{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c6a063ef",
   "metadata": {},
   "source": [
    "**--> Referências** Pag 73 do Guide v3.7. Observar o significado dos qc1a7 no Guide avançado v3.4 pag 67"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5991ef15-c4e8-4b29-8f55-0b3d430bed43",
   "metadata": {},
   "source": [
    "# Análise dos dados de Radiância - Fort.207\n",
    "\n",
    "O arquivo padrão de saída **fort.207** contém informações importantes sobre analises estatísticas dos dados de **radiância**. Este arquivo é dividio em três partes. A primeira parte lista o conteúdo do arquivo *satinfo* (arquivo para controle dos dados de **radiância**), a segunda parte lista os coeficientes para correção de viés após ler o arquivo *satbias_in* e a terceira parte fornece estatísticas detalhadas sobre os dado em três estágios (`it`):\n",
    "\n",
    "-> *stage o-g 01* (`it = 1`): dados antes do 1º loop externo (*outer loop*);\n",
    "\n",
    "-> *stage o-g 02* (`it = 2`): dados entre o 1º e o 2º loops externos;\n",
    "\n",
    "-> *stage o-g 03* (`it = 3`): dados após o 2º loop externo.\n",
    "\n",
    "A terceira parte do arquivo **fort.207** é subdividida em três seções. Cada seção trás uma tabela, sendo estas caracterizadas da seguinte forma:\n",
    "\n",
    "-> **Tabela A** (`tab = 0`): sumário para várias estatísticas como uma função do tipo de observação.\n",
    "\n",
    "*Colunas da tabela:*\n",
    "\n",
    "`'it', 'sat', 'type', 'penalty', 'nobs', 'iland', 'isnoice', 'icoast', 'ireduce', 'ivarl', 'nlgross', 'qcpenalty', 'qc1', 'qc2', 'qc3', 'qc4', 'qc5', 'qc6', 'qc7'`\n",
    "\n",
    "-> **Tabela B** (`tab = 1`): sumário para várias estatísticas como uma função do canal.\n",
    "\n",
    "*Colunas da tabela:*\n",
    "\n",
    "`'it', 'SN satinfo', 'nchan', 'type', 'sat', 'nobsused', 'nobstossed', 'varCH', 'biasBC', 'biasAC', 'penaltyCH', 'sqrt', 'STD'`\n",
    "\n",
    "-> **Tabela C** (`tab = 2`): sumário para cada tipo de observação.\n",
    "\n",
    "*Colunas da tabela:*\n",
    "\n",
    "`'it', 'sat', 'type', 'read', 'keep', 'assim', 'penalty', 'qcpnlty', 'cpen', 'qccpen'`\n",
    "\n",
    "## Leitura do arquivo **fort.207**\n",
    "\n",
    "Neste notebook serão apresentadas as funções criadas na classe `read_diag` para ler e organizar na forma de DataFrames as Tabelas A, B e C disponíveis nos 3 estágios.\n",
    "\n",
    "1. `fort_207_read(DIRdiag, dates):` esta função lê todas as linhas do arquivo **fort.207** identifica as tabelas A, B e C, organiza as informações dos 3 estágios (`it`) no mesmo dataframe. No final a função concatena as tabelas formando uma lista bidimensional de tabelas com relação ao tempo (range de datas) e tipo de tabela (`tab`). Abaixo você pode visualizar um exemplo das três tabelas no date = `'2024030100'` do EXP21.\n",
    "\n",
    "   -> **Tabela A** - `tab = 0`:\n",
    "   \n",
    "   <img src=notefigs/Fort207_TableA_tab0.png style=\"width: 800px;\">\n",
    "   <br>\n",
    "\n",
    "   -> **Tabela B** - `tab = 1`:\n",
    "   \n",
    "   <img src=notefigs/Fort207_TableB_tab1.png style=\"width: 800px;\">\n",
    "   <br>\n",
    "\n",
    "   -> **Tabela C** - `tab = 2`:\n",
    "   \n",
    "   <img src=notefigs/Fort207_TableC_tab2.png style=\"width: 600px;\">\n",
    "   <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a02883da-a398-407d-ab4a-1ec2d5a204e8",
   "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 # NEW !!!!\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d33b7fa0-6c74-4ced-9517-47dc76cc5278",
   "metadata": {},
   "source": [
    "## Leitura dos arquivos\n",
    "\n",
    "Realiza o processo de leitura do arquivo **fort.207** a partir dos dados iniciais fornecidos pelo usuário."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8e75c33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2025100112', '2025100118', '2025100200', '2025100206', '2025100212', '2025100218', '2025100300', '2025100306', '2025100312', '2025100318', '2025100400', '2025100406', '2025100412', '2025100418', '2025100500', '2025100506', '2025100512', '2025100518', '2025100600', '2025100606', '2025100612', '2025100618', '2025100700', '2025100706', '2025100712', '2025100718', '2025100800', '2025100806', '2025100812', '2025100818', '2025100900', '2025100906', '2025100912', '2025100918', '2025101000', '2025101006', '2025101012', '2025101018', '2025101100', '2025101106', '2025101112', '2025101118', '2025101200', '2025101206', '2025101212', '2025101218', '2025101300', '2025101306', '2025101312', '2025101318', '2025101400', '2025101406', '2025101412', '2025101418', '2025101500', '2025101506', '2025101512', '2025101518', '2025101600', '2025101606', '2025101612', '2025101618', '2025101700', '2025101706', '2025101712', '2025101718', '2025101800', '2025101806', '2025101812', '2025101818', '2025101900', '2025101906', '2025101912', '2025101918', '2025102000', '2025102006', '2025102012', '2025102018', '2025102100', '2025102106', '2025102112', '2025102118', '2025102200', '2025102206', '2025102212', '2025102218', '2025102300', '2025102306', '2025102312', '2025102318', '2025102400', '2025102406', '2025102412', '2025102418', '2025102500', '2025102506', '2025102512', '2025102518', '2025102600', '2025102606', '2025102612', '2025102618', '2025102700', '2025102706', '2025102712', '2025102718', '2025102800', '2025102806', '2025102812', '2025102818', '2025102900', '2025102906', '2025102912', '2025102918', '2025103000', '2025103006', '2025103012', '2025103018', '2025103100', '2025103106', '2025103112', '2025103118', '2025110100', '2025110106', '2025110112', '2025110118', '2025110200', '2025110206', '2025110212', '2025110218', '2025110300', '2025110306', '2025110312', '2025110318', '2025110400', '2025110406', '2025110412', '2025110418', '2025110500', '2025110506', '2025110512', '2025110518', '2025110600', '2025110606', '2025110612', '2025110618', '2025110700', '2025110706', '2025110712', '2025110718', '2025110800', '2025110806', '2025110812', '2025110818', '2025110900', '2025110906', '2025110912', '2025110918', '2025111000', '2025111006', '2025111012', '2025111018', '2025111100', '2025111106', '2025111112', '2025111118', '2025111200', '2025111206', '2025111212', '2025111218', '2025111300', '2025111306', '2025111312', '2025111318', '2025111400', '2025111406', '2025111412', '2025111418', '2025111500', '2025111506', '2025111512', '2025111518', '2025111600', '2025111606', '2025111612', '2025111618', '2025111700', '2025111706', '2025111712', '2025111718', '2025111800', '2025111806', '2025111812', '2025111818', '2025111900', '2025111906', '2025111912', '2025111918', '2025112000', '2025112006', '2025112012', '2025112018', '2025112100', '2025112106', '2025112112', '2025112118', '2025112200', '2025112206', '2025112212', '2025112218', '2025112300', '2025112306', '2025112312', '2025112318', '2025112400', '2025112406', '2025112412', '2025112418', '2025112500', '2025112506', '2025112512', '2025112518', '2025112600', '2025112606', '2025112612', '2025112618', '2025112700', '2025112706', '2025112712', '2025112718', '2025112800', '2025112806', '2025112812', '2025112818', '2025112900', '2025112906', '2025112912', '2025112918', '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",
    "DIRdiag = '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025'\n",
    "EXP19 = True\n",
    "EXP20 = False\n",
    "EXP21 = False\n",
    "\n",
    "#varType = 'n19' #['n15', 'n18', 'n19']#, 'metop-b']\n",
    "#varName = 'amsua' #'hirs4' #'amsua'\n",
    "dateIni = '2025100112' \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='6H').tolist()]\n",
    "\n",
    "print(dates)\n",
    "print('')\n",
    "    \n",
    "# Leitura dos arquivos de diagnósticos\n",
    "read = False\n",
    "    \n",
    "# Finalizando: liberando memória arquivos de diagnóstico\n",
    "if read:\n",
    "    for file in gdf_list:\n",
    "        if file._FNumber != None:\n",
    "            file.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd264cfe-cc42-4f0b-990f-7753c7aa12f6",
   "metadata": {},
   "source": [
    "Leitura e construção das tabelas. Concatena o range de tabelas na variável `Self`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e428fe3b-ac2d-4ef2-8992-3c21af61ff5f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">>>> Leitura: fort_207_read <<<<\n",
      "\n",
      "DIRdiag =  /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\n",
      "\n",
      "TABLE A: Names columns A =  ['it', 'sat', 'type', 'penalty', 'nobs', 'iland', 'isnoice', 'icoast', 'ireduce', 'ivarl', 'nlgross', 'qcpenalty', 'qc1', 'qc2', 'qc3', 'qc4', 'qc5', 'qc6', 'qc7']\n",
      "\n",
      "TABLE B: Names columns B =  ['it', 'SN satinfo', 'nchan', 'type', 'sat', 'nobsused', 'nobstossed', 'varCH', 'biasBC', 'biasAC', 'penaltyCH', 'sqrt', 'STD']\n",
      "\n",
      "TABLE C: Names columns C =  ['it', 'sat', 'type', 'read', 'keep', 'assim', 'penalty', 'qcpnlty', 'cpen', 'qccpen']\n",
      "\n",
      "['/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100112/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100118/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100506/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100512/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100518/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100600/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100606/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100612/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100618/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100700/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100706/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100712/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100718/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100800/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100806/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100812/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100818/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100900/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100906/fort.207', 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'/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110812/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110818/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110900/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110906/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110912/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110918/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111000/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111006/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111012/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111018/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111100/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111106/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111112/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111118/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111200/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111206/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111212/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111218/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111300/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111306/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111312/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111318/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111400/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111406/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111412/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111418/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111500/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111506/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111512/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111518/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111600/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111606/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111612/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111618/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111700/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111706/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111712/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111718/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111800/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111806/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111812/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111818/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111900/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111906/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111912/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111918/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112000/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112006/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112012/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112018/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112100/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112106/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112112/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112118/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112200/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112206/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112212/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112218/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112300/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112306/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112312/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112318/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112400/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112406/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112412/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112418/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112500/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112506/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112512/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112518/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112600/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112606/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112612/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112618/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112700/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112706/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112712/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112718/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112800/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112806/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112812/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112818/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112900/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112906/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112912/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112918/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.207', '/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.207']\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-01:12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-01:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-02:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-02:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-02:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-02:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-03:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-03:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-03:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-03:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-04:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-04:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-04:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-04:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-05:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-05:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-05:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-05:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-06:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-06:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-06:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-06:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-07:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-07:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-07:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-07:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-08:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-08:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-08:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-08:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-09:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-09:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-09:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025100918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-09:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-10:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-10:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-10:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-10:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-11:00\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-11:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-11:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-11:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-12:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-12:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-12:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-12:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-13:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-13:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-13:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-13:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-14:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-14:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-14:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-14:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-15:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-15:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-15:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-15:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-16:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-16:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-16:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-16:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-17:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-17:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-17:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-17:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-18:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-18:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-18:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-18:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-19:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-19:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-19:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025101918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-19:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-20:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-20:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-20:12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-20:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-21:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-21:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-21:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-21:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-22:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-22:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-22:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-22:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-23:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-23:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-23:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-23:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-24:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-24:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-24:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-24:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-25:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-25:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-25:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-25:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-26:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-26:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-26:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-26:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-27:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-27:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-27:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-27:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-28:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-28:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-28:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-28:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-29:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-29:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-29:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025102918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-29:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-30:00\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-30:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-30:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-30:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-31:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-31:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-31:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025103118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-10-31:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-01:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-01:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-01:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-01:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-02:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-02:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-02:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-02:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-03:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-03:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-03:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-03:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-04:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-04:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-04:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-04:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-05:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-05:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-05:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-05:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-06:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-06:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-06:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-06:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-07:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-07:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-07:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-07:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-08:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-08:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-08:12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-08:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-09:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-09:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-09:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025110918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-09:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-10:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-10:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-10:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-10:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-11:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-11:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-11:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-11:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-12:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-12:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-12:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-12:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-13:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-13:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-13:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-13:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-14:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-14:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-14:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-14:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-15:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-15:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-15:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-15:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-16:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-16:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-16:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-16:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-17:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-17:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-17:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-17:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-18:00\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-18:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-18:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-18:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-19:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-19:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-19:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025111918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-19:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-20:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-20:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-20:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-20:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112100/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-21:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112106/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-21:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112112/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-21:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112118/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-21:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112200/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-22:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112206/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-22:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112212/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-22:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112218/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-22:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112300/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-23:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112306/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-23:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112312/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-23:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112318/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-23:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112400/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-24:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112406/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-24:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112412/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-24:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112418/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-24:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112500/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-25:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112506/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-25:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112512/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-25:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112518/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-25:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112600/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-26:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112606/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-26:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112612/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-26:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112618/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-26:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112700/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-27:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112706/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-27:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112712/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-27:12\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112718/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-27:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112800/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-28:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112806/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-28:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112812/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-28:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112818/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-28:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112900/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-29:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112906/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-29:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112912/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-29:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025112918/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-29:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113000/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-30:00\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113006/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-30:06\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113012/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-30:12\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n",
      "Reading /mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025/2025113018/fort.207\n",
      "\n",
      " Preparing data for: fort.207 ---> 2025-11-30:18\n",
      "\n",
      "Nº total de linhas = 9113\n",
      "nl final = 9113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('')\n",
    "print('>>>> Leitura: fort_207_read <<<<')\n",
    "print('')\n",
    "Self = gd.read_diag.fort_207_read(DIRdiag, dates)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aac542-9e04-44ee-92a2-fa42a128f416",
   "metadata": {},
   "source": [
    "Abaixo segue um exemplo para visualização a Tabela A (`tab = 0`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3377eb2b-a93c-4587-ba3c-0f87dfa83caa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>it</th>\n",
       "      <th>sat</th>\n",
       "      <th>type</th>\n",
       "      <th>penalty</th>\n",
       "      <th>nobs</th>\n",
       "      <th>iland</th>\n",
       "      <th>isnoice</th>\n",
       "      <th>icoast</th>\n",
       "      <th>ireduce</th>\n",
       "      <th>ivarl</th>\n",
       "      <th>nlgross</th>\n",
       "      <th>qcpenalty</th>\n",
       "      <th>qc1</th>\n",
       "      <th>qc2</th>\n",
       "      <th>qc3</th>\n",
       "      <th>qc4</th>\n",
       "      <th>qc5</th>\n",
       "      <th>qc6</th>\n",
       "      <th>qc7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>22250.276089</td>\n",
       "      <td>8884</td>\n",
       "      <td>8464</td>\n",
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       "      <td>1</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>16160.727420</td>\n",
       "      <td>8927</td>\n",
       "      <td>8543</td>\n",
       "      <td>278</td>\n",
       "      <td>106</td>\n",
       "      <td>2883</td>\n",
       "      <td>4589</td>\n",
       "      <td>0</td>\n",
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       "      <td>7769</td>\n",
       "      <td>590</td>\n",
       "      <td>366</td>\n",
       "      <td>288</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>11249.671251</td>\n",
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       "      <td>10082</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>10843.862737</td>\n",
       "      <td>10496</td>\n",
       "      <td>9965</td>\n",
       "      <td>525</td>\n",
       "      <td>6</td>\n",
       "      <td>3505</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>10843.862737</td>\n",
       "      <td>8989</td>\n",
       "      <td>583</td>\n",
       "      <td>629</td>\n",
       "      <td>394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>10850.479046</td>\n",
       "      <td>10477</td>\n",
       "      <td>9917</td>\n",
       "      <td>555</td>\n",
       "      <td>5</td>\n",
       "      <td>3507</td>\n",
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       "      <td>719</td>\n",
       "      <td>416</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>14278.008861</td>\n",
       "      <td>8884</td>\n",
       "      <td>8464</td>\n",
       "      <td>312</td>\n",
       "      <td>108</td>\n",
       "      <td>2863</td>\n",
       "      <td>161</td>\n",
       "      <td>0</td>\n",
       "      <td>14278.008861</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>9975.615447</td>\n",
       "      <td>8927</td>\n",
       "      <td>8543</td>\n",
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       "      <td>7825</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>5548.944668</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>4615.754121</td>\n",
       "      <td>10496</td>\n",
       "      <td>9965</td>\n",
       "      <td>525</td>\n",
       "      <td>6</td>\n",
       "      <td>3505</td>\n",
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       "      <td>4615.754121</td>\n",
       "      <td>9041</td>\n",
       "      <td>656</td>\n",
       "      <td>587</td>\n",
       "      <td>394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>4618.602620</td>\n",
       "      <td>10477</td>\n",
       "      <td>9917</td>\n",
       "      <td>555</td>\n",
       "      <td>5</td>\n",
       "      <td>3507</td>\n",
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       "      <td>4618.602620</td>\n",
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       "      <td>416</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>13062.080497</td>\n",
       "      <td>8884</td>\n",
       "      <td>8464</td>\n",
       "      <td>312</td>\n",
       "      <td>108</td>\n",
       "      <td>2863</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>13062.080497</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>9192.676264</td>\n",
       "      <td>8927</td>\n",
       "      <td>8543</td>\n",
       "      <td>278</td>\n",
       "      <td>106</td>\n",
       "      <td>2883</td>\n",
       "      <td>4479</td>\n",
       "      <td>0</td>\n",
       "      <td>9192.676264</td>\n",
       "      <td>7838</td>\n",
       "      <td>577</td>\n",
       "      <td>311</td>\n",
       "      <td>288</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>4765.904743</td>\n",
       "      <td>10595</td>\n",
       "      <td>10082</td>\n",
       "      <td>504</td>\n",
       "      <td>9</td>\n",
       "      <td>3687</td>\n",
       "      <td>293</td>\n",
       "      <td>0</td>\n",
       "      <td>4765.904743</td>\n",
       "      <td>9037</td>\n",
       "      <td>651</td>\n",
       "      <td>550</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>3824.888809</td>\n",
       "      <td>10496</td>\n",
       "      <td>9965</td>\n",
       "      <td>525</td>\n",
       "      <td>6</td>\n",
       "      <td>3505</td>\n",
       "      <td>319</td>\n",
       "      <td>0</td>\n",
       "      <td>3824.888809</td>\n",
       "      <td>9023</td>\n",
       "      <td>624</td>\n",
       "      <td>541</td>\n",
       "      <td>394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>3963.466733</td>\n",
       "      <td>10477</td>\n",
       "      <td>9917</td>\n",
       "      <td>555</td>\n",
       "      <td>5</td>\n",
       "      <td>3507</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>3963.466733</td>\n",
       "      <td>9086</td>\n",
       "      <td>581</td>\n",
       "      <td>594</td>\n",
       "      <td>416</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    it      sat   type       penalty   nobs  iland  isnoice  icoast  ireduce  \\\n",
       "0    1  metop-b  amsua  22250.276089   8884   8464      312     108     2863   \n",
       "1    1  metop-c  amsua  16160.727420   8927   8543      278     106     2883   \n",
       "2    1      npp   atms  11249.671251  10595  10082      504       9     3687   \n",
       "3    1      n20   atms  10843.862737  10496   9965      525       6     3505   \n",
       "4    1      n21   atms  10850.479046  10477   9917      555       5     3507   \n",
       "5    2  metop-b  amsua  14278.008861   8884   8464      312     108     2863   \n",
       "6    2  metop-c  amsua   9975.615447   8927   8543      278     106     2883   \n",
       "7    2      npp   atms   5548.944668  10595  10082      504       9     3687   \n",
       "8    2      n20   atms   4615.754121  10496   9965      525       6     3505   \n",
       "9    2      n21   atms   4618.602620  10477   9917      555       5     3507   \n",
       "10   3  metop-b  amsua  13062.080497   8884   8464      312     108     2863   \n",
       "11   3  metop-c  amsua   9192.676264   8927   8543      278     106     2883   \n",
       "12   3      npp   atms   4765.904743  10595  10082      504       9     3687   \n",
       "13   3      n20   atms   3824.888809  10496   9965      525       6     3505   \n",
       "14   3      n21   atms   3963.466733  10477   9917      555       5     3507   \n",
       "\n",
       "    ivarl  nlgross     qcpenalty   qc1  qc2  qc3  qc4  qc5  qc6  qc7  \n",
       "0     347        0  22250.276089     0    0    0  303   33    0    0  \n",
       "1    4589        0  16160.727420  7769  590  366  288   57    0    0  \n",
       "2     535        0  11249.671251  8977  609  693  400    0    0    0  \n",
       "3     393        0  10843.862737  8989  583  629  394    0    0    0  \n",
       "4     406        0  10850.479046  8988  530  719  416    1    0    0  \n",
       "5     161        0  14278.008861     0    0    0  303   33    0    0  \n",
       "6    4498        0   9975.615447  7825  588  312  288   57    0    0  \n",
       "7     263        0   5548.944668  9056  626  579  400    0    0    0  \n",
       "8     200        0   4615.754121  9041  656  587  394    0    0    0  \n",
       "9     171        0   4618.602620  9130  539  636  416    1    0    0  \n",
       "10    156        0  13062.080497     0    0    0  303   33    0    0  \n",
       "11   4479        0   9192.676264  7838  577  311  288   57    0    0  \n",
       "12    293        0   4765.904743  9037  651  550  400    0    0    0  \n",
       "13    319        0   3824.888809  9023  624  541  394    0    0    0  \n",
       "14    234        0   3963.466733  9086  581  594  416    1    0    0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidx = 0\n",
    "tab = 0\n",
    "Self[tidx][tab]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9310f710-f9fb-4a84-a600-7b9205176ce7",
   "metadata": {},
   "source": [
    "Exemplo Tabela B (`tab = 1`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "eb87a0a0-0d66-4ddb-82b8-e316689b0006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>it</th>\n",
       "      <th>SN satinfo</th>\n",
       "      <th>nchan</th>\n",
       "      <th>type</th>\n",
       "      <th>sat</th>\n",
       "      <th>nobsused</th>\n",
       "      <th>nobstossed</th>\n",
       "      <th>varCH</th>\n",
       "      <th>biasBC</th>\n",
       "      <th>biasAC</th>\n",
       "      <th>penaltyCH</th>\n",
       "      <th>sqrt</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1610</td>\n",
       "      <td>1</td>\n",
       "      <td>atms</td>\n",
       "      <td>npp</td>\n",
       "      <td>241</td>\n",
       "      <td>3</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>0.779297</td>\n",
       "      <td>-0.644683</td>\n",
       "      <td>0.038427</td>\n",
       "      <td>2.017934</td>\n",
       "      <td>1.912182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1611</td>\n",
       "      <td>2</td>\n",
       "      <td>atms</td>\n",
       "      <td>npp</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>1.773720</td>\n",
       "      <td>-0.536676</td>\n",
       "      <td>0.037356</td>\n",
       "      <td>2.036618</td>\n",
       "      <td>1.964635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1612</td>\n",
       "      <td>3</td>\n",
       "      <td>atms</td>\n",
       "      <td>npp</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>2.094331</td>\n",
       "      <td>-0.233644</td>\n",
       "      <td>0.013920</td>\n",
       "      <td>1.046224</td>\n",
       "      <td>1.019802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1613</td>\n",
       "      <td>4</td>\n",
       "      <td>atms</td>\n",
       "      <td>npp</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>1.113997</td>\n",
       "      <td>-0.117500</td>\n",
       "      <td>0.015130</td>\n",
       "      <td>0.671868</td>\n",
       "      <td>0.661513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1614</td>\n",
       "      <td>5</td>\n",
       "      <td>atms</td>\n",
       "      <td>npp</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.046661</td>\n",
       "      <td>0.024827</td>\n",
       "      <td>0.023352</td>\n",
       "      <td>0.337791</td>\n",
       "      <td>0.336878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>3</td>\n",
       "      <td>3903</td>\n",
       "      <td>18</td>\n",
       "      <td>atms</td>\n",
       "      <td>n21</td>\n",
       "      <td>125</td>\n",
       "      <td>36</td>\n",
       "      <td>-3.50</td>\n",
       "      <td>-1.472483</td>\n",
       "      <td>-0.254654</td>\n",
       "      <td>0.024980</td>\n",
       "      <td>0.983737</td>\n",
       "      <td>0.950205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>3</td>\n",
       "      <td>3904</td>\n",
       "      <td>19</td>\n",
       "      <td>atms</td>\n",
       "      <td>n21</td>\n",
       "      <td>123</td>\n",
       "      <td>38</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-1.244452</td>\n",
       "      <td>-0.229094</td>\n",
       "      <td>0.031362</td>\n",
       "      <td>0.919147</td>\n",
       "      <td>0.890139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>3</td>\n",
       "      <td>3905</td>\n",
       "      <td>20</td>\n",
       "      <td>atms</td>\n",
       "      <td>n21</td>\n",
       "      <td>124</td>\n",
       "      <td>37</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-1.153265</td>\n",
       "      <td>-0.245837</td>\n",
       "      <td>0.031441</td>\n",
       "      <td>0.917143</td>\n",
       "      <td>0.883582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>3</td>\n",
       "      <td>3906</td>\n",
       "      <td>21</td>\n",
       "      <td>atms</td>\n",
       "      <td>n21</td>\n",
       "      <td>130</td>\n",
       "      <td>31</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-0.956399</td>\n",
       "      <td>-0.235594</td>\n",
       "      <td>0.036935</td>\n",
       "      <td>0.994983</td>\n",
       "      <td>0.966688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>3</td>\n",
       "      <td>3907</td>\n",
       "      <td>22</td>\n",
       "      <td>atms</td>\n",
       "      <td>n21</td>\n",
       "      <td>124</td>\n",
       "      <td>37</td>\n",
       "      <td>-3.00</td>\n",
       "      <td>-0.946384</td>\n",
       "      <td>-0.259568</td>\n",
       "      <td>0.032093</td>\n",
       "      <td>0.953206</td>\n",
       "      <td>0.917183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>267 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     it  SN satinfo  nchan  type  sat  nobsused  nobstossed  varCH    biasBC  \\\n",
       "0     1        1610      1  atms  npp       241           3  -4.50  0.779297   \n",
       "1     1        1611      2  atms  npp       244           0  -4.50  1.773720   \n",
       "2     1        1612      3  atms  npp       244           0  -4.50  2.094331   \n",
       "3     1        1613      4  atms  npp       244           0  -2.50  1.113997   \n",
       "4     1        1614      5  atms  npp       244           0   0.55  0.046661   \n",
       "..   ..         ...    ...   ...  ...       ...         ...    ...       ...   \n",
       "262   3        3903     18  atms  n21       125          36  -3.50 -1.472483   \n",
       "263   3        3904     19  atms  n21       123          38  -3.00 -1.244452   \n",
       "264   3        3905     20  atms  n21       124          37  -3.00 -1.153265   \n",
       "265   3        3906     21  atms  n21       130          31  -3.00 -0.956399   \n",
       "266   3        3907     22  atms  n21       124          37  -3.00 -0.946384   \n",
       "\n",
       "       biasAC  penaltyCH      sqrt       STD  \n",
       "0   -0.644683   0.038427  2.017934  1.912182  \n",
       "1   -0.536676   0.037356  2.036618  1.964635  \n",
       "2   -0.233644   0.013920  1.046224  1.019802  \n",
       "3   -0.117500   0.015130  0.671868  0.661513  \n",
       "4    0.024827   0.023352  0.337791  0.336878  \n",
       "..        ...        ...       ...       ...  \n",
       "262 -0.254654   0.024980  0.983737  0.950205  \n",
       "263 -0.229094   0.031362  0.919147  0.890139  \n",
       "264 -0.245837   0.031441  0.917143  0.883582  \n",
       "265 -0.235594   0.036935  0.994983  0.966688  \n",
       "266 -0.259568   0.032093  0.953206  0.917183  \n",
       "\n",
       "[267 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidx = 0\n",
    "tab = 1\n",
    "Self[tidx][tab]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80bbc3c-ffe8-451e-a288-da48e1d797a4",
   "metadata": {},
   "source": [
    "Exemplo Tabela C (`tab = 2`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b27cda0-f471-49e3-9f61-b2350ee0ed7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>it</th>\n",
       "      <th>sat</th>\n",
       "      <th>type</th>\n",
       "      <th>read</th>\n",
       "      <th>keep</th>\n",
       "      <th>assim</th>\n",
       "      <th>penalty</th>\n",
       "      <th>qcpnlty</th>\n",
       "      <th>cpen</th>\n",
       "      <th>qccpen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>o-g 01 rad</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>975900</td>\n",
       "      <td>106474</td>\n",
       "      <td>61860</td>\n",
       "      <td>22250.0</td>\n",
       "      <td>22250.0</td>\n",
       "      <td>0.359690</td>\n",
       "      <td>0.359690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>o-g 01 rad</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>967845</td>\n",
       "      <td>133535</td>\n",
       "      <td>53372</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>16161.0</td>\n",
       "      <td>0.302790</td>\n",
       "      <td>0.302790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>o-g 01 rad</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>595782</td>\n",
       "      <td>232650</td>\n",
       "      <td>84144</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>11250.0</td>\n",
       "      <td>0.133700</td>\n",
       "      <td>0.133700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>o-g 01 rad</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230502</td>\n",
       "      <td>83526</td>\n",
       "      <td>10844.0</td>\n",
       "      <td>10844.0</td>\n",
       "      <td>0.129830</td>\n",
       "      <td>0.129830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>o-g 01 rad</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230114</td>\n",
       "      <td>83207</td>\n",
       "      <td>10850.0</td>\n",
       "      <td>10850.0</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>o-g 02 rad</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>975900</td>\n",
       "      <td>106474</td>\n",
       "      <td>62046</td>\n",
       "      <td>14278.0</td>\n",
       "      <td>14278.0</td>\n",
       "      <td>0.230120</td>\n",
       "      <td>0.230120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>o-g 02 rad</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>967845</td>\n",
       "      <td>133535</td>\n",
       "      <td>53464</td>\n",
       "      <td>9975.6</td>\n",
       "      <td>9975.6</td>\n",
       "      <td>0.186590</td>\n",
       "      <td>0.186590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>o-g 02 rad</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>595782</td>\n",
       "      <td>232650</td>\n",
       "      <td>84615</td>\n",
       "      <td>5548.9</td>\n",
       "      <td>5548.9</td>\n",
       "      <td>0.065579</td>\n",
       "      <td>0.065579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>o-g 02 rad</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230502</td>\n",
       "      <td>83550</td>\n",
       "      <td>4615.8</td>\n",
       "      <td>4615.8</td>\n",
       "      <td>0.055245</td>\n",
       "      <td>0.055245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>o-g 02 rad</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230114</td>\n",
       "      <td>83348</td>\n",
       "      <td>4618.6</td>\n",
       "      <td>4618.6</td>\n",
       "      <td>0.055413</td>\n",
       "      <td>0.055413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>o-g 03 rad</td>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>975900</td>\n",
       "      <td>106474</td>\n",
       "      <td>62051</td>\n",
       "      <td>13062.0</td>\n",
       "      <td>13062.0</td>\n",
       "      <td>0.210510</td>\n",
       "      <td>0.210510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>o-g 03 rad</td>\n",
       "      <td>metop-c</td>\n",
       "      <td>amsua</td>\n",
       "      <td>967845</td>\n",
       "      <td>133535</td>\n",
       "      <td>53469</td>\n",
       "      <td>9192.7</td>\n",
       "      <td>9192.7</td>\n",
       "      <td>0.171930</td>\n",
       "      <td>0.171930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>o-g 03 rad</td>\n",
       "      <td>npp</td>\n",
       "      <td>atms</td>\n",
       "      <td>595782</td>\n",
       "      <td>232650</td>\n",
       "      <td>84702</td>\n",
       "      <td>4765.9</td>\n",
       "      <td>4765.9</td>\n",
       "      <td>0.056267</td>\n",
       "      <td>0.056267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>o-g 03 rad</td>\n",
       "      <td>n20</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230502</td>\n",
       "      <td>83806</td>\n",
       "      <td>3824.9</td>\n",
       "      <td>3824.9</td>\n",
       "      <td>0.045640</td>\n",
       "      <td>0.045640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>o-g 03 rad</td>\n",
       "      <td>n21</td>\n",
       "      <td>atms</td>\n",
       "      <td>605880</td>\n",
       "      <td>230114</td>\n",
       "      <td>83480</td>\n",
       "      <td>3963.5</td>\n",
       "      <td>3963.5</td>\n",
       "      <td>0.047478</td>\n",
       "      <td>0.047478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            it      sat   type    read    keep  assim  penalty  qcpnlty  \\\n",
       "0   o-g 01 rad  metop-b  amsua  975900  106474  61860  22250.0  22250.0   \n",
       "1   o-g 01 rad  metop-c  amsua  967845  133535  53372  16161.0  16161.0   \n",
       "2   o-g 01 rad      npp   atms  595782  232650  84144  11250.0  11250.0   \n",
       "3   o-g 01 rad      n20   atms  605880  230502  83526  10844.0  10844.0   \n",
       "4   o-g 01 rad      n21   atms  605880  230114  83207  10850.0  10850.0   \n",
       "5   o-g 02 rad  metop-b  amsua  975900  106474  62046  14278.0  14278.0   \n",
       "6   o-g 02 rad  metop-c  amsua  967845  133535  53464   9975.6   9975.6   \n",
       "7   o-g 02 rad      npp   atms  595782  232650  84615   5548.9   5548.9   \n",
       "8   o-g 02 rad      n20   atms  605880  230502  83550   4615.8   4615.8   \n",
       "9   o-g 02 rad      n21   atms  605880  230114  83348   4618.6   4618.6   \n",
       "10  o-g 03 rad  metop-b  amsua  975900  106474  62051  13062.0  13062.0   \n",
       "11  o-g 03 rad  metop-c  amsua  967845  133535  53469   9192.7   9192.7   \n",
       "12  o-g 03 rad      npp   atms  595782  232650  84702   4765.9   4765.9   \n",
       "13  o-g 03 rad      n20   atms  605880  230502  83806   3824.9   3824.9   \n",
       "14  o-g 03 rad      n21   atms  605880  230114  83480   3963.5   3963.5   \n",
       "\n",
       "        cpen    qccpen  \n",
       "0   0.359690  0.359690  \n",
       "1   0.302790  0.302790  \n",
       "2   0.133700  0.133700  \n",
       "3   0.129830  0.129830  \n",
       "4   0.130400  0.130400  \n",
       "5   0.230120  0.230120  \n",
       "6   0.186590  0.186590  \n",
       "7   0.065579  0.065579  \n",
       "8   0.055245  0.055245  \n",
       "9   0.055413  0.055413  \n",
       "10  0.210510  0.210510  \n",
       "11  0.171930  0.171930  \n",
       "12  0.056267  0.056267  \n",
       "13  0.045640  0.045640  \n",
       "14  0.047478  0.047478  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidx = 0\n",
    "tab = 2\n",
    "Self[tidx][tab]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e11f09d8-90b6-4a11-8ea1-fe1e38dfce19",
   "metadata": {},
   "source": [
    "A instrução **pandas.IndexSlice** pode ser utilizada para trabalhar com os multi-índices das tabelas. A seguir um exemplos, utilizando a instrução `.loc` com `pd.IndexSlice` do pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d666980e-3d9a-45ea-ab03-8a6bcc185be2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data =  2024030100\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sat</th>\n",
       "      <th>type</th>\n",
       "      <th>penalty</th>\n",
       "      <th>nobs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>n15</td>\n",
       "      <td>amsua</td>\n",
       "      <td>38637.231417</td>\n",
       "      <td>8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>n18</td>\n",
       "      <td>amsua</td>\n",
       "      <td>64181.464177</td>\n",
       "      <td>9076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>n19</td>\n",
       "      <td>hirs4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>n19</td>\n",
       "      <td>amsua</td>\n",
       "      <td>62841.750122</td>\n",
       "      <td>9217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>66132.389811</td>\n",
       "      <td>9033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>n15</td>\n",
       "      <td>amsua</td>\n",
       "      <td>15337.411502</td>\n",
       "      <td>8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>n18</td>\n",
       "      <td>amsua</td>\n",
       "      <td>32960.036424</td>\n",
       "      <td>9076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>n19</td>\n",
       "      <td>hirs4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>n19</td>\n",
       "      <td>amsua</td>\n",
       "      <td>24207.073446</td>\n",
       "      <td>9217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>24652.611498</td>\n",
       "      <td>9033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>n15</td>\n",
       "      <td>amsua</td>\n",
       "      <td>15133.217133</td>\n",
       "      <td>8583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>n18</td>\n",
       "      <td>amsua</td>\n",
       "      <td>27806.912701</td>\n",
       "      <td>9076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>n19</td>\n",
       "      <td>hirs4</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>n19</td>\n",
       "      <td>amsua</td>\n",
       "      <td>22265.953160</td>\n",
       "      <td>9217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>metop-b</td>\n",
       "      <td>amsua</td>\n",
       "      <td>23528.056263</td>\n",
       "      <td>9033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        sat   type       penalty  nobs\n",
       "0       n15  amsua  38637.231417  8583\n",
       "1       n18  amsua  64181.464177  9076\n",
       "2       n19  hirs4      0.000000  9330\n",
       "3       n19  amsua  62841.750122  9217\n",
       "4   metop-b  amsua  66132.389811  9033\n",
       "5       n15  amsua  15337.411502  8583\n",
       "6       n18  amsua  32960.036424  9076\n",
       "7       n19  hirs4      0.000000  9330\n",
       "8       n19  amsua  24207.073446  9217\n",
       "9   metop-b  amsua  24652.611498  9033\n",
       "10      n15  amsua  15133.217133  8583\n",
       "11      n18  amsua  27806.912701  9076\n",
       "12      n19  hirs4      0.000000  9330\n",
       "13      n19  amsua  22265.953160  9217\n",
       "14  metop-b  amsua  23528.056263  9033"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.IndexSlice\n",
    "tidx = 0\n",
    "print('data = ',dates[tidx])\n",
    "tab = 0\n",
    "\n",
    "Self[tidx][tab].loc[idx[:], idx['sat':'nobs']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee26f7d-aa37-4d7b-a7f7-5eef23d19727",
   "metadata": {},
   "source": [
    "Utilizando `mask` para trabalhar com os dados. Abaixo um exemplo para visualizar os dados da Tabela B (`tab=1`) do satélite `'n19'`, sensor `'amsua'` no estágio 3 (`it = 3`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a17dc943-dbff-4225-a322-26a537523601",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data =  2024030100\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>it</th>\n",
       "      <th>SN satinfo</th>\n",
       "      <th>nchan</th>\n",
       "      <th>type</th>\n",
       "      <th>sat</th>\n",
       "      <th>nobsused</th>\n",
       "      <th>nobstossed</th>\n",
       "      <th>varCH</th>\n",
       "      <th>biasBC</th>\n",
       "      <th>biasAC</th>\n",
       "      <th>penaltyCH</th>\n",
       "      <th>sqrt</th>\n",
       "      <th>STD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>3</td>\n",
       "      <td>1310</td>\n",
       "      <td>1</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>173</td>\n",
       "      <td>4</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-1.291502</td>\n",
       "      <td>-0.243553</td>\n",
       "      <td>0.047356</td>\n",
       "      <td>1.578102</td>\n",
       "      <td>1.559194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>3</td>\n",
       "      <td>1311</td>\n",
       "      <td>2</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>-1.347534</td>\n",
       "      <td>-0.232055</td>\n",
       "      <td>0.047241</td>\n",
       "      <td>1.590227</td>\n",
       "      <td>1.573205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>3</td>\n",
       "      <td>1312</td>\n",
       "      <td>3</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.074523</td>\n",
       "      <td>-0.045223</td>\n",
       "      <td>0.042064</td>\n",
       "      <td>1.143530</td>\n",
       "      <td>1.142635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>3</td>\n",
       "      <td>1313</td>\n",
       "      <td>4</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.174951</td>\n",
       "      <td>-0.064341</td>\n",
       "      <td>0.023566</td>\n",
       "      <td>0.356583</td>\n",
       "      <td>0.350730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>3</td>\n",
       "      <td>1314</td>\n",
       "      <td>5</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>175</td>\n",
       "      <td>7</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.688100</td>\n",
       "      <td>-0.057429</td>\n",
       "      <td>0.082893</td>\n",
       "      <td>0.277861</td>\n",
       "      <td>0.271862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>3</td>\n",
       "      <td>1315</td>\n",
       "      <td>6</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>8474</td>\n",
       "      <td>8</td>\n",
       "      <td>0.23</td>\n",
       "      <td>-0.906306</td>\n",
       "      <td>-0.016063</td>\n",
       "      <td>0.467666</td>\n",
       "      <td>0.174504</td>\n",
       "      <td>0.173763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>3</td>\n",
       "      <td>1316</td>\n",
       "      <td>7</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>8756</td>\n",
       "      <td>461</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-1.081586</td>\n",
       "      <td>0.115483</td>\n",
       "      <td>1.649195</td>\n",
       "      <td>0.298300</td>\n",
       "      <td>0.275039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>3</td>\n",
       "      <td>1317</td>\n",
       "      <td>8</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>4392</td>\n",
       "      <td>4825</td>\n",
       "      <td>-0.25</td>\n",
       "      <td>0.154102</td>\n",
       "      <td>0.028087</td>\n",
       "      <td>2.546014</td>\n",
       "      <td>0.398975</td>\n",
       "      <td>0.397985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>3</td>\n",
       "      <td>1318</td>\n",
       "      <td>9</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9216</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.426717</td>\n",
       "      <td>-0.014139</td>\n",
       "      <td>0.427173</td>\n",
       "      <td>0.163397</td>\n",
       "      <td>0.162784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>3</td>\n",
       "      <td>1319</td>\n",
       "      <td>10</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9216</td>\n",
       "      <td>1</td>\n",
       "      <td>0.35</td>\n",
       "      <td>-0.201580</td>\n",
       "      <td>-0.005228</td>\n",
       "      <td>0.371297</td>\n",
       "      <td>0.213271</td>\n",
       "      <td>0.213207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>3</td>\n",
       "      <td>1320</td>\n",
       "      <td>11</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9216</td>\n",
       "      <td>1</td>\n",
       "      <td>0.40</td>\n",
       "      <td>-0.188389</td>\n",
       "      <td>-0.001604</td>\n",
       "      <td>0.359950</td>\n",
       "      <td>0.239993</td>\n",
       "      <td>0.239988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>3</td>\n",
       "      <td>1321</td>\n",
       "      <td>12</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9207</td>\n",
       "      <td>10</td>\n",
       "      <td>0.55</td>\n",
       "      <td>-0.078107</td>\n",
       "      <td>0.007496</td>\n",
       "      <td>0.399726</td>\n",
       "      <td>0.347799</td>\n",
       "      <td>0.347718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>3</td>\n",
       "      <td>1322</td>\n",
       "      <td>13</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9204</td>\n",
       "      <td>13</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.164054</td>\n",
       "      <td>0.035962</td>\n",
       "      <td>0.373340</td>\n",
       "      <td>0.489258</td>\n",
       "      <td>0.487934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>3</td>\n",
       "      <td>1323</td>\n",
       "      <td>14</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>9207</td>\n",
       "      <td>10</td>\n",
       "      <td>4.00</td>\n",
       "      <td>-0.121716</td>\n",
       "      <td>-0.121716</td>\n",
       "      <td>0.050156</td>\n",
       "      <td>0.899785</td>\n",
       "      <td>0.891515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>3</td>\n",
       "      <td>1324</td>\n",
       "      <td>15</td>\n",
       "      <td>amsua</td>\n",
       "      <td>n19</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.892989</td>\n",
       "      <td>-0.033651</td>\n",
       "      <td>0.035532</td>\n",
       "      <td>1.300533</td>\n",
       "      <td>1.300097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     it  SN satinfo  nchan   type  sat  nobsused  nobstossed  varCH    biasBC  \\\n",
       "181   3        1310      1  amsua  n19       173           4   2.50 -1.291502   \n",
       "182   3        1311      2  amsua  n19       175           0   2.20 -1.347534   \n",
       "183   3        1312      3  amsua  n19       175           0   2.00  1.074523   \n",
       "184   3        1313      4  amsua  n19       175           0   0.55  0.174951   \n",
       "185   3        1314      5  amsua  n19       175           7   0.30 -0.688100   \n",
       "186   3        1315      6  amsua  n19      8474           8   0.23 -0.906306   \n",
       "187   3        1316      7  amsua  n19      8756         461  -0.23 -1.081586   \n",
       "188   3        1317      8  amsua  n19      4392        4825  -0.25  0.154102   \n",
       "189   3        1318      9  amsua  n19      9216           1   0.25 -0.426717   \n",
       "190   3        1319     10  amsua  n19      9216           1   0.35 -0.201580   \n",
       "191   3        1320     11  amsua  n19      9216           1   0.40 -0.188389   \n",
       "192   3        1321     12  amsua  n19      9207          10   0.55 -0.078107   \n",
       "193   3        1322     13  amsua  n19      9204          13   0.80  0.164054   \n",
       "194   3        1323     14  amsua  n19      9207          10   4.00 -0.121716   \n",
       "195   3        1324     15  amsua  n19       175           0   3.50  0.892989   \n",
       "\n",
       "       biasAC  penaltyCH      sqrt       STD  \n",
       "181 -0.243553   0.047356  1.578102  1.559194  \n",
       "182 -0.232055   0.047241  1.590227  1.573205  \n",
       "183 -0.045223   0.042064  1.143530  1.142635  \n",
       "184 -0.064341   0.023566  0.356583  0.350730  \n",
       "185 -0.057429   0.082893  0.277861  0.271862  \n",
       "186 -0.016063   0.467666  0.174504  0.173763  \n",
       "187  0.115483   1.649195  0.298300  0.275039  \n",
       "188  0.028087   2.546014  0.398975  0.397985  \n",
       "189 -0.014139   0.427173  0.163397  0.162784  \n",
       "190 -0.005228   0.371297  0.213271  0.213207  \n",
       "191 -0.001604   0.359950  0.239993  0.239988  \n",
       "192  0.007496   0.399726  0.347799  0.347718  \n",
       "193  0.035962   0.373340  0.489258  0.487934  \n",
       "194 -0.121716   0.050156  0.899785  0.891515  \n",
       "195 -0.033651   0.035532  1.300533  1.300097  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = pd.IndexSlice\n",
    "tidx = 0\n",
    "print('data = ',dates[tidx])\n",
    "tab = 1\n",
    "\n",
    "mask = \"(sat == 'n19' & type == 'amsua') and it == 3\"\n",
    "Self[tidx][tab].query(mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ba67a4-a566-4b17-85e9-bfaeb625bc06",
   "metadata": {},
   "source": [
    "## Plote fort.207\n",
    "\n",
    "A seguir é apresentada uma função elaborada na classe **plot_diag** do readDiag para visualização e análise das informações presentes na Tabela B do arquivo **fort.207**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "916a0be1-0751-4f05-b997-4ec6d62b6009",
   "metadata": {},
   "source": [
    "1. `time_series_fort207(self, Type=None, Sat=None, it=None, dateIni=None, dateFin=None, nHour=\"06\", mask=None, channel=None, Clean=None):` esta função fornece gráficos e diagramas (a depender dos valores da variável `channel`) das estatíticas disponíveis na Tabela B (`tab = 1`).  Caso o usuário faça `channel = None`, seis diagramas referentes as variáveis `nobsused` (nº de obs utilizadas), `nobstossed` (nº de obs descartadas), `biasBC` (obs - guess antes da correção de viés), `biasAC` (obs - guess após a correção de viés), `penaltCH` (penalidade do canal) e `STD` (desvio padrão) são gerados, sendo no eixo vertical a indicação dos canais e no eixo horizontal a indicação do tempo (date). O usuário também pode gerar os seis diagramas considerando uma lista de canais pré-definida na forma de um array (exemplo: `channel = [1, 2, 3, 5]`). Uma última opção é gerar dois gráficos, onde o 1º contém os valores de `biasBC`, `biasAC` e o desvio padrão `STD` e o segundo gráfico contém os valores de `nobsused` e `nobstossed`, ambos quando `channel` recebe um valor específico de canal (exemplo: `channel = 13`). Observe que previamente é necessário estabelecer o tipo de sensor (`Type`), o satélite (`Sat`) e o estágio `it`. Além disso, é necessário definir a máscara `mask` em concordância com o sensor e satélite estabelecidos.\n",
    "\n",
    "Abaixo segue um exemplo para `channel = None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ce022d2f-7ff2-4543-984a-0d5addf96bc4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " =============================================================================================================\n",
      "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15] 0\n",
      "\n",
      "mask =  (type == 'amsua')\n",
      "\n",
      "Stage it:  3\n",
      "\n",
      "Table:  1\n",
      "\n",
      " Preparing data for: channels2025-10-01:12\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
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      " Preparing data for: channels2025-10-01:18\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-26:18\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-27:00\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-27:06\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-27:12\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-27:18\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-28:00\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-28:06\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-28:12\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-28:18\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-29:00\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-29:06\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-29:12\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-29:18\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-30:00\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-30:06\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-30:12\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      " Preparing data for: channels2025-11-30:18\n",
      " channels:  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      "channels =  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      " Calculating for 2025-10-01:12\n",
      " Calculating for 2025-10-01:18\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Calculating for 2025-10-02:00\n",
      " Calculating for 2025-10-02:06\n",
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      " Calculating for 2025-11-27:00\n",
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      " Calculating for 2025-11-28:00\n",
      " Calculating for 2025-11-28:06\n",
      " Calculating for 2025-11-28:12\n",
      " Calculating for 2025-11-28:18\n",
      " Calculating for 2025-11-29:00\n",
      " Calculating for 2025-11-29:06\n",
      " Calculating for 2025-11-29:12\n",
      " Calculating for 2025-11-29:18\n",
      " Calculating for 2025-11-30:00\n",
      " Calculating for 2025-11-30:06\n",
      " Calculating for 2025-11-30:12\n",
      " Calculating for 2025-11-30:18\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      " Making Graphics...\n",
      " Done!\n",
      "\n"
     ]
    },
    {
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       "   136],\n",
       "  [172,\n",
       "   9022,\n",
       "   9005,\n",
       "   8984,\n",
       "   9048,\n",
       "   9047,\n",
       "   9045,\n",
       "   9042,\n",
       "   9044,\n",
       "   8599,\n",
       "   172,\n",
       "   172,\n",
       "   169,\n",
       "   172,\n",
       "   169],\n",
       "  [145,\n",
       "   9037,\n",
       "   9058,\n",
       "   9034,\n",
       "   9073,\n",
       "   9075,\n",
       "   9075,\n",
       "   9072,\n",
       "   9062,\n",
       "   8750,\n",
       "   145,\n",
       "   145,\n",
       "   142,\n",
       "   145,\n",
       "   143],\n",
       "  [143,\n",
       "   9050,\n",
       "   9025,\n",
       "   9014,\n",
       "   9059,\n",
       "   9059,\n",
       "   9053,\n",
       "   9059,\n",
       "   9055,\n",
       "   8710,\n",
       "   143,\n",
       "   143,\n",
       "   141,\n",
       "   143,\n",
       "   139],\n",
       "  [155,\n",
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       "   8990,\n",
       "   8977,\n",
       "   9011,\n",
       "   9013,\n",
       "   9011,\n",
       "   9007,\n",
       "   8989,\n",
       "   8675,\n",
       "   155,\n",
       "   155,\n",
       "   154,\n",
       "   155,\n",
       "   153],\n",
       "  [150,\n",
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       "   9000,\n",
       "   8989,\n",
       "   9022,\n",
       "   9019,\n",
       "   9015,\n",
       "   9018,\n",
       "   9016,\n",
       "   8556,\n",
       "   150,\n",
       "   150,\n",
       "   147,\n",
       "   149,\n",
       "   146],\n",
       "  [125,\n",
       "   7945,\n",
       "   7944,\n",
       "   7942,\n",
       "   7986,\n",
       "   7986,\n",
       "   7986,\n",
       "   7980,\n",
       "   7977,\n",
       "   8689,\n",
       "   125,\n",
       "   125,\n",
       "   121,\n",
       "   125,\n",
       "   124],\n",
       "  [146,\n",
       "   8998,\n",
       "   8989,\n",
       "   8986,\n",
       "   9022,\n",
       "   9024,\n",
       "   9023,\n",
       "   9021,\n",
       "   8990,\n",
       "   8585,\n",
       "   146,\n",
       "   146,\n",
       "   143,\n",
       "   146,\n",
       "   140],\n",
       "  [137,\n",
       "   8797,\n",
       "   8825,\n",
       "   8806,\n",
       "   8846,\n",
       "   8847,\n",
       "   8843,\n",
       "   8841,\n",
       "   8835,\n",
       "   8486,\n",
       "   137,\n",
       "   137,\n",
       "   136,\n",
       "   137,\n",
       "   131],\n",
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       "   8896,\n",
       "   8899,\n",
       "   8880,\n",
       "   8923,\n",
       "   8921,\n",
       "   8923,\n",
       "   8918,\n",
       "   8915,\n",
       "   8558,\n",
       "   89,\n",
       "   89,\n",
       "   87,\n",
       "   89,\n",
       "   87],\n",
       "  [138,\n",
       "   8995,\n",
       "   8976,\n",
       "   8942,\n",
       "   9008,\n",
       "   9008,\n",
       "   9007,\n",
       "   9003,\n",
       "   8996,\n",
       "   8712,\n",
       "   138,\n",
       "   138,\n",
       "   136,\n",
       "   138,\n",
       "   138],\n",
       "  [109,\n",
       "   9020,\n",
       "   9039,\n",
       "   9005,\n",
       "   9052,\n",
       "   9052,\n",
       "   9046,\n",
       "   9047,\n",
       "   9041,\n",
       "   8754,\n",
       "   109,\n",
       "   109,\n",
       "   105,\n",
       "   109,\n",
       "   103],\n",
       "  [163,\n",
       "   8951,\n",
       "   8967,\n",
       "   8958,\n",
       "   8994,\n",
       "   8995,\n",
       "   8994,\n",
       "   8991,\n",
       "   8990,\n",
       "   8690,\n",
       "   163,\n",
       "   163,\n",
       "   159,\n",
       "   163,\n",
       "   157],\n",
       "  [101,\n",
       "   9018,\n",
       "   9014,\n",
       "   8994,\n",
       "   9038,\n",
       "   9041,\n",
       "   9032,\n",
       "   9028,\n",
       "   9033,\n",
       "   8650,\n",
       "   101,\n",
       "   101,\n",
       "   99,\n",
       "   101,\n",
       "   98],\n",
       "  [161,\n",
       "   9039,\n",
       "   9043,\n",
       "   9015,\n",
       "   9057,\n",
       "   9057,\n",
       "   9057,\n",
       "   9053,\n",
       "   9051,\n",
       "   8689,\n",
       "   161,\n",
       "   161,\n",
       "   156,\n",
       "   161,\n",
       "   154],\n",
       "  [105,\n",
       "   9051,\n",
       "   9041,\n",
       "   9035,\n",
       "   9069,\n",
       "   9069,\n",
       "   9061,\n",
       "   9062,\n",
       "   9056,\n",
       "   8563,\n",
       "   105,\n",
       "   105,\n",
       "   105,\n",
       "   105,\n",
       "   100],\n",
       "  [155,\n",
       "   8950,\n",
       "   8965,\n",
       "   8935,\n",
       "   8993,\n",
       "   8992,\n",
       "   8990,\n",
       "   8990,\n",
       "   8980,\n",
       "   8730,\n",
       "   155,\n",
       "   155,\n",
       "   151,\n",
       "   155,\n",
       "   148],\n",
       "  [114,\n",
       "   8936,\n",
       "   8934,\n",
       "   8909,\n",
       "   8959,\n",
       "   8965,\n",
       "   8958,\n",
       "   8960,\n",
       "   8960,\n",
       "   8485,\n",
       "   114,\n",
       "   114,\n",
       "   113,\n",
       "   114,\n",
       "   112],\n",
       "  [133,\n",
       "   8862,\n",
       "   8860,\n",
       "   8837,\n",
       "   8897,\n",
       "   8897,\n",
       "   8896,\n",
       "   8890,\n",
       "   8886,\n",
       "   8448,\n",
       "   133,\n",
       "   133,\n",
       "   131,\n",
       "   132,\n",
       "   128],\n",
       "  [124,\n",
       "   8908,\n",
       "   8920,\n",
       "   8903,\n",
       "   8943,\n",
       "   8943,\n",
       "   8942,\n",
       "   8934,\n",
       "   8933,\n",
       "   8648,\n",
       "   124,\n",
       "   124,\n",
       "   121,\n",
       "   124,\n",
       "   121],\n",
       "  [118,\n",
       "   8952,\n",
       "   8971,\n",
       "   8924,\n",
       "   8999,\n",
       "   8992,\n",
       "   8992,\n",
       "   8989,\n",
       "   8982,\n",
       "   8666,\n",
       "   118,\n",
       "   118,\n",
       "   115,\n",
       "   118,\n",
       "   117],\n",
       "  [158,\n",
       "   7814,\n",
       "   7815,\n",
       "   7802,\n",
       "   7850,\n",
       "   7846,\n",
       "   7847,\n",
       "   7843,\n",
       "   7841,\n",
       "   8555,\n",
       "   158,\n",
       "   158,\n",
       "   157,\n",
       "   158,\n",
       "   153],\n",
       "  [117,\n",
       "   9033,\n",
       "   9021,\n",
       "   9018,\n",
       "   9058,\n",
       "   9057,\n",
       "   9058,\n",
       "   9045,\n",
       "   9045,\n",
       "   8792,\n",
       "   117,\n",
       "   117,\n",
       "   115,\n",
       "   117,\n",
       "   116],\n",
       "  [157,\n",
       "   9041,\n",
       "   9052,\n",
       "   9011,\n",
       "   9057,\n",
       "   9060,\n",
       "   9058,\n",
       "   9051,\n",
       "   9055,\n",
       "   8693,\n",
       "   157,\n",
       "   157,\n",
       "   156,\n",
       "   157,\n",
       "   150],\n",
       "  [158,\n",
       "   8965,\n",
       "   8987,\n",
       "   8957,\n",
       "   9003,\n",
       "   9004,\n",
       "   8998,\n",
       "   8997,\n",
       "   8987,\n",
       "   8726,\n",
       "   158,\n",
       "   158,\n",
       "   153,\n",
       "   158,\n",
       "   151],\n",
       "  [112,\n",
       "   9001,\n",
       "   8989,\n",
       "   8964,\n",
       "   9013,\n",
       "   9015,\n",
       "   9009,\n",
       "   9010,\n",
       "   9009,\n",
       "   8685,\n",
       "   112,\n",
       "   112,\n",
       "   110,\n",
       "   112,\n",
       "   109],\n",
       "  [107,\n",
       "   9043,\n",
       "   9032,\n",
       "   8988,\n",
       "   9056,\n",
       "   9058,\n",
       "   9058,\n",
       "   9049,\n",
       "   9045,\n",
       "   8689,\n",
       "   107,\n",
       "   107,\n",
       "   100,\n",
       "   107,\n",
       "   103],\n",
       "  [132,\n",
       "   8901,\n",
       "   8896,\n",
       "   8900,\n",
       "   8931,\n",
       "   8932,\n",
       "   8931,\n",
       "   8919,\n",
       "   8918,\n",
       "   8599,\n",
       "   132,\n",
       "   132,\n",
       "   131,\n",
       "   132,\n",
       "   127],\n",
       "  [164,\n",
       "   8770,\n",
       "   8810,\n",
       "   8791,\n",
       "   8821,\n",
       "   8821,\n",
       "   8818,\n",
       "   8814,\n",
       "   8812,\n",
       "   8524,\n",
       "   164,\n",
       "   164,\n",
       "   164,\n",
       "   164,\n",
       "   161],\n",
       "  [92,\n",
       "   8935,\n",
       "   8914,\n",
       "   8918,\n",
       "   8946,\n",
       "   8947,\n",
       "   8942,\n",
       "   8940,\n",
       "   8938,\n",
       "   8633,\n",
       "   92,\n",
       "   92,\n",
       "   90,\n",
       "   92,\n",
       "   91],\n",
       "  [144,\n",
       "   9039,\n",
       "   9051,\n",
       "   9012,\n",
       "   9067,\n",
       "   9067,\n",
       "   9066,\n",
       "   9061,\n",
       "   9062,\n",
       "   8714,\n",
       "   144,\n",
       "   144,\n",
       "   141,\n",
       "   144,\n",
       "   142],\n",
       "  [118,\n",
       "   9022,\n",
       "   9034,\n",
       "   9008,\n",
       "   9042,\n",
       "   9046,\n",
       "   9043,\n",
       "   9047,\n",
       "   9035,\n",
       "   8718,\n",
       "   118,\n",
       "   118,\n",
       "   116,\n",
       "   118,\n",
       "   116],\n",
       "  [169,\n",
       "   8973,\n",
       "   8991,\n",
       "   8934,\n",
       "   9013,\n",
       "   9007,\n",
       "   9005,\n",
       "   9009,\n",
       "   9004,\n",
       "   8728,\n",
       "   169,\n",
       "   169,\n",
       "   163,\n",
       "   169,\n",
       "   157],\n",
       "  [68,\n",
       "   8996,\n",
       "   8987,\n",
       "   8986,\n",
       "   9020,\n",
       "   9024,\n",
       "   9019,\n",
       "   9014,\n",
       "   9017,\n",
       "   6778,\n",
       "   68,\n",
       "   68,\n",
       "   66,\n",
       "   68,\n",
       "   65],\n",
       "  [134,\n",
       "   8995,\n",
       "   9015,\n",
       "   9005,\n",
       "   9049,\n",
       "   9049,\n",
       "   9049,\n",
       "   9042,\n",
       "   9042,\n",
       "   8660,\n",
       "   134,\n",
       "   134,\n",
       "   130,\n",
       "   134,\n",
       "   133],\n",
       "  [104,\n",
       "   9036,\n",
       "   9031,\n",
       "   9028,\n",
       "   9049,\n",
       "   9054,\n",
       "   9048,\n",
       "   9048,\n",
       "   9045,\n",
       "   8624,\n",
       "   104,\n",
       "   104,\n",
       "   103,\n",
       "   104,\n",
       "   98],\n",
       "  [144,\n",
       "   8933,\n",
       "   8909,\n",
       "   8927,\n",
       "   8951,\n",
       "   8947,\n",
       "   8949,\n",
       "   8947,\n",
       "   8946,\n",
       "   8638,\n",
       "   144,\n",
       "   144,\n",
       "   141,\n",
       "   144,\n",
       "   140],\n",
       "  [123,\n",
       "   8828,\n",
       "   8842,\n",
       "   8828,\n",
       "   8872,\n",
       "   8874,\n",
       "   8873,\n",
       "   8866,\n",
       "   8869,\n",
       "   8468,\n",
       "   123,\n",
       "   123,\n",
       "   119,\n",
       "   123,\n",
       "   118],\n",
       "  [140,\n",
       "   8863,\n",
       "   8877,\n",
       "   8880,\n",
       "   8910,\n",
       "   8910,\n",
       "   8908,\n",
       "   8905,\n",
       "   8901,\n",
       "   8609,\n",
       "   140,\n",
       "   140,\n",
       "   138,\n",
       "   140,\n",
       "   131],\n",
       "  [121,\n",
       "   8946,\n",
       "   8975,\n",
       "   8949,\n",
       "   9004,\n",
       "   9002,\n",
       "   9004,\n",
       "   8996,\n",
       "   8997,\n",
       "   8660,\n",
       "   121,\n",
       "   121,\n",
       "   118,\n",
       "   121,\n",
       "   117],\n",
       "  [93,\n",
       "   9036,\n",
       "   9051,\n",
       "   9021,\n",
       "   9068,\n",
       "   9070,\n",
       "   9070,\n",
       "   9066,\n",
       "   9056,\n",
       "   5048,\n",
       "   93,\n",
       "   93,\n",
       "   92,\n",
       "   93,\n",
       "   90],\n",
       "  [121,\n",
       "   8988,\n",
       "   9015,\n",
       "   8967,\n",
       "   9033,\n",
       "   9034,\n",
       "   9034,\n",
       "   9030,\n",
       "   9031,\n",
       "   7843,\n",
       "   121,\n",
       "   121,\n",
       "   120,\n",
       "   121,\n",
       "   117],\n",
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       "   9050,\n",
       "   9041,\n",
       "   8990,\n",
       "   9064,\n",
       "   9071,\n",
       "   9071,\n",
       "   9067,\n",
       "   9061,\n",
       "   6651,\n",
       "   91,\n",
       "   91,\n",
       "   87,\n",
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       "   88],\n",
       "  [139,\n",
       "   9029,\n",
       "   9026,\n",
       "   9022,\n",
       "   9048,\n",
       "   9049,\n",
       "   9049,\n",
       "   9055,\n",
       "   9047,\n",
       "   8809,\n",
       "   139,\n",
       "   139,\n",
       "   136,\n",
       "   139,\n",
       "   133],\n",
       "  [171,\n",
       "   8950,\n",
       "   8958,\n",
       "   8940,\n",
       "   8985,\n",
       "   8985,\n",
       "   8984,\n",
       "   8978,\n",
       "   8964,\n",
       "   8740,\n",
       "   171,\n",
       "   171,\n",
       "   167,\n",
       "   171,\n",
       "   163],\n",
       "  [104,\n",
       "   8987,\n",
       "   8981,\n",
       "   8979,\n",
       "   9007,\n",
       "   9013,\n",
       "   9009,\n",
       "   9009,\n",
       "   9005,\n",
       "   8632,\n",
       "   104,\n",
       "   104,\n",
       "   103,\n",
       "   104,\n",
       "   104],\n",
       "  [115,\n",
       "   8992,\n",
       "   8986,\n",
       "   8994,\n",
       "   9020,\n",
       "   9021,\n",
       "   9020,\n",
       "   9020,\n",
       "   9015,\n",
       "   8573,\n",
       "   115,\n",
       "   115,\n",
       "   113,\n",
       "   115,\n",
       "   110],\n",
       "  [147,\n",
       "   8865,\n",
       "   8860,\n",
       "   8832,\n",
       "   8896,\n",
       "   8896,\n",
       "   8894,\n",
       "   8893,\n",
       "   8885,\n",
       "   8617,\n",
       "   147,\n",
       "   147,\n",
       "   146,\n",
       "   147,\n",
       "   139],\n",
       "  [172,\n",
       "   8878,\n",
       "   8834,\n",
       "   8832,\n",
       "   8894,\n",
       "   8892,\n",
       "   8892,\n",
       "   8891,\n",
       "   8890,\n",
       "   8653,\n",
       "   172,\n",
       "   172,\n",
       "   170,\n",
       "   172,\n",
       "   166],\n",
       "  [93,\n",
       "   8948,\n",
       "   8955,\n",
       "   8955,\n",
       "   8987,\n",
       "   8991,\n",
       "   8991,\n",
       "   8991,\n",
       "   8984,\n",
       "   8713,\n",
       "   93,\n",
       "   93,\n",
       "   92,\n",
       "   93,\n",
       "   91],\n",
       "  [132,\n",
       "   9023,\n",
       "   9013,\n",
       "   9005,\n",
       "   9050,\n",
       "   9052,\n",
       "   9051,\n",
       "   9049,\n",
       "   9047,\n",
       "   8696,\n",
       "   132,\n",
       "   132,\n",
       "   126,\n",
       "   132,\n",
       "   126],\n",
       "  [94,\n",
       "   9021,\n",
       "   9020,\n",
       "   9011,\n",
       "   9053,\n",
       "   9053,\n",
       "   9052,\n",
       "   9053,\n",
       "   9048,\n",
       "   8801,\n",
       "   94,\n",
       "   94,\n",
       "   94,\n",
       "   94,\n",
       "   90],\n",
       "  [185,\n",
       "   8950,\n",
       "   8974,\n",
       "   8961,\n",
       "   9012,\n",
       "   9012,\n",
       "   9012,\n",
       "   9009,\n",
       "   9005,\n",
       "   8719,\n",
       "   185,\n",
       "   185,\n",
       "   182,\n",
       "   185,\n",
       "   177],\n",
       "  [119,\n",
       "   8995,\n",
       "   8975,\n",
       "   8960,\n",
       "   9020,\n",
       "   9022,\n",
       "   9022,\n",
       "   9019,\n",
       "   9015,\n",
       "   8693,\n",
       "   119,\n",
       "   119,\n",
       "   116,\n",
       "   119,\n",
       "   116],\n",
       "  [132,\n",
       "   9033,\n",
       "   9010,\n",
       "   9005,\n",
       "   9056,\n",
       "   9056,\n",
       "   9056,\n",
       "   9051,\n",
       "   9048,\n",
       "   8707,\n",
       "   132,\n",
       "   132,\n",
       "   130,\n",
       "   132,\n",
       "   131],\n",
       "  [92,\n",
       "   9001,\n",
       "   9011,\n",
       "   9001,\n",
       "   9040,\n",
       "   9040,\n",
       "   9039,\n",
       "   9038,\n",
       "   9034,\n",
       "   8675,\n",
       "   92,\n",
       "   92,\n",
       "   90,\n",
       "   92,\n",
       "   88],\n",
       "  [126,\n",
       "   8899,\n",
       "   8899,\n",
       "   8862,\n",
       "   8911,\n",
       "   8914,\n",
       "   8911,\n",
       "   8912,\n",
       "   8905,\n",
       "   8568,\n",
       "   126,\n",
       "   126,\n",
       "   123,\n",
       "   126,\n",
       "   123],\n",
       "  [129,\n",
       "   8807,\n",
       "   8820,\n",
       "   8817,\n",
       "   8853,\n",
       "   8854,\n",
       "   8854,\n",
       "   8852,\n",
       "   8848,\n",
       "   8553,\n",
       "   129,\n",
       "   129,\n",
       "   125,\n",
       "   129,\n",
       "   127],\n",
       "  [136,\n",
       "   8927,\n",
       "   8919,\n",
       "   8921,\n",
       "   8964,\n",
       "   8966,\n",
       "   8965,\n",
       "   8962,\n",
       "   8958,\n",
       "   8658,\n",
       "   136,\n",
       "   136,\n",
       "   135,\n",
       "   136,\n",
       "   134],\n",
       "  [142,\n",
       "   9038,\n",
       "   9037,\n",
       "   9008,\n",
       "   9062,\n",
       "   9061,\n",
       "   9060,\n",
       "   9058,\n",
       "   9059,\n",
       "   8707,\n",
       "   142,\n",
       "   142,\n",
       "   137,\n",
       "   142,\n",
       "   139],\n",
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       "   8990,\n",
       "   8966,\n",
       "   8961,\n",
       "   9016,\n",
       "   9018,\n",
       "   9017,\n",
       "   9014,\n",
       "   9007,\n",
       "   8644,\n",
       "   115,\n",
       "   115,\n",
       "   114,\n",
       "   115,\n",
       "   109],\n",
       "  [127,\n",
       "   8981,\n",
       "   8970,\n",
       "   8973,\n",
       "   9014,\n",
       "   9014,\n",
       "   9014,\n",
       "   9010,\n",
       "   9012,\n",
       "   8647,\n",
       "   127,\n",
       "   127,\n",
       "   125,\n",
       "   127,\n",
       "   123],\n",
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       "   8996,\n",
       "   9012,\n",
       "   8979,\n",
       "   9031,\n",
       "   9032,\n",
       "   9031,\n",
       "   9032,\n",
       "   9026,\n",
       "   8780,\n",
       "   119,\n",
       "   119,\n",
       "   116,\n",
       "   119,\n",
       "   114],\n",
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       "   9044,\n",
       "   9034,\n",
       "   9067,\n",
       "   9066,\n",
       "   9066,\n",
       "   9064,\n",
       "   9058,\n",
       "   8769,\n",
       "   136,\n",
       "   136,\n",
       "   134,\n",
       "   136,\n",
       "   125],\n",
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       "   8994,\n",
       "   8984,\n",
       "   8964,\n",
       "   9012,\n",
       "   9012,\n",
       "   9010,\n",
       "   9010,\n",
       "   8998,\n",
       "   8759,\n",
       "   149,\n",
       "   149,\n",
       "   146,\n",
       "   149,\n",
       "   145],\n",
       "  [81,\n",
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       "   8972,\n",
       "   8937,\n",
       "   8986,\n",
       "   8988,\n",
       "   8988,\n",
       "   8985,\n",
       "   8980,\n",
       "   8602,\n",
       "   81,\n",
       "   81,\n",
       "   78,\n",
       "   81,\n",
       "   81],\n",
       "  [92,\n",
       "   8912,\n",
       "   8886,\n",
       "   8909,\n",
       "   8933,\n",
       "   8937,\n",
       "   8937,\n",
       "   8934,\n",
       "   8926,\n",
       "   8561,\n",
       "   92,\n",
       "   92,\n",
       "   89,\n",
       "   92,\n",
       "   89],\n",
       "  [119,\n",
       "   8895,\n",
       "   8859,\n",
       "   8863,\n",
       "   8900,\n",
       "   8900,\n",
       "   8898,\n",
       "   8895,\n",
       "   8890,\n",
       "   8624,\n",
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       "   119,\n",
       "   119,\n",
       "   119,\n",
       "   113],\n",
       "  [167,\n",
       "   8907,\n",
       "   8930,\n",
       "   8906,\n",
       "   8937,\n",
       "   8937,\n",
       "   8936,\n",
       "   8931,\n",
       "   8928,\n",
       "   8733,\n",
       "   167,\n",
       "   167,\n",
       "   163,\n",
       "   167,\n",
       "   164],\n",
       "  [88,\n",
       "   8974,\n",
       "   8983,\n",
       "   8950,\n",
       "   9011,\n",
       "   9010,\n",
       "   9010,\n",
       "   9003,\n",
       "   9003,\n",
       "   8717,\n",
       "   88,\n",
       "   88,\n",
       "   87,\n",
       "   88,\n",
       "   87],\n",
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       "   9047,\n",
       "   9016,\n",
       "   9015,\n",
       "   9064,\n",
       "   9064,\n",
       "   9063,\n",
       "   9059,\n",
       "   9052,\n",
       "   8745,\n",
       "   114,\n",
       "   114,\n",
       "   110,\n",
       "   114,\n",
       "   106],\n",
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       "   9063,\n",
       "   9044,\n",
       "   9025,\n",
       "   9070,\n",
       "   9065,\n",
       "   9069,\n",
       "   9066,\n",
       "   9065,\n",
       "   6958,\n",
       "   75,\n",
       "   75,\n",
       "   75,\n",
       "   75,\n",
       "   74],\n",
       "  [151,\n",
       "   9034,\n",
       "   9042,\n",
       "   9011,\n",
       "   9065,\n",
       "   9065,\n",
       "   9064,\n",
       "   9063,\n",
       "   9059,\n",
       "   8710,\n",
       "   151,\n",
       "   151,\n",
       "   151,\n",
       "   151,\n",
       "   145],\n",
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       "   8985,\n",
       "   8981,\n",
       "   8952,\n",
       "   9017,\n",
       "   9018,\n",
       "   9018,\n",
       "   9017,\n",
       "   9011,\n",
       "   8629,\n",
       "   96,\n",
       "   96,\n",
       "   95,\n",
       "   96,\n",
       "   94],\n",
       "  [136,\n",
       "   9035,\n",
       "   9008,\n",
       "   9005,\n",
       "   9060,\n",
       "   9059,\n",
       "   9059,\n",
       "   9054,\n",
       "   9050,\n",
       "   8726,\n",
       "   136,\n",
       "   136,\n",
       "   133,\n",
       "   136,\n",
       "   130],\n",
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       "   8990,\n",
       "   9016,\n",
       "   8990,\n",
       "   9031,\n",
       "   9032,\n",
       "   9029,\n",
       "   9026,\n",
       "   9026,\n",
       "   8496,\n",
       "   105,\n",
       "   105,\n",
       "   104,\n",
       "   105,\n",
       "   102],\n",
       "  [120,\n",
       "   8824,\n",
       "   8841,\n",
       "   8824,\n",
       "   8876,\n",
       "   8876,\n",
       "   8876,\n",
       "   8870,\n",
       "   8863,\n",
       "   8561,\n",
       "   120,\n",
       "   120,\n",
       "   117,\n",
       "   120,\n",
       "   112],\n",
       "  [109,\n",
       "   8856,\n",
       "   8869,\n",
       "   8852,\n",
       "   8894,\n",
       "   8894,\n",
       "   8894,\n",
       "   8887,\n",
       "   8886,\n",
       "   8566,\n",
       "   109,\n",
       "   109,\n",
       "   109,\n",
       "   109,\n",
       "   105],\n",
       "  [130,\n",
       "   9021,\n",
       "   9014,\n",
       "   9000,\n",
       "   9048,\n",
       "   9048,\n",
       "   9047,\n",
       "   9046,\n",
       "   9041,\n",
       "   8775,\n",
       "   130,\n",
       "   130,\n",
       "   127,\n",
       "   130,\n",
       "   123],\n",
       "  [108,\n",
       "   9039,\n",
       "   9048,\n",
       "   8995,\n",
       "   9074,\n",
       "   9074,\n",
       "   9075,\n",
       "   9066,\n",
       "   9067,\n",
       "   8682,\n",
       "   108,\n",
       "   108,\n",
       "   108,\n",
       "   108,\n",
       "   100],\n",
       "  [135,\n",
       "   9018,\n",
       "   9027,\n",
       "   8999,\n",
       "   9047,\n",
       "   9047,\n",
       "   9046,\n",
       "   9045,\n",
       "   9028,\n",
       "   8757,\n",
       "   135,\n",
       "   135,\n",
       "   134,\n",
       "   135,\n",
       "   131],\n",
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       "   9004,\n",
       "   9008,\n",
       "   8990,\n",
       "   9021,\n",
       "   9022,\n",
       "   9020,\n",
       "   9016,\n",
       "   9018,\n",
       "   8621,\n",
       "   118,\n",
       "   118,\n",
       "   118,\n",
       "   118,\n",
       "   117],\n",
       "  [92,\n",
       "   9021,\n",
       "   9028,\n",
       "   9029,\n",
       "   9059,\n",
       "   9060,\n",
       "   9060,\n",
       "   9057,\n",
       "   9050,\n",
       "   8800,\n",
       "   92,\n",
       "   92,\n",
       "   92,\n",
       "   92,\n",
       "   90],\n",
       "  [109,\n",
       "   8327,\n",
       "   8315,\n",
       "   8316,\n",
       "   8344,\n",
       "   8343,\n",
       "   8348,\n",
       "   8342,\n",
       "   8345,\n",
       "   8437,\n",
       "   109,\n",
       "   109,\n",
       "   107,\n",
       "   109,\n",
       "   109],\n",
       "  [156,\n",
       "   8757,\n",
       "   8746,\n",
       "   8720,\n",
       "   8761,\n",
       "   8761,\n",
       "   8760,\n",
       "   8760,\n",
       "   8748,\n",
       "   8700,\n",
       "   156,\n",
       "   156,\n",
       "   155,\n",
       "   156,\n",
       "   150],\n",
       "  [85,\n",
       "   8932,\n",
       "   8956,\n",
       "   8926,\n",
       "   8981,\n",
       "   8984,\n",
       "   8985,\n",
       "   8983,\n",
       "   8979,\n",
       "   8531,\n",
       "   85,\n",
       "   85,\n",
       "   82,\n",
       "   85,\n",
       "   81],\n",
       "  [119,\n",
       "   8823,\n",
       "   8848,\n",
       "   8827,\n",
       "   8879,\n",
       "   8879,\n",
       "   8878,\n",
       "   8878,\n",
       "   8867,\n",
       "   8628,\n",
       "   119,\n",
       "   119,\n",
       "   119,\n",
       "   119,\n",
       "   112],\n",
       "  [118,\n",
       "   8922,\n",
       "   8910,\n",
       "   8901,\n",
       "   8943,\n",
       "   8943,\n",
       "   8943,\n",
       "   8942,\n",
       "   8933,\n",
       "   8694,\n",
       "   118,\n",
       "   118,\n",
       "   114,\n",
       "   118,\n",
       "   116],\n",
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       "   8994,\n",
       "   8983,\n",
       "   8980,\n",
       "   9013,\n",
       "   9011,\n",
       "   9013,\n",
       "   9008,\n",
       "   9004,\n",
       "   8695,\n",
       "   165,\n",
       "   165,\n",
       "   163,\n",
       "   165,\n",
       "   154],\n",
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       "   9012,\n",
       "   8974,\n",
       "   9040,\n",
       "   9043,\n",
       "   9043,\n",
       "   9036,\n",
       "   9037,\n",
       "   8598,\n",
       "   103,\n",
       "   103,\n",
       "   101,\n",
       "   103,\n",
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       "  [114,\n",
       "   8934,\n",
       "   8925,\n",
       "   8917,\n",
       "   8962,\n",
       "   8963,\n",
       "   8961,\n",
       "   8962,\n",
       "   8956,\n",
       "   8695,\n",
       "   114,\n",
       "   114,\n",
       "   112,\n",
       "   114,\n",
       "   112],\n",
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       "   9024,\n",
       "   9013,\n",
       "   8994,\n",
       "   9051,\n",
       "   9052,\n",
       "   9051,\n",
       "   9049,\n",
       "   9043,\n",
       "   8582,\n",
       "   76,\n",
       "   76,\n",
       "   76,\n",
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       "   76],\n",
       "  [117,\n",
       "   8961,\n",
       "   8981,\n",
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       "   9005,\n",
       "   9005,\n",
       "   9003,\n",
       "   8999,\n",
       "   8995,\n",
       "   8721,\n",
       "   117,\n",
       "   117,\n",
       "   115,\n",
       "   117,\n",
       "   113],\n",
       "  [100,\n",
       "   9003,\n",
       "   9005,\n",
       "   8984,\n",
       "   9029,\n",
       "   9030,\n",
       "   9024,\n",
       "   9023,\n",
       "   9024,\n",
       "   8599,\n",
       "   100,\n",
       "   100,\n",
       "   99,\n",
       "   100,\n",
       "   98],\n",
       "  [99,\n",
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       "   9002,\n",
       "   8996,\n",
       "   9045,\n",
       "   9046,\n",
       "   9046,\n",
       "   9042,\n",
       "   9031,\n",
       "   8670,\n",
       "   99,\n",
       "   99,\n",
       "   96,\n",
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       "  [107,\n",
       "   8919,\n",
       "   8943,\n",
       "   8916,\n",
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       "   8952,\n",
       "   8951,\n",
       "   8947,\n",
       "   8473,\n",
       "   107,\n",
       "   107,\n",
       "   104,\n",
       "   107,\n",
       "   106],\n",
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       "   8771,\n",
       "   8784,\n",
       "   8810,\n",
       "   8810,\n",
       "   8807,\n",
       "   8806,\n",
       "   8798,\n",
       "   8620,\n",
       "   124,\n",
       "   124,\n",
       "   122,\n",
       "   124,\n",
       "   119],\n",
       "  [99,\n",
       "   8885,\n",
       "   8883,\n",
       "   8845,\n",
       "   8909,\n",
       "   8912,\n",
       "   8911,\n",
       "   8908,\n",
       "   8908,\n",
       "   8625,\n",
       "   99,\n",
       "   99,\n",
       "   99,\n",
       "   99,\n",
       "   99],\n",
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       "   9022,\n",
       "   9027,\n",
       "   8999,\n",
       "   9052,\n",
       "   9052,\n",
       "   9052,\n",
       "   9049,\n",
       "   9036,\n",
       "   8730,\n",
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       "   91,\n",
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       "   91,\n",
       "   88],\n",
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       "   7930,\n",
       "   7928,\n",
       "   7915,\n",
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       "   7951,\n",
       "   7950,\n",
       "   7952,\n",
       "   8711,\n",
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       "   136,\n",
       "   133,\n",
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       "   135],\n",
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       "   8973,\n",
       "   8990,\n",
       "   8981,\n",
       "   9015,\n",
       "   9015,\n",
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       "   9009,\n",
       "   8741,\n",
       "   131,\n",
       "   131,\n",
       "   129,\n",
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       "   123],\n",
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       "   9015,\n",
       "   9034,\n",
       "   9002,\n",
       "   9057,\n",
       "   9058,\n",
       "   9056,\n",
       "   9053,\n",
       "   9053,\n",
       "   6415,\n",
       "   110,\n",
       "   110,\n",
       "   110,\n",
       "   110,\n",
       "   108],\n",
       "  [100,\n",
       "   8930,\n",
       "   8990,\n",
       "   8964,\n",
       "   9003,\n",
       "   9004,\n",
       "   9004,\n",
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       "   8755,\n",
       "   100,\n",
       "   100,\n",
       "   100,\n",
       "   100,\n",
       "   98],\n",
       "  [111,\n",
       "   9020,\n",
       "   9044,\n",
       "   8993,\n",
       "   9060,\n",
       "   9060,\n",
       "   9058,\n",
       "   9051,\n",
       "   9053,\n",
       "   8655,\n",
       "   111,\n",
       "   111,\n",
       "   111,\n",
       "   111,\n",
       "   108],\n",
       "  [139,\n",
       "   8919,\n",
       "   8932,\n",
       "   8923,\n",
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       "   8957,\n",
       "   8954,\n",
       "   8953,\n",
       "   8954,\n",
       "   8631,\n",
       "   139,\n",
       "   139,\n",
       "   136,\n",
       "   139,\n",
       "   134],\n",
       "  [88,\n",
       "   8830,\n",
       "   8841,\n",
       "   8806,\n",
       "   8883,\n",
       "   8882,\n",
       "   8881,\n",
       "   8882,\n",
       "   8874,\n",
       "   8419,\n",
       "   88,\n",
       "   88,\n",
       "   88,\n",
       "   88,\n",
       "   86],\n",
       "  [99,\n",
       "   8871,\n",
       "   8900,\n",
       "   8860,\n",
       "   8916,\n",
       "   8916,\n",
       "   8916,\n",
       "   8914,\n",
       "   8912,\n",
       "   8616,\n",
       "   99,\n",
       "   99,\n",
       "   97,\n",
       "   99,\n",
       "   96],\n",
       "  [102,\n",
       "   8996,\n",
       "   9000,\n",
       "   9004,\n",
       "   9021,\n",
       "   9022,\n",
       "   9018,\n",
       "   9019,\n",
       "   9014,\n",
       "   8738,\n",
       "   102,\n",
       "   102,\n",
       "   101,\n",
       "   102,\n",
       "   97],\n",
       "  [155,\n",
       "   8978,\n",
       "   8980,\n",
       "   8963,\n",
       "   8999,\n",
       "   8999,\n",
       "   8998,\n",
       "   8998,\n",
       "   8994,\n",
       "   8741,\n",
       "   155,\n",
       "   155,\n",
       "   152,\n",
       "   155,\n",
       "   150],\n",
       "  [95,\n",
       "   9028,\n",
       "   9018,\n",
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       "   9048,\n",
       "   9047,\n",
       "   9039,\n",
       "   9041,\n",
       "   8612,\n",
       "   95,\n",
       "   95,\n",
       "   95,\n",
       "   95,\n",
       "   94],\n",
       "  [119,\n",
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       "   9031,\n",
       "   8981,\n",
       "   9044,\n",
       "   9047,\n",
       "   9047,\n",
       "   9045,\n",
       "   9041,\n",
       "   8684,\n",
       "   119,\n",
       "   119,\n",
       "   117,\n",
       "   119,\n",
       "   113],\n",
       "  [63,\n",
       "   9030,\n",
       "   9048,\n",
       "   9048,\n",
       "   9070,\n",
       "   9071,\n",
       "   9070,\n",
       "   9067,\n",
       "   9066,\n",
       "   7265,\n",
       "   63,\n",
       "   63,\n",
       "   61,\n",
       "   63,\n",
       "   60],\n",
       "  [123,\n",
       "   8987,\n",
       "   8981,\n",
       "   8985,\n",
       "   9016,\n",
       "   9016,\n",
       "   9015,\n",
       "   9013,\n",
       "   9010,\n",
       "   8398,\n",
       "   123,\n",
       "   123,\n",
       "   123,\n",
       "   123,\n",
       "   115],\n",
       "  [48,\n",
       "   8994,\n",
       "   8996,\n",
       "   8982,\n",
       "   9033,\n",
       "   9033,\n",
       "   9032,\n",
       "   9029,\n",
       "   9025,\n",
       "   6308,\n",
       "   48,\n",
       "   48,\n",
       "   47,\n",
       "   48,\n",
       "   48],\n",
       "  [67,\n",
       "   8988,\n",
       "   8999,\n",
       "   8951,\n",
       "   9023,\n",
       "   9024,\n",
       "   9024,\n",
       "   9022,\n",
       "   9014,\n",
       "   8539,\n",
       "   67,\n",
       "   67,\n",
       "   66,\n",
       "   67,\n",
       "   64],\n",
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       "   8842,\n",
       "   8852,\n",
       "   8834,\n",
       "   8890,\n",
       "   8890,\n",
       "   8889,\n",
       "   8889,\n",
       "   8877,\n",
       "   8572,\n",
       "   100,\n",
       "   100,\n",
       "   100,\n",
       "   100,\n",
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       "  [120,\n",
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       "   8844,\n",
       "   8820,\n",
       "   8868,\n",
       "   8868,\n",
       "   8867,\n",
       "   8865,\n",
       "   8858,\n",
       "   8711,\n",
       "   120,\n",
       "   120,\n",
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       "   120,\n",
       "   115],\n",
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       "   9000,\n",
       "   8993,\n",
       "   9020,\n",
       "   9024,\n",
       "   9023,\n",
       "   9023,\n",
       "   9020,\n",
       "   8660,\n",
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       "   97,\n",
       "   96,\n",
       "   97,\n",
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       "   8999,\n",
       "   8991,\n",
       "   9049,\n",
       "   9050,\n",
       "   9050,\n",
       "   9045,\n",
       "   9045,\n",
       "   8792,\n",
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       "   106,\n",
       "   104,\n",
       "   106,\n",
       "   104],\n",
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       "   9021,\n",
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       "   9002,\n",
       "   9060,\n",
       "   9061,\n",
       "   9059,\n",
       "   9055,\n",
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       "   8704,\n",
       "   92,\n",
       "   92,\n",
       "   90,\n",
       "   92,\n",
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       "   8970,\n",
       "   8958,\n",
       "   8933,\n",
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       "   8994,\n",
       "   8993,\n",
       "   8986,\n",
       "   8982,\n",
       "   8751,\n",
       "   144,\n",
       "   144,\n",
       "   141,\n",
       "   144,\n",
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       "   9026,\n",
       "   9061,\n",
       "   9062,\n",
       "   9062,\n",
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       "   9054,\n",
       "   8705,\n",
       "   92,\n",
       "   92,\n",
       "   89,\n",
       "   92,\n",
       "   90],\n",
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       "   9062,\n",
       "   9060,\n",
       "   9023,\n",
       "   9076,\n",
       "   9079,\n",
       "   9078,\n",
       "   9075,\n",
       "   9073,\n",
       "   8762,\n",
       "   81,\n",
       "   81,\n",
       "   78,\n",
       "   81,\n",
       "   76],\n",
       "  [109,\n",
       "   9026,\n",
       "   9029,\n",
       "   9015,\n",
       "   9060,\n",
       "   9061,\n",
       "   9061,\n",
       "   9059,\n",
       "   9054,\n",
       "   8702,\n",
       "   109,\n",
       "   109,\n",
       "   107,\n",
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       "   104],\n",
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       "   8910,\n",
       "   8916,\n",
       "   8926,\n",
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       "   8952,\n",
       "   8951,\n",
       "   8948,\n",
       "   8951,\n",
       "   8570,\n",
       "   140,\n",
       "   140,\n",
       "   137,\n",
       "   140,\n",
       "   130],\n",
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       "   8823,\n",
       "   8838,\n",
       "   8816,\n",
       "   8847,\n",
       "   8849,\n",
       "   8850,\n",
       "   8845,\n",
       "   8843,\n",
       "   8570,\n",
       "   106,\n",
       "   106,\n",
       "   106,\n",
       "   106,\n",
       "   104],\n",
       "  [103,\n",
       "   8915,\n",
       "   8958,\n",
       "   8949,\n",
       "   8977,\n",
       "   8977,\n",
       "   8976,\n",
       "   8974,\n",
       "   8967,\n",
       "   8741,\n",
       "   103,\n",
       "   103,\n",
       "   99,\n",
       "   103,\n",
       "   98],\n",
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       "   9043,\n",
       "   9051,\n",
       "   9017,\n",
       "   9065,\n",
       "   9065,\n",
       "   9065,\n",
       "   9059,\n",
       "   9054,\n",
       "   8713,\n",
       "   77,\n",
       "   77,\n",
       "   74,\n",
       "   77,\n",
       "   76],\n",
       "  [152,\n",
       "   8962,\n",
       "   8975,\n",
       "   8970,\n",
       "   9008,\n",
       "   9008,\n",
       "   9005,\n",
       "   9006,\n",
       "   9002,\n",
       "   8718,\n",
       "   152,\n",
       "   152,\n",
       "   150,\n",
       "   152,\n",
       "   145],\n",
       "  [69,\n",
       "   8938,\n",
       "   8943,\n",
       "   8906,\n",
       "   8971,\n",
       "   8972,\n",
       "   8972,\n",
       "   8970,\n",
       "   8968,\n",
       "   7863,\n",
       "   69,\n",
       "   69,\n",
       "   69,\n",
       "   69,\n",
       "   68],\n",
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       "   7625,\n",
       "   7621,\n",
       "   7609,\n",
       "   7643,\n",
       "   7643,\n",
       "   7643,\n",
       "   7640,\n",
       "   7638,\n",
       "   8226,\n",
       "   91,\n",
       "   91,\n",
       "   88,\n",
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       "   7982,\n",
       "   7979,\n",
       "   7971,\n",
       "   7997,\n",
       "   7998,\n",
       "   7998,\n",
       "   7993,\n",
       "   7992,\n",
       "   8660,\n",
       "   103,\n",
       "   103,\n",
       "   101,\n",
       "   102,\n",
       "   102],\n",
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       "   8992,\n",
       "   8978,\n",
       "   9020,\n",
       "   9021,\n",
       "   9020,\n",
       "   9019,\n",
       "   9010,\n",
       "   8774,\n",
       "   151,\n",
       "   151,\n",
       "   147,\n",
       "   151,\n",
       "   148],\n",
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       "   9000,\n",
       "   9014,\n",
       "   8985,\n",
       "   9039,\n",
       "   9039,\n",
       "   9035,\n",
       "   9033,\n",
       "   9029,\n",
       "   8531,\n",
       "   79,\n",
       "   79,\n",
       "   79,\n",
       "   79,\n",
       "   75],\n",
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       "   8913,\n",
       "   8918,\n",
       "   8906,\n",
       "   8943,\n",
       "   8945,\n",
       "   8943,\n",
       "   8942,\n",
       "   8933,\n",
       "   8610,\n",
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       "   119,\n",
       "   115,\n",
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       "   119],\n",
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       "   7713,\n",
       "   7735,\n",
       "   7732,\n",
       "   7734,\n",
       "   7732,\n",
       "   7728,\n",
       "   8590,\n",
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       "   77,\n",
       "   77,\n",
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       "   8960,\n",
       "   8944,\n",
       "   8915,\n",
       "   8981,\n",
       "   8984,\n",
       "   8983,\n",
       "   8982,\n",
       "   8975,\n",
       "   8756,\n",
       "   124,\n",
       "   124,\n",
       "   121,\n",
       "   124,\n",
       "   122],\n",
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       "   9024,\n",
       "   9042,\n",
       "   9026,\n",
       "   9066,\n",
       "   9066,\n",
       "   9066,\n",
       "   9063,\n",
       "   9064,\n",
       "   8644,\n",
       "   112,\n",
       "   112,\n",
       "   110,\n",
       "   112,\n",
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       "   8972,\n",
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       "   8996,\n",
       "   8991,\n",
       "   8985,\n",
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       "   91,\n",
       "   91,\n",
       "   91,\n",
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       "   8985,\n",
       "   9001,\n",
       "   8975,\n",
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       "   9013,\n",
       "   9012,\n",
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       "   104,\n",
       "   104,\n",
       "   100,\n",
       "   104,\n",
       "   103],\n",
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       "   9008,\n",
       "   8972,\n",
       "   9031,\n",
       "   9030,\n",
       "   9031,\n",
       "   9024,\n",
       "   9024,\n",
       "   8808,\n",
       "   128,\n",
       "   128,\n",
       "   128,\n",
       "   128,\n",
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       "   9031,\n",
       "   9031,\n",
       "   9030,\n",
       "   9022,\n",
       "   9024,\n",
       "   8780,\n",
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       "   88,\n",
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       "   9033,\n",
       "   9029,\n",
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       "   9055,\n",
       "   9052,\n",
       "   9043,\n",
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       "   71,\n",
       "   69,\n",
       "   71,\n",
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       "   9000,\n",
       "   9040,\n",
       "   9040,\n",
       "   9037,\n",
       "   9035,\n",
       "   9033,\n",
       "   8598,\n",
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       "   101,\n",
       "   101,\n",
       "   101,\n",
       "   100],\n",
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       "   8837,\n",
       "   8791,\n",
       "   8861,\n",
       "   8862,\n",
       "   8863,\n",
       "   8858,\n",
       "   8857,\n",
       "   8618,\n",
       "   123,\n",
       "   123,\n",
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       "   123,\n",
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       "   8833,\n",
       "   8848,\n",
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       "   8860,\n",
       "   8867,\n",
       "   8866,\n",
       "   8860,\n",
       "   8860,\n",
       "   8665,\n",
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       "   74,\n",
       "   71,\n",
       "   74,\n",
       "   71],\n",
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       "   8978,\n",
       "   8972,\n",
       "   9001,\n",
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       "   9011,\n",
       "   9011,\n",
       "   9006,\n",
       "   9007,\n",
       "   8794,\n",
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       "   79,\n",
       "   79,\n",
       "   79,\n",
       "   76],\n",
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       "   9043,\n",
       "   9025,\n",
       "   9070,\n",
       "   9070,\n",
       "   9068,\n",
       "   9066,\n",
       "   9062,\n",
       "   8741,\n",
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       "   75,\n",
       "   72,\n",
       "   75,\n",
       "   73],\n",
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       "   9036,\n",
       "   9047,\n",
       "   9021,\n",
       "   9069,\n",
       "   9071,\n",
       "   9071,\n",
       "   9068,\n",
       "   9064,\n",
       "   8693,\n",
       "   130,\n",
       "   130,\n",
       "   128,\n",
       "   130,\n",
       "   126],\n",
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       "   9009,\n",
       "   9007,\n",
       "   9038,\n",
       "   9039,\n",
       "   9040,\n",
       "   9038,\n",
       "   9031,\n",
       "   8578,\n",
       "   81,\n",
       "   81,\n",
       "   78,\n",
       "   81,\n",
       "   77],\n",
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       "   9022,\n",
       "   9011,\n",
       "   9047,\n",
       "   9046,\n",
       "   9046,\n",
       "   9036,\n",
       "   9040,\n",
       "   8770,\n",
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       "   99,\n",
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       "   99,\n",
       "   99],\n",
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       "   9035,\n",
       "   9049,\n",
       "   9027,\n",
       "   9067,\n",
       "   9066,\n",
       "   9061,\n",
       "   9058,\n",
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       "   8644,\n",
       "   85,\n",
       "   85,\n",
       "   84,\n",
       "   85,\n",
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       "   8961,\n",
       "   8969,\n",
       "   9004,\n",
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       "   9003,\n",
       "   9001,\n",
       "   8996,\n",
       "   8717,\n",
       "   111,\n",
       "   111,\n",
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       "   107],\n",
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       "   8919,\n",
       "   8926,\n",
       "   8913,\n",
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       "   8954,\n",
       "   8949,\n",
       "   8939,\n",
       "   8944,\n",
       "   8462,\n",
       "   101,\n",
       "   101,\n",
       "   98,\n",
       "   101,\n",
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       "   8878,\n",
       "   8858,\n",
       "   8830,\n",
       "   8896,\n",
       "   8897,\n",
       "   8896,\n",
       "   8890,\n",
       "   8888,\n",
       "   8664,\n",
       "   91,\n",
       "   91,\n",
       "   89,\n",
       "   91,\n",
       "   85],\n",
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       "   8917,\n",
       "   8942,\n",
       "   8922,\n",
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       "   8952,\n",
       "   8950,\n",
       "   8949,\n",
       "   8945,\n",
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       "   95,\n",
       "   95,\n",
       "   94,\n",
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       "   94],\n",
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       "   8994,\n",
       "   8964,\n",
       "   9026,\n",
       "   9026,\n",
       "   9024,\n",
       "   9026,\n",
       "   9018,\n",
       "   8797,\n",
       "   97,\n",
       "   97,\n",
       "   93,\n",
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       "   92],\n",
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       "   119,\n",
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       "   114],\n",
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       "   9068,\n",
       "   9068,\n",
       "   9068,\n",
       "   9066,\n",
       "   9061,\n",
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       "   85,\n",
       "   85,\n",
       "   82,\n",
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       "   81],\n",
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       "   9033,\n",
       "   9039,\n",
       "   9017,\n",
       "   9066,\n",
       "   9067,\n",
       "   9060,\n",
       "   9060,\n",
       "   9058,\n",
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       "   100,\n",
       "   100,\n",
       "   99,\n",
       "   100,\n",
       "   95],\n",
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       "   9038,\n",
       "   9007,\n",
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       "   9046,\n",
       "   9040,\n",
       "   8665,\n",
       "   128,\n",
       "   128,\n",
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       "   128,\n",
       "   122],\n",
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       "   9020,\n",
       "   9016,\n",
       "   9060,\n",
       "   9060,\n",
       "   9059,\n",
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       "   9055,\n",
       "   8729,\n",
       "   87,\n",
       "   87,\n",
       "   86,\n",
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       "   84],\n",
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       "   9037,\n",
       "   9031,\n",
       "   9032,\n",
       "   9063,\n",
       "   9063,\n",
       "   9063,\n",
       "   9056,\n",
       "   9056,\n",
       "   8650,\n",
       "   84,\n",
       "   84,\n",
       "   81,\n",
       "   84,\n",
       "   81],\n",
       "  [85,\n",
       "   8933,\n",
       "   8954,\n",
       "   8937,\n",
       "   8972,\n",
       "   8974,\n",
       "   8973,\n",
       "   8974,\n",
       "   8964,\n",
       "   8553,\n",
       "   85,\n",
       "   85,\n",
       "   85,\n",
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       "   80],\n",
       "  [124,\n",
       "   8829,\n",
       "   8822,\n",
       "   8825,\n",
       "   8857,\n",
       "   8858,\n",
       "   8858,\n",
       "   8852,\n",
       "   8856,\n",
       "   8691,\n",
       "   124,\n",
       "   124,\n",
       "   124,\n",
       "   124,\n",
       "   118],\n",
       "  [79,\n",
       "   8925,\n",
       "   8930,\n",
       "   8916,\n",
       "   8950,\n",
       "   8956,\n",
       "   8956,\n",
       "   8951,\n",
       "   8953,\n",
       "   8694,\n",
       "   79,\n",
       "   79,\n",
       "   74,\n",
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       "   74],\n",
       "  [112,\n",
       "   7983,\n",
       "   7978,\n",
       "   7940,\n",
       "   8003,\n",
       "   8003,\n",
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       "   8002,\n",
       "   8000,\n",
       "   8699,\n",
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       "   112,\n",
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       "   106],\n",
       "  [83,\n",
       "   9030,\n",
       "   9036,\n",
       "   9005,\n",
       "   9052,\n",
       "   9052,\n",
       "   9050,\n",
       "   9048,\n",
       "   9042,\n",
       "   8708,\n",
       "   83,\n",
       "   83,\n",
       "   78,\n",
       "   83,\n",
       "   78],\n",
       "  [146,\n",
       "   9032,\n",
       "   9034,\n",
       "   9025,\n",
       "   9056,\n",
       "   9056,\n",
       "   9055,\n",
       "   9053,\n",
       "   9051,\n",
       "   8743,\n",
       "   146,\n",
       "   146,\n",
       "   143,\n",
       "   146,\n",
       "   136],\n",
       "  [93,\n",
       "   8975,\n",
       "   9002,\n",
       "   8997,\n",
       "   9032,\n",
       "   9035,\n",
       "   9035,\n",
       "   9032,\n",
       "   9030,\n",
       "   8715,\n",
       "   93,\n",
       "   93,\n",
       "   91,\n",
       "   93,\n",
       "   91],\n",
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       "   9049,\n",
       "   9034,\n",
       "   9029,\n",
       "   9070,\n",
       "   9071,\n",
       "   9071,\n",
       "   9065,\n",
       "   9064,\n",
       "   8720,\n",
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       "   98,\n",
       "   95,\n",
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       "   92],\n",
       "  [87,\n",
       "   9038,\n",
       "   9060,\n",
       "   9042,\n",
       "   9069,\n",
       "   9065,\n",
       "   9068,\n",
       "   9068,\n",
       "   9064,\n",
       "   8545,\n",
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       "   87,\n",
       "   86,\n",
       "   87,\n",
       "   82],\n",
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       "   8953,\n",
       "   8929,\n",
       "   8973,\n",
       "   8973,\n",
       "   8973,\n",
       "   8966,\n",
       "   8962,\n",
       "   8623,\n",
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       "   113,\n",
       "   112,\n",
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       "   110],\n",
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       "   8851,\n",
       "   8866,\n",
       "   8849,\n",
       "   8882,\n",
       "   8883,\n",
       "   8882,\n",
       "   8879,\n",
       "   8872,\n",
       "   8504,\n",
       "   104,\n",
       "   104,\n",
       "   102,\n",
       "   104,\n",
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       "   8878,\n",
       "   8885,\n",
       "   8869,\n",
       "   8905,\n",
       "   8907,\n",
       "   8907,\n",
       "   8904,\n",
       "   8899,\n",
       "   8728,\n",
       "   81,\n",
       "   81,\n",
       "   80,\n",
       "   81,\n",
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       "   8972,\n",
       "   8987,\n",
       "   8969,\n",
       "   9013,\n",
       "   9014,\n",
       "   9012,\n",
       "   9012,\n",
       "   9010,\n",
       "   8678,\n",
       "   94,\n",
       "   94,\n",
       "   93,\n",
       "   94,\n",
       "   89],\n",
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       "   9009,\n",
       "   9024,\n",
       "   8991,\n",
       "   9037,\n",
       "   9038,\n",
       "   9038,\n",
       "   9038,\n",
       "   9025,\n",
       "   8765,\n",
       "   103,\n",
       "   103,\n",
       "   99,\n",
       "   103,\n",
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       "   9011,\n",
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       "   9045,\n",
       "   9054,\n",
       "   9054,\n",
       "   9053,\n",
       "   8724,\n",
       "   118,\n",
       "   118,\n",
       "   116,\n",
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       "   117],\n",
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       "   9021,\n",
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       "   9058,\n",
       "   9053,\n",
       "   9053,\n",
       "   8727,\n",
       "   83,\n",
       "   83,\n",
       "   80,\n",
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       "   78],\n",
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       "   9013,\n",
       "   9027,\n",
       "   8990,\n",
       "   9057,\n",
       "   9056,\n",
       "   9054,\n",
       "   9054,\n",
       "   9054,\n",
       "   8764,\n",
       "   106,\n",
       "   106,\n",
       "   105,\n",
       "   106,\n",
       "   103],\n",
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       "   8989,\n",
       "   8995,\n",
       "   9025,\n",
       "   9021,\n",
       "   9025,\n",
       "   9018,\n",
       "   9008,\n",
       "   8724,\n",
       "   131,\n",
       "   131,\n",
       "   130,\n",
       "   131,\n",
       "   124],\n",
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       "   9001,\n",
       "   9003,\n",
       "   8970,\n",
       "   9034,\n",
       "   9034,\n",
       "   9034,\n",
       "   9033,\n",
       "   9026,\n",
       "   8587,\n",
       "   77,\n",
       "   77,\n",
       "   75,\n",
       "   77,\n",
       "   76],\n",
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       "   8975,\n",
       "   8949,\n",
       "   9007,\n",
       "   9007,\n",
       "   9007,\n",
       "   9004,\n",
       "   9000,\n",
       "   8522,\n",
       "   74,\n",
       "   74,\n",
       "   73,\n",
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       "   8840,\n",
       "   8885,\n",
       "   8869,\n",
       "   8901,\n",
       "   8898,\n",
       "   8899,\n",
       "   8900,\n",
       "   8893,\n",
       "   8608,\n",
       "   88,\n",
       "   88,\n",
       "   87,\n",
       "   88,\n",
       "   86],\n",
       "  [122,\n",
       "   8832,\n",
       "   8854,\n",
       "   8847,\n",
       "   8887,\n",
       "   8887,\n",
       "   8887,\n",
       "   8880,\n",
       "   8883,\n",
       "   8729,\n",
       "   122,\n",
       "   122,\n",
       "   119,\n",
       "   122,\n",
       "   120],\n",
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       "   8980,\n",
       "   8976,\n",
       "   8996,\n",
       "   9000,\n",
       "   8997,\n",
       "   8996,\n",
       "   8992,\n",
       "   8653,\n",
       "   73,\n",
       "   73,\n",
       "   72,\n",
       "   73,\n",
       "   71],\n",
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       "   9028,\n",
       "   9047,\n",
       "   9021,\n",
       "   9058,\n",
       "   9062,\n",
       "   9061,\n",
       "   9061,\n",
       "   9060,\n",
       "   8720,\n",
       "   90,\n",
       "   90,\n",
       "   89,\n",
       "   90,\n",
       "   86],\n",
       "  [77,\n",
       "   9026,\n",
       "   9042,\n",
       "   9037,\n",
       "   9073,\n",
       "   9072,\n",
       "   9068,\n",
       "   9066,\n",
       "   9063,\n",
       "   8683,\n",
       "   77,\n",
       "   77,\n",
       "   76,\n",
       "   77,\n",
       "   75],\n",
       "  [119,\n",
       "   8987,\n",
       "   8997,\n",
       "   8975,\n",
       "   9025,\n",
       "   9025,\n",
       "   9025,\n",
       "   9023,\n",
       "   9023,\n",
       "   8707,\n",
       "   119,\n",
       "   119,\n",
       "   116,\n",
       "   119,\n",
       "   116],\n",
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       "   9001,\n",
       "   8975,\n",
       "   9031,\n",
       "   9032,\n",
       "   9030,\n",
       "   9028,\n",
       "   9027,\n",
       "   8685,\n",
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       "   79,\n",
       "   78,\n",
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       "   78],\n",
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       "   9038,\n",
       "   9036,\n",
       "   9003,\n",
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       "   9058,\n",
       "   9057,\n",
       "   9056,\n",
       "   8742,\n",
       "   90,\n",
       "   90,\n",
       "   90,\n",
       "   90,\n",
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       "   9044,\n",
       "   9040,\n",
       "   9040,\n",
       "   9062,\n",
       "   9060,\n",
       "   9060,\n",
       "   9058,\n",
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       "   8474,\n",
       "   99,\n",
       "   99,\n",
       "   96,\n",
       "   99,\n",
       "   95],\n",
       "  [99,\n",
       "   8921,\n",
       "   8934,\n",
       "   8916,\n",
       "   8944,\n",
       "   8945,\n",
       "   8945,\n",
       "   8943,\n",
       "   8936,\n",
       "   8575,\n",
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       "   99,\n",
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       "   8835,\n",
       "   8865,\n",
       "   8823,\n",
       "   8869,\n",
       "   8869,\n",
       "   8870,\n",
       "   8867,\n",
       "   8864,\n",
       "   8596,\n",
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       "   98,\n",
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       "   98,\n",
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       "   8960,\n",
       "   8951,\n",
       "   8916,\n",
       "   8972,\n",
       "   8975,\n",
       "   8975,\n",
       "   8974,\n",
       "   8968,\n",
       "   8757,\n",
       "   100,\n",
       "   100,\n",
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       "   100,\n",
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       "   9034,\n",
       "   9036,\n",
       "   9011,\n",
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       "   9057,\n",
       "   9052,\n",
       "   9050,\n",
       "   8535,\n",
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       "   83,\n",
       "   82,\n",
       "   83,\n",
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       "   9003,\n",
       "   8983,\n",
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       "   9042,\n",
       "   9041,\n",
       "   9039,\n",
       "   9029,\n",
       "   8795,\n",
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       "   99,\n",
       "   99,\n",
       "   99,\n",
       "   94],\n",
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       "   8979,\n",
       "   9001,\n",
       "   8958,\n",
       "   9008,\n",
       "   9008,\n",
       "   9008,\n",
       "   9002,\n",
       "   9007,\n",
       "   8711,\n",
       "   110,\n",
       "   110,\n",
       "   108,\n",
       "   110,\n",
       "   107],\n",
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       "   9038,\n",
       "   9025,\n",
       "   9083,\n",
       "   9083,\n",
       "   9083,\n",
       "   9081,\n",
       "   9075,\n",
       "   8762,\n",
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       "   89,\n",
       "   88,\n",
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       "   9028,\n",
       "   9032,\n",
       "   9023,\n",
       "   9056,\n",
       "   9059,\n",
       "   9059,\n",
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       "   9059,\n",
       "   8730,\n",
       "   105,\n",
       "   105,\n",
       "   103,\n",
       "   105,\n",
       "   102],\n",
       "  [100,\n",
       "   8988,\n",
       "   8990,\n",
       "   9005,\n",
       "   9021,\n",
       "   9021,\n",
       "   9021,\n",
       "   9018,\n",
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       "   100,\n",
       "   100,\n",
       "   97,\n",
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       "   93],\n",
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       "   8967,\n",
       "   8953,\n",
       "   8945,\n",
       "   8979,\n",
       "   8982,\n",
       "   8982,\n",
       "   8977,\n",
       "   8979,\n",
       "   8491,\n",
       "   83,\n",
       "   83,\n",
       "   81,\n",
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       "   8901,\n",
       "   8893,\n",
       "   8887,\n",
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       "   8932,\n",
       "   8932,\n",
       "   8929,\n",
       "   8924,\n",
       "   8641,\n",
       "   69,\n",
       "   69,\n",
       "   69,\n",
       "   69,\n",
       "   68],\n",
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       "   8868,\n",
       "   8908,\n",
       "   8862,\n",
       "   8919,\n",
       "   8919,\n",
       "   8918,\n",
       "   8917,\n",
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       "   8687,\n",
       "   85,\n",
       "   85,\n",
       "   85,\n",
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       "   8965,\n",
       "   8974,\n",
       "   8948,\n",
       "   8979,\n",
       "   8984,\n",
       "   8984,\n",
       "   8979,\n",
       "   8982,\n",
       "   8732,\n",
       "   102,\n",
       "   102,\n",
       "   102,\n",
       "   102,\n",
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       "   8978,\n",
       "   9013,\n",
       "   9014,\n",
       "   9014,\n",
       "   9011,\n",
       "   9011,\n",
       "   8714,\n",
       "   76,\n",
       "   76,\n",
       "   75,\n",
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       "   9037,\n",
       "   9024,\n",
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       "   9055,\n",
       "   9053,\n",
       "   8748,\n",
       "   92,\n",
       "   92,\n",
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       "   92,\n",
       "   92],\n",
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       "   9025,\n",
       "   9023,\n",
       "   9034,\n",
       "   9067,\n",
       "   9068,\n",
       "   9066,\n",
       "   9065,\n",
       "   9060,\n",
       "   8631,\n",
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       "   65,\n",
       "   63,\n",
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       "   9029,\n",
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       "   9047,\n",
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       "   9043,\n",
       "   8633,\n",
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       "   114,\n",
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       "   110],\n",
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       "   9010,\n",
       "   8986,\n",
       "   9032,\n",
       "   9032,\n",
       "   9030,\n",
       "   9030,\n",
       "   9029,\n",
       "   8711,\n",
       "   96,\n",
       "   96,\n",
       "   95,\n",
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       "   93],\n",
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       "   9028,\n",
       "   9017,\n",
       "   9065,\n",
       "   9069,\n",
       "   9069,\n",
       "   9067,\n",
       "   9060,\n",
       "   8633,\n",
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       "   73,\n",
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       "   72],\n",
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       "   8965,\n",
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       "   9013,\n",
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       "   8410,\n",
       "   99,\n",
       "   99,\n",
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       "   8872,\n",
       "   8880,\n",
       "   8891,\n",
       "   8902,\n",
       "   8902,\n",
       "   8902,\n",
       "   8895,\n",
       "   8629,\n",
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       "   105,\n",
       "   103,\n",
       "   105,\n",
       "   102],\n",
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       "   8847,\n",
       "   8860,\n",
       "   8830,\n",
       "   8874,\n",
       "   8874,\n",
       "   8872,\n",
       "   8867,\n",
       "   8868,\n",
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       "   99,\n",
       "   99,\n",
       "   99,\n",
       "   99,\n",
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       "   9031,\n",
       "   9031,\n",
       "   9031,\n",
       "   9027,\n",
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       "   8711,\n",
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       "   90,\n",
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       "   85],\n",
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       "   9028,\n",
       "   9031,\n",
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       "   9069,\n",
       "   9069,\n",
       "   9072,\n",
       "   9070,\n",
       "   9070,\n",
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       "   90,\n",
       "   90,\n",
       "   90,\n",
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       "   8979,\n",
       "   9025,\n",
       "   9025,\n",
       "   9026,\n",
       "   9023,\n",
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       "   112,\n",
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       "   108],\n",
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       "   8985,\n",
       "   9030,\n",
       "   9031,\n",
       "   9030,\n",
       "   9028,\n",
       "   9025,\n",
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       "   95,\n",
       "   95,\n",
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       "   9051,\n",
       "   8770,\n",
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       "   54,\n",
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       "   54,\n",
       "   51],\n",
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       "   9051,\n",
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       "   8667,\n",
       "   88,\n",
       "   88,\n",
       "   87,\n",
       "   88,\n",
       "   83],\n",
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       "   9001,\n",
       "   8980,\n",
       "   9033,\n",
       "   9033,\n",
       "   9031,\n",
       "   9026,\n",
       "   9032,\n",
       "   8560,\n",
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       "   111,\n",
       "   108,\n",
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       "   106],\n",
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       "   8953,\n",
       "   8923,\n",
       "   8977,\n",
       "   8982,\n",
       "   8981,\n",
       "   8982,\n",
       "   8978,\n",
       "   8525,\n",
       "   79,\n",
       "   79,\n",
       "   79,\n",
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       "   76],\n",
       "  [56,\n",
       "   8836,\n",
       "   8863,\n",
       "   8846,\n",
       "   8887,\n",
       "   8887,\n",
       "   8887,\n",
       "   8885,\n",
       "   8879,\n",
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       "   56,\n",
       "   56,\n",
       "   53,\n",
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       "   55],\n",
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       "   8886,\n",
       "   8910,\n",
       "   8903,\n",
       "   8944,\n",
       "   8943,\n",
       "   8944,\n",
       "   8943,\n",
       "   8937,\n",
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       "   77,\n",
       "   77,\n",
       "   77,\n",
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       "   76],\n",
       "  [124,\n",
       "   8988,\n",
       "   9022,\n",
       "   9000,\n",
       "   9034,\n",
       "   9033,\n",
       "   9033,\n",
       "   9033,\n",
       "   9031,\n",
       "   8678,\n",
       "   124,\n",
       "   124,\n",
       "   122,\n",
       "   124,\n",
       "   119],\n",
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       "   9000,\n",
       "   9052,\n",
       "   9011,\n",
       "   9076,\n",
       "   9077,\n",
       "   9076,\n",
       "   9070,\n",
       "   9072,\n",
       "   8652,\n",
       "   92,\n",
       "   92,\n",
       "   89,\n",
       "   92,\n",
       "   91],\n",
       "  [80,\n",
       "   9047,\n",
       "   9050,\n",
       "   9022,\n",
       "   9071,\n",
       "   9071,\n",
       "   9071,\n",
       "   9070,\n",
       "   9068,\n",
       "   8684,\n",
       "   80,\n",
       "   80,\n",
       "   78,\n",
       "   80,\n",
       "   75],\n",
       "  [76,\n",
       "   8973,\n",
       "   9002,\n",
       "   8999,\n",
       "   9036,\n",
       "   9039,\n",
       "   9039,\n",
       "   9034,\n",
       "   9033,\n",
       "   8600,\n",
       "   76,\n",
       "   76,\n",
       "   76,\n",
       "   76,\n",
       "   74],\n",
       "  [108,\n",
       "   8971,\n",
       "   8997,\n",
       "   8971,\n",
       "   9022,\n",
       "   9023,\n",
       "   9023,\n",
       "   9020,\n",
       "   9018,\n",
       "   8735,\n",
       "   108,\n",
       "   108,\n",
       "   104,\n",
       "   108,\n",
       "   104],\n",
       "  [93,\n",
       "   8969,\n",
       "   8986,\n",
       "   8977,\n",
       "   9014,\n",
       "   9014,\n",
       "   9013,\n",
       "   9010,\n",
       "   9006,\n",
       "   8546,\n",
       "   93,\n",
       "   93,\n",
       "   92,\n",
       "   93,\n",
       "   90],\n",
       "  [75,\n",
       "   8958,\n",
       "   8984,\n",
       "   8983,\n",
       "   9014,\n",
       "   9013,\n",
       "   9014,\n",
       "   9012,\n",
       "   9005,\n",
       "   8494,\n",
       "   75,\n",
       "   75,\n",
       "   70,\n",
       "   75,\n",
       "   71],\n",
       "  [86,\n",
       "   8920,\n",
       "   8937,\n",
       "   8907,\n",
       "   8971,\n",
       "   8972,\n",
       "   8971,\n",
       "   8970,\n",
       "   8967,\n",
       "   8514,\n",
       "   86,\n",
       "   86,\n",
       "   84,\n",
       "   86,\n",
       "   85],\n",
       "  [114,\n",
       "   8857,\n",
       "   8891,\n",
       "   8864,\n",
       "   8898,\n",
       "   8898,\n",
       "   8897,\n",
       "   8897,\n",
       "   8884,\n",
       "   8686,\n",
       "   114,\n",
       "   114,\n",
       "   110,\n",
       "   114,\n",
       "   111],\n",
       "  [90,\n",
       "   8920,\n",
       "   8919,\n",
       "   8922,\n",
       "   8964,\n",
       "   8966,\n",
       "   8965,\n",
       "   8962,\n",
       "   8964,\n",
       "   8613,\n",
       "   90,\n",
       "   90,\n",
       "   87,\n",
       "   90,\n",
       "   86],\n",
       "  [89,\n",
       "   9025,\n",
       "   9028,\n",
       "   9023,\n",
       "   9065,\n",
       "   9065,\n",
       "   9062,\n",
       "   9060,\n",
       "   9056,\n",
       "   8751,\n",
       "   89,\n",
       "   89,\n",
       "   87,\n",
       "   88,\n",
       "   88],\n",
       "  [90,\n",
       "   9050,\n",
       "   9020,\n",
       "   9014,\n",
       "   9065,\n",
       "   9066,\n",
       "   9065,\n",
       "   9064,\n",
       "   9059,\n",
       "   8613,\n",
       "   90,\n",
       "   90,\n",
       "   88,\n",
       "   90,\n",
       "   89],\n",
       "  [104,\n",
       "   9026,\n",
       "   9033,\n",
       "   8997,\n",
       "   9049,\n",
       "   9054,\n",
       "   9053,\n",
       "   9052,\n",
       "   9041,\n",
       "   8730,\n",
       "   104,\n",
       "   104,\n",
       "   104,\n",
       "   104,\n",
       "   103],\n",
       "  [99,\n",
       "   8971,\n",
       "   8993,\n",
       "   8971,\n",
       "   9021,\n",
       "   9020,\n",
       "   9020,\n",
       "   9020,\n",
       "   9011,\n",
       "   8593,\n",
       "   99,\n",
       "   99,\n",
       "   97,\n",
       "   99,\n",
       "   97],\n",
       "  [59,\n",
       "   9016,\n",
       "   9048,\n",
       "   8996,\n",
       "   9054,\n",
       "   9058,\n",
       "   9058,\n",
       "   9055,\n",
       "   9050,\n",
       "   8720,\n",
       "   59,\n",
       "   59,\n",
       "   58,\n",
       "   59,\n",
       "   57],\n",
       "  [102,\n",
       "   9053,\n",
       "   9042,\n",
       "   9024,\n",
       "   9071,\n",
       "   9072,\n",
       "   9065,\n",
       "   9069,\n",
       "   9065,\n",
       "   8565,\n",
       "   102,\n",
       "   102,\n",
       "   99,\n",
       "   102,\n",
       "   101],\n",
       "  [113,\n",
       "   8984,\n",
       "   9002,\n",
       "   8955,\n",
       "   9019,\n",
       "   9019,\n",
       "   9018,\n",
       "   9012,\n",
       "   9014,\n",
       "   8508,\n",
       "   113,\n",
       "   113,\n",
       "   111,\n",
       "   113,\n",
       "   108]],\n",
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       "  [0, 51, 49, 52, 4, 3, 4, 10, 20, 12, 25, 0, 2, 0, 7],\n",
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       "  [1.8796699,\n",
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       "   1.9970772,\n",
       "   2.2398315,\n",
       "   2.2667694]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mask = \"(sat == 'n19' & type == 'hirs4')\"\n",
    "# mask = \"(sat == 'n19' & type == 'amsua')\"\n",
    "# mask = \"(sat == 'n15' & type == 'amsua')\"\n",
    "\n",
    "mask = \"(type == 'amsua')\"\n",
    "\n",
    "channel = None\n",
    "gd.plot_diag.time_series_fort207(Self, Type='amsua', \n",
    "                                 Sat='MetOp-B and C', \n",
    "                                 it=3, \n",
    "                                 dateIni=dateIni, \n",
    "                                 dateFin=dateFin, \n",
    "                                 nHour=nHour, \n",
    "                                 mask=mask, \n",
    "                                 channel=channel, \n",
    "                                 Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96041b3b-bb2a-4b6e-bc04-7ee472bc1712",
   "metadata": {},
   "source": [
    "\n",
    "Exemplo para quando `channel` é uma lista, no caso `channel = [1, 2, 3, 5]`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1e930c91-3c31-4057-a177-c426ac58a6d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " =============================================================================================================\n",
      "[1, 2, 3, 4, 5] 1\n",
      "\n",
      "mask =  (sat == 'n19' & type == 'amsua')\n",
      "\n",
      "Stage it:  3\n",
      "\n",
      "Table:  1\n",
      "\n",
      " Preparing data for: channels2024-03-01:00\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-01:06\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-01:12\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-01:18\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-02:00\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-02:06\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-02:12\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-02:18\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-03:00\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-03:06\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-03:12\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-03:18\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-04:00\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-04:06\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-04:12\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-04:18\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-05:00\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-05:06\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-05:12\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      " Preparing data for: channels2024-03-05:18\n",
      " channels:  [1, 2, 3, 4, 5]\n",
      "\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      "channels =  [1, 2, 3, 4, 5]\n",
      " Calculating for 2024-03-01:00\n",
      " Calculating for 2024-03-01:06\n",
      " Calculating for 2024-03-01:12\n",
      " Calculating for 2024-03-01:18\n",
      " Calculating for 2024-03-02:00\n",
      " Calculating for 2024-03-02:06\n",
      " Calculating for 2024-03-02:12\n",
      " Calculating for 2024-03-02:18\n",
      " Calculating for 2024-03-03:00\n",
      " Calculating for 2024-03-03:06\n",
      " Calculating for 2024-03-03:12\n",
      " Calculating for 2024-03-03:18\n",
      " Calculating for 2024-03-04:00\n",
      " Calculating for 2024-03-04:06\n",
      " Calculating for 2024-03-04:12\n",
      " Calculating for 2024-03-04:18\n",
      " Calculating for 2024-03-05:00\n",
      " Calculating for 2024-03-05:06\n",
      " Calculating for 2024-03-05:12\n",
      " Calculating for 2024-03-05:18\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      " Making Graphics...\n",
      " Done!\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = \"(sat == 'n19' & type == 'amsua')\"\n",
    "\n",
    "channel = [1, 2, 3, 4, 5]\n",
    "gd.plot_diag.time_series_fort207(Self, Type='amsua', \n",
    "                                 Sat='n19', \n",
    "                                 it=3, \n",
    "                                 dateIni=dateIni, \n",
    "                                 dateFin=dateFin, \n",
    "                                 nHour=nHour, \n",
    "                                 mask=mask, \n",
    "                                 channel=channel, \n",
    "                                 Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502ed794-ed36-4f4f-a8cb-04b9d9c5a2b2",
   "metadata": {},
   "source": [
    "\n",
    "Exemplo para quando `channel` recebe umm valor. Abaixo `channel = 13`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0b57ed0c-4e24-438a-a3bd-7023b9c719c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " =============================================================================================================\n",
      "13 0\n",
      "\n",
      "mask =  (sat == 'n19' & type == 'amsua')\n",
      "\n",
      "Stage it:  3\n",
      "\n",
      "Table:  1\n",
      "\n",
      " Preparing data for: 2024-03-01:00  - channel:  13\n",
      " Preparing data for: 2024-03-01:06  - channel:  13\n",
      " Preparing data for: 2024-03-01:12  - channel:  13\n",
      " Preparing data for: 2024-03-01:18  - channel:  13\n",
      " Preparing data for: 2024-03-02:00  - channel:  13\n",
      " Preparing data for: 2024-03-02:06  - channel:  13\n",
      " Preparing data for: 2024-03-02:12  - channel:  13\n",
      " Preparing data for: 2024-03-02:18  - channel:  13\n",
      " Preparing data for: 2024-03-03:00  - channel:  13\n",
      " Preparing data for: 2024-03-03:06  - channel:  13\n",
      " Preparing data for: 2024-03-03:12  - channel:  13\n",
      " Preparing data for: 2024-03-03:18  - channel:  13\n",
      " Preparing data for: 2024-03-04:00  - channel:  13\n",
      " Preparing data for: 2024-03-04:06  - channel:  13\n",
      " Preparing data for: 2024-03-04:12  - channel:  13\n",
      " Preparing data for: 2024-03-04:18  - channel:  13\n",
      " Preparing data for: 2024-03-05:00  - channel:  13\n",
      " Preparing data for: 2024-03-05:06  - channel:  13\n",
      " Preparing data for: 2024-03-05:12  - channel:  13\n",
      " Preparing data for: 2024-03-05:18  - channel:  13\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      "channels =  [13]\n",
      " Calculating for 2024-03-01:00\n",
      " Calculating for 2024-03-01:06\n",
      " Calculating for 2024-03-01:12\n",
      " Calculating for 2024-03-01:18\n",
      " Calculating for 2024-03-02:00\n",
      " Calculating for 2024-03-02:06\n",
      " Calculating for 2024-03-02:12\n",
      " Calculating for 2024-03-02:18\n",
      " Calculating for 2024-03-03:00\n",
      " Calculating for 2024-03-03:06\n",
      " Calculating for 2024-03-03:12\n",
      " Calculating for 2024-03-03:18\n",
      " Calculating for 2024-03-04:00\n",
      " Calculating for 2024-03-04:06\n",
      " Calculating for 2024-03-04:12\n",
      " Calculating for 2024-03-04:18\n",
      " Calculating for 2024-03-05:00\n",
      " Calculating for 2024-03-05:06\n",
      " Calculating for 2024-03-05:12\n",
      " Calculating for 2024-03-05:18\n",
      "\n",
      " =============================================================================================================\n",
      "\n",
      " Making Graphics...\n",
      " Done!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = \"(sat == 'n19' & type == 'amsua')\"\n",
    "\n",
    "channel = 13\n",
    "gd.plot_diag.time_series_fort207(Self, Type='amsua', \n",
    "                                 Sat='n19', \n",
    "                                 it=3, \n",
    "                                 dateIni=dateIni, \n",
    "                                 dateFin=dateFin, \n",
    "                                 nHour=nHour, \n",
    "                                 mask=mask, \n",
    "                                 channel=channel, \n",
    "                                 Clean=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2b358ef",
   "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
}
