From b8577bbbbe4e59c8f461a2ea4bbc42410614ac7e Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Wed, 24 Jul 2024 17:37:25 +0100 Subject: [PATCH 1/7] Adds jupyter notebooks for non polarised examples --- .../DSPC_custom_layers-checkpoint.ipynb | 861 ++++++++++++++++++ .../DSPC_custom_xy-checkpoint.ipynb | 782 ++++++++++++++++ .../DSPC_standard_layers-checkpoint.ipynb | 523 +++++++++++ .../examples/non_polarised/DSPC_custom_XY.py | 2 +- .../non_polarised/DSPC_custom_layers.ipynb | 861 ++++++++++++++++++ .../non_polarised/DSPC_custom_xy.ipynb | 307 +++++++ .../non_polarised/DSPC_standard_layers.ipynb | 523 +++++++++++ 7 files changed, 3858 insertions(+), 1 deletion(-) create mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb create mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb create mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb create mode 100644 RATapi/examples/non_polarised/DSPC_custom_layers.ipynb create mode 100644 RATapi/examples/non_polarised/DSPC_custom_xy.ipynb create mode 100644 RATapi/examples/non_polarised/DSPC_standard_layers.ipynb diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb new file mode 100644 index 00000000..08664421 --- /dev/null +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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 1 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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", + "problem.parameters.append(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True)\n", + "problem.parameters.append(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True)\n", + "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "\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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + " D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + " SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + " H2O_data = np.loadtxt(os.path.join(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 fourch 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 | uniform | 0.0 | inf |\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", + "| resampleParams | [0.9, 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 0.002 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/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb new file mode 100644 index 00000000..f7e1f5ae --- /dev/null +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb @@ -0,0 +1,782 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "956a341a-2a40-466c-b5c4-f8ea334ee81c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=15.0, max=30.0, fit=True)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.1, value=0.2, max=0.4, fit=True)\n", + "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"Lipid APM\", min=40.0, value=50.0, max=90.0, fit=True)\n", + "problem.parameters.append(name=\"Lipid Coverage\", min=0.9, value=1.0, max=1.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=3.0, value=5.0, max=8.0, fit=True)\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 SLD's. 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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + "H2O_data = np.loadtxt(os.path.join(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": [ + { + "data": { + "text/html": [ + "
import math\n",
+       "\n",
+       "import numpy as np\n",
+       "\n",
+       "\n",
+       "def custom_XY_DSPC(params, bulk_in, bulk_out, contrast):\n",
+       "    """This function makes a model of a supported DSPC bilayer using volume restricted distribution functions."""\n",
+       "    # Split up the parameters\n",
+       "    subRough = params[0]\n",
+       "    oxideThick = params[1]\n",
+       "    oxideHydration = params[2]\n",
+       "    waterThick = params[3]\n",
+       "    lipidAPM = params[4]\n",
+       "    lipidCoverage = params[5]\n",
+       "    bilayerRough = params[6]\n",
+       "\n",
+       "    # We are going to need our Neutron scattering cross-sections, plus the component volumes\n",
+       "    # (the volumes are taken from Armen et al as usual).\n",
+       "    # Define these first\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",
+       "    heads = 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",
+       "    # Start making our sections. For each we are using a roughened Heaviside to describe our groups.\n",
+       "    # We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),\n",
+       "    # which we will correct later for hydration.\n",
+       "\n",
+       "    # Make an array of z values for our model\n",
+       "    z = np.arange(0, 141)\n",
+       "\n",
+       "    # Make our Silicon substrate\n",
+       "    vfSilicon, siSurf = layer(z, -25, 50, 1, subRough, subRough)\n",
+       "\n",
+       "    # Add the Oxide\n",
+       "    vfOxide, oxSurface = layer(z, siSurf, oxideThick, 1, subRough, subRough)\n",
+       "\n",
+       "    # We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,\n",
+       "    # so we start the bilayer stack an appropriate distance away\n",
+       "    watSurface = oxSurface + waterThick\n",
+       "\n",
+       "    # Now make the first lipid head group\n",
+       "    # Work out the thickness\n",
+       "    headThick = vHead / lipidAPM\n",
+       "\n",
+       "    # ... and make a box for the volume fraction (1 for now, we correct for coverage later)\n",
+       "    vfHeadL, headLSurface = layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough)\n",
+       "\n",
+       "    # ... also do the same for the tails\n",
+       "    # We'll make both together, so the thickness will be twice the volume\n",
+       "    tailsThick = (2 * vTail) / lipidAPM\n",
+       "    vfTails, tailsSurf = layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough)\n",
+       "\n",
+       "    # Finally the upper head ...\n",
+       "    vfHeadR, headSurface = layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough)\n",
+       "\n",
+       "    # Making the model\n",
+       "    # We've created the volume fraction profiles corresponding to each of the groups.\n",
+       "    # We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions\n",
+       "\n",
+       "    # 1. Oxide ...\n",
+       "    vfOxide = vfOxide * (1 - oxideHydration)\n",
+       "\n",
+       "    # 2. Lipid ...\n",
+       "    # Scale both the heads and tails according to overall coverage\n",
+       "    vfTails = vfTails * lipidCoverage\n",
+       "    vfHeadL = vfHeadL * lipidCoverage\n",
+       "    vfHeadR = vfHeadR * lipidCoverage\n",
+       "\n",
+       "    # Some extra work to deal with head hydration, which we take to be an additional 30% always\n",
+       "    vfHeadL = vfHeadL * 0.7\n",
+       "    vfHeadR = vfHeadR * 0.7\n",
+       "\n",
+       "    # Make a total Volume Fraction across the whole interface\n",
+       "    vfTot = vfSilicon + vfOxide + vfHeadL + vfTails + vfHeadR\n",
+       "\n",
+       "    # All the volume that's left, we will fill with water\n",
+       "    vfWat = 1 - vfTot\n",
+       "\n",
+       "    # Now convert all the Volume Fractions to SLDs\n",
+       "    sld_Value_Tails = tails / vTail\n",
+       "    sld_Value_Head = heads / vHead\n",
+       "\n",
+       "    sldSilicon = vfSilicon * 2.073e-6\n",
+       "    sldOxide = vfOxide * 3.41e-6\n",
+       "\n",
+       "    sldHeadL = vfHeadL * sld_Value_Head\n",
+       "    sldHeadR = vfHeadR * sld_Value_Head\n",
+       "    sldTails = vfTails * sld_Value_Tails\n",
+       "    sldWat = vfWat * bulk_out[contrast]\n",
+       "\n",
+       "    # Put this all together\n",
+       "    totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat\n",
+       "\n",
+       "    # Make the SLD array for output\n",
+       "    SLD = [[a, b] for (a, b) in zip(z, totSLD)]\n",
+       "\n",
+       "    return SLD, subRough\n",
+       "\n",
+       "\n",
+       "def layer(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}{custom\\PYZus{}XY\\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{}This function makes a model of a supported DSPC bilayer using volume restricted distribution functions.\\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}{oxideThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", + " \\PY{n}{oxideHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", + " \\PY{n}{waterThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{lipidAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{lipidCoverage} \\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", + "\n", + " \\PY{c+c1}{\\PYZsh{} We are going to need our Neutron scattering cross\\PYZhy{}sections, plus the component volumes}\n", + " \\PY{c+c1}{\\PYZsh{} (the volumes are taken from Armen et al as usual).}\n", + " \\PY{c+c1}{\\PYZsh{} Define these first}\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}{heads} \\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{} Start making our sections. For each we are using a roughened Heaviside to describe our groups.}\n", + " \\PY{c+c1}{\\PYZsh{} We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),}\n", + " \\PY{c+c1}{\\PYZsh{} which we will correct later for hydration.}\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 our Silicon substrate}\n", + " \\PY{n}{vfSilicon}\\PY{p}{,} \\PY{n}{siSurf} \\PY{o}{=} \\PY{n}{layer}\\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{} Add the Oxide}\n", + " \\PY{n}{vfOxide}\\PY{p}{,} \\PY{n}{oxSurface} \\PY{o}{=} \\PY{n}{layer}\\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{} We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,}\n", + " \\PY{c+c1}{\\PYZsh{} so we start the bilayer stack an appropriate distance away}\n", + " \\PY{n}{watSurface} \\PY{o}{=} \\PY{n}{oxSurface} \\PY{o}{+} \\PY{n}{waterThick}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now make the first lipid head group}\n", + " \\PY{c+c1}{\\PYZsh{} Work out the thickness}\n", + " \\PY{n}{headThick} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{lipidAPM}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} ... and make a box for the volume fraction (1 for now, we correct for coverage later)}\n", + " \\PY{n}{vfHeadL}\\PY{p}{,} \\PY{n}{headLSurface} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{watSurface}\\PY{p}{,} \\PY{n}{headThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} ... also do the same for the tails}\n", + " \\PY{c+c1}{\\PYZsh{} We\\PYZsq{}ll make both together, so the thickness will be twice the volume}\n", + " \\PY{n}{tailsThick} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vTail}\\PY{p}{)} \\PY{o}{/} \\PY{n}{lipidAPM}\n", + " \\PY{n}{vfTails}\\PY{p}{,} \\PY{n}{tailsSurf} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{headLSurface}\\PY{p}{,} \\PY{n}{tailsThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Finally the upper head ...}\n", + " \\PY{n}{vfHeadR}\\PY{p}{,} \\PY{n}{headSurface} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{tailsSurf}\\PY{p}{,} \\PY{n}{headThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Making the model}\n", + " \\PY{c+c1}{\\PYZsh{} We\\PYZsq{}ve created the volume fraction profiles corresponding to each of the groups.}\n", + " \\PY{c+c1}{\\PYZsh{} We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} 1. Oxide ...}\n", + " \\PY{n}{vfOxide} \\PY{o}{=} \\PY{n}{vfOxide} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxideHydration}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} 2. Lipid ...}\n", + " \\PY{c+c1}{\\PYZsh{} Scale both the heads and tails according to overall coverage}\n", + " \\PY{n}{vfTails} \\PY{o}{=} \\PY{n}{vfTails} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", + " \\PY{n}{vfHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", + " \\PY{n}{vfHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Some extra work to deal with head hydration, which we take to be an additional 30\\PYZpc{} always}\n", + " \\PY{n}{vfHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{l+m+mf}{0.7}\n", + " \\PY{n}{vfHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{l+m+mf}{0.7}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make a total Volume Fraction across the whole interface}\n", + " \\PY{n}{vfTot} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{+} \\PY{n}{vfOxide} \\PY{o}{+} \\PY{n}{vfHeadL} \\PY{o}{+} \\PY{n}{vfTails} \\PY{o}{+} \\PY{n}{vfHeadR}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} All the volume that\\PYZsq{}s left, we will fill with water}\n", + " \\PY{n}{vfWat} \\PY{o}{=} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{vfTot}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now convert all the Volume Fractions to SLDs}\n", + " \\PY{n}{sld\\PYZus{}Value\\PYZus{}Tails} \\PY{o}{=} \\PY{n}{tails} \\PY{o}{/} \\PY{n}{vTail}\n", + " \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head} \\PY{o}{=} \\PY{n}{heads} \\PY{o}{/} \\PY{n}{vHead}\n", + "\n", + " \\PY{n}{sldSilicon} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{*} \\PY{l+m+mf}{2.073e\\PYZhy{}6}\n", + " \\PY{n}{sldOxide} \\PY{o}{=} \\PY{n}{vfOxide} \\PY{o}{*} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n", + "\n", + " \\PY{n}{sldHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head}\n", + " \\PY{n}{sldHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head}\n", + " \\PY{n}{sldTails} \\PY{o}{=} \\PY{n}{vfTails} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Tails}\n", + " \\PY{n}{sldWat} \\PY{o}{=} \\PY{n}{vfWat} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Put this all together}\n", + " \\PY{n}{totSLD} \\PY{o}{=} \\PY{n}{sldSilicon} \\PY{o}{+} \\PY{n}{sldOxide} \\PY{o}{+} \\PY{n}{sldHeadL} \\PY{o}{+} \\PY{n}{sldTails} \\PY{o}{+} \\PY{n}{sldHeadR} \\PY{o}{+} \\PY{n}{sldWat}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the SLD array for output}\n", + " \\PY{n}{SLD} \\PY{o}{=} \\PY{p}{[}\\PY{p}{[}\\PY{n}{a}\\PY{p}{,} \\PY{n}{b}\\PY{p}{]} \\PY{k}{for} \\PY{p}{(}\\PY{n}{a}\\PY{p}{,} \\PY{n}{b}\\PY{p}{)} \\PY{o+ow}{in} \\PY{n+nb}{zip}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{totSLD}\\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}{layer}\\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 custom_XY_DSPC(params, bulk_in, bulk_out, contrast):\n", + " \"\"\"This function makes a model of a supported DSPC bilayer using volume restricted distribution functions.\"\"\"\n", + " # Split up the parameters\n", + " subRough = params[0]\n", + " oxideThick = params[1]\n", + " oxideHydration = params[2]\n", + " waterThick = params[3]\n", + " lipidAPM = params[4]\n", + " lipidCoverage = params[5]\n", + " bilayerRough = params[6]\n", + "\n", + " # We are going to need our Neutron scattering cross-sections, plus the component volumes\n", + " # (the volumes are taken from Armen et al as usual).\n", + " # Define these first\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", + " heads = 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", + " # Start making our sections. For each we are using a roughened Heaviside to describe our groups.\n", + " # We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),\n", + " # which we will correct later for hydration.\n", + "\n", + " # Make an array of z values for our model\n", + " z = np.arange(0, 141)\n", + "\n", + " # Make our Silicon substrate\n", + " vfSilicon, siSurf = layer(z, -25, 50, 1, subRough, subRough)\n", + "\n", + " # Add the Oxide\n", + " vfOxide, oxSurface = layer(z, siSurf, oxideThick, 1, subRough, subRough)\n", + "\n", + " # We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,\n", + " # so we start the bilayer stack an appropriate distance away\n", + " watSurface = oxSurface + waterThick\n", + "\n", + " # Now make the first lipid head group\n", + " # Work out the thickness\n", + " headThick = vHead / lipidAPM\n", + "\n", + " # ... and make a box for the volume fraction (1 for now, we correct for coverage later)\n", + " vfHeadL, headLSurface = layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough)\n", + "\n", + " # ... also do the same for the tails\n", + " # We'll make both together, so the thickness will be twice the volume\n", + " tailsThick = (2 * vTail) / lipidAPM\n", + " vfTails, tailsSurf = layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough)\n", + "\n", + " # Finally the upper head ...\n", + " vfHeadR, headSurface = layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough)\n", + "\n", + " # Making the model\n", + " # We've created the volume fraction profiles corresponding to each of the groups.\n", + " # We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions\n", + "\n", + " # 1. Oxide ...\n", + " vfOxide = vfOxide * (1 - oxideHydration)\n", + "\n", + " # 2. Lipid ...\n", + " # Scale both the heads and tails according to overall coverage\n", + " vfTails = vfTails * lipidCoverage\n", + " vfHeadL = vfHeadL * lipidCoverage\n", + " vfHeadR = vfHeadR * lipidCoverage\n", + "\n", + " # Some extra work to deal with head hydration, which we take to be an additional 30% always\n", + " vfHeadL = vfHeadL * 0.7\n", + " vfHeadR = vfHeadR * 0.7\n", + "\n", + " # Make a total Volume Fraction across the whole interface\n", + " vfTot = vfSilicon + vfOxide + vfHeadL + vfTails + vfHeadR\n", + "\n", + " # All the volume that's left, we will fill with water\n", + " vfWat = 1 - vfTot\n", + "\n", + " # Now convert all the Volume Fractions to SLDs\n", + " sld_Value_Tails = tails / vTail\n", + " sld_Value_Head = heads / vHead\n", + "\n", + " sldSilicon = vfSilicon * 2.073e-6\n", + " sldOxide = vfOxide * 3.41e-6\n", + "\n", + " sldHeadL = vfHeadL * sld_Value_Head\n", + " sldHeadR = vfHeadR * sld_Value_Head\n", + " sldTails = vfTails * sld_Value_Tails\n", + " sldWat = vfWat * bulk_out[contrast]\n", + "\n", + " # Put this all together\n", + " totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat\n", + "\n", + " # Make the SLD array for output\n", + " SLD = [[a, b] for (a, b) in zip(z, totSLD)]\n", + "\n", + " return SLD, subRough\n", + "\n", + "\n", + "def layer(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": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": null, + "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": null, + "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, and then a Bayesian analysis:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "821571d9-3593-4ac6-a5db-4d83998ff4db", + "metadata": {}, + "outputs": [], + "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/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb new file mode 100644 index 00000000..d28c9c05 --- /dev/null +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1ca14405-4a7c-4588-93cd-46534c374a36", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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.\n", + "\n", + "Start by making a project" + ] + }, + { + "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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"SAM Heads SLD\", min=1.0e-07, value=1.75e-06, max=2.0e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"Bilayer Heads SLD\", min=5.0e-07, value=1.47e-06, max=1.5e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\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": [ + "problem.layers.append(name=\"Oxide\", thickness=\"Oxide Thickness\", SLD=\"Oxide SLD\", roughness=\"Substrate Roughness\",\n", + " hydration=\"Oxide Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"SAM Tails\", thickness=\"SAM Tails Thickness\", SLD=\"SAM Tails SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Tails Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"SAM Heads\", thickness=\"SAM Heads Thickness\", SLD=\"SAM Heads SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Heads Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Central Water\", thickness=\"CW Thickness\", SLD=\"CW SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"CW Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Bilayer Heads\", thickness=\"Bilayer Heads Thickness\", SLD=\"Bilayer Heads SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Heads Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Bilayer Tails\", thickness=\"Bilayer Tails Thickness\", SLD=\"Bilayer Tails SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Tails Hydration\", hydrate_with=\"bulk out\")" + ] + }, + { + "cell_type": "markdown", + "id": "356964f9-83a6-4a92-8092-4d250b68ac16", + "metadata": {}, + "source": [ + "Now deal with the experimental parameters. 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=5.50e-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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "\n", + "d2o_dat = np.loadtxt(os.path.join(data_path, \"DSPC_D2O.dat\"), delimiter=\",\")\n", + "problem.data.append(name=\"dspc_bil_D2O\", data=d2o_dat)\n", + "\n", + "smw_dat = np.loadtxt(os.path.join(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": 10, + "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 | 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', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\n", + "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | ['Oxide', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', '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": 11, + "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", + "| resampleParams | [0.9, 50] |\n", + "| display | iter |\n", + "+------------------+-----------+\n" + ] + } + ], + "source": [ + "controls = RAT.Controls()\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 our our initial starting position:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e59bf938-804c-458c-891c-c3e56ed32820", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.072 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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "895c9af2-2117-437b-a848-9d8e380329a6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/RATapi/examples/non_polarised/DSPC_custom_XY.py b/RATapi/examples/non_polarised/DSPC_custom_XY.py index fd350d78..e96dc330 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_XY.py +++ b/RATapi/examples/non_polarised/DSPC_custom_XY.py @@ -32,7 +32,7 @@ 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 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..08664421 --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -0,0 +1,861 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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 1 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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", + "problem.parameters.append(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True)\n", + "problem.parameters.append(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True)\n", + "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "\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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + " D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + " SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + " H2O_data = np.loadtxt(os.path.join(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 fourch 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 | uniform | 0.0 | inf |\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", + "| resampleParams | [0.9, 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 0.002 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_xy.ipynb b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb new file mode 100644 index 00000000..50b4c0ed --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "956a341a-2a40-466c-b5c4-f8ea334ee81c", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "attachments": { + "bf3e4c3d-0fc8-4565-8f2d-f4f8386d582c.png": { + "image/png": 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39VOMCROmrZl0vSqNKTEpMVRic2AUMsbb+EUj4VpxUsO14FpDC1sb4LrCvvvuS6VhPQfatZKtntJt37nc49zKKSn/nqvuqauKu55ZkYHKTEVfTgDAQF6abNM4GQ9KNEAaucmf7HDAkmePigGLbYKHDRs2yLOuvPJK3uD666/f0ivWajWxL5nLqFNUtBtcv349mWZ4NgQkLQLs6FiynBrKu7U5J+zwXPxEfq0vf/nLrE822brYI38cacUtwTeRH7tDPgjXXHNNedAte0B4n2xfLGZycOS98dP5zfMa2YMNuInEnnfeeWyMI0eO5Pcvr0ul4Z5bqSh0Bbb0pTfran2USK52Lir8toOEvJiasZ6ncamtgqQN7Joy6vZXuceWbdukMGHCtDWTPoFLjQmTUqPBgFSoT92wy7UUTw2dUBTAtYYS/KPLivXhD394n3322X333eWHdjt0azuCWsm2TNvhWqE+jbdqHpUbK/bRZlGVxnKJu+XNAQAMAJJtQpTAtiQUryibjOgBewVvUx7nyjpD2YLEyqQNnk0CZAP7NlAm2JaBYnQyDLHdoc0kIZdKOwKyrOQF3r/s2Qbl7F0tya8of5Ute1yQy2/MPovfj+zfbmCv0uXhkuVZLHXyluynsKEqeZY9Avbj8xo7XNh2tGsY4NLQVTXTV9/UXuFLPbe0eyXkh+T5FPB1PU9oFhY31Lyie65XrAmL/vph6a+YMGHq98krItFVog6iNqJNZmrnS6vpoxXKTRNWrNTcKMlQv4JrDS3KdzH32muvLfWKzgpa1rX8IArCuGtNaqakyNVeZMVJwj7y5KisKw4mv75OmHhRnObHx7Qz4Wfy7kLfBMF3hGuVv4vtCzaCN4IccxuUKFdzAQADEsHqMxcur4hVsKldB6xic41OdSZBviSnZpAe28PeSZRPGV/GN4a6Gse1us1E67K8YsfTBjNtMutlg4qp+a0mWtHXtGwL0/J+mlYRLcj0XJZXEi0merX/9o/pdY8/f5tLiBaa5fkq/y4w9eO01ExcqheZ4/wK0Tyil8z8FXP813HJj7SJ8ZnrR8az8EsL1xp62MY2++23n9zELdcve7hWugUStW1TpNIw29op0ttnSdrH5Pkhz/UvbqLytFRp1l5pxmbIFdvOpDx3Y6o2M57zNjz3Y/0jXTfNhJvFPRgxrqrryinvNZpOpWYSFSpyfT0f4GuBbTol/dflfnM5JxvYAUizsTAM29vbcfABGGjKoiW95nRkj38CvKa+w2U6ZQX1QBp7p1ne9qhTnKqoOr9ias88PVyjX871vzdl2SW3z7v4tpcvvP3lK6a8evn9S668b8mV9yy+8u5FV9y18F/uXHj5nfO/fd9Ll90/t/d06X0v9jlddv9L/TJdfM8L598x58oZC878w+MX3f385N8/9l9zNvByf+0f02tPZ/zvo/wVXD7tlW9Pefm03z181UNLL7jz2UvufRFHpr+neZfdN//iKfMvmjL/gqnzz58274JpL100de6/zlx42R1PXf77h34+be7zqxOdQdS00MVdTbjWUGbXXXd97V9BJtkCcbptU7jNk+pzSk1j/GrTWbVu/fzFS56c8+ztd9/zk//+1aGnnPtPky7++sSLv3bqJf886RKeH3TqpV8/7dL9jzv1gBPPmPjvP7/7lbVcj+Zpo7nN2VGEs2v6nmjcKHoHOEGc2iCYbsqi+2qFNXfH3HfhesaBBx7Ic9vTA+wwKpXKQQcdVA78QrcA2DHGpUqkWZ6dyAndmCI3crwsqiVBJ6lN5hrOfjXdo39/ce2kB+aNnblw0mNrRt+/cNz0ZTyNmrLkxKlLxj20+rSnNp/5bOcJ01/lafT0pSfNWDZm+rLxM5afMmPFpOlLzpr24nlT52z9dP605/tlOnfKs5c+OO9fn3j1nPvnXP7IQp5O+u307z21rL/2j+m1p+88vIAPPn8LVz62+KIH5k7808P8FfBDHJl+nC6c+ry9SXHx/S9eNEVPF9///GX3Pfsv9z3/r7c/ftUfH7r5/jk3/vHhNpe8iBwXYS241pDDdidoNBp77rknlXomvIZx9UbRNk/JtsxfI22G6FaQpLHpM80LbW7IBrW2mNYVk5jVCqI1ZmFhQNfe8+CR511xwLFjvnD0iKMmTvrDrAdleJa20NxiMbrV8HX3hXotiIMscJMeQ28NEFKtD8Pwox/9qMS4Wq4TxVAnCIIPfvCDsiwH/w2OiwAA2Mp7TOXGhPyfE3EdTDWyoGnaILSbizlfyX/w4MOXTXv4nCmPTZ727MRpL510/8ujpi46+YGl4+978fQZL5778KJzHl14xuz5J09/bvS9T4+889EJD8w9/aF5Fz2z8vuL2n+6LrmlQbenNI1oLtGCvqaFAzy9bBpW8fyZlF40b+PdB494buBfF5NMj7i60eZL5lt4MqK/PmI0fxE4LP07LTJB5lfNtKSYXi3aFq6Xtr4x3fibOxtukToUsgXXGnpIP2+u0B988MH887Z9A1kO6FjGW5kjPumeI35LUxCmcdJtTWLaDW4OUhatTX7yP/9354mnnnXEqPFHj5nQSKlusuQ4ad7ZOsjy/mMDXdGXhc9//vOyIPFDFNcdyX777Sc1v+0bCAEAsM0/JSao1b3Xlh4AUUe0zG2yuRmd+af7xvxp6gXPLDxp6jNjZ7xw9qOLL3381XPvnnP+nx79fy9uWmq6YHUUrRXazUPbHX9T94cbTftDZ8t9+nfw9Jfv29XbGa87nKeOMJMhdN//sb1wNPp9soOMN8yNEjs18hEbTAKbJL722v+iNKIs1iONI/cYXGsIs8suu9huQq+dCeONu9a2siW/qrueHye9B+PK4oiigMKI4pCiWA+LFZvBsfRoenpMvcx3k6ajtzGdr1hi/CJplZMRK5ZnrgK3z3z0yJMmXX/r7U3TvLBipo54R4xrLqGt3XbbjRXLfi9gh1X4Go3GRz/60XLfRbguAAONbjRoslB2XQkzfcldl+l+9tdMf/LKmc+dP3PuOY8sPmX6vMmzX73o8RW/XpsuNPktdLzLoY6GMqewnyROkro8xcpVuv13GMSNMGnyw6wrE2GokxtmqnfOpNeaZ0Tb05ij58S/U269poceMg+9Rv3rX/1Kv+wZ09ZMaRgkgS/Lse99/CO7R56Lw9LPkxGnLQ2sGpvR7fg0vPHGH5s6l0MYyxiuNfSQoVpkcJUvf/nL0qRQMhH3qVv91YZQ73mrR2jQ/cSojyFR4l5NCgOlvCTx03RLbQ6bQRx1XxPp9odZGJobp6keoyUspKuS6Psxa7ysSvS/s5/4u2NG/fahp9pM3tKB7rhjU0t/7Wtfk3zN6Cy04/nCF75gTwGb0RsAsMNci8+7TZFuznf29KWn3PbCOVMXXzBl0cX3LbjgD89M3UDzYx3pkrTRzYC4ntzVwiHOqNQeKTUplLrdHVSlGt823ivsr3poeZ+O51Zq1b//hy+2dbSjhr5jJp3BmHVLpVES8/QPB36J13iBjyPTnwfZVvbSrlFMM5P/mU/JSOkRjgMKr/7p1Q41qnGno5oZ4lpwraGHNI6y9+xl4XWTvPd0sO0Iam3LE16jGWGkMj/WyTOSUmPCwNwbsWncgyIpsB1xxe85PKb+F/hZbGJWYUpuko/Kwr/ia720ZhRrTUyHTph80Khx4ZY7j5U+o9rCRH2HyEsf2Q5Ruvvuu1NpPB+wY5CzYI899rDVviEe1NrW6PM21kzzKd26+XZManvfkur13G6f0g7HCfrTp/Ipy4c3zfIrpxnGUI9uGvJJJ+lh1yT0QkRn3rtozF2LL32s4+y7F57zv08vJ1oW6yiWY7KW8ZOVHZY+pqQRyw3zLMq/XN2UQYY9TvWUlUuauXBn21vu3uAUJLEfR7LshgHP37/r36Q76c0Mw4kr+uWH73rfe82vf4Ij049TRlu45JqZF4U6O7QKb/jNTQHFrh4dL05xyYVrgZaqDb72KF5bOc+28ELlccAi6hock6fjJ5z1hymPbvJ0g2OXqJaZudL1g4rvmfukKnQbuv2xijPf6Z7gQ5kaQmlU9bzmoXRS46LiKGOeSnXfjjQKdhisu5/73Occx7Frtq8r4+A4tVR3bykbiHz20Gu6DV1rVWZEYBlRwfR0jOsJL6RNlXd8TKi52ZFRZv3OQN/ViPMRFdw1weP3PfmDi/5jzOHjTjho9CnHTBz7zZN5ucecpwmHnzrxm6f3nk4+dFLvibc/8fAxp48947sXX/W7/771lWcWKsc0SzFD3+o3ZiLgUTWVN6OalDXMySzvLcxHyJUhyqudNbkDxR82ysJQ6dyj0K3+vZWh3SjxxHPSLL+u6mJEfKmsUtLMTG+rJUSn3r/sjBlrL5+66MJbHrxuxvyFke5qpZO8+/kY9NvfYL3X7bwdPwVRmPcZNqGVOE0+vc/e2U56M8Nw8gLfLjdd57Of/5z9RjD119T32SWrzY1MCTBef/MNaX4zRCE1BlwLAP0D32Ys66Szr3y1Uzdx4eV2RU07NpdKw9A3fmW6iqVcv4v1cqFbxrWStDCukmgpqX8kUd5izabC63uUTzCQ7LbbbuUw11C+jdEjvUyhW27TC33deDVKQimr7Z1tOh+j1I69PJKgYwjNvCv02iXrrjjvX4466OhjDzlu1NGjf/XT37zw6Nw8xOwZvakVg9kFxZB2PeavMdX7mhpFnLpJsezZo+YGf8Ura79z9r9MOOH0E7550rGHnnDBpEuefvB5e8sk4TPWpaSSiZJlpvlZ4usYRxrrPHj8SVNzknqxC9fq57hWFuqrYqrHI9aDHJovkI8zfw2xu5m/Ev4a5xNdMmvpRS9mR/32pf+atWRuW1Yxl9mNroxmbHeV2PYCWflW+mA5+bpf1e0FH+w49S8u7/aCD3Zk4Zf5jTfeiAMC1wKgm2tx9WxFJZZq3glnXNhuenZ1xGqD4wZ5o0T989+otOvkHCrJ41rdXEt1C21Z10rNQqY7aPE1SBLxb968mShvWAh2DK7rHnDAAXEcS2ISx3GGbGgx697GrxTXyop6q5S9rraspkqrQ0apjmIdd+TxE06cuHT+MrG1qBZLKMmvBdLPmU0sqSktNp5Rnfg157al71ZOYdEUOO42z4yApSadXFhXYcUETkJa8Oyrh3zxG5ec/m0+aVUj395pc+Xj+3wG54EOfUI6HjoPDEB5ixNJSuSnaZMyT0pZlAQdNV7qILpj5dpzpjxy5tTnJ/zumR/NXPNcp+6UVTceXQ31t53lVeTB7VpSy+driySe5UvN/vvvX46lgwGFL+lytPnI86WeL/hIQwXXgmsB0Cqu5RfJTJe3O+ubydizL1raVvWKil97vR6mcqtMS5Rfr3e5VpduqZ7NCEW3lMqTYhk6OztxwHcWGzduHEbV3z4yrRUFM9XVQX6oE4RkVK82eGWz4owdOW7siePI9IrRWqWMtyiKnSQ3t2TLPbIi050mKW2W9NVmt8cUb2EKi+dGxXJEOhyVFBpW3n+QP6u2oX7UQUePOnp029p26eHj1X1+h6tXrinXBpAWpZ/hMuDp0KjpZhW7pDMT6Yue+epX1+jJkC575LkLHnvhrPse/e7/PbJZsr9mtCnIVT01lqLSuLii5q6VDsK4VjnTKSEHDy74cC0A1wJA6qW+orZmInfVHRPdmrt81a133cMPV7W129pdpVoPgsjUWUseZWoGfbgWdeVI9X1f+mvZ3+MwDO2gW2BHVAjNyMXWdfnh0PferORdxeQ5fhzqAekSP02itNqmIw/HHjlCEgw06472lkyLUxTEeSqCMMtMndd3g9hLeD2j8s41OstcnEaSnYryCHC3+XaQRFnPoJyZgmaUjz9upjTqiuA5na6tmwfN8Jqrfnj0N48pN57UyXK8UD476OdiZlw30EFH36da09+kXI+/CL6QLlB09tQXL3lu5eRpj79kMhJVfCXjXwVmeMMoky9Ele5eDVbXksS/fCq0t7dTKTEVysiOgS/pdoR6ubxjwHq4FlwLgFapKqTm7jkbV8VPJFtGPU47g2j0qafLw07Xbwb6l7PpR0UeHmVqol01wT5cq1uuQpIfYGm6NmQTM7Qk5cSP/OtbqVSGYyk3k25TZ4pks+LwfNLYU2vtdanMatEiLWOyfRAEKtED0fYej1w6PvGU6BxT+XKogigL+5z4T31OW9g+LruiSiiKktBP8tscJJlmGk4jf59prHjKk+CxdDVcsa/QiQ775288//QL+SkYl5pWovbb3xdQpTu4+g41U6okSY2PM1/m5im6YOrCcffMO23KvJeJXmUN1n3lYz/LG4rq6GNiLo+5aMX56FiDNq5lkVtpjuNgeI8dDF/ey4qFrL9wLbgWAC1Bre7qDBmVum3x5KY6CeHq9s4vH3IYL3R4gTR6asYqiEtdYnrk4ek1mphsZ0NY5egW+mvtSHoHsobwLc9ehbBbG0K34sVOcubEs0YefYLX4ZvcLWnDqcsT6/W6F+hODhK5ivTNB5NnO9ENZZkwDkwShMBXXpD5ie6so6eYty2W38ikU1qELFZhkqgelheGsSxHSWzXy8nlG7heKw0jdQqQ4vMee/gI/rxRU8Z/oCyAa/X/vapIN8MO/Kyh05JkCXvwXI/OnT1/7AOLr3imcs2Dq1a7epvFG+cHVIv4kphkridXRcWlKeEip6LB7loS1+ICaftoSddcsMMu8uWH5TAXgGvBtQDY+dUFSQ7Lv+vt1ZoYVz2MpbfIyPGTeF4L9aAv1UhF9BquRX2O30ymt26PHwDc79yRVKtV+TGWhj1DOK4oRS7t0YfQFM00UDq9e0o/vvq/6hsbdiA5nRXTbNZRaZcFLVSZ3oPNWGgXJHm6DWdJREscqV9cKzHD1nYb7yFTMjipPIzTRNIK8znLViZvSZJexGl+TukGkKXc9wd95eAJJ03UzSA7A8S1+hc+nI3Ul4BVGDg6dUpGL9Wyq55YcPITi4+f8dIVM5e/6uc991i0mtQZUXGbKUt1S1CJaGU2uesgzkNIpRyzmzZtIhPaQiHZYciFXXf/Mz+yctkHcC24FgAt8ftYtAPsORJXW8Pli/fRJ471TVcuPRqy9PhQpldJ/uNq6hxpWq7slqu8OL5gB7uWGzllQdIt6zLdrI4L49GHHZMn9wsoaaSSb7vPewRbmrbvLQ3cVBa/bqE8ZTJ2SCwroSP/+SjJtOG7ge1F09HRYavIKDzbQUJJjRoe+XwFjKs6L0ZnRt+d9eLkxxcceueDVy+tLMooUjIafexRo06NRH8NqtRHqyRag9+1AIBrwbXgWgD0Fq18VGL7Gy8/8zXHT4rxjg8+ZmTDjHHM0/q2ir3pHga6rlqp1Mo7LFUEE5M9DYAdhyiWH3o8r9Z157Q0VrpZXUxHHnaU5OhrdjhyRyFohtukQ0R95Tl87fk2Dpa5Ha5le46VbbBWqUscT8fuzIDII484IfWU/F1afJHpSIn+k2/EtTqpEvB1MdDZCNua9IJH4+9/ZsyjL121ZOO0elajInd/GgfsueSnFHdP5Rp3T+va7SIM0QIArgXXAmBouFZcMq68PmnzTrc74aZmeNIZ524O9Cipkpmwo+rojgqxHlrGXGJsZVGcLR/jGK4FdjDSHZz9QfoE6tzHJofE/HkL2LgiN5YS6jcCbVxq2/SGqPv4XVsz36Zp23Wr77iW2UsUsGVm1bZa3l8rpcMPOSIOkzRN+fiUc4GiTe/2ulbcoEot7ZBshItCuuqpjcdMff70pxfPIT24llHuRKc40Q1EVaR6l4mknGSoKAA9GmgDAOBacC0Aho5r5XU1rhpEiipNX8ZircR0+8yHPaK1FUfyaDWDRPuYufuaplkP1ypNaJ4EdiidnZ06Ox9Ro9aUVO+njJ+gx9QySc91vr6qa0aQ7XKbrZx3udbWT+m2TMq+H7WV82xLrmUGEwu8UCf6MH3VOjdUePmkUWOoSMktg/DIMtgOTKIUJ6Jw/dq6S3T53a+cdNdLI6a9OIuoLb9lFVJad6jpUpwniJeLZN9zq8lwLQDgWnAtAIaKaxXRp5JrSW5r0zUryrRWra/oYMFNv/+/mmlJuNGJZKTjzobH9UPPj0sVBBlZKykpHFwL7DhsZoj2zR1+I2DZ+N6/fp+lS2ciMOnRdT7MzPQwLOZ56d2a+XZM2xPa2oYnbKlHmYwkqzvKS69J8054ed68ebfffrs9XJK3Bv21tvv62Vnb7GWqI6G1RJfOWj7m3nnfmbPuOXN/ihI+4o2MOoxrJfnwGtnrly8AAFwLrgXAUHIt1Ydrmclxw8S4loS2PKKvf+u4NU7A3rW65vKaRpil5cphXrNUXV0R4Fpgh/7i6RKXBsqv5UNp/fTHP9PtBrO8TZ1sJc3nwjDUqboGsktVVw16m/p3baOfbSl7h82B0Wg0yIwhxgu8kg2TFWv9+vXyV8S13lB5U/ryuI7oknueOfX+l8fcNudlok3s/PrKqO9HEbVHOiVGIn0FKevWHSvtu4gBAOBacC0AhpBrFQkDVQ/Xyky1wI10MulKGDdNboxjJp6+MSFe3uzp4YeSohlhqR9LOcUWXAvs2LpvSmEj0r1gAvral77OoiUZCPM2hESSNkPypOcZ4bcl6LRN6d1T219x4OJmW4aFqreDSU+2kSNHykPJIIr+Wtt7+aS0rhM93rKi/cyHXxxz+xN3tevWg24qAxbz/51at5SjY1wppVlXc9GkNC+PppWV946LJwBwLbgWAIO/dtpt6GHrWs26Hhplc1uHTZLRTJVH1CD66ogTmybM1YiLukJi79+rkmslcC3QL0V0m1wrqOlyeew3j4+dJM+E4QbSgyuMg4ZTz3TNN46yUEd0tynuRNvsWtucJr7/XEsOnes7MlzYqjUrZWUS6ZaTh3/jCM/xdQvDvva23Tnuh4qwqzRvBZ3fMLJelFFxodPfL3ERW0V03vRnTpv17L88tHAJUcBWH0hbAN+jqqKafhwZMcteq7NeBtcCAK4F4FpgeNY8bKOXyAy09ezSNWvqgQy35SS6JuLHqqgl9I4FAPCGRKvH2MTWAeyoblJAdWJ3Y0TNTp8La+xmkatVql61A6raoQjiTFeU9bxrD1s339ac7Nt/1m3lfBvRwT0ZekvRiKOO0yJaCyTConPEZzrwJcdcRm0ebrolOVM8nfTC13eWVIMSn1QSEHVk+gZTYPyLIl/n1adwAdHkKfMvnL3o/DufWJjoW1FJrI+kHuGNDzYrvd5adRn7dlozAACuBdcCYBiQmoSELtHxE85cuqkSsnop8lRXS5isyKIG1wL9IlrltOY9HEa6XenuRkWOwbWrN0hD1qOPON6EsyKpw1YqlcK1ksK1WCTCVOrBw+yY1jsacrrqxpamT2VlXZUfagGTvDhZEqR+e6Mt0QdKDadjYxICETUp0aNmUY3SGls7u5ZrUgu6Mr4Fb1Gt8l95zYNEo2cuvWDmov+YMbeDVzfNAdNDYSQmS6Hu6Rp1RcMgVQDAtQBcC4DXdK16oiscbX72Hzf+UkY69jOqOn73/gZwLTBQrmX/qnsZZeQ2PRlBy3N83ckwoj/+9vY4yAIT1OLNq9V68Yyya8XD07Wk0eCmtZv1547o6wf+c54WJ9UjOydRykc1SkI7RPLwdK0iruVQyhOXK+1abFeO5OWPKKw0AopXE33n2ZUn3vPSmXc8vZivjeYaaJpcB9a1wrJrIYAFAFwLwLUAeG3X0m1pQp0bY8S4SesrzdB0BI9KnRmKugRcC/SPbvXpWpI9T8JZehQpU/I2rm/jUjhqxFgVS4rzHlVbuBZlRSYbG91qbnacNteOvBzHse7iZQ54za0OQ9dKtCBx2Yl1IkEWLcUXudgjxboVRpn0XmWZaid6NE5Ove+pU6e+dOX0uWuJ1numuEWRfrbpIxcVrpXCtQCAawG4FgBb41rSKkYbl59OOudCXmjGquIFCfVoSUhwLdAvrtV3D6ii5qrDWaagBU09bu+YE8Z1Dcqt4116W8/z+nItyRM4/NoQVhtyfFJPsQcce/gIHdSqh/Z85b9IZy0vdoeha6V2mGslvq6LSpNY6E2oz/TX4vL0CtGP5y8/e8az33l4weOBzpChe3MFESWR7s1lXEuyCiVwLQDgWgCuBcBW1kXam55f6NaTL76yrrMRmdAWXAvsYNeScJbTcLUcBEpawTlVj2QwbVMKO9srpZ3Z3BjJsHUtaUPoVPVBkxANW9baZevk1HVrnt5AD8MbD888hN0GRjOxvtRkuXCokZKv3T3Ispi4VP1qdX38lCfPvOexW5a0sWh1mPtQRvrd1KmaBBp59sLctQiiBQBcC8C1AHg91+J6g5uR9EPg6aRJk9udUNrJ9GpJCNcC/elaXaWwSIbRrDi62ZuXJD5XiWnUcaNFIZp1z4YRoiiJ47iXa+XtEoedaymlj4aOuWRy0PiMPeLgI+ttukkhK6uOE5rk+K7vREk4zIqbDOyuchE117JI31fiQ9bUaQkjj5yES886ovMeW3bi1Be/+/DLC4lejXVnrbZaRSuW8vWUxWZvXSndAQBwLQDXAuD1XcuLdSeEihdWgqgapFwvGz3xDC+j3roF1wID51p2XCyn6upk5YoO/qdD8iqyaT1Y6ajnLQz72Jt1reFYPiVzo20tp3w96tP4E0/u3FCxbS/zqsOwOz6FaxXDaaV5DD9OWbTSCoVN8lKXaBlf96YtOv2Z9VfPfnET0UbTgJBLlNtop8ynqNnDtRDQAgCuBeBaLYdOMmbmSo8aqRekHAtFBwxyHD14jmzDhGHYYwH0r2tJJcRPMtYtPsSdXvyrP94pTQolTwbLmNQt6vUqXAv0c/krWRYrFk+hE0l9dsJJE22Chy2NW1Q2NzNe7fBuIyfHyhy90SNOSlx9PPxGYA+aJCAZVq6Vy7qMY6wkJ6F2Lf6piRrryddx1A6i781YcMbTnSdNX7zc5Cd0zdXP3HHyKXO1bhXDuGeI8wMA1wJwrVYmTdPeisVmxSv5T7VajR/qwTezzOpWYMh/ORV+2/qZOE2SwriqfsSVi2pEBx8zst1LgmIwGf5rw2ny4Y9jGC8YENfSHbSKVJfKz9yKZ8IKKVxra13L6lbRyu34o0ZGbpwnGvHCamdtWBYylTeVTvJUQJ6+psXap4IGpUkjpPVEZ/zfEyPuWjB51vJ1ZoNQb67vQRntcnUCwywPHnZ3rQS6BQBcC8C1WohmsymyxDbFlqXHfUkSZeCVruvakBcVt2D5Kfa5NBzvyw44OsBYuFZQpCX81rjTmqai0Uz00MZ+rBId3FKppIEDoP8sS2SArSAzWp96qtnhnDb+dBuL6LZxtsXdDM9eNJJDv1toK83P5xWLVuatCsNcERq15jAtaWke1JJLnO7XF3hk2l6u8bKnA7r4gXnnzl7zpEmJYe49scHGhWv5eUHsXgKl5SpcCwC4FoBrtRDSPrAHErOyTQobjUaP4u4b7EPQ36gsS/nINoM4MkMbb2jGnRGdMOnsjjCPa4WKvCj0fRcVCzBAriVtCMUTlJ/dduvtuoqbwrVeX7S6jVRWzt5gQltaLPy0fUPHME1QnnU1QzU3klQkQa5IC5SraHlG35v9/OT7n5l82/OvEhUyyoeUi6NvJhPl6r6rwrViXBIBgGsBuFarYHtk2YUwDG3jwI6OjvLGruvKgrQqLC+A/hUtrkbUax1SQ9tUa/L3UU30vdx//tbojY5uRlgNY52rMAx0fukkwiED/W5Z0m4w9ZRfC7ggjjl2rGRl0S0JddW22Aiu1ZdrmVGikh6uFdd1I7hvffPYZocjBy/0ozQefmJgR2bTjQNjTxtUTKmSFBl1IvarSXfOPOP+x38wY8EaM4y7KWPatVLyzRSnxThlJddSMqobXAsAuBaAa7UW0miQS3ClUklTXTXih/V63TqYrJTmhVSKZckC2hD2f01E90OI0pTrE3qsrcB0V9jsU0dEZ13+/aaiTj8sxpNRTT3ODAD971q25RtXX489bERYjfIMmHCtbXUt1dUmuN7W0GNtKZJ08EmUDrvCluYFh8uTS64naS9Mdy13E20iurM9OnP20xc8OOeVQP+tSHihY1YJhRHx1Y+PsMqoR1yL/wtNyAuuBQBcC8C1Wqns2maEklew0Wj87ne/+8///M/29nZpPSjeJYh3XW+wD0H/ulaW6N4IvueY6gixXHl5f3D6h8OOlu4N7bWmF4UmMQZcFwyUawX1UDnZA3fPzFyTyaCperpWr8xvcK0tuZbX4Yu4Hnfk8bGT5EkysuFX2IpU76HO797wqKH7X0WmsaBLK4kumvX8uJmPnT3jsXYduLcDFStxrVDrViL5gbo8VuWuRXAtAOBaAK7VOogpSaNBCWHxfO+9995rr7123XXXv/u7v1NKSdPBKIpkrFL2sf322++TBl5A2veBcC2ujgU+1z/0+LBat8ytWkflLQmriiphKlUN/e306DZTngOwrbpVEij2AbGlw772DVlIPVWKIby+aw3X7kiqW3+tkhJEzZjnV1x8Za6hcZEko9uRVEO8pKl8eC22JhatiBzdgFCnW+WfJHo6ozNmvXLBU4t+NncZXwSz0GYW1ANzscQa0dIK1i2ulbtWTGhDCABcC8C1Wg3J5y7LH/zgB9m1yMSyrr766s9+9rONRsO2EmT1Yr+iot3gHnvs8alPfUraH/Y4JQT7LJa63puB16iopVK/KN21TSUJIdE/jjiuw3RtiMymfSTgfo0JgK2oB9tEDrpnkVlwm14UxGgwvA3iWjqj7XIc6jx7iZ9KaEt7QR6xSYqpGOq3z2nwn8VFsnedizHV3atcbUeRCUfFehyt0x9ccNrTbeff9fJGovW1DhPOT4pPbYcT6HUYMnujCqIFwBCRK8mJLVVHyYYt3HTTTTYhNoBrDSaCIHBd9ytf+UpnZ6dtGbj77rtT0chQ1u+5555xHHMplzyEImZ8MpTHQeZdSRCsfG6QCaOhzeHW1EXMXVtT31Iqv1Gb5jm71vthnWiN5zumeWEUcr1NbYNuAdCX3nf5QHfX4um4o49n0SpGL0r90MMRex1KmfGo+4Bjjtfkx74bjDjiOIlr6VBhntTB6lYhI0PTtVSkY1Nxmn/GMG/6Z4bLXkc0asbLx9zz6r/f82pN/1mP5W6ugACAYYGtN3KVskcdUv7qed5vf/tbeWjzugG4VqvfOWAvsgNq7bbbbixRslyv1z/+8Y9LskFboD//+c9Lm8MwDA899NAPfehDolWWTZs23XfffTfccMP111//61//+r//+7+vvfbaa665huf33HPPmjVrcMxf17US7Voqdy3bXSGjTQ3HJd254bAxY/n78FLKsm2Ma8G4wFa7lvJ1tf/EEaOadSf0IxOqVhniBtvrWmyqYRzoZBiZHhI6qJuEEJGMxvt6lrWFFpuDsbAlpttVln+WJG/6l9I6P75pzgunPLjgrIfXLVXU1DYW+8kmuBYAw41169bdfffdXG/86U9/euONN/7sZz+76aabfvKTn3Dd8o477rjiiitsrxYcK7jWoLl/wAvSL2vXXXeVNRLL2n///WUbfhgb9txzz/3222/GjBk/+MEP3vve9x544IG9sxE6Bl7fY/AuBLW23rXS3LWo7Fr8TdRU1iSqmMyEm6o+H/ss2UbRgmuBrXMtnv70u9uoKGB8+qdZEkQ+jtj2uVZ+nDNyGi4f22MPH1Fb15DDO8xcK0wlg4XqSqISmqDWKbfcdvbDSybf9VI7X+sa5LKSkpMh/Q8Aw+fyqZStMdpb/1J75DlXNdmvbFwLwLUGB+W7Alymd9ttN8/zpCEsP3z/+99vfUmkiwv6jTfe+O53v1taD3784x+3BiUB3x49Onglb8CvwrvtHQ4GfbpWmrtWKZWbyl2rqXRcq6rokBGjnJRSXhnBtcDAuFZMo44dzQ872jq1D2RZomLEtd6Ia+l+lxk1645fC0aPOEkHbJI+c40MYddKUhmMWJHtlMrUieYQXfjQS+dOf+mHM5dUA0oDMmkwfLgWAMPo96jU27/HeutgN998M1cpbYMsANdqdYIgKDd4/exnP3vooYdSkZzwK1/5CnUfs/iQQw6x9jV37twDDjjAbtzjzkRnZ2efr4jo1la4VpEpu1uWLV0xq8epDLflmT4OqzbW88THBNcC/exays/a1rbb4IPubZklcK034losq4EXykkeNeOFzy5Wjs1DSMPHtXSin8S0nzTJ3yOKNxGd/9Bz5zy1Ytyvp60hE66PdM/VzX5biiIHwLC6giolSQGiKJJ5+a9JkvzqV7+CZcG1Bg22BHNpZjXKsuyXv/yl5MNgPvaxj33kIx/hbbhM77nnnlKy3//+97NfSfxq3333ve666+xOeAOWrvJZ4RtkDzja2+laJUEylRKtWIvWbKiE2bjJ5wVkW3htYV9wLbC9rnXs4SN4Hns6C5xOoNdjS7CNrhXGgdYt019Ld9aKaNKJp+lTWm3pDFXUvf3hkDj+ick96OtbR2HeetClcBXRiPsfHzVz0VVTX9DNKnhtLTWDPydwLQCGD72TqOkmFaUGU1yl/PWvfy1hrh75AgBcq0VvHnAJLne4am9v//SnP82K9bnPfW7vvfeWEBaXe15pi/UnPvGJz3zmMx//+Mf32msveS5voEeCKiK8vNxnc8EthYbBlhSpxwOuZ5mGXboewpZ12LEnOUqqK1vQKLgWeGOupbPk6VQtGU/67knowbW227WUHkYqb0MoR/h7F1+Vd87a4hk6dF3L3DfKUh2ir1C4iGjkI/OPn7F4LVG1Yi5wEYVZWtcZCwEAw+b3qKglcqW0XJOUoYPEtX7xi19gABK4FgD9qlultkO1WoNXBik5KVViPUloK1JpWkhXolLX97LXMC4AStSbNa7ByygOUtAk6lLuKyjFBnGtN+JaMnmOLwM76CmmV+bMzwKyOfkkIzyV7tfKs9J8SoaEa/lxUBfXchrUoWgtJZfOvvfEJ5cdcccL7WSODB+DUDckrOk4IAAADoaxjOFaAPS3aZXkSGXF6DpREJOJboWkRWvsud9esqmqKy1pIlMfMS6EtsDrFDfVcOpk7hdGbiyV/rEjx8G1+te1mm4jKw9QnlLn6uqEEyfyglf3s4iUJEI3RzsIouLctaKVj3I+yE9fZfprxeQluiFhorOqLiM6Y8a9Jz665PzH1+uC2Mz0/aREX808HfoCAMC14FpwLQAGwLWKzNsqTwadaNHi2nCS6Du/DaLzr/pxg/LQVj4yaJaKboVxBNcCW+laksldZyVVFDpR1IwP/afD4Fr961oyBV6oG2QWmR7nPPR83FTyMHRiXqOS8nOta/FJr6ch4FqJCvW1KoizZsofpo3oWaJTZs46/akV9zb1XSRydPshyuI0H34MAADXgmvBtQAYANcqDErXtHLXUrr2kRnXqqbUTrQ+1NIlLZJ0V3KVuHGo6yhJDNcCW4PjNbUDSDZRRfWOhlf1H5w6G67V767FTmvzENbbGrrzZUyTRp9a3+zIMHqJn486lSSKSiNAJN1cSw3qa1s+eGAUkrmuLQrpB68sP3n2nDMfmLeMTJHjkpjFGcV8EcO1CgAA14JrATAgdbWU8qzIUe5asXYs07uedMIC6oh085uvjR7f1JWTrBK6pgNI5kSB7bvV07UUXAv0Lm6pzpJnhtrz6j4/fmzW42Ejgmv1r2vlGfPlTkpAepzeiJKaOu4bJ+hsEXI7pXAt1TXAXpdrRbrN3RBwLRN1j/milbgJrSA67q5HR8185fT/na07a7km+aBWMe1auFgBAOBacC0A+rsyUriW6T2uIn0z29wA5xpXEIqBRYmuk6wO6fgLLnFN963OwHGyuHAznS0jryHDtcDrlDjl+vmgeeJaRx16tC5xcK1+dS1xWn4c+pHOhyFJRWNaOX+NPrllyDyTkiRL7QHPWxEPMddS+tOFSnn8iVcTnfDA4hNmLvvJrFcCca1Ypyl02cjisHwkAQBwLbgWXAuA/net0LpWFGrHivUoqOJaHUQr42zms88Gpp7mazHLu2z5cQTXAlvpWomKrSekgRo94iTbWRCu1V+u5YeePs5ZLhysW0FHpPNEuHTzf/3K74jy3I9Kq5VKe7pWNFRcK8pk/EA/Tmt8fXuqkZ38dOXEBzfMrUszaH2pq1NcpTBNY4oVrlcAALgWXAuA/netLG9DKHEt6bdl2hAmOl8ZV1Y6fd1Tq0o0+uyzNziO6BZv5/lhH/2zsq49o+4CeriWbuGW6n40kofQ6XR7ulbJGXDEXv8Uzvrur0Uy5rsX5qkIY5LWwYf/01G6+a+vV0ZuUpLbvBlhUoS2hkB/rWaQ5K6lfL58XXbHEyNnrR33wIoOMkfD11e9OkVNfXSUSZKBIgUAXAuuBdcCoH/rI93qbEXO96Kzh81SKHeBn125pq6TI1PdS2w66SzWdbJEq5qS2+UauBboCy1aGcVhEjRDHdRKKKrFkq2h7Frp4K/rt5yPKYqdZNwJ4/U5G+VnaByq7pGxruvAYD/4oo5x4lHkcPl6hej0e1aOm7rk4pnPV/XfnXIQtUgPhPIGAFwLrgXXAmAnwdUzFq2jTzu7YurGit3LTaV+khnRCsyUD7HeLWkGABrJQNioNaUifMTBR+aiZV0rLzsJXGtA5ENRo72ph5BuhnLmprEaqilDjbHHGQWUhlzsptbpgseSCx9e+3BETd0suiKFrih4SQbXAgDAteBaAOx01/rHkWM8UzfOo10pRZ4vrmV6fJVcC3Et0OfPGNd/vdA2bMvcUhtCuNZAykcW6b5b8+cu0C3m/LRtfTuvzNV3CJ6qMlqgzqq60acfz4kmz2xMvnf+Mh2Zz10rIeuZiGsBAOBacC0AdipcE+kkas9ok5vqOoqisJEn7zLZNcS1KHcttCEEvYtQoktHtbPGZeN/f/17q+s9zBxtCAfItXQ3uWZ8zOHfcmuebSw8dIfCU5lu1JwoytqIzrl72dg7Vl48Zf5ifbHy2bVMQqCizXOW6AnlDQBcKeFacC0AdqJr1UgnyfjGyHFuaCpnMdtV3gynGKEL/bXAFonDxOZNOerQo92Kl5cYuNaOca2EwkY0fvTJOgNhqI/uujXrh6xr6Q+lU767ROuIzrp36elT1tyzkdbqv7Br6cyErtUtnQ2ICyfKGwBwLbgWXAuAnedaHtHKmn/IMaO4huy6qVTR4jhNiyY4ia2zwbVAX9Vf3w24YGxcs+mIg4+UdOTdoiuFayEP4YC4h8lnk0XkN4KeQa2h6Vr6itRG9GxMk+9bdN4Dq5aafKrGsPQhaFA+trNJuwrXAgDAteBaAOw8UpOKkHWrmZKbUEfV84M0VXZknu7pMDKMrwV6UqvU85RvkurSZMOT5O9wrR3hHmnuWgf/0yEyvlnoR9p+h65r+UoHtX7yzKun3fvyhTMWLzUhLdPeWUt/3QTqWbx0S8M40aNAAwDgWnAtuBYAO8u1vFSLlpPSyPGnJaW+NtayIFrgteq+RrG8un/ySadw6Un81Aynm/UuLXCtgXKtUH8FJxx7orQh1ElKhu7ZmsaqSbSC6NQ/PHj+zEVXPbpskSNRLNZ7P6CkbvL96OSYug20QokDAMC14FoA7MyqWsNPpUP5kSPH8nxjzeWHmyv1btnd9ZLtdw9AV8Fgy5L0g+NPPDly49jT3bcyjA2wo45/6ETSNe4H378maIb6YWaCjUPUtfgzdSiaS3Tpw4tPu/OpRUTrY90tyzQXDENKGmXXiuFaAAC4FlwLgJ1aceF6WtPXDQhZtBw9cg1VgzTPh9Gt4wdcC/RVgBRVN9W4uDwx+0nbX0iPbgx21PFvtDc3r2vjhTXL1+oYo+MP3fG1tECtiuiap1aPv33OuXc/udz0zkpjSYPB/8We6bLV5VpwfgBwpYRrwbUA2IlEsdYqX+k+D4ceN7qqqDMqEnll5QwHCmODgt6VX+XrQqGH0031cLqSljCKIvljz5oyKr79Lh825aOi448ZKSdsnhxyyB3t1HQu3Uh0xl0vnj514Q8efmWlaFWmzMQXqDgi5ZnsqSZrCFwLAADXgmsBsLPrL3Gsqya1ODvutDM3x1lnQm7ZtfJoVgLXAr3r+rGTcOk5/KAjpJxEQcyi1XcmDLjWQLiWyl0rdKKjv3mMHmUr65kEcsiQmLwXi4nOnrrs0sfb7l3nb9Dtn81QFfl1So+lHUkeQr0CrgUAgGvBtQDY2XW1es2T5O9rvahO1DS3ilMqqnH5QElatOBaoGf5MQlVxp94su6pZbJipGkqotVtNK0MAwYM1PmrddecpMsXrxjaOd9Dog6iWxdunHTPssn3rVxmLlYxhVHaiPRwWvyRQ1KxDMWOsgYAgGvBtQBoiboaT1xNWVNrNoimz32503hX7lo2NaFpn4OxaEFv19q4dDPPlZ95dV9Eq+k27MjFusBkpRgpqsD9evyDZiiiJUkI29a367abfeWBHAIEZgjjf5v1/Fmz2ibdvnydsS8WrZTcvN1gGuowfcqVqu7jVQAA4FpwLbgWADurrqxTdRu/qhONOOuc5TWvl2upwrViuBboUX7OGHMm13N1dIVr/ErLVZSFcK0dd68k1QOa8ZQG6tzJ53UlIRxyh5qvS6uJvjvrhfMeaVz8QOeqmIIkTqiekNPMx9QKKQqlRWFk7AvFDQAA14JrAbCT62qeEybmnnF7mh14zAidNFkVHR7SYpjaTGf1yvJ+EWBIF4k+auqq70SUSqeBmzDiNF3PNQlVkihN01iam2Z5FFR1WQFqvv2OHTnaRLcO+seD81SEg6TLVv4eS/lOTevTIiTVPT0PX5peJbrwgbmjfv/Kv83c3JFv5Pqq0jQpMfSIxnEsl6wErgUAgGvBtYYkMf/UUZ6IzKZ+5pV22TXIMnJDt0JlTY+yFQT8hW1seB1hxt+NpCJM9LWpFI5g3crQX2vou3fRQ68r17+Ik6I4VaFSiRk/S+nVkXatkw4/VWexbBZR0NzME0lUIAUGQ2EPEEmSyIIewjiljWs2+Y2g60ss+W0LjiWdFS2Ui66hymS2SAItTpkZoZjIz4zGqyj12olmEZ328KsX3bdwnqNLJD8/aHZmug1h6JrAu15VfHCUNwAAXAuuNcSlq16v81wpZc3K083TNLwA0WoF0VKURhnXUUzPBxPd+tJBR3pmIdctW1PWSb0S1F+GuGt1RTK75fpPKTTNsqxOqXzjkBY/uTqPa8X5oEZp4EogVDbOih2j7AzAN5YbVK1W02NtdTav/eF1qadKWW3y496arpXnDJRSp8ckjkNKXB3CMrkuJDgVa9fyE3c90YVzlo+avuDK+16uFH8N65WMvIBcl/yA4kQ+o4LcAwDgWnCtoatYIlQS2mKCILB/Zb+KDBCtFnEt02JHxZmS2jJ/VYcde5J1rW6DGsO1hrFrJYpdK+6yLNkypjv/3zQuK2nF9PZzujKp9HYtVH0HgigJ/VBfb3WAy3w5Rxx8ZC5ag8S10i7XyuNano5r9XCtLCK1jmjclGfGTHvloj8+xSUu9uX5UZo5Afkehexp3VwLAADgWnCtoV2yhTAMZcFxnPI2PR6CneVaiqvKjTrXapwkc1Lq8HQzQrjW8HWt7m0IbQPCIuO/ylKVRLEKUy4lIw4ZrcuKa8qKuFai8qIC19oR31huUEopp+omfnrCMScOrrhWn66VDzvhZ3khyvLEGJNnv3zag4uvf2Ll+pC8hiliGZdM37pWKvcC0NIZAADXgmsNVdrb23lerVb1D6XvJ0kibQh1MmiDY5BlRLd2vmtlqR96aXEHuRqkXMt5YdEquNYwdS3Vt2vZ0dUybVppnicloZHfGKvHl5XggyfFKOGzveiypeBaO8a1GBYtXqxtrg+iuFbS07VUpEVLhbLeM8FUk9GyTvS4E06cMuf06fMWmTCqfoqbqNDLKAwo9CiOJB1L1pWQBQAA4FpwraGJNCOUPBkiXZIPwzYslAWbJAPsLNdy3WZhUXksq91Vx445tQ/XyuBaw8O1uuVUKLuWFq28xl7kqPz19bfW1wXkk+Jy5NvnqnLPLrjWQLtWfjmteXqUrZS+c+Hlg8y1VNeSuJaXRfpdB3nDVc+MrHXDsy+fM/PF0+6ds4qowzgYX6Gsa3V11ur6yAAAANeCaw1FVqxYQab/gO2pJUWcKwQSy5IYl/UusBNdKwp1Bdn1dWjLCVPpsjVi7GnWtUrpMeBaw9O18knp4HTaFfvium09CTpN7rc4z/leaFjSLYsGlSvAoD9xvGacFt1fCwfu6rLV8q6VdvUKzB9oeyLl60uREtfiXw8W+eVE35ny0Lcff+XCqc+uJNrsFNGwTCduCSkOpAFhUdTMzhWKHAAArgXXGmp84hOf+NznPkdFUEuSYVCRm5jL+h4GKfQ2YTHYWa7FUxLrDnV+rDujVwNdx6lF2riCsmuRrRCBoa5bXVakyq7F56ySEbNMVgyvw29b1a5MqvfUlQQGlEUq1/K8tKgeOwb9fQKnYlC6KYE53mEjOmX0hMHnWmleWMwYxColk17FDdnh/ZgqRMuILp76+Ol3zf63B19aY65OvElU1eUvNdkLQzGrYkiu1GT9QZEDAMC14FqDHhu8CsPwgAMOUEq5rvtZQ7PZlD85jsObLVq0aJ999pFbsHvuuecXv/hFXrYZ4ctYMZMIWI/1oH9dy46AlBDZbISHHz/Oulbdla9YVSttOGTDyrWsaOVR6IxiL5GQlbPJO3nkKV2xK9VzVC4kKNghX5cJOSql722ZQ+5V/eULV0TNmG1Df1lsHXrcDRVEftZ630jeILWna+lQldb3hN1RL20iurdDXTZ77qUz5rxK1EHUlDiqr3sNBpkfWNcqWrGmOidhkqEQAjDMkAFdlcGulCRtXJ+8+eabpVZZTpEN4FqtC5ddW5Rrtdq+++77yU9+UnSoUql85CMf4QWrW8wXvvCF3//+96Jet95664c//GHRJyEt4H3ymcA755VZAY72QLlWJlPuWlHhWieccqY0I6yHiRPEaaaCwENca1i4Vvd6fCnTnS4vsZdXg3kacdjI3K9USdK66RYYWBIV2zyE+SGPqNnhFGHGLnPmLeO01Zptq0zEvQiCFiNuJVHcVGGTa0Oxk9RT3YDw359eceo9T1941yPLTWKMUEYtdvVFyTQgVJG9fZTCtQAYjkRR1Pv2Pdckyz1Wbrjhhnq9jmMF1xoklXRjRLIsoaq9997behev+fznPy9Z3V3XlYK+zz777LXXXrK8xx57/P3f/31viYrjWFaWTxh+iu/7fUbAQH+5lml10+VaS9dXNjX1jSAnyaSdT0dHm8rQxW4YOVfZtcz5Tlmatw1UDZ3nffTRY/OqLG6G7CTCOBCd0JfNuDCNlNav2CALnuPzSt1XtvXaEJq3G+eulXZzLZ1oRflal1LdWWsJ0ZnTXjpz1iuX3PnQ0kwPMaDMU5Kmk5k08blr2UELMn5mHFEM1wJgGBIEAdc8y5mu+SeML4NcKb3lllvsGhwouFbLV8WK9q92+KxPfepT5fsH/LBHbgxZ+ZGPfORLX/rSbrvtRqaPQbPZZI+Sv7a1tc2YMeP666+/5pprfvnLX958883XXXfdj370oxtuuGHq1Knr1q3DYR8Q11J9uFabk46bfN76issP3SiNVJqoGHGt4SRaZdfK16ZBlkc/I8pqZlgtWNZOJU4jG9fSSQiTXDaOPOQonfw9Lr5IpdIskVGPW8+14l6uFeuylTTNiFk6irWYaOy9L01++NX/fOzl1ZnprCWu5XmJ7qylc8QnVHQSTHLXCvnwwLUAGE5wFXT58uX33XffTTfdxFXHG2+8kauUv/nNb37xi1/8/Oc//9Of/nT11VdLpRRtCOFag9K19tprL2kmy8uNRmP33Xcn05jQ3lr427/92y9+8Yuu67JT7bvvvgceeGDv/lfsXbJn3qz8V90vvxRJA/1W11F9uxZXyg4+ZqSkImwGcZialN9wreFiWXkaNzPpZc8L8raCZoziqFNNHn1O1pCEb11Klr3urkE/n8BpV1wrydOWZIF2LbGXNFaZztMXtmpcK+xyrawYZ0uvaZJyKY7ZoipE84hOfnDFaY8sv3d9s8NclAqtChPdgDDNXSvtcq1Ii1oI1wJgOF4YlSqrlMQAeE29Xr/11ltxfOBag6ko22VWLH74qU99Sh52dnayJrFNlQs6b/PRj37U6hMv8EMyHbocx7F76z3AMT+0Cgd2mGtVYvruj3/mpNRIVF57IRWnuA80HF0rLf6WRRTUUl1KXDr6KyMogmvt9O9L5+LPb0LZcc8iGnP82KAeOlXXdwObhzBKwkHjWlzClMMqlUY6E8b0Bk16sm387OUvkx46WxdIGfs4S1j8fd1lqxgTOYZrATBMqVQqLFSSK8i2sSonyeCa5//8z/+4rmtHfwVwrVYnDEOrQB0dHXvvvffHPvYxK0u2DSHblIymJbngq9WqPEVcqyxvkiRDzgrf93ucDBLawmEfONeyqQglrqXHD+105IZxzXNc30Fca5i4VppP3VzLdtbSeeI66bJTr1i/eLOp35YGO34N44JrDYxrSTr+bq4lPbWqvs1Zom+HUZpmSctdfyT1adGG0LpWFlcpqrNDhV62JqWrHlk56qENx06Zv5zIkXKUdrmWxLVyAetyLR+uBcDwvTZ2T6smudz4SvjLX/4SBweuNfjwPE/kynGc/fbbT3cMSFMWrT322IOLNevWPvvsI1u+853v/OQnP0kmVPXBD37w4IMPtp0Xe6R37+FUsk8c6gGp62RJPkJxKbTFrtXIqDOik8+6oBbqTueh+W6UgmsNC9cqBj7q5k5pXDTQcnX1uLHWo1ieUhYtuNZOcK38ZE6yLCpGNYvp+1depXtwGd3iq3RL3qgyOTDEtVR+uyfL1zt6SuPEjGJ86p0vnvH4prMfXLKWL02pvR+gL19RFkcmMUYe1+rqrxUiNwYAw40tjSQkYS6uyPz85z+XZZspAMC1Bh+f+cxndtllFy7utVpNyvdnP/tZ2whw7ty5+++//7777vvUU0/hWLWEa0nf9F66VYl1+q9jTzmtae48V5p+Jrm/wLCRrjJOw5X+WlKbv/dPUygsj0SLktFirpzQhLET88yEMYl0hX7UYrorrhVry8qKpqomsBWpaqp1K64TzSE6bfb6M++Y81BH0ii1aJWy122ggay8XkG0AACEsYzhWkOw8l7Ux21jWdtoUP7kOI7VMByuVnKtfHwevhxJaKsjpm9NPL09ynxjX+vWbkZcYjhX3+NQ5+aOXD1I7rhR45Wfx0xAC35fgRcee8yIekcjdKL8LE+p9b6vJCMuRrGOUeWupfS1RhcxN6RmRskGol83aMJDmy+744k2c98HAADgWnCtYY10spIhjGVcLF6QkeMajUZ5S+tgYKe6VtIV1yq51orNVZd0aGujF292gojQBmy4u5YObUmtXdHhhxyR5yFAkWjV7+uHP/iP3I3NWZ56qvVcS2WkE9VLUCt3rTh3rWrUxnK/lujCJ9vGTF912W2PVCQDIQAAwLXgWsOczZs3l4t4j7azLGM2TTxoFdeSoW1U/n9i4lrsWhuC5JDjRwcyohIq1sO57q5kfK08+PmH3/5RN/RyYxSJlnWtNavW5q07VZGgrxVdKxbX6mpDaLoFsmvVVSf/TmwkmnD/kjH3LPrh9Bc64VoAALgWXAvkP6FKua6rB3UxhVvGOOY15ewXPUbQAjuprtPTtSjLXavDdNkad86FbW7YMN9khu4Pw7XurhsQZnm3n2bF0Qup6bsF12rJ7yuN9Rn98MxHdKDIS/KwUYu6VlceVE0iCQn9gPw60Vqis2etmjT11XtX1NsUXAsAANeCaw17pGWgJHm3KzGUQSu7Via1m6K/lqTHqIXKM7o1/tyLTKYwSlNUrIdv3d2qOFuW1ODdiof+Wi37fUWBDjlOHDcpi/IumS3oWpkeUSDp6Vr5QFu+R/H6lOZ4dP6slWdPX7KUqE4YdAIAANeCa4HCrLhYJ0kSBEGlUqGiBxeZobttG0LHcXC4dnp1J8vbDnZzLf6G1tSaLkuXos4gT6mcoWI9jF1LcmOkgbr4vEt4oWv4JtCabpzRNw76pnYtVYw6Fbeaa+kx3DIbVC+985TCJiWriG6as+LUu14+f8qCdaZVM1wLAADXgmuhTGfWrJRSnudRKeUga1i50INWqet0b0PIixUvDIg6Yx3dGjFuks4CX4/wpQ3ruruixNdjw/7DAV/KuwAlcK0W/b6kDeG4UeNtF7uWjGvZUQPyEdUlb7tZH26MnLVEl9799IQ/zrlkykubjGthmEUAAFwLrgXA4HKt0ng1qis9BstVU1GDiF3rG8eN9k29ulYLcNCGZ909iVKeuzVPhdlJI8f4jaAl+/+ALjd2m54kIcxTEbaka+W2rvgdJpIENc0TeYR1oqVEZ9325LcfWPq96fM2mE6kcC0AAFwLrgXAoHctpuFHHUHgGtf60c9/sbkRdtYT9NgaznX3wAuleDQ6mzrG5aZwrZb9vjJzrmYRnTv5vK64VtqyrhWaKR9I3bRY1sMWLyKafPdzl9w//4HNVNUNmxFZBwDAteBaAAyqWlliu0CU2hCSWRmaG8n1jOqJXmiGaDI2rF1Luv2MOWHs5nVtkocQrtXK35duRmhGnZbz2ul0WzCulWeyVC6lPr/bNNOXGhO/SupEUz06a/qC8+94bhVRzaxEcQMAwLXgWgAMZtcqKtDrN7d5lAUmNwbPr/rxz50ErjV86+5ZmufGOPKwo3InjzCWcet+X5KHkL+m444+vllx9KnbknEtSbpDyqHUEdfydCxdRVm8meiKx16dPGvp+f/31Hqija6X5WOEAQAAXAuuBcAgqZVF9tZy3qDQdFLXuZjJy1RnGJqqD331sG+FRE6AqvUwrbtLnMSr++NGjfcbgQozLeKIa7Xq9yW5MbKI/vU7383zEKZmKOrWdK2sRmmDhTDJdAIMl1SoogrRqN8/eurslZfe8Sy7ViWQsSfgWgAAuBZcC4BB7Vqpzv8eRGFkGvOwaK3saIw9/Xyu5jR9VK2Had3dxklmz3goT22HsYxb243zUYwVtW/oyKJWzI0hY0uYuBa7Vk1cq8nXGVJxpjqITr7nhVNmr7z2iVWbpWdX0kTWdwAAXAuuBcBQcC35k2faEHpGutoaqFoP67p74IU6K4YZ+NpvBEE9RFyrZb8vncjExLX4Ozr6m8fkzYPjlnWtCiU1fnvsWg3iScfVNxKd+dDyk2evnt5GFRmdAq4FAIBrwbUAGMSupZR1LX5QbTqSHqPNz+qKvjlyQpj3+CiSFdo76NT7UY8HYHCLlk7yXqSz04kxzLLvRviKW/R7S4tM/abLFn9r2o3Tls2N0ZA2hKmJa7Fr8WVn1rLqaQ+tmvjw6uVmZC0v9gn9tQAAcC24FgCDqyKdlAekZcXKEpkypVN++UnGfsWixXWdvzv0eFfcLAvjxE+TSN99NiOQFsOP5j3wC3NDf57BWCRUN5cuhgFQvk48eMbEM0MnVqbEKAx11EoyXP76eArDMDeulI449Mj85GzB5Db5mw8pCyVkGlDMrtVBdNV9L018ZN3Y+1/erO8C6eIWJDEuJwAAuBZcC4DBVE9Lu1XAxLViff844+qN8mOlBzUmqmY08dKrNwTS5sfU2nhLFq3E3IvO8v1Edm9ZK+Y9A6+H1NSTQrBK4zKZkY++cdDhKs7rx2GUKHy5O/8b63ZHw7oWT6vWrJQ/NdqbeQNCv2VdyxQ5c7mIyGfXWk104R0vnjRjxYWPrazy31NfGhyixAEA4FpwLQCGhGslcZIkMrToRidl3Vq02W+axjyJDX3wLFF6gmsNB9eKacRRx0v1PTGZMlK052pV14oS09Azy4PXsZPwqZu5LXk+WteSokVhg4hd6/y75o6ftuwX67Ka/ph+ShhzAgAA14JrATAUXMtMpFzXtSMacyVtY0iHnjihmWWRtqs0U4n+X8ngS8q2UYJrDU3XMgHN235/h3zjcaR/tZIU326LupY+Q41r6S5bIf3w+/8RdsYtKitaCBPTgFnKWsKutYzo0umLzp616kkTV+eLUJCkGM4NAADXgmsBMMhdi7pcKzNXpiCljTXfJGKmb004M5AxbDMVx7EZjEsG80ls168UrjUkXSultrXtktfOxrWiGH22WtS18uiWZOpP6PijRuo2hK3nWsW1QkkTQnmD7FqzHTpn2ivnTV+8KHet0I8VLicAALgWXAuAwedaqe00YTKCWdfyGD+U0FYtonWN9JsnnuKZh4nprZ63JdT3zuM+cmPAtYaSa8V047U38UOvGUlijMxsDVrTtaQNoU5wk1GzwxHX8jr8VnUt/SmSYmjjOtFPnltz8h3PnHvHc6tMUD2jMExxOQEAwLXgWgAMFdf6/+y9iZ9cZZX//w/MjK/ZXiIqIIgMjuI4OKJfHUfFBUEwQAJhSchCNhJCwioiiIKDyqaioI7+xGUUBtkDCWEH2YNACCFk33qr/e7Pfn7PeZ5bt6o7CZgm6e6qPh+KSnXV7arbfZ/n9nnfc57PQZtByOPuzECM/mCwPeA9UZq0nJqhqCEEZ0pIPoTdyloq0ZOPP0VlJo2EHy7a0LEdu6xlb9V6Bas9mbSHzx47nMBjMa/l1vy5Eeebp9vhVgVYcM8LU+9cceEdz23CLn/23MI4sRaJRCLWItYikToUt8ygJVtYSSi4DXuwSCwVOlOur7GByMBfNmzuTzPfaAvXT6gWdXkbcGKtrmQtezv1xNP8M5Ib7/IvJJljjFHWqge13BvDMkwoXnl+5RisITRFsz7lWUv7EuUBgFn3/GX+o+t+sOylPg0MpGUty4wZtXMjkUjEWsRaJFLHsZYZwlr+ZlokVvheWMQ6fuacrTHzl8ij0PW35dL3PjYYEmlire5kLQYnT5isGajmkq0oTunYjlnWsrc4jfzE9l2M0xIbm6wlBPM/BQM8sSQA2wDOenjdpFuf2uIKCANe86ylKJVKIpGItYi1SKQOZ60ifBv0auFFd8zpMwMXAPECqCxrKWSxJmsNboNLwVF3sJaCuJpgBsLVj7JMkef7WGYt70PIUu5nr8Wt2afODfrCMTYf3QUaLFr2/dBFBLIKsGQTzHpw49R7/tLrm6fruoE04eT5TiKRiLWItUikrkYyG/cs+NZ/1wA2VuNq6pwRlA/1NLFWd7PWzT//jb2vDTREhkExZ5pYayyzlr9lCUMwDrg9did+edLYgxUcbG5Xvc8KS4y0fPXtpeum3LNu/vI3+l1/AcAOW8hajPJaJBKJWItYi0TqYlnWslFPj0YXZubpKxMglObem1C6JVtNdzpirS5irTOnzErrWROtfRYCGJf0KxubrMUlkzp3MQnLkT1wp0+YKhpyrLGWVPYMgj48DIuPsWHxNoCzb3910p/euPTZ/h7cxu58xbJWKnyPNxKJRCLWItYikbozoNOetY6de97mUDWEDX+MactrYdstl9oi1uo+1jpt4uk8FMVqPovUUhk6tmOWtSxo2fskSn0NYVxNvvfNH4zBvBYXsXKWGKk0oGL71BsAC5dunnLX1l9tgX7cpgqiH3RqYSwj1iKRSMRaxFokUreCFjhL9xrAYV+ZGDh3ZpOv5dKap64l1w6sZYi1uoS1jj/6BO+R0rN1oFivRcd2zLJW0ctYcuUbV6mGGXv9tTQYu3/aMlZqv+L2vKKXluHsRwbmPlh/xKD5O1jg4gOgiLVIJBKxFrEWidTp56Nd+WSYZtMtgE2pmXHZD/ok2oUpyFmr2f6YWKtrWet3v/y972ishVtDww15vo9l1kqyOOOpn4Aq0f7YfX3hxWOOtUDYM4a/dgNpHbS++qkNZz7YO/eBxkvuyg5AL4gBEMhaCbEWiUQi1iLWIpG6kLWgxVo2KlobGnsfuJCccY2NjD2M5c7wutUZmUCrU4P3NncT70PIsU0Tj3HBTxwxxiUd3rE1aQezlu8RnCQJWhHa2ZlBra8+7ZTprSsgZowMNqFB1HLWatidmnPrc6fet33WnRvXAFQw3dUPsgZSCE3rtUgkErEWsdY4G7tpmiqV/+2TMl8lf9BBB+23337+sX+1Xq/be8bY+eefP3fu3IULF86ZM2exk91Aa11sbJoLQIq3zf8ga93+Kmm0QnB7AGKDxhhljrbvvvFoPYrz3Jd2N+QuDOQkkEdzR0ownoOWhKgSY2sm74qhWimU9oCeNHYPpRDtPPalz33ZHkQZK5Vo47z77QZxHA8htB0Rbu+dVbSIDEj7fylOQKfbQzn3/t4T7+k/69Znt2OvLSZ0Bf96pBlduyGRSG8pG4va01oBV0XcyDn3z9xwww3+sY1g6ddFrNUxKpfL/kFPT88BBxzg/7ofeOCB//AP/1Bsk2V41fKMM864+OKLr7322ssvv/yaa675wAc+MOStlJOfG5bNiglT0F1BdKTRAWwHVwOBWnDh5dsaqa/8EVgG5NoZq+bN5Ftyio468Bh7vxPNjOcrHopjv3RcDlqtUNy5YwDNxw6QPf3a2MKePOfPW2Di5lUQ6aauO6/6Zlwt3DLNrOZexxuXcQPJACKsRxVVgOlLSlMeSq9/ZmMZF3ElXNftnqZCuhSrphMKiUTajT9ozbjRhpRaaxtV/uIXv6BfC7FWJ43g/KKpyzvZ+yOPPPLggw/2lwq2bNnyxS9+0W4QRZEf362/rlrbof/hD3/YvmQjAHufJEnBVPbBkKSWdKKk1hhhLWGQr4743DEJQEXoqrtA5K4gEWt1hXSrelAlWkTSUtZJXzuZWKvzjmSzZKC4aPXIQ4/imqcmaKks38CbFo4Oa4GQKmOuh0QYByWAqXf3nPlo+mAdjTEiHWoIpXeEx7bpgk4oJBJpV7JA5WNIG1iGYVicA4sHNpj82c9+Fsdxe1BKItYa08qyzJOVfWAH97777mtHsP3SjnJ7v88++0Azb9s+DeyDz372sx/84Aftl0Pe0M8QfxHCvlX798LgdDBpFFkr0XDc5OkJVvjgcooUTG9pwAVnnrWgqCEk1urEYyyYFIlPI+S2CpTX6kTQ2kkwoXPQYjXeWpdnzOiylpApcxnychz9paan3bXt9Hv71zhjjNRE6AZfsJavbiWRSKRdBKU7BTAfTNpXG43Gb3/7W/pFEWt1jHz2STjZB0EQHHnkkfaxHcrg1mgdeOCBnrU8UyVJ4jez9/vvv78f+mEY2mc8oVn19vYuWbLkuuuu+853vvOLX/ziJz/5ydVXX33VVVfdcMMN999///bt2+nXPuqsFTO8Ar21kgTOFizQmhWGhLq1pIfWa3VskO5AihksMFP57dILLyPW6kTcKh5YoMJLYAY2rtqEBy1tHU08gys+WqxlNNcguLtwU1Xwy8dfnXHXxsm3rtkA0MATCHY5Fr77Fl7pobwWiUR6M0kpV69e/fvf//573/veNddcc9NNN9lg8le/+tXPf/5zG0necsstV1xxBbhrTDsFMxKx1tiSpyZoWrvYP+T77bef5y6PW//6r/8Kg2sCmdO73vWuAw44wD8zJHPl//D7EKE9hWWfLHJipNFlLRunVSIdKbjs+9eXmPTZrcSvozODHKiJtToyQPdBrTuOLOCWuIJS2DLGINbqtLADmrZD6FFk4PyzLsiXbGnIQlYczdFiLa2YcU3SA3QdhG/8cfnsu9ctemDrFsdajvjxj0qmoGl2SnYsJBJp52oPKdvrBv2XftHKr3/96yEbkIi1xrqyLCv8KixEQdP5yt6/4x3vsA9KpVJxCcHD0mc+8xk/H3ymy7RJN2Uf+8Xc/jFR1hhiLQMJx9TWSdPn9CUsdAst8hGgB7GWj8/pyHXcMZZc4UoenRtj/OCKq4m1Ok7+Clf7hVsMLAx85mP/JRrSHjfNjGctPMeOGmuBdzdljqy2AVx8+5Oz7lz12+2wxV3E8VdshD/DKEmsRSKR3iaD/fjHP/b5ABKxVmfI55r8II7j+N///d8PPvhg/5L90rtfFH/mPYPdfPPNfh1Xke/aKUcVKw3ard7tgx2XeJFGOhR3bbXKsTzmpNN9UstGc4HrtjQkr0Ws1ZFXTxLmjyOLuD+Ep554WtZgxFqdJX8K9etpi8oCLc3k405ptsHLj2atVhtV1sIloMytzloNcMWjr8/604q/APS41Z54SVqwPEMuOLEWiUR6qxDFFG5q7eGljx7tMzfeeKN/nmoIibU6Q0VGy9cTbt++/eMf/7h/5r1O/nLC/vvvX/y933fffQtXDDJw78yDjlF3PTXPrVpblcZ32Yqk3oG1tAvHNbFWZ8mG4/bGY+HzWmE5mnz8KZTX6pYwBJC1UmQXw3MfQlc2MGqsFUZYHhgp2Q/wmzWVBfesOH/pypUOvdylOOyd7VnL1RAKYi0SiTQMAAPqZUys1YlqN7kqrptWq9V3v/vd73vf+3zFYJqmdjNvkmE1MDBguevwww/fsGGDf4auK3TgOQtjIO4Q69EXX64q8GWEamesRb1uOzEcVwILCG0gjuu1JMw/c0FjICDW6o6DKwOVs4vKj2aG7bfS0WItA/n5ZJOCK57aMGfJysX3PL0J8KziurzhUCTWIpFIxFrEWuNXlqZ8eioIgoK+7ANch91kMG/j7p8pigCL8kJSZ4kzwzmkGmKAqWefG7uoKFI7YS23totqfjpM3htDpip3lVSwdd02ymt1DWvZI/b7X/yvjkxu6+/qC0Yxr2XfPkjRh3CthDn3vDT1vlfPu+uJ7e5SDu6rG3jK+HGm3VUdOp+QSCRiLWKt8TR28ZJoc9Gh5S6LUmEY+i+rVd/ndpDZS5IkdpvCKZ7ayXXecdeY2rLHdSBmnz5mgj3YsbvJoawlibU6NByPghgzCs72/dUVqzC7RazVLQc3q7LTjj/dz0sk6ubRHBXW8kmtMJPcGWPMXPb6acvWXvvcqj7MdDnXQdnOWpJYi0QiEWsRa40jWY5qLbkebJ3ZbvniW3dbxLIPhizQKr6d1FnhmmnWEF527Y8HJPRnKivs3QezFhBrdZqwgNCydHO91sRjJ9n7HLeItTp/8tojduyRx3mGQZx2Gq3+WngmUb7pud4AMPepvlMe3Lw8UjVX7WjHoj+FEGuRSCRiLWKtcaqiDtA+sDRV0FfBWjumrSxuFYkv+y1k5t6BsTgICYlA1lpfbgQuqVV1QZEZmtcSxFqdJw1JlObHkcOxXzoOwZryWt3CWjoy00+eEZcSfzS90eso5rWYa1dcZ9GyLT1THt0+4f71q7HXlisXxHPNENZKgYYciUQi1iLWGj8q2hnnUbgDrXK57L+0xOVHtu9E7MnK98uiX10Hn7MURkiZQtaylPWXzdvtfYUPYS3dZC1ay95pqOXWa+V5LQmnT5qiU0N5ra5hLUsrD9y5vPB8t2fmUeyvZTCllrPWdUsfnnD/xuOWbX4DTywCd1QyfwpRmliLRCIRaxFrjT8VOas4jj1l+V4u4EoKfcrLL80aFMm5akMppX+e+nZ35KGXEAptWWt7mJw+f1FvohLIkx95cGacWfMIsZZu/wjTdsu/9gs/jMCbFvkD0+qLana47fSdx1E47n9ugUf0tt//Ka/bUq3OadC2woemQ4cdXAUq1PY+DTKsIXQHNONpcUyHTqS9v0eZwUbG5/5x6cSlm6c9vn0Ddu2LEat8DaFbr+XKzQWxFolEItYi1iKRujxUk1r4ZRPMLdn6zDHHW9CqSfwyT20VwbqWe7/3qC6oyX50nHDedLRmLiRjcYR8pWPT6AFZh6TP3fohq9kns7AGmJ/xK0bwhg+0jzC10Xzc1UC2jh00yoHvaIwOCiPY2Za011kr0fbmjzLPBEt586rEyEuCDu0UXWNZ68GNU5duWrT89R58PgaV2hOIcm2OVT7wGLEWiUQi1iLWIpG6O1TTqcxsfMTAhEqHypx5znlr++qJi4N4e5ctXOGjHW7t1T1yrCUyI2QzksS1ZGEm84/VotGzCWQIog6iAlkfRNtAVkDV08pWMCkYkWY8lcbufCPTodCyGXZKkY67fj6mrfOShkpf1d4LJr1hBoFWd7C0yWDGlJlxPakN1BGqa0GWsFE6vnZ+4dnjFYCpd2848/6Nv1odxLifoWMtvNqRFL47xFokEolYi1iLROp61lKgI57YGCg2eezT0FCwVsuNcIRYq7mArBkpGtdE2d7HYdKolDGpZYO2qAQyOOe0Cb/74Xeuu+TsOZO+AiqwP4dnLfcduL/ClSohbiiDnpn5TzLuYnHPWpY+r//BD9H8XRpira45vlnI7PE98biJvoUaLswzI1cxuDPWQrh6HOCMezctenDb0xzLBzEFbdCgxee1WA5YxFokEolYi1iLROpyae1WUcSChULZ2GdbLTxm8rTE1RPyIc7vI8JanEsfRCZRqmXBXS7fxVMQSdS/9cffvfTnV38HVGSJC5KqfbD89t9fdPaZabW33r9Vs9hubNp2tV4P8rcdf7F4zloWUTP9pc99uSgpJNbqkuPrluF95pP/5Q+rLxDFMsLROb52lsUDWv1skzzridKFD2xbD1AS9lwSIVa50WjPIpnDLLwz5PlOIpGItYi1SKSuZq16UGOa+wVO1YzbMOikmfMbzXgoZy0zcqzlQ0SljOQiRyzFs3oF0oDVS9dfeRmkjaS0HbNbKhX1AV4rgYj9lxDX/vzAvY2eLXZjE4c25HSrv/IFW/Ze8HGGF/ZHTmRRQ/iF//pi7gxOea0uYmkW8HlnnmWhi0Uc0ctgEniUji96u/cDLH5kzaxHt8/7/YptANvCukHWYgUZJq6SkFiLRCIRaxFrkUjdz1rerKyvXPJ5rf4oO3LCZJ/XGsRaZiRYK1+gxXT+oZLpJMTVZJajWKN/3avxwDbLWviMbnoPardXSvRveMNuFvZs+s55C3StDyR6TOsoyPcZnTbEuIMLg6WDBWstmHN2blVnKK/VPaxlj2+pp+wPMaL1qB5fKXkZYPq9L5x016tzf/l4CaCBPvCxAlfcyBC3YoDQO2Nqdz2FRCKRiLWItUikbmWtWq3CJfPeff2NyFLWyk19BWsNcn5X3iRw74aOdjci5s+jENXKkAag4qRv4+Jpk7FoUMUWtExqH2BNYBjGkhuZupY9SkAWYzlk3+YbLr842brOPra4ZdC60L1bJMcha/lY3IbgWcjuuu3uUY/FSXt+wigsEH3p+Zc9biVRyjMxKscXE1ca+gDOWPrS6UtX3/jk9gaeSXQKAXbsM3lpcozdjSWxFolEItYi1iKRup+1XFM0mbIMIUca7/z+51fe2Alr6RFjLbdMS0lMaqmUlbZ8e9FsXJoVlvCGpomyXq/7JJg92VYrgSMKCXEASQ1EBPW+ay9epEvbQKYmChwlAmcwTlmL5X3RsMbMYadgklira1jLG75POOZ4ez+wvTSKLK3cWFsHMP3BVxe/0Lsygyqzz8i6KmfOsQa9MJhnLdesT0uqISSRSMRaxFokUpezVhDUPeTYOKnOdAIwceocy1oNjqwVxLy5ZEuD4iNQQ9iIM19AiFiVVP/7grMhKSNrYV4r9dfC/Zbez7zplOhtPlJkLREAa1y5aJ5PbakwyFe2ZOPOhxDNElwN4W9/9TuMyzNNea0uY2nPWrnxiWoD6RE/vhKLA+EPr/ZOWfrSGUtf6gXMZzG8yhEn/qpNitdyEpzMrkJZaxqEJBKJWItYi0TqZtZSkqdpbM9YYco9biUGvnrSGZGGWmYKK0KlVO5UMTKxkf0sLVi9lA5sQcRijaKA0O+Dh0PZTG01i+UkEprlK4tbYRmS2kWzz9D1CjIYFyzKxl1g16whtAdy7sx5aPhO67W68fja+xOOPREProZRPL72dLFdwg8eem3mw69Nv/+FAXs+wV7krCoGYm/u7vJaDN1s7L5ScpVEIhFrEWuRSN3OWs0bRBn2D84A+gMx77xv2gehbNm+c86Rf/ZyXisvHWQJD2qIVSI+b+4MVe9DysKMVmGJkWfAVJHXynFLutQWemk4e4wYGuVH7/kTKAY8c6+q8RXbNWsIbRT+yY/9P99lS3JFrNU1x1dlWkRoNfnf377KHmXP0qO1XsueNLYCnH/bc7Mfen3aHX/usc9gVjUNdd23MPbdtRQwBilyIY1AEolErEWsRSJ1OWspqSTXWgvjmuNIDIde29Sfucgp1ZDKotuoTpNgr++PpSmOtYL17RvPnTMdEUskkIUt48Gmh3uLF5qW7nnmzROXZlml3377ZeedLeoDaBPPI7QlHFdr8R1ryRi73J543ETkLkms1XUszfG+sCIcxbxW4ljr7P97bu4Dq7/19Pot2l+pseMv9f368H/8J1WOtWgAkkgkYi1iLRKpy1lLZqlWwrIWLncCiAR4e4yN/QHilgHmnvcbK5nt5TOohKQCMgQV/eDSC3itnzfKTcoavNDIvGkAipfM5fZN67CBDw+//60LsQTRvrPCNsfjKxaXeY3ZbX/4k4/FjaIawu5iraan/4pnXrT3raTWiB/f2BljLLzr1YXL1y6XUEKw0kxUNITeaCcv/DV2w1gVLSVIJBKJWItYi0TqVtZyDIMYkzHlbcQiBZUUvn/DL2s8ryEUBpTx18z3MqgY4VZnVWVpc2Mr9stCZwuW+bCyFT22iMvTQ+u5QYWF+JKNO9O+Da8Cr0F9G4j6uGMtF4vXSw0eiyLv0cItUocfX9+rOg1wbeX82QuykGUJG61e1SHAMwIuWL5lwX2rVwHgei2QXFQsWeUJLX8KwUsesV8dSmOQRCIRaxFrkUjdzFoiTUAr0MazFve4peGE02eVU4VfamQtqRXn2UiwFq+CqFx90YLcdVCySqWmhrgO5mgl85vbK5/C4c0bWkpLLXhisjro6IJZk0FWQNXxI8Yfa/36Fze3luYRa3Xd8fWG/kd/8Rgc96atknBkFQD8Zk12wcM9C+545WXHWg0duadTPzeNb9MnI9CxXx2q6CCSSCRiLWItEqm7cUsrUaSGMgWNTFvcmnDqTN9iK5Y55AjB3j5rGXjTSkDDQFSTdc8Dr0DYj3ZlApNa0rS5Du6CtVSTtdzye1wOgoQmM3xPrEusQ2MriBp+2bQDgV2Fo13j1OcyfSaDycefgg4Kulm2RaDVRaxVLzXwyAo4/qsngGtYhWWEewq38nyyzuv/mk6kqoVJGq3b8YINFg3Ov/3FOfetX3zXq69a0AKoZxXLWopVfRZLgU9tMVBMFc+QSCQSsRaxFonUxdFaAV1FFGWJ5eIrrondYveaRRXXoimOw2GxVg42Q2r85KBiP2AsdUFbCrx81YJTHBfV0UjQPu++c3Bx4OAawuY7t+e1fAynWYImhPZ9otJ3zpqGfbd0JlVm2Y0Jew/CteoaFJT6t8VmYrrj+/8Yn+CDCV9wUbjIMx5a+1+abr/RXOhExWFiD10aZPZ+6slnaH+NQTQXcZliIAzrKBvMRLl5J9xqrAhnqMYnE1cx6M4XAm0FlWgYswlg+v2vTVu28et3Y3Mtl25OZdJnv5HbiddKbeXUZUDSESSRSMRaxFokUjeDVhtmOBM/Zw6GhoQG1g+EsYuofO2glkqKdPfZQ/v3NG2U5W8JV56d6vWqv2Re792ketdsfmoJiAoEvabeDy7npmT+7abd4X3w3pvBby5zn3qJV9BLvSCibOPrEJZlXLGhaJzUbZwXp5Hyq9Fg8GKwvDNyV7AWx0N40tGTsbWR+zmx1+0OoEWs1bmHOGxYBMKFWy8+/ZekltqjnBNXK32Lh1uBVLvJNv6yg5tWzJUC1kHH4J4JXdoKJSVkzJ4d7MtrAE6zrPXQlp+9WLFT2rXnSzFHjT2Nhc+Tq6Lu0RT0TyKRSMRaxFok0jhjLRsYzb3gst4YL2AnBmsL3XbD7GU8JKlVZKjSzC2Y5wwUVxmu4rhi0Qy25RWQVbDBW1yzMZxlvKKh1mDW0i1zwh1Yi2sTx851EDtupZa12JY3vjlvho1D46BkgzylmZTcfzfjcqjxhtb5rdNZy9lKXn/Fj/KCLWKtrpvDeEB9iZ8YnDLeI6xl/FuKHVkrbrFWZmdKDeB5gGkPvD77ka1PpmC/RN8OO/W0hbLQsxYfxFp+p2ngkUgkYi1iLRJp/LFWjcERXzg2dGWEuGrLN2XaQ6xlIO+tE4eRb4ql4gDSgFe2P7f0NgtasrwRnd8Nsy8xxkzbNzYbaul8sVbbupSd+RDqet929NhggcUtYI16z3rAFA9jiQ0BpSuMtEGo2aGMsCvWNZm8pLJv3QDGy5H0P5HBv1HEWt0gBC0DSQNL+1jA6/0NbKe2h1irqCGUmP11LhdN1ordDceREpBmRultAP+7qTT30XWzl69f57Je2FhZequdgDVZq72GkFiLRCIRaxFrkUjjlLVsYDTr3EtsOFXhwJpOf1qx4bCH2YklRr7SCi0QJbYqtjik4m+dOw9UYG+i0auSKmjs0ppxpoawlo/SjMhZq7DXa2OtKInzF+w+awZhJe5Bfls8ZwqYqN6/0RGXwKpItP0QO1u11RXeGAKCbTGmJaRrakys1XVzOI0zhuuh8FgvPuvctJ7tTdZKC9ZKsEw3Q9Zy02ethG8/umLuQ6un3/WX9S4L5pZ+pr6blmctCc2kFrEWiUQi1iLWIpHGM2uFCu5+5JkYXZuxhtCv1xpOXsvshLUw9JOejTRI7rzd49Ibr1z/7a+LRl9c3goGU0+CJ1LyVPCiOLC5t65eCrNeYlBYaVoJNHufJIkUzNgYTwvELcS5CHSQlDeBxnrFRrnHyLTdusN0mUufY62bf/y7fMmd+71I/HVSDWH3zGHspuXHP4fPHPFf3nlyT7GWd8KwrKW8N4YZxFpZGiJrKW3fdwPA4nsfP3PJi/Pv+8trxlUYKtzCzeWUuQyXGspaNOpIJBKxFrEWiTQuWStzsVRF5DWE+EySDpu1dnITaIcHgkMSOp/Ayk1XfEOVt+GFc/xATMQIxX1UFmRZG2vpN2Gt9g+0rOavnUf1Co8bSFwiAlm/4oK5EPcjbtko0DAbL/oyRbPDcjLTBQdYwIyJs0wI3hvDKBeaE2t1i6Igzt1cNNaInjxhcm4kugdZy+VElS8btJOuuV4LawJZjNNKItytBzj7nifm3bfihyv7N7hXcTtmJylTkHLHWgZaDcEgn8skEolErEWsRSKNP9ZKHWv97u6HMLXFm92CjdgzrOWiQB0nuLDexmC1PhDB+VMmWSxiEXoSZiyycVjMMmF0YXehckKQTR+AHVirDbeU8SvCtGsLpjXWMkmMFFkVVJX79WBpFXSGL7UZxw+5dfwBZjDh8yfib8stlzGq+BURa3XPHBaJ5DHOhYnHTsoabI/VEEI+pyROVExP+TMAspbGKyKYFrYzLOENgNUA8+556vyHX14WYBdj+yr+z3GqJqzOMX/dfENJrEUikYi1iLXGpYQQ+KdQaz+C0cZtsJIk8Q+UUmmKC12yLBsyAXzrHiu/QaVSKd4qiqL8r63W/hl05SaNSdaKNLLWqXPPa1+vtcfyWj4KVBIsCNlwLWusemwp8ACCAZ/wcqHhIF9B9SasJYeyVtvHami/2e3TMsjqeTNPwoVhOtZpwz6pXBzYnawl4LjPHu8PoV+vpYS2845Yq3vmsDN8Ny6XtW7V+j3pjTGItVie4XZ5rcg4msI5yKRA18H/b3109oOvLLr3z+vsad++4CdmhgY2AhjHd9BgWnkt094uj0QikXamer1eRKdWjUbDx5D+S/u37KabbrKv2gdKUWt0Yq0OuU5Q8FVfX59HqUMPPfRv/uZv2q8iFMTFGP61fe973/uhD33o5ptvNk72Heygl1J6DGv/xiEzoR3MSGONtSwo1xUcPXlm4h571jKa7ynWCspVdMXARqghsOCai8+FqAwiRE/CnbU8LlgLWv7WTW8M1da5dQfWGrS/aAEfWNYqv/Fi3LsOTdUMA5HutNuy6ooawrg/XTzzfF6VGCSn+YI2OxOJtbpmDuMCPDctsgZTiY4qsb3fYzWE7n0kZqWY8e2wXF4rwkSpNM4sNGG6B+DSJzfMXvbyhff9ebMzxhBugkIsnAGOdKzlLoo0L44Qa5FIpLeUjTNtPDnkSR+p+iDz+uuvb6cvErHWWJcfvkKIIAj8OP7Yxz7myeqwww474IADPCD56wp2M7v9hz/84eXLl9sH73nPew4//PCd5qnac192zkRR5N9hYGCAfudjlrUiDX2xnnXeZfbw1zITYQ8fg0vh92BeS0sTBaDSlU8+hCu1eB24fX/dvqEa6ldRJKlcdsvoQRmt1g+j23sot3lyaGB1SAZA1i89Z3bSvxk0w75eu7DH6I71WqX1Ve9DaLK8htBOQ2KtrprDdlxH3F8huPrKa/bkei0/YNASVNhvL2alW1IpjKhb1mIatgBMu/2FWQ+uvuDuh7a78mOs/bW74VKp7tsda+HFDl2wliTWIpFIu5YnKB9D4rJqY5RSPlItl8t+m+uuu65Wq0Ez5UUi1uoM+REchuH+++/v+crq+eefP/DAA4uMltf73//+iy66CFy5oEWvI444omCw4hoDc4JmEqxdO16rII0d1vLeGM+9vnUgfRs1hOZNbvhx0UAfsPicGaejPUa9D60C83VWsOM9DGItncdrZjBomUHb7Oh/iB8kAwj6Lkd/+RT9CTFZ1/KebmOPzvdJM7Dy6VW+xZavIcwSRuu1um0OuysPBWv958c/swc93/37cJwn9tt10d2Y+QbHso6dwAG2AUy69YWZD639xr0PbVaaQSv3jFfxhN0GdwAnGjKXLvJbxFokEmlXKkoH7YP2GNLHoj09PTbU/NGPfgRtS1RIxFpjXX5tVSEPWj7HZfWhD33Ij/ssyyKnj3zkI36Da6+91tcHlkqlIovlicu/p0cvC1cW4fyrfjWXnSr0ax+brFVjyFoxwLevuykx0EhYGie73V8rZ6rmzdORez6JQ2dWgXaCabVXhQOgYtkouV1Gk9AAAIAASURBVM12Rlq7Sjbt5Ml2Hhu8DUalqXMjjECEabkHRCzTsMlabRmzPE7s8P4/BqZPmgkRRH2JL8dEE0Jire6aw2hF6EauX6l10tdOxpG7p1hL5qyVuQVXqslaieEKmBZl0LE9428COHXJG6cte+NXK9f0G/fHIMsJ375HIHjOWmhamLOWz3sRa5FIpDeRjxL9OpRqtTokTLX66U9/Sr8lYq1OkuUfD0UbN26095/97GeL5VsWpSxZ9ff3Q5uhxbve9a6DDz74wx/+8H777fepT32qSIKVy2U/H+r1+ic+8Yn3O+27776HHnrov/zLv9h7+/iII47wS8JIY5O1fF7LHv4vHH/q2/DGcO9Z3AZlinSaRPVy//zZM3B5fVh2XbZSNII38Ba3Hfbf7DL3tUPtIksgLEFatx933twzMfgzMglqO1AWa1rPdzZrnfjlSRjSinzpm29r5no3E2t1yRxmKc8HOzY1kDNOm4kL8/YKa8Fg1krB1J2dKLwoYNYT5ZOXvbFCox08zrZYQ7NJhMMu52qDrCW8TYZ0E4xYi0QivbnCMPzYxz5m40YbT/7nf/7nP/7jP+6///777LOPDSzf+9733njjjT7lRUu2iLU6QNVqFdrq+pIkec973pOHrm4EH3jggcU1BhurWTD75Cc/adnJPzMwMGA3sN9e1Bn66xA+sdue7PIPhjxPGmusFWmoCihzOGby9EgBd9tJke4+a4n8lsf7OXFJyeMo0Ipd+o0LgkovmJRbBMLuPbqVDdNt9zsQ1647YumWNeGg5WH+FYFlhHFd1QfOnzcLnBe8zOI2y8OCtXynr85mrZOPOSWvIeS4XovyWl05h+N6IhJp50BQCq+6/HvA9mheS+WsxdtYy+W4UueCkdrJ84fV1TnPhictW7fBXaNxl+6atoXGjz7HWqrJWopYi0QivbX8KixfYFWr1Yqqwvw0k6a/+c1vvAkhsRaxVsfIwk9RFHvIIYcUrGWf/OhHP1q85Dnqne98JzTTXHabgw8+2D+PjtLugR/6fgPPYP6+/XnSqIdqzX9aa5x8GGQPVZnBVT/5pX2cKeezMPg0tzusJQvTC79SS6sMP8Te8xBkkieRJGtild7hNhgNd2CtnfhnDMlo+fuMYcCnGabRsvCN558C1nDW1Zh5a1JHXtzYqazV9lOf8rVT8UDKNqtG39eBWKsrhMXbxUxwR7ne32ABH2TOOWgV4m4OJO3nmuU0Mdgg1H4ZKl2XIHsNXHrvSzMf7Z9896tbbGwkE2OaVy0UMG5kzlzNGkJF67VIJNJfcQYy+RnCxx7+XkpZNCiyIev//M//FJuRiLU6Zlj7lVRWf/d3f3fYYYf5TJflIp/msriFBUhuy4MOOsizk/17bzewbLajAQapA+LywSZ+PkxKXK/SQGD9T2+Qba/GvElcu8laMndmN5D5DqfKJZfQNbp+8dQT0X6Q1e1TjEszBBX+Ck/A3TMM9H277HhmMRq+s/r3zp4BqgomtE+BQLrKWr8T2e6ZMeY4CgbF0DZ29VM1CmKVuUhW5ABrdjDEJ3XTSTu/musGbdJI7aG/YOGFeXbW90nQWmphx8TuspaB3NEQdIrzxc0e7plOxTjWFPp/bQaY/8fnZi3ZcO7SNeWiK50Z1Fu8dRGked2ExiOJRHo7wSr1MibW6kgVRi6FH8bBBx9s/05buPrIRz7yb//2b36F1X/8x3/YqM5j1bve9S6/5fvf/367Mf0OOxK09NDCPOyvZfnZwhCX9jCfNnuBJZC+QO5+zU8zK6Wx9ChpsRYzcT+oyg/PmwX1bcAb9qlGxPZ2vx375gnzsaBEwIv67r3xe3Y3OO5DCtL4bJ7Jw0QPimPskA1t2dxKTFVqZW/pjuuyUuNrNrU0RXdyUjfO4DxbhRd6dZ7amn7qjCFN59Ar2Uhldq+GULlLD/g9FrRkBMoOpJy17BlCsIEMRA/AExym/+7pix8fWPTHpwM6JCQSiViLWIv05iPY57XWrl0Lztzi0EMPPeigg/r7+7Ms8yPbYpV3HfRfHnLIIfvtt9+TTz4JzQ7fpC5gLVyypRCutlfDI4+bmLmoKxs2a5lWY2IHAwxkYPrXqp7XQFRBxbhoyuz1miLjUnPC56zQirCqe1fX1jwPooEdjbUZtJMdxVoaC7PycjKVaBGhI/fAphKxVtezlktiurWvInfIOOYLX22ZaLaSSkqo3SvbLrJYYGKcpLlPBrh3ibmo2GncD7AKYNEDay95rO95Z4xBIpFIxFrEWqSdyxcEFsuofHs4P5qllFiIIqV9tXC/8GaD3t/Cr02k32GnsxY0l374iKohLBXBrEUXJpBXAKrdfX9n9a4KJ0O/MMwwiCtXL5oFqgJJP7oCZskI9Db1H8GF67RqY0dVB1G67ry5wLGlcsEqOW4V9U6dwFr+Jpg0zSU1OoDTjj/dfkmg1d2sxUTWYiONVoSTjz/FuOlqeO486bfc3byWKS49QIy45a7BcJ+dRo/SKAXRB/AkwJwlK6f8+on17lkSiUQi1iLWIu1EnpSCIPCduf2T5XLZr0Fs72Lcbh7oeUw7AeW1uoi1fPcbz1e/u2PJtmoamGGxlvMVzK+O+xZP3nqDBdcvnqeDLWhlJlNWqZm932/Hf0SauQ4/MpVRL/CBZb/4MZS3G5MKEP4n9z97sfMdxFpJlObHj4Gs64lHTyLW6nrWSlnSYi03t/7nJ7/MF2u5jmpaGmirNtzdKaNyy8HUX4DxpwWF06hqP60EcNEjq+Yse+3cO15eW5gQkkgkErEWsRZpiDxTpWnqbdl7enr8M4wxn9HyI7tY01VY0m3ZYsPllpk7qSNxy+wEtyphaOOqcswjg5ersd2WHgZrgWct/Easa7Jxm/0nfeHO22BgG5gATTKkJQN0KUzlSNQQRjF+HKDPe4Q7kAU3XXKRMTFesrfPizyt1764f+yzVsZT07RezNsrCZg/fYFbKWd2+oeK1B2sJXVuVoT2mW6m9W8ZaDlPaihYa3enr/8ATJ1BqvD6iGN4dypIIZKQxFwPAJz9wMtzH3nj0iUr+7y1DIlEIhFrEWuRdhTnPAzzYvuiTUHRzaDRaNgnfXmhd8UY0r3bMpjFLfIh7EjWMjtnrUzrUGhvzbemp2oDrLoYRg0h+MVarhRVu4ao2LrquwvnAotMWgYVpeUBEBgPBikfgRpCR3XCRNgaSIR9djfmTzzO2ay5fq2O+hTsrsXhKLOWcob1SujMu38oyMr8S5/+8k4zGcRaXSN/3PMzdtMbw96SWlp4YwyftfI1nDK2Z33L7q7nnHSFggxiCSzhsF7CBc9sOvH2py6+7c/VfCkXiUQiEWsRa5F2IV8EaPmqffFVwWDF4G5vmVW05KLUVtewln9auJVLDW4sYn3+ayfboxsO48K4aaMEJdEVw/lH8/4tGMLZG/YvljJjSo0EAQjpolKp3JItG48muA9JDWSIj+3uKdneAnbMadc1hOh80MxrYYclAa+/uGbHH8E40cDvDsVp5CsD8Zj6LJbLam5bvz1rMD9UsIH18GoIfRktyMBilT0ZZMhaCvNaOoRapGN73t+gYc6jr897av2KFI0xJB0SEolErEWsRSKR3oK4vD06F7LZYuvYU2dY1qqr3WYtzBEpY1HKso3KAgQtGa94cAnmkezjkWebXbDf5efMdcTl2C9L/GY5mHUIa5UqA94IQcYqj4gjQz2Mun3ithGUHxsur3XKCafmtoStGd22suuvH20aexIHIPBCmstrGZf/jcGSnByIYRWHc1f0TVn60tpheOeQSCQSsRaxFok0HlnLAOeyudIeqsx8adLpZYXth9VuvmteFpgKUMItr69BVDv3tEn4GBd/jGzN3q5rGq8+fyGwKKtvx9ZiQR2SNEmxfHbMocqbemPkP6CCvs3937/06r1u7Egaq6z11S8ea8e1ZkZyhe6ULu21+94Y2k1by1oSWYvnbR84SA5xhJMZbnhsy1f/+PSCP69/g4Oi4UYikYi1iLVIJNKO9LET3EJvdCwkLQVZYu85BAAVuXs1QoWNu6wHkIS5F0W1D40xcLW9GGkvil17dZitGyEoD/HqkB3IWt7zXcZq2sQZxFrjlrW+dtSEIb2MU5YMm7VCt5wRJwrzlyqEgrhueAng4ttXnvCnl85/emMP1htSCSGJRCLWItYikUg7Za0dcMve1cOMO9uxsoJlL6yMYLdZC6lGuwxSWLUko4MtN128GOKqjdoQ3EbYY/1NPeh/cuFC0NUs2AZpABkfAQ/6PctaGU+9EYKvIZx6wjSqIRy3rHXmlFloj+Fg27hkdJLFw2Etg+aDiTdzd+aWbkQxO58tfW0A+MayDXMe75151/PY/UMlAJoOColEItYi1iKRSLtmLZ3fDwxYwoJUQ0PjqvfPTzo9huHktZJUtnoHJz3fOGMi9g42rKh5G7newW/SW7nac/3587C3MjY4jkTYQNYynZrX0qmZeNRJlNcat6z1/JMviEi6vJSzpmw6Fg6PtbwZqWqNvdRAYM8GrwGcdefqqfdvXLDkxQC3j8kdg0QiEWsRa5FIpEFspXZkLQ0icVaTTJdDvKptWevksxZvqATDYC30axAZslbcB6LEt72GxmaK+XVTsrVYywZ2e7nBVo5zeuhH252xu9TYWl/zLIgq7qrB5WqZ6iTW4jL3nfPR9r1/vG9MJuZII8Fa9rbyhVfbfQilFuhUuXuSfl2lX9SI1hf5tZjUiJrlqjUAc+9dN/WejXfUIXDLM13mi0QikYi1iLVIJNKbspZvgWqDNO7sB9dX4zPO+0ZJ7XZeK3PLsZRmuOIjK5defBRUhde34ZoobI+KtUkmD+BGkLWwwy9+tLvgD7gzWdnu2A/PmwXb16hwwLnCY4zZQazFRIZRtfN8xzJCQT6E45e1an31udPnRbXYKOCZ8FsOm7XyBLWv9cU2XSmIWgTwNMDZj/bNf6T0Cl6OsR9cJ9YikUjEWsRaJBKpxUL+InibPYbGWEpDrVS2XwYJs9FVYDCvtao/aPXPKarv8lsL21SrLlFDy15CQlKBpHrj1xfK2kYwEUiLAhi9xcU75amtvQ2X2vOkcR+d+I+2O2OirLT2+sVzIKtbLFQyMzAmfdWGtkFrJrV8y7so9Qfkvjvur22vkzHcOMGtQcOjiVvHf/kEbwLjawiH5UMo0SrGNdUybrYmfl7jpRNWA7jooTem3Lfu7Ps2rHKzCajDFolEItYi1iKRSLtkLfCZJbxpzpRraD3QSG2AVTXQAHjqta3+wnjcCByf2C3RZkJr6f0tfKGRq83T7gq374csQXEIKhBUv3fOfLSPzgIw+FLSvFjeArcR8CHUuWmH/3TcQyPwUj0PIaoDi9HGQwtX/Kg76FiGjQh/mBR/9187akJaz4i1xg9uKczENkesxvVVx31+Ao5yYce1KLbZrSFt0G/QTv0YZzUHV0sspZ8/mQ4ATvzjc1Me2XL+Hx6tgG8IoWm4kUgkYi1iLRKJNIi15FDWQkZyHhL4auhirIqCXg6nzf9G5qvSbEzltzQMlzYp4Vd0ZO7Gc9bCvlVKJ5wHeCFcMmzJUypB6BZrgf0WmYFO8LXmuqmR8nxXkJcvZujagd1a0duaM2DsktmzcFc1i+MK5O1gx/xRNMBSjg/c/gaVcNpp09GZO5HEWt0/h5scJRGNZG45I2DeaQtkSfsGw4K1vbob7804lIzFqAwnjJ0sAcTMAxWHGsCke1ad9sjGHy193r4eam8ITyKRSMRaxFokEumtWIsncZqmmTSeSUK37P0/vnhC6pZg+Y0tj3jWMkYVrMXyN8xZC28mMUkt69/2vfPORZ6pVl3THulZy974aLAWz8mwyVqSQ5bZ3fvmnNkQ1UCnIqu5H6EzWCvv0Zy5pWgcjjryK7gcLuIU/I5T1mLw3IMrfD4K3JIt+xLT2e6zVgXzWk3WCiHNWUtBH8CMhzZOfXjDkjfK9nMiQ6xFIpGItYi1SCTSW7OWu0Ge17KhWiWFqsQawmmLLrVBVy3l3qJdsshlwDCZ0l5DmBu4F6yF6z1SkOlVlrXKJRBCR4FnLYas1awhHEHWMk3WYrjn2lU5CrS2jsJo00a7q5DUvStAB7GWUX4pDYbTp588pdXKljQOWYvjLd6W+jJCNH8HyX2jhd14b8Hbaggz9LdB1lJu2eNrAcx7Yuv05WtWZ8VsouFGIpGItYi1SCTSm7AW5KyVJlGpVPI9phKTp7bWlpMYIFCFGTpmhAzmpbTKo7vCZtx15jEWV+Ks0QsshKQh+ntBMJCCBzXHWpq18mAjzVpNG+sma2mBK9CSyO5h3LMZd1iFYLJOYa04TPxPpzJtOKx45kWVaGKt8ctaAqfiFed+F6euW7ZXj2vDWK8loW4gBzaGFb+MuwsrmYElq4N5j2yYcf/KgeZsymi4kUgkYi1iLRKJNIS1Ws6BeWPfPK/FOcdaJJ1HUVvqIgJYM4A9TNPcbkxqlXlvDM9sskVujrV0CiYGVoe4dtU3zocsRp5JY//+ylUP8sKcw4xIpGZy1irgUOV2GdLEIe6eyL517gKXiAvxNvZ91QZ3oOaxyH+gjPJa45e1TIiDe+KRJ+E1Epdvilg4LNaKkLXcEk6OWWjmL5FUNPz4wdfPvG/l7CUvlJusFVOLARKJRKxFrEUikf4q1jIqSRIuFNN5cWDmbJ2//8vfBZB3psKeTtw+JznPTKuHqn9rjVV5KpVBP1YeqfSis87U9Yp9MqmWnfGGLvBs5N3yioSeLCoejeZBHfdZMruryFp2t03qsLATWMtAtVyzP0caZJW+qs/VEWuNW9YSNWXn7cwTZtl5iz6EuEZR7j5r2W+JcR2WmyeOsoRyp4J+gEv+9PzMu1YsWvpCyVl2EmuRSCRiLWItEom0c+owg5ZsFXF6/moj4bHEWGogNZ+bNLVPInRF2ijQSYorr7Is2aFVV85aoGKIy5DWlt95K9oPaqmzrP3Nm5/eatW1l6Wdi+LOeothYzGZ1QZwt6My8AY+6BDW0tIUNYTXff96zG4pIM/3cctamIlK4YTPTfSp2zTOhuGN4RnKQJ4pVVg9iKxlp/9WgIW3PjP7nue/9dBLZd1aAEnDjUQiEWsRa5FIpCGB+mDWKlpQtQGJTwHZWOrYmQtX11joHtcjX2InFBoygBnCWjYws3AlQpBR3LMRRGywrRZSnD9FwuDPHVav1WH8vNq0kWQRGgrG0VzCSJXUgUdXXrDQ7jbwoIPyWlEQ+z62x3/1BHtAdGoorzWuWSuCay65DldZpcjhpfqA2u2WcRrLCDUTDK+sKCyx5cpoO+1fBTjrzpfn3/vCKreS0348I28MEolErEWsRSKR3oq1oD2/VLzkicvy1ecnzywD2KgtcsiSOStCzmLYFWtFFRDBdy9a6HJETIo0igIz5OPzqE4WFLTX1P4puv3Htv82GjX0+TAMZHLBnKl2t7G7cYewFs9EvmpLwVe/fKx/gBbwFPyOT9aKMdNUXlv1eS1PSruf18JpIiV3Xc2hOXfQkvSP22HWktfOuffZtc7tHUcg5bVIJBKxFrEWiUR6O0jGANbU5EaGvbYaGiLOfV7L1xwOYi2/7suyVmMAeHDlefN1XDUytrFalKVqiPGgWyc2jPUku/9T5J+Ss5Zu2RKiVYZWXKaphUOdRlvXYlIrKHUWa1m40sycOuk0z1pUQzh+WStrNmFoeoMOb35prS1oNbPB2EnPPqwCnPfQhun3r1t091NbAV1Jwb1M67VIJBKxFrEWiUQavmzMVgOY/90b684ew1sR2hvLooK1VMuH0K2l5wHbvBoa/SBjbSxlyXoSDTJ5z+vcbCAoRoq1HByaQayVSpOv/3dFV3a3v79oHua1dMfUECJxORPC3//6fw0nb4zxzVpuvRY04OUnXs0azD6TiHg488tt7uYmdneQXCihBwAm/99Lpy/fvPiux+1j5eaRoLwWiUQi1iLWIpFIw1K+iMsG8OsaMGHepQFANXO2GUEFsGFW0u4r2DL3s6wVV749ewqufTKxa42qS1G4U9aSaDAt9jZr+U8ZxFo+DaBsaGoJBReosKAPWO2b0ycDjzqItTC1pSELWW77LvHnoeB3nLIWy/Na008802R5Xsvi1m7PL+NZi0lIsMJW4awpAZy65I2Tl2+5dNmzVb+N0r45BA03EolErEWsRSKRhgNa4LCjAfCpiXMta1VSBKoobLgqO1GwFm912XIttpLaNefMyXrWWdbKRMRccx7etJdvOr7r0WEtlSd/vItaIAUDxVWCXYxlgLk4FnQQa0mOrcL+9+Y/5IeL1muNZ9ZK/ZiGY/9rgs81DzOv5a6aSGStyGWqcWgNAEx7rH/C8q0/f3VLA/L6X2GItUgkErEWsRaJRHrroH0HnwyTN91SjrWOPGVRRRVxlWZxYGTazlo8ZyjHWtVeqGxHdDFxiMVMrhFqnLZYSzqz9ZFnLfuhchBr9cchw59C2F0NNr0GWV1sWgOqk2oI7b6fdPzJIpH5MjqqIRy3rCVwTCfbs7mnzkde0lBulIbDWsK1NIYkZy03yddzOOOJyleXbnmMoQmh4amdJopqCEkkErEWsRaJRBoOa0GLtQKAbc4bo+oDKy4wzDN5ish14MkzVxim6fSaC86BRhlEhJ7vYJlMBIK3agiV54GCtUZ2vVZ7Xst4StSVoIyIqGLdu/lnV1yGXcJ8c+e2DmBmh9toqM1K0TTJSsFRRx7tX4nrCbHWuGKtpu8LFKxlbyd8aSIut2I4s6Ik3O231/79ErSQx2suUAv1kwNm8iPbj7t/3XrHVzqJ7DTBk4PgNNxIJBKxFrEWaecSQvixmyRJK5rDhAMwZoNnyLAFbf6Mf+zv7bfEcexNge29fR+/jX9Gyjwt4B80Go3izTnn7Z9FGtvSPnMVA2wI2LzLf4hHLuF23NhXEoZprsQVFTJRscOiwVMVD0BSvvKCc3m1ahGMJ3HRRHgIn5gmpCm3wV4OTbUZQinNTmJJlvq2wIJnFq4a/b3fveRitKqPa+iWYZhL30mXHFNy8OI00+LSkeFh3fp8t+yNNTCXxRrwhU9/BX84rrEZ0kj0hiaNEeZqa09nH7kWxHZoPvfE827hJL6YROnuDjY77vE70wAErsmsKiwgvPDO52c8tfWku1f0+lGPvemYwispjFiLRCLtQaZqx6r2ALL48kc/+pH/0secJGKtMS0PToUGBuyfVAjDsMAh+zhN8U91EATFtxRD328WRZH/sq+vr+A3j2rFTLBzxr9DsTGpU2SPX1VlAwZOPPubePDCBKo1+yx3toTuMNelKnm7aVAByOrj9y/x2RWZoRmaErK9pZUZ3C55dC3K/XBtH+FbNqxV9QFQEagYcQtbhHEXx4rB1ZIjxFptDaO9B3ezGNKRV1JVJoHTTpwhUy0sBhsBe71fGWksUlfhlaIybTiUeyt2FKhE726eU+WsJSGLgeEZvh9gE8DZd7142oOrZi19Ec/ydoilwrGWRS5haLyRSKQ9JB9zxjE28OScF3+gi4v4WusbbrihVqvZB57HSMRaHSA7mouI0+LWEUcc8fGPf/xv//ZvP/jBD7bHoxafPCl9+tOffsc73nHYYYcdeOCBhx9+uM9xtee1Ck4rJol91b6J3dLe+ylE6iDW6s9Ci9dHzTgn9mGWwCUcMYNI+7pBSym1zDelYrV482s6CvLgT6O3umB8p/CgRrsdlB2uxenbjsz8sZFfnz9Llraqaq/vxQwumoxF2u4CMhZYy8Kgf0InxY5IKVJirXHLWniBA5sswDVXXYuprdQMg7Uyz1p2IHEmpa4BrAdYeO/K0+576YqXegfsLMh8IbFlLcH3cg0wiUQab7J/ju1fZxtz2qDRhp0+vPQY5oPM6667rvgjTr8uYq2xLh9c2tFsx7S/lnD33Xcfeuih/slDDjlk//3391t6yvLZqn322QfaEgJ+rPuX/H2pVGr/rmJLSmp15CBxNYSWtWZdfvUApnWkPZBYYSRw2QauwhI2GAuwFNBYAiv99NsXuvVOrgVPW8FeMQx2ZK1Rw0in4jxuHyRJItLokgVz0LBexpa1DI/CoOLWxujBjotjJa8VDgifymJRhg7dlNcar6yFppSuuDQNsgnHHJ8vpmS712zYe8ZIbQcVs5OWc2mn90qARctfn/vA6jtCrCdMmJvdmkmTSWItEom05xQEgb8i74tNfFRp/zoXl++3bt16yy23NBqNcrlMvy5irc6QHb71er14fNhhhx1++OH9/f3+mXe+852tv8EuV2vngN3GPvb1h35K+GjVkpt9UFxm8CWFnq8yJ/ptdy5r2XhrxfZa6GOxjEnhGwCjfx/IBq6hB5lFDRDBZXPOwKpBVz1YpLagrWDV7FBJOLpK07Q4ieMgV/yyc86Ceh+k9TyvZbgNPROX15JjjLXs7fijTkHoamRYQ9jse0bjdhyyFnboVvmQPPG4iSCG09va+LStb0quBMswr3VvA+YvW3nuA6+/BMhakXDNIBRTFrf2/npLEok0vqIOKYvqQasiRi3Q64YbbqDfErFWJw3o9tFsB/enPvUpD0senw488EAPVD7rZTd4z3veY1864ogj9tlnnyuvvNI+LpfLfuGWfeynR5G/8kWDQyYJQVfHsVYA2Mm0CrChlnovQRthcVdA6FbjpaBjG3iJRg148MQd/4usxYWL/DT30NVkdRgrhn6oYnAWlQlu/+zPFvu8lomqwEJfQ+jzWnLoGrNRZq2sbk6ZMD0YYEUNIbHWuGUtJTCTnAaZHSMTvzYJRwEfJms5E3mB5b8M5/73X9w8454XzrvrpQ3uVIDndy3slPfeOcRaJBJpD8al1WoV2hZrFcFD8Wf66quv9hf62yNMErHWWFej0fBw9YEPfMCSkk/dlkqlj370o+3Lq+w2hx9++D//8z/77d/97nd/5CMf8S/VajX/wG5vv+uggw6yVHbAAQd88pOffO9732vf9n3ve58ltJ6eHvptdxxr2RHQp/TWVE9b/PUtAxWmIFM5crhhkWH+RzPIgtWPLoV6PzbecX4YRZ5zpyfEUWetIdjvHTVFEqpq/+2/uEGWtiFoufVa2FJM8wK0xghr4TItCV/+zNcQukIe16OieRmN23HIWlmC1zWSRlobqH//yh/4fgB5EnZ33kn5RnkgjOD2cR/AvCXPnHn/Xy669flegO3OFAdnvUzBEGiRSKQ9rzRNbbS577772gDShpHvfOc7DznkEBtS2mDy7//+72+55ZYdPQJIxFpj+hJCMV6TJLH4VNCXvS/Wa0EzW9VeJWgnwz777GO/9FkvY4wf/cV6LWizd4NmwaHfmNQpQh9CqQKX3Trq1KmZKx3MmiGclsqkmNRybbTib82dhnyi5RCMaR8GY0p+DW776PV5rf8+bwFkDVyvlQWWs4Rgptm4eXDd4yjntWq9yewpC/NGW/Y5TDUQa41T1rKHHZdsuZGQhYzHYhjeGC6vZdy7YFU4B7B8dfrtj5396OuX3/JCFaDHQIyvZna+uyFHv30SibTnTmbG+KLB9lCz9RfX/Zm+5pprCLSItTqPtYr1KgVreR144IG+PrAY0z6Gtt/lY9NDDz2UPDe7nrUijK7w9okvHx26Bz6ixxMfF66ZqoCkDvWB6y5aDDz0loQde6ZvspaMgUfYZQiN1LVnLTXGWEuEcNtv783xyjW3I9Yav6zlcMtwO35FPjCHVUOoXMWsUoJrnOybASbf/vjsZS9vcxdc6ngGkAbcUkYF1DubRCKNDIMB9TIm1upEFeWw/oHFqs997nP/9E//5BdWLVu27KMf/WgecCvl81rve9/7PJjZb+nr67Mb0KWFrmetzOFWJc2u+umNFYDtmbZfKshjO/tIVquIJbzOezbiAyP/2ujLjL1AzbIW5ugiVwzpaiONSJMoTiMzpIAQd77ZInlUWMulF0tbQsxEcGKtcc9aunnv0q+PLn/MZMNhLW6EwsvHeB2uBvAawJkPvLj4kTW9dsRpXKyV4IJNd8lF7naNIolEIhFrEWuNLxXLsXzRYKlU+tjHPvaDH/zg4Ycf3meffU466SS/zUEHHeQ38z21/vSnPy1ZsuSwww5bsGAB/Q67PorzXufKpbPeaKRVt2DDfmkRJAsifE1oYME1i2dDbTvyCaa6du1+MXbMMXbBWqbSb1nrikVnQVz3rCVdDWGTecYQa+GSLdEsbdSgZEasNW5ZSzBZXP6wo2LR/MX52NhN1sq0q0TElsjYyPj23mz+Y68tWv7agH1X18ScQZxBJD1rcRpuJBKJWItYi/RW8q4vXlmWzZ07d+rUqeBqBdu7Y/mMVr1enz179sUXX5ymqe/NRb/A7o7ilMLmpqX+gUTKExec90ZqbITPtcIUkNIu82XjrvoVc04BGeAqjqY72V/LWmMKt4zr4lrr+/qsMzDBpVkaVH2t4CDWMk3WMqPHWhJuuflOSIHWa9EsLW7YUMuB95c/f5R9gMC02+u1sIYQBH7rBoArnnptzgOvzL3t2WreM09LiEIIMstawiVXqbKBRCIRaxFrkXYli08FLNkHhZ0g7FBh6FWgV7HEi9T1YVwS+VAeCer/TZo64JZshVmUhfWctdCscOC2H34LkooP/FVbuV1HshYL6utXQ9IAnkAWW6CKomDQ/o4N1poy6cyil7H3fJd254m1xjFrQbPF1lFHfmUY66lcL2ONXbMYgvvrAAvu+/OZd7+w8LbnLWth/y6sEA4aUA89a6XEWiQSiViLWIu0C/l0VrtHnK8kjOO43Z+waMNVNNrSWhcGbmPWYo60x8I4V5JkD7/ljS+ccdZ2O3K0i/uxx46ulVOM9evbIdoGKmp2o+pk1qqXISyDiG/9xU8hCfDHNDnKDC4gxJkwuqx19OeP90+kgSvoApnEDWKt8clavr9WTt0C5p15lkr07rKWwqpZZC1IpWWtVQDzlj01594Xf/jsQNXXzCo74OpVqNcsazF33UXSASCRSMRaxFqkt1JfX59nKu+B4f0Gt27d6l/1Nu6FDYY3J7Tcldtkk7o7jMsjKkxY/fGJF/sAeu2hB9G7cR3mtTRklfhn31wMwSbQURhVfdvfdtYyuwItPVbzWjwEHl181iy0s9cibtQL1oKxw1oCJh17un+Cx9KzFmcxsdb4ZC3JFeKWW6+lMn3XbXcHpXD4rJXYB/AXy1qPrph/38oHG5CzFiJYtQT1EmAVIUTEWiQSiViLWIu0a/k+BkWVoIcocAkuP6btS76w0Jca+ixWkekql8v0O+z+ME74G668X5tiv52Gcx/EiB9XaqE3xhVnz4BgG+i4Pa9l3pK1xmJey1KkDSIjkOkVFy4CmWDwaelLcb9ka3CAu9dZC9pYK7cZNLpoU3vqCWdgyVeWLygTnLwxxvVUNcqV+bnSQR4KFvD2xtt/5WBLwV1US4X9Y/AYwKwnVp19zysbnOG7OxvYAZ9WIa7akSecTw4NNxKJRKxFrEUikYYtIfKqJJbKAYBfPfECtiu29M3dKvwsgEbP5RcvVirW3rjPMclbB3hj1IcQfQcRI5XQYQVYA1TM6r0gI9c3TA/1IRwR1jJFPFsYzdmoOoN1r27EjFYzd6E1gdb4uPzRNnFcwlP7ESKESOMsLyNUcPW3rxkGayk/yrWsAZxy9zPTn+1bcOuzfc5xEOtp8doKElbsTwpa0JAjkUjEWsRaJBJp+HEd9+FaI7OsYVnrKwsvCkGWk5J9Oq3EkFWgb3Wl2sNthBaHWGJkdKf/yHjPU9Bp39pXQAe4Ds2l7Ao3wjbWknuftXSLtXSLtWSsCvMDnuH6SamFocC366UHOV4UrKWMVH40ujGa1NJpE2eoUA+DtQQyOysDnHz/ygkPbjr/9hf6jH2J4SxgSF3cGRC6JudUREgikYi1iLVIJNLbAA8beyWx8MbiJYDDJ09rgOTAUm45K4Ss+sML50jl0itaZJVSp7MWrkHTWqaRCErXfOvCtLRxbLLWD7//o7Se8VgYBVnChm5JGmesZVw2tsh8soAf9/kJw+iv5Zo4GKXEAMC0R9Z+9d7V1z21bQCGshbDZ1IyxyCRSMRaxFokEuntshZ2Sq1i0VAZ4BOnzgxAl5MKvpbYML966eyTbOgVstg5E4ruyGshRPHwqm8sBhWgl72NMo1o77LlthlN1jrhmBNbpt7udakFExkN2nHLWhI9MyGJUhwYHCZ84YThsZZbFwhbAeY+seGkpWsfqEMtZ63U9S7HV4m1SCQSsRaxFolE2jOsVa9FNsBSHL3Ilr2xuUdEDKTkxnnBVx77v1/a0CviCfbeiaMuYC2hpLHQYtKB9a+aRg/mtVh9rLHWVz5/dMFagkmDf4UorzWuWcvXENrB4Flr8azzWI3vLmtl0vgSwcd6gzMfWD3jwQ1vgGctgawlctbiOWulxFokEolYi1iLRCINX1xgYZIObaSFDoQDAK/0b2Wu2ylwLTatAVG3rIWm42iYLsec3cVuglYqbMyqG5UBy1qWsq66eGHOWnoH1sq9AUeHtSYdd1JcTbKQeQM6pRSx1nhmLaG4z2tp6boDKFj59Kph5LUSjrlRO9N/9uSrZ9yxYvbS19YBVDxrmdS/c5O1GLEWiUQi1iLWIpFIbxc/jKsrYonpybCM8PRzz9lSHcA2Pv2ln1xyIcjAslY5LPdu3eRaTnX2D5ujFJp8IGvNnHR0vl4rb2rcbgI3mqx1xx/vbNUQmpy1kiymETs+WcveMp561jJZ3oL8jZfW7i5rMWMid0nl0tsfn33Xi/PvW7kGXHMtvJjC/JDzdYYur83Ih5BEIhFrEWuRSKThK+Ms72iscXHGVglHTZ+Kq4Kkhkx8a/YMiKtKJ9K7Z4xBG/fdZC2mIJMCKwZNykpbbv/lj5G1wlKxFK3Nc3s0WSuuJvaexwJrxgw2x8vdEUjjlbUsaRuV9x/noYAUTj9hyu6ylnQVg1sAzr3lsQsfWH3RUmSthm88oIX/6GYDPd96j1iLRCIRaxFrkUikt4MfTPjYKgHYnLBpX/96TQgsFxwor1hyD/AoTqrSdTGO47jTf1iJ/Zi1kpnPa4EMXlh+D6gYWUvnrKVGnLWs8M+MAL8UJ29T25bXAvIhHPes1Qjrhec7tgQQMOXEqcbRd5qmfnshhHHa1fiPBbNk9ZplrdtfOP/ul//7odXr8/VazjDGNZRrjjhJrEUikYi1iLVIJNLbjfU55+Da9jKAAOD2Z55NXNT2zK23moF+CyE+r6U6O6eVx5rC85PLa0FWg7h0/WUX6lpfzlomZy2Vtxje66w1qGuWT2oxeHTZY8RaxFpDWMu4xDJ2Y1B5DeHnP3GkEoOGBNaaGmPv3+RaQ9Wx1tm3v3zh7SvsVN/uZn3OWs35Aa1rDTTkSCQSsRaxFolEGn5k58IpDVJA6tqXbhT64RUvQdD4zrQzgEtQUshIAotFx+MWXtdHNzfQyq3XYnVIyt89dz6k9Z2ylhmRvJZQvD3OjirxiV+dSKxFrAWDvTE8a+We79gnC848dZZRmMvyG+NFE/8eWu9q/GcgSwDLOSy4e+0lf1phQct+GeaspV0xMSt2hcYbiUQi1iLWIpFIbzOyyxdiaQVRlkau8c7Js+cDS29YtBC45lHiWSsFw03HsxZzcJmxyNUNYg3hSw8uAREja6pRYC2Lfj6itYFyGmQyVfazp50ynViLWKudtfJx0qwhFBGuoHz6oWfsl0Vlr2etXRUQ+vEfqWwA4MKHNi98YNuVd76yWaIdTr3FWjFeb2kNORKJRCLWItYikUhvI7IT3LUrtYxhMO5PAPoAPvr5r0BQDVc8h1YS+CKzrIUVhlx1OmtxfxMJ6MSzlo1b4y3rhrCW874eobyWy7SBlLLwxjjy018g1iLWgh1qCL1RSm6dInK7QMZYkdqCXSe1oJnXshN8ym0rZ93f8/27XukRaPhe5LUMRPaWu+AQa5FIJGItYi0SifQ2IztjsIUOLolHzJDM1RTNOO+S5+/4P4jr4Ps7AYtVYF+KO9wIT+U1Um7divfGqG6zrHXTlZflrKUHsZYFzb0dbnovbwStZpwdlqPJx59CrEWs1c5adubh0spMGNUcx84mcPvWnna+8g92XUOoOehtAGc/Vjrp1nU/XvK6pax+o5qsZf8LDK7eItYikUjEWsRaJBJpz0R2WFXnuuXaL2UGqh9g6bMvX7toAbJWDVv6ZCJITeBXcnQBa8VIUjL3xgj7LWudP/20HVlLjghrMZEVBBVWo0Y5QKv3UBBrEWvBDnktJbRxrhjeG0M05A0/GhSXZJnr1yDlm7DW6wrmP1E95c6tS15LE2xkbOJBrFV3Y99/SSKRSMRaxFokEultRXbCGGXcei30jMC2vlhld9nCcyDjvoaO8UABS1wE1+k1hLhYS0upMjBChxWQlrziay+5CFN6g+0xbLwpMcDd64dASo5LbkxeQKjiHVgrj7kp9h0PQcegdtqDTAiLDew4TY0fo8d9+Ws8RB9RLe0sVkHUsFOYKWna38q0rjXY2b0K4MS7Vi56trYVIOI42QXktcEKYoVLtvSQbySRSCRiLWItEok0nLjOhl/VoOEjM8zjpBHE/aCihRd+u66BYYCHOR7GEQZsPNcVeNkWSmJxlg01maz1I+VY0sSiSmFA+pVdZq/vjE+ygcqMz7tNmzyLNWQTtPINiLVIO+ExBVOOnuITUXaoVOr9HD1sWIz+oVgVnBN77h+qGcAAwNzfPDTl8VdOW/rnPm/zHktsXO5Gu3SJL+8fk6d3acyRSCRiLWItEok07JgNYy0hjE9pYVgmINveWLvi1EWX9eJKepAKF3SxLEEs0brDYy/XR8zoQZf8HW798IpL0AJe1kFHACmTsa+Z3NusxVkseKKZyrErhckTzvDLxdx+5i2PfN5R0ZAltUvBzGNmYq4qwiVdHBIGaQYsw5Rsk7VkkSDVdjpvBph32wunPv781AeWr+HuJTu/A6w8ZE3nmMTd562MibVIJBKxFrEWiUQaNmtxrWSTOFxFIYf6uuu/ufCIoyevExh1SbzMbYN+BwPadA9rwSDWmj7xq2iVIeqQVW34mWJh1kiwFsKszPJVOhI/csJRJxNrkf5K1vrRJT+2s1TXlWJcYe8Ci01ZqDMFzRElmrhlpGethfeunPzwM99ctfq1VOcLNiPms17EWiQSiViLWItEIu1J1koFbwXx2CA1htKab8477YxzL389dazlr4rbuN+CltLdylq33/wzZC0V6GgAsKuzGLEaQilSLFrUIEJfQzgnLCXEWqS/hrWiDWHORugRKhgusmTSe7q0sxbmtbCjwwaAs+9ffcqDT9/hOunlLecEsj7HG75T5lnLTnapibVIJBKxFrEWiUQaPmu5ykEdphxXBikDMgXWv+qJ+1/tCTZrrE4SYJRmiiUqk+0OaV3GWhCW054NlrWw45ZjrcToEWAtBC1n+6abLt53/OE+jHaJtUh/BWvZofLILY961hI4RRkHO5kzgNZiwCZrCTudlw3AvPtem/bwimcAej1kZXawa7dAscVabkRaZJNuxSCJRCIRaxFrkUikYcX6NsbyrOX8zWx0Ffc+/xAEfSHAbx5+oeECLxuLGZEZ3gUXuXXztgNryfiWm64HXsP1Wiaxv4tUqRFgLQd00jsQ2n/DAZHWNNUQkv7a4czgzONnJQOZHRxJFkvMx6ZC5wCfg5Zyg9xIO6m//cCqOfe/NueRl15weS3ugS3NHGt53LLUjy45oAUoYi0SiUSsRaxFIpHeFmtxbGHsnco4g7h64zfPgaC/KuGYWef2cqwwckZ56JUnRfewVsvWz7NWVPnWwtkgGlhJaFLGIzkSNmxaK4a4pdGHUMXw/7N3puF2lVWe/9Afqz9V9/PYTzlUq6WFWhb9lFZ1a1VZVdqWbZWKTIYIAcKYEMYgkyLIqCCTgCACDkAxJoHMcyATSQiZ5/nOwxn3vN959Vrvu8++594kwo1IeZP3/xwu5557hn32effJ+u211n89fM8vkW7JCtGzlte7Wc4RnP1vEygBXYyGk4oWjE2Sakl1gO5i2zEDgMnPr7hs8Z5JCze+bT0JUztpwDBmKIdVsBa3p2DACMItP9LNy8vLs5ZnLS8vr98jWCN/85QritTyBET4/Qu+AypJAT737+MC2ygvZOZYizMz9t+vs/g4jLVkctf3LqcCwrRGrJVHCDaplO+nN4aM4bSvf7fosfGs5fUulo8J4PIJVwMHYzPTzbBBrYY8RH6yiSmbm9LFmq8DXD17w5Rl+y+buWYXQBXvz1ykQycWFOEWgZZt97L1rMazlpeXl2ctz1pe70JSynq9bqNKbQfSQppiCA2cc3elvJu7gjcKIdz93S15ng/7J97ejgeGu1sYhuVx4jW2gjWtyf+CPnjKayWqb9+hdcuAx/h5/+s5l3ZkMJDb4jaR0ZDf46WG0JQzh8AiDYakLISkxvoPgIrTRp9jrThnf3DWonBWpkFC25XDmd+Y4NwIPWt5vbtTJfCNf/o2rhmWkkeo1IqLlBYQspbK8EDGBcZZhss4SNkgwBVzN0yYueHRzQMdAA1bIdwMg9YMN+nmd6sC993Fs5aXl9d7Jgw7j/hrGX8+8sgjWZb5kNKz1pjhq3IduyWbJEn7WQTGWHvE7e6D1FTezS13lAMqJ3zU4c9W0lfJZl5jJ1hjUmWpM3cW8ZaF09XAIdA5rpvtAyECOmJ0moUqD/EOcabNmH+/uuSWIdbSlNcCHj5+98262UfXQVDt1fvo+Z6FDEPcb3/trKEeG89aXr9bdMxC965e543RbLjvZF2v9NLYLJMZZC0QXArn5L4P4MrFm6Ys3bGkChV7aCNrpc6dxS4zZVnLDDkYetby8vJ674UBZAlXJVPhjUopl9diVn5HedYaSycP+vv73ZU4jl2Cy3EUrmykI2HVfgrBPRbv3I5PURSVSFayXBAE7qlGnKvwGitIbkeZUihGFBVX7ppyLqQNMIJpCsUm3f5g4O4mEvN+9C/9wVnLtFhLlrhVslbWmDL+FIgGgawFRJxk7w9rORsDnkiMmH/2kyfI+d2zlte7Zi1IqMGPpUVdLMtye2tGF2uSgdSVWLKasbvv0gXrr1q6vZP6vCDQlECVLjtm8OJYqzxSpCocDL28vLzei28sY9qLpDCAdCFlGWfilSeffNKxlt9dnrXGhpRS7dknhKWTTz75b/7mb06ycksc13p5B0dNmdWnPvUpfDi0pbzSNHUHCYJZeRiMIDSf1xp7rKVpqBO3HoTA6g9dezFV0xk5UA/wD5/79rlV+pgFsMiY44S1XEB5BNZKarN+8xh1TclUs8Td+w+9PY61Cj99CYMdock8a3m9a9ai0wIw7pRz3Bg8vCWNnQmhZS1gyEtIYQOKurN+OG3ZuTNXTpq9tkr/OkDIwJpg2jWmibXcMnPM1spxeXl5eb2nYUcro4VBqYshMeZ05+sxZEXWGtG04uVZa2wI1zGu6fnz5yNo4a/1ev3P/uzPPvaxj5WnFqCtUBCX/v/4H//jS1/6Ei59XPH4Kz68/RxDeWzgAePgqtlsQisP5jW2vvR0VsOALHfUwWty39tIHYrlwpC32T9OuHzAIRlPyCtCHdeshZQV9DUP7gSVqTyG9wMsCbDiqIH/zyNeTp6VsfGs5fWuWMsu5X/+P1+hJZSQEYb1sMlApXZMHH5x69CO0sLLNf+x6IK5a6YuXB9Zs3j8yrf5LOtbaF00XB+jYy35fszy9vLyOoHkDAJcfeCIPpTyxP3PfvYzB2POCMDLs9Yfu5RSZc8VQtEHPvCBv/u7v0Pocn/63//7f5d0hEzlqKm3t/cTn/jEihUr/ut//a8jngrvg0vfPcSddcDjwRFaefyMOHi8/vhZy0ZiGaMROzLetR5Yn+2npxjrYDW86oHf9BR3yzAQG+uxl3lH1uLNB354HWUCjODEm3949tOcxhnb+VrzX13mWIt2tGctr3fDWgrq3dVrp1yvspZrYDFwnAiK6SwB0wDoBzhEw7W2Tlm++ee7B7mw/VtUI4h01mItG98oKJ6BtyxavLy8vN6zmKOV1HKq1+sYpmIM6fpZ8Ja77rrL7yXPWmNGiEPtnhaoT3/60+5KpVLBn3/5l38JNmPrwAkhCq/cfffdn/rUp2q12le+8hU8JPAWvN7+JAMDA+V1hKvyJVxyzHdtjT3WgtixVpKKp340FdJuE9Qx0oqSOLUhWjdAFNaQtRQcV6zFy/azkrXyJkQDN1xyrolxD4g4TN6HGkI3yzgLUwxwJ46fhHGvSnwNodcoWEuG/Ov/8g28UrJWvVqzsxzyxKSJnaPVA7C4H25ZtB1Za6kDsdh2adG6sqwlBQg9xFq2lSv3S87Ly+s91eDgoEMssCfx2/tQMOZsNBrPPfecC019POlZayydP3CVfmmafuYzn8ErZTVge0dWWSn7yU9+Emwy93/+z//Z7orh7pllGd7h5JNP/uAHP/jxj3/8r/7qr0466aSPfexjSHF4e5lD8xpTrJVEzQHbnQo/uuwc4AMgcs0FfvgYjO3XsM8VTivRjDg/Hvq13BwhYq0WbtlOKcMgq4OOn3/qYR70F30wv9MYwLT4rUVxh+0cc5RL2/YQR2muGQIgnPK1M3gIhee7hhZoFaxlfEGX1+ELzC7e7et2IRW5YVrFmtaSG5ZBmoKs2wP5/pW9E19cO2nOij34d0ULnCdS01W7zPCqfaBpsZbPa3l5eb3ncoEihpEf+chHMAr93Oc+94EPfACvfPSjH8VIEkPKRx99tAxN/e7yrPXHLodP5fisIAi++MUvCiFcEaCjpnYkQ334wx9uNBruOq578Hmq4186SpoUaeHK6O/cuX4hax6ksTwKYzCocurx+OJZFzo04ULJ44e1JAOd20Kq1Dlfk0GIMIRYiRJVMBigNsk4xBQB6GEXyo/Zny7pRB5ueGHk6QhKmTjMXByc1tmQgbaCpEmTyoQog1jZ19vpnvHL//h/C4eDIdBi4Lzi4HBO8/KyZhhNTmCU09oJkpgOXkadV02WcoglNJvBAK7mKxZ3nTNn/9SX51fckpLQiFKXQJUIWkPFh8XPYXMRvLy8vP7Q/zxbuPr5z3/ud4VnrTH4b7ExYRgqpf7kT/6kHa6cN4ZzcndTs04++eT/9b/+10c+8pHPf/7zn/jEJz74wQ/6vXe8x2nkNka1R2F94+xpMukiwFAZqzWyhLwxkLW+dvFVyrbgI0VwI46D+Vqtkjztztw74opB5oAAxIRq/ubpB3TWB7oKKnABqDkMtxxZWcQqLtLiFpei0QhcwoFFWdF/JZ1PAZhElUEtY1R/KzjNklZC4i3fPfMc91eF3GdkC7Q8a3kdVYLbxCoDHkCuaUHKOEDcakackc/7gIIaru4GwL/99u2JK+p3zVnWETf78wSX/UAUtnUtjsy7mmLQlpeXl5dnLc9aXkdXEARlHtYlbcEmux599NEvfOELZRbLZWzLkVl5nn/yk58c0cLodVx+uXGZkRUESx77wffyoAN0g35VWktirX6Ar068HNdBWE0dYIztaN/5rLkAs/1EfhFrSuvKlk698BzyyRAhNXHpo1UCFq1fIy7u+ahhxkiZRWCYimv2eeywI8OQ6WSeuOpEzrmSdgyt3Zidm/fSGDO3YVCylvCs5fW7WMt2Uj798LOZsNifhHj84m1NzkJTSUwfQLI7Vlesrp8xZ//C3kZEZxaMXYu2XzNnzSA6Emv55ebl5eVZy7OW1+9UGIauNLZSqeAiRoL68Ic/fNJJJ33oQx/69Kc/fejQIbe48VdolQuWYw3+/M//HPw4uRPgy83avgcQNR66YSrkFQrDNMPoDT/51LbUr+2uSXvHKMvlccBarlWrTDep1kUqFYUmCCCOH7n1FogjYJmp1Y5SQHhk1sJb7BFHw2EVa+LOXLl0OrBePARB9uqsGwNgw2rUK2MIsViWG1uumYV5UI3clvDYopebMOtZy+vdLGkG3/rK6bj+MhaDyB1rZVQlGzMIMxUNAkxZOXjKK5u3akjobIo8WO3ioPvrg0zzgu89a3l5eXnW8qzl9e5Vtlo5H/bSMPDGG2889dRT3XV3oxti4FJb0DIkLB/odbyzlqB8C0u6334TTAwUiWUsSoyBKqP5p3jZuqc7y6imLdfyeGCtErQK3NJ0oRpAexEy6+6t7NwNXJIz21FZayR3tWqupGSx4gEeWB37VvNkF0Dnvh2zly14bMZL96xc9luRHgJVB5NaGzh6fpUJRKyJ4y/BjWn052RLkNnAd6jNSxYuHT749RqxnkVhJjru38fbmySN2dIyTLOcZmzhl3hy3Z23vlnLL1iw76KlB2767bM33XPzE88/xUBUkqqkulkpVN5aY7r0g/Gs5eXl5VnLs5bXO6gcxe04yjGVIygEKillyWPOf7Ozs7N8rAetE+TLLW4MYLDPOw+AiGU0yNMaRvquQwnjrz0BZWe+cea5uSiM+8Y8a+nhF6QsLUELFQXAMsgSyHPEpYfv/gkgAjEJQxz1DqzlkoRJOGDttMNa/7aFs58A6ObxFoAD9nJIpIhefV0H1lErjWxqFuE/L8BAp/DNr54hYjs1Oi2LG/VI1vLyGrGeVWHgfunZl1Knn+SgOIgcF2SkkbrY1Htv7jHyvkVrJs7dNe6VNT3UoJh1NTp+/puHJWQc4lyE0uRtZyA8a3l5eXnW8qzl9S7kclOuhtC5EWorsCkv91c32aCjowPa3AjbKcvbuB/vsZok10GZ/PT6ayBtQh5SEwfeqIErCPECEACcfu5FzkYi5HKMz9cq2rValgD4dgqvP61j6/POjMGf8pqrr6TqPutb2LroI9u2A5SWG/apkKB6QPW98vxDAAPWXoQuOj/grkS17etWzUiDvQA1UA0kPd5EuoMzvnG2HRnd6iUr8mm0he7V/Wr1OsKCloWT5uM//TmukWa1AloYnjbDGgM5qKN+0LjsfvDasrNnvH3t67sOEIol9hLded8Pc900ZNuStVWr+pXm5eXlWcuzlte7k5sv3H5FCFHmsspJxOUsOaWUc8vIrKCt8tDruGUtFoKIJ3/n2yAyUEyzlOz0XF2SoigOyfsb489DNG9wPdbn7RgopgYxuqKpXhCYgkxBYuyFDN9NnOXNG2+61lphM4s6ooVbR2StErSk3VsDiu9dtujXFrRqKu0Gg3u4ASZu9O0hm0c5oFnX6uXPa74fzIBJAxcun/XNCSIsQEuI8oU8a3n9jtgEl5V0pwvyWgJcU5pUMbB5qnrevPln9w4CbAf40bIN57361nUL3x6glR9KiAWEqaopiOthl135h+GWT2x5eXl51vKs5eXl9fuyVh5Atfvh227KKz00DFXrJMlc8qeeslDSEKreJI+RtTSh11hnLUlW73iRHAQGqI61mAns6Ga6GPJ5R+hJOvZvcwGoGs5aTlmSU+pJ6TBAjpJapJQckzWAjrffei5qbLF+GE3QouW9ASZlLKjjS2jWM3fWI4JtBegGneDTY8Q8/ZnXaKJsToGulEUKzrrJe9byOiprQYILBFTIca3s27oLb8mjBi7FjNV/8vA9g1r2AGwCmDTt9cvnb8RFiX8L2YCiEylxbmp33/8DBSHeEueDNrslcha7mEcw7VnLy8vr/fin2Z7xdz8fffRRv0M8a3l5HU+xmiBn82YvqBgUE42GcyNLhU51UWiHxDUgVB/XEUBTjG3WwjfcCKpJFjqGkSAymViYYUhPUobkDoKXtAZp5ZHbbxT1njbW0sNO9ONNXNjMgkzCWtQcCBu9uLc2vf1S0FhDEAV1myq0FhiZTRi4+kCR8aQToOv1pY8BdMpmtzXkgGQgwj2OrKXlkDliG2v5AQxeRz55AGkxM+DLX/iSRXKhVPTU0w82smoAsCHVvzrQuGLBhhuXbEHW6sljQ3wW5bJmiLjC+x+9HX/i9f0dWyvVbpo+Z5R78jz1dQ1eXl6etTxreXl5/T6slVbvveqibLCDrN5tHRzXVGhHo3etHTmGXVWtz7/x+7sq4VjPazk0IpoxsjC5ps5GbksXNHWySAYiobrKvHnnBWcBbwJVGMpydlb71NcoCK2RoLCTihKaVBQdfGPpUwCHAAZkXi1s4qzRo4yAh/ZX3KVk+z6QxZsbg+tBN3Wj+upvni3byPK8zISB9Kzl9Y6spWCwfwCMOuuUM8hWEESaVyrVfTlEFYAOgBvf2Hne9FXfX7i+w/Z2AbAoGbRFs1RA+OBjd8ZioBZ320palou42SSrpCzhPq/l5eXlWcuzlpeX1+8hJRAqHv7eZMprEXchVkCSK9vOZLFKGaUMMsLfjzsrsDV2YzvkNyDCCCxDCm6YhCijzhX3ZnmmTZwDF1AbgGb/f3z/Goj6aRhxMT4LRrCWdTUUKqeCQ8MqPO6eP+tX9cGN1KmlqiIPnO2htH7axjbC5Kl7ChbU9+Pd3lr1MmR9GBKf+a9fgTQ3edGs5dDKsZZz5fas5XXk9YzLV4Mt/JNnfOs0vJ7l0eNP3g9QR3bqzs02gEvnbL7uzc5zH3+5H2/NqSlLq1zb7kSb2op/+vCPFB0HEVchPmUQNAqQ84WrXl5enrU8a3l5ef1erFXpXPHM45DXVR5DTrZ8CSe3d2atzCWSiYGKUX8/fjzGX/2pGfMhv2UkBKfUQGRdFuv4vhRdyem73hVlxZA1oWO7Y61y4vEw1nKDuSSzpYGIW/XmwA6yFoQmqMBOK3bGMzQV2uAdBV3HS63WsA/OokZX2ji4fvErIOvf/cZX8LNgEU0xU6pIavHCwEN61vI66mJWoDnSOF0mjDt7oKvviScfM5Ak8UEFEa5LZK1zZ265cUPlkTd3DbhzJYa84ZG4wnggYRVBhqPxroMbkc0y3sCvACEzqo/V3hvDy8vLs5ZnLS8vr9+LtRjUuyGvADme2/yOgHpIPg+hFOTHkGWg9YCRl917T93WyY3pkN9Yn8Gm9bKvAvQCdNmCv60K9gIMAtFXRk6FGWQNyAfWzvgtItNI1hqazWXTVSap9mxHylo892mT9yKbpWHggtQwjspqwITlQRK7J7ETFzRLUvyx8815fGD7uK99kcz3lWaZTDPZAi1wNh6uiNGvVq/DF3S1r0blrDzEC08YLpNFS+aHDKmqUm124jrfB3DRwoPjX9u4354SyJluLWAqb7WG70k16nrs6fuZCfDXRljBBctzxuLcWxF6eXl51vKs5eXl9Xux1m3nnQlZhRzJ6wPNriowCvHJuTyJKJeSZyqJAoB+BJJKEo1x1uI2kdVhEWuDhjuXbr7gmcVT5269bsGODdYaGwPTLm7fo2iAqtxx7YUAw1mrNQ0Z41rNme3UigmxoJLUt4GuqkzovCi+SnNkLUZVmdZlW1LKMMW92myGPNP4jCbmoGq735xx13WXkCEHNZIhlRWsxTxreb3jyQOyecdFwvCSR9ndP7rTrpZYKVzjcUXCba9uvmhZ94R5O/da1rJlh5KWuKFBBXv2b7NWGclg1PnYUw8MVDusT4x0eS2eC89aXl5enrU8a3l5ef2uYGz4uWkLCsZaROBFsnumXAisLuJB2xxEeS2mocHcFDZJGR6jmxZO/u2SqRVDDGCKE+Mjn33ka70/J8Vbr+LKqYaYpHhpNx2L8KdqKWsbwE2Lll3y/Ks3L37r8unLr3h13Q1L9lz48tprFm69es66G2ev/NWGXZ02wZWw/Ibrr7HgyVzNoDQ2QFXFu6dKLEnVg2A6X3nqh2TyDqnNd0GzkWlqCpOZCixlhRnUJPXGZGFMzTBSEEXR3pRJ3LEFVD+YWlzvtC8CmSyTWsLWEGqfYPA64uIXyPxapyBTXC9cPfXgY7hOojwAaFbDfjyqb5qz9dz53VOWdh10da/CrjoNInXWF5orvIYrM3n6uUeVXauaziDoNM78mvPy8vKs5VnLy8trhHTZ0m6GrMMJkLTz3xMZBvxUsYY/02DrG0vS/h6LXkCTe210hXdluTXCMDphPLKgcvK3zq/apiZVGDdYZrNIAaZ4LdnK/0Ar//OHDdec0R+zFhQEVCy3XEQYZN2quRpUUOfAegFWA0xYmXxr+bZTl68+d9HqK+atuWnm2ltnrL35lTXXvbL6+gUbp8xbd+Gsld9bunnqjDc3A/QAvLmtB7JuUF1KKXzjQU7NVOQgwjm+0wrPGJFYN2Qrdy+8P86qiTMUGXrLujWYa2ggMrTToLI7tB6tmPNLEDtUtp/8RyzR2Z2JoJXZVpwjzlD28hGKXf8aKZ9GMgCT5//rKbhuslSrjKUA6wAuX7LjrNe6Jk/bQ0mtYmCxbPd3cSOz4xyPlPCV2U8xqFqLQlp1yidTvby8PGt51vLy8joiaxkY4h9pWStJA8tUjEBLJiBiCCuiWS95SfBWhkgyjaGakUpRBR0iRJ+Gb0y5dVtTpyVrkXm6ZS1duE24yjdZslZZePcHfa/WVN1QJR8LIM4gF0a72qcwDDHsPBB1I2i90JGc9tLB8Svzby/b8d0V6296e+9z/WybzdchUyHibAS4cf5aBLCzn1961bytNyzcd+VLW/Cvv/7tfWC60jwjEwwBLHF9VDKUFN1m0BTZlm2zbob+xbj/Rl1jqegpZj81bdPy5wF2Uu+YqrvMmN1/GO8mytKjMZ61vI64/gmr6tbi5al77r/m9Amyqd1UN7xlA8AFy7ZdMHfw4bV5QOms0B7NAtrOhrSYK5NQe/LZn+Yw0Mz6cNVlSii/5Ly8vDxredby8vI6BtbStV7R33HH1CmUALKspaWRbnKptTJ3RYZpGuNjmwYaAEv31myRHGWQijJCPZS8+k9jrdzWPZL9n0jt2CxEQakSIyNlZFVR3eCkWesvX7znullbbnl1w9Pruhb3yr3WEqPTUlaf7Ubrb/26B+DqmasnzV13zuw11yxaOe7Gqxg1w0A9sI1cDGRMbyqhVxbMII7t37LiCTD4fIke7bul4WVwybcn1A+t3rjmadwExSv0BngBv8rmtTxreR0lQqHUreasCqKXRc888ti4f/xXFRh36PUJWA9w/pJtVyysrEohpAxYYlNbR2AtRas3ONi3SZFZfGxZi3nW8vLy8qzlWcvLy+vdshb+X/NMxHVIGi6v9f3JFyAvaJYqpImy3o8eqYzIXX0SPjbQSDLUjTR7w97kcNYyQ6wl33/WionwmIVAXrwmpYO4bOQWqB7ZrMbNOnT+7B2/XrOloxLLnEAS/9SwxoM99oJRaVPSjZGFLsStKxesnLBs1XmLll47fcbmKAmoB0aTRyHXSai4fbn+uBegW6SbQHeAHtTpAJhsdDOJLCue+2/fATMw+5UfUzmibpAnQeY+B7KJ86zldfQIRYMg89AI2MJ1S0DI6ydeVtiqaOjV8HwEZ8/ddPnMfQfskAO7PlmRfB2Z10KyChU0Zy36j0RWq2E/HubCaL/kvLy8PGt51vLy8npn1orTiIZntdUQiv6O6y8+l2zftWhv3lBKIWvlcUD9W1riM0Sa7PuqAOOvviUpxxyX4VrrgXJEv9b7wFr27dJ7siEjtT5lYSIaEjJEqk4DtywdvGRefO06uPiFtwbx7euQ2MwWUtEgY2tL6N4Rbiqv84FDQW4zXRsBJi6ad8W6tXdv3HvDq2/stRgmqn3AY3zFeiZTejl86KEl8x7GlxWyDroJ0mYOR/VZMTjzX74Juta5bzGIDsrPOZItWIvZLi/fr+V15AVksiRJKjk0H3jyx6D1xmVr8XCVIR3EgwBT39g+bubbU15Y3wM0PA+MnepQstZQvxaxVpQNIG7d98ht1iEjlSBjlvgl5+Xl5VnLs5aXl9c7sxZ5Y2CkRd4YCV1kwvsO0cReZC0HKw6dTBvBgMizKBPSJXwqAF8Zf0kydGJ8JGup9521SntAVxiljExMyjBqpAwRTH1x1aS51fNea17z6sB+egspvYkmozdjT/wnjNfSKOEpvX2MQiOJt+uc/t5lLeAvnzf/vBfmTXx28U9XdW6nUccSgj4wwllacEhEumPFkl8qGmGc0QhjFY+atTK498Y7ZRO3t2/OjMeItaxPif2btD6K2rOW19EXELJTfKjydj0/6Cxiws4a3ibtIfDdaavOn7/tniW7+t0pEslb3wdQ4lZrabnmwGTb3rdSWVfkrxEJ4MYPG/Dy8vKs5VnLy8vrHVkLOaRgLYQJlYnBrl/cdQs0+gFJQzIMwoQQUrWieXpkcQrcQDFxa3t/OuGam6MCM9rMBlsjfpUtOHw/fQhVq3SQtikR1iw9CUFtyvQPlx28dG7vxfPqk1/Y223rAxMqtWoWloVJDlKSzxpushIQhXF3Dwgd9VWQbnCTu+qhA7ZbZi//3vwtE57fdMeyjoBMQ2Ie9tCoZ8gSXdu9a7FId1tjghjyBmjmqPXdf1YqMKbBqZHG1BfM/JUWg/guGvWBVv5h6KP0rOV1+AIKmhVcjT9+4HIFfVEeBvXs0TvuN5FJ7XiDifO2TV6yd9mgqmu7mHTJ8IezlrRlhBmH8FfPPRZkA4lq2vpVz1peXl6etTxreXl5vRNrFS7iRkbVXldDePPkCyAPiryWTZ64RBRei8NI8wwM1RzmeR5mwqW2DgYqsnjDSnPzgqa0fZxW7y9r4XY3FPKNBZMwR2KRwDaF0Z3Lt50/v+f06V1XzezussOs8K4BmAroKllouI3XwDlkGTlfCIHEVevpKooREcFiic8ZJdALcNW05ZPmd5330s4XdwYBvWwIlEOo1Bq71741C6DGk066EXeXkqN7vwZe+e00+ljikOoPIcyCDrIihGTImxvaA2Ivr2EHu1S4VOoP/2JqJdzMgTxVqCTVmhDO7tdT3jh02cJd3QBNYfPUcthXRBtwgdSCSzdgIPvhHTdwOpuQRKzmWcvLy8uzlmctLy+vd2YtoWz5ELVmMRp8mjXvvvZySBugnS+ZHln0V847tn9ybhAIWn0cosL8Dzgr7TEca0k7Tfh9zWu5gcBpxhTjstlEQHnpYO2KlQf/+YXtt22H3RyDSHzfVF3YsHmqbmuJETmzEIG4Jeki6Z0qmh1MqTnabF4wJQLQAYArZu+6bHFl8uyOtYqcCCHdQ56F2c79B9cZ6gALQATv0hUD/zlhjCmlOKfIeNy3x6XV2NIwfjTJjBd/oUU/VRJCMmwOUvF/H/ieuErT1F3hdtK4/SkNZEF6iMMeAYfwqMYlcuoX/w1XLy7y62dvunTR/svmbKrgAas13rU1L0uPmMLnLmEc4SGQqxQRq5H25tBUNGhL+j3v5eXlWcuzlpeX1zuwlvMhJKJIm5TXCgZre7aSG+Fw1pJDvhct1rJFcdKV6QF8fcJFe+sxRmHcvoZmyr6gsmGfY63C2OF9YC3XrxUwkZqifHFnHa5bvP2suTtv2CGn1SF21CRzQxV+1IXVbZ0GI/c2nUu9oF8UFGaGudtkWbAW3n5IwTM7G99b3HvatM6LX9u9sY5YNQDZhq5NL/QP7ORuwpflIprrdfR/S5CvhBBaFzEu/mqUPuNbp9FrZbQNwWD3knnP45NHwR471Fi2ZyBMaZrhdUIKFw/YOeMuIiH7C5BMBo8/dfdAcwOHbg5McLjijEuTCi31y2dunTB983VzN1ZoIFzm3CyHf0sU6WjT6tXERazoKM5uv+em1AxKCDxreXl5edbyrOXl5fXOrKVBJWkg84RYS6aP3XULFRAWrMUKWmo3tygM/px1YZFBQvr4h2+Pb7RK3OhceC1o5bX+c1gLNyxIaf4V4tO2CB5c0XnhzB2XLtq7wpJV4R1AnWp0h6r1IIzKGsi28/u8BZNJe4Wkodoq/PXyO+55amP/xW+k5y0Nbp69pq4j0Nv71v9KycGYGuDosZb8jlpD6P4VkVK6pIS7rqUad/ppxZQwSj6yPOwEc8ga0YfDWUt71jqRVS4bR1zuipCZhPCNtbMlDEioUQ9hM944dz2uZoT1qxZ1Xjpr69PbB2q0+PjwOlRdzmhwrCXsyo1zYQ9h9uQzD0moZ2bQgPA738vLy7OWZy0vL693YC2MqLgb9msYJI3JZ50KLIS8OYK1hoYRO9YqhhoXE7SQCP5l3AXdEjpC3mSWpyhUA54mZdym2rwM3wfWEnbrKynv1vDDuZunzN17zstbnu+lGiqbeWPAM6VbKGXaShyH2ye6oUS55cnywgtMY9dcM/mQgAtnHThtVs+UOdsP4EOibTCIQBc2BJUPZo6GlDiaN0YZKzNG/WVZlrl99Mxvn6YtEMADR3jNOTMfsIPBmlTPOMS+nrVOaCGZj2AtXEgIQg88dAeDOiN7zZAmbNN4B+g6AIsSuGRh93WL9m3UdPoA726LCNtZy+VjhRsqkOX0E//LpVJ21laDHWDgWcvLy8uzlmctLy+vd8daroaQmrV4vPy1l0DEqtrtWMvYPiXeYi3VzlpaOMcIY2mk0coOxRqBwShR9ms5g3L5frKWK/zrT03NwKzd9SuX7vvab1bcsbIPWYhaU3SMm4mBo9agbJxJZVeiKBosZ4K1v2vVVi0ZEm7pLG5ANBgcfMsAW5HBFUu7Ll146MZ5619a9BqQ1Tu5Hzq/EIMQpY7qQ+gQq4QuFzr39nYLnlAQLCCtuq1qvLHkMZ5uIm/DYawlbUzsWeuElq0bLFaRLUaVDzxI47BqJmgYOigpsxrDuefcf+sK/u0Xd39v9tZuuziJtfBYHToeZes0xdAANzLmtMstkwmy1lMv3KMADyzmd7uXl5dnLc9aXl5e74q1WBIiE8R9HRBVkbUgrY9gLTaMtUTBWlq7KI1ZvLjz1y+Hrq/JVdnlzJoWHom1zPvBWlUDnQCX/8cbExdsv3zFgTWcnCWA5SAQDENjc05Ei9ZtrUAri4hmZCqv+J99Wp2QVYYkG/esCsn+Zx+9tQ4wswpnvrj5wtm7b5mxJLDpKOSk3G4G7t3f7fnuAuU8z911pK/77/8pPjSJ0mKrqFJzEGDvlo3P0p7WssTCFmv55pkT+MDWukxqubWUpcRXEauEdAYko/MDjTr++McvXX/RjOop0zu/P3vzIBEYHsC5Yulw1mI2HdtiLWPNOBHMEMpAVsJD0xf8sj/Y4VnLy8vLs5ZnLS8vr3fLWga5wLCV816jTq20ATJ+Z9ai0+HSRWncmvj9v4uu7pfQkEBOEAbvJVuOhe83axkKDaFTwI/XdF26YMe5r6zeAtDnuqeo0z+n1Bad70fiylvDgS1xqXZzv9ZzISbp0hFEFraNlX5gdcg6f3Dh15F/ugAunrPjopXxVcv6l6f2hWRE1VwGStvGo22tS225vBbGys1m85RTvslFJHF7KMEAJoekcRBg96vTbiXWUrqNtYRnrRNcygrayghXr3oDSaprYH/DpmGp7jSLIYLTvvPIxFnJua9nd87f2m/zWniwyjw5EmtljrXwiXENMwH1ILX2GGEGnTMXPe1Zy8vLy7OWZy0vL68RoDVsXpaL1TnPi5pAmd1x/dXEWniJ69TRZCeWllQmR3hjEGhpByXcTu396oTJoZ3mKy2isCQ1grf6tWRpa3ZsoGVaTu6y+MWOG6axP1K2nrbEyNwWNN7yyooLX1138dzN2wEO2shSOeRRHHgMqgnQMJC2dowd6UrvSA9tXgla2r6cuyg75lgyyBqQHHp46nhQ3YGKtgJMmLtn3JwD179+aNA9ljiINjIXsRrx9tsuSRLBcGPBM0473c5HIioMK45xE6N2RM1VZHWosraBxs5SX/oRWyfGITyS2O3adOdQdBSRPaBQ+aM/vz/JBiWwroQ1aDReBIJBAv905o9Pn9E/cXl8z6yN/Zm2c7szm3cd0a/F2vNa2uZ+qYaQxZKKb+u33TPVFDDm6lf1aDfby8vLy7OWZ63j919srd1J0LIpH391Hfn1et3dEsdxeYrU3bPswHaPxTv4PTnWArXCgrxkLRfWmyyhbqI8RMS6derlGMezRq09FdPOBSPjp9at0p4+v+XBJ5KWc16YMlw15BQ9zEX6GGfvmlZZoLM6pPeRSap1NCEST01T/z4Gk87rosoIT1YCXL1486RXV18/bRWST6rwLbp0kG6l2oRxJYMlax1x44ZuLN817RbagSoG3g317cBruHV7RX7n8lVTVm64fPnO327LkJ+oYSsTTAYJZDkgd2rV/lEUuSmdpE375kSGn4WtXzz9m+OAfAiEsrOP7Atqoiw4wINNYAZoDLPNM1JdFz1YeNY63o/fIvcKLT/P9qMpyRCBJBUEQhKLyk8fvQPRKqMl5E5R4NKjiXCrNYxb1nnmzJ3UA8igkfVzOnB16+tgBB3p9kPAnWexcIWLLRMQZrqOL0eGGYZLLUrTwuKEwhE328vLy8uzlmetE0HtjlV5nrtRmFFEwWF/f3+JVQ6xwjAsH4i/4t3KPmyvMRWrMVcJaMrqM2e/rjBqqlEuKxyMeg+BZnnULDjqXUvZrFFDwaY93T31hLfPPh4eug0jvdGwlrOmCFxDPx9ircgEETVQEZPkDPobBnlsg5GTF666YsXOqa+t3insQ1wpJEaGqsiGFeOJR45qfrfv107iQuDpg/rO/W/MNeEgh2yHjs567pnvTFt8y+v9PWC3ONdhXk8gSfFoc6xVDlq2u0mxPM0aGLwyGTn/xKhP7N5wwG4kczlBVUS6dYDO6f9xG8S7CfMsY1nTbsgoC+F1QpwrOZy1GHczFYRNOoVrtyzpCfangODOi+nbnFKfOzXcs+3gOasOnT93e2hPWOSmxqHOBDd6tMej+OnDt0tocmjU414JnDZAt52XGMlawrOWl5eXZy3PWieQgiAok1pgbaZdX375K7fC6w7DUNVqFRHLFauUMOY1BllLj2StLDHNCrLWAz+8DrLAujjIoFkfLXvkFj++8Z3zmU0iCQNBGL+HrFWaRMAQJVJdHyIXkkYqpbKn6nND47Nuf2P9xBnLLp//1hpJE4pxk3hEj1f1yCW15AjWknAMbMnIKaMC0YFHrp8E9a6o0Y0ouAPg+2t3jH9+1Q3TN1W49cew7vfSvmjxcmLIh0Nw3GTGZEANMHjPtPVWibXE4ay1cvHjNHiZWEvLRDnW4r6G8ARmLSG1Yy3EHgHNux/8PqeuKvzuFgVrCYKh7QAXvbZ03Lytl8zaOmj7LxU0kZdyzowZ7fEo7r7/h/jYRA4qSBIeqRFZa89aXl5enrU8a3khONVqtSRJSnxCmmovDnQ3tgb+gLtnf39/ed1rzLMW4ooUkEUgku9PvgBUZtLgdxs5/A7W6k/0N75zXi1V3NYQGrdahqrvCko6thrCArRwg5V2mSFjisK/JA1SGSqQWaoRqX69rXrBi8snvbr8R8s2HALoVdarI29FqoaKoLhtUvl9WAufr4b7CpqgBh66diLwgdenPxvzdG2Y/+TtPZcs2HDV4q17bSLO3rswCLGI2BY2U8SMlMgGm71U0GjoecOunFdpm6iG0FmSFKzVtLOMD7HaVlChs+LGfZEpqk70rHVisVbrOMJVgj+jPMbVEuZIPuHPfnlXU/YzYNzWx+qYVhYeL/sALp274pyZG+9aPTDILeebOuGWVqNnLdnRvyuSfRIiqlpkYa7YsBrCIXnW8vLy8qzlWevEk8tZlYN9+vr6vvCFL/y3//bfPvvZz37oQx9yy9pluhx6DQ4OfvSjH8W/fvKTn8Q7+B04lmO1NtZyqaEsBZ4Cixa99Ayylo5DMJpl+WhZK5KU6jn/smtxVQU54RDjR0i2HDNrSfcuuABB2R7XGKaKNBG+ZiypTg82NeC6uftuWD7wo6VbtwNsD22xn2y5KFL1LKXC2O/NWngIpQSlGZjawRUvQ++mLbNfwoPKpba+O3PxuUvevHj+6v24Z+rW1806eRzewIIhcjOvSWAZi+mWBC469VKCYtPGWsXWhbZlq3v+9IfxQdAai4SsJeye8Dquj195RNZKM2qUillkBw1n/Y2DvY19EkJOBahU1ccTiYsVD5bNAFcufft7Sw8sDmglUfGhqOGi0tbVfbSspSDuqu7En2FetS8t0zw70tHt+7W8vLw8a3nWOsGUpqlbuGXb1Zw5cz7/+c87psIrH/vYxxxoubYu/HnSSSchZbk7//mf//nnPve5ZrPp9+RxwVqSzKBlknXvh6xJBnduQvEo0yT4hNVEIup01dJQEGs5QkjSHI6CW6P+znUbzImSFLWXEW7R2YLEcVhS10E/wO1z35z0yu4rXjuwNiMb+nrh805bE9ebwloI2Mo+8n0/ZtbC+2aKCI7pHPIKiAMrf3Ub9OzAzYtz2Gtg6hurzlyw9PTFa9fjJuvCXZvMGymROMRP1qZQhDLAnzRXmQAOvv3FUyxrOYPBdtaKyfBddi567XEyURSJSMhLgwzilO/XOhFYSx4xRWyXsExEgKz1s1/8RJIVaCyobFW7iQO4SBsGnukJLpq79gfz9+yz1I6IludV8rcEwjIzuoNRxqLyxDMPcqAXtY2FgktxJNZq33IvLy8vz1qetU4k4nIcxRj71Kc+dfLJJ0dR5ODq4x//uLtPWSjoQKujowN/fuELX/gv/+W/+B04RmO1IeP1krUky3sOPnLrjTRTS2XN/p5KX/9on92ZBA7GioYa2xpCfLEo4+q9mqRVNGhZJw9rRi8tvxBHcTckSPWCeRPg/BmLJ7+046653QhaPTFZztdqCVg3RM2ZTWpJZkHr92QtLuhcRYr/S6ug9k6792JgPcDIsiK0qa2JS5ae8vqGycv39DIb20qb2rI1kLbTzI41ozfCkP0yTTmBrJnh27nhvOtEb+b83FVr6+x5kcyWEVbi6k7QTdBx3KiAdetXf9hxZV5/1KzFRE5pJRkGWeXhJ+7BBRiJqmqtCmOzuQhV17++8cI56256YX2vpmXEjNEitHO0RmsNQ6zFIXzoibtDNoCsles44bFpa9nyrOXl5eVZy7PWiat2w3dHXJ/5zGfKekKtNaKXy2u55JX7U3kH5K4pU6a0W2t4jZVYrRiYM4y1hLWWCG+dcoGsdLXyWseCQmS2nhIGffVbZ5IznvVXF+a9Y61iwpdN0FncKiwkFJ25jwAOAFy2ct2Fr2/8wWu7Ow2lu3ChhwGNz0oagcYlbVTOE95iLQatAcCj9yF04lza6DKDYP2ORQ8C7yRrRAFxSnOTr5y78NTXN094fS+VEbJWkKxak8GM2z0IWhmnFFUmlXG1iV/97FdsfSS0k6D9jUolwdSQHwf2vw2maZN6ImOpZ60TBrd0+zHhLlwybpirIQzzQQkxMzRoC7/MhaAyVMQpXJAXzlx9zbLdd7+4MTGUjeJuRWnOhTkG1lKQVOIuMsYQDU5uOBLXIbV+HYG1/HwtLy8vz1qetU48leyUJMlf/MVfQJsR/Cc+8Qn82W71jugVRdGXv/zlP/3TP/30pz9d3l76v2dZhgx28sknf/CDH8Rn++xnP4vA9vGPf/yv/uqv/vIv/7Ld5NDrPytQaw0ebcMKrYm1yBgjXj9/BuW1JJkQhmF8DP1UsSjMN7727XGOghJeVBKqI87mGu0L2LldtP2uSwpipQKwbhODIfX9X7do11emvT51S/fmHBLZcs5weOMmFFOaSHOiLAKtIVdDDW2zXEezSW54GPKV3g35unBgDfEP1zrE/QiHAM6a9daFq7uumfZWv83Cabsj8jiyxYRkZChNKqn+qjWOWYKoWXDMSucMaCsjdG881En3ygXP23rCujEYOWuujWetE4C19IgFWLKWqyGsx/1IWTk0lW0QxL+lSSStQcsBgMmLt02ctn7NAbjlh78mNsL1yBOVJ8eW10plE6Hux/ff6moIU0Hr0E3Z8qzl5eU1WmGgqLX+zGc+g2Ekxo1/+7d/+2d/9mcnnXTSX1hhePnAAw/4veRZa8woCMgajXNeGgz+9V//tUMm5CskLlzl7ixCo9FoZ7Curi78iSv+Ax/4AB4VtVqt/WkHBgbK6+2zjx2zidE2X3u95+eHCLe0GmlCyEAkWdceqPcgM/DqALKW1scQezmvCOipJ1ff9KNqRKkWBu2tSaUT9DHGXs4cURd4RMgBpopX4hj6JGwBuHhp78UbKuNfeaNavkh7wV6LtWTLU1225/fMqN9ws1qjjhgFeaVz/eLHIVv3wE8nEf9wZj3moV/AbWs6z5m77arFO1Zz6hyjXF/xKkJxO+dYhQZElMeNKMbnZJGK+1PWlxX7rmAt3GZte29sZgORmA28tWI6+WTQHkjCOPAuhCfkEV0c1DbLhN+w2W0//oGiwXMJM5ExZFCoGcI89AIsCOHiRbsun7UzMnDu6dc2m7mlLWHbAiGI2ejzWlkOwU8euB1fK5VhrlLKpJFHi1+MXl5eoxMGjX19fdA2Z2jEbKH+/v6XX37Ztb2UqQIvz1p/1HJji52SJHENWiV6feQjH4FW4ssR1D/90z+Vfx0cHPyHf/iH8uEIachdCFTOsdClsKRVedjgUVQeP17/iaylRrCWtr1Ltd5f33ML+dqJWDWq1hIdcmlGy1pM2358XDPNtBILl+NqZuoIrIXYNEpb+SK94+YZ0/l6y1oat5aW6W4F41/edca83klLu5ZLqqujIFIJ6kZTtkXKFKOZ2wcXm+FB6+jjXA08t6/SnPnMbQA7r576TcYOQjiIsS7ZEyrYouGK+dsvXLDh+tc3d9umraawvmwyE3lA8CXCjIzjhywOTv3XM2gOWEO26sXwI5M28dUKYQ3Gx2FS29EY2AhQwWfFf318dHvCnj1BfmcyYZqSS0g+iECxrEnI6fSZLRLGQ3IvwO3rDpw/Z/c1r+1JOHzrS2fbpSXyLHHYNPrlL90LLVg2y+W17EQ9YYqOUJ/F8vLyOkYpK5cVwJCy7Fi577773IhXL89aY0AuH4VL2Z1FQH3+85//1Kc+5Zb1K6+84qr+jDGlD+Hf/u3f4i3luQSEMVz9eAcEsPYTDKVZPIKWu+46vvwwrj+CsAxK0GrLa2EMn0Gz77Yp50O1A2RojdD1sbGWtCOMaS4vwE2339PITSRdgqfdSQ9sIeCxsBa3bmmu8k9AnlG3EtU6djO4//WOSa9XzlnYu0QBIk2ihWEVWyoo7GvpYbGkGcFXx5Zn0zyNkPRqh3ZB1Bn0roF86223TQB1CFSdUlvIWikZEty36sClSzafN2vFsiaB0aC2lh6KKZVQdk5l1icOeKbd2NkzvjG+2GWiZC1h5yDblB6GsdR4FiHSvvbK/QB9UgwQWfrp4iecioyWsayFtHOod1+uQySfetqngBU1tATiZgfAOTPfPOfVXd+bto9LuHr8tRDnKo8dpTNu5Kg933E1Mg5RxGrdgwesD2Fu2nDLfzxeXl7vXmUBFEaPh6etXOHVvffe635tHwPr5VlrDKhMcP33//7fTz75ZISov/mbv9m9ezfYvNZHP/pR99ezzz77pJNO+uu//usPfvCDH/7wh6+//nqwLhoOqNI0LT3iy4PEtMXqCGb+bMQfAWsNq+izNwliLRFuWfgqBL3EWooZwRnXx1BD6J7fleedctZ5uA7qmX5vWSsr2r90bvIEaKm5RpQpL6+btODAxBfW91hbdJsSCm1erW2wT9ndolvEWfRo6WM6E++MCpCU6pB3gumBfB+IvSA7QFcgbeDmmoywanUVbll/4OIFb/101e4ugP5iyyjEBRERECokLaoexKdE8jrtG9+hrS4/LTvp2PZ0tT4UPOzyOlLWrOnIWt2C99JN/vA6UVmLU/GewEVy570/sikmPEqyTFIXFi0kznDZbAA4c+76C2Z3/WR+FQ/67Uu2QzMGlrhTJLgAWa6OgbUYxPhyjzxxP16vx4NtrOVdB728vEatchAR0hReR+5KkgSjR3f95z//eXkHL89aY0BIRLhkHWi5pixcx1OmTDnllFPcHVz2Flp2hS6pdd5551122WXlHUp8wucpuQtarVl4B1c36Ctr/8hZ69DaN4A3XQ0hDTVWUkg4NtbiBgaDrKO/fua5FxMI8SKvNaw5ylgLvtGzFpGNcEkqKRTP7eysbQYum7bqmsU7p87cfAggUZCnUE1rnKYGMedxr9rnibWPhG3dat2xR3syXjeCqnUF7Jn+3I/BIF91QrRn09yngHXysI/GXml6UrzTc/1s8qINU156Y1VCabc+NxYMhE6aIIVpJsXkM5vIOu3U8fjYOMjbWCuzvvDWP6Mw/EjBdDarb2qxB6AmBfMuhCcka1FSS+hMGkQl9rPH76/H/Y1kAFmrmdRpReABmRFFLQE4e9WeKxY1Xt0NcYN8WnrWb8Rjnyxt5LFYhTrWSnUgIb3jnh8KW0boMmxtrOVxy8vL690Ko1CMS13MWdoBlIFllmXPPPOMKy/0+8qz1tiQS9fW63X3a3nFLXEkK9XSCIIakd5td7zA292dR6R3HZL5w+OPlrXumXoZ8DqxFo/I/91oFyuNlrVyacq81rb93cy2VgVtZYT2CXWLtcRoQ7GW3xojiGISN7IT4MmD/PxFOyYt2nrPzLfqaVFhmIOOAL+wpe100rJ89RK0eHvGS7oiPTM69qPSvpQPABzcuPZ52TwEoo649cCkU0FTdg2PgbqEKKJKwR0AP1i557Jpb/5sXcchm9qyR4iAPCZHAzu/WFtLRZnbG5xT/hBrJZJ84QvWonnHKuHJLoB9WzfNANPPWebzWifKYXwYa9kLq0UD1bAfjw5hyZxOH7jVnqZ4vD1cqX3nzb1T5tV34qKL6CzFjWdPEP3d0mJ/nimeiFHOl9OCXosYb+nKhV0DB92ByWTkUlvDWMufCPDy8vqdQpRysWJ7DVRpE4ChZpqmTz75JLQ8Avwe86z1xy6XbnLwg1zkfg2CoDS0cGu93c2i5CvHUe39VyMWfXm0uJMTLj/mCwj/GIK0YaDlmEcxkMktUy6ivBa528XUwWXH6x7DvCZl3QuFhpAZXDrLNmx19hi5/dlmQminY5ki6VRsnGkzXjdD29x+oXHJGmO7zJgMEoWR3SoJk5cdGLdw5+ULtlbAvUPklZTRoF/BW66Dsj2vpY4lr2WG/3T+bymrY9CaRtsAujHWpbrBtPKTC0+HjFgrd3OWNUScclmTn53/vUXbL3lxBXIX/rlqX5Rs2/BtZNp9NjqlTUL+JeeBnLWGauGGZUO+8AbyxFVg1hTbt2DOE5Lj87E0ccXA+igXr+PiGG5rMnSgRcklRg7vazeukpZ8kLWCtEFnGZRd0ZmMJFy2fvvXl26f9PLefsv40DBnf/n/UUpMknVNnMnRe2No9+q5jmtR34w5L+I2ZCpw2S03W8LLy8vr3QuByhhTZq5G+BCC9cbwZ+09a3l5/VHHadaT3TUmCVAJ6ETUe/HK7vWr83rVmoprI6SL53jOjuElkpTOjtNQY4C/P52mbGHYFdnRUgXkFKCT2fGqdPbduQqORCA9IhFHQ7ESO3gVIU0FAYJhA+CqxTsmruyYOG/HeqAQT4Vkn0HjXLNAHYZqwwLW4VzX3q9lhllpu0sBZarNxtA+vqnTjrnTf0FpgqxJ4JqGENVVtYeuu5HQ9lVwyzsQt2ZvvGrZgSkvLD9kPbgTsCk/i0j4BjXP8H9PPzaXB/hR0WDYoqOsGEItVKtfS9kkHd5f8WDt6nm4GXnYabvARGuHHX7x0DX21TYFzqVVbSNfouzl4V/cF7GGrZsV2nCl8YCQWZIi8dcBTluy/dQ13XfM3dl0KWYG4//9u7QuKe/LY63SctbcKLZGDgx22wM5uf/ROxkN9Uoi0bRnLtqS5wbeo3HmXl5eJ17o4mcZe9by8horUs7Hj4ZnMUId1gAdgoobnXvS6iCFXBiaCeq3KNzMtRhteOQwDWyeBlnr7047M9eEPgVrOZRygd4Qawk3jPjw0j4zBAqUAuIY2PFmDjqu1PGmHGBDChfM3njqtDV3r+vZadNSPIjtc2ZcHrsH+uGsVZo3lhd7tk3KuANk9+tzngFeA5lal3lKPi2dPl0M9JGjY54hweYpdcXgDpn08qpzX91w7YIdO23LFk0yrmoqh+QyMbjPeNJMp170Y2gCBAnkQbt7BxRW2sXmkSkhZQ50s9olcnyyQZrrVcw/LplVHAm3vMY8a5kWa0k7SquR9zEIfvP8L5spLgORpE23YLIc8VvXB/M9Csat6v36or1bBeSc/ogH0cSzpii8Y60OKsOl2MMyeSwbROdNmKo/9st7BoODAiK7SbKcYN4GWn7teXl5edbyrOXldVyzVm5Zxjj3vBxjrBDS2k9vuZ6q1mjULwhulDIFaxk5WtbSdkoPfiXGipJOV//kPnzFyE67Ym4LhBtIXLKWeMe8lj077kiDTM84CKMglrAP4ObFey+ft+2WFXvWZ3TmPrfjgZ3DobTDvo5lYtbRLyNwi7aVdYPpObRrFTITmEzlsWOtSWedBY0asVaIQCsF48b6Je4BmLpo6yXzt056dfUebUsro6KxRRki4GaQjv/yudTLxe3oM31U1spy9+ngg5P1a+eC6RPpIbtXS/cPPTyd5fNaxxlrOXdKYi0JccAHBhpdihYALrdYSTwQFGVKAUIOv3mr47TF+767rKPHnUWR+CeR1rTtpJTJYG9qs6LHxFqSiSYe8XHeV4s7cUti2RjJWuBZy8vLy7OWZy0vr+P+C8vCzTDW4g1kresnT7QOGVIJ3daQdCx5LSqWi8lgmjmHwIE6UlZgoUIORYqOqFwnV6uHfoQPux4x/KosO4ypGQtgr4SpC/ZPWnDg4udWbrUYY7vCqFXLNY1Ja0j4B2YtfM0B3thKxEWpqMTw2PoFZHdeNxUjXkiaJqgTMllDQtwVGwNx05w3p67rOW/2poO2eJBqrTRtMMvIVjHJ1cX/djZxam6zacNYS5s23Cr+hztQNGZMexygyxpyZK2I9og/PWsdn6yVQ/PxXz9k26Uid7oiaNbtIawbUYpH4nXTl505b+eFC/cfyOlm4XxUhAWseoSHP669qjmGCW2a5TH1CvKKgfCRJ+9G5LPsJzxreXl5edbyrOXldcKxlnMIzHlS1BCqEGS86Y2FVKDHuSndMDCQV3LUeS3LS1kquE0xRfYye83GyCXThmZbaetA6FhLDnMqM21PBW3UR6N+Efwyw2rcGks83wenTNv93Zn7H3ijs2FZLU8HM9Ok/iemqRmtyOC9l6x1WPcXgk3fsrmPIXFhxGpEk9w+LGshYjU79lHDVRYDxbGaC3ICSWxq6/wFm8+eu/u2uQcqALUwtY+Skitm99KsR5+CBnVqcX1U1spybru27J5W9bffmg3tTvLvKmD33DU2j+EjsVYsazf+6BpkrVzHtm5Q2rwW5Xa5bRS8ataKy5Z1XLfwYICLJ6Gmx3rcxLVzx5W32RMh8hhZy+DxTrPiomxAQfOhX9wRq6qgEyLF3O1hrOX7tby8vDxredby8jruWYsyRJLq90DFIIPe7W9DWClrCKlcT1vzH6NGXVLUCgRTgMB26OOV/zvhAgzvlP1Ty/PPZsxK1jLDIEe1jzx2oEV5KmkvuJECcWUZgwmzdk1eL05/bst+pBzq709N3quhibGf4TQXGL+W1R+QtRyoJGA6pz1/t4WcmhJ1O9eYWS/H7PIJZxFrSUYXo5GQODGg7AW4bMHG8XP33jC/f78zKjS5dcCn90o5u0qFnORs+9lRWEtzKfC2NIm0jskYI93frLwFuusIrGU8ax1fx/DQ8dJeQ5i8ufF1O8WYjqkoCsifJsGlSAtsB8Ck+W9etWjv0pAOSS0hBE4eMylc890rZXeAR2ElDZNRe2O0jmfDDM2yCxPVj88diapnLS8vL89anrW8vE7EOE20TB0K1ooGHr7tJoiqBWvZcF5IN5NaHwtr2V6vzFXHIX9k8Pfjzm5A8WTFsCjlfPRoKJAtcwJo9Wq1Rh7rwuXPFkFZ1rIXSwdVZJUlW8a9vv/0V7b9dj9VKtrJApktWgztlZZvoDqGXTTMe/Aw+zTd1g1FRtkgDmzdMN2xFv2qE2ItHkPSuGHSRBoMrTKbkaNnY2RBn2FE+xbGvosPXDy3cuv8PhpyJ5lQIWeUbnj+kefJn0QylgEXR2UtTSyptWKcN235Yt/q5b8S2T5rfXLUNNzwdyE9a41l1ir81h1rvb5mQcAGm+mgY60wbIIbwiagpmA+wLmL1lwxYyOyeJTQx56ArCOlCzj1//y7bXOEehbXeTraIyaJcrspXOioGXflUNnXs0VSXovmE3jW8vLy8qzlWcvL68SK07jN9mCMLpOazWuF37vwbJAJpEgFFBcpXQZFmqL/UbMW5GmW24xWxinuu+WpX/VB4YrhUKqNtWSRChvySNTMersrxwMFaxV0gN+0QQ5v1mHCore/u2r7BS+sOogvZP+ktC3QM5FiTYdJMDS9+L1lrdLlDy9xs7oxj7Yi6nDKV8WMNeybyCEPaa+yEFlLJaHME2sZosBkiRZ7Aa5btOucmf3XLW32FlYgGYgEf5z9lfHIWgokjaw7ar+WRmyTlBuUWVazrNU7c/q9w2oI3xm3pMetsc9amWOtnzx0m81u5SnivT2hETYj+mw5nYG4cNHKC1dvvWHG2wcSOqTsBALRpB9w9v/9DhW2CurgYu7AGdXmyDJVnRuIA9b9xDMPNvI+z1peXl6etTxreXmdiHGaMC7ZI/NgAFnL1LuDQ7uICpRNMbWNkDq2vJaKMZoDN6uHK4gFVAAWb99NT5oUVYM2BBRcpMZZvbsNKswtHGtJ21GmWRTJNKdap4TbJ4ADAq59ecs5C9Zc8vrq3QB1DcxOBSoozo1IdumyY2Ut1eIZMzR8GVwnW05JKcnSADQVYWZJ74yXH7TtMBUuBuwGMpY2aBtUZsJBkBENMaP3IuLUJhRkjoyEeIQb/91p+8e9eODptytVJEZJVIZPgKxF+UDJ8J8VmcJwz/ch1hLKDoqlzcpA15ToBOjMY3zWrBwaLhFumfasdZwdw9boH5hIcxGXrPXIk/f1NzsULj9dmKPg3fAgYDnx9+mzXz994ZqfLtpJVYIMbPGgqIsAuOnZdNANZLDzFX7XLO8jimcuL62FTvC4Z1B94PHbJYSpDnKTcfweoOSsXXBSedby8vLyrOVZy8vrOGctjM0p6DcSWChq3Y/d9QNo9hMPtFhLDbdZH/ULcKpFjJV0WRmM63ZG6VcnTIxDYzKXuQKdk1Wgw4byxaQFrdy2NxXFhqYoGuzra+ADDzVoctC9iwaun9d1/vSlWwAOylxBkRVyvWGyxC0l2ihlFHGs7YE5jLVaF0FOgUJT6iCzZNVYvfJlMF0IR0ne52bI8jSy9hhEWT/5/jXZYCeBGXmBaCGYTR7q2IgdOVy/rO+ieX2TX9nY4bY8CSGAS06/xFlohEF2WF5LmuG271KwlpdchcU731zxEj7UNuMNsxvR6nDckp61xugxnKeZo26hs2Y6iKAV8gqClrTNWs2k7j5TyqHaFbkTYOLqHeMWbZizP+O6OAERQ8px5WjI+yM3ATtNU2nyUa+HIuFskrxhBz00D/RtieSAJUDRXsaLrGWUX2xeXl6etTxreXkd76xlv7QkAYCKr504DmRcspYZzlrmGFgLUUVrR1lBSmjSyfU3z7+E26i+KL9LpTVS00zkh7MWt7QjsjgLIgcbsSRLdwSttRlc8tLOic9tumfJhn4EHROmeYWGGjcVt3xim/sl8Zrt/B+9MUbBWsoWMY5kLU3mFSJ1LWGJ4hWR99WrO22nVshlo6BHDHKJtXCXJjdMPh94iLs6C6v41rWWWZLbJ2X4jtZyuHF55eJlHcvwKSIbF8fQ7Imci2MYpKCPylraOtEpQS1b9n3Xk8bOuTN/4TrWTOGxr927Etx41jo+JLlgVKJLBYS4YlLZRNZ6a+tKW0CYpTJMRVH3y3Oq08XV8PzO+sVrD42b9/Y+7QYMEKNnnMxr+ro68YAZ/82LZTF8QIxuPdgCYHLZpCR2aiiNXZdQf+aVxwTEmYmVG1ugoRjZ3rrcPQAAgABJREFUp31iy8vLy7OWZy0vr+NaNAAXY/NmTUc1pKzXfvUohf1xtZ21ZJErGj1r2cYpYBwKYwwW22hv2Ybt3D5zlJqyKk/ZpqN21rLNWlI6/w4h3KYYTSVOOyLYBvCjpVuvf6P/url7OgF6goDiS90kVzVOlhy5ZQ7aYnystZUYbQmhKyBUdD5+KBXUAq2WW72wFo46lGnvW6tn2V4pDFtj2zzTur9StD9FcnDTWtnoBdbkac3Wb7oaMPyZSdtcdcEzyy5c1XPxvO34HhG0XnvuNXyhIC6st03LDuTwWcbcTj6jEkKKuhMwTZAD+3ctbxmEJELGdpRagVuH1RB61hqz50vsSkC2kSaNeZ1DdNNtUwM2SGV7OraD5eioyRMaCDAIcMPMdeMXbJ8wZ9Oga5M09lSLYbbaVgZhNuHMK8Adn4aNlrUQtBxrWfajfq0U+q+7ZXIOAbIW01T6K4TCi2ctLy8vz1qetby8ToxQDWywpbKwYzfI8PC81u/LWkrLnMYZp+A6QSCUsP1gP4ZzaWxcusm+itJaj2AtTjghaPNa444zDn0aDgHc9vrGSa8unzRjzYIaPS0VUylbNBVUHQsZU0SMbhuOYb6WYy35O1irCFITEfWCqrz47EPOD0PpHP+YS8NcLMtx+6WoDYJMnrjvDhAuFSbcG09CAYyM4BsA977x9nnL9o6fueuggH374MrLbsyCSCSZG6AklGxtRNFeNTTI2JqGSOZyiVLzAAx+lP2D1R02z9ZECnXZLWUtTzxrHS9ybXsiF3gsMduvlTz+mwddXkvRrACOn7i0xxBllQEun7H2O69t/cGa/l6Xssb1YMjoEhQ+XAzGyTe/foEdusdssasc3bdJ6/gVKs9lTUGTQWX+8pdyXIG0PZQswwPTsRbPmf/8vLy8PGt51vLyOp4lJARBQLmVtPnj668EEUBUgbxZspZsYy01etYyWUJPrjXjVJcUIG5R2gmuuuWuSq6dE0aUcVk2crSxFqdHWsYyUtUj+j0hd7N+gFUMrlq0YeKMZd9fsLIbyI2QDA+tsWHAE/ID0LIwhbdQoVr28e8Ba+mh4kvD7fgsE9t0Vn3j2gXIWlwk7u9MQ8FatiuOwlkRf+fr/0xvw9pmKJvTUm60WJalwPYBnP7K8qvejG96qb9u4Ev/eIrtVmPAM/dZHI21aHoYBtpRq8hQYaCcGN735trXEnYAcctAw70o5/lwzi5Dds9aY5K1cvrUcSUwy1pJJeqOZKVkLW6YdoPyNDQ1Tda6ftmui+YferEGPca5gdrzBSxTg72Ropla3/rWJGv7rsmdZbSsJcvmTsFUXUGdQzWDgS1717mWLWkPdClpk9ToRyV7eXl5edbyrOXlNaa+swDyPLdkkt157RTIaiPyWr8na1FFkcUejK76reGetDO3vnTquKoo8llRTpYO3M3QGs5ayrGWwtBRuUlbKcB+gCunLb/mzb2XLVq9XvGGZa1Gk9jKtWkxcnsPaZgVRXd2kjIUHUvvGWshP5LthyBzQMta4eBO0HUweTnNy73TIgmWc/IBTOqU19IRTysGUuUSUcal/PBjqHWAuGLeutOf3XnJyyFy19e/NoE6zYI+oGIwa2cPR2ItYy+untEZOeJWa8airiWvPxfluwAGFJWPUT6Ni9Sz1vHEWiwnV3dp0owHzbT/qed+nkGDQRjLBje5KqtGNVQlvNwVX7l4xxVL+tcBnbOQbsodruEsgzyLIayC5BqSqj0zkYWjZS3mWiSpolVab4w6gpaA2i+ffSjVDYFIRy45bja693z38vLyrOVZy8vruJfzrogCjLeWvfqidXtPiFIoxtJqOGuN1gDahlM6rtfJ7t0WEEYWBMIUTrlgMl4nxlLFzOJ6s0bxYKsfqdVS5F6UKuSieozhW5+CH83cdPELa65dvvsN239S4+QeQaCV01P1s4hB1s5a5A9oU0dq1NGdbvN8b6sh1C2/fNx1aZNeS1dmvvQwVetRrxTdO+NGlZPDaPdpE4c0ZSutQ1oFQ6ktoynvRWOxGgaCWMLALtG1E+CyWT0XLOKTXjnUpMdmVHNIftmGmxGsNeSOmOdDc5VZzA3DaFcYWVv31iuKKi5rWU7zvjB8VkqYIdCSbd6S2oPW2Is5tJR5Rh4tPBVU9pfc/tCtGS4tEJzFdnoxYzKJyQyTzjhct3DzGS+9eeXcQ7vtCQihuET+yWqE9EnC6PiHOIYfXnsfHTuSOTPSURzvbgXmyh0EiRxU0BTQuP2eGyREinK9nFxwmK8e9PLy8qzlWcvL6wSI1ICnFM0njbWL54JKCRWsF4UzBm8lmcr5WqOPxdsc0h1TucFZCFo9GcQ2E2VnPmmFHKITJTMhRIESGSFXTqTEAwgRAZGsFnXz85/dPGVm180zN+8UzmlwiIJaRUw2G+bKCFuvq47tNHrb2fd23NLlPGXcbCog7Hv+6ZvxPRFEGV1Oax16ND7ASI24lQW/ffQ+imnxEtUpf4i4S8+Ge6WJtLipDj9eUTt1wb4zXlvTT2+DKZtgPMzQQh9tP7cl4BCuOvJkG7jxtNQkxhg3rT2Gn3LTJrtKe0PtUw1j6vi16wpXVihlImPA5ZW8sHJ6zUQ6s92KlDdtBuIQg/j/s/feX3JdVd73T/wHwGIewmKMwQwwzDIwwMysd+YF3oeBAQM2zkGWHGRbsiUHydnYBhtnAyYMOIANTnKUrNwKVrCVcw4ttdSxuvLN98T9nr3PrVvVrWB3Wy238NmrVKqurrp1wznV+3P23t9dr0EJYOLiA2fO3jX5tc0HbNYui6X2JFVSlTyPUfzVjJSffOdMTMr1a8OY8i2rJDazMZTm08MuDV6QFAyAcYnVjPV66gabM2fOHGs51nLm7O/7G0tgX10egFe8+uLzRFjFKg1lJc6zygsJrc79sFirhVgsCxl8KsRw4aRb95Qi2wULe0Axj6I9HIv1LWtRfVWC0aK0CqkBj80Ak19efuvS2vhnt3YSsI14wYca0JUrL45iKcQBlwZckLUqoA5ueOtp4W9tstbAtyjJ09Aj/EtvmXA5BBXwS3TyE4zuaYbtxjAcgSG4JT5cu6n3okWrVyvRC9Kcgf4yCb7zofKiOZ8H3nrzL6nXrxLbzEykKWY20pXlGuu4Knk4wqV1nWimSNUma26QQLrd27M37a6ZC80ItDGptBjC/gg8plHo8qI5+8+a2/6bVb1FO7aZeUEgIDRvCezcDHDeXXHeeFYPhs7eqoFYg3HLgNaBns3mXoKfYtdynEQ4+d14c+bMmWMtx1rOnP09s1biI2uJ8NG7b1MxZhX5fn2kWEs3Wctg0je/d5ZPDmElImlpnca1flQu5JkYNG80IzZAYwhkN8DEeeuuatt58/ztW0jqoehRsdPInqLDhbZsVIhRjRQyUiUobwK9F7UtBrJWSyhMUhMz49bWH77jZowflnsg8kgjUVDEKYYkY619ADcu3XH+tLYJL848QI60HubpN5vtmvvG71DyBKW46cxScVeDtWrG/XWsdQKzlkhJgNKMLB1B+MAzD3kQY5c6TuMGUarCoDeE0My15f0wbv6+S5ccXBTgMCNljATVXMCrSASfnv4Im2wzuGPyz4BpK4D5HljL4pYZZrEZZr/+/d3mnriLk1hmNo+cOXPmzLGWYy1nzv5+WUvEkHpp3wGI68eBtaCRQ1iV8IMLrvRIFaKeirpfI0E/zB7kzNAKoy5RIjXPaOOoqT6AZzvFhJWFs6av/8W8FQcMs3DQcuR9tVbWaukArJEJKaqgw9TreGvRMwAdIA8MyCFsebcQjKKIieErv2s/VAuEuCmpYxNrqRBiHofoBB8EmNnFHnh738SZa7eQhgGTEEd8WKzVW+heSVmO5lwmmSpjprNvfOKQXiOaRWjOTijWomuKl89Pgooo3v7IrdgWwcya1FYoYi1gCuUShAWAB+buHTd75xULd++kYi3SyoworuUloKpMCPtkCKzEc0XBY8VaDzx6q8DCRGStKIkZG/mFEmfOnDnWcuZYy5mz95+1vOJDt94AMrY5hFQCcsxYSx9CLJKcQOPjlxUc8ITx9aqxFX3gXqUIJPxHTlsaS1+iykWaUFDrspnt5y3svbxtZzttAUvwveOAB6qVtfIbpyIzjcIVhlG7F85+AuAAtiPW8SDWavirpBeHoa3UnPOHb7kB4hp2RQLwmMB34aYQNkNqP9zF4KFZ2y54edP42du6DCqlqlEyNyQz56lo9qpS2oERRK0wuqYsa9luafbGM9xyvu+JxlqkfsEZ5rTyFIIt+9Zh/SPjyubXKs5lNcGMVdgLMOG5ty98de3EuZt3UGCZcggDStyNA4m9rmIrIEPtEeL+ACuthjbijsJawc69a/y4x7AWk1HepcCZM2fOHGs51nLm7O+atSiH8JYrx6WlHowsoUKDYpqPKGsZX64/hgKD0y+5uq5pCV6qOKqbz1Q6hxMOCjXKY4CNdbhuTsd5Mw6OmbN/Ay3JM+YNqqQaQXc2V80efBMkJYJZkH0dq1AYA8GmyVq6pbmr2UjgV+ktEYQ1c8LNaU/KRYORdQx7RegqU4QppVif8Xq3FOH65f75M9o3aDxkczmisD7ES8BBlgAK8+Y8xZNezHvERkpW/Z63dKamDEbtZAhPPNZC2sc1B98Hv7O21yANxCFe2NBeTRHyagisH2ANwORZWye1bfvttmIH/Z60NEOQPujIQJVhfmlGWlXyGk7Ry84cp+p8WKylDstaCuqz5r0gMY0wta8JI+4uoTNnzhxrOdZy5uzvmrXCmnH61y+cDX7ZsJbC5ksqVWwEWEtZCNHEWnWF3t5pY66sSogyvuJJGnASm8cPVRK1zoFvivSD6wvjFhfPeaPjFwv3tDNSzQjNrxQ2aR15d3Ywa6lc/y3Sxi0Vpb4DG3TaiaDF+igTsslauZYj56kUSQa3Iirs3AQhihBaVsNtphJ8kmWE1JyHC864Yl8RJrzeO2GJd92M9bux7CahONWQDtmcKp+nXcuW/k1yw9JYv6OVIMGRtCWWZXsru7jWicdakoUa22AFvdDzv889mkQFXa1m11bZ9nYoetEN8Ndufu2i3be9uW2VwqxUbLKmDWHFIAMQvh96mNxLFYWG0HlZXfSDC3A0vlfWglweI5WVn/9yqoEu8yMTacqUcuPNmTNnjrUcazlz9nfOWiIOD+7BfLaWHMKRiWsRa4HK5TGMC3jxtbeGDT1ym32UAGO2wCnmwCLzsleLcOZrm8+e1zNxwYH9ymJYtmYvVDrCsRjVbO87iLXw1PkAnma9i+Y8C7IIugKijlmA9Ho9kLUkIqQIqiWMHMoYourNl48BHtWUyipnmFXEV1i4lYSnf+f8SMGk5/aOn9l3+Yyt20maXchw6KwVgypHodlAwbxfxKhmKESNYlmNbmaQEZ9jrROOtZDiIaxDrQrlX/7uNryOBpgCkiVMZazihKLDuwCmLNl18cz1k19ZvA9Q/N2M3SgKKJSNabrUaI+WPHQ2ai790TgMuR4D1sLQFle+oazH/nCvuY9ZXZE8hnRxVGfOnDnWcqz1wbEYc7XIOxOi9RmGrm/22Ixs+wA7IDWMk7kTOPrNXFkbCDLXMYsIEWtdf/G51MI41olHKUkq4vHIsJagqFWTtTxy86ss638lM9Zi9i3mvy6AsW/svOQtb+KSnhU26S3ygVQxIpZKGOmVcVt8IpotjJt6fanmVZl0A5Rnv/Zk6h0EHWAbaC0GsZY9jVxSazEtUq+ErCXCn028DKJaiOmDIFMCrZgwLPWAq/NPH+8xrAC7eXb3pfP2jZ+5smjOFfNz51e3CAsceQIqil8ZmuvcsGGWuQQKs7diOvdhC2vhM65c64RkLS08VjagVYCubQdX4pVN6HpKK0Ij6yArVO546dzNl83b8tuV29eXgjpNQByTNm2Yh6AQflIFWEFI5L9z2XbLbPYvQhAEkuqrjhpMPixrgU0jrPpdiSgSa1XNnOJSuPHmzJkzx1qOtT5wwzd34IrFYs5dURQlSZL/lbV/ceXh6pqlK3YerWaZuekTKWUurkyMqx//+o4bwSuil69TycIRYy2BMR+dSZ0VA8xWrGu446Hf9ceSKpWgHgaRNmNO9Ra6jO9vQOveJft/NG3HmPm9f+kQ/bgdTiE4TNKLGB95GUJbbSJ0q+o63sy/UMa9VKDVz/wDIKrIMFYUrsFassFadCaVNE6t+W3q46n2iq/86THDZhKFDSCyzq+kEjXjCdfZi8/NSxRUJNzz+u4LZ+y6YXXnHsr7YmYzZILsHfefYQZZoNTBWbMex4urac95JSvQsh2Z8RhDDS6l64TELWkwBgrL9s8LoU+xagbXEpgwQwujpmaMvhnAxbM2Xzx97bo4W+BAIsOkVvMSDoKJJITGWE0iCjQHUNjaaYZH61eHGXKVSuXIuHUk1hIKIgUY2lq/eRmVb5khpxKWuuvnzJkzx1qOtT4oZjHJjN00TXPKMn9WB/norT8aADOvT8jsA3caR/n1tVet6SoZ19+vtK9ehrVDiWdYi8XeiOkQCvTqdNY8KyFy6E/gxxeNNw8KQcJsdVGiyOFLSwA3tm2+cklh3NzC1DkHOrjdE9T9I08QUqZHWjNaU9EY9aGi3CqZx7UEuqK8z+z4qmUvUS9jD4y3qrI8SWgJajWU/kTKqW+zTm1cSxQOqmofi8oADSF8HUNahhQFu4MYnVMh4UAKkxYeOPf1tZNfnNljBRgHXtajngWlMGQWat27aNFzID0bwiIPnLewlsrlCpydaE6HuYZpV7rz7sevTzE3MKZqSPNNrQ2PG5AqUwuBB5a2Xzhj0zVt2/ppCGFUWclIJBp41vaN1ghKoWeVCPFfFc74jx+Y4eH7vhlmlvDfxdrE4VmLSY8qtYL7H74Da7eE4S4Ro+K8M2fOnDnWcqz1wTCbhmTGbk5Tnue1vqC7uztPVcrzScIwtM/YBy6uNcrNgJbxnFq+scRrf3nc9jKGqAYq4Yk/4qyllZXHMH5WmcMPL7qiEEMxErZeyQBGRMUka9L4xvX9p722++q5B3fZ3RCY7MRFYCUizGAc6V6oxFqpINYaUH1lfFNRxQoo6P7rkz8n1gqoK3EW/xoU1BKkw51prPOQOoeFEJQevWMqcpdUDREC7LJV3N1x9dhJXkJYxTGNa8ILK65ffmDyzBX7bF/jFrgy7q+ZlUeJM9jiG61KlfKuJET1eNA13AxejlbhD3OkgjpuuRqaE8kEpwYE0Pu7Z+8oJvtQ1DMr/APFYjNMiwDtAJNfWjVmxtZrZ2+p4HqZCKTIirTMIDVf2hlrYV9kMzLqLMVRkMD4n1yUD4c0TfOvjiMvqx2JtVQuj/HEXx41xOVHRTPodV4M6cyZM2eOtRxrfRDMeGy5G2fYyYBTrVbLI1259fX1Wayyf3FDMvsH2LHWaP5uas0RNdc6jmOZhJf89EdYIGRcfxbkmu9BGh5r1lIZaymel2z1Byj1UKfoFuYTMp0QZRl3cA/AQys3nz1jwxmvbNgOsLtOdfRJwFlC2nnIbFLAcYlrGdZKm6wlMtaSUQ9204KutSumGWbEkJRBrUQfylqsEddqslbiQVIHEdx59eVQLKBeN0EaIxHIpJ6efcZY867OXq/WH5gHq2tw87ytV8zb/hZ+0pAqJFE7IYqrJIbhrV87iyX7QVD0Y0DtWdbaWFJ0y02WE8jiCMdVyd9aY9ti6DNjlWW1jyr1Suaa1oi1bpm38+q5+2+fux3LbSlpwQaWDXHJlJmRZ2al0mkIaUn6CQ3g8p7es7/9o9SPW9fj7ErckUOpR2QtoSPKUw16+neV6wfNY3rGsZYzZ84caznW+gD+8Sb1C0tN3/3ud7/61a9+7GMf++xnP1soFPKEJfsac/9P//RPXyUzD3J1DWej87spJ2HjM2WlPlr88YF7kLUYCpHTSjfHUiDqMnqMWQtBKyVJdPzoRKK3Z1irM1BnjL3KFpBUBVQBNvhwy4xV46Ytu3pJ+693VXdSpAvfniY5GxjYsWGkEWYtYVkrEz+UWddf3BmoaNZe7V+t2V7QJZTKSPSguJZosBZ5v4pUE/McwsDcOtevwv5hifnRj6JKECHiphx+cNp5UQoW8BKOBTZ3TV95UVv7Wa+sKjb2zVzBd0FcCgkOI4FYfzNj+mMsRfV40kJoJBCS5gddaC6xkEa4yXJCzWszV/ljv5kK0CGgXBOYBJhyuzZR14CVea/uCya9tvmWpf2L6xSiFWZUq8gq/WuUmTEvjuoVxHJI+8HvZzXsYcyaLQaCIMjXaN5pvIkma+kmbqXCfJQZ3p6E2ivTnyHuShVwx1rOnDlzrOVY64NieZ19nh/y4osvfuhDH7KPDUp9/vOft390+/v77ZMGwD7+8Y/bPH7zwPzoTuMJcYnz7NDujnZW7MEcQrwZ/zuxrHWMcwh1C2upNI9rxRTFMrfTLriszLC5lE+1JY8s3T1++r6xr++5YcaqAwB9kNa1J6yb14gTaWHBR45oZMs4jqLBWlm9lmUtyTHCxPetXP40QJdKeww+yUgdylqshbXQDVVMswBkJGt9EJbBr/7u2inQdZDKampcoXNblfDEc6+Z7aRhYN5V9bFr82Mz3v7BjN3jVla2tlS4vDvWSqyOv9bhxo2vUpCjQnIIVuGbLqjE80isFTrWOpEcDrqGcRw/ct81It1uWMvHqkFAZRspUFkUeAngppeXXPrcypsX9Jhrj6m6SSIUJ31CjCTbrag0VNj6LaxDHAATZhgEHOqs1NNnvzrs3wUz5KIoGlTEewhriSZrNXBL4FJEHCSFIOl99LFfRKxkflRDb+DlzJkzZ461HGudwGb+mtp0QfOn1Pz9/vKXv3z77bcbD9f+Zf3iF7+YY5hNLDz11FPzZU7zwPw4KNvQ2fvifjXRSDcrcszzcZKJLHOGcnlef+/9d9xEvZ7qeK9SzVC8AbvuapGXJun3wlq6QWzZBgRSCkXPPOOzEWh1x3D+1bfWiDGqAPMORpOmrbzyja5Jb3R0GPTiZsyFDDxmdkzQOrhlF2KCCJIRzXmz2hiCWg9lOoSUDoiOLDN7t2/uG49hJqGqYk5johXPPc4BxVp0k7SrIglrtqkRqxaAx78YNxZb0aoiaN+AXUxBvNTCqBZlSgMzn2085vNm7jh34YHfrN61tVTT9mQKTqCEAQIN+f4JG62yLjT2TUK+NTM0oNzMds16kbWkyoNaOFK0zSF0ca0TbLL7OKkVD/ebmaShkkJaCmlNxUxhhkIzBYCJr6y6YdGBa6et3GPRRggr/B82pFZUwszrDZgVzbshrUPEgdmhPubcC/v7imDDX+9ckYtbb7LWgKZ0XFO7bg3Ba2/8jR4YJEwdazlz5syxlmOtD5a1rpSfdNJJlp0sYp188skwsKYLGpIY5pl77rnnlFNOgZZOXBbDrHa8tVaxDftGl3Z4zH2vBh0pVKFoydkzz/sM/R38rQEeHkCt66GpV2JrJ9v2qgXMoMVJek8mqSiEZB7CbP8USOP01yKoesDLAJ2knlak/qoHAW5+ae2Yx9+8783uzQQYAXJLRP2gYvQMtbC7JRt1UHrkT6nOT6ltSGU/GANEGyvlDWZcc+7TCR+wN4e6my282mg1JlN/zyaIS1S+hZsVDKa9sCi2SX8JQqnAnmOwX8K9y3Ze2bblkqXbF2OjLQUsRvxk0krnE36meKJ0CFyR6AGGERnxWBozugLtL79yD9Zr6Rh7m5l7Aa0lM65Ya5SZAlCHX+NozIIUxP7+vbYbtdJmPoXci6m5lvBIv2UXwIUz9kxY3Lkvr9KjYKZsjVo3GhqQSoaiGKyyhHTad37YHCQ0iM0fhYbcBb2sOaRbaP8wA0kFYY3gLq7UOvtL+7msZax3+C+aIx+7M2fOPiDfgEpVKhVoUcCOogHipcaHfOqpp6xQqtMLcKx1AlgekrKZZoaRLFxZ830/Ry87oPMYV7mMotUf+chHenp67DPVajUHqlNPPfUzn/nMJz7xiU9/+tP/9m//9slPfvJzn/vcP/7jP37jG9/IX+/s2LIWifc36oKwaxZe0FqQ9cuNYoFhGe5BZS+U95IYxoiZJERKrFPYcMawmZbn8T5aQcdY1rYKfOOs64xTeN+8Xde/suUXCw7MOqi7KeqFKmsK1flIIC226XAtTauOE2tZkQm8NVlr1/ZdfwHopiMU9pVJOsSm3uYa1Tt/e/sk4GnQW8fNJ3Dl2JvtRyGXAo9VbHzSfoC3PX3Fy4t/vHDrpYs3Yeyrs9vM1Thk/dQxiaS6LUFRChgmCWJKKA0Gs3mz036ablux4mmdFrDDLapsBFmYTrk2xicUa+kma0UQvjz/xZjVaQjQ1fcMbJsf1EEeFwHumbvjsqWVn05bR4teAQwpbqnh4jMvYVUdFVMRZW/Fot0W1tKtrKWPuM9xbGhQhFGN4cJE/NAjd5qdidOKYy1nzpwd3YIgMJ7k//k//+dTn/rUN7/5zQ9/+MOnnHKKcSONb/n1r3/9/vvvd6fIsdaJZPV63T4oFArm/lvf+pahJsaYlfo1w9pCVJ43aJ43NPXP//zPZg5krrWUdtUhzzzMi6q11q1Bs9aImbORYi2klCyuxXTGCHGErKWLB399w6XgdY4sa+XeF1d4s56ZjQ/hpTcwoHZXSgYVzrrp15c/v+KqGTsnvrD2hV3pLo166lG27p4iZemGSHXLkrw+Lqf0MKyFu7XnhZduINl3ZC2r8CiGWj5mjkhUHr7xSkhju/moAv/1r6c1WEtoFkjgZYnK3V0Ady7eNGbpjvOmL0clwTq34hbmf+xxTD1tScw9tnEtkQEYJIauMQMsEHyv522MvX3olEMFI2CtIQuHWyeamYvaG/c/8sSD1XoXi4oU1YxRGQML/5CnuwHG/2XRZQu7b3kbV7Z0Y13g3Y/+Dcs3N0Zj9lURRYnU6oisdYRRJLBQTPgBNtHmsvbEU79SUKMKTYdSzpw5O8JXnJR2NT8PBti8qtwLNT7kk08+aRzRIxeROnOsNcosjuPWgquPf/zj9oFlpC984QuDYl9m0BsAO+mkk+y4t0hmH5sXtIZ0rUy85S7b+9id7ZFjLfJ2RJZDqJo5hNWAZfEXyUF4919zIdQ7CWBGygTFpqgEMEW1Pd0iMsEgLgXVoBIC7AjYboBzXt54/crqfW8eWO9jGKdMqg4Z5GAmZCYW3/To9HE6pS2slVrBefIP2+e1PWBYSwiPs8iy1pD3SKdx7860Z0dS6JI1xC0VwDk/ujTTs8A2R9ht1vBTP2qEwEIPLpq18rrluxb3CXNiA5SIx5NcZykjyUTcw0YtlmUtYU5j1iY6sA3Btm6YR7r6HtF4ixqhY60Tj7VUBHx/eR9dXMqz1VwliZYqEkmdEghvnL1tUtv+uQyv/ZA1/Wm0L5+3yrJWWIvpz4E8WlzriKNIpSykBZ+4VDlQrLRjq7ehxtmcOXP2AbNBirt5SMC4l9alfOyxx3IP1p0ux1onEnEZajLj+6Mf/ajhKDOgDWL96U9/+vd///dW1jKj/Bvf+MZXvvIV+0ytVsuXHOzL7H1er2VnRWs3ZHeqR5YNNG/tZ2U8ai9k2EQ44cAiiErQswMVoRUfuZ1hmCKofNQLpP5d2CuViqyoUypQipv54twCcNW8lRcs7Lhwxq4VEZZpebZpL401VOxQLFf4aIKWPh6EcAhrUcBIqSjaHMVrzADXOpRYUYWsMwzWAlWBsBsV4ASkFZEpf9jwQxxaHRE6jbA/hg6AKYs2TF609eZZmzoNjqa2w5hKhU9iiXStEbQU5JKN2XJHKmQVdeplzxuv/RZ0AYNadmzolkZbzk401nrkqd921bqk6NdJX1wtNTRR8OqbefRmDFPnbZ86Z9s2wutARcNgrYvPvAwHJIOonrRMiqOy1iFzE9VNMS6WCmVmfJCKYn95NxMVmlbOnDlzdhgzvqhNlbIeYx68yvuqGw/z2Wef7e/vz59x5lhrVJsZxPmCQY5Dn/3sZz/5yU9+61vfOvnkk9euXQtUnWUe24DVqaeeesopp9gk2i+RAdVo2dBWa51iPg08zzO/NcRlUwqtQoazY8sGkLGWaBG9wOfDiKGLlCTKr95z/ZXAKuD1jjBrGccq9W1um4wNagM1IMY9ioAXsOLEeIR3rNg4fsWO8at63qJMOey1pYGpBjEaQmscQiNDEo4bIQxmLZtGqPmqlS8C7JOi1wYTkGaYGPq+cAQt1v/zqdf4vQXz04tPvMKrMlOWF+b8pXFQllR51RmIPoAFEVz2yrIJc3bMrOGpMx+phTlhnkRVN56XtzRL2rJZGDMUIgm1KK566wXUTsTmy1Z4nzvWOgEtk7K48d67UhyWFdAVxGwNkRZ1kQgKhN6zbO9187ZN+POcbmoaHmdqgkMZ/Qmc9u0zbFzLDi3z7Z6w9GisdThZGHKSMNRa93sonFX75QM3QlZp6IqynDlzdoQVJWrrmtektCoQ2t/++c9/dmfJsdYJZrZ9inmwd+9eO45vvPHG73znO/kLhBAWnMwrB4lb2LRaazZp0Eax8miYpS8zSawgoQttjbQrhoiic4kw8Gq+TBkJr8d3ThoPvAasZqNeI/UtiRp6QQRBislLYoASA8caJTMObnh66fmz1p29aNNX731iOwVwwlTgmMFcuBQks7EX3aKHkYEW9WA9XqwFjZZglqzCl6f9GqCbpchaSlKaJJfDYS1dA1G6acIl2MqLw7n/cw4vM2ItZVlL8UCq1CYEFjUcALjmtbcuW9g5ZXn5IIAvIKj3gSpQo1hhYdQqkdhzJTI9A0w5xPMJIYt3VovrSTpFZRFEdbyq35y9y5l7RPzIfyUoZqkeferxEGKedoEuW13OoJG4a1D84ufevGHRrkdnrzDTqjsREcBQWSvsT+6aeq+oa92QZ9fa9ggYGms1uuNxofy6b9Cvdtcvrm2wVl41+I5H7ZDMmbMPkFl31Fad5EEt6zoaJ8Eu1t900032+Vala2eOtUavDSoutOPYPmnuW+NUeVfcfHzbyWDuDV+1xrXyV9oHraEzs81B8p3OjrG7dpg2N+TB87B/1ybwSyDCEWUtje5gyCBMiBvo022LLYgEJjU9u5lfM33vZYs6xszZsBFgP+UVCm6rs1JgIXBsBaZlszWwaLKWQm7UI+t+HcJaIa3Ke/PmPGMIh5zFmKU4U4YZ19KeCrtABrxWkbX4wh+fi6AVCmStOMLES52a7dd9z5yBOkcH+vfrDo5Z2HnW9Pb1HOhvS6xEr0TWUjb4wOxptKVx2k7eOjKs71NSYlfb3D9SfqnICrcca51IrCUaXd64oesyLksIVJVUJQssPoWwbFvw6xbtvHz6yk7jsmh8chisZT6nY1t3FmglxhJCvUMO4RHaHWCgloc0iQImS0HcFbNCFlltRHIdazlz5iy3QTLuucZ19v1EjV6ffvppd6Icazlz9n75agN9JqV0HCLnyPjtOTOQssIKKcKPqPuiwtjD6BYLUxFqiIUwnn2sScT8pS44/fmNY+b0XvHSzu0AewG+d+mYaugj/hnGUDE2AVOpMgQfpe8ja6UkKcJFrLhHkhL9kvcQZQWEXk3N96HTCklWiJI5TzdfMxG4GnfGedQHjeJakgROgLd2FQupsdeYto7z5nXfPH170eyHQPajei1osFZD7V3Zdse4coKhLYwoCr+6dc2KaQa60nqFlN8rMhFOHmM0zV8xYApr63NY9a0MtBT2P4gf+vUDZjxUYlKVlCVD0+bFtUa3urteXHpN244pCzYWKSzsq2GylqjDrZPvyHMIk4QNI65FOT+K8Sgv2TK4df9Dt6YclQmT1LaIE3EcZtlB+tDvNOFYy5kzZ+B6GTvWcuZsFDhqA/X66AcELZVC4hnKuur8nwLzDWhJv34cWEUKCxXxwZ5tEoJCf5fxDdcDXNG266wFXZcvLu4l9KoCnHPlWJlVmlFcK65BGpCEw2HjWoIiMyO+/1FMbeWwsj9WsqhVR9v8pxrJhPF7Zi0KlMWVu66fbHxOqGJzJIi5VeCgqKPIckEJhwxRdQNcuWDvRQsO3jBzx34bWYOINN8zp5SRCInWeRWWwi1yn9xls6lCtbgRDKHJUMserfpFgpolSayT2PWCHCXzdyBrNQaWlDxJfcb9lBukCu++7w4zKBOcBR5oz6bUlqlSywySu9/YdP7zS25pW9cdYvi6zrGx3ZBZi8JOZ37/HDOq/IrNdBBDjmvloWGJCptm55moMlF57oU/2QULqYMoruYSn/owsjfqqIEvZ86cOdZy5ljLmbPj46tZPQzICpxkrtonGEaxkrpxsh+8bQq1OU5HnFU0ev0pKUWjxLPu1xD4ABsYjG/bcvaC3Re07VkLcCDBKI7hqK5yP8JHlCQowWel3lH6T3KhW1grOyIb1xp51opRMx0bAqNIZ3QAYN/0139NMo/QFMwANTzWKhUOYsqgXwKvuuXNpbyvlB1kU6yiUXpHSu7mihqfdA2R6jVvbLzt9be7CVPTRiAL8KQIYX3fJmtxrVjGWhj5KBc6d5Cb20+yCvhylDt0ca3RzVrUpYo3clm9t1a1Mdvv2zzD6zY7tyBVF8CbVbjnzY4pbdveDKl3gk5T1egnPlTWSmHMT8dhPzdMAca9igyXv0sdwsaNUXDYsGJrXK7j4LZybT+TJSwjFPWctZRyrOXMmTPHWo61nDkb3awlB5NJCkEZhLdm3mu8/6BhrahUpCjHCO8OZbMZT55rbKfTWenbreGOxVvOm7/l4qXt927q20dlJEmobPBq/Y69GD/Coj+RBbjMe1miW7T1siNSasT3v8F0YONaOlKqB2CX562n2jPqGf2eWKvRPcxsXPDvnHoqGJeUSRRhVC26i0Ay+Yr03AV0V3k7wHWzV10zb/2EV5btImBqpIcRVuFNtagLNrxhRbqEClXg5s54BhMIdR+ln0XYE7mWONYaHazFBxBFi5QfY7bzWqzA27bzLQ2BIKVJA9IqxUg1KF5UKKBy/Ywt41/ZcOOcjR0kB2pGaRgxMQzWoiTWO66/CyG/v258m6H112o8maaYzEpqSfkx4kT/2/O/56qYNQcDbmAs6wtySK2XYy1nzpw51nKs5czZaGCtrKGWhQCWC/elIXj9wGvXXXQG5o+JlNW9EdfxU1nRe6B5WUXVlFcBfrFw06UzVl08f/Ntq9pXVlVXKM1O+iEqNISB+O6ZF9fJ82KozuILLPBodjGWrSIONrNuhPEg75rFhXFkQ4BCZ/dMrJmyh0b95N4ba6mwVlaBZ/zQi77/P8haXKFqo85omeUQpTjGq4RxW7EaZyvAxPlvX7ds+y1ztqyooxYCvYxnWV+6wVqYTCgz6EI+THUcQFpdsuA5kAepXqsKytNK+B5zyu+jjrUGIoetd0pYOUr7Hv7VrUJXpGaN2LWl6NiMhH0Al87YefaL66e8urTTgHUaYJUUt2mmQ2QtTFIEVpW2XqtUqsDR+2sdWi86QPld2GL3NI3tsdx+5yRD+za0JZV5RmS6tY61nDlz5ljLsZYzZ6OUtTCskbFWmrOWjMHrg7T0+D03UYArNZ439jUe2W9EBUFoiC7QKHjXB7AmgSkLdo6fuX7qvLVvhZYQlIeV8VynWNl/xuVT9iaZZlrMs548DKv/m7jVbGc88o6XpVEpNbIWYkzvG7Pv0rA9IyEFDdHq4bEWVMsVc5aSStmwVnjgIBhfNmb2IK2coN16xlqSGz+cBZjUuAvk+NltF81be21bxy6SnqMdQJG3TM89EzPAfbPK+ZQryLAnl0rScGcabaPQVo3Y1nXZGtWsxVL7jGDct/2pnnr6IT/qFDLW+RxnRTOZQoCVEVy2pG/swoMv7UHdLl9FWabhUK+vbibJ9ndVbNthw0tcisOzVjPTTxwGF80kZ1rZKLGUSYJd9GbPew7VPVgvHVRqWauZQ+hYy5kzZ461HGs5czbKWCu1rJV76k3WCvvXzX5B9OxCfT9krePwjZhC1Gn8P6kRtFYAnDtt26SFB66fvroz0yUXQkcMJamxlRYjTfMV/bjKjQQghdIMD0Lb2JEtyj+u+uSWtTAghGX9hmR6Fi+9PwhXZdVj75m1MmdUiiceehhSXtm9F4IYZBZRiOiWtWJosJbyDE2FVRArAcYu33HuK7uueWF7lRQFNJbx1CgjMW8+S4FFhY3KsNuZtqob5l/XujXPalFIogIo8+6Ep1jT51hrlLOWkCimUqnv6+zdaK611AEXymYH6qgbNOpX/O+K7tNn7T9n/oFdVnpFx15cQ42VRA+DtXgN4hI744dnpwGP4xTTCI/GWrmA+4BD0MocgtaDC7F4KgrmQDSWHGasZQyLuxxrOXPmzLGWYy1nzkYla2GZ0yGsJSD1gHv3XjMOXSfDWmkgOTXKGY47eJgEJ0IgJRGHyNPSVFxk2EAEYbkYkqT7VUv7TpuxffKSvesIITBfDgXV69qunCtUwOjicNrVP9sTImuZ/WcahdcyB05nntZhOveMMGtRL1azyx4X7dX6YszSyuNa5GLq98BaPGK87l170cWQiusvvBhPi8jiWjlraXsRpUDFQXPPyhHwDQAXvbHy0oWFq9/o6s+yDVOi1NjKXZi3JdzTeUwDlRsp3mWgVna88tI9JA9eN2/Bo5MurjVKWKtBFAMHOnYd0GnCqikvP/Psb3ijvZvU1NEQa7JK5lV9AJOnbRy7pHDO9A3dGNRKMlzHCHI0VNaqFwI7oX/wf39sh0cUJYf7QmhtsswHQ1GOi40HOAylpsLC2h+f+qWAsgSPcV8DP/S7ZeCZcebMmWMtx1qOtZw5e9++gTJ3WRJlVdMkzuCHelXx4P7JE8CvYFBLc0li6kN0rRW5R3yA8yPt8rdOsZIoELbxVFiFco0SASFIYDfA9W2bLly24cK31/6+u7+dltvJN1MNgkI/TNPz37vgKrOJksR3l6Iwy6CzQnwyL0M6XgGu7ANSqUqM7Vu48HFqElvEz+ZNqUfaGXNKUz3EdXdSiNSiGl3y47OgGqydMQtSTl2xsFNtjDeMSEmMCwj6LOH5ZVREBOz2tTaBi2bvvnRB10oPEzIZ14VCu8SrEMdckJpIbG7S6mirRsUbShX28GQXQBdAAVQZIwyR62g8GmZwk9sxUKkz4ogSH9vTgSFnL0z7Hnjkdg1BmBaprxoXSWjlJ82vVyq4clbvpTO3bUGM5r4q16m5Fg5Xg0lqiHEhlQVI//rUs1k7Y9YCVjr/WhgY43p3swAjZODded+1Empm0Masap4RiuvWPGE3IJ05c+ZYy7GWM2ejyFD+ABJhc+8UA85EXcVliCvYU8urAYsx3KTSWKYM1NBZK22yVs4bGhJpQCtOwItQbiHAlEXjGUZIYEWA+5d2XD737YsXL7lpy/qV5OAnLWSYe17mYcHjhRiKKZQ1lIW2S/I1r56xlmjglh6okzGSzm8Q2J7FZcy7W/eSYgdAVq0kIB4+eaK0MwZpQj3EpfcklTbxShSqUI0g5teee64hLJnWuPITxFcUcLf5Wdh02PxOhAbqUono1SXg1tXF82bumPTipt4s29C8Pqjw/jQLbIYMDK82um9lwSuO8oN83/o100AdIDl+frxOqLN3ZK0W0NLNWidtLqUq+0mXwa2FS2ZwXTfDACe4mWbYqDpNYnlQwEOb4bSnd1w9Y8NWnIc1BiUD4aGtvPL8TFXzXZtg0twsPe3d1m5GGPM5DkA5KMKNMW05xMFvDipWpblLp5XDjoAXzQzyk7qmBRjHWs6cOXOs5VjLmbNR+R3EMtef40Pjdhs0MsQTgld4YOok7HXFzS1NozrpJAyZtWw6X+aWq1YhduNsGdYKQwhCHaD2HQceIWhN3w9Xv7hs0oxltyxagd20KPNJQKuXhjcbp0oJa346boJPD4pmTzXt5PvHWlGUsVa9trnYvwZUN+h6pvkuBrFWPFTWshGxsWddREmfChL+1P336Zo5bWZTMcOrxQIW5V2yBFhVwVQpoanMZVovjHt96+T57XPKUDJkFnMhAvNeOpO8lbX0INaCnmnP30fkW7PtwkTqXNvRy1o2qKWhFrECqb17hrWEuWgitIIo5urtVXDd3MK5L+5+cGXXbgAfPAF1g9yRnadxPOS4Fn22V/Zlos49/bxs1jWWGN47ayW6nED/nMXTOB5OHAtcrXBxLWfOnDnWcqzlzNkotTSS1vWvRUGiEyrgiZG1ZHDXNVdAuR+SCCSXaWT875hUNIbOWiLL39OtBSaKpQGDNAFeFnF/gp6fIYFlHK569a1fvrX757NXbY7RwUeJiYYGgyTeszdJig2GKgqR/tJ/fNuwVkUilZnXelGYNbqy0aTjyVrZUYeC9yxe9BeAbqpxCpr5eM0cwnR4rJUG/NzTzsLj7K9BXxEYAhPpcMSCAhe1sB6b80pHzbTtWYwVboJqrbYpuHtZ5/gFu6Yu2Wn2LIrsb3ggzZUWxFqxyK9XzlqsH6B3xbLnzD1KLCRe5kM713aUsBYMZi0JPtcVAeX7H77NsBamFOIEV6SQiUqSZrq1FWDiG/uvnntwoY9qNCFWc3k4rWxsaug9Hgq9/ZLj6oaZ3N//9v9kLQjksWItEcmygNqU266SENBqBa/WS83qR8dazpw5c6zlWMuZs9HmrKFHznXMGSlVpKDCpN67um2mLHUDj2WliDmEIIwHP+Tephlaqexh600rzoybr7BZFqCcoHHhVwNMXL7+hvU7xv75VfNjT6nR/ynUVJ+FiJWgOp4gDTyUwfMFwtirC5eHFEuKKNJVqlUhb2Ysjytr8ZR057XH0q7pr//WHJZg3bhrqsXjfC+sRdu56IzzIeCQKlStYOkV55+pk1qSVDh5nxj009rWrGViJwifmAJaJjH9Nw7CxQu2nP7KmxtSSs7Es6TKQZlSEA1rpaKVja04IWo9FpJgV1jfiXEtGTjN99HIWs1mVqnhK8NaTBcf+8O9FOMyz4gsu1R4AQ+6AO5auGPcy5uunr51HwUuzQhR0lB6ytJ4mNiiMZBqJz3zuYpx4iX19FixFtUW1n7zx19KCCNRM6O97lfei9KMM2fOHGs5c6zlzNnIOmtpIjNtdM0YQ6Uyg1t3Xj8BRAgsxHotlqCCuFZi6O12pK2AP0QvTRhQIlfeEJ5P0Z83GUxdueUHs+aet2Dhs/sK/YJCWn20BB9bWELEagEtYVv3Wq3z2W+txs7BcdIfhFlrrYF4c1xYy7iqtsEQhtna9yw1exR6+5G1dGv5k3WIUxJUHI72gKhRoh9XwLl/YP+jd98GmuJaMmQy0jZuKFAgg1OsA4LIONAppEWF13K3gMsXbjqvbf2U6auKYOXd6epjBDEWjZxPaCpuc0rkLJvDWbPiVcX7aZAox1qjlrVINyLQ4HUXdtKDkBQmVSbuB7GZMrsBrnh15VVvbHng7YO9tFQhbL8CGZsby1VJh2KBh0M99hIWYkXfRWePiWvJMYxrSRz3XqHWsefAlkR5Ege4cKzlzJkzx1qOtZw5G73OGsotkBfve2XFA+O1B8WDK+bPUPUCRNh8ScSBQSMUN2NDyyGUeWtdq3hOZRv2Sdbw4Q05GS5ZF8NdGw9899VZZyxZNGXT+g7j13vk/dUBeiLScFCWr4gE8EZV/irgmVr998++sIalZuApJVqwJOeB48NaVPiGe1Ep7zIHoZXtRhWDHqDGNmzWSkLVvq0Dz2loQAp6tu8wZzAu9fTt305ImpLqIPUj5sJmkuEnJOa8G/c6rJNwvtmha2etvnLJjglzNmyn0xx6BsyEgtCW9Mi8zVrmw2KkDqOPqnf+3KdV0mdOPMpScu483FHEWs2bwY9YgR/z/t//6X6NBNQfpR41ZssmnplYM+owoW37TYt2LA0xz9VnjaxRHmJwmybfUEXTtcyWOSxr/ff/+71s7h+rHEJRi3TFENeTf/1dPS4mMuIyYSJuqBo6c+bMmWMtx1rOnI0uXw1bVllOkCw0oJVU++6cerVxtuiWokstUq31MFaOrZeVsRYGYRSGWzAPECEDi8MCVK+oADy32xvXtm7sqs2XvL1sMUWz8C1WzI8KtgQ13JVZ51NONUbIWggdKW7tu6efY7tLmZeHUmfFG+r4s1aqZQ10rW3+CwCVamUvBbVYU0RR5y22rBq+GtrVkjDm7EvTKsPTmghiT4xC3PezqV7xAHZPYra9mEgTbI4keCZOwMJqCJ4HISciNSB406Kd581YffW8jeZtid0r42RTCqIcLDYgtPAptFVZu3oW5RDWUTJSMtcrdlSyFooQkiRG7f6Hb0tExSYQygbnR1p0A9z+1p7xbTuunbH8AMVh07SR8xuZ6+tF1HlNDH2HysWKXXNgPj/7x+eIUNpMwmPBWoqkFA0VBvc9cjdl4aYhShEKx1rOnDlzrOVYy5mz0WhBiIUZcRwrFhusiiu9xnH/+c2TM9bS1DW4pSPwUEGOC2U9vKw1ljZeoLLphCjOnuDaeSfAHfM3X/TGyquWblyPfX/R80PnzDIZyVskxFpUOEQ3bKrDSSEDCc6mEV596x01lb3pENZqqheOpAmFXazMrTb91SfJWfWUCrRihzRUVg1oPJqDmKL/2/xTEfqROQdn/+gC7OQcSMy/FAzPRlJHPEaZfs9cMmEQFkQcU+Ji3ioWUh+8OniMpDBqAn67cM9ly3efu2DDBqqXk4a3hICWjKyWK44MKXmFqrbKM15+Ao9RhSKsOtYafaxlMT5EOUBvXyKK5jFXMZfMToqI4yKGmXSXz1g9fu6WW2ct66Yp0+zKkHoga8OLa7GE5/NORqi6g6B1zHQIzbu4zyupDkOOEvbmgc761AkN4t236nLmzJljLWeOtZw5Ow6OGrpAvcV+6jkqUOEd+1xVMXsQQSsmH4yDRZThwVbWdhhimUbSuHlMoQKhjlNqPQywMYXbZ666cd6Gy19ZtoZcwHoeCqN8J6vqHlrWMjspOMKCtKEtZf5VPQNduLUzxl5q5THKcSoBBoSSjhdrUa+wUr3SLllBiWqSlJpANZi1WirYDjHOef5HQkpp/0jgexOY/VJblpSlqdYKITkOCp3zXnsuKfeiWqRMBY+wJk40m3oZv9aH0IMgRUEOJN1eBmPmb/jenJXXLtrcaZUwAg56cJDE7m0c1SlH0VCct3zRqwbWdNyPepVDj3w4G3nWSv24z8yMx/73F37ck/Ca4ZDEzLpsBMFBCS+3l65ftGdS27ZNpIoh8q8DXAXxQHi26HAYcS3cg7TBVxwe+PmDWcnWMWItaseccogf+NW9iQpIf4PT7WgTypkzZ461HGs51nLm7H1gLWyiwykTTHHwayDin02+EkSArEX1P3n7mmGyVoKfgdqAkFSBlMgV8RDps69O4aa2NTcsXDvxtTfn90HPwJ7FNrLlEX2FGA0jDXfjazFlIzCSMI4xFOQzr1yzc1+daCDNy41Ua86eIKHFEXXFUENCJl0L5r2AD1BVOyHmyVzZFnu3rGUeKOpxhBvR8Opfp5uzp6LMc1WS88hDKpbxg7dNgaBKxTacJz5eON3wPzUQjprfYU8zfKoOYQQT56+/YMWu8Ys2ryGgjesS9AAqzN13LiIKbVVB+5W+3Ya1mG87n3E3j0YZawkrQqih9ocn7tPgpaJuEwg59QDwaEVjymsrJs/ZNuGl5d2t8ausKwO2fBDZNB3yDkmuNEN5DJu2+93/+u9jp42hEhnXk5rAkC6/897bDHRFac2WPuosgOtYy5kzZ461HGs5czZqWMtPYwz4iATFBkUKaXjbhEshrlHIIta4hCzke2GtCCUcYtBV1IhAh91mz0UCRd7vX7lrYtvKS6Yv/NWaPR0Ma7eyxDqZYZoFLY8ea5tNaOu+MDERvUyWSEtlpRAl5P/nvDEHKkGS8cUAdQed+XYjy1oiNcBYXLzgxSTqJZEMLqVkXA7Mx1MD1PCPapbTjPm+ryWc9u2foPpGjG/1/ZCChgy4b1hr//oVWLslYttNC6lUJtRkLNMjoRt20ML/Y/z8zQATV+w+86Wl4/42fz/xqoRDWctWwuCpD8MiSEySLPdsBdELot+x1qhkLTNzvVK9veLvJ+hK/RCFMVJa9eilar3J0zfdOHfn33ZWehsCNs0lFRlT5h8MvccDCCbTmGUD3PBQIE7/nzP8UnCMWAtzEiMZ0Xt52S8ErBazeoO1UnC45cyZM8dajrWcORtVrBVplJ0QSYhZeTzdu2K57O/C7knEWlaVTuQK4GroH6DM1iWJx+NqeY2lhoSMT2+c9L9s6ZkwZ8V5L8+9Y/nGbQDlEDJJQZbVaIWUxRQetv6q4QNqerHZvXLA+kL+wwvGYk0/f79YK5WJOXtdPOmOwz78UST5XjSUORqgpd/hhCqlstRB+iNh41rXXHJ9FrbTEMWptuSqQvSPRbhhwWxDyyqsASr5R0xGWHGnhT2vDY7ChmXmqlY9vh9g6sx1V83c8MsNfVvJC892t7nPWe5llJAkuKb3yuqCWc8AFEAVbaWPs/dxCh9OGCNORPGPTz2goRam/QZCvKBu2SkEvTVK7lvRc9nL2298bWMnLWRQi3Cbl2pHJqfSymEpyWA5Ji5+8EjYei0RymMX1wLqYI5v9OKa+Xb69R8e9qMisWXsWMuZM2eOtRxrOXM26hw1kvLDOiheKwFL/3jX7YhFYcmylkDVr4GspYe6fZK14AnmFWmZSmQnQyGLIpgwY/nYGcumLl6/GVByGtGjwtDZ5+gqpY3exNlHNxLuNORpcU368v2sZKsvFsVEZXGtlpIjWuznx4G1QPQtXfAM6ScG5riTJEIHUeUqiHmMiFhLqyOdTxvOstmDjLEkSSxYdu8sZOr5WccwlfV7Dirgl//y4L2UJ+iDTphGoUfbniyxEJWVwOGTsawnhLLG2574/KoJszZPWbhtq1Ul0bkqvcpZSyj0YqWIKbPUW7X0NUKzfsdao4y1msIYD/7qtpy1NMkMUt9wYa74eX9bNe7V/Te9sLZkASyqp1m2L8/6EOhGH/Ah4pZgUtlEXQV5OPW8M84/VqyVKhawyNzHIjJA9/Bj9xvK4sq3uOVYy5kzZ461HGs5cza6HLU6Cy1rSa8CXn35yy9A7AH3LGsx4CmteQ+PtRgxRx1IxsLP8taMe75UwQWzFl69Zsf1S7Ztp0bGXq43jfmBhjOULUpCX0xYhfisJ9cABtMQVSNs5sNsPRL+6vSxV/iNjj6txVrEWnzEWQuK8974Y1RvNzxDeoD4+UHCW1hLtLDWEc+n4av8nnNMRDQPXn3xNaAAhIhQ1hErarDvGcNkRUNBPDy4+u3owF4QKY9qEQ8EnvGaxk7GhEQ2vIWVYF4VvH5I+nsFE/DUyspVb2y9cO66pdR6q6UDmHWIc00Rw46B7R4moo4DOxaA7HWsNTpZq1Tbv27zAk1Xn56BOEbI9kDsMnS9sG/S4uC13bLfhoy1sEqeDEPZcVZc+M4ymUfeIdHCWhJ+9N8/PlasxZQUGf9jGmFvubMR1HKs5cyZM8dajrWcORuNLpoIwjLGOkS65NVpKGrHKSGNhDEEaoqrLIikBqTw6UbHqgHVXKo1lISOVo9OScBdodh4isSzPIDb1+y47O0NZ73e9lyXv0tSyCXfuMRoD/XgUmmj33FWbMSzIq5W1rL1SuZBNWAJPf/9cy+OWkpQyFRD+3zozuMAHMqLrJo+Y8trFJ20Quxvp0CdFwQl82SSctniCjc3krOWPvQzs+xBz/Py7UdB/JPvny5qCmORSRbXSiVhm8HhyMNirVrpkVtvxP5I6FenCv1sT0JoQbWRoomRjBJU6+ZscGS0ToCrXlk9Zv6Gqxes6Wu0RJMtunTmA0LcJsero0QUVQAq0/72C4Cuw7GWGng7jKyhs2NkSufqNbopQmj46o9P3ce1uZjlhPdLycMgxa7l1FBh6oyFl87ed+nLe7uomlLG5le8kb1r29bZrFtSoFFqeN8vNrnXfkHc+7NfNtMIdWOED70jFn6lSGW31Ffupf5a8fRZz4dpkUJ5Td3UQ2auM2fOHGs51nKsdWKa7/t2xT0MQ5vvZH80ZrOeoLE2D42EqJQMGmLW7hyOqCs2wOVt8XhlrjCm4rTYgQVaPLh23IVIWTLVcYAFGy1l99AAm0yaQuPSsmWbtDXNj2XKFqAHZPpx6uJVB9gBcMP8LZfP3Xju8wv/sO1gh7aNfTikTLdEogbs4SEtiUUL4CWBj/cJ9vVpqMPD5v19HskA2OgWxoWwboo3HMChOHdNmqQ+xVQWYg4OG0DTjgV1qwSgeITdtObPfyqVew1xJazbaiIqzKk68nUZGC20DqgEQd2QVBRhP+KgGuI+xHDWD85usl4rAWqS7JfmFMQ3T7gCLyL3sN0WOqCYBZp1lM5TMZHQeGTYTOMutgPcNX/dhS8snDp/3doYhfjLhl1J4cD8baJ6OYEREQNoEZIeJSaa074V2CaU6VB2sGQ7k/JIZqJwvJn3qQa42s6OkZnzXJYQGFyyeaqpYjQ+aw8+OpWSPHsFL+hGYNoMiNUA17/dc9XMHXfM3tRN/cQRoWk9xE7fNIsfA15ZvA1ZKlC3rkQ0cKvcUzH3zOfZ4giNhLx93FCXPqgroCDlHv+XD95GCwo2gof67+YT/CBB+HSDzZkzZ461HGudwF68UqVSyT42oGUf1GrG3YR6vZ7/ETWP7RA3/m4QBDmGKeXSPEYDaymkLFHhhf0ggvVL5pU69yH5HCm9TRNr0Wq3prhTcihrscyrzoI5kXEDoeYnFrSue/2tyW07J87Z+tuNfUtLjAYEp5ZQTGdVSEM8QK2S2AzAEAXiidyKofr/Tj8fm3dFuppolm2R2iizaMis1URGQcKMIflzWSQBS/8p+zGqV9KacW0rS5Y/H7LdhrUiZnUHYixUy7c24NKIw7KWza0yt5qHCX31kpfxZWJcSPJjbaEXqNZ4EUYmtFCRb1D57XlvQFA0F7TStcd2fBatIvhZrEqClF6EXrbZ77ci+PmbW65+dendbWu3yIYIuILQV+R8m/2s4VAxjwLtI8emobfirQWPJdiKrRX/hALr7nM9iLWGVf/j7KjDM+XQm0LJDMUkAkmhzo6e3RoqqzdMx+Rc3UVpgyCw2YIsAlzy2qax87smTl+/i9Y+OCagxti8WCs9gLVUY+FCDGV/spWCjLhavmse/+0Tld5qthAjQCZqeHGnJFXNPgTKTMZgV/tabs4AspbHpEddm4Ui0Gos9zlz5syxlmMtx1on+Ag2EFWpVFrhynYHygr6W7TUfN+HlpAXLts7O97+WUs+l3HZ44phLfALt0+6HCR1DD46a5lfKXV4DT3dkkmomlRXq4bmqh8gpelb2jZd9fLy2+es20Sa741UQBvyICH3obNWFFKWoqGsWmDx79zLJ0UNDqxHRHEsIWk1cSziWtwKbcdxIyUQTwjKH6q0c9feJak8CFBORcFqKApxFNYSR2Ktum98R8UYU6nmgaj3es1SlEEpeTqP72FvZwirN4wfA14BhId0RB2fWyOBWaKmwrORpNjzuEhX57k95fHPzb165tuv98Ue9X7GzcZC2w6yMW4KQ21JTBlngYx3LGl7HHg9GxV0Ec3JEcqCltCteiZ6OPV+zt5peAoK5uBSV7kY2W5qCYQMShLTO3tBV0jAHfzIzG3YD3DdwvZL5uy95tVV+8x1l7S+gZHJ+HCsFR9D1jrztLOaOccqW1ZJoiHHtXA0U9ybAr+pVQF54umHE1kwD7iu2+K0lCkct25Bz5kzZ461HGud0JaHs3J26uvrw7/65bL9lXk+b8maR7rsA0dZowG30DvnHoia8ctvumosxHXDWioKjsRaDeFym/ymGglpTd0ylgeBGrlDSuiEhAc3Akx66a2739w1ddrindjaGCXvEspi4qohhq6HXE8leJpxGoAXc1/gNnt8MeGmu7oqcdqIlWEiqzlYkQ65XqtJCIpgo9ncWdKBx2GCUQMMEhTnznw8SjpIEMSQkm+rxrIpMPh85goZAyXsW3IIzY9m45gcSWfyv/+f7zXL/vWhl1PVin3IWhzV9XWlC4I+iFGT3XZIG4BbWc4hpinWGC8Ta21I4aG3dk6cv/GRzd3tfiP/irQ8sFWygEirEM+gcc0xFmqON/H3YzqhTil/EpMGlUxjbKOcV+O01msdTXfR2bAmspKaKZuuSWhhpkAC8a33T9LQq2UP9lmIE6AMWjMHX2ivXjNnx6WvbVhLgjQV1LpM40o38hjlCuasJTPWio8Va53943PMfViJgnJ4aOeGIUxHpHnaqsYcQkHZgzfedkUW11K2r7EKwpRaJDhz5syZYy3HWify2LWIldNUd3d367Du7e3NX1ytVu3LbFzLmpRSu4T69/kqcgx91Lruv+kaVe8DFjC/jKJkYXBYP0U2XLEMFoybhzew3XJz1YqmjhlKHILxyrcA3DJry9R52w1u7SKxu6SRy5YoKzYNDRGMobFQmmBaoAH4RKC3GEjwSWzjoiuuDVSmkCEzCXWl5NAb72a8kIWhctCySu7mP5Ya9vCRr/i+BXMfJ9DyGl3BuNZSiMNqYByNtWzFi5k1edaf8VB/+O3TLMhaXhpcSqcVj3wMS6aGnP1fXH8lIrT0SCRDyObn2beYz2UMZRJFLHlAO22wbFUC501bcumMlXN2l5NMmITjFeV42BW8cMaVrwHrhloHNlDWcVDaD2kBpNnVwB4yY4nW+pAjbpHtd3bsFk3MdzBD7RXK99MQSu2Bd+djNzLoU2kP6qCESGJmxq0DuHH+1steXn1r2452uty0+pUCq6Oupc4kPxvKKKqhSKOGsj9HZC2zpagatzbaknw44G3za20OIZOBYS0Nwc69a+phl3mQymoqEPWlctIYzpw5c6zlWOsEN8tOeQmWzSE0P9qIllLKkJgNXg0qgM5xC11JZ++bqSyOhIU93s0TxjUTCJWKw8NHHS1QJTkspApRqcFaUUvr4ewFCrFjXQD3vrnzxnm7Jk/fsIMW1OvY6YpE7XSm6M4y1oqH7osrrQTqrDQ8RZQC9MXBUogy1qRgLW3S3DAV1WzCZMYJuqEXorBfLDl9uPs1kh/cSN2qasp4vCrSg/TQBsPHQNY6xGHNFGWItbyiP+aci81r6yW/lbJ0HqfKPkXgFTTXUQZ71y7WxQ6MbmH3YU4ecFZMR560VWVM0hQrs1ijm9lBgIe3lc9/YenP56/fUGWkD6kgwMCJPUiDWQmUgO8HWTD8qqN42fwXRdCOsKYqMq1YFTgh1CAh8uHrQDo7KtxgSi+m4Xp+rd3W5q1sX13HHEJzrSo4V1EABXtq/elAeNnsrbcv2t1Whe0CuplONc04FUJUpszhXHbSFnMO+XodjbUkfO9b3zf3qceyrQ4Lhuwah5BaaqGwQjOkLErvT089XA97zI/mSaG43XaSOu0lZ86cOdZyrHXCmu21GsdxDlrGPvOZz3zta1/7xCc+Ye7zxMJcbNCQWLFYNA/MC1xEa2Qh6hA9jMG/tcVLOlX1Aoa2ZFjv6TCeuoh9TBfTR3DsGmvdGXhximthR6xMKsN2uEoaoS3zzG4OP5+z9meLd49/YflNM9fvA+gIsEwERfO4sDwms0w/MSxfXFEaIe5bmEiLDR6xwW8ef6YcMJ7Ll6ts0A7xZPLWpj2HcBNKCGjZF0c7liz8A9FKIGSisELKjPwMjLQ6Mmsd6XjpLUE15JHI/VT7pIQBN7vJJIpjj8JOMoakCrXOR26ZRCVbsc3MlI0MMYr1Kbo4cRKWoNG7th7wosKaugnT11zy2tt3v7mu0oRqMOxYx//NdiqElEV8Pkg2LH9dhduxNEgVNYZP0vxEteykbfqU6qHHLZ29A2tlpY4e9ToLqrz84DOPVKCGrdWweE9CjNos5gpNXr5v7II9U2espouH85SJOPX6kbXMNwD2qlatV20Yccijs9bY88a11muZPRdMDv2IaewyQUDFJYSprMa89OhjvyjVDhB6YZYvExyFGd1Yc+bMmWMtx1on9Ni1oGXDU+b+v/7rvwxr2fX4L33pSx/72MfsK+3L7P3SpUvNa0455ZRB+YfORoi1DpPKlVEWxxuFQe66YaLyyw1VDEES6snh3ZyG/5X9aN0ZihkBCTBYaYqIbgllD87uEZf9dcHk11f9anXHNgpqYaNhJkivTmRhMZUnL6lhVHCkSYT5qOTFMeNtiYwo/vsn51i68BMhh6mBZzOp4hwC8/PJMyDlXBhcKRjKmvn6A8RaYf5mKRotzPihq/jvkFPHU4Fa8XQZeSDaZi6wkDSQYVoOKotrcYQrTCP0HrrlGvD6sGLHXFksbsnaa1ldQSYjK35AQoJcmSsu8AV9AFNeWTlx+d4L31jeYX6MGR0t+utxVrcWc92jjLtujyBqh2QrxLsBm1R7igdJHOpDWEsia8WOtY45a2XnN8YkUA39CZRnr5vnA/eSiEqupPJTMyLN1Ltg/vYL5+/689aeAiW5luMkE8aIyqSfQYo4LYNKg4Ih9786Gmt5RT9L6jVDOhLDi2vFiWjt2lz1Cja0FST4wI+KYVLV+OUiDW659Txnzpw51nKs9fdgNlZQr9dPPvnkZ5991j4ZBMEXvvAFM7KtLGFMZh5cdtllU6ZM+fznP29+zIMM9lc21dA4zTbXq5XQMu/Tgdm7xAMtBrGWdZ7K5bL9bbHnIJbxxDXjY/FaP/XUilm9RNp0iqdZ9ppudekGB3TUQP0DpSkh0PymVI9DqsgyWLUH4Po31l/xyur7l+1YEaPwoKGvGiUcNrr5cGApxrga3brkMI63JZAiWpTnvVS/+PpsRqyVEs4lYqjhVNFQCGhhLZ0trlMgLk7SXlR4D7cnwQY66PSIJ+1wrHWYjq662aMZpbEVnHfm+ZVC1TxgiTwqaynUNVEpK3djaEt4i175K4jAXFyZBIwlLKup0xEGBHgmNKdiQm5BzIxSJXsBxszbembb5iunL+7Ei5L1AZM21xPjkL4kpNTMbKF7wcxHIdllToKMe20WqNKHZS0X1zr2rCVtiwVcIiky2P/QEzf5NLG9mEquIvz+LAJMffWtcUvax7Rt7Who0mRZglaEUDVZS7+H1tNHYa16v5cpZLDG2NdwmM5z74IuW/uw64zhDW6FL7z8FCb2cs/KYJarJcdazpx9sLyfRqWAXfS395zMslYYhk8++aRxPl1qlWOtE8ZsQmA+ZE866STzuLe31w73T33qU/b5QSGs/v7+r3zlK/mUyGnKspaNkuVP5lKH9sEwel9+EEFrIGs1F6q1Fjw1brcBrbRWABWqoHj/HTdRTy0MaokokinDmvVBnpYe2JmLtCKoNghvNhglE2F5JOa4ar6lzo1Xd/0rbZe/tPLnb3fOL2Jn33qjoCvrq4taGNioCWMvwG25lxjGIbdkS8oW4jJj5ZwxlxW9hDV6LDMN74G1YGCtlE3DC8L4oMGMObP/N8usI0W4dwFa78RajWsY1eMzTvspUpCf5l2e9UDWwpcLpYQUcZCFtpjnd+/91d03Q1oFv0QKGSpRxqfOheZEFrLLWIsTbuHumxl40StrLl3be/7slRsp0oVdj0lN3AYf0UkGRlVwUVTfemDXLIAOCu7VMCENBGPikBxCobMGx461jiVr8YQmnR8AKvJ1TLnnogjiwC4DYKUW1DVem1tmbblg9qarFmzqzRviZd8VHMdGFtBWLRotx5i1ENBLgU0jxJGsoFapD69ea+ActAwfFyudDz56l3kQxCXqu4W4JbVwY8SZsw+U5V1eoUWVLTff959//vkoitAdEu77wbHWiWCMMQtadnCfcsopnuflv/3MZz5jEcsO+lZS+shHPgIUELNvNPRlH5hp8LWvfe1zn/vcySef/PGPf/wLX/jCP5GZx9/4xjcKhYI758NmrUbpAoUnmI9xD+nJWm9Q6ERXm6fMq1lnC4UN9BFYSzWSBkExUFGWoKZkyydFAoNXhj+mvDb/1kXrb1+6/7Ui9vqpo1/PGIaxyNe3r085pAFwxL9jy1rYfEdBPVExZSlWYtHUox8Oa4lBrCUhTVD3vGY1/LZvbTP3POw+zBHoI+2zspLq1jdtvph+E9eSXMBt+6YdrbptejD1NUNbLApJzJ3jVZax39MOUQmDHJWCubipVB6TkbLVcSoTI9Fx09uWeMpCjuU9Y9u2j12795xZC9akMS2W0Glg2SnW5kKjEoMhq964vmnZoqc165Ks4Ne7bQzwcNoYTofw2LOWOZ2BF2sMNhq2avdgJ0M5GEThoocTylyPe6avnTpj69Xzt26ggLNuflfkGcXiUEX+Y8taKIlBmu+ZGqECvx4cC9bKQ1txzKoK6woxVTWIa2FS1268OXP2wbMgCP71X//V+I3f/OY3//M///PDH/7wpz71KeNVfvSjHz311FPvvfdeaAkSOHOsNarNRqusqKBdOfjyl79slxOklKVS6V/+5V/ysh/DYDaQZcNTJ510Us5d+WuspKF5IzQCwQbGctFCG+nq6elxZ/6dWQua6UDN9qHYfkdJQxyo8x6BDJK+PY/cOSUr1UhjVPe2XXETptRhWUtlrKVIKA+z/kREkhhWCaNaxnCGuWD7Fdz5+vJbF64b92Lb3AB2UDctlGhAbXR0BCOZh7aM4x8jsmmevrccwtY9tYdcjzCB4CfnXGTLt8qRZEN2HwcqX+vW5KU4Fn0ainV/z5LF00AVQXuoAfiufLsctLLOxQN8U9XsTlbv9+ZOn2fjACgkcEiULP8JS+CwKTR5zJKJoE4UHN479UqQNdtAKWWZ+HtkNQlIHCW7Wf0P7LemzJUwDvpj22tnL9pw4dK1D6xYW2qcCU6DxRJXgJqEIYXyehe3PZOL3cdJrRGvO5woi7Njy1paxVFgrmmVV6feOy6FzigpY26hwmTdPoDNVIB3+/xdN8/f+HaEUywjK1ADwUU1u+rpY89aIpT2wQVnXqgpysxTYUPo74G18jTCtOb3myl57/13VOo9NltV6CgrBnXmzNkHxgZ1G8qV26wZ7/SZZ545fEW6M8dao9BsIqxx3/OuxB/60IdsVqGlpk9/+tNANUIWqww15Rm0X/3qV/NXmh8NgOVaheZH+/pD0wVdwPc9shYiq1/DXrcqBFbFdrS1zmLHbhvTkGmClVrkvzCuD89autlmSmY3ZbdfrmPAqk4KCQ/OWnPDq8uven3VDbNReLBIq+mhMg5QgKp9xsuS+C6dc4VSuQbasOQr1KH1abbdlhlwv/njn2tppthhHsghbzzTC8kUBbP/ucTeqYYuCkL1zJz+FLb0jUMb8Wu4d4e9H0SGR2AtQqKohBGAH/7f09KAHeITD073stfXcDJkxVKChR7W4NW74t6dBgKZVzWuLRd0snXjtGurssipQyxeTR5jtKEYY3fjCYvWX7xw7SUvL9xKIUeNvEca8oTXPqQRxPXAvLAs0p7urs3EWuYSx0wGNomrqXqvWrtCOztm813EdcrEFSGkK3YsFCi+70PCpYZ2H9oBrnn9zUkz1k6ctmw3LXng+Ke+DnmrAD1ojoMaCdayPeLiWnLxuWOb2hhD56AjsZakcNbB7t2GuPyoWKp2mQfUR92xljNnHxTLvUTzIOubQmb9yVqt5nnek08+Ca4gxbHWiTiybVzrP/7jP774xS9a9PrhD39oHkMjUGvjVBaozON/+Id/aAW2QVvL4c0+Y3jMZhi6HMLhsZbN3KrWqBOaDWUIH/yeuyePg6hgIx7GQeJxZCWYG8IPh2MtcqQEsVYWBtEq14L3qLbn0fnrfjZr3fVvrJ+yYPdqQq8g61asKAstoBvpoAxo5gPD62V8qMZ9fuCM9jLScMYFl1USZK0Uhs5aeR1Lk7UUBbV6NFYo9aL+myhjhA8/zPqpeTmWatZdHX4/7fnkTd+0EdFCSYpAG/f0oV8+3PKmQUlfg71PA0w2wNV4ysBl7ZYrz4WwhFEoA0speFWGkhYyp9wMtBIMVCpSVIBisexRB+pzn1949ZL2a+dt3mUFFm3XXBvX0jzBSxYKZmjaW/zm64yXg6jPEDXX9UZ1lrC6GtlxOdY61l/AdD2CA17HDQ/e7GMr7xiDq4JHkTJktQHgujd3XLtw682zVxomLprfY2iaWx2U1v4BjTGjBo32Y8VamlQPbTLh6rfWvJf+WkdII0x7Ch0Swsd+/6DEBR3MKqx6Bcdazpx9gBwgcjUtR9kiF/OMTafKHctbb73V+qKtNS/OHGuN6jFtB7Flrd27d3+Z7HOf+5wVfLdCF1/60pfMC8xANxPADHFz//Wvf938yr7XoJR5Mo5jRdb8m3pI5SKmwEnXm3KYrKVt7Vy5P+u/FPT+5meTQdSA+WGtjKrk5GN5XtCiqtzaJxf9ME15g4llLfNBiAnKbh11pX1156sLH35715m/e/2WxfveMkMC2xajM68zpjKv9kFXicvSvP1xljqoQlBepkIxTNZqRreM85VI6K+Gxr3868uzUICeQ50NPUdRD0i1aiylBxqKHDoLpXVvLp1mRjELgqwRl4bGKect963VSoOY8HCsZZ6qY/Oxc35yrrmv9tcw1oShYIpEYVxC5KEJu2t+xIXOAoMpM39d4iCIUAdFVXavmgPSA5mGVV+njTEh833BwFqC/I0pnv8/e+8dZMWV5om+2H9fvI2NidiZnhez093zeqc32kzH9PZMz+7M7myPaaM2QkhCwggQ3gsrCQkhb5CEkBAgQN4iJLwpoPDee08VpnzVdXnT5/HvfOdk5s2qugVVdJVa0HniiyS5ru49ec7J73e+7/v9mjxboWKguKgn4rGVx/qvv3Tf+vPvXTVb9C+zMFx8UI2lSMX97KKE2I5h1ClYbcrO8XBWKx2F/I0s8UdTrNWtWIsHOSEMSxgvf/a6qdS2/Mar8ppwtQ0wZuWJ+5YfG7h691E15dRV0FhLDSVeGpe8h7FWsmpr5JBRsPVCuG063YW1JMhUoS1v/sLZjp+Tn+2hQsp7mba0/VE16TdKD7M9Z7UGWtqlTDnfU6x1K7U44VVvIehBrM/j3QI9viVGisO1EizJaRATbsoX6Dfq7Yf4vfqNcbGWfJfOS0zDvjf2RJiqqgJcVGI/B9VZYE1g4KZL/5s4M8Y+KPys8LIR3zdhBESqtAfjYZbkuItRjRIsJoFKNoNPw0wT/FGVKHgIixf2nuv//ront186LYA6vCnEE3CJfUIDFkoNCywdxIC3xVoeaKrePHVYGbileyDjsF/1GeSUqK5Zed+t/GPtKlsAP9iYNXPwZuvXrFkIEJEyPx9ETivpwMrG3xLcGAmsBXjGF7/6lztifKIGPwtFtMqxxkmMzFSIisbPSDQk4TTLPzlplEAe+Nkq8gHBvjC/j/GIUjJWRSuatgK9tkWAWnHohov9NldP2nr2aKAgvAl0hcDZoRJBgwBIGh0PRJsqty7zUVZlimpCkUgRLJlAmGKtro9qXrL2g1P2dm7tvs+KIl/kXKkpyIvp2YxelNdu7bn+G6onbDp2GZQP1IQlMNn1teC8NQTuBqylQ7XhV00Oae6XENekMZPDjQl2M0tcWbhle4ZEVqYjh5/XlLnKlcYxVwoEXVxA0pa2tN3CTW/xxzlT+r9xk7fRxYsXazyWlqWkWCttabtZrBXlbcEONzjQEIlSdOwYqnew4jcw869OnyaIB7iLk2Q1PC0HESLcxZSHji3HCDQkkm9VURAJY44LMXXn4Xu+3Pbo3qtHhahhsMnMaZx8mPQOS35Wa1cvZlDskm+XDJYkUuyiAjC54hYCMe6x5yV6KChODvn9lVPKIio2Bt5nu07QMNUOzCgdLk63AqwlSFEI48P3nhSijrhN3LMVYTpN5BB2mDrY+ieAY2q7sK0QeCjEWjhkIEQWlo/Ix+VdQe1uJD6Nl+mNMu4jA6bBpktnhJEXEkSBoBkRjqIrVOmgoGucjGxQgQzFGs/NvJ+TaHm3EMPXn5iy5fLDy461yEHF4T3IxwTYTKCTA+Zh0YxEzenzO2TPEN9R6Fpd5VZlWo4KrqQqeZ1w+Hk8NnTQJgDeS+pKnETVJkKg+PnV9ZLjsHnR+9ORqIv3RywKFJkPfbxixI7a4dsaz6odDbgoZgaCWtfhdue/Fx5OzsdWZWB6VCs6T6vRkkNgwJ2DYXa5pSnQxsp/+vV6jbiqdM1yWha/M0fthuQU7CdlvmKZ38hSnsy0pe229YxSLeMUa6Utbd2MtVQmGwmxFhTVUK2khB0Ibrj2C5MmStDlNDZo8eI2fhKJQmHtsZb8nwHuN/EJLrqBXLjkK+scfkmIKRXbR27aO3brqa0CaBWUGhpVW+ysR38ubcXkHkkGgZdPTdNUgCWMng1/9EV5zFiO7Jumlkb1Yqr2+GlpLz+StkIh/6DsIHBzGSRgUqxJzyXUIKZEmULkD+5ZYhVOAvM790DYt+t+KuUQ1ArDwrbCWki4BS8w0foVFcjBzY0t+jMRCXjXi9kQ6JZht6n+palThB8I3xOOBTCbA1dboMq0UBhXDHMeIZUQ1KVtE2UKgl2UN6fTDWOXH5+0/NS8nbVNKpKiblxMwip1CeQAk35tIxNN+/dWqCxHFmY5JoAhBQGoPE+xVhexlgJaXsAdDFmxJJ6PlOu5ZRbyJ5XIQgsiQPJZRJCku3Dv4Sd3Hu+zdP87DaC4YIPSggfjluGeXoFo2ZgwJPVGq5Mtht4zMiRSUaHdzmKtG8EtLU1gOU0t2csRUwspD7Q60BZPsVba0pZirbSlWCttabuup0NC70JJ1TIfRJCAc8LJ1oOHjQLheY8MHQYM7xI7mIU2O9w0EWtqrZZb0oNiHOlAkPSdqghQuk/beGro0t0vHriy3Qfh1Ix0+AjDNhYW6tGcsethLc48zwMHj4sCETUm//f7hueYpsdghm2Fb2Gc+EEbqutEXEu6tgERCBPGaFLNGftmlW+dRc4Vz5ReXT4s2eo690PsWUq4FWItGsbj7v7NPXHeneM4tmu5flfrWxjTBNsSO6Gg9tAhgFvgc4dYCyk0XMJaLMxPVH+UIAq5vPJqHgnEC9vOT1tzfOqyo1VCXFBc74q1ErsuUA5SXjTtK/K1u3d+wfwmv9gYKiNHfBgqtcxRpAVp2saNxjRvG9eSV8rHNlVZsT4NElQoEv03z58/XeWGQs0SVvp1V5gY9NmWqTurnt504rJK5TXkq50AZj1jX83Xb4u1FJZxmi3Y+UDCaQhoPtwVujHKaou1yrxGjkONtTA1mbA+/vQty61HpFAmVTHFWmlLW4q10pZirbSl7eY9nci9IUoCCymWOYBd1BNWQfpsT44ZLXxfEZTHylgdWsIRCQ121qmvsVYdB6A1ePXxBzdcGrvy1IenIeaVURrHOTsXwoY/ANYCc2wo/EOqoMxXW9zSpr3wuvT0WgwjSdNAEGaEthGtoiouo2jQpffmh0BL9yRRrNnk8unjXwgOFHyCZ1VZVMyN0YXmI4/EoQbVx07e1WJEk8ZMZgHnNOl5dzmuBRdLfi3Xxs3Ncx6bLoKgePmiShGEuJ3KISQJagRV7qVPo4qdQFH2Swg9cOGKh9adnl554aKil5Rv9i0jEf6Unykfrs80HACtLYoT6aE6OENoXMaTts7iFg23YNJpJosAApUwElT9t0fFtTfmP1bTVKcAs9fkZCSEeWbVgQlbrg364tBxB2QYGksckrynoURHWMvMGuFcxWoqIjHwziHAXtFlrNVROm60raBi+Lv2bFBBLafrWCttaUtbirXSlmKttKXt+s5OhBaUa6sT6gKRbxG2cX7XTrfmmiAYWwYKHJXA1kH9Qpu6o1BcixEWaIaxHBYXhJi49WK/LTX3rr04a3/uUAEiSNLXcf2M8rzFV+DYtcVagiTrvjCReElkbOyoNML/8Yve9YZKpwJNZW77mLLW7lcifJew+FmP0yLoPkMO4ZX6a5tVUMsO3EZF4wcQ5ibiWphGyge0xPm+5IPPJejSxVq6Gw2zEOCuKjBChzjFPEBr6ZoH/vuvvAwFexHWikTSSpyTPkS6VFEWiSrHfGCMlCh69q5TU3ZUP7juzJtn3XOq+E3jqJbmRk0f7xdlb1zdt22REHXwMbp0EIaNo2jiUhrCTo0HnuSWaL3fwRkCRMERJjYDNhMzZ5+hIqfDsHkrJ5HVeSGmb7s8pbLx+a21ebU3UOB2oNggkcl7+gKUwVoxwWYg/LynA+7S+v12kPBuYkB0iLU0kmMgAOcRVjx6fKcXZEsFrCnWSlvaUqyVYq0Ua6Utbd3l68TlVSHQIooSA3kPPzhY0WMEmqUPJco/eFtHhLW28HHPk76/aELAUDdl06lff7TjoaPG+B21x5VSqkIrQOkgXXlGqO0EPf1jW/ElarAUhrZUWpxifg8UPYaldrnvHDCkgEh93kJRqiTC3POCBLyMGdtxHPcTIc+HG+mD5XdueU+IK4IC/x5DBdCaQjcDJlTRDSUEWCBjrGVmrL69+zGPw049/33iWsJ1LEVdSCTcwpnM46NHysFAzazGWnoYlFgQVd6pq3gKkUaxEllbpnyLqRj8h606MHTj+SErjp1RuYU2jRkatDdrCFFjteywmvYDpSTQi+tgn8011uIp1roBkEggfNYGGxCk6P5BQsFRg9D0UOOs+U/4wvBpEDDIzqsXYtDSXX2WHh7x/pFTHuS2IghAOh5xvoI4c4dYS44/SF5l4dxCghY51rV7rLRD9PtgLUU7BtWJppWTcAtT8+13X1fcGLhMcmCKtdKWthRrpS3FWmlL201jDxIzm0snG1K5FLjy3FceniaMonBd6fkUzZx0wIvIQW10jzTKYrGVySysd4DPfez6Y0M2nJu8v3Hk8v1HVJ5SXrFIezlH8St4Islm/pVjLaOQ0w/UNGaQAg+a0/zwhSvNHtLk5iZhJiK0bbYkCTXKoOiL0LB4iRKsRJ8BrzV69pn1qxcquNEMxVrSA8YC2Tej1et4dqmLmEAOLjQZox4crX9YvrmgnGwAY/Jl8sU34b7DAKA4NOy/PGO68AAMK7iFS2T0Cgj56hcGOocQ3GIiAlcEjkl5vdLGnVB5ZvLWS5PXHTtCRIMAvgt4ly0KTfKrmsrbr96yZo5geRDVZZr83VFkeinW6gzWAiYb3kYGQBn2fDUCA4Jyio6lZc/BtfVuA1wetylr5puE+PSa+PkHu2eetBdurrKEqHWRo6S3gXjF9L6C3u8w9ziIahFRaSvjrl/0DqcMu0ES8w2xlud5CXoMSCOsa7gUCQ/gMtGtMh+bYq20pS3FWmlLsVba0nYjR0f5CywMakFZEQaghQJhW0+PGwuJZBgI26UzV3CKQcRs3gpraZRFWwGtmD/dB81iMW3z2eHbL/VddeThbefOK2oIQz2FfKEJEKVv4/hFIoKeLhBpyzGt0x0jv8qyXch/yxU1r6CENdIBvXvISAlZDApfU9FQCxfRdlmIWPPpURRWajFsKk/XNnIH9+99H340llirkZFGrTcGIkJdd9WY4pOTUArkvHVvUdHnzvuoy5gH/2UEeh9jfFM8hEK5ub4mBgwKUEZVe/EMtQsQfgS4pX5mpNalFc9sdaGh+X4oEkAwUcjyshBLavxRX+wau3Lf9I2HqqAwSw05PVSoKViTIFdO7F0ieKPgOcElJNDYDX5nirVuNJiZpndXfBgJLWA1KoiL1AjzHEuC3LzjV7+x6BlDuCa83pa9fIiJsevrx2zLjF5xvEE+hMMgLIGUUYj4/AGxln7Ctzy9iUBsjkze964BJU1tVg5xlQda5WeBnCNK7Y1Q5vsB8L8/+/xjYcLijeEWS/W10pa2FGulLcVaaUvbjR0dJJhHA+mXh0EtjDXQev3xx1BTA0Av4LKDXXMLKoFaY604qKWAlm0UGQnxm62QifRfDrSIRUfdQStPDK08+/ies6dVobvjF0LYwiIKP6rJOfBXgLV4G6yVkJ9KMiuSiP+91sZPvz6/QCLp3gAnInsJrCWRKlX19vALpBeXUYEgk9NzVvGQQpe2AiBGuGHPbiZBS/aPaReFlipWX3z9igrP8JMkfr9PDqF8G2AtgFJEutvgcxean5/xiMRazDHkCGG2qaj/CfE9HdfywwRUD3jtmaOoCYHhzlUD4Iwrlpxrfm732XGbjo5bsadRiGwg/LAyK9CyYwLX76xYCETkqFZ9iHAtIm9zhuGnk/RmsJYyZOu4lrxMcs7lT53bnPevuoJkGJJT+gITI9ZdGLqh8ZE1V84x2P5whWbd1I2Im9cPvunG2sOt0gNqWt772/v1r3MLAMPC6UbKYK1o/LMbQaY4tAV26sxB2WMKemE/gNCz67op1kpb2lKslbYUa6UtbTcJPCTQImFwC+NMRgIt0tggj0bVJZ1LBkwJijbDoziW0iohjTCBUFgFA9x06dN5TDrT0rmTbl29EM9VNg786PSgpUdGfrmjSj7IpWvoqIwmO6SeUx+npbeCnued65g1sdWzEY+6qCk48uv+2933W8oZrStYEO9SFBo0frtCJoBAFOYsFkzXyspXFfNXhMht2bQYuB8Uz7sy70ZZT9drLkhUMUopU92eby4M6T+0RLffHVjLt52wkM5X5Ss8yDdew0YWkgMl9iaIOxIOEey5rhcEYVBLs8UZKhaoOoeDEjJXRP+XAvHC5oP3L902fvOpBdur8uqlbpSA5RYhw+3Qzg+8wkFIKSQF7sNIam4y0qhWJ7AWJnEOYWusBdT/MHkDRPJFt+aVNx8nwjCpU1Txxln7Ltzxwe6J2xpWnvYzVOiLQkRSsCuI4rg9PyGvD7d4CW49O/05q9mOSicVl6IE5Ow6WIuUsl5vALcAcS1cPCdv1OtkQj+wCPVt21QUjh3BrbSlLW0p1kpbirXSlraO/RzFPShBAgLquUBlD2YzT4wZoYSUJNbyMfGUSwccfZiX1IpLMCNydNxAvgGS7vKK47tRiAkfbhm6tn5ERdMjK47I/9b6BYnCGgsXOc0DgzxmOk+Hl7SAvy7eNY8SLCVUKBCRDXi/UWN1BZd8xFbhK9ImkZIqC6lBsKBFqM4iV8yMzprMgTcbl7SJMNOyq41yksm1OAAAhWf6yz9fgW3SnViLyYHgRm8nnm/CZeHB0w9PFMgRvqsRuJvPQGiLhLBKRfAMjSf1daQg7aQIChlg671F9NThK4NX7H3ki/3HbGAprOfEUdcdI/32ms0Vr0Foi+VxMdCd6/tpXOuGWIuQmBsjkUNIPGwbcgRKmNUi8cgrC54vkKac3yixmbxIU7ae67/+/MTNF6Yt3Wmpq1BQBvOaxnEgOfGdPwTWImUAUkJ0a+h9w0MMSIQe/L4VtNZgSDLCd/BR5f9oUCg22G6z/O2NTVdFKIBx/TTCtKUtbSnWSluKtdKWto49a8sxSpVaCmi9NGUiaNcGNvaLDCrviWIpYGFZVlt9KhYXaNkUgFZWiAtUXBNixpfbH994tt/q6nGVNVe1Yo+wbesyFYbj5wCWgKMtXSWgxNAOOvmacXyDwhALgzCfrq2oMwGF2ByUl4Mol7LkdCkec6hWYoFbzCFPAoqaQ/s/E0FWUT6ouFZCQuqmwlrgR+aNnD6XWKvfvf2RhQMTdQ/Wkm8IaFRux6gOm2CXeEVBvecmTxCuA4PENAUOjGyz0JQY8EexZrrjwguxlv4+mKB8Xtdu7WLiqT0XZ2488drOM4cDVqt6JONr6kZK/GrbOOQVTwielw8VGw017lJf9sZ7JSTmfOeteUEFa2yuU5Jo+POKpS0k7ypG0fNE9F9/4bcrz41ZsqVFXrMAEg0tZVxExP0wCgICcOsPiLXaYSS5/lhs3ksLjDpTB6LcrNdq5HcJa5WJp0lwZc+e87QiJPTyhUb5iOuZHbPGp+MzbWlLsVbaUqyVtrRdF2upuBL2cxlhWSKXlZ70rpVfKuGnQDpbSPG8FxzL9b2E6nFSeDTEWlihKek9Vyuy76c2HJm+/ujEtUcnbjmzk8OWuWJGk7jOdHCRKPUeBTwCpa8KpGM00sP9enSLVs1SW/4IIjR1heLdDw7RoS3NVtaqbi3GWsJjSFejGdUXKwStIlYLZEtK740H2ovV5Bmaqrur7prmfC8UCqZprlm+lvqsFNTqDqylPyHAKGvmEfjxyqWV4wHZEkZ+OGuW8FzhOFC4hV1KccKzx/pHhTpbWPN/QBkbg4RRSKM8JcTwj9aNX7n9+e2H5Tg56cCAyToassqx0bh1w7sCN8Gfo6AAoCrH0kl6fayVKFZK8Gpi4M6EXYy8b7z69jxLsJw8F0AFOeXTbUO31g7fXC2vSJ1pepFIOdddzcJoM1U8J1+RmHEHWCuWDiv9QDmgDDG6/7hwxSEhMuwurAXabrz47POPOl5WrYEQwEbIT7FW2tKWYq20pVgrbWm7KVDBCbWKQIlhFkUQPDV2jHSpnWw9lHkI6eoSVxAL2Lp0ig3jMUF866CWRSD+c86GiNaruy+M+nzn47urB3+y4ahKJnT1WyDdifiBUgQOxakClaoEBSeJQpGvBdDS5iCqAzUWg73uqc88W2daOpMwiNMpEyUlxVydrlxyrCpVqVUvgYYnoSwjoS9HdY8RzWrQVXetUMzH7u89d94bsiBYuHuxFiZMueDAVkJVDiE3MgJ5n7z8CgAthIVjxTT3yWFQ8vhZIMEYsF+oCJ5HuMcg5vnMzkNjN+yZvuX4qA82XBWiVjE9uppNxKol7lXfuCS8Bmo1S7j19RgPX3esRVvpU8VMD7gpU+cD0yebMWeWhPsSaJ2n4pHlB2cdNIZ8eWSHuhwtnmEJ3OwW4nBWmA3LmQqIEf4V/IYuYS0XNjkuHKoCoBWTBZYb+ToJ9gZYq+3Pw4gU5Px1/RbD1FVbOAAmkTSulba0pVgrbSnWSlvabg5XSJ/K9zTP+8MDB+KGWkggFJ5HLQcyxrCrqA70jrJwoEaclirXQyebKuxRGwCsenTJ5uEfbppQcXzA8kNHAXZ4efuiK7CjdolZxPHHlR/vQ7WOZuYgoaAT/+rclw55MngkcKyiNHkfGCCAgZDzZ994I67aaoW1YmpBodIFybWmun1CZKzsBajUYk4rEukwSc+himG7i9+Zac53wzBYILEM5363xrUokF/wkK+fmcx2AxMicnJU+K7w3NlTJqOGesVOiR0nB8EFjrnWwgoJDJiKi8qf5rgiKDAoyFLjR4L2oNIP1ggxfOWhScuPv7zpvASm1TYKFaxV0deuyo8BoLJmVRuWYq2bwFqa0wKQvMXcWYvnFaE6jmWEeHbd3iEVDb0+OLn5qmuqKHSDQBJDwwWSVy1iO1eq5ZjoUPMfCGspZkUcyzTrlSNchVRM+bf/3MtssMM5FZQZ+TSMG3cJaxEfSQRqFq36t999HZZB34ypRdOWtrSlWCttKdZK2+3pTvEO8QFrbzxUBA51gWliV7g8tKAq3BQET08YA4CI2na+RnrJIGYssA1eMw85yeyQvIK3ZgmjCntIv+2IKZ5cuffJDccerTj6ypHaUxJ9Ab27PNS7ws1Q7nLlh5sqK01JJrvAv6E+Bxj8tILT1wBriRhrMR8FGh66gmq49bNedzsqfclN1piVuAsNYl8Rom7lF7MFL8hec8xr8EJSAlrR33O0mtH1CQmTCVHabNeSj/fq1SvWR2slOtQ6k6rLPSJ/Og0/WJX3yIEE7i3wEGJPIE+YBgwVefU8icE8JYeFOS+Dtbi8+ILpXgK3OYAg2FUhJALfDQGu2tEf7Xh8+Z5DFs+ATrT2pE3hXNu9frFwL8sPoZYdfWg5Kzuyy772Buq3rCety9gpvqS0vRZcicQ8Gk8sid6FosrwiES5PI+g/4Maz6jyA4lpX92y7+F1h4dvrB+/+oKjZNRauJvXZDa2CVFE0KHW2YMwN78inFs+l4/wksUjX6MhoVXGm6paIJ/QbD0FIqylqkxh6CYwGxPJP1V+0gFFPoJcS4cJSxESAgA1ipkyxPG83aKREsGnLW0p1kpbirXSdssBLdpGOLiVdjAJIQGPU26khyRhkoMUWELKV0YQQQq52h2MXYQ11wWPq68CumfTBu5kVc1MrmBXI2GoohwcEnNrPj5LoQMqVLE4YapWx0fwWJMQHxzPP7z+zNj1p8avP/bkxiMH8vB4gPRGtEdFSJLOS2LHmnohopeIqvm/NnGM6MuAAyf7ypcurAWBPlFVcO8aNjkX9YcnAgKhJeznLJUkmRHGObfhCDaqNDxVuFegBF1++PlcYcv2qqyqUdCPZq7vIGDJB8exIVMPnjSD0CIhRCmxAv0a8vHvQyJf1veNnX5VshXCLfi2gSUcQxQyH81+SQS2cIryeceyLdMHuKXSCSEs4mueOEaTJW3wy5lBIHAgEdeyFj5l67mhq45M33lx8emma2qIqSFsi+xl4dYLu1EifxY4pUEO7I4IeFzAQob9VjE9nQiHouwynEgzwwmEknxLWzKGbrfyEK+s6XkWI3k/KgskoYJZnB/ohUCdBfC0p4NR8HpTsILwWkQmKxockXvhrecuWE0SaI34cOWE7WfGbjg+Z9upMxlfxVKZS5D+cB8FsXhDPDf5H3z2lcMtHAkdyDUz1n2/vR+oCAtU9zR1WRJr+VxiTrkeyuVOE/zEe0+t8hLbqO0pVIaZ8Knw5s5/maktA2VhqjOmxPWw7Dg54D2fRgiNlKP0SEFX2tKWYq20pVgrbbco1tJASxtXNTPKl+SQOOQQYRM4aleD6Zt/wbKBVc/1uVpBAijBYlY+EzQ3qxp418nWSu/NMOuosNWOuKPyiCIfEAG24EXpb8MfYowFROR9IL24LMT0lQdn7rzS/9Ndj++u3sEgnFXQ7/LIrZ0DxiFPDjkmY6702ArIzjPYSa822G8Gjc8xYUCCEQNmEKhvIqzQJIIG0nAUNZ0WLC+4g5Fr+0HMaF+iLgTXFodFXG3gVviXQ6cQC2T5pnIZiR1YEmuZpnnnnXcKJWcsW/cToydknaNBJv+0GXr51BPEEfnma/t3CeQFQCweUnbIYz4v4brwvKBjIIeLrtnAAG5tomLaruoBXx6cuvniU+vP1EvQLiG+HQjXOL9/I8uehUxCYXLu+L7BiRciLsbKdBpPILr2gKrc6xPBYdJjR8a7grVoObe9dYpgjAyjYrmir9EYpZDrlqNWhuZM4RSE8+neVY2Kq+ahFRumbj/Ve+neCeuOXnZofDUkXKdU0bxTegtNSUZ4LLf12TtLNLpurG4CNg9XbQ0xgFgulStYEMEt0sbah4vjj1cSdrCW1jZcevGVGR7OIpbjwjadFgLSb3BNTRMVCjhkEymD5kmKtdKWthRrpS3FWmm7lbFW0nhrDy+MlkQb/9F+P5VgQX8WQDCmyNYZOM1B84Q7/5cwGiBvSEEBx6RWXnHDQVqRJYgl/3GFKCrOB3BKGHd9iOpI5/iIEAOX7Ri2+Ui/ZZUvHj530Ab0hYQoqy56CzYSkQ6G0qktNnVURKvOtpesrygiYQc8X7BUz0swmm86tyt7cS/OXBJcog4PIyfAiLSPa8UlYazD/E4AWhRxyGP0KGyoowADrOrfv7/EVwgh13X1K23b7s4xVw5r6dCWWWhQQ8KRGHTWpPEs0yifNI2iZ9m2aSk/mF4nY1VF8xxBihiSBaG6b5spnt1zrf9n+4atPDdi5YlTArhVQJ9NdqBfxfJyfDUEooUIS0UnINzgkzZohKltBROJHBJ5BCJxWhuOJAy2E/yEueoYxNwPvGeOXHQJayn2FAyJmkgp3clzQtrkiFIu4uGkobuPHAqpmh4Fxkt5abDvBlWG987h09vk9Ny4u8/aPWN2np1aeeZCKL0glw0m3QgJsSRWv5WAlvyxbkBxWEJVaDIG3T8YZp4iJPSLAVe62pYDexMeczXWuiHKam0ablkqiuVt3LJcoiyJtQKaV9GtQC6duUIe6F48jnC8SrDW4aw0rpW2tKVYK20p1krbLQXzQ65iAACAAElEQVS3WmEtrZbLStQUIrnzLRGU1gjGUT4Vik58CMDkrtULomq0LBM1X3tlYn+B6yDtyDTks0GWts68CgixXflS4WYFLkjg5UG2WFMgaoRYcM4csu7IyH0X+q7fMX3PwRMKj9mO5+aMiG3vlsdaWP5oEYADrCRjNUlGHts+SPXy9Vv2eFj/TEJ5Cw6unj1aaTddVF1lcKxK0wSjrFX5TXTBoisoyiATvcpblqV33B3P1g+OGzdOCxnHzTTN7hxtLFn3or+zEszlPuMqOEfsQk0VwC2j5diWjZCjGuf4UTj3bav009rCLYb9gk69tOyCr/Rzj/jihd1Xx2659MDaM6O2XRy1elcVPM727Fsl4TzhNVxkqDAIKHdBHZi2ZH4dBbfYhKCOyOs6w3YWKHwVuK2Pqhrtq5jCncZaDGomAxMY9pEDBXKgHB0Ccs5DGghfZRjqVMMmW4JMLFEBDfIAtAh2C14RiZkfVb59wRm68WyfdcdGbj+/U4gzivAdQpOeJ1G6HmASaHHOb60F0bW9OK4lf+yT056SJy21mVCuIAAKHogAw/X1OxPRaoO1MIU6MdszZK/mzbrnZz3GQTjOlka4TQFuUR/JvyIC1B5NseiYYq20pS3FWmlLsVbabg3XIsFwUC5iwBPhL+XzJza9g0TumqVAlK92ylEArib1nntsUr6pCuRMc9cE0R6eqvYi4Jz6DLw6P/wMk4gMeLQM/LzLQkzefGTg9lN9t50YvOFAJRfHEAAtKKbBlqrtwdTzAKDc2nhLulx5reZsWsLzw6gCFIl4WXlc/O5HkJkZEAzufu7L1fOtYk0IB7gvYarGrDpjs6Pa+rIMFlCLpeIPgLiKALSyLTlCyOzZs6V/rOGWdJr1y7ofa7WiW9CUblhiLXVxPUiYdArATEjcGZPGB0bGyTb6hRZ58Y2meqAZD9EXK9XgRUEeBfwpR65wTYgtMZB5vsDEzG2Hhqzd02fNzmFbD0/YuPOAinp9sn55gJqUEnLO9eodklWIi4BcdMxKwuKtBHXCYxVmlhAeC+MV7Y+lfLwesSAqr7oZUynBkAKHVKQuhlhad9hUR6jvQi6WUJwx6frLR4444ultJ0evOz1o+elRKy8+uraqShXINauAocfLeBV6mN0STc4FrqJ8yAl1DirXbpaIC5QPnHDbw7SLVEmzdw5ctY9rCdu1oOoQm04gFz175jNTmrKXPJylwgIAVmzRZBsSlXXlu6foK21pS7FWirXSlrZbC2slUFZc2tGq1L81PYCfzQvXBdkkZBsN1TOnjhFBQfrQiPtASs5tERhCOtOeA0K1HBw77dIpogOmKS4KQuxsEM/tOj9g5fb7K3aN3nn0uPLkTP1NJIQjWuzXI15RR3Vu5c7XKWqKc4GqnE0iLMuJe9k0jQED+0uHuMWsXbvjswKuVapZJW0fRn1Gg9a1HKz1xWxbMRI3CaXk07lMHtgvlJ921529MxkJPER9fX1P/WCWyFiLvqH6RQTDlQX8UGiqUfVpnp9rFIG14pO3IeDkGoDVgVcywJYBWay6toq3wloaMMCDRCXLScRFfPmIxAP7JIDfuXfIph0P7Tw6Ys2O8cu2yXFVceBIU65ZcasA2wFS8EPxy5FwVNJoW8ET5eXKrsNSyEWi0qb7j4oBL1C0Cp0yCoY1viIhygotSFgiGZLoyF7OBlmoOsXxOGXHqXu/3DP9WP7+j/Y+s7YKSrZycVUXjMjQ8WcMIRRHt26lOSmvPSxFkDQoTQ6AO3/ZyzFAIQAHsLljmqYctF7gJmdWJ4FWgAjCVFEHQRjbcjNM2Jgbi959VX6w7TcbdoO8UgGxqFYFFKLTzDQp1kpb2lKslWKttKXt69g6uEPzVhEtEgW0gla5VbF3wrBrqVIuIOle88X7x/ZtEsLAXoZy5ICHUqrMiQzYCx2VOmNxkWei0RenPTF905mpOy5NqTx236Ils3YdPqf2y4Ha3A9Z4LgZENtkWLo7JoXcG3Lrdj3EYZhoKnguZixK0gz1rKRvjLHqIeN3ff45ELlsUO9D6EsUMTV8pLnjMPEY9UPdIk1mEHFP00TUKLkBH192CGepi49dEpjof/73fwxsFPtzcbGW9Ji7jR6Dd6jTJccJE8gPYgnjoNhSpxAX6ARs+uLjw9sq4FwiLqrCToC1cKsAl/o0B1EUgzqkclmZJzusyI0G4VwTYkWmedgXFQ9tPTNy89Uxa6snfXzg473NZy2IyUiMa0Dmn0qUBTU2RdQhvwMPImwT+9Yd6Cm1w1q8h1kIk2HmGxopTfk2+yVxcEzrBGgDthLf4zrOfFCICbuqh+y52nfLxdG7rzy4ZOeBAJIGWwyiJqYcRthtatRiWtKHwKrdinMScgi5vP6wjwFh9qIn+6nPXfcB+nIDaSJKrC39QF5+D6tsUEtaNp+TUM1yCwoqO0QU5Wo2alx/eSw6EmvZVFiImpCTLcR1CxRTrJW2tKVYK20p1krbrYm1eCt/PfTLgijFyCmJ7eoAC7hrnvRmhX3y4BZq1wvU7BvVyl1jkZiSyEr/Qui8QaqklIimxGhR2lnvHMo9tPLUoMqzv/ti+6Nbj16Ur1feH4v5qgP1KViDvABLLAc+Cr51u55HZW7grBLbtVoAPwSha6wo+AwPXyWicUnFoi82LjGg1CbsfE+9iusUN+YlsVZrtrwOsJb08jwk/wf4ioq+vfvpV/MoAoGU0wzVKd1Yb9Mea6ksPR5yYcNXxsTlLMA+gC4r2yDsrMRaglg1pw89MXGUCIqQXhjDLUYS0a0wwCX/hwJQOI6k1PToM4mwr9j1LSrGNWHVgaFrLwxbf21SZcOYL08/saF6dR2ET1uULoFfqnyD2BEVgaLEAIsUbFmb2FyH6lsRgWGPHJVpqYPOHKNvyBI9lpTOY5FKb2iBmsBnfPHU1lPjt1385ZeHBu+pv3/1yWlbz1WpJEyVYIrhGmFbGDmY04y0SRq8tXIIubrwEmiFfYVLYdh/++d/B+LQgOicW6XTzq+DgspiLcqSAWcoUyQcaN9dBBnUs2bPsP1GXbvlBC1cd3CKtdKWthRrpS3FWrdiQ9H+a3Lo6wf/yCZ9exehZAjTGHQ5ASQUOUpjpwjMgcRUiS4IGYIUBTYEM5csenVPxVJh1gqaFzQnJHjwsHZNPdgdFlkL2xR82RylRVVMf1mIrViM33Bq2uHC3WtO9d92ts+yzWdUDQ14DkUHUBUiYeYUTWZnBWH64a3c94EW72FBpGjkKcEwAFJ+PqtKYBo/XvFaIHLNqOWuIQOk79uEw0KavKv1dwiN+cpVX1u2S0NKfK6xlsQumv0i9ia1HwmSQVQc3nnk0omqMETCVW6hos3okR/MymAt0SrRMRGzAcZLiGuB2RlBncfGDxe+CVgLlI4Bbrn5TEkIDqtiK6JkwcKxKrIKvZKIY8NncvQCxeVWQ7x4uHbkjlP3bTwwbOfpAesOjN58cvCy3Zrm4aRiLGxUgB8YWQQvEAsp3NUuY7Od8HESa2Gls4w6fZTIGzH1R3gYisJR9Kr961EXy7tCqnqqcansbo8Il4cdlVdCdtJO2ZAuWCPEzJU7plbsGrFm+/htJ4etOzpx86WJK09cUIjUCgeLo8ApzH1g3ZD9z9jtsB6WM9Ow7r7rHqAHipCYb4F+nZW39Xi2TUe/Mq5y5K0QLGmn/M7iLFAltGVfqT2958AmJopUGNK42msCnQMFqgPQw+hY8OCWaoTAui1BOKVUHnWBaDdXh6atE+6+7H+dwtD92h5pS7FWirXSpluxWESqyZM/1klfHmu5fljlrmVtNYkzh61unMP5AMiOPcohyoLMRhV5MGeOGyLMZoFM4RXgWGgAojM3jHsBVbaKLxgKYkln9wQXL28/PmnVrmcO1Q5ccXD4xnMPrDh4QHm68tkLjQa1MBBoZAxwCZGItZFp0mvnt3bfY02bDxKqLnBLOwUgZQQuDFfCDIqav1z+FhXSm8uZotgUFP/13r6FSIsW8jk5b85lKZfXCGqJMpkMoVwr7l5raAEZWYJ95CWLtUp79qoDray9/ssKarFY1FZE9Vry7is/sDs3IHg5HarWWIuXVJ4AqXC3oNIIJeQuUqNJoqyxg/sV6i7DSAxsFUjBHJgbMHUsSPwDuIU5ZTnPzymYpFFBGOmD0KgcySrKKsThgEtM9XZjy9jK7cPXbxuz9fDIrcfG7zr34NqD/Zduf+rAlbcu5fcKcVbhrlqF/03V80mTHorNwjoxv7WhZDSp546dNsrhWxXVHCyq1Nxm9buuCHFOhfv2E/HUxpOPrD0+ec2xGVurRqw+MHr3saFb907cdmjc0s2XFcqqylGL6JmHeZhzqDS3dQUdvy3Ww/YCCZEM2fChIxquNZa2DNRR13fpJMNwfnUWa4VFd4TbWte4puGc5TVJZCdBHOZFDwK8gEl0vqI+aqBySze5tmigFT9y9OjR1CH5iltNTY0+yWazaW+kWCvFWmnrkbHeZiNH/vcWoyfubqyVZFMA+mwcmMUCo5qEAKtwAUZeXgDEst38NdjMtpoFNmcMHTC5T2/h2yKfFS3NwOWWzQEnIQJGDMBIahe+1vTkin6CgPP6wtG6URtPT91dP7bi3PDP9sxYc+SYD56c9I+bfdCY+tH/+BfpGhYabXinjQF+qFQuF76H8nJ0Ih29pXufoZBWUSXCIZVcyQyBG6VjX7Hy3UP7NlPqUIGL2NRs47Lrew0akSPicqYY85IDTx4vJXw6iCVlakMvLSCe40PAx8F6Mx675PmZLwwdMExHSKBOTLnvuVzuZz/7WbLYptv2m/kNEqJau6FCh7aYldOxLDhR3BiomB0/bBCceybwq2viQKiwAuQKvRgCK5J1c7ZnQBgnLkqydGZqGCtSnHJAwVdLyYLtOx9fs/6+9z6ZvOf4iO1H799wcMiuCyP3Xxux80rflScGrT0/etWJbQVx0hOXOGj4VivWlmsqBHRNIRZ9jO2a4pNo6Io1dmAdvb5egaVOmvw+FxV0PKt2NHYy8f5lb/rms0M/2zF509kp2y8OWLp33JbzY7dcHLu96pfvVg7ZeLzfmm0Dliy7rKNe+bzrmVQEnnAVcwZD0RijUSUnvT3Ww3ZKdKFGQtGWk+ihMRNB19iGYDsu6nK1RO0ba6sYnmCEb689HcKtop2DwBUpeji3Y0/Fh58uuFZ/jgtPEcYQJfil0oMVSaIPbC+3Q8YgVU0uNa7ryjXnj3fH8ytv8Q6azmL4yU9+Ijv/1pLCS7FW2lKsdWs07UG6qok/2gSGclhL3teNYjYqAcJheQwNWDED3j41BMrJlwinxak689SwwYCyfE/kCwLkPSF0IN1YXMBegUi81GQCVXRWeXvSw5u56/i4zUeGVZ4esObo6PVnH91w7iSGZxtyUaIUgZL0//Qnf6lrZSKnAqsichMJB/aQaVTMf2vfHWQPm55drzqZ2RKmMgkzm53CsVVfzlZxFEiHc1xgwqjN1QRAlS/yhGcR7TdyvBy15+szcTgl7zENvfKWZ/mw9W1aHvKpFmaF2hJWSnySNqjf4MAE/n2iehRKuwKucwj/7M/+LHaGREQN31Nwq+Mn5V8mKmYFylpQnYVDBkJQEvZmTpu4b+tGjb6AAMPISieUKlTvBswLbORnIITDDVDrQiwE56qPmE8wcgV1NVELdyGKaytE8fmJ419cvvbs9v2DP1vT+4N143ZcGlxxfvj2+hHbs+O2t4xceviRNSdmbjr/+MazD689Je2RivOPbqyatPrsxDVnJ64+L23CmouhrT3zUMWRhzbsm1Sxr5PHqZsOTtp0YMrGAxM37p+8Yf8NXl9xYML6w+PXH52w7minjutOjF19duSy09LGrTn30PqLE9ade6ji3ORN54cvOzBmzeHnjzYO+GTL+PWHJ1ee2C/EYSGmLN90VkXAXOg+08icQaSBCMMXYe1lEFHmuFGd222CtRJgCfCSdkM5UGVIlNX7l3e34ifBaqtCYU3skra8L9fDWjHcgtUOM8dyJejymrPX3nl/num0yA913HyAgKckQDZUMwpEgA6H3OpYKxkwJ4T86Z/+6W0Qr7tlbjxqKy2upPjGN77RI0njaUuxVoq1UqAlEmRrf7xwqx3WijK4MMM2pAJKP9UrqCxBGyrg5e0/ew2qs6zmZ0YPka6HsA1QzULEz0qnVpgmM1xRZ4R87kWFGK4K8fT2Y/2Xbh27r6pPxZE7lmwatenQ+JWbjymU5emN4VBEFgH/XsH/2//vx5lG+R/RTEQN9pRYki4WsyHGhSNn49aORAaeeVmwJoBYEjAg2WdGMX/s6KElQtRw3igvAachtwHiWMICm6JAsRNKb+vDVRsGPvRIUwBd3eCFiYVuVOaDohwzDaggkiMxlYp8IQtDoX9cUBS9EvCYaj/84Q/1ncC27e4mNujafrznuBCLc0FFVxdc1VRfVOgLczn2PGvOczMbLp0BOQHiSlAoHd0g5FPxoGiweFmgZmE3CYnZHD9mvYBQDCe+yqbLqoFq0jA31clBAdjSTz+hIkw1HPv6x8+tOzxxyd4xXx4esfrEqM0Xx26rHrbh7IDVJ6QNWn9u8KaqwZWXB26+/MDmqw9srum/uU5avy11fbfW3Lvtyt3brnT+eP/Omnt3XLtvx7V7tl/ts/2qfrz31ssdvF6+rO7u7Q33bGvozLH/5oZx6xqnrW54ZF3d9LU1Dy+7MPnzY49+cfjpVUeeX3PwoCXOYgiUXSIQYZb22aYdrioN9AkNgAMz74sWKnIt+WrgdRChbIMma+Fc3Abi4mUiWqH+GwPuwTh12RO/+5deyz5YYTe54dpFSgWlmhq+DdYisHFErqM0bZiF2vqrsmMRlZM4cANjztwXZ70yU9NCMhWQJcwSpYrB8np6t0qTa0vS9fze976XuiVfcYsDiX/zN3+T9kaKtVKslbYe3Nohqokkge8fNdaK1VcDwFeamcDNCqtRoIIw6gWS/mv940P7vvP8E4DErBzk9GGi2QhM5Zs2qvSq8yph6ajaGh+9bPvw1QemHqobtOH4gxWHnz5UdVylV0H1C2ba47drDM0JwfJF+cd/9M3vycdtCmgtD5iN+dqpoR643ZTRsDr/1h6D8OOCWmbWACQQ+dore1etfJ3QS5Rd0USM8naYy5pQPQfcZBqW8mIgkado8lidK+4eOfmfeg+SvXTNAnfMoCLvUI9I0KYq9RVZJDFDlJVryJ85enbl56syDdmwrJ+HPNfSQdQkhJZlffOb30x6Qt2YW5Ikv+7k8AwQcV0/5CfI5xSvA6GOqYoHPTdTL7A1a8ZUnG8gxQzHoHSMCs3CzcOOADJglJpZlWFIMGFxKIKrZFRds2RBVFDTXRII0iJb2PlrR/c3VV8Qio4vy2HLoE5l3x0RYrsQn7aI2ScyM3ZVTd50dkzFidEVp0ZuODViw9lhYOeHbgQbsun8wA3nH9jYBRu46YK2zr6lsqrzNqLi4kNLj7++uWHdNXGah0VoDQpWNXLRSGLgROa9N3/fyd0w1qDACwqQfO5nUcYHrhtHjUHVg1HqIBFJ0prbZj1MRKW4igsrrIUs2OsJMlBE2vfXA2AS22EvwFOsbRphjLXIdbGWNjmzEZGznRhmTvEQ2q+89rjj10OU2pErq3Op+iTjXhm4dav1fBzXksuLXOWku59cc9LWoy2uAJRXQfb/n/zJn3iel+YQplgrxVpp6+aml5U4aUGf/DGuNR1gLYqK0kMXtEgLNcJrFjgvmi/Ne+Khx4ffjxur4CliSu9WSQ8Rh4taF7WoFMHDQrx2sn7iltP9VuwbvPlcn8pLfbdUD99WPXDpvofXHNrYEDRJxw7DjniTYSFQNDJNYnsQGsNEJQciUAoV3/mv33WJ73JkES/glCbcEeW4SDRhkltcXwsCNUCylxNe9bovX96/+wOJmBC6rMrWDMOqMcy6ktQzFZ4FA9VHDlGP5ggA0VoE3HGvfrbhN0OnnG6wncj3la6ZvpjMVI6ZLyaOmDRm8NiwmImJ5roW2ZvFgqkHQNLR+clPftJmS6K7xlrEJy5omxBCOcdTHjzEpYV8LQpxWYU8xDgpwmYOhLOJK6wMLzbJnnxq/MDHBv2LyB4RQb1wsqqqCP6SqRLgtFyBAREuGwmbMRuo8zxaIhSE/EkH1LepxPie6bdI9LF2w2eE53MgYGDkjfogytiEj2Kag09ovsisCuG2KCo/XWTVpB7JdsVaEpZJWEevz3TFsgpbFiODnRFOszyAIUhNH3rGeOHNJ9/88CWDymElx5fBBVIlQsIpch23MbI4Kg1kAFdBhUzLnd0WnOO8DEySJhdECG0ZFqTg6ogeCnlXJg2d+uC9Q2GkuWE6rl1wuoq1/IBKPFc0bSVzLPJGTlGZBJjJ65Z76ZWH58x9wvb09pSDSPFWx1ryVtuGBPinP/1p6pZ8lVhLRNyDpmn+4z/+I2OpYECKtVKslbaeWWu+/e1vf081eaIeJBExFJzETmHJL2xP6xzd4zpzvI5b2W3WGU+CJ5Vk26oVo1B3mBguxFUIMd5bPGfUkD6ffzA/cLLazai3g2bM5Q2/ymONisHs3aMXpy7b/GjloRGr94zfdnpI5YnBW87223T2gR3X7llx8qHKi0dUWKCog1Exe7t08ZFc71lR+EUInPmcKyY5If7s298KoPgevJy4w3U1jtpKD1CkZcx7uEe71vdlHo2J9ZKisVo3VsIhb+3S9y+e3BKRF8julA6ubbk5qnRObS9LqE+wQE6EVIC0G/qhiEPFWW3SNZYv+WWvob/pPaQ5F3C9B2/Du373y3sGPzAKSPsovNnzuOVinbkJcNdH+rpbBMu/JL3Db/3Vt2W3K7ePIYJVx4soHpU8dtloTDyemFZclAdaoDoge0FV57kB4/FEYpwhoBdvqa/R5VvMMRTuygv37LUdi6f1+dnipx/DLQazoJAQqRRBVVAkB5fEUQoicROAbuABiUvRlM4wDSSak/ifWAxQmgsC2hYF0Hp197YvDu9eJngOFM4IUB0qk1CZAOSQfdn6a8c/sMu8glhR3pEEzeb1GQhpR6yDLNLRIiUDd1/+tGKg1MYIYE5D71n4IvvaW88UcW0gMkTkichZtE4+i4BxVLgO1qsDMpRUgK6TpBjyiokZBsBL3v+tbK24MUnSRBTXcgsu/M+OMp+xyF4rPNB78D133Csf94tBG4G11iszaycYEDbbdeD6A+YKp5jp5KGeM6hTWN7YvmPFG3Of2bJ1JUL5qPQQJxQIOvgtHa5ISV010bljt5nWGUOYgvCAL3Gs+C9/+a2vz0p+25vsdj/AOmtAnv/5//sXCvDjtGd6wMrImqtbKlOsYeLNeQs66b6lLcVat17zff+v//qvv//97+v//tVf/dV3v/tdDg6U9Dyk/2EXSd4DovK4nF7lHfGE26wJ4HiJieuGx9BZ7GmHob3bShMQMfZ2FbeWz0Pq6iwWLYTFFVZVjjn2+WfGPP/UvprqFpVnlVd8a6eEWM/FrNPe2A0Xh648OWztmREV54evPzdlT+3witMDVxwcue7YhMpTEzadmLL55DM7zlYpqrRmFakp6u1gBkTnsQwrETF8CnnMLAce/MY3/6uFIeML0baSyhEgbOW195CRrhgLxYkjwu9QCFXpRIFrZqiulWiqHnnnz5xcteyLN06ePAh1IMAHgnxsKzYQ5qKAdvDrYPdc+b1UbZhzNRppEI5JxwSP2nWkEyPu7DXhN71G/+LOAc/OXlBf8HMIOr+mCAU4lrochYj4O6e+lkrUhEtgOIUf/O0Pc2ZWa3NZvk1DVIHbHWnX+o23faRL/U/LgbRWOA3GN6BOgQvS+2+5VvXktKnTx4/PXr0K8gNap4sZArVALFGa9FkldvIVt2UYnCHAwxFeTIkzvQC7+u9nM43HDu/Zu3WNn6+BWCExQrUDCTOQKbAFWa4cuDpDYMNCZ7fz60NXjxqblcgocWvpL4g7YZU8qZAjCF47VGSoyBWDawXvChX5xsL5Be/Nmvn8FLnuKbPkIMKQKBgajxEUb7O8EPVLA6iLg2PQ7s/fBtZenkzLvkUy0ExvZzCdTOkbFJn8wb4jnn/sJb2RAhnRBSBngYzsvK6NdATLCF5QqwGMIk2az7nsap+pbT4KSwbFnFGOGDCPQr0WDXL65GrV8Q/eeX3OK0/q9xI/m3hWpXwDXkeQTEtwu9sNV7MWCR6OGAYTuf28xpEGeptjt62rfhQclWuCXOHl9/hv3/8x7fn1PDVtbhBegkDxaH77O9+nkQBJat1tMTUOThjJFjNUUX7NXbBIXg59LVK4lWKt260FQfD3f//3lFKEkK3aj3/8Y0wREX4gXFexbMm7XAMTNbRtYk8+4admowc7c+xqQtFNmM5falYnzcr0n47Ps8q3zkVZT/JlZwOx4mTDM0sqJ8xbMvnt5Y99vvGJZVte235s1LtfTvm84oUdRx5etWXKysontx6YVLH3ns+29V1zYui2qhE7rwzedmFA5enBW84O33G215LKfssqp+8+9lFTYbeSgr2kAlkO+A1B5y1jNSHIDwz+83/5E/2I4eekdelDOjZPmdNDxpUzpAaOoTo4p3q9UcWsruYyu3dtn7d2zXMnj79PyEHdSaZ3Vb8XQxSleMM/gRTjNoElGvueReRQ1QhMazwh0a/P8J//+70//9X9732yTos+VRvZzzdv+t3Qgf/U63e/HTbww8qKc2ZGfrkr1LkYWM3RwFY6tn6eyK42//pHf6HDHQT876IPgQ6znNld76WgJ83Le3VUhWVcv1ajSG5fFayZZc8eqfxo7lOjpg791QvT+u5dt5DlTgimWdnrwVgND64w/6qeMb5Xo4MJKsxocgiM2T5uULHHqub67Zs2vLHii+ePHf7cNY+p+kT5+qssuOQ7ZwP7HEPVqhIqw6GXem68KZxDgzCXUvrlQUEELcJvhN/Om8AUtoefKX8sqxLi/LXLq95dPP7thePrazbCN+cXFXlNbURQ3wQ9wLMQxJNH0ihw/W1otLG8de1zGgXLA+axm4SbUQRCTsuFkwDszeKEe+8f+qvfDL/jTgCwBQ/YWQ15gbLCqBKFy4JmFODPC6eJFxuUiFxSuiDp9qpzeV9CGFZTRrBlMNeSl/7M0f0fvzNv7ivPrv7iI89oIk5O6CHBsVLpIImYOo9CX6yEk9WS1cHU7mi+e901Wx1qusySJ/r4H//0/+7hxSG1VoaFb2FDXgV5/p++8f8QILl10m7p3vsRKc0vQ1leGwIz9A3xjYWvI001hHCKtVKsdbs1jPEPfvCDuF7Ldd3vfve7KGCcQYhB3hyLykN5ceXBJ1Ydn15xdvy6UyMrzgzddGZw5ZkHtpwasPXEgK3H5MnQjedHVFSNXF/VmeOwiqrBm6oG9aQN2Xx52Naro7bVyOPQTdUPbrgwdNOlEZXVIzdfHrmlatTW6tFbLo3aXDVi0/lhG86OWHdh9JrL49fVjl1zbeiyi0OXXxpdUTtuU8PIipoByy8MXn9l9LamwZtq7ll+dsDGqw9urb9/xf7p27c+sXX51A2fjV729ogvF83YueLT5nP7hXVOkIsiuCycWmE0ilxGNDeKyxlx0REXAnGm80bFBSzO+eL0N7//f8nzmvxWeaRd/JCO7ZSyE503JE6WNV8cb29I7Lfw8rrs/KOnZq7fOmr5msErK0Zs2TFl96EnMF/HxGbFqrBLiL1C7JPms+1cnKTijPxAVxz1xDEiTlP4nuW/jC9O2OIYEmc8cXLv8XenPvabX93xrd/++jv97v/xXb/7/uK3Hhb8qhtc5KLR5lcsUWOJ+mb/miGyrrAdYdrCKoK7ZxeFufrQukfnPd5rQp9fj77z34b95n8P/vefDb3jVyN++c6yFzPu4W//4D944nw+OGbgE1RcDsRFJC6Vs4sd9WfZ/pHWTdexvPlwhK9KRLUcQgrvX1A6vSdULeFpdXJc2VHPrDx5ZO5j0//+8cf/+8yZ//DUU/9z5pP/+NzzP5u/8N5Plo5szC2z6WYm9iOxxxG7sDjAgeflEIeLuCVhcnyuy1qfbtg8ae2mhyoqJ1buePjI8ZeuNb7n47XyEmNxuEvjrUtG4IdIpHdIEXZok+cH1OjacfnKwpWrxrw+91cvvPS/Xp3zL++833v5Fw8I/wsh1ke2UZD1wpffc6samTuF2C34XsH3KTsAxvapR1LrwGT/sAPC3ibwPkEPCX+fIEcEPirQUdG4VTTtEN5p0bi7fsOCJ3v/7VO9/2Hqz3869Zf/PGfIgH1vzxdnjgGJqy1npC1MCyaoIc0VRiAk+pDYzFObJU4UuvSVXGEUZmOWsJrckJ+EgNLXx4s+nPPyi7NffvbVWc+9OefVL5d8durYcc90gWI0oKFSuaKWUZmgLiZZImrKTu0ApnZ7k+vwuW6ZqnpVd2CBPStPXHHyx//7P8pjj64PqcUmu1reZ3Xnm+zoP/3yG0V6pPvus6lpO6emzIVo4pTMgSlw2aRnsu6p1xc/SkUzBsblgsojSFuKtW6v9hd/8RdxPSjn/C//8i9h2w+keiB30OEQ9pnx/t5Hl54c9fmxMRWXHqys7r+lus/26rt3Xuq1+8Kde8713nWh35arAytrOmn9N9fct63mnu01fXrmKD+895oL91dckn9rQOWVfhVVfdfL86tDd9T2XXuu//rzAzdeGlxZLfGY/C2DNl4asu7imNVVk9Zdnbz+2vg11fJ83NrLY9ZWD19xfuLmunGVNcPXSXxYPX57w9jt9YPXXRz65c5hb86avGjmox88P+OTWTOWvPL4Zy899tnzMz59dvp7M6bMnzjp9ZGPvjHy6Xkjn58/Yvai0fPfHz970cjZi4e3tznvjCxrz70x4JWFQ19dNOzp1/q+9clD7yydKh+R1tHru24jumSvvT28rJV98RvvPrD4s16LP/3Foo9+seCDXyx471fz3r1j/ju/m/fOXa8v6PXWuw8senfInHn9Xnqlz6zZ/ebOGzrv7VFvvjtq9uJh0ua+P3r+R+Pe/GCM/HD53/Jf5t1hc94bOuvtQXPeGTZn8ZCX5vR9be6gtxePeXvxuNfnDH1j7ognn+rzwaczXn5z1OIlM15ZOP7Vt6e+sviJ2W8/88zcR19e/NSTbzzy0uKnZrw+bfrrU597+8mHXho74cUx0998+OE3p02bN+2JxTOee/eJ196bvuiTGY89N+itjx6bs2iqPD7y9IB57z/y2sIpry2a1O740Jy3R3exS0f2nL32ztjX3p383PyxryyeNP+jR15bMP7pFx546ZXB8xeMe3nWAy+92P+12YPmvvHgG68PXrRo5CL5zef0nT//3gULei18656Fi+6dN//u19/s9ebCPu98+OCrc+95dX6fV+bf//K8+2YvHPDme0Pmvjv0tYX931h4z2sLfjv7zV+/Ou/Xcxf2mvfO3fIozxe+1+fNt3vLq/zK3N+8/Pod8jhn/p2vvXXPG+8M6eqQ67zNfXvY/IWDFi0e+M67D3740YjPPh/9xbLxy1dOWLnmoXUbJq9eN37thvFbdz6ye//ju/Y9tnXn1O1bJ+9cO27v2rGHN0w+vHHKgbUTD1ZMPrV1xpkdT+9bNXXv6of3rXp476pH9q2cvnfV9H0rH5cnB9fMOLj2scNrHrvNjgdXPbp/9aPtj139nF3LJu9ZNW3vymlHNzxxZvOzlR+OPrZ25s6PJ51c/czZ9S+cW/fisS+fPrPy+bqtC3a/M237wuknlyza+87C3YsXHP/846q1q08vX7bz7cWrXn31/Sdmvjnl4WeGjJzU+/4Rv/jtoJ/9Slr/f+3V945Bd/3bwLt/PviBXmMeuGt0758P/M3/6XPnv/Yd2HtUnzsG9v3tgyP6j+v9i/vvvaP/qAcm/J9/+Nd77+p13z139ul9Z+/f/br37+687+57+t5z/329+9x/932D+w8aP3rM4488/NJzz74x++UFb7624K1Zr7/1aLl5PUnO/TmLJ7c7Tpzz9thumapvfjD20ed7zX1/zLtfTHtx3qBZCx58Zk6/l98a0qPrQ2qxLfpskrzPypvs6++Okp0/7ZnfypuyvBBpz3Snyckip4ycO2ATkyan2IIPp78yf/L7S1987a3HXF6HQE5D01ilLcVat1fTxVq+avLkBz/4AWCtUOUJZE4dX8z9YPdbq88889mhmcuOPbLyxOTVJyasPTF2vYReR0dvODxmw8GJ6w5OWre/8zZx3f4J63vqOHnt/qfXHnp54/G5287P331pwe5Li/df+fBYw5IzLTuzYk9BHHJgM/ycYmO/JMSVKOFQpxk1RImOLVEWYpN6sFHlGF2i8JTaYyWW8IrMKQrPFIE8GsLyhadUhnVVkqoAYo2CN3OgbCh03ijIpGYoUJeDFYM6fdKlD7mu5bpkGArZyhgV+fbGRcb2z/j0jBCXVVKZzk9rZLIjeYZBUpaputCLytM8xHIeb0Lwk/M6+S0QLdf5MkT+CVFr06tY1LPoimFXJ7zlCbA8GpZf11S8HIiCCx8IaX4GzsirY/J8AIPacZU5yixlpjpmWT7n5XU2uYMsecxb8sdCPQWGBKag3dEjkPdY5quW7R/1Aws9Z1hYnvBc2CrBvgh87il+ES/ARshEojI8fdyCWY6HqZ5qrPJsmFfLMhQ1M9yiCUeYMg4bL5Cw5wVZLn8Fb4mScHU2bobyZkoaKW+Mat9iy7EujrcuGQu/QJwXnIu+QHM0resYvUJwtTQ1IGE0+vQKZrXRa5oxra9vPhn9ojBtMrXOmeyujOVVyW6k0LcZtWRGOdoYpLSRWS+YSa1mEAbUJpdPXebHPKh2Yy7kf3KQ5KbICXwbVLZjjsGYwSZoVUrG3ET5mCeQScOKRqIK50I2FFUu6BMIbdG41o6p8quAYRcFNoWJ3H5eB6pwC7U7+mq+d8NUtXCDXtiz9pV4te/WdT6161nGuhz3eYtZLY8Y1ue0/7vX5GRx1dxBau6UzKeu6RULtiEnvIdJWIydMkGmWOv2a5zzb33rW/pcU73/+Z//ubwtAUtZVGgu70qNeXDQilFBS70qa6hVbkts1zp9rAlLQ3rKoDiDhiQHMT2d5kLQtdhOwtyQxKFU/Uwgxx9bgZO1sgVFyIaka8CgblOfwwsiyjXeugAU2C5AdpdoXjhg0/aLwpd/v2sV1XlTuvjCsB3QC1VrkO0Ht0oVLA+1gQFHYeIGsg+xKzRntmZ9pML3qGsh16HAW05adSPmchnGmF/vTzjEUZXrUK/FqC+dM1WboZgjOdG824o1Ax5ysOdT0OrxiKuuL5hJbIdLYEx8dZFyqJgNDOXugbYvsMkjxVDiUqK+SdHGt0r/xzwrmj0FhSzlouj4lAnPB32tJOWGYtNg8pJwiT0xSXL6MZKglklYzDFDCMMBkccE02iCBJAjSnxCEO/Z8SbCchxdgcMwZwhYGanPiSePCdIZfY7VdWfa8ti2oEhDXMeC1Do2VQ+Js6Qg4X0W5RxgWQHaS3niqwWzMZch0QqWK5ry8qidiwDI3CV0J45D7EDJtyNFv6rNh8mIDO4Xie+5IYkEcWhgBtznIfpSAw07AXFRyNHqYVA7D3lZMJbv9PyYG0PiOI4YrPRxGiEP2Xz+4NNW90/RcTUnUGpfmcm7rfT14/8WLDPtk6/ubsWB4EqbacP2JyUpF2GKtW67Jr2kn/70pz/72c+Uu8X+7u/+7kc/+lGApUvq6FBAERsBaMAG0pl1E1SEVCQ4/ZQz5yd8mesfUcxk2GN8xYEIeQXdUFCI663UhH8AgjguRw4LbOrLo0t8B8Mx4FT57CJ59GiAQl9eQiCLRfuj0nl1A4nDGE7QxAFwlf4s0nTYTL+Sd8Uk7NUnzc1W/CBjXfuQP6DJm5cHvCqMR3zK+ug4VvIRGjk68RtBfwff4JdqkGAHVGIHykrEkp7hO3k3hgpF0wWkSiL6SQkzXAnMRIBYzEgunRoXl4R9NTjJWI78ZPmCbM4MlVWVthXtmD76a9X5wK+FBHik0S6/HJ8x41mg9K+Tv9f0rHB3AAgJJAwJ5IQgrksDH8vOIqHaHhD00hBneT6i7ZQemKb1VJ/NAOIhlXaPeetL3P2mVrLOQwOuJJ4QSCmUzCJe3jN95eK3t4h543YzD/grylgXP0e+xSnyvA/RVImaPAQmF8xAgii1JwL8Y3qnQxHHh4zPekxp3mdFPCghlyVXYBReHYIU6JIXi1Jcoj2Mufspcs1CKzZ/6iNgyyDYszkjSc53uSC7ttOKllaObIJQ4AWe32UC624ausUizCjPE7bNogXfuVUW+dvA2txoknfb1Lp3ie5Q9oCWbv2WxUt7eWlLsdbtB7d++MMffu973/vOd77z7W9/Owik/4ldYTgiJ4+WKPiQcCVvonALpDHbLyWhX6yzKmgXEVGSL77bj4oePRDcV4RWyqHUMCnm7IZskEhDDPPIG4g1ZIACmAXaHLdIWZi5wjiK01o4L7FkaUfPJsyFLdvkdrvilKddxlrSDAPpE8uispv1YvQHvCeVteuvrfJ5Kj0aRlmrWAj3Aj/AKH4ELgYKP42Q0sde5/fqTIPwu2G9Nx0iLuIBuIVtSuVTu4jqfQGK1dWL1nFGwu9EIvwgYYXj+nFozvUdrtCgPBKGEQl45Bq2M/51g1tx38a/Tv4EafLn+tTTo92lEk5BeBBwEVaAosTSri38FIQQRK4iWJU0+UEIgxBSfLlj1zkxm2hPYy05xzS0a2NwnQnyfRec9YQaoC5QZSou18G6yNJj54+IShwlbxyBWlex/i+PFkzHNkuhThSUGNiR0mSLOPSJ78Uvk9cLS3RGfbn26usoCEauo97Owti1PhL1iJZ60+OW4BhlSTzFSEn2DQdIgi5OEwJiPFyjy07tsqJA3b69Eq94TU2mPMa4K7WvwBwH1iyEwttNLuelfdIzNyPS2kpTCWGa3Mr0pKeZthRr/TE0ChAlsICozXZBMSdwIdlG2PC4xjIJPR/WWpeyMxYm2/XgkYryEkYdyB2z1uq6ynhkrLVR9SA4FpDDppFblP0Smvqv2o5tDeE6Mssxyj6OiIepL08cT6sVE/1f/Xp5Hj/+dTXWVv0zXn9ZayuzFl//Y5PXMRnTbCUKFPncQZjTyLGIz6OjabbIo+sVMIHArQcJn550eHyS0SznHm6J6M5D3nP5lIuaOVQxFeUR82Is/P11sEhc1mltmojf5qGZscGzkUpSK/Go60jVdXkLk5X9qgQ8aeL6lsK8KDnU5YP6WfmIHvadHA83jD+E6Wdtwl3oj9dYPgil8FDrroA6C3XiqgHV0SfgG4lytXl92XBjqw9kJdOpAfT3FVe8rvg6UbTvZqfNuYn5ns03x7t4+hEvsPXAlo+w6AOLVr7NBNFPyaN8sT6XJ8kZIT/n630X+MObXlVk7yHYisPxjqqPHN2f8UKkh508t91i/DJ5DLCbduPNWoBYgQujYNWou2dOSSzI7rU9LM8dBiUrFmIgKC//S7itdrclDHYQAtwVBIHruqlnnmKt266ISwnDYpU3G2ZV2RZSHmhSPLetKnFXsBbnPau+Wbaog7f/nSXt14TzxcsZS1ioWKrXEZwEXSRCWUQVC8VBEu3xtzcPFeNzw2qJH/SxKY1BvpO81/oBkc5oIB+Rx6aMXLACwl39YsvNSSv74Z0w3DNH3NoXETeAW1x3UZc+nyUuaJuAacnRi4BW68vaztFrbLyiTwixo/xTR94eAlpQsMSRtwef5OXtQd4P9CPJI4NjV/uzBy36sXpTIGlO62NsXkmlt3PuaZeyRCIgXb431BTAagxjREHG2g2M+Fk18rEa/HbOaOygP0mX8rtKRAttAMP1jdyeR1L05NHPWTAKiEA5F1Tu4teAew/nfsZWgnYdfM4NBZDbZx901PNEYS0SGykRWlzn+HthLdhraz+vOz46v8/0xMyRllz5KdxaA73I6//ankRcQbbQoB8p2hl9I0i+Rd4C4rek1hmL+1ytMGGHy2uhT/RtVP5Xv0atReGJfoG+TKl13TwKIErv8TlOIAez15y9xqE/Q5P4Sh4Nq0nTL8kpWSgU9N3dsix90tzcnDrnKda67cBWAB5JvrGgkQq2QQ7SKQSt9hdp4qhz7ztpnPVgtVZEikBLKR+El3cBYm8b03I4rYMgWKlAKCbEaHuDb/+l2kKC0BDy5dG2TX2CcaDzZ/SzhUIuykFkOhtKv0xaJiPXHWYY+d+vr3oqp6ijOEYCiSVRB+7i97mh81RuSJczz7KFSoALHFcfOSaOpc79UupRNmNAAhLiXGUnIlXSJ49MkXxwdhNZZ6xnCxZJwnNNBJPbzdnoPAyFBQlyyOsYTlbdlLu4bfDkja+vGvNybSBBAFNA7WKyfD4vx7wLze7EeGNl4qgdFQmEScVxarE2nLCgzRHdjkeH2LqwyiRF/YhLHf1sHpTTsc2sxnzDjT4Ho3b1b+2NRMdklkEbTpfkMb5ANFHfxXX+thBljjyRbVFmKWi7HN94GbnBCtOFKZnPZ/U6r1d4aZZVlEfPc8J7FqfS4uU9vgvIt/i+GwT/P3t3AiTJWd99Pvvunu6euzX3ffXcMz0zQhLIBh+L4sVhOxy7mFjvRrxr7xsOOzZ8rP1iwGBjbIxBHEYIhJAEEjJI3DeIwwvG5lgbg993eQ0YkDTSHH1Vdd2Zldez/8x/1TM5NT33dGu6+vtxuZRdXd30ZGU++fzyuWr6HnmD/Ii9ItgrCI/LPiYmzqYFS/nkyafsVdXuPf2u7GcpgmQPl5u9XqenJ+1vkLKI3XjtNQ1JWiU31t7CfvLs1pIzdmK8UK0kl9qKFEVxcoM/ny+YdOnXbNay6xKBrNVWWSuohXoBLE+XWzbOO4PsK7G5Gc/wxmRT2f79jZnKGo+kwSoZ7hM35/ywN1sv0Up2Xl22pTp7sVa2Rk+V6MLncmWmUMjLdrE4I89p6Z9sV6qFtG7hpxXNIC3og2RcQVzX9+s8b+k1Y/bffPHnuc5a5+o9zZpQlDYD+mnjqFQvinE6VXTcmDF8pjmn2NxkrXi2uwPhuQO4kve8UtjoHZu+XpyRw8Pkpmr6g9VyPJPTCk9S+9PvnpexryVrBXP1HGcOZf/iTb325kPY6EZ1ybavlnYwbUBrNBtmnmvpXUl99uJzye2ix5tUOtPLapSuqN7IXXFj8hyT7nST3lyYTA/1i2WtC3uuXvQRJpX+cz1+vWRyfPuoZR76peeamtu+z/kwp9uny2cm3HE36T1ezAX57HsqpjwTFS7+e+ThX2xakfMffnOukSuZxqTRkSAdJ+xl7oK3tMz7F3YovaKsFV/y7tilOygac1VnZaVS0m35a6X6nszLmvmunAKlcl63pZwPIy9qzNdz7mel9p8Ofovku3ot0O/KCRKE7tyWJwv/Wa+habJtvJKGqGTbq1fSiVL1AhSkoSt5vVhK7mP6QU1+Vp7Zh9fxnCarqtEbC1Ki6xxMLfVJOeTPnJrR87FcriaT20ZRuVzW+bFNui4RdXOyVrs1ayWzk4W1cuxv2r1t4/bNuw+Mbtq+cWLqTGvXv+acufVLzpjc8qib2W5A3sBHNFsfoXpmYEDrbXo7G/mV3dOMM1XVMJ0mJEw7XSYzcfiN3pfyepApTVrbDRrPrlfQ7XpQkOLIrefHju0/dGTX0bF9u0c3JXOmhxJL3DXrlm7fsWHP3s3btq+X98jrxfK4PJerk0FYiXS5r9l+/0Wes//yuXhuxpiWcXKND6aSTsKvKUuXP2quWHalv3+Wfl3ZWldr2G1phzz/MXnG1Y38ZBClF4A1q7bt2rmvUva0dWvb1l23Pe8Fx8aed/DA0bNnps7NGOFFfj2eycvloNycHuXK99JVfV5X++yfdx/kwl5V2Rsljakzo+beazxfpB/Yhf29LvHIXnFn3w9eXTttJuPljh0/tGXr2k2b127dti6KvWJpsuYWd+3evHffzj2jW7dsXX+J4+EKm7Ps4LGwscbDpR9R9lk7BrfZczL3fexu2LZp5/6dB48dHj08+pkvfi5dUCHadWCXFPsr1q7YtX+PbF/y97QsaBGd32zY0oQYZftXNzcuvSSG7P9yughSMfMop49K+qhlHsnAm6vOWlfdqOVf7Vk5MfW0lNXZkn/z1hGT9AxslN7/x2/+rzt3b+jqdp5/55icAvpOKecPHd6zd//Wnbs2bd+5rlLN2euFbtf90lyWJG3yHDV6U+vNIHc6/+yJWw/JPvSD8lTuGXn9Z154YnCoe+z43gMHd+3aszEZjx2X/8//8r+feN4BKZQOHt65ecsaudqyJ6/tepS0SqXnjucmG/v3jflekr6kLCgXQ53k/ZWveM3GjZvl1JLrqed5Bw4cOHLkyLFjx3RybJu4QNZqq6w141XlFNl15MD/9Cv/SQcg7T+yb9e+Hc3Zvc57xM2sdRVzvs911srOc9gyRHvWoRqRaUzRYM5/nvU++nm3QrW5LDNCJnmk0zA0pnRLf/PFa95hVJWaZXox8NetX/mzL7zDT0YN+es3rJJ6p7znR//xfSno9f1vfNNfrR4ZTudvaPyGMHL1Z68+q8zdcLnWRcfO1fXPDY1rrKibNmqVW9covczDv2Byg2zvtdmyVnze7Cmtj/SPDNzG9q3HXrBt2460A095x45ta9euTW+Imr1799x+++2nTz/r+3LxCCqVSuaoCK7p75+j5+aEbM2aZdy6GtUF9/Jnbxqcdf7QaLZMdW5y7YsPiZllV9gZPrdt3+TV9a5zcHTs4Pf+7V9kY8fOLSduPSobflA7fGT/m9/yNxcLdVeSr5rPl24Gv2gzadyOz57nf+rTn/3Zn33R0yefrVaTqc8PHDiUTAVW846OHS+VKlGy/kS8dduOYrF8sd8ze25pdWFP7+BKP4LkzbVmrMqGKy9szlF0fkKe9S+5ZNa66nbpazw3JRoVilNHx/ZJ2S7V+kp1JglOfkWC046dm7UkL5WnDh4a1avAho2r/9NLflGCmfzU2LH9d7/5r8Nk/ebkt+nPul5pLkuS9nlOo2lytT1ydP/hI6Obt6yz3/3ev/3z8NLeMGosvrdt+3r9pI6OHZicelbfM7p3xxv+5rXsyWu8HqXdwn/v935P0tTmTdv37zvScutLCpl9iVHXK2tb7qFDh06ePKl9CCVx2S6FIGu1V9hK73avW7lenp/9ybPpYK3Cvt17MjWnyGSvUxemlOfuOW62MrREqov2CTTNdS2j82cTvkibeDzLrBvBBd0Ko9amsIs8Fwql5H6P5xdmKgcPHk5rCmZ6KumyvGvn3no9ODb2vK997eumue7Ejh279GfrnpRf7rXUsObhkd0bjV0atdbvw/P7oMbXMKI9ajkU41nrfzq1e9rjKDx/ZoXI1CtuXv7Impv0YyyWJqWWUyxNb9220aSr+hw/cSRptvWr2m9z48b19kjwfS+NClGxWLzGu+Nz99zotNmogGaXk7IDYLLr45kLBiheZOCbPcuC8z7Wy3bEushO0Dua//Gjp+64/WdlI58rm2S+qoqf3o9ZtnS1PFcrcgiZ06cmTxy/4+oG6Z1XyT4/Jbb85df697fB49V/+tod23br9ky+fPvzXqCNtxvWbykVqvr63tGD//gP38yOYLzUrovNuc7bs2T17B2HCxL7hZ9LNMuS8ec94lke531kV5q1rqoP9lWflVrO63Y+X5B/pcTaZJhKpZaMBfXD22+7M5l20A1le9PGbaOj++T1ffsOmLTDg5b/+/cdTkazxI1LVtLJ1g3b9k7AHDzLFVOum7Iph7fs56Rvgh+OHb311ltvM+lthVrVl88lN12U92zdsjNKDyT5jI4eOTE2dpx9eD3Xo7R/ZnIwHzxwtFqpy6EbNW/+bd261Q9q23dsipMb8v7hwwe3bduW/HRS6TEveMELPvCBDzBki6zVhlkrriTTT+1ev7OecxuXwlpwaPTA+R3Ws1kr7W8bXtnzJZPM9T/H6dz01fMf2U6M588qmNQ+m9XHK81agTlvSsZmN5ggblZkL5wF8RLVnUq5XvcaU7lpzVIe69Zu3rVzX60a7Ng+amul8nt379pv35Ncwmeq19UAOEfP6XpjdmxPs6Vltt5rvrmiecZani+oXrcm2+y/Me28EKYTiIWNacTOe3h+Tv7UICps3T4SxsWalztwcI8U/ZK1du/ZruPodKTE7t07ZTudkiR5pVortjZ1XtXfP2cPO2VF2Jz4od54ROlSBJnmnbhxe+JK80s26MaZtohLzw538fd4rnnln7zuwP5jw4O3HDxwfOf2g//L//y/xWHy+t49Y/oePx2UvmXz7kvtz0sFrSvIWtEizVryGN29XyLW5Hhuz659v/1fflde+YPf+6Mjh44lB0ZofC/asG7zHbfdGQXmqrNWnD1Oosaen7W7aXzJrJUu43HuOZ3FdJbnzP/mXGata9zPWsjb0nvrll1urRFf/+D3/6t8GTb38J7dB/btPSwby5eNpMsup4uD1Y1eCxrLW/qNK0Lc7sfnDXnoPEaNO6O+2bRxe3NJayMXWTs3g3w62uoie1s/Av28fvTDJ7dt3c1uvNZ7r1GhmEz0NTU1JV8k9xGa52Ct5v38z//80aOH5eRat361SdbAK+/Zs2v37t1pz5FkkgzZ3rFjBxVzslYbKhcrck1ZP7LOXj69mjt6rl1rthrYNbRBmTl6NuHVPOLWe/aX/QsvWp5frPXo0nRB1eQGZ9O6dRu0PKrXgy1btumL+oadO3fr6/piqVS5rgbMubyPdf4d64u0rUXX1M5z1bTPUnCJtQY2blp75OiB5NMI3Q0b1mmD1aZNG9K7a2F6Uy3avHmzPRJqtVpjKJ7t3jCHf/+1/JPN7MuwRvGFx8BcHz+XfP/ongMHDx7+mTt/To7qaqW+Y8euH/7gJ/L6gf1HwvDc/Cq7d49ex/48fwbIaxmi04a0GNm1a8/evfv37Nkre/iOO15Qrbq//du/s379Rr0JLR/BkSNjthQyl0jgl2+/jS4yi9IlW93tT8dX9HytH9nVzM1zlXw/zO5wlfRQaL7yn//zbyZNWCZpeJEdLh+EnBHlcjU55tNPIS2FTNq3OfltcfMfmJwguBzZS/oRxGnTlmysWbNOv5SLr+5DqfTLDt++faecC/pTW7dulxf1W3qasCev53pkx1yNjIzY62a1Wt21KzkRZmZyY2NHdILlw4cP33LLLdqo5bru8ePH165dyx4ka7UnqWiOjo5ml5DbuXMnu2UOLgN6DUjrIFEyz/XY2Nj+/ftPnTqlb9i9e3e2Qi+Fji7wp58RO/B6SF7SKWVllx45ckRfnJiYuOOOO86cOSPbP/MzPzM9PS2fi35MW7du9TwvnQyjgTUWr4cc1ZVK5aUvfenevXsLhaTfrDzfdtttcgrIft6wYYM9QeQD0ksybqAgCA4dOrRxYxKr5KiW41wL+b/5m79ZtWpVOv9+Ys+ePXfeeWf2sMc1kLJCDmPZyXKQS8lz4MAB2y3qz//8z1evXm3LEynz9+1L7rXJeSFXBNOchM2eAnFKfjyOyVpXRwqcfD7/vOc9Twr29H5Zo4SRfSt1evmu7Gq92ureli/lxR//+Mfbt29n713rPZ26Htha4MulVva57Fh51r6CenjLGaHvWbNmzbFjx2R7cnLSJO1go/LRcKkla7XpXYgo0tqnVOilVJKrslb6cWPr+vKsDeviS1/60uHDh3VbSh+tfUr9/jOf+Yy8UwomubJKpUfigVSDpEjSCy3DRq+H7kPZpVu2bJFavpbyUsuX8l2+JRta19F32tsNep3QCwb78Dr3//e+970XvehF+inIcS57ftOmTXqJtXtbTwR21w0nFcqvf/3rui2lilR9/v7v/162ZUMuAbbS+dWvfpV9df13FnRDK/cHDx60SUk2JFxpzXJiYkICsOx/W+Douq6nTp2S17VjVfYyzTiWK9nzUo1JV5UwWs684AUv0J2vr+/fv3/58uUmc+9M4oFci3VvyzulLiTvYU9e/8Ev5DC2B+26lFx8x8bG9uzZs2PHDqn2SBJOu5Akn4JcESRofe1rX2MfkrXa8z6EPK9fv/6uu+6yp4TeacONZRvWZZ9LmSLVSntJUP/0T/9kG9Df8573jIyMaNVT36aXZ1wb2YflctnuQ92Qsl4ql3ptkBJfyn39rgQAuRI888wzJrO0oq2P4nouwHKt1TsLJm0/v+OOO6TGeeutt/76r//69PS0fhAvf/nL2WM3POhKIX/06FH7QUgA0I0TJ07YiqnWMm1zOq6WHsOyJ7O5SA5p2f9SBOkdN73Br+xtTSlzXvayl+n26OjoK1/5ynQS1Cibtdi912Dp0qWarGQHvvCFL1yxYoVp3vq0cWvv3r1yqZWgq0OGfvM3f5P9dp2lvdZ2hoeHLyxMZLdrvtKiRna+fhDyI1IoyQdBLx6yVtuSY/1FL3qRVH0kaHFTZy5o8SGliexqz/OkWn/LLbfIrpYKkOSujRs3apG0detWuRJLfVRe17tx8n4puWZmZtiHN0T29rDsVe0uoh1+ZFs+CLnW2tCl75RKEinrRt1rkEupVO7l8Ja9vWfPHntqyJ6XxDU4OCj7v+UeBG4U2bdSp5fdLoXMP/7jP1YqFTnCpeTfkNqxY4d26WRH3airqpD0deedd0phbmv2J0+elP0sp8CaNWv0alsoFOQNUumU8n/16tVyRRgfH9dTRsurltyFi9GjV++seSkpzLX3uDhy5IjsZKnkHDp0aN++fbKh2finP/3prl27jh07NjIyQqee60xZeq9Bjmc5en/t135Nrq3yolZv5DjX9kMpgrSQl8vBb/3Wb8mJcPjwYTkXnnjiCfYhWattae98KZXk9NCiB3PB9gnRnuL29aeeesoO4tJXbIcTvUjYSwX9mK+Z7FI5wrVyqa/oR2B3qV23vlaraU8eORfsnmf/XyfZ2zrsxN7mlP1sPwvtUshemtNbDHpzwd760aO9ZeVQOfK5s3CdF1O9m5Z98dSpU3Y/nz59Wje0dVcvCi0D5Oy5kL3pwA2IKz/Osxu23/6TTz6pFwLd5/ZyrDtf9rneccueJrha2fJEzgJ7tMuOtRfTllHo9kIgZ4T8OFVQstZiCV1a0cRcFENS45QCKBu0Wq7Kcp2Wa+qFMYBr7XWy1Rcd/Gb7kOi1wV6YL+y9IG+2fd5w/Zdhvd9pX7E1Hvuidt3EDacJKlubkSLFVoCefvppw4jQG8HeKZOyXXsl6Iuyt20tU8t2LYWyNx1Mpi29pdiR30DfzivJWnpJlQNb9qGW59rGlY2schbYot7OTaUYl3s9ZPfqbC4t11zdmJiYuPA6qz+SvQS03OIEWasdaMmiJZGUU1oScY/5htPLZPauj41Surd1BJG+zV6tdbpC3abGf/2Huh7tWr9vubsmH41+EPZaKx+QvWyYzH1QXPMpYA9vLWe0Wml3sjxT0ZkjtrTRfW5nAjCZ2z22dYVP4XpoMWLrl3avyn7W499eZO3lINu6ZXOX/gb5kmaWq2IrM7YqL9t6/Nubay0ZQD4XLXxamnlxbXHX3i+25Yy9U2xv9Ng1texPyaeghzpTbpK1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAAJC1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAJC1AAAAAABkLQAAAAAgawEAAAAAWQsAAAAAQNYCAAAAALIWAAAAAJC1AAAAAABkLQAAAAAgawEAAAAAWQsAAAAAQNYCAAAAALIWAAAAAJC1AAAAAABkLQAAAAAgawEAAAAAWQsAAAAAQNYCAAAAALIWAAAAAJC1AAAAAABkLQAAAAAgawEAAAAAWQsAAAAAyFoAAAAAALIWAAAAAJC1AAAAAICsBQAAAAAgawEAAAAAWQsAAAAAyFoAAAAAALIWAAAAAJC1AAAAAICsBQAAAAAgawEAAAAAWQsAAAAAyFoAAAAAALIWAAAAAJC1AAAAAICsBQAAAAAgawEAAAAAWQsAAAAAyFoAAAAAALIWAAAAAJC1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAAJC1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAAJC1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAAJC1AAAAAICsBQAAAABkLQAAAAAAWQsAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAyFoAAAAAQNYCAAAAALIWAAAAAICsBQAAAABkLQAAAAAgawEAAAAAWQsAAAAAQNbClapUKroRRZHv+7odBEH2PXEcy3d1277HdV15rlar7EMANy0pqaQEC1L2xcnJSS3ZtACs1+v2W6VSSZ5rtZo8h2GoRZ/8Ei3r9Fl/IfsWAEDWwmVIZUKeZ2ZmtG4hdQj7La1MyLdsKtNaiM1dUv/wPI99COAmJCVVNiBpcScFmpZjln5Xijsp4mTbloF6O+lCGs/sXScAAMhamF29Xs+Gq2wVpFgs2mepc4yPj+vrJ0+elOeRkZG1a9cePXr0iSeemJ6eZk8CuNloiNJ7Q/l8Xu8raayq1WoSlvRb5XLZzHabyU/phoY0/a7elgIAgKyFy9PKhKSsXC5nms1cJtOpxt6+td1mfud3fkebs+TNK1asyNZRAODmLOWKxaJtqtJ8ZTLdCCVB6c0mL5WNW7LdUsrRdxoAQNbCFZHqhe0TqPnKViOmpqb0FW3U0hEOUvM4fPiwfOulL33p7/7u7zJoAcDNSeJTPp/XZCUbr3jFKxzH6ejoeOSRR0yzIUsCmBZiNn3p+C7ZmJ6eloJOXzx16pRJew/qm+k7DQAga+GK6iK6IVUHTVxah5CIZW8At9QtpLaxf//+HTt2yPaLX/ziffv22agGADcbbbZyUmvWrJHnnp6eZcuWSaDSKKXf0pRlOwpKuadvVt3d3UuXLpXnsbEx7VmtKQ4AALIWLkVrG5s2bdIKhElv9548eXLz5s2HDh0yaUOWRLLjx4+vXr16586d8sqWLVtsdURqHnYmQwC4edg4JGFpaGioo6NDG680Pknc0u9K9Ors7Mx2C9Q7TfKe/v7+5cuX/8Zv/IaUkPpTkrj+8A//UHtcAwBA1sKlaGuVhKgDBw6YtBdNGIZf//rXDx8+/Nd//de//Mu/fOTIEZMZsqVtXHv37tW6iHxpcxcA3Gy01V2zlhZ3+kp3d7e2ZUnhJhnMtmuZzDyr8uKSJUvk2Q5Vtc1chnYtAABZa/GQgGTjkNQn7PwWys5+YZpDFGxL1LFjx3bu3Llr165t27ZpJaNQKEi+0r6FEr0OHTokyUpHbcl39XWpkezbt08qHydOnNi+fTv7H8BNSIs+KfQ0WUncsvlKnqWss5mqt7fX/pSdjlVjVU9PT5zSkrOrq2t4eLhltS4AAMhabUWnqTDNaQOlHmCnDdR1h+V1SUpSaahWq1Kl0Du1+lNf+cpX9P6uvK61CqlA7N692060pR0FbWBbvXq1buj/hF0DVH8n0x8DuGlpWSfhSoPT4OCgPPf19WnokgJQ3qBZS7KTFIk2QUnJNjAwIMlK32makwbJl/KinUgDAACyVpuTUKSxR2/WStWhZZFNrU/o9uc//3kNVy11hQ0bNuhGsVg8ePCgaXYXFOvXr7dZSyct1NClv8EucAwANxspr6SAsi3/OuAq2xVQdHZ22m295aQFpo1nptm9UIOZvplxqgAAslbb0vEDknmkAmFzlNYS7GAD3dBugb/yK79i0oYsebNWGrSiYDscHjp0yFYyRkdHNVbpj99+++2y3ZLfNIxxcxfATcveZvr4xz+u87ybZi+A/v5+iVi5XE7Kw5GREYlPWuhJsWanYNVY1dHRYZqt+vKt7u7ugYEBO10hAABkrfYn9YDTp09ng5Ymq2wHvw984AN2pkENTtlUduDAgenpac1Ou3fv1tclhkmVQioi+tu036Bd8VOqHdr21TI8DABuritZszHKFonZdi0dlJUtTvWd8uItt9yib5PST4rHrtTKlSuZGAMAQNZqc3pjVWKPXW1Ts5PkHzvySuVyuUceeUTylXzLDtAyzV6C2ma1a9cum8G2bNkiG1KxkOeHH35YvqVrGZvMKlv21q9OXcjHAeAmLCQlMkk5JqlJIlNnZ6duiBUrVsiz3l0aGBjQIVv6LXmbbEvBKNurVq3KzquhzxrGpqen2cMAALJWe5IqwtGjR2+55ZYjR46MjY2tW7fuxIkTmn8k+ciGzT+nTp3SMVo2HT311FO6IVUQ++Ly5cs1rUk14tChQ5s2bdLX169f/6lPfcr+NtnQG8PytgsHhgHAzUZLOSkqNUqtXLky26glZakdwaVRatmyZRqo+vv79XUNY319fZs3b9bbVdnFuAAAIGu1Gxt+tFHrgQce0Mktsk1MdnILO0Wh1Dm0YapSqWS/K44fP24ykx3v2LFjV+ree++Vn9KGMjs6XONWdip5ALgJy0nb2c/eG7K3onR0lganKIqklJM36CSu2Wld7W/L5is7CzwAAGStNqRVAVsnOHTokJ8yzQEJmqnsFIWYH1oba8mf3AKf53sQUoHWeedYAQnAXGiZF0qnqmK3zBup2EgJr7UdHRNBVWeer7MmcyuKe+5krTYvbkzaBmXLfTvyW7OW3tOVF2PMi2xJJKWPnRWaPTOf+99kGnXZ+QDmqJzRK6+da4o9Mz/s2AczW2sz5prNWsViMTtiBWStdiOFu86N8cd//Md22kA1MTFhp804fvy4fHfnzp2jmBdr1qzZsmWLbOxN3XHHHStXrty8eTN7Zt7s2bPHcRzZOHTokHwE8qWcAuwWADfKjh07NmzYcPjw4V27dm3btk1eGRkZ2bdvH3tmfkjBfvDgwU2bNu3fv3/79u1ykZVX5Ev2zPw4duyYHPByCrz4xS9+85vfzFxBZK32J5X7n/70p5qsdFaM7P02Hc3F/bb5vN+p93jGxsakALKL8LBn5odO7vKSl7xE97ne8uT4B3ADaUOWbNjRgFLgU87Pm+zqdlLI/+Iv/qLWfNgz89muqFWd+++/P2bkKlmrXeVyOe0odfToUe0lqLFKKpqVSsU26epsgeyueWO77EsG1m5sclWmH/88N/muWrVKS3/GawGYC7Z/eJAaGRlhVt75JCV8sVjU6s3atWt1gC67ZT5p4nrDG96gl93saq4ga7WPfD4/NjamCw2rQqGQfYOdYot9NZ/0U1i/fr29HnMNmOe4e/z48exQXcZMA7ixtUwpVeQKq2WL1DJ1Il/MD9nz9qoqcWvLli3ZEVyYa7LPtWIpV9t7772XHULWanOjo6N/8id/kq1W6srCUvRHKfs6rd7z1odQp7+77bbb9HPRmz3smfmh9xek3iMp134cgj0D4AaW87bDSD6fn56evvPOO8+ePcuembf9r1MyaPF+++23G+YAey6Of9nnkrUkccmVly5UZK32pG0mOh5R21L0TpvW7DVoFYtFmrbmuVHFpP2Yb7nlFn2FnT+ftPfswMCAvcXAPgFww8t5e6nVMn/16tXslvls18p+uXz5ckOP8Xmkh70OhP7bv/1bkxk9AbIWMH/XgC1btmRLJfbMfF4GNm7caLe5AAOYo+qm3tyRbe00jnmTvYm5efNmdsg8H/z2mT6EZC3gOaAzUx0+fLhWq2m7IuZToVB4/vOff/bsWU1ZrDEKYC4Kebu4luu6hw4dYjnXeeN5nu2zIEX9sWPHuKdG1iJrAYuF7UO4c+fO7IWBPTOflwHd+dnx0wBwo+hIIa1ranf97du3s1vmOW6Z5rxTBw8e5CJL1iJrAYurGJIrsXYpOX36NFlrnpVKpf3790vQsjUhXQ4BAG4gKVsmJye1zJECn3J+3oyPj5vmvFPyKWgfQuabJWuRtYBFwc4CbJptXHRgm/9rgJLEy9wYAG44iVVS0dd+a5Twzwntn2+Xr6QPIVmLrAUsInaNy2KKHTLPdKEVO+e71Ir4FADc8LqmVPR1yJaWOazlOm9mZmZMc9bZarWqQUu/BFmLrAW0Oe1GIoW+XVpRyiOmfZ832n1fr8Sy51ngEsBcaFmn3t5iw7xdau1aT3qFZXQuWYusBSwW9Xo9u8wlO+Q5ibvlctkGLTr5ALjhhYzd1n7jxK35vMjqRhAE2m3B5i6QtchaAAAAAMhaIGsBAAAAIGuRtQAAAACQtchaAAAAAMhaZC0AAAAAZC2yFlkLAAAAAFmLrAUAAACArEXWAgAAAEDWAlkLAAAAAFmLrAUAAACArEXWAgAAAEDWAlkLAAAAAFmLrAUAAACArEXWAgAAAEDWAlkLAAAAAFmLrAUAAG7K6peJ4vOekwe7BSBrgawFAACuXGQfNlaFJgjPe04e1xC3pAIXhmEUnfvB7DaAm4Hrunq2vuMd7/A8T7Z932e3kLUAAMCNz1o2XAXnnv1ry1otoUvvnQO4edTrdbv99re/XZ4DOelB1gIAADcqZbW0a6UPbctqPMvDXGXWkmTl+77nedmqW3Rx4UVEAOaMnpiVSqVarb73ve/V7VqtRvlI1gIAAHOVtUwSrrKPa2zUytbnZCN7Ex3Ac851XT1D5dx88MEHJXGxT8haAADgejUz1ZU/rk52yEccx4zUAm5O9ty85557wjCUDR21BbIWAACYy6wV68M0HtdUjbM1uSAIarUafQiBm6oPoZxl+XxeNu6++249VWl/JmsBAIC5yVo2WcWZl68+a+lILZqzgJuZNmSJcrn8/ve/X75ksBZZCwAAzG/Wiq46a2mNrV6vf/rTn37JS17S1dXlOE5vb68D4KahJ2ZHR8fQ0JBt1+IWCVkLAABcl0qlpJNeBEFd41S5XNYQJo+gHupGIV+Wb0ZBkrTsuHmpigVBIF/GcVypVH71V39Vqms9PT1ae+vu7tbaG4CbnJyqnZ2dS5YskY1HH31UTmfKRrIWAAC4TtpWFdTrbq1W8X1PZ7+QEFWtuvqO6amCSdfbyk0X4/Q2tySrYrFYKpW++MUvSv1s3bp1Uj8bHBxsuU2+fPlymguABaGvr083Vq1a9brXvU7PfV3dGGQtAABw7VkrDFw/qGnrVqlUiI2ZKSbPQRifPjMpG1VPopYJ0w3P8yVHDQ8P9/b22jSl98U1ca1du1YbtV796lfbQSAAFoQgCB588EFdcJzzl6wFAACuM2sFUShZyks2ki6B9TiNVWFmUa0nz0xUQ3P41hf0DS1fMjicvR3e398voeuuu+4yzXnMWu6FhwBueiYzOuuv/uqvdAZC5nwnawEAgOvKWmEg0ciXSDU9dVaHbFVq1ZofTBcr05WaVLUe+9RneleM9Kxc6wyvcjr7nI5O7XH01FNP2dqY5KtcLqe/UWpptVotjmPZKBQK7GJgQZDEpUMx77//fn1FW7dA1gIAANeYtQK3YuJ6swUrmZOwUKnKlrwkKcrp6ulcvsrpHnD6Bp2egb6RtY8+9rjUyTRK6a+YnJzUDd/3s0Pq7c1yADc5ezoHQfDud7+7XC5nVyEHWQsAAFxL1jKxBCu/MDOlWcsP65KQcuXyx5/4otPd7QwNOT19Tr88D3zs69+6+z0PlTzP3u3WpYdNpq+RVNSyVTRmMwMWBO1JKCevpKz3ve997BCyFgAAuAFZq14rStYqFqaT2QgDT/LWVKXcu3yFs3SZ09OTBK1lq978vkff/ZFPFIyppuO4bILS0KUDtKSWJkFLX5dteZEOSMDCilsmvYHyzne+U7ftGQ2yVlux1zAdpJjP5+VYtwMWdVHI6elp+87GQijp+/UV2xAMAIClrU9Si9LLRJqRIklbJvYKufE4DqVi1TG4xOnucZatcHoGJGVNRuaev3t8yk9SVilKOhaSn4AFbWZmxqQ3RPRLHVRp65lSONx3333yXdlgHkKyVtteCCVQ7dixY2ho6Omnn7YBTA56CV16+1BvFs46I6e8yDrfAIAWevnQG9V6207kc1Mm9CRrJd+JfKer0xkcdFascHr7nN6BvDHv+/QTUi9z07FbQdqoRdYCFnpV88IGK71Zr6XE2972tmz6AlmrreiRvWnTJt3Yu3fvli1bZu2GkZ1UV86ZcrmsdyYmJibYjQCAi9GeEXIRmZqaSvoQVksmDnzfczocp683GaPV0zMeRkVjHv/yV//tmTNTfiRBa6JYkdpZrlghawELvZ6pdUgvHXsZhqEGrbRASLz1rW/N5/Om2eQFsla7Wbt2rd5xlIP++9///h133GGakzvZewxeysy29AGdawEAF9IuQ3J90QuH3sUbHx9Pa1+BBK3BFSucrm6nuycfmZIxf/nO++W5kjZq1WnUAtqoHNCNbB1SZ3s/ffq0FAdvf/vbTWaICshabUWS0rFjx/SIl+1isWjSe5C2FUsTl96V1OglbyuVSvpdDWlyqrAnAQBZ9spiX9G6lMSnZMmsjm6ns8fp6qmm+erND7y3ks6EUY5NMUjGcVU8dzqfYzcCC53WEvVuSy6X0ypl1jvf+U72ElmrbYVhuGfPnm3btu3bt082Tpw48aMf/Ui/NTU1pefDzMzMbbfdtjO1fv36vXv3jo6OyrNsP+95zzt79iy7EQAw6yXGZG5gy/N0bkZS1tCKEaer1+nsc4353n88ed8HHk9SVtqiVYmTRq1q3YtNlCzGFTD3ErDglUql48ePS71R6pN33nnnqlWrtmzZsnbtWqlYbtq06V3vepc2eTFki6zVnnbv3v2tb31Lj++77rpLjvtaraaXRnsfQm9GZhu7dKPldQAAlLZo2R5EOh7DcTqdjj6nZ8jpGpALSc2YBx/7mFxC8vVkjFYljLXrYLFcMmnWikKWNwUWNh2FpT2npBxoWbNY6pyPPPKITkJI1iJrtSE5svft25dd/HHjxo26Ice9Bi099HXGXs1g+px9HQCAWUnVSq8mjujscfqWOl2DxTjpMXjPQ49K0CoFyayDci1xo8iPo9hEYRKxokp5Jh23BWChstOtacTSZ7sEudQkXdd94IEHWByPrNXODh8+bEcrlsvl0dFRprsAAFw/ubhIXUp7QMiGRK3u/kGnf0XFmHxoZsJkpNapXKVUPzcTRlrhitJH0HwAaM8Mps/33nsvO4Ss1c5uvfXW48eP6zJZR44cGRsbI2sBAK6frmFq0t4QErR6enqcju58YM56Scp6y3veX21OOViq1Zu3tVuCFn2KALIWyFoL3MTExJ49e3bv3n327FmacQEA1097mJ88eTIIgv7+ficVppMNPluJ/+7TX05mIIxMvlwPG5UvbdWK7CNOHgDIWiBrLVizrmaQHb4FAMA10EkIxate9SpJWUuXLu3o6KjUwynP/OuTZ34yWXaNKbhRmKasoO63ZK047VXIElsAWQtkrYWtZdYX2rUAADcqa+Xz+a6uLm3UqtVqgTETtfCBj35WG7XkSz8wp5453QhacaTRSoOWdiLkmgSQtUDWWqh0Vgw7w7uuTczUggCAG5K1JGKNjIxI3NKLS8ULHv7YZ06W6mWTzD04mSslWSqS7wUmipJHM2sFzaFcZC2ArAWy1sKmk2/KVbBl0QMAAK792u84Q0ND8vyqV71KX3n47x6rGlNIly32NEdp1kr6C57LWtqopRPBk7UAshbIWgtVFEVyoNuJB3UtOeYhBABccaXp3GTtWXJBGRhckixe7HTqyKvJfPH7P/ppKTKFoNFm5dUj/dl6uZoGLdq1ALIWyFoAAMA0WqVqxapGpmIhbye36Orpdjo6uweG6mkLlWvMOx5+vBi2LKV1flqLL5vgAJC1QNYCAGBxZK2g5ku2iv1AZxGsVctevfJH//X/djocp7PzNa9/wzO54sl8rWLMvz05VU2DFgCyFlmLrAUAAC6TtSRo5c5OpVMHRn7dla9zualVIys7eroHV6w4O1N00zW1HvnEEz+ZdEshWQsAWYusBQAArixrJY80a0WhX62We/t7nK5Op7OzFPh1Y/JuWDPmtW95VzXtSUjWAkDWImsBAIDLZ62nf/xUOpFgVCuVky6FQdDT1+t0OMtGRp6ZmpSsVajH977vAxK08kEycIusBYCsRdYCAACXz1oStOplN6r7oR+4rispa2j5MqfTqQaBBK1KnLRlve/DnzyVd2WjFJC1AJC1yFoAAOAKslboBm6x2uhGmKyp1el0dDrd3WUv0BkI3/Xw+710oxKxXhYAshZZCwAAXFnWSiZxT4dsBUHU3dMnQaujr6+ero41USxLxHrwg4/naoEuTFz2QrIWALIWWQsAAFxB1grTrBWaMIwdp9vpSB612FTCpC3rgQ885jVXJZZHuRaQtQCQtchaAADgyrKWH8f1aGRkjWQtXbz4TKEkEetHz57++2/9vwUvmJwpV9wwpAMhALIWWQsAAFxp1ooaDwlanT1LnI6eUpDMhzHtBg89/tGpqquNWhK0gtC4yVcAyFpkLbIWAAC4XNaqVzy/Wu9wuvt6Bx2nJxmmVUqmHJTHhz77hGdMLU6yVhLKIhOGTEMIgKxF1gIAAFeQtZI1tWq+ZK2h4RXaqFWQR2SmvPipybxkrXytXnb9pLOhBC06EQIga5G1AADAlWStqbPjq5evkKzlON1BOrd7so6WMR//8j/kglgnxkimz4giz60Gvsc+A0DWImsBAIDLZy15dDlO0oewf6gem2psThdqVWP+n3/5b6V0Ta2Sn3QcLJfLzaFdAMhaZC2yFgAAuGzW8sPBnr6ert7/6w/+qJ42aknQetN9D1XTbXmlFiT1KdeVF4K6VyFuASBrkbUAAMB50rapc4rFYhxGSzq6ex1n6dCykutLuDpdrZaMefgTn3abvQebQ7Si5iJbZC2ArEXWImsBAIAmO4VgFEWTk5PynLzoBxK01ixd0eF0SorKBeFUFBWNOVXzNGs1glbyn4CsBYCsRdYCAACtqtWqPNfr5xbIcl23u7MryVorVpXL1alKWd4xHcdve+TRXJhMkhGYxoAushYAshZZCwAAXIrv+/Kcz+fdZA0t0+Gk8w86ThDG9XSkVsmYL33nuxXTzFo2btGHEABZi6wFAABmNTMzU6vV7Jevec1ruju71q1aI+lJakzlIJKgddp15TkXRV46WKsla8VkLQBkLbIWAADI8rzG0liFQkEbtRzHWdI/0OV0louVMG3Iqhhz90MPFdMGrvosWSuKCVoAyFpkLQAAkBUESZfAarU6OTkpG2fPnh0eHu7r61s2vFyi1GS+WAxCyVr/8uRP5LloJ8aIyVoAyFpkLQAAcLm4pbMRlstlx3G6u7sdpzPwkyQlOSzv+x/9ypdPVkpTQTL5e9CStdK4RdYCQNYiawEAgPPoJO++70viqtVqErR6enoka8VR0lewHMTTbv0Dn/tsSUJXHJRNdG5ijMhmLbvWFgCyFlmLrAUAAFKlUqlSqZh02vfXve51K1ascBxH41OQDtY6U6lO+nUJWtX0yzCbtSKyFgCyFlkLAABcnAStIAhWr17tpOp+6PpmphYWguiRT3xSothEvVZJ5sYI01gVNYIWWQsAWYusBQAAZuX7vgQtHa8lKaurq6tSqWijVq6SDNB6z4c/VEynIiwYv64BS/sXhmQtAGQtshYAALikYrHY398vWWvVqlXy5dR0XjsQlo15tlwqpVmraqJm1gpMGDWyVhq3yFoAyFpkLQAA0CoIAt/3V69e3dnZaQdr1SVx1cKHP/6ptEXLnPVKrolKXiVp8Wpkrcg2bZG1AJC1yFoAAOA8krIaV3THGRgY0PnfozjJWhMV/8EPfywfx9qu5SaTEAZkLQBkLbIWAAC4rKQXYL3uDg4O6KwYRpfbknAVmWJsJGXNxEm7VtWYMzOTYUvWagYtshYAshZZa3FxXVc3PM/Tgx4AsIgrQdnVh01zrFXg+dXYBE6H09np9PX1hKHve/VaPZZw9Yb7H5uOkpRVDk0lTC4kdb+aBK3YT58jDVo6TQaXGQBkLbLWYqEzSsnzzMyMSSfzle1cLseeAYBFKjpv9WENWvKIjV+s5AaW9HR1O9VqOZ36IslOBWM++MVv5+Mka1XT9qzkshK457JW8hsaQYusBYCsRdZaRDzPswe97YsPACBrtWStUmXG6XDk0b+kr1gsaiWp5AbTvvlxLiilvQfdNH1FUUTWAkDWImsh4bqupqxSMltvMvq5UCiwWwBgsVaCLtauFTidSdbq6HLkO7mZfHLJMObb/36y2JgSo5G10rt46YTvhqwFgKxF1lrEKpWKfRZnzpxhnwDAYs9a5w3ZamStyakzI7eskKwVxoEEqWRWjGq9bswjH/vipG+mPFOTrBUmP3Eua+lgLbIWALIWWWvRkny1devWQ4cObdq0ad++fe9973uLxSK7BQDIWtmsNbCkx3Gc4WVDkpdKNTedZ9CcHM9/+qvfLsSmEicrGnvJGC6dcqmZtRpdEpkbAwBZi6y1+ARB8OCDD/7pn/5poVCoVqvyij4DAMhazayVPLq7OzvS8Vqass7ki3Vj/vuPnz5TqEu08tJFjetxs10rDslaAMhaZC2YnTt31pNuIMloZtmws2UAABZ74moGLXl0dXVI0IriJGhVI1MMIrlavPm+B+S5FCRBy4tMzY/iRv0pSnsPBjZoxQQtAGQtstZiUyqVxsbGduzYsXbt2qNHj+7evdu2a9nQdfr06ccee+zuu++Wk+Ftb3vbAw88INtvfetb77nnHnn97Nmz7EYAaKM6kKnXg1iDkeSlMJBHh+P09vZK1pLXJvJFuTyU00ne3/7gw552MWxttmqGtPhccGMtY2BxGh8f/9CHPvT6179eq5H3ph588EH58pFHHnnjG99o0rnZ2FFkrfb0S7/0S1NTU7o9MjLyC7/wCxKu/JR9TxAE2vaVXew4DENGdgFAmwnDpJwP/UCC0czUpMQtv1Yd6u+TuNXXNzBdKAfplIMStE6VvVLaddCmrPPTVKP3IFkLWLTS0ZuNSqPnebY5SyqWpjn39aOPPkrWImu1LTnWoyjSZ52NcNOmTdk3yKGv6x2rcrksX2ru0h+3zwCANlCu1KQ2VKvVvJqbtGsF/r984xtdjjM4sEReSxYv9oKaMePV4D0f+XQlbdEiRAG4GKlJZiuKWqvM3rt/4IEHbMUSZK12IxFr79699uj/yU9+snPnznw+b9/geZ6902CnhpefYtcBQFtKJreoNypGxdy0xK1ux1m9fEWX03n2zFSYNmS56YJab3v/hybrhpttAC7NZi2pQNqmLalVSs1TKpnveMc7NGsxZQBZqz0dOXLk93//93V7165dBw8e1NyVveUwKz1DqtVqrVZjNwJAe6j7oZb+SS/xONKs1ZU8Ok06MUbeDfOBOePGBWNKae/BzBQaAHCO1BIlQWn/qTil3alM5sb9fffdp3VOpsIma7Whcrlcr9clbq1bt250dPTYsWMXHut670Fe1P61tp33smEMALCwSLEehMm8GEk/nyjOTYwHbu2W5Ssla0mSyk0X63GyZnHFmG//+JkZYwotWYvLAoDLkeJF6pO2t9Q999xDixZZq82Nj4/Ls4Yo20gVpex2Nnrpm/XEYCwjALRT1koWIw78pNiPjV+r9nV0aLvW+LNntV2rFCRZ6x2PffwH+VpJR2o1ltIKmA8DQJZWIOVZKo2u62Zv09vOhPfee69WPhn/T9ZqQ3pTQQ7uMAzt+WB705rze9a2hDElJw/DtwCgbYTN1qlScUauARK05LF0yaC8WvWiapjM9j7lm89+618LxhTPy1oRWQtAi5ZuUGHKNFu3JIA99NBD+i1u35O1AABo94pRuqpWFISx55nQX9rbJVkrlq/Ted6r6Ritb3z/R9NBLNu1RpRisBaAqw5grGVM1gIAYNFlLfn/8kxB8laf48ijx3E8r6bLahWNGffjhz76iam676ZzEtJsBYCsRdYCAACXVyy52vOvmptaNdC3aqh3SU+HMZEbJlmrlD4+/MWvVHXm98gnawEga5G1AADAFdSBjKlVA/lPZWZ6uKdzqLvj5E9+JFmrHjc6EI778U+m8rJdiSMvDslaAMhaZC0AAHB5fmACP4lcw/09venEGCYOqm6tng7Wmg7M+z/7RLKyVhzXk5kHI7IWALIWWQsAAFxBHcg0Zg+UlCVZq6/TqVXLYRyVo0a71pvuf6/OiiFZqxa4ZC0AZC2yFgAAuLxiqRaGsVurLOntWjrQk6xibKLQmEI9mXiwYszX/9sPZcNLG7W8ZIIMACBrkbUAAMBl60CN/0ZdzQ6Esl0NAglVU76ZjpK4VQiSrFVMljIOmO0dAFmLrAUAAC7PD5Ls1OE4ywb7JW759WqU5i3JWuNu/MBHPytZqxQZL45rXpWsBYCsRdYCAABXVgeSWOW6Q4N9ErSGBrolTU3lpuvpSK2CMW97+PEpPxm4Fco3QpesBYCsRdYCAABXJErSVpS0ay0d+OQnPlKvuxKrnp3KV9K49ZGvfHMmTtq4kqzl10wSt8haAMhaZC0AAHA5Na/qdMj/OUuH++OorhNjeOmsGM9Wwh9P16rGVNNAFtYrJvbJWgDIWmQtAABweVEUSNIaHurvSGYgDPL56cCYom+mffPYE1+fSVfZKgdxIBFMglbskbUAkLXIWgAA4IrSVle309vj3HriSK1ajE1UcpOZ3cvG3P/hzxaNKUTJylppZ0PPhDWyFgCyFlkLAABcUdZyOpzBJT3G+MYEkrUq9bAcJ30I/+WnE6W0XSvQrBVUTb1M1gJA1iJrAQCAy9aATFc623uH47he2Q/rQTpYq2jMeD2ZG6OSTv4epO8M3TLjtQCQtchaAABgFlEUua6r257nSYLqdzqWOF0DPb1BFErKygVBKZ2B8K0Pvb+c5i43NFFs6vVk2ow4CtiHAMhaZC0AAHBeysp+OT09Lc+F6dwSx1nqdH/ssY94YVRNW7TkMRHF933wQ4UgadFKq0gmDJP/pokLAMhaZC0AANBUqVRaNoIgWNLbt7p/qN9xTJSsoCVZa8aYZ2puwZgvfuufa8b4USNrRUGsP8KeBEDWImsBAIDzSFKq1Wpa3Uk6EJpksNaA0zHkdJsgyVq5ICwZc8qrP1kqn6m4Xpq1kvW2YhP6DNMCQNYiawEAgNlkuxFK6KpUKru37+hznL98xauTWQabWatozFve94hrkqnew8jE0bl2LQAga5G1AADA7EErDEOt8bzhDW/ocpy1wysqEzlJU0Hah/CM50nWuufvPlgzxo2TWTGMfVww6AsAyFpkLQAAYIrFogQt3c7n8yMjI/29fUs6uo0XBEFUM7FkrYkgGPfDgjHl0JS9uJGyokbWsj8OAGQtshYAAEjYaS08z/N9P7lUO05vd093OjFGnPYYnPYDSVnv/eSnK83Z3jVrebWAVbUAkLXIWgDakHZbKhaLWkF0XVeH9eu35EX7pb5BlMvllvqlnXsNWJzkFNCKjtZ45NyRrNXV0blicNirVDVrTdX9kjEPfuTjVR2sZdIWrehcuxYAkLXIWgDaiu22lB0rIiEq251JvlWv1zV02bdJ9KrVaqwIBGQT19TUlN56kKzV4TjFXF5y1GR+Rk4eefVkuXzvox+cPWsRtwCQtchaANqPbZ6SaqJtxbKByjZn6ZeFQkE2SqWSfYVFgYAwDCcnJ+2diO7ubslay5YtM+mYLDlD8r43HSbzEE4HsZu+EutIrZCsBYCsRdYC0L5c1y0Wi9lXKpWKXSlI2EatbDzTamWYYh8CeirJ6eD7vgStVatWyXOpVMoVC3X5lqSsMPzOk09WjanNmrUAgKxF1gLQlsrlcpyamprKvm7v02shPjk5KbmrWq1q3LJvpmkLsGeBJK6hoSEJWl/4whfSRq24FPjVtA/hux5/fML1y+kU8OaCrEXgAkDWImsBaCual2wTVq1W0y8lfUms0i8tbd2yc2PYUr6l1QtYbHzf1wbeQqHwF3/xF8kkhL298mWlVg2TlbXimTiYDsMPfv7z1XQSwjCbtUKyFgCyFlkLQJuamZmR5/Hx8Ze97GU6ziSZQq2ra9myZU7Tz/3cz/3whz806YyFJnMLX38WIGvJcy6XM+msGGJ4eDh5PQzcKPDShYxzUfSV73xnqh56dmKMsPmIyFoAyFpkLQBtx/O8np4evQ0vz0uXLrX5qrOzU577+/v1y/Xr12sNUuOWVi51qoyW5i9gEbLngpw4ek5FUSR1n0KtJuEqH4b/MT2dS3sSFuppnShKh20F2XYtltkCQNYiawG4uWkWknqeXfbKdV07OXsYhvK6RKwgCKRSqBFr+fLlzgX0xa6uLq04Dg4O6utDQ0NSpustfG3gavnlWuhPT0/zWWCxnXeSuOQc6e7ubtzLcJOehZK1Zoy55/EPyilRSuNWI2v5mrUiEwexCUITELcAkLXIWgBuXtrEpFFKX8l289MgpDOkaYKyLvxVkqB0bgxNTRKu+vr6bDOXRC/5n/B9X+d/t7lOXpRtu0QynwgWA50wRuo6S5Ys6ejoGBgYaIyBTOd8r8RJ1nrXRz98KvKL6ZyE59q1/KiZtXyyFgCyFlkLwM3OpqyTJ09q+RuGoXZw+tKXviQxSSqC2j9QOwqaZsOUZiRNTfl8Xl/X35ad/cLGM8ld9v69afahUpKysosjA4uhriMni54afX19ExMTmrVc3+hCxv/wg/8xk7ZrlbNZKyBrASBrkbUALBy2iSlbCmczUkdHhzxL7tLXbQ9Ak3YCNJmp3u2Ug9Vq1fd9eZbfJjlKUtaOHTu0Ttnb25v9DRrSWuIZsBgUCoWuri7bUJysQhcn/QRdY/799PiUiYtp6Krq8Kxs1jL0IQRA1iJrAVhQgiDQ7JTtK7hmzRr7hmxGsl0Nx8fHdcPOeCGvaPqSuCW/Uwt0SVw2uUn0kmT17LPP2rSmXapY5hiLhBzwer50d3evXLlS7zi4ni8ngJxj027w7sc+XDImlzZqNc66qDlYKyJrASBrkbUALAQ6PaCOoZLKn0SjgYEB7SvY1dU1ODgo+Uo7+GmXvwtHc5m0XUtjmOu6tkeiToah5HXdWLJkiSauZcuW2Z+1b6NpC4vnvoY833bbbZlGLQlOppp2IMx54f2PfVjOrqKcm2HzjIrToJV0IJRTJooJWgDIWmQtAAuizE0mm45jO2O7PEso0uRjR1VJHtNXJGJpM5RpToNhMoO+5D3yTtsR0fYqrKTkbZLftFOiVjH1V2VbzIDFIJfLdXV1DQ0NrV69Ws6jZHVjOV/8ZGUtefyPZ89U086ElSh0Az89V9MWraiRtZoPACBrkbUA3MS0aUtnv9D1ssIw1OykLVfFYtG2ONmOgrY9yrZ0SWqS9+tv0/ikfQLtNIOm2UtQexJq05lptqrpM7AY6FmTndVTzpEwDVe5sBG3zhbL9WSa9zi0ySomaAEga5G1cGXqKT3cT5w4sXLlStO8xw/MUdlq5wmMU/qtV7ziFcuWLbNTupvz+/XNVcHkONqfUEKXyXQyDFJ6LuifYVvMgLahYyOzHQhNOhqrECWzvb/3Y58uRckwrYpXT3oOxkFmJeNmyoobKxoDAFmLrIVLeeUrX7l///6DBw+2TAcH3Ni6XZBMcxZprU7qeb7vl8vl7DQYP/jBD+S7zzzzzDyVTY6zatUqyVoSukymt2GW/J0suoW2tGzZMjkFVqxYIWelzo0RpDO8F4y555HHJWsF58KUpqwgHaPVvPVA1gJA1iJr4RI0WcmxPjAwIBtjY2Na92XPYC7YToDaeUlHYUlVr6ura/ny5VLhk0PRzig4PyTU2V6L2QuA/IU6x4btjgi0mWq1qksgaJddk/ahDdKJByV1ve+TX5iq1JM5CV0v9FwTeGnQShbUSuceTGn7FnELAFmLrIVZaQfC0dFRk46B2bZtG/sEc51tdGNqakqDllb1Ojo6dIoLqfPZqTLm4e/RoV/yBwwPD2vcsjcg9A229yChC21GDumVK1f29va+9rWv1fsgcvbV00atZ0Nz1ktmI2y0XCWTYcjx72vWCpK4RdYCQNYia+EK3HHHHXKUawVXspYdsgLMUbbXKp3U8HS+QfH6179evyUVvpYZLObUxMSE/cP0L5HEZfTuvp3jOo7lpAhTfIJoJ29605vsYC1730EuAGci8+7PfKlkTDU0tUo9KFVNFNQrEsEkaPlBNmuFZC0AZC2yFi6uWCweOHDAHu5r1641rC+EuU9c2oikU1OY5nQsNvnIESjBZn7atbQVS5LVsmXLBgcHbb1T5PN5PReyCRBonwtzurJCX1+fHaarcyXljHnVfQ8Vdf3iOA1UYWAiX06F0Hh149ezWSskawEga5G1cHEbN24cHR3dsmXL9u3bJXetW7eOfYK5K1sl3ksNT5cqXr58ealU0mnZbZ89vbluv5xTOhmAaU4NL3/S+vXr5dku2EUzL9o7a4nBwUGTdtnVU88zZtKYd372i2d9U9MugjXZqpmkd2Eja3mm2exL1gJA1iJr4RJsRyntwbV161YGpWDueJ7X1dWliwh3dnba9Qa0e142X8m35qd9NTuZe29vr3YjfMlLXnLmzBkNfvKX6B9GH0Is1DpN5pERybnY09PV2ZFcoKXk16xV8sMpY54KTTHNXbWqn8StMDJRoOO1grQbYWiiWH81fQgBkLXIWrj0sR5FkfYhOXz4sF0TFrhmGqIat8kzcw/qdH+6iJa8rhOpP4d9VqV+mW25qlarfX19+udpp0Hbt8q2gAELLmi5oUnXIzZeGKTzXATVatmYoMNxJGjNTI3La3EU+PXkXKj4/pOlshzuk6GZdpNbEXIq1F0vyVZxlJn53U8fOv87PWwBkLXIWrj4sa7CMCRo4cYeWpJkZEOi18tf/vJbbrklOxBf3QwLDEjkkz9DQ5cEPx3EIvS7bspkJvYAFlbWkjBUi00p8GVjOp/zvJoGrZGVw11yPsZB3atEoWSxqFAoeMb83ec/fzaMSpK70iFbbpCezdqElUxIqHHLD9MuhWGSuMhaAMhaZC3MJggCnZbA9qTS1WbZM7geGkuKKdn4zne+oymrt7dX36A99PS7z+Hy2dl5EfXLUqm0ZMmSrq6u7KgtYEFnrUo9LNbqQZKaJBeZarVcmJlaMtAjcUseft0NgrpNZZK13vTgg09Xqsk8hGlIq0fNX9SatXyyFgCyFlkLl6E9uHSY1unTp9khuFH0oHrjG9+oQaunp0cjjbZlaSe953x8oP5Jmvrs39PZ2dnd3d3X16cvuq7L5JxYuFlLJ7CQeFQoJcf55OS4tmv1dDtLBnuTDoQmeurZZ+Q9XhyXwuiz3/zmdNqoVQzjamR0nGKtUk2HfJG1AJC1yFq4Sjr63zZnMb01rl8+n5dM5TRJdDHNJqxsU6ocbIVC4bn6I/Vv0Bwl25Kp5O+RuPXlL3956dKlOoeHpi/9m4EFGrcq1Xp8rn4TTU2e6e/rGljSE8XyelSPk+6F1TgqR/GzMzNnXbdkkrkxSmGygLH+oFutzZa1kqWNyVoAyFpkLcxOapl6oEtFU0f/69JG7BlcvyVLlmjQ6u/vlwyj07ubTIfV57D3YJakqez9BT0jbErUF+WP5wPFwg1bbi2Q5ygI5Tk3PdnX29nVmdJ7ORYAAIAASURBVPQgrAdemM4weHJq0pXjPI7vff+jOlKrHCf9CZOxXm69kdg0a5nITo9B1gJA1iJr4TJaJidgegxcv2q1aucbHB4eloilh5nGeEn1GrpunjknbJQ6deqUbhSLxeXLl3d3d3/3u9/VF2+SZAhcraAeS0zKT+Q0LwVubeWKZR3JMnd9QeRPlwr1NGVV04j19kceKUq+Sgdr1S+TtSS6BWQtAGQtshYuw87AprXh+VlGFm1MIsrQ0JAEraVLl7b0vrPJyo7Ueg7nYtH/abvMl2nOU68bOkOGNm3lcjk+VizUOk09agyqSqa/8CUvadB6zZ+/OmzOh1FOg9ZTxfKX//lf3fSVStjoLBimSSr0g+YSXVE6xKuRtQhaAMhaZC0Ac8V2OrXNPhJOdKSTphTNWnbyiQVB/0Vf+MIXJDTKv+Jzn/uc/DM1mNl24JutJyRwMX61nuYjU8rPpAOukqzV29utQ6/qaROWHMQlY36cL+VNo+tg0JxU49y9t/Oy1rkHexgAWYusBeDGa5lGZXp6WpKJHaYlZWi2yXRh0biVHbWVHdYl27VajRW3sEAqNUYbtUKv7pZLf/5nr5as1dfXk81aOhnGh77ytZk0a9mUFZO1AJC1yFoAnhMaoiqVimaqgYEBnXJw2bJl2SSmhenCSiZTU1Py/O1vf1v+RUNDQ1/60pfkH6tTJtrBXZK4WIwOC0BkysVKuVjSpNTX263tzp6fLLpVS1PWTJw8/9V9DxXT9HVexAIAshZZC8Bzwg7Huu2229atW9fb2yt1OIlezzzzjI1b+p6FOL9lLpdbunRpdkJCk/Ye1H9XmOIYwM0etexixLEpzOTS2wdLnA7HC5MZCCvxuT6Ej3z2y5W09yAAkLXIWgCe+yiiOUoHaA0MDPT09GQ7FupCAmahdSPUP1vHmGnQkn+gSZvm7OJg2vBlntO5PYArUat58bmTMUqCluPEzQWO5cx0026EZ9z42VpUMbp4cdR8AABZi6wF4LkrLmu1mjb+dHd3a2dCc/6CVLK9EBfItv+W5cuX26atljUS5J+/EEejYXGdpHIOlqvyXCyXnv/82+VI7urprro1zVqVsJG1vvqv/y4n7ZSnvQebWYuuhADIWmQtAM8J7SiYbfkxaeOPDSS+78u2FqYLaNp0iU/abCV/f6VS6ezs7Ovrk3+jtnTZhiwWo8OCCFqSqSbzuTRZxQOD/T193YPDQ36YHMf12JT8SLJWzjfv/ejnJGvl9A5DOl1hS+tWfOFsGQBA1iJrAZg7kkOyw5laJsCwrVvT09ML7p9m27V0aeb+/v7h4WGNW7o4mE6VYZfkAm7OrFXyPJ3AvVAqdnd3Oh2NEzZZJCudG0MO9EJk3vXoR2ci02ilJWsBIGuRtQDMD0lQmi6yy2RJEdnd3d3T02ODlvYnbIN/bxiGds4P2a5UKgMDA5Iq7agt7RKZ7ScJ3LxZq55MLRimB3O348jDhPWongzi8qIkaOWDZGKMb/z3/0g6E2q3QRIVALIWWQvAfDp9+rRJpxaU3OV5XjLqo6tLnu+++27TnCS9nUiwrKb0X+0ky7/2arDMjtFaWMs0YxFmLQlaT53Ju166hLHj9MkxHPmx79b9ULOWHOL/39PjExXfS6fKIGQBIGuRtQDMH9d1bedAzR7ap06eDx8+rK/YFq02WOFXopT+K/QyoNlycHDQ9pYMgiAMQ0lZC3HODyzCrFX2kw3JWks6neV9nWef+omJgzhNVjrb+/0f/Kib9icMyFoAyFpkLQDzSWeDkDLRBq3u7u7sfBjam07HL7XHklM6WKtcLmfTlE5t/81vfjP7b2TOd9zkWUuCVhK3Kl5XR+fqoYFexzGxX6sWwzSGzfjJYK23vOdhNx24RdYCQNYiawGYb5IudMiWhI0VK1bI8+DgoHw5mbJva48hTJKv5N+rHQXlWZvsZEMT5sDAgGlOQnj27FmODdzkWcsN0uWJY7N0yaAcwW9/41+bOIhC3w1iu7jWN/7t3920mStfrpG1AJC1yFoA5o8dnvSqV73KadJAYgtN+bLN5kDXBivbriVfvvKVr1y1apWdC0QxXgs3edZKJhsMTN2Luhxn1dLB0C1Xi3k5tHWwljx+PF6YrplymDRz1cKQrAWArEXWAjCvJFbVarVly5bpFBH2xXK5bBOXjnGyU6W3QdCysxGOj483yrIm/fe2xz8W7Z21ipVATtGe7gHJWn2dSQdCEwe+78shXoqSRq1HP/lEOTKVyOjU8GQtAGQtshaA+aPtWpoxBgYGJF9lM4bGEvtKG8QPqYZGUaT/av3XaZ6U13t6erq6uvr7+222bIO5QNDeWUuO3YobDw8v60onfA/dign8uDlYS7LWm+57WDsQThcrEUkLAFmLrAVg7so+CUvanmPHaBWLRYlYOuP5n/3Zn8kr+Xx+Ee4fHbjV19eXnY2wPeYCQRtnrWKlHif3SjoH+3r/8s/+NPSq6TrGphI0Bms98Y3vTruRrsHl+dw7AEDWImsBmAPaJKUtOXb94lwuZzvOLVmyxKTNO2axzr8n/2rZDytXrmwZtQXczHErly93d/Z0O069UjSRL1GrnnYalKBVNOZMOazJRj0kawEga5G1AMyhbCtNrVaTL1//+tcPDQ1JtFi6dGk+n5c8pp3rdIb3xaZUKq1evVqTp/YebI+pF9HOB23ZdZzuro7O/u4uEwfyKBaTCd+rscn55tv/frKSzpCRq3rJRBoBWQsAWYusBWAOVKvVIEgGzWuK0Cn4dFEpCRh2tWJ5w+LsOKchU9KmdqekaQsLo05jzED/sJzGPR1OrZj3a0kP4SDtPTjpmXc99qmZdELCcpB0LIxj+sQCIGuRtQDMJe1MOD09PTw83NnZuWLFipYBWpVKZdHGUUlcPT09doWxxTl0DQsrbDlOp2Ste976lrRdK8lU+bJbkxPZmPd85HMnZzydGyOMI2MidhgAshZZC8CNNzU1VavVoijS2cw3bNggiaK7u1s7E5p0EJe27dhVpxaVU6dOmbTToOyQ5cuX066FBVKnSbLW8qFhSVnF6Ql5rlTdetquJVnru09N5NJJMrzkVkI5bfEibgEga5G1AMwZiVJ2PgxdTUtbuuR12dDOhIt5rnMJnHb/lMtlDhjc1Bdgp3PFilVdjuNVyias6ySEtciUI/OvPz5TSBOXZq10sBZZCwBZi6wFYA7o1IIzMzN33XWXzjrY0dGRy+U0XcQpLRwldC3CeQjtSmKyB2T/dHVJ9dXRWRmBm1ZPT0+HnM79A2ESpZIcVfj/2TsTOMnq6t5Xz/Q2+8Kw+DASIyE+8zEP/AiaZ9yixhey6zOiMa4YRYI+EDGKLKIsRgGVRTZZhn3fREER1KAgxEQFlXXW3mq/+/Jf3/n/z61/36ruGaZn6WZ6zu9zKW7XVHdV3aXqfO8553cik9cC1rrkhjt9+FHqxDIW/KvgKbEWiUQi1iLWIpFIOyRgJ4QEtNHDVJUjKBwhhTVye2a54NZxS9vGLRPC9vU9+OCDujTB2T2A5m6RZl/ubHV1vxDKAGj12dNZamN8EWRZbrNYcMheefPtrVxmFrRyzgxlkTcGiUQi1iLWIpFIOw4MiAfO5cKtuOq4HnggYUZr8nOtUkE3fN/38V97NimJNMuxCxyKeA0FD1Q8eRfCgbpk0UknnQQUxSxlwdJmwhfqp7/8NVYNimJunvXGULQtSSQSsRaxFolE2l45owt3D4RZEJbBPdigBbdf/epXPc8j0Jr63QBbKY6Na/Yb3vAG2FADAwMIWuViQnwAiTTLgoMQa33hHMdLAMccc8xAX2XR8GCr1WJKRrnAjBYs//m7p8b80LGWPbyJtUgkErEWsRaJRNphYe4F8QDWsfRo6dKlCxYsAH7Yd999kR/gfupHKgsgCr8bkKaGh4dx/hgyGPpk4Baj2kvSnMQuujuzOmC7tfrsIQqsxW1Sq5EYk/dLr7vBFyrrsJY5aIV9CLEWiUQi1iLWIpFI2y03jBhWPM9DThgZGUG/hyVLliBIUFKrR+VMIDa84RaDaHbjxo2IWLjRYPMSo5JmWW7aeJqmeIKvX79+8eLFCyuVvVavNHdarAK4GvdDYK0LrlwLt5FQ3GCY+UXBc2ItEolErEWsRSKRdlTcCtfRHmP16tXLli1zJubAFViPRB4PU78boihC7oJ1LLnEB0gpgcFcyEsizf55rTutmDhl25S59lVOPeUkODiNMUbK0o4xxk9+8d+F1bvWcc7gATbvRTWEJBKJWItYi0Qi7YBc2gqoAGcTOz+M448/Hh+DLOEyYKTydsMfn3vuObfp1qxZMzY25h7pyjJJpFlmLThh8dgLwxB+hINz9fJlmzdt0LZQcKLpoTfGmB+2mQDWwn6t1PyeoH4tEolErEWsRSKRdoIajQauQFg2ODiIo6LQ5gHDNQKGaQXwiRSKjAqwCttt1apVzh8fUwoEqKTZFx57GLtgCSu2Xy40B6dE1sqkAS2fy+/95CF4dD3JEtXtjaEEsRaJRCLWItYi7TShV5XuONFhmFg2tnaCf1UduTuxaIo24wtTrkXepWKwPhD3YK1WQ5TCKrj+/n7aYlsXbkZTiyUEEtf4+Pg73vGO4eFhZC3YsG7QFlkRkmZZ+FEMByGuwBGISVdgrXptwtQDA48JjXWD515yGboR5iVvDMprkUikXaEtXbeFjyn4pwsvvBB/pMuUxFrz+Rxw5U8YncO3sktu6JK9lRO2/tNZsVsIqCBJEmfVALsb7sG8FuxZCMXw4jelsLZF7lxw2zMIAth6S5Ysed3rXtfTAkcizbIgcMHrKcVXr01Wn/2VszCvxbXKlY6UjrW++rY7iLVIJNIsfCjBNyMEGOUu8Z6o8hvf+AZFIMRa81aukEzb+pOyzZrpk7YnA6zAlzcwGJBVlmXEV7uX4KOtTAVwi8aD2rbOL168uDy2mLQt2xOTBrBVm80m3ol5LbcZ4TwiC0fS7CtJElf9iwcnut1kUehYC30IPSY2NdvEWiQSaTblgArHyTgfqXPOOYe+NIm15vlx/+IXv3jp0qVve9vb4FbbQhS8CDH18eVLEZj7gge7oinSC1BCYMu7xqtK7s44joeGhiAOe8lLXgK3PaWhpGmFdbblAx5+hC355je/GTYmgKsu2W23223aYqTZVPksDsPwtNNOW7BgwZIlS7SSeZaYUgW0wdD6kV8/kXYmGhNrkUikXfq9mec5xoo91+vd+vnnn48xZ3k2IIlYa/6w1ktf+lI83OFL+k/+5E+OO+44OB/cFzasu6yI6z+huHx3kYOrer2O67ATXfbSeQ9OBWnStHKU1XMFDgdtlTcmlUOQZl/uqgr+uGjRouKYFMbJHS+6hBlvptk3L7s8IdYikUizGG1OXceoEr5AL7zwQoxMytVVJGKt+aMDDjgAq03gEN9///3/+I//2J0G2OhfHsmaJAk8rJwFLpeokV5oyrKsh6Cwl8PzvJUrVw4NDa1atarVauEOpQz+tguv0sHp4AoyIaLda6+99t57b9/3IaYNgoDYlTTLQqMjPDh1p1kLznTFIHwxXZoAVLkyNYRrb7nNF4pYi0QizQJlwSdSOVDEyin3FQk/Xn755TSXklhr3sr5W7Tb7S9+8YuHHnqou7RQvrowLVCh4wJdv3/hyxWzIU1BQLZ06dKeNq1Go0FssC2a1vQCNuxvf/tb3KRuM9L1OdLsy8U0cKD29/cPDg7edtttMge8Mt0RmeDYr7W+WqcaQhKJNAtyl3HTNA3D0AWN8IlkzFGFGB8fv/baa7HPmRwBiLXmrWq1GlDWK1/5yn322Ud3DNYcXI2Ojt54443nnnvuxRdf/I1vfOO888775je/+bWvfQ3W77jjjs2bN9MGfMEKLxRh7gV3KIAWtssPDAwsXrzYJbXIuH9bBF8MLgeIOQQ0jMGtNzQ0hGPKyq1xJNJsghYen41G48wzz0R/UXvg6jiMAKBSKROlR30fECu00OVAS5VZi0QikXaS8EOp2Wzeeeed55xzzgUXXPDtb3/7/PPPh5WLLroIbi+55JKvf/3rmOmia77EWvP2HMAL8BA1vu51r/vDP/xD2izzD7fcCkByubPIueppmmux4x9zVkCwGzdupO1JmhPhhRU49tAbE3DLxC5KJzHLlcliRVqfd/XatlaJBa3OWI/SQiKRSLteNMuYWGsPYq3DDjssTVPnAHPIIYfQZplnarVabvBuMdt04UJdGmONe5+uJ223wjCE6HbRokV9fX2Dg4PaVnBReS1prs53XXK+4dzUBCqbv0osa5279kpPy9jeo3pAiz4DSCQSsRaxFmnnHutr1qxx8SKA1ste9jLycJ83YoyhGQasAEgPWQ0PD2M0Njo6qjtGQAQGOy4AWuTYV73qVbQ9SXP4qQ4n9YoVK+BovPXWW+GeNGYQzsRMNrIMPg6uvPvOhshbkk2yliTWIpFIxFrEWqRdI8/zli9f/trXvvaAAw549atfTTHivPw4830fjfLg9i1veYsuTTTWnWataV0fSNvItNo2/sLmfdGLXrRkyRI6j0hzcrLjufy5z33O1Qm3Wh7mtXLrhLE5DDaEfmQSXGoa1iKRSCRiLWIt0k4UfjGjo6A76Kmtfz4xAOzTdrv9V3/1V9i8sWrVqnq9Xq1Wy8cAZjJpv++IcHDCG9/4RhfjUr8Waa7CF/Ro2W+//ay/qIGoaq0NrNXm/LJbbvZtJWEtiYoTvpu1CLhIJBKxFrEWaWfKRYQ03GBeSkp5xhlnYG1b2eQd2ACbuBASSDso2Jg48H716tWwnZcsWUK1uKS5OuXhCFy+fDncFoMHlI4TjnmtMy68AA7T2JoQCmItEolErEWsRdrVqtfr5QvwLsdFmgfCskDMtOy99944Ns3zvEajgQ9wU9sJtnd8O+PGxPxhX18fbRbS7AM/zqvBU37VqlXwY6vlMa5TZuAq1Pqau+4ajaO24imyliqxFhUSkkgkYi1iLdKuEHw3Y5wNMbfzpiPNk3OvUhkaGoLoP8syhGpsJcKsCwRneCfN19pBuQatH/3oRwMDA0uXLsU0F4k0J2f94sWLm80mnt1cGKxqJRmc89U0DTpTjCebtUqsJQi3SCQSsRaxFolEmvqZFccxhlZAzhjoY90ggNZnP/tZ3SkXpPzVrhMgK2MMawh1Z3Id7hqEW5dRJJF2ujjn8Alw+umnA+3DQYgXUMIwZtzYu8MP377xxtji1pZYSxBrkUgkYi1iLRKJNFW1Wg1XXJvQkUceuXTpUgi5cOITFgrC5xpZ5O10uRQWbuTFixfDZh8eHtY2aYy2+yja+KRdKjTDROHIB0xVeXEOnwvnXXFFZCsJgbtYcUQSa5FIJGItYi0SibRVuQjegdadd94JiAXx1qJFizjnOFBLU1JrF6vdbsM3B2z2ZcuWwa3b7OUSTcIt0q6LXUZHR/fff3/MZhd32pnFVT+CQ/CWe+/1lEJjDD4da3E34JhEIpGItYi1SCRSWRjZQ7gfBAGWsQ0MDJQ/1OB+CvR3kZrNJm7k4iNvcrpRC/l2fHwc/wktNEiknS48u+HA6+vre+SRR+DYg3uQtWJbNDjq+3Enr1UYY0i7ZllLEWuRSCRiLWItEok0rdAKD27hk+v3fu/3sFMLG7Qw2cUYwxXybNh1Ao6C7Y91XMC6cOvIClNbZARP2qUfAg8//LDjfFSaMWzW+uWzz8YWulqM5T2sJSZZKyfWIpFIxFrEWiQSaarQiQGbhfr6+t761rc6AHA1bJxzSm3tCvm+jxCL9iQY72JeEf4J3UpgR8D2J9wi7cLvWutACKc/HpBwNCrbmhUIdflNNyFreULAJ4V0oMUnWSsn1iKRSMRaxFokEqlHiFLVahUiLfTDWLJkCQRbY2NjWMAGIX4YhogBFOvvdKElBsjzPFx56KGHELfQGIPqBkmzA/xDQ0M9ea2Jaj1ROpT6xnvuaTCGNYScWItEIhFrEWuRSKRtFEIUGj339/fDrZtPDRhQzNjhnIL+XSdn5g7xLqLXoNXrX/96nFkHiAvcJaUk1iXtIsE5jmntL3/5y/bH3PNacPLHyqSzntg81pbGGCNSuvh0cAWEkliLRCIRaxFrkUgkm7zSJS87l1HBijVs08LPL7IcnE1h/gqwNs9zZ1EAWrNmDbMCyqU9QtopEowDD/Gc2eSUAy35gx/eX+mrLF48rJThp8RMLeawFml97X0PAfHjFGMDVJwZwFK8WOCIVcUgY/J8J5FIxFrEWiTSHiqsFewJ2Vut1imnnLJy5cq+vj4ELUxhkWZTaZo69xHQpk2bVq9evWDBAhxz5B7WaDTKP5JI2xOjWNaCxUCXufKibVuWXrZiecV8BlTyPPX9NtoKtuMYKOsb19/TtKyFbu82142gxextwVqIWyQSiUSsRaxFIu2JcpWBsILJE7QfBNByTRro0OAah0izA1q4ghsff6zVatg+p61tCYh2CmnnSAJmScda2l5/gc8EAC3ELdUZm8VYlgqDWFfc8xPPdmrFqjNZi1iLRCIRaxFrkUikqbiFfOXcBZGyALfwAW5+LmnW5Gwe3UrZjRCnyqJLJO47EmmHUIsLTG1J3kVGJqnVV1m11+ooidM8SbK40agBWW2O1ONjUWBNCOEAjXL8LTnJWmbdRj9UQEgikYi1iLVIpD1WExMTuAKBOzYIVTq6/fbbnZ84Bvou7ifNgrApy40zrlarvu8PDAysXr0abtGSBB5ALVukHZfo1BDynKVxkiXmrIcPBDQgve6G612GCu0ubvn+f/i2aytU5semb4BfyU6zlpLEWiQSiViLWItEIhVqtVrO6Q6iq7322qv8r2iegVOMSbP2neHKOwGxXL+c53kur+XyjYRbpB0+4LQSEhZjj2HX4b7Pfe5zcLDBZwJSVpxnrSiCA3HCC0/7+sXAWi2u28ywlhcZ8hc8n2QtVbAWVhKSSCQSsRaxFom0Jwor0LDtB40H4fYzn/kMkBXSV9kVg7qDZk1SSsdaTti7hVnHxYsX4zcK7CaaJU3aGYecDv2oyEMpnWfJoHEhNTakjEscmoWWg5nWN373gWpmurZCYe0y7F/YEmsRbJFIJGItYi0Sac8VJqxcBH/qqae6f8ICQpzgRAH9LLNWeQUzV9IKy7rQtgR4jCwiSTsuzs1hpkRRRhiFPhxuffiZsHQJF6YBKxIKKKudc/hQ8KRJaqWWu1phIopAR3RatqRjNgItEolErEWsRSLNfzlbcJebgs8jCNMhdocVBK3h4WHdGetEemEK4OrBBx+EPQX7CykL9iyaZJBIOyJjL6iKGcQGlqRA1jrn6+cKaYAqs3ktoKxHfvMMUlZm01x80mmw8Cok0CKRSMRaxFok0h6nNE1dWRq2YGG2BMKpRYsWrVy58uSTT9aUKtkdvkuQjf/yL/+SKIu0c46rArG012oXrKX4wkpl9aoVo6OjWECYYYOW0hdefTNSlgOtElIRa5FIJGItYi0SaU+Vy1llWSalBLiCkH3NmjVwe8IJJ+A/kdHCC1y+7yNrLVu2DHcW7TLSDorbnFaWsQKQpOivVBYav3fzVZty4eci1Xos5IHWF113O1IWgpaYilRqykIikUjEWsRaJNIeAlrYAoR2dosXL8bOH92pM5zWlYH0whFw8vDwMLZsoVsGibSDsnOMgbCMF45ixuJi75XLgLXWrFyBrJXbAkI42nytx2OVdxBrepgi1iKRSMRaxFok0p6pOI7zPMfESF9f3+DgYLvdhvAd+Mp5iJMNxgtWaZriFwnsO2ePQWxM2tEYBRg+L5wDRZZWRzcN9lX6K5XTTj6JMZZJ4zcYaj2R65t++FhgM1rKlgsqs2wDcZFIJBKxFrEWiTSPxTkHyhodHYX1JUuWLFu2DFnLeWagAMPI2P0FriiKYG/C7lu1atXChQsBlWmbkHYQtHJmPATzTMZhhM1aSwYXLASYF6Z1E5u1MKn1tStuDQqT98IJY1uzWyQSiUSsRaxFIs3/M8cKYvSVK1ficC1tywuBxHQno4UfVaQXoBqNBq4AaCEta7KOJO0waykzjzgt7DEa9f5KZWhBpb+vAtwFnwyp0O1MtqX2tL7inv+ocWQt3lkmiYtYi0QiEWsRa5FIsyM5w2U2KAsnk6KcoQIO0dKdQVu6ZBA/lxutN0rbuVus969NExMqXbwM92KmjVJndLszhLvpoIMOwl47Mo0k7Thr5dL0XzGusyTVUq5YPAS4NdBXybNIdaYYhzav9cR4HBUm75Og9fysRSKRSMRaxFok0s5kBjPQ0y3MLllpwXtKj9kueBBCOC5yMbfv+/gjGoIrq+HhYQAtbPK55557XgjhneqNxno2WodwrBu1zIEMmVlUnseBMPNUiwfwXEj7GMEL62q4jWKGvz1RbeJKFKedkE/6QdOGiQyXjMW8a1IQwp55PTKKteQQfYosxfvzHOA0zdOsCDLhjzKVp6zwCrC3MhMsg9/SUQC/Dk+Q21e2TYy9LaEp7GLY71gI+vd///eUhyTt4MmYKB1yac9COHzl0v4Fg5XK4sGFWR7BcexzFmtdz8VjT29sC1NPOHmakEgkErEWsRaJNBesJWfAWhDkzzBaRo5yoBVFUZZlOJ4YGcwBGPoNwu3Q0JCzHMSKwTn0CjdEMS1oFUBlsamEJHEUGChKwzBouQvqWRpzYB5VAE+BSLnmiXGw9toRAI8SWjAJGweft16v2r9o/oLFtLhYsR0pk1NZzbNLQ1nC3gIswaZWAkALzQDcfo6ixGCVfUtBEBm4suvw7pLIJAzhOSbf4A6zFuxiV+3pEpV0vpF28GTEYVlBGOP1AvikeNGqFf92/LFMpMBamNRqS3X1bXf5TMM5QHBPIpGItYi1SKQXBnTtsupBiLax/M8ZfwN6YQvWxMQEZrcQtLC3B1Z0dypsjuM7Va6vk6X2D15kgbprkMI4StNidK/f9gTPkYhY4Mk4NLiURDqNAa3MgivSLnmCIBf7niG0LIFNFyWxMD0qYZTlvDSYVbmXZ16C5T3AWi6UkMYO28JYzHMvMk0sooNn5dvy/UHKEibF9AfD1jbM1r9LTGItz11FKJ1mpB08F/3cXG5gXDrWGoTjSnGTUbasBZ8mgPiXXndTBmdiRolUEolErEWsRSK9MFBiG5ftEKa2dCe75RzbEb2Gh4chCl+0aFF/fz+G40mS1Go1xC2AjTkmrml6sXh3t32JxJSrzuuqPIzgLZvUE9M801movarmoVm8CR03tQh1WNOZp/y6FpnOU4gdI69tnNYgXowy0XkyoCM3mLWLtextGif4AgC4cml82VppkuG4IWVuE2A2xn0Oz6Gr8HfhAVluQlKpMpsuCwVXO4m1UMjY8I2CZYSu445E2r5zET5KvDwzzu9JCpS1YnBo+fBwnkVCy0yr2IIWLP/5u6cye74Qa5FIJGItYi0SaY7DF7HNy3YELujMzjmv1+t4DxYExnGMTVmDgxAyVVasWAG3QGXoX+d6uvDDaA5rCLuqB0tFfZ1bOZnxspyD/VBSalMMWBQNQhzI81ZDp4GOmpr7Om/d8M3Tj3rn2771xU9/48RjPv6uvzjnxGN0MGb+iUdpbcQkvhQgkWHULDfpJiZN7SErvxzVxXimMjCM0R4gk7oRR0hZE0kS2ugTboNOJIrX/kfDCO+3LS6m0SV1pYk7QyMjI464MF15xhln0BlH2pFzMZWySFYpvWjhAHx29FcqLE9NnlarQInIglbaKbUl1iKRSMRaxFok0guItfiUZQdZC0NtbN2BFfRqdx6Dy5cvX7Zs2ZIlS1xcjsIKQ8bY3LuET1KWRL5yi+h2BXQIVK8F2PHUrnmGkIRsbtyo0+j04485+/Ofjjc+qaNx3d6oWU0nY9H6/9K8psMR7W0C1vrO1ZeeffJntchUFBqji5SjhYbvZYCfarpUo+rsRNxfARNITbA8NT4O6zXG7vrJT8694orvPfwwgO8Gz1t7112X3HTTGRdeOJFlm8PQsyOJxpIk2oG9PK1MNaP9OlmzZg2VEZJ2/FxkNrUFatWM4TssZ335S/ABg5O14JhvCXHVLbeZzK2XEGuRSCRiLWItEukFEcG4Zdpc1o4UEAJfQcCNiAXghO4XWDSIbTzVahXNwYGs4MHw6eNGMznbOtfoNReShSlIJ5dlKcuAFteyZ1vhFkwaEUJq3goAtFirdc6pp/zyxw9oDveHbGKdzV+1tb9Z86ZZmuvNj3BnVNMZrETN9c+e++XT4mpVJyn8nbQV4hOIWExyX6lkEZ4tZBrrAKsR/I5JVa1vtm79wf3nXbX2l+vWxyYGldU0w7xWLctHjL2AyXSNhtFZF34LE1xpqUBxxytIcSC1trlN2NHDw8MrVqyg0420gxeGUs7Gx8fHNm0G0DrgRft7DfPpwc31AlND2JbyzvsfgJUol4y8MUgkErEWsRaJNNfq6sxRuqvjSE33mBkJqwF//vOfQ7SNHVnLly/HdcdUU4Eqz3MhxNwXEE66Dk6ylgUteFfSLl1Eav4DhGFcB6G55fzb557zxU8fp4FxeKaDhhaJFpGOGjqsmxUgq7ipWVDcCbeJZx7DUlaf+OVPfnz6Zz4jmy3zp/xAR7GO0gKGOriFRYNoTugKAmH5xTPP/vAXv9jY9lyOK7H3J3Yd7wyk9rjEla9eeFFsurkE3zJpzyhsxYQk7H1nNdlnRecbaUdYqxX4eDwttEmtfmOMoZPMeNE0sgQP/vW1BhYQBiH1B5JIJGItYi3S3AkxII5jjObRwsFF9rjyAgj3NfpDuHKsncwSouTtLpmSJjjmzpFOCTMwiiUs9nUewmOkME4VWZbBq3LW7W5DoakgVgziPZi8QspCDQ8Pu/h7NwDRwt69zFqGssI85sY60PzIRF7sGnhwFpklaH35U5/4xsn/lo1tgq1nQAu2M/oNds3m4l2e++ifgRYaLDW/mIY6j0752EfM32xWjVGh5+mc6SRH1oLt2E4EgtamthneGmj9jSvW1jLelirtmBZOXRJerOSqWFm3efw79/0wk3rDaLVcQdrwQjE9a23VOaP0XYK32J63YMEC5wWvO0WGu8/xQJpj1ory2M5MKFhrzcpV7aZn7hcKLyKcc9llqR3DlUtKapFIJGItYi3SnKonwnM/CisXC5bvnH3hy5j2xc9IqiNpVfxxYadpyaSDW9xltzg3j8mzpDN3C1Ah1iyyQ3Lz8isZGxtzrxNu4V/f9KY3QVQ9MDDgBitBhL18+fLXv/71cw6u28landSWMHWDmNGSYRbFLLFjrAyMNSZGrYugr7Pm5z/wjzrzzToLdWtcp5EhKCSr6Wo1S6aCctKxsD2h45b5I82xM47+MPxZWLHkxnUKT8TDII1SATtjAsjP9lzB8rWLr4jNPTHaD26pIFDYSBReepZPdp1N1L2rb7gN/uBoI4B3GzMVZuasyMSMWQuOBGy6w90Nt4jca9asgSMEvSWBzDH99YIw9yftHrglheLV8Ym9VqxcPrz4s58+Hu5qBWFm/F3gVNGX33ILXmIwx23OaYuRSCRiLWIt0twIgQHCwcSqHPPFcVzOICGfzOHrdM+OjLRzWUImnopbcCvSULFUK4EkIItWB4CnRGYRPMDAg/EGl+12G0fuwoZys3fvu+8+CKb7+voArlasWIGIhVOJ99tvvzRN0YcQfhdusUFrt2Et1Zmj1WnQsvbrme2d4knsGxgzA7Iy05EVTZx97Ed0OKa9zTptWW/3yDCYLEBLlKr+cMnLNu6IW4LrODC/CKhW3ajzpk6q//7JD8pNvzO468Of5cyWXHJVWAIAX/3ov3/77ZvuwPpAjxkGy4TcuoW/EjpPGcsmHxYz/civnvzxo78yCNeOzdzYlOeyh7W2taYUzzKsDv3Vr37l7DHgMHbI3VNKSiI9H2vBQSUxr+XV25jdzewIYzjOHt+8GY7/dprjBSPaYiQSiViLWIs0ZwJOcEyFCRm3rjtWDXiPmDuVXzCO90WrCT5DTZ80w7zWZFKrcPpKrW84txeGzWZxqS2Vwa8Alz722GMYN69evRpX9t57b6wPdLWCe+21F8bTrVYLNyluTywh210iO1vjJxGTdFdKSoZxEEcebhkZNHQeJKPPnfWZj2nWMI4XrGXsLkTkj23olA4Wv+5AK53KWsWTmtQWb01YOw1fszawls5ql53xOVnbrGPPJL4AkrIcfmtT3WsLfdq538ICwnrCEvsH/Tjp/LXJGVy9t0jVTIksl5mAd5nw4rVdfr1JcLUTkQpzSIgZshacXC4nrG1+eOPGjXB4YDUpgRZp+6595Dzxg+ZpXzwFWOt/rNlPW/uLhh9hXne970f2tGpnKbEWiUQi1iLWIs2lHFlB6A/wgFTjKMs9DPMwcyh4eVEUoUFfGbp2VuzC04glIUTGjEvesVjwpIlX7vzBQ294+99W+gYqFWMeuNAufdZCEO0EFy9ejPkrl8WCf/rTP/1TfIUu64WbGkcYw7aF8Hq3KSOc4gvhWCvJYmmurwPzhKI9bvJXzD/nC8fpoAoUptBRMPdzYLDu4sweb/1ea41JwDPtYMpgVQx/2cw7FmFrw+8u/+qXeW2TZlF7fATzbLCnrrrtOy2uxxNp7DHE5Fghr9UuPOun3krFWSa5KN8D/49FkSV79DfPwJ8Kch1mgs+ctdyhi5WEqDVr1ixduhRTW3C/S9juPnlO0hyzFhfGLBM+hRYPDvRXFsABmKSG6dvMJLUuu+22OudYQ2iGccUJbTISiUSsRaxFmjO5tBUe8c4Hz5XqAR4cfvjhb3jDG1772te+YY4EL+Btb3vbn//5n7/pTW8CjHn5y1+OY3+3Q30lLejIOhYsMEC1cLgytKqyaE1lcK9K/2qzLFxZWbi8smjV4LK9kbiwJhAbsfDXYR2ICxNu2qYppk7EgpgbccuZv+8+n4ilFFCJtVSBuzxsjGkZ6RSwqnXse/5Gh+NaJc3WhM0OMmeq0WzWt0gm0xirFxijpMl4ZSmwCktiT0KUqTLZGDn+w0f86qH7Yd1rNzMh7/7hg+sabaCjRq4xyhyrNTt/TXZzUddik6O5UmLyGS1sN5LCrvDKG+/AtNtorSW6xhxvaw2hu0CA55o7DjHlBa+gnFsmkZ6XteCMaLWrCyuVwQV9wFpe3VemYpClNq+19jvfqbE86czgInMMEolErEWsRZozoQHawQcfDPTyspe97KCDDsKqJ8zGuNTWkiVLnH/anMiRElANvJhykd6OCImr0rdwaOnKyuDSyoIllYVLK/17VQb3rgzsO7nAj4v3qSxYDg8YWLr6f7/xLcyW5QBTwdaDbQUxtGPUdevW6U5uELYh/KurzMQ7sWULtGHDht2GtUrDrFwRYKfYj/OgqUU48cRj5372aJ3VgLUYTzxhjAHXTYw34iiSrJO5KqYhdxrAWHErrZ2hLDkTdhgmylLTiKIU9ne1sjw3foah5m1v89P/duxR8NeuuvaaMc9Hq+uqNSSse1HxsrkQadQzf3nqwkSa84TLDJZEMExs1mOF9Y3fvHTtSN13+bcu1uok67YkPDDgUEGywnMKHf91KWMM9/e4rZBIW2It60PDkLXwMIxilttLA0+O19Z5nqmkVSqBUwuON2ItEolErEWsRZpDvfSlL0VnPAj1DjnkkP322093yvOwxwnOBIQcxK05ETx7mbiQ+vr7+/u2V4hwC1H9gyZh1TdUGVi6aJ8DXvq//vehb3/X24742F9/4JP/92MnvP7vP3D4+z5xyQ13VxMTdodMp8oE/bCCnTxmpk1owmS839XF+QnDlawIz6XxM7SRUZakU3Fm2glOzz9OV+3669aqa5KVKDtYCKmTyLgCRq2Tjvqw9sZMUkuGuTaFfIBbyCqh4KmUHTxzrFU47HdbwLMybgnBcHZW2gEtHDQssyiujWiVPffkE1decSmXzPj0d7Y8mlhk6AhflBLCs3M15TbNE7cu4ai36860I5Idyw2lb/7OfbiXS5WEHW7UfEsJLhxUgF8nhrXszvJaPpabIm4lSYKURZ7vpG1krSBsnXnWlwC04DDKggQLYDOb1Lr45pt9+FzqjORmWa4YJ9wikUjEWsRapDnTi170It253P7P//zPBx54IF6Ax24i7DVybubOutr9+vwwqlZTmohyuyRS226kyXadsfHqfT/62V8c8S9vf///O+T/vPcfjznlJ7+baGk9zrSnzTKa6rFM+9ahoSlN9ANbMIli03eUJwVFmD8m8zSzHzTmqb0ozYXtgOKTJKOmvB4+jYHErsctNQlaEcsimZn5xCy21hSZrjV0nJ70wQ9DWKelsZAAJt3SLGAXLE4dHl2aXtWBMc14FiLJ5DxLOXObIuQKN9RVV6/97AnHhcY/IzSu/TIRcROAjaewqXW94WXc/WV8om25ndzsAZPIXZua/lW33BEqnXR2hJ0nK8PYc0WSNkUmS8sUYu68477KAsSt3Ep3ph6TSNsiP/QWLRpCE0I4WFVqTs7csta3br15RDIf53QnsTneBM3YIpFIc3dxqNM0gV92eMuskLWiKLrssst2zfRUYi3SC0N4NR0rBv/oj/7o4IMPhiPe0RSyFjo6uDtdvRyuOMfz+bpx4NZ1tYGqXgJkVdN6BEJwqeta3/nouiM+9cWD3/6e/3vU51v2ojIE4BNcbwgMdDVyE+q06xNayVatavzim60OqYogjMuTprzQpsFUF2vlpWUOWMvij98Oaq1mbjzdmZkiHLU0T3Tg6zg588iP6/GqSYOKdCINIvsiZ4RyvPet8c705AxJJssSZRJW5l4ALfRbu+qm29dvhp3AT/3csVqEyhvVvKWFr3kc1MbRBRtoOZl5qGnyuVK4lBpmt0bD5NxvXwkrG+pN7OCKmEmLxZmPbWmqF7e6WatEl29/2/9xeS344qnVavRBRJrJhSFZ6avss3qvwUqFtVM4QJW9KlTn/J7Hfr6RJaPCpM7R6chM/aYAhkQizZ2wWt7zPIwne67RB0Fw3XXX4ZyhsnMviVhr/shdUL/jjjte8YpXuCsQ1WoVTw84DV796lcfdNBBBx544P777w+P+Z9WsP6a17xmYmJiPoc19ioLVoK5Ei8T7msDUU3YStys+CbKMbfVXANF/enb33H4ER+5/JbvYQdRKzNGdmXPPYjKM1FwRZZzZZ/I/n3jR8JdbNSd2uJzldcyB0kBhFEep7nN1QFzRW01NnLWvx6t277Os8QAAwcIaebxjIrh1DRvzbGWWeq1MdgyfhgkzHwM+wC6qR4N2AMP/6f9VOZha+LfTz3B+MvHNetVmNi6RD02Xud60pBwRnCZ56k0k7fgLYt2blzdYAmVvu+hnxncqlUtHMrN1VFbeVjeP8/v/5Gm+dKlSxcvXnzDDTdQASFppqyVsXTZiqUDfQu+dsZZKio+SmKpn2s2IZZpmUU51koLH04SiUSay1Dz0EMPhbgRXdbWrFnzkpe8BKLKffbZ55BDDjn99NN1yUeKRKw1r4Q208AS++6772GHHYbX1yH4c2kcJDG0c0AM45w7A2vMdI2Ojs5j1oJ37c5/eO8GuqT2UhmqydlQ5aWVKlxJbLfPoX/21vd/7JPIZpu9PDZZET8tfO2aHa9xmaRBFLYRLXgaWbvzAhHKxY1d3uh6lkDL2EZzY27R8ppmG2ShQSpYWHLhl07Roae9tma5bXTifhLMlG1Ud7VhqQmKCY5TsmS92cAt8PSmUTSu+PF//TbDB+WxSbLJ6LNHf8AktYC1WCTjAKeB+ZFh2RluIfMCOEuMP6G15fBzHkoDWo2UP/zrJ5C72nmWammTjTNkLZvPxBFbq1atgiOKCghJM9Ixn/rXxUsXLaxUbIlg8SGSKH3hdde1bFIdzgffjjKwM8GphpBEIs2lxsbG3Hqr1eoZcCKEuOqqq8rVQyRirfkmOL5///d//6c//Wl5iBbgVhRF7lo7/IiFglPLBed3whctBMvv0XEX/o9L4fthFKfGGozLNDNeDs2WlzCe5cYNQtjanicn4r/76KcvufUH2MfV4IYWYFO2o5Sbji0T3CszsRfuTuy4ZN6ZHTxpRC6mzWPNCmslpsPM2KMnfsu0aQEr1Ca8dc/WnnnSQJdgKjcNaZwl2+KBvlUMKRtOGKsMrB704wR+rvoRbLTfbBi7YO1Ndes3CHfaScqZGeQV1//t6A9rHqqwafNaElu2tiOpBdtfmuY65rY87EQf9q/WGxut86+4CnGrmZrzwWepKl7z9C7wU61NhFArVqxYtmwZlhHiaUipLdI2anixGTuxasVK06YVmRRunBnsv+iGG8wgbzOBAT5hlBnLzZnBLWItEok0R3IRlJmvUrLbxXgSIk/f9y+77DI9rxtSiLX2aDUajf333x/X0Q8tCIKeNC6eJ3Ecl+8JwxDZbH7XEOpOAWH3RCwgsITFvoZoH8309KR7XhwFPaF2mAksOITlz4/46N986BPwiI1e2mYKaSEy6S6ToZEsFHlgWatszedwS84JawlbMliPTVILmEvV6hDZwYfiZWedabYAj+MEeIYlWShVruCzcntiO8cqvQki+Eu1VhvbtMY9M5b1tvse3NgIAHXG20Gcm24uFrd10tY8gCAza42aGsLEV3mimGmV43ym7GdmW5uUlalj5Gkau46yVmZGGDXS/Pbv3z/mh/BiApu93EbWmvQLsTk3bNmCc1DbSl36OCJtoxYvNkMv+ioVJWQam+s7cANH5mbfdEta0CoGGRuTGLJ9J5FIcxpEOY6CIBPLhbDb3wWWn//85/FqI3AXbTFirXmoV77ylYcccshLXvKSP/iDP3jFK15x6KGHQtgHKAXHPdCXtJqMGqd0LrqRx/P+wgy2bJl+KpYZHJKJzgMZNXnUkomnsxjRKw09F3BHofnU4NYtY0Ni7DSwwue5VvLufzkm1Prp0XH4+PHyzE9jYbiC5zwp+K1Mcc9bn7ZrWUsCUXnMOkCEoW60IbL79Hvep4NQpxGAVo41hTYLxDx/5jVLCCrTvNPc1i7CD17Gm4lpmvr6pVf5wrZOCRNNmuY3lvmtqt0jkYrqnzn6w7ZfK9PCwFLQas4cR6UBLZki8SZxaHY9fFVIMy4WvhkAkh/+9W9jWxcaW9wSxbvoRivVdUdX3505acSqVasWLFiwdOlSV8pLH0ekbfpCtdp3731M7MJUjsnzTSN4QE4kSaxVKq07C1C9IM93Eok0Z4K4sTyv1QlBC0NK8nwn1prPwnyum/ATRRFee8CjHy8/uCsNeGK4C/AQLGKfyTxO+5bd7Sc/OCQXcRsdxu1ivM4Na0FozlIcDAXbpNPeJpkNgNo2NK9aa/hI67GYtYT+xyM/NtI27VsJjsexPWAcA/eu1NYcsxa8PT/zTGFeFOkw/fKRR+mEwaFg3NU1b4gg1gBC9uWZiVvZjNmm5IRRzmgBnzT8CF3Xx4Psypvv9FUx8MoMNBMOcrhmEdq+y6Dx+U8dZaoxgVrztDMfeaavh+ksMPb1nUlfppDSOnNgtxi8hstvvrOWyqAzyGiaZGOJtUTJUtL1s7lhcZo830kzUV+lsnLFslqtpoq0sylwvfzGm1NdDJkwp4yQxdUwwWiLkUikORR6WbsCQvzRCeKlSy65BHmMfAiJtUikUiyuss5Szj7JcrBdjrDL/hnY7QMhez0XEBv99T99sG37uGqySJWYMElxlsT2uUQxBxlWpqRoZmWUsUxVwmRkqDJNnvr+g9pPNBCQsZXmuWkOkbbkThasxWec12Jp0LF3L1gLt14rTGDTNWND8wBagFvwlK28a9RYKS2W2YxWIvz62ad9AegrnBgxyUbAYFmkXtHvEbRVvyPLfrJrqrLu+CXWwxj3YD1Tl1x/K+y+WsZjm9rKpUjzYh6deQfTuZvkneIu3/dXrlw5ODjY19eHSF8u0yWRdMedEg8Md1X4jW984+JFQwv6KnC/OUeSDA/IM795ftoBrUmr0rm4OkMikUjPH1rQLGNiLRJpq7G47A7E5dSCsbJ5oG21MpOXhHXryzvEFdoFOOPt/3Tk4R86qmqLDMesQbybuRRFkZlKsQV791lgLfsGAWPC5oante+ddfSnbFIr1txOGdYytsska21PDSHyas54kucpl4Ipk7YqJkprPe7Fjzz+FFCWx4vUH5/GutBWXbJI50F13ZOaBZpHOmwYAFOy5yO+pzh2GtbqWoqANUhS7l5SkGz2kl88uxltTnyWY4zLLdcJxsv7q1NDaLZSjiQpBOcc81pBEFBPMGlLQsrK8xzrC4YGBhfaZi1YT6WZsgCfIRta7Xga0CLWIpFIxFrEWiTSPPjImOJ/0DMOy7JE4ibzInFlttVnc5AHtolrQ6Lf+Yl/a9hqQyxLyyVATeGHMTY20ftMxVNLtctjKc6Spsld8ejUY44SIyM6YzrnwURV29eJybqiNA4ARm5Xv5YyNobCQNZkCsjLOP7xK2641WUFYzkJWqLsXmigiKnUN4WdIjr2I+81VYXcz+qbLRL3fspvtVZBFj6QbrFPgUlGLGvEzORVt30XyxqrcYhW/sBjCoslJveU+V2706W1xpeq44G7YsUKYK0XvehFujNZgUTqiUVcQyx2kwNlLRkYANyCo9H4tQiTGz//mmuaXEyeht3HHm1JEolErEWsRSLtrnDVazE3ZVGOtYrJW5jgMlERxusQKj3bTFtaT3A9LvVfvPejBsCkbqUiNy6FMky4mvqsFglmg7UAVCCc82uP3HObqo9rlmnG8ranbV5I9I4h3i7PdyUYy8qghVsqFNrL1eXX3+Iz82MjSu0GET2b15VLRZ51e5eZ4au0/aVjP6rDCUNcwhj3g8o+Ls8zNnFyU0+yFgS7cEeYZrBD0AJ+cyu6YO0NaP5mHSS1lySYtur6C3Y3WdwCwuM5N5kKzGuVnd9JpC7i74w0dMdtq9UaGhhcPjwMBxUcWc+Ob47s1Znzrr026iS17MQ+u6jiI4K2JIlEItYi1iKRdjPW6on1t7IUuDLZjDTZj8Rtlua5sTpCV1uYkH08Vu875jOB1qOhwLqgemB88MIoL/5c6Yr1bLAWAELs6cz/8v87ylTlxb5IQ1dWV7yeUpmc7VWa+ZOYOWPFNomEDriZHQyb5ZvfvjqwdYN+LnjHMF0VCT2teoc725Fc7ZpOWpr7urX5v753o1kxPXUaO7W2xe5P9SQHOryENgPojgg7LhAGri6+/qbfjI/7sJtUsb/4VFrDEWompWkW+AvwBcMYe8tb3gKgtWLFip7ZjiSSEw6lQdz6whe+0L9g4WClkvp+ys0EAvig2JwnVSmaUublVJZlLUWsRSKRiLWItUik3Ze1puuikltedE8QnkuFfyfjZkIOxOjNIPFytSFkbz3iI2iYAQBmkjxCJ/k01UG2DWwX+/YA44xtuuzE4+XYhnDkOZu84VESShvKmVchisAOJ3Flzoli5tsztX7uzj7kJ794vJFKrM2reyHWRyVJ5IwKe3eE5LHXMC+BBbo9qrP6aR//J+2PmWSX2z1b69Tq3blTs1vwFL7vo09GM06BAGEf/fDxX23I45oyg9QmWcvUirraw2J0WGd/yUajgS9jwYIFgFv33nsveb6Tes88y1duOg1A15o1a/oqlQP22RcIDP7NlzkccuffeuPGNMa81uTh22EtQaxFIpGItYi1SKTdjrWmM6Vwngqs26iQqylVhfi7aIEqmDGDMEGRtdwQ1h2+qfWTLf53H/pULdONpNTy3tuJwfWuZi3Ozz76KB21dRZqbrJN3NoPFtVKvJOuswyBPhkzZS1hffyYHVjsQGtjK/n1c5vRw7oWGMu1jZs3RWZUNO9wi1RTG7dg+ye+KXrMGpo1dDJ+4sffKyLfVWFhp9ZWCgin/E3HWjxsNzAtGcZRlOWYloy1/lVt/LZHfurZvrvCAl451pJl1sKNFQSe6xZDe4y9996b+rVIU848DqCFxyoSFxwqCyp9g5UK/Bzn2VjQgkPulIvOh9uWUrk7JTtFvcRaJBKJWItYi0TaXVmrF7S2ylp8agSvC2RKAh8ekwZtnoRaMjNSXeuq1HVp8lp//d6PpcYlYrInakoOjU9Nnc307Ux5eyWvRZ7psRE9PmpGTuWB0ixSySRrlcwWTWLKDOOaGWuZ/J4qvBlxTnFqGlGadz/wEKxM+Ak6vPthgG82DFqdOszeMkITj+IL5rEWxhhDt0d0XG9sXhd7TZxuDK9O8NzVOU6LXNOwFm5nKZS0T22/H9CWsMXMmOevXbsWWavN4a93XtlkNVcXaymTztQ4TmRoaAhTW3RakabFrfJ1AdPgt2QpHJpeoyls4XFVi5YpI0wjvOjiLn+Yw7foEqRRxiQSiViLWItEmh/aYvWg2mI2rKfHwjwYwaOZqVZmwOOtf3sEEkjTJriiXDOp04x1npH3GtB3j/naIhl2HuNYheNoLClF4AGxqLYxlvjScUcbehG8NNhXqq6cT7Goafwqnn/7KPvWIFJc3wrh1gPITMSl192SdSb/dg8L6ppjNh0vdUOvtcr41AffYzrN/Bprjps0YhIavp3a7oW/rraM09MZ7nPTWWfSbdfc/V0cExYym6bjPTugY0yvmbB9X0EQYCQ9PDxcZq2e8V9lPw/SniZmhcfDa17zmsWLFy+o9GEaGY60ap5tTOMaZsK5KI4y7Aw1oxeKFkFq2SKRSMRaxFokEmlSbS9wvuf1iDdT/Rd//56xUJiRXLKYohPnIjG2fDpL4xJr8e1jLcczLEkZgJbIdB5o7h///nfq1vhUz/Tn+QzddtaySb8mM8EigMpIyGKtL73hNp91gVaXsbueoac8/AoL//34o8ysLR55oxuN24eSCRoG7vA8ItulpttSX3fn92AH1fw8yDt/tpe1MApmnOdlO2/nRgj05f5sakXnwp6sMAwRt3D8GjL5ypWrtUUon5sE+DnXrN0sTNdWUNQaItHLDmsxYi0SiUSsRaxFIpF6WcWYYTDZjrJUFV4R73jfR+G2netnRxuOQwC37EdVmV625Rm2zGNKGhQB1ko9YK2vfPrjOvd3HmtNQ1zKlkLVrQfjRi+99q5720I3E74F1pLbw1o8vO6bZwJxmexWEsAbjH1Pb83aZGasBVEvevdfe8d38471NkS4SnVa7HQXa5WfAviq0pG2NWNRFOH4WtRW54CR5rOSJHGOKe12e9GiRatWrapUFsARFWfmYwEOuQtuvhFNdKJy9pQTa5FIJGItYi0SibRVXPGDyORMpK56ycaaD6B1wqlfueLGu8wQZK7biUgF1qrJboDZTtYCZUlqejySKG/VAEuO/eARmvmGT3Yla8FbmAjTwMaLF1xzI0CLx7tmFqvyb20na0U6qp/yyX8xqS2R1Tatd571O4W14NVuavqJ6S7Lbv/uA8BaDS/HvFY3axU1hGHo53mepmkcw9vVNoA2CsMQ7ea07T0D4sLxtaQ9VnCEwGGAxLV06VI4SL7whZPNkLdMwQfCz37zZFUa98u2tQfVPaylqYaQRCIRaxFrkUikLXwGobe4mayVmaHGPtPNRK69+a6sM+o3Foa4uNYz732fjig6/hIiCmxey9dBXbfHjaef2oWpFeuooUfCbCzMH3tyXah00KmT3FmsxZqjZqJx0jrluH81b800caH5x7ZtmecDy3aau51y1rnfwlce5z3da7Jk2ihd5mrTpk3alhH29/e7O4G4sLyQmrVISN133303eqgoi1CpMgPozvn2lYHW47mpIczLrCWs+yWxFolEItYi1iKRSNOzkBJJbLs1uEQ/9CAvPDP+12vf2IgFTvjN9KQd/Pa7jWHtkZIiSbTk/vgo9xrHHfkhzRPNIxk0dhFrqRJrNXJ92Y23x1rXYsMtjSh1zo16KmvN7Gm4FomOmvBeLvzKlwxrGYd9bt7sNN1s28Na8FITrVsxxwLCcy74NlOTzpNqireHUgJoCmJowOniI7KjJ598sud7iLq29lhh2hORe8WKFQjk6NtpLFiUvvUHD7RNr6CEHxNruGL7L7GA0M3fI9AikUjEWsRaJBKpC7SMcbm1FzdXtTG7BZFUPSjat878+rd8XtijV4OcT5mtPJUc1JSl9HQd4748NRwi2BdPOE7nMbyGYHS9GQ2sdjRcU9MtogMq8C4uuvr6SOvNLQ+b03KTzeNdrNU1VWxGzy3NWwibhriS4ISjj9JJZGwVYfMW2a1y85hU28Va7TRHL5NY6Nu/e3/I9EQ75VvwUQTWSoD0OoL1hQsXQhi9fPly3OPAYIRYJCwd5JwDdMERAqwFt+Z8UeYc+d3oRDVlYed8SVjeOdrdSLcdGgVBIpFIxFrEWiTS/AyxWBIi/ORZokzfha43Ws6Z0M+Uz/Q/ffSTHitwKy/NL+0tuus2MZ/ihKE7oGUHguWRZonm6f133qojD7lLS77j4dpUynIL/PXLrrsBQKvNhGlFEyrvGse6w6wFj7fO72ZJAnhHz/zyv3Qca4hNEbdKgen2sVYmzDZKhY5yiamti6+8Ie/eL907RSNKeZ6HIfWjjz7q7DFcjxY8howx9nBt3LhRWytCODYGBwdPOukkzAPXmb72rntx0reXZ83QF5PnO4EWiUQi1iLWIpFIW2GDXg/3IqaHMKvhx8Zh3M7devs/vLdth/+iEXzItBfnRXZLKQmYpNyk417IQb94+xiehW3gkMzMnko0iz73yU+YpBZSlpAqy2ZeniinjsCCJeqkqsJMJNy8sroXXXnDza7ZaSqfqGmhbaYvBnu0eKZZlrcaXz31ZO614A0m9erk3DAzX9iAVsZmnFDiQuH7AtYKWVHq6eUq4Na134vcTnFbpux7gdyFwfTHP/7x8p2kPVlo9S6EOO644wYGBuAI4Ra+Q62bSl//3R8AYMEBlnLBpVCTk81Lgx92qLaYRCKRiLWItUik+YpbZkKxnDqbOJdFdiuShrLedPg7q4nxTA+EtYZXJruirJP4JLOV5ikjEmSik2kBRpA2o8UBriLeGvvR3TcDcfnjozIONcR1+Ks7ibUsjYhcaQStWOjrb/tO3kGsfLoE3c5hLcVVHid+07xZwWTgnfSpT+rA15zpKNA8x63EJRPGF1HO9Bmk1DkTWS5d7vE36zZfd/t3zLTZIMU3FcUpjooWPHe/mCQJzlACLV++fOHChXCLboR4J6yXqw1Je5rQLgXHry1duhQInFnHzmfayfpWnOIAAUxcy+LssT1afAfOFxKJRCLWItYikfYE3JosmevAklJoBN8MkpqfYmvTOz941CaPR1qPtMw9mdTNlqeRo6ZjLYSZIGWeH7aadVM9KBNh/AZDWE485sjWxmds9aCUgAlKh36yPS9+CiWWnz2RBrSqQT7upcCHDrF4d3nhzgsUkV2B8PK83QTEGvvdE5pnzXXPZDV44wxILI48oXJuaWz7npRxGWUcWQt2RDMT66pNWNkwUUcAxg0RhxFEzGgw6KYnPf300+UpW1EUxXGMX0KkPfcjQBZnPRwVixYtGhoaqtfrudITXK+970HPHmaiOLtkHgcWtMxALes9WDoR6TgikUjEWsRaJBJp8jNoeosLQ1+c81bbR6sMiOk3VL220B859uQ6N9mtVqqcBbyxMSyq4wrmcf1Rmb0tjMgl415N+xOa+2d/4Tgd1U0lYRw2x8c1M/OhtitOm4a1hO1oQhSphVk7079+ZlMoihezS1nL5APztOhM47n2Pc2y4z74frg1hYVRoDPYeDxjsZcEfOZ5rTwtyizhNXtx3kxz7KO76Z77gLg8Znz5c2DgZptlHB+JjVhAXC6FBfdASL1kyZKJiYmpATdpD1Se53icIIQbmxxluhmrSl/+vfvHMx3jGQJsBQcJN1kuZC1u7iLWIpFIxFrEWiQSaTowENO7XHB0gcfHpFz51noMIqzRWP/FEf8SdHwyIKy3hWcWLTTrpLa6WCuxSOF7LZ0F0jOgpeOqv/7xbPw5M/k3iaxjhJnGK7Z/flcva8FTj9XbxqxP6UuvudXnGn5ICsSSnfo9XKbzY9/e7RnnmSgGc3EFZJXEhrKSoPXM7zRLRKuu88TOf2V+Gs54Xhm83JwhoKnO5vW4HotS2FvnXb42s5ApOg/227CjTHGgm1EL8TRE1XEcDw4OQkh9+OGHu78NJAb/RCfFnoxbyFqLFi3Ce2pBNCr0M4kMbM2wcXpPbWulMmOyhWa8zFqCWItEIhFrEWuRSKRuNuC9/nWTtux5ltSq47o0d2ukCfSgfa0/dcrXYGWiHWOYlaVhx2Cwl7XyybwWN+58Iqw99d9nnfAJzVoGuniUtRoYorX8bMdZS5XyWqZ6UOmL197YTI2lR2q5a1ezljD8qUwPm8BANNPwBlnylROOy8Y2mR/zMI8DkzUECsrTmbIW5hayJM9yjqxl3pddbvneD1JLtsDGcZTjg1mWu/pACKaBu8rpC1CapkhfdDrsyQIUbzabq1evRmMMrDsNmLjk7ntr9pSHE5ll0p7YtvrVdGJmuXHYLLGWINYikUjEWsRaJBKpm7XyLbCWjahsMkQoZpNOZvKv0A2grEzf9eAj8IubxqpZlple+S2wFpr+AdAksZ97dc2Dpx7+gW6s0wwIpJXXNmvB2tV6GGTGypxvF2t194m5vNbGidbam++CZ8f5YOFkQSNXk6DVRVw7vj1TKQuvANiMaRqObNR5ptMoH9l4/43X8mZNpb7xY2Qx+nPMlLXMWGQuMYfHrW19K5dNrnwLXbfc8/1GlGJfjSpVRgJNuQJCbQfX/tmf/RmE1HvvvTdjTFnpTrUhaQ8UGmPgFGNs5DPHjNZnXXPDk6lA1upcmOG2DrZgrUx3DhpiLRKJRKxFrEUikaay1jRzmRSXeWKq4CTHIAz+qdE0JXlNbqzJPKU/eeKXqkGOnJZLVXBaMTlqsjqRm7aiLIwjC2+ZDmpf/vTROm/rtKl5qPPQ1NTZTq0gnzl7uAk/nbleomMXsbkV3Hn/j4BDApvdQpP3tDBFlLuOteDvx0lmoEVJZQYZMx0HJiJl2Yn/epQO2iJsAZQGflMZZhIzS6kpN0BWYcmlo9nYstYzI9VUaSz1EhKbxybLAiVwYM6K55MKywgfeOABTGqZB3NGJ8X8P+fdoVScC5M/L6xUBhYO/vXf/F3CDEHBYTGudcuucDy/M24qB23BsLBJrZz6tUgkErEWsRaJRNpa9D5NuD/9cFIkGYjpx/y4LfU/vP/ISOtqpiPrDR13EkdRlsZpYK6V81ioXJo+rhwna33t5BNlq26nGFuvCFGkwiyHFV5naktYNXVRskA6qTM72xdeW4PrmtTXf/+Bp5oteGGJGbFVWKVlSTqrG7ScIZRMJt4pJxyrYk9GZlNonmg7X6tszqGmf79T8LJ7bDRCV2zNP667/bvwrkOlq4kovON6thguQvaZwLofcAuzWmKSmUnz92y3ONRJaEtLSvbAAAjneqiyYGGlH0cY+1rf8+hjsT3lO2MbusZ8l69TTB6LJBKJRKxFrEUikbY7VEtY7tqEfjtae88njoWYbCQzF78Rt5optl1xpRIhIvtAi1EiSRq1B+6+00yaiiNTeSSKGcrKXB0H+JAzYy20PbSljbBqxhYL/Wzdg9dz9b0/fCaM21rj08ecm8o+kxFiuzYcVFOWyZGvLA1aj//nz4z1Iix5oJO29XPbkhfillnLDZAtPVEUM7h3vJVUI/aT//5dYHdHhOmIycfLTlpMQoy9fPGShX0LcGqt1IpKwPYc1rIXTfCM4wKPT8aHK5UllYF9V+6XMj0SZnWtT7/iSjJLIZFIxFokYi0SafZCNaZkpsSEF/hcAlmtb8VHHH2cbyP7lixyKak0V7urE5uRsiJvXMNjeXTK8f9Pi0z4bTtTi2tZeMQrs1bMF36+cL+XuEzazAztNcO+sJTustvu2hCmSBqBfQVMWR92RDu167fRtKwFYa3pZ8uO+9iHgLXisfXGg1EkumTOsW0q0+bkE7nWO1/osy9ZOxKLhixYS/UyqrkrDSO47atU9tlnH/tHVSsIibXm/wmsulgrd6wl5JBhrcHFC5cIe+40ta5hmxaJRCIRa5GItUik2YrWpE1IFcWEwDZP14O3/eP7WzY+gx8jZVirFfiYzMnaNc1jAxVZcPcN1wBr2Ram0IIWLw8+3rY5V3Kq8WCYMvTtiJV+6L8f92Thy9dmCudNCTtdSgo2tSpyF7JW12uGN5sDaDVG15tNETZMrxoLFWwctXNYa6LacvnGJtM3f//HwJmb/GhLrAVLu95YubzwQkBUC9OMjvA9Abds0anMy6wl5b7LVw1WFnz1jLO9mK9rBpfc/b0WsRaJRCLWIhFrkUizKIMNUuWj4yPc+PtJxK220uua0Xgia6nEcB8JJ2jVTVNW3JaN0ZM+eZRt0EpF5Bvi6oBWVxQ487gxAYLpmJ5fddPtG6qt3JrRJ3JyWnHR1CQYY7ueJVTR2VIiqIK18qhlcgl5eNInP6bDuqhuMpWEis98F0yf1zLpiFyPtSPY/pfedPvTDS/uSRV259ySIOyrVPr7+4eHhx3ukuY/axVXTIoF+7XuufOu/kplqG/AtG7Z6yaX3v290UyQMSWJRCLWIhFrkUizx1ph0LKjtjharWfWhqHF9buP/ARa4TUzEXJVC0xGxZQIJpFuVnWrqlnkXPiwg6i3q34myR18ILCBLyXm027+7r01PzU9SzXfWOqxjs+aNPZ6uCrULg8dzRPZuczdbwhxK9NZqNO2jlunHXuUZma8WMdYf9sTbtOzFiyNdoQOGbAX2kKf8+21jrW2hFuD/WaY0tDQ0LoN6+Ex7TCgQ3wPYa3ylDk4llYvXzFQ6V9Y6W97sZ+pR3739G8mGkHR70cikUjEWiRiLRJpVlgLQMtvVYG4GMsyYfqs2rlxeIDl4Ne9uZXLNlN5Z2aX8R1v1AG3TjvqozqPTF5LZiKPsjyy0V7J30wWdoJTcUtNt+Av5rYjy9f63kcfrefMPG8njSUzVfT8Z8a2XnVGGO/ydi01WQ8pylPLNOeJZ3BLhIBY915/BWxIXttk+7h4aUSYLL/rafhqOsoyeS17X70V5ljGCcwZZ54qdgSfiltSR0FsWrb6+rCGkPJae8wp3DvtANRXWQBLpWNCeN5V12DHIx0SJBKJWItErEUizWKght7sNhtje6UynGcVCg2gdca55ydaV/2oYC0udJad8qEP6MjXeZJWx+AXOUuUncnDe1iLT++MMZWy3JJZ0Prh4798YmK8LVXqjCbceGZe/EFrBsCZ5rvchnDLrGXygcBaUcuks9L25z/2QdO1JRPrmcFKxLV1//fpWcvYXSTFVg1YUcl5/hVXZXqLuAUUivYYCxYsqPRVmOBCSTrE5/0p7CirzPZwFAz0L1qwcNhPWMALE8uUWItEIhFrkYi1SKRZZS1TQFh0W5UHFid2XvBJp58FkX09TCKBw09VvH7j2jNO115bR6HNa5kCQibSLtaSnbzW5M+9rGVmBOdClIYvQ1AIseADv/7VYyMbR9IY6+W6XNb5ZKLM/eIcslYatQ1rAVyFdR0b4pKtcS0jg148Nv7vigueg8q5u0nWmsx9dVOWnCSz8jRnXAKhYGmnxhE+TAxqyk7qL/DCyTLCRcPNdkNpYq15fv4KBUcX98MWrOdZwrIcj5xKpX/x8lWtMIWj5YnnNo16Ec5qIF9KEolErEUi1iKRZpG1FMd+rXIGpkinpKwZJO9+3wcSXoBNFISX/fu/s02bdMY0Y1qwqN2wA7VkF4qo52ethJl6xZiZp2/4ccQNUdz30CNjLK93rsGb1yRL/DfrrOVop0xcXQ4ZMs+Dhs5DLRJ/03Nf/swnRXtcZ22b3bKm8Pa9Zznv+l3V2fLOUKSHtbpxyxnoQ6x84RXXIHQltqYyM4OUJpNaoR/wnC1atMiWEUpirXkecJhLHbnQLIo920BorP8BuU1Sa9HSyoJ+TIeed/laBK2RapNYi0QiEWuRiLVIpNnFLc3Lluu44HQmYYdZHf63/1DwTs7GnnzSUBYE+JxLY/Xu2kVKZXKqVB83mefq6lwyo4qtK1rICop49PGnf/nM+qjjNW+iQyE5txO0pJxc7N8RWyxR3PnxbI8PoXNBtNEt1yJLGjXrfZ/5I+uuv/jrOm+JoAq4FbXrLDfTjdtesBXWUj0Y2u0s4tAXt1IzExddfeO4l3qpgdXUDnN2qS3YQXA7MLAQWGt48VBmMoWk+cxaXtwS9lrJxPhmY3Bjz48FfYOVvoG9fu8AOKHssAQzJa9tRykQa5FIJGItErEWiTQHUdvUn4AGhFBJljImzvzKWb4ffuHzJxp7jDgBDNJMsigx46SEnCaC6xr+28ta1s3coIKXYruYfuK5EWCtBO7hxggxdXksaV5FQTWqiwnFbLFWudeq7PweRYGSxQsTUWCmlInspE8dKRrrtQhF2MC8VhiGapppY3ybWKsbt2BD1eL80htua+XaNW7FmUwzaaY/J6Z+zG97AFp7771Xpa9Cea15z1pwHgqdRXEbjiUFJ2YqWMyHBpdUBoY9biw9z7zw4onEzFnjSo+MVmmjkUgkYi0SsRaJNIec1ZFUWZZ0MjD6xz9+8Oyvfi2O45GRsTIVQJRnE2Oih6ym9Ov3shZWD0IIOO6lv3xq0xPrxlNryMF01xwty3uMi7TwoijM5WdqKb8Dm6YrQTcVvrQZqQygo6QWyh8f1WnzC594n2Zt07jFIoiAkyTx/HBKIs6xlt5qXqvYmMKOTsptxq+e6YuvvgW2yEg97PIj0TqJjBXhm9/8RsCtweGBKAnp8J7frJXplJtLE0xmsbkOwvW+K/etVAYq/YvruQi0vvjmW33X+khZLRKJRKxFItYikWYZtHq5xYb4BiG0bDXrnCVh23Ten/jZEywiaC9jGOLbEapq0t5d2gFcBRtwXEoFir39YO1ExEr//NdPPfabdWaQV2p7953puQJ4kYZgzN9hnQWej7uSxdnYOhIrGLuyTMYZsZjxpeM4DcO480iumptl/TnNWzpthY0xpCkxTdHjDFjL4RZEzA0mRyP+018/HVnzkoZnXPEhxma5glei7BOEob9y5fJKX+Vnj/yUjvD5zVqxiLjO0swM0WaenzXD/kr/gr6hyvAyAK2nmq2qVMDnkb0YEocJbTQSiUSsRSLWIpFmD7TE1GK8IpclZZ4UzhmKn/z5z7Zr4+9+97sBija2vdAOemq1oy4bDIFeFhzpKDdNXZ2mpimslSudSP3jR3+1seab4VE+C3FQLzxbbi7XCyZBiG3WAAIW4wQhOqw1SxvIgBY3NCMd9hRvIWE8SFLXghVHOY9jzX1RffqME45S/oRWmchiHxQnM2ctieBaYi2Oea3Ijjb++iVXJ5jsk2bQc54VHhvV8Ql4fKVSWbV6WV8ffX7Oc9bKzUmRMR6nQRtTv8hax514qklq3Xa7b4+ZCetRSXktEolErEUi1iKR5gy3SvOapEhDSzyJzgLmN0TkA2xALPeX//TPHlCWtFzEy5SFSS3uWEsZ0MIxU3Kqgzlwwvd+8rNfP7cxLRmaC907BBmd1hIeI25hrmyyDWkWXAiRtWSZfMzdaC3oyCjLZZYK849RW+ew+Jt/85+ujNDk/zpegtzZGBa9XltmLS17ajJx67VShcOmv//Tx56baGHjlnExyXXoR4jKlb5K30KzaGrZmteslaok5r490SSLsvvu+v5eq/9HpbIIDo/fTjQuu/3utoVzbh9vHOFJJBKJWItErLW7yHjEQdCcJFIav7g8z92dcA4YVwOtfR/iAB2baUk6CAL8RSFEGJpOkizLaDPOqQoTQme90OnrkIayIIZj7WzkmXNP/qwWrFVtwr8efeZXn+Vm4nCqzBCpooWKCYsTPLNLUTpo7pBFNoepODGBXi1JIPLblEQ33P+Dx8fH0Ns9txWD8FpEPk0nlip1f82Bj3mnY23aoczTbM6E2UxT9tF3/JXmbd3epCUc9gm37zR282SNlTu3WTw5/d/dAkaKFDas2ajNTHztiqshkobFs/WESe5gUP7Z619bWWjadgyhCYHnphCi/EVFmh/nbxh7Zi9Lc9xV+hZVFi6tDCyHM+v+Rx+P7EWNSJj8Kec5gTeJRNq95HleOVbEH50YYxdddBGEnbCCMSeJWGu+qdVqYQx34IEHujsBoiCkQwArH/oQ3iGGTYYJUrrgjzR3sRqzvu6GsiannUJsFjY0a+q88dxP72s+/bhOE3hswPUmrd95/InPtfOaJwAa0maSNANtxk/JrJjgyzmG/ABaNrMVTHj2AboeF6B12V23PxsHXmeOVsxUMZ9rdx+2isOw2rlutzULv3TMBzVviNZG2y/DkbViTDJshbWe9+8L7QcJ/B3YgKdfvrZpVyKlg8hmyzKDrTmLKv0VWAaG+jdv3uyuccCJSQf9fDp/fc/0UmYsh+OiGqSVpasNaw2uHE30dx/6xYZamHVSqXEcWmNMEolE2k2+UZUCyuoJHXXnwj1exD/77LN15yo/iVhrfipN05e//OX7778/QhdmsZzw6Mfzwd0DMNaGSFTriYkJ2oAvKNYq5bW45pFO6nr8mRM/egRgA1CByo03A0T2Na3f9u4PFT4WlqrGR8fQKC8zDSRS4L2yg0+2IHC84WWWNM679tpnvXbQmaMVShXnQvBeu7/dlbVMI5cwI8jaE1/9zMeBtYxPRtbWttsq7cxottuemwLJGbKW8GNtfQ5auSkdu/3hx37bDP1OuqxRDTCvpTQzrDVQqdiWrdDKXdroOU9Ju/P5Kx1rwTFQWWjzWkMrf/bEc9XMJo3hflGUrWYp+VKSSKTdRoyZy0OY1MJ4Em7x+6ter+Njzj33XAwpe1JeJGKt+SAgqJe97GWHHXYY3B588MHuejlAVxRFLqqDH/E8mVouSNchXjCsxVVnWm6xS8yI3ijc+LsLTjnO2JfzKGvW4d8SrlvAWkpP5NpY4XHtR3mS5h27CIlL2buCC+vwnhlOmwiiB37+2Fhk4BtYyxPC5yJTSjhQmQelbYBDCTMJq6iZjz595vH/YkoxMzv+qKdlyzRrsZmVdZn9pFiUKMuwVS7hu+XWnzw8znQr07lEZ0jjQyhU/rfv+pvKQjPRmL6B5vP5q9CRRiVc5QVrLYm0vuDqWyZi6QsdclVrB0WLoKaPXBKJtNvIxY2uEh6Vpincjo+Pw+15550HtxB20uYi1pqfgsMdrzrst99+utMNgueAtpfS3bUHLCYEuHItW8hmo6OjtBnnOlZDN4tJkwvLPFzzECCh/dzjBhUSX8chPBb2dtaxwvvUyacX3VbwI8tUNy+JDrwF3PxKKMztlTfeBj/CnRD/GTMMS1noMCHUPCkoTVJjd6FZqvy6bXjzR375sEkSisIGo+RB4gzxZ8JaNpMRxXlm90JN6qcawc33/wemy5rNGP90kPpcM9Oy1Vd517vehd9S2FdJ1zjm0/mbZwmylgGtgeHK4NKhNS8eC7knzXnaygu251IAgadJQJuMRCLtRhobG3PhZcOq5wHkjUGsNZ9Vrgw8+OCD3bUHuK1Wq5jSBbJ69atffdBBBx144IH777//K17xiv9pBeuvec1rqIbwhcNaupxVUlJLlrcmTj3uKJ22dR5okaXtprZGeErpViATG+gf+peHQ+w2EgR5NwkgRqT2MbhMxPyy62/OrQ2GF2ZSdWWwTKTIMyZytZv37qvJycIcNpqOmro5duw//6NmieFUIbUsP8wYNs6YtXLDbEFoLmmEdtvCLrjqju8Beo1WA243bMZyYSYvxUtWL9lr79WVSgXO1iRJ3HUQqiGcT6ewEEwomSlVWTCwYr/fqyxcdPF1t2xqxHCSbqi2Qm7O8DTPrI0o9Y6TSKTdTPCF9apXvWq//fY77LDD3vCGN+yzzz4HHHDAvvvuC4Hli1/84gsvvBC/2sgbg1hrPhMXABWwVhiGmONyxYSY/G21/j977x0lV3Xl/1YrgEQwDmPPzLPf88S1xv/81rw18/zm/db6/dbkscfjwTgHbM/YJphkTDBJEijnnCWQEEI5C0UkEBljA8YIIZTVueLN4eTzzj7nVupuiW4Z2ZJ8tjfXperqW7duqN6fu/f+bqfxyVqe1zywOoQXAB9U1czrY52gj4j5JcmitNgGgoSQ1KKC6IyNFjNLdYjfJcT//MqXzACfDLRoViGH9Qsc7eoFUx9b4TJ40o9o9kZEy2UwziimFPPeeu4XJ2uBnKNgrleCXiyqdp2C1ejgs/tgBhbVc7qyvB90a7GBDgrTu65SKhdLrtGNVAeijGV3RNZs3Y2rI7wSAqkrN/b/5u/+NteiUKvnt6i5VK1dAkYI8jzHnHiDrrgqN3ioOiuWrFpn8smgTqkvRkxJ/UaINWvWrF0kZmrgzVLFkz3+eKkYcuXKlZRSK65rWevSNCOyKXUu6zOf+UztyRpZGY5SPzU3G8yTjTJoNuC7UPjA4JahLA1akqMRd9+ROgUtLoj1MF9WJQlYQigvQCejIOWs1U9GJpnDqhLwAhhAUVZJkYCUc9duKgnp6OpBxRZpTDIkyzpNiO7qgvFZKUsudtaKONE7gDMUQ9dWGtOuzin33iMxgtQW1VL4uqXNPBro3wczIglk5TBLqHQRN1Wduw684mHhY1aJ4RUBQmYHK9YaPnx4GIaN9z6sXUIG51LZqcCBvuaa3KAhqzZtraRQM+qmRF1OMYVBcJDqFLTWGmnNmjVrF77VOgvMAxNhYm2yqqy7ZMmSHqrX1ixrXVJm8rZCiD/7sz8zkZytTbo4WUvSOIbMVRRIliq+2r15ndYUJBl91WBMfZvFUPgGkneEljRN/d+f/7xiLS8kpkmLRBDQqWe6tWLhimeefdfxjOqgj4SZVsVDWm1WyooYDWtRyHZd9Hktmg0B09OK1d8GhVgYzXzwAdzRrlBJAVgSuGa6MZLnci+uNoGMVfOI6s+OAq1l6zapC9JnwuS7IkLCNDF5rcbUVmP1r7WL//LlhGHK2eVXXpEbNGjI8KsWPfY4rp4YtXsaojqnzu4xa9asXfTfe3aWsWWt3ysrFouu6/77v/+74i7bc38xGkqwLv+jxHOgXFDXDU4aeV8VtGpjfA1rcRlgFb4FfqKi+QJneSk8Kb/0/R/EupUr1SOeUDWpNX7liqNR7PaYo8UbwsBLjrVqxYHVf/MMtyL/1/t3S7eQdLepfZuiMEDJOeS1RD10ljWxDZNu7I7RiVK5iLBDCKoG2Yq1PvaxjynWYtoa/1BZuzRYS+iraOjll+VaBv3bF78IEilB3Au0LGtZs2bNspY1y1oXlSmyquW1FG6ZgkCLWxdjsMYQDop50HJIPUmCmaPvhy6j6uinBiV2neNSixDwrOxHsR63Feh6wi9ff4OPILZzYihpKzC5ePuuVsq8qn6Div4JlxhxWRulxTVryTprXez9WrqgCxQvWG2ncYNbirW8MXfcJElIi+0Sx0YCPsZEDOxYZWTVpPqoG98cRNRuX7huXaBLCktxWgmSCMG7KNAaNGiQrFbw1hQyrF0CRhilnL351q8UVH/ij/7w8SdXIsZZg6yoaHZr1qxZs6xlzbLWxWE9FN5lcy+WtYsGDgz5MC3kkJSf37IKlMpZpJNaTRGbZi0tpqf/HYSxSae4BIL7xzbsyCeZ6mCByKmPr3I0ial/upR6Wq0BVsUEN2J5tXSZds0n9BJgLYOOTdru8A8KHBsVR9z8fYkcKNmsFNXzYTQwbRhRFx+RjfrxZtxWd5r6Ui7bvu2k46TVnIbr+h/+8Icvu+wyU0bY2toqbV7rErIUI0SwAS21jFOYveaGgehFWZa1rFmzZlnLmmWti88cx+GcE0IUaNkA7uL80tKAEAW83Cnj4upZY/VUqECX9tUHHNdYS5BUCs4QhnFbiAutcna021VR/hd/dNuv8u7bJW/Wmk2elF0YQCsRWdBfu8WuKL2HLEqtwOnir3HSUh+iPhsa1yXgIxnkSfcR6bTDuC2cCsQGGvyKxnXy6m7VZZ4gRiJ4gUMisQuRUEikoVjqvJayD33oQ1b285K8fAmjLYMHKdC6+poP6aEABFMkLWtZs2bNspY1y1oXuzVWDJowzqa2LsZgDYUp5LVIuGjMvTLokMSVYdkAA9YJE5zNODZ5LIJRJDlnITJ9QmHIVaDfgUAJ4zsjRs/dsfckhX6tQOtoYGI6tGD2hYoCq3rvvM8ap4vfOOxJTmS1h8rMeoZZslFJMld6bWNv/y+1q7UmoWTxwHCLVQ9HxloNw5CNCr8rZRtKZq9cFatnRMZaas8b3KploW2t7yVjKUZxmlw27PLLhw8bP3GC47mU4oZ0sWUta9asWdayZlnrIg0qG5TcS6WSBa2LNVZLtI4DwTTfKsNuGeezxItmrRot1FhLgOo4sJbEyqUIIKI/1g5S4q+Vg0fWbPzn2+44TmUrgsJCYAHMJMOSUYJTQpCeh5zJnffo3b8k/gJo1lIusmnOsV5CbaQiWF6RQbskldOvvyw9D3ZrIs+BtVCthrC2B/WTvpRedaDZ+t3PqNWHCXccGEuiQOsjH/nIH/zBH9hmrUvuVom86sMfAbXJlpznOQaxgsBr7IkUjVO2rVmzZs2yljXLWhfLuW5wKwgC84yZeGDtdx17VQXHa1J4Z7+zDQrskaTolm9dJ1FFUlcyn0cVmYmSm3laZtVUTyAmXGrZDDNNC8k0htd0EfnY/uff47JDyoU79ndxwAyFY7pGkUPDkt4wBVoYJnbB0C7cwFqXyI13IausBYm7VLerxVlqi0TlVkhtlU/NHXGPjH3pBiAIAgrtSdVR3UXD4+oLhEywRBiOgpaIpPX+LZ3XEr4GrQLlrx9r63YJFBzqzFkuN0j5kCFDpJ5rxzm1cfcFbAOopEWYwsFtyQ2/cpj6RcaR55cbMp59Z7dEszihEXQR+urWjhqcVH/a6PwM3v8PyOs5WWvWrFmzrGVZy5q1iwW0GKj5QcCUhTIia+yJKpFaCsxD37CxQTIqcSJDd8oDd8mgKDk0WKXIU2vAMGAra9bSL9e8BDF6VROlGqyp8F8F+kvWbPG1znteyM9//xZPyiKTWqdSBhXfRPYpTlKGqKQ+DXE2I9lkzKrdR/xiwy0hm4BW7e8oBNbSQSpuSG0h89FYKGlF5o/Mv/dmWemSSUkmJyXwaYdAJ6Xofmbn47s2LHz71Z0dh1+RUVvqHlV7VNJWkhyBOk18lMi2lLdjtRLoAYPGOqhFxKZliyYgcQhH8Y3j7Qd+dbIzkIE6wkJecc3Hci1DVFAORxJQ0IhM8lqwa2vMLqR7JU1KnZleJ2XmCKVxQhCgcr6rAOo1TAwbdkUul3vggfv0xUrCyNFNlPANgFCS1YsK0MI0ujZlzzflu17shyjQ3xgJEuqq9xNRYLLy3umfz1n6yKZdi1/91e724nvqRyl3lauXKQ9QmUmkPEAOFqn+dYq4ekCxIEKeGezqoEWrdxCQxS1r1qxZ1rKsZc3aRcdaqIm1qq1RIhU4QjrqgmQV3P/miUSeJL6koYgUKCWYxT4C8fZUDzPG9bG8mrUYrDDFCCJ7ylRgVQkUasknNmz1iSxj6QnoGmoL5FdvvCdPIKvDZP3+OZTAcYwk0iN+wetJrYuOtc4UUEKBJLRsEYJiSmPNWqGoNlmRSHpdEhdl2jH75q9KdEqKE20n9x7+9ZYXn15Kg0NSdEjeJf3j0jsmRV7KLhYc/uVLa57ZswgnB6U8JWW7oi/PPwVsRWCfxi7olERRoucjcwfHJp92tEzW7H4F9PcDHGCZaxn68U/80d69e4GWsxHVtZi+fppY+50ar7JWU2IKnmfgzWcaFOi2tAy+5pprtMikwrEU4ZCyBJKX1NTwcgxGCRZM5zIxF2bcdohjfQ2ituIJDxVS6W7atWL01LuPd/2SyrxLjkfyFJVFKt1T+UNUhh7OPzL5/gXLZxAZUhlRmWhHTlKiMKI7Vcsg9c/GWvUxCDohrqVzajdzrFmzZs2ylmUta9YuilitVghUZS2RBWxeyc1ghjOGYhR7UiSShbd++0vM7TJJLanvUqs4DDcpEFYHGfMsLg8jpOLyGEFiauaCR0OWdXapVTgMlnkkP/fNG4A0MJQcMT1LSlcdKr7iVXl3Wp8T1ZRluZjotpcjyTDXaiNM1NQIEYLivoiEFYliiXzZ9c7027749LIH9m+aJtmpIP+Gwiopimn5qEy6oLNLVCQtBqUjUpYlyI7k/co7WzcvjMJTcdKhjoAUQejktQo/ZiiFvIeGpVjPko71sXi7NV8hOqsm5aDh1+RahgwbfqX6+5TEYXPaxLLWhcxa9fBC6JsTOCW+G5jzbdCgQQq0hgwZJIHvUdYjSeI48c2vM0ZqUihq5QmN1MUaYlcnpspMJq3dR55Yu2TrrrWVqINCHtQLcHdEC0z6SFYCrB6ERAZO1KWu7K7yyckzx06ZNa6jeFL9bkS8mPpqVV4CyS4Cd3nOejMiYy1axS2b17JmzZplLcta1qxdVLGabOqpqGeJoiiBUAtD8ZiX79K5F6RCr0fuvgUK23jkF9ukSClMxDWNVQ3NVNmauRnlpDDOCWKTOJs2ez6uyhV2uihtaFIqJHLpqs0+0dVCuqEIc3gDWANB9UD/ohZK6x1NMgZi7riqxg6BM1NRaYKK0EtFPcl8WT7x3Pq53W9sXDLu+xKfgvwVL8q4XXJXMgXAIU/KUuhZZUJBUZSG3XHQDr/OfRQ7L7zwNKduEnXCbLO0C6d5gGTBkwhD9lFAaNwdJKZ88dF129o9rB7nBg3PDR3eMnSYaDpbepYRWvtdn1B9s5aZlEAxq51pppLwmmuuVqz1yCOjfN+VVYEi0zqrEEvDWU2wkhAeCRkVvNMKn7D0IlpatGwWlaHiqJiaEkEoDkQ8MXdD3KiiqEz9CEMuS13ykVqaSkLl46aMMq9345JaVkJFZaRnF9bZygiJbdmyZs2aZS3LWtasXYy4lfWpm0xFbbyVisDCwNN9PgQ6LJxC5fR7QfGUTpJEOC5LEG8ncRSYqKh3G71ZmxdjqiXFp85ZBFp6XJYC5CMQcwgokFUhwOaW9RvvnVTh/tGC4wnpU0iCpYjAFGxRTZQJfkm1CtViyKwYklIC3TUMNDCoQGUp82HxtYOvrJTkiH/6FRkcH3nLt6CDy23jbgcUcwrTxKI1SATW+t1ZoCwFIVEkVNjremlY3L93NaTCZHsSHdNi7wpguUlAIgLZLSy5Q5LOiO544Y2ukKkoOzf4ilzL5es2bU1SK2NzQV+/PbKO+kziiuIbASaO8MiRDyvQ+rM//7/C0DfJK8X0olqIi8y1BrddMKYe1zkrJivKneTUstWzV6xdUAraQlzS7ESwQDFJslauEBFuvjcUdKGUhwkLDFlRmap/OlGx6HWpX1y+arEhMd3K5TQQVF/EdQbOt2bNmjXLWpa1rFm7WL6EZI2UauN0lQcoycQwOCm3npAkkSSadN+dkqjwKGTYY1ghFoncchXW6uthzatS7qV80fJVCreQhq6IQi5FvSahGRmUvMgUCf3rN6/39eingMu8F6q1JVFancPLwXmTWtpFv/NZtVGOJlCWqQgU4JaHpbwklRf3Py7lcZb+WqLDOp1VefWptTIswcQtGmk5EpKkgU6FQdBq8ooQNBNeG14cVGC1cXBq39NL0/Sgwi3ffQ9SWyTrrRPCbAj1cKRYt9UXinVjSG1dmbvs6suuvMbKf190rGVM4ZbQKheup84BkJe86uphuZac6XoCGDNanyyDMYNpKXaytKqsxKSdyfL0+aOYdMthK1PXvkxSFgdpyOqtndqFLFaiiu+ZHJdx9cqYQMmwKR1MmBdTd8ykESlX/0wopGGRPnP7ylnZc86aNWuWtSxrWbN2ibCWqLNWVbedR9DCQUCeAYVq+ZOvfxlCfBkFUbeK1AlI2hEFBiRKQKy9qlTBGsbyplXtsBXrt7lIIxzO4ApxXVgo6rEawlThXWuC/8e/fcGVsismRh8Cp4xEqDobqsZatKpWf/GGyHr3s0zng6daZ5EnUoRh8ZhkhRf3rUbeCZF26BYsxbR5ErZK4j/wX9+BA6FYF4cYhawqiO8RjGotcwaeYi08yGTslXXo3NV26rmndy8CrULhAqoRYY4+ZNK09EgxBdAdO/tRtfyXa7+ZGzR80PBr8hW/Gbe4zTNckKzVeEGrwylSBLktIzPjuOGw4Vcq0Lp82CDDWihlurYQXOFWAnW+Us/OM0PXyt2ld/Y+u3bC1PuE9IOkW2G7gFsfHiJxvQaWyzCkYciB2AW8qdDCOEyYK5QSlnLQtiRhUql4nXoN5ZlzJgoZhUmpYXRBX8RlJytbs2bNspZlLWvWLvpQrQ/WUlE/VUGVekj8ono45vabZRpKRV8c4i0vLBrQEghpTqCNrFXTLo+hLPD4/pdfz6Y+8Uw/w08w0+1YJoKKE2S2hejpuir++pdvfz+C9BmsLXCTDOBMdSM3SS3oMWMXZf9GY3BMhc5NwYdKtD4Gj5z2X7+87wnJ2ml4EiZgMbUnUBSVoXRTRcDYVeh73w+vB9bSBYRIUxrMJubUyO4nGATjhNldphMMVAQVnhUUTNH05P49ywkgnA+vjXSZKOccI6ziYt0+V6Fy3a4D5UTmhl6Va7n8Qx//ZB/NeJa1LmDWQpjWKKtQ8RRTX/e1b+ZaBl02bLCRHATlDN4gMyP0TDt4hCh3pXTL3uHlK6cJEMVMtMP4LEQjdY6YkmNCGyZxQd2hjGN4JkpiJuBBgswreRh7KY7MJK6UuGptQVwkPFj06Ixek+IsblmzZs2ylmUta9YuJWN11ooxQVLoJAklkKtKIc3Ck6T79Fv7dkm/Ajku3RAvTFQk6vO4Mpl4HSipeD2fJOqlnXFcjLGTCiM8WNMqpH0ETloDTYf/CuO6qLxr9KTUKN1xtQlZ/7+IkRn0xAS+cFnrDBEh51R5s/wASlFed09FiqlI0C25s3vjAknaoGJQVPT+zyRGmKFRqpDJl07H0TdekkSRKcjEYchMgOx+LFnIMmV8wWsgXVVsF0THtVAbtnXzQkZaBetUj9V6SOAaWsaakH0hPQJSJVf+wScVaymvsZbneSAXTkA+0bLWBXGyiT6eMFekHyNTqeuEaa5laG7w4FxLjov6vHHFV3EE120KjVcKlkJdgqrAvvLE6plh0q4BSf4G3nv2sRl5bPgtmTVnPDSG8cBAF9bZcsYRjM82Vw2DjUximibCspY1a9Ysa1nWsmbt4ovVYKQoYdX4jMcsjnBARWxyUanTPXvMQzIoSxRB15bud8/mcdUE4oUMvbSxgFD98rq9e148eDCgMuJNoMVkrXKtTlmmY0n9qAsRU8C0eud+yNXEwrxFuduppc5wCrKEhVKXuABZ64x337miLMAtUeNNqrui3DQ9xYhinrJAbbs2L5aiTJyTkvvQvsVJVuNnomNIFvgyLUnqj3/oJ2r3dHa9J2TkxoWQBwELTTEhgxJPjrDZDt0HJ2jtSOlnQim633h9M0zfoidp1AZQR2FqWoKli+B4KTx+/eARAK1Bw67++B9FmKujhvV5ksbqNOCRV8nOAWsXHmuFKTEn2amObrVctWlr7vIrWi4bPnjoIDOz2HXd+qv1GoIABl0j4jLhzZzzcMU7pie99ZOjmrRM+3pZ38QVxsUVKxdgqgA+KZZbqzkukiIYM6CM0kwORxEXSi3bW7NmzbKWZS1r1i6y76BsCYrPmX50NsqmXDgtRXLbD6+XTAXiCYAWAgCrDj6mjbGdqPJAR8n3sFizfUeFsq4obsxl1RQOuWgIDI0Wnk68CA1pKuIrEXhw76jx6ne7i2GSZHDmu9DirzaVMaLnrqYXaOzbB3EBTyrQ4gLpScXQnSJAqN3ntEPK1ih4/cC+RQo2S62HIa7MEkmmiYWbxhi1jqC7E1TgUTkunRo78idO6bgmUxUTRwRckTAlCp8502korksCozprZbiFOOtWxyry33zjF+sk61LwJjEFOUKhOU1XbypYm7f4sU/96V9CSiQ31KiTIBhzy1Eaq3VSkljWutDOt1peq7NYMVXB0+YtvPHOu3KDFDYPUT8ql4vmVNApSpB9930XgRYOTDQOosL8hVN0GaGvZwM0lCieUYo9qxHWj/u+0dBYN9vgkGituB1bt6/Rgx5isyQ0rBYTyjTFtfdldo6xNWvWLGtZ1rJm7eKyFGfjRFW8RXBcjZwSqFXjybSxI2TiyciRDMnIg4aihhb2RslBo+FupmOt3rLDwdzBFDXwFauXK/ZSWagVuem0TAUx0+6llp+77psmJ9be7Zk1JCnNvjTV/9gFWUB4RsVqUNOugpYJTyOKukGpQrZvXDdJwY8URa52eOP+qs4OFqZWEzJdYep2QCuX8MeMuKPc9R4DdXjNVBJRlgSRa7TlUgXJ2fM9WIsTWoJKz/iwoMf371omcRGOMrR58XJZrQ0GNHHNXbmWIVde89GPfPyP/BiZLp3sE6l1WNa6IFmr1t4YEH6iu3C4tf2yD390+Ef/MDdoaLni6oNr5gzQMHJrZyPlQRCW3nzrZagTBoXARDF1v1mr1nZ1lkuSNy95NQsOvzth0sjX33zRC7rjtAytncKQG6eUcg73Dxi1/VrWrFmzrGVZy5q1iy1Oo6CExwnDOm4moDmOQ1LphPahSufpg28wvwTKCwJGbDVCkWmvqslgqHC+hCBN8+SWnUaEUP3ICdMaX/Ee3RZC9iVXDfyGdXItoNmav/DN/2r3IKFWiIhJkXl+DCkbxC7Edvn3Y63m2NRj6IQkp08cflWkLkzESsK6SEAtzGWN5X+o0HUC9jcPg8JpFuR3b1qpHmO3S2IPDp9ud0kZIlryQmRr4yb3mDlsEhGQuFAUrUivuH3NfMnLMBxZS/lLkuA44BCRw4tzgy9TYXrJDYzogoQ+s1i9wNYQXljnW7Xx0rBWiKCj7omNmz0scpdfmRt8ee7yKxAG1AmCQNeykhQ76lxy/XYv6Ji/cLK+jrOmqSgK+lh/r1O6evOlpmwxMMWUIHQEFKWqzUBhXJy3YKopJkyRh0lE4a4LrE33lRF7slmzZs2ylmUta9YuOtbiIQmz0kEVqSceDG5S7uTnjH5Q4ggyWpykSaBjL52D4lQnWTIZjEhX/amwvcTlvJVrCwkrRTBSF9EqYjVYr1itl2vFaMp0roxpGQchr/vRLd0I3qjTS0C8IaFZRMkvItaioh6V6kIp5hNUCCuH3nl9N7RLMU4T+PhpGlc7W3qIzIE0o4pB1XpKxU44WOq3ovJLT22UTrc+aomMfV12CEfIT2Pa0C3DeiUYEdLBMfEkK0qRP7BjEXHekVGXZKGWnge0rkC9GcxlunzYVbmWIY0fS+EWKGRYu5BYq1ZAGBOhrs1la9erI9Tlx8BaV15TijDROSJtVGeQwhgyq+6CxeOitEMRlxeof1JTXnjmi4ufoSyQ90ujsukC4YbWEPbNDYjRY++vKRMSGmsmNOcbs6xlzZo1y1qWtaxZu/hYK6KRxgCE3LxMPUki3nV6yl13AGgFjgrfQ6ekw3fo1dEDhakWXq9q1mmh9jbClm7dmcfSSGr4sUhTHfyJ2gypfrjgkeOYII1okHP0yo+F6X/ecGuB6exZRI04oUiFZBcTa9UKrhRoIeJ6QadTPt16+E0ZFEHsUe1kzUaKJgmDybDVD8dNtaHpy2KSFr0yZPUiH0o6Q19G4YQ7bhNdHRIrHo2I55oslocRboArCgWCVIOyXrOQUZBp6Ielk1C+iN9+++UnJCtAPxj21CYRIDfIYuVacnoM7jUI0zhBlFKtrKC7tqz9zs833gdrqfPs3ZOtitpdxD/0x/9nbvDlQz76ccVaUIUbkzCEhqhS5bSZozV34aiKfxQUKU2Plr4CK+WomnyulQ33YKozYdiZX9YrC1epVMx1ESdOgiowujwpzJozPogKGS9cPQAAazxJREFUJlGGScwYq/+uNWvWrFnWsqxlzdrvNPQyE355HzFZr4BHd/XQlOk2LRaL2AEZjHLXpLvvkCjmxS6oG1QBvVb3hpC9JvKuozpclbJQAdqCdZsqJvOiwrtEB0dCIpOA6oOp2JlYS6pf5dytOFzX2xVSFFRx7u++cF1kShaxRjguCeJ9hXo9b6uft/E8fRRA9rpnT5sc0AUJ5iVRZ6n4XmfbQZ05pDzWlXt6rzoeA9Cqs5YBrVC72qFQ2AeFfADKWpyfEBU+T77nblEsmMfQ0wWzkRPW9N6KURXEUQNaaVIlsITAnC7aKeUpiQ/t37pI+K2SuzwsgNY+jo2yf0vL4FxuyOf+41qYeKbHoyHGG2jwjFlKa+eG6mfhk+qp1fMXGkfkFWP8yq8PRZqcQBXj8quNcL/QnXiMEaFz0ikpTJ52HwahlErZPWla/iCDLWr9UbR50DCVfYp/9jznz5Dd6uuzBIEXw9h0qrNYkNqK0/Kjy+a2th01b61TW9zIEg78wrRmzZplLctalrWsWfvgQEsH07Quhi7q40pRzAgW1dveZtStDoxoqgN9AqoMHN110w0SpzIONWXpRIiK4KnpxJAxAvmKUgCtU0bMvd0JVm7ejhok3cUZw8Z+BEmCVydDNcWODuYqNvzsP/ybUeAw8oZBmoX7hJCeMhtZU1mTVMC5dNefQVGwl9q1ma0MmSO1zzJ1R5lykG7XjXCKfxgXYcp99Yx/6Be7Oo49K2WXlnrrYxZtsyZ+PcZlPfawij655i+SjPjp7XrIFuKRK5GnCCrVIhnmuGDOjMAG6xEJZ21gJm9WRv7hF3cvl6IoeUVERUXg8LG4vGLY1bkcDNpCIF4CAwFKhINsHAMBFK3QTaVuAWo6BKLn57J29nOtSRWl6frletJa7eqgtdsTSeCbqQClODH3PvJUPrZ1x+kgMUnmq675xKCWy6HpEpmMF0LEBQFCVJk7f5I69HFarJbt0Z4U1zdLvy/qnPPNi6wBLAhLq1YvK5bazYaZAVyURUZZPkoyMfowSvqSmK8pyxOLW9asWbOsZVnLmrXzwlqixlo6OsJJFsGZeA5LnnIMOnUkAayi6gni5Tt/+uMbaejrfh8KQ26CAFapwjuzYh2OlTHcEm+vJC6SKzc+9eqb75BqgNhIIzXaGXC8JerVR6IBlo63F9S7f/uHt7hYOigTKmzPl1l2t54k6oMI6leKGh1pNmpZ1MUSP1DWog3zgmBpdhAMWxZZ9gCjCEBLBYt6UpaIAmh+Y9Gm5XO4d0KKDsm79B4dcDjeRGJaMZ+jCAfl6eNGQhUovF0UdZ9UuKU2LMRxRFAWvotM4qQBtLLwVA+GjvQo6Y51y1QIXoYZX8gFRk9QS27IZZdfNWTYR2YtXdEZgHiCp8P6gCJWL48kOPJirwxgyausJXgt62JZa2CsVQMtI7dfw62s/Q8eqJPeNE8mDHQkCpSr47Ji7zNljc6RSWrlhg7ODYHjo2ifBlCJKnw/7Bo99n5dtodk8xSH+tb81j52j68OfTp5fmH8xBFCxvozKcoK9BLBKUxiM8srRcSMQ+iLtZBlLWvWrFnWsqxlzdoHa7zHUNGm0TT6AcZU1EsNqUhCBQCoUpActR4+JBkhgQrWKVBWlVWECtGU6/il24M+LOikQnLttj2QcYop7ZO1zrGGp+l3a6xltNLV23U68Tf++xazDaeKnsmnlXwfqtpQIjgG8MBJPcoXfaULfmPWas5u1ZbahUjTmGJW5RkkqSt5DCrtsmvLqiky7pSoDBIUinUFPYfN6ZO11BuV2o7dd8dNCrREVAJM0sLZTP1U6lnV6hHpW02hOlotwVEraNCzwpa1CySpyLSsVkugxBEUMlTUfuDnbyxfvzVUnyQVFWamVPMIx8VyQQiW1UkK2isPY3aOtX6xVq+kVrPqena86icm5tl0KgVXmw68eMwJTEFvKSFDr/6wOnAtuZweCN6hR7qBz547wWhRdOdP981av6NvsBprERoqFJw4eZTaWkQqJv+WEl/A3AGUndL6EqfM5rWsWbNmWcuyljVrv8VgpaEjq2mwVZKgKIqyaIZhyP8o0Cp2q2Wl7eRdN9+oWEskkc59AF9RTFSAXltViJjRcy8nctXWXQA81cHGrPfUrA+CtWQDa6l36aqERlv6c1/+jq9F4U/ki0ZfPkA6daM7wRjFjayVQYU4pzv178NaPeAHmlsw9pPYN+kIrcGuNjDkqFMxzKa1kyQ9oVNGaidzGqQD3SbR54wywBuCQgVXqOPYO1MfeVBrCeqsGk2zSFow13UJYT3OjYa1GRUEv5I/JKWrfMfmxyV3Y18n3wTOKRs8KNcyJGBi2uLlZaqzYIk6XUztYLY2jHE2Dkz0VfNm7X2Ob7XfUvRirYa+rEZJSVMxHDO4HHa+8PPDHcVYN1KWU5obdkVu8GCFWqBtQ8zEKhi5NnrszzS6lKv6E1HfXVi/s28wmqS+KXfE1Ht4NGwtpo7CyRhVBAzsRimODHDCGSb6LCNsKKW2Zs2aZS3LWpa1rFn7oIK1xoCs1ujD9EAlZgJfGKAU0aAi09BvO6ECm70b1v7yuf26QYtAsxZMIYYpoirSMSsJUmqQpttLT3Y7v3rvlArZij4OcH3955W11Fu0dZewnrtlWra+9r0bWsuBiik7oijVMWMliUFmDaVN2gz8NxTHGNCn4IzAwCJddak2GgH/QC9ZwujxLVsm6wYtBVpFaKaqJOdQ1HgW1ip3twPbkUCyePyIe6GYEPksdGTi6wnIpDZ+OmVNJ0Y9i1LVPKRpiSRFtZsPvnXgrTeeTeO8wsUv/ue/QmZrcA7rgP3nB4+8farT5FJMXxAkHrn0orhhQ7O0W0PmxMa+74MZ9R3VeL+A9xTAyFoWsTlgsHzmlTdO5V2o8g1xpA/xx/74j9UhGzJ8cIz8GLmYhWWnffzEEVrur0QojBDQ4hPoQmMtU5IaxRWTfJs6fQzXs79E5ojwhAkMVdOCiz5EN3iVWq1Zs2ZZy7KWZS1r1j5A0OJ1nUDWEJNBHMx0jwc0YkUy9WEcE9GeBodefUm3EhHkVUybTRzHNchJq1p4nV6yYv02xTkqoItZtlqYmpoScT5ZS2jtO/VGZT81Mb2nQ8wvfee/uxMcKAzzIcAPtWBZppfYyCU9pSA+aNZqDPFANzFRbIMjR9CyThCFpfyhX7y2XspWxaeCuaD/Fnr1TNBvzlr6sIJEu2IqHAJxIX/E3bfBY3WUaSLCipmXpTYwTJPGs6LeGpTtHJCDE1B8GAVeK046FcPu3fOk+iCu13bNRy4D3BqU0/oo6LV3jp6sBAHoZMgKFcUYo8ZaNCHraiWZkJ2t6eonZhjVyubcrKxf1Ca7a4Z9+wJwZNOuAx0lmKiGmYxjwI8oTnMtucHDBj0yYQQDBcsECW/thsc7uo7VBlgp7+w6fYFlHbNEKOMpJpEerKwQPpo9d1Jn93Eho4SUa7gFfYZQIiv6wi3bH2jNmjXLWpa1rFk7H6xVrQyjDWEZljw22u4qRnELMPqWRNGpo++88Oz0UQ9obXeEnTJIGnCOUWKiOh9TBxET0qmoeuPeA0gH2QaxYiLKPqSSIkT7imx+E9bqSRcgs+aFVOdk3AgZ0lP497Wbbs1TkAHoxtSUTqltpr07Xtg5zz7uh251j7GunPKkC0cnIYUlO5L0jUPvbi4WDsFdeQCtBEfEbEkCCblzYa0+tpBqhQOtJ0mTQJFV4hZe3b8raT8BaK0Od+SYgjEvVaDKq2eF5qLaLtYf1At8UwyJcEmzYrlSObRm9WwFXS2DcsovH36Zeq3jwyzkxRu2+Ppzqoi4gHhcHdXcQJE11kKWtfpxspFmjXXeI02Nq5RlrsqKruR86rnXDh7vpFrNX1bnKbTkYC7alR8aGlM3pOV258SkuWMcUxEqUcXJm4xWEDoXJmvFiWf2Q5idumjBoulMBsdO/ZpqwEQ0MMo0pnfLspY1a9Ysa1nWsmbt/LMWqwfNDbfAOZbUT1wTCcvEUcG36G6d/8hD0isb0KKeY4YUu6WiWZMTJomewtMZoHV7ntn8zEvtXqJ+X6EOrbIW1OwRzs7CAB8ca8WY1aoiE6qXOsT///7zOl+rrlVYvZitLyW3cxPHeD/WagQto6YPObYKTAeW7V0dzxw7saE7/6zabakeIlT7lTSh53aE+97PwjTgUb+cV7ilxxATEbozH7xXFjs0a5VNNiMmIYZDzms7KlsHywJ7s59iZHTs3AS1gVqGLO7a+XiSdn/4w0NUCE8oZE58BK/ownza8pXm1bV6wobqxFquxvCdZa0BstaZQUvt7YqQ21/8ZSGB52Mk4wgUWWisaFoOH9Jy5bDBuUE5JqN5y2ce7T4UgpCN+hIgCIe1t6AsxSS+IPNa5jwi1d4ttdl+yW19/uVdbtgJCS7sGLHBRjUgy1rWrFmzrGVZy5q188ha0EGlg2bOYaQShoFPyonyBHuQckgrkkFS657vfgMG6eppxdh39BytlGMkdV2O4imsQ7q2ir9843ZQQcAQ3uHmvvzfQmTT4y0a3x1Xx0Jd+4Mbj5Y99bhMYAxXomM0jYQqQAO8IVECmmVGtuE3ZBujtIF5LRmgAkB4YJJERGs+po7ik/cOr3vpxblB8KZu0+qCLW2o8Pzg9liP8cEN461UtBpWJt55C6S2kCcJnABUxETimEX6xOApSBPqicb60LJ6H5eKXyOT19JC8AUpS88/t3nwYMiWDB06VG19yUsMViqft3KtYd2AQ0+dWlnRDTO1DBILnUgL/HK9NFTUPz1CyF672aEUNAYRQTgIGKcKIdRVbFDVgFZZd06mRnBfyrV7n+9KYbeH+thVgZ9flsuBD85h7C54dAaWUWfUGkmPSdQ8X5tfeJN/ea9Mcc3V1ZUwGUydOZoID1FPp7ZQjPwabmFKcIMAjDVr1qxZ1rKsZc3aB8olvI5BQjAjf1zxuoXJaMnI6z4pU3fCvXfQrlaZhhKlpi3EcytprF4AI2uYThyZ2G7usidKenatuY9O+0Ks3yZriYYZU2pjHCZbw1gFnRuffe6fv/L1WKOXR0EgEfq7vLD5djf/oFjL6Ex7xXIWECaAuELtQAUVCrR49Ny+NU75FSlPhP57ilJ0asjNwunaR/hg9hrvy3W3D4cZx6LQcfcPvqMAOy6362OoolVodwkTN8URpZgBiUMrH44IqzIjhcgVCRka3BJcbX+ls/2dz/7tXynWGtwyqFzymD5DzLlRwmTmY49XqAhFlsBKNI0WHdcUtLZ3nFRr5TzL5jFt9pLtdSgVK8SGgvS4POklSTmGYlOf8lJCDGi5BEZNLV27VZ35x0ue0a0pOWH1G4B/6LLBirU++UcfmTRpVEzdUpJHMkJw6ElfiHUhs1ajjiXhMi06p4WMFi6dbnQywgSUCRMc9CgmrDiBZS1r1qxZ1rKsZc3aB8wlaRhlzVppwhkSWWNOEnrdkvmJ26kwYMRPbpJBCerKtN4gKE+EsQG0ouPTarXS4hVPnugomt8/VXTSxtq8CwAqpekoo9Aj5GmGUHHnF775Pa1NAfFoqHu6EioYl4hg9T0bx/FvQnpNsaDgiedBrkxBF04lo2mlrOvyXJa2b9u4CBBFoNDtgjovAJVQZtwSCk1ZmRz6+dqbBukoHOLYkTi47fvfEHFZsiB0Ozjxq+oIJE0CM6gaK1AU9aHPuueNaNmFUGfkHHAJU5KHQhWh8kGu66o38EgQC1xJQI6/nKDlazf5JJOLNOeM1mEkUOMIWohNRqmiL1tVWDeEEnOCqTM2QikSzFyAiqx8JlI9Mssl4u0TrQtWrNGIy0zRZhnKB2GitxGlHJLLXdaSWzR/BiaQ+aFwTyXp9vPiYlXeN9tsBmcBYgVxcfS4B/yooB47fp5BLXNSquRrxYRhlFjWsmbNmmUty1rWrJ0HCqkJbYNSApI0ZGFRT1uKuo4f/MkN18NjlkgU0CTwotiEyGrp6bpBtdyx/8DSlatjDoGyj4RyUHt3InqBleWY1Jba8nwQeZRD9ZqU37/trmUbtqkA9HTJT6vtW+ZmPuZMnMPu7EtjXcC4ZCJRDDCTBpKlAFrUi9wjB559Qu1RHBdNQR5JDQkaKceibrHh55+1sk318p3qWDO/pA76U+se37d9nTkTiJ+PKp1anJCwNDJbwxirsZaG6lr7EOhuC6qoLMGJP+zyoVddcSWMx9UZPfW53KCbSeLGvvn1mYse8/T0s04vcTBPqkcB6/HGjekseEdK7VXb45RTeykh2OzMgFGfQ2WsOnvyIUwSn/3o46GuBYy41M/TWNBK6CpiVscyib2Wltw1Vw0dflkOaUl3OMA4jAn0cl3k+MHVZ1Ho6IXFlLiKsvY9+1SYlASAVmB6t1KFqCSlnNkaQmvWrFnWsqxlzdp5CUdQqhvfBYrLnTKpQP6KhTIsTh15j9N6hHjdKmJ2ix0qBFfBdDFOo6qqQSGFCqV5y1eqMM5JcMGPTMydYGHSWdVIrV91R+cW67zfb/V864rrECEjzHC1nq2M4bN8/YbbQp3pUs90hklazbF8UKxlONbpPgXzgoFeQpmUJSnu2b1Uyi6OK6Cf5gR1WUjYOpPaMuhxXjToe5d0Bn5FTyJGxdajmrGjB279gYxKcEpQgC4eVaCbSxJ12gho2mtkLdnYA0aSEMbGKgbABPJaLS2DB+UkLuiGLhfhEuOxiuadKIqEbC17kxc8GjdUGJY0zwudyyKEmHRWY9eWNQm9bZkATCx42iCDUUS4TKiD6aKVq1B1zlQ2ywG6Mali3YQBb3AZX33NkNyg3OAhuXyhHXJBgnmegi6ZpviDOtM+wO+rAb1LdU4xdJ8qyipW2kpu65QZj+jvAL9RKoMwrEftWbNmzbKWZS3LWtasfXCBS5IG0NVAQp44oIFBw7jtmAxKY+64SQX/Egcmo2V0vbCR6hZQg3fCSxav32Yq8bJ0kFbuVqENJirAruo59LvH47fCWuBBAI0ZThB3Ob4JTEtEdiP51Ztun/IY6OOpT5QnrC0Ikprs3rmzVnUyr0AsrgC9iFDGBeSefuOlnQr9CDqFSAeoMwKZ0FS9ZyYLaXJEtQfkfMSmffW2Uc8vwbEOyjBuC/nS6Z414l7p5sPTR9TpYU4SIEYdpBrBjKYESA/pDQ1hV1xxRQ7GGuee2fk4cg4pFih3v6OBnURE50i1LLfD5YzHnjxZiQzMpw37nzFWIy5rtcMXJLgmg+Fz7nKuSRVauNbu2u0zmDqc18MPzLgFJimGWXfQg+dEXSEuff7af1AHZtCwnAYPClWFetXdHaUBsv2FyVqy4pYTFGqmQho5o4rfPnXmaAF5vtgNCqZ3q7X9pJ1lbM2aNctalrWsWfuAAxdd94e4wSoake7Tr21d98Kax1WQTTtOGtaK3AJkXVDgMdLNQLdh0orVS7fv8XTjU1cMCtDFCCTJEeY1jXIcpjpWM7Dx/sT1QbMW71NwL3BKapMCzzf1hKfylVjDla8/i/Lrbrz1SNnLROE5PwfWYn2xVhoWpYgkqeCgTSadrz23xe96R+JuKUPKTdFgVCy2GZF334nr4om8R5proBV0Z9vbvQUby4HDZAaHMGKLRoa4Du7ZtnDUfdIvqmeSSpfUXT/t+VYjWVlnraraCg456NRHWtQRUcdxhg4dPLgld/2X/k5Gh7etnKzLI/0wLBBJYw5NaXG1iU49NX/1przWscyXK7VtxhibP35WJKO2s83o8Eh3uwVc5pP0pOMs27z5pAMn9sli0UG41lCH4G4Jcv0OIcNy2L5977rt+9bnhuUGX5nLDcmlPNSsxWqa8TQeUCr1AmMtvdmmC8v1HSZwlDpRWmZSkVWk9sCEKSMWPzZT4ZZO7mH12f3QsyeVNWvWLGtZ1rLWjz/GomGZtZGcp6Uc0PKMce7Z/ey/1SvQ7zkX6gy/ov8fpM67Tr8thS9R+b4ff2/SfbdLr1tFHQLi6UTquToq+PYwDIHqwumSzVt+1dHtaNEGyP8k2JQt6e4mWCMjsOeFisA5TD+uthmdRwWzOtU0LfuU2qu65ExwL4qNTn0JM4Nb3UQYyvpf133ZsFYPzfoeyvW936XKWlxkQ6ISTRBaLgL2mbt72woad0tShn0uoih0dQd/THkASgdpwGhaH5BUZy2i1+APIMHVlFd8f9aqvWfKoaSKoRhwS50DTjdkOIkvS21vPP3Uwz+9WWfnoo7Th4xKocjmO9VHgdXG41JUF6zPteSuuHLwsFxOopNS5Ncsn7Zv5xq1hjR1IcTXyZlygszELV/rx89bvmLt5s2Hjr6LOCYC5OYzZhCspsjS6DS7/Hv7+1w1A7t+P3h4qH9Tsb6c6lrN3g7tkSRTdunw00fXb1q+eVuFSTO7zGHMJJxDRqviJTTFZfVLR0++MXn6CPXCctw25GoALQLJLoTgtom6hNWVK95vvtwHpEzY9F19JkXBuusriw/0G5RlpwCHBi2YFxdTndwLUHn2wqkJC0o+NBAmNDb1hEbUsWF55vPhfWRVe3wKmzezZs2ylmUtaxeSmbvXSZKYk54QIs82A7dZvbq61OHL+VvyAS2zabl9RhSs+heZNSyrLToEQXWZCjbNM/CAiIZAvA5apiguoDJIQVAhWzPVITEzZV/QAKN+4kUhRGJJq2StpcMHJvzs+zLtkKKS+p1ajS/FkjsEGQhpo2jqqieOB17aEPiwphxO7zxSlbKEPF/eM5ppXJ7JTT0e0fE9p/XxzUY2QJYJ3M13uXhy+1P/8e3vOCzrIGr3obDN9E75WodAAYCOzKij835CJNXVIq1krtylMGYqr+f3tu07sPqFl7cyDhOiFco0fArec3hR49Tj+mvMlnNxFmX5PnYRP6vLxvhP9AT16lhhBV08SSsd0LXF/dF33vjLfZtlWpJpUULsHpKwILGvX0bUTtVa9jCDK41RdlpqbYaWy4e2tOReemGX4c+gfPrnzz1FQ0X1vsQerEEzG1G4SYGmIq5ogTk0WLphxcLVy1u9gkl8OZKX1fPVVKSjc2RlvdJMStG4ggZwCrE2a/7c1T2oN6//1y+XA/M+jwlvdlpbMr3dKSQTUQpZJXCYbSepBylmheO8pPe73vUw61p99vmrt858fP3RUmjKXw3Zx9XeLQelmkJ5rDhLpES48xaPrwQnQ9QhpD/4CgCtlstzRta/ShqNPGG2rZ8+MAqqnuRUa6DU7oOQhknWtV4z40jDp/wNXb1HiBVu0VQkIQmwRA9PHhUwxa0pgepKFCYVIE8aMX1F6ys9uy5YwwAG8wWrTjHQCOIZKwve+IeJNqrF6EtJWrdu/UJxebY7aqZkff78+QPLqFuzrHVxmUKCcllFULImvd3V1dUP1rqQXTaF0WciroYlTsn7/JbJT3GmIlTEVETMGkMZFcagBDfcooV7vFGprH+AIW0Vd0y7+7tJ65uSlmRSNDOxUq4iPIhiVfT28onW+Zu2HfaiU4iVs9QB7Ytt+lzy39J3Zb9zCM1xIW+IvTBu5i6k1dYXrNr4het/dKqS1EJYl4gabjEIEmEFKYIEIMGRIq4kKevXVoTMC9kRocMV/60XX1nTmf+lJoIwCh0OoCedUjDQD3vGj/4+ONrPZR9Vhfq8oZokE5k6uNwuqS+9zrG3/8A/9hYpnIq6jgGDKUcuEAFIWaaVQldtOlkUY7Weih/lBg2+fPiwlkE5taPioAB7iTsn3nrul89ulrwioy7JQxoVKVLYRhBKUkpCoaiD+zBFW3iS7njp+VkrV8xbu+a1Uyc6MFGkoYihU0Bdq/JuBscRNPvNzRoO0hFIj4hKBXhS1d4w5ZjBAD2s/nr/PTyDn+n1juDK/Wo5pbpQvWpCs4uBroijp0Rv+8Wrczaue3zPntYEm592qstTqF8XFQo3CzxG9GlsppOTiHjKZ8ybNH32WH0XIAzTQpDkc4NyH/34VVd/ePhZSYn328/pVgnTk9R5TQ21CbeqTVYZaw00Z2aIqOa16135sdYTFCiJnug+qXBrzLTRB36xP6YVJDwmIyIDxZ9qpyVAXBQLUsO8hHAvVDxG1VcGqGn0+lRxnDb8YWq8xUNtdGvd+gXHWmcKFyxrWdb6/bEoigxrFYvFcylQ4b/xXdAP0PmASwhZg8gbpVz9FVchKNeDcQWH5ooqM8DNYQhWoDqGyiAm5bLAiVYvUFEXKSNH0VWh0CoZkhjx1tML7vrZ5B/fJj1XUqKnekqasGLBD2NYwekCWrFp37FuUB0sJDACuEGrYACx+4DrtAa0/A2+XhtxokddVsykk4pUZEKF6rOr4Pifrv3Otd+6oRzJkEo3ySrWCmUf1VRAjCt85aEUrmSl1D365qub9u9cKtnpxD0ECS5RUT9lfkUqUqPnMn1M9FXKeDbuOodLxgSmTTkukyjGJKyAKoaWx0g7TqgPu3DUfSumjEnbj8uoDLWF2EdxWVarCl2nmCZRdhnqz9uSg0lbzz+/F67scqukZaAsVj744lOvPr1e4hKPiyZ9GIQVo3NIFEppdQcDwyZdY3DlRClav+/luSu3Lt2097Etz0x7YtPrBf8wkh2KTNRPpXyPy+NStkl5Wvsp/aTy49pP6B91DMS7BuhnWs/pvrxTo1SNu450JDuef/vRdU/PXrFtzhPbl2858MybJ1sDuAmSx7IzkQquNARCvivK8mDEYS6SCMkkFE5Iy+rkbSscWfzYtGee34oYyF3q+wI0TeNc1YIgajpneicABxa4DOT201lrO03WsebZfR7Sb+81Sb35q5UXnLy6Dt0EvhsrcZHI6JU3npk9f0LROcV1W1eEiorxMAsRjaLUixKfiUYorQ05QJRFBEoQSVNM1mOvioHun/P8/WmXdvl7vhwAa1mzrHUpmjrLa63wZgmVb2dVfBbNkSirlVCdV3YakIusnZ311XrR6KghxxJy4ROMexXscYEph7IfLnGEg4iGEIUmoVR8hBJJEmi2UWEr9+MUElah3wnyDKkzc8TP3tr9lPRdKJmB6cQyjQG4YNCukMtW7n505S71dHuRVyKZr8iUZ5kgJuoR0cCW4rwsf3NvPGdixKOU1Y5IkPJSQDwEcJUlu7i88fb7/vPL36UgYwgtRr4PxUWFvKMiwSjUGRWFItQlzukDu1a9/PRaEpySqAvyE6IsaZG67ToqRjKOcbECGCEGtidZFn1my2rY10y59R6YAf7dEX2zFlScek7W8EYRCzwgdqh3i2Sli+VPvb53+/03ff+Xz+5Kyu2Q3RIJZLdQoEsKSZDvQo76+BQ5lQ9fNXzoEBAkjP0uzQiuYi3hnFS4BYWBtLR76+Onj78lmMdYEPhlXemZbQRGPE0YJrDbqe6HQ0x6qXRjGekaxUBjTJtmqtdDsvXdUwsPvDr1qWcmbt/38IZdIzfsGbnh6REbnx6xYd9DG8FHbtg3du3TE9bsmbh6T3+WZ/FJa/b25fsmrtnf5Gv3GZ+++fnpmw9U/Rnjszc+M2/lru1P/+rQicBNASkTAYoXoQCdRgNgigAcUucxRbeOLMcy9GTR4wUkfSzdruA4kkXlIWqbv2TsM8+vl7KYIEVzFYQ8rTQIXyBGiH/61Gl9U8E5LDMYQv12Kt/vTlOj62TlWQuEe3uf3Ch5HIcmy62/QkkQO9Wpx2pn+wfffXni1AfjtKD+maASgV7KRKdFIc+GcKi4i3GT/I4SVESkYmbiYRJpZdderNVQg6B5jPZvyc/r96dd2uXv87Kh0LfH94VlLctav0/pLLXs7Oz8i7/4i0996lOEkDRN+8zk9kgB9aAXeuE5boarWtFaKhtDqPqDWptQ9uuUwPBNluo/9pmQsRFxpjLCKhAlnX7QJpkLtV7lTlnqgs4Oz3no+m+NufUGGRViFfHLEMswlqANXY7k0y8cWr7m6UUrd6v36qxIJ5EFD7ZNwZXrsEznIJZxiGTvvoXf3VKcw43uM+S4CKNMcNGrryPDMMwqvornZVex5Ibw4Lqvff2rX//ageef88MgiMIq8+C20++sX7Pg+Ls/h8IuWslKxqhHoyJU2ZEA9qMiEKH1DnRvx4A+tU5gUlpdMs0irKHnRzTXcA54f/ZC0NqPEIIeMxxHuuKLdh0/JmkC9K4+oFCE70gaTrv/p+NuvfGlDWukW5JIaxhinXTJzuIE4vtc7lN//NF862FQB+GVpPCe5EXlzD0uhWm8cletnLdv/2b4LZbCejg0X4ko0DsN2nsEwfDhmjOcHC4GUbugfM5LmBQRLWIWVesA/QbNyaB6WTHR3+U5XOx9enbF8l7ezCJptdFHy2DgiDP9IHVRrA4GhjEMbiE5imURyzyV+YCfYjKvfMqse8ZPu73iHYzSo8oVhDJ+UrcOqk8fJlG+pSV3+VDFWjmoiSNe9X16vD8ZGH3Bf6hhVe/rSGQnMO+zEav3QAL9W2G/PYKzpVaj2KNS0Uy9qx6rQrEzCEsVV6F6iHApjEEjdOHiqSuemGe+hin3CHH0/POQCV+IIGvBapLuIGe/3yZAagj1e0nFhfR9a5d2eSkt9V8PUq3CaMyGZ1erZS3LWpe+UUr/5V/+5ZOf/KQ63Rljn/70pz3P0/IYvEc3f2MlGO514/SCwi125pIweobtR7WIn0PnFcfERLoSxRCAKgdZdt1Lwv0wziMZxQBdEMr86pXnJ/3snmUTJoRHjsogloio2IzEfsrio90nZ61eMn/j6glLHzcCeWUmu9NsmK8JuApOhDA385Fw5OswF/Y/G5if16rMmtzfubJWjbh4dvM5xVGc+CZhSAXEuo6n0DRiwq+Sb+TH3UJGiDkxLqkH/33DN3bv27RyzeKUVnQIDS1CIQqI4DEmYUoat5nwbNZzw3EXvf0MPf28Sll117jVJFHQEPnRge7Ss6SIy06FMFotzRVZzAqahAF0VKmTkIVB6xEQLYxdmfrjf3rzw7f/sPWtVyR1QQBD+jguqDNr2NDckFzuk39wheReXD4mSR5YS+SV8/hkWDms3iqNWqEzLu16dvsTT62ef/CVnXHxPZiDzCsyzeuyQ9dgg0BlmhpdjIQkleyi0Pk0zbQaa9WmUsbBBWPgZq+xM11yZ/EzwVM/S9r6zLc0JF60HiNPOQYpC11pqlX1UYAcLynqsysykuXK3bCNSXXKqWu3HLEOJzw+ecZ9cxY87IbHNbLWPQ5PGojFukRzcC43dBBkFw3zCBo08E9UXUZ6qjUfUN6+mWTez6sKQ6w6ou2MFYXVfJTeAz5TtNOPpWatmkSKdq3dkrmglXxnw9ztmnBoaD5+6Hd6Xod64LrtUyaNWPnkgoO/fpnUbqDAF4KCrmzaeBRXwqjMgfSzKs3e1YDiQqpnt2799965bhSImu801b6pLWtZ1vr9sE984hOmaDCO48985jOf+tSnmovdm4LCWtTUmCBC8vz3aw2ohpD2Fbr1bjBoWK2IePajWNZL2bxYK2YpsPJlqVtGHsyc9Qr5g2+Nu2/UHTff9eADo/c++5JHof2jLWUlKVfuf37qE2sXbtixevfzx/MR0mGOGwNcHSp3eJkkuYqXeVdSTuF+OaIweSaqJgNUhFHx3BMCQI4MxOn5c1aTduy/nylGpLLa3l8/WvrWdZcfvCVlq5AnuDxJ5XHEjknZHqSHN22f88Jra9dvm9XlvoWkinq7sHT/38/947U/uPlv/vWLD0xdWBGw48osy6WUaCY3UqTSrSZYUp076O1nwHUIErFu6MfvR1wiY62BebOyNq+Fwh2FTrUMUWSedDxopsJxAKmtxNX6hKFiqjACpmLSZdIhtADnlHBR/sjsUXc+cvP1o2757z1b1i5dNFux1mW53P/6f/4KGthEWZIuybsladUtTnntJsGVh3pAdEiyI5X2A28+v/yFPfPffGkFj3+t6wSVtzZ4O7ye+ZAHSwKZBlKFvCSCuxLI1MgS2FWMVpUJ+bmVAX8g3dga+WpGjFNOEGiMqgNBtCPtUZgWqmMDQHrQ9wxKVTgrLls2b8nSudOmjZs1a1L2JcIwzJGrFFWoEDkO9hQGq0d6YhvUAauvCjQkN/SKwVcMyQ1R/yx25Js/I68ueV2wZ0Aaf7T6Vfa+S9bUQ9WEVb313vXrRXYV9Gup72PouQUMNbg+E5KI+i6cCanaM1zECeyoLNNFcOQloSOzFKmpxIb8beR1v/LSnrkzx02e+NALz243EphUtnHZZY6IzPT2s/LIps+iJQoZH4CqY/WjU+vWrZ+H+IGITLc1ai4hynCL628Ry1qWtS5lUwHIn/7pn8oGEcK//Mu/bGYtLptLB2tVN7Xqu1RX/hhl3vO1HFB9MG+SpKoPUOLZ2livAkjOMzlBrPgoZUG+sG/jllF33PGzG2985I5bxt55y/QH7965cvGxXzz39gs7Nyybv2rpo+tXrVuybPmcZY/u+PkrJ0jcpYPQdh2NdkvFClq4TMgAwe5yZezLsCTL3TwfQQtXKZWuJ7oruJVB732ZiE4VT2DWyrlaQZFB+1IycEfnY6krbfhAtDpoQ41TVNXH1lEsd3So5OplCZEORtWe60zSXz29f/ruvVN275m2a/fUvftnFisvUHmIySNqX2L5HpKHmTylvBC9jmReUa8i29MclzQoqOW7nvzn7/70H759x99/6/bP//Cef7z+x//2g1tnbdyqDoRb1YHuP2vhgbFWY8jeLxd18TfjpBr3k5iE5l38RFG8Yhd9HotEUqgSTIIuLdwY+ihPpZ+IgtA7k6NORVO8cgJkMEgFZmQj/8rLgLWuuTz3yH0333Pbt0be+18THrrh0XkjvfyveXoqdN9VO4+QNjhtxTtSKNY9KJJXJf6llO9I8rpaCvLGwV89sXPrhJ3bJ+7eMXnPzimH3l4jybuStUtmEmXaM2bTzWDKhdPgUEho+qH66VSG5AyOZdDbU/Col8PzSIbaA+1ezVNZJLJCZTGhnYibCF59hPx7x55duOiB8RNuXLL4wXnz7lq98pHTJ57Wl3WZIEViPsXqZaFfac1ObDguYUYaiQ8NnAAYQFwP3fvAkNygj119DYmSalldtaBOaH3/mtcvk356Al9V/QczOM2jKkP6DYV/evt5omtHq3RkODnbVN6/JdGgFellUp0E0LBORkUUZuBN9GXEROIFWvNCVxAwDCl9vWcEDpVne4YGgsAGF0tH1qyf/8jYO6fNHDVr7phpMx55ev+2Kmv17NoSA28301f3+fr+tEu7/H1e6kEXYdM3T3ajBDWwFp8/f6ENyC1rXcr26U9/2ozYMtmtv/qrv9ItW5xl00x4sVjetn3HgiWPLVu5+rEn1i5ZqXzj4pUbFz65SfmCJ7fMW7VpzupNs9ZunLNm4/lZrp+zZu2sdavnrF3dr+XatTPXbpq+dovyaevAp67fMnn9lkkbtkzavG3iFvAJW7aN27pt7DbwcVu3jN+8dtKm1ZM3rpq6ceW0DSunb1g+a/3yOesfnfL43EnLp49fMnHMorEPzx81aoHyEY8sfHDMkvvHLbmn6j8DX3y/enLMkgdHL3nwkaUjRi0dMfLRkQ+BPzxq6Uj1/PhF90/UPmkh+OQF4FO1T5uv/MG6zxsxdd7DU+eNHriPPR/LafMenjF35My5D86cO6Jfy3n3zF1w69yFN2j/UeaLfjB30X9p/x744u/OW/TduYu/M2/Rd+Yt/N78+T+cP+9HyufOv1H5HPCbZy24ZeaC25TPWHC78mkLfwK+4KeTFv105KN3PrDsrgeW3fvAYw888OioB5aOfWDJxAeWTH7o0anaJz702LiHlo16aPlDDz1+38OPjpy0YPLkmdMnTp86ZdaMGfPmKFcPxk+drHzekkUTpk0ZPXH8gkeXzF44f8ykCTPmzZq9cO6M+TOmzpk6ccbECdMnKJ88Y9KUmZNnzp42bcbkiZPHLVoyf/KU8fMXzJ4xc8r0WZMnTHtkxrwJU2ePnTl/4six906YNmrx8tljJj04cfrD6vnJM0erx2qpfqr+OWvBpBlzxjb7OOPT5kxQPnXuJOOTtU+aN2n8ggnKJ6m3mDtu5uxxc2aNXTBj7MLpoxdPf3jhjIcXzBw5f9bIObNHzJozYsa8B9XBmjz54RlTxy2eN+Myndr62JW5WZMfmDX5vgUzHpox6a6l8x+eMfnupQsemTbl7rlzH5oz++6F8368aN6PGn3h/BvqPu+mBfPrPm/BTbMX3jRrUbas+o9nNvv0xbcan7b49olLbh+39Cf99zGP/mT0QHzMY3eNXtbD72zwO+q+/Pbxj90+cd6NU+beOH3ezXOW3Lp4xZ1Prn9w047RO/aM3/n0uF17x+za+8ge8If37nl4796Rarlzz9idu8fv3D1B+e5dk7RPUf7cM/N2PzV91/bpT++afWDfgh1bpx/Yu/DpnQt3bVs6pCV31fCcWj619bE1q2a99urWHdsX73xqyc4di7Qv0D4ffOfsnbsn6fX30yft3jljz46ZvX3vzlnP7V+otkc9eP6ZRft2z1EPdj415Zn903btGvPss1Oe3jN+25ZRLz0368C+abu3jjmwZ9r+pybt2jTu+Z3Tf75/wfM7Z+7bNOWlnQv2rpn7ytYnfrFjzUubH39p84rXnlr16rYnn1275OUtTzy/4bFn1y5Vj1/YuPzlLSvUMy9sWbF9xcLntq977emnnt++YceaFcr3b97wwo5tezZt2Llu3eYnnly1eOnSWfPmTJw6bfT4iaPGjhs57uH7H7nvzvvv/PFPb7vh9ttuuO0nN//kp7f+5PabfvzjH/7wB9df/52vf+UbX772a1/6D+Vf/dK1X77229dd+93rrr3+OvXgS1+/7rqvXKfsy1/8+jeuvfmW7z485qfTZz2kvnmmz75/+uyfzVxw37QF90xdePfUfi4X/Kz6fTvWLu3SLj/g+GHu6OmzH5mhfNYY7WPBZ05UPm36JPVndNasGdOmTVGsRQizAbllrUtWG+Ozn/2selAqldSytbX1r//6r9UDjBLltVuGmDBCz1hDqMee6uq3C8NLOrPUKAbdXhWkPtWgRn2s6sf186f1a9qrktMFuGlPS5KUIIUSdEu3SzpdstwtK0VZdGW7L0+H8nQiTyPZRmQble1ctlPZSWRnKjsj2e3J7oosFGWhAnLu3UT/SDkF79ZeILKkvYykk8LtdnOTPiDSIyCEdkE4lWUuC0J299vbuDzE5Vuy7m9K+br2X1T9Ne2v6qV6/nD1UBg/qf20WpVeW7Zvza5Dst2TJxx5zJGnHNnmyC5HlhzpOtKvSK8inYosV2C3t1fk6Yo8rg6WSysRQcrLgff2e++u3bzx/lEjvvm96//xc//6pa9/9dqvfeWLX7nu3774hf/59//7pttvfe6Vl7wkCHCQ0JRIM/DHFFJSP/SyebgcpWmsCyAZZYkAib7Qj7uYTjqpJWKV2uPaMiTFCBWY9Ln0enmgHbI6OrETK0cyTmWcQFI09mQcyTTV0waE0cmADi4vSx/p7CiFE0mdTuqfSRxUBI3+j09cfdUwaNzSQ4mhgtUvH9FXSZnijlo+R+/zI81+tJcfN0suj1J5GMtDuL48jPrwI4k8opdHE3ky0hdLP5fhQFxdiW4ffrLBjzf40UAeZfo7QMjjTB4R8j2Z+buQ0AM/KOWvtf+q6uafB7Wb17yr/T29T6q7S7wH3y702JBBucuG5NSyuoeLKDnB2enm76T2alnmcb2qgwPxd2V9sxv9XcnfBhF+8iv94B3zJE1fhe0n6rp7G36dvp44z8FP0ZvwK+yQelnU8ZwURyU9LPkpibplWpZRUVbapNMhw7x0O2X7ew999ys/++YX7/zy5277z3+69Yv/dMt//MPN//73P/r8//7hlz4/ZeS9L+7YVDxxWPglqCZNQhH6WZaMmoyWyQGLerlBbbZAWi0wqBUgqF8xiV4GokFCJ5p5rRGMmUwhRdjXgvwVvZPV1ae+MZSfovI4kcdIf5enCXwVO9atWz8P8YPDpMvhr15Q9YiLhAuk/oxygdWF3NHRsX79esVaQouW2sjcstYlqI3xJ3/yJ7WklrI///M/1/Nms5oxQghCqNavpf5Q4uq40prHF57XJqgaJTRXu1MlsVI1wGzsWXG0u9k0VR5DiwzWEmQIwyCduFqPFGIIrHuJiWUVQfWeMKYl7DDEBkTn0N0GNwWGKgSP9LRjgiRPG2oy9Vv4A3H3fLovmoqt++NhQ82Sn/WpNbnh4nKvZaVaZFhrtgq1SkHEMlU5pKUgDZNENGts430VBSH9AngZ6yVfQQRPCDaPU0pqj41gBuYQ4sVpsm7z+q9+/Wt/+9m/uf573503b44QmRIcZH0BudTlAUEiE6HeRaEXdWJaMY9TqhjVD9N8iktwjIRnnjeC13qv+qJnZVcGZnrLI3UaIFiaT60+DtFjXSGSjcCjGIrlQOuSNe3ksCb65Dvdg3O5q69o+cOPDaNpWRAnezsBr8Qkn7V79esmhttwaGrVIGc7MUSP6nxzdfRnOWCnzf2atHm4bZ/6G2arYiEizn3QHeGeYBXlkpUkK2jPGxess8G7wWle0KJbPmb2Z6n7GOwKXNGFeaGC2498aPhlgxXipmmknjRKhxGnLqeO9jI4K2Zu/tk////bO/Mgv6rrzmsXIBYJgVglEAYkS+wyMmIbBDFCEtpAC/ti8AIpu8aT2EPsmsSeeJLx2DVylcm4KDM2kwlkjLfyBKpCMn/EMzUeUzhjx46djCGOsYSkVi+/fXn7nHvP+92+/etfN93qXzfq9udTV7dev9/rbvV97917vvece6783iw6kkWHO5Skp1H+5WC6jvBQGrxltkMwWVUqZrlpVDWl2GM2q6gUTG22oWuW3nhdai3f+2/feHLH7oduu+OR9935hY994h/+x/+yEcpJ1lu0ORzts2lmhALbHYZZpW6mODSAcGjooH1aA5vFJsmTq+tr5jKdRJ1u1/BbmnTcXixq3dx6nJaCaCAwWXMG0sEucew1hUKZbPuhc26MzObHePbZZ00OXkBrzVSWL1+uMYR9fX1bt25dv369feJzrSUfiR7TYPhavTlSzvfAy7H+jtf51pp2oyo/hXQwNB1awyt+NkVv59tkqLnmeopwfAsCEl0y0WxP9JxpvsF8eVPDS0MfmRldFRhjqdVAL01Snea2e9PL3Dp6HWXZqDkz/JQncVv7tKWTyzOqt+VGH/EHtiVT0YxtI+dkT6yyipK4UCnrmUbQLJSKUtebDTPN5r0pYRjWq43Pf+4/3LDhxgfvf+DX//SmWRwSJDbRXrNc7cnTzaVVTZvWDAvumQnjUr3ep8eNoC+2d83evkpHiWIvMBZuYGt1B8Rm/VjYyMKayWrftKVes8lbIn9dXFo1KQeyqFwym+p+4fN/NHvWrDlzZt166/X6fwiCguZ7jM1iKv0PhMMyKowmbLx9ZvNbnY2ya2U6zi0DjkFnRZ2SEI6SqDQeJQNH0ipRK2lep4V+aZ4W/603f5XnNmxEdgFSOm/W3BPnL5A2ty93oqsCywO9w4SfW9DYHF/Od31fUpsHclgdlPukttthh3bL7zALo6CnKr+k8IvefONwv+87mkj95J4PP3HXB/du3N18s2YfosQ41wuNpK9q9hLLM+inWS0xpWqXYDR9yRSYbQOCRj4JIJotthMgsZ/8IzXpMQIpnW6W33nEw7Y/75w20biX46SR+Nk18hz3msk9Glsd2jeuNKm9KDX1b2Ztx7h6x5zvaRo3GjWVWPv372+Ns0QSorVmIhs2bLjkkktsnvfsoosuevHFF1ViqQAbnksjMqmcjYXpSjPJIwmrk1WnNuN1PMa6lqWaabqtmJNJa4X20KJ/iP5pcZwm+UAc2mgVk3MsTaQ0ZVA347pJq9XK2u60WjBMzDmvn924x3hewpbNnBfzi2J1wdhdgIwZk5oQsSCJo9Rs7TXmuhallUmqRTmIaS52+Vhr+ZbExP64Mpj/ebjfwvbA9tPUFmuhDb1rWoJWiUbJId4xRXgkRmCzGQby9A7uFJWmmlo9iEKznbc9X2vUhzz2qen3q+VGtVwLm4laulE9TJr5DP03X3jxsQce27drjzwqQdPsMtR79JA8MFFYVwvbpBBs5eCIm7U4ashxFDeCtNHMalJHqbSVuYlJbEpm68Q0oDR+LchMqZskbk2b6U9+bjNIm5ExMbU0XJHHILBhh4F5gsJmWLMOtyAIa7Nmz1pwwlyp5RuDqK4bc9fqRd0x1jzrSdaxdNheQM6nJnFIo1WCIXUwvITy68adqXE8xQSnGdE8rCSDpfV45Q9Z217bcRLLTQtF88Rp6y11xeZ+GNIuacsBKvfKafdqqSl3WHqLObPmitA64cTZtVqvau9S5YjV24U0LSfWmenVBfPSx/nbMZa6ldHFxua11UMyHGZhtSp1otuEV1uJ1nttx9rIdt68995ND3/x9/9T81CqZ4yz0/Zs0hwucYs8h2FSTYekcjEldruXGX9+NPiSm2Illnn5vVybbekoO+ZUHCkvZYc5lDwyw4ZZ1FqOdON6jZKCvBqR2cQkHlsd2tenMqm9KDX1b2ZtSyMeHLOarZSw+VCrOd+d1rJxVYDWmllo6ODzzz+/du3aFStW9PX1Dc4ZJol6tPTpr9fr7svh09bj3T5nUkuUD8SJ7hUzhnpYNuQhsSutK3MfSzI4Tx8NG/7d3LPaELmYe7u09Wm+549zzaXjvpPJZJZoXHunWjXZ8kGleensgxqak3p4nQ7dRNv9zA6+rFFMtMRPAh63Pcbypfb1Sq1WGxgoDrk17lhM7kaqPzOuxSav90C90V/X2/adF7+95bYt73/wUf2NYSVslOt63Cw1rfvLPm6NtLW/01AP6hCPSuJ27kqtHa27WdnHzf5XdTumXA8lLd9d0tqCOSrWCo2gmrRMXflFIrQWLztj3gkLy/VaPQxamxInjaAuCjNv5NSrsxHTqaf5Xs9Div7qNgEU52t08uz22djq8ZexvBRRh423OrlK/BT8bkOqIW+3V+LQbv0bpGmSt8ysWfNPOOGkWWbz4jBJ68VSj3WzmPmYRrPoxS7WvXcqHHdG+xFaotJfMn9WM98Y3WS2N3NI5mmrH06bR7Iv/Junn7jvY/vueLB4oGF+f2Vw1qPZP7iSyt7NZiUqNmyQc2SDqHsrR/RYS5g1bGR1LcxsHEQUxvVm3GzY7crc7sxpu/fT72Pj9m3Q4oaZRrC+L13fZY7zgMTA28FLJ2LiPOestLPOB9k1IVKqoz4VU99/Uii/4aUzDYuOv5/73OdGsjABrTXt0ZQYorja5hLyUTJN20ejTrPHpiTHUzH/JV1WNlib5dVSG5PV5DhO0/rgcSK2Upo4VeCb6VFLXqVpnCZhKlfHpgwuz0rDwY2EdCJcfp2/6Fv+A2Fub+ZWsf2ZZtJXLYjWuu80lF9iLrEb2E7uFmTjLOl4SmJstsCucwvCVrEuQ6NqktZD5OzeNNZAqzjJW1KP0xF8LXHSYQ9iTV8RhYO/MQrN/TL3Q76hWq026w3jXWn5MaLALLVq1OotozkqDhQGbVk/htE9Ca3/c1Ixa3w6LC+pGb/qP/zwF/t23Pf1r35TztR7A70sLCd6TbM/MrmsbQSWSpA0GWp3ejc0TfO29zwDrTrJvN252ov8ayZmBVq12ZD6aLE4e+HCWXPnzJo9T39JLYjrYVSq1fNwyiTTW+PVnkfI8wuZ/N1J1OGeRPbuufT48lSHrrYZRMacQdy+beMjafvvpIlXoraSyM1vNOXuh80oDhMx2VNvwiUeFiYdqwpu7T/hihVhJiTUJBmqNeTLOQsWmmjNufPlk2LJeHdT85E8g6JLEs2v0rl08LWPWNI0zh+DkTuBqB7qwZEDh+V8WIw+/sTHt9+2S5RdaLYSyKr9Nb1eOiH5eUFgfH1u0qFWq7n5hzQdjOqRA29eIktTf3Iq9WMdoyA236XPsN4U2/pmd7PI9hFu7BjS0bSFCybDxPBIk4bZsMEqHXPBGKZQJrOM4RXev3+/7Xai2MxgAVprxlEul3WI1S22KpVKZm2vtOX4iqIotnF1ZoLRnydOW3WqQSManT8J9XhLGo28IL5jCbPhu3wOWyHg7+ZsVVSi0i1ozei7NFtDw2yaGvXWvhJMd9pJvJ12/CXh6fHXV4652IaK2raT8hKQJcOtK+tJiNrqkXcfTeIOuw9Hw3Z2TlyzZyN7aUSDmfwWQ5wVyaAFaOLE0tydNVR3BYVm/pTFZpsif7lTYpfciWn7wfufOPR6rwmSLLdCHKv2pUk9H1061BE3dHBKne0Yjzx0pR0GtkK1mNq0H9VmoP9lEVpS5p6waKBcq4eJ/gcq1WacjD61Hw3xC+XrlDrFdw2WtkhEu/Kz03q5jvX4nbpv+0i2WdZDvscJpzb36RCtNcJvFMnmfsu/++M/mjV7lpRFJ5+q7kGNpNNnUb5sNIZItcFitcnwIL1WH6Kl7upU/WAd71Vodwi0y8ZS6zndvWPPA/vubxQbcUt9aamVyvI3B4HZ3sME0ZnJKfNyhUndRmTmP22Idzdt+dxbZ9J48J6ZiEr74ogkFxE7RJINd8sZ3R60Zlx0HDFDjNct5COOiWaUA+PqaupiL01cptMBIhGT2LvNdroljo5p82sMYgplUrVWp3W9ut18s9n8yle+kkcWpKzXQmuBP3OYTlU93tLNMKTOJl08NFXDSLOjLnqqQ7DShP8zx3Vs6mjzxsPatPUtbfUo7fO29rTf5ukE/owxP3LDvmswjjTbcvOdH37gyXpPaPIRahaUOLcIG/XcrhWrUn+O7m5nHVMNMR2rQaUVNzjq3zPs/5N2Oj1r1pwTTlwkdaVq4gaDKGyLj5djXbrpne90L8b9SnYYbUeq3+lHt/Pt7e3tVf9XsVj0Hf6uuU455RQRWieeeKKJbRvLczKkHxh0krat6Sw2CpFJiFINzGq9WmCzPmgfFJRDl88nMrk2B3ulP/3Kn9619W49ky8qe5v/TKeXdHLv2fA90Mce4wcA099OsB2p1k8//TQNgtYCABhNW8aDq5I847CZW9HNvvCRux7bsfGuPO2fruOqDPrTSgXjXq7VauZHpVGhOmDs56gS5RnVumNfLlmyZJaJcpsjtQsY06HOaQkNmreSD3JEX+lBtVp1iwrq9XpmAwGkJU866SRp0hNOOCFf8D2avmpbQtaeks/5b+W+i94OrZ82yKKj1b7AHpQaFfmxxZ6Syqq0YYVWmB9/5pP/ds/2vWHFLLqS2lyWHB8qFgAArYXWAgA4Nq3VHqKma/1rZllXozeIi6mmRiy8Wb7r9t3NgUADtIq9JVVc9WrD/1GVZrnl1Iq6qLVKpdLpp58+e/bs+fPnz507V4TB4cOHs1b8sOorqcPQ5Gb0U4aAtIa0nvuyXC7rgUiv0047TYTWsmXLpN3GoLWyEdJ1tMkt8xiY6L4s6Rk4OlAtag6ScrNqdHor2k4eHrP41G6IeMdtm7/99e/IQbVQk7rcX4nqsT5jaC0AQGuhtQAAZpDWspGBZvFLK5IwLWb1I4FLTH/vXfepTSylUTZrbN46cEi+6+DBg/oD4zRSazvtXtBUvW6S2oswWLBgwSyLnBwYGHDpd6vVqguQw7Xl2wTaGqK4/GStolHFRJBmFOF60kknaaoh4++amNZy28rJL/V/Umhz9hcKRjv1HxnQx0aeoscf/sDf/+hnqqwGV9iZDBnxsa+BAwBAa6G1AACOZ60llm7hSFF3p9bk3oO7ikXG6/XIvY+acC8v6Ywmx3MjUMdt7iZIuVxWoXXGGWe4n3/o0CE90AVICK3hNoG2iR6LJJaG2rp1qzTjwoULpXaxhWmavp28GSkZ/bD1SPbnFPrL5WJN80+0LVy6e/vu//i5/aK71LVlFnFVQ6fepZgv0VoAgNZCawEAzDStlWYDPQX1azX6m3nCy0ZWPVozMYR2dU25t5IF2RPvf/K/fvXPxCxWQ1nqWqVu3GJRmlvYXSJJkmq1qsfz5893ri11yPT19TmB19PTw211qP50loFGVy5evFha76STTlq6dKme19jLsdyHEeTW0LwUVmXl52wOjKZN114+WhMpteV9W//P//yB/2OcvjJ7bXuJNJt19gYFALQWWgsAYJprrbZT6toygYK6b3HTbMw1PF6sUWzqBffsujeqxkfe7JFj811xFtaiPPX2+PP+jT68Cc1mc86cOaeeeqoIhs9+9rP1el2kgrpu5IDFWsPRZBiaHmNWi3nz5kmTuuVb2fhcgiNk//NSkPce7MszaNRtVpUwu3vz7td//kZrx4JABbkWk2w9SoOG8WXJQaG/KGdwagEAWgutBQAwE7VW1NqnyKaJy+ugFVIYZergkmIcXLHZYfbhex4xezPbhV5po7XBU9I1rRVaMpvS/dVXX1XPjKbRy1r76ellkxG+OH3RjQdVbs2ePVvXaAmZlydD5Osx/ezOKt3lVslqdlPsINu96Z7f/5efsZtohboRnGoqrVO7NEuFlpRquTa4WRwAAFoLrQUAMJPk14g7pcYjlzDTbN077tiZKzG7ebZZjeMZ0Pk2smkWBbGzuc1uXbVmbmqPgEoFzWCuiqtUKp1yyim64mj+/Pl6magsvVKuUekltUbHtQkwvSzztpmaLjjvk3rw9O/y/zoXDagHToVKQ51++uma4X2Cyk1/XWuBXL4BgIYCpnYTtnylX5jduv63Xnrh5Xwb9bCb2hsAAK2F1gIAmOZaq6PoGi69mvlBo9is9FTv3rxbarG2a8W6M69LhbK6L9ShIUIrbEYivY4e6dULzFqvUSkUCnqg4kHklm66pe4apwGc2HBLvOS8hheKUHGpIHwdMl1wQZKiFVV0yZ8mf6aedH4qNQKct+qVV15ZsGCBtpIwqhdrxC13tfF1cZcvUAv9RSue09wR2swTqNx567bH73lczlQO16ROCmnuLEVrAQBaC9BaAABvo8E83WX22koGFZcJIYuzDz78oTyl4dGiXpYnnVOd5oIMrY3ec/DoKLa1yAO3XktRB4uwf/9+twbp1FNPbRNjw3+UE2DOtTV902mIuHKyx1+oJifVB/jSSy+dcsop2jgitz7/+c+/3Y9MhuYVTIZaGNaL1fY8JFm9ZDxapb6K3tktt239y+++YoR3f938MBtQWj9az9O4AwCgtQCtBQAwVgHmJZFL6mm90JCD2kBdjkVl7dy6qy3dnNtPSc5UBqp6XC81RnFlOO9Tb2+vHtdqtSAIRC/JgdSiKGZbVFeICNHNjoX+/n5fnKhyUw+YfDm9csS7+Enn4lPR6Od2z1pbPC9ZsuTkk0+W1pBa2uev/uqv5OTRo0dHFVqdtZaq1mKxrIrr8OEem9AisTI7TJt2jVYj3Xr7tm+98O1O3s5Grq7RWgCA1gK0FgD8ZomlET9MRi/uut6ePhVdcSMZ9FyFuedqoKew9fY79bzJJm8PnMoadIslo/1nYktmHThOerkD+ejEE09U19acOXMWLVq0cOFC0R7q7ZHLhq/LcspEE8dPC9qyLMqf4AICVVJWq1U5I3++c/RJfeutt2Zjct+NnMndtrD+8qiVXrLQV9F0KUkt27V59wcefkJdlHI31ZOZBomURqWaRXHQrGcZKSIBAK0FaC0AQGt10lqxmMxeaZNbjVrTSSbjudK8hS1xZdxWSbbtju3bN+9wvhO5rFqomTVd9kuTKX7k/4yvl9Q9pWFyzlGjvpe5c+e6kMIFCxa4nbiyllNIvle/UcSDqqzjMD1GOgKZFzwp/39dPaVfDgwMyF+qglNThgirV6/WTzVgUuXWyH9vxx2Kh4i6o0f76jWTxeTNX70lH772v//unp0P1vpDzYTRrIR5oKDJ6m4dlVki7a0/cKBwFLkFAGgtQGsBwG+61hruwnISK8pCLW2Ky/hV7E+rlKp5YGE8mDAjTwdv9+PauXnXPbvubZbyrPFRNdaDPGnhyGjQoL9CyQktzYchekBFxaJFi+bMmaMpCkVxOT+PHGj0naosGSzdqq3porX6+vpU/Gig4B/+4R9q5KT8pfPmzXO+rJNPPtlpVF+LjkqSZw8corWS4Q9HuVT/Vx/713fv3Jdf7n1HHGTVslFZtp2TZlhLs7ARlkR8JSq+AQDQWoDWAgC01uh+LSe0zEc2uq9Ra+b7JiWtTbqGGu0aN1gvNaJ6/N+/9Rd3b9/drAQm7DCyy71GTlJnUhTaA5cmPk8i38q25+9irGJMNIlz76gUUfW1aNGJ1v+z0OYwNLshN5v1ltJoq487rfXyyy9rrgvVV7ocS/9Mt1ztxhtvbFNWlUpF26RarbalGOkkt5KOt6BRC5PI1Du37b7/noejZp7gJCwnLu9/GrUutvGZckfDuCFaK0gbcSZSO0RrAQBaC9BaAABjEmBjEnIjlLBpIgaLA6Ud23Zuet8duYiKvUwb/kHkrQRLvIVhrZ8WNMJ6tZHGQ3647uKlX86eNUc1ybKzl84SSTJ71sITF0g9Z95sqectmKsn58439aw5g5+Oq8g3diwjXT9bfm2nYr5rdueyZOlic8Fsc9nc+XP0D5H//0CxPzV+pEaUhKVKcZRVdtJ4co0eV2plPag1qkaPafJA66eKy1FWs4naRUFXs7s3775/1wPFQ6U8tWBo6+RtU7Sbu5WO5CIDAEBrobVoAgCA7uJnw3Phf3fcvvn+vQ/86v+9KRZ/pa/qYgs18nCwRK0zVomZTZA9i180m8o2kV56oPt3lYsVXT7064Nvbt56hwoqqRedcpJqGDlQxSXS5aSTTxxJ6kxZmb9wnv6XRFPJsVODWlavWfWNb72oMkmEkyglOejpPRKng85GOa+58gVpZBd7qVGFlUpFvU+D+TasK1JkldmZuhhpcOBPvv/3e+/c1/NPvaXDZdVghQOlpJLmcYPkFQQAtBagtQAAjkN08VUcx3m+B7vK6y//4pW7tt39kQ9/tPdQn3xZ7q/km+RqusJo2KZeNvGdcX9FZsWXpp5v23NZ0803ak3NWm5T6iUq8ORAoxD7+vo+9alPLVmyxIUdnnbaafMnmXkjsHTpUvd/eOqppw4cOJC1dmpWyuWyyieXh1COdQWXWAADAwN52ox00A3oOwYHEz9an2FQNR6tvOmirO/X/ft23rN7y54//vS/z5pmt7SgEJpbUG8tzQqtJGO/LABAawFaCwDgOByrROS4nOyZW19kkxMalWU11YvPf0N0193bd//4tb/LPVr2gv4jA4P5yZ2/y9NXGnlYK9bbRMVAX2lIHGOQ6kFkD9I4G+gvuzPyX0ui1AizMddREIvCGV6PdH0cmvz1w+v+3oFcfGbmf2UG9STLtVNmXHZmR2bVOYnZTUu/S8+Y3CRZ628M8q2E8yKNUUmNVyrKKj1VGyhoFssZL1Yje3DvQ9s37Xh6/5+Y7dGKZuthI3Rj0+BpI99yWlWZuQvVOAvRWgCA1gK0FgDAcYZLXB4EgRyL6NB8gGkwRC+JZa8OK/nyp//37x9/+AObf2vLkx/47eefe6EyUDWZNtwuXrbUCw3dSdmPPDRZEO011UK95eRJw4ZRL0kovyUrF+pBNapXwiHeMPtpHp34TtQiseTPNwvPvPOlvnIat64JzTo00z6t640ek+ttaF+j3DTevCDPE9jsC4P+PCxQTyalbOdv3XX/jge3bdzx5s8OpOVMNZhJOuL9H6QOGmGeItKeyVPzJ1mzHvAkAwBaC9BaAADHHQMDA7pN1pABLDVeFHWemE26WrLHnEkGlVh+EGbP/sl/fuL9T2665Y4737dtz/a9H370iW/82TeDcmgSyls3jhyr+tIU841SlDRa3rBgqFusVZrltNLfGH5+Uko0QkmGOOukKeqlxpDsINEQV54GSaorL/92e0Hprcrv/6tP337d5g/d/8RvP/jRO2/Z/rlPfUFUVloUvZuLrqA3FqFl2qoQ+lkYRUqpX87em+zIkSOF/qIeOx8aAABaC9BaAADH76DldriqVqv53lw2nM9F+vUdzXfuSqJUinyktSaLFynlsmjIsYgrPS73Vp575r88sOfB3dv23Hf3/bfecNvD+97/6L0fvGvLvl2b75F6953379vxwM479m36F3fu2LR35x17pN61ee/dW+/T+r67Hhb9NqlF/m8dy/ZNO6Ts2nLX3h379u28R+p7dt0rf4t8i5y8a+vdUrbdvn3HHTvlU/lIDu7f/cDOzbvkys9/9gs/fvXvagP1Sl/VyKe6SSSYl2jQr2WOa1mjvzm4ECs2+sokag9tlkIbuai1VVh5LferEdSdqQEAgNYCtBYAwHGEpsKr1WouR0U+hsW5ptKk7VLn+2h5Cdz9jbbiWr65kxw4xWU2TY4HPzIxhDaesNJrfD5JLfcdmYNmrjrSen6yPpDoQVhuuZjG5acKRyjj9Wu1fq/8UWElyr+0W1qZpVP+t/t5GoO8Kdzfnvu+3MXJ4C/VTBj5+iv9XXWTMqQtQXwQNV2m+FKl6HLEh3GQksMdANBagNYCAJg+g9g4S1cC9qK31zzTsiTaosnY6/EWHlgAQGsBWgsAYIZqrVZW9zHV8W+S0LIeQuMjzMI4i8ZSS0FrAQCgtdBaAAAwVHGNpU6OyTU0fYsJEoyCrClSaiy1Ki60FgAAWgutBQAAnugaS50eo2KZrrVdhKUiaozFKaix1AAAaC0aBK0FADCzddY4tcHYAxSzaV/LH67BgWMpg6009t8CAIDWArQWAMBMFVrEvHW3QcdXAADQWoDWAgCYqVpr7H6bY8j9MK1Lrp0mcz0YAABaC9BaAAAzWWuNsU7HXU9/rTXZ69MAANBagNaakRQKBXes26HW63Wa5R1H70WpVNID/yRMGfouNJvNKIp0PJjZcisb63qt8dYzoXWOJSyQ9VrwdtRqtbaePwxDmmUqzX3p3nUXeIwftBZaCyaLIAikc5daj2mQ40RrlctlGQPkWO5OpVKJ45i7M5VjgFo8AwMDekbaX6QvLQMA3UJNfKG/v19q6eex+KcS6dKlY9dj7eql25/x02poLbQWvDNay9n3bQMAvOM0LMjgqde6zvTJ8CgCwOSgE2qONjcXTFlv7zp8enu0FloLuozfs7tp+7beH94RiVUsFuVgw4YNmZ1yc6ILpgZp/5tvvlknO1Xo9vT00CwA0C3UuJeO/fDhw5l1qqxdu5bxd8rQLl27d+nqpcPXYRfQWmgt6CbqworjuF6v63QOweLHFWeeeSZuxneK5cuXu7eD1gCArtMWsHDuuefSJlOMdO8aSagdPqC10FrQfaSXcatT9Eva5HhAI/jPOeccqcvlcmanP2mWKaNWq8nQ61SWND5hnADQ3U4miiLpWNSXValUrrnmGpplKoWuG1Wlq5cOnxhOtBZaCyYFF5186NAhFVo4Uo4HRP3Krbnhhhu098e1MpXoALxhwwaxgfRG8FIAwGSY+3pQKBT6+/tvuukmjSeEqUE6ds39KF29Ruwzp4nWQmvBpNiUYsfL4/7UU0/pki3y8BwPQiuzC+fOOOMMPYPWmkp0mnnx4sX+SVy+ANBdoaW9ioy5Krpchw9TQFuXrh0+6+XQWmgtmBDqwnKZxPVkoVCQ848//vjll19OL3NcaS1hxYoV2hPhWpl6zjvvvLZZCQCALqKeczdAn3/++bTJFMtdF9ojHT5JCKcYtz7imWeeYZBFa0171EyXbsWlsdZZtFKpdPvtt5955pmrVq3SXr5arWLTHw+oj/Gaa66RkZgg8qlHXoTrr7/+4MGDqnV1R2OaBQC628lrzIJY+XEcb9iwgfF3yvD9WkePHr355pul26dZpgyNz9dj0Vpu9oGWQWvNBAtSu3UXJq5zCfJ8X3LJJUQPHie4XMArVqxwo4LOAMEUoC/Cu971Lj2m9weAyehnpJNXc1OGZunkly9fTirgKaZWq6ncveCCC1yeEpgCXFPLW/DFL35R87TRLGitaYzvKPeDFjTVu8qtCy+8UE9qBjw4HtCb0tfXR1NMMfJqrFmzxs0xyyvDewEAXUS7FxmR3bZO5CGcStqmL6+++mqSzb4jiqtarX7zm9+k8dFaM4FKpaJOrZ///Ocly+uvv+73+GvXrpWTJAA4HqjVaiKAVRLreCC3j/m2KcMtWFcyL10nAEC3EPtSe5j+/n45lrGYOZ2pRAZZaXOXfJ910VOJ+nX12D322DlorendobgH+sYbb1y5cuXFF1985ZVX6r7patBfddVV2ukjt46TMVgP6hYaZIrRHl8XVKj6dWsdAQAmjstWxYz+O4JmYPZtJObUphh/VHVTnDQLWmsaI8+xmuxHjhzJvFUociBCS574devW6S4TtNU7jibD8HOZyK1hvdaUoVObAwMD/pcAAN1Fp/M1+buOxbTJ1Jv7avb09vZmbK8ytahrS71bjLNorWmPztn4MzeuQ3EHq1atYm3icXXL/G0uaZApRkffYrHofMKsWQeALuIbl7VaTQdfss5OsV2kWldXWOBgnEq0tf1AKhl2cS2itWZmX+Oe7IsvvrjNh+t8XDi7Jg+no3SFqDtfKpXcSKDzbW5Idn2TDNUujRVMpLt3a9N9QeVbQjoN4TKG6Un9kpjbCTZ+ZmOYtVXVkeseaX+WgWmgSSK26LFzpLt1FBoHQftPsIW19dxI6kevubb1N9qSfl6/1EXUbY2vXZPOkzIHNJb2d+2mzSVn/HbTjkjPaEekK7javh2O2c50D7A8tDqboJ28trzrbZxfVxq/7ZlnDgKtNb1xgVIazKDdinv0/Whatp6YDFtTuiHpfXw1K3fEt/K1M5Iz0g257sbf9Q+tNUF0UZbLQdJmVoq5r7dDTqJyJ8MMcta8jsraI/n2DdbkJOG6EX2S//mf/7lNX+kF9PwTx8WPuGQY2uxt/vO27Vy1w3ex/XKZ26Ml6+QWgJHQpenSbtpcLhODdPt6O3S6TSd3/DBCffhZU9ct0aUHb731Vls7u5dCGty3P+XYn18GtNZ0NXT0sXbvgEsGkA3LiAqT3QdJn3L48GF3a1wX4xSv3h25ZXqeXPBdtIEyb9ZZ+33X7Hpep0JdWkI39DLf3y3cVII0uOt89NVwWXyg692O/2D7b4Gb8WFOYeK4LdGdR8s1rz/mul5FTf+jR4+6Dsqf15chYPj9gtGRhvLbUO1492zLW+ArWLk70sLudjhVDBPp2KWfkXZWoavtKbV+KSpXL1Pbxu/w9QwzbmitaYw/VfPWW2/5VqPzd2lf7zQAdLf9ZTT1OxTtU6rVqhsD/MASPXCBVeppIUfwROYa3FvgJu/lyXcTyb7p4y72556Z8p8gLnpTOx+xO331q8d+eCF0F/cAu6darEx30gX8SCfD1HJXlK17qqU99eHv7e3VfkbnF6T/0UHBDcHu1sj17mSGs2Wc/bxvrEuTOu105MgR7Xxcz6NDrdt2BSaO7jPkP8wuPZs/EEjP4x5pN9S694VmRGtNY3znlfbjOt8m3Y28GzIMXHDBBZdccgkNNXmoNSNdj7b86tWrly9frt2TU2Lnn3/+eeed99Of/lTne+RKt6CLPmiCw7ALxXEj65VXXpl5qyyWLVt21VVXOXNfRwg/uB+ODedU1+1cL7vsMt28Wx5vJ4DlyZf2z8hSNTmIHSNDwIoVK1auXPnUU0/5H+3Zs+eaa67Ztm0brTRB2vboq1hefvnlNkG1c+dO6fy1q3EWqigBuTuLFi1yJ92io2xYzCGMMqEg/blGgsgDf9FFF7nWSyx79+6VlpenXU/qhObXvva1c84559JLL6X/mQj+Qy7NeMUVV0i3455h6fl1SNV+3pk00vPLQHzzzTe7+ThAa03jMUDXgGo/Is+09js6kfDXf/3XN9xww6c//WmRW2IDsTax67gpZK03bNggsva5555bv369dDR6TX9//5o1a6S7f+GFF9797nfLsV4s945Zt66oXLcdgpr4Z511lo7E2s5i6GzatOlLX/qSnPyDP/gDNxXn4vhpxokgNo10OB/72Mfe8573iGWzdOlSlVvCueeee8YZZ3zve9+7+OKL5XXArJwkrSUSd926dd/61rfe9a53iWWphpE0uJx/9dVXly9frnNt9P8TNPd121wXMSgdizz5cl4FgDTye9/7XmlwOZCPdEReu3bt2Wef/cwzz0jnI0pArxSd5n4IedvGjj9cnnfeeW7tlhzIgLt48eLnn39eGlzugkYPSsvLly+99JIMuytXrqQBJ4I0snbgjzzyiJu719FTH3UZdqXzSS36pVz24x//WCwfuSM0IFprJsz3ZNZd2xY01dvbq9M50rPL03/LLbesWrWKFuu61nV3QYbM1atXu4++//3v6+B65ZVXSqej/ZEMsWKStgVZ4dc6ZpyZokt11b6XWgZXnUu74IILvvzlL2sLyzVijLo5Nj3Zls8Kju0tEFNSGlZfBzFrZDyWR/2mm26S5pW3Q87Ljbj66qtpq64Lrfvuu0/sTpcMQyxOfaqvueYa189ov4RNPxFDM/NC1MSCP+2006SFXdfd39+v8zuacvbyyy//+te/LmPxFVdc4YYJGX9FEvs5aWVopvMZIzpTIC22ZMkS6d4vu+wylwdPboeIW11HJyf1WMcCbfmBgQG5XoNN4Jg7ebEnpfGvv/566cz9hXBSHzx4cOPGjfLYZ9b39bOf/ezaa6/VlpcrdaKNddForemNTjb4i4VcvjUZg93Ej1hCa9asobm6jsZwqgUv1qQfji+WvbS/aC3fyjnnnHN0hFD1hdCauBnkwmh/8IMfiFkpt0CnFXp6euQWZF6crQzSbW3OeokJal3pbX71q19dd911agy5MVXui9wLffLVVJXuiBbrOqJpdc5e2l+efLHmpWP54Q9/KAZo1nIFyFvwyU9+krY6Zty8jO+bvfjii50A+8lPfiJ9jj75alyKEhNhoALMJWmQb3HbTjiPOl3Q26JN5O8pqg2bWaeKGDbasetHYvHr9fIu6HAsbf7000+7a+DYjEw3QfDe975XQzTdTIHGMogGU6tG7ogoW/eEy0DwiU98gmZEa81M5KF/z3ve478t6uaC7uIvOvdX/0tf8zd/8zeZneP09z3zzX2yYkwQl1JZm1e6eI0dF+tTZxxE/QYWvV4ucDfLCWDm+yeCDLobN26U0fSss87SyWO1QeVGXHvttdK2bl0cts5kIH2OPNVi6J977rli36t989hjj8m9cAtU3E2BY0YNfdeTSLNfcsklLv/7gw8+qJNo2iPJo66jrXRELkWk3BqnEDIvJoXptnEpLu2uNX4hay0WdT6TN954Q724OtfjJ+J3sc1wDGjHoqOqPsa6dEUe4yuuuEI3F9UpTjmQzkevUUkm52l8tNZM5rTTTtPMp5qE0I9wg67gunLXEwmvvvrqsmXLbrvttszGrWmn49K7n3766W2DK+soJoLuryLt70JkxSpSs146/XXr1jkpJbdAzsunuqxRV1/QgBPn8ssvl4dcrE99C+RLXR0q9r1aqFqjtSZjoue73/2uLg2VxhdNpTbNQw89pKa/GvRiDLll6zCRrt6fmrnuuuvcp3v27JHHXldhSa8iT/7SpUsz61F03VRm/VptW9778eQwdnQe2e/Ae3t7ZQgQASzDge7ptHbtWpekR9pfo9pgIlpL8VtSrB3pavRT97SvWbPGBTVI+8urIffL34YR0FozBHWwaBiJgxieyeDAgQOZNzd56aWXaqCIW0EnvYxOf0q/L+PB9u3bxejXbIQMtF0xgLQZpeUfe+yx3/u933v00Udl0H3yySelweWk3AW34YEbDKAriE0j9uXv/u7v6jyOGJHy5S233KLzC2L3+J5ef1IfusW6deukS5FHXWdztmzZIrfgueeec1MP0jVddtllcp62mggu36kzPc8880xtczn+9re/vX79eml59bFIg2/YsEFuypVXXqlTaZoDw58P0q2KmO4ZI85zJe1WLBalYVXZOg1w1VVXXXHFFf4202L/OFn753/+52RjPmb80A/p0i+44AKdO5Dj5cuXf/SjH33ooYc+8pGPSPvv3btX2lzG2QsvvNDlB160aJF8SjOitWYsa9as6e/vl95HenY/phm6bvFrTiTpYqQbcuc1RPDcc8+VodcZnTJC+9/rL5WG8eKL1TfeeONHP/rRL37xizfffFNuhBxkNhvSa6+9ltl5TRmb/dl9TduQ2f1GacljNkD1OZe+xcX0y9OuJqYYN9LCumZ948aNfkgzdAWx1MXW+dCHPuTOyAOvBytXrhRbR2+Q+liIlT1mfJWlXvGs5dcaGBiQR/3IkSO6LlffArkLH//4x1UJO4fY9ddfL2/EwYMHM29uzgXZwuhCq02U6q4eOnpKm7uQQm1/rZ2yldFZlBgTbROUW5plLbORC+6+/PKXv5SH/8CBA6+//vrZZ5+tQYP/+I//KLrLrd3VTAH0P2itmTk2CNdee632Lxop/sorr9AyXW9nfy2Wy3QkX/b19elH0vusWLFCRwW5HWLu6/jKphNdQRpWN4920fwuDYzoK+nuV69eLWJALP7169ffdNNNoqzkYqaTu6i1Mhscpem/PvOZz0jji6zVtJwaSStfyknyUE0Gzz77rM7vyFMtHb6GDkrnI2b9pZdeKndEzFBVudj0x4w2ndSaDEZneaRhfbetGJdqg375y18WGaYJZuV2yHvRlhtGf4L8NLJijB1tQ9f+MtS6/mTlypVbtmxxcz1yU9SXKC0vd0TO79+/n8RgE+/nnQvxrLPOcrOc2tQ6nsoo4DYOFVPnd37nd+QbL7zwQjcBBGitmTkPkdk55jPPPFN6+a997WtuyRB0EdfXSINLh36RRQ6kl3fS6zvf+c673/3us88+258QcqYPNuhE8FXTr3/968xOPPuLVcQAlWaXF+Hqq69uCxlXZxdbbE1wGNa5TFFW8thrPgz3YIsZtGzZMjn5pS99Ccuy66h9+dprr51//vnr169X/5W7L9LnyElRXLR8VwZT3cpSzxw6dOj666/XPkSbV+7F2rVrFy9eLLUva1etWiVvwemnn/7Vr35VZbBLi6qwnfrYu5rQIg2+adMmbTe5KdLCYuGIcS+2/rp163TphNwg0QM33HCDNL50TXJZW7PDePsZRRp/48aNOnes46ncFw2e8hPwyLisW/xpChnaEK01k8cG3X7R9fvkO5okxNZsE04qAPRY/Vd6RzRGXxPR6rDNADDxMUBadcDif6RTzv4goWOz3Bppc6fQELoTRFdNyFMtT7gmxdZxt61t3QZo0PXOJ/O283YxyW63XCd98WtNfEJHHun+/n6XWjPzcs/KjdAuXcdZndmUrqYtt7u+FFK7uAZiq8bS/tKq/qTY3/7t3+ojrQOo6+flS21Pt3GihvfTz0/8FvT09Ggno2vU/VdD3wXX+TtxpXdHhC4POVprZtI2Va+5AehuJgNt1diiPi6/nV1378ZgZ/Fn5MboBv4Mgo64GtjZlmbQmUTa/m7kJhvhRHBDaeaF9/gWpD7hfmp+6G7n4/ZuUnGVeiQWvZKuZoL4W0e4x9vtu+U+0hkfN+PmbFD3/Mu7oHfHf4NgLLiH2eV79M19f1rZfeSb+HhXJmjk+I2vXbr2Ktra+pD7JpA2uI68buoH0FoAAAAAAABoLQAAAAAAALQWAAAAAAAAoLUAAAAAAADQWgAAAAAAAGgtAAAAAAAAQGsBAAAAAACgtQAAAAAAANBaAAAAAAAAaC0AAAAAAABAawEAAAAAAKC1AAAAAAAA0FoAAAAAAACA1gIAAAAAAEBrAQAAAAAAoLUAAAAAAAAArQUAAAAAAIDWAgAAAAAAQGsBAAAAAAAAWgsAAAAAAACtBQAAAAAAgNYCAAAAAAAAtBYAAAAAAABaCwAAAAAAAK0FAAAAAAAAaC0AAAAAAAC0FgAAAAAAAFoLAAAAAAAA0FoAAAAAAABoLQAAAAAAALQWAAAAAAAAWgsAAAAAAADQWgAAAAAAAGgtAAAAAAAAtBYAAAAAAACgtQAAAAAAANBaAAAAAAAAaC0AAAAAAABAawEAAAAAAKC1AAAAAAAA0FoAAAAAAACA1gIAAAAAAEBrAQAAAAAAoLUAAAAAAAAArQUAAAAAAIDWAgAAAAAAQGsBAAAAAAAAWgsAAAAAAACtBQAAAAAAgNYCAAAAAAAAtBYAAAAAAABaCwAAAAAAAK0FAAAAAACA1gIAAAAAAAC0FgAAAAAAAFoLAAAAAAAArQUAAAAAAABoLQAAAAAAALQWAAAAAAAAWgsAAAAAAADQWgAAAAAAAGgtAAAAAAAAtBYAAAAAAACgtQAAAAAAANBaAAAAAAAAaC0AAAAAAABAawEAAAAAAKC1AAAAAAAA0FoAAAAAAACA1gIAAAAAAEBrAQAAAAAAoLUAAAAAAAAArQUAAAAAAIDWAgAAAAAAQGsBAAAAAACgtQAAAAAAAACtBQAAAAAAgNYCAAAAAABAawEAAAAAAABaCwAAAAAAAK0FAAAAAACA1gIAAAAAAAC0FgAAAAAAAFoLAAAAAAAArQUAAAAAAABoLQAAAAAAALQWAAAAAAAAWgsAAAAAAADQWgAAAAAAAGgtAAAAAAAAtBYAAAAAAACgtQAAAAAAANBaAAAAAAAAaC0AAAAAAAC0FgAAAAAAAKC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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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=15.0, max=30.0, fit=True)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.1, value=0.2, max=0.4, fit=True)\n", + "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"Lipid APM\", min=40.0, value=50.0, max=90.0, fit=True)\n", + "problem.parameters.append(name=\"Lipid Coverage\", min=0.9, value=1.0, max=1.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=3.0, value=5.0, max=8.0, fit=True)\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 SLD's. 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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + "H2O_data = np.loadtxt(os.path.join(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, and then a Bayesian analysis:" + ] + }, + { + "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", + "Elapsed time is 99.984 seconds\n", + "Final chi squared is 8.39155\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "data": { + "image/png": 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KsNgdpGfruZyxkdIj5C8kE8wD4qNDePu27gC8fVt3jwQY8dEh3NyjmUeDFaNBoXUjfRrsiQt55NscHju3EP7m/Pnz9OvXD7PZzE8//cSuXbuYMWNGiTVzRO1w4nzhpJdGkVI/yF9Ij5CHxEYGuX33Va0bhbPjRCaqBvtPZ3tlGq4QdcGrr75KXFwc8+bNc+2rLbVyRMlOXtCLpdYPMRNokmVm/IUEQn6mbePC6rBr95+VQEiISvr+++8ZNmwYt9xyC6tWraJp06Y8/PDD3HfffaXex2KxYLEULtGSmZkJ6MPeNpvN7VibzYamaaiqWu4aPtXJmaDsbFNtoqoqmqZhs9kuWSriVIZe8iQmPLDY3+NiztvLOs4f+OpjUd72SCDkITHhgTw+qBUxFawAXNOiiiza99rPu7m2Y2PJFRKiEg4ePMg777zDxIkTefbZZ9m0aROPPfYYAQEBjB07tsT7TJ8+nalTpxbbv2zZMkJC3P8fmkwmGjduTHZ2NlZr8Rpk3pKVVf46Zr7CarWSl5fH6tWrsdvtpR73e6qCAQP23CyWLFlSrnMnJSV5qpm1nq89Frm5uWUfhARCHhMTEcQTQ3x/vaaM/MIXAbuqyRR6ISpJVVV69uzpWsi2W7du7Nixgzlz5pQaCE2ZMsWtwnJmZiZxcXEMHTqUiIgIt2Pz8/M5duwYYWFhBAV5f8hd0zSysrIIDw8v9yriviI/P5/g4GCuvvrqSz6WfybtI+D4EeKbRDFiRPdLntNms5GUlMSQIUNq5UKjnuSrj4Wzx7UsEgj5mcFtY/jnD7tc2zKFXojKiY2NLVbJuF27dnz33Xel3icwMJDAwOK9xmazudgbiMPhQFEUDAaDTyzT4hwOc7apNjEYDCiKUuLjXNSFPDtGo0JEcEC539DLOqc/8bXHorxtqV3PZlFlCQ1C6dysMC8oMth3nrRC1Cb9+vVjz549bvv27t1LQkKCl1okqupMthUDCmGB0kfgTyQQ8kNdmtVz/bznVO0b7xfVQ9M07r//fqKiolAUxbWekyjZE088wfr165k2bRr79+/n888/57333uORRx7xdtN81l133cWNN954yWMGDBhQrgrn1eFsjgUNCAuSQMifSCDkh1oXmTm2J618Y6ii7lu6dCnz58/nhx9+IDU1lY4dOxY7ZuXKldxwww3ExsYSGhpK165d+eyzz7zQWu+7/PLLWbhwIV988QUdO3bkn//8J2+++Sa33367t5tWpy1YsIAhQ4bQsGFDIiIi6NOnDz///LNHzn0ux4pD1QgLkEDIn8hf2w8VnUIvPUJ1n9VqJSAgoMzjDhw4QGxsLH379i31mLVr19K5c2cmT55Mo0aN+OGHH7jzzjuJjIzk+uuv92Sza4Xrr7/eL39vb1q9ejVDhgxh2rRp1KtXj3nz5jFy5Eg2bNjgthhrZZzLsWJzqNIj5GekR8gPtY4pDITW7D/L0bPlm2IoaocBAwYwfvx4JkyYQIMGDRg2bBgAO3bsYPjw4YSFhdGoUSPuuOMOzpw5A+hDFo8++ihHjx5FURQSExNLPPezzz7LP//5T/r27UvLli15/PHHufbaay+5mKvwL99++y2dOnUiODiY6OhoBg8eTE5OTonH5uTkcOeddxIWFkZsbCwzZswo8/xvvvkmTz/9NJdffjmtWrVi2rRptGrViv/9739VareqamTl27GrmuQI+RkJhPxQZIiZhgX1jg6dyWHoG6skGKpjPvroIwICAlizZg1z5szhwoULXHPNNXTr1o3NmzezdOlSTp06xa233grArFmz+Mc//kGzZs1ITU1l06ZN5b5WRkYGUVEy+1BAamoqY8aM4e677+bPP/9k5cqVjBo1qtTV4idNmsSqVatYvHgxy5YtY+XKlSQnJ1fomqqqkpWVVeXnYLa1sLRIuPQI+RX5a/upZvWCSc/SK9zm21WpJ1Re7/aH7NNlHqYAEZqKonjos0ZYDDywqtyHt2rVitdee821/fLLL9OtWzdXzRuADz/8kLi4OPbu3Uvr1q0JDw/HaDTSuHHjcl/n66+/ZtOmTbz77rvlvo+oImsunNlbs9ds0BpMZdcySk1NxW63M2rUKNfsuU6dOpV4bHZ2Nh988AGffvopgwYNAvQAvlmzZhVq2uuvv052drYrqK+srCI11sICZTatP5FAyE8N69CYrccuAGAyKFJPqLyyT0PWyTIPUwq+vKVHjx5u29u2bWPFihWEhYUVO/bAgQO0bl3xYqArVqxg3LhxzJ07lw4dOlS6raKCzuyF9/rX7DXvXwWNO5d5WJcuXRg0aBCdOnVi2LBhDB06lJtvvrnEhWgPHDiA1Wqld+/ern1RUVG0adOm3M36/PPPmTp1KosXLyYmJqbc9ytJZl7hcgyhgbLOmD+RQMhP3dCtCa8s3Q3oydPSG1ROYeV7sdUAraBHyCMBUTmv6xQaGuq2nZ2dzciRI3n11VeLHRsbG1vh5qxatYqRI0fyxhtvcOedd1b4/qIKGrTWA5OavmY5GI1GkpKSWLt2LcuWLeOtt97iueeeY8OGDR5fkPbLL7/k3nvv5ZtvvmHw4MFVPl/RHiEZGvMv8tf2U7GRwbRsGMqB9Bx2pWay62Qm7ZtElH1Hf1fO4SlNVcnMzCQiIgLFB6rwdu/ene+++47ExERMpqr9t1+5ciXXX389r776Kvfff7+HWijKLSAEmnSt+euWc6FVRVHo168f/fr144UXXiAhIYGFCxe6LS0C0LJlS8xmMxs2bCA+Ph6A8+fPs3fvXvr3v3SP1xdffMHdd9/Nl19+yXXXXVe53+cizh6h2MggWjQo3nMq6i7vv0ILr+kaVw8AVYMbZ6+RhOk67JFHHuHcuXOMGTOGTZs2ceDAAX7++WfGjRuHw+Eo93lWrFjBddddx2OPPcZf//pX0tLSSEtL49y5c9XYelFbbNiwgWnTprF582aOHj3KggULSE9Pp127dsWODQsL45577mHSpEn8+uuv7Nixg7vuuqvM5Ts+//xz7rzzTmbMmEHv3r1dz8GMjIwqtT3LogdCd/VNxGCoXWupiaqRQMiPRYcVrnlkdegJ06JuatKkCWvWrMHhcDB06FA6derEhAkTqFevXoXWjfroo4/Izc1l+vTpxMbGur5GjRpVja0XtUVERASrV69mxIgRtG7dmueff54ZM2YwfPjwEo//97//zVVXXcXIkSMZPHgwV155ZbH8tou999572O12HnnkEbfn4OOPP16ltjuHxoLM8rbob2RozI/9rWcc760+6Nru3DTyEkeL2mLlypUl7m/VqtUl6/1MmDChzKUN5s+fz/z58yvfOFGntWvXjqVLl5Z6+8XPnbCwMD755BM++eQT175JkyZd8hqlPb+rKiPXCoDZKInS/kZCXz/WIiaM6zoVJspuOCQ9QkII/5SRp/cImY0yLOZvJBDycwPbNHT9/OL3OyRPSAjhlzIKkqXNRnlb9Dd+8Re/6aabqF+/PjfffLO3m+JzHEUKvqoarNhTdrFAIYSoa3IKKkubpEfI7/hFIPT444/z8ccfe7sZPqlPi2hMRWZI/L4vXXqFhBB+Jztfnz1p8oFyF6Jm+cVffMCAAYSHh5d9oB+Kjw7h/Tt7uraT/jwta48JIfxOnk1yhPyV1wOh1atXM3LkSJo0aYKiKCxatKjYMbNnzyYxMZGgoCB69+7Nxo0ba76hddiAtjE0qx/s2nauPSaEEP4ix1LQIyQ5Qn7H63/xnJwcunTpwuzZs0u8/auvvmLixIm8+OKLJCcn06VLF4YNG8bp04W5LF27dqVjx47Fvk6eLHtNKKG744oE189GRdYeE0L4lzyrHgiZpZii3/F6HaHhw4eXWmwLYObMmdx3332MGzcOgDlz5vDjjz/y4Ycf8swzzwCQkpLisfZYLBYsFotrOzMzEwCbzYbNZivtbjXCef3qaMftvZoxZ9UBzufaUDWNE+eyiY3w3xWYbTYbmqahqipqOZcWKErTNNf3yty/rlBVFU3TsNlsGC+qz+Lt/09CFJVrcyZLe71/QNQwrwdCl2K1WtmyZQtTpkxx7TMYDAwePJh169ZVyzWnT5/O1KlTi+1ftmwZISG+sTBpUlJStZy3d5TC0lwjGjB3yQZOJ/rvG7jJZKJx48ZkZ2djtVorfZ6srCwPtqr2sVqt5OXlsXr1aux2u9ttubmShyZ8R57NOTQmPUL+xqcDoTNnzuBwOGjUqJHb/kaNGrF79+5yn2fw4MFs27aNnJwcmjVrxjfffEOfPn1KPHbKlCluiwNmZmYSFxfH0KFDiYjw7qKkNpuNpKQkhgwZgtns+d6ac+uPsvT4n4DCr6kKg3p14taecR6/Tm2Qn5/PsWPHCAsLIygoqML31zSNrKwswsPDUZTa8cKqaRoPPvgg3333HefPn2fLli107dq1SufMz88nODiYq6++utjj6OxtFf7jrrvu4sKFCyXmgjoNGDCArl278uabb9ZYuwDybc6hMekR8jc+HQh5yi+//FLuYwMDAwkMDCy232w2V0vwURnV1ZZB7WN5eckeHJoGKOxLz/OZ37mmORwOFEXBYDBUaC0uJ+dwmPMctcFPP/3ERx99xMqVK2nRogUNGjQo1vY9e/bw4IMPsmvXLjIyMmjSpAm33XYbL774YonPFYPBgKIoJT5n/fW5Jarm999/Z/LkyezevZvc3FwSEhJ44IEHeOKJJyp9ToeqYbXrw9nSI+R/fDoQatCgAUajkVOnTrntP3XqFI0bN/ZSq+qu+OgQpt3UkckLtgMwf+1h7rgigZYxYV5umagKq9VKQEBAmccdOHCA2NhY+vbtW+oxZrOZO++8k+7du1OvXj22bdvGfffdh6qqTJs2zZPNFqJEoaGhjB8/ns6dOxMaGsrvv//OAw88QGhoKPfff3+lzukcFgOZPu+PfPqjakBAAD169GD58uWufaqqsnz58lKHtkTVGC9KFJy84A+pKVTLDBgwgPHjxzNhwgQaNGjAsGHDANixYwfDhw8nLCyMRo0acccdd3DmzBlAH7J49NFHOXr0KIqikJiYWOK5W7Rowbhx4+jSpQsJCQn85S9/4fbbb+e3336rqV9P+Lhvv/2WTp06ERwcTHR0NIMHDyYnJ6fEY3NycrjzzjsJCwsjNjaWGTNmlHn+bt26MWbMGDp06EBiYiJ///vfGTZsWJWeg7mWwvw1Kajof7zeI5Sdnc3+/ftd24cOHSIlJYWoqCji4+OZOHEiY8eOpWfPnvTq1Ys333yTnJwc1ywy4Vm9EqMINBqwOPShnc2HzzP0jVUse6I/8dG+kSzuTaN/GM2ZvDPlOlZTNRQPTcVtENyAr67/qtzHf/TRRzz00EOsWbMGgAsXLnDNNddw77338sYbb5CXl8fkyZO59dZb+fXXX5k1axYtW7bkvffeY9OmTcVmeJVm//79LF26lFGjRlXq9xIVl2fP41DGoRq9ZvPI5gQaiqcMXCw1NZUxY8bw2muvcdNNN5GVlcVvv/3mmkV5sUmTJrFq1SoWL15MTEwMzz77LMnJyRXKTdu6dStr167l5ZdfLvd9LpZjLewRkqEx/+P1QGjz5s0MHDjQte1MVB47dizz589n9OjRpKen88ILL5CWlkbXrl1ZunRpsQRq4Rnx0SEkTezP6PfWkZqRD+gFFn/akcoD/Vt6uXXedybvDKdzfX89tlatWvHaa6+5tl9++WW6devmNnz14YcfEhcXx969e2ndujXh4eEYjcZyDTv37duX5ORkLBYL999/P//4xz+q5fcQxR3KOMToH0bX6DW/uv4r2tZvW+Zxqamp2O12Ro0aRUKCXpusU6dOJR6bnZ3NBx98wKeffsqgQYMAPYBv1qxZudrUrFkz0tPTsdvtvPTSS9x7773l/G2Ky7UW9gjJoqv+x+uB0IABA0r9tOA0fvx4xo8fX0MtEvHRIbw4sgMPfrrFtW/msj0M7xjr971CDYIblPtYT/cIVUSPHj3ctrdt28aKFSsICyue73XgwAFat25dofN/9dVXZGVlsW3bNiZNmsTrr7/O008/XaFziMppHtm8Qr2DnrpmeXTp0oVBgwbRqVMnhg0bxtChQ7n55pupX79+sWMPHDiA1Wqld+/ern1RUVG0adOmXNf67bffyM7OZv369TzzzDNcdtlljBkzpny/0EVyi/YISUFFv+P1QEj4pms7NqZlw1AOpOtj+xaHxjebj/HksPK9SNVV5X0DUlWVzMxMIiIivDJrLDQ01G07OzubkSNH8uqrrxY7NjY2tsLnj4vTyyq0b98eh8PB/fffz5NPPlnuITVRecGmYNpHt6/x65anMKjRaCQpKYm1a9eybNky3nrrLZ577jk2bNhA8+blC6bKy3m+Tp06cerUKV566SXPBELSI+R35C8uSjX5Wveu8Dmr9kvidC3VvXt3du7cSWJiIpdddpnb18VBU0WpqorNZvPrCtqikKIo9OvXj6lTp7J161YCAgJYuHBhseNatmyJ2Wxmw4YNrn3nz59n7969Fb6mqqpuKwJUVJ5VZo35M+kREqUa2qEx7RqH82eaXh3ZpsLGw+f8fnisNnrkkUeYO3cuY8aM4emnnyYqKor9+/fz5Zdf8v7775e7J+ezzz7DbDbTqVMnAgMD2bx5M1OmTGH06NFSF0iwYcMGli9fztChQ4mJiWHDhg2kp6fTrl27YseGhYVxzz33MGnSJKKjo4mJieG5554rswd19uzZxMfH07at/kFt9erVvP766zz22GOVbrfFXnRoTPoH/I0EQuKSXhzZgb/NXe/abh5dtd4D4R1NmjRhzZo1TJ48maFDh2KxWEhISODaa6+t0NCdyWTi1VdfZe/evWiaRkJCAuPHj69SMTtRd0RERLB69WrefPNNMjMzSUhIYMaMGaWuJ/nvf//bNWwbHh7Ok08+SUZGxiWvoaoqU6ZM4dChQ5hMJlq2bMmrr77KAw88UOl250sdIb8mgZC4pCtaRnNLj2Z8s+U4AF9sOkqPxOKJj8J3rFy5ssT9rVq1YsGCBaXeb8KECUyYMOGS5x49ejSjR9fsjCVRe7Rr146lS5eWevv8+fPdtsPCwvjkk0/45JNPXPsmTZp0yWs8+uijPProo1Vq58XyrA5MBgUNas2SOMJzpA9QlOnZEe0IC9Rj5m+3HCf56Hkvt0gIITwn365iMioyY8xPSSAkypSVbye/SJ2Nhz9N5lB6yZVihRCitsm36T1CATJjzC/JX12UaePhc9iLlHpKy8xn0IyVrD9w1nuNEkIID8mzOTAZDVJV2k9JICTK1CsxiiCT+1NFBcbMXS/BkBCi1rPYVEwGRWoI+Sn5q4syxUeHsOyJ/kwZ7l5XSAP+/sH6Ol1bqKyq5+LS5PGrOnkMq66sxzDP6sBgUDBLjpBfkkBIlEt8dAgP9G/J7DHd3PbbC2oL1TXOmji5uXU3yKsJVqsVQCpOV4I8Bz3H+RiWVusq3+7ApEiPkL+S6fOiQq7r0oTNR84zb+1h1760jDyOns2tU4UWjUYj9erV4/RpfYHVkJCQCk2rVVUVq9VKfn6+V5bY8AWqqpKenk5ISAgmk7zUVFRVn4OeVhuf05qmkZuby+nTp6lXr16pAXm+Te8Rkhwh/ySvTqLC/u/69mw8fI6dJzMBeH3ZXt7+dT/Lnuhfp4Ih5yrszjeiitA0jby8PIKDg/26LonBYCA+Pt6vH4OqqMpz0NNq83O6Xr16rseyJHk2FaOiYK4lAZ7wLAmERIUZDApvju7KtW/+hqNg7D3frvLTjlQe6N/Sy63zHEVRiI2NJSYmBpvNVqH72mw2Vq9ezdVXX+3XS08EBATUmt6DynjppZeYOnWq2742bdqwe/duj5y/Ks9BT6utz2mz2Vzm0Gy2xcaRc7m0j42ooVYJXyKBkKiUVo3CuaNPAvOLDJHNXLaH4R1j61SvEOhDFBXNcTEajdjtdoKCgmrVm4aouA4dOvDLL7+4tqtjGLAyz8HqaENdfU6fz9Fz2bon1PNuQ4RX1N2PaqLaTRnRlujQANe2xaHVycRpIS7FZDLRuHFj11eDBg283SRRQRa7CsBj17TyckuEN0iPkKi0QJORf/ylA498sdW1T1U1vt1ynF6JUXWuZ0iIkuzbt48mTZoQFBREnz59mD59OvHx8SUea7FYsFgsru3MTD3PzmazeX3oqyzO9vl6OyvDatMDIdVhL/fvV5cfj4ry1ceivO2RQEhUyXVdmrB63xm+2nwMgMnf/YEGBJuN/DzhagmGRJ3Wu3dv5s+fT5s2bUhNTWXq1KlcddVV7Nixg/Dw8GLHT58+vVhOEcCyZcsICakd/1eSkpK83QSPy8k3AgrLly8nrIKjfnXx8agsX3ssylt6QgIhUWX3XtWcrzcfQ0Mvsgh6yfqNh89JICTqtOHDh7t+7ty5M7179yYhIYGvv/6ae+65p9jxU6ZMYeLEia7tzMxM4uLiGDp0KBERvp2oa7PZSEpKYsiQIXUuR2jy5l/AoTJs6BAig8v3u9Xlx6OifPWxcPa4lkUCIVFl245ncHHdVrNBoVdilFfaI4S31KtXj9atW7N///4Sbw8MDCQwMLDYfrPZ7FNvIJdSm9paXjaH/goWGFDx360uPh6V5WuPRXnbIsnSospKWossNNBEbpEV64XwB9nZ2Rw4cIDY2FhvN0WUk6ZpOFQ9EDLKEht+SQIhUWXOtcheu7kzbRrpeREX8mwMn/Ub7646UKfXIhP+7amnnmLVqlUcPnyYtWvXctNNN2E0GhkzZoy3mybKyepQXT8balmhSOEZEggJj4iPDuHWnnGM6t7UtU8Dpv+0m2tmrJBV6kWddPz4ccaMGUObNm249dZbiY6OZv369TRs2NDbTRPl5Jw6DxII+SvJERIeNbxjLK8v2+Macwd9YdY7PtzA8okDJHla1Clffvmlt5sgqshaJBCSoTH/JD1CwqPio0NYPnEAXZpFuu23OTR+2pHqpVYJIUTJ3HuEvNgQ4TUSCAmPi48O4cv7+9CsfrDb/jeS9kq+kBDCpzh7hBSodYvJCs+QQEhUi+AAI/PuuhyzsfCFJd+uMnvlfgmGhBA+w2J3APpi0sI/SSAkqk2rRuE8NbSN276vNh1j2JurJRgSQvgES8HyGhIH+S8JhES1uv/qFozo2NhtX57NIflCQgif4Jw+LzPG/JcEQqJaKYrCzNFdadvYfd2lGT/vll4hIYTXFfYISSDkryQQEtUuyGzkw7suJyTA6NpnVZFeISGE11kdeo6QTJ33XxIIiRrRpF4wr4zq5LbvtaW7pfK0EMKrnD1CEgj5LwmERI35S9emDG4X49p2aHrl6aFvrJJgSAjhFYU5Ql5uiPAaCYREjfq/69oXe9Ll21U2Hj7nlfYIIfyb9AgJCYREjUpoEErSxP4kRLkvtVE/2OylFgkh/JnF7sCgSCDkzyQQEjWuZUwY34+/kiaRQa5993y8md/3pnuxVUIIf2SxqxgVBaPMGvNbEggJr4gMMTO2b6LbvjvmbeTwmRzvNEgI4ZcsdhWjUZHlNfyYBELCa4Z3jMVY5BmoaXDfx5s5IsGQEKKGWO0qRoMiQ2N+TAIh4TXx0SF8ds8VbsHQvtPZDHh9JesPnPVew4QQfsM1NCaBkN+SQEh41RUto1nx5EB6NY9y7dOAMXPXS40hIUS1c/YISRzkvyQQEl4XHx3C6zd3cXsh0tBrDMkCrUKI6qTPGlNkiQ0/JoGQ8Anx0SF8fu8VxT6VyQKtQojqZLGrGCRHyK9JICR8hj5MNoDLE+u77X/lp92SMySEqBZWu4pBkUVX/ZkEQsKnJDQI5esH+kjOkBCiRljsDoyKgkHeDf2W/OmFz1EUhX//tXOJOUOyLpkQwpOszqEx6RHyWxIICZ+U0CCUz+7pXSxnKN+u8uP2k95plBCizrHYVRRFwSA5Qn5LAiHhs/pc1oCVTw1kQOuGbvtfXbqHVXtOe6lVQoi6xJkjJD1C/ksCIeHT4qNDmH93L67rFOu2f+y8TcxctkeGyYQQVaL3CEmytD+TQEjUCpOvbetWgRrgP7/uZ/DMlRIMCSEqzWJ3YECSpf2Z/OlFreBajuOiD21Wh8ajXyRLMCSEqBRrQY+Q1BHyX3U+ELpw4QI9e/aka9eudOzYkblz53q7SaKSrmgZzYqnBjJ+YEuKvmRtO57BwNdXsP9UttfaJoSonVzJ0jI05rdM3m5AdQsPD2f16tWEhISQk5NDx44dGTVqFNHR0d5umqiE+OgQnhrWlmCzkX8v2+va79Dg5jlr+dvlcQxoE8PxC3n0SowiPjrEi60VQvg6q10lLFByhPxZnQ+EjEYjISH6m6HFYkHTNDRN83KrRFWN7NKU//y6H4tdde27kGdjzuqDzFl9EIBgs5GfJ1wtwZAQolQWGRrze14fGlu9ejUjR46kSZMmKIrCokWLih0ze/ZsEhMTCQoKonfv3mzcuLFC17hw4QJdunShWbNmTJo0iQYNGnio9cJb4qNDSHqiP1OGt+XKy0r+e8o6ZUKIsljtKgrSI+TPvB4I5eTk0KVLF2bPnl3i7V999RUTJ07kxRdfJDk5mS5dujBs2DBOny6sI+PM/7n46+RJvfBevXr12LZtG4cOHeLzzz/n1KlTNfK7ieoVHx3CA/1bMu2mTgRcnEVd4NWfdvPKkj8lmVoIUYyqalgdeo7QxbNShf/w+tDY8OHDGT58eKm3z5w5k/vuu49x48YBMGfOHH788Uc+/PBDnnnmGQBSUlLKda1GjRrRpUsXfvvtN26++eYSj7FYLFgsFtd2ZmYmADabDZvNVq7rVBfn9b3dDl8TG2Hm58euJOnPNPacyubHHafIt+lDZiowZ/UBPlpzkA/G9uTyImuYVSf5W5VNHhvhbVZH4dC69Aj5L68HQpditVrZsmULU6ZMce0zGAwMHjyYdevWlescp06dIiQkhPDwcDIyMli9ejUPPfRQqcdPnz6dqVOnFtu/bNkyV66RtyUlJXm7CT6pEdAoCLp2hq8OGthx3vkRT8Gmanzz6wZONdaKLdtRneRvVbrcXOmlE95VNMdQltjwXz4dCJ05cwaHw0GjRo3c9jdq1Ijdu3eX6xxHjhzh/vvvdyVJP/roo3Tq1KnU46dMmcLEiRNd25mZmcTFxTF06FAiIiIq94t4iM1mIykpiSFDhmA2m73aFl/X4uBZxn20GasKoGDXFBYeNrLwMBjQCAswsPiRK2kWFVwt15e/Vdmcva1CeIvF7nD9LEts+C+fDoQ8oVevXuUeOgMIDAwkMDCw2H6z2ewzb2i+1BZf1a9NY3558hrmrT3I/DVHKDpPUEUh06rxXcpJEhuEVes0e/lblU4eF+Ft1qI9QhIH+S2fDoQaNGiA0Wgsltx86tQpGjdu7KVWidoiPjqEF0d2ZFzfFrz0v538utt9oda3VxwAIMhkYNkT/WWavRB+RobGBPjArLFLCQgIoEePHixfvty1T1VVli9fTp8+fbzYMlGbxEeH8NLIDgSWMrMs364ybckuvtl8TGaXCeEFfxy/wKRvtvFG0t4arfPm7BHSkKExf+b1HqHs7Gz279/v2j506BApKSlERUURHx/PxIkTGTt2LD179qRXr168+eab5OTkuGaRCVEe8dEhJE0cwOyV+/lq07Fity/deYqlO09hAB7o35IxveKlh0iIGrDvVBZ/fWctNoceADUMD+TvVyTUyLWL9ghJQUX/5fUeoc2bN9OtWze6desGwMSJE+nWrRsvvPACAKNHj+b111/nhRdeoGvXrqSkpLB06dJiCdRClCU+OoRHBlxGsNkIUGwBV9Cn27+z6gCDZ6yQ3iFRIa+88gqKojBhwgRvN6VWeSNpLw5VY3C7GABeW7qbXKu9Rq7tyhHSQJEeIb/l9R6hAQMGlNkVOn78eMaPH19DLRJ1WXx0CD9PuJqNh8/RrF4wd83bSH6RT4VOVhWeWfAH02/qREKDUC+0VNQmmzZt4t1336Vz587ebkqtkp5l4acdaWjAQ/1bsu90NkfO5rJ0Rxqjujer9us7Z41paFJQ0Y/Jn174nfjoEG7u0YwrWkaz7In+vH5LF/79185c/Hlw7YGzDHh9Jc9894f0DolSZWdnc/vttzN37lzq16/v7ebUKr/tS0cDIoJMdIuvz4DWDTEaFA6fyamR61uchVc1yRHyZ17vERLCm+KjQ1y5QL1bRPPZhiMsSD5BerZeXVwDvtx0jK83H+OpoW24vnMTyR0Sbh555BGuu+46Bg8ezMsvv3zJY325cn1ZqqNa+ordpwgLNNI1LhKHw06bRmE4VI0Dp7Nq5PHIs+rX0EcltApdU6rHF/LVx6K87ZFASIgC8dEhTBnRjtt6xTNwxkrUIiO2qgav/byHWcv3kSRT7UWBL7/8kuTkZDZt2lSu42tD5fqyeLJa+prdRvJtEJJ3miVLlnA+B8DE1oNpLFlywmPXKc2WMwpgJCsrmyOHs1iy5GCFzyHV4wv52mNR3ur1EggJcZGEBqF8fu8V3P7+ehwXpa9Z7CqPf7WV0T3j6NuygQREfuzYsWM8/vjjJCUlERQUVK77+HLl+rJ4ulp6Vr6dc+t+BeDG/j0Y1DYGi13l33/8QpbDzIgRw6p8jbLkJZ+AfTsJCQ2lZYuGjLi2TbnvK9XjC/nqY1He6vUSCAlRgitaRrPiqYH8tCOVfaez+HZL4afTrUcvsPXoBYwKfHbvFVzRMtqLLRXesmXLFk6fPk337t1d+xwOB6tXr+btt9/GYrFgNBrd7lMbKteXxVNtPXAiy/VzXHRYwXkhMthERp4dBwaCzMZLnKHqVAwoCmgamE2mSv1etelvV9187bEob1skEBKiFPHRITzQvyUALRqE8drPe9xud2hw+/vr+fXJATKzzA8NGjSI7du3u+0bN24cbdu2ZfLkycWCIOHuz9RMjAZwqBAbWbjmX0x4EBl52aRnWYiLqt4eV5tDJcBoQJVZY35N/vRClMP1nZu46g8V5dDg/k8288XGozKzzM+Eh4fTsWNHt6/Q0FCio6Pp2LGjt5vn846czaVecAABJgP1Qwo/uTetpwdFp7Mspd3VY6z2gkBIBYPMGvNb0iMkRDkUrT+kqRqTvvvDddueU9lMWbAdkwE+vUeGyoQoj6PncgkJMBISaHQrZpjYIBT2ppNeE4GQQ8VsMuBQNQmE/JgEQkKUU9Gp9udyrUz/abfb7XYVbpu7ns/vu4Ie8b6d+Cqqx8qVK73dhFrj2Dm9B7VxhHuieYuG+jDzqcy8am+Da2hM02SJDT8mQ2NCVMLwjrElDpWpwG3vr2femkM13yghaglN0ziUns2x83muoTAnZ77Q0XPVP9RstauYTYoEQn5OAiEhKsE5VPb6LV34143u+SB6zaG9ABw/V/2faoWobc7n2rAU1KZ4dkQ7t9uiwwIAOHkhv9rbYXOomI360JiMjPkvGRoTopKKDpXF1Q/hznkbXbfZNdiUrnDqzzTubyTDZEIUlZahBznRYQHEXDQ0Fh2qB0I/7Uhjw8Gz9G5RfTl3NodGQEEgJEts+C/pERLCA65u05Bfn+xP17h6BXsUPttvYGbSPt5ddUBmlAlRxKksPRAKDSj+WTw6rLDO0p5TWcVu96Q8q4N8mwOrQ5WhMT8mgZAQHtKiYRgLH+5L9/h6AGgo5Ds0pv+0m2FvrpZgSIgC6Zn6jLCwwOKBUGiAkQCTgQCjAatdrdZ2nLiQx+GzueTb1GI9U8J/SCAkhAcpisIbt3YlwPU/S/+UmWdz8NOOVK+1SwhfcjornwCTgZCA4hMOFEWhQWgABoO+pE11stodAKyeNJC/dGlSrdcSvksCISE8LKFBKD89diW9G7q/iM9Ytkd6hYRAL5YYaDQQXEIgBBAVFoCCUu09QraChO3IYN9ZFkLUPAmEhKgG8dEhjG6h0rzIoqxWh8Z3yce82CohfMOpzHzMJkOJJSgAokP1PCGro3oDIbuqn1+Rd0K/Jn9+IaqJ0QBvje7itu+tX/dzKD3HSy0SwjekZ1kwKkqpPUL1Q8xoaNXfI6TqPUJSVdq/SSAkRDVqExvOIwNaurZVDR79MlmGyIRfO5djBYUSc4QAIoLNaBrVHgjZC3qcZMKYf5NASIhqNvryeAKKLG2940Qmg2aslGBI+K1zOVY0TSOolKGxiCAzqlb9PUJ2h/QICQmEhKh28dEh/DKxPx2bFhZWtKka326RfCHhf2wOlcx8O6pGqTlCEcEmVFWr9hwhhwyNCSQQEqJGxEeH8NbfuruV8X/r1/3SKyT8zvlcK6AHRKUOjQWZcWhgsTmqtS02V49QtV5G+DgJhISoIc0bhvLowMtc2xowa/le7zVICC84n2MD9ECo1KGxgunsudUcCDlnjUlVaf9WqUDo4MGDnm6HEH7h5h5xmIq86H6XfIKdJzK82CIhata5HL1HyGpXS501FhFUEAhZqzcQcg6NKTI05tcqFQhddtllDBw4kE8//ZT8/OpfIViIuiI+OoSnhrZ22/fol1tliEz4DWcgpGqXmjWmL72RV82BkF3VkBBIVCoQSk5OpnPnzkycOJHGjRvzwAMPsHHjxrLvKIRgRKcmBJoK/+sdTM9hyMxVEgwJv5CakefKlSs1WbqgRyi/mofGHKqGdAaJSgVCXbt2ZdasWZw8eZIPP/yQ1NRUrrzySjp27MjMmTNJT0/3dDuFqDPio0NIeqI/3QoWZwWwOFSWbD/pvUYJUUPW7D+DpukJyk3qBZd4jDNHqLrXGtMDIYmE/F2VkqVNJhOjRo3im2++4dVXX2X//v089dRTxMXFceedd5KaKotMClGS+OgQZtzcxa1bfsayvdIrJOq8zHwbBgV2TB1G52b1SjwmPEgfGrPYq39oTPKkRZUCoc2bN/Pwww8TGxvLzJkzeeqppzhw4ABJSUmcPHmSG264wVPtFKLOaRETxk3dmrq2barGuoNnvdiius9ms3Hs2DH27NnDuXPnvN0cv5RndWA0KIQEmEo9xmw0YDQUTm+vDpqm6T1CkiXk9yoVCM2cOZNOnTrRt29fTp48yccff8yRI0d4+eWXad68OVdddRXz588nOTnZ0+0Vok55fFArt5fhX3efkl4hD8vKyuKdd96hf//+REREkJiYSLt27WjYsCEJCQncd999bNq0ydvN9Bu5VgcmQ9lvPQFGA7ZqLKhYWEyx2i4haolKBULvvPMOt912G0eOHGHRokVcf/31GC56YsfExPDBBx94pJFC1FUJDUKZfVt31/bPO08x9A1JnPaUmTNnkpiYyLx58xg8eDCLFi0iJSWFvXv3sm7dOl588UXsdjtDhw7l2muvZd++fd5ucp1nsanlqtsTYDK6lsCoDs6q1ZIjJErvm7yEpKQk4uPjiwU/mqZx7Ngx4uPjCQgIYOzYsR5ppBB12YjOsXRaFcn2gnpC+XaVn3ak8kD/lmXcU5Rl06ZNrF69mg4dOpR4e69evbj77ruZM2cO8+bN47fffqNVq1Y13Er/km93YDaWHXwEmgzkWu3V1g6bXXqEhK5SPUItW7bkzJkzxfafO3eO5s2bV7lRQvibCYPc33xnLtsjvUIe8MUXX5QaBBUVGBjIgw8+yN13310DrfJvFrtKgKnst55Ak8E1fFUdpEdIOFUqENK0kp+c2dnZBAUFValBfiVth7dbIHzEoPaNuKxhqGvb4tDYeFiSeUXdY7WrBBjLfusJMhtRtdLfb6rcDocsryF0FRoamzhxIqBH0C+88AIhISGu2xwOBxs2bKBr164ebWCddeBX+OQm6HgzDJsGaLB5HvQcB+GNvd064QVPDGnDI58XTjBoGikfKqoiLy+Pc+fO0bRpU7f9O3fuLFcvkfA8h6phVzW3gqKlCTLrx9hVrVxDaRVlszt7hDx+alHLVKhHaOvWrWzduhVN09i+fbtre+vWrezevZsuXbowf/78ampq7ZKem85/U/5Lem4JxSVt+fCDHlSy41t4oyOsngGrXoGstJptqPAZIzo1pmWRXqGx8zbJ8Fglffvtt7Rq1YrrrruOzp07s2HDBtdtd9xxhxdb5t+y8vUFV0tbbLUo5/T67PzqyRNyzkgzSCTk9yrUI7RixQoAxo0bx6xZs4iIiKiWRtUF6XnpvLPtHQbEDaBhSEP3G02BcPUkWDoZLFmgWmHTe/ptR9dBk676z1lp0kvkRxRF4YoW0RxIzwH0rvuNh88RHx1Sxj3FxV5++WW2bNlCo0aN2LJlC2PHjuXZZ5/ltttuq7ahFlG2zDw9qClPIOQsqjjhqxQ+uruXx9tilUBIFKhUjtC8efMkCCrDqZxTbt/dKAp0ux1Gf1r8tqXPwPePgjUHTiTrvUQnpB6Tv7inn/tkgxYNQks5UlyKzWajUaNGAPTo0YPVq1fz7rvv8o9//EOSY70oI0/vESptjbGiWjUKw2RQqm3mmNXuzBGqltOLWqTcPUKjRo1i/vz5REREMGrUqEseu2DBgio3rDY7lnWMJ1Y+AcCTq55k8Y2LiQuPK35gUL2ST5D8MexLgpyCYbVvx8HD6yG8WfU0WPiMFjFhjO2TwEfrjgDw7uqDPDeinfQKVVBMTAx//PEHnTt3BiAqKoqkpCTGjh3LH3/84eXW+a/MgqGxkMCyA6GwQDMGhWqbOeasWi09QqLcsXBkZKTrk1RERASRkZGlfvm71cdW49D0NXJsqo33/3gfVSujQqpyUUyalQpqwSche74+ZCb8wv39W7qqTf+8M00KLFbCJ598QkxMjNu+gIAAvvjiC1atWuWlVglnj9ClltdwCgs0YVe1agyECnqEJBDye+XuEZo3b57rZ0mIvrQAY4Db9oL9CziYcZDnrniOtlFt2XZ6Gx/s+IAHjI1wzV1RAA24ciL8PrP4SdN3g8NWzS0XvqBpvWDaNA5nd1oWoBdYlFyhimnWzL33NC0tjcaN9Ty7fv36eaNJgqKBUHmSpfXp8/ZqCoQKh8YkEPJ3lRodffnllzl06JCn21Jn3NLmFl7s86LbvpT0FEb/MJr/W/N/jPt5HCuOreCZ3R/x33qRpBsNhb0/IQ30730edR86WzML0wcDicreUzO/hPCqv10e7/rZqCj0SozyYmtqv6FDh3q7CQLIzNNXng80lWdoTP+cXl3rjbmSpSUQ8nuVCoS++eYbLrvsMvr27ct///vfEqtM+7v20e0BuKX1La59qqayaP8ibKr+qchiNPBO/UjSjUY2Bgbx33qR7Fn5T/3gTXNhxOtu51TSd3PVvn9h/N+jkHMGjm2EL27Tv4s65W+94ggt+NRsNCjk2xxeblHtJjPFfENGng2DomA2lR18BBc8/6trvTGbFFQUBSoVCG3bto0//viDAQMG8Prrr9OkSROuu+46Pv/8c3JzJZehqIX7FwJgVIwEGNyHzM4XWattfr1w3qkfyVuRBcMf9nzIPQP9n4FR70NY4fR5wx9fwH+6wYfDYc+P8NFIOCc9dHVJkNlI/zZ6jovVoTLiP6slT6gKZKaYb8jIs6EolKuytHNmWbX1CNmd0+er5fSiFqn0xMEOHTowbdo0Dh48yIoVK0hMTGTChAmucXh/1zC4IQPjBmIvGPJyaA66x3Sne0zhSuP5Rv0/+oagIJzD4HZF/5OkBQRD62th4BQICIXsNBzdxmIzFgRKlkzQiiRTp3xWM7+YqDHxUYU5QXYVWXJD1HqZBcURyxUIFfQIVXeytMkg8+f9nUeeAaGhoQQHBxMQEIDNJgm9AA1DGjLp8kmuxGmjYmR92npiQmKYfPlkwgPCXcfOjK7Pn0GBdM3PJ0uB/9aL5P+i65Ox6V192OvbcQCoOxayvN2rqB1vKX7B1TPg5LYa+d1EzRjd073kQuemMiNT1G7OZGlzOZbYcCZUV1uytEPDoIDEQaLST4FDhw7xr3/9iw4dOtCzZ0+2bt3K1KlTSUuTJSKc4sLjeP1qPc9HKZgQvfTwUgJNgXx/4/cMbFjYO3TOaCAlKIjTZhPv1I/E6LARue4dfU0ye75+kD0fizkSxw3vwNgfILhoAq0K80dAyucg+RB1QvOGoYzsHOva3nBIeoQqy2gsOzlXVL+MPBuaBuZyLroK1dcjZLWrGBRF6giJygVCV1xxBZdddhnffvst48aN48iRIyxfvpx77rlH6ghdpFGoXt3WrhVWR522fhp59jxmdZnAtNNnCCkyBp5mNtM530KwUvDC3bgzmAoW33R+z0qDw7/D6M/c6w9Zs2HRQ/DhtZC2vVp/L1Ez+rcuXJ7lpe93Sp5QJW3durVazvvOO+/QuXNnIiIiiIiIoE+fPvz000/Vcq26ICvPhkPTCChHj5AzR6i6eoRsDhWDIvljopKB0KBBg1yLrj711FPFVncWxRkp/ERq1+wkn0pGURRG5uTy+mn3WXd/BAVyODKGg+aCIOfmghpOw/5Fm9QFcHCFvvRG/gUY/Yl+W7Mia/EcWw/vXg0/TYa8C9X3S4lqV3TCjEPTWHfwrPcaI4pp1qwZr7zyClu2bGHz5s1cc8013HDDDezcudPbTfNJF3KtaBoElGM1+WrPESroEaqGhe1FLVOpQOhf//oX7du393Rbqk1iYiKdO3ema9euDBw4sEav3TC4IQ91eYjHejzm2hdgDKB7o8JhsZAWeptGXTaKAIMZgP22C/ytSWMWL3mo8GQGE23TFunrkYGeOxTaQJ9ZNvoT+PsCqJeg36apsGEOvN0TUr6Q4bJaqk+LaLf/pKHlKEQnas7IkSMZMWIErVq1onXr1vzrX/8iLCyM9evXe7tpPkfTNNcSG+UaGiuoNaRW02uX1SFDY0JX7srSEydO5J///CehoaFMnDjxksfOnFlCZWQvW7t2LWFhYTV+3YYhDXm468PsOrvLte/1q1/X1x7L0j/dx1/+IA9l7OKW1rdwW8Iwnl7+GAexkGcw8Hx0BCt3zGFsx5E0zjtDYwC7RT+RPR/O7tdnlmWl6YnVVz4JPzwGxgBwWPX1yhY9CFvmw4h/Q2znGn8MROXFR4cweXhbpv+0G4CFW0/QuVk9qTLtIRkZGWzbto2UlBQee+yxsu9wCQ6Hg2+++YacnBz69OlT4jEWiwWLxeLazszMBPRFYn19oomzfZVtZ67VTsGMdYxo+nlSt2H69Aaw5qC1vhbHzR+BUhgkmQwKDlWrlscm32pHj4Eqd/6qPh51ia8+FuVtT7kDoa1bt7pOWl3j7f7CmTfk1DCwHg93fVj/OaQhXw7/lKe/u56VIcEA/JJziF+A1ju28l3RO5qCIL7gBde5Ur2hyJ+05SA4sFz/2Tlcdvk9cM3zEFy/en454XGjujdzBULLd59m7YGz/DzhagmGLuHAgQM8//zzBAYG8uabb1KvXj0OHTpESkqKK/DZtm0bR48eRdM0QkNDKx0Ibd++nT59+pCfn09YWBgLFy4stcd8+vTpTJ06tdj+ZcuWERJSO/6eSUlJlbrfBQs433L+2LYVjjoYtOtpTNZsVMWEce9P7J73CPsaj3Tdx4ARuwOWLFnigZa723vYgMOhcOH8uSqdv7KPR13ka49FeesaljsQWrFiRYk/V9Xq1av597//zZYtW0hNTWXhwoXceOONbsfMnj2bf//736SlpdGlSxfeeustevXqVfIJS6AoCv3798dgMDBhwgRuv/12j7W/vBoGN+TO9ne6fr6UM8GhnDAXJEZrGgUfW9gXqJAUEkxXi4OGDvTcoajmejHFgin2rqU6nL1BXW6Hbc4aQxpseh/++AaG/hO63SFzR2uBhuGBNKsfzPHzeQDk2Ryy9lgZbr/9dm6//XYSEhLo2LEj2dnZZGZmEhkZSfv27enYsSPHjh3jgw8+YNCgQcTFxZV90lK0adOGlJQUMjIy+Pbbbxk7diyrVq0qMRiaMmWKW496ZmYmcXFxDB06lIiIiEq3oSbYbDaSkpIYMmQIZrO5wvffeyoLkvXFo6/o1ZOBylZMKafRFAPa9bPgf4/QLm0Bre6YAWb9uf1/W38lx+pgxIhhHv1dADb8bxcB59No2CCCESN6Vvj+VX086hJffSycPa5lKXcgVNTdd9/NrFmzCA8Pd9ufk5PDo48+yocffljuc+Xk5NClSxfuvvtuRo0aVez2r776iokTJzJnzhx69+7Nm2++ybBhw9izZ49rdemuXbtit9uL3XfZsmU0adKE33//naZNm5KamsrgwYPp1KkTnTvX7BCRs65QeSSfSsam2QEzDe120gueWJqiMLFRQ5pb7Cw4eZIzJqM+VHZ0XeEU+6LS/tC/LmbJgP89Br+/AeGxMGQqxJU/sBQ175q2MXy87ggAJgOy9lgZTp8+TceOHWnRogVpaWlMnjyZhx9+2G1ix4cffkivXr2qFASBvqr9ZZddBkCPHj3YtGkTs2bN4t133y12bGBgIIGBgcX2m81mn3oDuZTKtvWX3YWJ/qFBAZi2L4GAUJTYbpi6/g1+fgbFmoU56wQ00oPIAJOBbIu9Wh4bh6pgMCgYDYYqnb82/e2qm689FuVtS6UCoY8++ohXXnmlWCCUl5fHxx9/XKFAaPjw4QwfPrzU22fOnMl9993HuHF6j8ecOXP48ccf+fDDD3nmGT1pOCUl5ZLXcL74xcbGMmLECJKTk0sNhGp0DN9uxwzY7HYocu4u0V2IMATz0PmzJIVEcLYgXVZFH2A/FGiif3wzQn+fwpjckwys15qm5gjMtkxsxhAMUYmo6fswY8OGCTN6kFj0ZwDOH4Lzh1A/HI5j7BJoWpjALarG02Pmg9s2cAVCw9o3JjbC7HPj8RVVne3/z3/+w0MPPUSDBg2YM2cOs2bNYufOnbz22mu0bt262q4LoKqq22uI0O09lQXAlOFt6REfCQt+BlsedLgRjCZI7At7f4ZzB90CoWqaNIbNoaLg6nAXfqxCgVBmZiaapqFpGllZWQQFBbluczgcLFmyxNVL4wlWq5UtW7YwZcoU1z6DwcDgwYNZt25duc6Rk5ODqqqEh4eTnZ3Nr7/+yq233lrq8TU5hh9ou0Bi4xs5vHEnFvMJt9vuC7mLAcdeoH2jJ8kISUTTNNZnLWKJYzOaopBpNOAggN93/o45yEyzFk8xYM8LrLnsGTJCEomMOsyAPS+wLeFeeh6Zw+HoARxqMIjYjC3YlEA6pX7lupZBs6PNH8HexiPZ13gkmlKp+FiUwFNj5laHXp3coSls2JfKkiXHPXJeb6rOdQmvv/56rr/+etf2uHHjeOedd7j66qv561//yosvvuiR60yZMoXhw4cTHx9PVlYWn3/+OStXruTnn3/2yPnrkmyL/iFsXL/mBJzbqw/dKwbocJN+QKtheiB0Zg+g/+2cS3GoqubxVeItBbPGZNFVUaF3vHr16qEoCoqilPipSlGUEoOIyjpz5gwOh4NGjdyTixs1asTu3bvLdY5Tp05x0036fzSHw8F9993H5ZdfXurxNT+GfxstS9qdug32QL9+/SC2CwA99pm4+fsl3Ne4EXYFcshho3UjW6xbeKXLEzhiJtGv2ygIbwzHN+PQrqNL01g4Ak2vuZemDdvBHmDVqwA4MGBA/1RkxE67tIW0taTguPY1tBY1W2agrqmOMfOvT29i0+HznLEoNO7Ql+4J9TxyXm8p7/i9JxiNRsaPH89tt93GSy+9RNu2bVFVFYfDUaXznj59mjvvvJPU1FQiIyPp3LkzP//8M0OGDPFQy+uOXIsdBb2Xh5PJ+s7wxnoJECjskV7/DvS6HwLDXYUXHZqGAc8GLDa7iqIg0+dFxQKhFStWoGka11xzDd999x1RUYV5CgEBASQkJNCkSROPN7IqWrRowbZt5V+Dy2fG8Os3g/7PYK7fDAqu27hxF3LqDef9K8fwdPK/OZ13GgAHDjbmHWHYNf+H0fmfunkfaN4HY1YaqHmY4y/XX3RiHoPEK+D3NzF2Hg27f4DtX7suq5w/hOmLW6DNCBj2L4hqod9wbCP8/iZcOUHyiSrAk8+bDrGRbDp8HoC/z9vE8okDanXCtDdyCaKiovjPf/7Dgw8+yBNPPMGgQYN4+umneeSRRwgODq7w+T744INqaGXdlGN1YHJWLzyxBQIj9RxFp4btAEXvKfrzf9D1NgKdgZCqYfZwCS2bwxkIefa8ovapUCDUv39/QF9nLD4+vtpLkzdo0ACj0cipU6fc9p86darur3If3livD3TRvj2xoxiRMIgfEvvzyPJH2JS2CYBv9n7Dn2f/5NHuj5Kem073Rt31WkUlnSeuF4z5XP85trP+omPPcz9mzxLY8xN0Hg19H4OPRuoJ2QeWw8Pr9dlqokYFFSmmaHNoMnOsCtq3b8/PP//MDz/8wFNPPcWMGTNITU31drPqtDyrvbCQ4skUCAiBsCK9/eYgiGkHZ/ZCnh7wB5qqr7q01aGiIAUVRSUrS//66698++23xfZ/8803fPTRR1VulFNAQAA9evRg+fLlrn2qqrJ8+fJSC5b5i2BTMH9p8Re3fTvO7uCBpAd4fs3zjPp+FMeyjpV9oqjmcPs3ReoPGcHg/KSuwR9fwvvXuC38ytHy5WcJzxrVzX0pG5k5VrajR49e8vbrr7+e7du38/TTTwNw4sSJSx4vKi/PruqBkKZBekFqQ5h72gMx7fS8oXx92NSZI+SohurSNruGggyNiUoGQtOnT6dBgwbF9sfExDBt2rQKnSs7O5uUlBTXzC9n0TPnC9jEiROZO3cuH330EX/++ScPPfQQOTk5rllk/qxH4x4EGosP4wHk2/NJPpVcvhNlHCusP4QD1Itm8xSdmm8wFxZxFDWqdeNwWsXo1dEVoH6o70xT9VWXX345DzzwAJs2bSr1mNzcXEJDQ+nYsSPfffddqceJqrHYHASZDfrrjTVbf125OBCKaqkvD2QpCIScQ2OOauoRUhQppSYqN33+6NGjNG9efGgkISGhzE9gF9u8ebPb+l/OROWxY8cyf/58Ro8eTXp6Oi+88AJpaWl07dqVpUuXFkug9kdx4XEsvGEhvxz5hf8k/8dthXuAPef2cCzrmD5EdinxffQq1SXVIrqYaoOV02HwSxDhW/lg/qB3iyj2nc5GA7Yfz6DvZcU/kIhCu3bt4l//+hdDhgwhKCiIHj160KRJE4KCgjh//jy7du1i586ddO/enddee40RI0Z4u8l1ltWhEhFkhvQ9+o78TAi/OBBqoX8oy9UXog4wV1+PkNWugqw+L6hkj1BMTAx//FG8UN+2bduIjo6u0LkGDBjgmpJf9Gv+/PmuY8aPH8+RI0ewWCxs2LCB3r17V6bZdVJceBzjOo7j+5u+54nuTxATUli+4JM/P+EvC/9S9hBZVHMY+z9oMaBwWMwYAP0m6N8v9sdX8EYH+P4xvQ6IqDHd4gqXRvlk/RGOnq2+Keh1QXR0NDNnziQ1NZW3336bVq1acebMGfbt2wfoFai3bNnCunXrJAiqZja7qq8ov2OBvkNzuCdLQ+HkjCw9LzTQWJgs7fH2FNQRMkog5Pcq1SM0ZswYHnvsMcLDw7n66qsBWLVqFY8//jh/+9vfPNpAUT5x4XHc3elubmx1I39f8ndX8GPX7Ly49kWm9p166Z6huF5w5+Lis8PaXqdv930U/vxBX9Fes+vd18kfwbYv4aonof/TUpmsBnSNr+f6+acdaazYfZplT/SXpOkyBAcHc/PNN3PzzTd7uyl+y65qhAQY4fQuPQ/o+jfg4jIdzkAoJx0oMjRWTYEQyKwxUckeoX/+85/07t2bQYMGERwcTHBwMEOHDuWaa66pcI6Q8KyooChmDZyFocgKzpvSNnHdguvYmLax7BM4Z5Q5p8g7t8Mbw+b39SCoKIcFVk7TF3Q9vtmDv4koSfPoUIKLzCPOt6tsPHzOiy0Somw2h4qqQVigCXLPgjkYetylzxQrKrQBKEbIzwCqedaYXZVkaQFUMhAKCAjgq6++Yvfu3Xz22WcsWLCAAwcO8OGHHxIQUMJQiqhRreq3YvENi0mMSHTt09C45+d7+HD7h+WbTXax0tYzc0r7A94fBF/8Dda+rS8EKzzOYFDo0KSwsGegUZHZY+WwfPlyrrjiCoKCgggPD+fyyy/n1VdfJSsry9tN8wsXcvUJGGFBZsi7AObQkg9UFAgM15OpQU+uprqmz2uSIySASgZCTomJiXTu3Jlrr72WhIQET7VJeEBiZCKzB83GcNGf+I3kN7hx0Y0VD4acCdWg5xHF9S4yzb6IPT/BsufgrR6QtrOSrReX0j2hME/o5Zs6ybBYGTZs2MDw4cMJDAzk+eef5//+7//o3Lkzr7/+Oh07diwx31F4VkaeFYD6gehBTtAlqvQHRbpyD509Qs5hLE9yntMos8b8XqWeArm5udxzzz2EhITQoUMH10yxRx99lFdeecWjDRSVFx8Rz9xhc1EuKk1vVa2uQozl5kyobnMdjFsC9yzTvzcuefFaNAfMuxaSPwa1yDIG5w5ByufSY1QF7WILFzs+k231Yktqh9dee40bbriBVatW8fzzz/P000/zwQcfcOTIEa6++mquu+46Lly44O1m1mnnC3qEmhnOARoEX6IXM6QBOGygaQQW9Ajl2aq2FEpJrHZnjpD0CPm7SgVCU6ZMYdu2baxcudJt4dXBgwfz1VdfXeKeoqb1atyLH0f9yLDEYW77P9n1CQcvHKzYyUrKH7r1YzAVLE2gmKBo0GXJhO8f1fOHDvyqBz/v9IVFD+nfJRiqlHaxhZ+ml+1Mk5ljZVi3bh3jx48vtj8kJISPPvqIZs2aMWfOHC+0zH+cz9ED9mak6TtCG5Z+cHgjQIPpzWiRqX9gy6+GQMhWUFlahsZEpQKhRYsW8fbbb3PllVe6PYk6dOjAgQMHPNY44Rlx4XG83v91nu/9vCuJev+F/dy4+Eb2X9hftZNHNYeH18GQf4DBCGiAAo06Fh5zagd8chPMvw5sBW/atlypUF1JLRuGYS5Ys2nrsQsMfWOVBEOXkJ6eXmLdMwCDwcDjjz/Ojz/+WMOt8i9nC3ouGzkKAqGwmNIPTuinzyqz5xNj0UcbPN0jpKoa9oK8IxkaE5V6CqSnpxMTU/yJnJOTI9G1DxvddjR3tLvDta2hcf+y+9l3fl/VThzVXP+E57C4zuwqmlZUZpHlC4xBUqG6ksxGAw3DCyuKy8yxS3M4HG491xfr0aMHe/aU8HwVHnMmW39tqG85oS/nc6kcofqJenkOUyCBij6klmf1bCBkLcgP0tBkaExULhDq2bOn2ycoZ/Dz/vvv+/0aYL5udNvRBBQpkpiel86o70fx7rZ3KzebzCm+T+EQmcFcfJmOYlTY8C5Ycyp/TT/WuWk9188BBpk5VpaPP/6YDRs2kJ9ffOZjRESE5AhVM2cuW3jecaBgZlhpnL1FBjMBil6uI9/m2WTposnXEgiJShVUnDZtGsOHD2fXrl3Y7XZmzZrFrl27WLt2LatWrfJ0G4UHxYXHseiGRby26TVWHlvp2v92ytu898d7LLpxUdlLcpTEOUR2dB1ExsFntxRf0b4ohxU2vANbP4Urn9ALOBqMpR8v3PRMrM/Snfoww5AOstzMpVx11VX885//JCsrC5PJRJs2bejRowfdu3enR48eNGrUCIfD8zkootD5XL1HKCznqN7bE3CJQMiZP2QwEoD+gcrTOUI259plmtSBFZUMhK688kpSUlJ45ZVX6NSpE8uWLaN79+6sW7eOTp06ebqNwsPiwuN4+vKnWXNiDbYiPTdW1crnf35O26i2dG/UveIBUVRz/Qvcg6KMY4Xfzx+CVa8V3seaBb/+A1I+g2unQ6uh8spUDi0LFl8F+HF7Gsv/lArTpXF+ONu3bx9btmwhOTmZ5ORkvv/+ey5cuCDD+TXgfI4N0AjKPqbPKA0MK/1gZ4+QYiAQZ4+Qh4fG7M6hMVliQ1QyEAJo2bIlc+fO9WRbRA2KC49j8Y2L+eLPL/jkz09c+z/981MAAo2BLLxhYeV6h8A9KCrq3CFY81bx3qJzB+DzWyHxKuj9AFiy9OG2ks4haNnA/Y3EmSckgVDpWrVqRatWrdyWATp06BCbN29m69atXmxZ3Xc+10oUWZjsBUPhAZcIhAJCwaw/j83OHiF7NQ6NyRobfq/cgVBmZma5TxoRcYlEOOEz4sLjeLrX0wBuwRCAxWEh+VRy5QOh0jiH0P78Hn79V5EE6wKHf9O/QC/g+PB6CYZK0LR+MAFGgyvpM9BkkDyhSmjevDnNmzfnlltu8XZT6rQLuTb+alxduONSOUKgD4/Zcl1DYxZP9wg5k6VlaExQgWTpevXqUb9+/Ut+OY8RtcuYdmMwl1AlOi0njY1pG1m8f3HVEqkvFtUc+j0Oj2yA7neWfpw9X1+y4/gWz127jjAaFBIb6J+aFeCze3r7VG/Q6cx83kjay+nMSyzL4iP27dtH//79vd2MOu18npXehj/1jf6Ty54xGhYDmopJKwiEPNwjJENjoqhy9witWLGiOtshvMg5TPbjwR+Zv3M+OTa9+/rtlLddx1R5qKwkUc3hyonwxzelJ1an74b3r4F2N0DLAfpq1dJDBECLBmHsPZWNBjSMCCzz+Jq07XgGs5bvo2PTSIa0L33qui+wWq38/vvv3m5GnWW1q+RYHDQKuIBmMKEMfNZ1m6o5qztf9Jk8NAbO7HMNjVnsnk6WdvYIyfR5UYFAaNasWcyfP5+IiAg+/vhjRo8eTWCgb734isqLC4/jwS4P0r9Zf+786U7yHe6f5Gt0qMwQANHN3WsR/blY/zKY4aG10LC1Z9tRC7VoWLhw5cH0HBKiS1nIsoYdPZvL+M+TARj/eTJJksTt19ILagg1MGSiBBQ+RzelbeLR5Y+Sa8+lXXQ7Ph7+MYHGgveUkCjQHJhU59BY9eQIaYCkCIlyD4398MMP5OToPQXjxo0jIyOj2holvKdddDteubr4enEBhgC6N+pePRctOlR24zswfiOM+apgyY6LqDa9h2jFdNj6mV8v09GiYWHC6YH0bC+2ROccDvvlz1OuoQyLDxR7fPDBB5k7dy6bN2/GapW12Wqac3g0ghwI1lMnzuSdYfzy8eTYc4gIiGDX2V3M2zGv8E4hUaA6UApyCD09NOY6n6bJrEFR/h6htm3bMmXKFAYOHIimaXz99delJkXfeecl8j6EzxsUP4gZ/Wcw5bcpWFX9jaNTw06uhVo93ivkdPFMszsX6UtzXFyc0ZIFqwqCNWOgHkD54XCZW4/QmUsXpjydmc9nG47SLjacrzYdIy3TQnRoAHf0SWBo+0YeeTNwDodNv6kTgSYDFrvqE0nc27dv57PPPiMnJwez2Uz79u1dNYS6d++OwSBrLFSnU5kWQCMYiz7kBbyT8g4Wh4UO0R14Y8AbDPtuGHP/mMstrW8hOjhaX5TVYcXg0F9/rB6fNabXEVI1Pd9O+LdyB0Jz5sxh4sSJ/PjjjyiKwvPPP1/ii6eiKBII1QFDE4dSP6g+D//yMPmOfLac2sKWU1sIMARUvuhiRTW/CsZvKqxHtH427PnJ/RiHRV/DrP8zYDT51ZT7olPoD6VfOhByBikX+33/Ga66rAGd4yIZ2yeRmIiK5/OczsznvysP8PmGIwC89L+dvDSyA1MWbuft27p7fVhszZo1aJrGnj17XDWEkpOTWbhwoauitPQKVJ/0rHwacQEDGkQ05UL+BRbuX4hDc3B/5/uJDYulb5O+rD25lqWHl3J7u9sLeoTsrh4hq8PDgZBdhsZEoXIHQn379mX9+vWAvlDh3r17S1xvTNQdlze+nDcHvskjyx/BoenJilbVypa0LTUTCIF7L1FkM9i/Ai7KXyLzBPzvUf1nP+ohigwxEx5kIivfzokLpVfxPno2l4c+LX3m3W/7z/Db/jMcTs/hxb90qHAwtO14BvPXHnZtW+wqeTYHjw9qRZdmkRU6V3XYuXMngYGBtG3blrZt23Lbbbe5bjt48CBbtmyROkLV6FSmhTamk/pGVCLfH/geh+Yg0BhIv6b9ALgm/hrWnFzDxtSNeiBUMITmXKTZ08nS1iLJ0hIEi0r1CR86dIiGDRt6ui3CB/Vr2o8pvaa47dt+Zrt3GhPVHB5Zr+cR3bFYX6X6Yg4LfHs3bJzrF/lDzerrvS0nL+ThKFhN+2LLd59yrbQNMLRDIw5MG8Gcv3cn2Fy4rMmPO/QK1VD+6e9FE6OdAk0GBrdrxBNDWleqh8nTJk6cyH//+1+3fT/++CO33347b731FpdffjnTpk3zUuvqvqPncmllOK5vRLXk6z1fo2oqlze+3JUc3bdJXwA2n9qMpmn60BigFMwmtdlLfm5XljNZWtVkrTFRyUAoISGB33//nb///e/06dOHEyf0VcU/+eQTmYZaB41uO5onez7p2v5679dMWz+Nx359jG2nt9VsY6KaQ9fb9Kn0N8zWV7G/2MlkWPIUvH05nN5ds+2rYc3q6wvd2lWNU0WCltOZ+bz8wy5e/mEX24+7T2y4tXsz/rN8H93j63PPle49Z88t2s7Rs7muobRtxy89KWLj4XPFEll9YTisqG3btvHXv/7Vtf3nn39y0003sWrVKj799FN69erFyZMnvdjCuu3ouVxaaUcBOG40cCRLH0J9qudTrmOahjUlzBxGpjWTC5YL+tAYoNj157Snh8acOUeqpmGUFDG/V6mnwHfffcewYcMIDg5m69atWCz6OG5GRoZ8sqqjBsUPwqgU9h58secLVhxbwd3L7vZsscWKKNpD1Puh4rc7Z5j98g99cdc62EPkDIQAjp8vHB7bdjyD938/xPu/HyJpV5rbfbKtDmYt38fpLAu39oyj6OdhVYPFKSfcpr8fPZtb6vV7JUYRaNJfRgKMCnf1TfSJ4bCiMjIyiIsrHMr9+OOPadGiBUeOHOH48eN06dKFV14pPlNSeMbRszm0RP+wvCxzHwYMDIofRMt6LV3HKIrCZfUuA+BE9glXjxA2PRCy2ByopfR4VobVoWJQ9MrS0iMkKhUIvfzyy8yZM4e5c+diNhdWJO7Xrx/JycmXuKeorZJPJbvyhIqyOqwkn/Li39zZQ9T7ATAFF7/dmgO/z4DFj8Ds3nUuGGpar/B3PnFBD1guHq7Ksuh/twZhAQDsP61Ptf8zNZPvko8z+do2buf8ff+Zck9/j48O4e3b9LIKs2/vwUuVyDGqbs2aNSM1NdW1vXz5cm655RaMRiOBgYFMmTKFZcuWebGFdVe2xc65XBstDPrjv/TcH5gMJlrVb1Xs2M4NOwNwPPu4q0cIhxWjQSHleAaPfuG5PC6bXcVsNKBKjpCgkoHQnj17uPrqq4vtj4yMdM3CEHVL90bdC4udXSQlPcV7vUJOzsKMN74DY3+AtiOLH+OwwEfXw4G6UyXdmSMEcPyc3iNU0nAVwPkcfSry2yv2A/Dswu3MWr6PxpHuAeSWI+cxG/U3h4unv5eUOxQbGeT23dcMHjyYmTNnAnDkyBGSk5MZOnSo6/aWLVty7JiXn7911JGzOTQgg2glix/DI/jzwj6sqpWWkS2LHdupYScADpw/AEYzGAPAYaVLs0jqh5g5fokJARVlc2gEmPRAyChxkN+rVCDUuHFj9u/fX2z/77//TosWLarcKOF74sLjWHjDQka1GlXstm/3fssNi27wjWCo6236tPuh/yy5hyjjOHxyI7zbH1ZOr/U9REWHxpbuSON0Zj69EqMwlfA/23HRyIKzlkpUaABjLi8cOrKrGl3j6gHF831OZ1lcw2pOMeGBPD6oFTHhvllp/vnnn2fFihW0aNGCPn36EBcXx5VXXum6/dSpU4SFXWI1dFFpu05m0tlwAIBP6usB9cgWI7ki9opixzaP0PPVFh1YRL49X190WbUTEx5EkNno8aExk0FBVWX1eVHJQOi+++7j8ccfZ8OGDSiKwsmTJ/nss8948skneeihEnI1RJ0QFx7HvZ3uLbFnyKbaSDqc5IVWlaJoD1FJM8xSU2DlK/Cf7vDbzFobEMUV6RHamZrJ7rQsvks+zqRhbcu8rzNYsthVnhjSmocGtKR+sD7UvenweaB8vTwxEUE+M0OsJE2bNmXTpk3cdNNNDB8+nAULFrgNh/z666+0bi1LtlSHzYfP09WwHytG9hk1moY1ZdpV06gXVK/Ysc6SHGk5aSQdSQJzCKh2gsx6z01psyIr48DpbM7n2rA6VIKKzJwU/qlSgdAzzzzDbbfdxqBBg8jOzubqq6/m3nvv5aGHHuLee+/1dBuFD3H2DL3c72XeGfyO220f7viQP9L/8FLLSlCeGWaosHwqvNUT1syqdQFRRLCJ8MDCcmDncqzMWr6PRpcISsYPvMx9+/Nk8m0qk69ty4QhhQFB03pBNCzIK3JKzdCHxN5ddaBWrCzvlJCQwIwZM/jggw/o1q2b2227du3i5ptv9lLL6rZNh89xjWErKUFmrGj0ju1d6rEh5hBigvXadJnWTAgIAU0lyKigqhqq5rlAKMtiB+D9O3tyQ9cmHjuvqJ0qFQgpisJzzz3HuXPn2LFjB+vXryc9PZ3IyEiaN6/7hez8XVx4HDdcdgNXNr2Sr67/iuigaAAyrBncvuR29pzbU8YZvMA5w2zIP0pew0yzQ9IL8HavWhUQKYpC0yLDY+mZ+pBVVGgAYYHun3SdeT+XxejDQM40oqIJ0YPaxRAZrD8+Jy7ksys1y3X/oknY//sjtcSp9eWtP+RLPv74Yx5//HFvN6POybXauXA2jQ6GI3xRsBxT/2b9L3mf5pHNCTAEkG3NhoIFWkNMDhwaHu0RclaW7ndZAwJN0iPk7yoUCFksFqZMmULPnj3p168fS5YsoX379uzcuZM2bdowa9Ysnnjiiepqq/BB7aPbc3fHu932PfPbM9guXh/MFzgXd310sx4QdR5d/BjVqgdEs6+oNcFQdFjhUOWrP/8J6HVSsgtmi8VH6YHStJs68figVkSF6sNfJSVEX8i1kZFnd53vlZ92u96ALk7C3nmyhECohBwi4Z9+23eGmwyrSTMa+TU0CINioEejHpe8T7PwZgDk2HIgQA/Yu2Usx6GqHu0Rsqv681iWmRNQwUDohRde4J133iExMZFDhw5xyy23cP/99/PGG28wY8YMDh06xOTJk6urrcJHDYwfiNlQWEZh/4X9vLT2Jb1CrC9yBkSj3oNHk6HdDcWPceTD+v8W3++DiqZ6OuOUjYcKp7x3i9eXK2gXG8ETQ1rTtnEEjw9qxbSb9Fk6JRVAdAZLe05lsSBZrwpctGYQQIcmvlUvSPiWT9fs5QHjD/wruj6gcFXTq4gMvPRzJjY0FofmINuWDfUSAOh29gccql7jylOcEwWkhpCACgZC33zzDR9//DHffvsty5Ytw+FwYLfb2bZtG3/7298wGqWL0R/FhccxZ8gcDEWeTt8f+J63tr7lxVaVU3RLGP0x3L8Kml3uftvG92DRw3BqF6R87rM9RM4ZXkV98PtB188XFzh0Jje3i9WHK0pKiD6XU9ijNzNpL7lWu1vNICEu5Ydtxxl+9HXebWBgVWgIEYGRTLp8Upn3axLWBIfmIMOSoa8taA7FqOiJ0p4cGnMuOWOUQEhQgUVXAY4fP06PHnrXZseOHQkMDOSJJ56QglSC1OxUVNxr18zdPpfTuad5oMsDNbdIa2U16Qr3/qIvzbFxbuH+lM/0IAhNn8478Flo9xefWtS1Y9Pin7KLTpV35gSVhzMZGuDyxPpsOnye1Ix8Zizby/9d397t2PGfJ5P0RH+fWk6jpk2fPp0FCxawe/dugoOD6du3L6+++ipt2rQp+84V8MJHt5BpPY/zz6rhHhRcvKVddItW8E/R4zRFQ3Hbd/FZ3fekk0Ge4mDO/OcuOs/FbdCwKxoXLjNgI4z65gi+HvkNjUMbl/l7xobGAnAu/xyEJ4DDikmz41BVzwZCBUt2yFuXgAoGQg6Hg4CAwlkkJpNJ6m8IoLDgosXhnhuy+MBifjz4I9/f9L3vB0MAVzwCWz65aIX7ghdge76eP7TyFXhorc8EQ0VrCV2scUSQayisrDo/F1ekvqFrU/44noHFrjJvzSFGdIp1ywtyJln7cyC0atUqHnnkES6//HLsdjvPPvssQ4cOZdeuXYSGhnrsOofzD3PO4J6ArpTy8yVvU0ApsqekOEAp8l0rsn3G6CDPUPy4ku5n0Aw0MYZxRdxVPNrn+TKHxJyahjUF0NcbaxgJqg0jduwenjXm7BGSD/ECKhgIaZrGXXfdRWCg/oKan5/Pgw8+WOw//IIFCzzXQlErOKfVv7/9fRbsc//72zU7Pxz4gYe61oIaU87ZZb/PhOSPSz7GlqvffuVEnwiGLhUIdWoW6RoKK8vFydBnsi3ce2ULZq/cj6rpPUCThhb2dFxcddofLV261G17/vz5xMTEsGXLlhKr71fWxw9s8ti5Kstms7FkyRJGjBjhtrSSJzUMaQhAljULgvTgyaTaUD08a8yhaiUGgcI/VShHaOzYscTExBAZGUlkZCR///vfadKkiWvb+SX806UKLn608yO2nNrihVZVQlRzPcgxF/R0GExguCj/Lflj+G8fn8gbigw2E1xKUbjOJQybOV1cEbqkZOgh7WNc26kZ+bz32wHXdklJ1s6htaJDbP4kI0PvMYuK8u8AsbJMBhPBpmDy7HkQqI82GDU9X82TPUI2hyrDYsKlQj1C8+bNq652iDrC2TOUfCqZBsEN+Me6f3Ay5yQ59hzuWnoX34z8hrZRZVc99rqo5vrw19F1EN8HHDb4Ziyc3lV4jD0PVk6DAc96tWdIURQaRQRyuIRV4juVshL86cx8PttwlNt7x7sqQjuToe/7eLPrOJNRD4zqh5g5n2tjd1o2DcICOJNtLZZkXXRozR/zh1RVZcKECfTr14+OHTuWeIzFYsFiKRw+zszMBPTeFpvNB0tOFOFsX3W3M8wcRoYlA7sxGBNgKCjFYXdoHru23aGiULXfpaYej9rAVx+L8ranQoGQEOURFx7nygca22Es0zdOd932yPJHeH/o+zSP9P6QUpmimrsHOPevgp+ehi1FPhD88TXsXAyPbPBqMBRXP7jkQKiUHiFnvZ8h7RuVujTG+M+TXbPEXvpLB15YvJOMPBtnsvXFW5OPnidp1ylXMFV0aM0f84ceeeQRduzYwe+//17qMdOnT2fq1KnF9i9btoyQkNrxWCUlVe9SOopFwapaWbU+mUGAw6ovtmqxWlmyZIlHrpGVY0QDj5yvuh+P2sTXHovc3OKviSWRQEhUq6uaXcWMLTOwOvQ3z9O5p7lp8U18f+P3xEfEe7l1FWQKgJFv6tPsf5yoJ0+Dvqr9kknQcZTee+SFgKhlTDi/7T/rtq9pvWC3YovlcXEytHO7ZcMw3hrTjQc+2UyeTQ92/vHDTuwOXMFUr8Qogs1G8mwOgs1Gv8ofGj9+PD/88AOrV6+mWbNmpR43ZcoUJk6c6NrOzMwkLi6OoUOHElFQfdlX2Ww2kpKSGDJkSLXlCAEs/GUhp0+f5iW+JDwokG6qPoZlMJoYMWKYR64xfecqjHZrlc5XU49HbeCrj4Wzx7UsEgiJahUXHseiGxbxysZXWH18NQAOzcGrG19l9uDZXm5dJXW7Xa8/9GGRF9H9SfqXKVhf7DW89DfD6lBSwnRpvUGXUrRIYqDJ4LYdFRpAnk0lLiqYY+fysOuFq5mRtIe3xuj5Qj9PuJqNh8/RKzHKL3qDNE3j0UcfZeHChaxcubLMJYYCAwNdk02KMpvNPvUGcinV3dZODTux5/wesm05HDCb6ZGvVzp3aJrHruvQ9NlznjhfbfrbVTdfeyzK2xYpMC6qXVx4HM/0esat4OLqE6v5as9XXmxVFcVfAYNeKL7fngerX4PzR2q0Oc3qFw86SssPupSieT9v39a9xGKLjw1yX7R1xe50rv/PbyQfPU98dAg392jmF0EQ6MNhn376KZ9//jnh4eGkpaWRlpZGXl6et5tWazUNa0q+PZ8gUxBWRUFR9Yjbk4XqHaokS4tCEgiJGhEXHseEHhPc9k3fMJ1fj/7qnQZ5QodRhTPLikr5HOYO1H+uoYCoT8togkzu/507VyIQKqqkIAjAXDCD7u+9C4c2D5/N5a/vrGXKgj84dq584/J1wTvvvENGRgYDBgwgNjbW9fXVV7U4yPeyhiENsWt2zIYALAYDiqYHQp4tqKhhkEBIFJChMVFjBicM5u2tb2NV9Xwhh+bg8RWP8/Hwj+kW083LrauEojPLcs7C8qlQMMPFZMukw/HP4P0N8ODKas8bysi1YXO4V/auzNDYxYpOh784MOqZGMWnG47SqlEY+05lo2nwxcZjfLnpGJ2b1aN/64bc1TeRqNCAkk5dJ/jsenq1WMNgvZaQ0WDEYjRBQSDkyenzDk1zL6ZozYHv7oe9S/SuJ4MJTIFgNOs/o4BicCtFbQKG5udj2j+ZkktTelj0ZRBSv/qvU6JL/35GTaNnairGBd/VXLnuFv2h591lH1cOEgiJGhMXHseiGxfx7rZ3WXxgsWv/M6ufYeENCwkpqXfF1xWdWdawNXx+K6C/bCScXQ2qXQ+UqjkQ2nj4nNuyGg3CAqgXUvEAJCY8kDGXx/HFpmOkZuS7TYcvbZ2xf/+1M5uPnOeNpL3kWB1oGmw7doHtxy9wT79aMDtQ+BRnIGRQDNgMZtD0AN+Ti66qKhiLdgmt+jfs+VEv4tikG+SdB0sm2PJAdQCa/iHnomDMoNrAXkNv/NZsMProW7amYXbk6o9ZTQVCNs/VKvPRR1XUVXHhcTzQ5QF+OvSTq2foZM5JJq6cyH8H/xeDUotHa1sPg7E/wtd3QN45zGoeGgqk79ELL1ZjMFR0xpZBgSnD21XqPDERQdx+RQJfbDrGzpMZbtPhnTPIokLNPD6olWuFepPRwL1XteCv3Zvx0brD/LQ9jT2nsugaV4/IEN9JnBS1Q4PgBoCezGwxmvQlO1BRMaCqGgYPjGnZVRWzc5FwWz6s/y+gwehPoHn5KoLbbTaWVnOl7drCYbOxruCxMNTCx6IWv+uI2srZM3Rn+ztd+9acXMP9y+7nWNYxL7bMA5pfCQ+vR6uvBz0KGqx5E97uBWtmVVslaueMrddv6cLKpwby1x5Vn7XWoUmkq2J1sNnomkEWFRrIE0NaExXqPvupfmgAEwa35ucnrmbdlGv4540lFxUU4lLMRjP1AusBYCnIRzNTOHPMExyqhsHZc3F0nV4CA0UvfyH8jgRCwiviwuNoXd99/asNaRv4y8K/1P5gKLwR9jt/JDOoaeE+1aov2PpO32oNhjw5Yys2MsgVXP084epSk6dLvm+w29R7ISqiQXADVE3FWhAIGXEOj1U9ENI0DVWjsGfpwK9gDoWwRnpOkPA7EggJr3GuWF+UXbPz2qbXan8wFBbD2sueQbt4qM+Wq38CrSX8bTq88A0xITE4NIcrEHL2CKnqpe5VPs6V510jbGnbISQKIpuWfidRp0kgJLzGuS7ZE92fcMsNWnlsJTctvqnWB0MWcyT2v38PgRdVDA5v4p0GCVFLRAdFY1ftWAz660KQUY+APDE0Zi3Ie3MNjZ3Zpyf4Rkgg5K/qfCC0Z88eunbt6voKDg5m0aJF3m6WKBAXHsfdne5m4Q0LXUmSABaHhZ8O/eTFlnlI/BVwTxIEhBbu++yvXl+13t9XiRe+rV5QPRyqA6srENIDIE/UEnIFQgYFLNmQeRyyTkFkzVaDF76jzgdCbdq0ISUlhZSUFH7//XdCQ0MZMmSIt5slLtIisgVvXfOW2763t77N0cyjXmqRB8W0hcvvK9xW7bD6da815+JV4o+WsFhrSSR4EjWlfmB9bKoNa0FPcbChIEfIE4FQQb0to6LA2f36TocFolpU+dyidqrzgVBR33//PYMGDSI0NLTsg0WN69igI3e0v8O1raHxxpY3vNgiD+pxFxiKJGJu+0Jftd4LSlolviyVDZ6EqIzIwEisqhVLwehVoMFzydLOHiGjQYHzBT2z9RL0/6PCL3k9EFq9ejUjR46kSZMmKIpS4rDV7NmzSUxMJCgoiN69e7Nx48ZKXevrr79m9OjRVWyxqE5j2o7BXCRg+OXoL3yz5xsvtshDoprD+E0Q11vf1hzwzZ1wMqXGm+KsOQSUe5X4ygRPQlRW/SC9gnIeeuDjyRwhS9FAKCsNUCChr8wY82NeD4RycnLo0qULs2eXvBL5V199xcSJE3nxxRdJTk6mS5cuDBs2jNOnT7uO6dq1Kx07diz2dfLkSdcxmZmZrF27lhEjRlT77yQqLy48jsU3LqZfk36uff9Y/w9+P/G7F1vlIVHNodsd7vu+f8yzq0mWQ9GaQz9PuLpcM8IqEzwJUVnOOkLOQChE0Zeu8cSsscJkaSArVV9CIyS66icWtZbXK0sPHz6c4cOHl3r7zJkzue+++xg3bhwAc+bM4ccff+TDDz/kmWeeASAlJaXM6yxevJihQ4cSFHTpWigWiwWLxeLazszMBMBms2Gz2cq8TnVyXt/b7ahujYMac23ctaw9uRat4IXwud+e47vrvyMysHbUpin1b9WsDwTUw2S9oK/ek7YNx4pXUK96qkbbFxth5obOjUpso91ud3133hYbYWbJ+L5sOXqeHvH1iY0w8+eJCwCcOJtDm5iKT6+v689jUXn1A/UeIUtB/aBIo/6a7JFZY84cIYOhoEcICK5X5fOK2svrgdClWK1WtmzZwpQpU1z7DAYDgwcPZt26itVi+frrr7n//vvLPG769OlMnTq12P5ly5YREuIbtVSSkpK83YRqp6AwIXwCc7Pnkq1lc85yjnsX38udoXdiVIzebl65lfi36vAfGmWkcMXBmQBov81g1ZlocgIb1XDrSnYsG8DE77//zpEw99vMwB8n4Y8ix+3bvon8SkyCy82VPCNRsnpB9QCwFqwzFmnQnyueSJa2uQIhIDNVn7wQ7K3FTIUv8OlA6MyZMzgcDho1cn+DaNSoEbt37y73eTIyMti4cSPfffddmcdOmTKFiRMnurYzMzOJi4tj6NChREREXOKe1c9ms5GUlMSQIUP8Ym2bE9knyDqQxfs73wfggP0AydHJvHjFi15uWdnK/Fttz8Fx0IARFZNm45oj/8Zxxw8Q7f2ZKztPZvL69vVceeWVdGhS8nN+ccpJ/rNwO0ZF4z87jUy7qRM3dK1YfSRnb6sQF3P2/DoDoQhFn6noyenzJoNBD4TQJBDycz4dCHlKZGQkp06dKtexgYGBBAYGFttvNpt9JvjwpbZUp8T6iSTWS3Tbt/jgYi6rfxl3dbzLK22qqFL/Vol9wBQI9jwADDmnMbzXT0+oruaV6svSpH4ojw9qRZP6oaU+z3q1aIjRaMJqcxBgMtKrRcMKPyf94TksKsdsMBNoDMSm6sOnYej/Tzw+ayzDOTQmgZA/83qy9KU0aNAAo9FYLIg5deoUjRs39lKrRE0qaRmOGVtmMG3DtNpdeTqqOTy8DloNK9yn2mCP94tIxkQE8cSQ1sRElJ5PV5mEayEqIsQUgkNT0YDwgh4hTwRCzlljgYodrFn6TgmE/JpPB0IBAQH06NGD5cuXu/apqsry5cvp00dWCfYHzmU4RrUa5bb/i91f1P5lOKKaw/BXcftvuPVTr1edLi9Zh0xUpzCznqBmB0JdQ2NVP68zWTqS7MKdEgj5Na8HQtnZ2a7KzwCHDh0iJSWFo0f1isITJ05k7ty5fPTRR/z555889NBD5OTkuGaRibovLjyOezvdS4AhwG2/xWFhY2rlakr5jKjmcN+vYC4IJk7vhNm9a00wJER1CQvQA6GVIcGE4PkcIbdAKCC8yucVtZfXA6HNmzfTrVs3unXrBuiBT7du3XjhhRcAGD16NK+//jovvPACXbt2JSUlhaVLlxZLoBZ1W1x4HItuXMSDnR9027/25Fq0Gq7D43FNu0HHvxZuOyxwZI332iOED2gcqqc/fBoZ7gqEPJkj5BYImYrnhQr/4fVAaMCAAWiaVuxr/vz5rmPGjx/PkSNHsFgsbNiwgd69e3uvwcJr4sLjeKTbI8waOMs1hX7ZkWV8uedLL7fMA658wn07J9077RDCR8SHxxNmDsWOQhAFdYQ80iPkQFEgXMsq3CmBkF/zeiAkREW1qt8KBcW1/cqGV1hycIkXW+QB0S1h1PuF27+/Aaf/9F57hPCyekH1sDqs2BSFQKyAZ3qEbA4No6IQrjoDIUWvLi38lgRCotZJPpWMXbO7tlVUJv82mc2nNnuxVR7Q+RZoMVD/OT8D5lwluULCb9UPrI9VtWFTIFDTe4Q8MjTmUFEUCNOywBio9wYpStl3FHWWBEKi1ilpSj3As789S15BXZ5aK6FwjTVUGxxc6bWmCOFNzvXGLIpCgKb3CHli1pjFrmJQFMLVTDAH68GQ8GsSCIlaxzmlfmKPiZiVwqJ8qTmpvLDmhdqdPN3pZtz+W0qPkPBT4QUzuayKQj3bKdorhz02a0xRIFTNBlMQmALKvpOo0yQQErVSXHgc4zqOY/FNi3ms22MEm4IBWHp4KU+uerL21heKag5//xacOVBbPwFbvlebJIQ3OAMhm2Ig0pLKVPN8HB5Yft5qV1FQCFMz9WEx6RHyexIIiVotLjyO+zrfx6Sek1z7ko4kccOiG2pxMNQCVyCUdw7WveXV5gjhDa5AyGgiO7ARAdhdxRCrwurQZ40Fq7lgNEuPkJBASNR+x7KO8eqmV9322VQbPx3y/nIVlXJ0HVDkBX/je+CBT8JC1CauQAgwaA5MOMi3eahHSIFgNQcUk/QICQmERO2XfCoZi8NSbP/HOz9m55mdXmhRFcX3gYKhPgCyT8OeWl4eQIgKci6x4UDDgB4IOYshVoXVrmJAIUjNBYNReoSEBEKi9is6i8ykmIgOigYgw5rBmB/HcCijliUcOxdkveKRwn0b5nivPUJ4gdFgJNAQqAdCakGPkN1R5fM6p88HqTn6tHnpEfJ7EgiJWs85i+zlfi/z/U3fM65j4Tp0Ghr/WPeP2jeTLKo5DPsX1EvQtw//Bul7vdsmIWpYkCmoYJDYjhEVi0eGxvTXAj0QMkhVaSGBkKgb4sLjuOGyG4gLj+Oa+GswGwqn1W8+tZlP//zUi62rpPOHIeNE4faaWV5rihDe4JwNqqp2TIoDq8MzPUJGVH1oDAWMMjTm7yQQEnVOXHgci29czM2tbnbte33T66w6tsqLraqEo+ugSAVtdi4AWy0vGClEBQSb9UDIoek9QlabZ9Yacy7iioL0CAkJhETdFBcex92d7sZQ8BRXUXly1ZPsPrfbyy2rgIuTpm258NtM77VHiBoWYgoBwK6ACQcWT0yft6uEk6tvaJr0CAkJhETdlXwqGbXINHSLw8KDSQ+SlpPmxVZVgDNp+vJ7Cvet/rdUmxZ+I9QcCoBNARN2z8wac6iEKgU9q5omPUJCAiFRd3Vv1B2T4r6q9Nn8szyQ9AA5thwvtaqCoppDkx5Fdmiwd6nXmiNETXJOobejYELF6olZY249Qqr0CAkJhETdFRcex7tD3y0WDB3MOMiDSQ+WWHvIJyX0BcVYuJ2T7r22CFGDQgOcPUKKXkfIQ0NjoTh7hBzSIyQkEBJ1W6/Gvfj+pu8Z1WqU2/6U9BTuX3Y/DrXqnzCrXVRz+Pt3hdv7f/FeW4SoQeFm53pjYETFZvfMoquhzh4h1S51hIQEQqLuiwuP495O97qKLjoln07miZVP1I4aQy0HQpNu+s+p2yB9j3fbI0QNcAZCeYqCEc9Nnw/RCmaNOWxSWVpIICT8g7PoYo9GPdz2rzi2gtkps73UqgrqdGvhz+v/6712CJfVq1czcuRImjRpgqIoLFq0yNtNqlMiAiMAyDIYMCoaNg8tsRFKPlZDMDis0iMkJBAS/iMuPI6Huz5cbP+7f7zLkyuf9P3V6pt0Lfx5y3z4+AY4ttFbrRFATk4OXbp0YfbsWhJM1zIRAXoglGHQ36ocdvulDi8Xq0MlmHxsxmCwW6RHSEggJPxLr8a9mDFgBm2j2nJ106td+5cdWcbIhSN9Oxg6f9h9++BK+GikTKf3ouHDh/Pyyy9z0003ebspdVJkYCRQGAjZHbYqn9NqVwkhD5sxRHqEBCCBkPBDQxOGMnPATNanrXfb79AcvLrxVRbvX+ybAVF8H7hoBhz2fL0CtRB1kGtozKjPmtx+9CzvrT5Q6fPZHSqqBk04TYQlFazZYA7ySFtF7WUq+xAh6p7kU8lYHdZi+1cdX8Wq46sIMASw6MZFxIXHeaF1pYhqDjfOhoUPFO4zBekBkqgVLBYLFkth2YbMzEwAbDYbNlvVezuqk7N9NdnOcKOeLJ1l1NcONOJgQfIJxvWJr9T5cq360FoDzmPQHDgG/h9q6+uhEr+TNx4PX+Wrj0V52yOBkPBL3Rt1J8AYgNVhxagYiQ2N5Xj2cdftVtXK5rTNvhUIAXQeDSv+BReO6tt/+1wPkEStMH36dKZOnVps/7JlywgJCfFCiyouKSmpxq6V5cgCCofGIhUL5zIyWbJkSaXOl2MDMBGoWsAAS84loq7cUKU21uTj4et87bHIzc0t13ESCAm/FBcex4dDP+SDHR9wT8d7iAqO4voF17stybH86HKua3EdAb5UeVZRoM11sOEdfdtWvv/owjdMmTKFiRMnurYzMzOJi4tj6NChREREeLFlZbPZbCQlJTFkyBDMZnONXDPHlsOr37xKnln/PxgZHky+GsqIEVdW6nynsyyweRVBit5TcO11I0GpXIaINx4PX+Wrj4Wzx7UsEggJv9Ulpgv/ueY/ACzev9gtCAJ9mOy+Zffx5sA3qR9U3xtNLFnLawoDoQMroN1I77ZHlFtgYCCBgcWTc81ms0+9gVxKTbY1xKD3kuUZ9Bwhg6bi0Kj09VX0ACgAKxpgDqh6onRt+ttVN197LMrbFkmWFoLCoTIAk2JyFV9MPp3MmB/HsOecXsDwWNYx7ydTJ/QFQ8FnmIMrvdcOQXZ2NikpKaSkpABw6NAhUlJSOHr0qHcbVkeYCp7nuQVDY4pmx+6ofAFUW8ESHYHY0OTtTxSQHiEhKD5UduDCAf614V9YVSsnsk9w+5Lbebjrw/w35b9YHBYCjYEsvGGhd3KIAsOgWS84uhbOHdDzhepVLnlUVM3mzZsZOHCga9s57DV27Fjmz5/vpVbVHYaCYavjBtgdYEbRHNjVyhdVtBYNhCo5JCbqHnkmCFHAOVQWFRzFvzbqQZCTxWHhjS1vuBZqtTgsJJ9K9lZTocWAwp+XPA1ZaV5rij8bMGAAmqYV+5IgyHNCTCEcN6hMi66PotpxqJXvEbIWVKYOkEBIFCHPBCEuUtrU+qJMionujbrXUItKUDQQ2vsTnPBiUCZENbqy6ZXEYCJfMehDYx4JhOxoitFTTRS1nARCQlyke6PuxRZovZhdszNvxzwyLBk11KqLNO0O5tDC7a/+XnPLbWSlwYrp0gslakR4QDgO9BXoFdWOowo5Qla7igEVs+JAu7g4qfBbEggJcRHnAq0Te0zEbCh91sE3e7/h2u+uZf7O+WiaVrOJ1Eaze/0gzQEHfq3+64IeAK16RQIhUSNCzaHYADsKiuqoUo+QxaEvuAqgSo+QKCAhsRAliAuPY1zHcXSP6c7bKW+zJW0LNs2GSTGhaRoOHABk27KZsXkGn+z6hHN557BrdgKNgXx73bfV38gWA+HUjsLtsEbVf00halhYQBg2VOwKKJqjSjlCuRY7IQWBkGaQtz+hkx4hIS6hS0wX5g6dy+KbFvNyv5d5rPtjriCoqNO5p7Frevl+i8PCttPbAFhycEn19RC1Hua+vfSZmlmANfOk+3chqlGYOQwbGnZFQVHtVZo1tuXIeUIVPRBSjL5T70Z4lwRCQpRDXHgcN1x2A4MTBper0vRX+77itP00L65/kZsW31Q9wVDT7oBSuF0TC7CeOwTfjtN//nZczQRewq+FmcOwo2FDAc2OqoFayV6h7CI9QkFBstiq0EkgJEQFOOsNDYwbyIt9XuSK2CswlTDCvP3sduZkz8GBA4vDwi9HfvF8/lBAKNRLKNw2BpZ/AdbSEp7LSoQ+uk4PuKBmAi/h98ICwgA9Wdqo6pWhK5snZLVrhFKw6K3Bh5bOEV4lg6RCVFDRpTlubn0z205v44MdH/C3Nn/j0z8/5bcTvwFgpXAK/qwts3DgwGww82i3R+nQoAOp2al0b9S9akUZE6+ElMP6z1c+Uf4FWJ0Jz22GQ3jjsvc7xffRV7y35+vfyxt4CVFJYWY9ELIrCgFqHkCl84Qsdgehin4OTBIICZ0EQkJUUdHAKC4ijg2LNrgVYwRceUU21cbMLTNd+6tcobrFAEj5VP+5tF6crDTYPA96jise3OSe1XuASrqtJFHN4eZ58OUY/Xt5Ay8hKskZCNkUhSBN743U84QqPuvLai+cNUYZJTKE/5ChMSE8KC48jkU3LuLlK17m4bCHyzze4rAwK3kW83bMq9ywWVyvwp9Tt5Y+3FXadPfcsxWfCh/RxP27ENXIOTRmB4IKgpjK9ghZ7SqhSsHQWDly/YR/kEBICA+LC49jRIsRNDE1IdQQWubxPx/+mZlbZnLjohsrHgzVi4eg+vrP6Xv1oObUTs8VPJTiicLLnD1CmqIQjLNHqJKBkEPVZ40pRr0WlxBIICREtfry+i95ud/LLPjLAu7rdB+mS1SztarWiq9fpijQuKP+s70g96EyvTxOF0+Nl+KJwsucPUIAgYoHeoTIB4NJAiHhIoGQENWoaVhTbrjsBlrVb8Vj3R/j65Ff0yG6Q4nHBhgCKrd+WbOeJe935v/kni1+mzPQyU4v3CdT44UPCjIGoRSUiQgoGNaqbI+QzdkjZDDqwZAQSCAkRI1qVb8Vn474lIe7PIyhyOrXYeYwXuv/WuWSpht3Knm/s2fo4kCoaMCz/KXC/TI1XvggRVEINumJzQGKPgnB7qhcUUWbQyMMCygGCYSEiwRCQtQwk8HEQ10fYt6weTQMbgjoS3VM+W0Kq46tqvgJG10UCBXt5Sm67ewFKhrwOIrMbnNOjYfiU+M3z5PhMeE1QcZgAEye7BGSoTFRQAIhIbyke6PufHX9V3Rt2BWAPHsej/76KDO3zKxY8cXoloUBDLj38hTddg53FQ14nDNncs/Cti/huoKp/RdPjU+eXxgIhTeG/s+Ub7q9EB4QYtYnHTh7hCqbI2R3qAV1hBS4xILKwr9IICSEFzUMacj7w97nqqZXAaChMW/HPJ5f83z5l+YwGN2DEod7DSPXtnO4y1kLCKDvo/r307v1YTTnp+RLTY0PbwwDp0ggJGpMUEHgblQKKks7KtkjpGqEKfmgGEjFzk8Hf+JC/gVPNVPUUhIICeFlgcZAhiQMKbbf4rDw/vb3yxcMNe1R+PPFM9OcvT5Fh7ucgc7at/TvF/ciCeFDQgt6hJoaTtFV2V+FHiFNnzWmKEzK28vTvz3NVV9dRZ5zxqXwSxIICeEDejbuSUAJax8t2LeAGxbdUHbBxaL5PC3669+duUGDXtK/l1QJ2tlbdHEvkhA+JMQUAkCsks4/zPMqvQK9XVUJJQ8VhT2ObNf+fef3eaSdonaSQEgIH+CsSP3Pvv9kUPwgt9ucy3JccqiscefCnw/8qn939vKE6QnZJQ53FU2OFsJHOWsJ7SOWQGyV7xFS9R6hwyaFfAqDqQMXDniknaJ28otA6PXXX6dDhw507NiRTz/91NvNEaJEceFx3NjqRmYOmMmAuAHFbrc4LKUXXGzUvshGwZtEeXp5bp4HN75TmDN0MedMMyG8yDk0pikqZuzYKpkj5FA1Qshjt0l/64sOisagGCQQ8nN1PhDavn07n3/+OVu2bGHTpk28/fbbXLhwwdvNEqJUBsXAzP4zixVevGTBxcBwCG3ovq88vTwRTaDrbSX3FhWtNwQSFAmvCQ8IB5yBkKPSPUIOVSWEPNIMCkYU4sLjUDWVPef3eLK5opap84HQn3/+SZ8+fQgKCiI4OJguXbqwdOlSbzdLiEsyG83MHjSb2NBY176RLUdeuuBivfjCn4e/Bte+ov98cV2h8ipabwgg7Y/KnUeIKnKuN4ZBw6Q4Kp0jZFKtmFBJMyoYUGgSpn8A2H9hv6eaKmohrwdCq1evZuTIkTRp0gRFUVi0aFGxY2bPnk1iYiJBQUH07t2bjRs3lvv8HTt2ZOXKlVy4cIHz58+zcuVKTpw44cHfQIjqER0czRsD38BUUAH3u33fcfuPt7Pt9LaS71AvofDnkGhY+oz+c2VnhMX3AXNI4XbRPCQhalC4We8RsioaJuyV7hEK0XIBSDWAA831weJM3hlybDmeaayodbweCOXk5NClSxdmz55d4u1fffUVEydO5MUXXyQ5OZkuXbowbNgwTp8+7Tqma9eudOzYsdjXyZMnad++PY899hjXXHMNo0aN4oorrsBoNNbUrydElXSI7sAT3Z9wbf9x5g/G/Tyu5KTpoj1CB5aXXD26IqKaw0NrYcAUfftStYWEqEbOoTGLQSUAe6UqS2ua5lq9/qTRgIpGi8gWrtslT8h/eX2xleHDhzN8+PBSb585cyb33ff/7d15fFTlvfjxz5k1CdnJRkxYBBQpS1hMQFsFjSyiP1Gx2lpFsGgx3KpRrNxXgdrqpVfQohZJL1fA6k+x9CfYIrVAZFFEloQgS4jIGpYsELIns57fH5OZZMgOSSYz8337ysvMc57n5DnnhJlvnnUWM2Y4xipkZGTw+eefs3LlSl5+2fEXb05OTos/4+mnn+bpp58G4Je//CUDBw5sNq/JZMJkMrlel5eXA2CxWLBYLG26ps7i/PmerodoXUc+q0cGPsJnP3zG96XfA2C1W8k+n03c9e4LGiqh17n+QdtUBbsxEizVoDWgt5RjsVrBWR+rFT3UpzV47ZYekgDXp6Lftsi9fAeQ32PRVmHGMABqNCq6qxwjZLLaCcaxXlCBxrGJa6/gXkQYI7hsuszx0uMMi5ZWT3/k8UCoJWazmaysLObNm+dK02g0pKamsmtX2zeELCoqIiYmhry8PPbs2UNGRkazeRctWsQrr7zSKH3Tpk0EBQU1UaLrbd682dNVEG3UUc9qqn0qb/EWJkyoqOQdzIOj7nmiywu5pe77iycP8u3gpQCEVZ9iXN4Cdu7cSVnQufo0YOfOnVQEnCKk9hzjgAMHDjC6Lr2pvM60jlBdXd1h5xK+LdQQCkBtXSB0NS1CNWYbPajFApRrHZ0hMUExxATFUGWt4lLtpZZPIHxWtw6ELl68iM1mIzY21i09NjaWo0ePNlOqsfvuu4+ysjJ69OjBqlWr0Omav+x58+aRnp7uel1eXk5iYiITJkwgNDS0/RfRgSwWC5s3b+auu+5Cr5d9crqzznhWpiMm3sp5C4AtbOFXE35FoC6wPsOFeDi+BIAYTRl33313XfoByINbb70Veg13S7u9YAVKWT5qsKN1afhN/eF003nd0jqAs7VViNaEBdS3COmxYbXa2n2OaouNHkoNlxsMjYgwRhATFMOp8lNUWyQw91fdOhDqKO1pPTIajRiNxkbper2+2wQf3akuomUd+ayeHPYkW/K3cPjSYS5UXeCDvA9IS0qrz6Cr/zlK6Rn0WEEfCHWBv16nA2ddvv2zI1+ZY6yRUunYUFX/1euN8zZVvgPI77Boq3BDOAC1iopGUbHZrO0+R43ZSjC1XNbWD40N0AUQHeRYdqLaKoGQv/L4YOmWREVFodVqKSwsdEsvLCwkLk42fBT+5WzlWbf1Tt47+F4L226ocKmZwZ+5G+DIuvrXEf1AWxf8V9VNta+4cO0VFqKDOMcI1dZ9Yqm29o8vqzHbGaI5SYnGcRKjokOjaIgMiMSu2mXWmB/r1oGQwWBg1KhRZGZmutLsdjuZmZmMHTu2hZJC+J7swmys9vq/hC12C3/K+lPzBS42sUicqQL+9VL967Fz4Nf74eEP3PNt/S+wt7/7QYjOoNc6Wg9r6gY5268iEKo2W7mOi66usYC6pSEiAyKxqTbpGvNjHg+EKisrycnJcc38OnnyJDk5OZw5cwaA9PR0VqxYwfvvv09ubi6zZ8+mqqrKNYtMCH8xMnYkBq37xqybT2/mQHEz6woVf984bdsfobxuwHNEP0cgpCgQXDcOr66boLj4EO/+8wmKq69yMUYhOsFRg4FvAgNQLe1fEqLaYiNIqaVEq0GDhqC6bTucLUIV5oqOrq7wEh4PhPbt28eIESMYMWIE4Ah8RowYwYIFCwB4+OGHWbJkCQsWLCApKYmcnBy++OKLRgOohfB1iSGJrJywkvGJ43niR0+40t/c9yaq2sQsmuIrJhRYa2Bf3Z5iugB4bB2E9nLPc+cCQKFYq2V5aQ65J7d06DUIcbUCNQYu6HT8KSIc+1WsjVVjthGEicsaLQG6AALqtqCJDIgEoNwsg/f9lccHS48bN67pN/EG5syZw5w5c7qoRkJ0X8NjhvP2HW9jsVvYlr+NU+WnyC7KZvvZ7YzThjsyafRgt0DREffCJ78G5ziIYQ87Fky8QnFkbz4cfBvJJ74F4Pjm33DDPSuQEXnC064Pvg5zcR61igJX1TVmIx4TJVotBq3BNePSGQhVmis7tL7Ce3i8RUgI0X56jZ7nRj7ner0sZ1n9HxQRdVttXDoOlgZ7hR37d/33w38GQHF1Me/mvEuxqRSAo2XHWVlzkvy6mWIp1dWcP76psy5DiDYL1gdjV8CsKPxl2/eUVrevVajGbCVAsVCi1aLX6AnQurcIyWBp/yWBkBBeqLi6mKMlRxkQPgCAoyVH+bI4y3Ewom7bANUGl47VFzq7z/H/8D7QewwAhy4eYvmB5ey++B3vhoexOGspAJt71C8eOiSvbrKC7D4vPCjYGIYFBZOioMfK+dLa1gs1UGOxEYCZy1otWkVLoN7RIhQeEO44bqvp6CoLLyGBkBBe6NDFQ2R8l8EdiXe40pYd/xQ7uHd5FR6BkDi4fjxQ12I07GFQFPIr8nlxx4sAvPvdCpZHhFGrOroc9gYGuE5hKDnp+ObvM8D5vRBdrIcxDKuiYNIo6LFRY2nfrMZqsw2jYuGyVoOiKARqHYGQs3XIZDW1cgbhqyQQEsLLNAxgVh1exY0RNwJwrDKfLUGB0LN/feaiukCotsyVVHzjBN7NeZft+dsx1w06tdYFQNq6YYM6rYHp0clE2xwfNsVaDe8GGyk+Ltu7CM8IMYZhVhwtQjqs1LYzEKox2zBgoVSjQUFxW5W9h74HVtWKTZaM8EsSCAnhZbILs10BjMVucY11AFh2XX/s142uz1x0BMrOwflsx+u4oRyyVbL8wHKMOmOj6fjOVqM3bn+DFyetIDqsL4BjFllEGMU9r0e4W7ZsGX379iUgIICUlBT27Nnj6Sr5pB6GYMyKguUqA6Fqsw0tVso0Ciqqa9YY1O9lVmmRAdP+SAIhIbxMw/WEdBodBy4eICE4AYAT1nK22crAWLcvXlEu5G10lS3r9xNXa9If9/yRl5Nfdju3DceHS2yPWNBo4I75bsfXFnwtaws18Mknn5Cens7ChQvJzs5m+PDhTJw4kaKiIk9XzeeE6EMwKY4FFbWK+aq6xmo0dlRFwaba3FqEIgIiAHh+2/MdV2HhNSQQEsLLJIYksuQ2x+aqM37kWFh0cM/BruN/OfgX1JhBjhdl+XBgjetYdmS8qzXJbDOz8YQjSHIOEzUBP+85iuhAx8KKDJ4K0Te5yv/92P+juEYCIac333yTWbNmMWPGDAYPHkxGRgZBQUGsXLnS01XzOT0MPVyBkE5josbcvkCo1mKjXOto8bTZbW4tQv3D+2PQGiipKem4CguvIYGQEF4otodjQdFVhx0LJG46XT/F/cilI2wPj6nPfK5utlhYbwbceJ9ba9K+wn1M6z2RF8ododDvSquZd8tC10aUaDRcHPuU288urCrojEvyOmazmaysLFJTU11pGo2G1NTUdm30LNomRB8CjjgIncbS/jFCJjPlOscJzHazW4tQz8Ce6BQdtbb2zUQTvsHjCyoKIa5ew73HGnrXeoHbcX1uOAyaQmJob5bctoRfb/01z498nsX7FvPQsJnQZxJ89TyxD6xstNDizpBItPr6t4ozBz6E3nfg7y5evIjNZmu0yn1sbCxHjx5tsozJZMJkqp+dVF7uWM3YYrFgsbR/kcCu5Kyfp+oZoGkwk1FrobK2fffMUl3K5boNV01WEwbF4Cofpg+j1lZLtaW6zef09P3oTrrrvWhrfSQQEsILRQdG8/NBP2ft92ux2Bv/Y8+tLWZr3ADuKPihPvGme4H61iSnwqpChsQNZ/bw2UTHDW90rpFxo1gYFu563efIRvJ//AOJEQM65mL8yKJFi3jllVcapW/atImgoKAmSnQ/mzd7ZuZgvjXf9b0GEweP5LKx/EgLJdxVXrzEvkhHMGW2mzmRe4KNxx1dw6fNp7GrdipNlWzcuLGl0zTiqfvRHXW3e1Fd3baNdCUQEsILRQdFMy9lHmN6jeHXW3/NrKGzWHFwBU8NfYr/Ofg/APy5Vx9uH/8a2sPrIGoA9LnF7Rx/ynbsXP/ijhdZf996nkl6psmflRiSyA3Dp7P36EcAxNisVGxfBFPf68Qr7P6ioqLQarUUFha6pRcWFhIX1/SmJPPmzSM9Pd31ury8nMTERCZMmEBoaGin1vdaWSwWNm/ezF133YVer+/yn3+m/Ax/2fAXAAwGO4l9B3D3hIFtLv/pwVWc12lBhbfHvc3NcTdj1BoBiC2MZU3mGixYmDR5Ehql9VEjnr4f3Ul3vRfOFtfWSCAkhBdztu5cH+6Y1n5H7zvYeX4nhy8d5ljpMdYplUx74C9uZaIDoxmfOJ6t+VsBx6Dp7MJsEkMSm/05Kb3G8H/rAiGAQYc+h/HnIOy6jr4kr2EwGBg1ahSZmZlMnToVALvdTmZmZrN7IxqNRoxGY6N0vV7frT5AWuKpukYFR7m+1ygWTDa1XfXQWsoo12hQgPF9x7sdiw6Odn1vU2wY9Y2fUXO86dl1tu52L9paFxksLYQPKTWVurbdAHhn/ztUmCvc8kQHRTP35rmuQdMGrYGRsSNbPO+V3Wkaaw1snt9Mbv+Rnp7OihUreP/998nNzWX27NlUVVUxY8YMT1fN54QYQlxj3jTtHCytqipGSwWVGg1aRWl03LnfGECNVbba8DcSCAnhxaIDo5k9fDYRRsc6KJdNl/ns+GeMiXPsJVZSW8Lb2W83KtdwCv6S25a02BrUiDHM8ZWYAs6NXv3Uww8/zJIlS1iwYAFJSUnk5OTwxRdfNBpALa6dRtEQ4how3b51hKrMNsKVSio1GnRNfOyFGcNQ6sKsamvbxpUI3yGBkBBeLDoommeSnuGGiBvcAqJHBz/qGv+wJm8NX5z6on6n+boFEZ2tPFe29tRaaymsKqSgqoAyUxl21U50YDTTBk5zZJjwKvw6G1Kehib+uvY3c+bM4fTp05hMJnbv3k1KSoqnq+SzwvSOAeU6pbZdLUIVtRbCqKJao2BQtI2OaxQNIYYQQFqE/JGMERLCBzgDoiOXHLNoYoJieOnml/jDt38AYOHOhcxLnsfyA8sZlziufp0g4ETZCXac3cH+ov0cLTlKSa37onI6RUd0ULRr9d3Pqk9xtjibgdaB9Atzn2ovRGcKN4SQbypBp9RQY7G3uVxlrZUwpYpaRSFMafpjL8wYRrm5XAIhPySBkBA+6qEbHmJ/0X42nNhAtbWahd8sBGBPwR6+v/w9O87uAGDeV/NaPI9VtXKh6gIXqi4A8NHRj/jo6EdM6DOBN8a90bkXIUQDEcZIqDhNNEVYq08AyW0qV15rJZRKzIpCoLbpAbSRxkjyK/IlEPJDEggJ4aMURWH+mPkUVBWwr3Afdhx/Qb+xr/ngJTIgkr6hfQk3hqPVaKm2VlNSU8KFqguUmkrd8vYO7d2Z1ReikfDAniiqSm8ln4cuLwIeaVO5iloLem0lqqIQrG16RljPwJ4A1FgkEPI3EggJ4UMKqwpd/48OjGbt92v5rx//F+uPrycjJ8MVDDWUFJ3EPdffw48Tfkx8j3iUZsb97C3Yy8x/z+TZEc9ix86o2FGdei1CXCmybjzbXs113Gm/2OZyFbVW0DnWlAltsLVGQ87uYmkR8j8SCAnhI/Ir8l07y7+440WW3LbENSZo9vDZDOk5hGcyn+HBgQ/SL6wfRo2R1/a8xryUeW6btjanb2hfZg+fzX0D7nMbYyREV4nqEYcKbIqsIa3SRFt/C8trLRh0lYCWMG2PJvMkBCcAMmvMH0kgJISPyC7MdttZ/miJ+35Xzqb/n974Uwb3HExxdTElppL6neZb4RyQLYSnRAVFu2Yqlmgt9LXZ0Whbn/xcUmkmSlcNhBARENZknrhgx2rga/LW8ODAB5ttGRW+R6bPC+EjRsaOdFskcVDkoBbzOwMbad0R3iIqsH516XKdhoqKtm2hcLmymkqdDUVVCWmweGJDcUGOQCivJI/jpcevvbLCa0ggJISPuHKRxKY2V234fyG8TcPWyzKNhrKyy20qZyor5JJWiwIEBoQ3mSeuR/3+cEU1RddSTeFlJBASwoc0t0jileOH8ivyG5UVortzdu8a7SqXtRoqytsWCFnLCinWalCBwLr1sK4UFRjlWl36Yk3bB2IL7yeBkBB+4MrxQ9mF2R6ukRDtF2oIxYCGAFWlVKOlqry0TeVqywq4oNOhKgpBdcHUlXQaHXE94jBoDBRVS4uQP5FASAg/MDJ2JAE6xz5NAbqAVjdZFaI7UhSFWH0IetXRIlRd1bYxQoHmS+TrHXODgpsZLA3QJ7QPeq1eWoT8jMwaE8IPJIYk8un/+ZTswmxGxo5s3yarQnQjCYHRHKspoVSjobayrNX8NrtKhL2AUq1jj7GYoJhm8/YJ7cOB4gOu/fiEf5BASAg/kRiSKAGQ8HoJIQkcuZxHqVZLbXXrgdDFShMhxguu17FBsc3m7RfWD5PVRHGNBEL+RLrGhBBCeI2EyBup1mgo0mrb1CJ0+lI1QfoCwPGB17OZMULgWDTUjp2TZSc7qrrCC0ggJIQQwmskRN6ARVG4oNNSXVXSav5Tl6owGcrRqio99cHoNU1vugq4VlgvNZVK95gfkUBICB8l6wYJX9QntA8AqqJw0dr67K4zhSWcNCgEqBBTV7Y5EQERrq02cktyr72ywitIICSEDyqsKpR1g4RP6h/eH2ebjonzqKraYv7q/O/YF2ikSqOQEJLQ6vlHxY5Co2jYV7CvA2orvIEEQkL4oKMlR2XdIOGTdBodgwJi0dtVbIZLlFZbWsxvuJhFkVaLES3zx8xv9fypfVKxq3b+9v3fqLbIBqz+QAIhIXzQoMhBsm6Q8FlDo4ehQaUgoIbvC5ofMF1SZSZAuw9VURjfcwhhxubXEHK69bpbiTBGUG2p5tNjn3ZktUU3JdPnhfBBsT1iZd0g4bNuv+EBPsrfzIEAA1t2fktK/3ubzLf3eCEXgi8AQdzW/542nVuv0TMvZR4v7XiJxfsWs+n0JgZFDiJIF4RW0bp2pVftKsdqjnHmuzNotG1vU3Bu49FeCSEJhBpCr7q8Wx2Uaz9HQzarjTxLHqHnQtHqtG2rwzVeR1yPOAZGDLymczhJICSEj5J1g4SvSo4fQxAaKjUQcnYxh8/9hB9dF94oX/a21WwJCUBR4Sf9JrX5/JP7TabKXMXS7KXsL9rP/qL9zebdemjr1VyCT/pg+wdd9rMeHPggv7vldx1yLgmEhBBCeBWdRkfaoF+wOPd93u9VQvEn9zL+loUMHZhEpbmG0stF7MjewD7jx5gVHSmB1xHezK7zzZl24zSm3TgNi83CpZpLVFmrUFUV538Wi4Wvv/qaH//kx+h0bfsobW1gd0tCjaEYtIarLt+RrrwOi9XCl5lfcsedd6DXNb88gas8V38fnJxd/x1BAiEhhBBe59Gb0zl4LosvKg6zvmcp6/Oeh7wrMgXo6U8PFt3z16v+OXqtnrjguEbpFouFH3Q/cEPEDej1rX/4+zKLxUKIJoTowGivvBcSCAnhQ6IDo5k9fDbRgdGerooQnUqr0bL4gTX86nwOa795l3Olx7HYzWgUBY1GT6QxkjE3TmLS6BloFJkXJJongZAQPiQ6KJpnkp7xdDWE6DL945N4edr/eLoawotJmCyEEEIIvyWBkBBCCCH8lgRCQgghhPBbEggJIYQQwm9JICSEEEIIvyWBkBBCCCH8lgRCQgghhPBbEggJIYQQwm9JICSEEEIIvyWBkBBCCCH8lgRCQgghhPBbEggJIYQQwm9JICSEEEIIvyWBkBBCCCH8ls7TFejuVFUFoLy83MM1AYvFQnV1NeXl5ej1ek9XR7RAnlXrnP+mnP/G/FF3en9pjfxOu5P7Ua+73ou2vsdIINSKiooKABITEz1cEyF8U0VFBWFhYZ6uhkfI+4sQna+19xhF9ec/x9rAbrdz/vx5QkJCUBSFm2++mb1797ZYpqU8zR1rS3p5eTmJiYnk5+cTGhp6FVdz7dpy/Z19rraWay3f1Tyn5o5dmSbPqvUyqqpSUVFBfHw8Go1/9tJf+f7SnXWH3+nuRO5Hve56L9r6HiMtQq3QaDQkJCS4Xmu12lYfdEt5mjvWnvTQ0FCP/bK15fo7+1xtLddavqt5Ts0day6/Pz+rtpTx15YgpyvfX7yBJ3+nuyO5H/W6471oy3uMf/4Zdg3S0tKuKU9zx9qb7ikdWZ+rPVdby7WW72qeU3PHuttzAs8/q+54T4QQ4krSNeZFysvLCQsLo6ysrNtF3cKdPCvha+R32p3cj3refi+kRciLGI1GFi5ciNFo9HRVRCvkWQlfI7/T7uR+1PP2eyEtQkIIIYTwW9IiJIQQQgi/JYGQEEIIIfyWBEJCCCGE8FsSCAkhhGjRsmXL6Nu3LwEBAaSkpLBnzx5PV6lT7Nixg3vvvZf4+HgURWH9+vVux1VVZcGCBfTq1YvAwEBSU1M5duyYW56SkhIeffRRQkNDCQ8P58knn6SysrILr6JjLFq0iJtvvpmQkBBiYmKYOnUqeXl5bnlqa2tJS0ujZ8+eBAcH8+CDD1JYWOiW58yZM0yZMoWgoCBiYmKYO3cuVqu1Ky+lVRII+aj8/HzGjRvH4MGDGTZsGGvXrvV0lUQz7r//fiIiIpg2bZqnqyJEI5988gnp6eksXLiQ7Oxshg8fzsSJEykqKvJ01TpcVVUVw4cPZ9myZU0ef/3113n77bfJyMhg9+7d9OjRg4kTJ1JbW+vK8+ijj3L48GE2b97Mhg0b2LFjB0899VRXXUKH2b59O2lpaXz77bds3rwZi8XChAkTqKqqcuV5/vnn+ec//8natWvZvn0758+f54EHHnAdt9lsTJkyBbPZzDfffMP777/P6tWrWbBggScuqXmq8Ennz59X9+/fr6qqql64cEGNj49XKysrPVsp0aStW7eq//jHP9QHH3zQ01URopHk5GQ1LS3N9dpms6nx8fHqokWLPFirzgeo69atc7222+1qXFycunjxYldaaWmpajQa1Y8//lhVVVU9cuSICqh79+515fnXv/6lKoqinjt3rsvq3hmKiopUQN2+fbuqqo5r1+v16tq1a115cnNzVUDdtWuXqqqqunHjRlWj0agFBQWuPMuXL1dDQ0NVk8nUtRfQAmkR8lG9evUiKSkJgLi4OKKioigpKfFspUSTxo0bR0hIiKerIUQjZrOZrKwsUlNTXWkajYbU1FR27drlwZp1vZMnT1JQUOB2L8LCwkhJSXHdi127dhEeHs7o0aNdeVJTU9FoNOzevbvL69yRysrKAIiMjAQgKysLi8Xidj8GDRpE79693e7H0KFDiY2NdeWZOHEi5eXlHD58uAtr3zIJhDyktb5o6Lh++aysLGw2m+xwfRW68jkJ0d1cvHgRm83m9kEGEBsbS0FBgYdq5RnO623pXhQUFBATE+N2XKfTERkZ6dX3y26389xzz3HrrbcyZMgQwHGtBoOB8PBwt7xX3o+m7pfzWHchm656iLMveubMmW59qk7OfvmMjAxSUlJYunQpEydOJC8vz/UPLSkpqclBZ5s2bSI+Ph5wDNx7/PHHWbFiRedekI/qquckhBDdVVpaGocOHeLrr7/2dFU6hQRCHjJ58mQmT57c7PE333yTWbNmMWPGDAAyMjL4/PPPWblyJS+//DIAOTk5Lf4Mk8nE1KlTefnll7nllls6rO7+pCuekxDdVVRUFFqtttFMoMLCQuLi4jxUK89wXm9hYSG9evVypRcWFroNQ7hyELnVaqWkpMRr79ecOXNcg74TEhJc6XFxcZjNZkpLS91ahRr+bsTFxTVqIXf+LnWn+yFdY91QR/TLq6rKE088wR133MFjjz3WWVX1azJ+Qvg6g8HAqFGjyMzMdKXZ7XYyMzMZO3asB2vW9fr160dcXJzbvSgvL2f37t2uezF27FhKS0vJyspy5fnyyy+x2+2kpKR0eZ2vhaqqzJkzh3Xr1vHll1/Sr18/t+OjRo1Cr9e73Y+8vDzOnDnjdj8OHjzoFhxu3ryZ0NBQBg8e3DUX0gbSItQNtdQvf/To0TadY+fOnXzyyScMGzbMNa7lgw8+YOjQoR1dXb/VEc8JHIMpDxw4QFVVFQkJCaxdu9bvPmRE95Wens706dMZPXo0ycnJLF26lKqqKlcrqC+prKzkhx9+cL0+efIkOTk5REZG0rt3b5577jleffVVBg4cSL9+/Zg/fz7x8fFMnToVgJtuuolJkyYxa9YsMjIysFgszJkzh0ceecTrusHT0tL46KOP+OyzzwgJCXGN6QkLCyMwMJCwsDCefPJJ0tPTiYyMJDQ0lP/4j/9g7NixjBkzBoAJEyYwePBgHnvsMV5//XUKCgr47W9/S1paWvfaoNXT09ZE42ma586dUwH1m2++ccs3d+5cNTk5uYtrJ5zkOQl/9c4776i9e/dWDQaDmpycrH777beerlKn2Lp1qwo0+po+fbqqqo4p9PPnz1djY2NVo9Go3nnnnWpeXp7bOS5duqT+7Gc/U4ODg9XQ0FB1xowZakVFhQeu5to0dR8AddWqVa48NTU16jPPPKNGRESoQUFB6v33369euHDB7TynTp1SJ0+erAYGBqpRUVHqCy+8oFosli6+mpbJ7vPdgKIorFu3zvVXhdlsJigoiL///e+uNIDp06dTWlrKZ5995pmK+jl5TkII4XtkjFA3JP3y3kGekxBCeD8ZI+QhrfVF+1O/fHcmz0kIIXybdI15yLZt2xg/fnyj9OnTp7N69WoA/vznP7N48WIKCgpISkri7bff9rqZB95OnpMQQvg2CYSEEEII4bdkjJAQQggh/JYEQkIIIYTwWxIICSGEEMJvSSAkhBBCCL8lgZAQQggh/JYEQkIIIYTwWxIICSGEEFdpw4YN9OvXj+TkZI4dO+bp6oirIOsICSGEEFfpxhtvZNmyZRw+fJhdu3axZs0aT1dJtJO0CAkhhBDNuHTpEjExMZw6darJ4z179mTAgAH07dsXg8HgSn/kkUd44403uqiW4lpIi5AQQgi/s3HjRqZMmdLs8Z/+9Kd88sknpKenU1FRwYoVK5rMt2LFCn71q18RGxvLoUOHiIyMBODQoUPcdtttnDx5krCwsE65BtExpEVI+JRr7a+///77iYiIYNq0aZ1QOyFEdzF+/HguXLjg9nX27FnuuusuevbsyX/+539SXV3Ne++9x5NPPtnkOaxWK2+99RYvvfQSlZWVREREuI4NGTKE/v378+GHH3bVJYmrJIGQ8CkvvPACK1as4NFHH2X+/PntLv/ss8/y17/+tRNqJoToTgIDA4mLi3N9RUdH88ILL5CdnU1mZibDhw9n48aNGI1GxowZ0+Q5MjIyuP7660lLS6OiooITJ064Hb/33ntlzJAXkEBIeJ2W+uyb669vq3HjxhESEtLkMenzF8I32Ww2fvGLX7BlyxZXEATw1VdfMWrUqCbLlJSU8Ic//IH//u//JiEhgbCwMHJyctzyJCcns2fPHkwmU2dfgrgGEggJj8jJyeGRRx4hLi4Og8FA//79+f3vf4/Vam217GuvvcZ9991H3759Gx2bMWMG/fv3Z/bs2SxdurRD6/zb3/6W1157jbKysg49rxDCc5xB0KZNm9iyZYsrCAI4ffo08fHxTZZbuHAh999/PzfddBMAgwcP5sCBA2554uPjMZvNFBQUdN4FiGsmgZDocitXriQ5OZnY2Fg2bNhAbm4u8+fPZ+nSpc32xTu11GffUn+9U1JSEkOGDGn0df78+VbrLX3+QvgWm83GY489xqZNm8jMzCQpKcnteE1NDQEBAY3KHTlyhA8//JDf/e53rrQhQ4Y0ahEKDAwEHO9bovvSeboCwr9s27aNWbNmsWrVKh5//HFXev/+/bFYLDz11FPMnz+fAQMGNFm+pT77hv31f/zjHzlx4gT9+/d3y3PlG1V7Ofv809LSruk8QgjPcgZB//73v9myZUujIAggKiqKy5cvN0p//vnnKS0tJSEhwZVmt9tJTEx0y1dSUgJAdHR0x1ZedChpERJd6tlnn2Xy5MluQZDT7bffDtCoebmh5vrs29Jf3xGkz18I72ez2Xj88cddQdCIESOazDdixAiOHDnilrZhwwaysrLYv38/OTk5rq/33nuPM2fOuAVOhw4dIiEhgaioqE69HnFtJBASXWb//v189913zbam1NTUAKDTNd9Q2VyffVv669siNTWVhx56iI0bN5KQkMCuXbvcjkufvxDezW638/jjj7N+/Xo+/PBDevXqRUFBgduXzWYDYOLEiRw+fNgV3FgsFl544QXmzp3bqJv9zjvvBNz/kPvqq6+YMGFC11+kaBfpGhNdxtlC01QTNEB2djYAw4YNa/YcTfXZO/vrc3NzXWlN9de3xZYtW1o8Ln3+Qni3vXv38tFHHwFw9913NzquKAqlpaWEhoYydOhQRo4cyd/+9jeefvpp3nnnHUpLS5kzZ06jcomJiQQFBZGTk8O4ceOora1l/fr1fPHFF51+TeLaSCAkuozZbAZocvAhwLvvvsttt91Gv379mj1HU332be2v7wjS5y+Ed0tJSaE9GyosWLCAuXPnMmvWLNLT00lPT28yn6IoVFVVuV6vWrWK5OTkZtcgEt2HBEKiyzinpW7fvp2pU6e6HVuyZAm5ubl8/fXXgGO8kHOa+sGDB9m9ezejR49mxIgRbrO2GvbXN+xS27t3LzNnzuTy5ctNzh67WtLnL4R/mTJlCseOHePcuXPt+uNKr9fzzjvvdGLNREeRvcZEl5o0aRIHDx5k6dKljB49msLCQv73f/+XNWvWsG7dOu666y63/AsXLqS0tJS33noLcARFI0eOpKioiODgYIYMGcLMmTP5zW9+41buzJkz9OnTh61btzJu3LgOq/8TTzyBVqvlvffe67BzCiGE8BxpERJd6tNPP+WVV15h7ty5nD17FpvNxqRJk/j+++8bDYJeunQpp06dYvXq1a60hn32VVVVbe6v7wjS5y+EEL5HWoSER/3yl79k69atZGVlER4e7kpfvXo1//jHP1i7di1ardatzOeff87cuXM5dOgQGk3XTXxcvnw569atY9OmTV32M4UQQnQumT4vPGrZsmXMnDmT/fv3u9LWrVvHmjVr+PjjjxsFQeDos3/qqac4d+5cV1ZV+vyFEMIHSYuQ6HYiIiKIjo4mKCgIgFdffZV77rnHw7USQgjhiyQQEkIIIYTfkq4xIYQQQvgtCYSEEEII4bckEBJCCCGE35JASAghhBB+SwIhIYQQQvgtCYSEEEII4bckEBJCCCGE35JASAghhBB+SwIhIYQQQvgtCYSEEEII4bckEBJCCCGE35JASAghhBB+6/8DLnozwK1vnoYAAAAASUVORK5CYII=", + "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..d28c9c05 --- /dev/null +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -0,0 +1,523 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1ca14405-4a7c-4588-93cd-46534c374a36", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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.\n", + "\n", + "Start by making a project" + ] + }, + { + "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": [ + "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"SAM Heads SLD\", min=1.0e-07, value=1.75e-06, max=2.0e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"Bilayer Heads SLD\", min=5.0e-07, value=1.47e-06, max=1.5e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(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", + "problem.parameters.append(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\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": [ + "problem.layers.append(name=\"Oxide\", thickness=\"Oxide Thickness\", SLD=\"Oxide SLD\", roughness=\"Substrate Roughness\",\n", + " hydration=\"Oxide Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"SAM Tails\", thickness=\"SAM Tails Thickness\", SLD=\"SAM Tails SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Tails Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"SAM Heads\", thickness=\"SAM Heads Thickness\", SLD=\"SAM Heads SLD\", roughness=\"SAM Roughness\",\n", + " hydration=\"SAM Heads Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Central Water\", thickness=\"CW Thickness\", SLD=\"CW SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"CW Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Bilayer Heads\", thickness=\"Bilayer Heads Thickness\", SLD=\"Bilayer Heads SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Heads Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Bilayer Tails\", thickness=\"Bilayer Tails Thickness\", SLD=\"Bilayer Tails SLD\", roughness=\"Bilayer Roughness\",\n", + " hydration=\"Bilayer Tails Hydration\", hydrate_with=\"bulk out\")" + ] + }, + { + "cell_type": "markdown", + "id": "356964f9-83a6-4a92-8092-4d250b68ac16", + "metadata": {}, + "source": [ + "Now deal with the experimental parameters. 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=5.50e-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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "\n", + "d2o_dat = np.loadtxt(os.path.join(data_path, \"DSPC_D2O.dat\"), delimiter=\",\")\n", + "problem.data.append(name=\"dspc_bil_D2O\", data=d2o_dat)\n", + "\n", + "smw_dat = np.loadtxt(os.path.join(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": 10, + "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 | 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', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\n", + "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | ['Oxide', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', '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": 11, + "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", + "| resampleParams | [0.9, 50] |\n", + "| display | iter |\n", + "+------------------+-----------+\n" + ] + } + ], + "source": [ + "controls = RAT.Controls()\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 our our initial starting position:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e59bf938-804c-458c-891c-c3e56ed32820", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.072 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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "895c9af2-2117-437b-a848-9d8e380329a6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From c19dd5c8564529e29390f08961447255f6cdacab Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Thu, 25 Jul 2024 16:21:28 +0100 Subject: [PATCH 2/7] Adds jupyter notebooks for absorption and domains examples --- .../absorption-checkpoint.ipynb | 311 +++++++ RATapi/examples/absorption/absorption.ipynb | 827 ++++++++++++++++++ .../absorption/volume_thiol_bilayer.py | 2 +- .../domains_custom_XY-checkpoint.ipynb | 162 ++++ .../domains_custom_layers-checkpoint.ipynb | 133 +++ .../domains_standard_layers-checkpoint.ipynb | 196 +++++ .../examples/domains/domains_custom_XY.ipynb | 500 +++++++++++ .../domains/domains_custom_layers.ipynb | 448 ++++++++++ .../domains/domains_standard_layers.ipynb | 341 ++++++++ 9 files changed, 2919 insertions(+), 1 deletion(-) create mode 100644 RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb create mode 100644 RATapi/examples/absorption/absorption.ipynb create mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb create mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb create mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb create mode 100644 RATapi/examples/domains/domains_custom_XY.ipynb create mode 100644 RATapi/examples/domains/domains_custom_layers.ipynb create mode 100644 RATapi/examples/domains/domains_standard_layers.ipynb diff --git a/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb b/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb new file mode 100644 index 00000000..11f314af --- /dev/null +++ b/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb @@ -0,0 +1,311 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", + "problem.parameters.append(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)\n", + "\n", + "problem.parameters.append(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True)\n", + "problem.parameters.append(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the bulk in and bulk out parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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": 6, + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "\n", + "data_1 = np.loadtxt(os.path.join(data_path, \"D2O_spin_down.dat\"))\n", + "problem.data.append(name=\"D2O_dn\", data=data_1)\n", + "\n", + "data_2 = np.loadtxt(os.path.join(data_path, \"D2O_spin_up.dat\"))\n", + "problem.data.append(name=\"D2O_up\", data=data_2)\n", + "\n", + "data_3 = np.loadtxt(os.path.join(data_path, \"H2O_spin_down.dat\"))\n", + "problem.data.append(name=\"H2O_dn\", data=data_3)\n", + "\n", + "data_4 = np.loadtxt(os.path.join(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": 8, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Code' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m problem\u001b[38;5;241m.\u001b[39mcustom_files\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m 2\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDPPC absorption\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m path\u001b[38;5;241m=\u001b[39mpathlib\u001b[38;5;241m.\u001b[39mPath\u001b[38;5;241m.\u001b[39mcwd()\u001b[38;5;241m.\u001b[39mresolve(),\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m \u001b[43mCode\u001b[49m(filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m'\u001b[39m, language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'Code' is not defined" + ] + } + ], + "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\", resampleParams=[0.9, 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.ipynb b/RATapi/examples/absorption/absorption.ipynb new file mode 100644 index 00000000..49dbcbbb --- /dev/null +++ b/RATapi/examples/absorption/absorption.ipynb @@ -0,0 +1,827 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pathlib\n", + "\n", + "import numpy as np\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "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": 2, + "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": 3, + "metadata": {}, + "outputs": [], + "source": [ + "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", + "problem.parameters.append(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)\n", + "\n", + "problem.parameters.append(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True)\n", + "problem.parameters.append(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True)\n", + "problem.parameters.append(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set the bulk in and bulk out parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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": 6, + "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": 7, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "\n", + "data_1 = np.loadtxt(os.path.join(data_path, \"D2O_spin_down.dat\"))\n", + "problem.data.append(name=\"D2O_dn\", data=data_1)\n", + "\n", + "data_2 = np.loadtxt(os.path.join(data_path, \"D2O_spin_up.dat\"))\n", + "problem.data.append(name=\"D2O_up\", data=data_2)\n", + "\n", + "data_3 = np.loadtxt(os.path.join(data_path, \"H2O_spin_down.dat\"))\n", + "problem.data.append(name=\"H2O_dn\", data=data_3)\n", + "\n", + "data_4 = np.loadtxt(os.path.join(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": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n",
+       "    """VolumeThiolBilayer  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",
+       "    subRough = params[0]\n",
+       "    alloyThick = params[1]\n",
+       "    alloySLDUp = params[2]\n",
+       "    alloyISLDUp = params[3]\n",
+       "    alloySLDDown = params[4]\n",
+       "    alloyISLDDown = params[5]\n",
+       "    alloyRough = params[6]\n",
+       "    goldThick = params[7]\n",
+       "    goldRough = params[8]\n",
+       "    goldSLD = params[9]\n",
+       "    goldISLD = params[10]\n",
+       "    thiolAPM = params[11]\n",
+       "    thiolHeadHydr = params[12]\n",
+       "    thiolCoverage = params[13]\n",
+       "    cwThick = params[14]\n",
+       "    bilayerAPM = params[15]\n",
+       "    bilHeadHydr = params[16]\n",
+       "    bilayerRough = params[17]\n",
+       "    bilayerCoverage = params[18]\n",
+       "\n",
+       "    # Make the metal layers\n",
+       "    gold = [goldThick, goldSLD, goldISLD, goldRough]\n",
+       "    alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n",
+       "    alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n",
+       "\n",
+       "    # Neutron b's..\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",
+       "    # Work out the total scattering length in each fragment\n",
+       "    # Define scattering lengths\n",
+       "    # Hydrogenated version\n",
+       "    COO = (2 * bo) + (bc)\n",
+       "    GLYC = (3 * bc) + (5 * bh)\n",
+       "    CH3 = (1 * bc) + (3 * bh)\n",
+       "    PO4 = (1 * bp) + (4 * bo)\n",
+       "    CH2 = (1 * bc) + (2 * bh)\n",
+       "    CH = (1 * bc) + (1 * bh)\n",
+       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
+       "\n",
+       "    # And also volumes\n",
+       "    vCH3 = 52.7  # CH3 volume in the paper appears to be for 2 * CH3's\n",
+       "    vCH2 = 28.1\n",
+       "    vCOO = 39.0\n",
+       "    vGLYC = 68.8\n",
+       "    vPO4 = 53.7\n",
+       "    vCHOL = 120.4\n",
+       "    vCHCH = 42.14\n",
+       "\n",
+       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
+       "    vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3)  # Tail volume\n",
+       "\n",
+       "    # Calculate sum_b's for other fragments\n",
+       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
+       "    sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n",
+       "\n",
+       "    # Calculate SLDs and Thickness\n",
+       "    sldHead = sumbHead / vHead\n",
+       "    thickHead = vHead / thiolAPM\n",
+       "\n",
+       "    sldTail = sumbTail / vTail\n",
+       "    thickTail = vTail / thiolAPM\n",
+       "\n",
+       "    # Correct head SLD based on hydration\n",
+       "    thiolHeadHydr = thiolHeadHydr / 100\n",
+       "    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n",
+       "\n",
+       "    # Now correct both the SLDs for the coverage parameter\n",
+       "    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
+       "    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
+       "\n",
+       "    SAMTAILS = [thickTail, sldTail, 0, goldRough]\n",
+       "    SAMHEAD = [thickHead, sldHead, 0, goldRough]\n",
+       "\n",
+       "    # Now do the same for the bilayer\n",
+       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
+       "    vTail = 28 * vCH2  # Tail volume\n",
+       "    vMe = 2 * vCH3\n",
+       "\n",
+       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
+       "    sumbTail = 28 * CH2\n",
+       "    sumbMe = 2 * CH3\n",
+       "\n",
+       "    sldHead = sumbHead / vHead\n",
+       "    thickHead = vHead / bilayerAPM\n",
+       "    bilHeadHydr = bilHeadHydr / 100\n",
+       "    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n",
+       "\n",
+       "    sldTail = sumbTail / vTail\n",
+       "    thickTail = vTail / bilayerAPM\n",
+       "\n",
+       "    sldMe = sumbMe / vMe\n",
+       "    thickMe = vMe / bilayerAPM\n",
+       "\n",
+       "    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "\n",
+       "    BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n",
+       "    BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n",
+       "    BILME = [thickMe, sldMe, 0, bilayerRough]\n",
+       "\n",
+       "    BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n",
+       "\n",
+       "    CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n",
+       "\n",
+       "    if contrast == 1 or contrast == 3:\n",
+       "        output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
+       "    else:\n",
+       "        output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
+       "\n",
+       "    return output, subRough\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k}{def} \\PY{n+nf}{volume\\PYZus{}thiol\\PYZus{}bilayer}\\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{}VolumeThiolBilayer 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}{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}{alloyISLDUp} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{alloySLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{alloyISLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", + " \\PY{n}{alloyRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", + " \\PY{n}{goldThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", + " \\PY{n}{goldRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{8}\\PY{p}{]}\n", + " \\PY{n}{goldSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{9}\\PY{p}{]}\n", + " \\PY{n}{goldISLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{10}\\PY{p}{]}\n", + " \\PY{n}{thiolAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{11}\\PY{p}{]}\n", + " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{12}\\PY{p}{]}\n", + " \\PY{n}{thiolCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{13}\\PY{p}{]}\n", + " \\PY{n}{cwThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{14}\\PY{p}{]}\n", + " \\PY{n}{bilayerAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{15}\\PY{p}{]}\n", + " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{16}\\PY{p}{]}\n", + " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{17}\\PY{p}{]}\n", + " \\PY{n}{bilayerCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{18}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the metal layers}\n", + " \\PY{n}{gold} \\PY{o}{=} \\PY{p}{[}\\PY{n}{goldThick}\\PY{p}{,} \\PY{n}{goldSLD}\\PY{p}{,} \\PY{n}{goldISLD}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + " \\PY{n}{alloyUp} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDUp}\\PY{p}{,} \\PY{n}{alloyISLDUp}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + " \\PY{n}{alloyDown} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDDown}\\PY{p}{,} \\PY{n}{alloyISLDDown}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Neutron b\\PYZsq{}s..}\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{} Work out the total scattering length in each fragment}\n", + " \\PY{c+c1}{\\PYZsh{} Define scattering lengths}\n", + " \\PY{c+c1}{\\PYZsh{} Hydrogenated version}\n", + " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\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}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{3} \\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}{CH} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\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{} And also volumes}\n", + " \\PY{n}{vCH3} \\PY{o}{=} \\PY{l+m+mf}{52.7} \\PY{c+c1}{\\PYZsh{} CH3 volume in the paper appears to be for 2 * CH3\\PYZsq{}s}\n", + " \\PY{n}{vCH2} \\PY{o}{=} \\PY{l+m+mf}{28.1}\n", + " \\PY{n}{vCOO} \\PY{o}{=} \\PY{l+m+mf}{39.0}\n", + " \\PY{n}{vGLYC} \\PY{o}{=} \\PY{l+m+mf}{68.8}\n", + " \\PY{n}{vPO4} \\PY{o}{=} \\PY{l+m+mf}{53.7}\n", + " \\PY{n}{vCHOL} \\PY{o}{=} \\PY{l+m+mf}{120.4}\n", + " \\PY{n}{vCHCH} \\PY{o}{=} \\PY{l+m+mf}{42.14}\n", + "\n", + " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", + " \\PY{n}{vTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{vCHCH}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Calculate sum\\PYZus{}b\\PYZsq{}s for other fragments}\n", + " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", + " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH}\\PY{p}{)} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Calculate SLDs and Thickness}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", + " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{thiolAPM}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", + " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{thiolAPM}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Correct head SLD based on hydration}\n", + " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{thiolHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{thiolHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now correct both the SLDs for the coverage parameter}\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{SAMTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + " \\PY{n}{SAMHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now do the same for the bilayer}\n", + " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", + " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", + " \\PY{n}{vMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\n", + "\n", + " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", + " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\n", + " \\PY{n}{sumbMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", + "\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", + " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{bilHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{bilHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", + " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + "\n", + " \\PY{n}{sldMe} \\PY{o}{=} \\PY{n}{sumbMe} \\PY{o}{/} \\PY{n}{vMe}\n", + " \\PY{n}{thickMe} \\PY{o}{=} \\PY{n}{vMe} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldMe} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldMe}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{BILTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{BILHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{BILME} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickMe}\\PY{p}{,} \\PY{n}{sldMe}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + "\n", + " \\PY{n}{BILAYER} \\PY{o}{=} \\PY{p}{[}\\PY{n}{BILHEAD}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILHEAD}\\PY{p}{]}\n", + "\n", + " \\PY{n}{CW} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cwThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + "\n", + " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{1} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{3}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", + " \\PY{k}{else}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDown}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", + "\n", + " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{subRough}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n", + " \"\"\"VolumeThiolBilayer 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", + " subRough = params[0]\n", + " alloyThick = params[1]\n", + " alloySLDUp = params[2]\n", + " alloyISLDUp = params[3]\n", + " alloySLDDown = params[4]\n", + " alloyISLDDown = params[5]\n", + " alloyRough = params[6]\n", + " goldThick = params[7]\n", + " goldRough = params[8]\n", + " goldSLD = params[9]\n", + " goldISLD = params[10]\n", + " thiolAPM = params[11]\n", + " thiolHeadHydr = params[12]\n", + " thiolCoverage = params[13]\n", + " cwThick = params[14]\n", + " bilayerAPM = params[15]\n", + " bilHeadHydr = params[16]\n", + " bilayerRough = params[17]\n", + " bilayerCoverage = params[18]\n", + "\n", + " # Make the metal layers\n", + " gold = [goldThick, goldSLD, goldISLD, goldRough]\n", + " alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n", + " alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n", + "\n", + " # Neutron b's..\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", + " # Work out the total scattering length in each fragment\n", + " # Define scattering lengths\n", + " # Hydrogenated version\n", + " COO = (2 * bo) + (bc)\n", + " GLYC = (3 * bc) + (5 * bh)\n", + " CH3 = (1 * bc) + (3 * bh)\n", + " PO4 = (1 * bp) + (4 * bo)\n", + " CH2 = (1 * bc) + (2 * bh)\n", + " CH = (1 * bc) + (1 * bh)\n", + " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", + "\n", + " # And also volumes\n", + " vCH3 = 52.7 # CH3 volume in the paper appears to be for 2 * CH3's\n", + " vCH2 = 28.1\n", + " vCOO = 39.0\n", + " vGLYC = 68.8\n", + " vPO4 = 53.7\n", + " vCHOL = 120.4\n", + " vCHCH = 42.14\n", + "\n", + " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", + " vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3) # Tail volume\n", + "\n", + " # Calculate sum_b's for other fragments\n", + " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", + " sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n", + "\n", + " # Calculate SLDs and Thickness\n", + " sldHead = sumbHead / vHead\n", + " thickHead = vHead / thiolAPM\n", + "\n", + " sldTail = sumbTail / vTail\n", + " thickTail = vTail / thiolAPM\n", + "\n", + " # Correct head SLD based on hydration\n", + " thiolHeadHydr = thiolHeadHydr / 100\n", + " sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n", + "\n", + " # Now correct both the SLDs for the coverage parameter\n", + " sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n", + " sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n", + "\n", + " SAMTAILS = [thickTail, sldTail, 0, goldRough]\n", + " SAMHEAD = [thickHead, sldHead, 0, goldRough]\n", + "\n", + " # Now do the same for the bilayer\n", + " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", + " vTail = 28 * vCH2 # Tail volume\n", + " vMe = 2 * vCH3\n", + "\n", + " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", + " sumbTail = 28 * CH2\n", + " sumbMe = 2 * CH3\n", + "\n", + " sldHead = sumbHead / vHead\n", + " thickHead = vHead / bilayerAPM\n", + " bilHeadHydr = bilHeadHydr / 100\n", + " sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n", + "\n", + " sldTail = sumbTail / vTail\n", + " thickTail = vTail / bilayerAPM\n", + "\n", + " sldMe = sumbMe / vMe\n", + " thickMe = vMe / bilayerAPM\n", + "\n", + " sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + " sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + " sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + "\n", + " BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n", + " BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n", + " BILME = [thickMe, sldMe, 0, bilayerRough]\n", + "\n", + " BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n", + "\n", + " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", + "\n", + " if contrast == 1 or contrast == 3:\n", + " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", + " else:\n", + " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", + "\n", + " return output, subRough" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "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": 9, + "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": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "Elapsed time is 0.098 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(parallel=\"contrasts\", resampleParams=[0.9, 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/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/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb new file mode 100644 index 00000000..16508865 --- /dev/null +++ b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[IMAGES!]\n", + "\n", + "# 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": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(calculation=\"domains\", model=\"custom xy\", geometry=\"substrate/liquid\")\n", + "\n", + "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=20.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Layer Thickness\", min=1.0, value=30.0, max=500.0, fit=True)\n", + "problem.parameters.append(name=\"Layer SLD\", min=-0.5e-6, value=-0.5e-6, max=0.0, fit=True)\n", + "problem.parameters.append(name=\"Layer Roughness\", min=2.0, value=5.0, max=7.0, fit=True)\n", + "problem.parameters.append(name=\"Domain SLD\", min=1.0e-6, value=1.0e-6, max=5.0e-6, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now set the SLDs of the bulk phases for our samples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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 = 1), or the domain (domain = 2)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "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": null, + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "controls = RAT.Controls()\n", + "problem, results = RAT.run(problem, controls)\n", + "\n", + "RAT.plotting.plot_ref_sld(problem, results)" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb new file mode 100644 index 00000000..0b6e223a --- /dev/null +++ b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb @@ -0,0 +1,133 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[NEED IMAGES HERE]\n", + "\n", + "# 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 1 or 2 [IS IT??? We have to resolve this satisfactorily], 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", + "We start by setting up the project:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem = RAT.Project(calculation=\"domains\", model=\"custom layers\", geometry=\"substrate/liquid\")\n", + "\n", + "# Make some parameters\n", + "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD up\", min=9.0e-6, value=11.0e-6, max=13.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD down\", min=5.0e-6, value=7.0e-6, max=10.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Alloy Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.5e-6, max=5.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Gold Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\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": null, + "metadata": {}, + "outputs": [], + "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": null, + "metadata": {}, + "outputs": [], + "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/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb new file mode 100644 index 00000000..fd0588a9 --- /dev/null +++ b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb @@ -0,0 +1,196 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import RATapi as RAT" + ] + }, + { + "attachments": { + 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" + } + }, + "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": null, + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem.parameters.append(name=\"L1 Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", + "problem.parameters.append(name=\"L1 SLD\", min=3.0e-6, value=4.1e-6, max=5.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L1 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L1 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"L2 Thickness\", min=5.0, value=60.0, max=100.0, fit=True)\n", + "problem.parameters.append(name=\"L2 SLD\", min=2.1e-6, value=3.0e-6, max=5.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L2 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L2 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"L3 Thickness\", min=5.0, value=200.0, max=300.0, fit=True)\n", + "problem.parameters.append(name=\"L3 SLD\", min=3.0e-6, value=7.0e-6, max=8.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L3 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L3 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now group these into layers as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "problem.layers.append(name=\"Layer 1\", thickness=\"L1 Thickness\", SLD=\"L1 SLD\", roughness=\"L1 Roughness\",\n", + " hydration=\"L1 Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Layer 2\", thickness=\"L2 Thickness\", SLD=\"L2 SLD\", roughness=\"L2 Roughness\",\n", + " hydration=\"L2 Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Layer 3\", thickness=\"L3 Thickness\", SLD=\"L3 SLD\", roughness=\"L3 Roughness\",\n", + " hydration=\"L3 Hydration\", hydrate_with=\"bulk out\")" + ] + }, + { + "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": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(problem)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, make a couple of Domain Contrasts" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "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": null, + "metadata": {}, + "outputs": [], + "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_XY.ipynb b/RATapi/examples/domains/domains_custom_XY.ipynb new file mode 100644 index 00000000..4ff1e19d --- /dev/null +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[IMAGES!]\n", + "\n", + "# 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", + "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=20.0, max=50.0, fit=True)\n", + "problem.parameters.append(name=\"Layer Thickness\", min=1.0, value=30.0, max=500.0, fit=True)\n", + "problem.parameters.append(name=\"Layer SLD\", min=-0.5e-6, value=-0.5e-6, max=0.0, fit=True)\n", + "problem.parameters.append(name=\"Layer Roughness\", min=2.0, value=5.0, max=7.0, fit=True)\n", + "problem.parameters.append(name=\"Domain SLD\", min=1.0e-6, value=1.0e-6, max=5.0e-6, fit=True)" + ] + }, + { + "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 = 1), or the domain (domain = 2)." + ] + }, + { + "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 0.049 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..da83a3f0 --- /dev/null +++ b/RATapi/examples/domains/domains_custom_layers.ipynb @@ -0,0 +1,448 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "from IPython.display import Code\n", + "\n", + "import RATapi as RAT" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[NEED IMAGES HERE]\n", + "\n", + "# 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 1 or 2 [IS IT??? We have to resolve this satisfactorily], 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", + "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", + "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD up\", min=9.0e-6, value=11.0e-6, max=13.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Alloy SLD down\", min=5.0e-6, value=7.0e-6, max=10.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Alloy Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\n", + "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", + "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.5e-6, max=5.0e-6, fit=True)\n", + "problem.parameters.append(name=\"Gold Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\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 0.002 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..4df3a645 --- /dev/null +++ b/RATapi/examples/domains/domains_standard_layers.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import RATapi as RAT" + ] + }, + { + "attachments": { + "f38e04ec-f12b-4e68-b486-6cf8bffef1bd.png": { + "image/png": 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" + } + }, + "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": [ + "problem.parameters.append(name=\"L1 Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", + "problem.parameters.append(name=\"L1 SLD\", min=3.0e-6, value=4.1e-6, max=5.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L1 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L1 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"L2 Thickness\", min=5.0, value=60.0, max=100.0, fit=True)\n", + "problem.parameters.append(name=\"L2 SLD\", min=2.1e-6, value=3.0e-6, max=5.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L2 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L2 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "\n", + "problem.parameters.append(name=\"L3 Thickness\", min=5.0, value=200.0, max=300.0, fit=True)\n", + "problem.parameters.append(name=\"L3 SLD\", min=3.0e-6, value=7.0e-6, max=8.0e-6, fit=False)\n", + "problem.parameters.append(name=\"L3 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", + "problem.parameters.append(name=\"L3 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now group these into layers as usual:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "problem.layers.append(name=\"Layer 1\", thickness=\"L1 Thickness\", SLD=\"L1 SLD\", roughness=\"L1 Roughness\",\n", + " hydration=\"L1 Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Layer 2\", thickness=\"L2 Thickness\", SLD=\"L2 SLD\", roughness=\"L2 Roughness\",\n", + " hydration=\"L2 Hydration\", hydrate_with=\"bulk out\")\n", + "\n", + "problem.layers.append(name=\"Layer 3\", thickness=\"L3 Thickness\", SLD=\"L3 SLD\", roughness=\"L3 Roughness\",\n", + " hydration=\"L3 Hydration\", hydrate_with=\"bulk out\")" + ] + }, + { + "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 0.058 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 +} From 563e2c0a9ed3e47f9580d10dff3b88c62b40dbee Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Thu, 8 Aug 2024 16:06:57 +0100 Subject: [PATCH 3/7] Uses extend method in jupyter notebooks --- RATapi/examples/absorption/absorption.ipynb | 43 +- .../domains_standard_layers-checkpoint.ipynb | 229 ++++++- .../examples/domains/domains_custom_XY.ipynb | 43 +- .../domains/domains_custom_layers.ipynb | 57 +- .../domains/domains_standard_layers.ipynb | 70 ++- .../DSPC_custom_layers-checkpoint.ipynb | 78 ++- .../DSPC_custom_xy-checkpoint.ipynb | 569 ++---------------- .../DSPC_standard_layers-checkpoint.ipynb | 147 +++-- .../non_polarised/DSPC_custom_layers.ipynb | 78 ++- .../non_polarised/DSPC_custom_xy.ipynb | 36 +- .../non_polarised/DSPC_standard_layers.ipynb | 147 +++-- 11 files changed, 701 insertions(+), 796 deletions(-) diff --git a/RATapi/examples/absorption/absorption.ipynb b/RATapi/examples/absorption/absorption.ipynb index 49dbcbbb..627eda65 100644 --- a/RATapi/examples/absorption/absorption.ipynb +++ b/RATapi/examples/absorption/absorption.ipynb @@ -12,7 +12,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -52,26 +53,28 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", - "problem.parameters.append(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)\n", + "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", + " 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", + " 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", + " Parameter(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True),\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.append(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True)\n", - "problem.parameters.append(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True)\n", - "\n", - "problem.parameters.append(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)" + "problem.parameters.extend(parameter_list)" ] }, { diff --git a/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb index fd0588a9..6fa72d5e 100644 --- a/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb +++ b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb @@ -2,11 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" ] }, { @@ -33,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -49,24 +50,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"L1 Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", - "problem.parameters.append(name=\"L1 SLD\", min=3.0e-6, value=4.1e-6, max=5.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L1 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L1 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "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", + " 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", + " 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.append(name=\"L2 Thickness\", min=5.0, value=60.0, max=100.0, fit=True)\n", - "problem.parameters.append(name=\"L2 SLD\", min=2.1e-6, value=3.0e-6, max=5.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L2 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L2 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", - "\n", - "problem.parameters.append(name=\"L3 Thickness\", min=5.0, value=200.0, max=300.0, fit=True)\n", - "problem.parameters.append(name=\"L3 SLD\", min=3.0e-6, value=7.0e-6, max=8.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L3 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L3 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)" + "problem.parameters.extend(parameter_list)" ] }, { @@ -78,18 +81,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "problem.layers.append(name=\"Layer 1\", thickness=\"L1 Thickness\", SLD=\"L1 SLD\", roughness=\"L1 Roughness\",\n", - " hydration=\"L1 Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Layer 2\", thickness=\"L2 Thickness\", SLD=\"L2 SLD\", roughness=\"L2 Roughness\",\n", - " hydration=\"L2 Hydration\", hydrate_with=\"bulk out\")\n", + "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.append(name=\"Layer 3\", thickness=\"L3 Thickness\", SLD=\"L3 SLD\", roughness=\"L3 Roughness\",\n", - " hydration=\"L3 Hydration\", hydrate_with=\"bulk out\")" + "problem.layers.extend(layers)" ] }, { @@ -101,9 +103,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "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)" ] @@ -117,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -134,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -161,9 +285,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.011 seconds\n", + "\n", + "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "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", diff --git a/RATapi/examples/domains/domains_custom_XY.ipynb b/RATapi/examples/domains/domains_custom_XY.ipynb index 4ff1e19d..e1e2ac29 100644 --- a/RATapi/examples/domains/domains_custom_XY.ipynb +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -9,7 +9,8 @@ "import pathlib\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -35,11 +36,15 @@ "source": [ "problem = RAT.Project(calculation=\"domains\", model=\"custom xy\", geometry=\"substrate/liquid\")\n", "\n", - "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=20.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Layer Thickness\", min=1.0, value=30.0, max=500.0, fit=True)\n", - "problem.parameters.append(name=\"Layer SLD\", min=-0.5e-6, value=-0.5e-6, max=0.0, fit=True)\n", - "problem.parameters.append(name=\"Layer Roughness\", min=2.0, value=5.0, max=7.0, fit=True)\n", - "problem.parameters.append(name=\"Domain SLD\", min=1.0e-6, value=1.0e-6, max=5.0e-6, fit=True)" + "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)" ] }, { @@ -450,16 +455,36 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.049 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.061 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/RATapi/examples/domains/domains_custom_layers.ipynb b/RATapi/examples/domains/domains_custom_layers.ipynb index da83a3f0..06195d84 100644 --- a/RATapi/examples/domains/domains_custom_layers.ipynb +++ b/RATapi/examples/domains/domains_custom_layers.ipynb @@ -9,7 +9,8 @@ "import pathlib\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -148,11 +149,11 @@ "\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", + "| 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" ] @@ -162,13 +163,17 @@ "problem = RAT.Project(calculation=\"domains\", model=\"custom layers\", geometry=\"substrate/liquid\")\n", "\n", "# Make some parameters\n", - "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD up\", min=9.0e-6, value=11.0e-6, max=13.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD down\", min=5.0e-6, value=7.0e-6, max=10.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Alloy Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.5e-6, max=5.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Gold Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\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", @@ -398,16 +403,36 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.002 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.010 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/RATapi/examples/domains/domains_standard_layers.ipynb b/RATapi/examples/domains/domains_standard_layers.ipynb index 4df3a645..6fa72d5e 100644 --- a/RATapi/examples/domains/domains_standard_layers.ipynb +++ b/RATapi/examples/domains/domains_standard_layers.ipynb @@ -6,7 +6,8 @@ "metadata": {}, "outputs": [], "source": [ - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" ] }, { @@ -53,20 +54,22 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"L1 Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", - "problem.parameters.append(name=\"L1 SLD\", min=3.0e-6, value=4.1e-6, max=5.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L1 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L1 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", + "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", + " 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", + " 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.append(name=\"L2 Thickness\", min=5.0, value=60.0, max=100.0, fit=True)\n", - "problem.parameters.append(name=\"L2 SLD\", min=2.1e-6, value=3.0e-6, max=5.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L2 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L2 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)\n", - "\n", - "problem.parameters.append(name=\"L3 Thickness\", min=5.0, value=200.0, max=300.0, fit=True)\n", - "problem.parameters.append(name=\"L3 SLD\", min=3.0e-6, value=7.0e-6, max=8.0e-6, fit=False)\n", - "problem.parameters.append(name=\"L3 Roughness\", min=2.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"L3 Hydration\", min=10.0, value=20.0, max=30.0, fit=True)" + "problem.parameters.extend(parameter_list)" ] }, { @@ -82,14 +85,13 @@ "metadata": {}, "outputs": [], "source": [ - "problem.layers.append(name=\"Layer 1\", thickness=\"L1 Thickness\", SLD=\"L1 SLD\", roughness=\"L1 Roughness\",\n", - " hydration=\"L1 Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Layer 2\", thickness=\"L2 Thickness\", SLD=\"L2 SLD\", roughness=\"L2 Roughness\",\n", - " hydration=\"L2 Hydration\", hydrate_with=\"bulk out\")\n", + "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.append(name=\"Layer 3\", thickness=\"L3 Thickness\", SLD=\"L3 SLD\", roughness=\"L3 Roughness\",\n", - " hydration=\"L3 Hydration\", hydrate_with=\"bulk out\")" + "problem.layers.extend(layers)" ] }, { @@ -291,16 +293,36 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.058 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.011 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb index 08664421..38be0712 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb @@ -13,7 +13,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -430,14 +431,17 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", - "problem.parameters.append(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True)\n", - "problem.parameters.append(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True)\n", - "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "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),\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)" ] }, @@ -480,16 +484,16 @@ "metadata": {}, "outputs": [], "source": [ - " # Read in the datafiles\n", - " data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - " D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - " SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - " H2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016607.dat\"), delimiter=\",\")\n", + "# Read in the datafiles\n", + "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + "H2O_data = np.loadtxt(os.path.join(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])" + "# 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])" ] }, { @@ -743,13 +747,13 @@ "\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", + "| 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" ] @@ -813,16 +817,36 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.002 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.009 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb index f7e1f5ae..13bd5e3d 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb @@ -13,7 +13,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -70,12 +71,16 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=15.0, max=30.0, fit=True)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.1, value=0.2, max=0.4, fit=True)\n", - "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"Lipid APM\", min=40.0, value=50.0, max=90.0, fit=True)\n", - "problem.parameters.append(name=\"Lipid Coverage\", min=0.9, value=1.0, max=1.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=3.0, value=5.0, max=8.0, fit=True)\n", + "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)" ] @@ -144,509 +149,7 @@ "execution_count": 6, "id": "60a4b771-7967-4b46-bd3c-1c7cb4eaa24b", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
import math\n",
-       "\n",
-       "import numpy as np\n",
-       "\n",
-       "\n",
-       "def custom_XY_DSPC(params, bulk_in, bulk_out, contrast):\n",
-       "    """This function makes a model of a supported DSPC bilayer using volume restricted distribution functions."""\n",
-       "    # Split up the parameters\n",
-       "    subRough = params[0]\n",
-       "    oxideThick = params[1]\n",
-       "    oxideHydration = params[2]\n",
-       "    waterThick = params[3]\n",
-       "    lipidAPM = params[4]\n",
-       "    lipidCoverage = params[5]\n",
-       "    bilayerRough = params[6]\n",
-       "\n",
-       "    # We are going to need our Neutron scattering cross-sections, plus the component volumes\n",
-       "    # (the volumes are taken from Armen et al as usual).\n",
-       "    # Define these first\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",
-       "    heads = 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",
-       "    # Start making our sections. For each we are using a roughened Heaviside to describe our groups.\n",
-       "    # We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),\n",
-       "    # which we will correct later for hydration.\n",
-       "\n",
-       "    # Make an array of z values for our model\n",
-       "    z = np.arange(0, 141)\n",
-       "\n",
-       "    # Make our Silicon substrate\n",
-       "    vfSilicon, siSurf = layer(z, -25, 50, 1, subRough, subRough)\n",
-       "\n",
-       "    # Add the Oxide\n",
-       "    vfOxide, oxSurface = layer(z, siSurf, oxideThick, 1, subRough, subRough)\n",
-       "\n",
-       "    # We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,\n",
-       "    # so we start the bilayer stack an appropriate distance away\n",
-       "    watSurface = oxSurface + waterThick\n",
-       "\n",
-       "    # Now make the first lipid head group\n",
-       "    # Work out the thickness\n",
-       "    headThick = vHead / lipidAPM\n",
-       "\n",
-       "    # ... and make a box for the volume fraction (1 for now, we correct for coverage later)\n",
-       "    vfHeadL, headLSurface = layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough)\n",
-       "\n",
-       "    # ... also do the same for the tails\n",
-       "    # We'll make both together, so the thickness will be twice the volume\n",
-       "    tailsThick = (2 * vTail) / lipidAPM\n",
-       "    vfTails, tailsSurf = layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough)\n",
-       "\n",
-       "    # Finally the upper head ...\n",
-       "    vfHeadR, headSurface = layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough)\n",
-       "\n",
-       "    # Making the model\n",
-       "    # We've created the volume fraction profiles corresponding to each of the groups.\n",
-       "    # We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions\n",
-       "\n",
-       "    # 1. Oxide ...\n",
-       "    vfOxide = vfOxide * (1 - oxideHydration)\n",
-       "\n",
-       "    # 2. Lipid ...\n",
-       "    # Scale both the heads and tails according to overall coverage\n",
-       "    vfTails = vfTails * lipidCoverage\n",
-       "    vfHeadL = vfHeadL * lipidCoverage\n",
-       "    vfHeadR = vfHeadR * lipidCoverage\n",
-       "\n",
-       "    # Some extra work to deal with head hydration, which we take to be an additional 30% always\n",
-       "    vfHeadL = vfHeadL * 0.7\n",
-       "    vfHeadR = vfHeadR * 0.7\n",
-       "\n",
-       "    # Make a total Volume Fraction across the whole interface\n",
-       "    vfTot = vfSilicon + vfOxide + vfHeadL + vfTails + vfHeadR\n",
-       "\n",
-       "    # All the volume that's left, we will fill with water\n",
-       "    vfWat = 1 - vfTot\n",
-       "\n",
-       "    # Now convert all the Volume Fractions to SLDs\n",
-       "    sld_Value_Tails = tails / vTail\n",
-       "    sld_Value_Head = heads / vHead\n",
-       "\n",
-       "    sldSilicon = vfSilicon * 2.073e-6\n",
-       "    sldOxide = vfOxide * 3.41e-6\n",
-       "\n",
-       "    sldHeadL = vfHeadL * sld_Value_Head\n",
-       "    sldHeadR = vfHeadR * sld_Value_Head\n",
-       "    sldTails = vfTails * sld_Value_Tails\n",
-       "    sldWat = vfWat * bulk_out[contrast]\n",
-       "\n",
-       "    # Put this all together\n",
-       "    totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat\n",
-       "\n",
-       "    # Make the SLD array for output\n",
-       "    SLD = [[a, b] for (a, b) in zip(z, totSLD)]\n",
-       "\n",
-       "    return SLD, subRough\n",
-       "\n",
-       "\n",
-       "def layer(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}{custom\\PYZus{}XY\\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{}This function makes a model of a supported DSPC bilayer using volume restricted distribution functions.\\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}{oxideThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{1}\\PY{p}{]}\n", - " \\PY{n}{oxideHydration} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{2}\\PY{p}{]}\n", - " \\PY{n}{waterThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", - " \\PY{n}{lipidAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", - " \\PY{n}{lipidCoverage} \\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", - "\n", - " \\PY{c+c1}{\\PYZsh{} We are going to need our Neutron scattering cross\\PYZhy{}sections, plus the component volumes}\n", - " \\PY{c+c1}{\\PYZsh{} (the volumes are taken from Armen et al as usual).}\n", - " \\PY{c+c1}{\\PYZsh{} Define these first}\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}{heads} \\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{} Start making our sections. For each we are using a roughened Heaviside to describe our groups.}\n", - " \\PY{c+c1}{\\PYZsh{} We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),}\n", - " \\PY{c+c1}{\\PYZsh{} which we will correct later for hydration.}\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 our Silicon substrate}\n", - " \\PY{n}{vfSilicon}\\PY{p}{,} \\PY{n}{siSurf} \\PY{o}{=} \\PY{n}{layer}\\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{} Add the Oxide}\n", - " \\PY{n}{vfOxide}\\PY{p}{,} \\PY{n}{oxSurface} \\PY{o}{=} \\PY{n}{layer}\\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{} We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,}\n", - " \\PY{c+c1}{\\PYZsh{} so we start the bilayer stack an appropriate distance away}\n", - " \\PY{n}{watSurface} \\PY{o}{=} \\PY{n}{oxSurface} \\PY{o}{+} \\PY{n}{waterThick}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now make the first lipid head group}\n", - " \\PY{c+c1}{\\PYZsh{} Work out the thickness}\n", - " \\PY{n}{headThick} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{lipidAPM}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} ... and make a box for the volume fraction (1 for now, we correct for coverage later)}\n", - " \\PY{n}{vfHeadL}\\PY{p}{,} \\PY{n}{headLSurface} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{watSurface}\\PY{p}{,} \\PY{n}{headThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} ... also do the same for the tails}\n", - " \\PY{c+c1}{\\PYZsh{} We\\PYZsq{}ll make both together, so the thickness will be twice the volume}\n", - " \\PY{n}{tailsThick} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vTail}\\PY{p}{)} \\PY{o}{/} \\PY{n}{lipidAPM}\n", - " \\PY{n}{vfTails}\\PY{p}{,} \\PY{n}{tailsSurf} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{headLSurface}\\PY{p}{,} \\PY{n}{tailsThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Finally the upper head ...}\n", - " \\PY{n}{vfHeadR}\\PY{p}{,} \\PY{n}{headSurface} \\PY{o}{=} \\PY{n}{layer}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{tailsSurf}\\PY{p}{,} \\PY{n}{headThick}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Making the model}\n", - " \\PY{c+c1}{\\PYZsh{} We\\PYZsq{}ve created the volume fraction profiles corresponding to each of the groups.}\n", - " \\PY{c+c1}{\\PYZsh{} We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} 1. Oxide ...}\n", - " \\PY{n}{vfOxide} \\PY{o}{=} \\PY{n}{vfOxide} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{oxideHydration}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} 2. Lipid ...}\n", - " \\PY{c+c1}{\\PYZsh{} Scale both the heads and tails according to overall coverage}\n", - " \\PY{n}{vfTails} \\PY{o}{=} \\PY{n}{vfTails} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", - " \\PY{n}{vfHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", - " \\PY{n}{vfHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{n}{lipidCoverage}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Some extra work to deal with head hydration, which we take to be an additional 30\\PYZpc{} always}\n", - " \\PY{n}{vfHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{l+m+mf}{0.7}\n", - " \\PY{n}{vfHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{l+m+mf}{0.7}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Make a total Volume Fraction across the whole interface}\n", - " \\PY{n}{vfTot} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{+} \\PY{n}{vfOxide} \\PY{o}{+} \\PY{n}{vfHeadL} \\PY{o}{+} \\PY{n}{vfTails} \\PY{o}{+} \\PY{n}{vfHeadR}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} All the volume that\\PYZsq{}s left, we will fill with water}\n", - " \\PY{n}{vfWat} \\PY{o}{=} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{vfTot}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now convert all the Volume Fractions to SLDs}\n", - " \\PY{n}{sld\\PYZus{}Value\\PYZus{}Tails} \\PY{o}{=} \\PY{n}{tails} \\PY{o}{/} \\PY{n}{vTail}\n", - " \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head} \\PY{o}{=} \\PY{n}{heads} \\PY{o}{/} \\PY{n}{vHead}\n", - "\n", - " \\PY{n}{sldSilicon} \\PY{o}{=} \\PY{n}{vfSilicon} \\PY{o}{*} \\PY{l+m+mf}{2.073e\\PYZhy{}6}\n", - " \\PY{n}{sldOxide} \\PY{o}{=} \\PY{n}{vfOxide} \\PY{o}{*} \\PY{l+m+mf}{3.41e\\PYZhy{}6}\n", - "\n", - " \\PY{n}{sldHeadL} \\PY{o}{=} \\PY{n}{vfHeadL} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head}\n", - " \\PY{n}{sldHeadR} \\PY{o}{=} \\PY{n}{vfHeadR} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Head}\n", - " \\PY{n}{sldTails} \\PY{o}{=} \\PY{n}{vfTails} \\PY{o}{*} \\PY{n}{sld\\PYZus{}Value\\PYZus{}Tails}\n", - " \\PY{n}{sldWat} \\PY{o}{=} \\PY{n}{vfWat} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Put this all together}\n", - " \\PY{n}{totSLD} \\PY{o}{=} \\PY{n}{sldSilicon} \\PY{o}{+} \\PY{n}{sldOxide} \\PY{o}{+} \\PY{n}{sldHeadL} \\PY{o}{+} \\PY{n}{sldTails} \\PY{o}{+} \\PY{n}{sldHeadR} \\PY{o}{+} \\PY{n}{sldWat}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Make the SLD array for output}\n", - " \\PY{n}{SLD} \\PY{o}{=} \\PY{p}{[}\\PY{p}{[}\\PY{n}{a}\\PY{p}{,} \\PY{n}{b}\\PY{p}{]} \\PY{k}{for} \\PY{p}{(}\\PY{n}{a}\\PY{p}{,} \\PY{n}{b}\\PY{p}{)} \\PY{o+ow}{in} \\PY{n+nb}{zip}\\PY{p}{(}\\PY{n}{z}\\PY{p}{,} \\PY{n}{totSLD}\\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}{layer}\\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 custom_XY_DSPC(params, bulk_in, bulk_out, contrast):\n", - " \"\"\"This function makes a model of a supported DSPC bilayer using volume restricted distribution functions.\"\"\"\n", - " # Split up the parameters\n", - " subRough = params[0]\n", - " oxideThick = params[1]\n", - " oxideHydration = params[2]\n", - " waterThick = params[3]\n", - " lipidAPM = params[4]\n", - " lipidCoverage = params[5]\n", - " bilayerRough = params[6]\n", - "\n", - " # We are going to need our Neutron scattering cross-sections, plus the component volumes\n", - " # (the volumes are taken from Armen et al as usual).\n", - " # Define these first\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", - " heads = 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", - " # Start making our sections. For each we are using a roughened Heaviside to describe our groups.\n", - " # We will make these as Volume Fractions (i.e. with a height of 1 for full occupancy),\n", - " # which we will correct later for hydration.\n", - "\n", - " # Make an array of z values for our model\n", - " z = np.arange(0, 141)\n", - "\n", - " # Make our Silicon substrate\n", - " vfSilicon, siSurf = layer(z, -25, 50, 1, subRough, subRough)\n", - "\n", - " # Add the Oxide\n", - " vfOxide, oxSurface = layer(z, siSurf, oxideThick, 1, subRough, subRough)\n", - "\n", - " # We fill in the water at the end, but there may be a hydration layer between the bilayer and the oxide,\n", - " # so we start the bilayer stack an appropriate distance away\n", - " watSurface = oxSurface + waterThick\n", - "\n", - " # Now make the first lipid head group\n", - " # Work out the thickness\n", - " headThick = vHead / lipidAPM\n", - "\n", - " # ... and make a box for the volume fraction (1 for now, we correct for coverage later)\n", - " vfHeadL, headLSurface = layer(z, watSurface, headThick, 1, bilayerRough, bilayerRough)\n", - "\n", - " # ... also do the same for the tails\n", - " # We'll make both together, so the thickness will be twice the volume\n", - " tailsThick = (2 * vTail) / lipidAPM\n", - " vfTails, tailsSurf = layer(z, headLSurface, tailsThick, 1, bilayerRough, bilayerRough)\n", - "\n", - " # Finally the upper head ...\n", - " vfHeadR, headSurface = layer(z, tailsSurf, headThick, 1, bilayerRough, bilayerRough)\n", - "\n", - " # Making the model\n", - " # We've created the volume fraction profiles corresponding to each of the groups.\n", - " # We now convert them to SLDs, taking in account of the hydrations to scale the volume fractions\n", - "\n", - " # 1. Oxide ...\n", - " vfOxide = vfOxide * (1 - oxideHydration)\n", - "\n", - " # 2. Lipid ...\n", - " # Scale both the heads and tails according to overall coverage\n", - " vfTails = vfTails * lipidCoverage\n", - " vfHeadL = vfHeadL * lipidCoverage\n", - " vfHeadR = vfHeadR * lipidCoverage\n", - "\n", - " # Some extra work to deal with head hydration, which we take to be an additional 30% always\n", - " vfHeadL = vfHeadL * 0.7\n", - " vfHeadR = vfHeadR * 0.7\n", - "\n", - " # Make a total Volume Fraction across the whole interface\n", - " vfTot = vfSilicon + vfOxide + vfHeadL + vfTails + vfHeadR\n", - "\n", - " # All the volume that's left, we will fill with water\n", - " vfWat = 1 - vfTot\n", - "\n", - " # Now convert all the Volume Fractions to SLDs\n", - " sld_Value_Tails = tails / vTail\n", - " sld_Value_Head = heads / vHead\n", - "\n", - " sldSilicon = vfSilicon * 2.073e-6\n", - " sldOxide = vfOxide * 3.41e-6\n", - "\n", - " sldHeadL = vfHeadL * sld_Value_Head\n", - " sldHeadR = vfHeadR * sld_Value_Head\n", - " sldTails = vfTails * sld_Value_Tails\n", - " sldWat = vfWat * bulk_out[contrast]\n", - "\n", - " # Put this all together\n", - " totSLD = sldSilicon + sldOxide + sldHeadL + sldTails + sldHeadR + sldWat\n", - "\n", - " # Make the SLD array for output\n", - " SLD = [[a, b] for (a, b) in zip(z, totSLD)]\n", - "\n", - " return SLD, subRough\n", - "\n", - "\n", - "def layer(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": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "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())" @@ -662,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "57303283-9319-4b1c-817b-04d6441a2992", "metadata": {}, "outputs": [], @@ -696,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "6815648e-ad4a-4193-ba39-83f5f15781b1", "metadata": {}, "outputs": [], @@ -747,10 +250,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "821571d9-3593-4ac6-a5db-4d83998ff4db", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "Running Differential Evolution\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final chi squared is 8.39155\n", + "Elapsed time is 103.555 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", diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb index d28c9c05..f94fe68c 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb @@ -12,7 +12,8 @@ "\n", "import numpy as np\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" ] }, { @@ -72,26 +73,30 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"SAM Heads SLD\", min=1.0e-07, value=1.75e-06, max=2.0e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"Bilayer Heads SLD\", min=5.0e-07, value=1.47e-06, max=1.5e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0),\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\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\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)" @@ -112,23 +117,22 @@ "metadata": {}, "outputs": [], "source": [ - "problem.layers.append(name=\"Oxide\", thickness=\"Oxide Thickness\", SLD=\"Oxide SLD\", roughness=\"Substrate Roughness\",\n", - " hydration=\"Oxide Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"SAM Tails\", thickness=\"SAM Tails Thickness\", SLD=\"SAM Tails SLD\", roughness=\"SAM Roughness\",\n", - " hydration=\"SAM Tails Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"SAM Heads\", thickness=\"SAM Heads Thickness\", SLD=\"SAM Heads SLD\", roughness=\"SAM Roughness\",\n", - " hydration=\"SAM Heads Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Central Water\", thickness=\"CW Thickness\", SLD=\"CW SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"CW Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Bilayer Heads\", thickness=\"Bilayer Heads Thickness\", SLD=\"Bilayer Heads SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"Bilayer Heads Hydration\", hydrate_with=\"bulk out\")\n", + "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.append(name=\"Bilayer Tails\", thickness=\"Bilayer Tails Thickness\", SLD=\"Bilayer Tails SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"Bilayer Tails Hydration\", hydrate_with=\"bulk out\")" + "problem.layers.extend(layers)" ] }, { @@ -259,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "e0409648-05f4-448b-93d6-5c4382e0dad6", "metadata": {}, "outputs": [ @@ -396,12 +400,26 @@ "\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', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\n", - "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | ['Oxide', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\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" ] @@ -423,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "c9ec7e39-48a8-4651-b74c-19d5800c67d7", "metadata": {}, "outputs": [ @@ -444,7 +462,7 @@ } ], "source": [ - "controls = RAT.Controls()\n", + "controls = RAT.Controls(display='iter')\n", "print(controls)" ] }, @@ -458,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "e59bf938-804c-458c-891c-c3e56ed32820", "metadata": {}, "outputs": [ @@ -467,16 +485,35 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.072 seconds\n", - "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is \n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -489,14 +526,6 @@ "problem, results = RAT.run(problem, controls)\n", "RAT.plotting.plot_ref_sld(problem, results)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "895c9af2-2117-437b-a848-9d8e380329a6", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb index 08664421..38be0712 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -13,7 +13,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -430,14 +431,17 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=20.0, max=60.0, fit=True)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", - "problem.parameters.append(name=\"Lipid APM\", min=45.0, value=55.0, max=65.0, fit=True)\n", - "problem.parameters.append(name=\"Head Hydration\", min=0.0, value=0.2, max=0.5, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Hydration\", min=0.0, value=0.1, max=0.2, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=4.0, max=8.0, fit=True)\n", - "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=2.0, max=10.0, fit=True)\n", + "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),\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)" ] }, @@ -480,16 +484,16 @@ "metadata": {}, "outputs": [], "source": [ - " # Read in the datafiles\n", - " data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - " D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - " SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - " H2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016607.dat\"), delimiter=\",\")\n", + "# Read in the datafiles\n", + "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", + "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", + "H2O_data = np.loadtxt(os.path.join(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])" + "# 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])" ] }, { @@ -743,13 +747,13 @@ "\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", + "| 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" ] @@ -813,16 +817,36 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.002 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 0.009 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb index 50b4c0ed..13bd5e3d 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -13,7 +13,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -70,12 +71,16 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=15.0, max=30.0, fit=True)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.1, value=0.2, max=0.4, fit=True)\n", - "problem.parameters.append(name=\"Water Thickness\", min=0.0, value=5.0, max=20.0, fit=True)\n", - "problem.parameters.append(name=\"Lipid APM\", min=40.0, value=50.0, max=90.0, fit=True)\n", - "problem.parameters.append(name=\"Lipid Coverage\", min=0.9, value=1.0, max=1.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=3.0, value=5.0, max=8.0, fit=True)\n", + "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)" ] @@ -257,9 +262,20 @@ "\n", "\n", "Running Differential Evolution\n", - "\n", - "Elapsed time is 99.984 seconds\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Final chi squared is 8.39155\n", + "Elapsed time is 103.555 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -267,7 +283,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb index d28c9c05..f94fe68c 100644 --- a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -12,7 +12,8 @@ "\n", "import numpy as np\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Layer, Parameter" ] }, { @@ -72,26 +73,30 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"SAM Heads SLD\", min=1.0e-07, value=1.75e-06, max=2.0e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"Bilayer Heads SLD\", min=5.0e-07, value=1.47e-06, max=1.5e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(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", - "problem.parameters.append(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", - "problem.parameters.append(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + "parameter_list = [\n", + " Parameter(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0),\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\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", + " Parameter(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\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)" @@ -112,23 +117,22 @@ "metadata": {}, "outputs": [], "source": [ - "problem.layers.append(name=\"Oxide\", thickness=\"Oxide Thickness\", SLD=\"Oxide SLD\", roughness=\"Substrate Roughness\",\n", - " hydration=\"Oxide Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"SAM Tails\", thickness=\"SAM Tails Thickness\", SLD=\"SAM Tails SLD\", roughness=\"SAM Roughness\",\n", - " hydration=\"SAM Tails Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"SAM Heads\", thickness=\"SAM Heads Thickness\", SLD=\"SAM Heads SLD\", roughness=\"SAM Roughness\",\n", - " hydration=\"SAM Heads Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Central Water\", thickness=\"CW Thickness\", SLD=\"CW SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"CW Hydration\", hydrate_with=\"bulk out\")\n", - "\n", - "problem.layers.append(name=\"Bilayer Heads\", thickness=\"Bilayer Heads Thickness\", SLD=\"Bilayer Heads SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"Bilayer Heads Hydration\", hydrate_with=\"bulk out\")\n", + "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.append(name=\"Bilayer Tails\", thickness=\"Bilayer Tails Thickness\", SLD=\"Bilayer Tails SLD\", roughness=\"Bilayer Roughness\",\n", - " hydration=\"Bilayer Tails Hydration\", hydrate_with=\"bulk out\")" + "problem.layers.extend(layers)" ] }, { @@ -259,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "e0409648-05f4-448b-93d6-5c4382e0dad6", "metadata": {}, "outputs": [ @@ -396,12 +400,26 @@ "\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', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\n", - "| 1 | SMW | dspc_bil_smw | SMW Background | add | Silicon | SMW | Scalefactor 2 | Resolution 1 | False | ['Oxide', 'SAM Tails', 'SAM Heads', 'Central Water', 'Bilayer Heads', 'Bilayer Tails', 'Bilayer Tails', 'Bilayer Heads'] |\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" ] @@ -423,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "c9ec7e39-48a8-4651-b74c-19d5800c67d7", "metadata": {}, "outputs": [ @@ -444,7 +462,7 @@ } ], "source": [ - "controls = RAT.Controls()\n", + "controls = RAT.Controls(display='iter')\n", "print(controls)" ] }, @@ -458,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "e59bf938-804c-458c-891c-c3e56ed32820", "metadata": {}, "outputs": [ @@ -467,16 +485,35 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.072 seconds\n", - "\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is \n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] + }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -489,14 +526,6 @@ "problem, results = RAT.run(problem, controls)\n", "RAT.plotting.plot_ref_sld(problem, results)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "895c9af2-2117-437b-a848-9d8e380329a6", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From c149fc3c2b3e207471abc1259b583dd80f363a3a Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Tue, 29 Oct 2024 09:38:01 +0000 Subject: [PATCH 4/7] Updates notebooks --- .../absorption-checkpoint.ipynb | 575 +++++++++++++++++- RATapi/examples/absorption/absorption.ipynb | 12 +- .../convert_rascal-checkpoint.ipynb | 169 +++++ .../convert_rascal.ipynb | 8 +- .../examples/domains/domains_custom_XY.ipynb | 24 +- .../domains/domains_custom_layers.ipynb | 24 +- .../domains/domains_standard_layers.ipynb | 24 +- .../non_polarised/DSPC_custom_layers.ipynb | 27 +- .../non_polarised/DSPC_custom_xy.ipynb | 15 +- .../non_polarised/DSPC_standard_layers.ipynb | 64 +- 10 files changed, 757 insertions(+), 185 deletions(-) create mode 100644 RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb diff --git a/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb b/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb index 11f314af..ceb686b9 100644 --- a/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb +++ b/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb @@ -12,7 +12,8 @@ "import numpy as np\n", "from IPython.display import Code\n", "\n", - "import RATapi as RAT" + "import RATapi as RAT\n", + "from RATapi.models import Parameter" ] }, { @@ -52,26 +53,28 @@ "metadata": {}, "outputs": [], "source": [ - "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=135.6, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD up\", min=6.0e-6, value=9.87e-6, max=1.2e-5, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD imaginary up\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD down\", min=6.0e-6, value=7.05e-6, max=1.3e-5, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD imaginary down\", min=1.0e-9, value=4.87e-8, max=1.0e-7, fit=True)\n", - "problem.parameters.append(name=\"Alloy Roughness\", min=2.0, value=5.71, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=154.7, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Roughness\", min=0.1, value=5.42, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.49e-6, max=5.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD imaginary\", min=1.0e-9, value=4.20e-8, max=1.0e-7, fit=True)\n", + "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", + " 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", + " 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", + " Parameter(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True),\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.append(name=\"Thiol APM\", min=40.0, value=56.27, max=100.0, fit=True)\n", - "problem.parameters.append(name=\"Thiol Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Thiol Coverage\", min=0.5, value=0.9, max=1.0, fit=True)\n", - "\n", - "problem.parameters.append(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer APM\", min=48.0, value=65.86, max=90.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Head Hydration\", min=20.0, value=30.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Roughness\", min=1.0, value=3.87, max=10.0, fit=True)\n", - "problem.parameters.append(name=\"Bilayer Coverage\", min=0.5, value=0.94, max=1.0, fit=True)" + "problem.parameters.extend(parameter_list)" ] }, { @@ -184,15 +187,508 @@ "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'Code' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[8], line 7\u001b[0m\n\u001b[1;32m 1\u001b[0m problem\u001b[38;5;241m.\u001b[39mcustom_files\u001b[38;5;241m.\u001b[39mappend(\n\u001b[1;32m 2\u001b[0m name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDPPC absorption\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 4\u001b[0m language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m path\u001b[38;5;241m=\u001b[39mpathlib\u001b[38;5;241m.\u001b[39mPath\u001b[38;5;241m.\u001b[39mcwd()\u001b[38;5;241m.\u001b[39mresolve(),\n\u001b[1;32m 6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0m \u001b[43mCode\u001b[49m(filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvolume_thiol_bilayer.py\u001b[39m\u001b[38;5;124m'\u001b[39m, language\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpython\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'Code' is not defined" - ] + "data": { + "text/html": [ + "
def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n",
+       "    """VolumeThiolBilayer  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",
+       "    subRough = params[0]\n",
+       "    alloyThick = params[1]\n",
+       "    alloySLDUp = params[2]\n",
+       "    alloyISLDUp = params[3]\n",
+       "    alloySLDDown = params[4]\n",
+       "    alloyISLDDown = params[5]\n",
+       "    alloyRough = params[6]\n",
+       "    goldThick = params[7]\n",
+       "    goldRough = params[8]\n",
+       "    goldSLD = params[9]\n",
+       "    goldISLD = params[10]\n",
+       "    thiolAPM = params[11]\n",
+       "    thiolHeadHydr = params[12]\n",
+       "    thiolCoverage = params[13]\n",
+       "    cwThick = params[14]\n",
+       "    bilayerAPM = params[15]\n",
+       "    bilHeadHydr = params[16]\n",
+       "    bilayerRough = params[17]\n",
+       "    bilayerCoverage = params[18]\n",
+       "\n",
+       "    # Make the metal layers\n",
+       "    gold = [goldThick, goldSLD, goldISLD, goldRough]\n",
+       "    alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n",
+       "    alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n",
+       "\n",
+       "    # Neutron b's..\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",
+       "    # Work out the total scattering length in each fragment\n",
+       "    # Define scattering lengths\n",
+       "    # Hydrogenated version\n",
+       "    COO = (2 * bo) + (bc)\n",
+       "    GLYC = (3 * bc) + (5 * bh)\n",
+       "    CH3 = (1 * bc) + (3 * bh)\n",
+       "    PO4 = (1 * bp) + (4 * bo)\n",
+       "    CH2 = (1 * bc) + (2 * bh)\n",
+       "    CH = (1 * bc) + (1 * bh)\n",
+       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
+       "\n",
+       "    # And also volumes\n",
+       "    vCH3 = 52.7  # CH3 volume in the paper appears to be for 2 * CH3's\n",
+       "    vCH2 = 28.1\n",
+       "    vCOO = 39.0\n",
+       "    vGLYC = 68.8\n",
+       "    vPO4 = 53.7\n",
+       "    vCHOL = 120.4\n",
+       "    vCHCH = 42.14\n",
+       "\n",
+       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
+       "    vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3)  # Tail volume\n",
+       "\n",
+       "    # Calculate sum_b's for other fragments\n",
+       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
+       "    sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n",
+       "\n",
+       "    # Calculate SLDs and Thickness\n",
+       "    sldHead = sumbHead / vHead\n",
+       "    thickHead = vHead / thiolAPM\n",
+       "\n",
+       "    sldTail = sumbTail / vTail\n",
+       "    thickTail = vTail / thiolAPM\n",
+       "\n",
+       "    # Correct head SLD based on hydration\n",
+       "    thiolHeadHydr = thiolHeadHydr / 100\n",
+       "    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n",
+       "\n",
+       "    # Now correct both the SLDs for the coverage parameter\n",
+       "    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
+       "    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
+       "\n",
+       "    SAMTAILS = [thickTail, sldTail, 0, goldRough]\n",
+       "    SAMHEAD = [thickHead, sldHead, 0, goldRough]\n",
+       "\n",
+       "    # Now do the same for the bilayer\n",
+       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
+       "    vTail = 28 * vCH2  # Tail volume\n",
+       "    vMe = 2 * vCH3\n",
+       "\n",
+       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
+       "    sumbTail = 28 * CH2\n",
+       "    sumbMe = 2 * CH3\n",
+       "\n",
+       "    sldHead = sumbHead / vHead\n",
+       "    thickHead = vHead / bilayerAPM\n",
+       "    bilHeadHydr = bilHeadHydr / 100\n",
+       "    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n",
+       "\n",
+       "    sldTail = sumbTail / vTail\n",
+       "    thickTail = vTail / bilayerAPM\n",
+       "\n",
+       "    sldMe = sumbMe / vMe\n",
+       "    thickMe = vMe / bilayerAPM\n",
+       "\n",
+       "    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
+       "\n",
+       "    BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n",
+       "    BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n",
+       "    BILME = [thickMe, sldMe, 0, bilayerRough]\n",
+       "\n",
+       "    BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n",
+       "\n",
+       "    CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n",
+       "\n",
+       "    if contrast == 0 or contrast == 2:\n",
+       "        output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
+       "    else:\n",
+       "        output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
+       "\n",
+       "    return output, subRough\n",
+       "
\n" + ], + "text/latex": [ + "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", + "\\PY{k}{def} \\PY{n+nf}{volume\\PYZus{}thiol\\PYZus{}bilayer}\\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{}VolumeThiolBilayer 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}{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}{alloyISLDUp} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", + " \\PY{n}{alloySLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", + " \\PY{n}{alloyISLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", + " \\PY{n}{alloyRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", + " \\PY{n}{goldThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", + " \\PY{n}{goldRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{8}\\PY{p}{]}\n", + " \\PY{n}{goldSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{9}\\PY{p}{]}\n", + " \\PY{n}{goldISLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{10}\\PY{p}{]}\n", + " \\PY{n}{thiolAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{11}\\PY{p}{]}\n", + " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{12}\\PY{p}{]}\n", + " \\PY{n}{thiolCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{13}\\PY{p}{]}\n", + " \\PY{n}{cwThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{14}\\PY{p}{]}\n", + " \\PY{n}{bilayerAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{15}\\PY{p}{]}\n", + " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{16}\\PY{p}{]}\n", + " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{17}\\PY{p}{]}\n", + " \\PY{n}{bilayerCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{18}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Make the metal layers}\n", + " \\PY{n}{gold} \\PY{o}{=} \\PY{p}{[}\\PY{n}{goldThick}\\PY{p}{,} \\PY{n}{goldSLD}\\PY{p}{,} \\PY{n}{goldISLD}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + " \\PY{n}{alloyUp} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDUp}\\PY{p}{,} \\PY{n}{alloyISLDUp}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + " \\PY{n}{alloyDown} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDDown}\\PY{p}{,} \\PY{n}{alloyISLDDown}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Neutron b\\PYZsq{}s..}\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{} Work out the total scattering length in each fragment}\n", + " \\PY{c+c1}{\\PYZsh{} Define scattering lengths}\n", + " \\PY{c+c1}{\\PYZsh{} Hydrogenated version}\n", + " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\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}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{3} \\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}{CH} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\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{} And also volumes}\n", + " \\PY{n}{vCH3} \\PY{o}{=} \\PY{l+m+mf}{52.7} \\PY{c+c1}{\\PYZsh{} CH3 volume in the paper appears to be for 2 * CH3\\PYZsq{}s}\n", + " \\PY{n}{vCH2} \\PY{o}{=} \\PY{l+m+mf}{28.1}\n", + " \\PY{n}{vCOO} \\PY{o}{=} \\PY{l+m+mf}{39.0}\n", + " \\PY{n}{vGLYC} \\PY{o}{=} \\PY{l+m+mf}{68.8}\n", + " \\PY{n}{vPO4} \\PY{o}{=} \\PY{l+m+mf}{53.7}\n", + " \\PY{n}{vCHOL} \\PY{o}{=} \\PY{l+m+mf}{120.4}\n", + " \\PY{n}{vCHCH} \\PY{o}{=} \\PY{l+m+mf}{42.14}\n", + "\n", + " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", + " \\PY{n}{vTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{vCHCH}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Calculate sum\\PYZus{}b\\PYZsq{}s for other fragments}\n", + " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", + " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH}\\PY{p}{)} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Calculate SLDs and Thickness}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", + " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{thiolAPM}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", + " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{thiolAPM}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Correct head SLD based on hydration}\n", + " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{thiolHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{thiolHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now correct both the SLDs for the coverage parameter}\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{SAMTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + " \\PY{n}{SAMHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", + "\n", + " \\PY{c+c1}{\\PYZsh{} Now do the same for the bilayer}\n", + " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", + " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", + " \\PY{n}{vMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\n", + "\n", + " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", + " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\n", + " \\PY{n}{sumbMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", + "\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", + " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{bilHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{bilHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", + " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + "\n", + " \\PY{n}{sldMe} \\PY{o}{=} \\PY{n}{sumbMe} \\PY{o}{/} \\PY{n}{vMe}\n", + " \\PY{n}{thickMe} \\PY{o}{=} \\PY{n}{vMe} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", + "\n", + " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + " \\PY{n}{sldMe} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldMe}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", + "\n", + " \\PY{n}{BILTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{BILHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + " \\PY{n}{BILME} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickMe}\\PY{p}{,} \\PY{n}{sldMe}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + "\n", + " \\PY{n}{BILAYER} \\PY{o}{=} \\PY{p}{[}\\PY{n}{BILHEAD}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILHEAD}\\PY{p}{]}\n", + "\n", + " \\PY{n}{CW} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cwThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", + "\n", + " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{0} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{2}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", + " \\PY{k}{else}\\PY{p}{:}\n", + " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDown}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", + "\n", + " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{subRough}\n", + "\\end{Verbatim}\n" + ], + "text/plain": [ + "def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n", + " \"\"\"VolumeThiolBilayer 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", + " subRough = params[0]\n", + " alloyThick = params[1]\n", + " alloySLDUp = params[2]\n", + " alloyISLDUp = params[3]\n", + " alloySLDDown = params[4]\n", + " alloyISLDDown = params[5]\n", + " alloyRough = params[6]\n", + " goldThick = params[7]\n", + " goldRough = params[8]\n", + " goldSLD = params[9]\n", + " goldISLD = params[10]\n", + " thiolAPM = params[11]\n", + " thiolHeadHydr = params[12]\n", + " thiolCoverage = params[13]\n", + " cwThick = params[14]\n", + " bilayerAPM = params[15]\n", + " bilHeadHydr = params[16]\n", + " bilayerRough = params[17]\n", + " bilayerCoverage = params[18]\n", + "\n", + " # Make the metal layers\n", + " gold = [goldThick, goldSLD, goldISLD, goldRough]\n", + " alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n", + " alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n", + "\n", + " # Neutron b's..\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", + " # Work out the total scattering length in each fragment\n", + " # Define scattering lengths\n", + " # Hydrogenated version\n", + " COO = (2 * bo) + (bc)\n", + " GLYC = (3 * bc) + (5 * bh)\n", + " CH3 = (1 * bc) + (3 * bh)\n", + " PO4 = (1 * bp) + (4 * bo)\n", + " CH2 = (1 * bc) + (2 * bh)\n", + " CH = (1 * bc) + (1 * bh)\n", + " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", + "\n", + " # And also volumes\n", + " vCH3 = 52.7 # CH3 volume in the paper appears to be for 2 * CH3's\n", + " vCH2 = 28.1\n", + " vCOO = 39.0\n", + " vGLYC = 68.8\n", + " vPO4 = 53.7\n", + " vCHOL = 120.4\n", + " vCHCH = 42.14\n", + "\n", + " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", + " vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3) # Tail volume\n", + "\n", + " # Calculate sum_b's for other fragments\n", + " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", + " sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n", + "\n", + " # Calculate SLDs and Thickness\n", + " sldHead = sumbHead / vHead\n", + " thickHead = vHead / thiolAPM\n", + "\n", + " sldTail = sumbTail / vTail\n", + " thickTail = vTail / thiolAPM\n", + "\n", + " # Correct head SLD based on hydration\n", + " thiolHeadHydr = thiolHeadHydr / 100\n", + " sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n", + "\n", + " # Now correct both the SLDs for the coverage parameter\n", + " sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n", + " sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n", + "\n", + " SAMTAILS = [thickTail, sldTail, 0, goldRough]\n", + " SAMHEAD = [thickHead, sldHead, 0, goldRough]\n", + "\n", + " # Now do the same for the bilayer\n", + " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", + " vTail = 28 * vCH2 # Tail volume\n", + " vMe = 2 * vCH3\n", + "\n", + " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", + " sumbTail = 28 * CH2\n", + " sumbMe = 2 * CH3\n", + "\n", + " sldHead = sumbHead / vHead\n", + " thickHead = vHead / bilayerAPM\n", + " bilHeadHydr = bilHeadHydr / 100\n", + " sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n", + "\n", + " sldTail = sumbTail / vTail\n", + " thickTail = vTail / bilayerAPM\n", + "\n", + " sldMe = sumbMe / vMe\n", + " thickMe = vMe / bilayerAPM\n", + "\n", + " sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + " sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + " sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", + "\n", + " BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n", + " BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n", + " BILME = [thickMe, sldMe, 0, bilayerRough]\n", + "\n", + " BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n", + "\n", + " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", + "\n", + " if contrast == 0 or contrast == 2:\n", + " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", + " else:\n", + " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", + "\n", + " return output, subRough" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -214,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -276,11 +772,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for Controls\nresampleParams\n Extra inputs are not permitted. The fields for the \"calculate\" controls procedure are:\n \n [type=extra_forbidden, input_value=[0.9, 150.0], input_type=list]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m controls \u001b[38;5;241m=\u001b[39m \u001b[43mRAT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mControls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontrasts\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresampleParams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m150.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m problem, results \u001b[38;5;241m=\u001b[39m RAT\u001b[38;5;241m.\u001b[39mrun(problem, controls)\n\u001b[1;32m 4\u001b[0m RAT\u001b[38;5;241m.\u001b[39mplotting\u001b[38;5;241m.\u001b[39mplot_ref_sld(problem, results)\n", + "File \u001b[0;32m/mnt/c/Users/gnn85523/env/wsl/lib/python3.10/site-packages/pydantic/main.py:176\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 175\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 176\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mValidationError\u001b[0m: 1 validation error for Controls\nresampleParams\n Extra inputs are not permitted. The fields for the \"calculate\" controls procedure are:\n \n [type=extra_forbidden, input_value=[0.9, 150.0], input_type=list]" + ] + } + ], "source": [ - "controls = RAT.Controls(parallel=\"contrasts\", resampleParams=[0.9, 150.0])\n", + "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)" diff --git a/RATapi/examples/absorption/absorption.ipynb b/RATapi/examples/absorption/absorption.ipynb index 627eda65..16f255fb 100644 --- a/RATapi/examples/absorption/absorption.ipynb +++ b/RATapi/examples/absorption/absorption.ipynb @@ -395,7 +395,7 @@ "\n", " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", "\n", - " if contrast == 1 or contrast == 3:\n", + " if contrast == 0 or contrast == 2:\n", " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", " else:\n", " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", @@ -537,7 +537,7 @@ "\n", " \\PY{n}{CW} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cwThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", "\n", - " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{1} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{3}\\PY{p}{:}\n", + " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{0} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{2}\\PY{p}{:}\n", " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", " \\PY{k}{else}\\PY{p}{:}\n", " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDown}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", @@ -678,7 +678,7 @@ "\n", " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", "\n", - " if contrast == 1 or contrast == 3:\n", + " if contrast == 0 or contrast == 2:\n", " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", " else:\n", " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", @@ -781,7 +781,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.098 seconds\n", + "Elapsed time is 0.079 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -789,7 +789,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] @@ -799,7 +799,7 @@ } ], "source": [ - "controls = RAT.Controls(parallel=\"contrasts\", resampleParams=[0.9, 150.0])\n", + "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)" diff --git a/RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb b/RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb new file mode 100644 index 00000000..d437a732 --- /dev/null +++ b/RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "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", + "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)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simply give the file path to the function `r1_to_project_class`, and it returns a RAT `Project` that you can use exactly like any other." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from RATapi.utils.convert import r1_to_project_class\n", + "\n", + "project = r1_to_project_class(\"R1monolayerVolumeModel.mat\")\n", + "print(project)" + ] + }, + { + "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:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project.parameters[\"Head Thickness\"].prior_type = 'gaussian'\n", + "project.parameters[\"Theta\"].mu = 2.0\n", + "project.parameters[\"Area per molecule\"].sigma = 50.0\n", + "# etc...\n", + "print(project.parameters)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Also note that any custom files must be available to RAT. By default, RAT will assume these files are in the same directory that you are running RAT from, but if they are elsewhere you may change the relevant file location: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# e.g. if our model is in the directory `my_models/`\n", + "project.custom_files[0].filename = \"my_models/Model_IIb.m\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As well as MATLAB functions, RAT can also run custom files provided in Python or C++ format. This may be beneficial if you do not have access to MATLAB, do not have access to the custom files from your old RasCAL project, or find it more convenient to use Python. This is done similarly to changing the file path: if we have a function defined in the Python custom file `Model_IIb.py`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "project.custom_files[0].filename = \"Model_IIb.py\"\n", + "project.custom_files[0].language = 'python'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RAT to RasCAL1\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, + "metadata": {}, + "outputs": [], + "source": [ + "from RATapi.examples import DSPC_standard_layers\n", + "lipid_bilayer_project = DSPC_standard_layers()[0]\n", + "print(lipid_bilayer_project)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`project_class_to_r1` takes parameters `project` and `filename`, which are the `Project` object and filename for the produced .mat file respectively. This .mat file can then be loaded into RasCAL-1.\n", + "\n", + "Alternatively, if one sets `return_struct=True`, the struct is returned as a Python dictionary instead of being saved.\n", + "\n", + "Note that a MATLAB engine is used to save the project to a .mat file, so the Python library `matlabengine` must be installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from RATapi.utils.convert import project_class_to_r1\n", + "from pprint import pp # for printing the struct\n", + "\n", + "# save to a file called lipid_bilayer.mat\n", + "project_class_to_r1(lipid_bilayer_project, filename=\"lipid_bilayer.mat\")\n", + "\n", + "# return as a Python dictionary\n", + "struct = project_class_to_r1(lipid_bilayer_project, return_struct=True)\n", + "pp(struct)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": 2 +} diff --git a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb index d437a732..ae368a15 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb +++ b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb @@ -1,8 +1,10 @@ { "cells": [ { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ "# Convert between RasCAL1 and RAT" ] @@ -147,7 +149,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -165,5 +167,5 @@ } }, "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 index e1e2ac29..81292e67 100644 --- a/RATapi/examples/domains/domains_custom_XY.ipynb +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -455,33 +455,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.061 seconds\n", + "\n", + "Elapsed time is 0.087 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", diff --git a/RATapi/examples/domains/domains_custom_layers.ipynb b/RATapi/examples/domains/domains_custom_layers.ipynb index 06195d84..75ef358c 100644 --- a/RATapi/examples/domains/domains_custom_layers.ipynb +++ b/RATapi/examples/domains/domains_custom_layers.ipynb @@ -403,33 +403,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.010 seconds\n", + "\n", + "Elapsed time is 0.015 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", diff --git a/RATapi/examples/domains/domains_standard_layers.ipynb b/RATapi/examples/domains/domains_standard_layers.ipynb index 6fa72d5e..de04b310 100644 --- a/RATapi/examples/domains/domains_standard_layers.ipynb +++ b/RATapi/examples/domains/domains_standard_layers.ipynb @@ -293,33 +293,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.011 seconds\n", + "\n", + "Elapsed time is 0.004 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", diff --git a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb index 38be0712..69a77d49 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -787,7 +787,8 @@ "| procedure | calculate |\n", "| parallel | single |\n", "| calcSldDuringFit | False |\n", - "| resampleParams | [0.9, 50] |\n", + "| resampleMinAngle | 0.9 |\n", + "| resampleNPoints | 50 |\n", "| display | iter |\n", "+------------------+-----------+\n" ] @@ -817,33 +818,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.009 seconds\n", + "\n", + "Elapsed time is 0.025 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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ViRWHbuHrasNLgbWNbYqgAKqkQLKwsCAwMJADBw7o80A0Gg0HDhxg4sSJxjXuMT6uNux9vys/HY9m5ZEoAG49yDCyVaVn57tdjG1CqcjxdmRlZRVr/tGjRxk4cCAjRowAdPk9N2/epGnTpgbzxo0bx6JFi4iNjaVHjx54e3vr9wUEBHD16lUDoVYatFotO3fuZN26daU6/scffyQ5OZkhQ4box2JjYwkODiYwMJA1a9bk8QS9+uqrDBs2jJ07d+ZZNff111/j6upKz549S2WPQFBdkKvUHLh8n7eD6mImM9nF5FUekxVI6enp3LhxQ78dFRVFZGQkLi4u+Pj4MHnyZEaNGkWbNm1o164dixYtIiMjQ7+qrTLg42rDyI5+TwRSYrqRLar6yOVy4uN1qweTk5NZsmQJ6enphS6Rz039+vXZvHkzf//9N87Oznz99dckJCTkEUjDhg1jypQprFy5Ur9yLYdPP/2U/v374+Pjw0svvYRUKuXcuXNcuHChWF6sHE6fPk1mZiZduhQtTjMzM4mPj0elUnH37l22bt3KwoULefvttwkODgZ04igoKAhfX1+++uorEhMT9cfneF5fffVVNm3axKhRo/jyyy/p3r07qampfPfdd+zYsYNNmzYVy/MlEFRnjt18SIZCTY8mNYueLDAaJiuQTp06pf9gB/QJ1KNGjSIsLIxXXnmFxMREPv30U+Lj42nVqhV79uzJk7htbGo5WWMuk6BUa7l2P83Y5lR59uzZg6enJ6BLvG7UqBGbNm0iKCioWMdPnz6dW7duERISgo2NDePHj6dfv355Vl05OjoyZMgQwsPD9V7MHEJCQti1axefffYZ8+fPx9zcnEaNGjFu3LgS3cv27dvp27dvsWo2rVy5kpUrV2JhYYGrqyuBgYFs2LCBwYMH6+fs37+fGzducOPGDX0iew45tbokEgkbN25k0aJFLFy4kHfeeQcrKys6duxIREQEnTsXL2FeIKjO/HklgdrO1jSsaW9sUwSFYLICKSgoqMgCixMnTqw0IbWCiE3OQqXW3Uf0w0yiEjPwryF+gZcHYWFhhIWFFTrHz8/P4HX19OvMxcXFoBiiRqMhNTU131VbsbGxDB8+PN/k/pCQEEJCQgq0ozjFQ7dv38706dOLnBcREVHkHNBVGh89enSR88zMzJgyZQpTpkwp1nkFAoEhkXdTaOfnUulXLVd3RPDTyJyITiL3V+G+y/FGs0VQNiQnJ7N161YiIiKYMGFCuVxDoVAwZMiQQoulCgSCyodSreFyXCrNvByNbYqgCEzWg1RVaOfngplUgkpXVRJXGwsjWyR4Vlq3bk1ycjLz58+nYcOG5XINCwuLAitrCwSCysuNhHQUKo0QSCaAEEhGxsfVhv/2asj8PboClulyVRFHCCo70dHRxjZBIBBUUs7HpiCRQJNaRRdTFRgXEWKrBLTxc9b/P8qEl/oLBAKBoHCuxKXh62KDnaXwT1R2hECqBPi7PUnKNuVaSAKB4AmxsbGMGDECV1dXrK2tad68OadOnTK2WQIjcycpAz83sRDHFBASthLgamuBvZUZadkqbiUKgSQQmDrJycl07tyZ4OBgdu/eTY0aNbh+/TrOzs5FHyyo0txJyqRjnYL7fgoqD0IgVQIkEgmNPOw5GZ1M7KMsYpIy8XaxKfpAgUBQKZk/fz7e3t6sWbNGP+bv729EiwSVAa1Wy52kTF5u4130ZIHRESG2SkK3BjX0///t9F0jWiIQCJ6VHTt20KZNG4YOHYq7uzutW7dm5cqVxjZLYGQS0+RkKzX4uooQmykgPEiVhCaeT5Z8Lj5wnRcDaudpcisQCEyDW7dusWzZMiZPnszHH3/MyZMnee+997CwsGDUqFF55svlcuRyuX47NTUVAKVSiVKpLPA6OfsKm1MZMVW74dlsv5mge169HCwq/N5N9TEvD7uLey4hkCoJDzOefDhqgSM3Ehnu6ms8g6op0dHR+Pv7c/bsWVq1akVERATBwcEkJyfj5ORkbPMEJoJGo6FNmzbMnTsX0NXGunDhAsuXL89XIM2bN4/Q0NA84/v27cPGpugfSvv37392o42AqdoNpbP9RKIEkHHxxGGuy8repuJgqo95Wdr9dGuoghACqZLQ3t8VmVSC+nHBSCmiBH1ZM3r0aH788Uf9touLC23btuWLL76gRYsWAHh7exMXF4ebm5uxzCwVwcHBDB8+PN9+blFRUXzyySdERESQlJSEm5sbgYGBzJ8/n0aNGgHoWx4cO3aMDh066I+Vy+XUqlWLpKQk/vzzT4KCgujQoQOtWrVi+fLl+nnLly/n7bffZs2aNQbtSkaPHs3Nmzc5cuRIOd155cTT05MmTZoYjDVu3Jjffvst3/nTpk3T95MEnQfJ29ubXr165dvGJgelUsn+/fvp2bMn5ubmZWN8BWCqdsOz2X7rz5u4xsUwaECvcrKuYEz1MS8Pu3M8tEUhBFIlwcfVhlkDm/Hx1vMARMY84rX2Pka2qurRu3dvfeJsfHw806dPp3///ty5cwcAmUym71xfmVAqlQV+OCQlJXH06FHWr1+f73E9e/akYcOGbNmyBU9PT+7evcvu3bt59OiRwdycpOLcAmnr1q3Y2dmRlJSkHwsODmbr1q0Gx/755594e3sTERFhIJAiIiLy9ZhUdTp37szVq1cNxq5du4avb/5eYUtLy3x79pmbmxfrS6G48yobpmo3lM72h5lKajpYGfWeTfUxL0u7i3sekaRdiRjc2gtrc53fdde/98hWqo1sUdXD0tISDw8PPDw8aNWqFVOnTiUmJobExERAF2KTSCRERkbme/zDhw957bXX8PLywsbGhpYtW7J582b9/rVr1+Lq6mqQTwIwaNAgXn/9df329u3bCQgIwMrKijp16hAaGopK9aSKukQiYdmyZbzwwgvY2toyZ86cAu8pPDycgIAAatasmWffxYsXuXnzJkuXLqVDhw74+vrSuXNnZs+ebSCEAEaNGsX69evJysrSj61evTqPwAkODubq1avExz/pG3jo0CGmTp1q0Bg3KiqK27dvExwcXKDtVZUPPviA48ePM3fuXG7cuMEvv/zC999/X269+QSmQUKqnBr2eYWwoHIiBFIlIjFNjkKlASBDoeaXf+4Y2aKqTXp6OuvWraNevXq4uhavLkl2djaBgYGEh4dz4cIFxo8fz1tvvcWJEycAGDp0KGq1mh07duiPSUhIIDw8nLFjxwJw5MgRRo4cyaRJk7h06RIrVqwgLCwsjwiaOXMmgwcP5vz58/pj82PHjh0MHDgw3301atRAKpWyefNm1OrCBXdgYCB+fn76MNCdO3c4fPiwgbADnXfE3NycP//8E4BLly6RlZXFG2+8wcOHD4mKigJ0XiUrKys6duxY6HWrIm3btmXr1q38+uuvNGvWjFmzZrFo0SKGDx9ubNMERiQhTY67EEgmgwixVSJORCeh1mr12+tP3mFsFxOpnbKiG6QnVPx17dzhP4eKPX3Xrl3Y2dkBkJGRgaenJ7t27UIqLd5vBS8vL6ZMmaLfnjhxIuHh4WzatIkOHTpgbW3NsGHDWLNmDUOHDgVg3bp1+Pj4EBQUBEBoaChTp07Ve2bq1KnDrFmz+PDDDw0a0A4bNowxY8YUao9cLmfPnj3MnDmzQHu/+eYbPvzwQ0JDQ2nTpo0+X6lOnTp55o8dO5bVq1czYsQIwsLC6Nu3LzVq1DCYY2trS7t27YiIiOC1114jIiKCLl26YGlpSadOnYiIiMDf35+IiAg6duyYb+ioOtC/f3/69+9vbDMElYjENDmd64kikaaC8CBVItr5uWBl9uQpuX4/nXuPsgo5ohKRngBp9yr+r4SiLDg4mMjISCIjIzlx4gQhISH06dOH27dvF+t4tVrNrFmzaN68OS4uLjg4OHDw4EF9DhPA+PHj2bdvH7GxsQCEhYUxevRofSL0uXPn+Oyzz7Czs9P/jR8/nri4OIPVFW3atCnSnoMHD+Lu7k7Tpk0LnDNhwgTi4+P5+eef6dixI5s2baJp06b5rgoZMWIEx44d49atW4SFhRXouQoKCtKH0yIiIvTir1u3bgbj1TG8JhDkh1arJTFNjru9lbFNERQT4UGqRPi42rDvg27M/v0S+y7eRwssP3STzwY2M7ZpRWPnbhLXtbW1pV69evrtVatW4ejoyMqVK5k9e3aRx3/55ZcsXryYRYsW0bx5c6ytrXn33XdRKBT6Oa1bt6Zly5asXbuWXr16cfHiRcLDw/X709PTCQ0N5cUXX8xzfiurJx+etrZFF5PbsWMHL7zwQpHz7O3tGTBgAAMGDGD27NmEhIQwe/ZsevbsaTDP1dWV/v3788Ybb5CdnU2fPn1IS0vLc77g4GDmzJlDbGwsEREReq9at27dWLFiBTdv3iQmJobnn3++SNsEgurAo0wlCrVGhNhMCCGQKhk+rjb857m67Lt4H4C1x27zegdf6te0N7JlRVCCMFdlQiKRIJVKDRKTC+Po0aMMHDiQESNGAKBSqbh582YeD864ceNYtGgRsbGx9OjRA2/vJ60FAgICuHr1qoFQKw1arZadO3eybt26Eh0nkUho1KgRf//9d777x44dS9++ffnoo4+QyfIv1tKpUycsLCxYunSpPi8LdLk3iYmJrF69Wh+KEwgEuvwjAHcHIZBMBSGQKiFRDw0b1n689TxfD20lKmuXAXK5XL/6Kjk5mSVLlpCens6AAQOKdXz9+vXZvHkzf//9N87Oznz99dckJCTkEUjDhg1jypQprFy5krVr1xrs+/TTT+nfvz8+Pj689NJLSKVSzp07x4ULF4rlxcrh9OnTZGZm0qVLlwLnREZGMmPGDF5//XWaNGmChYUFhw4dYvXq1Xz00Uf5HtO7d28SExMLrb9jbW1Nhw4d+Pbbb+ncubNeSFlYWBiMm+JyYoGgPEjMEUgixGYyiBykSkg7PxcsZU+empPRyfRaeIg7D4tX/VNQMHv27MHT0xNPT0/at2/PyZMn2bRpkz6HpiimT59OQEAAISEhBAUF4eHhQb9+/fLMc3R0ZMiQIdjZ2TFo0CCDfSEhIezatYt9+/bRtm1bOnTowMKFCwuskVMQ27dvp2/fvpiZFfw7p3bt2vj5+REaGkr79u0JCAhg8eLFhIaG8sknn+R7jEQiwc3NDQsLi0KvHxwcTFpaWp7Hrlu3bqSlpYn8I4EgFwlp2QBimb8JITxIlRAfVxv2T+7G4KVHeZihy23JVmk4EZ0kvEjPQFhYGGFhYYXO8fPzQ5trJWFQUJDBtouLC9u2bdNvazQaUlNT8/W2xMbGMnz48HxXcYWEhBASElKgHbmvWRDbt29n+vTphc5xc3Nj8eLFRZ6rsOs5OTnlu3/mzJn5rp6bMWOGwWo8gUAASRkKbCxkWJkbqceIoMQID1IlxcfVhsk9GxiMtfV1NpI1gpKQnJzM1q1biYiIKLfCgAqFgiFDhtCnT59yOb9AIChbkjMVONsU7pUVVC6EB6kS81o7H1b9FUXUA11OUkxyFr5uRa9sEhiX1q1bk5yczPz582nYsGG5XMPCwkJ4aQQCEyI5U4mTjcjJMyWEB6kSI5VKDLxI8/dcKVboRWBcoqOjSUlJMSgoKRAIqjePhAfJ5BACqZLTrJYjksf/Px+bwsaTMUa1RyAQCAQlJzlDeJBMDSGQKjmn7yST22c0d/cVoh9kFDhfIBAIBJUPkYNkegiBVMl5uv1ISpaSnmLJv0AgEJgUjzKVOAsPkkkhBFIlJ6f9SFDDJw1DlWotf914YESrBAKBQFASkjMVOAkPkkkhBJIJ4ONqw2cvNNPnIgHsvhAnvEgCgUBgAmQp1MhVGpxthQfJlBACyUTwcbXhx7HteNwQniPXH4jq2gKBQGACJGfqCv4KD5JpIQSSCdG1QQ061XXVb2erNOy+EGdEi6oe0dHRSCQSIiMjAYiIiEAikfDo0SOj2iUQCEyXHIEkkrRNCyGQTIxpvRsbbH+59worDt0UnqRiMHr0aCQSif7P1dWV3r178++//+rneHt7ExcXR7NmzYxoackJDg5m1apV+e4LCgri/fffzzMeFhaGk5OTfnvLli307NmTGjVq4ODgQMeOHdm7d2+e42JiYhg7diy1atXCwsICX19fJk2axMOHD8vqdgSCKsWjTCWASNI2MYRAMjGa1XakTzMP/bZKA/N2XxHhtmLSu3dv4uLiiIuL48CBA5iZmdG/f3/9fplMhoeHR6ENYI2BUqkscF9SUhJHjx5lwIABz3SNw4cP07NnT37//XdOnz5NcHAwAwYM4OzZs/o5t27dok2bNly/fp1ff/2VGzdusHz5cg4cOEDHjh1JSkp6JhsEgqpISpbu/etkLTxIpoQQSCbIR70b6XORcshpZisoHEtLSzw8PPDw8KBVq1ZMnTqVmJgYEhMTgbwhtqd5+PAhr732Gl5eXtjY2NCyZUs2b96s37927VpcXV2Ry+UGxw0aNIjXX39dv719+3YCAgKwsrKiTp06hIaGolKp9PslEgnLli3jhRdewNbWljlz5hR4T+Hh4QQEBFCzZs3SPCR6Fi1axIcffkjbtm2pX78+c+fOpX79+uzcuVM/Z8KECVhYWLBv3z66deuGj48Pffr04Y8//iA2NpZPPvnkmWwQCKoiqY8Fkp1V5frhJSgc8WyZIH5utqx8vQ3j157SF5E0l0lo5+diNJte2fUKD7IqvvSAm7UbG/pvKNWx6enprFu3jnr16uHq6lr0AUB2djaBgYF89NFHODg4sGvXLt566y2aNWtGhw4dGDp0KO+99x47duxg6NChACQkJBAeHs6+ffsAOHLkCCNHjuSbb77hueee4+bNm7z55psABv3VZs6cyeeff86iRYsK9Wjt2LGDgQMHluoxKAyNRkNaWhouLrrXVVJSEnv37mXOnDlYW1sbzPXw8GD48OFs2LCBpUuXInlawQsE1Zi0bBV2lmbIpOJ9YUoIgWSi9GhSk3HP+bPySBQADWraU9vZuoijyo8HWQ9IyEww2vWLy65du7CzswMgIyMDT09Pdu3ahVRaPGeql5eXQY+1iRMnEh4ezqZNm+jQoQPW1tYMGzaMNWvW6AXSunXr8PHxISgoCIDQ0FCmTp3KqFGjAKhTpw6zZs3iww8/NBBIw4YNY8yYMYXaI5fL2bNnDzNnzix03tKlS/PkKKlUKqysrAo85quvviI9PZ2XX34ZgOvXr6PVamncuHG+8xs3bkxycjKJiYm4u7sXao9AUJ1Iy1ZiL7xHJod4xkyYD3o2YM/FeGKSsrh4L5X1J2MY1t7HKLa4WbuZxHWDg4NZtmwZAMnJySxdupQ+ffpw4sQJfH19izxerVYzd+5cNm7cSGxsLAqFArlcjoODg37O+PHjadu2LbGxsXh5eREWFqZPEAc4d+4cR48eNQibqdVqsrOzyczMxMbGBoA2bdoUac/Bgwdxd3enadOmhc4bPnx4nvDXli1bmDt3br7zf/nlF0JDQ9m+fXsesSMaJgsEJSM1W4WDlUjQNjWEQDJhbCzMmDe4BSN++AeA2eGX6NawBl5OFe9JKm2Yq6KxtbWlXr16+u1Vq1bh6OjIypUrmT17dpHHf/nllyxevJhFixbRvHlzrK2teffdd1EoFPo5rVu3pmXLlqxdu5ZevXpx8eJFwsPD9fvT09MJDQ3lxRdfzHP+3B4dW1vbIu3ZsWMHL7zwQpHzHB0dDe4bKNDLs379esaNG8emTZvo0aOHfrxevXpIJBIuX77M4MGD8xx3+fJlnJ2dqVGjRp59AkF1JlV4kEwS8YyZOD4uNkgBDZCpUDN85XF+HNMOX7eiv1wFumRoqVRKVlZWseYfPXqUgQMHMmLECEAXprp582YeD864ceNYtGgRsbGx9OjRA29vb/2+gIAArl69mkewlBStVsvOnTtZt27dM50nN7/++itjx45l/fr19OvXz2Cfq6srPXv2ZOnSpXzwwQcGeUjx8fH8/PPPjBw5UuQfCQRPkZatEgLJBBGr2EycE9FJaHJtRz/M5PmvI0RtpAKQy+XEx8cTHx/P5cuXeffdd0lPTy/2Evn69euzf/9+/v77by5fvsxbb71FQkLe3Kthw4Zx9+5dVq5cydixYw32ffrpp6xdu5bQ0FAuXrzI5cuXWb9+PdOnTy/RvZw+fZrMzEy6dOlSouMK4pdffmHkyJF8/fXXtG/fXv84paSk6OcsWbIEuVxOSEgIhw8fJiYmhj179tCzZ0+8vLwKXW0nEFRXUrOUOFiLEJupIQSSidPOzwUrM8OnUa3V1UZ6/us/OX5TFO/LzZ49e/D09MTT05P27dtz8uRJNm3apE+gLorp06cTEBBASEgIQUFBeHh45PG0gC6kNWTIEOzs7Bg0aJDBvpCQEHbt2sW+ffto27YtHTp0YOHChcXKgcrN9u3b6du3b5nVbPr+++9RqVRMmDBB/xh5enoyadIk/Zz69etz6tQp6tSpw8svv0zdunV58803CQ4O5tixY/oVbwKB4AnCg2SaiGfMxPFxtWHfB93YfSGO+buvGHiTVBoY8cNxDv43GB9XG6PZWFkICwsjLCys0Dl+fn4GSchBQUEG2y4uLmzbtk2/rdFoSE1NNUjSziE2Npbhw4djaWmZZ19ISAghISEF2lGcROjt27cXy+sUERGR7/jo0aMZPXp0kfOextfXt8jHUSAQPCFNrsReJGmbHMKDVAXwcbXhP93q8vuk53B6qpS9SoMoIFnBJCcns3XrViIiIpgwYUK5XEOhUDBkyBD69OlTLucXCARlR2qWWMVmigiBVIVo5OnA6tFtyV2LzEwKD9PlIh+pAmndujWjR49m/vz5NGzYsFyuYWFhwYwZM7C3ty+X8wsEgrJBq9WSLhchNlNECKQqRoCPM+91r6/fFr3aKp7o6GhSUlIMCkoKBILqSaZCjVqjFUnaJogQSFWQicH1CPR1NhjLVmn4LuKGEEkCgUBQgaRm6/qwCQ+S6SEEUhXETCZl0SutsLM0fENuOBlTZp4kUU1Z8KyI15CgOpCWrWtC7SAEkskhBFIVxdvFhi9eapFnPFuleaakbXNznZs4M1N4ogTPRs5rKOc1JRBURdL0HiTxOjc1hKStwvRt7smIDj6sO35HP2Ypk1DbyZrNp+/Szs+lxMv/ZTIZTk5O+uKINjY21bpyskajQaFQkJ2dXeyGt9UdrVZLZmYmCQkJODk5IZPJjG2SQFBu5HiQnvboCyo/1fYZi4mJ4fXXXychIQEzMzP+7//+T999vSoxvV8TTt9+xOW4VAC8XW0Y+cNxFBqwMpOy74NuJRZJHh4eAPlWkK5uaLVasrKysLa2rtZCsTQ4OTnpX0sCQVUlQ64GwFYIJJOj2j5jZmZmLFq0iFatWhEfH09gYCB9+/YtVoNQU8LKXMbS4QH0++YImQo1NxIy9Ptywm0lFUgSiQRPT0/c3d1RKpVlbbJJoVQqOXz4MF27dhWhohJgbm4uPEeCakGGXOdBsrUQr3dTo9oKpJw2CqDziLi5uZGUlFTlBBKAv5stLwXWZu2x2wbjlmZSfY2k0lTalslk1f5LTiaToVKpsLKyEgJJIBDkIV2uwspciplMhOBNjUr7jB0+fJgBAwZQq1YtJBKJQXuHHL777jv8/PywsrKiffv2nDhxolTXOn36NGq12qDjelVjXJc6BgUkbS1koNUyb/cVQhYdFsv/BQKBoBzIkKtE/pGJUmkFUkZGBi1btuS7777Ld/+GDRuYPHkyM2bM4MyZM7Rs2ZKQkBCDvJhWrVrRrFmzPH/37t3Tz0lKSmLkyJF8//335X5PxsTH1YY/PuiGv5vOQ5ahUCNX65ZZZynVokaSQFCGzJw5E4lEYvDXqFEjY5slMALpCpXIPzJRKu2z1qdPn0L7TC1YsIDx48czZswYAJYvX054eDirV69m6tSpAERGRhZ6DblczqBBg5g6dSqdOnUqcq5cLtdvp6bqkp6VSqXJ5OF4O1vy89g2DF52nPtpunuRoMVMAptP3WHn2RhWjWxDW3/Rkb245Dz3pvIaMBWqwuPZtGlT/vjjD/22mVml/bgVlCMZchW2FuK5N0VM8llTKBScPn2aadOm6cekUik9evTg2LFjxTqHVqtl9OjRPP/887z++utFzp83bx6hoaF5xvft24eNTcnzd4zJMF/45qIMtVaCFgn9fdRkqSV0qqkm8fJxfr9sbAtNj/379xvbhCpFVaizZWZmJlbpCciQq0WIzUQxyWftwYMHqNVqatasaTBes2ZNrly5UqxzHD16lA0bNtCiRQt9ftNPP/1E8+bN850/bdo0Jk+erN9OTU3F29ubXr164eDgULobMSKup+4yffslALbelmEm0bI/VoZMAi8F1mZ8lzrUdrE2spWVH6VSyf79++nZs6dI0i5Dcjy0psz169epVasWVlZWdOzYkXnz5uHj45Pv3NJ6qE3Vg2mqdkPJbU/LUmBtITX6vZrqY14edhf3XCYpkMqCLl26oNFoij3f0tISS0vLPOPm5uYm+cU4oqM/V+9n8NNx3co2lVaXwa3Wwi8nY9lyNq5UNZKqK6b6OqismPpj2b59e8LCwmjYsCFxcXGEhoby3HPPceHCBezt7fPMf1YPtal6ME3Vbii+7bfvSbEzh99//72cLSoepvqYl6XdxfVQm6RAcnNzQyaTcf/+fYPx+/fvC5d2Cfh0QBOuJ6Rx/Jau9YiTtTmPsnTKOqe57YSgekIkCQQlJHf+ZIsWLWjfvj2+vr5s3LiRN954I8/80nqoTdWDaap2Q8lt/+HOcep72NO3b9MKsK5gTPUxLw+7i+uhNkmBZGFhQWBgIAcOHGDQoEGAruXDgQMHmDhxonGNMyHMZVKWDQ9k0NKj3H6YyaMsJVIJaB73EN1wMoYtZ+7y09j2dKjralxjBQITxsnJiQYNGnDjxo189z+rh9pUPZimajcU3/YMhRp7a4tKc5+m+piXpd3FPU+lXeafnp5OZGSkfiVaVFQUkZGR3Lmj6ys2efJkVq5cyY8//sjly5d5++23ycjI0K9qExQPZ1sLfhjVBvvHnaY1WmhQ006/X6nW8vrqf0QJAIHgGUhPT+fmzZv64rSC6kOGXC2W+ZsolfZZO3XqFMHBwfrtHPfzqFGjCAsL45VXXiExMZFPP/2U+Ph4WrVqxZ49e/IkbguKpp67PUuHBzB6zUnUGi3X7qcbeJKUam2pWpIIBNWVKVOmMGDAAHx9fbl37x4zZsxAJpPx2muvGds0QQWjKxRZvTsOmCqVViAFBQWh1WoLnTNx4kQRUisjnqtfg9mDmjFty3kAtKAXSRYyyTO1JBEIqht3797ltdde4+HDh9SoUYMuXbpw/PhxatSoYWzTBBWIVqslQxSKNFnEsybQ81o7H+4mZ/LdnzfRasHcTMorbWqz6dRd5u2+woL915jcswF9mnkKoSQQFML69euNbYKgEpClVKPRIuogmSiVNgdJYBym9GrIoFa1AFCoNPx2JpZsla4cglylYd7uK/RaeEjkJAkEAkERpMtVAKKStokiBJLAAIlEwhcvtaRLPTcAMhVqJE/NyVZp2H0hruKNEwgEAhMiQ64GECE2E0UIJEEeLMykLBsRQDMvXf0VLeBia4F5rlfLwv3XhBdJIBAICiFT8diDJJK0TRIhkAT5Ym9lTtiYdvi72QKQlKHA2fZJnZacQpJCJAkEAkH+ZCt1HiRrcyGQTBEhkAQF4mZnyU9vtMPT0QqAhDS5wf4NJ2NEPpJAIBAUQKbisUCyEALJFBECSVAotZ1tWDeuPW52Fvox11z/F54kgUAgyB+9QBIeJJNECCRBkdStYcdPb7THyUZXnv1hugJprsztDSdj6LEgghWHbgqhJBAIBI/JCbHZiFVsJokQSIJi0djTgZ/HtcfRWieSNFpwtX3iSVKotczbfYXnv/6T4zcfGstMgUAgqDTkeJAszcRXrSkinjVBsWlay5Gfx+XyJGUo8pQAUGlg+KrjwpskEAiqPVkKNdbmMqTSpz8pBaaAEEiCEtHMy5Ffx3fA5bH3SIvOk2SW6/2v1sK83VcIWXRYiCSBQFBtyVKqsREJ2iaLEEiCEtPY04GN/+mAh4NuddvDDAUONhY87UXOUqpFArdAIKi2ZCnUWIkEbZNFCCRBqajnbs+mtzoa1EmyMjfj1bbeWMqeuJM2nIwReUkCgaBakqkQHiRTRggkQanxdrFh81sdaVnbEdD1Hdp8+i7vdm/AK2299fNUGnh99T/CkyQQCKoVWUqVqIFkwgiBJHgmXO0s+fXNDvRoXBMAlUbLV/uuIldqDEJuSrVWhNsEAkG1IidJW2CaCIEkeGZsLMxY8Xog47r468e2Rcbi72ZnIJJy10s6fvMhm0/fFYJJIBBUWTIVauFBMmFE9SpBmSCTSpjevwkNPez5ZOsFFGoN1xPScbQ2p0VtR45cfwA8qZeUg7W5jL3vd8XH1cZYpgsEAkG5kKVUY28lvmZNFeFBEpQpQ9t4s/ntjng5WQOQkqXkyPUHyAqoA5KlVLP7QlxFmigQCAQVgi7EJgSSqSIEkqDMaVHbiV3vdtHnJQGoNVqcbcwxz+cV9/W+q6KwpEAgqHJkKdVYW4ivWVNFPHOCcsHZ1oKVIwP5bGBTrB6rouRMJUoNdKjjwphOfvq5OWG3ngsPCaEkEAiqDFkKtejDZsIIgSQoNyQSCSM7+vH7e8/R2sdJP378VhJbzsYaVN8GkKs0zNt9he4LIth+NlYkcQsEApMmUxSKNGmEtBWUO3Vq2LH5rU78dCyaL/deJUOhJiVLCYCbnQUpmUqUGq1+vlKtZdKGSEAkcQsEAtNFtBoxbYQHSVAhyKQSRnf25+CUIF5oWUs//iBdgVKjxdPRKt8Xo2hXIhAITBVRB8m0EQJJUKHUdLDim9da89vbHQ3CbnEp2WgKOCanftKEn8+IliUCgcAkUKk1KNQaUQfJhBECSWAUAn1d2PJ2J1aObEMjD/s8+52szWlay0G/rVBrCT8fx6srjzN/9xXhURIIBJWaLKUaQHiQTBiRgyQwGhKJhJ5NatK9kTsHriTw3Z83iIx5BMCjLCWPHucpPc2yQzdZczSKsDHtuPsoi3Z+LiJHSSAQVCpyBJLIQTJdhEASGB2pVCeUejR252R0MmuORrH3Yjy58rbzkK3SMGzVcTRasDKTsu+DbkIkCQSCSkOW4rEHSQgkk0UIJEGlQSKR0M7fhXb+LsQ+ymLDiTusPxlDQpo83/k5AipbpeFEdBIAJ6KThEdJIBAYnUyFCLGZOkIgCSolXk7WTO7VkPe61+fQtUQ2nIzhz6sJKNX5u5W2R8byyZZ/kau1WJpJmdyzAX2aeQqhJBAIjMKTEJv4mjVVxDMnqNSYyaR0b1yT7o1rkpyhYOe/99hwMoaL91IN5uU0w4UnBSe/2neVn8a2p0Nd14o2WyAQVHOyhAfJ5BECSWAyONtaMLKjHyM7+nHsxkN+PBbNyegkHmYo8p2vVGsZtvI4H/ZuSCtvZ5HQLRAIKgyRg2T6CIEkMEk61nOlYz1Xbj/IoNeiw8hV+VdR0gCf77mq3xYJ3QKBoCLIVAqBZOoIgSQwaXzdbNn/QTdORCfRxteZuJRsVhy6ScS1xHznZ6s0rDkaxYwXmlawpQKBoDqRpVABIsRmygiBJDB5fFxt9B4hPzdbOtZ15WpcGt/+eZ3wf+N4Oq17zd/RHLhyn1fb+lDD3pL2/q7CoyQQCMqULIUaSzMpMqmk6MmCSokQSIIqSUNPe5YMC+DDkEx2nb/HnvPx/Bubot9/JymLL/bqQm9mUglTejWgb/NaQigJBIIyIVOpFuE1E0e0GhFUaXxcbXgnqB5LhgVgKcv/l5xKo+XzPVfpseAQtx9kVLCFAoGgKpKtUGMjwmsmjRBIgmqBj6sN+ycHMa1PowKFkkKt4bVVx1l84Lro9SYQCJ6JTIUaK+FBMmlEiE1QbfBxteE/3erSp5knJ6KT8HK0YmtkLBtP3dXPufcom4X7r7Fo/zUm9ajPi61ri7CbQCAoMVlKtejDZuIID5Kg2uHjasNLgbXpWM+NL15qydTeDfPM0QKL/rguwm6CZ+bzzz9HIpHw/vvvG9sUQQWSpVCLFWwmjhBIgmpP3+a1CvwgU6g1jAk7yY9/R4uwm6DEnDx5khUrVtCiRQtjm1IlSUjNJl2uMrYZ+ZKpUGMt2oyYNEIgCao9Pq427H2/K18Nbcn68R2YEFTXYP+tBxnM2HGR7l9HsOLQTSGUBMUiPT2d4cOHs3LlSpydnY1tTpXjQmwK3b8+RJf5Bzn5uFl1ZSJLKZK0TR0hbwUCDGspdajrir2VmUEFbgClRsu83Vf4et9V/turIX2aeeLpYG4McwUmwIQJE+jXrx89evRg9uzZhc6Vy+XI5XL9dmqqrtegUqlEqVQWeFzOvsLmVEae1e6kDAVvhJ3Ev4YN6dlqwo5G0crLvixNLJDi2p4hV+JkbVZpnpvq+lop7JxFIQSSQJAPfZvXYvGBG/qO3LlRqLX6Zrg/jgo0gnWCys769es5c+YMJ0+eLNb8efPmERoammd837592NgUvUhg//79JbaxMpCf3XGZsDtGyu10CVot1LCGJk4aWrhoqWENCjWsvColPUvChAYZ/BUv5c/L6ewKv0tF1mQs6jG//0CGRXYSv/8eU0EWFY+q9FopLZmZxYsCCIEkEORDTtjtRHQStRytWHH4Foeeal+iVGt586dTzGljJCMFlZKYmBgmTZrE/v37sbKyKtYx06ZNY/Lkyfrt1NRUvL296dWrFw4ODgUep1Qq2b9/Pz179sTc3HS8mQXZfe1+GlNX/ENNByte7VATmVTC1fg09t18yI47Guq42ZIuV5GWrWTlyADa+7vQ4HYy+1adpHaLTrTydjKa7U+z+PpRGtZ1o2+fvItAjEFVe608Czke2qIQAkkgKIDcYbdO9dzYcvouH287T7bySWNcuVrXyORuUhb+NU3nQ0dQfpw+fZqEhAQCAgL0Y2q1msOHD7NkyRLkcjkymWFuiqWlJZaWlnnOZW5uXqwvheLOq2zktlur1fLfzRfwdbVlyzudsMmV4JypUPHnlUT+iXqIuUzKK229aVBTF1Jr4++GpZmU8/fSaVunhlFszw+5SoOdVeV7XqrCa6UszlUchEASCIrJi4G1aePnwqsrj3HvUTYASg1cT5Hwf9/9xYTuurwkUTepetO9e3fOnz9vMDZmzBgaNWrERx99lEccCXSciEriSnwav4xrbyCOAGwszOjXwpN+LTzzHGcmk1Lb2ZqY5Mq1eCJTocJKJGmbNGIVm0BQAnxcbdj9Xlda1nZ8PCJh+WUpWSoN83ZfIWTRYbHKrZpjb29Ps2bNDP5sbW1xdXWlWbNmxjav0rLhZAx+rjZ0rOta4mO9XWyIScoqB6tKT6ZCFIo0dYRAEghKiKONORv+05FOjz/IVVoJOVG3LKWa3RfijGidQGB6aLVaIq4lMqBlLSSSkmdaezvbcLcSeZA0Gi1ylUYIJBNHCCSBoBRYmcv4cWw7utV3ezzy5EN94f5rwoskMCAiIoJFixYZ24xKS9SDDJIyFLTxcynV8d4u1sQkZaLVasvYstKRs/pVhNhMGyGQBIJSYi6Tsmx4K1q6aAzGs1Uavou4IUSSQFBMTt1ORiKB1j5OpTre29mGDIWa5MzKUeMnRyA9nUslMC2qvUDKzMzE19eXKVOmGNsUgQliLpMysr6Gtr6GlZI3nIwR+UgCQTE5HZ1Mw5r2OFiVbpWSt4tuYURMUuV4v2UpdAJJ9GIzbaq9QJozZw4dOnQwthkCE8ZMCqtGtqaxp2El3yylWniSBIJicDk+lWZejkVPLIDaztYAxD6qHInaOR4ka5GDZNJUa4F0/fp1rly5Qp8+fYxtisDEsbEwY+3Y9ng4GBYG3HAyhl4LDwmRJBAUgFarJepBBnVq2Jb6HI7W5sikEpIyFGVoWenJFB6kKkGlFUiHDx9mwIAB1KqlW9Wwbdu2PHO+++47/Pz8sLKyon379pw4caJE15gyZQrz5s0rI4sF1Z0a9pb8OLZdng9FkZMkEBTMwwwFadkq6riVXiBJJBKcbSwqkUBSAYhVbCZOpRVIGRkZtGzZku+++y7f/Rs2bGDy5MnMmDGDM2fO0LJlS0JCQkhISNDPadWqVZ56JM2aNePevXts376dBg0a0KBBg4q6JUE1oKGHPQtebplnXHiSBIL8iX6QAYDfMwgkAFfbyiOQskWIrUpQaVPs+/TpU2joa8GCBYwfP54xY8YAsHz5csLDw1m9ejVTp04FIDIyssDjjx8/zvr169m0aRPp6ekolUocHBz49NNP851f2m7bgqpNfp2mezRy443Ovvxw9PbjES1SQKNRs+f8XcZ09q94Q00M8Z6qPtzKEUiuzyaQnG3NK41A0ofYhEAyaSqtQCoMhULB6dOnmTZtmn5MKpXSo0cPjh07VqxzzJs3Tx9eCwsL48KFCwWKo5z5z9JtW1C1ebrTdFMN+NrJuJ0uASS0qaFheD0NpFzm998vG8dIE6K43bYFpk/Ugwy8nKyfuWaQq61lpRFIYhVb1cAkBdKDBw9Qq9XUrFnTYLxmzZpcuXKlXK5Z2m7bgqpNYZ2mW3TKZOB3x8hQqDmRKOXMQwkyCQwJqM34LnWo7WJtJKsrP8Xtti0wfe4kZeLj8uw/Mp1tzfXeKGOTpVRjLpNgLqu0WSyCYmCSAqmsGT16dJFznrXbtqBqk9/roF5NR2YMaMqHv/0LgEojQQX8cjKWrZHx7H2/q2hsWwDiPVV9iE/JxrcM3gcutpYkVyIPkvAemT4mKW/d3NyQyWTcv3/fYPz+/ft4eHgYySqBIC9D29TmOX07kieIGkkCgY74lOw85TFKQ06SdmVoN5KpUIv8oyqASQokCwsLAgMDOXDggH5Mo9Fw4MABOnbsaETLBAJDJBIJnw9pgW0+H5ai2raguqPRaElIy8bD8dkFkrOtBQq1hnS5qgwsezaylGrRZqQKUCqBdOvWrbK2Iw/p6elERkbqV6JFRUURGRnJnTt3AJg8eTIrV67kxx9/5PLly7z99ttkZGToV7UJBJUFLydrPuj5pJyEs82T8FGWUs2J6CRjmCUQGJ3kTAVKtZaaZeRBAkjOMP4KyCyFWjSqrQKUSuLWq1ePbt268cYbb/DSSy9hZfXsL+6nOXXqFMHBwfrtnATpUaNGERYWxiuvvEJiYiKffvop8fHxtGrVij179uRJ3BYIKgOjOvmx8VQM1+6nk5ypxEwKKo2uTUltJ5GsLaiexKfqSqfkF2LTaDQoFMXPKXK0AC97GQ9S0nC3Lb/giFKpxMzMjOzsbNRqdb5zzLQqvB10cyoLxbG7MlIau83NzZHJnl2glkognTlzhjVr1jB58mQmTpzIK6+8whtvvEG7du2e2aAcgoKCiowlT5w4kYkTJ5bZNQWC8sJcJuWzgc149fvjANhbmZOWrUSlgTFhJ0XCtqBacj/tsUB6KsSmUCiIiopCo9EU/2QaLTOD3ZGkJxIVVX5eWa1Wi4eHBzExMUgkknzn9PCWoKltTVRUVLnZUVKKY3dlpLR2Ozk54eHh8Uz3WiqB1KpVKxYvXszXX3/Njh07CAsLo0uXLjRo0ICxY8fy+uuvU6NGjVIbJRBURTrUcaVnk5rsv3Sf5MwnYYCchO0JQfWESBJUK+6nZiOTSnCze7JCWKvVEhcXh0wmw9vbG6m0eN4gtUaDOiEdT0crHKwtystkNBoN6enp2NnZFWibeXImEsDLufK8n4tjd2WkpHZrtVoyMzP1XTU8PT1Lfe1nyiIzMzPjxRdfpF+/fixdupRp06YxZcoUPv74Y15++WXmz5//TMYJBFWNj3o34uCVBNQaQ+/ohpMx7Ii8JzxJgmrF/VQ5NewskUmf/MpXqVRkZmZSq1atEhXh1Wq1SMwUyMwtsbLKW5KlrMgJ/VlZWRX4hS0xU2Euk5ZL+klpKY7dlZHS2G1trUtbSEhIwN3dvdThtmd6lE6dOsU777yDp6cnCxYsYMqUKdy8eZP9+/dz7949Bg4c+CynFwiqHPXc7Xilrbd+u7Gnvf7/ImHbeCiVSmJiYrh69SpJSeI5qCgepMtxdzAUMzl5JhYWJfMCSSQSZFIJ6kqwzF+r1SI1nShWlSRHXD9L26JSCaQFCxbQvHlzOnXqxL1791i7di23b99m9uzZ+Pv789xzzxEWFsaZM2dKbZhAUFWZGFwPc5nu0zP6QSaWj/9vIZPwMF0ulv1XEGlpaSxbtoxu3brh4OCAn58fjRs3pkaNGvj6+jJ+/HhOnjxpbDOrNEkZSlxs8xdCpckdkUkkaDTGF0gaDUhNKM+nKlIWeValEkjLli1j2LBh3L59m23bttG/f/88ri93d3d++OGHZzZQIKhq1HKy5uU2Oi9SllLNy219mNanEVKJhHm7r4jaSBXAggUL8PPzY82aNfTo0YNt27YRGRnJtWvXOHbsGDNmzEClUtGrVy969+7N9evXjW1ylSQ5U4GLTdnlC0mlEtTG10dohAepSlCqHKT9+/fj4+OTRxRptVpiYmLw8fHBwsKCUaNGlYmRAkFV453gemw8FYNSrWVbZCwf9W5Etkq3Yicn1CZykcqPkydPcvjwYZo2bZrv/nbt2jF27FiWL1/OmjVrOHLkCPXr169gK6s+SRkKWnk7l9n5ZFJJnvw+Y6DR5u9Bio6Oxt/fn7Nnz9KqVSsiIiIIDg4mOTkZJyenijdUUCil8iDVrVuXBw8e5BlPSkrC39//mY0SCKo6Xk7WDG7tBUBator4lGyszHRvRyszKe38XIxpXpXn119/LVAc5cbS0pK33nqLsWPHVoBV1Y/kTCXOBYTYSoMxQ2yjR49GIpEgkUho6uVIIz8vevfuzb///quf4+3tTVxcHM2aNTOKjaUlODiYVatW5bsvKChIf9+WlpZ4eXkxYMAAtmzZYjAvOjqaN954A39/f6ytralbty4zZszIU+tKrVazcOFCmjdvjpWVFa6urrz00kscPXq03O6vIEolkAqqT5Senl6psvYFgsrMuOfq6P+/9Wwsv096jq+GtiRsTDtORCeJMJugSqPRwqMspb4CdllgbA9S7969uXfvHgdOX2HLrt2YmZnRv3//J/bJZHh4eGBmVrnakBSWyJyUlMTRo0cZMGBAgXPGjx9PXFwcN2/e5LfffqNJkya8+uqrvPnmm/o5V65cQaPRsGLFCi5evMjChQtZvnw5H3/8sX6OVqvl1Vdf5bPPPmPSpElcvnyZgwcPUrt2bZ5//nm2bdtWJvdbXEr0LOVUs5ZIJHz66acGSzDVajX//PMPrVq1KlMDBYKqSoOa9gQ1rEHE1URiH2VxKS6Vdn4uhCw6TJZS1w1cLPsve7KyskhKSsLLy8tg/OLFi8XyKgnKhgwVaLWUqQdJauRVbJaWltSs6UGi2hpvF3+mTp3Kc889R2JiIjVq1MgTYnuahw8fMnHiRA4fPkxycjJ169bl448/5rXXXgNg7dq1fPDBB9y7dw9Lyyer/wYNGoS9vT0//fQTANu3byc0NJRLly5Rq1YtRo0axbRp0/TzJRIJS5cuZffu3Rw4cID//e9/zJw5M997Cg8PJyAgoNAuFTY2NvpG8bVr16ZDhw40atSIsWPH8vLLL9OjRw969+5N79699cfUqVOHq1evsmzZMr766isANm7cyObNm9mxY4dekGk0GhYtWkRqairjxo2jZ8+e2NraFuPZeHZK5EE6e/YsZ8+eRavVcv78ef322bNnuXLlCi1btiQsLKycTBUIqh7jc3mR1h67zYnoJLKUumXOYtl/2bN582bq169Pv379aNGiBf/8849+3+uvv25Ey6ofOS3TytSDVAlWsWkeC7TMjAzWrVtHvXr1cHV1Ldax2dnZBAYGEh4ezoULF3jzzTd5/fXXOXHiBABDhw5FrVazY8cO/TEJCQmEh4frw8BHjhxh5MiRTJo0iUuXLrFixQrCwsKYO3euwbVmzpzJ4MGDOX/+fKEh5B07dpSqZM+oUaNwdnbOE2rLTUpKCi4uT9IJfvnlFxo0aJCvt2ry5Mk8fPiQ/fv3l9iW0lIiD9Kff/4JwJgxY1i8eDEODg7lYpRAUF3oVNeVujVsuZmYwYmoJCYG18PaXKb3IIlcpLJl9uzZnD59mpo1a3L69GlGjRrFxx9/zLBhw4psbSQoW9IfC6SClvnnJkuh5mZiepHzkjPkJGUqUZWRSKpbww5ri+IXGdy1axdOjg5otJCVmYGnpye7du0qdoFDLy8vpkyZot9+99132bt3Lxs3bqRdu3ZYW1szbNgw1qxZw9ChQwFYt24dPj4+BAUFARAaGsrUqVP1i6Tq1KnDrFmz+PDDD3n//ff15x42bFiRzd3lcjl79uwp0LtUGFKplAYNGhAdHZ3v/hs3bvDtt9/qvUcA165do3HjxvnOzxm/du1aiW0pLaUKhK5Zs6as7RAIqiUSiYTX2vkwO/wyABFXE9n7fld2X4gzsmVVE6VSqQ8VBAYGcvjwYQYPHsyNGzdMqj9VVSBdpXu8iyOQbiam0//bv8rbpDzsercLzbwciz0/ODiYRd8sIepBBg5SOWtWfU+fPn04ceIEvr6+RR6vVquZO3cuGzduJDY2FoVCgVwuN0hnGT9+PG3btiU2NhYvLy/CwsL0CeIA586d4+jRo8yZM8fgvNnZ2WRmZuodG23atCnSnoMHD+Lu7l7q0LNWq833fRUbG0vv3r0ZOnQo48ePz3NMZaHYAunFF18kLCwMBwcHXnzxxULnFuZSEwgEhrwUWJsv9l5FodLw25m7vNbWm4X7r5Gt0rBw/zX2fdBN5CGVEe7u7vz777+0aNECABcXF/bv38+oUaMMVhsJyp90pS6p2sHKvMi5dWvYsevdLkXOS8tWEp+STZ0atsjKoJ1G3Rp2JZpva2uLf926qO3Tqe9uR+cO7XB0dGTlypXMnj27yOO//PJLFi9ezKJFi2jevDm2tra8//77Biu9WrduTcuWLVm7di29evXi4sWLhIeH6/enp6cTGhqa53tao9EYLKIqTh7Pjh07eOGFF4pz63lQq9Vcv36dtm3bGozfu3eP4OBgOnXqxPfff2+wr0GDBly+fDnf8+WMN2jQoFT2lIZiCyRHR0e9EnRwcBC/tgSCMsLJxoJ+zT3ZejaWlCwlYcei9TWRslUaUROpDPnpp5/yrCCysLDg119/ZeLEiUayqnqSoQIna3OkxaioaG0hK5YnJy1bia2lGY08HLAwM06/sZzoXs7Sd6lUSlZWVrGOPXr0KAMHDmTEiBG6c2k0XLt2jSZNmhjMGzduHIsWLSI2NpYePXrg7f2kfVFAQABXr16lXr16hnZpNKSmphb7PrRaLTt37mTdunXFPiY3P/74I8nJyQwZMkQ/FhsbS3BwMIGBgaxZsyZP6PHVV19l2LBh7Ny5M08e0oIFC3B1daVnz56lsqc0FFsg5Q6riURsgaBsebmNN1vPxgJwKzFD5CGVE7Vr1zbYjo+P16++6dy5szFMqrakKyW42BbtPSoJOcUZjbWSTS6XExcXx4OkTLTJsXy/fCnp6emFLpHPTf369dm8eTN///03zs7OLFiwgPv37+cRSMOGDWPKlCmsXLmStWvXGuz79NNP6d+/Pz4+Prz00ktIpVLOnTvH+fPn+d///lfsezl9+jSZmZl06VK05y4zM5P4+HhUKhV3795l69atLFy4kLfffpvg4GBAJ46CgoLw9fXlq6++IjExUX98znvw1VdfZdOmTYwaNYovv/yS7t278+jRIxYtWsTOnTvZtGlTha1gg1LmIM2ePZvhw4eLopACQRnR3t8FT0cr4lKyORGdxNa3O3EtIZ12fi7Ce1SO9OrVS4TWjESGCpztym4FG+hCdoDRVrLt2bOHBv4+ANjb29OoUSM2bdqkT6AuiunTp3Pr1i1CQkKwsbHhzTffZNCgQaSkpBjMc3R0ZMiQIYSHhzNo0CCDfSEhIezatYvPPvuM+fPnY25url9yXxK2b99O3759i1WzaeXKlaxcuRILCwtcXV0JDAxkw4YNDB48WD9n//793Lhxgxs3buT5oZKTdySRSNi4cSOLFi1i4cKFvPPOO1hZWdG2bVsOHjzIc889V6J7eFZKJZA2bdrEjBkzaN++PSNGjODll1/Gzc2trG0TCKoNUqmEga28WH7oJmqNlrMxjxjVyY87DzPZfPquEErlRGVKCK1upCvBvwyX+AP6/mcaIzyvYWFhhIWFkZyhICY5k2Zejnnajfj5+Rm85oKCggy2XVxcil0MMTY2luHDhxvUQ8ohJCSEkJAQg7HcIbbivO63b9/O9OnTi5wXERFRLHtHjx7N6NGji5xnZmbGlClT9Kv5cuw2xqr5UgVpz507x7///ktQUBBfffUVtWrVol+/fvzyyy9kZorqvwJBachpPQK6ytp3HmbSa+Ehpmw6R6+Fh0Rl7XJA5FIajwyVBGeb8gmxGbMUkkarRYKE8nplJScns3XrViIiIpgwYUK5XEOhUDBkyBD69OlTLuc3FUqdxda0aVPmzp3LrVu3+PPPP/Hz8+P999/XxxIFAkHJaOhhTyMPewAiYx6x52J8nmRtgaCqkK4s3hL/kiA1cogNchrVlp/4bt26NaNHj2b+/Pk0bNiwXK5hYWHBjBkzsLe3L5fzmwpl0hDG1tYWa2trLCwsSEtLK4tTCgTVkn7NPbkSr3sPpWUr9cnalmZSHqbLufMwU4TaBCaPVqstWCAl34HsVEiNg0wJSGVgZgFWTmCWN5yUG+njlWPGCLHloNFqkRRjZV5pKajwoqDsKbUHKSoqijlz5tC0aVPatGnD2bNnCQ0NJT4+viztq3rEnIBfh+n+FQieIqTZEw/siagk9r7flWl9GiEB5u2+QsiiwyLUVobIZMWvkiwoOzIValTafEJsx5fBLy+DPBWUWaBRgiID0uIh4TJkPCjy3FKJ8VaxgU4glaM+ElQgpfIgdejQgZMnT9KiRQvGjBnDa6+9lqfxoyAfkqLgxwGgyobr+8CvMwR/At7tjG2ZoJJQ390Ofzdboh5kcDI6CVtLGa52lvpQW05/NuFFKhvOnj1rbBOqJUmZusKHBh6k63/AnqnQ6X/g4AWudSCnsKFGDan3ICUGzKzAsuACjrp+bOVpfeHoQmxCIVUFSiWQunfvzurVq/PUZhAUwp3j8Pv/dOIIdL+MbkXoxodv1r3xzW3h3w3Q5X0hmqopEomEXk1rsuLQLTRaOHAlgQ7+rliZSclWabAyk4q6SAKTJ/lxp1q9B0mjgd+nQJ1g6DIZbt82PEAqA8faOq9SSgzUaAQFiBCpsUNsGq0QSFWEUoXY5syZI8RRSbhxEFaHQHw+9VZU2fDTYNj2NmwaCVfDdV6mpKiKt1NQKQhp+iTMtu/ifXxcbdj3QTe+GtpStB0xEZYtW0aLFi1wcHDAwcGBjh07snv3bmObVWnI40G6eRCSoyD4YyioRYhEAvYeus9MZcFhZqlUYtQkba0IsVUZiu1Bmjx5MrNmzcLW1pbJkycXOnfBggXPbFiV4uhCw20rJ5Cng1YFUjOdNyk3qmzYOBL6fS08SdWQVrWdcLOz5EG6nGM3H6BQafBxtRHCqAJJSUnh3LlzREZG8t5775X4+Nq1a/P5559Tv359tFotP/74IwMHDuTs2bOlbvxZlXjiQXoskM78CDWbQ+22IJcXfKClPcgsIPMhWORfUVkqMU4dpBxEiK3qUGyBdPbsWZRKpf7/gmKi1epi5rnJfqT718YNAkbCse9A/dSHQvy/Ok/SO8fBRVQsr05IpRKeq+/G1rOxZCjUnLmTTIc6rsY2q0pw8+ZNpk+fjqWlJYsWLcLJyYmoqCgiIyP1gujcuXPcuXMHrVaLra1tqQTS060l5syZw7Jlyzh+/LgQSOg8SJZSLZZmUlCrdOkGnd4rMGymRyLR/cDMStZ9tuYzXyqRoDZyHSQz4UKqEhRbIP3555/5/l9QBBIJDN8E57fAgZmQfv9JHlLmA/hrAdjXAmsn6DQJ7hyFM49766iy4c6xwgVSUpRujk9HIaSqEDkCCeDI9US9QLrzMJMT0UmisnYpGT58OMOHD8fX15dmzZqRnp5Oamoqjo6ONGnShGbNmhETE8MPP/xA9+7dDZqAlha1Ws2mTZvIyMigY8eO+c6Ry+XIc3lOcioeK5VK/Q/T/MjZV9icysiDtGzszHV2SxLOYSZPReXbBe3j+9VqtWg0GjT5ZFtLLOyQZCSgUcl13qSnkEpAqdbme2xZkFOFOsfGp9FotUgk0nz3RUdHU7duXU6fPk2rVq2IiIige/fuPHz4ECcnp3Kxt7h2V1ZKa7dGo0Gr1aJUKvOsVi3u+6VUSdpjx45l8eLFeYpIZWRk8O6777J69erSnLZq0/xF3V92CpxdB/+sgEePExHT7un+fv8vNOoHMkudR0lqoUvadq33JNQWcwL+WqRL5LatAUs7gioLzKzhnSLElMBk6FL/Seuew9ce8L8Q9JW1c5K1RT5SyUlISKBZs2bUqVOH+Ph4PvroI9555x2DVbirV6+mXbt2zyyOzp8/T8eOHcnOzsbOzo6tW7cWmLs5b948QkND84zv27cPG5uin+P9+/c/k60VzYWbUuzMJezfv58G8dupJ7Vmd2Qc2nO/Y2ZmhoeHB+np6SgUirwHa9U4AtkpiSjM8hYyVKlAqaZEnetLw9M1/9555x1+/fVX/bazszMBAQHMnDmTZs2aAboealeuXMHV1ZXU1FR954m0tLQ8ne0ryu7iMGDAAIYOHcrIkSPz7Lt9+zazZs3i6NGjJCcn4+rqSsuWLZk5cyYNGjQAdI8F6F7Pbdu21R8rl8tp3LgxycnJ7Ny5ky5dutCzZ0+aNWvGwoVPUlNWr17Nf//7X7777juGDRumH3/nnXeIiorKN79PoVCQlZXF4cOHUalUBvuK2/GjVALpxx9/5PPPP88jkLKysli7dq0QSIVh5QgdJ0D7t+DaHp1Qijqk26dI1wkiAJe6kHz7yUq3wd/D6dUQfVSXs3Rjv05MqbJ081VZRXubBCaDu70VjT0duByXyoV7KTxMl3MiOilPZW0hkErGN998w9tvv42bmxvLly9n8eLFXLx4kS+++EL/YV5WNGzYkMjISFJSUti8eTOjRo3i0KFD+YqkadOmGeR2pqam4u3tTa9evQrtQaVUKtm/fz89e/bE3Lxs23aUJ9vXncFWkUDPnj2x2vwj+HemTz9dWDI7O5uYmBjs7OywsrLK/wTKRKxlGqzyeWwyU+Uos5U4OBRcCuBZ0Gq1pKWlYW9vb1At29zcnJCQED6Z/y0OVmaoM5L5v//7P4YNG2ZQ3DFHLAB68Wtvb1/uvcYKsht0r6OCXj9JSUn8888/bNy4MY+NSqWSIUOG0KBBA3777Tc8PT25e/cue/bsQaVSGcz39vZm06ZNdO/eXT+2fv167O3tSU5OxsbGBgcHB7p37862bdv0x2q1Wv766y+8vb35559/eOutt/TH//3334wcOTLfxy47Oxtra2u6du2a53VUXPFcIsmamppKSkqK/oFOTU3V/yUnJ/P777/j7u5eklNWeTILWm0hlekEzqgd8PYxCBwD5rm+7JJu6pK4QRdq2zxWJ5ZyErrVCri49cl8MytdmE1QZej62Iuk1cJfNx7Qzs8Fa3Odq9jaXCaW+5eC/v37c+XKFf766y/GjRtHZGQkPXr0oGvXrkyYMIGEhIQyu5aFhQX16tUjMDCQefPm0bJlSxYvXpzvXEtLS/2Kt5w/0H3pFvVX3HmV6e9Rlgo7c53d0vvnkXq1NtgvkUiQSqUF/kksbJAos/LdJ5NKdInShRz/LH854uJpGyUSCVZWVrjWqImHpycBAQFMmzaNmJgYHj58iFQq5c6dO8hkMv7991/9cfDE1uTkZIYPH463tzd2dna0bNmSDRs26PevW7eOGjVqoFQqDa794osvMmrUKP32zp07adOmDTY2NtSrV49Zs2ahVqv1dstkMlasWMGgQYOwt7dn3rx5Bd7v7t27CQgIwNPTM8++y5cvc/PmTZYtW0anTp3w9/fnueeeY86cOXTq1MngHkeNGsWGDRuQy+X68bCwMEaNGmXwGDz//PNcvXqVhIQE/eN69OhRPvroIw4dOqSfd/v2bW7fvs3zzz9f6HNV2PumKEokkJycnHBxcUEikdCgQQOcnZ31f25ubowdO7bcmueZIteTr9Njcw+m/zWdO6l3Cp5YswkMWASTL0GvOeDsl3eOVpV3LAePFjBqp/AeVTFyh9n+idJ5i/a+35WvhrZk7/tdhfeoDJDJZEycOJFLly4hk8lo1KgRGo1G/2VSlmg0GoM8o+pMcoYCWzN0FbIzEnSfYSXBzFr3wzGf1WpSSWVY5i8hPT2ddevWUa9ePVxdi7fIIjs7m8DAQMLDw7lw4QJvvvkmr7/+OidO6DovDB06FLVazY4dO/THJCQkEB4eztixYwE4cuQII0eOZNKkSVy6dIkVK1YQFhbG3LlzDa41c+ZMBg8ezPnz5/XH5seOHTsYOHBgvvtq1KiBVCpl8+bNRb5nAgMD8fPz47fffgPgzp07HD58mNdff91gXufOnTE3N9fnOl+6dImsrCzGjh3Lw4cPiYrSlcD5888/sbKyKjCvrywoUYjtzz//RKvV8vzzz/Pbb7/h4vLkF6yFhQW+vr7UqlWrzI00RbJV2XwQ8QFpijS239zOjps7+LXfrzR1K2QFi7UzdJoIHd6BG3/AoS8g9mTeeeY2oMwGNDrP0ctrDcWRSNyuEgT4OCOTSlBrtJx63Kg2RxSdeGpb8Gy4uLjwzTff8NZbb/HBBx/QvXt3PvzwQyZMmIC1tXWJzzdt2jT69OmDj48PaWlp/PLLL0RERLB3795ysN70SMpU0MxWi+T+ed2AZxECSZEJD6492VZm6gpGqhS6Pm25MM9WYpGajVZih4RnXE3m1gAsiv8e27VrF/v2eyGV6HJyPT092bVrV7Hzi7y8vJgyZYp++91332Xv3r1s3LiRdu3aYW1tzbBhw1izZg1Dhw4FYN26dfj4+BAUFARAaGgoU6dO1Xtm6tSpw6xZs/jwww95//339eceNmwYY8aMKdQeuVzOnj17mDlzZoH2fvPNN3z44YeEhobSpk0bgoODGT58OHXq1Mkzf+zYsaxevZoRI0YQFhZG3759qVGjhsEcW1tb2rVrR0REBK+99hqHDh2iQ4cOWFpa0qlTJyIiIvD39yciIoKOHTtiaVl4f75noUQCqVu3boCuD5uPj0+5dSuuKtS0qcntVF0ithYto/eOJrRTKH39+xZ+oFQKDXrp/pJuwanVcGqNLkcJnhRJk8igfi9ddVnQCaPLO+DgnCdJ3n6dRDsTE8XW0oymtRz4924K1+6n8yhTQWqWSiRqlyNNmjRh79697Nq1iylTpvD1118TFxdX4vMkJCQwcuRI4uLicHR0pEWLFuzdu5eePXuWg9WmhUqtISVLha0ZSO5fAEtHcPIt/KAH1+D7bsU6v9PjvzLhzUNQq1WxpwcFB/P+p/PxdLRCI89g6dKl9OnThxMnTuDrW8Q9olvxOHfuXDZu3EhsbCwKhQK5XG6QqD9+/Hjatm1LbGwsXl5ehIWFMXr0aP338blz5zh69Chz5swxOG92djaZmZn68G2bNm2KtOfgwYO4u7sXWppiwoQJjBw5koiICI4fP86mTZuYO3cuO3bsyPN6HzFiBFOnTuXWrVuEhYXxzTff5HvOoKAgNm3aBEBERARdunQBdBokIiKCMWPGEBERwfjx44u8h2ehVEnaBw8exM7OTq9gc9i0aROZmZl65VqdsTKzYmanmbyw9QVUj8Nj2apsPjr8EVuvb+WTDp/g5+BX9Ilc6kCv2TqRc2ELnFwF987o9mnVOkF0eQd4toT7lwyLTmoUT5K8RT0lk6SNrwv/3k0B4PTtZJIzlSJRuwy4c+cOPj4+Be7v378/ISEhLFmyBED/ZVRcfvjhh2e2saqSnKn7jLIzB0niFXBvXHT9I7cGOrGSm4c3dOVRbNwMhtPlKuJSsvBzs8P8WesRuZUscd/WxgYf/zr4utjgaGPBqlWrcHR0ZOXKlcyePbvI47/88ksWL17MokWLaN68Oba2trz//vsGq/lat25Ny5YtWbt2Lb169eLixYuEh4fr96enpxMaGsqLL75ocG6NRmOQrGxrm3+hzdzs2LGDF154och59vb2DBgwgAEDBjB79mxCQkKYPXt2HoHk6upK//79eeONN8jOzqZPnz75rqoLDg5mzpw5xMbGcujQIX1idrdu3VixYgU3b94kJiaG559/vkjbnoVSCaR58+axYsWKPOPu7u68+eabQiA9xtvemx2Dd/Dt2W/ZHfVkGeLxuOO8sPUFxjcfz6D6g/C2L8ZyYnNraD1c93fvLJz8AS789sSbFHeu4GNFZW6Tpa2fM6uP6mLuJ6OTGdbOB0szKXKVBkvRl63UtG3blkGDBjFu3DiDZce5yczMxNbWlmbNmvHmm2+WqmCkIC/Jj9uM2JlrdR7ymsVoW2Vhk9eTY2Gr60Tw9A+/bBVZ5uloatqDuWH9m/ImJ/NJIjVM5M7KyirW8UePHmXgwIGMGDEC0Imaa9eu5Vn5OG7cOBYtWkRsbCw9evQwKEkREBDA1atXqVevnsExGo2mRKUPtFotO3fuZN26dcU+BnT33KhRI/7+++98948dO5a+ffvy0Ucf5alPlEOnTp2wsLBg6dKlZGdn06pVK0D3vk1MTGT16tX6UFx5UqrCC3fu3MHfP683wtfXlzt3CklGroZ423vzbut3sZAaxsm1aPn+/Pe8sPUFYtJiSnbSWq1h4BL47xXo8yW4F+MDJqcyd06Pt5gT8Osw3b+CSkubXALo1GNv0a/jO9CzSU1+Hd9BeI9KyaVLl7C1taVnz554eHjQr18/xo8fz7vvvsuIESMICAjA3d2dNWvW8MUXXwhxVIY8TNcJJFuZFknyLZ2XvDSYWehW8z5FTrqPMdqNyOVyHiTcJ/H+fS5fvsy7775Lenp6nsrqBVG/fn3279/P33//zeXLl/nPf/7D/fv388wbNmwYd+/eZeXKlXkSrD/99FPWrl1LaGgoFy9e5PLly6xfv57/+7//K9G9nD59mszMTH14Kz8iIyMZOHAgmzdv5tKlS9y4cYMffviB1atXF5jY3bt3bxITE/nss88KPK+1tTUdOnTg22+/pVOnTnohZWFhoR/PSeYuT0olkNzd3fn337yNV8+dO1fsbP3qhLe9N9sGbWNy4GTMJIZOO5VWxRcnvkCtKcWqGStHaP8mvP03jN0H9XpQ6FOqyobbf+tE0o8DRGNcE6CGvSX+bjpX+L93U8hWqgnwdWblyDYE+DoXcbSgIFxdXVmwYAFxcXEsWbKE+vXr8+DBA65fvw7oKm6fPn2aY8eO0bdvETmDghKR40FykaYjyU4B17qlO5HMMn+B9DhcZ4yFbPv27qV7YCPq+XnTvn17Tp48yaZNm/QJ1EUxffp0AgICCAkJISgoCA8PDwYNGpRnnqOjI0OGDMHOzi7P/pCQEF2y+OOijB06dGDhwoWFhpTzY/v27fTt2xczs4IDTbVr18bPz4/Q0FDat29PQEAAixcvJjQ0lE8++STfYyQSCW5ublhY5K2Cnpvg4GDS0tLyPHbdunUjLS2N4ODgEt1PaZBotSWX2R999BEbNmxgzZo1dO3aFYBDhw4xduxYXnrpJb766qsyN7SykdOeICUlpUQFvmLSYth5cyfLzy1Hy5OHvo5jHWZ1nkWLGiVc7vo0Wcnw7yZd88f7F/Lud/IBW3eIPfVkbNAyaDUs71yxGq5IlEolv//+O3379i23XzP/23SOTafvAvDb2x0J9NV5lapy25HSvr+qCsW9/4p4/ZU1Px2/zcwdF1nd9DLdrn8G/zlisIotOzubqKgo/P39Cy4UCZCZpOtG4NFCV1fuMUqVhsvxqfi52eJgVfaPSU6oysHBIc/qtAy5ipuJ6TSoaY9VOYf3unfvTtOmTQtMdH6awuzOjxYtWjB9+nRefvnlZzX1mSip3TkU9joq7vurVDlIs2bNIjo6mu7du+vVpUajYeTIkXlqLQgM8bb35p1W79CvTj++PfMt++/sR6PVcCvlFsN/H86XXb+kt3/v0l/A2lnnVWo3HmLP6ITSvxufVNx+dEf3l4PETFeLJOqIbtlsTrHJ3KvhRBsTo9LC20kvkC7EphLo6yLajghMluQMBc425tgpHoeOSh1ie7y8Wy0H6ZPXvj7EZgQXUk5YT1qOK7yTk5OJiIggIiKCpUuXlss1FAoFQ4YMoU+fPuVyflOhVALJwsKCDRs2MGvWLM6dO4e1tTXNmzcv1jJGgQ5fB1++CvqKxacXs+rCKv34h4c/5Pqj60xsNfHZyihIJFA7UPcXMhcubYMjC3QVunOjVcGBXD2gZLk+dHIQbUyMSrNaT37hXIjVrWgTbUfKhgMHDvDJJ58QGRmJubk5jRo14qWXXuKdd97J00pJUDYkZShwsbHATh6P1q4mEstStgTJaVSrUhh0ITBmiC3nms+6eK4wWrduTXJyMvPnz6dhw4blcg0LCwtmzJhRLuc2JUolkHLw8/NDq9VSt27dQuOUgoJ5scGLrL20FoVGF0vXouX7f7/nUMwh5j03j/rO9Z/9IpZ20HoE+HaG79rlG7fXo86n0q+5jWhjYkQaezroC0aefyyQ2vm5iNVsz8g///xDnz596NixI9OnT8fCwoKrV6/y1VdfsXTpUnbu3EmLFs8Y8hbk4WGGAmdbc2yz7qN1qVP6Uo5SM5BI83xmSSQSXTVtIyRpV4QHKXdfN0H5Uqok7czMTN544w1sbGxo2rSpfuXau+++y+eff16mBlZ1chK4B9UbZDB+NfkqQ3YM4Z+4f8ruYi7+MDocGvaDMXvghSUU/hKQQLMhMHStzoMkkrmNgpW5jPruul/Z1xPSyVaqxWq2MuCLL75g4MCBHDp0iOnTp/Phhx/yww8/cPv2bbp27Uq/fv149OiRsc2sciTn8iDhXMrwGui85LICVrIZqd1IzjVFDeWqQakE0rRp0zh37hwREREGyU89evRgw4YNZWZcdcHb3ps3W7yJpcywZLoWLW//8TZbrm8hJi2G7Te2l7wkQJ6LtYPXfgHfjhDwOrx3Bvp+Be3fAeenQ2haXa2lX16CbW/Dt20eh+mEUKpomtZyBECt0XIlXldYLWc1G8D4tac4czvZaPaZIseOHWPixIl5xm1sbPjxxx+pXbs2y5cvN4JlVZuHGQqcbcywld9H61LKFWw5yMxBrcwzLJWC2igeJJ04E10mqgalEkjbtm1jyZIldOnSxeCF0LRpU27evFnIkYKC8Lb3ZuvArXlKASg1Smb8PYP+W/oz/eh0Bm8f/OwiKTcu/rqE7j7zYFIkTDwNXf+nW+32NDn5St8GwolVcPZnIZYqiOZeefOQQLeS7bWVx9l/6T6vrTzOnYeZxjDPJElMTMy3nhvoOotPmjTJoEKxoGxIzlBQ2yIDc00W2tImaOdQkECSSIyUg6Qt1/CaoGIplUBKTEzE3d09z3hGRoZQzs+At703Y5qNYcfgHQyoY1hYTIMuIVeulnPm/pnyM8KtHjw/Hd47B6N/h6Yv5p2jVcPv/4Xt7+jE0vX9OqEU+YsQTOVEMy9H/f9zC6QT0UnIHydryx8nawuKh1qtLnQZeWBgIFevXq1Ai6o+Wq2WpAwF3sTrtvMRSFqtFrVWTZYyi3RFOmmKNLKUWeRbkUZqUbBAMoJC0mq15ZqgLahYSiWQ2rRpY/DLKkcUrVq1io4dRTLvs+Jt783brd7OU307h+iUaE7EnyibkFtBSKXg1xmGroF3TkDLVyG/dEqtGn5+Cb5ppQvDfddeVzJAiKUypUktB31ew/lcAqmdnwtWZrq3sZVI1i4xa9eu5Z9//iE7OzvPPgcHB5GDVMZkKNQo1BpqqWN1A85++n0KtYIlZ5cwavco7mfc5276XW6n3uZO6h1updzixqMbKJ7ON5KZ6/pPPiWeZFJjJWmDVCikKkOplp7NnTuXPn36cOnSJVQqFYsXL+bSpUv8/fffHDp0qOgTCIokJ3n7zP0zKDVKvjj5BVmPaxmturBKXxrAysyKLS9sKV4/t9Li3hAGr4BuU+HGAV0dpb+/AfL5AFLLddW50epKBjz/CTR+QZQIeEZsLMyo42bLzcQMrieko1JrMJNJ8XG1Yd8H3apswcjy5LnnnmPWrFmkpaVhZmZGw4YNCQwMJCAggMDAQGrWrIlaXYoK94ICSXrcZsRNEUuWuQtmj5fny9Vyxu8bz4UHFxjTcAwuVi5423tjY22DBAkKjYLY9FjupN6hrlPdJ5EK2eNCkBrlk2X/6JbZGy/EVvHXFZQPpfIgdenShcjISFQqFc2bN2ffvn24u7tz7NgxAgMDy9rGaou3vTcD6w3kpQYvsb7felyt8rZxyVZll2/ILTcu/tBuHPT6DN47C82HFjDx8SeTWg77P4UlbeDgbOFRekYa1NTV5VGoNMQkP2l+6eNqw0uBtYU4KiGHDh0iJSWFq1evsnbtWvr06cPdu3eZOXMmzz33XLnVmKnOJD1uM+KYeYd0y5r68S9PfsnFBxdZHbKa8S3GY2VmhZWZFRYyC8xl5tia21LbrjZytZw0Ra7u79LHAumpMJtUoiuLUdFoNBSaZhIdHY1EIiEyMhKAiIgIJBKJ8FRWUkolkADq1q3LypUrOXHiBJcuXWLdunU0b968LG0T5KKOUx2W9liK5KkwlxQpLWu0rHiDXPwh+JMnBdpkFlC/J+S3KkWjgsNfwjetYcMIXYVvQYnJWeoPcP1+WiEzKy8Jqdks3H+NhNS8IS1jUb9+fV599VW++OIL/vjjD5KSkrh58ybr16/no48+MrZ5VYoHabqaRTbpt8mw9AAgKiWKTdc28V7Ae7Ryb1XgsTbmNlibWZOUnSvPTlaAQDJCiG306NH4utlSz90eiUSCq6srvXv3Nuhb6u3tTVxcHM2aNatQ256V4OBgVq1ale++oKAg3n///TzjYWFhODk56be3bNlCz549qVGjBg4ODnTs2JG9e/fmOS4mJoaxY8dSq1YtLCws8Pf3Z+rUqTx8+LCsbqfYFFsgpaamFvvPVIiKiiI4OJgmTZrQvHlzMjIyjG1SoTRxbcKOQTvo6Pkkz0uDhs9Pfm74q6qicPHXNcodtAwmnIDhm3VlA17fBnXyaySohcs7YWUwfB8E2yfCvXMVbLTpUq/mk8rO1xPSjWhJ6Tl3N4XFB65z7m5K0ZONiL+/P0OHDhWtk8qY+NRsZFIwT4nSe5BWnV+Fu407rzZ6tcjjHS0dyVRlotHqFiYgNQMk+XiQMEoOUtfne3Li4k3i4uI4cOAAZmZm9O/fX79fJpPh4eFR6QorK5V5E91zSEpK4ujRowwYMKDAOcXh8OHD9OzZk99//53Tp08THBzMgAEDOHv2rH7OrVu3aNOmDdevX+fXX3/lxo0bLF26lEOHDtG5c2eSkip2EUqxBZKTkxPOzs6F/uXMMRVGjx7NZ599xqVLlzh06BCWlpZFH2Rk/Bz9+L7X98zoOENfDuBo7FFe3fUqd9PuVrxBLv66Rre5c4zqBsPIbfDuWegwAST5NG28dxbO/gTfd4WwAfD3Eji5WoThCiG3B+nGMwikM7eTC62bVB5enoTUbGbuuMiEn08DMPGXM5W+JMH169fp1q2bsc2oUtxPzaaxXSYSZSYZljVJV6azL3ofrzR8JU8duPywMbNBq9WSrXr82pRIHidqGyZvyyQSNJryuIPCsbCwwMPDAw8PD1q1asXUqVOJiYkhMTERyBtie5qHDx/y2muv4eXlhY2NDc2bN+fXX3/V71+7di2urq7I5YbVwwcNGsTrr7+u396+fTsBAQFYWVlRp04dQkNDUalU+v0SiYRly5bxwgsvYGtry5w5cwq8p/DwcAICAqhZs2aBc4rDokWL+PDDD2nbti3169dn7ty51K9fn507d+rnTJgwAQsLC/bt20e3bt3w8fGhT58+bNu2jdjYWD755JNnsqGkFFvG/vnnn+VpR4Vz8eJFzM3Nee655wBwcTGt1T8vNXgJKzMrph2ZBsCdtDu8Gv4qS55fUqibukJxrQO95+rqLF3arstD0uTzSyX6sO4PdGKqzxe61ijmhXTyrob4u9nqk0+vJ5TOY5hTN0mu0nD4WiL782lym+PlaeblSM8mZfMcnLubQtjf0fptuQn0j1MoFPz111/GNqNKEZ+STQvrh6CADMua/HHnD+RqOf3r9C/6YMDSzBKJREKmKhObnPC+NG8tJGOE2ECXfZmzii09PZ1169ZRr149XF3z5o/mR3Z2NoGBgXz00Uc4ODgQHh7O66+/Tt26dWnXrh1Dhw7lvffeY8eOHQwdqssBTUhIIDw8nH379gFw5MgRRo4cyTfffMNzzz3HzZs3efPNN9FqtQahsJkzZ/L555+zaNGiQj1aO3bsYODAgaV7QApBo9GQlpam/+5NSkpi7969zJkzB2tra4O5NWvWZNiwYWzYsIGlS5dWWDmhYgukxYsXExYWhoODA2vXruWVV14pV4/L4cOH+fLLLzl9+jRxcXFs3bqVQYMGGcz57rvv+PLLL4mPj6dly5Z8++23tGvXrljnv379OnZ2dgwYMIDY2FheeuklPv7443K4k/JDrTFcYZMiT+GNvW8wu8ts+vhXoi7MLv7Q5X1oMlDXssShtq40QH5933JqLO37BOr3gjZjwL8bSPPxQlUzrMxl+LraEvUggxsJ6Wg02hIvKc6vblJukXLnYSYTf9HliE34+TTD2vvyTlBd3B10QikhNZuf/7nD8PY++rGiyH3OHET/uOpJfGo2PS0S0SIhw9KdgzEHaePRBg9bj0KPy1JlEZWi8y7fS7/H/Yz71LR97NFIu6crYqt54pFMzVJyPzUbiaXdM32Z+jv6Y21mXfTEx0Ts30OD2jWQoKsL6Onpya5du5BKixes8fLyYsqUKfrtd999l71797Jx40batWuHtbU1w4YNY82aNXqBtG7dOnx8fAgKCgIgNDSUqVOnMmrUKADq1KnDrFmz+PDDDw0E0rBhwxgzZkyh9sjlcvbs2cPMmTMLnbd06dI8OUoqlarQOmNfffUV6enpvPzyy4DuO1mr1dK4ceN85zdu3Jjk5OQC6zCWB8UWSLt27SIjIwMHBwfGjBlD7969y9XIjIwMWrZsydixY3nxxbzFCjds2MDkyZNZvnw57du3Z9GiRYSEhHD16lW9Xa1atTJwK+awb98+VCoVR44cITIyEnd3d3r37k3btm3p2bNnud1TWRNQMwArMyuyVdlIJVI0Wg0KjYIPD3/IhQcXqO9Un0CPwPItAVASXPyfhOIm/AN/LYAza/Ofq8qGyzt0f7Y1oPnL0PIV8GhRrRsd1XO3I+pBBtlKDbGPsvB2KdwD87Sgyd3kViYBJ2tzFu6/pt+fW0Ap1FrC/o6mcz03vSepNN6l3OfMYcmwAKN7j9566y0CAwNp3bo1LVq0wMIi/7pjgrLjfmo2daziwcELuUTCqfuneKfVO0UeF5USxSu7XqkACw3Z0H8DTVybFHt+u07PsWDxEtzsLUlOTmbp0qX06dOHEydO4OvrW+TxarWauXPnsnHjRmJjY1EoFMjlcmxsnrxXxo8fT9u2bYmNjcXLy4uwsDBGjx6tF4Lnzp3j6NGjBmEztVpNdnY2mZmZODjoqvK3adOmSHsOHjyIu7s7TZs2LXTe8OHD84S/tmzZUmAO3y+//EJoaCjbt2/PoyPyLQhqJIotkBo1asS0adMIDg5Gq9WyceNG/QP9NCNHjnxmw/r06UOfPgV7QRYsWMD48eP1Cnj58uWEh4ezevVqpk6dClBgnBd0Sr1NmzZ4e+vEQ9++fYmMjCxQIMnlcoO4b04yulKpLDTBrTzxsPJgU99NnEs4h6u1K+/++S4qdIJw7SWd8LCV2rK+/3q87LyMYmOB2NeGDpPg4i5QZoKZFXR+H0nyLaSRPyFB+2S9XkYiHP8Ojn+H1t4TTbOhaNqOB3tPY94B8CS5saJeA3XdbNj/+P+X7z3Cw9680Plno5NYfOA6N++nMa1vQzwdLPnm5Zb855ezqLWQkiln8YHrBDdwxdlaRmBtB+zMJaQrtZhJtKi0Ej749TQ7J3YB4INfT+v/3TmxC7Vdiv51nfucNmYwtI0PTTxsC33MKuLxPH/+PD///DMZGRmYm5vTpEkTfQ2kgICAYv/qFxSf+JRsalncQ+tSh2hVNNnqbLp4dSnyOH9Hfzb01/X5TJGn8DD7If4O/jpRkJkEmQ/Atb7+x1OGXMW9R1n4u9lhJns2D1JJsLKxoW69etSw10VXVq1ahaOjIytXrmT27NlFHv/ll1+yePFiFi1aRPPmzbG1teX9999HoXiSY9W6dWtatmzJ2rVr6dWrFxcvXjQo3Jyenk5oaGgex4JGozHw6Nja2hZpz44dO3jhhReKnOfo6Ei9evUMxgpyoKxfv55x48axadMmevTooR+vV68eEomEy5cvM3jw4DzHXb58GWdnZ2rUqFGkPWVFsQXS8uXLmTx5MuHh4UgkEqZPn56v61IikZSJQCoMhULB6dOnmTZtmn5MKpXSo0cPjh07VqxztG3bloSEBJKTk3F0dOTw4cP85z//KXD+vHnzCA0NzTO+b98+A3VvLB7ykPF241mWvsxg3EnixD+H/sFKUknzeZosevL/R4CkDpbN2uCfuB+F1Aa/pMPYyu8jfdxqRZIWh+zYN0iPfUuCQ3Nuu3TlvmNrNNLChUJ5s3///qInlQHpiRJAF27cdfgUWTcL/7UVkw5gxq4L8dRW3SU6XYqfvYact35k5FnAjL/++ovbj3PA32oEX50347W6an66YcZbjZT8e/xPg305Y//md9F8yDnuncYqvCW3OHXkVqHzMzPLP4H76NGjaLVarl69ypkzZ/R/W7du1delEa2Tyo5MhYrUbBWu8rtovTtxLeMaNW1qUs+pXpHHWptZ6z05aYo07qTeob5zfSxkFmCTDMnR4NLw8ao2nUCy1KTTwNkeK/MKDM9rMSgUKZFIkEqlZGVlFXxMLo4ePcrAgQMZMWIEoBM1165do0kTQy/WuHHjWLRoEbGxsfTo0UP/Qx8gICCAq1ev5hEsGo2mRKvMtVotO3fuZN26dcU+pih+/fVXxo4dy/r16+nXr5/BPldXV3r27MnSpUv54IMPDPKQ7t+/zy+//MLIkSMr9D1ZbIHUqVMnjh8/DujEyLVr1yosDvg0Dx48QK1W58mqr1mzJleuXCnWOczMzJg7dy5du3ZFq9XSq1cvg+WYTzNt2jQmT56s305NTcXb25tevXoV6EmrSGLTY/k8/HNkyNCi1fdui1XHslazlmXPL8PT1vgel2KRfBuupsCRr0GVjRozQAlo9csuJWipmfovNVP/RWvtgqbFK2hajQS3+hVqqlKpZP/+/fTs2RNz8/IXab73Ull3Q/c+NHfzpm/fguupbI+8x6It5/Xby6/o3u5DA7wxl8Si1MKvN2WP95mzc2IXLMyl/H3wBhDL5mgzg30Anyz5C9Dqx4rjQQK4eC+Vr84fp0uXLjStVfT7pSLKhVy8eBFLS0saNWpEo0aNGDZsmH7frVu3OH36tMESZMGzEZ+SDWixy7gDLiO4nrKN57yfK/EXXk4LJoVaoRNIuYtFPhZIOSKlIhO1tVpQKOQkJtxHmW5BcnIyS5YsIT09vdhL5OvXr8/mzZv5+++/cXZ2ZsGCBdy/fz+PQBo2bBhTpkxh5cqVrF1rmKbw6aef0r9/f3x8fHjppZeQSqWcO3eO8+fP87///a/Y93P69GkyMzPp0qVoD19x+OWXXxg1ahSLFy+mffv2xMfr+vFZW1vj6KjrNblkyRI6depESEgIs2fPxt/fn/PnzzNlyhS8vLwKXW1XHpSqGENUVFSFurnKi6LCeLmxtLTMNynd3Ny8Qr4Yi8LP2Y+NAzey6vwqtlzfYrDvTtodRu4dyfKey2nk0shIFpYA93rg/h40HVBAnpIUeJLTIslKQvbPMmT/LAOvNtDxHV17E1nFPS8V9Tqo5/Gkae3th1n6a+aXPO3lbIdKm/fLZ9OZWP3/c/anK7WcvptKIw97NpyK5bV23gT4OPO/zf+y8LVA/GvqRM3C1wIZv/aUwVgOhSVw13K2ZVL3+tRyti3W41QRj+XkyZNp2rQpCxYs0I+Fh4fzyy+/4O7uzqRJk/SJsIJnJyY5i5okI1NnE23jxAPNAzrX6lzi85g/fl8rcpb25y4Waa4T7NLHoqsii2lr0XI04gCN6+pyjezt7WnUqBGbNm3SJ1AXxfTp07l16xYhISHY2Njw5ptvMmjQIFJSDOuGOTo6MmTIEMLDw/MsXgoJCWHXrl189tlnzJ8/H3Nzcxo1asTYsWNLdD/bt2+nb9++ZVaz6fvvv0elUjFhwgQmTJigHx81ahRhYWGATiCeOnWKGTNm8PLLL5OUlISHhwd9+vRh9uzZFb7avFR37uvry5EjR1ixYgU3b95k8+bNeHl58dNPP+Hv719mirMg3NzckMlk3L9/32D8/v37eHgUvhqiKuNt78245uMIvxWOXC3HTGKGSqvLSXqY/ZCRu0eyKHgRnWp1MrKlxcTFH7pMhvObn+QpBT9eabj/0yfzJFLIKRwXewo2jwU7D90KuDZjwc44ns7ywM7SDHd7SxLS5EQ/fFLYNCFNl0vUs0lN3B2sUKo1zNx5sdjnzVlVdvVxhe7nG9XE01EncnL+zf3/3GMF2ZAbdwcrPujZoPg3WgGcO3eOTz998jrKyX1wd3dHLpfz888/ExkZSa1atYxoZdUhKjGdBua6z+yjyiSkSGnnUbxVx7mRSqSYy8xR5iztz92PLWfOYxeSpgIV0qofVjNlzmL83Wyxt8pf4Pv5+RkkIQcFBRlsu7i4sG3btmJdLzY2luHDh+f7wz0kJISQkBCDsdwhtuIkQm/fvp3p06cXOS8iIiLf8dGjRzN69Ogi5z2Nr6+vXjDBE7uNEakpVRbib7/9RkhICNbW1pw9e1afvJySklIhlWctLCwIDAzkwIED+jGNRsOBAwfo2LFjIUdWfbztvdk6cCuzO8/mvYD3DPZlqbKY8McEdtzcYSTrSkHuat3vHIfOk3TeodxLb7WavMelx0PEPFjQBLZNgPuXKs7mcsbPTZdc+SBdQVp2/snM3/15Qy92CqPm42TSJcMCAPTL8Sf+cgaFSsOk7vVxt3/yAexub5lnLIe4FF3xvstxqZWunUh+pKSkGORurF27ljp16nD79m3u3r1Ly5Yt+fzzz41oYdUi+mEmHW3ugZkVR1Ov4Wvmi525XdEH5oOF1OKJB0ki1dVPy1UL6YkHqeIEUo4Wk5ZzjkxycjJbt24lIiLCwBNTligUCoYMGVLsCEtVpVQCafbs2SxfvpyVK1cauMI7d+7MmTNl02crPT2dyMhI/Uq0qKgoIiMjuXPnDqBzj69cuZIff/yRy5cv8/bbb5ORkVFkXYfqQE6T2x6+PfJUp1VpVXzy1yes/HdlpVpOWShPV+t28Yd3jkFAMRYDaJQQuQ6WdYSw/nC7eEn8lRl/1yerT6If6JKZc8RJXEo2iWlyvj+UNwna/PFqHnOZBAcrnfP4/uPeWJ6OVnlqJN16kKH3+uQInhxP0NMeoty1jj7eep7FB66TkJZPnatKRO3atYmLi9NvHzhwgKFDhyKTybC0tGTatGn64nuCZ+fWgwxamccgd2/MyfunqG9W+nxBc6k5ytxFZ2Xmup6PjzFGDlJOc9yS1iYrKa1bt2b06NHMnz+/3BoqW1hYMGPGDOzt7YueXIUpVYjt6tWrdO3aNc+4o6NjmXUlPnXqFMHBT/p55SRI58QrX3nlFRITE/n000+Jj4+nVatW7Nmz55nLoVclcrxJf9z+g2/PfItS++QD5Zuz35CQmcDUdlORmWIRxvzCbw37wMWtBR8TfQTW9AbfLtDtQ/DvapI1lXI8SAC3HqTjaG1u4Pl5MaA2mUpdEdGu9dzwcrbm15MxfNS7EbPDL7N0eCCWZlLGrDmB+vH3x+/n43i1rY++RlLuQo45oTN/N1sux6dyMyEDByszmtRyYHh7X6wtZAbiSqk2DeHdo0cPFixYwIYNG7h9+zZnzpzh66+/1u+vW7cuMTExRrSwahH1IJ262luccWtCdlokDWxKH3I1k5qRocrVO1NqZuBBkkgkSCu43UjOD85y1kdER0eX7wUEekolkDw8PLhx4wZ+fn4G43/99Rd16tQpC7vyxGbzY+LEiUycOLFMrldV8bb3xsXKxUAc5bD+6noeZj9k3nPzitUHqdKRE367cwx8HodWr+3VCaYcJGa6Kru5uf0XrP0LfDvD8/8HvqYVlvV3e1JWIvpBJkq11sDz8/v5J16RF1rVomuDGrg7WNGgpi6c4eloRTMvR74c2pLJG3XNglccukm3BjVYMiyA8WtPGRRyjEvRLVF+f0OkgR1bzsay6kgUM19oalCA0lwmMQmRNH36dFq3bk2dOnXIzs7G29vbIH/y/v372NmVLgQkMESuUpOQnEoNq2jWmjfF3dqdmtLS/5g1k5qh0qjQarW6VXCyfNqNSCq23Yi6gkJsgoqjVCG28ePHM2nSJP755x8kEgn37t3j559/5r///S9vv/12WdsoeEYCagboBZClzJI3mr2BVKJ76vff3s9b+98iVVH+y6rLhdzht9z5SqN26f4due1JvpLEDGxy9US6fVTnUfp5KCRcNor5pSG3Byn6YYZenACYSSWkZD35ovhk2wWylRo+6NkAF1tDEfxiQG3GdtaFLdVaePvnM6Rn68Skp6MVao2WlYdv8c7PBYfN41Ozefvn05yPTdHnMf2na13gSdivsuLl5cXJkycZPHgwffr0YcuWLQZLzg8ePEiDBpUrsdxUuX4/nUbcRqpV8Vd2PJ1qdSpyeX9hP5DNpGZotVrU2sftlvLtxwbqCl3mn+NBEgKpMlAWKSSl8iBNnToVjUZD9+7dyczMpGvXrlhaWvK///2PcePGPbNRgrIlJ9R25v4ZPO08eeePd9DkSmw+df8UL25/ka+6fVV5Gt2WltztTHJ455iuZcnBOZD5UCeUrB11/we4vg9u/AGtX9d5lOwqdwkLv1w5SFEPMvBxtdF7ftrXceXojQf6/UU1hf24byOuJ6Rx5PoDkjIUTN2iK/24+0I8H289z793nywvdrAyIzVbxQ+j2uBmZ8miP67x59VEtFp4f8NZZgzQtSNYcfgmoAv35dcMtzLh6+trEFbLzaVLl3jppZcq2KKqyenbyXQxu0SstQO3MuN4y7MTioeKfOfKZLqQv0KhyNO0NAezx/WOVBqV7v8yM4McJMjxIJXhTRSBuoJCbILikVNs9llKhpRKIEkkEj755BP+97//cePGDdLT02nSpAkrVqzA399fXwBKUHnwtvfG296b7Te2I8+nSez9zPuM3D2S73t9TwfPDkawsBxx8df1c8u5b63qsVCS6Zrjgm4l3Jkf4cJv8Px0aDte96FbCbEyl1HL0Yp7Kdn6pf45y+7P3k42mJs7lyi/FWhmMilLXgtg7I8nOX07WR+q++7PG3mum6nQfQHVdNCF6FaPbstHv/3LxlN3Uaq1fL3vKvAkB6kocVbZeboAn6D0nL6dzBtWV9hXqwEWPKK9R3uOXDiS71wzMzNsbGxITEzE3Nw835YvarUajVJDRmYGWABKLShVkJmhb2ytVSlQaFVkZ5etYtFoNCgUCrKzsw1sk2crQK00aElVmSjI7spOSe3WarVkZmaSkJCAk5OTXnCXhhJ9A8jlcmbOnMn+/fv1HqNBgwaxZs0aBg8ejEwm44MPPii1MYLyJyfclp9I0qLl/T/fZ3XI6hI1aDQJfDqCuY1hflKOOMqNIh32TIXIX2DAYvAKqDgbS4Cfmy33UrJ5lKkkOePJL/Gc5OxAH2dO30k2yCUqqBaRo405P49rz7Qt59l6NtZgn4eDFfGPl+vn7jebUxTy/e71iUvJ5sj1ByRn6kIcOTlIucWZoHpzITqeJqpLzDBrTPda3bG3KHh1lEQiwdPTk6ioKG7fvp3vHI1WQ0JGAgpLBTbmNqDM1vVsTDPXV9N+kC5HIpGQnVy2TYi1Wi1ZWVlYW1sbhAlTs5VkyFWYZxavunxFU5DdlZ3S2u3k5PTMdRFLJJA+/fRTVqxYQY8ePfj7778ZOnQoY8aM4fjx43z99df6JbKCykvulW1Lzi55UkvkMRnKDEbvGc2S55fQzrPkRdwqLTn5STmhNrUcZJa6VWyqfHJl4v+FVd2hwzs6j5J55frQ83Oz5e+buhBh1MMMLGSGv6xaejty+k5yvgUd88PKXMbCV1rxSpvarDwSRf2advRu5omztTm9Fh3Ok3yduyjkvBeb03PBYbIei7PX2/uy+u9oA3EmqL5ciU+lVVoE5xwk3FQkM6Ve0c1PLSwsqF+/vkGT1qf5eNfHvNLgFV70fxEe3IB9/4Uhq8GzBQBh2y8iV6n5fEjZLoVXKpUcPnyYrl27GoRvvj98i8PXklk3rnL+uCzI7spOaew2NzcvEy1SIoG0adMm1q5dywsvvMCFCxdo0aIFKpWKc+fOmZQire5423szptkYevj20OclnY4/zdJzSwFdQcn//PEfvur6Fd19uxvZ2jLExf9JocncK98u74A/5+YVSloNHFuiWxk3eDnUblPxNhdAnVyJ2tfvp9G0lqPB/vo1S1e/pENdNzrUdTMYy8lv+k/Xuiz58wZxKdkGwqu2sw2TezZgzu+6RPcT0UlA/tW2BdWP9SdiGGVxgAWevjR28adTrf9v777jo6zSho//pmXSJo00kgwQOoiGGmTZVRCkuYLY0LUgdoW1xLKrj8q6i4/rqsiuUnZdFfd1VR4VEBCRJiDSpAQJTTohIY0Q0pMp9/vHnZlkUkhhJpMJ1/fziZm56xkZhmvOuc51foXNWk/vbS1ardZl9fna7Do7ZyrOqMeEd4TidCjLAn/1i51FoyOjqOKi12gJnU6H1WrF39/f5R/sc2V2iq0at9/PXRpqd1vnzXY3ayDyzJkzDBo0CIB+/fphNBp5+umnJTjyUY6CksmxycQFuy6nYLVbSdmYwpIjF6kr5Ktqz3wb/qRapbt24cmqmX6cOwIfjIFNb4K98Q/21tAvvjog2nO6oM7+gGauYJ5TWN5g9WtHoFMz+br2DLX7hnfBXLVwbVqmOiMyv6TSJypqC8/JKCijfNdnpIWcZTflPDHwCecM2ksV4R9BfrkajBMQrg6tFVcvPxXkp6ekovX+vpZU2gj0kxGU9qRZ71SbzYafX/V4rl6vlzoh7UTNUgAaHGX67byy5RU+3v+xN5vWOhyFJw01hoQUO1T9v0CxwfpZ8J9JUOT9SQhJCWHoqqbL7D59nsNZjS8rcjGOIbOLVb+umXy9P7N6dltOYTnvrT/K3UM7uxyfX1LpExW1xcUpioJdsWNX7NjsNqx2K1a7FYvdgsVmodJWSaW1ggprOeWVpZRXllJQcoEfU3ez8N9PYurwKW90iGBq36n8Ot5963SGGcMoqChQn2i1EBQNxTnO/YFGnXNiQWsoq7QR6Nc2J3aIlmnWn6aiKNx3333OxfHKy8t59NFHCQoKcjlu8eLF9Z0u2rCapQBigmJ4ZPUj2FGzct/a+RbpRen8z9D/ad+9hY48pc2zYbdjBpOCGiRVzRc++QMs+DVM+QQ6eW+2X4Cfjr4dQ9iXcYFfsotdikN6Ss3k65pDeo7gasnjv3LOrgPILCjzeJvaqtdff53Fixdz6NAhAgIC+NWvfuWRpSF+/9F1pBryef2TF6k5o10BlKrgvs52TfW2uudUPXbX3/MoCCCEJ5MeZVrSw+65ZpUw/zB+yf+lekNwdN0epMrW7EGyNt6DVFEE+75QlzzKP6bOprVZwVap/niwbpMehQlWK/oDepxf/HxAs9sd1x+mume90WYFSFOnTnV5fvfdd7ulEaJtqFkKwBEcOSw6vIgSSwmv/fo1t3WRt0m1lzDR+akfXDWV5MLCG2D836B/E9aD85BBncPZl6H25Kw7pH5z9tdrKbd6Zn0Fx1Il7/1uYL35RQadlkeu7cbMZfsB2Hg4p84xl4uNGzcyffp0hgwZgtVq5cUXX2TMmDEcOHCgzhfKS5EcOxpT+k6CTSY0aNBoqnuAqfFbU/Vfher9GjTOpXaq46HqY2s+dz7WaFyOUs+tcaxG/a3TaAgJCaN3z+Ekdb6WAL37JzmEGkOre5AAgmNce5D8dJRWtF4PUmmljbDQi+TInNoKX05T2xg/CKL6QFAk6I1qoUudXi094iF2u43DBw/Rp09vn1peqtntNl3azLWamhUgffTRR267sWi7BsYMxF/vT3mtpOUVx1dQYilh9ojZzkJt7VLNJUxCzfDp7a7lAUAtSvdNCtr0naAdA+dPQXT3Vm3mwM7hLNxy0mVb37gQdp8uwGKz16l5dKkig9VrXSz5+tZBCbyx6hCllTZ2nj7f4HHt3apVq1yeL1y4kOjoaHbt2lXvOpYtdc/4F1m5ciUTJkzwqcRbdwgzhnGhonqoF1MMZKU5nwYZ9ZRabNjtiscXkIWqITZjA5+L2Qfgv7dC7FXwwGoI6+Tx9tRmt1g4lr+SXldPQOdD7xVvtrsd/ysnWspsMrN44uJ6SwF8n/49j619jPdGveeb67c1Vc2K3I7yAPXMdNP9/CnJoQfhwEl49Pu6Vbw9aGCnMJfnGnD2KL20NI01T19LdEjrzqgJMuoZ0zeGpamZ2FpxodC27sIF9c8lIqL+ulAVFRUuBQYLC9VEd4vFgsVSdx1FB8e+ix3TFrmj3Sa9iSJLEWUVZei1erQBUWiLs7FWXdOoU0esisrK3Zob1FDbSyos+Os0dV+ToqBb8TQaU0esUz4DvyDwwp/X5fxeaeiajZEASdSrZimAf+/7N4uPVOeVbTu7jRnrZvD3kX9Xi7S1dzXLA7jkJ6k6XtijDkgeWw8RD7Ras+LDXIctBnQKY3fVjDZ3VrF2VOCOCKr+9uaYxVZ7yj/ADVfFsTQ10/nccplHSna7naeeeorhw4fTr1+/eo95/fXXefXVV+tsX716NYGBjf8Zrlmz5pLb6Q2X0u6jlWq198UrFxOsDSYxN5d+RVms/OYb0Gg4mK8BdCxbuZoQ99aKBOq2Pb9QR8bpYlauPOayPfrCXoalb2NLt+fJXbvR/Q1ppsvxvVKbYxmSxkiAJC7KbDLz4JUP8s3xb1yqb287u41H1jzC3NFzCfEL8WILW1Ht/CStAcVuQUPVdNBNf4O4Aa1WfVuj0TBlsJlFO9Px02t5fmxvpi38iTKLjQCDzm1VrB0VuNOqeqfOXihnxqfqArYzPt3tXKTWoXbA9MORXAZ0CndLW3zR9OnTSUtLY/PmzQ0e88ILL5CSkuJ8XlhYiNlsZsyYMYSENPz3y2KxsGbNGq6//nqfGmJzR7tjc2L5dO2nDP7NYLqGdkVzyIb2q/8w4bpfQUA4Ecfzef/wToZdM4LOEe77ItdQ21/es56r+iYy4Teuvci6r75Eib6CIVOeq5ns1eou5/dKbY4e2sZIgCQaVbP69rt73sViV7snU3NTuW3Zbbw94m36Rdb/zbjdqZmf1GkYtoyfsS19HKOtWJ3+//5IeGgDxA9olea8OKEPXaOCGNq1A/3NYXz31DXsOJlPcpcIj1Wx3p95wblmW+0p/6D2ON06KIEvd50BYPGeTH5/XY/2PQOyATNmzGDFihVs2rSJhISEBo8zGo3O2cE1GQyGJv2j0NTj2ppLaXdkkFrQtMRWol4jVK3lZijPh5BoQgLV/5+VNo1H/t/UbnuZxUZIgJ/rvUrz4ZdvYcxfMPh5oBurBS7H90p912qKdjwdSbiT2WQmwj/CGRw5ZJZk8rtvfsfenL1eapkX1Cg0qfSewOae/+MyXZpPJkNmaqs0JTTQwCPXdqO/OQyATh0CuXVQgkeX+LgiLtRZiDLAoKtTxTs6xJ83b72KHjFqjbSTeSXOZVEuF4qiMGPGDJYsWcL69etJTGy93LTLRahRfd85Z7IFR6u/q6b6BxnV92hr1EKqtNqx2BQCauc6ndgIdgv0neTxNgj3kwBJNFnNYpI6qqdbKig88f0TZBZnNnRqu1bsH4/VUCNIKDsP/xqhzlxphzqG+vPdU9fw1m1JfPfUNfXOatNoNNw8IN75/O3Vh7HbPVfjpa2ZPn06n3zyCZ9++ikmk4msrCyysrIoK7t8a0O5myNAcs5kC3IESOpU/6CqGWWtUQuprOoedeogndgEHXpASFw9Z4m2TgIk0WSOobZZw2fxr7H/wqCt7qbML8/n7pV3c7rwtBdb6EUPra/1LVGBxQ+BzbdmjDRVU3qqhnXt4Hy8+3QBX+0+0xpNaxPmz5/PhQsXGDFiBB07dnT+LFq0yNtNazcMWgPBhuDqAMkYDH7Bzh4kx8y11qiFVFLVS1UnQDq+ERLdV9ZBtC4JkESz1Fy/7eubvuahKx9y7ssty+X2FbfzY8aPXmyhl4R3htGvgr5Gb0p2Gnw8Ec4d9167vEivc/14+eu3h8gpujzWZVMUpd6f++67z9tNa1fqFouMrhEgqcFKa/QglTp7kGoMsRVlq9Wyu7hveRXRuiRAEi1mNpnpHOK6/laJpYRH1z7KxnTvT2dtdRGJ6qK3w59WF84EOL0F3hsC+Se827ZG1Jy27+5zf91dTaY9V1LJTe/9WCepW4iWclmPDVyqaRt0Wvz02lbJQap3iC1rn/o7rr/H7y88QwIkcUkcVbdre27Tc6TlpdVzRjsXkQjX/wkG1liCRLHCN8+02SDp9LlSl2n7p881rUZIU8998DeJxFXlKWVeKOfW+VtZfyi7znFCNFedatp11mPTUVLh+R6keofYsveBnwnCunj8/sIzJEASl8RRdTtlUAoGTY0pr9YyHlr9EKk5qd5rnDf96onqXiSAY+tg3tVtMkjacTLfZdr+jpP5bj03MtjI0hnDnTPtFBSiglu3wrdonxpfj01PSSvkIDl6kIJqLjWSlQYxV4BW/pn1VfInJy6Zo+r215O/5uWhL9MrXF2xvNhSzMNrHuanrJ+83EIviEiE6TsgtMaaS9ZydVZLG5PcJcJl2n5zCkw29dxokz+fP3w1kwfE8/Zt/bkyIbTe44RojrpDbLV6kIw6Z++OJznuEeDSg5QGsZdJfbh2SgIk4TZmk5lh8cM4caG6l6TMWsbjax9nS8YWL7bMSzp0gzs/c92W+mmb60Xq1CHQZdp+c2ooXezc2rlJ/gYd70zpzw1XdXTvCxCXrbpDbDFQmuecPRrop6e0FYbYnEnaVV8WsFZC3hG1B0n4LAmQhFvtzt7tsrgtQLmtnBnrZ1yeidux/eDOz6uH29K3wdzkNhkktbTAZH3nXkpekxBN5RhiU5SqGlvBservklyg9XqQSius+Om11TM3C06BYoMO3T1+b+E5EiAJt6qZtG3UGRkYra7TZbFbeGrDU6w7tc6bzfOOXuPhytuqn9sq4YhvLhjZVJeS1yREU4UZw7DYLZRZqwpwBkepv2vUQiptjWn+FhtBNYfXHF+AIrp6/N7CcyRAEm7lSNqeNXwW80bPc5nJZrVbeWbjM6w6scqLLfSSa57H5a/b7o/bXC9SY6JNRp4c1YNoU901w2q7lLwmIZoqzBgG1FhuJKgqQCpRl7ZRZ7G1TpK2Sw2k/OOgM4JJKmj7MgmQhNs5ikmeLT5bZ7jNptj4ww9/YPmx5V5qnZd06AoPrAZDgPo8O80n6iPVFB3iz9PX9yQ6pPEZaJeS1yREU4X611qPLVCtueUYYgs0tk4PUkmFzXWK//kTEN5FZrD5OPnTEx7TUI0ku2Lnfzb/D1/98pUXWuVF5iGQdGf1c7sFjq33Xns8rDUWzhWXt1C/WuuxGfzBGFKdg9RaPUgWq2uAlH9cnckqfJoESMJjHMNtN/e4uc4+BYU/bf0Tnx781Ast86JhvwdNjQ/S49+3yZltQvgCxxCby0y2oMgaSdr6VknSLq6oZ4hN8o98ngRIwqPMJjMPXvkgRl39eSuv73idj/d/3Mqt8qIOXWHqctBVFdU8uByWPgbzhkmQJEQzBRmC0Gv0rrWQgqKgJA+AYKOe4vJWqINUYSXYvypAUhQoSIewzhc/SbR5EiAJjzObzCyZtISUQSn4af0A0NZ467218y3+9fO/vNW81tdlOFz7R9dt1jI4vdU77RHCR2k0mrrVtIOinD1IwUY9JZU27HbFo+0orrAS7KiiXZIHtgoIjffoPYXnSYAkWoWj2vbSm5Yya/gs3h/7PqM6jXLuf3fPu8zaNqu6nkl796vfu37D1Bqg0zDvtUcIH1WnWGRgB5chNsDjw2zF5VaCjFVD54UZ6u8QCZB8nb7xQ4RwH7PJDMDkrydTYatAixY7ar2cRYcXUWGr4M+/+jMajcabzfQ8vREmvAWfVtVHCo6Ck5vVx5LcKUSThRpDa+UgRUGpOs3fMexVUmHD5G+o73S3KKm0Emysur4ESO2G9CCJVrc7ezcVtgoAZ3DksPToUv6y7S/YFXt9p7YvPa6Hzr9WHxdmwrIZkoskRDM1OMSmKJiqepCKKywebUNxuZVgZw9Sptoj7KjJJHyWBEii1dWc/u+n83PmJTl88csXvLT5Jax2zydXepVGA9e/6rpNcpGEaJYwYxgXKmvNYrOWQ2Wxc4ityMOJ2i45SIUZENJRaiC1AzLEJlqdY/r/7uzdDIxRlyLZnb2bwspC3t75NjbFxvLjyym1lvK3a/6Gn86vkSv6sITB0G00HFurPtfo1G+/+SdkqE2IJqh3iA2gJJdgo7o2W4kHF6y12OxUWO3OYIwLGTK81k5IiCu8wlFt22wyOx+PMI9gSq8p6DXqB8260+v4/frfU2pp5wudjnsdqMq5Umyw5hWY/ysZahOiCRoOkPIw+Xt+iM1RiNJxLwozJUBqJyRAEm1CelE6Ny+7mU8PfYpVqe4O35K5hUfWPOL6AdjeRPWEvhNdt1lKZahNiCYINYZSWFlYnbdYowepNYbYHNcOqj3EJnyeBEiiTdidvZtya3m9+1JzU7n/u/vJK8tr5Va1ol+nuD7X+cu0fyGaIMwYhl2xU1RZpG4IjAA0UJKLQafFqNd6dLkRRwkBZw5ScQ6YJEBqDyRAEm3CwJiBdaptG7QGTH4mAH45/wv3fnsv6YXp3mie58X1h+6jq59f96LkIAnRBHXWY9PqXGohBRv1FHswQHJU6g426qGiGCwlEBzjsfuJ1iNJ2qJNcFTb3p29m47BHTlbfJaOwR15dM2jzmPSi9K5+9u7WTB6AX069PFiaz3kN8/A0apk7Z+/gN43Qvo2tSdJgiUh6hVqVAOkgooCOtFJ3VhzuRF/PcUeTNJ2BF/B/noozlQ3Bkd77H6i9UiAJNoMR8K2w9dHv8Zid02uzC/PZ9p303hnxDsMi2tnQ1CdfwXxgyBjF2Tvg7lDwV4J+gB4fKsESULUwxEgNbRgrdqD5LkkbUeAFGTUQ0G2ulF6kNoFGWITbVbNYTc/rR99ItReoxJLCY+vfZzlx5Z7s3mekfxw9WN7pfpbaiMJ0aCaPUhONdZjC/LwgrWO/KYgPz0UOwIk6UFqD6QHSbRZtYfdTl04xepTq9l2dhtWxcqLm18ksziTh696uP0sTdL3JvjuRedSCQDo/KQ2khANCNAH4K/zrxUgRULuIQBMRs8OsRWVWwn006HTatQEbZ0R/MM8dj/ReqQHSbRpZpOZgTEDeXzt4/x525/ZmbWTpKgk5/73Ut9j5paZWGyeXUqg1Rj8YeC91c/Du4CCWhtJliERol4R/hGcLz9fvaF2D5JH6yDZasxgy1aH19rLF7bLnARIos2ruXabVbGyN3cvOo3OuX/J0SXc8+09ZJdke6uJ7jX4ftBU/dUsK5ChNiEaEe4fTn55fvWGoEi1F9ZuI9hf79E6SMUVlloBkgyvtRcSIIk2r74SADbFRnJsMgatuoL2/nP7ufObO0nLS/NGE90rrBP0GKs+Li8Ax1p1+gCpjSREPeoESIGRoNih7Dwh/gaPF4p0VtEuzpEE7XZEAiTR5jlykVIGpbgsbLsjawegdq8D5Jblct+q+1h6dKk3mule/e+sftxzDNw0X2ayCdGAukNskervkjxCAvQUlXtuiK2o3EpIgPpFTe1BivLYvUTruqwDpHfeeYcrrriCvn378sQTT6AoirebJBpgNpmZ1m8aS29ays09bnZut9gtLh+MFbYKXv7xZf605U8NVub2CT3Hgb86O4dj30OfiRIcCdGACP+Iuj1IAKV5hPgbKCy3euzzvbDcQoh/VYBUcq56qRPh8y7bACk3N5f33nuPXbt2sW/fPnbt2sW2bdu83SzRCLPJzINXPugcctOhQ6HuB99XR77idyt/x7GCY63dRPfQG+GKqkDQUgKHVni3PUK0YeH+4Q32IJn89djsCqWVnpnJVlhmUYfYFAVK86qDM+HzLtsACcBqtVJeXo7FYsFisRAdLcl1vsAx5DZr+Cym9pvqsm9cl3H46/wBOHL+CFNWTOHzQ5/7Zu9g0h3Vj/d+Duk74LPfqb9rPhbiMhfhH0GRpah6Nqt/GGh0ag9S1fBXoYeG2ZxDbJXFYC2vDs6Ez2uzAdKmTZu48cYbiYuLQ6PRsHTp0jrHzJ07ly5duuDv78/QoUPZsaPp/1hERUXx7LPP0qlTJ+Li4hg9ejTdunVz4ysQnmQ2mZnUfRK39rzVmait1+jp26EvL179IjGBaqJkha2C17a/xsNrHuZs8VlvNrn5zEPVaf4AJzbCwhvg8Dfqb8fjj2+Uqf/isufIQ3QOs2m16qK1Jeecw1+FZZ5J1FaH2PTOpU0I7OCR+4jW12YDpJKSEpKSkpg7d269+xctWkRKSgozZ85k9+7dJCUlMXbsWHJycpzH9O/fn379+tX5yczM5Pz586xYsYKTJ0+SkZHBli1b2LRpU2u9POEmZpOZj8Z+xNUdr0aDhtm7ZvPKj6+QXZqNtsbbe9vZbUxeNpn/HvwvNrvnisa5lUYDV01RHyt2sFVN97dVVj+2lsvUf3HZcwRI5ytqDLMFRkJpHqEB6gwzTyRqK4pCYVlVD5KjuKvkILUbbbaS9vjx4xk/fnyD+2fPns1DDz3EtGnTAFiwYAHffPMNH374IX/84x8BSE1NbfD8L774gu7duxMRof7FuuGGG9i2bRvXXHNNvcdXVFRQUVHhfF5YWAjgHJ4T3tM3vC+/7fxbdp3dBYAePVas6NBxT+97+PbUt+SU5VBiKeGvO/7KimMr+MPgP3BFhysu+d6OP3uPvQd63oBh4xsA2NGhxYZFHwyAwVqMxRACccnQzt6D8ndKNEe4fzgA+WW1aiGV5GHy99wQW4XVTqXNruYgOXqQZIit3WizAdLFVFZWsmvXLl544QXnNq1Wy+jRo9m6tWnfps1mM1u2bKG8vByDwcCGDRt4+OGHGzz+9ddf59VXX62zffXq1QQGBjb/RQi3eyj4IeYVz+OuwLs4bTtNsjEZU5aJBEMCy63L+dnyMwBp59K457t7GOA3gOv9rydEG3LJ916zZs0lX6NeisJov2iCKnMAO0eib+BYtFojqUveek5GXkfF1v3Afs/c30tKS0u93QThQzr4q8NaeeV51RsDOzhnsYFnhtgcQVeIv8FZuVuG2NoPnwyQ8vLysNlsxMS4FuSKiYnh0KFDTbrG1VdfzYQJExgwYABarZZRo0YxceLEBo9/4YUXSElJcT4vLCzEbDYzZswYQkIu/R9Ycen25e3j4P6D9EvsRw9rD5Ki1SVJPt7/MQePHaxz/J7KPRy0HeTOXndyX9/7MPmZmn1Pi8XCmjVruP766zEYDJf8GuqjNW6D7fPRopB49Q10ufL2qj2/o71mzTl6aH3Vpk2bePPNN9m1axdnz55lyZIl3HTTTd5uVrvlr/cnxC+EnNLqFAuCIiHvCP4GLQadxiM9SI6gKyTAAOfy1NIcOs98DojW55MBkru89tprvPbaa0061mg0YjQa62w3GAwe+4dRNM/AjgMZ2HGg83l6UTqTv57sXKYEwE/rx3397uOzg59RZCmi3FbORwc+4ssjX3JX37u4u8/dztXBm8Oj74O+E2H7fAD0R1bBwLs8c582xNf/TjlyKO+//35uvvnmxk8Qlyw6MNp1uaGqHCSNRuOxatquPUgyxb+98ckAKTIyEp1OR3a269pb2dnZxMbGeqlVoq2puYYbwM09bubBKx/EbDJzd5+7+dfP/2LR4UVY7BaKLEUs2LuAD/d9yG+7/ZbHkx4nJqiNLBlgHlo1XHAOjq4DSxkYArzdKnERjeVQCveLCYyp24NUeg4UBZO/nsIy9/cgOYIuk79evZckaLcrbXYW28X4+fkxaNAg1q1b59xmt9tZt24dw4bJWlVCNTBmIH46dWkSP50fD175IABfH/2aYksxf0j+A8snL+fmHjc7F7+ttFey+Mhixn01jhd+eIED5w54rf1OWh30qvrH1lICxzd6tz1CtEHRgdF1AyS7FcoLCAkweGiIraoHKaAqB0kStNuVNtuDVFxczNGjR53PT5w4QWpqKhEREXTq1ImUlBSmTp3K4MGDSU5OZs6cOZSUlDhntQlhNpn5cMyHfJD2AQ/0ewDAOeRm1BlZMmkJZpOZV3/1Kokhiby9623nuVbFyorjK1hxfAUDowdyZ587GdVplLPmUqvr/VvY84n6+PBK6DVOfVyUBTs/gsHTwCS9p76qpbNkPT6L0kM80e5I/0g2l252XlNjDEMPWC5kEWzUUVBS6Zb71Wz7+ZJytBrw09ixF+dC7FXY2uifhbxX6l6zMW02QNq5cycjR450PnckSE+dOpWFCxcyZcoUcnNzeeWVV8jKyqJ///6sWrWqTuK2uLwlRSfxj+v+Aag9R44htwpbBSkbUnhp6EtEBESgoOCn9aPSXokWLUF+QRRVFgGwO2c3u3N2Ex0Yze09b+eWnrcQGdDK3xQTrwWdn1r/6PiG6u1FWbDxr2oPkwRIPutSZ8l6bBalh7mz3dkV2eSV5bH8m+XoNDpMZWe4Dti2bgWl53uTZ4OVKzPcdr81a9awM0ODv1bLt99+y/XnznBG6cTBlSvddg9PkPdK02fJttkAacSIEY0uDzFjxgxmzJjRSi0Svs4x5FZZVWTxUP4h7vvuPrRoqbRXYtAaSBmUwujOo4kMiGT5seV8evBTjl1Q13PLKc3hvdT3+OfP/2R84nju6nMXPUJ6tE7j/QLVXKSTP0DBKbV6dkQiFGaq+wszIa5/67SlMdKr1WwtnSXbGrMoPcET7Q7OCGbZxmUMHTmU6MBodcjr0IsMS+pJb/9O7Mu4wIQJl56CUbPtaetPEFmczYTxv0a/7xG6XXU1ickT3PBq3E/eK9WaOku2zQZIQribY8ht1vZZHMpXy0FY7dUzWyx2CxH+EZhNZgBu73U7t/W8jR1ZO/jvwf+y8cxG7Iodi93CsmPLWHZsGYOjB9PH0odxyjjPv4CuI9QACap7kb6sGlL+4j4YdB/8JsX7QYn0ajXbpc6S9dXZtO5sd5wpDoD8ynziQ+MhJAbQoK84T4fgXhSUWt36/8hgMFBYbiM8yIhBqQRrGTpTDLo2/ucg75Wmz5L1ySRtIVoqKTqJ2SNmY9Sp/xgZtAZnXpGfzo+BMQNJL0rn66NfsyNrB8uOLSMuOI5/XPcPPhjzgcvyJQA7c3by/0r+H3d8ewerTqzy7DIm3aqHnDm+QV1ixFquPrdVwI5/qsGJ8Lri4mJSU1Od1fwdOZSnT5/2bsPasbhgNUDKKKkaRtPqICAcSvIID/TjfGml2+95vrSSiEADlDqqaEuRyPZEepDEZcdsMrNk0hJ2Z+9mYMxA8svy603kdjDqjMwbPY9vjn+DHbtze2RAJHll6gfj0YKjPLfpObqGduWJgU9wnfk6NBqNexvesb9aiK78grp47ahXQO+vBklavTpjpy0NtV3GGsuhFO4XagzF5GfiTNGZ6o1VU/3DovworbRRYbVh1Ovcds/zpZWYwwOhpGodNqmD1K5ID5K4LJlNZiZ1n4TZZHYmcidFJ9WpnQRqQvcjqx9h8ZHFzm1GnZGF4xbyUvJLmHVm5/bjF47z1PdPcc+395CWl+beRmt1kFi1VmDZeagogls/cj3my2lqfpI31cyLukw5cihr/0hw5Flmk5n0ovTqDYGRVT1Iai9xQal7Z3CdL7UQFuhXvcyITPNvVyRAEqKGgTEDncNvDnqNHqtSnat0c4+bmTd6HutPr+edne/QXd+dAE0AfSL6OI/Zm7uX333zO/605U8UlBe4r4FdR1Q/Pr4BQtRhBRy5VNZydejNnYqy4PvXmzZ8l3+iOi+qLQRr4rJSJ0AKUtdjCwtU66G5e5itoLRSDb4cQ2zSg9SuSIAkRA2O4bdZw2fxwdgPmDV8Fv8c809n0GTUGbmh6w08vvZxZu+aTYW9gu8rvseqWBnXZRyTuk1yXktB4asjX3HT1zex7vS6hm7ZPF1r5SGZYiH5EXAEdXp/6NSCmToXC4IcSddNCZBq5kV5IlgT4iLMJrPrEFutHqTzJe7rQVIURe1BCvJTlxkxhoLez23XF94nOUhC1GI2mZ0z2Rxq5izVHoYbaRzJdst25qbOpdJe9xvqufJzPPX9U/SP6s/Lw16mZ3jPljcuoiuEJEDhGTi9TV2CZMLf1J6lz+9Uh9wiEpt/XXfNPOs0rDovqqXBmhAtlBCcQFZJFpW2SrWKflUOUnhVD1KBG3uQisqt2OwKEYF+kJUnCdrtkPQgCdEENXOWag7D+Wn9GBUwikeTHq03OKopNTeVW5bdwuaMzc6Zci7DAU2h0UDnqqDDWgZZ+9THjqE2vfHiPUFNGSprzpBabRGJ1XlRLQ3WhGghs8mMgsKZ4qpepKoepBB/PVqNmjPkLuerlhkJD5SFatsrCZCEaKaaw3CLfrsIgJHmkc513/RaPWO7jG3w/BnrZjBx6URe+vElbl52c/ODJPPQ6sfpO1z3lZ5reDisqUNlzRlSq48jWHP8FqKVdAntAsDJCyfVDUGRYKtAZy0hNMDg1hwkR8K3mqSdB8HRbru2aBskQBKiBRw9SvHB8QDEB8fz4ZgPGWkeycKxC3ly4JP46/0Btb5SzTXcbIrNWaCy3FrO7uzdzbx5cvXj9O0NH3cpPUGN8eS1hWihqIAogg3BHL9wXN0QWDXsVVULyZ1DbI5rhQfJQrXtleQgCeEmNdd9A1g8cbEzbwngn3v/ydfHvnY5R4uWAdEDmnej6CvALxgqi6sDJFMsXPvH6n8QoHl5RTWn5tfu+alvOROpli3aII1GQ2JoIicuVM2edAQtpecICzSQ78YkbUcPUrijBykoym3XFm2D9CAJ4SE185bMJjOPJD2Cn9Z1losdO4uPLG503UEXOj3ED1IfF2bAhTNqkDLyBdcAyaH0nNrbU3qu/uvVnppfs36RTNsXPiYxNLF6iM2RF1SSR2SwkXMlFQ2e11znSioJ9NPhr9eqPUiSg9TuSIAkRCsxm8wsvWkps4bP4tGkR53bP0j7gHf3vNvMi9XMQ7rIMBtU5yXlqOvP1SngWHtqftbPDe+TafuijUsMTeT4hePqlw7HF4bSPKJMRnKL3Bcg5RZXEm0yqgVbbRUyxNYOSYAkRCty9CpN7z+dl4a+5Nz+/r73WXRoUTMuVCNAOl0jQLpYFet1f1J/1+4JckzNB/V37FUN76s9bd/ROyW5SKKN6BralWJLsboMkN5PrU9UogZIOe4MkIoqiDIZa1TRliG29kYCJCG8ZErvKTyW9Jjz+f9u/19e3/5602a1JQwGqtZ6c/QgXWyoDMBWlaBauyeo9tR8Rw5S6TnY+zncMLt6X+1p+/XNmnPkQ0lukvCCxFD1PepM1K6qph1t8udccQU2ezOGsy8it7iCaJO/mn8EEiC1QxIgCeEl6UXpfJj2ofO5HTufHvqUG5fcyEdpH108UAoIg+iqpU2y9kFlycWHygCqyhDU2xNUc2q+I7DKOaQGPzqD6zGNceRDSYAkvCDBlIBeq69O1A6MhJJzRJmM2BXIL3HPTDbpQWr/JEASwkvqWxgX1DIAs3fNZtLSSRcPlBzT/RUbZOy++FAZwKg/qb8vVsCxMLO6F8oxJCeEDzFoDXQydarRgxTpzEECyCkqd8t98oorawRIGgiMcMt1RdshAZIQXlKzIrdeU7fihsVuYfau2Uz+enL9QVLCkOrHGTvrDpU5FFd9ww2u+oZ7sZ6grJ+re6Fs7l3YU4jW4jLVP7CDMwcJcEuittWuVuVWA6Q89R5a3SVfV7QtUgdJCC9xVOR21ErKLM7k8bWP11mypMJWwe7s3XXWhyN+cPXjMzvV3zWDn5b0BMVeBYZAsJRWr6kmhI/pGtqVZceWqU+qepAig9UhZncESEVV5ZSiTUbIyZXhtXZKAiQhvKjmwrhmk5m/XfM3ntrwlMsxRp3RWWzSRWRPMIZARSFk7HLd19KeoJA4eGyLms/kH6YugFsfR56So3dKiDYkMTSR7NJsSiwlBFXlIBn1OsICDW6ZyVZYFSA5h9hkin+7JENsQrQhozqPYmK3ic7nPcN7snji4rq9RwBaLcRVVeEuOgsXMqr3OXqCoDovqakiEqH/7xoeiqs5W07ylEQb1DW0K1C1JltQJFhKwFJGtMlITuGl94oWVqozSKsDJLUHya7Yuf+7+/nL1r9gs9su+T7CuyRAEqKNeX7I80QFqB+4v5z/hfXp6/n66NcN5CHVGGbL2Fn92NETdNN8GPdXdZu7entqzpaTPCXRBjkWrT1+4bhLNe34sAAyCsou+frnK8BPryUyyOiyzMiaU2v4KesnvjzyJZ8f/vyS7yO8SwIkIdqYUGMoM4fNdD5/e+fbvPTjS/Una9eXh+QQkajObFv1R/W5u3p7Og1ree+UEK0gyBBETGCMmqgdVF1NOyE8kPT8Sw+QzlVoiA/1R6vVQGl1gPTfg//l6o5XM6zjMH4488Ml30d4lwRIQrRB15qvZVSnUS7bHMnaLhIuEiBBA709Cpw7BgXpYLM2v3ERidW9UzVnywnRhjhnsjl7kM5hjgjgzPnS5q19WI9z5ZAQHgB2m1osNSiSSlslaXlpjDCP4OqOV7MrexeV0sPq0yRAEqKNembQM+i11fMo9Fp93WTt4GgI7aQ+PpuqfmDXVLO3p6qkAIsfhncHwpx+MCsaPhgDZ2sVlWxMY3lKQnhZ19Cu6hCbI4G6NA9zeCAllTYKSi2XdO38Cg3x4QFQdh4UOwRFcTj/MBa7hasir+LquKspt5WTmpN66S9EeI0ESEK0UeYQM/f0vcf5fFD0oPqTtRMGqb8tpZB/3HWfo7dn4nvQMUndlvdL9X7Fpi5Vsvz36vPyQje+AiG8p3NIZ04XncauN4IhCErUITaA9POlLb6uoiicq4CEsACXKtr78vZh0BroFdGLnuE9CdQHsi9vnzteivASCZCEaMMevvJhIvzVCr3bs7az4tgKnlj/BHtz9lYfVDMPKedA3YtEJELuITizo3pbzJXQ+7cQXqui9uKHILueawjhYxJMCVjtVnJLc53rsZkjAgA4c77leUgXyqyU2zSYw2sGSJGk5aXRO6I3fjo/tBot3cK6VVfzFj5JAiQh2rBgv2Ae6PeA8/mLm1/k+/TvuX/1/dUJ252urj6h5iK0Dj9/AVvfq34+8iV4ZBPc8V/4/S51MVqjSd1XnAUfXA8Hl3vg1QjReuKC1OHfzJLMqvXY8ggNMBBs1HM6v+U9SI7gKiHctQfpaMFReob3dB7XLawbRwuOtvwFCK+TAEmINu62Xrc5e5EU1OTSSltldcJ23EAwdVQfn/nJ9eTcX2DZ71239bheraEE6vIIQx6AW6oXzaWyGBbdDZvfqb9BigJ5RyH1M/jx7+q27Qtg+7+gKKulL1MIt4oLVgOkM0Vn1FlmJXloNBq6RQdzJLu4xdc9lque26VDoDrFX2dE8QvmZOFJuoR0cR7XPaw7Jy6cwK7YL+l1CO+RAEmINi5AH8DUK6a6bPPT+VUnbGu10HeS+theY1aaosC3z4G1ajih1/iGbxIcrf7udl31tgNL1d+HV0K/W+DkZlj1IvxjALw3CJY+CvsXq8fs/Uy91zv9YPEjkJnaotcqhLsEGgIJN4aTWZypvr+LswHoE2viUFbLc+0OZRcTYVQICTCo1wyOJrc8jzJrGZ1DOjuP6xralTJrmXp/4ZMkQBLCB0zpNYVQY6jz+QvJL7gmbPe9qe5JB5fB8Q3q47BOMPzphm9gioVr/wiT5sFv57jWN9q/BNK+gtX/Q+5P85mn5JOra+Cjw26Bnz+Hf10LR9Y29eUJ4RHxwfHqEJupo7N3s3esiSPZxVhtLevZOZRVRFxgVZmAoiwwdeRU4SkAOodWB0jdw7oDSB6SD5MASQgfEGQI4q4+dzmfHzl/xPUA89DqYTZQa7OserH6+djXQW9s+AamWBj5AoR0hMHT4OENkDCkzmG5Oh3zw0PJiO8P170ME95Wd9zwDvzmGQgIr7peHHS9tnkvUgg3iwuOI6M4Q31/F2eD3UbvjiFU2uycyCtp0TUPZxURV1U5g8JMMMVysvAkWo0Wc3D1l5aYoBj8tH71V8AXPkECJCF8xG09b3PWRVp1cpVrETqtFvpUr+HG/90LhWfUx91GQe8bmnyf3NJc5mV+T+6ol9Tnv3qceUkTOHJtCrMj1YrBD/gVkj5gSnWhyviBMOoVePqA2gN13UugM7T4tQrhDvHB8WQUZahfHhQblOTRJzYEgANnmz/MlldcQW5xJfFBtXqQLpwiPjgeQ433vFajJd4ULwGSD5MASQgfERkQyfWdrgcgvzyf1adWux5wxeTqx5VVSag6Pxj/Bmg0Tb5PWl4a8/fO59CFYwAciu7O/MI0vgoyst2oBmguSeI1+QWqPVAD7qq7T4hWFhccR1ZJFjZTjLqh6CyhgQa6Rgax40R+s6+38+R5AMzOAOksmGI5XXSaTqZOdY43m8xqkrjwSRIgCeFDpvSe4nw8Z9cctcaLg3kohHWpft51JExdAZE9mnz99KJ0nt30LABv7prDvLBQ3tw1B4BFhxdh0KrfkJ1J4oVVCaiFkogq2p644DisipVcfVXPTtFZAIZ3j2Tz0bxmX++HI7l06RBIB3/AUgblBRCiDuPFB8fXOT4hOEF6kHyYBEhC+JCB0QPpEa4GPNml2aw6uap6p1YLN7ylPr7jU7h3KXQaWr3fkYhtinVuyi3NZV7qPGegtTt7t3PorlyxMD88lHJFXZbBardye6/bAXjrmrcwW6zw5TT1Ql9Og/wTHnjFQrScI2jJsFeARusMkH7dI5JT50pJb2Y9pB+O5PHr7lWL3xarSd9KcAyZxZnOsgI1OXqQZKq/b5IASQgfotFoGNN5jPP5Wz+95foNNfZKNQiKH1T3ZEcids0AqSyX+Xvnk1umBkgDYwbip/Or995+Oj/GdxnPY0mP0S+yH5zeSq5SybywUHKVyvqLVArhRY6gJbM0C4JjnDPZru7aAYNOw8p9Z5t8rQOZhZzOL3UGSJqqaxX6myi2FBNvqtuDZDaZqbRXuvb0Cp8hAZIQPibMGOZ8bMfO33b8jdzSXLU36Nhicoc+6BIEXUx2SbbLb7PJzFvXvFXvsW9d8xZJ0Uk83v9xogKjoNMwco1BzA8PJdcYpC6MK0QbEqAPIMI/gjPFZ9S/E1VDwaEBBm64siOfbD+Fza406VoLt5wgNsSfa3pULX5b1YOUoVHPTwhOqHNOgkndJsNsvkkCJCF8zPD44WioTrrecGYDaXlpdXqDGlMz3+jZTc86P8RjgtSEVn3VqIDjt2O7U0Qi2b9Vg6lfxsxk3ulv5ZuyaHPig+PVYo0h8VCY4dx+3/BE0vPL+GJn48HL0ZxilqZmcs+wzhiqaoBpCjPAL5iMSjVxu74hNscQnwRIvkkCJCF8jNlk5r4r7nPZ9uymZ9l/bj9Q3RvUmJr5RpW2Sjamb2Re6jzOl6sf+HcWqtOgHyko4LGYXxMVEOVyfnpROs/ufhOAV/fNa1ZwJkRrcdZCCusMBaed2/ubw7h9cAJ/XnGALccaTtjOLCjj8f/uIiE8gGnDu1TvKEiHsM5klpwlQB9AuDG8zrn+en+iA6PVHizhc/TeboAQovmu63QdH+3/yPm80lbJ/277X0ANlpZOWupaabsejnyjSlslfjo/4oPjeeP7N1gwegGP9byDrrkLAOiBnknJz0Oga4BUM8Cy1lziRIg2JC44jrS8NIhPVoMau925FuHMG68gs6Cc372/neTECPp2DCEmxB+NBgpKLRzPLWbTkVw6BBn5aNoQAv30WCzqpAXNhdMQ1okzRWeID45H00ApDZnJ5rukB0kIHxQfHE9iSKLzuRYtVkUNUhqsUVRLzXyjt655yzmEFu4fzuPD/ofwm6sCsFsXQkRinfNrJnTrNDqg6b1XQrSWhOAEskqysIbGg60CSnKc+4KMehZOG8Ls25MIDTCw+WgeCzYeY/6GYyzfm0lppY3fX9eDb574NT1jTC7X1VxIh7BODU7xd5BaSL5LAiQhfFBUYBR/SP6D83nvDr2dwYrLQraNcARFMUExdRK2o2KTeCzpMaJik+o9t2aA5ciJqpnLJGDu3Ll06dIFf39/hg4dyo4dO7zdpMtOXHAcNsVGjn+wuqHGMBuAXqfl5oEJvH/vYNamXMvemWPYO3MMP/7xOj55cCjTR3YnLLDWzE5FUa8jAVK7JgGSED5qaMehBOmDADhx4QRv/OYNoKpGUSPDa7Vll2TXSdiOCoyqnrHWAEeA1dzeq8vBokWLSElJYebMmezevZukpCTGjh1LTk5O4ycLt3HWQtJVDYHVCpBaws9ahMZSihJqJrM486IBUoIpgfMV5yl2VLcXPkMCJCF8lF6rJyla7d0ps5ZRUFEA1DPb7CKiAqJ4LOkxMoozXBK2mxvkONaIa07vVXs3e/ZsHnroIaZNm0bfvn1ZsGABgYGBfPjhh95u2mWlY5C6iHNGZQH4h0HBqUu+ZmClmtR9LjCUclt5vTWQHBxfVqRn1fdIkrYQPmxQzCC2ZG4BYFfWLgDOl59nXuo8but520V7fwBnL1F6UTrv7H7HmbDd3CDn6YFP8+bON1vUe9UeVVZWsmvXLl544QXnNq1Wy+jRo9m6tW5BzYqKCioqKpzPC6tmEFosFmdScH0c+y52TFvUmu3WoSPSP5L0C+nYwzpD3jFsl3Bfi8VCYKXaC3iyalusf2yDryXWX61JdqrgFN1Durf4vpdK3it1r9kYCZCE8GGjOo3i3T3vArA3by+PJT0GwPy98xlhHtFogOTgyCd64vsnWhTkRAREAM3rvWrP8vLysNlsxMS4/v+IiYnh0KFDdY5//fXXefXVV+tsX716NYGBgY3eb82aNS1vrBe1VrsDLAHsOLyDmyqCCDy6g80rV17S9XqVZ1KuD2Hlzk0ApG1J46jmaL3HKoqCESNrd66lIq2i3mNak7xXoLS0aUvMSIAkhA/rFtaNgdED2Z2zm/SidH7b9bcUWxrPdcgtzeWLX75w6WWqmbDdVI4huvpqwIime+GFF0hJSXE+LywsxGw2M2bMGEJCQho8z2KxsGbNGq6//noMBkNrNNUtWrvdm3/cTE5ZDnFxI9Fue48J48dDA9PyG2OxWDj/r/cwxF1JdI9oQg+GcvMNN1/0nE++/YSgDkFMSJ7Qonu6g7xXqjl6aBsjAZIQPu6ahGvYnaPmDG06s6lJw2OOqtvN6WWqj2OI7sC5Ay2+RnsUGRmJTqcjO9u17EF2djaxsXWXgTEajRiNxjrbDQZDk/5RaOpxbU1rtTshJIG9eXvRdbkNyi9gqDgPppb3dprKM6DbOLJKs4g3xTf6GjqFdCKzJLNN/BnJe4UmX0eStIXwcdckXON8vOnMJi+2RDj4+fkxaNAg1q1b59xmt9tZt24dw4bJmnWtLT44nuzSbCwduqkbcusOczaZzUJwRRZE9uJM8ZmLzmBzSDBJsUhfdFkESJMnTyY8PJxbb721zr4VK1bQq1cvevTowb///W8vtE6IS9M9rDtxQeo6UDuzd3K6UJ3GLEUbvSslJYX333+fjz/+mIMHD/LYY49RUlLCtGnTvN20y05ccBx2xU6WXwBoDZB7uOUXO38SrWJDiepJRlFGvYvU1uYoVmmx+1aC9OXusgiQnnzySf7zn//U2W61WklJSWH9+vXs2bOHN998k3PnznmhhUK0nEajYVic2ithsVt44Qd15pSjnlFuaS7zUufJQrKtbMqUKbz11lu88sor9O/fn9TUVFatWlUncVt4niOIySzLhug+cDa1xdfSZO0FwBrZk6ySrHoXqa3NbDJjU2xkFWe1+L6i9V0WAdKIESMwmUx1tu/YsYMrrriC+Ph4goODGT9+PKtXr/ZCC4W4NINjBzsf1y7a6Mg3amwhWUfCde1FaZviUs5tz2bMmMGpU6eoqKhg+/btDB061NtNuizFBsWiQaMuWmtOhvSWVzTXnPmJYmMs2VixKtYmDbFJLSTf5PUAadOmTdx4443ExcWh0WhYunRpnWM8Va4/MzOT+PjqN3d8fDwZGRluubYQrWlIzJA625pbz6gplbM9ca4Qnuan8yMqMKoqQBoK545AaX6LrqU9s4P8oB5kFmcCXLRIpENsUCx6jV4CJB/j9QCppKSEpKQk5s6dW+/+ppTr79+/P/369avzk5mZ2VovQwivigmKoXNIZ0AtjActW3JEiPYqIThBXRMtoerLxJmfmn+RimLIOcC5oB5qsAXO/L+L0Wv1dAzuyJliWZPNl3h9mv/48eMZP358g/trlusHWLBgAd988w0ffvghf/zjHwFITU1t0b3j4uJceowyMjJITk6u99iWVroV7Vtbqk47KHoQpwpPYcMGQAdjBywWC1arOuRmtVqd7cwszHT+7hHSwzsNvoi28P9TtC+dQzrzy/lfILwLhCTA0bXQc2zzLnJiExrFRn5wL04XnaZjUEf89f5NOtVsMksPko/xeoB0Mc0t199cycnJpKWlkZGRQWhoKN9++y0vv/xyvcdeaqVb0b61heq0ukqdy/MfN//ICf0JMq2ZLs8B57Yju49Q8nNJ6za0CZpa6VaIpkoMTeS7k9+hAJq+EyFtMYx7A7TNGEjZvwQlqjfF/h05WbiOLiFdmnxqQnACqbmpzW228KI2HSA1t1x/Q0aPHs3evXspKSkhISGBL774gmHDhqHX63n77bcZOXIkdrud559/ng4dOtR7jZZWuhXtW1uqTjukbAhfLPnC+Xz4r4fTJ6IPB/MPMm/VPOfzlcdX8u9t/0aHjn8X/5tXrn6FCV29V+G3Pk2tdCtEUyWGJlJqLSWnNIeYvpNg2zw4vQW6/LppF6gshcPfYr/6cSiCk4UnnbNHm8JsMrP8+HIURUHTwireonW16QDJXdauXdvgvokTJzJx4sRGr3GplW5F+9YW3gdxhji6hHThZOFJAKxYMRgM6PXqX3O9Xo/BYGBg3EC0ei2V1koMevW5t9teW1trj/B9jt6ek4UniUlIhqje8MPspgdIP70P1jLsV96O7cefSS9O53ehv2vy/buGdaXMWsbZkrNNKg0gvM/rSdoX09xy/UJc7pJjq3Pofsn/pd5jzCYziycuZtbwWSyeuFgSucVlId4Uj16r58SFE+qw2sgX4dg6OLi88ZPPn1KDqYH3QlhnztvPY7VbSQxNbPL9e4X3AuBQ/iVU8Ratqk0HSFKuX4jmqVkP6WD+wQaPM5vMTOo+SYIjcdkwaA10MnXiWMExdUOfidB3Enz1EOz6GKwVdU9SFDjxA/y/myAgDEb+DwC5drWmWHNykKIDowk3hnP4/CVU8RatyutDbMXFxRw9etT5/MSJE6SmphIREUGnTp1ISUlh6tSpDB48mOTkZObMmSPl+oVowMDo6rpHFwuQ6lNuLWdH1g52nN2Bv96fXhG9uCryKmKCpPKzaB96RfSqDlA0Gpj8T1jxNCx/Ar59HsI6Q3C0ur/8AhSchvICiBsAt3wAQZFgsZBpzSTMGEZ0YHST763RaNT750uA5Cu8HiDt3LmTkSNHOp87EqGnTp3KwoULmTJlCrm5ubzyyitkZWXRv39/KdcvRANigmKIDogmpyyHI+ePUGmrbPQcRVH4IO0D/vXzvyizlrns02q0TOw2ken9pxMbJMPawrf1jejLhvQN2Ow2dFodGAJg8gIY/pQ63FZwGkqrlpuK6Ap9J0JCspqnpK2eJXrWdpbekb2bnWzdO6I3a055f8araBqvB0gjRoxAUZSLHjNjxgxmzJjRSi0Swrf16dCHnDM5WBUraXlpXKi4AKiL1/bt0NflWKvdymvbX+PLX76s91p2xc7So0tZeXwlj/d/nGn9pqHVtOmReSEa1LtDb8qsZZwqOkXX0K7VO6J7qz9NlGnLZFhE89M8roy8koX7F5Jdki09sz5APumEaGf6RPRxPl5/ej3PbnoWqF681sGu2Hl+0/MuwdHEbhN5Z8Q7vDPiHab1m4bJoK5hWGmvZM7uOTy85mFySqur2AvhSxx/Nw6ea97wc03nys5RqBS6/D1rqkExgwDYmb2zxfcXrUcCJCHamZq9RFsytziH2RyL1zp8+cuXzu5+vVbPG795g9d+/RqjO49mdOfRpAxK4dtbvuWevvegQR1K2H52O7ctv41tZ7e14isSwj1CjaGYTWb25u5t8TXSzqUBtChA6hDQgW6h3fgpqwXLnIhWJwGSEO1MzcTRM8VnMOrU+l3+en/n4rXZJdnM3jXbedw7I96pt1hkqDGU54c8z7/H/JvoAPW6+eX5PLLmEf6595/Y7DZPvhQh3C45NpntZ7e3+PztWdsJ04Y1aQ22+gyOHcy2s9saTS0R3icBkhDtTM3E0TJrGW9c84ZLzSNFUZi1fRYlFnWJkZt73MwI84iLXjO5YzJfTfyK4fHDAXV47r3U93hg9QPOVc2F8AVXd7ya4xeOk12S3fjB9dietZ3u+u4troY9uvNoMooz2Je3r0Xni9YjAZIQ7dypwlMuNY++T/+eDekbAOjg34GUQSkNn1xDmH8Y80bNY0b/Gc4ht13Zu7hl2S0sOrRIepOET0juqBZT3ZK5pdnnZpVkcaLwBN303Vp8/yExQ4gKiOKb49+0+BqidUiAJEQ7t+zoMmd3vs1u4++7/+7c98LQFwg1hjb5WlqNlkeSHuGjcR85hxiKLcXM2j6Le769h3258q1YtG0R/hEMiR3CiuMrmn3uiuMr8Nf508PQo8X312l1TOw2kaVHl5Jfnt/i6wjPkwBJiHaqd7g6bfnYhWPOVcSXH1/O8QvHARgQPYAxnce06NqDYgbx5cQvmdRtknPbvrx9/G7l73h247OcLjx9aY0XwoMmd5/MjqwdpBemN35wFbtiZ/GRxYzuNBp/jf8l3f++K+5Dq9G6fFkRbY8ESEK0M1EBUTyW9Bg39bjJue3LX76kwlbBvNR5zm1PDnzyklYVN/mZmPXrWXw49kOXmjLfnfyOSUsn8e2Jb1t8bSE8aXTn0UT4RzBv77zGD66y/Nhy0ovSuaX7LZd8/zD/MJ4Z/AyLjyzm77v/jsVmueRrCvfzeqFIIYR7RQVG8Xj/xym3ljM3dS5FlUV8d/I7AM6WnAXgN/G/cdZkuVRDYofw5cQv+eqXr5i/dz755fnotXq3XV8IdwvQB/DUwKd4ZcsrjDSPZEyXi/eknio8xds732Z8l/EkRSWRQcYlt+HWnrdSUFHAe3veY/GRxVwZeSVxwXEE6APQaXTotXq0Gq0z3+9S2e12DpcfJjMtE63Wd/pGmtvu6MBoJveY7JZ7S4AkRDvlr/fnxq438umhT6mwVbDs2DLnvicHPunWexm0Bu7ofQc3druRhfsXYtAamrVOlRCtbVL3SWzN3MofNv2Bvbl7mZA4gQRTAiF+IWg0GkotpWSVZrE1cyv/+vlfhPmH8YfkP7i1DQ9e+SAjzSNZdmwZRwuO8lPWT1TYKrDZbVgVq9snPlRUVJD6S6pbr9kamtPuPh36SIAkhGjcLT1v4dNDnzqf++v8eW7Ic/SK6OWR+wUZgpjef7pHri2EO2k1Wv73N/9L17CuLNy/kP8c+A8AGjRoNBrsih0AnUbH2C5jeW7Ic3QI6IDF4t7hsG5h3Xh60NNuvWZ9LBYLK1euZMKECRgMBo/fz1282W4JkIRox3qG92RC4gRWnljJdebreD75eeKD473dLCHaBL1Wz6NJj3J/v/s5lH+I7NJsCioKAHUYLiYwhp7hPZs101O0HxIgCdHOvXHNG8wcNpNAQ6C3myJEm+Sn8+OqqKu83QzRxvhOppYQosUkOBJCiOaRAEkIIYQQohYJkIQQQgghapEASQghhBCiFgmQhBBCCCFqkQBJCCGEEKIWCZCEEEIIIWqRAEkIIYQQohYJkIQQQgghapEASQghhBCiFgmQhBBCCCFqkQBJCCGEEKIWCZCEEEIIIWqRAEkIIYQQoha9txvgqxRFAaCwsNDLLRHeZLFYKC0tpbCwEIPB4O3mtBuOv1eOv2eXm6Z+vvjq+89X2w2+23Zpd7Wmfr5IgNRCRUVFAJjNZi+3RIj2q6ioiNDQUG83o9XJ54sQntfY54tGuVy/ol0iu91OZmYmJpMJjUbj7eY4DRkyhJ9++qnNXrsl12jqOU05rrFjLra/vn2FhYWYzWbS09MJCQlptI3e4IvvCUVRKCoqIi4uDq328ssEaOrniy+8/+rjq+0G3227tLtaUz9fpAephbRaLQkJCd5uRh06nc5jb353XLsl12jqOU05rrFjLrb/YvtCQkLa7IeOr74nLseeI4fmfr605fffxfhqu8F32y7tVjXl8+Xy+2rWzk2fPr1NX7sl12jqOU05rrFjLrbfk/9vPak9vieEEMLTZIhNiEtQWFhIaGgoFy5c8MlvZcK3+er7z1fbDb7bdml380kPkhCXwGg0MnPmTIxGo7ebIi5Dvvr+89V2g++2XdrdfNKDJIQQQghRi/QgCSGEEELUIgGSEEIIIUQtEiAJIYQQQtQiAZIQQviouXPn0qVLF/z9/Rk6dCg7duzwdpNc/OlPf0Kj0bj89O7d27m/vLyc6dOn06FDB4KDg7nlllvIzs5u9XZu2rSJG2+8kbi4ODQaDUuXLnXZrygKr7zyCh07diQgIIDRo0dz5MgRl2Py8/O56667CAkJISwsjAceeIDi4mKvtvu+++6r8/9/3LhxXm/366+/zpAhQzCZTERHR3PTTTdx+PBhl2Oa8t44ffo0N9xwA4GBgURHR/Pcc89htVrd1k4JkIRoJZMnTyY8PJxbb73V200R7cCiRYtISUlh5syZ7N69m6SkJMaOHUtOTo63m+biiiuu4OzZs86fzZs3O/c9/fTTLF++nC+++IKNGzeSmZnJzTff3OptLCkpISkpiblz59a7/29/+xv/+Mc/WLBgAdu3bycoKIixY8dSXl7uPOauu+5i//79rFmzhhUrVrBp0yYefvhhr7YbYNy4cS7//z/77DOX/d5o98aNG5k+fTrbtm1jzZo1WCwWxowZQ0lJifOYxt4bNpuNG264gcrKSrZs2cLHH3/MwoULeeWVV9zXUEUI0Sq+//57ZdmyZcott9zi7aaIdiA5OVmZPn2687nNZlPi4uKU119/3YutcjVz5kwlKSmp3n0FBQWKwWBQvvjiC+e2gwcPKoCydevWVmphXYCyZMkS53O73a7ExsYqb775pnNbQUGBYjQalc8++0xRFEU5cOCAAig//fST85hvv/1W0Wg0SkZGhlfarSiKMnXqVGXSpEkNntMW2q0oipKTk6MAysaNGxVFadp7Y+XKlYpWq1WysrKcx8yfP18JCQlRKioq3NIu6UESopWMGDECk8nk7WaIdqCyspJdu3YxevRo5zatVsvo0aPZunWrF1tW15EjR4iLi6Nr167cddddnD59GoBdu3ZhsVhcXkPv3r3p1KlTm3oNJ06cICsry6WdoaGhDB061NnOrVu3EhYWxuDBg53HjB49Gq1Wy/bt21u9zTVt2LCB6OhoevXqxWOPPca5c+ec+9pKuy9cuABAREQE0LT3xtatW7nyyiuJiYlxHjN27FgKCwvZv3+/W9olAZIQND6WD20/30NcPvLy8rDZbC7/OADExMSQlZXlpVbVNXToUBYuXMiqVauYP38+J06c4De/+Q1FRUVkZWXh5+dHWFiYyzlt7TU42nKx/9dZWVlER0e77Nfr9URERHj1tYwbN47//Oc/rFu3jjfeeIONGzcyfvx4bDYb0Dbabbfbeeqppxg+fDj9+vVztqux90ZWVla9fyaOfe4gi9UKQfVY/v33319vDoQj32PBggUMHTqUOXPmMHbsWA4fPuz8gOnfv3+9CYKrV68mLi7O469BiLZm/PjxzsdXXXUVQ4cOpXPnzvzf//0fAQEBXmzZ5eGOO+5wPr7yyiu56qqr6NatGxs2bGDUqFFebFm16dOnk5aW5pKb1lZIgCQE6gd5zQ/z2mbPns1DDz3EtGnTAFiwYAHffPMNH374IX/84x8BSE1NbY2mCkFkZCQ6na7OrJ7s7GxiY2O91KrGhYWF0bNnT44ePcr1119PZWUlBQUFLj0Fbe01ONqSnZ1Nx44dnduzs7Pp37+/85jayfFWq5X8/Pw29Vq6du1KZGQkR48eZdSoUV5v94wZM5yJ4QkJCc7tsbGxjb43YmNj6/TiO/4+uKvtMsQmRCN8Kd9DXB78/PwYNGgQ69atc26z2+2sW7eOYcOGebFlF1dcXMyxY8fo2LEjgwYNwmAwuLyGw4cPc/r06Tb1GhITE4mNjXVpZ2FhIdu3b3e2c9iwYRQUFLBr1y7nMevXr8dutzN06NBWb3NDzpw5w7lz55yBnrfarSgKM2bMYMmSJaxfv57ExESX/U15bwwbNox9+/a5BHhr1qwhJCSEvn37uq2hQogaqDUbJCMjQwGULVu2uBz33HPPKcnJyU2+7qhRo5TIyEglICBAiY+Pr3M9IZrj888/V4xGo7Jw4ULlwIEDysMPP6yEhYW5zOrxtmeeeUbZsGGDcuLECeXHH39URo8erURGRio5OTmKoijKo48+qnTq1ElZv369snPnTmXYsGHKsGHDWr2dRUVFyp49e5Q9e/YogDJ79mxlz549yqlTpxRFUZS//vWvSlhYmPL1118rP//8szJp0iQlMTFRKSsrc15j3LhxyoABA5Tt27crmzdvVnr06KHceeedXmt3UVGR8uyzzypbt25VTpw4oaxdu1YZOHCg0qNHD6W8vNyr7X7ssceU0NBQZcOGDcrZs2edP6Wlpc5jGntvWK1WpV+/fsqYMWOU1NRUZdWqVUpUVJTywgsvuK2dEiAJUYunAiQh3O3dd99VOnXqpPj5+SnJycnKtm3bvN0kF1OmTFE6duyo+Pn5KfHx8cqUKVOUo0ePOveXlZUpjz/+uBIeHq4EBgYqkydPVs6ePdvq7fz+++8VoM7P1KlTFUVRp/q//PLLSkxMjGI0GpVRo0Yphw8fdrnGuXPnlDvvvFMJDg5WQkJClGnTpilFRUVea3dpaakyZswYJSoqSjEYDErnzp2Vhx56qE4A7Y1219dmQPnoo4+cxzTlvXHy5Ell/PjxSkBAgBIZGak888wzisVicVs7NVWNFUJU0Wg0LFmyhJtuuglQh9gCAwP58ssvndsApk6dSkFBAV9//bV3GiqEEMJjJAdJiEb4ar6HEEKIlpNZbEKgJo8ePXrU+fzEiROkpqYSERFBp06dSElJYerUqQwePJjk5GTmzJlDSUmJc1abEEKI9kWG2IRArTY7cuTIOtunTp3KwoULAXjvvfd48803ycrKon///vzjH/9oUzNUhBBCuI8ESEIIIYQQtUgOkhBCCCFELRIgCSGEEELUIgGSEEIIIUQtEiAJIYQQQtQiAZIQQgghRC0SIAkhhBBC1CIBkhBCCOEBK1asIDExkeTkZI4cOeLt5ohmkjpIQgghhAf06tWLuXPnsn//frZu3crnn3/u7SaJZpAeJCGEEKIFzp07R3R0NCdPnqx3f4cOHejevTtdunTBz8/Puf2OO+7g7bffbqVWipaSHiQhhBCihpUrV3LDDTc0uP/2229n0aJFpKSkUFRUxPvvv1/vce+//z6PPvooMTExpKWlERERAUBaWhrXXHMNJ06cIDQ01COvQVw66UESl4VLzQWYPHky4eHh3HrrrR5onRCiLRk5ciRnz551+Tlz5gzXX389HTp04MUXX6S0tJQPPviABx54oN5rWK1W/v73v/P8889TXFxMeHi4c1+/fv3o1q0bn3zySWu9JNECEiCJy8IzzzzD+++/z1133cXLL7/c7POffPJJ/vOf/3igZUKItiYgIIDY2FjnT1RUFM888wy7d+9m3bp1JCUlsXLlSoxGI1dffXW911iwYAFdu3Zl+vTpFBUVcfz4cZf9N954o+QktXESIIl242L5AA3lAjTViBEjMJlM9e6TfAIh2i+bzcbdd9/N2rVrncERwA8//MCgQYPqPSc/P5+//OUvvPHGGyQkJBAaGkpqaqrLMcnJyezYsYOKigpPvwTRQhIgiTYlNTWVO+64g9jYWPz8/OjWrRt//vOfsVqtjZ772muvMWnSJLp06VJn37Rp0+jWrRuPPfYYc+bMcWubX3rpJV577TUuXLjg1usKIbzLERytXr2atWvXOoMjgFOnThEXF1fveTNnzmTy5Mn06dMHgL59+7J3716XY+Li4qisrCQrK8tzL0BcEgmQRJvx4YcfkpycTExMDCtWrODgwYO8/PLLzJkzp8FxfoeL5QNcLBfAoX///vTr16/OT2ZmZqPtlnwCIdofm83GPffcw+rVq1m3bh39+/d32V9WVoa/v3+d8w4cOMAnn3zCn/70J+e2fv361elBCggIANTPLtE26b3dACEANmzYwEMPPcRHH33Evffe69zerVs3LBYLDz/8MC+//DLdu3ev9/yL5QPUzAX461//yvHjx+nWrZvLMbU/vJrLkU8wffr0S7qOEML7HMHRd999x9q1a+sERwCRkZGcP3++zvann36agoICEhISnNvsdjtms9nluPz8fACioqLc23jhNtKDJNqEJ598kvHjx7sERw7XXnstQJ0u6poaygdoSi6AO0g+gRDtg81m495773UGRwMGDKj3uAEDBnDgwAGXbStWrGDXrl3s2bOH1NRU588HH3zA6dOnXQKqtLQ0EhISiIyM9OjrES0nAZLwuj179vDzzz832PtSVlYGgF7fcIdnQ/kATckFaIrRo0dz2223sXLlShISEti6davLfsknEML32e127r33XpYuXconn3xCx44dycrKcvmx2WwAjB07lv379zuDHovFwjPPPMNzzz1XZ8h+1KhRgOuXvB9++IExY8a0/osUTSZDbMLrHD069XVjA+zevRuAq666qsFr1JcP4MgFOHjwoHNbfbkATbF27dqL7pd8AiF8308//cSnn34KwIQJE+rs12g0FBQUEBISwpVXXsnAgQP5v//7Px555BHeffddCgoKmDFjRp3zzGYzgYGBpKamMmLECMrLy1m6dCmrVq3y+GsSLScBkvC6yspKgHoTHgHmzZvHNddcQ2JiYoPXqC8foKm5AO4g+QRC+L6hQ4fSnMUlXnnlFZ577jkeeughUlJSSElJqfc4jUZDSUmJ8/lHH31EcnJygzWURNsgAZLwOsfU2Y0bN3LTTTe57Hvrrbc4ePAgmzdvBtR8JMd0+n379rF9+3YGDx7MgAEDXGaR1cwFqDk099NPP3H//fdz/vz5emeztZTkEwhx+bnhhhs4cuQIGRkZzfriZTAYePfddz3YMuEOshabaBPGjRvHvn37mDNnDoMHDyY7O5t///vffP755yxZsoTrr7/e5fiZM2dSUFDA3//+d0ANlgYOHEhOTg7BwcH069eP+++/nz/84Q8u550+fZrOnTvz/fffM2LECLe1/7777kOn0/HBBx+47ZpCCCG8R3qQRJuwePFiXn31VZ577jnOnDmDzWZj3Lhx/PLLL3WSr+fMmcPJkydZuHChc1vNfICSkpIm5wK4g+QTCCFE+yM9SKJNevDBB/n+++/ZtWsXYWFhzu0LFy5k2bJlfPHFF+h0OpdzvvnmG5577jnS0tLQaltvgub8+fNZsmQJq1evbrV7CiGE8CyZ5i/apLlz53L//fezZ88e57YlS5bw+eef89lnn9UJjkDNB3j44YfJyMhozaZKPoEQQrRD0oMkfEZ4eDhRUVEEBgYCMGvWLH772996uVVCCCHaIwmQhBBCCCFqkSE2IYQQQohaJEASQgghhKhFAiQhhBBCiFokQBJCCCGEqEUCJCGEEEKIWiRAEkIIIYSoRQIkIYQQQohaJEASQgghhKhFAiQhhBBCiFokQBJCCCGEqEUCJCGEEEKIWiRAEkIIIYSo5f8DX2W04AcnE5EAAAAASUVORK5CYII=", diff --git a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb index 13bd5e3d..a3b603ee 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -262,20 +262,9 @@ "\n", "\n", "Running Differential Evolution\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Final chi squared is 8.39155\n", - "Elapsed time is 103.555 seconds\n", + "Elapsed time is 119.992 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb index f94fe68c..15fb93b4 100644 --- a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -74,26 +74,26 @@ "outputs": [], "source": [ "parameter_list = [\n", - " Parameter(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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=\"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", + " 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=\"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\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0),\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", " 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\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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 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", " 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=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\n", + " Parameter(name=\"CW Hydration\", min=99.9, value=100.0, max=100.0, 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", "\n", "problem.parameters.extend(parameter_list)\n", @@ -303,7 +303,7 @@ "| 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", + "| 11 | SAM Heads Hydration | 10.0 | 45.45 | 50.0 | True | gaussian | 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", @@ -455,7 +455,8 @@ "| procedure | calculate |\n", "| parallel | single |\n", "| calcSldDuringFit | False |\n", - "| resampleParams | [0.9, 50] |\n", + "| resampleMinAngle | 0.9 |\n", + "| resampleNPoints | 50 |\n", "| display | iter |\n", "+------------------+-----------+\n" ] @@ -485,32 +486,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is \n", + "\n", + "Elapsed time is 0.056 seconds\n", + "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", From 842c6b751279d33e618175036ecf41268ce4cb0c Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Fri, 1 Nov 2024 15:19:01 +0000 Subject: [PATCH 5/7] More changes to notebooks --- RATapi/examples/absorption/absorption.ipynb | 8 +- .../convert_rascal.ipynb | 26 +- .../domains_standard_layers-checkpoint.ipynb | 26 +- .../examples/domains/domains_custom_XY.ipynb | 6 +- .../domains/domains_custom_layers.ipynb | 13 +- .../domains/domains_standard_layers.ipynb | 2 + .../DSPC_custom_layers-checkpoint.ipynb | 239 +++--------------- .../DSPC_custom_xy-checkpoint.ipynb | 17 +- .../DSPC_standard_layers-checkpoint.ipynb | 76 +++--- .../non_polarised/DSPC_custom_layers.ipynb | 16 +- .../non_polarised/DSPC_custom_xy.ipynb | 6 +- .../non_polarised/DSPC_standard_layers.ipynb | 70 ++--- cpp/RAT | 2 +- 13 files changed, 141 insertions(+), 366 deletions(-) diff --git a/RATapi/examples/absorption/absorption.ipynb b/RATapi/examples/absorption/absorption.ipynb index 16f255fb..7b5ff978 100644 --- a/RATapi/examples/absorption/absorption.ipynb +++ b/RATapi/examples/absorption/absorption.ipynb @@ -60,14 +60,18 @@ " 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", @@ -781,7 +785,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.079 seconds\n", + "Elapsed time is 0.077 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -789,7 +793,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb index ae368a15..9c2f39f4 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb +++ b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb @@ -1,27 +1,13 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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)" ] }, @@ -48,7 +34,7 @@ "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:" ] }, { @@ -102,7 +88,7 @@ "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." ] diff --git a/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb index 6fa72d5e..8da0409e 100644 --- a/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb +++ b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb @@ -59,10 +59,12 @@ " 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", @@ -293,33 +295,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.011 seconds\n", + "\n", + "Elapsed time is 0.004 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", diff --git a/RATapi/examples/domains/domains_custom_XY.ipynb b/RATapi/examples/domains/domains_custom_XY.ipynb index 81292e67..45101425 100644 --- a/RATapi/examples/domains/domains_custom_XY.ipynb +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -72,7 +72,7 @@ "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 = 1), or the domain (domain = 2)." + "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)." ] }, { @@ -456,7 +456,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.087 seconds\n", + "Elapsed time is 0.058 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -464,7 +464,7 @@ }, { "data": { - "image/png": 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", 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Lk5OTYVpncddtySHO3IkG4N9pHbAT3WMZJCcnEx4eTsWKFc1u5kRByO75LGl/W3mR3XMgXouFT/04nGvJjugki2LZ/ZgfJfH1ltP3GLHFhpmp7u5g+P3KgzgTRiIIglC0KDRJaFGgVIhZteZEJEJmppqHSIQEQRAykWUUWjUyEhYiETIrIhEyM9WfS4QuRYpESBAEAQCtmmT0U/QtleKj0ZyI/20z4/tcInRZJEKCIAh6mhTCZf2eZCpL8dFoTsT/tpkpba+itL3+W49IhAQhf+7du8f777+Pq6srNjY21KlThxMnTpg6LCEPZE0K2rSPRJVF/rcTEYoPkQiZofRxQk8S1DyOL/w9tAShJHj69CktW7bE0tKSHTt2cOHCBebNm0epUsbbDFIoPFrNs/dCS6UYI2ROxNxpM+Tr7sCha08AfatQ6Sqm2UJCEIqz2bNnU758eVavXm0oM/ZCeULh0WlSDb9biDFCZkX8b5shMWBaEPJv27ZtNGrUiB49euDm5kb9+vVZuXKlqcMS8kin1QBgoVBgbSE+Gs2J+N82Q9U8ni0sdTky1oSRCELxdePGDZYtW0bVqlXZtWsXQ4YMYeTIkaxdu/aFx6SkpBi2knh+SwnB9GSdFgAnW8ssN1wVSi6RCJkhX/dnO0lffhBvwkgEofjS6XQ0aNCAGTNmUL9+fT766CMCAwNZvnz5C4+ZOXMmTk5Ohh+xZ1sRIcvIabu7F/bwoP79+xs2fxVMQyRCZsjWyoIKLrYAXH0Qh04ndlkpCR49esSQIUOoUKECKpUKDw8POnTowKFDhwDw8fFBkiSCgoIyHVurVi0kSWLNmjWAftfvjh07Zqizc+dOJEli6tSpGcqnTp1KhQoVCuQxFWWenp7UrFkzQ1mNGjW4ffv2C4+ZMGECMTExhp87d+4UdJiFrn///kiShCRJWFpa4u7uTrt27Vi1ahW6tGQDICoqihEjRlCtWjVsbGyoUKECI0eOJCYmJtM5165dS+PGjbG1tcXBwQF/f3+2b9/+0ljSX/OSJGFjY4OPjw89e/Zk7969GSvKOnToMyBFIS+muHDhQsPfXV6tXLmSVq1aUapUKUqVKkXbtm05fvy4cQI0AyIRMlPpM8cS1VruPE00cTSCMXTv3p3Tp0+zdu1arly5wrZt22jTpg1Pnjwx1Pnv4F6Ao0ePEhkZiZ2dnaEsICCAQ4cOodFoDGXBwcGUL18+0yaOwcHBGTawNBctW7bk8uXLGcquXLmCt7f3C49RqVQ4Ojpm+CmJOnbsyP3797l58yY7duwgICCAUaNG0aVLF8NrKiIigoiICObOncv58+dZs2YNO3fu5MMPP8xwrrFjxzJ48GDeffddzp49y/Hjx3nllVfo1q0bixcvfmksX331Fffv3+fy5cusW7cOZ2dn2rZty/Tp059V0qYaps4rC7lbzMnJCWdn53ydIyQkhN69exMcHMyRI0coX7487du35969e8YJsqSThWzFxMTIgBwTE2PqUIxq7q5Lsvfn22Xvz7fLO8/fN3U4RUZSUpJ84cIFOSkpydSh5MrTp09lQA4JCXlhHW9vb3n8+PGySqWSb9++bSgPDAyUR4wYITs5OcmrV6+WZVmWL1++LAPykSNHDPWaNGkiL1myRLa2tjY8P0lJSbJKpTIc91/ZPZ/F/W/r+PHjsoWFhTx9+nT56tWr8oYNG2RbW1t5/fr1OT5Hds9BcX0t9uvXT+7WrVum8j179siAvHLlyhce+9tvv8lWVlZyamqqLMuyfOTIERmQFy1alKnumDFjZEtLywyv5f/y9vaW58+fn6l88uTJskKhkC9duqQvSI6Vt21aL9fyayBbWVnJHh4e8ueff26IQ5Zl2d/fXx4+fLg8atQo2dnZWXZzc5N/+OEHOT4+Xu7fv79sb28vV65cWf7rr78Mx2g0GnngwIGyj4+PbG1tLfv6+soLFizI9vny9/eXR4wYIY8bN04uVaqU7O7uLk+ZMuWFjzErGo1GdnBwkNeuXZvjY4rr6y07OX2PES1CZqqaWGG6RLG3t8fe3p6tW7eSkvLitaHc3d3p0KGDYUBvYmIiGzduZODAgRnq+fr64uXlRXBwMABxcXGcOnWKHj164OPjw5EjRwA4fPgwKSkpZtki1LhxY7Zs2cKvv/5K7dq1+frrr1mwYAF9+vQxdWhF0quvvoqfnx+bN29+YZ30XcItLPQru/z666/Y29szePDgTHU//fRTUlNT+eOPP3Idy6hRo5Blmf/9738A3Ltzm159A6ntV59Dx06wbNkyfvrpJ7755psMx61du5bSpUtz/PhxRowYwZAhQ+jRowctWrTg1KlTtG/fng8++IDERH0ru06no1y5cmzatIkLFy4wefJkvvjiC3777bds41u7di12dnYcO3aMOXPm8NVXX/HPP//k+PElJiaSmpqKi4tLLp8Z8yTWETJT1Z7bhf6y2Hw1eyv8If5h4V/X3g0G78tRVQsLC9asWWMYrNugQQP8/f3p1asXdevWzVB34MCBfPrpp0ycOJHff/+dypUrU69evUznDAgIICQkhAkTJnDgwAF8fX0pU6YMrVu3JiQkxHB/xYoVs+0OKsm6dOlCly5dCvei6kR4fKVwr1naF6xs832a6tWrc/bs2Szve/z4MV9//TUfffSRoezKlStUrlwZKyurTPW9vLxwdHTkypXcPxcuLi64ublx8+ZNAJau+BEvL08mfPMtVdwcaFSvDhEREXz++edMnjwZhULfZuDn58ekSZMA/XivWbNmUbp0aQIDAwGYPHkyy5Yt4+zZszRr1gxLS0umTZtmuG7FihU5cuQIv/32Gz179nxhfHXr1mXKlCkAVK1alcWLF7Nnzx7atWuXo8f3+eef4+XlRdu2bXP93JgjkQiZKZ/SdlgpFai1OtEi9DLxDyEuwtRRvFT37t3p3LkzBw4c4OjRo+zYsYM5c+bw448/0r9/f0O9zp07M3jwYPbv38+qVasytQala9OmDaNHjyY1NZWQkBDatGkDgL+/PytWrAAwJERCIXp8BX7wL9xrfrQPvOrl+zSyLGc5NT02NpbOnTtTs2bNTIPxZblgJnM8H8uFS5dp0KAhkiSRPla6ZcuWxMfHc/fuXcNkgOe/VCiVSlxdXalTp46hzN3dHYCHD599cVqyZAmrVq3i9u3bJCUloVars/zi8bz/fnnx9PTMcM7szJo1i6CgIEJCQrC2ts7RMeZOJEJmylKpoLKbPRfvxxL+OIEUjVbsr/Mi9m7F5rrW1ta0a9eOdu3a8eWXXzJo0CCmTJmSIRGysLDggw8+YMqUKRw7dowtW7Zkea6AgAASEhIIDQ0lODiYcePGAfpEaODAgURFRXHs2LEsuy2EAlTaV5+YFPY1jeDixYuZVt+Oi4ujY8eOODg4sGXLFiwtLQ33+fr6cvDgQdRqdaZWoYiICGJjY/H1zX1sT5484dGjR8/FIiOnzRrLbg2h52NLr/t8Wfqx6bPjgoKCGDt2LPPmzaN58+Y4ODjw7bffcuzYsWzjy+o6z8+4e5G5c+cya9Ysdu/enSmZEl5MJEJmrLqHAxfvx6LVyVx7GE8tLydTh1Q05bB7qiiqWbMmW7duzVQ+cOBA5s6dy7vvvvvCvbEqV65M+fLl2bZtG2FhYfj761shypYtS9myZZk3bx5qtVq0CBU2K1ujtM4Utr1793Lu3Dk++eQTQ1lsbCwdOnRApVKxbdu2TC0YvXr1YtGiRaxYsYIRI0ZkuG/u3LlYWlrSvXv3XMeycOFCFAqFYf2e6lUr8du2XciybGgROnToEA4ODpQrVy7X50936NAhWrRowdChQw1l169fz/P5sjNnzhymT5/Orl27aNSoUYFco6QSiZAZ831unNC/EbEiESrGnjx5Qo8ePRg4cCB169bFwcGBEydOMGfOHLp165apfo0aNXj8+DG2ttmP+wgICGDp0qVUqVLF0OwP+lah77//3jCoWhCel5KSQmRkJFqtlgcPHrBz505mzpxJly5d6Nu3L6BPgtq3b09iYiLr16/PsNJ2mTJlUCqVNG/enFGjRjFu3DjUajVvvvkmqamprF+/noULF7JgwYKXLkoZFxdHZGQkqamphIeHs379en788UdmzpxJlSpVAPi4fy8WLl/NzC8/Y+K4T7hx7SpTpkxhzJgxhvFBeVG1alXWrVvHrl27qFixIj///DOhoaFG35Nu9uzZTJ48mV9++QUfHx8iIyOBZ5MohOyJRMiMNfJ51hKw9+JDejYSq9wWV/b29jRt2pT58+dz/fp1UlNTKV++PIGBgXzxxRdZHuPq6vrS8wYEBLBu3TrD+KB0/v7+rF69mvfee88Y4QslzM6dO/H09MTCwoJSpUrh5+fHokWL6NevnyGxOHXqlKGLKD0hSRceHo6Pjw8ACxYsoG7duixdupRJkyahVCpp0KABW7dupWvXri+NZfLkyUyePBkrKys8PDxo1qwZe/bsydCS6e7mzuK1m/hu+mQaNaiPi4sLH374oWFgdF4NHjyY06dP8+677yJJEr1792bo0KHs2LEjX+f9r2XLlqFWq3nnnXcylE+ZMiXTmCshM0kuqJFoJURsbCxOTk6GaZ0liVYn02T6bp4kqLGxVHJ6cjusLc17nFBycjLh4eFUrFhRDDQ0guyez5L8t5VT2T0H4rVYeGIirnFLVxqA2mWdUJjhXmMl8fWW0/cYs1hH6K233qJUqVKZsmVzp1RItK2h7+5IStWy/8ojE0ckCIJgAs+1B5hfCiSYRSI0atQo1q1bZ+owiqQOtZ+N+9j17wMTRiIIgmACsgzoZ2Qp0vYlE8yLWSRCbdq0wcHB4eUVzVCLyqWxs9J3h+259ACN9uVTNAVBEEoMWfvc1HkTxyKYhMkTof3799O1a1e8vLyQJCnLqb5LlizBx8cHa2trmjZtKnbVNSJrSyVtquvXq4lOTOV4eJSJIxIEQShEuucSIdExZpZMngglJCTg5+fHkiVLsrx/48aNjBkzhilTpnDq1Cn8/Pzo0KFDhlU269WrR+3atTP9REQU/dWAi4IOtTwMv+/6N9KEkQiCIBQyWUv6CCGFyIPMksmnz3fq1IlOnTq98P7vvvuOwMBABgwYAMDy5cv5888/WbVqFePHjwcgLCzMaPGkpKRk2LQyfV2LkiygWhnDdht/X3jA1DdqiX5yQRDMg05naBEyx9liQhFoEcqOWq3m5MmTGTaOUygUtG3b1rD7tbHNnDkTJycnw8/LFusqCRysLWlRRb+mzP2YZM7ejTFxRIIgCIVEp0VD2rIhIg8yS0U6EXr8+DFarTbDirag39gufeXMnGjbti09evTgr7/+oly5ctkmURMmTCAmJsbwc+fOnTzHX5w83z22U3SPCYJgLmQtqbK+c8TFLvMu90LJZ/KuscKwe/fuHNdVqVSoVKoCjKZoalvDnYnSOXQy/O/0Pca2r4ZSdJgLglDSPTdY2snG8iWVhZKoSLcIlS5dGqVSyYMHGde3efDgAR4eHi84SsiLMg4q/H3LABARk8zBa49NHJEgCEIhkLXoDLPGTG/q1KnUq1fP1GGYlSKdCFlZWdGwYUP27NljKNPpdOzZs4fmzZubMLKS6d3Gz8ZD/RZqHl2CJUn//v0Nu2k/LyQkBEmSiI6OJiQkhG7duuHp6YmdnR316tVjw4YNmY6Jiopi9OjReHt7Y2VlhZeXFwMHDuT27duF8EiE4uzRo0cMGTKEChUqoFKp8PDwoEOHDhw6dMhQx8fHB0mSCAoKynR8rVr6yRpr1qwB9DvQd+zYMUOdnTt3IklSpn20pk6dSoUKFV4YW5s2bZDSFk1UqVSULVuWrj0+4K+0vb+KwljpsWPHZvjMy4v79+/z3nvv4evri0KhYPTo0cYJroQyeSIUHx9PWFiYYeZXeHg4YWFhhjfcMWPGsHLlStauXcvFixcZMmQICQkJhllkgvG8Wt0d17Q+8r8vRBKVoDZxRIKxHT58mLp16/LHH39w9uxZBgwYQN++fdm+fbuhTlRUFM2aNWP37t0sX76ca9euERQUxLVr12jcuDE3btww4SMQirru3btz+vRp1q5dy5UrV9i2bRtt2rThyZMnGeqVL1+e1atXZyg7evQokZGR2NnZGcoCAgI4dOgQGo3GUBYcHEz58uUJCQnJcHxwcHCGzVSzEhgYyP3797l+/Tp//PEHNapVYdSwoXz1+WiKQpuQvb19jjZEzk5KSgplypRh0qRJ+Pn5GSmyEkw2seDgYBnI9NOvXz9Dne+//16uUKGCbGVlJTdp0kQ+evRoocUXExMjA3JMTEyhXdOUvtn+r+z9+XbZ+/Pt8k8Hbpg6nEKXlJQkX7hwQU5KSjJ1KLnWr18/uVu3bpnK0//Gnj59muVxr7/+ujxgwADD7Y8//li2s7OT79+/n6FeYmKiXLZsWbljx445jim759Pc/raykt1zUBxfi0+fPpUBOSQkJNt63t7e8vjx42WVSiXfvn3bUB4YGCiPGDFCdnJyklevXi3LsixfvnxZBuQjR44Y6jVp0kResmSJbG1tbXh+kpKSZJVKZTguK/7+/vKoUaMylGke35Bnzp0vA/KuXX8bys+ePSsHBATI1tbWsouLixwYGCjHxcUZ7k//e5s+fbrs5uYmOzk5ydOmTZNTU1PlsWPHyqVKlZLLli0rr1q1KsP1PvvsM7lq1aqyjY2NXLFiRXnSpEmyWq023D9lyhTZz88v03W+/fZb2cPDQ3ZxcZGHDh2a4ZjsZPWYs1IcX28vk9P3GJMPlm7Tpg3ycxveZWX48OEMHz68kCIyb+82Ls/KA+EA/HbiDgNa+pj9mkLvbn+Xx0mFP2aqtE1pNnbZWODXiYmJoUaNGoC+6zkoKIg+ffpkGodnY2PD0KFDmTRpElFRUbi4uBR4bEJmSZokwmPCC/WaFZ0qYmNh89J69vb22Nvbs3XrVpo1a5btxBN3d3c6dOjA2rVrmTRpEomJiWzcuJF9+/Zl2BvS19cXLy8vgoODadasGXFxcZw6dYrt27fz/fffc+TIEQICAjh8+DApKSkvbRHKRNbxVo9ezPx6Glu2bKZ9+3YkJCTQoUMHmjdvTmhoKA8fPmTQoEEMHz7c0GUHsHfvXsqVK8f+/fs5dOgQH374IYcPH6Z169YcO3aMjRs3MnjwYNq1a0e5cuUAcHBwYM2aNXh5eXHu3DkCAwNxcHDgs88+e2GIwcHBeHp6EhwczLVr13j33XepV68egYGBuXusQpZMnggJRUsVNwcaVHDm1O1oLkXGcfZuDH7lnU0dlkk9TnrMw8SHL69YBGzfvh17e/sMZVqt9oX1f/vtN0JDQ1mxYgWgH98RHR1tSIz+q0aNGsiyzLVr12jSpInxAhdyLDwmnHe3v1uo19zYZSM1XWu+tJ6FhQVr1qwhMDCQ5cuX06BBA/z9/enVqxd169bNVH/gwIF8+umnTJw4kd9//53KlStnOVA4ICCAkJAQJkyYwIEDB/D19aVMmTK0bt2akJAQw/0VK1bE29s7dw9O1iIpFHhXqsKtW7cA+OWXX0hOTmbdunWGbrrFixfTtWtXZs+ebVjSxcXFhUWLFqFQKKhWrRpz5swhMTGRL774AtAvxzJr1iwOHjxIr169AJg0aZLh0j4+PowdO5agoKBsE6FSpUqxePFilEol1atXp3PnzuzZs0ckQkYiEiEhk3cbl+fU7WgAgkJvm30iVNqmdLG5bkBAAMuWLctQduzYMd5///1MdYODgxkwYAArV66kVq1aGe57WSutYDoVnSoWSkvhf6+ZU927d6dz584cOHCAo0ePsmPHDubMmcOPP/5I//79M9Tt3LkzgwcPZv/+/axatYqBAwdmec42bdowevRoUlNTCQkJoU2bNgD4+/sbkvj0hCjX0laWlmXZ0Pp98eJF/Pz8MoxVatmyJTqdjsuXLxsSoVq1aqFQPBtq6+7uTu3atQ23lUolrq6uGbaE2rhxI4sWLeL69evEx8ej0WhwdHTMNsRatWqhVCoNtz09PTl37lzuH6uQJZEICZl0ruvFV/93gQS1li2n7/F5x+o425rvQmOF/aGTH3Z2dlSpUiVD2d27dzPV27dvH127dmX+/Pn07dvXUF6mTBmcnZ25ePFilue/ePEikiRluoZQeGwsbHLUOmNK1tbWtGvXjnbt2vHll18yaNAgpkyZkikRsrCw4IMPPmDKlCkcO3aMLVu2ZHm+gIAAEhISCA0NJTg4mHHjxgH6RGjgwIFERUVx7NgxBg8enPtgZR0arY7b4dfxb9ksV4daWmZcd0iSpCzLdDodAEeOHKFPnz5MmzaNDh064OTkRFBQEPPmzcv1ddLPKeSfyWeNCUWPvcqCHo30U+mTU3UEian0JUpISAidO3dm9uzZfPTRRxnuUygU9OzZk19++SXT6u1JSUksXbqUDh06iPFBQq7UrFmThISELO8bOHAg+/bto1u3bpQqVSrLOpUrV6Z8+fJs27aNsLAw/P39AShbtixly5Zl3rx5qNXqvLUIyTq2bAoiNiaa7t27A/ou4DNnzmSI+dChQ4YusLw6fPgw3t7eTJw4kUaNGlG1alVDd5xgOiIRErLUr4WPYU2NdYdvotGKbx8lQXBwMJ07d2bkyJF0796dyMhIIiMjiYqKMtSZMWMGHh4etGvXjh07dnDnzh32799Phw4dSE1NZcmSJSZ8BEJR9uTJE1599VXWr1/P2bNnCQ8PZ9OmTcyZM4du3bpleUyNGjV4/Phxpqn0/xUQEMDSpUupUqVKhm2X/P39+f777w2Dql8mMTGRyMhI7t69y9GjR5nwzTymfTGOnh98aEik+vTpg7W1Nf369eP8+fMEBwczYsQIPvjgg0xbPuVG1apVuX37NkFBQVy/fp1Fixa9sBUsv9KXpYmPj+fRo0eEhYVx4cKFArlWcScSISFLFUvbEVDNDdCvNP33hQcvOUIoDtauXUtiYiIzZ87E09PT8PP2228b6ri6unL06FECAgIYPHgwlStXpmfPnlSuXJnQ0FAqVapkwkcgFGX29vY0bdqU+fPn07p1a2rXrs2XX35JYGAgixcvfuFxrq6u2NhkPystICCAuLg4w/igdP7+/sTFxeW4NWjlypV4enpSuXJl3n77bS5eucG8JT8yacaz7ilbW1t27dpFVFQUjRs35p133uG1117L9jHkxBtvvMEnn3zC8OHDqVevHocPH+bLL7/M1zlfpH79+tSvX5+TJ0/yyy+/UL9+fV5//fUCuVZxJ8liVGS2YmNjcXJyIiYm5qUD2kqaA1cf8cFPxwFo7FOKTR+3MHFEBS85OZnw8HAqVqyItbW1qcMp9rJ7Ps35bytdds+BeC0WAlmG+2H8q/NGJympU9bJ1BGZTEl8veX0PUa0CAkv9EqV0lR100/FDr35lHN3Y0wckSAIghHJ6V3+5r1WmrkTiZDwQpIkMaDls2mzKw+IrRUEQShB5GdrbIlUyHyJREjI1lv1yxr2H9t+NoJbT7Ke+SEIglDspLUIySINMmsiERKyZWOlZEBLHwB0MvywX7QKCYJQQqSvxSOJFiFzJhIh4aU+aOaDnZV+VdNNJ+/yMC7ZxBEVPDGHwDjE8ygUafJzy4KITMhsiURIeCknW0veb6bfv0et0bHq4E3TBlSA0ldwTUxMNHEkJYNarQbIsD2AIBQZslgfTRBbbAg5NPCViqw+dBO1Vsf6o7cY4l8ZJ1vLlx9YzCiVSpydnQ17A9na2hr2HxJyR6fT8ejRI2xtbbGwEG81QhH03Bgh0SpgvsS7k5Aj7o7WdG9Yll+P3yE+RcOPB2/wafu8LzVflHl4eABk2ChRyBuFQkGFChVEMikUTc/NGhN9Y+ZLJEJCjg1tU4XfT94lVSuz6mA4A1tWpJRdyduMVZIkPD09cXNzIzU11dThFGtWVlYZducWhCJF1qFLS4CKSq4+depUtm7dSlhYmKlDMRviHUrIsfIutvRM24w1Qa1lRQmfQaZUKrG2thY/+fgRSZD5efToEUOGDKFChQqoVCo8PDzo0KEDhw4dMtTx8fFBkiSCgoIyHV+rVi0kSWLNmjUA9OrVi44dO2aos3PnTiRJYurUqRnKp06dSoUKFV4YW5s2bZAkCUmSUKlUlK1alzf6jWL3X9vy/oCNbOzYsezZsydf59i8eTPt2rWjTJkyODo60rx5c3bt2mWkCEse8S4l5MrwV6tgpdS/bNYevsmjuBQTRyQIQlHSvXt3Tp8+zdq1a7ly5Qrbtm2jTZs2PHnyJEO98uXLZ9po9ejRo0RGRmJnZ2coCwgI4NChQ2g0GkNZcHAw5cuXJyQkJMPxwcHBL91zLDAwkPv373P9+nX++Hkl1X0r89mwD5kybmQeH7Fx2dvb4+rqmq9z7N+/n3bt2vHXX39x8uRJAgIC6Nq1K6dPnzZSlCWLSISEXPF0suG9pvpvXEmpWlbsu27iiATBdKZOnWpoYUj/qV69uqnDMpno6GgOHDjA7NmzCQgIwNvbmyZNmjBhwgTeeOONDHX79OnDvn37uHPnjqFs1apV9OnTJ8Pg+oCAAOLj4zlx4oShLCQkhPHjx3Ps2DGSk/XLeSQnJ3Ps2LGXJkK2trZ4eHhQrlw5mjWqz/QvxjB55nw2bVjL7t27DfXOnTvHq6++io2NDa6urnz00UfEx8cb7u/fvz9vvvkmM2bMwN3dHWdnZ7766is0Gg3jxo3DxcWFcuXKZUr2Pv/8c3x9fbG1taVSpUp8+eWXGbrgp06dSr169TJdZ+7cuXh6euLq6sqwYcOy7bZfsGABn332GY0bN6Zq1arMmDGDqlWr8n//93/ZPjfmSiRCQq4NbVMZlYX+pfPz0Vs8iC356woJwovUqlWL+/fvG34OHjxo6pBMxt7eHnt7e7Zu3UpKSvatxe7u7nTo0IG1a9cC+iUrNm7cyMCBAzPU8/X1xcvLi+DgYADi4uI4deoUPXr0wMfHhyNHjgBw+PBhUlJScrwLPQCyFi0Sb/TojZOzM5s3bwYgISGBDh06UKpUKUJDQ9m0aRO7d+9m+PDhGQ7fu3cvERER7N+/n++++44pU6bQpUsXSpUqxbFjx/j4448ZPHgwd+/eNRzj4ODAmjVruHDhAgsXLmTlypXMnz8/2zCDg4O5fv06wcHBrF27ljVr1hi6DnNCp9MRFxeHi4tLzp8bMyIGSwu55uZoTd/m3qw8EE6KRsfS4GtM61bb1GEJgklYWFgYZhoWBl1SEik3Cnd8nqpSJRQ2Ni+tZ2FhwZo1awgMDGT58uU0aNAAf39/evXqRd26dTPVHzhwIJ9++ikTJ07k999/p3LlyhlaQ9IFBAQQEhLChAkTOHDgAL6+vpQpU4bWrVsTEhJiuL9ixYp4e3vn+HGlaOGO7IakUOBTqQo3b94E4JdffiE5OZl169YZuukWL15M165dmT17Nu7u7gC4uLiwaNEiFAoF1apVY86cOSQmJvLFF18AMGHCBGbNmsXBgwfp1asXAJMmTTJc38fHh7FjxxIUFMRnn332wjhLlSrF4sWLUSqVVK9enc6dO7Nnzx4CAwNz9Djnzp1LfHw8PXv2zPFzY05EIiTkyWD/ymw4dptEtZZfj99hsH9lvJxf/kYpCCXN1atX8fLywtramubNmzNz5swXDthNSUnJ0FISGxub6+ul3LjBze7v5DnevPD543dsatXKUd3u3bvTuXNnDhw4wNGjR9mxYwdz5szhxx9/pH///hnqdu7cmcGDB7N//35WrVqVqTUoXZs2bRg9ejSpqamEhITQpk0bAPz9/VmxYgWAISHKjWStRAqWWCjSZ47p/7148SJ+fn4Zxiq1bNkSnU7H5cuXDYlQrVq1MkwIcHd3p3btZ18KlUolrq6uGZbi2LhxI4sWLeL69evEx8ej0WhwdHTMNs5atWplWJTU09OTc+fO5egx/vLLL0ybNo3//e9/uLm55egYcyMSISFPStur6NfCh2Uh11FrdSwOvsaMt+qYOixBKFRNmzZlzZo1VKtWjfv37zNt2jRatWrF+fPncXBwyFR/5syZTJs2LV/XVFWqhM8fv+frHHm5Zm5YW1vTrl072rVrx5dffsmgQYOYMmVKpkTIwsKCDz74gClTpnDs2DG2bNmS5fkCAgJISEggNDSU4OBgxo0bB+gToYEDBxIVFcWxY8cYPHhwruJM3wJGQubmjeu80rxpro5PX4k+nSRJWZbp0vY0O3LkCH369GHatGl06NABJycngoKCmDdvXq6vk37O7AQFBTFo0CA2bdpE27Ztc/KQzJJIhIQ8+6hVJX4+cov4FA2/hd5h0CsVqVTG3tRhCUKh6dSpk+H3unXr0rRpU7y9vfntt9/48MMPM9WfMGECY8aMMdyOjY2lfPnyubqmwsYmx60zRUXNmjXZunVrlvcNHDiQuXPn8u6771KqVKks61SuXJny5cuzbds2wsLC8Pf3B6Bs2bKULVuWefPmoVarc90ilJ4Ibdn4CzHRT+nevTsANWrUYM2aNSQkJBhahQ4dOmToAsurw4cP4+3tzcSJEw1lt27dyvP5svPrr78ycOBAgoKC6Ny5c4Fco6QQg6WFPCtlZ0VgK/03RY1OZvbOSyaOSBBMy9nZGV9fX65du5bl/SqVCkdHxww/JcmTJ0949dVXWb9+PWfPniU8PJxNmzYxZ84cunXrluUxNWrU4PHjx5lmV/1XQEAAS5cupUqVKoauKdC3Cn3//feGQdUvk5iYSGRkJHfv3uX4iVPMnzGFqZ+P5v0BgYZEqk+fPlhbW9OvXz/Onz9PcHAwI0aM4IMPPshw7dyqWrUqt2/fJigoiOvXr7No0aIXtoLlxy+//ELfvn2ZN28eTZs2JTIyksjISGJiYox+rZJAJEJCvgS2roibgwqAXf8+4Hh4lIkjEgTTiY+P5/r163h6epo6FJOwt7enadOmzJ8/n9atW1O7dm2+/PJLAgMDWbx48QuPc3V1xeYlg7EDAgKIi4szjA9K5+/vT1xcXI5bg1auXImnpyeVK1em36CPuXH1MvNXrGHG3AWGOra2tuzatYuoqCgaN27MO++8w2uvvZbtY8iJN954g08++YThw4dTr149Dh8+zJdffpmvc2blhx9+QKPRMGzYMDw9PQ0/o0aNMvq1SgJJTm8bFLIUGxuLk5MTMTExJe7bm7EEHb/N+M36gXv1yjuzZWgLsbeU8FIl4W9r7NixdO3aFW9vbyIiIpgyZQphYWFcuHCBMmXKvPT47J6D5ORkwsPDqVixItbW1gX1EMzak4gb3NO5YG2pxMZSSXkXW1OHZDIl8fWW0/cY0SIk5FuPRuWp5q4fGBp2J5o/z903cUSCUDju3r1L7969qVatGj179sTV1ZWjR4/mKAkSioDn2gHEdzfzJQZLC/mmVEiMf706A1aHAjB75yXa1XRHZaF8yZGCULxltVeWUEzIMvBcImS6SAQTEy1CglG08S1Dyyr6/XHuRCXx85GCmQkhCIJgFLKMjPQsARJNQmZLJEKCUUiSxIRONQzvJd/vvUZ0otq0QQmCILzQs3V4ZFm0CJkzkQgJRlO7rBNv1S8LQExSKt/9c8XEEQmCILyALPP8TCHRIGS+RCIkGNVnHapja6UfG7T+6C0u3s/9FgKCIAgFTtaR3g6UMSUSzI1IhASj8nCyZlhAFQB0MkzZ9i9ihQZBEIocWfcs/ZF5frSQYGZEIiQY3aBWFfF21a/HcTw8iu1nxXR6QRCKGFnmWYuQ6BozZyIREoxOZaFkcpeahtsz/rpIolpjwogEQRD+47kWIdFobd5EIiQUiNdquBNQTb+o3P2YZJYGXzdxRIIgCM+RdRmmz5uqQUiSpBduSCsUDpEICQXmyy41sVTq315+2H+DG4/iTRyRIAgFrX///rz55puZykNCQpAkiejoaMPtbt264enpiZ2dHfXq1WPDhg2ZjouKimL06NF4e3tjZWWFl5cXAwcO5Pbt29nGkX49SZJQKBQ4OTlRv359PvvsM+7fv49hMUVJP1jaVF1j9+/fp1OnTvk6xw8//ECbNm1wdHTM8BwLOSMSIaHAVCpjz6C03enVWh2Ttp4XA6cFQQDg8OHD1K1blz/++IOzZ88yYMAA+vbty/bt2w11oqKiaNasGbt372b58uVcu3aNoKAgrl27RuPGjblx48ZLr3P58mUiIiIIDQ3l888/Z/fu3dSuXZtz586jkZXoZNDqno0XKmweHh6oVKp8nSMxMZGOHTvyxRdfGCkq8yISIaFAjXi1CmWd9btKH77+hK1h90wckSAIRcEXX3zB119/TYsWLahcuTKjRo2iY8eObN682VBn4sSJREREsHv3bjp16kSFChVo3bo1u3btwtLSkmHDhr30Om5ubnh4eODr60uvXr04dOgQZcqUYcioT9GgX+qjlK0li+fNpFy5cqhUKurVq8fOnTsN57h58yaSJPHbb7/RqlUrbGxsaNy4MVeuXCE0NJRGjRphb29Pp06dePTokeG40NBQ2rVrR+nSpXFycsLf359Tp05liO/5rrH062zevJmAgABsbW3x8/PjyJEj2T7G0aNHM378eJo1a/bS50PITCRCQoGytbLg6zdrGW5/s/0iMYmpJoxIEISiKiYmBhcXFwB0Oh1BQUH06dMHDw+PDPVsbGwYOnQou3btIioqKlfXsLGx4eOPP+bQkWM8evwYgI2rV7Bg/nzmzp3L2bNn6dChA2+88QZXr17NcOyUKVOYNGkSp06dwsLCgvfee4/PPvuMhQsXcuDAAa5du8bkyZMN9ePi4ujXrx8HDx7k6NGjVK1alddff524uLhsY5w4cSJjx44lLCwMX19fevfujUYjJpwUFLHpqlDgXq3uTsdaHuz8N5InCWpm7bzEzLfrmDosQSiWUtVaoiMTC/Wazh62WFrlfBPl7du3Y29vn6FMq9Vme8xvv/1GaGgoK1asAODRo0dER0dTo0aNLOvXqFEDWZa5du0aTZo0yXFsANWrVwfg7p27VC5dgfnfzePzzz+nV69eAMyePZvg4GAWLFjAkiVLDMeNHTuWDh06ADBq1Ch69+7Nnj17aNmyJQAffvgha9asMdR/9dVXM1z3hx9+wNnZmX379tGlS5cXxjd27Fg6d+4MwLRp06hVqxbXrl0zxC0Yl0iEhEIx5Y2aHLj6iAS1ll+P36Z7g7I08nExdViCUOxERyby24zQQr1mzy8aU6aCQ47rBwQEsGzZsgxlx44d4/3338+yfnBwMAMGDGDlypXUqlUrw30FMa7QcE5JQXxcLBEREYZkJl3Lli05c+ZMhrK6desafnd3dwegTp06GcoePnxouP3gwQMmTZpESEgIDx8+RKvVkpiY+NKB3s9fx9PTE4CHDx+KRKiAiERIKBSeTjZ82r4aX22/AMBnf5zlr5GtsLbM+bdMQRD0rTM9v2hc6NfMDTs7O6pUqZKh7O7du1nW3bdvH127dmX+/Pn07dvXUF6mTBmcnZ25ePFilsddvHgRSZIyXScn0s/pVb482bdTZWRpaWn4XUqbZvbfMp3u2Wau/fr148mTJyxcuBBvb29UKhXNmzdHrc5+Q+qsrvP8eQXjEomQUGj6tfDhf2H3OHM3hhuPEpi/+woTOmXd7C0IQtYsrZS5ap0pykJCQujSpQuzZ8/mo48+ynCfQqGgZ8+ebNiwga+++irDOKGkpCSWLl1Khw4dDGOKciopKYkffviB1i2a4upamkRUeHl5cejQIfz9/Q31Dh06lOsut/86dOgQS5cu5fXXXwfgzp07PE4blyQUHWKwtFBolAqJb3v4YaXUv+xW7r/B6dtPTRyVIAimEBwcTOfOnRk5ciTdu3cnMjKSyMjIDIOfZ8yYgYeHB+3atWPHjh3cuXOH/fv306FDB1JTUzOM33mRhw8fEhkZydWrVwkKCqJly5Y8fvyYZd9NR06bMj927Fhmz57Nxo0buXz5MuPHjycsLIxRo0bl6zFWrVqVn3/+mYsXL3Ls2DH69OmDjY1Nvs6ZlcjISMLCwrh27RoA586dIywsLNcDyc2VSISEQuXr7sCotlUB/aas434/S3JqbhqnBUEoCdauXUtiYiIzZ87E09PT8PP2228b6ri6unL06FECAgIYPHgwlStXpmfPnlSuXJnQ0FAqVar00utUq1YNLy8vGjZsyKxZs2jbti3nz5+nZrWqhi02Ro4cyZgxY/j000+pU6cOO3fuZNu2bVStWjVfj/Gnn37i6dOnNGjQgA8++ICRI0fi5uaWr3NmZfny5dSvX5/AwEAAWrduTf369dm2bZvRr1USSbJY4S5bsbGxODk5ERMTg6Ojo6nDKRE0Wh1vLT3MuXsxAAxtU5nPOopBgOZG/G1l/xwkJycTHh5OxYoVsba2NlGEJdjTm1xOsCMFS+qWczZ1NCZXEl9vOX2PES1CQqGzUCqY28PPsP3G8n3XOXMn2rRBCYJgVuTnNl0VzJtIhASTqObhwMhXn+8iO0OKRnSRCYJQSGQdpttqVShKRCIkmMzHbSpTy0vfXHnlQTxzdl42cUSCIJgNWRYtQgIgEiHBhCzTusjSZ5H9dDCckMsPX3KUIAiCEYgWISGNSIQEk6rh6cj4Ts8GSo/ddIZHcSkmjEgQBHOQPkZIpEKCSIQEkxvQ0oc21coA8DhezdhNZ9DpRKO1IAgFyDBhWqRC5k4kQoLJSZLE3B5+lLZXAbDvyiNWH75p2qAEQSjZZJ1hQUXBvIlESCgSStur+K6nn+H27B2XOJ+2zpAgCIKxSXLa3l0iFzJ7JT4Rio6OplGjRtSrV4/atWuzcuVKU4ckvEBr3zIEtqoIgFqrY8iGk0QnZr85oSAIQt6IWWOCXolPhBwcHNi/fz9hYWEcO3aMGTNm8OTJE1OHJbzA2A7V8CvnBMCdqCRGBYWhFeOFBEEwtrRZY6ZuEJIkia1bt5o4CvNW4hMhpVKJra0tACkpKciyjNhVpOhSWShZ9n5DXOysAP14oYW7r5g4KkEQcqp///68+eabmcpDQkKQJIno6GjD7W7duuHp6YmdnR316tVjw4YNmY6Liopi9OjReHt7Y2VlhZeXFwMHDuT27dvZxpF+PUmSUCgUODk5Ub9+fT777DPuR9wzeQKU7v79+3Tq1CnPx0dFRTFixAiqVauGjY0NFSpUYOTIkcTEiKEFOWXyRGj//v107doVLy+vF2bGS5YswcfHB2tra5o2bcrx48dzdY3o6Gj8/PwoV64c48aNo3Tp0kaKXigIXs42LO5dH0XaO9WivdfYfeGBaYMSBMGoDh8+TN26dfnjjz84e/YsAwYMoG/fvmzfvt1QJyoqimbNmrF7926WL1/OtWvXCAoK4tq1azRu3JgbN2689DqXL18mIiKC0NBQPv/8c3bv3k3tOnU5d/Fqkega8/DwQKVS5fn4iIgIIiIimDt3LufPn2fNmjXs3LmTDz/80IhRlnCyif3111/yxIkT5c2bN8uAvGXLlgz3BwUFyVZWVvKqVavkf//9Vw4MDJSdnZ3lBw8eGOr4+fnJtWrVyvRz7969DOeKjIyUW7RoIUdGRuY4vpiYGBmQY2Ji8vU4hdxbHnJN9v58u+z9+Xa59uSd8rWHcaYOSTAi8beV/XOQlJQkX7hwQU5KSjJBZHnXr18/uVu3bpnKg4ODZUB++vTpC499/fXX5QEDBhhuf/zxx7KdnZ18//79DPUSExPlsmXLyh07dnzhuV50vcTERLlaNV+5ZeN68tk7UfL5u9GyVquVp02bJpctW1a2srKS/fz85B07dhiOCQ8PlwF548aN8iuvvCJbW1vLjRo1ki9fviwfP35cbtiwoWxnZyd37NhRfvjwoeG448ePy23btpVdXV1lR0dHuXXr1vLJkyczxPP85176df744w+5TZs2so2NjVy3bl358OHDL3ycWfntt99kKysrOTU1NcfHFNfXW3Zy+h5j8hahTp068c033/DWW29lef93331HYGAgAwYMoGbNmixfvhxbW1tWrVplqBMWFsb58+cz/Xh5eWU4l7u7O35+fhw4cOCF8aSkpBAbG5vhRzCNj1pX4vU6HgDEpWgYsDqUJ/FisUVBKKliYmJwcXEBQKfTERQURJ8+ffDw8MhQz8bGhqFDh7Jr1y6ioqJydQ0bGxs+DgzkUGgYTx4/BmDhwoXMmzePuXPncvbsWTp06MAbb7zB1atXMxw7ZcoUJk2axKlTp7CwsOC9997js88+Y+HChRw4cIBr164xefJkQ/24uDj69evHwYMHOXr0KFWrVuX1118nLi4u2xgnTpzI2LFjCQsLw9fXl969e6PRaHL8GNN3W7ewsMjFM2O+ivSzpFarOXnyJBMmTDCUKRQK2rZty5EjR3J0jgcPHmBra4uDgwMxMTHs37+fIUOGvLD+zJkzmTZtWr5jF/JPkiS+fceP8MeJXLwfy+2oRALXneCXwGZYWypNHZ4gmERqSjJR9+4W6jVdypbDUmWd4/rbt2/H3t4+Q5lWm/2myr/99huhoaGsWLECgEePHhEdHU2NGjWyrF+jRg1kWebatWs0adIkx7EBVK/mC0DEnduUcXNj7ty5fP755/Tq1QuA2bNnExwczIIFC1iyZInhuLFjx9KhQwcARo0aRe/evdmzZw8tW7YE4MMPP2TNmjWG+q+++mqG6/7www84Ozuzb98+unTp8sL4xo4dS+fOnQGYNm0atWrV4tq1a1SvXv2Fx6R7/PgxX3/9NR999FEOngkBingi9PjxY7RaLe7u7hnK3d3duXTpUo7OcevWLT766CPDIOkRI0ZQp06dF9afMGECY8aMMdyOjY2lfPnyeXsAQr7ZqSxY1b8Rby45xIPYFE7djubTTWf4vld9FIqiMtxREApP1L27rJ8wulCv+f7MBbhXqpLj+gEBASxbtixD2bFjx3j//fezrB8cHMyAAQNYuXIltWrVynCfXACTW2SdPimTJImEuFgiIiIMyUy6li1bcubMmQxldevWNfye/rn0/OeJu7s7Dx8+2y/xwYMHTJo0iZCQEB4+fIhWqyUxMfGlA72fv46npycADx8+fGkiFBsbS+fOnalZsyZTp07Ntq7wTJFOhIyhSZMmhIWF5bi+SqXK18A1wfg8nWz4qV9jeq44QqJay59n71OulA0TOmX9TVEQSjKXsuV4f+aCQr9mbtjZ2VGlSsbE6e7drFux9u3bR9euXZk/fz59+/Y1lJcpUwZnZ2cuXryY5XEXL15EkqRM18mJi5cuA+BVvgK5WVHR0tLS8LskSVmW6XQ6w+1+/frx5MkTFi5ciLe3NyqViubNm6NWZ78+WlbXef68WYmLi6Njx444ODiwZcuWDOcQslekE6HSpUujVCp58CDjjKEHDx5k6jMWSrbaZZ34vnd9AtedQCfDin03cLG1YrB/ZVOHJgiFylJlnavWmaIsJCSELl26MHv27ExdOQqFgp49e7Jhwwa++uqrDO/5SUlJLF26lA4dOhjGFOVUUlISP/y0itbNGlDKtTQWCgVeXl4cOnQIf39/Q71Dhw7lusvtvw4dOsTSpUt5/fXXAbhz5w6P08YlGVNsbCwdOnRApVKxbds2rK1z3o0pFIHp89mxsrKiYcOG7Nmzx1Cm0+nYs2cPzZs3N2Fkgim8VsOdaW88azafueMSvx7PvolZEISiKTg4mM6dOzNy5Ei6d+9OZGQkkZGRGQY/z5gxAw8PD9q1a8eOHTu4c+cO+/fvp0OHDqSmpmYYv/MiDx8+JDIykqtXrxIUFETLli15/OQJy2Z+oa8gwbhx45g9ezYbN27k8uXLjB8/nrCwMEaNGpWvx1i1alV+/vlnLl68yLFjx+jTpw82Njb5Oud/xcbG0r59exISEvjpp5+IjY01PJcvG5cl6Jk8EYqPjycsLMzQfRUeHk5YWJihD3XMmDGsXLmStWvXcvHiRYYMGUJCQgIDBgwwYdSCqXzQ3Iex7X0Nt7/Yco7/OxNhwogEQW/WrFlIksTo0aNNHUqxsHbtWhITE5k5cyaenp6Gn7fffttQx9XVlaNHjxIQEMDgwYOpXLkyPXv2pHLlyoSGhlKpUqWXXqdatWp4eXnRsGFDZs2aRdu2bTkfepiavvpjJWDkyJGMGTOGTz/9lDp16rBz5062bdtG1apV8/UYf/rpJ54+fUqDBg344IMPGDlyJG5ubvk653+dOnWKY8eOce7cOapUqZLhubxz545Rr1VSSXJBjETLhZCQEAICAjKV9+vXzzD6fvHixXz77bdERkZSr149Fi1aRNOmTQslvtjYWJycnAzTEQXTk2WZmTsu8cN+/WJqSoXEol716VzX08SRCblRkv62QkND6dmzJ46OjgQEBLBgwYIcHZfdc5CcnEx4eDgVK1YUXR3GlhgF0bc4q6uIpVJBDc/i/fozhpL4esvpe4zJxwi1adPmpbMChg8fzvDhwwspIqGokySJCZ2qE5uUSlDoHbQ6mZFBp9HodHSrV9bU4QlmJj4+nj59+rBy5Uq++eYbU4cj5IT8bOCxmHsqmLxrTBDyQpIkpr9Vh56N9LNZtDqZTzaGseV04a6vIgjDhg2jc+fOtG3b9qV1xYKtRYSsQydSICGNyVuEBCGvlAqJWW/XRalQ8Ovx2+hkGPPbGVI1Mj0bi7WfhIIXFBTEqVOnCA0NzVF9sWBrESHLaElblFXkQ2ZPtAgJxZpCITH9zdp80MwbAFmGz/44yw/7r5s4MqGku3PnDqNGjWLDhg05HlMxYcIEYmJiDD9iMKtpJGjgok7/ZUkSmZDZEy1CQrGnUEh81a0WlkoFqw6FAzDjr0s8jlczvmN1sQK1UCBOnjzJw4cPadCggaFMq9Wyf/9+Fi9eTEpKCkplxq1gxIKtRUNq2hAhpSRhqRTvD+ZOJEJCiSBJEl92qYGzrSXf/XMFgB/23+BxfAqzu9fFUikaPwXjeu211zh37lyGsgEDBlC9enU+//zzTElQXpl4Ym+JlP6cSiIHMjDn15lIhIQSQ5IkRr5WldL2KiZtPYdOhs2n7vE0Qc2SPg2wtRIvd8F4HBwcqF27doYyOzs7XF1dM5XnRfoWCYmJiUZfhM/sGT70JcMWFuYuMTERwCy35hCfDEKJ817TCrjYWTIyKAy1Rkfw5Ue8/+MxfurXmFJ2VqYOTxByRKlU4uzsbNjE09bWVnxoG0lKaiqyRomsUKCTFCQnG6f1rjiSZZnExEQePnyIs7Oz0VoyixORCAklUsfanqwdYMVH604Ql6Lh1O1oeqw4wtqBTSjrLL5dCwUjJCTEqOdL31/r+R3NhfxLiIvmqUaFUiFhpZRQR4txW87Ozma7h6dIhIQSq3llV4IGN6P/6lAexaVw7WE8by89xNqBTajuIVaSFYo+SZLw9PTEzc2N1NRUU4dTYvzfzwtY8NCPMvbW+Ho48FW36qYOyaQsLS3NsiUonUiEhBKtlpcTm4e0oO+q44Q/TuBBbAo9lh/hx76NaFrJ1dThCUKOKJVKs/6gMrbk+Ggi41KRFVZ4pkolZksJIW/EVBqhxCvvYsvvHzfHr5wTAHHJGj5YdZyd5yNNHJkgCKag1WlRICNJiOU1BJEICebB1V7FL4HNaO1bBgC1RsfQDSdZf/SWiSMTBKGwaTT6RAj0awkJ5k0kQoLZsFNZ8FO/RrxdX78xq06GSVvP890/V8x6DQ1BMDc6nRYJGQkJ0SAkiERIMCuWSgVze/gxuHUlQ9miPVeZuu1fkQwJgpnQavWJEKJrTEAkQoIZUigkJrxeg0mdaxjK1h65xZf/O49OJ5IhQSjpdFqt/hcZFKJrzOyJREgwW4NaVWJeDz9D0/j6o7eZuPWcSIYEoYTT6nSGFiExRkgQiZBg1ro3LMf8d+sZkqFfj99h4tZzoptMEEowrU5n+F10jQkiERLMXrd6ZVnYqz7KtDfEX4/fYfbOyyaOShCEAqHTZmj1FXmQIBIhQQC6+nmx4N16ht2ol++7zg/7r5s2KEEQjE+TjDbto0+WMXwBEsxXnhKhGzduGDsOQTC5rn5efN3t2a7hM/66xKYTd0wYkSAIRpf6XCKELAZLC3lLhKpUqUJAQADr168nOTnZ2DEJgsm838ybT9v5Gm5P2HyO4+FRJoxIEASjeq5FCMSsMSGPidCpU6eoW7cuY8aMwcPDg8GDB3P8+HFjxyYIJjH81Sr0a+4NgEYnM2T9Se4+TTRxVIIgGEVaIiST3jVm6oAEU8vTS6BevXosXLiQiIgIVq1axf3793nllVeoXbs23333HY8ePTJ2nIJQaCRJ4ssuNWlVtTQATxLUBK47SaJaY+LIBEHIt9QkdPqdxgDRIiTkc7C0hYUFb7/9Nps2bWL27Nlcu3aNsWPHUr58efr27cv9+/eNFacgFCoLpYLFvRvg42oLwMX7sUzYLKbVC0KxZ2gRkpBlMX1eyGcidOLECYYOHYqnpyffffcdY8eO5fr16/zzzz9ERETQrVs3Y8UpCIXOydaSH/s1wl5lAcD/wiLYfOqeiaMS8iM1NZU7d+5w+fJloqLE2C+z9FzXmE6WxfR5IW+J0HfffUedOnVo0aIFERERrFu3jlu3bvHNN99QsWJFWrVqxZo1azh16pSx4xWEQlXFzYGZb9cx3J78v/OEP04wYURCbsXFxbFs2TL8/f1xdHTEx8eHGjVqUKZMGby9vQkMDCQ0NNTUYQqFJTU5Q9eYWFlayFMitGzZMt577z1u3brF1q1b6dKlCwpFxlO5ubnx008/GSVIQTClrn5e9GhYDoAEtZaRv55GrdG95CihKPjuu+/w8fFh9erVtG3blq1btxIWFsaVK1c4cuQIU6ZMQaPR0L59ezp27MjVq1dNHbJQ0DRJaFGgQ9K3CIkmIbNnkZeD/vnnHypUqJAp+ZFlmTt37lChQgWsrKzo16+fUYIUBFOb+kYtTt56yo3HCZy7F8OKfdcZ8VpVU4clvERoaCj79++nVq1aWd7fpEkTBg4cyPLly1m9ejUHDhygalXx/1qiaVLQyQpkFOjEpqsCeWwRqly5Mo8fP85UHhUVRcWKFfMdlCAUNXYqiwzbcHy/9xrXH8WbOCrhZX799dcXJkHPU6lUfPzxxwwcOLAQohJMKjUJTVqLkCzLYmVpIW+J0ItmzsTHx2NtbZ2vgAShqKpTzolBr+gTfbVWx4TNYqd6QciPVLWa7wcNos2ixvitqku9VXVpu9yPY2HbC+6immS0kgUgoZNBNAgJueoaGzNmDKBfZ2Xy5MnY2toa7tNqtRw7dox69eoZNUBBKEpGt/Xlr/P3uROVxPHwKDaeuEPvJhVMHZaQhaSkJKKioihbtmyG8n///TdHrURCwVs5ZAi/NDxFvK0GlyQrtLKCB7bJTDw+nt2124OFlfEvmpqEJu2jT5ZlMVhayF2L0OnTpzl9+jSyLHPu3DnD7dOnT3Pp0iX8/PxYs2ZNAYUqCKZnY6VkxlvPZpF9u+syscmpJoxIyMrvv/9O1apV6dy5M3Xr1uXYsWOG+z744AMTRiaku3/zOhcc7hBvp6GV2pF9Q04yruYCyj905oFKYs228QVzYU0KGkmfCOnEpqsCuWwRCg4OBmDAgAEsXLgQR0fHAglKEIqyVlXL0KWuJ9vP3icqQc3S4OuM71Td1GEJz/nmm284efIk7u7unDx5kn79+vHFF1/w3nvviUUxi4j/mzOP6z5PQYbJry8DoFublpz6vRF33Hfz65O/6V8QF9YkocUahQRanYwkWoTMXp7GCK1evVokQYJZ+7xjdazSNiladShc7EVWxKSmpuLu7g5Aw4YN2b9/PytWrOCrr74SH3xFREp0Mg9LpVJKp8Wj7LNWVsm5HKoUBRFWEvtO/s/4F05NRiMpUUj66fNK8XIwezluEXr77bdZs2YNjo6OvP3229nW3bx5c74DE4SirLyLLQNa+rBi/w3UGh1zd11mQa/6pg5LSOPm5sbZs2epW7cuAC4uLvzzzz/069ePs2fPmjg6ASBeEYvaUkvjJCnDiOUy9WpQ/pIj18pFc/DC7/g3NPIOBZokkmUrdLKMTitmjQm5aBFycnIyfJNydHTEycnphT+CYA6GBlShlK0lAFvDIrgUGWviiIR0P//8M25ubhnKrKys+PXXX9m3b5+JohLSndyzi4sVIkECf6emGe6rWbkCnjH6Ae4Pku8a/+KaFJ7K9uhk8HV3oFZZ8Zll7nLcIrR69WrD72JAdEYxKTHsvb2XN6u8KZrdzYiTjSXDAqrwzZ8XAVi89xqL32tg4qgEgHLlymW4HRkZiYeHBwAtW7Y0RUjCcy6GBBPpkgIy9OixIsN9TWtUZX9seZD+5Z5cAPvBpSahQQnA5qEtsLXK07rCQgmSpzFC33zzDeHh4caOpdiadXwWkw9P5qN/PiIiPsLU4QiFqE9Tb0rb66f4/nnuPtceikUWi6L27dubOgThOfH3o0iw0WAt67CwtMxwn621CvtUJyQdPFZqjH/xtE1XQawqLejlKRHatGkTVapUoUWLFixdujTLVabNxeWoy2y/oV/86+j9o7z1v7f47fJvYmaKmbCxUjKoVSUAZBmWBl8zcURCVsTfY9GiSdKSrNLhqM36/0VjpcRSKxGrkJB1Rt7XLzVJJEJCBnlKhM6cOcPZs2dp06YNc+fOxcvLi86dO/PLL7+QmGhes2equVRjedvluNvqZ6gkahL5+ujXBP4dyN24AujfFoqc95t545w2Vuh/ZyK4E2VefwPFgeiyLlq0Wi2pSh0u2qz/X5LsrbFSK9EoJGKN/T6qSUGXtvO8GCctQB4TIYBatWoxY8YMbty4QXBwMD4+PowePdrQD29OWpZtyZZuW+hetbuh7FjkMd7e9jZBl4LQyWKn8pLMXmXBwJb6rTe0Opl1R26aNiBBKOLUimRkCdx1WY/PSXZ2RJWqz1KiY+4Z9+KaJHSy/qNPzBgTIB+J0PPs7OywsbHBysqK1FTzXGXXwdKeqS2msqLtCjzs9MlgkiaJ6cemM+jvQdyJu2PiCIWC1KdpBaws9H9OG0PvkKgugLENglACpKrVPLZ/ChKUk+yzrGPhYIV1qn7sXeRTI4+7TE1Gm9YiJFoKBchHIhQeHs706dOpVasWjRo14vTp00ybNo3IyEhjxlc83DsFP7WHJ9dpUbYFW97YQg/fHoa7QyND6b6tO79c/EW0DpVQrvYq3vDzAiA2WcOW00b+Fivki1KpNHUIQprr504Tb6sGwM0y66nrNvbWWKtVADyJNfJniiYZnXHaAIQSIk+vhmbNmlGlShV+//13BgwYwK1bt9izZw8ffvih+a0jlBwLvw+Eu8dhRWs4uwl7K3smN5/MD+1+wMtO/+GYpEli5vGZfLjrQzGzrITq38LH8PvawzfFAN0i5PTp06YOQUhz5fBBEqz1LaZl08ZW/pezkx12KfpNvZ/GGXkyjibZMEZIECCPidBrr71m2HR17NixmXZ3NisJj0CR9m1THQ+bB8H/hoE6geZezdncbTM9fXsaqp94cIJ3t7/L4YjDJgpYKCi1yzrRyLsUAFcexHP0RgGsgSIIxVzU7XskqrQA+JTyzrJOGVcnbFL03WZPE42cCKUmoZVFIiQ8k6dEaPr06dSsWdPYsRRPrpXho33g996zstPr4YcAePAvdpZ2fNn8S35s/6OhdSg6JZohu4fw47kfRatBCdP3uVah30+KWYMl3bJly6hbty6Ojo44OjrSvHlzduzYYeqwirTk6ASSrLUgy1QuUyXLOuXcSmOn0e9n+STxgXED0CQjI4k2IcEgx0tqjhkzhq+//ho7OzvGjBmTbd3vvvsu34EVKyp7eGsZVPKH7WMgNQEeX4aVr0LHmdBwAE09m/Jb19/44uAX7L+7H52sY+GphZx7dI5vXvkGBysHUz8KwQja13THwdqCuGQNO87f56tutbBTiZVri6qYmBjOnDlDWFgYI0eOzPXx5cqVY9asWVStWhVZllm7di3dunXj9OnT1KpVqwAiLv6UqWpSrEACLOxds6xTtawHllp911hcqhG3rpFl0TUmZJLjd+jTp08bZoSJ/vYX8OsFZRvCpgHw4BxokmH7J3BjH3RdiJONM9+/+j0rzq5gWdgyZGT23tnLe3++x/w286lSKutvR0LxYW2p5A0/LzYcu02iWstf5+7To1F5U4dldq5fv86kSZNQqVQsWLAAZ2dnwsPDCQsLMyQ+Z86c4fbt28iyjJ2dXZ4Soa5du2a4PX36dJYtW8bRo0dFIvQiOh1qS51+kwufVllWKePshIVsA0C8xoirtWtS9CHIEmLCmJAux4lQcHBwlr8L/1G6KgzaDf98Ccd/0Jdd2AoRp+Cd1SjKNWKI3xBqudZi/IHxxKnjuBl7k/f+eo+vWnxFx4odTRq+kH/vNCzHhmO3AX33mEiECl+fPn3o06cP3t7e1K5dm/j4eGJjY3FycqJmzZrUrl2bO3fu8NNPP/Haa69Rvnz+/4+0Wi2bNm0iISGB5s2bG+FRlEwanUSqpQ4LWdK3pr+AEv30+SRtkhEvrj+X6BoTnpenMUIDBw4kLi4uU3lCQgIDBw7Md1DFnqU1vP4tvLserNNm0UXfhlUd4NBC0OloXa41G7tspFqpaoB+Vtm4/eOYEzoHjU6sQVOc1SvvTKUydgAcC48SK02bwMOHD6lduzZ+fn5ERkYybNgw7ty5w9OnTzl06BArVqxAkiSaNGmS7yTo3Llz2Nvbo1Kp+Pjjj9myZcsLx1CmpKQQGxub4cfcaHQ61BY6LHXZpyJK9Ku1J2LE98PUZABkxBpCwjN5SoTWrl1LUlLmLD0pKYl169blO6gSo0ZX+PgglG+qv63TwD+T4ZceEP+I8g7l+fn1n+la6Vnz+s8XfmZ08GgSU8WHZ3ElSRLdGzzb/fzPc/dNGI15WrRoEUOGDKFPnz4sX76cbdu2MWzYMK5cuWL0a1WrVo2wsDCOHTvGkCFD6NevHxcuXMiy7syZM3FycjL8GKMlqrg5UO02yVYaVPLLOiT09ycpjLj2miY9ERJdY8IzuUqEYmNjiYmJQZZl4uLiMnyrefr0KX/99Rdubm4FFWvx5FwB+v8Jr4yB9MbYa7th+StwYx82FjZMf2U6E5tOxEKh/8Pfd3cfH+76kCdJT0wXt5AvXep6Gn7fIRKhQtelSxcuXbrEwYMHGTRoEGFhYbRt25bWrVszbNgwHj58aLRrWVlZUaVKFRo2bMjMmTPx8/Nj4cKFWdadMGECMTExhp87d8xvxfloe/2XvE/8pmZfUdK/H6qNmbBonrUICUK6XCVCzs7OuLi4IEkSvr6+lCpVyvBTunRpBg4cyLBhwwoq1uJLaQltp8AHm8EuLVGMj4R13WDvN0g6Lb2q92J52+XYW+r7zM8/Oc8HOz7gduxtEwYu5JW3qx21vPTTf8/cjeHuU9HCZ0pKpZLhw4dz4cIFlEol1atXR6fTodVqjX4tnU5HSkpKlvepVCrDVPv0H3OSEBdHqlKLQgdvNH4j27oaC2v9v8YMIPXZGCGx87yQLleJUHBwMHv27EGWZX7//Xf27t1r+Dl48CC3b99m4sSJBRVr8Vf5VX1XWaWAtAIZ9n8La7tCzD2aejZlTcc1uNnqk6U7cXd4/6/3OfvorOliFvLs9TrPWoV2njfDrWeKIBcXFxYtWsTBgwdp27Ytr732GnPnzs2yqz8nJkyYwP79+7l58ybnzp1jwoQJhISE0KdPHyNHXjJcPXkUrVLO0QeP1jItESqgFiGRBwnpcpUI+fv706ZNG8LDw3nzzTfx9/c3/DRv3hwvL6+CirPkcHCH9zfDa1NASluR+vZhWN4SLu+gmks1Nry+gSrO+qn0T1Oe8uGuDwm5E2KykIW86VTbw/D7X6J7rEipWbMmu3btYtWqVfz4449UqlQpT+d5+PAhffv2pVq1arz22muEhoaya9cu2rVrZ+SIS4a7Fy+gVcgoc9A3JVspQQZtASRCgJg1JhjkabD03r17+f333zOVb9q0ibVr1+Y7qBJPoYBWY2DADnBKGyyZ9BR+7QU7xuOhKsXaTmtp7NEYgGRtMqOCR7HpyiYTBi3kVqUy9lT30C+Ueep2NJExyS85QjC227ez71ru0qUL586d47PPPgPg3r3cbZb7008/cfPmTVJSUnj48CG7d+8WSVA2oiPu5zgRkiwVSLKRW4RSn/0Niq4xIV2eEqGZM2dSunTpTOVubm7MmDEj30EZm4+PD3Xr1qVevXoEBAS8/IDCUqEpDN4P1bs8Kzu2DH5qh2PcI5a3XU4nn04A6GQdXx35ip8v/GyiYIW8aF/rWatQyGXjDdAVcqZx48YMHjyY0NDQF9ZJTEzEzs6O2rVr88cffxRidOYn+Wk8WoWMZQ6GKyuslCh0kpFbhJ51gYrp80K6PK39f/v2bSpWrJip3Nvb+6XfwEzl8OHD2Nu/ePEuk7F10a83FPoj7PoCtGq4fwZWtMaqywJmtZ6Fu507a/5dA8Cc0DmkaFMYVGeQaeMWciSgWhkW7bkKQPDlh/RqUsHEEZmXCxcuMH36dNq1a4e1tTUNGzbEy8sLa2trnj59yoULF/j3339p0KABc+bM4fXXXzd1yCWaJkmNTgGWOdhj0cJKgVJWoFMYb0D7k7gUVqf2ACQUIg8S0uSpRcjNzY2zZzMP4D1z5gyurlnvHSNkQ5KgSSAM2gOuadtspO1kr9g2gjF1BjPUb6ih+sJTC1katlRs2FoM1C3njIudfoXcQ9eeoNYYcU0U4aVcXV357rvvuH//PosXL6Zq1ao8fvyYq1f1yWmfPn04efIkR44cEUlQIdCqdcgKsMzBCB0rG0sUOgUvWXcxV/bd07FY+5b+/BZ5+vgTSqA8vRJ69+7NyJEjCQ4ORqvVotVq2bt3L6NGjaJXr165Otf+/fvp2rUrXl5eSJLE1q1bM9VZsmQJPj4+WFtb07RpU44fP56ra0iShL+/P40bN2bDhg25OrZQedbV72Rf97nn8PR6pJWvMsSzNaMajDIULzuzjIWnFopkqIhTKiT8fcsAEJ+i4cStKBNHZJ5sbGx45513WLBgAVu2bGHnzp2sX7+eTz/9lNq1a5s6PLMha2V0koyl/PLsxtrGCoVOadRESJO2XyaAjaXSeCcWirU8JUJff/01TZs25bXXXsPGxgYbGxvat2/Pq6++musxQgkJCfj5+bFkyZIs79+4cSNjxoxhypQpnDp1Cj8/Pzp06JBhQbR69epRu3btTD8REREAHDx4kJMnT7Jt2zZmzJiRZWtWOpMvga+yh7dXwJvLwVK/TUP6TvaDkuGzRuMMVX86/xNzQueIZKiIa1OtjOH3kMuPTBiJIJiWTtYhS2Apv/yjx9raCqWsMOrihxpNKhL6Vlml6BsT0uQpEbKysmLjxo1cunSJDRs2sHnzZq5fv86qVauwsrLK1bk6derEN998w1tvvZXl/d999x2BgYEMGDCAmjVrsnz5cmxtbVm1apWhTlhYGOfPn8/0kz6dv2zZsgB4enry+uuvc+rUqRfGU2SWwK/XGwbvA/c6+tuaZPjzUz44u5Mv639iqLb+4npmh84WyVAR1rpqGcOaJcGXxIBpU9izZw/NmjXD2toaBwcHGjduzOzZs7PcM1EoODpZiyzJWObgo8fGWoVSpwQJ1OqsF6jMLa0mFQuRCAn/ka9O0vTZWB07dsTb29tYMRmo1WpOnjxJ27ZtDWUKhYK2bdty5MiRHJ0jISHB8GYXHx/P3r17qVWr1gvrF6kl8NN3sm8c+Kzs8p/0/Hs2X1V9z7B/8oaLG1hwaoFIhoqoUnZW+JVzBuDqw3gexIpp9IXp2LFjdOrUCZVKxaRJk/jyyy+pW7cuc+fOpXbt2tm2EAvGJaNBBqx4ebeUnbU1Sp1+Pk9iinESVq0mFWVaG5OYPi+ky9OsscTEREaMGGFYM+jKlStUqlSJESNGULZsWcaPH2+U4B4/foxWq8Xd3T1Dubu7O5cuXcrROR48eGBobdJqtQQGBtK4ceMX1lepVKhUqrwHbWyW1tB5rn5V6v8Ng6QoiI/krb9noaj3JpNi9K1bq86vwlppzZB6Q0wcsJCVllVcCbsTDcDRG0/oVq+saQMyI3PmzKFbt25s2pRxHa7ExEQGDx5M586dOXfuHM7OzqYJ0IzoZH3LjtVLN1wFOzsbQyIUnxSPs0PmJVtyS6PRoJB0IIsWIeGZPLUITZgwgTNnzhASEoK1tbWhvG3btmzcuNFowRlDpUqVOHPmDGfOnOH8+fOMGjXq5QcVRdVfh6FHntueA7qFbeXLVFvD7aVnlrLq/KqsjhZMrHmlZ2/iR66LzXQL05EjRxg+fHimcltbW9auXUu5cuVYvny5CSIzL3FPo9CQBBJYSS8fQuFgY40yLWFKTIwxSgxarcbQImQhEiEhTZ4Soa1bt7J48WJeeeWVDItS1apVi+vXrxstuNKlS6NUKnnw4EGG8gcPHuDh4fGCo0owBw/99hztp4PCEoCedy/xWXS8ocr8k/PZcLEIz4wzUw29S2Gl1P+5HRaJUKF69OhRluuegb6rfdSoUfz555+FHJX5uXTiKGor/fgcVQ4SISd7OxSyvgst4XHuVvx+EY1Gi1LSJ0KiRUhIl6dE6NGjR7i5uWUqT0hIMOpqnVZWVjRs2JA9e/YYynQ6HXv27KF58+ZGu06xolBAi+EQuAdK+wLwwdMoRkVFG6rMOj6LLVe3mChAISs2VkrqVXAG4HZUotiNvhBptdoMLdf/1bBhQy5fvlyIEZmn+5cuEm2vn75uo7B9SW0o5WCHIn2M0JMIo8Sg1WpEIiRkkqdEqFGjRhm+QaUnPz/++GOuE5T4+HjCwsIICwsDIDw8nLCwMMMK1WPGjGHlypWsXbuWixcvMmTIEBISEhgwYEBeQi85PP30aw411D8Pg2JiGfz0WfPx1CNT2Xt7r6miE7LQvNKzxUZF91jhWrduHceOHSM5OfNAdUdHR6Kjows/KDOzSf0HfzfTz5p0UDm/tH4pBzsUacNY46MfvKR2zmi0WsOK0iIREtLlabD0jBkz6NSpExcuXECj0bBw4UIuXLjA4cOH2bdvX67OdeLEiQz7f40ZMwaAfv36sWbNGt59910ePXrE5MmTiYyMpF69euzcuTPTAGqzZGULXRdAlbawbTjDop+SoJBY7+SITtYxbt84VrRbQSOPRqaOVABaVHZlYdp2G0duPKFHIxMtzWBmWrVqxddff01cXBwWFhZUq1aNhg0b0qBBAxo2bIi7uztarfG2cRCy9kSVCjJ0SCrH6L6LX1rf4blZY0lxT40Sg1arfW6MkFhZWtDLUyL0yiuvEBYWxqxZs6hTpw5///03DRo04MiRI9SpUydX52rTps1Lp30PHz48y8GOQpoaXaBsA6QtgxkXvp9opZLt9naodWpG7BnK6k7rqO5S3dRRmr16FZyxUipQa3WcumWcN3bh5dK/nF29epWTJ09y6tQpTp06xbZt24iOjhabbxYStSSj0MHMD/+HZQ7Wm1MqlSjTFl5MTjDWYGl9i5AkiRYh4Zk8JUIAlStXZuXKlcaMRcgPRy/44H8ojizmq71fE61QcNDWhnhNEh//1Zefu/5GeScfU0dp1lQWSuqUc+LkrafcfJLI4/gUStsXoaUaSriqVatStWrVDNsAhYeHc+LECU6fPm3CyMyDWiGjkKUcJUHpFGnrDaUkJxglBq1WhyTpx4SI/FdIl+O2wf9uO5Hdj2AiCgW0HIllYDDzcMMvWb9mxxNtEoO3vElU5BkTByg08i5l+P2kaBUyuYoVK9KjR49cbw0k5F6qQkahy113lDJt1lhySpJRYtDo0hIhhSRahASDHL8qnZ2dKVWqVLY/6XUEE/OojW1gMEt83qaKWj9L446kZeS2d0k+sQrECtQm0+C5REh0jxUNV69exd/f39RhlHhahYwyB5utPi+9forGOKuxa3U6JCQkSRIrSwsGOe4aCw4OLsg4BGOzUOHUYRbLLr/Ce4cn8EgBZ1SWTDz2Dd9e+QfFG4vAvszLzyMYVcPnEqETIhEqEtRqNQcPHjR1GCWeVpJRaXPZIiTpW4RSNer8B6DTodHJSEpQSGKLDeGZHCdCCxcuZM2aNTg6OrJu3TrefffdorUVhZAlj2pdWOzgTv9/BpGEjr/t7Sj76DBjljSB17+F2t1FZ3khKm2vwsfVlptPEjl3N4YUjRaVxcv3XRKE4k4n6bDU5W5YqpTWIpQqG2FWnyYZLcrnWoTyf0qhZMhxer59+3YSEvQD1gYMGEBMjHFG8QsFr6ZXY+a+9j2KtE1aVzs78puFGv74EDa+D3HGWaNDyJn07jG1Vsf5e2JMXUH7+OOPWblyJSdOnECtNkLLgpAnOgksc9k1ZpHeNWaMRCg1CY2sAElCgZg1JjyT4/S8evXqTJgwgYCAAGRZ5rfffsPR0THLun379jVagIJxtC7XmglNv2D6sekAzHAthadGQ6tL2+HmQeg0B+r2FK1DhaCRtwubT+m3DDh5KypDd5lgfOfOnWPDhg0kJCRgaWlJzZo1DWsINWjQAIVYT6bAxTx5jCzJWGlz9/6S/uVNgy7/QaQmokUJkhgjJGSU40Ro+fLljBkzhj///BNJkpg0aVKW629IkiQSoSKqV/Ve3Iu/x5p/16CVJMa6lWHt/UiqJ0fDlo/g3y3QZT44epo61BKtoZg5VqgOHTqELMtcvnzZsIbQqVOn2LJli2FFabGWUMG6HHoYWYLcDqZQSIAMD+zUXLkVhq93vbwHkZqEFgUyEpIkvvMJz+Q4EWrRogVHjx4F9BsVXrlyJcv9xoSi7ZOGn3Av/h7/3PqHRIXEsHIV2HDrJh5aLVzZAUsPQ8dZ4NdbvFMUkKpu9jhYWxCXrOHkrafIsiw+iAvQv//+i0qlonr16lSvXp333nvPcN+NGzc4efKkWEeogN399wK4gnUuu8aktBahIxWSmbxrAEEf5eP/Ka1FSE5rZxJdY0K6PLUJh4eHU6aMmHFUHCkkBTNemUHdMnUBeCinMqxaQ+Lt05La5BjYOgQ29IAY4+z4LGSkUEg0qKBvFXocr+Z2lNiAtSCNGTOGpUuXZij7888/6dOnD99//z2NGzcW6wgVsEd37oIEtorcTQyQAGSoGWlFMvkcJ5SahAYFOjF9XviPPCVC3t7eHDx4kPfff5/mzZtz757+A/Pnn38W01CLAWsLa75/9XvK2ZcD4EpSJOPrBqCt0/NZpWv/wNJmEPoT6IzQPy9kIBZWLDxnzpyhe/fuhtsXL17krbfeYt++faxfv54mTZoQEWGc3c2FrMWl6CcF2FnZ5Oo4SQIUoFJLaKV8rn+W1iKkS2sREomQkC5PidAff/xBhw4dsLGx4fTp06Sk6FcwjomJEd+sigkXaxeWtV2Go5V+wPu++0dY5FMTem8Eh7QxQimx8OcYWNUeIs+bMNqSR6wnVHhiYmIoX/7ZBrfr1q2jUqVK3Lp1i7t37+Ln58esWbNMGGHJl4D+M8LW0i5Xx0npH1E6BVrymwjpW4S0aWOERM+YkC5PidA333zD8uXLWblyJZaWlobyli1bcurUKaMFJxQsHycf5vrPNSxatur8Kv7PIhWGHoV67z+reDcUVrSGvyeB2jh7/pg7v/LOhjEKYoXpglWuXDnu379vuL1nzx569OiBUqlEpVIxYcIE/v77bxNGWPIlK/Ur3NtZZD3T+EXSG21kWSKXE84yS01KaxHSv9+JMUJCujwlQpcvX6Z169aZyp2cnAyzMITioblXcz5r/Jnh9tTDUzkbfxveXAL9toNrVf0dshYOfw9LmsLlHSaKtuSwU1lQw9MBgMsP4ohJSjVxRCVX27Zt+e677wC4desWp06don379ob7K1euzJ07d0wVnll4aqvfK8zD0TtXxxm6r2TyP4E+NVHfIiTrh2CLCQpCujwlQh4eHly7di1T+cGDB6lUqVK+gxIKV+/qvXnH9x0A1Do1o4JHEZkQCRVbwZBDEDARlGkTX2PuwK+9IKiPGEydT428XQD91m9hd6JNG0wJNmnSJIKDg6lUqRLNmzenfPnyvPLKK4b7Hzx4gL29vQkjLPke2+tbkv3rvJWr4yRJ/xElywojjBFKQoslWhn9oooiDxLS5CkRCgwMZNSoURw7dgxJkoiIiGDDhg18+umnDBkyxNgxCgVMkiS+aPIFDd0bAvA46TEj944kSZMEFirw/wyGHoFKbZ4ddGk7LGkChxaCMfYBMkPPb8B68maUCSMp2cqWLUtoaChvvfUWnTp1YvPmzRlaA/bu3Yuvr68JIyzZHty5SYxdIsjgW6lhro5VpCdCOim/c8b0g6UlC8NII9E1JqTL3cYvacaPH49Op+O1114jMTGR1q1bo1KpGDduHIMGDTJ2jEIhsFRaMr/NfHr/2Zt78fe4GHWRyYcmM6f1HP2Hhmtl+GArnPsddk2AhEegjod/JsOpddBhJvi2f+l1hGfEgOnC4+3tzbx587K878KFC7zzzjuFHJH5OL5tCwk2WlQ6cr02mUKZPt1eQmOMMUKSDTqdDEoxa0x4Jk8tQpIkMXHiRKKiojh//jxHjx7l0aNHODk5UbFiRWPHKBSSUtal+P7V77G1sAVg582drDy38lkFSYK6PWB4KDQaSNoqH/DkGvzSAzb0hCfXCz/wYqqssw2eTtaAvmtMoxXLFJjCunXrGDVqlKnDKLEeXrpOspUWR13uu7aUacmKBEZpEdJIFuhkfRwiERLS5SoRSklJYcKECTRq1IiWLVvy119/UbNmTf7991+qVavGwoUL+eSTTwoqVqEQVC1VlVmtZhlWdF18ejH77+7PWMmmlH4rjsH7oELzZ+VXd+kHU/8zGVLiCjHq4iu9eyxRreVSZPbPmSzLRMYkc/dpov5brSAUA8kxyWgsZMrkYdqXUpHWaSFLaPObuKQmocWC9D8d0TMmpMtVIjR58mSWLVuGj48P4eHh9OjRg48++oj58+czb948wsPD+fzzzwsqVqGQBFQIYHj94QDIyIzfP57bsbczV/T0gwE7oPtP4OClL9Ol6scNLWoAJ1aBVlOIkRc/zy+seOT6kyzrJKm1LNpzlaYz9tBs5h5emR2M31d/8+lvZ7h4X+xeLxRtTyzikSXwJndT5wGUSn0iJMkYpWtMI1mgTcuExBghIV2uEqFNmzaxbt06fv/9d/7++2+0Wi0ajYYzZ87Qq1cvlMrcLZ8uFF2D6gzitQqvARCXGseo4FEkpmaxFYQkQZ139N1lrcaC0kpfnvAQtn8Cy1rop9vLogUjK62qljb8HnLlYab770Ql8tbSQ3z3zxUexqUYyuOSNfxx6i6vLzrAF1vO8TRBDFgXip6EuDhuu+pf135uTXJ9vIXy2TBWowyWRmnoGhPT54V0uUqE7t69S8OG+lH/tWvXRqVS8cknn4gXVAmkkBR80/IbKjrpx3xdi77G5MOTkV+U0Kjs4bUvYdhxqPHGs/LHl/XT7dd0hnsnCyHy4qVyGXvKldJvO3A8PIr4lGctaBHRSfReedTQZaZUSLSqWpq2NdxxtNZ/QMgy/HLsNq/OC2Fj6G3RZSYUKf+3cB4PXBJAhs6tcz8Oy9JCv2CvBMiShFqdkv0B2UlbR0gnA7IsusYEg1wlQlqtFisrK8NtCwsLsf5GCWZvZc/CgIXYpS2Lv+vmLtb8uyb7g1wqwrs/w8C/odxz3wBvHYKVr8KmARAVXnBBFzOSJPFqdf2Gt6lamZ3nIwGISUql76rj3H2qX4iuYmk7doxqxc8fNuXHfo04+sVrfPF6deys9K2wTxNT+fyPc7yz/DCnb4sZaELREH7tKrc9ErHX6XB29cn18emJUPqc96T8rGyv1rcIpZ9OdI0J6XI1fV6WZfr3749KpV9cLzk5mY8//hg7u4z7x2zevNl4EQomVdGpIjNfmcnI4JEALDi1gGou1Wjh1SL7Ays0hQ//hov/B7unQlTabLJ/N+vLGvaH1mPBwaNA4y8O3vDzYt2RWwBsOHaLN/y8GLL+JNcexgPg42pL0EfNcHe0Nhxja2XBR60r061eWab/eZFtZ/Sbhp66Hc1bSw/T0LsUXet60tq3DD6udijEm36xplFn7PrU6DKOvdNo/3s7Y33dfzZO/u/xWk3Glc21uowdUTpZS6pajU6rIVWtJlWdjEaTikajITU5idTUVLTqVGStBq0mlYT4GA7+u4eQVrfRSTDc44OcP9jnWFqpQG2Yn4o6NTlP59EfHIdGTluXiLx1jWXVIv7CVvICoFDkaaJ38fKi5zOrciM9H7lKhPr165fh9vvvv/+CmkJJElAhgI/9Pmb5meXoZB2f7f+MjV02Uta+bPYHShLUfAOqdYKTayBkFiQ+1g+oDl0Jp3+GJoHQ8hOwcy2Ux1IUNfQuRXUPBy5FxnH6djSt5uzlQay+C8DVzop1A5tmSIKe5+5ozaLe9enVuDyT/neeG4/035hP3npq2NXePm07jwoudpR3saF8KVvKlrLB2dYSR2tL7K0tUEoSCkm/GaV+Q8pn2xAoJDGe4kVmzpzJ5s2buXTpEjY2NrRo0YLZs2dTrVo1o16nyYYGpBa3ZLY6IENv5Sv06TQ+T6ewNiRC+seenJKfRCgBLekrVT+bmv9fmqdPid4wG/W/W3BzeYBCIeuXP3rB019o/yvF7L+/wLlWhREnjHKqXCVCq1evNspFheJniN8QLjy5wP67+4lJiWF08GjWdVqHjYXNyw9WWuoTnrrvwuFFcGQppCaAJlm/f9mJ1dBsKDQfBjbOBf5YihpJkhgaUIWRv54GMCRBVhYKfujbiAquti89R4sqpdk5qjVbTt/lp4PhXHkQb7gvPkVD6M2nhN7Mf5dZelKkT5L+8ztShvvtVBYc/eK1fF+zKNu3bx/Dhg2jcePGaDQavvjiC9q3b8+FCxcytZTnR4C2HAmpGbuFMn8uZv9JKf3n/ky3/3O4JGcsSIjWotVKIGW8kvSff0lLGtyVjnQKGErrBp2yjSs7NipriAcprTUgNTUpz+ciJf65FqGsxwglnTnD02m98KobgewGOiTiJCd0Cis0Cuv/PElSlr++oCBLco6zGwn7Mp7YOpR6rui/x77ktpTdfS+KI9P/7gsu9YLnJcvzveRcGV5g2Tymsg2yuU7u5GllacH8KCQFM1vNpPf23tyOu82lqEt8deQrZrwyI+etBdaO8OokaDIYDi2A4ytBm6JfoXr/HDj+A7Qcqb9fZV5jz7rW9eTEzShDF5mzrSVL+zTIsPr0y1hZKHi3cQV6NirP5Qdx7Ln4kNO3o7kQEUNETD6+ST8nfaDpMy/uFtCZwUzBnTt3Zri9Zs0a3NzcOHnyZJYbU+fVvEE7X16pBLJV6b9opSdtKfnqGotHm5bcyTKZuotTHzwkfnZ3POs80tdR2aMcuBMnjzp5v6ZQLIhESMgxRytHFgYs5L2/3iNJk8T2G9upXbo2fWr0yd2J7MtAh+n6FqD9c+HUWtBpIDka9nylbzFqNQYafQiWWXcJlTSSJPFVt9q827g8D2KTaezjgoO1ZZ7PVd3Dkeoez9ZtiUtO5e7TJO5EJXLnaRKRMUnEJmmITU4lPkWDTpaRZX3yopP14x5kWZ/myOllAIbf0+6XnyU8clp5+vG2Vub39hITEwOAi4tLlvenpKSQkvJs5lNsrFgHKjv2tvrW0PSFWVLy2iKk04E6AQ3PJUL/+QL35Nsv8PB9xBOFAieFCotPL4HKIa+hC8WI+b1TCflSpVQVvmn5DZ/u+xSAb0O/xbeUL409Guf+ZI5e0OU7fSvQvjlw5leQdfpxRLu+gMOLofWnUL8vWFi9/HwlQC0vJ2p5ORn9vA7WltTwtKSGZ+4XtRNyRqfTMXr0aFq2bEnt2rWzrDNz5kymTZtWyJEVXw42+pbh9BahVE0ep8+nJgLycy1CMsrnxtmm3LiBxa2dJNeyIFplg0uVjiIJMiNmMARdMLb2Pu35sPaHAGhlLWP3jSUyITLvJyzlA28u1a9BVOvtZ+VxEfDnp7C4IZxeL1apFoq0YcOGcf78eYKCgl5YZ8KECcTExBh+7ty5U4gRFj+OtmmJkJTeNZbHRCht2n36dn46MrYIRf+2CbtyqZxVWVA5KQ6povG6NYWiTyRCQp6MqD/CMIU+KjmKT4I/Qa3N5+rGpatCj9Xw8UGo1vlZefRt+N8wWNIEzm4CXb7XmBUEoxo+fDjbt28nODiYcuXKvbCeSqXC0dExw4/wYrY2+gHnhhahvHaNqeP13bg8axFKT65krZa4nVuxdkyhfvqstMqv5i9woVgRiZCQJ0qFkjmt5xim0J9/cp45oXOMc3KPOtD7FwjcC1XaPiuPug6bB+m37bjwP32/vyCYkCzLDB8+nC1btrB3714qVqxo6pBKFFXaGMFnXWN5/LKljjdMnQf9oH9lWoNQ0tmzWBGJJMlYAvRYC04vTmaFkkckQkKeOamcWBCwAJVSv8Dmxssb+b/r/2e8C5RtCO//AQN3gU+rZ+WPLsFvfeEHf7i8U+xjJpjMsGHDWL9+Pb/88gsODg5ERkYSGRlJUlI+pnkLBpaW+vcWWdJ/VKVq89Y1dvxmNM1TvjfclmXZMGssPmQfqtIqDF+rPOvmOV6heBKJkJAv1V2qM7HpRMPtr458xZWnV4x7kQrNoP926Lst47YdkWfh13fhx7ZwPVgkREKhW7ZsGTExMbRp0wZPT0/Dz8aNG00dWomgSkuEJEk/b0yTxxahyw8TeYITPevouyJ1umdjhBJDQ1FVLkOCQkKrsABnbyNELhQnIhES8u2tqm/xdlX9IOdkbTJjQsYQr45/yVF5UMlfv23He5vA0+9Z+b0T8PObsLYr3D9j/OsKwgvolxnI/NO/f39Th1YiWKV1jclpSYs6j4lQijoFa9T4++r39dMho5AkZLWa5PPn0TnrSEEitZQ3KJQvOZtQ0ohESDCKCU0mUMOlBgC3Ym/x5aEvC2YPHkkC3/bw0T54dz241Xx2380DsMIftg6D2PvGv7YgCIVKZZm2cn1ai1Beu8bUajVWpPJErV8xRpZBqYDkixeR1Wq0VonIEli5VDZK3ELxIhIhwSisLayZ12YeDlb6tTd2397NugvrCu6CkgQ1usLHh6D7T1AqfZCqDGHr4fuG+rWJ8rMkvyAIJmWhTEtc0hIhrTY1u+ovpFarUUkazkboW6pL26uoVMaepLAwLJ0UWGifYokCheNL9k8USiSRCAlGU96hPDNfmWm4Pf/kfE5EGmdTvBdSKKDOO/o1iNpPB1XaYoSpCRA8HZY2g2u7CzYGQRAKhEKpRJIlpPTB0nn8YpOiVmMl6UjR6IdEH53wKo19XNCc3UGVTncplfgUaxkQiZBZEomQYFT+5f0JrBMI6BdbHLd/HI+THhf8hS2soMVwGHkamnxkaErn6U1Y3x02DYC4fCz6KAiCSSh1CkIr3AQgNTlvYw/V6lRUCh1qjX4NMmXajDHd3QsATPSujrVOo1/tXjA7IhESjG5YvWE09WwKwOOkx4zdNxaNrpBWhbZzhde/haFHwPuVZ+X/bobFTeDkWjG7TBCKkRb3XsMpWb+wYmpiXJ7OkZKqwUopk6rR/+1LkoTm6VNIeoqssOK0Lu28jp5GiVkoXkQiJBidUqFkdqvZuNnqZ2icfHCSRacWFW4QZarpp9x3Wwo2aRtgpsTA/42EoPcg/lHhxiMIQp44JdShTHwpANR5bRHSaLFSgFr7bBHWlKtXUVrp0Fo5UEaTNvbIQbQImSORCAkFwtXGlXn+87CQ9IMdV/+7mt23CnmsjiRB/T4w/ATU6/Os/PJf+rFDl/4q3HgEQcg1WSmj0K/5jEadnKdzqDVaVEoJjTZ9kw1IuXYNpUpCbW2HY3qCZOtqhIiF4kYkQkKBqedWj7GNxxpuTzo0iZsxNws/EDtX/aauvYPAroy+LPExBPWGHeMhjzNRBEEoeLJCRkL/hUqTx01XUzQyVhYKUrU60jOhlKtXsXSxJclShW16d7mVnTFCFooZkQgJBeq96u/RyacTAAmpCYzZN4YkjYmmtFfrBEOOQLXXn5UdWwZr34C4B6aJSRCEbMkKkLAEOe8rS6u1OlQWClJ1sqFFSH31GhaOKmIVStwt7AEJ0tctEsyKSISEAiVJElNbTKWSUyUArj69ytdHvi6YxRZzwr4M9PoFOn8HSit92e3D+n3L7hw3TUyCILyYkmctQnmcdJGilVBZKknV6pAkCVmWSbl6FQtbBdGSjLuFnb41SJJefjKhxBGJkFDgbC1tmd9mPjYW+m9b/3fj/9h0ZZPpApIkaPwhDNjxbHBk3H1Y0wUuGnHTWEEQ8k8ByPoxQkddHjN9ff/cHS/LqHVgZWlBqlZGkkD7+DHamBgUljqeSjIuSmvRLWbGRCIkFIpKzpX4qsVXhtuzjs/i/OPzJowIKNcIBu97Ns1em6Lf1T70R9PGJQiCgaSUUGCJVapEqgRHkk7m7gSpSaTIFqgsrdBodSiQSLl6FQAFKTyRU3GSLEQiZMZEIiQUmo4VO/J+jfcBSNWlMiZkDNHJ0aYNyt4NPtgCdd/V35Z18OensHe6WG9IEIoAyUJC0imx0iqo+diGVCmXf5dJT0nBEisrKzQ6fYtQ6sUjVHg1CpIe80CnxhGlSITMmEiEhEI1puEY6pWpB8D9hPtMPDQRnazL/qCCZmEFby6HlqOele2fA3u/FsmQIJiYQikhyRaAjEJDHhKhKNRYorK21rcISRLcOoKdWzJy3Z7sVIE9EljZF0j8QtEnEiGhUFkqLZnrP5dSKv0Cafvv7mfNv2tMGxTo9yxr9xV0nPWs7MA82DfbdDEJgoBCqUCSlSDJKDUS6tyOZ058QopsiZXKFo1ORiGB9tE9ZFlJVMdvuGWhxE6WRYuQGROJkFDo3O3cmdlqJlLaRNZFpxZx6sEpE0eVptkQeH3us9shM/UJkSAIJqG0tECSLZAlkDT6cUK5khiFGgtUNs8SIflpJLLS1rAPorVOB5a2xg9eKBZEIiSYRMuyLRlUZxCQtjnrvnFEJUeZOKo0TQKhw8xnt/d8BWG/mC4eQTBjFhYKQyKk0Eqo06a/51ha15iVygatTqZMUjQSKcgqR0MipNKmiq4xMyYSIcFkhtYbSmOPxgA8THrIFwe+MP14oXTNh0Lbqc9ubxsJ4ftNFo4gmCsLKwskFMiSDFoJjSSRkJyY8xMkRpGCFVYWSrQ6mQqxkSgtZSR7F0MiZKFJEV1jZkwkQoLJWCgsmN1qNq7W+v19DkUc4sdzRWjqesvR0DhQ/7suFYLeh0eXTRqSIJgbSwslCllK6xrTf2TFJjzN8fFyQlrXmKUCjU6mQkwkChsJyaEMj5Me46RyQqFOFImQGROJkGBSZWzLMKv1LMN4oSVhSzh+v4is8CxJ+sHTVTvob6fEwK+9ITnWtHEJghmxtLJAkhXoJECr/8iKT8z536AmIQodCqyUCrQ6mfIx97F0tEayduRJ0hOqK+wgJU50jZmxEp8IXb58mXr16hl+bGxs2Lp1q6nDEp7TzLMZQ+oNAUAn6/j8wOeGJmuTU1rAO6vAvY7+dtR12DZCTKsXhEKisrLSd40ByEoAEpJicny8OlFfV2WhT4TKxdxHaasEaycc7p/jxwtH9Zsw27oUQPRCcVDiE6Fq1aoRFhZGWFgYBw8exM7Ojnbt2pk6LOE/Pqrz0f+3d+/hUVT348ffM3vNnYRAQgQMoqIYJFwD9YaKIFq+xdpW+7NKQalaqNpYKqiArSj1+sULlYoitn6tqE9BixZRiiCCXALhLqJcBZIAIdnc9jYzvz8mWYi5kIQkk+x+Xs8zD7szZ3Y+M2R3P3vOmXMY0mUIAMcrjjNl1RQ0XbM4qkquWLjl7+BKMJ/vXAzr51kakhCRwuWyo1Q2jaFXJkKNqJUtKykCwGlXUQydLkV52JwGuBMwSisnW759EfS7vZkjF+1F2CdCp/vwww+59tpriYmRtuC2xqba+MsVf6FTVCcA1uWt429b/2ZxVKdJOg/GzDn1/JOH4egW6+IRIkKYNUIKugJ2zWxCL68oa/D+RSVmx+oYl53U8kJcWgBF8YM7Hq+vsmap+1BzYFURkSxPhFatWsXo0aNJS0tDUZRam63mzJlDeno6brebrKws1q9vWh+Sd999l1tuueUsIxYtpWNUR56+8mlUxfyznLtlLmuOrLE4qtNcPBqGTjIf6wFYdA8EfdbGJESYi3a7Qn2EbHplIuRrYNOYFuBkRQCAGKedzLI9pA4sQgmWgrsDPl+JWc7maonQRTtheSJUVlZG3759mTNnTq3bFy5cSHZ2NjNmzGDTpk307duXkSNHUlBQECqTmZlJRkZGjeXIkSOhMh6PhzVr1nDDDTe0+DmJphuYOpDf9fsdAAYGU7+YSkF5wRn2akXXzjjVX6hgp4w8LUQLi3Y5UQ3zq0qtHF3D62tgjVBpAScNsxN0tNPGdeSQeH45dB9CoOsg9GAFumIzR5YXEctudQCjRo1i1KhRdW5//vnnmTBhAuPGjQNg7ty5fPTRR8yfP58pU6YAkJube8bjfPDBB4wYMQK3211vOZ/Ph8936le+xyN3CLW28RnjycnPYfXh1RR6C/njqj/y2ojXsKuW/7ma1ec3vQKvXm3WCq3+X+h1I3QdYHVkQoSlaLcLhapEyKwR8gYaOI5QSR4njVgUwGFT6RYswB+w4xz3McUVx3EaBrrNYX2NgLBUm/7/9/v95OTkMHz48NA6VVUZPnw4a9eubdRrNbRZbNasWSQkJISWbt26NTpucXZUReXJy58kJToFgJz8HObk1l5jaInUPnDVQ+ZjQ4d/3wda0NqYhAhTsdFuFMNMgGyVNUK+hiZCpXmcJJYOUXaCukGqXkiZYU6l4fF5cBoG2KRvUKRr04nQ8ePH0TSNlJSUautTUlLIy8tr8OsUFxezfv16Ro4cecayU6dOpbi4OLQcOnSo0XGLs5foTuTZq57Frpi1QK9te40vvv/C4qhOc/kDkHqp+Th/O2xoQwNBChFGYt1RoXHGXEHzTlJfoIFNY54jnCSBxBgXvuPHiVMrKFHNuz+L/cU4DKSTtGjbiVBzSUhIID8/H6fzzH/wLpeL+Pj4aouwRmbnTO7vf3/o+cOrHyavrOEJcIuyOeDG5089X/EElORbF48QYSo+2o1S2UcoSjOrhHxBb8N2Prmfk45UEmOcBHd/jc2lU+Q0R7Iv9hXjNAwUe/3dJUT4a9OJUHJyMjabjfz86l8w+fn5pKamWhSVaE1jLxnLsK7DACjyFfHQqocI6m2kGarboFNjj/g88Ol0a+MRIgzFRUeHEiE3BooBAa2iQfvuzzvO+xX9SYx2YHyzG8UNJ13mEB1ViZAqiVDEa9OJkNPpZMCAASxfvjy0Ttd1li9fztChQy2MTLQWRVGYeflMusR0AWBTwaa2Nb7Q8MfA3cF8vPUdOJJrYTBChB+304FijiuN02HDrkNFoGE1Qh8diQPghj5dUPZ8je5WKbdXNo35iolSbFIjJKxPhEpLS0MjPwPs27eP3NxcDh48CEB2djbz5s3jzTffZNeuXdx7772UlZWF7iIT4S/BlcDTVz6NTTFHlX1166tsyNtgcVSVYpJh2NRTzz97zLJQhAhfZiLkctnBUPgPufi8ZxhLyDD4vlwlI8HHTf3OwbFzG3aXFkqEinxFxKoO6SwtrE+ENm7cSL9+/ejXrx9gJj79+vVj+nSzmeGWW27h2WefZfr06WRmZpKbm8vSpUtrdKAW4S2zcyYTMycC5nxkU1ZN4aS34TNQt6iB46DDuebjvSvgu/9aG48QYcfsGxTdIZauBW58qsKevcvq38VzhO+1RM5JcBHYtoruF3+L2+anojIR8vg9xCh2sMtgipHO8kRo2LBhGIZRY1mwYEGozKRJkzhw4AA+n49169aRlZVlXcDCMuMzxpOVav7fF1QUMP3L6RhtYfJTuwuumXbq+WePga5bFo4QYUcx3+fRKSkM2ml2di4o2l//PnlbOWwk0zUlmeAXC4jr6mWl1pe9MeaP7mJfMdGKTWqEhPWJkBANZVNtPHnFkyS6EgH4/PvPefvrty2OqlLGzeb4QmDOQfbNUmvjESKsmD8s3N3TcftVVMPgwImD9e5hHNlSmQh1Rj+4hQpvHOODD3EyqjtgJkJuVKkREpIIifalc3RnZl4+M/T8uY3PsevELgsjqqSqcPWjp56vegbaQm2VEOGgskbI1fMiVAPiAnC0pP6hNI4e+AYfTrrHqKhlhymO6oaigKqYYxIV+4txK6rUCAlJhET7c2XXK7mj9x0ABPQAf1z1R8obOtJsS7pw5Kl5yI5sMvsLCSGajzuK6GCQaL+N497CustpATYcKAKg13ebcET7KejcB1VRsFV+6xX7inGhSI2QkERItE8P9H+A3h17A7Dfs58n1j1hcUSAosCVfzj1fNWz1sUiRFgxa4SCeoBODgOX10aRVlJ38YNrWevvwYVJdowP38ERrXO0w0UoCthUs0bI4/PgMpAaISGJkGifHDYHz1z5DNF2c96gD7/7kCV7l1gcFXDx/0DyhebjA1/C/i+tjUe0qFWrVjF69GjS0tJQFIXFixdbHVJ4MnMXdF2jyzmpOCqcnDTqHkuoaON7fKQP5WfOk0T7v8BAZU/sYBTMprGgHqQkUIJTEiGBJEKiHese351Hh5zql/P42sc5VGLx3HCqClc8eOr52peti0W0uLKyMvr27cucOW1oUuAwZFR2lg5qAXqO+gkdi11859RZlvtJzcLHdvPXLTrRgSA3b3uMjheVQa/rKVQTURQFm6rg8XsAsBu6NI0JSYRE+za652j+p+f/AFAeLOeR1Y9YPwVHxs0Qf475ePd/4MR31sYjWsyoUaOYOXMmN910k9WhhLfKGqGgFiT1upu5fJ+dDmV25n71CIXFBQDoQQ3fd6tZNHcm5UdtLD4xg6RzjqJd9AuUn87DVzlhq6ooFPvMwRgduiY1QkISIdH+PZz1MF1juwKwuWAz87fPtzYgmwMGT6h8YsD6Vy0NR7QdPp8Pj8dTbRENUPlNpekBAEbe/VuG7ujAHrePq/91LZe/0peRrwxgzJJJ/F/cFuIvXEpMt2PoF96E7ed/BVcsvqAe6iNUlQjZdE1qhIQkQqL9i3HEMOuKWaiK+ef8Su4rbD++3dqg+o8Fe5T5ePP/gVe+8ATMmjWLhISE0NKtWzerQ2ofKmuENM2s7U297mYe/f3LPPz1BdzwXRwZx12klTpJqnDhC0bxdic3V/XoxksXXmr+MAF8QbN57fSmMVUPgk0SoUgniZAIC5mdM5nQx6yFCRpBpn4x1dpb6qOToO+t5mN/CWx+y7pYRJsxdepUiouLQ8uhQxb3aWsvKsf+CVbWCAHEXZDBL//yL2bNXMPcaet5c+p6/m/KOj588Cv+eslfSC+I5fVdb7D+6HoAfAEzETq9aUzVAmCXprFIJ4mQCBt3972bjI4ZgHlL/fM5z1sbUNY9px6vf1Wm3RC4XC7i4+OrLaIBKhMhQ9caUFThR1k38My1z5HocfDoiqkc8hzCF9RQAJsKnoqTZAUMlKBXaoSEJEIifDhUB7OumEVUZZPUwt0LWfX9KusC6nwR9LjKfHxyH+y3MBYh2rHKVu9G3Qhx/sAhDPNncDRQwMyvZuINaICCTVFI+H4Dr31/CIJeiO3cMkGLdkMSIRFW0hPS+cPAU4MaTvtyGicqTlgX0MBxpx5vfMO6OESLKC0tJTc3l9zcXAD27dtHbm4uBw/WPw+WaBxFNb+qqvoINdTYYZMYuKsDOfkbKQ+Y4w6pqoJWXvmZ8LtN5l2eIqJJIiTCzs8v/DlXdTVrYgq9hTy25jHrZqnvdSPEdDIff70ESgusiUO0iI0bN9KvXz/69TNnNM/OzqZfv35Mnz7d4sjCS1UipBuNS4TS+/YjvTwJn+7Ho+/FwMCmKPj8pWaBxB6hZjcRuSQREmFHURQe+9FjJLmTAHOW+vf3vG9NMHYn9PuV+VgPSqfpMDNs2DAMw6ixLFiwwOrQwoqq2gDQGjlGmM3uoP+Fl4MBXvIrX0sh6C9DUxRzAFQR8eSvQISl5Khk/vSjP4WeP7PhGQ56LGqu6H/Hqceb3pRO00I0kmqvbBprwmCpPTL64far6PoxDMO8fT4QKENTbM0dpminJBESYWtYt2H8/MKfA1ARrODRLx9Fa8BdJ80u6Tw472rz8cn9Miu9EI2k2uwATXr/ds/oS5TPhkM/igHYFAUtUI5e+ZpCSCIkwtofBv6h2qjTb+2yqGnq9E7TOQusiUGIdspuNwdFDAQDZyhZU3xyJ+K0KFSlEMMwzKaxYAW66mjuMEU7JYmQCGvRjmgev+xxlMqhaV/c9CJ7i/e2fiC9boDYFPPx1x9BSV7rxyBEO2Wzm7U3voC/Sft3dHdEsZdiGKCgYQR9GDLHmKgkiZAIewNTB3LbxbcB4Nf9PLr60dafmNXmONVp2tBg8z9a9/hCtGMOZ9U0GY2vEQLoHNuFoNOLrusEKcdh6Cg2qRESJkmERES4r/99pMenA7Dt+DYW7FjQ+kH0H0to0qScv4MV/ZWEaIecDnP056Y0jQGkdDiX8qggMRdO50BFLg4DsLubMULRnkkiJCJClD2Kxy97PDQx619z/8qek3taN4jEc+H84ebj4oPw3X9b9/hCtFMul9mMFQg27cfDVemjGbotCdQAx3z7cBgGqsw6LypJIiQiRmbnTMZeMhaAgB7gkdWPENCb9guzyWSkaSEaze00p81papN2TIdunPd9Iopmp0Lz4DQM1MqpeISQREhElImZE+mZ0BOAXYW7eG3ba60bwAUjIS7NfPzNUvAcad3jC9EORUdVJkJa02qEPL4ghY5E7JoNr1aKwzCwOyQREiZJhEREcdlcPHH5E9gqB1N7dcur7Dqxq/UCsNmh/+3mY0ODTdJpWogziYoy+/MEm9ivbuXuYxQ5OuAIKuhKOW5FRZU+QqKSJEIi4lySfAl39rkTgKAR5JEvHyGgtWITWf87Tk2nvUk6TQtxJnFRMQDoTRyVvcQXpNgRT4yuYrP7iFbsILfPi0qSCImIdM+l99ArsRcAe07u4ZUtr7TewRO6mk1kAJ7vzclYhRB1io2OQjFUtCYmQt6ARrE9HrtPp8xfShSqOQ+gEEgiJCKUw+bgicufwK6aA7W9vv11th7b2noBDJ5w6vHq2WAYrXdsIdqZaLcLxVDQm/g+8QZ0PPZ47EEFr7cct2KTGiERIomQiFi9knpxz6X3AKAbOo+sfgRv0Ns6B+95DaT2MR8f2QT7V7fOcYVoh+Ki3KiG2uSmMW9Ao9gRjzOo4g/4cKOATW6fFyZJhEREu7PPnWR0zABgv2c/L21+qXUOrChw2QOnnq96WmqFhKhDQkw0CmrTa4SCGuX2GJyGnaAWwIlijvYuBJIIiQhnV+08cfkTOFWzmvwfO//BxryNrXPw3mMgMd18vG8VfLu8dY4rRDsT53aj6ja+6LCdu/42tNH7ewMaqqoS64xDMzRcBiADKopKkgiJiHdeh/O4r/99ABgYPPrlo5QHylv+wDY7XDPt1PNlj0DQ1/LHFaKdcTjsXLvndnqUx5OnljZ6f19AR1UgPjoBDR0HhvQREiGSCAkB/OriX9G/c38ADpce5rmNz7XOgTNuhnMGmI+PfQ0rnmid4wrRzqSWnEeiLxlNaXzzmC+oY1MVEmKSCNp07LouiZAIkURICMCm2ph52UyiKofdf/ebd1lzeE3LH1hRYPSLpz6Uv3wBtr7b8scVop0JqkEUbDRl1C2/pmNTFBLiEtFVGJ4cxfrAyWaPUbRPkggJUalbfDeyB2SHnk9fMx2P39PyB07NgGunn3q+6B4zIdKaNq+SEOEoqAZQdFuTaoT8QR27TaF/cn+GbE/Cq8BerfFNbCI8SSIkxGl+0esXDOkyBID88nyeWv9U6xx46CQYON58bGjw6XSYMxhWPQvHdssdZSLiaaqGYjiaVCMU0HTsqoozIY6LDsYRpUOF0uwhinZKEiEhTqMqKn/+0Z+JdcQC8OF3H7L8QCvczaUocMOzcMUfTq0r/A7++7iZEM2+FJZkw+7/gL+s5eMRoo3R1CDoNoJNSGACmoHdphCIMb/ynDpUGE0bk0iEH0mEhPiBLrFd+OOgP4aeT18znbyyvJY/sGqDa6fB+E8g/Yrq24oPwsbX4Z+3wlPp8PcxsHYOFO5r+biEaAM0m4Zi2JtUIxTUdBw2Fb+/AFQNhwZepJZVmCQREqIWY84fw3XnXgeAx+/hoVUPobXW5Kjdh8Cvl8D9W2HETOhxFainDf6m+WHvCvjkYXgxE14dZvYpKjrYOvEJYQFN1VF1B0Gl8VVCQd3AaVOJObiWJLsPu+GgPCqhBaIU7ZHd6gCEaIsURWHG0BlsO76NvLI8NhVs4tWtr3Jv5r2tF0TiufCj35mLr9QcdHHPMvj2Myg+dKrckc3m8ul0SOoJXQdBp16Q0A3iUsEVC86qJdqcWsDmBFV+B4n2Q1cNFMPRpKYxTTdw2lV83iJiHT5s9g5UuGKaP0jRLkkiJEQdElwJPHXFU4z7ZBy6oTN361wGpQ5iYOrA1g/GFQsX3WAuhmGOObT7P7BjEeSdNlls4Xfm0hBVE0/anObgjjanWfOkKKCodSxK7dtRTm2n8pvK4YbbFzX3lRARyrDpKLqdoKKgaxqqzdbgfasSIb/PQ6zDj+q3UxGsaMFoRXsiiZAQ9eif0p97+97LnNw56IbO5FWTWfjjhXSO7mxdUIoCnS82lyuy4fi3sHMRfPMJHN1iNp01hKFBsMJcWoIztmVeV0QkwwaqbjYRewNeom0Nq9HxB3UMwGVXCfhLiXP6UbzO1ptgWbR5kggJcQYT+kxgY95G1uWt43jFcbI/z2b+yPk428rItMnnw5WTzSXog/wdZn+h4u+hrMC8y8xXCv5S87EWAD1gJkxa1b+nPTYMMHSg8t+q56HlB8+l06loDXZQNTMR8vnKiXY3LBEq95vjcbntNoL+UqLdGoo3SHlA7r4UJkmEhDgDm2rj6aue5pYlt5BXlseWY1uYtX4WM4bOsDq0muwuOKe/ubQWw6gc56gqgRKiBdgV1ID546PCX05iA3cr8ZqJUJTThlZaRozbwK6plHlLWihQ0d5Ib0khGiDJncTsq2fjspkzVr//zfu8u1umwgDMpjpVNW//tzmqL0I0E9WuYtPNRMjnb3hzrscbACDKYUMPVBAdBXZNodzfChMri3ZBEiEhGuiSjpdUqwV6ct2TrDy00sKIhIgcNoeCTTd/iPgbkQidqhFSMIJeYqJtZiIUkERImCQREqIRRvcczdjeYwHQDI0/rPwDW49tPcNeQoizZXPYT9UIBRqfCNkdXlyGgcMZRZQjBq8mnaWFSRIhIRope2A216dfD4BX8zJx+UT2FcsIz0K0JIfTFqoRakwi5Kkwm8Zs9jKchoHqiCY2Kh6fEWiROEX7I4mQEI2kKipPXP4Eg1IHAVDkK+KuT+7igOeAxZEJEb4cTgeqYX5l+QK+Bu9XUtlHyLCV4TIM7M4Y4qITCChBdOncL5BESIgmcdqcvHD1C/RK7AVAQUUB4z8Zz0GPTHMhREtwuRyohjmIYqAJTWN+owSnYeBwxpIQa95zNm7pOHae2Nn8wYp2RRIhIZoozhnHvBHzuCDxAgAKyiUZEqKlRLldoUTIH2xEjZCvKhHy4DbA7oylX1ImF30fz9ZjW8nJz2mReEX7IYmQEGch0Z3IayNeCyVD+eX5jFs6jr3Fey2OTIjwEhvjPpUIBRre0dlTYY607tM9RKGi2N1073weQ7YmEueIlak2hCRCQpytJHcSr414jfM7nA+YzWTjlo7jm5PfWByZaA1z5swhPT0dt9tNVlYW69evtzqksJQUH4uqm4lQsKHTyADFFWaNkFf3EK2oYHcTl9wJAJfqktvohSRCQjSHJHcS80fO5+KkiwEo9BYy/pPx0v8gzC1cuJDs7GxmzJjBpk2b6Nu3LyNHjqSgoMDq0MJOUkJ8qEaoUXeNVfYRqtCLcRuA3UV8ZSLkNGxSIyQkERKiuSS6E5k3Yh6XJl8KQLGvmLs+uUvGGQpjzz//PBMmTGDcuHH07t2buXPnEh0dzfz5860OLeykJiaE7hprTI1QUZnZn6hCK8YFYHcTFZ+AzeHAodkoD0qNUKSTREiIZpTgSuBv1/2N/p3Nub5KAiVMWDZBOmSGIb/fT05ODsOHDw+tU1WV4cOHs3bt2lr38fl8eDyeaotomJTEDqfuGmtEIlRYbpYtDZ7EqRtgd6MoCvHJnbAFkRohERmJ0LPPPssll1xCRkYGb731ltXhiDAX64zlleGvkJWaBUB5sJx7P7uXr45+ZXFkojkdP34cTdNISUmptj4lJYW8vLxa95k1axYJCQmhpVu3bq0RalhwOx3oigY0LhEqKjebxor9x3EYmjkxMRDXsRM2vyF9hET4J0Lbtm3j7bffJicnhw0bNvDyyy9TVFRkdVgizEU7onn52pe57JzLAPNX58TPJrLq+1UWRyasNHXqVIqLi0PLoUOHrA6pXdEUM6nRGpgI+YIaFQENFB8VWhl2TQO7G4C45E4o3qDUCInwT4R27drF0KFDcbvdREVF0bdvX5YuXWp1WCICuO1uXrz6Ra7udjUAft3P/SvuZ/nB5RZHJppDcnIyNpuN/Pz8auvz8/NJTU2tdR+Xy0V8fHy1RTScppgjQTe0j1BRuTmqtGIvIVHTsOmBUI1QfHInqAhIHyFhfSK0atUqRo8eTVpaGoqisHjx4hplzub21IyMDD7//HOKioo4efIkn3/+OYcPH27GMxCibk6bk+eGPceIc0cAENSDPPj5gyzdL8l4e+d0OhkwYADLl59KbHVdZ/ny5QwdOtTCyMKXppqJUEBv2DxhhWVmwjTS/hWrDlZ+7rvN5DOuYyco91MhTWMRz251AGVlZfTt25fx48fz05/+tMb2qttT586dS1ZWFrNnz2bkyJHs3r2bzp07A5CZmUkwGKyx77Jly+jduzf33Xcf11xzDQkJCQwZMgSbzdbi5yVEFYfq4Kkrn8L1pYt/7/03mqEx9YupJLmSGNxlsNXhibOQnZ3N2LFjGThwIIMHD2b27NmUlZUxbtw4q0MLS5rN7COkNTAROlmZCKXb8qjQFJRb/w93z+sAs2nMEVQ57i9rmWBFu2F5IjRq1ChGjRpV5/bTb08FmDt3Lh999BHz589nypQpAOTm5tZ7jLvvvpu7774bgLvuuosLLrigzrI+nw+f79Tw7XJXh2gOdtXOzMtnYlftLPp2EUE9yAOfP8Bbo97ivA7nWR2eaKJbbrmFY8eOMX36dPLy8sjMzGTp0qU1OlCL5qHZDWy62uBEqOqOsUS7Bw82UnrdGNoWn9wJu6ZIZ2lhfdNYfZpye2ptqgY32717N+vXr2fkyJF1lpW7OkRLURWV6UOnc8U5VwBQ4i/ht8t/y4mKExZHJs7GpEmTOHDgAD6fj3Xr1pGVlWV1SGHLsOuohkpQr9kCUJvCMj+qAvFqCb7KvkFV4jomY9dUKrSGT9chwlObToSacntqbX7yk5/Qu3dvfvWrX/HGG29gt9ddESZ3dYiWZFftPHPVM1yUdBEAh0sP89Cqh9B0zeLIhGj7DAeohg3NaFiNUF6xF7fDRhylBJ3R1bY5XG5iXLFoaAS0hr2eCE9tOhFqLmvXrmXnzp1s2LCBAQMG1FtW7uoQLS3GEcPL17xMpyhzmP91eeuYkzvH4qiEaPtUt4oBvG9s53DB/jOWz/N4cdpVYihHd9X8LE+MTwbgDyv/IHMDRrA2nQg15fZUIdqDlJgUnrnqGWyK2XF/3rZ5rDy00uKohGjbnDEOLi4YAsC2b788Y/mdRzwUlfuJ0b2o7g41tmck9uaC4mRWH14t778I1qYTIbk9VYSzASkDeKD/A6HnU1dP5WjpUesCEqKNi4mLone+2ceuqPTME9vme7wotlJi9SDO6I41tvfochFXrU/knNhzOOk72ezxivbB8kSotLSU3Nzc0J1f+/btIzc3l4MHDwLm7anz5s3jzTffZNeuXdx7771ye6oIG2MvGcu13a8FzM7TD69+WPoLCVGHxA5xOINRAHgqCustaxgGJd4gyYlFxOkGMXHn1CiTdE5XggE/sbZoin3FLRKzaPssv31+48aNXH311aHn2dnZAIwdO5YFCxbI7akirCmKwp8v+zM7TuwgryyPjfkbeXPnm4zPGG91aEK0OSnJiVRoBgCl3vprcDzeIEHdICq2kLhynbj4rjXKJKWZyVGU5uTkGV5PhC/La4SGDRuGYRg1lgULFoTKyO2pIpzFO+N58vInUVAAeGnzS+w8sdPiqIRoe85NTcZm2HHqUO6vf4y3AyfMgRJd7mPE6Tq2qMQaZeKSO2F3OHH7FakRimCWJ0JCCBiUOohxGWZzb1APMuWLKTIZpBA/0DPNbAmI0uxUBEvqLfvdsVIA3PYj2ADcCTXKqKqNxC5p2Mo0inxFzRytaC8kERKijZiUOYmLky4GYF/xPp7f+LzFEQnRtsS6o/DaynHpDiq0+keE/q6gFDCI9e4xV8TX7CMEkJjWFaXYJ52lI5gkQkK0EQ6bg79c8RdcNnME3Hd2vyO39ArxA6WuEhyaG69ef43p13klKI6TpPkrm7w69aq1XOf089AKiinxlzR4xGoRXiQREqINOa/DeTw48MHQ80e/fJS8soaPoi5EuKuI8mIPxuDFV2+5HYc92NyHOM8fQI/uCNFJtZZLOe98bKVmAiTNY5FJEiEh2phbe93KsG7DAPODecoXU+SXqhBV4sAejMNbzzQbJ0p9HPV4SY/eyr1FxajJtdcGAaT0vIDYCvMG6kMlMqVSJJJESIg2RlEUZl42k9QYc/T0nPwc/rb1bxZHJUTbENsxGkcggQql7vG2cg6cBAz+n7HR/JLrc3OdZaNi40iPT0c1VL4plGk2IpEkQkK0QQmuBJ6+8unQFBx/2/I3lh9Yfoa9hAh/53ZPJToQzzGbgS/gr7XMpzvzsEUfZJC3mOPnXw2D7qr3NXv0ziTR65L5xiKUJEJCtFH9OvdjUr9JABgYTPliCtuObbM4KiGsNfrygaR60im2G3y4emGN7d6Axr+3HKVz/Ap6+/0kXTzmjK/ZvU9fEgoVdhTI+ysSSSIkRBt2Z8ad/Pi8HwPg1bzc/dndbDm2xeKohLBOh5gYVNWNQ7Oz5pv3amyft+o7oh1beca3CsPmQL1o9BlfM71vf1JLYvm6aDflgfpvyxfhRxIhIdowRVH404/+xKDUQYA5H9mEZRNYun+pxZEJYZ2OlyRz/vHBrLMf5JM1bxMM+vkmz8OMJWv41/qX+LvjOS6v8KIOvhtiak62+kNOdxRZXbLQ0Hl6/dMYhtEKZyHaCsWQ//F6eTweEhISKC4uJj4+3upwRIQqD5Rz34r7WHd0XWjdiHNHcH//++ke393CyJpO3ltyDZpK0zSm/OkFVnddTKnrJA7NiVNzoqCgGuaiqE4cipNEJRm300XfpEzSUjvz40uuJ6GWUaYP7drO/1t+B0VxAV6+5mWu6naVBWcmmlND31+SCJ2BfFCJtsKn+Zj+5XQ+3vdxtfWDUwczNG0ofZL7kBabRkp0Ck6b06IoG07eW3INzoamabz6wcfs2beKUuUohgJ2ux2XMwZbdCI64NO9nAgcpzRYwiH3txiKjkN3EavGMeacn5F1YT8GnjMgNIjp+7Om80r8J5xz7oW8eePfURTF2pMUZ0USoWYiH1SiLTEMgw+++4D/zflfCr2FdZazq3ai7FFE2aLq/TBXFAWbYkNBwaZW/qvYQutVRa17oZ5tiorb5ubJK56s89jy3pJr0Jp85QG27PqGxbs+5NuiPeyK2wBAJz2Nc2POZWiXoVwU34MPFj7Hsl77SVDjuPPCsfTvOpD4mCQ6uDsAhCZHrnKm91e15z/c9wyv9cPtjXrtxhz7B4c5mzjbUvIoiVAzkQ8q0RaV+kt5Z/c7LP52MQc8B6wOp1ZR9ijW37a+zu3y3pJrYBXDMPj2wCE2btvOB0ffp9BXyNH476wOSzRCz4SeLB6zuN4yDX1/2Zs5NiFEK4h1xnJXn7u4M+NODngOsLlgM3uL93Kk9AjHKo5REazAG/RSEazAoI7fOoZ5W75u6OaCjq5X/lu5TjM0DMNAM+oevK4uVWMgCdHWKIrCBenduSC9O7/kBvzeIIeOHuXrvG8pqjiJrhn4vV4OFe2mNFCEVy/HR9VI1gbmW6r6+6rmu+xst1fflpSWQkyH2Dq2/+C1zlC/Ua28Uc+2BuzflNcKra/nUDXO6QfPL02+9IxxNpQkQkK0Y4qikJ6QTnpCeosfqyohMgwDHR1N1zAwqiVLoaTK0Fs8HiGai9Ntp2ePbvTs0c3qUIQFJBESQjSIoijYldM+MqTCRwgRBmQcISGEEEJELEmEhBBCCBGxJBESQgghRMSSREgIIYQQEUsSISGEEEJELEmEhBBCCBGxJBESQgghRMSSREgIIYQQEUsSISGEEEJELEmEhBBCCBGxJBESQgghRMSSREgIIYQQEUsSISGEEEJELJl9/gwMwwDA4/FYHIkQ4aXqPVX1HotE8vkiRMtp6GeMJEJnUFJSAkC3bt0sjkSI8FRSUkJCQoLVYVhCPl+EaHln+oxRjEj+OdYAuq5z5MgRrrnmGjZu3Fhv2UGDBrFhw4ZGbatt/ZnWeTweunXrxqFDh4iPj2/M6TRZfefW3Ps3pGxzXeva1su1bniZxm47fZ1hGJSUlJCWloaqRmYrfdXnS1xcHIqiWB1Ovax4L7Rlcj1OaavXoqGfMVIjdAaqqtK1a1fsdvsZ/4NtNludZeraVtv6hq6Lj49vtT+6+s6tufdvSNnmuta1rZdr3fAyjd32w3WRWhNUperzpT1pzfdCeyDX45S2eC0a8hkTmT/DmmDixIlnVaaubbWtb+i61nS2x2/M/q15rWtbL9e64WUau83qayuEED8kTWPtkMfjISEhgeLi4jaXfYcbudZCmOS9UJ1cj1Pa+7WQGqF2yOVyMWPGDFwul9WhhD251kKY5L1QnVyPU9r7tZAaISGEEEJELKkREkIIIUTEkkRICCGEEBFLEiEhhBBCRCxJhIQQQtRrzpw5pKen43a7ycrKYv369VaH1CJWrVrF6NGjSUtLQ1EUFi9eXG27YRhMnz6dLl26EBUVxfDhw9mzZ0+1MoWFhdx2223Ex8fToUMH7rzzTkpLS1vxLJrHrFmzGDRoEHFxcXTu3JkxY8awe/fuamW8Xi8TJ06kY8eOxMbGcvPNN5Ofn1+tzMGDB7nxxhuJjo6mc+fOTJ48mWAw2JqnckaSCIWxQ4cOMWzYMHr37s2ll17Ke++9Z3VIYe+mm24iMTGRn/3sZ1aHIkSzWLhwIdnZ2cyYMYNNmzbRt29fRo4cSUFBgdWhNbuysjL69u3LnDlzat3+9NNP8+KLLzJ37lzWrVtHTEwMI0eOxOv1hsrcdttt7Nixg08//ZQlS5awatUqfvOb37TWKTSblStXMnHiRL766is+/fRTAoEAI0aMoKysLFTm97//Pf/+97957733WLlyJUeOHOGnP/1paLumadx44434/X7WrFnDm2++yYIFC5g+fboVp1Q3Q4StI0eOGJs3bzYMwzCOHj1qpKWlGaWlpdYGFeZWrFhhfPjhh8bNN99sdShCNIvBgwcbEydODD3XNM1IS0szZs2aZWFULQ8wFi1aFHqu67qRmppqPPPMM6F1RUVFhsvlMv75z38ahmEYO3fuNABjw4YNoTL/+c9/DEVRjMOHD7da7C2hoKDAAIyVK1cahmGeu8PhMN57771QmV27dhmAsXbtWsMwDOPjjz82VFU18vLyQmVeeeUVIz4+3vD5fK17AvWQGqEw1qVLFzIzMwFITU0lOTmZwsJCa4MKc8OGDSMuLs7qMIRoFn6/n5ycHIYPHx5ap6oqw4cPZ+3atRZG1vr27dtHXl5etWuRkJBAVlZW6FqsXbuWDh06MHDgwFCZ4cOHo6oq69ata/WYm1NxcTEASUlJAOTk5BAIBKpdj4suuoju3btXux59+vQhJSUlVGbkyJF4PB527NjRitHXTxIhC52pPRqar20+JycHTdMiepbr1rzeQoSD48ePo2latS8ygJSUFPLy8iyKyhpV51vftcjLy6Nz587VttvtdpKSktr19dJ1nQceeIDLLruMjIwMwDxXp9NJhw4dqpX94fWo7XpVbWsrZNJVC1W1R48fP75au2qVqrb5uXPnkpWVxezZsxk5ciS7d+8OvdkyMzNr7Xi2bNky0tLSALPz3h133MG8efNa9oTauNa63kIIEU4mTpzI9u3bWb16tdWhtAhJhCw0atQoRo0aVef2559/ngkTJjBu3DgA5s6dy0cffcT8+fOZMmUKALm5ufUew+fzMWbMGKZMmcKPfvSjZou9PWqN6y1EOElOTsZms9W4Eyg/P5/U1FSLorJG1fnm5+fTpUuX0Pr8/PxqXRB+2Ik8GAxSWFjYbq/XpEmTQp2+u3btGlqfmpqK3++nqKioWq3Q6X8bqampNWrVq/6W2tL1kKaxNqo52uYNw+DXv/4111xzDbfffntLhRoWpC+EEDU5nU4GDBjA8uXLQ+t0XWf58uUMHTrUwshaX48ePUhNTa12LTweD+vWrQtdi6FDh1JUVEROTk6ozH//+190XScrK6vVYz4bhmEwadIkFi1axH//+1969OhRbfuAAQNwOBzVrsfu3bs5ePBgteuxbdu2asnhp59+Snx8PL17926dE2kAqRFqo+prm//6668b9BpffvklCxcu5NJLLw31h/nHP/5Bnz59mjvcdq85rjeYHSO3bNlCWVkZXbt25b333ou4LwwRXrKzsxk7diwDBw5k8ODBzJ49m7KyslDNaTgpLS3l22+/DT3ft28fubm5JCUl0b17dx544AFmzpzJBRdcQI8ePZg2bRppaWmMGTMGgIsvvpjrr7+eCRMmMHfuXAKBAJMmTeLWW29td03nEydO5O233+aDDz4gLi4u1KcnISGBqKgoEhISuPPOO8nOziYpKYn4+Hh+97vfMXToUIYMGQLAiBEj6N27N7fffjtPP/00eXl5PProo0ycOLFtTdBq9W1rwsQPbtU8fPiwARhr1qypVm7y5MnG4MGDWzm68CPXW4iGe+mll4zu3bsbTqfTGDx4sPHVV19ZHVKLWLFihQHUWMaOHWsYhnkL/bRp04yUlBTD5XIZ1157rbF79+5qr3HixAnjl7/8pREbG2vEx8cb48aNM0pKSiw4m7NT23UAjDfeeCNUpqKiwvjtb39rJCYmGtHR0cZNN91kHD16tNrr7N+/3xg1apQRFRVlJCcnGw8++KARCARa+WzqJ7PPtxGKorBo0aLQLwu/3090dDTvv/9+aB3A2LFjKSoq4oMPPrAm0DAh11sIIQRIH6E2S9rmW5dcbyGEiEzSR8hCZ2qPjqS2+dYg11sIIcQPSdOYhT7//HOuvvrqGuvHjh3LggULAHj55Zd55plnyMvLIzMzkxdffLHd3X3QVsj1FkII8UOSCAkhhBAiYkkfISGEEEJELEmEhBBCCBGxJBESQgghRMSSREgIIYQQEUsSISGEEEJELEmEhBBCCBGxJBESQgghmmjJkiX06NGDwYMHs2fPHqvDEU0g4wgJIYQQTdSrVy/mzJnDjh07WLt2Le+8847VIYlGkhohIYQQog4nTpygc+fO7N+/v9btHTt25Pzzzyc9PR2n0xlaf+utt/Lcc8+1UpTibEiNkBBCiIjz8ccfc+ONN9a5/Re/+AULFy4kOzubkpIS5s2bV2u5efPmcc8995CSksL27dtJSkoCYPv27Vx55ZXs27ePhISEFjkH0TykRkiElbNtr7/ppptITEzkZz/7WQtEJ4RoK66++mqOHj1abfn++++57rrr6NixIw8//DDl5eW8/vrr3HnnnbW+RjAY5IUXXuCPf/wjpaWlJCYmhrZlZGTQs2dP3nrrrdY6JdFEkgiJsPLggw8yb948brvtNqZNm9bo/e+//37+/ve/t0BkQoi2JCoqitTU1NDSqVMnHnzwQTZt2sTy5cvp27cvH3/8MS6XiyFDhtT6GnPnzuW8885j4sSJlJSUsHfv3mrbR48eLX2G2gFJhES7U1+bfV3t9Q01bNgw4uLiat0mbf5ChCdN0/jVr37FZ599FkqCAL744gsGDBhQ6z6FhYU8/vjjPPXUU3Tt2pWEhARyc3OrlRk8eDDr16/H5/O19CmIsyCJkLBEbm4ut956K6mpqTidTnr27Mmf//xngsHgGfd94okn+MlPfkJ6enqNbePGjaNnz57ce++9zJ49u1ljfvTRR3niiScoLi5u1tcVQlinKglatmwZn332WSgJAjhw4ABpaWm17jdjxgxuuukmLr74YgB69+7Nli1bqpVJS0vD7/eTl5fXcicgzpokQqLVzZ8/n8GDB5OSksKSJUvYtWsX06ZNY/bs2XW2xVepr82+vvb6KpmZmWRkZNRYjhw5csa4pc1fiPCiaRq33347y5YtY/ny5WRmZlbbXlFRgdvtrrHfzp07eeutt3jsscdC6zIyMmrUCEVFRQHm55Zou+xWByAiy+eff86ECRN44403uOOOO0Lre/bsSSAQ4De/+Q3Tpk3j/PPPr3X/+trsT2+v/8tf/sLevXvp2bNntTI//KBqrKo2/4kTJ57V6wghrFWVBH3yySd89tlnNZIggOTkZE6ePFlj/e9//3uKioro2rVraJ2u63Tr1q1aucLCQgA6derUvMGLZiU1QqJV3X///YwaNapaElTlqquuAqhRvXy6utrsG9Je3xykzV+I9k/TNO64445QEtSvX79ay/Xr14+dO3dWW7dkyRJycnLYvHkzubm5oeX111/n4MGD1RKn7du307VrV5KTk1v0fMTZkURItJrNmzezdevWOmtTKioqALDb666orKvNviHt9Q0xfPhwfv7zn/Pxxx/TtWtX1q5dW227tPkL0b7pus4dd9zB4sWLeeutt+jSpQt5eXnVFk3TABg5ciQ7duwIJTeBQIAHH3yQyZMn12hmv/baa4HqP+S++OILRowY0fonKRpFmsZEq6mqoamtChpg06ZNAFx66aV1vkZtbfZV7fW7du0Krautvb4hPvvss3q3S5u/EO3bhg0bePvttwG44YYbamxXFIWioiLi4+Pp06cP/fv359133+Xuu+/mpZdeoqioiEmTJtXYr1u3bkRHR5Obm8uwYcPwer0sXryYpUuXtvg5ibMjiZBoNX6/H6DWzocAf/3rX7nyyivp0aNHna9RW5t9Q9vrm4O0+QvRvmVlZdGYCRWmT5/O5MmTmTBhAtnZ2WRnZ9daTlEUysrKQs/feOMNBg8eXOcYRKLtkERItJqq21JXrlzJmDFjqm179tln2bVrF6tXrwbM/kJVt6lv27aNdevWMXDgQPr161ftrq3T2+tPb1LbsGED48eP5+TJk7XePdZU0uYvRGS58cYb2bNnD4cPH27UjyuHw8FLL73UgpGJ5iJzjYlWdf3117Nt2zZmz57NwIEDyc/P57XXXuOdd95h0aJFXHfdddXKz5gxg6KiIl544QXATIr69+9PQUEBsbGxZGRkMH78eB566KFq+x08eJBzzz2XFStWMGzYsGaL/9e//jU2m43XX3+92V5TCCGEdaRGSLSqf/3rX/zpT39i8uTJfP/992iaxvXXX88333xToxP07Nmz2b9/PwsWLAitO73NvqysrMHt9c1B2vyFECL8SI2QsNRdd93FihUryMnJoUOHDqH1CxYs4MMPP+S9997DZrNV2+ejjz5i8uTJbN++HVVtvRsfX3nlFRYtWsSyZcta7ZhCCCFaltw+Lyw1Z84cxo8fz+bNm0PrFi1axDvvvMM///nPGkkQmG32v/nNbzh8+HBrhipt/kIIEYakRki0OYmJiXTq1Ino6GgAZs6cyY9//GOLoxJCCBGOJBESQgghRMSSpjEhhBBCRCxJhIQQQggRsSQREkIIIUTEkkRICCGEEBFLEiEhhBBCRCxJhIQQQggRsSQREkIIIUTEkkRICCGEEBFLEiEhhBBCRCxJhIQQQggRsSQREkIIIUTEkkRICCGEEBHr/wP36JO4iSYKxwAAAABJRU5ErkJggg==", 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" ] diff --git a/RATapi/examples/domains/domains_custom_layers.ipynb b/RATapi/examples/domains/domains_custom_layers.ipynb index 75ef358c..1466f2f2 100644 --- a/RATapi/examples/domains/domains_custom_layers.ipynb +++ b/RATapi/examples/domains/domains_custom_layers.ipynb @@ -14,20 +14,23 @@ ] }, { - "attachments": {}, + "attachments": { + "33c727cd-f7da-4589-aef2-f96e0f70c4d2.png": { + "image/png": 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" + } + }, "cell_type": "markdown", "metadata": {}, "source": [ - "[NEED IMAGES HERE]\n", - "\n", "# 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 1 or 2 [IS IT??? We have to resolve this satisfactorily], 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", + "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" ] }, @@ -412,7 +415,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/RATapi/examples/domains/domains_standard_layers.ipynb b/RATapi/examples/domains/domains_standard_layers.ipynb index de04b310..8da0409e 100644 --- a/RATapi/examples/domains/domains_standard_layers.ipynb +++ b/RATapi/examples/domains/domains_standard_layers.ipynb @@ -59,10 +59,12 @@ " 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", diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb index 38be0712..89eef82a 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb @@ -43,7 +43,7 @@ "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", + "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", @@ -57,7 +57,7 @@ "\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...." + "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:" ] }, { @@ -421,7 +421,7 @@ "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 1 as before, and that we are setting a Gaussian prior on the Head Hydration here)." + "We need to add the parameters we are going to need to define the model (note that Substrate Roughness always exists as parameter 1 as before, and that we are setting a Gaussian prior on the Head Hydration here)." ] }, { @@ -429,13 +429,26 @@ "execution_count": 4, "id": "70494ef9-6cc5-47dc-9d02-6506645de46b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for Parameter\nprior\n Extra inputs are not permitted [type=extra_forbidden, input_value='gaussian', input_type=str]\n For further information visit https://errors.pydantic.dev/2.7/v/extra_forbidden", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_list \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 2\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOxide Thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 3\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOxide Hydration\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 4\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLipid APM\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m45.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m55.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m65.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m----> 5\u001b[0m \u001b[43mParameter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHead Hydration\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgaussian\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 6\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBilayer Hydration\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 7\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBilayer Roughness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 8\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWater Thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 9\u001b[0m ]\n\u001b[1;32m 11\u001b[0m problem\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mextend(parameter_list)\n\u001b[1;32m 12\u001b[0m problem\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mset_fields(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m)\n", + "File \u001b[0;32m/mnt/c/Users/gnn85523/env/wsl/lib/python3.10/site-packages/pydantic/main.py:176\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 175\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 176\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mValidationError\u001b[0m: 1 validation error for Parameter\nprior\n Extra inputs are not permitted [type=extra_forbidden, input_value='gaussian', input_type=str]\n For further information visit https://errors.pydantic.dev/2.7/v/extra_forbidden" + ] + } + ], "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),\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", @@ -455,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "453fe3d2-162a-42bb-91ee-b1d020ffd29e", "metadata": {}, "outputs": [], @@ -479,7 +492,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482", "metadata": {}, "outputs": [], @@ -506,7 +519,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "2e649c26-b32b-4c79-8ae7-fa701c87e6c2", "metadata": {}, "outputs": [], @@ -524,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "5d51954f-469a-4044-9a7d-1b6e30474a6b", "metadata": {}, "outputs": [], @@ -550,12 +563,12 @@ "id": "a69a6d51-202a-4834-a6be-5c30f67d9107", "metadata": {}, "source": [ - "We need to use the data resolution (i.e. the fourch column of our datafiles). Do do this, we need to add a 'Data' resolution object to our resolutions table" + "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, + "execution_count": null, "id": "b1e4d313-8450-459b-b60e-868fe82f06b0", "metadata": {}, "outputs": [], @@ -573,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "efc7b351-2112-40c4-862b-a47e4570d173", "metadata": {}, "outputs": [], @@ -624,141 +637,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "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 | uniform | 0.0 | inf |\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" - ] - } - ], + "outputs": [], "source": [ "print(problem)" ] @@ -773,26 +655,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "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", - "| resampleParams | [0.9, 50] |\n", - "| display | iter |\n", - "+------------------+-----------+\n" - ] - } - ], + "outputs": [], "source": [ "controls = RAT.Controls()\n", "print(controls)" @@ -808,53 +674,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is 0.009 seconds\n", - "\n", - "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "problem, results = RAT.run(problem, controls)\n", "RAT.plotting.plot_ref_sld(problem, results)" diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb index 13bd5e3d..7c2fee74 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb @@ -90,7 +90,7 @@ "id": "b1fe7c6e-2200-4c83-9485-ec3590e950d5", "metadata": {}, "source": [ - "Need to add the relevant Bulk SLD's. Change the bulk in from air to silicon, and add two additional water contrasts:" + "Need to add the relevant Bulk SLDs. Change the bulk in from air to silicon, and add two additional water contrasts:" ] }, { @@ -262,20 +262,9 @@ "\n", "\n", "Running Differential Evolution\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Final chi squared is 8.39155\n", - "Elapsed time is 103.555 seconds\n", + "Elapsed time is 119.992 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" diff --git a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb index f94fe68c..447a1c29 100644 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb +++ b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb @@ -33,9 +33,7 @@ "\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.\n", - "\n", - "Start by making a project" + "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." ] }, { @@ -74,31 +72,35 @@ "outputs": [], "source": [ "parameter_list = [\n", - " Parameter(name=\"Oxide Thickness\", min=5.0, value=19.54, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Oxide SLD\", min=3.39e-06, value=3.39e-06, max=3.41e-06, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails Thickness\", min=15.0, value=22.66, max=35.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails SLD\", min=-5e-07, value=-4.01e-07, max=-3e-07, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Tails Hydration\", min=1.0, value=5.252, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Roughness\", min=1.0, value=5.64, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"CW Thickness\", min=10.0, value=17.12, max=28.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"CW SLD\", min=0.0, value=0.0, max=1e-09, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"SAM Heads Hydration\", min=10.0, value=45.45, max=50.0, fit=True, prior_type=\"uniform\", mu=30.0, sigma=3.0),\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\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Roughness\", min=2.0, value=6.014, max=15.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails Thickness\", min=14.0, value=17.82, max=22.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails SLD\", min=-5.0e-07, value=-4.61e-07, max=0.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Bilayer Tails Hydration\", min=10.0, value=17.64, max=50.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\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=\"CW Hydration\", min=99.9, value=100.0, max=100.0, fit=False, prior_type=\"uniform\", mu=0.0, sigma=np.inf),\n", - " Parameter(name=\"Oxide Hydration\", min=0.0, value=23.61, max=60.0, fit=True, prior_type=\"uniform\", mu=0.0, sigma=np.inf)\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", + "# 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)" ] }, @@ -154,7 +156,7 @@ "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=5.50e-06, value=5.98e-06, max=6.4e-06, fit=True)\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)" ] }, @@ -303,7 +305,7 @@ "| 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", + "| 11 | SAM Heads Hydration | 10.0 | 45.45 | 50.0 | True | gaussian | 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", @@ -455,7 +457,8 @@ "| procedure | calculate |\n", "| parallel | single |\n", "| calcSldDuringFit | False |\n", - "| resampleParams | [0.9, 50] |\n", + "| resampleMinAngle | 0.9 |\n", + "| resampleNPoints | 50 |\n", "| display | iter |\n", "+------------------+-----------+\n" ] @@ -485,32 +488,13 @@ "output_type": "stream", "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time is \n", + "\n", + "Elapsed time is 0.056 seconds\n", + "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - }, { "data": { "image/png": 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", diff --git a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb index 69a77d49..681d5a9e 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -43,7 +43,7 @@ "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", + "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", @@ -57,7 +57,7 @@ "\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...." + "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:" ] }, { @@ -421,7 +421,7 @@ "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 1 as before, and that we are setting a Gaussian prior on the Head Hydration here)." + "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)." ] }, { @@ -435,7 +435,7 @@ " 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),\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", @@ -550,7 +550,7 @@ "id": "a69a6d51-202a-4834-a6be-5c30f67d9107", "metadata": {}, "source": [ - "We need to use the data resolution (i.e. the fourch column of our datafiles). Do do this, we need to add a 'Data' resolution object to our resolutions table" + "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" ] }, { @@ -657,7 +657,7 @@ "| 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 | 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", @@ -819,7 +819,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.025 seconds\n", + "Elapsed time is 0.023 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -827,7 +827,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb index a3b603ee..e6cf9d64 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -90,7 +90,7 @@ "id": "b1fe7c6e-2200-4c83-9485-ec3590e950d5", "metadata": {}, "source": [ - "Need to add the relevant Bulk SLD's. Change the bulk in from air to silicon, and add two additional water contrasts:" + "Need to add the relevant Bulk SLDs. Change the bulk in from air to silicon, and add two additional water contrasts:" ] }, { @@ -264,7 +264,7 @@ "Running Differential Evolution\n", "\n", "Final chi squared is 8.39155\n", - "Elapsed time is 119.992 seconds\n", + "Elapsed time is 127.146 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -272,7 +272,7 @@ }, { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb index 15fb93b4..29a8def4 100644 --- a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -33,9 +33,7 @@ "\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.\n", - "\n", - "Start by making a project" + "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." ] }, { @@ -76,29 +74,33 @@ "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", - " 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", + " #\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", - " 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=\"CW Hydration\", min=99.9, value=100.0, max=100.0, 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", + " 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", + "# 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)" ] }, @@ -107,7 +109,7 @@ "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." + "Now we can group these parameters into the layers we need, and add them to the project:" ] }, { @@ -154,7 +156,7 @@ "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=5.50e-06, value=5.98e-06, max=6.4e-06, fit=True)\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)" ] }, @@ -163,7 +165,7 @@ "id": "420b57a9-4fc7-49d5-acaa-68570f1876d2", "metadata": {}, "source": [ - "Likewise the scalefactors and backgrounds." + "Likewise the scalefactors and backgrounds:" ] }, { @@ -192,7 +194,7 @@ "id": "a1b04d8b-8cc8-4e35-9e06-a6be3de90c53", "metadata": {}, "source": [ - "Now load in and add the data." + "Now load in and add the data:" ] }, { @@ -295,24 +297,24 @@ "| 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 | gaussian | 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", + "| 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", @@ -487,7 +489,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.056 seconds\n", + "Elapsed time is 0.048 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" @@ -495,7 +497,7 @@ }, { "data": { - "image/png": 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", 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", 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" ] diff --git a/cpp/RAT b/cpp/RAT index 8bcffd98..ee47ff97 160000 --- a/cpp/RAT +++ b/cpp/RAT @@ -1 +1 @@ -Subproject commit 8bcffd98722d0c7b1e69a51d357b6f81cd1613ec +Subproject commit ee47ff97b070131ee05e14c16720d42aac736a4f From 67a47a18f9bb262ed63b94cf5aa6213dd82fb0bd Mon Sep 17 00:00:00 2001 From: alexhroom Date: Mon, 4 Nov 2024 13:55:57 +0000 Subject: [PATCH 6/7] remove ipynb checkpoints --- .gitignore | 3 + .../absorption-checkpoint.ipynb | 820 ------------------ .../convert_rascal-checkpoint.ipynb | 169 ---- .../domains_custom_XY-checkpoint.ipynb | 162 ---- .../domains_custom_layers-checkpoint.ipynb | 133 --- .../domains_standard_layers-checkpoint.ipynb | 345 -------- .../DSPC_custom_layers-checkpoint.ipynb | 708 --------------- .../DSPC_custom_xy-checkpoint.ipynb | 312 ------- .../DSPC_standard_layers-checkpoint.ipynb | 536 ------------ 9 files changed, 3 insertions(+), 3185 deletions(-) delete mode 100644 RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb delete mode 100644 RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb delete mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb delete mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb delete mode 100644 RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb delete mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb delete mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb delete mode 100644 RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb 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/.ipynb_checkpoints/absorption-checkpoint.ipynb b/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb deleted file mode 100644 index ceb686b9..00000000 --- a/RATapi/examples/absorption/.ipynb_checkpoints/absorption-checkpoint.ipynb +++ /dev/null @@ -1,820 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "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": 2, - "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": 3, - "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", - " 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", - " 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", - " Parameter(name=\"CW Thickness\", min=1.0, value=12.87, max=25.0, fit=True),\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": 4, - "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": 5, - "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": 6, - "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": 7, - "metadata": {}, - "outputs": [], - "source": [ - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "\n", - "data_1 = np.loadtxt(os.path.join(data_path, \"D2O_spin_down.dat\"))\n", - "problem.data.append(name=\"D2O_dn\", data=data_1)\n", - "\n", - "data_2 = np.loadtxt(os.path.join(data_path, \"D2O_spin_up.dat\"))\n", - "problem.data.append(name=\"D2O_up\", data=data_2)\n", - "\n", - "data_3 = np.loadtxt(os.path.join(data_path, \"H2O_spin_down.dat\"))\n", - "problem.data.append(name=\"H2O_dn\", data=data_3)\n", - "\n", - "data_4 = np.loadtxt(os.path.join(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": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n",
-       "    """VolumeThiolBilayer  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",
-       "    subRough = params[0]\n",
-       "    alloyThick = params[1]\n",
-       "    alloySLDUp = params[2]\n",
-       "    alloyISLDUp = params[3]\n",
-       "    alloySLDDown = params[4]\n",
-       "    alloyISLDDown = params[5]\n",
-       "    alloyRough = params[6]\n",
-       "    goldThick = params[7]\n",
-       "    goldRough = params[8]\n",
-       "    goldSLD = params[9]\n",
-       "    goldISLD = params[10]\n",
-       "    thiolAPM = params[11]\n",
-       "    thiolHeadHydr = params[12]\n",
-       "    thiolCoverage = params[13]\n",
-       "    cwThick = params[14]\n",
-       "    bilayerAPM = params[15]\n",
-       "    bilHeadHydr = params[16]\n",
-       "    bilayerRough = params[17]\n",
-       "    bilayerCoverage = params[18]\n",
-       "\n",
-       "    # Make the metal layers\n",
-       "    gold = [goldThick, goldSLD, goldISLD, goldRough]\n",
-       "    alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n",
-       "    alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n",
-       "\n",
-       "    # Neutron b's..\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",
-       "    # Work out the total scattering length in each fragment\n",
-       "    # Define scattering lengths\n",
-       "    # Hydrogenated version\n",
-       "    COO = (2 * bo) + (bc)\n",
-       "    GLYC = (3 * bc) + (5 * bh)\n",
-       "    CH3 = (1 * bc) + (3 * bh)\n",
-       "    PO4 = (1 * bp) + (4 * bo)\n",
-       "    CH2 = (1 * bc) + (2 * bh)\n",
-       "    CH = (1 * bc) + (1 * bh)\n",
-       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
-       "\n",
-       "    # And also volumes\n",
-       "    vCH3 = 52.7  # CH3 volume in the paper appears to be for 2 * CH3's\n",
-       "    vCH2 = 28.1\n",
-       "    vCOO = 39.0\n",
-       "    vGLYC = 68.8\n",
-       "    vPO4 = 53.7\n",
-       "    vCHOL = 120.4\n",
-       "    vCHCH = 42.14\n",
-       "\n",
-       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
-       "    vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3)  # Tail volume\n",
-       "\n",
-       "    # Calculate sum_b's for other fragments\n",
-       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
-       "    sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n",
-       "\n",
-       "    # Calculate SLDs and Thickness\n",
-       "    sldHead = sumbHead / vHead\n",
-       "    thickHead = vHead / thiolAPM\n",
-       "\n",
-       "    sldTail = sumbTail / vTail\n",
-       "    thickTail = vTail / thiolAPM\n",
-       "\n",
-       "    # Correct head SLD based on hydration\n",
-       "    thiolHeadHydr = thiolHeadHydr / 100\n",
-       "    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n",
-       "\n",
-       "    # Now correct both the SLDs for the coverage parameter\n",
-       "    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
-       "    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
-       "\n",
-       "    SAMTAILS = [thickTail, sldTail, 0, goldRough]\n",
-       "    SAMHEAD = [thickHead, sldHead, 0, goldRough]\n",
-       "\n",
-       "    # Now do the same for the bilayer\n",
-       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
-       "    vTail = 28 * vCH2  # Tail volume\n",
-       "    vMe = 2 * vCH3\n",
-       "\n",
-       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
-       "    sumbTail = 28 * CH2\n",
-       "    sumbMe = 2 * CH3\n",
-       "\n",
-       "    sldHead = sumbHead / vHead\n",
-       "    thickHead = vHead / bilayerAPM\n",
-       "    bilHeadHydr = bilHeadHydr / 100\n",
-       "    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n",
-       "\n",
-       "    sldTail = sumbTail / vTail\n",
-       "    thickTail = vTail / bilayerAPM\n",
-       "\n",
-       "    sldMe = sumbMe / vMe\n",
-       "    thickMe = vMe / bilayerAPM\n",
-       "\n",
-       "    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "\n",
-       "    BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n",
-       "    BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n",
-       "    BILME = [thickMe, sldMe, 0, bilayerRough]\n",
-       "\n",
-       "    BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n",
-       "\n",
-       "    CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n",
-       "\n",
-       "    if contrast == 0 or contrast == 2:\n",
-       "        output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
-       "    else:\n",
-       "        output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
-       "\n",
-       "    return output, subRough\n",
-       "
\n" - ], - "text/latex": [ - "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", - "\\PY{k}{def} \\PY{n+nf}{volume\\PYZus{}thiol\\PYZus{}bilayer}\\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{}VolumeThiolBilayer 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}{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}{alloyISLDUp} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", - " \\PY{n}{alloySLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", - " \\PY{n}{alloyISLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", - " \\PY{n}{alloyRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", - " \\PY{n}{goldThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", - " \\PY{n}{goldRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{8}\\PY{p}{]}\n", - " \\PY{n}{goldSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{9}\\PY{p}{]}\n", - " \\PY{n}{goldISLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{10}\\PY{p}{]}\n", - " \\PY{n}{thiolAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{11}\\PY{p}{]}\n", - " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{12}\\PY{p}{]}\n", - " \\PY{n}{thiolCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{13}\\PY{p}{]}\n", - " \\PY{n}{cwThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{14}\\PY{p}{]}\n", - " \\PY{n}{bilayerAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{15}\\PY{p}{]}\n", - " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{16}\\PY{p}{]}\n", - " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{17}\\PY{p}{]}\n", - " \\PY{n}{bilayerCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{18}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Make the metal layers}\n", - " \\PY{n}{gold} \\PY{o}{=} \\PY{p}{[}\\PY{n}{goldThick}\\PY{p}{,} \\PY{n}{goldSLD}\\PY{p}{,} \\PY{n}{goldISLD}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - " \\PY{n}{alloyUp} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDUp}\\PY{p}{,} \\PY{n}{alloyISLDUp}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", - " \\PY{n}{alloyDown} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDDown}\\PY{p}{,} \\PY{n}{alloyISLDDown}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Neutron b\\PYZsq{}s..}\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{} Work out the total scattering length in each fragment}\n", - " \\PY{c+c1}{\\PYZsh{} Define scattering lengths}\n", - " \\PY{c+c1}{\\PYZsh{} Hydrogenated version}\n", - " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\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}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{3} \\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}{CH} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\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{} And also volumes}\n", - " \\PY{n}{vCH3} \\PY{o}{=} \\PY{l+m+mf}{52.7} \\PY{c+c1}{\\PYZsh{} CH3 volume in the paper appears to be for 2 * CH3\\PYZsq{}s}\n", - " \\PY{n}{vCH2} \\PY{o}{=} \\PY{l+m+mf}{28.1}\n", - " \\PY{n}{vCOO} \\PY{o}{=} \\PY{l+m+mf}{39.0}\n", - " \\PY{n}{vGLYC} \\PY{o}{=} \\PY{l+m+mf}{68.8}\n", - " \\PY{n}{vPO4} \\PY{o}{=} \\PY{l+m+mf}{53.7}\n", - " \\PY{n}{vCHOL} \\PY{o}{=} \\PY{l+m+mf}{120.4}\n", - " \\PY{n}{vCHCH} \\PY{o}{=} \\PY{l+m+mf}{42.14}\n", - "\n", - " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", - " \\PY{n}{vTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{vCHCH}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Calculate sum\\PYZus{}b\\PYZsq{}s for other fragments}\n", - " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", - " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH}\\PY{p}{)} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Calculate SLDs and Thickness}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", - " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{thiolAPM}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", - " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{thiolAPM}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Correct head SLD based on hydration}\n", - " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{thiolHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{thiolHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now correct both the SLDs for the coverage parameter}\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{SAMTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - " \\PY{n}{SAMHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now do the same for the bilayer}\n", - " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", - " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", - " \\PY{n}{vMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\n", - "\n", - " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", - " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\n", - " \\PY{n}{sumbMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", - "\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", - " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{bilHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{bilHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", - " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - "\n", - " \\PY{n}{sldMe} \\PY{o}{=} \\PY{n}{sumbMe} \\PY{o}{/} \\PY{n}{vMe}\n", - " \\PY{n}{thickMe} \\PY{o}{=} \\PY{n}{vMe} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldMe} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldMe}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{BILTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{BILHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{BILME} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickMe}\\PY{p}{,} \\PY{n}{sldMe}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - "\n", - " \\PY{n}{BILAYER} \\PY{o}{=} \\PY{p}{[}\\PY{n}{BILHEAD}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILHEAD}\\PY{p}{]}\n", - "\n", - " \\PY{n}{CW} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cwThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - "\n", - " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{0} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{2}\\PY{p}{:}\n", - " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", - " \\PY{k}{else}\\PY{p}{:}\n", - " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDown}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", - "\n", - " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{subRough}\n", - "\\end{Verbatim}\n" - ], - "text/plain": [ - "def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n", - " \"\"\"VolumeThiolBilayer 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", - " subRough = params[0]\n", - " alloyThick = params[1]\n", - " alloySLDUp = params[2]\n", - " alloyISLDUp = params[3]\n", - " alloySLDDown = params[4]\n", - " alloyISLDDown = params[5]\n", - " alloyRough = params[6]\n", - " goldThick = params[7]\n", - " goldRough = params[8]\n", - " goldSLD = params[9]\n", - " goldISLD = params[10]\n", - " thiolAPM = params[11]\n", - " thiolHeadHydr = params[12]\n", - " thiolCoverage = params[13]\n", - " cwThick = params[14]\n", - " bilayerAPM = params[15]\n", - " bilHeadHydr = params[16]\n", - " bilayerRough = params[17]\n", - " bilayerCoverage = params[18]\n", - "\n", - " # Make the metal layers\n", - " gold = [goldThick, goldSLD, goldISLD, goldRough]\n", - " alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n", - " alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n", - "\n", - " # Neutron b's..\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", - " # Work out the total scattering length in each fragment\n", - " # Define scattering lengths\n", - " # Hydrogenated version\n", - " COO = (2 * bo) + (bc)\n", - " GLYC = (3 * bc) + (5 * bh)\n", - " CH3 = (1 * bc) + (3 * bh)\n", - " PO4 = (1 * bp) + (4 * bo)\n", - " CH2 = (1 * bc) + (2 * bh)\n", - " CH = (1 * bc) + (1 * bh)\n", - " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", - "\n", - " # And also volumes\n", - " vCH3 = 52.7 # CH3 volume in the paper appears to be for 2 * CH3's\n", - " vCH2 = 28.1\n", - " vCOO = 39.0\n", - " vGLYC = 68.8\n", - " vPO4 = 53.7\n", - " vCHOL = 120.4\n", - " vCHCH = 42.14\n", - "\n", - " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", - " vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3) # Tail volume\n", - "\n", - " # Calculate sum_b's for other fragments\n", - " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", - " sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n", - "\n", - " # Calculate SLDs and Thickness\n", - " sldHead = sumbHead / vHead\n", - " thickHead = vHead / thiolAPM\n", - "\n", - " sldTail = sumbTail / vTail\n", - " thickTail = vTail / thiolAPM\n", - "\n", - " # Correct head SLD based on hydration\n", - " thiolHeadHydr = thiolHeadHydr / 100\n", - " sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n", - "\n", - " # Now correct both the SLDs for the coverage parameter\n", - " sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n", - " sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n", - "\n", - " SAMTAILS = [thickTail, sldTail, 0, goldRough]\n", - " SAMHEAD = [thickHead, sldHead, 0, goldRough]\n", - "\n", - " # Now do the same for the bilayer\n", - " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", - " vTail = 28 * vCH2 # Tail volume\n", - " vMe = 2 * vCH3\n", - "\n", - " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", - " sumbTail = 28 * CH2\n", - " sumbMe = 2 * CH3\n", - "\n", - " sldHead = sumbHead / vHead\n", - " thickHead = vHead / bilayerAPM\n", - " bilHeadHydr = bilHeadHydr / 100\n", - " sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n", - "\n", - " sldTail = sumbTail / vTail\n", - " thickTail = vTail / bilayerAPM\n", - "\n", - " sldMe = sumbMe / vMe\n", - " thickMe = vMe / bilayerAPM\n", - "\n", - " sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - " sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - " sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - "\n", - " BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n", - " BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n", - " BILME = [thickMe, sldMe, 0, bilayerRough]\n", - "\n", - " BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n", - "\n", - " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", - "\n", - " if contrast == 0 or contrast == 2:\n", - " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", - " else:\n", - " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", - "\n", - " return output, subRough" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "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": 9, - "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": 10, - "metadata": {}, - "outputs": [ - { - "ename": "ValidationError", - "evalue": "1 validation error for Controls\nresampleParams\n Extra inputs are not permitted. The fields for the \"calculate\" controls procedure are:\n \n [type=extra_forbidden, input_value=[0.9, 150.0], input_type=list]", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m controls \u001b[38;5;241m=\u001b[39m \u001b[43mRAT\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mControls\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcontrasts\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mresampleParams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m150.0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m problem, results \u001b[38;5;241m=\u001b[39m RAT\u001b[38;5;241m.\u001b[39mrun(problem, controls)\n\u001b[1;32m 4\u001b[0m RAT\u001b[38;5;241m.\u001b[39mplotting\u001b[38;5;241m.\u001b[39mplot_ref_sld(problem, results)\n", - "File \u001b[0;32m/mnt/c/Users/gnn85523/env/wsl/lib/python3.10/site-packages/pydantic/main.py:176\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 175\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 176\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValidationError\u001b[0m: 1 validation error for Controls\nresampleParams\n Extra inputs are not permitted. The fields for the \"calculate\" controls procedure are:\n \n [type=extra_forbidden, input_value=[0.9, 150.0], input_type=list]" - ] - } - ], - "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/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb b/RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb deleted file mode 100644 index d437a732..00000000 --- a/RATapi/examples/convert_rascal_project/.ipynb_checkpoints/convert_rascal-checkpoint.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "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", - "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)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Simply give the file path to the function `r1_to_project_class`, and it returns a RAT `Project` that you can use exactly like any other." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from RATapi.utils.convert import r1_to_project_class\n", - "\n", - "project = r1_to_project_class(\"R1monolayerVolumeModel.mat\")\n", - "print(project)" - ] - }, - { - "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:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "project.parameters[\"Head Thickness\"].prior_type = 'gaussian'\n", - "project.parameters[\"Theta\"].mu = 2.0\n", - "project.parameters[\"Area per molecule\"].sigma = 50.0\n", - "# etc...\n", - "print(project.parameters)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Also note that any custom files must be available to RAT. By default, RAT will assume these files are in the same directory that you are running RAT from, but if they are elsewhere you may change the relevant file location: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# e.g. if our model is in the directory `my_models/`\n", - "project.custom_files[0].filename = \"my_models/Model_IIb.m\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As well as MATLAB functions, RAT can also run custom files provided in Python or C++ format. This may be beneficial if you do not have access to MATLAB, do not have access to the custom files from your old RasCAL project, or find it more convenient to use Python. This is done similarly to changing the file path: if we have a function defined in the Python custom file `Model_IIb.py`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "project.custom_files[0].filename = \"Model_IIb.py\"\n", - "project.custom_files[0].language = 'python'" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### RAT to RasCAL1\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, - "metadata": {}, - "outputs": [], - "source": [ - "from RATapi.examples import DSPC_standard_layers\n", - "lipid_bilayer_project = DSPC_standard_layers()[0]\n", - "print(lipid_bilayer_project)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`project_class_to_r1` takes parameters `project` and `filename`, which are the `Project` object and filename for the produced .mat file respectively. This .mat file can then be loaded into RasCAL-1.\n", - "\n", - "Alternatively, if one sets `return_struct=True`, the struct is returned as a Python dictionary instead of being saved.\n", - "\n", - "Note that a MATLAB engine is used to save the project to a .mat file, so the Python library `matlabengine` must be installed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from RATapi.utils.convert import project_class_to_r1\n", - "from pprint import pp # for printing the struct\n", - "\n", - "# save to a file called lipid_bilayer.mat\n", - "project_class_to_r1(lipid_bilayer_project, filename=\"lipid_bilayer.mat\")\n", - "\n", - "# return as a Python dictionary\n", - "struct = project_class_to_r1(lipid_bilayer_project, return_struct=True)\n", - "pp(struct)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv", - "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": 2 -} diff --git a/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb deleted file mode 100644 index 16508865..00000000 --- a/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_XY-checkpoint.ipynb +++ /dev/null @@ -1,162 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pathlib\n", - "from IPython.display import Code\n", - "\n", - "import RATapi as RAT" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[IMAGES!]\n", - "\n", - "# 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": null, - "metadata": {}, - "outputs": [], - "source": [ - "problem = RAT.Project(calculation=\"domains\", model=\"custom xy\", geometry=\"substrate/liquid\")\n", - "\n", - "problem.parameters.append(name=\"Oxide Thickness\", min=10.0, value=20.0, max=50.0, fit=True)\n", - "problem.parameters.append(name=\"Layer Thickness\", min=1.0, value=30.0, max=500.0, fit=True)\n", - "problem.parameters.append(name=\"Layer SLD\", min=-0.5e-6, value=-0.5e-6, max=0.0, fit=True)\n", - "problem.parameters.append(name=\"Layer Roughness\", min=2.0, value=5.0, max=7.0, fit=True)\n", - "problem.parameters.append(name=\"Domain SLD\", min=1.0e-6, value=1.0e-6, max=5.0e-6, fit=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now set the SLDs of the bulk phases for our samples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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 = 1), or the domain (domain = 2)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "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": null, - "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": null, - "metadata": {}, - "outputs": [], - "source": [ - "controls = RAT.Controls()\n", - "problem, results = RAT.run(problem, controls)\n", - "\n", - "RAT.plotting.plot_ref_sld(problem, results)" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb deleted file mode 100644 index 0b6e223a..00000000 --- a/RATapi/examples/domains/.ipynb_checkpoints/domains_custom_layers-checkpoint.ipynb +++ /dev/null @@ -1,133 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pathlib\n", - "from IPython.display import Code\n", - "\n", - "import RATapi as RAT" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "[NEED IMAGES HERE]\n", - "\n", - "# 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 1 or 2 [IS IT??? We have to resolve this satisfactorily], 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", - "We start by setting up the project:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "problem = RAT.Project(calculation=\"domains\", model=\"custom layers\", geometry=\"substrate/liquid\")\n", - "\n", - "# Make some parameters\n", - "problem.parameters.append(name=\"Alloy Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD up\", min=9.0e-6, value=11.0e-6, max=13.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Alloy SLD down\", min=5.0e-6, value=7.0e-6, max=10.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Alloy Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\n", - "problem.parameters.append(name=\"Gold Thickness\", min=100.0, value=150.0, max=200.0, fit=True)\n", - "problem.parameters.append(name=\"Gold SLD\", min=4.0e-6, value=4.5e-6, max=5.0e-6, fit=True)\n", - "problem.parameters.append(name=\"Gold Roughness\", min=5.0, value=7.0, max=11.0, fit=True)\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": null, - "metadata": {}, - "outputs": [], - "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": null, - "metadata": {}, - "outputs": [], - "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/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb b/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb deleted file mode 100644 index 8da0409e..00000000 --- a/RATapi/examples/domains/.ipynb_checkpoints/domains_standard_layers-checkpoint.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "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": 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" - } - }, - "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/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb deleted file mode 100644 index 89eef82a..00000000 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_layers-checkpoint.ipynb +++ /dev/null @@ -1,708 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "4b988c4a-3a09-4b75-8a87-8ba8402635ba", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "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 1 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": [ - { - "ename": "ValidationError", - "evalue": "1 validation error for Parameter\nprior\n Extra inputs are not permitted [type=extra_forbidden, input_value='gaussian', input_type=str]\n For further information visit https://errors.pydantic.dev/2.7/v/extra_forbidden", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_list \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 2\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOxide Thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m60.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 3\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOxide Hydration\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 4\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLipid APM\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m45.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m55.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m65.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[0;32m----> 5\u001b[0m \u001b[43mParameter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mHead Hydration\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mmax\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprior\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgaussian\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msigma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.03\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 6\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBilayer Hydration\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 7\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBilayer Roughness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m8.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m),\n\u001b[1;32m 8\u001b[0m Parameter(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWater Thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m, value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m, fit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 9\u001b[0m ]\n\u001b[1;32m 11\u001b[0m problem\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mextend(parameter_list)\n\u001b[1;32m 12\u001b[0m problem\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mset_fields(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.0\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10.0\u001b[39m)\n", - "File \u001b[0;32m/mnt/c/Users/gnn85523/env/wsl/lib/python3.10/site-packages/pydantic/main.py:176\u001b[0m, in \u001b[0;36mBaseModel.__init__\u001b[0;34m(self, **data)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[38;5;66;03m# `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks\u001b[39;00m\n\u001b[1;32m 175\u001b[0m __tracebackhide__ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 176\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__pydantic_validator__\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_python\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mself_instance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mValidationError\u001b[0m: 1 validation error for Parameter\nprior\n Extra inputs are not permitted [type=extra_forbidden, input_value='gaussian', input_type=str]\n For further information visit https://errors.pydantic.dev/2.7/v/extra_forbidden" - ] - } - ], - "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": null, - "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": null, - "id": "fa4c1b96-3a1b-4aa6-8d61-68f24b0cb482", - "metadata": {}, - "outputs": [], - "source": [ - "# Read in the datafiles\n", - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - "H2O_data = np.loadtxt(os.path.join(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": null, - "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": null, - "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": null, - "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": null, - "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": null, - "id": "ee889e55-8357-4363-860d-fb1c13bb8e8b", - "metadata": {}, - "outputs": [], - "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": null, - "id": "154a33df-06b9-4035-aa4c-a0e095c1bb06", - "metadata": {}, - "outputs": [], - "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": null, - "id": "d5d9a782-0fb1-40b6-b1fa-86307abe32a6", - "metadata": {}, - "outputs": [], - "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/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb deleted file mode 100644 index 7c2fee74..00000000 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_custom_xy-checkpoint.ipynb +++ /dev/null @@ -1,312 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "956a341a-2a40-466c-b5c4-f8ea334ee81c", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - "H2O_data = np.loadtxt(os.path.join(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, and then a Bayesian analysis:" - ] - }, - { - "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 119.992 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/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb b/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb deleted file mode 100644 index 447a1c29..00000000 --- a/RATapi/examples/non_polarised/.ipynb_checkpoints/DSPC_standard_layers-checkpoint.ipynb +++ /dev/null @@ -1,536 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1ca14405-4a7c-4588-93cd-46534c374a36", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "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 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 = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "\n", - "d2o_dat = np.loadtxt(os.path.join(data_path, \"DSPC_D2O.dat\"), delimiter=\",\")\n", - "problem.data.append(name=\"dspc_bil_D2O\", data=d2o_dat)\n", - "\n", - "smw_dat = np.loadtxt(os.path.join(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 | 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 | gaussian | 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": [ - "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 our our initial starting position:" - ] - }, - { - "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 0.056 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 -} From 11c07aae2568bc379814f99e172cbc43c12fd334 Mon Sep 17 00:00:00 2001 From: Paul Sharp <44529197+DrPaulSharp@users.noreply.github.com> Date: Mon, 11 Nov 2024 15:06:25 +0000 Subject: [PATCH 7/7] Addresses review comments --- RATapi/examples/absorption/absorption.ipynb | 563 +----------------- RATapi/examples/absorption/absorption.py | 11 +- .../convert_rascal.ipynb | 345 ++++++++++- .../examples/domains/domains_custom_XY.ipynb | 4 +- .../languages/run_custom_file_languages.py | 1 + RATapi/examples/languages/setup_problem.py | 9 +- .../examples/non_polarised/DSPC_custom_XY.py | 9 +- .../non_polarised/DSPC_custom_layers.ipynb | 11 +- .../non_polarised/DSPC_custom_layers.py | 9 +- .../non_polarised/DSPC_custom_xy.ipynb | 13 +- .../non_polarised/DSPC_standard_layers.ipynb | 13 +- .../non_polarised/DSPC_standard_layers.py | 7 +- cpp/RAT | 2 +- 13 files changed, 391 insertions(+), 606 deletions(-) diff --git a/RATapi/examples/absorption/absorption.ipynb b/RATapi/examples/absorption/absorption.ipynb index 7b5ff978..d0af24dd 100644 --- a/RATapi/examples/absorption/absorption.ipynb +++ b/RATapi/examples/absorption/absorption.ipynb @@ -2,11 +2,10 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pathlib\n", "\n", "import numpy as np\n", @@ -33,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -49,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -109,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -129,7 +128,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -159,22 +158,22 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "data_path = pathlib.Path(\"../data\")\n", "\n", - "data_1 = np.loadtxt(os.path.join(data_path, \"D2O_spin_down.dat\"))\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(os.path.join(data_path, \"D2O_spin_up.dat\"))\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(os.path.join(data_path, \"H2O_spin_down.dat\"))\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(os.path.join(data_path, \"H2O_spin_up.dat\"))\n", + "data_4 = np.loadtxt(data_path / \"H2O_spin_up.dat\")\n", "problem.data.append(name=\"H2O_up\", data=data_4)" ] }, @@ -187,514 +186,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n",
-       "    """VolumeThiolBilayer  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",
-       "    subRough = params[0]\n",
-       "    alloyThick = params[1]\n",
-       "    alloySLDUp = params[2]\n",
-       "    alloyISLDUp = params[3]\n",
-       "    alloySLDDown = params[4]\n",
-       "    alloyISLDDown = params[5]\n",
-       "    alloyRough = params[6]\n",
-       "    goldThick = params[7]\n",
-       "    goldRough = params[8]\n",
-       "    goldSLD = params[9]\n",
-       "    goldISLD = params[10]\n",
-       "    thiolAPM = params[11]\n",
-       "    thiolHeadHydr = params[12]\n",
-       "    thiolCoverage = params[13]\n",
-       "    cwThick = params[14]\n",
-       "    bilayerAPM = params[15]\n",
-       "    bilHeadHydr = params[16]\n",
-       "    bilayerRough = params[17]\n",
-       "    bilayerCoverage = params[18]\n",
-       "\n",
-       "    # Make the metal layers\n",
-       "    gold = [goldThick, goldSLD, goldISLD, goldRough]\n",
-       "    alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n",
-       "    alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n",
-       "\n",
-       "    # Neutron b's..\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",
-       "    # Work out the total scattering length in each fragment\n",
-       "    # Define scattering lengths\n",
-       "    # Hydrogenated version\n",
-       "    COO = (2 * bo) + (bc)\n",
-       "    GLYC = (3 * bc) + (5 * bh)\n",
-       "    CH3 = (1 * bc) + (3 * bh)\n",
-       "    PO4 = (1 * bp) + (4 * bo)\n",
-       "    CH2 = (1 * bc) + (2 * bh)\n",
-       "    CH = (1 * bc) + (1 * bh)\n",
-       "    CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n",
-       "\n",
-       "    # And also volumes\n",
-       "    vCH3 = 52.7  # CH3 volume in the paper appears to be for 2 * CH3's\n",
-       "    vCH2 = 28.1\n",
-       "    vCOO = 39.0\n",
-       "    vGLYC = 68.8\n",
-       "    vPO4 = 53.7\n",
-       "    vCHOL = 120.4\n",
-       "    vCHCH = 42.14\n",
-       "\n",
-       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
-       "    vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3)  # Tail volume\n",
-       "\n",
-       "    # Calculate sum_b's for other fragments\n",
-       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
-       "    sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n",
-       "\n",
-       "    # Calculate SLDs and Thickness\n",
-       "    sldHead = sumbHead / vHead\n",
-       "    thickHead = vHead / thiolAPM\n",
-       "\n",
-       "    sldTail = sumbTail / vTail\n",
-       "    thickTail = vTail / thiolAPM\n",
-       "\n",
-       "    # Correct head SLD based on hydration\n",
-       "    thiolHeadHydr = thiolHeadHydr / 100\n",
-       "    sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n",
-       "\n",
-       "    # Now correct both the SLDs for the coverage parameter\n",
-       "    sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
-       "    sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n",
-       "\n",
-       "    SAMTAILS = [thickTail, sldTail, 0, goldRough]\n",
-       "    SAMHEAD = [thickHead, sldHead, 0, goldRough]\n",
-       "\n",
-       "    # Now do the same for the bilayer\n",
-       "    vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n",
-       "    vTail = 28 * vCH2  # Tail volume\n",
-       "    vMe = 2 * vCH3\n",
-       "\n",
-       "    sumbHead = CHOL + PO4 + GLYC + 2 * COO\n",
-       "    sumbTail = 28 * CH2\n",
-       "    sumbMe = 2 * CH3\n",
-       "\n",
-       "    sldHead = sumbHead / vHead\n",
-       "    thickHead = vHead / bilayerAPM\n",
-       "    bilHeadHydr = bilHeadHydr / 100\n",
-       "    sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n",
-       "\n",
-       "    sldTail = sumbTail / vTail\n",
-       "    thickTail = vTail / bilayerAPM\n",
-       "\n",
-       "    sldMe = sumbMe / vMe\n",
-       "    thickMe = vMe / bilayerAPM\n",
-       "\n",
-       "    sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "    sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "    sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n",
-       "\n",
-       "    BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n",
-       "    BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n",
-       "    BILME = [thickMe, sldMe, 0, bilayerRough]\n",
-       "\n",
-       "    BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n",
-       "\n",
-       "    CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n",
-       "\n",
-       "    if contrast == 0 or contrast == 2:\n",
-       "        output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
-       "    else:\n",
-       "        output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n",
-       "\n",
-       "    return output, subRough\n",
-       "
\n" - ], - "text/latex": [ - "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n", - "\\PY{k}{def} \\PY{n+nf}{volume\\PYZus{}thiol\\PYZus{}bilayer}\\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{}VolumeThiolBilayer 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}{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}{alloyISLDUp} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{3}\\PY{p}{]}\n", - " \\PY{n}{alloySLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{4}\\PY{p}{]}\n", - " \\PY{n}{alloyISLDDown} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{5}\\PY{p}{]}\n", - " \\PY{n}{alloyRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{6}\\PY{p}{]}\n", - " \\PY{n}{goldThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{7}\\PY{p}{]}\n", - " \\PY{n}{goldRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{8}\\PY{p}{]}\n", - " \\PY{n}{goldSLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{9}\\PY{p}{]}\n", - " \\PY{n}{goldISLD} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{10}\\PY{p}{]}\n", - " \\PY{n}{thiolAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{11}\\PY{p}{]}\n", - " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{12}\\PY{p}{]}\n", - " \\PY{n}{thiolCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{13}\\PY{p}{]}\n", - " \\PY{n}{cwThick} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{14}\\PY{p}{]}\n", - " \\PY{n}{bilayerAPM} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{15}\\PY{p}{]}\n", - " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{16}\\PY{p}{]}\n", - " \\PY{n}{bilayerRough} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{17}\\PY{p}{]}\n", - " \\PY{n}{bilayerCoverage} \\PY{o}{=} \\PY{n}{params}\\PY{p}{[}\\PY{l+m+mi}{18}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Make the metal layers}\n", - " \\PY{n}{gold} \\PY{o}{=} \\PY{p}{[}\\PY{n}{goldThick}\\PY{p}{,} \\PY{n}{goldSLD}\\PY{p}{,} \\PY{n}{goldISLD}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - " \\PY{n}{alloyUp} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDUp}\\PY{p}{,} \\PY{n}{alloyISLDUp}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", - " \\PY{n}{alloyDown} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyThick}\\PY{p}{,} \\PY{n}{alloySLDDown}\\PY{p}{,} \\PY{n}{alloyISLDDown}\\PY{p}{,} \\PY{n}{alloyRough}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Neutron b\\PYZsq{}s..}\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{} Work out the total scattering length in each fragment}\n", - " \\PY{c+c1}{\\PYZsh{} Define scattering lengths}\n", - " \\PY{c+c1}{\\PYZsh{} Hydrogenated version}\n", - " \\PY{n}{COO} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{bo}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\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}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{3} \\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}{CH} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{bc}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\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{} And also volumes}\n", - " \\PY{n}{vCH3} \\PY{o}{=} \\PY{l+m+mf}{52.7} \\PY{c+c1}{\\PYZsh{} CH3 volume in the paper appears to be for 2 * CH3\\PYZsq{}s}\n", - " \\PY{n}{vCH2} \\PY{o}{=} \\PY{l+m+mf}{28.1}\n", - " \\PY{n}{vCOO} \\PY{o}{=} \\PY{l+m+mf}{39.0}\n", - " \\PY{n}{vGLYC} \\PY{o}{=} \\PY{l+m+mf}{68.8}\n", - " \\PY{n}{vPO4} \\PY{o}{=} \\PY{l+m+mf}{53.7}\n", - " \\PY{n}{vCHOL} \\PY{o}{=} \\PY{l+m+mf}{120.4}\n", - " \\PY{n}{vCHCH} \\PY{o}{=} \\PY{l+m+mf}{42.14}\n", - "\n", - " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", - " \\PY{n}{vTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{*} \\PY{n}{vCHCH}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Calculate sum\\PYZus{}b\\PYZsq{}s for other fragments}\n", - " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", - " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH}\\PY{p}{)} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Calculate SLDs and Thickness}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", - " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{thiolAPM}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", - " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{thiolAPM}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Correct head SLD based on hydration}\n", - " \\PY{n}{thiolHeadHydr} \\PY{o}{=} \\PY{n}{thiolHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{thiolHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now correct both the SLDs for the coverage parameter}\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{thiolCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{thiolCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{SAMTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - " \\PY{n}{SAMHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{goldRough}\\PY{p}{]}\n", - "\n", - " \\PY{c+c1}{\\PYZsh{} Now do the same for the bilayer}\n", - " \\PY{n}{vHead} \\PY{o}{=} \\PY{n}{vCHOL} \\PY{o}{+} \\PY{n}{vPO4} \\PY{o}{+} \\PY{n}{vGLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCOO}\n", - " \\PY{n}{vTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{vCH2} \\PY{c+c1}{\\PYZsh{} Tail volume}\n", - " \\PY{n}{vMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{vCH3}\n", - "\n", - " \\PY{n}{sumbHead} \\PY{o}{=} \\PY{n}{CHOL} \\PY{o}{+} \\PY{n}{PO4} \\PY{o}{+} \\PY{n}{GLYC} \\PY{o}{+} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{COO}\n", - " \\PY{n}{sumbTail} \\PY{o}{=} \\PY{l+m+mi}{28} \\PY{o}{*} \\PY{n}{CH2}\n", - " \\PY{n}{sumbMe} \\PY{o}{=} \\PY{l+m+mi}{2} \\PY{o}{*} \\PY{n}{CH3}\n", - "\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sumbHead} \\PY{o}{/} \\PY{n}{vHead}\n", - " \\PY{n}{thickHead} \\PY{o}{=} \\PY{n}{vHead} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - " \\PY{n}{bilHeadHydr} \\PY{o}{=} \\PY{n}{bilHeadHydr} \\PY{o}{/} \\PY{l+m+mi}{100}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{n}{sldHead} \\PY{o}{*} \\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilHeadHydr}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{n}{bilHeadHydr} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{n}{sumbTail} \\PY{o}{/} \\PY{n}{vTail}\n", - " \\PY{n}{thickTail} \\PY{o}{=} \\PY{n}{vTail} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - "\n", - " \\PY{n}{sldMe} \\PY{o}{=} \\PY{n}{sumbMe} \\PY{o}{/} \\PY{n}{vMe}\n", - " \\PY{n}{thickMe} \\PY{o}{=} \\PY{n}{vMe} \\PY{o}{/} \\PY{n}{bilayerAPM}\n", - "\n", - " \\PY{n}{sldTail} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldTail}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldHead} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldHead}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - " \\PY{n}{sldMe} \\PY{o}{=} \\PY{p}{(}\\PY{n}{bilayerCoverage} \\PY{o}{*} \\PY{n}{sldMe}\\PY{p}{)} \\PY{o}{+} \\PY{p}{(}\\PY{p}{(}\\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{bilayerCoverage}\\PY{p}{)} \\PY{o}{*} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{)}\n", - "\n", - " \\PY{n}{BILTAILS} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickTail}\\PY{p}{,} \\PY{n}{sldTail}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{BILHEAD} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickHead}\\PY{p}{,} \\PY{n}{sldHead}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - " \\PY{n}{BILME} \\PY{o}{=} \\PY{p}{[}\\PY{n}{thickMe}\\PY{p}{,} \\PY{n}{sldMe}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - "\n", - " \\PY{n}{BILAYER} \\PY{o}{=} \\PY{p}{[}\\PY{n}{BILHEAD}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILME}\\PY{p}{,} \\PY{n}{BILTAILS}\\PY{p}{,} \\PY{n}{BILHEAD}\\PY{p}{]}\n", - "\n", - " \\PY{n}{CW} \\PY{o}{=} \\PY{p}{[}\\PY{n}{cwThick}\\PY{p}{,} \\PY{n}{bulk\\PYZus{}out}\\PY{p}{[}\\PY{n}{contrast}\\PY{p}{]}\\PY{p}{,} \\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{bilayerRough}\\PY{p}{]}\n", - "\n", - " \\PY{k}{if} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{0} \\PY{o+ow}{or} \\PY{n}{contrast} \\PY{o}{==} \\PY{l+m+mi}{2}\\PY{p}{:}\n", - " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyUp}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", - " \\PY{k}{else}\\PY{p}{:}\n", - " \\PY{n}{output} \\PY{o}{=} \\PY{p}{[}\\PY{n}{alloyDown}\\PY{p}{,} \\PY{n}{gold}\\PY{p}{,} \\PY{n}{SAMTAILS}\\PY{p}{,} \\PY{n}{SAMHEAD}\\PY{p}{,} \\PY{n}{CW}\\PY{p}{,} \\PY{o}{*}\\PY{n}{BILAYER}\\PY{p}{]}\n", - "\n", - " \\PY{k}{return} \\PY{n}{output}\\PY{p}{,} \\PY{n}{subRough}\n", - "\\end{Verbatim}\n" - ], - "text/plain": [ - "def volume_thiol_bilayer(params, bulk_in, bulk_out, contrast):\n", - " \"\"\"VolumeThiolBilayer 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", - " subRough = params[0]\n", - " alloyThick = params[1]\n", - " alloySLDUp = params[2]\n", - " alloyISLDUp = params[3]\n", - " alloySLDDown = params[4]\n", - " alloyISLDDown = params[5]\n", - " alloyRough = params[6]\n", - " goldThick = params[7]\n", - " goldRough = params[8]\n", - " goldSLD = params[9]\n", - " goldISLD = params[10]\n", - " thiolAPM = params[11]\n", - " thiolHeadHydr = params[12]\n", - " thiolCoverage = params[13]\n", - " cwThick = params[14]\n", - " bilayerAPM = params[15]\n", - " bilHeadHydr = params[16]\n", - " bilayerRough = params[17]\n", - " bilayerCoverage = params[18]\n", - "\n", - " # Make the metal layers\n", - " gold = [goldThick, goldSLD, goldISLD, goldRough]\n", - " alloyUp = [alloyThick, alloySLDUp, alloyISLDUp, alloyRough]\n", - " alloyDown = [alloyThick, alloySLDDown, alloyISLDDown, alloyRough]\n", - "\n", - " # Neutron b's..\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", - " # Work out the total scattering length in each fragment\n", - " # Define scattering lengths\n", - " # Hydrogenated version\n", - " COO = (2 * bo) + (bc)\n", - " GLYC = (3 * bc) + (5 * bh)\n", - " CH3 = (1 * bc) + (3 * bh)\n", - " PO4 = (1 * bp) + (4 * bo)\n", - " CH2 = (1 * bc) + (2 * bh)\n", - " CH = (1 * bc) + (1 * bh)\n", - " CHOL = (5 * bc) + (12 * bh) + (1 * bn)\n", - "\n", - " # And also volumes\n", - " vCH3 = 52.7 # CH3 volume in the paper appears to be for 2 * CH3's\n", - " vCH2 = 28.1\n", - " vCOO = 39.0\n", - " vGLYC = 68.8\n", - " vPO4 = 53.7\n", - " vCHOL = 120.4\n", - " vCHCH = 42.14\n", - "\n", - " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", - " vTail = (28 * vCH2) + (1 * vCHCH) + (2 * vCH3) # Tail volume\n", - "\n", - " # Calculate sum_b's for other fragments\n", - " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", - " sumbTail = (28 * CH2) + (2 * CH) + 2 * CH3\n", - "\n", - " # Calculate SLDs and Thickness\n", - " sldHead = sumbHead / vHead\n", - " thickHead = vHead / thiolAPM\n", - "\n", - " sldTail = sumbTail / vTail\n", - " thickTail = vTail / thiolAPM\n", - "\n", - " # Correct head SLD based on hydration\n", - " thiolHeadHydr = thiolHeadHydr / 100\n", - " sldHead = sldHead * (1 - thiolHeadHydr) + (thiolHeadHydr * bulk_out[contrast])\n", - "\n", - " # Now correct both the SLDs for the coverage parameter\n", - " sldTail = (thiolCoverage * sldTail) + ((1 - thiolCoverage) * bulk_out[contrast])\n", - " sldHead = (thiolCoverage * sldHead) + ((1 - thiolCoverage) * bulk_out[contrast])\n", - "\n", - " SAMTAILS = [thickTail, sldTail, 0, goldRough]\n", - " SAMHEAD = [thickHead, sldHead, 0, goldRough]\n", - "\n", - " # Now do the same for the bilayer\n", - " vHead = vCHOL + vPO4 + vGLYC + 2 * vCOO\n", - " vTail = 28 * vCH2 # Tail volume\n", - " vMe = 2 * vCH3\n", - "\n", - " sumbHead = CHOL + PO4 + GLYC + 2 * COO\n", - " sumbTail = 28 * CH2\n", - " sumbMe = 2 * CH3\n", - "\n", - " sldHead = sumbHead / vHead\n", - " thickHead = vHead / bilayerAPM\n", - " bilHeadHydr = bilHeadHydr / 100\n", - " sldHead = sldHead * (1 - bilHeadHydr) + (bilHeadHydr * bulk_out[contrast])\n", - "\n", - " sldTail = sumbTail / vTail\n", - " thickTail = vTail / bilayerAPM\n", - "\n", - " sldMe = sumbMe / vMe\n", - " thickMe = vMe / bilayerAPM\n", - "\n", - " sldTail = (bilayerCoverage * sldTail) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - " sldHead = (bilayerCoverage * sldHead) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - " sldMe = (bilayerCoverage * sldMe) + ((1 - bilayerCoverage) * bulk_out[contrast])\n", - "\n", - " BILTAILS = [thickTail, sldTail, 0, bilayerRough]\n", - " BILHEAD = [thickHead, sldHead, 0, bilayerRough]\n", - " BILME = [thickMe, sldMe, 0, bilayerRough]\n", - "\n", - " BILAYER = [BILHEAD, BILTAILS, BILME, BILME, BILTAILS, BILHEAD]\n", - "\n", - " CW = [cwThick, bulk_out[contrast], 0, bilayerRough]\n", - "\n", - " if contrast == 0 or contrast == 2:\n", - " output = [alloyUp, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", - " else:\n", - " output = [alloyDown, gold, SAMTAILS, SAMHEAD, CW, *BILAYER]\n", - "\n", - " return output, subRough" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "problem.custom_files.append(\n", " name=\"DPPC absorption\",\n", @@ -714,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -776,32 +270,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "Elapsed time is 0.077 seconds\n", - "\n", - "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "controls = RAT.Controls(parallel=\"contrasts\", resampleMinAngle=0.9, resampleNPoints=150.0)\n", "problem, results = RAT.run(problem, controls)\n", 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/convert_rascal_project/convert_rascal.ipynb b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb index 9c2f39f4..c3b5e343 100644 --- a/RATapi/examples/convert_rascal_project/convert_rascal.ipynb +++ b/RATapi/examples/convert_rascal_project/convert_rascal.ipynb @@ -6,7 +6,7 @@ "source": [ "### 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", + "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)" ] @@ -20,9 +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", @@ -39,9 +170,24 @@ }, { "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", @@ -59,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -95,9 +241,173 @@ }, { "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", @@ -117,9 +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", diff --git a/RATapi/examples/domains/domains_custom_XY.ipynb b/RATapi/examples/domains/domains_custom_XY.ipynb index 45101425..9e19f2ed 100644 --- a/RATapi/examples/domains/domains_custom_XY.ipynb +++ b/RATapi/examples/domains/domains_custom_XY.ipynb @@ -17,8 +17,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "[IMAGES!]\n", - "\n", "# 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", @@ -456,7 +454,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.058 seconds\n", + "Elapsed time is 0.071 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" 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 e96dc330..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 @@ -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 index 681d5a9e..e4ac99ad 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_layers.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pathlib\n", "\n", "import numpy as np\n", @@ -485,10 +484,10 @@ "outputs": [], "source": [ "# Read in the datafiles\n", - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - "H2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016607.dat\"), delimiter=\",\")\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", @@ -819,7 +818,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.023 seconds\n", + "Elapsed time is 0.020 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" 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 index e6cf9d64..c792fb13 100644 --- a/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb +++ b/RATapi/examples/non_polarised/DSPC_custom_xy.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pathlib\n", "\n", "import numpy as np\n", @@ -125,10 +124,10 @@ "outputs": [], "source": [ "# Read in the datafiles\n", - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", - "D2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016596.dat\"), delimiter=\",\")\n", - "SMW_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016601.dat\"), delimiter=\",\")\n", - "H2O_data = np.loadtxt(os.path.join(data_path, \"c_PLP0016607.dat\"), delimiter=\",\")\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", @@ -245,7 +244,7 @@ "source": [ "## Running the Model\n", "\n", - "We do this by first making a controls block as previously. We'll run a Differential Evolution, and then a Bayesian analysis:" + "We do this by first making a controls block as previously. We'll run a Differential Evolution:" ] }, { @@ -264,7 +263,7 @@ "Running Differential Evolution\n", "\n", "Final chi squared is 8.39155\n", - "Elapsed time is 127.146 seconds\n", + "Elapsed time is 108.162 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" diff --git a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb index 29a8def4..34a81e93 100644 --- a/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb +++ b/RATapi/examples/non_polarised/DSPC_standard_layers.ipynb @@ -7,7 +7,6 @@ "metadata": {}, "outputs": [], "source": [ - "import os\n", "import pathlib\n", "\n", "import numpy as np\n", @@ -142,7 +141,7 @@ "id": "356964f9-83a6-4a92-8092-4d250b68ac16", "metadata": {}, "source": [ - "Now deal with the experimental parameters. We need a bulk in of Silicon, and two Bulk outs - D2O and SMW." + "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." ] }, { @@ -204,12 +203,12 @@ "metadata": {}, "outputs": [], "source": [ - "data_path = os.path.join(pathlib.Path.cwd().parents[0].resolve(), \"data\")\n", + "data_path = pathlib.Path(\"../data\")\n", "\n", - "d2o_dat = np.loadtxt(os.path.join(data_path, \"DSPC_D2O.dat\"), delimiter=\",\")\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(os.path.join(data_path, \"DSPC_SMW.dat\"), delimiter=\",\")\n", + "smw_dat = np.loadtxt(data_path / \"DSPC_SMW.dat\", delimiter=\",\")\n", "problem.data.append(name=\"dspc_bil_smw\", data=smw_dat)" ] }, @@ -474,7 +473,7 @@ "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 our our initial starting position:" + "The default action (\"procedure\") is just a single calculation. So call RAT.run() with this, and then plot the results:" ] }, { @@ -489,7 +488,7 @@ "text": [ "Starting RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n", - "Elapsed time is 0.048 seconds\n", + "Elapsed time is 0.027 seconds\n", "\n", "Finished RAT ───────────────────────────────────────────────────────────────────────────────────────────────────────────\n", "\n" 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 diff --git a/cpp/RAT b/cpp/RAT index ee47ff97..8bcffd98 160000 --- a/cpp/RAT +++ b/cpp/RAT @@ -1 +1 @@ -Subproject commit ee47ff97b070131ee05e14c16720d42aac736a4f +Subproject commit 8bcffd98722d0c7b1e69a51d357b6f81cd1613ec