diff --git a/.gitignore b/.gitignore index 5cab62d3..093e835f 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,6 @@ dist/* # Local pre-commit hooks .pre-commit-config.yaml + +# Jupyter notebook checkpoints +.ipynb_checkpoints/* diff --git a/RATapi/examples/absorption/absorption.ipynb b/RATapi/examples/absorption/absorption.ipynb new file mode 100644 index 00000000..d0af24dd --- /dev/null +++ b/RATapi/examples/absorption/absorption.ipynb @@ -0,0 +1,305 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Absorption (imaginary SLD) - effect below the critical edge\n", + "\n", + "RAT allows the use of an imaginary, as well as real part of the SLD. The effect of this is usually seen below the critical edge, and must sometimes be accounted for.\n", + "\n", + "The example used here is Custom Layers. It analyses a bilayer sample on a permalloy / gold substrate, measured using polarised neutrons, against D2O and H2O, leading to 4 contrasts in total. Absorption (i.e. imaginary SLD) is defined for Gold and the Permalloy, to account for non-flat data below the critical edge.\n", + "\n", + "For absorption with standard layers, an additional column appears in the layers block to accommodate the imagainary component of the SLD. For custom functions, we add an extra column to the output.\n", + "\n", + "For all calculation types, to activate this functionality it is necessary to set the 'absorption' flag when creating the project." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(name=\"Absorption example\", calculation=\"non polarised\", model=\"custom layers\", geometry=\"substrate/liquid\", absorption=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now define our parameters, noting that each SLD parameter has both a real and imaginary component:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True),\n", + " Parameter(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True),\n", + " Parameter(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True),\n", + " Parameter(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True),\n", + " Parameter(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True),\n", + " Parameter(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True),\n", + " #\n", + " Parameter(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True),\n", + " Parameter(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True),\n", + " Parameter(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True),\n", + " Parameter(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True),\n", + " #\n", + " Parameter(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True),\n", + " Parameter(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True),\n", + " Parameter(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True),\n", + " #\n", + " Parameter(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True),\n", + " #\n", + " Parameter(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True),\n", + " Parameter(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True),\n", + " Parameter(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True),\n", + " Parameter(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the bulk in and bulk out parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.0e-6, value=2.073e-6, max=2.1e-6)\n", + "\n", + "problem.bulk_out.set_fields(0, name=\"D2O\", min=5.8e-06, value=6.21e-06, max=6.35e-06, fit=True)\n", + "problem.bulk_out.append(name=\"H2O\", min=-5.6e-07, value=-3.15e-07, max=0.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use a different scalefactor for each dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "del problem.scalefactors[0]\n", + "problem.scalefactors.append(name=\"Scalefactor 1\", min=0.5, value=1, max=1.5, fit=True)\n", + "problem.scalefactors.append(name=\"Scalefactor 2\", min=0.5, value=1, max=1.5, fit=True)\n", + "problem.scalefactors.append(name=\"Scalefactor 3\", min=0.5, value=1, max=1.5, fit=True)\n", + "problem.scalefactors.append(name=\"Scalefactor 4\", min=0.5, value=1, max=1.5, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the backgrounds and resolutions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "del problem.backgrounds[0]\n", + "del problem.background_parameters[0]\n", + "\n", + "problem.background_parameters.append(name=\"Background parameter 1\", min=5.0e-08, value=7.88e-06, max=9.0e-05, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter 2\", min=1.0e-08, value=5.46e-06, max=9.0e-05, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter 3\", min=1.0e-06, value=9.01e-06, max=9.0e-05, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter 4\", min=1.0e-06, value=5.61e-06, max=9.0e-05, fit=True)\n", + "\n", + "problem.backgrounds.append(name=\"Background 1\", type=\"constant\", value_1=\"Background parameter 1\")\n", + "problem.backgrounds.append(name=\"Background 2\", type=\"constant\", value_1=\"Background parameter 2\")\n", + "problem.backgrounds.append(name=\"Background 3\", type=\"constant\", value_1=\"Background parameter 3\")\n", + "problem.backgrounds.append(name=\"Background 4\", type=\"constant\", value_1=\"Background parameter 4\")\n", + "\n", + "# Make the resolution fittable\n", + "problem.resolution_parameters.set_fields(0, fit=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the datasets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = pathlib.Path(\"../data\")\n", + "\n", + "data_1 = np.loadtxt(data_path / \"D2O_spin_down.dat\")\n", + "problem.data.append(name=\"D2O_dn\", data=data_1)\n", + "\n", + "data_2 = np.loadtxt(data_path / \"D2O_spin_up.dat\")\n", + "problem.data.append(name=\"D2O_up\", data=data_2)\n", + "\n", + "data_3 = np.loadtxt(data_path / \"H2O_spin_down.dat\")\n", + "problem.data.append(name=\"H2O_dn\", data=data_3)\n", + "\n", + "data_4 = np.loadtxt(data_path / \"H2O_spin_up.dat\")\n", + "problem.data.append(name=\"H2O_up\", data=data_4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add the custom file. We can see that we add an extra column for the output in our custom function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem.custom_files.append(\n", + " name=\"DPPC absorption\",\n", + " filename=\"volume_thiol_bilayer.py\",\n", + " language=\"python\",\n", + " path=pathlib.Path.cwd().resolve(),\n", + ")\n", + "Code(filename='volume_thiol_bilayer.py', language='python')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, add the contrasts:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem.contrasts.append(\n", + " name=\"D2O Down\",\n", + " data=\"D2O_dn\",\n", + " background=\"Background 1\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"D2O\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " resolution=\"Resolution 1\",\n", + " resample=True,\n", + " model=[\"DPPC absorption\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"D2O Up\",\n", + " data=\"D2O_up\",\n", + " background=\"Background 2\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"D2O\",\n", + " scalefactor=\"Scalefactor 2\",\n", + " resolution=\"Resolution 1\",\n", + " resample=True,\n", + " model=[\"DPPC absorption\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"H2O Down\",\n", + " data=\"H2O_dn\",\n", + " background=\"Background 3\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"H2O\",\n", + " scalefactor=\"Scalefactor 3\",\n", + " resolution=\"Resolution 1\",\n", + " resample=True,\n", + " model=[\"DPPC absorption\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"H2O Up\",\n", + " data=\"H2O_up\",\n", + " background=\"Background 4\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"H2O\",\n", + " scalefactor=\"Scalefactor 4\",\n", + " resolution=\"Resolution 1\",\n", + " resample=True,\n", + " model=[\"DPPC absorption\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now run RAT and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "controls = RAT.Controls(parallel=\"contrasts\", resampleMinAngle=0.9, resampleNPoints=150.0)\n", + "problem, results = RAT.run(problem, controls)\n", + "\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RATapi/examples/absorption/absorption.py b/RATapi/examples/absorption/absorption.py index 0656d1dd..cbf9dc63 100644 --- a/RATapi/examples/absorption/absorption.py +++ b/RATapi/examples/absorption/absorption.py @@ -1,4 +1,3 @@ -import os import pathlib import numpy as np @@ -78,18 +77,18 @@ def absorption(): problem.resolution_parameters.set_fields(0, fit=True) # Now add the data we need - data_path = os.path.join(pathlib.Path(__file__).parents[1].resolve(), "data") + data_path = pathlib.Path(__file__).parents[1] / "data" - data_1 = np.loadtxt(os.path.join(data_path, "D2O_spin_down.dat")) + data_1 = np.loadtxt(data_path / "D2O_spin_down.dat") problem.data.append(name="D2O_dn", data=data_1) - data_2 = np.loadtxt(os.path.join(data_path, "D2O_spin_up.dat")) + data_2 = np.loadtxt(data_path / "D2O_spin_up.dat") problem.data.append(name="D2O_up", data=data_2) - data_3 = np.loadtxt(os.path.join(data_path, "H2O_spin_down.dat")) + data_3 = np.loadtxt(data_path / "H2O_spin_down.dat") problem.data.append(name="H2O_dn", data=data_3) - data_4 = np.loadtxt(os.path.join(data_path, "H2O_spin_up.dat")) + data_4 = np.loadtxt(data_path / "H2O_spin_up.dat") problem.data.append(name="H2O_up", data=data_4) # Add the custom file diff --git a/RATapi/examples/absorption/volume_thiol_bilayer.py b/RATapi/examples/absorption/volume_thiol_bilayer.py index f5ac1bbc..f64f16ff 100644 --- a/RATapi/examples/absorption/volume_thiol_bilayer.py +++ b/RATapi/examples/absorption/volume_thiol_bilayer.py @@ -130,7 +130,7 @@ def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast): CW = [cwThick, bulk_out[contrast], 0, bilayerRough] - if contrast == 1 or contrast == 3: + if contrast == 0 or contrast == 2: output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER] else: output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER] diff --git a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb index d437a732..c3b5e343 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb +++ b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb @@ -4,22 +4,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Convert between RasCAL1 and RAT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RasCAL1 (R1) project structs can be converted to RAT `Project` classes, and vice versa.\n", - "This is done via the functions `r1_to_project_class` and `project_class_to_r1`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RasCAL1 to RAT\n", + "### RasCAL-1 to RAT\n", + "\n", + "RasCAL-1 (R1) project structs can be converted to RAT Project classes, and vice versa. This is done via the functions `r1_to_project_class` and `project_class_to_r1`.\n", + "\n", "Converting from R1 to a `Project` is very simple. We use the example R1 project in the file `R1monolayerVolumeModel.mat`, which is a project for analysing a monolayer of DSPC with various deuterations (tail-deuterated, head-deuterated, fully deuterated, hydrogenated)" ] }, @@ -32,9 +20,140 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: ----------------------------------------------------------------------------------------------\n", + "\n", + "monolayerVolumeModel\n", + "\n", + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "non polarised\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "custom layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "air/substrate\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 2.9979642781948908 | 8.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Area per molecule | 47.0 | 53.052680457664785 | 100.0 | True | uniform | 0.0 | inf |\n", + "| 2 | Head Thickness | 7.0 | 12.276333836779942 | 20.0 | True | uniform | 0.0 | inf |\n", + "| 3 | Theta | 0.0 | 28.870541049836262 | 50.0 | True | uniform | 0.0 | inf |\n", + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+-----+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------+-----+-------+-----+-------+------------+-----+-------+\n", + "| 0 | Air | 0.0 | 0.0 | 0.0 | False | uniform | 0.0 | inf |\n", + "+-------+------+-----+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+---------+----------+---------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------+---------+----------+---------+-------+------------+-----+-------+\n", + "| 0 | D2O | 6.3e-06 | 6.35e-06 | 6.4e-06 | False | uniform | 0.0 | inf |\n", + "| 1 | ACMW | -5e-07 | 0.0 | 5e-07 | False | uniform | 0.0 | inf |\n", + "+-------+------+---------+----------+---------+-------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+-----+--------------------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+-----+--------------------+-----+-------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.1 | 0.2272676786810902 | 0.4 | False | uniform | 0.0 | inf |\n", + "+-------+---------------+-----+--------------------+-----+-------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+------------------------+-------+------------------------+-------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------------------------+-------+------------------------+-------+------+------------+-----+-------+\n", + "| 0 | Background parameter 1 | 1e-07 | 2.2653463958223856e-06 | 7e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | Background parameter 2 | 1e-07 | 5.7431759430575025e-06 | 7e-06 | True | uniform | 0.0 | inf |\n", + "+-------+------------------------+-------+------------------------+-------+------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+-----------------+----------+------------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+-----------------+----------+------------------------+---------+---------+---------+---------+\n", + "| 0 | Background D2O | constant | Background parameter 1 | | | | |\n", + "| 1 | Background ACMW | constant | Background parameter 2 | | | | |\n", + "+-------+-----------------+----------+------------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+------------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution parameter 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+------------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+------------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+------------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution parameter 1 | | | | |\n", + "+-------+--------------+----------+------------------------+---------+---------+---------+---------+\n", + "\n", + "Custom Files: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+-----------+-------------+---------------+----------+------+\n", + "| index | name | filename | function name | language | path |\n", + "+-------+-----------+-------------+---------------+----------+------+\n", + "| 0 | Model_IIb | Model_IIb.m | Model_IIb | matlab | . |\n", + "+-------+-----------+-------------+---------------+----------+------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------------+----------------------+---------------------+---------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+------------+----------------------+---------------------+---------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "| 1 | d70acmw20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 2 | d70d2o20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 3 | d13acmw20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 4 | d13d2o20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 5 | d83acmw20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 6 | d83d2o20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "| 7 | hd2o20 | Data array: [51 x 3] | [0.051793, 0.58877] | [0.051793, 0.58877] |\n", + "+-------+------------+----------------------+---------------------+---------------------+\n", + "\n", + "Contrasts: -----------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+-----------+-----------------+-------------------+---------+----------+---------------+--------------+----------+-----------+\n", + "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | model |\n", + "+-------+--------------+-----------+-----------------+-------------------+---------+----------+---------------+--------------+----------+-----------+\n", + "| 0 | d70, acmw | d70acmw20 | Background ACMW | add | Air | ACMW | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 1 | d70 d2o | d70d2o20 | Background D2O | add | Air | D2O | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 2 | d13 acmw | d13acmw20 | Background ACMW | add | Air | ACMW | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 3 | d13 d2o | d13d2o20 | Background D2O | add | Air | D2O | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 4 | d83 acmw | d83acmw20 | Background ACMW | add | Air | ACMW | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 5 | d83 d2o | d83d2o20 | Background D2O | add | Air | D2O | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "| 6 | fully h, D2O | hd2o20 | Background D2O | add | Air | D2O | Scalefactor 1 | Resolution 1 | False | Model_IIb |\n", + "+-------+--------------+-----------+-----------------+-------------------+---------+----------+---------------+--------------+----------+-----------+\n", + "\n", + "\n" + ] + } + ], "source": [ "from RATapi.utils.convert import r1_to_project_class\n", "\n", @@ -46,14 +165,29 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Note that there are various features of RAT which do not feature in R1, such as `prior_type`, `mu` and `sigma` for parameters. These are given sensible default values (again e.g. for parameters, `prior_type = uniform`, `mu = 0.0`, `sigma=inf`), but you may change these if you would like to use these new features:" + "Note that there are various features of RAT which do not feature in RasCAL-1, such as `prior_type`, `mu` and `sigma` for parameters. These are given sensible default values (again e.g. for parameters, `prior_type = uniform`, `mu = 0.0`, `sigma=inf`), but you may change these if you would like to use these new features:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 2.9979642781948908 | 8.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Area per molecule | 47.0 | 53.052680457664785 | 100.0 | True | uniform | 0.0 | 50.0 |\n", + "| 2 | Head Thickness | 7.0 | 12.276333836779942 | 20.0 | True | gaussian | 0.0 | inf |\n", + "| 3 | Theta | 0.0 | 28.870541049836262 | 50.0 | True | uniform | 2.0 | inf |\n", + "+-------+---------------------+------+--------------------+-------+------+------------+-----+-------+\n" + ] + } + ], "source": [ "project.parameters[\"Head Thickness\"].prior_type = 'gaussian'\n", "project.parameters[\"Theta\"].mu = 2.0\n", @@ -71,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -100,16 +234,180 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### RAT to RasCAL1\n", + "### RAT to RasCAL-1\n", "\n", "To demonstrate the other way around, we will use the DSPC lipid bilayer model project from another tutorial." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.029 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Name: ----------------------------------------------------------------------------------------------\n", + "\n", + "original_dspc_bilayer\n", + "\n", + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "non polarised\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "standard layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "substrate/liquid\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 3.0 | 10.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Oxide Thickness | 5.0 | 19.54 | 60.0 | True | uniform | 0.0 | inf |\n", + "| 2 | Oxide SLD | 3.39e-06 | 3.39e-06 | 3.41e-06 | False | uniform | 0.0 | inf |\n", + "| 3 | SAM Tails Thickness | 15.0 | 22.66 | 35.0 | True | uniform | 0.0 | inf |\n", + "| 4 | SAM Tails SLD | -5e-07 | -4.01e-07 | -3e-07 | False | uniform | 0.0 | inf |\n", + "| 5 | SAM Tails Hydration | 1.0 | 5.252 | 50.0 | True | uniform | 0.0 | inf |\n", + "| 6 | SAM Roughness | 1.0 | 5.64 | 15.0 | True | uniform | 0.0 | inf |\n", + "| 7 | CW Thickness | 10.0 | 17.12 | 28.0 | True | uniform | 0.0 | inf |\n", + "| 8 | CW SLD | 0.0 | 0.0 | 1e-09 | False | uniform | 0.0 | inf |\n", + "| 9 | SAM Heads Thickness | 5.0 | 8.56 | 17.0 | True | gaussian | 10.0 | 2.0 |\n", + "| 10 | SAM Heads SLD | 1e-07 | 1.75e-06 | 2e-06 | False | uniform | 0.0 | inf |\n", + "| 11 | SAM Heads Hydration | 10.0 | 45.45 | 50.0 | True | uniform | 30.0 | 3.0 |\n", + "| 12 | Bilayer Heads Thickness | 7.0 | 10.7 | 17.0 | True | gaussian | 10.0 | 2.0 |\n", + "| 13 | Bilayer Heads SLD | 5e-07 | 1.47e-06 | 1.5e-06 | False | uniform | 0.0 | inf |\n", + "| 14 | Bilayer Roughness | 2.0 | 6.014 | 15.0 | True | uniform | 0.0 | inf |\n", + "| 15 | Bilayer Tails Thickness | 14.0 | 17.82 | 22.0 | True | uniform | 0.0 | inf |\n", + "| 16 | Bilayer Tails SLD | -5e-07 | -4.61e-07 | 0.0 | False | uniform | 0.0 | inf |\n", + "| 17 | Bilayer Tails Hydration | 10.0 | 17.64 | 50.0 | True | uniform | 0.0 | inf |\n", + "| 18 | Bilayer Heads Hydration | 10.0 | 36.15 | 50.0 | True | gaussian | 30.0 | 3.0 |\n", + "| 19 | CW Hydration | 99.9 | 100.0 | 100.0 | False | uniform | 0.0 | inf |\n", + "| 20 | Oxide Hydration | 0.0 | 23.61 | 60.0 | True | uniform | 0.0 | inf |\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "| 0 | Silicon | 2e-06 | 2.073e-06 | 2.1e-06 | False | uniform | 0.0 | inf |\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "| 0 | D2O | 5.5e-06 | 5.98e-06 | 6.4e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | SMW | 1e-06 | 2.21e-06 | 4.99e-06 | True | uniform | 0.0 | inf |\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.05 | 0.1 | 0.2 | False | uniform | 0.0 | inf |\n", + "| 1 | Scalefactor 2 | 0.05 | 0.15 | 0.2 | False | uniform | 0.0 | inf |\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "| 0 | Background parameter D2O | 5e-10 | 2.23e-06 | 7e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | Background parameter SMW | 1e-10 | 3.38e-06 | 4.99e-06 | True | uniform | 0.0 | inf |\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| 0 | D2O Background | constant | Background parameter D2O | | | | |\n", + "| 1 | SMW Background | constant | Background parameter SMW | | | | |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "| 1 | dspc_bil_D2O | Data array: [82 x 3] | [0.011403, 0.59342] | [0.011403, 0.59342] |\n", + "| 2 | dspc_bil_smw | Data array: [82 x 3] | [0.011403, 0.59342] | [0.011403, 0.59342] |\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "\n", + "Layers: --------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "| index | name | thickness | SLD | roughness | hydration | hydrate with |\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "| 0 | Oxide | Oxide Thickness | Oxide SLD | Substrate Roughness | Oxide Hydration | bulk out |\n", + "| 1 | SAM Tails | SAM Tails Thickness | SAM Tails SLD | SAM Roughness | SAM Tails Hydration | bulk out |\n", + "| 2 | SAM Heads | SAM Heads Thickness | SAM Heads SLD | SAM Roughness | SAM Heads Hydration | bulk out |\n", + "| 3 | Central Water | CW Thickness | CW SLD | Bilayer Roughness | CW Hydration | bulk out |\n", + "| 4 | Bilayer Heads | Bilayer Heads Thickness | Bilayer Heads SLD | Bilayer Roughness | Bilayer Heads Hydration | bulk out |\n", + "| 5 | Bilayer Tails | Bilayer Tails Thickness | Bilayer Tails SLD | Bilayer Roughness | Bilayer Tails Hydration | bulk out |\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "\n", + "Contrasts: -----------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | model |\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "| 0 | D2O | dspc_bil_D2O | D2O Background | add | Silicon | D2O | Scalefactor 1 | Resolution 1 | False | Oxide |\n", + "| | | | | | | | | | | SAM Tails |\n", + "| | | | | | | | | | | SAM Heads |\n", + "| | | | | | | | | | | Central Water |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | Oxide |\n", + "| | | | | | | | | | | SAM Tails |\n", + "| | | | | | | | | | | SAM Heads |\n", + "| | | | | | | | | | | Central Water |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "\n", + "\n" + ] + } + ], "source": [ "from RATapi.examples import DSPC_standard_layers\n", "lipid_bilayer_project = DSPC_standard_layers()[0]\n", @@ -129,9 +427,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'NoneType' object has no attribute 'result'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpprint\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pp \u001b[38;5;66;03m# for printing the struct\u001b[39;00m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# save to a file called lipid_bilayer.mat\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m \u001b[43mproject_class_to_r1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlipid_bilayer_project\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlipid_bilayer.mat\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# return as a Python dictionary\u001b[39;00m\n\u001b[1;32m 8\u001b[0m struct \u001b[38;5;241m=\u001b[39m project_class_to_r1(lipid_bilayer_project, return_struct\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "File \u001b[0;32m/mnt/c/Users/gnn85523/projects/python-RAT/RATapi/utils/convert.py:497\u001b[0m, in \u001b[0;36mproject_class_to_r1\u001b[0;34m(project, filename, return_struct)\u001b[0m\n\u001b[1;32m 493\u001b[0m filename \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.mat\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 494\u001b[0m \u001b[38;5;66;03m# scipy.io.savemat doesn't do cells properly:\u001b[39;00m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;66;03m# https://github.com/scipy/scipy/issues/3756\u001b[39;00m\n\u001b[1;32m 496\u001b[0m \u001b[38;5;66;03m# rather than fiddling we just use matlab\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m eng \u001b[38;5;241m=\u001b[39m \u001b[43mwrappers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart_matlab\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m()\n\u001b[1;32m 498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m eng \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 499\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatlabengine is not installed.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'result'" + ] + } + ], "source": [ "from RATapi.utils.convert import project_class_to_r1\n", "from pprint import pp # for printing the struct\n", @@ -147,7 +458,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -165,5 +476,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/RATapi/examples/domains/domains_custom_XY.ipynb b/RATapi/examples/domains/domains_custom_XY.ipynb new file mode 100644 index 00000000..9e19f2ed --- /dev/null +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -0,0 +1,503 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simple example of a layer containing domains using a custom XY model\n", + "\n", + "Domains custom XY models operate in the same way as domains custom layer models, in that there is an additional input to the custom model specifying the domain to be calculated:\n", + "\n", + "This is then used within the function to calculate the correct SLD profile for each contrast and domain. In this example, we simulate a hydrogenated layer on a silicon substrate, containing domains of a larger SLD, against D2O, SMW and water.\n", + "\n", + "Start by making the project and adding the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(calculation=\"domains\", model=\"custom xy\", geometry=\"substrate/liquid\")\n", + "\n", + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=10.0, value=20.0, max=50.0, fit=True),\n", + " Parameter(name=\"Layer Thickness\", min=1.0, value=30.0, max=500.0, fit=True),\n", + " Parameter(name=\"Layer SLD\", min=-0.5e-6, value=-0.5e-6, max=0.0, fit=True),\n", + " Parameter(name=\"Layer Roughness\", min=2.0, value=5.0, max=7.0, fit=True),\n", + " Parameter(name=\"Domain SLD\", min=1.0e-6, value=1.0e-6, max=5.0e-6, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now set the SLDs of the bulk phases for our samples." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "problem.bulk_in.set_fields(0, name=\"Silicon\", value=2.073e-6, max=1.0, fit=False)\n", + "\n", + "problem.bulk_out.append(name=\"SLD SMW\", min=2.0e-6, value=2.073e-6, max=2.1e-6)\n", + "problem.bulk_out.append(name=\"SLD H2O\", min=-0.6e-6, value=-0.56e-6, max=-0.5e-6)\n", + "\n", + "problem.scalefactors.set_fields(0, min=0.8, value=1.0, max=1.1, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The custom file takes the parameters and build the model as usual, changing the SLD of the layer depending on whether we are calculating the layer (domain = 0), or the domain (domain = 1)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
import math\n",
+       "\n",
+       "import numpy as np\n",
+       "\n",
+       "\n",
+       "def domains_XY_model(params, bulk_in, bulk_out, contrast, domain):\n",
+       "    # Split up the parameters for convenience\n",
+       "    subRough = params[0]\n",
+       "    oxideThick = params[1]\n",
+       "    layerThick = params[2]\n",
+       "    layerSLD = params[3]\n",
+       "    layerRough = params[4]\n",
+       "    domainSLD = params[5]\n",
+       "\n",
+       "    # Make an array of z values for our model\n",
+       "    z = np.arange(0, 141)\n",
+       "\n",
+       "    # Make the volume fraction distribution for our Silicon substrate\n",
+       "    [vfSilicon, siSurf] = makeLayer(z, -25, 50, 1, subRough, subRough)\n",
+       "\n",
+       "    # ... and the Oxide ...\n",
+       "    [vfOxide, oxSurface] = makeLayer(z, siSurf, oxideThick, 1, subRough, subRough)\n",
+       "\n",
+       "    # ... and also our layer.\n",
+       "    [vfLayer, laySurface] = makeLayer(z, oxSurface, layerThick, 1, subRough, layerRough)\n",
+       "\n",
+       "    # Everything that is not already occupied will be filled will water\n",
+       "    totalVF = vfSilicon + vfOxide + vfLayer\n",
+       "    vfWater = 1 - totalVF\n",
+       "\n",
+       "    # Now convert the Volume Fractions to SLDs\n",
+       "    siSLD = vfSilicon * bulk_in\n",
+       "    oxSLD = vfOxide * 3.41e-6\n",
+       "\n",
+       "    # Layer SLD depends on whether we are calculating the domain or not\n",
+       "    if domain == 0:\n",
+       "        laySLD = vfLayer * layerSLD\n",
+       "    else:\n",
+       "        laySLD = vfLayer * domainSLD\n",
+       "\n",
+       "    # ... and finally the water SLD.\n",
+       "    waterSLD = vfWater * bulk_out[contrast]\n",
+       "\n",
+       "    # Make the total SLD by just adding them all up\n",
+       "    totalSLD = siSLD + oxSLD + laySLD + waterSLD\n",
+       "\n",
+       "    # The output is just a [n x 2] array of z against SLD\n",
+       "    SLD = np.column_stack([z, totalSLD])\n",
+       "\n",
+       "    return SLD, subRough\n",
+       "\n",
+       "\n",
+       "def makeLayer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R):\n",
+       "    """This produces a layer, with a defined thickness, height and roughness.\n",
+       "    Each side of the layer has its own roughness value.\n",
+       "    """\n",
+       "    # Find the edges\n",
+       "    left = prevLaySurf\n",
+       "    right = prevLaySurf + thickness\n",
+       "\n",
+       "    # Make our heaviside\n",
+       "    a = (z - left) / ((2**0.5) * Sigma_L)\n",
+       "    b = (z - right) / ((2**0.5) * Sigma_R)\n",
+       "\n",
+       "    erf_a = np.array([math.erf(value) for value in a])\n",
+       "    erf_b = np.array([math.erf(value) for value in b])\n",
+       "\n",
+       "    VF = np.array((height / 2) * (erf_a - erf_b))\n",
+       "\n",
+       "    return VF, right\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k+kn}{import} \\PY{n+nn}{math}\n", + "\n", + "\\PY{k+kn}{import} \\PY{n+nn}{numpy} \\PY{k}{as} \\PY{n+nn}{np}\n", + "\n", + "\n", + "\\PY{k}{def} \\PY{n+nf}{domains\\PYZus{}XY\\PYZus{}model}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{,} \\PY{n}{domain}\\PY{p}{)}\\PY{p}{:}\n", + " \\PY{c+c1}{\\PYZsh{} Split up the parameters for convenience}\n", + " \\PY{n}{subRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n", + " \\PY{n}{oxideThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", + " \\PY{n}{layerThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", + " \\PY{n}{layerSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{layerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{domainSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make an array of z values for our model}\n", + " \\PY{n}{z} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{141}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the volume fraction distribution for our Silicon substrate}\n", + " \\PY{p}{[}\\PY{n}{vfSilicon}\\PY{p}{,} \\PY{n}{siSurf}\\PY{p}{]} \\PY{o}{=} \\PY{n}{makeLayer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{25}\\PY{p}{,} \\PY{l+m+mi}{50}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{subRough}\\PY{p}{,} \\PY{n}{subRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} ... and the Oxide ...}\n", + " \\PY{p}{[}\\PY{n}{vfOxide}\\PY{p}{,} \\PY{n}{oxSurface}\\PY{p}{]} \\PY{o}{=} \\PY{n}{makeLayer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{siSurf}\\PY{p}{,} \\PY{n}{oxideThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{subRough}\\PY{p}{,} \\PY{n}{subRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} ... and also our layer.}\n", + " \\PY{p}{[}\\PY{n}{vfLayer}\\PY{p}{,} \\PY{n}{laySurface}\\PY{p}{]} \\PY{o}{=} \\PY{n}{makeLayer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{oxSurface}\\PY{p}{,} \\PY{n}{layerThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{subRough}\\PY{p}{,} \\PY{n}{layerRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Everything that is not already occupied will be filled will water}\n", + " \\PY{n}{totalVF} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{+} \\PY{n}{vfOxide} \\PY{o}{+} \\PY{n}{vfLayer}\n", + " \\PY{n}{vfWater} \\PY{o}{=} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{totalVF}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now convert the Volume Fractions to SLDs}\n", + " \\PY{n}{siSLD} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}in}\n", + " \\PY{n}{oxSLD} \\PY{o}{=} \\PY{n}{vfOxide} \\PY{o}{*} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Layer SLD depends on whether we are calculating the domain or not}\n", + " \\PY{k}{if} \\PY{n}{domain} \\PY{o}{==} \\PY{l+m+mi}{0}\\PY{p}{:}\n", + " \\PY{n}{laySLD} \\PY{o}{=} \\PY{n}{vfLayer} \\PY{o}{*} \\PY{n}{layerSLD}\n", + " \\PY{k}{else}\\PY{p}{:}\n", + " \\PY{n}{laySLD} \\PY{o}{=} \\PY{n}{vfLayer} \\PY{o}{*} \\PY{n}{domainSLD}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} ... and finally the water SLD.}\n", + " \\PY{n}{waterSLD} \\PY{o}{=} \\PY{n}{vfWater} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the total SLD by just adding them all up}\n", + " \\PY{n}{totalSLD} \\PY{o}{=} \\PY{n}{siSLD} \\PY{o}{+} \\PY{n}{oxSLD} \\PY{o}{+} \\PY{n}{laySLD} \\PY{o}{+} \\PY{n}{waterSLD}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} The output is just a [n x 2] array of z against SLD}\n", + " \\PY{n}{SLD} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{column\\PYZus{}stack}\\PY{p}{(}\\PY{p}{[}\\PY{n}{z}\\PY{p}{,} \\PY{n}{totalSLD}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{k}{return} \\PY{n}{SLD}\\PY{p}{,} \\PY{n}{subRough}\n", + "\n", + "\n", + "\\PY{k}{def} \\PY{n+nf}{makeLayer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{prevLaySurf}\\PY{p}{,} \\PY{n}{thickness}\\PY{p}{,} \\PY{n}{height}\\PY{p}{,} \\PY{n}{Sigma\\PYZus{}L}\\PY{p}{,} \\PY{n}{Sigma\\PYZus{}R}\\PY{p}{)}\\PY{p}{:}\n", + "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}This produces a layer, with a defined thickness, height and roughness.}\n", + "\\PY{l+s+sd}{ Each side of the layer has its own roughness value.}\n", + "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", + " \\PY{c+c1}{\\PYZsh{} Find the edges}\n", + " \\PY{n}{left} \\PY{o}{=} \\PY{n}{prevLaySurf}\n", + " \\PY{n}{right} \\PY{o}{=} \\PY{n}{prevLaySurf} \\PY{o}{+} \\PY{n}{thickness}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make our heaviside}\n", + " \\PY{n}{a} \\PY{o}{=} \\PY{p}{(}\\PY{n}{z} \\PY{o}{\\PYZhy{}} \\PY{n}{left}\\PY{p}{)} \\PY{o}{/} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{2}\\PY{o}{*}\\PY{o}{*}\\PY{l+m+mf}{0.5}\\PY{p}{)} \\PY{o}{*} \\PY{n}{Sigma\\PYZus{}L}\\PY{p}{)}\n", + " \\PY{n}{b} \\PY{o}{=} \\PY{p}{(}\\PY{n}{z} \\PY{o}{\\PYZhy{}} \\PY{n}{right}\\PY{p}{)} \\PY{o}{/} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{2}\\PY{o}{*}\\PY{o}{*}\\PY{l+m+mf}{0.5}\\PY{p}{)} \\PY{o}{*} \\PY{n}{Sigma\\PYZus{}R}\\PY{p}{)}\n", + "\n", + " \\PY{n}{erf\\PYZus{}a} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{[}\\PY{n}{math}\\PY{o}{.}\\PY{n}{erf}\\PY{p}{(}\\PY{n}{value}\\PY{p}{)} \\PY{k}{for} \\PY{n}{value} \\PY{o+ow}{in} \\PY{n}{a}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{erf\\PYZus{}b} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{[}\\PY{n}{math}\\PY{o}{.}\\PY{n}{erf}\\PY{p}{(}\\PY{n}{value}\\PY{p}{)} \\PY{k}{for} \\PY{n}{value} \\PY{o+ow}{in} \\PY{n}{b}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{VF} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{(}\\PY{n}{height} \\PY{o}{/} \\PY{l+m+mi}{2}\\PY{p}{)} \\PY{o}{*} \\PY{p}{(}\\PY{n}{erf\\PYZus{}a} \\PY{o}{\\PYZhy{}} \\PY{n}{erf\\PYZus{}b}\\PY{p}{)}\\PY{p}{)}\n", + "\n", + " \\PY{k}{return} \\PY{n}{VF}\\PY{p}{,} \\PY{n}{right}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "import math\n", + "\n", + "import numpy as np\n", + "\n", + "\n", + "def domains_XY_model(params, bulk_in, bulk_out, contrast, domain):\n", + " # Split up the parameters for convenience\n", + " subRough = params[0]\n", + " oxideThick = params[1]\n", + " layerThick = params[2]\n", + " layerSLD = params[3]\n", + " layerRough = params[4]\n", + " domainSLD = params[5]\n", + "\n", + " # Make an array of z values for our model\n", + " z = np.arange(0, 141)\n", + "\n", + " # Make the volume fraction distribution for our Silicon substrate\n", + " [vfSilicon, siSurf] = makeLayer(z, -25, 50, 1, subRough, subRough)\n", + "\n", + " # ... and the Oxide ...\n", + " [vfOxide, oxSurface] = makeLayer(z, siSurf, oxideThick, 1, subRough, subRough)\n", + "\n", + " # ... and also our layer.\n", + " [vfLayer, laySurface] = makeLayer(z, oxSurface, layerThick, 1, subRough, layerRough)\n", + "\n", + " # Everything that is not already occupied will be filled will water\n", + " totalVF = vfSilicon + vfOxide + vfLayer\n", + " vfWater = 1 - totalVF\n", + "\n", + " # Now convert the Volume Fractions to SLDs\n", + " siSLD = vfSilicon * bulk_in\n", + " oxSLD = vfOxide * 3.41e-6\n", + "\n", + " # Layer SLD depends on whether we are calculating the domain or not\n", + " if domain == 0:\n", + " laySLD = vfLayer * layerSLD\n", + " else:\n", + " laySLD = vfLayer * domainSLD\n", + "\n", + " # ... and finally the water SLD.\n", + " waterSLD = vfWater * bulk_out[contrast]\n", + "\n", + " # Make the total SLD by just adding them all up\n", + " totalSLD = siSLD + oxSLD + laySLD + waterSLD\n", + "\n", + " # The output is just a [n x 2] array of z against SLD\n", + " SLD = np.column_stack([z, totalSLD])\n", + "\n", + " return SLD, subRough\n", + "\n", + "\n", + "def makeLayer(z, prevLaySurf, thickness, height, Sigma_L, Sigma_R):\n", + " \"\"\"This produces a layer, with a defined thickness, height and roughness.\n", + " Each side of the layer has its own roughness value.\n", + " \"\"\"\n", + " # Find the edges\n", + " left = prevLaySurf\n", + " right = prevLaySurf + thickness\n", + "\n", + " # Make our heaviside\n", + " a = (z - left) / ((2**0.5) * Sigma_L)\n", + " b = (z - right) / ((2**0.5) * Sigma_R)\n", + "\n", + " erf_a = np.array([math.erf(value) for value in a])\n", + " erf_b = np.array([math.erf(value) for value in b])\n", + "\n", + " VF = np.array((height / 2) * (erf_a - erf_b))\n", + "\n", + " return VF, right" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Code(\"domains_XY_model.py\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, add the custom file to the project, and make our three contrasts." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "problem.custom_files.append(name=\"Domain Layer\", filename=\"domains_XY_model.py\", language=\"python\", path=pathlib.Path.cwd().resolve())\n", + "\n", + "# Make contrasts\n", + "problem.contrasts.append(\n", + " name=\"D2O\",\n", + " background=\"Background 1\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"SLD D2O\",\n", + " domain_ratio=\"Domain Ratio 1\",\n", + " data=\"Simulation\",\n", + " model=[\"Domain Layer\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"SMW\",\n", + " background=\"Background 1\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"SLD SMW\",\n", + " domain_ratio=\"Domain Ratio 1\",\n", + " data=\"Simulation\",\n", + " model=[\"Domain Layer\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"H2O\",\n", + " background=\"Background 1\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"SLD H2O\",\n", + " domain_ratio=\"Domain Ratio 1\",\n", + " data=\"Simulation\",\n", + " model=[\"Domain Layer\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, run the simulation and plot the results." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.071 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls = RAT.Controls()\n", + "problem, results = RAT.run(problem, controls)\n", + "\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RATapi/examples/domains/domains_custom_layers.ipynb b/RATapi/examples/domains/domains_custom_layers.ipynb new file mode 100644 index 00000000..1466f2f2 --- /dev/null +++ b/RATapi/examples/domains/domains_custom_layers.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter" + ] + }, + { + "attachments": { + "33c727cd-f7da-4589-aef2-f96e0f70c4d2.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Analysing Domains Samples Using Custom Layers Models\n", + "\n", + "For custom models, all the work with calculating the reflectivity from the different domains is done within the custom model itself. To do this, there is an additional input into the custom model file which denotes the domain to be calculated:\n", + "\n", + "The final 'domain' input is always either 0 or 1, denoting which domain is being calculated. Then, within the custom model, we can calculate the layers structure for whichever domain structure is required in this pass through the function:\n", + "\n", + "We will make a simple example of a permalloy layer on silicon, which has spin up and spin down domains, each with different SLDs\n", + "\n", + "![image.png](attachment:33c727cd-f7da-4589-aef2-f96e0f70c4d2.png)\n", + "We start by setting up the project:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "domains\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "custom layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "substrate/liquid\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------------+-------+---------+---------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------------+-------+---------+---------+------+------------+-----+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 3.0 | 5.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Alloy Thickness | 100.0 | 150.0 | 200.0 | True | uniform | 0.0 | inf |\n", + "| 2 | Alloy SLD up | 9e-06 | 1.1e-05 | 1.3e-05 | True | uniform | 0.0 | inf |\n", + "| 3 | Alloy SLD down | 5e-06 | 7e-06 | 1e-05 | True | uniform | 0.0 | inf |\n", + "| 4 | Alloy Roughness | 5.0 | 7.0 | 11.0 | True | uniform | 0.0 | inf |\n", + "| 5 | Gold Thickness | 100.0 | 150.0 | 200.0 | True | uniform | 0.0 | inf |\n", + "| 6 | Gold SLD | 4e-06 | 4.5e-06 | 5e-06 | True | uniform | 0.0 | inf |\n", + "| 7 | Gold Roughness | 5.0 | 7.0 | 11.0 | True | uniform | 0.0 | inf |\n", + "+-------+---------------------+-------+---------+---------+------+------------+-----+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+-----+-----------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+-----+-----------+-----+-------+------------+-----+-------+\n", + "| 0 | Silicon | 0.0 | 2.073e-06 | 1.0 | False | uniform | 0.0 | inf |\n", + "+-------+---------+-----+-----------+-----+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "| 0 | SLD D2O | 6.2e-06 | 6.35e-06 | 6.35e-06 | False | uniform | 0.0 | inf |\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.02 | 0.23 | 0.25 | False | uniform | 0.0 | inf |\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Domain Ratios: -------------------------------------------------------------------------------------\n", + "\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "| 0 | Domain Ratio 1 | 0.4 | 0.5 | 0.6 | False | uniform | 0.0 | inf |\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "| 0 | Background Param 1 | 1e-07 | 1e-06 | 1e-05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Background 1 | constant | Background Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Custom Files: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+------------------+---------------+----------+-------------------------------------------------------------------+\n", + "| index | name | filename | function name | language | path |\n", + "+-------+---------------+------------------+---------------+----------+-------------------------------------------------------------------+\n", + "| 0 | Alloy domains | alloy_domains.py | alloy_domains | python | /mnt/c/Users/gnn85523/projects/python-RAT/RATapi/examples/domains |\n", + "+-------+---------------+------------------+---------------+----------+-------------------------------------------------------------------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------------+------+------------+------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+------------+------+------------+------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "+-------+------------+------+------------+------------------+\n", + "\n", + "Contrasts: -----------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+------------+--------------+-------------------+---------+----------+---------------+--------------+----------+----------------+---------------+\n", + "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | domain ratio | model |\n", + "+-------+--------------+------------+--------------+-------------------+---------+----------+---------------+--------------+----------+----------------+---------------+\n", + "| 0 | D2O Contrast | Simulation | Background 1 | add | Silicon | SLD D2O | Scalefactor 1 | Resolution 1 | False | Domain Ratio 1 | Alloy domains |\n", + "+-------+--------------+------------+--------------+-------------------+---------+----------+---------------+--------------+----------+----------------+---------------+\n", + "\n", + "\n" + ] + } + ], + "source": [ + "problem = RAT.Project(calculation=\"domains\", model=\"custom layers\", geometry=\"substrate/liquid\")\n", + "\n", + "# Make some parameters\n", + "parameter_list = [\n", + " Parameter(name=\"Alloy Thickness\", min=100.0, value=150.0, max=200.0, fit=True),\n", + " Parameter(name=\"Alloy SLD up\", min=9.0e-6, value=11.0e-6, max=13.0e-6, fit=True),\n", + " Parameter(name=\"Alloy SLD down\", min=5.0e-6, value=7.0e-6, max=10.0e-6, fit=True),\n", + " Parameter(name=\"Alloy Roughness\", min=5.0, value=7.0, max=11.0, fit=True),\n", + " Parameter(name=\"Gold Thickness\", min=100.0, value=150.0, max=200.0, fit=True),\n", + " Parameter(name=\"Gold SLD\", min=4.0e-6, value=4.5e-6, max=5.0e-6, fit=True),\n", + " Parameter(name=\"Gold Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)\n", + "\n", + "# Set the bulk SLD\n", + "problem.bulk_in.set_fields(0, name=\"Silicon\", value=2.073e-6, max=1.0)\n", + "\n", + "# Add the custom file\n", + "problem.custom_files.append(name=\"Alloy domains\", filename=\"alloy_domains.py\", language=\"python\", path=pathlib.Path.cwd().resolve(),\n", + ")\n", + "\n", + "# Make a contrast\n", + "problem.contrasts.append(\n", + " name=\"D2O Contrast\",\n", + " data=\"Simulation\",\n", + " background=\"Background 1\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"SLD D2O\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " resolution=\"Resolution 1\",\n", + " resample=False,\n", + " domain_ratio=\"Domain Ratio 1\",\n", + " model=[\"Alloy domains\"],\n", + ")\n", + "\n", + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the project, we are using a custom function which we have called 'alloy_domains':" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
def alloy_domains(params, bulkIn, bulkOut, contrast, domain):\n",
+       "    """Simple custom model for testing incoherent summing.\n",
+       "    Simple two layer of permalloy / gold, with up/down domains.\n",
+       "    """\n",
+       "    # Split up the parameters\n",
+       "    subRough = params[0]\n",
+       "    alloyThick = params[1]\n",
+       "    alloySLDup = params[2]\n",
+       "    alloySLDdn = params[3]\n",
+       "    alloyRough = params[4]\n",
+       "    goldThick = params[5]\n",
+       "    goldSLD = params[6]\n",
+       "    goldRough = params[7]\n",
+       "\n",
+       "    # Make the layers\n",
+       "    alloyUp = [alloyThick, alloySLDup, alloyRough]\n",
+       "    alloyDn = [alloyThick, alloySLDdn, alloyRough]\n",
+       "    gold = [goldThick, goldSLD, goldRough]\n",
+       "\n",
+       "    # Make the model depending on which domain we are looking at\n",
+       "    if domain == 0:\n",
+       "        output = [alloyUp, gold]\n",
+       "    else:\n",
+       "        output = [alloyDn, gold]\n",
+       "\n",
+       "    return output, subRough\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k}{def} \\PY{n+nf}{alloy\\PYZus{}domains}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulkIn}\\PY{p}{,} \\PY{n}{bulkOut}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{,} \\PY{n}{domain}\\PY{p}{)}\\PY{p}{:}\n", + "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Simple custom model for testing incoherent summing.}\n", + "\\PY{l+s+sd}{ Simple two layer of permalloy / gold, with up/down domains.}\n", + "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", + " \\PY{c+c1}{\\PYZsh{} Split up the parameters}\n", + " \\PY{n}{subRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n", + " \\PY{n}{alloyThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", + " \\PY{n}{alloySLDup} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", + " \\PY{n}{alloySLDdn} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{alloyRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{goldThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", + " \\PY{n}{goldSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", + " \\PY{n}{goldRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the layers}\n", + " \\PY{n}{alloyUp} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDup}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + " \\PY{n}{alloyDn} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDdn}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + " \\PY{n}{gold} \\PY{o}{=} \\PY{p}{[}\\PY{n}{goldThick}\\PY{p}{,} \\PY{n}{goldSLD}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the model depending on which domain we are looking at}\n", + " \\PY{k}{if} \\PY{n}{domain} \\PY{o}{==} \\PY{l+m+mi}{0}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{]}\n", + " \\PY{k}{else}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDn}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{]}\n", + "\n", + " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{subRough}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "def alloy_domains(params, bulkIn, bulkOut, contrast, domain):\n", + " \"\"\"Simple custom model for testing incoherent summing.\n", + " Simple two layer of permalloy / gold, with up/down domains.\n", + " \"\"\"\n", + " # Split up the parameters\n", + " subRough = params[0]\n", + " alloyThick = params[1]\n", + " alloySLDup = params[2]\n", + " alloySLDdn = params[3]\n", + " alloyRough = params[4]\n", + " goldThick = params[5]\n", + " goldSLD = params[6]\n", + " goldRough = params[7]\n", + "\n", + " # Make the layers\n", + " alloyUp = [alloyThick, alloySLDup, alloyRough]\n", + " alloyDn = [alloyThick, alloySLDdn, alloyRough]\n", + " gold = [goldThick, goldSLD, goldRough]\n", + "\n", + " # Make the model depending on which domain we are looking at\n", + " if domain == 0:\n", + " output = [alloyUp, gold]\n", + " else:\n", + " output = [alloyDn, gold]\n", + "\n", + " return output, subRough" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Code(\"alloy_domains.py\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the main difference between this and a 'normal' custom function is the extra 'domain' input, which we then use to select which domain we compute using the 'if / else' instruction at the end of the function\n", + "\n", + "To run this, we make a controls block as usual, and send it to RAT." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.015 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls = RAT.Controls()\n", + "problem, results = RAT.run(problem, controls)\n", + "\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RATapi/examples/domains/domains_standard_layers.ipynb b/RATapi/examples/domains/domains_standard_layers.ipynb new file mode 100644 index 00000000..8da0409e --- /dev/null +++ b/RATapi/examples/domains/domains_standard_layers.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" + ] + }, + { + "attachments": { + "f38e04ec-f12b-4e68-b486-6cf8bffef1bd.png": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtAAAAGVCAIAAABYSFGJAACAAElEQVR42uydB3hVRdrHJz0hJAQIvYUOoTcpIoIKdnRd+8ruurb9dm3r6lpWlF6S3NySShcUqSItvRdAAakhvd7c3ns99873zjk3lyC4i67ugpn/8z7zzJk7Z865J2V+Z+addxCmoqKioqKiovqFhegjoKKioqKioqLAQUVFRUVFRUWBg4qKioqKioqKAgcVFRUVFRUVBQ4qKioqKioqChxUVFRUVFRUVBQ4qKioqKioqChwUFFRUVFRUVFR4KCioqKioqKiwEFFRUVFRUVFgYOKioqKioqKigIHFRUVFRUVFQUOKioqKioqKgocVFRUVFRUVFS/RuDwUvsxxlxXgrHnWqOioqKioqLAcR1tMNijMxkZtis12ZyQmm3Y5sQuhjXP942r2XXMhbHB4nFiDKax2HUOJ5d3w4NyWuHpebHbYjUwbjuUuV02yhxUVFRUVBQ4bgAcbrb7tDGMXG+EvMHuK3F7O6W466acSbQ2BwCZBwNWaBwOrdOushqd2GNjHHanBeqaTVqAEw/joMBBRUVFRUWB4wbAYWNccqMB+lToKgsrzjy57NXHn/7T6Enz4sbcMWzszLgxM4eNnR43ZvqwcdPixk67mnIlv/YULKbviBHjZo6eNKf/8PiU7V+sThYBc9jZxwUPzeSwOVxw5LFaDNjrpsBBRUVFRUWB48bA4WS7z8KT3zz67AsPP7ns1MWGw7knGmUmmdYj1XlIqnfLdG6p3inTOSGVahiJlukiKZjNg6sbVfAEFAb3OsGmNfy0wWMn8DI3A1kojAa7x8NgbLWzMylexsO46K84FRUVFRUFju8LOkul2ZqQuaXv8LHZFWeMbny+Qd6itMrNuFnuaFTYmuW2RoWlWWHpSG1NUkeDzNUkdXWFFL5sXaupSWpSGDxVjfJ6iU5mcAo2fz5k3OTCE6ctDLYz5BnqjQYADpY26PAGFRUVFRUFjhsBh87OTL7z3vfX8CRGV73MVCsxyEz4UrOmVcOw5uowB2uuFpW3RYW7jHlVZnz6YpvGjCU6l1TvrmvXVjUrskq+efbFPzswNjjcxLfUgxmGYZesUOCgoqKioqLAcSPgEGzZ+XFCisyCa6SGWplZYsJNKpfagVs0zDWmdRHTMM1qMG8XsRYV0yi1Wdz4UoNKoiXzLKerxVaMG5WmZa/9Lb/irMmJHW52sQ/jdTtd3DJZKioqKioqChzfB46ViWlJm/dUS4ytOnejyi6FF/oaucQIfarz+6ayd+RdXcaczXKbwogbJNb6dovE4NU48Ll6RaPCsv946bJX3+IWs9hZ0jAYTIQ5qKioqKioKHBcDxxD4+9o1nnqlI4GtQusScvUKuyNGneD2sGa61pjCzW2LmJNalur0sGaq1nV2Rx5FecffeqPABxGGwOP0WK1YxKIw01/xamoqKioKHDcEDjmNOu8dUpng9rdCTicHcDh+D5waCwNGnMXSZvUlmYVYQ4gjGuBw3Wmqn3efUtNbmx1kcdod7g8Ho/XS+dUqKioqKgocNwIOOLGz2nV4gaFu0kFRrw06hXWZo0dXu47zOEzlYvNWJo0xiaNuWuk5maWOVjgYJ8AMfKg4HDkpLk6OwlFCo/RzXjtdit1GqWioqKiosDxQ8Axr1XTCTi0rnqFuVljJWDhs2uxw9cZdxED4LjKHD7qYh+UwoL7xk20eNhQpF7yJG0OM+vRQZmDioqKiooCx3XAMXzcvDYNbpS7m5XuZhVZAdugMLZoLGxH29HdXn3Lt7GH5q5mHeDlZw6XxODtM2yCgw2bpreSTVVcHpsX01AcVFRUVFQUOP41cLDTBK0aBwscpma1kbXvMYfNl+8qZuaeAxnX8TMHN7GitA8cOxWAAyhDazYy2O0hIx0UOKioqKioKHD8AHC0q3GzzN2qdLUqHWKNrUluaNOYWtXGVrWeTY2tKnOrytKqsnU9s3QGDrJ0hfWiBeBokFuHTZzFAYfV7WT31rWxm7tR4KCioqKiosDx74HD0Qk4/LTRCTjYZaJtimtTla1Nabthyi3xuEXSH21kRIcb5PCNcDT4nWeV1lGT5xhdZBc3N9nF3uny2DAd4aCioqKiosDxQ8BxoymVG/pwEHpQGr11bTqxwtaudtQ2a9RmDPnaVnVts0pt9rZIjdXNSqnGJlZbFHqnWGWtadcrbLhN677QrG7XeyQmfLlF26Zn4Fpig6deZtG4MOQbVXbI1yusdVKz2omhJnTqVa06pR2frZVB/Ua4isxc026UWfCFRhW0yXlxwll1ckuL2gktQwnUsWB8Ge5Qx1RLjEYPPlsvb9W4oIXzDUqVDTcqLPUyk9wMt6GulRikRs/FJmWb1umbMLrOWNIisOX3G+VWrMDhiImzrF7fRvYeEvrLw06pUFFRUVFRUeD4N8BB9kxpkJtbNNZrXBk6ulsAjoZ2g0TlMNqxQsdcaVDqrfj8lTZIVUa3TGtvlurVJkZn9VyuF9e2yAE7oC+Hjr9JaQcgkBi8UjOBCQCC7+rkJowb5NaTVW3Q/QM0mLy4Rmoi27jo3CcvtQKCAEYoCCLY4CylFUNnDynUAXwBsIAWAFmgtRatC/ACwAXOqhLrdUAwGieAC1wR0ARakxqx1k2uDo1rHBgIo6pVY8VwJ27ADoASSH8YOK6O63SghoMrB+CweQht+IADkMPL0F9xKioqKioKHD8DcOiAAMSGC9WSZonJYMMAH30GjUWoOwqIDI7s89qbH9Q0K85eboD3/gs1LVobNrrxlTbtxQY59O4NUuOpyy1NcjOwhYnB1WKdwuSFVGUhAw/f1bRLDIxU7wb+qJcYDAxJ4VOlGZ+rl1U1q+qkxkaZCdLqVsINde16mcmbnPlFi9p+4nwD3LYdYyCJs9ViaEFmYAAsoARaaFPba9r1LUorXAUK4U6gGuThFLg3qAyHFDioqKioqChw3ELAUS/WyzQuhY4RK2w1TeqqesXAuIlv/2PV2ctN63gZgB281O1qk6u2Rd4gVonVlgu17VK9U+cAAnBBJ62xYbUVn61urSUcYAUQASaAfL1EB0Bg8WLAkXatXaxx1LVrAVCutCgBQRQmht2s1Qlps8IEp8C5UD+79HRg9/47D2RByYV6yZUWtcGF27XORpmhgdCJAdBEonOcrmpVmj1QH/gG6ERhwUAzQB5AG1KjB1K9G1PgoKKioqKiwHELAUeL3NKqsEo0TqXRK9W6mqSmqD5xKzakNMsNVU2yuPHTFy995uyVxtfefn/WgsVgKxNS9A5c9m3VO/9ck5S649Gn/zB93uIrzfJX3/xw7NS5Cx/47XrBpitNinaNdcGS36Tv2LvowScnzLxr35HCvPIzv3nu5XsfeSpjx/4mmb6qUX65UfrCy2+Nnjz73oefPl50sq5Nc8/DT6KgaLji4kefhRu43CD77QuvzF30yP2PP1d++orOjnfuOw7X/efqZGgT8h+u5k2cc9+wCbMT0ndJDAxwhspGhkCAOShwUFFRUVFR4LhVgANMrmcaJUbADrHK3qa0QR6A48OVScAQRhceNm7aq2998P6K9fc9+iQvfds7/1yFAiO//DpXa8OABZCfe8/Dy15969MNwgef+F3a9j1//L93Arr13n0oB3AhOKpvSHS/d5evW/jgE1AzZsCIZ1/8CwAHlNeJ1UqTO6rvsNkLH9z25dfjps3rNWjUxfr2F//yd6j5witvvvn+CqgwYsLMWQvuF27+HK4SGBnbINGu2CiCCrFDxrz+3iefH8wK6t5HuGX3xrSdou37zzfI4UtdaFRIjZ56mYkCBxUVFRUVBY5bCDgkGmdVo7JJagLmUBg8SqO3R78RC5b85r1P1vcePBoFda84e6VRqtHaPM1yXZ1Y2bN/HC91a2O7euH9j02aOb+mWaa3eZulWsgYHbi2RT5w+PgN/IwGsQoFRKxPTodM8YnvInsNXLGerzI6T5ytgvKC8tP89O1Q89yVphaZrrpJCoW7Dx6Hw6jYwcfyy50YCzM/g8ILNS1tCsPJ765ExPQXZOxIFG2Gpk5frIPCXfuOoOCofceK4LvUtOsbFRa1HbdpnTKTt0lppcBBRUVFRUWB4xYCDrHG0Swza624XqyvblY3tBti+o8cNXH2c3/661/fXV7TqmxVms7Xtjzxuz9F9xsa3rM/QMDKDQLAi3se/M2Tv3uJW8AC/PHUCy8DE3TrOQAFRX60cmOTRAM1vzhwTKqxQD4gPGb/kTzItMr1XPnapFTIoLAeJA2KDO/RL3PHnvPVzXCYX/Yt1ARAgU/hxMCInqROQMSBo/kAHFATuAQo51Jd20O/fQGFxkyet+SrvBMAGdziFCCPH6INChxUVFRUVBQ4/jfAUcsuG6lqVilM3natEyykx4D3VybWitU6B4a0VWV+7uW/9B85vuT0hUapZujoSR+vTgTgWPzIk489/XvgCaXBsfSpZcPHTS375gIQw8j46bzUrSe/uwK4sO9wbrvKxMHH5/uPyrRWOIT8l19lCTJ2oNBosdIIhfVtSshI1OYrjRL49HBOCXT8y9ckhXSPhcahHAxIBepsFGT27B/XItNBOZzYojAWnDgPwBHZb2SL2t6mdXLeG/+COShwUFFRUVFR4PgfAAdwRpPcLNG5atjFrjIDEzt0XELq9halCUxjw5ebZPGz7py3+GGpwZaxay8KjFjHSzM6MNAGGEBAbYt8+txF8dPmai3M9t1fATEAkQANdOs5AA6hAmBE7KCRew5la8zuwoozKLDbkdzS8m8vQuE7H64ERuHmRy7UtFyqa4uI6f/me8uBV7jRjhde+it8Wt0kBYiB0wFTovsMkets0OaWXfvzK86qrfgv/1gVGzexTkrCglW1aiwY10oMFDioqKioqChw3ELAcalRAahh9pDVrUAewBwjJt7xu1fe0tqxRGc/V9umc+Ad+w8HRvVGQRH9RozrPzw+MXXbpQbJkseeffoPfxarLTaMU7d92XPgSBQYOWfRQwNGTPh0g7C6RQGHuWWnm+WGVqUJ8gezir+rbrF6cVjMgC1ffCXV2TcINwdGxsJHKKJX32HjoFp9O7tQJTCy16BRcoNzo2hLZOyQ4Ki+UDJmypxvLzXApaGktk3lwPjDVYmQR4Hdw3oP++f6FLHOBczRpLSerZXAt6bAQUVFRUVFgeMWAo52rRPY4nydtLpVw0W2qGpWWDG+2CCR6h1tagukYq35fH1rdZtcbnIoTe5zNa0ynfNCnbhFboK+/0Jtu8LoqmvTnKlqqmqUS7Q2scqqMjMaCwbCMLkwwEejRA8nqs1eOBfqtKnMSiMDeKG1YmCXk+fqGqW6NqUFzqoXaxsk2tJvLkN9oxMDhXx7sRFK5HoXXBHOgvahznm4sMlT1649Vy8juKBxtGmdQBtSo0frJCtjKXBQUVFRUVHguIWAo05qVNlI8NAWtR0qQ1fdqDRVt2vaDY7v6sWtWqvagWskqjqZRqy31sl01a2qeglx+7hQLwE6sXgxdNUNUr3RjRtlBpmBaVNba9q0tWKNyoIVJqauXa+yeKV6N5wIqdqKuTqNMhO0UyvWSXQOKJEZXFXNKsgbXLhJTmKPXm6SN0iNYo3tu5p2ssurwgKtARLVtBHnEqXZw4YCM11uUde067llKWKdqwqwSW03emjgLyoqKioqChy3EnBABiBDYmCa2EjhSisG4Gg3OIEtDNBtq80nLzc0q40So/1yq0xh9ajsxEOCCytOPD/a9ZcaFWRDEzYMhljjIL1+s0rnwgANULNeYoBUwS5VhXIgGzgLrggdPJTrnfhSswrKrRhDO+1697laCdyMxoalRk+T3NzGDsC0sd6sFxoVbaxn6IV6GbQGaAJ4obCQlMRNl5ngdK0Tl31XRwN/UVFRUVFR4Li1gKNFbYfuGSgBMlCT7KYm0UL9Np2tVqpt1VoBMlq1ZrB2g61err/YpKxXWKGbb9G6mpR2qRm3qJ1QonfjRhW0YNW4cE27sd3o5XZ8hTpqJ9ldtkZqUtsxt2csnAXlMgvZaVbrJvnTtVKo36xxQk2Vg7RQKzNzu8I2KmyX23T1Mgu0LzF4oQUob9W5IQ+XO1PTbvKSuOYAQGB1UqMFYxqHg4qKioqKAscvDRxzWOBwNivdLHC4bgAcV5nD1qZ1Km1kNzWx3gEdNtCGxOhiBzkcgBcAHBKj81KLtEll0LnxxWYZoEatzEj2XVNZIW3VkY3gIQ9kUyXWykxeSIFarrTraiWGNr1LYmBO17ZDTbkVf1cnhRJAkwvNhA/q5CY4C1oA4mk3MlzLcjM+UyeTGrHSDhihBnABqgC8EOsYQA2Akgalja1phvJ2vRuu1aolgzRyiweYCb4pac3Abk/Pfc3r00570/u3pwfgGDnhjqvA4SXP0+v10l9xKioqKioKHDcAjrixsy826lpUTL2U7GpG+mZJpzjfV5nDzFmrijNLp/RfGzn9l0x94xA3k7Jmue7+fzCF79tmcp1tkTUbXGANakerwVPVZmxU2No17vHTFhgd2OIgj9HlJgMcDoeD/opTUVFRUVHguAFw9I+bZMW4us1c1Wqol9kut+ilRtyqcV3HHP8COP6HqaX1KkncpP044KjTmC+rDO12XKO2XJGbW/RMvZzM1LQo7ANHTOGGNyx2xknmVLDdSYGDioqKiooCx42AY+q8Jeca1E0qF7u6xFIl1osNHm7uoElt62SWJrVvXKGjm78V0qsOFjdlV5njplL4vtUKU5PR1Wb1XpQaqhUWsQnXyS3NKkd1i27+fY9JFGaF1gyPUaPVExcO+gtORUVFRUWB44bAseTx54+WfgfAQdwqTbheYQXggD61gzk47Liacu4Lvs77f57+NLvp9ptVtnqNrdnkrlHbLoh1zTq3zILrpOa6dn2T1DRs9DS7m4xwcKhhd9p0Bi39FaeioqKiosBxA+D40+vvHcirbFDaTlwGlsBVYi1gR6PK3gk4HIAj/pRdzHJLpT/Nbqp9+L6NGmeT3l2tsNQqrC16BmhMYmDkRs8n64TbvjgEtKHQGq02h8li9mKP002nVKioqKioKHDcCDiKTl146qXXpWav3IpbtGQNyIVmNVmG6keNa635lrOfgBo3a/B9a+SWJq0LrN3orVeQaCJaO26WGVM2f/H+J+t0Zjs8Q8brAdqw2k1eboUsFRUVFRUVBY7vAQe8kj/49O8/P5xXLzfUyfQqO1nyCi/x13TPSvdV60rAAVYnszWr3S1al9SIa9r1LUprdatqg3DzsDETFVojwz5Du9PmdNswdtvsJgocVFRUVFQUOG4AHFqb+9vqxtHT5723KqFJZQLs0Ln9e4tcxxy3KG3Ybjr90czRonY3Kmz1MotY42iQGqVaR1bxqZHx09O37HBjrNLpXezwhhe7XW4rpiMcVFRUVFQUOH5ohKNVrd+8+8Cw+KkfrkngZXx2uUkmN7rb1FYSFxxM5WTNzVmLwtmisN86BndYK9a0a51ija2mTduiNEv17kYZCaYO+etS649tv10D39p5qUF1tqpt35HC51/86wsv/V9R+Uk3+/RYd1FCG15fADAKHFRUVFRUFDiuE/SXjRI5MEc92cAVb929P+OzPXHjpw4dO3XkxFnEJtzB2hxi8fPAxk6ZP2bq/FsnnXzHol6DR4+bNi+kR38UGDlo9OR+cePjZ941durcMVPnXpf+uPsHGz9twfDxd0ybvfi+h55+6S/vHssv5bCC8QGHp8M41KC0QUVFRUVFgeNGwAH9pJM1wA6rB6uMdrXJAYd2743N7MIm9y2Uqowk2BbccHBULAqIkOltkFcYHT9L+2Dcw3F6sMHiMTu8HGoYrRZvJ9qgqEFFRUVFRYHj3wOHWmd2MhgMulKrw8N0vL5fb+5b0rQmmwvjkeMmdu/Zp6FVIlPrf8bGTTZsdWKn1zeqYbLZTTYrc3Vgw0ODfVFRUVFRUeC4OeZgsM3uhozV5uJiWFmsTqfLyxrzPXMzXqfnFjICSU4G0nETp6DgsPOXq002YCcyJvED9qPaJ5BhMDs43pLKFFa7DSBDb9SwfhudBja8HUZFRUVFRUWB43q5XdjpIBuBOOwMpC4XSRnG6+s7Ser5nnlvoZTI5rBD+thvHg8Li9h3YL/dzm1s0ukbfD/9Ee0DigHQcCwB+MH5h5qthg5H0WtpgwIHFRUVFRUFjhvKYrJDN+ll+2irweJlsAs6WJcbSMTDOLDbQVLG7mHsvvSWm1FhMciL33nn7eDAoI8//sgHST+U/sjGXW6CHS7GbTDpueheKrUMCjoqdAIODwUOKioqKioKHD8ktrMEyHA53L4uk2H7Zi/bN3u5btV11by3lnk9BAssZuNnO7b17dP70UcewmSaxUPSG9uPaRy7QB7yLLxOt4Px+pa/2h3ma1bA0hEOKiqqX1z+YWb3tUPOXfjfj/cH7LqB+U6u/deXXN+qP9jB7b0aAN26P7YbpJ7bwbDBYGAYpr6+HiE0atQo3xdidfX7eb0ej4dhmJ/Q/g/8zdNlKVRUVL/4P2afd7rH7fE64cUHYzvGto51hE4Pdrkx42bfkNxsZfi3R96QukB6dcC689IG8rAY7HHd2LyM93uvzdz8OYspflxhsIvBNrCr8+YUOKg4krDZ4M8P63S66OjoHj16NDU1AVgAXng7RJ8SFRXVbQwc0L1eBQ4rCxxWL4ljYHdjpwsz0HM6OiZ6O6+g+3UbYQ7mWuDw0wZ5VvZOxj46dtC6Azh8JdcAh7czcFjAKHBQXSO32+1yubj8kiVLEEJ79uwB4PB2En1KVFRUty9wsAO0BDi8ZIU+iZrUeXiD7T9JICWWORg3KewixlzlD4IfDDwQdjGi3UNorLPZWXN6vVzXwHDkQYzrIrzfG+Rwe8nwho1OqVDdgDkcDrIv/Pbt2wE4Xn755e8NgVDmoKKiul2Zg+0gCXB43L4XdNIdQs/KdEymdHayA/5wurCra6SuTlMjTEe5nTVrh3GHpD4HKF6v75F2sus7CE/HE6XAQXWtGIax2WyAHXK5HIBj+PDhdrv9euag5EFFRXW7ycO9jgNtMF63x+fA4NtM4XqDfteBXY6ukoJ5O4wrBHN2MlenOj5S+96gCMcc/tWO1zmf3t6iwPEzyz+fAsAB6fjx44E58vPzf9hplIqKiur2oA12qaCzAzg8XE/ZOejzNR0nGfZwdakpFdZ5xctiFhnhcLAjGQ528MPRybXF4R+v+J4jCPZNr1wTZ8HbaWHAbc4cFDh+Zjmdzs5UkZiYCMDx5JNP2u12+KgzYZDfLAocVFRUtxNwuDDnrgHA4WG7RA44vL6XdS+31J8sXTFjr7mTg2SXMI6x2NTJOrVwri1cOZkr4XxLufR71sEcxPOj06yUm/Ma9eEHBQ6q68UNb1itVolEEhUVFRAQYDKZ4NA//uFnDvqsqKiobh/gcBCY8DoZr5tz3/C9jF+lDfjUhLEGe+XEPErsUXcV84JpyHf3mbrD4GnosNeAPWbssWKPHTNOMt4BL5weNhoTWcLYwRzs+FDHOIiDPF1u8YuLAgfV9X+R7PJXyABhQB4y99xzT2Rk5IcffshVUKvV/oEQQBD6xKioqG4b4ACewFY2FDTTOQIj43t9snnscoylV87s/fPzM5YtHXnnOHRXlzH4svPGozlg8Z2MLVkwJXTyMPTI/IHThqP5E3qs+ftz2V8kYacMY6NBK2EcVswCh9FghYfocLELWMgoETs+xHiv7qJOgYPq+kELboSDG8/IysoKDAzs37+/zWbT6XRcNcARbiULFRUV1W0IHCTOsd8dweliLBYNY5dUf3f4pacnPnN/b+Hye1KXz9uXfP/+5Pu6iO3j37eHv/hLAWf3dRg53JUIdv8ewaO7Eh7JXHHfhrfu+PvzI155fOSlb/djrMXYYjcbnHYX8IROb2Z9NlzsdIyVMIeHocBB9YPAwbDi8hxVLF68GCH06quvciCiVqs5LjGbzfSJUVFR3VbAAa/dLi7UtqvDv5FNjOe/OTBvElr99qwdGxd+mTRnf+Lkw/wxx/jDu4gd4Q8/LBj5tXD49ZadMaF4x6zPVgzITp9RvG3+3vXjDiZOXvFav3tnh1w8/ZXXLsFOI2Y8OrWJi71BVsz6XEA6AYeLAgfV9/4iPR4OMrjpEm7q5MSJE+Hh4bGxsefOnfN7lXJupPSJUVFR3T7AwbpGeplrgYNMphg0l3/3xNjlb0w7kLrkaNrs3IyJR3h98kQ981O6FYq6d4lU1D031W/dOln3rxNCSjb33b0C5af2Kc4c9NX67hVbR+5ZP2L5m+P/8PQEjMXYroLOAx6wzQIvom7WQ9TFxQdjV710zF1R4KDqLC6KuR84vF6v0WiEzLJly0JCQh5//HGu2vUOpFRUVFS3A3CQaJjuTss72VUV+sS1L63/aHHm2rlfCad+vqL7kYSIisyoIgEqEqLiLpMWCFG+6AZWlIZObOtWvimiLLNbniD48DqUlRh0KLHP4e0P/faBGHlDFvaqGIvJpLG5HCSwCTtdxbh9gxyuq3FOKHBQdVZnHw7/AAYUSqXS0NDQoKCgiooK3OHewVWjoqKiuj2Ag9tvzNcj+oHDrlVX7935/pt/GPp54oyjouHffj40OwGVpaAyUdeyUhEqSUGlKR1p6tU0ayP5NCcRFfDQN1u6V6SGf7Nr9N6UO1b/Y/qTD/THtkbMuBgb8b9Va5mOoSOnuyMCum9zlttcFDh+EeDgUMPPE9wky8qVK0NCQsaMGcMFHuWWq1BRUVHdPsjBruH0XAMcrG+jZPYktIO3eJ9gYsmWuBweOrISnduMSnioJKmrWBGxgEJeAGQKeaiIhwqTfWm5KBBKTm+KPpkRdTI16kRaj2Mr0f5V4bt5EzIS5r/45DDsbcMuGyCGSY/dDJmrYp+tyw8cXKwOChxU3wcObkqF2zMWpFQqMbssBdL+/fsHBwdv3769s4cH1S33Q7zhhgW/itDCVFT/4f83/64MHh92QD8I/80UM+PR0W2PfJU8IlcQVZmBTqWjkkRUnhxczosq50WUJ4eW8wNLBahUiEjKDyQfQSFrFTywMC5fyg++1gJZuz5PDsv5qJw0xbZD8qhTs8EdJdynpHIpP7Q0OYwz9lrcFf0XZT/lh3KZEkFoiSCwRIg467g0dw+dz7pq7NWJXXvDgSXJQSUC0n5hUnDBxsCixNBKUVTF5sFHMsZnrpv+4NxQ7G7DjM1qsGMGe92snyh5ti524xUuaNjtvo8KBY7/rtRq9bFjx6KiosLCwurq6ux2O4cmHIvAXzA38gEU8tPcO+BEv/sIlwExHfH4gZr9ebuDYdiAdtcbeYHpSimBCzfZiYqLMqzSKL2+P3Zul+hOOxr8KkILU1H9ByDu23qdDG1wnoxcsFGykal21iS0jz/nKK9/RWqv4kQyn1ICYJHUqzRxAEmhIxeSyQWfiQh2sH1zGNDGiaSwk4kRwBzQSRcLUbGINWEgsevz5JCk0MjJTFSZhsqF4XCijwxYqqgQRVSkBJWnktvwkYSQne8QBJfwI0qSIyGFQgIBABb8MAIWgsBiQVixIKJQGEZMEFEgCs0VooI0VJSBitJRgQjlC0halIoKhcFgvlsVEithQQqYpoI1jnV8tAEXFUDlqyl3uQJhRE7awC95M+ZPCsbY7PHaSMgvb0eYLwa7fLuxkH9P7H8hD/XhoLpZGoBUq9U++uij4eHhkyZNslgsuGNlrE6n4+ZfOA/Tn6bvBfZQKpVut8dihn8NWKsx+rf/Uav0N9gTqKuazWQG4IB/nDqdxmIxAWQ4XFaG4Jnby21kQIGDiop7e2HXZrLzxBbssXGdIrtg0+HBxlkTg/YLZmTxep9IiSlJIL17EQAHL6Y0qS+kZLQA+uZUVJxGUkIGpLOPIMCRHHyCF3iSh04kkx76poCDM4CJVHbIBKAhOQSuWJaKKkQhlaKoMn63MkEQIQy4EC+UmIDFgqvAEcaiABm08A9jkEsIQlmYCAUa4IAjL90HHIWpxErSUVlGEFQAY+/Kd28EOIQ+2rgKHALEsgXhDNYCO04hl4MW8lP77OFNnT+xGwn3RYx1iWF8IUbh0E6MPHaWNtwUOKhuShxYABMAWwBtIISWLl3KDWn4o4HBoX8i5iczh5msqerkssp2lhfPX3rh+WUPPfDwxPhJUydPmzZl+tjR425kY8Z3MZszffq0CRN2bNq0OT2Ni9FsMGrsTssNRji8dFaFigLHjwGOjtkTX4/eAQpkbCA5uoTXC0CknBcB3fMJATohJCk7KnDt1ImAa6Rznv2UzRezy0OKeVHFvO4lLHCUi7pXCGNLAHH4EeQj9tOipO5Fyaio85QKn0y4ACKUc7M8rL9nKQsNvpESQSjAByED1gO0RBRADgVhZaLwipRIgizJUQBMnSdouFmbcoGPNjoBR2AHalwFDjAAGgocVL+IAAL8EyUKhaJHjx4hISFvv/02HMpkMq6cm1sBUPgJq1cYhuHwhbuWSqXiaKOipPiZ3/zm4cWLT5WVXTxzRt7WJm1p0chkBpXqBqZWmFRdyMxKhV4qUbe1Jq9Zu+6Tj+NHj9i9c/s1215eDxxUVBQ4bhI4/F24yEcbhYQ2QgsFkYX8XsW8WMIcyZGkbxaiihRUIWKHB3ihFcnBxHihV/M3MtLBk0sElfJ7QPdf4oOP7sVJPYkBgrCjC3AVcpgMuOCb9fi+CVgTXmssiJSLAstEoWWCkJLkkOKkkKLE0OKksLKkbqxjClhkBQATsTBirNeIj4pYY8GFAgcFjv+FOOCwWCwABKdPnwbgCA4O/uyzz7iRCW7Ryk/eQtbvf8rBCrdYZv26NQvnzCrLPsoYtdVnTmHGYZSJFU11Tp0KMjc0s1RslrV2FZOKXSqVTSbz6PXYZkvZuKFvz+jL58+5nPaOGZROosBBRYHjJ4xwCK4FDlFggTCsQBhZKIgCKxZEkLmVjm7+BC/wRFKYz3ihJ1j3jh/KQwdfxi5DLRF0h3aK2SsW8iIKk6KLeD2K+VElopBSUXipoFcJv3exIIzMeqSgcjARSxjJYQQakqLBKpIiCdwA7ghZ7gH6IbMzqFKEygRhpKbfADKS/JwR0XE/wWAVxFc0kCOMq+YfMukwChxUv7i4uRKj0chhh8lk2rp1K0KoZ8+eeXl5nQdC/pOlKx6PR6FQ+MdLZk2bzFv1ifjyWewwYpP6fGmeSy3BBqVF2mxXtN3QnIrWrmRtTkmbvV3sUSqavvvOIpNW5OV+9O67DouZAgcV1c8AHOxUSAnrllGYggpSSC+bJwpjLYLDDiADgh0snRB/DkIVXEcezIJFBDG2R2fzkX7gKE8OLuKjfCEqEgSRaRr2EgAWBfxuRcndC6FNUUhJaliZKKZYGFMiDCN+pqms2wfrslrKiyb+JUmxnAFJ+MZa/MzBcgk3C1MuiDgpCj+VEnlSGFXJj2SHWAK5MZgTyYizckIboZx7R2GH0wYFDgoc/wMBCnT2z4BDuVz+wQcfAHNMnjz522+/hUI9vGdf5/t589LpdP5Zldra2p07dyasXYWtOqe0EWslJDUp7e31bnmzpva8Q9LwA1bnkNZ0FZPUmeur7S1NWK8F7AAexGbznClTpS0tN8ILz69hXRoV1X8TOIh/Q4QPOITBBcKwPGFUrjA6VxQBlieMzhPEFiQPKOD3ZUc7wri1oz7fCM5dIzmSGOe34cuz61HJstVgsnIkhXAG8ehMIfl8AcrlB+UlB+cJggvJipLgktRuRSkR7MhKhxOJKJBM6/AGFPIGFfJ7FfKjC/kxHPRwy2e4mRd2EW8Q4QZBWKkovDwtuDQlsIgXkpfgmyri7so3HcNOoADlwBcEkCoQRnRgR2DxjYGDjPRQ4KD6Zf5W2YkSLvwGIAV3aDabFyxYEBQUFBcXl5+fz4EI/kkRSDufwjXO5/OFSRuNrbUeeb2p/py9rQpr27CyEfJY2+pur3FJam6QSqpd0qouksJXxjq5vaHW3lzvVcrMbS0mSfvFUyefWfqYb0GKlwIHFdVPBw42lEUoN5lSIIjK4/fNTY7L4cflCPplCfvmCAbl8Ifm8kZBIZAHGfMQBRMaIAtTQwlGwIlABmAifz6KVGB9QQpEocXpAYUZqDAjoCAtMFeEsvjoaCI6vBEd5wVn80NzhaH5KeEFqZH5Kd0gnyMMzEsBC4YL5Qr65iaNz02aRG6GPyQ3eUQuf1CeKKyAYxF+dDGfXLc4leyQkieIIZwkCjqejA6tDtjzMYs4Kaw/CjtwUpxCOAZuPlcYkyOMzRX2ApbimIMCBwWOW0jAHM888wxCqF+/fp9//jkHJdxOK/5wpZ2jl3IzMoAUHLuYTCYXq+sHVCZMmKBVSByyRkZa45XU3Fx6xSu5wkgvd5EUvjJub8DiRq+01dZcj40GeU3NpZMnf/vQw9cBh6dj3QoVFQWOm3YaZSN8F5EFsYG5ibF5iRNSXuk/LQRNCkXTItEDI9Bz09Gh9fFFmVOzRLFHBEH5mQE5aehIMirIDM3JQEVb0TF+SH5694LNqHgbyhaF56REHgewSCV18tIi4VO2MOIYP6x4U7+8lNijST2emIiem4FyRf1yhf0h/Xpj95JNA3d/GliU2btke8jna1HZZ72P8YekvzUqHqFZPdC4QLR4CHrrAVS8eWJuWr8cwYC8lP6V2wYe44V/sSq4ZMe4QwkD//ksKtzSt3zn4NfvRzOi4cZ67N2AKrcPz+LHZglD8jPCDiWgvPS+h3n9ireM3b8uumJbXElG7IGV6Ltd/YoEAT8aOJzcs3VT4KD6mUc+QC+//DIwR7du3VatWuX/CDiDm4UBnuAWsMChP6KXf/bkhvIDh1Naz/asN2+Xu5Jdwe21WNKI5a3G+iqsUZjE4taqqnvn3UWBg4rqZwGOklRUBMyRFpidOCh342zhS2OnBiH+nwdsem/IGw+iJSPQ/SPQzuWjy3dO3Z/Yfc/GsK9FvQ7xBuRsGrYvKfBIWtRRwYistDEH+WGfrUHZqWNy0id8ldzzcErv4m3jd6/pfXBD/5yU4SVbx3yV0Pc4f3S2YMLhxOF7Vw8Ay02ZsOvjAcWZM47whhVmjvtqw5C89LgjouDS3TF7E4KzUiekv3VHHEIZ784TvD0L7mF8KNqyfNCOlT0P8cZU7rxz24cRuRljSnfO/mL9yLd/g56YidL/GXosffje9fGpf+91SNQnZ/Pg7LSJe9fHHUyOzd82LC9z1MGkYUdE44q2zoB73vVpZOX2kWWb+h5dh3ITb+jGQYGD6n8hzmkjPT0dsUpKSmpra/M7fPhDgfmdSbnxDEAKs9kMhVar9Xr4+MnAAa/+Xcfg+3raq7GkDitbdPWXbZJWRqc1SGRTx8ZfDxzYFweMiooCx037cLCrSIpYX4qsxCE5G+8UvDRucjDK+Puw3Ix5R/hzEl4dPC4YvXJPYOHmRfs2jH9lMXphPnpsAlr78vDjqXd9tnLyn+4KX//y5BcXocemoo0vTxG8Pu+R8ei+UUj4xpSDGx7avXLWi3ehB8egB0ejvz0cWJTx+K7lU955FH36fLc9q2Yufybmw992e2YGemIKemkB2rt2/N71sd/un/TF2p7ZaXOgqcEI7U5Y/NmGBZ+vfWwQQinL+x/dMu0vD6OnZ6D7RqBld6LtK2d98qcBM/qgoQHouYXob8+glX8a/vuFaJ9g/Bcb49a+0v8vD6H5w9BjM5DwbwOPpi4o2HbvS/eh5+ehB8ci0RuoJGPQ12sCzu/sx4UFK6XAQf+K/rfi6IHbnGD37t3BwcHAHG+++SZm43N0DhfmP0Wr1XLjHP9iPUunKRUOOGpuJmWkABxX3DLSGXeFlPu+TnEVVjVZxLXmtiaXRu3Q6ccNG3FdUFGPPz4H/aWlosBxs8AhDCsRhRSk+IAjK3G24OXRk0LRId7kHasGHkudt2/DoqenBT8/J3L7x/PvHobujkNvPBq7dCIaH4Y+/n3fPRsWD0FoYjh6YgZ6bh6C/PQo9Mp9Pe4agoYh9PnKJ/+2tP/SCeijZ6Pffyp6WneU8PKM/eseenIaenYWOpTwyMNj0KQI9OwdaNl8NKsXeuNBVLx1cpZo4MHE/rtWjF3/p1kTItDGv03IXDV74Rg0YxDa+F5syoqBD01CLy6MWvGH+Ok90e8Xha1/Y+bCkWhMJHrvhe5r/jLkg+fix0eiXRumpf5z+MLh6MF49O4zkc/ciabEoK3L5//jd9GTe6Hlvw9+eyn68El0dGPvQmHPw6tINA7fPjIccLAB0fNEFDio/uviFqdoNBqghPLy8tjYWGCO+Pj4S5cu+f7OGYYb5zAYDLjTtnD+iB2cP8cPAAfx4WAktTeZumVXnLIrXSQF8yhqLC3n3Ip6j7bVLGnStbc49Ib4UWMocFBR/afAQZaZRJcIuheIUEFaYFZS/6ykqcmvDQQI2Lmu9+6kPgW77vpiw913xKJ7h4Wtf/WuEcFI8N6YY5n3fy16fMFw9PQCdCRz/uhQ9H8P9T6+5W7Rh0MGI/T+M6OOpD28bdW0Kb0Q783ZJTve3LX60eMZT237dNGIAPTnJXFHRC88Mhk9dyfKzvjjktFoYRzatfbuQylLlk5FcHg4udee1ejUF6Py0+9IeX0REMykGBQXjvojtP6tRUe23ff1lvl7Eh85mPS7bR/f/+zcwEVj0AHhH5ctGjxrINq58e69ggeW/3HO2G7o6/T7HpiCFo9BO1c9vSdhiegfk2b3R289PmDdX8ZP6gnAEZ2bfldh+rSSjDEVGYPhCZDVNzcAjlAKHFT/VQE9eL1ebl8VzIYFa2xs5GKfg1asWNHa2uqv2flEbhqF8+3wn34dcEjtsma3tBZg4iZTp6zGSfrjrpK6FFUW8Tmb9Ao2SmzKNmljjU2rmxw/4bolKm4KHFQUOH58HI7uJcJwEn4jHWXxYrN4k5Nf6z+xG9qT3O9A+oDPNo7ck7QYgOOPd8W/85vJ8VEoZ9uDKR+OOJaxdNk9aHYc4r/fe0w44v9txra1w49vnTOuG/rkD+MOihbsTpwJ8LH65QlJb85/bCaa2gfNHorGhKJ3n556WPTCkkno+QVo2+oHHpqAXlzS84uEu/elLHzloZAHJ8Bt9CpOD8gWRBzeOErwyrzxwBn/N4H37sypvdFT83rsSJy+PWHCS0u6LZ1EhlgmdkcL4tDRzNf+eM/QGX3RPtGSXYl3v3z/MLjQXv6dSyajJ2eGZKX+9WvRUsCmydHorccGf5Xy/OKJCO7zyZlo4ysh+1f2O/hp+KnMviWCUAocFDj+9/IPTjAMA5QAGMH5ZKxbty4mJgaYY8CAATt27MAdEdC5QKXcKQ6HY8uWLe3t7dc32xk4nNJ6t7T+plIZAEetQ17j6DKpXXbJqbpiaL/k0DTbNO3S5lqLTjdpfDwFDiqq/xw4SlinUaCNgkx0nBd7PGkm79WR8eEo+R/djm2fcCBlxuZP583ui167f/KyRb1mD0Y71836SrAwK+3Z5+ahJRPRfv7MWbHo708MO5Z512dr48eGoIQ/37ll+UyoM3cAWv77cY/PQo/NRonvjvvkz8MWjkKvPTJk25r7HrkDvfpYUObKO2cPIcMkOxKmfZky85XH0OJxKDu52+mtETlJgTlJI1NemTEGoc9WzNjLW/D7u2Mm9kSf8yeKPu43Jgyt/sOdKW88+OLdvRaOQPv4T/z1kaF3DUMHUu48nHnPP54dM38wyt91z0PT0T3DUXbqy/uSFu5Nmju3H/rDwogdax47nPF70XszloxBCwehAtH4b7aMKeH3oMBBgeNWEeCFy+XyD2BwfhsAFidPnpw8eXIoqzvuuOPSpUscnfgXqlRUVACRLFu2rLGx8YeAwypvJhjBwcTNpNAN/7i0BrrtjvTf1/95zXkj+xH3L6+xSi5gY6NRfN4orQHgUIubbAZD/JixNwrCQYGDqkur0/b0NuxxcQcMt4861s6eiPYJpx3l96xI616YRNanFLOhxEvS2B3euRGOpOnJrw6fGIbWvhaybdWAd55CYyPQvP4oK+WlTR/NHheOXlkCBDD7n88MWjgMvbEU7dk4bXQA4r8x9SBvxn7etPhQtObFCTnpD+5aPW0EIuX3jEJzh6JjmQ9ueGvYqFD0f4/EHEl75MGpaOlMlLX1oYcno2cXoCObZu8VTXzyTvRQPMrlRZYLQ0tFkflJQzb9efSsCLT9oxGH+LO2fzx/bDh6/bfELXRkAMp454GdH/32vqHokQloX+K9bz/eZ1I0Wvd6j53rJvz9N7Hjw9D2VcM//iOaHInW/WnMto8nvP9MYBxCh5IeXfPK5MQ3Z36+ZtHfHg+Y2QMd3RhXmhKXlxRVwg/rHOYcnkyhKDBPFJaT3udLAhyR8Dxd2OnCjJd70AxJ3fBSiW1O7pn/KrZzosBxq75MsP4ZwA18Pn/YsGEAFuHh4fPnz8/KyvIPcmzatCkwMDAmJiYqKur48ePcahf/ipX4+HiTVmmVN7JzB7U3nRJzS282tYsvelR1hobTkJoaz2Fts77uDLbKsLpJW38Ga1vtkmpL80Wsb7M0X3ZI6rBRZmm94mTHVIxNl7C61dB40dpWjU1y+eVvTM2XPcpmt7zRJWuAalgr9qhblDVnsL7dIas3NF/CRinWtjDKBo4t4B6ut5u/f7fsikt6yS696JLXmturTdImo0xs0agmjB5rtzq8jO/PG34KDOPyeNxg3Mpkr9frYMWFSKGi6iryeDH8YXid2ONjDS/xdnJhr2zOZLQ7ZdJXoqjCzUG5AhJBHHpWbuOSIj7KSQwuSx1emTkr+cXu83ujKd0QYMeCAeidh9C+lROL0+768pPBa5ehewejkQjdOwj9fg46xpvzxcqxd8Qg3v/1Lt48d8uHfSZHoM3/iDu4fuKeNePmxaKNL8cmvTpkejQaG4juHoyADz56utvm90b9fj56/QG06f0hf5yPfjcPfc0bfyh57DuPo8fi0cFPQi/sGJa3MSp3Y79tb0TO7oY+/7BfVvKUg+tmPDkFPTEZpbw9+Olp5B4mBKPH49ETk9DuFVN2LZ+ycBAaF0zcTj/4bcjcWJSXPn/TPwa/ejeaFY1GI7R4GFr1QuTRpEXvPRa9aBA5fW5vtOJZlJ04oiJ9eD4vulAQwe49G8iGNQsuEIUWCMNITJFNfT/nT74rvjsmW3Z6bNgL/77tbNwvF3a5scaNdR7sIg+Z8bEeBQ6qX0QajYZbw9Le3v7BBx8MHDiQ2+9t5MiRCQkJtbW1n376KefqERYWFhER8c4773DMwY2XQDWzTtUBHDdrN+zC/4Vhdb2t7cK0wZEpn7zhURD+sLddVl4+AcABnxobzrmltdilNdSfx7p2YAh9wwUgDK+qhdCGUwd4AeWAF1BOsMOlZxRNbd+VA39ANRJ6vO0KdukU1aexTuxSNMqvfOuQ1phbL0GzrJF7YHzGLrH5Ufcvu4KVNVhVgzXNADSm9ka9pNWsVABwcGFFbVZ4kg74R9AxyHGD4Q34P8ENSlFR/fqHOIAzPC5iXoaM8TPcoIcXeyRzJxHg2J/SI39LUI4QlaWgMj4qSiQxwk+lR5UIoo+tjcrZODA7ceTeTwaXpM7K50/NThqXzx9bmjq2PGNUaeqgvOShx9ZP3v3RhMPrpu/9dGxJxpyyzbO2vte9fOuUY8mDT+6ccjhxwJGkgTnCEYXp43d/GpuVPD4nmdTMSppemDL34Oqxx5MmlW2afTRxzK5/xlZum3k4YciBtX0L0kcd5Q06tLHvofUxFen9TmX2zU/oVpDc4+TmYTveQSc2jy8Qjq7YPO1IwujjvMllWxdt/SDu4IaZWYK7D6yf9tW68XnCKQUp075ePxqQ6OCaEVm8CVACli+aCunxpPhD60Z9tXbk0YRxOYJp+aK7jm6cfWj9RDjMTR6Rw+uXm9QjP5kNc84FSOVCvIuCOeA4ujn2c/7EBeO7YYeHcWGbF9vIfBVmycPuxmoPVhOeYzoGl+gIB9UvIf9Ahf8dGghDIBD07t07NDSUWz0LaSAr1KE5c+YUFxd7WC1evFinlP7SwGEVX4azRsYE8D76K+TtkivQeWOXRlt/1iWvA7CQXDhB2EInVV45iw1yh6QBu42a+gtuZYuq9pz4wkl1HbCIxC5tADO2XMEmBdZLwaCCRVzr1bTqmy5ibZux5TI2SIA5sLaFHdv4eYDD1X7RJb2EVY1OeYNV1mJWSBw67dT4iZ3jcHg8hCrcbic7yOEBqoOfjtNJXkkYhqGDHFRdDji8TugFOwEHmGLuRLRbNPOAsG/hpshcPqog27GiEyJUkIBKBOjUpvASYXg+P6o0Y1DF5lFfr+uVzRuQLxqQndT7qzXBB1eirI2BRaKYPP6wkvSJJenxRWmj9q/ufmBteFFm3682BB0XhB9OCj65a2CWIDJbGHWUF/nNrpE5wr77V0fni4bnCYcdBxARDPp6Q88DayJzRf1KNg3OFfXOTelZvg0YJWzfGlS6NbZsa0y+AOUno4qMoOwkEon8xJaeecLIY0kRX2+IOLQh6vDGXtnCoV+u6pUtGpWbPi5LODw/Zeih9dGHN8YUpQ8BO5LQ81hS77LNw7P5fY/zYiEPaY6gX55oQK6wfza/P4dE2fyBecJ+OfzuxzYGHE9ABXzkC3+e0hETnY3anpsSfHxzz938+IXjI7DD7XV6HB4/cGAHdrqxxoM1BDjYR+29/adzKXDcouJcOuDV2WAwWK3Wzr1aTk7Os88+O3r06JCQEG4NrV8AH0AkW7ZsgWqDBg1StLf80sDhUTUYWs5PGRy1S7jS1HIBO9VuRf3Z/IMfvPzcI/MmPzp38uHtqeqac5++8dKOxFXahotOedO3uYeeWXxny7lKU2v1+6++8Nz9d/3xscXNZ8vtssbig7teeuKBpI/enjlyQMWRPZs3fPLonVOWzBx3ZEcKAYL2Go+6xdh8AVDm5wIOp/iCXXyeIV4d9U5Vu00tt6iUo4cNxy6vf0oFnr2TyA7Acc3oMrsh30/Y+IaK6jadUPHRBrbD3wT8XXg51yYXpOo749GXgnkHBIOKMnrl81BlclglD53fGliwEeVvRKcy0aktYSWpYTm8oAOrifNmvii6JKNn5eZelVt6VGRGlqQFFYlC8oTRxxN7HEvodvaLuLLNPbOSA0/tjD6WjCp3hAM0HFiHKraHZwtQriggTxSalRwELVRs6XcsMezQOlS2qXf5ll75KeEntkOmx8F16NAGVLIpNEeIjieT/VaOJKE8ISrPRKd3BRaloX2rUNnmgKMJqCgj+MSO6Pw0aDmscnu/sq39jiV3O7AhtGRzn6KM6NJN0SWZUQWpEdByYVq3PFHYcV4gFHJ5sKL0yOKM7pDmp0WUb4/NTQ2HC8EVi9ODKreEnNgUWJFO9oTjjMMObg+5vBSUtTnqS/7YRePDiLeGE95pyHyKzTerwrixwYt1XvLPiPUZpcBB9YvKP1bP7QTLLX/l4n3JZLKAgABug/uwsDCADzgMCgqKiIiAwj//+c9z5879pYED6jOqRpeqIRqhjPUfWMRVDkUD9Nxbkz5d9vCiz/hrV7z5cgxCV8oLXnnykTG9u2OTCutky996dUj3YKdaPH34gHkTR36Zxrt31sQ+wcgkaUhfu3xg96DJQ/okLn+3YP+uHght2bh8l2BN0kdvYofG0HwJsEPfeM6javzZplQ09Yy8yiOv44DDJG+XNtRHhUUM6jvwjplz33j9b4cOHW5ra2epgkypwE/B4XD4/XbZkQ8KHFRdDTisXmAOkB84XNr549Fe/t1f8YcXp/UpSAw6yet2IjHoGxHZ7f1kGnHmyElAeTzo70PLMsMrt0QWpQZn81A2QACfrGcp4KPsBATkcTwRfb0O5fChw0Y5AnScB9CAABrO7A6GTOU2sjfbie2AJgigITsZ5QpI116SEQiZLB45EQqzklFBKirfgko3oZJM4rKaK0SF6SQDh9AClEPLlVtRQQopgfpQAg0eTkT5qQGAAsd46CiPbEKbxbZWmEYaPJZE8sUZKJtP7i2P3asWUshDOXx6jI9yU0kFuPlCIcpLQrkJKGdjx+6y18YbhUtnb47cwx99z7ggbLdhh5NhvC7WgYP14YDHbfIS5nCxgSGJ/wwFDqpfRP4ootDD+bs3zlGRO6yvrw8NDQ0MDIyOjvaPcIwbN+6111775ptvoMLQoUM7OY3+UsBhar2EjeL4gZHCle+axFf0LZds0gaXssWjEUPGKmkcGBGw8YO3678tg7ss/mq3Xdk6a+zQf77x8r4ton4RyCCuk9acUzZcGhQV/GUmf3dGcp8wdDL368ZzJ0qP7uuO0Lc5X7mVTabWKmt7jVfTig0Sq7gK69t+NuBQ1TolF12SapukFoDDrJDImxqjw7tFR0QFohCEghEKjInptXTp0gMH9lmtZv88l8vl8gdeo6LqMrMqxGPUywKHBzM+Z0YCHPr54wP2Ji86xBtRnNavMCHoFC/8ZGJQ8Xqyb/vpzJAyEcpeT3rZMzsiT2wJy08mnFEoIEbig4kCKtNDTm0KL08LPb0t+rvPeuQmkSW1p7airAR0ZmfA0Q3o9I7AE5vR8Y2+1bbALmTNbQqCmpWZQd9sDYESoJbKzABo/NgGYBdUuYlkikQkD+2UZBAcAarYuwKd3E5gAhgFDArB4NPSDHQsAZVvCihl8/lCVJaOStNICi1AWpJKMqd3hJRnkAwYfFqc4is/uT0AQASYpmwzGUepSEcnM8h3/zY9pCKZ+LKU8wPL2R3twUoEgWR32U2R+5JH3jcuANtN2GH3uBkfv3mx24vZ52zigAOetuf29xqlwHHrAofZbOYWRBD3Aaezc+AvKPn888/Dw8M5zpg/f/7q1aubmpr8b9uQmTJlilom/qWBw6msVzWenzay79r3XzdL67FJbmyvb7t8+rFFc+bED+8ZiPqEom3J69WNV+IHxf71hafKju0fERt5uvAYf+UHkQiBjenfg8vsEG44/uW2AZGB+rZazJhbL337wtJ7AVMWTRl5vuiItuE8kIdDVq+pO/Pz+nBYW79ztl8BoLEr2mxquVmpGDU0rqW+ubS4gpckfPTRxwA42K31widNmlBWVsYFRMEdAelxJz8bKqpf/RgHcRclUyp2DzvU7/Nn7BjhOJQ8vDStd3EiOpUcdDIxoDQhtGhDcEVyt8qUbmWCsBPp3SrTwrI2oDzo/kVBpzK7g5WJwosSgwsTQoqTwr7+FBUlB5/MiMzZQMYGylMD8hIR2MnM4MJkVMAjC17ObutJtrznh1SmRULN/ESCMlCnKBlB46cyu5WJoIWIYn4AZLLWoVJh0PnPepHD1JBCETqxrVtJRvDZL3qWpAXk8Ai+lKUFnNoSlr2BNA52Zlu3nI2ohE/W10Ah8X5NQVnr0bG1qFhADDJH15DbK4XWMoIr04MApOAGoM7JLYRLchPRsTUoZz0q4wWVJ4UWrgs8kRRZkRQFVs6LKk2OBCvhRxQII3Ize/iAwwbAYQPg8LCo4fYQ1zEvmVoxs8ABtOHycKMeFDiofgn544dyQxpcUFHo27hoHB9++CFwxqFDh9ra2gAv/DvU+4dAxo8fb9QofmngcKmaTJKaoTEhm5NW2pXNyoYLHr3s+UfunTthxHcl2drmmqnDB676++sWabNo9SexIWj1u2+M7hcNUPJlJr9XMHKo2hT1F70GuVnaiE3K5BXvx/WKcGnalQ2X7MpWp7r1SkXefdPHzBrZDzs0sqpvsLYNmyQOcvWfBzi88iuMvAorG2ySWrOkySRvt2s1U8ZPINF3XD4fjtZWcXJy8rRpUzi8W7Zs2blz5xiGgWdOV8ZSdS0xpMvzks7PznaBXhY4oIdUdwDHsOL0mEIeOsVHJ5LQKUHPooSosuQelaIegBRFSUHlKWHlKSGFvAAwYIX8xAAWNSLKkqMrhT2/29K3NDksf0MA0ElRUkC5MPTctphiHmItsJQfemZTr2MrUbkgqkIQA912wcbAk2kEZYp5wSfTukOFnHWohBd6IqX74U/QydSoCmG3vPWoUhQJNFMmjCzgh2cnBebxQ45tRNnAQ2ndivghJcKwyrQIoJZyYXD+RnQiNawoAZ3OCC9JQidEIXBdsMqU8FPpkRWisJLkoFJ+8JnNPeC6kC9MRGCQgVutTA0GHKnMQKcyQqCp8uTQk8Kob4S9TvF6nUiMqUjsVZEYU54UU8qLLkmOKoGbF0Zmb+q5J3nsfeOCWOCwYLeLAw7im8utN8ZWljYYN/EhdXopcFDdWkOebP8HzDFx4kStQvJLA4dVWmuW1sb1Cvv7K8+fyjt0viz7YkX+4tmTH1kwq+3y6cM7Nw+JDlv7j7ewSd1edT4SoV5B6J9vvIzNqgvleQAcH/71TwZxnbzuAvCHRdaUmbBycHQIAAeASPHhPWXH9tqlDYJP3hkYjkytVYyqmaxYsch+xmWxbsklAA5GVmttr3EoxUaZ2CCT+lepwJ+92Wx1uRgWO5p37NjWu3dvYI64uLiioiIADm5ICbAD8n7yMBqN3GzL96LRU1Hd5v9cfNEg2C7QyRrDuja5sFczpg/K2bn0aFpcQXpEWRo6vwUVQRfOiyzh9SrhxUAXW5ocAUBAJhTIHivczEIwKYFyeOmHV39eVDkvwmfJYayFdhh7SD6KLCfVosqTosu5+v5P/eZvhFT2G/TxUYWCqDxhVIEgCjr7QgGxYhIhI6KEHwZ3wjYFoBBckRwIRmZArt7Avzf4OpxzBve92BuIqkiKBs44xY89mdy7ktfDN8IhCC8ThRemdd+bGFH4xX3z4hA26rDHAeym1esY7AcOdgLrKnDYKXBQdWngwDa5suHcvInDYgIRNzNy76yJh3dt6heBJsf1G9G7O9iWpLW6llpDW/1TS+4GyGj4rtwsrvcapLtTk+IH94wCCglGc8YOq/6maE968tCYEEXNeezQ8j5+d1A3FI3Q2D4RohXvYrfepWi0SWoVV079jFMqPwQcXie7JMVLlqj443BoNCq5XP78888DcwQFBW3evJkDCw477Ha72WzuvC0O/W2k+rUBh5vtBIlLgZPtAtlZFQAOl/zpBwalfjp5x7pe2elhX61F53ag3HWInfuIKuZHsqE2O2iD850UoA7y4LAjjBghEq7zJn0/fNSRciWkDnTk0GcDQwCpcN38tXW+VzOiI40sSY5kgYMMLbDAEcHG4woDY++NtAOQwdIG8nldXHMP/z4l4USJ+b6RH3oq+VFgZfxu8ByK+UGFfOLCcpwfVPLluE2rJ909NgrebDAD7ycuh8vqZpfidwIOLqo5BQ6qLg8c5rbLFskVk/gytsit0lpslEguf+uQN7WeP9FyrhLr5V6t1CFvsUmbADJee3rp7DGDMaM3NFfpWy5hvVTTeF7bcFHdcA5KsKEdG+XiSyewU29urza2XDG3XTE0X2o9Vw60oW04L7l0Eju1bkU9dqj+CyMcLoeHW4fmcjFsRDVf4C+tVpuYmOifXuGCrcFj58YznE4n9yPwR1KhovqVAofdTQY5vGykUd2TD8St/fvYyoMLC3f0KdsW8PVadPYzEnCigA14xS0ELRERvwewciEx35INEfLH/PaNEAj+Zcp6XPpSwY+r7wu6RYwN+ikKLGYDjfuuy1q54KqV3sz9dEqL/d+040RuLKeIx/p/wEdpxIrSUX46ykoLTV0ecWznU3PG9MIu7LQYMHbAI7W57Z2Aw8WtT6FTKlQUOGos4ipsFNulNdgilV85ha0KgIPWCxXYrtU1XbZLGzQNl+2SJuw211QWRSO0S7BGXXfG2lYNp5Dg5SaJTUwaMTZd0jZ8h01yj7pJfvkbh6wWO7QkwKi2zSlv8KhbdI0XIAWziqu09Wf/C8DhsLmdDvLnzTBeoAcu8Jd/GiU3NxeAo3v37rNmzdLr9bgjGj3utMGef2c+KqpfDXAQ5vB6WdqwukgvyP7P8dpee2FB0vL56auGfyWM3Z+AznweVSBCeanEClJ90a7Igo4O4CDkIbxBdIpf1AB9yNa1rHFxP69BBNF161d/UvudW+bIo1AA4EWeAPdActNQTjo6khZ1fOecPzzWU7TyfezEVouBwTY3tpldJje3CJaQHAmwxgIHQ8aTKHBQdVngAFPXfosdCtnlSpv0irH5fNv5Mo+qkYxAyBst4mqsFVvb6/QNlyBVVJ1JfP9NrGu1ii9jXRvWtWBlI9Y0tZ4uwjY5dmmhRHq+HBqBTxllvbbmNDa2a+rPeTWtpCmd2K1sUtWeNTZfgEb+C8DhtDPMtYG+7HYr94Th8UokEpVKxQVee/rppzUaDe5w7AUZDAZMV69Q/SqBgyFOo25sBmP3G+PexXFlwf7f3Ntrb9qSvO0zPlsZfHLnkIPrUQ7bs0L/Cr1s4fde/QW+UQH/wEBhR1f9y5mfNgquu1ZnPvDf1Y9tPC8lEKxAFOhrvyOuaHE62TI3LwVlpwRliYKPCkOPCMMPCPvvEMxa8e7ChE+Xs0/V48YWuanNSbDjeuAAyGNu938oFDgocPxne6k4VWQLN8kVbJGCedWNmrozXFxzS9tle3stkAdWtzKKFq+qzdleKz5bbBdfxIp6SK2ADvpmrGm2tV1QXqokeWUD1jS4JdWMvFpX8y20T9oRV/8/e18BX8Wxvk2VUveiRUuLB6fFIUQI7pV7b+1WKJATI0DtVmlLcWkpRRp3d3dPiGPBQiBux8/Z3TPfO/ues1kC9J7c/yXfLez7ezKZMzu+szPPePvFMnowV+05wrSCt//FczhuRThY6L9xxl0qarUWCQdAoBR4xmhlZeWwYcOAc6xfvx5HOHCGRXw4mCSS3HGEQ8ORNj2RI+FgcLKR1by3ZtJ3TpP9909POj7Z59un43/tG3mgZ+ShHtEH7o3ZT28sS9jbk67Q5JeIggrg12z2pFeNGCc7Hozd9+CfqdSf+0XqrWzef4NN40yKCfdDiDwevB50SQe/tqMX6OP39ozlL1ozT+0Zs68XxX5qwpOPHoiYfffE7H8w5sCj0Qeejj7YJ+rQgMhDQ4IPjf/k70+uXjKMaDmtkmgNei1RqkiTmrQBt6DX5BET4eBMQ0sS4ZDkbiYcwAyqCxPw2rba0lTSXEXaqjWXy6AJN9SdJQ3ngRZoqyuAaigvlGkulxN9A2k8w1wuBVVxJq/1dBZ/tewZ0nxBeT5fXVUIeuXZfGrSdtlQe4qpO0vvb2upBtrRcLqAtF5pPJ2nqam87SMcLBEIh0ql4Xcdc3iXijCAgZKYmNivXz/gHDt27MBlHGgHOId0Mpgkd5oYt8UqONKiJ21aoqM7K1ja+4b2sLoibcLgHhvXPOn+3bjQXRaRe16K2P90xIFHIvc/Hr3vydg9zybsfCHp5z5JP/dL3jGAAvRgsvP5hN1Px+8BCxQxe5+O3fP0DSqYPxm79/GYvY9TdR/dbELVm9i/3iZVH0P7dH8KvzmFLhrd85gJj0PQFBAHimcTdj0bv/v5hF3Pgwpx5v00T93zbPTe56P3ggoxgUg+YuQf+x6J3PVI1J5nIvf1D987NHjnCN8fRnh8PeLwvyyWzL8vL8udcjiWNCrbNUTRTmoVpEnPbzk2EB1ekkcJBx3kIBLhkOR/mnBorlXhno7r1VMm9frr2rsO7ZVSoqoBVX6+AKgGPUHrYgmlGs0X1ZdKdVcq2WtngGcY6s4Z6s6TpkvqiyXaCyeBcIBKWi+R+rPy07mGmgr6s+UiuVrJVpeBIbl2CmgHEA59zSnV5Ur9tXOGhouggt7QeB78vBnhoDBF7LQ5KtjnIP5XgXCcUV05g4Sj9VrtuFGjaSeOX8Nh+sL5zX96OnqBh8AyDCPsevX19b3vvvuAc3h6euLgB1ANHOqQRJI7Suh3AFWMiiNyfkpFr+dPqYImk+6x0LRUFYWvf33U1vdGbn17kPfOeX57J/vtG+u/d0LgnklBu6aG7Hw19OcZYTsAs6j68/RQMNk5NWTX5JBdk4J2TwjaPYnavIkKPkyg6l6LoD0WvDqWqkbzSSKVNzHaEWyOBRUQsnts6O4xoPKwMGEChM5jsglTEUG7Jwfunmy+6r9nqv+eyRBhiAMEDeHysAg9MC34wHT/PbPdf5x14NNpX380ymHdwHeX9CvMOMwxNYS/J0WuVWuJup2r19HpKj09yJXoOwgHkQiHJP9LPAOawJaWFsKfAwGEY/bs2VWnynV1l5irZ7mrtL1na86qL1XoLp8iDVRPW9ya09judjTbNae7BnF7/x84+beoEXbAdsCMUM6aoxqunNZVFRGgR43Vyktn9I3XGi9dqLlwacyosXigEdtxIzRnXC93i0VbX331Fa7nyM/Px+ENacWoJHeuMHjal3FpIyF6Hd6uzBj07YRpPPbLtz/865OpFs9PGPHQhJEPTBj5IGDiKw/xeHjyyyYYTR6aOOJBAdTmn6kPTBzxgKBOGf3Q5DEPXqeOeRDU18Y/PGn0A1PH9pwz7alH7u3x6P09Rg3pMW54D3B1MzzYGaKI/bv4iNQRD00c+QiPXkaM6kkxsteUMY9OHv3Y1LFPTRv/3IKZwx3Xr85I8CakGWpr/uIUfgc+rW0Yg/FYc87E70w1/F+fbUiE4w6qAPhtEcL+CCAcffv2he9fU3MB2AZ7pYq5csZwrYrUXQCNsqr4hua/oms84H8TXSccRFHXWpLVWp6vunxW31jXerUGPuuXXnpZOLKZNX7qzJ8TDpD33nvv6aefHjZsmLDOQxJJ7qr6R9jDBX0eQjeQNwp3u90m8Iesq25UW1prCVGznKqu4eKkyWOefrZXckqM6U602wfGtHm+M1QqBYBhdPhTrVbyK9DvumVeEuG40wQ4B86qDB06tLb6or7+Mmm6aKg7r7xQAuBqzxrqzmkul123joGizKT5i6OLUypa4F6XKujJHxdPA+Fou3b1wtlzkydPNZ2p2AXC0dLSMmTIkAceeOCbb75BE2mQQ5K7SnAmUXSvIadn1LcTKr6Z195K5S1op706scc9PSpPlag17WByW6Ok02luCtGcLE5KsaDHKVqJcEjylxQ8DYKYNkrY2tpmpyW1XqjU0fWb57RXT6kvlaqv0KvYDY1V0Dbr+BaaV8t0tWV3gsoTjhvXptxKpRNJ1RVEWd9Qlk9a6pTXrtRfvqhVqgYNGvIfEA7o28XGxvbo0eOxxx47f/68xDYkuXtEzwvqFQp6qXJDQ4OpE8/cZuhvVDmDTqdXNrfUg36cxSggHJerzwMhuP2RufkIh1arVquVoALVALAsvY9NGuGQ5K8qHMeJFyrC1y6TyZJiI9uqz8gvlWpqKg1NF0jzRV3tGe3VSl3daWibtbUVRrW2TFtbZlTF5n9J9ZS29pSZKoC7ekZ3+ZSh4UrbhbNsS3PTlZqtLps9PLz+A8KBw8grV64EzvHee++Jd7JIIsndwDlQg4up+ZN2oVLSs5z2tgNDuV7luwA6htUMGzYECMfZs6fpfprbHBPkEzeCj08H/9DpNEA++BkWiXBI8tckHMI3j9Oo8fHxH73/DtG2krYrmtqzqquntXXndPVVoKqvndLUntbUnupAXYURYsO/JE6bD+21s1z9Bf2180TedLE4v/pUBdHptn/3/c5de24gHP9m0ShWsk1NTXV1dYMHDwbOERcXJ4w5SSLJ3VAFoeAaJiDcfKXEdctwAumk8nM6HP9hKseOHfvUU0+UlZWB/naPuNyKcCD36rSSQxrhkORO6GFgOwe0Y+3KZUnhgVfKC+mGz+pzjVUV6rqLTHON8tp5Ve0FVW2VCGdNqPqL40KXQOQNl4tzSXsT296sbKr/8ZtvZk6f0dom7yrhwJFkqGGVSuXnn38OhGPu3Ll4/KgkktzxAg08VDjCnnysjkBzqwb4vwXGKFwnVTh1T6PRPPfcC/A9Xr58hdDdfLc3PvxRPTeBXq/FuxEEqgE/FYp2iXBI8lf94PFTFw9ylBQWjH95+JE9uxR11zh5W2tNtaapTtfS0HipSl57RV57+TrUXaToZPjXw5Wu4HJ9VSXRyq+cLm+pqU5Pih85/KW9u/dwHLmecHAmwnFLtoGTWZj5oE6dOhXquBMnTkglU5K7QZBqCIRDOGaXTnDcThj4+5xvBE81oEWn66gGDRrSo8e9zc2tQD5YKrcxPrciHMKUCpASrVaNizmkEQ5J7rBqgBzYsWfamIl7v/9p+2f/artar2lqq79QrahvaK+tM+HanYW6ruDaxYqyS5Xl3sd/f3PNyo/efycjPdWUc2Ig4fizIVDhXHNgGzqdLjw8HAjHmDFjiGm2BQyFg8Lw6DBJJLkLhLvN+BMORO95Bs2jjz5+330PtLa2m2gQ978EiXBIcgcRDijSnNqw/+cDHkc9x748buaUmYP6DJo8dvK4V8aNGzHmDgSky3yMGGMxcqSt5dxv//X5yfw8ht+0xjDQHWG7SjiAZ+C2FKzU6uvr+/Xr9/DDD0dERAg9Pxx2ktiGJBLh6J7WmqFnoJKHH34UCEdbm7y7OJBEOCTCcbdKa5OKsKS9UQOqVs7WXKgDDacVTRh0AnOXwUC0GlVba7NarQbSoNZq9Cxj6DzC0YE/q1l5qqFUKvGnq6trjx49Vq5cKVANtCMRDkkkue1dLTwemG/Qe/bsBYRDLlf+TxIsiXBIcscNcgD0OtNtZBxpamwX9J2bU8NfHFzXVK1Wi+50jF6gGm3y9q4SDpxSkcvlwmKOc+fOPfnkk7179y4pKUEuIkxsS9fWSyLJbRXWdJE7fHT33//g/fffr1JpJMIhEQ5JbjvhkCu0eFC/vF3D33pAmztGT/R0OTfHq4yeX+4NKrS6Oj37F1Z1Bp3eYLZKaQLuoDeN7xj4Sxq7PMKBhx3xZw8QZB7EdCbH1q1b0ZCSG9HciiSSSPJfauM7q/wnxsHnptdr77333gcfvL+7LlOUCIdEOO7uAQ6lSiO0l03NraCqtRphaQIPRgTuL44ujIbgDJKG41R8/QQ/VTqtRqf9z6ZUxAcfIaWIiYkBwvHKK6/gmWAqlQprPWmEQ5K7vV76L8mtGnLcFcJxjEqluOeeHo880su0T+T/T3wkwiERjruEcHBaTldTf1VPmHZ1G0sYhJ5oTVCLoTUo/9rg1F2BTs6vFNUBLeNYYB7szRaKmkk4kGQAqwDCIYxkANt44IEHEhMT8Sde6isVS0kkwvHfa+BvsjiLM+joMC6jbpc397inxxNPPkJvYTWwEuG4OwiHwWz1zhjiM5itkturGggj17QbeJKh0razRK9Qt6r0Cvj8WAJdee3/svp/gN4cFfIEKicd/WHAKRW1VqPRaW9dNXC3er9aNT3IWd7Wjib0MEHe/J1/vN2z5wM/bv9BMAGbNDRDF8uJpP5nKukW1dz4SHKbCEdnlf+gGa1W3drW2KNHj6efeRwHPCTCcacTjq4u+rsD2AauBDBTJbdbZQy3VPF+5/9d9f92T7Q56q0uW7rVOcrMn79frVKBeqWiFU3cjh19qOd9b65d09hwDU1U7W1dKyGS+n9RSbeo5saHk6jG7alyb+hoGQxILy5dugCEo2/f3kRaOyURjjuTcBgYc0EkmAHDXwCMRkk1jJbVqtAENbVXLt3Xo8eIl4YY9BoAWABDtbz1L5Govzz+18qnRDi6UXDj2KVLl+69995+/foR0TYxSSTCcSeNcEi4S6HTqmnvimNUSjloFPI2ltE9/dQT9/To4evj5enhtnHD+k/Wf3Sl+pKUV3crJOkmwfGMCxcu3H///S+++KLENiTCIREOCXcINGqlWqUAqsGxetAkJsS9/947dgttnn3mKWAbDz5wHwA0gFEjX5GySyIcktxuwV1jp06devDBB4cNGyZsIpNEIhwS4ZDw1wZnupypuakB1Pq6a/379QF68cTjjz7cq+cjDz+EbGNA/77bv/+2ob5WyjGJcEjSDSMcBQUFQDgsLCykBRwS4ZAIh4Q7h3CwjE4hbwPgOIfbH8cfe/Rh5BmA5559+v777gHNyaICKbskwiHJba+I4aNk2aSkpPvvv3/WrFlShtwdhEOS/yolk+R/WfC8UYPBgBemrF69+oEHHnjhhRd6mGTOnDnQ05KmkyWRpBtEr9eHhITcc889dnZ2RFo0KhEOSQwS4bhTulNYnbW0tOC1sUA+QNO7d2/gGY888gjUekA+du/eTUQnoEsiiSS3T4D3+/j4CHco4r4VSe5wwoH3ShD+yGd45VDbYtWM3UHxnd148DMWC7x1E562trai8/b2dlz4A4ZgU7iWE6StrQ016BsEgR5CiGiIlkEVSC62CkVFReAWLIOHGC4GgdbQBD0BO+hEMAGbaEE4qRqPmBR+Cn3ZTkdZCwkHH/iTYgysgdPotHKlQrg/DAwFPcOxwgVjGDeILbiFKKHPN95+junF6ImtgR6ci+MjntrEN9XU1ISpw8hjqtEJxlzclRcsgGXMOnSOh3kL/YzrOBa94EDf6SlGG1prjHYnJ5BeCIXhRfyaxJUInuMpGEIOCAkX/L/RZ3QOqnCJK1IHyAqxb0Io4qNCMTMFvZAugU/AU19f3549e957773ANqDiu3z5spBv9O2bNJjbGAdjwTAY8C2LYwsFDKOHJfDfmoNzvMAFNELxwDeFPovfRV1dnfhjQSdCAgUNGAoZC9kiXFEhrs0F+iWUFrEJOBeyXchVofhhwRbMQQ/xuWn+C4aYBHrZr1qN37K4hIsLmLgwdyqEnT5SM78LFKEuEldKYFOo+qS+dfd3ALB47N27F747mUxGpCsF7hLCgZ+60DwLTQhWlNAy4bctkAZszODR119/DZX1U089BfX1yy+/HBERAVX5pUuXhA8Ya3akEUJhEmpArOOENhKJi4YXNKmqqurVq1dwcLBAaIQIY8UNXjU3NwuPwDeB/WCNI9RZndo2gbVgkkEDlrHaFVdn9Co1ltHqdaK7Tg1AL/A6D6VahRowBD1YAw3ERFypYVuITE7cYqH/At+CJHeqQCEyWMkKljsJRhi5nWAB/FHzIuShYAE79Df6gxQBGzawI2QymmALIdTU4IOY2YAFoVQIvoEd9BApINpHRtKJBAj1vlDwMJdQI46wmFsIiQVD4VYUcUmG3BOSL2Qsegth1dfX4yMgT2hoY2OD8ykLFy7EcoJvBAstaMQlVlw+hWhAiJjtnUiYgpdOt1KhTTFhEqIqcH10Dj/585FooOJmHt6FEAewgJ8AtrhYsAXOJ0QJI4OhiIkFeCVkoEAWxRkO9sXFRnB7IzUUirf4ShrQY5aK44yWkbFhJXDT7wI/czHVgML53/ouwFonyiX1sLtZMLd/+OEH+PQ+/fRTIh38dTcQDqGbjhpoNaERbW5tqW9swJ9CWwt66MGLP+MNGzaMHTu2tLQ0JSVl06ZNjz32mKurK9Z0WE3jvRXi5h8/eHGzgZ+9uKihEzBvaGhISkoSOASOmoA59rM7NTPCtePoVggXanahjYFqpa1dAWlRqbUNjc2YrvqGJnpfmsaY0rr6RrRw9VodTbWOgc+iXaWuvlYLGh1nuFrfgCZ42LZSq2tul6Oe+kmPzcZRE2gY1NAHFuLZ2tqO5vX1jXzz0MJHW2+q4qkFyOIbugLUjuBVW5tcCELUZrMaDbAoJE8EK1KxoYgQQEVPPeHzDciBppMFU1brW1rahAoZ/MGgQW1sbDZlpoqI7pxrbmkz3rjG0evohOxtbZMLeatQqjGHWf4gachhvLhOp6dDRJeraxjWgE7QPrwF4QUZ+OxFE3yJAsAQnGi0dDhKrlChIQRaW9fAGYzhtrS2w6sULOgZ4w0s4Or8hUuDhwzr9fCjBw7+0mmCDAJCVxDPylNnjANarAEADsErwR/hEj5wghp0KGjAEKMtvqsPnIMn4BX6iYYQkOCDkFLMMUiIYA2eQqKER0Ki4A9eByYT9JAJnfIfggNvMSfFKf3z7wL8wcjXXK0VXhn8xM8E4oAfDgaBDoX3iH5iPDl6PbJKCJ2nEcYuB34XDQ1NQpHD8gxFUfwddem7gIAw/4Uchp9C6JhX0pTo/69BDpCtW7cC4dixY4dQk0ty5xMOIBN4Q6lKoxa+PaAdtJowcPC0Td7e6YOEpv3NN9+cMWOGQBo++OCD55577vTp09DlKiwsdHR0nDVr1ocffpiWlgYlCUjJxo0bw8LCpk+f/tZbb5WXl/v6+o4bN+7999/HQZHs7OyVK1daWFi88cYb4eHhGMqyZcvOnTsHHlpaWhYVFcEja2trNzc3fLpnzx7olc6ZM8ff3x+oDFJmgbtgv7NTerGWMVx/TSs2llAPwiOojLAyxUpTpdO3KpTQlIHXGoZFYlHf3IKcA9gGyx/0rdYz+AjrQb6H31H9wU9sngU+gbUktujwVNznB31tbT3aEbMB+Cl4KNhXq7WCIXgu9JlBI4QFFsBaJ4cQqJjcgB38WVfXcH333egEVaEZQI1crhQaJKjNsSoXt2SUrnHGnMSmSKjfhdYRsh2sIV3ANo/eYs9w6E9jUwuaC0wCmw3wDeyA5+ImH51AO4ctsTga+LpBgyqat8uVqImOievR4150hSkCFaMEkblWWy9ED0IUEoh6ob0X63V8UydOUZecQ3AQKLoCPUQG4gwpBRVzshOHQA3YB3N8JEQYAUnD5l9wKDAAJIhCxoqzGjIfMhe4CDwC/wU/MQ/xexGzN3y5QvTAZyGrwQY4B5tYTtChhmcMyAzE5YplDVjkOg2xY+Hs0neBoQgZCxpMBUZGSDXoIIt0emmEo/sER+8+/vhjIBxHjhyBn9II051POHBGQK5UCOMZ0PzBT+AfSDhA09pO22PkHDj3gVTUxcXltddew8l4KDrADO69996cnJySkpJ77rln7dq1hw8fBs7xwgsvAAnw9PSEgjVgwAAwfP7555999tlRo0Z99913Y8eOXbduHThftGjRTz/9BCzE1tZ28ODBV65cgSDAn+joaKhgwG3Pnj2/+eab1atXgx5oTVBQUK9evaCkHj169PvvvxcGOXAQRTxeLR5jh1oSakOhAhLXs+J+m9ANYkWAJq6hpRU67KgX+IfeYDxFmVITOj8gDEcbBxiQXkCLDoD+nMAwoMHGHh7QEagfVSoNmGDEhTq0tbW9ublVqE/RDlapSCOQKKAqdBZRI34k2Afn4IkQyaamFuhBCj1C06A9UDUt2AENBmccz+BZFHgONjFRQvMptDqYt9CW42304jYYmx9wAgxPaIMF0OaHz3nwSiCFYoKIeiQNAiMEf+ANgipuVDA+4sEVgVvg+4WwcLQASQb0zoOCQwVr2FKKqQy6Elp35BNCAwwNMz5CJ/AU+/SgwWTCT9SgBbAsECnwRMxI4JF4CASdYL4JXA0tQNJAA0kADQ6TdBquEIYZMFBIFOYkDs9AiJjhaIg5f6tsx28E49mJymAy4YViSQBPxOwKywDkqvidgh30UGf6OgD4XWDhx8IGBQ+KHxhCUURe8p98F3xkhLePGsEQo4EvC9MlSXfOp4C8/vrrUJ9DtS8to7krCEdjs3G8GljF8FdeHj12zICBL06cPGn23DkjRo3csfNneHT67JknnnoS7QhtObTuMpls3rx52JYDpYCGC4oONP9/+9vfpkyZgv5XV1c/9thjQDKAE/Tv37+hoaG9vX3Xrl1PPPHExYsXgans2LHj0UcfxfIHPoC3mZmZTz75JDxqa2sDD4G+qFSqZ555Zvv27egnsJb9+/dHREQ899xzRUVFneiFeK0reFtfX49PgbhYWFhMnDTllRGjJkycPGLk6NffeAvaG8C0V6cPGfrS8JdHjBo9FsznzV8AlTvUSn97593hI0cNGDxk2Csjnnz2uUnTXj11rgoi+s32H57r03fClKljJ0x8/OlnLCZNPubmXn2tFp6OHjPu5ZdHDB48tG/f/mPHWmzb9tmZM+cg9Oeee8HS0mro0JdGjRozZsy4CRMm4TDGqlVrZs+eO3HiZHDVr9+ABQusg4NDwXz//oMzZsx6/vneU6e+On36TAuLCV9//S2Y5+UVzJo1BzwfMWIU+A9evfPOe1i3Dhs2fObM2f37vwgADfzEahcsgDWwDE7AITgHT+DRV199A4avvTZjypRpED3Q7Nt3AMwDA4MhqmDzpZdehniC/ZUrV4P5lStXwebo0WNHjhwNsZ0zZx5ED3L21OmzMgcnyNWBg4ZgBg576eUzZ6tqrtae+MMdsvrJp56xGD9x0uSpL/Tu+/32H8HJ2XPnp0577Zlnn3/5lZGDBg8FJ3//xzs4yWK7cBHk9/gJk+CNgD/wavAdwcsCa/ji4CW+OHBwekYWOPn18BEwAYCH/fq/OODFQV7evtCQhIVHzpo9F36OHDUG/IGX+9bf/oG9/Nemz3z8iadAhYDAK3CFreC61998+pnn4O1DlOA9jh03PjgkDJrJn3fuhnhC0iAVvfv0A9/27T8ITvLyCyFRvR5+FJyA/1iioI2HwmNlbQueQNAA0MBPMIRHmAqwDE7AITgHT8Ar8BC8Bc8hCAgIgoNAIWiIAEQDIgMJmTvP8qXhr4BXGFvIIrAGSYBkPtizFzzFVhNSAf6DTfAHcgmeQlZAhkC29B8wsG+/AVOmvgpxgPf1y6+/gRPIRrAG+SBkr/i7GDpsOKZd+C6AOrz9znvwE9IFj+AlQs7DCwWv4OXCK4ZEQSrgpYNvUACgGEBhgCRArMAJRABStO3Tz8EQnDwDGT7PcsiQYfBdQGmcNGkKTqxAkYOCB8UPCiEURSiQUCz/g+8CgoCMnTlrDqQdABr4iaM4Lpu3QMLHjLUYZzEBCSsrNXndJcICIOheQiUfGxsrLeC4W0Y4oAVtbG1R6bSz589ramsFDZhcvlrDL1lgAVqWAcAjg2lpJw4bfPDBB3PmzMG1e2BSWlr6/PPPQ9GBdv2TTz4RVuAPHjwYGIaXl9eQIUNwTeIff/zx+OOPI1EIDQ295557QJ+SkjJ37lyw/NBDD0ERLC4uBh9AHxgYCLwBCyUG/fTTT3t6eoJXCxcuBPNXX30VLOPSNrCJGlyrfyOtpp05LaPSsThLgmhV0AUZjOmnXK1DDZpoWOMARptKC6C3pXPXXQ+l1LFoBzKuXa6mt07yJE6l1Om0xn43oycsQ+gQEv8TzRvqW8CyQq6BR4I5x1KNvF2NJvBTqdBqNSx6C34CBD/RBJ2D58Ij0GBY8AgN0bLYB/BQo2bAcwxRCBScgDmagCdgCBaaGtuEyCPUKr2BTgroMa+ELNUwBtBDVrB8hoBePEqEo0GQw0Im6zgiznyFRs+IMr+5XSm4Bd/gqeAQfcMgxC8U3i/EQRwomAie1za2CHrBf/QT1TalRghOcI7+oxMBEIqxYCg1mAohbvXNbZg0NMefggUwBCfotlNUIQgICIIT4ixEA0NX6znUCEkWbELShNzulO2CKyHmAPEngHEDO0JwQubo+NIOloWcwUShypreJtoReyguBvCJMaLPzRgZ7pbfBRQ5KHiCORRILORd/S4MfGKF9wIazHCIPGogkhOmvNrQ0g6pk0Y4unmEA+r/1157Derw3NxciXDcLWs4hAqi38AXoQ0R1X1qfqUC1sgqll/hIR5F2Lx5szCSAbJp06b77rsPeMDq1avxIBfc/vrUU0/t3bv38OHDTzzxBN/n1p04caJ///64wQHMn332WdD06tVr48aNuFD0gQceUCqV4BVOqUBZxEKJ29heeumln376CctrZWXlzJkzx4wZQ0ybNolpR5ywaVC8op7OIptollKl03H8OjKWjvfS4WlaAekFPc6eKDRarDeF+tRU0euwzsU1HKAHfwz8qC9UeVqtntaePKdTKFQGXgvcgnK1djUdGTYYGR8dScbFHIzRhE5t8CrYpzMXJpsGvp6lWn56G6pjOufNP0VzRm+0if5znHHdDdTX4Bu6EpujCvFUKtXoVvBBsIM+C/bp0lF+gQjGWaNlka5AvjHYVnH87fUMXWCr1XF6vskAvVpDSxrHZ52Bb2vF5qjydFBnnMni/RH8hLeA5ugK/NHz8zWs6W0KKr47lo9bG7xjPaUFEBN4m/iuDfyMGL5fAz8RZiDX+QZPIVyMOcu7RXNwC+ZqfoqC0gK1Hn3Dp5SRKLVCKjD+qAqpo2uNRXGGXIJ0sXyLDqFAusBEiIOQD+JSivHBXAI76DNrKnvip+AK4kPTL0oFxlx4CxiikM9oE9IlvLVOOYZ69F/JL6i+8Z0K5hArzEkhbvhlCfnZypclOoWn0OJ3gSX/ViUQS3WXvgsoC6xp6TemApd+s6beAqS074uD8buGtyNxjm4e5xg/fjzU7eXl5eJt85LcySMcwpSwxfiJdKq4pVnUj+U6Qby9c/369ZMmTSouLs7JyXF2dgbGcOjQIa1Wu3v37ieffDIkJAR4CZCSRx55pLq62t3dvU+fPlikjh8/3rdvX+QEQDhGjBgBNnHpEJCGL7/8Ek9EwKUbUVFRqCkrK8PxlcGDB+/ZsycmJqakpAQ4zbFjx5DK4DY88u8ObjJ0RZUO/jKnCEmqpP4HarcIrbVu/KKNe5r4HkVxeQX8UOqM1FmS7qg0+IYA2hFoFO6///6amhoibYu9GwhHY1Ob8BEOf3mEeIvsDYSD4YfDO8602LZtG5QVPIdj2rRpPj4+9fX1OPixZs0aoAh4DSAwA7AfHBz8wAMPIFPx8/MDioD6ffv2Pfroo0qlEvdHgd7S0hI0uIMfya9cLn/ooYdCQ0ORTAwdOvTEiRPoIR4TefToUSFFtbW1UrmRRBJJTNejMOLbUoTqrrXdOM/V1NZOR2JMoziSdBvngBr+mWeegWq8sbGRSOeg3A2Eg6454CcaVGptQmKycfU4o78V4QBmICzjwPMQxUdAogaIKk6XNDU14YEceJKVMPYgnD4kPvUSTEDTxgv8xL2yKBAiDl3gPhQ8xQj8xKONrl69Svj5FJxwkcblJJFEkn9LODQ6Lc6w7P/lV1yqJY1wdKfgqda9evWCzqR0mcDdQjgMuJTBdGwA3RpHDKLzvm4ywoECXAFPn0RKIRwRKBzAdWMZAirQ6bhSsIYHNhPRqS94OifyBqAdSCMEwiE+t1E4tlI4ohil0/GXkkgiiUQ4xIQDq7hWBV2gNmT4y3ThfJtCIhzdKXgtxj333PPkk08SaT7l7iEcrW3G/ehbt32GZxjgid03JRxAEYRzODo+a44D8iEszBSohjAggQwAVeEo605HI3fiEwKHAN/wqFAwwUIJ5KOhwXg+lXCFBzEdvy2cKyqJJJJIhOOmhANPOMTT/F4cMpQuW2YMuEhWkm4jHJcvX+7Ro8fAgQOJdMzoXUI4lCqdcLDPkKEvXT+fcvMRDuF27/b2dpwTEc+94WQHsgewiZNzxLS0QnxIBl7UQq6/dw3vN8H7KXDgBHkJzpsQ0f1k4FZ8KZpAUAQLkkgiiUQ4bjWl0q6Q6/k9OxOmTAWqwZh22UjSDYKXaBYWFgLhGD9+vEQ47hbCoWc6ToF89bUZLEeEU8xvSjjEl0/i5VLCSIZwG5ZACMQsRBjDwCUgyBLE8yO4D5aY7lIhogu3xBGGcilcxyXcpQkmeBPsjaMmkkgiiUQ4biQcWr2urqkZukqvjB6j4Y/5kKZUuk2wjxofH3/PPffMnTuXSFMqdwnhEJ9LPWToS3iNwp9MqRDT3arIFfCKNYFMiDmEUICERaZ4raUwHIL0QljtIb7ZEnmDcFUpXjktvtKdiC7C7jSVI60/kkQSSf6ccBivceAxZvyEprZ2hhjPepGkGwSrbtxpuGjRIly3J2XLXUE45Arj3Q0vDX8FbxlgOPbfLhqVRBJJJPmLEg68H0rDsDrOsHv/Aco2tIw0wtHNIxyHDh3q0aPHxo0b8XgnKVvuCsJBD0zUMYCRo8bgXVamEyklwiGJJJLcgYQDb6O87kID/rR1iXB004vh6cVPP/0EhMPV1RWvqpey5c4nHPQIar0BN6ccOPgLPWWZo7eZSoRDEkkkuYMJh47R4x0FialpuEtFIhzdTDhcXFyAcOzYsQOvqpey5c4nHFodZ7h+JUe7Qv7nu1QkkUQSSf7ShAO3xap0dFRj1DgLehOQ6e4YSbpN8G56T09PIjo3UpI7mXAYjMs4VCq1Nik51ZxtsZJIIokkf/URDo1Oi4TjpREj21Vq/tZflUQ4uk1Ylp0+ffr999+flpYmEY67i3AolJTvvzZ9Jp43qtZqJMIhiSSS3KmEA2eN+UueDVNem04vf5Yub+te0Wq1AwcO7NmzZ1VVFZEuUrlLCIdawwgHfz3z7PO4S0WpVkmEQxJJJLlTCQdOHAPb0LJc/0H0enppl0o3i1qt7sULnkAtXGchyZ1MOHC5qEqnV2p1k6a9Cl9gA72enhN9q4x4HFL86ZoHRvTB31gRMLewcIPc/A54jlwXpc5eXW+9qzFHjoWnEYrDhyD0PBhjQOJgqJ7Dn9dHWeRz5yu6bxWBP82HP7Vs6JzJHVWtyAYnjrlIa34Wda7fRfT0hjjfwolI7cgVww0v/Dap3YP/Y3xuKCd/0riafDMY1e5uQm/+nd7aDoLrYm52IS6c4XrCIYRf29iA+1NeGT0GR3b5CRZJuklUKhXe9Y0HKUmE464gHDqGfmRqlm3X64aPtWjR0LlMhtNCg6pWNBFGRVgttKAtzXJ6j3OrkucierOh5YiKh4aDZtvAFykDSwz0EQRLiJKHmv+ppy0Wd12rZQR3PdCQkiU9qsSgIwYNMagoONDoOI5hDRySBTp8yjGE1RE+XQazwRK1jrSqSYucAQ29T1ID3hK1lm3kSAtH5NQaxyeIpTQD4qRpbiEso+fPKJOrdSo9x++74zpoioEx1rD85mM+/rrrAYnSswYda2A4oxCOpaDL6DkTpyGYh1oT9bmuJuf7asCW4KkCYKD5r4MsYHg2RN8C9VEPXzmvMY1f0VaqS++XE5oKpGIszTQAwyeZviwwVcsVRAeZzxBGeyNHxCZBx2iRpjS1tWL8G/gih7HizFT5TGVolrIilea48ekNahdKA/+yIRk6iO0NgKy8GfgDs7sSf7kWShqHXw5AZzAef4nl3EAN9LQcXE+M6ZfFFxtqjfdNoaBn6fAvm2g0xn6FcarUYDZuTkD/lEawIos34bJEKPwU7PWdDsa0S1Xsg2CTNT4yP/6895zGAKnm9HqtSinnPyF66he9RYVj2tS0urOytjWmUjrbvBuluLgYCIeFhQUe+Sit4bgrCAc0zAqVnK7QJmTwqNFa/qNTaxWcXqFTNRGDmtZ+nEGnpVWa7mb1w78DrY471ScmCM0cJ/TJ2Fv5Y7IqNtTx7a2u40zijh6M8FRrChRtsLy5mdASTkkUKqJtZgyNKlLfbuBzQM+QNoZQznED4TDQNk6jNfB7f8ByG2+1WaMzxsFU+7J8Wv4k6JvUw7wrg6ifh7yLFWWaTpRqPmc4bCgZ2kwKhny9KupZonPxU/NBY8XdJOhWnV6hZRVqju4G4M+SA8LBaVXXE47rXiw81rKMjqOHTKt0rFLXqbSYD85Mm10tzLquo0tJAMsani+3QZPIO4emEppEhQaYhMFEOHRGwmFqgAnl8Ug4KFvFPIXfciVRaeBbNvnMas3PGQEiOnTd8NUthx6uZxsGI1U2wiAeETQmgSPs9dnU4Vw0Ish1sI0u5aeW9jOYpvZmBuohStsYjUqN9YOc0YJRq0qFlQ8r10tnm3ffQJjBkJaWBoRj1qxZOMIhDW/cFYQDP+n65ib49uYstIOmoqGtxdjNpVWfVt3eIm9TQWE4e6lFA9+nnrQwXUArR5o50sryYETQ3/CTRxtYNtwAzgSWV9GQGJoJaTZQtHCknSNKhkLBknaWtOhJk540MlRt4X2Wa0m7jkapiTUXwCnqgC5A3QQBaanPkAMlZ6oYomWIvGOEA4EjNxynaqN3Xp++1KiE7ALOQUg74bOCIe16Hjpoj0mLjsatmaX5cx1YCsiHNlYEdAsqnwoKUwa2sHyiGGN6jWBJA0fj38zQ5INJA0PqeTRAoDrSBhmiJwodRRvvVTPmDNPF94sJ4UNs5DoAZUhJm09SXaeFxk+p0mvVGjrOdMOcGrINaCn1rEap1WBToeeZ5XnqmHQB/KgAcGOtwdABjnTG9U6g/ekCOAINlJlQc6Sr8adBEFrS6NiUjtS3MqY+fyfCIZr27Ew4DMjL1Xq+WdVRPxuVrU3qRh3R8uMxHM9mzFMNep1BK4aWaLVgaBzaoUM+GhNAz9LzFBh+fJHRUws6DdGpeWjoMJdOTxeMMbwd3ia8HwDY54xDNHwKwSuwrAW+xIPR8GUJYHpqlgpRatZCBjAqvRozS6dStjTRCxaUekgI7T+oWbahsVkPHzRknEovEY7uESAZfn5+QDjWrFkjjW3cPYSD02nVOLtPayhaS9I+kAbqB6JXtzdr5K30IV9/vTx27tODXhtgsbyvxWrz0W/CKorxa/qNX9dv/BsmrOtrsZZaGL+SgtqEn6+DZsCExQMmLrwedtRwwtIBE5bzWEpNJtoMmGTVb9LCF8avem78ut4Wa/qMW9Vv3Ir+Y1f0A83YNeBbH4o3+o57o/+41weMWztw7OoB42iU+ow3Hyv7WCztPXrp8CkfPj903aPP2w0dtWLC9EU6Ssc0DJ2nYE1sg/CNAVSJHDSurRoy8rWFw6Ytf37c4idH2DwwYHbf8cv7WywfYLGMB+jB87UQw74T1vWdsEYMPqMwfzBPXu9PkyDG6+AcfOs/fknfCUv6jl9G4zl+be/xb/S2eIuq419/fuLq5ycu7z1xWe/xS/tYLOttseqF8WueG//6cxZvvjDuzd6QIRZvvDjujUHj1gFetFg3wML4dozvxVxgDNdCXvXG4Hj0mbDshbFLnhw6v/8omwEj5l5p4nTGVpO9FeHQ6qGBZjQ4wafnlFqmWa61tls1dOQkihGTzVMnDB01duioMbxqwkgLHhN4mHxDjJw0bNTkYaMmmomhoycPHTVl6KhpN2LIyKk3w2SzY25UXxo7ccSEqSMnznjFYvrgl6f2HTR26nTr5/oO4meLWH686gbC0TGlYiQceroEkramjUqGn7k0aIhWQVpbDfX8kIe+K9CJoTEBHmlEUPEAjY5GQM8zG0pNIFwV0SqJWsVrNLwhPuVHCkGj1FJoeVYBrIWoCMf7plYShQlqOdEr6XAjfGBMF+PPtOqgA8I1NzdydI6Lo6ccQjdAraQDkDqIBpk5a45aqaEjYxpGIhzdIyzL7t69GwiHo6OjRDjuIsLBajX89Bld7iDX6JV6WuPrgf4r2/lFD3Q8s11BmpVk6Birt+z3rJKdWCbzNR8rHPyMkAUgljtQLJP58/A1aQIRYHOlzIeHH48ACvugDlAT3o6D9woH70Uyv0WyAFAXy/yW2vsAliF4b5fbU6yw9wWs2uS9wt4bQlziYC6WOXi//1Xs/DcPW7/psfbDiI9dEv+x3m3A8AW1rXp+pkbPGYxLN/i5Ydqt1bIMHWgh5LkRc19ds2WFw9G3vgha5eK3XOa1Qua1yt5jlcxthcxjuYPHEkevJQ7eSx39BCx3EEHmS2GMfweW2wdSQ5k3+LDc4cQyR4Ab75XvYodAE/ztnLztnDwWO7ktdvBcLPNaLPMxZVTQYvugpZuCVsiMObna3odC5g35CQCfu/R+IYuWOXgto8kBuEGIoIGfKx08V2489pbT0VGz/tFO6FiQ5rop8s6EQ65spSs56HyKQcOf+ahhycNP9QmPz+KRY66akC5CZgc6/LkOEQm5XUB8fkR8YXh80Y0Iiyu8CeILuhBzXvUJi/cKifMMjPMKTAyPKQgKz/L2i50wZS4/p2MwDgHgWijx4ubrCAe0q3Q6pk6uHPDKyInzZj419IUBE18c8mr/Z0Y9MmhSv0ETB5iJwRMGDrEADEYMGj8QMXDCwEETB73IY8CkDsDPQRMHDqZPKV6cNHDAZEJJt8QAAIAASURBVIr+UwagBgCG+JS3NuDFyb0HTOk9YPKA/pMH9Z88pP+kYVSdPKjv1H59p/YG9JkG6PPCtAF9pgzqO3kI79z8+A96ZdqYkZPHMDgTxDF6tUpPJ1KMU6stGjWwseEjRzEMp27XiNedSHK7xdnZGQgH0A6GYXBPrDSrcucTDpwPuHr1qp7hXps1F157c0sbP8kG1ZfOoOMUcj3UZNBaDBhlteqTg0tkwbYOsWZioSzWziEBsEgWZ+fQgYWOcbayeIS1Q6IAW1kiPAXLi2TgJInCPsUI/EmRYAL1ysYxwdqpA/DTxpH6v1AWDbDjsdg+igfVQ6yszQZNwoaIpRvi/ra5+B3n4r9vSl/7vk/f4UtUxqUhBhHh0PNrMxVQtTVpOGhinxpps8rp+OtfhC/fGm1jH4GRWSyLsAM4hNk6hlk7hVk5Rdg4xUCEEQtNoJngGL/YIX6xYyxiqQMFaBZivjlE2zpG2DqG8ACvIiC2Vo40M0G1coy1coqycg4DLHCMXOAYZSWL4REHsLFPANjJkvC9LHKIXuIQvtQhdLlD0FKHkIUOUea/XxoNsE9jQpNjhGMUeLLu02TrD7ys3jnU80WbFn5Oqkmvr5c3G/50hAMIhzChD+qoCa8FRCT7R6b6R6Sbq1Ikm5DaAXhqRKYYAeFdgH94ln94jm9Enn94njkqWKahdCX+EUl5kUmF0UnFoTH5iWmn41Mqo+MKXxwylp8lMREOfv31TdeN8sMGdJk3rhYeMnFMQEqkd7JvcJ5/aLF3cJFHUJ5HcK5XcI6PWWqOX3BWQGhGEEVmAOgBQVkBgdl+QTn+Abl+Abn+/nl+YgTl+iAC8ig6PaVOeHOTHS//fDffAjfffB/fPH/fvEDfvGAe/mDiU+jhU+jmXeTmXejhWejjWejvk+8PbrsSf3+3CI8+wwe0qeU6XLDM7yDTaDR8/4Au62jX68ZNnAQlUiIc3Sx4zGhAQAAQDrwVXCIcdwHhYFlNeztdM8UaBg17GVdNQSeAn6VleXO6cq2VIc8Pt17r+McS53gblxyzkbfQKR9g55S30DmLwiUDYOOSBU+tXfKsXfIFWG3Ot96cY7M53WZzKq+iNR6gN5qnmp7yFgCu2dZbshdsyadwLQDV2jUXDG2d0xc6pS1ySlvsmLKI1yx0Srd1yoQgzAdE0nJTip0sx/bDXLv3c1Z9mP4PWczQ8X/H/roe+5bGGh8Jh5whTLOWbSbk2TGL3voiaKlr5PyN0XPWJyx0hhzIszPmQLq1a6rVllTLLelWrlmi4PJ45Cx0zrFzyV5EkbnIJQOwxCUNABrwxMY5n88TPkNck0G1dk233pxltTmPz8Y88NPKNd1qSzJgvmvags3pVi6Z1s4UNAecs62dc00BZdk5py92SVzqHLPCOXypSxT87Mr7pdGAtEAEaIiuGQBICHhra5+yzDHun1+l9Lb4eyO/1EZF+5SM4daLRgF6fkavTam5Wt8k13ADho0OS8gJScgLSSgwTzUD8YVihMYWdQnBccVBcaXBsaWd1JC4suD4sk5qSHwJDaUr8Q+IzAqMzIlMLPULy/ULyfMKyEhIrRg5ZgZj3IPTQThwI8atCIeSMPVaxdCpo6OLEoILgjwyjhzP2BNUdjSg6PeAwmMBhSfMUQML3IwEhYKSFUBAngewhIACd78iN79Cd98iN4DPScQJ35PH/IoAoOFx3VOqR3OwANZ8T/7uU8wDnhZ5+hb6GgF6cFV8zKvkd0+KY54lJ9xL3L1OQqAnuhD/fM+sczmvTBurYDWUoBlYRqOmC1k5TqFRCwt7n+7d27iFWG+QCEe3ydy5c4FwZGRkQNlFwiHNrdzphAO+PY2GvmedXqvVjx4zXqGkO5RYWmsxjEYLH6laSxvXZoa8MGrJKkcPG8ek+U458x3zzFEtAfZFlvaFlrJcS4es+Y5p8x1TeGRYOvLWHIvmOhbNdyyY65Q31ylnrnPmPKdkHmnzHDPmOWbzyKJ6p5R5zolGgJ5ayDI+csyY7ZQ90yl3hnM+ADRznLLmy1Kt7JNt7JNsNyXZbEpeYJ86T5Y5F+IAoVDkmaOCZfB88dZim00nVztWLl+fseKD4L6j1jazdGVfB+GgyxNY3IDK0mljukr08eE2yx29rTeF24AP2yosZScBC2QFkHBLx+y5zulzXNJnuWTOccye45iLmOeQP88hdz5F9nx7SEImRHuBLHmBLMlKlmAli6N6hzxLB94Tp0xLp1RL5xSqOqXPd8rm87OAz/zs+c7p851T5rqkzHZOmQN55ZRm6ZBmJaOgL8IhGwKicMyydEyxdoi3cwhbIgtcLAuxdkw1//1S1SkTMNcZUpQ9xzlnjjN9pxDDRc4F1uvj1zhFPvHSmhZ+67PCoOXXL96ccOhZjVqr6LTFYPArE4JjsgNjcwNj8sxSO1Bwg9rpqRFBUYXmIyCawv9mCIgpugliC8yNuSn+0clANbJDoktCY8qj4k8D54hPqRwwdLzp1Bcx4TDcSDg4I+EAkKvK5mGvjvw96qh/vldIpbtb/m7vkr2+xb8A/E4eNkcFBBQcBQTlH0WNf8FR36IjPkVHfIuP+PDwKgEcBniWUniVHgT4lPzC47APfXREAHVCDX/xKoWnB9Ey1VPOAezEnacaPIkBLmK0edCz9Bf3ssPupZR8gKH58QfO8Ues+4hXx11rbwTCoVLKdSol34litSxdjdusVkHlNm7yZNx4Q7ckS9ItwjDMxIkTgXCUlpbiT4lw3B2EQ0eP2QCGodMxQDjkCuiI0hEOOl1Ad1PS0xtUHN1q8ezIpSudvGwcE6C9xFbTDDXP2rHEckOBnWsRdK9f+yh86RdZU/4ZaO2StsAxfa59ht3WktmbspZ8VkrbLedUaCMXfQHtVvJMWeoC14KZ9jkzNmXbbCuZKUufIUuY5Rg/zzXRckvqXJdUoCZ2n5dP/yRl9TfFY9/ytt2Wu2BrwUyXvHnbTo55P2rJl0XLPs213hS7YH3oEseERY6Jdpsz5tinQu+fNsYUOeaoEMocl1TaYG/KXuRQsHRT6soNQX3HrG41IOEwdBAOOjREV7xxBrqzX05IH4tly2Vedg4xdi6582VAucoAC+xLLB2KIGdo8+ySCYRjrpEAUfCMgbK0BQ45C+zT56xPWPNl0Zz1UV8HKNd8kbFgY9iybWm2TrnWTgV2WwqBUS3ckmnrmmbpmLz08/x59mk2LnmzP0lb6Fpguzl75icxiz8DnhFj+2naAtekBQ5xVrIYm03RlusjFzqkLNqcY7U5f8pHiUu+Kp2xIcbaPmKpU/AH38d+73Vx0eY0K+dcfIMLnHIXOOWA+qdvmeYn8rM5TgVznIrmO5QAtbLelLt4U/JqWfgLo99sZHCLgZKlxIO5KeEwnj5CCLQEeIcn9EmHjZoMLXFAbB603OapJvCNvVj1jsiOyz4bFF8UllzqFZ7lH5PvGZYZFFsYEJEfn372mFdiZGI5sorQ2OKIhLKYlFNufqnwMzGzClS/sNywuBLvsCxwEhiXHxQPHmb6RedAuKDGZp0Cw/CUEtC7h6QGJxSChT+CksGcj1hX4h+dH5pQGhRZEhxVFhpVHhZTGhyR99Ko6aaT5ujtzqbjZ4zFjx/quI5w6AmrIayS6PtPHByYF+Cdf9y//Hf30l2e5T8D5wD4FO8zRwX4Fe73Ldjnlv5TaPlv3nl7/EsOueXs9C05GFjxi1/5wT/yf/6jcFfkZbej+Ts8y/YfL/zZu3IPwL14p3f5Xs/SvT7lB/0qD/+W9eMfhXtA71124HjBDp+KfR5lu4/kfBN07tDxgh9Dz/3uX0pJjHf+UYhqWJmXR+ahoJIj3oX7/Er2+ZTuOVH4s0fpXq/KX91oxPaZH3/gRgFZgYPGv6wmjM6gx22xdPs9S9emqVk9v5OFvDJ2LCW4eqm56VZ5/PHHgXDodDq1Wo07Y3GcQ5I7mXBAjaVR0FOqGNYweMhwPCFSr1YB26SEw0AJh4LuQSXPjFoOhGOhYxz0rRc4ZpqjQsNpIyue+3GOjWOOnWvaPPuwJZ/FWTlFfB0sf/vnivmbEld8Vmwly5i7IX7Ohmi7LQlTP/Sd7xQ/3ynZblvBrA0Z0E4v3np6zsYcm835NlsyFn+eOcs+YuamcBvXzOkfJ019P9XKMffVd3xXf5Gy+LOM2U7J0+yT527JW/x18cyNsdPf8/1kT/mPPvVrXONmvOM9f2O0jSv0wjOxR25pnjrXOX2Wa9JMl+Q5sgxbp+wl9gnLN/n1Hbu8lSDhoIcsdZwiwAED0RigHuPHOvqNW7ZC5rXIIboT4VggA8JBeQ8QDgCODQg0CFrxBY7Z1g6Zy7YVzP4oerFL4urP0/LayZF0svaL2Onvui92TreyT535QeTKbZk2sqiZ//QDLjLj/QBbWaL1psRFTmmrP8ud+nffZVsSV3+RZOMcOk8WNGdTwPKt0au3Rr+5Lc5ufeCCD4PBspUTHRlasCX7lXUn3v85L+oUuaAjlUqyZHOSlUs2vkErp6wFzlmg3vL9UmTjYBUdFqLjVSeBcEAabTflLtmYvFYW1mfU600MfziHATcI67tOOAq6gFuMNMTnnEsuuHgiMMUzLB0IB5ADIB/BcUVBUfmxqZXB0QWRiaXewRmg8QpK9wvLDogAhnEyJKbQPzwH1Ojk8vD4Yv/wLL+oLO/ItKC4nODEvLCkgpCkfJ+INK+IVL+ojBNB8ZGpJ9OKz2eVXgJz38h0/5iczkzo3yK68D8iHGLOIRAO3YAJAwPz/KEh96341b30J8+KHz3LdniX7vQu2W2O6gOaop3BZfvDKg9l1PkEVx4MqjjgWbAzsPKAR+GO4znfAxs4lvO9V9me33O/B4bhd2rfwfRtvqd+div+wb14xx/5P7oX7vQ8uTvq0h8+pftBfzR7++9Z3/pU7Ak8t8+j9Eev8h1+FbvAq8PJ34ZXuGXXxqRXR2VcDo8qcw/MP+iduzPg5O6A8l3exT97le3yrjgAhMazdHcX4n/yN7+swBcnvKwijPZ6wiEc1dPMz620qlQcTlFJ0i2iUCgeeuihBx98kGVZPS9Euk7lLljDYTyE59q1OiAcM2bOhl/NzY382YwM3QlvIhzNhDw9GgiHx0LHmAVOqdaOqeao0HDa2BfMX59tuTHRbnO8jUuQtYvvyi/C05uJVyGZ91Hw7H+G2NrTBZ6LnSP/8UPKim2RM973nb8xdvXnpfPXZy74pGCRY9lr7yQtdS169b3gRa4Ji1zjrRyirGXJdk558HSZU+Fq5+h1n4Yv2Ohv6RRtuSV92sZYy83JSz9LCSonp7XkgpZ8+G2SzXo/iPaSL/KmfBjFT0DQ6JmjzndJmrk1ZvqW2JmOSdYu6YscYpbbe/Ydt6SV4J4LvUF8VBmtsfAwU7pfpf+4pavsPZY4RC5yyba0z6dUw77E2p7OqgAP4wcGMvmJDwpLJwpovyHHrB3SbRxSFzunWm2ItJNFrNoWW0WIbxF5/fNg6/Vuq1zjl29OXCgLXftp7OufRy/fHGhn77t6a6TVRz5ArVY6R8973+Odf6Ws2xI66x8Hljl7LdsWYOvg8cZnAZZv71tn777G3nutU8Tf/5W95l8F09fHWLmmr/425/d0tqCR1OjJZR1Z5hILKcU3aOOUZuWcBuqt3286H+FsPkU4oVMEWOCQZ7spa8mm+HWyoL6j1rbyB0Iw3H9COIJjcsUzIGagSIQO88tN5HKTISq1LDK1JDiuICypyDci64/AxMCo7OConMCIrJDo3IDwzIT0irDY/PC4Ap/g1LjUstCYPA//RDAHC4CEzPKQuCyfyKSAqJSg+Izg2PSw5JzY9MKQxKzwxGz3kNiKi/XNenquSXJBRVJOqX90eqcZnH8LHOEIiCoJjC4NiYYIlAZF5A0b/apWRDhwoQYSDkQH4TAYT1bVElbdQTiO+Fccci/b7lm53bPsJ+9SnnOYofqU7HTP2x5++mBGvddpLi7u4u8hFft8C3cC8wg/exi4SMS5331O7gmu/MW/dD+obvk/ehb/EHHpoHvR9oCKvQFl+8DwSNK3gcWH/IsOAALKDviX7nXL3+5e9L1fBYTyU+iZ/f4nd4eU/HqeyW4nFy+zJVdJaSMpjy49GpS/N6RoV3DZTu+TP/qUADXZ43byJzNjboz/yV99swMHTLyecNAhSU6t1TD8hmEtPQFMD4SDFjmtNMrRTVJVVXXvvff26dNHmGGR8uSuIBw4ioXHJis0WjXdJcuwOiXddqHXUMLBCoRj6QpnDzvHaGvHFHPhkGm1MW+RQ4HVxgQbh9CFLj7zNx1Z8bnfeUL2x6lsNwSt2ZK8ZmviUodQq4//WLjp2EL739/+Nmala/iUtb+/vi177ebcOW+Hv/tVieUHgX/7MsFO5rFI5vbmvyJWbY5cuzXV5sO4JZ/Evr01zPLvu1dvCbFzjbLcHD9/S5LtloQTuaSohXbZL6rJWy7+q5xD7Rwjl/0rb7ZTMs8kUswEEI4Z26Knb4me6ZRo7ZK2yCF65Sb3vuMW8YTD0Jlw4CAHS1mamuMJh8xtESUcmfOhSZYV8SiwluUtcDQOIZioRiquw+Dbb8o2FjqkWG2IXuoct9w5YrGDf4mSHIprWOpw7PVP/W0/dvvy+NmCZlKqIJcIiT5N3vrc74yWJFWRd/8Vtcre69vj5RcZ4rg7/kDI6YuEVBOSdpW4JV19e/Nxj8hL17TkvIKkXSbfBapsXTNmyxJWfJW55Wj5tl/SA1MuVWvJCpdoW+c0fH02lHNQ3PoVp9rIMm1k2dayHNOiHOOUkK192pJN0etkAX1HrUHCwbJIOJjbTDgE2lEgXr1R3UzqVSQmozwsqcAzNNk3Mj0ipSguvTQ+vTg8PueET2RQVHpEQi6oAP/wlKikfO/gBK+g+JiUwsTM0sDINM/AOP/wpODotLCEzMjknKiUXO/QOI+g6ND4DJ+weP/IJDCsadEA24BPKDm3NC6j0CcsqavxD4jODU0oDog6GRhdTFdyxBYHReYg4eA5x60Ih0EgHPCPo1+vQUN0AycMDM7z9c0DwnHQoxQIx3fQxvONsVmAZt4t93sgGbkt/tUkI+HSiZCy/T55O4OK9/+e9LVXzs9BJw+FlfwaUfabR9rP8Wfcj6ds9y3e7XPyZ/e8H8Mqf/XI/Dm64rh35v6w4mNBeb+Bxi9nX3j5Ed9COnASevrg8eyv3PO/A8IRUfF7SXNCYW1cQpnv2dYsFTmfcyEwpuTX0KJdQSd/8sr/zqv4B//K3b7lu82PPI3/yUNAOPpPellxA+FgOFbN6pFwzLNbiGfCahhWGuPoHsnMzOzRo8fEiRNxZ4qpAEvZf6cTDrVGJ7SY/LGM9FArRttOv0T6PXKUcPAnZj49evEKZzeecKSaC4dMy0/ylm+pWOiQtGBTwKLNXlYOR5ZtcztHyJEk5d8+j1suC7V8x/0Hz4sFDbRpvEZI7Bn5B98fryUk6RxZ9vHxf/4rcuuBdHj01fHYsLKaChW1k3aJ3XooafEHvwdm6KEFvawmVwjZGSm33Ro/zzl2gWPkF141jntzvJPkp1vI35x9VjkHz/zAZ8HmNJvP83HowkxQwrE1TjTCAYTDEwhHO6396R7FjvsgWONxCHSaxUBXvfSnUyrAz8LtNmfwazNNizMo2zASDrrYs2OEo2N4AwiHnUPCyi1Jy5zCV28Jg+R752qWyo6scDr2xlYf9+TWzGryvUdeQG4jZNR3bmkJlborLHnT+ejqDYcSytjTbeTTA+FVSpJyTv2te1r8WV0NQ376Lf6PoJJrClJUTeIqieOvl1d9fdLSJXXSe76WH7mtdTwelHSxgUPCkY6vz8YpDXHr95tuJBwOpsUc/PQQHaqRJS+2j1rr4NdntEA48N6crhGOkOjs4Oi8rqPAqInJ5ZF9qV57tYXzDUv1i0gBHpCYXVbTopezpEVDN2GdvljbKNcDIhMyEjMKsosqocMLatnZanpwFkcPeL1c1xYWn1FWdbVVQ9e+KhhScb4uJiU/IeNkSEw6EJFzV1oa5Nz5q23gJCWnLDIxJzg2k4ZudrSBGAXE5IQkFgZE5wNnCok5GRpbFBSZPWz0VJ5w0E6A3kQ4GLyoxbiY1Mg28EwwEH6QQz/EYmBorq9f3pGA8oNepdu9KijhABphNnYCMwgs2Z9dH1hD8hLOe4QW/+Kfuz8g71BRQ2w7qaojZXJyHtTkyoAWcq68JRUYBlCKKn3GNVKSdNq/SpELzF9JahpJVeGVROAT5zV5reRsC6m8Ropiz58AthFYsheoSUDOLz6pv/ilHC6+mtRITmVVBUYW/RKcvyv45M8+hdu9S34EwuFT9jPSIHMhIhxqch3hIJRe0AquTa8bOmY01Hr1Le3SyebdJv7+/kA4VqxYgZMphF8xKk2p3OGEQ+ifX21uhU9uvq0tVFetbQ38EgU1PdzPwDAsnSCgazhG2610clvoGIMdcfOQPW99wTLXSltovRyCl2zzXbz12MrP3SsZ4pXNrnIMWrLe/03nCLdEZcRJw76gMrekykZCfgmOTSyru6Iljt/7r9uwN/Os7irYT866zOj9MlO8U1OaCbmkJm9uOvSrb0UTdNmbSECWbsvvp4FqTP0o0G5L/NwPvZZv8vJPVYBv724NXOEYaLUpfLZzykznrPl08UG6mZjvnDpjS9J01+SZDvwaDlncyo1e/cYuEREOQwfh4Kt7SjmgNeJIv3Grljt4LHQKt9mSNscpg2+J+ZWYjpkmtmFcUELXi/CrL3H1BjThC2WpdrKkJU5xi2VhJsKhXu54ZJXzkVlvfvP2Z+5vbT3yuvPe7/6IhVeVcrZ9+/HYi2riFX92265gyLeIvGq36IIWQo7F5IKdpNOKRpYc98uKSqiSM+S7gznW7xyz2RRt5Zxmuy13vlP8qi0R73zmH5Fec1kBhCOWbp3FsRbHDMSt3y9lGyLCkTnXmQJYnZVjop0sYrWj3wtj1rTo+VtFWA3fS+e6hXB0UA2K2MwGJWnREmAbofGZ4YnZSTklZ2uaKy9eO1lZ1a5ja5paG+QquZ4LjIwJjU04c7mmUaEuPXu+rk0B5rkl5VcaW5QsSczIKz51GdhkbSt7tro5o+CMR0B0QESqb2hCcnY5WLhUp8ovuwDpTM+vDI/PCYvLDonO7VKcA2KyQpLyA6LpuEhITKF5hMPA4ZUkohNI6SU6RD/UYmB4tn9A7pHA8oPeJdt9yr/z6Qrh8C3eGVL+i3/RgazakFpSknDWK6zoaEjB78G5v5fWJ9eTM4UXEs63FAGfKL6YpiTXqhVlQBq80w42kFMN5Ez51fQa5akr7adLL2WrSePZuqKkotArmnIlqT6ryC1uSAzM/yX81OGgikOeWbvjTnmXN2de0ZW1kcvXmPKEUo+AjD3+2T+Hl0HFsMu3bId3xU634h+6wpZ2+Bb96p1DCYf8BsJBCRo/Mdqi1YyeMlnL67UsJxGO7pEffvgBCIerq6swmYKLOaScucMJh9ZgaFZp8NqtoaNH8Z+jltG20p38ermRcBAkHAt5whFn6qObAYe8BfYVNrJSS/skO9eopZ/62bn+vvpLz1MscUvXWv/zjzWO4as2Br6zLezvmz3e2vzLHt/kNkICk3IO+yS3ccQtqHLDlhNX5STvTFvhhWtyQg4H+Dh//31BVX0rRz7dHhwYeaW2lez4NW3pB7+9/mnMkk+TLZ0Tln2ZPvsD72UbPQNS1TVasuqTPxau9171RdbcLTkzXeh+V2zdzQG0nTNc06dvzpgpy7V1LFhin7Jyo1+/sUvpHSo090yEw7hLhW5W5Fjafso50ttizRJHLxvncOutabOd03CJKK5FNcLRuFCU7k/hl4viEIi1LIdyDvvkhZtiFtmHL3MOKdeSXxLqlzkdXbftxHtfeuzxz82tIUA1zqlJlZocicza+O2hekLKaskut/g6lhwJSfWIzoScvKyhdi7rSI2SuAdmBUeUNMuJ/edBqzb4rf40a75jxvRNqQs2p63cGrNi04mgpOpmA1nhnGDtnIuvz8opB3Gr9ws8w0ZGI7zAOLwBaaTbfee6pFg6xS90CFvl5Pf8mLVNen46gDX1xrtIOKDN/o+Q3YGYzKvN9Lx5//CksPiMgMgk37DY5JyT4QmpQCYaWuVtKm1J5RmdgZw+fykxLROidfZidXVtA7zl2qbWlMyc89VAegkQkVPn67UGklN0ISIuJzb5pHdgfGR8fmJ6SfnZhlYVyS48n3vyfGM7ycg7HRGX5xWY2KU484QjIyQpF1ebBsUWBscVBEZlDhsz5WaEw3gBcQfhwPkV0yVnYGfYuMER2f6BOUeCyw75FP/oW7YdaYTZ2B1SdsQv79fca1GN5HR8pW9o3onIfM+IHM/wDI/EwrCYjICM4jg9aauXX26UV+tIa3plVOa5aCW5WnktX26o05K2c1fKU7PjGaJq1dSdPJvbwl5tYauj8/wic71C8o+55ez0KNjlmbMvrTr8oq60lpxrJzXX9BUZZwOCcg/45+4MKdtL142W7vCq2O1R8rNPyc4uxJ8nHH1NhMMgIhxyuRwJB2TsMwP6y1l6CJiGkwhHN8m7774LhOO3334jpvO+gHBI22LvfMKh4W8TUDBcu44ZPtaiXcOvhuTUlHDo2qCKgy8URzjolEpXt8U6FNhtrpr9SdGsT1IWb0tavCXY0v731Z95n+OIR4Zq0YfH/u4asuKTP77+LeecgragF1X03i+obTdt21crJxUXyWH37CYVOfhHbDN/XniNGkgQ3XR6vols/ibYI+RSO0c+2uq5fMMfSxxDZnwcaLs11dIhcsW2GOhbB2XqrunJ266Bizb4Lt2WPHVDwpwthXONTbtZgEZ01ub0GS5AOPKtnIoX2aev3BDw4uilKg6vBdEY+PXuxnl0uk9Wj4SjzUB6j1u3xMHX2jnaekvmTOesWS7Z/J4UOmpi6ZwynyIVftJdKsK2WB58Q56+bFuWzaZIO4eQN76MukSIe458xebfFm3ctcZhd1614QpDNu92f3frj82EBGdUvCn7JiL3wlUNAdSoyXubtx8LpdTty4MnPv72l6WffPWG7IcPtxzwCC8Hw7ddAlY7R9o5JloBB3JKm+8Ub+cUtGjDkaC0q3WELhoVjXDQLSoC4eigYiLaxNPKHONGX+dsSCY9XMQldf71hIOfqONMhIO78RpgbDINfC8TLxCFcjl85GR6DodpHcZNxjA6RjKMCI3K45FrRHR2aHQm4NI1uUJPQmPSopKzgqKTYlJzmtW0jZFr9MBsIB4hkTFKHW3FY5NSQQX+UXW5pl2toxfk8BZAX1J59tzFBnAVk1QEZCI563RQZHZYbH5UQoGOj3DByQu1zdTbqpr2worq0JgcY8TEsf1TFQhHaCKoOZBennAUBUZlA+Hgd6mw/AIOJBwG3LdiIhx8j91YFjsIx9Bxg8OzAwJyjwaW/epd8qNP+Y++XSEc/if3hhT95p/9S2FtXCupSijzDck8BkQhJte3sCq5urUCGEaLpoYhiqorZSlZMaApr86tkZ9WkYbgeM9mNZRTRbumniVKPZE3Kq+kFMTUyM80c5dCMzwCU4+FF7j5FR4MKj8ccPJwZKlHaL5HSI5HZVNOG7l0ujU9vsIzKH9/UOFe7/wdnkU/+VfuDTy9vytsiRIO32z//pOGK+jtcVphlwpO60FJa9frwHTstKm4UUWaUuk2sba2BsIRGhqKVIMuPJLYxl2whoMzAPWnl2axcg03bsoc+PxaWhVQg+kVjYRV0KXufD8AGrYXJv19kczPyjnZ0jnL0jnHLNUpf65jia3rmXn2eXM/SVzknLB2W9y6LaFVeuKe1PwPV/flH+1dJ9t3po2cV5GPvz604ZuDV7QkMLH8H/Y/BMZXArG4qiBNGvLW+m/La7iL7eRvDj+9s3n/35x+XfzBrne3+R2LrK3Rk3c/DV20wXuxc+T09UG2WxKsHMOXuoQv3egelssAiXnTxWfxJ95LN8dO+yhswZZciBV/vIR5cM6ydk22dEmd6ZS/YEuZnUv2KlnokJFLdJAjBhWh2xH+H3vnHdbGkf//XC53KbbjgBuuNINx77333k3v1TUuYDt2ckku5RI3bIMLmA7qWvUumgQqqDeae9ziAjamg2jzm90FQnLfu8O/J/fPxfu8n3lGq5V2ELs7r5n5FNS8Fk+L2oXn18WG6a87wIipe3bFMjefKFp/Qr/4sHbZMd2yWPXyONnKuNyVx0UrjwtXHucvPy5adrxwWVxRH8lWxhauis1dd1Ky/jh7y2f0nZ+T7wKgrAYE7YOzNPk1ZknpE/DcDuIzxXzVPQgQRJEp5tTlb64g1e2oTSazoCIi9vzZGxx4THkV+DZb+C05n1v+2vd02kXOvZ8A8P4yb12ceE1c3rqThRs/L9jxlTQyvuA892bhgw5INt9QH57OfrbumAiLtp6/ZK9w59fG5Z/mrTpWtDK2CLYfCm0zqnxYroyVrTymWH5MvSRWszhOtzjWAEv4l244pVx7iL3jMHHMrEDccKOlqRVNvAXa+qh3zgNLHYo9cNq6+090LA+BA5FoqVIjLpoEl75bUi0i0cMDesUQa3kCI58PpUcl0PIFGr5AzRcqq2raWtoBk5tPZYsLlAZL5T3YFI25nC/Jf1XbVPWqPl+mvHnnAfzf1TWiVk25BcXwZbO9C5YEMoKw+MUqHYsvtVX8BDsnpkDJEmtoAjQsB42rLFRV1jWiuNTYAl7W2vHoDprye/wiA6RnrG36/pSoAUeumpdbQmYVsXMtvPxyltDMFJS4eszsJlrU4KQFzazbkz21vW9qeDyTO/YCt+FwmenK0TL6usVSrecRS3/FMl3KL8viq5NtP0tetFv1d3l8RZpEQxCqspvB45f2OwU6lszAq269X/FAU6QTNnY+r2v7uR28fvzyVp6S/XPNrUZQJVEyeDKSpIQuVJPZ8oxnHeXPQTlbmyYwZQqtWRzjNbLqAlN7PbeCLDASKPLkwkrOU3DzdoM+t4xCKUpgG66LSm8wDPGUkjMMc3z/G4/KdJ1RQh03262pO34hZmuLRhxFoyjjAy14GUyZO6exrQ2F3dbWt13O77vhKya/SZUCb/Vx48ZB4KiqqqqtrcXfakHHum+3/3XgaGt6jbE+evsNdBzX1I4+65saMPPtzkZgr2u3dzR0gGdtwHFa8MZPaevjCvsfxwKO11ccMy75VLvySAk6bj6Uu3Yfw/8z4VMAdI8AIfdxlvhWGt+mvtP8oBmQZeUs9f1qAMSG6pBjCacv0B83gqctQGapiTp+jZZ/72kHEOibTl8tuoDcpmnbQr4WpEhf320Bgad4a/dTfP9evPlU/oqjvDVH2bE3yuKRnxT3wH10xeHpOdqjjUdYS/ax159UYmsZ/Z7kiFNt+Ey29kTRwmMa2JWuOKzYeojpOW03toaCAkcL/IEAGl8C5TI0N31LS/3Ldiy+1aipvpv301bulaw5ZloVZ1sZa14ZZ0DtQ48XrjkhWXNCvPqkeOXJ3JUn5PgyRK9WnkCNVZcf5q0+xt58krn7S6auDsCxJGSFJwDcagBZItgTAvhDlVejiybUwscBR68FxSbdqUPN8y6T9Tv3XvA+eJlS8Mj2EjXF1dejvPIFuTRJ2Zn/Anj/w7z8aMG2L/XzYjizQgmBPyqJRnAH82e5DcA9ONavhFCiWPupcMdnaFR177+bZ4aw13RHFFVgMVKLVpyULzsphyUWrg2dp1l2XLf0uGnpcfOy46YVx9EQ9btO5/p/hjh6bn5Wj81ddOEZZ1p79BvgQBMFonmJ27BYalgFAgczV0/PM0MhuUYkFy/R0Fu9YuXqesWR6AVCqxCVWSg0ohLphCKNUKx++HNdawcov/v09sMXN396Zr39U2MneFRd+9Pjl7CraWwFuYUleTINmre0A9x7WEVBBIoSS/XrVihr+f3KO09u3XsqyVfdvvsctlicbxbLbAhfx5SYEK5WlGeFZxGLlCKRvKzs7uuGDlPlfSJXikiV3W2T6vtZMoRKkczCybMyRNYcphYRWiTy8kkzlmChMO1oLlXUIvkX4Pj1oByfPcKCdKNeKm3OM9HAX2R9JrU8JavsAqHiPMV2Dp3k6J9gB88quYzILloe8WqA9UGdsvSptPxpvvYO94XdVgfu3Xulvf9a3wqe3qsx5OsZ1geKZlBlB6/Mt4pECtrdatOrrvsP6syV1erKV2rDz3m5ZaQHHfo7dhVVfZmiupQt/5GlT2DoLubdzCp9nX+rSWV9KbvdpHsNfjI8yxdaCBRFIlN3lW+5zjJcRLRn2eYLECP6337EdJWpprrMcmvuBo7OXuDApznq29CQrPnFxR3dXePbQfZ/BTjwshc4GhoaBmBbU1NTY2Nj93T7WxeVPwJwoKsnoK2pGV1Ymb1wLXzgwv87mqwZA47O5hrYFbRiuTCcZgTtjmNtiS3YEKveeEzdn3JdnHrTKSMaHfwL7e4vSzYdE20+zAr7WqJ+CCAolL8Et2tBWRXglLy4VQvuNoA7DcBaBSjF6OSH37GUO/WoX+uXVwu9D10PjMsUmLsgqfzUBUx1QHIXbIljnWdXG6vwSBu0LXGCtUcFyw8xvb/KT5O33bKDZwDcbQSw0829BXYeF2z/XLYmVra2ewKmn1KvPFq4Ik656IR12enyVce0Gw+wR0/YifoOo8wOmbyjAQvz1YrBG2i340nEa1qA0yQ/nzjxrpO6zSfLlh404vE3UaOW2O4Fi7Wxir6WHLhPKR6bHLLOltOmbac1G48WLAolBX9VtCuWvesY3eczZtDXwgXBiTtO0IO/FW8/Ttv1GWNrLGX9oeyVMamVHaDwMYD7Nx0hrtmfAfcH/V0Q/BXX/3P2jjjm8mjyluP5Pl+bVx1WrY3Tb/msdNNJ8+Y4zdbjRTtO5O04ztt2mLLpAGX1PvbuU+p1B/O2HVdtOKxYFCHdccqyKc64/qgBi8uuW9Md0RxvdrcFLmrvEofGNV923IBGN49TrTos3nCY4X2cMHr6jho7aqDR3tb6a9r4z8AxYeJcrkTTDRNSDUeKlxquVN0rnkTZRxqeyMwVW7liWBpRSXToTon6/tM6+E+ptaPdTlVTe/mDp88b7Ki/dzOoaQSvm4BUpke4BS2d6OpPUUkp/LYChfn+k9r6VlAL+6sOUF3XqTHctJY+qnreKZGaCopuI2wDR2jj8a08nkEsNHJZCgG3uLL8cc2rdp3pNoMnL1DZ4KmxhvWrRP9YqZ4s1NElpayC20S+DZFWsKTmEc5TsCQ0bThwdKGxvzrwX/BfAUd7D3CwtSyKLotWlpZdehECBxaHo7+Cfbao7LrAct1axa0CBqgXwFgNzDcb5NYX0oft+sdthsedllfgdtkrRa6VUlDKaASPX4H7AhWBWZSmviOwvZA9A2V3WkqeAuvdNrXiCfN2p+JWR7Hobqbkfjan7CqnNIFbllj4gFDWnPsIGB4C471OPZTkJpllTiGXXKRoL0DuYVjOMSxnGLazVNubtN98lVHyn4GjCTVUwzq8t13e775qj2EEvL37er3eunXrnXfe8fLyAr+OLvp2VeUPABzY07+xqaWusT23UIf2AN2ztK2go7HL3gCwI151Aqdpvt6x9E1HxeuOyfupNbHyZbGFi47mrv1MvuRT3pL9jC0n+FuOMiL+UbAnju5znBpwkhp0irrE58c9R7P2fyeAZcjfOH4n6WvDrvmfoNxtBYbnIPQ0Y+v+1JDTrJ1HSOv3Zn16Wed9Wrz6IG3VQcT7y7x9Z1RbjzA2HOGsOsJbEyddFSdasp/u/61842GS90lkx2Fi6FfCqB8UC0Jzdn2jXXRAhOVzQROL9LNce7x41Qn10pOWtX+r3HTKtOOYyHmKH2pki85et7Vgjp54XhW0C2hra61rbevAA38FbD9IX39QuPmEFnbtEDW6rUGPKjahpWrTkRL4EoWPnghaqNMsJBL0SN2SyML1h5Rbj6m2Hy3acbRg6wHRln3cbUcECyOIEZdNW7+Qzo8mwHLjZ8KVR5h+PyhPkZ9UAHBNARbEENef4O/8umDZIfriqJwdkFSOsnbGCvecLFx3ULo4XLDqUPHGWMOqg6o1n6o2HlZvOiLfejhv2xHRriP83cfEGw8KIXBsOVbk/bke0tKaA7KtEDL2F29AG4/ah64/htqK9sYo23SsEGp9rBz1Iu6OY1a09nj++jgepKKg09ljZm553Y6RRVsTtg71ZsDBFmkYYi0ulkjLEmtwwf1skRoXB5WSg9Y1LJEei1Cux6TFjlSzxUpBoYYlKRbJtbkqvVCuZkllTElhnsoklukE+VpZSRmRIaVyCl42guoGQGHn03mybJoI7oHvimUGiA4SuZGE5EnzzaqSuwxuiVRWSWZquZIyScFtrtCCsDUCkZ7DV3MFqly5iZ+nIbEKyOzCnub1S7C1gqKyVKSYKi4Tq3+mS29TRaVknm7chHnYj9WBJQ2EF1cLGlG023LoXwIHvIFdZrhzNByKLgcpzSRYEwjlF4ml56FI/SsptvPZ6u8yZV9RNT+yTJcYhnhBaZK4IpVacgHRJbAM17nmVL41nWfJoGuuI9okcRmxGtwqf13M0qQyNMlcY7rAlqV4wpbcIjLMSVTTlRT5dxTzZXrZ1UztWYr1UrbuB4r5LNl0hmw8h9gSBLfTxfdyuJUZVNM1mimZboEfSSAZzsMD6LazSNkZqu0HCEz9bz/VfJ2hprnMGt8MWlu7bTi6gQPz4kEni+De+CtX7Ji1aGf7W7fM/yJ54NgB60KhEALHpk2beokEdeRub3/rpfIHWFJpqccM99ChUnMb5tLZgNlVddpBVzNohz1EJxz2PW8EwyZs3n0kc8sp4frThf3Ums/lK79ULTpVtOor9cLjuatO52/9e/Hq46IF0eQNx4Wb4vgbj3I3HeN5fw47LYHfl4p1+5ENeynbDlH3HEO+ybhpew0I8tbwLwS7j9J94pgBpwRLgm74f5m36Sgbfir6omlpDHVpNH1DnGTzF4rlJ2TrvtJu/M44/6h4wafcdSdFG45zt30mXH2IsfIwb0Vs3tZvLfMOwh5aueWkfNsJeT/LjXEFyz/NnROTt/igfNXBvK2HmGOn+DbZ0d6xE3v8N2Mz21ioZMyHAJ/hsAOnyVv2HM3cFkeHf+ay/cyNx6Wb43oTzedthwxxtGjrMdmWONGWOAFWSjbH5W2OlW0+Vrz5mBJ29rtOlOw4qth1TLF5n2jHQcmuQ9Kdx3I3fyZZEctddAjZ+EXu+tOSlXG8dafE08Iy96fe/orzOuiicc1J4davC1cd58O3An9U+5wSrovK2X6Ytfu4eNdnBf5fq3eeUmyMk+3+Urv1eNH6TyUbPxXvisv1PSn1jhXtPMzxPindEstfEk5atZe55Zhk2/GCXaeKNhyGOwt6lAcF/5YtsVLY5u2xAijY/s1xkg0ncIk2nuSuPZzjc5rgf/L6AJfZj2vtuJVee3tdH9roz5LKPKrYQJRYcZHFPZJYKGIoEy4qKgMs6SIDItIhIg0iLumRiilSMcUKfkEJjV/AkRbTeFKGMF8kUyICKVsiQ7gylkApLjByxSWmsieNbeDe4wZJoalAUQr3MPkKKAavGB4DX0J6oAiUVIkmm6dky8uyeBp6vo1XfIsmtdByTWLNbZ6ilChSUqQqvsrKVZno+Tq8Yf0Xq8BG4JXw5HdY+bcyGFqawIKIDLMWbmzFGKILzdyGraV02QEW4wt0/SvgABhweHA0PLqGzLARyOZr5FI0iUlO2aV+liRbPKsygVVxmVF2kWQ4m1XyPdl4AZIBCZLH7XSK8TJkCG5lGsN2AyJC3gN6ZZvyPtDl3iGLKnNY5mSqNpFlTSLqLkFRzAmMiuto9riKK9TyRJLtEnL7Oq0igVp6gV4aTzajgdKzNOdIxgSi4XKa8gzJcAUpTWKUXUPjmlvO0ErP0svOkKzfE0vj+99+ijmFoUZcZnn+M3CgeXWx0ObwAecxeXIr5igL3jqp/Df6GGzeopc24JaQkPDuu+8eOHCglzBgpbW19e2qyh8AOOyNHe0tXRh0JFzJhCXqI4CmbUMjHoGOpvZ2OwSRBvj0n7k99Hjm5qOUtcdYa49x+lOuiuWs+Vyy4Chz+Qn+yhPczV/mLj1MW32EsSmOuzmOu/EYe+MRzpq9tOC/K9ftY289Itp0kBP0ea7/SZ5fLPvkZWMi41nIKf6ew7Qt+wkrgpOivi0M/EK0bh9p/UHKpqOsDYcZfl/B7lC87YRo5SHOov3MFbHilXGSVSfE274q3PyZYOVBiveXkk2x7A3HuDu+lG/+vHjLF4oNRwTwpJsP8/pTbj7M8Tsp8j4u2BEr9Tkl8z8pCThGGTtp6+tGgKcLx93323BO77CjSypYNotXbWDktOV+pxJ8vkzb/XXOxhMpm0+kbz2euSMWKmvnMeLOo5QdR5EdxyjonrgbO+JSt8Vlbo3L2RxH3hxL2xzL3HGcsy2WtekQsv0IY9dRhu8JLkSuPScQv28FSw7eWBNL2PUNe+nBtI2nyJEJxetPEhfvT4H718YRt32JbDpFWxOXvfpYzurDqYFfIlsO3vA9Rdp0KGX1/uvbT5FXHkxdcSBt3dGcbafo3l9ydp+mbzuSs/lgxs4jBN/PqNuOpvt9TQ0/ww/+nr3tePbq/cnrP03bdpy0NY7Yo5xebYvL2hmbCbUtFn256Thx4wmonE0nMn2/Jmw5Eu8T++PkZVua8KQVtS/s9rp/YTQKuo1G/8mGgyEx0aSlvaJLbKikFoakV7+JZY5lXpWW9BVbUkIXyNlSlUSuZ4nlDGGhsFDFlchZQhmdUwiRgs6RU5gFasNdS8VTjkiNcwZbqOJJNHypFpawDvcLsIz2dKkByTVyZDaKSM/Is7AKymgSM0loIIv1VKmelqujSkooYiVNXEwTK/H0LkyxpT8lbD8/V8eXlkgLrWRExuRpikvuURiyMS5TerxR2rDpDTsWRL8N7yB/sRjt/j17gaPLbbonV81nlFDZZjLVmEK1JpGs14m2q/0UyZpINJ6n2S5DyCCb4yFnkE2XaOZEmvUK3XItXXU2Sx3PKE2hm2+kK+MphmT5z2z1Cz5Zm8CvyKAZrsIKtzIjR3MJsd2AJEEvTaJXJFHLrhHMl7NMF3MsiQRrAtlykVlxlV2ZjJReo1uT2RXpnMosZlk6xZQEP8UoS6ZZEymWi2hcDds5kuUs2qR+t59iTmeo2FjyNjsaabSrJ0paj9EovCxfNre4eXnB4VZ9ff3bJZX/Bm3gprh9g3odOXLkvffeO3v2bFNTEw4Z7dj29uf6IyyptLe0NrS1dza1dk6YNLumDrXWhlcJOgEOWjs7Wuz2FtgTwP518vQNvuH/iIijhBxnhRzn9KcMOsHcHZu9/WjqjmNpWw8lh/6Nuik60ftoevhpSnBcju+nGSGxpIDD2UGfEqCCDxODjlB27cvZtjdra0zmroOEqC+FW2MyAuJoYafZ/rHUPUfIOw4TN+3LiPpOGvaNaGnIFe84iu+nmd77U2EZ8Td22N9YOw5nbT+cvudops+xjO17rwTGZvgfyYAn8vk0c1PkDd9Ymt9nHN/PIEbw+lMGnUD8Y66EH7gSfjglOi4r/HBy2IF4zylr2rGxUHt3t9mFdZEd6JxQZ0sbGr4S/nadgzzdXFYvf3eK119mzxi4aMHABQs/nr/wk7mLHecsHTJ7hePsNY6z1qMl3DN3vuNc9K3Bc5d9PG/VwHnrBs5d/+cpKz6asXbgrI0Dpq8duXS349yNH09bPXD66vcmLHKcv95lre/QhRs/mLr8o+nL35+y7L2JC8eu2gP3DFu05YOpS9+dsHDU8u3Oa3w+nLr0w0kLB01fPGT+6kGzl3w0a9GgBcsclq0Zs3H7oEUrPpy77K8zF300a5nDvHVD5m0cPGP1+5MXw8P+PH3Wu1NnvzN55l9nzv9k8aqhS9Y4rdj08bwVUIPnQi3r1Sdzlw2ZvWrIrDWfzF4/eM76gXM3Dpi7GQpWBs5e8+fxs50XrR08fiK8kho725pbG/pARnsfF1kA+riptLd39gLHxIkz+UIlT6juFXyJqVgo6JGwSCToFnzJxhYmGBJUPR4i6HIMVaAUFJhEBUZBvha1lhCrxHklfJFKKjOL883CXCOTp4LdfKGiAuEo+BI97OY5Qg2sQOEHcEVakdQglVrFQqNUbBbzdWK+AYrH1vH5ZrGkgsM1C0W2/MJKsdjMYyv5rGIBB7YKb2G/SrGgQCYSF3C5Mr6YT2arpAqjwkQnslYuW4tdb114LhXcARsDjo5/CxzAbboXXyVmqhCekY7oMuimDKop/U2UStReoZmSEVMypAqOLROxpCHGFHZZFsuSwSrNYpmzOGVEUSWVZkynaFIF5ZBsMmjaZCiqJgnRpzDN6XRDCtOWiSe1Z8CKNZ1iTqVa0kjGVFhPK4rPKblC1ieR9ckk7Q2KPp1qyKIZs+kmWKbDT8EzwlNTjFcpxkSq6RrVnNL/9tMMRIaK5zJzYjMa2ryjL3DgZvKoX3RH+8z58zGj0c63wPHf2HDg6J3ngOX27dvfffddBEHwdZa+kx9vt/9x4IDPscbmBnwl2N19chse0Apu7bhlQhtq6wf713YwYrjXvFkbJ7sumerSX012XTTZfcG8WSunT140eoTnwlmrpnnMmzVxyYRxM2d6LPQYOXW256KFk1c5O3gtn7F+vtfyqc4LprovmT1p7cIZm91GzZo7Zf1Uj6UTnOdP9lwyY/KqCW4Lpk9ZOWPqqvFu891d582cvsbLY8F09/kzMM3xWjLRdZ7nuNkzpiyd4rVwqueCOVOXTvecDwXPBQ+b4jJvzqRVE9yXeLov66cmuS1cMGHhAs85M9xmTnaZMcF1+vw5y0eNcAHd8Q66sPhLHe3duWhwd8jWpvbXL1qeT1s/57o0m3WrgHEn77qKlGlg5uiZBB2bpOGQSwSkEhGpREoqEZPhSw2TpGUTtByClpelE2VpxZk6aZY+l2JVkMxF12Vsmq04Q8XPVAtopgKeTZGWxyAqBDSNlKHPR3R5OUU8rqWIrs3NLGQzDQU8azF8i6IWk1UiKjzGVMiwypMKkSydgF6Rl6FnZRjYN0ro6XpWlomXYxJm6kTpakGWWkrRFzEsSrIlN8vAJVrE8C1Ykm3SNDUrVcXM0sG28XL6CLaWoBWQ4YnUeSR1QU6JLEsjz9QUZ2iUmSXqrBI122rJyc+bvnpVVWurHbvAamtrfh2E4z8AxzSvSfM9PBa7u2JyxrXEfRzUMrdxy9zH4Fru1i1YXzx+3AIPZ6j5Hq5Qcz3d53p4QC3wHL/Iy3P6qJEL3F2WjHeZN3bkiglui1zHLHIbN2eM0/IJ7vOdR88aNXypB3oiWC7zdFs9eQJ8d/boEXAPfLnQdexCV+clbu5LnN1XuLktGj1yrbvbGvhynMtarylL3CfOd/FaPH7K0vGTFo11XenqtsHDfeU4pxWuY3rb+R+13N1pjceQZWMHbpwwerXbyFXuLjOGD1s6cdJ0d3eARSdpw4Qac6BmCB3/Hjjg3es+bZJIkctRsPkGNktDYOsJDB2RoSP3W0SeicwxEKnKNLo6g2sk0dVZ5OI0rpHC0OSIbWymhkSSp3P0dCiqIoejp9KVmXnlLJoijWcgia0MmiKDZ6LCT/GtCKIlMY1URE+GJdvEIGtIsM410/hmGuQh+FmWlsrU0FhaOtfA5OgZdBWRrsrhGMjwvEhJBr0klaXLfqP2szQIUyFymTG1GYtw2Bc4OrHbtaEDddQeN358I3qFYt55b7ffe+v1iYUbhA+73T5//vw//elPSqUS39/rjfw2tPn/OHBgphvoc8vehvoturlPaGxoxYEDDUWNRZXoggN3LBLTNM+Z3x46cnXvrvSodf3W+rTQTWmhW26EbiYd9CXs904KWJ8etpkQszMleANp/87U0PU3gtdmR2/OjtwElRWyhhS2mh29IcN3ITF4qejYLtb+DcTQZdnBizIDFrIObcwOXZLkPYu2bxU5enlqwCzK3jUpASt5x0PTQzdd8VmJHIGn2Hk1aO2NyM1JYZtvRGxNCd+aFrWVsH835eBuQsSW1MBVhIh1xPA1/VROxPq06J3Xo/ckRO1MOOh37qD/1/sD509yBR0NXd1Zwtt6YjHZsRCQ9s4uewdoawD1o2aNohkQoplItGXnWFOJ1hQKHJyZU+CgDR26GeEILBMKHcPBoR42/oNjMpI5E4poyUQ/ZclM093I1CdnG5KopRlkayrZmMyxZHNNWSx9OhqvWpcG61BsQwbHmIloUqDgS4GVAF/CY1hwpGgkEPWZJEM6xZpJLk0jWJKIZcnInUxKZWqW5TrJlkorzyGbCTQzmW5BskuyCKasTFMKqSyLUpGTbUkjlmbCA3JMqSRz+m9EMaODSMSQjehz4FCSYiSTjHSikZljZBMNPLIBgoiYrs4dN3sK7sXT0Z3CotfUoPNfAgceSaK9c76n8/WY7YTIFX20nBiBihyxtEeLKT2iRSxghc3ihE3nhM6AFWbYHCRsHhK2AIoaPJcZsYAbsZATPo8VPIO0y5MTNI0fOlMQMYsVMpUfOYsdOg2KGz4DCZwEv0EUM5ceMJERNBl/Cz+GEzWbEDqDe2R5yh5XeuRURuSUtN3jBIcX5YRNI+5dRNy/PD1ycXbkopzQWcSgSXmHFzBCJ9Mi5sEW9lPEyMXEmKVpYYuJ+9alRqxNi95xfa/vuX1Bi9xHw2sKXlqQNlqxcXlHz3jhN7npsQU9NKlbazto7QQjXd1nLZ3v5DnSdc64iUtdh0wa5DJntOvssW+gOeNGT3Nyn+fiscDNedaYUVNHusx2hnKeNc55lnO3Zrricpnp7DrL+ZfPznLuFfYpF1zjULnhGjll5LjpY91mu7vOcnOejsp15nj32Z6whHswufR852hMb9B4l1lubnOnD/f0gD8aGmmjo/uK62zvamhsbu8xYHZ0coLvNDW1vJ3h+G/QRu+tjVtswPr777//zjvvNDc3982fgof/evuL/Y8DR0snatDX3GZvtbcvXrwUXhj19Y3YW53tna1oFEgUOOwQP+Z4TLq4P4wePk8S4tJP5QaPzwucKA+bIfWbLPHzyg2cmhc0pTB0en7I5KLoWfLoGZJgr/zwSbKoyZJAF3GAszxsvMx/dHHAaEOUR573MN6WgbKg0UUhY2Ep9h5aEuOm3Tdet9fdeMBTFjxcsnuQOtJF5Osq8p0g2OOWGzBBGujB3jNWFDRBvn+OMGKGOGoWP2waL2iyONBL7O8hC5ygi54qC3SRBTr3U3lB42GfBDstSshMcsyC1H1L4/euXj7RAaARxZo6UebA1dKGusjaW+Fdg83LNoKWUbPGMPQ0igl28ylEWyLFmkC1XkLM3UKTalrREjEnMEyoYIVmSaBaEuCRJFtCjvkCufQipewSuewCDar0LJoLw/IjSfM9w3CeY7nEtV5mmy8i+nNUzY8k1fdM4wW4B+6n687StGfgW1AU3TnEep1svEo2XaaVXqGXXyZZzmSbv8+x/AMqy/QPgulstv58uuosQXeFYcukW9OQyjSSNZFSiq7fZ+jP5Zgv0iquUW2XYaso1ktY2S3YVNhg+Lcw0L8ogWZOpECCsaSgOGLKoZpoFA0TUfOdZ3s19QJHx5sBx2IPJ3LkQkmwZ4/GQ+V2yy0vyC0v2KVX+UHwPztWFTBc4z9U7T9c5e+kCBgJr6XigLFFAc66SC9ViLvMZ4zMe6Q6YIw2cIwhYLQ+cKQpfExJ0HC592Bz1DhT5NjC3YNUAUN1oSO1IU7Fvg5wP3zXED4a7oF14Y6P2LsGyaOdC4KHGPaN0UUPy/f7SLNvdH64EyfQiR3izAlzl0R5Fka4ykNGqAIHS3b+tTBodN9G/ntJQtxEEZ6soPHCyJmMkBlI+BJC1Npr0dtXujoCe21XJxwJduHRuNv/HXB04PHN4UW5aPnqLBqVwqNns7Oy+elEfgaZS6CyKf0UmUMhMEk5TBKJTSFxqLCEdQIDlhQql0HhMahcFoXH6i3hTjKHCj+Ff5bKhnUqXpLZdEwMKBIHE5sFxRILaDw2hckkMhlEGiOLSoclmc2isljwSCoL1uk930N5U8E2XyHkzFmzphGztoV3Zkt9cyMa2BC9SSG31bXZqxobbmRmNtnbcBOit9vvbsPRG0UU94Ctqqr685//7Ojo2BsTrNd19u3P9b8PHHUtTfAqsHd2YEsq7mg+ejSaM2aS0IUDBxYgsq1l4Xj36zF+4qDpGr+R/ZTWd6TW26kizM0CH/rbBqv3DDeHOusCRhV7Dy3Y9XHBroGyPQNl3gOK/QaqAgaWBH2sC3NUhwwT7fjAuNdZGTpCvGtgScQYyBaiPYPyA4YIdg3g7Xg/329QcYgjfNDn+XxQHPiJNcataM8Q2HOURo1Veg8q8h6kCRupDB2d5+8kC3PJDx5bEDBSGTiyCPYTm/+igYziP1wR4NRPFQeOLA4aVxg0VhowXBw5lh7lmRw9Y+3E90D7z3j8Jew5hjoOtIOWZjS7LhY6uRM0osnbxjN1LKoxh2KDXfh1qiUR654T0O7ZEk/DjOCoaGylSwxT4i/AATt1WzxUjvEHyBmMm/EU6z8YlWfI5r9DMSt+RMrPMiouMCvj6WXnIIKQLT/ACuvmRSj2rUuwRMrPYyb95+BhjPJLnMpkxHoVfjOjNJFVcZledgH71PcE43eMiovc21cZ5Veo8OzW60wbuljOLEslmRKptitQkEWIJviFV+ilKB5RrfFY2SMUOOIZlh8Y1u8Q23c02w9U2xk0LTjEEct1uimDps1hljBcZ3s0oeYGqPNOx7+OHd0HONp77ciWjR/CDJ9eHOjUo+FQigBMgUMhGagCHHulxqTxd+wFDkgbRYFjUcQMcBPvGSv1GSvdM6bAZ4wqyE0XNj5/m0PhLkfhtgHK0JEQXvN9HQr8HGGpjRqX6z1YuudjRYhTUdBwWIf7C/2HyAOHaSJcVOHu8Dvzdw4q2vmhZs+Hau8P5XsGirYPVEZ55YVNFAV5SQI8lcHutgh3i6/jnYiRWn+Hvo3891IEOipDhsPTKcKccwMgf0xhhS3MiFy7zmUAsL+EdyLuht2CdZb/DBz4lFsvcDyrahg/YRoZYfOl4ix6dgaSQuER6QwK8gYbk0pjMjlCCpVJprGYLD6LK6LSWBQ6m45wqAjnNyUm+BYTfhAt0cPYeEn/5YAe0XlQbLaUSuVBNEAQIRMR0Wh8OpXPZAoR7F20RDi934Ng39z/1lMZTLpE6jZzzs81De2gT7JKbD20DXXD66jHRlzokgp82NnfTun//sDRW+LAodVq8cT0fW/8XyINv93+t4EDtdNuqm9Dl83BF6c/xz3Rm1tb0GQWXW0dOHC0NQJ7w3K3cSkxO/MCvXS+Qw2+Q/tTQmm3D6wIHFERNFKzc5DRb4gxcJhi18dwWGkIc9KHj7DuHavwHyT3ft8cNVQf9nGx/wclYUPyfQYWBTvq944piR5TGDI0L2CILGSEZq9LYfDwgkBHWbCjPOgTZZijNmq4MmQI7Hjg01/t+4nKe4DK+0NjqKMy4JNc70EQEQrDx+UGjYHkoQ0bUx4zrjJiWFko7I0c8M6pPyWUzn+I1n+w2vevirDBgginjMgJG7z+hAJHl70dT2CBRs+EwGFvxuLAd7ShK8RNHWDsNE8cOFArOct1NFO2+SrDeBXiBTrDYT1PKT0DmQP5NXBgMx/xaLdtO08vj4fokKX5mnfnEtX0Dc38LfvmBfipHMuPWaZ/ZJt/gBUoUuk5+s1LlPILGYbvMo3fwzq1Ij5N9w18ybyJfafxMkV/kaKPpxou0ExoyGemLZ6g+Y5pu8SwXCZpL1B0l+nGa1T9NbLmGtWYlKO5RDNf51SkoqRiTUSJBEWNXqG0gc1wJMKXDOv3DNvXiO1rWuk31NLvqKXfo38X3G9OomvTWSUU1zmuzag9xv8XcHg4sMIn97DF0D5g4YBLEzD4N4IdvNYPZQ4cOGSBYwsD3fIDx4v9XJTRUwuDPQuC3YuC3QzRk2S+o/TRHsWhY1QxbqooF1nYmKKwMcpo15IYN1iX+A6B+xUR40S+Q/IDRxSEjILvag94KSInFgQ4q4JGl/g5GAMdy6JG6cOctDEe7D2jBSGTeUFTRUGTikO9jOHueWveKQscqvMb/M+N/NcapAsapPT50BA8rMhvuCzInR86Iydi+Ubn90Hry84ueI2hwNHUDRyd2HJoD3B008YvwAE1eepCAplNZ3HpHBYLdugcGpvJ4jJ4/RafQeMJ+HlUEodO47IQkVCQTyOzmQwBk85nIPzflFBsBp/NRD8IK70lKkT4f0lMI/GpRB6LLhHx5EKujEkX0ik8eCI2Q8xmCHvE79WbNJ4HCSmDxh47ZWYzhhn25k48sbO9pbX61UvckacVAJlK1YrNTHa8DQPx3wEOfN0En9IgEAh/+tOffHx8uvsgbOu9699u//vA8dre2trR3tzcDMfm7S3NcKQEgQO9G7s60OcZChz1oLV2tatTevQWcch4RZCDKtChPyXsHoq9P1H4DNGFjC32d1KFjs3zG5HnP6ruirf52Gyezwhx8Dhh0Ci+7/D8sLFC/+HigKG8XR/o9jnzdw0Q7hmUFzwC2fahOGBEcYy7wG8YBIiSfeNl4aMFuz/O9XOQBw6BlaKoCYVRXvKI8VLf4YqIsbmBI+jbBpk+X/QsOfhxcuiti74Fh+dRdg7l7nIsChhW5D0I66jQh3t/SogaRj9Hs88gg/efNSEfScKHZkV4bJjwHmh/iQcOwjJPwuESOpXdjA6YsGdWB5rua9w0d7aWgdrbWyBwJFPNyTRTMsOYjJggeaBdNan0DOUX4IAUkog6HGKrKujiS1kivRTljxztGV0NS3w7BZ0LsVym2hKzTPHZ5ou0yuvMOynUimsE62X4klx2BVZIpYn0m0nwrRzLJbiTartCNiSwzMm80jQotiWJBUnCCl+mwArXlkpSJ+QoLgrLCfIH7OJHXH11rugmmaZPphtucMsymJZkdOaj7BoVflU3Z+CrKonosovlKqwjlnOI9QfE+j3N9j2kDVS2H9CZG0siQ5fMLiG4zRnXDFrbepdU+gUc6GJ7e7t9iccQesSM/OCRhX0kC3LqVvDwoiBcQ6GKg4YqAh3x/xqGlXCnE76ikRvsdvvrdY/Obud4j2FsH8rc7pgbMFbsPbwowkUZ40nb8bEkaHRuuHNe6DhB4EjqlgEFUW6igJGS0LFwP7w+C8Jd8iJc4FVK2+lA9hnNCnFVH5mk2DsmP3Cg2O8DjvdAfuS4vLiFN69HGM4FmM54l367qfz0IuH2982Ro4rRtg3vp2D7dUGDVT7vG4OHKH0diwNdhKHTCOHL+gJHfU8KFcxk2f6vgKOuzg4f714T5lKpAiqVw2Tx2FwWgZAN6YND4/RTbDqHTmJwmAJKNsJji2gEJuzySVkUhMrmIBALeL8uuVDwI/gpYKW3/EXwrb6icZgULsQOIVsKxaZz4ekQMrPne3Cx+qr/jYdiIFwqX+o2cwG+CFX1rAZPpdvR1t7WAX+ezlp7cwvomrdkSUNL229iqL3dfi8bjl7UwLcvvvjivffeO336dN+dbW1tb4Hjfx844LVQ3dTYiuUEb2lpkufnYWEQYP+AGXYAHDiaQVsdaHm5zmVIRvQGYahbcfDg4iCHfpVBDuqQYXneg4qDR+ZhUw4Cv5FF+6YAQwJAYvnhE6HkR+bnH5glippC9xtXdHCaIHCEMXZKQTgKIspDU1RHZ8gPTOUEjRNFeIrDPbl+Y5i7h0kCxyljvJTRHuJgZ8TflervKgr3EoU4y6I9md7D2CHOQHEW3CGDO1RQnQeMabov13MDxymi3BRBIyAGqQMH9VOw9zL5DjH7OBj3/Fkf9FFu6PCsiIlrvT4C7Q0QMVrxGVrMvhZ0deBBwDoxM/iWTvu4GaPYegLNlEK3JlMtV2nm66h7oTEFMSWj5g62eGLZGVLZD1hOzm6rDtywo3fBAophu8ayJj0FJu0zLtd8g1xymX8rC34bQZdANiVSzddJxgSy8SpSmkQxXWNXprLK0/A9rIoUxHYjSx1PUF9EDNcgW3CsaRxTCsuQzNBdp6qvsnQ3eOZMpi6l4Db9XpvuJ2C6Zdc8B5XlTUrpLSqiv0HXX6VqL9OMlxmlV+nWy/j0BgQOkg0qgYQyBwSORHxWpnuFyHYGF816DrIRPBFbk+02Z1QzGlIOi1Ty/wEckdMhcPyaObpp4zfAUdQNHPC/BrFyMKzDw/JCRktCnOFFC9TngOWq/NAM2f4p8n0Ti/Z65Yc5s32GsgNGMf1HiyLGswPHcoOdC/ZPhnVp9ARJlCd8mRvjVXhgCi/EBSp/36TcA1PZe6eyYiZz4PUZ7pQfMTQ3cmjuPmfevgllSSHghQQ84IFaOfiZDaRfag94KoKH9Z82oCCdaIIclD4fGoOHKX2HyoNQ4MiJwIDD/rKjq72xBziw+ButAF503asqOHDYe4GjE7UdBZ4ec8UiJZMpJhHpdDqDTqUxqQwoVv9LGuz+2QI232qwwP6ex+RSCRQuk8WiI/8kGhSTRmdRcVF7SlQcCibMnKNXcD88BYfFh6egkKgIhSoRibUqpcmoZ9KoCJ3KpJEROrm3hDvfqP10GhsRFjo4e9W04DMcPVZDqA1HZ1Xty8ZONN7+gE+GtGJX5tsZjt9965tLBd+8vb3/8pe/ZGZm4jakvdOZb3+rPwRwPKl51dpt9N45ydMDHTbBAXsHmmK7D3C8Bi3V610csqPWSkJgt+0An+b9LEvCHHJ9/1oUNkQS8ElBxGjIE8oj08EjMsj7jhM2mezjigR6MQMn5u2fzw2dKjs8X7p3cuqmAfLDs0QxkzO2j2CGTSL6jUdCp0JxImayQiYLo6YXH1ooDJmI7BwjjpjCjZqBhE0s+WxJ3t6J3KDRvNCxhUdngeIzloQg5Y9+wJABnonvJAYzA53lkS55/o6QgRRBg/spVYCj3neU0dvJsPsjXZCDOGx0RsS0VV4OEMtaukBzr/chpI+O7rxkuDl2K6h2mTmIY0xCYcKGrpKgsxfmq9j0xlVIEqTS8zkV3xPLUdMHxHoGsZyDnTQUGuDIeh4147BcRIMz2pKYlpQacNdUnSewZCElSVxjCkt/jWO4IbCmwjq9JIGtT867mSO0ZtDUl4nyeESTyDOlwlPD/TzzDUFpCteShEBAUV5kaK4JLFiKTkMaXXmNoUmmKK5IK8iVjUrdc5HsHlLZpKgCFarHbKb+GlV3kaqLp5suMkovUy3nMJ5A81PAlpMgLZXGo5EorYnYzE0KxClshQU1QKHieb/QGQ4IHFluc0e2gHoshi3mxfkfgAPljF7gWObhwIyYWBTsCFUc1Ct0/gyXKnBwH+GYOAAK/u/gR/JDnCShowVhLtxwNyD7BtxJp/iPZoW5MIJH0/1HFB+ZLI4ZL947RXliCT3QTXpgpjBmatZOJ1iSfMbClymbHan+LoVH5rHDJrBCPcX7ppMC3EmhUzj759L8x0giXQojR/EDHfIOetIixj+mnADP8mTno3indkhj1+Tvn83fPbzYfxg6z/erRv4nhQyR+X6sDXGS+48oCHbnhc3Mjli+zuVDYK9p6+pswIADcyJsBx2NEHzhb4oBR8f/CRwjh08QcIsRmohCZPK5olyJlM1koasq/SxZDD6PxWEjJerilub6/DyxSMhlMqgCPovJoPwiJqlXbBaN2y1KT0nBjEpJfUSAEjBhSaLTiBwug4FQiKRMsZh9735FU1N1Y3M1m01gcQg9JYnFIcGSzabAJvW//ei8jrR4+PipTVjUHJw2aqtftzTBe7ezprHWjkcrmTCpA7s4W5vfdnv/lRmO3oii8B6fO3fuu+++q1Ao8MWUvlMgb804/hBLKjUt9fD2a21pmjtzBnxK1dTUdGD7m1H6gAMoDDhaq9e5OmRFr5cEj++XBRy2ngIfoIrgAXl+f1FHD5cGDZZFjRWGjIaDSHAnq4USy4uYLoiaLYqZe/MHH2DJAncY4C4CSq7mn1wNKslAcSUnYh7ryBrT5f2gWqk9G2mXXAb3+OB5IVCn6b/aJT24tCr9CHgsAqUkcIdSlRwiCnOH2AE7DyRqhuDExrPbJ9zK+QK8kD/P+FQQ6lkSPb7IHyOhNwEOrf8onc9I3Z6PdYHDIWxlhc5d6zEcEkZrJwocXbgNWjvqXIxHHe1EpziaW8Bzl1kDYa+PmoiWYn2wJR4zGk3EbDUuoVRR+gMUzXqmj87hlqQQUBBbAt2UgBiuUVRXXoE7up9zRWaK0JDD16aq7jAeteqawN3XoPJ2bZH1mbTiZcFju16oT2MqrsE6fLfkHkv3gFcDyuEx99tKNE+4hbcp+meih8D4ElS+AOXGFxJIGKSiixTFZUlZNlWZACu2mrxXoKLkCYtpuMIyJrBtlzmll9jlF0imbzGb0B/wNpNKz+ApLdAlFWMKw5CKTdtcxReDcDsPhuUKXf8LcKCBpLswmn1D4GCFT+yDF92dd58pqL4aAJmjOHhQETq75lgUNDw/GF0W4Ua4MCPHA8U/QA2XFuAqiPDihnoUH52D7rmTBV4IgSFdfz4Y3GUC7Y1kPy/533dYLoeBm9SyaxF30w+AsmzwkAVuU2q5XxGi5z2mfw3u88ADBrid2ZIVLAgeJdnrRQrxvJV5DDyTZcXuuBa5grxvOXfvAmGgZ37AaHgJ/bqR/07wLyoJdijy+VgX7FTkN0IW5AGBIyNy5Rq3D0FbTRs2RG/qdlFpQ5MGdtVjAc57gQMX6hZrt6NLLS7jpoiFCiYiYSB8LltAIpCZDPqbiMpAyCRipl6ngmcsLJBw2HQqJQdCBotF/rWIeKUviLC7SxImwq/EzOZgFYRO4gtYLPi1NIJSVdDY9ArePp2gkcEiMFjZWNlHkGkY1P63H2GwiEzB2Emz7Vgk5eYm+DwD9qZm1F2lpQnetK1dHS/qX3tMnoy6xTa3v11S+e8BBz6TYbfbXV1d33nnnTt37uB3fd8pkLfA8T8OHFi6osbOLizGV1e7h6sb7jDW0oEliepeKraDjnrQ9nKV27DU6I3CUE9F4NC++rWngEOPBndbS4Q7yoM+Lgj8pGS/qyzGjR0wShQ1CTxAgPh7ZvAUJGBy3v6FbcQTIP/C62uRQPQjqFJYUk8DKwe8MtL+Fnp139Y6FQNU237i3gA/Kevzs17yr4EKPvi5mBC6oIl/Gby2ggpxG/9s+T/25IZN5vuP5wRPZoQvJEavu+iztEvDAdWm8h/DxP4eJf6jlLsGoKv7gf2VGgJHwDCd71Dd7sF6/9H5AZPJgcu2uowGTV2w72ztzp6FJ4FqwoxGu7ApD9gx1LnPdmKVZDL1SdzyJIoZzXIJeQKxoDabaNZvUzyaDQtdSUnAF1NomOsKfgBiiUdMF2GXz9Jfk9hyqsHNsiql1IxIDDReSVb5M9mjRpPxJ/H9Wl0DuPewwXjvdUkLeKyqZEt0xPu1mkbwoMBMgXseN5kN96Qvwe0XoEL9gKt/IX4Oyp4Cs601T3ovi1eRzLZeY5uuCC03TE95Tzv0z4HpYadOfp9KUZ+n685D2kDMP1IMX1PNf0ds39BKMdm+www1MItXSzzbkMjRX4Ulavdq6TEsRdeDrtINKWwt0W3OmGbQ0I6GxQT4Cvr/Ab49G/7o6V1bwbxUpqLOKUE9V1pgtwlOX6m75VAU7CgJGy4JcyoKHKlA/avH5geh0xtI1HhguAz0V4xxK3k7x4t9Jj6PDwPm9LLre205fwPVOnAnrxVeci/UhC/8Lu9bU63IBI/yGP8IggwBfhJXML5t1GWBmmLehegK4XXQWA5u8VoF3909t4vlN1YcM40aMe0W4TNQo2ktFzZZuS1aCv/0znQfL37IhKIgJ/UvLfwPgsyhDxqk9n6/NGCIxnuoIsCVFzYjNWbZ8vF/BZ1VHaCxrQtfsoNP8TY7aITqwBIhYcKzuYFeQQyeNHkGnc5jMHk0OpvJ4CEIE8eI/otMJgqFfJ1OA08rkxXQ6VQiMQdBaCqVAvbZaKKllqZXr6pl8jwIPXq9FkEQgUAAuxPYx4gEwrt37zbWo26oDXX18kKZgMd/WVUN6/CDTQ2N8AA6nU6hULhcPoPBePToEfy///TTT7DkcDhcLpvJZKJLQQwGl8uFe+DZ36j98INUJt9jyhzUIg2DXQxlu/C5jtramrYO1C/Fc9LklvaOVvjQa39LHL/z1tTU1FuHtPHw4cP3339/0KBBb3+ZPyZwYKYaoLH+9fP6murpk6bBO/Hx01d4yMw+wNEAB1jL3Uckx2wWhHrhT///xByDe8rBxX4D830GinYPFviNEIS4S/bOgMPKOspJRtScvOPrkna60EJnpO92K/3RW/nNHvBM8XNRNvvCUfDSqqLEf7d3O2h9XF9aWGfNBS0/yZK+FPx4ANySgAYj5dP1N7O+AM9KCn+IZO9bkndgtijIRRrkzA/y5ETOTfKdU57xd/DM1CXL4EXNY20bagwcWhHyCepy8gsV/WeVBKKOLdo9g3X+Ywv9p1ICVmx3Hg0a0Snati48vUVTFzqCr29GOaML9TFGV6FaXGePZamJTF0KtyyFbD6P9dDofAZiPcMynWcbL3EMibCfxiYGEqk9lhA9RIJG2hCWJsG+XGrNfg3ulr5QcJVEVlE2X0nMNzJlFo5UR7c9VjxpKjfeL9TdyXvWetNwrwC+VQcevey8Z31U3Ayelf+ssT1QP6qvrO66b61SlNWpfgLGgsd0pDSJVYam4iRrLnAtSdon3IctmgZwswXce9xlVT7kQk5C0BmORNgSyBycirOIDfV9xYxD8VkZdM4GAhPHkMDTJ3AMlyBCwZZ3rwdZE6jm61RjCktHdJ0LgaOpHfOn+P8Cjumwz4ZCHWKxK+03zin4vwm/9iBw5IUOLQwervEbbvR20vmgzCEId0ai3EDZdXCfRNk6uuKzzbxdXjSfSZmh06VnAxln9oHHurbbRYKrXwD7vRLq+SvHvEF9KbhfUJB2GjxV3M29hpyN+qkgCbRYzeyzRlYCfEsZH8U/tCAvwou1wzEvcjw7bELJWb/WkjS7DanXE8HTwkZpPCtmTm70FAw4+n+9DdYHDdB4v1cW4KDzdsSBI3nvsqWefwVdzyFwoPNnaF+JJgNpAq1NaDf6K8j4NXB0TZo8nY6wGUwujc6C2IGg2PFmwMFDFzwYBQUFECDkcjmPx+Nj2/3792/duqXT6SBSwH9WZWUl3ruQydTCguK62qbnz17evnW/zd5V86q+rPRm7evG5qY2DltQX4dOMDx5/Px1TYNUUsBAOAK+5EZy+q2b9+DBRoP10cOn8EgyiQ5bC49ns/g0KhO+pJARyB5vChw0BnfClFmozx22nNeJJ10EnfbW5h766AwOCWvtwGYm294Sx+89iY7d1L2xROEl9O67786dO/ftL/MHneGwt9W3tLxCzQ5amhbOX2Jv7YL33OtmNL5Qcxdu29GCz3CscB+GAwdq3RbYo27vAFxoIIG+Syqwqzb4DLQFOVbEjCvyG6oId1MenMkKnwye8upF3yMn1p0PnEz922ZdxqegWgqeCsBzEWgxPtRlJ/7NF1Rrmh8XS6g/gtbbnPSvQZUe1FrBSx14VgReFoNaZUbs2prCi6Amn3FkASlwFCvAgbnj3ZIDo3IjnOhBo3Rnd4MHbGAjqc94i/Z5ySOHG4Lf0+15B51473dcBPhXKIM/UQUOUvkOVAWNyg2aSAhassnNCbSgYYJaujOGQzCr70LT27XhxvAtcPTWAxyIPoVdjgIHpfQMvh4ByaN7ksOIxvtCDUixmFrY3MB5bBbkHOzmKboz/NKr9JJ4vim1Hty722QUGahCHcItopQ/MtV1VT1veljb+aIV1N16atOUF8FKE6gx34Xc8BK+hDvhnp/r779ue/G49n5N53PbU11lnekxqGSaskm6JKL6KtuayTGms3XpIgOJq8rK09Eqn6urwSP9CwXPSmEaM1imNJohkWFJ5FcmobFDUOHWrKi9KtWMetxAZoLwBGkDthn19bWhFh4kGxqKoy9w2HHg6HxT4BiGhM+QBaGeKZgTR+8kxy/Cr7Ru8A100AYMMPt9cHPXgNs7B1XscjD4Ds0LceJGjgbqb8C9NCRoPHGPKydqluDk6q7SNNCsAA9loLYCPDeei94EnuvBvQID6QfwTC1NOIzOczSXgtsC8FoHms3gNt9C+Pyx4Bx4mZ97ZB7Xe6g+zEnjNxBSgsT3EyRgZFagS1roxOzomaAiG9QINYeni/c4/AbH/9P1NrgkZECx33umYAe1z1BZkDs7fEbS3uVLPd8HXVWdXc1duLcrdlu2gvYWbDHvTYGDjVD7KRw4aBSqSqGE36gsViA0OhNhkIkkComcK5GKhaIimRy+9eD+T1Avq6rZTI7NUgr3WM22509f1NbUWUxWsVDSUNcId9KpiB3zByERyFDZmTnEHBJ8l0ykNDU0w4Nh5d6d+/AYHoeP0Bg0Cjwdi8Vgwwp8CRuDhePod/uRfwkcaMYG0NnR0dba1mJv68CBo/2tn8p/Z7NjG6xcvXoVAkdMTMzb3+SPuqSChS5EM6d0gd07fdA8DWjSNoDliu3C3PzhY60bOG7EoEsqOGT0cIbjL5zxy0pEt9DphK3vGXYPLI0YV7BnSH6IqyRmWoavO3giqJUnXty3+MKnK78/tKLpAR+0aBP+vuvad97glaKE9cN3B9dWyFNBazmo1jU9KLrwmR94oW24JUk67Z14dNPl/SuuHVh+MWL2XdbfwAM6+egc7mEvySEXYdiggn3DiN4f5J2cCR6SwTNB3vmASwHjWQcmcoId8v3e0Yf+BQIHOkT2d+hPidqghAwsDvmw2P8DRchw+Ldnhi5c5zEUdeTHpobacOBAl9Kburo62n8NHMwSCBxpEDhIlvOk0nOksh+g8PAbNCzkF2LGQ4viwb5wRw985QVdfBHdukFRn6eqL1eDCtsrOU2ZyijKkmq4T+p+agA1mnKFyiZ/3vS48olNpODefVHZBGp/rn8AS6laYLmnf91RVf7IojAVsvMRVVkRs4iqulfwM7jDNhHZFgJRlSwoI3FNORx9jsREJUqu5XCvKsvEdaDK8tooLGUyjDmIIZ2mT6YZriJWzJUXnYm5iqMGGqbdlEozpaA+veZe2jiH2Xag9qR9gGNUE2iw47k6MR/ON5rhQCKm47SBe73iSyrq366nOPTMrg02+H9g9fnrzd0oc5TuGazxHyoJc0KixwLLBVBFz4qcRNo/Nyly5qOCs8BeJKGeoMQfuClNAy+M5/Zvui1NAfU28FQN6syXD214XJAMHhWyfgy7sn/1xahlVw+syj6+uTznBHiVK9o7Teg3zBI1xhTiqA38RBE+qvDgRGb0JOremcToGUB5AdzOLI7yVISM6p6S6e/1NkgZ9lFhwHuGEAeF39CCYHdWxMxr+5YvRoGjurOrtTdhIOjJPdbxL2jjdwEO2LvTyRQSgWg1W+A3QuyAdQGPDyUrKERzSneBmpevYGkxmUtUali5VXnz0YOHjfVNEpG4pQnNk2Dv8Thta7VLxbnPnz6rfvGSQUcgWxh0eoggsGXlpRXt9raXVa8qysqf/fy8q6PzVuVtqVhCyCZSyRQ+V4CiBpPDYbF/L+DoRD1SOpubG+saajvg466js6G1/S1w/O5br7loc3MzvMH37t37l7/8JTEx8e0v80cEji7UNBtN1FxbX4cO2ZtREx/4fOjOMoD2AD3AYcdsOGLWS0Lc+iw5/xKwSOvfo4ABOv9eDTLCEZ6PQ1GAE3/3cE6gGyt6Bnn/ItCkaytj6lhnKxVZucxzDdUlHa3ldMr3N0100FZRrci6ELlaePEIOuJsvv1Kyz4fs6VaQQMNla/kBNopv5uEb1rlqckh82rzEkBjSdYX6xNDPbOjPZmfemSHjyQfnAiqeeBlgf0Wr5j4lY7ytTHjQFaEByXAUfupizpwEGyYwW9Af0pN4IeK0A+LQv8qD/yzPNyBF+6SFjFn9YTBoAPN8oSlCGlDgaOzAXS2wGc/ljQWgkh7I2hy6QEOVkU3cBDLzhDLf8DDb0C8wJ1gexxN40mlaIeNO5ei1qOWc/yb16iGi0zTtVpw+2dgM1YVlr5U3amxvex4VNP1xHC3uPxnfQt49aC2gpGfU2Dkv+p83AxePmm8LVIzlGW59eD5k4a72opimUlc+cKca+NZqlS14LGglELVJTNMaQxDKkGeKC2jPAflzzsr7lbrXnTefgEeaqpUbDOZrEujm7sTdZKM6KwGBY+9YblOsSSTLCkUcyqsoDYo1j60UXYup+z/AA40OTh2wb0pcGCBvxz7WG/gF9iHBv8PdZjgS6heo0tFMGo6CklX6+dYHOgkCR1Li3FLO+j1WnsOVHFsrL89Kr6iF5zRy6+0thuKSlL5yI+gsRTUmr+L24kkfwZaKsAr3RMT7VzstpxzUaDBBGrU0vSTuRknK6QJqad3P5ReA40G9pGVVD93aZCrxG+0wGekJHJSp/i7KuZXtvS4B8xvwUs5qKDkHV7E3TMSDZT+Rtdb+Ed5ge9pQx2K/B0hoDMiZ0DgWOrxIeh8hSYZaMMoAwMN3F75TYGDjTA4dHo/BXt3PpvDoiMWg7Gj1V5cKCNkZgk4XB6LDU/w7PETWBfzBbD+8N59+M2vXlShzqWtLXcqb7LotKePHjfV1TJpdGJWZnZ6hoDD5jJZda9eVj97Ls/PQyjUG9euFkhzGVTKg7v3GmtfN9XVowavKMd0ws+qiuQMKo1Jo8KzcBgIRB8aidj/xkP9G+BobmrADVDgvoWLlmDLo6DJ3vYWOH7nEW2fbClwmz9//rvvvltYWPj2l/ljAgd8drU3d+A3I2i1d7Wg/SZ4Vd/aAxzw5m/qBY6eGQ7HvnMb+Lizz9zGr3DEFjbSGOSk8ndShbvLIiYKIqdx9y8EVhKoKgZ1hs4KIXgot/KugGdadMWkqRQ8KbhN+pJ9enfO4Y3AwoSdgfTHmOtRqxOCloCbIvC4ENTqQa0G5CcUnN5ivBQGrDn873YlB3tc9x7GP+DJ2evOj50FHjNAnQq8VIEaHWg0tRQl8mMXEPY4cLwHwtaivVH/BDswZciHxaHvFQf8qShsMDfCOTVy1iqvQaCzthF01f0CHPAnwjqDjv8LOMrT/h971wFfVZH1IUSE0HsJkNDSSEIIoRcFLFiwoEB6QrGs3VU/3WLbdV0VFRtFkd5SIEAokRZqaKEXEcQGSAupr9f7/e897x0mLwRBAgnLnN/k/ibz5s6dO/fMOf85M3Nm3l4VT6iAQwuqtUPd4sFogwEHY46P5uT+J2Xvx+l7P198YMrvyv7flUO/KQfOKEd+0u0+VrCjSPlNp5zQKacMyqkfLuxYtTtlw6GlxcqvBuX05iOZGZtmLM2ZecJ48Kz9aKFy4ozth3zlp5xfV+w6t+Y3ZW/m4Rnzcr9I2zcxbfeX87ZOWHN0zq/2HeeVAyfMO88qB/cWbPrueMaC3Knzdk5K3z9l0cEpaQe+dLv8cjn+Iicc8/YDeXylbUvRZlK0fSuzD41XAYfqqGPSgt3fAnD49dQAB+3ldCqXdO9z+UWj7KOCLRyldnZozEbstzm+0caERuBPqPnNsS3Xx7VdkeyfNi5g5tNhyr4pimmrotuhHFmsnNuS/vkLeT99pyjHFNv3CCe3zfz0rw+8//RgJW+LcmbjnHeip7/x6Pujex3O+I9ybr3y41KlcItyYeOaT584vfIzZe/85S8NWpLcJXt0wPqkDiuifRfHtle2faGcXqHkZSsnVygbv9j4j/vnj+y4YWzQ5tjmV8VvG0sDjowxEZOfHjiw8+2KPV9182VxHzimurm9FsBxhdeFABwIABworjDvwg+HDn9/4CCQB7CFSacHzvj52I942E9Hj61Ymnn21O9AGDaTcc/OHatXrjjx80+ADse+P7x9y+Zzv586vH8f0INit505eSJl7pz1a1YDRgCLfDtlMq7Ij38BSk7+8rPFoF+36rsVS5csXYQKZOCaNn8eAjK4wcQV1X9RekZ6+rJLAg51d4+6Q8KCtGrVvNR9edpRNBJwVDjpdDrVq6SimEymBg0aeHt7nzlzRjbLLQo4Cs0GiK8TZ8+iy4V0ibDbL06plAIc2pTKpKeGLRrd9btEv9UJ/qtxdYc1CW0prFWDL4XseF9I220P3X5wRJ1tD9Y8lNR6Z3Tr9cNbrRjWct8LA9bER3wX32N1cr+sxP5LYntlRPfa/4/Yiff4rXrlzq9Gtk19JmrOkxHKninK2eVfjuiQ/nzfZa8NnjSqw9IX+mz426C58R2WjAuaH91682u9vvtL+HfjgjaOa78+tunusa32PuG77FGfrLg2S5JCZkcHLxrXa+m4niuSQ6AYtic0O/S0H0a96+N9rzBAz21JrLs1vmZOdI0tSQ2Wj/b7dkzEkKA66h5+1Rua06yKqVKAwyYCjq3z03dOX3xo+ry9nwFVuPXxeJcTiwOfu4MLc7gmVmhb7KHP5u/9JG3fl7O2fZp1bG7GvulLD8xJz/1mxf7p8zZ9/N2hWd8dnrFg82cLtnyatvXLzL1fp2/76oSy4xfb9uX7py7fNw15UnO+yNg5efX3c1YenJa5f8q09f+et2N85tFJc3f/N/3wJwv2f4hrxvcTFh+asPjAZ0v3f7Hi4MTVR6et+GH6wgOTF+z+LGXvp+kHJqQf/Djt0Efphz+kBShacAEmcshBJ79onjlUqKGGg5/PPfDVvH1TFuyenrFjvl/PNgQ4nMIisisEHP06t5g7rndWYgcE9SxAjdNU1hL8gBGzEfsRy22Pbr5zVMudI1ttH9V2c7T/2riArIQu0+9tveXFe6bd22nNuHvW/WXY8nEPzIm5c/mLj8xN7jM7KXLJ83dmPDtw6qgg5Uiq8mPGmpcHpyV0zUiOWJrcbe1z/Ta8MHD1k72WxIfNebT9smd6rH6xZ8bIFuviW+5MbL5/dKvdo1tuSWy+Iqb50mS/lc+Er3u5x6qng5fH+mbHtd4xxn9jnO+V85vqymx0PZpS2TKq8QZ1i034N0/1H9TJWz0v0G4UAYf9T02pqM46VX18hdeMOdNnL0lfvG3T1pL8Ys3tsN2sM/3+66kjB76nVcC6ghKL3nzs8NEVS5Zr1g4HMAdww7xZM4EYfvnxmNNqUU1/NuvRw4eylmXi31O//gKEgQwLUxas/S5rU/a6xelpiABSzPx2Kn4FKJk9fRpACdKBNjR/YimIrMxcSjDoCuu/KH1JuYCDTqlH1zXp+/YboLpALNLZ5ZTK9ZlVoUWjx44dq1Gjhq+vrzyG/hYFHOruc/WsFM3rhsXZuXMYOKFYb1O3818EHNqiUYu6S2WiG3Boh8ES2mh7EXAk+iK4AYeqCTAqzY2+/fiTDbY8Uu1gcoPc2Pp7k1rsTmy1fnijDcOb7UrovHG4777RoeseaHFwdNiGB1sdeKpryoN1Nr4ckhrT+KdPhirn5irLXkqLa7n8iQ4rn+iYmeS7LKnVlucDVsY1zR7Tav3oFusSmmwb1z57RJNtIxvmDq+99YFq+0bUOpSoqpztowPXxAVtGNN1ySNt149se3hsp81DvXIevC0ntrm2CNH3Sq5AJ9viGu+IrbNtZK1t8U2zkvxnjI68O1AFHEb1DHoRcKgTUU4NcBhcgMNv0daURTtmLT04a/5u1Smnhjm0cAAqmYwEX5G/Ti18TifKko/R9ANfzt35yZLvp83Pnbho77fzt08C4MB12aGpc3I+WHb468xDkxfs+ATXxfsnzt8+fuPJBReU/bn5S+du/TDz0JSMfV/Oyflw2eEpqbkT0nd/lr53wqIDE5b88EXKgY9m7no37ej4tO8/nLf/vfn73pu3+725O/69cP/4zMOfp+76aEHu+LQ9n2Yc/AxAJH3fR/P3/CdFOz2O3ZYT5tBmhT7SjmqbQMfbut9LRRtz909asHdqyq6ZGTtS2veoeMCxtnzAkRPbdEd0Y3Un8ygVdmyPbrU1um1OtP/W6M6bHw/IGRm8I6bb2gcD1j0csjup/9oRoesSQrIS2i8d5YvryfcfUH6a6Zg2dk20euRbTmLHdSN9c+I7bEtovyW23bZYv41xbVcnt88Y1TRnnB/Qbc6j3tserr4nrk5uQsPcp9osj26w7km/rORWSx6rtyWp1ba4Jusfq6MB3CvlN3VhbHKD9TG374kHSzfdFNd+WXL41Cf7D+4IwHFWZSv1cGIX0LC5z+C9GsCh+Rr38C9+2ZCRumjV8u9UmJK++LtlWYtSFmYuWorE+bPmrV6xauGC9KzMlWtWrk6dm4IM27fk4ME/HzsKPAG08d3yZWS6yMxYhH+BJwAg0hfMR9i2eRMhEmAOgAlko1+BKnAXEAZuWbY4g/IT2qBZlauq/GUAh8moJ9hhsZkDg0JolCXXcFQ4mUwmiuj1+lWrVvn4+PTq1Us2y60LOEq05dqQYAazA4DDQa4KtelhncXkVMW+TnFAgeYN7Njy62ceWa5NqWiLRhuzR1H3lIrmlImmVOJUW3dOQq0NSdWyR1dbxyG5WnZS9eykGhsSa2xMuG1L/G05cbdti71te+xtO2Jq4LoryWfDSK9dY+rn/aur6dN+v74ekBNfZ+1jXjmxPttia22PuX1HzG25MTV2RSNUB5rZFt1k+6hmu0Y23Pe4z8HHaiHsfbx+7sgmW0a12xDdMTs6cEN05y2j/HaMaLnvsUb78NOo+tuj626PvsJrwx3RTQ+O8d8X13z/2E5rkrvMSOw5tHMDwC+z6ujRDTic2nmodtW9Y4lVdTdtUqzNgpqt3r9i8Y6587ZMWXJghuaL86sF+whkTFINAHu/UZde7p2iLcN0hdS9mhP0vZPS9yBortB3T124a9rC3BkLc2epqzjVX7/QspW6bjidvua3ucuOTl94YGLKni8X7P4sbd9ExOlX2nmrzeBokzsHx88/9DGFlIMfpx74FCFt/2eqe9A9FNQdNHSwLZ1tqwV1rat4hBv529BWdUxyvZfr1aYuyJ02bc3EFXuWBPbpWOIsKtDlO11Hz18acIDPWDYx9WvfMH1MmObCXOO3+IuOv8QpFXVtr8s5mOpobnNCbQrgHATXAqNR4IqGuSMbIewa0QRh72NNwCe7n/TNTqi/asTtW8c0PfV2t5Lx/X9+PWRzTB31+OIYH4TtWtgR7ZMbXSsnpv6ahBarEltmxzfbEgveqJs7Sv0JmTXHpk1XJzVfndRybaJaYe3o2kbaXEm9K7xuj22wcVSdvU/47ni80Z7YtmuHt8uMD5v97ODB7asp9jPq7nSbXXOlr7nPVy0edlv5aMNqcwSHhK/MWpO+cMnsOQvmzE1dumTl0oWZmelXEZYuXHLJAPChnjs7P21ZRibwR8qcBeu+W3v8h6NWo2n3ju2AEcAKWcsygRsQVw81WbSwIkJG5sIlV175xQvVg2eDwrobLdrOHqe6P5O8gNAuFRV5WIydA4LQjMVGiwQc14PQ5tTr33rrLW9v71dffVUem3Irr+FQx0kQYiUGa7++d6rLzguMdEKIST061qa6lnAaFWN+L/+WXz/72IrkwC0JjRG2xiM0pLAtruG2+PoUdsTV5bAl0WfNmJpZ47yzxuFa87uxtRBWj62JsHZ0zezkmuuTvTcmeW9K8tqcVD0nsfrWBK99oxtsHu6d/XA1SPndCY22jVLPgN0V33BnTD2EXdF1dkfX3juq9r5RtRB2R9ehs8h3RjfaParevpF1EHaParQjuvnmmLYbYzpkxwasj+20KQYDXN9dI5vjp53astYrDNtjGm98vNWWmI6bHm21Zrhv5qiQeWMHDQtupVh02tnWqo8vBhxOOq5eUc7q8i9YCluFtF67d9X87Ompm79dvn8eQAOgQ+qeqermjt3TUvbMSNk9K3U3MMSMdO0nDgt3UXCBDISMnbMzds5FWJg7G/lxe9mw5NDcjAOz0/eqvy7InZqy61tE0vZMT9mDZ6kPJXyjrfecAlgwf//X8w9MTtn/NYXUfQhT0/ZOzcidunjntMW45k7N2PUNhYW7p1BwYyAOU+mlXO+lXlEZ1GFW5r60lfuXrNq93DekhUkxGm0GMFtRUUl5gIOXmNGk79mzZ41GY6+2dTPGhm5KaLY5oYnGcsRmKmvtjPPRghpH4lY1NNyS0HBzYsNNSfXXJ9dfN1q9Ir41QcsfQ2tLXWFnTG0wEthp3WO110bXy46tvwHAJalp7rhWW5Oarh9Rx2NZNK2xAKoGmACkyE5oDgBEeAJXYJ0NiU3WJTVbk9wCYV1Siw2JzbbEN1HNY3H1d6q1vaKAfrQuuvH2MR02PdoiNz5wQ0xo1ri+c56/967OtRT7OXVtst2qOvqyq35FTarfF8tl/HAgtPPrOGPm3JTURYAdSzOzUlMyFmkhY8GVXhHSU9WjSTyuwBlpKenaSSiqwWPBvJQVS5Zv37pj367dy5ctnTtz1vx5cxalpqUsmJc6bz5uQFw7G+Warmq4mvqnpyyePW8hWTjU9S7kuNzhNJsMBr0KO06fPuVUHCFdwmjRqFzDUeGEHs3Gy4SEBC8vr0mTJknAcesCDrOmOE0O9ZiLatW81UXmDsVCaxHsVqPNpNMXasdEGfsGtp/w5PCM0WHqdAkGeQkcmq93haYIfJIWwvr4lqvj/bLiO2fFd0RYmYDQfmWi38rEdmpIarMyqfXKpJZZSS2ykpohYOy4aUzAxoTgDfFBCIAL62I6b04M2flEt+yYzggbojtvHNURYZMWNsT4rYtrg0FndnyTjXENN8XVx7hzY1yj7LgWq+PbZCX4aU/suCreb018mw2xLTfGNtsQ1+TKw7q41llxod/FRW57ovfmsX2Wje4/JXbQkPYtyBOv5ofjIuCAIqDp9RKHxazYug+KytyYsSxn0Zp9S2dkTczYPmfRjlkI6TvmLNo+L33HgvTtqQiI46dLhXlq2LbAHVIRFm1LS9++MO1SIXVbOq7pOxYt3JmxKHcxrogjJWUHQhrCgp1iSE3JTU/JTdOuakjdqYb0nekZWxcuzbkYFm+lkJaxzR22Lly0jUPaou0LFuGNdsxJ5+v2eUics3Y6bl+4Li28b6hNsZodFrvTYTZbywMctE2fJnc5z5DQNgvGRmpTdcRyKo95HK2i/gtOIw6Mb7Ux1m99nP93if4rkv2zktQpP21qDOktwRWrEputTEZokpXcaF2Cyidr4zptHBuR81TUhuTwVTGBq6MDs+O7bEwKXxcTJIbsaDWsi+24KqEN2FVjuWZgp02xTXAF+2lApLnm51SN4F/32XJNrzxkJ7TKSuy0bmy3rOEB62O6rYiJSk3qP2H0nf3a11Kceeq+KNWQZtdOJ7aYVOe2hvIARyGwnaIEBYelpmUszVyZuSwrNW3xgvkLMxevRFh+hdclKxCWXuqarp7Ups5ZLFq4FPEF89MXpi1eunTZsqXLMzIyVOcZGRmLFy1ZuFBFJMuWLctcsmzp0qXXeNXCiiuv/9IlKxcuXtkhMAxcpTfbigr12kl3isNuRbBaTOT4y2K1GyxW9OhCnV5qwoolsV9HRERUr15969at0oX5rQs4Cg06dT7Fok6xD+g3kI5SNBitRQazSTsXBB3SpCuylxT1Cw3+4NmEb58cPH9c1IKxCJEUUtUQQSFtTETa2K4U0sd0TR8dlZZwR1r83WkJQ1IREgelJN0xP3ng/OT+80b3nTOm95yxvWaP6zF7XHctdEP8m9iec0YPSn3q3pQn75k7ZjDCvORBcxLvwBVhflLpkNx/ztgec8aFzx8bmjI2KHVsbC3xIwAAgABJREFUYNqYwNQxIfh3zriuKHPWuF4IiMwb2y1lTNeUMeFXFeaP6Z712mMTH+n27bDQlPj+mc8Nnzxm+B3t/cyFeiudnKL5NVcBh9NCgCPfpK6PKrIZgqJC/EPbNfSrG9a/c+RdIf7dW/ipoaVf99Z+kW3adm/XNtIPV/VfNdEjtHaFyDZC0G7p1rlNZEDZ0LZ7YLuoIL8ewf49Q3DFv77dOreO6OTbvXOrqEuENlEBrXsEtI4KVK/u0LZ7gH9EQIeungGJCH5CaNtNDX7dOqJW7tqqNW8b5ap8xODQjpF+QVEBfsH++SUX3As4yl3DodPpSDAhTnv3Aera1vWa8tQ9c8f1njuup8Z1KqdprBXGQWW2MW72Gx21JLH3oqQ+c8f2mflEn5lP9p7zRE8kLkqOXJjcdf7Y8OlPh055JnTKsyFTnwma/WTQvHHhC8beOXf0XXMT75qTOAjX+cn3LBh9L67zku72CPMT7543eiAYSWO20LTRoQuTwykgjpT540LmacGdIVwNY7umXnHAC84b13fhk4NTY3ovjh+4IP6OqUl3vzfmvnt6dQBPOVUPL3YtWNXDQBQdAIfg2vwSoUtoxIKUhQAcs+csmDZ9zrLlq4A5EFLmXekVSGL+gvSyV+CMxUuWZy5dmbF4WcaiTLoC3MycMXdBShp+TU2je1PVE15T6a7Ua7xq4arqn5GekdWuYzA5S0PLqZ62MeQmB/Gad3Obw2oyW9UGtSty0WiFEx2SAtiBTl23bt1atWrl5eXJRaO3LuAw2lXvyEazQa/Xqwu3LQ6jwapNpSgXDPp89bwjbVG3w9E3PDysnW+/oPb9gv36ByG0pTBQDb4U7gj0vSOoNYVBga2HdPYd6t/uAT//B/za3effbmj7Nvd2aH13R4RWd3VqNbhzi0GdW9wZ0GxgoBoGBDXrG9yiZ3Cr3qHtegT7du3YLKJDs55Bvr1D2uLaJ7iNGoLalQpqYqs+Ic36hjTpF9xoQHDDgUFqGBDcqG9wsz4hLdRfg1v1DW7VL6gFytdCiwGBra4wDAxsMbhzk3s61I0Pa/Kwb4076le7D5WPjFK0XTwuwOHU/HA4TbSID+it2GqhI/Es6oEXxgumM+f0J81KiUkNOgxMTerw1KrNwVu1f3XuYHAHk/HSwWrUHI6VDSYt0OFeOqdS4lBDscOhx9juUoFc13sEk3t+jQPZbExC8HiuWfWvajKr+s9AY26KnDOc0TnUM9tMdiM0pN5o0OkMSvmLRmkkRFADcZPJVFxc/ND99/YI6dQ3uD0CuA6cRgw2KLAlB2I2Yj+w3NBOze/p3Lh/SONeYY17hDfuG9p4cFCjuwMbDA6oPyCkbmSET2h3ny7dfbp2q90rrHa/kLr9glr16tiyl39LXMHVA4L9+wW2693JF9e+QaVCnyD/vkFtBgQ1AYPdGdjozsAmgwKagYFxVXk4COyncqDGhBd/vSOo5UC1elcUBgU2v7N93WGd6j/m5xPjX2+Ef8NHApoPiWjfsX1Tq2IxU+OpytOOjmpVl+JeDnDkFxRFdu81ecq3a9ZuWLU6e1HGsuUrVmdkrERYfIXXxcuBJBYuWVb2mpK6KC1j6cJFS+enLaJ4+sIlc1PScV2yPGvFyjUZmSsy1GPtVy7NzEpfDESyXLv3mq6ucOX1z1gJwNGtZ/9fT563Ot1oAgMqfYl2HrbDalUPRHp0+ONySuW6ktFoPHHiRLVq1fz8/IA25GH0tyzgcBhtqsyyk4ERo3SLg2ZVbG4PQwV6fVGxevzSuV9Pqmctmp1GaFEtmC1ODhazg4PVbKNghxbWQ9VqAeMxCnoEPTo9gtNgtBvVYDOZLSazyWLWDiKx6ZzmYqtB7zBbVecDNoPNpLcaEQwWs96qhhIbgpUC/jXgJ6vebNG5g95kMSIgv8FiRbEIZrMRfxjNIOUKA0rQGc4rtjx1j4DhpGK6oJj1xw8cs9pd2lc91xSAw646/nJox7iZ3LBDXYZmLtZbSsxOvR1NpRgsTpPWVFaLQ21mMwWnS2GbFbMa8K/TqgELu3b1CHazNuFVNti000IBHk1WNSCCf1Wji/oIpytoT7RoJfDRohZnqWCzlwpcvtlxMRidrqCV5rQ57Ajqe6nvYqZABWgrDpQSvY7keEFB0WV2qYjrRsno+utvvxssqtN4A/GbWdG4S2Uwu9liVYPNYrEh0WxxqC2L1wZfmYoMjqJCBaEEoMdhLlGMRYqh0GIpLFAKz2ghTyk0WQtt4DKLQdU97vNpjUZwjln1dqdyjovZ3CwHTjOarOCuYjAY2MliQqexms1Wjbd0+NYIRkuJyVxiNumQF1nMFpvReqUBQMusz1OMeUrxOaXwtFJ4VtHlA61Z1HPoVZhoI8Chjs/x6TAYUM9dLw9wnDl7vl//O0O6dPVv39nPv5N/+wCE4OBuVx4CQxC6BnQJD+wS7nENCe/WJaJ7aET3oLCI4LAIxMO6RXWJiOoUGNopOKxTYHiHwC64BnTpGhAc0T4gBEUhfo1XCldV/7btQ6L63FGks6r7UEz2wsJCPkuFfH9hrNWrd1+TTV3goTOZJeC4HoQOsmLFCgCO++67j2dYJN2igMNB+tHpGB0X5zSrp3pCiBusjhKrnXWnukzN4dqExxJNcQomyEvIPIcmIa3OUmNmK51MqwZ1i5riKtZl9MSDrHaHWVt86crssOqgGErd6KRt9ErpKqjncl8s2UlBqI9DEW+5koBKFEFfKlaLLd9hPK0Un1VsNqc2dUJGAs35uwtwODXZrzPbCKiZVU/nTu0AT1t+4fmL54hwZdwv4RQaQAy2MsF+VbV3fwLFtbHB5moTbnYxp6s9He5HlTr1hHN5VM9VH8fFtuXMBpOR/JjbbA6DweRwP+4yi0Zp6EOzKmaN+IlOpcx7OdzB4yvbkGgzq65jbHrFoR197TbUqBuJGClqH8mkGlUU1a+/mRYq2U3FTpuReY+4tzQGM2uJjjKtgWdh4Ga1afmd7m99VZ9L21OtuUhTHVfYKGAwDu7P11jR1SIWGowDH5qc5azhwCBBtV+a0KCl0u1XGWxuCeBxNdnVI5LN2olCiOtsTqON/IOoVhejVT1j3qr1FABck7Pccq7qavtT9S822mkNh8loo4+intymcW1BwYWCovwuoeHIcPJsnpxSuS5axunE+OGtt97y8vL629/+JgHHrQs4CHMUleRrgzxHaECAw2J1AOyrh3sqequVZLJdk1/qBpZz+aV65CU1nBjUMa7F7Bp1m2za6NoFQZx213narFpJLaruwS2K3aTpB7PdpFMsmsMLNdGirtJHcC2doxI05UFF0Qy3XQzOUkFdMW13KFcaTIqzQFHOK7ZCVeYb7ZYSDKzVvcRWmk2g55oUu0Gtm8NJb6MCDoeTzLMFGGZrZ0SVwj0Msy6Lgeylr26963AjqtJXq2YM0lYUai9uAWxUZ2xcEyMizrNdfEW61ylcXbrWVhp5uILzYrCRqnNVTkQwbgJysFrtJpM6V1JSordZ7JcBHHzmgjoG1Sg/P18Roa0i8phNqHlpwGFXUywqDHSxrgtwmFxuK1Bvm8Ot2w0qoFVMJbaSfBebqYHayqJpdDU4LtqDVJOQZjxyuhrI6WomPNGien9Do1us6qZUBzWwcHD8Hwd1Pg48o/mEoA2vKnSw25FYqP1kpda2ag92EJR3lLcttkSn7gwymdWb9AaLyWynjmJzXsW1zFjhIn+IXGK0OU12V5zMYK5zhQA+bOUW8ieC7Srrb9Y8pOlMLp/l0Hya10t1W6zd7mo9v46dXNDKIbdPVDzaoMioUaO8vb2nTp3K9ktJtyLgMBrNBDmLC4u6BIdAVpEbWg/55TIhKI4/WBLieZu6zZ3labGuiAqxqpvgFbPRxDmLCopJISHoSooAR2iS1XXGkmb8VDW3zUIb6J2aLNeX6DRlVkJWO7qdirWaLeKVn0VvR5u1PDRcWbJfPLnC4dK+ztLq32VZsXHLlGm0P2orN1ksNtWMpJFVNQCTPscg1cLZrerEl3bKpdVVmeKiAnXzshZXJ8W0q9o47hSnw6a2odOOJnA1nUNdlmOzuDAQfkV+daOgzuUKyaa1PC2pYxxAkx20pV5LdCh86rTzBg4MPewxTk8zm+plQTVK2CwOu92d16IzqaedGa1UAFSvygmFRtWiZtb8YNptaCtiNrv79QkpqrON2pyJm28hLO3qSVT4ZPjRoap2F9RxaphXPW6ZvCtaqfVohKeUXrFf3suVPbNUNDOUsvT8YX8s/ynlXVVHKUA2xa4pMIPRTHhFVd52J97XqmUwWy7O41zIL3Q4XRFqE45TTpvWLmx0oTwIeoOJC6FiCS4ixr/i6gZtiphfueLrJTsj/aszqHtS9GZL1+5RaOTzhSXSwlHhRMs10AW6dOlSrVq13bt3yz2xtzTgQA8rKTZSNDAwmAQiaWUBZDgEY/tV0+nTp1lvXbhwgREue5Z06S3VT0MRsynhAORUF5Yr6vJyu0aMFXj9s1thW86fP0+bHVhBMpRG5uLiYvqXzi1EUVQy6kaRyiIAJmpwVBItQO9FL0LppKLwduIbiecRIDOvfqDGxMuKQArvyB48qQ2pKBA3PiL0ddA4XBp9uLy8PGphulFdX+xu/ypIJ0+ePHXqFL0IGpBbDHG8Ar0LcR29HRoE70tMRe+OdGoKRXCVKDIStR79RK2BW+iT4RFC93Hdjk/M7UyWm6pGqDYB94KCAk7kphNxJzUUZeY3/f333z0KBPvxr7hddCNLnMzPEhtE1Ebnzp3jNkfb0hOvklhwiVY6FVWcv5DHI4dOQcGE6sx2h1SG14PwoX18fG6//XbqO8xOkm5FwFGQX2JTrcxKgwaNKCIwxLUCDgYEDCYgs2zqSginKEFIe0F2i3qU7kVOYlMWRlQ9qATK8P333x89etSDv1mpsMJgBUmKgRVAWQeXN5LKjndFdcXvjpaheiI/BDQ776P3EqEbNQ5BKKSoRhG3LkEi7kWBP/74I6lJagfyskUKBo1DpSHlnEb8mfgLUgNyCVVwRMVIjnHGJS29+An5+UXwXiJKY3WLCIrCr/yxjh8//tNPP+GLMCwWPxzVAe1MFaA2RB2QmVKqLFajF+TqqX453W9NW4foVxEHM3AnkEFM4mE1xI0oCiXQ0mCxk3oMRXhogUcw44mDiooCHMU61U8JEIbebFmdvV5d+4wvqDrTkVTBhN6xZ8+eatWqdenSpWwnlXRrAQ6rxalNNECBKf36DaAZljI99s8DjldffRXAFtxWq1atyMjI2bNns8YiouEOJJQICDBIJZEkmlsYYbCAo1uioqJGjBiBbBDxjB5YO9KIliQjSr7vvvtatmy5du1aUkgk70ipV1ZvpBOc6TV37txZo0aNahoFBAQ8+eSThBJYTYpDQNyC92WIwF6zSNzzXlO6klpFaX379k1ISBBNGlQNbjHKn5OTExoainEJajJgwIATJ05QPXlXmwiMqhStXr26evXq1IYRERFPPfUUcQWu1ES//PILvYI4lyy+DlAvGMNDv1KTotHQUD169IiJibkk0OGdvQSF0YZUkz59+tCQ/fTp01VQ4FKV0DIMNcoidQ9rByMGah+xAQnNo3+B6xilod2OHTtm04g7Pm4n5mf2Ewn8STbOP9tDywUctK4ZgEOnTrCpB9NDEAJ8SMBR4XwFZpg6dSrEGqSZ3BN7qwOOvPOF2lI+Nb5ly9YKt3A888wzEPoQsuvWrfvrX/8KyfuPf/yD9B9EDAsjaE2CER5TAx7DGlaiPBuC6969e3/99VfOA6HmYZKlSXQIU4Ceu+66C3VYunSpxzi4sogGf/y+69evRyX3aDRlypQmTZrcc889tHHjt99+47sAyKih+EsRnoAOYNFMYIKgG14fKIT06759+9BEdDuKpVZitUHwBf+mpaW98847yJmZmQlh8f777yulp9sQv/oR5/U32Dmdmzdv9vLyAmDav3//J598EhgY6O/vT4zETMUKT3HPJtC/iDNPIoXiuAtNR9NS1G7gJRTOsw/IyTYntCfPWH377befffYZmB9tDuj2z3/+U6nCS/Tbtm3boEGD2rVro4MMHz78+PHjNPnIDCb2FJ65E83j9OLEUWX9O73xxht16tRRtDkUgheke0TeY8SD5wLSeXt7k7gQTWsVBTiKdSVWbZX36//4J1k4JOC4TgOq+Pj4mjVrTp8+nb6gXMZx6wIO98YOJy1UpD0FFQg4XnnllaFDh0JU0bD75Zdfrl+/PmnB3NzcpKQkjPyeeOIJ0o4Q3A8++OCqVaseeughIAPoPCi8Bx54AInLli2DMjh69Ojf//733r17h4WFTZgwgeTgu+++m5GRAQH3xRdfzJo167///W+/fv2QjQUfARS81NatWzF4bdSo0bZt22jIRbWqCqCbWgAVg7g/deoUfQI0Ef6dPHky5XnttdcGDRqE4TXgCP5FNrTt9u3bBw8ePGDAgPT09J9++mn06NF333032pCwFxqnb9++aE8qBBrirbfemjZtGuLQfwBeyHD//ff/5S9/UQSzNiMYWuzSrl27hx9+WLSjkL6pgoMVMMnOnTvRaPi4BA6ys7OhRCdOnEga66mnngoODgbLgaPwFmCMjz76CE0Htunfv/977733+++/P/vss2AwgBV639mzZ4MJIyMj0f5kbYIWnDNnDiJZWVnjxo0DmEYGSNUdO3ZQHdB6JFt5EUybNm3efPNNPA7pVdM4hI756quvAr6vWLECfIU2PHDgAMNiihDCIE5gmwReB4mM78mKw/iDbqHZGRTOK2DQFCiHCkEimy2RGf+iI7Rq1app06b/93//RxlEsHjtgENvVLfwAGeAm/07dVaNHFaHXDR6XYzoVmunTp3ATuiY4gI1Sbci4ND2Kag4o6REP336TIvFVnrP0rUCjjFjxkBB8jTw4cOHwXmQaBggIoJf582bBwVQt25djOAhgDAGQjrG09CRkIAY4nz44YePPPIIUAKkNqT8sGHDoCahJJBt06ZNKPbee+997rnnEIGiRWJsbCzURr169WhsxNsrSCxiCIU8a9asqdylGx6imQlytkaNGmfPniVFBb2Fl3rppZcw3AzTaMaMGRDBeIX169fjFapXr46me/vttwEImjVrhvQXXnghOjoaEdz+8ccfP/roo0Bjf/3rX9GSaHN8gscff5yaC0CkVq1aQDDAHJDszz//PGkCWvnB8h2gp3HjxvgKDC/wNenXqjlYz8nJwevzmgNQ165dExISwF1BQUHADQsWLADAQp7vvvsOv955552IA3VB3SKCJh05ciSG4z4+PqmpqQcPHgTABVZYuXIlGJKQGT5KYmIiXj8lJQX5fX19wXIoOTAwUFyjgCf+/PPPu3btAiuiZFRMqaoT2ND3eIsvv/ySB6YBAQFAsVThzz//HL0MY4APPvgAgwc07ObNmwG/gO8BTcBvGCpMmTIFwBeoi5ZhAVuA07p164aG2rJlC9oKHJucnEzlo7XRGmjnmJgYgr9gdZHr8DhkRsvjQ1zLeKo8wGG1q8s1yN9a7/4DrE6lQGeUgON6ELoA5EzDhg158vdPYUdJ/xOAQ1di4k7WokUrEj2CyetaAcfLL788cOBAGg9BB0B4QfJiQAlpAgFNj4OEgiiH7IYsw69ff/01JBpEGOK05gPq1svLCyiBR7G4Kzw8fMKECRBkDz74IE2ox8XFQfxR5RGHDiaNyIMn/FtYWIhiIQ1pZEYZKn39Iy80wbAbAILnQUg0DxkyBPL99ttv/+GHH+hd7rvvPgh3tBLeZfr06ejA5DmYZPehQ4cQ37BhA2s+XDHC/uyzzxC55557gC0g3NFoQHWklRHHGJ2mSHj8QZEnnngCcIRG9tRc5c3uVwVC3cjCAfzKG3PQhsCpgKdIpxk3ZIP6BLcgA36FyqQX7N+/P9iJ3qtDhw7QqdwOuAX/osXwdZD/6aefBv+AaQFt6XstXrwY5ZNTdp4fRKsCkUDgRkVF0Ypd5c8sfrwRhBchNwnUT4EtWrduTUbKli1bfvLJJwCd6KdPPfUU+A3jBFrcg18BTTAeAKr7z3/+g5wjRozAXX369MG/S5YsAaIF76FX4pYGDRrQog20CRDw66+/Pm7cOJSDcYgibFb/5ptv8CA0O8oH1BNNbhUFOCiYbHaycBQbVKdg+FcCjgqndevWQXqDPUjkVs3F5pJuEOBQfTbaVScQmoTtRFMqAk9cK+CAjMZImqQJBNn3338P2ZSVlYUxIjQZ25Yh7N566y1IIkgfDKnpsB+K0ww6xNOOHTt+/fVXDDEh1Jo3bw4m/uKLL5AT46QxY8agECiVl156idQhntuvXz9FWGpAWuTUqVOQZbyGg0bzlfiBXV5PNDmLmmzfvh1vCoCluLcaQnBDt+FN0W5s1v773//evXt3gCegk8zMTNJtZPYgBYk4hpV4WajG4OBg/Hvbbbd99dVXaMzY2FgapkPjvvjii9Q4//d//0fDWa4VqcyJEydCWaK5cCObqXgHadXsM9nZ2eANce8llP2rr75K6pASAW3/8Y9/oGUIgaGFKR0Q5Nlnn+VBNlgUkXfeeQfgGCzq4+ODovDiw4cPJ5YD2qPvgkQgPAzjeBUCr1TFT+Dbxo0b9+zZkzpCFdwWiFo1adIEWJ+N3pMnTwbGRZysjJQNEL9GjRrgjYULF4KpMHhF4r/+9a+aNWuCLSE3Pvroo86dO4toHt0Wt+BzzJ07t23btrQWBI2GpqPVQojPmjWL1x6Bq5GNZgDB/AA0lM4zOLwQlQcS4soAcZu9RTsPtjzAUaLX0bbYjoFB5NpcOv6qcMKXevfdd9EfgVOrLNSWdOMAB89bAnP06NGrzLD1WgFHcnIyhowQKzSC/Otf/1qnTh1IHAjusWPHIuWnn37CFeJ49uzZNEeAkSg5BIREg/LAvWTtgEBPSEjAuPP06dOQPoGBgRh1obbQqc899xxY+Y477qB5ASS+/fbbBDhoREUz+oicO3dOHP1XBd1JUIxWtm7cuBGdkxeX4PVRWwCFBQsWQKYXaYR0YCyyD+HXPXv2kJcCaMS1a9dCHEN2AyUsW7YMLRwSEkJj+qCgIAhx/Dp48GA0F54F5cr4DGNNjER5Ay0ZBlJTU1E+BqZlQRKvOa2CfWb16tVoqxMnThBuBoaDwps2bRp0JFCX6uNO021JSUkDBw7EK9x///3PPPMMmUPAqwAcBEMfeuihF154Yf78+UB1K1euBHD529/+RkyFbATaZsyYQVqZHgTuBUwkqcobO8m9CkbqGN9XWWMymgVAgWYhCQ8BOrRo0YIsZzt37kTN8SL79u0jKxF4qVOnToTn0LBkMMe/U6dOxV0oAXgXSA5NB7ZEswByLVq0iGb6iG9XrFhBDdW6devx48dT+tmzZwGm0cfxiMOHD6OP43P8+OOP3G6M1URfMorb7Qfv1EVl3O7pLrdLhaZUgkLDLhQVl3W5JqlCCOgcn5uMZ/QdpR+OWxdw2N2dTKczBAQEKZ67Nq4VcEBeQDVC3OTm5kKs04IDpGNMAy5MS0sD873xxhuIY2gIAQTdQOYHSA2oDVr8CK2AYda2bdsSExN79eqFkU1WVhZugUaEEMRInUalUMOIEDdjRNujRw+uBiVCGWC81bx5czxi//79vOmgEhfxiT5PQUBCeFPgjN27d0+aNKlp06bdu3dXtOWfaA2M9vLy8tasWUNLvklwk1XjwoULAG203ReKFlpw+fLljz322AMPPADhu2TJEjQsLbMdNWoUreHAT4TPCAiiGWkBB8nrzMzM2rVr//Of/9y6deuhQ4cOHjwI1cKmL2qxqjleycnJQc3Bb6g2IGlYWFiXLl3AAMCpaC68ERocrcdYatiwYU8++STdC5BBQzEQEBgQG5AKtCmUH3i4Z8+eAwYMAPsBq6Hp0FYAgviVGAyfDGWyzxi0FdgMWhb/gtmgVvv370+wpgq2GyoGZgPLUTfBOwKq3n333eAEoDR0NNLrixcvBmsBP82cObNNmzakP3CXv78/7eJBk6KPgz1wBVzD7cDQaBYAffxEczS4Hdhr3bp1ZK4Dqpg4cSI5jEFKbGwsbSRu1aoVReLj40VTJS31oE3a5HiGl52hO4te6RyuY+gvvYbDYrPSLpUvJk22aicRytNir4d8w0fHRwRqZMBRRdbPSaoEwGGzameeaCdfBAd3UX0bl3L2cK2AA/ACo20wHAY6GPFAcpGigviGkoPwgu5s164dxpGUjpzQbQQFaJ1dfn4+zREgDoBSv359xLtp9Pnnn5NJHCN1DHGgMmncCaEDwIFhKNWBrOvff/99NYFatmxJ1oKqIP1RYRKRGCWjbmgTtEz79u0/+OADen38NHv2bIhp/ApBD+QBrYAmQgqwF9mW8RMG4op7egWjUoy/URQ0YkBAAEr79NNP8bKDBw9++umn8TgoWihUgmLQstAuFCehMHr0aPJm0ahRI1yhP2gPi7gZoQoKDlSJFD+5fgHoBJvRT2glNEjbtm3xE5TrF198AQYAq4NPAGRJaVGcXJQ+/vjjGPEDB/fr16+mRnfccQeYDZ8jLi5uzJgxyAOGRMPSbpfffvsNJWOMrrg9u7z++utAgUj08fFBTdjyXwUX2+LT+/r6vvPOO5s2bQITohEIJeAnDBjQ144ePXr8+PGeGpFpB7xHNonVq1fjBcmLGjoyWhivDwZ+77330CsBO9BnUdTXX38N3YNsP//8M1JoLAFCA2L4i8YR94dTyYBo//3vfxW3t0Aew+BepLz55psLFy4kIfPRRx+JHZkAMbmivyTgMKt+N1yeRs121c4rF41eDyILGbobG5sVObFyKwMOOv2K3G98/vmXdJyHIBCvFXC4e77LhzSkLXsvQBw6jH01sj1fdHXMUoY9SbPOI19VvKDP44n4lYQ+XZFImkCUsCB2wVlZH1j0yehRDfdBUxenqFFhaDVIZLY08LCPnaPTyI9fHPG8vDx+SlmIILqVZFnACyBE+ycy0LdDHgIfVVBwiPY5XhaAavMgGIloEGRDpOzhJuJ8Ij4HLeZFa9MtyEktz+tq0ezMz7wmlHYecU1EfzCi75mqRoGBgYQsgcUffvhhIAyaWtq/f//AgQOB3mrXrj1o0KBDhw7hFYAeaAUMaPLkyQDBFJ84cSJKQGTEiBEox9vb+95770WxaByAM+ASagdaY4T2R4OEh4d/+OGHxFG8Yp0ejXv//ve/8/hHpxFwMK1GArJ5+eWXkY4S3njjDWJacR+E+1SgcheNFukNOpN57YaNdu38OQk4KpxoJfWQIUNEySz9cNy6gMN14JmZ3CPaFM+J+Qo4S4W1HasuSA1WALQknhUhK04+tKKs3wIIL5TJJ2MpbufTrGLpJ9HWyjdCopEXRX4i7xCpRGs2YyOqFVkvWF8iQi4+uYnwXqTnRIhGupAyi+qWNR9FqFn4UCWa+SaPapyuCMtpyV8WKVHWuJf0F15FCC3Dryxqd7wjuc0WOZymkBirkeMvhnrMJIzwmKMYn4nuuqkBGY4w6sXtDKOr5lkq9EbcVdlbBv8KnvTYwo3BK23PIbflnJk7FCLkrY5amIAXnXkktq34jWhihdYz0ak0ZT3rk08dei6vCSCwzrKCPvHlF42qvr8MqlWjd/8BgB3S8df1oBdeeAFQ9d133+Vjm+QCDgk4XIBjw4ZNSkUvGmWxQs7LxWPb2IhKwgI/QUKRuCk7CuSpWdHJMR0jIppbyfm0x11gcTKeexgSqDKVe3KbCAhQEw8LhHjiDClLcQRPR5R5GDaU0idikPwlEc+AjGfNuPMT7GOhz/rVA42R2VyEhlWNREsG/UsK3mPlBH7Fi/BhPSLMIv4RD+4B8zDMYpZmXFuW67h9RAkrZqiCx3Pz18SbkrGB3040dyml59T4vdi7OfEnIWZayKK4XZJ4aBpiWrodGXgQckkjHH1EdAEGzexXjVI87sJzNTep9vIAh9FsYsdfjZo1p10qJUaTBBwVS1FRUTVr1ly3bh0NmUjyyOPpb13AQXMoZNuIjIwi5CEIR0eZcPWQxi2OPYQOH15KcX4o4w8+F4qkiUUjFiisEngpGWtNPmOWx0CUx/0Ih2bnMGrPonPIStwvWwlXnY4mfeiNaExMDubNfKU8molY8cjDcVy1gaZNW1Jn4/eid9T8YKkDPnqKpiHobBQrPwV5tGanb+TQtAUN+q2a/8dCMV2szxVf/ySKuPKrxmOur6zppIvpVHO0pMZ4pc5BJcuEe1hsYR0ssqt4hJi4LZNPw1FKH6TCI3J2482sWNn+Sy7RbhrZNOsOHcJs0qpNe0AcGnqwa6uJCbNamQfQ2poSIbuRg9tWsy5c5FWtfRzaDKCJn0ucxr2P+JZK0AxsLv6ne4Wv5tCUls2jHK458blycUrFURZw2J1qxGCx6s2WqN59gDzOFxTKKZUKpyZNmpBTHLG7Scdfty7gQAezOewmixmRqJ49+BzFCgtXSxX46EsFYcQjrzf+6vhT2vE61ud685vk/8rlN2c5nxhCj/aqQPSFhHYp0etUg4dNWvuvQZUI51fTv3RIbHBwsDi2lFMqtzTgoJ5GeL9l61aEPGgJ9/+0wJWhUsKfBhzXqz63KuC4VfitvHaAxNMZ9BQPCgl2OR61yJF3BUxo8tzujBkzvLy8Ro4cSRCEj7OWi0ZvXcDh0E6KxVVvNHTrHkldkQ4a+N8VuDJUYvgTgOM6hlsScNxCobx20DbnOc/lnUf83vuG4lpYXCTV4DVCDbZt0L+0uXry5Mk0ycj7U6rsRi1JN2JKxWKz2hx2o9kU3CUE/14oyJcCVwYJOCT//w8DDoPJKE6v0BDLbJX+Ia4JcHjsXAsKCiJXyJzO+wdli92igIM87tGMJiF9giBS4MogAYfk//9VwEEijuwcqo9zbdOKBBzXuIBDRBInT56sV69e/fr18/PzPZz9SMBx6wIO6mkEONDfaF7zf30NhwwScEjAcUsDDgq/nzkN0ddvQH/E8/IvyCmVayFyYMPgIz093dvbe9CgQQAZHjsK5ZTKLT2lQkifoAYvpPrfFbiSJHny/60EOOT3dQWSdQAc7Tt2kItGK2Ds6nblQqjiqaeeql69+ocffii6BZJuRm91wGF3OlzHCjgdXcJC2dohBa4kCTgk//+vfl+eQwHICA0P0xsNJANlj/jTJPr7B7zo06cPnctNZo+yvnol3boWDsIc3XtEnfz9FE1wUocUd5EhD2ERXBGnnbS8q4X+5XS+sj8lg0aK27Ejb5FSBIdgSEThXLJDcaImXBRGJLx7Xhyj4KEGkxEZaMcNvw7+VSug1Zbzq49w79HiHkL+zhme00H2Ykfy8FTD7pPZB5ToIbusgVHE/sXFxaKHSjojhh01UpXY+Sn1UvZjRo9AYklJCft0pwex/zS6EWWiKPLZyic00sgDVzrYk9ypcTofrYTCWToQkTd08Y04D7vA8hAr5P+RvW2yz2mxfdj3PH8L/MqH7/CxGoijkjQ24hc0asTNiAzswotuYY+09Gm4SfEg8ZOx1xkwFQXiItofTvyGOK2qRpw0k7jeEOm0IADMhggzIfEevS+dbqoIi/nL6xcey6foX1plVSH9gnmGHJGxz1/ujOTNluL8+S7ZL/CTB9fR16+sfoH89EZIZ9ailuGmw8cq0euoQc6eP0eJ7Tt24O8u6c+ReKITPsEvv/xSq1at+vXry5aRgKM0LDXoi0qKaVtsW792tH6bnHOQMGV5SgHZWLpd0haCdMg1FEj+xOgp4rEgrBFJBrGe8xiRcPm4Un1YjqMOIh4iOYt0EiukKvBSYp15UTopYHYQTo6ruc+IWoF+5aO5SMPxmSaiH3FRmbE68WhnD7fldHScIhw0Q7egBHIsjYjom1zRDs9UBMfSdEIpD4zJVyYV6zGGEA/M4wgRn+shun4XAQG9C/skpuNIWAlRZfhUPIYU1A54EXYtz0+hU2DEaV3Rw6xocaW3YIYhz+6iQqKSqblE7hLVmLg8DU/nf8EApC/Zlk662XXEhoZCWH9ThHsBsiE/grh7HCzK7Cqmi1+B1TMv2i/bL8DYvGPikrvTr7VfuLmdPqUI2thf+JX0i7KuxEUAeuP7BT+L29bl718bDuHbMUQTxyQUSO5RiqQ/P3bVgCa1/KJFi26//fY77rhDNosEHKVIBA2BwUHAHzwegvziLkqjpXN555GB5SD6MP5FNkToV1HAuZz3aY6caUTFEeJLHleRjoH2Uo9lsttQICQ+y3cSrHn5F3AVhT4JCLoiDyqAmvO6V1YkPIRFXB2AOhiS24qLdW5xaUQVaCQoql0IQw0bWRBBOtQTHaurdSqHTmcgl/DaWyhUMpePDGaoJLtTsJQY8ESkA2K5xaud4shZWMi6R8e3aNYHG64lJXquGP3LT8G/XBrlQQoC4riKz8J4VdME6lvwmyKR8yA//nVqZ+uQk3u8jvgrvz4qwM9FfvqXqiqof51b05dQQ3FzkcoXT/qFxvXwBU5Df7s2QhdsS2Y0o4CKVN6hb0Fvh0/pxisXP4SYjlpxNZCHjOqus0NLisXdkoQhyELGIID6hWgzwC0YKzPgoH4BrkM2lZn1ejC2eIQEgd3y+gXbJwhYkK8IQiEV0i/cIM/KLISK4BNfsl8Q15XXL6gBEecvYtUqXSn9gvKjWNzCz7JrHYLNTjR8QpyuSPn1xG9o0j79+iJFAo5rJDbjgZnHjRtHCzhks0jAcYkpFVqh3aRZ067dIhDJLyx4ZPij6Ie9+vQOCgmu37DBXffcvWHTRvw0a87s/gMH1K7jA3TSvUdUZFT3D8d/hPQ9+/YOvf8+/w7tQ8PDwrqG467E5CR0ZjzCz8+ve/funTVCBP+SgklMTAwODu7atWt4eHiHDh2GDh26Z88eFIUCIyK7oWQU4lO3Dh43Y9ZMpK9eu2bI3Xc1aNSwQ6eOUT179O3f78GHhpHjEGTuHBiAlGYtmj8w7ME27doi/dD3h59/8QUUEhLaBVXCXV3CQvfs2Qf9N2XKN0FBIQg9evTq0KFTcHCXjz/+FLXauXNXRERk27Z+eAd//w69evVJTEyGaMvLy3/00ceQ3q1b9y5dwjp27Ny//0CSnjExcZ06BQQEBLVu3Qa/osz16zfip0mTpoSGhqPk3r37IkPDho3nzp2P9O++Wx0ZGdWunT8KxyNwy8iR0ZCMkJ79+g3w8anbs2dv1CEwEG3Thb7Rww8/GhXVMzw8AvmRiFvoERMmfI7KIPj6tkXdOncOnDz5a6Rv3Li5T59+zZq16Nu3f0hIKPKj8pDIkOyDB9+FbAgoH3UeMuRuJOInZMBDUXnUtnnzlni7zZtzFPW08cmoPJ6LR9Cz8FCkowIoFpXBT6gYqodKUm2RiMLxjniRWrV8UAfCLqNGxXTt2q1BgwaRkZH43P369Ttw4ABk0wcffFCjRg0vL68BAwaAQ3x9fT/++GPkz83Nxb+4BY9GxVDn6OhY0mQDB96Jf/HKeAoiQ4fef+6cesgcMuAtqJ5NmzZHI+zatQcy8IsvvkKt8KHxuem7gwHABmAG8EbHzp3AHoiApZ974XmwDY16wc+t2/iCycE2yEA2s2EPP4RO0aNXz4CgwLr16w2+a8i69dlIB4uCUcGu4DdwIxiY+sWuXbvuvfdef3//0NDQsLAwtHt8fDwN3y/dL3Ql6DioCfoRnosOhWrs3b+vovoFHnHkyNEXX3wZjAH+RPOiAcHtaCiodjQLGgrtgw+KL4ufyusXycljADVOnz772GMj/PzaoyiwE/JXVr/AFelr1qwjpgXPIBHviFfuFNB50pTJaBBIsN59+zRt3gySza+9P5rrL88+w7Yfukr608TmSUTA0tWqVQPzy2aRgKM0l7j3p7AxlgwVCAVFheKAj8Z85I+PJpJ5SIe+itsBU3iCmS0cZGETT6xmsyoN9fjfgoICdRbWbeoU7cmoCUoTZ6O5JpwCzES1PX32DKejYlQrfkEadZFMFAfc2hRAAY+xiCCCecgO2UdjaB5q8xiRB2FsAOBxWH5+IZeAOD+RrAU07KbRHt+LkRxSeDxH6agY3UvKm4eh+JVrxYWLo3lxcEljU86POA9VuVn4XfgnLhY3UmXYSMCVoXYTXwGlQaNz0/FVnEPhuRgfHx8AkSNHjvCuOahkt3dCxc0eRTwgxgtSBfAUfDVuUhpwi++LGopfjWpL9aT3FTkfrEJah5iH1jMR74kHcECRsxWNjHlgRWTzmMsgMwNeli03HstcLtkveEKHjYt4nN5oqKh+wY3AH5pNWdRu+EyiBai8fiEaRUSjQmX1C7oF1Th16rRojBFnT/irifOt+PpoH2rVM+fOSpXzJy3l7k0oYPhz585h/NCwYcNKP4hbUlW0cDB0gFwjgUvzneiW1A+pT4qzxZSBJ7PFORT6FYFEMM8Ki2e9UiIQBq0SIFxM+AOVIfhCq+R4OSojG6oS2YrxE4lX9o5K9YeQ5RTKj6trtsjpMvJDzJF8PHPmnKhfz549b9OeTSKMRC2ysfSkwTTLyry8fI6TPZlmNEQ0g8dBFCKRpDl0JxVLlcGD8CthCFE0k6Wa1fYvv/zGdmauCZWAV6B5DZLRKJDvomqzVj5//gKVQO9L/3IGto2jJpQB9SERT62Ep/DrUzVQJVSMX1MshH7F7VRtak8a3NNqGLQRtC+ujRs3xniIoScfHUyzLfSlKEKPoLfmlkciFU5VQjuQ+sT4m3JSlfAKBD7EckgP8QQEK2my+bGuIhQOlgYLARDwEmlxzQSzKy1+Ais6lItLMkkog+GpBcrrF8TMItuTdqyofsG4jTiNtDvPmKBZKANZLy7TL4grcBdloxTmzErpF2B1KhwMQDVBgfS96MviQ/N4Ca0kTr/yWmBJf3o+RXEv3MnMzER3vvvuu2WzSMDhSbQyn4dH4rhNjPCcsbjemye/PXwGl7KLuI1s4iBPXJ9oMpnEVWOiDOXySYKLYzsewLGcLeuxmHGGWE91OG5x0j92myu1sECHqxPjY73FYXclmow2XDkzQkmxUd03p92IOCVCreBfpHNpHHBvcZHBpXdKBySaTXZkMBqsYjqejkQ8Wq8zizVEORTBXQX5JVQNZPYoHCm4l+/C6+CqKzFRfgr8RPHRuAXZxESbVX2WeCNVm9oH6agGMnhUjx6NyqMcvAjfThEe1IrLWhH39/evWbPm2bNnPYwBJMXwRNyO+vC3KCrUc5n0yvwdPRoEP1Hd6BOLTYrSwAzMTmAwcdHSJXvEJT3w0oJr0don8hu/Ke9GcduTLt0vLsn/zO3X3i+oj+Hd8bm53ehzc8tcSb8QI7iX2aay+sWFvCLidmJ4V90ELEhYjeEFtyFgBy+OsTmkQ6o/P5/iHmNYx44dC8AxYcIE2SwScFzCwkHClDaRkpBC32PnHGRvpHEbCTJR/FE6G3XFPYEk5hRhD5toUibhy2zK8pfMLeJSVp6jKbW7VVh4z3sKeC6WasWCWBQ0riGUZntXJZTWBA5NdanDIxoQl5jUEZVTNWwo2gpKyC96MXWQZ3M1HEZatG8fKeqIilaVWrVxs6NUTtVOUGSguGo0tl5MpzieqC7PhBrQqQKUnoXxGf2KWlP56uDS6UpRlbdWZ1UrKJrCsDshrKnmFKc35eeqUljT3+K7owRVnWgtQM81qzMBDnovdaW/3dUy6ghSS0c51DLq19SpmADPohTkBxChe6me9I7qeNS9fpC2IRiNRnACLZkMDg728vI6ePAgLyBlkwANbVXVRbrKaqeWpBV+qAm1DK6c7tQUEurM7a8aPOyuOL07l0YmCvHAQtrUwEAcPxHsoLk5donNhj3GImK/IB52KJc+GJN2WFyyX3B9xC7JZsgK6Be0NpNmFa2uGqu2BKF96JsyP5TXLxwOF9fxvZXeL9AXPPiQ1t6KFiBxw7Oz9HHZ8nj6ayHG09Sdt27dKj2KSsAhSVJVkU20ysxisURGRmJIBMDBMsvD8YkkSZKqMvFe5WPHjvn4+LRo0YL3LUuSgEOSpMoksmSwp6levXoBcOzdu5dxhjzeSZKkm4jYL05KSgr68rBhw6R5QwIOSZIqmQhkuHZkuP3M3nnnnTVq1MjJyWEHUx7e1SRJklTFiXprUlISAMc333wjG0QCDkmSqgTgIC9Y7CbowQcf9Pb2Xr58ueL2V1j2tGtJkiRVZaJxgp+fHwDHDz/8IBtEAg5JkqrQeIiGRGazOTExEYBj2rRpBDjkxIokSTfjQOLHH38E2gDmQBcu68ZekgQckiRV2niI976+/vrrNWrU+Pe//804w2PXqCRJkqp+j548eTIAx7hx4xThUBtJEnBIklRp5DH0wb9ffvll9erVn3/+eT7xVUINSZJuLtLpdEOGDMHIITMzUxEOyZIkAYckSZVGHlteIZXmz5+PgVFSUhK7gONDzCVJknRT0Pnz52vXrl23bt0zZ86Ia8MlScAhSVLlEMsgXsNRUlKyc+fOWrVq9enTh89SUaRJVpKkKkm8phtXq9VKa0UNBsOKFSswbHjggQfQx8llMPvvlyQBhyRJlUPsfoOBxfHjxyGqgoODKYVAiVwxKklSle3C6KQeu9ZHjRqFXvzpp5/Sv9J3nwQckiRVPtF0CWQWC6ySkhKIqqZNm6qezB0OAhzSHitJUhXsvBQxGo3iSUAYNtStWxe9GIMHSuRTkSVJwCFJUqURoQrF7dqcEr29vWvUqFFUVMSWD1zlojNJkqoU8UQnjweQgq66adMmLy+v4OBgRbNtUDbxVE5JEnBIklQJJO56ZbEVGBiI4dGhQ4d4cbtoApEkSVLVIR4J0OGLiDz//PMYM7z66qsEQQhqyFlRCTgkSap8wEERdmGOyMiRIwE4UlJS+CdFzqpIklT1iC2UPHJASocOHapXr75p0yaeZ8nPz5dtJQGHJEmVTGy3YDyByL///W8AjjfeeEMEJXJnrCRJVY14NSif7Zybm4vOGxAQUFhYyH32woULsq0k4JAkqZKJRBKdmcIQZNq0aRghxcTEUIp4nKykSv9iN+1VUsV3Xj53ngCHXq//6quvADhGjx5NYwlkoJ4rT6iXgEOSpKpIO3fu9Pb2DgsLUwRj7M1l4WC3BPzvZXYGsl26bCFK6fUriBQXF19muEm30KYAileoCxNU0nbTXh1ojusbrpausvwqaN7gFd/MZt26dQPg2LBhAxkmie0LCgqkWJOAQ5KkqkiFhYU1atSoW7cuNDFpTValNwVB+Iq7BK9kpMhvJygjVxwgQ8QuV0IESq7DmJKU900aJOCoeAuHB6I9fvx448aNW7Ro8dNPP7GLHUXOh0rAIUlSVSbILIyTILZ4LHXzvgtgE3R/SUnJHxpFHAKJEERxL6r9Q9TFSEXRpqIqWtA7buogAcf1IHIkSjRlyhRyMAreI6hh1UiRu1Qk4JAkqWoS1OSgQYO8vb2XL1/ONtubqP4Y85k1uqqtvB44g0g8R4ZMHZcvk63cbGip0KaTgEMCDk8LB+NgRB599FEAjkmTJrFtgwAHO9SRJAGHJElVi6A1X3zxxZo1a77//vtsJLh538VoNP5h/cm84YE2aCEIH1dxJYRn6XQ63FJSUnIdGk0CDgk4LmFRo1mVvLy8Jk2a1KtX75dffvGAudLTqAQckiRVXZo2bRqGSsOHD78ZBRaZN0R9D4l8mcWeDDg8bBscp2HilcAORic8CVWhs1EScEjAUYpocwrx2LJly9Bne/bsyem8GlqcdpEkAYckSVWLDh8+7OXl1bp1a9K7vMv/piDGCpC2JSUlkLZXaOEQcYNOp2MxjQLJTEI2j/IKEUHJ+fPnKSJP6bzYyBJwVDTROiFa2ozhAQDH559/rri3soNjiSevEC5LkoBDkqTKUdi0bvTkyZPKzbnK/fTp0++//z4wE96iVatWI0aM+APrgXtKBa+v1+svXLiwdevWvLw8BhMMTf7QvgK0kZ2djQoo0n+JBBzXmYgzwa7g87p164L3xO1RYGZC2/LAWAk4JEmqolRYWBgXF+fl5TVz5kwPs8GNJzIS0JyIuHmEBavHJliq6rhx4xo0aPDtt98eOXIE6r9Xr14Envh2Ps4KspjivM8QMvrLL78MCAhQ3BM0JNlF4EXDSkrhKSeylBw+fNjHx2f37t189B2PR8lWRJPuHhM9fBYGXVEmrQdkGwlttMFbU32QTi2AMtmEw5WklbPcIFxzfhceHPNPJo1E5ETFsvNsri2dh852L9SE3tEjnVqSD083mk34eFa7zanFETGYjIjrjQZRr9scdvxqsVkpbrZa7E7XdAwiuIv+5XS+8i5ug0ZK6aPbeQMRaWhVEzvsXLJDceKJ9C8eYbKY8SsSESgDqiqegkblXH6e7roDOHdfwLt8/PHHt99++/Dhw+VuFAk4JEm6yQjK5v333xcdnFeikYNULKsQUpas+aCYWYWQNi0qKjp9+nS9evXGjx9PZgaRPvvss2HDhj344IP//ve/SZd/8MEHK1asePnll/v27fvKK68AbC1ZsqRPnz6Q4KNGjfrmm2927tz5/PPPv/feewMGDFi+fPmGDRtiY2MHDRp03333AVXQE+Pj4yMiIkaMGAFYcP/999MKmOjoaOgk4I+hQ4fefffdr732GoE50dUYwQgGQ6TDzp07x+9Or0lVxUCWwA3rFZr3QWloE9ZApOk91BLt7MWV1SSalMEQex/BjeLRG4xyuGRRpeHp/C9yijuDcAvK56cjJ2IEIzgC0MBIAjoegeAIBQARaH1CABTxCEhHCcAE+BX3ig0oRtAy1Boem4zEcqh8XEv0OjGdakVAhOEaFULxs2fPVmI/ZfYYOHAgnX8kzzySgEOSpJuJSElArUKEQcXSwLFyZ1VIxEOFlF1N4mHzoHpmZ2ej8jt27KCf9BohAjxRv379CRMmfPXVV3Xr1n3iiSdwFx1WhyvSvb29//WvfxFEaNCgwSeffLJp06aJEyciQ3Bw8EcffZSbmwuogZxz5syBlO/UqVNBQcHrr79+xx13bN68+Z///OfGjRuBXZo2bYrEb7/9Fg/18vJ68803U1NTAVNE7X7mzBlFWNnn4ZMUCAMqjbbJcGJxsc6tv0sAKrS7ALmMpW804FfBcOJAMXY7j4ad6rjeISotM27hf+kn5EGxJpNFaz2j+BOVKaajVlQZygO1jie6UYief0INinUlUOoEFAhzQJ0TtmBbAn4ymIxnz5/TGfSMP5AZ/yIbIvj1XN55gikEBQglEMyiSQSOUOsRXGNQBTwHRkLhKBBVYhhEgAOF4xEiGCIbDEErXhKEoir9UDTialpx1bBhQ+lRVAIOSZJuSsCRl5dXs2bNevXqkVStxCkVOmIbsjUqKioyMjIgIKBt27YdO3Zs1qzZzJkzkf7zzz8fO3ZMhCZABoAL5CCV1QzK8fHxGT9+PCl4YA6IaeiPu+6666GHHkI5yJCUlBQXF4dh6+zZs3v27FlYWKi49+ycOnWKJ0F+//13xI8fP450qK7o6OiIiAi2pvz2229IX7t2LdkzWrduDfBBN/IAlIw0eGhQUBBgDeI5OTkAQ3hHf3//rl27IvLII49Q5pCQEGSLjIzq2bN3rVo+ffv2J40+alRM167dAgOD+/Tp16ZNu9DQ8MzM5UifPz+lRYtWnToF9O7dNzi4C9InTZqC9PXrNwYFheAWX9+2iHTuHBgdHUuAYODAOwMCgtq18+/evQci9933wPnz6olfMTFxERGRXbqEITRt2hwP2rVrD9rviy++QskdOnTq0aMXikKYMuUbAJ09e/YhZ8eOncPDI/DckJDQl1766w8/qF+nrV+7offf17qNb/ceUV3C8GNYUUkxPsywhx/q1ad3j149A4IC69avN/iuIevWZyN91pzZ/QcOqF3HJzA4CLdERnX/cPxHSN+zby/K8e/QHiWEdQ0PCglOTE4CbsAj/Pz8unfv3lkjRPAv4dTExETgRbRqeHh4hw4dgCb37NmDolBgRGQ3lIxCfOrWweNmzJqJdFQA1UBlOgcGRPXsgeo99MjD9C1QMr4F2C8sLAxlVm5XJW6n0xaTk5PlgSkScEiSdFMCDmjl0NBQthNUrqkWlcH4ErKeLdi8vICFLOAFsBGJ4BUrVnh7e//www9kRaD1FufPnwcKWbZsGd27evVqAA4glXvvvffll19WtCHsK6+8MmTIEDoyF4CGSk5LS/P19eXKbNy4cdiwYdBbuJ0Ax759+6CBMMSEYgNQAxwBVjty5Ag9aPLkyagMKo9ySkpKeD6IdrJ4OCij10EiDc252dUpCafCpguyWNCVDBgGg8kNZSyFhTybYMnPLyRDhYcNQ1GP2Chio4heb7RaXdXALe4mteKhKFm0iCDRaDSXRoQ2snnwAll+ImrCxeLHk7+fwrWgqJBnLshEcaEgH1UQUwqLixB4gQVPqZgsZqPZlF9YwAsv2MJBzcXrfBHh1qOJIf63oKAAX4FtGOI8DupGMEgzA6kvhIqhek4NHXIJNLUElFmJB7ES2+BFgKJuu+227Oxs1FAuUpaAQ5Kkm4zIReYLL7wAhfrBBx9UroWDF9jTgBUKnkzHoqyn5YG0TBL0/fff16hRY8KECYpwEAzAh4+Pz+LFi+ld1qxZU7t2bWj9hx56CG9K0AQ4Y+DAgShhypQpGBATOHjnnXfatm1LaADDZQj3l1566YhGiAMDkSFk1apVaC5kRpkM1KDqyEXp+PHjkYhbCNkwTjpx4oTiPiSP1qOgwj///LNbYReyviSC/i4u1pEWp0kNghp4RVbzBCAYdjBKQKBbGFIgQiCG9NSFCy6bPB5BxZL9o6RED/WLcPr0WcpJszBnzpwj8CGWg2x4CkEQuhK4IbXN8xc0LQJUAb1erCvhpaC0tpTBB63l5EUe4hwK/YoAlOAUdiBbNHI3hY4QBhqWV5VSe+K5BF9o9SgvR6WAdJruwa+onkObkKEZPVrMW6HH8v1Jyx+uALvgK/AnQ1UpviTgkCTpZkIbNBzMysrC0Lxv376V6/iLhvsQ8bRtxEOqom6saUSnWzExMRDEwA2//fYbXqR79+6AFBiShoaGHj58+JdffunTp0///v2Rc/Dgwa+99hrd+9RTTyEPlNOsWbOqV6+OnMeOHdu0aVPdunXpKXhczZo1Cco899xzgCw//vhjWlraqVOnioqKIiMj3377baglPPq9994DjDh37hyKQub9+/cDnWzYsIHRA2ELj2Zn+weveSR4hOcatXUFTodLJVLEZLTharcpBr0FcUQ4AwLy60pMSPRYb+mwq9lsVjUDpRQV6vlX/GQ22TndYnaI9+IpxUUGRAoLdJTC5aM0MTNy4umuR7gLYEsG2Q9E6wIH/KQ3Glj9s+73CAaTkfa58LZYpcyWJXHE77ENh28kKMPLODx2zYjbYhlw0B4f8ZjAygIcL7/8MpgNCFjkFkkScEiSdHMQn8KAsXu9evWAOXjTROWO54KCgmgvBlm22UiACMtZUuFk7Xj22WdReeADSOQXX3wRmv7gwYMDBgyoptHAgQOBJ5B5xIgRwBk0bH333Xf79euHxJ9//tnX1xfZXnnllU8//ZSMK7Ql5NVXX61RowZ+evDBBwFKTp8+HRsbS2X26NEjJycHSig+Ph6Pbtiw4cmTJ9u0aYOfEI+OjuYdrYyQRDdlZLPhrbOkHZGB7Bw0gWI0YKTuAFZQLQqaOUHV8Zr+RAYVKGjxwsJiTlcXclrUtaJOzUph0xZ0qtCEUILVrgIIxZWOElSrhlO1bZQUqy1s1bynIj9S7DaFy3TYXXGDwQS4w6WhhpSumjec6lNQJtKhy2lmBKkEO8gyQTYPsjeIO13VOxSnaO0gUwQl8k+UGchDcS+i5CNs6F9qT0YGjEtopkbc/8JzNPQU2qBLD0I6fTu6Usn4oJXYI2gRa5MmTby8vPbt2weGubkc9EmSgEOSJNcgm4z8Dz30EJTl4sWL+QRUcRRV0X4IyvrDLoWBQkPDSWtA811mYMknvtKZVbRngaQz7aol1xp/6H6RBsSUkzaUsmcw2qZL7kfxL8l6ABreI4oM0Ek0DqaciFM5cpb9JiVa80H2D5oeCg0PE00vot+OCvzGxDDU18jLPgFucNSaNWvQPTt27KiU8RMjSQIOSZJuDiKJBu344YcfQqLFxcWJ6eRUSimzk7Mi0IbtkoBDca9XwNX5R+KctqWImIP9LZJ1hKX25XU/ZxMdZzEAYrkv4hhxjy57faCcXI4EHDcpMdqgFSTAFs1aNPeYCboegIMXhzL4YNaiHd3vvfce9UTpwlwCDkmSblYjB6TYkSNHatSo0ahRI9KXNILHlScFKm5QVS7gIGEKqEELGNWZBWGB5OVhByMPntF3CHR56Sxuqb2kHUX86apOor9MsZKqMuCgCRdaOGJz2MMjut4AwFF2gxhZzvR6Pc14/vbbbzxtJNdwSMAhSdLNRLxKkSLt27evXr36wYMHyZ0lj/5FNHBdAQc/a+jQ+wlwWC1O3v5wFQrjUjr+zyl+0a+5iD88kER5DtMk4LhJAQcvLCVg0c7fT9xTc/2mVHgujydTcP3mm29q1arVr18/XuWt3JzHHkmSgEPSrQ44eEnm008/7eXl9e6774q/cuQGWDgU94KSkJBQk8lCgOPPCe4/DThKn/l10VIi/qsI7sP5Lpq4kQjjfwNwGExGu9PBDkI6BwbcAAuHIuxdEl3L9+zZ87bbbgPsoBQPhy6SJOCQJOnmIJ4x0ev12dnZNWvWDA0NFQ/oIgFXocOpcgEHO/+OjIwyGEwk0S+jvsvT7lcLOMoeL3ol8yZljRy0iORKJnEkVWkg7t4mQ0YOq92Wu3vXjQEcQP+8uYYsGbt378YwoH79+uyNRjznT5IEHJIk3TTk8oykeRooLi7u0qULpFtOTg7JNZowruhV8X+whsNstrZq5auQo0ynIh7/4VmQW7WX1e6XOqL8iqwaV2uiKLu8Q9o5bnbipRvkGkTctHK9p1T4JGEX9LHbn3zyyWrVqj3//POUwocAy88kAYckSTcfmUwmNmkkJiaSy1ECIuy6isdb1xVw8BN79OhFyMN+2WeK5gQPBS8aKq4ccFwGTIgZxBkWsmqUV75EHjcjsZN19sv+z7fevDEWDqX0Yqm8vLx69eqhS549e5ZmGxn9yzUcEnBIknSTEXmaIoIsO3LkCKRb586d+XhMyDUaVN2ANRxuh0vWyMgohZxtOxU+/lSSpBsGOPLyL4jextp37CB6Qy/rmbRCSHRzh76g0+lmzJiB/jh06FDR5z15pZNGDgk4JEm6mYgP9WbPzefPnwfaqF69+tGjR00mE506prjXct4ACwfJ3A0bNhHyUCTYkHTj+4U2mULOSckpavceUZf0uX49LBziXvSwsDAAjjlz5ijCwTHumUd5looEHJIk3TzE5lm24iLyxhtvQMa99tpr4rkq4skU1xVwuM+Xt0nAIamyiLAFQQ2aOgnrGl4e2qjwbbE8B5eZmenl5eXn50c2D/RHAhmUoXJPdZYkAYckSVdN4g4Ug8EAKbZ79+7bbrutZcuWBDiQQibciluLUC7goKUkdrvz88+/1B7tcE2VS5J0A0k8eY5CoyaNbwDgEJE9+sLgwYNr1qz55ptvUjfkDsh++SRJwCFJ0k1m5OChkk6no1WQ/v7+1atXX7duHZ0PQr+yu47rBzhoeAecERzchZZuqCed2uTiOEk3GnCQmy9at2GxWcO6hotnv10/wEFI4vTp07/88gtAv5eXF+KKsH2dMsgVoxJwSJJ0swlW95jJ44SzTz75pFq1asOHD1fc20ZEN0TXD3Dw4wICgly7YeWiUUk3nGgfrMFkpLkVQI2JkyfdAAsHWfjogMC3334bfZBPqaXeR8cHipklScAhSdJNBjggxdiAYbPZoPVr165dp06d48ePV+jqjT8GHGRu6dGjFy3joOPRJUm6kUTmDfbAgfDTLz/fAMBB1kRcT5w44evrC8Bx5MgRNkA6HA7RI44EHBJwSJJ0kxHwhHg0A/kdQvzhhx+uWbPma6+9dh2eWS7goFUjJpOlQ4dOCp1Nb6c1HMhspWyqoVu92ii47iWfpMplgqN0sAlXT9VxaY0iKJbSBdrc5TjKf6hSccHmUeCl2vYPnuiJO8sJdq0cuzv8oX79o2a/3LOcQhv/4bX0XZc+c7icPGXrTM3l+ZnyCwtwtdptNJPy/Q9HbsyUCmEOOrr5zjvvpB7qsSEFeaRzFwk4JEn636HNmzd7e3s3b96cHHVA6t2Aw9vYt2mLFq1YIeh1xYqiU5RixakZum2WYrvRohiL7Xl2Re9UrOSJCcGuFuqwqEEpHZBisyhWLZi1YOQrboH0ptWprGjdwa1rne7gRjx2xWaHPlLMdsWoBbOWopQONjHdViZYygk2dfWAuxBEHFpQUxwmR7HRXoKIwaz+qjfbND/Ymrp0IosVwaFYbYoThehMGlJzqCAFcaNVveKF7dq74UolW+k2pwNNaVXsZncwKVbt6gpm9Ve1WAc1l/vTaS2jJqBgClYNHmqloSYWd7A7NIRIAM2dRw3UGvTrxasHMHRcvHIDEvTUyjBrD7TTp3SUfoRDsegN+erciMNhMtuNJhU6qMeyqc2gZtQ+pY2/OB1MTwiD3JwHdwmpWAuH0Wj0mKO02+1kZSwuLm7atCkAx6ZNm66DfVGSBBySJFUlIptHREREjRo1vvnmG6WCF6ldbkpFc7Jumz59ZkmJHiDHYaf8OmPRKbvNgv90NmeJw5JnPmNRCs1Kvs1R4rQoTk1D2qyK2WEyOaFQFFeAgnHYTA6LmqjoNYShF4IKXKx4XauKR+wWzcBjN1kdei0YrXbL/7P3HtBR3Fz7+EAIIYEESIAkBAgkNBcw3bRQbWwwHWyKKe69bHcv9A6h91BDCaElIRA6xrhQ3LcX93Xfvjvb9ZNm1otJe9/ky/c//5Nv73mOLGs0Gq1mV3p0dXVlNFngmIkyEDCj3BY48YUPghn0VpXRqtBbFUaLSo/ym/RmlB+FtjwavVVjMBlxkxVCh0Kzzmy2hah6ltZKWrRWk9Zq0VoQJ4BAT4cwEkRJj1J0ZoUZyM2QNph1cpVJi6PREQ6NbQkHHFxJwkEO5EaiNNz4q0H6DYVQKx+ytNI1BNicrbDgBPQEGbLf/qsh12qjaCaiHPJZMGI0vSZkRvPrZ70B+zD/X4avaRn62LBecJzWEOEbajBTazaYxwq0RJQ4et5AnARrJpklyTZgI72uOfmZyPUU9B6NhiFOQ/9BDYddPwEjKpWKPD/Fzj/279/fqVOniRMnkrvDHP42HITDIQ7598vp06fhNGvs2LGg1bnh/zbhIPtcyDOIOPIYjpZU4MCAa4waHakxIAcbBY7GXaT2gCONqXUmi9gJOdm1tHmW/bLeNg9+Pa9uXY6xEAe3QNqCxi1lK9TEv3o0dbfBiPKgkJzam4gCNa9Hu1bH17YQkBl0rY/+E6A8VqCz2v61EDWUE3odU2sLwRGv3mAuV2jEAOW0aVx0evz3CAdyCg8JmkJtNpiAUmMhR1Bcr7JYdWYL3hYEF8INVkhL9EZgMKIR2UDohGzACRgI0mWxmKwEWtUZKA4TzVY7KzMRyhITURTkcyqzDQTtsJre1KkglmgGGpjBQmR7M4Tpmt+Eer3VrLda9VbIxLRmq9JilUEAiwJYNFarzmoxwPrAwlvrr8eNckhMNVqFgeAaWo1Rb7C2qrJ+h3DocAPphwOyDXJftrOryz+o4Wi7x9VuO0VudsVxfOjQofB398MPP5CJDlsNB+FwiEP+5QLnVbDvIzfmZWVl/aMzrf9AOEwmS3Z2DjH/Q915c6PcNv6aQUMj0oDL1WatHg7GLcBSB0wNQK+EJAXgeqDXAnMzMMNMMmBSEKGMSGmFgYQMwNvhXXo1wLUAVwNjMyrHVAvM1cBcCSyVRKQWmOqAuQEYG1EGFDaibCiiBEZ4lwKVaWpEeUzEQw0wUQsMOAEtymZqRldhPf8CGtBHMNcBqwgBRowyVBRMBOUAiACohlykpqpardITQxdaRyCWNl4TDmKpBI7tNm0EHGBhC+p1sCawkOY2dIqkSro2VMz0u+YOVvu7IxdPLG1BJv7qFdtXOnRvPqLte2+bR/N70P0e9K0rLUbiX/WbBFFHEEejfemGUG+Ql5DFJaypTmvSaI2thMP0K8IBh3e9wURuToGcg1Rs9P9iwD+7pELuC2ubQq6enDt3DrINJycnkm20db7nEAfhcIhD/oVC9n2wv9u8eTPs/mbOnAnaHOT2v0c4Wud8+kmTvoKDKIygJXoDWqa34GgosJrRHNzFdYT7+MEjhnUZ79Zp8rCOU527Th/64VSnHlOcP5w07L1JwzpNdn1vsmtnImyLzlOGdZ0yrPuUYR9Nce0xxfWTKS69p7j0meLaa4prpynD2tsxeViHKa4dJ8NEF3TjFJfOBN5DcO2ESoYPcukx2aXrJOIp5BNR3KU7SnfuRYQfTnLtCjPDq5OGdZw0vAPERHs4rOPE4R1bw04Th78GkR9+rg6T3TAEGEEld4UfbdRQzGUg5jQImzDm4wljhhNjnUWv17XaoCAvKpZWzgHjKpUCZmisq8LV9QTPgExFDEx8xGkskIopEEyQFamBCdIjnLBvMQG9BQFvHd/xtoDpBoLeQegI4EQKeReRB+loTKgoG/HCUcltw9eXdDbA21HJv4XpdwA5gEFvMegsRq0VMjyDkuCOcgIqYIDQACOEzmrSWY04HLUhF6lvEMJqKRQyu30xrieXSOy2OL+24SC1RyTnGDRkMHmE7D9IOOwMHq3jEeoNGOnbt+9bb71FqjdI3xuOJRUH4XCIQ/7lQp6zWl1d3aNHj/bt22dnZ/+DZf9HDUfXrt3JiKwFsRydRgv7dYPeZto5cpRz3rPzFfxv6/jHmrhH5GUnlSXn5KXnm9nnmrgnmrjHmjknWnGsDeC/J5vZp5rZZ2DO5rILzWWXmku/ay4738w90sw90Mw9hCKvs52SlZ0hMpM41YozTewLCJwzTZyT8HGtOIFS2OeJqzA818Q5RdaniXeyiX/8ddiKRu6JRu5JErAoMiRwCn0K3h4EWCX0UFRaI/+AvOqEovbqi6ffjHEdAKwmtaIJTeVbTy+1Ew5iZo+0GsqWmgmjB00ZN8BtYIexQxGDmTAMG+/WYeyI99zdOrsPf999eNfxw7qPd/1wvGuPCc4fT3D6lEDviUP72DHBCWHi0N4TnT6e5NRzklP3Sc7dSEx07j7R+SN4o7tz73HOfcY59R/n3M8dluPcE8IdpdswzoWIuPR0d/nI3aX7BIRuBLpPdOoJS/499PwtYLHjXD8aM+wjGELA0ibACrxRYDdY/jjX7mS20cM+dnbqOXrMQKNZSZpxqFV6o+H1rh/z7xmNyhRy0oCDzBYdG/PPajjs6o22O9IvXboE+f2wYcNAm0VMhwtzB+FwiEP+5WI/RZZGo7333nvTpk37/+B4etjzkivWQ4Y4kSkqJXFypkFphTNoK9J1y+R1Li7dpdXfy2qPyMTr1KJUnL9Oz9uE87ZqeVvVwg1q4TqNYIPWhnVaQYYdKm4qgXQVN1PFWa/ibFCxNym56zXCDLUoA96lEWzW8nZqubu13D0QOGcPEd8NEzW83RBqvh071YLN8FlqUXorMtDTYaJgK4HN6F+UuE4p3KwUblUKtsNQLdj+BmA5BDRtQjV/D4oLM7TCdC1/M1GHvRreHqVwk7QsvVl8RC39xd21NzCodcoWO+FAhhzoL2kjYQCkjsLQ5DlxcNHTc1XF32qrLslFh5sFuxvFe+okexrEewnsbxQdbBQeahQeaeIfbuIfbeYhtNhwGIXc4wgofrCFv69FsLtFsKtFsAOGsLRmwddNggPw3gb+8Qb+Nw38k02Cg838A2RpTSjxJES94HiD4GiD4GCDcF+D8OsmhN3oduGuZuGOZuG2NyDa8htsItEk2gZvrxcdqBcebhCghyIahx50GD4X1USwj3gEzHAQ5qkRnn+Zf2nadDettgHXKe00AX61WjfukhtVXhMOg8mIzIcIkNkLigr/QcKh1+vtvybyC2+1WhsbG0eOHNm9e/edO3eCVtsOpVLp2AHrIBwOcci/X8NB7oOVSqVdu3aFE6+XL1/+bxMOez/r7OyqUKiQj1G0sKIxWhVWoK5rlhITd/m4MR9IuHtreYlKcYxOFGrkR5i5sUYuXc9jagVUrSBex6fqeQT48Xp+rF4QRSDCIAq3QRhJIJqEVhilFcbAu3R8pp6TquekGdgZBnaagZNq4CQbOIl6bqKOm6zjpmq5aVpeqpaXrOWz0LOEMVpRhFYUZgP8VxhL1IGKIuhfVLJGyNIIkkjo2kDLT4TQ8V6DeBB8yjr4ILL+Bi4LVYOdASum4dOUwkQpG46+l5z7dQImDbAatRrVHxMODTA1jvyyk7ryF4XwrFp0SPoqSSlIlYsTWspZLZIEGJGLkxSiFAiVMEUpSFYJEiHUApZawITQCugo5Ceq+WQiXS2MVwtj1aIoCJU4SiWKUYlilSKqUsiEdZMLk2GoEtIRBInwWUp+hlywDkImzJCJkmUillxEV4riIVQEYIFEIW0gjmqDiDYIIxClkNAU4gRUbWG6Qri+FZnw6XJUPpMAS07Up1GwU1p9Z4hTDyuy88DRRmsV3nZzzW8JB+n4S4vrIPOATUrqOWD8nyIcOI63HlJo22peX19/+fJl+Ctzd3cnd4ZDUkJGHGYcDsLhEIf8m0UmkwHiqHpy+kWhUGBXuGDBAtDGWwDsMUlTj79+xsofEg7SgbrBYBo61JlQO5vlMjXhlEFjBmqDidwJ0jzStUOdZG+zhGmpDQc1AbpSXyAO05cE4+xIDS9KyQ7H2VGQgpg40aCGpSpYBapjgSRYz1kOKtaCqrVW8Upt2QLQEgHKV2pK54O6YFzkD+qjlSWrQRUdVKXixXGAywR8moUbAyooJm6opjTILIrXc+OskiRLRbKiNNoooWj4IabKMAV3ual6bVPpAtAYpi8P1EuCG4tWgEY6Lg4xVYXgkrW4JMBYHmMqp5oraHpRHM6PBnVJTS/WgAq6jhMJ6pKVhYGgnIqzw2HEwA7Xs2MtglSzMBkSKRMvBPDjYE10hVFASNfzQ/Fyagt/fYv4zJjBXYBZC0dDYs/IHxAOC5zQN4536tTIvqASHlFw1kPqgIuocl5EMztYK4lT86NAUypsMVg9ZWkwqE9sfrXCIAwGFaGK4qWgIhBUBeq4y2Hracr8DXxY52BlyRLYYkr2fFP5MtAQKH0+EzSH64TLQV2YQbxWw/MH1fClhCuKfA2CUFDF0MNqixIhVWosDLHW0LWiUAV3hYq/3AJL5vvjKH8Uzg0zCWMURYGghqnjhBvgBxdGWSWxEOqyAPipUVH1dHmRv7psrbUyUssLkLPXmCspzUWhemECqFmvZtPN5Um4MA4XReLiMNj4KBSF46JoSEQqhVeGD++FDEstGmQHRNiLWm1fOkQ4bFYvrb6/SMUGabRBso0+/fr+s7tUQJtz2uDXHsY//PBD+Ct78OCBPYNjMcVBOBzikP8TAtmGvb9TKBRdunTp0KED2RtaLBaSiPzdvbJ/SDhI+zjIM1avXktwGjTlJM6z0JuB3mAkhwrFaJeOjaL9cBbbWLDQIvS1cHxBEw3wYoCYCWrSdJx4UJlm5tCNpXFGdjQc19WFq9WFy0BtpKZ4ibZskZ6zCNSuqXg4HtT4G7gLrBJfDW+JuTLALAlTFQdY+XGK/AArO9pYEgiEIRbuKngXkASBBhrOjWgpCFGVRIP6DVpODM6HI1+QURII+QouWqHhL9eL1loqwzTcQAggjZOVLANNMPS1VsaIHs61lMe2FK42i6LN4khQFW8ShAFJVPNLX1Abr2ev1RavAA1UUBNj4UcauCycTcc5wbDaFnYwEMdby8IAP9zEX22siFTwU+Ti42MGdwZmtX3nhY1wtHIOYgcHSTjqJzi93cQ5oxXuVXOS9fxYOJaD+lTQmGkW0hrzg7RlsXhZDBAxrPx4adZSIEs1c4Pw0uWIbdSHmLmLQeVqIIBULNzEXQXKA4GCpi5aqCqeD2rWmoV+sCUtomW1T6cYYM76YBPPrznXEy9ZaGIvB+IovCRcVRBr5CZZRGmw0eRFIbgg1FoRgvNWAEkAqIkEvLWG4rXqVwGgNsVYGqUrDteXhAMhRfUy0CqIMZaFA1EMgKyoIk75ciWooQBJjLIA1i1CXbRSD9ukcZOqgKItYuIlCerCOBOPYhJEmoQhJmGQRRBiEYTBxtTwM6sEl4cP74G2GbchHMBs/zbaCYfNi6vJYib9fdl2/wAw1n3cP0g47DtdlUolqcPYvXs3ZBvz58+vrKwErW5vWk2aHLTDQTgc4pB/tdiN2mQyGezy0tPTYYc4e/Zs+8IHqdiAk7O/7hPszzyNEj2sBcJgMJFLKkaTzcG2HicX2I3jhnRr4Z9Q8VONAjgXDzSXrAT8GHluuLYosf45Q1mSIX1CAdVfA96Gppw4IM4EDVtBZVJT/mrQmCx9thgoU0B5GKinqOGwKg5SlfpZq0JN5SGgMlZTEmZkx4JyFhBTQXk04PqDcn+zwNckWAGfZRRE41waqNkizYnX89YZhClaHg3UJevFMVpBOJDSTeXU6txVoDpNw6VqeRQNL9ZSyZCXhrYUB0ufr1LAAbIpzSSObS5YZRSGg4oYUM8w8YIsvGBQHQ2H3qZsH9BIlT1f2fI8zshPBcpMIA61sgMBP8JaGgQzmPnLzBXBCgFTLjk6ZvC7SMOBHGPYvKDaHK2TYyRKMgCzChEO57eaOCe1op0aDsMgCDMLo6oeBBlK1wP+Vl1hJpDswQsyFLmJgLsJCDYCfgao2qDICdK8CASCaHmuLyRe2pdBQMRS5AfJcgOAhAWheBEMCUrTs5VAmgQk8QCSGF6YuXSttWwNqINNFwcE8bAcwN9kKt2hK9xZ/zRDW7wFiLfBz6UtjYf8BvCo1jIKKGUCQbqxkGLlsCylVMBjAQ7DymFU3l4CM8CHQhZiLI4AVQmyZ/7wLQMRYpa6V8GgKhVw0gwv0mRZKbKnGUBwCNSdtLAzLZwkC5dm5cYBDgUWBcoScPaOGv6V4cM/RPuorSrCP1kbX2M2JYfRattJa/Nxbl9G0eI6hUrpNnIE6ez8nzIatZP1+vp6GP/oo4/eeuutX375xVahNoTjn/Pw6xAH4XCIQ/7/J2QfZz8RWy6Xt7S09OzZs3PnzqdOnbIvP0P5W3tl/8yGQ6/XQ54Bn6BWa/V65ErLTMzdYYpeC8h5u/vgXjLeKTUvA9QxAHc1/nIF4MaD8q1lN8Kc38eGdsbce2CTemJpyz6wcHfqitdJs2JA1VZdKVNbSlMWxpr4TFVxlKIwAufEKovC4XRfw4tS86MtFYlmQWJTbjio3aR5EaR5uVJbuBiIlpv4S8ySVZAcQA6RHvj25xg28VMMhn7jMPGjSBU7qakwRsWhKdk0DTcJtOxVlCQ3vWIW3lxR9MPyyuwI0LhtH+ujL9/BYJ6GgnCThKUXUUEFwyyKl79aCxoS9WVBqkJ/IzsAiEK0xSuubevn1hVj+bWry1tl5ocCPpzxRwBuCCIcgqXmygCFgCGXHBk15F1gQWv85t8QDpPtMBUjMMMJfSMiHNwTOuF2SIMgbbIKIoBkM6g5+vL8qtGdsWFvY07tsCUuWMqSztJHDBlkHsLNQLwJ0gVNQTSoSAQ1GUCSZipjWvgpQLRO+jQSlG+RPaehQqq24UV01fMoc0kM4EZbS4ONr1Zai9cAfqQmP7ApK2pfRB+3d7Ch7bBJvbCFwzH2rRizaJumJBFUbWx+FlF7PwiyHGthBuBtLLy4UHBzBajdpXvFzFiOzeqLKZ/HAm4i4CdDLmLm0EFFupXH1L6K1hVGIQJUv/7VmTnefbBpH2GDMWzxYEzwQ6ylbKOFk2LhMiw8SivhSEKEQ3B5uFs3tDHYqgQW4xuEw/o7hAOyjbbrKRCQcPzjh7fZ1yJjYmLeffddT09PMoV09gV/A46FFQfhcIhD/v0CJ1gk2yAN1siZ1tdff03u2ZPJZG07RDsv+Z8TDpLowLFz8uQpBPlAhEOrMyLOYfkdwqEuWQvEQaAlBS+MVRekPzq9tm8H7PHF+OKbjAsbPZw6YXOcMFnBJqNgT+OLFFC73yjchvM2m0TbtJwNMLRIdsJ/a/IYQLpT8ixaL9qKczYB0U4g2QYEcOJO1RT5AWkoqAlSs/3rXwY2FTF3UYfMH4Wxf8588E3EzEHYLCesqWgdaD5SX5BkrNhR/ypVJ9ypF+2DCJqFxS99B9QeaihIq3nOyvl+maVyl1awQV6cpOWmGYXpihKaWZQEqQ8oTwASOqiBT6QsccO8B2NjPsaS1nS3VqSDmgQ0reeEWUsDLOwVZsFic+UahZDWUn5k5FBIOJBto7HNaSZtCYcVuWiFA1gzWlKxEY54oyDQLApWFcXh7PSccwuGvYc9+mYe51bY6YxRw7pg/hMxA29LfR5TU5ZmkWxQs5nmitTa/FCDOLmpMM4kyTQI1+GiTfLSTGC+LHwUo+NurH4SAWo2gMokY1koqI0D9XHGEj91oS+oosH0jFWd57lixTdZz6/Sx36KeQ3D9BVbldxENZeB7mrYBRoOGIrWGdlbFg3DNq3toS3eBOOyF8nPTi8EFbuBaKu6KFVVkKjnpDXmxDU/pxg4qaAqE4giZPlLHx4deYTem30t+PbXPmO6Ynvi+hp4SQYBxSgMN4ps6ylWbpyOt75G8K3b8K4ANKI1pj8lHPaj70iqQTobhZF+/T+3k4//OeGwKy0gwygoKPjkk0/efvvt3NxcUslHejq36zksjuOSHYTDIQ75d4v9CNnm5mb7zr2xY8dCzrFt27a2PIO0pf+nllTI7hXD2pNrK6Rln8liRHtiNaTfadP4wd0UvGNaXrJFHGJBJo2RsrxgULG34Ad6Twwrzz8sKzus5R14eGb10M7Y4ZRRBvEBwUMGa2U3/6nYsknY1a/n1L/YZJIcSwvodyLjq+OZX810wfYkuNXmbQ6d9bb/eCxzZXcTJ90qZIIaioG/pqVwCWGKmKoV7UwLHjxjKIYLrkEwVw5y74vJuXsFTxO20QZ6j8RWeWBfJ44oz9mQuLbP0K7Y6N4YbeWH2d+HPL4UFDK3o4K9W/pyfUV28toZ2GxXLDPwA8nDcBM/o+l5rK6MbhYkgoZN13YMMvLSF47FWKt7KzlpsoJIdUEI4EYDSQyoCHuTcHQGVnS4jMFqc/mFKGCr804TuT5l1hOE450mzslWwrHWLAo0ipmyYmbedwsHdMTKn1KUnC06wdcvb4T3bYedWD/eXHW4qXgjc1UX/xnY3HHYL+d9VOL1hpptiydjDy8v95uGzRuH7UsZX5W/OcynY5gPdmWXu5GfLH8Z0pTra2D7A1EgqIxoeOFf/yI2eXXXZROx6ucHZNxzrKAhI/phDdwN/OzIHYxeni6YtzO2L66/oWwXc2nXCb2xGV9i4d5v51wMKbwZHenzlqpsR+PLDYU/hDGXv+czHEta1UXJXg/qDlQ8DdULgnD+ah0bcrX1OGcLqDgMKVqoD6YRJmjEsToJstU1iIOMwjCjIFotyKwRnndzex8RDqscrTTZCYflDwkHyTm0uE6nx2HEa7a3Uq36BzUc5JnMMDJ+/Ph33nln1apV5Pcfprf1QOrYE+sgHA5xyL9fYE/X1NTUNkUmkz148OD999/v2LGjSCQiEysqKv6GAuVPllTIDTKTJn2l1eKkA0141WhVmIFKr8NRRr1i/NCOCsEenZCC832BZAmoDLSI47SCDbe+Wdm9PcbJ3V1XulMt2m2uPTzNCUsP/7TyeSocIz2GYd9snhLn123w+9i1g756yblArw8+74BNH4plRo3shWEjemChnj22BLtN6ImdThlm5Capi4P1/BBrdRyQb1Tz10tfbY9bMtDD+W1J1vGH5xI9nCHD6CTOWb8/bVTownbndk9NDfvM7VOs9H7ioYzJ4z7HQua9uyFmMPdx6i7GpMlfYhr+2WdXKEPewyhLP7q809tjMDa8OyYrzMC5G428dc3PqaBiIxBlqoozZ7liEYs/MVbuAdXrQU0mkDAt7BDVi8Um4VJjZYBMyGguP+o2tAuyb4GzcCtptPEm4SAtEcxwJt2CdqlwT2lFNsJhEgdaajIq8+PvnZv35ftYbcH6ivwNjSX7tOXnpjlj0b59agoPeY7A3AdiZ79eHL3i0487Yoe3TK5lb/+yB/ZZZywjbvzaBf3c+mA9MIy5dvjK6e8O/xCrzE4E8oOgKl1XFgYksc3FAaB5E2jYH7Hg/Tmj2guyjz++umnsQMxncrs6wYG960b7eWCPL/sfSB7RF8PYt5Lvnwp164lNHYQdSZ9eX7D37LbZAztD9nAk73rMp+2wmKVdvtvnPfELrO/bGD+LBRq/1pdHG8rDiO3H66TP14mepI/rh62L+UwhSlNIKKrycHV5kEYSphNH4kKKQrCxChGOzgDU/xHhQJt9bAe9oW8jJBkmi5k8vI3Eq8KCf3BJhaQRdXV1V65c6dKly7vvvgsIA1I71bAvozgMOByEwyEO+fcL2fcRFhVmkgSQsnLlSgzD/Pz87H3iP7ikAud8xOEgyPGXwUCUbwVarZogHEocl6NjMgwtE4d2UAh260RxoHo1KF+sLJgPKuIVnKRbZ5a79cdePdyirT7eyN6oq/zaZxw2ZyyW90PYJ+2xsof0uqLtuvLjC8ZjiyZgCt6JpZPaB83uJuecbCo5umgcNs8Nw7kXGp7tXTEWS1nW2cBLAeUMc3mUihuiEsQ1lyYZy4/EL/18SBes/9vYl+9i7v2w+qKvFaLDmorDdaXbm3l7lOJDQz7C9iS7q8THIMVJCOitLT8k4+7//tDqvh0xffnlON++4/pgWv4xNedAbd76AW9hN76e2ZCXAGq/NgvWaUuTtCUsg2DL/HFYxJIehordKnaSujBO9yoEcMNBZaSV72stD1LymXIJsaRiNdgIx+sD4lsPRHlNOOSthGOnmkc1CAON4lA5h6aTbM277g9rJchK1kqOKXjHGkqPzhmNBc375NL+Nf3ewwrvbZa8OqiuvuQzCVvp06k0e8Pgj7EN9Kmqqut5t3d8iGEX99OaORd5T3YM6IjlXo5Ul603CFPkhTF6Ht1cmSxnMxqK0hn+n/Z5G/v8PezjDthkF6yi6KTw5V68/mID96C0ZH/1q/1zRmBboic0lx7zHIbtZk1sKj1cX3TgxEZv98+xptKDYfM/WDgegw1Y/XJzY9kOl57Y0Q3j1ZJNeCVVLQ7Tl6fIyjL04kPMFZ/064g1lKxH3kQkceryEE15gE4SpBOH4cI4lWB9jfDsyOGthONXSyrE+G5FeiKScCAht8UaTEZIO2CrKlRKu7Pzf2qXCvyq4zg+aNAg+Gs6duwYSSzs9AL+pki1ouN4egfhcIhD/o8K7AThPKxbt26wl3z06BG5i5XsGe0GbqSimNzv91cJBxw1tVotJByDBw9FBhwE4TAY4QMsODraXY0O6NLLJru831K2Vy9MwNkrgDQIlIfpy8Is5WnPrgXBaferu18rhEekhet05fu/Goyx1vQ4nOH+xXtoxqziH5Jz9icH9vtqIFZXsCN8wbtBczpYq0+oubuWT8RS/T9Rl+zRle5dPQnLDHhfw2EpS0N1wjCdKFTFD4WjNajaSvN9Z/5IjHcvYS9rJBzkHl4I0IiPip6tX/wVNt0Vc+mF9WqHHd84vb54F+Q06yI/byzapBbuP7XFa0hXTMU77jsZi1vcXcH+Wi/aLyveNKkPtj36E1C9TV3CBKJ0PYcBqtLhc+dPxEKXtMPFm0HFBmNRHCiNseSvAKIwUOYPBOF6doJGdGjc4PbAokLtYyaOKWtzKi5JOGxLKlZCw8E5oxbtVvIYemGYURRlLV8vL818cHph/7ew2tx0LW+fsmyfhnd4xiCM4vfpxV0Lh/XE1MKTotytDWX7E0O/dB+ItQhP9uuEfXcguJG7X/J8F6Rcv5xIVwoOt7B39MWw51eYetHWxqJ4NTvFIt4My6/Li7ZKtu2I7j/LCat6tmMXbVLvt7AXP62vKz5V+mhXwNzek52xL7phkLgcylzQWLzXyxXbSXVVcQ/Aalzdu3hAB8woPuPtgjGWf6pg74NUT87eAdlJWkgPBS9dIYjWV1KbS6ha/qZvMid+2QHLubS26VWCTsjAxWEGib9RsswkXmESrTYJwjSCFKngm9HD3kWEw6IE6NvUdhcxpBSQs+GIcFjN9i8jSS80Oi25M3apn+/f03DA77NdbwHZQ1sawWKx4O9o5MiRwHFgioNwOMQhDvmtyOXyrVu3durUqV+/fi0tLaDVqI20dLN3qbCT/ePl5z/TcBAhcHFBx0lIpXBKCtQaPZrHW+DsjzgUVC+f7PxhS+khPT8dVEQbS/wAL8zKjTPwErIurPryXayp7KeaggPW+lP3vw107YkdSp9yeqs3HEGby460sI+aay6Ezns/bEGXxtKv57tjaWH9G0u24oLdy9yxBL+uOGevgXdwxXgsaeV7OD/DUpWil8Tj4hhzFd0gSdDx02LmYXPdMKPkQHXeuhlDsEDvjnLu0Wjfz/w9u9y/SOFm7Zg4CM7CZ+skp6cPxZICPwPSU3Lu3jPb5g39EE7ZD/tNwdbO6gh5DxxBVZydw7phl3dNbC5IbHhO0ZUl45wkTTFdUZowYxSWHN3bVLtPXZrekh0FRMmAHw9erQalAUAQB7OpRYfGDsWAVWYmzlMjz0U1WdsQDpsNBzIaHe/8TiP3FCQcai5JOGJ0nAx16da874IgV+DeYer4h5Wl+3O+i4b848rupTtp7p+1wyQ520HDVUPNt6u8Ovp7v8V9uvnT9ti1I+G66sO1xTuHvIv9cJCp4O+ofJE4oD12/5sYBW99M4durtotzU+SFSQA9VF5YTJtSbtpX2A6/sGavG1TBmIznTCgyIpc6jS6P/b05jZ1zb0BH2KHNy3TV5yd44Ztjh6qZB+Q5u+6tGPRlP6YhnN69dR3V0zuYJKcrcnbgosOD+6CHUwZARr266vSZDy6qXL77eOL4Ee4dXBlXc4GUL0f5ycZhNFGUQCkGhbhWuSKgx+t46XX878ZPbzTbwmHCemEjBaghbARjlb/HOTB9HqjQYvr5ErFDI+ZTS3Nf1vDQWoK4debJOIwwmazO3ToAAmHUCiUSqUOZYaDcDjEIQ55Q8jusrm5eerUqbCvXLp0qd2o3m46ShIO2KX+8aTtDwkHucmWOLjb5hnJoLeYzIBYOMANQNFKOHo0lx7T89YBMUX7fBkQUIEorSUv/vahRf3bYdf3p7z4ccPBjJkDP8BmuGLSwuO5N1JH9sailgzgZ3199VDIoA+wrdRJKuG3iya1Wx8zqvrFrqaSfaumtWOt+Ly58LCs6Nia6e8krPq0uWRTzFLsxxNTZNwElTCtqTQNF3+dGfrForFYQ+E+neTsdup4lx4Y5/Fur1FvLZj4vij3xO2zCV+8j62P+aqZc37eOGy1R+eCn5JET3ec2rbM9WPMWn87LXzskG7Ys+ss3qPNyQFfDuyE1eRvVHF2gZZz1sqDdc9T6/OTWkq3TnDBVszr/uR6vIK3z8jfaShOtpbRrOxoMyfSLKbL+OmNFUeGuWImINMSh7IbwesNsW3OmLciP1eIcLxN+OGwbYs1C6NA1b6mwt3PLsWM7oXlfE9/eTP5UMost56Yz4h2zWXnK/KOOHXHKCuH1hScuHIweMAH2KFN0zjZG4d/il09GtYi2i1+sWFIF+zynpgm9jZtxa7+HbHz25dJi1I207pf2jdVxd+mFW7U8DNbitNSA7v5Tcas0tMt7IOHMme59cZKHu6cOfKtuZO6VpdcOH8w6qMO2CbGLGnxMd+v2q326JJ/I7n2xbH9ibNH9MRqn586nrnki3ew7O+SGoqOxfv1hbUSZW+UFm1p4q5rZK9/8l3IJxi2Psz95dV1+ZeT86/Ea7g7IL3ABQxcSDHwGZCDGrjpau4uKf88oeGQAguxpGJtSzgM5Em1aFWlVcOBXJsA0uMcUGs1MDJuvPvfW1Ihabf9t0CuTsIfi5ubG/wFsVgsUi/oMA51EA6HOMQhvzNXg2FJSUmPHj3I5WdSn2EnHKR/DmLfxF/WcADbogziHGazVaFQkSeJt2o4SMKhnOTcq7n0OM7fAPgx5uI1+vxApAMQp/N/jBj2HtYfw77ohE38EttOH4GXn4Kz56bSg2e2zRv9GfbZW9j4/hhz9eeNJQcaS/at9X471u/DFvYeNW9v2Oy3Ev0/VbMParmHw2a/k7jqQ41g88DO2J6EnnIu3SrdqOCmKdgbN0X29xmO6YSHavI3Nxft+eJdbCdj/L1vY1x7YkM/wuCAOmkwdiDd01h14dpBX3g7HC/zb8Sd3j6/zztY9av9ptrLK6a/1a8jBi+N6YPdPbW8KidVx99Rm5doEGy1SHbMGoL164AN7IV17YC59cVOb5+u4mywijOBiKFjIzedeAWlUZQirT7g7IbpQZ0KmFSkPsM2gr5WchCWpJBwNI4n/XCItuo48Rb+Wqsw3FqxVV66/fG51Z93wPq2w3q3w5ZMwJIDBrWUHec/3qLknTq+fvbwT7A+HbH+72FpkQMri9Kl7A0922MPLofopFtUldvgSA/JQX1porYy81MMK77DtNZv6vcuti7qPSDdpuIzZWyKkstircbmjcGaStJVwl2CrMThH2ObqcNzfqD3/wDr0R6bMBib5IId2eytER+7sm8BLAc2S9al2DNb5o/+BGsuPqHmnVkxtSNMH/Up2vLz7S5Pc80xpWC7tmKLWrwlfMHbgzpjzl0wt25YHwxz64ppuNsIwsEy8GkGPsvASzJyMtXc3VL+t20Ih6EN4TCThIPgHEZkeEsoP0hfcyThIPH5gP5/Y0ml1ZGdqa0CDyaSbvRGjRplt8v+64ZQDnEQDoc45F8tpPaCNKe/dOlShw4dPvjgg5qaGrJXJdeq25KPv72ksnjxUqIoM+FplCQcJmJJRQ0Jx0SXXo1lx7WCdU25y0ALA3BCgDhOnb/CWEaX5W9SF+9rKUwCuoPyUlp1ToSKnQQadjUXMFRlyTCsyIrQ8lJB7TYdP0XNZVVkB4O6jRoOo+lVtJaboGGjQ9oaX8ZouFSdIFYnjFByA3SiYI0gWMkOB9UpNTlhyhKmkZ8IpPAulqVio7lysygruqk4lXM/3Fixo7kkTS/erOGnc++uUZTQlEXx0rwIULejoThBX7FZwU9tKUuQcViNRbD8pJbCWFC3DvIALSfGLKRYJVSzKN5YnljHSZVX7JAJM7Tlqc0lIU0Fy82SVbhwqVbsq6sObiiPk9ZudR6JGYFQA+SwRdCE3Gzz/QXfgZ6A6Q3CcQwXbsE5sYC/BgjXyAuDcAED1GbgwgRLZaalYr2Gm4KL1pkrt6q46UpeuqVme10BreZFvIKbUp4brK+iNXGCQdOW5tK42kLfJvYag2CLoiRVKwrVigPlxVRZEUXOXomX+6v5AbCtDOIAXLgaF64B2lQlJ7ChYLWpgmmqSKrODVbxElpKGcbKjc2lLKUgrbaAquSlQGqiFSbL2az6AmpjIQMXbajOpZvKdzQWJoLGvUpOCv9RAJBuMlen1xWEN5dFKPiRzWVhyIFs7W5pTpKmZLu2bIuek2HksUz8aJMgzCIgXJvzw0k/HPW886OHEUajlhZgwZFew0La2L4mHMTaipU07LBvi7Uf2PZel85/g3CQVIP81cjlctKvV3Z29ocffti+ffvbt2+3zexYUnEQDoc4xCFvCCQT9pMg5syZA/vNKVOmkPMztDWTuGTP8FcJB9kjQ54xfvxEHEcW+1qNAddbEOEwAwNACwhWo3K8a6969nG1cJ2OHQSqQ1qezALVYUDiDyQhoHqTuogFxMyGvGUGdihoTgeVdAM3WvkyENQmAUG8RRivLQrVlobKX6wGDYmgLsHICQOiKBha+NFAFAckFH1ZiJEbDCoiQFWYkb8cSJBBgJkfDKrpyGN3bTLgR2kKVyue+2uLgy2QKIgZoGG9lh2nLokGkgQ9Nw5nR4GmNFDDbMnzA43J1U/9gHKjUUJVloTB+ihKghRFgTgnFIhitMVrgZQOyqOs3ADVi6WAH2QWRii4FEvd+hZ2tIIdouOtAUoKUIQbxYv0Il+8KrRJTGuo3jV8OGYFEhNQGmDDWKzkJs+2hMOI5uqIcLi7vNVIaDhwTjzgBQLBGgt/LaiCFCfcIopoeelvFsRoysLVJeHKolBQwTTw4huer4EESC+IQcfLNSRaKpDfi4bcFaAyDlStAXURTbnwg2TquX6gfJUsfy2oTgJ1YVaJL5DHqwoX4WWLQdVqi2iFuniJ/NUiUBkJ6uiEl7ZY2Czq4lAjL1ZRGAaqkhrzA0BVgo4TbhJGmYQxBn6MVUwDNSnaslgIs5AhLwgF0iQ9L7LhuZ+scCVQp4DaaGtFCKiBdQ6tfrLazIX0cae2gArKU628SNiAgL8aCPxRyAsA3DAjN60BEg5X0vFXC7AS/uPQLmJ0yh1BOEiYiRUW9L00o9No0FkqpO8NSES+mjrFflT9XyUcGo3Gru0Ti8WTJk3CMGzx4sVkCmkIBf7+yUQOcRAOhzjkXyrkkgrpoaupqalXr16w99y8eTOpHG5LNf6GDQcgnBPA0NV1uFyulMkU5JKK2UoQDituBRqLUT12WK9q7nGZeJ2hPMLIX6opnAPq/A28qRrODINoDTEoUgAvFELxbKmlcC06AKw0RPt8JSiDiREwDupYgBtuLQ0AnGDkirs40HZIWEkwYIcBfiTgBgHOWvzFYsAPAPXxoGy1Lm85YEcCdqzxRSAQx4HSQJRfEg/EFFn2cmtZBJCmGuDoCPPwY/EXa1A5oljTy5XmwlWghq4tDNAVBQJhDBBGWSHVqKBYyuAjQoEkWpvnC0ThoDJGnbMIyJPNxQGQEpn4cYgAVVMtbH9LmV9LtoeZs8LMDTGLEpScTQrh2XGD3gEmNWpFPTlIIjsOKCThwNGgCl8GMhod6/JOPfeMVrhbx2ZZuBFAEKh5vhiwVwFBEBCEAE4Q4IWB8nhEtjgRgB0Oyulm2FD1yUDKrL3nA/jh+oLlQATbBLZqPGwQ2G7mV3QAiV3pGtBAs7yMNb+IgS1peLUUz12OmkUUqcxdBPhhgBcOKqigiqV55g/vNT4PAZUphvxQwKEBSar2WTBo2aHJDQC1TH3halQNcayxKEiVt8pSGg6aM9GBfJwIA3yD9SwACZ801liyXPV8IWEpvJbYKpwIajaqn8GnZBpfhQFOOHpxXHhpNXx9APJRdpiFndbAvTjKtQdybW6RA7TXSQ+sBitSaRgsNrZhhM1lRN7giWUpdG6bhTQaJXnG9Zs3/t6SitFohDSaVN3JZLKYmBj4e3FzcwNtNq2oVKr/xNEd4iAcDnHI/z2xb38lfQZkZ2e3b98e9qHXrl2DnSbsQy2EgD9zkviHhMPe7X7xxUBbChwNjK8JBxxBjSbdyOGfSfhnG8q3mxrSGkuW6wVwlhygFXqDZn8ZZ7lWEGrhhakgP5DEoXFUHK99sRqUU9H0mhNhLgmBKTDUvvCHKdayENtAK4hDXIEbDeAwzw03QQoijITEBX+12lwCx7BIIKKjc8tELFAWC3MaX64G0mTAjtA8XwPq0wEn1lACqQbFyotF031YTlk44EQiBiOMhjwDnUFfzcRLQ828KOIc1FBEVrgReshCqhj6gjWW0lBQw9A8XwU4kMcwgYCiL4H0KFxfvBKUh6lf+gIpw8ChGAWblKX7lbzrE774EOiNiFlAUmFoSzisepuSA46g8Jp8lMsHtdxLCuEAPIZcAAB7G0lEQVQxBWe9nssyw08qpRtK/I2la3SFK0EtXfXKHwjjTOwIEzsKVCU05/iDSgZ8uvLFKtCSjheuAZww1D6lNHNRLGwuY2EYEG42F8CPGad7GQC4GYCXqX8RAiqYgE0BwkR9QQRoyMQR/YoHAobyWRAoT4W3A0maJi8SlK8DJQxrIR2IMkFpAihjGItDQXksbBDFc/hSKECaoi8JN5ZFmjnosF8TO8zIDtZDdiiJtPAD0YF28I1XR5u5oRZujL4AkpIEaxkNPYuL3h06dwYhDMXZ0UbOein32kjXT4nj6YlVOdQsOME50CGxhJ4DqTfs5i9tyYRGpyUPjP17rs1JpR38RUBWcezYsS5dunzwwQc5OTm/4uXwB+XgHA7C4RCHOOS12D0K2D0USaXS8+fPQ8LRqVOnR48e2XlG28MgYGb7jcTmQNOfaDhIDfPIkaPtSypos4UFjacNSqkJ6JU4PtTli4qKu1WSU/XiXc3i9QrhepUwRSWJVZTHysUpclG6RpiAtioIaG9ASPl1CgLMxtAJEgiwEIQMnZDWCgaRCC8lIfBTINDeSz6DMEsk72VphCzkVLsVZFHwEsxg5KOnqEUMlZgBw1+BuJEEGWfY6mArAdY5DhdF46JInSgW5RFnyvm76kqPSEuvTHLqCUwaRDXUZjRIthIOEyIcJMwaAxxcNYMHvF/B+0lefq2BfUgp3KMWrkNPF8eqxXEQGhENgaiGio+qoRMnakVMtYCu5MeTgE2Bw/rw0hBgi/ETcM4mBH4SjOu4GxFg4wjQzmEdL10jSLMDpbRCy0kBVVvVxYmKAiao2QUkW1peMHRlyagNUR0Yre1gg6EiRcWnKbgUopIUBS9KJYw2VNHUkmgEMYVowwSNIAlBmGB7d6I4BCGFqDZDLthRX/7wy/4fW5F9rU6rbYChSlUPx/pWwmG1L6bYCYcW15G6Dcgz9EbDzt27yNPq/4hwQN5A/ijgd5788sOvuv1nAuOlpaWQbcBfyrlz5xw9iUMchMMhDvmvBPansCe1n3Wp1Wrj4+Pbt2/fq1cviUQCZ3IajcY+XWs7b8NxnLjrDzUcdnv+IUOcWh8GmpvQwoq0vtYEdDgwKAxg9FdTC9jPyni3K8Q/1Iiu1glu1Auu1ItP1klOSkVXpMKrdaLz9eJTBE624ngb2BNRnjrxaZgfQXy2Dv4rOWkDunSWuHShTngRhQgoW6PoFAnydqnktFR81g5UoPg0vGTPI5Wcqi4/VfMboBt/A3hvo/Bsk/B0k+h4o/hwo+RAffm+uvJD0vLjEt43DdU/NtZkicoeTRgxxKhSIOcb5rZLKsj8wGiDVYecc5uGDx/I5zyVlmeVvDjDKzheKzpTU368puIoQvlxKWw0ia0mlfzjFbxjEFWC47XiE3Xl39RVnKiTtDaa6Gy96DQRP1UvvIiAPv4p1DgQ8LOjysMWON8GZ+vaoIpzoqXyolRwurLkaB3vTA37m8qS44rKS3VEE7XidVNU8I5W8o/ViI7XV5yqrzxZIz5cKThQwd9bU3GIAFl/+11kBL6747CtUEi8x2rxVXZp9tixY62QMJu0Gl0ToeHQEYSD2Jny5kG7JkKfQZqLkj5GIfNwGeba1tP5r6BWq+1fXdKSCQq52VVHCPxFjBo1CpLyoKAgRwfiEAfhcIhD/iuxT9pgpK1d/dSpU+EEztnZmfTVASd8ZIerJITcK9vqouMPNRykIsRstj548EingyWg7ReQwJDHa2nM8kZcMWSM54ARnr2HjnYaM36Ak8vgwcOHfunu/OXYoYOdIE0ZPMj9y0ET+w+Z8LnTWAKjP3caheA84jXIFHSJyDPU/fMhE/sOndjXyb2v8+i+ziMIjOrrNLav04S+Qyf3HTqFwGQyD7xlwBCE/kPJR4wlco7t42xDX9ujUQYiGyxzdJ/XGPsbuLcFrM+XgycMHOQ+cPCoL4e6DnB26u8y6HPXQZ+7OA0cNfqj/oO79R7oNt6r28dD9MRaSrPCZLXYdsGi4ZOwS7AQawTwD2zlj3t/Pmb8hM8H9hsxznn81GFfuvTp6+rU23UEgsuoNlVyHzRy6hfDJw5wcf/CddzA4e6D3MYPdBv7xbCR/Z1dCLjCEFbjcxcXW0u6uPSFIJvLxbWPq2tfl+F9XEaQQOkuw2E6AZSzv6vrwBEjIL5wHUaU5jpw2Cjn0RPhu+jb2ow2oBcxesBw9y/cxg8YPq6fy+h+LiMHDB/zhdvYAW6j+sCnuLr2ef2sUW0BK9M2hO96zMRZb7/7IenTS6WV6fQteqNchzzlG0lvo1a771EialdmyJWK/2aXit2wifR3Zz+DELTuPZk3bx6GYU5OTo2NjY7TXx3iIBwOcch/JXC6Zp/Mwc5Uq9XC2RvsQyGfGDJkCOxVIfMgt8W23RwLM9g1Ikql/E+OpyfUJybiQXqj0YyWVOCDdJBzGA1A32QwdP9icljyqYXh2/xi9wTQjgTHnwqJuRIWfSUk7kxw/JmAuKur435cQb3uR7+6nHbVj35lOf07P8al5YwLfowLtpB+yY/x3XLaFXQV5bnuR/sRgX4VpsMMfszz8BZ41Y9204/6kx/1jh/1ZyLDdXQj/dIK2hUIsgQIXwbCUqYtAkGUjEDmJPJ814orv8HVtoDVWBV/c3X89VXU7/xp51cwzixnnvRjIcyN3OufcCos89uV1L2fj55brQMqALStjr8IzydGq81Rt5HcuaLSA5dRHkvW0JYGs5ZHJ6+iZfqzdsCilrIuLGVeWsr8rm0dljGu+dK+942/tDT+wpK4b5fEnVsSf2Zp3KkVjFMrmCdJLLfhlB/rFFkrP+YZXxbEqVbA+DkIPybEmdZsJyB8mcfnxx1YQj0MS1jGOLGCdRqWMz/qKGp2+pU3gF7Ed4spF3zpl5dQv50XfWpe9Mkl1HO+9G8XU876Mi8RuPAmiMTX7UyAeQk2oM8KlvNID4NNE4QOHibUG7jNl3nrQW7osEBi9wrpgYMMtbgOhm4jR/xHo1H4c7AvHZKGoiQRCQkJgb+Lzz77TCKRgP+8h8shDsLhEIc4pFUJAWmB3WiUXAcht8WKRKI+ffqQx0OQKQqFwt69knf9uYaD7KDhDDA5OZVUdSjkGrJTb2lpMgBLswl8OWHN/IjjK5K+D1h3ezHlxpLYu75RuUui8hbH3l0Ud3d+bK5PXN4cyhMv+n1v2n0v+l1v+h0vxm1vxi0vxi1bSL/txbjjTbtL5HnoTX3sTX2KAOP0O96MHxHot2EG2yVKrjflmTf1CSzQmyhkDu3OHOp9CJSCyrnrybjrwUQhBPwXPZd2n8wzB5bTBuTV34K8FwLe6EN5PDf+sQ+6F9b8Ry/mTQI3FqX/sjDp+jzaqelBmwZO860HoAkYqzWNOHlaGxoxjcQOFS2yiLQCpRpU1lmGjV++Ku5QaOaVFckX51JOe1HOw9I8mbc9GXcgiKcT7UB7ODPq1qzYO7Pj73jH3ZoV86NnzE3v+B/m0n/yod/0od+Yw7hGAEW8md8TuObFQvB8Aze8mEQbMm56M2/APCgn6zuI2QlXZtIuwH99Eq/Pon/vxbgOM0yP/Z5omfutIBoKvgj6nalR1z3if5rNuGOHR/yPMJGsMwoZtwncskWIdE+GHbA978ylXwtKPDN22qp6NZDK9TJco9DKIOcwmrS27cSm1jUpRDvMhPGohfQxShqNQkDC8efH07c9Vh5SDci2SQ3fli1b4C/i/fffz8/Pd7ANhzgIh0Mc8hfYht0m9He9ewmFwkGDBpFrKzBOJsKcrQ42jISG+c+2xZK99pdfDiIJB+zOdRoDnJGaDNbaFjmc0L/dd/ZCypUZERc8427MoT6cE583J5btHcv2oj7zoD+dTiuYTiucSXs+k/4MgfF0JuPxTOZDAvdbIw9RIrxE5qHneVALPKgvPejPPBiPPRj3IXXwYDz0oD/xoOWhdEohygDjMIVJEAv6E0/aM5SfDst/Op3xdBoT4gkRon9hInH1GczmSYPx1yCqROJJ2xDeNZ35BAL+60nNQ6Ch+k9nPp7GejyNuDQ29ObM2FvzGNc9w/Z/5r6g2giUyHBUj7fujUWEw6pFsKBD2GH7ac1gyNigaX57PMMvLkh+NC8jbwrjISyTgO1xM+nkZ3nmxcidzcqdl5A/NyHHm/5kFu2BF/UBJCJetMcQnvSHBFCEaIc7RJPen866S+D+NNbDaaz701HzPkFthRrzoa09mXdQftad2akPfdIfeyU9nMG4N4V2z5OVNS/9hUdrBVpbyXa7T3KeV8JTT+YTGNojMBG+C/jWZtJzX79f9EKfEG8zdzo9D4GRO53xDLYq4o5r9w5wW6whtEGwoXQWne3YGZJw2L3BI8JhROfXA4se8mqTkbQbhcxjxKiR5EFuf2I0at/AZacd+/fvh2zjnXfeuXLlCplHqVQ6vJg7xEE4HOKQ/yzkGVR2xQYZt2/8Q1skTCapVEqurUyaNKmqqqqt6gIQLgdMJsOfEA7YI1ssYOLEyai/JzyNoiPWcTQYwL8KALo6rfRL+mVh6r2J0VdnMB5Mo+dMoxZ9RX81OeHBhKTb45IeT0jInkbP9qBCZHnQHnvQHnnQHnjQ73nQfyHCe+hflPgYZUDZcmbFv/CKy/eiZMHx1Yt224v2sxf1Fy/KI6/4HJQe98or7oVXfDa6Sv8JXYWXKFmzCMBCZtIez6BDPCLCxzPRQx+TV71e4/Es6q/wqLV6r8OZ9Ecz0b05sFawbjNo6LNMpedMpb2YRn3lTS/2TS4NSHuxgnr9s2G+dRo0guLAhNscbwDijHU1cjVhwZHvUTNoVICPBi5fnXhvWWaRe9TTkXEvpqdzpzKyZ9JtjdC2nrPjs7xiH3vFPPChPF7AyFrIeuITf88z6u7suJzZsB3icxGISOtnR/WcSYMtkDWDlkMA1hm2Zz5qMVvObLKV4LMmhN+aQXk4Keru5OgHc5MLvRNfTY7KmhYHM+Sg5oVobSvUwtQHc5nZM2N/gZiX8BQCxWPu+TBzYfnwlc0iC7e3JJWoDyV3JjWfAIygus2m3g5N/+kT54XlctBsQA0FuZjJiquULTb1hoEAecYu/LYh5ZBtVYU0FLUA69f79v75kgrpVMNgMJDnAdXX1x89erRdu3adOnXKyMgAbXavOMQhDsLhEIf8V0LaitqdbdindG0VHuXl5WPGjIGcAzIPcq8svErmb1U7W36D17xErdaSGg5kNErmNQOdBvnUhISju1vwXPpPU+NveqU+nprwZEpC3leswq8SXkxKfjAp5ZeJKY8mJWXNYGZ7MrJnMbJmMR55M+7NZvwym/mzD+MnH8YPRPgzoZ//ZTb9njfjgTcdjkn5sykvZlNzZtOezKY/IPBoNhqr8lA65dXrq/AuCBiBV2lZ8N5ZjCfwKZ5MiAeteAQTvekQWW1ApDDIwu/50H8hcI98FlInMLI8GVkeTARUeXqOJyPHg5Ezg5E/nfFqOqNoBq1sZnyJZ0y+T9QD35jrn7ku1xIDJW4xtboWRUoNYMUJDQcOp+8aHToFtbdL8KLYnz1jn02llkxJKh9DKZrOyiEq/IiofNYserY3LRtWcn5Cjg/t0Tz6/VXrcin72cknRKyD7OidhfOoj+ZSsn2oWT7UbB9Kng8lH7UGBOWFN/WFNy0H3g4rTNQZNghqNyJbng81xwe1WzaJOfRn8xPyp0Xdn5/4MvFbPeOMZm5SwbSohz60J3NpD0jACvi0Ni9s57n0h4HbCjMuNGRcqAve+nI+/Ze51Eew/Fm0fG9aHqo5arrXHwTVgZ6HwICVgV+DLB/6Hd+4Cz0HL1IC5HhVpjfgZr3tW2duXUkxEd7l0EGyiHAYjbZTVMxWS4tcRtqQtj1a5beEQ6PRkN9hkoWfPn0aI2T//v2gNUPbs+kd4hAH4XCIQ/7mUoudjpDWG3w+n3Th/P7775MzPNDq45m0DCUOaXttK9qWrygUilGjRslkMvvRKrBT15vRbL7eAglH4HzWTx7Mu1NYjycnPJuYkDuZ9fwrVu4U1lP7AsE0etbMxOyvqHdnMm4vyngwL+GmX/oPyacLWcfz0s4Whu28N5fx7VfhR2czLi7O+BlZddCy5ycUL0gs8ox/NjMWco7cKREP4XjmRcmbEv5kHqtoUXKZR+yzuayXaOEm7vEcZvaksJ+WZDz/KvrnWcwH89OezmT8vOEnXeLlJhiZHHtj0fpnM+h3ZtLvQf7hwXo0nfl4Kv3+NNptv83Z40NPT406E7z9ScpZYfTeomnh38+MuuVJfzwl/uFURvZEetZXrGfT7UstxIrPdPrLafRCiKm0Am/WS2/a/fnUKx+PWC4D5HwdkPtgTaTpATIfha9DawEGQt0Beg3x94v72Sc2xyPmpQetZCrtBVpMIdY+yMWaKawnxJLNw4nRNxamPZzP+pF1vKgavkQdqAOgBoA8GUg6z50YesKHeXPtlpdzaHcnBV1flJA3l/ZsVvwTH1jP2Nsz436cEXfdJ+GnuYm35rBuLUi5Pz3mxlzG3fmsh16xdxYlZHnH3psT/8CHeh9+Xm/KHTYAz1RgTODZBSl351GvL6Lf8Io5t4B1c3rEFR/mo2XruJNC7s+iZB/JAvkq8EoHKgBg4yDtROmcmO+96Hdhk85kPpxOrJShz0KCSHkDLGR2s4x2+ROnpSpiL6zRSmjXTDZzUbQpxWZpS2iLLMiotNWuGZlikBqLrKxs9NW0wu+wzk4sZLJmmIfI/JpwQD7BYrHeffdd+OW/evVqY2MjIJYg7Uo++5qLQxziIBwOcchfFj1xonxbLQWUhoaGxMTEdu3ade7cee7cuZBG2EkGDO/cubN48WKpVNrc3NyWuFRWVsLIuHHjyD6dNDu1AoveggaEBjPoPmINHJY8GXfhGGkjHAm5U5jIGsOT9sybkutFzZ0GZ9sp+dMYj6bE/+BBuT6f+f2Gi/wiFagkhk++ERQowa4fJJ7RR6ZFHZtFvTAl7Mq4gO88Y24vTs2BU+rp0ch2cl7CE9/U3AVJTycHX5sR8/Mc6v1FKc/g1fmQPYRfX7Eudy7jl5kx19ZsyR0XdGp63MUyAH6uBgvSf54Ycc6bdXMm9eo0yrUFGY9mJz+YynwwPeHB+OjvplEvL99469xLfb4M8Mwg5RvOfNqNubRf5icjc40ZSflTkvKnpz6fhpjTY8IS4qkHLXcm7fl0WsE0egHkCl6JuV6MO/NoF3uOWiojFBtogYBwzm2x2rZ3om2ZQGsEaCoNp/W9hvr5xf8wPybLKybXm/oCLTQwniDTEMLoBDYdpDiQrkHy4Z38xJv58xz6lc3fCSQmsOtiXureG1vO3BcCABF75N6E4F1Tw4/NZ15bmvzLAsadhcz7E9d+Oyvmqm/KL3Pp3/um3ZjHOj+XeW569PEpUcfXbLm3gHXVM/KcX/KduXFXfRNuL2b8tJB2M2jjU6/ob4O2PQ7c/nAW5axf+vW16276RB8L2HB1Rcb3njHn5tBuz4iGNPHVpJCfNnwvO52vDdnxfcbZh+VWcJcNFlMveTFu/w63+APAzMuo35GEAy3NkS6+jFaScBBbYXETUFmAwgrpGfE/ueRXW1tN6t6MRrPT0GFovUpvIbdNwe8tDAsKXqamJRLqEdvCImQnc+bM6dixI2Qb33zzjf2clLaGHQ7C4RAH4XCIQ/5HArtRUlcMCYdMJiMTYb/8ww8/vPPOO2+99Vb//v0fP34M2QPMAEfE3bt3d+vWrUePHuTUELITu6oZpmzfvt2u8zAakUEkZB3wT7MJ9Bq+cjHzqjf9NhyS4TCJBkvmMzhwwrHZi/p0DgVxjq9isj1Yr7xYeXNYT+ZQbsMJ9O6rUkg1Mo8/Tz2cvf+GiKsDbC2g7MvyjjyxmHUpfMe9lRnfL2WdRxtAgvfCMGb3/Wmh+7wiD4ftuLOQdto3+eIC6qm51G+WMM8tZV0IXH9nIeXyQurFFcnX50R/s4h+LnDDzeCttyJ335tPP7163U3P6BOr1t1axLqykPn9jJhLHsy7s1MeezJ/9KRfpB3PzWkGxRoAR7ON59lLWNfmM24tSHg0k/pwJvlZEp9BNuDBeEyYZz4hzE5zPWjPIe2YQcudnZA9h35rIfX8pyMWK2xLASbS8QaiGua2hEOLE1YvPZyXLKVcnxd3zzPusScjewY9ywMVSNhdQipDf0ng+Ux6rjczZ2bUrXmUH47cVlRbAGvX7eVR+2LXn9lw5pdyAE49EfinHT14S8jTA9iAAj249hJQdmcFZty8zQGvmoHACHKkoEQDDtwRCa3oAz6sAOFbbqxK+ja3CpQbkb4ktwYw9j5YkXQ2WwouvVDG7Lm1/5agVAEeiUGRErxSghNPWhYm3Jgefm1RQtbKjGdTgo9BtrGAvi1owwGBDuRXgrkRx+bQ7hIf4b/CHOrDZdTv/yrhMBi15HYqqbQe/j/MdaRcpjYQ5/Kq1eibefnyZcgqRo9xg6WRWpCysrLevXt36NCha9eut27dsv804DeZ/G5DtmHn5Q5xiINwOMQhf0dIT0ewV7WbxRkMBtIDGCBOyHRxcYG9MOyg3d3df/rpJ8hIFi5cCP/94IMPOnfufP/+ffJGO1MhiQu5OkMY7emByQi7f7kJfDJsuS/jig/tlicdzl9tG0PQzg4a2trqE/9sTnzujNj86XH5HnHZixJzlyQ+WhB3fd03PJEOrDuav5J60jf26L7vOLUAnH8s90+4uO1Cyf9j7y3g4kjSxv/s3t5mk40Rd09IIC4bD4FAILi7BSe4x9Zyq3EshrsO7jrIDDPDDDJoAnGDBB/X+ld3A2Hl3tu797f3/i/Xz+fZ2urq6mph0vXtp6qeh94P2kdBJwtUPOD53iR4XEntGAPkF+A1OqyQQHxc3s2FFeBmzWOxuvPPVgEJZUzwUgheiEDTS3ApjGjocbt9BGTUDZsHxFY/BC2DgN4H2obBMzHwvtWILNANqNW7RNS/UOh4tezHdObleGo7G3yf0GHgl6nlmavhU6boXq7kU3fcm3TCD7FAQNpA14bUQIpS9WnA1q0o+9RrBtZq+ubq+8Su3KnDwQKpS7A4ZO+BA/XVzeED2DFKhgFYIK9j4Jum4V2s7F2uHFip5FeNtOZNV/FuQlbfYGtwfJpUfGhq3iQtX6K+X9m11DcPR8GlYKK11337gHuW/tdaBwCjT/LV3dyWftDDApBASpr5b6QgufKNvmto+zvwFoDqLlEW+W0/AF2jgPYGxJb1vJSC8jbO+Vt5tMcgvqgrNIUGP/nreySm3iFPhYD0BBh4h4TltsFDWt4CAr3/OQDdAqDnn258vvKkI8H0fIVBUMbF6IqIiuZnUgCrfRteZOydoOldBa9W1Zv2R1INb6KJT9ZSOf0/ABwcbFvA5yIzSKVizMIhkQB5uZ3YfI2xUcSScfnyZQgW8Ac88/NP3757DWnbz89v1qxZn3322a5duzCjHWb2wxaQY4Y9LpeLT+DABQcOXHD5X8mkU6/fjRCL2TC++eabFStWzJ49G76mv/jiiwULFmBR36B89NFHUVFRWCOvXkESAEVFRViz6BC4CEi5QARpBowJwIptJqZ+6do++bAXV/afXNtZMwkcGl4NmgEMJU/ScedSda9KPf8SPS/Cz4kP4Ieq/88FRm4hmnY/GrsHP+IA2gsQeLP4wShoeCwh1D4qbervk4CUii6Py1Gwx4BdYwaxp60PybwRI/m6bhbMf3kr58fbNQ/egJJ69pW7pAEBeM0GFmfDG7pB8yPgEJDQ8hT0CUHHa1BAEjweBuVMoOFZq+JB0vKvU/fKV/dIMg5McrqcC/HlSlKvsX+ermexlnftKbd6Vd8WFR/mKX8msp7WrwbSBuL8AwEOEmQOqKe8a7UDiDo+OUbeMWt2aPEk2MxGDDiQ/vE3wCGCwDF/m5a+X6q6T56ST7FSUOmJgHLYlJoXXc2rSRVVSB6q3nQ1b4qqB6QNopFf6c8JTyFw/HiPaucXbekWYucb+oIN4L3cTWt4zQV/Cy3Utf3O3vfe81Hwig3sfO53vwHNj4GDX7T7xWR4429FwMb3vqVn2HM2IHXwLN2uuQSEe1y86/dNLPMJeDoMbLyC6zoE9Z1iu4CojKr+J2zwzZ2ak1aXbxc87BUBp++Izt83qTnmn7BOtriQ39iPoF7rOwR0UktfWvhkaHsRIUmoedH+SKrpRTT1zvyngEMqEYnEyPptFmscmhfMXyJCVwGJReDsWXcseNCCBTLTPpp24WLg/v37Mf7w8PCY/OfAQ2XqP4RfOefFBRccOHDB5V8RbNUffJ9C5oCEMWmrmPDlJcHyP/zww549e6ZNyIwZM2RkZGBm7ty5lpaWmEkDNrJhw4b3pg4eCwEOIQ/2BCwBWLXNxMw3Xcd7HDgm3Vqo+MJeGTFvqKOrJSF/qHpX6wRUaPvkarjHX0nuhN/Hl0KKXb6Ocf0qysY/GG72jIGf4qrfwY4/psLc46qtXzDzFYB6Jar8tQAkl3YZOH333Z3CJ2Mgu/appdf1H+4Vw7sKTah3DYhS07vk7H3vTiyV0QlaeoFbUEz3S8B4AE4bX346CJ4MAiu3SD3rkJ4+0PYaaJyt1/Jv0/SuV/MoOO2aaBKUGhBMhJ3oz4k9Rn65mu6FEDhU3Ukafm2qPl2nfNuUfSnI9ftgOk4bk8Ch551j4jUBHEiviQUh+7WFgwc4bCCBhDRvu46uf+pp3wJF32LFc6UKCHCQYE+MdcmYQtpQ8yad9qxSdy/Q9coJzup7KYIQUGfsGmzodM3WOwx2vOR2Xk7VM5hxCboL1c47tKZ57J0AOPrdb+oBlA5g4hDs4p/Y+gSUNnB1bK/ZeN57wwVl1LcBf4ttfyJ6x0MWibwZQx6O3+WEx4OA3gOMnUNj857DP4HDhTRDj9s/JTQ9lYLzd9r0PUuMfYgmfhVabilGvvcNfK7fSifBah1vgI5DgrZXFeqN7Q+ppneVqU/6Ujndvw8cAhFgScAYChzjRo2RUWRCqFDI7+tDZn1uk98FaWN0hKN6SmPGjM+hoKA87aOPp03/7C+Y6Y5IJE5OMsUEW5aCRVSZdGCDj6rgggMHLrj8rwSywtQgEZgfUpFINDmwMjnI0tfX5+bm9ikq8E398ccfY/5J//KXv5w6derZs2fwNS0nJwff3Rh/jI0Oo0Mq/AkLh6mpb4a2T6GabxXi6Qtx7oToSb8GFd8GVfiZ7tNw3KP8VGCV5vlKvQtl2gFZ6p7Rl+Mbe4Ug4BZB9+zftJwu2Z6/3jkMqrsHUus6YX9i5R9q6HLVLvBe2xvAfA2+Di16wQPBSRT7oPtfhRS+EoDb6Qzjs9cDrxBgPjiR5P+3RPilDjuWFyNICvtO58A7zKeA1Mb3uJTwsB/2smw7n0iHgDhyF3gwDE67E/XPd6p5Vuufr9DxTTU+l3TuPrGNA75PbtP3S1P3yNbwqVB2r1H1oyt5MhS9GKjjsgbUbxhyU+N+wMaHVFALh1fsmh06PMzkj1g4IHsI3wMHEocMfkrz2KidZt42Q10/gqpP6QmfSoVz1ccCK1GaIU0qsulbA5+n3vlqDZ88Ta+0qxmPu3ngXFi5mU+o/YXI9Mo3kLSisrticrv6RcD/xxRLr5umbld7hkDrS+B8Pqb1OWh6ApwC09wuZjf2guanwOPbfC3bq83PAalbkFHRDf/wt5Mrbb1/Jrb09wwAU7drcFddJ7DxS8qq53YMACPvGPtv0r9PauoVA7erZAWrZEOfah2PQkPfLIuL8WrO32k6f1fexuuXANdvazS9y5DBpj+mmj4lpj5pU4ADXdbzj4BDjPgl54rFQhSUhVaWZ16+6Nu758DnM+d++uln2O92zpxZ0z6aNmfuzCtXrkx1UYONoUDIwBzlwczE4CAAE4GUccEFBw5ccPlXBL5qMTPyZGYyfhVkjskBFzDhhMDCwgIbT/kYFZiZN28eZvPYs2dPY2Pjrl27MLsI2ghUoUSMzOEYEYDl28yN/QiaPsWqvkRkBax/wwl/ClRFdAqkMqIUtfMU1fO1J7zylbyz1AMy9c6n/0To6pGA83cLTQJuunx3P7G6/Q0A0aX0DMrDHh74+l6Zmd9dfffg1nfI/AOPHzN7uSC65JGRVxjMPxeDq0k052+T/W/kd4+BuznM6DzGCAB302sdzt2q7x7tGQYO50Lpz6TlLcP2QXe73gHKE+B4MdbA9Qah7u1jAVD3qlTzJR84k2bwZam6X4yKx61zMRXdEvB9Rov+uSR1n3StwJJTvpVqAWRFH/IxL7KiHw25HT/EpSaqyH2hczzrNYLQORze8at36HLfD6lwJAAZcpoMQiYCAh4QjgOHvLmuT4Gqd/UJ73qFQPKxgFoIahAy3qtfDTKC41el6luM+Cnxzbic0vMUgETyQCKpD14ofFY1D8DZy1lf3q56KQVNfeBaSn0xc/g1AJFFXVZBka1vQRkTaDveM3KPb3kNytqAsnWwy99y2gZAcfNoQVP/Ex5IremKL299LQUdg8Aq4M6DMVDSBqyC0sOyXz6RALvLuepuYReiSPC8zldq9f3LtbxKDQPKih6AzGbOj+mUFPKLXj6gvgAGPrnqvmXojNo/pBq+xciyWDntfwQcLCS6imQ87i6HOzqxMhb5xb562b961fq/fDx92rRPpk+f8de//hWLNT/9s7988tdpWJCUSZPGpNeZX8Vpm5xYjQsuOHDggsv/e8HWAU5988LPvjVr1sBXNjakgnEGLLGysgoODmYymbD+jh074LsbW6jC48FUJJBI2SJkkefKPWe03NLUPIuPu1UoBVAUAqkKAY2YKvozlPwZyn6ME74NSgFk1XP16uerlbxyVLzSvic8fQRAEmUkpOBBcbcI9pQVPXzH7+K+iip7BpCFFV8nNCTThp4DEFrc4xNeCru9nzJbNL3DXK/nwgO/iidr+96G5bDC5aS6xLqupxKQVNtyv4z6SIysttVy/6YRckYfOO16mdoHKh4LdHyuGwaGFD/g0IeB/lfVh12zdb+tOH0u2eDbhItpNffIDx4AkN45+mN+p+E3hGPu8afPF2t9U3sioPyAe4liAHXiXhqhKqF5Jf/GI56Ib2/NgGI936Ql8rpDAoC5RpMiUxDGkMBj74FDyAPiYSkYAmDhdnttz2JVd5KKb7NyUMthz3qINUreJCVvMqokJZ86VKtVA4gnPHJ1zpV8m/aS2AeeAPAQosZrcL9i4OzVqpOOoSYXEu9XvmjnA/gcusQgr4utFxBucjGm8iko6gaaHrH6fmnEFyCjBdj9WK7kHEYbBBVPgdPPKaQ3yILkLiGydKXiicTofAR8LBXPwEGr4ASKuF0IDC+knPa+cyGR1iwAPvcYukElii4EwwuloeXDLXzECUcPANXPwN8SOlWcU1R8KhR9a/6gnvIuMfZJXb5NdxiAUcSHF+a4BPBZwr8HHBIR5gtMIhAgfMDl8sNC72qo68yds+Cz6bM++ugvmEHuk08+nvbRNKiRkZEYbWDrYPGAKbjgwIELLv83wAHfvxhwYAwxPDwMX9affvrpypUrDQ0NExISXrx4Men7C2YgkcjKymL5/v5++A0vkArhJ/ywCLwTg2W7zxj45Rh9SdK5xFD0bzgeQBlXf9oJ//FFnkp+zQo+NAWv+pO+9fAbV+di7XeEQeoo0oP2AkB9CyKrhpx+KNRwv2f1ZeaXMXTaKOgGAIJFPJ2rfS7e825tJwDfZnRoBMS43yYyxeCnvF69i0mmlzOr+sCtkl7v8Bz6CNILwn4XftvW9QG7H5JJAyCm7o3z9dwmNsjuEll8m6x3PiqRNkwbA2oBJUfc8k/65R5xi7yUyexBL4MhQtLC58A3lnnSP+uYV5aif5FSUOXpr2iIzQZdqoqsVkVWriIKCxX8yGrnSRpBJXoBacv3mI6hzrhR39vYnEchZuTAgIOPOBxF5m9+vs7cwLNc06vxhBtNxb9F48vOk+gi25O+dFTRPGJNaVD0RNzAqwdWap4r1QzM0/DP1PBP1wwgGFws0TlXpOmfrR6QqRWUpn0hRfdiks6lJI3AWK0LifqXMvW/zNE5l6PunwNBwfjrGu0LpYoe6UccY6x/LNcMTFZwDXUOLta9GG39Q/pJj1CrH3MVXO9p+KfqnM8/6pKk4pOlFZSr7JFg8WPBKd8Yix9LDtlHaQdWaPpVnfYqMrhYZPq3LMOv4+HD1AtK1PXNPe1RrOJbh4ZK+UOq5luhczZu3gblUdRtCXxKfN4Yb5QzxcIhFCEmIhbiGB51PCoVSzALB58/bpzbuGGLUICsNn7x/E1OTp6Xl9f+/fvnzZvzl0+mQbW0tJzqTgb7JePYgQsOHLjg8u8TbGBlqhN0FosFX83p6ekQMrBCWDLVFRI2z0NDQwNMWKS5fJ4I8dSNzJZ4LQBztpqqn0057VVy9GyJsj9ZMYCCqVIA4ghc2Z+i7MdQ8mYqeXUed29ScKMpnCUfsi896V5l/T1T1aNI26/MKKjKILBM16dI16fAKLDc7FIV7Cl1LhWbfFet+2WJgnvKXtsIg2/KVf2ztS4UHnaOh+XKPplqATnqQXnOYc2K7gmKrpGGFzL0AtONLmQbXcg1/bpkn8Vt3XOFlpdrjjrEG14qO+aUqOadpeKZafxVhe65Ku1zTacD6CaXW4+6Eo64JBh+XaDqnajul6J9rkDnXJmyZ9ExtxLEx5c/WcGXpBxEU/KnKiNKgTru4By9weN+NSpBRGXfHA2vhAXb9d/BD3MhYPPhB/qYCAwh3jiwURW0E4Xf70MCAD/rl247Y+VPNPRv1PBtPuVLPeJGRCLSYQtiMcWWxXrT4V5V/wZV3xoVr3Il9wJl99zTvgUageUn3MrQ6SMU9XMkVb8KRe8CZZ8CZf8iFd/Co+5ZCm55qoFVan41R13LTpytUg+CjZOVvGr1v2lS868+fjZX61yFql/BQYeEk+6Zx1zTNYPK9b6sPx1AVPAoN/vhATyjekC9oluhik/eIcdEuPeUN3xoLae9GzW8KcpuJfAxagdlQ9xRdiWonq0yONcGL3WClv6xqvkQXb8rX7vXcFACBnl8gRiN4CZ5H6t+Ajg4k8DB4yDLYlEHMJKBgSEOh3f8mBLmhEMsQmMKTuB0ZxczvyA7NDR0ZGQEg4zJQMr45FBccODABZd/q3njVzGrsGFszAPSZIBNgK4k7OvrwzYn55liE+7EUhFXwsWAYxCAZbutjPwJhuerYbeERDubCJyGhiKrPuUDy+sVXBtPeXee8m4/7dehd75b3Yd+wqVa+WylwTnqCcfC43Y5On41RoEkVZdiZccCLZ9K7cCqkx6FCq65im75Jt9SDb8iq/qUagRU6pyvgTQAd53yLtEKgi2XqHgVn/Is0gusNAio1PAoPu1RquZepuVdA1vW8q3T8iHpB9J0AignnIpNv2pW967RDSIjoe29mvZbVKi6N+gFwb1E5bO5R2yTjS/WqrqV6vg1aPvRYeeq7gdRhnLIqeaYWw0SUs53PMAbGpkMCeoG71HRr0L1XDlEHx2/hKW7DIcngsSiwDEimXQ2inyKC1AjBzITUma9roFLhrpzoYpLqbo/Uf0csvhlfILtpKKrYDQg3HhUKrgUq3iVGlysM/2GZHCReNqvXCuoUdmbevxszQmPWsgHmudpmhcoaoF1J5FRmDqoJ73hNZPVAxnqgZBgqOrnWo6erYWMBc+i7k/WPk+BbRpcajzpXm7yTfNB+7yDdgXaF1uOuRKPudUpepD1LjBVPImqPuUGX5IMLjQoOpcpOZH1Ajr1/JmnPWo0fIuNLlUanq+Ej/r0WapBUDe8YCwS7x9JES9hNmGz1ypiFg4JgD8/jpQv/o2FgyNFh1oQRX6wIolEhA7nIbJp41YeVyQUSBEViuGvF3GAKx2PbY+RMURnDDKwFJ8cigsOHLjg8n9g5PjtyxcbasEqYNFVJnex2WxseQv6Tkc+17lSFvwmhe/+dxKwSF5fzz3OwDdPzSMfMbwHVmkGVcDPYp1ARHUDynX9iapuNRrejSddahQcKtQ969FOvU4/iKQXUKcfWKvtVaHilK/mUqjvW20cWKfvV6vjW6fnTzYIpCg6FB21ydX0qjnpVKLmVqniUmZyga4f0GAYRFX3qFZ2LlWwKzC9yFC2KzENpBv5Nep4NOh7Nao41Oh5NWu5NR6zrtbybNb3Y2p5Np52J+81zlFzr9fyput6t2l5tBj4tGqcrdf3qbe61GToXa/uUqXhUq/jwVB3blRxpGh6tmj5tOr4N5t+1YZEag2o0PcvhwrvCN4XvEeNcxUK7tlq/rkafim6PpGLt2sOi7GQ9DBlobMQxJPAgU4j5QiAZFQKFm485XiRYBVUoOKcrOKeoHsxTxMNYqfpV4RqyYQWnfbM0/Ir0A0s1PLLPeWRqugSp+ASq+gKjyqG8KTtT9Lyq1fzqlJ2K4N6yqNMwalQ7xxJN6j+mEPuCecio0uNBudpRx0LFc6WqgfUn3SvgHkVtwp4yCm3cnWPKouvmjW8iac8KjR8ak95Veuep+pcoJ7yrj14Jl/Dt0b3XKVWYImSS7aaR7mGBxk+Q3V30mmY98zR9M7U8MxSdc1Xc64/fbYBCXoXUABv4Y+kOn4El8v5C2XV+gXYkArEXBZvlPVL4OAhs24ngGPqHA74KEUiCbYslssRgik/ZJFIIBKjR038pLHMVA94uOCCAwcuuPxb7RziCZGigq0bxOwck0Pd8DUNIQPzEnbgwIFJH2JcPksMuCLAh8AxIAZL5DR0HMOMvFKN/PI1PbNgP6ThRdDwytD0zND2zNDxgEow9q8wv0Ay8KvQ8CjU9i7W8i447Z6l5Z2n5Jii519kdqFSz7dQxzPXwKdY17PglEOm1tmik7aZ+l6Vrj8wTWBn71F62qnAPKjOwLvKBOKLQx6soOtRbnWBrHW2RN250NiHaB3UcNquQNk6z9yfauRF1nWt03Kpsb3Yru9B1jlbYxJINT/faOBba3GRouNZdcIy28Cjxsy/Tt0xT9k6Vcc139i70tCjUv8s0cSbZuBO1nMnGfqRNT0qTthlnrBL0/bIgXeh654BFd4RvC94m2reWbrnCg2/LDQISjH2v798h+qQEInPBr/CRWjkFCx4GwocYinCHyNvhp+zpWCFnIKhy1U913CzC0kW3yad9gnT8o/Q8o/S9oMag2qsrm8sTLW9o/QC440uJOsHxWn6RGj63jO4GGNxOeO0F9ybanCOYHAuWy+QoB+UZXwxz+KbYrh52jNJzSNePyjT4FymulfCac943XMZqt6Jxl/nn/ZJ0j+fY/5ViZZPhvG5QgO/HHgjx2wi9QKyDc/lHbW/r3MuU80n6bRvqlZAjlZgprJHhKJbuKZvnNXlYtMLZafPEk67pev6pZleTDC9FIuk57P0fEvV3SAVJWoERGgERP2RVNcvwsT73tq9BmPw98PlsHlD2JCKgC36NXBIxRhwCHiI5YjDYaGx2VhjY+y9ew4MvBvBnI3yeJP+u7CBGdFU3xuTqPG7TvBwwQUHDlxw+dPtHNjwNiaToTWnvqAx8oAUAt/Uk46/3r17hwRSAaM8yTB8x0Pm2Lxf38glxMwzwSow2ywwxzQIapZZEMEikGAZkGEdkAFTVbs72u7Rup6x+r4JpudSDPziNTzu6/rGGAUl6fjEaHlF6fkl6HonaLrH6PkkWl/KM/ZJtT2fq+MSp++WoG4faRWYZeqTruMSa+iRbOqTZuYL2yRoOkab+2VY+BMMPZK0HO6b+yTZBhHOXMizDEAqa9jHGHtn6Lgm6rklG3un6Xomap6N1PGM0vaIMPCNtb2UaewTq+USbuoXfeZ8kqV/vIVvooVvqolHmrFnppZzoppjlK5nvL5frL7/fYuLiciN+GfZ+BNs/GEGuTV4myZBORreSZre8addwgzcrq/fcxrp0JAuUAD7SyHsNaWIE24kGCpi6h+RIlNsh+FX/YJ1chsPac6WVVp6UG+5gs5HW/fO3X8C6rx9UJVQVZ6/VxlNT83erThj+zGon+86MWefItTZe48vOnpyzv7D03fs/0Ru72c7Dn2+++isPQozdx2bsfPo/IMn5x9UnLn74Ixd+2UOHYE6c8++WfsPfbxj97StO5ccO7XsmMancofn7FRaclBjhtwxmC44oLrokOrsvUcXHFWaJisvc/Tk3CNqM79QnHdUYc7hQ3MOHPpo6+7Pd51cfFh/4QH1efuPz9y949Mdm6fvkJ+1+/C8vToLvjCcs//UnC+Oz/nixB9J5+09NXer+mcrjoyhQypDo31SKRtdag3+HnDApzoyOiCA3DExaRQL3jYyzJZOLLdCAyOPCkVcNNDPuK2OxWJhv2c8bAouOHDggsu/VTCTxlS2mBxJwcBicg7HpOUD25SXl4cVhoeRwN8ikUAK+ALA4UvFcPfilXK796huXn9s05ojcuuOb1l/VHbjIdmNB7ZsOCC3/oD8ukPb1x34Qv7o3u1HNm/Zv05276btB2G6ZtPerTuPbZQ/snn7sS3yxzfLHpGTPSq35di6LYdXrN+3Zu2eA1+cWrVy2+4dCru2H9+57dim9Xu3yx2BmXWrd8pu3L9T/ujaldt3bTu2f7fS6uXy+3efWL9629rV22Q371u5Qn77jhPr1u/bsV1h86YDu7af2CF/dPUqebmtBw4ePrV+044t2/Zv2rpPbvsXclt37oRtyG3fuG7L1q375bYe2bj+4Da5E/Jbjsht+WLHjoNb5Hetkd26WX73lo374Y3sWAv1gPx6eGuHNm84vnHjcbktx7duObxJdt/Bo6qLlsoKUduQQMgRwWQiPP0EcIxJwRBX2P+O27fz+JHU8ur8ZmZEZcW92oL0tpp4anECZYo2lGIaU1cI05TGylR6FUyTqOXJlLIkWkkMOSOJnp3BLMxsK0lrLkqm5yfTC1MYRUmNBYm0/KTGvNTmAlghjpKW0JiW0pKV3pYfR8tMaS5IoObE1Gam0IrSaKWp1JJ0WnlOKzGKmJnVWh5LJqS1FiU15cTRc2JpRXGNhRmdZTGN6WntRfG0nJSmiuj6wrSmikRabnJTRlZnblZnATxpXH1lPKk6nlIaSyuAR/2RNIFcnVrPlD9h8GyEx0aW9UAO4HHHWOD9EmL46AQSIJgcUkH9dEnQVSp8zHrBZnMlmNcTBDVEE79bCbbue9J0N+l7ZnIeEi644MCBCy7/tyL5jb7HDvjS3rpVHnO4hIygo5+dPDGAtCEQs/bJrf7Bzfy2vW6ig0aajVK6jUKi/ZF4hyOxjkcS7I+lnFEgWCsU2CgRziiGnzl83Unhrof6bVe1UNuTt+3Vbp/VuWqlEmGvmeZimGh1OtZW9a6H1lUXpVuOR8IdjoTaHUrwUo1xO3nX4Wi8h8odu8ORjsfv2R25b380zlkxwfVkrNOJKPtjUG9bH4pzU411V41wUwl1Ugi2V7jnpnrbQfGu3Yl4Z9Vo+5NJZ9Uj7JVCrY6E25+446p67YzibRfVJE/VGLsvEpwOJ7mdDLFVuOuqdddJK9FT/47FgQTH4xnep0Ks9t48oxDlpRXjqJBsdyzDBuqRZPuD8O5iHE7F251Ktj6Y7nwsyuX49bPaB7ZuFaP9o3BivQXqdxSdv4Ga+tEnJxIA0dpdm3MailPJmRnNGQmMqKSWiJSmuJSmeFTjUI3BNIOZkEC9n8KIzmImpjfGZjbGFbam5jOTM1qi8nuSE+l3YimheQ+T01qjIupuZrTFQE1pjoCa0Rad1hoZTwtPoIfBTFZ7XAojMp5yJ5l2n9Aal9OemN4UndBwh9CaADMpjRFQYSa9JSatORrWTKJGZbbEZzHj05ojUxj305nR6czYlOaonO7kKFJwBjMqnhoGm0qov1fUmZvUEJ/ahJ036o+kqfTkdHLJ6j272UDKQx4L5pAVfVioOQOddSsRY/9Hf41CoZDD44qlEilmQoL4xmZN5n+ruOCCAwcuuPz/mTZEvwUOzIEBl8vfvHnLuI1EhKzvhFU5KHCIxAPHts4LdVFJs95XaSVfZ7qWZLas2mpxuc3iEtul5dbLaixWUExWUHWX0C3WlJxZnXlmda79phzbTfmWshVOezNt5Kt9j9eePVhkuKFCf22d7dYCp80pZ1bm2a+u9dxS6rim8MyKbNMFmUbzKh3WltutrrBdVeO4vtZpQ7n1ikLTRSUWS6tsVlXZrq2x39zgsTNJd16y0ULaxUPFrrIJ+jLVbvI1zvJ1DnJFxiuhEs9sJTpuK7GTzbRcRww4GqW9oMx6Rb3dshzt6UT7tbXeXxBs5XJs5QttZfMM5hYbzaqxWVxhszzPbhPUEpsV1ZZLyabLyGZLq6znl9ksLrZeX265vtZkAdl2aaHdyminAwqb10kEyJPhT1ngiTg4n4hxivWmAiBZu2tDDiUrjQJ5IiKp+XoS83pqS1hq821EW8JSWkNSWoMxJXSGpbTcyGgOJjSHplGup5KuZ5JuJNddjSP9mNEekkC/cr/u20TG1eTm6zAPK8N8HO0nqElN11Jbb8Jjoaa1hsQ1XEltCia03U5rCY6j/BxD/jGZcSO7805i47XU5lvprSGwPK05JIVxC1bLYoanUm9lNoVATaZdS6Bdha1BjaX9lMa8ldB4LffB/fSW0LyOiFRqSF5bdHpjGDwcnu4PalrT/QxK5up9smg8HtSEhhiCEJaVjtMGBhzYbxLjNwQ1kPXYEjGLwxaIhGfs7QaHh3DgwAUHDlxw+UCAA6UNrlgsPXFCCSBx7Z8AtFcQS5EpkbBXkIgGjm+dE+ZyMsN6X5WVHMl0DclsabXVwnKbhbBXrrBeWmu+ggL7ab1ZDKtFdTYLKy0X1dqsgBRSZbys2mJNgeXKApuVGTqzcrRmNpgvqTOWKTeZxXBbW2Ywj3FmXbXBgko9GbL5cqhUi5XVevNrDBbCDNQ6g4VEXRnYMsNiFc10eY3OAprZqsYzG6pMlxbqy1RZrqi3Xl2mv6BSZyHNZDXDbC1JbyndYh3JdBU8b6X5slrH9RWW8ylWn1NMP6FbzmywkiHofp5jurTYanWJ2aJG2/mwnGw4jWE7G15zken8CuvFNZZLKaZQF1dbIcBRYg1bgMAhQ7ZdXGi/PNppv+KmdZjbiH8JOEIQ5kA0ZApw3IS9eyL9enoTAhz5zHulnbEV7fGFzEhICbk99/J67yN1GFdhmtaG1M/oCIWayrwF8+ntIYSucLiZ3HKD0HE7nRma0nwLKuSPzI5QSCeYprcFx1J/hIyS030v72EE3JvcdBOeMbMlBIJOStN1iAiZXSFQU9pvJjZfgSfKfnAvpTk4tysyhRaSRg/LaPrTgUMsFsJdbC4Hs4PAVG6bPG7hwAUHDlxw+XCAY3KqHYvFEaIzFPg8sViCdqvIkgKRVPT2xJZZ4S6KmVZ7IHCQTVeRzWDfvLDCZj5kjiqrxXXmiFWAZCbTYLWg1mQu7KEbrVdBYqgyXFxusqzCfl22xaJsi/l1LmsgZ9RazKvU+bjecGad/rwW6zW1uvMpxkva7TbSzVfQzVfCPGQLqslSqsmyRrPlzRarW6xWN5osJxssbDZbUa05u9lmTYPF8kLtzxlOG9rObinR/BweQtFf2G27scV8ZafdpnrDhVX686l2a3I1P2l0WMS0mVWrPa3Tfl6b64ois4WNgV8UWK7K0ZvNdF7WaTe7QWcazXhavdkMovlciFC1FouppospZgtrrGQQnLJaW2Wxts5kboPtoiK7ZTGOe5U2rYGsIR2f+/jPAEfb1VTYYSPMAXUcNcYV5YNU+s3khhvZjDu5jPsEUnhK3a0E6rVY2pVUZkhGR3hS802oyS23YAnMpLVB4AhJYFyPp1+DhbAElsNqcDOC9H0M9WdYIav7NkxjaT/dr/8OEkl6e1hi0w2sBai5D+5DoEml30iEuNN4Jbn5emrbjeS2G4nMK4mt1zK6w2HlmIafczoj0lvCkyi3YPpnAwe6zAfwhQKxVAJTSB579+/7H0ZVcMEFBw5ccPkPAw6xWIwBB58/PsNfwJeIhEjfIBQhTqmBqE9xy8w7zgpZVruIllsoJitgl1xrOb/KWgYyB+ynSWZL68yXltksK7dfSTRfWWu0kmSyvt58c6P7F0+v6DH/pkb/XqXxJ+U8ry0Rep/lWS9uc99KsVheb7GcbLOqwnBRvdWKGrPlueqfVRotbrBdXWu5ssJ4YZXpUpLNKpgv0Z9XrCdTZbqYbL6s0nBepel8xILitCpTd3q2wUyQZC0M1SaaLKgzWVyuP4fmuK7UVKYQ1jmztN5pNdFShm49t0xrWpHOxw8vffH4pmHrFcPGy1pZ1hvKzREjB9n443rjj0gWc+ttF9VYza+zWEgzWUg1nV9rNRfBKatV1RarSMZzqDYLSuyWxDnuPrlpFeZWQoTOEf3ngGNKf5wyRSF/ZLSFp9FDslvu1T/PIT/Pq+nOrH2cldcdE0O9Ek35Ob09HGpaW1hm5x1C191Y2tXklmC4CdPEpptYHlaIa7yKskjYxK4b6C6IJhA+7iYwbmR03KaxCxtG8whddyDiZLXfyWKGpbeGpLfeSm8LhsCRxEQsMQkt19M6Q2Fr6cww4uuM2r7snLaorPaIPxs4JBIRsksixpTL523fuYMn4OPAgQsOHLjg8oEAB5gIJ6uhoQXTFy9ewde5BF2mKEI5BAjfKMnOuOt8PNtqR43lZqrJMqrpwjpLmWrruZA5YD9NNltca7E013p5ocP6ehvZerNNZXrrik1kn96wBJ0JYKQCvMkF/QTADKXeNkxxls0xWVVguJxovznfbEW20ZI6V/laF7kS23XVzltyTJflW6/Os1pVbLe+zHFToe3aLPPlhWfW1XvtqHXaVGa9gui6udhhdb7DmmQzmTL3jaDlZ1B5rtJhbYPblgKLJQW2yzMsFhafXV/usSnXdGGl5ZJ664U0h+XSWHPwLB68KwXDtaA99UG4Q6bJkirLBXS7hfVmMxrOLK40nQdvhGQ+n24yn2YqU2s1G8EpK2RgiGw8i2YjU2q3KN5xp8rGFf8L4Lg+qSlMVFHgSKRfT2sNzWy+U/cy+w1ofwM6nvGbBkHPM9DSMFSY0xGT0hSeQr+dQAtOpIamNt9Oa7oLS2Ca3no3lXEHlifRwtJa7iTSb6U0B6czb6e1hic3hSTRQ5MZyKwRWA3q3eofYHkvoDwEZELHvcja75OpN1OptzLooVnM8IxWZMQEUgihKxwxq7SEZDLvUIYKewH1GWiqfZ6T2x6d1hzypwKHUMgXSyUQNSbnjS5ZthQfUsEFBw5ccPlwgAPzzyEUirdt2zFeVYwYOeB+sRDWgV3GK2XZGfedjudYbq+z2EgzWUoznU+ymFtjNQ4cFLOFdeZLC+1W59quqTXfRLHcDmmDYLHtcaQ7YNW1hjiTrtl0pASCd8WAW10VbnNTc02e44Fcl71pZ2QzHeSyXLbfM1iaYLW+LOBwhPGKDNftUJMct8Tbbcp035nnfyDZRT7UYHG63ZZYoxW57ruTHGWTz27L8NkddWZ9zXcqHaHGV07NqDp3OMcdHrUpwWVzVtC+u2aLs61XMXx2VVsvK7FYzIqyHcsIqvjRpiXxW8BpA9TYDNutBRbLGK6rK01mNTqtLTSYhQCHhQzdRIZuOrfO6nOIUxhwNBjNolvPLT+zIMFhh8qm5YhvTGTRhfifBI4rqa1XJzWFiSnCHJnt4Rltd9IZd0iv8l+B9taBmvLWdNqL8pegDTJH7ZPchLqQ9IZ72c3RBFokTIvaklIabqfU385tjS3tTM1piclhxBS0J0AmyOuIyW2LJTRHpjXeTWu8R2iOymuPj6q+XtqTBjOZTRFVTwk5bVExtdcKOmKzG+/nMu4XMWOK2qMJTXcSSdfiSVfT6CFJlFuQfsp6kp+Dlj7QMQwe0V+VZ9Mj/2zgAKhDE7iXxWHzhQKBSHjk2FGYx4EDFxw4cMHlw7FwoC7CpFu2yLHZqM8l6fhyT7FQhACHAALH9AinozlW8vUW6yFwwF6ZbD63xno27JVrLWWopvPJZgvrnVYXGs+v0lpAt9pMtNuWabutKdwWvMyr+84sx1fjmsUeUqwfEDR0ll2Jcj/ZFu4O2lPBy3wwUC6qvJblrVD/kzHoSADMWHgIeJQOGsLY1VfBgyTQXzBY9n2qn2LxeXVQHQxYJKTCw5T68DPh9rtAS8RIyffZfoqAHAaYUeBRCnibB3oTiT9pFTjI5hnNLzNbWGGzvNJVPs1pzw2r/ZF+BuANCTDT08/I5ZoupjksL9X5S7v7hkqT2fBG4E0xUOAgWc6otp5dZbWs1mIZ1WgmAwWOJIdtpzYuRYFDggSJ/WeBg/lepwIHOoUzJJka2vCm6A3obHhemlp1t7w1g/a0rA887BpsKG5Ja3lN7Ac9A6B3ADxuf1tf2Zld2UboHKa8Az1vwIM+afcgeNzUX/FU0jwIeh4Jm+oe55d3ZbUNkofA8yHw7DV4WN5JyKbHPuTQevi0mt7shueFA6BrEHQNgM4XQjrjTUkRMw4iSF5LdDYjIr85pu5x7gDoeSFgvgUP6U/L0+vuZTSF/anAIRIJODwutlAFmSUjFlXXEHELBy44cOCCywcFHCMjI7AnOHjwMJ8vRGZySDGHVkD6e8BBN14Mu2Syxexaq9k1VuPAARGEavxXhvGnXYazOiyXVJ9ZUeC66WGsLXiV0fy9cbnHqQS7w2H2B8HbMm5vRsb3pqCPCJgEbnEYqyAY9NWLqiNKvzYHL6rAKF1QEymoiwKDDWCEOlweBrpzwOOi+htnST+5gZaC1rsXG0J8wWgLeFp13fIIaM2WkpNiHU+CliwwQANtWc9yfgIvK0BLbIntujqrRZUGM2ot59e7yaZZb4pxOlQX6gHe1g7nfV9oL1tjs7jJZk6F2jSG+YwOpyUki9kYcDBM4N3NQHDKEgGORpNZVPMZFbYLUp13Kq9fjDh8l0jEEv4/Dxw/TWrKOHMgwBFHvZLfE5NGv016VdAHHpCeFmfWRpY1ZRTUp4yAF8PSZzWtBWPg5TvR4+bHNa843bCkX9CbVHBnFLzkg7cvWJ1Ph5nD4OkwePxK1NH5lgSh5NFIS8sr8jvp85qO0qZnpD7x0xfc7rLmLFjtEYuRVRvV8rJ6GPQMSh48HaFzwfNXvNbSpqQiRgLUPGpMDjmGA14+GqLXtRfAU1Q1ZpcyUjMZt//sIRWMJMRSyeDwkEiCzCHF53DgggMHLrh8OMCBTeAQiSSLFi0BqF0bibXFE08Bjhf/EDiajec+sf78ofFHHVof0fQ/zTOekXxmcXuEMXiZWup+PMNsb5LVgTu2hwCLLH5ZRCP8DcJEfYhPsPmRe7YnwbNa0F5U9O0Z0Ed9lHHlpuUx6p1AMNr6pij8hq1CdbAXeFrdHP3NDSPFTD/bRB/LxqgfJG3lkraySG8T0Evi1WXcNDkO3jSDZ+RYD91gO2XwvA48Kc42W93ktKbBakGV6Zxii6WRekuKv9IDjwrB0+KGKxbZFquIljItVrO6rD+nG/2l4OQ08r8bOK5gwJHcdBMDDkpfMQSOmt78pPLw3PrE/JoUPhh4y33c+ojEAf30bmJ+dUpeVfKY9DUszCyJ5YF3g4JnhTVppSTCqPTlsPRJCTWloC6RDd50v6ZHZ4URm4opXUTGQ/Ir9qNnw53Z1Yn9/J6XnI6CuvgnQwyIFIzustyq2LZnxAHRw4bughJqUnzxrRJ6Suur2kHxo8aH5URGvgAMMHvrMysj/mzggM8Qwwu+UICtjL0VEoxbOHDBgQMXXD404ODxBEePHgeoB7DxN/r7IZUX/3BIpcVobof+J0ydaa0G05vslpS7riF4bHqQaAeGS55H+OQ6K8dbHU9w1wJjTf2tGd2kKMBuvuWqlv6l5b2zmuyGZNBRlPuDA+B2MlO+v+uhlfatNXhRR4v/+oq9UsGNs4DTzkj8IedvnoLGcvC6HQx2gXftUMO8TYepeYBZHnxGHXQRx4gpIQ7qYW5anKZs0JaRb78jS2cuxXEd2XlDtPrMUv9jYmo06K9vSfwy1Gpntu2mMsvFJVrTupwWdzqtbLRZXGc593eHVFDg+Lz8zCRwIN65/yXgmDqk8h44EunXM9ruJDYEN74rGwS9lBdlOeTYMgYhpypRAAb7OY9ejnTDTBUtr7whu7guA9IG3KxtKoK08Wywo4CYWkbOgsDRx+nKq4kta0jjg7ewDrWLOCLuGxS+7mM954NRARgubcgakTyDXJJbHf14oJEPXtXQsyBwND+qYENGa8+pas7Ib4gvb87kgNcs8OL5KLPzBXUMvBrgPqY8KP2zgQNxao5O3cAmjbI47F17dmNuOXDgwAUHDlxw+RCAAyAzRpHlKERiLcQOxLv5r4FjYkjFcjsGHDRTGWzSKASOGisZitlChvF8puHMZoMZNPP5ZMfV2XYrY2zW0MPMwLOskvO6tw33pLlqPc+4DsaY1IyfGcUhgNOc+r1NuJvKFdvD0vYs0F+b+q05eFpaHxV400Ux7qIh4LU0pX77s/3Ru74aYJjSmPQtM/0W6G+mJF67f9HmKSkV8B9e89AFj6qHyamRXrqgv1HKINxzUw9zPfW2Pgb0EwvOHkrRXlxhs57osrXKax8ghQI2vbcg+K63Zqjt3nK/Q/VusqUGM6qNPi/Tm12s+3mt5ftJoyRLZNJolSUyaZRqMoeCAkey8+6TG5ZiwCGSCMX/W+AYH1IhdNyGwBFPutnwpvAFYBJ7cjJrIwsoSb0DzTzQ/6Cvsf0FeQQCAbOwkp5dREp9zemGm2XUzMeDLS/GOspphGJy2htuV7+go4SSWEZN7ec/eDLY9mSwnQeG2p8xyG3VT4e63gmeZpbFvuZ0Dop70stut78kDkl6i0hIfcbjsiHQk0OKzm2ILWpOqWonDIMnL3hMFnjOBq+4oG9A+LR3hPFvAA6MMzD3X1B37t41yhrDgQMXHDhwweXDAQ4sVpZYPPEaxxbESpFxFnRZ7CsldJVKthWySoVqsgxdpTJ1WexCqulihuXyeotlpZbLcmxWJ5qtSTwj9yTGC4yRh4pvvSsMFtUngaEmYUNqYpBxUZgneFMF3pZTY7wGakMAn/K44Lvs741gpjHOJ9ztSO6PJmCkpu6OS5SPUk24E+BRqkKd+qpuA35rT0lITdw5MFQPWA3f2OwBPVmgJ/eWzT7QnACaY8Pt9sZ4KYC2eNCbkWy3l+itkGG8Jt10tSDnPBirA49KWglXGSl/Y8ady3Xfn2W2jOSwhnxmFdV5C81FFnEoggAHsiy2zurzKpvxVSoocMwuP7Mo2XkPAhw89MlI/p+tUsnqvAOBI5kaTB8oewcePmBTO97VP2Ix2ODlwxFaBTOzpCl1CDx+B3q6BhsYLypZ4EXHOxKhLgqWwDrZpJjUqruDoHcIPEgjhmWRIt6CB69EXa2vG55zHz4a7Wx+QYGZQfCimJH2WtzxDnQXNMa0v6tmg2e5lOg8akxtb/YweFTxIC2bHplKuZNSH5bDiImtDC5sSWkfJL8FvcTOvH/DpFE+nzs5aQObPQqBAx9SwQUHDlxw+XCAA4slC7vN27fvYiUiLArqe+B4oyQ7876TQrbVDgw4qKbzUT8cMlXWiOMvstliiuniGqOFVVbLiuxW5jusL7DbUu6y7024I2AmgLdl4G0pYMQIKm5VBGnHmO8i+Kn0JvqA55ngQTwYK5fUX49y2F76pSp4lPIw2iXGblvNd9rgQULHXbt0ty/y/RUALfxZgneGjyJojABDlWCkCnCI4HFK3jcagvobowVfVV9SBc13QUNwkc/BJPut7KKLoDc+Wn9tjtXWLKMVFK+9gPg9GCoFA0QwWA/eVIJHBMqFo9n6s0k2i8p0ppcbLSBaLa+xXFhnMZ9mspCG3B3mhwNx/EUxnUe2mF16ZkmSyx7FDcv+VeCY6ofjF8CR3HQztSUEAkdDX/5L0Ppc0jIAHgyBR60D1fktsQWtcTmMqAdcMrILNPeDzpbByurezNym6F4B9bGoMZN6D1Z4BhhdLGJGQ1hpR+JrwHzEb6a+qHjIbh0Gr/vAs3fg+VvwqKY3t5dH62LVlncl0l7DczVnMyIKmLEVj1KfAkZtX3ZWe0Rq822Y5nXFZbZGpdLulvdkvgU9dU8LMukRf/ayWMy1OdTh0REsM3PW5zhw4IIDBy64fFAWDiijo6zt23cKhWKoXI4Q6xsmPI2+Udwy8y7iaRRx/IXEaTNF+uZJT6PjwGEym3RmUbnDonLbJTXmSyu0F1TrLqM578h22Zzivjnt7KYM21X5BovrrddWWq8ss15V7SRX57Evx3xdhdP2ErutJTayFfbypbZbapx2ZhmsrLKVK7eSrbTeUmq+ke72RYX1llzzTTXeh4h+R9KsN8Wbryl025Fhua7wzKZaR7kS05XVpquqTFZSHWWLDBeVWy6vPrOu0WNXifESoskCiu2SIuNZWeYLCc7ycfZyqU7bqrx3QVSimE1/5Di303IG3WRmm/2yWksZDDhQT6OzK2xkyieBw3zuL4ADIsX/Cjh+4fgrnRlK6LiTRLmV3RJR0pGU3xRX1pqS0xid0XQ3rfl2dkdkalN4JvNeDOlaUW9C8aNEmElqDElhhBHa7sMKyfTQvO6Y3K7I3I67hNbQgq6ovA6EFZIb7qbRYnKZKVlNSemNsemNkTE1N/I6YtLoIVnMMKiwZiz5ak5nREZbeM7DiJT20AjKj0ltIUjaGpLSdjuOdius/Lvi3tSstpgkGhIC5s8eUhGIEOYYGRvF1qrIb9+GTSDFgQMXHDhwweVDAA40tDoyniIruxUrYY3xsF6BJ0adagreKsnOuu+EAcdGiini+Av2wXWWyASOWktkSIViNp9kNqfeZm655axy89lNVktazZc26s2vM15QaLM4xWJ2ldfaAvNZFDOZRpM5DSazyeYyNaaLW87K5WnOoTlsrDZZXmu+gmy5ttZ0eYX+YrrtegoSxGRZndFymuUakuHyWqPlJJt1pSZLi02XQpgguW7JN15Qbr6YYreWarUS4g7VeBHFaCHZYF6d/pxej80lWtPL9eeRrCAJzSEZT2+wmlttNT/PYnGhw/oSx3XZBjMb7ebSTT9q0p/Waf4JWWtak/lskoUMyXw+DV3iSzH/vM5ydq3F4jpzpIU6S5kiu+WJLvsUNy4EfEgcIoGUj9EGChxiFDjAL4Bj97pcCBwNcZmt0UnNt1C2CE5pnQykgikSxS2x6Sah4x5kiHTGHQIjgkC7X9CakEwOJ7RHETrvR5J+Tm4OzXkQdb/ux7IXKTAtepKQyAhOagrJ6opIb7sD92Z3R8ZSrycxbiY33UpuColtuJHafB+LVp/VnpDWHBtPuRNPCU9vvZ//MC6K9HN2171o8k+pLWEJ9Ft5PTGJLcHRtKupnbcjaT+ndN9O774b13zzPuVK3uOEmMZbqa33CJ3RSU3jQW6xyHP/ME1riiA0ZK3bswUChwAJTz/+M8OAQzwe+g71oDaJH0AyGZ5eKBZB1Lh24zq2OBYHDlxw4MAFlw8BOAQC5AOUzeZu3LgZIw/EDwcAHPitiQVX4Q8rrf403uVkuvlWovW6BtMFdONZdJPP6CbTqaafUcxmUkxnNZjNQYDDfE6tBaINpvMoqJLM5hEtZaqs5kGtsYCbcxpQRSqbyaC64LcpyUyGZPrL1BTuWlBrjmiNBaYyteZIC0gFs3nwjJMKN6EiTZnPQ083C56u1gK9EssFROTYeSSEfmbC68duAVYjm8+H2MQwmdtiPKPV5JM2449bjT9tNv2szW5hmfHnSSaLIh12H1k5DUjfAjDKl44htp/JJznRB2K9aT/rnfwhuZiiyMyG5FxmSgL1dkZbNOy5k1vvYemvNLUlAtO05shJhZvJrf+EpqKNTzQVOUWjJzLjZ5msOXHs+ytJYk7mf9M+Uv/e1Lv4H9L0pviEooTtB/f0jw3C3xybz+OgkzMgSfwGGpBJuIgBBEUSTCcXp+DAgQsOHLjg8uEAB0AnjcKX/ZEjx4SwQxAh3adAjAAHSwL4XAHgDmnIyhCCjDLt9haYra2xWAb7ZqqZDNVsLsUMsQrUWi6ssVwM01rL+f+hKbx+otVyqPUWS6jm85vMZraazoDKMJtVb7mgwGJZrvvelKDTx9ZOA5xHLPbzCeAQ/QI40M93nkg4wB3avG9TGaM4r5GQQoqLr72XxUxOa47971FCYwqlu1Fu764xPhc+I75UjGakggmAAJMWoQkVCoUQL+CjxALGwrKcvFx8DgcuOHDggssHBRwiESwHmIUDc/zF4oresSVjAoB8fYpYh5b+9Wfz4xEW+4vcj+Vabymy2lRsuaHIakOB9aZcG9ls2y05NrJF1mtKrZaXWq38T0wLbNZkndmaYSefc2ZLge36Mus1FVYroMJMqsHyVJttad4qYW4aB9fMAJJhdKwJfqkj+lvgQIxDIt76HesJVZkZtakFjOzCFgKhMSmLmpxFTf0v0ZwGQlphxppNG4Y5LLaQj853QZQnEv494BCLx+PETi6FPX5CAYuuggMHLjhw4ILLhwAc2JAKfK9fvPglBhyjIxxsDgc6uYMPxCz9g1tD3E3CHU5FOSrF2R9NsDuIKczH2B+PdFCIdjgab38gyX53kv3e/8Q0zmH/Xeej4S7H7zsfjnQ6EO+AlCfb7U6y25/iopDkpRXuqu2tc3T3qoWIcxKBmMcXosAh/BVwYMzxbnho5ebV+xX3L9gos/6LVRuPrF4gN3vt3lXroO5Z89+Qbti9dtfhPTv378VmZwilEo6AzxeLxL/ChSn4gLGFQCQUTEDJ57Nn4RYOXHDgwAWXDwc4MK9fQsSRFeJvVCSSIKtUELOHAFk+IOWOverdt3bR0fULDi2dfmrdnNMbZmmsRxRmVDfOU9k0T3mTjNJmGcXNCxT/Y9MTsjJH5OYckZt1TG6m4pYZKpunq22arrFxusaGmceWfHx8+cwTG5ep7dp2fOce4Qify5Kikx8lvwscHDaysGKEMypCFmhw+YDNBkNjYEgA+CJk3Y/ovyOFKhnmsJAZLYMD2KIedLxO/PdwAeMMLHIbpmvXr8OBAxccOHDB5cMBDmw8RSIBVVVECBzYpFEWiyWV8EX8YSF3EEjYoy97gYgNRCwgHAHst4iyBgAb6iDgQB0G3GEpb1TMY/2HKrxTvqCfJ3zDF74S8V8B7ivAeQXYbwCrHwhZgDUMhkfgd/qrBy/gIxTxJhnjl8AhGZ8BKRRgH+t8npj7bvTNELdfBPhiqQg+5P8SFUsgZEHkAhzB+HgKm897NzjwP+ACxiIYcGAR6g8cOoiHp8cFBw5ccPlwgEMsHl/SuWWL3HhdKRgcHJRIOIhfc0T5YgFbxGNLkRWO4t++8t8vN3i/xPE/T+GnOR9Zwwk/0IXiqfMzRGIxTyQVjN+fgIWoiA/AVAvH1GcsARwWX8CXYEYjqVQsFHHHoUTyX6MT01mGWWNCdAbor6wXv6tYIBWIGhwed2Rs9KSK8sDQIA4cuODAgQsuHwhwQBkaGoLpnj37kNj0sGNAh1TESDfJlUi5LPYw0lsIJdiqDP4vVTjptglxtSD+D1X4TS4AEu64jnMWD1Mx6lpDAAR85Cb5LCHc5o1xUeAQ/eJJokjBhRUmOl2RYJzP+CzefyFwYJM22HwepAfMgDE5P+N3FZsuiq2JhT8nQ2MjfEgFFxw4cMHlwwEObEgFyvr1G8erihGn5vDzXQI7CCAUAil/oved2hnzp6AGEuhNLJAiziL5mDustwP9MBUh3/kCxDUWWjgyNowV8oXwY1fC5rKwcpgRS0VYC3AvVJjh8jmTDfIEXJiOskY4PPZk41jJZLNSJNwoDzsWNoW1hjUCFbaPVYAXM8YexQpZnDFYLkJQSjJBHlLRhPPLqVA1HlsMbQk2j6gUnkg4gSyoS6txjxJ/V6W/p5ORa6DykKWjgM1CAvZi/l75mPM1tMLwEAurhlVAVnaIxg/BqiFjPAKpRPy+DuaiXsCXTJ1ignjvnNIOtnfcmb3wfTlsZ3BgdPLA314PdsG/e1PIc/r92/3FqhPxFJ20gmC+OrDBFMzrKA4cuODAgQsuH4iF4/Xr1zDdtm3H8PAo1IlVA+KptMH9PdpA3FVLxVjXK5AgfRZPImEJBFifDTN86fv+G4k8KxYLJsYwRnk81EODdHJUo394mC0UYvm3IyMwHWKzJ/Pw2KlDIHATK8cUnleEuPgEv6oJ24ebHJEIqzBVJ08NrwRztgqpCUkRH1Tj5gvhexOOFMUmIXL3Ug6qginAIX0PHFPmc4z3peP2n9/vgyd9YQ2NDE+mv3KBxRcKxtgsrATrg2E5FuEM6uDwEOYja5Q1hgUigSVY1w7rYCWwft/bfqwQnkIgEmJh0iYjpU2dYAF3iSfGQTCzBHaKSQ8Z2FVhV/73wOKfBQ54GfAWpppA6E0M3MKBCw4cuODy4QAHh8OBvSWEhIMHD/N4yBJZ+C3LF4hECGq8t23wJjhj/Isf7ZIBQhsCIIF9sGCEL2RPVIN94zuuECrMvxxmwbSPxcN2jUmQdFgohenrUQ6siZW8GmFjFWAhVgJ3wXRIAFsHAzzRIF8M8/DAfjYfK8TawQ6Epx4VI8oD4ymsD3lk6sXDo2AhbLzn9VusBB4LW+MhsVGQ8GHIwAlELBGGHeNDLSJUJai9B70iFrwJJIU3/n5EBu1NpVjAEFSmYgdm3pCON/UrhWfiCLl8CaQ0MVvAGeOxhtkj6CIXiD+iUe6YGDWqwPLJFCqsie3CanJFPKwcU9ggi8+Gh8Nm4eZkBQHEmynVpjaI7YXVYAlPjDhuh+2/evv67fC7gdFBrMLg2BCWwU6Npb97U+i0UemvZCpwTEUN0RQLB4Y7EGi4fN4pNdVJl6M4cOCCAwcuuPzHAwc2aVQslurrGwJsfSz8uBdL0TEF6a+ma2CcIZGOe6PG+ikMODgCPqQVjgT0vnwNMyyRBB4yKhCxxdK+EcSFGFT4KQ3TJ2/6YfpqcBgrhHWwclhzkMPDCqGOCcWwZIDNHeLysRLYOJaBJcM8wa9KMDaC54WbsB0eWg43YU3YDlYNy2DXM9ksPBGXh6IGAhxoRvx+0ARDDVR5UsT/KgdlDg7ci/LWFOBAhmOk77tD6RTskI5bjH6rYiB62f8CprlFOfK75Kb9ddq0j6cdPHagrZvJEbJhOVsAEQT29BAZuDAdHBuAKbYLK4c6whmOjIvAKrwZeI0VckUcLIOpQMp/9fYldsYx3ihfwoP6drgfbj55+biksliMAAoPOyNMa8jErTu2fDZ7OryewwqHYB3kAUn5sA52Iqza794UGl/mnwAOzAaEzfPgCwWYnWP12jVTDS04cOCCAwcuuPxnAwcKGUKJBHHCgXj9GmWBiZCeQrRTFU3MYPi9OQrSyc9jIOX2P+2C395QJex3MG2oKjywY9P0adOWy3wGM3ATiFlYBUx5Q68AbwigK28FI2+wQjHrrXC0D2ZSosORClhleKBobLwaPAQt5A6+xDJj/U+RRmAd4ShW0kyu7GFSsdawkjePO+DewZc92ipHVy6YWVeWCwQjUs4Adqli1gBq20C7Zow2EOAQSMdR471KxxWlDRS+0K503BaC2kYA+kSwyGRgfH3K+FrR39FR1sgYe5TaSPnLXz+2OWNNaqhvYTY7uzrFJcSyOGMY4IkkQlhnYOgdtonNQcGmsyCrbYH4dd+rBYvm15PrsGkxMIWFI2PDyNJcdPoLl8+ZnDGDHTipfCEvOPTW8pXLsPkuWOOwMC0j9etvv+p+2JVXkPvxJx/98NP32JVMvSpsgs7v6vi41MQjmgSO36LGuErEmD1jclRl5+5d+JAKLjhw4ILLhwMco6OjMOWj35Xj/CFAvtlF6Pe6CPsqndJ5vO8upswQhP+JOcMYE2BKLCLITJ+mrXzk5cPWTnqdu51ZMSFRPNaP1Rnre4JwyQQNwL4fYoQIcgbMwE3+sHDkzWfTpj3vboabQy97kMIJmIB1Bp4/mMyz3z4bJxIJW4hSC2z26J6tP37pj1AIb2i8goT9tJOxcv4MteP7Ycst5EqsUMp+Jxh+DXvY8WDp4x0kMl1D+t62IUDtHOMjBdhjmFJZOkkbQiQjwdxeoWtrp6DZ+9kev0ixv46qqurWrbIDAwPY32VkZGhsbAzmi4qKfHy8jhw54uTkMDQEC0eqq6vd3FyLi4sPHNivoqLS2/uwp6dHW1tz2rRpMLWwsIDH3rlz59atG46OjidOHId/38uXvzl8+LCKysnQ0FAul83lcsPCQtTV1deuXZ2Tk5OXlyMvL798+VI9Pb3o6MiJXwuANcG4/zewcuVyXV1dLMSaUMhHfzBcFKNEv70jDC+mIM0fAg4xihqwAk8AIYnP4rAhcLwdeIcDBy44cOCCywcCHNiQCvwAl5ffDpAJpH3IWgYOD1t7gs4JRb/YxRO2ebF0kjZEaLeMDbhgw/DYJAYOl6+uobVj526sb3jT9xamwyPIokcKtdH4/2vvSqCrKLN0oFkDGAhhDVsgkIUQQJKgQAKNENGg3YqOZ7Blm1Gn1XFBW6fBPto2qEjjwhpBAYUjokfBQVxaaUZBFBBBpkEaWWwkZCN5SV7evtR8Vd97l+KBPadphGO436lT569b/3//v2695H51/+2Wfx191dibbr7lb8eOn6xyfLrls//49d0f/unjX/zyRhxHvzv2/fETQ3LyEtomjhw1etjwfJQqGn/9yldW4Txj5u/qnK7fzng0a8DAwquveXHpS7h74K/frlr92nXX/zI374qPPv7zA9MfapeYlNi+w333T/9y1+7IWMta5+Ej323Zus1Z727eIv7jTZtr6+q9vsiAzXpv4PRpKWH/aT0gwQAjPeHIEZIxoacRDq7kEbA6ZgK29Twss0e8rxFzdtbWowUtm8fP/sMsM7YUDHlckUko27Z+1qF9x19NvHXxwiVX5A1tFNfY5/Ei3bljp4Q2bVe/umrYFcMz0tKh5zcPPoS7D9x3/9LiZSg4quDnTX/W5Prxv4Dk9489Mf3+B15csvS/Hn6kRbOWH77/wd+OHmsc12jJouJ3/3vDvLnPfr17z7XjiqBz+Usr3lz7BqfyupzugM+clhIKBPfu+d92CW3/+Mw8pGW6r9ftsY1TCZ/t/PcIh51qcKivLxTZSyUcXeY8d2ieRjgUSjgUioZDOEKhUH19PTxov37pkSBH2JwWK+tqmM6Xc0EDFu2IEo6Q5XplVKk5osJvuAMRL5KQ1PV3Tzxd7zO7H0x3Yk3w2LX3QOPmbW685bZX17w1dMToLj1Sq52+BcXLm8a37dSt9+Oz52YPuTI1Y2BJRc2cZxfGNWo+47HZzy54EcWRbtSs9R13PzBvfvH8JS//y61T17797v2/mRnXJH7jn/5n6/bdyLBi9RurXn/7iafmrV67LqVfVmHRDbOfeb6syok24DBnzXhN0vB9WTUy79yzP+LtwuZd31kGxpJz8AhH1tz4fwiHyTkswsG1vQNnEI5IZOi0c8gAA0iIv2zRgsWRDWwCBlhIvcM5bcq/5Q7KMWef1rnBAJDn5WXL33ztDXCLk6WVoUB40wcfIw095aUVzRs3+8vefWFfCGxgwvU3FowY6aioJiEoO1HOinp3TwHP2Lj+3SaNm+74bDvvouyLC4sz0jJrKh0cXuH3BgJuk2346r2o5b677k1s276uypy+BP32ZUXCwbM9UdCI4RwxYziCZxvDwTkv7E/xmjN3jeTu3ZRwKJRwKBQNinAEAgH4gbS0DCEcLpfntLJhI2bHEMI+ADAyxcDrD5jLUhhxcY0ZfjDjBy5z3qnb45s67d8Z9giGzEvk2fjeB8UvLmveIr6qusbnD368aTOEyLBv/wEk/rLvG+Ssc7qQnvvHZ1HE9EZWWAIV4bJzl+Tnnp+//p0NP2vS7N2N77MWl9s7tnDc75+YVVNrTiWFWjYDBZGG5vhWbT7b9kVkUqjbGwqfNozxh9xb2PbIp4ls8nDMp/0Zw2VidRnmZnA4t2zeYtasWdw0NRQIhky+Z2RlZd3967skT+9eKU899dTK5Svat29vBhgMY+f2HXFxcbWOmmPHzKDF9u3bw0GTPo4rvPqhhx5yVJnruZV8f/z2229H2Y4dOzaKi1uzZk1FWfmkSZOQvvnmmzdv+rPL5Vrx8vIOHTrYW1VT7XC73Ug/99xzbVq1fuutt9z1ruhvw8U8ZgjkbE90pnFicFYLcwAHzmAbMoBU5gwr4VAo4VAofvKEw4iuNDp8eD54BteTOAfgE9Z0UWbfv9vhcPTs2XPhwoVIQHL8+HF4QSRGjRo1ffp07k8Lz5qSkvLII48sWrQIiZqaGgi3bNkCD3rkyJHS0tJmzZp9/fXXKAgPl5SU9P7777OWioqKoqKivLy8xMTEli1brlixory8/I477mjVqlV+fv6XX35pmMu0p991l+mq/X6/1+tlFIdt8Hg8qGLz5s0ecyKLqdCIbpl7UYAGgPANHjw4JyfHa061MYQIXnfddVOnTqVJYa42bdosWLBg9erVnTp1oqn37duHZ8Hjl5SUIPHVV1+x7IQJEx5++GE8FOw/bdq0QYMGwaR4CzDL/PnzaQdIUOnQoUORfvzxxzMzMzmah2ahTV599VWoXbJkib211iRqn72p5wVc5IMHKcXur/dohEOhhEOhaDiEw4x1mz0EZkzC/GwNhM6NcBjR8acEvs5zc3PtwsrKysLCwhtuuMEaDmmucJqQkLBs2bJ169aBOtDD7dy5E+n9+/eDAzVp0gSXYAxoIdzehx9+SD0TJ06Enm3btoGItGvX7plnnkH74f+++OKLbAvIM2zYsBkzZkhjZL8Yp9N56NAhaENxkiHx7hfrxdBzgzahVffcc8/27dvBme688845c+bMnTsXJOOll14CRXjssceQAYkXXnghOTkZhAMFYZ+mTZuCgVVVVeHu4sWLQdGg7Zprrpk8eTL1g+TB5si/YcMG2Bb0DlV8/vnnKP7oo4926NABZl+/fj2Kl5WVfffdd2YAwzDAYN57773LLrvs3nvvhU5Qmb1798J6vCvNFsOeB+JlGFyjzB8MsOfl7v+8RwmHQgmHQtFwCAc/7vEPfsSIAsuRmJ33cGfn8KVODwS3hMTy5ctbt24Nz7d79+6DBw+CJRQXFy9duhSObceOHfCRM2fOpAd9+umn4UGpYc+ePRCeOHGCJGPhwoWlpaXInJSU9Mknn7AifJSPHj0aBSGBHB5006ZNu3btwi3Ql5YtW6LsmDFj4Gi//fZbPB18LSkO/ejhw4fhpF977TU4adIgO0+6KACpIufIzMzEUzdv3hwPeOTIEbCuKVOmdOvWDcLevXt/9NFHR48eFX4G0Fw0eFFRESha586d8fjjx49/8MEHQSAg37hxY9u2bZGtT58+PXr0WLVq1cqVK3EJKtOsWbO1a9fCROXl5fHx8Y0aNUIp4YiTJk2Ks9CqVSueKee6tGRpbPn5IhzsVXF7PR6fF7QjtV9f3S1WoYRDoWg4hINfq0Fr3QR8wZJwnNsHf8ACHBjJx/z587Oysui0CgoK8FUNH48vZly2aNECdIS9JK+//nrHjh2RAC3Al3Tjxo1RHE0CR0FOc2yBGX2JQ3FQBzhXeNmEhARIsrOzQTjmzJnzzjvvQBsrgjaQnjfffBNVwHPjFoqzEwHf6FDObEwcOnTo/H6mn3NYiI/scDjwgMePHxd7om0QulwucfPsewJFgLm4Dw4MTkYF8oQnBT+zqzWsXhLqRE6wELIEKGS4QvpQaizwrj38Q+uxPdbE3VOJ80g4OFw0GA6BbXAAR8+UXhrhUCjhUCgaDuGgTwoEQjh8Pni4MGepnBvhYAL+Tzr74c9CFsR1gXbQQSJDSUmJ3d/LKBDGXVAWH/pUaETD+HCrKAKJ34Jhrc4OP3348GEqQSm0BOejR4/aHWpkfYhoLSzLMMBFR8wgEhlfIs6ej085MksfEJ+F8QZSDQBsAAWFDdjzUC2e2hydGgqRl9BcJBl2FoKcvIS1jx07RqOR8UCnvW3/PLh5PWMbHMaR0T9TCYdCCYdC0XAIB7243x+87bbJlo8x58Byn/p/CAzsy3e5Oac2yiTwOU45hQyESNX0Z/JJDT3ifckPrMWm/MIb+C0u+UVOWmMnEKyI3+LwrOxDkSDBmWGGiwLGNqQZbCQkdscPg5A90HrSe8UBLjEDUFBEHgdpDpu1PzUYm9348uKolnxFrGc3O+mISKSj6vzYgTOJrAmx4By+gD89M0OXNlco4VAoGg7hoDPz+QLp6ZlkHuY26OcE+jk6QvFP4pbskXlxpYx/SGbxefCCzCajFKWpopycg5fwkUJxUCMdsz3oIhRHJOI4S0pKLuKLoX1gOmlYDJcSukYeIBA7yOwbezBDOlwM25BYYXIsizyUSCkSlJiAE4r7LEg2ISvyvs4L4ah11nEeLHhGIBTMzOqvEQ6FEg6FouEQDnHqWVnZtbXOkLWUZH29W02muKCRnugsFVlvdN83+7nY+VkPtZhCCYdC8RMjHFzjwTDXrsiMfBzXKttQXGh4fF6O4WCvCo5DRw6HT+3Yo4RDoYRDofiJE45o10MoIaGdYXWpVFfVqb0UFxjkGS6Pm8wjEAouXLxICYdCCYdC0XAIB+F2e4cNGxEKWYuamwts6z90xQVFODpRhQNFfQF/VvYA0A4lHAolHApFAyEc7E/x+4OffrrVsLaNxT/zqiqHmkxxgQlHzDJf7donaoRDoYRDoWg4hIOzEjwec7YC1+GI7OmpUFxAyDocjGognT1ooBIOhRIOhaLhEA7DCnKAaqxY8QpXGg0F1VyKCw2uNOoPBkA4QDuC4VBOXi53jlXCoVDCoVD8FDnHWbpUuJBDly7JkrW21qn2UlxIgEFUnKxkZwqHcfTpm6oRDoUSDoWioaGurq6goMDhcNhX4gJc9b6Q9Q/e7HMJRz2DYXjcAa5JykGmIvf7wl6vH/n9+Fb1RyTIibS5XLqVh3KkT5wo4zLqbpcp8nlDXFidGgDIWQq1UxIMRLaXY1rqRUs4+oQSp9MV6RiK1si7IuHZWefhXbSWO8hAJ+qqq3VTgvzQIy2XGnGXvpDbqbs8bpnMyc90WTST4xK4sAS/11mQu7FLGzlMUi4dteaqoLJfCd8Il0vhWluyPCgTXLMdzbA34GR1lSxuwUoZRZDDLuEmJhTKPmp8BObhave0D96U11yCPPJ+aRO+O9pT7IycIK9eT5CWpIR6aOFTv5zogmOyvtnzzz9/HvdqUSiUcCgUF59qcO3wnj17ihBuDB4FfsK+uCNIRm2NCwcSdjmy4YCTDoeMmPw4THphXYI9gL7A00Bi8pigIXpwCweLQ1jv9DINtaZXiyqEbzOpSbRSky5Y+c0+IEsIZ4ayUiMuTXdoESPqMRlSvQ8FpYhkRsL0hdG6whb5oARFaAoIkYYQ7hP+tq7eaXfh8Nnw8fz+lq3ISDXoy1HE7fWQDYSja2uCkVAiC17FrKTJnWICgUDMMqluC7JeKpRwIxIoFOZhP0Q/86DxdoaEhrHNHEsRMLlfSNgGngjM0mSN/lMa8S5gSTwtDAKzyCtDmi8LBhQ721+HyU6it5CAEpO2GkZ1dbU8Mh5N2YZCCYdC0TCRlpZG/iFLfXMkaV1dPRcelQ1WmIAQtyQbge9gDgSBv5K5tQcOHKRHwUctiwjw+SvZoMfhOLWniQxchUKUwpnCsrIKqVEaBj1utzdGs2FN962sjGyhUlFx0t7a8vJKZjADLdEveLlERcwgi66iYE1NZIUS6IRjloBElaOa4QQe1TUOJsorKyTSQM5B540M9oiIPd4ArkD+UVNTAyIoDCPCKqw9ZeiJZUs8CmMUQgkUMryBNNTaIyj2sAoaI3KyDXZtlFWUU1hTVysECKbmqzSMyJbCYl4YJ+a9wIB8uaWl5TIqGedjx45TLvbku6u3IA+7d+/e87s/nEKhhEOhuPgRDri3QYMGyR4Z1hbzptsQNoCEuBMk7HJkIzmAB4LnkA4ZexpUA0SBpZCG4+HmtB5raUnktLMW4S4gDeQZdrpDgJrQXdkLsrrIaiI2yiI+srq6hgwGt1CpGdWPPhEaQ7mQD8OITA/GA7IWaKbLNKI7jdl3N6XDpo+XJSUYbJAYhn03MqRPlJXC2de7XUJHSAIC0YG79l1hhXxYbyccpWVh7ofCglBF9iDBEiTsYRhUxDyl5WV2/oHGsJHMjDxsKh+k1lknbxzGCVvdT2I9Mgm2FIYi26Cp+bqFOJphM6/sYWva3+xQs9gGN+LB83KL2mAwOGLEiPO7P5xCoYRDobiY4Doc8GcrV67E/3r5F89QBL0FvMLJk9WkF3Q8uKRXZgZkZgYyA7svZ4BEmEfYGgBB/y0fx7xEKTIDqALVCIdPOX75FGaYgfJQdPoumoRKoZbf3xTCvSGzfSNV+2pmbB4yw1PGhEb4CKAadJzIIM/CGnHgoZAUpy6DMzhiA04aR3WNwyp6KtJQU1dbV++EBO4cLpxF6NEZCKmsOikMQPa9Y9eJEd16jWfZD5YJk4JEVaENHIGB6uwrW0C5hGGkUrQHmdEktEcaKfu14hFAmMiZYCU0124u2JMhK9rETi5Jy9AOvOIzqSRfE0mnfQK2fac9/GgyMjL0z1OhhEOhaFCEQwIbW7Zsyc3NTUlJycrKysjon5WVfeutt8FhwFMVFo7r2TMFQhxI4BJC3EIGZIPwqqvGxse3zs0dunPnLqhasGBR375pXbokDxp0ef/+A9LSMubNew4+ad26dwYOHDxgwMBevXqPGjW6T5++48ZdS7+OnNDTtWu3MWMKmzRplp8/ks5p/PjrMzOz+vVLR0WoeuTIn2/YsBHebs2atd2790xO7j506JXIgLLFxUtRZOvWbciWk5MHyZAhubg1ceKvHI5aHFdeORw1ojFQBTnaDGIBBzllyrT09MwePXqhlsTEpLy8Kw4cOAhVs2c/1blzVzzU4MFD2rVrD22vvLKqpKT04MFDqf36ZvTP7D8gq2dKr/TMjPseuH/fN/vhPZM6drhq7Jg+fVNxa8DA7MtzhpSWl0E+4eabCkaNxGW/9LSu3ZLHFI59e/06yOcvXDBsxPAOnToOvfKK4fkjBg+5fNaTsyHfsWNHQUFB165d4Xezs7MHDBgwdepUso3U1NT8/PxuFpDApWGFLqZMm8pK0Z4uyV3zRxZ8sWM7VEEh1EI5qkBFqA6VQo4GjL26MLl7t75p/ZAB+W+8aQI7U4bk5kBPZlb/3ql9Ro+5KjGpPar45pu//va3M/Hu8FpTUvrQjDAFDAKzwDgwEQwFc8FoTz75NIvgEiaFefFSUGTy5KmQnzhRdu2143v3Tr388hy8EfxUZAhRUVERrvE77NWrl/55KpRwKBQNDRwWgM9K7o0eDnPew6mNyysrK5mNOXkpGZDZ6XSyVMxYP7IZCGVvdOkmYA89bjGPx+NhpdQDJmQfOSisiLDvli4Ns8+voUKUsgsZM5D+CM77EIl8YXMwAQoy/GN/Ftn13t4/In0WbnM+Rsge/GA/RcXJSgjr6p32oAiLy+BN9q3IUAyXBfunPzsa+LByCwmaCNoY2JAqcEkJq5PeFqmUk2WknRBCCaMsMrKVjyldPLCAfVwFR7OyhTEviENMYHCZUMOcYny8PnndMa8DBfly+WtUKJRwKBQNAT4Lhm0QohHtZ8FZvEWMO5FLZLB7ZTgVuAr2xEMtbp3JGCCk78EtcVecbRFd9tQjwyHpjXiXS4bYB5rQXbE6u3JIcLZ7MjaSQtYCPZKmq0MtzGYfIWGXQy0fyk44OCQzwhI8bjp1enR2oNjnicQMsCBTgdevddZJN0ckp2UB4XAyG4Vye+8DhTJEw16jnfqQD6Eikgw7pWBOe0E0DDnty43LL4E/GBhBWmItHOfjJdLSVHuDcRZaKXTQTk14lzyDbzCGPioUSjgUCsWliPCPffyj+JHbo1Ao4VAoFAolHEo4FAolHArFTxUhPf7OcekRjh/bpAqFEg6FQgmHHko4lHAolHAoFAolHEo4lHAoFEo4FAolHEo4lHAo4VAo4VAoFD9IOAJ6/MBxaRKOH9Ge+vemUMKhUFzihEPPZz1fsoTjx7Kn/r0plHAoFJc459DzWc/6e1B7KpRwKBQKhUKhUCjhUCgUCoVCoYRDoVAoFAqFEg6FQqFQKBSKfwL/ByW5rX6a9809AAAAAElFTkSuQmCC" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Domains Samples Using Standard Layers\n", + "\n", + "Domains standard layers projects proceed in much the same way as a normal standard layers problem, except that there is an additional grouping step between layers and contrasts.\n", + "\n", + "Layers are grouped into 'Domain Contrasts'. The model for the actual experimental contrast is built from these domain contrasts rather than from layers. There are exactly two domains for each contrast, with the the ratio of them controlled by a fittable 'domain ratio' parameter.\n", + "\n", + "![image.png](attachment:f38e04ec-f12b-4e68-b486-6cf8bffef1bd.png)\n", + "\n", + "In this we will set up a simple example of a simulated system consisting of two layered domains to illustrate this process.\n", + "\n", + "Start by making the project, specifying that this is a domains calculation:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(calculation=\"domains\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define the parameters we need to define our two domains:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"L1 Thickness\", min=5.0, value=20.0, max=60.0, fit=True),\n", + " Parameter(name=\"L1 SLD\", min=3.0e-6, value=4.1e-6, max=5.0e-6, fit=False),\n", + " Parameter(name=\"L1 Roughness\", min=2.0, value=5.0, max=20.0, fit=True),\n", + " Parameter(name=\"L1 Hydration\", min=10.0, value=20.0, max=30.0, fit=True),\n", + " #\n", + " Parameter(name=\"L2 Thickness\", min=5.0, value=60.0, max=100.0, fit=True),\n", + " Parameter(name=\"L2 SLD\", min=2.1e-6, value=3.0e-6, max=5.0e-6, fit=False),\n", + " Parameter(name=\"L2 Roughness\", min=2.0, value=5.0, max=20.0, fit=True),\n", + " Parameter(name=\"L2 Hydration\", min=10.0, value=20.0, max=30.0, fit=True),\n", + " #\n", + " Parameter(name=\"L3 Thickness\", min=5.0, value=200.0, max=300.0, fit=True),\n", + " Parameter(name=\"L3 SLD\", min=3.0e-6, value=7.0e-6, max=8.0e-6, fit=False),\n", + " Parameter(name=\"L3 Roughness\", min=2.0, value=5.0, max=20.0, fit=True),\n", + " Parameter(name=\"L3 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now group these into layers as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "layers = [\n", + "Layer(name=\"Layer 1\", thickness=\"L1 Thickness\", SLD=\"L1 SLD\", roughness=\"L1 Roughness\", hydration=\"L1 Hydration\", hydrate_with=\"bulk out\"),\n", + "Layer(name=\"Layer 2\", thickness=\"L2 Thickness\", SLD=\"L2 SLD\", roughness=\"L2 Roughness\", hydration=\"L2 Hydration\", hydrate_with=\"bulk out\"),\n", + "Layer(name=\"Layer 3\", thickness=\"L3 Thickness\", SLD=\"L3 SLD\", roughness=\"L3 Roughness\", hydration=\"L3 Hydration\", hydrate_with=\"bulk out\")\n", + "]\n", + "\n", + "problem.layers.extend(layers)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we look at the project, there are two extra groups as compared to a normal standard layers - Domain Contrasts and Domain Ratios" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "domains\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "standard layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "air/substrate\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------------+---------+---------+-------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------------+---------+---------+-------+-------+------------+-----+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 3.0 | 5.0 | True | uniform | 0.0 | inf |\n", + "| 1 | L1 Thickness | 5.0 | 20.0 | 60.0 | True | uniform | 0.0 | inf |\n", + "| 2 | L1 SLD | 3e-06 | 4.1e-06 | 5e-06 | False | uniform | 0.0 | inf |\n", + "| 3 | L1 Roughness | 2.0 | 5.0 | 20.0 | True | uniform | 0.0 | inf |\n", + "| 4 | L1 Hydration | 10.0 | 20.0 | 30.0 | True | uniform | 0.0 | inf |\n", + "| 5 | L2 Thickness | 5.0 | 60.0 | 100.0 | True | uniform | 0.0 | inf |\n", + "| 6 | L2 SLD | 2.1e-06 | 3e-06 | 5e-06 | False | uniform | 0.0 | inf |\n", + "| 7 | L2 Roughness | 2.0 | 5.0 | 20.0 | True | uniform | 0.0 | inf |\n", + "| 8 | L2 Hydration | 10.0 | 20.0 | 30.0 | True | uniform | 0.0 | inf |\n", + "| 9 | L3 Thickness | 5.0 | 200.0 | 300.0 | True | uniform | 0.0 | inf |\n", + "| 10 | L3 SLD | 3e-06 | 7e-06 | 8e-06 | False | uniform | 0.0 | inf |\n", + "| 11 | L3 Roughness | 2.0 | 5.0 | 20.0 | True | uniform | 0.0 | inf |\n", + "| 12 | L3 Hydration | 10.0 | 20.0 | 30.0 | True | uniform | 0.0 | inf |\n", + "+-------+---------------------+---------+---------+-------+-------+------------+-----+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+-----+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+-----+-------+-----+-------+------------+-----+-------+\n", + "| 0 | SLD Air | 0.0 | 0.0 | 0.0 | False | uniform | 0.0 | inf |\n", + "+-------+---------+-----+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "| 0 | SLD D2O | 6.2e-06 | 6.35e-06 | 6.35e-06 | False | uniform | 0.0 | inf |\n", + "+-------+---------+---------+----------+----------+-------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.02 | 0.23 | 0.25 | False | uniform | 0.0 | inf |\n", + "+-------+---------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Domain Ratios: -------------------------------------------------------------------------------------\n", + "\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "| 0 | Domain Ratio 1 | 0.4 | 0.5 | 0.6 | False | uniform | 0.0 | inf |\n", + "+-------+----------------+-----+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "| 0 | Background Param 1 | 1e-07 | 1e-06 | 1e-05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+-------+-------+-------+-------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Background 1 | constant | Background Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------------+------+------------+------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+------------+------+------------+------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "+-------+------------+------+------------+------------------+\n", + "\n", + "Layers: --------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+--------------+--------+--------------+--------------+--------------+\n", + "| index | name | thickness | SLD | roughness | hydration | hydrate with |\n", + "+-------+---------+--------------+--------+--------------+--------------+--------------+\n", + "| 0 | Layer 1 | L1 Thickness | L1 SLD | L1 Roughness | L1 Hydration | bulk out |\n", + "| 1 | Layer 2 | L2 Thickness | L2 SLD | L2 Roughness | L2 Hydration | bulk out |\n", + "| 2 | Layer 3 | L3 Thickness | L3 SLD | L3 Roughness | L3 Hydration | bulk out |\n", + "+-------+---------+--------------+--------+--------------+--------------+--------------+\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, make a couple of Domain Contrasts" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "problem.domain_contrasts.append(name=\"Domain 1\", model=[\"Layer 1\"])\n", + "problem.domain_contrasts.append(name=\"Domain 2\", model=[\"Layer 2\", \"Layer 3\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now make a contrast as with standard models, but this time also including the default domain ratio (\"Domain Ratio 1\"). Note that the model for each experimental contrast **must** have **exactly** two domain contrasts." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "problem.contrasts.append(\n", + " name=\"Domain Test\",\n", + " background=\"Background 1\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " resample=False,\n", + " bulk_in=\"SLD Air\",\n", + " bulk_out=\"SLD D2O\",\n", + " domain_ratio=\"Domain Ratio 1\",\n", + " data=\"Simulation\",\n", + " model=[\"Domain 1\", \"Domain 2\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can run our simulation as usual, and plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.004 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls = RAT.Controls()\n", + "problem, results = RAT.run(problem, controls)\n", + "\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/RATapi/examples/languages/run_custom_file_languages.py b/RATapi/examples/languages/run_custom_file_languages.py index 0e9ae47d..e343d68d 100644 --- a/RATapi/examples/languages/run_custom_file_languages.py +++ b/RATapi/examples/languages/run_custom_file_languages.py @@ -11,6 +11,7 @@ project = setup_problem.make_example_problem() controls = RAT.Controls() +controls.calcSldDuringFit = True # Python start = time.time() diff --git a/RATapi/examples/languages/setup_problem.py b/RATapi/examples/languages/setup_problem.py index dff7a1d6..2ab6f4fd 100644 --- a/RATapi/examples/languages/setup_problem.py +++ b/RATapi/examples/languages/setup_problem.py @@ -1,4 +1,3 @@ -import os import pathlib import numpy as np @@ -39,10 +38,10 @@ def make_example_problem(): # and H2O. Load these datafiles in and put them in the data block # Read in the datafiles - data_path = os.path.join(pathlib.Path(__file__).parents[1].resolve(), "data") - D2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016596.dat"), delimiter=",") - SMW_data = np.loadtxt(os.path.join(data_path, "c_PLP0016601.dat"), delimiter=",") - H2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016607.dat"), delimiter=",") + data_path = pathlib.Path("../data") + D2O_data = np.loadtxt(data_path / "c_PLP0016596.dat", delimiter=",") + SMW_data = np.loadtxt(data_path / "c_PLP0016601.dat", delimiter=",") + H2O_data = np.loadtxt(data_path / "c_PLP0016607.dat", delimiter=",") # Add the data to the project - note this data has a resolution 4th column problem.data.append(name="Bilayer / D2O", data=D2O_data) diff --git a/RATapi/examples/non_polarised/DSPC_custom_XY.py b/RATapi/examples/non_polarised/DSPC_custom_XY.py index fd350d78..567c765b 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_XY.py +++ b/RATapi/examples/non_polarised/DSPC_custom_XY.py @@ -1,4 +1,3 @@ -import os import pathlib import numpy as np @@ -32,7 +31,7 @@ def DSPC_custom_XY(): where VFn is the Volume Fraction of the n'th layer. """ - # Start by making the class and setting it to a custom layers type: + # Start by making the class and setting it to a custom XY type: problem = RAT.Project(name="Orso lipid example - custom XY", model="custom xy", geometry="substrate/liquid") # We need to add the relevant parameters we are going to need to define the model @@ -59,10 +58,10 @@ def DSPC_custom_XY(): # Water and H2O. Load these datafiles in and put them in the data block # Read in the datafiles - data_path = os.path.join(pathlib.Path(__file__).parents[1].resolve(), "data") - D2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016596.dat"), delimiter=",") - SMW_data = np.loadtxt(os.path.join(data_path, "c_PLP0016601.dat"), delimiter=",") - H2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016607.dat"), delimiter=",") + data_path = pathlib.Path(__file__).parents[1] / "data" + D2O_data = np.loadtxt(data_path / "c_PLP0016596.dat", delimiter=",") + SMW_data = np.loadtxt(data_path / "c_PLP0016601.dat", delimiter=",") + H2O_data = np.loadtxt(data_path / "c_PLP0016607.dat", delimiter=",") # Add the data to the project - note this data has a resolution 4th column problem.data.append(name="Bilayer / D2O", data=D2O_data) diff --git a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb new file mode 100644 index 00000000..e4ac99ad --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -0,0 +1,865 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter" + ] + }, + { + "cell_type": "markdown", + "id": "793d9c50-698e-438b-87f7-85e3a9f11d6b", + "metadata": {}, + "source": [ + "# Custom Layers Example for Supported DSPC layer\n", + "\n", + "Example of using Custom layers to model a DSPC supported bilayer.\n", + "Start by making the project and setting it to a custom layers type:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9a60cd45-0e1d-448a-b4bd-4c02bd6a3475", + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(name=\"Orso lipid example - custom layers\", model=\"custom layers\", geometry=\"substrate/liquid\")" + ] + }, + { + "cell_type": "markdown", + "id": "9cc56e51-3d52-460a-bbb1-6d68571887c6", + "metadata": {}, + "source": [ + "For a custom layers model, rather than being forced to define our layers as \\[Thick SLD Rough.... etc\\], we can parameterise however we like and then use a function to calculate the \\[d $\\rho$ $\\sigma$\\] arrangement for each layer. So for example, if the volume of lipid tails are known (from the literature), then all we need is the Area per molecule, because then:\n", + "\n", + "$$\n", + "d = \\frac{V}{APM},\n", + "$$\n", + "where d is the thickness and V is the volume.\n", + "\n", + "Likewise, the SLD is:\n", + "$$\n", + "\\rho = \\frac{\\sum_{i}n_{i}b_{i}}{V},\n", + "$$\n", + "\n", + "as usual.\n", + "\n", + "In this folder there is a pre-prepared Python custom model for a DSPC on a Silicon substrate. We can display it here to see what we mean:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9038b77f-e3fc-4946-87fe-af4addf8ee84", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
import numpy as np\n",
+       "\n",
+       "\n",
+       "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n",
+       "    """CUSTOMBILAYER RAT Custom Layer Model File.\n",
+       "\n",
+       "    This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n",
+       "    The final parameter is an index of the contrast being calculated.\n",
+       "\n",
+       "    The function should output a matrix of layer values, in the form...\n",
+       "\n",
+       "    Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1\n",
+       "              ....\n",
+       "              thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]\n",
+       "\n",
+       "    The "hydrate how" parameter decides if the layer is hydrated with Bulk out or Bulk in phases.\n",
+       "    Set to 1 for Bulk out, zero for Bulk in.\n",
+       "    Alternatively, leave out hydration and just return...\n",
+       "\n",
+       "    Output = [thick 1, SLD 1, Rough 1,\n",
+       "              ....\n",
+       "              thick n, SLD n, Rough n]\n",
+       "\n",
+       "    The second output parameter should be the substrate roughness.\n",
+       "    """\n",
+       "    sub_rough = params[0]\n",
+       "    oxide_thick = params[1]\n",
+       "    oxide_hydration = params[2]\n",
+       "    lipidAPM = params[3]\n",
+       "    headHydration = params[4]\n",
+       "    bilayerHydration = params[5]\n",
+       "    bilayerRough = params[6]\n",
+       "    waterThick = params[7]\n",
+       "\n",
+       "    # We have a constant SLD for the bilayer\n",
+       "    oxide_SLD = 3.41e-6\n",
+       "\n",
+       "    # Now make the lipid layers\n",
+       "    # Use known lipid volume and compositions to make the layers\n",
+       "\n",
+       "    # define all the neutron b's.\n",
+       "    bc = 0.6646e-4  # Carbon\n",
+       "    bo = 0.5843e-4  # Oxygen\n",
+       "    bh = -0.3739e-4  # Hydrogen\n",
+       "    bp = 0.513e-4  # Phosphorus\n",
+       "    bn = 0.936e-4  # Nitrogen\n",
+       "\n",
+       "    # Now make the lipid groups\n",
+       "    COO = (4 * bo) + (2 * bc)\n",
+       "    GLYC = (3 * bc) + (5 * bh)\n",
+       "    CH3 = (2 * bc) + (6 * bh)\n",
+       "    PO4 = (1 * bp) + (4 * bo)\n",
+       "    CH2 = (1 * bc) + (2 * bh)\n",
+       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
+       "\n",
+       "    # Group these into heads and tails:\n",
+       "    Head = CHOL + PO4 + GLYC + COO\n",
+       "    Tails = (34 * CH2) + (2 * CH3)\n",
+       "\n",
+       "    # We need volumes for each. Use literature values:\n",
+       "    vHead = 319\n",
+       "    vTail = 782\n",
+       "\n",
+       "    # We use the volumes to calculate the SLDs\n",
+       "    SLDhead = Head / vHead\n",
+       "    SLDtail = Tails / vTail\n",
+       "\n",
+       "    # We calculate the layer thickness' from the volumes and the APM\n",
+       "    headThick = vHead / lipidAPM\n",
+       "    tailThick = vTail / lipidAPM\n",
+       "\n",
+       "    # Manually deal with hydration for layers in this example.\n",
+       "    oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)\n",
+       "    headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n",
+       "    tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n",
+       "\n",
+       "    # Make the layers\n",
+       "    oxide = [oxide_thick, oxSLD, sub_rough]\n",
+       "    water = [waterThick, bulk_out[contrast], bilayerRough]\n",
+       "    head = [headThick, headSLD, bilayerRough]\n",
+       "    tail = [tailThick, tailSLD, bilayerRough]\n",
+       "\n",
+       "    output = np.array([oxide, water, head, tail, tail, head])\n",
+       "\n",
+       "    return output, sub_rough\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k+kn}{import} \\PY{n+nn}{numpy} \\PY{k}{as} \\PY{n+nn}{np}\n", + "\n", + "\n", + "\\PY{k}{def} \\PY{n+nf}{custom\\PYZus{}bilayer\\PYZus{}DSPC}\\PY{p}{(}\\PY{n}{params}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}in}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{,} \\PY{n}{contrast}\\PY{p}{)}\\PY{p}{:}\n", + "\\PY{+w}{ }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}CUSTOMBILAYER RAT Custom Layer Model File.}\n", + "\n", + "\\PY{l+s+sd}{ This file accepts 3 vectors containing the values for params, bulk in and bulk out.}\n", + "\\PY{l+s+sd}{ The final parameter is an index of the contrast being calculated.}\n", + "\n", + "\\PY{l+s+sd}{ The function should output a matrix of layer values, in the form...}\n", + "\n", + "\\PY{l+s+sd}{ Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1}\n", + "\\PY{l+s+sd}{ ....}\n", + "\\PY{l+s+sd}{ thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]}\n", + "\n", + "\\PY{l+s+sd}{ The \\PYZdq{}hydrate how\\PYZdq{} parameter decides if the layer is hydrated with Bulk out or Bulk in phases.}\n", + "\\PY{l+s+sd}{ Set to 1 for Bulk out, zero for Bulk in.}\n", + "\\PY{l+s+sd}{ Alternatively, leave out hydration and just return...}\n", + "\n", + "\\PY{l+s+sd}{ Output = [thick 1, SLD 1, Rough 1,}\n", + "\\PY{l+s+sd}{ ....}\n", + "\\PY{l+s+sd}{ thick n, SLD n, Rough n]}\n", + "\n", + "\\PY{l+s+sd}{ The second output parameter should be the substrate roughness.}\n", + "\\PY{l+s+sd}{ \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n", + " \\PY{n}{sub\\PYZus{}rough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\n", + " \\PY{n}{oxide\\PYZus{}thick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", + " \\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", + " \\PY{n}{lipidAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{headHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{bilayerHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", + " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", + " \\PY{n}{waterThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} We have a constant SLD for the bilayer}\n", + " \\PY{n}{oxide\\PYZus{}SLD} \\PY{o}{=} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now make the lipid layers}\n", + " \\PY{c+c1}{\\PYZsh{} Use known lipid volume and compositions to make the layers}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} define all the neutron b\\PYZsq{}s.}\n", + " \\PY{n}{bc} \\PY{o}{=} \\PY{l+m+mf}{0.6646e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Carbon}\n", + " \\PY{n}{bo} \\PY{o}{=} \\PY{l+m+mf}{0.5843e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Oxygen}\n", + " \\PY{n}{bh} \\PY{o}{=} \\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.3739e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Hydrogen}\n", + " \\PY{n}{bp} \\PY{o}{=} \\PY{l+m+mf}{0.513e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Phosphorus}\n", + " \\PY{n}{bn} \\PY{o}{=} \\PY{l+m+mf}{0.936e\\PYZhy{}4} \\PY{c+c1}{\\PYZsh{} Nitrogen}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now make the lipid groups}\n", + " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)}\n", + " \\PY{n}{GLYC} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{3} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", + " \\PY{n}{CH3} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{6} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", + " \\PY{n}{PO4} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bp}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{4} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)}\n", + " \\PY{n}{CH2} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)}\n", + " \\PY{n}{CHOL} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{5} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{12} \\PY{o}{*} \\PY{n}{bh}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bn}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Group these into heads and tails:}\n", + " \\PY{n}{Head} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{n}{COO}\n", + " \\PY{n}{Tails} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{34} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} We need volumes for each. Use literature values:}\n", + " \\PY{n}{vHead} \\PY{o}{=} \\PY{l+m+mi}{319}\n", + " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{782}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} We use the volumes to calculate the SLDs}\n", + " \\PY{n}{SLDhead} \\PY{o}{=} \\PY{n}{Head} \\PY{o}{/} \\PY{n}{vHead}\n", + " \\PY{n}{SLDtail} \\PY{o}{=} \\PY{n}{Tails} \\PY{o}{/} \\PY{n}{vTail}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} We calculate the layer thickness\\PYZsq{} from the volumes and the APM}\n", + " \\PY{n}{headThick} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{lipidAPM}\n", + " \\PY{n}{tailThick} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{lipidAPM}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Manually deal with hydration for layers in this example.}\n", + " \\PY{n}{oxSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{oxide\\PYZus{}hydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxide\\PYZus{}hydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{oxide\\PYZus{}SLD}\\PY{p}{)}\n", + " \\PY{n}{headSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{headHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{headHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDhead}\\PY{p}{)}\n", + " \\PY{n}{tailSLD} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerHydration} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerHydration}\\PY{p}{)} \\PY{o}{*} \\PY{n}{SLDtail}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the layers}\n", + " \\PY{n}{oxide} \\PY{o}{=} \\PY{p}{[}\\PY{n}{oxide\\PYZus{}thick}\\PY{p}{,} \\PY{n}{oxSLD}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\\PY{p}{]}\n", + " \\PY{n}{water} \\PY{o}{=} \\PY{p}{[}\\PY{n}{waterThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{head} \\PY{o}{=} \\PY{p}{[}\\PY{n}{headThick}\\PY{p}{,} \\PY{n}{headSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{tail} \\PY{o}{=} \\PY{p}{[}\\PY{n}{tailThick}\\PY{p}{,} \\PY{n}{tailSLD}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + "\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{p}{[}\\PY{n}{oxide}\\PY{p}{,} \\PY{n}{water}\\PY{p}{,} \\PY{n}{head}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{tail}\\PY{p}{,} \\PY{n}{head}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{sub\\PYZus{}rough}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "import numpy as np\n", + "\n", + "\n", + "def custom_bilayer_DSPC(params, bulk_in, bulk_out, contrast):\n", + " \"\"\"CUSTOMBILAYER RAT Custom Layer Model File.\n", + "\n", + " This file accepts 3 vectors containing the values for params, bulk in and bulk out.\n", + " The final parameter is an index of the contrast being calculated.\n", + "\n", + " The function should output a matrix of layer values, in the form...\n", + "\n", + " Output = [thick 1, SLD 1, Rough 1, Percent Hydration 1, Hydrate how 1\n", + " ....\n", + " thick n, SLD n, Rough n, Percent Hydration n, Hydration how n]\n", + "\n", + " The \"hydrate how\" parameter decides if the layer is hydrated with Bulk out or Bulk in phases.\n", + " Set to 1 for Bulk out, zero for Bulk in.\n", + " Alternatively, leave out hydration and just return...\n", + "\n", + " Output = [thick 1, SLD 1, Rough 1,\n", + " ....\n", + " thick n, SLD n, Rough n]\n", + "\n", + " The second output parameter should be the substrate roughness.\n", + " \"\"\"\n", + " sub_rough = params[0]\n", + " oxide_thick = params[1]\n", + " oxide_hydration = params[2]\n", + " lipidAPM = params[3]\n", + " headHydration = params[4]\n", + " bilayerHydration = params[5]\n", + " bilayerRough = params[6]\n", + " waterThick = params[7]\n", + "\n", + " # We have a constant SLD for the bilayer\n", + " oxide_SLD = 3.41e-6\n", + "\n", + " # Now make the lipid layers\n", + " # Use known lipid volume and compositions to make the layers\n", + "\n", + " # define all the neutron b's.\n", + " bc = 0.6646e-4 # Carbon\n", + " bo = 0.5843e-4 # Oxygen\n", + " bh = -0.3739e-4 # Hydrogen\n", + " bp = 0.513e-4 # Phosphorus\n", + " bn = 0.936e-4 # Nitrogen\n", + "\n", + " # Now make the lipid groups\n", + " COO = (4 * bo) + (2 * bc)\n", + " GLYC = (3 * bc) + (5 * bh)\n", + " CH3 = (2 * bc) + (6 * bh)\n", + " PO4 = (1 * bp) + (4 * bo)\n", + " CH2 = (1 * bc) + (2 * bh)\n", + " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", + "\n", + " # Group these into heads and tails:\n", + " Head = CHOL + PO4 + GLYC + COO\n", + " Tails = (34 * CH2) + (2 * CH3)\n", + "\n", + " # We need volumes for each. Use literature values:\n", + " vHead = 319\n", + " vTail = 782\n", + "\n", + " # We use the volumes to calculate the SLDs\n", + " SLDhead = Head / vHead\n", + " SLDtail = Tails / vTail\n", + "\n", + " # We calculate the layer thickness' from the volumes and the APM\n", + " headThick = vHead / lipidAPM\n", + " tailThick = vTail / lipidAPM\n", + "\n", + " # Manually deal with hydration for layers in this example.\n", + " oxSLD = (oxide_hydration * bulk_out[contrast]) + ((1 - oxide_hydration) * oxide_SLD)\n", + " headSLD = (headHydration * bulk_out[contrast]) + ((1 - headHydration) * SLDhead)\n", + " tailSLD = (bilayerHydration * bulk_out[contrast]) + ((1 - bilayerHydration) * SLDtail)\n", + "\n", + " # Make the layers\n", + " oxide = [oxide_thick, oxSLD, sub_rough]\n", + " water = [waterThick, bulk_out[contrast], bilayerRough]\n", + " head = [headThick, headSLD, bilayerRough]\n", + " tail = [tailThick, tailSLD, bilayerRough]\n", + "\n", + " output = np.array([oxide, water, head, tail, tail, head])\n", + "\n", + " return output, sub_rough" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Code(filename='custom_bilayer_DSPC.py', language='python')" + ] + }, + { + "cell_type": "markdown", + "id": "002b67c8-1091-4544-9325-58227a012e4e", + "metadata": {}, + "source": [ + "We need to add the parameters we are going to need to define the model (note that Substrate Roughness always exists as parameter 0 as before, and that we are setting a Gaussian prior on the Head Hydration here)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "70494ef9-6cc5-47dc-9d02-6506645de46b", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True),\n", + " Parameter(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True),\n", + " Parameter(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True),\n", + " Parameter(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True, prior_type='gaussian', mu=0.3, sigma=0.03),\n", + " Parameter(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True),\n", + " Parameter(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True),\n", + " Parameter(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)\n", + "problem.parameters.set_fields(0, min=1.0, max=10.0)" + ] + }, + { + "cell_type": "markdown", + "id": "a11897b0-244b-46c2-8bcd-a3d65bd8fc5c", + "metadata": {}, + "source": [ + "Need to add the relevant Bulk SLD's. Change the bulk in from air to silicon, and add two additional water contrasts:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the bulk in from air to silicon:\n", + "problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.07e-6, value=2.073e-6, max=2.08e-6, fit=False)\n", + "\n", + "problem.bulk_out.append(name=\"SLD SMW\", min=1.0e-6, value=2.073e-6, max=3.0e-6, fit=True)\n", + "problem.bulk_out.append(name=\"SLD H2O\", min=-0.6e-6, value=-0.56e-6, max=-0.3e-6, fit=True)\n", + "\n", + "problem.bulk_out.set_fields(0, min=5.0e-6, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d767523b-70ab-42a9-b28f-cd013a8b177e", + "metadata": {}, + "source": [ + "Now add the datafiles. We have three datasets we need to consider - the bilayer against D2O, Silicon Matched water and H2O. Load these datafiles in and put them in the data block:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the datafiles\n", + "data_path = pathlib.Path(\"../data\")\n", + "D2O_data = np.loadtxt(data_path / \"c_PLP0016596.dat\", delimiter=\",\")\n", + "SMW_data = np.loadtxt(data_path / \"c_PLP0016601.dat\", delimiter=\",\")\n", + "H2O_data = np.loadtxt(data_path / \"c_PLP0016607.dat\", delimiter=\",\")\n", + "\n", + "# Add the data to the project - note this data has a resolution 4th column\n", + "problem.data.append(name=\"Bilayer / D2O\", data=D2O_data, data_range=[0.013, 0.37])\n", + "problem.data.append(name=\"Bilayer / SMW\", data=SMW_data, data_range=[0.013, 0.32996])\n", + "problem.data.append(name=\"Bilayer / H2O\", data=H2O_data, data_range=[0.013, 0.33048])" + ] + }, + { + "cell_type": "markdown", + "id": "e60cd052-54f9-41b4-ab8b-6d4dde1c50fa", + "metadata": {}, + "source": [ + "Add the custom file to the project:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2", + "metadata": {}, + "outputs": [], + "source": [ + "problem.custom_files.append(name=\"DSPC Model\", filename=\"custom_bilayer_DSPC.py\", language=\"python\", path=pathlib.Path.cwd().resolve())" + ] + }, + { + "cell_type": "markdown", + "id": "19a57f11-3d3c-49c5-b7a6-52bf449a3878", + "metadata": {}, + "source": [ + "Also, add the relevant background parameters - one each for each contrast:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b", + "metadata": {}, + "outputs": [], + "source": [ + "problem.background_parameters.set_fields(0, name=\"Background parameter D2O\", min=1.0e-10, max=1.0e-5, value=1.0e-07, fit=True)\n", + "\n", + "problem.background_parameters.append(name=\"Background parameter SMW\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter H2O\", min=1.0e-10, value=1.0e-7, max=1.0e-5, fit=True)\n", + "\n", + "# And add the two new constant backgrounds\n", + "problem.backgrounds.append(name=\"Background SMW\", type=\"constant\", value_1=\"Background parameter SMW\")\n", + "problem.backgrounds.append(name=\"Background H2O\", type=\"constant\", value_1=\"Background parameter H2O\")\n", + "\n", + "# And edit the other one\n", + "problem.backgrounds.set_fields(0, name=\"Background D2O\", value_1=\"Background parameter D2O\")\n", + "\n", + "# Finally modify some of the other parameters to be more suitable values for a solid / liquid experiment\n", + "problem.scalefactors.set_fields(0, value=1.0, min=0.5, max=2.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "a69a6d51-202a-4834-a6be-5c30f67d9107", + "metadata": {}, + "source": [ + "We need to use the data resolution (i.e. the fourth column of our datafiles). Do do this, we need to add a 'Data' resolution object to our resolutions table" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b1e4d313-8450-459b-b60e-868fe82f06b0", + "metadata": {}, + "outputs": [], + "source": [ + "problem.resolutions.append(name=\"Data Resolution\", type=\"data\")" + ] + }, + { + "cell_type": "markdown", + "id": "ddde7088-1382-4f56-9e05-6f1683ec2260", + "metadata": {}, + "source": [ + "Now add the three contrasts as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "efc7b351-2112-40c4-862b-a47e4570d173", + "metadata": {}, + "outputs": [], + "source": [ + "problem.contrasts.append(\n", + " name=\"Bilayer / D2O\",\n", + " background=\"Background D2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD D2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / D2O\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / SMW\",\n", + " background=\"Background SMW\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD SMW\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / SMW\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / H2O\",\n", + " background=\"Background H2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD H2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / H2O\",\n", + " model=[\"DSPC Model\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "89f110e4-c3f8-488d-91d5-4f5fb5fbe9d7", + "metadata": {}, + "source": [ + "Note that the model is simply the custom file we've just added to the project.\n", + "\n", + "Look at the complete model definition before sending it to RAT:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: ----------------------------------------------------------------------------------------------\n", + "\n", + "Orso lipid example - custom layers\n", + "\n", + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "non polarised\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "custom layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "substrate/liquid\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 3.0 | 10.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Oxide Thickness | 5.0 | 20.0 | 60.0 | True | uniform | 0.0 | inf |\n", + "| 2 | Oxide Hydration | 0.0 | 0.2 | 0.5 | True | uniform | 0.0 | inf |\n", + "| 3 | Lipid APM | 45.0 | 55.0 | 65.0 | True | uniform | 0.0 | inf |\n", + "| 4 | Head Hydration | 0.0 | 0.2 | 0.5 | True | gaussian | 0.3 | 0.03 |\n", + "| 5 | Bilayer Hydration | 0.0 | 0.1 | 0.2 | True | uniform | 0.0 | inf |\n", + "| 6 | Bilayer Roughness | 2.0 | 4.0 | 8.0 | True | uniform | 0.0 | inf |\n", + "| 7 | Water Thickness | 0.0 | 2.0 | 10.0 | True | uniform | 0.0 | inf |\n", + "+-------+---------------------+------+-------+------+------+------------+-----+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", + "| 0 | Silicon | 2.07e-06 | 2.073e-06 | 2.08e-06 | False | uniform | 0.0 | inf |\n", + "+-------+---------+----------+-----------+----------+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", + "| 0 | SLD D2O | 5e-06 | 6.35e-06 | 6.35e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | SLD SMW | 1e-06 | 2.073e-06 | 3e-06 | True | uniform | 0.0 | inf |\n", + "| 2 | SLD H2O | -6e-07 | -5.6e-07 | -3e-07 | True | uniform | 0.0 | inf |\n", + "+-------+---------+--------+-----------+----------+------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.5 | 1.0 | 2.0 | True | uniform | 0.0 | inf |\n", + "+-------+---------------+-----+-------+-----+------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", + "| 0 | Background parameter D2O | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", + "| 1 | Background parameter SMW | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", + "| 2 | Background parameter H2O | 1e-10 | 1e-07 | 1e-05 | True | uniform | 0.0 | inf |\n", + "+-------+--------------------------+-------+-------+-------+------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| 0 | Background D2O | constant | Background parameter D2O | | | | |\n", + "| 1 | Background SMW | constant | Background parameter SMW | | | | |\n", + "| 2 | Background H2O | constant | Background parameter H2O | | | | |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", + "| 1 | Data Resolution | data | | | | | |\n", + "+-------+-----------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Custom Files: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", + "| index | name | filename | function name | language | path |\n", + "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", + "| 0 | DSPC Model | custom_bilayer_DSPC.py | custom_bilayer_DSPC | python | /mnt/c/Users/gnn85523/projects/python-RAT/RATapi/examples/non_polarised |\n", + "+-------+------------+------------------------+---------------------+----------+-------------------------------------------------------------------------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+-----------------------+------------------+----------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+---------------+-----------------------+------------------+----------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "| 1 | Bilayer / D2O | Data array: [146 x 4] | [0.013, 0.37] | [0.0057118, 0.39606] |\n", + "| 2 | Bilayer / SMW | Data array: [97 x 4] | [0.013, 0.32996] | [0.0076029, 0.32996] |\n", + "| 3 | Bilayer / H2O | Data array: [104 x 4] | [0.013, 0.33048] | [0.0063374, 0.33048] |\n", + "+-------+---------------+-----------------------+------------------+----------------------+\n", + "\n", + "Contrasts: -----------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", + "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | model |\n", + "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", + "| 0 | Bilayer / D2O | Bilayer / D2O | Background D2O | add | Silicon | SLD D2O | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", + "| 1 | Bilayer / SMW | Bilayer / SMW | Background SMW | add | Silicon | SLD SMW | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", + "| 2 | Bilayer / H2O | Bilayer / H2O | Background H2O | add | Silicon | SLD H2O | Scalefactor 1 | Data Resolution | False | DSPC Model |\n", + "+-------+---------------+---------------+----------------+-------------------+---------+----------+---------------+-----------------+----------+------------+\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "861b6e03-773a-46c3-b3fd-0df47c99d27e", + "metadata": {}, + "source": [ + "To run it, we need to make a controls block" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------+-----------+\n", + "| Property | Value |\n", + "+------------------+-----------+\n", + "| procedure | calculate |\n", + "| parallel | single |\n", + "| calcSldDuringFit | False |\n", + "| resampleMinAngle | 0.9 |\n", + "| resampleNPoints | 50 |\n", + "| display | iter |\n", + "+------------------+-----------+\n" + ] + } + ], + "source": [ + "controls = RAT.Controls()\n", + "print(controls)" + ] + }, + { + "cell_type": "markdown", + "id": "384f0a34-1a2b-40f7-a945-6d44db9391ab", + "metadata": {}, + "source": [ + ". . . and send this to RAT" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.020 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "problem, results = RAT.run(problem, controls)\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RATapi/examples/non_polarised/DSPC_custom_layers.py b/RATapi/examples/non_polarised/DSPC_custom_layers.py index 1b88ebd3..ddbac6fb 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_layers.py +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.py @@ -1,4 +1,3 @@ -import os import pathlib import numpy as np @@ -39,10 +38,10 @@ def DSPC_custom_layers(): # Water and H2O. Load these datafiles in and put them in the data block # Read in the datafiles - data_path = os.path.join(pathlib.Path(__file__).parents[1].resolve(), "data") - D2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016596.dat"), delimiter=",") - SMW_data = np.loadtxt(os.path.join(data_path, "c_PLP0016601.dat"), delimiter=",") - H2O_data = np.loadtxt(os.path.join(data_path, "c_PLP0016607.dat"), delimiter=",") + data_path = pathlib.Path(__file__).parents[1] / "data" + D2O_data = np.loadtxt(data_path / "c_PLP0016596.dat", delimiter=",") + SMW_data = np.loadtxt(data_path / "c_PLP0016601.dat", delimiter=",") + H2O_data = np.loadtxt(data_path / "c_PLP0016607.dat", delimiter=",") # Add the data to the project - note this data has a resolution 4th column problem.data.append(name="Bilayer / D2O", data=D2O_data, data_range=[0.013, 0.37]) diff --git a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb new file mode 100644 index 00000000..c792fb13 --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -0,0 +1,311 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "956a341a-2a40-466c-b5c4-f8ea334ee81c", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Parameter" + ] + }, + { + "attachments": { + "bf3e4c3d-0fc8-4565-8f2d-f4f8386d582c.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "2c6e70fd-f282-47e6-b310-8f4a3e305b7e", + "metadata": {}, + "source": [ + "# Custom XY Example for Supported DSPC layer.\n", + "\n", + "In this example, we model the same data (DSPC supported bilayer) as the Custom Layers example, but this time we will use continuous distributions of the volume fractions of each component to build up the SLD profiles (as described in Shekhar et al, *J. Appl. Phys.*, **110**, 102216 (2011).)\n", + "\n", + "In this type of model, each 'layer' in the sample is described by a roughened Heaviside step function (really, just two error functions back to back). So, in our case, we will need an oxide, a (possible) intervening water layer, and then the bilayer itself.\n", + "\n", + "We can define our lipid in terms of an Area per Molecule, almost in it's entirity, if we recognise that where the volume is known, the thickness of the layer is simply given by the layer volume / APM\n", + "$$\n", + "d = \\frac{V}{APM}.\n", + "$$\n", + "We can then define the Volume Fraction of this layer with a roughened Heaviside of length dlayer and a height of 1. Then, the total volume occupied will be given by the sum of the volume fractions across the interface. Of course, this does not permit any hydration, so to deal with this, we can simply scale the (full occupation) Heaviside functions by relevant coverage parameters. When this is correctly done, we can obtain the remaining water distribution as\n", + "$$\n", + "VF_{water} = 1 - \\sum_{n}VF_{n},\n", + "$$\n", + "where $VF_{n}$ is the Volume Fraction of the n'th layer.\n", + "![image.png](attachment:bf3e4c3d-0fc8-4565-8f2d-f4f8386d582c.png)\n", + "Start by making the class and setting it to a custom XY type:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d53c3ea9-b06f-4bf1-b7cc-da2264ca7322", + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(name=\"Orso lipid example - custom XY\", model=\"custom xy\", geometry=\"substrate/liquid\")" + ] + }, + { + "cell_type": "markdown", + "id": "f73d2471-a59c-4394-bd9f-7bed3f7f6057", + "metadata": {}, + "source": [ + "We need to add the relevant parameters we are going to need to define the model (note that Substrate Roughness always exists as parameter 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ba58003-096e-45cb-b384-f82d66259fed", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=10.0, value=15.0, max=30.0, fit=True),\n", + " Parameter(name=\"Oxide Hydration\", min=0.1, value=0.2, max=0.4, fit=True),\n", + " Parameter(name=\"Water Thickness\", min=0.0, value=5.0, max=20.0, fit=True),\n", + " Parameter(name=\"Lipid APM\", min=40.0, value=50.0, max=90.0, fit=True),\n", + " Parameter(name=\"Lipid Coverage\", min=0.9, value=1.0, max=1.0, fit=True),\n", + " Parameter(name=\"Bilayer Roughness\", min=3.0, value=5.0, max=8.0, fit=True)\n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)\n", + "\n", + "problem.parameters.set_fields(0, min=1.0, max=10.0)" + ] + }, + { + "cell_type": "markdown", + "id": "b1fe7c6e-2200-4c83-9485-ec3590e950d5", + "metadata": {}, + "source": [ + "Need to add the relevant Bulk SLDs. Change the bulk in from air to silicon, and add two additional water contrasts:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d0ef585b-4893-440b-9e63-6dcc8102d4c6", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the bulk in from air to silicon\n", + "problem.bulk_in.set_fields(0, name=\"Silicon\", min=2.07e-6, value=2.073e-6, max=2.08e-6, fit=False)\n", + "\n", + "problem.bulk_out.append(name=\"SLD SMW\", min=1.0e-6, value=2.073e-6, max=3.0e-6, fit=True)\n", + "problem.bulk_out.append(name=\"SLD H2O\", min=-0.6e-6, value=-0.56e-6, max=-0.3e-6, fit=True)\n", + "\n", + "problem.bulk_out.set_fields(0, min=5.0e-6, value=6.1e-6, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "643dd278-57d7-4756-b568-824e0b3cb2d5", + "metadata": {}, + "source": [ + "Now add our datafiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "372cf5bc-5ec5-4e96-8ade-05a8b0baa3a2", + "metadata": {}, + "outputs": [], + "source": [ + "# Read in the datafiles\n", + "data_path = pathlib.Path(\"../data\")\n", + "D2O_data = np.loadtxt(data_path / \"c_PLP0016596.dat\", delimiter=\",\")\n", + "SMW_data = np.loadtxt(data_path / \"c_PLP0016601.dat\", delimiter=\",\")\n", + "H2O_data = np.loadtxt(data_path / \"c_PLP0016607.dat\", delimiter=\",\")\n", + "\n", + "# Add the data to the project - note this data has a resolution 4th column\n", + "problem.data.append(name=\"Bilayer / D2O\", data=D2O_data)\n", + "problem.data.append(name=\"Bilayer / SMW\", data=SMW_data)\n", + "problem.data.append(name=\"Bilayer / H2O\", data=H2O_data)" + ] + }, + { + "cell_type": "markdown", + "id": "7f4a1730-f6af-40f4-b1dc-1d76eeaaa08e", + "metadata": {}, + "source": [ + "Add the custom file to the project. We can view the code first." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "60a4b771-7967-4b46-bd3c-1c7cb4eaa24b", + "metadata": {}, + "outputs": [], + "source": [ + "Code(\"custom_XY_DSPC.py\")\n", + "problem.custom_files.append(name=\"DSPC Model\", filename=\"custom_XY_DSPC.py\", language=\"python\", path=pathlib.Path.cwd().resolve())" + ] + }, + { + "cell_type": "markdown", + "id": "c4157f30-47c0-476c-b9d3-736f0af21e79", + "metadata": {}, + "source": [ + "Add and modify the remaining parameters - backgrounds, scalefactors, and resolutions:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "57303283-9319-4b1c-817b-04d6441a2992", + "metadata": {}, + "outputs": [], + "source": [ + "problem.background_parameters.set_fields(0, name=\"Background parameter D2O\", fit=True, min=1.0e-10, max=1.0e-5, value=1.0e-07)\n", + "\n", + "problem.background_parameters.append(name=\"Background parameter SMW\", min=0.0, value=1.0e-7, max=1.0e-5, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter H2O\", min=0.0, value=1.0e-7, max=1.0e-5, fit=True)\n", + "\n", + "# And add the two new constant backgrounds\n", + "problem.backgrounds.append(name=\"Background SMW\", type=\"constant\", value_1=\"Background parameter SMW\")\n", + "problem.backgrounds.append(name=\"Background H2O\", type=\"constant\", value_1=\"Background parameter H2O\")\n", + "\n", + "# And edit the other one\n", + "problem.backgrounds.set_fields(0, name=\"Background D2O\", value_1=\"Background parameter D2O\")\n", + "\n", + "# Finally modify some of the other parameters to be more suitable values for a solid / liquid experiment\n", + "problem.scalefactors.set_fields(0, value=1.0, min=0.5, max=2.0, fit=True)\n", + "\n", + "# Also, we are going to use the data resolution\n", + "problem.resolutions.append(name=\"Data Resolution\", type=\"data\")" + ] + }, + { + "cell_type": "markdown", + "id": "d941b284-13b0-4866-90c7-765fb2dc4ed1", + "metadata": {}, + "source": [ + "Now add the three contrasts as before:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6815648e-ad4a-4193-ba39-83f5f15781b1", + "metadata": {}, + "outputs": [], + "source": [ + "problem.contrasts.append(\n", + " name=\"Bilayer / D2O\",\n", + " background=\"Background D2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD D2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / D2O\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / SMW\",\n", + " background=\"Background SMW\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD SMW\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / SMW\",\n", + " model=[\"DSPC Model\"],\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"Bilayer / H2O\",\n", + " background=\"Background H2O\",\n", + " resolution=\"Data Resolution\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " bulk_out=\"SLD H2O\",\n", + " bulk_in=\"Silicon\",\n", + " data=\"Bilayer / H2O\",\n", + " model=[\"DSPC Model\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ca0e6c93-e617-482c-b8bd-41dc0df4f586", + "metadata": {}, + "source": [ + "## Running the Model\n", + "\n", + "We do this by first making a controls block as previously. We'll run a Differential Evolution:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "821571d9-3593-4ac6-a5db-4d83998ff4db", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "Running Differential Evolution\n", + "\n", + "Final chi squared is 8.39155\n", + "Elapsed time is 108.162 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "controls = RAT.Controls(procedure=\"de\", parallel=\"contrasts\", display=\"final\")\n", + "problem, results = RAT.run(problem, controls)\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb new file mode 100644 index 00000000..34a81e93 --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -0,0 +1,535 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1ca14405-4a7c-4588-93cd-46534c374a36", + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "\n", + "import numpy as np\n", + "\n", + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" + ] + }, + { + "attachments": { + "e72d4765-3d29-4d8b-a0c5-2b9ba546588c.png": { + "image/png": "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" + } + }, + "cell_type": "markdown", + "id": "74ca6ea0-5261-4712-b202-043c00b7e4c2", + "metadata": {}, + "source": [ + "# Standard Layers Analysis of a DSPC Floating Bilayer\n", + "\n", + "In this worksheet, we will carry out an analysis of a floating bilayer sample using a 'standard layers' model. \n", + "The sample consists of a DSPC bilayer, on a silane SAM on Silicon:\n", + "\n", + "![image.png](attachment:e72d4765-3d29-4d8b-a0c5-2b9ba546588c.png)\n", + "\n", + "So we are going to need layers for Oxide, SAM tails, SAM heads, and the Heads/Tails of the Bilayer. We also need to consider hydration of the submerged bilayer." + ] + }, + { + "cell_type": "markdown", + "id": "be9b7c0d-dff4-4971-9efa-722215eb5227", + "metadata": {}, + "source": [ + "## Making the Project\n", + "\n", + "Start by initialising a project:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "24510c3b-eb41-4981-ac34-9503f742a8dc", + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(name=\"original_dspc_bilayer\", calculation=\"non polarised\", model=\"standard layers\", geometry=\"substrate/liquid\", absorption=False)" + ] + }, + { + "cell_type": "markdown", + "id": "31584d08-aea4-4411-9b3c-84f4eadbef66", + "metadata": {}, + "source": [ + "The add the parameters we are going to need:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f75a8713-0e9c-4972-a803-fae5b5028056", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\"),\n", + " #\n", + " Parameter(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\"),\n", + " #\n", + " Parameter(name=\"SAM Heads Thickness\", min=5.0, value=8.56, max=17.0, fit=True, prior_type=\"gaussian\", mu=10.0, sigma=2.0),\n", + " Parameter(name=\"SAM Heads SLD\", min=1.0e-07, value=1.75e-06, max=2.0e-06, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"gaussian\", mu=30.0, sigma=3.0),\n", + " #\n", + " Parameter(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\"),\n", + " #\n", + " Parameter(name=\"Bilayer Heads Thickness\", min=7.0, value=10.7, max=17.0, fit=True, prior_type=\"gaussian\", mu=10.0, sigma=2.0),\n", + " Parameter(name=\"Bilayer Heads SLD\", min=5.0e-07, value=1.47e-06, max=1.5e-06, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"Bilayer Heads Hydration\", min=10.0, value=36.15, max=50.0, fit=True, prior_type=\"gaussian\", mu=30.0, sigma=3.0),\n", + " Parameter(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\"),\n", + " Parameter(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\"),\n", + " Parameter(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\") \n", + "]\n", + "\n", + "problem.parameters.extend(parameter_list)\n", + "\n", + "# In addition to these, there is also Substrate Roughness which is always parameter 1. Increase the allowed range of this a bit\n", + "problem.parameters.set_fields(0, max=10)" + ] + }, + { + "cell_type": "markdown", + "id": "52f6752b-ce20-4c36-b357-988eb8ee178b", + "metadata": {}, + "source": [ + "Now we can group these parameters into the layers we need, and add them to the project:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f9fb80fe-41a3-4062-b84d-1e4ed524d02b", + "metadata": {}, + "outputs": [], + "source": [ + "layers = [\n", + " Layer(name=\"Oxide\", thickness=\"Oxide Thickness\", SLD=\"Oxide SLD\", roughness=\"Substrate Roughness\",\n", + " hydration=\"Oxide Hydration\", hydrate_with=\"bulk out\"),\n", + " Layer(name=\"SAM Tails\", thickness=\"SAM Tails Thickness\", SLD=\"SAM Tails SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Tails Hydration\", hydrate_with=\"bulk out\"),\n", + " Layer(name=\"SAM Heads\", thickness=\"SAM Heads Thickness\", SLD=\"SAM Heads SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Heads Hydration\", hydrate_with=\"bulk out\"),\n", + " Layer(name=\"Central Water\", thickness=\"CW Thickness\", SLD=\"CW SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"CW Hydration\", hydrate_with=\"bulk out\"),\n", + " Layer(name=\"Bilayer Heads\", thickness=\"Bilayer Heads Thickness\", SLD=\"Bilayer Heads SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Heads Hydration\", hydrate_with=\"bulk out\"),\n", + " Layer(name=\"Bilayer Tails\", thickness=\"Bilayer Tails Thickness\", SLD=\"Bilayer Tails SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Tails Hydration\", hydrate_with=\"bulk out\")\n", + "]\n", + "\n", + "problem.layers.extend(layers)" + ] + }, + { + "cell_type": "markdown", + "id": "356964f9-83a6-4a92-8092-4d250b68ac16", + "metadata": {}, + "source": [ + "Now deal with the experimental parameters. We will delete the predefined default parameters and add new ones for this specific problem. We need a bulk in of Silicon, and two Bulk outs - D2O and SMW." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b197f3ea-c6ef-4831-9500-9ff0cb5011f3", + "metadata": {}, + "outputs": [], + "source": [ + "del problem.bulk_in[0]\n", + "problem.bulk_in.append(name=\"Silicon\", min=2.0e-06, value=2.073e-06, max=2.1e-06, fit=False)\n", + "\n", + "del problem.bulk_out[0]\n", + "problem.bulk_out.append(name=\"D2O\", min=5.5e-06, value=5.98e-06, max=6.4e-06, fit=True)\n", + "problem.bulk_out.append(name=\"SMW\", min=1.0e-06, value=2.21e-06, max=4.99e-06, fit=True)" + ] + }, + { + "cell_type": "markdown", + "id": "420b57a9-4fc7-49d5-acaa-68570f1876d2", + "metadata": {}, + "source": [ + "Likewise the scalefactors and backgrounds:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "92a26ca2-b1ee-41c8-89ce-8b72c0892438", + "metadata": {}, + "outputs": [], + "source": [ + "del problem.scalefactors[0]\n", + "problem.scalefactors.append(name=\"Scalefactor 1\", min=0.05, value=0.10, max=0.2, fit=False)\n", + "problem.scalefactors.append(name=\"Scalefactor 2\", min=0.05, value=0.15, max=0.2, fit=False)\n", + "\n", + "# Now deal with the backgrounds\n", + "del problem.backgrounds[0]\n", + "del problem.background_parameters[0]\n", + "problem.background_parameters.append(name=\"Background parameter D2O\", min=5.0e-10, value=2.23e-06, max=7.0e-06, fit=True)\n", + "problem.background_parameters.append(name=\"Background parameter SMW\", min=1.0e-10, value=3.38e-06, max=4.99e-06, fit=True)\n", + "\n", + "problem.backgrounds.append(name=\"D2O Background\", type=\"constant\", value_1=\"Background parameter D2O\")\n", + "problem.backgrounds.append(name=\"SMW Background\", type=\"constant\", value_1=\"Background parameter SMW\")" + ] + }, + { + "cell_type": "markdown", + "id": "a1b04d8b-8cc8-4e35-9e06-a6be3de90c53", + "metadata": {}, + "source": [ + "Now load in and add the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "73681532-2688-4bfd-8153-1ab763f5687a", + "metadata": {}, + "outputs": [], + "source": [ + "data_path = pathlib.Path(\"../data\")\n", + "\n", + "d2o_dat = np.loadtxt(data_path / \"DSPC_D2O.dat\", delimiter=\",\")\n", + "problem.data.append(name=\"dspc_bil_D2O\", data=d2o_dat)\n", + "\n", + "smw_dat = np.loadtxt(data_path / \"DSPC_SMW.dat\", delimiter=\",\")\n", + "problem.data.append(name=\"dspc_bil_smw\", data=smw_dat)" + ] + }, + { + "cell_type": "markdown", + "id": "0668e70b-3d7a-4d35-bb37-90f73c17cb77", + "metadata": {}, + "source": [ + "Finally, we build everything up into the two contrasts:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e5475631-2aa2-4419-9227-aa41cd94b4ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Set the model\n", + "stack = [\"Oxide\", \"SAM Tails\", \"SAM Heads\", \"Central Water\", \"Bilayer Heads\", \"Bilayer Tails\", \"Bilayer Tails\", \"Bilayer Heads\"]\n", + "\n", + "# Then make the two contrasts\n", + "problem.contrasts.append(\n", + " name=\"D2O\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"D2O\",\n", + " background=\"D2O Background\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 1\",\n", + " data=\"dspc_bil_D2O\",\n", + " model=stack,\n", + ")\n", + "\n", + "problem.contrasts.append(\n", + " name=\"SMW\",\n", + " bulk_in=\"Silicon\",\n", + " bulk_out=\"SMW\",\n", + " background=\"SMW Background\",\n", + " resolution=\"Resolution 1\",\n", + " scalefactor=\"Scalefactor 2\",\n", + " data=\"dspc_bil_smw\",\n", + " model=stack,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "85c539d2-84f2-4a0d-a62b-666fbe9b2407", + "metadata": {}, + "source": [ + "Print our project, to check what we have:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e0409648-05f4-448b-93d6-5c4382e0dad6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: ----------------------------------------------------------------------------------------------\n", + "\n", + "original_dspc_bilayer\n", + "\n", + "Calculation: ---------------------------------------------------------------------------------------\n", + "\n", + "non polarised\n", + "\n", + "Model: ---------------------------------------------------------------------------------------------\n", + "\n", + "standard layers\n", + "\n", + "Geometry: ------------------------------------------------------------------------------------------\n", + "\n", + "substrate/liquid\n", + "\n", + "Parameters: ----------------------------------------------------------------------------------------\n", + "\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "| 0 | Substrate Roughness | 1.0 | 3.0 | 10.0 | True | uniform | 0.0 | inf |\n", + "| 1 | Oxide Thickness | 5.0 | 19.54 | 60.0 | True | uniform | 0.0 | inf |\n", + "| 2 | Oxide SLD | 3.39e-06 | 3.39e-06 | 3.41e-06 | False | uniform | 0.0 | inf |\n", + "| 3 | Oxide Hydration | 0.0 | 23.61 | 60.0 | True | uniform | 0.0 | inf |\n", + "| 4 | SAM Tails Thickness | 15.0 | 22.66 | 35.0 | True | uniform | 0.0 | inf |\n", + "| 5 | SAM Tails SLD | -5e-07 | -4.01e-07 | -3e-07 | False | uniform | 0.0 | inf |\n", + "| 6 | SAM Tails Hydration | 1.0 | 5.252 | 50.0 | True | uniform | 0.0 | inf |\n", + "| 7 | SAM Roughness | 1.0 | 5.64 | 15.0 | True | uniform | 0.0 | inf |\n", + "| 8 | SAM Heads Thickness | 5.0 | 8.56 | 17.0 | True | gaussian | 10.0 | 2.0 |\n", + "| 9 | SAM Heads SLD | 1e-07 | 1.75e-06 | 2e-06 | False | uniform | 0.0 | inf |\n", + "| 10 | SAM Heads Hydration | 10.0 | 45.45 | 50.0 | True | gaussian | 30.0 | 3.0 |\n", + "| 11 | CW Thickness | 10.0 | 17.12 | 28.0 | True | uniform | 0.0 | inf |\n", + "| 12 | CW SLD | 0.0 | 0.0 | 1e-09 | False | uniform | 0.0 | inf |\n", + "| 13 | CW Hydration | 99.9 | 100.0 | 100.0 | False | uniform | 0.0 | inf |\n", + "| 14 | Bilayer Heads Thickness | 7.0 | 10.7 | 17.0 | True | gaussian | 10.0 | 2.0 |\n", + "| 15 | Bilayer Heads SLD | 5e-07 | 1.47e-06 | 1.5e-06 | False | uniform | 0.0 | inf |\n", + "| 16 | Bilayer Heads Hydration | 10.0 | 36.15 | 50.0 | True | gaussian | 30.0 | 3.0 |\n", + "| 17 | Bilayer Roughness | 2.0 | 6.014 | 15.0 | True | uniform | 0.0 | inf |\n", + "| 18 | Bilayer Tails Thickness | 14.0 | 17.82 | 22.0 | True | uniform | 0.0 | inf |\n", + "| 19 | Bilayer Tails SLD | -5e-07 | -4.61e-07 | 0.0 | False | uniform | 0.0 | inf |\n", + "| 20 | Bilayer Tails Hydration | 10.0 | 17.64 | 50.0 | True | uniform | 0.0 | inf |\n", + "+-------+-------------------------+----------+-----------+----------+-------+------------+------+-------+\n", + "\n", + "Bulk In: -------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "| 0 | Silicon | 2e-06 | 2.073e-06 | 2.1e-06 | False | uniform | 0.0 | inf |\n", + "+-------+---------+-------+-----------+---------+-------+------------+-----+-------+\n", + "\n", + "Bulk Out: ------------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "| 0 | D2O | 5.5e-06 | 5.98e-06 | 6.4e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | SMW | 1e-06 | 2.21e-06 | 4.99e-06 | True | uniform | 0.0 | inf |\n", + "+-------+------+---------+----------+----------+------+------------+-----+-------+\n", + "\n", + "Scalefactors: --------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "| 0 | Scalefactor 1 | 0.05 | 0.1 | 0.2 | False | uniform | 0.0 | inf |\n", + "| 1 | Scalefactor 2 | 0.05 | 0.15 | 0.2 | False | uniform | 0.0 | inf |\n", + "+-------+---------------+------+-------+-----+-------+------------+-----+-------+\n", + "\n", + "Background Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "| 0 | Background parameter D2O | 5e-10 | 2.23e-06 | 7e-06 | True | uniform | 0.0 | inf |\n", + "| 1 | Background parameter SMW | 1e-10 | 3.38e-06 | 4.99e-06 | True | uniform | 0.0 | inf |\n", + "+-------+--------------------------+-------+----------+----------+------+------------+-----+-------+\n", + "\n", + "Backgrounds: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "| 0 | D2O Background | constant | Background parameter D2O | | | | |\n", + "| 1 | SMW Background | constant | Background parameter SMW | | | | |\n", + "+-------+----------------+----------+--------------------------+---------+---------+---------+---------+\n", + "\n", + "Resolution Parameters: -----------------------------------------------------------------------------\n", + "\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| index | name | min | value | max | fit | prior type | mu | sigma |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "| 0 | Resolution Param 1 | 0.01 | 0.03 | 0.05 | False | uniform | 0.0 | inf |\n", + "+-------+--------------------+------+-------+------+-------+------------+-----+-------+\n", + "\n", + "Resolutions: ---------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| index | name | type | value 1 | value 2 | value 3 | value 4 | value 5 |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "| 0 | Resolution 1 | constant | Resolution Param 1 | | | | |\n", + "+-------+--------------+----------+--------------------+---------+---------+---------+---------+\n", + "\n", + "Data: ----------------------------------------------------------------------------------------------\n", + "\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "| index | name | data | data range | simulation range |\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "| 0 | Simulation | [] | [] | [0.005, 0.7] |\n", + "| 1 | dspc_bil_D2O | Data array: [82 x 3] | [0.011403, 0.59342] | [0.011403, 0.59342] |\n", + "| 2 | dspc_bil_smw | Data array: [82 x 3] | [0.011403, 0.59342] | [0.011403, 0.59342] |\n", + "+-------+--------------+----------------------+---------------------+---------------------+\n", + "\n", + "Layers: --------------------------------------------------------------------------------------------\n", + "\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "| index | name | thickness | SLD | roughness | hydration | hydrate with |\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "| 0 | Oxide | Oxide Thickness | Oxide SLD | Substrate Roughness | Oxide Hydration | bulk out |\n", + "| 1 | SAM Tails | SAM Tails Thickness | SAM Tails SLD | SAM Roughness | SAM Tails Hydration | bulk out |\n", + "| 2 | SAM Heads | SAM Heads Thickness | SAM Heads SLD | SAM Roughness | SAM Heads Hydration | bulk out |\n", + "| 3 | Central Water | CW Thickness | CW SLD | Bilayer Roughness | CW Hydration | bulk out |\n", + "| 4 | Bilayer Heads | Bilayer Heads Thickness | Bilayer Heads SLD | Bilayer Roughness | Bilayer Heads Hydration | bulk out |\n", + "| 5 | Bilayer Tails | Bilayer Tails Thickness | Bilayer Tails SLD | Bilayer Roughness | Bilayer Tails Hydration | bulk out |\n", + "+-------+---------------+-------------------------+-------------------+---------------------+-------------------------+--------------+\n", + "\n", + "Contrasts: -----------------------------------------------------------------------------------------\n", + "\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "| index | name | data | background | background action | bulk in | bulk out | scalefactor | resolution | resample | model |\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "| 0 | D2O | dspc_bil_D2O | D2O Background | add | Silicon | D2O | Scalefactor 1 | Resolution 1 | False | Oxide |\n", + "| | | | | | | | | | | SAM Tails |\n", + "| | | | | | | | | | | SAM Heads |\n", + "| | | | | | | | | | | Central Water |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | Oxide |\n", + "| | | | | | | | | | | SAM Tails |\n", + "| | | | | | | | | | | SAM Heads |\n", + "| | | | | | | | | | | Central Water |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Tails |\n", + "| | | | | | | | | | | Bilayer Heads |\n", + "+-------+------+--------------+----------------+-------------------+---------+----------+---------------+--------------+----------+---------------+\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "id": "136e2c63-f439-4c2d-bb10-453950bbf41b", + "metadata": {}, + "source": [ + "## Running the Project\n", + "\n", + "To run a project in RAT, we first need to define a controls block:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c9ec7e39-48a8-4651-b74c-19d5800c67d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------------+-----------+\n", + "| Property | Value |\n", + "+------------------+-----------+\n", + "| procedure | calculate |\n", + "| parallel | single |\n", + "| calcSldDuringFit | False |\n", + "| resampleMinAngle | 0.9 |\n", + "| resampleNPoints | 50 |\n", + "| display | iter |\n", + "+------------------+-----------+\n" + ] + } + ], + "source": [ + "controls = RAT.Controls(display='iter')\n", + "print(controls)" + ] + }, + { + "cell_type": "markdown", + "id": "05f44162-c5b2-46e4-9233-b63e81089fd8", + "metadata": {}, + "source": [ + "The default action (\"procedure\") is just a single calculation. So call RAT.run() with this, and then plot the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e59bf938-804c-458c-891c-c3e56ed32820", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.027 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHLCAYAAAAk8PeNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAAC2x0lEQVR4nOyde3xT9f3/n7m0Se/QlhYKlPv9fhdvgKCIyrwypjgR1KkrDq1uP/nOoW5T5qYMnZ14GeJd5o05L6hDLqLcodzvlGvpjV7Sa+6/P06StrQpaZvkpO37+XjkkeRzTs55J9Dkdd5XjdPpdCIIgiAIgtAG0aptgCAIgiAIglqIEBIEQRAEoc0iQkgQBEEQhDaLCCFBEARBENosIoQEQRAEQWiziBASBEEQBKHNIkJIEARBEIQ2iwghQRAEQRDaLCKEBEEQBEFos4gQEgRBEAShzSJCSBAEIQRYv34906dPJyUlBY1Gw8qVKwN+zrNnz3LnnXeSkJBAREQEQ4YMYdu2bQE/ryCEEiKEBEEQQoDy8nKGDRtGRkZGUM5XVFTEZZddRlhYGF9//TX79+/nhRdeoH379kE5vyCEChoZuioIghBaaDQaPvvsM2666SbPmtls5ve//z0ffPABxcXFDB48mOeee46JEyc26RyPP/44P/74Iz/88IN/jBaEFop4hARBEFoA8+bNY+PGjXz44Yfs3r2bGTNmcO2113LkyJEmHe/zzz9n9OjRzJgxg6SkJEaMGMHrr7/uZ6sFIfQRj5AgCEKIcaFH6NSpU/Ts2ZNTp06RkpLi2W/KlCmMHTuWZ599ttHnMBqNAKSnpzNjxgy2bt3K/PnzWbp0KbNnz/bL+xCEloBebQMEQRCEhtmzZw92u52+ffvWWjebzSQkJABw8OBBBgwY0OBx/t//+3/85S9/AcDhcDB69GiPiBoxYgR79+4VISS0OUQICYIghDhlZWXodDq2b9+OTqertS06OhqAnj17cuDAgQaP4xZNAJ06dWLgwIG1tg8YMIBPPvnET1YLQstAhJAgCEKIM2LECOx2O3l5eVxxxRX17hMeHk7//v19PuZll13GoUOHaq0dPnyYbt26NctWQWhpiBASBEEIAcrKyjh69KjneVZWFpmZmcTHx9O3b19mzZrFXXfdxQsvvMCIESPIz89n9erVDB06lOuvv77R53vkkUe49NJLefbZZ/n5z3/Oli1beO2113jttdf8+bYEIeSRZGlBEIQQYO3atUyaNKnO+uzZs1m+fDlWq5U///nPvP3225w9e5bExEQuueQSnn76aYYMGdKkc37xxRcsWLCAI0eO0KNHD9LT07nvvvua+1YEoUUhQkgQBEEQhDaL9BESBEEQBKHNIkJIEARBEIQ2iyRLXwSHw0F2djYxMTFoNBq1zRGEVoPT6aS0tJSUlBS02rZ5TSbfL4IQOHz9jhEhdBGys7Pp2rWr2mYIQqvl9OnTdOnSRW0zVEG+XwQh8FzsO0aE0EWIiYkBlA8yNjZWZWsEofVgMpno2rWr52+sLSLfL4IQOHz9jhEhdBHc7urY2Fj5ohKEANCWQ0Ly/SIIgedi3zFtMzAvCIIgCIKACCFBEARBENowIoQEQRAEQWizSI6QIHjBbrdjtVrVNqNFEx4e3mZL4wVBaBmIEPJCRkYGGRkZ2O12tU0RgozT6SQnJ4fi4mK1TWnxaLVaevToQXh4uNqmCIIg1IvMGrsIJpOJuLg4SkpKpKqjjXDu3DmKi4tJSkoiMjKyTVc1NQd3s8CwsDBSU1PrfI7ytyWfgSAEEl//vsQjJAg1sNvtHhGUkJCgtjktng4dOpCdnY3NZiMsLExtcwRBEOogwXtBqIE7JygyMlJlS1oH7pCYhJgFQQhVRAgJQj1IOMw/tIXP8ezZs9x5550kJCQQERHBkCFD2LZtm9pmCYLgIxIaEwRBaCJFRUVcdtllTJo0ia+//poOHTpw5MgR2rdvr7ZpgiD4SJvwCH3xxRf069ePPn368MYbb6htjiAIrYTnnnuOrl278uabbzJ27Fh69OjBNddcQ69evdQ2TRAEH2n1Qshms5Gens7333/Pzp07+dvf/sb58+fVNksQ/M7dd9+NRqNBo9EQFhZGcnIyV199NcuWLcPhcABQWFjIQw89RL9+/YiIiCA1NZXf/OY3lJSU1DneW2+9xZgxY4iMjCQmJoYJEybwxRdfBPtthTSff/45o0ePZsaMGSQlJTFixAhef/11tc0SBKERtHohtGXLFgYNGkTnzp2Jjo5m2rRpfPvttwE736nzFXy8/QynzlcE7ByC4I1rr72Wc+fOceLECb7++msmTZrE/PnzueGGG7DZbGRnZ5Odnc3zzz/P3r17Wb58OatWreKee+6pdZzHHnuM+++/n5kzZ7J79262bNnC5Zdfzo033sjLL7+s0rsLPY4fP84rr7xCnz59+Oabb3jwwQf5zW9+w1tvvVXv/mazGZPJVOsmCIK6hHyO0Pr16/nb3/7G9u3bOXfuHJ999hk33XRTrX0yMjL429/+Rk5ODsOGDeMf//gHY8eOBSA7O5vOnTt79u3cuTNnz54NiK2nzlcwdcl6Kq12DHot6Vf3ZdrgTgBsOVHI2O7xpCZINZIQOAwGAx07dgSU/+sjR47kkksuYfLkySxfvpx7772XTz75xLN/r169eOaZZ7jzzjux2Wzo9Xo2bdrECy+8wEsvvcRDDz3k2feZZ56hqqqK9PR0brzxRrp27Rr09xdqOBwORo8ezbPPPgvAiBEj2Lt3L0uXLmX27Nl19l+0aBFPP/10sM0UWjlVVjuf78pm/eF8juWXU1Ruodxsw+F04gQcTicOJ9CKugZeO7gjL90+wi/HCnkhVF5ezrBhw5g7dy633HJLne0rVqwgPT2dpUuXMm7cOJYsWcLUqVM5dOgQSUlJQbHxH6uPcCSvjH3ZJVRalTJhs83Boq8P8pevD6IBHEC4TsN3j0ygW2JUUOwSBICrrrqKYcOG8emnn3LvvffW2e5uNqbXK18HH3zwAdHR0dx///119n300UdZvHgxn3zyCQ8//HCgTQ95OnXqxMCBA2utDRgwoJbYrMmCBQtIT0/3PDeZTCIohWZxKKeU+97expmiCoZ2acfI1HYkRIUTbdSjdYXKtRrQgCt0rrbF/iE13n9OhZAXQtOmTWPatGlety9evJj77ruPOXPmALB06VK+/PJLli1bxuOPP05KSkotD9DZs2c93qL6MJvNmM1mz3NfXNf/yczmaH5ZvdtqinCL3clN//yR5XPGMqxru4seVwgdpv9jA/ml5ovv6Ec6xBj470OX++VY/fv3Z/fu3XXWCwoK+NOf/sSvfvUrz9rhw4fp1atXvWMxUlJSiI2N5fDhw36xq6Vz2WWXcejQoVprhw8fplu3bvXubzAYMBgMwTBNaAPkmqq481+bSYgK57v0CfTqEK22SS2SkBdCDWGxWNi+fTsLFizwrGm1WqZMmcLGjRsBGDt2LHv37uXs2bPExcXx9ddf84c//MHrMZviuq6w2Hzet6jCykurj/Cvu8c06hyCuuSXmskxValtRpNxOp31jri4/vrrGThwIE899VSd/YWL88gjj3DppZfy7LPP8vOf/5wtW7bw2muv8dprr6ltmtAGePq/+3A64Z17xtEhRgR2U2nRQqigoAC73U5ycnKt9eTkZA4ePAiAXq/nhRdeYNKkSTgcDn73u981ODqhKa7rv942jHve2orZ5sCg1/LKrFEA7M0uYXBKHDaHg4+2n+G7/bkArD6Yx6nzFZIv1IJQ40vGn+c8cOAAPXr08DwvLS3l2muvJSYmhs8++6zW+Iu+ffuyYcMGLBZLHa9QdnY2JpOJvn37+s22lsyYMWP47LPPWLBgAX/84x/p0aMHS5YsYdasWWqbJrRyDuaY+GpPDs/PGCYiqJm0aCHkKz/72c/42c9+5tO+btd1Y6bPX94nke8emVAnIfqqAdU5SqYqm0cIgZI8LUKo5eCvEJUafP/99+zZs4dHHnkEUMT91KlTMRgMfP755xiNxlr7/+IXv+Cll17i1VdfrZUsDfD8888TFhbGrbfeGjT7Q50bbriBG264QW0zhDbGWz+doGOskRuHp6htSounRQuhxMREdDodubm5tdZzc3M9lTNNJS0tjbS0NM/02ouRmhDZoLAZ2z0erQYlcx/onxzTLPsEoT7MZjM5OTnY7XZyc3NZtWoVixYt4oYbbuCuu+7CZDJxzTXXUFFRwbvvvlurhLtDhw7odDrGjx/P/Pnz+e1vf4vFYuGmm27CarXy7rvv8uKLL7JkyRJJ8BUEFbHaHXy1J4c7L0klTNfqu+AEnBYthMLDwxk1ahSrV6/2lNQ7HA5Wr17NvHnzmnXsxniEfCE1IZLrh3Tiv7vPAeBoTXWMQsiwatUqOnXqhF6vp3379gwbNoyXXnqJ2bNno9Vq2bFjB5s3bwagd+/etV6blZVF9+7dAViyZAlDhw7ln//8J0888QQ6nY6RI0eycuVKpk+fHuy3JQhCDTYfL6Sk0uppzyI0j5AXQmVlZRw9etTzPCsri8zMTOLj40lNTSU9PZ3Zs2czevRoxo4dy5IlSygvL/dUkTWVxnqEfGFASqxHCJ08r5Q6CoK/WL58OcuXL29wn4kTJ/qcCD137lzmzp3rB8sEQfAnPxzJJynGwKCUWLVNaRWEvBDatm0bkyZN8jx3JzLPnj2b5cuXM3PmTPLz81m4cCE5OTkMHz6cVatW1Umgbiz+9ggBdIuv7h90qlA6TwuCIAiNZ3NWIeN6JtSpBBWaRsgLIV+uYOfNm9fsUNiFBMIj1K1GDpGM4BAEQRAaS4XFxt6zJdw2qovaprQaJMsqiHSt0QnzZGG5ipYIgiAILZH92SZsDicjU9urbUqrQYSQFzIyMhg4cCBjxviv8WFcRBjtIpV+LacLK/12XEEQBKFtsC/bRLhOS+8k6SLtL0QIeSEtLY39+/ezdetWvx63m8srlF1Sidnmv/wjQRAEofWzP9tEn+RowvXy8+0v5JMMMu7wmNMJZ4rEKyQIgiD4zv5zJgZ0kmoxfyJCyAuBCI3BBQnTUjkmCIIg+IjT6eRYfhl9kyUs5k9ECHkhcKGxGiX0UjkmCIIg+EiuyUyFxU7PRBFC/kSEUJCpVTkmQkgQBEHwkeP5ZQD07BB1kT2FxiBCKMhIaEwQBEFoCscLytFrNbUuqIXmI0LIC4HKEeoYayTcNSRvz9kSCY8JfiM/P58HH3yQ1NRUDAYDHTt2ZOrUqfz4448AdO/eHY1Gw4cffljntYMGDUKj0XhGdPziF7/g2muvrbXPqlWr0Gg0PPXUU7XWn3rqKVJTUwPyngRBqCaroJyu8ZEyaNXPyKfphUDlCGm1GjrGGQHINVVxzd/XiRgS/MKtt97Kzp07eeuttzh8+DCff/45EydO5Pz58559unbtyptvvlnrdZs2bSInJ4eoqGp3+6RJk/jxxx+x2WyetTVr1tC1a1fWrl1b6/Vr1qypNQZHEEKBKqudp/+7j093nFHbFL9xpqiCLu0j1Daj1SFCSAWiDDrP4yqbgy0nClW0RmgNFBcX88MPP/Dcc88xadIkunXrxtixY1mwYAE/+9nPPPvNmjWLdevWcfr0ac/asmXLmDVrFnp99cSdSZMmUVZWxrZt2zxra9eu5fHHH2fz5s1UVVUBUFVVxebNm0UICSHHy98f5c0fT5D+711kF7eOViVniyvp0l7CYv5GhJAKjKrRGj1Mq2Fs93gVrRFaA9HR0URHR7Ny5UrMZrPX/ZKTk5k6dSpvvfUWABUVFaxYsaLOlPm+ffuSkpLCmjVrACgtLWXHjh3MmDGD7t27s3HjRgB++uknzGazCCEh5Fi1L4frhnTEoNfy1Z5zapvjF84UVYpHKACE/NDV1silvRN5d/MpAGZd0o3UBFH4Ic+rE6AsL7jnjE6C+9f5tKter2f58uXcd999LF26lJEjRzJhwgR+8YtfMHTo0Fr7zp07l0cffZTf//73fPzxx/Tq1Yvhw4fXOeakSZNYu3YtCxYs4IcffqBv37506NCBK6+8krVr13q29+jRg27duvnjHQuCXzh5vpyjeWX8dmo/CsstbDpeyL1X9FTbrGZRZrZRXGGlczsRQv5GPEJeCFSyNMCglOquoNJduoVQlgel2cG9NVJ43XrrrWRnZ/P5559z7bXXsnbtWkaOHOlJgHZz/fXXU1ZWxvr161m2bFkdb5CbiRMn8uOPP2K1Wlm7di0TJ04EYMKECZ48IbcgEoRQYteZEgDG9Yjnkp4JbD1RiMPhVNmq5nHW9VshHiH/Ix4hL6SlpZGWlobJZCIuLs6vx+7aPpIYg55Ss4192SV+PbYQIKKTWsQ5jUYjV199NVdffTV/+MMfuPfee3nyySe5++67Pfvo9Xp++ctf8uSTT7J582Y+++yzeo81adIkysvL2bp1K2vWrOG3v/0toAihuXPnUlhYyObNm7n//vub9PYEIVAcyjGRHGugXWQ4w7q2o6TSytniyhZddu7Oc0oRj5DfESGkAlqthgEpsWzJKuRcSRWF5Rbio8LVNktoCB9DVKHGwIEDWblyZZ31uXPn8vzzzzNz5kzat29f94VAr1696Nq1K59//jmZmZlMmDABgM6dO9O5c2deeOEFLBaLeISEkONQTin9Oiqe994dlC7MR/PLWrQQyi9Vcv8Sow0qW9L6kNCYStQMj4lXSGgu58+f56qrruLdd99l9+7dZGVl8dFHH/HXv/6VG2+8sc7+AwYMoKCgoE4p/YVMmjSJf/7zn/Tu3Zvk5GTP+oQJE/jHP/7hSaoWhFDiUG4p/TvGANC5XQQGvZZjeWUqW9U88kqriI8Kl6nzAUA+UZUYlFIdbtuXbVLREqE1EB0dzbhx4/j73//OlVdeyeDBg/nDH/7Afffdx8svv1zvaxISEoiIaNjNPmnSJEpLSz35QW4mTJhAaWmpeIOEkMNic3C2qJIeiUpfLK1WQ88O0RzLL1fZsuaRX2qmg3iDAoKExlSitkdIhJDQPAwGA4sWLWLRokVe9zlx4kSDxyguLq6zdvfdd9fKL3Ize/ZsZs+e3UgrBSHw5JRU4XDWTiru1SGKY/kt2yOUX2amQ4wIoUAgHiGV6J0U7XFxSmhMEATBP5wpVjr11ywz79I+ssU3VcwzmUkSIRQQRAh5IZDl8wBhOq0nhp2VX87Bc+IVEgRBaC7uliQ1q6s6tzOSU1KFvQWX0ItHKHCIEPJCoGaN1aSbq4LBCfzs5R9l5pggCEIzOVtUSYcYA8aw6lFGKe0isDmcFJR577oeyjidTvJMIoQChQghFQmrkf1vscvMMUEQhOZytriyTvdlt3fobAsNj5Vb7FRa7SKEAoQIIRWZNriT57EGZOZYCOF0tlwXeighn6MQbHJNVXSMNdZacwuhlpon5O4hlBRjvMieQlMQIaQik/sn0S4yDICIcB2d2sl/crUJC1P+PSoqJEzpDywWCwA6ne4iewqCf8gvrRtCijXqiTboW6wQyjNVAYhHKEBI+byKaLUaJvTtwH8ys6mw2Nl5qpixPcQrpCY6nY527dqRl6fM+YqMjESj0ahsVcvE4XCQn59PZGQker181QjBoaCsbnWVRqMhKdbg8ay0NPJduU1JsSKEAkGb+Ha6+eabWbt2LZMnT+bjjz9W25xaXNlHEUIA6w/nixAKATp27AjgEUNC09FqtaSmpoqYFIKCze7gfLmlXs9Jh2gDeS1VCJWaMei1xBjaxE920GkTn+r8+fOZO3cub731ltqm1OGKvomex+uP5PPY1H4qWiOAcvXYqVMnkpKSsFqtapvTogkPD0erlQi8EBzOl1twOusPIXWIabkeoTxXuE8uKAJDmxBCEydOZO3atWqbUS9JMUYGdIrlwDkTe86WcL7MTIK0UQ8JdDqd5LYIQguioaTiDjEGDuWUBtskv5BfKs0UA4nql2rr169n+vTppKSkoNFo6p2UnZGRQffu3TEajYwbN44tW7YE39AAcqXLK+R0wgvfHZZ+QoIgCE3ALYS8eoRaaB+hgjKzTJ0PIKoLofLycoYNG0ZGRka921esWEF6ejpPPvkkO3bsYNiwYUydOrVW/sbw4cMZPHhwnVt2dnaw3kazmNCng+fx+5tPMXXJehFDgiAIjcQtdBKiw+tsS4oxUlxhxWyzB9usZlNUbqF9ZN33JPgH1UNj06ZNY9q0aV63L168mPvuu485c+YAsHTpUr788kuWLVvG448/DkBmZqbf7DGbzZjN1VcNJlPgR1+M6t6eMJ0Wq90BQKXVzpYThaQmRAb83IIgCK2FonILsUY9Ybq61/huL1FBmaVOw8VQp6jCSruoMLXNaLWo7hFqCIvFwvbt25kyZYpnTavVMmXKFDZu3BiQcy5atIi4uDjPrWvXrgE5T00Meh2X9KyuFgvXaaW5oiAIQiMprLAQH1W/5yTR5SUqaIEJ00UV4hEKJCEthAoKCrDb7SQnJ9daT05OJicnx+fjTJkyhRkzZvDVV1/RpUuXBkXUggULKCkp8dxOnz7dZPsbw22jungeXz+0k3iDBEEQGklRuYX2XoSQW0gUVViCaVKzsdkdlFbZaB8pHqFAoXpoLBj873//83lfg8GAwWAgIyODjIwM7PbgxJMnD0gmXK/FYnOw4WgBdocTnVZKJQVBEHylsNxKvBfPiVsIFVe0rJYYxZWKve3EIxQwQtojlJiYiE6nIzc3t9Z6bm6up+ldoAjG9PmaRBv0TOirJE3nl5rZfrIoKOcVBEFoLRRVePcIRYTrMIZpKSxvWR6hYpcHS0JjgSOkhVB4eDijRo1i9erVnjWHw8Hq1asZP358QM+dkZHBwIEDGTNmTEDPU5PrhlSLu5dWH5HKMUEQhEZQVO49RwgUMVHcwkJjRS4PloTGAofqQqisrIzMzExP5VdWVhaZmZmcOnUKgPT0dF5//XXeeustDhw4wIMPPkh5ebmniixQBNsjBEp4LEynhMM2HC3g6sVrRQwJgiD4SOFFkorbRYZ7hEVLwR3Kk9BY4FBdCG3bto0RI0YwYsQIQBE+I0aMYOHChQDMnDmT559/noULFzJ8+HAyMzNZtWpVnQRqf6OGRyjWGEafpBjPc7PdyZYThUE7vyAIjeOpp55Co9HUuvXv319ts9okNruDkkor8Q2UmcdHhVHY4jxCir3txCMUMFRPlp44cSJOp7PBfebNm8e8efOCZJFCWloaaWlpmEwm4uLignbeW0Z0Zv85pXeRTqORMnpBCHEGDRpUqyBDr1f9a7VNYqqy4XQ27Dlp1wJDY8UVFmIM9fdGEvyDfLIhxh2XpBIRpsy3MoZpSYqVtuqCEMro9Xo6duzouSUmJl78RYLfKXFVV8VFePectI8Mo6i8ZYXGpJli4BEh5AU1QmMAkeF6rh/aCYByi51v9+de5BWCIKjJkSNHSElJoWfPnsyaNcuT3ygEF5NLCMUaGwiNtVCPkFSMBRYRQl5QI1naza0jq5srfrL9TNDPLwiCb4wbN47ly5ezatUqXnnlFbKysrjiiisoLa1/yrnZbMZkMtW6Cf7BVOUSQhHeQ5PtIsNbXo5QuVUSpQOMCKEQZFyPeM8snPWH89khPYUEISSZNm0aM2bMYOjQoUydOpWvvvqK4uJi/v3vf9e7vxojfNoKpkobALENhcaiwqiyOqiytpzBq8p4DQmNBRIRQl5QKzQGoNVqmDIgCQAn8PNXN0oZvSC0ANq1a0ffvn05evRovdvVGuHTFjBVWdFoIDrcu0eoJY7ZKK6wSmgswIgQ8oKaoTGATjWmI9scTjZnnVfFDkEQfKesrIxjx47RqVOnercbDAZiY2Nr3QT/YKq0EmPQo21gNJFbULSk7tJFFRYpnQ8wIoRClOsGd6Lmn3O4lE4KQsjx2GOPsW7dOk6cOMFPP/3EzTffjE6n4/bbb1fbtDaHqcraYFgMWt68MafTKR6hICC/riFKakIkC28Y6Hn+3QGpHhOEUOPMmTPcfvvt9OvXj5///OckJCSwadMmOnTooLZpbQ5Tpa3BijFQcoSg5YTGKix2LHaHeIQCjHT+8kKwp8/Xxx2XpPLymqOcL7ewam8Ob/6YxeT+yaQmRKpmkyAI1Xz44YdqmyC4UDxCDf+kRRv06LUailpIaKy6q7R4hAKJeIS8oHaOEIBBr2PGaKWqxOZw8vR/9zN1yXpJnBYEQbgAU6X1oh4hjUbTouaNlVa5KuGM4rMIJCKEQpzbx9Yur6202mX+mCAIwgWYqmwXzRECZd5YSwmNuYVQzEUEntA8RAiFON0SohjTvb3nebhOK/PHBEEQLsAXjxBAu4jwFpMsXepuEikeoYAiQqgF8Ksre3keD+0SJzlCgiAIF+BLjhAonafdAiPUEY9QcBAh5AU1GypeyFX9k+jmEj/bThZxOLf+9v2CIAhtFV+qxkDpPO3uQh3qlJpt6LQajGHyUx1I5NP1QigkS7vRaTXcfWl3z/M/rNwrCdOCIAguLDYHlVa7TzlCscYwz1yyUKe0ykqMUY9G471JpNB8RAi1EG4b1YXIcB0Am7MKuebv60QMCYIg0LhcmtiIMEoqW4oQshEj+UEBR4RQCyHGGMaobtVJ01U2h1SPCYIgoFSMQcMDV93EGvWYWowQshJjkPygQCNCqAWRNrF3refDOsepZIkgCELo4Pbw+JojVG6xY7M7Am1WsymrshEtHqGAI0KoBXFJrwQm9UvyPN8sHiFBEASPh8enqjGXWHJXZIUypVU2KZ0PAiKEWhiPXtPX8/i19cdbxFWNIAhCIHEnP/sUGnOJpZaQMK3kCEloLNCIEPJCKJXP12Rw5ziu6JMIwKnCCp767z5JmhYEoU1jqrSh0UB0uO8eoZZQQm+qshJtEI9QoBEh5IVQKp+/kAcmVDdYfHfTKa5evFbEkCAIbRZTlZUYgx6t9uJl5nEur1FL8AiVmaVqLBiIEGqBXNorgS7tIzzPzXanVJAJgtBmUfrt+BZCqvYIhb4QktBYcBAh1ALRaDQ8WMMrpNHAmBql9YIgCG2JcrPd5xCSuwor1D1CTqdTPEJBQoRQC+WOcan0SYoGwOmEo/llKlskCIKgDmVmG1EGnU/76rQaYgz6kM8RqrDYsTucIoSCgAihFopGo+Gxqf08z5/8fB8nC8pVtEgQBEEdys02ohqRVNwSukuXmd0DV0UIBZpWL4ROnz7NxIkTGThwIEOHDuWjjz5S2yS/cc3AZI9X6ExRJVMWy9gNQRDaHmVmW6Oqq2KM+pAPjbnHhkiOUOBp9UJIr9ezZMkS9u/fz7fffsvDDz9MeXnr8JxoNBou653oeW51ONmUdV5FiwRBEIJPYz1CcRFhIZ8s7R4bIh6hwNPqhVCnTp0YPnw4AB07diQxMZHCwtZTYTXn0u7UHExsqgjtP25BEAR/05hkaVBCY6YQ7yxd5rJP+ggFHtWF0Pr165k+fTopKSloNBpWrlxZZ5+MjAy6d++O0Whk3LhxbNmypUnn2r59O3a7na5duzbT6tChW2IUi2cM9zzPWHuMY3mSOC0IQtuhMcnSoJTQh7pHqNTjEZLQWKBRXQiVl5czbNgwMjIy6t2+YsUK0tPTefLJJ9mxYwfDhg1j6tSp5OXlefYZPnw4gwcPrnPLzs727FNYWMhdd93Fa6+9FvD3FGxuHtmZ0a7y+aIKC1P/LrlCgiC0HcotjU2Wbjk5QuIRCjyqf8LTpk1j2rRpXrcvXryY++67jzlz5gCwdOlSvvzyS5YtW8bjjz8OQGZmZoPnMJvN3HTTTTz++ONceumlF93XbDZ7nptMJh/fibpc3ieRbSeLALA5Yd3hfH45vpvKVgmCIASe8kYmSyseodAOjZVWKe9J50O3bKF5qO4RagiLxcL27duZMmWKZ02r1TJlyhQ2btzo0zGcTid33303V111Fb/85S8vuv+iRYuIi4vz3FpKGO2WEV3Q1UgW2nOmWD1jBEEQgoTZZsdqdzYhRyjEPUKNFHdC0wlpIVRQUIDdbic5ObnWenJyMjk5OT4d48cff2TFihWsXLmS4cOHM3z4cPbs2eN1/wULFlBSUsLzzz9Pv3796N27d7PeQ7BITYjkw19dgt519fDpzrMclyaLgiC0csrNdoDGhcaMeiosdqx2R6DMajbK2BARQsEgpIWQP7j88stxOBxkZmZ6bkOGDPG6v8FgIDY2lkcffZSDBw+yffv2IFrbPMb0iOfXE5XRGzaHk7T3d0qukCAIrZpyc+Orq9wJyKUhXDmmzBkTIRQMQloIJSYmotPpyM3NrbWem5tLx44dA3rujIwMBg4cyJgxYwJ6Hn/zwMRedIg2AHDgnEmaLAqC0Kpxd2BurEcIqkvUQ5HGDJIVmkdIC6Hw8HBGjRrF6tWrPWsOh4PVq1czfvz4gJ47LS2N/fv3s3Xr1oCex99Ehuu5emB1KNFid7DhaIGKFgmCIASOao+Q7+XzLWHwapnZ5rFTCCyqC6GysjJPyAogKyuLzMxMTp06BUB6ejqvv/46b731FgcOHODBBx+kvLzcU0Um1OX+K3tSs87gv7vOildIEIRWSVM8Qu4wmvu1oUhplc3juRICi+pCaNu2bYwYMYIRI0YAivAZMWIECxcuBGDmzJk8//zzLFy4kOHDh5OZmcmqVavqJFD7m5YaGgOlyeI794zzVJFtPF7I1dJbSBCEVkhTkqXdIafQDo3ZJDQWJFSXmxMnTsTpdDa4z7x585g3b16QLFJIS0sjLS0Nk8lEXFxcUM/tDy7vk8hVA5L4br+SX2W2Odh4/DypCZEqWyYIguA/3KGxqPDGDV0FKDWHbmistMpKjJTPBwXVPUKhSkv2CLn53dR+tZ5LOb0gCK2NMrONiDBdoxoPGvRawnSakPcISY5QcBAh5IWWmixdkz7JMfzzjpGefKE3fzzBoZxSVW0SBEHwJ42dPA+g0WiINuhDdvCqxebAbHNIaCxIiBBq5Vw3tBO/mtATUCrI5i7fKkNZBUFoNZRZbI2qGHMTYwwL2WRp95wx6SMUHEQItQEemdKX7q7coLPFlVwjidOCILQSmuIRAqVyrDREy+erJ8+LEAoGIoS80BpyhNwYw3TcNKKz57ndCcs3nlDPIEEQBD9RbrY3TQgZ9SGbI+T2VMUYJDQWDEQIeaE15AjV5JYRXQirkUz4weZTbDtRqKJFgiAIzaesicNJY436kA2NmSQ0FlRECLURUhMi+V/6BPokRQNQabUz49WNHMmV5GlBEFouzQmNhWqytITGgosIIS+0ptCYm26JUfxyfDfPc6cT/u+zPRft4yQIghCqlJubkSwd4kJIyueDgwghL7S20JibiX2TCNdVh8i2nijikRWZkjwtCEKLpMxsa1QzRTfRRn3INlQsq7ISrtdi0Dde4AmNR4RQG0MJkU1k1rhUz9rKzGyZUi8IQoukycnShtBNlpY5Y8FFhFAbJDUhkmduHsJV/ZM8axa7g493nFHRKkEQhMaj5Ag13nPiTpYOxdSAUrPMGQsmIoTaMAtvGFjrP8DbG0+QVVCumj2CIAiNwel0UmG1E9HE0JjV7sRscwTAsuZRWmWVROkgIkLIC60xWfpCuidG8e0jE+iZGAVAcYWVX7y2kbPFlSpbJggtj7/85S9oNBoefvhhtU1pM1jtTuwOJ5FhTUiWdvXoKQ3B8FhpVdNaAghNQ4SQF1prsvSF9E6O5h+3j/A8zzWZueGlH9h5skhFqwShZbF161ZeffVVhg4dqrYpbYpKix2AyPDGCyF3RVYodpcurbKJRyiIiBASOHDBINaiCis3v/KTiCFB8IGysjJmzZrF66+/Tvv27dU2p01RYVW8OcYmCCG30AjFpopKaExyhIKFCCGBsd3jiajHtXzv29tYtiFLqskEoQHS0tK4/vrrmTJlitqmtDk8HqFWGBoTj1DwkE9aIDUhkm8evpKv957jhW8PYbErVRTnyy388Yv9PPf1Ab5Ln0iqa3CrIAgKH374ITt27PA5hG42mzGbzZ7nJpMpUKYFnd1niln4n30cyytjeGo7nrt1KCntIgJ6zgpPaKxpydIQmkKozGwjRnKEgoZ4hARAEUP3T+jF/9In8n/XDSAuovqP0Gx38tyqg+IZEoQanD59mvnz5/Pee+9hNBp9es2iRYuIi4vz3Lp27RpgK4NDfqmZO17fjN3h5IGJvTiWV8bc5VtxOAJbml5pVYRQRHjjf8rcycihGRqT8vlgIkJIqEVqQiS/urInr/9yDJoa61/uOceUxWtFDAmCi+3bt5OXl8fIkSPR6/Xo9XrWrVvHSy+9hF6vx26313nNggULKCkp8dxOnz6tguX+Z+m6Y2g08M49Y0mb1JslvxjBwZxSNh4/H9DzukNjTSmfVzo3a0MuWdrucCoeIQmNBQ0RQl5oC+XzDTG2ZzyfPHgpsTU8Qxa7kzc2HOfj7WdEEAltnsmTJ7Nnzx4yMzM9t9GjRzNr1iwyMzPR6ermrRgMBmJjY2vdWjpOp5Ov9pxjxqiutIsMB2BM9/b07BDFpzvOBvTcFc3IEYLQnDfm9lCJRyh4iOT0QlpaGmlpaZhMJuLi4tQ2RxVGdmvPe/dcwvSXN3jW3t54EjiJUa/l20cmSN6Q0GaJiYlh8ODBtdaioqJISEios96aOZpXxrmSKib06+BZ02g0XNE7kQ1HCwJ67kpX1VhEE6rGQKkcKw2x0JhbCMnA1eAhHiGhQYZ0iWPV/CvonRRda73K5mDLiUKVrBIEIVTYePw8YToNY7vH11of0CmWrIJyqqx1Q4T+otLiQKMBg75pP2UxRn3IJUu7Q3USGgseIoSEi9K/UyxfPHQ5V/ROrLX+3f4cCZEJQg3Wrl3LkiVL1DYjqBzJLaNnYnQdr8yATrE4nHDogj5l/qTCYiMyTIdGo7n4zvUQbdCHXI6QW5jJ0NXgIUJI8AljmI4354zh+iGdPGvf7Mtl0vNr2Hi0QPKGBKGNcjSvjF5JUXXW+3WMQaMJrBCqtNibHBYD1wT6EAuNVXuEJEcoWIgQEnxGr9Py8h0jmFQjF8DuhFn/2sxjH+1i6pL1IoYEoY1xNL+M3h2i66wbw3QkRhs4V1IVsHNXWpsnhEIxWdrtEZJZY8FDhJDQKDQaDU//bDB6bbUr2t0qpNJqJ2PtURFDgtBGKKm0kl9qpldSXSEEkBRjILc0cEKowmInMqzpgiE0c4Rs6LSaJs1PE5pGqxdCxcXFjB49muHDhzN48GBef/11tU1q8aQmRPL9oxP5+eiuXBiaX7H1tHiGBKGNcKZI+TtPja+/ejQ51kieKYAeIYu9SXPG3MQYQzE0pkyeb2rek9B4Wr3vLSYmhvXr1xMZGUl5eTmDBw/mlltuISEhQW3TWjSpCZH89bahXNU/iYc+2IHVXt1BttJqZ8uJQimtF4RWTp5JGRfSMa7+ztpJMQb2nwvcGJFKq73JPYRACT+ZQi5Z2ioVY0Gm1XuEdDodkZHKD7LZbMbpdOJ0Brbte1vi2sEdWT5nbJ2hrWcKK8QrJAitnFxTFRoNJEYb6t2eFGskN4AeoQqLvVkhpBhjGGVmW0j9JpSZbZIfFGRUF0Lr169n+vTppKSkoNFoWLlyZZ19MjIy6N69O0ajkXHjxrFly5ZGnaO4uJhhw4bRpUsXfvvb35KYmHjxFwk+c1nvRN6/b1ytq5glq49wtYzkEIRWTa7JTEKUgTBd/T8lSTEGCsos2AM0c6zSamtWaCzaqMfphHJL4HodNZbSKhuxUjEWVFQXQuXl5QwbNoyMjIx6t69YsYL09HSefPJJduzYwbBhw5g6dSp5eXmefdz5PxfesrOzAWjXrh27du0iKyuL999/n9zcXK/2mM1mTCZTrZtwcUaktufjBy6tJYbMdidf7zunolWCIASS3NIqkmPr9waBkiNkdzg5X24OyPmVZOlmeITcg1dDKGFaQmPBR/VPe9q0aUybNs3r9sWLF3PfffcxZ84cAJYuXcqXX37JsmXLePzxxwHIzMz06VzJyckMGzaMH374gdtuu63efRYtWsTTTz/duDchAErfkFfvHMWsNzbjvv5buvYYVpuT0d3ac6a4krHd4yV3SBBaCXmmKpJj688PAmgfqXg2iiusJMV436+pVDY7NOaeQG8F/G9fUyitstHJS86VEBhU9wg1hMViYfv27UyZMsWzptVqmTJlChs3bvTpGLm5uZSWKg29SkpKWL9+Pf369fO6f2udDh0sLu2dyMcPXEpSjHKVWFRh5flvD/GL1zdJryFBaGXkmswNeoRiIxQhFKjuzZXW5lWNued5mULKI2STOWNBJqSFUEFBAXa7neTk5FrrycnJ5OTk+HSMkydPcsUVVzBs2DCuuOIKHnroIYYMGeJ1f/d06HfeeYdLLrmEyZMnN+s9tEVGdW/PqoevpEv7iDrb3BVlgiC0fArKzF4TpaHa42KqDIzQaH4fIUWohVRozGyVrtJBptXLzrFjx/ocOquJTJ9vHvFR4bxx12imvfgDNdMk9Rro0i6Cj7efkTCZILRwiiostIsM97rdnfQbqBL1qmaGxtzVWaHUVLG0yiY5QkEmpD1CiYmJ6HS6OsnNubm5dOzYMaDnzsjIYODAgYwZMyag52nN9O8Uy//SJzCiazvPmk1GcghCq6DKaqfK6qBdhHfvRWS4Dp1WExCh4XQ6qWhuaMxQM0dIfZxOJ2VVNvEIBZmQFkLh4eGMGjWK1atXe9YcDgerV69m/PjxAT13Wloa+/fvZ+vWrQE9T2unV1I0nzx4KfdP6OlZc5fSSphMEFouxRWKeGgf5f1HW6PREGMMTNNCi92B3eFsVtWYTqshKlwXMh6hKqsDm8PpqWYTgoPqn3ZZWRlHjx71PM/KyiIzM5P4+HhSU1NJT09n9uzZjB49mrFjx7JkyRLKy8s9VWRC6KPValgwbQB9k2J4/NPdtbpQrz2UR+c4I2dLqmqFyk6dr2DLiUIJnwlCiFJUYQFoMDQGgZvnVWVxADR7Jld0CM0bq548r/pPc5tC9U9727ZtTJo0yfM8PT0dgNmzZ7N8+XJmzpxJfn4+CxcuJCcnh+HDh7Nq1ao6CdT+JiMjg4yMDOz20Gm01dK5dVQXenaI4sH3dpDjmkj9xe5zfLFb6TVk1GtZPmcsu84U8/fvDlNlcxARpuPNu8dwpriSLu0iPCX4gAglQVARj0foIkIo1hiGqdL/HqEKqyJemhMag+ru0qGAu3pNQmPBRXUhNHHixIu2N583bx7z5s0LkkUKkiwdGEaktufbR65kzptb2X6yqNa2KpuDO97YRM0mtJVWO79ctrmWFylcpwwjtNidhOk0PHBlLy7tlVDHqyQIQuAodnmE3L2CvBEoj1CFqxt0c0JjoOQJBaq8v7G4BZmM2Agu8ml7QTxCgSPWGMbffz6cKYvXYbE7am2rrxN/TREEigCque0fa47yjzVKeFWngcU/H47V4RRRJAgBpKjCikZzce9FrDEsIDlClW4hFN68n7FQmkAvoTF1kE/bC+IRCiypCZH8L30CG4+f53h+Gf/dnU12cfOHM9qdMH9FJgAGvZbvHpkgYkgQAkBRhYW4iDB0Wk2D+8UYwzhxvtzv56+0KkIoIrx5NT+B8lg1BbcdMmssuDRJCB0/fpyePXtefEdBaIDUhEiPSFlw3QCO5pXywZbTbMkqpLTK6hnWaHcoIbDYiDDCdVo6t48gxqDnXEkVe8+W4PByfLPNwaKvD/CP20eg9zIUUhCEplFSaW2wdN6NIjQCkCNkcQuh5l3PRxv0frkI8wfuz0k6SweXJn3avXv3ZsKECdxzzz3cdtttGI2tby6KhMaCT++kGP5ww8BGvcZdXdalXQR7s0s4llfGh1tPe5o4fr03hznLt/Kv2WMI14sYEgR/Yaq0EueDEIoy6Cg3+/97tNJPOUKhlCxdWmXz9F4SgkeTfhl27NjB0KFDSU9Pp2PHjtx///1s2bLF37apivQRahmkJkRy26guXNIrgXuv6MmiW4eyav6VjO3e3rPPD0cKmPnqRj7YckoaOAqCnyg1+zYTKzJcT5U1AELIVTUW0dzy+RBKlpau0urQJCE0fPhwXnzxRbKzs1m2bBnnzp3j8ssvZ/DgwSxevJj8/Hx/2ykIPtOvUww/H5Naa23n6WIWfLqHa/6+TsSQIPiBsiobMYaLe4SMYTpPGMufVFjsaDRKLmBziDHqQ2bWWKl0lVaFZv0P0uv13HLLLXz00Uc899xzHD16lMcee4yuXbty1113ce7cOX/ZKQiNYmz3eCLqcZlX2RzSzVoQ/ECZzx4hHZVW+0XbpDSWSoudyDAdGk3zwkgxRj3lFrun472alFZZxSOkAs0SQtu2bePXv/41nTp1YvHixTz22GMcO3aM7777juzsbG688UZ/2Rl0ZNZYyyY1IZJvHr6S52cM4w/XD6i1zSi5Qm0Wq9XK6dOnOXToEIWFIoibQ2mV1ad+N+4LErPNW1lD06i02JsdFoMaE+hDIE+ozGyTHkIq0KRPfPHixbz55pscOnSI6667jrfffpvrrrsOrVb5genRowfLly+ne/fu/rQ1qEj5fMunZlXamaJK3vzpBADPfHWAy/skXnQ0gNA6KC0t5d133+XDDz9ky5YtWCwWnE4nGo2GLl26cM011/CrX/1KLnoaSZmP+SxusVJhsWNsZmJzTSqs/hFC1RPofUv+DiSlVTbVbWiLNOnS+JVXXuGOO+7g5MmTrFy5khtuuMEjgtwkJSXxr3/9yy9GCkJzeeKGgVzSUxnNca6kiqc+36+yRUIwWLx4Md27d+fNN99kypQprFy5kszMTA4fPszGjRt58sknsdlsXHPNNVx77bUcOXJEbZNbDKVmH4WQS/xU+jlhWgmNNd974n4PodBLSEJj6tCkT/y7774jNTW1jvhxOp2cPn2a1NRUwsPDmT17tl+MFITmotNq+O01/bl16U8ArMw8yy0jO3Nl3w4qWyYEkq1bt7J+/XoGDRpU7/axY8cyd+5cli5dyptvvskPP/xAnz59gmxly8PpdLrCOBf3XriHolZa/Cs0Ki32Zs8Zg2ohFAqhMakaU4cmfeK9evXi3LlzJCUl1VovLCykR48e0ntHCEmyLuhu+5evD4oQauV88MEHPu1nMBh44IEHAmxN66HCYsfp9K3xnzscVmnxb45QhdXe7B5CUJ0jFAol9KU+ikvBvzQpNOYt+7+srKzVNFeUZOnWx9ju8bUSpfefM/HTsQIVLRKElok7jBTjQ2JvpCdHyP8eoUi/5giFgkdIQmNq0KhPPD09HQCNRsPChQuJjKye4WS329m8eTPDhw/3q4FqIcnSrY/UhEi+fWQC/1x7lA+3ngbgj//dz9fzr2h2Ca4QelRWVlJYWEjnzp1rre/bt89rqEzwjTKz76Mg3AnNfs8Rstr8UvDg7uSsthCy2h1UWR0ihFSgUR6hnTt3snPnTpxOJ3v27PE837lzJwcPHmTYsGEsX748QKYKQvNJTYjkgQm9cMuegzmlfLFb+l21Nj7++GP69OnD9ddfz9ChQ9m8ebNn2y9/+UsVLWsdeDxCvvQRciU0V/q5qWKFxT+hMY1GQ7RB/Qn01Z+phMaCTaOk55o1awCYM2cOL774IrGxsQExShACybaTRdQM7r66/jjTh6WoZo/gf/785z+zfft2kpOT2b59O7Nnz+b//u//uOOOO/ze2K8t4hYNvvS8MbqmwwekaswPoTEIjTEbZY0Ql4J/adIn/uabb/rbDkEIGmO7x2PQaTDblR/EfWdLOHW+wtNzSGj5WK1WkpOTARg1ahTr16/n5ptv5ujRoxIG9QPVOUIX916E67TotBq/j9motPqnagxCY8yGySXERAgFH58/8VtuuYXly5cTGxvLLbfc0uC+n376abMNE4RAkZoQyXfpE1n09QG+3puDE3jzpyyenC55I62FpKQkdu/ezdChQwGIj4/nu+++Y/bs2ezevVtl61o+btEQZbi4ENFoNESE6fw+eLXCT32EQBEfaucISWhMPXzOEYqLi/NcScXGxhIXF+f1JgihTmpCJM/ePARjmPIn8O+tpympVL98VvAP77zzTp32HuHh4XzwwQesW7dOJataD6VmG5HhOvQ6335CIsL9P3i1yo+hsRhjGKWq5wiJR0gtfP7Ea4bD2kJCdEZGBhkZGdITqRXTPiqcW0d24b3Npyi32Pl8Vza/vKSb2mYJfqBLly61nufk5NCxY0cALrvsMjVMalWUVTVuJlZEmM6vOUJOp5MKP4bGog168kqr/HKsptKYvCvBvzSpj9Cf//xnsrKy/G1LSJGWlsb+/fvZunWr2qYIAeSOcamexx9sPqWiJUIgueaaa9Q2oVVRWmX1qXTeTUSYzq9VYxa7A7vD6ZeqMXDlCKnuEbIRrtP6dR6b4BtNEkIfffQRvXv35tJLL+Wf//wnBQXSlE5omcQYwjyl9PvPmdhwRP4vt0akUsy/lJltPjVTdGMI02Kx+6+zdJWrS7XfqsZCIkdImimqRZOE0K5du9i9ezcTJ07k+eefJyUlheuvv57333+fiooKf9soCAFjy4nCWqX0b/7Yuj2dbRWpFPMvysBV35N6DXotZqv/hFCFVREt/gqNxRrDVK8akzlj6tEkIQQwaNAgnn32WY4fP86aNWvo3r07Dz/8sCcOLwgtAXcpvZs9Z0twOMR7IAgN0dgcIYNeh9nmv9CYO/HaX6ExpY+QykLIbGtUuFHwH00WQjWJiooiIiKC8PBwrFapvBFaDu5S+gGdYgDIKzWz8fh5la0ShNCmTo6QwwE/vABLhsKirvD2jXD+mGezQa/FbPOfR8idbxQZ7r/yeYvd4fcS/8ZQWmXzqS+T4H+aLISysrJ45plnGDRoEKNHj2bnzp08/fTT5OTk+NM+v1FRUUG3bt147LHH1DZFCDFSEyKZN6mP5/kn28+oaI0QCHQ6SUD1J2XmCzxCq5+G1X+CXpPginQoPg3LbwCLkioR7m8h5BIsEeF+uZb3vBc1E6YlR0g9mvS/6JJLLqF37958/PHHzJkzh5MnT7J69WruueeekO0j9Mwzz3DJJZeobYYQokwekOT5Mly1L0fVK0PB/+zcuTMgx33llVcYOnQosbGxxMbGMn78eL7++uuAnCuUKKuyEev+0S46CRszYNLvYfqLcPkjcOfHUJYDu94H3DlC/g+NRfjNI6R4YtQMjyk5QuIRUoMmCaHJkyd7hq4+9thjdaY7hxpHjhzh4MGDTJs2TW1ThBAlz2SmwnU1WGGx8/E28QoJF6dLly785S9/Yfv27Wzbto2rrrqKG2+8kX379qltWkCplc+y8x0Ij4Txv67eIb4n9L0W9nwMuHOEAhAa82P5PKBqwnSZJEurRpOE0DPPPMPAgQP9YsD69euZPn06KSkpaDQaVq5cWWefjIwMunfvjtFoZNy4cWzZsqVR53jsscdYtGiRX+wVWidbThRS82v64x0ihISLM336dK677jr69OlD3759eeaZZ4iOjmbTpk1qmxYwnE4nZWYbUe7Q2OFvoPfVEB5Ve8ful0P2TrBbMYT5OzSmCJYIP84aA1QdvCqhMfXw+VNPT0/nT3/6E1FRUaSnpze47+LFi302oLy8nGHDhjF37tx6Z5itWLGC9PR0li5dyrhx41iyZAlTp07l0KFDnhb6w4cPx2arq+S//fZbtm7dSt++fenbty8//fSTz3YJbYux3eMx6rVUub6sD+aYqLDY/JaMKYQmJSUl7Nq1i8zMTH7zm98061h2u52PPvqI8vJyxo8f7ycLQ49yix2n0xVOKs2FnN1w6UN1d+wyBmxVkLMHg97o96oxjUYJufkDd1hczTEbUj6vHj5/6jt37vRUhPkz3j5t2rQGQ1aLFy/mvvvuY86cOQAsXbqUL7/8kmXLlvH4448DkJmZ6fX1mzZt4sMPP+Sjjz6irKwMq9VKbGwsCxcurHd/s9mM2Wz2PDeZTE14V0JLIzUhkm8fmcDvPtnFpuOFVFkdrD6Qx/RhKWqbJjSBY8eO8cQTT2AwGFiyZAnt2rUjKyuLzMxMj/DZtWsXp06dwul0EhUV1WQhtGfPHsaPH09VVRXR0dF89tlnXj3mreH7pcwzHFQP51y/Ban15F92GgZaPWTvxKC/3K99hCotdiLDdH7rDxXt8QipI4QcDidlFskRUgufhdCaNWvqfRxILBYL27dvZ8GCBZ41rVbLlClT2Lhxo0/HWLRokScstnz5cvbu3etVBLn3f/rpp5tnuNAiSU2I5DdX9WHT8c0A/HdXtgihFsqsWbOYNWsW3bp1Y/DgwZSVlWEymYiLi2PgwIEMHjyY06dP869//YvJkyfTtWvXJp+rX79+ZGZmUlJSwscff8zs2bNZt25dvWKoNXy/lJldw0ENeji5G4ztIK6ez09vgNjOUHwqIOXz/gqLgZLDFK7XUqZSaKzcYsPplDljatEkv+LcuXMpLS2ts15eXs7cuXObbZSbgoIC7HY7ycnJtdaTk5MDVqa/YMECSkpKPLfTp08H5DxCaDKuZwKJ0QYA1h7Ox6RizoDQdPLy8hg8eDDDhg0jJyeHtLQ0Tp8+TVFRET/++COvvvoqGo2GsWPHNksEgTLVvnfv3owaNYpFixYxbNgwXnzxxXr3bQ3fL26vSbRRr4TFOg4Bb56ZuK5QctqVI+TH0JjVv0IIIFbFMRulNb1sQtBpkhB66623qKysrLNeWVnJ22+/3WyjAsXdd9/N888/3+A+BoOB2NhY3nnnHS655BImT54cJOuEUECn1XDD0E4AWGwOvtx9TmWLhKbw0ksv8eCDDzJr1iyWLl3K559/TlpaGocPHw74uR0OR63wV03c3y81by2NWlPSc/cpQsgb7bpC8emAVI1FhvlXNMQYw1TrI1QthCQ0pgaNEkImk4mSkhKcTielpaWYTCbPraioiK+++sqTwOwPEhMT0el05Obm1lrPzc0N+CgPmT7fdrl1ZBfP4/c2nVTREqGp3HDDDRw8eJANGzZw7733kpmZyZQpU7jyyitJS0sjLy/PL+dZsGAB69ev58SJE+zZs4cFCxawdu1aZs2a5ZfjhyKeH+0wDRSfhIRe3neO6wolZzDotVhsDr8Nv6202P02Z8xNtEGPSTWPkOJ5jhWPkCo0Sgi1a9eO+Ph4NBoNffv2pX379p5bYmIic+fOJS0tzW/GhYeHM2rUKFavXu1ZczgcrF69OuBVGRkZGQwcOJAxY8YE9DxC6FHzy2hvtokfZSJ9i0en0zFv3jz279+PTqejf//+OBwO7PbmhWvy8vK466676NevH5MnT2br1q188803XH311X6yPPRwJ0tHm3PAYYP23b3vHNcFSs9h1Cmv8ZdXqMJq91sPITcxRr16HiFzjXCjEHQa9amvWbMGp9PJVVddxSeffEJ8fLxnW3h4ON26dSMlpXHJpWVlZRw9etTz3F3ZER8fT2pqKunp6cyePZvRo0czduxYlixZQnl5uaeKLFCkpaWRlpbmSbAU2g5bTxbVer7sxywu65OokjWCP4mPj+ell17igQce4JFHHmHy5Mn87ne/Iy0tjYiIiEYf71//+lcArAxtSs02IsN16IpPKAvte3jfOa4L4CTOqszvM9scGP0gYCotig3+RBm8qk5OoITG1KVRQmjChAmAIlZSU1P9Urq4bds2Jk2a5Hnu7lE0e/Zsli9fzsyZM8nPz2fhwoXk5OQwfPhwVq1aVSeBWhD8xYU9hXafLcHucKLT+qdUV1CfgQMH8s033/DFF1/w2GOP8cILL3DunOSD+YJn8nzRCdDooF2q950jE5Q7m9ImQEmYbv6PfaXVTrvI8GYfpyYxxjBOni/36zF9pbTKilYDUX4Wd4JvNClZ+vvvv+fjjz+us/7RRx/x1ltvNepYEydOxOl01rktX77cs8+8efM4efIkZrOZzZs3M27cuKaY3SgaFRqz2+DAF+Cn+LegLu6eQgM7KYms+aVmfjwq4bGWyKlTpxrcfsMNN7Bnzx5+97vfAXD27NlgmNWiKTO7Js8XZUFcZ9A1IGwilahBpKMEwG+9hCosgQmNqVU15haX/uqLJDSOJgmhRYsWkZhYN1SQlJTEs88+22yjQoFGJUuv/yusmAUf3Q0VhQG3TQg8qQmR/GZyb8/zl78/6rdETyF4jBkzhvvvv7/Bv+OKigqioqIYPHgwn3zySRCta5mUVtmUHkKm7Pr7B9Ukor1yZ3V7hPwjhCotdr+HxlTNEZKBq6rSpMysU6dO0aNH3bhwt27dLnoF1lLIyMggIyPj4smU54/B+r8pj/evhDNb4eal0OPKgNsoBJbJA5LpkRhFVkE5W04UsmpvDtOGdFLbLKER7N+/n2eeeYarr74ao9HIqFGjSElJwWg0UlRUxP79+9m3bx8jR47kr3/9K9ddd53aJoc8pWbXj3ZpDsRc5O8hPBq0YRhtJUAHv/USqrQGqmpMrRwhmTOmJk3yCCUlJbF79+4667t27SIhIaHZRoUCPnuEEnrBjOVKd1UA01l462ew7q/g8F/fDCH4hOm03HdFT8/zRV8f9GtTOCHwJCQksHjxYs6dO8fLL79Mnz59KCgo4MiRI4DSgXr79u1s3LhRRJCPeHKETNkQc5E2JhoNRMZjsCqhMYu/qsYC2EfI4Qi+51fmjKlLkz7522+/nd/85jfExMRw5ZWK52PdunXMnz+fX/ziF341sEUw8EaISob//gYKDgFOWPMMnNkGt7zqcQ8LLYtT5yt4+vO91c8LK1i24QQPTmygb4oQkkRERHDbbbdx2223qW1Ki6fMbKNDdLhvHiGAiPaEW4qB0A6NRRv1OJ1KaX6wR12YqqzESmhMNZr0r/2nP/2JEydOMHnyZPR65RAOh4O77rqr1eQI+RwaAyjMgnduAlslaPTgtANOOPINvDYRZr7bcPdVISTZcqIQs7321eHfvjmIVgO/urKnJDYKbZLSKisJYWawlkOsL0IonjCL4hGqsjbfo+p0OgMSGovxDF61qiCEbKTEGYN6TqGaJoXGwsPDWbFiBQcPHuS9997j008/5dixYyxbtozwcP+WNKpFo5KlT21URBCA0waXPgQRrh5LRSfgjath14qA2SoEBncZPYC7ct7hVEJkD32wk/Nl9Y9REEKP1atXc8kll2A0GomJiWHMmDE899xz9c5MFBqmrMpGR42r15YvHqHIeHRmZX+rvflhJ4vdgd3h9H/VmEv8lKlQOVZaZSM2QjxCatEkIeSme/fuDB06lGuvvZZu3br5y6aWR+p40LvUvN4Io+fC/eshZYSyZquEz34FX/8/sMsQz5aCu4z++RnD+P7Ridx9aXfPti92n2Pi39by6rpjfst7EALD5s2bmTZtGgaDgSeeeII//OEPDB06lOeff57BgwfXm+8oeKfUbCMJV3WsT6GxduiqFCHkj7+VSoviVfJ/1ZgiRNQYs2GqlGRpNWmSEKqoqOCee+4hMjKSQYMGeSrFHnroIf7yl7/41cAWQXwPmP1f6Hc93Pya4iFy2GDOKhh5V/V+m5cqidRl/plzJASe1IRIbhvVBa1Gw4dbaldElpptLPr6IHOXb5Uk6hDmr3/9KzfeeCPr1q3jiSee4He/+x3/+te/OHnyJFdeeSXXX389xcXFapvZInA6nZSZbcQ73ELIh5mPEe3Rml3J0s0caQJKxRjg/6oxlxBRo4ReqRoTj5BaNEkILViwgF27drF27VqMxuq45pQpU1ixonWEgBo9a6zrWJj6DHx2P6x8EP45HkrPwc/+AdNfBJ0rZHjqJ3h1ApzZHjjjBb+z5UShp9M0QM/EKNwZQhuOFvDYR7tVqTYRLs7GjRuZN29enfXIyEjeeustunTpwtKlS1WwrOVRYbHjdEKc7bxSKRvmw1iS8Bg0FqVjsz88QhVuj1AAGioCQR+z4XAo4lKSpdWjSUJo5cqVvPzyy1x++eW1EkYHDRrEsWPH/GacmjRp+nzNXCFbJWxYrCRSj7ob5nwNMa45bKXZ8OY02Pmu3+0WAsPY7vFE1PjiPV5Qjk4Lelfy0H93ZfPMVwfUMk9ogPz8/Hr7ngFotVrmz5/Pl19+GWSrWibuzsux1nyI9XGuZHgUGksZOq3Gz6Ex/4aSosLVyREqt9hwOJHQmIo0SQjl5+eTlJRUZ728vLxtV9Kkjgd9jSukHW8rnqGsH6DgMPz8bWUfALsZ/pMGX/1O8oZaAKkJkXzz8JXMHFPdSdfmAFsNL9C/NmSx+fh5NcwTGsBut9fyXF/IqFGjOHToUBAtarmUmZXvqkhLvm9hMQBDNFjKMOjA4odkaXdoLCK8WSmuddBpNa7Bq8EVQtUDV0UIqUWT/ieNHj261hWUW/y88cYbjB8/3j+WtUTie8CvN9bOC7JVwjs3K+Gyt6bD9JdgzL3V27e8qmwvlx/QUCc1IZK0ib09lWT18dr640G0SPCVt99+m82bN1NVVVVnW2xsrOQI+Yj7RzuiMrfaw30xwqMBiNVZ/Roai/CzRwhc88aCnCPk8bJJ1ZhqNOl/0rPPPsu0adPYv38/NpuNF198kf379/PTTz+xbt06f9vYsojvAZenw+6PFBGkDQOHy+Njq4Sz2+D6F6DjUPjyUWXbiR/g9Ulw+4eQPFBd+4UGcVeSfb33HIu/PYTZ7sSg0xAXGU5eqZnVB/M4mldK76QYtU0VXFxxxRX86U9/orS0FL1eT79+/Rg1ahQjR45k1KhRJCcn+9YvTPAkEodV5ELMZN9e5BJC7fQWP4XGFBv8nSMEuDxCwfXQu8d6xIpHSDWa5BG6/PLLyczMxGazMWTIEL799luSkpLYuHEjo0aN8reNqtDoZOmauD1DN70Cv/ysOlymj6gOjfW4Ei6bD5GukSTFJ+FfV8PBr/zzBoSAkZoQyf0TevFd+kSenzGM79InMmNUF8/2N37IUtE64ULWrVtHSUkJhw4d4u2332batGmcOXOGp556iiuuuIJ+/fqpbWKLoazKhgYHuoq8xoXGgDit2a9VYxF+rhoD1+DVoIfGrK5zi0dILZosQXv16sXrr7/uT1tCirS0NNLS0jCZTMTFxTX+APE9lBsooujURmVS86mNUHIG3puheIh0BugwAPIPgKUMPrwDpjyliKS2nG/VAkhNiCQ1IZJT5yt444fqkNgnO86Qfk1fkmKkU2wo0adPH/r06VNrDFBWVhbbtm1j586dKlrWciitspFAKRqHrRHJ0i6PkM7st9CYRgOGBkLUTSXaGBb0HCFTpSs0JkJINXwWQiaTyeeDxsbGNsmYVotbEP1zfN1wmd0MnUdAu1RlJAdO+N+TkH8Ipi8BvUEtqwUfuXAUh9Xu5N1Np0i/uq+KVgm+0KNHD3r06MGMGTPUNqVFYKqykhqm9ATy2SPkEkIx2iq/dJautNiJDNMFpDAnxqinpCK4obHSKit6rQZjmP+FneAbPn/y7dq1o3379g3e3PsI9VCztN5hVcSQm8z34fg6GPdg9dqu9+HtmySJugVQcxSHm2/35ahkjdBYjhw5woQJE9Q2o0VQUmmlp8F1UexrsrTBLYTMfhm6WmmxByQsBkqejinoOULK5Pk2XXGtMj57hNasWRNIO1o/7tJ6W6VyP+sj2PNvpcQewF6lDDAcc6/SX8hWpTRffGOysm9iH3XtF7ziTqDecqKQN344zsGcUg7mlJJnqiIpVsJjoY7FYmHDhg1qm9EiKKqwkBpuArsWojr49qLwKEDxCJn8ERqzBlIIqRAaq7JKxZjK+CyEXnzxRZYvX05sbCxvv/02M2fOxGCQsI3PuBOoT21URFF8D4jrUl1dpjPAmmcVAaQzQGQiVBRAURa8MQV+8T50v0ztdyF4wZ0vdKKgnIM5yiDPDUcLuGVkl4u8UhBaDkUVVkboiiA6GXQ+/nyEKUIoGjMWu388QpFhgamwio0Io6Qy2KExm/QQUhmfQ2NffPEF5eVKm/Q5c+ZQUlISMKNaLfE9YPgd1TlDNavLrvq9IoJAyRu6/BFIHqw8ryqGd25SRJMQ0lzRJ9HzeP3hfBUtEdw88MADvP7662zbtg2LxaK2OS2a4goLHTXFvucHAWi1EB5NtKYKix9m8lVa7H6fM+Ym1qjHVGnF6QzeuJzSKhsxBvEIqYnPMrR///4sWLCASZMm4XQ6+fe//+01Kfquu+6qd70lkZGRQUZGRuD7i7irywqzYM2i6tBZ/+uVxowfz4Gj/wO7BT69F0xn4LKHpaIsREmMNmDQazHbHGw4WoDD4USrlX8rNdmzZw/vvfce5eXlhIWFMXDgQE8PoZEjR6LVSpKqrxSVW0l0Fvo2db4m4VFEUumXZOkKqz0gPYRA8QjZHE4qrXa/j/DwhqnSSmyEeITUxOdPf+nSpaSnp/Pll1+i0Wh44okn6k3u0mg0rUIINbt8vrHUFzoDuH0FfPUobF+uPP/fU1ByFqY9B9rAfBkITePU+Qquf+kHT0JoQZmFAzkmBqUE4f+P4JUff/wRp9PJoUOH2LFjh+f22WefeTpKS6Kqb5RUWmmvLYCYRjZ+DY8m2lHlt4aKkQHMEQKlpD1YQqi0ykqHmOignEuoH5//pS+99FI2bdoEKIMKDx8+XO+8MaEZ1PQOZb6v9B0qOQ2XzlfK61f/Udlv6+tQlgO3vAFhkowbKlw4oR7ghyMFIoRUZt++fRgMBvr370///v254447PNuOHz/O9u3bpY+QjxRVWIgxFDTBIxSJ0eyfPkKVVjvtIsObfZz6cHtmTFVWOsYF57tVcoTUp0mfflZWFh06+FgxIDSOwqzqfkNuwiLhwZ8gtrMyqNVhgwP/hXdvhdvfB6P80IYC7jL6mmJo/eF8HpjQS0WrhPT0dAYNGsTixYs9a19++SXvv/8+SUlJzJ8/X/oI+YDZZsdmqSJCW6RUuDYGfQSGKgtmPyRLV1jspMQFxiMUF+H2CAUvYdpUZZVmiirTpOB4t27d2LBhA3feeSfjx4/n7NmzALzzzjtShtpcavYbcmOtUNaH/QLu+LenCoOTG+DN66EsL/h2CnVwl9H/7bahnqvJbSeKqLLKHCs12bVrF7feeqvn+YEDB7j55ptZt24d7777LmPHjiU7O1tFC1sGxRVWOlCsPGlMsjRAWARG/OQRstgDHxoLYi8h8QipT5OE0CeffMLUqVOJiIhg586dmM1mAEpKSnj22Wf9amCbw91vqCY1Z5T1ngyz/1s9oyx3DyybCsWngmunUC+pCZHMGN2VK3or1WMWu4MD53zvyi74n5KSErp27ep5/vbbb9OzZ09OnjzJmTNnGDZsGH/5y19UtLBlcL7MQrKmSHnS2NBYWAThTgtWf5TPWwNYNRZRnSMUDGx2BxUWu3iEVKZJQujPf/4zS5cu5fXXXycsrPof8LLLLmPHjh1+M85fdO/enaFDhzJ8+HAmTZqktjkNU7OkfvYXyv2vN1YnTwN0GQVzv4FYV4+awuPwr6mQf1gdm4U6DEqprqjcly1CSE26dOnCuXPnPM9Xr17NjBkz0Ol0GAwGFixYwLfffquihS2D3NKqZgoh/80aC1QfIYNeS7hOGzSPkLt5o1SNqUuThNChQ4e48sor66zHxcV5qjBCjZ9++onMzMyW0SHb3W+oxxXVfYfcCdSFrsnmiX3gnm8gwdVxujQbll8HufvUs1vwMKhzdd7WlqxCFS0RpkyZ4skPOnnyJDt27OCaa67xbO/VqxenT59Wy7wWQ56pik6aQpx6I0Q0cpSSPoJwp7+qxgIXGtNoNMRGBG/emFsIyeR5dWmSEOrYsSNHjx6ts75hwwZ69uzZbKOEC3AnUK98ULl3i6G4LjDna+g4RHleng/Lr4dsqYBRmxhD9RXe57uyOXW+QkVr2jZPPPEEa9asoWfPnowfP56uXbty+eWXe7bn5uYSHS3lyxcj12Smb3gBmnbdGt/HzO0RamZozOl0UmGxBSw0BkqeULA8Qu7zSGhMXZokhO677z7mz5/P5s2b0Wg0ZGdn89577/Hoo4/y4IMPXvwANVi/fj3Tp08nJSUFjUbDypUr6+yTkZFB9+7dMRqNjBs3ji1btjTqHBqNhgkTJjBmzBjee++9Rr02JKiZQG2rhA2Lq8VQdAclZ6jzKOV5ZRG8fSOcDb0QZVti7wXhsI3HZXiuWnTu3JmtW7dy8803M23aND799NNafYO+//57+vbtq6KFLYNcUxU99QW1w/S+EhZBmKP5oTGL3YHDScAaKgLERIQFLUfILYQkWVpdmvTpP/744zgcDiZPnkxFRQVXXnklBoOB3/72t9x7772NOlZ5eTnDhg1j7ty53HLLLXW2r1ixgvT0dJYuXcq4ceNYsmQJU6dO5dChQ54+RsOHD8dmq/sf99tvvyUlJYUNGzbQuXNnzp07x5QpUxgyZAhDhw5tyltXh5oDW0EZ1Lr7o+rcoYj28MuV8P5MZVBrVYkyuf6uz6oFkhBUxnaPRwu4v/Y7yvBVVenWrRsvvPBCvdv279/PbbfdFmSLWh65JjNdnDnQfnTjXxwWgd7RfI9QpUWpwAxUaAyCO4G+OjQmQkhNmuQR0mg0/P73v6ewsJC9e/eyadMm8vPziYuLo0ePxl0tTJs2jT//+c/cfPPN9W5fvHgx9913H3PmzGHgwIEsXbqUyMhIli1b5tknMzOTvXv31rmlpKQAyhUhQKdOnbjuuusaTOg2m82YTKZaN9VxJ1CPrNGx21YJBz6vzhsyxipT6ru5BrOaS+DtmyVMphKpCZHce0V1mLiwwqyiNUJDvP3228yfP19tM0KefFMFHey50L5741+sN6K3KzlCzZnjVeESQgENjUUEMTRW6fYISWhMTRolhMxmMwsWLGD06NFcdtllfPXVVwwcOJB9+/bRr18/XnzxRR555BG/GWexWNi+fTtTpkypNlirZcqUKWzcuNGnY5SXl1NaqkwDLysr4/vvv2fQoEFe91+0aBFxcXGeW82yW1WJ7wGXp1eX1usM8P0ztfOGDNEuMeTKfzCXwDs3Q85e9exuw1zaO8HzeN/ZEBDUgtAMLEVnCXNamhgai0TvUIZKN2feWKWrJ1cgQ2NxQQyNlVbZMIZpCdfLvDs1aZQ/buHChbz66qtMmTKFn376iRkzZjBnzhw2bdrECy+84ClJ9RcFBQXY7XaSk5NrrScnJ3Pw4EGfjpGbm+vxNtntdu677z7GjBnjdf8FCxaQnp7ueW4ymUJLDLnnkZXnw3cLlXV33tDl6co+s/4N796mhMncOUNzvoIO/dS1v41Rc7SGlNALLZnCcgsdq45BONChf+MPEBaBzl4FOLHYHU3+4a8OjQUulBTMZGmlmWIzvUHmUig4AhXnwVIGlnJw2IEagtPjhXNe8LwF07670lfPDzTqf9NHH33E22+/zc9+9jP27t3L0KFDsdls7Nq1K2SHFvbs2ZNdu3b5vL/BYMBgMARv+nxjqW9aPdTNG5r1byVP6Ow2qChQHt/zjTKzTAgKHWIMJMUYyCs1sy+7BKfTGbJ/J4LQEMfyyxioOYk9PA5dU75DwiLQ4MSAVUmYNjTNDndoLCI8cB6U2Ah90EZsKOM1miDqnE7Y/W9lGPepjdQSPV5xffd4voNa+HfRwBvVEUJnzpxh1Cgl+Xbw4MEYDAYeeeSRgH25JyYmotPpyM3NrbWem5tLx46NbPHeSII+fb6xuL1DGxYrIggUUXTKJYQMMXDnJ/DWdMjZrfQZevsmpRFjtMyJCxaDUmLJO5SPqcrG2eJKurSPVNskQWg0R/PKGKw9AZ2GNL50HiBMCekbaF53aXdoLCLgHiFbUC5cSqusjfcIlebCx3Pg5I/Q6yqYvkRpoRLdEcKjlJtOco4aQ6Nktd1uJzy8euqvXq8PaP+N8PBwRo0axerVqz1rDoeD1atXM378+ICdF5SS/YEDBzYYRlOdC/OGao7iKMyCQ1/BDUsgobdr7Ri8e4viShWCgoTHhNbA4RwTI/RZ6DoNa9oBXN9REViaVUJfaVFyd3zKETqzHTYthdONa7cSGxGG3eGk3BL4aECj54xVFCqNcwuPw13/gV9+BqPuVqqD4zpDRDsRQU2gUbLa6XRy9913YzAofs2qqioeeOABoqKiau336aef+nzMsrKyWs0Zs7KyyMzMJD4+ntTUVNLT05k9ezajR49m7NixLFmyhPLycubMmdMY0xtNyHuE3NTMG0odXx02c0+w10fALz+FT+4F01nFO/Tv2XDHCvmDCQIDLxi1MXVQYD2ZghAIzh7JpKMzH3pObNoBXB4ho8aCuRlCqDo0dhEh9P0zsP6voNWDwwZj7oPr/uaTN8sdqjJVWok2BLas3VRlpV1k+MV3BCUc9umvlLzPe76DhF4Bta0t0ah/5dmzZ9d6fueddzbbgG3bttWa/+VOVJ49ezbLly9n5syZ5Ofns3DhQnJychg+fDirVq2qk0Dtb0I2R6g+auYNZb6vJFLXbMBYdEK5cvjXNVBVDMdWw3/nw40ZTXNzCz4zsFO1EJLhq0JLJM9URc/CH7AZjeh7XNG0g4T5ySNktaPRKDPBvLL/P4oIuuoPcNnDsGM5fPkoxKbAFeneX+fCM3i1ykoKERfZu3mUVFrplhB18R0B9n4CR7+DO/4tIsjPNEoIvfnmm343YOLEiRftKzFv3jzmzZvn93M3RIvxCLmp6QXSGZSb3VwdLovvAbd/qFSQ2c2Q+Z6SdT/hd2pb3qpJjY8kKlxHucUuQkhokXy85Riz9d9i73s9+rAmCgOPEGpeU8VKi53IMJ333B2bBb75PfS7Dq54VLnQG3MvFJ+GtYtgwM8gsXeD53CPuwhGCX1xhZW4CB8883YbrHkW+kyFvlMDbldbQ9pZthZqjuGwm+HqP0JUh2oRBNBtPNzyGnx0N+CENc8o+UOD63b0FvyDVquhX8cYdpwq5kxRpatKREKSQuNw2O04na5mhE4n4FTqhFwXke51ZdX1WNmhVum066Wu19c4lhPX612vwYnd7mD34Sx6/fA0SZoSdJMXNP0N6JXO6kZN85KlKyz2hsNiez6CkjMw6+Pa3u6JjyselTXPwIyGL+jdk+CDUTlWUmmlnS9CaPeHSo7nRWwXmoYIIS+0qNAY1B7DoY9QrnzieyiJgt/8Hi5/GLqOhUE3QdFT8L8nldetfBDad5NRHAFkQKdYdpwqBuDguVLG9ohX1yDBbyxatIhPP/2UgwcPEhERwaWXXspzzz1Hv37+7dl1eNFl9Lcd8OsxfeEKoEIbieWmfxGR2KfpB6pRNdbc0FiDQmjnu9BzAiRd0OsoLAIuf0QJkV31RIOhJY9HKMC9hOwOJ6VVtot7hJxO2JgB/a6HpiarCw0iQsgLLS405i1p+q3pYKtS8oJ+vUlZv2w+FBxWwmO2KvhwFvxqHcQENu+qrdK/Rp7QwRyTCKFWxLp160hLS2PMmDHYbDb+7//+j2uuuYb9+/fXKSJpDlXjH2aLKY/q3i8a10ON6/GF68ooJCcaNK7tTty7Kevu/T1hJo2Gmr1mtBotHRLa03Xw5WiM1f+Hm4TLI+TpI9RElNCYl5+tkjNKE9mbX6t/+/A7YPUfYcdbisfcC8YwHQa9NuAeoVKX0LqoEMrZDXn7YcpTAbWnLSNCqDXhTpp2c2qjInRAuXf3GNJo4Ia/K0Lp1E9Qek4Jl83+XCrJAoCuhote8oRaF6tWrar1fPny5SQlJbF9+3auvPJKv51n+ORf+O1YqlBDCDWvaszmfc7YkW9Bo/OeQxMWAcN+AZkfwOQnQevds6TMGwtsjlBJpY9CKPMDiEqCXv5pHijURQaceKFF9BG6GO5wGSj3cV2rh7TqDfDztyBGGUzLqZ/g2yfUs7WVcup8BU99Xj3rLfN0sXrGCAGnpKQEgPh48frVQq+0XDFqLM1LlrY6vPcQOvIdpF6i9NLxxqBboDwPzmxt8DyxRr1HqASK4gqXEIpsQAjZrUre09Cfg078FoFChJAX0tLS2L9/P1u3NvwHE9K4w2U3vaIMY33/50pO0CuXKmIoOglmvgM6Vx+LzUthz8fq2tzK2HKiEEuNIZNH88qwO+qvktxxsoj73t7GjpNFXo+XZ6ri798dJs9U5XdbhebhcDh4+OGHueyyyxg8eHC9+5jNZkwmU61bm0Crw6kNw4AVazMbKkbW5xFyOODEjxfvc9RljOJdOfhFg7vFRoQFPDTmk0fo1CZlRNLgWwNqS1tHhFBrJ76HEhsvOQ3WCmXNWqGM5ijMgi6jlUZjbv77MJw/poqprZGx3eNr9Tyx2p2cOF9eZ79T5yu4/fVNfLc/l9tf38Sp8xX1Hm/XmRJeXH2EXWdKAmaz0DTS0tLYu3cvH374odd9Fi1aRFxcnOcWMgOdg4HeqOQINbNqrN7QWP4BMJcoHqGG0Gqh/3Vw4IsGB48GY/CqT0Lo2GqITIROwwNqS1tHhFBboWaYDJT5ZG7P0MjZMHSmsm4pVebY2Mzq2NnKSE2I5IP7LqFnh+rE2fryhLacKPTkTphtDracKKyzz6nzFcx7fwcA897f4VUsCcFn3rx5fPHFF6xZs4YuXbp43W/BggWUlJR4bqdPnw6ileqiCTMSqbU1u2qs3tDYqU1KfpAv1a/9b4CiLCUB2QuKRyjwOUI6rabh7tVH/6fME9PKT3UgkU/XC60iR6gm7jDZyLuq16wVcOBz2PUBXPqb6plk53bB/55SxczWyMhu7fl/11aX89YnhMZ0a0+YTkmqNui1jO1eO8ckz1TFn77cf1GxVB8STgscTqeTefPm8dlnn/H999/To0ePBvc3GAzExsbWurUZ9EYitX6oGqvPI5S9E5IHKgNHL0aPK5Xk7WNrvO4Sa9QHxSMUFxHmvTlkWR7k7PHbhHXBOyKEvNAqcoQu5MIhrTqDMpNn5YPwxhS45s/KGsCmf8LxtaqZ2tqoPWqj1JMP9O8tp7nlnz8y87VNWF25RGmTevHJjjO1hMuuMyV8tz8Xd5StPrHkDQmnBY60tDTeffdd3n//fWJiYsjJySEnJ4fKykq1TQs99AZFCAUiNJazBzr62GNHb4DOo5UqWi8oVWPBEUJeOfa9ct/rqoDaIYgQanvUTKC+6vdKF2pQGjFWFtXuVbHy11BZrIaVrY4u7SOIcbnAt2QV8ovXNvLd/lx+9+ludpwqJqeG6Hl1/fFawqVmSMzNy3eMJDUh8qLn9Xc4rT7vUlv2OL3yyiuUlJQwceJEOnXq5LmtWLFCbdNCD72RCI0/QmMXhJLsVsg7AB2H+H6gbuMVIeQlTyguIsxT1RUoSiqsnrlm9ZL1AyQPVopahIAiQqgt4k6gHvCz2uX1qeNh3AOK6xiUafVf/z/17GxFaDQarhqgfKGVmW21KsncJEYr1XvlZqWb+a/f286p8xW18ofcvyGd4ow+ndeX3KPGkFdq5sXVR8grNTe41lZwOp313u6++261TQs99AYiNM3zCNUbGis4olzQday/Uq9eUsdDxXmlsWw9tIsIo7TKhq0Ztl6Mi3qEzm5TqtyEgCNCqC1zYXn9qY1QfBJu/CcYXKGc3R/Coa/VtbOV8MT1A0mICve6ff7kvnRpX53QbrU7WXckv1blWbhOw92XdicpxuCTJ6bmaxsKp/nq6TlXUlXr3tuaINRBH4FR0/QcIafTSYXFVnfExvkjyn2HAb4frOtY0Gjh5E/1bm4XqfydBrKXUINCqMoE+YeUql4h4IgQ8kKrS5b2RnwP5erovRlKrtA/x4PDBtOeq97ny0fBXKqeja2EDjEG/nrb0Hq3GfRaJvTtwNJZtatevt5zji7tI3j5jpEAZMwaxVM/G0RSrNEnT0xqQqTnte5wWr2ixwdPT31htpprae9t56nP97XJEJngA3pDs4SQ2ebA4YQow4VC6BgY4iCyEU0sDTHQcajXPKH2riaHxQEWQl4HrmbvAJxKLpMQcEQIeaFVJkt7o+bkelul0mOo6yXQc5KyZjoLq/+knn2tiCGd4xjauXp23ZQBHYBqkTK4SxzP3lSd6/DTsfPc9M8fsTmUH4+aITG3B+bAOVODniH3a9z3TQ1l1Rdmq7lmsTtZ/tMJ8krNbTpvSPCC3tgsIVRhUULGERfmCBUeh4SetafN+0LXcXB2e72b3B6h4gpLo+30lQY9Qme2KV75xL4BO79QjfTsFmpPrgelx9Duj1zhsk3K+pbXYMgM6NrKPWQBZteZEnafLeHGYSkM6hzLDUM6MSjlDMO6VIujoV2VxxrACew+U0L6ikyGdI7lo22nWXMwj3C9lr99cwiA//tsD1a7k6sHKkNz39t8iqmDkvlmXy6zxqXWsaFmKCspxsB7m095QnLe1gZ3jvOE2cw2R60wm3tNr1VymNzHf3H1Ea4emExSrG/5TEIrR29QOks3Me+m3Kz09anjESo8DvE9G3/AjkOU7zVLeZ2ye7dHqKhcpdDY2e3QeaT0DwoSIoSE6lyhDYsVEQSK+Ck5DZP+D777A+CErx6F+9Y0OKxQ8E7NMNKqfTk8ek0/UtpH8sjVta/6kmIMzJ/ch3C9hue/OYwTZcbSnrMm9pyt24PIXXb/3NcH6dTOyL+3neFscSUfbz9DRLiO5BgDt4zozNmiSk4UlPPwikwAHnx3O3df2p03NmThamHEg+9uY+5lPXnth+M11rbz5PRB7DlbzP1X9OSlNUdJm9iL1344xvShKfzhhoE8sXKvpwDn1+9t5883Komrzga69wptDL0RAxbMTRRCbo9QZHg9HqFulzX+gB2HAE7I3V/nAi8uwKExm91BmdlWvxByOhWPUM2eb0JAESEkKLh7DO3+SBFB7iqyuK6wewXk7lUaLe58F0bNVtvaFkl9oaX6SuCTYo08cnVfTp2vYMn/jniEzsX44WiB5/HH288A8JevD3rWPt15ttb+NoeTNzZkAeA+hc0Br/1w/II1J3/4z95ar138PyVB9d1Npzxr7v2tdif/79M9AGQXVzHEe6NloS0RZiSc5oTGFI9QraoxSzmUnoOEXo0/YIf+oNVDzu46Qsig1xEZrgtYaMw92b7egavl+cpg2E4+9kUSmo0IIaEat2fo1EZFBAHs+Tdc9jB8eq/yfPUfYeCNDU94FurFW2jJG1tOFNYSQfMm9ebSXgmUmm2cKapkw5F81hzKD7TZzeKhD3bwv/SJPvU8Elo5eiPhTkuzc4SianqEChUh36TQWJgREvspzRjroX1kOEUBEkJugVWvR8g9+iN5UEDOLdRFApBCbdw9hkCpIFv5IHz+EPS+WlmrKIB1f1XPvhZMfRVcDXFh6fvPR3fl0t6JTB3UkXsu78Gj1/QD8HSb1nnJFb1mUDLzJvXmlhGdAbi0VwIAo1Lb1dn3+iEdPcdxH25Svw611volRzOue3sAxvdMYNrgjvRLjma4K8+pe433ZbE7m927SGgl6A2E03whVKt8vtA1IDq+CR4hUMJjuXvr3dQuMnBNFYtcx42vr51G3gFlBEj77gE5t1AXEUJeaDPl8964sJKs5HT1+I0trypxeaHRDOsSx/zJfWolR3vDV+H0+DSlf8qfbxpSa9I9KALqiesG8tjUfkwb0gmAq/orjR0fmNjbs797zlmf5BhPiMvtixrWtV2ttVtGdiHT1fV6x6kiFkwbwDePTODPNyvVbr+/fqBPvYuENobeSJjT0uRkaXdorFay9PljYGxk6XxNOg6B3H3gsNfZ1D4yPHBCqNziOUcd8vYr1WKSixk0RAh5oU2Vz9fHhdPq8w8q/YVAuV+zSB27Wjju/B9fK6kuLH2vdSxXUnXf5GgAhnSJ8wineZOUAbpuAVUzUfu5VQc9x3Tv7x4KOyglro44qrnmvm+oW3XN4/o6CkRoA7iEUFM7S7s9QkZ9TY+Qq2KssaXzbjoOVoZP13NhFxcZFrDQWKHruO3qyxHKOwhJAwNyXqF+RAgJ9VPftHqnvbrMdM9HkFO/S1nwH26xkxRjqLvNJario6q3uQVT76ToWs9rJmrXzDtyb++bHOPxVLlFzLM3D6mz9vIdI5k2uNNFPT4NCTihjaI3EuZoemis3GwjMlyHVltD9JScgXZ1W0T4jLsbdT2jNtpHhnlCWP6mqNxCrFFPmO6Cn2CnUwmNJfUPyHmF+hEhJHjHM63e9WOmN8LYB1wbnfC9NFkMNI31IHnD25gON/FR4Z7zuMXLgE6xddY6xRm9huwaEm2CgN6A3mnBYq0bhvKFeueMlZ6DmJSm2xSdBOExyryyC2gfGU5JAD1C9eYHlZwBS6l4hIKMCCGhYeJ7wOz/Qr/rlfsJv4NYJemWw6vg1GZ17RN8EiA1xUvNMR1NpT6Pj79Em9BK0RvR4MRha5q4KLfY6/YQMmVDbDOEkEYDib3h/NE6m+IiAusRal+fEMp3tbtIasTcNKHZiBASLk7XsXD7+8p96bnq0RsA6/+mnl0CUFuA1MwbulAc1SteAuTFEe+QUAe96/+CvWljVyotttoeIXMpmE3NE0IACfULofaR4VRa7VQ10YPVEIXlFuLrS5TOPwhhUUr/NiFotIk+QllZWcydO5fc3Fx0Oh2bNm0iKirq4i8UalOYpZTUu6vJAI5+pzRalOZfIYFbFAEMTLl4ZVrN/T1rfhAx9R1XaOOEKcUXWnvjZty5Kb8wNGY6p9zHdGqeXQl94NiaOsvto5RE5pJKK8Yw/1ZwFZZb6Nkhup4NzUz+FppEm/AI3X333fzxj39k//79rFu3DoNBrlKbRM2S+pps+HvwbRECRn0hrgvFkXh8hEbj8gg1VQhVWuxEGWpcu5dmK/fN9Qgl9lb6o1UW1VqOi1A8NoGoHCuqsNafI1R4XElHEIJKqxdC+/btIywsjCuuuAKA+Ph49Po24QjzP6njIcxVCq2PgAhXtdD+/yj9PIRWy4XiSPKBhEbjKrrQ2JrqEbIRUdMzY3IJoWZ7hJRWExd+hwVy8GphuaX+HkJNHSArNAvVhdD69euZPn06KSkpaDQaVq5cWWefjIwMunfvjtFoZNy4cWzZssXn4x85coTo6GimT5/OyJEjefbZZ/1ofRsjvgc8+BPc9IpSWn/pQ8q60wE/LlHVNOHiiBdHUBWXRyjMacbhaPww3ooLPUKmbIhMUEZlNAd3V+oLKsfcQsXf88ZsdgcllVbioy7oIWSzKFVjIoSCjuqukfLycoYNG8bcuXO55ZZb6mxfsWIF6enpLF26lHHjxrFkyRKmTp3KoUOHSEpSOuQOHz4cm81W57XffvstNpuNH374gczMTJKSkrj22msZM2YMV199dcDfW6skvodyK8xSOrqGR4OlDHatgMlPQVSC2hYKXpC8HUFVXB4hA1YsdgfGRnZOrrDYao/XMGU3r3TejSFaOc4FCdNxEWHotBrOl/tXCLkn2tfxCBWfUi4qRQgFHdWF0LRp05g2bZrX7YsXL+a+++5jzpw5ACxdupQvv/ySZcuW8fjjjwOQmZnp9fWdO3dm9OjRdO2qZOFfd911ZGZmehVCZrMZs7nadWsymRr7llo/NZOmNa4vJrsZdr4Nlz+irm2CIIQmbiGkcQmhRiYgV1jsRNUUQqXnmp8f5CahV/XcMhdarYb4qHAKypoWyvOGe7xGQvQFQsjd3VqEUNBRPTTWEBaLhe3btzNlyhTPmlarZcqUKWzcuNGnY4wZM4a8vDyKiopwOBysX7+eAQO892hYtGgRcXFxnptbQAk1qJk07bTjGc+5dVm9M3sEQRBqeYSa0F26wmwnomYfIdNZiG1mfpCb9t0Uj8wFJARACBV6mzNWeFz5jJqb8yQ0mpAWQgUFBdjtdpKTk2utJycnk5OT49Mx9Ho9zz77LFdeeSVDhw6lT58+3HDDDV73X7BgASUlJZ7b6dOnm/UeWiU155DpI6D7ZcrjklNw+Bv17BIEIXTxCKGmjdmosNhqe4RMzewqXZN23aDoZJ3lDjEGzpf5NzTmrkKrUzVWeFyZOK8N6Z/lVonqobFgcLHwW00MBgMGg4GMjAwyMjKw28XDUQf3HLJTGxVRVHAETmxQtm15Dfpfp659giCEHq5k6SZ7hGr2EbJZoDzPf6Gxdt2UEnpzmZIz5CIx2sCZogr/nMNFYbkVrQZijRckS0vFmGqEtPRMTExEp9ORm5tbaz03N5eOHTsG9Nxtfvr8xYjvAcPvUO4TekFkorJ+fE29c3sEQWjjuDxCRk3jJ9BbbA5sDmf1iI3yPOU+xk+/A+7BrSW1IwCJ0eEUBMAj1D4yvPbwWICiLGgvPYTUIKSFUHh4OKNGjWL16tWeNYfDwerVqxk/fnxAz52RkcHAgQMZM2ZMQM/T4inMglcuU66m3GS+r549giCEJjo9To2+SR6hCotSFRxlcHmEyvOV+6gO/rGtfTfl/oLwWGK0gYJS/+YInS+z0C7yAm+Q06mUzreTnFQ1UF0IlZWVkZmZ6an8ysrKIjMzk1OnlMS19PR0Xn/9dd566y0OHDjAgw8+SHl5uaeKLFCIR8hH6us2vXuFJE0LglAHp97gKZ9vDOUW5fvEkyxd7rrwikr0j2HRHUEXXidhOiHaQKnZ5td5Y/llZjpc2MurohBsVdUDrYWgonqO0LZt25g0qXqIZ3p6OgCzZ89m+fLlzJw5k/z8fBYuXEhOTg7Dhw9n1apVdRKo/Y3kCPmIO3HaVolSPeZUqjlO/AA9J6psnCAIoYRTZ2iSR6jS7RFy5wi5hVCkn4SQVqsMOi2+0COkJDSfL7fQuV2EX06VZ6oi+cKO7KYzyn2cCCE1UF0ITZw4Eaez4S6j8+bNY968eUGySCEtLY20tDRMJhNxcRcfXtlmqZk4bS6Fr3+nrGd+IEJIEIRaOPVGpY9QI4VQudntEaoRGjPENr+rdE3ad6tHCCmem4JSs9+EUH6pmSGdL/hNKTmr3Md28cs5hMahemhMaAW4E6d7TKguqz/wuVKBIQiC4EZvaFL5fIUrNBblCY3lK+M1/Em71Do5Qu4Qlj97CeWVmuvxCJ0FbZj/cp6ERqG6RyhUkdBYIynMgtcmVucLWSsUMTT8juCcv8oEh76Co/9TSvmdTohJVqowRs9RRJpGc/HjCIIQMDRhERiwYm1kjpA7WdpTPl9x3v+ioV032PdZrSV3rx9/CaFys40ys42k2AtyhEpOK60ApIeQKogQ8oKExhpJvUnT/w6OEDrwBXyZDmW12yxQlgPndsH+ldBlDEx5urr5oyAIQUejNyoeoUYLIeWCNNJQwyPkdyGUClUlys2ofOeH6bS0jwzzWwl9nqsCrU6ydMlZiJOwmFqI/BT8Q81u025O/KBUQwQKSzl8ci+smFVbBIXHKG3qNTW60J7ZCm9Nh53vBs4eQRAaRBNmwKCxYm5i+XxEWI0cIX9VjLlxCxF3vo6LhGiD3zxCeaYqAJJi6gmNScWYaogQ8oL0EWok7qTpm16BEb9U1hw2OLwqMOezmWHFnbDno+q1vtfCnK/h/2XBowfhiTy49V+QNFDZ7rTDf9Jg7XNK6EwQhKCi0RsxNqFqrLRKGa+hczchLD/vfyHkFiKm7FrL/myq6PYIJdcJjYlHSE1ECHlB+gg1AXfStFsIAez/3P/nsdvg47lw7HvleXgM3Pwa3P4hdLsUdK5mZTo9DLkNHtgA4x6ofv3aZ2HTP/1vlyAIDRMWQaS28UKozGwj2ugKizmdgQmNxXQENNWl7C782VQx11RFRJiOaEONrBSHHUqzpXReRUQICf6nyxilQRkoYsVc6t/jr/p/cPAL5XFYJNz5MQyb6T0ZWquDa/8CV/+peu27hXB6i3/tEgShYfQGjJrGJ0uXVdmqxYOlXMlH9LcQ0oUpYuiC0FjHWCO5rpBWc8kvNZMUa0BT87uqLE/xnkvpvGqIEBL8T/FJ6NBXeWw3w5Fv/XfsI/+DrW8oj3Xh8Iv3IPUSpWot833lvuZjNxoNXPYbuPwR5bnDBh/drbjYBUEIDnojRmxN9Ai5PL3ucT7+Lp8HJTx2QWgspV0EZ4srL9rvzhfySs0kXZgobXIJL/EIqYZUjXlByuebSGEW/HN87Qqy/Z/D4Fubf+zKYvj8oern056DXlfVPqfO9SVjNyuPr/o9DPiZErYDmPQEnNoMp35SvoA+fwhul9loghAUXB6hxlaNlZptRBsu6CodiJ47sSl1QmMp7SIw2xwUlltIiDZ4eaFv5JVWkXRhD6ES1/kkWVo1xCPkBckRaiL1ldEf+Q6slfXv3xi++b0SSwfoOQlGzal7TrtZubkff7dQEUlu75BOD7ctq/4SPfRlda6RIAiBxV0+31iPUM3QmL8HrtYkrkud0Ji7o3R2cfPDY7mmejxCJWeUEH9E+2YfX2gaIoQE/1KrjN7138tarjQ5bA4nf4JMV+m7IRZufFkJdxVmKV+Mbk+QzgD6C664bJWKWHIT2wmueab6+Te/lyGxghAM9EYMGkujy+fLzTaiDa7QmGfOWLyfjaM6NFYjDJbSTvk+OVvc/Iu5PFOV99J5afiqGiKEBP9Ss4z+ur9Vrx/5runHdDrh+xrC5eo/Kldu7pDYdwur19M2w683KY/dgigsUhmoWDNvaMgMSBmhPM7bDzvfabp9giD4ht7YpM7SZWYbMcYaHqGI9tXVof4kNkW5cKsq9izFR4Vj0GvJbqYQqrTYMVXZ6imdPyP5QSojOUKC/4nvodyqTEqFl8PmSpj+a9OOl7UeTro8Sgm9lfL8wizYsLh2SCyqQ3Uu0GXzldygUxsVEfTejOocInfe0NRn4c1pyv7f/1nJYzLENOutC4LQAHoD4c6m9RGqDo0VBG4mV82miq5QlUajobMrYbo5nC2uAKBL+8jaG0xnocOAZh1baB7iEfKCNFT0A8ZYJVQGUJQF5481/hhOJ6yp4Q2a8Lgyl+eVS2HH29XrYZHV53Lj7mtUcrq2YPpuofL6mE4w8EZlvTwftr/VePsEQfAdvZHwJozYqNVHqDwfIv3cTNGNl6aKndtHNNsjdLpIeX2X9hd04C85Kx4hlREh5AVJlvYTvadUP25KeOzYaji9WXncoT8MvkXx8lgrqvcZeRc8+FO1N+hCUscrQqkm1grlOJN+X722eanSrFEQhMCgNxDmbFyytNPpVISQ2yNUUeD/rtJuopNBo61bORbXfCF0pqgSvVZTe/K8zaKMB5KKMVURISQElj7XVD9uSj+hDUuqH4+eC7tXKKEud0K2PgIuT/cugkDZ9uBPSt6QO6laH6EIpA79qm0sOQ0H/tN4G4U2y/r165k+fTopKSloNBpWrlyptkmhTVgEOhzYrb6PrKiyOrA7nLVzhAIVGtPpFU/xBZVjSi+h5lWNnSmqoFM7Y/WYEIDSc4BTPEIqIzlCQmBJGuCqxDirVI5ZKiA88uKvA8g/pAxuBWjXDb57UglxhUXCrI8U4ZI6vmER5Ca+R+28oZqvGz+vWqT99DIMukUqOASfKC8vZ9iwYcydO5dbbrlFbXNCH71yIeK0+S4qSs1WgODkCEH191UNUtoZKSgzU2W1YwzTeXlhw5wtqqRLu3ryg0C6SquMCCEhsJzZqriaQcnPOfED9J3q22u3Lat+3GUM7P1YeWytUETQ8Dsab487kbsmPa6E5CGQuweydyihuNRLGn9soc0xbdo0pk2bprYZLQdXJafT6rsQKqtSwtXRBr1rzlgAQ2PgaqpYfy+hnJIquidGNemwpwor6N/xgmKMEukqHQpIaEwIHIVZ8NZ0RbS48TU8ZimHzA+Ux3ojXPpQ7XDYhYnRTbHNXU6v0cCl86q3bXqleccWBKF+9DU6v/tImdklhIx6qCoBhzWwQqiepopd4xVPzonz5U06pNPpJCu/nJ4domtvKDkNxjipVlUZ8QgJgePURrjQBX50tW+v3fsJmEuUx32uUXr9NDYc5o2aIzn0EUrfo0G3wLd/gPI8OPQVVBQGpmGb0KYxm82YzdUiwGQyqWiNCrg8QppGhMbcHqEYQxiUu6q5ghEaczo9IfKUdhEY9FqO55czsV/jD1lQZqHUbKPHhd4k01kJi4UA4hHygpTP+4HU8TW6PLtybnwto9/6r+rHh1bBygeVXkDNFUFQeySHu+u0PhyG/lxZs1tgz8fNO4cg1MOiRYuIi4vz3Lp27aq2ScHF5dVtjBAqrekR8gxcDXBozFYFlUWeJZ1WQ4/EKI4XlDXpkFkFiiep54VCSErnQwIRQl6Q8nk/EN8DZv8X+l0P/a+vXr/YbK9zu+FcpvK4XSo4XBUmF47KaCo1x4DoI6q7TvecWL2Pe5yHIPiRBQsWUFJS4rmdPn364i9qTbhCY9rGhMZcHqEogy6wc8bceJoq1i6h79UhmmN5TQuNHc8vQ6uB1IQLk6XPSOl8CCChMSGwdB0LU5+BjLHVa/s/h7H3eX/Nrg+rHw+/Ezb8vTqM1dzcIKgeA3Jh12l9BCQNVMJw53ZBzl7oOLj55xMEFwaDAYOheRPMWzQuD3GjhJDZRrhei0HvEkIabWAHlHqaKp6FTkM9y706RLHlRGGTDnkot5TuCVHKe6hJydnqpq6CaogQEgLPqY1KuMnN6c1gM1cnTtbEboM9HymPdeGKYBr687ol783FXT2W+X7tMFnHoYoQAmXbtc/653xCq6SsrIyjR496nmdlZZGZmUl8fDypqakqWhaiuP7mNY0UQtWl8+chMgG0AQxmRCeBVl/HI9SvYyz5pWYKyswkRjdOzB44Z6J/pwsSoi0VUFkoOUIhgITGhMBTK1cIpWLk1Kb69z2+RklYBkgepFSJuEdl+EsE1bGtRpjskgcVAQZK80a71f/nFFoN27ZtY8SIEYwYoQzwTU9PZ8SIESxcuFBly0KUMOVvTevwvaFi7TljAWym6Earg5i6JfSDUmIB2JfduAR3p9PJgXOlDOgYW3uDe4yH5AipjgghIfC4c4U6Da9eO/q/+vfd9UH14+ydSnWXe2J8oGz79Ua46RXlPmV4dT5TRcHF85mENs3EiRNxOp11bsuXL1fbtNDE5RHS2RtRNWa2XiCEApgo7Sauc50S+tT4SKINevY3UgidK6mipNLKgE4XCiGXx0lyhFSn1QuhQ4cOMXz4cM8tIiJC2uCrQdexcOeneKrH6iujryqBg1/WXvNXgnRDuD1OoITDekys3ibVY4LgP1yeYX0jPELlZnvtgauB9ghBvd2ltVoNA1Ni2XO2uFGH2nFKqT4b2jWu9oYSEUKhQqvPEerXrx+ZmZmAEs/v3r07V199tbpGtVWiEqDzSDi7HfL2Qd4BZQSHm32fVfcd0ujAafdfgvTFqNlbSGcEQyyYTYowa8xYEEEQvKPV40RLOFZsdgd63cWvxUurbMR4Bq6eVwoaAk1cZ6Ur/gWM6d6eFVtP43Q60fg4hmfbiSK6JUSSFGOsvaHkrNIGIMxY/wuFoNHqPUI1+fzzz5k8eTJRUU1rkS74gaEzqx/veLv6sdNZu6Pzdc9Xh6sCkRt0ITV7C9mrlKRpAGs5HP468OcXhLaARoNdZ8CABYvdtwn0ZWarCh6hLkoOj6O2jWN7JFBQZuF4ge9l9NtOFjKqWz1VbqYzkh8UIqguhHyZ3pyRkUH37t0xGo2MGzeOLVu2NOlc//73v5k5c+bFdxQCx5AZ1RPgd32gVI8BHPkO8g9W7/fN//m3SuxipI5XhrmCcj9qdvU2CY8Jgt9w6AwYsGKx+SaESqtsRBn0iiipOB+8HCGHtbpvkYtR3dqj12r48WiBT4fJK61i71kTl/aqx+YS6SodKqguhNzTmzMyMurdvmLFCtLT03nyySfZsWMHw4YNY+rUqeTl5Xn2GT58OIMHD65zy87O9uxjMpn46aefuO666wL+noQGiIyHgT9THlcWwcEvlMc/vVR7v2DkBtUkvgc8+JPihXrwJxh8K8R0UrYd+a5Wl1lBEJqOQ2fAqLH4LIRKKq3ERYQpf4NOR3CEUGyKcm+qXUIfbdBzSc8EvtmX49Nh1hzMQ6OBSf3q8WKZpKt0qKB6jtDFpjcvXryY++67jzlz5gCwdOlSvvzyS5YtW8bjjz8O4MkBaoj//Oc/XHPNNRiNDcdj2/wsoGAwcnZ1r6Adb0N8T2UqfU2ClRtUkwsn0w++FTa+rFwZ7v8PjLo7uPYIQivE6fIImRshhNpFhAWnq7Qbt6em5Cx0HlVr09TBHXnq830+9RP6Yvc5xnSLJ6G+/UrOVnexFlRFdY9QQ1gsFrZv386UKVM8a1qtlilTprBxY+O8Bb6Gxdr8LKBg0P1yRfwAHF8Lb9bw0l31RHBzgxpiyG3VjzM/8L6fIAi+ozf6LITsDielVTbFIxRMIRSVqITwL6gcA5g+tBN6rYYVWxsej3LyfDk/HClg5ph6fkOqSsBSKhVjIUJIC6GCggLsdjvJycm11pOTk8nJ8c01CVBSUsKWLVuYOnXqRfdt87OAgoFGAyN+Wf3cWqHcRybC+HmBa57YWDoNr65QOb0JCo6oao4gtAr0ikeoymq/6K6lVUpD07iIsBoDVxMCaZ2CRqOExy7oLg3QLjKcW0Z2ZtmGLEoqvTdcffF/R0iMNnDdkE51N7qPKx6hkCCkhZC/iIuLIzc3l/Dw8IvuazAYiI2N5Z133uGSSy5h8uTJQbCwjXF6i9JZuuslyqyv+F6QMlLxBLk6z4YEGg2MuLP6+c531LNFEFoLYREYNFYqLBcXQm6hoXiECkAbBsa4i7zKT8R1qdcjBDB/cl8qrXae+nwfTqezzvbVB3L5dOdZHrm6DxHhuroHKHZdYMdJxCEUCGkhlJiYiE6nIzc3t9Z6bm4uHTt2DOi5Zfp8gCjMgremw5FvlAnzs/8Lc76CPtfUGnCoOoVZSnPFrpcoc4dACY/JyA1BaBbaMCNGLFT64BEqrnAJociw6tJ5H/v3NJvYut2l3XSMM7LoliF8tvMsD32wk5Pny3E6nZSZbSz/MYsH39vB1QOTuX2Ml3lzJaeV75WYwP6OCb6herJ0Q4SHhzNq1ChWr17NTTfdBIDD4WD16tXMmzcvoOfOyMggIyMDu/3if6xCIzi1sbppoq0Kvvm9MtJi3V+g0zDoHwJVfTWbK+ojoMcEOLZamYF29H/Qz3tyvyAIDaMJM2KgnMqmeISighAWcxPXGU5s8Lr5xuGd0Wg0LPzPXr7YfQ6tBty+odvHpvLk9IFotV5EW8lpRWhp6/EWCUFHdSF0senN6enpzJ49m9GjRzN27FiWLFlCeXm5p4osUKSlpZGWlobJZCIuLkiu2LaAewCrrUq5Ijr0peIdAvh4Dvx6k/r5QTWbK9oqoUN/RQgB7HhHhJAgNANdmBEDFgp98AjVFkJ5wUmUdhPbGUrPgcPuVbD8bFgKk/sn8dOx8+SVVhGm0zK+ZwJd4y/Sib74NLTz4i0Sgo7qQmjbtm1MmjTJ8zw9PR2A2bNns3z5cmbOnEl+fj4LFy4kJyeH4cOHs2rVqjoJ1P5GPEIBIr4H3PYmfHh79ZrDptzbqhQRorYQck+kd3uERs+BfZ8qX4qHvoKcPdBxiLo2CkILRRtubFSOkE6rUYauluVD++6BN9BNXBdlzE9pToP9fqIMeq4e2Mjfo5LTkNC7mQYK/kL1HCFfpjfPmzePkydPYjab2bx5M+PGjQu4XZIjFEA6j4R+11cLIDd6Y/B7B9XHhRPpE/vAJQ+6NjqVcF49CZKCIFwcjT6CCI3NpxwhdzNFjUaj5AhFB9kjBF4TpptF8WlJlA4hVBdCQhskpiNMfcYzidqTjHzbm+p7g9y4J9K77Rl7P7TrpjzOWgdHvlXPNkFoyeiNRGgsPpXPe7pKgytZOinAxtXAXdpe4ucWKjYzlOVAOxFCoYIIIS9kZGQwcOBAxowZo7YprRN3iAxg+ksw4XHFUxSqhBnh6qern3/7hFSQCUJT0BswaKw+JUufL7MQHxUO1iowmyA6iEIoop1Sql900r/H9fQQEiEUKogQ8oKExoJA55GKAOo9GSYtCM1SUncZfWEWDLwJurrCsgWH4cNZYC5T1TxBaHHojRix+hQaK6pwCaFy12zJYCZLg5KTVHTCv8d0e5gkWTpkECEkqEdMx9AVQFBdRr/yQeW+6ARc+xelqRso1W5vToPiU6qaKQgtCr2BcHxLlj5fbiE+MlxJlIbgeoQgMELI3UxRxmuEDCKEvCChMaFOGf2pjYoX685PwOBqqZCzG/4xCr58DEzZ6tkqCC2FsAjCfRyxUVRuIT66pkeoFQihktMQnayE24WQQISQFyQ0JnjK6EG5d1e09ZwA93wLcS7Xtt0CW1+Hl8fCvs/UsVUQWgp6A+FOs085QoUej1AeoAnOnLGatO+h5PT4Mx+w6KSExUIMEUKC4I0Ly+hrVrQl9Yf718Hlj0BYlLJmKYWP7oavHwebRRWTBSHk0RvRY8dsafhvxGyzU2a2VecIRSaALsit79p3V3oJ+bNyrPCY9BAKMUQICUJDXFhGX5PIeJjyFDy8G4bMqF7f/Ar8d770GhKE+nC1zbCZqxrcrahc8cLER7lyhIKdKA3VDRz9GR47f1QZNC2EDCKEvCA5QoLPRCXCLa/D9YtBF66s7Xofdrytrl2CEIq4hJDdWtngboXlisfI4xEKZjNFN3FdQKPznxCqKITKIkgQIRRKiBDyguQICY1Co4Ex98DNS6vXvvotnNulnk2CEIq4hZClYSFUUGYGanqEgpwoDaALU8RQYZZ/jld4XLkXIRRSqD5rTBBaDIVZSuVY6njvHbAH3wonNyrJ03azkjP04EapEBEEN66/BYelosHd8koVIdQhxqB4hDoNDbhp9RLfw38eofPHAHC274HNapVZls1Ep9Oh1+uVESzNQISQIPiCu6eQexDrhcnTNZn6DJzdDtk7lCvATRlwxaPBtVcQQpVwpbjAcZFmpHmlVcRFhGEM07nGa6gQGgOlcuyMnyID549iiR/AudwiKioCMMOsDRIZGUmnTp0IDw9v8jFECAmCL9TXU8ibENIb4MYMWHoZOB3ww2IYfifENHJCtSC0RsKjAdBaK7A7nOi09V/N55nMJMUYlNL1yqLgN1N0kzQAMt8Du63ZVWuOwiyyRv4endVKSkoK4eHhzfZmtFWcTicWi4X8/HyysrLo06cPWm3Tsn1ECHkhIyODjIwMcV0KCqnjldwGW5Vy7+4p5I3kgTBqDmz7F1jK4Ps/wY0vB8dWQQhlXEIoSlNJWZWNuMiwenfLK60iKdbg6iGE0oRQDZIGKL3CCo9Dh77NOpSlrAhHRHu6pqQQGRnpJwPbLhEREYSFhXHy5EksFgtGY9NSECRZ2guSLC3UIr4HzP4v9Lsebn5N8Qjt+w98cAec3lL/ayb9X3UH6p3vSuK0IIAnNBaFGVOV90aFikfIWN2xPTYlGNbVJWmgy6D9zTuO0wklZ0Eb1mTPhVAXf3yW8q8hCL7SdayS//PZ/cr8sY/ugkNfwlvT668qiUqECb91PXEqjRalt5DQ1nEJoUhNFaVVNq+75ZW6QmMmVy6NWkIoKlHJT8o70LzjmLLBWqZUogkhhQghQWgMNXOF3NiqlPX6GHt/dRfZUz/Bno8Da58ghDpaHQ69kWgqKfXiEXI6na7QmFERQmGRYGwXXDtrkjSg+R6h3H3KvQihkEOEkCA0BneuUE30ERDXFTLfr+sZ0ocrE+vdfPcHuEi1jCC0esKjicTs1SN0vtxCldVB53YRiiclNkXp1aUWSQOb7xHK3Qth0aBtmam5d999NxqNBo1GQ1hYGMnJyVx99dUsW7YMh8MBQGFhIQ899BD9+vUjIiKC1NRUfvOb31BSUlLneG+99RZjxowhMjKSmJgYJkyYwBdffBHstwWIEBKExlEzV2jG28ocslkfwXszlHDZy2Ph7Rtr5w31uRr6TlMel56DtYvUsV0QQoXwaKI0VZSa6/cInSlSvK5d2kcoHqHYzsG0ri5JA5QZYdaGx4I0SO4+SGzZM8auvfZazp07x4kTJ/j666+ZNGkS8+fP54YbbsBms5GdnU12djbPP/88e/fuZfny5axatYp77rmn1nEee+wx7r//fmbOnMnu3bvZsmULl19+OTfeeCMvvxz8opKWKU0FQU26joXb369+nvl+dbjMYYHja+HUJvj1puoS+2ufhWPfK00WN76s5Bxc/nCwLReEkEBriCZG490jdNYlhLq2j1Q8QmrP5koaqLTCyD8IKcObdozsndDvZr+aFWwMBgMdO3YEoHPnzowcOZJLLrmEyZMns3z5cu69914++eQTz/69evXimWee4c4778Rms6HX69m0aRMvvPACL730Eg899JBn32eeeYaqqirS09O58cYb6dq1a9Del3iEvCCzxgSfqS9cdmHeUHxPZUCrm/89Cd8/U10RIwhtifBo2um9C6EzRRVEG/TERuirQ2NqkjxYCWmd3da011cUKh6ljoP9a1cIcNVVVzFs2DA+/fTTereXlJQQGxuLXq/4XT744AOio6O5//776+z76KOPYrVaa4mpYCAeIS+kpaWRlpaGyWQiLi5ObXOEUMYdLlvzDJz4SfEK6SPq9hoa/2vFc7T6j8rz9X9VbnFdlWZxxnbQcQgMuU354pVGa0JrJTyKWG2F1/L5M0WVdGkfgcbpCA0hFB6p/E2e3gpj7m3868/uUO6TBkFx3d50lRY7x/KDnzvYq0M0EeG6Zh+nf//+7N69u856QUEBf/rTn/jVr37lWTt8+DC9evWqtxN0SkoKsbGxHD58uNk2NQYRQoLgD7qOhbv+U3seWXk+fPN7JQTWdayy3xWPAhpY/XT1a0tOKzeAY6vhxyXQYQBMXgj9r/N+ztIc2PYmjJ4DMR0D9MZ8IFTsEFoO4VHEaIspLLPUu/lkYQVd2kcqzRSddvVzhED5Gz76v6a99vQmiEyAdl2h+ESdzcfyy7jhHxuaZ18T+OKhyxncufkX+k6ns06HbJPJxPXXX8/AgQN56qmn6uwfSogQEgR/Et9DuRVmKf2FbFWKuKmZL3RFOvS4Eg59rYimnL1gvqCqIv8AfHg79L9BqTprV0+8/OwOWPcX6DSsYcHUWBorbAJlh9B6CY8mVltFvmvC/IUcyytj+rAU9Zsp1qTbZbDlNSg+Xf/fY0McXwfdr/Dq5e3VIZovHrrcD0Y2jl4dov1ynAMHDtCjR/XIodLSUq699lpiYmL47LPPCAurbhnQt29fNmzYgMViqeMVys7OxmQy0bdv8zp4NxYRQoIQCE5tVEQQVOcL1ZxN1mW0cnPjsENZLhz8EnZ9WJ2LcPAL5Sp0fBpc/ggYYpT1wiz4eI7y+OM5tYVWc3ELm3apUHyqYUEUSDuE1otBqRrLM9UVQuVmG2eLK+mTFA0mV4gkFDxCPSeARgvH18DIu3x/XZVJGcI8/Havu0SE6/zimVGD77//nj179vDII48Aiido6tSpGAwGPv/88zpjL37xi1/w0ksv8eqrr9ZKlgZ4/vnnCQsL49Zbbw2a/SBCSBACQ2Nnk2l1ypdseQHMfBdO/girFkB5nnKMH16ALW9At0uh6xilp0lNofXlo0qJr1YPYRFKA7qwCOXceoNybPfVaE23tEYDaKofl+XDNwuU558/pIQlItpBQh9lu+c4GuX+6Pe17djxNvS5psbncInkOgl1McQQ5awgr7SuEHLnyvROioaz2cr/4cj4YFtYl4j20HkUHP6mcULoyLfK31GvyYGzLUiYzWZycnKw2+3k5uayatUqFi1axA033MBdd92FyWTimmuuoaKignfffReTyYTJZAKgQ4cO6HQ6xo8fz/z58/ntb3+LxWLhpptuwmq18u677/Liiy+yZMmSoFaMQRsRQn//+9954403cDqdTJkyhRdffFEm/gqBxZ1AvWEJ9JlaN1eoPmqGmIbcBr2nwPq/weZXwWFVwmeHv1ZuF3JstXLzJ05XUueqx31/zYbFys3NwkLQND8ZU2hlRLQn4v+3d+9hUZxn/8C/nAU5i5wEBbEaiQp4QG3fSyBikBAbrdqYWk8YNCn2F4Oh1VTENrEmalKiwZDXqJjkSrTmLcQYY1UENVFR0U1E0ZiK9YBAFJGDclrm98c4yy7scpBld2G+n+vaC5l5dvae2WW897mfeUZZgbvVtWhQNsLSoukC5islYiIU4G4PXLxh/MkU1QU+B2S9Ifby9HJs33Mufgl4hwAuA4CaTsxDZAL2798PLy8vWFpawsXFBUFBQdi4cSPmzZsHc3NznD17Frm5uQCAQYM050wqLCyEn58fACAlJQUjRozA5s2bsXLlSlhYWGDkyJHIzMzElClTDL1bPT8R+vnnn/H+++/jwoULsLKywoQJE3Dy5EmMH9/GN3SizpLuTbZ5nPaxQup0lZii1gBjFgI5b4vfLB+WGXYfiLqCrQusGqphITSgrLpOvJXGI9/fLMdAt96wt7EU/y5cBxox0GaenAYcSAIuZACj5rXdvvqO2IP01Mquj62LpaenIz09vdU24eHh7R4IHRsbi9jYWD1E1nk9PhECgIaGBtQ8ysTr6+vh7u5u5IhINnSNFbpxSuwtknqJWhtT5DoQ+M2HYknr58viVP2A+K302rfA0Cnit2ahEWhsEJ9fVw3UPxQncGyoFdcJjY+CelTWEgSxl+nGKfEqtdKLYiz3b4iTRKobEiPOgdKoBCA8Kq89+llXBdz+Qbz039qu2QEwkW/yZFpsXQAATqgWb66qlggpbpQjuL+z+EvZVcDP8IOIdXLyAYZEAyc/AELmAG3d+fzMdvFvLeT3homPHovRE6GjR49i/fr1yMvLw+3bt5GRkYGpU6dqtElNTcX69etRXFyMoKAgbNq0CaGhrZQY1PTt2xevvfYa+vfvD0tLS7z00ksICDDyLKUkH83HCln1Fm/Bce07sdwl9RK1Z0yRmRng/oT4kIxpxzeq1q4Cu7QPyHlL7Lq/mg2ELgKGPCMmQhbWgLJOjCdqjWZPFi+Z19CZc5QsPUqEnM2qcKv8oWqgcE29EheLKjBzlA/Q2AjcK+zYeBxD+NUrwLYo4PvPWk9wqkqB4xuBUfNNY4wT6WT0maWrq6sRFBSE1NRUret37dqFhIQEJCcn4+zZswgKCkJUVBRKS0tVbYKDgzFs2LAWj6KiIty7dw979+7FtWvXcOvWLRw/fhxHjx411O6R3Knfm2za/wIZi8RbcDQ+mkhOvfdHajfvK/1eeSWNPZImdZOol+OOvSP+/GKBmPyELQeeTRGXzdguDrzOXiv2/GSvBX7K0r5NGWrPOYqaeZQI+faqxaXblarFJ67eRUOjgFD/PkBVsfj3YUqlMUC8AGD4b8WLGUovaW/TUAv834vil4mwPxs2PuowoydC0dHRePPNNzFtmvZ7sLz77ruIi4vDggULEBgYiLS0NNjZ2WHbtm2qNgqFAvn5+S0e3t7eOHToEAYNGgRXV1fY2toiJiYGJ0+e1BlPbW2taqS7+oh3oscm3Zusvrqp/CVR7/2R2rU2oFqbymIxOaksbrnsxinNsUdlhU1t1MtxkoYa4O5PQMQKYNBEMSHqN7IpmbqYKf786v+13Ka2OGSgPecoasZW7CF50kWJgttN59jDBaXwcbHFYA978XMImOZ0DDHviDPCb4sCTm8Fqu+KyxtqxXJ1eox4v8GZ29kb1A0YPRFqTV1dHfLy8hAZGalaZm5ujsjISJw4caKVZzbx9fXF8ePHUVNTA6VSiZycHAwZMkRn+7Vr18LJyUn1MPRlfNSDqd+TzNwKGBiun96fymIxOaksbkpGpB6b/P9rOfZIatNnUMt7pKknZg6eYkJU/7Blz1Fjg+Y2dcXRw5MifZyjZMnWGQDwC8cGXCoWE6HaBiW+yS/GpEAP8are0kvi34mp9QgB4hVjC74Wvyx8nQCsHwj8vR/wd28xCaqpAObvFSdOJZNn9DFCrblz5w6USiU8PDw0lnt4eODSJR1dks2MGzcOzzzzDEJCQmBubo6JEyfi17/+tc72K1asQEJCgur3iooKJkOkH+qX1EuDpKWEoTNjbaTZd6WfR94Su+QB4My2prE+FjZAkQKw9xDbDIkWy147XxBv/XHsHfH35omZtp4jiXripC2OIdE9egxRR89RtbW1qK1tmjtHtj3OljaAVW8E2Nfj2qUH+O/dauQWluFOVS1mjx0gtvm5AHAbDFhYtb4tY7F1AWZsA6LWiuPrqu+IJWSvIMB7ZNsDqclkyOKdWrNmDQoKCnDhwgVs3Lix1TmEbGxs4OjoiE8++QTjxo3DxIndfxIsMiHNy1/axu90pDel+WX3RefEfyvrmn6OfjSgemIycOrDpnEN6jez7PtoALa2Wxmo92RJzB99h5ISJ11xSEkRAWCPswZbFzzh1AAnWyv8fV8B1u4rQMxwL3EiRUD8nKpfGGCqHDyAoFnAL5eIU134jGYS1M2Y9Lvl5uYGCwsLlJSUaCwvKSmBp2fXfsuMj4/HxYsXcfr06S59HZKx5smDNNZG1+BmoGWS1Pyy+32vaba37AX4h2kuO7Sq6TWlgdF9nxB/auu9cfUXEx7g0U1jAUT+TfwpJU664mg+LqmH6eg5asWKFbh//77qcePGDUOFanrsXGFVcxevRQ1BVkEp+rnY4o2pw8R1giBO59B3qHFjJFkw6UTI2toao0aNQlZW04y5jY2NyMrK6vIJEVNTUxEYGIgxY8Z06euQjGmbO0hXciRRH4cDNBt3ZNk0dkcyY7s42Dl0cdMd79XH90gDo71GiD91lbGa9xzZ99VcrysO9TFEPVBHz1FSj7P6Q7acfICKW5gzbgDOr47Cnvj/gWvvRyXde4VATTngHWzMCEkmjJ4IVVVVQaFQQKFQABCn4VYoFLh+/ToAICEhAVu2bMGOHTtQUFCAl19+GdXV1ViwYEGXxsUeIepy6smDNNameXL0437NHqDm43DUe2ue2dC0PWmMkKO3mNx4B4uTK6przz3QdLHro9mDpCuOzrxGN2Gsc1S35+QD3L8JQLzpqLm52pAFqTe03ygjBEZyY/RE6MyZMwgJCUFISAgA8aQSEhKCVavE7vvnn38eGzZswKpVqxAcHAyFQoH9+/e3GJyob+wRoi6nnjxIY22aJ0eWvcQeoJ+ygH1/AnbPF9ep9xZJvTXeIU3bm7ha87Wa99iov2Z7OHhqltA8nmzZg6Qtjo68RjdlrHNUt6eWCLVw8wzg4s9Lz03Mzz//jJdffhn9+/eHjY0NPD09ERUVhe+++w4A4OfnBzMzM+zcubPFc5988kmYmZmpbtMxa9YsTJ48WaPN/v37YWZmhtWrV2ssX716Nfr3798l+wSYwFVj7bk3yZIlS7BkyRIDRSSKj49HfHw8Kioq4OTkZNDXJhmRkgfpp5Qc7XwBmPxW0w1P9y5tGgANaE7EKCUp6kmJ+xPae2x2viCO7znwuvaB0bpIl9IDYhmto/vVwxnjHNXtOfYDaiuAmvtAr2bn2GvfihMXkkmZPn066urqsGPHDgwcOBAlJSXIysrC3bt3VW18fX2xfft2zJo1S7Xs5MmTKC4uRu/evVXLIiIi8Nprr6GhoQGWlmIqkp2dDV9fX+Tk5Gi8bnZ2NiIiIrpsv4zeI0Qka9qSGCl5qCppKpNJSZDUm6Ntvh/1bdj10d1j03x8j75o2xciXZweXTF3/5bm8soSoOQ8EPCU4WMincrLy3Hs2DG8/fbbiIiIwIABAxAaGooVK1ZoTEkze/ZsHDlyRONCgG3btmH27NmqhAcQE6GqqiqcOXNGtSwnJwfLly9Hbm6u6v6gNTU1yM3NZSJkDCyNkUFoS2KkhCLgqZaXrUt0lZzak4w0H9+jL+r7wqSI2uL8qNRRdlVz+U+HxJ8Dww0aDrXO3t4e9vb2yMzM1JgLqzkPDw9ERUVhx44dAIAHDx5g165dLe40P3jwYHh7eyM7OxsAUFlZibNnz2LmzJnw8/NTTUh6/Phx1NbWdmkiZPTSmKliaYyMRr0MJZWzJNLVWLpKTurP1bZOGt8zqIvnx2otDiJA/IzYewBFZ4GhzzYt/2EXMOB/AHt348VmaHUPgDs/Gv513QYD1nbtamppaYn09HTExcUhLS0NI0eORFhYGGbNmoURIzTL5bGxsVi2bBn+8pe/4IsvvkBAQACCg4NbbDMiIgI5OTlYsWIFjh07hsGDB6Nv376YMGECcnJyVOv9/f0xYMAAfeyx9n3rsi0TUedJCY9qdmhrzbFCHcHkhEyJmRnQbzRwU+3K3Lv/AQqPAr/eZLy4jOHOj8D/hrXdTt8WHenQFAXTp09HTEwMjh07hpMnT+Kbb77BunXr8NFHH2H+/PmqdjExMVi8eDGOHj2Kbdu2tegNkoSHh2Pp0qWor69HTk4OwsPDAQBhYWH48MMPAUCVEHUlJkI6pKamIjU1FUql0tihEIlXgR14XbwjfPl1lpyoZ/AZLd7apf6heHuKnLXiZ3v4DGNHZlhug8WkxBiv20G9evXCpEmTMGnSJCQlJeHFF19EcnKyRiJkaWmJOXPmIDk5Gbm5ucjIyNC6rYiICFRXV+P06dPIzs5GYmIiADERio2NRVlZGXJzc7F48eLH2r32YiKkA0tjZBKkcpZ0qwGPJ4GQ2caNiUhfnpwKHH4DOLUFsO4NnN8NPJcqJkVyYm3XbSePDAwMRGZmZovlsbGx2LBhA55//nm4uLhofW5AQAB8fX2xZ88eKBQKhIWJvWL9+vVDv3798M4776Curo49QkSyJpWzihTGjoRI/1wHAiFzgINJ4u9jXgSCmeibort372LmzJmIjY3FiBEj4ODggDNnzmDdunV47rnnWrQfOnQo7ty5Azu71scgRUREYPPmzRg0aJDG3FthYWHYtGmTalB1V2IiRNQd8Cos6qmeTQGGRAM2jsCAX4pjh8jk2NvbY+zYsfjHP/6B//znP6ivr4evry/i4uLw+uuva31Onz592txuREQEPv74Y9X4IElYWBi2b9+O3/3ud/oIv1VmQluzGcqU+hihH3/8Effv35f3fYGI9EwqO8v5b4vHQF5qampQWFgIf39/9OqlY2oM6pDWjml7/744j5AOvNcYERFRz8dEiIiIiGSLiRARERHJFhMhIiIiki0mQjrwXmNEREQ9HxMhHThYmoiIugIv1tYffRxLJkJEREQGYGVlBUC8Izvph3QspWP7ODihIhERkQFYWFjA2dkZpaWlAAA7OzuYcQLJxyIIAh48eIDS0lI4OzvDwsLisbfFRIiIiMhAPD3F2eGlZIg6x9nZWXVMHxcTISIiIgMxMzODl5cX3N3dUV9fb+xwujUrK6tO9QRJmAgREREZmIWFhV7+E6fO42BpHXj5PBERUc/HREgHXj5PRETU8zERIiIiItniGKE2SJM1VVRUGDkSop5F+puS8+RyPL8QdZ32nmOYCLWhsrISAODr62vkSIh6psrKSjg5ORk7DKPg+YWo67V1jjET5Px1rB0aGxtRVFQEBwcHVFZWwtfXFzdu3ICjo6NR4xozZozexy897jY78rz2tG2rja717V1eUVFhMu8joP/3sru8j6NHj8bhw4fh7e0Nc3N5VunVzy+tTaxnap9ZU8Jj0zo5Hx9BEFBZWdnmOYY9Qm0wNzeHj48PAKhOVI6Ojkb/QFlYWOg9hsfdZkee1562bbXRtb6jy03hfQT0/152l/fR0tJS9bclV+rnl/Ywlc+sKeKxaZ1cj097epvl+TWsB4iPjzeZbXbkee1p21YbXes7utxU6Du+7v4+EhEZEktjHVBRUQEnJyfcv39flpl1T8H3kbobfmZ147FpHY9P29gj1AE2NjZITk6GjY2NsUOhTuD7SN0NP7O68di0jsenbewRIiIiItlijxARERHJFhMhIiIiki0mQkRERCRbTISIiExYamoq/Pz80KtXL4wdOxanTp0ydkgGcfToUUyZMgXe3t4wMzNDZmamxnpBELBq1Sp4eXnB1tYWkZGRuHLlikabsrIyzJ49G46OjnB2dsbChQtRVVVlwL3oGmvXrsWYMWPg4OAAd3d3TJ06FZcvX9ZoU1NTg/j4ePTp0wf29vaYPn06SkpKNNpcv34dMTExsLOzg7u7OxITE9HQ0GDIXTEJTIS6wI0bNxAeHo7AwECMGDECu3fvNnZI1AnTpk2Di4sLZsyYYexQSGZ27dqFhIQEJCcn4+zZswgKCkJUVBRKS0uNHVqXq66uRlBQEFJTU7WuX7duHTZu3Ii0tDTk5uaid+/eiIqKQk1NjarN7NmzceHCBRw8eBB79+7F0aNHsWjRIkPtQpc5cuQI4uPjcfLkSRw8eBD19fV4+umnUV1drWrz6quv4quvvsLu3btx5MgRFBUV4Te/+Y1qvVKpRExMDOrq6nD8+HHs2LED6enpWLVqlTF2ybgE0ruioiLh3LlzgiAIwu3btwVvb2+hqqrKuEHRY8vOzhb27NkjTJ8+3dihkMyEhoYK8fHxqt+VSqXg7e0trF271ohRGR4AISMjQ/V7Y2Oj4OnpKaxfv161rLy8XLCxsRE+//xzQRAE4eLFiwIA4fTp06o233zzjWBmZibcunXLYLEbQmlpqQBAOHLkiCAI4rGwsrISdu/erWpTUFAgABBOnDghCIIg7Nu3TzA3NxeKi4tVbT744APB0dFRqK2tNewOGBl7hLqAl5cXgoODAQCenp5wc3NDWVmZcYOixxYeHg4HBwdjh0EyU1dXh7y8PERGRqqWmZubIzIyEidOnDBiZMZXWFiI4uJijWPj5OSEsWPHqo7NiRMn4OzsjNGjR6vaREZGwtzcHLm5uQaPuSvdv38fAODq6goAyMvLQ319vcbxeeKJJ9C/f3+N4zN8+HB4eHio2kRFRaGiogIXLlwwYPTGJ8tEqK3aM6C/unxeXh6USiXvLt1FDPleEhnSnTt3oFQqNf6jAgAPDw8UFxcbKSrTIO1/a8emuLgY7u7uGustLS3h6urao45fY2Mjli5dil/96lcYNmwYAHHfra2t4ezsrNG2+fHRdvykdXIiy5uuSrXn2NhYjZqpRKrLp6WlYezYsUhJSUFUVBQuX76s+sMKDg7WOqjswIED8Pb2BiAO1Js7dy62bNnStTskY4Z6L4mITFF8fDzy8/Px7bffGjuUbkuWiVB0dDSio6N1rn/33XcRFxeHBQsWAADS0tLw9ddfY9u2bVi+fDkAQKFQtPoatbW1mDp1KpYvX45f/vKXeoudNBnivSQyBjc3N1hYWLS40qekpASenp5Giso0SPtfUlICLy8v1fKSkhKNYQnNB5U3NDSgrKysxxy/JUuWqAaB+/j4qJZ7enqirq4O5eXlGr1C6p8dT0/PFr3j0metpxyf9pJlaaw1+qjLC4KA+fPn46mnnsKcOXO6KlRqA8dYUHdmbW2NUaNGISsrS7WssbERWVlZGD9+vBEjMz5/f394enpqHJuKigrk5uaqjs348eNRXl6OvLw8VZvDhw+jsbERY8eONXjM+iQIApYsWYKMjAwcPnwY/v7+GutHjRoFKysrjeNz+fJlXL9+XeP4nD9/XiNZPHjwIBwdHREYGGiYHTERsuwRak1rdflLly61axvfffcddu3ahREjRqjGrHzyyScYPny4vsOlVujjvQTEAZbff/89qqur4ePjg927d8v+PyIyjISEBMybNw+jR49GaGgoUlJSUF1drerh7Mmqqqrw008/qX4vLCyEQqGAq6sr+vfvj6VLl+LNN9/EL37xC/j7+yMpKQne3t6YOnUqAGDo0KGYPHky4uLikJaWhvr6eixZsgSzZs3q9iXv+Ph4fPbZZ/jyyy/h4OCgGtPj5OQEW1tbODk5YeHChUhISICrqyscHR3xxz/+EePHj8e4ceMAAE8//TQCAwMxZ84crFu3DsXFxVi5ciXi4+Pld4NWY1+2ZmxodlnmrVu3BADC8ePHNdolJiYKoaGhBo6OOoLvJfVEmzZtEvr37y9YW1sLoaGhwsmTJ40dkkFkZ2cLAFo85s2bJwiCeAl9UlKS4OHhIdjY2AgTJ04ULl++rLGNu3fvCi+88IJgb28vODo6CgsWLBAqKyuNsDf6pe24ABC2b9+uavPw4UPhD3/4g+Di4iLY2dkJ06ZNE27fvq2xnWvXrgnR0dGCra2t4ObmJixbtkyor6838N4Yn+zvPm9mZoaMjAzVt4i6ujrY2dnhiy++UC0DgHnz5qG8vBxffvmlcQKlNvG9JCKijuIYoWZYl+85+F4SEVFbZDlGqK3as5zr8t0N30siIuoMWZbGcnJyEBER0WL5vHnzkJ6eDgB4//33sX79ehQXFyM4OBgbN27s9lca9ER8L4mIqDNkmQgRERERARwjRERERDLGRIiIiIhki4kQERERyRYTISIiIpItJkJEREQkW0yEiIiISLaYCBERET2mvXv3wt/fH6Ghobhy5Yqxw6HHwHmEiIiIHtOQIUOQmpqKCxcu4MSJE9i5c6exQ6IOYo8QERGRDnfv3oW7uzuuXbumdX2fPn0waNAg+Pn5wdraWrV81qxZeOeddwwUJXUGe4SIiEh29u3bh5iYGJ3rf/vb32LXrl1ISEhAZWUltmzZorXdli1b8NJLL8HDwwP5+flwdXUFAOTn52PChAkoLCyEk5NTl+wD6Qd7hKhH6Wy9ftq0aXBxccGMGTO6IDoiMhURERG4ffu2xuPmzZuYNGkS+vTpg9dffx0PHjzA1q1bsXDhQq3baGhowHvvvYc//elPqKqqgouLi2rdsGHDEBAQgE8//dRQu0SPiYkQ9SjLli3Dli1bMHv2bCQlJXX4+a+88go+/vjjLoiMiEyJra0tPD09VY++ffti2bJlOHv2LLKyshAUFIR9+/bBxsYG48aN07qNtLQ0DBw4EPHx8aisrMTVq1c11k+ZMoVjhroBJkLU7bRWs9dVr2+v8PBwODg4aF3Hmj9Rz6RUKvH73/8ehw4dUiVBAHDs2DGMGjVK63PKysrwxhtv4O2334aPjw+cnJygUCg02oSGhuLUqVOora3t6l2gTmAiREahUCgwa9YseHp6wtraGgEBAfjb3/6GhoaGNp+7Zs0aPPfcc/Dz82uxbsGCBQgICMDLL7+MlJQUvca8cuVKrFmzBvfv39frdonIeKQk6MCBAzh06JAqCQKA//73v/D29tb6vOTkZEybNg1Dhw4FAAQGBuL777/XaOPt7Y26ujoUFxd33Q5QpzERIoPbtm0bQkND4eHhgb1796KgoABJSUlISUnRWYuXtFazb61eLwkODsawYcNaPIqKitqMmzV/op5FqVRizpw5OHDgALKyshAcHKyx/uHDh+jVq1eL5128eBGffvopVq9erVo2bNiwFj1Ctra2AMTzFpkuS2MHQPKSk5ODuLg4bN++HXPnzlUtDwgIQH19PRYtWoSkpCQMGjRI6/Nbq9mr1+vfeustXL16FQEBARptmp+oOkqq+cfHx3dqO0RkXFIS9O9//xuHDh1qkQQBgJubG+7du9di+auvvory8nL4+PioljU2NsLX11ejXVlZGQCgb9+++g2e9Io9QmRQr7zyCqKjozWSIElYWBgAtOheVqerZt+eer0+sOZP1P0plUrMnTtXlQSFhIRobRcSEoKLFy9qLNu7dy/y8vJw7tw5KBQK1WPr1q24fv26RuKUn58PHx8fuLm5den+UOcwESKDOXfuHH744QedvSkPHz4EAFha6u6o1FWzb0+9vj0iIyMxc+ZM7Nu3Dz4+Pjhx4oTGetb8ibq3xsZGzJ07F5mZmfj000/h5eWF4uJijYdSqQQAREVF4cKFC6rkpr6+HsuWLUNiYmKLMvvEiRMBaH6RO3bsGJ5++mnD7yR1CEtjZDBSD422LmgAOHv2LABgxIgROrehrWYv1esLCgpUy7TV69vj0KFDra5nzZ+oezt9+jQ+++wzAMAzzzzTYr2ZmRnKy8vh6OiI4cOHY+TIkfjnP/+JxYsXY9OmTSgvL8eSJUtaPM/X1xd2dnZQKBQIDw9HTU0NMjMzsX///i7fJ+ocJkJkMHV1dQCgdfAhAGzevBkTJkyAv7+/zm1oq9m3t16vD6z5E3VvY8eORUduqLBq1SokJiYiLi4OCQkJSEhI0NrOzMwM1dXVqt+3b9+O0NBQnXMQkelgIkQGI12WeuTIEUydOlVj3YYNG1BQUIBvv/0WgDheSLpM/fz588jNzcXo0aMREhKicdWWer1evaR2+vRpxMbG4t69e1qvHntcrPkTyUtMTAyuXLmCW7dudejLlZWVFTZt2tSFkZG+8F5jZFCTJ0/G+fPnkZKSgtGjR6OkpAQfffQRdu7ciYyMDEyaNEmjfXJyMsrLy/Hee+8BEJOikSNHorS0FPb29hg2bBhiY2Px5z//WeN5169fx4ABA5CdnY3w8HC9xT9//nxYWFhg69atetsmEREZD3uEyKD+9a9/4a9//SsSExNx8+ZNKJVKTJ48GT/++GOLQdApKSm4du0a0tPTVcvUa/bV1dXtrtfrA2v+REQ9D3uEyKhefPFFZGdnIy8vD87Ozqrl6enp2LNnD3bv3g0LCwuN53z99ddITExEfn4+zM0Nd+HjBx98gIyMDBw4cMBgr0lERF2Ll8+TUaWmpiI2Nhbnzp1TLcvIyMDOnTvx+eeft0iCALFmv2jRIty6dcuQobLmT0TUA7FHiEyOi4sL+vbtCzs7OwDAm2++iWeffdbIURERUU/ERIiIiIhki6UxIiIiki0mQkRERCRbTISIiIhItpgIERERkWwxESIiIiLZYiJEREREssVEiIiIiGSLiRARERHJFhMhIiIiki0mQkRERCRbTISIiIhItpgIERERkWz9fxzHXqWL2OInAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "problem, results = RAT.run(problem, controls)\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.py b/RATapi/examples/non_polarised/DSPC_standard_layers.py index 2beb7ce0..f583a1b3 100644 --- a/RATapi/examples/non_polarised/DSPC_standard_layers.py +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.py @@ -1,4 +1,3 @@ -import os import pathlib import numpy as np @@ -310,12 +309,12 @@ def DSPC_standard_layers(): problem.backgrounds.append(name="SMW Background", type="constant", value_1="Background parameter SMW") # Now add the data - data_path = os.path.join(pathlib.Path(__file__).parents[1].resolve(), "data") + data_path = pathlib.Path(__file__).parents[1] / "data" - d2o_dat = np.loadtxt(os.path.join(data_path, "DSPC_D2O.dat"), delimiter=",") + d2o_dat = np.loadtxt(data_path / "DSPC_D2O.dat", delimiter=",") problem.data.append(name="dspc_bil_D2O", data=d2o_dat) - smw_dat = np.loadtxt(os.path.join(data_path, "DSPC_SMW.dat"), delimiter=",") + smw_dat = np.loadtxt(data_path / "DSPC_SMW.dat", delimiter=",") problem.data.append(name="dspc_bil_smw", data=smw_dat) # Set the model