{ "cells": [ { "cell_type": "markdown", "id": "c6a063ef", "metadata": {}, "source": [ "# Convergência do processo de minimização\n", "\n", "O arquivo padrão de saída **fort.220** contém informações sobre a convergência do processo de minimização em cada loop interno (*inner loop*: iteração de minimização) executado pelo GSI. Com os dados deste arquivo é possível analisar a contribuição de diferentes observações na função custo (`J`) em relação ao custo total (`cost`). Informações sobre a norma/produto interno dos gradientes (`grad`), stepsize e indicação da nova direção de busca (`b`) do método dos gradientes conjugados também são fornecidas. Mais informações sobre os dados fornecidos pelo arquivo **fort.220** estão disponíveis nos guias do GSI: User’s Guide Version v3.7 (seção 4.6) e Advanced User’s Guide v3.5 (seção 3.1).\n", "\n", "\n", "## Leitura do arquivo **fort.220** \n", "\n", "Para leitura do arquivo **fort.220** foram elaboradas as funções dentro da classe `read_diag`:\n", "\n", "1. `fort_220_cost_gradient(DIRdiag, dates)`: esta função lê todas as linhas do arquvio fort.220 e identifica as linhas com as expressões `Minimization iteration`, `costterms Jb,Jo,Jc,Jl =` e `cost,grad,step,b,step? =`. Utilizando os valores de cada variável (identificada por `Label`) são constuídos dois dataframes (duas tabelas) onde as colunas são, na ordem, as variáveis `'InnerLoop', 'Jb', 'Jo', 'Jc', 'Jl', 'cost', 'grad', 'step', 'b' e 'step?'`. O primeiro dataframe corresponde aos valores de cada variável nas iterações internas (*inner loop*) do **1º outer loop** e o segundo dataframe contém os valores das variáveis em cada iteração interna do **2º outer loop**. No final a função concatena as tabelas formando uma lista bidimensional de tabelas com relação ao tempo (range de datas) e outer loops (1º ou 2º).\n", "\n", "**Exemplo:** pedaço de uma tabela do **1º outer loop**. \n", "\n", "| |\tInnerLoop |\tJb |\tJo |\tJc |\tJl |\tcost |\tgrad |\tstep |\tb |\tstep? |\n", "|:--- |:---: |:---: |:---: |:---: |:---: |:---: |:---: |:---: |:---: |---: |\n", "|0 |\t0 |\t0.000000 |\t872614.730155 |\t0.000000 |\t0.000000e+00 |\t872614.730155 |\t116.153929 |\t0.650383 |\t0.000000 |\tgood |\n", "|1 |\t1 |\t1023.257157 |\t862816.657900 |\t0.019797 |\t2.893432e-07 |\t863839.934854 |\t71.176122 |\t3.941374 |\t0.375492 |\tgood |\n", "|2 |\t2 |\t5344.739506 |\t838526.699901 |\t1.334128 |\t2.372508e-04 |\t843872.773772 |\t108.896822 |\t1.020004 |\t2.340786 |\tgood |\n", "|3 |\t3 |\t3468.606202 |\t828305.357196 |\t3.075260 |\t1.170560e-03 |\t831777.039828 |\t83.265426 |\t1.454311 |\t0.584654 |\tgood |\n", "|4 |\t4 |\t1759.696994 |\t819930.295887 |\t4.117129 |\t3.667851e-03 |\t821694.113678 |\t68.440676 |\t1.471768 |\t0.675615 |\tgood |\n", "|5 |\t5 |\t2921.504980 |\t811875.004067 |\t3.650240 |\t7.889535e-03 |\t814800.167176 |\t79.142060 |\t2.093740 |\t1.337168 |\tgood \n", "\n", "\n", "2. `fort_220_Flags_V3_4(DIRdiag, dates)`: esta função lê todas as linhas do arquivo fort.220, gerado pela versão 3.4 do GSI, identificando em cada *inner loop* as matrizes dos parâmetros `'J', 'b, 'c' e 'EJ'` (`Labels`). Tais matrizes, ou lista de valores, contém a contribuição de cada observação (aqui nomeada de `Flag`) para estes parâmetros, como descrito na rotina `sptcalc.f90` do GSI (foi inicialmente pensada conforme a rotina disponível em https://projetos.cptec.inpe.br/projects/gsi/repository/entry/trunk/gsi/src/main/stpcalc.f90). O GSI identifica as observações através de números inteiros. Na versão 3.4 utilizada como base para o desenvolvimento desta função, as flags são valores inteiros de 1 à 43, sendo, por exemplo, a flag 26 a contribuição da radiância. Pode-se verificar que a soma dos elementos da matriz `J` resulta no valor da variável `cost` lida na função `fort_220_cost_gradient`. Assim como na função `fort_220_cost_gradient`, uma lista bidimensional, em relação ao tempo (data) e outer loop (**1º outer loop** e **2º outer loop**) é retornada.\n", "\n", "**Exemplo:** pedaço de uma tabela do **2º outer loop**. \n", "\n", "\n", "
\n", "\n", "3. `fort_220_Flags_V3_7(DIRdiag, dates)`: realiza o mesmo processo de leitura e retorna a lista de tabelas como explicado na função `fort_220_Flags_V3_4`. A diferença entre as funções está relacionado a quantidade de observações (flags) analisadas na função custo. Na versão 3.7 do GSI são consideradas 56 flags (o significado de cada flag pode ser observado na rotina `sptcalc.f90` do GSI disponível na branch https://projetos.cptec.inpe.br/projects/gsi/repository/entry/branch/gsi_t11824/src/gsi/stpcalc.f90). Por exemplo, nesta versão, a flag que contém a contribuição da radiância na função custo (`J`) é a flag de número 32, na versão 3.4 a radiância é identificada pela flag 26. " ] }, { "cell_type": "markdown", "id": "b83c561f", "metadata": {}, "source": [ "## Importa as bibliotecas utilizadas no readDiag" ] }, { "cell_type": "code", "execution_count": 1, "id": "e6c764fb", "metadata": {}, "outputs": [], "source": [ "import gsidiag as gd\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime, timedelta\n", "from gsidiag.datasources import getVarInfo\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "2afd9826", "metadata": {}, "source": [ "## Leitura dos arquivos\n", "\n", "Realiza o processo de leitura do arquivo **fort.220** a partir dos dados iniciais fornecidos pelo usuário." ] }, { "cell_type": "code", "execution_count": 2, "id": "e8e75c33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['2025113000', '2025113006', '2025113012', '2025113018']\n", "\n" ] } ], "source": [ "# Rodada do Experimento 19 (Fazer 1ª rodada para FEV e 2ª rodada para MAR)\n", "# DIRdiag = '/home/radiancia/Exp_SMNA/data/EXP19/gsi_dataout'\n", "# Rodada do Experimento 20 (Fazer rodada para FEV)\n", "# DIRdiag = '/home/radiancia/Exp_SMNA/data/EXP20/gsi_dataout'\n", "# Rodada do Experimento 21 (Fazer rodada para MAR)\n", "#DIRdiag = '/home/radiancia/Exp_SMNA/data/EXP21/gsi_dataout'\n", "EXP19 = False\n", "EXP20 = False\n", "EXP21 = True\n", "\n", "DIRdiag = '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025'\n", "\n", "dateIni = '2025113000' \n", "dateFin = '2025113018' \n", "nHour = '06' \n", "\n", "datei = datetime.strptime(str(dateIni), '%Y%m%d%H')\n", "datef = datetime.strptime(str(dateFin), '%Y%m%d%H')\n", "dates = [dates.strftime('%Y%m%d%H') for dates in pd.date_range(datei, datef,freq='06H').tolist()]\n", "\n", "print(dates)\n", "print('')\n", "\n", "read = True\n" ] }, { "cell_type": "markdown", "id": "6353269e", "metadata": {}, "source": [ "Leitura e construção da 1ª lista de dataframes com os dados sobre a função custo total (`cost`), norma do gradiente (`grad`), `step`, direção de busca (`b`) e termos da função custo: `Jb`, `Jo`, `Jc` e `Jl`." ] }, { "cell_type": "code", "execution_count": 3, "id": "606d428d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", ">>>> Leitura: cost_gradient <<<<\n", "\n", "DIRdiag = /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\n", "\n", "Names columns = ['InnerLoop', 'Jb', 'Jo', 'Jc', 'Jl', 'cost', 'grad', 'step', 'b', 'step?']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.220']\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:00\n", "\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:06\n", "\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:12\n", "\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:18\n", "\n", "\n" ] } ], "source": [ "print('')\n", "print('>>>> Leitura: cost_gradient <<<<')\n", "print('')\n", "Self_1 = gd.read_diag.fort_220_cost_gradient(DIRdiag, dates)" ] }, { "cell_type": "markdown", "id": "03768db7", "metadata": {}, "source": [ "Leitura e construção da 2ª lista de dataframes com os dados da contribuição das observações (`Flag`) para a função custo (`J`), termos para estimar tamanho de passo (`b` e `c`) e estimar termos em penalidades (`EJ`). Neste caso, a depender da versão utilizada do GSI no processo de assimilação, deve-se optar pela versão de leitura: `fort_220_Flags_V3_4` para a versão 3.4 e `fort_220_Flags_V3_7` para a versão 3.7. \n", "\n", "**Obs.**: no experimento EXP19 foi utilizada a versão 3.4 do GSI e nos experimentos EXP20 e EXP21 a versão 3.7." ] }, { "cell_type": "code", "execution_count": 4, "id": "9fa51a28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", ">>>> Leitura: Flags (versão 3.7 GSI) ---> EXP 20 e 21 <<<<\n", "\n", "DIRdiag = /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\n", "\n", "Labels = ['J', 'b', 'c', 'EJ']\n", "\n", "Flags = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56']\n", "\n", "Columns: ['InnerLoop', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56']\n", "\n", "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.220', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.220']\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:00\n", "\n", "Nº total de linhas = 13541\n", "nl final = 13541\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:06\n", "\n", "Nº total de linhas = 13541\n", "nl final = 13541\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:12\n", "\n", "Nº total de linhas = 13541\n", "nl final = 13541\n", "\n", "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.220\n", "\n", " Preparing data for: fort.220 ---> 2025-11-30:18\n", "\n", "Nº total de linhas = 13541\n", "nl final = 13541\n", "\n" ] } ], "source": [ "if EXP19:\n", " print('')\n", " print('>>>> Leitura: Flags (versão 3.4 GSI) ---> EXP 19 <<<<')\n", " print('')\n", " Self_2 = gd.read_diag.fort_220_Flags_V3_4(DIRdiag, dates)\n", "\n", "if EXP20 or EXP21:\n", " print('')\n", " print('>>>> Leitura: Flags (versão 3.7 GSI) ---> EXP 20 e 21 <<<<')\n", " print('')\n", " Self_2 = gd.read_diag.fort_220_Flags_V3_7(DIRdiag, dates)" ] }, { "cell_type": "markdown", "id": "2d4d5b3a", "metadata": {}, "source": [ "Os dataframes `Self_1` e `Self_2`, são listas bidimensionais de tabelas, por exemplo, `Self_1[tidx][Outerloop-1]`, que armazenam para cada data do período `dateIni - dateFin` (índice `tidx`) um array com duas tabelas (índice `Outerloop`):\n", "\n", "--> se `Outerloop = 1` é possível manipular e visualizar os dados referente ao *1º outer loop*,\n", "\n", "--> se `Outerloop = 2` é possível manipular e visualizar os dados referente ao *2º outer loop*.\n", "\n", "Abaixo segue um exemplo para visualização da tabela do *1º outer loop*, `Outerloop = 1`, no dia e hora `dateIni`, ou seja, `tidx=0`, da lista de tabelas `Self_1`." ] }, { "cell_type": "code", "execution_count": 5, "id": "a6512d53", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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costgrad
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" ], "text/plain": [ " cost grad\n", "0 739400.245276 4399.916423\n", "1 735738.134748 4203.220077\n", "2 721878.525642 3103.296084\n", "3 708924.987866 3492.013851\n", "4 699584.901413 3769.097513\n", "5 694316.991340 3506.484380\n", "6 683380.838089 3023.744706\n", "7 676158.568412 3142.115768\n", "8 669771.465586 2953.372595\n", "9 664943.860285 2631.615802\n", "10 658356.140604 2779.922133\n", "11 654588.184743 2824.707194\n", "12 647450.550232 2606.608845\n", "13 639238.918163 2474.928760\n", "14 634898.973829 2583.825392\n", "15 629389.906422 2606.630216\n", "16 624940.844973 2528.988430\n", "17 620114.459463 2180.499365\n", "18 615643.183081 2031.250513\n", "19 610046.096517 2286.393103\n", "20 607300.748934 2334.346060\n", "21 603200.024649 2104.912481\n", "22 599063.079690 1963.239098\n", "23 594346.957538 2247.099289\n", "24 589872.943517 2240.454683\n", "25 586395.114002 2079.359516\n", "26 581386.294580 2048.174168\n", "27 578509.006834 2057.008026\n", "28 575092.406535 1860.903182\n", "29 571140.046920 1812.033698\n", "30 568815.223847 1944.412767\n", "31 565304.418894 1872.094759\n", "32 561926.863095 1720.561671\n", "33 557569.793207 1779.741591\n", "34 555192.053675 1731.808521\n", "35 552725.509596 1619.908350\n", "36 550344.434839 1572.549695\n", "37 548446.572263 1615.285583\n", "38 545775.844663 1672.887521\n", "39 543832.305873 1650.656398\n", "40 541514.954714 1451.805198\n", "41 539527.753041 1406.081524\n", "42 536949.012753 1617.683339\n", "43 535633.460197 1595.512797\n", "44 533632.348876 1479.684733\n", "45 530982.677185 1414.568934\n", "46 528677.627681 1484.115136\n", "47 526589.659194 1461.030286\n", "48 524466.834472 1479.349496\n", "49 523072.763407 1472.913217\n", "50 521793.343549 1380.545018" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = pd.IndexSlice\n", "tidx = 0\n", "Outerloop = 1\n", "Self_1[tidx][Outerloop-1].loc[idx[:], idx['cost':'grad']]" ] }, { "cell_type": "markdown", "id": "cfd3c40c", "metadata": {}, "source": [ "Observe que no caso das tabelas do arquivo `Self_2` as linhas tem multiplos índices. Abaixo um exemplo para visualizar apenas os dados da função custo `J`." ] }, { "cell_type": "code", "execution_count": 8, "id": "12fbf92a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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101 rows × 2 columns

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" ], "text/plain": [ "Flags 25 26\n", "Inner loop Label \n", "0 J 2.379999 0.0\n", "1 J 2.377490 0.0\n", "2 J 2.368076 0.0\n", "3 J 2.356854 0.0\n", "4 J 2.323822 0.0\n", "... ... ...\n", "96 J 1.316311 0.0\n", "97 J 1.316756 0.0\n", "98 J 1.316537 0.0\n", "99 J 1.315555 0.0\n", "100 J 1.314314 0.0\n", "\n", "[101 rows x 2 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = pd.IndexSlice\n", "tidx = 0\n", "Outerloop = 2\n", "Self_2[tidx][Outerloop-1].loc[idx[:,idx['J']], idx['25':'26']]" ] }, { "cell_type": "markdown", "id": "54461e30", "metadata": {}, "source": [ "## Plote fort.220\n", "\n", "A seguir são apresentadas algumas funções elaboradas na classe **plot_diag** do readDiag para visualização e análise das informações presentes no arquivo **fort.220**." ] }, { "cell_type": "markdown", "id": "2df5cd1b", "metadata": {}, "source": [ "1. `fort220_plot(self, dateIni=None, dateFin=None, nHour=\"06\", Label=None, Flag=None, cost_gradient=False, vmin=None, vmax=None, Clean=None, **kwargs)` pode ser utilizada para visualizar as variáveis das colunas dos dataframes `Self_1` ou `Self_2` em função dos *inner loops*. Para cada data-hora é elaborado um plote com as curvas do 1º e 2º *outer loops*. \n", "\n", "No primeiro exemplo é utilizado o dataframe `Self_1` para visualizar a função custo total (`cost`) em relação aos *inner loops*. Observe que neste caso é necessário atribuir `True` ao termo `cost_gradient` e fazer `Flag=None`." ] }, { "cell_type": "code", "execution_count": null, "id": "5f12635b", "metadata": { "scrolled": false }, "outputs": [], "source": [ "gd.plot_diag.fort220_plot(Self_1, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='cost',\n", " Flag=None, \n", " cost_gradient=True, \n", " Clean=None)" ] }, { "cell_type": "markdown", "id": "3ac3a8cf", "metadata": {}, "source": [ "Agora, utiliza-se o dataframe `Self_2` para visualizar a contribuição da radiância (`Flag='26'` ou `Flag='32'`) na função custo (`J`) em relação aos *inner loops*. Neste caso é necessário atribuir `False` ao termo `cost_gradient`." ] }, { "cell_type": "code", "execution_count": null, "id": "b23f7f7d", "metadata": { "scrolled": false }, "outputs": [], "source": [ "if EXP19:\n", " Flag = '26'\n", "else:\n", " Flag = '32'\n", "\n", "gd.plot_diag.fort220_plot(Self_2, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='J',\n", " Flag=Flag, \n", " cost_gradient=False, \n", " Clean=None)" ] }, { "cell_type": "markdown", "id": "d1005ae0", "metadata": {}, "source": [ "2. `fort220_plot_lines(self, dateIni=None, dateFin=None, nHour=\"06\", Label=None, Flag=None, cost_gradient=False, vmin=None, vmax=None, Clean=None, **kwargs)` retorna uma figura com o gráfico da variável `Label` em função do *inner loop*, onde cada linha contínua refere-se aos dados do primeiro *outer loop* em difrentes datas e cada linha pontilhada identifica os dados obtidos no segundo *outer loop* no range dates fornecido. Como na função `fort220_plot`, o usuário precisa setar `cost_gradient=True` quando utilizado o arquivo `Self_1` e `cost_gradient=False` quando utilizado o arquivo `Self_2`." ] }, { "cell_type": "code", "execution_count": null, "id": "f126267b", "metadata": {}, "outputs": [], "source": [ "gd.plot_diag.fort220_plot_lines(Self_1, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='cost', \n", " Flag=None, \n", " cost_gradient=True, \n", " vmin=None, \n", " vmax=None, \n", " Clean=None)" ] }, { "cell_type": "code", "execution_count": null, "id": "2e7bceaf", "metadata": {}, "outputs": [], "source": [ "if EXP19:\n", " Flag = '26'\n", "else:\n", " Flag = '32'\n", "\n", "gd.plot_diag.fort220_plot_lines(Self_2, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='J', \n", " Flag=Flag, \n", " cost_gradient=False, \n", " vmin=None, \n", " vmax=None, \n", " Clean=None)" ] }, { "cell_type": "markdown", "id": "aa54650a", "metadata": {}, "source": [ "3. `fort220_time(self, dateIni=None, dateFin=None, nHour=\"06\", Label=None, Flag=None, cost_gradient=False, Clean=None)` retorna uma figura com gráfico de linhas da variável `Label` em função do tempo (`date`) no primeiro e último *inner loop* de cada *outer loop*. Como na função `fort220_plot`, o usuário precisa setar `cost_gradient=True` quando utilizado o arquivo `Self_1` e `cost_gradient=False` quando utilizado o arquivo `Self_2`." ] }, { "cell_type": "code", "execution_count": null, "id": "dada6696", "metadata": {}, "outputs": [], "source": [ "gd.plot_diag.fort220_time(Self_1, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='cost',\n", " Flag=None, \n", " cost_gradient=True, \n", " Clean=None)" ] }, { "cell_type": "code", "execution_count": null, "id": "cb02c981", "metadata": {}, "outputs": [], "source": [ "if EXP19:\n", " Flag = '26'\n", "else:\n", " Flag = '32'\n", "\n", "gd.plot_diag.fort220_time(Self_2, dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='J',\n", " Flag=Flag, \n", " cost_gradient=False, \n", " Clean=None)" ] }, { "cell_type": "markdown", "id": "33303716", "metadata": {}, "source": [ "4. `fort220_time_Obscontrib(self1, self2, dateIni=None, dateFin=None, nHour=\"06\", Label=None, LabelObs=None, Flag=None, Clean=None)`\n", "\n", "A função de custo total (`cost`) é a soma da lista `J` (lista de contribuições de cada observação). Esta função plota a taxa (DQ) da contribuição da observação `LabelObs` em relação à variável `Label`. O quociente DQ é calculado para cada loop externo.\n", "\n", "`DQ = ( J_last - J_first )/( cost_last - cost_first )`\n", "\n", "onde,\n", "\n", "last --> valor no último loop interno\n", "\n", "firs --> valor no primeiro loop interno\n", "\n", "Se `Flag=None`, são plotados gráficos de barras, onde cada barra representa uma flag, e uma figura para cada data é retornada. Se Flag for um número, dois gráficos de séries temporais são retornados:\n", "\n", "1º) Q = (J/cost)100 no primeiro e último loop interno de cada loop externo.\n", "\n", "2º) DQ de cada loop externo." ] }, { "cell_type": "code", "execution_count": null, "id": "7f440f41", "metadata": {}, "outputs": [], "source": [ "# Gráfico de barras de todas as Flags\n", "# Flag = None\n", "\n", "# Gráficos de linhas para uma Flag\n", "if EXP19:\n", " Flag = '26'\n", "else:\n", " Flag = '32'\n", "\n", "gd.plot_diag.fort220_time_Obscontrib(Self_1, Self_2, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='cost', \n", " LabelObs='J', \n", " Flag=Flag, \n", " Clean=None)" ] }, { "cell_type": "code", "execution_count": 10, "id": "37287809", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "x_Flags = [1.0, 3.0, 4.0, 5.0, 18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "flags xx: [18.0, 19.0, 20.0, 21.0, 25.0, 30.0, 32.0]\n", "yy_mean antes: 4 0.095333\n", "5 0.086978\n", "6 0.382948\n", "7 0.018704\n", "8 0.000131\n", "9 0.383077\n", "10 0.227029\n", "dtype: float64\n", "yy_mean depois: 4 0.095333\n", "5 0.086978\n", "6 0.382948\n", "7 0.018704\n", "8 0.000131\n", "9 0.383077\n", "10 0.227029\n", "dtype: float64\n" ] }, { "data": { "text/plain": [ "
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", 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", 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", 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", 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", 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", 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KSkqUZMjDo7hKlfKRi4vrv+pIUGJirFJSkmUY6XJ1zdh2iRKlZLFwkA4AAMAeCBYFJCUlSUlJCTbL0tPTlJSUoKSkRJUrV0lubhlf0NPSUnTpUoQMI906Lj7+ivX5nEpMjLvh3BBJionJCEvly9+W4+1aLC6yWCz/X9dVlSpVLtuxhmHo0qUIpaQk2SxPTU1RXNxlJSUlqmxZP5twcd2lS+E2wSgpKUGxsRabQJXV/Je0tFTFx19RcvK1bLcNAAAAcwgWJl27Fq9r1+JtluXk6IG7u4dKliwrV1c3WSwuMgxDqanJiou7LCnjN/rXv6DHxV22hgo3N3eVKFFG6empio29bIeOcs9ikUqUKK3Y2BglJsZlcWrY/yQkXLWGChcXV3l7l5XFIsXGXlJ6eppSUpKUkHA1y20YhqFSpcrJMAzFxsZIynj9S5b0kYtLxpGW66EiY9tl5OLiqoSEWCUnJ95w2wAAADCHYFFA3N2LKTk5SYmJl///dCDD5vmUlGRJGV+mM079yVC6tK/c3DJOk0pLS1NCwtUc79Pbu4y8vctk+7zFIvn6llRUVKwMI9thWSpe3Fvx8VesRy08Pb2yHPfPEFaqlI+KFfP6/31bdPlypHVMVl/+S5b0sW43KSlBycnXJGUckXBx8bDZdvHiJeTqmvHx9vLyVnJy4g23DQAAAHMIFiZlNXn73+f8Z+XKlSibwPBv/zvtKV2G9Vu+xRoqpIxwkhv2nGNhsVhUokQpxcZeUmJinDw8sl7/+pwKybb+f/7/P8f80z+3+c/X+Ppr9c+rUsXHX1V8fObQxZWrAAAA7INgYVJeJm+npaVaQ4XFYpG3d1nrfInr934wsjhkYHZqgL3mWFz3v6MW6Vl+qTcrJ4EtJwzDYJ4FAABAPiNYFIC0tDTr/3t4FJeXV0lJUnJyUqaxLi4ZE6MNw/j/eRgp1hDy7wnQBc1icZGXVynFxV1WampylmPc3NysRw1SUpKsp0L9sxc3t7x9LN3c3K2nR5UqVU7Fi3tnGmMY6YQKAAAAOyBYFABX1//95j05+ZoSE+Nlsej/J27bslgsKlasuK5dy7iC1JUrUSpRorTS01OVkBCbq/3ac47FdcWLl1R8/FXr6Un/5ulZwtrn1asxKlkyY0exsZdsxuSFp2cJ62uSMRk8XW5u7jKMdKWlpSo5+ZpcXFy51wYAAIAdECwKgKurmzw8iis5OVGGka6rV6MkZcwzyGoOhLd3GSUlXZNhpCs1NVlXrmRMcnZzcy90cwZcXFzk5VUy21OuvLxKKSkp4wpN6elpunIlyuZ5d/di8vIqlad9X78HSHz8FRlGuuLiLmUak9fQAgAAgBvjbmEFpHTpcvL0LPH/94BwkadnCZUpUz7Lsa6u7ipb1u//Jzhb5OLiqhIlSttMGi9Mp/d4eZXMth6LxaKyZf3k7V3m/0/psihjUrq7vL3LmL7PhLd3GZUpU14eHp7Wm+Fdv/HgzY7YAAAAIO84YpEHufmC6uHhKT8//0zLszslJ6uxUsbRCR+fijbL/nn60M3udp3fXF3dsq3VxcVVFSpUy3bdjCtIlc7RZV+zm0ReurRvtqc0FSvmZZ27AQAACsjrpWXPk48jh/1lx60jLwgWRcSlS+Hy8iplvdxscnKizRyL7O4bAQAAANwKBIsiIiUlOdN8hOtKlvSx3gwOAAAAKAh8Gy0iihcvqZSUa0pLS5VhGNZ5A15eJXN9Hw0AAAAgvxEsiohSpXwKugQAAAAgW1wVCgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmOVywmDdvngICAuTp6almzZpp9+7dNxz/7rvvqlatWipevLiqVq2ql156SdeuXbtF1QIAAACOwaGCxerVqzVixAhNnDhR+/fvV8OGDdWlSxddvHgxy/ErV67U6NGjNXHiRIWGhmrx4sVavXq1xo4de4srBwAAAIo2hwoWs2bN0pAhQzRo0CDVrVtX8+fPl5eXlz766KMsx+/YsUN33323Hn74YQUEBKhz587q37//TY9yAAAAALDlMHfeTk5O1r59+zRmzBjrMhcXF3Xs2FE7d+7Mcp0WLVpo+fLl2r17t+666y6dOnVKGzZs0KOPPnrDfVks+Vp6oXG9L0ft75/o1XE5U7/06picqVfJufp1xl6L+j5ywpne15txmGARFRWltLQ0+fn52Sz38/PT0aNHs1zn4YcfVlRUlFq2bCnDMJSamqqnn376hqdC+fiUkKurQx3oyaRcuZIFXcItQ6+Oy5n6pVfH5Ey9Ss7VrzP1ak++voXrdeR9daBgkRdbt27VtGnT9P7776tZs2Y6efKkXnjhBb3xxhsaP358luvExMQ7bCK1WDJ+KKKjY2UYBV2NfdGr43KmfunVMTlTr5Jz9et0vdp5H1FRsXbeQ844w/ua0xDnMMHC19dXrq6uioiIsFkeERGhihUrZrnO+PHj9eijj+rJJ5+UJNWvX1/x8fEaOnSoXnvtNbm4ZH1kwlE/NNcZhuP3eB29Oi5n6pdeHZMz9So5V7/O1Ks9FbbXkPfVgSZve3h4KDg4WFu2bLEuS09P15YtW9S8efMs10lISMgUHlxdXSVJhrN/MgAAAIBccJgjFpI0YsQIPf7442ratKnuuusuvfvuu4qPj9egQYMkSY899piqVKmi6dOnS5J69OihWbNmqXHjxtZTocaPH68ePXpYAwYAAACAm3OoYNG3b19FRkZqwoQJCg8PV6NGjbRx40brhO6zZ8/aHKEYN26cLBaLxo0bp/Pnz6t8+fLq0aOHpk6dWlAtAAAAAEWSxeCcn1yJjCwcE4XswWLJmJwTFeW4k4+uo1fH5Uz90qtjcqZeJefq1+l6nXubXfcROewvu24/p5zhfS1fPmeTtx1mjgUAAACAgkOwAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkOFyzmzZungIAAeXp6qlmzZtq9e/cNx1++fFnDhg1TpUqVVKxYMdWsWVMbNmy4RdUCAAAAjsGtoAvIT6tXr9aIESM0f/58NWvWTO+++666dOmiY8eOqUKFCpnGJycnq1OnTqpQoYK++OILValSRWfOnFGZMmVuffEAAABAEeZQwWLWrFkaMmSIBg0aJEmaP3++1q9fr48++kijR4/ONP6jjz5STEyMduzYIXd3d0lSQEDArSwZAAAAcAgOcypUcnKy9u3bp44dO1qXubi4qGPHjtq5c2eW63zzzTdq3ry5hg0bJj8/PwUFBWnatGlKS0u7VWUDAAAADsFhjlhERUUpLS1Nfn5+Nsv9/Px09OjRLNc5deqUfvzxRw0YMEAbNmzQyZMn9eyzzyolJUUTJ07Mdl8WS76WXmhc78tR+/snenVcztQvvTomZ+pVcq5+nbHXor6PnHCm9/VmHCZY5EV6eroqVKighQsXytXVVcHBwTp//rzefvvtbIOFj08Jubo6zIGeLJUrV7KgS7hl6NVxOVO/9OqYnKlXybn6daZe7cnXt3C9jryvDhQsfH195erqqoiICJvlERERqlixYpbrVKpUSe7u7nJ1dbUuq1OnjsLDw5WcnCwPD49M68TExDtsIrVYMn4ooqNjZRgFXY190avjcqZ+6dUxOVOvknP163S92nkfUVGxdt5DzjjD+5rTEOcwwcLDw0PBwcHasmWLevXqJSnjiMSWLVs0fPjwLNe5++67tXLlSqWnp8vFJeMoxPHjx1WpUqUsQ8V1jvqhuc4wHL/H6+jVcTlTv/TqmJypV8m5+nWmXu2psL2GvK8ONHlbkkaMGKEPP/xQS5cuVWhoqJ555hnFx8dbrxL12GOPacyYMdbxzzzzjGJiYvTCCy/o+PHjWr9+vaZNm6Zhw4YVVAsAAABAkeQwRywkqW/fvoqMjNSECRMUHh6uRo0aaePGjdYJ3WfPnrUemZCkqlWr6vvvv9dLL72kBg0aqEqVKnrhhRc0atSogmoBAAAAKJIcKlhI0vDhw7M99Wnr1q2ZljVv3ly//fabnasCAAAAHJtDnQoFAAAAoGAQLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkECwAAAACmESwAAAAAmEawAAAAAGAawQIAAACAaQ4XLObNm6eAgAB5enqqWbNm2r17d47WW7VqlSwWi3r16mXfAgEAAAAH5FDBYvXq1RoxYoQmTpyo/fv3q2HDhurSpYsuXrx4w/VOnz6tl19+Wa1atbpFlQIAAACOxaGCxaxZszRkyBANGjRIdevW1fz58+Xl5aWPPvoo23XS0tI0YMAATZo0SdWrV7+F1QIAAACOw62gC8gvycnJ2rdvn8aMGWNd5uLioo4dO2rnzp3Zrjd58mRVqFBBgwcP1i+//JKjfVkspsstlK735aj9/RO9Oi5n6pdeHZMz9So5V7/O2GtR30dOONP7ejMOEyyioqKUlpYmPz8/m+V+fn46evRoluts375dixcv1h9//JHj/fj4lJCrq0Md6MmkXLmSBV3CLUOvjsuZ+qVXx+RMvUrO1a8z9WpPvr6F63Xkfc3HYHHt2jVZLBYVK1YsvzZpV7GxsXr00Uf14YcfytfXN8frxcTEO2witVgyfiiio2NlGAVdjX3Rq+Nypn7p1TE5U6+Sc/XrdL3aeR9RUbF23kPOOMP7mtMQl+dgsXXrVn399df69ddfdeTIESUmJkqSvLy8VKdOHbVo0UK9evVS27Zt87qLXPH19ZWrq6siIiJslkdERKhixYqZxv/55586ffq0evToYV2Wnp4uSXJzc9OxY8dUo0aNLPflqB+a6wzD8Xu8jl4dlzP1S6+OyZl6lZyrX2fq1Z4K22vI+5rLYJGSkqIFCxZo1qxZOn36tHx8fNSkSRM98sgjKlu2rAzD0KVLlxQWFqbly5dr9uzZ8vf318iRI/XUU0/J3d3dXn3Iw8NDwcHB2rJli/WSsenp6dqyZYuGDx+eaXzt2rV18OBBm2Xjxo1TbGys3nvvPVWtWtVutQIAAACOJlfB4vbbb1dycrIef/xxPfTQQ2rSpMkNx+/bt0+ff/65pk2bppCQEJ0+fdpMrTc1YsQIPf7442ratKnuuusuvfvuu4qPj9egQYMkSY899piqVKmi6dOny9PTU0FBQTbrlylTRpIyLQcAAABwY7kKFmPHjtXAgQNzPI8iODhYwcHBmjx5sj7++OM8FZgbffv2VWRkpCZMmKDw8HA1atRIGzdutE7oPnv2rFxcHHviNQAAAFAQLIaR97PBzp49q/Lly6t48eJZPp+YmKjIyEhVq1YtzwUWNpGRhWOikD1YLBmTc6KiHHfy0XX06ricqV96dUzO1KvkXP06Xa9zb7PrPiKH/WXX7eeUM7yv5cvnbPK2qV/fBwYGas2aNdk+/8033ygwMNDMLgAAAAAUAaYuN3uzgx0pKSmcegQAQG69Xlo5vxB67hWW3/QCcCy5DhZXr17V5cuXrY+jo6N19uzZTOMuX76sVatWqVKlSqYKBAAAAFD45TpYvPPOO5o8ebIkyWKx6MUXX9SLL76Y5VjDMDRlyhRTBQIAAAAo/HIdLDp37ixvb28ZhqFXX31V/fv3z3TZWYvFohIlSig4OFhNmzbNt2IBAAAAFE65DhbNmzdX8+bNJUnx8fHq3bu36tevn++FAQAAACg6TE3enjhxYn7VAQAAgFvozpnb7Lr905523TwKIVOXbPrjjz/06aef2iz7/vvv1bp1azVr1kzvvfeeqeIAAAAAFA2mgsWrr76q1atXWx+HhYXp/vvvV1hYmCRpxIgRWrhwobkKAQAAABR6poLFgQMH1LJlS+vjTz75RK6urvr999+1a9cuPfjgg5o/f77pIgEAAAAUbqaCxZUrV1SuXDnr4w0bNqhTp07y9c24rU+nTp108uRJcxUCAAAAKPRMBYtKlSopNDRUkvT3339r37596ty5s/X5uLg47rwNAAAAOAFTV4W67777NGfOHF27dk27du1SsWLFdP/991ufP3DggKpXr266SAAAAACFm6lgMWXKFEVGRmrZsmUqU6aMlixZIj8/P0nS1atX9cUXX2jYsGH5UigAAACAwstUsPD29taKFSuyfe6vv/6Sl5eXmV0AAAAAKAJMBYt/iouL07lz5yRJVatWlbe3t0qXLp1fmwcAAABQiJmeWb1nzx61a9dOZcuWVVBQkIKCglS2bFm1b99ee/fuzY8aAQAAABRypo5Y7Nq1S23btpWHh4eefPJJ1alTR5IUGhqqTz/9VK1bt9bWrVt111135UuxAAAAAAonU8HitddeU5UqVbR9+3ZVrFjR5rnXX39dd999t1577TVt2rTJVJEAAAAACjdTp0Lt2rVLTz31VKZQIUl+fn4aOnSofvvtNzO7AAAAAFAEmAoWLi4uSk1Nzfb5tLQ0bpAHAAAAOAFT3/pbtGihefPm6cyZM5meO3v2rN5//33dfffdZnYBAAAAoAgwNcdi2rRpat26tWrXrq37779fNWvWlCQdO3ZMX3/9tdzc3DR9+vR8KRQAAADIqztnbrPbtk+/2d1u2y5KTAWLxo0ba9euXXrttdf0zTffKCEhQZLk5eWlrl27asqUKapbt26+FAoAAACg8DJ9g7y6detqzZo1Sk9PV2RkpCSpfPnyzK0AAAAAnIipYJGamqqEhASVKlVKLi4u8vPzs3n+6tWr8vLykptbvt3gGwAAAEAhZOqwwvPPP68WLVpk+/zdd9+tkSNHmtkFAAAAgCLAVLDYuHGjHnzwwWyff/DBB7VhwwYzuwAAAABQBJgKFhcuXFCVKlWyfb5y5co6f/68mV0AAAAAKAJMTX4oV66cjh07lu3zoaGhKlWqlJldAABQKNn10pWedts0ANiNqSMWXbt21YIFC/T7779nem7//v1auHCh7rnnHjO7AAAAAFAEmDpi8cYbb2jjxo2666671LNnT9WrV0+SdOjQIa1bt04VKlTQG2+8kS+FAgAAACi8TAWLypUra+/evRo9erS+/vprrVmzRpJUqlQpDRgwQNOmTVPlypXzpVAAAAAAhZfpG0xUqlRJS5culWEYNjfIs1gsposDAAAAUDTk253rLBaLKlSokF+bAwAAAFCEmJq8DQAAAABSLoNF3bp19cknnyg5OTnH6yQlJenjjz9W3bp1c10cAAAAgKIhV6dCDRw4UCNGjNALL7ygnj17qmPHjmrSpIkCAwPl5eUlSYqPj1dYWJj27t2rzZs3a926dfLw8NArr7xilwYAAAAAFLxcBYtXX31VzzzzjBYvXqwlS5Zo2bJl1knabm4Zm0pNTZUkGYahoKAgTZo0SU888QQ3ygMAAAAcWK4nb5csWVIvvviiXnzxRZ0+fVo7duzQ0aNHFR0dLSnjbty1a9dW8+bNFRgYmO8FAwAAACh8TF0VKiAgQAEBATccc+nSJZUtW9bMbgAAAAAUcna5KlRSUpI+//xz9erVixvkAQAAAE4g3+5jYRiGtmzZohUrVmjNmjWKjY2VYRjcKA8AAABwAqaDxb59+7RixQqtWrVKERER8vT0VLdu3fTQQw/p0KFDmjJlSn7UCQAAAKAQy1OwOHXqlFasWKEVK1boxIkTcnd3V9euXdW3b1/17NlTJUqUsI4DAAAA4PhyHSyaN2+u3bt3y93dXR07dtTYsWPVq1cvLicLAAAAOLFcB4tdu3apWLFiGjdunIYOHary5cvboy4AAAAARUiurwo1d+5cBQcHa/z48apSpYo6duyoRYsWKSYmxh715dq8efMUEBAgT09PNWvWTLt378527IcffqhWrVqpbNmyKlu2rDp27HjD8QAAAACylutg8eyzz2r79u06deqUJkyYoAsXLmjo0KGqVKmSunXrpqVLl+rKlSv2qPWmVq9erREjRmjixInav3+/GjZsqC5duujixYtZjt+6dav69++vn376STt37lTVqlXVuXNnnT9//hZXDgAAABRteb6PRUBAgMaNG6cjR45oz549GjZsmA4cOKBBgwbJz89PPXr00Pbt2/Oz1puaNWuWhgwZokGDBqlu3bqaP3++vLy89NFHH2U5fsWKFXr22WfVqFEj1a5dW4sWLVJ6erq2bNlyS+sGAAAAirp8uUFecHCwZs2apXPnzun7779Xv379tG3bNq1fvz4/Np8jycnJ2rdvnzp27Ghd5uLioo4dO2rnzp052kZCQoJSUlLk4+NjrzIBAAAAh5RvN8iTMr7Id+rUSZ06ddL8+fP19ddfa+XKlfm5i2xFRUUpLS1Nfn5+Nsv9/Px09OjRHG1j1KhRqly5sk04yYqj3vPvel+O2t8/0avjcqZ+6RV5VZheR2d6b52p11uhsL2Oha2egpDrYBESEqJ7771XtWvXvuE4T09P9e3bV3379s1zcbfSm2++qVWrVmnr1q3y9PTMdpyPTwm5uubLgZ5Cq1y5kgVdwi1Dr47LmfqlV+SWr2/hex2d6b11pl7tqbB9jnlf8xAs3nrrLY0aNUr+/v7q3r27unfvrnbt2qlYsWL2qC/HfH195erqqoiICJvlERERqlix4g3XDQkJ0ZtvvqnNmzerQYMGNxwbExPvsInUYsn4oYiOjpVhFHQ19kWvjsuZ+qVX5FVUVGxBl2DlTO+tM/V6KxSmz7Ekh35fcxrich0sIiIitGvXLn333Xdav3693n//fRUvXlzt2rXTvffeq3vuuUfVqlXLdcFmeXh4KDg4WFu2bFGvXr0kyToRe/jw4dmu99Zbb2nq1Kn6/vvv1bRp0xzty1E/NNcZhuP3eB29Oi5n6pdekVuF8TV0pvfWmXq1p8L2GvK+5mHytsVi0X/+8x9NmjRJe/fu1YULFzR79mx5enpq1KhRCgwMVP369TV69Ght375d6enp9qg7SyNGjNCHH36opUuXKjQ0VM8884zi4+M1aNAgSdJjjz2mMWPGWMfPmDFD48eP10cffaSAgACFh4crPDxccXFxt6xmAAAAwBGYnizg5+enJ554Qp9//rmioqK0efNmde3aVevWrVPr1q3l6+urfv36adeuXflR7w317dtXISEhmjBhgho1aqQ//vhDGzdutE7oPnv2rP7++2/r+A8++EDJycl68MEHValSJeufkJAQu9cKAAAAOJJ8vSqUm5ub2rVrp3bt2untt9/W6dOntX79em3YsEG//PKLmjVrlp+7y9Lw4cOzPfVp69atNo9Pnz5t93oAAAAAZ5BvweLatWuyWCw2k7gDAgI0bNgwDRs2LL92AwAAAKAQynOw2Lp1q77++mv9+uuvOnLkiBITEyVJXl5eqlOnjlq0aKFevXqpbdu2+VUrAAAAgEIqV8EiJSVFCxYs0KxZs3T69Gn5+PioSZMmeuSRR1S2bFkZhqFLly4pLCxMy5cv1+zZs+Xv76+RI0fqqaeekru7u736AAAAAFCAchUsbr/9diUnJ+vxxx/XQw89pCZNmtxw/L59+/T5559r2rRpCgkJYU4DAAAA4KByFSzGjh2rgQMH5vhmeMHBwQoODtbkyZP18ccf56lAAAAAAIVfroLFU089laedeHh45HldAAAAAIWf6ftYAAAAAECurwq1f//+XI13dXVVqVKl5O/vLxcXcgwAAADgiHIdLJo2bSqLxZLrHZUoUUIDBgzQrFmzVLx48VyvDwAAAKDwynWwyO0kbMMwFBsbq927d2vhwoUyDEPz58/P7W4BAAAAFGK5DhaPP/54nndWpkwZrV69mmABAAAAOJhbOumhXbt28vb2vpW7BAAAAHAL3NJg0bt3b506depW7hIAAADALZCrU6Hq1q2r0aNHq1+/fvLw8MjROklJSVq5cqXefvttHTlyJE9FAgAAAIXW66Xla8fNRw77y45bzz+5ChYDBw7UiBEj9MILL6hnz57q2LGjmjRposDAQHl5eUmS4uPjFRYWpr1792rz5s1at26dPDw89Morr9ilAQAAAAAFL1fB4tVXX9UzzzyjxYsXa8mSJVq2bJn10rNubhmbSk1NlZRxNaigoCBNmjRJTzzxhEqVKpXPpQMAAAAoLHJ9VaiSJUvqxRdf1IsvvqjTp09rx44dOnr0qKKjoyVJ5cqVU+3atdW8eXMFBgbme8EAgH+w4+H3onLoHQBQOOQ6WPxTQECAAgICbjjm0qVLKlu2rJndAAAAACjk7HJVqKSkJH3++efq1auXKleubI9dAAAAAChETB2x+CfDMLRlyxatWLFCa9asUWxsrAzDsM7BAAAAAOC4TAeLffv2acWKFVq1apUiIiLk6empbt266aGHHtKhQ4c0ZcqU/KgTAAAAQCGWp2Bx6tQprVixQitWrNCJEyfk7u6url27qm/fvurZs6dKlChhHQcAAADA8eU6WDRv3ly7d++Wu7u7OnbsqLFjx6pXr15cThYAAABwYrkOFrt27VKxYsU0btw4DR06VOXLl7dHXQAAAACKkFwHi7lz52rlypUaP368Jk2apNatW6tfv37q3bu3fHx87FEjABRZd87cZtftn/a06+YBAMixXF9u9tlnn9X27dt16tQpTZgwQRcuXNDQoUNVqVIldevWTUuXLtWVK1fsUSsAAACAQirP97EICAjQuHHjdOTIEe3Zs0fDhg3TgQMHNGjQIPn5+alHjx7avn17ftYKAAAAoJDKl/tYBAcHKzg4WCEhIZnuZcF9LAAAAADHl6933nZxcVGnTp20ZMkSRURE6NNPP9W9996bn7sAAAAAUAjla7D4J09PT/Xt21dff/21vXYBAAAAoJCwW7AAAAAA4DwIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA00wHC8MwdPbsWSUnJ0uS0tPTbR4DAAAAcHymg0VMTIwCAwOtd9mOjIy0eQwAAADA8eXLqVCGYdzwMQAAAADH5lbQBQAAkCOvl5avHTcfOewvO24dABwfk7cBAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBqXmwUA5Is7Z26z6/ZPe9p18wAAk0wHi9KlS+unn35So0aNJEk+Pj42jwEAAAA4PtPBws3NTW3atLE+dnd3t3kMAAAAwPE53KlQ8+bN09tvv63w8HA1bNhQc+bM0V133ZXt+M8//1zjx4/X6dOndccdd2jGjBnq1q3bLawYAAAnZ8e7qnNHdeDWcajJ26tXr9aIESM0ceJE7d+/Xw0bNlSXLl108eLFLMfv2LFD/fv31+DBg/X777+rV69e6tWrlw4dOnSLKwcAAACKNoc6YjFr1iwNGTJEgwYNkiTNnz9f69ev10cffaTRo0dnGv/ee++pa9eueuWVVyRJb7zxhjZt2qS5c+dq/vz5We7DMNLt10ABMwwpPT1d6emO2+N19Oq4Clu/xVwtdt1+uqv9ZjTn9u+7otyrVLj6LWy92pNhFK7PsT3x91P+4me28LEYhmEUdBH5ITk5WV5eXvriiy/Uq1cv6/LHH39cly9f1tdff51pnWrVqmnEiBF68cUXrcsmTpyotWvX6sCBA1nuh6MZAAAAcCZBQUE5Gucwp0JFRUUpLS1Nfn5+Nsv9/PwUHh6e5Trh4eG5Gg8AAAAgaw51KtStUKFC1QLdf6v3frXr9kM9B9lt21FPHbfbtnPLYpHKlSup6OhYOcYxu+w5U6+Sc/VLr47JmXqVnKtfenVMztTrzeRLsIiPj1dsbKx8fX3l5lYwWcXX11eurq6KiIiwWR4REaGKFStmuU7FihVzNV6SLJaCPciTlGbfT6xL2jW7bbugX7t/slgkFxeXQlWTvThTr5Jz9UuvjsmZepWcq196dUzO1OvN5PkVOHPmjIYPHy5/f3+VKlVKVapUkaenpwICAvTqq6/qzJkz+VnnTXl4eCg4OFhbtmyxLktPT9eWLVvUvHnzLNdp3ry5zXhJ2rRpU7bjAQAAAGQtT8Fi3bp1atCggd5//325urqqR48eevjhh3XvvffKxcVFISEhatSokdavX29dZ9y4cflWdHZGjBihDz/8UEuXLlVoaKieeeYZxcfHW68S9dhjj2nMmDHW8S+88II2btyomTNn6ujRo3r99de1d+9eDR8+3O61AgAAAI4k1+cthYaG6qGHHlJgYKAWLFigVq1aZRrzyy+/6Omnn1bfvn21d+9eTZ8+XcuXL9eUKVPypejs9O3bV5GRkZowYYLCw8PVqFEjbdy40TpB++zZs3Jx+V+WatGihVauXKlx48Zp7NixuuOOO7R27docz3wHAAAAkCHXwWLatGkqV66ctm/fLh8fnyzHtGrVSr/88osaNGig4OBgJSUlafr06aaLzYnhw4dne8Rh69atmZb16dNHffr0sXNVAAAAgGPL9alQP/74owYPHpxtqLjOx8dHTzzxhBITE7VkyRK9+uqreS4SAAAAQOGW62ARHR2tgICAHI0NDAyUq6urHnnkkdzuBgAAAEARkutg4evrq7CwsByNDQsLU4UKFXJdFAAAAICiJdfBom3btlq8eLFiYmJuOC4mJkaLFy9W+/bt81wcAAAAgKIh18Fi7Nixio6OVuvWrbVjx44sx+zYsUNt2rRRdHS0zeVdAQAAADimXF8Vqm7dulq5cqUee+wxtWrVSgEBAWrYsKFKliyp2NhY/fe//1VYWJiKFy+ulStXqm7duvaoGwAAAEAhkutgIUm9e/dWo0aN9NZbb+nbb7/V2rVrrc9VrlxZQ4cO1csvv6waNWrkV50AAAAACrE8BQtJql69uubPny9Junr1qmJjY1WyZEmVKlUq34oDAAAAUDTkOVgkJSVp+fLl+uGHH/Tnn39ag8Udd9yhLl266OGHH5aHh0d+1goAAACgkMpTsDh48KDuu+8+nTlzRoZhqHTp0vL29tbFixe1f/9+ffbZZ5o6daq++eYb1alTJ79rBgAAAFDI5PqqUHFxcerZs6ciIiI0depUnTt3TpcuXbL575QpU3ThwgX16NFD8fHx9qgbAAAAQCGS62Dx8ccf6+zZs1q/fr1Gjx6tKlWq2DxfpUoVjRkzRuvWrVNYWJiWLFmSX7UCAAAAKKRyfSrU+vXr1blzZ7Vt2/aG49q3b69OnTpp3bp1GjZsWF7rw7/sGdnabtu2WCTNtdvmAQAA4MByfcTi4MGDNw0V17Vv314HDx7M7S4AAAAAFDG5DhYxMTGqWLFijsb6+fkpJiYm10UBAAAAKFpyHSySkpLk7u6eo7Fubm5KTk7OdVEAAAAAipY8XW729OnT2r9//03HhYWF5WXzAAAAAIqYPAWL8ePHa/z48TcdZxiGLBZLXnYBAAAAoAjJdbD4+OOP7VEHAAAAgCIs18Hi8ccft0cdAAAAAIqwXE/eBgAAAIB/I1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0hwkWMTExGjBggEqVKqUyZcpo8ODBiouLu+H45557TrVq1VLx4sVVrVo1Pf/887py5cotrBoAAABwDA4TLAYMGKDDhw9r06ZN+vbbb7Vt2zYNHTo02/EXLlzQhQsXFBISokOHDmnJkiXauHGjBg8efAurBgAAAByDW0EXkB9CQ0O1ceNG7dmzR02bNpUkzZkzR926dVNISIgqV66caZ2goCB9+eWX1sc1atTQ1KlT9cgjjyg1NVVubg7x0gAAAAC3hEMcsdi5c6fKlCljDRWS1LFjR7m4uGjXrl053s6VK1dUqlQpQgUAAACQSw7xDTo8PFwVKlSwWebm5iYfHx+Fh4fnaBtRUVF64403bnj61HUWS57KLPTs3Vdhet2u11KYarIXZ+pVcq5+6dUxOVOvknP1S6+OyZl6vZlCHSxGjx6tGTNm3HBMaGio6f1cvXpV3bt3V926dfX666/fcKyPTwm5ujrEgZ5bzte3ZEGXkEm5coWvJntxpl4l5+qXXh2TM/UqOVe/9OqYnKnX7BTqYDFy5EgNHDjwhmOqV6+uihUr6uLFizbLU1NTFRMTo4oVK95w/djYWHXt2lUlS5bUmjVr5O7ufsPxMTHxDptILRapnB23HxUVa8et547FkvEXQHR0rAyjoKuxL2fqVXKufunVMTlTr5Jz9UuvjskZes3pL4cLdbAoX768ypcvf9NxzZs31+XLl7Vv3z4FBwdLkn788Uelp6erWbNm2a539epVdenSRcWKFdM333wjT0/PHNXlqB8aeyuMr5thFM667MGZepWcq196dUzO1KvkXP3Sq2Nypl6z4xDn9NSpU0ddu3bVkCFDtHv3bv36668aPny4+vXrZ70i1Pnz51W7dm3t3r1bUkao6Ny5s+Lj47V48WJdvXpV4eHhCg8PV1paWkG2AwAAABQ5hfqIRW6sWLFCw4cPV4cOHeTi4qIHHnhAs2fPtj6fkpKiY8eOKSEhQZK0f/9+6xWjbr/9dptthYWFKSAg4JbVDgAAABR1DhMsfHx8tHLlymyfDwgIkPGP41Nt27a1eQwAAAAg7xziVCgAAAAABYtgAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMc5hgERMTowEDBqhUqVIqU6aMBg8erLi4uBytaxiG7rnnHlksFq1du9a+hQIAAAAOyGGCxYABA3T48GFt2rRJ3377rbZt26ahQ4fmaN13331XFovFzhUCAAAAjsutoAvID6Ghodq4caP27Nmjpk2bSpLmzJmjbt26KSQkRJUrV8523T/++EMzZ87U3r17ValSpVtVMgAAAOBQHCJY7Ny5U2XKlLGGCknq2LGjXFxctGvXLt1///1ZrpeQkKCHH35Y8+bNU8WKFXO8P0c9uGHvvgrT63a9lsJUk704U6+Sc/VLr47JmXqVnKtfenVMztTrzThEsAgPD1eFChVslrm5ucnHx0fh4eHZrvfSSy+pRYsWuu+++3K8Lx+fEnJ1dZgzyG4pX9+SBV1CJuXKFb6a7MWZepWcq196dUzO1KvkXP3Sq2Nypl6zU6iDxejRozVjxowbjgkNDc3Ttr/55hv9+OOP+v3333O1XkxMvMMmUotFKmfH7UdFxdpx67ljsWT8BRAdHSvDKOhq7MuZepWcq196dUzO1KvkXP3Sq2Nyhl5z+svhQh0sRo4cqYEDB95wTPXq1VWxYkVdvHjRZnlqaqpiYmKyPcXpxx9/1J9//qkyZcrYLH/ggQfUqlUrbd26Ndt9OuqHxt4K4+tmGIWzLntwpl4l5+qXXh2TM/UqOVe/9OqYnKnX7BTqYFG+fHmVL1/+puOaN2+uy5cva9++fQoODpaUERzS09PVrFmzLNcZPXq0nnzySZtl9evX1zvvvKMePXqYLx4AAABwIoU6WORUnTp11LVrVw0ZMkTz589XSkqKhg8frn79+lmvCHX+/Hl16NBBn3zyie666y5VrFgxy6MZ1apVU2Bg4K1uAQAAACjSHGYW8ooVK1S7dm116NBB3bp1U8uWLbVw4ULr8ykpKTp27JgSEhIKsEoAAADAMTnEEQtJ8vHx0cqVK7N9PiAgQMZNTny72fMAAAAAsuYwRywAAAAAFByCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAExzK+gCUMi8fkVRUbEyjIIuBAAAAEUJRywAAAAAmEawAAAAAGAawQIAAACAaQQLAAAAAKYRLAAAAACYRrAAAAAAYBrBAgAAAIBpBAsAAAAAphEsAAAAAJhGsAAAAABgGsECAAAAgGkOEyxiYmI0YMAAlSpVSmXKlNHgwYMVFxd30/V27typ9u3bq0SJEipVqpRat26txMTEW1AxAAAA4DgcJlgMGDBAhw8f1qZNm/Ttt99q27ZtGjp06A3X2blzp7p27arOnTtr9+7d2rNnj4YPHy4XF4d5WQAAAIBbwq2gC8gPoaGh2rhxo/bs2aOmTZtKkubMmaNu3bopJCRElStXznK9l156Sc8//7xGjx5tXVarVq1bUjMAAADgSBziV/M7d+5UmTJlrKFCkjp27CgXFxft2rUry3UuXryoXbt2qUKFCmrRooX8/PzUpk0bbd++/VaVDQAAADgMhzhiER4ergoVKtgsc3Nzk4+Pj8LDw7Nc59SpU5Kk119/XSEhIWrUqJE++eQTdejQQYcOHdIdd9yR7f4slvyrvTC53pej9vdP9Oq4nKlfenVMztSr5Fz90qtjcqZeb6ZQB4vRo0drxowZNxwTGhqap22np6dLkp566ikNGjRIktS4cWNt2bJFH330kaZPn57lej4+JeTq6hAHerJVrlzJgi7hlqFXx+VM/dKrY3KmXiXn6pdeHZMz9ZqdQh0sRo4cqYEDB95wTPXq1VWxYkVdvHjRZnlqaqpiYmJUsWLFLNerVKmSJKlu3bo2y+vUqaOzZ89mu7+YmHiHTaQWS8YPRXR0rAyjoKuxL3p1XM7UL706JmfqVXKufunVMTlDr76+OQtNhTpYlC9fXuXLl7/puObNm+vy5cvat2+fgoODJUk//vij0tPT1axZsyzXCQgIUOXKlXXs2DGb5cePH9c999xzw/056ofmOsNw/B6vo1fH5Uz90qtjcqZeJefql14dkzP1mh2HOKenTp066tq1q4YMGaLdu3fr119/1fDhw9WvXz/rFaHOnz+v2rVra/fu3ZIki8WiV155RbNnz9YXX3yhkydPavz48Tp69KgGDx5ckO0AAAAARY7FMBwjW8XExGj48OFat26dXFxc9MADD2j27Nny9vaWJJ0+fVqBgYH66aef1LZtW+t6b775pubNm6eYmBg1bNhQb731llq2bFlAXQAAAABFk8MECwAAAAAFxyFOhQIAAABQsAgWAAAAAEwjWAAAAAAwjWABAAAAwDSChZPYs2ePnn76abVs2VK1atXS5s2bb7rOrl27dP/99ysoKEidOnXSV199dQsqNW/lypXq0aOHmjRpoiZNmqhv3776+eefb7jOd999p65du6p+/frq0aPHTccXVgsXLlStWrU0derUG44rqv3OmTNHtWrVsvnTtWvXG65TVHq92c9ofHy8Jk+erNatW6tBgwbq1q2bPv3005tutzD2f7Neo6KiNHr0aLVs2VINGzbU4MGDdfr06ZtutzD2umDBAj3wwANq3LixmjdvrmeffVanTp2yGZOUlKRJkyapWbNmaty4sZ577jlFRUXdcLuGYei9995Ty5Yt1aBBAw0cODBHr5E95aTX1atX69FHH1WTJk1Uq1YtXb16NUfbXrFihdq3b6/69eurT58++u9//2uPFnIsJ70++uijmf6+mjBhwg23WxjfV+nm/646ymdYunGvly9f1htvvKEuXbqoQYMGatu2raZMmaLY2NgbbrOw9moXBpzC1q1bjVmzZhk//PCDUbNmTWPTpk03HH/27FmjYcOGxvTp042TJ08ay5YtM+rUqWNs27btFlWcd1u2bDG2bt1qhIWFGadOnTJmzZpl1KtXzzh+/HiW4/ft22fUqVPH+PDDD42TJ08a77zzjlGvXj3j2LFjt7hycw4cOGC0a9fO6NGjhzFlypRsxxXlfmfPnm10797duHjxovVPdHR0tuOLUq83+xkdN26c0bFjR+O3334zzp07Z6xatcqoU6eOsXnz5my3WVj7v1Gv6enpxkMPPWQ8/PDDxoEDB4w///zTGD9+vNG2bVsjPj4+220W1l6feOIJ48svvzSOHz9uhIaGGkOGDMnUy4QJE4w2bdoYO3bsMA4ePGg89NBDRt++fW+43QULFhjBwcHGpk2bjNDQUOPpp5822rdvb1y7ds3eLWUrJ71+/PHHxoIFC4wFCxYYNWvWNK5cuXLT7a5fv96oV6+e8cUXXxgnTpwwxo0bZzRt2tSIioqyZzs3lJNeH3nkEWPcuHE2f1/FxsbecLuF8X01jJv/u+oon2HDuHGvx44dM4YPH25s2bLFOHPmjLFjxw6jc+fOxnPPPXfDbRbWXu2BYOGEchIs3nrrLaN79+42y1588UXjiSeesGdpdnPnnXcan332WZbPvfDCC8bQoUNtlvXp08cYP378rSgtX8TFxRmdO3c2fv31V+ORRx65YbAoyv3Onj3b6NmzZ47HF9Ves/oZ7d69uzF37lybZffff78xa9asbLdTFPr/d6+nTp0yatasafOLgLS0NOM///lPtj/DhlE0ejUMw4iOjjZq1qxp7N692zAMw7h69apRr14947vvvrOOOXnypFGzZk3j999/z3Ib6enpxt13320sWrTIuuzq1atGUFCQ8e2339q1/tz4d6//9Ntvv+U4WDz44IPGpEmTrI/T0tKMli1bGgsWLMjXes3Iqteb/V38b0Xlfb3u+r+rjvwZvu5G3yE2bNhg1KtXz0hJScny+aLWq1mcCoUs/fHHH2revLnNspYtW+qPP/4omILyKC0tTevXr1dCQoIaN26c5RhH6HXy5Mlq06aNWrRocdOxRb3fM2fOqGXLlurQoYNGjhypCxcuZDu2qPf6T40bN9aPP/6oiIgIGYah3377TWFhYTe8oWdR7D85OVmSVKxYMesyFxcXeXh4aN++fdmuV1R6vX7KROnSpSVJhw4dUkpKis3Pbo0aNVS5cuVsa//rr78UGRlps07JkiXVsGFD/f777/YrPpf+3WteJCcn6/Dhwza9uri4qEWLFkWi13Xr1qlZs2a69957NXPmTCUmJma7jaLyvv7731VH/gzn5DtEXFycvL295ebmluXzRaXX/JL1qwCnFxUVJV9fX5tlvr6+iouL07Vr1+Tp6VlAleXMsWPH1K9fPyUlJcnLy0vz5s3T7bffnuXYrHotV67cTc8PLSzWr1+vI0eO6IsvvsjR+KLcb4MGDTR9+nQFBgYqMjJS8+bN04ABA7Ru3Tp5e3tnGl+Ue/238ePHa/z48WrdurXc3NxksVg0ZcoU3XnnndmuUxT7r169uipXrqyZM2dq8uTJKl68uJYsWaLw8HBFRkZmu15R6DU9PV3Tpk1TkyZNVLNmTUkZdbu7u6tUqVI2Y8uVK5dtv9eXlytXLtM6haXfrHrNi0uXLiktLS3LXv89p6GgZNfrvffeq8qVK6tChQo6duyYQkJCFBYWprlz52a5ncL+vmb372poaKjDfYZz+h0iJiZG77//vvr27Zvttgp7r/mNYAGHFBgYqLVr1yo2Nlbff/+9Ro0apeXLl2cbLoqqv//+W1OnTtVHH31k8xteR9WmTRvr/9euXVsNGzZUu3bt9N1336lPnz4FWJn9LVu2TH/88Yc++OADVa5cWXv37tWkSZNUoUKFHB2pKirc3d01Z84cvfbaa7rrrrvk6uqq5s2bq3Xr1jIMo6DLM2XSpEk6ceKEVq5cWdCl2B29yubLZq1atVS+fHkNHDhQZ8+eVbVq1W51maZl9++qI8rJd4i4uDg99dRTqlGjhoYPH16A1RYuBAtkydfXN1OSjoqKkre3d6E/WiFJHh4e8vf3lyQFBQXp4MGD+uSTTzR58uRMY7PqNTo6OtNvPwujw4cPKzo6Wr1797YuS0tL0549e7RixQodPHhQrq6uNusU5X7/rVSpUgoICNDZs2ezfN5Rer127ZreeecdzZ07V23btpWUEaxCQ0O1ePHibINFUe0/KChIX3/9tWJjY5WSkiIfHx/16dNHQUFB2a5T2HudPHmytm7dquXLl6tixYrW5b6+vkpJSdHVq1dtfuMbHR2t8uXLZ7mt68ujo6NVoUIFm3Vq165tpw5yLrte86Js2bJydXVVdHS0zfLC8t7mpteGDRtKyjidM6tgUdjf1+z+Xb3nnnsc7jN8s+8QcXFxevLJJ1WiRAnNmzdP7u7u2W6rsPea35hjgSw1atRIv/32m82yHTt2qFGjRgVTkEnp6enWc7f/rSj3+p///Efr1q3T2rVrrX+CgoLUo0cPrV27NlOokIp2v/8WHx+vc+fOZfuPl6P0mpqaqpSUFFksFpvlrq6uN/wtflHvv2TJkvLx8dHp06d16NAhdejQIduxhbVXwzA0efJkbdq0SUuXLlXVqlVtng8KCpK7u7t27txpXXbq1ClduHAh29pvu+02lS9f3maduLg4HThwINvzwG+Fm/WaFx4eHqpXr55Nr+np6dq5c2eR6zU0NFSSsv37qrC+r9m5/u+qI32Gs/PP7xBxcXEaPHiw3N3d9cEHH9z0bIGi1qtpBTlzHLdOXFycceTIEePIkSNGzZo1jY8//tg4cuSIcf78ecMwDCMkJMR45ZVXrOOvX252xowZxsmTJ43ly5cXmcvNhoSEGLt37zbOnTtnHD161AgJCTFq1aplbN++3TAMw3jllVeMkJAQ6/h9+/YZdevWNRYvXmycPHnSmD17dqG4TGVe/ftKJI7U75tvvmns2rXLOHfunLFv3z5j4MCBRrNmzayXnC3Kvd7sZ/SRRx4xunfvbvz222/G2bNnjS+//NKoX7++sWLFCus2ikr/N+t1w4YN1j43bdpktGvXzhg+fLjNNopKrxMnTjSCg4ONXbt22Vx2NDEx0TpmwoQJRtu2bY2dO3caBw8eNPr27ZvpUp1dunQxfvjhB+vjBQsWGE2bNjU2b95sHD161HjmmWcK/PKVOen14sWLxpEjR4zPPvvMqFmzprFnzx7jyJEjxqVLl6xjHnvsMWPZsmXWx+vXrzeCgoKMr776yjh58qQxfvx4o2nTpkZkZOStbM/GzXo9c+aMMXfuXOPgwYPGuXPnjM2bNxsdOnQwBgwYYLOdovC+GsbN/111lM+wYdy419jYWKNPnz7Gvffea5w5c8bmvU9NTbVuo6j0ag+cCuUkDh06pMcee8z6ePr06ZKk+++/X2+++aYiIyP1999/W5+vWrWqFixYoOnTp+uTTz5RxYoVNWXKFLVq1eqW155b0dHRGjVqlC5evKiSJUuqVq1aWrx4se6++25JGfMSXFz+d7CuSZMmCgkJ0bvvvqtZs2YpICBA8+bNMzXhsDBxpH7Dw8M1YsQIXb58WT4+PgoODtZnn30mHx8fSUW715v9jM6aNUuzZs3Syy+/rCtXrqhy5cp66aWX1L9/f+s6RaX/nPx99Oabb1pPpbjvvvv07LPP2myjqPR6/SaGjz76qM3y6dOnW09hHDt2rFxcXPT8888rOTlZLVu21MSJE23Gh4WF2dyEa8iQIUpMTNSECRN09epVBQcHa9GiRQU61yonva5atcpm8vKAAQMyjTl37pwuXbpkHdOtWzfFxMRo9uzZioyMVJ06dbRo0aICPRXqZr1e/w3+J598ooSEBFWqVEmdO3fO9DkuCu+rdPN/Vx3lMyzduNddu3bpwIEDkqROnTrZrLdlyxbddtttkopOr/ZgMYwiPhsOAAAAQIFjjgUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAKFCPPvqopk6dWtBlAABM4s7bAAC7Gz16tNasWZNp+Q8//FAA1QAA7IFgAQC4JVq1aqXp06fbLPPx8SmgagAA+Y1ToQAAt4SHh4fKly9v88fV1TXTuLVr16p3795q3Lix7r77bo0cOVLR0dE2Y7Zs2aLOnTurfv36evTRR7VmzRrVqlVLV69elSSdP39eTz/9tO688041atRI3bt3188//3xL+gQAZ8URCwBAoZKamqoXXnhB1atXV3R0tN58802NHj1aH374oSTp3LlzeuGFF/Too4+qT58+Cg0N1YwZM2y2MXnyZKWkpGj58uXy8vLSyZMn5eXlVRDtAIDTIFgAAG6JrVu3qnHjxtbHrVq10uzZszONe/DBB63/X7VqVb322mt68MEHFR8frxIlSmj16tUKDAzUqFGjJEnVq1fX8ePHNX/+fOt6Fy5cUJcuXVSrVi3rdgAA9kWwAADcEs2aNdPrr79ufVy8ePEsxx06dEhz587V0aNHdeXKFRmGIUn6+++/dfvttyssLExBQUE26zRo0MDm8WOPPabXX39d27dvV4sWLdS5c2fVrl07fxsCANhgjgUA4JYoXry4/P39rX8qVKiQaUxCQoIGDx6sEiVKKCQkRF988YXmzp0rSUpJScnxvvr06aPNmzfrvvvu0/Hjx/Xggw9q2bJl+dYLACAzggUAoNA4deqULl++rJdffllNmzZVjRo1Mk3cDgwM1KFDh2yWHTx4MNO2KlWqpP79+2vu3LkaNGiQPvvsM7vWDgDOjmABACg0KleuLHd3dy1btkznzp3Tli1b9P7779uM6du3r8LCwvT2228rLCxMGzZssN4jw2KxSJKmTp2qX375RefOndPhw4e1a9cu1ahR45b3AwDOhGABACg0fHx89Oabb2rjxo3q1q2bPvzwQ+sk7euqVq2q9957T5s2bVLPnj316aef6umnn5aUcUlbSUpPT9fkyZPVrVs3PfnkkwoICNDEiRNveT8A4EwsxvVZcQAAFFEffPCBVq1axb0qAKAAcVUoAECRs2LFCtWvX19ly5bVvn37tHjxYg0YMKCgywIAp0awAAAUOWfOnNEHH3ygK1euqHLlyho0aJCeeuqpgi4LAJwap0IBAAAAMI3J2wAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADCNYAEAAADANIIFAAAAANMIFgAAAABMI1gAAAAAMI1gAQAAAMA0ggUAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADTCBYAAAAATCNYAAAAADDNraALAOB40tPTde3atYIuAwBuysPDQ25ufB0C8gM/SQDyVVJSko4cOaL09PSCLgUAcqRcuXLy9/eXxWIp6FKAIo1gASDfGIah06dPy8XFVb6+VeTiwj/SAAovwzCUlJSo6OgoSVJAQEDBFgQUcQQLAPkmJSVFcXFxKleuojw9ixd0OQBwU8WKZfxdFR0dpaSkJNWqVauAKwKKLiZvA8g3qampkiQ3N/cCrgQAcu56uPj55190/PjxAq4GKLoIFgDyHecpAyhKrv+dlZSUqN279xZwNUDRRbAAAACQ5OnppcuXL3PxCSCPCBYA4MT279+rli2bKjY2tqBLgRPasGGdunZtW9BlWGUcuTAKugygyGLyNgC7u3Pmtlu6vz0jW+dq/B9/7NfKlct07FiooqOjNG1aiFq3bmszZsmSRdq69Ue5urqqWjV/vfLKGHl5lch2mwcPHtDSpYt16NBBJScn6bbbqqpbtx7q06e/XF1dc1zbhg3rNHv2TG3cuDVXPeF/ys+77ZbuL3LYX7kav2zZx/r555905sxpFStWTPXrN9AzzzynatUCJElpaWmaPXumDh48IEkKCmqgF154+Yafo19//UWffrpMx44dVXp6mgIDa6h37z7q1q1HrmpbvHiBfvnlZy1ZsjJX6wFwThyxAOD0EhMTdfvtd2jEiFFZPr9r1079+us2LVjwsRYvXqbSpUtr2bIl2W7v559/0vDhQ1W+vJ/mzJmvFSu+UJ8+/bV06UeaOHGsDOPW/0Y0LS2N0zsKqd9/36/evftowYKP9c4785SamqqXXhquxMRESdK6dWt18WKEFi1apkWLlikyMlLffvt1ttv74otVGjNmpOrXb6iFC5do6dJV6tChs0JCpmvu3HdvUVe2UlJSCmS/AG4tggUAp9e8+d0aOvRZtWnTLsvn//zzhBo3bqpixYpJkpo1a66TJ7O+ckxiYqLeemuKWrZsrVGjXtMdd9RSpUqV1aNHL7322uvaunWLfvxxk6SsT0M6ceKYWrZsqr//vqD9+/dq2rRJiouLU8uWTdWyZVMtXrxAkpScnKy5c99Vr173qGPHlhoy5HHt3/+/SafXTzHZvv1nPfJIH7Vv30IREeE5ej22bt2iRx55SO3aNdeDD/bQp58ut3n+6tWreuONCeratZ06dLhbI0c+r3Pnzmba97ZtW9Wv3/1q376FRowYnuP9O5tZs+aoW7ceql69hu64o6bGjn1dERHhOnYsVJL0558nddddzeXi4iIXFxfdeWcznTx5IsttRUSEa+7cd9WnT3899dQwBQZW1223VVX//o/o2Wef16pVy3X48CFJWZ+GtG3bVrVs2dT6/Mcff6iTJ49bP38bNqyTJMXGxurNN9/Qvfd2VOfObfT880/rxIn//UwsXrxAAwc+rHXr1qpPn55q375Fjl+PNWu+0EMP3ae2bf+j/v17a+PG9TbPh4eHa/ToEerUqZU6d26j8eNHKyYmOtO+1679Ur17d1eHDndr/PjRiouLy3ENAPKGYAEAN1Gjxh3at2+PkpKuSZJ27PhVNWrckeXY3bt/05UrV9S//6OZnmvZsrWqVq2mzZu/z9F+69dvqOefH6kSJUro66836uuvN1q3+847b+nw4f9q0qRpWrp0ldq166iXX7b9gn/t2jWtWLFUo0aN07Jlq1W2rM9N93n0aKgmTBijjh07a+nSVXriiaFatOgD6xdKSZo27XUdOxaqGTNmaf78j2UYhl555QXr5Yav7/uTTz7SuHGT9P77ixUbG6vXXx+bo76dXXx8xhfgUqVKSZJq1Lhdv/32q9LS0pSWlqZdu3aoRo3bs1x369YtSk1NzfLzd999D6h4ca8cf/46dOikfv0eUWBgdevnr0OHTpKk8eNH6dKlGIWEzNbixctUs2ZtvfjiM7p69Yp1/fPnz2nr1h81depb+vjjnJ1K9fPPP+m990LUr98j+uST1brvvt6aPn2yNTSnp6drzJgRunr1qubMWah33pmnCxfOa8KEMTbbOX/+nH76abNmzJilmTPn6MSJY5o5880c1QAg75hjAQA30axZc4WGHtaQIY/LxcVV/v7+evrpYVmOPXfujCTJ3z8wy+f9/QNsvvzfiLu7u7y9vWWxWFSunK91eXh4uDZsWKcvv/xWvr7lJUkPP/yodu3aqQ0b1umppzJqS01N1YgRo3XHHTVz3Ovq1SsUHHynBg58UpJUrZq/Tp8+pZUrl6lbtx46d+6stm/fpg8+WKz69RtKkiZOfEO9e3fXtm1b1b59R+u+X3rpVdWrFyRJGjdukgYMeFBHjhxS3bpBOa7H2aSnp2v27JmqX7+hqlfPCA89evRSWNifeuKJAZKk+vUb6d5778ty/XPnzsrb21u+vr6ZnnN3d1flylWsn9GbKVbMU8WLF5erq5vN5+/AgT8UGnpY69ZtkoeHhyRp+PAX9csvW/XTT1t03329JWWc/jRu3CSVLVs2p+1r1apluueeHurdu4+kjM/f4cOH9Omny9SkSVPt27dbp079qc8++1p+fhUlZXy2Hn30IYWGHladOvUkZRzRGzduksqXryBJevHFV/Tqqy9q+PAXbXoBkL8IFgCQAwMHPmn9sp0T9pxHcerUSaWlpal//942y5OTk1W6dGnrY3d3d91+e9ZHVrJz5kyYWrZsY7Osfv2G+uyzT5WWlqYzZ8Lk6upqEw5Kly6jatX8deZMmHWZq6ur6tSpa33s7x8gb++SOnPmNMHiBmbNmqFTp/7U++8vsi5zdXXVSy+9WoBV2Tp58rgSExPVvXsHm+VJSUk6f/5/E9crVqyUq1AhSadPn1bPnraf6/r1G+rzz1dZn69Qwc8aKiQpMLC6vL1L6vTpMGuwqFChojVUSBkT3tPT03X27BmCBWBHBAsAuIkTJ45r4kTbUy0eeqi/evV6MNPYqlX9JWV8Qb/+G/1/On36tAIDM45muLhknI36zxDyz9OJspOYmCBXV1ctXrxMLi62VwYqXry49f+LFSvGzQqLkFmzZmjHju2aO3ehKlTwsy7fuXO75sx5x2bss8++oJYtM1/9rGrVaoqLi1NUVKT1aNZ1KSkpunDhLzVpkjGHwmKxZArAOf38lSvnqzlzFmR6ztu7pPX/PT2LZ3oegGMjWADATdxxR02tXPlljsbeddd/VKpUaa1atTxTsNi+/Wf99ddZDRnytCSpTJmM3+ZGR0dZz6f/5wRYSXJzc1damu3VnO64o5bS0tJ06dIlNWzYOE89ZcffP9B6WdPrDh48oKpVq8nV1VX+/oFKS0vTkSOHrP1duXJZZ8+eUUDA/07/SktL09GjR6xHJ86ePa24uFj5+wfka72OwDAMvfPOW9q2bavmzFmgypWr2DzfvHlLNW/eMkfbatOmgz74YI4+/XS5nnvuJZvn1q79UomJierYsYukjM9fQkKCEhMTrYH0xIljNuu4u7srPT3NZlmtWrUVExMtV1dXVapUOVe93kxAQID++98Duueee63LDh48YA3jAQEBungxQhER4dajFmFhpxQXF6vAwOrWdS5eDLcJV4cPH5SLi4uqVfPP13oB2GLyNgCnl5CQoBMnjlm/VP3993mdOHFM4eG5v4pR8eLF9corY7R9+zbNmDFVJ0+e0N9/X9C3367V1KmT1LZtB7VvnzEB9rbbqqpCBT999NFCnTt3Vjt2bNeqVbZXYKpUqZISExO0d+9uXb58WdeuXVO1av7q3PkeTZkyUT///KMuXDivI0cOadmyj7Vjx3ZTr0W/fo9o3749WrJkkc6ePaPvvvtWX375mXUycNWq1dSqVRvNmDFVBw78oRMnjmvy5AkqX76CWrVqa92Om5ub3nnnbR0+fEhHj4Zq6tRJqlevPqdBZWHmzBn64YfvNHHiFHl5eSk6OkrR0VHWiwXkRsWKFfXss8/r888/1YIF83TmzGmdP/+XVq1arg8+mK1+/R6xznupVy9Inp6eWrBgns6f/0s//LBR33337b+2V1l//31BJ04c0+XLl5WcnKymTZupXr36GjPmZe3e/Zv+/vuCDh48oAUL5uno0SOmXov+/R/Td9+t05o1X+jcubNatWq5tm37Sf36PSJJatq0mapXr6HJk8fr2LGjOnLkkKZMmahGjZqodu3/nXrn4eGhKVMm6sSJ4zpw4He9++7bateuI6dBAXZmMQriguoAHFJCQoJCQ0NVsWI1eXh4FnQ5ObZ//149//zTmZbfc8+9eu211/O0zQMHftfSpR/p8OH/Kjk5+f9vkNdTDz1ke4O8//73D82c+abOnTunOnXq6sEH+2r8+NH6/PNvrL8NDgmZrp9+2qwrV65o0KAhGjz4KaWmpmrp0sXauHG9IiMvqnTpMqpXr74GD35KNWrcnuMb613v/bvvflLJkhmnsWzdukWLFi3QX3+dVblyvnrggb56+OH/XWXo6tWreu+9EP366zalpKSoYcMmeumlV1S1ajVJ/7up35gxEzRv3nuKiopUgwaNNHr0BFWsWDHLOpzZ9cu7/tvYsRNzfUO767Zv/1mffrpcx48fVVpaugIDq6t37z7q3r2nzbht27bq/fffU2TkRQUH36WWLVvrrbemavv2jKswJScna/Lkcdq7d4/i4mKtNSUkxGvhwve1deuPunz5knx8yqlRoyZ66qlh8vOrmOMb62X1OV2z5gt9+ukyXbwYoUqVKuvxxwera9fu1ufDw8P17rtvad++PbJYXNSsWXO99NIr8vEpJ+l/N/W7777eWrp0sa5evaoWLVrq1Vdfsx4Z/Lfk5GsKDz+r338/oGvXEvXss89YT1UEkHMECwD5pqgGC+Qv7haOgpSXu4UTLID8wU8NAAAAANMIFgAAAABM41QoAPmGU6EAFEWcCgXkD35qAAAAAJhGsACQ7zgQCqAo+d/fWfzdBZhBsACQb9zcMu65mZqaUsCVAEDOJSUlSpKuXUsq4EqAoo07bwPIN+7u7vL29tbly1FydXWXi4uloEsCgGwZhqGkpERdvhylS5cuKS0t1eY+MwByh2ABIN9YLBYFBATo0KHDunjxXEGXAwA5cunSJf39d7hSU9Pk5VVCFgu/FAHygmABIF8VK1ZMNWpU19dff6MrV67Iz8+Pq6sAKJQMw1BKSorS09OVkJCg+Ph41a8fRLAA8ojLzQKwi/PnL2jdum8VGRlZ0KUAwE25urqqfv0gdenSWR4eHgVdDlAkESwA2M2lS5cUFRXFhEgAhZqLi0UlSpRQ5cqVCRWACQQLAAAAAKZx4jMAAAAA0wgWAAAAAEwjWAAAAAAwjWABAAAAwDSCBQAAAADT/g+Sdmr+CtLB7AAAAABJRU5ErkJggg==", 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fc8iQJF9fX61fv14//fSTateurSlTpmjBggVq1apVnvQfAAAAKCyseo5Fs2bNdLfLbGR0Ve1mzZrpwIED97FXAAAAAP7Lqo9YAAAAAMgfCBYAAAAADCNYAABgRZYtO6KyZaeqbNmp+vjjHXndHQDIMqueY/GgXLlyU7Nm7dWePRd08GCEbt68LUnq1q2GPv20dbr2N28m6rPP9mnt2pMKC7uqhIQkubk5KTCwnF5+OUiNGj38oHchx8LDr2n+/APau/eCDh2KVEJCkiRp8OBH9e67DdK1T0hI0pw5+/T990cVFnZVDg528vcvq9deq6vHHvPN8uNu2HBKhw9fkiQ980xNPfxwiRzvw+nTV/Tdd0e1Y8d5nT17TZGRcSpa1F7+/mXVt2+gnniicrp1IiJiNWnSTm3efFqRkXEqU8ZZLVtW1DvvhKhs2WI57sv9smnT3/rf/05r375/FRERq+vX41W2bDE1afKwBg8OUYUKrhbtv/nmsDZvDtOePRd08WKsefmlS4MedNcztWPHOa1ff0q7d1/QhQsxunr1ltzciiokxEtvvllfNWuWsWjfseMK7dhxPtPt5dW+zZ27X9eu3ZKkDN8z2bFz53ktWHBAhw5dUlTUTd26dVtubk6qXdtDffsGqEWL9O+xP/6I0JQpv2vXrn8UF5cob+8SeuqpGnrllWA5OtoZ6o81W7/+pJYtO6K//opSdHScEhOT5e7urDp1yuvVV+soOLi8ue316/FatuyItm0L14kTlxURcUOSVLGim556qob69QuUvT2/swGAUQQLSefPx2jGjD1Zbt+jx2r93/+FWyyLjIzT//53Wps3h2nJkifVsmXF3O7mfXH4cKTmzt2fpba3byfr2Wd/0LZt58zLbt1K0m+/nddvv53X5Mmh6tnTP0vb+vHHU1q+/C9JUsOGFQwFi3XrTmry5N8tliUkJGnbtnPatu2cxo1rppdeCjLf988/MWrb9htduHDDvOzChRtavPhPbdkSpvXrn1H58lm7wuSDMmbM/+nUqSsWy/75J0bffHNEmzb9rR9/7C5f35Lm++bNO6AjRyIfcC+zZ/r03frll7MWyy5ditXq1Se0adPf+v77p1S3rmce9S7r5s3br3PnrkvKnWCxdu1Ji2WRkXHavDlMmzeH6bPPnlCXLtXN9/3yyxn16LHa/IOAJJ04cVkffLBdv/12Tt9800l2dgXzC/PmzWHatOm0xbJ//72htWtP6scf/9b333dVSMhDkqQTJ6I1YsTWdNs4fDhShw//qp07z+vLL598EN0GgAKtYP6Lk02OjnYKCfHSG2/UVffuNe/a9siRSHOocHCw1YcfttDy5Z3VuHEFSVJyskmLF/953/ucW5ydHdS0qbcGD35UrVtXumvbFSv+MoeKSpXctHhxB02f/riKF3eUJI0cuVX//BNz3/ucEVfXIurfP1BffdVRX3zRXsHB5cz3TZy4XbGxiebbw4f/Yg4VbdtW1pdfPqm2bVOOapw/H5PhFxBrUKOGu8aPb6Zvv+2i8eObycUl5Xm/fPmWpkyxDFaVK7upe/ea+vjjx/Kiq1nm7V1Cw4c30ooVXfTJJy3l4ZFytOjWrSSNH78tw3X8/MpozZpu6f4rCMqXL67+/QM1d24bff99V82a1VpVqpQy379gwUHz3zdvJmrgwE3mUDFoUH19/nl7Va9eWpK0detZLVqUfz6LssvXt6QGDqyn+fPb6vvvu2rq1JYqX764pJQfQRYt+sOivb29rTp2rKq5c9to2bJOevrpGub7Nm78W9u3W/5YlF1pP2MAoLDiiIWkqlVLa/XqlC8mixb9oa+/PpJp25iYePPfNWuW0YsvBkhK+Ycs9Uv37dvJ5jbh4ddUp85CSVKDBg9p1aqnzfeVLTtVklShgqu+/baLRo7cqt9+Oy8nJzs9+WRVjRrVRMWKOZjbr117QnPn7tfRo1G6deu2SpZ0kq9vSdWr56mRIxvLxsYm2/verJm3mjXzliSNG7dNGzf+nWnbLVvCzH8PHFjPPMRo//6LWrz4T928eVsrVvylt96qn+m+p12WqlOnb81/r1z5lBo2rJCtfWjS5GH16FFLbm5FzctCQh6Sn99c3b6drLi42zp+PEpBQeUVERFr3kdX1yL67LM2cnKyV7Nm3vLzm6vr1+O1YcMpXboUax4SFRy8wPyLdOpwmwULDmjYsF8kSVOnttTzz9eSJLVrt0y7d19QhQqu2revr/l5e/bZlZKk11+vo5Ejm2Rr/yRpwoQW5jpJUtOmKX+nhqCDBy9atJ8/v50k6dat23r3Xcur0f/Xb7+dM9fgv8P/YmMTNX78Nq1adVw3b95W48YV9MEHzdWp07fpnpPseu21ugoJechiCEqpUkXVq9ea/79PERmu5+paRI8+6nXXbd/tfffbb+c0deouHToUoRs3ElWypJMqVHBRnTqeGjKkgVxdi0i69/tt+fK/9MYbmyweN/U9LeXseXn2Wb90y4oVc9QLL6Q8J7GxCebl//vfafMwt+bNvTV0aENJUrlyxdSmzTJJ0pdf/qE+fQLu+ZwkJ5s0dervWrLkkK5evaWgoHIaO7aZRo7cah5+tndvHz38cIl02xk9uonef/9XHTwYIVfXInruOT8NHhxiUdfFi//UV18d0smTl3X7drJKlXLSI4+UVrNm3nr99brZfp4k6Y036lncbtw45fU6cuRWSdKNG3eeK09PF/3ySw9VrVravKxFC18dPRqlQ4dShmQeOBBhHsaa1c/tJUue1KhRv2rv3n8VEOChVaue1oYNp/TNN4d19GiUoqNvKiEhSR4exdSo0cMaPPjRdEdnt20L17hx23T0aJTKlSuul18OtvjcB4D8hGCRTTVrllGJEkV07Vq8jhyJ1Bdf/CEfnxKaM2efuU3XrtXvsoX0rl+PV/v2yxUZGSdJiotL1Bdf/KGzZ69p2bLOklLGo/frt17JyXeu6xEZGafIyDjt3n1Bw4Y1kr199oNF9vp55x/qtP/wOTvf+Xv37n/uax8yEhBQLt2yUqWKqmTJIoqKuinpTh/37Llgfg79/cvKySnlLeDklDInY/v2c0pKMmnv3n/Vpk36uRmp6te/88V2375/9fzztZSYmKQ//0z5Mnzu3HVFRNyQh0dx7dlzwdz20UcfytE+pg0VqSpWLGn+O20NclPfvmu1ZcsZ8+1Nm07r8OFIxcUZ/3W2ceP0c5EqVnQz/53ZPv3xR4SqVZutGzcS5eXlojZtKmvQoPpycSlyz8c8deqyundfaZ5HJUlRUXGKiorTgQMR6tMnQK6uRbL0frvfkpKSdf58jJYvv/NDR9rQvWvXnfda2iFjAQHl5OBgq8TEZB09Gq2rV2+pZEmnuz7WyJFbNX/+nev//PbbeXXq9K1Klrz7c3rmzFV16vSt+fVw8+ZtTZ26S1FRNzV5cqiklCOd77yz2WK9ixdjdfFirE6dupzjYJFWYmKSwsKuas2aE+ZlaZ8rT8+Mhzb6+pY0B4vsvoeuXYtX587f6vLlWxbLf/75TLohWufPx2jZsiPasiVMW7f2VJkyzpKk3bsv6NlnV5qPOp09e03vvfezatRwz1ZfAMBaECyyycWliBYt6qCBAzcpPPy6hgy582twhQquGjWqsZ58smq2tnntWrxq1/bQ5MmhunAhRuPGbVNc3O3//w/U32rVqpI2bTpt/pIzfHgjBQWVU3T0TR07FqX1608pBwcrsq1yZTf9+mvKmPglSw6pYcMKunr1ltavvzMm/MKFuw+F8vAopjVrumn69F3mL6wTJjSXn19ZScq1f1B///28OVQ8/LCrqlRJ+aUy9Vd2SeZ/3FO5u9+5HR5+7a7br1mzjFxdi+j69Xjt3fuvJOnQoUu6devOWPfduy+offsq2rMn5X5bWxvVr597cwbWrbvzvLdo4ZNr2031889h5hoVLWqv4cMbqUIFV33yya5MjyYYlZV9io1NNA87CQu7qlmz9urnn89o/fpnzMPyMvPrr2fNoaJ//0C1bl1Jyck22rfvgn788ZT5qF9W3m+PPearNWu6qW/fdbp0KeXIQW4NyapZc475hwYpZRhPp05VNXz4nUBj+VouZtG2ZEkn8/rnzl2/a7A4deqyFixICRW2tjZ66636Cgwsp/nzD5jf75m5cOGGmjXzVt++gTp8+JImTdqppCSTvvzyT/XuXVs1a5YxHyG0t7fVxIktVLFiSV26FKs//7yk/fsv3nX793Lr1m09/PAMi2VFi9qrZ09/9e8flMlaKa5du2Ue/mRjk3LUJzuuX4+Xu7uzpkwJ1UMPuSoqKuX5btbMW/7+ZVWuXHEVL+6gW7eS9OuvZ/XZZ/sUGRmnpUsP6c03U47qjh79qzlUNGnysPr3DzI/jwCQHxEscsDd3VmVKrkpPPy6xfLz569r7dqTCg2taP5F/+GHS2RpSMTcuW3Mv9ZeuhSnTz7ZJSllknOrVpXk4HBnWEHFiiVVs2YZlSpVVFJV8xCI+61379pauvSQbt1K0tatZ1Wt2mfp2qT9Yp3RvhcpYq9HH/XS0qV3vsRXr+5+z6Et2XH27DW98sqPklK+MHzwQXPZ2qZ8YUz7S7uDg+UZc9I+x2nbpQ5pSsvW1kb16nlq8+YwnTgRrWvXbpkDRrVqpXXsWLT27v1Xbds+ogMHUr48+fmVVYkSTrrLxeSzbMmSP81D9ry9S+jVV+vkeFsNG1bI8DW6ceOdX11ffDHA/EWtatXSevTRL3L8eJnZvPm0+XXv5uaU7nVdtmwxvfRSkIKDy8vVtYh27/5HM2fuVUJCko4ejdK8efs1aNCjkjJ/39nb36n5ww+XUJUqpVWzZjk1bVrBPIRPUpbeb2XKOKtMGWcVKXJnm7n5Ok7Lzs4m3STstK9RR0fL+9KeDSo1hGX2nPz449/m12SbNpU1ZEjKBPR69TxVu/Y8i6M7/+XsbK8FC9rJ1bWIHn+8ok6evKzvvz8mKWXeQs2aZczPpaOjrXx9SyogwEMuLkUsJqHnJjs7W9nY2Mh0lzfazZuJ6tNnnflow8svB1scLcvq5/bs2U+kO5rYsOFD+uST3ZozZ5/++Scm3fOXGsojI+O0b1/KZ0aRInaaP7+t3NyKpnseASA/YfJ2Nl25clMdOizXL7+clbu7szZvfk6nT7+ml14KkskkrVlzQqNGbc3WNt3cnCz+UQsMvDO05+zZlF/Ou3Spbv4C06fPOlWr9plq1JijF15Yc89fFXNLlSqltXRpJ/n43Bkj7OBgazHpu0SJew9HuZ9OnIhWhw7LzZPIP/iguVq1utO/tMMd0p5JR5ISE5MzbJeZ1C+RJpO0b99Fc7BI/QK+Z88F/fVXlHmsd0ZDf3Ji3rz9Gjw4ZWhJ2bLFtHx5Z/O8gNx09uxV899pX5MVK7rdc4hMdq1de0IvvLBWCQlJKlbMQV991THdKXTnzWurceOaqWPHqmrRwkdDhza0GEaTdshWZlq3rqRSpVJ+vR8xYqv8/OaqVKmP1K3bDxbDaPL6/bZkyZP64Yeu+uSTlqpWrbTi45O0bNkRizkdaV+j8fGWr+W0r+17jddP/YyRpKCgO3UuWdJJlSuXymgVs8qVS1m89tKun7rdZ56pKRsbKS7utrp2/U6VKs1SQMA8vfLKhnRzg7KrSBE7rVnTTd9911UTJzaXl5eLbtxI0Jw5+zR+/PYM17lxI0HPPrvSfBKODh2qaNSoxtl+bCcnu3ShIikpWV27fq85c/bp1KkrGYay69dT5umlfX/5+JS0mCOW9nkEgPyEYJFN69ad1JUrKb9ytWv3iPz9PVS8uKPFRMIffzxl6DEyGtZUvbq7fvrpOfXrF6jg4HJydS2iqKg4bdhwSt26/aDduy+kX+k+aNz4Ye3a9aJ27uytzZuf0/Hjr6pdu0fM96edHPmgHTp0SR07rtC//96QjY304Yct1LdvoEWbtF9WIyNjLe5LHc4iKUunv009laUk7d17QXv3XpCdnY06dqwqd3dnHTp0STt23Dk1b24Ei08+2aURI7bKZJK8vFy0evXTFqH0QcnJiQIys2zZEfXvv14JCUkqUaKIVqzokuXTzKb9AhYdHXeXlik8PIrpp5+e1+uv11X9+l4qVcpJV67c0s8/n1Hfvuu0cmXKr8R5/X4LCiqvRo0e1nPP1dKSJR3Ny9evP6lbt1K+rFq+lu/s++3byebPqP+2uxejdc1o/ebNfbRu3TPq0aOWatUqK2dne124cEPff39MHTuu0JkzVw093qOPeqlJk4fVp0+gPv20lfm+H35I/4v/1au31LXrd+YJ6V26VNOcOW1ydEretEMnU+3efcE8Z8PDo5g+/bS11qx5WnPntjG3STtv5277BQD5EcEim9JO1Et7hpa0ZyDJ7mkHr1y5pdOn71yjIO24Y2/vlC+4JpNJ1aq564MPmuvHH7vr1KkBWrgw5cw/yckmw2EmO2xsbFSpkpv8/T1UtKi95s27cx2Mxx/P2vU7UocmSVn7h/Zedu++oE6dvlVU1E3Z29tq5szW5jN2pVW3rqf5sQ8dijR/Sbt167b5C4GdnY3q1Cmfbt3/CghI2X9JWr/+lM6fj1H16u4qXtxRdeqUV3x8ksUpL5s0yd4Y7v8aO/b/NHHib5JShuesXdtNlSrdv1Dh7V3S/HfaX5ZPn75i8cXViIULD2rgwE1KSjLJ3d1ZK1c+nWGouHjxhvmiZmmlDiWRLOcZZMZkMqlCBVeNHNlYa9d20/Hjr2rPnn7m+9evP2Vul9X3W9ovgUZfyzdvpv/sSPsd02SSYmJSPmvSnkAg7QkCDhy4aD4zXfXqpe85cTvtEci0db569ZZOnbp813VPnbpicaa8tPVI+9lVt66npkxpqS1bntfp069rzJimkmSeS5Zdt28nW5x9L1XaWqQeGUh16VKsnnxyhfnz9YUXamv27CdyfGG8jL78//vvnddo587V1K1bjUxP2JD2x4uzZ6/q6tU776m0zyMA5CfMsVDKWOXUU6mmfrmUUudMpAyPCAgopwoVXC1+kV+z5oSCgsrJ17ekZs++c1aotFcMvttpC9N65ZUNeuutR3XhQozFBetat045M9Gnn+7Rjh3n1bKlr7y8XOXs7KCtW+8MyfjvsJ6sioqK086dKb/epf0SceLEZfO+h4Q8ZP51rm3bZXriiUqqXt1dV6/e0qJFf+rPP1Oes+rV3S2ucn23fS9R4s6Xne++Oyo7OxvZ2trmaIz677//o2ee+cE85rx//0BVqFBCv/9+56w5NWq4y9W1iDw8iql160rasOGUrl+P10svrVf37n765psj5i9sbdpUtrj6dkanm5VS5mgEB5fX9u3ndPRolCSpTh3P////8tq48W/zRe18fEqofHkXRUXl7Dofw4f/Yj5rT4kSRTR8eCOdPx+j8+dTtufkZGdxdqwdO84pOvqmxfAuSeaali5dVA0apJw1J7PTzbZuXdEcjD7//KA8PV300EOumjZtV4724b/mzNmnUaN+lZQypGXEiEa6cSPBom6pr4e//76i555bqU6dqql5cx+5uDhq166UORap0g7Jy+y198MPx7R48Z964onKevhhV5UoUUT79t2ZiJ76PsrO+61kySIK//+XQFiw4ID8/T3k6uqoGjUsrxyeFbVrz1PXrtUVGFhOHh7FdeFCjD777M4+enm5yN09ZcjM449XVLlyxXTxYqy2bj2rCRO2y9/fQx9/vMPcvmfP2vd8Tp54orLGjdsmkynliOyUKb/L37+s5s07cNf5FVLKZ2e/fuvVp0+AjhyJ1KpVx833pdZj2LBfFBERq6ZNveXlVVx2drb6/fc7V1DPyWfXhQsxatt2mZ5+uob8/MqodGln/f33Fc2ceedCp7VqlTX/HRkZpw4dluv06auSUo4edu5cTbt23QlkDz2U8vq+23N1L6nrSylHl+rX99K1a7c0blz6YVllyxZTcHA57dt3UbduJal///Xq1y8w3fMIAPkJwUIpX6779FmXbnnqFaUlacaMVnrmmZpq2dJXdet6as+eC4qPT9J77/1isY69va3eey97k6nd3JwUERGrnj1XWyxv2tRbrVqlHAG4fTtZP/98JsNf92xtbdShQ5VsPWaq48ejM9z3NWtOmMecr1z5lDlYHD8ebfHraKpy5Ypp4cJ2Wf71r1GjCuZT9H7zzRF9803KROScnPv///7vrMVE1tmz91kEvdR9SD395AcfNNfBgxd14cIN/fjj3/rxxzvX7njoIReNH98sy49dv76Xtm+/M9wp9UjHf391z+lpZlOlvb7ItWvx6WqW9roZkvTxxzvNwz3SSl0v5cvS3a8X0qKFrx57zEdbtpxRXNxt83U7ypcvLjc3J8NHLdLuU3x8kt5883/p2qR9PcTF3dbSpYe1dOnhdO3q1/cyX6/hbpKTTfr9938swktanTqlnNEtO++3hg0rmMN16nVFsvNlNK2rV+MtLoKXloNDylmVUn8pL1rUQdOntzJfeXvatN0W7Zs189YLL/jf8zErVXJT376Bmj//gJKSTProo5Rg4uLiqAoVXC3OPvVfFSq4as+eC+mep+ef9zP/wHLr1m2tW3fS4oxfqYoWtb/nhTkzExERq08/3ZPhfcWLO2rs2Kbm2ydORJtDhZRy7Yht2ywviDd48KOGr5weHFxONWq466+/ohQeft18/ZF69TzNZ41Ka/ToJurS5TslJiZr69az5vBasWJJi/4CQH7BUKhssrOz1bffdtG774aoZs0ycna2l729rcqUcVbbtpW1dm23bI+lL17cUWvWdFOrVhXl7OwgNzcn9erlry++aG/+EhEa6quePf3//9CGIrKzs5Gbm5OaNfPW8uWdLYZF3E/PP++n6tXd5eLiqCJF7OTrW1KvvBKsX37pec+Jnmk9/nhFvf9+E/n4lMjxUISc8vJy0aZNz6lnT3+VL19cDg62Kl++uHr29NfGjd1VvnzG57zPSNp5FtKdYFG7tofFft2vswXdbwsWtNeLL9ZWqVJOcna2V8uWvlq9+ul7ntY1t6Wejvmxx3z08MOucnKyk7Ozg2rX9tD77zfR9993NV+T5G7q1PFU//6B8vcvq9Kli8rOzkYlSqRccG/+/Lbq1KmapOy93955J0Q9etRSuXLFDJ/2efDgR9WgwUPy8CgmBwdbFS1qr4oVS+q55/z0v/89l+5LePPmPlq//hm1alVRJUsWUZEidqpSpZSGD2+kr77qmOW5A2PHNtU774SoXLlicnKy06OPemnlyqcsTsZQtGj6SeAVKrhq1aqn1bDhQypa1F5lyxbTm2/W08cfh5rbdOmSMiSocmU3ubqmPJfu7s564olKWrOmm3x8Smb7eSpVqqhee62OgoPLy93dWfb2tnJ2tle1aqXVt2+Atm7toaCgew9nzG12drb6+utOat26klxdi8jdvaj69w/UJ588nmH7Rx99SF9/3Un+/mXl6GhnHqY3cGC9DNsDgLWzMd3tnHyFQGRkzoam5Ia0V3DN6JSmOWFjI7m7pwy5KdyVtS4FrS6ZDQ/LbwpaXXLKZDKlmzNw+fJNBQXNV1zcbZUoUUTHj78qW1ubHA8TyipqYp2oi/WhJtapoNalTJms/ejKUCgAKORmzdqrq1dvqWXLinrooZThTx999Jvi4lLmWHToUMXihAsAAGSEYAEAhVxcXKJmzNijGTPSz1lIHVoFAMC9ECwAoJBr2LCC/vgjQocPRyo6+qYcHGxVsaKb2rSprJdeCnrgc2oAAPkTcyzycI7F/VBQx/bld9TFOlEX60NNrBN1sT7UxDoV1LpkdY4FZ4UCAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYVYfLGbNmiUfHx85OTmpfv362r17913bT5s2TVWrVlXRokVVoUIFvfXWW7p169YD6i0AAABQOFl1sFi+fLkGDRqk0aNHa//+/apdu7ZatWqlS5cuZdj+66+/1tChQzV69GgdPXpUCxcu1PLlyzVs2LAH3HMAAACgcLHqYDF16lT169dPvXv3Vo0aNTRnzhw5Ozvr888/z7D9jh071LBhQ3Xv3l0+Pj56/PHH9eyzz97zKAcAAAAAY6w2WCQkJGjfvn0KDQ01L7O1tVVoaKh27tyZ4ToNGjTQvn37zEHi9OnT2rBhg9q0afNA+gwAAAAUVvZ53YHMREVFKSkpSR4eHhbLPTw8dOzYsQzX6d69u6KiotSoUSOZTCbdvn1bL7/88j2HQtnY5Fq381zqvhSkfSoIqIt1oi7Wh5pYJ+pifaiJdSrsdbHaYJETW7du1YQJEzR79mzVr19fp06d0sCBAzVu3DiNHDkyw3VKlSomOzurPXCTY6VLu+R1F5AB6mKdqIv1oSbWibpYH2pinQprXaw2WLi7u8vOzk4REREWyyMiIlSuXLkM1xk5cqR69Oihvn37SpJq1aql2NhY9e/fX8OHD5etbfoAcflybIFKlTY2KS/m6OgYmUx53Rukoi7WibpYH2pinaiL9aEm1qmg1sXdPWtByWqDhaOjo4KDg7VlyxZ17NhRkpScnKwtW7botddey3CduLi4dOHBzs5OkmS6S3ULUuFTmUwFc7/yO+pinaiL9aEm1om6WB9qYp0Ka12sNlhI0qBBg9SrVy/VqVNH9erV07Rp0xQbG6vevXtLknr27CkvLy9NnDhRktS+fXtNnTpVgYGB5qFQI0eOVPv27c0BAwAAAEDus+pg0a1bN0VGRmrUqFG6ePGiAgICtHHjRvOE7vDwcIsjFCNGjJCNjY1GjBihf/75R2XKlFH79u31wQcf5NUuAAAAAIWCjeluY4QKgcjImLzuQq6ysUkZBxcVVbDG9uV31MU6URfrQ02sE3WxPtTEOhXUupQpk7U5FgXvdEgAAAAAHjiCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAsFwLFrdu3VJ8fHxubQ4AAABAPmKf0xW3bt2q1atX67ffftNff/2lmzdvSpKcnZ1VvXp1NWjQQB07dlSzZs1yq68AAAAArFS2gkViYqLmzp2rqVOn6syZMypVqpSCgoL0/PPPy83NTSaTSVeuXFFYWJi++uorzZgxQ97e3nr77bf10ksvycHB4X7tBwAAAIA8lK1gUblyZSUkJKhXr156+umnFRQUdNf2+/bt07fffqsJEyZo8uTJOnPmjJG+AgAAALBS2QoWw4YN0wsvvKAiRYpkqX1wcLCCg4M1duxYffHFFznqIAAAAADrl63J2y+99JJFqAgPDzfPrcjIzZs3FR4eLkdHR7300ks57yUAAAAAq2borFC+vr5auXJlpvevWbNGvr6+Rh4CAAAAQD5gKFiYTKa73p+YmChbWy6VAQAAABR02T7d7PXr13X16lXz7ejoaIWHh6drd/XqVS1btkzly5c31EEAAAAA1i/bweKTTz7R2LFjJUk2NjZ688039eabb2bY1mQyafz48YY6CAAAAMD6ZTtYPP744ypevLhMJpPeffddPfvss+lOO2tjY6NixYopODhYderUybXOAgAAALBO2Q4WISEhCgkJkSTFxsaqc+fOqlWrVq53DAAAAED+ke1gkdbo0aNzqx8AAAAA8jFDp2w6ePCgvvnmG4tlmzZtUpMmTVS/fn1Nnz7dUOcAAAAA5A+GgsW7776r5cuXm2+HhYWpU6dOCgsLkyQNGjRI8+bNM9ZDAAAAAFbPULD4448/1KhRI/PtL7/8UnZ2djpw4IB27dqlrl27as6cOYY6OGvWLPn4+MjJyUn169fX7t2779r+6tWrGjBggMqXL68iRYqoSpUq2rBhg6E+AAAAALg7Q8Hi2rVrKl26tPn2hg0b1LJlS7m7u0uSWrZsqVOnTuV4+8uXL9egQYM0evRo7d+/X7Vr11arVq106dKlDNsnJCSoZcuWOnPmjL777jsdP35c8+fPl5eXV477AAAAAODeDE3eLl++vI4ePSpJ+vfff7Vv3z717t3bfP+NGzcMXXl76tSp6tevn3mbc+bM0fr16/X5559r6NCh6dp//vnnunz5snbs2CEHBwdJko+PT44fHwAAAEDWGAoWTz75pD799FPdunVLu3btUpEiRdSpUyfz/X/88YcqVqyYo20nJCRo3759eu+998zLbG1tFRoaqp07d2a4zpo1axQSEqIBAwZo9erVKlOmjLp3764hQ4bIzs4u08eysclRF61S6r4UpH0qCKiLdaIu1oeaWCfqYn2oiXUq7HUxFCzGjx+vyMhILVmyRCVLltSiRYvk4eEhSbp+/bq+++47DRgwIEfbjoqKUlJSknl7qTw8PHTs2LEM1zl9+rR+/vlnPffcc9qwYYNOnTqlV199VYmJiZmeGrdUqWKyszM0IswqlS7tktddQAaoi3WiLtaHmlgn6mJ9qIl1Kqx1MRQsihcvrqVLl2Z63/nz5+Xs7GzkIbIlOTlZZcuW1bx582RnZ6fg4GD9888/mjRpUqbB4vLl2AKVKm1sUl7M0dExMpnyujdIRV2sE3WxPtTEOlEX60NNrFNBrYu7e9aCkqFgkdaNGzd07tw5SVKFChVUvHhxlShRIsfbc3d3l52dnSIiIiyWR0REqFy5chmuU758eTk4OFgMe6pevbouXryohIQEOTo6ZrheQSp8KpOpYO5XfkddrBN1sT7UxDpRF+tDTaxTYa2L4TFAe/bsUfPmzeXm5iY/Pz/5+fnJzc1NLVq00N69e3O8XUdHRwUHB2vLli3mZcnJydqyZYtCQkIyXKdhw4Y6deqUkpOTzctOnDih8uXLZxoqAAAAABhn6IjFrl271KxZMzk6Oqpv376qXr26JOno0aP65ptv1KRJE23dulX16tXL0fYHDRqkXr16qU6dOqpXr56mTZum2NhY81mievbsKS8vL02cOFGS9Morr2jmzJkaOHCgXn/9dZ08eVITJkzQG2+8YWQ3AQAAANyDoWAxfPhweXl5afv27emGJ73//vtq2LChhg8frp9++ilH2+/WrZsiIyM1atQoXbx4UQEBAdq4caN5Qnd4eLjF6WwrVKigTZs26a233pK/v7+8vLw0cOBADRkyJOc7CQAAAOCebEymnI8Ac3Fx0ahRo/TOO+9keP/HH3+scePGKSYmJscdvN8iI623bzlhY5MywSYqqmBNGsrvqIt1oi7Wh5pYJ+pifaiJdSqodSlTJmuTtw3NsbC1tdXt27czvT8pKcnQBfIAAAAA5A+GvvU3aNBAs2bN0tmzZ9PdFx4ertmzZ6thw4ZGHgIAAABAPmBojsWECRPUpEkTVatWTZ06dVKVKlUkScePH9fq1atlb29vnlgNAAAAoOAyFCwCAwO1a9cuDR8+XGvWrFFcXJwkydnZWa1bt9b48eNVo0aNXOkoAAAAAOtl+AJ5NWrU0MqVK5WcnKzIyEhJUpkyZZhbAQAAABQihoLF7du3FRcXJ1dXV9na2ppPA5vq+vXrcnZ2lr19rl3gGwAAAIAVMnRY4Y033lCDBg0yvb9hw4Z6++23jTwEAAAAgHzAULDYuHGjunbtmun9Xbt21YYNG4w8BAAAAIB8wFCwuHDhgry8vDK939PTU//884+RhwAAAACQDxgKFqVLl9bx48czvf/o0aNydXU18hAAAAAA8gFDwaJ169aaO3euDhw4kO6+/fv3a968eXriiSeMPAQAAACAfMDQ6ZrGjRunjRs3ql69eurQoYNq1qwpSTp8+LDWrl2rsmXLaty4cbnSUQAAAADWy1Cw8PT01N69ezV06FCtXr1aK1eulCS5urrqueee04QJE+Tp6ZkrHQUAAABgvQxfYKJ8+fJavHixTCaTxQXybGxsDHcOAAAAQP6Qa1eus7GxUdmyZXNrcwAAAADyEUOTtwEAAABAymawqFGjhr788kslJCRkeZ34+Hh98cUXqlGjRrY7BwAAACB/yNZQqBdeeEGDBg3SwIED1aFDB4WGhiooKEi+vr5ydnaWJMXGxiosLEx79+7V5s2btXbtWjk6Ouqdd965LzsAAAAAIO9lK1i8++67euWVV7Rw4UItWrRIS5YsMU/StrdP2dTt27clSSaTSX5+fhozZoxefPFFLpQHAAAAFGDZnrzt4uKiN998U2+++abOnDmjHTt26NixY4qOjpaUcjXuatWqKSQkRL6+vrneYQAAAADWx9BZoXx8fOTj43PXNleuXJGbm5uRhwEAAABg5e7LWaHi4+P17bffqmPHjlwgDwAAACgEcu06FiaTSVu2bNHSpUu1cuVKxcTEyGQycaE8AAAAoBAwHCz27dunpUuXatmyZYqIiJCTk5PatGmjp59+WocPH9b48eNzo58AAAAArFiOgsXp06e1dOlSLV26VCdPnpSDg4Nat26tbt26qUOHDipWrJi5HQAAAICCL9vBIiQkRLt375aDg4NCQ0M1bNgwdezYkdPJAgAAAIVYtoPFrl27VKRIEY0YMUL9+/dXmTJl7ke/AAAAAOQj2T4r1MyZMxUcHKyRI0fKy8tLoaGhWrBggS5fvnw/+gcAAAAgH8h2sHj11Ve1fft2nT59WqNGjdKFCxfUv39/lS9fXm3atNHixYt17dq1+9FXAAAAAFYqx9ex8PHx0YgRI/TXX39pz549GjBggP744w/17t1bHh4eat++vbZv356bfQUAAABgpXLlOhbBwcEKDg7W5MmT013LgutYAAAAAAVfrl5529bWVi1bttSiRYsUERGhb775Ru3atcvNhwAAAABghbIdLCZPnqxjx47ds52Tk5O6deum1atX56hjAAAAAPKPbAeLjz/+WDVr1lTFihX1+uuva+PGjYqPj78ffQMAAACQT2Q7WEREROi3335Tjx49tHPnTrVt21alS5dW+/btNXfuXIWHh9+PfgIAAACwYtkOFjY2Nnr00Uc1ZswY7d27VxcuXNCMGTPk5OSkIUOGyNfXV7Vq1dLQoUO1fft2JScn349+AwAAALAihidve3h46MUXX9S3336rqKgobd68Wa1bt9batWvVpEkTubu765lnntGuXbtyo78AAAAArFCunhXK3t5ezZs316RJk3TkyBGdPn1a48aNU0xMjLZt25abDwUAAADAiuTKdSwk6datW7KxsVGRIkXMy3x8fDRgwAANGDAgtx4GAAAAgBXKcbDYunWrVq9erd9++01//fWXbt68KUlydnZW9erV1aBBA3Xs2FHNmjXLrb4CAAAAsFLZChaJiYmaO3eupk6dqjNnzqhUqVIKCgrS888/Lzc3N5lMJl25ckVhYWH66quvNGPGDHl7e+vtt9/WSy+9JAcHh/u1HwAAAADyULaCReXKlZWQkKBevXrp6aefVlBQ0F3b79u3T99++60mTJigyZMn68yZM0b6CgAAAMBKZStYDBs2TC+88ILFPIq7CQ4OVnBwsMaOHasvvvgiRx0EAAAAYP2yFSxeeumlHD2Io6NjjtcFAAAAYP1y9XSzAAAAAAqnbJ8Vav/+/dlqb2dnJ1dXV3l7e8vWlhwDAAAAFETZDhZ16tSRjY1Nth+oWLFieu655zR16lQVLVo02+sDAAAAsF7ZDhbZnYRtMpkUExOj3bt3a968eTKZTJozZ052HxYAAACAFct2sOjVq1eOH6xkyZJavnw5wQIAAAAoYB7opIfmzZurePHiD/IhAQAAADwADzRYdO7cWadPn36QDwkAAADgAchWsKhRo4a+/PJLJSQkZHmd+Ph4ffHFF6pRo0a2OwcAAAAgf8jWHIsXXnhBgwYN0sCBA9WhQweFhoYqKChIvr6+cnZ2liTFxsYqLCxMe/fu1ebNm7V27Vo5OjrqnXfeuS87AAAAACDvZStYvPvuu3rllVe0cOFCLVq0SEuWLDGfetbePmVTt2/flpRyNig/Pz+NGTNGL774olxdXXO56wAAAACsRbbPCuXi4qI333xTb775ps6cOaMdO3bo2LFjio6OliSVLl1a1apVU0hIiHx9fXO9wwAAAACsT7aDRVo+Pj7y8fG5a5srV67Izc3NyMMAAAAAsHL35axQ8fHx+vbbb9WxY0d5enrej4cAAAAAYEUMHbFIy2QyacuWLVq6dKlWrlypmJgYmUwm8xwMAAAAAAWX4WCxb98+LV26VMuWLVNERIScnJzUpk0bPf300zp8+LDGjx+fG/0EAAAAYMVyFCxOnz6tpUuXaunSpTp58qQcHBzUunVrdevWTR06dFCxYsXM7QAAAAAUfNkOFiEhIdq9e7ccHBwUGhqqYcOGqWPHjpxOFgAAACjEsh0sdu3apSJFimjEiBHq37+/ypQpcz/6BQAAACAfyfZZoWbOnKng4GCNHDlSXl5eCg0N1YIFC3T58uX70T8AAAAA+UC2g8Wrr76q7du36/Tp0xo1apQuXLig/v37q3z58mrTpo0WL16sa9eu3Y++AgAAALBSOb6OhY+Pj0aMGKG//vpLe/bs0YABA/THH3+od+/e8vDwUPv27bV9+/bc7CsAAAAAK5Ur17EIDg5WcHCwJk+enO5aFlzHAgAAACj4cvXK27a2tmrZsqUWLVqkiIgIffPNN2rXrl1uPgQAAAAAK5SrwSItJycndevWTatXr75fDwEAAADASuTKUCgAAADc3TvrfsjrLtzTpHad87oLyMfu2xELAAAAAIUHwQIAAACAYQQLAAAAAIYRLAAAAAAYZjhYmEwmhYeHKyEhQZKUnJxscRsAAABAwWc4WFy+fFm+vr7mq2xHRkZa3AYAAABQ8OXKUCiTyXTX2wAAAAAKNuZYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAyzN7qBEiVK6JdfflFAQIAkqVSpUha3AQAAABR8hoOFvb29mjZtar7t4OBgcRsAAABAwcdQKAAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIbli2Axa9Ys+fj4yMnJSfXr19fu3buztN6yZctkY2Ojjh073t8OAgAAAIVcrgSL2NhYXbx4Ubdv386NzVlYvny5Bg0apNGjR2v//v2qXbu2WrVqpUuXLt11vTNnzmjw4MFq3LhxrvcJAAAAgKUcB4uzZ8/qtddek7e3t1xdXeXl5SUnJyf5+Pjo3Xff1dmzZ3Olg1OnTlW/fv3Uu3dv1ahRQ3PmzJGzs7M+//zzTNdJSkrSc889pzFjxqhixYq50g8AAAAAmctRsFi7dq38/f01e/Zs2dnZqX379urevbvatWsnW1tbTZ48WQEBAVq/fr15nREjRmT7cRISErRv3z6Fhobe6bCtrUJDQ7Vz585M1xs7dqzKli2rPn36ZPsxAQAAAGRftq+8ffToUT399NPy9fXV3LlzMxxqtG3bNr388svq1q2b9u7dq4kTJ+qrr77S+PHjs/VYUVFRSkpKkoeHh8VyDw8PHTt2LMN1tm/froULF+rgwYNZfhwbm2x1y6ql7ktB2qeCgLpYJ+pifaiJdaIuhQc1Nqawv1eyHSwmTJig0qVLa/v27SpVqlSGbRo3bqxt27bJ399fwcHBio+P18SJEw139l5iYmLUo0cPzZ8/X+7u7llap1SpYrKzyxdz2LOldGmXvO4CMkBdrBN1sT7UxDpRl4LP3Z0a54bC+l7JdrD4+eef1bdv30xDRapSpUrpxRdf1Pjx4/Xll1/q+eefz3bn3N3dZWdnp4iICIvlERERKleuXLr2f//9t86cOaP27dublyUnJ0uS7O3tdfz4cVWqVMlincuXYwtUqrSxSXkxR0fHyGTK694gFXWxTtTF+lAT60RdCo+oqJi87kK+VlDfK1kNnNkOFtHR0fLx8clSW19fX9nZ2eUoVEiSo6OjgoODtWXLFvMpY5OTk7Vlyxa99tpr6dpXq1ZNhw4dslg2YsQIxcTEaPr06apQoUKGj1OQCp/KZCqY+5XfURfrRF2sDzWxTtSl4KO+uaOwvleyHSzc3d0VFhaWpbZhYWEqW7ZstjuV1qBBg9SrVy/VqVNH9erV07Rp0xQbG6vevXtLknr27CkvLy9NnDhRTk5O8vPzs1i/ZMmSkpRuOQAAAIDck+1g0axZMy1cuFBvvvnmXYdDXb58WQsXLlSLFi0MdbBbt26KjIzUqFGjdPHiRQUEBGjjxo3mCd3h4eGytS14cyQAAACA/MTGZMregZq//vpLQUFBqly5subNm6cGDRqka7Njxw699NJLOnnypPbv368aNWrkWodzW2RkwRpLaGOTMg4uKqpgje3L76iLdaIu1oeaWCfqkjveWfdDXnfhnia165zXXcjXCup7pUyZ+zTHokaNGvr666/Vs2dPNW7cWD4+Pqpdu7ZcXFwUExOjP//8U2FhYSpatKi+/vprqw4VAAAAAHJHtoOFJHXu3FkBAQH6+OOPtW7dOq1atcp8n6enp/r376/BgwenOwMTAAAAgIIpR8FCkipWrKg5c+ZIkq5fv66YmBi5uLjI1dU11zoHAAAAIH/IcbCIj4/XV199pf/973/6+++/zcHikUceUatWrdS9e3c5OjrmZl8BAAAAWKkcBYtDhw7pySef1NmzZ2UymVSiRAkVL15cly5d0v79+7VixQp98MEHWrNmjapXr57bfQYAAABgZbJ9ntYbN26oQ4cOioiI0AcffKBz587pypUrFv8fP368Lly4oPbt2ys2NvZ+9BsAAACAFcl2sPjiiy8UHh6u9evXa+jQofLy8rK438vLS++9957Wrl2rsLAwLVq0KLf6CgAAAMBKZTtYrF+/Xo8//riaNWt213YtWrRQy5YttXbt2pz2DQAAAEA+ke1gcejQoXuGilQtWrTQoUOHsvsQAAAAAPKZbAeLy5cvq1y5cllq6+HhocuXL2e7UwAAAADyl2wHi/j4eDk4OGSprb29vRISErLdKQAAAAD5S45ON3vmzBnt37//nu3CwsJysnkAAAAA+UyOgsXIkSM1cuTIe7YzmUyysbHJyUMAAAAAyEeyHSy++OKL+9EPAAAAAPlYtoNFr1697kc/AAAAAORj2Z68DQAAAAD/RbAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBh9nndAQDIqnfW/ZDXXbinSe0653UXAADIExyxAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhnEdCwAAABRa1n6NpPx0fSSOWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAAAAwjGABAAAAwDCCBQAAAADDCBYAAAAADCNYAAAAADAsXwSLWbNmycfHR05OTqpfv752796dadv58+ercePGcnNzk5ubm0JDQ+/aHgAAAIBxVh8sli9frkGDBmn06NHav3+/ateurVatWunSpUsZtt+6daueffZZ/fLLL9q5c6cqVKigxx9/XP/8888D7jkAAABQeFh9sJg6dar69eun3r17q0aNGpozZ46cnZ31+eefZ9h+6dKlevXVVxUQEKBq1appwYIFSk5O1pYtWx5wzwEAAIDCwz6vO3A3CQkJ2rdvn9577z3zMltbW4WGhmrnzp1Z2kZcXJwSExNVqlSpTNvY2BjuqtVI3ZeCtE8FAXUpPKixMbxXrBN1KTyosfXJTzWx6mARFRWlpKQkeXh4WCz38PDQsWPHsrSNIUOGyNPTU6GhoRneX6pUMdnZWf2Bm2wrXdolr7uADFCXgs/dnRrnBt4r1om6FHx8hlmf/FQTqw4WRn344YdatmyZtm7dKicnpwzbXL4cm6+S4L3Y2KR88EdHx8hkyuveIBV1KTyiomLyugv5Gu8V60RdCg8+w6yPNdQkq+HGqoOFu7u77OzsFBERYbE8IiJC5cqVu+u6kydP1ocffqjNmzfL39//rm0L4oekyVQw9yu/oy4FH/XNHbxXrBN1Kfior/XJTzWx6jFAjo6OCg4Otph4nToROyQkJNP1Pv74Y40bN04bN25UnTp1HkRXAQAAgELNqo9YSNKgQYPUq1cv1alTR/Xq1dO0adMUGxur3r17S5J69uwpLy8vTZw4UZL00UcfadSoUfr666/l4+OjixcvSpKKFy+u4sWL59l+AAAAAAWZ1QeLbt26KTIyUqNGjdLFixcVEBCgjRs3mid0h4eHy9b2zoGXzz77TAkJCeratavFdkaPHq3333//QXYdAAAAKDSsPlhI0muvvabXXnstw/u2bt1qcfvMmTP3v0MAAAAALFj1HAsAAAAA+QPBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGH2ed0BSO+s+yGvu3BPk9p1zusuAAAAwIpxxAIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhGsAAAAABgGMECAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGCYfV53ALBW76z7Ia+7cFeT2nXO6y4AAACYccQCAAAAgGEECwAAAACGESwAAAAAGEawAAAAAGAYwQIAAACAYQQLAAAAAIYRLAAAAAAYRrAAAAAAYBjBAgAAAIBhBAsAAAAAhhEsAAAAABhmn9cdyIpZs2Zp0qRJunjxomrXrq1PP/1U9erVy7T9t99+q5EjR+rMmTN65JFH9NFHH6lNmzYPsMcAUHi8s+6HvO7CPU1q1zmvuwAABZ7VH7FYvny5Bg0apNGjR2v//v2qXbu2WrVqpUuXLmXYfseOHXr22WfVp08fHThwQB07dlTHjh11+PDhB9xzAAAAoPCw+mAxdepU9evXT71791aNGjU0Z84cOTs76/PPP8+w/fTp09W6dWu98847ql69usaNG6egoCDNnDnzAfccAAAAKDyseihUQkKC9u3bp/fee8+8zNbWVqGhodq5c2eG6+zcuVODBg2yWNaqVSutWrUqw/YmU3Ku9TenHGytPt9ZxfP0oFl7XaiJdaIu1qkw1iU3mUxScnKykpN5Ho3gvWKdrL0u+akmVh0soqKilJSUJA8PD4vlHh4eOnbsWIbrXLx4McP2Fy9ezLD9pUvncqezBgwMDszrLtyTNTxPD5q114WaWCfqYp0KY11yWyYjkJENvFesk7XXxRpqUrZsiSy1s+pg8SD4+fnldRcAAACAfM+qj/24u7vLzs5OERERFssjIiJUrly5DNcpV65cttoDAAAAMM6qg4Wjo6OCg4O1ZcsW87Lk5GRt2bJFISEhGa4TEhJi0V6Sfvrpp0zbAwAAADDO6odCDRo0SL169VKdOnVUr149TZs2TbGxserdu7ckqWfPnvLy8tLEiRMlSQMHDlTTpk01ZcoUtW3bVsuWLdPevXs1b968vNwNAAAAoECz+mDRrVs3RUZGatSoUbp48aICAgK0ceNG8wTt8PBw2aaZzd+gQQN9/fXXGjFihIYNG6ZHHnlEq1atYi4FAAAAcB9Z9VCoVK+99prOnj2r+Ph47dq1S/Xr1zfft3XrVi1atMii/VNPPaXjx48rPj5ehw8fzrdX3d6zZ49efvllNWrUSFWrVtXmzZst7o+NjdXYsWPVpEkT+fv7q02bNvrmm2/uud0ff/xRrVu3Vq1atdS+fXv9+uuv92sXCqR71SUqKkpDhw5Vo0aNVLt2bfXp00dnzpy553apS87NnTtXXbp0UWBgoEJCQvTqq6/q9OnTFm3i4+M1ZswY1a9fX4GBgXr99dcVFRV11+2aTCZNnz5djRo1kr+/v1544YUs1RIpslKX5cuXq0ePHgoKClLVqlV1/fr1LG176dKlatGihWrVqqWnnnpKf/755/3YhQIpK3Xp0aOHqlatavHfqFGj7rpd3i859/XXX6t9+/YKCgpSUFCQunXrZvFvAJ9feeNudbl69arGjRunVq1ayd/fX82aNdP48eMVExNz120W9Lrki2BRWMXFxalq1aoaPXp0hvd/+OGH2rZtmyZNmqQNGzaoV69eGjduXLo5Jmnt379fb7/9trp27apVq1bpscce04ABA3TixIn7tRsFzt3qYjKZNGDAAJ07d06zZ8/WypUr5eXlpd69eysuLi7TbVIXY3bv3q3nnntOK1as0BdffKHbt2+rT58+Fs/5hAkT9Msvv2jatGlasmSJLl26pNdee+2u250/f76WLFmi999/XytWrFDRokXVp08fxcfH3+9dKhCyUpebN2+qcePGevnll7O83Q0bNmjixIkaMGCAVq5cqWrVqqlPnz6Kjo6+H7tR4GSlLpL09NNPa/v27eb/3n333btul/dLzpUrV06DBw/WDz/8oO+//16PPvqoBgwYoJMnT0ri8yuv3K0uly5d0qVLlzRkyBCtW7dOEydO1LZt2zR8+PC7brPA18WEfKFKlSqmn376yWJZ27ZtTTNnzrRY1qlTJ9PUqVMz3c7AgQNN/fv3t1j21FNPmUaOHJl7nS1E/luX06dPm6pUqWI6ceKEeVlSUpLp0UcfNa1YsSLT7VCX3BUdHW2qUqWKaffu3SaTyWS6fv26qWbNmqYff/zR3ObUqVOmKlWqmA4cOJDhNpKTk00NGzY0LViwwLzs+vXrJj8/P9O6devua/8Lqv/WJa3ff//dVKVKFdO1a9fuuZ2uXbuaxowZY76dlJRkatSokWnu3Lm52t/CIqO6PP/886bx48dneRu8X3Jf3bp1TStWrODzy8qk1iUjGzZsMNWsWdOUmJiY4f2FoS4cscjHAgMD9fPPPysiIkImk0m///67wsLC1KhRo0zXOXjwYLozZDVq1EgHDx68z70tHBISEiRJRYoUMS+ztbWVo6Oj9u3bl+l61CV3pR6KLlEi5YI+hw8fVmJioho0aGBuU6lSJXl6emb6HJ8/f16RkZEW67i4uKh27do6cODA/et8AfbfuuREQkKCjhw5YlEXW1tbNWjQgLrkUGZ1Wbt2rerXr6927dppypQpunnzZqbb4P2Se5KSkrR+/XrFxcUpMDCQzy8r8d+6ZOTGjRsqXry47O0znsJcGOpi9ZO3kbmRI0dq5MiRatKkiezt7WVjY6Px48erbt26ma4TFRUld3d3i2WlS5e+51hNZE3FihXl6empKVOmaOzYsSpatKgWLVqkixcvKjIyMtP1qEvuSU5O1oQJExQUFKQqVapISnl+HRwc5OrqatG2dOnSmdYldXnp0qXTrUNdsi+juuTElStXlJSUlGFd/jtPAPeWWV3atWsnT09PlS1bVsePH9fkyZMVFhammTNnZrgd3i/GHT9+XM8884zi4+Pl7OysWbNmqXLlyjp69CifX3kos7r81+XLlzV79mx169Yt020VhroQLPKxJUuW6ODBg/rss8/k6empvXv3asyYMSpbtqxFGsaD4+DgoE8//VTDhw9XvXr1ZGdnp5CQEDVp0kQmkymvu1cojBkzRidPntTXX3+d111BGtTFOmVWl7RfjqpWraoyZcrohRdeUHh4uB5++OEH3c1CwdfXV6tWrVJMTIw2bdqkIUOG6KuvvsrrbhV6mdUlbbi4ceOGXnrpJVWqVOmec18KOoZC5VO3bt3SJ598ovfee08tWrRQtWrV9Pzzz6tNmzZauHBhpuu5u7unS8XR0dHpfi1Hzvn5+Wn16tXau3evtm/froULF+rq1auqUKFCputQl9wxduxYbd26VYsXL1a5cuXMy93d3ZWYmJjujEPR0dEqU6ZMhttKXf7fCcHUJfsyq0tOuLm5yc7OjrrkguzUpXbt2pKks2fPZng/7xfjHB0d5e3tLT8/P7399tuqVq2avvzySz6/8lhmdUl148YN9e3bV8WKFdOsWbPk4OCQ6bYKQ10IFvnU7du3lZiYKBsbG4vldnZ2d/1lPCAgQL///rvFsh07diggIOB+dLNQc3FxUalSpXTmzBkdPnxYjz32WKZtqYsxJpNJY8eO1U8//aTFixenC3F+fn5ycHDQzp07zctOnz6tCxcuZPocP/TQQypTpozFOjdu3NAff/yR6fhaWLpXXXLC0dFRNWvWtKhLcnKydu7cSV2yKCd1OXr0qCRl+kWW90vuS05OVkJCAp9fVia1LlLKc9qnTx85ODjos88+s5hfmZHCUBeGQlmx2NhYhYeHm2+fP39eR48eVYkSJeTp6al69epp0qRJcnJykqenp/bs2aNVq1Zp6NCh5nXeffddeXh46O2335aUcqXyHj166PPPP1fTpk21YcMGHT58WGPHjn3g+5df3asuP/74o0qVKiVPT08dP35cEyZMUGhoqMWkeuqSu8aMGaN169Zp9uzZKlasmHkcq4uLi5ycnOTi4qIuXbroww8/VIkSJVS8eHGNHz9egYGBFv8wt27dWm+//bZatmwpGxsb9ezZU5999pm8vb310EMPafr06SpbtqxCQ0PzaE/zl3vVRUoZcxwVFWV+T504cULFihVT+fLlVbJkSUlSr1691LJlSz3//POSpN69e2vIkCHy8/OTv7+/Fi9erJs3b6pz584PfifzoXvVJTw8XGvXrlXTpk1VsmRJHT9+XBMnTlTdunVVrVo183Z4v+SeKVOmqEmTJipfvrxiY2O1bt067d69WwsXLuTzKw/drS43btzQiy++qJs3b2rSpEm6ceOGbty4IUkqVaqU7OzsJBW+uhAsrNjhw4fVs2dP8+2JEydKkjp16qQPP/xQU6dO1dSpUzV48GBdu3ZNnp6eeuutt/Tss8+a1/n3338trkweFBSkyZMna9q0aZo6dap8fHw0a9YsQ5MpC5t71SUyMlIffvih+TD1k08+qVdffdViG9Qld6VeGLJHjx4WyydOnGj+sjls2DDZ2trqjTfeUEJCgho1apTuWiRhYWEWFzfq16+fbt68qVGjRun69esKDg7WggUL7vmrFFJkpS7Lli2zmBD83HPPpWtz7tw5XblyxdymTZs2unz5smbMmKHIyEhVr15dCxYsKDBDCe63e9Ul9dfxL7/8UnFxcSpfvrwef/zxdJ9jvF9yT3R0tIYMGaJLly7JxcVFVatW1cKFC9WwYUNJfH7llbvVZdeuXfrjjz8kSS1btrRYb8uWLXrooYckFb662JiYUQoAAADAIOZYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAwwgWAAAAAAwjWAAAHogePXrogw8+yOtuAADuE/u87gAAoOAYOnSoVq5cmW75//73vzzoDQDgQSJYAAByVePGjTVx4kSLZaVKlcqj3gAAHhSGQgEAcpWjo6PKlClj8Z+dnV26dqtWrVLnzp0VGBiohg0b6u2331Z0dLRFmy1btujxxx9XrVq11KNHD61cuVJVq1bV9evXJUn//POPXn75ZdWtW1cBAQFq27atfv311weynwAASxyxAADkidu3b2vgwIGqWLGioqOj9eGHH2ro0KGaP3++JOncuXMaOHCgevTooaeeekpHjx7VRx99ZLGNsWPHKjExUV999ZWcnZ116tQpOTs758XuAEChR7AAAOSqrVu3KjAw0Hy7cePGmjFjRrp2Xbt2Nf9doUIFDR8+XF27dlVsbKyKFSum5cuXy9fXV0OGDJEkVaxYUSdOnNCcOXPM6124cEGtWrVS1apVzdsBAOQNggUAIFfVr19f77//vvl20aJFM2x3+PBhzZw5U8eOHdO1a9dkMpkkSf/++68qV66ssLAw+fn5Wazj7+9vcbtnz556//33tX37djVo0ECPP/64qlWrlrs7BADIEuZYAAByVdGiReXt7W3+r2zZsunaxMXFqU+fPipWrJgmT56s7777TjNnzpQkJSYmZvmxnnrqKW3evFlPPvmkTpw4oa5du2rJkiW5ti8AgKwjWAAAHrjTp0/r6tWrGjx4sOrUqaNKlSqlm7jt6+urw4cPWyw7dOhQum2VL19ezz77rGbOnKnevXtrxYoV97XvAICMESwAAA+cp6enHBwctGTJEp07d05btmzR7NmzLdp069ZNYWFhmjRpksLCwrRhwwbzNTJsbGwkSR988IG2bdumc+fO6ciRI9q1a5cqVar0wPcHAECwAADkgVKlSunDDz/Uxo0b1aZNG82fP988STtVhQoVNH36dP3000/q0KGDvvnmG7388suSUk5pK0nJyckaO3as2rRpo759+8rHx0ejR49+4PsDAJBsTKmz5QAAsHKfffaZli1bxrUqAMAKcVYoAIDVWrp0qWrVqiU3Nzft27dPCxcu1HPPPZfX3QIAZIBgAQCwWmfPntVnn32ma9euydPTU71799ZLL72U190CAGSAoVAAAAAADGPyNgAAAADDCBYAAAAADCNYAAAAADCMYAEAAADAMIIFAAAAAMMIFgAAAAAMI1gAAAAAMIxgAQAAAMAwggUAAAAAw/4fxYl78r3JiFgAAAAASUVORK5CYII=", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Gráfico de barras de todas as Flags\n", "Flag = None\n", "\n", "gd.plot_diag.fort220_time_Obscontrib(Self_1, Self_2, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Label='cost', \n", " LabelObs='J', \n", " Flag=Flag, \n", " Clean=None)" ] }, { "cell_type": "markdown", "id": "c7de69f2", "metadata": {}, "source": [ "5. `time_series_fort220(self, OuterLoop=1, Label=None, Flag=None, dateIni=None, dateFin=None, nHour=\"06\", Clean=None)`\n", "\n", "A função plota uma série temporal para dados do arquivo `Self_2` em diferentes flags dos termos de contribuição `J`, `b`, `c` ou de penalidade `EJ`. Um diagrama de Hovmoller é retornado se `Flag=None` ou se Flag for uma lista.\n", "\n", "**Obs.:** considerar os nºs das flags no formato inteiro (`int`) (nesta função não utilizar o formato string para identificar a Flag). " ] }, { "cell_type": "code", "execution_count": null, "id": "8467b823", "metadata": {}, "outputs": [], "source": [ "# indicar a flag como inteiro\n", "\n", "#----- Flags list -----------#\n", "# Flag = [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 14, 15, 16, 24, 25, 26, 27]\n", "\n", "#----- all Flags ------------#\n", "# Flag = None\n", "\n", "#----- number flag ----------#\n", "if EXP19:\n", " Flag = 26\n", "else:\n", " Flag = 32\n", "\n", "gd.plot_diag.time_series_fort220(Self_2, OuterLoop=2, \n", " Label='J', \n", " Flag=Flag, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Clean=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "2604f3b3", "metadata": {}, "outputs": [], "source": [ "# indicar a flag como inteiro\n", "\n", "#----- Flags list -----------#\n", "# EXP19\n", "# Flag = [1, 2, 3, 4, 5, 6, 7, 11, 12, 13, 14, 15, 16, 24, 25, 26, 27]\n", "\n", "#----- Flags list -----------#\n", "# EXP21\n", "Flag = [1, 2, 3, 4, 5, 18, 19, 20, 21, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32]\n", "\n", "#----- all Flags ------------#\n", "# Flag = None\n", "\n", "gd.plot_diag.time_series_fort220(Self_2, OuterLoop=2, \n", " Label='J', \n", " Flag=Flag, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Clean=False)" ] }, { "cell_type": "markdown", "id": "996aa0ae", "metadata": {}, "source": [ "6. `Mean_std_fort220(self, OuterLoop=None, cost_gradient=True, Label=None, Flag=None, dateIni=None, dateFin=None, nHour=\"06\", Clean=None)`\n", "\n", "Esta função plota um gráfico dos valores médios e dos desvios padrão da variável `Label` calculados no intervalo de tempo e para cada *inner loop*. No final a função retorna uma figura onde os pontos são os valores médios e o desvio padrão é representado na forma de uma barra vertical, indicando para cada *inner loop* a variabilidade do dado no intervalo de tempo." ] }, { "cell_type": "code", "execution_count": null, "id": "a649aa0a", "metadata": { "scrolled": false }, "outputs": [], "source": [ "gd.plot_diag.Mean_std_fort220(Self_1, OuterLoop=None, \n", " cost_gradient=True, \n", " Label='grad', \n", " Flag=None, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Clean=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "41d2634c", "metadata": {}, "outputs": [], "source": [ "Label = 'J'\n", "if EXP19:\n", " Flag = '26'\n", "else:\n", " Flag = '32'\n", "\n", "gd.plot_diag.Mean_std_fort220(Self_2, OuterLoop=None, \n", " cost_gradient=False, \n", " Label=Label, \n", " Flag=Flag, \n", " dateIni=dateIni, \n", " dateFin=dateFin, \n", " nHour=nHour, \n", " Clean=False)\n" ] }, { "cell_type": "markdown", "id": "94a029b9", "metadata": {}, "source": [ "Caso o usuário queira gerar um GiF animado com as figuras geradas pela função `fort220_plot`, basta adaptar o caminho do diretório." ] }, { "cell_type": "code", "execution_count": null, "id": "0fbcfe74", "metadata": {}, "outputs": [], "source": [ "from PIL import Image\n", "import os\n", "\n", "imagens = []\n", "path = \"/home/radiancia/DiagRadi-V2_issue45/notebooks/\"\n", "\n", "Label='cost'\n", "\n", "imagens, frame_one = [], []\n", "for i in range(len(dates)):\n", " tidx = i\n", " datefmt = dates[tidx]\n", " \n", " new = Image.open(path + '/' + 'Fort220_'+str(Label) +'_'+datefmt+'.png')\n", " imagens.append(new)\n", " \n", "# Salvar imagem no formato gif contendo todas as imagens do \n", "frame_one = imagens[0]\n", "frame_one.save(\"GiF-Fort220_\"+str(Label) +\"_\"+datefmt+\".gif\", save_all=True, \n", " append_images=imagens, duration=1000, loop=0)\n", "\n", "print('Gif criado')\n", "print('')" ] }, { "cell_type": "code", "execution_count": null, "id": "b1955770", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "readDiag.Radi", "language": "python", "name": "readdiag.radi" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }