{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Segregation Inference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of Contents\n", "* [Inference Wrappers use cases](#Inference-Wrappers-use-cases)\n", "\t* [Single Value](#Single-Value)\n", "\t\t* [Dissimilarity](#Dissimilarity)\n", "\t\t* [Relative Concentration](#Relative-Concentration)\n", "\t\t* [Relative Centralization](#Relative-Centralization)\n", "\t* [Comparative Inference](#Comparative-Inference)\n", "\t\t* [Comparative Dissimilarity](#Comparative-Dissimilarity)\n", "\t\t* [Comparative Gini](#Comparative-Gini)\n", "\t\t* [Comparative Spatial Dissimilarity](#Comparative-Spatial-Dissimilarity)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is an example of the PySAL *segregation* framework to perform inference on a single value and comparative inference using simulations under the null hypothesis. Once the segregation classes are fitted, the user can perform inference to shed light for statistical significance in regional analysis. Currently, it is possible to make inference for a single measure or for two values of the same measure.\n", "\n", "The summary of the inference wrappers is presented in the following Table:\n", "\n", "| **Inference Type** | **Class/Function** | **Function main Inputs** | **Function Outputs** |\n", "| :----------------- | :------------------- | :------------------------------------------------------: | :----------------------------------: |\n", "| Single Value | SingleValueTest | seg\\_class, iterations\\_under\\_null, null\\_approach, two\\_tailed | p\\_value, est\\_sim, statistic |\n", "| Two Value | TwoValueTest | seg\\_class\\_1, seg\\_class\\_2, iterations\\_under\\_null, null\\_approach | p\\_value, est\\_sim, est\\_point\\_diff |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly let's import the module/functions for the use case:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import geopandas as gpd\n", "import segregation\n", "import libpysal\n", "import pandas as pd\n", "import numpy as np\n", "\n", "from segregation.inference import SingleValueTest, TwoValueTest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then it's time to load some data to estimate segregation. We use the data of 2000 Census Tract Data for the metropolitan area of Sacramento, CA, USA. \n", "\n", "We use a geopandas dataframe available in PySAL examples repository.\n", "\n", "For more information about the data: https://github.com/pysal/libpysal/tree/master/libpysal/examples/sacramento2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['FIPS', 'MSA', 'TOT_POP', 'POP_16', 'POP_65', 'WHITE', 'BLACK',\n", " 'ASIAN', 'HISP', 'MULTI_RA', 'MALES', 'FEMALES', 'MALE1664',\n", " 'FEM1664', 'EMPL16', 'EMP_AWAY', 'EMP_HOME', 'EMP_29', 'EMP_30',\n", " 'EMP16_2', 'EMP_MALE', 'EMP_FEM', 'OCC_MAN', 'OCC_OFF1', 'OCC_INFO',\n", " 'HH_INC', 'POV_POP', 'POV_TOT', 'HSG_VAL', 'FIPSNO', 'POLYID',\n", " 'geometry'],\n", " dtype='object')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s_map = gpd.read_file(libpysal.examples.get_path(\"sacramentot2.shp\"))\n", "s_map.columns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "gdf = s_map[['geometry', 'HISP', 'TOT_POP']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also can plot the spatial distribution of the composition of the Hispanic population over the tracts of Sacramento:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf['composition'] = gdf['HISP'] / gdf['TOT_POP']\n", "\n", "gdf.plot(column = 'composition',\n", " cmap = 'OrRd', \n", " figsize=(20,10),\n", " legend = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Single Value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dissimilarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **SingleValueTest** function expect to receive a pre-fitted segregation class and then it uses the underlying data to iterate over the null hypothesis and comparing the results with point estimation of the index. Thus, we need to firstly estimate some measure. We can fit the classic Dissimilarity index:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.32184656076566864" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from segregation.aspatial import Dissim\n", "D = Dissim(gdf, 'HISP', 'TOT_POP')\n", "D.statistic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The question that may rise is \"Is this value of 0.32 statistically significant under some pre-specified circumstance?\". To answer this, it is possible to rely on the **Infer_Segregation** function to generate several values of the same index (in this case the Dissimilarity Index) under the hypothesis and compare them with the one estimated by the dataset of Sacramento. To generate 1000 values assuming *evenness*, you can run:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fd0bcddf3b4441a0b83286bde09b23f1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "infer_D_eve = SingleValueTest(D, iterations_under_null = 1000, null_approach = \"evenness\", two_tailed = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This class has a quick plotting method to inspect the generated distribution with the estimated value from the sample (vertical red line):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "infer_D_eve.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to see that clearly the value of 0.3218 is far-right in the distribution indicating that the hispanic group is, indeed, significantly segregated in terms of the Dissimilarity index under evenness. You can also check the mean value of the distribution using the **est_sim** attribute which represents all the D draw from the simulations:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.016109671121956267" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infer_D_eve.est_sim.mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The two-tailed p-value of the following hypothesis test:\n", "\n", "$$H_0: under \\ evenness, \\ Sacramento \\ IS \\ NOT \\ segregated \\ in \\ terms \\ of \\ the \\ Dissimilarity \\ index \\ (D)$$\n", "$$H_1: under \\ evenness, \\ Sacramento \\ IS \\ segregated \\ in \\ terms \\ of \\ the \\ Dissimilarity \\ index \\ (D)$$\n", "\n", "can be accessed with the **p_value** attribute:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infer_D_eve.p_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Therefore, we can conclude that Sacramento is statistically segregated at 5% of significance level (p.value < 5%) in terms of D.\n", "\n", "You can also test under different approaches for the null hypothesis:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "116d6e27ddb6443c9a7f6233c6205053", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=5000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "infer_D_sys = SingleValueTest(D, iterations_under_null = 5000, null_approach = \"systematic\", two_tailed = True)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "infer_D_sys.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusions are analogous as the *evenness* approach." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relative Concentration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **Infer_Segregation** wrapper can handle any class of the PySAL segregation module. It is possible to use it in the Relative Concentration (RCO) segregation index:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from segregation.spatial import RelativeConcentration\n", "RCO = RelativeConcentration(gdf, 'HISP', 'TOT_POP')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since RCO is an spatial index (i.e. depends on the spatial context), it makes sense to use the *permutation* null approach. This approach relies on randomly allocating the sample values over the spatial units and recalculating the chosen index to all iterations." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "24b830ffe15f4024997e61c73212589f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "infer_RCO_per = SingleValueTest(RCO, iterations_under_null = 1000, null_approach = \"permutation\", two_tailed = True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "infer_RCO_per.plot()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.452" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "infer_RCO_per.p_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analogously, the conclusion for the Relative Concentration index is that Sacramento is not significantly (under 5% of significance, because p-value > 5%) concentrated for the hispanic people." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Additionaly, it is possible to combine the null approaches establishing, for example, a permutation along with evenness of the frequency of the Sacramento hispanic group. With this, the conclusion of the Relative Concentration changes." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "93c551d12b524fb0ba904c2ad6339854", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "infer_RCO_eve_per = SingleValueTest(RCO, iterations_under_null = 1000, null_approach = \"even_permutation\", two_tailed = True)\n", "infer_RCO_eve_per.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relative Centralization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the same *permutation* approach for the Relative Centralization (RCE) segregation index:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 1000 iterations out of 1000.\r" ] } ], "source": [ "from segregation.spatial import RelativeCentralization\n", "RCE = RelativeCentralization(gdf, 'HISP', 'TOT_POP')\n", "infer_RCE_per = SingleValueTest(RCE, iterations_under_null = 1000, null_approach = \"permutation\", two_tailed = True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "infer_RCE_per.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusion is that the hispanic group is negatively significantly (as the point estimation is in the left side of the distribution) in terms of centralization. This behavior can be, somehow, inspected in the map as the composition tends to be more concentraded outside of the center of the overall region.\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparative Inference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compare two different values, the user can rely on the **TwoValueTest** function. Similar to the previous function, the user needs to pass two segregation SM classes to be compared, establish the number of iterations under null hypothesis with *iterations_under_null*, specify which type of null hypothesis the inference will iterate with *null_approach* argument and, also, can pass additional parameters for each segregation estimation.\n", "\n", "Obs.: in this case, each measure has to be the same class as it would not make much sense to compare, for example, a Gini index with a Delta index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example uses all census data that the user must provide your own copy of the external database.\n", "A step-by-step procedure for downloading the data can be found here: https://github.com/spatialucr/geosnap/blob/master/examples/01_getting_started.ipynb.\n", "After the user download the zip files, you must provide the path to these files." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "import os\n", "#os.chdir('path_to_zipfiles')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\renan\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\pysal\\__init__.py:65: VisibleDeprecationWarning: PySAL's API will be changed on 2018-12-31. The last release made with this API is version 1.14.4. A preview of the next API version is provided in the `pysal` 2.0 prelease candidate. The API changes and a guide on how to change imports is provided at https://pysal.org/about\n", " ), VisibleDeprecationWarning)\n" ] } ], "source": [ "import geosnap\n", "from geosnap.data.data import read_ltdb\n", "\n", "sample = \"LTDB_Std_All_Sample.zip\"\n", "full = \"LTDB_Std_All_fullcount.zip\"\n", "\n", "read_ltdb(sample = sample, fullcount = full)\n", "\n", "df_pre = geosnap.data.db.ltdb" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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5 rows × 192 columns

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" ], "text/plain": [ " n_asian_under_15 n_black_under_15 n_hispanic_under_15 \\\n", "geoid \n", "01001020500 NaN 1.121662 NaN \n", "01003010100 NaN 609.000000 NaN \n", "01003010200 NaN 37.567365 NaN \n", "01003010300 NaN 374.853457 NaN \n", "01003010400 NaN 113.203816 NaN \n", "\n", " n_native_under_15 n_white_under_15 n_persons_under_18 \\\n", "geoid \n", "01001020500 NaN 1.802740 3.284181 \n", "01003010100 NaN 639.000000 1407.000000 \n", "01003010200 NaN 564.014945 686.748041 \n", "01003010300 NaN 981.543199 1523.971872 \n", "01003010400 NaN 796.944763 1029.919527 \n", "\n", " n_asian_over_60 n_black_over_60 n_hispanic_over_60 \\\n", "geoid \n", "01001020500 NaN 0.301098 NaN \n", "01003010100 NaN 221.000000 NaN \n", "01003010200 NaN 27.861793 NaN \n", "01003010300 NaN 103.848314 NaN \n", "01003010400 NaN 37.127235 NaN \n", "\n", " n_native_over_60 ... n_white_persons year \\\n", "geoid ... \n", "01001020500 NaN ... 5.794934 1970 \n", "01003010100 NaN ... 2003.999981 1970 \n", "01003010200 NaN ... 1757.910752 1970 \n", "01003010300 NaN ... 2835.404427 1970 \n", "01003010400 NaN ... 2323.133371 1970 \n", "\n", " n_total_housing_units_sample p_nonhisp_white_persons \\\n", "geoid \n", "01001020500 2.166366 NaN \n", "01003010100 1106.000000 NaN \n", "01003010200 619.433984 NaN \n", "01003010300 1025.805309 NaN \n", "01003010400 780.370269 NaN \n", "\n", " p_white_over_60 p_black_over_60 p_hispanic_over_60 \\\n", "geoid \n", "01001020500 6.433142 3.514090 NaN \n", "01003010100 8.299712 6.368876 NaN \n", "01003010200 13.313281 1.480888 NaN \n", "01003010300 8.023381 2.788906 NaN \n", "01003010400 11.072073 1.427952 NaN \n", "\n", " p_native_over_60 p_asian_over_60 p_disabled \n", "geoid \n", "01001020500 NaN NaN 4.737847 \n", "01003010100 NaN NaN 5.821326 \n", "01003010200 NaN NaN 6.248800 \n", "01003010300 NaN NaN 7.214156 \n", "01003010400 NaN NaN 11.205555 \n", "\n", "[5 rows x 192 columns]" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_pre.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we are interested to assess the comparative segregation of the non-hispanic black people in the census tracts of the Riverside, CA, county between 2000 and 2010. Therefore, we extract the desired columns and add some auxiliary variables:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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n_nonhisp_black_personsn_total_popyeargeoidstatecounty
geoid
01001020500NaN8.56830619700100102050001001
01003010100NaN3469.99996819700100301010001003
01003010200NaN1881.42475919700100301020001003
01003010300NaN3723.62203119700100301030001003
01003010400NaN2600.03304519700100301040001003
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" ], "text/plain": [ " n_nonhisp_black_persons n_total_pop year geoid state \\\n", "geoid \n", "01001020500 NaN 8.568306 1970 01001020500 01 \n", "01003010100 NaN 3469.999968 1970 01003010100 01 \n", "01003010200 NaN 1881.424759 1970 01003010200 01 \n", "01003010300 NaN 3723.622031 1970 01003010300 01 \n", "01003010400 NaN 2600.033045 1970 01003010400 01 \n", "\n", " county \n", "geoid \n", "01001020500 001 \n", "01003010100 003 \n", "01003010200 003 \n", "01003010300 003 \n", "01003010400 003 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df_pre[['n_nonhisp_black_persons', 'n_total_pop', 'year']]\n", "\n", "df['geoid'] = df.index\n", "df['state'] = df['geoid'].str[0:2]\n", "df['county'] = df['geoid'].str[2:5]\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filtering Riverside County and desired years of the analysis:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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n_nonhisp_black_personsn_total_popyeargeoidstatecounty
geoid
0606503010158.832932851.99997620000606503010106065
06065030103120.1517641739.99997320000606503010306065
06065030104367.0152895314.99981520000606503010406065
06065030200348.0011054682.00789620000606503020006065
06065030300677.9989014844.99220320000606503030006065
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" ], "text/plain": [ " n_nonhisp_black_persons n_total_pop year geoid state \\\n", "geoid \n", "06065030101 58.832932 851.999976 2000 06065030101 06 \n", "06065030103 120.151764 1739.999973 2000 06065030103 06 \n", "06065030104 367.015289 5314.999815 2000 06065030104 06 \n", "06065030200 348.001105 4682.007896 2000 06065030200 06 \n", "06065030300 677.998901 4844.992203 2000 06065030300 06 \n", "\n", " county \n", "geoid \n", "06065030101 065 \n", "06065030103 065 \n", "06065030104 065 \n", "06065030200 065 \n", "06065030300 065 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_riv = df[(df['state'] == '06') & (df['county'] == '065') & (df['year'].isin(['2000', '2010']))]\n", "df_riv.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Merging it with desired map." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "map_url = 'https://raw.githubusercontent.com/renanxcortes/inequality-segregation-supplementary-files/master/Tracts_grouped_by_County/06065.json'\n", "map_gpd = gpd.read_file(map_url)\n", "gdf = map_gpd.merge(df_riv, \n", " left_on = 'GEOID10', \n", " right_on = 'geoid')[['geometry', 'n_nonhisp_black_persons', 'n_total_pop', 'year']]\n", "\n", "gdf['composition'] = np.where(gdf['n_total_pop'] == 0, 0, gdf['n_nonhisp_black_persons'] / gdf['n_total_pop'])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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geometryn_nonhisp_black_personsn_total_popyearcomposition
0POLYGON ((-117.319414 33.902109, -117.322528 3...233.8248792537.09678420000.092162
1POLYGON ((-117.319414 33.902109, -117.322528 3...568.0000006556.00000020100.086638
2POLYGON ((-117.504056 33.800257, -117.502758 3...283.4395453510.68101020000.080736
3POLYGON ((-117.504056 33.800257, -117.502758 3...754.00000010921.00000020100.069041
4POLYGON ((-117.472451 33.762031, -117.475661 3...273.5604553388.31899020000.080736
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" ], "text/plain": [ " geometry n_nonhisp_black_persons \\\n", "0 POLYGON ((-117.319414 33.902109, -117.322528 3... 233.824879 \n", "1 POLYGON ((-117.319414 33.902109, -117.322528 3... 568.000000 \n", "2 POLYGON ((-117.504056 33.800257, -117.502758 3... 283.439545 \n", "3 POLYGON ((-117.504056 33.800257, -117.502758 3... 754.000000 \n", "4 POLYGON ((-117.472451 33.762031, -117.475661 3... 273.560455 \n", "\n", " n_total_pop year composition \n", "0 2537.096784 2000 0.092162 \n", "1 6556.000000 2010 0.086638 \n", "2 3510.681010 2000 0.080736 \n", "3 10921.000000 2010 0.069041 \n", "4 3388.318990 2000 0.080736 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gdf.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "gdf_2000 = gdf[gdf.year == 2000]\n", "gdf_2010 = gdf[gdf.year == 2010]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map of 2000:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf_2000.plot(column = 'composition',\n", " cmap = 'OrRd',\n", " figsize = (30,5),\n", " legend = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Map of 2010:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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59yyg6K1XLChveic+LjB9UAOi+3nb9862qSp7pa3GGGOMMcbsGUGpxxIg9QSSo5YJb7bVYI7dndVJim4BeI+qropIEviciPyBqn5eRE4C7wOuNJpRRHzg49E014AvisinVPWbA2q/2REDCo6ERRecT01AZhrUg2QaiouDWX6FF9VRFg/KeZcln8xVs+U9Dzn7PnTjPiy8CvmFBguRatZ+WHLlH8aOI7mZxhn8GmXJa1j7xXTttWpAHiAM3O3u6+4GkEuia57Lvk+k0OVrsH7Plelp5sR3IL4f9UgLev1rQABr813sKGPqHOTsZC+9cyWohs7DgtzGGGOMMcYccGEJLawh6ZH+luMlLCBvtpn0NtDrLtM2KK8uRa1SfDYZ3Sp/zf9r4J8A/7HJ7M8Ar6nqGwAi8pvAhwALyu9lg4jlZGdh5kEkNwcb96BwHwhcZq6XrCkv01ZQcCVx/JQL9IMLpvlp19jK4LAbd9z/mdma2QVAxAXYczNoJZgelFybVNksReMl0JWrLkBfzruAfHIUinXZ6BrA+m3XhtQkoK5szaUXOtgghdIa3P5K57v67ovo4i1csG0IpUbyS4Qv/ofoi1aiOvxZ5NTbkD4uRVMNYemN+mejYyy+9eJKbkjlJrHp6uZtOH+0DIn+r2zD5vOx+wikxpDUWPTex5dTv3zdGrgWD8obLdaxufHVZW/ej49JUPlHqoFhBRLp+o1usA/i+rlUMHTbo7us8Np22fvf8Y3tuffTOhCMMcYYY4wZipVbkD7f3zLCkouP+GkLzpttc1Ay5SsZ7y8AF4CPq+oXROT7geuq+tUWO+I4cDX2+Brwtj7aa3aFHgMkY6eQ6QuQnqj58OhmfePQlatRIDnhytw0qg8vCUiOuBO+lwBVRASVRBSUjwKgzWrQ5+9DdrppMwV1baoE8+PrXb8TxXV9NvdDaRXSU25ATC3XzhMUYOO2K9UjGeSR96I3X4KFqzTkJXCj13apsATTp2D1PhSHMIBjWILla1ueVoCTT/ccuxRAM9Ow8HIfjRuC8bPgJ3ucOVZOaCCxxLrOhy6zt4UQTU9CoYcrULx4H6zZN8ob7afpgqqChmhYRvxkXx11TVYw2OUZY4wxxhhjANCl68hsn0F5cPGTYAMSuShB0pghEoaWKd+uDLuIfAj4WVxGbBn471T1c53MW6+joLyqBsCTIjIJfFJEHgd+Cnh/u21ptLiGE4p8GPgwwKlTpzppltkp3WRZpidh9BgyfgJJjTaZSLY+LK+6QExqwgWQkiOunEQUzK/pCIpnI/tRBnt9cDyuUoYmRt0n2s3rpeoC+lGAv3jbPUyOQakuM764BOlpVxanuLw1ADp2Cs9LoIu3YSEKbnsJyE1GA6BEHRMzc0CPA85u3IXsOJQL1cFVhu3eS2giDUff0ntScWoMxs/A8qXBtatfy5dR8ZHMZIczeFvHFRgW1a6zD8RLoOnp6IqUDnlpN/CPHtRBXvcrD7SLK5Ea0DBwJbzKebS05s55QR5Sk6gojB5H/NSA2guEdgwaY4wxxhgzFPkBjeMHLg5RWnUJjZvJl8YMnjCcTPkOy7D/KfApVdUoPv7bwEO9lHDvKChfoaqLIvIXuBI0Z4FKlvwJ4Esi8oyq3orNcg04GXt8Amg4oqaqfgL4BMDTTz9taXG7WbusxdwcMnIEsjNIpdSG9HBCFnEn9NQ4+Jn2HzgN3PTtpMZqAtYqHqxcdevxklGGvIKfdRniWna9vRpCad0Fnxop3Hc9wskR15kQlmHtpsui9xJuH6zeZ7NfKjsBiSg4NnkE0qnespnjisswcdita+Fq7WCxw3Lrq24fHnm0p8C8AJqdcx0ipRXAi/7fSSGs30Qzk51v03YFr6XHeuBewv04atVhFZcaQfZcmRPTXvPjVIMyWlyOxsKIxqjIV85ZXrWUUbOrkLwElJbRxdfQzDSSnUMG8mPcfhIYY4wxxhgzFOUG1Qn60qB0qzFDMKRM+bZl2FU1HngcofoHa9cl3NsG5UVkDihFAfks8F7gY6p6KDbNJeBpVb1XN/sXgYsicha4Dvww8CPt1ml2uRYnWDn0BDJ6ZLDrKy5DJtU+sK9BFDRqExxdv43mDiMaoHiwciUqM5OH3BGXKR+WooFVQ/BzLpBZWnVZ8sUWPcnlDUhm3fQCjJ2ExIgr51AJ9jeSTkOh0QCzPchHg7tOHm9eJmfQbn4ZDUtw9Mmevn8FhbETm4+1uLLzJW1Ka7B2yx0rbTdqG4OG/XzvdBqQ99IWkN+vgeAWpWvET7irm4or6Oq17sb28NPVMT0A8vfR/CI6egxJjfWXxbBP3wpjjDHGGGN2XLnYfpp2vJQb4w9cItigy1kaU0+k178xZ0Xk+djjT0RJ4hUdlWEXkR8Afh44BHxvN/PGdZIpfxT4tSgN3wN+W1V/r9nEInIMVzfng6paFpGPAn+Eq6fzq6r6YgfrNLtaqwO/yWuqEDYJ8nXyQSouo+nJlh86EUH9TPMszor0JKi6kjWFRReQr1iPLvTIHXUlasR3XypB3mXRNw3IR19ANaVxPPBTSNBBOZpyCOPHYf3+4Oo95+dh/Ji7HxRgbX4wy23m9jfQ299ET38HMnF8syZ7T6fJ1HhUJmiHM+ZXr8HqNfTQt7U54SskohHr68ciGCQvFWUxDznzwH5E9VQmaPfzagPnDYjnQWYCUmNoYQlW21xx4yXc1UCFpQZXEYXu8+NnYPQY+OkefzhZVN4YY4wxxpihKOfRaJy+nogPydF9+LeT2e283jLl76nq0y1e76gMu6p+Elfe/Z24+vLv7XTeuLZBeVX9GvDWNtOcid2/AXww9vjTwKfbrcfsflrOu1rb91/tbQH1ddg3dRAADIudle3w2gzOmRhx9bU1hI07rkRNI+s3q/cr5RxSY7UB/Ng69cW/hNIGTBxFzj8L6RyU8q1LN8S/tOYvQe4xyE6CNxsFwTxYu+Neb7TethRKUfa9nyEqFNPDcroRwuXPVdfiJdCRQzB6CLLTSG4Gkum2gXpB0ekHISjBwivbV6+9mflvobOPNG935QqNYQ5IWck46PW3TptgbA2vq8pmZs/ovMSSeB6SnUKTOfT+txpP5KXcsVJcan1cBnl06Q3XIZo73H1JGxvo1RhjjDHGmCFRV3rXbxNLaUhc+V4LyJttNqya8nRRhh1AVT8rIudFZLbbeaHLmvLm4NHCsgvEL12GtVu0D+oOIHiSO+KCNxGpWaa0LvPSKqMzNQ6JLFpar2a8hx0EuzV08zbJktc711xAHmDpJvqlT8LjfwtvbKr9suOufRNOPFItPwOQ8F0W6vL17pZVL8jD9ElYuL49A8BWhGVYueFuVI8OHTsGMxeQ0UOQzDSM5wmAn0RnH3X15ovL1U6X1RvbG6gP1qG44jpmahrpuYBhaYjZ8VAdvb6f75xiB+MtVARF8LYOiHywKP3t8F2oh6twJJFuPkiw+J2XRAIoLLpBYkePgvhd/IiyoLwxxhhjjDFDE5S6D8p7yShD3q6yNjtAhlZTvm0ZdhG5ALweDfT6FK50xjyw2G7eehaUNzVUFTbuuiD88mXId1nnvGlGYxdBFS9VF4jvnBC6UgnxMgqqkJmuDrxa4Wchf6f9QlsE5AkTcCOWRaoKUyfhG39AOHse7+Lb3D6NspQ1HsASgdnTtcvbWIFcXQCsvO5K2xRWoNDsaoMObNx1g8Au3dq+QUmbiQL1m0H68eMwfQEZmYOkC0BXAnYCrixQrDSQZqZckH7hlegZz/XQl9boJhu4K4uvoYeeih2b3vCD8RBd5bDNpVQ0QMVDdvo4MQMk3V0tEVfpFNqih/N0aQVdWHFXGKUnkeSoK2vj2Y95Y4wxxhhjdkTQRaKN+K50a0+Z9cYMzjAy5ZuVYReRj0Sv/xLwt4EfE5ESsAH8kKoq0HUJdwvKG5e5uHoDXb4ES1fa12TvyTYGFL2Uy6z2ElFmdehqk4dlNku4eCnI322/LGXL/tCSgiqSyaLf+mzt9FOnYOGKu78YjekQlqrLCOuCnOUGQfblZcjNuQFj8/dc6ZqgAImx/oLy4K4yyE7Begf15SuBuJ5K53Rp+TosX3e5yQ8/BwiayIGfaXiiFYDUOHr426NnNHpnBZYvuQ6IQdPA7f/MhBsfIdiGgHwiy8Aytr1E5z+2tAz5JTSZcwP2aLDfcsY7sN+ys/vYnqZvfh/LDEuuAzj6rGpyFNITSCIHXmJYlyIaY4wxxhhj6gVdJO+kxi073uwCMqxM+YZl2KNgfOX+x4CPdTpvKxaUP6A0LMPaDXT1Jtz7lguQDHeNg1uOJEGj9koSkrmojEhUlkUVSqtRVqi6Wtz5eRdkDsuQno4y6Vu0SYHUqMsoL61CIucCpH4GWVtA33weXanLss9NVwPylXYAunAX1pfc40I8wN/iBLJ+172ejtW8b1cvv1OdfoEGKfCHfVw0IL57L8trEBbR5HjTAF39FRWCouOnYfSEe6+17K72qAzg26/Fl9GZx3q+kqMbCrWDIwtIjz9+FGkwCGc70bFfwl1psp1lj3YDDd2xuF+Uh9C5pjq4/tbSKpRW3XEvCTQziSTH3HnbasobY4wxxhiz8/y0BeTNrrEfErksKH+AuNI0d9ClN2HxVRe0zM7BzMVoCnE1h+PHtUb/eClYvhZ7wauMrFCdF2D0SE2ZkeoycBm3m2Ir8RKQnqCzwL3nMsjJxRauNQFDEUH9FOSXawOJlYzvZhnyknRtL60BZfd/ahJy4665fgaCEjI6jY7MQn1QPjUCa7EM9PEj7v/LL8LNl9z9wxc62MbNDaFmcNYBZayXXnyN8NZNUs++HTwBUURKUFqvrsubQF/7FnLu2EDW2THxa0vrhCVX7iI52nFAWgA8391IQ3IEHTvpgn6q7odEUHCfgV4CzfPfQMdOQ3p8+OVdVi9X7ydyLps4kevqy0cBNu71144DGRSNzi374hoBgbDbTpm6+Rsa0v7RMmzcQyvHrZeFY49Cfg1WbrsxPLJTsLHI/ruiwRhjjDHGmO0jZ74TGZ1tP6GfccmKxuwCMrya8tvKgvIHhN79Knr/pQalaaSuVHXQOMYhHtpJWRvxOqtXHguwViuHdxJcCV0HQbvayIlc5wFXP+vaU1qBYsE9Ts265+L7Jiwj4tooZ5+EqaPom89DfgnGjtRmyQNsrBH+6a9BocMyJ4ls7UCMGkZ10qMBOr0BfVyDEL1zi8J//N3a58cnybznafDH0b/8Qzj7yGDW1425B9hyHIRFKC6iyfEuB4esEtTtywo/iR56ygWrl9/svp0rl2EFdOpBZFDvSzvldXcbO9PDzH0GLkvraDK7LVcImN2o2fve44+gytUwnQo33K+V0RyMnAFNgIQgF+He652V4zLGGGOMMcbUGj8Ohx5oP12lcoAxu4hlyps9Q5feaFIrfsBBtnYZteK7eu/h6tbXkuNQGexvMwGzEryPBfq1HAWopXnZHT+1NfCz2WHgRb28Wfe6+NWRwyudA/Ufbj+LhNVMdREPpo7C6HPo/DW4/rWtbdAQNuoGiPU8qPRCZ0ao2f+5KcgnoLgS7Y9cNSDvZ2DtduNt7Vaz3sRiHvXG4YtRnfzbV+DIY9s6KKzMnKXhQK0aQnER/DSaGOm5jEvNulDIzqDZGVi+Chs97N+Fl9HxM5Ac3bUB6067u1oKCyAjB6+EzX7RdemiOk1j8vVHl8Do8dZlf7wk4nno5iDNXRIBccehTJ2A9Ah66QvRlT7GGGOMMcaYzghy5tn2gc3kaHW8OWN2Ec8y5c3eMeBMx6ZaBXA9KC41f7m03DyD0s/GBr2kGij2khCWXGmeoOSC2OlJN11iDDTKqFdxwfxExn2hVIL/xOu015Xi2Xzad+VOGuwqSSbR3JgrlbP11a1PJRIwNRo9aJARnpuuBuW9JK6gN+6LcP1+g3X0oGlA24O//pNoQFxgfRW8cQgWB7Pedo49Revjh81Bb9XPRmWSvL57RwXQ8ZNuANdeAoWZGaQS+u43+NlW/9trurAvqtf0Mp5AvVbfH9Fr6Wkkd8idZ9strdMBhztol4zOwEPvR++8Are/OaDlGmOMMcYYs7/J6WeR7ESrKSA1Nrix7YwxW1hQ/qAIO8lwbZVP2+C15CjcX6id6j9NcoUAACAASURBVBufQRMZQMBPIA+/G8FzQaF2JWegeSZusOFuFV4KvIQL7qzfcsuu1Fxfu958+aVVl5GfHt+aDd+MJBG2tl1V3X5NZ+D4W+D61ztbXiuFBZg8A4VVSKRcdrhbWf/LbkPGxiGs7VzQGzeQE7Pbk4XazWDDlePBS6OJbM9lbSoEIDWOzrwF5rt8H8VjM3rrZ6PjcEhXF+xkQP5A1pU3HZm84D5DHQTknQEdS2EAno8kEsixR9DJ4+jVFwbXgWmMMcYYY8w+JKe/AznycOuJLCBvdjORfZGwaEH5gyJeq7wHqgLJ6dongwAWbrae8aF3uXIpnQTkuxEW3U08FyzsZhDU0rILHmUPA+2CSB4EG41DSMUVCPJIagI9dBLGZ13QNJGAhTsQKsydhmQKUmlYnHftnJxusLAQCtFVBIX7brsSo5A4HMV7KwHf/t5HAP/4cfxsGg00uuJAEN8jDKFwM+pkUUg9eR6Z9CA1BfevtFzmQMy/DlMn6SqgHRbcOAAImppEvBZlMzogibSrNX/3q52Xall6DR0/574QBFdiJyzF3qvY0SN+Zx1kIo2n6+FLZ2Ch9MIimp7YtWV6BsZL0WC0671rEANEJ9KQmXH364/BqCRZ670krqNRovuldQiiORIp0FiHnCTcse+nWw9OG3XM1qwlNwEX3w3zl9HrX7FyS8YYY4wxxtSRs9+FtKoj7yVdDfntGjvNmB4INtCr2UuaBifq6gE3IcU8+vpXultnKgdB2Dqw0ifREM3NQjAGa206COLCMhC6Wu1BHg0DNksxqEa7RUE8pNBuEEFFfA9GYgOJHj6xdbLcCTQMYeVqtURM00WGkI+yPROjgA+pcZBRl+0vvuu5Xu1imyMSbsDSla3vtjdG8WvV9zj19ovuJFdagOw05GNlbHqtM+8lXb1/iEoIRa2olNQpbrgOjK4pFBeiwHx/pzURDz30Vlh4rXqlQiv5BciuuvcD3CZ5CSDjOli6DQxKstrptPXF7pY1UBp1gu33QOdAqvDvEjKQjjyCYuvPQv5e6/m9DCw0GVB54nTUERIpr0MhWtfo0cZX0KTGkZGjiLe1FJd4HsydhfEj6PWvwtK11m0zxhhjjDHmgJBz70LmLlQeub/PNXA3L+VKBlsw3uwRlilv9gTtuOzEAAJR596GHH8UJBpIk6ILMlbaoYELeot0Hzz1sy5js9JUEbS4gmgZ/BQ6cQ6Wr3RRQiGs1lr+0h/D/atbJzl0Fh54ovHs4kNqtKt6zeJ56ORFJCyjWoalS1T3u0B21j0urkFyBJbuw5VYZ8jRB112OLjg1cjhrgeAlalZ9HInU0YlWURgfBqS0X71UqBZWIr2VzLn/g/LLnjXat3n3+WuJBiW4iKamuo/Yx7QqQuwcs2VR2qn7jMmIu6YzB6C4jKU1zpcs+eyhgd9ZcmgAs1hsA9qrHdAK8XkGwz6fCD1e+y0mH8pdjIaOQKlePC/wfgXfgYZOdYwIB8n6SycfRssnUWvPt/31WLGGGOMMcbsXYKcfzcye849TOTclamV5Di1v3vMHiMthkvcQywob2KaDHSKtg7sj84hT31fNKeydQDTEipJ9NbnawLYOnYKGTuxtXfLS8XqdOPKL3gpF/TVVPV5BdbvorlZRANEA3T8NCy93n5T++79jc4AheXqYxTGTtTWXSsuQ2mldk7Pg3Le7eXJc9XOiuIykojmnXrABeVnSuihR+CNz8HaPbj5Mpx6xGVgA2zcc/Xx/ZRbb2EZSm0CwEnPldoBGJlwA+RurCLlunpxzb6UwyJoAUYPuTZt1psXyM64fVtYgvKwBz1toriEpieRPs/QAujYCdcRtNymF2PlCprPQRi6/SYeELoOqcIS5KLSH+2Cm54PpVbBwx3+oRQUXbmR/SyMlXuRPV5DsU0nWce2azyBLV899VfkeMjYqbYB+c3FicDkYRh9P3rrZbj70kCaaYwxxhhjzF4iF96DzJxxD5Jj1avXNyewgLzZeyxT3uw7+rXPdT2PPPb+KBgvrscV2ZIZrGFxa0b5yhVIZNDsLJLIVp8P8tREZ7xUVF87CnhGASJFoLwKi4vo5AVEg86z5DP1dd2bfJibBXYTOVhvUDpGTrt2RDQ1viUo72rFj7hll1aQ4rKL1Wbn3PbEW5RIIpNH0Qffh37pN9wL62uwmQiurpxNpcJDds79X97YGtDyM5Cdgo0NZKJSIibvzgKZESh2EYAUIdaIals25mHs+M4F5AEIobyGJkb7PkkLQHYOTeTg/reaTxjkW18xsXrDDYycnWmeBe9n25bLcR04YZcdDgMsx+InK5/0A2KPb+0gStcMk5dynV7F6BxZXxopfx9yh6LPliLjpxG/+58tkkgiJx5Dp0+iV16ADRsI1hhjjDHGHBBHn6wG5BO5rQF5Y/Yoqylv9pkuD+hkFh58N5IZjbLbiYLmtZMpUq2PXkcXXnFrzh2KnonVGa8IS9HzXu2yy+vVwTCLq649HbV7xGVzxwNAzTa9Ue10L9lZrXFw2fsjx2HtunsiPQ1+IqpbH7pAbWUbSsuxhtQPphh7fO8yHHsIdH1rgHfjrvs/M+UC88mcy2RP5lzga/02YTmD1HwRu2UrHjIyFtt2D8IGwV/xoo4SHxKZWKZ8JCi67P2w7Er8VN60dnX0BykoAB6ayPadMQ8gyRF09gm493W6Gog2rrTqrmIYPxW9b+IuGYTqALCp6MqFRkF0ESgX0Rf/HzQ14mrYjx9BkllIjaF+pi7BIXYsNcv4Ftz6Ov3oF5dRybiM/gafdRL+3gxhS7LJPohdsbPnDLLtLZbjZ2P7Tqg+iM+TqI65AK6D0PfcVSSpEXe+ACgs1C47KLgxOLyUu3ookelrKyQ3AQ+8G+avoDe+sr3nJGOMMcYYY7bb6CHkxJPuvpd0v8ON2QcEwbNMebPnlUvoV/48etAmgPPUDyKTh6EcAGWoBDzrPwhBoeahoJA74oLzRIOorlxxQXWAhVcIywVk6oGoLEGUDa9l97+fqt6PZSOL+DD9UKzlngsYT5yrrqdeUIT8vKvDvhmUVzh7EU6fj02oLsivAZpfqC4vMwmFJVfHviG3jRoUXQDWS7je6NyxKIgZuCk0gLUb1dn8DGTmYp0T9YutCyzfeMk9N3MKpo8DAeSXqkGtyv+V/RXbb5/5N7/HF37lt7asYvr0MT76sR+uPvHG16v3H39H9X5qEu5earL9wHrUMXDmO5HsaPPphi3YAC2jybHBBOb9JHr4Kbd9K1dpHZz3mryu7tibvAB+unEmfxhGHTQNhKCEroOluAJ+iBajaacfqo650I3kaHeDt85fIfzLrccPgPeB/wbGJrpvw45r2iu3ra0YrB47j7ohHt7hb2s7mW4soCuxElBBCSpja3fSwRkWkWR2IJcnuoFgz8DEEfT612GxowE2jDHGGGOM2WMEOf+u6nhriayVqTH7h1imvNkvOqk7/OSHkMnDmyVVUN8FhRsGFRssT7xoUnEx9/FTsPBKNRO9sBDFs+PZ69Ego5sBcKnWa69bh2tF6JYXlmjK81yQv5JRXm0eVD7QXhL1M7BxJzaBD5kZV/e92RfZ2CkICmhYcFnPlQFCx06Cl6wpa1PNNo+2IyhBpnGmvyroN/7fBi+EcO8SFAvIsTOQm4TcNPgpNu6tcOk3P8X4ucOMn5pk5FC2dR1mEbITY+BXMoY9NCghlfenl9jkbvjCD0tQXEFT44MJ6AHk5tDcnHtjwlL0OYgyx6NOmUa5wtUlaOts8pbtjL0mfm0H2LbFj1u0bze85z0ZYImf3SJocR7cdgPoIBjwsSWpDJx5GsK3ojdehOWb7oorY4wxxhhj9oOxw0gmuiJVEu5mzD5iNeXN/nfxncixhxA/Vj9cpHYw0x6ICJocqdYSzh1GyhtR1m6TLHQBtOSC2X6mJoCvm2VmOvhQZqZd8GVLvWXPlQHZuOcGaN2cfgZS48j6reaBIT8L6UlXomTlau1rK9dc7fzx8whBtP0emp2rlrVJZl2d+SbbLQ9/D/rKn8Pa3a2vL9+EExejzogQgjxewucbP/9vqluWTpMcHwOEt/3Kv+J7fv6fARAuLUDhLlIpna3VsQCCP38BXVtG5o4hr11BRjKQ8GBkDS8X1a5X0HypcThzcQkZix030VgAUilztPn8ljvR3cpjiapieJDJIF6XwVMtDbw0uOtfkgb1+HTrdC1eb6zFNPEFTp6AfOx4kB6CyuJFZYa62Tmt2rdXvxT3WUAetqmefIfvt5+qXsEUBu5KqV1ARMBPICefAJ5ASwVYvY8uXYfsBKzNw9K1nW6mMcYYY4wx3cvGxtFL5vbw32rGNGaZ8mb/eeL7kVQWLawh08cbZ1drVHegm5N63aSqWg3IA6xcRbNzSFBw9aobLUC8qHdXoyBtbLr8YtQub2sWepyXgsKiC8yv36xm6qcm0OJSNbsdXNmZ0ePgp1ypnNJ647IiuSOuU4HQZdhvoa6+e/4+ZOKlPaKdkhiB4hKMHG0wbzTV6Ayam2wclAdX/71mptodHhYKFO66rGoNytHAvJD/0kus/ta/d804cZzpn/hg7XLya+jVV9Grr24+5T36FByKOmXEY/XX/7RhkxIPP45399Wa57yzj8C1lxpvQ6dSGciOQHaM1I/+Y5DQZYyHZVwvQdRZ46ViNdv7W+WuVC7VHue9xJWTo62vLOlWD4Nw7gr77vgYdC38PgdMTo0hs48CoKV1tKegfP8lqNqRZBqmjsL0SQg23Dl3YwGKa+1nNsYYY4wxZheRXBSU99N9J1Uas9u4whp7/w/5PRpBMQOzEdUanzwO44ddIF5AxqZbz9dF0EclUTOgnqrC/brAbJB3gVXPcyVUEBeADwMgjD5x0TorHzxVEA8NS7VlbzSEwrwbVLUScPSSLjBeiNUvHjkO+ftovNRMZdrRE5CMDxIawuhRdGEdIdqW1ASMn44C3GF1O5rJ30XTrr651tXHd3o/oejaIpKLlb9ptagw9r61OIn5b387wYuurrx//gKSiPZlahxWbzSdr1UjdH0V0jmksN5g+g4V8+62NI/evoZ37PTmjwzVyra51Pj9cJJuauUGTJ2uzZbvlvaSXd+qfM3wA6emE9uV+d/DesIuxi+I285jKzrGZWQSfeg9sLYE85dgcXdk+BtjjDHGGNNWNkoItMFdzb4klilv9jgdhatfh8w48tbvr60ooh7hWjw7UAmWl0keOepK2WwGyRss1kvXDqK6frN2gmaZucGGK+NCIiphU67UCYkWFbrMYEL3XJB3y69kx2voJi1F7S7ch/SMW99Gg8BlsIGmxmD5zegJcZnx6fEtA4NqGLgyNOU8msohkxfA8zczzgG0sAhrddtav90b91Etu/0T30eVe0ER8vcgPQVeunUd+Lhbr8C5JzYftgpGaywo72WrX9BaF6AV8iTOzUYvLkLlbUs1KbPTAb19BTIjyNQRWLjVfoY2Sn/8f5L68Z/aDP9Xt3tvnpwVcZ1HGtIo4KkL9YNSapP7na4wgELl6g+t+7/JIsutyqLs0TIwYYnNY2bz0JHaq3H2kkFe/QA0f197+Jx1M6hwzaoG/5nWSvkmcAMvhyVU/JrOKhEPRqdgdAo9+ggs3YR7r1n2vDHGGGOM2d0268nv0b9pjGljPyRhWlD+QGgQUPEm4PWvwNlnkLNP1waXxaN8/U1ufvQfbJnt8L/8RdIPPeSC5s3iNH4Klt9o3pxS4yxpzS8imWkor0Mi3XjesBiVsKkGLYUQTU24OvHigZeGMBoAs7TcMhtYtIzmjrnl5uaqI5NX2hSGsDEPC69VgzfpB7ZOV863DshX5O+6sjh+1HGhVMuPrN10+7W06ga+9VKuDn0UmJcHvhtdm4f1+1uXW1x3X7ZRwKtVj2F5ZQ1w7ZdchuQDFwDBn5qorWvvFyAdfZGXC7WDim5ueKsgbJPX8mtw+ORAgvLcuUr5rz5N4h0f3KNh+CoF15FUbnEVQbzk00BWqpCIDzCsjd/T+Dio6RaDdvaSeb8rKJsbWLMJzTsfd7WWHScD1NO+6fGTOowfXBordxUGqJd0nb3a+BiXVBrmzqCzp1z2/P3LsHCFPdsZZYwxxhhj9i/7iWr2M6HzJNZdzILyB9XKEvLtfwcZm6FytlbxXeA5KEKicW+qlgaQgZnI1T4ePw3Ll2H9Njp22nXkSq5xRqWXhLASkK9GCiUsQ8Jlfaufgo07bvqwVB1pvNEAsn4aNI9kD9cEfVzN+1W4/2o1875i4RVXhiY364L2aG1ZnHbK6y5olp4GT1wQHqDYYBlLr7pa+5lp1xHwbT/kAknlIrq+AC/9cSxjs7OgVelrX6R8568BSD7zTqZ++Luq++LKy9UJ/RE30CHA8YdgrVEQvbdvel24ObAgevhXn6L45oskv/e/RKZm93BwXloH5AEmjsLIZGW0XHcsjB5z97MzdZ8tjQ2YG900+j8oueCjAEF3pYQknT5Av+/24tHksVlOa1Cadbb0EpTvap7KWCIerd+LeK9Re6q4sUvi3zFhoePdtiV7fvmOy57PL3XcBmOMMcYYY4ZJL38eLrwT0TKI1ZQ3+4+VrzHbIrzyJ7B6nc3Ag8ayOnt16BiMzgLqSmbgQaEaUJAmQfn1v/kcmbe8pc3C27TNT9U94cHMoy7oXOnpKm9AMsrajteFr9wXv+F6FA826oLHfhoyI1FQWWPzC6xec6Ge8hqMHHPBTg1h4z4sXmqacaq3vwzHvsNl4jcKprelru59arz5oLSV52L1+F01H0ETKWT8EHr6bfDqn0UvyubmJTIe3/vC74HnIZ6w+H//Byi5TPfRk0egMtBip4fR/A23DwHC2hNf8sK5unIrUVBwpUUmfKlB1n0/brxO6d/+j3D8AskP/vgeD863oGVX5qkBER9pdBw14idRP+lOJ/nl2phmJeDfKDNZFS0X4fG31608uqWaXOFittHgu0xk9CiMHo0tu1JSjOjqjW7Wqcjx79zyXGxtm2XLOrocMTnWvJRaad0tu3JeD8tumX6yfQdYhySZgpkT6PRxyK/A/asw/2bvZXqMMcYYY4wZhIU30ZfycPE9SCrn4g5+xiWnGLPnNYlZ7DEWlN8TpOnl9D1JjiJzb0FEohrW+S1BYfEbHxrFV19q+HyNfIPyKjUUsnPVOu/Ll5DZx1zAvbSMJidchnZwG5k66Xp1NaytkxyWmwezK7wUpCeRsARhCc0dhvXbLkhfKRVTUd5wZWo2s+w9FzBvVgZCQ1f7vcss4y2Ky66diXTzOtBadoPZFlcgNeku0Qk2YPkyMnMS5T1RYL56QhICcnPVevHeWAnWXVu9kRRsVkHpMJiWX3Y3iAbirZLFDsr2bJfrr7ng/ImLJD/wX+yx4LzCyInaZ+5/q/az36T00+b8XWytACrA6rVuGunW0Cz2vnd29mBotM9303aHDa4I6lP9GBvVF6DbrHzX5zPIywxb7Hwt135HiO8GHh9CwFxEIDsOxx9FDz8IK/dg/g1Y62MgZmOMMcYYY/qxchO98p+Q8+90V0oHeRcPSeT2ZplOYyJumMvd9Id4bywovxcMsvdn6iFk4rSrIa/lqA55NYijqhReeZ3g7nzD2UtXLlG8fJnU6dNRhj2g4WZGoyJQbH0Jv4ig6cnY4KuVTEYgLKOrd+D68y6AsnoT0hNIehRSo5AeQbxENfNxS1ma0JWFiQZ+lXigW3xITcDSq40bVlyMyhqkYOVGB4MlDuh9CYsgI0CZhkHywoK7AeTvufI8lQFvVy4jmUo5kRZfqjW94fERfWN3VaN6/RW+uzphc7bK8gWplApShdzo1vVVsq09Lzpb1u2r4mCyVJu69qoLzp98gNT3/QQyOjbc9Q1A5RMUp4WFwQQQ/UzdE5X66SHMvqXuuco/1Yzlml7o8gYsvNJ/m3YTScaO0fh74NG040oVCKOxFsTV5lfd+d76IL+z699urXZ3/VunAQTDz2CXRAKmjsDUETS/Dks34N7rrgPcGGOMMcaYbSSjh2qfCAqunGlqDDwLCRqzk+wTuCcMqAfTz1UD8hHRAE3m0MIaIkpw7z5rf/InrP/ln+ONTZB+/Cm0XMLLZMg+8wyJw4dITGfR9XkoR+nW4qOVWtadBhA9v2ZgUrecBFoqVDMLNYDFy+5uZZqxY+jUSSSZdT28XhL8VLRNinhRVn18uV4SxEPCMppIQ+4IrNeWVlFV8DKQn+98MM1eYm9eBl25A2E0wKCGEAbgL0ImBekJt7WV7OjNUkWxskWqhPfXCb/5dVfTvlyClWX0ld+CRNJNEwQQlKFUcl+4ax1sU1CG67Gs6YnDsBJ1zsyeg9tRZ4Y/AhfPu/thSGruxa53Q3ned30QHZAjJyGZRK+2GDy4mauvUPzf/jHJH/9pvEPHup9/B2m52F1AXrX5MdmoYwQQfGTseHftAnTfBeVjwfewXK2h3izAvjlAaOXMpFE5FK96bhNv+4P04jUeO+PA2vnRDySTg8wFdO4cFPNw9zUorLorjxoNnm2MMcYYY8wgjc41eDJ0yZTJURdXMWYPsoFezfYYRFDHzyEnv6smIL+pnIc7z6Ozj5OYm2HmJz/C6PveDyKkL56ta4vnAtfxuI8GUOowkF2/LA0gO4uGZVh4HZaaXOo/cggmjiK+B1qEYqwkQXIETY0j4tdmt4vnMsqDIogLcgug2Tk3eGvUZpWUe7xxqftt6JKWCrDYpFxI4nDD/aglRfNRUL4cooUSeuce4evdB8OBusCtNrlfP0/stZrjscdjs0l5JO/8w3hzU5vLleKKC2AB4VPPEnzpb3paXenXfo7UR/8Vks32NP+2U3XHQmLUBe60/QDLldEhGtuuwPDev3ys5hzSdUw3jAaiji3DS4GXcHH+4oLrJExkQRKNz8f9CAYwEPeeszeOOfE8yOTg5OMA6P0bcO1LVnveGGOMMcYMV6nF1ZqlVZcQmNgjfycbE9PRGGS7nAXl94Q+D7TsIeTwWxsGgLS0jt74a/fg/rfQaMDV9APnmjRlUIOCeNVs8I177obnLp+K10TOTLog1tRpRBpkgHpJdGMBygEyMl1ppOvtDYouwFb3QRUUnTjn6nUHZVi+PKBtGiwNlfD2BsGXn4eVhcEtOFD0yEV3f7VIsPmeSvNrMmpi9/2Pb+BdeBgJY8GoKKNYRrLIyo3G8xRuI29/B+VXXoH7XdZp1pDir/0cqQ//3N6oO6Yh4vnIxJnqU5v3pPZ/id1vGuDrrt58W5lZN6bCsNez3YaRWB0W0bDoMumLy9XnxUdHTw3wh4RAeXVAy9pDwqD5YFV+CjS6eqimbFe59Xgk20Cmj6Hjc7B4A26/ZKVtjDHGGGPMUOjyTWSyxdXR5XUX5/FT29coY/olAnshttNG26C8iGSAz+KG9ksAv6OqPyMiPwt8CDfK2x3gx1V1SzRNRP4h8BO4v4j/rar+rwNs/8HQzwAcM48iY8e3BOQVXDD7zgvVJ0urLjg+csSdkIMiA4lS+ZmoTEs8CKKQPQzrsUNGw2pAPj0OyRysRmVm1u/B6bdBGF3uLx6qArdf3CzXoKffhSRH3XrK6+BnG5Zy0DBwA70ud1I3fsC62J3hrQ2Cv/zMwJsQXmkyWK+XwDs92/i1eOBwADWZPW8NCeoG0VVgtXXng6zcJHlyjpIIzN/pbqXL85R/95dJ/MBHdrzsd0uq7sqNOtUma+3/bY+pwY5KLgBHn0av/RVkZ2D1uguMEu7hmHxUMmpY9dg1rA3Ig+tAWXkTzR6G5Ej/uy08oBnXYbFp9k+zfare7viDQxJJmD2NTp2A5btw5yXItx6TxRhjjDHGmK7Mv46eeCvSLJEFoLwWlf3dk3/MmQPqoJSvKQDvUdVVEUkCnxORPwB+QVX/JwAR+W+BnwY+Ep9RRB7DBeSfAYrAH4rI76tqk5E2TUO9nBgzM8iRb3dBCS8Rq1EeBbDX5+Hul+vW4yHZ2aj+eskFLsK6mrcdZ0kLeCm0tAZLL8PEWWQzECJQXGVLNFFwmfGJLKzehEI8iKXo9a/C8ccRBJ1/1XUiVIwehcIiilQ/mMFGbWBeFQ2K6K0v1c67nVq+lT28z2OT7v+VxV5a01r8uMtk4PQpd39kuhoE0x47bXqdD6CcJ3H2AuVug/JA+NpXKP32L5L8O/9w9/7eCAq4vs4BGcKGCiAn3gGAzjzkngwDN7DvXhQOL3Naxd8yhkWNjdtQzKC5w32UtPGgfACDuV6qx7rs9ftYWrw2fOL7blDYyUOuY/LOK7Da/fnNGGOMMcaYLYqrcPtlOPpI82kqSZRWX97sIQeifI2qKlCJYCajm6pqPGI6QuO/ZB8GPq+q6wAi8v8BPwD8L/00+uDp4kDzs5CbQ2YeqQZ3auqsJ4AQyc2iI0ddxniFhuj8N5CZR93gqVpC/awLbldEy1LcwIgNA0iSRBdfp+aQWHqzdkpVSE9BZsbVqK/IjMDSTRpKjcL9N9BKxmkiCxPRoK/BhrttlNDc4eqHM8jHvlhCtytTo4MJyksCEiPV8iFhQFS1vn7C6t1Ci+B5yQOSm+O6SiqN//CThPO30TuxfTI1R+KBh8Dz8CYS4KUpfvrTA9geRd7y7OZDXVx2VzkAeD4SVAa/hHi29uLdVOUuoEgygXgeuaNjJKJgpKbHWLpeDXqOjQck+qmE1EfwVy9/i+CFvyDx9Lv7aMCQqPY/+KOXcFkO22Tz6G6VebEXNPwBKn1lz6v4sNa4HFONIA8r0aDW6RlIjbvOlMqxsDnIM9H76wNe1OEiUF5v+y2hmx1hUp2v8n/leTclhOWd6bTyUt2Vx+r1vQmKbgySDqlqVBJto/3EfRDxYGwGHX075Ffh7huwcGmo6zTGGGOMMfufXv0CjB1GRmdoHLPAxjoye4oIe6M0cRsd1ZQX99frC8AF4OOq+oXo+X8B/BiwBHx3g1m/AfwLEZkBNoAPAs83WceHgQ8DnDp1sVSn0QAAIABJREFUqrut2O+6KF8jJ98VBcqbZPtFJ1oRH2Yec9nwK9Wa6jJ2GonXEguL1Jy0g4ILyK/fgVx1FO+oJLi7v3Spdv2SgEQGiitudO/imrvlo9G+R0/AajT4abACk8dgITYYamoMMqMueO+Puwzd0nI1iz8eKNGyC+psBlwqQU6NttuDqYsug7/Yw+C0cWMnEL8a/AwXr8Glz/a+vDe+XjNQoweQBDlznnIlKD86QerZb3cDRgKUi5AaUG+2KhRidcLXN+D2JXc/93jDviER4eb/9bsNM99P/eTfI1E5lPwUN/6P39h87cGf/nvQV1C+v5Nv8Ge/iYxO4D/01r6WM3AaMJBM3X3QY7y9wsa7XXrv3FCJzpPdvp+FeXfDg+SI62DRSmkggbI2/CyqJKLvCq0G8SuB/PrvhA6apF4aEKSDQYYHR7al1nu3GRUiAx+St+36yI7BqSfQIw/C/BW492rteCvJkYZlrowxxhhjjNlCQ/Tuy8jYd0Fqolryt5IQ4yWrCXnG7AnDqykvIs8Bv4iLWv2Kqv7Lutd/FPgfooerwD9Q1a9Gr10CVoAAKKvq063W1VFQXlUD4EkRmQQ+KSKPqeo3VPWngJ8SkX8GfBT4mbr5viUiHwM+EzX0q0CD0TpBVT8BfALg6aef3v7rx3e1Dg40P42ceGfnpQ80cD1L0w+gmWk36OnYCSQ7XTOZoKifAS2jQRFWb7iTN8DqTXT0mDuRr91EJ84i4iNTF2D9tluNn4b8Mixfd3XqK/VyvSSMn4XMmHucnYONu+ClITMFEx6s3ITpM+5jkJpEjr7VZWyjaCpX05lQo7yOJkdjgZfafSK+D3OPoTef77OmfN0AsrnpoQRuvEMzpN7nSoW42tQDHPQ1bkvjm3Xs1JcdkoZBeS0H0KB08+wPfh+JZL97qv/yLuVP/TIy98/xZg71vayBqXy2zJ6mAIUl6CugHUKpUcehQGoy6nysG7x0kCegKEtf/SyERevn2SGSysDRB9BD5+D+dVd3vpyHB98Nt15xwXpjjDHGGGPaWblVzYb309FVqmXA2/tXPZsDSfoZf7PpMsUHPg68D7gGfFFEPqWq34xN9ibwLlVdEJEP4GLZb4u9/t2qGst4ba6joHyFqi6KyF8Az+Gy4Ct+Hfh96oLy0Tz/Dvh3ACLyP+M2ynSjWTQkOQbZGWTyAni91iIGyc1C9h1RVmgI5Q2X2Y7ngoRB3mXHxwPy4E7gK1fcfT/jsvZEIJHbrOMu5TIkfHTqPCy85jLYJ89CMldzqYnmDkNmGimtghbQTAYy5xCJtqm0BP4xpJLpD2juyJZazapuwMZ28SNJpGHmYZfVXz8AY8fqgv2pHJqdgY35JtO3W1yT9y8Muyvp0KtMXc94vDlazc4t3Jzn8j/9ZdfpIeLaB5BIknviKcoL99FCnjuf+Rvmsy4qHxbc+evEf/13mTgxgC/8fmrSx5T+6N+T+pH/fheNTbp7WmL6IL4bLGko1HXM+ZnoXDvkc0OwAZJAJbHNWfO7TKfnHD/r/h9wqRvxEzB3Gp0+AUu33bn3yAMwdgiufRnSo7B2b3u+K4wxxhhjzN6zsYCWCkii4OI9In1dGWzMjhpe+ZpngNdU9Q0AEflN4EPAZlBeVf86Nv3ngRO9rqxtUF5E5oBSFJDPAu8FPiYiF2MDtn4/8FKT+Q+p6h0ROQX8IPBso+lMK3UH2sR5ZOpiXRC+vyCl62GKek0TmWidYXTfh4WXoHC/+QKys0gle7m8DplZyFc7hkQL6PQDkMjUfXA891xx2WXSlyRqT3Wb1M9AWEIWX0cnz28G5vEz7tKrosu+Vy/tOhRWr8NElmrtlK1UQ3TtVh8B+cbk3LvRm1+D+3swe7HV2IdxQUj5drUzxJ+eJX32PPk3XmPt+c+3XEVxKQ8nRvprJ/TcAbXFtVcp/9Gvk3jvD7krKHaS6mDq+IXl2jIXSPVHlxk6Fb/12BGDEuTdLTkelesa4gVmWoag7K58Ei+qyd7NAjxA3HJazrgPAsoCEhSj8j9sHSy938X7PkwfA0DLZfCTbpwBGxjWGGOMMca0k1+EZNplxm/jOGTGDEOPQflZEYmXVf9EVLml4jhwNfb4GrVZ8PX+PvAHsccK/LG4DONfrlv2Fp1kyh8Ffi1K4feA31bV/5+9N42VJMvu+34nInJ/e21de1XvPT3T08MZDocz5FAkJYqkDMmkSJGmFgsQIVCwQMAwYMuQTcEWBBiQP3iBBIoWBBg2ZMlaCBAjUiQNiqQoidSQHM7SM90zvVV3dXXt9eptuUXE8Ycbub5cIvfMV+cHRNXLyIgbNyJuLPm/5/7PF0TkX4jIC7hf0TeAnwEQkQs4z50fTtb/F4mnfB34L1R1Rt4bq4uGZReF3o9EdG4gG5enJ0r2r1Vre0SweR2Nqr0TpLYL8g08H3JbHeKUaB38NSc8elkn7tQPoNbwhq9CYQeO7rqgxOy68zqrPYagiMYRcvABWjqPJElmNb+DRqETPsr3WrXfewe2nkW6hmGpKhzeRXffmSiBYz8kk4PL346OI8r3jXAcdq6n1BbqNZdgEnDezi3rDI0UTb6LOCS4eAXZ2CSqRxx+85vE0beId4ePELj7T36RvRde5On/7JOIvxwicfzl36b25d+Gp67jXX8Z/5XPIps7849Zn8hKaRDmBjZ35mlDVN9zonxm47ilzbRpS0KsXh5QRHs60nXiBcn91kte/hPv+zikQ4iPZu8nPzYag19Mtxw0j4s7TvFUvfK1egS334DHH7hnbek0VKfbwWwYhmEYhmGcQKoHsH7OBSdmN917umGsJDJS/s027g/xee/1g7qnqCIi34sT5b+rbfbnVPWWiJwFfl1EXlfVvsknh16BqvoV4Fg2RFX9s32Wv4VL6Nr4/N3DtvGkI0EB9QL0/d9ozfRzsPm0+4HfJdjrwYfI5rX51tEL4NTLTlDXGC0/hP13nX2Cn4gO7URV3EUSNK1s0NAN7dewj1cyzg+++JRL6toWaU94BH4BjUOoPHS+80nEJuW7xyOMNUIfv4NmSk6wEj8RLRTCKuQ2gQ2OJUXEg6AAm9eQTLsAo23/JX/7fXqWBeSVnzg+E9Dd9+G9f9d7vT72CPpoj/offQWKJYgVPEGyWYhi1yFRm1Ikph+gf/R7Pb+q3LjNrV9wiVq9q8+y9/o3O74PPvZRailEeYDKG68Dn5yoqoSVvl72Y3P7HeLb7xD/hy/gfexzBD/4F0ZOCDkRM+gkapFEKrfTtCRasGjf7O8l8aVqT0gaJ3Vs1L0tT0S/UQUy6Ytl+3Ea99jM+ZhqlFja5MAvMZeI86S9upwj0fGO2Z7EHcI+4M6/F+COu0zd9mVaCBHEo49kaVj+qJ8DvIn3T4/24O1/1+rAiCLYG9CpbxiGYRiGYRgJ+uFXYfuqs0asPXaWyH5/hwHDWFpmZ19zE7jc9vkScOwHl4i8AvxD4IdUtSmGJZo4iWPML+LscMYX5Y35IBtX4ey3oXf/EHZeRApnaAorpbMusvvgJuR2kOJZVOPE7iJMhCuvFX2u8UzEROch7pqMrD2FFnZQPKS26+rgBYkA7rX+X7uUiC5OGBdAawf9Rfmg4ET3XhH5UdlN1Ufo0d3hkYdxDarJMpKBgw9JLZYd3ILrf9I9rEZEoG+iFM2Oad1SPnRTo5zxShmbjvbUJYQHp04R3nh7pPIqhz6FjQmEw8pj/Jc/SfS13x++7BjEX/131G6+RfYv/Q0kN4cs9EkuhJnRz99cfAhys9tuGlQhHlGo9Aeck7g+HRugsVlgJ0dUdVNmPblXz6EuUQUQNMhDVB392aORE5ZPOJJYSqlk3GiBuJL6mleN4WgP7r8Du+/NspqGYRiGYRjGSebogQtw89fc5/o+aCmxLTaM1UGYmSj/ReA5EbkOfAD8JPBTHdt29uz/EviLqvrNtvklwFPV/eTvHwD+x0EbM1F+mTj9MXjwGlI4TUeko8bI5lXYvJaI317iG11z/0sSBRvVmt5gGotbDi+JKAbQVmLOuOaE76jibspx3YnqhTNI8RxJyGqSZDSx1WhL9gkgftaVm92AoIR4Q4aORFUIj1zJhXMuGr4RRe8FTuvdT5kHeFQrAK3DxmU4uJ1y3Rh999fQK9+HZAotv++o5o5Zu2d38ayz0qF51AbUI24bYpMMt5EkQjRXTOZJa554zqN64bRHL3fuYenqFWrf/DqjcOP/+CW2Pv9Zzn1ye+wbqZetExXX4KhHB840eHSb2v/+X5H5838D7/zl4cuvGhIkgvyi7W1OgI94O4s+nJB0eoobktq4Mc0Udfk8vAwqfmdC2Fl3OK0YQgxx1d1G/SQpep9nkqrC0WO49Roc3e+5jGEYhmEYhmGMRtcPlvDQva8HBctDZqwUswhGVtVQRP468KuAD/wjVX1NRH4m+f7ngZ8DTgF/P6lDmFjinAN+MZkXAP9YVf/1oO2ZKL9E6L0vJ57ebVYpQa51z9SIpvjRsWJblGEUOvHby0KcJGs9tqGG8Os5Mb4hCMT1JNI+KU/VebV32A1IYjcBaJhoPeLsYqLI3dCjGlo/AvGQ9cutC8XPN72WRQQtnIbaPhpWobI3e9uCsAzZNReJnwaN4IN/i+68NDgJ6OGt1ikaUqR4wLPf03tzp9/tPf+dJUjg1+dmV/r4J0YW5AGi3V0e/NIvE2z8GKdfGLNXvrpP8PKrhF/8nfHWT0McUf+//g7Bj/0s3tMfmaG2OQclV7xWhHnT+mcZFOQxjmoc9vc/nHJizdFZhmMKoFDbTSxtisylXnEdqHda2vi5GVszrSYikojz2rou246Tlvfh9jdg78MF1dAwDMMwDMM4mfTQNqKy04Uya+Yzb6wG0ghEnj6q+svAL3fN+/m2v38a+Oke670NfHyUbc1mD4yR0doB3P8KFM8mczx3MwzLLduWuH48GaT4va0c4hotv/Qu/IwTx+OaE++zm63vvHY/MTnu/0vDMifsnBceIOEBIOjut+DwAzh4Hz38EI0ST10N0cxGa7Wwih5+COU78/ERbljjjEJYgftfQ8uPnHgyTbwMGtXROGweo97Mqbd6jP0LipMNczv85mRWDCLzSQwZ/vP/jfDX/+n020CDeditiN+07XajZma/yXSM8RjS0AmYvaaFi+KL3n4XUdWJ8+JNNwfDwG260UTqZdExfNifJEQEietIXAfJoH4evfUGfPM3TJA3DMMwDMMwpkthG7KF3t9p5HzmbZSrsSKIJyNPy4Z1gS0LGZcwVfLbTjzT+PiQdi/jIuE75mWdeN8Q0xviPTgRGprR6c2kq5UHLnqxbb56OfB8JLPW5kUcJ3XpElUa3vUN2qMguyOq995B996BjafRw1sQVdDSRVf+4Yokp4vrsPsWZNfRjasQ5KY0TEZc58VQFi/yte9vfOcDdj73WWoHZXRM65jc1WuUXn6BnVfOMZF9yeF95PIz6PtvjV9GSuIv/Rtqt94m+xf+68EjJ0ZFFepH0ytv1XiShkjmT4HXx8Nf+nwIy1AdsTPxGOrKyKyBBnPqkElGdUkG9bLIqJZjTyBCjPgl9Oon4fzL6M0vwcPR8nUYhmEYhmEYRj/k8rfPxPLDMIzxMFF+WTi8i5z7pBPY+w31lx4RpVEZvPWmrqV+HoKiWxx1fuSNpKu1PSfK+7mORKpa3YXyfcCD7edpiqQiLmFgeAhxHUVcx0FU7RTSvHzSgRD37VXVvTZhIZUQPQMGRqOnoLYP978Gm0+jhe3VfJj5GbzrzwKg9Tp6482271IKzbUq8ZuvkXn+FcLd9GKht7bO1vd8js0Xz1DYbCQjnrwXPrhwjvocRHkA7tyg9g/+OzI//rPIqTPIJEP7NBnJEh4yn46XZW2vy1qvGeDlnKVLL/o0AfWnmIi3fuA6dzNrPaLmk2EUUejsgTSmMdqqOUJExLV5P5Mks05x/WodouTZpCGy0CS8q4C6KyKbR57+TvTaZ9APvwYffmXRFTMMwzAMwzBWmfWnYOtSigUXHxRoGGlYSU2uCxPllwDVGOp7QHw8Er6dqOq8gaMyHTdK8ZqfXaNsfScaoUHiJ6yhi4JvF+SjMBHkgdzmcXlMBDJraL3cspjpbvja8KJPvusVXb9o/DzsvT+dsh6/DZVtdPs60qujZJmJ6gTPXITqYwDC4AXit95w3w0SmHve6wRvfWvw9nyfzNlzFF98nnOfu0Imlwh90xRiD24n7W8+w+z8F1+Cm7+F3vLh6T+GrJ9B4zCdQK/JdRhHifA54Hp/YjhpL32D2vYS7Gtch+qjxLbMCfF6/y24/83OBNZ96NiDC59ETj2dzn4sqrhtBXmIKk9SV8wIyLFnp3iCXPwYev5l9MZ/hAdz6oA0DMMwDMMwThRy5vl0ImYcpQ/YM4xFIYLMyFN+npgovxSkjRhOEq82kzSSRF4OFnokEf40KCXCZRnw0LAMh22etYVTAzadiDVhBfUDpEsAVY2gfuQ84pdNkIfpa2HVR/DYRzevzrx3zju3g/zQjySfJNHRpPmRXAlvazv53KhLm/Dd8BBv/BfG8O7vAhA8e43a+zegVoGwzrv/7Kvo0RFxGCURs4MOnHL01lv4qq1j4Hns/Mk/TvHqaep7FbaeKRFkG2XMSDSfkyAvV58heOk5d+6TZMv6+hdQL3D+2aWzyOVPIevn+hcS11u2UYugcSpMEV0Q49yIZnSyao9BfLR8APfeGO++HZYRIjRTgnqZ4dd4w9ImQL0AWXhS3iUj6J+jQzwPuf4Z9PIn0bd/B/ZWxP7NMAzDMAzDWDziwdaV4ct5GTcZxpIjsJQe8aNiovwSICLOZia1KDJ6w1NIIuQFxENrh3B0p3OhZsJXLxF33XZUtS3BbIzL1h23vouqriwNkUwBFYGwCvkdZ3NQedTfkmdezEI4L9+H4hnIlqZfdhtSDJDiWv8FsusIA6x5unXAjO8sMaIqVB+S+fwfo/7//WuII4JCnuqD9JY09Tu3yZzdQY8OIQi48J//GNvXktvKxWKPja8gxTWC7/xuJH7kBPluGtHFh3fR138ZPfcycvETiN/rZWaBD4245iY/39sKy5g9qsvloa8Rki/As9+Nlvfh1pdTRcs38VwEjcR1NMglI0BSeMdrCFHo7kMaNzuOn3gaeV8GLpKB578Xqkfom78F5UnzDRiGYRiGYRgnnu1rSGaALaYXuN+JXna5fq8YxiBOQFs1UX5ZEC+9KO/naYmdHoqXRM435iV/N+Z5AQJo8QJEZbT8CBAnmkdVN8Uhkikm68edWmq393BUdv7AYRnKdzsF96jqksVuPIMELvmsFs+h5QdwMCX7mLGYkTi8+zZ65uUF29iMuG8awcWX4b0/BEDCB3jXnyOOhVJwhs0f/j7C+48IttfxikXEl8SWqPOGd3DjAeVbtyhcPA31Gud+4JMUt5ZQhPd8vI99Fv8jn4bSRksXj2Kolol+71eJ3/xyz1X9T3833qYH4QjC153X0N334frnkfUzrfmqzQh744ST3XD/a/s9GWftNeqIERGXILbZcNuuQ5nesFLJbkBhHb37Jjy+2fnl2RdaETPNDltF2kZXiUauQzYouHbuZWmO7GlHtSXcRy5SXoOC+9vL0K/jqv+IpH52WNos/6QhALkivPxDcPQY/dZvPNnJog3DMAzDMIyByKmn3R9+zv2GiOutwEu/4N7hT4DAaTxBiEXKG9OkdNFFyGmc+E5Hidd7DwGnO+p8mJYvfiKqKkR1F2kXbDe/1oNbENXRB685sb30FJJdc1F7np9Yg7R51avC0YcucWwvwsNmBCXgBOv8KbT2uP8600KBqOasFBrikXig+7PZXlSF219Csxsd++zq0kqO2DkvmZ/f7ly+XnPnXRX8rIvAT2Xv0JlHIA2SL3asEVx13vA715PREs8Vj6/URen8ac595keTD+fgvW+MVIeZsraN//k/g3/1BSiVBsany3/6V9D9PcJf/cfou193864/T/D8dajtwjhBvNU99PUvwHN/AkmS6SgkbcRv6ogST5h8eBwW8bIlnrufNNpqe0ffCYuSFrSZbPs4o9ssiUb9h5BOMQmskofqQ+TsMy7PSZs9imyc7R0Bn+vcT0GdlVlQdPfhXvevXnUOy66NxPU+VlQCwaB97XH/01V7QfNGvhYEoLgJH/9RdO82vPlbbaPaDMMwDMMwDCNh/Sxk1p3OAEAh+U0WTzXQxzDmh3nKG1NEenjJqp+Dg5tMFOUtXkuEC6vHRBJtj96tH7jJz0L59vGygiJ6sAeBjwRDmo42bG4a1fCgdAGdtSgf5OHo3my3cQx1/syjIgLZ9STxZw3qh63REuGRm0pPQTSDCMi4CmeehXtvTr/sedPs/PDwPvOD+B//LmRtLbVRjACyvkHmx34GffyY8Iu/QrAdOUF+0qq99W/g+R9A1s85m6qo2mGBo5kNCPJOeJ014rkICLfl2W+vY9uN+0WbIJ8mOagxfzRCzj2HxnU4GHIv7XGRuRQWYf8W1u+LOSVqXloyxbHzsQiKbJxDv+0n0Ac34J3f4URYhxmGYRiGYRiT4+ecTtId6CNCu2ZjGKvGrPM7zgMT5ZcYyZTQ0gU4/GD8QjR2yVkzJQh7RYv3aMR9ovX08DHcdJYnCrB5Hjl9HeIe4lqv3taggJz6mIvkEx8t34XylAR0TaJTqzMW/adJ5ZGbNq6A1pMEvm3fRzXYew9K5503f1Rl+LCI9Mj2eXRqovwCb4Y7l8n81I8g569MFAQugGxuEnznD8DrvzKdusUh+vqvwPXvRk4/A7mtTl/6+h7U99HCGeetPQ0hzWtEpLcdDL9hC7IEwmfjJAXFxIP8ZFqMrDQaIedfRm9+GcqP6C/wDrjgPK9PcxujjY91Ya+QKC3BVDolBEVOXUF3fgq98wbc/P0pVM4wDMMwDMNYaQpbTrxUi4o3ThACmH2NMWsku47qeWcXMy4aOduYTAFqIR1iRbcQIIFbvpcIcueNzs+PP0QffwiFbTjzNJIJBib5ExEnDiYJMNU/PjpgbPyiE7BXkWH1PkzO/cbl6UYXxxVYOzM8GjYVCxDAxEO++y8jpY3pFqvxlPdG0Xf+Layfg9wa5La7Esaqy83gZdH8zuSWNn6O4+dDe8xbAsRLxPkwXYJQY4Z0tQ8NkUuvog/HzQWy+i9IcyMoTNXGSQTkqRfQc8+j7/0B3Htj+EqGYRiGYRjGyaSQWNR2uRkYxqpzEuxrVn8PngAktwmFs5MXFFWdABaUWvO6h8tnivQVU/r5F5cfwXt/gL71e2i5miSiHY5kpySm+nnYX2QS2TkxA01VLrw4/ULnQbaEfP9fm7ogD7jo7amj6Fu/Cfv3UOlzfcQ1OLqNRjXUe0L6Sxudf+IniUGNpULryPZTLP5VYdqR8pLkWukxnSBEBLn8bbB9DYqnhi5vGIZhGIZhnEByiShvI5SNE4Qg7vfOiNOy8YQoP6uP5HdcAr3Kg8kK0hD8HIoH5QdQuQ+5nSShawRhBWoHaOmM2267aH/lVaReRvfuwf0+tie3v+6kkOfWYP304ASbQQ7NrEN9wiSs9TJLGQU8bcKy6xjRKT5M4yrkN6Eyhid+gsYluHsHgkQg93yXALLyEKnPwDf8wkvIK39y9ZLDH95zyV8Bffa7+vfq1vedpU3+lBsNcCK8tockIxZJEksHLpeCsRjWr7X+3n+39XeuPSm1tjRyCdC4V4S39u/cimuJ+H38Ataju+651CjDbQT8HHL6pRQ70IZI0tHT1vYaNw0/19+/PY6OJ1NfYcTzkGc+547A0WP06/+KJ+J5aRiGYRiGYQAg+eR3eiNIUxYdcGMYU0BAzL7GmCv5004wmCQBpZdzVjYK5NadKF99eHy5gw8hs45mCq1gVhHQKrK+AVufgzB0nuSHPToKvvXr8PEfB79/E9M4collJyXIQmZtOmUtM+X77iGaK06xUHXD2SYQ5aVQRN9/vZXAs0GuhG7uIOVHvVccRmkb1rYhm0/89hVyG8iz3zFbQV48lwRnUhuZQaTRxCoPQDw0f8ZZiQxbPlNyBWs8pgf3FPD6jKZR0gc7+0nCS4vkmCvu9Gjy7/GT1WpSre8UGe866SuIh30E8TFF5Gall/xlLa7P/JoVgOImfNtPoq/9q9XKv2IYhmEYhmGMT2699XdUddaJhnESWLlIzeOYKL9CiAhaPOcEjbGjy+OkLCCzAadfQe9/pfei9f3EzqYhiLQJI1HZ9UydvQr+82ilAh98pSW2nP84MkCQb5IpTS6mRzWemMi/kW46vZaVZLaXlCVw+io8ep+xj2H1HvLiJ9FvdCUVrB7CrqJbp0YT5i+95JIS148g3IVGIG6mgFx+HvbeQbObroPCc9HV0xyGJOtnkE/8ObRehYN76J2vw+GUEhKPisZQvgN+Ac1tpvObX+SDSbVvs0uNgPM6HBJdbywBU25rfh4y68eLPel2TlHF7fcUfeX7IZ4HH/vT6Lu/23/Em2EYhmEYhnFyyK+1/g4r7p37BIiZhmGR8sbcERG0dAEObkJ4OHoBcb0lZiZitpx5Fb3/1d7Ri+LTVEWjPoJgVEUyAs98B/rOH7jP519mmKAmng/rV9CHXx99P1aRTAkKp3CCePuxkSSSOJmnCsTJ/+p6sqNacvw1eYBqcngVavtoY9lxOff88US+oyB9tl07SoT508OF+SALF56BwzvHv/NzyNOfbkVPVx6A7MLaRWZ1G5NMDrYvwdZFeHgDff+LyTUzDUY8V1EZjspodgv8bKet1ElEAL+Aa+fhbEctGD1YzMuNbD2NcK3XNyfKUqYnc4iWbyAoXPsOdOMCvP3bc9mmYRiGYRiGsQCCPBK0j2aOnV1optR3FcNYCZIY01XHRPkVRETQtYsuuWk0hme3RhC1iXoaIqc/hj78hhvO1I7ngzpPX6pDRNWoglz/JHrzNfejPw1eZrS690LECdf1MTopRsUvwM6zSHY9SQwryXSyAAAgAElEQVTYuAtoEiXsobVD2L/pRJbMmhOsNQKNEK01Fz9Gn0OmXgDV3aHLTURxwofyoDrVyrB7f7Awv7YDG5u9BXlAnv3McTuT0gUkSJdUeBJEBE5dg42n0Jtfgodvz3ybfantAoIWToNq+utsFZHkH8m4+0RYoTHSx5g1i2pX/bZ7gts5zC1Kvh0BZOcyWvpR9LVfctZBhmEYhmEYxskiv3l8XtNb/gQomsaTzQlowybKrygiHrp2Cfbfm473sobIzkvo7lsd1jjS8PhNm3wxqiDnP5J+u6MI6RIk0eMR5Hdc8sHHb9OMJp8Vfg62nkUKO3SKgvFxrUgjJJOHnWehfK/l3w8T3DDUPTQbdh6zSIQZHsLWJdi92fv7/KbzntckUr/hEV7Zh/Ij0Bpy/aM9Vmzznw4yaCaD7N3tXOTMNZAKVHv72sszn+3dxg9uomsXkDn18ksmj1z/TnTnafS934XaonIYqGtbkkELOxCn8Js/Cfi58TohjcmJa2iPhMNK1dlMJZ86CApuNFQ34kF2ozXSpz3RVJ8EsK48r2sTjZFFsUseOy0ao1D8HOC5NpfdcJ2s4QzaX1CcuyDfjuQK8ImfQN/4dTi4O3wFwzAMwzAMY3Xo8Q4Pmgjzsw9wM4xZcgI0eRPlVxnxfHT9EuzdmM6Peg2RrafR/ZvOHmTt0ngCsIxQl4avebva4uegcAbJOCFa4xDxc8k+dn3efs5VHZCt6279sII+esvtwzjktiC3AZk1pHAqKT2xlBmFfnY/IyK1VkI+9fsk0pwG2+f6i/JrZyDf445XXIMPHqVKFCt1IJ+DwtNw5x1nV3P+GTi63X+lCx8Fr99xF+eHN2dk8xx85E+ht78Ot7869+030TocNfzmt13dFlebOeFh0fIL4Oheb7s0vwCHt3qvs/MRN9KqGwmagyDc+fRaDXeQLVO/Z5xMYbRVZ4GuA9jP0szBoVEyqmvKonxmbfA+zwkR4MUfQD/4Knz45UVXxzAMwzAMw5gWh/fRsIYE2c75cRUwUd4wFo2J8iuOeBl0/TLs3+jTCzoiGiFrF10keuX+mJXyE4/zZqEgfm+rjdwmnHkVKg8BcUK8RrQnlxXPbxNkuj8nm0zqDoCfQU6/mERdgtYOXbki6P4HsPvW8XoEBdh5Ecmtd4kkExzTjauw++Z0oyDDahJlmnQUaDy9yPnwAC69Ajf7JP7thdRh4wLs9RHmuomqQBVO7QA6WJDfOI+UNuh7Dopne0fizgHxA+TiK+j2VfS931tcIlho+s2T20Fz68g07gPLiIhFy3ewql0wXc8Gz59QmJ6ytY2fiPyN66gZyT/N4y2JIL88ljGCwsWPohtPwRu/uujqGIZhGIZhGFNBYe827FzpnB0vPjDEMCZD4AQkevWGL2IsO+LnXFT71ESD2EUJFs42he2R8AIXVdmcjjo6DBRBxUfDClQeILVdJ3IG+USkmJLI0vBxz+TdPmmErD0Fp17qXO7sJ5Cnvg3JFqcbtRjXIX9qeuXhdEmJykhcQeJqp/XDNPAiFyV6bMMD1lnfGmNDQ85xpoCcf46BnSJHd9AZWMioqptSXE9S3ERe+BPIs398xK2McK32Oh+9qD6EvRtoVE9V99XkpO7XqjLG+ei+9OMaqqBR5fgUtk/l1lRvm6aWfHnUio9JUIDMYi1r+iGAt34aefXPzdYSzjAMwzAMw5gbuvv+8ZkLCm4zjGkhMt60bJgof0KQoAilC1MsUFzEYPEpKJ5Ln5C1dMHZv3QTHiWCSsV51Ffud0a8apw8GGZ/lUjxDM2mXzrvxPiZbWzWl9iUj5eGsHV+xJVq43XeDECe/nQ6n+gJffBU20T49tEdcehskHS4ECcikB2xHke7qcoGnJVSbie9Vc/RbTj4IOlYOGkk+RW87PBFjTkwoIWN8sYjuPwb3VO9Me07P/fGFLVN43SkRtX0nV0dlZyQzJr7f8lHs0iQQV79s7B1ZfjChmEYhmEYxnKze+P4b89ghhqIYcyLE6DKmyh/gpDsuhPRp1ooLvK9cBaK55Pkd134OSfcF8+7FXo2dE0i10MXQR6sHV8kPOpd/rTRCLn8XcjFzyHbz8x2W9mN2ZZ/jMTiI20nSi/WdnqX23eTU765Xf9MuuTFhTOIl15YU1U0jtA4bonw9YNkOoLIRewikhy/RjbbNIy4/x9+De6/j8YpxLmwAkRu9EpuG4IUiW01goObcHS3bZ/jjg6I1sQUI+tn3A3QaGcz7+wy0jFOuzneRkSjOXe0SCLmz/ulbPleAvshKPLs5+HKdyy6KoZhGIZhGMYk1Mtw9Mj9Lb7TKCbRCwxjWfDGmJYM85Q/YUhuC43D8f3g+xaME8LySeLTyiMXpdzoYR1VlI2rLklgtz90PKch/Ro1jOhnvKEZR0SKOF9+cL5wUcXtW24LymO2Ab9Pp0pDUPKycLTb9pVO77ydezF9AGv5HhrXIb+T0lteBvjvxy4yvzs6P47QbMmlRZi2nvboXageoOdfRPwBT4eo6katiLh6ej5kN6E2PLkuUbV/Is52gjW0cApZmSSqqyNuzo7ZHQNB0fWrnTN7RKSL+LDWe2SN+gU673/S+r/efR0m95eg5KLiUchstkbCqCJaT/5s3bNl3IvSz7kfJGk6/zrqOCHhodvHjrwpy4ugyNln0fVz6GtfwJIsG4ZhGIZhrCiPP4DSjvsduYTRwoYxDmP/HlwilrCfwJiY/CnIjuPznQKRRJzfgUxpsijp7vW8zFL67E7EjG0KJCoj4ZGb4ioi4m5Mk4jkURnOPjtoq1A/7Jymwfo5ZHMUD36F6qNU+6rgIuJHJobaPtT3Ses2MxJH9+G9P0Drg6x6ujYs4vTN7IaLnM9MYTRGeADl+1OImJ/nQ3H1H8ATMePdl45Juz43Jmnec7onT7yuee2PC+2akhcqkeZnN+gqWddrvaq0lzn+znluO14uvY2Nxu6aa0zjoHHLikcCVqUNS2Ed+bafgMKM3isMwzAMwzCMmaK777k/ospiK2IY00J4MuxrRCQvIv9RRL4sIq+JyP+QzP/bIvIVEfkjEfk1EelpaC4i/2Wy3tdE5P8RkclMoI2hiIizk8n0sIiZ3kYmLyOud1pxTNmXfHlYwIVfP5wsUV9xrfN8KC5C3svO5jz5OeTCC9NNtNuOyuSWJ+HhbIT5ehne/SJ6NGKnQTNyXpzn/KT2H+FhIsyvQl9tm3j7xDK/+4riueTc3dNAT/m+hQ3a0JxJIvS9PMNfhxoWbNEU7lPaJs77Kba9eMTzkJf/Ezjz/KKrYhiGYRiGYYzK/m20XnEjx+MZ/eY2jDlzAjT5VL8Eq8D3qerHgVeBHxSRzwB/V1VfUdVXgS8AP9e9oohcBH4W+JSqfhTwgZ+cWu2NvoiIS7o6iTA7F9oiyWclyC4SCViEeCjCZMlbojJc+mjrcxy6IW+PP4DdG8c35k/mSSfPfCZdYtduvKzLeTCs/CCfDNWboENBY4jr6RO0jlR2BDf/EK2Uhy/bk8id7+zmeJtveMvXD9H9mx3CfCuWeYkQLNnrPKk+dAmEu6dpjZJZJI3OLQlc5PzghdumaZDktQgPAX/pcyUIilz9dnjmjy26KoZhGIZhGMaoHCT2tt0Wwoaxqngy+rRkDFWz1ClQjRDOTDKpqu61LVaiv24TAAURqQNFIIXBsTENRDy0dAn23xvRO3eOxKFL+hoeJJHzxcSOZOlkwDFZnAev1PfQzHri0TxOAaETsZv++zPi2qdBx2yfcQ3qB6j4znc6t976TvyWcKuKiKCZDajtMnb7iirg9RqBMoX2evZFyE0wkEiSOuS2na3PqDx+u/mn7r6Jell3TRLD5nVk48r4dZsF4oNfBNReLFeKlNfKvB8BTVudIBmI0WWLJT7U9o6vNy3C5DUrKDlxfkmt3ASQ7Yvox38M/eovjdeZahiGYRiGYcyfKHlvi6pOd1nygBDDGMryaewjk8pMVUR84A+AZ4G/p6q/l8z/O8BfAh4D39u9nqp+ICL/M/AeUAZ+TVV/bUp1N1Igno+uX4K9G4v7kS8eGoWIn6W30hLTtKMIjyCzfnK8zsIF74efG12UDwrg510E+uVXnSgfK7TvSvdpLK3R4e0iQG4NSu3ey8fvmOL54A9Q34ptSSTbV1d1LxP1Azi64+bltoFElJeg1aHQsT0PzW6kS5LaD3WmHVNPKpIt9i5zVHFS40TUS9chpOWH7lh2b7S9I+/oPnFmPZkftjrONEpGECgQOSueZlbctsSemSLezgsj7sgQGsdKk22cmI68JSS33XUtNY597BKLd5CcCxWXCFY7VxmIn4XgXLK81+H3HoePaRbWfqvJFPoXHaz13q5C/w5TdfdtvxE131huTm98YRmy685SR6OkY2z5kEwOPvHj6Jf/pauzYRiGYRiGseS0vUSHZZcj0DBWlUnzjA0sWn4Q+F9xbi//UFX/p67v/zzw3yQfD4C/pqpfTrNuN6lEeVWNgFdFZAv4RRH5qKp+TVX/JvA3ReS/Bf468Le6KroN/BngOrAL/DMR+Quq+n/32Om/CvxVgCtXliwic8URL4OuX4b9GzNPPHp84wFavpt4VQ9g8xmksUR9/+QI8/6iUyiMIVT6eajcRwpnwKs25+l7b41WzuVXkcwwu5gh9ctvIwN68FXVtelGu24IaRr3LVu8AM2sjZn4FWc14edQL4tIUodp+Oz3tcWJwS+kjwYXXD6JFFG9qgrV3eFl1vfh/lfSbb8X8RSS0fZDxLXZxvHxcuAl50MBrXcKm37efV55u6z5hQUc35Jrq+oF/UdlZDZa0Thpt9Nh9Kfu3DV4+Dq9hHTdeQnpZ9MmjP/Mi6qugCCfdGjPq9On0amlrmPCzycdX8sXkS4C+szn4Y1fXXRVDMMwDMMwjGG0/2aNKu4998Tm9TOeCGbwkzgJSv97wJ8AbgJfFJFfUtWvty32DvA9qvpIRH4I+AXgO1Ku28FI41VUdRf4TeAHu776x8Cf7bHKHwfeUdV7qloH/iXw2T5l/4KqfkpVP3XmzJlRqmWkQPwcrF1hbgnlJEAru+jeu1C+P3z5vXc7oiJXXzBLWHgmiQVufw7D4UQE8XzEzyB+plOgH4SXZaJjE1Whvo9GNSfwTsObbFC948gJ82nrvHQvVzMWNEXc8QmKLUEeEu/5jJvvJxPiOm8kaIuGNpYCHdS++10fA9aZuNmpiyKKdfB2pkp3pbXV8eRNlrtjFsj6GRv6bBiGYRiGsQoEXTm5jo2WNozVQjwZeUrBp4E3VfVtVa0B/wQXbN5EVf+9qjai034XuJR23W6G/pISkTNJhDwiUsAJ7a+LyHNti/1p4PUeq78HfEZEiuLGFXw/8I1h2zRmgwR5WL/ErMUFrR+hD78O5bvOjibVShFaaYvYXdIh++MjULwApYudnQ+zZphAnduG3FYybbpEob0ezj7I85+F0ukRNj6Ndjaz4UjTSYIcVef3MhNVk86ENIJ7nCrR76yGex1jHkHGw/almaMzWc7L2ovosjHt5ji1fKyhE+f9wnzv352VWMLONpf8lSufXnQ1DMMwDMMwjGEcPuj8fMyG0jBWDBljGs5F4P22zzeTef34K8CvjLluKvua88D/mYThe8D/q6pfEJF/ISIv4MLXbgA/AyAiF3C+OT+sqr8nIv8c+EMgBL6EC+s3FoQERXTtEhzcZPpKmaC1x3B4e7zVK/egsOOG6cd1F8U6b7udWbD1PCAtT/+tZ51n8cH7sx8RMCyCMa6lS16YWAnJUxfBfxb95u+m2PY0FLEZRqj6ufSdRnNhwPXYuA7ieuK1nR1uZ+HnU+7fPPzYl8jvXdUdu5NwbzlxjHO9z3E0kEZue0Ex8VGfUbtWndL9cz7I6afRGymeCYZhGIZhGMbC0Mpe55tzHK7ce6dhNGkPuBuN0yLy+22ff0FV23XqvlnJjlVB5Htxovx3jbpug6GivKp+BfhEj/m97GpQ1VvAD7d9/lt0ec0bi0UyJbR0AQ4/mHLJCsH4yUJk85lOodHLrH4kaz/RPa5A6ZwTV4ndAzGqu5tK+e70tj/sHjXLyMulf7iLi3pdVALktIhHh3WHxs7OZv+GG+mQKdE80V5bJ4yGLhq87ZrS5j9PKErSwfQkH4RxSdqhl01GMg0/htq8v7QnaNVW/pCe9HCwV20b+dEZ4iDi9bmPSVsS4K6yVQfcG4fsl4i7X3tZ9/dJyH0yISKCnn4O7n9r0VUxDMMwDMMw+nFwF60dIdnGe7W634yyfBaJhjGM8TV57qvqpwZ8fxO43Pb5EnDr2PZFXgH+IfBDqvpglHXbWdQ4bGPBSHYd1fNw9OE0S4Wje+OtWjzPMc/g+oFL2ije8guno9CIXtcYtE3Q8X0nLq0ldlT1I6jtThbRW90FP/GOa4j+0Eoq2i9JYz9ypyCqIi9+V9cXsxA5vYFJXidFRFA/B+GStK1+hzB3qn/7rz5qncOgBMXEXkjVjQTwMlB50JxF/QCOptjpk5plEsGXqS4TMO9OLxEgcB2NzetywPVZ7zcCx2vdk45vpOd+CSBbz/ZeJbNOX7957WODJsGAkSZpj6sm11khSRy8JPeRBSEXP46aKG8YhmEYhrG8lB9CdQ+ybTanYRmyJsobK8o0cvsd54vAcyJyHfgA+Engp9oXEJEruJypf1FVvznKut2YKP8EI7lNlBiO7kypQJ+xBS+vj7gTVwFx4qL4LTFomHXHMlN73P+79sj6IAvBWdcxEVXcAzQqj7Qp5xmuaJz0gg86PX4OyW7QXCjsta1GhOk8hM25GJHPYRtp6bO/lXsuIn4Ynt/mC6itzobsJlrdhdp+uqTLs0CXSAj3CyNfR0aDJTqPy0LD0sYvJu3qCT1GmRwUtqE8YkevYRiGYRiGMT+8LgE+rrupe75hPKGoaigifx34VVxCv3+kqq+JyM8k3/888HPAKeDvJ3n6QlX9VL91B23PRPknHMltoxpDecwI93Y0RNbOg1x2iV5HIawMiJ7ULlubfsutCJlNpwVH1ZZ//iDiqosezW8CW+44lR84oTMqp4ukT6M9Zzegvj94Gc8HcklnyQzIbLiRAjJDL/kEVYVoRp7yGxch17ByahPpJPGjbkQEN200BIpbkFuH+hHaHnWrQL3SPBwSFDrPuV9wVkjQu7NK65Bdg8fvTG33RmdJhMrGsQ6KECXH6gmPcF4J5joqYIy2KknOEC+X3NsntLRZeuuv4wigVz8Dr//K0GUNwzAMwzCMBbH3IZROdc6zaHljRZnVzyZV/WXgl7vm/Xzb3z8N/HTadQdhoryB5E85Yb7yYPjCadDQWWeM4gfv50Yov90+YQVpF7S9bBLdnFYIUndcs2tJWaWUHvTD71aSJvt6+Z4TNFMd/zESiIog/UZNTJswZYfGOBTWIWiU3XbsxUe2rwxYUdHD24M7RzafhrAKpfOtspvnrnHsBGfpocknhdMfRe9/bdQ9mQ7LFCnfoNEJqFnXcTGsc8xYHEvYfHoTu7r6BfccfMLalKydQr2g7X5kGIZhGIZhLBN675vI+Y92zozrSc4kkweNFUJ6W5+uGiusbBpTJX86nUXGUASN6qMnaB3lYtII/Pxo5S8rEjCZ4pTyuPlZyG65Kb9zvJTiU9Ox9PBykNtyyUdzW5OXh4to11mJ53NnSuqi4DoVwiM31Q+6pn0XCS5Bxzpy5uMsxrJH3ciEpUSfOPF05Zh6MtUBbXEa7VSjRJwvMvJrVr+RYMrSJz4XFK58etHVMAzDMAzDMPpRPNV7/tTftw1j9jR0+VGmZcO6wgwgSXpZOOsihwd5nvctIEDLdyfwrB7x6lhycSI1k94VMgWINqDWL6lispk2WxP11qFxriv3IbOGApJtiejHZamGnYwifs4Jwd34BbdI/aA1b9SOHq81YkLrR1C+4861l0VzW5DdQKbVg+9np+YtrgAXXuqeMxay/Syoovf+aLJKxXWohZDdBG2c/wg5+yp6/7X55mWIqq69iT+/baZBWd0XUI1cNMsiO6zCwzFWitFj7UCTnCF+55NApeXTHhTGrmYnXv9OGC/bSpI8DeoHST4UL/29pu9+ep2dbANJ7j/izT3/ipy6hr777+e6TcMwDMMwDCMdsnG+9xdRdYQR8YaxJMwm0etcMVHeaCIiaPEpN3RpRLFF998f7kc+aNuZ0ojiwbJG3Y7IpIJaVIHM+lBRvh0J3XlSVcisNaOrRzDQgcI5xPNb5ywoODGr3bYgqo7eeeLXUD3lEtRWd1vrxzVn0+MXpjesboovHAKoREOXS4VGw8U3jSHYaG283u96TeyO/MSiBUDryOmPoI/e7OxAmTnLes0ua72GoDELT1Q8bvvp9Xwpnkfouh+2J07tO5qhnkSXS7rDITLglCtNC5qJaVRGk9FdBff/uCK5MMbzYv7tQ0TQ7avw6Mbct20YhmEYhmEMwRsQJBWW3Yh3w1gVljH0fUSsG8zoQERg7SKULjn7kQ5xsKvBN76TYCJBHqSHUCFOaOk3lP+k2NdMI8p1zPuQiIw/4qB8Bz267XrTMyW3H9NImBlVWwk4uwU6mK7GNE0tdmyBX3C3YY/Rdk6h+shNtWHCaJyI9m3b0BDZfhrWL49R5zFZWvua1X+QL44pHbvMRqcgr7gfBWkv0rg2narMsolqlEThFwePGDkBfuxy8dVFV8EwDMMwDMPoge59iFb7/H6Mqkv8m80wuhjDumYZNXyLlDeOIeK5RKLZNbSgzqqkto8e3XHiY/3QRUhGNWT7eTSc1AJE0ThGghwgaHUPjj50XwV5pHSBY2rJSfEY97MwsQbT787iwUZbUlHV47lXd98cf7Mao2EZyay5zpMpWcFweBP1sr0tcpY0+Yym7dzofsep7bvkuQCb18fbeFxPRL62wlVbnxudHOERBCVneaQhaIzkN9FMCR6+Pt62R2IJX/AE18E3rbZrjEemSLN9KOOdD42XbLhtj/uySNJ56SWJhjW5PtueZ0t6jxuJ/Lqz7bFcDYZhGIZhGMvFvTfQyh7ykR/u8WWSa8vvExhpGMvGMqrsI3ICfv0Zs0REXCR0poSUnnKWJ2EZqo/Q6iO0fA8qDybf0P6N3pJdWEGrj5HCaRq+5hpVIawgfnY60dlzRxI/8ypUH06hvBjWLuGOj3YJsjP0yy4+5WSnhnie3XB/64Q2LnF99cScsXMptNGw7R9r3ZTHPDx058gvQOkpEHEe/UnEvO6+BY/fHrMSw+q4hKI8YJHykzDlczquIA/JD4g8k9VpTm1UpHXNiu9yaQgQ1SGO3LDiFX7BFBS9/Cm48R8WXRXDMAzDMAyjm/0P0coBkl879pVGVaezGMYKsFQxWWNiorwxEk6kL0KmiKxdBEA1dp7mFSfUU308oZ1NF5UHaA/h33mbn0GKZ1ZryL+XmZIYn6AxPa1eBq4SQX0CAb10CdEu4by25wSmzPp0z387UxJ2NY4hnJKfunjORibdlt35dyt2jPjQ6j6ENbePs4yWDYqQP+UEeY1c00levLRZN8NIQZB4TkrzH/d/VBmtY+3oNprdOu4pPypxrc1fvlvUbvR6DRK7+41pHCaQ97gvqXTarw0U2ZO6BT4a1qBahuxaMnpsAsRL7HJ6bDGuQRwhM+qIkFPXUBPlDcMwDMMwlhK9/RpsnIfqHlrZh8pjN5XOIK/8+KKrZxjpWOFApgYmyhsTI+I5//ncFsJ1VGP0nS/MZ+Pley5af/1aImAsazRuG3FttsJ1CsTzIbfuOlFGtQJau4LEfexaNHLivJ9zwnIvC5plQKMpRuOP6AXffkzaHyL7d+DeG+7vwjasrbXKlpbg6eS7fuJiv3Mprc6A7Driea0OGY1wfvOxS9ibP53MD5NjlHge9U0mm5ZlvTbVRSr3a9NGXyRT7DlfvSxUpzCCalQ0dh0CXn68UVRRtfd6QaF/GEZY7R3d7+UgSBtl5K5lVdB3f7N5rWnxLLJxEdaeQnIbKctqLza5zntdetUD0DoalJJOwPYOCZecdpJXXPE8dOMC7N2aoBTDMAzDMAxjJtz5Gnrna8fnTxoUYhjzYli81YpgorwxdUQ8VIL5Wsvsv4vmtpHiOUaNGl8IK+kbLMjGtXRCe1R1U3bd/b9sdjTdUf4LQ3r+iXjIzvP9V/PWettG5bahdgTSpcLltloCqp8/PkIiqoCfwyuegeIZINHxEr97jSO4/9VUe9SfJRXlRYCuxJteNrEYiZev7RrDiatJlPqoz4IFt1GNOju/ju6iR3fdV7ktZPs6rF9AUov9KQj7dLaJj2bX3WiaMZHLn0Jf+6Wx1zcMwzAMwzDmzEnwAzGeCARxTh4rzioqg8YqEORdMth5Un2EZkp9ozeXiihJzjmp//rEpLyJeVmXcHfUyPfavnuwL3hkQDsuL8IUo6InsNTRw12oJMe02na9jFtm9RHkTrljHodA7MTJ7FrbiIheVhuRE+aDQu+REyJw6qNuXY3h0TfHaLtL/sD0siCB69BQkvTsvhthENVWNH/FKjFNQTxJUuVlWIlO2gaDDkF1F739JbjzFXTrKrJ1DXIb47+IigzenkZQ3YNsabzyAQobrgN6lezlDMMwDMMwnmRWQUsxjAbekmsMKbBuMGM2LCo5yMFNtH7oxLSlRiG7uehKkEooLZx1IxDGTcDYyDng5Zyf+YIREddpNDViZOel8VaN6q6DIKx1RWSnECizmz2mraTcshsNoFFy3lKcZ42TKN3jjwURD/EDxM84m6iNq7B2ueUpvsqoOiFegpY3erfQacmO5sC0o9RjFzGvCnhJJ+iUNwHzjybSCB69jb7zG+i7v4U+vulGsvReeEBBaV5gYxRBx9xHQeH8K2OtaxiGYRiGYcwfOf3coqtgGOkRGX1aMixS3pgNixTFDz5AuQXrl5HM2vJGuC48Sp7+0ZKFs0im5CIc45qbJiWqQARkNhLReIHnRaZ961MXZR4O67joFLPCCAcAACAASURBVLdk5xJwyZWwdw9ufSnd5kSOHz8v487XpMc1PEKDAoMEvYa/tRZ2XMdC+T6U70623UXReDAPzMO5pNY7xnC0/ZqQJH9A1Od8j3GeR83JMbiw0RavPEJvfdHdey5/Dsmvt74Tb0jdUm6rtueWDkouOfSoL7LtdTIMwzAMwzCWF/HhzAuLroVhpEM4EZHyJsobs2HhkeoK++852aFwFsnvJJGSIQv3DW6gOpOh/arSGXXdISgmf4skWQWPdwzIxnVnUzONJK1eDq11lRPutupAYhVCnNRHkeJ2f8uENGK6Nv6RljDVfa9OE/np5xNf6oEbals+hSg/TNAqnYGN81AcYxRFWuFYvFbS137EIVQfDi9KxCWzXL+Als65/Q/LLuK+tr8cHU+ToNqW8NaYKVEdbR/B0mjOwsRJRzsKjSquUMm2dUoqzuZmii91cQ0i6XFdav+RF56PvPQjyYcR66Kg4RH4OZd8XQIGPutGFdfDQ9cJID7ktyEopBToV/9F2TAMwzAM44ngqY8hk1gXGsZckRORA8FEeWM2LNPFUb6LNqJ4FWddUnoKWbQlhUYQrEF9b2rRlqrAwQfjR7aXLk5HjG/Up3oAvbK6D1rn4rcj2T42N8JoAmlT2EvOdcdxbgxfSibBddxIkHQq6RDdqvPLdBYPxwtUVaiXIVdAzj2dzI1AArRyCI8/bBMOaYl8Z650liYe5He6Svdcexevte1GAtMpI57vvOuza8AZt18aQViB2gGKt4LynJggPxZjdHxG5f4WWdnNMcoU17EWVTnuK6/O2qZBUAT1XCdp86KPXUT92Oe/0QHQAy/oUSeXrKj17Bxtf7Vyr5n8WYMSrF1E/DzJTbNjK0gyYoDkXqBR+g60+r6bgoLbTqYEQX6AQL8kneCGYRiGYRhGf4Iccu2zi66FYYzGEtrRjIqJ8sZsWHikfB+CAhzdhqO76MY1yO8g3gI7EOI6ZLagNjwqOV150WiCvF+AwmknyMR1F2E5TQ1lrM6ZGdxYu46JAGSH2SqMWo80B84to6rO9qX6CCrJuS8+BX5yvDSC2i4SKXr7626en4HiTqte8XkX0d4Q/iSATKFzc17W+Tp3CG5DouSnhIi4OjWEes9PRi00HjvJaIbG36ru/2V5sCrj51EwpkwyqmYUgkSQ9nODz2OjPYp0XfKeux695Ps4TP72krpMcqOcrlCtUdgU5AEX1R7V20ZhNZIWt6LnxQs6hnuqNkYXictz0fOYtdW7MSqmch/Wr1hSMMMwDMMwjBVGrn0eya4tuhqG8cRhorwxG5YpUr6dZhS4wt47cHQH3bgKQXFx4nxchew21B5NVIwCHH6YcmlB1q86QbchWIs3He/4J5YhYrIqZIpoZReO7h6PTB2mRRdPIeevtz5HFcis9Y/GdRsdUmhvRATNrLuI2GkyNCJXQDIsR3TtpMLrk8yUj9uomnwjqWvjmmrYUHlB4lRTbbXDflZOUc2J2323kYEgjyajbWRYBYNia4SKxtPtfIp6jW7q6vTy/M7RAX6+45g2It1VPCfI+/kh95Y2Dm6i61eQqSbQNgzDMAzDMGZO8RQUNtGnPrqCo5qNJxrzlDeMASxjpHxQPC5ah0fw8BsAqN82nF88yG64KPJMwUWQz5I4ZKxo0ARV4PB2+iSf61fmEwU89ajnFRRJ/YLzkK7tdUazHkPAL7q217h+Mop87E8BOC/nyr3Z17eBl3HXzBTtjIajrmPIy7Lwcy3izp1Fy68QSWR8+/1aBPA7E/p6uUTIlgH3qCHtT+tQryfXaoT6BYgjpO89uD0Sf8r3xfrB8GWiWtIp4erXLtn3Xr6SXpjXGPbeRfOnktFnS/j8NwzDMAzDMI6TX4fKXbjxm+ilz5qnvLFaLGsw8AiYKG/MhqX8UT7kgo2qnZ/LFSjfBS+D5rYhv42sXcRZbcTOemZqftPaloh2xDVVofo4fVQjQPl+4v09AuK1Ik7jsHWOpc2HWduji/XJEjTbxT0JICgliWajJEp+PZm6LGYaHvFBEYj6RpP3FLq074feeNnUfvIigvp5t/wobWsaxLXE83r63vepSZs415gDKURs8ZyI3PO7rvWFpMOls11rx3ZkSKLnxkqxm5KoevXzzU416Sgvpb2VFxybdbzyXR/DHtdno3O5Y9k2e7I4RvGOH9q47d4TVRL7nzqprsXKA6g8RAunIbuB+Jnj9TUMwzAMwzCWiOQd7+G30EdvwbXvRU69sNgqGUYqxCLlDaMfIt6i41x7MGaN4roT56MKsnbezRMBP4t62UT4UCfqjyvS+1kIU0Q79iO/BbktQJwtzwBk/ZqL8oxH7ADIbUMfix/pYwGhuoydMzNCcXYyClQeQb2X9YUgxT4vOX5+cMRrs/yG97PnfObjglvXzx8XEcXvHLWiMaNcByKCBkXXVsboMJoIrQMByILuJCJzHiVgTISfm3yd6oPjnbPDCLqiiZo5Hjw0t+MkaR0hYWwcjXGt9bhGao/d9psjBKTDnkwEVEMgcPedfvWLqkmHsfQW/3vVpXwPyvfQ7CbiadLZsXxvBIZhGIZhGE887e+AGqPv/AasnUdyG4urk2GkZVny0U2AifLGbFhG+5o0Q/wHUdtD62WkLdLZ3QNc77J6GSeMhoepo5Hbyx4XabNlGB7cm9g1RClFHz/XSnAoPv2Elb5WCNO+SS67rlMeZE8zIV6AFK8cn188N0Ihox9AEUGzG1DdZa6R6xoDYeIxP+eIedUpjoIxZoZfSNTlMdZtJCJuH5USD8p3MCJeDmkv28u2nosaJZ2iPdq1Rkm9JuwEqz12HXe57b6LuA6DEA3yIGtQ2++93TiEw1vj1QGQ5z6HvvP7KUV9wzAMwzAMYy6snXM6hHSO7tRbX0Suf//CqmUYqRD6Bo2uEibKG7OhX/K8RREUnef6hOjeu8ipl3p+58Tx2EUWR5URItFljlHIiu69g6xfGR4RmlmDyn0nyBd2GKx8+WjHszyJyI5SCpvrF9xLgQiSG+BjF1VQP48s0takH6k6YsbsVSicO25rMUdEBM1tQW139A6niYhdckovR4ct0qyReV6TxnCSm0uzk7DRHqTj65HL1Mj93xDLc9sca1/dPZ3dHY2phWbtbFOeD2QSS5k4iWLX1nJeNn3i7dIlegr87R1aA0zkm+J8dtNtu37YWVcRKD4FR7ddfTeShNONY6MR7N/oX7+ojFz/FPqt30m3P4ZhGIZhGMZs2bwE0X7vILoHbxB7AXLxO5Cgjz2kYSwDFilvGH1YJlFeNYlgnoKYd3SHOLOGrF9A+owGENT5CseH6bbpBZA/44SZ6iMmigpOc0/ysj09yzvIrCV1aSyf7Ee/m17lbp+CeuyL34gYVchvItvXQBvikw6+sUZlJ8xnt4D4yXErFq81ImJRVRBxol1tb3j7mTZxWwdSsz0OaSuToLiOPI1GtzMxmLqPuJfDCdgCfuO+O+k2Gnk8ErsYVXeNDRPhu0vpm1woTf3iVieXl0nqk3RoKq36DUH8PvkXJGjzzVe3bwP2R8RtU7Prrh7t4rznQ3YL6vvHfpxpmg7oqAylM3A4x2TVhmEYhmEYRideAFsXXfDdoPfce6+hR/fhxR9d+O9Qw+iNWKS8YfRlmUT5oDiRPcwxHr+F1g9h+7neyTdJhPlMabhljnjg55BEINL8KagMFi00dtYaEvRIQjjMvyazhuR3BguNXsYl7Gt0KIRHcHDkbFL8lNsUzwlKUd353SOwdgYp7nQKrNAmyKdFofYIvByaKXVaRIyJij95OSlfVrQh/nUTVl1nCOqib+ctfKdAxHPCfP0gfRTvtGls18+5tqc6/VySzfL8ViSzsTik+c8sCk7+9EbPszGQtvuieHQmwe6zfFMAz7rPkQIT3Ac06oq4d/urjeum+96t6lx9RFrifBxCfT+pl9f7GQBJour2fUnyrLTts1x4Hn3z4VLe2wzDMAzDME48hW3IZl0epTS/XQ/vwOFdN6LdMJYNwSLlDaMvy9Rj1Uc4n4ij22jxLFLYGbBQiij5oJTaikXjCA7uwNFdCIroxmX3hZdxWaeVoZYbUrrgblyZNouYRjXb72fdnQniHU8iOggvi+7dgOI55PT11vxuQb4nKUc0xFWoVt2oBEgsIOouOrSRfLdZpLp5QcH9X9tLjoEkkaTSX2xKTcoHQnXP5R8I8kj7dSLaFtVKYj2RFCsBy2KoLyJoZs21tdo+C6tXo2NJPFBvNg9kwUVpR+Xpl30CUPGOJzRuirErQMf9MuWoJk0i7JsJvtssurycu494mZbdVKNDp9k+pRGSPqBebaJ1ymj5PgW5JKsat52TZD/j/ePteu0S0LoniQj4GdTbgupjKD7Vs0NRvAA2rx+brxo7n/7anht5FVWQZ74T/fAbcDjD/BuGYRiGYRhGJ9tXXGBbOGLerMPbJsoby4uJ8obRh6AE+VNOsIkWnNxtGsnltl9wgm75ARy8D4WzEORRvL6iugDq5/oLVJm1gYK8qkKsLsqx/ACO2iLowyN4+Ib7u3jWCdQpbkgquG32ivxt16SKZ+HoTutzUOqvIYmgfsGVG9c7oy+ru0kE5SjC7Yg31u72pfXem1Og1nYukiSEgEsYOS8OP2xVKbft/Pq9oFPsEqEz0rbr84JxkbQZV/+wvFjRumH3IQFOUEzMs1WB+EQ8qJeWOJw8gfaiEd/tx8COx0a7ilvXYkMoz5Qg46LEO1pa39EVPSLU+xGVk3plhgjz/csTYhTv+DK9Los+vvMinsspMeKIEREPskXIFNHiOfcsqx0gVz7h/OUt8athGIZhGMZ0yG1AcRsI3XujZHDvnbH7vVx7OFaxWnn85FjGGivGE2JfIyJ54LeBXLL8P1fVvyUifxv4Mzgj07vAX1bVW13rvgD807ZZTwM/p6r/y5TqbywpEuSRC58Dkgjv8MhN9UM0PIJ64/PR0OjuySriJ1YsE5LdQDwPXTsPxFA674TJ8AgNSkg/i4GGyNOIdG4IK17GWdb0Iiihlcfw+J3jUai9OLrrIhyjIeLY2uX0D9TuqvlD7Ij230d7iUZRFc2fQsKjtFt2kadBdr6WIWlFskFUd8dY55GbvAxaOAPZNZfsdkWEZBFxgpufSzo5FtVx4CydjiOtEQc6gkDfECdVLUq+DwrTubcuHElE7wHPIdG+96O+9/GJ8dxzQ0PXsevnaSnmndtUhnTaaY9ro2fHZR2NfRf53oWIoGSA8Z7XLuo+B4UcFE7Bxy+gX/8ClMf7gWgYhmEYhmEACOxcdRa4tRm8my/KstQwhvEE2ddUge9T1QMRyQC/IyK/AvxdVf3vAUTkZ4GfA36mfUVVfQN4NVnGBz4AfnGK9TdWAPF8yK67ic5APBcNXm8J9OEhGtUgqrkHQMf/Iwy1UoXMuovibWf9amJBkAzn9zIuyjEsw+Ett51uiueaNiMigq5dbBNNFcKDRJg/LtoIccvGAEnE+WS9HogIsQrcf63vMr33d4i9wdolJJNPX2b3zc3Lpa9LN4e3oHTBneM0xHX0jX/t/s6uI9e+c/xtp2YyYU2RySw74ro7Toc4a6LCacgUVkec9/wkav5o8SNj2mn3gxefwUmU26KJRdO314WyGu1j6RnWAbiQvqbYidhhIoIPuq7icHCnQlrrsaM7bmRT4XTPr10i3HRFDUOCDHzsR9AHb8FbvzmdQg3DMAzDMJ4ksmtQ2obqkKStkxDMcUS5YYyIeKv/e3ioKK+qCjRCcDPJpKranjmzxPCfat8PvKWqN8apqHEycdFzWTfltty8Pss2BfzD2+j9r9BXYFN15R1+0Dm/cAZyG21C53rrOz/TSmpXfeyil+PQifalTg+1nkKpxlPRxzSqwb2vMrLyMeQhLNm10XyJuwXJuA7+gNvFELFGKw+RTDFd9HtcndBHeRzGV5oUQR+8Plqn0SDCI9h/z5Wd23adRrkKMrHn/WwRETQounMXphjhMbOKBK3Emh2XRVvbU2l9blw77fkOVJ0gGodLnpRykZZGq/8CBCT3rkbbWLJ9OuEJhkWA08+g1UO4+cVFV8cwDMMwDGM5CQr/P3t3HitZfhV4/nvujT3e/vLlvldmbS67yoWXMtV4wUDbNRqMxPTItDSomZE8ltrN9H8zjNWNNC0kRt3/8Ic1bosB0RIGMSC3EO0BWjDI3TON8YIxtinjclVWVVZmZWVWZr79xXLvmT9+N96LiBf7euPG+UiR+V4sN34RL+LGjfM7v3Mgu+BWk/u1sjRVt1K7/GCsdy2L58e6fWOGInNQvgYOs9y/AVwDPqeqX43O/xXg54FN4CNdNvNJ4HcGH6qZd4cB/KWLkFlA73zteHayl4tqsN9rPN/PQP5Ex8zjWlM7CifcCSCz1FuwVTw6Z+F2p9USevuvjmf392LvHhRPQmW7dYC+n/Ispa3jWZkH9+BAXK15SbtMYqiLCXYJZgUHaHYF6SWL2svW1WsuuMeWWXDZnvWPrfkhSXSm9lp/va6AcniAhtWj87X+/7Du+kSrKxaiYJ6ib33LPe/jEJW3keLpETSiHT8RifooHEwvmK3V6E/rA7XgfF2TTQCNVsTU6ok3OwzUins99tSgOJmU2T/Q6ah+39gu43ySPSfGwc+5viCH6ialDn+vnR2i7Vb9SGbkSVgCyLl3oaceR//m9wf7/DPGGGOMSaL8GuQXoXQf2HfHSfWHqpNYVV1YG/99GDMIERhTpryIfAz4NcAHfl1Vf7Xp8seB3wSeBT6rqv+m7rIbwDYQAFVVfU+n++opKK+uWPQzIrICfElEnlLV76jqZ4HPisgvAZ8BfrnNA8oAPw38Urv7EJFPAZ8CuHjxYi/DMnNMcmtw7oMuMF+r5Z0quIaoYdkFcBcvQCp3OHs2UCmQbjNvknIZtRr0/KGoiKsbXpcFqZUD9M43Bg9IhGXYvumaDqby7nFrANWSq+te2kFWLrvGepWt9ttR7VAmQRubv/Zr782eytjoXt1sf3XfjWc/RiVR0gvove+OLjM+YUQEzSxF78spZnJrEK1g8d37OAyigKs2XqcTiYKXXg7CGL0GJyksu8mhpOra06Q5gD1BozrG9HxcbkXDmS2vqgKU20wyZlZHOKhGksrA0/+NBeaNMcYYM+cEls6A70UJWlOs6Z5fczEWY+JqDBNTUVL654CfBG4CXxORP1TV79Vd7T7wi8DPtNnMR1T1XpvLGvSVAqeqD4G/AD7WdNEXgZ/tcNOPA99U1bYRPVX9gqq+R1Xfs7Gx0c+wzJySVB458zwsnHOB6N3bLoDk52D1OpIpIp6PDNM4s7R5vJ66V9f01M8ghG23r7hMU42C+wou4LB14zADVcs76O2v9tbUtZvKrlslsH0Tdm7DwX133sF9xEu5Mjb5U+1vP2gwJFV0Jz8XZSi3pqUH3Sc6MgtHPwcxCHz7eReI9wtoUEW3XrOAfBcinpsgmjo9apTZHJDvazPV4foqzLSYlXQZKXH7mDA6tcySn+AKFfEgDN0EUu00RySVQd79Satdaowxxpj5ky7C2mVYXIVgC8oPp9fEcuUKcu0F5Mn/1vUHNCauPK//U3fvA15S1ZdVtQz8LvCJ+iuo6luq+jVg6MBQ10x5EdkAKqr6UETywE8A/7uIXFfVH0RX+2ngxQ6b+TmsdI0ZA/F82HgWffhD2I3mfFauHTZmHZ66D8TDMjYelLeOGueF4bElM4rA/l3X4LUuw1QXL7syJwdRV/StHxLmz8Ctr46/1EdtNUE0wgb19ds7BNQ7kfzq0da1VhZGXaayqtt+WIGgjKYKSGWn/ba8EF25Ag9fGWgsI+Nn3WPZeaP7dc1xXobD18GsE3GvYS/jJv5MMmgVCDu/RCfV30K84xnqc/glSDzPZcx/47eZ2goFY4wxxphJER9Wz7sSNaWeEmtHz8+6UriFE8jqI0jx5HTGYUy/xjNxdQ54ve73m8D7+7i9An8qIgr8W1X9Qqcr91K+5gzwW1EKvwf8nqr+kYj8gYg8hvvW9CrwaQAROYurufNC9HsBl/b/P/bxIIzpmYggq9fQ7CK6c2uEAfk65S3XiLZWf7gWmJOFhqspHmz9sPU2tm8cO0v2b6NLF2Dz+GUjlV1FVV1Gvx6d50rvREFzosBjZaf/IFRdeXapr999GORPAVGmsaqryd4hMI/XU2Wt8fDSqKRg5xaJCChPiYig2RX3d07CyoJanXk/16HEk5kpkgLfx+37wuMTLpIac0C+1otEXGmxlpdPWAwWRkgqA8/+HPrN38EC88YYY4xJpMXTkM6BVqD09nTGUDiBnHwnrF5D/HT36xuTDCdE5Ot1v3+hKXDe6htRP4Gh51X1loicBP6jiLyoql9pd+WukS9V/Tbw7hbntyxXo6q3gBfqft8D1nsZuTHDkMIpyJ9wZWwORvzBJmlXAqO5vEt1F00Vj/WR7IufgsXzruTMOGQWkVPPuCCpBq6mvJcBP+cC6AKufwVAGvUyEIyxpq+ImwxoF5T3s3D/r8d3/52kiujOrdEE4sSj8QWhUdNRzz0HrRqMItGERH2GuczQ3IDXMFst4rsJobDMDD2I9g4z5nNAMMXJBi8qqzKJCGoC/m6tSKquXE3UDNjPQdTzA21q8DwWOpqyZSOiqv01BW8gaP1zptFni+pAr1JJ5+Dpn0X/5v8acDzGGGOMMTHjZ+H8M0hhCX3wGmxPaUV2bgU595wrUzOtEjnGDEtk0Ez5e12ar94ELtT9fh641evGo5g4qvqWiHwJVw5n8KC8MbNExIeF867R5M7rbQKfA/AzUG0RPAlKUfMTtzMQDdHla64MwX5vTVFFBE3noHgadt8czXjrt3/y2aOZb8XVxJdU+w/g5augIXr32y7o2O56XtodWFR3QUOEplILHecXFdIFqLYoBRJWkQs/El1NG+9fQ/ecj4sGgwfkJeWa4Xiuz0DrbGqBxYuIn0bDkMYlBu6fln+XWWmwI9KyvIt7REkK7kavkcM68+FkA/R+xu3bPIXm911HTa+5XsKlXgry7Zaw1k0cNTTQVTiY0vLbXrV8n7suIJN7qSqkF93TF1RGsAKjzd+z9BBtniBs+XNNXYa+yFGgXrzOq5hUYefVxvNSBTR/crDAfG4Jnv5H6N9+aXSf5cYYY4wxk5ZfQc49DWk/Ku9a6bW29eitXkUuf9Qy400yjOd99DXguohcAd4APgn8415uKCJFwFPV7ejnnwL+t063saC8SSTJLKErj7l64OWH3W/QVZsl9JllF5gWzwWyw6qrJ59dcdn1qbzLTO82XhE0uwDhCdeodVTyG5ApRgG8DOL7Lks+ymA8ely1WUZBfFdSQcNK7ysOCmfB6zNoogrBXn+3GXezzeAAssuuwW8/CqcRrUZNRTs9D0eTDGMps2QmrG6/IBnXX2Kck0b1DrO5x1veY5Bgqo687MqIexM0ZMlPW1TTvlsD7J50eo76eP6aJwZrv3fJRBERtHAO9uqyvqp7UN5CM0uDB+Z/5L9Dv/8nsNVzgooxxhhjTDycfxZZWOaw31rNSI79+rR8Ebn6Dy073iTHGF7LqloVkc8Af4LLgPsNVf2uiHw6uvzzInIa+DqwBIQi8s+BJ4ETwJei91gK+KKq/nGn+7OgvEks8VKwdAktLbng/KAZ0F66db3f9AKk8tGHmrog9tYr7sO21li1XHZ11dMLUUZ5+zGICJpfdbc/uD/YWJu3uf7E0YduqwaV4kfZzS7LVzWAIHCZuP0ErRL0uS5+Fi2ccn+D7DJH5SyiLNrGa0Mqi1Q7TC5kliFdPLr+QAdgCWmYmmQSlf6YRM35uL8URr4/GPUDjvsTGFf1fUKaRJ9t4qfR3HrjhG7pPoiPpheQAZ578Tx44uPot/897E+p5qoxxhhjTD9WLyGnrgNVWh57TjooLx5y+aMWkDfJIbikuDFQ1S8DX2467/N1P7+JK2vTbAt4up/7sqC8STzJrqKpBVfOprLd/waay1Kk8u5zNb1w+KGmYRW2X2uc/a7RAMqbUXB+KRpD68CECOjKJdjORqVshgse6fbrkHmi/YevBod3oQeb6J2vJaMp5zC06mKK2SUOs5A7/Rk6BeTBBWjTRSQ9IyVozHDCqlvREY4pY96LJszsgHpAMubmrX0Sz03mDLPfndSc3WFT8FYDiIYQtFlhdXAXypto8dxggXlALz8Hf/cf+r6tMcYYY8zEFVZwAfk22iU6jMvCaSSdn+x9GjNWgyY8xosF5c1cED+NLl1xwYLd2wxV8qFagvzJuoB84ALy3YIqteC8l4FUru0EgYQBZIto9hpUK268g5bE2HwF1agUTRgghZNQPAl7b0VBPS96DGV48NIQJR3q61QnxYiiXEEJdm+hS5fd6g2TbIfNYMcQmI/KZMU/IB/j8Ykfj9I14kEYHP8c8DJRSbTaGTn3n+JeV616m0zr+ZZUFKSP9pVBGfY6lJgJy7D9CrpwwfV/6ffulk6jl34UXv3/BhuvMcYYY8ykdAuAT7qUaf7EZO/PmEkYU6b8JFmEyMwNEYH8CTSz6ILo3TKc28mvIdGbXzWE3Tf6C5qHZVfWJr3ognb1wXw/fzguQSGVQpfOu5jH9s3BgvNbR4339OAebL8KlZ3+t9OOl4LiWfe4gkrdbGWnoLage/cRiWrEa7egvjb9P4OqB5BZGOy2wb4ro3TsOao1YEwx089N0ogAoQuwQuvSUQNtN824a8ibCalG+/Laa6TGj/7Grd7OIm61VfOFI+mbMoimcfgZKF44bFrteC127YOl9gsgp59AF06g3/3Dvm9vjDHGGDMpkurWC22yQXnJFLtfyZhZE/tkte4sKG/mjvhZdPka7L8Fe3foKzjgZ6MAKKiqu32lVeZiDyrbUUmbxaNsSe94F3QRcRUXli/B/b8f7L5qli7Dzs3httEsrCJ4SHoB+mjirj/8Knrvpb7uSt7x4f7GFifl7cGD8hp2npDxk7F0Hfv3MAAAIABJREFUK3mifYuXjUpFDZmhLTIjcy8xHqRWj1YcTFNYbb2Swl/pdkP3X8NTPKGD0WNzgkGULR8C2hiLP3zt+y1K3gz5+ljYgLPPwK1vDbcdY4wxxphRWzkHy2chnQHtsJJ+wt/d9OENOPWM1ZQ3ySEy+RUnYzD7j8CYAYgIUjgFK9ddY8ZeBSUobboM+YP7Rw1d6+XWotN640lazIFpAOUtF5j30h0Dr0IIq9c715/Lrbma960snIPqjlu6ttCqJ8UwBgmyxDhwNw7VXVfqaByCA2JdMmTuhUR5vkNuZ1beMzF/LWro9sfTnMhKD5GtpOrK2NRO03y+NWpeptXWk05heeQ1UwWQjesj3aYxxhhjzFDyK8gTP4mcvo7ki50D8jD5DN+d2+jLf4IGI1rBa0wciPR/ihnLlDdzTVJ5dOW6a6p6cLe3GwX7sHcQBfObluGnF9uXxckUobLfupRFecttq8tOQkTRlUdg6zVYOu+C+F7WBWbSBVeWJpWG1OmjsYnnfq6VrNGqK5lTPAu7Her/jpvOSoBxhMo7kFsez7aru5CyZrKxJYBkhqszHwaxPJCYSYcBZI+ZLwkU99dEWI4aFI9wUtIaZxtjjDEmLjKLyOX3dg/E15tGcsiDl9Ht2+jCGaRwwo2heBJZGnXCnjETEvfvQT2woLyZeyIeLJxFMwuu3npPZQ3UBefzJ1wZgtIDd7afcc1ZWwlKrsFr4EWZzS222UOgWiREl69A+b47oxbkr68T361evggsnHXj2Xzl6P4nKRyklMeMB/IHLV/Ts8FqNZsZ4aVGG9icGWN8TU+plI0GpeP15CM9HVpKqm5iYUIHo8P08w7LbjXYqJ5rP0UiJlSMMcYYM9v8HPLIc/0F5GF6Kzar+/DwZfThy0fjeNfPI5bwYGZRAkr4WlDemJrDzPc64kPhNBzca11aJqy44MjqE7D3ZvdgeFiOGvnpYE1ba8OigmZXjyYD+pVbxyueguIpdPVRd97ubfTut5lYUDcYsr62OU419pVD5poGLjAZ9nnQDm7/NDMB+VmZGJriJFZlq+XZPX8hqi8VI9JUIqbFTkCazu+pt3bTc1NbddVq420vq99m7U6Hf84F0Ivvhde+OvS2jDHGGGMGIj7y6I/1H5CHmGT4Cqw/5hL1jJk1o6gOGwMWlDcGXI347RvHg17FM+7Lf/4k7Lze+sbFMwiKLpyD6gHsdmmkGlZcw1gNBwvO1XhDvH3rZhTFi4I5i+fBy6Jbr7jn4eD+4NvvRT8ZkytnkXPXh3u+4mDcJXu0CvgxOcgzx9QatTZkOffAz85QQH4M9t9G0eg5qHsPZZaRdj00uvKi/aC6gPakn99U4fgkbu4EXQPWenxCV1TbZt23lS4OeBDbany9ptCPdv8nZ55CN9+AzRE3LzfGGGOM6YE89uHBAvIw+QxfPwsb70CWLkBuCZdU4SPpQY+ljZm2eNaI75cF5c3cU1XYecMt5fLzkF5wZWjEQ+qDCJLu+KErAKkcunwd9t+C8mb7Ow3LriZueWfwYFB13wVWKrv937ZNIEuKG0hxAwDdu4ve+WZvwcNB9oX93ObhLTS3iKyuD3BHMbJzE128iIyrS3hYhlSR2clUnkMCruxGj2VTvOwM1pIXyK7hSnLB0etRoDzA6p5Bv+x0VNd8t58JklHxMkBdUD5VaPy8aUfErZqo7jHv73MBePwfojf/Gt745rSHY4wxxpg5Itc/DAyT1DHZY3t57BOujrwxSTJT35Fbm/0CPMYM6+AelO7DwgUkv46ksohIQ4BEUKR4ChYuQPEc5E8e3b6yg9ZdW1DIb8Di5c73G5QgszTwLLmIRAHYfm/oI7nuwW0pbCAXPoisPwUL3Zq/TGBn+Ob3XYbxLNOqa8g61vuY70DdbFA3ydftfSNpN2k3YwcbooHbh4qHeB7i+dHJG/F7eNDnxT/KktdqU+mXCfHSblI1XXQNwvtp0iy4bKdhzNZLqi0B5Py74dGfmvZQjDHGGDMvrj4PQx4+yqSP7/MzntxmTCsi/Z9ixoLyZq5pZQd2b0F2tacsRUHdB2h9QKT0AHZuue3VIh3VfUBg+TrkT7ffYLAP2RUGjpAEB32XsZHFi4if7u26qTyyfAlZeYTOY4zfzi12vDQsXUEyi2O+IwvKz4bQrcjp9N7xkliKKA6PJ8StVIh+nUJpICFE/Fx0yvSWJd+wgSEP3xK0mxDAW70AZ5+Z9lCMMcYYk3SX3otkevsu3dGkj/HvfGuy92fMRMgAp3ixoLyZWxpUYOtVyK67bMVhLJyN3uIu0iGpvMsMRZHMAixfa5/ZWN1zgXm//wYrgkZlInqUWYJcH9ev3U9mAQqnOlwhfju32CmcPqrfP06zXnd/nmjoJmtaZWontY78SOtnDrrfiTLk6aOvRezYPreZnHnntIdgjDHGmCQ7+TiSHzJucGiyoTi9+ZfowxsTvU9jTHczXgvCmMEcNXatQrrHWr51BEULp6G8DdmVjrdXxAXXgnL7DQb7UVZssf/yJtX93htHeqneA8P1gUIFWb6KZpbc7dNFl2lZ3Ue1ijuo6DNIlFsD9TneJLCu/nTD8ypoZjUqgdHivmpNNFudX0/VPcflrf7GOyg/5zraT2riQqtAn00fzRQp4IHn1U2oSHLLEA0SlM9tuP3j8Y0xXMp3fbaENp3f5WbD6qfRdesNjGAQCZPKwOolePDqtEdijDHGmKRZPoesnxtd0szE8ysUfenLcPZ9yNn3TPrOjRmPBOQpWVDezKfdW1GjPBg0uCFeCnKr3a8YlmG7hyCBBoAHmUXXBPDg7d7GIaCpHFR2ulzTQ4pne9omeMcayEo6j6QvNF1v2e0HB6mT/tZr8OBmXzeR6x9A0iMIOKeyaPGse87277pGmn01exS36iC75ppQVnZcENWLljJ6KRdgVYXcCjLS7OAeNM9zmHgTjl4/4kNYJblB1wFemF6qzcTnCJ6jQZrIDnu33pD7MNVoks9j8Gz/5O0kBODRn0Bvfwde++q0h2OMMcaYpFg5j5x5bMSrWKdzHKa3/gpOPO5Wwhszy4REVGywoLyZO3rw9lHAu3hu/B+HXh815/wM5E8gGqK5Vdi9A5Xt7rfrFvQtnI5K5PTQHFB8CCZQAmWgHejoApWCQqqALpxHRNCg6lZP9GLhHJKqlRvyXSZ8rAQM3X3ITJbUsuNnr7HrTBLfTZjOqsqYG0bPKAHkzFPomaeOzizvoy/9OWy/ObVxGWOMMWZGLZ8bQ0Ce6R7vb92EE49P7/6NGYl4Nm7tlwXlzVzRyh7svOF+kdSE3sQCkoaFs+4+AUr3XYNYPMifhP033fXyG0j0gS8aooUN0BMusz8otb+H5avUz7ZrdR9Km7XfEK3CwT2o7KD5jQ6NXmU+gj2pogvI+VkkOHDneT5kV6O/Swu5dbeKQQXxYx7wDkrgF4YouR1NfhwGigHElVip32gYgiQ1o3sKEnBQYSZAxE3EDl0CJ7ka3kmZPDzxAvpXvzGt4RhjjDFmFi1sIGcfn3yfJ/Hd9y7xOSxzKb4bR7nb6vgepPPDb8OYOEjA92cLypu5oWElyoSOgoh+biKLxgRFly43ll/IraPZVRcAzyy6U1BGmkopCIAIunAeqgew+8bxO1i6Gm27bvteqnVZhnTRXdZOdb+fhzZ5o6iznSocZcjWTXSIiFudAI2B+cwSZFc7TGTE1DAvbi/lniP1oP61FTYfkApoMmaozbyZ8ckkP+96kVhgvje2jzLGGGNMP3IryIVnBit1WM9LR8drWvddViEsI+efjc6Pzut2fOpl0df/aojBCKxfR5YvDbENY+Jk9o/xLShv5oKqwtardY0UgdTkZohb1UMWierHH04SpIFsVJ+98fqu1EoWXbnuaqCXHroLFi70HmsQDzJL7n7Fa5rx9+pq7I+etgymT2EH6ucaXwNNRDzIr7vgvIYuCO1nkJkM6AzYBFOpq6/fQ8BvJp8bMzUDvV76eS23atza6XozSnCBeUI3YTvrkwxjJoCeegrufGfaQzHGGGNM3KULyJX3Dh+QBwjKffYuG5PMAvLEP0IsS94kyYx/pQMLypt5sXvreDPSWGY+hy6TWwOolVWpIxqguTVIL7rgctvHUBegyZ86rDkvQfnoMvFcw0ENIDx+XyNV3YOtV5qGOMAywGEy5WuPtQeHExedVhXEneoAH1IC9HPQqEeNSS04b3ogqRykctFeqPaaEffj/t3WNworNE4Q1VZnRMF6bZFZ5Gc7Z5GPYtXNtAmA51ZABQdRg2DTjlx+P7p/H7ZuTXsoxhhjjIkrP4Nce340AXmIyoGOZlNDySxaQN4kTwJiEF26Qxoz+/Tggaun3uygTe3wqVMXEE4vRHXkGgm4YHy60FvMNXrsLtu77ohAwyiQM4Gmrq2CRZMsuyApjkWovQzxOEIak4ESkge4UViuC5Aa0xv3itHoFNL59dP8PlW3/9DgaDlwT2r1OMPRfdGKA5Hos8Leg50IINd+fNrDMMYYY0xciY88+qERHyfG5Phs/z66H9f4hzEDEKLvQX2eYsaC8ibRtLoPO6+3vjC7HO+QrIYu2zNVPHZR1xBofRaoBnDwdpsSMh02EVTRg020vItGAXRVJdx8hfDBD9rcbYgGJfd/GKLVfXT3zdZ/g0EyVR/cQTff7vNGtYz3ukkALwNh+8a5iTDIZMug2cMauGWZtVr9xsRVWLdaKElEwM9MexSxJ+ksXH5+2sMwxhhjTAzJYx8ZfeJGXIKAQQl9/T9PexTGjJAMeIqXGa7NYExnGlZh6wZtAzDlHcittr88NhRSCy6YdCzo2a5+ctPvYRnKD9HMSs/10fXt78BBFABfedTVWj+4D5svg3jo8mXEOyqfo6qwfw/23+rtYXnHVwF0HdO3vgxeCvmpT/V+o1TnOvKJFVZcY6FOak2FRFxA3vPi/3YwpidN+znx52PSyM+1LH1mjsipx9H9Lbjzt9MeijHGGGNiQh79CP2V8eyRn2vznazpWDVdbDyv9p1d684Oh1xpvvU6eu9F5MTjw23HmLiIy6TXECwobxJJVWH71c5BmNxqywas8RS6bG8v09QIVtxjTOUbA8+tMp6r+4CHZpa6BubDrdehVLe87eHfow/rrqAh+uAHsPwIksqiGsDOLShv9v6QBt2BhgGq2tvkQqrY+jWgVQZuhDpL6g/iDs8T8CSqr187sPPd32MeJy9MQjW9twfpYTFrapNrqXy0vzetCMDl96GoNX41xhhjDHLtQ+CN6Xth9QCqO92vlyoAdcer9cM5/Orff1JbM73x5+j2G8ilDyMDJMkZEysWlDcmpvZuQ6Xpwy+34YKO5YetbzMTao1gQ/dYas1rKzvg5yGKybSsoZ8qQGYBxCPcvHHY/PWQuH+keBY2f9i95vvubVfWZvUx2Hq5/4DuwI0WFd58Bc5c7Xy1VKH9pIyG81HCRjUq3aNHv1Nt7JkJ8xGwNPGmIeRPtrpgsv0nJqHtZLE0/iytzq8/r5ZBVbcvrfUOSeXpPPEoHFUw7LQvbnXfo/jS2m6VV/19eF3uq8Nl2vk6AnDxvajnwe1vd7gPY4wxxiTa1echNcbAXq9Bw57yxUY0cfD29+HkO6HY6tjbmBky+zF5C8qb5NHSQ9i/23hmZhlSWSAL5S1YODdDWfLNokCr+BCUj+reBftRbKZFAMvPQv4U4kVBmOwS+tY3W2/94Q97D9KKDzuvDZZhXR28lIR+7y/gxDlXH7gVP999TEkvZeGlQMLkP06TCIKOb3JoZkrXNKVE9foRJa0O5ZTOGwjiPxkn3ljHKAKcfxYNKvDW343tfowxxhgTU5feh2S6lPsckm4/hIdvuiQTjY7PVI9+r/2cu+1uEAYQVNz3/DBoPKXzkB9yQIUN5MyPQGFj2IdmzPTNQ6a8iOSArwDZ6Pq/r6q/LCL/CvgELgL4FvBPVPVWi9uvAL8OPIX7hvjfq+p/Gd1DMOaIVg9gu66paP4U+JnDALwiUDg5wwH5mmgqPbPgPsij7H+3T/LQ/AZUdqG65wI2hdNHAXlAFs66Zi9vf/f4prWPWnriDV4mIRiiVErlAB68BScvHL/My/T3GJJK0rScoDFmrsiMBOTNMRNYISEicOn9sHgKfe2rULGyP8YYY8xcOP9uJF8Y//3s3oe3ftD9er1UgQ0DyC8MPpa168jlH7eyNSZBZj8o73W/CiXgx1X1aeAZ4GMi8hzwr1X1Xar6DPBHwL9sc/tfA/5YVR8HngYsHcmMhYYBbN/ALfv3YeEC4qcbAvCCIl4CFohocFR+RgSy6+AfHVRIrXxB7gQUz7Z+zEuXYeX64GPILCPDdKevDlc6Ru/eOH6mpOhvxzz7O/GWvHT8s2CNGafD2pu9HOaYeJrMPkxEkPUryLt+1pZxG2OMMfPg9DuQheXJ3NcI6sAfGub73cJpLCBvEkVksFPMdP22qk6tOHc6OqmqbtVdrUiLddIisgR8EPg/o22VVXWWC3qbuFJ19dXzG8jCGSieSUA2fAfiN2WDh648T3bt8INfRNx+Jyy3DEyJCLL6KCxeGmQAkFsZbOyAVkIoD5mRWGq+vUT10y073JWuid8HjjGTJXMyOZXg9/oov8h2vas08uhHoWjLuY0xxpjEOnEVWZngJPwog+CDriL00siVn7SAvEmeBATle0oZFhEf+AZwDficqn41Ov9XgJ/HLbb5SIubXgXuAr8pIk9H2/ifVHW3xX18CvgUwMWLF/t/JGa+BSWkroZ4/N5qI3QsIF9PXf38sAKV7eisKpQeuPObNyUCJ96BhmXYjerYiUfDMyi1hoAhh80F00Uk6LMkhPiQW3Ol9O58DwrLbtsa1ZIW3/1e2j/Kol87A5mcW6YXhuBH1xEPvKyrBRw9bHLLEFbrJiB6eBWE1Tjul4eX4PkoMwltGou2bDxa//MY68L3S6J9VWJK10iLALU2/Z9Ek91BSzoH1z+KfuffQ/Xg+BVWL9HQiU2jf0o7sH9/cgM1xhhjTP9WLyEblydSIu/QKFdthoMdZ8uF55Hs4ujGYUzCicjHcFVffODXVfVXmy5/HPhN4Fngs6r6b3q9bbOegvKqGgDPRPXhvyQiT6nqd1T1s8BnReSXgM8Av9xi+88C/0xVvyoivwb8L8C/aHEfXwC+APCe97wnyd8wzaiFVZclPxd6aXwXNYLNrrvAfC0oVd509dbTxYYDEREPNp5G0wtIcNBHECvahpeBzBKHTWvQKHCePsrY1tDVsA+rrtb9Iu0z9L0seDkggOpO3QW1gJS6y9hD3/yqewxrT0Dp7ei56WP3sVgLsCSN7UJNLzwaXis9z1Bp658lFZ+gfHDgyjglRcfJ2ASbwmOWTB4u/yj60p8fv+yRD7XMMtPtt9C/+w+TGJ4xxhhjBrFyDjl9bfLHqt4Ig/K15rD9ZJWtXIETT4xuDMbEyRhCOVFS+ueAnwRuAl8TkT9U1e/VXe0+8IvAzwxw2wZ97SGi0jN/AXys6aIvAj/b4iY3gZu1zHrg93FBemNGQ8OjjPDEE/orzRK6AHx2te6sssuar9t7qSqEZSS7HJV/6VNYhlQO0gXIFCEbZeRrBYJ912w2OOAwi3bnjS7bK0F1sykg34aXQdafcPejVfoPRic1eJ3EiQYzerUJtHgu5TNmavvo1YuwdvX4+ZrUzwxjjDEmwRZOImcen07yyMhL8fVxzJ5ZQi59yK2ONyaJxlO+5n3AS6r6sqqWgd8FPlF/BVV9S1W/BjQ3Wex622Zdg/IishFlyCMieeAngBdFpL5D5E8DLzbfVlXfBF4Xkceisz4KtJ0hMKYvqlDZmezys6mJSjEMKrsGqeLR7+WHEJRRxP1c3nKbz627QH6vYyqchoULRzs38aHcepJEAbZeG/wxNPOyyMoVqA5Tm94OUIxJJPHdKqrEsGDwJIkIcuV5ONHcDL3d38E+S4wxxphYyq8gF941vdWco8yUh77K4cjlDyPpwmjv35hYkQFOnBCRr9edPtW00XPA63W/34zO60Xft+0lLfYM8FtRGr4H/J6q/pGI/EEUbA+BV4FPA4jIWVzdnBei2/8z4LdFJAO8DPxCjw/GmM6CfVc7fS54uJItg1LwM+BnXRkbDdzzJ54L1gdRDXfBZdb7eTi4135zfhYKLRrkeGnaBi1UR/j3EmTlqnsMQ1ESGUwJDsDPYU1vTWcJff1DlJVkgWwzOPFTcOV5yK+iN78eLRmf9qiMMcYY07NMEbn8XreCe0pkYR197ENHfdFEXPlYEfTejdblaKR2wFHXxwZ1P1a3ej98rwz7XdmYmBtsFcg9VX1Pp622OK/XbwF937ZrUF5Vvw28u8X5rcrVoKq3gBfqfv8W0OkBG9O/oDxkhvQMkdQI6+rWGsFWobKFeK4GtOZPuSC8Bm7HlsrBwjnYuc2xwG5mBTIDNIpp25hG3IRBbWKgB7L2+AgC8kmmFpg3CSWNzahb7Ru9tJsAtKW6yTDSz8A+71oEzrwDTj7mvtj6o16Cbowxxpix8DPIIz861YA8AF6AFNutRN/tP2mtn8PbVLa/bRszS8ZXgvUmcKHu9/PArXHddoAC0sZMWRi4sjXzYCzBCAXPdyVtoqauQojm1l3998pOtHMTF5jfvxvVhAdya1DZA6LGrs36CKwDkMrD6nXYfwA7r3e/PkDhNFobX62xbK3OryouCF3/e93/h2V23P+imthEYfccWEDedNFvs6hpED9qdBrWNXOu7X88tz8Dd14YHE0uJokG9NUGSKJ/4tJ4dxgxeAzip8DvMBldWEGe+hncpBHUl5zTV/8LbPV6HG+MMcaYUZDrH5x+QL4bSXO8JPUI+ZnxbduYOBjPd76vAddF5ArwBvBJ4B+P67YWlDezRTVq7DoHa8jHmh0ohwH5o3MUTRUbJzwEKJyA8q5rEFs9cGPavwOFUxz7O/gZ0DxUS3QNCGdXYOUq4vnowgbk11wT2Oo+VHbb306rcP9YC4vBLD8ymu3EVVCKsuXn4P1ikqFWAktxGfEaAuHRvvDY8l4ag7ZJC8Y36GOSLVFv+dB9aZ1StnwvxE9DYbXlZZrcmV9jjDEmni69H4jvccMhPzXeYZY2YeH0GO/AmORR1aqIfAb4E8AHfkNVvysin44u/7yInAa+jstWDUXknwNPqupWq9t2uj8LypvZoeqCtTHImhu7cS/Xb9sgJoTMkmv8enRl1/w1vQC7t4+CYLu3oXCmruYdLqAfHLia9GG19d/Ky8DSRcguI1HjGxEfUj6sXEHLu/B2u37Q0jQ205UGuOxNCwyZVqb9uogy3Q9XtdTtM+aiibfpSYwD8sYYY4yJkdVLSL7ATGQoyHjDcfrgZWT9sbHehzFTNaYYh6p+Gfhy03mfr/v5TVxpmp5u28mIW0EbM0bBAYR9lkeZReKPOSDvH5Wjab4IXBPX7ErTBdGS/IWz7nIAFPZuHR3v+Nmj7dY3ka2X34CzzyGFjcOAfF8ySy7YPyrzUAYprHSYhDFmSsR3E3QQBeKjjHhjWlLwZjSPxPa/xhhjzGSkC8jp68xEQB7G36vm4Svog1fGex/GTFOtrnw/p5ixbwpmNgRlV+886cQf70qADgH5w6uAKyGRXW9xqbq68pnlo7P2brss1+YM9rAE1W1IFVzAPr+BrD6KpyFQC9i32Cl2yo5N5TuOvV9699uED19GS1to9QCt7LmfdUYO5HoVHOB29/H7EDLTNsHXhPguI0hSUX34qr0kTe/CytgzysbCgvLGGGPMRLjGrjO0um4CxzW6/cbY78OYqUlAUH4Gv92YuTMvjV3HHZDH6xqQPxwKgOeh+Q3X6LVZKg+pHOzdcb+H1WM16g9VdwBBMkuH8TcRgbCKpgpQbaof76VdIF/EjbnW1FWO18EfnsLOTXTnZuPZhdNQPNMUTNGmn5t26H7W1RWOq2A/yk623f7ckFpZmA4TXaM8LgkruNJ5tfsTGjKcw+qxZsvG9EWjwPwMlLHTygHcvwEHD6c9FGOMMTGmqXzdMZsiwYGV8BvE5eeYiTry9bwxZ8oDbN5Aww8gk7gvYyYqihPNOIvOmHibl8au464hDwPtrwTQ/EnYf6vpgmgHWDwL5e0eguXt/n7HByXpHGy84/gWVOHOt3oZ9vD23kT33uzvNrl15MTxcceKBkAGKxOSUBI1Sa0FvGulnryM27+M+wueREF4rQXeo39qAVQLxJtR0AC38ife+zFJ59C1y7B9B0rbje+/dDSxLZ5bCZhdcpNXQQkq+1DamYmJB2OMMcO7+ztfofT9Hxyd4ftkr18l+8hlMmdPklpbwM/7iFRBPddAPBQ0VDRQtBqioSIpj3R6e3oPZJrWriC53LRH0b9JBMpLW+iNP4MrP+kS44xJCiER3y8tKG/iS9VlyCf9i+m4a8jXaDhQNr6gaP4U7N9pvU0/77apA9T715DDbNpuggqxDsLMSkZLWAIvS6yfS9NCD+8TiQLgWvc7RAF5jYLzYSwnAM24tPpjNJ/X/HvcP3OVo9VK8Z6wl3QOufYhNHwe9regcgDZIuSWOn4x1tIO+oM/h723JzhaY4wx07DyX3+E3fNn2Pmzr7gzgoDSiz+g9OIPOt+wmedx4X/95OgHGHeZInLqkdkqW1MzqRJ391+C089C4cRk7s+YSUlAmcjZfwQmuYKDMZQriRPPnSY56TBg6RIhPF5jPlTYf9sF6w/uuUBvn7XYRYPjzWDbqex2v47pTkOXjTmPu38vfVTTvP5UazgaR15tnBKNv0NGTad9iUj0ZSWaCPPS7pS0/gmmibY4hU2noOk0C8KZOggXL4UU15CVs0h+uWummmQXkCc+DhuPT2iExhhjpiWT26H49GPDbygMOx8nJpRcnbE68vUm+PfS299Awxl9noxpSQY8xcvsfKMx8yWsJLuxq9TqLk8yW1mims/9UUDDEEr1GXsCe282Tprs38PVk+59t6I9TkqoKuxoXb4vAAAgAElEQVS2yNQ3Awqj/gLx+1Aaq7A5GFk7xTAwLX4UNK/bT2jgfvfSHP7txHOTCl6694dxmFEfzN1LwCSIVhMdfBA/jVx+DtavTXsoxhhjxszLjubzTOetd9SVHwX6/34bG5Os8/7gh+j3fh+t9tZjzpiZkIBGrxaUN/GjoatTnmhT2hn0WWJF8VyGenNAfv8eLSOA5U2oHkTlUZpUD9DyDlrZRYMyGip4EjUgTUO1FG3Sc7/Xd6PX8HhD2LiZuYNgdZM0linN6N6PI5h9rwXZNWw9YVULqNey3VWj8jQBiP0tzZzRauNnRcKICHLp/bB6ZQY/Y4wxxvRKUiMIy+RyM7WKbGgnriLZGK927cWkA4QH99GbfznZ+zRmnMTr/xQzdoRv4kU1CsgnObhUKyMxSZ6rJd63EDKLkMpHgXhg9zYd/z5BCfZLkFl2QQSNsumDfdirbxgrkF2BMIDKVuttpRegcKrzZEJ6EZbOI4cBCwEN0dI27N8d/4qLxUtI8XSUvTxjNLBAD9D//kZchm6t4XEtgH54YN3nAXbzxIhWux+kC033acyc0qo7wJ6Vvh59klQGuf5hVEPY34StW2ilBA9uwMHmtIdnjDFmBLxUyOJPfBg8wcvn8HIZJJvBy6SRdIq7v/G7bPwPP0dmowiiiCjgVj1KGEBYBRSq+1N+JBOSWUA2rs5u2ZqaaQQI730PXX8MWTwz+fs2xhxj0RgTL8H+7H+4djOpxq4N9zlY4E4gahDroYWTcPdver9xOQoWZFfA81rEPRVKDzpvo7IDmztI8QysPcphsLO+fEdYcsHJoDGrWNJZSJ+Pruuh9/6297H3Qnxk/R1IbmW02500DRJdAqI3Pbw/JHUU+NMACBtf08MExy2wbswQak1fk03Eg8IqFFbdnNyZp9CX/h/YujXtoRljjBmSVLZYee5sm0ureJk0/mIeL3g40XHFlTzyAdAZLltTM+mg/MoVZPkSFDcme7/GjM3sfweIX+6+mV9hdT5m9yfZ2HWE9ykAJ56m791G6SHsP4gaVQ6wxDCVr/slalQYll0wvpfs/7AyhqY2gpx8dvYD8hBl1pjj6pqh4uGC8FGjVAuiGxMz8/eelFQGufh++2JtjDFzQKsBYSkBQehReOQfJCMgD5MNyp94Eu/ax5GNJ+tWmBszywaoJx/D7/H2bjTxoOqyopNO/CkE5aWxIeswWxLQjXfB29/ts2msuvI32VU4uNf5qqmiqx/vZ2HpistMbqhp3wMv65rYbL3qyrOMuoRNKo+k892vNzOEZJeM6pH40aoBdZMVtfdq/D67jTEGKazAk/8V3P0BeuP/nfZwjDHGjMnZ//kXEMow77k0p55A0gkKYU0iKF88jZx/DllstxLDmFk2+3nmCdqjmZlW3Z1OBvk8GPGHvaDo+jvg/osQ9Nm9vbLtmrjWZzf4eSiedvXmFy9ETV33XD35w9dEl6Cxn3cZ9eLBwcPGUjUjmpBokE1AhnyDOQ/KH5bw0aPSUjGcRTfGmGYiAicfRatluPm1aQ/HGGPMGHhV6yFCcR1ZO5uwmMEYv2/kVpHzH4DlS+5YwZikERLxnd2C8mb6qgeuOWjiyZRK14y++Z2g6Nrj8PClxhUO6UUXeG8nrEJmydWSX7jobls45QKhC3UHWan84c/64AZUdpGVS1EwP6ofnMqDlz1sVFo72NDsMiw/4nbQYQBbr4z2wS9eQBYvjXab06Y6e9ngx0ohdZpUqPUfqND6gc7xhIQxJhHk9BPo2z+E/fvTHooxxhgzWuIjF59NTtmamnEEFL0Ucva9cPJdiDfvfcNM4llQ3pghhVWXJT8PptHgdYwERVeuweYNF4hfjJqqVhZht0PjufIWLF4GPw3pYpfnxIMHrwABunMbUgXk0gcBbTvjL56PLLhu8rp/f3ThVvGQtSdd1kECdv4NZvHhhNX+x520v5sxxpnzxT7gPvs4/Q70lf807aEYY4wxIyXXfix5AflxWLmMXPgxJLs47ZEYMwHCbAYyGllQ3kzPvNSRrxlDxvq0ucD8FShtIV5UJidTROUC7Lze+kb5DUjn3d+/Y0Be0PsvAUerC+Tc+6K4am87Xx1VLfn0IrL+BJLKjWZ7sWLRLGPMjAuTtJR9CGuXYfdtePslCMZQus0YY4yZtDPvhMQmfI8ooJhZcMH41Suj2Z4xs2KSzZLHxILyZno0SFhNuA6m0uC1ZpwBV0GqB+A17gwlnUMXL8H2a433n12B7JIL5vsZqO633a5uvQEPXz466/SzEAXFtbyHBmUkv4bQYbKj3DTpI35U1qaPFQtrTyLFUwiSqJUOh/zmMjDGGDNrQpBUNPk9v5OM4qeQy+9HTz2O/u2XmOfnwhhjTAIUTyArG4lMbgNXfnXoT+r1R5GLH0L89CiGZMxsScBKeAvKmyma/TdQ78b8WMVrf7DSTwC6Xxq2DVRLKoMuXXYNXIMDV4u97mBBwjLq5443i/UyaFCGzVcbz3/zmyigfvaoB8G1F1CE1oczgqw9Hm0zBZ6PRDOpGlSgvIXe/Zv2kyWLl5Cli+42GrrSPEmMbwQl1yj3UDIPeo0xCXfYpDmdzAnUPkh+GV29BA9uTHsoxhhjzMDk/NPJTuIbQZavXPgHFpA3c2z2Y4oWlDdm7MadYS0usCoplwnuQtfuorDK2CLJkoJq5/JD4qfQwikQWjea0aBxFYGfheqeK6iydB7e/ntYOg+L5+DO37jM+rqmwPraf4JT72qsm+dlXPa3atssevHTkF9Hc2uwfzc614flq0jxlGuQ0/y8HTYJTWBkPqhbseDnSORjNMb0aMb3cxrtq6e6Qm365PIHYO0yerAF+w9h/4H7f5b/tsYYY+aL5yX7s3zYoHwqn9Dyqsb0Qqx8jTFDmf1Jrd6Ms8Gr+EeZ5lqdbHZgZZtevtyL374IoBC6MjZByT2W+hrwC6dg4RTi+WgYtC51U96Gyj7UgvKpghtTrwdv5e3DevGkCnWB+DaPK2HNeo+LVgUkYBmYMWZQ3fbrUVOlrvuJXoO/4nqMjHSVTq1nSW2iOjpoT/T+u5Gkc7B+peFQS8MAyrtwsAUHW+j+Jhw8hL37VoPeGGNMDNlEckcLp6c9AmOmR1wJqFlnQXkzRbP/BuqJjvFgIpz9L9ESllEvA9XdxvPrM+vFg9PvdhnwD15xJXFOvhNZOg+oKy3jZ3sMxkcBJQ2QMx8AwuiV2MPfKelBeS89Q2/LGc/mNWZmRauxRvn2O6wHP2p1nwm2u3Cfq7kld+Jod6+qUDmA0rYL1h9sHmXXl7ZdCbjcsvvdz8Di6egztwqpPKQy7vkNyrD3NmzfntpjNMYYkxQJ7edVb9jvXdbo3sy9mQletGVBeTM94kFu3f1c2T1eWzwRPBqCAqMmKdApBeZlhMsJm8vYNN+VCORX3FU3Hgd9HMlEme1eBrzOZQpUFaoH4GeRuoO7/nfhCY/qzP5nmjFmFmk1+jxL+JfvmBIRyOTdafFkY3Z9UHHX8dOoateMJFWFO99Db/+tW8lmjDHGDGLhxLRHMAFDR+VHMgpjZpOVrzFmhBIa7OzUgLXzDZt2MBpl3Dc9T1NbriMjzWoUQjSVh0rnGvUQZfplltwhTFB2AfkOtHoAu29CeRNSi+jShTaNYY17PtNWvsYYM3ljyZSvY4trBtLQoL2HzwYRgdPvgI1H0Tsvwu1vW2kcY4wx/SusT3sEsSfLl6c9BGOmbPbjFhaUN/EwzhIv09RvJnktiN+xLI3ngqbiRc1Hp0A8Rh3dkLCM+vnGpqPN0kugVaT2uL00bjVCYzDHLcXfdU1cy1tHF1S33YoMPzvYIJPc7BUAjZoFxzzrwktHTYxn/0PYGFMTgqSjRq1jkNTjjJgSP42cfSd66nHYugMHD9G9+7B9B8rdJ+CNMcbMufzitEcQX14KufhB5MTj0x6JMdOVgGRCC8qbmEjgl+UO5VjaXr+nEj5hVNJ3SjXkxDtW/31kPK91tZ9UAcRDmoM1GlIfQHZlavZg543WjWEB9t+GhbODjU9S4wsYxYVWOJz4iSMv5QLycR2fMWZwOs6JzzD6XI75pGPCiJ+G1fPAefeXVYXd+7BzB62WobLnms+CW/kmKXcsUFu1Vd6FzTeml4RgjDFmKiRTmPYQ4snPINdeQBYH/D5rTJLMQ/kaEckBXwGy0fV/X1V/WUT+FfAJXETsLeCfqOqtFre/AWzjQm1VVX3P6IZvZl4tizmc8zqyPQfkJ0h8l1koUjcBIC7YPaaghoQV1M9CUGq8QBVEXZgmKENQgVTWZVVWS26M1RKUHrhTJ7m1IQboJXL+qEFYHXwlwbiJ7xoaWUDemAQbZy8W23dMm4jAwjosrPf811ANXSPaagl983tw7+/HOkZjjDEx4GcAm5A9lC7CyiXk1LuR3PK0R2OMGZFeMuVLwI+r6o6IpIH/LCL/N/CvVfVfAIjILwL/Evh0m218RFXvjWTEJllqwd0EzHA1EOl9oiGOAXmAyvbRz17GHRhpOP4MfS99FJRXdc/N9uu0jIb7OdfAtVukPLsG2SVI5V3WXjDgAd60VidM3PGSQLEwhrJJxpg5YlnyM0nEg0zBnc68E7WgvDHGJF+SK4bWdCqtJx6sP4aceALya4ifmdy4jJkJQhISbroG5VVVgVrxx3R0UlWtK9RMkeTvMs04ZFemPYLxqK9jjudqxEu0HLs+sCt+/Bqgid8YkAc3/o517kcojJqNAuzcPJ41Xy84gMwSlLdpGUROFSCzAOrKnYjnQzhoUEbcdhJPopUJ0x5HE/GtbI0xZjjiz8l+PMHCeZkcN8aYOebNQcnQTlavIuc+YBnxxnSTgNhATzXlRcQHvgFcAz6nql+Nzv8V4OeBTeAjbW6uwJ+KiAL/VlW/MPSojYmzMGisfSoA6oLxQRA10qwFeOf4YKMNAdfw1UvB0hXYfNkF6RfORWVV1AVnq3tQ2Yd0Hopn4OFL7jnOLrvMfnAHc4cBGDn6T9ID1Ked/R1+T/wc8ciSr5v5rq2kScCHrjHGmCGUNqc9AmOMMeO2dGbaI5iMFl9t5NxzyJlnJz8WY2aNMLaKGyLyMeDXAB/4dVX91abLJbr8BWAPV879m9FlN+ijhHtPj0BVA1V9BjgPvE9EnorO/6yqXgB+G/hMm5s/r6rPAh8H/qmIfLDVlUTkUyLydRH5+t27d3sZljHxFDQ1GG1elqZBfDP1xItFiRYJDxAESeVh9TFYvoaki4iXQrw0ksojuXVk8bz7P11w11s87xrEUXWn+iBufbbFIDvveQgI+3mmH5CXo0mVw7h8Y0NfY0yShRy9+cVNoo5KQ9ZdwsrmzQvxpz0CY4wx41ZYnfYIJqP5O+n6oxaQN6YvMsCpyxZdUvrncDHsJ4GfE5Enm672ceB6dPoU8H80Xf4RVX2ml56qfX0jUdWHwF8AH2u66IvAz7a5za3o/7eALwHva3O9L6jqe1T1PRsbG/0My5j40LBzuZU4E99ln1f3pj0Sp+qqZrlAfPddlaSykDvhSta04tXV4dOqy76XusVC4oGXddn4fsb97GXd7bwM+CMMDMWNl3IB+anVWxb396iVLVIrU2PM/NLGk1ZGG5g/3LaZScUT0x6BMcaYccsuTHsEk5fKIeeem/YojJkh4mIG/Z66ex/wkqq+rKpl4HeBTzRd5xPAv1PnL4EVERloiU/XSJeIbIjISvRzHvgJ4EURuV53tZ8GXmxx26KILNZ+Bn4K+M4gAzVmJlSbGraKT7y//NcyET1XEiZWTfDUTRB0aoDTREQg06ZPgdcc1AmjrHo4DApTdQFhDahl2ysBECS7jq2kgHA6FXrEi+o8B+5kwXhjTDOtNE6imrklmTwsnZ32MIwxxoyRZPLTHsJk1H3NlQvPI5k5nIwwZijeAKeuzgGv1/1+Mzqv1+vUSrh/Q0Q+1e3OevmGcwb4rSiF3wN+T1X/SET+QEQew60zfhX4NICInMXV3HkBOAV8yZXbIQV8UVX/uIf7NGY2eSnQunrlwUHn609Tq4aucTNIKR0v5bLdm1csBGV3WfP2/az7e0UlhRTvqIxPUIKwgqYXISwns6q8n2NqE0dexj33EqfJIGNMPMV5gttMkqxfQ7duTXsYxhhjxsWLR0nV8YuObfLrsPbodIdizCwaLKHvhIh8ve73LzT1Pm210eYvIp2u87yq3hKRk8B/FJEXVfUr7QbTNSivqt8G3t3i/E7lal6Ifn4ZeLrbfRiTGH7GncIQwpILygdlYhlMmJUDHa32VbpAxEPzp93tDu67Gv/Z1ahJ7HFhUAYvjaBREL58/EqVbfDSaG0bYSUZAXrx3UqEiT8YiSawrEyNMWZaYvi5bHqzdhHeWIDyzrRHYowxZizmJGFHK8ilD0BqGbHvRMb0b7D3zb0utd5vAhfqfj8PNGeDtL1OfQl3EamVcG8blLcuV8aMg+dBKu+CwfmTkF2JmmjG6C3XR1mYqQpKfcdORATx0pBbh/wG+DkkaqSjGqKVPcLdO4T3vw93vgb7d13gvVVAviasQGXHnTRAj5XDmUFeZoIBeXHlJ7yM+3lWJoWMMckhqcb+FWYmiZ92QQxjjDHJ4+fm63uCBrB8ofv1jDFNhDGVr/kacF1ErohIBvgk8IdN1/lD4OfFeQ7YVNXbg5RwtwKdxoybiDu48HMuEB5Wogz60nQPOJq7vceQaq3RX4CroNUfER/1MlDZQyvbaGkTSg+O187fuYkuXzkM3HcVViHcQdMLEFZdlr1prVYHWgMgdM+9JYIYY6ZBxO2/zexbOQerl+HBjWmPxBhjzCgtbkx7BBPkweJll0xmjOnfGFaYqGpVRD4D/AngA7+hqt8VkU9Hl38e+DKuQsxLwB7wC9HN+y7hbkF5YyZJ5KjEjaor31GtBegnHCiYwhI5F2QXQHtboicCe3eBe2h2BTLLiNc5OK+1iY/qXpT9HqDbr3Ms3X79HYiXRjdvQPkhPHwJXbqC+H0cFFV2AEFTeUCQSf8N485LuwBY753OjTFmfGLVzNwMQ0Tg0vvR7TfdcZQxxphkKKxMewSTs3IdyS5PexTGzLDxxBhU9cu4wHv9eZ+v+1mBf9ridn2XcLegvDHTIuJqpWfSwKILXgZRHfpao9hxmlZwYv8tyJ+gpx2oqqs9HlZchnvpAZpdg1QRCKMmoZ7bVq28THnr2ASHFE6hhLD3ljtj/Z14qag+/Oo1dPcO7Lwx4ANSNwEAqJ8H8WYkOC/jrSdfH5A3xpg40MB97s7EPtp0I5kCXPtx9Pt/bBMuxhiTFNnFaY9gMhbOW0DemGEIiYg1WFDemLjwUu6ULkIYRCVuxhSgF99lkU9arY79wUM0v9Y2HnyUUd9C6b47+Tkob/ZWAigsufz8hQtI/gTUZdsLQPEM6mf7y5JvJdh34/fcagiZxOTKoMTDNVEawweZpCwgb4yJp3mqUzsHZOmUC8z/4M+w5r3GGDP7JJ0j8Y1eC6ehcGbaozBmxglJqItrQXlj4sjzwSu6AL2GLjhfPejciLQT8XE7rNAFxqcQkFcFDu5Fv1ShWkbFcwESLxWNUd0Vy5vdJyOCA0gV3G0qO90H4OeQ4um29d8llevn4XQWliEsu8x5L+X+hlod+UeGgguua9j/tjU4qvc+DhaQN8aM1Kj2KaHb92mIBXGTQVYvwLWPoC/9+bSHYowxZli+n+zVT4sXkcLpaY/CmGRIQMzBgvLGxJ14LvicKtQF6EsQlnq4re8ylqv9B+Fdj9XAlWMZxc6ueUKh/LDpClHd8X4OwjRwA82uuez5dsSDhXMtA/Ja+9eLunGPMosy2Ifa5sRH/Wz0wVE7NY1HQ6SH+1cAL+PK5mgACOpn3Phr2/Tqgk6qrScjDrPlR2kc2zTGmBEG0Gvla7y0W5lWv+1aybSWPCyQH1OrF6B4EnbfmvZIjDHGmNayKxaQN2akvGkPYGgWlDdmlhwL0Ec16IOmAL2kXEA43BvoblQVyttu25klGDKLXLWXbHY9Km/TDxE3zuyqu4/mYEp2zTXWDSuolzkMTqsXlVip7tZvDPyC296oZ101OKw93/FqkgI/e5gB74ZVG4scTcw0/M31+GugIbYvqJ9D6oPlkoomXUb8OD2vv4kVY4yZlrCCmxBOWZ35GSfiwaMfRb//p7D39rSHY4wxZiACGuPyn8OQFCxcnPYojEkWy5Q3xkyNeJDKu9NhgD4K0le2+tqUahQQDw6ihrNlDrOdxe9422PbqpbASyF1ddsRcdsdp6Dkgtl+7qg8T+H0UcWDKFNfvXS02qBVRroCQZRBPqVsSK1CddTPlYIIKlHz1bDiJpX9Qk+Z+T0Tf7CJFWOMmRp1AQDxLQl+xkk6B0++APdeRt/4a6gMlphgjDFmShZOTHsEYyKw8uhoy6UaM/esprwxJi4aAvTqMrKru9EX0s5Zy6oKpc329erDAPVSiMhhvLXdhKSCqwcPaHYFEFcaoIcM8ZHQ8KicjZc+vo+Oar0f4+c4LJ9TLSVixvWY5r9BEEBQQjPLdXXph4xIyYjL/xhjzKTU9l2hReZnmXgpOPkorF+Buy+ht77levIYY4yJv8LatEcwHosXkczCtEdhjIkhC8obkzQirkFsugg5dWVsKrvu1C5A36mBbGUL/BNRvfcADt5Gs6uAuNq7aBSwj4L7NaXmmvETUitnIz6Qpqf65mF4dL0kBuQ7KR/9zTS96HoIDBqctyx5Y8zM6/SZ0aIXiIkl8dNw+gnYuAZ3f4Dee8nK2hhjTNzll6Y9gtHLrkL+5LRHYUwyJSB2Y0F5Y5JM5KgGfe5EFKCPsuj7yWiWpgYapQd1l/loeqEhuBsL1V0IUpBdbP9YU0UXkA/LidihD62y7Z4Tr7+SRYdELF5ljEkGSTf9TtQU1swSF5x/Ejn9JFrahe076OYbsPkGVPenPTxjjDF1JF2Y9hBGr3gOse+ZxozJ7L+3LChvzLyoD9DruqvBXt2Fci/155XD8i7HLgriF5Cv0SocPHDlbKir0y4+eBk3dq1aQL5edRfNro621rwxxswcbZzQtQnHmSfZImSvIieuutV9+5uwdx99+2XYfH3awzPGGJNK0/CdLQnCEpDAyQZj4iABcRyv+1WMMYkjAqkc5NZh8TKsPwXFs1Ft9RYqe64yycGUStIMq3QfVI7Kq3gZ1+x03M1nZ1X1AB1k1jmsWgkbY0wyJOAg37QnIkhhBTl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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdf_2010.plot(column = 'composition',\n", " cmap = 'OrRd',\n", " figsize = (30,5),\n", " legend = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A question that may rise is \"Was it more or less segregated than 2000?\". To answer this, we rely on simulations to test the following hypothesis:\n", "\n", "$$H_0: Segregation\\ Measure_{2000} - Segregation\\ Measure_{2010} = 0$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparative Dissimilarity" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.023696202305264924" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "D_2000 = Dissim(gdf_2000, 'n_nonhisp_black_persons', 'n_total_pop')\n", "D_2010 = Dissim(gdf_2010, 'n_nonhisp_black_persons', 'n_total_pop')\n", "D_2000.statistic - D_2010.statistic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that Riverside was more segregated in 2000 than in 2010. But, was this point difference statistically significant? We use the *random_label* approach which consists in random labelling the data between the two periods and recalculating the Dissimilarity statistic (D) in each iteration and comparing it to the original value." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 1000 iterations out of 1000.\r" ] } ], "source": [ "compare_D_fit = TwoValueTest(D_2000, D_2010, iterations_under_null = 1000, null_approach = \"random_label\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The **TwoValueTest** class also has a plotting method:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "compare_D_fit.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access the two-tailed p-value of the test:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.26" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "compare_D_fit.p_value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusion is that, for the Dissimilarity index and 5% of significance, segregation in Riverside was not different between 2000 and 2010 (since p-value > 5%)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparative Gini" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analogously, the same steps can be made for the Gini segregation index." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 1000 iterations out of 1000.\r" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from segregation.aspatial import GiniSeg\n", "G_2000 = GiniSeg(gdf_2000, 'n_nonhisp_black_persons', 'n_total_pop')\n", "G_2010 = GiniSeg(gdf_2010, 'n_nonhisp_black_persons', 'n_total_pop')\n", "compare_G_fit = TwoValueTest(G_2000, G_2010, iterations_under_null = 1000, null_approach = \"random_label\")\n", "compare_G_fit.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The absence of significance is also present as the point estimation of the difference (vertical red line) is located in the middle of the distribution of the null hypothesis simulated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comparative Spatial Dissimilarity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example of a spatial index, comparative inference can be performed for the Spatial Dissimilarity Index (SD). For this, we use the *counterfactual_composition* approach as an example. \n", "\n", "In this framework, the population of the group of interest in each unit is randomized with a constraint that depends on both cumulative density functions (cdf) of the group of interest composition to the group of interest frequency of each unit. In each unit of each iteration, there is a probability of 50\\% of keeping its original value or swapping to its corresponding value according of the other composition distribution cdf that it is been compared against." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Processed 500 iterations out of 500.\r" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from segregation.spatial import SpatialDissim\n", "SD_2000 = SpatialDissim(gdf_2000, 'n_nonhisp_black_persons', 'n_total_pop')\n", "SD_2010 = SpatialDissim(gdf_2010, 'n_nonhisp_black_persons', 'n_total_pop')\n", "compare_SD_fit = TwoValueTest(SD_2000, SD_2010, iterations_under_null = 500, null_approach = \"counterfactual_composition\")\n", "compare_SD_fit.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conclusion is that for the Spatial Dissimilarity index under this null approach, the year of 2000 was more segregated than 2010 for the non-hispanic black people in the region under study." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }