{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b8fe8bda",
   "metadata": {},
   "source": [
    "# readDiag - Versão Gerd - Convencionais\n",
    "\n",
    "Notebook para gerar resutados das análises do processo de assimilação pelo GSI no SMNA com os arquivos prepBUFR\n",
    "elaborados por INPE e NCEP.\n",
    "\n",
    "\n",
    "## Utilização da classe `read_diag`\n",
    "\n",
    "### Bibliotecas necessárias\n",
    "\n",
    "Para iniciar a utilização do `readDiag`, carregue primeiro as bibliotecas necessárias para a sua utilização:\n",
    "\n",
    "* `gsidiag`: é a biblioteca que contém as classes `read_diag` e `plot_diag`;\n",
    "* `pandas`: é a biblioteca que fornece as estruturas de dados tabulados utilizadas pelo `readDiag`;\n",
    "* `matplotlib`: é a biblioteca a partir da qual são confeccionadas as figuras;\n",
    "* `datetime`: é a biblioteca utilizada para manipular datas.\n",
    "\n",
    "A instrução `%matplotlib inline` é um comando mágico do Jupyter e apenas ajusta o ambiente para que não seja necessário utilizar o comando `plt.show()` sempre que figuras forem mostradas dentro do notebook. Se você estiver utilizando o `readDiag` dentro de um script Python, esta diretiva pode ser suprimida e o comando `plt.show()` deve ser utilizado, a depender da situação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36e5dd3a-77f6-47d2-b503-2a0184a245a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Descomente a linha a seguir para utilizar este notebook no Google Colab\n",
    "#!pip install readDiag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bd343fb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2083386/1422729586.py:1: DeprecationWarning: You are importing the legacy package 'gsidiag'. This interface is deprecated and will be removed in a future major release. Please migrate to 'readDiag.open.open_diagnostic' and the DiagnosticAPI.\n",
      "  import gsidiag as gd\n"
     ]
    }
   ],
   "source": [
    "import gsidiag as gd\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime, timedelta\n",
    "from gsidiag.datasources import getVarInfo\n",
    "\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c524c794",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2026093006']\n",
      "\n",
      "['/mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026093006/diag_conv_01.2026093006']\n",
      "\n",
      "['/mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026093006/diag_conv_03.2026093006']\n",
      "\n",
      "Aguarde, o tempo total estimado para a leitura dos arquivos é de 0 minutos e 20 segundos.\n",
      "\n",
      "Reading /mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/2026093006/diag_conv_01.2026093006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026-10-23 16:36:42,145 readDiag INFO read_conv_file completed in 1.217s\n",
      "2026-10-23 16:36:43,905 readDiag INFO read_conv_file completed in 1.040s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pronto!\n"
     ]
    }
   ],
   "source": [
    "# Para uso em máquina local (neste caso, o diretório remoto deverá ser montado localmente com o comando sshfs)\n",
    "# Exemplo: \n",
    "# $ sshfs username@egeon.cptec.inpe.br:/pesq/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout\n",
    "# ou\n",
    "# $ sshfs username@itapemirim.cptec.inpe.br:/share/das/dist/joao.gerd/EXP18/GSI/dataout /extra2/EGEON_EXP18_GSI_dataout\n",
    "# DIRdiag = '/extra2/EGEON_EXP18_GSI_dataout'\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_amsua_INPE/')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_amsua_NCEP/')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepbufr_conv_NCEP/')\n",
    "# dateIni = '2025082012' \n",
    "# dateFin = '2025082012'\n",
    "# nHour = '6'\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/IBIS/ATMS_AMSUA/gsi_dataout/SMNA_v3.0.0.t11889-atms-amsu/SMG/datainout/gsi/dataout/')\n",
    "# dateIni = '2025050906' #'2025082012' \n",
    "# dateFin = '2025050906' #'2025082012'\n",
    "# nHour = '6'\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepINPE_press')\n",
    "# # DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepNCEP_press')\n",
    "# dateIni = '2025090412' \n",
    "# dateFin = '2025090412'\n",
    "# nHour = '6'\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepINPE/i79') # Eduardo\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepINPE/DIMNT') # Sergio\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepNCEP') # NCEP\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepINPE/i80') # Eduardo\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/2025090412prepbufrINPE_i84') # Eduardo\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/2025090412prepINPE_i88') # Eduardo\n",
    "# dateIni = '2025090412' \n",
    "# dateFin = '2025090412'\n",
    "# nHour = '6'\n",
    "\n",
    "#DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/2026021900satwndINPE_i90') # Eduardo\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/2026021900satwndNCEP_i90')\n",
    "# dateIni = '2026021900' \n",
    "# dateFin = '2026021900'\n",
    "# nHour = '6'\n",
    "\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepINPE/i81') # BGFE\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/prepNCEP/i81') # NCEP\n",
    "# dateIni = '2025102912' \n",
    "# dateFin = '2025102912'\n",
    "# nHour = '6'\n",
    "\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP20/gsi_dataout') # EXP21\n",
    "# dateIni = '2024021912' \n",
    "# dateFin = '2024021912'\n",
    "# nHour = '06'\n",
    "\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_controle')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026041906_satWnd_PTC_i97')\n",
    "# dateIni = '2026041906' \n",
    "# dateFin = '2026041906'\n",
    "# nHour = '6'\n",
    "\n",
    "\n",
    "# # DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026051906_prepbufr_CNT_i99')\n",
    "# DIRdiag = os.path.join(os.getcwd(), '../../Exp_SMNA/data/EXP_OBS/Exps_DISSM_ATMS/2026051906_prepbufr_CPT_i99')\n",
    "# dateIni = '2026051906' \n",
    "# dateFin = '2026051906'\n",
    "# nHour = '6'\n",
    "\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/Exps_DISSM_ATMS/2026061506_controle\"\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/Exps_DISSM_ATMS/2026061506_satwndLW\"\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\" # teste com modificação no read_satwnd.f90\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/Exps_DISSM_ATMS/2026061506_IRVISWV\"\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/Exps_DISSM_ATMS/2026061506_NCEPkx251\"\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout/Exps_DISSM_ATMS/2026061506_DIPTCkx251\"\n",
    "# dateIni = '2026061506' \n",
    "# dateFin = '2026061506'\n",
    "# nHour = '6'\n",
    "\n",
    "\n",
    "# DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\" # UMD\n",
    "# # DIRdiag = \"/mnt/beegfs/caroline.viezel/SMNA_v3.0.0.t11889/SMG/datainout/gsi/dataout/CNT_ATMS_warm_OCT-NOV2025\"\n",
    "# dateIni = '2025102812' \n",
    "# dateFin = '2025102812'\n",
    "# nHour = '6'\n",
    "\n",
    "#DIRdiag = \"/mnt/beegfs/luiz.sapucci/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "# DIRdiag = \"/pesq/share/das/dist/luiz.sapucci/EXPS_OBS\"\n",
    "# DIRdiag = \"/pesq/share/das/dist/caroline.viezel/EXP-ATMS/ioper_SMNA\"\n",
    "# DIRdiag = \"/mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "\n",
    "# DIRdiag = \"/pesq/share/das/dist/caroline.viezel/EXP-ATMS\"\n",
    "DIRdiag = \"/mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "dateIni = '2026093006' \n",
    "dateFin = '2026093006'\n",
    "nHour = '06'\n",
    "\n",
    "# DIRdiag = \"/mnt/beegfs/ioper/SMNA_v3.0.0.t12717/SMG/datainout/gsi/dataout\"\n",
    "# dateIni = '2026082712' \n",
    "# dateFin = '2026082712'\n",
    "# nHour = '6'\n",
    "\n",
    "\n",
    "varName = 'uv'\n",
    "varType = 220       \n",
    "vminOMA = -2.0       \n",
    "vmaxOMA = 2.0        \n",
    "vminSTD = 0.0        \n",
    "vmaxSTD = 14.0       \n",
    "Level = 1000\n",
    "Lay = 15           \n",
    "SingleL = 'OneL' \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",
    "paths, pathsc = [], []\n",
    "\n",
    "# OuterL = '01'        \n",
    "# [paths.append(DIRdiag + '/' + dt + '_satWnd_CPT_iC61' + '/diag_conv_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "# OuterLc = '03'\n",
    "# [pathsc.append(DIRdiag + '/' + dt + '_satWnd_CPT_iC61' + '/diag_conv_' + OuterLc + '.' + dt) for dt in dates]\n",
    "\n",
    "OuterL = '01'        \n",
    "[paths.append(DIRdiag + '/' + dt + '/diag_conv_' + OuterL + '.' + dt) for dt in dates]\n",
    "\n",
    "OuterLc = '03'\n",
    "[pathsc.append(DIRdiag + '/' + dt + '/diag_conv_' + OuterLc + '.' + dt) for dt in dates]\n",
    "\n",
    "print(paths)\n",
    "print('')\n",
    "print(pathsc)\n",
    "\n",
    "read = True\n",
    "\n",
    "if read:        \n",
    "    gdf_list = []\n",
    "    print('')\n",
    "    \n",
    "    print('Aguarde, o tempo total estimado para a leitura dos arquivos é de ' +\n",
    "          str(int((float(len(paths))*20)/60)) + ' minutos e ' +\n",
    "          str(int((float(len(paths))*20)%60)) + ' segundos.')\n",
    "    \n",
    "    print('')\n",
    "    \n",
    "    for path, pathc in zip(paths, pathsc):\n",
    "        print('Reading ' + path)\n",
    "        \n",
    "        gdf = gd.read_diag(path, pathc)\n",
    "        \n",
    "        gdf_list.append(gdf)\n",
    "        \n",
    "    print('Pronto!') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "655590bb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<gsidiag.legacy_api.read.read_diag at 0x7f980f027f50>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e3569a-1b5d-451d-9e15-720a9153e6b7",
   "metadata": {},
   "source": [
    "Fazendo `tidx = 0`, obtemos o primeiro objeto da lista `gdf_list`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88d8af81",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gsidiag.legacy_api.read.read_diag at 0x7f980f027f50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tidx = 0\n",
    "gdf_list[tidx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4ad8170-bffd-4c93-a51a-5747b88478b1",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Obtendo informações dos arquivos\n",
    "\n",
    "Utilize a função `pfileinfo()` para obter uma lista das observações e seus respectivos tipos (`kx`) que estão contidos dentro do arquivo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83af7ed8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Name : ps\n",
      "              └── kx => 120  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : t\n",
      "              └── kx => 120  126  130  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : q\n",
      "              └── kx => 120  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : uv\n",
      "              └── kx => 210  220  221  224  230  232  240  242  243  244  245  246  247  250  252  253  254  255  260  280  281  284  287  \n",
      "\n",
      "Variable Name : gps\n",
      "              └── kx => 3  5  42  43  44  66  267  269  750  751  753  754  755  825  \n",
      "\n",
      "Variable Name : sst\n",
      "              └── kx => 181  183  187  \n",
      "\n"
     ]
    }
   ],
   "source": [
    "gdf_list[tidx].pfileinfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9be857b1-4371-4528-ac22-477878108426",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ps': [120, 132, 180, 181, 182, 183, 187],\n",
       " 't': [120, 126, 130, 132, 180, 181, 182, 183, 187],\n",
       " 'q': [120, 132, 180, 181, 182, 183, 187],\n",
       " 'uv': [210,\n",
       "  220,\n",
       "  221,\n",
       "  224,\n",
       "  230,\n",
       "  232,\n",
       "  240,\n",
       "  242,\n",
       "  243,\n",
       "  244,\n",
       "  245,\n",
       "  246,\n",
       "  247,\n",
       "  250,\n",
       "  252,\n",
       "  253,\n",
       "  254,\n",
       "  255,\n",
       "  260,\n",
       "  280,\n",
       "  281,\n",
       "  284,\n",
       "  287],\n",
       " 'gps': [3, 5, 42, 43, 44, 66, 267, 269, 750, 751, 753, 754, 755, 825],\n",
       " 'sst': [181, 183, 187]}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# listas das variáveis com seus kxs\n",
    "var_kxs = gdf_list[tidx].overview()\n",
    "var_kxs\n",
    "\n",
    "# ver tabela de dados\n",
    "# gdf_list[tidx].obsInfo\n",
    "\n",
    "# print('Variável: ', varName)\n",
    "# gdf_list[tidx].obsInfo[varName]\n",
    "\n",
    "# print('Variável: ', varName, ' e Tipo: ', varType)\n",
    "\n",
    "# gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "\n",
    "# varTypes = [220,230]\n",
    "\n",
    "# print('Variável: ', varName, ' e Tipos: ', varTypes)\n",
    "\n",
    "# gdf_list[tidx].obsInfo[varName].loc[varTypes]\n",
    "\n",
    "# print('Variável: ', varName, ' e Tipo: ', varType)\n",
    "\n",
    "# gdf_list[tidx].obsInfo[varName].loc[varType].obs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c1b8c83a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "tidx = 0\n",
    "varName = 'uv'\n",
    "varType = 243\n",
    "gdf_list[tidx].pfileinfo()\n",
    "# gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "# df = gdf_list[tidx].obsInfo\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "#df.iqc\n",
    "\n",
    "print('')\n",
    "print('==================')\n",
    "\n",
    "print('iusev Max e Min')\n",
    "\n",
    "param = 'iusev'\n",
    "\n",
    "aux = df[param].max()\n",
    "\n",
    "print(param,': max = ', aux)\n",
    "\n",
    "aux = df[param].min()\n",
    "\n",
    "print(param,': min = ', aux)\n",
    "\n",
    "\n",
    "print('')\n",
    "print('==================')\n",
    "\n",
    "print('iqc de todas as observações')\n",
    "\n",
    "# param = 'iqc' #'analysis_use' #'wpbqc' #'qc_setup' #'iqc'\n",
    "\n",
    "param = 'iqc' #'qc_setup'\n",
    "df2 = []\n",
    "df2 = df\n",
    "\n",
    "aux = df2[param].max()\n",
    "\n",
    "print(param,': todas max = ', aux)\n",
    "\n",
    "aux = df2[param].min()\n",
    "\n",
    "print(param,': todas min = ', aux)\n",
    "\n",
    "flags_unicas = []\n",
    "flags_unicas = np.unique(df2[param].values)\n",
    "\n",
    "print('')\n",
    "print('df2 values =', df2[param].values)\n",
    "print('')\n",
    "\n",
    "print('')\n",
    "print('flags_unicas =', flags_unicas)\n",
    "print('')\n",
    "\n",
    "total_linhas = len(df2)\n",
    "\n",
    "print('')\n",
    "print('total_obs =', total_linhas)\n",
    "print('')\n",
    "\n",
    "\n",
    "\n",
    "print('')\n",
    "print('==================')\n",
    "\n",
    "print('iusev == -1: iqc das observações rejeitadas')\n",
    "\n",
    "# param = 'iqc' #'analysis_use' #'wpbqc' #'qc_setup' #'iqc'\n",
    "\n",
    "param = 'iqc' #'qc_setup'\n",
    "df2 = []\n",
    "df2 = df.query('iusev == -1')\n",
    "\n",
    "aux = df2[param].max()\n",
    "\n",
    "print(param,': todas max = ', aux)\n",
    "\n",
    "aux = df2[param].min()\n",
    "\n",
    "print(param,': todas min = ', aux)\n",
    "\n",
    "flags_unicas = []\n",
    "flags_unicas = np.unique(df2[param].values)\n",
    "\n",
    "print('')\n",
    "print('df2 values =', df2[param].values)\n",
    "print('')\n",
    "\n",
    "print('')\n",
    "print('flags_unicas =', flags_unicas)\n",
    "print('')\n",
    "\n",
    "total_linhas = len(df2)\n",
    "\n",
    "print('')\n",
    "print('total_obs =', total_linhas)\n",
    "print('')\n",
    "\n",
    "\n",
    "print('')\n",
    "print('==================')\n",
    "\n",
    "print('iusev == 1: iqc das observações assimiladas')\n",
    "\n",
    "# param = 'iqc' #'analysis_use' #'wpbqc' #'qc_setup' #'iqc'\n",
    "\n",
    "param = 'iqc' #'qc_setup'\n",
    "df2 = []\n",
    "df2 = df.query('iusev == 1')\n",
    "\n",
    "aux = df2[param].max()\n",
    "\n",
    "print(param,': todas max = ', aux)\n",
    "\n",
    "aux = df2[param].min()\n",
    "\n",
    "print(param,': todas min = ', aux)\n",
    "\n",
    "flags_unicas = []\n",
    "flags_unicas = np.unique(df2[param].values)\n",
    "\n",
    "print('')\n",
    "print('df2 values =', df2[param].values)\n",
    "print('')\n",
    "\n",
    "print('')\n",
    "print('flags_unicas =', flags_unicas)\n",
    "print('')\n",
    "\n",
    "total_linhas = len(df2)\n",
    "\n",
    "print('')\n",
    "print('total_obs =', total_linhas)\n",
    "print('')\n",
    "\n",
    "\n",
    "#df1 = df.query('iusev == 1')\n",
    "\n",
    "#aux2 = df1['iqc'].max()\n",
    "\n",
    "#print(aux2)\n",
    "\n",
    "#mask = None #\"((-60 <= lat <= 30) & (180 <= lon <= 360))\"\n",
    "\n",
    "# df.query(mask).iqc\n",
    "#df.query('iusev == 1').iqc  #qc_setup\n",
    "\n",
    "# df.query(mask).wpbqc\n",
    "# df.query('iusev == -1').wpbqc\n",
    "\n",
    "# df.query('-90 <= lat <= -60')\n",
    "# df.query('lat >= 19')\n",
    "# df.query('lat >= 10 & iusev == 1')\n",
    "# df.ksub\n",
    "# df.end_err"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baaf3d6a",
   "metadata": {},
   "source": [
    "## Utilização da classe `plot_diag`\n",
    "\n",
    "### Distribuição espacial\n",
    "\n",
    "Observações: na tabela dos convencionais o `iuse` é `iusev` (mudar no mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6297ca3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# varNames = ['ps','t','q','uv','gps','sst']\n",
    "# varNames = ['ps','t','uv']\n",
    "varNames = ['uv']\n",
    "\n",
    "for varName in varNames:\n",
    "    print('varName: ', varName)\n",
    "    try:\n",
    "        varTypes = var_kxs[varName]\n",
    "        print('varTypes: ', varTypes)\n",
    "        print('')\n",
    "        \n",
    "        # Todas as obs da variável\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas da variável classificada por tipo\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes, mask='iusev==1')\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap_ASS.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas sem classificar por tipo\n",
    "        varNames = [varName]\n",
    "        idschar = '_'.join([str(item) for item in varNames])\n",
    "        gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iusev==1')\n",
    "        figname = idschar + '_pvmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "    except:\n",
    "        print('warning --- not kx varName ', varName)\n",
    "        print('')\n",
    "        \n",
    "\n",
    "# Gráfico de barras do total de obs por variável\n",
    "gd.plot_diag.vcount(gdf_list[tidx])\n",
    "\n",
    "idschar = 'All_varNames'\n",
    "figname = idschar + '_vcount.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64b605a2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "varNames = ['uv']\n",
    "\n",
    "# var_uv = [220, 221, 224, 230, 232, 280, 281, 284, 287] #[210, 220, 221, 224, 230, 232, 280, 281, 284, 287] # rodada BGFE\n",
    "\n",
    "# var_uv = [220, 221, 224, 229, 230, 280, 281, 284, 287] # NCEP -> controle\n",
    "\n",
    "# var_uv = [220, 230, 280, 281, 284]\n",
    "\n",
    "# var_uv = [253, 252, 245]\n",
    "\n",
    "# var_uv = [247, 246, 245, 240]\n",
    "\n",
    "# var_uv = [245]\n",
    "\n",
    "# var_uv = [210, 220, 221, 224, 230, 240, 245, 280, 281, 284, 287]\n",
    "\n",
    "# var_uv = [210, 220, 221, 224, 230, 240, 244, 245, 246, 247, 260, 280, 281, 284, 287]\n",
    "\n",
    "# var_uv = [245, 240,  246, 247, 244, 260, 251]\n",
    "\n",
    "var_uv = [243, 253, 254]\n",
    "\n",
    "\n",
    "\n",
    "for varName in varNames:\n",
    "    print('varName: ', varName)\n",
    "    try:\n",
    "        varTypes = var_uv\n",
    "        print('varTypes: ', varTypes)\n",
    "        print('')\n",
    "        \n",
    "        # Todas as obs da variável\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas da variável classificada por tipo\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes, mask='iusev==1')\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap_ASS.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas\n",
    "        varNames = [varName]\n",
    "        idschar = '_'.join([str(item) for item in varNames])\n",
    "        gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iusev==1')\n",
    "        figname = idschar + '_pvmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "    except:\n",
    "        print('warning --- not kx varName ', varName)\n",
    "        print('')\n",
    "        \n",
    "\n",
    "# # Gráfico de barras do total de obs por variável\n",
    "# gd.plot_diag.vcount(gdf_list[tidx])\n",
    "\n",
    "# idschar = 'All_varNames'\n",
    "# figname = idschar + '_vcount.png'\n",
    "\n",
    "# plt.tight_layout()\n",
    "# plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4383ff41",
   "metadata": {},
   "outputs": [],
   "source": [
    "varNames = ['q']\n",
    "# var_uv = [247, 246, 245, 240] # vento GOES\n",
    "# var_uv = [254, 253, 252, 250, 245, 246, 247] # vento GOES\n",
    "\n",
    "# var_uv = [253, 252, 245] # vento GOES\n",
    "\n",
    "var_uv = [120]\n",
    "\n",
    "\n",
    "for varName in varNames:\n",
    "    print('varName: ', varName)\n",
    "    try:\n",
    "        varTypes = var_uv\n",
    "        print('varTypes: ', varTypes)\n",
    "        print('')\n",
    "        \n",
    "        # Todas as obs da variável\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas da variável classificada por tipo\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes, mask='iusev==1')\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmap_ASS.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "        # Todas as obs assimiladas\n",
    "        varNames = [varName]\n",
    "        idschar = '_'.join([str(item) for item in varNames])\n",
    "        gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iusev==1')\n",
    "        figname = idschar + '_pvmap.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "    except:\n",
    "        print('warning --- not kx varName ', varName)\n",
    "        print('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86d4e3f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "varNames = ['t']\n",
    "# var_uv = [247, 246, 245, 240] # vento GOES\n",
    "# var_uv = [253, 254, 252, 250, 245, 246, 247, 242, 243]# 240, 244]#, 255, 260] # vento GOES\n",
    "\n",
    "#var_uv = [253, 254, 243, 252, 250, 242, 245, 246, 247]\n",
    "\n",
    "var_uv = [180, 181, 183, 187]\n",
    "\n",
    "# instrument_title = 'Global spatial distribution of AMV wind vectors from geostationary satellites.'\n",
    "\n",
    "instrument_title = 'Global spatial distribution of the temperature observations.'\n",
    "\n",
    "for varName in varNames:\n",
    "    print('varName: ', varName)\n",
    "    try:\n",
    "        varTypes = var_uv\n",
    "        print('varTypes: ', varTypes)\n",
    "        print('')\n",
    "        \n",
    "        # Todas as obs da variável\n",
    "        idschar = '_'.join([str(item) for item in varTypes])\n",
    "        ax = gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "        figname = varName + '_' + idschar + '_' + 'ptmapNew.png'\n",
    "        plt.tight_layout()\n",
    "        plt.savefig(figname)\n",
    "        \n",
    "#         # Todas as obs assimiladas da variável classificada por tipo\n",
    "#         idschar = '_'.join([str(item) for item in varTypes])\n",
    "#         gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes, mask='iusev==1')\n",
    "#         figname = varName + '_' + idschar + '_' + 'ptmap_ASS.png'\n",
    "#         plt.tight_layout()\n",
    "#         plt.savefig(figname)\n",
    "        \n",
    "#         # Todas as obs assimiladas\n",
    "#         varNames = [varName]\n",
    "#         idschar = '_'.join([str(item) for item in varNames])\n",
    "#         gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iuse==1')\n",
    "#         figname = idschar + '_pvmap.png'\n",
    "#         plt.tight_layout()\n",
    "#         plt.savefig(figname)\n",
    "        \n",
    "    except:\n",
    "        print('warning --- not kx varName ', varName)\n",
    "        print('')\n",
    "        \n",
    "# ###################################### NEW ########################################################\n",
    "if ax != None:\n",
    "    datefmt = dates[tidx]#.strftime(\"%Y%m%d%H\")\n",
    "    date = datetime.strptime(str(datefmt), \"%Y%m%d%H\")\n",
    "    date_title = str(date.strftime(\"%d%b%Y - %H%M\")) + ' UTC'\n",
    "    plt.title(date_title, loc='right', fontsize=11)\n",
    "    plt.title(instrument_title, loc='left', fontsize=10)\n",
    "    #plt.annotate(forplot +' - '+ forplot2, xy=(1.02, 0.5), xytext=(0, 0), xycoords='axes fraction', \n",
    "    #             textcoords='offset points', color='gray', fontweight='bold', fontsize='10', \n",
    "    #             horizontalalignment='left', verticalalignment='center', rotation=90)\n",
    "    #plt.annotate(forplot2, xy=(1.005, 0.885), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n",
    "     #        color='gray', fontweight='bold', fontsize='10',horizontalalignment='left', verticalalignment='center')\n",
    "    \n",
    "    # Obter os handles (elementos gráficos) e labels atuais\n",
    "    #handles, labels = ax.gca().get_legend_handles_labels()\n",
    "    # Ou definir uma nova lista de labels na ordem correta\n",
    "    #novos_labels = ['Curva A', 'Curva Z'] # Ordem correta dos handles plotados acima\n",
    "\n",
    "    # Chamar legend novamente com handles e novos labels\n",
    "    #ax.legend(handles, novos_labels)\n",
    "# #################################################################################################\n",
    "\n",
    "figname = varName + '_' + idschar + '_' + 'ptmapNew.png'\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56fc3405",
   "metadata": {},
   "outputs": [],
   "source": [
    "varName = 't'\n",
    "varTypes = [181]\n",
    "\n",
    "# Todas as obs da variável\n",
    "idschar = '_'.join([str(item) for item in varTypes])\n",
    "gd.plot_diag.ptmap(gdf_list[tidx], varName=varName, varType=varTypes)\n",
    "figname = varName + '_' + idschar + '_' + 'ptmap.png'\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0eaa77-fca0-4be6-8d82-1ef7794bab0b",
   "metadata": {},
   "source": [
    "Gerando uma figura com diferentes variáveis, considerando a máscara `iuse==1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8bdc6d6a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "varNames = ['ps'] #['uv','ps','t']\n",
    "idschar = '_'.join([str(item) for item in varNames])\n",
    "\n",
    "gd.plot_diag.pvmap(gdf_list[tidx], varName=varNames, mask='iuse==0')\n",
    "\n",
    "figname = idschar + '_pvmap.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0824e199",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# varName = 'ps'\n",
    "# varType = 183 #181\n",
    "\n",
    "# var_uv = [245, 240,  246, 247, 244, 260]\n",
    "\n",
    "varName = 'uv'\n",
    "varType = 254 #181\n",
    "\n",
    "# Statcount do readDiag versão do GAD (Old pois o João fez um rotina completamente diferente que não está funcionando)\n",
    "gd.plot_diag.statcount_Old(gdf_list, \n",
    "                        varName=varName, \n",
    "                        varType=varType, \n",
    "                        noiqc=True, \n",
    "                        dateIni=dateIni, \n",
    "                        dateFin=dateFin, \n",
    "                        nHour=nHour, \n",
    "                        channel=None, \n",
    "                        figTS=False, \n",
    "                        figMap=True, \n",
    "                        markersize=5.80)\n",
    "\n",
    "# gd.plot_diag.statcount(gdf_list, \n",
    "#                         varName=varName, \n",
    "#                         varType=varType, \n",
    "#                         noiqc=True, \n",
    "#                         dateIni=dateIni, \n",
    "#                         dateFin=dateFin, \n",
    "#                         nHour=nHour, \n",
    "#                         channel=None, \n",
    "#                         figTS=True, \n",
    "#                         figMap=True, \n",
    "#                         markersize=5.80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2e46ee21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable Name : ps\n",
      "              └── kx => 120  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : t\n",
      "              └── kx => 120  126  130  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : q\n",
      "              └── kx => 120  132  180  181  182  183  187  \n",
      "\n",
      "Variable Name : uv\n",
      "              └── kx => 210  220  221  224  230  232  240  242  243  244  245  246  247  250  252  253  254  255  260  280  281  284  287  \n",
      "\n",
      "Variable Name : gps\n",
      "              └── kx => 3  5  42  43  44  66  267  269  750  751  753  754  755  825  \n",
      "\n",
      "Variable Name : sst\n",
      "              └── kx => 181  183  187  \n",
      "\n",
      "columns ['lat', 'lon', 'elev', 'prs', 'hgt', 'time', 'iuse', 'iusev', 'wpbqc', 'inp_err', 'adj_err', 'obs', 'omf', 'kx', 'ksub', 'iqc', 'qc_setup', 'analysis_use', 'rwgt', 'errinv_inp', 'errinv_adj', 'errinv_fin', 'obs_u', 'omf_u', 'omf_wob_u', 'obs_v', 'omf_v', 'omf_wob_v', 'factw', 'spread_u', 'spread_v', 'omf_wob', 'dhgt', 'pbqc', 'emark', 'end_err', 'geometry', 'oma']\n",
      "\n",
      "Formato da matriz gerada (Latitudes, Longitudes): (90, 180)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = None\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n",
    "mask = None\n",
    "#mask = f\"((nchan == 13))\"\n",
    "# mask = f\"((iusev == 1))\"\n",
    "\n",
    "#mask = f\"((950<=prs<=1050))\"\n",
    "\n",
    "# mask = \"((-90 <= lat <= -80) & (240 <= lon <= 300))\"\n",
    "\n",
    "param = 'obs_v' #'obs'\n",
    "varName = 'uv' #'uv'\n",
    "varType = 254\n",
    "\n",
    "\n",
    "# 1. Definição da malha de retângulos (Resolução da grade)\n",
    "resolucao = 2.0  # Tamanho do retângulo em graus (ex: 1° x 1°)\n",
    "\n",
    "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n",
    "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n",
    "\n",
    "# 2. Configuração correta da figura e do eixo com projeção Cartopy\n",
    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "tidx = 0\n",
    "df = []\n",
    "gdf_list[tidx].pfileinfo()\n",
    "gdf_list[tidx].obsInfo\n",
    "#df = gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "\n",
    "df_aux = []\n",
    "if mask is None:\n",
    "    df_aux = df\n",
    "    del(df)\n",
    "else:\n",
    "    df_aux = df.query(mask)\n",
    "    del(df)\n",
    "\n",
    "\n",
    "# 3. Cálculo do valor médio de OMF em cada retângulo\n",
    "# 'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n",
    "estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n",
    "    x=df_aux['lon'],\n",
    "    y=df_aux['lat'],\n",
    "    values=df_aux[param],\n",
    "    statistic='mean',\n",
    "    bins=[lon_bins, lat_bins]\n",
    ")\n",
    "\n",
    "# 4. Organização do resultado em formato de matriz\n",
    "# Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "matriz_media = estatistica.T\n",
    "\n",
    "# Substitui retângulos sem nenhuma observação (NaN) por zero\n",
    "matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n",
    "\n",
    "print('')\n",
    "print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "print('')\n",
    "\n",
    "\n",
    "\n",
    "# 5. Plotagem da matriz de OMF médio \n",
    "pcm = ax.pcolormesh(\n",
    "    lon_bins, \n",
    "    lat_bins, \n",
    "    matriz_media,  #matriz_oma_media.T,\n",
    "    cmap='RdBu_r',       \n",
    "    vmin=-60.0,           \n",
    "    vmax=60.0,            \n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "\n",
    "# 6. Adição de elementos geográficos (linhas de costa e estados)\n",
    "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n",
    "# ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n",
    "\n",
    "# 7. Configuração das linhas de grade e rótulos de Lat/Lon\n",
    "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n",
    "gl.top_labels = False\n",
    "gl.right_labels = False\n",
    "\n",
    "# 8. Barra de cores (Colorbar)\n",
    "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "# cbar.set_label('OMF Médio (Valor da Observação - Forecast)')\n",
    "# cbar.set_label('Count Obs')\n",
    "cbar.set_label('Mean Obs')\n",
    "\n",
    "#plt.title('Distribuição Espacial do OMF Médio por Retângulo', fontsize=14, pad=15)\n",
    "#plt.show()\n",
    "\n",
    "forplot = 'varName ='+ str(varName)\n",
    "instrument_title = str(varName) + \" \" + str(varType) + \" | \" + str(param)\n",
    "\n",
    "            \n",
    "if ax != None:\n",
    "    datefmt1 = dates[0]\n",
    "    datefmt2 = dates[-1]\n",
    "    date1 = datetime.strptime(str(datefmt1), \"%Y%m%d%H\")\n",
    "    date2 = datetime.strptime(str(datefmt2), \"%Y%m%d%H\")\n",
    "    #date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%d%b%Y %H%M\")) #+ ' GMT'\n",
    "    date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%H%M\")) #+ ' GMT'\n",
    "    #ax.annotate(date_title, xy=(0.75, 1.015), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n",
    "    #           fontsize=10, horizontalalignment='left', verticalalignment='center')\n",
    "    plt.title(date_title, loc='right', fontsize=10)\n",
    "    plt.title(instrument_title, loc='left', fontsize=11)\n",
    "    ax.annotate(forplot, xy=(0.65, 1.075), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n",
    "                color='gray', fontweight='bold', fontsize='12',horizontalalignment='left', verticalalignment='center')\n",
    "\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"SulAmerica_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"Melissa_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"word_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(figname)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3f78485",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "from PIL import Image\n",
    "\n",
    "# 1. Configurações de caminhos e nomes\n",
    "pasta_origem = Path(\"/home/caroline.viezel/readDiagGerd/notebooks\")  # Substitua pelo caminho da sua pasta se necessário\n",
    "nome_base = \"TotalObs_ps-181_202608\"\n",
    "\n",
    "# 📂 COLOQUE AQUI O CAMINHO ONDE DESEJA SALVAR O GIF FINAL\n",
    "pasta_destino = Path(\"/pesq/share/das/dist/caroline.viezel/arquivos\")  # Altere para o caminho que desejar\n",
    "nome_gif_final = \"animacao_intervalo.gif\"\n",
    "\n",
    "# 2. Gera os nomes e carrega as imagens no intervalo de 1600 a 2700\n",
    "imagens = []\n",
    "\n",
    "for numero in range(1600, 2701, 100):\n",
    "    nome_arquivo = f\"{nome_base}{numero}.png\"\n",
    "    caminho_arquivo = pasta_origem / nome_arquivo\n",
    "\n",
    "    if caminho_arquivo.exists():\n",
    "        img = Image.open(caminho_arquivo)\n",
    "        imagens.append(img)\n",
    "        print(f\"Adicionado: {nome_arquivo}\")\n",
    "    else:\n",
    "        print(f\"Aviso: {nome_arquivo} não encontrado. Pulando...\")\n",
    "\n",
    "# 3. Cria e salva o GIF no caminho especificado\n",
    "if imagens:\n",
    "    # Garante que a pasta de destino exista no sistema\n",
    "    pasta_destino.mkdir(parents=True, exist_ok=True)\n",
    "    \n",
    "    # Caminho completo do arquivo final\n",
    "    caminho_salvamento = pasta_destino / nome_gif_final\n",
    "    \n",
    "    # Salva o GIF\n",
    "    primeira_img = imagens[0]\n",
    "    primeira_img.save(\n",
    "        caminho_salvamento,\n",
    "        save_all=True,\n",
    "        append_images=imagens[1:],\n",
    "        duration=500,  # Tempo de cada frame (500ms = 0.5s)\n",
    "        loop=0,        # Loop infinito\n",
    "    )\n",
    "    print(f\"\\n🎉 GIF criado com sucesso em: {caminho_salvamento.resolve()}\")\n",
    "else:\n",
    "    print(\"\\n❌ Nenhuma imagem do intervalo especificado foi encontrada.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04b6be80",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ax = None\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Extrai o mapa existente e forca a primeira cor (indice 0) a ser branco puro\n",
    "cmap_original = plt.get_cmap('OrRd')\n",
    "cores = cmap_original(range(256))\n",
    "cores[0] = [1, 1, 1, 1]  # Define o valor zero/minimo como Branco Puro [R, G, B, Alpha]\n",
    "\n",
    "# Cria o novo mapa de cores customizado\n",
    "meu_cmap = mcolors.ListedColormap(cores)\n",
    "\n",
    "\n",
    "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n",
    "mask = None\n",
    "#mask = f\"((nchan == 13))\"\n",
    "# mask = f\"((iusev == 1))\"\n",
    "# mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) and (iusev == 1) )\"\n",
    "\n",
    "#mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) )\"\n",
    "#mask = f\"( ((kx == 247)) )\"\n",
    "\n",
    "# mask = f\"((950<=prs<=1050) and (iusev == 1))\"\n",
    "\n",
    "# mask = f\"( (0 < iqc < 8) )\"\n",
    "\n",
    "# mask = f\"( (8 <= iqc <= 15) )\"\n",
    "# mask = f\"( (iqc == 15) )\"\n",
    "\n",
    "# mask = \"((-90 <= lat <= -80) & (240 <= lon <= 300))\"\n",
    "\n",
    "#mask = \"((-60 <= lat <= 30) & (240 <= lon <= 340) & (iqc == 0))\"\n",
    "# mask = \"((-60 <= lat <= 30) & (240 <= lon <= 340) & (wpbqc == 4))\"\n",
    "\n",
    "param = 'obs' #'iqc'#'iqc'\n",
    "varName = 'uv' #'ps' #'ps' #'uv'\n",
    "varType = 245 #180 #253 #181\n",
    "\n",
    "\n",
    "# 1. Definição da malha de retângulos (Resolução da grade)\n",
    "resolucao = 4.0  # Tamanho do retângulo em graus (ex: 1° x 1°)\n",
    "\n",
    "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n",
    "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n",
    "\n",
    "# 2. Configuração correta da figura e do eixo com projeção Cartopy\n",
    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "tidx = 0\n",
    "df = []\n",
    "gdf_list[tidx].pfileinfo()\n",
    "gdf_list[tidx].obsInfo\n",
    "#df = gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "\n",
    "df_aux = []\n",
    "if mask is None:\n",
    "    df_aux = df\n",
    "    del(df)\n",
    "else:\n",
    "    df_aux = df.query(mask)\n",
    "    del(df)\n",
    "\n",
    "################################################################################\n",
    "    \n",
    "# # 1. Identificar todas as flags únicas do seu vetor 'iqc'\n",
    "# flags_unicas = np.unique(df_aux['iqc'].values)\n",
    "\n",
    "# # 2. Criar a função que conta quantas vezes cada flag aparece em cada bin\n",
    "# def contar_cada_flag(valores_no_bin):\n",
    "#     # Se o bin estiver vazio, retorna zeros para cada flag\n",
    "#     if len(valores_no_bin) == 0:\n",
    "#         return np.zeros(len(flags_unicas))\n",
    "    \n",
    "#     # Conta quantas vezes cada flag única aparece neste bin específico\n",
    "#     contagens = [np.sum(valores_no_bin == f) for f in flags_unicas]\n",
    "#     return np.array(contagens)\n",
    "\n",
    "# # 3. Sua chamada adaptada para contagem\n",
    "# estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n",
    "#     x=df_aux['lon'],\n",
    "#     y=df_aux['lat'],\n",
    "#     values=df_aux[param],\n",
    "#     statistic=contar_cada_flag,  #'mean',\n",
    "#     bins=[lon_bins, lat_bins]\n",
    "# )\n",
    "\n",
    "# # 4. Organização do resultado em formato de matriz\n",
    "# # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "# matriz_media = estatistica.T\n",
    "\n",
    "# # Substitui retângulos sem nenhuma observação (NaN) por zero\n",
    "# matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n",
    "\n",
    "# print('')\n",
    "# print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "# print('')\n",
    "\n",
    "# import matplotlib.cm as cm\n",
    "# import matplotlib.colors as mcolors\n",
    "\n",
    "# # Pega as cores originais do mapa 'tab20'\n",
    "# cores_originais = cm.get_cmap('tab20').colors\n",
    "\n",
    "# # Substitui a primeira cor (índice 0) por branco\n",
    "# # Modifica a lista de tuplas RGB transformando a primeira em (1.0, 1.0, 1.0)\n",
    "# cores_modificadas = list(cores_originais)\n",
    "# cores_modificadas[0] = (1.0, 1.0, 1.0) \n",
    "\n",
    "# # Cria o novo colormap customizado baseado nas cores alteradas\n",
    "# cmap_customizado = mcolors.ListedColormap(cores_modificadas)\n",
    "\n",
    "\n",
    "# # 5. Plotagem da matriz\n",
    "# pcm = ax.pcolormesh(\n",
    "#     lon_bins, \n",
    "#     lat_bins, \n",
    "#     matriz_media,  #matriz_oma_media.T,\n",
    "#     cmap=cmap_customizado,       \n",
    "#     #vmin=0,\n",
    "#     #vmax=20,\n",
    "#     transform=ccrs.PlateCarree()\n",
    "# )\n",
    "\n",
    "# # Define os limites exatos da área que você quer exibir\n",
    "# # Formato: [lon_min, lon_max, lat_min, lat_max]\n",
    "# ax.set_extent([-120.0, -20.0, -60.0, 30.0], crs=ccrs.PlateCarree())\n",
    "\n",
    "##################################################################################\n",
    "\n",
    "#3. Cálculo do valor médio de OMF em cada retângulo\n",
    "#'statistic=\"mean\"' calcula a média. Pontos fora da malha são ignorados.\n",
    "estatistica, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n",
    "    x=df_aux['lon'],\n",
    "    y=df_aux['lat'],\n",
    "    values=df_aux[param],\n",
    "    statistic='count',  #'mean',\n",
    "    bins=[lon_bins, lat_bins]\n",
    ")\n",
    "\n",
    "# 4. Organização do resultado em formato de matriz\n",
    "# Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "matriz_media = estatistica.T\n",
    "\n",
    "# Substitui retângulos sem nenhuma observação (NaN) por zero\n",
    "matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n",
    "\n",
    "print('')\n",
    "print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "print('')\n",
    "\n",
    "\n",
    "\n",
    "# 5. Plotagem da matriz de OMF médio \n",
    "# pcm = ax.pcolormesh(\n",
    "#     lon_bins, \n",
    "#     lat_bins, \n",
    "#     matriz_media,  #matriz_oma_media.T,\n",
    "#     cmap='RdBu_r',       \n",
    "#     vmin=150.0,           \n",
    "#     vmax=350.0,            \n",
    "#     transform=ccrs.PlateCarree()\n",
    "# )\n",
    "\n",
    "# pcm = ax.pcolormesh(\n",
    "#     lon_bins, \n",
    "#     lat_bins, \n",
    "#     matriz_media,  #matriz_oma_media.T,\n",
    "#     cmap='tab20',       \n",
    "#     vmin=0,\n",
    "#     vmax=20,\n",
    "#     transform=ccrs.PlateCarree()\n",
    "# )\n",
    "\n",
    "pcm = ax.pcolormesh(\n",
    "    lon_bins, \n",
    "    lat_bins, \n",
    "    matriz_media,  #matriz_oma_media.T,\n",
    "    cmap=meu_cmap,       \n",
    "    vmin=0,\n",
    "    vmax=2000,\n",
    "    transform=ccrs.PlateCarree()\n",
    ")\n",
    "\n",
    "\n",
    "# 6. Adição de elementos geográficos (linhas de costa e estados)\n",
    "ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n",
    "# ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n",
    "\n",
    "#ax.set_extent([-120.0, -20.0, -60.0, 30.0], crs=ccrs.PlateCarree())\n",
    "\n",
    "# 7. Configuração das linhas de grade e rótulos de Lat/Lon\n",
    "gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n",
    "gl.top_labels = False\n",
    "gl.right_labels = False\n",
    "\n",
    "# 8. Barra de cores (Colorbar)\n",
    "cbar = plt.colorbar(pcm, ax=ax, orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "cbar.set_label('OMF Médio (Valor da Observação - Forecast)')\n",
    "# cbar.set_label('OMA Médio (Valor da Observação - Análise)')\n",
    "cbar.set_label('count obs - células 4°x4°')\n",
    "#cbar.set_label('mean (iqc)')\n",
    "# cbar.set_label('count(0<iqc<8)')\n",
    "# cbar.set_label('count(8 <= iqc <= 15)')\n",
    "# cbar.set_label('count(iqc == 15)')\n",
    "\n",
    "# cbar.set_label('VEL U - média (iusev == 1)')\n",
    "# cbar.set_label('VEL V - média células 1°x1°')\n",
    "\n",
    "# cbar.set_label('Qntdd ' + mask)\n",
    "\n",
    "# cbar.set_label('Pressão (hPa) - média células 1°x1°')\n",
    "\n",
    "#plt.title('Distribuição Espacial do OMF Médio por Retângulo', fontsize=14, pad=15)\n",
    "#plt.show()\n",
    "\n",
    "forplot = 'varName ='+ str(varName)\n",
    "instrument_title = str(varName) + \" \" + str(varType) + \" | \" + str(param)\n",
    "\n",
    "            \n",
    "if ax != None:\n",
    "    datefmt1 = dates[0]\n",
    "    datefmt2 = dates[-1]\n",
    "    date1 = datetime.strptime(str(datefmt1), \"%Y%m%d%H\")\n",
    "    date2 = datetime.strptime(str(datefmt2), \"%Y%m%d%H\")\n",
    "    #date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%d%b%Y %H%M\")) #+ ' GMT'\n",
    "    date_title = str(date1.strftime(\"%d%b%Y %H%M\")) + ' - ' + str(date2.strftime(\"%H%M\")) #+ ' GMT'\n",
    "    #ax.annotate(date_title, xy=(0.75, 1.015), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n",
    "    #           fontsize=10, horizontalalignment='left', verticalalignment='center')\n",
    "    plt.title(date_title, loc='right', fontsize=10)\n",
    "    plt.title(instrument_title, loc='left', fontsize=11)\n",
    "    ax.annotate(forplot, xy=(0.65, 1.075), xytext=(0, 0), xycoords='axes fraction', textcoords='offset points', \n",
    "                color='gray', fontweight='bold', fontsize='12',horizontalalignment='left', verticalalignment='center')\n",
    "\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"SulAmerica_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"Melissa_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    #figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"CH\"+str(channel)+\"_\"+\"word_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    figname=varName+\"_\"+str(varType)+\"_\"+param+\"_\"+\"word_dates_ALL\"+\"_\"+\"plot.png\"\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(figname)\n",
    "    \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4bff0d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = None\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Extrai o mapa existente e forca a primeira cor (indice 0) a ser branco puro\n",
    "cmap_original = plt.get_cmap('OrRd')\n",
    "cores = cmap_original(range(256))\n",
    "cores[0] = [1, 1, 1, 1]  # Define o valor zero/minimo como Branco Puro [R, G, B, Alpha]\n",
    "\n",
    "# Cria o novo mapa de cores customizado\n",
    "meu_cmap = mcolors.ListedColormap(cores)\n",
    "\n",
    "\n",
    "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n",
    "mask = None\n",
    "#mask = f\"((nchan == 13))\"\n",
    "# mask = f\"((iusev == 1))\"\n",
    "# mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) and (iusev == 1) )\"\n",
    "\n",
    "#mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) )\"\n",
    "#mask = f\"( ((kx == 247)) )\"\n",
    "\n",
    "# mask = f\"((950<=prs<=1050) and (iusev == 1))\"\n",
    "\n",
    "# mask = f\"( (0 < iqc < 8) )\"\n",
    "\n",
    "# mask = f\"( (8 <= iqc <= 15) )\"\n",
    "# mask = f\"( (iqc == 15) )\"\n",
    "\n",
    "# mask = \"((-90 <= lat <= -80) & (240 <= lon <= 300))\"\n",
    "\n",
    "mask = \"((-90 <= lat <= 90) & (0 <= lon <= 360))\"\n",
    "\n",
    "param = 'iqc'#'iqc'\n",
    "varName = 'ps' #'ps' #'uv'\n",
    "varType = 181 #180 #253 #181\n",
    "\n",
    "\n",
    "# 1. Definição da malha de retângulos (Resolução da grade)\n",
    "resolucao = 2.0  # Tamanho do retângulo em graus (ex: 1° x 1°)\n",
    "\n",
    "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n",
    "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n",
    "\n",
    "# 2. Configuração correta da figura e do eixo com projeção Cartopy\n",
    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "tidx = 0\n",
    "df = []\n",
    "gdf_list[tidx].pfileinfo()\n",
    "gdf_list[tidx].obsInfo\n",
    "#df = gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "\n",
    "# =====================================================================\n",
    "# FILTRAGEM DOS DADOS (Seu bloco original)\n",
    "# =====================================================================\n",
    "df_aux = []\n",
    "if mask is None:\n",
    "    df_aux = df\n",
    "    del(df)\n",
    "else:\n",
    "    df_aux = df.query(mask)\n",
    "    del(df)\n",
    "\n",
    "# =====================================================================\n",
    "# CONFIGURAÇÃO DO MAPA DE CORES (Zero em Branco)\n",
    "# =====================================================================\n",
    "import os\n",
    "from scipy.stats import binned_statistic_dd\n",
    "\n",
    "cores_originais = plt.colormaps['tab20'].colors\n",
    "cores_modificadas = list(cores_originais)\n",
    "cores_modificadas[0] = (1.0, 1.0, 1.0)  # Força o índice 0 (ou seja, contagem zero) a ser branco\n",
    "cmap_customizado = mcolors.ListedColormap(cores_modificadas)\n",
    "\n",
    "# =====================================================================\n",
    "# PREPARAÇÃO DOS DADOS E BINS PARA O ESPAÇO 3D\n",
    "# =====================================================================\n",
    "# Identifica as flags únicas contidas nos dados filtrados\n",
    "flags_unicas = np.unique(df_aux[param].values)\n",
    "\n",
    "print('')\n",
    "print('df_aux =', df_aux[param].values)\n",
    "print('count df_aux =', df_aux[param].values.size)\n",
    "print('')\n",
    "\n",
    "print('')\n",
    "print('flags_unicas encontradas nesta região =', flags_unicas)\n",
    "print('')\n",
    "\n",
    "# Cria as arestas das flags com segurança\n",
    "flag_bins = np.append(flags_unicas, flags_unicas[-1] + 1)\n",
    "\n",
    "# Empilha as 3 coordenadas para a função multidimensional\n",
    "dados_3d = np.column_stack([df_aux['lon'], df_aux['lat'], df_aux['iqc']])\n",
    "\n",
    "# Executa o agrupamento 3D nativo\n",
    "estatistica_3d, bordas_arestas, binnumber_3d = binned_statistic_dd(\n",
    "    sample=dados_3d,\n",
    "    values=None,             \n",
    "    statistic='count',       \n",
    "    bins=[lon_bins, lat_bins, flag_bins]\n",
    ")\n",
    "\n",
    "# =====================================================================\n",
    "# LOOP DINÂMICO PARA GERAR OS MAPAS\n",
    "# =====================================================================\n",
    "os.makedirs(\"mapas_flags\", exist_ok=True)\n",
    "\n",
    "# Converter a grade de longitudes (0 a 360) para (-180 a 180) apenas para o desenho\n",
    "# lon_bins_plot = np.array(lon_bins) - 360\n",
    "\n",
    "for idx, flag in enumerate(flags_unicas):\n",
    "    \n",
    "    # Abre a figura corretamente configurada com Cartopy\n",
    "    fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "    \n",
    "    # Extrai a fatia da flag atual e transpõe para o Matplotlib (.T)\n",
    "    matriz_da_flag = estatistica_3d[:, :, idx].T\n",
    "    # Substitui retângulos sem nenhuma observação (NaN) por zero\n",
    "    #matriz_limpa = np.nan_to_num(matriz_da_flag, nan=0.0)\n",
    "    matriz_limpa = matriz_da_flag\n",
    "    \n",
    "    # Plota aplicando a longitude convertida para casar com o enquadramento do mapa\n",
    "    pcm = ax.pcolormesh(\n",
    "        lon_bins, \n",
    "        lat_bins, \n",
    "        matriz_limpa,\n",
    "        cmap=cmap_customizado,\n",
    "        vmin=0,\n",
    "        vmax=100000,  \n",
    "        transform=ccrs.PlateCarree()\n",
    "    )\n",
    "    \n",
    "    # Centraliza perfeitamente na região da sua máscara (-120 a -20 de longitude)\n",
    "    ax.set_extent([-120, -20, -60, 30], crs=ccrs.PlateCarree())\n",
    "    \n",
    "    # Adiciona detalhes geográficos essenciais\n",
    "    #ax.coastlines(resolution='10m', color='black', linewidth=0.8)\n",
    "    #ax.add_feature(cfeature.BORDERS, linestyle=':', edgecolor='gray') # Adiciona fronteiras de países\n",
    "    \n",
    "    # 6. Adição de elementos geográficos (linhas de costa e estados)\n",
    "    ax.add_feature(cfeature.COASTLINE, linestyle='-', linewidth=1)\n",
    "    ax.add_feature(cfeature.BORDERS, linestyle='-', linewidth=0.8, edgecolor='gray')\n",
    "    # ax.add_feature(cfeature.STATES, linestyle='--', linewidth=0.5, edgecolor='gray')\n",
    "\n",
    "    # 7. Configuração das linhas de grade e rótulos de Lat/Lon\n",
    "    gl = ax.gridlines(draw_labels=True, linestyle='--', alpha=0.5)\n",
    "    gl.top_labels = False\n",
    "    gl.right_labels = False\n",
    "    \n",
    "    # Barra de cores (Legenda)\n",
    "    plt.colorbar(pcm, ax=ax, label='Quantidade de Ocorrências', orientation='horizontal', pad=0.08, shrink=0.6)\n",
    "    \n",
    "    ax.set_title(f\"Distribuição Geográfica - Flag iqc: {flag}\", fontsize=14, pad=10)\n",
    "    \n",
    "    # Salva e limpa a memória do gráfico\n",
    "    nome_arquivo = f\"mapas_flags/mapa_flag_{flag}.png\"\n",
    "    plt.savefig(nome_arquivo, bbox_inches='tight', dpi=300)\n",
    "    plt.close(fig)\n",
    "    \n",
    "    print(f\"Sucesso: Mapa da flag {flag} exportado para '{nome_arquivo}'.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b83eb307",
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = None\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "# Extrai o mapa existente e forca a primeira cor (indice 0) a ser branco puro\n",
    "cmap_original = plt.get_cmap('OrRd')\n",
    "cores = cmap_original(range(256))\n",
    "cores[0] = [1, 1, 1, 1]  # Define o valor zero/minimo como Branco Puro [R, G, B, Alpha]\n",
    "\n",
    "# Cria o novo mapa de cores customizado\n",
    "meu_cmap = mcolors.ListedColormap(cores)\n",
    "\n",
    "\n",
    "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n",
    "mask = None\n",
    "#mask = f\"((nchan == 13))\"\n",
    "# mask = f\"((iusev == 1))\"\n",
    "# mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) and (iusev == 1) )\"\n",
    "\n",
    "#mask = f\"( ((kx == 253) or (kx == 252) or (kx == 245)) )\"\n",
    "#mask = f\"( ((kx == 247)) )\"\n",
    "\n",
    "# mask = f\"((950<=prs<=1050) and (iusev == 1))\"\n",
    "\n",
    "# mask = f\"( (0 < iqc < 8) )\"\n",
    "\n",
    "# mask = f\"( (8 <= iqc <= 15) )\"\n",
    "# mask = f\"( (iqc == 15) )\"\n",
    "\n",
    "# mask = \"((-90 <= lat <= -80) & (240 <= lon <= 300))\"\n",
    "\n",
    "mask = \"((-60 <= lat <= 30) & (240 <= lon <= 340))\"\n",
    "\n",
    "param = 'iqc'\n",
    "varName = 'ps' #'ps' #'uv'\n",
    "varType = 181 #180 #253 #181\n",
    "\n",
    "\n",
    "# 1. Definição da malha de retângulos (Resolução da grade)\n",
    "resolucao = 1.0  # Tamanho do retângulo em graus (ex: 1° x 1°)\n",
    "\n",
    "lon_bins = np.arange(0, 360 + resolucao, resolucao)\n",
    "lat_bins = np.arange(-90, 90 + resolucao, resolucao)\n",
    "\n",
    "# 2. Configuração correta da figura e do eixo com projeção Cartopy\n",
    "fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "tidx = 0\n",
    "df = []\n",
    "gdf_list[tidx].pfileinfo()\n",
    "gdf_list[tidx].obsInfo\n",
    "#df = gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName] #.loc[varType]\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "\n",
    "df_aux = []\n",
    "if mask is None:\n",
    "    df_aux = df\n",
    "    del(df)\n",
    "else:\n",
    "    df_aux = df.query(mask)\n",
    "    del(df)\n",
    "\n",
    "################################################################################\n",
    "    \n",
    "# 1. Identificar todas as flags únicas do seu vetor 'iqc'\n",
    "flags_unicas = np.unique(df_aux['iqc'].values)\n",
    "\n",
    "# 2. Criar a função que conta quantas vezes cada flag aparece em cada bin\n",
    "def contar_cada_flag(valores_no_bin):\n",
    "    # Se o bin estiver vazio, retorna zeros para cada flag\n",
    "    if len(valores_no_bin) == 0:\n",
    "        return np.zeros(len(flags_unicas))\n",
    "    \n",
    "    # Conta quantas vezes cada flag única aparece neste bin específico\n",
    "    contagens = [np.sum(valores_no_bin == f) for f in flags_unicas]\n",
    "    return np.array(contagens)\n",
    "\n",
    "# 3. Sua chamada adaptada para contagem\n",
    "estatistica_contagem, lon_arestas, lat_arestas, binnumber = binned_statistic_2d(\n",
    "    x=df_aux['lon'],\n",
    "    y=df_aux['lat'],\n",
    "    values=df_aux['iqc'],          # Mudado para 'iqc' para alimentar a função\n",
    "    statistic=contar_cada_flag,    # Sua função de contagem personalizada\n",
    "    bins=[lon_bins, lat_bins]      # Seus bins originais de longitude e latitude\n",
    ")\n",
    "\n",
    "# # 4. Organização do resultado em formato de matriz\n",
    "# # Transposta (T) necessária para alinhar o shape [Lat, Lon] padrão de mapas\n",
    "# matriz_media = estatistica.T\n",
    "\n",
    "# # Substitui retângulos sem nenhuma observação (NaN) por zero\n",
    "# matriz_limpa = np.nan_to_num(matriz_media, nan=0.0)\n",
    "\n",
    "# print('')\n",
    "# print(f\"Formato da matriz gerada (Latitudes, Longitudes): {matriz_limpa.shape}\")\n",
    "# print('')\n",
    "\n",
    "import matplotlib.cm as cm\n",
    "import matplotlib.colors as mcolors\n",
    "\n",
    "# Pega as cores originais do mapa 'tab20'\n",
    "cores_originais = cm.get_cmap('tab20').colors\n",
    "\n",
    "# Substitui a primeira cor (índice 0) por branco\n",
    "# Modifica a lista de tuplas RGB transformando a primeira em (1.0, 1.0, 1.0)\n",
    "cores_modificadas = list(cores_originais)\n",
    "cores_modificadas[0] = (1.0, 1.0, 1.0) \n",
    "\n",
    "# Cria o novo colormap customizado baseado nas cores alteradas\n",
    "cmap_customizado = mcolors.ListedColormap(cores_modificadas)\n",
    "\n",
    "\n",
    "# # 5. Plotagem da matriz\n",
    "# pcm = ax.pcolormesh(\n",
    "#     lon_bins, \n",
    "#     lat_bins, \n",
    "#     matriz_media,  #matriz_oma_media.T,\n",
    "#     cmap=cmap_customizado,       \n",
    "#     vmin=0,\n",
    "#     vmax=20,\n",
    "#     transform=ccrs.PlateCarree()\n",
    "# )\n",
    "\n",
    "# # Define os limites exatos da área que você quer exibir\n",
    "# # Formato: [lon_min, lon_max, lat_min, lat_max]\n",
    "# ax.set_extent([-120.0, -20.0, -60.0, 30.0], crs=ccrs.PlateCarree())\n",
    "\n",
    "\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "# Certifique-se de que a pasta para salvar os mapas existe\n",
    "os.makedirs(\"mapas_flags\", exist_ok=True)\n",
    "\n",
    "# Loop dinâmico por todas as flags encontradas no seu vetor\n",
    "for idx, flag in enumerate(flags_unicas):\n",
    "    \n",
    "    # 1. Configura a figura e o eixo com a projeção do Cartopy\n",
    "    fig, ax = plt.subplots(figsize=(10, 8), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "    \n",
    "    # 2. Extrai a matriz 2D da flag atual e aplica a transposição (.T)\n",
    "    matriz_da_flag = estatistica_contagem[:, :, idx].T\n",
    "    \n",
    "    # 3. Plota o pcolormesh usando as configurações anteriores\n",
    "    pcm = ax.pcolormesh(\n",
    "        lon_bins, \n",
    "        lat_bins, \n",
    "        matriz_da_flag,\n",
    "        cmap=cmap_customizado,  # Aquele com o zero branco\n",
    "        vmin=0,\n",
    "        vmax=20,                # Ajuste o teto máximo de contagem por bin aqui\n",
    "        transform=ccrs.PlateCarree()\n",
    "    )\n",
    "    \n",
    "    # 4. Aplica os limites geográficos que você definiu\n",
    "    ax.set_extent([lon_bins[0], lon_bins[-1], lat_bins[0], lat_bins[-1]], crs=ccrs.PlateCarree())\n",
    "    \n",
    "    # 5. Elementos estéticos do mapa\n",
    "    ax.coastlines(resolution='10m', color='black', linewidth=0.8)\n",
    "    plt.colorbar(pcm, ax=ax, label='Quantidade de Ocorrências', orientation='horizontal', pad=0.05, shrink=0.7)\n",
    "    \n",
    "    ax.set_title(f\"Distribuição Geográfica - Flag iqc: {flag}\", fontsize=14, pad=10)\n",
    "    \n",
    "    # 6. Salva o gráfico dinamicamente com o nome da flag\n",
    "    nome_arquivo = f\"mapas_flags/mapa_flag_{flag}.png\"\n",
    "    plt.savefig(nome_arquivo, bbox_inches='tight', dpi=300)\n",
    "    plt.close(fig)  # Fecha a figura para liberar memória do computador\n",
    "    \n",
    "    print(f\"Mapa da flag {flag} salvo com sucesso em: {nome_arquivo}\")\n",
    "\n",
    "##################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37e6dda2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ax = None\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import binned_statistic_2d\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "\n",
    "#mask=f\"((iuse == 1) & (lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "#mask=f\"((lat>-60.0 and lat<15) & (lon>260.0 and lon<350))\"\n",
    "# mask = f\"((iuse == 1) & (nchan == 3))\" #None\n",
    "mask = None\n",
    "#mask = f\"((nchan == 13))\"\n",
    "# mask = f\"((iusev == 1))\"\n",
    "\n",
    "\n",
    "param = 'iqc'\n",
    "varName = 'ps' #'uv'\n",
    "varType = 181\n",
    "\n",
    "# 1. Configuração correta da figura e do eixo com projeção Cartopy\n",
    "# fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.PlateCarree()})\n",
    "\n",
    "tidx = 0\n",
    "df = []\n",
    "gdf_list[tidx].pfileinfo()\n",
    "gdf_list[tidx].obsInfo\n",
    "#df = gdf_list[tidx].obsInfo\n",
    "df = gdf_list[tidx].obsInfo[varName].loc[varType]\n",
    "col = df.columns.tolist()\n",
    "print('columns', col)\n",
    "\n",
    "# df = df.query('(-63 <= lat <= -61) & (295 < lon <= 300)')\n",
    "\n",
    "# df = df.query('(-90 <= lat <= -70) & (240 < lon <= 300)')\n",
    "\n",
    "\n",
    "# df = df.query('(-90 <= lat <= -80) & (240 <= lon <= 300)')\n",
    "# df = df.query('(-71.4 <= lat <= -71.3) & (240 < lon <= 300)')\n",
    "# df = df.query('(950 <= prs <= 1050)')\n",
    "\n",
    "# df = df.query('(991 <= prs <= 993)')\n",
    "\n",
    "df\n",
    "# df[param]\n",
    "\n",
    "# df_data = []\n",
    "# df_data.append(float(df[param].mean()))\n",
    "# df_data\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48cf904-ad49-4d2e-9415-6bec88ab48cc",
   "metadata": {},
   "source": [
    "### Histograma\n",
    "\n",
    "Utilize a função `pcount()` da classe `plot_diag` para obter um histograma com a contagem do número de observações para uma determinada variável:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "078f84a8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "varName = 'ps'\n",
    "\n",
    "idschar = '_'.join(str(varName))\n",
    "\n",
    "gd.plot_diag.pcount(gdf_list[tidx], varName)\n",
    "\n",
    "figname = idschar + '_pcount.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "368a57ec-9e04-4394-a9d7-4cb4bba1b0a3",
   "metadata": {},
   "source": [
    "Utilize a função `vcount()` da classe `plot_diag` para obter um histograma com a contagem do número de observações para todos os tipos de variáveis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1d44398",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gd.plot_diag.vcount(gdf_list[tidx])\n",
    "\n",
    "idschar = 'All_varNames'\n",
    "figname = idschar + '_vcount.png'\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(figname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50747e4-44c9-48d3-a150-ec640573cb18",
   "metadata": {},
   "source": [
    "De forma semelhante, utilize a função `kxcount()` da classe `plot_diag` para obter um histograma com a contagem do número de observações por tipo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46deda00",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gd.plot_diag.kxcount(gdf_list[tidx])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb35f54",
   "metadata": {},
   "source": [
    "### Série temporal\n",
    "\n",
    "<a id='time_series'></a>\n",
    "A seguir são apresentadas as opções de figuras com a função `time_series()`, incluída na classe `plot_diag`. Inicialmente é gerada uma figura com os parâmetros já fixados nesta seção. Em seguida são apresentadas figuras alterando os parâmetros `Level`, `Lay` e `SingleL`.\n",
    "\n",
    "Plotando uma série temporal do OmA e OmF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "241bc9fc",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# varName = 'uv'\n",
    "# varType = 245\n",
    "\n",
    "varName = 'ps'\n",
    "varType = 181\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType,\n",
    "                         mask='iusev==1',\n",
    "                         dateIni=dateIni, \n",
    "                         dateFin=dateFin, \n",
    "                         nHour=nHour, \n",
    "                         vminOMA=vminOMA, \n",
    "                         vmaxOMA=vmaxOMA, \n",
    "                         vminSTD=vminSTD, \n",
    "                         vmaxSTD=vmaxSTD, \n",
    "                         Level=Level, \n",
    "                         Lay=Lay, \n",
    "                         SingleL=SingleL,\n",
    "                         Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "905097b2",
   "metadata": {},
   "source": [
    "No caso anterior, o parâmetro `Level` estava fixo em 1000 hPa com `SingleL` igual a `All`, ou seja, toda a atmosfera foi considerada como uma única camada e o valor 1000 hPa funciona apenas como uma flag para indicar que não existe variação em altura. Ainda com `Level=1000`, pode-se usar `SingleL='OneL'` para uma única camada em torno do valor de `Level`, neste caso 1000 hPa, variando entre `Level-Lay` e `Level+Lay` (na definição de variáveis, `Lay` foi fixado com 15 hPa; no caso de ser `None`, o valor padrão de 50 hPa é utilizado)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "565623bd",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "SingleL = 'OneL'\n",
    "Lay = 15\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType,\n",
    "                         mask='iusev==1',\n",
    "                         dateIni=dateIni, \n",
    "                         dateFin=dateFin, \n",
    "                         nHour=nHour, \n",
    "                         vminOMA=vminOMA, \n",
    "                         vmaxOMA=vmaxOMA, \n",
    "                         vminSTD=vminSTD, \n",
    "                         vmaxSTD=vmaxSTD, \n",
    "                         Level=Level, \n",
    "                         Lay=Lay, \n",
    "                         SingleL=SingleL,\n",
    "                         Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8771f5b9",
   "metadata": {},
   "source": [
    "Observe como no exemplo acima a quantidade de dados diminui, uma vez que agora está sendo utilizada uma camada entre 1015 e 985 hPa.\n",
    "\n",
    "Agora vamos alterar para não fixar em um único nível ou camada, ou seja, altear a variável `Level` para `None` ou `Zlevs`. A opção `None` busca os dados em cada nível existente nos arquivos e faz a figura para todos esses níveis, porém os valores no eixo y (ordenada) são apenas dos níveis padrão. É importante esclarecer que os chamados níveis padrão estão definidos na classe `read_diag` e podem ser acessados utilizando o método `zlevs` da seguinte forma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "269a1812",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gdf_list[tidx].zlevs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f63a5f-8d2c-4e8d-a292-89a484740c06",
   "metadata": {},
   "source": [
    "Dessa forma, pode-se utilizar o parâmetro `Level='Zlevs'` para produzir uma série temporal por níveis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b8695b3",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = 'Zlevs'\n",
    "Lay = 15\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType,\n",
    "                         mask='iusev==-1',\n",
    "                         dateIni=dateIni, \n",
    "                         dateFin=dateFin, \n",
    "                         nHour=nHour, \n",
    "                         vminOMA=vminOMA, \n",
    "                         vmaxOMA=vmaxOMA, \n",
    "                         vminSTD=vminSTD, \n",
    "                         vmaxSTD=vmaxSTD, \n",
    "                         Level=Level, \n",
    "                         Lay=Lay, \n",
    "                         SingleL=SingleL,\n",
    "                         Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "168e33f3",
   "metadata": {},
   "source": [
    "Se `Lay=None`, então as camadas são preenchidas variando entre o valor médio considerando as camadas inferiores e superiores. Por exemplo, para o nível de 700 hPa é construída uma camada entre 750 e 650 hPa, uma vez que os níveis inferiores e superiores são 800 e 600 hPa, respectivamente. Para o nível de 1000 hPa a camada varia entre 1050 e 950 hPa.\n",
    "\n",
    "O mesmo exemplo anterior, mas considerando `Level='Zlevs'`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "369095b9",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = 'Zlevs'\n",
    "Lay = None\n",
    "\n",
    "varName = 'ps'\n",
    "varType = 181\n",
    "vminOMA =-5.0\n",
    "vmaxOMA =5.0\n",
    "vminSTD = 0.0\n",
    "vmaxSTD =5.0\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType,\n",
    "                         mask='iusev==-1',\n",
    "                         dateIni=dateIni, \n",
    "                         dateFin=dateFin, \n",
    "                         nHour=nHour, \n",
    "                         vminOMA=vminOMA, \n",
    "                         vmaxOMA=vmaxOMA, \n",
    "                         vminSTD=vminSTD, \n",
    "                         vmaxSTD=vmaxSTD, \n",
    "                         Level=Level, \n",
    "                         Lay=Lay, \n",
    "                         SingleL=SingleL,\n",
    "                         Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f65d75eb",
   "metadata": {},
   "source": [
    "Finalmente, existe a opção de se considerar todos os níveis, ou seja, `Level=None`. Esta opção traz certa dificuldade na visualização da informação na figura por conta da grande quantidade de níveis e distribuição dos dados em todos os níveis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a84f15",
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [],
   "source": [
    "Level = None\n",
    "\n",
    "gd.plot_diag.time_series(gdf_list,\n",
    "                         varName=varName, \n",
    "                         varType=varType,\n",
    "                         mask='iusev==-1',\n",
    "                         dateIni=dateIni, \n",
    "                         dateFin=dateFin, \n",
    "                         nHour=nHour, \n",
    "                         vminOMA=vminOMA, \n",
    "                         vmaxOMA=vmaxOMA, \n",
    "                         vminSTD=vminSTD, \n",
    "                         vmaxSTD=vmaxSTD, \n",
    "                         Level=Level, \n",
    "                         Lay=Lay, \n",
    "                         SingleL=SingleL,\n",
    "                         Clean=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ddd69e-cecb-458b-b25f-27820feaa636",
   "metadata": {},
   "source": [
    "Finalizado o uso dos arquivos, feche-os para liberar a memória utilizada:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e95c31d-1cff-4047-a53e-5d5b11534aa2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for file in gdf_list:\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab17d898",
   "metadata": {},
   "source": [
    "O `readDiag` é um pacote em desenvolvimento e em constante atualização. Novas funcionalidades serão adicionadas e demonstradas por meio deste notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eefbad7e-cccf-4a99-be5f-aa4d5c2dd642",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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