{ "cells": [ { "cell_type": "markdown", "id": "0caebf0f-de3c-43c5-b50f-593f893471a9", "metadata": {}, "source": [ "# Strain & Stress fields\n", "\n", "In this tutorial you will learn to measure liquid fraction and individual bubble radius from respectively phase-segmented (cleaned) and bubble segmented (no-edge) images.\n", " \n", "The tutorial is divided in the following sections:\n", "- Import libraries\n", "- Quantification folders\n", "- Get familiar with the input data\n", "- Strain - Shape tensor\n", "- Strain - Texture tensor\n", "- Stress - Batchelor tensor" ] }, { "cell_type": "markdown", "id": "39129563-f38d-4083-be8e-acebe8490101", "metadata": {}, "source": [ "## A) Import libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "cbeba4ed-7c0c-44cf-80af-38e66215ebd9", "metadata": {}, "outputs": [], "source": [ "from FoamQuant import *\n", "import numpy as np\n", "import skimage as ski \n", "import os\n", "import matplotlib.pyplot as plt; plt.rc('font', size=20) \n", "from tifffile import imread\n", "from scipy import ndimage\n", "import pickle as pkl\n", "import pandas as pd" ] }, { "cell_type": "markdown", "id": "220cf32d-17c4-4cfb-aa46-4f66291610c7", "metadata": { "tags": [] }, "source": [ "## B) Quantification folders" ] }, { "cell_type": "code", "execution_count": 2, "id": "d2cdab91-3980-4643-b250-7dd7c4268e1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "path already exist: Q3_Bubble_Prop\n", "path already exist: Q4_Topology\n", "path already exist: Q5_Texture\n", "path already exist: Q6_Stress\n" ] } ], "source": [ "# Processing folders names\n", "Quant_Folder = ['Q3_Bubble_Prop','Q4_Topology','Q5_Texture','Q6_Stress']\n", "\n", "# Create the quantification folders (where we are going to save our results)\n", "for Pi in Quant_Folder:\n", " if os.path.exists(Pi):\n", " print('path already exist:',Pi)\n", " else:\n", " print('Created folder:',Pi)\n", " os.mkdir(Pi)" ] }, { "cell_type": "markdown", "id": "7da34ce8-d2d1-4ad7-894d-5496cd525061", "metadata": {}, "source": [ "Let's read the first bubble-segmented image of the series (with no bubble on the edges). " ] }, { "cell_type": "markdown", "id": "dd13c67c-d424-4b7d-b972-593b288e6878", "metadata": {}, "source": [ "## C) Get familiar with the input data" ] }, { "cell_type": "code", "execution_count": 3, "id": "d81102aa-72d7-4164-a4d7-c27ad9792cc1", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/gpfs/offline1/staff/tomograms/users/flosch/Rheometer_Jupyter/Jupy_FoamQuant/FoamQuant/Figure.py:90: UserWarning: The figure layout has changed to tight\n", " plt.tight_layout()\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Name and directory of the no-edge bubble segmented images\n", "dirnoedge = 'P5_BubbleNoEdge/'\n", "namenoedge = 'BubbleNoEdge_'\n", "\n", "# Read the first image of the series\n", "Lab = imread(dirnoedge+namenoedge+strindex(1, 3)+'.tiff')\n", "\n", "# Since we are now looking at more bubbles let's create a \"larger\" random colormap: here 500 random colors\n", "rcmap = RandomCmap(500, verbose=False)\n", "\n", "# Show a 3D-cut view of the volume\n", "Cut3D(Lab, \n", " nameaxes=['z','y','x'],\n", " cmap=rcmap, \n", " interpolation='nearest', \n", " figblocksize=4)" ] }, { "cell_type": "markdown", "id": "6441839c-44f0-475b-9046-10abc12d0fea", "metadata": {}, "source": [ "## D) Strain - Measured from the bubble region (shape tensor)\n", "The shape strain tensor $U_S$ is computed from the shape tensor $S$. Each bubble is represented by a set of coordinates {$\\textbf{r}$} of all the voxels defining its region in the image. From this set can be defined both the center of mass coordinates, $\\langle \\textbf{r} \\rangle$, and the shape tensor as\n", "\n", "$\\textbf{S}=\\langle(\\textbf{r}-\\langle \\textbf{r} \\rangle) \\otimes (\\textbf{r}-\\langle \\textbf{r} \\rangle)\\rangle^{1/2}$\n", "\n", "The strain tensors $\\textbf{U}_\\textbf{S}$ is derived from $\\textbf{S}$ as the deviation from an isotropic shape $\\mathbf{S_0}$ state (a perfect sphere):\n", "\n", "$\\textbf{U}_\\textbf{S} = \\log(\\textbf{S}) - \\log(\\mathbf{S_0}) $\n", "\n", "with $\\mathbf{S_0} = S_0 \\textbf{Id}$ and $\\textbf{Id}$ the identity tensor. The isotropic state is build from the three shape tensor eigenvalues $\\lambda_i$ as $S_0 = (\\lambda_1 \\lambda_2 \\lambda_3)^{1/3}$. \n", "\n", "The shape tensor is already included in the saved properties by the **RegionProp_Batch** function." ] }, { "cell_type": "code", "execution_count": 4, "id": "0356ba92-49ca-4206-b962-b92d3474ef7c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Path exist: True\n", "Bubble_Prop_001: done\n", "Bubble_Prop_002: done\n", "Bubble_Prop_003: done\n", "Bubble_Prop_004: done\n", "Bubble_Prop_005: done\n", "Bubble_Prop_006: done\n", "Bubble_Prop_007: done\n", "Bubble_Prop_008: done\n", "Bubble_Prop_009: done\n", "Bubble_Prop_010: done\n" ] } ], "source": [ "# Name and directory where we are going to save the bubble region properties\n", "dir_Bubble_prop = 'Q3_Bubble_Prop/'\n", "name_Bubble_prop = 'Bubble_Prop_'\n", "\n", "# Indexes of the images of our time-series (we are working here with 10 subsequent images of the same foam sample, evolving over time).\n", "imrange = [1,2,3,4,5,6,7,8,9,10]\n", "\n", "RegionProp_Batch(namenoedge,\n", " name_Bubble_prop,\n", " dirnoedge,\n", " dir_Bubble_prop,\n", " imrange,\n", " verbose=True,\n", " endread='.tiff',\n", " endsave='.tsv')" ] }, { "cell_type": "markdown", "id": "706000a7-b10e-45ed-8055-757689d06e2d", "metadata": {}, "source": [ "Let's open the first saved bubble-properties table with **pandas**." ] }, { "cell_type": "code", "execution_count": 5, "id": "4cebf7e6-40f0-4fe8-a85f-abd8ac3bb434", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " lab z y x vol rad area \\\n", "0 1 15.286670 79.575571 183.687352 7622.0 12.208438 1889.990904 \n", "1 2 16.593385 130.636373 17.162980 9584.0 13.177087 2204.247246 \n", "2 3 15.635897 172.321484 101.120593 10324.0 13.507857 2309.744140 \n", "3 4 15.538057 175.370390 217.745069 9328.0 13.058701 2154.914928 \n", "4 5 15.121800 200.194312 163.705468 10936.0 13.769663 2403.553261 \n", ".. ... ... ... ... ... ... ... \n", "936 937 232.931714 209.884521 86.382444 9387.0 13.086175 2168.907873 \n", "937 938 234.656031 34.253836 50.915761 9841.0 13.293833 2235.291662 \n", "938 939 234.123875 156.617150 218.298716 9889.0 13.315411 2238.448309 \n", "939 940 234.464956 224.364352 219.393250 9274.0 13.033453 2143.488520 \n", "940 941 234.799707 24.502990 72.834127 8193.0 12.505991 1977.594288 \n", "\n", " sph volfit S1 ... e2y e2x e3z \\\n", "0 0.997457 7696.702997 11.356878 ... 12.123131 0.558382 -0.565461 \n", "1 0.996115 9674.503042 12.035502 ... 12.727413 -0.753596 -0.022763 \n", "2 0.997574 10400.092065 12.805426 ... 13.886619 -0.036291 -0.224959 \n", "3 0.997516 9371.282698 12.381921 ... 12.635434 -0.351062 0.549606 \n", "4 0.997433 11037.743266 13.050254 ... 14.409886 0.621144 -0.254747 \n", ".. ... ... ... ... ... ... ... \n", "936 0.998400 9475.291923 12.489027 ... 13.894511 -0.170001 -0.174207 \n", "937 0.997166 9895.232739 12.680207 ... 12.997291 -0.130880 -0.529484 \n", "938 0.998684 9938.866176 12.619337 ... 13.528568 -0.152264 0.059711 \n", "939 0.998308 9307.919924 12.256650 ... 12.866831 -0.005062 0.036705 \n", "940 0.999019 8257.341727 11.949345 ... 12.912100 0.136215 0.314278 \n", "\n", " e3y e3x U1 U2 U3 U type \n", "0 0.558382 12.288122 -0.075555 0.016856 0.058699 0.118985 1 \n", "1 -0.753596 12.968875 -0.093752 0.022430 0.071322 0.146864 1 \n", "2 -0.036291 13.241960 -0.055850 -0.019896 0.075747 0.117809 -1 \n", "3 -0.351062 12.901907 -0.054760 -0.022566 0.077326 0.119294 -1 \n", "4 0.621144 13.336950 -0.056747 -0.021472 0.078219 0.121240 -1 \n", ".. ... ... ... ... ... ... ... \n", "936 -0.170001 12.962521 -0.049827 -0.009192 0.059018 0.095265 -1 \n", "937 -0.130880 12.878073 -0.049090 -0.035787 0.084877 0.127836 -1 \n", "938 -0.152264 13.724473 -0.055368 0.015737 0.039632 0.085592 1 \n", "939 -0.005062 13.650618 -0.062668 0.017493 0.045175 0.097010 1 \n", "940 0.136215 12.591211 -0.048139 0.014913 0.033226 0.073929 1 \n", "\n", "[941 rows x 26 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(dir_Bubble_prop+name_Bubble_prop+strindex(1,n0=3)+'.tsv',sep = '\\t')\n", "display(df)" ] }, { "cell_type": "markdown", "id": "bec42bc3-173f-49c2-ab3d-3ac854b05768", "metadata": {}, "source": [ "Let's keep the shape tensor tensor columns" ] }, { "cell_type": "code", "execution_count": 6, "id": "6081d6d8-106e-4417-a40b-f89c7cd51bc7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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e1ze1ye1xe2ze2ye2xe3ze3ye3x
012.3906940.206741-0.5654610.20674112.1231310.558382-0.5654610.55838212.288122
114.053116-0.391930-0.022763-0.39193012.727413-0.753596-0.022763-0.75359612.968875
213.5575470.846910-0.2249590.84691013.886619-0.036291-0.224959-0.03629113.241960
313.761952-0.2562400.549606-0.25624012.635434-0.3510620.549606-0.35106212.901907
413.758225-0.470152-0.254747-0.47015214.4098860.621144-0.2547470.62114413.336950
..............................
93612.564094-0.058917-0.174207-0.05891713.894511-0.170001-0.174207-0.17000112.962521
93714.1528350.463591-0.5294840.46359112.997291-0.130880-0.529484-0.13088012.878073
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93912.6711460.5021820.0367050.50218212.866831-0.0050620.036705-0.00506213.650618
94012.135317-0.1064190.314278-0.10641912.9121000.1362150.3142780.13621512.591211
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941 rows × 9 columns

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" ], "text/plain": [ " e1z e1y e1x e2z e2y e2x e3z \\\n", "0 12.390694 0.206741 -0.565461 0.206741 12.123131 0.558382 -0.565461 \n", "1 14.053116 -0.391930 -0.022763 -0.391930 12.727413 -0.753596 -0.022763 \n", "2 13.557547 0.846910 -0.224959 0.846910 13.886619 -0.036291 -0.224959 \n", "3 13.761952 -0.256240 0.549606 -0.256240 12.635434 -0.351062 0.549606 \n", "4 13.758225 -0.470152 -0.254747 -0.470152 14.409886 0.621144 -0.254747 \n", ".. ... ... ... ... ... ... ... \n", "936 12.564094 -0.058917 -0.174207 -0.058917 13.894511 -0.170001 -0.174207 \n", "937 14.152835 0.463591 -0.529484 0.463591 12.997291 -0.130880 -0.529484 \n", "938 12.792572 -0.396828 0.059711 -0.396828 13.528568 -0.152264 0.059711 \n", "939 12.671146 0.502182 0.036705 0.502182 12.866831 -0.005062 0.036705 \n", "940 12.135317 -0.106419 0.314278 -0.106419 12.912100 0.136215 0.314278 \n", "\n", " e3y e3x \n", "0 0.558382 12.288122 \n", "1 -0.753596 12.968875 \n", "2 -0.036291 13.241960 \n", "3 -0.351062 12.901907 \n", "4 0.621144 13.336950 \n", ".. ... ... \n", "936 -0.170001 12.962521 \n", "937 -0.130880 12.878073 \n", "938 -0.152264 13.724473 \n", "939 -0.005062 13.650618 \n", "940 0.136215 12.591211 \n", "\n", "[941 rows x 9 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Shape tensor\n", "df[['e1z','e1y','e1x',\n", " 'e2z','e2y','e2x',\n", " 'e3z','e3y','e3x']]" ] }, { "cell_type": "markdown", "id": "ba2e7771-93de-40ea-b821-1281600da008", "metadata": {}, "source": [ "Individual bubbles are often highly deformed in what looks like random deformations. In order to extract the strain field of the structure, without this local \"noize\", we spatially average the shape of the bubble inside boxes. Only after averaging, the average strain is computed from the average shape tensor.\n", "\n", "First, let's reshape our table in form of two lists: a list for the centroid coordinates and one for the shape tensors." ] }, { "cell_type": "code", "execution_count": 7, "id": "3ecbfa52-001d-43d5-ba47-c6d58235076e", "metadata": {}, "outputs": [], "source": [ "LCoord = np.asarray(df[['z','y','x']])\n", "\n", "numpy_shape = np.asarray(df[['e1z','e1y','e1x',\n", " 'e2z','e2y','e2x',\n", " 'e3z','e3y','e3x']])\n", "LShape=[]\n", "for bbli in range(len(numpy_shape)):\n", " LShape.append(numpy_shape[bbli].reshape(3,3))" ] }, { "cell_type": "markdown", "id": "5522f40a-7492-462e-b096-a4ac815407ba", "metadata": {}, "source": [ "Now that we have the data in this format, one can perform a cartesian-grid average. Here we choose to have 5x5x5 boxes over the pixel coordinates [0,125] along z,y and x. We choose to return the result in a structured format." ] }, { "cell_type": "code", "execution_count": 8, "id": "96b4a4bf-ac76-4ac6-8f41-cf3877849870", "metadata": {}, "outputs": [], "source": [ "Grids, CoordGrd, ShapeAVG, ShapeSTD, ShapeCount = Grid_Tavg(LCoord, \n", " LShape, \n", " Range = [0,250,0,250,0,250],\n", " N=[5,5,5], \n", " NanFill=True, #<- if no bubble in a box, fill with a nan value\n", " structured=True)" ] }, { "cell_type": "code", "execution_count": 9, "id": "6db37f4c-d565-406a-b0eb-fd7d2d68f1b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[13.0621894 , 0.35516098, -0.34083759],\n", " [ 0.35516098, 12.82388882, 0.36474759],\n", " [-0.34083759, 0.36474759, 13.22112962]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Averaged shape tensor in the fist [0,0,0] box\n", "ShapeAVG[0][0][0]" ] }, { "cell_type": "code", "execution_count": 10, "id": "91477362-9765-4340-b427-35e20c43c878", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.00213077, 0.02782792, -0.02633693],\n", " [ 0.02782792, -0.01634475, 0.02838876],\n", " [-0.02633693, 0.02838876, 0.01421397]])" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The associated strain tensor\n", "USfromS(ShapeAVG[0][0][0])" ] }, { "cell_type": "markdown", "id": "d51a014d-ea97-4be3-ba75-64a8f2a958af", "metadata": {}, "source": [ "To have a bit more a feeling of this strain tensor field, one can use the following representation, using ellipses. If you wish to learn about this representation, have a look at the following paper: [Three-dimensional liquid foam flow through a hopper resolved by fast X-ray microtomography](https://pubs.rsc.org/en/content/articlelanding/2023/sm/d2sm01299e). **CutTensor3D** work similarly than **Cut3D**. It represents the three orthogonal cuts of the tensorial space." ] }, { "cell_type": "code", "execution_count": 11, "id": "aeba89d8-884f-49aa-9fb7-cb2acea76730", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal min/max ax0 -0.014470684848635748 0.03758690547066675\n", "Normal min/max ax1 -0.042390339076687135 0.012313103703006308\n", "Normal min/max ax2 -0.03931547642670907 0.022374484108615564\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "CutTensor3D(Grids,\n", " CoordGrd,\n", " ShapeAVG, \n", " ShapeCount,\n", " scale_factor = 150,\n", " figblocksize=4,\n", " vmin=-0.1,\n", " vmax=0.1,\n", " Countmin=5, #<- shows a symbol if there is al least 5 bubble in the average box\n", " showscale=False,\n", " nameaxes=['z','y','x'],\n", " DeviatoricType=2, #<- 2 for dealing with shape: Shape -> Strain\n", " colorbarlab=r'$U_S$') " ] }, { "cell_type": "markdown", "id": "d0424d35-befe-4366-8ddc-9091e50ebebc", "metadata": {}, "source": [ "We can also do the same for the whole time-resolved data. Since we have only a small number of bubbles, it is going to smooth a bit the dentencies.\n", "In addition we can use **ProjTensor3D**, which average even more along the whole volume." ] }, { "cell_type": "code", "execution_count": 12, "id": "25c41e40-5029-4d1c-aaa5-0ace80e14293", "metadata": {}, "outputs": [], "source": [ "# Take into acount the whole time serie data\n", "LLCoord=[]\n", "LLShape=[]\n", "for imi in range(len(imrange)):\n", " df = pd.read_csv(dir_Bubble_prop+name_Bubble_prop+strindex(imrange[imi],n0=3)+'.tsv',sep = '\\t')\n", "\n", " LCoord = np.asarray(df[['z','y','x']])\n", "\n", " numpy_shape = np.asarray(df[['e1z','e1y','e1x',\n", " 'e2z','e2y','e2x',\n", " 'e3z','e3y','e3x']])\n", " LShape=[]\n", " for bbli in range(len(numpy_shape)):\n", " LShape.append(numpy_shape[bbli].reshape(3,3))\n", "\n", " LLCoord.append(LCoord)\n", " LLShape.append(LShape)\n", "\n", "Grids_2, CoordGrd_2, ShapeAVG_2, ShapeSTD_2, ShapeCount_2 = Grid_Tavg(np.concatenate(LLCoord), \n", " np.concatenate(LLShape), \n", " Range = [0,250,0,250,0,250],\n", " N=[5,5,5], \n", " NanFill=True,\n", " verbose=False,\n", " structured=True)" ] }, { "cell_type": "code", "execution_count": 13, "id": "ed06287f-c612-4b5d-9e8a-f1176ecf4f44", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal min/max ax0 0.0018644002701723728 0.02751263607400842\n", "Normal min/max ax1 -0.025305090187731964 0.008156738035116365\n", "Normal min/max ax2 -0.023519841267799376 0.010775489595964052\n" ] }, { "data": { "image/png": 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uLsWUlJTEm2++yWeffcb+/ftJS0ujRo0a9OjRgyeeeIJatWq59Vp9MVsvUhL44mezJLWd4Jv/I5GSwBc/myWp/fTF/49ISeGLn8/smHe52ZOr6SX05JKSx2eTXCWJLzZkIiWBPpvmp/+RiDnps2lu+v+ImJcvfj6V5BJvCjA6ABERERERERG5vLmzWqK7qytKyeWzqyuKiIiIiIiIiIhkU08uEREREREREfEo9eQSb1CSS0REREREREQ8Skku8QYNVxQREREREREREZ+nJJeIiIiIiIiIiPg8DVcUEREREREREY/ScEXxBvXkEhERERERERERn6eeXCIiIiIiIiLiUerJJd6gJJeIiIiIiIiIeJSSXOINGq4oIiIiIiIiIiI+T0kuERERERERERHxeRquKCIiIiIiIiIepeGK4g3qySUiIiIiIiIiIj5PPblERERERERExKPUk0u8QUkuEREREREREfEoJbnEGzRcUUREREREREREfJ6SXCIiIiIiIiIi4vM0XFFEREREREREPErDFcUb1JNLRERERERERER8nnpyiYiIiIiIiIhHqSeXeIOSXCIiIiIiIiLiUUpyiTdouKKIiIiIiIiIiPg8JblERERERERERMTnabiiiIiIiIiIiHiUhiuKN6gnl4iIiIiIiIiI+Dz15BIRERERERERj1JPLvEG9eQSERERERERERGfpySXiIiIiIiIiIj4PA1XFBERERERERGP0/BD8TQluURERERERETEozQnl3iDkly+7MwZoyMomn37IDLS6Chcl5AANWsaHYXrLBYoV87oKFxntUKAjzVBvhavOJeVZXQERZKeAVh8Z4aBrCzw9zc6CtdZrb4Vb1YW+PnO2wHwvXjFOR9rPklOtp0i+QqrFYKCjI7CdefO+c7p0eHDEB5udBRFU6+e0REULyW5xBt0yiEiIiIiIiIiIj7PR/LuIiIiIiIiIuKr1JNLvEE9uURERERERERExOcpySUiIiIiIiIiIj5PwxVFRERERERExKM0XFG8QT25RERERERERETE56knl4iIiIiIiIh4lHpyiTeoJ5eIiIiIiIiIiPg89eQSEREREREREY9STy7xBvXkEhERERERERERn6eeXCIiIiIiIiLiUerJJd6gJJeIiIiIiIiIeJSSXOINGq4oIiIiIiIiIiI+T0kuERERERERERHxeRquKCIiIiIiIiIepeGK4g3qySUiIiIiIobJyIDkZFiy5HeysoyORsQ1iYmQkBBvdBgi4kBJLhERkeJitRodgcseeWQxY8ZsY+/eFC5cMHfoVquVQ4fMHaMvO3gwhauvfoVVq06Smmp0NFKSrFsH3bpBWBiUKfMZAwZcQ6lSI/jrr0SjQyuU2iPPefRRePxxo6MoWHKylWuvnUuLFrUYOvRXo8PxGRY3byJFoSSX5GW1wsaN8NNP+gYXn7Pjyy9J2L3b6DCkpLlwgYU9etAvIIBve/UiMyHB6IicysyE555LZN68e5g5szXXXBNGeHgUBw4cNDo0p15+eSX16kUQGNiEUqV60ajROKNDKpDVCgcOpDBmzM+MHr3U6HCcio+3csMNS6lbtxHR0c/Ro8dYKleGadNQbxrxuP/+F7p0gdWrIT39LPBI9haioprw7LPrjQyvQHfc8Qjly3/AN98kcfq00dEU7PRp7MnrRJPnDs+ePUPVqmVZsiSSTz6JpGrVSP7555zRYeXx449xNGvWHxgOxLJu3d0cOGDyP65JKMkl3qAkl+SSHhvLw5Urc/COO+D222HMGKNDyi0zE37+GZYs4czUqcxo04Z5N9/M5wMH8v0zz2A9ccKcZ+ZpaeyeMoWZjRvzSq1aPF+tGnObN4cNG2x99E0qMzmZn556inG1anHit9+MDqdgW7dy4uqrueXOO7m2RQt2jB1rdERSUvz1FzRuzDurVvE50OPbb6kZGcmiBx80OrJ8jR4N06f/DmRfxMgC/mL27MoGRuXc6tXw8suHgXjgN2A5+/b9wJ9/GhyYExs37iYgoCn165fh1Vfb8Nprg/j5ZxN+LwG9e89g8+Y7gEMXS94nPv5nxo4FNaHiST//DCNH5ix5DIjJ8fggX311wqsxuWrGjN9Zteot0tIG0b9/Za69dgjnz583OiynrryyMWXKXE1gYEvKlbuBvXv3Gh2SU1u3WklPjwXigDjS0+PYts18F9y/+GI2sCRHyV90796ctLRMo0ISkRyU5BI7a1YWjzRowDvAdcAmgI8/hvR0YwPLlpUFjz0GQ4bASy9x8MMPGZ2YyNDjx+m3axd9ly/H0q0btGkD48ZBUpLREdts2wZt2rBx9myePH+e5zIymAR8dOYM9O9vSyZeuGB0lHk8VrUqlcqV44Y5c5h8+jTLW7WCuDijw8rfqVNkduzIvX/9RQywH2g7YwavN2qE1YxJT7l8WK1w//3sOXiQLTmKTwBlvv/eqKic2rcP3nwTYJfDlgbMnRvMqVMGBFWArCx45BGAIw5bavLccwYE5II//rgC2A1k/9hJ5Prr95my+QwNvQ8o41D6OJDFq6/a8rcinjBtWs7BAp+TO2EAcBuHD99NSop34ypMTAxMmLAwR0kicXErCQ8PNyqkAh05YiUj4w/gT2AH8BNbt6YZHJVzI0bkV2a+JFfv3mOAVg6l+2nf/k4jwhERB0pyid1rVaow7+L9s0Bn4NPMTChVysCocvj9d1vPp4tiHTZHZt9JToavvoKpU70TV2GefBJOnybEoTg5+86OHbBsmXdjKkxmJjH//EPODuLfAHz+uUEBFeLbb5mckcGaHEXpwLp9+8CiTs7iQceOwbZtLHAorgR0P3nSdAnsH37I/mHpmORqSmYmbN9uQFAFiI6Gv/+G/JJcP/5oQEAu+P77ykAVh9IdfPONEdEUbO/eysCLDqVbgQ+xWmHFCgOCkhJhvX0kYgz/DlPMVg6Yw4ULFtMlWt98Mx340KH0fvz9/Y0Ip1CvvZY3oTVvXmkDInHN2LF5z9nMNqgEoGvXQF5++RMgIFf5+fPLeOklx/eH5KThiuINSnIJAGm7d/OBQ1kWUKFTJwOicSIyMtfDWMfNjvvv2+exUIrkzBkA50kuMN8QywsX6OVQ9ANgrp/r/9pw5AjjHcpqAO+1bIlFSS7xpKws0iFP+3k/EGC1mi7J9e9UYXmTXAB+JjsrSLY3lIcdttQ0XbOZzTYCvYVD6Q5TzoUTEAC2nlv1HbY8CyRg4hFY4uP+/TyMwHZpNaf/Arbh02b7Cv/ll5XAGYfSwQZE4pqtW/N2hdu7N9iASFwTGOhamRncfXcdKlXql6f8o48e4/Rpc333i5Q0JjudFaME7trFBuCWHGVzgBs//dSgiPJRs6atV9TFb7tYh80Rjvv36OGFoFxw331AAUmuJk2gd29vRlS40FBu6dQp15WTACD6yiuNiqhAh6tXJ2d/Qz9gEVDujjsMikhKjJo1WVmzJo7zDj8A0KxZnuS80Ro3BtslDMfFGZoQEACtHEdfGCwqCoKDIW9Prlq0b29AQC649VaAax1Kd9CliwHBFKJjR4BA4HWHLaeA/9CokddDkhKifn2ApcBihy19gf4AhIZCw4ZeDatQBw869ttty6pVDQyJxRUTJuRNtrz9tnmTXGUcR08DoaHmG66Ybc2aj4BqDqXxDB8+2ohwfIJRPbmOHDnCqFGjiIqKIjQ0lHLlytG6dWtmzJhBcnJy4RW4aPHixdx8881UqVKF0qVLU7t2be677z62bNlS6HNr166NxWIp9Fa7du1ii/dypSSX2Fx3HeGlS/M1tmu6TwFDzbhczJAhtpUfFy6kxcMPM751a55o2JBBNWvSvX596NQJ7rkHZs+Ge+81Olqb55+HBQuo3KsXXcqXp1eFCvSrWpXeTZrAggW2oZVhYUZHmUeFb75hYM2aPHXttfw4fTox8fE0M+OvNODeIUPY+vnnNCxt64I/Ebhh8GDzrz8tvs9i4eebbspV1AaIKlMG3nrLmJgK0LUr1KlzEHDsVtSU//s/qFjRiKicCw2F0aMzgOO5ygMCajJhgiEhFWrwYLjxxtw9uSIi9lG3rvl+qD37bPaPyh4Xbzm9TosWR70flJQId9wRA/yfQ2lZ4C2yf9IOG2auXjxZWVlUqJB7cr0bbxxMu3YGBeSCBg1SKVUq9/DErl3NO1wxKipvOsNsic6cAgL8+OyzDUBQrvI///wv33+/zJCYzM6IJNfy5ctp0qQJr776KtHR0SQnJ3P+/Hm2bdvG6NGjufbaa/nbNjeC2y5cuMCtt97KgAED+O677zh16hSpqakcPnyYjz76iHbt2vHyyy9f4isRV1msVqv5zrokl/j4eCIiIoiLi8s9seUZx+7Sl+jXX+Hbb6FJE6zdu2Mp7nEr+/aZrldDgRISbL3HfIXFAuXKGR2F66zW7LEyxSYpKYm3XnuNJ0eMwN8T7zWHeJ1+NsU0nP6Pinms275Nm1j43HO8v3074zt3Zuibb0KNGsVWf3oGYCmeNnnevG945JFbc5SU54EHzjJ7tqXYflBmZUFxTVETE3OOa6/twqlTh7FabT8wv/jiDL16FV9GzmotvngBTp06xbPPTqVChRb06tWC9u3rF+ucPVlZxTe0dPNm27WhI0f+AhoD6ZQqVYeZM19n+PBexTbk2zFetZ/mVtD/pziaz59+2saNN/YhPf1kjtKPgIEAtGgBa9bYEt2XKjm5eIc9btmyjzfeeJ9ffvmMn3/eSkREnrEEl8RqhaCgwvcriqwsK6mpqaSmXiAiIqJYp3I4d674TudiYs4SFXVFrrI//zxNxYpXOHlG0Rw+DJ5obj788Hdefrk1YBseWqlSKz7+eDE1a151yXXXq5e3zBfbz+yYzy1eTHiI4/iWQp6bnEz5/v3der27du3i+uuvJzk5mbCwMMaOHUvnzp1JSUlh8eLFvPvuuwA0bNiQbdu2EeZmx4OBAweyaNEiADp37swTTzxB1apV+e2333jllVc4cOAAAO+++y4PPfRQvnXUrl2bw4cP06dPH/7zn/84PVZgYCD16ztOMyA5KcnlA7yW5PI0Jbk8S0kuz1OSy+d4K8mVLTMzk8zMTAKLuftBcSa5AM6cOcdnn+3m1193UbFiGq+8Urwz+xZnkiunuLg4jhw5QuPGjYv1R1pxJ7k8rTiTXACpqfDjjzBnzgQqVSrNrFlPEhpavL+yleTyLZ5OcgEcOfIPN9/8JH/99QHQG1hGYKCFBx+EKVPyH7rmjuJOcmWzWq0emffTE0kuTyrOJFdmZiaHDx+0T2cZHGyhRo1aBBTTATyV5AJ48835zJr1EO3bj+K//51EcHDxnAcoyXVpSa7OnTuzdu1aAgICWL9+PW3bts21ffr06Yy5uLrBxIkTefFFx8VYCrdu3To6XZzH+tZbb+WLL77IdWErJiaGFi1acOTIEcqWLcvff/9NZD6/ibOTXIMGDWLhwoVFjkP+5WO/MEVERMzL39/ftKts5XTFFeV59NHO2NbR9R0RERFcc801Rodx2QkKgu7doXv3CUaHIiVIzZrl2LPnfZYs6UdQUAsqVrRwzTXFl9zyNC1sU/z8/f256qq6RofhlkceeZBOnZpz9dWOczJKTu4MP3T3k7Zt2zbWrl0LwJAhQ/IkuACefvppFixYwJ49e5g5cyZjx46lVKlSefYryLRp0wDb+3fOnDl5zgMrVKjA1KlTGTBgAOfPn2f+/Pk8/fTT7r0ocYnm5BIREREREUPcdVcv+vSpwvXX+06CS8SRxWJRgstkli1bZr//wAMP5LuPn58f999/PwDnz5+3J8VclZiYyOrVqwHo2rUr1atXz3e/22+/3d4LbenSpUU6hhSdklwiIiIiIiIi4lHenHh+w4YNAISGhtKiRQun+3W0LTUMwMaNG4t0jK1bt5KampqnHkeBgYG0adPG/pz09PQiHUeKRkkuEREREREREfEobya59uzZA0DdunULnNetYY4lPLOfU9RjONZT0HEyMjLYt2+f0/3Wr19PkyZNCA0NJSQkhCuvvJK7776bZcuWoenUXaMkl4iIiIiIiIhcFi5cuEBMTAyA0yGE2cqWLUvoxaVcjx49WqTj5Ny/sOPUyLHqdkHHOXjwIL/99hvJycmkpKRw6NAhlixZwm233Ub79u05fvx4kWIsiTTxvIiIiIiIiIiYVnx8fK7HQUFBBDlZijQhIcF+PywsrNC6Q0NDSUpKIjExsUgxFeU42Yk0IN/jBAYG0rt3b7p160bjxo2JiIggNjaWzZs389Zbb3H06FF++uknunbtyubNm4mIiChSrCWJklwiIiIiIiIi4lGXsrpizp5QAOPHj2fChAn5PufChQv2+4GBgYUeIztZlpKSUqTYinKcnAm5/I6zdetWIiMj85R36tSJxx57jDvvvJPvvvuOPXv2MHHiRF577bUixVqSaLiiiIiIiIiIiHiWxeLeDdsQv7i4OPtt7NixTg9TunRp+/20tLRCw8qePD44OLhIL6cox8k+hrPj5JfgylamTBmWLFlC+fLlAZg7d65Lr6uk8ukk1y+//MIrr7xC9+7dqVGjBkFBQYSFhVG/fn0GDx5sX1HBVStXruT222+nevXqBAUFUb16dW6//XZWrlzpch3JyclMnz6d1q1bU65cOcLCwoiKimLUqFEcOXKkqC9RRKTYqe0UEXGP2k8RkUtwCUmu8PDwXDdnQxXBlhTK5soQxKSkJMC1oY3uHif7GO4cByAiIoL+/fvb69q+fXuR6ygpfHa4YseOHVm/fn2e8rS0NPbt28e+fft4//33ue+++5g3b16B3QetVisPP/wwc+fOzVV+/PhxvvjiC7744guGDRvG22+/jcXivIPlgQMH6NmzJ3v37s1VHh0dTXR0NPPmzWPRokX06NGjiK9WRKR4qO0UEXGP2k8RkUvk52e7FfU5RVS6dGkqVKhATEwMx44dK3Df8+fP2xNQjkMiC5Nzsvljx47RsmVLp/vmnGy+qMfJ1qhRI/t9TUDvnM/25Mr+p1atWpUnnniCzz//nK1bt7J582Zee+01qlWrBsCHH37I4MGDC6zr+eeft59kNG/enE8++YStW7fyySef0Lx5c8DWJfCFF15wWkdiYiK9evWyn2QMHTqU1atXs2nTJiZNmkRYWBhxcXH069eP3bt3X+rLFxFxi9pOERH3qP0UEfEdUVFRAOzfv5+MjAyn+0VHR+d5jqtyJp1y1lPQcQICAqhbt26RjpPNarW69bySxmL10b9Ur169uP/++7njjjvw9/fPsz0mJoZ27drx119/AbB+/Xrat2+fZ7/9+/cTFRVFRkYGLVu2ZP369bnGyCYnJ9OxY0e2b99OQEAA0dHR1KlTJ089EyZMYOLEiQBMmzaN0aNH59q+efNmOnToQEZGBp07d+bHH390+bXGx8cTERFBXFwc4eHh/244c8blOkxh3z4oYKyx6SQkQM2aRkfhOosFypUzOgrXWa0Q4GOdSR3idfrZNLGS1HZCAf+jrKwi1WO09AzA4jvXpbKyIJ+3l2lZrb4Vb1aWWxe2DeUYr9pPc7efBf1/fKz5JDnZPtrJJ1itUMAoLNM5d853TucOHwYfaW7s6tXLW+aL7ac95s8/JzzHKoMuPTcpiYg77yzy6x03bhyTJ08GYMuWLVx33XX57jdlyhT7/F6rVq2iW7duLh8jISGBChUqkJaWxi233MK3336b735paWlUrFiR+Ph42rZty6ZNm1w+Rk6PPvooc+bMAWDjxo20a9fOrXoudz52ivSvb775hrvuuivfkwyAChUq8Oqrr9off/755/nu9/rrr9szu7Nnz84zCVxISAizZ88GICMjg5kzZ+apIz09nVmzZgG27O/TTz+dZ5+2bdsyZMgQANasWcOOHTsKeYUiIsVPbaeIiHvUfoqIXKJLmJOrqPr27Wu/v2DBgnz3ycrK4oMPPgBsE7937ty5SMcoU6YMN954IwA//PCD06GRS5cuJT4+HoDbbrutSMfIFhcXx6effgrYvicKGhpZ0vlskssVnTp1st8/cOBAnu1Wq5Uvv/wSgIYNG9KmTZt862nTpg0NGjQAYNmyZXm6Ca5du5bY2FgABg0ahJ+Ty6s5u64vXbrU1ZchIuJVajtFRNyj9lNEpABeTHK1bt3a3pt2/vz5bN68Oc8+r776Knv27AHgiSeeoFSpUrm2L1y4EIvFgsViYcKECfkeZ9SoUYDtosSjjz5KZmZmru0xMTE888wzgC2R9tBDD+WpY+XKlaSkpDh9LQkJCdx1112cO3cOgCFDhhQ48X5Jd1knuXIuq5nfl//Bgwft8yt07NixwLqytx87doxDhw7l2pZzJZ2C6mnZsiWhF7tnbty4seDgRUQMorZTRMQ9aj9FRArgxSQXwKxZswgODiYjI4Nu3boxefJktmzZwpo1axg+fDhjxowBoH79+vn2iHVFly5d7KsefvXVV3Tt2pWvvvqK7du3s2DBAtq0aWNf6XbKlCmULVs2Tx1TpkyhevXqDB06lPfff5+NGzfy66+/snbtWiZPnszVV1/Nd999B0CDBg2cJtzExkdGULtn3bp19vsNGzbMsz07a+tse045t+/Zs4crr7yyyPUEBARQp04ddu/enes5IiJmorZTRMQ9aj9FRArgpdUVszVv3pxPP/2Ue++9l/j4eMaNG5dnn/r167N8+XLKlCnj9nHee+894uPjWbFiBWvWrGHNmjW5tvv5+fHCCy8wfPhwp3X8888/zJs3j3nz5jndp0OHDixatIhyvjQPswEu2yRXVlYWU6ZMsT++66678uyTcxnPnMt/5ifnMp85n5fzcWhoKJGFTKxeo0YNdu/ezdmzZ0lNTc23m2Fqaiqpqan2x9njd0VEPM2X205Q+ykixvHl9lNtp4hcrm699VZ2797NrFmzWL58OceOHSMwMJC6devSr18/HnvsMUJCQi7pGMHBwSxfvpxFixaxcOFCdu3aRWxsLJUqVaJ9+/Y89thjtG3b1unzZ8yYwerVq9m8eTN79+4lJiaG2NhYQkJCqFq1Ktdddx0DBgygW7duWHxpJQ2DXLZJrtdff52tW7cCtsnd8puYLSEhwX4/LCyswPpCc6wCkZiYmG89hdWRXz35nWhMnjzZvlpOgU6eLHwfMzlxAtLTjY7CdQkJcAkZfa9zGP9teqmpvrV8EECVKkZH4HG+3HZCEdrPHD/mfEFmRgBZ/r41w4Avrf7na6vFZWT41mqQAIGBRkfgeb7cfrrcdgK+lv+Ki/Otz0tWFrjwbzWNxETfOZ1LSQGHdR7E29wZflgMSZ1atWrx2muv8dprrxXpeYMHD841t2Fh7rnnHu65554iRmcbWq6J5IvPZZnkWrduHc8++ywAV1xxBW+99Va++124cMF+P7CQs6+cJwSOk8Jl11NYHYXVk23s2LE89dRT9sfx8fG5rubZXWLG2etq1oSqVY2OwnXx8VChgtFRuO6ff4yOoGjOnwd1tTUVX287oQjtp8MkzmZnsWb51I808K0flVAs59BSgvl6++ly24ktyepLUlMhPNzoKFyXmOhbX1EhIb5zTbhMGSW5DGdQkktKlssuyfXHH39w2223kZGRQVBQEEuWLKFSpUr57lu6dGn7/ZwTheYnZxdux6Wes+sprI7C6skWFBSk1RJExKsuh7YT1H6KiPddDu2n2k4R8QolucQLLqsk18GDB+nWrRvnz5/H39+fTz75pMAVZ3JOLufYDdxRUlKS/b5j1/Dsegqro7B6RESMoLZTRMQ9aj9FRIpASS7xAh+aMaNgJ06c4KabbuLEiRNYLBbee+89brvttgKfk3PCz2PHjhW4b84JPx27b2fXk5SURGxsrEv1VKxYUVfMRMRwajtFRNyj9lNEpIiyV1cs6k2kCC6Ld0xMTAxdu3bl77//BmD27Nncf//9hT6vUaNG9vvR0dEF7ptze1RUlFv1ZGRkcODAgXzrEBHxNrWdIiLuUfspIiJiTj6f5IqLi+Pmm2/mzz//BGDKlCk8+uijLj33yiuvpOrFidDXrVtX4L7r168HoFq1atSuXTvXthtuuMF+v6B6tm/fbu8y3q5dO5diFBHxBLWdIiLuUfspIuKm7OGKRb2JFIFPJ7mSk5Pp2bMnv/zyCwDPPfcczzzzjMvPt1gs9OnTB7BdBduyZUu++23ZssV+laxPnz5YHD5onTp1IiIiAoD3338fq5MlURYuXGi/X1h3dhERT1HbKSLiHrWfIiKXQEku8QKfTXKlpaVx22238dNPPwHwxBNP8J///KfI9YwcOZKAANv8+yNGjMiztHJKSgojRowAICAggJEjR+apIzAwkMcffxyAPXv2MGPGjDz7bN68mfnz5wPQsWNHWrVqVeRYRUQuldpOERH3qP0UEblESnKJF/js6ooDBgzgu+++A6BLly4MGTKE33//3en+gYGB1K9fP095/fr1GTVqFFOmTGH79u20a9eOZ555hjp16nDgwAGmTp3Kzp07ARg9ejT16tXLt/7Ro0fz6aef8tdffzFmzBj2799P//79CQ4OZs2aNbzyyitkZGQQHBzMzJkzL/0PICLiBrWdIiLuUfspInKJtLqieIHF6qx/s8k5dtsuTK1atTh06FC+27Kyshg6dCjvvfee0+cPGTKEuXPn4lfA6g779++nR48e7Nu3L9/t4eHhfPzxx/Tq1atIscfHxxMREUFcXBzh4eH/bnByHNP65x+4OA+FT4iPhwoVjI7Cdf/8A5GRRkfhulOnoFw5o6Momlq1cj10+tk0sZLUdkIB/6Pk5CLXZaTUDH/wsVXRSpUyOgLXZWb61jl0ejr4+xsdRdEEBuZ+rPbT3O1nQf+fmJgiVWW4f/4BH3mLAZCYCGXLGh2F61JToUwZo6NwTWYmBAcbHUXR5PfV74vtpz3m1asJDw0t2nOTkoi48Uafer1iLJ8drlic/Pz8mD9/PsuXL6dPnz5UrVqVwMBAqlatSp8+fVixYgXz5s0r8CQDoG7duuzcuZOpU6fSsmVLIiMjCQkJoUGDBjz55JPs3r3brR9pIiJmpLZTRMQ9aj9FREQ8w2d7cpUk6sllEPXk8iz15BIvUE8u46gnl+eoJ5d4mnpyGUc9uTxHPbmMk6snV1hY0Z6bmKieXFIkPjsnl4iIiIiIiIj4CM3JJV6gJJeIiIiIiIiIeJaSXOIFSnKJiIiIiIiIiGcpySVeoCSXiIiIiIiImEL2jNHKbVyG/Pxst6I+R6QI9I4RkcvDmTO22WVFpFDPP/8lFSq8Spky06lYcRqffLLJ6JAKdeGC0RFc3n75JYFnnvmf0WFICZWYmEhCgpVTp4yOxDU7d27l00/P8csvEBtrdDTOpaXBM8/E0K7delq3fpuffzZ/Ww9w223PERExkcmTo3VqJyJFpiSX5JaVBdOnw9tvw5o1kJJidEQFi42F336DJUtg2zajo7k8JSUZHUHhXnkFrrsOWreGN97AmpVldERSwsQePcq+zz4j+dgxo0Mp1N9/w4wZC0lMHEV6+hgSEp7hgQd+NPUPtTfe+IHQ0Mb4+/fA3///uOWWN40OqUBJSVZeeul3ypefwksvfWJ0OE5ZrVZGjFhPUNBg2ratzMyZd3LDDbs4ftzoyKQkiY6GG298gKuuas4118xl+vREo0Mq0LJlWfTocRcjR1ahZ8/eDBmymGSTrtg7cOCDzJ9fkb17O/L33//Ho49+ZnRIBcrKgieeSObHH98AJjB5chS1azdj06ZfjA6tQOfOpbFnT7zRYfiG7OGKRb2JFIGSXGJjtcInn0DDhvDuu/DaazB8OPTogWnPdtPToUkT6NkTRo2CO+6AadOMjsq5ffvgmWdgwgR46y3YuNHoiJyLj7f9TatWhXr14OabwaQncJw8CXPngtVKVkYGL772GuO6djU6KilJ/v6bbxo3pv7gwYQ2aED50FBuDw21ratuMpmZMGAAgOMJYxaPPWZAQC7YsgWefHI/8AfwLfA233+/ELPmE5955lMiI2vz8svXEB8/lpdfnsPmzUZHlb/MzCzmzh0IvA/Y2vht2xbQsSOcPm1oaFKCPP/8D/z99/+AXcBwpk2rxk8/rTM6LKdefvlN4DCQDnzNpk0D+OOPvQZHlb9Nm2rmenzo0J8cOGBQMC547TVYsGAFkDPR+RtpadWMCqlQ8+b9RbVqbWne/AGysqxGh2N+SnKJFyjJJTZz58L48XnLjx+HWbO8H09hrFaoUydv+dtv2xI0ZrNsGbRvD++/b4tx4kS48054/HHbZSuzeeklWLTo38e//QZ169piN5uLX3yJwJ3Ay8CUAwf4aPRoI6OSkiIuDrp143hGhr3oHy6ent9zz78Ti5jEjz/Crl2Q9+vfyuefw6FD3o+pIFYrDB0Kth+UOdVi7FgDAnJBdHQZ4EiOkk106HCORBN2Ttm92x8Y5FD6EUePpjFhggEBSYm0bt0YIGdbmcrRo80MiqZwJ07McCipyZ9/NjMilEKlpl7tUPIHTzxhSCiFOncOpk4F+NRhS2e2bq1kQEQFy8qy0rjxAh577FrgF2ApDRq8a3RY5qckl3iBklxi+xWxdKnz7UePei8WVzlLYGRk2CYgMJvnnsu/fMkS+OYb78biir//zr/8pZdsXUHMpHJljl59NTcAX+Qofuizz/j5f5pfRjzs++/h5EkcOxVVB1i5En4x1xCL7duz7+XtyQWwY4c3oyncwYPw55+QX5Lr++8NCMgF6emdgeAcJVnAKlPGa0t4DnYoPQd8zbJlpsvRymXo77/PAL86lF5B27YRBkRTuD//PEHuJDZABe6/36w/whs5PD5OUFCcIZEUZuVKSE1NABzPi+825UWCnTvPs3//aODfaT2OHh3J5s1/GheUL1CSS7xASS6BY8dsvyScadXKe7G4KiQk//JmzaBCBa+G4pKCfimYsedZy5bOt+3b5704XBQ4fjyO85JmAH8+/bRmqxbPCgwEyD/JBbZL0ybyb446b08usF0nMJN/R3w6/qisabp8e7Zy5YKBmxxKvzHl5Mnh4QB1gfYOWxbwzz/mu6Yhl5/HHnuW3L24oHfvp6lVy5h4CjNmzH/ylA0b9qRpf4Pfems9wD9X2YEDe4wJphC208uvgZznbQHA7Vx5pSEhFahFi3J07brAoTSFwYP7c0HnniKGUpJLoEwZKFUq/2316sGwYd6NxxVhYXnLAgNtPY3MaOLE/Mtr1YJbbvFuLK546ino3j1veWAgVKzo/XgKUal1a7584gmyU59lgVXAA2AbKiriKd26Qb16eZJc1QCuuALatTMgKOeaNs2+l39PriZNvBlN4erVg0qVIL+eXF26GBCQC26/HaCnQ+m3tGxpsgwi0KkTlC4N8KDDlm+pWfMEAQHej0lKlsOHf3coCeStt0w6QSBw9Gi0Q0kpnntugCGxuKJVq0CgXq6ytLQ/jAmmELZrRosdSrvi71+em282ICAXfP31rVSqlPP9WoqBA+8n8OIFMMmHn597N5Ei0DtGIDIy/0RWz57w8cf5J5SMdued2ZegbcLCbPNFNWtmWEgF6tfPNubm449ts2pOn26bn+vHH83Z86x0aZg3z3Zr29ZWVqkSvPMOlC9vbGxONH/yST5o0YIo4GfgxuwNMTHGBSWXv9Kl4dtv+Sc0NFdx9bp14auvwKHcaDffDLVrQ96v/yw6dICoKO/HVJCAAJg2LR04kas8IqIWr7xiTEyFue02uO++3EmukJBSBAc7GQZuoPLl4cUXwTajYc73ahZNm35oTFBSYsTEnCUuLvfQrjZtBhIY6O/kGcZKTk7i/PmtucoaNOhj2ngB+vYFiyX3kMWwMHMOp2vRIhZY6VB6N0OH2tZBMqvo6OlUr34N5crVY/Hizbz44ij8lJRxTsMVxQt0jU5sHn8crr/eNmFLWJhtknTbLyFzuuoq21xWX39t+5HZu3f25X7zKlcObryx8P3MwmKxra7Zo4dtuKUPfMHc8b//ceuSJQQ+/7xtbrYrroD+/Y0OSy53Vaqw//Rp4k6d4tivv3I8MZEWnTubMoEdGGhbSLdTJ79ciz9GRlqZP9+4uArSu3cKXbvexy+/HCEh4TAZGUf58cea+a49YgYWC7z3XnXOnLmHoKA63HlnL+6+u6Vpf/Q89RSEhobxzDN3ceHCAiCMli3vZuxYxyGXIsWrQoWK/PHHMcaPf59vvplDVtY/fPbZHKPDKtC4cVOZPftj/vlnMxaLPx999F+jQypQ1arQuXMjfvzx37l3K1Y0Z5IrIuJPgoLKkJqaPbY7kFtu6cvLLxsaVqGCg0vzww9fUaFCBcLM2DHAbNxJWvnAbxAxF4vVqmlFzS4+Pp6IiAji4uIIz9l7yYRzIxXon3/MfSnGUXy8KX+kOvXPP7Zeeb7i1Clb4s8Tzp6F/fttPfuCgwvd3WUOk4Q4/WyKaTj9HyUnGxeUG1Iz/CEoqNjqmzNnAZ99tpXERAvlyvkxdGhX7ryzT7HVD85HwV+qrKwsLBYLlmI86c3M9K1z6PR08C/mziM7d/7Kli27ueeeO4iIKP4eiI6jd9R+mltB/x9PdJC2Wq0cPnyQ2rWvKva6//knd+f/4nD48N9s376ZO+4YWLwVA4mJULZs8dX300/r+OyzrwkJacQttzSiWbMowsOLb2L/1FTbzCfFIT09nTff/IGvvvqUiAgrS5e+X6xtc2Zm8Z4WekN+X/2+2H7aY962jfAiJgPjExOJaNXKp16vGEs9uUTk8lOxoinnDhMxi0ceeYBHHnnA6DDcYtYeUb6uefNmNG/ezOgwpISyWCweSXB5Sq1aV1Grlm/E265dR9q162h0GC4pVaoUI0d2Z+TIfOaFlcuDenKJF+hMUUREREREREREfJ56comIiIiIiIiIZ7mzWqJ6cEsRKcklIiIiIiIiIp6n4YfiYUpyiYiIiIiIiIhnaU4u8QIluURERERERETEs5TkEi9QkktEREREREREPEtzcokX6B0jIiIiIiIiIiI+Tz25RERERERERMSzNFxRvEBJLhERERERERHxLCW5xAuU5BIRERERERERz1KSS7xASS4RERERERER8SwlucQLlOQSEREREREREc/S6oriBXrHiIiIiIiIiIiIz1NPLhERERERERHxLA1XFC9QkktEREREREREPEtJLvECJblERERERERExLOU5BIvUJLLl8XGGh1B0Zw7B8HBRkfhuuRkCAszOgrXJSVBqVJGR+G6mBjIzDQ6iqKpVcvoCKSYpKYaHUHRJKQAaUZH4To/Pyhd2ugoXJeeDv7+RkfhupgYyMoyOoqiueoqoyOQ4pKRYXQERXPhgm+dHl24AGk+1N4nJ/tO+5mVBQE+9us3KMjoCIqZklziBT72MZdcfO0Hd3g4REQYHYXrMjKgbFmjo3Bd6dK+9U2Yng6hoUZHISVUZlCI0SEUSfoFCPKhpWLS032vOfKVH2lgu8blS0lEubyUL290BEUTFORbiY3ISN+6JlyqFAQGGh2FazIzfSvheVnS6oriBXrHiIiIiIiIiIiIz/Oh6xoiIiIiIiIi4pM0XFG8QEkuEREREREREfE8Ja3Ew5TkEhERERERERHPUk8u8QIluURERERERETEs5TkEi/QxPMiIiIiIiIiIuLzlOQSEREREREREc/y83PvJj5t1apVPPXUU/z5559eOZ7eMSIiIiIiIiLiWdnDFYt6E5+2ZMkSZs2aRalSpfJs27t3L4MGDaJjx44MGTKEHTt2XPLxNCeXiIiIiIiIiHiW5uQqkbZt20bLli2pV69ervLTp0/Ttm1b4uLisFqtbNiwgQ8//JD333+fAQMGuH089eQSEREREREREc9ST64S6cSJEzRu3DhP+Zw5c4iNjaV58+Z8+OGHTJ06lfDwcIYOHcqRI0fcPp56comIiIiIiIiIZ6knV4mUkJBApUqV8pQvW7YMi8XCp59+Sp06dQC4/vrrad++PW+++SZTp05163jqySUiIiIiIiIil6UjR44watQooqKiCA0NpVy5crRu3ZoZM2aQnJxcbMdZvHgxN998M1WqVKF06dLUrl2b++67jy1btrhcx7lz5xg/fjxNmzYlIiKC8PBwmjZtyvjx4zl37lyxxepNFSpU4MSJE7nKzp8/z++//06TJk3sCS6Adu3a0bZtW7777ju3j6eeXCIiIiIiIiLiWe6slniJqysuX76cgQMHEhcXZy9LTk5m27ZtbNu2jXnz5rFixQquuuoqt49x4cIF+vXrxzfffJOr/PDhwxw+fJhFixYxYcIEXnjhhQLr2bZtG3369OHkyZO5ynfv3s3u3buZN28eX375JS1btnQ7ViO0bNmSVatWkZaWRmBgIADff/89VquVLl265Nm/WbNmfPTRR24fTz25RERERERERMSzvDwn165du7jrrruIi4sjLCyMSZMmsWnTJlavXs3QoUMB2+p+PXv2JDEx0e3jDBkyxJ7g6ty5M8uWLWPr1q3Mnz+fOnXqkJWVxYsvvsi8efOc1nH8+HFuvfVWTp48SUBAAGPGjGH9+vWsX7+eMWPGEBAQwIkTJ+jVqxfHjx93O1YjPPjgg5w+fZqnnnqK9PR0MjMzeeutt7BYLHTr1i3P/mXLliUtLc3t411ykmvKlCl5Mo0iIiJiXk888TFVqsygfPmXKV9+HLt27TY6JDHQjh0xtG49i3vvXWx0KCKmd/58HIsWnSMhwQqA1WpwQCK+xMtJrpEjR5KcnExAQADfffcd48aNo23btnTp0oW5c+cybdo0AKKjo3nttdfcOsa6detYtGgRALfeeivff/89ffr0oVWrVjz44INs2bKFmjVrAjBmzBhiY2Pzree5557j9OnTACxatIipU6fSvn172rdvz9SpU+3HOH36dKE9wsymT58+3H777cyZM4fy5ctTsWJF1q9fT5UqVbjpppvy7H/ixAmuuOIKt493yUmucePGUatWLW699VaWLVtGRkbGpVYpIuK+PXvg44/h8GGjI5ES5PBh2L8/i++/j+H116Mv6Wqgpx0/Dh99NJW0tNHAi8Bk7rnnd8z69Z2eDidPWnnnnYO0avUhn3yyyuiQLhvz5v1CVNSd3HtvVRISRrJjxytkZekXu3jXp59upUuXz3jssS188slxjhzJNDokp6xWuOuuD3j44QpUqxZJeHhzrr9+nNFhFWjp0r28884B1q37hy1bMklPNzqigj3yyJtEREygcuU3ad9+CX//bd7zOasVfv0VDh3K4McfY1m9+rTRIZmfF5Nc27ZtY+3atYCtp1Xbtm3z7PP0008TFRUFwMyZM0l34wOSnSjz9/dnzpw5+Pv759peoUIF+wTq58+fZ/78+XnqOH36tH143s0330y/fv3y7NOvXz9uvvlmAD744AN7QsxXLFq0iJEjRwIQGxtLZGQk7733Xp6/F8CGDRuoUaOG28cqluGKGRkZrFixgjvuuINq1aoxatQo/vjjj+KoWrztzBn47DPYu9foSAp36BA89BAMGADbtxsdjZjB5s3QoweMGwc9e8LffxsdUYFeeuklXnrpJd5//32Xn3P27Fn788Q8GjVqRNOmpejbtyLPPx/Fu++6PsGoN1mtMGwYQHCu8lOnUnj1VUNCKtTYsfOpV68GTz99FXv23M/QoW9yCatKe8WZMym88kocb799ntmzY0hJSTE6pHz99tt54H9A9kn9b8ya9YuBEblO7efl4e+/4b773mPjxruYO7ctgwZVp127/zM6LKdefhl+/jn73CIe+JU//jiMWUcOJSfDwIF3M3JkXW65pTydO5di8uRPjA7LqXPn4OOP5wMTSUl5jN2772bWrB+NDsupjz/+jjZtStOwYSl69ChLz57XkZpqdFQm58Uk17Jly+z3H3jggXz38fPz4/777wdsCajspJirEhMTWb16NQBdu3alevXq+e53++23Ex4eDsDSpUvzbP/qq6/IzMwsMFaAwYMHA5CZmclXX31VpFiNFhgYyGuvvca5c+c4duwYZ8+ezXeo4oYNGzhw4ACdOnVy+1iXnOT67bffGDlyJBUqVMBqtXL27Flef/11mjRpQps2bXj33XdJSEi41MOIN2zdCtdeC489Bh06wCfm/RIEbHEuXw5r18Ktt8KxY0ZHVLCMDHjuObjqKmjUCPbvNzqiQiUePMjtZcvya9mycPfdkGPCRjN6p39/tmZlYQVISIB164wOqUATJkxg4sSJPPjggwwaNMilsednzpyxP0/MYePG7HtZ9rLnnz+LGUfyb9sGtgV+gh22pDBnDiQlGRBUIVavDgZy/oL8ia5ds7hwwaiICnbmDERFdWXy5EhGjy7HuHEVeeAB1xMx3jR1amegVq6yuXNHGRNMEan99H0JCdC9O0Du87fTp6tTxN+ZXrFrF8yYAeB4Ae0qpk83ICAX2C5q/JOjxMrkyWVM23N37FiAs7nKFi6saMohoVYrPPVUEJAzq5XA448bFZE42rBhAwChoaG0aNHC6X4dO3a039/470mdS7Zu3UrqxcxmznocBQYG0qZNG/tzHHuMZcdaWD2XEqtZlCpViqpVq+LnZEGBQ4cOccMNN3Drrbe6fYxLTnJdffXVvPbaaxw/fpylS5dy66234u/vj9VqZdu2bTz88MNUqVKFQYMGsc7kPzhLtKQkW6Io5wdu5EjbJSAzslphx47cZa1aGROLq9q2hVmzbL+AjhyBG24wOqICJR44QM9rr+UL4EZg53ffYdquHkDMhg08AlwH1ABGAKcc3yMmZbVa+eijj+jUqZPPdT0W+O9/ASo4lJ7l7bcNCKYQ69dn38ub5EpMtHWGNJt9+xzbyn84fjyaxSadPspigbS03F3vly835/Cr0qX9gDscStdy5IjvLFGu9tN3LVgABw8CHHXYUoOLo39M5d9Og3mTXN9+6+VgXPDnn/C//wGcd9hSlpUrDQioEElJ8OmnVhyTXFDRlB3zN26E+Pgwh9JE3n8/+30t+cpeXbGoNzfs2bMHgLp16xIQEOB0v4YNG+Z5TlGP4VhPQcfJyMhg3759+dYTERFB5cqVndZRpUoVe4+wosZa3Pr06cOECRNYtmwZh4txmpj77ruP9evXc91117ldR7GtrhgQEEDfvn358ssvOXbsGNOmTSMqKgqr1UpycjIfffQRXbp0oW7durzyyis+tyLAZS/Y8QfPRVu3ejcOVznrtpppzh8SgG2uqJxMPGdP0unT9GzZkuzfw/9wMdFlxrOii7669157P5rjwDtA8COPGBiR62655RasVis///wzrVu35tdffzU6JCmC338HqOhQGmPKhNG/HV7zJrkADhzwZjSFy8oCqHnxltMGVpl0aq6KFcHx9CoiIivffc1g9OjuecoefNB3JrRV++m7/r327dgTv7opZ6GwXTezkl+S6+TJ7PbKPH76CWxDkR3PN8uxc6f34ymM7TQ5kdw9owAqmnKwhu394JjkSgPSTPn9bxqXMFwxPj4+1y21gLGhFy5cICYmBsDpEMJsZcuWJTQ0FICjRx2T7gXLuX9hx8k5x5TjcbIfF1ZHznqKGmtx+/rrr3n55Ze54447uOqqqyhXrhxdunThqaee4sMPP+S3336zD8H0tmJLcuV0xRVXMGrUKH7//Xe2bNnCsGHDCA8Px2q18vfff/PCCy9Qu3ZtevTowf/+9z+3JniTYuYsQ27miU/KlMlbZrtkZU7Nm+ctM9sZUba9e3GcAjAZiOnZ04hoXPKFw+MbgYhGjYwIpchmzJjB7Nmz8ff35+jRo9xwww38z8zvZcklMBDyJrnOYsZpmP6d2zP/JNclrNbsEX5+UK4cgGNvro2Yu9NO7ha0Vi3zXoB58MGbgPBcZcePm7SbXD7UfvouW3uTTO7hdAA1yGceYsPZZmw4gy3mnK4iJMTtziYeY/v979iLC6AsBXRqMYzt2q9jLy6Aik6vxRvJ9v92THIBJJp+cn8jWbG4dQNbciciIsJ+mzx5stPj5JwuKSwsv/9TbtlJrqIuHFSU42QfI7/jZNfjyVg9wWq12m+xsbGsW7eOWbNmMXjwYJo1a0ZYWBgtW7Zk6NChzJkzx2sXojzeHLdu3Zq3336bkydP8sEHH1C5cmWsViuZmZmsWrWKu+66i2rVqvHss89y0owTmJQktWrlLbP9ujCn/MYh/2LiCXNnzcpb5mQJWaOFVq7MN0Dni4+DgK86dqTr+PEGRlWAlBRaAg1yFN1uVCxuevTRR/n2228pW7YsycnJ3HXXXZoc2Ue0bg35JbkuLtZjKlddlX0v/yRX1arejMY1XbtCfkmuS1hZ2uMqV879Cz0qyrxJLoArrnAcsnie77839gpxUaj99E3NmkHeXlwA1WnXzruxuKJKFcjbiysQqMq113o/nsI0bQrOklwXpwYyFdv3j2OSKwh//zBTfp/a/ob5XHAnMd/r2mJjtbp3A1vPpbi4OPttrG0St3xdyDFxZ6DtamSBgoKCAIq8UExRjpN9jPyOk12PJ2MtbvHx8axfv96e1GratCkBAQG5El+pqans3LmT9957jxEjRtCiRQsqVKjAY489xp9//umx2LxyzeHQoUNMnTqVF198kdOnT2O52OUw+8XHxMQwffp06taty2uvveaNkCQ/c+fmftywIdx4ozGxuCK/Xzj5LA1rGs2bQ5cutvsNG8KaNeZNItavT8gzz/BNQAA9gC8ffJBuOVYoMZ3YWMYD0cCfwH+APkVYccssbrzxRrZs2UL9+vWxWq1MnDiRu+++O9cXqJjP44+Dv3/eObmGDzcknAL926SXdtiSgsViziZ01CgoVaq9Q+khunQx4fiVi+rUyZ3katDA3Emu+fNfAUrlKlu06ENjgnGT2k/fM2wYBAc7JlMjCQgIo4DfroaxJdwdk1y1AX/uu8/r4RSqXTto0MCxl1wwbdoEUcC81oapVw+qVMk7H1ffvpZ8B28YrVUr6NIlNE9527aJXHONAQH5iKws924A4eHhuW45k0aOSpf+9zzHlYVJsoc+Bhex22BRjpNzeKXjcbLr8WSsxS0sLIwbbriBESNG8N5777Fz504SExPZsWMH8+bN49FHH+X6668nJCQkV+Lrn3/+4a233qJp06aMHDnSpddcVB5LcqWkpPDRRx9x4403UrduXV566SUOHTqE1WqlYcOGzJgxg9OnT/PDDz8wYMAAAgICSElJYfTo0Xz00UeeCksK0qyZbXjinDnw5puwYoXzubrM4u+/4YEHICoKnngCevc2OiLnLBb46itbf+zt280/Uf6zzxJy9izfnDvHzSaecB6wTYLTpAkAUcBzAwZwxSUsO2ukevXq8fPPP9O1a1esViuff/457du358SJE0aHJk40agSPP567J1etWgkUsJCPYRo2hB49IL+eXHfcYc6eXFFRsGhRIyyWSHtZaGg1rrnGvMPpFy6cz2+/HWTr1sPs3XuM//s/c88PWLduZcqUGZyrLDp6AVYzLmlWALWfvqVGDRgxIneSy9+/BkuWYMr2c+hQiIzMOx9X1662BajNxs8PxozJ3ZMrKKgcn35qvqGVYDtNvvvu3EmuwMCKvPKKQQEVwmKBRYv88ffP/X36wguJTqcOFu8pkyMz6sqwvqSLy0u7MlzQ3eMk5VjC2vE42fV4MlZvKFWqFM2bN+fBBx9k9uzZbNy4kfj4eKKjo1m0aBH/93//R+3ate0j+2bPnk337t2L/YJUsY/I3rx5MwsWLGDJkiX2saVWq5WQkBDuuusuHnroIa6//nr7/l26dKFLly5MmjSJO+64g507d/L6669z7733Fndo4oqgILjDcdiCiQUHY9pvv8uExYxnQo4CAuCDD2xJxIgIMPHcYa6IiIjg22+/ZeTIkfz3v//ll19+oVWrVixbtoyQkBCjw5N8jBzZjT59tlKqVEXq1KlImTJ5r+6axcyZsGNHcK45rSIiLjBlimEhFap7dz8mT36Z5ORIunS5gRYtatl7hZtR5cpVjA6hyKZOfYBHHnkXAD+/snTpcjMpKcmEhJj3vZwftZ++5YUXBnDffR1Ys+YoZ84co27dUvTqZXRU+StTBoYPD+XNNxuQmHgQSKNp06v4+GNzJo0ABgzoTu/eCfz993ni4v6hdOlUUw/1vv/+9pQtO48tW86Snn6WNm0qUcBCc4aLjISdO3fi7x9CaGgZypcPpVSpUoU+ryTLOfywKM8pqtKlS1OhQgViYmI4VsjKBefPn7cnjnJODu+KnBPFHzt2jJYtWzrdN+dE8Y7HqV69OqdPny401pz1FDVWo1gsFurXr0/9+vXp378/AN999x1jxoxh9+7drF27llGjRvFf23LlxaJYklzZ820tWLDAvhxm9tW/Fi1a8NBDD3HPPffkynQ6ql27NlOmTOHmm2/mr7/+Ko6wRKQkKVsWBg0yOopi4+fnxxtvvEHjxo0ZMWIEJ0+epGPHjowcOdLo0CQfFSpUoEIFxyGL5lS+PLz2Wlvee+9xEhODqVSpNL17X0PZskZHVrDHHnvM6BAua506teGOOx4iKuom+vbtQ2io45BW36H203cEBQXRoMFVNGhwVeE7m8Bzzz3Fc889RVZWFidPnsBisVDaxB8Vi8VCWFgYTZqEAeb/QVynTl1GjqxrdBhFUr9+g8J3EjtvJbkAoqKi2LBhA/v37ycjI4MAJysuREdH53pOUTTKschVznoKOk5AQAB16+Z+nzdq1IgdO3YQFxfHqVOnqOwku3vy5Eni4+PditVMunXrRseOHenXrx/ffPMNb7/9NiNHjszzd3HXJV936NmzJzVr1mTcuHH89ddfWK1WIiIieOSRR9i5cyfbtm1j+PDhBSa4sl155ZUAJCc7rloiIlIyDRs2jFWrVlG+fHkuXLjA1KlTjQ5JLgO33NKdJUtmsWLFFBYsmMBtt/lQD17xCIvFwn/+8y4DB97t0wmunNR+iqf4+flRrVp1qlatZnQoIj7lUiaeL6obbrAtWpOUlMSOHTuc7rdu3Tr7/XZFXPWiVatW9snic9bjKC0tjS1btuR5jmOshdVzKbGaTVBQEIsWLaJ8+fJYrVYWLFhQbHVfcpLr22+/JTMzE6vVSocOHfjggw84ceIE//3vf2lqW9bDZSEhIXTo0IEOHTpcalgiIpeNTp06sWXLFho2bOhzc+SIiBhJ7aeIiHl4M8nVt29f+31nCZSsrCw++OADACIjI+ncuXO++zlTpkwZbry4qs8PP/zgdLjh0qVL7T2wbrvttjzbe/fujd/Fcc8FJXsWLlwI2BLtvc08F7WLwsLCuPfee7Faraxfv77Y6r3kJNcVV1zB6NGj2bt3L2vXruXee+/NtcpAUVStWpW1a9eyZs2aSw1LRMT0FixYwHvvvZdrPL8zderU4eeff+bee++lY8eOuhggIiWa2k8REd9zKasrFlXr1q1p3962OvP8+fPZvHlznn1effVV9uzZA8ATTzyRZ061hQsXYrFYsFgsTJgwId/jjBo1CoCMjAweffRRMjNzr6gcExPDM888A9gSaQ899FCeOipXrszAgQMBWLVqFZ9//nmefT777DNWrVoFwH333ed0SKOvaXVxMbbsaa+KwyXPyXXs2DGn41tFRMS5QUWcQ6xMmTL2q00iIiWZ2k8RESnMrFmzaNeuHSkpKXTr1o1x48bRuXNnUlJSWLx4MXPnzgWgfv36PP30024do0uXLvTv35/Fixfz1Vdf0bVrV0aOHEnVqlX57bffmDRpEkeO2FaEnjJlCmWdTII6adIkVq5cydmzZxkwYADbt2+n18WVOL755htevbjafcWKFfnPf/7jVqxmlD2tVWxsbLHVecnZKSW4RERERERERKQg3px4HqB58+Z8+umn3HvvvcTHxzNu3Lg8+9SvX5/ly5e7NIe4M++99x7x8fGsWLGCNWvW5BmZ5ufnxwsvvMDw4cOd1lGjRg2+/vpr+vbty6lTp5g6dWqeuSQrV67MsmXLXOrF7CuyV4sszpVJlaESEREREREREY/ydpIL4NZbb2X37t3MmjWL5cuXc+zYMQIDA6lbty79+vXjscceIyQk5JKOERwczPLly1m0aBELFy5k165dxMbGUqlSJdq3b89jjz1G27ZtC63nuuuu47fffmPWrFksW7aMQ4cOAbYF+vr06cPIkSMpX778JcVaXCIjI2nWrBnNmjWjefPmNG/enEaNGhW5E1R2QrBGjeJbAdZi1SycphcfH09ERARxcXGEh4f/u+HMGeOCcsf58xARYXQUrsvIACfdSU0pMRGCgoyOwnUnT0JoqNFRFE3NmrkeOv1simk4+x/52iK+cXG+9fFOT4ewMKOjcF1KCrg5nagh9u/3rXgBGjbM/Vjtp7kV9P9JTzcoKDclJoIvDTzJyIDgYKOjcF1iIjgsFGdamZlwibkMr8vvu98X28/smI8dK3rM8fHxVK/uW6+3JPDz88NiseQqK1WqFFdffTXNmze3J7+aNm1KmJOTwm+//ZaePXtisVgYNmwYb731VvHEViy1GOTMmTN88803vPjii3Tv3p0KFSrYJ4YbPHhwketbuXIlt99+O9WrVycoKIjq1atz++23s3LlSpfrSE5OZvr06bRu3Zpy5coRFhZGVFQUo0aNso/FFRExktpOERH3qP0UEXGfN1dXFM+69tprCQoKwmq12m9paWn8+uuvLFiwgCeeeIIOHToQGRlJ/fr1ueuuu5g4cSLvvPMOb7zxBnfddRd9+vQBbAmzhx9+uNhi86HrGnlVqlSpWOqxWq08/PDD9onnsh0/fpwvvviCL774gmHDhvH222/nyVbmdODAAXr27MnevXtzlUdHRxMdHc28efNYtGgRPXr0KJa4RUTcobZTRMQ9aj9FRERg+/btZGZmEh0dza+//mq/7dq1i5iYGPt+VquV/fv3c+DAAf73v//lqiN7UOG4ceNo2rRpscXm0z25cqpRowbdunVz67nPP/+8/SSjefPmfPLJJ2zdupVPPvmE5s2bAzB37lxeeOEFp3UkJibSq1cv+0nG0KFDWb16NZs2bWLSpEmEhYURFxdHv3792L17t1txiogUN7WdIiLuUfspIlI0WVnu3cSc/P39ufrqqxk4cCDTp0/n+++/58yZMxw9epSvv/6al19+mTvvvJO6desC5Or1ZbVaiYyMZNasWUycOLFY4/LpnlwvvvgirVq1olWrVlSqVIlDhw5x5ZVXFqmO/fv3M23aNABatmzJ+vXrCb44EL5Vq1b07t2bjh07sn37dqZOncoDDzxAnTp18tQzY8YMoqOjAZg2bRqjR4+2b2vbti2dO3emQ4cOJCcnM3LkSH788Ud3X7aIyCVR2yki4h61nyIi7jNi4nnxvmrVqlGtWjV69uxpL0tKSuL333/n4MGDZGRkUL16da6//noCPTCpn0/35Jo4cSK9evW6pK7jr7/+OhkZGQDMnj3bfpKRLSQkhNmzZwOQkZHBzJkz89SRnp7OrFmzAIiKiuLpp5/Os0/btm0ZMmQIYFtBYMeOHW7HLCJyKdR2ioi4R+2niIj7NCdXyRUaGsp1111H//79uffee+nUqZNHElzg40muS2W1Wvnyyy8BaNiwIW3atMl3vzZt2tCgQQMAli1bhuOClGvXriU2NhaAQYMG4eeX/58154SkS5cuvcToRUSMobZTRMQ9aj9FpKRTgks8rUQnuQ4ePMjx48cB6NixY4H7Zm8/duwYhw4dyrVtw4YNefbLT8uWLQkNDQVg48aN7oQsImI4tZ0iIu5R+ykiJZl6cok3lOgk1549e+z3GzZsWOC+ObfnfF5R6gkICLDPqeBYh4iIr1DbKSLiHrWfIiIinlWik1xHjx61369evXqB+9aoUSPf5+V8HBoaSmRkpEv1nD17ltTU1Hz3SU1NJT4+PtdNRMQszNp2gtpPETE3s7afajtFxBu0uqJ4g0+vrnipEhIS7PfDwsIK3De7qzfYlmzOr57C6sivnqCgoDz7TJ482bVlNC/OxeAzjh8Hh7+dqflaixobCyEhRkfhOl97/4qdWdtOcL39PHeu0F1M5dw53/p4BwRAjn+Z+WVlQqbRQRTBhTQfHL/hQ29gDzJr++nyuSdApi99WCD1AqRa/I0Ow2VZWbY21FckJ0NamtFRuCY52XbzJdWqGR1B8dLqiuINPtSEFr8LFy7Y7xc2s3/OE4KUlJR863FldYCC6sk2duxYnnrqKfvj+Pj4XFfz7MLDCz2eqYSGggsnY6aRkAD+vnNSBEDp0kZH4LpKlaBcOaOjEDeYte0E19tPi6XQQ5qOwwJsphYY6FtJuZAgK34+9J5o1jDV976fBDBv++nyuSdQKsC3fnGWDrL41NiVrCxbG+orQkJ8JykXH+9717AvN0pyiTf4SJPkGaVzJATSCrkEkbN7t+NSz9n1FFZHYfVkCwoKctpLQUTEaGZtO0Htp4iYm1nbT7WdIuINSnKJN5ToJFeZMmXs9x27gTtKSkqy33fsGp5dT2F1FFaPiIgvUNspIuIetZ8iUpIpySXe4EOdd4tfzgk/jx07VuC+OSf8dOy+nV1PUlISsYXMM5RdT8WKFXXFTER8ktpOERH3qP0UERHxrBKd5GrUqJH9fnR0dIH75tweFRXlVj0ZGRkcOHAg3zpERHyF2k4REfeo/RSRkkyrK4o3lOgk15VXXknVqlUBWLduXYH7rl+/HoBq1apRu3btXNtuuOEG+/2C6tm+fbu9y3i7du3cCVlExHBqO0VE3KP2U0RKsuzhikW9iRRFiU5yWSwW+vTpA9iugm3ZsiXf/bZs2WK/StanTx8sDstyderUiYiICADef/99rE4+iQsXLrTfv+222y41fBERQ6jtFBFxj9pPESnJlOQSbyjRSS6AkSNHEnBx3dsRI0bkWVo5JSWFESNGABAQEMDIkSPz1BEYGMjjjz8OwJ49e5gxY0aefTZv3sz8+fMB6NixI61atSrOlyEi4lVqO0VE3KP2U0RKKiW5xBt8enXFjRs3sn//fvvjmJgY+/39+/fnunoFMHjw4Dx11K9fn1GjRjFlyhS2b99Ou3bteOaZZ6hTpw4HDhxg6tSp7Ny5E4DRo0dTr169fGMZPXo0n376KX/99Rdjxoxh//799O/fn+DgYNasWcMrr7xCRkYGwcHBzJw585Jfu4iIu9R2ioi4R+2niIj7tLqieIPF6qx/sw8YPHgw77//vsv7O3upWVlZDB06lPfee8/pc4cMGcLcuXPx83Pe+W3//v306NGDffv25bs9PDycjz/+mF69erkcM0B8fDwRERHExcURHh7+74ZTp4pUj+EOH4bISKOjcF1CAlSrZnQUrjt3DsqVMzoK11244FvxQp73r9PPpsmVlLYTnP+PClnUzHRiYqB8eaOjcF1gIFSsaHQURZCZgZ+l8N1MIzER/P2NjqJoypTJ9VDtp7nbzwL/PxkZRarLaLHxFvDznc9LVhaEhhodhesSEiDAR7pNnDrlW39bAIeFVQHfbD+zY969O44yZYoWc0JCPE2a+NbrFWOV+OGKAH5+fsyfP5/ly5fTp08fqlatSmBgIFWrVqVPnz6sWLGCefPmFXiSAVC3bl127tzJ1KlTadmyJZGRkYSEhNCgQQOefPJJdu/e7daPNBERM1LbKSLiHrWfIlISaXVF8Qaf7slVUqgnl0HUk8uz1JNLvEA9uYyhnlwepp5c4mHqyWUc9eTyHPXkMk52zDt3uteTq3lz33q9YiwfaZJERERERERExFdpTi7xBiW5RERERERERMTjlLQST1OSS0REpATLzPS9kW8iIiLie9yZY0tzcklRaeJ5ERGRYhAfH8emTVZmzoSUFKOjKdinn66kZs2PqVnzHW6//VWOHDlodEiXH12qFrksJSYmsWJFGhcu2D7mmZlGRyRGysryuWnqRC57SnKJiIgUg/btW3P33RV59dUu9Oo1kr///svokJwaPfoR4F7gYXbuHMWKFb8bHdJl5e+FC+kfGMjqm26C2bPhyBGjQxKRYjJo0CwGDixNlSrVKFfuegYMmGJ0SAVas+YUP/4Yy+nTWfz1l7mTcn/8kcX48fu45pol1K49ll27dhodUoHOnbNSu/Y+rrrqDf7v/4YYHY5PyJ6Tq6g3kaJQkktsrFbYtg2+/57M+HijoylcQgK88QaMHAnjxsHixUZHVDirFdLSjI6iaHzxW+XYMYiONjoKKWHefz+Zf/7ZB5wD1vDXX7OIiYkzOqx8nTgBkHt5qVdeSTQkFlft3JnO3LkHuPPO7+jZ8y1SU1ONDilf5376iSdLlaLh0KF8Coxet46sp56Chg1h1iyjwytQZkoKv0ydyg/PPeebbb/4tNQLF1j9yiumf+/98AP8+OMhwAqcADbz/fd/m7YnT1oadO/emR49ylKrVgBNmkQwd+7XRoflVN++XXjjjfocO3Y3cXFTGDBgtdEhOXXq1Anatq0L1AeeYPny9xgw4IDZ38KGU5JLvEFJLrF56SXo3ZuY+++ncYMGfHLjjUZH5JzVCsOGwZw5sHIlLF0KEyZAly7mHrT99ddQuzZUrfrvzayX0/btg4EDoXp1qFYNxo/3jb7YzZrBNddA27YwYoTR0UgJ8vzzf2D70ZPNwg8/XG1UOAV64w2AMIfSRBJNmudKSEikZctg/u//6vLFFzezcuUjzJ59yOiw8vXF6NHMBNIvPt4JfAKQng6jRsGyZQZF5ty2t96iR3g45SpVosWkSTw6ezY0bmz7fhXxtPR0ePZZnihThpvGj2dsYCAZJm2MMjLgqacADjtsqcU33xgQkAueeAIg+4KLFYhn7Ngg0yYNYmIa5Hp88uROvv3WoGAKERdXmQsXknKV/fTTBCwWgwLyEUpyiTcoySW2RMt772EFHgKigXv+/JP7qlQhbt8+g4PLx8GDsDOf7ssnTkCjRpjyl9qOHfDww3nLx471fiyFsVph8GBYu/bfsnnzoFat7F/H5vT773A4x4nnRx/Brl3GxSMlyhVX7HYoqUdERIghsRSmShXIm+RK4i+Tjq5cuTIMiMxV9swzf3PsmCHhOJeSwuB9+4hyKB4HXMh+MG2ad2MqzPHj+D3zDN8C2X24/wJOHT0K996b3e1PxHNefpn3X32Vdy4+nALcXLYsZzZtMjKqfG3aBEePQt4kV23WrzcgoEIcPAgLFgDE5iq/cCGCDRuMiKhgcXEAzR1Kd/L00wYE44ILF/yAOg6ln5OVpYxMQZTkEm9QkkvAzw8qV+Zd4MscxR8BT3foYBsaaCYhhfxwNGNi4+OPnZf/8493YylMUhIcOpT/tqlTnW8zWvv2ecuCg70fh5Q46elw5oxjkqsJ99xjSDiFso2aDnUoTeTUKQOCccFXX0HeHxIHWL7cgGAKsmMHAbGxTHUoPgL8N/vBr796NaRCzZhBM6CMQ/EGsL1RTJhokMvLr6++iuMlwM3A6QsX8tvdULbrq1by68l17pz34ynMli0AaYDjSiiR/PKL9+MpTHIyQDOH0r2cPZtMenre/Y3WtCmUK1fBofQCy5eb8I8rUsIoySVgsRA/aRJjHIorApPA1ovHTCpXho4dnW//7DPvxeKqwMD8y61W8w2xDAuDm25yvn3dOu/F4ipnl3jq1/duHFIi2a7s506u+/k1oWxZQ8IplC33m3e4ogl/UwIQEAD5JbnM1nRe/IVGL8DxG2oS8A9AqGNy0WCHD+MP3OBQbG/l6zj+3UWKT2xsLHf4+eHY9MwFrmnZ0oiQCmSbsvYM5Im4FhER3o+nMLZZJvKbGzKCUqW8HIwLKlSAUqWakPvnaRYZGbuJiTEqqoI99FDeyeZnz37bgEh8R1aWezeRolCSSwAIv+kmVr/9NjlTAguASgAnTxoTVEH+8x+oWTP/bWb8pfbgg/mX9+5t+1Y3mzlz4K678t/WoEH+5UbK79uvc2fvxyEl0unTViB3T65rrmlqTDAuqFQJ8huuGBRkQDAu6NcP4KpcZRbL33Tvbkg4zjVpAn5+WIDpDptiuXjR6JZbvB1Vwbp0AaCDQ/F6sA1bb+44dEik+ISGhtK3W7dcZY8A9954I4SHGxNUASpWhLy9uAKAqqb8qNjyhLH5bImkXTvvxuKKUqXg+utDAMfzzJ2muz6Q7cEHuwP+ucpiY03aLdokNFxRvEFJLrFr0acPv/z2G8OrVOFRoCfYevWYccxNxYqwaBH5fkvffbf34ylM/fqwfj0MGGCb0Ld1a3j+efPOcRUaCq+/bltx85lnoEwZ2+pgkybBddcZHV1e/v62oZ/+OU40liwxLh4pUWJjjwPnc5XdeGMTY4JxwTXXQH49uapXNyAYF/ToAXfckbtH0VVXnaR2bWPicapyZVtiCGgF9HfY/F/g8EMPeTmoQjz8MIwYQcccvY2jgoJo1749Wa+/bmBgUhKUKlWKV//3P5aMGkWYnx/XBQTwWrdutjk1Tcg2K4JjkqsGoaH+9OljQECFiIqCTp0ce3IFcvvtpWnWzIiICvfII+A4L1f58jvNmPMEICSkFNWq9cxVlpy836BofIOSXOINAUYHIOYSWqECb//yC1lHj8Iff9iuTFetanRY+atQwTaUcvNm25rOKSlw661w/fVGR5a/unXh1VeNjqJoqlaFxx+33cyuRw/Yvx9iYmxDbLS8jXiN43xcZRg8uJYhkbiiQQOIjAwlNvbfssDARBo1MiykQk2b1plu3T6idOk6dOhQhxo1TNgDFmDmTNs8lp99xiTgf9hWWqzh789/nn2WGvnNHWikgACYNIkWY8fy2eLFdOjShSuuuqrw54kUo36TJ9P4/vsJCwsjqEYNo8Nx6uqr4ZprDvHbbzlLa/Hyy1CunFFRFWzMmFS2bKnIhQuxQDrBwZHMn290VM516wadOjVn7dpF9rKaNc2dNHr55ed48MGv7I8rVAgkMTGBsDDH2Q4F3EtaKcklRaUkl+TLr0YNMPGJhp3FYktqmTWxJd4VGWm7iXhR7do1uOOOp1i3bjfx8buoV68+ZcuaN8lqsUCvXmG5OkvUrZtoyjlastWuXZuHHqptdBiFCw629TJ+6imuWruWcStXEly9Oo/PnEmwidumwLAw7jRbLzMpUaKiHNclNaf/+79qTJ16EydPHiY9/Qjt2tXigQeMjsq5Ll3aERt7BqvVyoULF0hISDD9mjwvvdSVjz9+kQoVmtO7d3Pq1XMyPYlJdOnSkttuG8RVV3WgR4+bqVevmtEhmZqSXOINSnKJiIhcgqioa5g5899emsnJSQZG45p77rmJKlX+y9mzYVx1VRhNm5p0rKKvatkSWrZkwqhRRkciIsVowIB7GDDANo1HVlYWF8w4D2w+LBYLwcHBBJs9w4VtTsspU8w7r6UjPz8/Zs1aaHQYIpKDklwiIiLFKCTEpDPk5tC4cXMaNzbhTMkiIj7Cz8+PkJAQo8MQ8SnurJao1RWlqJTkEhERERERERGP0nBF8QYluURERERERETEo5TkEm9QkktEREREREREPEpJLvEGJblERERERERExOOUtBJP8zM6ABERERERERERkUulnlwiIiIiIiIi4lFaXVG8QUkuEREREREREfEozckl3qAkl4iIiIiIiIh4lJJc4g1KcomIiIiIiIiIRynJJd6gJJeIiIiIiIiIeJSSXOINWl1RRERERERERER8nnpyiYiIiIiIiIhHaXVF8QYluURERERERETEozRcUbxBSS4RERERERER8SglucQblOTyZYmJRkdQNCdPQlKS0VG4Lj0dypUzOgrXXbhgi9lXxMeDn49NCxgZaXQEUkxOnTI6gqKJj4fQUKOjcJ2fn2+dlKZe8K3hECkxgL/RURRNxTJGRyDF5Xys0REUTUIiBJY2OgrX+aWlgDXT6DBclnS+FAQGGR2GS86fh4wMo6Momho1jI6geCnJJd6gJJcvK1/e6AiKpnp1CAszOoqiueIKoyNwXblyEBxsdBSu8/eHUqWMjkJKqLJljY6gaCIjoXJlo6Nwnb+/b+WwUzN863QoJj2SMN/4TSmXIV/7vFj8fev00z/hAkE+1H4GBwDBvtEgVagAERFGR1GyKckl3uBDTaiIiIiIiIiIiEj+lOQSEREREREREY/KXl2xqDezSk5OZvr06bRu3Zpy5coRFhZGVFQUo0aN4siRI8V6rM2bN3PfffdRu3ZtSpcuTZUqVbjllltYvHhxoc8dPHgwFovFpduhQ4eKNW4j+FZ/YxERERERERHxOZfTcMUDBw7Qs2dP9u7dm6s8Ojqa6Oho5s2bx6JFi+jRo8clH+ull15i4sSJZOXI+J06dYpTp06xatUqFi1axJIlSyhd2ocmIPQgJblERERERERExKMulyRXYmIivXr1sie4hg4dSv/+/QkODmbNmjVMnjyZuLg4+vXrx+bNm2nSpInbx5o3bx7jx48HoE6dOowbN45rrrmGEydOMGvWLNasWcPXX3/NQw89xEcffVRgXVWrVmXVqlUF7lOtWjW3YzULJblERERERERExKMulyTXjBkziI6OBmDatGmMHj3avq1t27Z07tyZDh06kJyczMiRI/nxxx/dOk5sbKy97po1a7JlyxYqVKhg396rVy9uu+02vv76az7++GOGDRtGhw4dnNZXqlQpGjdu7FYsvkRzcomIiIiIiIiIFCI9PZ1Zs2YBEBUVxdNPP51nn7Zt2zJkyBAA1qxZw44dO9w61rvvvktsbCwAU6dOzZXgAvD392fOnDn4+/sDMH36dLeOc7lRkktEREREREREPC67N5erN7NZu3atPfE0aNAg/PzyT6kMHjzYfn/p0qVuHWvZsmUAhIeHc/vtt+e7T/Xq1bnpppsA+P7770lMTHTrWJcTJblERERERERExKMuh9UVN2zYYL/fsWNHp/u1bNmS0NBQADZu3Fjk46SlpbF161bA1jMsMDDQ6b7ZcaSmprJt27YiH+tyoySXiIiIiIiIiHhUUXtxmbE31549e+z3GzZs6HS/gIAA6tSpk+c5rtq3bx8ZGRmFHsdxe0HHOnfuHO3btycyMpKgoCCqVKnCzTffzH//+1+Sk5OLHKNZKcklIiIiIiIiIh51KUmu+Pj4XLfU1FRDXsPRo0cBCA0NJTIyssB9a9SoAcDZs2eLHG/2ccA2JNGV4zg+z1FiYiIbN24kLi6OtLQ0Tp06xXfffceIESOoX78+mzZtKlKMZqUkl4iIiIiIiIh4mNXNmy2RExERYb9NnjzZ++EDCQkJAISFhRW6b/ZwRaDIc2VlH8eVYxV2HIvFQps2bZg0aRLffvstv/zyC5s2beKdd96hdevWABw/fpxu3bqxc+fOIsVpRgFGByAiIiIiIiIi4szRo0cJDw+3Pw4KCjIkjgsXLgAUOEdWtpwxpqSkuHUcV45V2HFef/31fHudtW3blqFDh/L888/zyiuvkJSUxEMPPcT27duxWCxFitdM1JNLRERERERERDzM/Z5c4eHhuW6FJbkyMjKwWCyXfFu4cGGuekuXLg3YJoYvTM4hisHBwYX/efI5jivHKuw4BQ2rtFgsTJo0yb5CY3YvL1+mJJeIiIiIiIiIeJj7SS6zKFOmDODa8MOkpCT7fVeGN+Z3HFeOdSnHyTZ8+HD7/XXr1rlVh1louKKIiEgxsFqt7N37N9HRdejd27bkdYCJv2WzssBPl7pKvFOnwGI5TaVKlYwORUqw+HgICrLdpGQ7f/48S5emcuutV1C6tB/+/pBjuiHxee4krdxLcgUEBLi1qqGjKlWq5HpcvXp1fv75Z5KSkoiNjS2wl1T2JPAVK1Ys8vDKnJPNHzt2rMB9c042n3MS+qJo1KiR/f7x48fdqsMsTHz6LSIi4jt27YrmzjsbAXUYNao7Dz98C0891R0/E2aSNm2K5uabHwEqAVfQtGlVNm58xuiwLjtWqy2Z6O9vdCT527kziT59RgGLGTlyN8OG1cDNC8AibsvMzOKaa1qSllYfuJY+fa5l5swbcg3VMZs//zxJenpFmjbVT6niNnjwQrZseYpx44KAGjRufCurV79mdFj5slph4cLTNGt2nObNrzU6HMlHw4YNi73ORo0a8b///Q+A6Oho2rRpk+9+GRkZHDhwAICoqKgiH6d+/fr4+/uTmZlJdHR0gfvm3O7OscB2sfZyYb4zb5GiuIw+jCLiuzIy4M47v7346ADwX+bOfdiUk3auXQs333wEWAMsBt5g167X+OILY+MqyJEjKTRpsgg/v7vZvDnD1E1/QkICDzywhipV5lG+/Fj69BlpdEj5euGFn+nTpznwNhDLzJmDadQoi82bjY5MShKrFe65Zz9paTuBT4Fn+PLLrsTEnDc6NKf+/huuu24AN9xQiTJl7qdTp884dCih8CcaKOXECTY88wynd+409bnzrFmwZcvhi49Sgf38/vtZTpwwMqq8rFb48stkKlf+D88+W5dbbhnAmjXpRoflI3x/uOINN9xgv1/QsL7t27fbhxG2a9euyMcJDAy0r3y4efPmAuflyo4jKCiIli1bFvlYAH/++af9ftWqVd2qwyyU5BIbqxU++QQGDybrhRfYvHy50REVLCEBRoyAa66BG26A2bPBhcn/pAgyMuDLL+Gtt0jauZMzZ84YHZGIab30EsDKXGVZWbeYLsl18CD07w9w2mFLJR56CPbuNSCoAqSnpxMY+Ai1a1fh998HAkto124VbdrYXosZ/frrbr78sgupqUOBKWza9BYHDmQaHVYeP/00B9iXo+RHYBb33gs//mhQUFLifPEFrF//i0NpZdavr5Lv/kb76Sdo2vQfYCPwD/AhO3bcxVdfrTU2sPxYrUyuX5825coR0bgxHd59l29uvBEaN4YZM0x33rx3L0yZAnDYYUstXjNZR65jxw4zbFgD4AUgEfiL/v3f4Z13DA5MvKJTp05EREQA8P777zvtAZVzwvrbbrvNrWP17dsXgPj4eJYuXZrvPseOHeOHH34A4MYbb8w1l1dRvJPjDdyxY0e36jALJbnE5qOP4NFHyfjqKx54801uuO8+lowfb3RUzr38MvzwA2RmwrlzMGcODBsGOVaWMJ2TJ+Ghh6BnT1usX35pdETOZWbCbbdhvftuPnnySRq0bcujPXsaHZVzViv8739QubLt1r697bHJTuDk8mS1wubNSYDj1bzuRoRToClTwHZR0THJdQVpaTBzpvdjKsiff5YiI2M7EJej9D22bYNu3bJfi7ls2lTHoSSNjh3NN7dF376zAMd5O8aSnv47jz4K+/cbEZWUJFYrvP46wE6HLc1Zu9b78RQmIQEGDQL4FsiZuA5mx44bjQnKmYQE6NePXTEx/Axk9zH6CWzno6+8AsOHm6pX17x5tuHd+SW5VqwwVahUq1YDcEzETuDFF2N1kaBQvt+TKzAwkMcffxyAPXv2MGPGjDz7bN68mfnz5wO2hFGrVq3y7HPo0CH7Co6dOnXK91gPPfSQPaH27LPPcu7cuVzbMzMzeeSRR8jMtLVJo0aNylPHli1bOHnypNPXY7Vaef7551m9ejUATZs2davnmZkoySU2n39OGjAA+ADIAgbOns03trMP8/npp7xlP/8MU6d6PxZXDRoEn34K69fbes3dey/ExBgdVf5Wr2bn99/TAbgHOA58/ttvrJk92+DAnJg6FR599N/H+/bZHn/wgXExSYnx++9w8OAaIGdSNYCKFc31oyczE776KvtR3p5cYL4ePJMnAzzoUPoVcJYDB2DBAu/HVJivv64E5J4lOTn5AGfPGhOPM7VrRwLvAzl7G6YC95KUlMqoUeb6USmXn+jo7GSqY0+uazl1yoCACvHxx3D6NNjaoJy6smFDiAERFeCxx+DHH7neoXhTzgdffgm7d3sxqIJd7IhCfkmuc+ds17TNwjbX5qsOpeeAyYwZY+5r7sbz/SQXwOjRo6lfvz4AY8aMYfjw4axZs4YtW7YwefJkunXrRkZGBsHBwcy8hCuI5cqVY+rF37eHDx/muuuuY8GCBWzfvp2vvvqKrl278vXXXwMwYMAAOnfunKeOlStXcuWVV9K3b1/efPNN1qxZw86dO9myZQtz586lTZs2TJo0CYCQkBDeffdd041EKColuQSAlLAwbgM+z1GWAdzzn/9wzmwD4cF2hSo/n34Khaw+YYiEBPKd6OTdd70fiwvG3XcfLbB1xs/p8ZdfJiMjw4iQCuZsMqGJEzHdL0u57KxZA45DFeF6brghwoBonDt6FP5dgdpx+LEtyXXiRPaVdHPYtAmgP5BzAuoM4MMc280lMNACXOVQ+je//mpAMAW4/nqAzsBTDlt2AS/yyy+wapXXw5ISZNs2sP14dezJdS2Bgd6PpzDffw+2ixnfOmzp7fS01BB//w0Xf/Q6Jrn2Arkur+7bhxnEx3Nx3q1EbMNAc6oFQEqKl4MqRIMG7QHHIWgzOXr0UPafX/J1eSS5ypQpw/Lly6lXrx4Ac+fOpUuXLrRt25Zx48aRmJhIeHg4S5YsoVmzZpd0rOHDh/Piiy9isVg4cOAADz74IK1ataJPnz6ssZ2E0qNHD9577z2ndaSmpvLll1/y2GOP0aVLF6699lratm3L8OHD2bp1KwA1a9bku+++y7fXma9RkksAWBQTwwqHsiDgE6uV8rt2GRFSwdKdTO6YlQUFTABomAAnq++YdK3sq6pVy/frpIrVyvnzJpwM9ppr8i9PTzdvbzm5bOzYYSXvj57uuDnvp8fkHr2bd7giQOnSYKaLd7amMxK402HLfMCKCReuxHZu6Dhk8YCpeiEAVKgAvXsDTAIc29DpwHo++sjrYUkJYluh/ii2HjA5NadmTe/HUxjbaJ91gGNGq6e54j10yH63KRDssHlLzgcNGng+HhfE2UekO/biArD9ccuV81Y0rnnoIYCpQM5z/DRgLN86nhJIDpdHkgugbt267Ny5k6lTp9KyZUsiIyMJCQmhQYMGPPnkk+zevZtevXoVy7EmTpzIxo0bueeee6hRowaBgYFcccUVdO3alUWLFrF8+XKnK9I+8MADzJkzh/vuu4+mTZtSpUoVAgMDCQkJoWbNmvTt25f58+ezd+9enx+mmE3r3goAD65Ywe6KFXnj4uNQ4Gts13hNOenJkiVw1135bzPjPEzBwdCjB6xwSCXaviFN54FNm3grMtI+gOBK4HWgd/fuWCpWNDAyJ1580dbP/cKF3OU33ggXuxKLeMpff+0H/nYovQUn0ysYplo18Pe3DVt0NlyxcWNzJbk6d84ekvggkDPj8iewlQ4drjMkroK0agXvvps3yRVhro59AIwaBatWBZGa+hHQin+H3FqB+9m8eReZmRH4+xsXo1y+bG2R41DFSKA2FxcUM5XISMg7VPE6oDLdzTQFY4MG9sa+FNCa3DNG/gT0Atscsc4uEnpZqH2Et2OS6wogmFq1cu5jDv36weuv1+PEiUeBWTm2LOb330die29IXu4krcyZ5AIIDQ1lzJgxjBkzpsjPrV27ttNJ6/Nz/fXXc/31jv0zC1erVi3+7//+j//7v/8r8nN9lQmvgYoRLP7+vH76NEOACOB7Lia4rrjCNruv2VxzDYwcmbe8VClM98sy26ef2q6urVhh620WGwvh4UZHlS//0qV5Y8QIQrFd4/8T6FOlChazLkZQs6ZtqbXly2HCBHj8cXjtNdsspvp1Jh4WG+t4ybYy11/flEqVDAnHqdBQ6NAh+1H+wxWdXTswyvPPczE51BFbuv1fZcu+x/33GxFVwW65BUJDcye5LJYDpvzRXru27RoBNMHW2ud0mPT0J0hO9npYUkLYVqjPO+l86dIWbr7ZgIAK0bWrFdsl4Jx6Ex6ee1pQw1WrBk8+aX+Y77xcPXvCW295M6oClS1rWzcov/m4AFO+H4KDYfp0sK2wGJlr2z//PFWk5IWIFC8lucTOr1Qp3jl2jB0zZtB2xAh46inbJOkmTcQwfDi89BL06gWVKkFUFMyeDVdeWfhzjVK+vG3lv2uvNX3ypd20aRzdt49xK1ZQ+rPP4LffoI5j7wQTsVigRQt4+GEYNw7uucd2BiLiYf7+jvNx3cLw4SbqDpXD2LFgsWSRN8l1BQ0bwgMPGBGVc1deaZvzrGlTP3JOQB8RcR0TJ96Ak575hgoLgyeeyD0nV+nSByhf3qCACnHvvbYFf+FJbMnEnN5n2zaNuxHPuPFGyG/S+aFDbUkPs2nf/ncckzBBQb1ZsACqOC60Z7SxY23zvrZqxfUXp8woZ7HQq1Il+g4aZFuYJyzM4CD/ZbFAnz6QX5IrKMi0Ax+46SaYMqU88Hyu8ooVszh/3nFuMRHxFg1XlFz8Q0Ko86DjSlYm1q+f7SbFz2KhbI0aUMNxiXkRyXbhQgrp6WtylV13XXfMOqVB27Ywffp5Ro3KvYBEgwaVWLoUUyaNmjWDX36BH38cxNtvn2fYsAfp2vVqo8Mq0MCB1xATMxY/vzq0aHEV111XB6vVasrViiwWeO45qFzZnylT3ictrQkQD1jo3n0M7dp1MTpEuUzVqgVlyuzMNWl748bXMmqUcTEVpEWLxvzww04mT/6KX375iqys86xffzVXOa4zYQYWC9xxB9xxB50TE9lz7BgNGjQwZRuUbeRI+PDDww69R2sxaZLtvWJWDzwAjRo9xj33vInVauWhh6Ywduxdpv5bG+vyGq4o5qQkl4iIiJsCA4P45JM1zJmzkl9//ZaEhJ288cZNRodVoIEDg0hJeZeffjrN2bOnCQw8wxdfXGG6+U5ysljgxhtrcOONjku2m1OVKlWZMuUVo8NwmcVi6ynRu3ctpk17k1Wrnufllz+gb98OhT9Z5BL88MMGli/fyYYNOzl//hf++99WplxZEcBisXDddc1YtqwZ8CJJSUmEhpo/kREaFkbDhg2NDqNQ5crBgw9ezZIlHfjnn8NkZBxj8OBa3Hef0ZEV7rrrgli5cgU1atR2Ovm3ZFOSSzzPYtWAYdOLj48nIiKCuLg4wnMOHTTjKncFOXDAVF2jXVK9utERuC4jw7eG5509a5tDzZc4TLLk9LMppuHsf3TggGeOl5AQR5kyxT/DuNWaPV+Jb/D3N2evMGdiY42OoGhOnfLc16nVaiUlJZmQkOLNejp2Clb7aW4F/X9OnTIoKDelp5tz+KMz/gnnCfah9vNMbCkILf4GKSMjg4yMjGJNGsXFYcpFQApyxRV5y3yx/cyOefHiY4SEFC3m5OR4+vev7lOvV4ylnlwiIiLFxBMJLhFvslgsxZ7gEhEpqoCAAAIC9FNVRIpOLYeIiIiIiIiIeJiGK4rnKcklIiIiIiIiIh6mJJd4npJcIiIiIiIiIuJhSnKJ5/kZHYCIiIiIiIiIiMilUk8uEREREREREfEw9eQSz1NPLg84cuQIo0aNIioqitDQUMqVK0fr1q2ZMWMGycnJRocnImJKajtFRNyj9lNERMRGPbmK2fLlyxk4cCBxcXH2suTkZLZt28a2bduYN28eK1as4KqrrjIwShERc1HbKSLiHrWfIuI71JNLPE89uYrRrl27uOuuu4iLiyMsLIxJkyaxadMmVq9ezdChQwHYu3cvPXv2JDEx0eBoRUTMQW2niIh71H6KiO+xFvEmUjTqyVWMRo4cSXJyMgEBAXz33Xe0bdvWvq1Lly7Uq1ePMWPGEB0dzWuvvcaLL75oYLQiIuagtlNExD1qP0XEt6gnl3ieenIVk23btrF27VoAhgwZkuskI9vTTz9NVFQUADNnziQ9Pd2bIYqImI7aThER96j9FBHfU9ReXOrNJUWnJFcxWbZsmf3+Aw88kO8+fn5+3H///QCcP3/efmIiIlJSqe0UEXGP2k8REZG8lOQqJhs2bAAgNDSUFi1aON2vY8eO9vsbN270eFwiImamtlNExD1qP0VERPLSnFzFZM+ePQDUrVuXgADnf9aGDRvmeY6ISEmltlNExD1qP0XE92hOLvE8JbmKwYULF4iJiQGgevXqBe5btmxZQkNDSUpK4ujRo/nuk5qaSmpqqv1xfHx88QUrImISxd12gtpPESkZdO4pIr5JSS7xPCW5ikFCQoL9flhYWKH7Z59oOFvKefLkyUycOLHwA5ct63KMptCypdERiJkUclIul7/ibjvB9fazTh3XYpSSwde+Tn0tXil+hp17ApUruxajuCnMtz7gV/hQuFdcYXQEoiSXeIPm5CoGFy5csN8PDAwsdP+goCAAUlJS8t0+duxY4uLi7LeCei2IiPiq4m47Qe2niJQMOvcUEd+k1RXF89STqxiULl3afj8tLa3Q/bO7gwcHB+e7PSgoyH4yIiJyuSruthPUfopIyaBzTxHxTerJJZ6nnlzFoEyZMvb7BQ2jyZaUlAS41r1cRORypbZTRMQ9aj9FRETypyRXMShdujQVKlQA4NixYwXue/78efuJRo0aNTwem4iIWantFBFxj9pPERGR/CnJVUyioqIA2L9/PxkZGU73i46OzvMcEZGSSm2niIh71H6KiO/RnFzieUpyFZMbbrgBsHUH37Fjh9P91q1bZ7/frl07j8clImJmajtFRNyj9lNEfI+SXOJ5SnIVk759+9rvL1iwIN99srKy+OCDDwCIjIykc+fO3ghNRMS01HaKiLhH7aeI+B4lucTzlOQqJq1bt6Z9+/YAzJ8/n82bN+fZ59VXX2XPnj0APPHEE5QqVcqrMYqImI3aThER96j9FBHfoySXeF6A0QFcTmbNmkW7du1ISUmhW7dujBs3js6dO5OSksLixYuZO3cuAPXr1+fpp582OFoREXNQ2yki4h61nyIiIrkpyVWMmjdvzqeffsq9995LfHw848aNy7NP/fr1Wb58ea6ln0VESjK1nSIi7lH7KSK+xZ2eWerJJUWj4YrF7NZbb2X37t08+eST1K9fn5CQECIjI2nZsiVTp05l586d1K1b1+gwRURMRW2niIh71H6KiO/QcEXxPIvVatW7xuTi4+OJiIggLi6O8PBwo8MRkYv02TQ//Y9EzEmfTXPT/0fEvHzx85kd8+LFuwkJKVqv0uTkBPr3b+JTr1eMpeGKIiIiIiIiIuJhGq4onqfhiiIiIiIiIiIi4vPUk0tEREREREREPEw9ucTzlOQSEREREREREQ9Tkks8T0kuEREREREREfEwJbnE85TkEhEREREREREvUNJKPEtJLhERERERERHxMPXkEs/T6ooiIiIiIiIiIuLzlOQSERERERERERGfp+GKIiIiIiIiIuJhGq4onqckl4iIiIiIiIh4mJJc4nlKcomIiIiIiIiIhynJJZ6nJJeIiIiIiIiIeJiSXOJ5mnheRERERERERER8npJcIiIiIiIiIiLi8zRcUUREREREREQ8TMMVxfOU5BIRERERERERD1OSSzxPSS4RERERERER8TAlucTzlOQSEREREREREQ9Tkks8TxPPi4iIiIiIiIiIz1NPLhERERERERHxMPXkEs9TkktEREREREREPExJLvE8DVcUERERERERESmC5ORkpk+fTuvWrSlXrhxhYWFERUUxatQojhw5UizH2L9/P5988glPPvkk7dq1IyQkBIvFgsViYeHChUWqKyMjg3feeYcOHTpQsWJFgoODqVu3Lg8//DB//vlnscRrBurJJSIiIiIiIiIedvn05Dpw4AA9e/Zk7969ucqjo6OJjo5m3rx5LFq0iB49erh9jHXr1tGpU6dLjNTm3Llz9OzZk59//jlX+YEDBzhw4AALFy5kzpw5PPjgg8VyPCOpJ5eIiIiIiIiIeJjVzZu5JCYm0qtXL3uCa+jQoaxevZpNmzYxadIkwsLCiIuLo1+/fuzevdvt41it/752Pz8/rr76alq3bl3kejIzM7n99tvtCa7bb7+db7/9lp9//pk33niDK664gtTUVIYNG8aqVavcjtcs1JNLRERERERERMQFM2bMIDo6GoBp06YxevRo+7a2bdvSuXNnOnToQHJyMiNHjuTHH3906zjVqlVj+vTptGrVihYtWhAWFsbChQvZunVrker58MMPWb9+PQCPPPIIb775pn1b69at6d69Oy1atCA+Pp4RI0bw559/EhDgu6ki9eQSEREREREREQ/z/Z5c6enpzJo1C4CoqCiefvrpPPu0bduWIUOGALBmzRp27Njh1rHq1avHqFGj6NixI2FhYW7HPH36dADKli1rv59T3bp1GTt2LAD79u3jyy+/dPtYZqAkl4iIiIiIiIh4mO8nudauXUtsbCwAgwYNws8v/5TK4MGD7feXLl3qhcjyt2/fPvuk8nfffTchISH57meWeIuDklwiIiIiIiIi4mG+n+TasGGD/X7Hjh2d7teyZUtCQ0MB2Lhxo8fjcsbVeCtXrkz9+vUBY+MtDkpyiYiIiIiIiIiH+X6Sa8+ePfb7DRs2dLpfQEAAderUyfOc/2/v3oOjKu8/jn82LLnfqEFpIAUkhISZzsC4cWgTS3AsILSl6MhAqYSLCY5SsDNVAafF2pECdZSq4JRpuYhO5Ga1Foepw0i4JBopaVGBJhuJJNBUaLmEJGRNcn5/MLu/hGw22SS7e87m/ZrJzAnnOc9+Nz58DN89l2Drab3t99fU1KihoSGgdQUSTS4AAAAAAIBu1NTUSJLi4uKUnJzsc2xaWpok6eLFi2pubg50aV6565WkESNG+BzrrtcwDNXW1ga0rkCy7i3zBxD3o0OvXbsW4koAtOf+O9n+8b4wF/ITMCfy09zITsC8rJyfjY3+nx3kPubWPIqKilJUVFS/1OWP+vp6SerRjeDdlytK0vXr10Nar9R9zbfWa1U0uSzAvTDdnVUA5lJfX6+kpKRQlwEvyE/A3MhPcyI7AfOzUn5GRkZq2LBhWrz4R706Pj4+vlMerVmzRs8++2w/VOefGzduSLr5nrrTvqnV1NQUsJp8cdcrdV+zGertDzS5LCA1NVU1NTVKSEiQzWbz/Pm1a9eUlpammpoaJSYmhrBCwH/hsH4Nw1B9fb1SU1NDXQq64C0/w2HtYeAKl/VLfpobv3siHIXL+rVifkZHR+vs2bNyuVy9Ot4wjA5ZJKnbs6JaWlo0ePDgXr1ee9u2bevw5MHo6GhJ6tF7aX+JYkxMTJ9r6Q13vdLNmtt/fysz1NsfaHJZQEREhM/rZxMTEy0d1BjYrL5+rfIJ2kDlKz+tvvYwsIXD+iU/zYvfPRHOwmH9WjE/o6OjfTZYrCIhIUFSzy7na3/z9p5c3hgI7nqlmzX7+m9ghnr7A00uAAAAAAAQNux2e7881fCb3/xmh+9HjBihjz/+WA0NDbpy5YrPm8+7b/o+dOjQkNyPS+p4s/na2lqlpKR0OdZdr81m6/Ym9WZGkwsAAAAAAISVzMzMfp9z/Pjx2rdvnyTpzJkzmjRpktdxLS0tqqqqkiRlZWX1ex09NX78eM/2mTNnNGHChC7HnjlzRtLN+zG2vwm91USEugD0XlRUlNasWROyrjDQF6xfhAprD1bG+kUosf5gZaxf9Ifc3FzPdnFxcZfjjh8/7rn8LycnJ+B1daWn9dbV1amiokJSaOvtDzbDis8eBQAAAAAACCKXy6Xbb79dV69eVVZWlj7//PNON8WXpEcffVR/+MMfJEllZWXKzs7ul9ffvn27Fi1aJKnzTfG7Mn78eJ0+fVrf+MY3VFNTo9jY2E5j1q1bp1WrVkmSdu/erYceeqhf6g0FzuQCAAAAAADoRmRkpJYvXy5JOn36tF544YVOY0pLS/WnP/1JkjR58mSvDa7q6mrZbDbZbDbl5eUFtOZf/OIXkqT//e9/euqppzrtr6qq0m9/+1tJ0pgxYzR79uyA1hNo3JMLAAAAAACgB5588knt2rVLFRUVeuqpp+R0OjV37lzFxMToww8/1Nq1a9XS0qKYmBht3LixT6+1d+/eDk9yPHr0qNdtSRo2bJimT5/eaY78/Hxt3bpVx44d06ZNm1RXV6eCggINGTJEZWVl+s1vfqNr164pIiJCr7zyiux2a7eJuFwRAAAAAACgh5xOp2bMmKHKykqv+xMTE/Xmm2/qBz/4gdf91dXVGj16tKSbZ3sdOnTI67hRo0bpyy+/7FFNvua5dOmSZsyYoU8++cTr/sjISL366qsqKCjo0WuZGZcrAgAAAAAA9FB6errKy8u1fv16ORwOJScnKzY2VuPGjdPPf/5znTx5sssGVyikpKSopKREmzdvVm5urm677TZFR0frzjvvVEFBgU6cOBEWDS6JM7ks6dy5c3r55Ze1f/9+nTt3TlFRUUpPT9ecOXP02GOPeb2RHNAbJ06c0IEDB3TkyBF99tln+uqrrzR48GClpqbqu9/9rpYsWaJ77rmnx/MdOHBAW7ZsUVlZmS5evKihQ4fq7rvvVmFhoddTa71pbGzUpk2btGfPHjmdTrlcLqWlpWnmzJlavny5vvWtb/X27WIAID8RLOQnwgnZiWAiPwH0iQFL+etf/2okJSUZkrx+jRs3zqiqqgp1mQgD3/ve97pcZ+2/Hn74YaO5udnnXG1tbUZhYaHPeQoLC422tjaf8zidTmPcuHFdzpGUlGTs37+/P38MCCPkJ4KF/EQ4ITsRTOQngL6iyWUh//jHP4zY2FhDkhEfH288//zzRklJiXHw4EGjoKDAE7SZmZlGfX19qMuFxY0ZM8aQZKSmphorVqww9u7da5SVlRmlpaXGiy++aAwfPtyz5ubNm+dzrtWrV3vGTpw40SgqKjLKysqMoqIiY+LEiZ59zzzzTJdz1NfXG5mZmZ6xBQUFxsGDB42SkhLj+eefN+Lj4w1JRmxsrPHPf/6zv38csDjyE8FEfiJckJ0INvITQF/R5LKQvLw8Q5Jht9uNkpKSTvs3bNjgCeBf//rXIagQ4WTmzJnGrl27jJaWFq/7L168aGRkZHjW3OHDh72Oq6ysNOx2uyHJcDgcRmNjY4f9DQ0NhsPh8Kxtp9PpdZ41a9Z4XmvDhg2d9peUlHheZ8qUKX6+W4Q78hPBRH4iXJCdCDbyE0Bf0eSyiLKyMk/ALl261OuY1tZWIysry5BkDBkyxHC5XEGuEgPNe++951mXy5cv9zrmscce84wpLS31Oqa0tNQzZtmyZZ32u1wuIzk52ZBkZGVlGa2trV7nWbp0qWee48eP9/6NIayQnzAj8hNmR3bCrMhPAL7wdEWLeOeddzzbixYt8jo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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjTensor3D(Grids_2,\n", " CoordGrd_2,\n", " ShapeAVG_2, \n", " ShapeCount_2,\n", " scale_factor = 150,\n", " figblocksize=4,\n", " vmin=-0.1,\n", " vmax=0.1,\n", " Countmin=5,\n", " showscale=False,\n", " nameaxes=['z','y','x'],\n", " DeviatoricType=2, #<- 2 for dealing with shape: Shape -> Strain\n", " colorbarlab=r'$U_S$')" ] }, { "cell_type": "markdown", "id": "f28178ff-1266-4eb5-bf17-38cbd452e738", "metadata": {}, "source": [ "## E) Strain - Measured from the distance between neighbooring bubbles (texture tensor)\n", "We can now do the exact same steps, extracting the strain field but working with the texture tensor. \n", "\n", "Each bubble is also characterized by a set of link vectors \\{$\\mathbf{l}$\\} between its center and the neighboring bubble centers. The texture tensor associated to each bubble is defined as\n", "\n", "$\\textbf{M}=\\langle \\mathbf{l} \\otimes \\mathbf{l} \\rangle$\n", "\n", "with $\\langle ... \\rangle$ the average over the set of neighboring bubbles. \n", "\n", "The strain tensor $\\textbf{U}_\\textbf{M}$ is derived from $\\textbf{M}$ as the deviation from an isotropic texture $\\mathbf{M_0}$ state:\n", "\n", "$\\textbf{U}_\\textbf{M} = \\frac{1}{2} \\left( \\log(\\textbf{M}) - \\log(\\mathbf{M_0}) \\right)$\n", "\n", "The isotropic texture $\\mathbf{M_0}$ is constructed in the same manner. It is important to note that the factor $\\frac{1}{2}$ accounts for the difference in dimensionality: the texture tensor has units of length squared, whereas the shape tensor has units of length. Note that the texture tensor tends to be less noisy than the shape tensor in dry foams, but more noisy in wet foams. This was attributed to the decrease in the average coordination number $Z$ and area of contact $A$ between the bubbles when the liquid fraction $\\phi_\\ell$ increases.\n", "\n", "Anyway, let's get this texture tensor from our images. For this we have to start by extracting the contact topology between our bubble (which bubble is in contact with which). We use **GetContacts_Batch** and indicate that we just want the contact table **save='table'**. This function is wrapping **spam.label.contacts.labelledContacts** from the SPAM toolbox [spam.label.contacts.labelledContacts](https://www.spam-project.dev/docs/spam.label.html#module-spam.label.contacts)." ] }, { "cell_type": "code", "execution_count": 14, "id": "deb1a413-93c1-4051-b141-1b8fc04b5ed8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Path exist: True\n", "Topology_001: done\n", "Topology_002: done\n", "Topology_003: done\n", "Topology_004: done\n", "Topology_005: done\n", "Topology_006: done\n", "Topology_007: done\n", "Topology_008: done\n", "Topology_009: done\n", "Topology_010: done\n" ] } ], "source": [ "# Name and directory where we are going to save the contact topology\n", "dirbubbleseg = 'P4_BubbleSegmented/'\n", "namebubbleseg = 'BubbleSeg_'\n", "\n", "dirnoedge = 'P5_BubbleNoEdge/'\n", "namenoedge = 'BubbleNoEdge_'\n", "\n", "dir_Topo = 'Q4_Topology/'\n", "name_Topo = 'Topology_'\n", "\n", "GetContacts_Batch(namebubbleseg,\n", " namenoedge,\n", " name_Topo,\n", " dirbubbleseg,\n", " dirnoedge,\n", " dir_Topo,\n", " imrange,\n", " verbose=True,\n", " endread='.tiff',\n", " endread_noedge='.tiff',\n", " endsave='.tiff',\n", " n0=3,\n", " save='table', # <- save the topology table\n", " maximumCoordinationNumber=20)" ] }, { "cell_type": "markdown", "id": "2322e04d-243f-4d84-ad8e-8fc4fe7650f2", "metadata": {}, "source": [ "The contact table is looking like this, for each central bubble, we get it's coordination, and the label of all its neighbooring bubbles." ] }, { "cell_type": "code", "execution_count": 15, "id": "b182b9b9-43f3-4013-90c7-52484916f853", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1662 rows × 46 columns

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" ], "text/plain": [ " lab lab_noedge Z z y x lab1 cont1 \\\n", "0 1 -1 3 2.692405 2.768354 8.936709 10 6 \n", "1 2 -1 3 3.463628 2.487876 34.752667 10 7 \n", "2 3 -1 7 4.504314 6.833565 61.568884 4 1 \n", "3 4 -1 6 4.723270 2.845283 88.178616 3 1 \n", "4 5 -1 6 4.585279 6.343482 164.845108 6 3 \n", "... ... ... .. ... ... ... ... ... \n", "1657 1658 -1 9 244.488782 228.989763 43.207362 1491 8364 \n", "1658 1659 -1 6 246.556267 238.599726 20.218664 1506 8529 \n", "1659 1660 -1 5 244.988495 243.245025 224.386505 1449 8404 \n", "1660 1661 -1 5 245.887428 247.547247 39.662284 1504 8455 \n", "1661 1662 -1 2 247.550136 247.308943 246.818428 1513 8610 \n", "\n", " lab2 cont2 ... lab16 cont16 lab17 cont17 lab18 cont18 lab19 \\\n", "0 153 318 ... 0 0 0 0 0 0 0 \n", "1 118 10 ... 0 0 0 0 0 0 0 \n", "2 118 16 ... 0 0 0 0 0 0 0 \n", "3 134 2 ... 0 0 0 0 0 0 0 \n", "4 9 8 ... 0 0 0 0 0 0 0 \n", "... ... ... ... ... ... ... ... ... ... ... \n", "1657 1504 8366 ... 0 0 0 0 0 0 0 \n", "1658 1518 8559 ... 0 0 0 0 0 0 0 \n", "1659 1513 8426 ... 0 0 0 0 0 0 0 \n", "1660 1520 8531 ... 0 0 0 0 0 0 0 \n", "1661 1576 8667 ... 0 0 0 0 0 0 0 \n", "\n", " cont19 lab20 cont20 \n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "... ... ... ... \n", "1657 0 0 0 \n", "1658 0 0 0 \n", "1659 0 0 0 \n", "1660 0 0 0 \n", "1661 0 0 0 \n", "\n", "[1662 rows x 46 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(dir_Topo+name_Topo+'table_'+strindex(1,n0=3)+'.tsv',sep = '\\t')\n", "display(df)" ] }, { "cell_type": "markdown", "id": "dc650341-c093-4856-8dd8-2a74b1d4a774", "metadata": {}, "source": [ "This table is perfect for us who want to compute the texture for each individual central bubble. We can run **Texture_Batch** on the whole time series." ] }, { "cell_type": "code", "execution_count": 16, "id": "5db93589-9e02-449e-b9f7-0dafcb64c989", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Path exist: True\n", "Texture_001: done\n", "Texture_002: done\n", "Texture_003: done\n", "Texture_004: done\n", "Texture_005: done\n", "Texture_006: done\n", "Texture_007: done\n", "Texture_008: done\n", "Texture_009: done\n", "Texture_010: done\n" ] } ], "source": [ "dir_Texture = 'Q5_Texture/'\n", "name_Texture = 'Texture_'\n", "\n", "Texture_Batch(name_Topo+'table_', \n", " name_Texture, \n", " dir_Topo, \n", " dir_Texture, \n", " imrange, \n", " verbose=True, \n", " endsave='.tsv', \n", " n0=3)" ] }, { "cell_type": "markdown", "id": "317e38a6-6ac1-48c6-b9b6-dd8a6e8d87e1", "metadata": {}, "source": [ "The texture table is looking like this, for each central bubble, the texture tensor is saved in as 'e1z','e1y','e1x', ..." ] }, { "cell_type": "code", "execution_count": 17, "id": "6a0c7445-438f-43c5-a6ed-0853cc88564d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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941 rows × 23 columns

\n", "
" ], "text/plain": [ " lab labnoedge z y x rad \\\n", "0 154 1 15.286670 79.575571 183.687352 13.244031 \n", "1 155 2 16.593385 130.636373 17.162980 13.412385 \n", "2 156 3 15.635897 172.321484 101.120593 14.441475 \n", "3 157 4 15.538057 175.370390 217.745069 14.293863 \n", "4 159 5 15.121800 200.194312 163.705468 13.813899 \n", ".. ... ... ... ... ... ... \n", "936 1512 937 232.931714 209.884521 86.382444 13.615627 \n", "937 1514 938 234.656031 34.253836 50.915761 14.232325 \n", "938 1516 939 234.123875 156.617150 218.298716 14.098111 \n", "939 1517 940 234.464956 224.364352 219.393250 13.813096 \n", "940 1521 941 234.799707 24.502990 72.834127 13.206825 \n", "\n", " M1 M2 M3 e1z ... e2y \\\n", "0 143.146698 188.665207 199.824185 145.088144 ... 199.761173 \n", "1 145.059897 173.953140 230.704676 166.649686 ... 224.300849 \n", "2 176.124295 211.680976 243.314619 176.488827 ... 236.743360 \n", "3 159.800474 219.541577 243.110006 161.102709 ... 228.860442 \n", "4 136.649861 182.360569 278.841570 138.356440 ... 268.554950 \n", ".. ... ... ... ... ... ... \n", "936 154.233618 193.452225 213.536990 157.115806 ... 192.189452 \n", "937 174.557681 207.576403 229.370816 190.732572 ... 226.062002 \n", "938 130.697754 235.697297 254.884119 153.221315 ... 232.118401 \n", "939 137.489777 204.724088 246.778179 158.126016 ... 210.912977 \n", "940 129.720257 191.731632 213.348160 129.916408 ... 208.388919 \n", "\n", " e2x e3z e3y e3x U1 U2 \\\n", "0 0.895467 -9.191918 0.895467 186.786772 -0.101612 0.036440 \n", "1 -16.785618 16.282702 -16.785618 158.767178 -0.107605 -0.016786 \n", "2 -12.915726 -2.816887 -12.915726 217.887703 -0.084509 0.007436 \n", "3 -12.302243 5.740241 -12.302243 232.488906 -0.122867 0.035941 \n", "4 27.195228 -4.772531 27.195228 190.940610 -0.166964 -0.022682 \n", ".. ... ... ... ... ... ... \n", "936 -5.140028 1.909386 -5.140028 211.917575 -0.091984 0.021297 \n", "937 1.767641 -16.343852 1.767641 194.710326 -0.074388 0.012234 \n", "938 8.278592 6.961134 8.278592 235.939454 -0.209597 0.085233 \n", "939 -14.217263 -41.189681 -14.217263 219.953051 -0.163842 0.035214 \n", "940 9.135482 2.690941 9.135482 196.494723 -0.148044 0.047315 \n", "\n", " U3 U type \n", "0 0.065172 0.154436 1.0 \n", "1 0.124391 0.202486 -1.0 \n", "2 0.077073 0.140379 1.0 \n", "3 0.086927 0.189517 1.0 \n", "4 0.189647 0.310703 -1.0 \n", ".. ... ... ... \n", "936 0.070687 0.144454 1.0 \n", "937 0.062154 0.119665 1.0 \n", "938 0.124364 0.316217 1.0 \n", "939 0.128628 0.258736 1.0 \n", "940 0.100729 0.226832 1.0 \n", "\n", "[941 rows x 23 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(dir_Texture+name_Texture+strindex(1,n0=3)+'.tsv',sep = '\\t')\n", "display(df)" ] }, { "cell_type": "markdown", "id": "ad820758-70ce-485b-bd89-1e75057e7285", "metadata": {}, "source": [ "As above, let's reshape these table data in two lists, one for the coordinates, one for the texture tensors.\n", "Then, let's average these in space and time in a 5x5x5 grid over [0,125] pixel range over the z,y and x coordinates." ] }, { "cell_type": "code", "execution_count": 18, "id": "f3f688d4-3fd4-4154-b374-4a523621f323", "metadata": {}, "outputs": [], "source": [ "# Take into acount the whole time serie data\n", "LLCoord=[]\n", "LLTexture=[]\n", "for imi in range(len(imrange)):\n", " df = pd.read_csv(dir_Texture+name_Texture+strindex(imrange[imi],n0=3)+'.tsv',sep = '\\t')\n", "\n", " LCoord = np.asarray(df[['z','y','x']])\n", "\n", " numpy_texture = np.asarray(df[['e1z','e1y','e1x',\n", " 'e2z','e2y','e2x',\n", " 'e3z','e3y','e3x']])\n", " LTexture=[]\n", " for bbli in range(len(numpy_texture)):\n", " LTexture.append(numpy_texture[bbli].reshape(3,3))\n", "\n", " LLCoord.append(LCoord)\n", " LLTexture.append(LTexture)\n", "\n", "Grids, CoordGrd, TextureAVG, TextureSTD, TextureCount = Grid_Tavg(np.concatenate(LLCoord), \n", " np.concatenate(LLTexture), \n", " Range = [0,250,0,250,0,250],\n", " N=[5,5,5], \n", " NanFill=True,\n", " verbose=False,\n", " structured=True)" ] }, { "cell_type": "markdown", "id": "2d3b97b6-6478-4d56-b2b3-c600ec152198", "metadata": {}, "source": [ "Once again we can represent the obtained strain $U_M$ by using the **ProjTensor3D** function. " ] }, { "cell_type": "code", "execution_count": 19, "id": "78cb7534-414c-43d4-b235-6b9daa6c23bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal min/max ax0 0.0023403027427819747 0.04642474069427069\n", "Normal min/max ax1 -0.034138627203550244 0.020506781868971365\n", "Normal min/max ax2 -0.025567206792855193 0.01330556953933863\n" ] }, { "data": { "image/png": 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aBJ06OfqznZ0N48YZm3TSSdCqFfTrB+++a0wYcJrw8HBcrv4eS6fx8MO/cOiQLSGJVDtVctnh+++NCq6/8u231sTir5wcWLny2B773nvGUYmTpijk5vpOzrlcxpFVt26OrE777auvGNanDylAAyAFSHn5Zc7o3JnWvXvbHF0V7drlveyHH+Cnn6B1a+vjkcD3zTfG6fTDtmMcUB364w+eOPNMxjRvztWffEJ4XJxdEfrtjz/gpJPmAQOAnXTsuIVNm5aRmOigcbiSQ4fKaN16C1u3/gT8BKxl/vyfGDXqF77/fjlt255hd4jHrajIxd13Dwc2ACcBl/Phh32YMyeNpUvDaNnS5gCP1x9/0GbyZBoA+ZUWPwBcCkSWL9i3Dz7+2DhpJPL9916LlgNPeSxLAU6cONHIcDtof/K112D4cIBc4FPgfWA2UMy2bfDPf57J55+3tTPEKvl3+/Zs+eknIjE+2xFA5I8/Ennppdw0axYJCQk2R3jsysqga9cHWLHiWyARqHP430R+/70Oc+Yk8vzziTz5ZD3S0501+yElZQSbNr0A7D+8pIyyskf573/f+dtDUql+mq5oPlVyWa2wEJ599u8f55SDqj17vJf9+99w++3ey3fvhs8/r/aQTDVgwNHX/f67keRzoK0zZ/Il8DrwL+BujEPgV53c72LbNt/Lv/zS2jjEOTx2vicA5Sc1twA3bdrE7717G9N6HaioqIyTThoPXADsBGD//rX07+/MPdqiIoiLO5OtW5sDlwGjgXeA1ZSVFTJt2lp7A6yif//7Z4wEF8CvwNNABvn5jejd+ya2bt1qX3BVMXo0kbt3M8Fj8WaMShw3f/xhTUwS+DymtxVGRjKkSRMq12dGAK8B4T//bLT1cIgdO/IZPvwd4HKgPnAN8BlQXPGYhQvfw+XAizZN/+knngMmAuOBscA/gfu+/558h+0jh4bCihUrgfnAJxh7yc8AY4A7gMGsWdOLAQOusi3Gqho1Khm402Ppu7zwwjqnHsqI/CUluaz23ntulQNHdcEF1R+LGRITvZdt3Qo9e8KMGd7rliyp/pjMUlJiTCv9K+HOLIb888cffS5PiYmxOBITderke/myZdbGIc7RoQNceCEA24BXPFZfDzTdsgUuv9xIajtIcXExLVv2xjjccJ/Gt2bNV+zevduWuPwRGQlw8lHXr1jxk2WxmGncuE+OsmYXW7b8jygn9bbZvRtmzgSgL+DZcWssRh1LBfXAk3JpaW53Hysq4iePk1djgNPL77z++t+3yggQp512CzAImAEU+XxMUdGvrFy5wsqwTOF7awyRDpsLZ+QYc/72cSUldao5EvNddBG0bHkPEF9pqYuCgrFMm2ZXVCLVR0kuqx1LJVNKClxzTfXHYobkZGPCemXPPWf04CoogLvucl/npIqI8PCjJ04Amjd3zsUBPOw8yvuQ0qSJxZGYKD7e9/ING9x73omUCw2FF1+E66/nMdx31iOBB8vv7NzprD6JgMsVwe+/+0ogXMycOSupV6+e5TGZ4+hTj/fvd2Yl1/nnxwEn+lwXH59GSkqKtQH5o9J4G4JR3VHZXozqYcDouu3U6fFivnbtKhJdK8GrErBd8+aMrLygqMgxJ07POKPvX6yNAi6nY8d3OOUU5zXUL/6LdREOu8K1kVPd97ePi4vzcYLfAYYNS8K7musDpk515ndnMAkx4SbulOSy2t+ddUpMhGeecVbyZOBA9/ulpfDmm8ZUv3//231dnTpWRWWOM888+rrrr7csDLOl33kn4zG+6q4EMoHTQkJI7drV1rj85uvvq6DAmCYs4ktkJDz0EH0ffJCzKlUyDgVSKz8uK8vqyPwSGQlr107CuK4dGF/3j/HUU7No27aujZH5p2vX8jqOJKAzMAx4kTZt5vPJJ/+zLzA/fPrpnQwcuAVYATwEHOkr9o9/XG5TVFXkcVCbDvTxeMgzQBYYnZ19VYNLzRQSApMmURQfzxCgcn/vcGDK5s1EeD4nP99zSUB6/fUeQK1KSyKA3sBbwC5q1/6YV14ZQK1atXw+P5D1bdCAm4BrMdpe9MXYsu4pKUQ7bHtOPBEmThwPPIvRofMujC27DOP7pg0hIamcddYJtsXoj969oXnzu4HKrRpcZGWNZft2u6ISUJKrOijJZbU2bY6+rmVL+O9/nVe+n55+7Gdj09OrNxazXXGF753w3r2NKUwOddYttzDqttv4N/Au8H/Az088wQWXXmpvYP764APvZXFx5fOcRI7q4htuYPnatXx8662cFxfHKM8H9PE8VA98LVpEMWrU+0RFtWT48Lns2/dP7rjD2V/7H310Cd9+u4P+/ffQoMF8mjZ9iXvvHc7ChZ2pXz/Z7vCq7L//DeG++9pRp86jwI80abKRQYOe4qGHPK+IFeBOOw3aujfPngCEVbp/CHgzM9O4sq9IZSefzLLRo1nnsXg00NZXRXarVlZE5bfmzWOIiekL9MTo87QLo/n81TRtGs/nnxuTA5zowS1beKV7d17H6JA4Hfi0SRO+WLyYGAe2wLj55n5s334Ho0c/TIsWz2C8X58QHz+fvn1X8/3323j33TdtjrJqwsJgwoREjE68lU1n2TLfbUxEnCrE5cQuhx7y8vJISEggNzeX+KNNWTpe1dXHJysL7r3XvUl2w4bGJXcvv7z6ezx9+y2cWg3l0GVlRvXW++8ffWrYSScZU37M/tLLzob27c19zcp+/dWIe80aqFfPuPbzlVcaU52qS0gING5cfa8Pxvs0axasXm1cJzkjo3r/PzB+l9HR1ft/fPAB/POfR+4PGwZ3e36hm6xFC9NeqlrGM4tVyzb89ps5r3M8/9+8ecZUxbPOMnp3mXwl1aLwGKjfwNTX9KWsrIzQ6hyvfHBou8KjKiur3iG/8v9z6JBFF43LzSG0Ok7/btoEffu67efcCrwEnBIZyb/uvpvLRo4kpDqvTGxi1bjTx+Rqi9/XFY1NsnbOHIYMHcr3BQWcAXxPpatyluvWDd56y9T/t5BISKhj6muWc7lc7NsXwnvvwbp1xq5whw5wySVePfdNFVpSRIRXCZzJSkqMNiXffQdNmxozHaq5SjP/QAiEV/eGGRMBCguNCTbVfTH1HTvMP0Ty5fHHc5k8uSmV+49ddtl1PP/8lGr7P1NT//4xx8rpY3Jl5duy6913iffjiz+voID6V10VFL8TswTZbqgDpKbC1KlGYiE317jfooU1e8/VKTTUOCubnm4kupYvPzJFLDraaKR/3XXWjN5mO+kkcPJVB48mJMTYu7rkErsjMVf//tCoEXz9tZHQveIKuyMSJ2raNGgqTaxOcEnVhYZalOCqTs2bw9Kl8OGH8P33cOgQDycnc3pICDeOGUOEkxrpiy1O79aNpevXM+nOO7lw0yYi1641MsBgVGZfdRWMHWtvkMcpJCSEpCTjvFvQCQ+HwYONW5CJiqreJKQdRo9O4Jdf7mH+/DGEhdWjf/8HGDv2FrvDqtH8nXKo6YrelOSyQ2QknHOO3VFUjxYtjCqa4mLjKktgVD9V+2kkkUrS0503NVZEJFjExrod9NbH6J4mcqzCIyMZ+fLLxp39+41KcICTTzbaEIhIlYSEwEsv3cm778YxaNBQYmP1eZLgoySXVI+ICKOaRkRERESkqmrV+uuetiJyXGrXjuemm+6xOww5TJVc5tMcBhERERERERERcTxVcomIiIiIiIiIWEyVXOZTJZeIiIiIiIiIiDieklwiIiIiIiIiIuJ4mq4oIiIiIiIiImIxTVc0nyq5RERERERERETE8VTJJSIiIiIiIiJiMVVymU+VXCIiIiIiIiIi4nhKcomIiIiIiIiIWCzEhNvxGDFiBBkZGQwaNIiioqKK5SUlJVx33XVkZGRw5513ViyvXbs2mZmZZGZmsmbNGgDmz59PWloa6enprF69uiqbXa2U5BIRERERERERCWIrV65k586dLFy4kFatWjF9+vSKdTNnzqRx48YsXLiQgoIClixZAsCpp57KvHnzmDdvHmeccQYADz74ILNmzWLatGk88MADtmzLX1GSS0RERERERETEofLy8txuhYWFXo9ZunQp3bp1A6B79+4Viay/Wrd582Y6d+7MsGHDOHToEAUFBURERJCYmEiTJk3Yt2+fBVt3fJTkEhERERERERGxmFnTFVNTU0lISKi4TZgwwev/ysnJIT4+HoCEhASys7P/dt2mTZtYsGABDRs25KWXXnJ7HEB4eLjbtMdAoCSXiIiIiIiIiIjVQkL8vwFZWVnk5uZW3EaNGuX1XyUmJpKXlwcYSa2kpKS/XZecnAxA//79WbVqldvjwOjlFRkZWT2/mypSkktERERERERExGomJbni4+PdblFRUV7/VYcOHZgzZw4As2fPJj09/S/XHThwgNLSUgAWLFhA8+bNiYmJobi4mH379rFt27aKJFggUZJLRERERERERCSItWvXjpSUFDIyMli3bh19+/Zl6NChAPTu3ZusrCwyMjKIiYkhLS2NjRs3cs4559C5c2dmzZpVcdXFxx57jJ49ezJgwADGjx9v5yb5FG53ACIiIiIiIiIiNU5oqHHz5/nHYeLEiW73J0+eDBi9td544w23dWeeeSYrVqzweo0uXbqwdOnS4wzUOpZUcv3www9kZGTQpUsXrrjiCoqLi3nvvfdIS0uja9euZGVlAbBu3To6depEWloac+fOtSI0EZEaR2OyiEjg0JgsIlLD+TlVUdxZkuQ64YQTmD17NvPnz6d58+Z88sknPP3008yfP59x48Yxbtw4AEaPHs2UKVOYPXs2Y8aMsSI0EZEaR2OyiEjg0JgsIlKDmdSTS46wJMmVkpJCbGwsABEREWzYsIHWrVsTGRlJeno6a9asAWDHjh20aNGC+Ph4kpOT2bNnj8/XKywsJC8vz+0mIiLHRmOyiEjgMHNM1ngsIiI1naU9ubZt28bcuXMZP348u3fvrlhe3rHf5XJVLEtISCA7O5u6det6vc6ECRMYO3Zs9QZ7uDQ86OzYAQ0a2B2FuQ4cgOJiu6MwV2Eh5OTYHYX5srIgIsLuKMzXooXdEVSJo8bk/fur9/VtsL8sjOIg/DgAREfbHYG5SkogPNi6mOYH53AMEFvH7giqxowx2ZLxGNi1++8f4zS5ByE00e4ozOUqhLhadkdhvj17ITzIvmfy8yEy0u4oxBb+VmOpksuLZbtseXl5DB48mClTplBaWup2ZiksLAyA0EpN03JyckhKSvL5WqNGjeKee+5xe+3U1FRzA27d2tzXCxTNmkHjxnZHYb46deyOwFy7dwfhERVQu3bwvVcO5bgxOQjHrcI94VBmdxTmKygIvuTJ/v3G8BVMDobVoXac3VFIObPGZEvGY+Bgrfqmv6bddmZDvSBLCP25O5LGUXZHYb69uZASY3cU5qpXD044we4oxBZKcpnOkqPo0tJSBg0axJgxYzjllFMoLi5m3bp1FBUVsXz5ctq0aQMY5dobN26kQYMGR60YAIiKiiIqKghHbBERC2hMFhEJHGaOyRqPRUSkprMkyfX++++zZMkS8vPzGTduHMOGDeOuu+6iS5cuREdH8+abbwIwfvx4hgwZQmlpKY8++qgVoYmI1Dgak0VEAofGZBGRGiw01Lj583xxE+KqPMHfofLy8khISCA3N5f4+HhzXvTnn815nUBTUBCU036Cbgrc7t3BN98HYMOG4HuvAM44w7SXqpbxzGLVsg1B2KNux57woGyWUlAAiUHW1yY/PwinKx4Mvm0qZ+bQ6fQxubri37rVtJcKGNu2GVPGgsmffwbnbn9WFqSk2B2FuaKigvO9AnMPaZw+JldWsS2ffkp8XNX7B+QdOEDCpZcGxe/ELEHY9EdEREREREREJMCpJ5fpVNsmIiIiIiIiIiKOp0ouERERERERERGrqZLLdEpyiYiIiIiIiIhYTUku02m6ooiIiIiIiIiIOJ4quURERERERERErBYaatz8eb64UZJLRERERERERMRqmq5oOiW5RERERERERESspiSX6VTbJiIiIiIiIiIijqdKLhERERERERERq6mSy3RKcomIiIiIiIiIWE1JLtNpuqKIiIiIiIiIiDieKrlERERERERERKwWGmrc/Hm+uNFvRERERESqRVERrF693+4wREREAlf5lMWq3MSLklwiIiJB6pdf1tGo0YM0ajSORo2e5Lbb3rA7JKkBDh06xIUXTiUhYRj16rWhc+c63HJLNsuW2R2ZOM2MGZ+SkfEeL7yQxZIlUFZmd0Tiy969sH07lJTYHYm5cnP3sWvXXl59FTZssDsaCVr+JLiU6PJJSS4RERHAtX07Jbt2cXDbNnK/+46SoiK7Q/JLXh707bsOeBwYA4zko4+eYPlymwMzwZw5vzJlShbLlu1nwQIXLpfdEZmjrAx27bI7Cv9NnRrK8uU3Af8B1gClTJu2hG7d4IYb4MABmwMUR9i8Ge6880mysq5i4sQmDBzYmHffnWZ3WH7Lz8/l0KFDjB9vJIecbtasDznvvCfo3Hk0F1xwG8uWzbc7JNNMnvw6XbrU4+mnz+ayyx5g5coldodUZWVl0Lz5OCIjbyUy8iYiI6/h7bdX2R2WSLVQkktERGq277+Hu+4ipnVrIk49ldi2balz8cWsPvdceOEF2LfP7giPm8sFQ4fCvn2HPNZEcc018McftoRlitxcGDCgLyNGNKFnz9r06RPJpEnv2h1WlblcLl544RdOPvkV6tUbTMuWJ/LTT9vsDqvKZs6Ee++NBM71WLMYgOnToX9/YxqjyNHs2gUXXFAEfF9p6R98/nmCXSGZ5oknJtGuXR3eeut8OnV6hJ9++sHukPzy4IMvA6OACfzxx4tMnrzS7pBM8cMPMGXKXMAF/AD8i+efn2JzVFXzyy+Qlgbbtr0BvAT8F3iLIUO2csstUFBgc4A1nSq5TKckV4Aoys6mVHt8Isfvxx9hxgxYssQoXRE5VtnZcO21cNFF8MYbXldiKd66FR56CNq1g/fesyXEqvriC5g/H6DQY00UubkwaZINQZmgsBD+8Q+AyonHEiZMiHNsRYTL5eLhh9PJyRkKTAW20bnzQn7+2e7Ijt+hQ3DvveX30j3WLj7y02J49FGrohInGjcOYBWeY9jixR0cPWVxxgz46KN5GNs1DxjLf//r3CT9gQOQlxfvtmzBgjzy820KyETPP18EuFelLV16oT3B+GHjRujaFVauBIj0WFvEa68Z36vFxTYEJwYluUynJJcddu3C9eGHDGzVirRWrTihVSuiO3Vi1ZlnwrnnQq9ecN99xhGKU+dg7N0Lc+YE3YhZVlDA7u++szsMcxw4YFSoOHlv8d//hquvNhIRt9wCnTtTPHgwz3TqxO4VK+yOTgJZbq4x1n76acWiCI+HFFd+7C23wGuvWRWd315/vfwn7yQXGAdaTjxzO3kyrFoFkOOxpg7PPWd5OKb44YdQoJPH0gVcfbXz3qNPP4U//yy/55nkWg4cOZn38svw22/WxCXO8uuvRkUgLPVYcyqQxPbt1sdklgMHDgLuzem+/DLTsbtiP/4IEO+xNI/cXBuCMZHLBb/99i3gObf6AjvCqTKXC266CfbsKV8S5fEIYx/hm2/g2WetjEzclF9d0Z+buNFvxEo//GA0o8jMJOShh1gEfAtsxyiE3Qawf7/x7f755zBsWPmpLOfYuhVOOAFat4ZrroHUVGNP1sG2//gjmdHRnBwdTUxSEvU7d+bgm2/aHZZ/xo2D5s2hZUto1AjGjHFeQnX/fvB8H8rK+GT1au7Jy6PxdddxTZs2LHv6aXvik8D20EN4lsocNclVbtQoRxyVFxTAt9+W3/NMckUDcPAgrF5tZVT+c7lgyhSAUsDzCCqR6dOdN4wBfPklQGePpQv47TfvIS7QzXcrekjzWHuI8PAjJx9KSuCtt6yISpxm+vTynzyTXB0ByMmxMBiTvfvu/VRO9kIol17aybGFGImJ4CvJ9fvvNgRjop9+gj//nOuxtB2PP17XlniqaulSY6LDEd6VXOWeeUbTyCV4KMlllTffhMGDjdHmsCYeD8ny9bx333XOkciePXDeeVBa6r587NjyU+/OU1ZGreHDmQ/8ypGvgt9vvhk++sjGwPzw00/w4ovuR4OvvAING5YfbTlDaKjP8twXD/9bBLwF3PP668b8mW3O7XEjJtu3D955x31ZcjIRcXFui4o7dHB/TFGRI47KN22qfIUr755c5TZvtioic2zdWv4x9jUtuQ47d8Lu3RYHZQKjP5pnkusXYBfvOmwW09atle8lUa9eK7f1Z5+92O3+nDnVH5M4z9dfl//kmeQyEqcRnmckHKKszMXGjZ5XuI3n2mtLHJvkKiwEX0kuJ/d9hPK/Qc8k14U0b25DMH744gv3+1FRR09y7d5t1GOIDTRd0XRKclnht9/gqae8Fqd63D/qIfjOnSYHVE3+6uBv/Xrr4jDTsmXEf/+919d3FjiuR0+F/fuPvu6665zTZDs21qjBrmQtnt0T4FaAr74yGg689ZYzSz3EXGvWuCfjQ0Lg66+JSEpye1jxnXfCFVd4PzfAHZkuBkebrgg4bjrJli3lP/kaoxIBZ1Z4REUBtAPiPNYsZM0aZ71Phzxyqief7D5lMSTEPcn188/e58WkZsvPL99l3I73nrGR5Kpf3+KgTPLtt5sAz2ZVBznllDo2RGOOsDDwleTKzrYhGBMtXJgLuLcnCQu7kFNPtSeeqvIsPq9fP8rjvvs+wqZN1RyQ+KYkl+mU5LLC2rU+9+KOqZIrOho8qwkC1dq1R193rudVlhzicDmEz/cqOdnqaMxxzjmQ7tkrpZJ58ywLxW9Dh8KDD0JMDGBcL6ay+kDf8jtFRUay+e23LQxQApKv3gUHDxLhUR5QfPAgXs1foqOrMTBzuPd2OXqSK8qzNUeAOzKNwjPJFQHEAkbu22lOPhkgHO8eVgsBZxWhNmjgfj8mxn2b1q9fjNGgwVBc7J0Yk5rt11/Lf/Ks4ooHWlG3bvkUOeeZMeMrr2W1a7clPDzMhmjMUasW+EpyHfBsZeUgBQXw88/zMabGl4ukTZtOjvveDPe8oo7HdMWSEvf5iWrtJMFCf8pWaN3a1yjz95VcMTHw5JOQ4JDLJXfs6L0sLAyefhqaNbM+HjN07AhnneX1XmXFxla+hJSzhIbCtGkwfLjv9fXqWRuPP0JCjEqbmTM5ePnlTPVYfRPeLTYdN/9HzHfGGRBZaUfP5YILLiDC49Rz8ciRsGiR+3PTPPsMBR73gjTfPbkAUlKsiMY8dStaoeR4rKkDhBAXZ8y6dpouXcp/8u7LBc66NkibNu731651T3JlZ+8CjsyTDQlx7tQzqR5HGmR7JrnOA0I57zxr4zHTH394n/Bu1663DZGYxzjf653kcnLR/Pr1UFbmOVUxnS5dnHcW5bTT3O/v3Om+V5yd7b6P4Pl4sYgquUynJJcVmjUzrpbokR73rA6qSHIlJMDAgcZlii500KVqhwyBu+82Gs+fcAL062dMExs40O7Iqi4sDL74guZnn02z6Gg6JyQwqGVLWj/9dPnpd2eKjDSaza9eDbfeaiyrVw/uvBM6eV7lywHq1yfm0UdZ+frr3NuqFYkhIYQBQ309VkdUkpBg9EisrKCACI+5bsWeDZ4aNHDEeOY+NB29kuuMM6yIxjytWpUX0nlWchllHZmZ5VNnnKVNG2jbFryTXKuAXE480fKQqqx7d/f7u3efTEyM59yyI1MWmzZ1zzeLHEnq+u7HddFFVkZjrl27PMeuJgwZMsCWWMxSuzbExXknuRxQ9HxURr9Kz6q7ixx1SFaut0cOtbj46D25UlPhzDOrPSTxRUku03mXF0n1GDwYMjJg1iyj8ffu3TTNy6PFzp2kxsTQpE4dmjZujOvWWwlp3dqZB+KhoTBypHELJvHxPLdoEQ69Ov1fa9DAuMrcQw/ZHYkpTm7fnonvvsujeXksfvNNUrduNS4rk3+4B0Z0tHMr8MRcY8fCypWw4sjV3v7y6oq1asFrrzmisjYpyUicGJd29914/tRTcVTyBIzi5n/8A955J8djTR3AuCCxE4WEwOOPQ+/e5+ByRXLkoMNF27aLqVOnp53hHZe2bY3Z8Isr8lghHDyYDnxc6VGLgWsB6NbN2vgk8BkVm4WAZwfsjiQmwsUXWx+TWfLy3LuGJif3pUMHB58wPaxp03h++qnykjzH9k0D2LLld4yLfxzRtOmFjjy33aqVMeHh/ffLlxw9yTVqlDNPFAWF0FD/5opqnqkXJbms1LSp2xSxNsAG24IRCV6x8fFcdNttxp2yMqOpTW6uUeJiNJCQmi4uDmbONHq6vfEGlJUdPcl1zjnGtbVbt7Y4yKobPNgoID5aJdeNN1odkTkefBA++2wfeW4XWEzk1lsdMZP0qNLS4MUXo7n11vNwuRZWLO/QYTngnCQXwBNPQNeuRr8tg68kl9HFwal/h1J9WraEqKiVFBZ6jl3nMXx4RQtOxykqKqSgwL06rX//Lkd5tLOkpXknudq3dwHOrC7588+vPZbU4YYb2tsSixleeMFom7xuHXg38TA+ZwMGwA03WB2ZSPVR2k9EgltoqJFgbttWCS5xFxtr9Az8/nv45z9pULcuJ4SH0zQ8nBbR0dTq2hU++wxmz3ZUggvgyivh7LPBV0+u9HS46iobgjJBgwZw553xxMe3JCwshZCQaNq3r8PYsXZH5r8rr4QHHuhHhw5DGDJkCosWbeLxx8fYHdZxa9MGnnuu8uwJz4b664BsRo+GU06xNjYJfNHRcPbZnlMVW9GxYx2GDLElJFOEh0cwbdoi+vadRNOml1KrVhLXXpthd1im6NvXc7piKSkpB22JxQyFhe79uBISunL55c4tcapTx7im1NVXg2clV3h4EWPHwpQpmvFmK01XNJ0quUREpGZr1gxGjOCTESPsjsQ04eFGgVqXLoWVGjnDqadG8b//OXtKwl13DeWuu4503CspKQma/bsRI+4gGP4MBw40roB3112wc2d7jAseHKq0fin33HOJXeFJgOvTpz6//tqJHTu+Bw7RokUa//ufz2s4OUZoaCitW7fnscfaA/dQVlZGaJBMMWrUqD4PPPAMu3bVJjExntat4wkPd2DblcP++c8JtG59ITNnfsXOnXMZNuxCR//tgZHoeu01uPDCy/j88yYUF0eRnBzJ5Ze3cfQU4KDhb6LqOJ87YsQIli1bRpMmTZgyZQqRh5tjlpSUcOONN7J582bat2/Ps88+y7Zt27jmmmtwuVzExsYybdo06tSpQ2ZmJqWlpYSFhXHDDTcw2LPXrc0c/pEVERERXxITYerUh1m16ia2by8kKamQzp3bEu950t3hwp1+9BGkevSAzp3h888jefTRCyguLuK009IZODCdXr3SgiYxKebr128Q/foNoqioiJ9/Xk1MTKxjpykeTbAkuADi4mpx44132R2GaVJSGnPLLddyyy3X4nK5KCkpsTsk0wwceDEDByqrVZOtXLmSnTt3snDhQh5//HGmT5/OwMMXVZo5cyaNGzfm9ddf56abbmLJkiW0atWKjz76iKSkJF555RVeffVV7jP6YfDFF19QK0BnyWjPUEREJEi1aXMWbdqcZXcYUkPFxRlNj6+44jO7QxEHioyMpG3bc+wOQ2qwkJAQIpx4MTBxFpMqufLcG5YSFRVFVJR7H7alS5fS7fBVX7p3786UKVMqklxLly6lV69eFeuWLFlCx44dK54bERFRcWIxNDSUnj17UqdOHZ5//nlODLCrGQXPaQQREREREREREacwqSdXamoqCQkJFbcJEyZ4/Vc5OTnEHy7pT0hIIDs7+5jW5ebmMnnyZK677joAPvjgAxYsWMC9997LHXfcUV2/mSpTJZeIiIiIiIiIiNVCQ42bP88HsrKyKpJUgFcVF0BiYmJFxVdOTg5JSUl/u664uJiBAwcyceJEEhMTAUhOTgagS5cu3HvvvVWPvZqokktERERERERExKHi4+Pdbr6SXB06dGDOnDkAzJ49m/T09L9dN3z4cK644go6depU8djyZNjPP/9ckfgKJEpyiYiIiIiIiIjYwc+piseqXbt2pKSkkJGRwbp16+jbty9DhxpXrO7duzdZWVlkZGQQExNDWloaS5cu5Z133mHKlClkZmby7LPPAtC1a1cyMjK4+eabmTRpkum/Dn9puqKIiIiIiIiIiNVMajx/rCZOnOh2f/LkyYBxteo33njDbV1aWhoHDhzweo3vv//+OIO0lpJcIiIiIiIiIiJWszjJVRNouqKIiIiIiIiIiDieKrlERERERERERKxm0tUV5QgluURERERERERErKbpiqZT2k9ERERERERERBxPlVwiIiIiIiIiIlZTJZfplOQSEREREREREbGaklymU5JLRERERERERMRqSnKZTj25RERERERERETE8VTJJSIiIiIiIiJitdBQ4+bP88WNJb+R/Px8zjvvPGrVqsXatWsBaNGiBZmZmWRmZvLVV18BsG7dOjp16kRaWhpz5861IjQRkRpHY7KISODQmCwiUoOVT1f05yZuLKnkiomJ4bPPPuO+++6rWJaQkMC8efPcHjd69GimTJlCgwYN6N69OxdeeKEV4YmI1Cgak0VEAofGZBEREfNYkuQKDw+nXr16bsv2799Ply5dOOGEE3jhhRdISkpix44dtGjRAoDk5GT27NlD3bp1vV6vsLCQwsLCivt5eXnmB71nj/mvGQjy8yE21u4ozBUVBbVr2x2FuQoLoaTE7ijM9/vvsG+f3VGY74wz7I7guDhxTC4tM/0lbbdrNxQF4cfh0KHgO6m4d6+xXcFk9+7g++os17at3REcHzPHZEv2kQFKS6vndW2UmwMQZnMU5tqxAyIj7Y7CfH/8EXzfM3FxUKeO3VFUj8REuyMIcGo8bzrbenItXryY5ORk3nzzTR555BGee+45XC5XxfqEhASys7N9HlBNmDCBsWPHVm+ATZtW7+vbJTsbEhLsjsJc4eHB9w2enAxhwbWjBRifq5gYu6MQHwJ9THbF16nW17dDeDxEBeHHPDTU2FkPJvv3B9/QdehQ8Ca5gkFVx2RL9pGBE5u4/v5BDhMSEhJsOS5iYsAjfxoUQkODb7sq5aalplGSy3S2dSlLTk4GoH///qxatcoIplLTtJycHJKSknw+d9SoUeTm5lbcsrKyqj1eEZFgpjFZRCRwVHVM1ngsIuIw6sllOlsquYqKinC5XERFRbFgwQKaN28OQEpKChs3bqRBgwZHrRgAiIqKIioqysqQRUSClsZkEZHA4c+YrPFYRERqOsuSXD179mTVqlWsX7+eyy+/nPfff5+4uDiioqJ47bXXABg/fjxDhgyhtLSURx991KrQRERqHI3JIiKBQ2OyiEgNFRpq3Px5vrixLMk1a9Yst/sjR470ekyrVq1YtGiRVSGJiNRYGpNFRAKHxmQRkRpKPblMZ1vjeRERERERERGRGk2JKlOptk1ERERERERERBxPlVwiIiIiIiIiIlbTdEXTKcklIiIiIiIiImI1JblMp+mKIiIiIiIiIiLieKrkEhERERERERGxWmiocfPn+eJGSS4REREREREREatpuqLplOQSEREREREREbGaklymU22biIiIiIiIiIg4niq5RERERERERESspkou0ynJJSIiIiIiIiJiNSW5TKfpiiIiIiJSLUpKoLTU7ihERESkplCSS0REBNi71zggz8uDn38uxeVy2R2S31wuF6tXw/r1sHkz/P673RFJTfH99wVceeUyzj57MgMGDGPv3j/tDklERCTwhIb6fxM3mq4oIiI1Wk4OPP/87zz66FnAIeAgUMzNN+dz4421aNfO3vj8sW7dL1x1VVugFhBHeHgtVq/+iVDtEEk1crlc3HzzCZSV5QCwYQM88khvnn++p72BiTPs20fxxx/TeehQwqHi9vpLL3HCoEEQG2tzgP7Ztw/i4yEszO5IzFNWFlzH2WVlZcyff4C9e12UlUGDBi46d44nxOHTwoqKSli+PIR27cIoKTHetzp17I5KNF3RfEE0HImIiByfTz6Bli3h0UcjgF1AHlAMwCuvHOTcc+Hmm+HAARuDrKKyMrj99gMY27MP+J2Skt+YO1df/YGqsBB++QWysuyOxD/79oVQVtbabdnChauYP9+mgMQZiorg8cehWTOKhw7lW2ARMA+YCxQNHw7NmsF//mMMcA60Y8cfnHXW+Zx00kBOPHEEAwY8TUlJid1h+eX337dy4YWP0rTpkzRt+hxjx75ud0h+2759JzfeGM/IkQmMGpXA9dfXYe/eHLvD8suhQ3D55W/Ru3c4jRvH0bRpCied1IfCQrsjk4oklz83caM9XRERqZFefRX694c9ewCifTziEABTpkDPnpCfb2V0/vv8c9ixY7/H0jhGjDCmZTrV0qUunnrqd84+eyY33jieMoce7HpavPgb2rXrwcUXX0unTiN4443/2h1SlS1ZAnCmx9JVPPig+nPJUeTkQPfu8MgjcOAAvoaocIDsbLj9drj6aiMp5jAjR2ZRWjoPmAZMYsmS0ezf7+ySrhkzNvPrrw8DI4E7mTLlfkd/xwA88YT3sn/9y7ktDPbuhR494Ntvy3dkCoA/gRx69zbaNIgEEyW5RESkxlmyBG69tfKSGB+POuT2+DvvrO6ozPXOOwCeSa5aFBfDihU2BGSC7Oy99OlTn0mTUvn990v57LN/8sknW+wOyxTPPLOeAwe+BN4EJjFu3Gt2h1Rlv/4K4DnPdyX5+bB4sQ0BSWArK4OrroKFCysWHTXJVe6DDzwH8YC3aBHMn7/dY2kjJk92bhVGSQk89ZRnKVA006bZEo4ptm+HL77wfk8++sjFzp02BOSnsjK4/npYvRqMavXKavP9987bvwk6quQynZJcVisrg3nz4OWX4e23+ePddznwxx92R2WeuXPh8sthwIDy0VQCncsFf/5p/Busdu2C5cuNf6XGc7mMHTr3P/kIwH0nITb2oNv9t95yzgH63r2wahWA5zzLOMD4ODhRUVES4D5WDR++kuxse+IxS3Y2LF++221ZcXE9Dh06yhMCnDG990yPpZuAfGbNsjwcCXSvvgpff+226G+TXACvvw5ffllNQZnvpZcAPJNcDfmvc4s2D38neg5U0Tz0kA3BmGT2bPDcHzC4Dq9zlk8/rZw/9ixJrw3AjBnwww9WRiVulOSq0KtXLx566CGmT5/Ob7/9VuXXUZLLSi4XDBsG11wDEybAqFHcdf/9NExLY2iTJnw3fjwuJ9fx//GH0bxm+XJYsMCY37Npk91RmeOdd+Dkk41OoYMHB01d72sXXMDkhARcLVrAOecEzaXXyoqL+e7JJ/k1PR3atYOLLzb+Nnv1gm+/tTs8sdl335UngI4YOTKE2Fj3aq7Jkw8RH+/+uJdfrt7YzPLjj+U/eVdyAWxxaPHTvHkh+KoQevVVO6IxjzEV1jMJX581a2wIxgRGA+rWeKclfmTRImdPlxWTlZXB00+7L2vQgJJPP/V6aPi0aRAX577Q87kBat++8mm8OzzWNKKoCHJzbQjKBMY2eSe5wsKcOzXZqHT2neRyYiLoNbeiYM8k15GdHCdX38nxGTFiBBkZGQwaNIiiStO+S0pKuO6668jIyODOSuV9zzzzDOnp6fTq1Yvcw4PV/PnzSUtLIz09ndUmFrbceOONREREUKdOHb704ySGklxW2rwZvvii4u4eYAbGcPMKcN5//sPUZs2c23H255/x6l54//2OrxC6vWFDWl97LW23baP9wYOc++67pJ1yCllOfZ8OW/H44wxfvpxbgGuBgg0b4H//szusqisp4dt//pPb2rWjybnnct60abxcUOD+mOJimD7dnvgkYHh+Z558stEGJjravS9Xo0YHefBB98d+9ZUzhjRjuhgcLcm1d6+V0Zhn+3bwleT6+GMbgjHRxo0Auz2W1qNhQxuCMYGRHI4GWnqsWcn+/cbVFkUA+OmnygOW4bXXKGnd2uuh4RdfDE8+6b7w//7PyCAFuB9+KP/u8K7kAtjt+fF3COOz7J3kKi117vfM1q3gO8kFfhSW2KKgAJYurbzEdyUXwLJlVkQkPoWG+n87RitXrmTnzp0sXLiQVq1aMb3ScdHMmTNp3LgxCxcupKCggCVLlrB7925mzpzJokWLGDBgAC+++CIADz74ILNmzWLatGk88MADpv0qLr/8cq644gqefvppunXrVuXXOa4k19ixY9m8eXOV/7Mar3Ztt7tTKb+GlyEK6AVG1Yn7iOQMvq6FvGyZW48FJ9q6Zw/rgB+BlcBy4Nu9eyl2cDXXvnXr6Pevf1GeknwLOA/YsHKljVFV0Y8/QseOcM45fDZrFi8C5ROAP8ZzYhOQkmJpeNVp/373BEaBZ1JPfPIsMO3RA8LDISbGvZLr0KFDXH65+2Ozs43ZvYHuyMxc39MVi4txpIgIgPYeS1fy22/lCTBn2rYNfCW5jmO/NaA0aFD+05kea1YBOLZC7e9oTK6CtWvd76emwkUX+bziYHh4uDEbwhgIjvj552oM0BxHvne8e3KBc6uejO9D7yQXOPOqxGBcA+FolVwOyKe62bLF82/LuydXOSd/hzqeSdMV8/Ly3G6FPi6duXTp0orkUffu3VlilGMedd3y5cvJzMwkJCSkYllBQQEREREkJibSpEkT9pn4wWjWrBnPP/88jz/+OCeddFKVX+e4dp/OPfdcHn74Ybp168bLL79MttObYFitfn04/IfjAjxrZvoCiQD79xvNNJ3WjKNTJ9/L33jD2jhMdrRZFeEHDx5lTeD79Lrr8JyttBb4+MiRiTN89BFcey0cfi8u91i9Gfip8oLMTBg61JLQrFC/fn3Gjh1bcb9Xr142RuMcnkNrnTrGv56VXAcPHiQx0fv5TkgQHTm29l3JFe7V3MYZGjUC70quncBOR1cHGVf49J6u6Dld1imaNCn/ybvqDoKnk4EnjclV4HmFxORkCAk5epIrOhoSEtxXOGB/+ciJB+/pigC1alkZjXmMX73vJJdT2wQZSSHfSS6nJSM98u6EhR19uqKmkdvHRYjfN4DU1FQSEhIqbhMmTPD6v3Jycog/vHORkJDgls/xte7vloExNheZdLXb+fPn07VrVz788EPuueeeKr/OcSW5evTowdSpU/nggw9YuHAhjRs3pn///nz33XdVDqBGCQmB55+H/v1ZjpFUqOyGynf27IH1662LzQwREXDBBd7Lv/zSGfN7juJo32dhTksIVXJtjx5Mp/L5G7gkKYn7jK6ozjF1qtvds4DUSvdDgO8ALrzQmCr8zDNeFZVOdt5555GSksKgQYM4dOgQLgd/zqxUr577/fJCguho70qun37CS3lSzBk8k1xGJZfnMaJTtG0L0ILy7ThipePOsFdmHCi6V3JFR9fzaj/kFKeeWv7TmR5r1gLFBNP1dirTmFwFngPyzz9Dbq7PJFdYWJgxtdHICh/9NQLQkZMj3tMVIyIqVz86i3HCxHeSy6nfM7GxcLQkV1SUxcH4yXOXt7T06NMVg2iiQ42VlZVFbm5uxW3UqFFej0lMTCTv8GyknJwckpKS/nLd3y0Do5dXZGRklWJu164d/fr145FHHmH69OmUlpbSt29fHnvsMZ72o+ficSW5li1bxu23306fPn0488wz2bRpE88++yx33XVXlQOocWJiYNIkiseNI71SBrQZkOn5uBNPtDg4E/i6REx0tKOTXP985hneBd7GuLD7a8CrN91EYnKyvYH5o1Mn+gLfA2cATcPCeHPePEJ9TTkNZB47tiHAVUBP4NWTT2bHBx9w/cqV8NRTQfvtPXToUG644QYuvfRS9jq1AYbFzj3X/f5nn8Evv/iu5HruOffHtm7tjDzpkeSI53wRo1ygcWMrozHPySdD06ahQFuPNSvxePscJTS0DKNT5xGpqfUcWwlRuzY0bw7eSa4i4BevHEUw0Zh8nDp2dG93UVgIDz5IqUfJTGhoqHHQcv/97s9PToaWnr3fAo+ROCkEPP8mGnHaac6trjWqnT2TXFHUru20E0JHGAlH30kupyUjmzXznN179CTXWWdZEZH44nL5fwOIj493u0X5yMp26NCBOXPmADB79mzS09P/ct3ZZ5/NvHnz3JbFxMRQXFzMvn372LZtG8l+HBOvWLGCf/3rX7Rv356NGzdy//3306VLF3755Zcqvyb4uBrvX5k2bRpDhgyhfXv3fhgTJ070K4iaKP3aa1l07bWsnzeP1557jtRduwjds8eYY3LyyTBxojO/HSIj4fPP4aabjkzuvv7642qIF2g633YbXH21Me1y717o3du4EqGTXXQRvPkmp8yfz7fJyfzeuzdJTZvaHdXxGz0aXnjBOPP7xx9w99082a9f+d5k0Bs8eDAAXbt2JTU1lX/96182R+QMvXpBVNSR62SUlMDll0Pt2u5ZkvfeO8Tcue7P7dfPmhj9daRhue/piqedZmU05gkJMb5SxoxpByyptGYFrVrZFZX/4uKygTK3Ze3a1bcnGJOkpcGmTYnAicDWSmtWcujQGTZFVb00JldBnTpw2WVG+4Fy//kPJR6XgA0LDTUG6koXcALgyisdkSEypvDu9LGmIZ07WxyMiZo3h2XLPPv+RNOmjXOnK7ZsebTWyK5KVarOEBMDGRnwzTflS44+XfHSS62KSjyVlRk3f55/rNq1a0dKSgoZGRk0adKE++67j6FDhzJ58mR69+7NJ598QkZGBu3atSMtLQ2A3r17k56eTmJiIm+//TYAjz32GD179iQ0NJSX/JgJFBISwsknn8zJJ5/MpYf/CLOyshg2bBifffZZ1V/XFQS11Hl5eSQkJJCbm+s2P9Qvdlw5z+UyjrY8G2qaKTvbmrLuAwdg3jzj//IsmzBbeDhUKrUMCvv3+27k73Rr1xrfuMHmzDNNe6lqGc8sVh3bYHaviJEjfV15vhvwVaX7TwN3V9xLSDBmkZtVxLl+ffV9zL/7zmhXB5cAsyqtmQA8wP/9X/UVNx44UL2VYkVF0KnT/9i27caKZbGxJ/Hrr9V3YZw//6ze806zZv3MsGHuWbr33jtEhw7VNzdmw4bq3R1YtQquuw6MbokzKq25m5NOetotp2G2tp6Ffn5w+phcbfGbOSj/8otRRlKpp8tyoPLeYwzg1ca/Th1Yt860P+RtWSHVNihv3gxduy4FOlZaGgUc5P/+LwQ/+iv/pV27qvdz/uWXcMstdwHPVlp6I2PHvnr4O6h67NxZfds1fz5cf/0+wPPY4g9efrkRflzw7S8VFlbP9/I331Q+QVcb95Nfi4GOnHGG8bjq2ifx1d+0qpw+JldWvi179vi3LXl5edSt68zfSVFRkc+pjj179mTWrFk+nnFsAv/UR00SElK9CS4rxcXBJZfYHYWIiE8PPQRz5xoX5zzCc76b+8UlXnnFvARXdWvbFuLjIS/Pe7ri2Wc7e/ZuZCQ891w7+vQJIza2JU2btqNPn/a4XC5CHFo6kJzs3o8rMjK+WhNcVmjbFk46CX79tR3uSa5VjvkciUVOOw1efZXKWRHPFJrXAUtEBEyb5oh+XGBM0jj99O0eF5NsxODB1ZfgssIFF0B8/CEqX3A8Li7aMVXPvqSnQ+PGIfz+u/vy+vVdZGbaEpJfunaFAQNg2rQyvKu7a1OrFrz0UnCeW5fAl5mZSWFhISeeeCKtW7emZcuW7Nu3jzJ/SttQkktERGqgWrVg5kxj9vGRRJdnlaHRZyQszJgV+49/WBmhf6Ki4Kqr4JVXvBvPDx9uS0imOuectvz6az4xQVIZeu65Gfz0Ux5bt+5m377deB+IOE9ICNxwA/zzn2d6rFnJ6ae78N3zRmqsgQONgevmmyEv76+TXCkpMGWKcVEZB7nsst1uSa66dRsyZox98ZghIgLOPx8+/TQSl8uoxLvkkmjHXjQDjAki48aFMGSI+/LRo11Usbe27f79b4iMPOB1wfsmTWozZYrRb1TsU7mvVlWf71RLlhitJ7Zs2cLatWv55ZdfiIyM5N133/XrdZXkEhGRGqlRI1i8GMaPN85i5uZ6V3J17gwTJlT/rOvqMGwYvPnm/sNX7jP06lWLwy0WHC0sLCxoElxg9KSoVas2rVvXBhxc1uGhZ0/49NMzWbas8tIcOnfehtGrS6SSvn2hQwf4978p+d//IP9I/6BwMJJb118Pd9zhnLLaSm6++Rauvnow69fv4MCBHURFhTo2aVLZs8/+h2ef/Q9lZWUUVZpy6mTnnhtJz543sG0bQAhNmoSQkeHcfq8RETB+fDj16j3H2rX55OfnU6tWPi+9lGzqVEKpmpqc5CrXrFkzmjVrRu/evU15PSW5RESkxoqOhkcfNXp0Pf54D5Ytq09oaAwJCdFcfnkHrr7a7girzti2R1i16k/+/PMAycn7GTxYp2vFOiEh8NxzTcjISKSoKI+YmFa0a3cmdeua3GRPgscJJ8BTT9Hm/vuZ9fHHlO7ZQ0lJCeEnnGAkuBw6JblcbGwc7do1B5rbHYrpQkNDva5S7FTR0TE8/7yPK8Y7WExMDKNH3253GCKWUJJLRERqvLg4GD/+KuAqu0MxVe/eV2HSSTGRKomKCuGDD74lJaUJUVHBcQAs1S+5Xj163Hyz3WGIiFQ7VXKZT0kuEREREak2J554it0hiIiIBKSyMuPmz/PFnZJcIiIiIiIiIiIWUyWX+ULtDkBERERERERERMRfquQSEREREREREbGYKrnMpySXiIiIiIiIiIjFlOQyn6YrioiIiIiIiIiI46mSS0RERERERETEYqrkMp+SXCIiIiIiIiIiFisrM27+PF/cKcklIiIiIiIiImIxVXKZTz25RERERERERETE8VTJJSIiIiIiIiJiMVVymU9JLhERERERERERGyhRZS4luURERERERERELKZKLvOpJ5eIiIiIiIiIiDieJUmu/Px8zjvvPGrVqsXatWsBeO+990hLS6Nr165kZWUBsG7dOjp16kRaWhpz5861IjQRkRpHY7KISODQmCwiUnOVlfl/E3eWJLliYmL47LPP6NevHwDFxcU8/fTTzJ8/n3HjxjFu3DgARo8ezZQpU5g9ezZjxoyxIjQRkRpHY7KISODQmCwiUnOVT1f05ybuLOnJFR4eTr169Srub9y4kdatWxMZGUl6ejojRowAYMeOHbRo0QKA5ORk9uzZQ926db1er7CwkMLCwor7eXl55ge9a5f5rxkIdu0Kvk9CdDRERdkdhbny8iA8CFvm7dkDMTF2R1HjOXFM3r3b9Je03aZNEBqETQPKyiA+3u4ozFVQALVq2R2FuUoPFcGhYD39G213AMfFzDHZkn1kIMj2JAHYlwOFxXZHYa5duyAszO4ozLdjR/Adzhw6FHyHM+USE+2OQGoaW46ic3JyiK+0B1xaWgqAq9JolZCQQHZ2ts8DqgkTJjB27NjqDTI5uXpf3y6lpcE30hQVBd83XUiIkbwLNs2bB9+RYhBwwphcVFStL2+L8HCoX9/uKMxXUhJ8XzOJicGXuEtNLiYiCM+lBAN/xmRL9pEBwoLvj6d2AtS2OwiTRURASordUZjP5YKkJLujMFdOjs4D11RqPG8+W84hJyYmup1ZCjt8iiG00intnJwcko4yeo0aNYrc3NyKW3mvAhEROX4ak0VEAoc/Y7LGYxERZ9F0RfPZchqmefPmrFu3jqKiIpYvX06bNm0ASElJYePGjTRo0OCoFQMAUVFRRAVrPaeIiMU0JouIBA5/xmSNxyIizqJKLvNZluTq2bMnq1atYv369QwdOpS77rqLLl26EB0dzZtvvgnA+PHjGTJkCKWlpTz66KNWhSYiUuNoTBYRCRwak0VERMxhWZJr1qxZXsuuuuoqt/utWrVi0aJFVoUkIlJjaUwWEQkcGpNFRGqmsjLj5s/zxV3wdY0UEREREREREQlwmq5oviC8eLmIiIiIiIiIiNQ0quQSEREREREREbGYKrnMpySXiIiIiIiIiIjFlOQyn5JcIiIiIiIiIiIWU5LLfOrJJSIiIiIiIiIijqdKLhERERERERERi5WVGTd/ni/ulOQSEREREREREbGYpiuaT0kuERERERERERGLKcllPvXkEhERERERERERx1OSS0RERERERETEBuXVXFW5mWXEiBFkZGQwaNAgioqK3NaVlJRw3XXXkZGRwZ133gnAtm3byMzMpEuXLvTo0YOcnBwAMjMzycjIIDMzk7feesu8AI+DklwiIiIiIiIiIhYrbzzvz81fK1euZOfOnSxcuJBWrVoxffp0t/UzZ86kcePGLFy4kIKCApYsWUJ8fDwfffQR8+fPp0+fPrz66qsVj//iiy+YN28egwcP9j+4KlCSS0REBONsWEmJ3VFUjyVLdPUdERGRmmrnzlLef38njzyykn/843M++eQ7u0MSk+Xl5bndCgsLj/m5S5cupVu3bgB0796dJUuW/O36OnXqkJSUBEBERATh4Ua799DQUHr27Mmll17K1q1bzdi046bG8yIiIsDYsf/l9ddnA7FADLfdlsmIEVfZHZZffvhhHpMmzWbTphBCQkIZO7Yl3bsPsjssERERsUhREVx00Uj27p1Usezbbwfx229TufVWiIiwMTgxrfF8amqq2/KHH36YRx555JheIycnh0aNGgGQkJBAdna21/r4+Hif63Nzc5k8eTJffPEFAB988AHJycnMnz+fO+64gxkzZlRls/yiJJeIiNR433wDr7/+A3CkPPuFF8Lp2/cqmjWzLy5/TZ68hE2bngCMnaDHHruUiy8eREiIzYFJ8HO5KN23j80ffMDqt9/mhPPOo+Mtt8BJJ6E/QKnJZs78hvffb0Tnzk0566xo2re3OyIJZkVFMHgw7N3b0GPNDp58EhYsgLfegrg4W8ITzEtyZWVlVSSiAKKiorweu3PnTvr16+e1vEePHuTl5QFGQqu8QqtcYmKiz/XFxcUMHDiQiRMnkpiYCEBycjIAXbp04d577636hvlB0xVFRKRG27gRhg0DKPBYE8tNN8FxVHsHlJ07YdUq972moqIQFi2yKSCT5OVBTg5s3lzGlCl/sH//frtDMsWKFQf44IMgmT7y5588Fh9PfJMmnHrvvVyxYgWvvvwytG0Lp5wCY8bA3r12RykB7ptvfiEt7X3uv/8HFi7MYfduuyPyX35+IXfddSFLlrTkiSdi6N+/Eb/9tsnusPyyc+d2LrpoEo0avczJJ7/Oo49+aHdIfnO5YPx46NNnH/PmLeTgQc/9A+eYMAEWLgRo5LFmOwDffgt3321uA3M5Pv40na+cIIuPj3e7+UpypaSksGjRIq9bz549mTNnDgCzZ88mPT3d7XkdOnTwuX748OFcccUVdOrUqeKx5cmwn3/+uSLxZTUluWxWvHevRhURERs9/nh5IstzJzaGTZvgQ4fur6ekAHg24grlvPNsCMZEPXveTIsWrenQIY7772/Miy9+YXdIftm0qZD69Z8jLe1krr76EmrXzqdPH5ybjPzgAzjzTGq7XG6fqFXlP+zYAU8/DWedZTSLE/GwfTtcdx1ceOEMli27kokTz6ZLl0QaNLiUPXvsjs4//ftvBSrv9+8gJ6e+XeGY4tNPN/PTTyOA4Rw8OIT//OdGR/e3dLlc9O59Ka++msqqVUkMGdKZ//1vtSP7Wv7+O7z2Wvk970qucp99Bl99ZVVUEojatWtHSkoKGRkZrFu3jr59+wIwdOhQAHr37k1WVhYZGRnExMSQlpbG0qVLeeedd5gyZQqZmZk8++yzAHTt2pWMjAxuvvlmJk2adNT/szppuqKV8vLg3XeNutDNm2H/fk7dv58woA3QJiSEIS++SJMePeyOtGoKCuDTTyEqCnr0gOhouyMSEflLW7fCvHnl97wruQCmT4eBAy0MylSeJ1FC2LsXGnru6zrEwYOwfv0mYF3FsokTN9OvH5x8sn1xVdWePTm0bt0W2FaxrKjo38ya9RCzZsHYsfDAA/bFd9xeew3uuAOAMz1WrQOKgYrWL3v2wGWXwf/9H5x+umUhSmD76Se44ALYtQtgs8fa+px7rjG9vGlT62Pz1549sHHjFo+lSbz8cjyTJ9sSkt+Ki+GRR7y/O99+G6691paQ/PbLLyH89NMm4PeKZZMmrWHYsDT7gqqijz4y3iODZyVXLnAAMOYpPv00HO4rLhYza7qivyZOnOi1bPLhwSk8PJw33njDbV1aWhoHDhzwes73339vTkB+UCWXVTZsgIsuMmpGFy+GnTvJ3b+fLcAm4CPgEZeLnOHDYdAgWL7c5oCPU0kJXHGFsTd+991w8cXw6692R2W+ggL45Re7oxARk8ydW/me7yTXypXgxBlxxpl07yTX4SpyR5o5E8Azm7WZMWNsCMYE33xTB+900ETAaOj68MMwdaq1MVXZmjXG9/9hbTxWFwFe354HD8Ktt6qiXQCjorZPn/IEF3gnuU7mt9/gyiudebVYY6aPZ5KrGRs22BCMSf7v/8DXd+e4cTYEY5KYGIAzPJauISzMhmD8dOQkHnhXckHlaq4ffzTaN4j1ysr8v4k7Jbmscs89lb+1AVjj8ZAI4DQwyvevuMJIiDnlr3bLFli79sj9rVuha1f3ZU61ezckJhq3E06AtDT48ku7o/LPH38wp2NH7khOZmRyMo8kJ/NBerrje6TkrF7N15deyqQmTRjcpAkPNmkCTZoYCeZg+FsU061YUfme7ySXy+XMnP3Bg+AryXXokA3BmOTbb8FXkmvOHKMHmdP85z8AnkeDeRiJLsPIkcb5lYA3bhyUllbcTQSaREa6PWTVI4/AhRe6P++HH2DVqmoPTwLf1Kmwya09lefAa3z2ly+HLxw4S3npUvBOcjV19MVNFi8GX9+dBQVuw4GjGFWC3kkuJ07BdE+g1iYqqpbb+sTEHW73HTtNXsSDklxW2L3bqL/28KPH/ZaA2+7gK6/gmPrl+vXBY2cWgF69nH9aYMIE72UDBhiJPSfKzobOnVm2fj3PA08CY4G3f/kFOnQwmmE4zXffQffuvN27NxeuWsUIYCrwafn69evhoYfsi08CVlZW5Xu+k1zgzPyv0WfM80RJSKWpC85j9OPxTnJB+QGks6xcCUbN05Uea54F/gSMbZ4929q4jtv+/d4NXe68kzMvusht0ersbHjvPePkQ2XfBUnDffHL559XvldE5Wm8hiOffScmuYxDAe9KrogIHw92CGMX/6DHUuO7c98+q6MxT1qa5xTqtWza5KyKU5fL2OWvrF499ymLp5zivs+/xrMCQyxhVuN5OUJJLivUqgWh3r/q1R5XPPAs7Qfg5ZeNa78GuoQEo0uoLxdd5MwjxHKffOJ7udOmlJb79lvIycGzmCMGjG9DYz6QcxQXw223wbp1XpN+foYj2+ljzrhITk7le0dPcjlhGPZkFAJ7V3I5eWfIOBj0THJlAYUeFSDOcCThOBb3XbIC4MgJFiMZFsC2bMEtexoSAiNH0taj19bqNWuMvp0tWrg/PzfXgiAl0LmfdNiKd5L+yGd/xw4cpbTUaAIOv3msaUadOpaHYxojkeV90RbA0SdUMjI8K7n28t13DiwX9pCc7J7kiotzT3I5/cIOTqUkl/mU5LJCTAz07u21+EeP69L7THLl5ztn52/EiKOvGz/eujjMdsIJvpe3b29tHGY54wyIi/M67xZT/sOpp1ockJ9CQiqmAnt+hko43J46OtqY8yPiwX3HwPfZaIData2Ipjp47vmEOrKvSLkTTwTvJJcL+I38fOvj8deRXM+pgGeX5pcxEngE/hTTkBD3+y4XFBXR9gz3A8VVP/6Ia+tW76sqGm+s1HDuyR73flzh4YlAnaM8NvDl5JRP3/Ou5Grc2Pp4zGJ8h/o+QRQfb3U05jnvvGaUN2Qvt2SJs8qcQkIgKcl9WUyMe1+uQ4d2eNyv7qjEFyW5zKckl1UefRTOPLPibhnePbl8JrmSk41eUE4QGXn0puxOnEdS7pln3O/XqgX//S80b25PPP5KTYWPPqJZeDgZwNnA6UATMLY1M9PO6I5feDgMGwZAbaAF0Azog1Ebkdyjh/H3d8EF9sUoAct9eD16JVcjz4sSOYCRzPKu5HLy1JhzzgFIAJI91mx25AFVnz6V742h0rUHMaZrPQZ4Fz4FnObNjQqtym65hTM9kld79u5lR8+e5Q3jDOHhzvvekWqRnl75nnuSq6TEPbnduXP1x2Mm47z2fsCzVKYZp5xifTxmMZIo3t+dyckQF+fjCQ7RqlUo0Npt2eLFzkpyAZx0kvv9gwfdd2Z273av5HLKIafI3wm3O4AaIz7e6EPxn//AK6+w5cABPCdPeSW5QkON64eHO+htio42El2nnea+vFKCz3HOPtuoFFq/3jhqbNHCWe+JL2efze1//sntdsdhlpEjjcstbdnCj6edRnRDX1eQEfHWtKlxRSGD7ykXtWt7txByAiPn4J3kio318WCHOP98Yyd8376TgcrT4DdzumcLFQe47Taj/ebu3QBNgZuAlyo94jUiI+/nsss8q9cCTHQ0XHYZvP/+kWVffkmzBQuoFRbG/kodqFdt3ep+Ifurr4YGDSwLVQLXjTfCxInlF1rwvrJiucaNjeszOYkxHvvq5XoiZ59tcTAmatkS5s3zroI2Tkg4V3Q0GKeAj/QLjIx03gWM0tPh+++P3N+0yX3/eOdO90ouo+m+WM3faixVcnlTJZeVIiPhjjtgyRL23347GXXqkHB4VV0gpfLjOnc2dhYvucSeWP0RHQ2//Wb0E+vRA4YMgccftzsq/0REwOmnG9/mTk9wBaumTeH885XgkuNy5OCi+PCtMiMb1Lmzz7aKAc9IZnk3nq9Vy8eDHSI6Gu6/HzynLNaqtZnzz7clJL8kJRnnv468J/8Eois9ooRzzplE/frWx3bcHn7Ya35SaEEBbTwusba68p3mzZ3dzkBM1bixseto8H1lxehoeOstoxOIkyQmQmSkZ5IrhXPOiSHZszDVQXr0AF8niPr1syEYk3Xq5D7durTUeZVcvXq53z9wwL2SKz/fvZLrvPOqOyLxpazM/5u4c+BuexCIj6ftPfewYOVK9m3ezNZPP2XGU08RMmMGzJ1rlBW88QacdZbdkfqnRw9jb+Xhh1X/KiIBqXv38ml9nmeioTzJdaXnhe8cwkjMeVdyOfmACuCGG6BDB/ckV/v2271myzlFerpx2XZjRnUj4DYAQkIS+cc/nmDmzKfsDO/YnXgiTJ/uleg60+NhFUmutm3hyy+d3bhHTDd4MHz0EYSHe1dytW4N33wDXbrYEppfQkPhpJO8+3Fdf70t4Zjm7LPhxBPdk1xNmsTSvbtNAZno6qvdk1xlZVsoc1g2oXVryMiovMSz98KRJFf9+p6PFXEulaTYLCQ0lCZnnEGTMzyv4iEiItWtfn0YOBDeesvzTDRALB07Onunr0EDF3/+eeR+XFyI44tRQ0LgiSf6s2pVa2rVOon09JNJTk76+ycGsJYtYdYs44pxy5ePZPbs2owdeyd16yb8/ZMDSceOsGwZjBplXKm3tJS2Hg9ZFRZmtGIYPtyoXBfxcPnlsGjRm3zyyQZWr95MdvZmhg5tzzXXOLOqtlzr1r+5ta5t1qwZF11kXzxmufXWTkyfDjt3FhAXV8D115/u6Pep3Lnnnsmtt44hKekMzjnnDFq1ak6oAzds3Di4+OLyvnCesx3ygANAHLffjqN7djqZpiuaz+G7uiIiIv4ZORK++66A9evdl6emxvL0094XjnOSk05yT3I1buy8HXRfWrduQ+vWPi/X4mgNG8Kll9bl0kvH2B1K1aWmwtSpsHMnLFxIh8WLGbB4MWc2a0bb9HTa9ut39KsWixx27rntOPfcdnaHYap//nMMffoMZNGiLRw6tIW0tJaO/n4pd/XVN3D11TfYHYbpEhOTGTFirN1h+O2UU+Dpp+H226GszFdLjx1ccEFzhgyxPDQ5TEku8ynJJSIiNVqtWvDccy4GDGhJfn4BZWUFhIQUMGNGtNflt52mY8cmbN3agfx8FzExZXTufNLfP0nEDCkp0L8/bfr35x27YxEJAAkJiaSlnUNamsO7sovj9OkDderAyJG1+f332kB+xbpu3bYzeXLzoKi+cyolucynJJeIiNR4p556MitWrLM7DNNdeeXtXHll0FxHVURERKrg/PNh8WK47bZh7NsXSr16jTjnnIb07t3SsT0tRY5GSS4RERERERGRIBYRAZMn/8vuMMQHVWOZS0kuERERERERERGLlZUZN3+eL+6U5BIRERERERERsZh6cplPLeZERERERERERMTxVMklIiIiIiIiImIxVXKZT0kuERERERERERGLKcllPk1XFBERERERERERx1Mll4iIiIiIiIiIxVTJZT4luURERERERERELFZWZtz8eb64U5JLRERERERERMRiquQyn3pyiYiIiIiIiIiI46mSS0RERERERETEYqrkMp+SXCIiIiIiIiIiFlOSy3yarigiIiIiIiIiIo5nW5Lrt99+o169emRmZpKZmcnu3bt57733SEtLo2vXrmRlZdkVmohIjaMxWUQkcGhMFhGpGcorufy5iTtbpyt26dKF6dOnA1BcXMzTTz/NwoULWb58OePGjeOVV16xMzwRkRpFY7KISODQmCwiEvzKyoybP88Xd7ZOV1y8eDEZGRmMHj2aDRs20Lp1ayIjI0lPT2fNmjVHfV5hYSF5eXluNxER8Y/GZBGRwFGVMVnjsYiIs6iSy3y2VXI1bNiQTZs2ERsby0033cSMGTOIj4+vWF9aWnrU506YMIGxY8dWb4C7d1fv69tl/XrIzrY7CnOFhEBYmN1RmCs3F4qK7I7CfFu2QFyc3VGYLzXV7gj8FuhjcjB+HA4eNG7BxuUKvrOKRUXB9zVzKBvCg2ybytULgq+Zqo7JluwjE5wHVYcOBd92FR8shsIgG5CBA7lhhIQE1/XT8vOhdm27oxAJDraNDlFRUURFRQHQt29fXnvtNWpX+mSH/cXe5KhRo7jnnnsq7ufl5ZFq9kFmSoq5rxcotm8PvhH04EGIjrY7CnMdOhScyaDoaKi0ky6BI9DH5CDII3pJSoLISLujMF9oKMTE2B2FuXJzjfMpwSSnKC4ov2aCRVXHZEv2kYPUCScY41cwCSsqJjbIxmM4/D5FBVeSq7AQ6tSxOwqxg66uaD7bhvL8/PyKnxcsWECvXr1Yt24dRUVFLF68mDZt2hz1uVFRUcTHx7vdRESk6jQmi4gEjqqOyRqPRUScRdMVzWdbCnzRokU8+OCDxMbG0qxZM8aNG0dUVBRdunQhOjqaN998067QRERqHI3JIiKBQ2OyiEjNoUSVuUJcLuf/SvPy8khISCA3N9e8M1bBemnmFSugbl27ozDXwYPQqpXdUZgrJ8eYyxRs1q2DxES7ozBfu3amvVS1jGcWq45tOHTIlJcJKAcOaLqiUwTjdMUdO4JzVjxAkybmvZbTx+Tqij/Y+u6B0RMp+KYrFgTldMU/94TB4em8wSKYpyuaOXQ6fUyurHxbpk/PJTa26ttSUJBHv37B8TsxS3BNZhYRERERERERcYCyMv9OHATjSQd/Bdn5ChERERERERGRwBcoPblGjBhBRkYGgwYNosjjsuYlJSVcd911ZGRkcOedd1Ysr127NpmZmWRmZrJmzRoA5s+fT1paGunp6axevdqc4I6TklwiIiIiIiIiIjXQypUr2blzJwsXLqRVq1ZMnz7dbf3MmTNp3LgxCxcupKCggCVLlgBw6qmnMm/ePObNm8cZZ5wBwIMPPsisWbOYNm0aDzzwgOXbAkpyiYiIiIiIiIhYzqxKrry8PLdbYWHhMcewdOlSunXrBkD37t0rklh/t37z5s107tyZYcOGcejQIQoKCoiIiCAxMZEmTZqwb98+E35Dx09JLhERERERERERy7lMuEFqaioJCQkVtwkTJhxzBDk5ORVN6xMSEsjOzj6m9Zs2bWLBggU0bNiQl156ye1xAOHh4V5TH62gxvMiIiIiIiIiIpY7kqiq+vMhKyvLLcEU5eMKpDt37qRfv35ey3v06EFeXh5gJLSSkpLc1icmJvpcn5ycDED//v2ZMGECw4YNq3gcGL28Im24lLiSXCIiIiIiIiIiDhUfH++W5PIlJSWFRYsWeS1fuXIlkyZN4pprrmH27Nmkp6e7re/QoQNz5syhc+fOzJ49m+uvv54DBw4QHR1NWFgYCxYsoHnz5sTExFBcXMy+ffvIz8+vSIJZTdMVRUREREREREQsZ850RX+0a9eOlJQUMjIyWLduHX379gVg6NChAPTu3ZusrCwyMjKIiYkhLS2NjRs3cs4559C5c2dmzZpVcdXFxx57jJ49ezJgwADGjx/vd2xVoUouERERERERERHLmTNd0V8TJ070WjZ58mTA6K31xhtvuK0788wzWbFihddzunTpwtKlS02JqapUySUiIiIiIiIiIo6nJJeIiIiIiIiIiDiepiuKiIiIiIiIiFguMKYrBhMluURERERERERELKckl9k0XVHMVVYGK1bA2rV2RyIiIiI2Ki62OwIRERGpaZTkEnM9+ijccQfccgs8/7zd0YiIiIjFCgtd9O//Dc2bd2HixAUUFNgdkTjV778X6u9HRIKcy4SbVKYkl5hnzx6YO/fI/ffeg3377ItHjq6oCD76CJ54At5/H+1Bihzx88/5QVOBcujQId57bw9r1hxgw4YytmyxOyIJdi++uJBTTunMd99dACzg+efH0qGD8VUjcixcLmN3cuhQOPHE7tSu3ZywsME0avQSS5dm2R2eiGPNmrWbq656gZKSMrtDETdKcplNPbnEPIWF3st++AEuvND6WMxWWAiffQYhIXDppRDu4I9OQQFccYXx3pSbOBHefBNOO82+uMy0dy98/DHk5UGXLnDWWXZHJA6wdi289loBL7+cCpzHKadcwwsvXE5GRpzdoVWJywV33z2bt9++vNLSluzcuY7YWLuikmC3aNGPwKJKS74hN3ch992Xwa+/wsiRxlepiC87dsC118LXXwOUAN8BBcBm/vxzKpdc0piffkqlYUNbw5QaxOVyEeLwQeuRR1byzDPPAdOAQpKTT+Hmm7sxahQkJdkdnagnl/lUyWWH3bvh9ddh2rTgqnRKTPRetmyZ9XGYraQErrrKmIZ5++3QqxccPGh3VFX3/PPuCS6ArCwj8bVzpz0xmWnDBmNbXnoJpk6Fm2+G+fPtjkoCmMsF48bBuefCyy9/AuQCc9iw4Wq6dTuBvXvzbY6wah5/HN5+23OsCqVPH9/nJJyioMDFvn25dodhqvx8+OmnNXaHYYr9+28AGnksHQvAyy/DG29YHpI4xO7dcP755QkugDUYCa4jcnM7cP75xrksJ9u0aSNvvLHR0WNxTXDwYBHnnXcOTZs+SUrKIbvDqZKyMhfPPDMYeB0o/4N7jldeMc4Dq8JbgpGSXFbLzjaSJGPGGKczO3ZkxZAhFOc78yDKja/SgKVLrY/DbP/3f/Ddd0fur1ljTMV0qk8+8b18924233knRUVFloZjqu3b4a673JPHLpeRVBY5iqeegvHjjT8VeNNj7Xk891xtG6Lyz5Il8OST4HmACLEsXQrPPmtDUCZp3Hgideu2JSxsLXXrwo03wp9/2h2Vf5o0mU6nTm1p2HAUy5Y5+4zsr79GA6M8ln4NLAbgsceMcxEinm6/HTZurLzEcx+yOVCfjRvh3nuti8tsq1YV0b79AG6//Uzq1ZvMxo3O/syX5OYy76abuCsujmvj4uCdd4KiDcbmzdCs2Uts2/YDhw6NBE7jv/+dhsvlrPfrm29CgNs9ls4CNrFtG1x5ZVC8XQ6n6YpmU5LLat98Y9RiH7bqwAEyvv6a7q1bk/3jjzYGZq5sYBnwaU6OzZGYYOVKSoEcYCvGecXvZ8ywNaQqKy6G336ruOsCVgOPAG2B5gsWMO+dd2wJzRS9evmuRqv0mROpbMsWo4rLsB34yuMR1/Dii87bATySxPKs5DJORrzyCpSWWhmROdLTPyE3dyTGaNyRffu+ZMoU6NgRdu2yO7rjV1gIp546DxgEuDh06Am6d7+efv2KHfn+gHGRZbiRo1VzFRcb16gRqWz9evjgA/dlsbFLPB6VVvHT1Knw++/VH5fZ3n8fOnceB/yAcRLiFs46qxd7HVqatuLll0lp1Ijz33mHZ4G3geybboJzzjFmCTjYxo3ZQOXBaiv//rfzLqr1+ecAVwN1Ki11AS8CxmfvuecsD0ukWinJZbUGDSp+3An0xviK+wbo0KsX66dOtSkwc9wLJB++dQCuAMpKSmyNyV8Dnn2WcCARaAq0AS7+/ntbY6qy0lLKMM6nj8A4J3omxqFHeYr1Y6fOJVm37ujr6tSxLAxxlv/9z5iRbHgHqNyMtRZwOQcOwIoVlodWZSUlRgGqwTM7FwMYCaH1662Myn8vvbSSb781kkGGfOAS4EV++w3GjrUttCrbv38Pu3ZdDlSuoH2dr7++nKFDD+CwggEAGjUCiAZGeqz5CjCSFgsXwurV1sYlgc2zyDwxEerWda/kioo6kuRyueDLLy0IzETbt8ONNy4Bxnus+ZVYJzZKzM3ltEmT3L5lSoHPwDih+s9/2hKWGUpK4LrrHgfc28qUlU1yXH+ubdsA4oAbPNa8hvE9apz4cvJEDhFPSnJZLT0dzjmHg8DlQOWTUBuB50ePdnRfpDKMKq5yhUDWzz/bFI05onwsyysrw1XmvCuTHNi9m8ZAJ2AS8KuPx3yycSNlDtw2rr766Otat7YuDnGUhQvLf3IBngnefhg7huCkotTt2+FQResQ7+mK5ZxWBXHBBY2AMzyWlgG3AXfy+efOK31KTq5L9+5PA2Eea2bx4YcX8MwzzqvuOPPM8p9uAlI81h7JRDp51r+Yz3NX8ZJLdrFtm/teSseOHd3uO62X0Lhx+4HBuJ9MCad167eJiYmxKSo/TJ1K7I4ddPNY/En5D049IQy88MJmwLNqqz9Tp6b5enhAi44u/+lWoHKCLo/yFg179zr67QoCmq5oNiW5rBYaCi+/zD21auHZkr088cAttzhzHgnQwsclOjY6fBpmvI9lJcDB/futDsVvcY0bc+LfPKastJStTktM/l25Q2amJWGI8xyZ4rYaWOux9pqKn+rXtyggE7h/ffierggQEWFFNOY58cQGwP8BV/pY+xx//nkp+Q7sb/nmm9djHBZ6HuQuY/z4TmRlbbM+KD9cdFH5TzF4V3PNAb41fprz90O31ByefwvZ2e5VXLVq1SIi4nS3ZU4aw1wueP/9u/E+vTiWl15qb0dI/jucZezjsfhLDp9eOekkiwMyR1kZPPHEKKC40tIIRo9+gvYOfKuOXDi9GXCpx9rnKU+6/vKLdTGJJyW5zKYklx3q1+fe11/ntMjIikXNgI84XDW0YoVjG7a3aNu24ucwjOlwhxxcmQaQ4GNZPLDfiVdYDAmhj4+zhScCdwMLMLoSNYvyVb8WwMrKIDzc97pGjSDNeWfexBpH8vKeDeebAF0AiI+HSkNbwGvY0DifYvA9XRHg5JOtisgc0dFw8cUxGNNKH/JaHxb2I/sdePIhIgJmzepFbOzXGBPjjygt/YXnnnvKnsCq6IILoF698ntDOVo11+7d8McfFgYmAc0zH7JggXs/rpYtz+Wbb9wrHo8cvAe+zz77lOLi/3os7cigQffTrp0tIfnv3HMB6IV7LepBYE5oKIwebUdUfnv77SWAR4M4bufWW52ZtOvRo/K9OzzWrgfmAs6+6rLzKcllNiW5bNL83HNZOm8e3ZKSqA3MBOpVfkB2tu8nBrhz+vdnFsbUy4OH/+114IC9Qflp2NixrAWygFyMfgO5iYnUr9RfzUn6PPIIAK0xDhNXAFuAp4EMDu+oHC1hFKgKCio3VnJ3443O2x6xTOfOYNRmvu2xZjDlX5E33ABOyvtGR0OHDuX3fE9XPO00SE21Mipz/PvfkJQUitEM+E2g/GRRHG+99RkNGza0LTZ/pKXB0qVptGy5GDjyxjRseAGPPTbRvsCqICIChg8vvxcD3O/xiC/hcC27w8+BiYl69nS/v3+/+8neH3/s6PY1HxMD3TznyQWw+PhaREdXvhhDLc4++y0mTXLw/km/ftC/P8lA50qLY0JC2Hb99cYVQRzG5XLxv/95XroziWnTHiTMc1a5Q5x1VuVzvedjHAFUZnSdP/HvpnqIOIiSXDaq07gxny9fzuLbb6d1fKVJcamp5UdejlPnjDPogVHBVVFF7tCqtHKNBg6kdVwcjTEquEIBR9YrH9Zi0CA2X3opazEOE9vhPkOf6Gg44QRbYquyuDij3MbT2WfDpZ6l2SJHDB8OUVFzAM9L8w0G4JRTYNQoy8Py2623lv/kWXFqVHLdfTc4rHcuYLwfy5bBVVdBUtJgYmO/JjKyPs8/P40+fRxUbudDkyawcGFLJk5cQmJiaxo3bseSJR8R5aQM62FXX125ymYo4HlSyKjmCtVeqBx2zjlGFaChGHBvEFRY6F6RfccdlStxA1+XLl354Yc1tGx5BQD/+Me/+fLLk3Biv/kKoaEwZQrMmcPgyy7jmjZt+Pi++9iTlcUdRy7x6zgjRjxAbOwpFfe7dx/D+ecn/sUzAltICDz9dHlvrhDgdo9HfE5c3EYyMqyPTcqpksts2r2wWXhEBGfcdx8sXmyMQI8/Dp9+6tyrwfk6anJgjxQ3sbHuV4hJTIR77rEvHn+FhXHSM88cvc7/0kud1egCjB2tGzyuGtOiBfzrXzqKkr+UmgqXXLLAY+l5wKm0bw+ffQa1a9sRmX969YLBg8FXJddVVxlJIqc66SR4+21jult+fid2797M8OG97Q7LFGFhcMMNjVmxYiFff/0Fder46goZ+CIj4dlnOXwAHwvc5/GILwgN/c5xU2alev3vf5CSAkaPRM8EfUV5Kunp8PDDFgZmktTUJL799l1mzvyaKVOup1LXEucKCYH0dIa88w5vLF3K5Y88QmyikxNCIfTqdRnr1q3ljjuep02bDrzyyjC7w/Jbq1bw5ptGBSRcDdRxW3/22S85cl8neCjJZTYd/QWKhASj7HfwYEhOtjsa/zRvbncE5rv2Wvj8c3jqKaNb7pHLRzlTbCxMm4ZXI4hTToGHvHvdOMLgwfDCC3DNNUbpzRtvGAlJkb/x9ttP8Pnna0hLu5/o6Eacd941TJ0K8+c7c0ofGMcdzz8Pp58eSkjIkUqgSy6J5T//cWYV19HUqlXL7hBMV6dOIvXrO3NKfLnTToPJk8urB24B3K/eULfuoyT4anopNVZqqnHF29TUJR5rTgOMsq1Bg2DWLGdNIa8sJCSELl26EhJMg3AQio6OYPTo25g9ewmRQZGNhIsvNvZreveOIyzsRgDCwxvzj3+MZ8qUf/7Ns0WcxcETwSVgXXmlUZFWzkmdQf/KmWc6P7lVWUoKzJwJc+fCTz+Vl7Tg6Nr5jh0d2QNC7Ne16+l07fovSkvHU1JS4tgDqMpCQ2HJkg8BKCsr4+DBg4SFham4USzTuTN8+CGMHBnH2rX3UV7RFRXVnNtvvwKXy6WDfXFz0kkwd24vnnkmkrlzl/DHH0tJTOzIwIFGBapjm7SLIwXb+HTqqTB1KmzdejvLlp3HP/5xOeHqWxsA/K3GUiWXJ/1Vi/kuuACmT4f1641ygUGD7I5IjiYszDi1c/HFdkciEhDCwsIIc2p32b8QGhpKXFyc3WFIDXT66ca03+++G8bIkZ/Qq9dQ7rhjAJGR2gUV35o3P4kXX7wFowIQSkpKdP0YEROdeGITTjyxid1hiFQbfWWI+SIj4T//gVWroFEj5zUxFxEREdOEhMB558Uxb94iu0MRB1KliYgEN1VymU3fGlI9IiKMS+WIiIiIiIiIiFhASS4REREREREREcupkstsSnKJiIiIiIiIiNhCiSozKcklIiIiIiIiImI5VXKZTRcSFxERERERERERx1Mll4iIiIiIiIiI5VTJZTZVcomIiIiIiIiIiOMpySUiIiIiIiIiIo6n6YoiIiIiIiIiIpbTdEWzKcklIiIiIiIiImI5JbnMpiSXiIiIiIiIiIjllOQym3pyiYiIiIiIiIiI46mSS0RERERERETEcqrkMpuSXCIiIiIiIiIillOSy2yarigiIiIiIiIiIo4XkEmuESNGkJGRwaBBgygqKrI7HBGRGkvjsYhI4NCYLCIi1eGvvl9KSkq47rrryMjI4M477wRg27ZtZGZmkpmZSdu2benTpw8AmZmZZGRkkJmZyVtvvWX5dkAAJrlWrlzJzp07WbhwIa1atWL69Ol2hyQiUiNpPBYRCRwak0VEgpHLhBvk5eW53QoLC485gr/7fpk5cyaNGzdm4cKFFBQUsGTJEpo0acK8efOYN28e/fv35/LLL694/BdffMG8efMYPHhwlX4j/gq4JNfSpUvp1q0bAN27d2fJkiVejyksLPR6E0VExFzHMh6DxmQREStoH1lEJBiZk+RKTU0lISGh4jZhwoRjjuDvvl/+bv2MGTO47LLLAAgNDaVnz55ceumlbN269ZhjMFPANZ7PycmhUaNGACQkJJCdne31mAkTJjB27NjqDSQ1tXpf3y7Bul3B5vBnIOikpNgdgRyHYxmPwZoxOTq6Wl/eFsG4TcEqMdHuCMxXp47dEcjxCph9ZCA04E6T+y8hwe4IqkOs3QFUiwZN7I5AJPBkZWURHx9fcT8qKuqYn/t33y85OTkVr+25fsOGDdSvX586h3csPvjgA5KTk5k/fz533HEHM2bMqOomVVnAfUUlJiZWnHXKyckhKSnJ6zGjRo0iNze34paVlWV1mCIiQe9YxmPQmCwiYgXtI4uIBCNzKrni4+Pdbr6SXDt37qRTp05eN5fL9ZffL3/1/fPBBx/Qv3//ivvJyckAdOnShT/++MO/X00VBVySq0OHDsyZMweA2bNnk56e7vWYqKgorzdRRETMdSzjMWhMFhGxgvaRRUSCkTlJrmORkpLCokWLvG49e/b8y++Xv/r+qTxVEahIhv38888k2lQKH3BJrnbt2pGSkkJGRgbr1q2jb9++dockIlIjaTwWEQkcGpNFRKQ6HO37ZejQoQD07t2brKwsMjIyiImJIS0tDYCNGzdSt25dt2RW165dycjI4Oabb2bSpEnWbwwQ4nK5jj31F6Dy8vJISEggNzdXZ6xExNGCYTwLhm0QEQHnj2dOj19EpLJgGtPKt+Xdd9cQG1u7yq9TUJDPVVedERS/E7MEXON5EREREREREZHgd3xTDn0/XyoLuOmKIiIiIiIiIiIix0tJLhERERERERERcTxNVxQRERERERERsZymK5pNSS4REREREREREcspyWU2JblERERERERERCynJJfZ1JNLREREREREREQcT5VcIiIiIiIiIiKWUyWX2ZTkEhERERERERGxhRJVZtJ0RRERERERERERcTxVcomIiIiIiIiIWE7TFc2mSi4REREREREREXE8VXKJiIiIiIiIiFhOlVxmUyWXiIiIiIiIiIg4niq5REREREREREQsp0ousynJJSIiIiIiIiJiOSW5zKbpiiIiIiIiIiIi4niq5BIRERERERERsZwqucymSi4REREREREREXE8VXKJiIiIiIiIiFhOlVxmUyWXiIiIiIiIiIg4niq5REREREREREQsp0ousynJJSIiIiIiIiJiOSW5zKYkl4iIiIiIiIiI5ZTkMpt6comIiIiIiIiIiOOpkktERERERERExHKq5DKbklwiIiIiIiIiIpZTkstsmq4oIiIiIiIiIiKOpySXiIiIiIiIiIg4nqYrioiIiIiIiIhYTtMVzaYkl4iIiIiIiIiI5ZTkMpumK4qIiIiIiIiIiOOpkktERERERERExHKq5DKbklwiIiIiIiIiIpZTkstsmq4oIiIiIiIiIiKOp0ouERERERERERHLqZLLbKrkEhERERERERGxnMuEm/9GjBhBRkYGgwYNoqioyG3d5s2badeuHdHR0ezfv79i+TPPPEN6ejq9evUiNzcXgPnz55OWlkZ6ejqrV682JbbjpSSXiIiIiIiIiEgNtHLlSnbu3MnChQtp1aoV06dPd1vfsGFD5s2bR4cOHSqW7d69m5kzZ7Jo0SIGDBjAiy++CMCDDz7IrFmzmDZtGg888ICl21EuKKYrulxG9jIvL8/mSERE/FM+jpWPa06kMVlEgoXTx2SNxyISTJw+JvtSUHDAlOd7jvNRUVFERUUd02ssXbqUbt26AdC9e3emTJnCwIEDK9bHxsZ6PWf58uVkZmYSEhJC9+7dufbaaykoKCAiIoLExEQSExPZt29fVTfLL0GR5MrPzwcgNTXV5khERMyRn59PQkKC3WFUicZkEQk2Th2TNR6LSDBy6phcWWRkJCkpKVx//aV+v1atWrW8xvmHH36YRx555Jien5OTQ6NGjQBISEggOzv7mJ4THx/v9pzKywDCw8MpKioiMjLyGLfEHEGR5GrUqBFZWVnUrl2bkJAQU14zLy+P1NRUsrKy3N4oJwvGbYLg3C5tk3OYvV0ul4v8/PyKLxon0ph8bLRNzhGM26VtOjZOH5M1Hh+7YNwubZNzBON2aUz+a9HR0WzZssWr/1VVuFwurzHeVxXXzp076devn9fyHj16VFSC5eTkkJSU9Lf/Z2JiIps2bXJ7TmJioltFWUlJieUJLgiSJFdoaCiNGzeulteOj48PmoGmXDBuEwTndmmbnMPM7XL6mSmNycdH2+Qcwbhd2qa/5+QxWePx8QvG7dI2OUcwbpfG5KOLjo4mOjrasv8vJSWFRYsWeS1fuXIlkyZN4pprrmH27Nmkp6f/7WudffbZPPXUU4wZM6biOTExMRQXF7Nv3z7y8/NJTk6ujs34W2o8LyIiIiIiIiJSA7Vr146UlBQyMjJYt24dffv2BWDo0KEA7Nu3jwsvvJDVq1fTu3dvvvjiC+rVq0fv3r1JT09n2rRpDB8+HIDHHnuMnj17MmDAAMaPH2/L9gRFJZeIiIiIiIiIiBy/iRMnei2bPHkyYExNnDt3rtf6u+++m7vvvtttWZcuXVi6dGn1BHmMVMl1FFFRUTz88MPHfEUCJwjGbYLg3C5tk3ME63YFmmD8PWubnCMYt0vbJFUVrL/nYNwubZNzBON2BeM2iTOEuILp+psiIiIiIiIiIlIjqZJLREREREREREQcT0kuERERERERERFxPCW5RERERERERETE8ZTkOooRI0aQkZHBoEGDKCoqsjucKvvtt9+oV68emZmZZGZmsnv3bt577z3S0tLo2rUrWVlZdod4zPLz8znvvPOoVasWa9euBfC5LevWraNTp06kpaX5vApEIPG1TS1atKh4v7766ivAWdv0ww8/kJGRQZcuXbjiiisoLi52/Pvka5uc/j45icbjwBOM4zFoTHbyNjn9fXISjcmBJxjHZI3Hztgm0JgsAcglXlasWOEaNGiQy+VyuR577DHX22+/bXNEVbdlyxZX3759K+4XFRW5zj33XFdhYaFr0aJFrptuusnG6I5PcXGxa9euXa5rr73WtWbNmqNuy2WXXebasGGDKzc315WWlmZz1H/Nc5tcLpfrrLPO8nqck7Zpx44drgMHDrhcLpdr1KhRrvfff9/x75OvbXL6++QUGo8DUzCOxy6XxmQnb5PT3yen0JgcmIJxTNZ47Ixtcrk0JkvgUSWXD0uXLqVbt24AdO/enSVLltgckX8WL15MRkYGo0ePZsOGDbRu3ZrIyEjS09NZs2aN3eEds/DwcOrVq1dxf+PGjT63ZceOHbRo0YL4+HiSk5PZs2ePXSH/Lc9tAti/fz9dunRh4MCBZGdnA87appSUFGJjYwGIiIg46t+ck7cpPDzc8e+TU2g8DkzBOB6DxmSnbpPGZOtoTA5MwTgmazx2xjaBxmQJPEpy+ZCTk0N8fDwACQkJFR9MJ2rYsCGbNm1iwYIF7Nq1ixkzZlRsG0BpaamN0fmn8vsER7bF5XJVLHPi+7d48WLmz59P9+7deeSRRwBnbtO2bduYO3cunTp1Cpr3qXy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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjTensor3D(Grids,\n", " CoordGrd,\n", " TextureAVG, \n", " TextureCount,\n", " scale_factor = 150,\n", " figblocksize=4,\n", " vmin=-0.1,\n", " vmax=0.1,\n", " Countmin=5,\n", " showscale=False,\n", " nameaxes=['z','y','x'],\n", " DeviatoricType=3, #<- 3 for dealing with shape: Texture -> Strain\n", " colorbarlab=r'$U_M$')" ] }, { "cell_type": "markdown", "id": "87a98bcb-52ff-4248-8ec9-ebed0917642a", "metadata": {}, "source": [ "## F) Stress - Measured from the interface curvature (Batchelor stress tensor)\n", "The last but maybe one of the most interesting tool. \n", "\n", "Batchelor established an expression for the elastic stress emerging from the local structure of a suspension of fluid particles in a continuous liquid phase, such as liquid foams and emulsions. The elastic stress tensor induced by the surface tension over all the interfaces bounding any given bubble takes the following form:\n", "\n", "$\\sigma_{ij} = \\frac{\\Gamma}{V} \\oint_S (\\delta_{ij} - n_i n_j) dS$\n", "\n", "where $V$ and $S$ refer to the individual bubble volume and interface area, and $\\Gamma$ to the surface tension.\n", "\n", "This stress tensor measure $\\sigma_{ij}$ was implemented in 3D and evaluated for each individual bubble by integrating over all liquid-gas interfaces surrounding it. The local stress measurement was validated *via* a tomo-rheoscopy setup under a quasi-static liquid foam flow regime [Multiscale stress dynamics in sheared liquid foams revealed by tomo-rheoscopy](https://www.nature.com/articles/s41467-025-64412-z).\n", "\n", "The individual bubble stress tensor measure can be obtained batchwise with the function **Batchelor_Batch**. Each bubble interface is meshed thank's to a scikit-image function [mesh_surface_area](https://scikit-image.org/docs/stable/api/skimage.measure.html#skimage.measure.mesh_surface_area). The tensor is integrated thank's to a modified function form PoreSpy [mesh_surface_area](https://porespy.org/autoapi/porespy/metrics/mesh_surface_area.html#porespy.metrics.mesh_surface_area)." ] }, { "cell_type": "code", "execution_count": 20, "id": "077ba69d-e111-4de3-9294-f66f1cc9af10", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Path exist: True\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 941/941 [02:16<00:00, 6.91it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_001: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 938/938 [02:13<00:00, 7.00it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_002: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 942/942 [02:13<00:00, 7.04it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_003: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 943/943 [02:13<00:00, 7.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_004: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 956/956 [02:17<00:00, 6.97it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_005: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 945/945 [02:13<00:00, 7.05it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_006: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 941/941 [02:15<00:00, 6.97it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_007: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 950/950 [02:15<00:00, 7.01it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_008: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 948/948 [02:15<00:00, 7.02it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_009: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 945/945 [02:14<00:00, 7.00it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Stress_010: done\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "dir_Stress = 'Q6_Stress/'\n", "name_Stress = 'Stress_'\n", "\n", "Batchelor_Batch(namenoedge, \n", " name_Stress, \n", " dirnoedge, \n", " dir_Stress, \n", " imrange, \n", " verbose=True, \n", " endread='.tiff', \n", " endsave='.tsv', \n", " n0=3)" ] }, { "cell_type": "markdown", "id": "977b21c5-f5f1-433b-970f-8e6912cce491", "metadata": {}, "source": [ "The stress tensor table is looking like this. The integral tensor term is saved as *'B11','B12','B22', ...* (1,2,3 for z,y,x). The *'bii'* are the 'Bii' divided by the bubble volume $V$. In order to obtain a physical result in $Pa$ we multiply the *'b'* tensor by the surface tension and divide it by the pixel size. " ] }, { "cell_type": "code", "execution_count": 21, "id": "31195327-d968-49e9-8be5-6432615bf2c6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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936937232.931714209.88452186.3824449387.02241.8176781448.171000-13.260882-11.0954061547.387234-10.6068711488.0771210.154274-0.001413-0.0011820.164844-0.0011300.158525
937938234.65603134.25383650.9157619841.02288.5343211587.04390228.740443-42.7654221497.399214-8.7639971492.6255250.1612690.002920-0.0043460.152159-0.0008910.151674
938939234.123875156.617150218.2987169889.02289.0198961486.954152-30.9774249.2027531532.810831-12.4272191558.2748100.150364-0.0031330.0009310.155002-0.0012570.157577
939940234.464956224.364352219.3932509274.02195.0876051427.19994335.6013123.4853791451.8998781.8544031511.0753880.1538930.0038390.0003760.1565560.0002000.162937
940941234.79970724.50299072.8341278193.02036.4181821324.486919-5.87022716.2838921381.0724893.0349541367.2769550.161661-0.0007160.0019880.1685670.0003700.166884
\n", "

941 rows × 18 columns

\n", "
" ], "text/plain": [ " lab z y x vol mesharea \\\n", "0 1 15.286670 79.575571 183.687352 7622.0 1959.851467 \n", "1 2 16.593385 130.636373 17.162980 9584.0 2296.892631 \n", "2 3 15.635897 172.321484 101.120593 10324.0 2388.240172 \n", "3 4 15.538057 175.370390 217.745069 9328.0 2214.715346 \n", "4 5 15.121800 200.194312 163.705468 10936.0 2483.656637 \n", ".. ... ... ... ... ... ... \n", "936 937 232.931714 209.884521 86.382444 9387.0 2241.817678 \n", "937 938 234.656031 34.253836 50.915761 9841.0 2288.534321 \n", "938 939 234.123875 156.617150 218.298716 9889.0 2289.019896 \n", "939 940 234.464956 224.364352 219.393250 9274.0 2195.087605 \n", "940 941 234.799707 24.502990 72.834127 8193.0 2036.418182 \n", "\n", " B11 B12 B13 B22 B23 B33 \\\n", "0 1311.222441 9.783312 -29.556724 1300.680149 32.160212 1307.800343 \n", "1 1586.908722 -18.623406 3.384973 1496.785631 -50.586303 1510.090909 \n", "2 1592.128459 55.777156 -17.082450 1615.968433 3.985240 1568.383452 \n", "3 1527.999650 -18.222971 35.719418 1441.040353 -26.531295 1460.390689 \n", "4 1662.292886 -43.144426 -15.439269 1706.953553 50.088335 1598.066836 \n", ".. ... ... ... ... ... ... \n", "936 1448.171000 -13.260882 -11.095406 1547.387234 -10.606871 1488.077121 \n", "937 1587.043902 28.740443 -42.765422 1497.399214 -8.763997 1492.625525 \n", "938 1486.954152 -30.977424 9.202753 1532.810831 -12.427219 1558.274810 \n", "939 1427.199943 35.601312 3.485379 1451.899878 1.854403 1511.075388 \n", "940 1324.486919 -5.870227 16.283892 1381.072489 3.034954 1367.276955 \n", "\n", " b11 b12 b13 b22 b23 b33 \n", "0 0.172031 0.001284 -0.003878 0.170648 0.004219 0.171582 \n", "1 0.165579 -0.001943 0.000353 0.156175 -0.005278 0.157564 \n", "2 0.154216 0.005403 -0.001655 0.156525 0.000386 0.151916 \n", "3 0.163808 -0.001954 0.003829 0.154485 -0.002844 0.156560 \n", "4 0.152002 -0.003945 -0.001412 0.156086 0.004580 0.146129 \n", ".. ... ... ... ... ... ... \n", "936 0.154274 -0.001413 -0.001182 0.164844 -0.001130 0.158525 \n", "937 0.161269 0.002920 -0.004346 0.152159 -0.000891 0.151674 \n", "938 0.150364 -0.003133 0.000931 0.155002 -0.001257 0.157577 \n", "939 0.153893 0.003839 0.000376 0.156556 0.000200 0.162937 \n", "940 0.161661 -0.000716 0.001988 0.168567 0.000370 0.166884 \n", "\n", "[941 rows x 18 columns]" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.read_csv(dir_Stress+name_Stress+strindex(1,n0=3)+'.tsv',sep = '\\t')\n", "display(df)" ] }, { "cell_type": "markdown", "id": "80866c65-2725-403b-aee9-b2827d009c6b", "metadata": {}, "source": [ "As above, let's reshape these table data in two lists, one for the coordinates, one for the stress tensors.\n", "Then, let's average these in space and time in a 5x5x5 grid over [0,125] pixel range over the z,y and x coordinates." ] }, { "cell_type": "code", "execution_count": 22, "id": "13aca886-592e-4a96-89c7-2cf7ac4a3124", "metadata": {}, "outputs": [], "source": [ "# Take into acount the whole time serie data\n", "pixsize=2.75e-6#um\n", "surftension=21.1e-3#N/m\n", "\n", "LLCoord=[]\n", "LLStress=[]\n", "for imi in range(len(imrange)):\n", " df = pd.read_csv(dir_Stress+name_Stress+strindex(imrange[imi],n0=3)+'.tsv',sep = '\\t')\n", "\n", " LCoord = np.asarray(df[['z','y','x']])\n", "\n", " numpy_stress = np.asarray(df[['b11','b12','b13',\n", " 'b12','b22','b23',\n", " 'b13','b23','b33']])*surftension/pixsize\n", " LStress=[]\n", " for bbli in range(len(numpy_stress)):\n", " LStress.append(numpy_stress[bbli].reshape(3,3))\n", "\n", " LLCoord.append(LCoord)\n", " LLStress.append(LStress)\n", "\n", "Grids_sig, CoordGrd_sig, SigAVG, SigSTD, SigCount = Grid_Tavg(np.concatenate(LLCoord),\n", " np.concatenate(LLStress),\n", " Range = [0,250,0,250,0,250],\n", " N=[5,5,5], \n", " NanFill=True,\n", " verbose=False,\n", " structured=True)" ] }, { "cell_type": "markdown", "id": "7d6ee09e-bfd4-421a-860e-bd2af4ffe373", "metadata": {}, "source": [ "Once again we can represent the obtained stress tensors $\\sigma$ by using the **ProjTensor3D** function. " ] }, { "cell_type": "code", "execution_count": 23, "id": "f272e0ed-876c-48a9-89c7-8f023656ad69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Normal min/max ax0 -0.5432484576144363 20.837223257146253\n", "Normal min/max ax1 -21.085453366830986 6.273667281737579\n", "Normal min/max ax2 -17.612397861857293 9.448668331923121\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ProjTensor3D(Grids_sig,\n", " CoordGrd_sig,\n", " SigAVG, \n", " SigCount,\n", " scale_factor = 0.1,\n", " figblocksize=4,\n", " vmin=-40,\n", " vmax=40,\n", " Countmin=5,\n", " showscale=False,\n", " nameaxes=['z','y','x'],\n", " DeviatoricType=1, #<- 1 for dealing with stress: Full stress tensor -> Deviatoric stress tensor\n", " colorbarlab=r'$\\sigma$ (Pa)')" ] }, { "cell_type": "markdown", "id": "91f77830-4ff5-4c58-8f8b-1baebfafbddf", "metadata": {}, "source": [ "You have now completed this tutorial. I hope it has been helpfull to you. Go back to [FoamQuant - Examples](https://foamquant.readthedocs.io/en/latest/examples.html) for more examples and tutorials." ] }, { "cell_type": "code", "execution_count": null, "id": "85e39c05-be74-4b32-a591-d79b68f3306f", "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": "3.12.8" }, "toc-autonumbering": false, "toc-showcode": true, "toc-showmarkdowntxt": false }, "nbformat": 4, "nbformat_minor": 5 }