmgjmtB8fH1bVGXuI7RNHwxNW1OTdYjAhPZowoX4\nbS08h1ypJijJ/+K3PK4Juvt/0xYH96H6pS1+bY9fsS57H+24yah7+1/8bqxIyYZGTnSz7rTrOYPj\nPy4GmYyinH3ccs8GgiOS6uqotUGERrUUUeWVaRLbne4dZvDTEcFDbsE+pt+6gfAQwUOr5qPYsHMe\nwYFNcLntpCQM9bu7GgDUnbqjnzgD02eCD8ehfUR8tgFls+YAGO5+BGf6Yez7duC1Wgh+5nWRFV+Z\nCmsB63IWUW0v4ftzC7G5THSPvoFHuyxn5dl/0zq0NycqdjGv2zdiS70slZYCtp1ehNFWwqbMhdhd\nJjrF38BPOSvIKt0LMhkrjzxJy6h+DG01R2y5l6XYVcCntYso95Sw1LgQi9dEirINM0tHY/GaAeEt\nAjJknGjqn88R+3ioXYjZa2JwwA1MNMxgWe1iZMg4ZN/HJ1EbaKZM+uMPFIlCTwHvOxZR5i1hsUPw\nMVx1wx+W+RuFngI+tC2izFPCEpugdZj6BjoqurLEuhA3bs57zvK54TuxpV6WSksBm7MWUWMrYcMZ\nYYzqEncD9/VaxpqMFyioyaTAeIq5/df65XoNv1JuK+D7/EVUO0r4Nlfw0SNSOG90yiDidf4bv3+l\nxFnA8qpFVLhK+KxyIRaPiesNN9BfP4rXS+cRrWyCw2unt26o393V8FuqzAWkZi7CaC1ha8ZC7E4T\nHZvewNB2j5BbfpjTRTuwuyxM6OGf8TtaFU9bbVc+q1iI2+sm13GWRU2FPqyQKXglfjkflv+bTgG9\nOWjZxRtN/C9+q6Pi0bfqSuGKheBxY8s/S8sF9eNQ+ZYV1KYL8Tvv3ScJ7NSPmPH+F78VsfEoO3bF\n8u5CcLtxnz9L0DLBhzs3B+OU0XgtQvzG6wWZjPBz/hW/tRHxBKV0JefbhXg9biyFZ+nyL8FDcMvu\nxA+fQd66xYCMqsx9dHt+A7pY/4vfith4lB26YnlvId5f2iL4U9/Y5i4qwPrRIjzlJUI9swnNUP+M\n340N2e+f35L4/49MJvMCPPJm4/3u33pUeLf0q/9ovB4AnnhN8NEkv/H6uNhU8PDD6MbrAeDmdYKP\nz+9s3D6mLhd8XEhovD4ScwUPuc0arweAhDzBR01Q4/URbBQ8VIQ1Xg8A4ZWCj69ub9w+Jn0t+Ng6\nrPH6GL5N8HCqdeP1ANDmtODj4xmN18fdnwgeTrZtvB4A2mUKPnrtb9w+DvQWfESUNV4f5ZGCh5Gb\nGq8HgM2jBB9RJY3XR2m04MHr9cpEliIq0gKREhISEhISEhISEhISEhIS/1OkZIOEhISEhISEhISE\nhISEhMT/FCnZICEhISEhISEhISEhISEh8T9FSjZISEhISEhISEhISEhISEj8T5GSDRISEhISEhIS\nEhISEhISEv9TpGTD35i3HpOTnb5GbBkSEn7F6ZLdTFsux2SvFFuKhIRfEVEpZ53j7xUzzlceZvLX\ncsrNeWJLaUC28Sgjtsl57GB/saVcU7plBfJ9zediy5BopBwd15yir/4jtgwJCYmrRCm2AIm/xtav\nppN5cBkyZMhkcnSB0TRtMYR+Ny1AHxwrtry/hNlSzta9z3ImZxO1piK02hBiIjowqNeTpCQMEVve\nVVP58J04z2QQtf4gMmV9F7Pt3U751FFErt6DpmsvERVenozy3Ty9fxAyZHhp+LqhDhGDeKn3dhGU\nXR0f7ruLfec/Z3znF7m5w9N1+0+X7OaVrYNYMrFc2CHz37cQzS2/izXmz7nNcDcLwpf6lL1S9QQf\nGl9nSMBNfBT1g0gKr57EPPllzyUZMsbrp/FG+CciKPtzzLFOZ4VTGG/lyImSRXO9cgjzNQuIkTeO\n8fYB03S+dtR7iJRFM0A1hGd1jccDwPtp09mTswxkMuQyBeEBTenRdBy3tn8ejVJ36YP8tL9vKviI\nVsE9OFVzgHzzGZrqW4kt6U9T4Srlg4p/s9u0gWLXRcIUkbTUdOSO0AcYYBgltrw/xSc/Tsdkq+Ch\nYf4/tl6KKlc575Q9y17TJspcRQTKQ2ip7cDM8CfpZWg8cyhndTkXlz5L9f5NOCqKUBpCCEhqT/zU\npwi+zv99lEfJhTHHe4lXNspkaG6bRuAi/497V+LEf6bjMFbQbb5/9xWv10v12IHIAoMJWV6v1Wu1\nUjmkM+r+Qwh89V0RFf7fQEo2NGKatRzGyClf4HE7qSzOZNvXM9iyYhrj7tsqtrS/xPLvx+F02Zgw\n6lPCQ5IxWUo5n78bs7XikvXdbicKheoaq/xjQl5cTMnwjhjffJ7geS8C4DHVUjXvbgLve8JvEw0A\nbcL6smx4cYP9acVree/EfdyQeP8lj3N7XCjk4g8nMpkMlSKAjSdfZ1DL2QRqwn9bKJ6wP4EMGXGK\nZmwwr2R+6CK08gAA3F4335mXE69IEFnh1XMovv5cSrWu46nKezkUX1yXfNDKAsSS9qcZpBjG0oAv\ncOLktCeTB6wzmO2Zxvf6xjPeDlQO432D4OGMO5OHzDO43zSNb4MajweADjHDuL/XF7g8Dk6X/ciH\nP9+Nw21lerfFYku7ahxuGzuLV/BUh69Yk/cmmws+5p6Wr9WVn6pJ451Tc8gzZ5JoaM9dyS/x9NFR\nvN59Fx1DB4iovJ4CZy6Tc/sQKA9mbuSrtNR2xOv18JMlleeL72N7ygWxJf6f4uGL47B7bLwU9ylN\n1clUuko5aNlNtfvScyh/JeupcXjsNpKe+RRtfDLOqlJqj+7GVdM4fISdrI97ji3rMM29V9j3a/Ih\noPHEvcaOTCYj6O3PqBzcCevXnxFw+10AmF74B3i9GOYvFFfg/xHE/3Ug8ZdRKjXoDJEAGILjaNF5\nIhn7P/SpYzYWsXbpTeRn7yRAH0nfG/9N6253iCH3iljtNVy4uJeZt6WS3GwgACFBTWkS062uzoIP\nmtOt/V1UG/M4mbWGFonDueOWb0RSfHnkQcGEvvEJ5VNHETD8FtSdulM9/xHkIWEEPfqc2PKuiEKu\nJEQT5bMvv/YUn2Y+zsQWT9Mndhylllzu2d6cuV1XsDVvKWeqDjC97evckDhHJNW+tIkZRJXlImvT\nX2DKdW9ftl522X5WH3uGIuNp4oPbMaPXhySGd72GSi9Pa3UHSt1FrLes5FbDNAB2WDeglQXQQzOA\nao8w6fJ6vbxT8xJfm5ZS7i4lSdWSuSEvMUx3s5jy64hQ1J9LQfIQAMIVkQ3qFbku8u/qx9lrS8Xm\ntdJC1ZbnQt+mu6bPNdP6R2hkGiLkgvZYeRxjVBNZ5qgfb0OMcj4PWM3NqnF1+zrUNmeW+kEe0Dx2\nzfVeigYe1BNZZvONGVWeCmaYJpLq2EikPJonA15ggsa/YoZSriFIK/jok3A7maU7OXTxe6Z3W8yx\nos0sP/ooZeYLJIf1YGjKbJHVXpo9JavQK4O5LmIkVreJJacfYEaLV1DIFFjdZp49OpruESN4ssOX\nVNgKeC/rEWT4V8L0+eL7kCNndeLhuqQoQHNNK24JuhOAPMc5nim6m3RbGvGqROZFvSGW3D+FcJdD\nOW3jhrH5xGvYXRa6JIzhzt7volJqxZbXgFp3DUcse/koIZUe+oEAxKqa0i6gfg41/GxzxofMpNiV\nz8aarzDIg5gS9jDTIx4XSXVDXKYaao/vpc2iVIK7DgRAE90UQ+tuPvXcllqyn7+Tyj3fowgwEDv5\nceImzxVBcUPkkfVxTxYsxD15RMO4576Yh/m5x3Hu3gZyOaqe/dD/+20UzRKvldT/EygSmmN47nVM\n/3oEdf8huM9lYV3+AaHf7Ub2S+LHXVyI6dnHcOwWEu+q7n0wvPQWyuYpYkr/2yCt2fA3oab8PLmn\nNxPd7Dqf/Qc2zye5wximPH6cDr3vZcuXUynNPyKSysujURlQqw1kZv+Ay2W/bL29h94kKrwND047\nzMgBL19DhX8Obb8hGKbOofLRqVg2fot17VeEvf2Fz2MVjQGzs4Z/H7yFjhGDmdzqeZ+y5af/yY2J\nD7BkYCY9Y8aIpLAhcpmciV0WsCPrfcpqcy5dyevl6yPzuL3b67xww2GiApN4c+doHG7btRV7GWTI\nmGi4m5Wmj+v2rTR9wgT9dJ96H9e+xVLjQp4KfZ2tcRkM141ldtk4TjnSr7Xkv4zJU8utJf0od5fy\nSeR6tsZm8GDQM5d89MJfyPGcZ7trM10U1/1xZT/lgvs8252b6aL09fCG7UVuVI1lT3A6Y9S38ZB5\nBgWeiyKpvDqUcg1Oj50Ky0X+s3csHWNG8OqI44xs+SArjv1DbHmXZEvhJ4yIvxuAvpFjkCFjf+la\nALYXfYEXD4+2/Yhm+tZ0CR/CpMR/iim3ATXuKvaZtzA59AGfRMOvGBRBeL1eHigQYsM3CWm8FPsJ\nS8rn4/Q6rrXcv0RWyY8UVp/k8VHbuW/QSo7mfse2zMsnsMVEJzegkxvYWfsDDs/l51DLK9+ipaYj\nq5OOMiPiCRaW/oN0S9o1VHplFAEGFAEGqn78AY/j8j6Kvn6TgObt6PjZUZrc8wL57/+Tyt3fX0Ol\n/x1ek4maMQORh4UTvGEfwRt/QhYcSs2tw/A6nWLL+9sRMHUWqu69Md4/BeOjM9DNnovqut6A8EhF\n9bhByHR6Qtf+SOjGA8hj4qieMAyvzT/mhI0dKdnQiLlwahNLngxk8T90fPpyCkFhzblx2iqfOikd\nx9O+10xCIlPoMeyfNG0xmKN73hJJ8eWRyxVMvGEZRzO/4LlFISz5og8bds4jv+hnn3pJTa/n+h6P\nEx6SRHhoskhqr47gJxeA10vlnNsImvcSqpZtxZb0p/B6vbxxZBIquYbHunzRoPym5g/RO3YsUboE\nwrVxIii8PB3jR9Iisi+rjj192TpjOjxL+9ihxIe05Z7en2J3W9ifs+IaqrwyN+snke44RK7zHKXu\nYvbYtnCr4S6fOh8ZFzIreB6j9beRqErhsZDnuU7Tn6XGxnH1EGCV+TNqvUY+ilxLV00vmimbM1x3\nC9dp+ootzYdtrk3EGwOJMeroYkohQd6cZbpVf3ygH5Hq3ESzykCaVOroXiN4+NTg6+E29VTGayaR\nqEjinwEvokTJfucekRT/MdkVP7MvdwXto4eSmv0ekboEpnV9i9iglvRseqtf3tlQYMkmo3ovI+KE\n5KFCrmRY3DQ2FwrJxYvmMyQa2qOWa+qOaR3c068ScHmObLx4SVK3vmydnyzbyLGf5vW4L2ml7UiX\ngN48FfUWLm/j+DEVoArmzj7vExPcirbxQ+meOIFThf65ZpFCpuDluGWsr/mCXmdCuCOnD2+UzCPd\n6juH6qMfzqSwOTRVJ3FH2AM0U6dwwOw/nmQKBcn/Wkb5li84OCyEjHv6kPvOPEwnfX0Y2vUkfuqT\naJumEH3LPUSOmkrR141n0UjbquXI9AYMr7+HslVblCmtMLy5FG9ZCc6dW8SW97ck8NX3cKbtRabR\non/ihbr9tu++AiDorY9Rtm6HMrklga+9h9dci33berHk/q1oXJdZJXyIT76eobctxeWwcOLAUjJ/\n/hSLqRStPqyuTkyi7/oAsYm9ycnceK2lXhXtW46ldfKNXLj4I7kF+8nK2cyPBxcyYsDLDOr1JADx\nMd1FVnn1yLRaAmc9TvVzDxN4r3/cRv1n+Pz0U2RVpbGw/0G0Sn2D8pTgbpc4yn+4reurvLi5Dxfa\nzWtYKJORHFnfNzQqPU1DOlBYk3kNFV6ZYHkIIwLG8o3pY4LkIfTSDCRW2aSu3OSppcRdSNffPWpw\nnbYfu6ybrrXcv0ym4xgd1N0wyAPFlnJF+iquZ1HAUqxeC8ucS/nS8SnlnlLCFGF/fLCf0Ed5PW/p\nBQ+f25eywvEpZd5SQqn30FbRoe7fCpmCcHkk5d5SMeReluNFm5i+OhC314XH46J7kzHc1e0dPjp4\nLynhvjGvRURvkVRens0FH+H1epi6t3mDsnJbgQiK/jxXk/g4bz9NlDKeaFV83b6OAT2RN5LrXHEh\nbZH9Zq2fEF0cOeU/X+EIcRkaNJYBgTdyxPwjx6z72WvazGcVC3k46mXuiRDmUC21HX2OiVLGUeH2\nr/4dNnAsIX1vpPbYj9Rm7KfmwGaKvlpI09kvEz9V8GFo79uvDe17U7n7OzHk/iVcxw/jzjpFRaJv\n3PParLhzzomk6u+NdcXHyAJ0uAsv4s7LQZncEgDXiSO4c89TlnSJtrggtcX/AinZ0IhRqXUEhwuT\nlYFj36K8MJ1d3z3MuNmNNyuqVKhJSRhCSsIQhvR5htWb7yF133wGXCc8i6dWNfzR69colCBvHBOr\n37Kn4GvWnvsPz/bcSIw+6ZJ1tAr/boukiOvo1mwcXx+ex5iO/xJbzl9iomEGcyumoZMZeDzkpas+\nzt+e7f47oJPpSJQL4+0CxVtkuNN5wvYw3+mF8fZSb91w4V9XcHUyHQkKwcPLyrc46U7nn+aHWR1U\nHzOUMt9Fd2XI8Hg911TnH9Em6nruuW4pCpmS0IA45HKF2JKuGrfXzbbCz5nRYgE9I270KXs14062\nFH5KU31rUos+x+Gx193dcLomza/6dYK6BTJknHecYgi3iC3n/wsK+e8WoJbJ8PpZX/g9apmaXoYh\n9DIMYXbkMzxbeA/vls3nrnBhDvX7/g0yvPifJ7lKTfB1Qwi+bghNpj/D+Vfu4eLH84n1k3UZ/ms8\nHpTdehG45PMGb62QhUeIJOrvi/PoQSyLXyV4+Tqsn72H8YGphG7cLyQTPR6U7bsQ/OE3DdsitPFc\nTPBnGt+vIInL0mvEc+RlbaMk/3DdvuILB3zqFF04QFh0m2st7S8TFd4Gj8eFy335Z/ck/recrznG\n4uMzmdbmVTpHDhVbzn/FhC4vk1X6I+kFm30LvF7OldX3DbvTzMXqDOKC/etRl74BQ1DJ1NR4Khmm\n853QG+SBRCviOGzb57P/oG0vKSr/8nEl2qm7kOE4Qq3HKLaUP8WTmufY6d7GUbcw3kbIIinxFtWV\nl3pKKP7Ntj/yj4Dn2OXaxjHX4T+u7EeoFTqiDM0J1zf1STTEB7Uhu9L3+fOz5fuvtbwrkla2nlpn\nBaPiZ5JgaOvzNzDmNrYWfsrgmMnIkPNm5kzyTKc4UpHK1xdeAfwnkRisCKWvfgRfVi3G6rE0KK91\n15CkaUOpq4ASZ/3dGunWNDx++OP270qSpg1urwuHt3HPoQIS2+B1u/D+so6DKcN3bmvK2E9AYuOZ\n2yo7dsV9Pgt5RBSKxCSfP3lgkNjy/lZ47XaMD01DO2kGmkEjCHr9A9y557AsFt7+o+zQFfeFbGRh\n4Q3b4pcFPiX+O6Rkw9+IJinXExXflUM76l+flX1iDRkHPqK6LJufU18hP3sHXa9/VESVl8ZireTD\nr4dw9OSXFJWdoLLmAumnV7Hn59dJSRyKRm0QW+L/CYyOCl4+OIYOEYMY0GQyVfaSBn+NiejAZAa1\nmMXW0w0X9Vp74iUyilK5WH2SpftnoFRo6J04SQSVV2ZL7An2xJ9H1eCKFNwbNI8PjW/wg/lrcpxn\n+U/1sxyy72VW0CUeHfFTxuunYpAFck/ZGA7ZfyLPlcMWy/cctO/744NFpJ/yejrJu/K2XRhvBygG\ns9SxhKPuwxx3H2WObToB+Pcrzvqqrqejoivv2F7748qNgKHJsykzX2DZkUcoMmaRlr+a7ec+EFuW\nD1sKP6FT2GACVaENyvpHT6DEeoFTNQd4sct6ck2ZzEnrykdnn+DO5Ofx4kUt9583ITwbvQQvXiZc\n6M4W42pyHFnk2M/wVdV7jMnpRF/9MBLVrXii6E5O245z1LqfV0sfu8TVdYn/lmp3JTMuDGF9zZdk\n2U5Q4LjAFuMqPq14nV76oejljWMO5aqpJPOBIZRv+RJL9glsRReo2L6Kwi9fJ7j7UBQ6wYfp5AEK\nlr+KLT+bkrVLKdv8BbG3N57HVTW3T0OmD8Q4bQzOA3tx513AuW8i8/n3AAAgAElEQVQ3pqcfwV2Q\nL7a8vxWml57Ea7cT+MtrLuVR0QS+vBjz68/hyjqFdvwdyCOjqZl6C479e3DnXcCxfw+18x/HJT1G\n8T9Beozib0bXQXPZ+uVUasrPI0NGrxHzOXv8W3ateYgAQxTDJ31GVFP/eL3fb1GrDSTE92bfkUVU\nVGXjctsJMsTTpe0UBvcWFvnzlys6f2cOlWyg3JpPuTWf6Vt9F3304kWGjA+HnG9UbXFLx3/x4/nP\nkHl+swK6TMbErgv46vBcio1ZxAe3Y+6gDaiV/vfjUCe//OMq0wMfwuI18WrVE5S5S0hSteL9yDW0\nUre/hgr/OwLlQayM3sO/qx9neumNuHGRomrDc6H+uer7b3lQM5dZ1qlc8OTwb+1CHrTNZLR5EJHy\naF7QvEaW+7TYEv+Q+7VzmWOeygX3pft1Y+rr4fqmPNZ3DcuPPcaOcx/SPLQbkzq9ypIDU8SWVsfz\nnddetiw2oDmbh7nrtt/tVX/HyU+la5HJ5MTq/Gdh5Cbq5nybeIQPK17mP2VPUuIqIEQRTgtNe56K\nFhaiXtzke54tvofbc3sRq2rGP6IWMq9wssjKL89v12hoTOjkBjrpevNFxSLynNk4PHaiVfGMDp7C\nvRGXn0P5W/+W6wwEduhN0cpF2C9m43HaUUfGEzFiCvF3/bLgs0xG7O2PYclOp+Czl1AEGGh674uE\nDRwrrvg/gdwQSMj6vZhfeALjjPF4TbXIo+NQDxiCLChYbHl/GxwHfsT66RJCVm9Hpq+fS2nH3IZ9\nwxqMD04jdNMBQr/fg+mlJzHeMxFPbY3QFn0HIQ9umBSW+PPIvF7/Wd34/woymcwL8Mibjfe7f+tR\nIUC9+o/G6wHgidcEH03yG6+Pi00FDz+MbrweAG5eJ/j4/M7G7WPqcsHHhYTG6yMxV/CQ26zxegBI\nyBN81AQ1Xh/BRsFDRVjj9QAQXin4+Or2xu1j0teCj63D/v/62Fb4ObEBSURqm5JjOsG7Zx4iObAz\nz3Va819/9vBtgodTrRt3W7Q5Lfj4eEbj9XH3J4KHk20brweAdpmCj177G7ePA70FHxFljddHeaTg\nYeSmxusBYPMowUdUSeP1URotePB6vf6V1bvGSHc2SEhISEhISEj4EVWOEj4/9xxVjmJC1TH0iryJ\nGS0WiC1LQkJCQkLiTyElGyQkJCQkJCQk/IiJifOYmNh41l6RkJCQkJC4FNICkRISEhISEhISEhIS\nEhISEv9TpGSDhISEhISEhISEhISEhITE/xQp2SAhISEhISEhISEhISEhIfE/RVqzoZFiM1dy9MdF\nHNq+gKR2NxMZ3xm3y0Z1eTbN242mdddJHN/7LvvWP8nY+7YRm9BTbMmXxWgqYt/hRRh0UcjlCmQy\nBUWlxxk/8kOxpf0pbD/txPTpO9i2fI9uwl0EP/4iith4ABzphymfdgPqDt3QT3+QgEGjRFZ7ad45\nPpPjZal0j76REE0McuQcLF1PraOCRdeno1b4zzveL4XZXsXW04vYfOo/tI0ZzMMDv6srW3N8PttO\nv0Pv5pOICWzJ6uPP8MSQbSRH+k/fsHosfGB8nS9q36WTpgcfR61rUGerZS2zy8Yx2TCL4boxDAgY\nLoLSq2NyyVAiFNEkq1pT46nkk9q3mRH4MMHyMC44z3LUcYBdcVliy7wsld5KPrAv4k3HAkYpb6aD\nojN2r43znmxGqUZzq2qS2BKviipPJR/aFvG2bQEj1TfT/hcfOZ5sRqhGM14ziVJPCf+yzOWw6wCH\nQrLFlnxJjLYytpxdzNpTr9Cr6QSaBLXD5XFQas4hSt+cce2fRS4TrqGsyXiR1Sfn887oXMJ1TURW\nfmUcbhv3p3VnSc9Dfj/G/haX18V3NZ+RaTtCuCIKndyARq7lOt1AdpnWcW/4U3V13y1/kSXl89me\nnEuMyr/ao9pSxO7TH7D5xGu0ih1I69jBjOzwuNiy/jRlziK+qFxEmDIKBQrkMgVnbMd5Pq5xzaUc\n5UUUr1yEKjQKFApkcgWW7OPoWnQm/90nab1oG4Ht/Cdu/56a8UORR0ajaNEaT1Ultg/fRnvvw8hD\nw3CfP4vz0AHC0vw37l0JW2UR+Rs/IGf1a4R1GEh4p8E0v9U/+4rXbMb85ktYv1yKTG/AMH8h2pvG\n4zzyM9VTbkKZ1AL18NHoH3pSbKl/e6RkQyNFqw+jQ+97+XnbSwwavxhdYBQAVnMFH/wrksDgJrS5\nbippW18gplkPkdVeHpvdyNfrpzD55q8x6CIBSD+zGpu9RmRlfx5tn0FouvWhqHsc6i496xINAB6T\nkZBn30Q31n/fL+72ujE5qnhvcBYquRqAtOIfSM3/lJf77GoUk2C9JpRBLWfh8tjZmPkGJcZsooNS\nABjXaT4apZ4b283D5jSxLuNlkiL8q28EyHXMDHqMInc+abbdDcqLXQWk2w/STJnES+HviqDw6slz\n5XCTfiKTDfcCsMmyhk2Wb3k29M26Oi9V+eck5VfCZGHcpb6X1x0v8YZ2MZFyYZyt9FSQZIokTtaE\nPsr+Iqv8Y0LlYUzV3stC20u8qvP10bI6kjh5E3qr+tNfNYggmf++4z1IG8ngpJmsPfUKM7q/h04V\nVFc2c00Y4fpmDEqaAUDvhNvZl7fC7xMNAOsuvke++RRl9ovE61LElnNVXHCc5fHCSUwKmcNzMfVj\nUbmrhBvPt2ZB3HKf+jcE3c4G4wq/SzQAhOhiGdRmDuuOv8gdvRYTGZQktqQ/jclt5ImCKbzR5GvC\nlMJcaotxNSZP45pLucxGsudPocWLX6MKFXxU7FiNy1RD5KipFHzyAoa2/hW3f4s7NwfNLRPRThXi\nnn39Ghzrv8XwUn3cMz/n33HvSmjDYml20xzOffUibecsRhfrv31FptdjeOYVZEHBWJd/iObGcUKB\nx4PhiRcImDZbXIH/h5Aeo2jE5GWlEh7Tri7RAGCqKUCGDC9eLp7dSXzSAGQy/32969HMLwkNTqhL\nNADERXaidfINIqr668g0GgJumYT5q4/q9jmOHcRTWuzXiQaAU5X7uK3lv+oSDUfLtrE042Ge77WF\niAD/myBejsyi7Qxr9SAd40ay+VR9gC8yZhEf3BaAU8U7aRXtn31jv20nkwz3UuDKxe11+5QdsO3C\nhYu+2qEiqbt69tm2M1E/o257ry2VvtohPnUSlf7/w2qXK5U28nZ1P9ABCr3142xjYbczldYKXx9F\nHsHHr+x0bmWwaoQY8q6a9JJtpIT19Ek0GO3lWBzVqOX1CdHjRZvpED1MDIl/irPGI3QIHYBSrqbc\ndlFsOVdFibOA6XmDmRL6IONDZviURSij6RjQk166wT7795o200fvv+2RWbiNcH2zRploAFhf8yVx\n6oS6RANAa00n+hsa11yqfMuXaGIS6hINALoWnQjtfQPGwzsJ7OyfcftXnHu2o5k84zfbqaj6+8Y9\neXP/j3tXouLINgIim/l1ouG3BEydhbe8FPv6b3EeOoAr56yUaLjGSHc2NGLyzmwjoVX9LdR2aw37\n1j9Jj2FP0yR5ADu/fRBkMk4fXkHBuT106v8A4THtOL53CS6nFYDug8V9tZZWE8zx098QHpJMq+aj\niI3qRHhoCiqVjh8PvcXJrDUE6mOJCGvB/qPv8sycIpRKDXZ7LTvTXmHkgJdF1X8p9JNmYl7+Ho5T\n6chUapyn09HffjderxfzsiV4bcJ3Hzjbv15r1j58QN2/Myv2svj4TJ7ruZFYfTIV1gL2Fq1if9Ea\nwrSxxOlbsPHCu3w6rIBteR/jcAuexqWI76nKWkCILpaRbR7jPztvYnznFzFowjhdvIs+ze8A4ETR\nVkDGTzkrOFOyh2GtHiA+pB2pZ5bUebmxnThecpxZDNPdTLA8jFzXOZJULQHYblnPYN1NfFzyJvcH\n/7Ou/kfGNwEvQfIQsp2n+Gfo63i9Xj6vXYLNK3iZFXztvUwyzPTZ3mvbxiPB8332TQmczae1i3B5\nXRjkQYCX2/UzWWaq1z47SNxzaqd7G4OU9eNsjbeG+fYneVz9NL0V/VjqWMIx9xFmqufQRdGNck8Z\nk61j2Krfh9frZalzCdZfvDysEc/LLuc2BqnqfRg9NbxgfZLHtE/TW9Ufr9fLPucuBqtG8oNjNdsc\nG5miuZueqr51x9R6a3nL+gr/0ok37p4o3kan2JF12y6Pk5XpT9Oj6XgKa8+Qmv0BLo+dA3mrGN3m\nHz7HWp21rM18hds7+UfccHoclNsL6B05mjBNLOX2Ap/y87XpbCtcRougbhRYzlLtKOXBNksAsLhq\n+TrnFWa0uPZeXil9hHhVImOCp12yfFroo6hlGhaVPUu0Mh6H186W2lXcHSa0h9frZUX1EmweoV/c\nHS5+3MgsTKVtnJAMMdur2Jv1CWdL9nJDp6e4WJmOzVlLlaWA23q8gdfrZcepJTh/iRUjO4iv36AI\nZnPNNzRVJdPfMIpW2k4kaFoQqAjhs4qFHLHsZWbEU2TZ0jF7ail1FjAvRvCyomoJ9l/aYkaEuF6U\n+mAqtn+DtkkyIb1HoUvpREDTFmjjkrjw1iMgk1G+ZQXGY3uIufUBdMntqdq7HntRDgAxEx4UVb/2\nTt+459i1Dd0/5vvWmTYL63v/AYUSWVAw3upKtFPuwb52JWg0uPbvQf/6e8jk/nk9uOJYKuFdhL7i\n9Xgo2vUVMqUKR00ZCaPvF1ldQ+QhoWgnTsW84Bl0jzxNwIQ7fcq9Xi/WD96sb4+qSnSzH60r95hq\nsbz9Coan/SNuNEb880yWuCryz25HrlCRdXQle9c/xbavZzBkwgf0HvUCAHlZ2+g68DFad5tMUrvR\n/LTxaS5kbiSlw1i6D55HcW4apRePiuqhc5tJDOv7PBlZa1j8RU9e/TCJwpKjFJdn0K/bw9gdJtq3\nGs+I/i/x8LSjKJUaANLPrKLWXCyq9suhbtcZVbvO1L71AvYft6G//W4AbNs3EDBiLIGz5+E4moYj\nQ9zv/nJkVx/mjSOTeLL7tzQLbAdAbm0GNzd/GJvLRJ/Y8Uxp/RJvDThKevl2eseMZVzKPLKq0zhX\nI74n2S/DWpuYgcQGtWJH1nsAWJw1aFR6AE4WbWNUm8fo03wyXZqMZtWxpzlesJFuzcZyY7t5nC9P\n40KlOF5+vWrTXNWCC86zAOS7LhAiD8PjdXPakU5vrXDV8JmKObi8TmYGPcZEwwwq3GWsN69kh3UD\nI3RjmRU8j2OONDIc4rZLgSuPPNf5Bnc2PFU5C7vXxj1BjxEhj+I78xfssG1kZMBYZgfN45hdfO27\nXdtRoWKNcyXzbU9xv3UGb2k/4GntC6x3fc941SQc2Mn1CJPdXe7tJMibA7DFtYGblGN5WDOPQ+40\njrvF87LHuR0lKr63r+QFy1M8aJ7Bf3Qf8JROiBfH3IfRygK4UTWGm9W30lXZg+8c3/h8xg+OVZR6\nxB13T5bswOo0sj37QzZnvcP602/QJ2EyOlUIgZpwhqbMol/CFM5V/ky7qEE+xx7IX0W1zX/iRnrV\nbnpHjgYgUtPU586Gi+YsFmTcwbSUFxkcO5kqRzFN9C3ryveUrKLKce29VLkr2F77PbeG3HPZOv0M\nI5hfPJtQRQS3hc5idPAU0m0/00MvtMcu8waGGsZyd/g80m1pZNrEjxunCrfTNl74AXUk9zuGtnuY\n4prTlNWeZ0CrmfRvNZM9Z4S1D9LzN9A1YSwjO8zjfFkauRXi678xaBL3Rz5Pau0aJuX0ZER2EpnW\nI+yqXceUsIfJsZ/mouM8t4bO5NaQmayqFrzsNm1gaOBYZkTMI92aximruF7Ch0+iycznqdy1hoy7\ne3L01iTMZ44iUyio+XkbsZMeI2LEZEL7jSb/g6dx1lRQsf0bYiY8iCnzIC6zUVT9v8V9MQ9P7nnU\nv7uzwfTYvXjdbgLufQjNzRPwVJTj/GkX7qxMtLfegev4YdynT4oj+iqoOLad8C7C3ZXlhzZjSGxP\n7ICJaMJiMZ47JrK6S6MeOAJ3TjbKlm0blNU+Pguv243unofQjp6Ap7Lcp9z+wyo8pf4TNxojUrKh\nkVJelIHNUkmPYc/QsstE+t30Cg67iZKLhwGorcoHr7duYUiLqQSrqZzqinOcOfoVAMERydRW54vm\nweV2IJPJuL7H4zw07TDPPVhB09ge7Ep7lVbNR2C1VWE0F9Kp9UQAQoMTAKgy5mHQR4um+2rQDhiO\np9aIYXp9lt2Vew7LWuG7VyQk4y4U77u/HHm1J3n54Fjmdl1Bi5DuAJyuOkDXqBGYndVU2gvpFye0\nR5QugSLzOfYUCp5idMmUW8X1VFBzitjg1nXbI9vOJfXMEuwuCwq5CoAKcz5evHULQ9bYSjDZyymt\nPceBHMFLVGAyleZr76XUVUS0QljrI1HZghyXkGw4Yt9PN20ffrLtoK26M8HyEE7YD7PZ8i13B9Vn\n4Gs8VRS588lznecHs+ClmTKZIpe47fKjbRspqjZEKWLq9h23H2Sr5XvuDhT099MO5f2Ib8l1ZrPW\nImhPEFl7pjuDKm8l8zTPME41kfnaVzB7TRxzC+PsYOVwVKjY6drGCOWNAOxx7WCAQkgG5XjOsdop\neGkuT6bAI46XU64Mqr2VzA14hjGaiTyr8/UBwiMUd2ruIUQeCsBR10FaKOr70kV3HpEyccfdC1XH\nsLlqmdjxJYak3MvIlg8ypu1TGNRhHMj7hiHJswDIq04nJbwnWpWh7thycx7BWv+JG3mmUxyv3MnK\nC6/xTc6rWNxGyu31yYZPsv/JkJgpaBU6AC6YMugYej0ApdY8QtXieMl3nMODh/ba7pcpP88ZWzqb\nar/hthChPbJs6XTU9kQvN9R9xgaj0C+aqpIpdoo7PhVVn6baUkibWKHfdm8+AZO9ArvLQs+k2wHI\nLT9cF1tKa8+Rdl7QHxmYTJUIseK3OLzCXGp6xOOsSjrMvlYVdND24OOKVxkRNIFqdwVWr4UbggUv\nJ22Haa4WvOQ7zrGx5pe2UIs73nqcgo+4Ox6nw2eH6balAkObHhQsX4C9JB/w1i0M6awswVldTuX2\nVRja9QIg6emPUeqDrvA/XFucu7ahaNkGeXR93HNln8H+7ZfIo2Kwrf4S+5qvCHjoCdTDb0L3j+fx\nOhwgl6NIbnmFTxYPU/5p7BWFhHcWEigKXSDnVryI22bBUVVMQFSCyAob4jx5HE9tDeohN2B59w2f\nMlf2GWzfflHXHrbvvkL34BN15e6Lecgj/SduNFakZEMjJS8rlZiEnqjUurp9dmsVNeXCKuIl+YeI\nS6pfuCz3zFYS24ykY9/76NjnPgDKC4+LunjkjwcX+mxrNUG0TByBRh0IwLm8nTRvMqDBcUWlx4mJ\naH9NNP5VHEfTCBg51mefYeoc9FOF796ZeRx1Z/9a5KjQnM0LaTfyQKeltAsXzh2ry0RWVRoA6eU7\naBfm2x43JM5hVILgKcd4nJYh4no6U7KbNtED67Z7JkxELlOw/OcHSQq/DoCcikO0iqrvGxmFW+kQ\nN5LBre5jSEvBS17VcVEWj/zJtqPu6n+iqgU5ziy2WtYyLOBmQFj3oJ9WuPp2wL6LntqBqGRCEsXq\nsXDQ/iN9tEOYEngfUwIFL6ccx+mkEbdd9tq20e9360yk2ffQ6zf6A+Q6whQR3Bk4hzsNv2h3iqt9\nlyuV7oqe6GT142y1t4rzHmGcNcgMbHVtpI9iAAGyAAB+dO9kgHIw1d5qZqrncLda8JLhPk43hThe\ndrtS6aZs6CPHXf/Wid3ObQxQCeeezWtjk3MtN6tvpcZTDQj62yjEHXczSlJJCe+F8pfE4W/3t4rs\nh0qh+WV7Ox1ihmNx1C+Ol1t9nKbB/hE3XB4nBdZsZrR4mYmJ/+C25k/QOXQwZb+5s+FwxRa6hAt9\nxuo2c9GSRXJgZwDOmY6TaBDHS4yyCTJkeH63nsyv7DD9wH7LdroG9EMtF9pjv2U7ffXDqXUL7TEp\ndA63hwr94oz9OB0CxB2fMgtTaRrWCYM2HIAAVSCnClNpE1e/7sShnFVcl3Q7VoeRQW3mMKi1oP9i\n5XGai7zQ8LIK37mUQRFEX8MI9PJA9IpADphT6fmbNTS2GFcxKvh2TG4jt4fN4bawX9rCJm5bFK3w\n9aHUBxHSawQKXSDmU4cI7FQft2t+3kpIr5FYzmdgL86lOm0rJWveu9aSr4hj9zZUA3zjnjvjOMrW\n7dFOvBPtrXegnXI38kAhQeKtNWJd8ga6fy1AptGIIfkPqTiaSmBSJ9RBQl8Ja98fpS6Ivfe1R6kL\nQhUYKrJCX1yZJ3CdPE7AxKnoZj2KfeMa3IX146zr5HGUrdoTMEFoj4A76tujrry1f8SNxoyUbGik\n5Gel0qxl/SDmcbsozT+MVh8BgNVcjiYgBICqsrOUF56g68C5KBQqVBo9Bef30iRlEPqgmEt+/rXg\nyMnl1Jrqb01yOq2kn1nJkL7PApCdm0pKM99FpgqKjxAX3QUQnrPyRzxWC/bD+9H09dUuU6mQ6/TY\nf96Lps8gFFHiffe/p8yaz/wDI5nRdiFdo4QF4jxeDx+ffIwukcJz3sfKU+kY4etJKVehVerJrNhL\nx4hBhGrF9WS2V/lczVTIlQxr/RAH81aTEiFc/QhQBaFTC32j2HiWi9UnGNV2Lkq5Co1Kz5nSvbSJ\nHkRIwLX3UuIuJFwhLIyVqEwhzb6bKEUsOrnw+Mc+Wyp9A4R+H6WIRSfT1x37ofENJuin007dGZVM\nhU6u56BtL721g3zuKLjWeL1efrLtaJBsiFbEEfAb/QCplnV+pX2XO5WBynrdLq+LY57DhMuEcfac\n+ywFnnyS5MKCX9nuLGxeK81kCaxxfoNKpkIv07PftZf+ykFEy8XxstuZyvUqXx/H3YcJkws+TrrS\nOePOrEuGpDo30UnRDb3MwA+O1Rx3HaGD8pdxV8RFMU8Up9I2amCD/XpVKCHaWABsThM/X/yWdlGD\n2Jv7JQA5lUdIDO3yS23x48a6i+/RPdx3Ic5QTbTPnQ2h6hhCVMJY8GPJKtoEC+PXWeMRUgLFa4so\nVRy3BE/jq2rfH3Zur5uvq95nZOAEguWhRCqF9jB7TGyr/ZaeukGsMwrt8WsfP2zZSw/dICKV4saN\nzMJU2sQNbbgvtv7294MXVtIj6Xb2ZH1UFyvOFu+lVewggnXi6v+hejllrvq5lM1jZYtxJbMjhbnU\nflMqvfT1XrYYV3JD0O2srv7Ity304rZF+eblOCrqfXhsViq2r6TJ9H+h0AehNAhx25p/Fsu5E8RO\nnovX7UJpCCGk53DsBeex5p4RS74PXq8X594dqK/3Pa8UyS1B4/tmL9sXHwMgj4lF9+g/sb79Cp7K\nimum9c9QcSyV8M71nuyVxYS260vzCU9wdvlzuCy1IqrzxXlwP84jaQRMnAqAut8glK3aYV36dl0d\nRXJLZFrf9rB+KbSHM/0Iyg7+EzcaM9ICkY2M4tyfOZfxPXlnt6NQask/u4OmLQYjVyjp0Gc2xRcO\n4HE7iU/qT3XZWTJ/XkZJ/kHGz9ledxeE3WbkYvYueg5/RjQfFdXn6dhqIkcyv0CGDIfLgtVayS1D\nFxMa1AyA8qqzXN/zCZ/jSitO4SnPwGwpo7L6HHmFaTSL85/3LZu/XY5t12ZwObGs/QrDHbNQRMfW\nlXtqjdj37yLoYfG++9/j9Dh4dv8wdMogcozHOW88islZxYnynSjlapoGtgGgyHyW8SlPNDje4jRy\nomIXt7UUz9P5ikNsP/MuJ4u2oVHqGd7mobqyQS3uJb8qHblcAUDbmMGcKNzCj+eWkVNxkCeGbUej\nFPqG1WHkdPEubul4bb2ccWSwrHYxqdYf8ODhvuAnSFG14XrtSDprerDTuokdlvXku3LYZd1InKIp\nN+smccaRwVe1SzF7TYQpInk45Nm6z6z1GDlg28WDIeK0y0VXLt+aPyfPdY4qTwWp1nWcc57h3qC5\nANyin8QZZwZfmZailemwe20MDRhdp32/fRcPBYuj/bD7Z9Y7v2e3azsatOx27eB65WCUMiUzVLM5\n6D6A0+Gkj6I/N6vGM9/2JN87VwNwnaI3HzjeYaJKWIzU6DWy172LeZpr7+Ww62c2Or5nj1Pwsce5\ngwEqwcddmtkcdh3A5XXSWtGOQaoRyGXC9YcEeXNC5WF8Y/+cOzQzWOtYxSl3BhWeMi54znHIlUZ3\n5bUbd7Mr0jh48TtOle5CqzSQXrSVjrH1i132SZjEmfJ97M/7BofLSt+EOzhZupOk0G4AFBhPkV+T\ngdFeRonpHNkVaaSEX/u4kVG1l28uLOCM8SDN9G3oFi7cpbSz+Gt+Kv2efPNplmU/yx1J/2JO60Vs\nKPiAREMHthUuo+cvazvkmU9xwZRBjaOMQss5TtWk0Sb42np5IeZDPq58jWeKZpKgTkEnN6CRBXBT\n0GQMiiBuDJrEEes+Nhq/we6xclPQHaRZdtJO263uM0xuIz9bdnFfhHhx42zJPk7kbySzcBsARy58\nR9dE4Y7EUmM247svqKubHNmbkwVbaRn9y11/v8SK0Z3FjeX5jvOMDJrI+uovkMlkWD0WatyVPB2z\nmDiVMJfKc2TzaHS9l04Bvdln3ko3neDF5DZy0LyL2ZHiebEVnCdsyETKN38BMhkemwWXsZLEuYvR\nxCagjmlGddoWyjYs+3/snXd4FNX+h9/dzZb03ntCDQRCL6J0kXoRKQoCIqiIqIjEq+DvihcVBLFS\nxAhcAcEriCJVem/SSwiQEEhISO+bzfbfHyOJK6Epl9nFeXnyPNlzZpbPJ3POnDPfU4aKc7/ScM52\nFBoXVH4hKP2EPpbC1QNdejLOkfVF82HOvIL++yWY09OwFhVi+GUtptTzuIwT2j2n+ATUfZ5A99Xn\nyHx8QVeJsntvm++Q+wVgPLgHda/+YlioleKz+8j/dQOFx4S6krvvRwIfepzMTUnEDpmMTKHASeNG\n0eldBLTpI6pW/Zb1VH23GP2mNbj9u+aNZPptG7EaDOiWfnwx9gwAACAASURBVAUyGS4vvYGycQLq\n3k9QmfQ5cm9frLpKVL9dD/OFc5hSzmAtzMd8OQ3j0UMoW9jP84YjIbPX0eEHGZlMZgWY8Ik4f/uT\ne+cR3+55rFjJSttNRL2utz/pD3z6mrCJ3YdviOOhuPQKW/ZNZXCvxX/pe/45U/ARlnl/fFR8Mw/X\nYc+D1Yr+0G40He7+b/9HroYLHn7uK8612HB5Hj0ihPJ0tnA3Tf3/nKd+awUfS4aLd0/aen4enesK\nXs7n7qZR8N17GbFU8HE5Utx769LyeTzlJng5XLWbh5zv3EvUFcHDlQhxPCwpn8fQ37Qf0u+mg+bP\nlanIDMFHqYd41yLJMI9RSsHLPvNuOjndnRfPMsFDoY/4bXWm+Qof6qYyx+3u77u+RYKPFU+K5yNf\ne4UfzkxlbJs/32489Z3gY3P3++fj1cPteKnBHOp51Dys5+qusPTSVCY1unsvj24RPJxrIN61WF48\nj8FeQr04Urmbdq53X8cbpgg+Fj57/31sPzePjvUF/RdydhMX8ufuUaMXCR7Oxol3LVYUzWOQt+Dl\nqHY3bd3u3kujZMFH2wP310fZyb2UHdtJ2Ki3ufzxKwQOGIdzVIPbn3gTDrYTfPjl318f2n//E3lk\nDM4jX6Ckz8O4Tf8Cp/iEP/VdBf6Ch8c2/u89pC6fRvTARBQqDaUXj2Ix6vGOa39PvntTT8FHQK64\nbZ858wraj6bi8dnd32vzAgUPVqvVft/Xeh+QllH8zUg5toJ96yeT9E4wSf8KwtUj+PYn2Rlms5H9\nx+aQlXOE9Kt7xZZzx1T+tILSmZO51iKYa82DUAQ43t/+j+zKWsHSc5MZuSWYkZuD8NY4rqcD6StY\ndXwyr6wK5uWVQXg5O66XNdoVzCyeTOurwbS6GkSAwnG8rNGuYFbJZFplBdMyy7G0/5FVxhVMq5pM\nvYpg6lYEESRzXC9Gq5Gv9XM4YT7CQaPj3HevY7IY2XxxDpeKjpCSb9/6V17+iF053wOQoU1Bb9bZ\nBBpMFiNrMudwoewIZ4rt20ttrC9bwaf5k3kkNZiHLwZVL7lwFA6lrWD10clM/C6YiSscu61YX7qC\nz/Im0+lCMB0vBOGndCwvHk07YDUayFu3GOfY+L8UaBAT9ZCRyD08qVryFeq+A/90oOF+E9l3PBk/\nf0H29mXoci7ds0CDvWA1GtEtmoPxxBEMhxzvXmsvSDMbREDsmQ33ArFnNtwr7vfMhv8FYs9suFfY\nw8yGe4G9zGz4K4g9s+FeYQ8zG/4q9jSz4a9gDzMb7gX3Y2bDVxcScXPyItA5irTy4zwRMRFfTcg9\n+357mNlwLxBzZsO9wh5mNtwLxJrZcK8Ra2bDveR+zmz4X2IvMxv+CtLMBgFpzwYJCQkJCQkJCTvh\n+Xqzqn/vGjxMRCUSEhISEhJ/DWkZhYSEhISEhISEhISEhISExD1FCjZISEhISEhISEhISEhISEjc\nU6Q9G0Tg+p4NEhISEhISEhISEhISEg8mf/c9G6SZDRISEhISEhISEhISEhISEvcUaYNIERk733En\nOHz5ohCkc2QPUOPDkWf4yGSCh2XD7p2HV34MR2/SYqXmO9tHDuWZ1nNJKzjM+fy96IxlXMzfT//G\nb9Mg8JG//H8+/a3gY85LjnstAMbPFXwkznRcH7PecPy3OEDNmxySxjiuj+e+Fjzsb+e4HgDaHxB8\nbHzMsX303CT42NHJcX103il4ON343ni4ZshgV/k6nGRKikx5POLemwbOwqv7zuqOsrZkCY00LTle\nuY9RfomEq2Pvyf8bf0bwsb6X416L3hsED+lRjusBIPryg+Xj930fR0OG4/droaZv+9YHjutj+uS/\n9YSGaqRgwwPAzqWjuXBwCXKFEp/QeB4Z+iV+4c1um2dP3E7nhUNLqSjKxNkjEENVGU27viai2geb\nUl0uvRsm0jysn9BoyWSsT57FwKbTMJh0HLn6E0MSPgDgcMYqZu3oyUf/SMXbzt41/vY34RiMtgGT\nlnWHMqTjXC5m7aJUm43BVMnFrB20bTCK+uFdRVR7c758PxyjwdZHw4ShdH98rs1xO9ZNol7jAYRG\n2d97ruPKw9FabT0MUg5ltvNcXtKN5jvjEpQoiZPH84nzlzRV2N89CuCNFUIQjt914lrHDmXYQ8K1\nOHBxKUXaTDycA6kylNE93v7uU/2PhqMza+F316K731Amxcy9ZZ69MXxnOFVm22vRKWQoL8XNJU+X\nweH8dShkSkoMebT2702sh32+t37wAcHH7+tG14ChTKg3lwvlx1iX/RUxbk3IrDzPo4HDqe/RUkS1\ntfPfovlMCJpe/fmtzOFMD1+K0WJgYsYTLI89jK9TADGahrxx9SlWxB4WUe3NydNlcDhPCJqUGPJo\nFWBbbnSmCj45NYrnG36Cn3OYiEpvTZYpg+2Vgo9Ccx5dnHsTpxZ8nNAf5kjVXiosZRzV72e819u0\n0fz1wYJ7za08HKzaRZ4pG521kgNVOxjkNoqHnO2z/ZawH0pLMkhNWYdCoURbkUed+r0JDKmp36eP\nL6W8NBNXt0D0VWW07mB/7bejIgUbHgDcfCIZPiMbq9WCi0fgHefZE7fSmbJ/MaV5F2nT/wPKC6/w\n3dT61G87Eo2rj0hqb054eDhardYmojx06FDmzhU66xUVFYwaNYpPPvmEsDA77azIZHSIGYGryguA\nQ1dW0j5qKK4qLzKLT7Mu+UM6x44hwD2G+OAeGMw6Lubvo3XEQJGF11BWmUu3hETio2sCJluPz6Jv\nm/cAWLhpII8/NJv2caNxVnuxYEM/pj+bh1rpKrJyW7TlubTqmEiduH4gkyFDxuFds+jQY5rNcZlp\nu0g+tkw4zs7Is+TyiiqRnkrhWsiQ8bl+FlM0gocIeSTn3bKxYCFAbr/3qLLKXHrEJ9I0sqZM/XJq\nFv1bCD72XVhMbulFBrT6gMLyK7y9qj7t647EVWM/96kiQy5DQxJ52LumPC3PnsVz4dNumWdvFOtz\nGRiVSNuAGq2r0mcxoq6gdX3GfEbVr3n4nXVqOIlNlool96YUGXIZEp7IQ741Pv6bOYtno6ZRYSrl\njVM9+DRhF1GucRQb8njpWFu+bZNWPeJnL2wt+4G+XsOJ1cQBoJZrADhSuRsXuTu+TgEANHJuySX9\nObIMlwlVRYkl96ZsuDKfZxrUlJuPTgxnUoJQbn7JXEhh1VUO5KxmTMPZYkm8I74tn88b3jU+Xssf\nzif+S6my6NhS+ROJ3sJgwQbtKkbl9mRnaCoBTvY1WHAzDwAv5Q1kss9shriPxkPuxXN5/TgSnoeL\n3L7ab4AMMljHOpQoySOP3vQmgRsDn5OYxAAG0B77GyzIyMhg3bp1KJVK8vLy6N27NwkJgofRo0ez\nZMkSlEol8fHxfPnllzRrZp+DBccPzadTj5oy9fP3w+k3WChTp44upqjgIp16fEBp8RUWfFKf+OYj\ncXaxn/bbkZGCDQ8CVivO7v53n2dP3ESn2Wzk4I9vMPCtYwC4+0YyZGqKXQYacnNzSUxMpF+/fshk\nMmQyGbNmzWLaNKEDvHDhQq5evcrq1auZPdt+OyuemoDq34srs8kqTaZN5CAAwr3jeefRfQS4xwBQ\nVJkJMhmB7nVF0XozZMho3WAELmohYHIsdSUt6w3FWe0JwKuP78LXIxoAq9WCxWISTestkclo1GIE\nGmfBx/lTK2nYbGj1ZwB9VRn5OafwC4wTS+UtkSHjSdUIvGSC5h+NKxmkHFr92YoVP7kD3KNkMtrV\nrSlTRy6tpE3sUFzUXpgsRlYdfoP/6y/cp3zdI5k2MMWuAg0AyGT09B+Bu5PgYXvhSrr7DcXdyYsi\nQ+5N8+wNGTK6ho7ATSlo25Ozks7BQ6s/78v9gS6hw4l0E+qE6reHX3tDhowegTU+duatpGuA4GN3\n/mrKjUVEuDQAwFsVgMFSRXLZQRp5thNT9g0M8RnH4LTmPO07ARe5G0/5jAcg23AZL4WvzbEeCm/S\n9GftMtiwL0coNxHuv5UbRU256RE+GoDlF98VRdvdsFH7A4+7Dqeu6rfgj0zwcdmUypelHzLEbQwR\nyhgece5BlVXHEf0+ejnZz2AB3NwDwHdBuwh3EtpvCxbMVjttv4H5zGc6NQ+4wxnOUmwDn7vYxTKW\n0Q/7GywAmD9/PtOn/87D8OEsXSp4iIyMJDs7G4vFQmCg/Q4WAKSc+YHGCcOr+0pOTkKZMpuNbN/4\nBs+OF9pvT+9Inn8tRQo03EOkYMMDgMmoI3lvEiq1O1fPb6Np14l4Bze8bZ49cTOduWn70WuLKC+8\nTE76AfKvHCGsYXc8fKPElnwDMpmMESNG4OUldBxXrlzJ0KFDqz+PHi10Vt591/47K9f5/uQUBsS/\nY5NWx69t9e9rz86gV4PXifRuer+l3RJ3l5qASYk2m5yiZJrXGVSdFuxT82B+6tJP9Gr9rt3NagBw\ndavxUVGaTUFuMvWbDLI55tShJJp3eIULp1ffb3l3hL+8xsM1SzbnLck8rq7xoLPq+I8hCTeZO7tM\n2xivmkh9hf3dozycbctUdkkyLWMEH2m5+9HqiyiouExa3gGu5B8hLrQ7fu5RIqmtHR9ljYd8Qzbp\nlcl08RU8+KgCb5pnb3ipa3wUVmWTUZHMw0E1WntHjOPl/c3pHzkBZyc3+kaMF0PmbfFW1fgo0Gdz\npTKZTgGCD1eFBwAmi6H6obfKXElaxUm7Czb09hpGsu4oW8pWobdU0dK1IwDF5gI0chebY9UyDVpz\nuRgyb0vvyHG8sq85/4gSyk2fSPssN7djuPs4+mQ351kPIfgzwkPw0UAVz6qgfUQohcGCa6ZMZMiI\nUtrXYAHc3ANQHYAA2FL5ExO83rXLWQ0AP/ADwxlOHIJmDbaBzzLKOMWp6nx75IcffmD48OHExf3m\nQVPjwWq14u/vAIMFQPO241g0tzmt2k9ApXKjRTuhTGVd2U+VrojS4stkZRzgWtYRout0x8s7SlzB\nDxBSsOEBwDe0CTEtBqFQKHF2D2DTl/156t3zt82zJ26mU1uaDYBMrqBOi8FExvdh6ZuhDPnXWVy9\nQkRWbUtAQE3HMTs7m+TkZAYNss/O+p1QVpXPudwd+LdbXGv+rrRFeDmH8GSzGfdZ2d2x9uAUerV6\n54b0q/knOH91G2qlG52bThBB2d2x55cptO9m6yM1eS3RDXqiUChFUnV3TNNP4Z9qWw+NFU3o7zQI\npUyJvyyAobr+HHWzv3vU7/nxyBT6Nq/xUVIp3KfkMgWtYgbTJKIPbywP5d0nzuLlal/3qessyJjC\n6LAb68Xt8uyNby5OYVisrdbOIcNILTvK3txVGMxVNPa2vzXpf2Rh+hRGRtb4SPDuTB23ZpwvP0K8\nVweSSw9ixYLWXCqiyhuptGiZlj2WGWHfIkdOUv4HvJLxD36IPYm73POGjfYqLRV4OfmJpPbWdA4Z\nRmrpUfblrMJgqaKxj/2Xm9r4h9swThuOsrFyFXprFa1/tydDM03NYMH80hmM8XidOJV9DRbArT0A\nJOtPsK9qGy4yN571sN/2exzjaE5zJjABN9wYj20AK4kkXuEVVmOfgwUA48aNo3nz5kyYMAE3NzfG\nj6/xoNPpSEpKwt3dnW3btjFx4kQaNrS/wQKAxk2HkZN1lJQzqzCbqgiPFspUeXnNc0bDJoOp06AP\nc2aEMmbCWdw97LP9djSkV18+AMS2HFL9sOHhH0tp/kUKr566bZ49cTOdKmdh2rt/RAsAlCoXnFTO\nXDm9VjStd8KUKVMYOXKk2DL+EiezN+Km9q0173jWekDGk81mYDTrKai4cn/F3SHlunwuZu3A1yPq\nhrww/wS6NnudiICWfLy6AwZj5f0XeIdUVuSTkbYDT5+o6rSKsmvodSV2u3zijxRY8tlj2kGkPMom\nfYDTEJQyoe5Hy2NJs1zkjNn+7lHXKdflc/7aDptZCy4q4T4V6Sfcp9ROLiidnDmZYZ/3qWJjPsfK\ndhCsibqrPHujxJDPycIdBLpEVadVmbTMOTuWVxslsaBDMo+FjeHfx/uTr8sUT+htKDHkc7x4B0HO\nUdVpCpmCjxO2k1pxgh1536OSa9AoXPFV2Vfn90DFZlq6dkQt16CUqxgXOJUhPuM4pTtEtLoBBaZr\n1cearWZKzUWEKCNFVFw7VSYtc86M5ZX4JOY/kkyPsDFMO2rf5aY2Ki1a3i4cywzfJDaHJjPEbQwv\n5PUn22Tr4/vyRQQqQnjLZ6ZISm/OnXiIUyfwnOfrxKtbMiinAzqLfbbfwxjGIAaxilV8yZeUUhMs\nXMtaetITJfY9WDBs2DAGDRrEqlWr+PLLLyktrfHQpEkTnnnmGZ588kmeeuop+vfvL6LSm2MwaNm0\nZiy9Hk/i+QnJNG05hh+W9aesJBO1Rmi/g0J/95yhdCb1nH22346IFGxwcHLTD7F4ohdmox4AY1U5\nMmTInVS3zLMnbqXTL0zYhMZiNf/uDBkWs/2u0cvPz2fHjh1ERUWJLeUvcbXkDCqFyw3p53J3UVaV\nS0JIL0p0OZzM3khJVY4ICm9P8pWNuGpsAyaXcw7x1qIgisqEAEmdkEe4mn+M5IxNYki8Iy6d34iz\nyx98XPiFirJsDu2cyaEdH1JccIFzx5dzKWWjSCpvzRbTRrxlth6OmA4RUe6F3irU/QqrUPdV2Nc9\n6vecvroR1z8E4cJ9brxPyZBhsdO1xAdLNuLpVHsg8VZ59saR/I14qGy1Hi3cTLx3R1QK4eH36bpT\n6RM+jpTSQyKpvD2Hijbiobzxb+7q5MHjYePpHDAYf004WlMpLb27i6Dw5oSr6pCiO2GTZrVaaOLc\nhhauj1BsKiDHeBWAX7U7iVU3IlJtf9P2jxVsJt63ptwMqzeV3hHjOF9iv+WmNvboNtNaIwR/VDIV\nE7yn8rT7OE7oa3xsr1yPDBn/9JmB3qrnqsm+Bgtu5eG4/hCtMoKqNbfWPMIZwzF26eyv/daiZSxj\nSSKJZJIZwxj6059MMrnGNUoosevlEwBarZaxY8eSlJREcnIyY8aMoX///mRmCoGfIUOGoFQKwZLY\n2FguXrzIqVP2N1iQfnEz4dEdcVJqUDipeLjbVJq3GUd25iECg4X222r53XOGTGa/e3k5INIyCgfH\n1TuMpt0TUSjVAORc2kdgTHu8gxpQUZJ10zx74lYeAELqdSInbR/hDbujK8/HZNASnfC4mJJvycaN\nG/H1dYzO+q1wVnoQ7FHPJi2vIp3ZO/uiN2uFBKsVZDKSBtnX1N7rXCs6g8rJNmAikysI8W2M52/T\n2wtK01AoVIT52eer8QAKcs7gpLT10bjlMzafTx78kobNhhIeY59Tf5MtZ3CR2XoIkYfxijoRtUyo\n+wfN+2ijaE89hX3do35Pdi1lyss1hPrBnUjL2UdcWHfKdfnoTVqaRdrnfepS5Zkb1tLfSZ69caXi\nDOo/BERDXOpwKM92RMqKhQaebe6ntLsiXXsGTS2B3SEHIngn7nviPNuyNnsB/UJexEcdJILCm1NP\nE08H957Mzkkk0CkMg1VPW7duBKnCAZgetpSk/Pdp6tyOX7U7+Sj8vyIrrp0QlzocrqXc1Pey33JT\nG5HKOmzT3egjQS34OFi1iwJzLp1depNvyuG4/iD+TsGEOdnPbJPaPVhJULehwJxLfVVjAhVC+51h\nTEOJijiV/bXfm9lMRzpW79MwlamYMHGIQ1RQQS65zGQmVqxc4ALLWY4WLT3pKbLyGjZv3kzHjh2r\n92mYOnUqJpOJQ4cOkZ2dTbdu3SgoKECtVlNeXo5MJkOlsr/BAm/fOqSm/KFMWS2EhLfB3SOEiOhO\nXL2yj+i63amsyMdo0FKvkX22346IFGxwcNy8QvGPaM7JrbOxWMyU5l2kx9gfb5tnT9xOZ+dnlnB0\n/bsUX0umJOccj41dY3f7NfyeM2fO4OLiGJ31W9E//u0b0gLcovl6SJkIav4cGpUHAV62AZPIgJa0\nbfgsu07PQSaTcenaPl7svR4/zxiRVN4etcYDH/96teaVl2ZxbN/naCtyObJ7NkZ9BTENe91nhbfH\nQ+ZBrNzWQ4g8lKby5nyhn40ZM2mWi3zrbH/3qN+jUXkQ6HnjtXi24xLWHnuX7JJkrpWc46Xua+x2\nvwYXhQfhmtrL063y7A0XJw9CXWy1RrvH08q/J1+nJOKnCcNo0ZPg2w1/53CRVN4eV4UHYc43/s2f\nCJvA+YqjHCvZjt5cydjYWSKouz09PAfRw7P2/Ylau3WmtVtnAPp5j7ifsu6KKI94Wvj3ZOG52svN\nzqzlnC3eiwwZ/0l5kzifDvSJHCey6htpoIqno3NPPihKJNgpDL1Vz0OaboQ4hZNpTOe53L5UWrVQ\nKDzAy5BxKsK+Bgtq99CVEKdwQpzCGeT2LEvK5iBDxhH9PhYFrq/e9NKeqEMd1mL7gGvBQhvaEI7t\n/ehLvmQoQ3kE+xosqFOnDmvX/sGDxUKbNm2Qy+UkJiaiVguDBfv27aN9+/Y0aGB/gwUBQfHE1OvJ\n9o2JuHuEYTbriarTDQ8v4Tr0HbSEvdvfpSAvmcL8cwx8eo20X8M9RGa1Wm9/lMQ9RSaTWQHGznfc\nv/2XLwrv+XZkD1Dj417Wg/fee4/09HQWLlxok758+XL27t3LggULGDJkCB06dGDcuL/eWbn+zvVl\nwxz7Wjz9reBjzkuO7WP8XMFH4kzH9THrDcFDqYfjegDwLBN8JI1xXB/PfS142N/OcT0AtD8g+Nj4\nmGP76LlJ8LGjk+P66LxT8HC6seN6AIg/I/hY38txffTeIHhIj3JcDwDRlx8sH3/c2PTPspKVHOYw\nYYShR09LWtKFLtX5WWTxOZ/zBV/Qne68wAv04q8NFsi4t/3alStXcvjwYcLCwtDr9bRs2ZIuXQQP\n69at4/z585jNZi5evMj06dPx87s3G8Be79u+9YHjlqnpk6uvhUxkKaIizWyQkLjHvP32jTMCAIYO\nHcrQoUOZN2/efVYkISEhISEhISFxPxn027+bEUooH/72z14ZNGjQTd+s1qdPH/r06XOfFUk4GtIG\nkRISEhISEhISEhISEhISEvcUKdggISEhISEhISEhISEhISFxT5GCDRISEhISEhISEhISEhISEvcU\nKdggISHxp7iQv5/J65vyzAo1H2ztcvsTJCQkJCQkHJg+G+Tsy1kttoy75mDVLmIuyykxF4kt5Z4R\nc1nOJq3jXQsJib8b0gaRDwC6sjyObnqfjDPr0RZfRePuj29oExp3HE9EY/t5X29t7FgyivMHv6n+\nrHH1JTC6Le0GfIRXUH0Rlf19WXBgFBX6Ql7v9LNNenrhUf61qRWf9L+Mn2sES4+8SqRPMxI7b0Tl\nZB+v+nx5rhxkMqhtF2aZjDb1R/J010X3X9if5OTBr9ixbiKvvFuCXCHcrs1mI5//yxMv3zqMmniq\n+tjiwjS+nlmXIc9tI6JOZ7Ek2zBON4rlxm+QIUOOnABZIB2dujJVPYMgebDY8u6KEm02Px+bypmr\nGynX5eGm8Sc+vBd9m7+Dt2uo2PL+FO+ljqLMVMjMBj/f/mA74+PTo9ia9VvZkinw04TzUOAAnq77\nLhdKf+XNw535b5cC3FU+Yku9KRfLj/PC0RY09nyIz5vtEVvOXfP21VGUmgv5ItK2/JzVHeWptFb8\nUu8ywaoIkdTdHX02yJEhq/UtBDJkdA0byYQm9t12JBaM4ocKoU4oUBDsFE4PlwG85vUuUPOWAkfg\nupfreMt9SVC3ZbLPR8Qqhb7h4fAcPOXeYkmUcBDWrRrF6eNCvZDJ5Li6BRIZ25XOPWbg5uFY/RBH\nRQo2ODjlhVf4cVZ71M6etO3/IT5hTcBi4WrKVnaveJGn378stsTbEtagO11HLQOrFW1pNgd+mMQv\nXw1gyL/Oii1N4o/IajoruRWpdK8/Hm8X+3kX8Qejcqp/P315LSt2Pi+k/RZ8UDo5iyXtTxER2xmT\nUce1zMOERrUH4FrGIdQaL0oKLqLTFuLs6gtARup2nJw0hEY9JKbkG+is6E6S8zKMGEmxJDNe9yxj\nLSP5yXWz2NLumILyy8xY2x5/9xhGd1yKv2cd8svS+PHXyby/phVv9TuIr5tjPFTdKSaLESe5UmwZ\nt6SZb3cSmy7DZDFwpngPn54ZjcGio0PgwJs+ONoT6699TQP31iSXHSSj8jwRLg9OgN2RHmwBlnWt\naTsO5a1lzunnWdY1p7oMqRSO0XZ00HTnE/9lGK0Gfq3awz8LR1Nl1dHL9eZvRLBXrnuxYiXPlM0H\nxZN4MW8Am0OFvqGfIkBkhRKOQnSd7vQbtAyzxUhBXjIbfniWtatG8tSzjtMPcWSkZRQOzu4VLyKT\ny3niraPENH8Cr4C6eAXVp3Gnlxj8tjDqmbxnASveqU/SK878J9Gf9V/0xGqxiKy8BoWTGmd3f5w9\nAvALT6BJ19coyUnBbNQDcOint/huagO+fsWFb9+O5uCP/8RsMois+kZGjRpFv379xJZxXxj+rRyd\nsYykA6MYvlzBnktLxJYEgLtLQPWPs9pLSHP2r07TqNwBWL33dd5dVo/XFrjwztIY1h58G7PFJKb0\nWvH2r4urRzAZaTuq0zLSdhBZtxuBYS3JuLSzOj0zbSchke1QOKlEUHpz1DI1fnJ/guUhdHbqRn/l\nYI6YD1bnl1nLeEX3PHXKAwkr86CPtjPHzUdFVHwj3+4bh1ymYGKvbdQP6YSPaxj1gzsysddWZMhZ\nvu8lAC5c2830n9sx/ht3XlnixQdr2pJdnCyy+jvjvdRRJKb0ZVnWTPofDaf/sXCxJd0WpVyNl8of\nP00onYKfpEvw0+zP/ZE3fxWWdT253Z/emxR8fPpZkZXeiMFcxba85TwTNZVmXl3YcG1hdV5O1RW6\n7JRzofyYzTlddsrZne9408Z3l6+n74UGtDzrzOj0Lmwq/Z4mZ+RcM2SILa0aL3VA9Y+bk9B2eKr9\nq9NcnNxFVnhnqGRqfBX+BDmF0tftSfq7Ps2Wyp8ATWKw/gAAIABJREFUsGLlmP4AvbOb0eCKM/2y\nW3JGf+w23yge1734KQKIUyfwrMdrpBlT0FuFvqE9L6MYxSjkyFGgQIWKWGJJJJFKKsWW9qc5fvw4\ncrmchx9+2Cb9ypUryOVyjh2z37KkUKhxcfPH3SOE6DrdaBA/mOzMmn7I9ClyTvyaxI/LB/PRVDfm\nfxTLmRPfiqj4wUKa2eDA6LXFXE3+hdb/+AAn1Y1Rd5WzB/lXjrL3u/F0eWYpQbEPodeVkHV+uwhq\n7wxDVTmpR77DJ7QJCqUaACe1G51H/AcXrxCKryWze/lYFE4aWvV9V2S1fy+uj/BYrGbmPJHDxDUx\nDEmYQZvIwbgoPUVWd3c4q70Y2X0ZHi5BZBee5rudL6BSutKjxVtiS7uBiNjOZKTtoF3XKQBkpu0g\nrvnTlHhFkJm2g/rxTwjpl3aS0O5FMaXelnTLJbaZNtFM0ao6bVBlL7xlPqx02YCXzJsVhm/4h7Yr\nR9zOEyAPFFGtgFZfzNmsX3i85QcoFWqbPJWTM53ixvHz0X9RXlXA3K39ebj+czzXeQUmi4GMgmPI\nZQqRlN89x8t24abw4pOGv9j9rIDaUCk0GCxVvN1sNe8ff4KvOpzDTeltl6PSO/NX4qrwpLXvY+jM\nFXx2cTzPxUxH8Vt5cbSZAbbUlJ1rhgxey3iCob4vM9D7eS5WnWZWzkQH9+c4qGUaDL89nANML07k\nHZ/PCVSE8GnJVMbk9WVXaBpquUZElbenwlLOWu13NFA1QS1T3/4EO6A73VnGMgwY2MMeRjMaHTrm\nMEdsaX+Kr7/+mtatW3Pw4EHOnz9P/frCTCyr1YpM5jj1ubjoEpcubCI4tJVN+r7t0+j02Id0emwG\nJ3/9mg0/PEtEdEc8PMNEUvrgIAUbHJjS/FSsWPEKanDTYyqKM3BSuxHZpC9KtStuhOMbGn8fVd6e\nzLMbWThBGDUwGrS4eUfQa/yG6vwWPadU/+7uE0Hzx97i5NbZDhVskMvlrFq1igEDBlSnRUdH8/LL\nLzNx4kQRldXOqeyNjPmv7UiO1SrMhpHLFHhqApAhw1npgafG8aYy9mz1f9W/+7hH0LVZIgfOLbTb\nYMO2n17GbDZitVrIvnKAHgO/xt0znO0/vwpAYV4KFeXXiIi1v406t5g2ElrmjhkzVVTRw6k3852F\ntbi7TNs5az5Fmnt+dQdysuZdNph+5jvjUl5RTxJTOgB5pRfBaiXYq/b7bIhXHFasFJSnozOU0iSi\nD37uUQAEeda7j0r/Omq5M1PqLMZJ5nhdg/Mlh9mR/S0t/HrgphTWcXuq/O12z4aNOYvoFTwagA5+\n/fk89WX2FazhEX+hjXCUYM/e8o20Sa69rQD4b9F8wlWxvB40C4BIdV0uG87zRe7b91Xn35ET+sOs\n0X7Lw86PVqe94vkvOjh3A2CW32LaXQ1jjXY5g93tb/bPLt1GGl8RylalVUuIIoLFgRtuc5b9oEaN\nP/4APMmT7GAHP/ETc5jDbnbzBm9wkpN44slQhjKTmTjZ6WNZVVUVy5cvZ8WKFXzyyScsXLiQmTNn\nAhATE4NMJqNly5YAdOrUie3b7WtQ89KFjcye6o7FasZkqqJO/d70GfiNzTGNm42gUdOnAHik+zSO\n7P+MzPTdNEoYKobkBwr7LNUSd8SddEbCGnTH3TeSb9+OIjyuB2ENHyUmYQBKjdt9UHhnBNftSMen\nk8BqRV9ZzNnd81j3WXcGvHkYN69Q0o6t4vT2zyjLT8Wor8BqMdt0ZiTuPQ0COzK6TZLNRouZJaf5\ndM+AW5zlOBy5sILdp+dQUHYJvbECi8WEk8I+R0siYrtgNOnIvnIAq9WCi1sAXr4xuLoHUVJ0CW1F\nHhlpO1AqXQmOaCO23Bt4SNGRz52T0Fkr+caYxLeGxRRY8vBR+HDSfAwtWmLK/WzO0aMn3ZImkuI/\nhwwZ7es+w6cbH6VBSFcahnSlRfRAfNzsfznCdWKcGztUoOFIwUYGbHHHbDVhtppoF9CfsQ0/50qF\nfe/3k1WZyunSvUxusBQAhdyJHoEj2XBtYXWwwVFo4dqRqaFJNv2Ri1WneS1D8HHZcJ5GzrYjiPHO\n9nefelC4/oBuQqgT3V36847P51wwnkWGjGbqttXHushdqa+M56LRPpd6tdF0ZLqvULZKLcUsK5/H\n8Nzu/BR8mCAnx9uUV40aPXqyyaYXvRjJSL7hG9JIYzSjUaBgFrPEllkrK1euxNPTk8cee4yKigpe\neuklpk+fjkKh4PDhw7Ru3ZrNmzfTpEkTVCr7WsoJEBHdkZ6PJ2E0VnLi1yROH11MZUUezi41wWj/\noJqBWLlcgYurP1ptnhhyHzgcp1chcQNe/nWRIaMk5xw0/Uetxyg1bgx86xjZqbu5em4LJ36ZweE1\nk3nizSO4eAbdZ8W146RywcMvuvpzx2FJLJroybk9XxHRuBfbFj5Fyz7vEh7XA5WLF5dPruHg6kQR\nFT/4qBQuBLhF26RpDcUiqbm3XMjayZJtI+jX5gPqh3dFo/LkWOr3bDoyTWxpteLpE4WnVySZaTux\nWi2ExXQEQKlyITC0BZmpO7h6aRdh0R2Qy+1vyr6LzIUouVCWZig+5Yz5FP+sepUfXX/BgoVAWRCb\nXPfeEDz1wEMMuTcQ4FEHZDKyi5NJiLzxPptdLHTiAzzq8MwjC+nWeAJnr27iZMbP/HRkCi91X0Nc\nWHcRlN89zgpXsSXcFfE+HXm1URIKmRM+mpDqJQj2zvprX2O1WnjqUPQNefn6LOS/baf1+zphj3vK\nADjLXQhT2fooMz8YbYUjcv0BXSFzIlDxuzphFFfXn0EjcyFcWVO2pquSaJLhyYryr3jN23FmtgIc\n5jDLWU53ujOPeYQSylzmAlCf+sxgBmMZyzSmocH+lrQsWrSI0aOFmVj9+/fn5ZdfZs2aNQwYMAB/\nf2H2ho+PDwEB9jnT1UnpgpePUJa69/mU/JxTbFn/Kk+O+qX6GMUfN0SWyUAa2LwnSBtEOjBqV2/C\n4npwZuccjIYbN53R60oBkMnlhNbrRJt/vM/At09i1Gu5cnrd/ZZ7d8hkmAyV5KTtw9U7jOY9J+Mf\n2QJP/1jKCy+LrU7CgUm/th9/zzp0a55IuH9z/D1jKSxLF1vWLQmP7UxG2nYy0nYQEdupJj2mI1fS\ntpORttMul1DUxpvqd9hp3spx81GaKpqTZ81FhoxoeYzNj6/c7/Zfdh9w1fjQKLQHO8/Nw2iqssnT\nmyrZeW4e8eG9cPltQ9Iwn3h6NElkUu8d1AvuxP6L39T2tRL3ALXchSCXaPydw20CDUqZMLJmxiyW\ntJtitprZnLuE52Jm8HXLkzY/Ma5N2HRtMV5KofNepL9Wfd7FiuNiSf5LRKsakKw7YpN2WndIJDUP\nPtcf0EOcwm8IvlmxclxfsylepUXLBeMZ6irj7rfMP40MGVVWndgy7oiNbMQdd5xx5iEeojOd+YIv\nOMc52tLW5tgOdMCAgVRSRVJ7c1JTU9m7dy+jRo0CwMnJiZEjR7Jw4cLbnGm/dOjyDumpW7iWZV+b\nUT+oSMEGB+fhJ+ditVpZPb0lacdWUZJ7gZKc85zdNZ+V7zXhypkNnN7+OQWZJygvyuDi4W8x6Svw\nCm4otvRqzCY9lWW5VJblUpyTwt7/vozJUElkk754BdZDW5LFxcPLKStI5+yu+aQe+U5syXeNTCbD\narUduTUaHXCoweoY64hvRYBXPQrL0jmWupKC0kvsPPk5Jy/Z547W14mI7Ux2xkFyMg8THtOpOj08\npiMpJ79Dp80nIrazaPruhg5OHWkib8Zn+pl0dupGG0V7hlb+g62mTVyxXOaw6QDTq6Zy0LRPbKnV\nDG0/B4vFxMcbu5GSvYMi7VXOZ+/k042PAjKeavcFBeWXWf3rW6TlHqCwIoOU7B1cLTpFiHcjseX/\n7QhwjgSZjF/z1lNqKKDKpBVbUjUHCtdRZiykd/AYolzjbH46BwxhU85iVAoNcR5tWZH5IZe1yZwp\n3c+XaYkOtani9VkZg33GkmFIZXZOIpf1F9hauppVRV8Bjr4JpmMyp/Q99uq2csFwljcKnkUlU9PP\n9SmxZdWKwaon35xLvjmXNEMK7xS9jM5aSVeXvmJLuyM60pFTnOICF6iiipWsxI+bB9GtWO2yTnz9\n9ddYLBaio6NRKpUolUpmz57N5s2bycrKElvenyIipiNBIc05tHum2FL+FkjLKBwcD79oBk4+xrFN\nH3DopzfRlmShcfXFJ6QxDw36DJWzJ+knf+LoxmmYDJV4+MfScfhCgmMfElt6NVkpW1n6ZggASo07\nXoENePS5VYTUfQSApt0T2b/qNUxGHeENH6V1v2nsWTFOTMl3jb+/P9eu1YxS5ebm2nx2GGx2HLa/\nRvFOaFZnIJdzD/H97pcwmfXERfSkV6up/HTgDbGl3ZSI2M5YzEbcvcLx8o2pTg+N7oDJqEOt8SQw\nrIWICu+Ol9Wv84JuBJct6axy2ch7+rd5Vfc8+dY8AmSBtFE8xFPykWLLrMbfI4Yp/Y+w7vi/WbRr\nBOW6PNw0/jSJ6M0LXb7HyzWEMl0euaUXWLB9MBVVBXg4B9Ku7nAea2K/5UrAMevxrfDVhPB0nXf5\n5uIUPj07hq4hI5gYv0hsWQBsvLaIZt5dcP9tE8vf08l/EF9feoujRVt5o8FiPjo/hhePtibEOZYJ\n9eYx4fgjIij+c1x/aApWRfBJxGpmXZvId4VzaeTcirEB7/BO1mhUdv4GhNqwx4fBO0WGjDe8Z/B+\n0eukmy5QT9mIRQHr0cjt720tAPuqttI2U+gbusrciVU2YJ7/KlprhFcv2vu1cMGFaG5cKtWQhqxk\npU3aHvagRk0ssfdL3h1hNptZsmQJM2bMoHfv3jZ5w4cPZ/HixdXLK8xm+5tJditad3iddStHUFx0\nqdayZO/ly5GQ/XG0VeJ/j0wmswKMne+4f/svXxQqoSN7gBof96IejBo1ivT0dD777DObdC8vL956\n6y2OHz/OsmXLkMvlTJkyhd27d/Pvf//7L7+N4vorh5YNc+xr8fS3go85Lzm2j/FzBR+JMx3Xx6w3\nBA+lHo7rAcCzTPCRNMZxfTz3teBhfzvH9QDQ/oDgY+Njju2j5ybBx45Ojuuj807Bw+nG4nhYVvAZ\n8/Kmsj/ur+3tEH9G8LG+l+Nei94bBA/pUY7rASD68oPl46++DWYUoyikkJ/5+Ya8bLKpT32e5mle\n5VXSSGMMYxjOcGby10farz8k34t+7Zo1axg8eDA5OTl4e9sGSGfOnMmCBQs4f/48np6evPXWWzz/\n/PNoNBo8PP76nkvX+7ZvfeC4ZWr65Opr8beOXEjLKCQk7iF79uyhefPmNj+JiYl8/PHHREdH07lz\nZwYPHsxzzz1ntxvpSEhISEhI3Cu+K5zHmcpfyTJcZkPJCr7Kf4/+3qPEliUhIQohhLCRjZzgBM1o\nxhjGMIxhvM/7Yku7gUWLFtGlS5cbAg0AgwYN4vLly+zcuZMvvviCpKQkQkND6d+/vwhKJewZaRmF\nhMQ9YvHixSxevPim+Rs22L4f+vHHH/9fS5KQkJCQkBCVDEMqSfkfUGYuIlAZxhCfcbwQ8H9iy5KQ\n+J+xmJv3BUHYEPIAB+6Tmj/PmjVrbpoXHR1ts3Ti2WefvR+SJBwQKdggISEhISEhISHxP+GN4I95\nI/hjsWVISEhISIiAtIxCQkJCQkJCQkJCQkJCQkLiniIFGyQkJCQkJCQkJCQkJCQkJO4pUrBBQkJC\nQkJCQkJCQkJCQkLiniLt2eDAWMwmzh/4D/mZx3B2D0CpdsNJqSGkbicun15L88feElviLTEb9Rzf\n/CGnt39KcN2OBES2woqVsvw0LGYDjzz1JUqNm9gy/xZoDSVsSvmUdckf0jCgEwmhvXm0/ngOXP6O\nrRfmcbX0DN3qvcQjMc8Q6C68B9piMbP8+CTkMgUaJ3cGNHlHZBfwxZpuuDsHEujdgEp9ETtPfkan\npq/iovYhv/Qi6TkHeefpC2LLvC3lpVnsXPc6Kae+J7reY3Tq8xHevnXYs2kKv+6ZTZ24fjRr9xJR\n9bpTkJvMltUvYjJW0q7r/1GnUT+x5QNQZC1igf5zPjHMoKdTP+IVCeitVVyypNJT2ZeByqfElnjH\nGE1V7L2wiCpjOR7OAVRUFaDVF/FYk3/iovYSW95dUWC4xk+5C/g2eybNPTrRwrMLQ0MmiS3rjikx\n5LP2yhy+vzSdDkGDiHRrhMliIEeXTpBzNF1CnqbEkEecd3uxpd4RBnMVLxxtyYIWR1ApNGLLuWPK\nzCUsK/iURQUf0tK1E4+492ao73g2lnzHd0XzSNWf4Umfl+jv9Qzh6lix5d6SyYe64a0OJMy1AeXG\nIn6+/Bn9ol7FXelDduVFdmQto55Xa+K8H2JMw9liy62VQnM+S8rmML90Or1cB1FX2Qij1UCmKZ1d\nuo1orRUsD9pBM3UbsaXeEr1Vz4LSD1lU9iltNB1pohL6hRmmNAxWA+/7fomr3I1k/QleK3iaX0LP\niC1Zwk7RVRZxZP/nHNg9g7oN+hEYkoDJWEVxYSp1GvalUVPH6YM4OlKwwUEpybvItoVP0ajjOB55\nal51emVZLt9NbUDXUctEVHdnKJRqEroncuKXGbR/YjYe/jHVef/9dyNObJlJq77/FlHh3wdXlRfd\n6o3jpzPTGNHqC4Lc6wDQLupJTmStJ8K7KYOaTrM553DGKsqrCuhS9wWc5CoxZNtQUJZOszqD6dDo\neQBOpK3mRNoPPNHhk+pjVu9zjIcqd89Q2nefyvlTK2n58Gv4BcYB8HDPD/h1z2wS2o4lql53APwC\n42iY8BTxrZ5F4ST+dbiOj8yHZ1TPM8vwHh9p5uAvF171WmQpJKbCnxBZGO2dHhZZ5e3JK01l8e5R\n/KPFv2kQ0rk6/Xz2Tr7Y3Jd/9t0jnrg/gZ8qmAFB4/jP1WlMjJ5DqCbm9ifZEV4qfx4LG8P3l6Yz\nvtF8XJ1q3uc+aJsP3116n5cbfekwwYY12fPJqDxHvv4qoS51xJZzx3govBjiO44F+dOYHPwFEWpB\ne0+vJ9ldvp76mqa8HDjtNt8iPjmV6TwcPJieEUK7sS9nNftzfuD5uJp2w1PlT07lJZr6dhVL5m3x\nVfgzxH0M80unM813Pu7ymnqRkOGDs8yFBFVrERXeGWqZmuc9EplfOoMp3rOJUNbcnx7NasSC0plM\n9P43dVWNWBS44RbfJPF3x9nFh4TWz7Nv53s82m8Orm5CH6SyspDP3vfHwyOM8Gj774M8CEjLKByQ\nipIs1n7ahcadXqZBe9tXzbh4BBIY3YaQ+p1vcrZ9cS11Dxo3P5tAg9ViwaivQKF0FlHZ348z17bg\npQmuDjRcJzl3B42Dut1w/KlrvxAX1IX6AR2I9RO/E3Ph6jbaNaypD+evbqVemG3n0N/TcTrzLq7+\nWH/7d52TBxfg7OxDZUV+dVrWlQOERLW3q0DDdXaattJQ3qg60ACQbc1ChszGl71SUVXIp5t68FiT\nN2wCDQD1QzqRW3qe89d2iaTuz/NryRYC1REOF2i4zrHCLdT3amMTaCg1FKA1lgDQ3PdRsaTdFRfK\nj9HE6xGc5Cry9VfFlnPXHKjYgp9TcHWg4TqHtTto63Zjm2GPnCzcxqNhNe3GiYKtNwQVApwjOVu8\nl3ifjvdb3l2xV7eFpuo2NoGGInMBZZYSKixlyGQyEdXdOYf1e/CR+9kEGixWC1pLBRqZ0C9UypSE\nOkWIJfGOySefd3gHFSqGMYwP+ICpTGUkI5nKVCxYxJb4QHM5dSv+AY2qAw0AFaWO0wd5UJBmNjgg\n+1dOwN0nivrtRtaa36TrayhVLlw5s4HygnSUamEpQv12I7FarZzdNReTQQdAwqOJ9013bWSd30Zw\n3ZoG3GqxsOe7cfiFN6Np90lYLGaOrn8XV69QzCYDV89t5qHBn+PuG2VXPh4EzuRsJS7Q9oHqWtkF\nyvR5NAyqSa8yVvDL+c84evUnPDT+7E77Dw/HjGTLhbkYzML16BN3/69H+7gxNp9TMrfQq9VUm7SH\n4p5j3aF/4eUaisliICVzMwMf/hxf9yh2n56L0STo79Zc/PKkcfFBJquJB1/L/BV3zzBcPULQaQsA\nMJsMFOWlEN9qFFarleP752IyCh5adxLfww7zFjo71Tz4lVpLmap/k0mqKTzk9AhWq5W5hk9wwgkP\nmSfF1iJeUr+G1WolyTgXnVXw8qpaHC+rDifi7RpG08i+teabLUbKdXkAnM7cQEF5Omon4X7bvp5w\nv92RPBfjb/WiRxPxrwnAr6VbaeUpzIzJ12exvWglOwtX46cKJkxTl9U581jb8hoquRqtuZylWdMZ\nG/GByKprOF6whRZ+j1V/NlmMTDjQmgTfrmRUJLMteymNfR6hsXeH6mMqTeV8f2k6z9SzDx9Gi4EC\nfRbt/friqwom35AFCA9Va7Lnc7H8GP8IHUd99xaUGPJ5+0x/5jTfV31+pamcbzOm81yMeH4OVmyl\ntattm3FZf4Eic55N+vmqU/xc/A1xzi3IMFyk0JTHlOA5rCiai94i1I1R/uLUjR7htu3GiYItDK07\n1SYtxiOBcNeGaJxcsVqtrLsyF8Nvup+IsY86DbC3agsdnWvqhdFq5KPiKbTXdMVL7sOaiuUcrtrN\ncI+XaKCKB6DCUs780ukkettHvQDYr9tGa01Nv9BitfB/heNopGrGc56T+E/ZF6QYTjHMfSzx6hYA\nbKtcR6YpHYBnPF4WRXdt+OPPGMYwnenMZz4e1ASCfPAhggie5dlbfIPEXyE9dQvRdWv6IFVVpez4\n5U3ad5qCf1A8h/bM5uqVvbTr+BZ5Oacw6MspL8uia6+PbL5Hry/nwM7pdOphP/XEkZCCDQ5GVUUh\nl0/8RMfhC296THhcD4xVFRxeM5lBU05QVVHI9m9GUL/dSDLOrCcq4XHcvELZ/NVACjKP4xfe7D46\nsCUrZRv+ka1IPfo9VrOJjLMb0Lj589jYnwDYuew5fEPiiXv4Baq0RRxZPxUPv2iunF5nVz4eBJJz\nthHr15Z1Z2cCYMVKWuEhYnxa4qKsaSA1Sjd6NZzE+uRZPNnsQwCOX11Hy/DH8XEJ5bPdA7lcdJwo\nH/GuR1F5BgVll6j/h5kNK3a+QKhvEzo0fgFtVREbDk/FzyOa05fX0TTmcbzcQvl600Ay848T7i9u\neZLJZDg7+wBgMum5cHoVHXt9yLH9c6jUCjMbzp1YTsMEYd3hpXPrqdv4cdw9Q1mzdCC5WccJDBXX\nwy7TNoYqn2G18XtOmY+TarnAp5oFhMnDAXi16gVi5XUZr55IpbWSj/TvA/CLaT19nB4nRB7K8MqB\nnDQfp6ni/nqpMpRzKPVbhj+cVGv+teJz6AylBHvHUWWsYPWvk3lnwAkqqgpZtGsE7euN5FTmeppF\nPY63ayjztw4ko+A4EX7i36eOlm7j5Shh7Xma7gyDg15lY/4SOvk8QVe/wfQLfA6VXA3AjsKVFBpy\nxJR7AyeLtuPvHMGGzK8wWvRUmSp4LX4xubrLpJQc5MnYyTecsydnJUV6+/FxsmQX7f2EIJa/OpyC\n32Y27C34ia4BT5FceoAcXTr13VtwrHgbwZpom/N35q+kSOTrcki7jSbObVmUX9NmnNYdopGmJW4K\noc24rL/Am5nDWB57CGe5C9OyXyRa1YDd5evp6vE4gcpQJmYM5JzuOA2dxa0beboMYbmEn227cSx/\nMwm/pf2at552QY/jpwnlg2MDSSs9Tqyn+HUaYL9uOyGKCJaXf4XBqqfSUkE/t6FMKXiB1/3fo5m6\nDe5yT2YXv01S4BoANmhXkm+2n3oBsL9qG03UrVin/R6z1cQO3QZ8Ff585fcTv2h/pJ/rU5zRH+Wq\n6TLx6hYUmwtZp/0vn/gvZWL+CMotZTazO8RmC1toQxubQEMBBZRSigbH2afFEbmSto345s9w7tT3\n5F47TlHBBXr2X4CHVzgnjyyiVftXOXnka0qKLpHQagx6fTlzpofcEGxIOb0SbYV91RNHQgo2OBil\nBWlYrRb8I1vWml+Wfwk330hkCicMujJWvp9AWIPudB7xH+H8/DSKc1JI6D4JD/9YKooyRXtIr9IW\nUXD1BI+NW4urZzAAdVsPZeO8fuz9/lUaPjSGtKP/5eEhcwAozDpFcJ1H7M7Hg0B2aQpFuiymtvgU\nb5eQ6vRPdw+gUS1LKK6WniXEs2H159yKNLLLUugdN4lA91gKKzNFDTakZG4hyLshHq5B1WlZBac4\nnvo9gzvOFT4XnqJOiFCeCkrTyC1OoVuzSfh5xFJckSl6sAHA2c0fgMM7PqRZ+/GAsLxCpy2gMPcc\nHt5ROCmFzkpJURqF+Sm07jgJL99YyksyRQ02JJvPUGwtIlH9Ni4yFwYoB/O4tgcnzEcJk4dz0Xye\n743L+FSzgO+N31JlrWKC+p8ApFvSuGBJ4RX1JKLlsWRZMu97sCG37AJmq4lQ78a15u85/zUxAW0J\n9W6E0VRFlbGMd1cnEBfanVGP/AeA/LI0ckpS6NFkEv4esRRpM0UPNlzWpVBgyKaFRxcA2nr1oMxU\nTIEhm65+gwEIVkcCkKPPwFsZKJrW2kgrO4HOVM7Iuu/hJFfa5H18+hta+fe84Zw8XQbeKvvxcUV7\njuMlO7hYcRywUmkuq15G0cr7UaxYOVq8hUn1hUDXsZLtNPPuUn1+bpX4fi7pU8gzZvHPmE8JUNa0\nGRMyBtgsofg8dzJ9vJ7GWe4CQGrVGQZ5v8AR7S7SDSk84zeJMFUsOcZM0YMNxwu2EO7WEB91kE36\nsYJfeL7hpwBcq0zj/9m787ioyv0P4J8ZtmFYVVREUAFxB/dQc0PFNS0tNc3KpUXNzFRSs3sFvXZd\nUq9d81ampZbmmqXmTmCgoqCAiCsKLggqKPvO8/uDHyMjMIMwcWamz/v1mtdLzjky38885znP8Mw5\nZ+5kXcGrbnPhqHTHw9w7ejHZEJsXiSyRgTn2/xEIAAAgAElEQVR1/gUz2dN+kVh4BwJCdWPIR0XJ\neFxccmbcvcLbcDDRn34BAE+KUhGbH4nvGuxHA9OS94UvW4/HO8kjEJDyEebWWQoBgdO5gVjq8A2A\nkgmTjhbdAADLHTaq5dcHx3AMg1HmjBMUYCEWYgzGIAlJWI3VsIc9LuMyVmKlhJUal4dJMcjJScWL\nPp/BzFyJ1l5j8PP3g5CUGAFbexe08hyNnOwUFBRko0371wEASfciUK9Ba7Xfk/bkNqys9aufGBre\ns8HAWNs7AzIZRHFRhevjL+6HXG4CUzMFXl90GV1fWoxHdy4gOrBkoGzbZzra9p4GAEi5G4UGzaS7\n1v7e1UDUcWytmmgoZV23CZJuhODuleNwdO8JE7OST9juXTmBxi37IS/7iV7lMAYxScfR2LaN2kRD\ncXERYpMC4dmo/PXPtx9HoWmdDqqfB7SYjv4tStoj4XEU3OtJ2x5X7hxDS2f1SZKrd0/ArVFPmJmU\n7E/X7p5AC+d+yM57gt7tpqNXu5L676VEoVkD/diflFb1kRh/ChaWdrC1LzkbQGldH9lZD3D7ZhCa\nuPdVbduh+3R06F6S4UFiFBybSJshqPA4uph4QylTqpY9EY9xs/gGAOBicRRay9vhdfM3McbsDbxl\nPgW2spJPft4xn44p5iVZYoqi0Nmk9rNYWdQDAMhlJuXW3X98GWdu/Kg668HMVIHFr13Gy50X407K\nBRyPKTne9m0zHX1bl+S4mxoF1/rS71fhT46juVV72JnVUy2LSAtEB9ve5ba9kRUFN2XFky1SuZBy\nHK3su5WbaACAmMcn4VXXB0IIpOenqJbfzIhCUxv9yFFYXIB7OTfwrtvnGNfkE4xrMg8d7fupJhss\nTa1xJvV3eNn3hoVJyfXpkU/+QCf7fsj8/3tSxGVGwdVK2jxnMo/DzaKN2kRDkSjC2cxA9LB+OmaE\nZh5RTT5kF2chPu8aWll2wNh60zGmbknfuJYbBU9L6ftG5KNj6OCgPm6k5T9CYtYNtKzTDblF2RjW\ndDqGNSmp+1Z6FFrYS183AITkHkdHi27l/tC+mBeOroqnN8H7M+co+liWTMhdzo9CC3P96BelTuUG\norlZa9VEQykn0yYIzwuBldwa+zJ/xGCrVyFEMQpFIa4WxOBeYQJO5hzFjxn/k6jyygUiEOlIx7f4\nFv/Ff/EFvsB4jEcd1EEhCjEbszEZk/EQD7ELu6Qu12jcijsOJ2dvmJk/fQ+Sm/MYj1NK3oNYWNjg\nVtxxNHV7OpF75eIutPF6HXm56aplD+5HoX5D/eonhoaTDQbGyt4JLbu9jUsn1Q+oxcVFuHTya7h1\neg0ZKQn4Ya4DZHITNGs/Am16T4WVfWMAgImJGcwsrHD/RgicWvhAaedY0dPUintXTsClzWC1Zan3\nYnDj3DZ0GPgJLJR1VBMRBbmZuHVhDxzdX8T1sz/pVQ5jcCnpOLycBqktu/YwFMWiGM0dupXbPuFx\nJJqUmWwwlZtBYWqFqw9C0KahD+wtpWsPIQSu3QtESxf1N41KizqwsyrZn/LyMxEZtwfujV7EuWsl\n+5OFmRXiEkPQorGP2hkRUrK0qo9b1w6jU4+n16AqrRvg/u2zaOn5mtq2JiZmMDe3wt1bIWji7gNr\nG2kzBBUdR1/Tp21QKAoRWRyBejIHAEBTeTMoZOqnkG7JL7k8zExmBiuZFU4XhqCXqQ8ayms/i4NN\nM7RvMhznbu5QW56Udg0//DkZ031/QeM6bZGSkYCPtzpALjNBh6Yj0Kf1VNhblRxvTeUl+9X1pBC0\nbOQDO6X0+9W5tOPoYqfeN8LTjqvOdCh1NfM8WliVfGKrTzfSikw5Ds+6fcstz8hPhanMHHbmDjiR\nuBX5xbkAgBtp5+FuW/rJs/Q5fk38H7rWVT/W1jFvqHaDyIe5d9DYsuSmi3eyryGvKAcNFU0R+HAH\nrmWch4e19HnOZB7Hi9bqOS5kh6IYxfBSPh0zHEwdUdek5Ayto2m7VOvMZGZQyq1wPisEXa184GAm\nbd8QQiAqJRAd6qn3jdjUELSq0w1JWXG4/PiUaqy7lBoCr3o+5c6CkEpoznF0U/Qtt9xabgtbecnX\n894quI6rBRfxju1sxOSdR1tz/evfp3JPoLel+vvCq/kx+C1rG963+wQA8GvWT3jVeiK2Z34LACgS\nhbCV26O35UDcKbiJuIKrtV53ZSIRiQxk4F/4F97De/gQH2IBFsAGNtiDPfgYH6u2fYzHuI3bElZr\nXOJvHEez5k/7c3FRIZLuRcBSWfIeJPXR9ZJt3J9eNnX54k609nodUeHfAQCS7p1HQ6f/7ydCf/qJ\noeFlFAao9xvfIvLoCgRtfQd2DZrDzMIaJmaW8Og6HuaWtijIy0L7gZ8gLmInCvKzUJifDa9+H6n+\nf35OOhKvBaHz0M8kqT/1fiziwn/GzfO70MijD8IPLgaEQG5WCrKe3MPgqb+ikUcvFBXkISkuFDfC\nd6CwIAfNX3gDty8dUl0uIXUOY3ArJQJn7+zBxftHUSyKEZV4GO2dBuPXmM8RlXgI5qaWOBC7HMPb\nLoC8zM0K41PP40XXCWq/K7sgHZeTg/CKp0T7VXoCwq5uwaP0OGTlpiDm1n48eHwV/TvOAQB0bjEO\nN5NCEXF9BwoKc9ClxRu4lHAILv9/WntOfjquJwZhcBf92Z9s7ZvAu+88yORPX3trWyf0HvI5lP9/\niUVZebnpuHMzCN37S5chougsDhTsQ3DhCVhAgeDCQPQx7QdTmSkmm03FuaIzKMgvQA+TXhhh+iq+\nzvsSdeX1kC2yMch0mOr3pIt0hBQFwc9Cuizv+GzDvvDPsPfcAtS1boq8gkwUFOVi5qDfYWVRBwBg\nrXDAIK9PEH5zJ/IKs5BfmI0B7Z4eb3Py03H1fhBe6ijtfhWdHopTT37HubRjAIDglF/Qp95IAMCd\n3OuY4DRPbfv4nMu4mRODxwUPcS83DpcywtDWxrvW6y515UkYTiX/gujUIChMrBHx6Cg6Ozz9BN3K\nzB7uth1x7O4PcFA4w0FRMuFzO+syijNjkJb/EPez43DlSRha2dd+jotPQrDt9jJczTiHpsrW6FK3\n5Aadgck/I/TRPtzOvoJNt/6Jt5r+A73rv4pvb85H8IPdAIC2dt2x995/MaDhGzibehi3RAzSCh4i\nMScOselhaGNbe3ku5UTgeNoenM48CmFdjJCMw+hpMxgbHnyOkMxDsJBZYtPD5XinfsmYMb/Rl9j1\n+Bt4WHjityeb0cfm6c1WM4vScS4rCO83kK5vPMhJwIm7W3A/Ow4Z+SkIe7Afd7OuYpRbybjRzMYT\nJjIzRDw6ghHNSiZ9swvScTE1CK83l36suJAXhqNZv+BMbhCUcmuczDmK3pZP+0UPRT+czDmCPZmb\nEZV3Dj81PAFLuRI3Ci7jakEMUoseIqEgDhfywlSXWkjhen4s9mf9jN+zdsFb0QdfPlkMAYHHRSlI\nLrqHbxv8ihf+/wyN1uYdEJkXhhZm7WAqM0VDUyc0MCn5IMFabosb+bFwN2spWZayjuM4uqEbzKB+\nxskf+AN90Ve1PBvZ+BN/IgABUpRpVBLvnMW12H1IiDsBU1MF4uMC0cy9H+Qmpuj4wlQk3jmD4uIC\nODfticcpN+AzaJnq/zZu0h23rh+Fc7OSfe3Rw8sQyTHIznqIJ6lxuHcnDI1dpOsnhkrGmZraJ5PJ\nBABM/Z80r31M8Hq06fkeBATuXz8J51bP/93RX08r+QolqTIAus1hyP2g9OusfnyjdjIIIfDxr65Y\n8/Itta/SOnZtPfo1L2mPKw9Oop3j87XHhJ9Kfte6D6Rpi5MX1+PFtu8BQuBG4km0dKned6rP+Kok\nh9+K2s9x4dR6tPcuaYO7N0+iqUf1Mqz8pCRDmq10/WJD/npMMivJElp0En1Nnz+LXXpJjg3vSJfj\nj9j16N2qJMf1+yfRuvHz5Xj3u5IMp7pLk+F+XgI23vHHZ82/r9Hv6XG6JMehwdLkSM5JwE83/DHb\ns2Y5hhwuyfFHX2nHjKTcBGyO98e8Vs+fxyeoJMPFdrWb4Y247vjUaR3aWpZ8g8DPKevxWt2SvhGR\ndRLdrJ+vb3jGlOQ4OLR2cxxIWI8hLiV1x6SeVN08sjqG/V6S4VYzafanu4UJWPvEHysdatYvXOOl\ny3EuNwRncoPwof1n8E+ZiTdtpsPdvFW1fldpDl2d7TEYg9EDPfBP/FNt+TZsw3EcxyZsAgAsxmKk\nIQ2rsKrGzymD4b+vBZ6+t13wufQ50h4n4M8T/njptefrJ//+VNUWhvG9s38RXkbxN3P93Hac/fVT\nbJnfCFvmOUL5zP0SDIWx5DAkqdn3MH13A8SlnEV7p6FqEw2n4rdjV+SnmLG3EWbscUQdS8Nqj/Br\n27H/zKdY+H0jLPjeEbZWhlU/AFy+sB1/Hv4U6//VCOuXOMLK1vAylNpdsB1Lcj9Fi8xG8Mh0hKPM\nMLOExW3HL+GfYu62Rpj7kyPslIaVo7C4AHvur8OVzHBEpYdIXU61FRYXYH/COlxPC0fMY8PNUaqw\nuAD77q3D1YxwXHyiv3l+ePQFDqftBFByQ8k8kaOaaPj9yXZ8mfwp+l1pBJ8rjqhvahh9IyhxO7Zc\n/RQTAhthwglH1LUwjLorUiAKsCV9HS7mheNcrv7uR9p0VfREgcjHrozv0dLcs9oTDboUhjDMx3wE\nIQjRiMZRHFVbPw7j4AhHbMAGrMEa1Ed9nUw0kO4VFRUg4sw6JN0Lx514w+0nUuKZDRKQ+swGXdCH\nMxt0gWc2VF167kPsivoMthb1MbjVLNgoHHT6+6U+s0FXpDyzQVf04cwGXdCHMxtqSuozG3RF6jMb\ndEVfzmyoido6s2FVkh9s5PZwMm+GKzkX8JbDbLUbStaUVGc26JLUZzboipRnNuiSrs9skALPbNAf\nPLOhBO/ZQERVYquojyne30hdBhERGYA5jk+/xu8l+zckrISIiKTCyyiIiIiIiIiISKd4GYUESi+j\nICIiIiIiIuP0d7+Mgmc2EBEREREREZFO8Z4NEjLks0pKb9xiyBkA48hhDBkA5tAnxpABMI4cxpAB\nYA59YgwZAOPIYQwZAObQJ8aQATCOHGW/te3vjGc2EBEREREREZFOcbKBiIiIiIiIiHSKkw1G4Pbt\n21i/fj02bNiApUuXIjIyUrXOxcUFdevWRZ06dVSPDz74QMJqK6Ypg6Z1+iAzMxOjR4/G3bt31ZZH\nRETgo48+wtatWzF16lTExcWp1gUHB2P79u3YuHEjJkyYgBMnTtR22eVUJ0dZc+fOxalTp2qjVI2q\nk2PKlCkwMzODUqmEt7c3Lly4UNtlq6luW2zduhWff/45Nm7ciDVr1tRmyRWqTg59O2ZVJ4M+HrOq\nk+PChQuYOnUq1q9fj48++gjh4eG1XXY5leXQtK6qx7DaUp0M2tZJoTq1nj17FqtXr4a/vz8GDhyI\nkydP1la5FapOBkMav7WtK6Xv47emdYYyfmtbZyjjt6Z1hjJ+a1qnj+O3wRNC8FHLDwCi5KXXjfnz\n56v9PGHCBCGEEElJSWLt2rXi1q1bIj4+XiQkJIgZM2aIx48f1/g5ayuDtnU1VdMc3333nfD39xdy\nuVwkJCSolufl5YmmTZuK5ORkIYQQ586dE127dlWtd3BwEJs3bxZCCLF7926hVCpFZmamJBmEqH6O\nUkFBQaJhw4YiODi42jVImSMgIEA8ePBAJCUl1ej5hZBun9q0aZNYsGCBEEKI+Ph4YWFhIVJSUiTJ\nIET1cuj6mCVVW+jymCVVWzx+/Fg4ODiIS5cuCSGESE5OFq6urqK4uFjvcmhaV9VjWFX9VftUTdY9\nL6naIjs7W3WMEkKIXbt2CaVSKRITE6tVg1RtYSjjt7Z1pfR9/Na2zhDGb23rDGX81rTOUMZvbev+\novFb8r89pXxIXsDf8aHrP9Q9PDxUbwiFEOKdd94RQpR0/LKdfOfOneLUqVM6ec7ayqBtXU3pKodM\nJlM7YB07dky0a9dObRtra2tx69YtIYQQly5dEtnZ2UKIknYxNzeX9M1KqefNIYQQaWlp4ssvvxQ+\nPj6Sv1kp9bw5/P39dfK8QkizT+Xn5wsHBwdx+/Zt1bqybfS8pGoLXR+zpOrfujxmSdUWe/bsEXK5\nXBQVFanWNWrUqNrt8Vfm0LSuKsew5/FX7VO6WFdVUrVFdHS0kMvlIi4uTgghRHp6upDJZGLXrl3V\nem6p2sJQxu+qrDOE8VvbOkMYvzWtM6TxW9O65ORkgxi/ta37i8Zvyf/2lPLBb6MwAtOnT0enTp0w\na9YsWFtbY8aMGQCAhg0bqrZJTExEbGwsRo8eLVWZGlWWQds6fRUfH4969eqpLatTpw4uXbqEZs2a\noU2bNqrl+/btQ0BAAKysrGq7TK205QCADRs2YObMmdi7d68EFVaNthw5OTnYsGEDbGxscOLECcye\nPRutW7eWqNqKacqQkJCA1NRUxMfH4/Tp0wgPD4evr6+qjfSJphzDhg1TLdPnY5a2/clQjlmactja\n2gIA8vPzoVAoAADZ2dmIiopC9+7da73W6qrKMYxqh6enJ0JDQ+Hm5gYAuHPnDmQyGTw8PCSu7PkY\nyvhdFYYwfmtjCOO3JqdOnTKY8VuTBg0aqP6tz+O3NoYyfhsSTjYYgTfeeAMRERHYvXs3cnNz0bt3\n73LbLFy4EIsWLZKguqrRlKEq+fTNo0ePoFQq1ZYpFApkZGSofo6MjMSJEydgbW2NWbNm1XaJVaIt\nx/79+zFkyBCYmZlJUV6Vacvh5eWF0aNHw8zMDA0aNMArr7yCq1evSlFqpTRlSE9PBwCYmJhgzJgx\neOmll9C4cWNcunQJTk5OUpRbqar0DUC/j1naMhjKMUtTjsGDB6Njx44IDw9Hz549cebMGRQXFyMt\nLU2iaqunqvsb1Y5u3bqp/r1s2TLMmTMH7du3l7Ci6jGE8VsbQxm/tTGE8VuTxMREAIYxfleVPo/f\n2hjK+G1IeINIA5eVlYWpU6diw4YNiI2NxTvvvINXXnkFd+7cUW3z8OFD/PHHH3o7S6opQ1Xy6SM7\nO7vSS2ZUMjMz4eDgoPq5Q4cOmDNnDrp06YKePXsiOzu7tsvUSlOO+/fv48mTJ2qf8ugrbe0xduxY\n1Rsud3d3XL9+HdHR0bVepyaaMtjZ2QEAOnfuDABQKpWwtLTE/v37a71ObarSN/T9mKUpgyEdszTl\nMDExQWBgICIjI7Fz504oFApYWVkZ3JvfquxvVPs2bdoEJycnrFixQupSqsUQxm9NDGn81sYQxm9N\nDGn8rgp9H781MaTx25BwssHAHT16FH369IFCoYC5uTn8/f0xffp0hIWFqbY5dOhQudNI9YmmDFXJ\np49atWqFpKQk1c9FRUVITU1F06ZNERYWBkdHRyQkJAAAevfujfPnz+Pw4cNSlVspTTmOHDmCxMRE\nrFixAsuXL8e1a9ewbds2HDp0SMKKK6atPezt7ZGXlwcAyMjIgEwmg7m5uVTlVkhThg4dOqiWlZLJ\nZCgsLKz1OrXRlKOUvh+zNGUwpGOWtrawtbXFjBkzMGbMGLi4uCAtLQ2+vr5SlVstVdnfqHYdPHgQ\nMpkMy5YtQ15enmosNASGNH5rYkjjtyaGMn5rYkjjd1Xo+/itiSGN34aEl1EYuObNm5eb/SwuLoa3\nt7fq55iYmHKnkeqTijIIIeDt7Y0nT55ozaePevfujYcPH+Lu3btwdnZGUFAQ2rZtCw8PD4SHh6Nd\nu3aqTwjj4uJgbm6uGnD0iaYcz15n+/XXX2P8+PF6ecqZphz37t2Dn58fLCwsAAChoaHo0aMHWrVq\nJXHV6jRlAIC+ffsiNDQUvr6+ePjwIbKysjBy5EiJqy5PWw5A/49ZmjLk5uYazDFLW1s0adIEO3fu\nRLdu3fDNN99g2rRpcHR0lLjq51OV/Y1qT3BwMJKTkzFs2DAkJSXhzJkzaNSokcFM/piYmBjM+K3J\nxIkT1X7W5/FbE2dnZ4MYvzVxcnIymPG7KvR9/NakKn9T0fPjZIOB8/T0xJAhQ+Dn5wdnZ2fk5eVh\nwIABcHFxUW1ja2uLFi1aSFilZhVl6N+/P1xcXODi4qI1n5S2bduGkJAQyGQyzJ8/Hz179sT06dNh\nYmKCrVu3YunSpejevTuCgoKwY8cOAECXLl0wefJkrFu3DjKZDKGhoTh48KDqplmGkqPUvXv38OWX\nXyI5ORmrVq1CZmYmhg4dajA5GjdujE6dOmHVqlUoKirC9evX8csvv0hSf3UzAMCWLVsQEBCA2NhY\nXL58Gb/++qukp7zXZJ/Sl2NWdTJU5ZhsCDkAYNasWYiIiEBgYCCys7OxcuVKyTIAlefQtK4q+5u+\nZ9C2zlBy3Lp1C8OHD0dWVhaAkg8VZDKZZPcBqU4GQxq/ta0DDGP81rTOUMZvbesMZfzWtg7Q//Fb\n0zp9HL+NgezZaxnpryeTyUq+/9KAX3uZTAbAsDMAxpHDGDIAzKFPjCEDYBw5jCEDwBz6xBgyAMaR\nwxgyAMyhT4whA2AcOcpkkElciqR4zwYiIiIiIiIi0ilONhARERERERGRTnGygYiIiIiIiIh0ipMN\nRERERERERKRTnGwgoueWnJyMgQMHwtraGiYmJlKX89xcXV2xevVqqcv420tISIBcLsf58+elLoWI\n6G9h0qRJGDFihNRlENHfBCcbjMCkSZMgl8thYmICuVyu+nd0dLTUpWlVtnYzMzM4Ozvj7bffxv37\n96UurVqMYRCvSpt88cUXSEpKQnR0tF61Vdnazc3N0bBhQ/Tr1w/r169HYWGh1OU9l0ePHmH69Olw\ndXWFQqGAo6MjfH19ceLECalLq7LS9ih91K9fH8OHD8fVq1dV25TerVmfVdavIyIiIJfLcfv2bQmq\nen4TJ06EXC7Hu+++W27dvHnzIJfLVTn19VhmDH3cmMa9Bw8e4KOPPkLz5s2hUCjg4uKCYcOG4dCh\nQ1KXppGPjw9mzpxZbvnmzZthY2NT5d9jCP1EX98X6qoN9JW+7huVMYb2MIYMxoiTDUbC19cXSUlJ\nqsf9+/fRrl27ctsVFBRIUJ1mpbUnJCTghx9+wB9//IG3335b6rL+cvrYFqW0tcmNGzfQuXNnuLm5\noUGDBhJWWl7Z2o8dO4YRI0Zg0aJF6NWrF3JycqQur8pGjRqF8PBwfP/997h+/ToOHjyIIUOGICUl\nRerSnouvry+Sk5ORlJSEY8eOIScnB6NGjVKtN+SvtQIMY7KklEwmQ5MmTbBz5061vlBUVIStW7ei\nadOmElZXdcbQx41h3EtISEDHjh1x7NgxLF++HBcvXsTx48cxdOhQTJs2Teryqs2Q+rQmFb0vbNu2\nrdRlVYmxtIGxMIb2MIYMhoqTDUbCwsIC9evXR4MGDVQPuVyOXr164cMPP8ScOXPQoEED9O3bV+pS\nyymt3cnJCQMGDMCYMWNw5swZ1fo1a9agffv2sLa2hrOzM959912kpaVJWHH1yOVyrF+/Hq+++iqs\nra2xcOFCqUuqlKY2cXV1xW+//YbNmzfDxMQEkydPlrhadaW1N2rUCF5eXpg1axaCgoJw/vx5rFix\nQrVdRkYG3nzzTdjY2KBRo0ZYtWqVhFWrS0tLQ0hICJYtW4a+ffvCxcUFnTt3xuzZszFmzBgAJZ8o\njhgxAkqlEm5ubti6dSs8PT2xePFiiatXV/bY1KFDB3z88ce4cuUK8vLyVNvEx8dj4MCBsLKyQtu2\nbXH8+HEJK66ewsJCzJw5E40bN4ZCoUDTpk3x6aefSl1WOZ6envDw8MDOnTtVyw4ePAhLS0vV+BAQ\nEIDNmzfj4MGDqk9ET548KVHF5VWlj+/duxft27eHUqlEvXr14OPjg4cPH0pc+VPaxr3r16+jT58+\nsLS0RNu2bXHkyBHY2Nhgy5YtElatbtq0aZDL5YiIiMCrr74KDw8PtGzZEh988IHqE3S5XI4NGzZg\nzJgxsLa2hru7O3766SeJK6+aiRMnlvtkOiAgAJ6enqp/G0I/Kfu+0NAue9TWBkDJGQTDhw/H0qVL\n4ejoCBsbG0yePFltjNEnb7zxBl577TW1ZUIINGnSBP/5z38kqqpqqtIeMTExGDBgAOzs7GBjY4OO\nHTsiODi4tkutVFUyCCGwZMkSNGnSBAqFAl5eXvjtt99qu1SjwcmGv4EtW7bAzMwMoaGh2LRpk9Tl\naHTz5k0cPnwYXbt2VS0zMTHB2rVrERsbi+3bt+PcuXMVniZlCBYvXoxhw4YhJiYGH3zwgdTlVMmz\nbRIeHo7+/ftj7NixSEpKwtq1ayWuULu2bdti8ODB2LNnj2rZmjVr0LZtW1y4cAGLFy/Gp59+in37\n9klY5VPW1tawtrbGb7/9Vukbprfeegt37txBUFAQ9u3bh82bN+v96fwZGRn4+eef4eXlBQsLC9Xy\nzz77DLNmzUJ0dDS6du2KcePGITs7W8JKq6bsWRlr167Fr7/+ip07d+LGjRvYsWMHWrZsKWF1FZPJ\nZJgyZQo2btyoWrZp0yZMmjRJ9bOfnx/GjBmDAQMGIDk5Gffv30ePHj2kKLfKyvbx5ORkjBs3DpMm\nTcKVK1fw559/4s0335S6xEo9e4wVQuCVV16Bubk5zp49i02bNmHRokXIz8+XuNKnHj9+jCNHjmDG\njBmwtLQst97W1lb17yVLlmDkyJGIjo7G2LFjMXnyZNy9e7c2y62Wyj4JLV0+d+5cg+snhkZbG5QK\nDg5GdHQ0AgMDsXfvXhw9ehTz5s2rjRKf24QJE/D7778jIyNDtSwoKAhJSUkYP368hJVpV5X2GD9+\nPJycnBAeHo6oqCj4+/tDoVDUVolaVSXDf/7zH6xatQorV65ETEwMRo4ciVGjRunVZUiGhJMNRuLQ\noUOwsbFRPYYNG6Za5+HhgWXLlqk+ddA3pbUrlUo0b94crq6u2LVrl2r9zJkz0bdvXzRp0gS9evXC\n8uXL1T6VMySvv/46Jk+ejGbNmun1Kd46vYAAACAASURBVMua2qRevXqwsLCApaUl6tevbzDXwbVp\n0wY3b95U/ezt7Y358+ejefPmePfdd/HWW2/pzU0jTUxMsHnzZvz444+wt7dHjx494Ofnh7NnzwIA\nrl27hqNHj+Lbb7/FCy+8AC8vL/zwww/IysqSuPLyyh6b7Ozs8Oeff5b7ZHP27NkYOnQo3N3d8fnn\nnyMlJQWRkZESVVyxZ4+xNjY26NOnj2r97du30aJFC7z44otwdnZGt27d9Pa0+HHjxiE8PBxxcXFI\nSkrCkSNHMHHiRNV6pVIJS0tLtU9GTU1NpSu4ikr7eGJiIgoLC/Hqq6+iSZMmaNOmDSZPnoz69etL\nXaKKpmPs0aNHcf36ddXZSt7e3lizZo1eXXp348YNCCHQqlUrrdu+9dZbGDduHNzc3LBkyRKYmprq\n1RkA1WVlZaXX/UTT+0JjY2pqih9++AFt2rSBr68vli9fjm+++UYvL6saOHAgbG1tsXv3btWybdu2\noV+/fnp3WWp1JCQkwNfXFx4eHnBzc8PLL78Mb29vqct6LqtWrYKfnx/Gjh2L5s2bIyAgAL169cIX\nX3whdWkGiZMNRqJPnz6Ijo5GVFQUoqKi8N1336nWde7cWcLKtCutvfSMhZMnT+LBgweq9YGBgRg4\ncCBcXFxga2uLUaNGIT8/H0lJSRJWXT363haltLWJIRJCqM1cd+/eXW199+7dERsbW9tlVWrkyJFI\nTEzEgQMHMHToUJw+fRrdunXDv//9b1y5cgUmJiZq+5OzszOcnJwkrLhiZY9N586dQ//+/eHr64t7\n9+6ptil7+mJpBn3b3549xkZFRWHbtm2q9RMnTsSFCxfQokULzJgxA7///rve3o/C3t4eI0eOxMaN\nG7Flyxb07dsXzs7OUpdVY6V9vEOHDujfvz/atm2L1157DV9//TUePXokdXlqNB1jr169CicnJzg6\nOqq279q1K+Ry/XnL9jz7dtn+bWJigvr16+td/zZGmt4XGhsvLy+1M2y6d++O/Px8xMXFSVhVxUxM\nTDB27FjVpHt+fj727Nmj12dfPY/Zs2djypQp6N+/Pz7//HO1G0IbgoyMDCQmJpY7S6lnz5569R7R\nkOjPyEU1olQq4erqCjc3N7i5uaFRo0aqdVZWVhJWpl1p7W3btsV//vMfdO7cGR999BGAkk8LX3rp\nJbRt2xa7d+/G+fPnVZeC6NMppVWl721RSlObGKrY2Fi4ublJXcZzMTc3R//+/fHZZ58hJCQEU6ZM\nQUBAgNRlPZeyx6bOnTtjw4YNSE9Px7fffqvaxszMrNz/Ky4urs0ytXr2GOvm5qb2B3rHjh2RkJCA\nZcuWQQiBt99+GwMHDpSwYs0mT56MLVu2YNOmTZgyZYrU5ehEaR+XyWQ4evQojh07hvbt22Pjxo3w\n8PDAxYsXpS5RxdCPsR4eHpDJZLh8+bLWbZ/t3zKZTPL+bWtrW+G9n548eQI7OzsAJfebeHZSRZ/O\nLtFG0/tCffBXt4G+TvYCJZdSBAcH4/79+zhw4AAKCgowcuRISWvSVXssWrQIly9fxsiRI3Hq1CnV\nmZe14a/ep3iTyerhZAPpnUWLFuH48eOIiIhAeHg4CgoKsHr1anh7e6N58+Zqn4hS7Vi0aBGOHTuG\niIgIqUuplpiYGBw+fBijR49WLSt7MzYAOH36NFq3bl3bpT2X1q1bo7CwEK6urigqKlJrj7t37yIx\nMVHC6qpOJpMZxD0ZnpeVlRVGjRqFr776CgcPHsSJEydw48YNqcuqUP/+/WFubo7U1FS8/PLL5dab\nm5ujqKhIgsqqp6I+7u3tjX/84x84d+4cnJycsGPHDgkr1KzsuNeqVSskJiaqnb137tw5yf9AL6tO\nnToYNGgQ1q1bV2Ff1vebOLds2RLnz58vtzwiIkJ1uWn9+vXLfR3ps5d3GVo/0Se6agMAuHjxotol\nE6dPn4aFhQXc3d11XLVudO3aFc2bN8e2bduwbds2vPzyy1AqlZLWpMv2cHd3x4wZM3DgwAFMmTKl\n1s6q0UUGGxsbODk5ITQ0VG2bkJAQtGnT5i+o2vhxsoH0Tp8+fdCxY0esWLECHh4eKC4uxpo1axAf\nH4/t27fr/Q0J09PT1U61joqKQnx8vNRl1UifPn3QqVMntW9z0Fd5eXmqm3VFR0dj9erV8PHxQdeu\nXTFnzhzVdmfOnMHy5ctx48YNbNiwAT/++CNmz54tYeVPpaamon///vjpp59w8eJFxMfHY9euXVi5\nciUGDBgAT09PDBo0CO+//z7CwsIQGRmJyZMnQ6lU6t3Me2l7JCcn48qVK/jwww+RnZ1tUN8/rknp\nJyRr1qzBzz//jCtXruDGjRv46aefYGdnp9eXJ1y8eBE3b96s8MySZs2aISYmBteuXUNKSgoKCwsl\nqLBi2vp4WFgYli5divDwcNy5cwe//vor7t69q9df+1d23PP19UWLFi3w1ltvITo6GmfOnMGcOXNg\nZmamV/37q6++ghACXbp0we7du3Ht2jVcvXoV//vf/9C+fXupy9No2rRpuHnzJmbOnIno6Ghcu3YN\na9aswY4dO/DJJ58AAPr164cLFy7g+++/R1xcHFauXFnuDxB97if6TldtAJR8G9DkyZMRGxuLY8eO\nYcGCBXjvvfcqvHlpbaro/WBCQgKAkhspfvfdd/j9998xYcIESesEdNMeubm5mDFjBoKDg5GQkICw\nsDCEhITU2rFXV/uUn58fvvjiC/z888+4fv06/vnPfyIkJAR+fn61ksPoCCH4qOUHAFHy0uvGxIkT\nxfDhwytc16tXL/Hxxx/r7LlK6SpDZbVv27ZNmJqaips3b4r//ve/wtnZWSiVSjFgwACxa9cuIZfL\nRUJCQo2f/69oC7lcXu7x2muvCblcLvbs2aOz5ypVW/tT2TZ56aWXxKRJk3T2nELoJkfZ19/MzEzU\nr19f+Pj4iPXr14uCggLVdq6uriIgIECMHz9eWFtbC0dHR7Fy5cqaRhBC6CZHXl6eWLhwoXjhhRdE\n3bp1hZWVlWjRooWYO3euePz4sRBCiOTkZDFixAhhaWkpmjZtKn744Qfh7u4uVqxYoRcZhCjfH+zs\n7IS3t7f45ZdfhBBCxMfHC7lcLiIiItT+n676yl99nAoPD1cdizZs2CA6deokbG1thZ2dnejbt684\nc+ZMjZ9bl/1b01jx7PqHDx+KQYMGCRsbGyGXy0VwcHCNnvuv2Kcq6+OXL18WQ4YMEY6OjkKhUAgP\nDw/xxRdf1Pi5hdDdcUrbMfb69euiT58+QqFQiFatWokDBw4Ic3NzsXPnzho9txC63aeSkpLEzJkz\nhbu7u1AoFKJx48Zi0KBBqj5eUV92dXUVq1atqvFz1zRHeHi4GDx4sHB0dBT29vaiW7du4rffflPb\nJiAgQDg5OQl7e3vxwQcfiIULFwpPT0/V+pr2E12P36U09XVtx4HqqG4OXbRBaZ4lS5aIBg0aCBsb\nGzFp0iSRk5NTazkqUtn7wdGjRwshhLh586aQyWSiUaNGoqioSCfPKUTNMtS0PfLz88X48eOFq6ur\n6ngwdepUkZGRUWs5dLFPFRcXi3/961+iSZMmwsLCQnh5eZX7Hc+ZQfK/PaV8yEpeC6pNMpmsZMbB\ngF/70k9XDDkDYBw5jCEDwBw1lZKSAicnJ/z88881vvaTbaE/jCEDwBw1FRUVhY4dOyIiIgIdO3as\n0e9iW+gPY8gASJtj0qRJSElJwW+//Vbj32UM7WEMGQDjyFEmg/6ckiYB/fmOHiIiqrI//vgDGRkZ\n8PT0RHJyMhYuXIgGDRpg8ODBUpdGRDW0b98+WFlZwcPDA7du3cKcOXPQsWPHGk80EBER1SZONhAR\nGaCCggJ89tlnuHXrFpRKJbp3747g4GDJr1EloprLyMjAvHnzcPfuXdSpUwc+Pj5YvXq11GURERE9\nF15GIQFeRqE/jCGHMWQAmEOfGEMGwDhyGEMGgDn0iTFkAIwjhzFkAJhDnxhDBsA4cvAyihL8Ngoi\nIiIiIiIi0ilONhARERERERGRTnGygYiIiIiIiIh0ipMNRERERERERKRTnGwgIiIiIiIiIp3iZAMR\nERERERER6RQnG4iIiIiIiIhIpzjZQEREREREREQ6xckGIiIiIiIiItIpTjYQERERERERkU5xsoGI\niIiIiIiIdIqTDURERERERESkU5xsICIiIiIiIiKd4mQDEREREREREekUJxuIiIiIiIiISKc42UBE\nREREREREOiUTQkhdw9+OTCbji05ERERERGTEhBAyqWuQEs9sICIiIiIiIiKd4pkNRERERERERKRT\nPLOBiIiIiIiIiHSKkw1EREREREREpFOcbCAiIiIiIiIineJkAxERERERERHpFCcbiIiIiIiIiEin\nONlARERERERERDrFyQYiIiIiIiIi0ilONhARERERERGRTnGygYiIiIiIiIh0ipMNRERERERERKRT\nnGwgIiIiIiIiIp3iZAMRERERERER6RQnG4iIiIiIiIhIpzjZQEREREREREQ6xckGIiIiIiIiItIp\nTjYQERERERERkU5xsoGIiIiIiIiIdIqTDURERERERESkU5xsICIiIiIiIiKd4mQDEREREREREekU\nJxuIiIiIiIiISKc42UBEREREREREOsXJBiIiIiIiIiLSKU42EBEREREREZFOcbKBiIiIiIiIiHSK\nkw1EREREREREpFOcbCAiIiIiIiIineJkAxERERERERHpFCcbiIiIiIiIiEinONlARERERERERDrF\nyQYiIiIiIiIi0ilONhARERERERGRTnGygYiIiIiIiIh0ipMNRERERERERKRTnGwgIiIiIiIiIp3i\nZAMRERERERER6RQnG4iIiIiIiIhIpzjZQEREREREREQ6xckGIiIiIiIiItIpTjYQERERERERkU5x\nsoGIiIiIiIiIdIqTDURERERERESkU5xsICIiIiIiIiKd4mQDEREREREREekUJxuIiIiIiIiISKc4\n2UBEREREREREOsXJBiIiIiIiIiLSKVOpCyCSkkwmE1LXQERERESkK0IImdQ1EAE8s4GIiIiIiIiI\ndIxnNhABEMJwT3CQyUomrw05A8Ac+sQYMgDGkcMYMgDMoU+MIQNgHDmMIQPAHPqkNAORvuCZDURE\nRERERESkUzyzgUiHpkyZgi1btsDMzAyenp74+uuv0bFjRwCAi4sLsrKy1GbMx48fj6+++kqqciul\nKcft27dx4MABmJmZ4cGDBxg2bBg6dOggccXlacpw4cIFfPPNN/Dy8sLVq1fx5ptvokuXLhJXXLmt\nW7fizp07aNiwIdLT0/Hxxx8DACIiIrBlyxZ06dIFoaGh8PPzg7u7u8TVVq6yHACQmZmJSZMmYc2a\nNXB2dpawSs0qy3D27FmEhIQgPT0dp06dwmeffYbevXtLXG3lKssRHByMxMREZGdn448//sCkSZPQ\nv39/iautmKb9qdTcuXMxatQo9OjRQ4IKq6ayHJqOYfpGU1tUpZ30RWW1GtL4DVSew1DGb6DyDIY0\nfmvab8LDw7F161aDGb+Jqk0IwQcff9sHAFHSDXQjICBAPHjwQCQlJaktT0pKEmvXrhW3bt0S8fHx\nIiEhQcyYMUM8fvy4xs+p6wxCVJ5DCCHmz5+v9vOECRN08py11RZPnjwRDg4O4tKlS0IIIZKTk4Wr\nq6soLi7WyfPqOsemTZvEggULhBBCxMfHCwsLC5GSkiLy8vJE06ZNRXJyshBCiHPnzomuXbvq5Dn/\nin2qshxCCPHdd98Jf39/IZfLRUJCgs6es7baIjs7W7VcCCF27dollEqlSExMrPFz1nZbODg4iM2b\nNwshhNi9e7dQKpUiMzOzxs9ZW21RVlBQkGjYsKEIDg7W2fPWZg5Nx+GaqM0MVWmn6qqtHIY2fmt6\nzQ1l/K4sgyGN35XtN0+ePKmt8Vvy99h88CGE4GQDH3/vh64HSH9//wqXJycnq70x2blzpzh16pRO\nnvOveLNSWQ4hhPDw8FAN9EII8c477+jkOWurLfbs2SPkcrkoKipSLWvUqJFetkd+fr5wcHAQt2/f\nVi27deuWEEKIY8eOiXbt2qltb21trVpfE7puC005ypLJZHo72aApQ3R0tJDL5SIuLk4IIUR6erqQ\nyWRi165dNX7e2m6LS5cuiezsbCFEyXHK3Nxc7yYbqrI/paWliS+//FL4+Pjo7WSDthyajsM1UVsZ\nqtrvq6u2chjS+K3tNTeE8VtTBkMavzXtN7U0fkv+HpsPPoQQvIyCSJdycnKwYcMG2NjY4MSJE5g9\nezZat26NBg0aqLZJTExEbGwsRo8eLWGlmlWWAwCmT5+OTp06YdasWbC2tsaMGTMkrrZilWWwtbUF\nAOTn50OhUAAAsrOzERUVhe7du0tZcjmnTp1Camoq4uPjcfr0aYSHh8PX1xfNmjVDfHw86tWrp7Z9\nnTp1cOnSJTRr1kyagiuhKYeh0JTB09MToaGhcHNzAwDcuXMHMpkMHh4eElddnra2aNOmjWrbffv2\nISAgAFZWVhJVW7Gq7E8bNmzAzJkzsXfvXukK1UJbDk3HYX2hKUNoaKjB9HtNOQxp/Na2TxnC+K0p\ngyGN35r2G0Mav4lqipMNRDrk5eWF0aNHw8zMDA0aNMArr7yCq1evqm2zcOFCLFq0SKIKq0ZTjjfe\neAMRERHYvXs3cnNz9fa69Moy+Pj4oGPHjggPD0fPnj1x5swZFBcXIy0tTeqSy0lMTAQAmJiYYMyY\nMXjppZfQuHFjXLp0CY8ePYJSqVTbXqFQICMjQ4pSNdKUw8nJSeLqqkZbhm7duqm2XbZsGebMmYP2\n7dtLVW6lqtIWkZGROHHiBKytrTFr1iwpy62Qtgz79+/HkCFDYGZmJnGlmmnLUZXxRGqaMty/f7/S\ndfrW76t6jNL38VtbDkMYvzVlMKTxu6xn9xtDGr+JaorfRkGkQ2PHjlW9wXV3d8f169cRHR2tWv/w\n4UP88ccfej9zXVmOrKwsTJ06FRs2bEBsbCzeeecdvPLKK7hz547EFZdXWQYTExMEBgYiMjISO3fu\nhEKhgJWVld69+QUAOzs7AEDnzp0BAEqlEpaWlti/fz/s7OwghPrXc2VmZsLBwaHW69RGUw5DUdUM\nmzZtgpOTE1asWFHrNVZFVXJ06NABc+bMQZcuXdCzZ09kZ2dLUmtlNGW4f/8+njx5onaGhr7S1hba\nxhN9oO0YVdk6fVOVWg1h/NaUw1DGb00ZDGn8LlXRfmNI4zdRTXGygUhHwsLCYG9vj7y8PABARkYG\nZDIZzM3NVdscOnSo3Klz+kZTjqNHj6JPnz5QKBQwNzeHv78/pk+fjrCwMImrVqetLWxtbTFjxgyM\nGTMGLi4uSEtLg6+vr5QlV6j0LuFFRUWqZTKZDIWFhWjVqpXqk8PSbVJTU9G0adNar1MbTTkMRVUy\nHDx4EDKZDMuWLUNeXh4SEhJqvU5tNOUICwuDo6Ojqu7evXvj/PnzOHz4sCS1VkZThiNHjiAxMREr\nVqzA8uXLce3aNWzbtg2HDh2SqtxKaWsLbeOJPqgsQ1FRkUH1+6rUagjjt6YchjJ+a2sLQxm/S1W0\n37Rq1QpJSUmqn/V5/CaqKU42EOmIs7Mz/Pz8YGFhAQAIDQ1Fjx490KpVK9U2MTEx5U6d0zeacjRv\n3hyRkZFq2xcXF8Pb21uKUiulrS2aNGmCM2fOAAC++eYbTJs2DY6OjpLVWxknJyf07dsXoaGhAEo+\nIcnKysKoUaPQu3dvPHr0CHfv3gUABAUFoW3btnp5n4DKcowcOVLiyqpOW4bg4GAkJydj6NChSEpK\nwqFDh9TeTOoLTTlMTEzQrl071aeEcXFxMDc317uvxtOUYeLEiZg3bx4++eQTzJs3D+bm5hg/fjyG\nDBkicdXlacpRlfFEH2jKYEj9viq1GsL4rSmHoYzfmsY9wHDG71IV7Te9e/fGw4cPDWL8Jqop3rOB\nSEcaN26MTp06YdWqVSgqKsL169fxyy+/qG1ja2uLFi1aSFRh1WjK4enpiSFDhsDPzw/Ozs7Iy8vD\ngAED4OLiInHV6rS1xaxZsxAREYHAwEBkZ2dj5cqVElar2ZYtWxAQEIDY2FhcvnwZv/76Kxo1agSg\n5HvIly5diu7duyMoKAg7duyQuNrKVZSj9I/abdu2ISQkBDKZDPPnz0fPnj0xffp0iSsur7IMt27d\nwvDhw5GVlQWg5FueZDKZ3l5HXFkOJycnTJ48GevWrYNMJkNoaCgOHjyouvGlPtG0PwHAvXv38OWX\nXyI5ORmrVq1CZmYmhg4dKmHFFdOUQ9t4oi80HaO0tZM+0VarIYzfgOb+bQjjN6B5nzKk8RuoeL8x\nMTExqPGbqCZkz14zRPR3IpPJSr7/0oD7gUwmA2DYGQDm0CfGkAEwjhzGkAFgDn1iDBkA48hhDBkA\n5tAnZTLIJC6FCAAvoyAiIiIiIiIiHeNkAxERERERERHpFCcbiIiIiIiIiEinONlARERERERERDrF\nyQYiIiMREREBuVyO27dvS10Kkd6xsbHBli1bpC6jShISEiCXy3H+/HmpSyEiHQoKCoJcLkdqaqrU\npRDVCk42EOnIpEmTMGLECKnLqLEHDx7go48+QvPmzaFQKODi4oJhw4bh0KFDUpf2XCZNmgS5XA4T\nExOYm5vD3d0dfn5+yM7Olro0rSZOnAi5XI6lS5eqLQ8ODtb6JqX0TtRSMuTXvqxHjx5h+vTpcHV1\nhUKhgKOjI3x9fXHixAmpS6uWsu0il8tVjx49ekhd2nN58OABPv74Y7Ro0QKWlpZwdHREz549sW7d\nOtXXj+qzitqg9GFiYoLJkycD0I++rMmz/bxhw4bo168f1q9fj8LCQqnLq5LKxm1Dnbg1xD5e1f5g\nKN5880107NixXB84ceIEzM3Ncfr0ab3v20S6ZCp1AUSkPxISEtCjRw/Y2dlh+fLl8PLyQnFxMY4f\nP45p06YhPj5e6hKfi6+vL3788Ufk5+fjzz//xJQpU5CTk4N169aV27awsBCmpvpxSJTJZLC0tMTK\nlSsxdepU1KtXT22dIXie115fjRo1Crm5ufj+++/h7u6OBw8eIDg4GCkpKVKXVm2l7VL2q93Mzc0l\nrOj5lB6j7O3tsXTpUnh6esLS0hKXLl3Cd999BwcHB7z++utSl6lRUlKS6t/79+/He++9h6SkJFWb\nWFpaIjU11SC+fq90fyosLMTDhw8RGBiIRYsWYevWrQgMDISlpaXUJVaboRxrn/U8fbygoABmZma1\nVVqFqtIfDMm6devg5eWFgIAALFmyBACQkZGBKVOmYN68eXo98UP0V+CZDUS1YM2aNWjfvj2sra3h\n7OyMd999F2lpaVKXVc60adMgl8sRERGBV199FR4eHmjZsiU++OADREdHAwDS09Px3nvvoWHDhrC1\ntYWPjw8iIiIkrrxiFhYWqF+/Pho3bozXX38dEyZMwL59+1RnCBw6dAje3t5QKBQ4evSo1OWq8fHx\nQbNmzbB48eJKtzl8+DBat24NS0tL9OnTB9euXavFCjXT9tofPnwYnTt3hlKpRO/evXHv3j0EBgai\nffv2sLGxwYgRI/DkyRPJ6k9LS0NISAiWLVuGvn37wsXFBZ07d8bs2bMxZswYAICrqyuWLFmCSZMm\nwdbWFk2aNMHOnTvx5MkTjB07FjY2NmjZsiUCAwMly/Gs0nZp0KCB6mFvbw+g5BPGvXv3qm3v6uqK\n1atXS1FqhaZOnQpTU1NERERg9OjRaNWqFZo2bYqhQ4di7969qomGuLg49O3bF5aWlmjdujUOHjwo\nceVPVfTal20TGxsb1bZXr15Fr169VDmOHTsmVdkVKt2fGjVqBC8vL8yaNQtBQUE4f/48VqxYgSVL\nlsDT07Pc/3vxxRcxa9YsCSqunpMnT6Jbt26qM2lmz56tt2dvaOvj69evx6uvvgpra2ssXLhQ4mqr\n1h9KLyvasWMH+vbtC6VSiU6dOuHixYu4ePEievToAWtra/Tp0wd37tyRNI+dnR02bdqE5cuXIzw8\nHAAwa9Ys1K1bF4sWLVJtd/r0aXTs2BGWlpbo0qULL5kio8XJBqJaYGJigrVr1yI2Nhbbt2/HuXPn\nMHPmTKnLUvP48WMcOXIEM2bMqPCTBFtbWwDA0KFDkZSUhN9//x2RkZHo3bs3+vfvj+Tk5Nou+bkp\nFArk5eWpfp4/fz6WLl2KK1euwNvbW8LKypPL5Vi2bBm+/vpr3Lp1q9z6O3fuYOTIkRg0aBCioqLw\n4Ycf4pNPPpGg0qp59rX39/fHf//7X5w9exaPHz/GmDFjsHTpUmzcuBHBwcGIiYlBQECAZPVaW1vD\n2toav/32m1rdz1q7di26deuGCxcuYOzYsZg4cSLGjRuH4cOHIyoqCr169cKECROQn59fi9Ubp9TU\nVBw9ehQzZsyAQqGodDshBF555RUAQFhYGDZt2gR/f3+DbIN58+Zh1qxZiIqKgq+vL15++WXcv39f\n6rI0atu2LQYPHow9e/ZgypQpuHLliuqPLqBkAuXMmTN45513JKxSs7JnBSQmJmLo0KHo3LkzIiMj\nsWnTJmzfvh0LFiyQsMLqW7x4MYYNG4aYmBh88MEHUpfzXPz9/bFgwQJERkbC3t4e48ePx4cffohl\ny5bh3LlzyM7O1ov3Vv3798f06dPx1ltvYc+ePdi+fTt+/PFH1dmTQgj4+flh5cqViIiIgJubG4YP\nH47c3FyJKyf6Cwgh+ODjb/sAIEq6Qc1NnDhRDB8+vErbHj58WCgUCp08r64ynD17VshkMrFv375K\ntzlx4oSwsbERubm5ass7dOggVq5cWaPn12VbCFG+PcLCwkS9evXEuHHjRFBQkJDJZOKXX37R2fOV\n0kWOsrX7+PiIcePGCSGECAoKEnK5XKSkpIgFCxaIli1bqv2/f/3rX0Iul4uEhIQaPX9NM1TltT92\n7Jhq/bp164RcLheRkZGqZf7+Ecc9MQAAIABJREFU/sLT07PaNQhR8xx79+4V9erVEwqFQnTv3l3M\nnTtXhIWFqdY3a9ZMjB8/XvVzZmamkMlkYtasWapl8fHxQiaTiYiICEkylDVx4kRhamoqrK2tVQ8b\nGxsxf/58IYQQMplM7NmzR+3/NGvWTKxatarGz62LHGFhYRUeo5ydnVV5pk2bJo4ePSpMTU3F3bt3\nVduEhIQImUwmNm/eXKMadH2c2r17t5DL5eWWl+43//73v1XLiouLRYsWLcQ//vGPGj2nrjJoGvPm\nz58vrKyshBBCvPTSS2LatGmqdZ988ono2rVrjZ9fV8faZ/uEtbW1UCqVqmPpp59+Klq0aKH2/374\n4QehUChETk5OjZ7/rxj3tPXxjz76SGfPV0pXObT1hw0bNqiWHThwoNzx4IcffhC2trbVfn5dtkdO\nTo5o1aqVMDExUTuGlo6B27dvVy3LzMwU9vb2YuPGjTV+3jIZJH+PzQcfQgie2UBUGwIDAzFw4EC4\nuLjA1tYWo0aNQn5+vtq1ilITQvv1wefPn0dWVhYcHBxgY2Ojely6dAlxcXG1UOXzOXToEGxsbGBp\naYkXX3wRPj4++PLLLwGUXI/buXNniSvUbvny5di1axcuXLigtvzKlSvo1q2b2rLu3bvXZmkaaXvt\ny55a3bBhQwBAu3bt1JY9ePCgdot+xsiRI5GYmIgDBw5g6NChOH36NLp164Zly5aptvHy8lL928rK\nCkqlslwOAJJnKdWnTx9ER0cjKioKUVFRiIyMhJ+fn9Rl1UhISAiioqLwwgsvIDc3F5cvX0bjxo3R\nuHFj1Tbe3t6Qyw3vLU/ZPi6TyeDt7Y3Y2FgJK6oaIYTqngfvvvsufv75Z+Tl5aG4uBg//vijXp3V\n8GyfiIqKwrZt21TrKzrW9uzZE/n5+bhx40Ztl6uVtj5uCONeZZ4dN2QyWbnjbWZmpl6cIaBQKDB3\n7lwoFArMnj1bbZ1MJlPbp6ysrODp6WkQfZvoeenH3dCIjNjt27fx0ksv4f3338eSJUtQr149RERE\nYPz48Xp1Wq+HhwdkMhkuX76Ml19+ucJtiouL4ejoiJCQkHKTE6WXWeiTPn36YMOGDTA1NYWTkxNM\nTEzU1ltZWUlUWdV17doVo0aNgp+fH/7xj39IXU6VaXvty96UrPSPkrLbyGQyFBcX106xGpibm6N/\n//7o378/PvvsM7z77rvw9/fHnDlzAKDczdVkMlmF2fQhCwAolUq4urpWuE4mk5Xr1wUFBbVRVpU0\nb94cMpkMV65cUTtGNW3aFEBJNtIPsbGxcHNzAwAMGzYMSqUSe/bsga2tLdLS0jBu3DiJK3yqoj7x\n+PFjrf+v7ISKPtHUxwHDGPcqU9GxVZ+Pt6ampgY5yUmkS+wBRH+x8PBwFBQUYPXq1fD29kbz5s1x\n7949qcsqp06dOhg0aBDWrVtX4VcUpqWloVOnTkhOToZMJoObm5vaw8HBQYKqNSt90+Xi4lLuj11D\n8vnnn+PPP//E4cOHVctat26NsLAwte1Onz5d26VVylhe+2e1bt0ahYWFGu/jYKjq16+vdj+A5ORk\nvbo/QN26dTFw4ECtX3HZunVr3L17V+04GxYWpjd/gDyPM2fOqP189uxZtG7dWqJqqiYmJgaHDx/G\n6NGjAZRMIr799tvYuHEjNm3ahFGjRqndBFPftW7dulw7/Pnnn7CwsIC7u7tEVZEhE0Ko7VNZWVmI\niYlBmzZtJKyK6K/BMxuIdCg9PR1RUVFqyzw8PFBcXIw1a9Zg1KhROH36NNauXStRhZp99dVX6Nmz\nJ7p06YLFixfDy8sLQggEBgZi+fLliI+PR48ePfDyyy9j+fLlaNWqFe7fv48jR47A19cXL774otQR\nqqwql43oC3d3d7z//vtq+83U/2vv3qOiKvc3gD97QK4jYnLRyRte8wIKYiGVgoaa5bVVptjyEqSY\npqYUZp6DHk3M1DQ1lfDW6iJii1OanSNHRcXjMVHESFQMhBDwrgFKKu/vDxfzY2QYbtvZe6bns9Ze\nS/YM7u8z7+z9Dt/Zs2fKFKxYsQIzZ87E1KlTkZ6ejg0bNihYZe1ZwmN//fp1vPrqq5g0aRJ8fHzQ\nuHFj/Pzzz1i2bBleeOEFaLVapUusl7KysioXc7WxsYGbmxv69++PtWvXok+fPtBoNJg3b57qvnZu\n3bp1+mPU3//+d/To0QO2trY4fvw4Tp06hcGDB+OFF17AU089hTfeeAMrV65EaWkp3n33XcW/4q8+\nPv/8c3Ts2BHe3t5Yu3YtcnNzERERoXRZehXPp/Lycly5cgVJSUlYsmQJevfurT/7BwDCwsKwdOlS\n2NjYqO6bf6pTcZyaOnUqVq1ahYiICMyYMQMXLlzA3LlzMX36dJMXKlWKqX3c2ljCXFKdRYsWwc3N\nDS1atMDChQthb2+vqjN+iOTCZgORjA4dOgQ/Pz+Dda+88gpWrVqFmJgYzJ8/H4GBgVi+fDlGjx6t\nUJXV8/LywokTJ/DRRx8hKioK+fn5aNasGbp3745PP/0UwMPP4n/44Yd46623cPnyZXh6euLZZ5/F\n+PHjFa6+btR4+qsp8+fPx5YtW/QfvWnVqhW+++47vPvuu9i4cSN69eqFpUuXYty4cQpXWjNLeOy1\nWi369OmD1atXIysrC2VlZXjyyScxbtw4/dfFGctR23VKSUpKgk6nM1j35JNPIjc3F8uXL0dYWBiC\ng4Ph6emJjz/+GJmZmQpVapyXlxdOnjyJJUuW4G9/+xvy8vLQqFEjdOnSBdOmTcPbb78NSZKQmJiI\n8PBwBAQEoHXr1li+fDnGjh2rdPl1IkkSYmJisGLFCpw8eRJt2rRBYmJilfFTUsXzycbGBq6uruje\nvTsWLlyI8PBw/ZX3gYfj1q9fP+Tm5qJfv34KVlx7FfutTqfDnj17EBkZCV9fX7i6uiI0NBSLFy9W\nuELjHt3HhRBo2bIlcnNzVXUsqiu1H1vromLfnj17Ns6dO4du3bph9+7dqmvuEslBsuSuIFFDSZL0\n8CspLHg/qJhsLTkDwBxqYg0ZAOvIYQ0ZAOZQE6UydOvWDW+88QaioqJk+f84FurBHOpRKYNldmLI\n6vDMBiIiIiJ6LK5evYodO3bg4sWLeOutt5Quh4iIzIjNBiIiIiJ6LDw8PODu7o6NGzfiiSeeULoc\nIiIyIzYbiIiIiOixsMRvASEiInnwqy+JiIiIiIiISFZsNhARERERERGRrNhsICIiIiIiIiJZsdlA\nRERERERERLJis4GIiIiIiIiIZMVmAxERERERERHJis0GIiIiIiIiIpIVmw1EREREREREJCs2G4iI\niIiIiIhIVmw2EBEREREREZGs2GwgIiIiIiIiIlmx2UBEREREREREsmKzgYiIiIiIiIhkxWYDERER\nEREREcmKzQYiIiIiIiIikhWbDUREREREREQkK0kIoXQNRIqRJIk7ABERERFZDSGEpHQNRADPbCAi\nIiIiIiIimdkqXQCRGljyGT6S9LB5bckZAOZQE2vIAFhHDmvIADCHmlhDBsA6clhDBoA51KQiA5Fa\n8MwGIiIiIiIiIpIVmw1EREREREREJCt+jIJIRrm5udi1axcaNWqEy5cv46WXXkLPnj0BACdPnsSG\nDRvg4+ODs2fP4o033oC/v7/CFRtnqtbU1FRs27YN/v7+SElJQWRkJNq3b69wxVXV9HgXFxdj4sSJ\nWLlyJVq2bKlgpaaZynHs2DEcPnwYt2/fxpEjR/Dhhx+ib9++CldclakMycnJuHTpEkpLS7F//35M\nnDgRAwYMULhi42q7D8+ZMwejRo1CYGCgAlXWzFSON998E9u2bUOjRo3g7e2N9evXw9fXV+GKq6pp\nLL788kvk5eXB09MTt2/fxqxZsxSstnqmcrRq1QolJSUGp3SPHTsWa9euVapco0xlMDUnqo2pHGqf\nv6ubz0zN12qcy+uTw9TvKaE+GSxlLieqFyEEFy5/2QWAeLgbyCMqKsrg53HjxgkhhLh586Zwc3MT\nGRkZQgghioqKhJeXlygvL2/wNuXOYKrWsrIy0aZNG1FUVCSEEOLnn38WvXv3lmW7cuao6fH+4osv\nRHR0tNBoNOLixYuybLOCuXKUlpaKuXPn6u+7Y8cO4eTkJC5dutTg7ZpzLNzc3MTWrVuFEEIkJCQI\nJycnUVxcLMu2zZmjwoEDB4Snp6dITk6WZbvm3L+FEGLBggXi8uXLorCwULZtCmHesdi0aZN+38jJ\nyRH29vbi2rVrsmzbXDkKCwvFqlWrRHZ2tsjJyREXL14U06ZNEzdu3Gjwds05FtXNiXIwV44bN26o\nev6ubj4zNV/LPZcrlcPU79VHQ3PUJ4Pcc3mlDIq/xubCRQjBZgOXv/Yi9wv5jh076l+QCCFEWFiY\nEOLhH1EajUY8ePBAf1uLFi3EkSNHGrxNuTPs3Lmz2lr37t0runfvbnB/rVYrsrOzG7xdOXOYylCZ\nJEmqbjaYypGeni40Go24cOGCEEKI27dvC0mSxI4dOxq8XXOORUZGhigtLRVCCBEfHy/s7OxU2Wyo\nzXPq1q1bYvXq1SI4OFi1zYaackRHR8u2rcrMNRZ//vmncHNzE7m5ufrb5Dg+VTBXjqKiIoPGQnx8\nvCzzhRDm3S+qmxPlYK4ctZ1P6kPODI/OZ6bma7nncqVymPq9+pArR10yyD2Xs9nARW0Lr9lAJKOp\nU6fCz88PUVFRWLRoEaZNmwYAaNKkCQDgzz//1N+3tLQUp06dUqROU1xcXAAYrzUnJwfNmjUzuH/T\npk2RkZFh1hprYiqDJTGVw9vbGykpKWjXrh0AIC8vD5IkoWPHjorUWp2axqJr165wdHQEACQmJmLB\nggVwdnY2f6E1qM1zKjY2FlOmTIEQ6r2SeU057ty5g9jYWHz77bcIDw/HmTNnFKnTFFMZjhw5guvX\nryMnJwfx8fF47733cP78eaVKNclUDg8PD7i6ugIALl26hF9//RV9+vRRpE5Tano+VTcnqo2pHJY6\nn5iary1lLgdM57AUpjJYylxOVF9sNhDJKDQ0FK+++ioSEhKwfv163Lp1CwAQHBwMX19fHD9+HABw\n9OhRlJeX629XE1O1Xr16FU5OTgb3d3BwwB9//KFEqdWypMfblJpyBAQE6O8bExOD2bNno0ePHorU\nWp3ajEVaWhqWL18OrVaLmTNnKlWqSTXl+OGHH/Diiy+iUaNGSpZZo5py+Pj4YMKECXj99dcxZswY\njBgxQslyjTKV4dKlSwAAGxsbvPbaa4iOjsZrr72mX68mtT1OzZs3D+PHj1eixBrVlKG6OVFtTOWw\n1PnE1HxtKXM5YDqHpagpgyXM5UT1xWYDkUxKSkowZcoUxMbG4tdff0VYWBhGjBiBvLw82NjYYN++\nfUhLS0N8fDwcHBzg7OwMnU6ndNlVmKq1SZMmVd61LS4uhpubm0LVGmdJj7cptc2xadMm6HQ6fPzx\nxwpVWr3aZOjZsydmz54Nf39/PPfccygtLVWwYuNM5SgoKMDNmzfRtWtXpcusUU3jMXr0aH3DpH37\n9jh//jzS09OVLLmKmo5RANCrVy8AgJOTExwdHfHDDz8oWbJRtdk3rly5gv3796Nt27bKFWqCqQym\n5kS1MZXDUucTU/O1pczlgOkclqK2GdQ8lxPVm9Kf4+DCRckFMn7O8LvvvhOrVq0yWDdv3jyjn7u7\nevWqcHR0FAUFBQ3erpwZjKlc6759+0SPHj30t92/f1/Y29uLc+fONXg7jzNHdY+32q/Z8ChjOXbt\n2iU2bdokhBDi7t27Iicnp8HbMVeGo0ePCk9PT33NmZmZQpIksXPnTlm2Za4cmzdvFjExMWLp0qUi\nJiZG6HQ6MXnyZPHjjz82eDvm3L+PHj0qtFqtuHv3rhBCiNOnTwuNRiPOnDnT4O2YYywKCwtFfn6+\n0Gg0oqSkRH+7TqcTa9askWVb5t6/t27dKvz8/GTdjrky1GVOrA8l5oyabqsrOTM8Op/t27dP9OzZ\nU/9z5fla7rlcqRymfq8+5MpRnwxyzeXgNRu4qGzhmQ1EMunQoQPS0tIM1pWXl+OZZ54BALRu3RpH\njx4FAGzYsAERERFo3ry52eusjepq7du3L65evYrff/8dAHDgwAF069ZNlZ8ttKTH2xRTOZKTk1FU\nVIQhQ4agsLAQe/bsQWFhoZLlGlVdBhsbG3Tv3l3/DuGFCxdgZ2en2q/Gqy7HhAkT8P777+O9997D\n+++/Dzs7O4wdOxYvvviiwhUbV12Oli1bIjIyEvb29gCAlJQUBAYG4qmnnlKyXKOMZfD09IROp0NQ\nUBBSUlIAPDwzoKSkBCNHjlSy3GrVdJz65Zdfqpx+rTbVZahpTlQbU2NhifNJ3759ceXKFaPztSXN\n5aZyWIqaMljKXE5UH5IQ6r2QFdHjJknSw9MbZNoPduzYgWPHjqFly5YoKyuDv78/+vfvDwBYsWIF\n7O3tcevWLZSWlmLhwoXQaBre75MkCYB8GQDTte7fvx/x8fHo06cPDhw4gA8++AAdOnRo8DblzmEq\nw9dff43Dhw9jw4YNGD16NJ577jlMnTpVlu2aK0d2djZ69OiBkpIS/fYkScKtW7eg1WobtE1zj0VR\nUREkSUJKSgqmTJmCAQMGyLJdc+YAgPz8fKxevRqfffYZQkJCMHnyZAwZMqRB2zT3/r1r1y6cPXsW\nDx48wPnz57FkyRJZTlc251jk5+djwYIF6NatG86cOYMxY8agX79+smzX3M+pRYsWITs7G3FxcbJs\nDzBvBlNzYkOZM4ea529T85mp+VrOuVzJHHLO5w3NUZ8Mcs/llTJI9QpBJDM2G+gvTe5mgxIexx8j\nSmAO9bCGDIB15LCGDABzqIk1ZACsI4c1ZACYQ03YbCC14ccoiIiIiIiIiEhWbDYQERERERERkazY\nbCAiIiIiIiIiWbHZQERERERERESyYrOBiGrl4sWL0Gg0OHHihNKlEBFZNWs73hYVFWHgwIHQarWw\nsbFRuhyyQtOnT0dwcLDSZRDRI9hsIJLRyZMnodFo8PzzzytdSp1oNBrY2NhAo9FUWWxsbDBp0iQA\n/3+VY7WbOHGivnY7Ozu0b98ekZGRKC0tVbq0egsODsY777xTZf3WrVvRuHFjBSqq2YQJE6DRaLB4\n8WKD9cnJydBoNLh+/bpCldWNNTyfLPXYVNnly5cxY8YMdOjQAQ4ODmjVqhVeeukl7NmzR+nS6qzi\nOVWxuLu7Y+jQoTh79qz+PpZyvAWMj82QIUP0Y/PJJ5+gsLAQ6enpKCgoULjaqirv45XHJTAwUOnS\namSsdhsbG6SnpytdWp1UzBfh4eFVbnv//feh0WgwbNgwk/+HGvYZa5n3iOTCZgORjL744gs8/fTT\nOHr0qMGLRrUrLCxEQUEBCgsLERsbC0mSUFRUpF+/atUqAJb1dVAhISEoLCxEdnY2Fi9ejHXr1uG9\n995TuqzHQg0vsIyRJAmOjo5YtmwZrl27VuU2S2LpzydLPTZVuHjxInx9fbF3714sXboUp0+fRlJS\nEoYMGYKIiAily6uXkJAQ/XF27969uHPnDkaNGqW/3VKOt9WNzUsvvaQfm6ysLPTq1Qvt2rWDh4eH\nwhUbV7GPV15+/PFHpcuqlUdrLygoQPfu3ZUuq04kSULr1q0RHx+PO3fu6Nc/ePAAX375Jdq0aaNg\ndbVnTfMekRzYbCCSyd27d/H1118jOjoa/fv3R1xcnMHtBQUFCA0NhZubG5ydneHn54fk5GSFqjXk\n4eGhX1xdXQEA7u7u+nWV3znPycnBwIED4ezsjG7duiEpKUmpsk2yt7eHu7s7nnzySbz++usYN24c\nEhMTIYTAm2++iXbt2sHJyQmdOnXCsmXLlC7XagUHB6Nt27ZYuHCh0dsr3u3ZvXs3fH194ejoCH9/\nf9WdPl7d88nYu1VqOwXe1LGpotbt27cjKCgITk5O8PPzw+nTp3H69GkEBgZCq9WiX79+yMvLUyxD\nREQENBoNUlNT8corr6Bjx47o3Lkz3n77bf07uBqNBuvXr8fw4cPh7OyMzp0748CBA8jLy8OgQYOg\n1Wrh5+enmnd8K55THh4e6NmzJ2bNmoXMzEyUlZVVua8QAm+//Tbat2+PCxcuKFBt9WoaGy8vL3z/\n/ffYunWrwZlyalN5PB6dDzds2IDOnTvD0dER7u7uePHFF1FeXq5wxf/PWO0ajQbBwcGYOnUq5syZ\ng2bNmsHDwwOfffYZysrKEBERAVdXV7Rp0wbffPON0hEAAN7e3ujYsSPi4+P163bv3g1HR0cEBQXp\n15WXl2POnDl44okn0KxZM8yaNQsPHjxQoGLjapr3AODgwYMICAiAo6MjmjdvjnfffRf37983Y5VE\n5sFmA5FMduzYgSZNmmDw4MEIDw/Htm3b9JNfaWkp+vbti9zcXHz//ffIyMjAggULFK64fj788EPM\nnDkT6enp6N27N8aMGWMRp5Pb29ujrKwM5eXlaNWqFRISEpCZmYmPPvoIS5YswebNm5Uu0SppNBrE\nxMRg/fr1yM7OrvZ+kZGRWLZsGVJTU9GuXTsMHToUd+/eNWOldePg4KD/o9DYu1VqegfL1LGpQnR0\nNObOnYu0tDS4urpi7NixmD59OmJiYvDzzz+jtLTU6Md4zOHGjRv417/+hWnTpsHR0bHK7S4uLvp/\nL168GKGhoQbHp0mTJmHatGlIS0tDixYtMGHCBDNWXzt//PEHvv32W/j4+MDe3t7gtvv372Ps2LE4\ndOgQjhw5gvbt2ytUZVW1GZvjx49jwIABGD16NAoLC/VnylmK1NRUTJs2DQsWLMC5c+ewb98+DB48\nWOmyau3rr7+Gi4sLjh07hrlz52LGjBkYNmwYunbtihMnTmD8+PGYNGkSLl++rHSpkCQJb775pkFD\ndNOmTZg4caLB/T755BPExcUhNjYW//3vf/HgwQN89dVX5i63WjXNe/n5+RgyZAh69eqFtLQ0bNq0\nCd988w3mzp2rQLVEj5kQgguXv+wCQDzcDRouKChILFy4UAghxL1790Tz5s3Fzp07hRBCbNy4Ubi4\nuIjr16/Lsq3K5MwghBAJCQlCo9FUWZ+TkyMkSRKxsbH6dfn5+UKSJJGSktLg7cqZY8KECWLo0KH6\nn//3v/+JZs2aiTFjxhi9f1RUlAgJCZFl23KPR4WgoCAxffr0Kuu3bNkiGjduLOu25MpQeRyCg4P1\nj/+BAweERqMR165dEwcOHBCSJIlvvvlG/3vFxcXC1dVVxMXFNWj7jyOHEIbPp8pZKlTsK6mpqQ3e\nthwZTB2bjO3Xu3btEpIkicTERP26LVu2CBcXl3rX0JAcx44dq1KPMZIkiXnz5ul//uWXX4QkSeLT\nTz/VrzM2XnUh53PK1tZWaLVaodVqhSRJok2bNiIjI0MI8XBcNBqNSE5OFoMHDxZ9+vQRN2/ebPB2\nhZD3GFXbsXn55ZfFxIkTZdlmBbnnjMrjodVqRePGjUVUVJT47rvvhKurqyguLpZlW5XJkcFY7UOG\nDBFCPNz3AwMDDe7v7u4uhg8frv/53r17ws7OTn9MqA+5cgwdOlTcuHFDODo6iqysLFFQUCAcHBxE\nXl6ewXFYp9OJJUuW6H+3vLxcdOrUSQQHBzeoBjlzCFH9vPfBBx+ITp06Gfzeli1bhIODg7hz506D\ntl8pg+KvsblwEULA1gz9DCKrl5WVhcOHD+PLL78EANja2mL8+PGIi4vDqFGjkJaWBh8fHzRt2lTh\nShvO29tb/2+dTgcAqnhH5FF79uxB48aNcf/+fdy/fx8jRozA6tWrAQDr169HXFwcLl68iDt37uDe\nvXto27atsgVbuaVLlyIwMBCRkZFVbpMkCQEBAfqfnZ2d4e3tjV9//dWcJZpU3fMpIyND6dJMqunY\nVKHyfu3p6QlJkgw+8+3p6Yni4mLcvXsXDg4O5guAul274NEcAKrkAB4es5544gmZKqyffv36ITY2\nFkII3LhxA+vWrUNISAiOHTsG4GHucePGQafTYf/+/UbPHFBaXcZG7SqPRwVXV1fY2dmhdevWaNu2\nLQYNGoSBAwdi1KhR0Gq1ClZr6NHaKz9XfHx8DO7r4eFhsJ/Y2tqiadOmqpnHXV1dMXLkSMTFxcHV\n1RVBQUFo2bKl/vbbt2+joKDAYM6QJAnPPPMMfv/9dyVKrlZ1815mZqZB/QDw3HPP4c8//0RWVpbF\nXW+DyBQ2G4hk8MUXX6C8vBxeXl5VbsvPz1egosenUaNGVdap6bOrFSpefNna2kKn0+m/bm379u2Y\nNWsWVqxYgT59+sDFxQVr1qxBYmKiwhWb5uLiglu3blVZf/PmTTRp0kSBiuqmd+/eGDVqFCIjIzF/\n/nyly6mz6p5PGs3DTyNW/gPl3r17itRoTG2PTZX364qPgBhbp8S+3rFjR0iShDNnzmD48OEm76vm\nHI9ycnIyGJfY2Fg0adIEGzdu1F/X4OWXX8a2bdtw+PBhhISEKFVqteoyNmr36HhUdvLkSRw8eBB7\n9+5FTEwMPvjgAxw/fhzNmzc3c5XGmar90TlbkiSj69SwT1SYNGkSxo8fD61Wi0WLFildTr3Vdd4T\nQqjqI3hEcuA1G4ga6MGDB9i2bRtiYmJw6tQpg8XHxwebN2+Gr68v0tPT+ZVHZlTx4qtVq1YG3+ue\nkpKCgIAAREREoGfPnmjXrh2ysrIUrLR2OnfubPSCg6mpqejcubMCFdXdRx99hEOHDuGnn34yWC+E\nwNGjR/U/l5SU4JdffkHXrl3NXWK1qns+ubu7Qwhh8HV+J0+eVMULxtocmyxB06ZNMWjQIKxZs8bo\n9WGMNeEslSRJBhnDwsKwcuVKjBgxQpUX4/2rjI1Go0FQUBAWL16MU6dOoaSkBLt27VK6LKs1YMAA\n2NnZ4fr161WaWC4uLmjRooXBnAFAf0aQ2hib97p06VKl/kOHDsHe3l5V12QhkgObDUQNtGvXLly7\ndg1hYWHo2rWrwTJ69GizpVwiAAAEbUlEQVRs3rwZoaGhcHd3x/Dhw3H48GFkZ2fjhx9+UM23UfyV\ndOrUCSdOnMBPP/2ErKws/OMf/8DBgweVLqtGERER+O233/DOO+8gPT0d586dw8qVK7F9+3aL+QrG\n9u3bY/LkyUYvELdo0SIkJSUhIyMDkyZNgr29PcaMGaNAlXXToUMHtGrVCtHR0Th//jz+/e9/V/l+\ndaXU5thU3Wnwajs9fu3atRBCwN/fHwkJCTh37hzOnj2Lzz//HD169KjT/6WWbGVlZSgqKkJRUREy\nMzMxffp0lJaWYtiwYQb3Cw8Px8qVKzFy5EhVNhzkHBslVR6PiuXq1avYvXs3Vq9ejbS0NOTm5uKr\nr75CcXExunTponTJVu306dP47bffjJ5NOWPGDHz88cfYuXMnzp07h5kzZxo0fNXE2Lw3depUXLp0\nCREREcjMzMTu3bsxd+5cTJ8+3ewfUyN63NhsIGqgTZs2oX///kavx/Dqq68iJycHKSkpOHjwIFq2\nbIlhw4bB29sb0dHRqnj3sy7UftX92pg8eTJee+01hIaG4umnn0Zubi7mzJmjdFk18vLywsGDB3H+\n/HkMGjQIzzzzDOLj45GQkICBAwcqXV6tzZ8/H7a2tgbPG0mSEBMTg9mzZ8Pf3x8XLlzQf92Z2tna\n2mL79u347bff0LNnTyxYsABLlixRuiwAtTs2JSUlWcR+7eXlhRMnTiAkJARRUVHo0aMHBgwYgH/+\n85/49NNPAdT++KSWbElJSdDpdNDpdAgICEBqaioSEhLw/PPPAzCs86233sInn3yCkSNH4j//+Y9S\nJRtVm7GxBJXHo2Lx8/ND06ZNkZiYiJCQEHTp0gUrVqxAXFwcnn32WaVLrpGl7ROVOTs7V3tdjNmz\nZ2PixIkIDw9HQECA/vomavXovKfT6bBnzx6kpaXB19cXYWFhCA0NVU2jmkhOklo6/ERKkCTp4VdS\nWPB+UDF5WXIGgDnUxJwZkpOT0b9/f1y5ckX2C/ZxLNSDOdTDGjIA1pHDGjIAzKEmlTKor4NEf0k8\ns4GIiBRlyS/siIiIiMg4NhuIiEhRajyFl4iIiIgahh+joL80foxCPZhDPawhA2AdOawhA8AcamIN\nGQDryGENGQDmUBN+jILUhmc2EBEREREREZGs2GwgIiIiIiIiIlmx2UBEREREREREsmKzgYiIiIiI\niIhkxWYDEREREREREcmKzQYiIiIiIiIikhWbDUREREREREQkKzYbiIiIiIiIiEhWbDYQERERERER\nkazYbCAiIiIiIiIiWbHZQERERERERESyYrOBiIiIiIiIiGTFZgMRERERERERyYrNBiIiIiIiIiKS\nFZsNRERERERERCQrNhuIiIiIiIiISFaSEELpGogUI0kSdwAiIiIishpCCEnpGogAntlARERERERE\nRDLjmQ1EREREREREJCue2UBEREREREREsmKzgYiIiIiIiIhkxWYDEREREREREcmKzQYiIiIiIiIi\nkhWbDUREREREREQkq/8Dfoqc1OoybnYAAAAASUVORK5CYII=\n\"></div>",
+ "result": "<div class=\"output_subarea output_png\"><img 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gRERERERER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T8tCFurwN13vMHsV7hSxfdvSzNm8s9+kHoFe6PJgxWhKJGTYbTvY9nOFIBCFQEl1ut/4sg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\nGRkZKCoqEjjqzkml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"selectedType": "Html",
"pluginName": "IPython",
- "shellId": "7D657CFFBE3442BB86E5CDE1D2A08A35",
- "elapsedTime": 8215,
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 9116,
"dataresult": {
"type": "OutputContainer",
"psubtype": "OutputContainer",
@@ -1051,13 +978,13 @@
"height": 660
},
"evaluatorReader": true,
- "lineCount": 67
+ "lineCount": 62
},
{
- "id": "markdownANJbsd",
+ "id": "markdownQWiEkl",
"type": "markdown",
"body": [
- "<div style=\"font-size: 150%; font-weight: bold;\">Calculate error</div>"
+ "<div style=\"font-size: 150%; font-weight: bold;\">Estimate error for binary systems</div>"
],
"evaluatorReader": false
},
@@ -1124,6 +1051,352 @@
"lineCount": 39,
"initialization": true
},
+ {
+ "id": "code2MruHL",
+ "type": "code",
+ "evaluator": "HTML",
+ "input": {
+ "body": [
+ "<script>",
+ " ",
+ " function add_estimate_plot() { ",
+ " beaker.ctrl_xc_estimate = document.getElementById(\"errorbar_estimate_xcfunctional\").value;",
+ " beaker.ctrl_kpt_estimate = 8;",
+ " beaker.ctrl_prec_estimate = document.getElementById(\"errorbar_estimate_precision\").value;",
+ " beaker.ctrl_tiers_estimate = document.getElementById(\"errorbar_estimate_tiers\").value;",
+ " beaker.ctrl_rel_estimate = document.getElementById(\"errorbar_estimate_relativity\").value;",
+ " beaker.ctrl_pred_estimate = 1;",
+ " beaker.ctrl_quant_estimate = document.getElementById(\"errorbar_estimate_quantity\").value;",
+ " beaker.ctrl_code_estimate = document.getElementById(\"errorbar_estimate_code\").value;",
+ " beaker.ctrl_button_estimate = 1",
+ " beaker.evaluate(\"exe_cell_estimate\");",
+ " }",
+ " function clear_estimate_last() {",
+ " beaker.ctrl_button_estimate = 2",
+ " beaker.evaluate(\"exe_cell_estimate\");",
+ " }",
+ " function clear_estimate_plot() {",
+ " beaker.ctrl_button_estimate = 3",
+ " beaker.evaluate(\"exe_cell_estimate\");",
+ " }",
+ " function error_estimateUpdateForm() {",
+ " var code = document.getElementById(\"errorbar_estimate_code\").value;",
+ " ",
+ " var dprec = document.getElementById(\"errorbar_estimate_precision_name\");",
+ " var pprec = document.getElementById(\"errorbar_estimate_precision\"); pprec.innerHTML = '';",
+ " var prel = document.getElementById(\"errorbar_estimate_relativity\"); prel.innerHTML = '';",
+ " var ptiers = document.getElementById(\"errorbar_estimate_tiers\"); ptiers.innerHTML = '';",
+ " var pxc = document.getElementById(\"errorbar_estimate_xcfunctional\"); pxc.innerHTML = '';",
+ "",
+ " switch(code) {",
+ " case \"VASP\": ",
+ " dprec.innerHTML = 'Precision:';",
+ "",
+ " addDropdownChoice(pprec, \"Low\", \"Low\");",
+ " addDropdownChoice(pprec, \"Normal\", \"Normal\");",
+ " addDropdownChoice(pprec, \"Accurate\", \"Accurate\");",
+ " addDropdownChoice(pxc, \"PBE\", \"PBE\");",
+ " addDropdownChoice(pxc, \"LDA\", \"LDA\");",
+ " break;",
+ " ",
+ " case \"FHI-aims\": ",
+ " dprec.innerHTML = 'Integration grid:';",
+ "",
+ " addDropdownChoice(pprec, \"light\", \"light\");",
+ " addDropdownChoice(pprec, \"tight\", \"tight\");",
+ " addDropdownChoice(pprec, \"really_tight\", \"really_tight\");",
+ " addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");",
+ " addDropdownChoice(prel, \"zora\", \"zora\");",
+ " addDropdownChoice(ptiers, \"minimal\", \"minimal\");",
+ " addDropdownChoice(ptiers, \"standard\", \"standard\");",
+ " addDropdownChoice(ptiers, \"tier1\", \"tier1\");",
+ " addDropdownChoice(ptiers, \"tier2\", \"tier2\");",
+ " addDropdownChoice(pxc, \"pbe\", \"pbe\");",
+ " addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");",
+ " break;",
+ "",
+ " ",
+ " case \"GPAW\": ",
+ " dprec.innerHTML = '$E_{cut}$:';",
+ "",
+ " addDropdownChoice(pprec, \"300\", \"300\");",
+ " addDropdownChoice(pprec, \"400\", \"400\");",
+ " addDropdownChoice(pprec, \"500\", \"500\");",
+ " addDropdownChoice(pprec, \"600\", \"600\");",
+ " addDropdownChoice(pprec, \"700\", \"700\");",
+ " addDropdownChoice(pprec, \"800\", \"800\");",
+ " addDropdownChoice(pprec, \"900\", \"900\");",
+ " addDropdownChoice(pprec, \"1000\", \"1000\");",
+ " addDropdownChoice(pprec, \"1100\", \"1100\");",
+ " addDropdownChoice(pprec, \"1200\", \"1100\");",
+ " addDropdownChoice(pprec, \"1300\", \"1100\");",
+ " addDropdownChoice(pprec, \"1400\", \"1100\");",
+ " addDropdownChoice(pprec, \"1500\", \"1100\");",
+ "",
+ " addDropdownChoice(pxc, \"pbe\", \"pbe\");",
+ " addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");",
+ " break;",
+ "",
+ "",
+ " }",
+ " }",
+ "",
+ "",
+ "",
+ "</script>",
+ "",
+ "<style type=\"text/css\">",
+ " ",
+ " .error_estimate_table th { font-weight: bold; padding-right: 2ex; }",
+ " .error_estimate_table td input { margin-right: 1ex; }",
+ " ",
+ "</style>",
+ "",
+ "<!-- Controls area -->",
+ "",
+ "<div class=\"error_estimate_control\">",
+ " <table class=\"error_estimate_table\">",
+ " ",
+ " <tr>",
+ " <th>XC-Functional:</th>",
+ " <td>",
+ " <select id=\"errorbar_estimate_xcfunctional\">",
+ " <option value=\"PBE\" selected>PBE</option>",
+ " <option value=\"LDA\">LDA</option>",
+ " </select>",
+ " </td>",
+ " <td id=\"errorbar_estimate_xcfunctional_description\" style=\"white-space: pre;\"></td>",
+ " <th id=\"errorbar_estimate_precision_name\">Precision:</th>",
+ " <td>",
+ " <select id=\"errorbar_estimate_precision\" >",
+ " <option value=\"Low\" selected>Low</option>",
+ " <option value=\"Normal\">Normal</option> ",
+ " <option value=\"Accurate\">Acurate</option> ",
+ " </select>",
+ " </td>",
+ " <td id=\"errorbar_estimate_precision_description\" style=\"white-space: pre;\"></td>",
+ " </tr>",
+ " <tr>",
+ " <th>Tiers:</th>",
+ " <td><select id=\"errorbar_estimate_tiers\" ><!-- content inserted programmatically --></select></td>",
+ " <td id=\"errorbar_estimate_tiers_description\" style=\"white-space: pre;\"></td>",
+ " <th>relativity treatment:</th>",
+ " <td><select id=\"errorbar_estimate_relativity\" ><!-- content inserted programmatically --></select></td>",
+ " <td id=\"errorbar_estimate_relativity_description\" style=\"white-space: pre;\"></td>",
+ "",
+ " </tr> ",
+ " ",
+ " <tr>",
+ " <th>Quantity:</th>",
+ " <td><select id=\"errorbar_estimate_quantity\">",
+ " <option value=\"E_tot\">Total Energy</option>",
+ " <option value=\"relR\">relative Energy</option>",
+ " </select></td>",
+ " <td id=\"errorbar_estimate_quantity_description\" style=\"white-space: pre;\"></td> ",
+ " ",
+ "",
+ " <th>Code:</th>",
+ " <td><select id=\"errorbar_estimate_code\" onchange=\"error_estimateUpdateForm()\">",
+ " <option value=\"VASP\">VASP</option>",
+ " <option value=\"FHI-aims\">FHI-aims</option>",
+ " <option value=\"GPAW\">GPAW</option> ",
+ " </select></td>",
+ " <td id=\"errorbar_estimate_code_description\" style=\"white-space: pre;\"></td>",
+ " </tr>",
+ " ",
+ " </table>",
+ " <table class=\"error_estimate_table\">",
+ " ",
+ " <tr>",
+ " <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_estimate_plot();\">Compare Reference/Estimated Error in binaries</button></th>",
+ " <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_estimate_plot();\">Clear plot</button></th>",
+ " <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_estimate_last();\">Clear last set</button></th>",
+ "",
+ " </tr>",
+ " </table> ",
+ "</div>",
+ ""
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": {
+ "type": "BeakerDisplay",
+ "innertype": "Html",
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n \n function add_estimate_plot() { \n beaker.ctrl_xc_estimate = document.getElementById(\"errorbar_estimate_xcfunctional\").value;\n beaker.ctrl_kpt_estimate = 8;\n beaker.ctrl_prec_estimate = document.getElementById(\"errorbar_estimate_precision\").value;\n beaker.ctrl_tiers_estimate = document.getElementById(\"errorbar_estimate_tiers\").value;\n beaker.ctrl_rel_estimate = document.getElementById(\"errorbar_estimate_relativity\").value;\n beaker.ctrl_pred_estimate = 1;\n beaker.ctrl_quant_estimate = document.getElementById(\"errorbar_estimate_quantity\").value;\n beaker.ctrl_code_estimate = document.getElementById(\"errorbar_estimate_code\").value;\n beaker.ctrl_button_estimate = 1\n beaker.evaluate(\"exe_cell_estimate\");\n }\n function clear_estimate_last() {\n beaker.ctrl_button_estimate = 2\n beaker.evaluate(\"exe_cell_estimate\");\n }\n function clear_estimate_plot() {\n beaker.ctrl_button_estimate = 3\n beaker.evaluate(\"exe_cell_estimate\");\n }\n function error_estimateUpdateForm() {\n var code = document.getElementById(\"errorbar_estimate_code\").value;\n \n var dprec = document.getElementById(\"errorbar_estimate_precision_name\");\n var pprec = document.getElementById(\"errorbar_estimate_precision\"); pprec.innerHTML = '';\n var prel = document.getElementById(\"errorbar_estimate_relativity\"); prel.innerHTML = '';\n var ptiers = document.getElementById(\"errorbar_estimate_tiers\"); ptiers.innerHTML = '';\n var pxc = document.getElementById(\"errorbar_estimate_xcfunctional\"); pxc.innerHTML = '';\n\n switch(code) {\n case \"VASP\": \n dprec.innerHTML = 'Precision:';\n\n addDropdownChoice(pprec, \"Low\", \"Low\");\n addDropdownChoice(pprec, \"Normal\", \"Normal\");\n addDropdownChoice(pprec, \"Accurate\", \"Accurate\");\n addDropdownChoice(pxc, \"PBE\", \"PBE\");\n addDropdownChoice(pxc, \"LDA\", \"LDA\");\n break;\n \n case \"FHI-aims\": \n dprec.innerHTML = 'Integration grid:';\n\n addDropdownChoice(pprec, \"light\", \"light\");\n addDropdownChoice(pprec, \"tight\", \"tight\");\n addDropdownChoice(pprec, \"really_tight\", \"really_tight\");\n addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");\n addDropdownChoice(prel, \"zora\", \"zora\");\n addDropdownChoice(ptiers, \"minimal\", \"minimal\");\n addDropdownChoice(ptiers, \"standard\", \"standard\");\n addDropdownChoice(ptiers, \"tier1\", \"tier1\");\n addDropdownChoice(ptiers, \"tier2\", \"tier2\");\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n \n case \"GPAW\": \n dprec.innerHTML = '$E_{cut}$:';\n\n addDropdownChoice(pprec, \"300\", \"300\");\n addDropdownChoice(pprec, \"400\", \"400\");\n addDropdownChoice(pprec, \"500\", \"500\");\n addDropdownChoice(pprec, \"600\", \"600\");\n addDropdownChoice(pprec, \"700\", \"700\");\n addDropdownChoice(pprec, \"800\", \"800\");\n addDropdownChoice(pprec, \"900\", \"900\");\n addDropdownChoice(pprec, \"1000\", \"1000\");\n addDropdownChoice(pprec, \"1100\", \"1100\");\n addDropdownChoice(pprec, \"1200\", \"1100\");\n addDropdownChoice(pprec, \"1300\", \"1100\");\n addDropdownChoice(pprec, \"1400\", \"1100\");\n addDropdownChoice(pprec, \"1500\", \"1100\");\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n\n }\n }\n\n\n\n</script>\n\n<style type=\"text/css\">\n \n .error_estimate_table th { font-weight: bold; padding-right: 2ex; }\n .error_estimate_table td input { margin-right: 1ex; }\n \n</style>\n\n<!-- Controls area -->\n\n<div class=\"error_estimate_control\">\n <table class=\"error_estimate_table\">\n \n <tbody><tr>\n <th>XC-Functional:</th>\n <td>\n <select id=\"errorbar_estimate_xcfunctional\"><option value=\"PBE\">PBE</option><option value=\"LDA\">LDA</option></select>\n </td>\n <td id=\"errorbar_estimate_xcfunctional_description\" style=\"white-space: pre;\"></td>\n <th id=\"errorbar_estimate_precision_name\">Precision:</th>\n <td>\n <select id=\"errorbar_estimate_precision\"><option value=\"Low\">Low</option><option value=\"Normal\">Normal</option><option value=\"Accurate\">Accurate</option></select>\n </td>\n <td id=\"errorbar_estimate_precision_description\" style=\"white-space: pre;\"></td>\n </tr>\n <tr>\n <th>Tiers:</th>\n <td><select id=\"errorbar_estimate_tiers\"></select></td>\n <td id=\"errorbar_estimate_tiers_description\" style=\"white-space: pre;\"></td>\n <th>relativity treatment:</th>\n <td><select id=\"errorbar_estimate_relativity\"></select></td>\n <td id=\"errorbar_estimate_relativity_description\" style=\"white-space: pre;\"></td>\n\n </tr> \n \n <tr>\n <th>Quantity:</th>\n <td><select id=\"errorbar_estimate_quantity\">\n <option value=\"E_tot\">Total Energy</option>\n <option value=\"relR\">relative Energy</option>\n </select></td>\n <td id=\"errorbar_estimate_quantity_description\" style=\"white-space: pre;\"></td> \n \n\n <th>Code:</th>\n <td><select id=\"errorbar_estimate_code\" onchange=\"error_estimateUpdateForm()\">\n <option value=\"VASP\">VASP</option>\n <option value=\"FHI-aims\">FHI-aims</option>\n <option value=\"GPAW\">GPAW</option> \n </select></td>\n <td id=\"errorbar_estimate_code_description\" style=\"white-space: pre;\"></td>\n </tr>\n \n </tbody></table>\n <table class=\"error_estimate_table\">\n \n <tbody><tr>\n <th><button type=\"button\" class=\"btn btn-primary\" style=\"margin-top: 2ex;\" onclick=\"add_estimate_plot();\">Compare Reference/Estimated Error in binaries</button></th>\n <th><button type=\"button\" class=\"btn clear-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_estimate_plot();\">Clear plot</button></th>\n <th><button type=\"button\" class=\"btn clearlast-primary\" style=\"margin-top: 2ex;\" onclick=\"clear_estimate_last();\">Clear last set</button></th>\n\n </tr>\n </tbody></table> \n</div>\n"
+ },
+ "selectedType": "BeakerDisplay",
+ "elapsedTime": 0,
+ "height": 179
+ },
+ "evaluatorReader": true,
+ "lineCount": 159,
+ "initialization": true
+ },
+ {
+ "id": "exe_cell_estimate",
+ "type": "code",
+ "evaluator": "IPython",
+ "input": {
+ "body": [
+ "\"\"\"Add a new set of points (error or relative error) to the left plot\"\"\"",
+ "# Code specific parameters:",
+ "keys, ref_keys = get_keys(beaker.ctrl_code_estimate,beaker.ctrl_prec_estimate,beaker.ctrl_kpt_estimate,beaker.ctrl_xc_estimate,beaker.ctrl_tiers_estimate,beaker.ctrl_rel_estimate)",
+ "beaker.ctrl_sys_estimate = \"binaries\"",
+ "if beaker.ctrl_button_estimate==1:",
+ " # Database for code",
+ " db_con = con_code[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate]",
+ " # el. solids or binaries",
+ " mono_or_bin=ref_dict_binaries[beaker.ctrl_sys_estimate]",
+ " # The plot label generated from the settings of the drop down menus",
+ " lab=beaker.ctrl_quant_estimate+', '+beaker.ctrl_code_estimate+', '+beaker.ctrl_sys_estimate+', '+', '.join(array(keys).tolist())",
+ " # Error:",
+ " if beaker.ctrl_quant_estimate=='E_tot':",
+ " if beaker.ctrl_code_estimate=='FHI-aims':",
+ " ref_data=data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]]",
+ " else:",
+ " ref_data=data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],ref_keys[0]]",
+ " data=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code_estimate,",
+ " beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],keys, recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " xylist_bins_two[len(xylist_bins_two)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
+ " elif beaker.ctrl_quant_estimate=='E_coh':",
+ " db_con_mono = con_code[beaker.ctrl_code_estimate,'monomers']",
+ " db_con_bins = con_code[beaker.ctrl_code_estimate,'binaries']",
+ " if beaker.ctrl_code_estimate=='FHI-aims':",
+ " ref_data_mono=data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]]",
+ " ref_data_bins=data_ref[beaker.ctrl_code_estimate,'binaries'+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]] ",
+ " else: ",
+ " ref_data_mono=data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0]]",
+ " ref_data_bins=data_ref[beaker.ctrl_code_estimate,'binaries'+name_base[beaker.ctrl_code_estimate],ref_keys[0]] ",
+ " data_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
+ " beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate], keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_bins=get_data(db_con_bins, name_dict[ref_dict_binaries['binaries']], Z[ref_dict_binaries['binaries']],",
+ " beaker.ctrl_code_estimate,'binaries'+name_base[beaker.ctrl_code_estimate], keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw) ",
+ " xylist_bins_two[len(xylist_bins_two)]=get_xy_Ecoh(Z[mono_or_bin], (ref_data_mono-data_mono)/N[ref_dict_binaries['monomers']],ref_data_bins-data_bins,zeroinds),lab ",
+ " # Relative error",
+ " else:",
+ " if beaker.ctrl_code_estimate=='FHI-aims':",
+ " ref_data=(data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base_expanded[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]]-",
+ " data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]])",
+ " else: ",
+ " ref_data=(data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base_expanded[beaker.ctrl_code_estimate],ref_keys[0]]-",
+ " data_ref[beaker.ctrl_code_estimate,beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],ref_keys[0]])",
+ " data_one=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code_estimate, ",
+ " beaker.ctrl_sys_estimate+name_base[beaker.ctrl_code_estimate],keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_two=get_data(db_con, name_dict[mono_or_bin], Z[mono_or_bin], beaker.ctrl_code_estimate, ",
+ " beaker.ctrl_sys_estimate+name_base_expanded[beaker.ctrl_code_estimate],keys,",
+ " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data=data_two-data_one",
+ " xylist_bins_two[len(xylist_bins_two)]=get_xy(Z[mono_or_bin], N[mono_or_bin], ref_data, data),lab",
+ "",
+ " # Database for el. solids:",
+ " db_con_mono = con_code[beaker.ctrl_code_estimate,'monomers']",
+ " # Database for binaries:",
+ " db_con_bins = con_code[beaker.ctrl_code_estimate,'binaries']",
+ " # Error:",
+ " if beaker.ctrl_quant_estimate=='E_tot' or beaker.ctrl_quant_estimate=='E_coh':",
+ " # The plot label generated from the settings of the drop down menus",
+ " lab='Binaries pred., E_tot, '+beaker.ctrl_code_estimate+', '+', '.join(array(keys).tolist())",
+ " if beaker.ctrl_code_estimate=='FHI-aims':",
+ " ref_data_mono=data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]]",
+ " ref_data_bins=ref_data",
+ " else: ",
+ " ref_data_mono=data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0]]",
+ " ref_data_bins=ref_data ",
+ " data_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
+ " beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate], keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_bins=data",
+ " # Relative error",
+ " else:",
+ " # The plot label generated from the settings of the drop down menus",
+ " lab='Binaries pred., '+beaker.ctrl_quant_estimate+', '+beaker.ctrl_code_estimate+', '+', '.join(array(keys).tolist())",
+ " if beaker.ctrl_code_estimate=='FHI-aims':",
+ " ref_data_mono=(data_ref[beaker.ctrl_code_estimate,'monomers'+name_base_expanded[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]]-",
+ " data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0],ref_keys[1]])",
+ " ref_data_bins=ref_data",
+ " else:",
+ " ref_data_mono=(data_ref[beaker.ctrl_code_estimate,'monomers'+name_base_expanded[beaker.ctrl_code_estimate],ref_keys[0]]-",
+ " data_ref[beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate],ref_keys[0]])",
+ " ref_data_bins=ref_data ",
+ " data_one_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
+ " beaker.ctrl_code_estimate,'monomers'+name_base[beaker.ctrl_code_estimate], keys, recommended,",
+ " name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_two_mono=get_data(db_con_mono, name_dict[ref_dict_binaries['monomers']], Z[ref_dict_binaries['monomers']],",
+ " beaker.ctrl_code_estimate,'monomers'+name_base_expanded[beaker.ctrl_code_estimate], keys, ",
+ " recommended,name_dict_monos_gpaw,name_dict_bins_gpaw)",
+ " data_mono=data_two_mono-data_one_mono",
+ " data_bins=data",
+ " # Get the predicted error",
+ " data_pred=get_binary_error_from_solids((ref_data_mono-data_mono)/N[ref_dict_binaries['monomers']],binaries_to_monos_min,",
+ " N_bins_min,binaries_to_monos_max,N_bins_max,beaker.ctrl_pred) ",
+ " xylist_pred[len(xylist_pred)]=get_xy_predict(N_bins[zeroinds], (ref_data_bins-data_bins)[zeroinds], data_pred[zeroinds]),lab",
+ "elif beaker.ctrl_button_estimate==2:",
+ " if len(xylist_bins_two)>=1:",
+ " del xylist_bins_two[len(xylist_bins_two)-1]",
+ " if len(xylist_pred)>=1:",
+ " del xylist_pred[len(xylist_pred)-1]",
+ "elif beaker.ctrl_button_estimate==3:",
+ " xylist_bins_two={}",
+ " xylist_pred={}",
+ " ",
+ "# Matplotlib figure",
+ "fig=figure(0,(15,10))",
+ "# Axes",
+ "ax=fig.add_subplot(121)",
+ "ax2=fig.add_subplot(122)",
+ "# I like the grid.",
+ "ax.grid(True)",
+ "ax2.grid(True)",
+ "# Labels",
+ "ax.set_ylabel('$\\Delta$E per atom [eV]')",
+ "ax.set_xlabel('Z [#]')",
+ "ax2.set_xlabel('$\\Delta$E (estimated) per atom [eV]')",
+ "ax2.set_ylabel('$\\Delta$E per atom [eV]')",
+ "",
+ "#Plot",
+ "for i in arange(len(xylist_bins_two)):",
+ " ax.semilogy(xylist_bins_two[i][0][0],xylist_bins_two[i][0][1],'o',label=xylist_bins_two[i][1])",
+ "for i in arange(len(xylist_pred)):",
+ " ax2.loglog(xylist_pred[i][0][0],xylist_pred[i][0][1],'o',label=xylist_pred[i][1]) ",
+ "",
+ "# Diagonal line for right plot",
+ "ax2.set_ylim(ax.get_ylim()[0],ax.get_ylim()[1])",
+ "ax2.plot(ax2.get_xlim(),ax2.get_ylim(),'-k')",
+ "# Legend",
+ "ax.legend(numpoints=1,loc=4)",
+ "ax2.legend(numpoints=1,loc=4)",
+ "# Figure title",
+ "fig.suptitle('Observed/estimated error for binary systems')",
+ "# Show",
+ "fig.show()"
+ ],
+ "hidden": true
+ },
+ "output": {
+ "state": {},
+ "result": "<div class=\"output_subarea output_png\"><img 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NjI/X19URE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\nYZQkSZIktev/AS8r8r1JEYT9AAAAAElFTkSuQmCC\n\"></div>",
+ "selectedType": "Html",
+ "pluginName": "IPython",
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 1142,
+ "height": 710
+ },
+ "evaluatorReader": true,
+ "lineCount": 134
+ },
+ {
+ "id": "markdownANJbsd",
+ "type": "markdown",
+ "body": [
+ "<div style=\"font-size: 150%; font-weight: bold;\">Estimate error for arbitrary system</div>"
+ ],
+ "evaluatorReader": false
+ },
{
"id": "code1sT7ei",
"type": "code",
@@ -1135,11 +1408,11 @@
" function process_form() {",
" beaker.query = $('#query').val(); ",
" beaker.ctrl_val_xc = document.getElementById(\"errorbar_val_xcfunctional\").value;",
- " beaker.ctrl_val_kpt = document.getElementById(\"errorbar_val_kdensity\").value;",
+ " beaker.ctrl_val_kpt = 8;",
" beaker.ctrl_val_prec = document.getElementById(\"errorbar_val_precision\").value;",
" beaker.ctrl_val_tiers = document.getElementById(\"errorbar_val_tiers\").value;",
" beaker.ctrl_val_rel = document.getElementById(\"errorbar_val_relativity\").value;",
- " beaker.ctrl_val_pred = document.getElementById(\"errorbar_val_formula\").value;",
+ " beaker.ctrl_val_pred = 1;",
" beaker.ctrl_val_quant = document.getElementById(\"errorbar_val_quantity\").value;",
" beaker.ctrl_val_code = document.getElementById(\"errorbar_val_code\").value;",
" beaker.evaluate(\"process_formula_cell\");",
@@ -1156,7 +1429,7 @@
"",
" switch(code) {",
" case \"VASP\": ",
- " dprec.innerHTML = 'Precision';",
+ " dprec.innerHTML = 'Precision:';",
"",
" addDropdownChoice(pprec, \"Low\", \"Low\");",
" addDropdownChoice(pprec, \"Normal\", \"Normal\");",
@@ -1166,7 +1439,7 @@
" break;",
" ",
" case \"FHI-aims\": ",
- " dprec.innerHTML = 'Basis set';",
+ " dprec.innerHTML = 'Integration grid:';",
"",
" addDropdownChoice(pprec, \"light\", \"light\");",
" addDropdownChoice(pprec, \"tight\", \"tight\");",
@@ -1181,16 +1454,9 @@
" addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");",
" break;",
"",
- " case \"exciting\": ",
- " dprec.innerHTML = '';",
- "",
- "",
- " addDropdownChoice(pxc, \"pbe\", \"pbe\");",
- "",
- " break;",
" ",
" case \"GPAW\": ",
- " dprec.innerHTML = '$E_{cut}$';",
+ " dprec.innerHTML = '$E_{cut}$:';",
"",
" addDropdownChoice(pprec, \"300\", \"300\");",
" addDropdownChoice(pprec, \"400\", \"400\");",
@@ -1239,15 +1505,6 @@
" </select>",
" </td>",
" <td id=\"errorbar_val_xcfunctional_description\" style=\"white-space: pre;\"></td>",
- " <th>k-point density:</th>",
- " <td>",
- " <select id=\"errorbar_val_kdensity\" >",
- " <option value=\"2\" selected>2</option>",
- " <option value=\"4\">4</option> ",
- " <option value=\"8\">8</option> ",
- " </select>",
- " </td>",
- " <td id=\"errorbar_val_kdensity_description\" style=\"white-space: pre;\"></td>",
" <th id=\"errorbar_val_precision_name\">Precision:</th>",
" <td>",
" <select id=\"errorbar_val_precision\" >",
@@ -1259,20 +1516,12 @@
" <td id=\"errorbar_val_precision_description\" style=\"white-space: pre;\"></td>",
" </tr>",
" <tr>",
- " <th>tiers:</th>",
+ " <th>Tiers:</th>",
" <td><select id=\"errorbar_val_tiers\" ><!-- content inserted programmatically --></select></td>",
" <td id=\"errorbar_val_tiers_description\" style=\"white-space: pre;\"></td>",
" <th>relativity treatment:</th>",
" <td><select id=\"errorbar_val_relativity\" ><!-- content inserted programmatically --></select></td>",
" <td id=\"errorbar_val_relativity_description\" style=\"white-space: pre;\"></td>",
- " <th>Prediction formula:</th>",
- " <td>",
- " <select id=\"errorbar_val_formula\" >",
- " <option value=\"1\" selected>1</option>",
- " <option value=\"2\">2</option> ",
- " </select>",
- " </td>",
- " <td id=\"errorbar_val_formula\" style=\"white-space: pre;\"></td>",
"",
" </tr> ",
" ",
@@ -1290,7 +1539,6 @@
" <option value=\"VASP\">VASP</option>",
" <option value=\"FHI-aims\">FHI-aims</option>",
" <option value=\"GPAW\">GPAW</option> ",
- " <option value=\"exciting\">exciting</option> ",
" </select></td>",
" <td id=\"errorbar_val_code_description\" style=\"white-space: pre;\"></td>",
" </tr>",
@@ -1313,7 +1561,7 @@
"<code>FePO4</code>",
"</fontsize>",
"</p>",
- "<button onclick='process_form()'> Predict error </button>",
+ "<button onclick='process_form()'> Estimate error </button>",
"",
""
],
@@ -1324,14 +1572,14 @@
"result": {
"type": "BeakerDisplay",
"innertype": "Html",
- "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n \n function process_form() {\n beaker.query = $('#query').val(); \n beaker.ctrl_val_xc = document.getElementById(\"errorbar_val_xcfunctional\").value;\n beaker.ctrl_val_kpt = document.getElementById(\"errorbar_val_kdensity\").value;\n beaker.ctrl_val_prec = document.getElementById(\"errorbar_val_precision\").value;\n beaker.ctrl_val_tiers = document.getElementById(\"errorbar_val_tiers\").value;\n beaker.ctrl_val_rel = document.getElementById(\"errorbar_val_relativity\").value;\n beaker.ctrl_val_pred = document.getElementById(\"errorbar_val_formula\").value;\n beaker.ctrl_val_quant = document.getElementById(\"errorbar_val_quantity\").value;\n beaker.ctrl_val_code = document.getElementById(\"errorbar_val_code\").value;\n beaker.evaluate(\"process_formula_cell\");\n }\n \n function error_valUpdateForm() {\n var code = document.getElementById(\"errorbar_val_code\").value;\n \n var dprec = document.getElementById(\"errorbar_val_precision_name\");\n var pprec = document.getElementById(\"errorbar_val_precision\"); pprec.innerHTML = '';\n var prel = document.getElementById(\"errorbar_val_relativity\"); prel.innerHTML = '';\n var ptiers = document.getElementById(\"errorbar_val_tiers\"); ptiers.innerHTML = '';\n var pxc = document.getElementById(\"errorbar_val_xcfunctional\"); pxc.innerHTML = '';\n\n switch(code) {\n case \"VASP\": \n dprec.innerHTML = 'Precision';\n\n addDropdownChoice(pprec, \"Low\", \"Low\");\n addDropdownChoice(pprec, \"Normal\", \"Normal\");\n addDropdownChoice(pprec, \"Accurate\", \"Accurate\");\n addDropdownChoice(pxc, \"PBE\", \"PBE\");\n addDropdownChoice(pxc, \"LDA\", \"LDA\");\n break;\n \n case \"FHI-aims\": \n dprec.innerHTML = 'Basis set';\n\n addDropdownChoice(pprec, \"light\", \"light\");\n addDropdownChoice(pprec, \"tight\", \"tight\");\n addDropdownChoice(pprec, \"really_tight\", \"really_tight\");\n addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");\n addDropdownChoice(prel, \"zora\", \"zora\");\n addDropdownChoice(ptiers, \"minimal\", \"minimal\");\n addDropdownChoice(ptiers, \"standard\", \"standard\");\n addDropdownChoice(ptiers, \"tier1\", \"tier1\");\n addDropdownChoice(ptiers, \"tier2\", \"tier2\");\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n case \"exciting\": \n dprec.innerHTML = '';\n\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n\n break;\n \n case \"GPAW\": \n dprec.innerHTML = '$E_{cut}$';\n\n addDropdownChoice(pprec, \"300\", \"300\");\n addDropdownChoice(pprec, \"400\", \"400\");\n addDropdownChoice(pprec, \"500\", \"500\");\n addDropdownChoice(pprec, \"600\", \"600\");\n addDropdownChoice(pprec, \"700\", \"700\");\n addDropdownChoice(pprec, \"800\", \"800\");\n addDropdownChoice(pprec, \"900\", \"900\");\n addDropdownChoice(pprec, \"1000\", \"1000\");\n addDropdownChoice(pprec, \"1100\", \"1100\");\n addDropdownChoice(pprec, \"1200\", \"1100\");\n addDropdownChoice(pprec, \"1300\", \"1100\");\n addDropdownChoice(pprec, \"1400\", \"1100\");\n addDropdownChoice(pprec, \"1500\", \"1100\");\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n\n }\n }\n\n\n\n</script>\n\n<style type=\"text/css\">\n \n .error_val_table th { font-weight: bold; padding-right: 2ex; }\n .error_val_table td input { margin-right: 1ex; }\n \n</style>\n\n<!-- Controls area -->\n\n<div class=\"error_val_control\">\n <table class=\"error_val_table\">\n \n <tbody><tr>\n <th>XC-Functional:</th>\n <td>\n <select id=\"errorbar_val_xcfunctional\">\n <option value=\"PBE\" selected=\"\">PBE</option>\n <option value=\"LDA\">LDA</option>\n </select>\n </td>\n <td id=\"errorbar_val_xcfunctional_description\" style=\"white-space: pre;\"></td>\n <th>k-point density:</th>\n <td>\n <select id=\"errorbar_val_kdensity\">\n <option value=\"2\" selected=\"\">2</option>\n <option value=\"4\">4</option> \n <option value=\"8\">8</option> \n </select>\n </td>\n <td id=\"errorbar_val_kdensity_description\" style=\"white-space: pre;\"></td>\n <th id=\"errorbar_val_precision_name\">Precision:</th>\n <td>\n <select id=\"errorbar_val_precision\">\n <option value=\"Low\" selected=\"\">Low</option>\n <option value=\"Normal\">Normal</option> \n <option value=\"Accurate\">Acurate</option> \n </select>\n </td>\n <td id=\"errorbar_val_precision_description\" style=\"white-space: pre;\"></td>\n </tr>\n <tr>\n <th>tiers:</th>\n <td><select id=\"errorbar_val_tiers\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_val_tiers_description\" style=\"white-space: pre;\"></td>\n <th>relativity treatment:</th>\n <td><select id=\"errorbar_val_relativity\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_val_relativity_description\" style=\"white-space: pre;\"></td>\n <th>Prediction formula:</th>\n <td>\n <select id=\"errorbar_val_formula\">\n <option value=\"1\" selected=\"\">1</option>\n <option value=\"2\">2</option> \n </select>\n </td>\n <td id=\"errorbar_val_formula\" style=\"white-space: pre;\"></td>\n\n </tr> \n \n <tr>\n <th>Quantity:</th>\n <td><select id=\"errorbar_val_quantity\">\n <option value=\"E_tot\">Total Energy</option>\n <option value=\"relR\">relative Energy</option>\n </select></td>\n <td id=\"errorbar_val_quantity_description\" style=\"white-space: pre;\"></td> \n \n\n <th>Code:</th>\n <td><select id=\"errorbar_val_code\" onchange=\"error_valUpdateForm()\">\n <option value=\"VASP\">VASP</option>\n <option value=\"FHI-aims\">FHI-aims</option>\n <option value=\"GPAW\">GPAW</option> \n <option value=\"exciting\">exciting</option> \n </select></td>\n <td id=\"errorbar_val_code_description\" style=\"white-space: pre;\"></td>\n </tr>\n \n </tbody></table>\n \n</div>\n\n<br><br>\n<p>Enter: Formula:</p>\n\n<p>\n <input id=\"query\" value=\"H2\" size=\"40\" onkeydown=\"if (event.keyCode == 13) process_form()\" type=\"text\">\n</p>\n<p>Examples:</p>\n<p>\n <fontsize=4>\n<code>O2</code> <br>\n<code>FePO4</code>\n\n</fontsize=4></p>\n<button onclick=\"process_form()\"> Predict error </button>\n\n"
+ "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<script>\n \n function process_form() {\n beaker.query = $('#query').val(); \n beaker.ctrl_val_xc = document.getElementById(\"errorbar_val_xcfunctional\").value;\n beaker.ctrl_val_kpt = 8;\n beaker.ctrl_val_prec = document.getElementById(\"errorbar_val_precision\").value;\n beaker.ctrl_val_tiers = document.getElementById(\"errorbar_val_tiers\").value;\n beaker.ctrl_val_rel = document.getElementById(\"errorbar_val_relativity\").value;\n beaker.ctrl_val_pred = 1;\n beaker.ctrl_val_quant = document.getElementById(\"errorbar_val_quantity\").value;\n beaker.ctrl_val_code = document.getElementById(\"errorbar_val_code\").value;\n beaker.evaluate(\"process_formula_cell\");\n }\n \n function error_valUpdateForm() {\n var code = document.getElementById(\"errorbar_val_code\").value;\n \n var dprec = document.getElementById(\"errorbar_val_precision_name\");\n var pprec = document.getElementById(\"errorbar_val_precision\"); pprec.innerHTML = '';\n var prel = document.getElementById(\"errorbar_val_relativity\"); prel.innerHTML = '';\n var ptiers = document.getElementById(\"errorbar_val_tiers\"); ptiers.innerHTML = '';\n var pxc = document.getElementById(\"errorbar_val_xcfunctional\"); pxc.innerHTML = '';\n\n switch(code) {\n case \"VASP\": \n dprec.innerHTML = 'Precision:';\n\n addDropdownChoice(pprec, \"Low\", \"Low\");\n addDropdownChoice(pprec, \"Normal\", \"Normal\");\n addDropdownChoice(pprec, \"Accurate\", \"Accurate\");\n addDropdownChoice(pxc, \"PBE\", \"PBE\");\n addDropdownChoice(pxc, \"LDA\", \"LDA\");\n break;\n \n case \"FHI-aims\": \n dprec.innerHTML = 'Integration grid:';\n\n addDropdownChoice(pprec, \"light\", \"light\");\n addDropdownChoice(pprec, \"tight\", \"tight\");\n addDropdownChoice(pprec, \"really_tight\", \"really_tight\");\n addDropdownChoice(prel, \"atomic_zora\", \"atomic_zora\");\n addDropdownChoice(prel, \"zora\", \"zora\");\n addDropdownChoice(ptiers, \"minimal\", \"minimal\");\n addDropdownChoice(ptiers, \"standard\", \"standard\");\n addDropdownChoice(ptiers, \"tier1\", \"tier1\");\n addDropdownChoice(ptiers, \"tier2\", \"tier2\");\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n \n case \"GPAW\": \n dprec.innerHTML = '$E_{cut}$:';\n\n addDropdownChoice(pprec, \"300\", \"300\");\n addDropdownChoice(pprec, \"400\", \"400\");\n addDropdownChoice(pprec, \"500\", \"500\");\n addDropdownChoice(pprec, \"600\", \"600\");\n addDropdownChoice(pprec, \"700\", \"700\");\n addDropdownChoice(pprec, \"800\", \"800\");\n addDropdownChoice(pprec, \"900\", \"900\");\n addDropdownChoice(pprec, \"1000\", \"1000\");\n addDropdownChoice(pprec, \"1100\", \"1100\");\n addDropdownChoice(pprec, \"1200\", \"1100\");\n addDropdownChoice(pprec, \"1300\", \"1100\");\n addDropdownChoice(pprec, \"1400\", \"1100\");\n addDropdownChoice(pprec, \"1500\", \"1100\");\n\n addDropdownChoice(pxc, \"pbe\", \"pbe\");\n addDropdownChoice(pxc, \"pw-lda\", \"pbw-lda\");\n break;\n\n\n }\n }\n\n\n\n</script>\n\n<style type=\"text/css\">\n \n .error_val_table th { font-weight: bold; padding-right: 2ex; }\n .error_val_table td input { margin-right: 1ex; }\n \n</style>\n\n<!-- Controls area -->\n\n<div class=\"error_val_control\">\n <table class=\"error_val_table\">\n \n <tbody><tr>\n <th>XC-Functional:</th>\n <td>\n <select id=\"errorbar_val_xcfunctional\">\n <option value=\"PBE\" selected=\"\">PBE</option>\n <option value=\"LDA\">LDA</option>\n </select>\n </td>\n <td id=\"errorbar_val_xcfunctional_description\" style=\"white-space: pre;\"></td>\n <th id=\"errorbar_val_precision_name\">Precision:</th>\n <td>\n <select id=\"errorbar_val_precision\">\n <option value=\"Low\" selected=\"\">Low</option>\n <option value=\"Normal\">Normal</option> \n <option value=\"Accurate\">Acurate</option> \n </select>\n </td>\n <td id=\"errorbar_val_precision_description\" style=\"white-space: pre;\"></td>\n </tr>\n <tr>\n <th>Tiers:</th>\n <td><select id=\"errorbar_val_tiers\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_val_tiers_description\" style=\"white-space: pre;\"></td>\n <th>relativity treatment:</th>\n <td><select id=\"errorbar_val_relativity\"><!-- content inserted programmatically --></select></td>\n <td id=\"errorbar_val_relativity_description\" style=\"white-space: pre;\"></td>\n\n </tr> \n \n <tr>\n <th>Quantity:</th>\n <td><select id=\"errorbar_val_quantity\">\n <option value=\"E_tot\">Total Energy</option>\n <option value=\"relR\">relative Energy</option>\n </select></td>\n <td id=\"errorbar_val_quantity_description\" style=\"white-space: pre;\"></td> \n \n\n <th>Code:</th>\n <td><select id=\"errorbar_val_code\" onchange=\"error_valUpdateForm()\">\n <option value=\"VASP\">VASP</option>\n <option value=\"FHI-aims\">FHI-aims</option>\n <option value=\"GPAW\">GPAW</option> \n </select></td>\n <td id=\"errorbar_val_code_description\" style=\"white-space: pre;\"></td>\n </tr>\n \n </tbody></table>\n \n</div>\n\n<br><br>\n<p>Enter: Formula:</p>\n\n<p>\n <input id=\"query\" value=\"H2\" size=\"40\" onkeydown=\"if (event.keyCode == 13) process_form()\" type=\"text\">\n</p>\n<p>Examples:</p>\n<p>\n <fontsize=4>\n<code>O2</code> <br>\n<code>FePO4</code>\n\n</fontsize=4></p>\n<button onclick=\"process_form()\"> Estimate error </button>\n\n"
},
"selectedType": "BeakerDisplay",
"elapsedTime": 0,
"height": 331
},
"evaluatorReader": true,
- "lineCount": 186,
+ "lineCount": 161,
"initialization": true
},
{
@@ -1399,14 +1647,14 @@
"outputdata": [
{
"type": "out",
- "value": "Total energy error for H2: -22.8954475 meV (per atom)\n"
+ "value": "Total energy error for H2: -22.199865 meV (per atom)\n"
}
]
},
"selectedType": "Results",
"pluginName": "IPython",
- "shellId": "7D657CFFBE3442BB86E5CDE1D2A08A35",
- "elapsedTime": 2341,
+ "shellId": "27A64174BE534D8A81CD4007A8828BAE",
+ "elapsedTime": 2252,
"height": 55
},
"evaluatorReader": true,
@@ -1427,13 +1675,37 @@
"ctrl_button": 3,
"query": "H2",
"ctrl_val_xc": "PBE",
- "ctrl_val_kpt": "2",
+ "ctrl_val_kpt": 8,
"ctrl_val_prec": "Low",
"ctrl_val_tiers": "",
"ctrl_val_rel": "",
- "ctrl_val_pred": "1",
+ "ctrl_val_pred": 1,
"ctrl_val_quant": "E_tot",
- "ctrl_val_code": "VASP"
+ "ctrl_val_code": "VASP",
+ "ctrl_estimate_xc": "PBE",
+ "ctrl_estimate_kpt": 8,
+ "ctrl_estimate_prec": "Low",
+ "ctrl_estimate_tiers": "",
+ "ctrl_estimate_rel": "",
+ "ctrl_estimate_estimate_xc": "PBE",
+ "ctrl_estimate_estimate_kpt": "2",
+ "ctrl_estimate_estimate_prec": "Low",
+ "ctrl_estimate_estimate_tiers": "",
+ "ctrl_estimate_estimate_rel": "",
+ "ctrl_estimate_button": 1,
+ "ctrl_xc_estimate": "PBE",
+ "ctrl_kpt_estimate": 8,
+ "ctrl_button_estimate": 3,
+ "ctrl_estimate_pred": 1,
+ "ctrl_estimate_quant": "E_tot",
+ "ctrl_estimate_code": "VASP",
+ "ctrl_prec_estimate": "Low",
+ "ctrl_tiers_estimate": "",
+ "ctrl_rel_estimate": "",
+ "ctrl_pred_estimate": 1,
+ "ctrl_quant_estimate": "E_tot",
+ "ctrl_code_estimate": "VASP",
+ "ctrl_sys_estimate": "binaries"
},
"locked": true
}